5688 lines
210 KiB
Python
5688 lines
210 KiB
Python
# pylint: disable-msg=W0400,W0511,W0611,W0612,W0614,R0201,E1102
|
|
"""Tests suite for MaskedArray & subclassing.
|
|
|
|
:author: Pierre Gerard-Marchant
|
|
:contact: pierregm_at_uga_dot_edu
|
|
"""
|
|
__author__ = "Pierre GF Gerard-Marchant"
|
|
|
|
import sys
|
|
import warnings
|
|
import copy
|
|
import operator
|
|
import itertools
|
|
import textwrap
|
|
import pytest
|
|
|
|
from functools import reduce
|
|
|
|
|
|
import numpy as np
|
|
import numpy.ma.core
|
|
import numpy.core.fromnumeric as fromnumeric
|
|
import numpy.core.umath as umath
|
|
from numpy.testing import (
|
|
assert_raises, assert_warns, suppress_warnings, IS_WASM
|
|
)
|
|
from numpy.testing._private.utils import requires_memory
|
|
from numpy import ndarray
|
|
from numpy.compat import asbytes
|
|
from numpy.ma.testutils import (
|
|
assert_, assert_array_equal, assert_equal, assert_almost_equal,
|
|
assert_equal_records, fail_if_equal, assert_not_equal,
|
|
assert_mask_equal
|
|
)
|
|
from numpy.ma.core import (
|
|
MAError, MaskError, MaskType, MaskedArray, abs, absolute, add, all,
|
|
allclose, allequal, alltrue, angle, anom, arange, arccos, arccosh, arctan2,
|
|
arcsin, arctan, argsort, array, asarray, choose, concatenate,
|
|
conjugate, cos, cosh, count, default_fill_value, diag, divide, doc_note,
|
|
empty, empty_like, equal, exp, flatten_mask, filled, fix_invalid,
|
|
flatten_structured_array, fromflex, getmask, getmaskarray, greater,
|
|
greater_equal, identity, inner, isMaskedArray, less, less_equal, log,
|
|
log10, make_mask, make_mask_descr, mask_or, masked, masked_array,
|
|
masked_equal, masked_greater, masked_greater_equal, masked_inside,
|
|
masked_less, masked_less_equal, masked_not_equal, masked_outside,
|
|
masked_print_option, masked_values, masked_where, max, maximum,
|
|
maximum_fill_value, min, minimum, minimum_fill_value, mod, multiply,
|
|
mvoid, nomask, not_equal, ones, ones_like, outer, power, product, put,
|
|
putmask, ravel, repeat, reshape, resize, shape, sin, sinh, sometrue, sort,
|
|
sqrt, subtract, sum, take, tan, tanh, transpose, where, zeros, zeros_like,
|
|
)
|
|
from numpy.compat import pickle
|
|
|
|
pi = np.pi
|
|
|
|
|
|
suppress_copy_mask_on_assignment = suppress_warnings()
|
|
suppress_copy_mask_on_assignment.filter(
|
|
numpy.ma.core.MaskedArrayFutureWarning,
|
|
"setting an item on a masked array which has a shared mask will not copy")
|
|
|
|
|
|
# For parametrized numeric testing
|
|
num_dts = [np.dtype(dt_) for dt_ in '?bhilqBHILQefdgFD']
|
|
num_ids = [dt_.char for dt_ in num_dts]
|
|
|
|
|
|
class TestMaskedArray:
|
|
# Base test class for MaskedArrays.
|
|
|
|
def setup_method(self):
|
|
# Base data definition.
|
|
x = np.array([1., 1., 1., -2., pi/2.0, 4., 5., -10., 10., 1., 2., 3.])
|
|
y = np.array([5., 0., 3., 2., -1., -4., 0., -10., 10., 1., 0., 3.])
|
|
a10 = 10.
|
|
m1 = [1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0]
|
|
m2 = [0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1]
|
|
xm = masked_array(x, mask=m1)
|
|
ym = masked_array(y, mask=m2)
|
|
z = np.array([-.5, 0., .5, .8])
|
|
zm = masked_array(z, mask=[0, 1, 0, 0])
|
|
xf = np.where(m1, 1e+20, x)
|
|
xm.set_fill_value(1e+20)
|
|
self.d = (x, y, a10, m1, m2, xm, ym, z, zm, xf)
|
|
|
|
def test_basicattributes(self):
|
|
# Tests some basic array attributes.
|
|
a = array([1, 3, 2])
|
|
b = array([1, 3, 2], mask=[1, 0, 1])
|
|
assert_equal(a.ndim, 1)
|
|
assert_equal(b.ndim, 1)
|
|
assert_equal(a.size, 3)
|
|
assert_equal(b.size, 3)
|
|
assert_equal(a.shape, (3,))
|
|
assert_equal(b.shape, (3,))
|
|
|
|
def test_basic0d(self):
|
|
# Checks masking a scalar
|
|
x = masked_array(0)
|
|
assert_equal(str(x), '0')
|
|
x = masked_array(0, mask=True)
|
|
assert_equal(str(x), str(masked_print_option))
|
|
x = masked_array(0, mask=False)
|
|
assert_equal(str(x), '0')
|
|
x = array(0, mask=1)
|
|
assert_(x.filled().dtype is x._data.dtype)
|
|
|
|
def test_basic1d(self):
|
|
# Test of basic array creation and properties in 1 dimension.
|
|
(x, y, a10, m1, m2, xm, ym, z, zm, xf) = self.d
|
|
assert_(not isMaskedArray(x))
|
|
assert_(isMaskedArray(xm))
|
|
assert_((xm - ym).filled(0).any())
|
|
fail_if_equal(xm.mask.astype(int), ym.mask.astype(int))
|
|
s = x.shape
|
|
assert_equal(np.shape(xm), s)
|
|
assert_equal(xm.shape, s)
|
|
assert_equal(xm.dtype, x.dtype)
|
|
assert_equal(zm.dtype, z.dtype)
|
|
assert_equal(xm.size, reduce(lambda x, y:x * y, s))
|
|
assert_equal(count(xm), len(m1) - reduce(lambda x, y:x + y, m1))
|
|
assert_array_equal(xm, xf)
|
|
assert_array_equal(filled(xm, 1.e20), xf)
|
|
assert_array_equal(x, xm)
|
|
|
|
def test_basic2d(self):
|
|
# Test of basic array creation and properties in 2 dimensions.
|
|
(x, y, a10, m1, m2, xm, ym, z, zm, xf) = self.d
|
|
for s in [(4, 3), (6, 2)]:
|
|
x.shape = s
|
|
y.shape = s
|
|
xm.shape = s
|
|
ym.shape = s
|
|
xf.shape = s
|
|
|
|
assert_(not isMaskedArray(x))
|
|
assert_(isMaskedArray(xm))
|
|
assert_equal(shape(xm), s)
|
|
assert_equal(xm.shape, s)
|
|
assert_equal(xm.size, reduce(lambda x, y:x * y, s))
|
|
assert_equal(count(xm), len(m1) - reduce(lambda x, y:x + y, m1))
|
|
assert_equal(xm, xf)
|
|
assert_equal(filled(xm, 1.e20), xf)
|
|
assert_equal(x, xm)
|
|
|
|
def test_concatenate_basic(self):
|
|
# Tests concatenations.
|
|
(x, y, a10, m1, m2, xm, ym, z, zm, xf) = self.d
|
|
# basic concatenation
|
|
assert_equal(np.concatenate((x, y)), concatenate((xm, ym)))
|
|
assert_equal(np.concatenate((x, y)), concatenate((x, y)))
|
|
assert_equal(np.concatenate((x, y)), concatenate((xm, y)))
|
|
assert_equal(np.concatenate((x, y, x)), concatenate((x, ym, x)))
|
|
|
|
def test_concatenate_alongaxis(self):
|
|
# Tests concatenations.
|
|
(x, y, a10, m1, m2, xm, ym, z, zm, xf) = self.d
|
|
# Concatenation along an axis
|
|
s = (3, 4)
|
|
x.shape = y.shape = xm.shape = ym.shape = s
|
|
assert_equal(xm.mask, np.reshape(m1, s))
|
|
assert_equal(ym.mask, np.reshape(m2, s))
|
|
xmym = concatenate((xm, ym), 1)
|
|
assert_equal(np.concatenate((x, y), 1), xmym)
|
|
assert_equal(np.concatenate((xm.mask, ym.mask), 1), xmym._mask)
|
|
|
|
x = zeros(2)
|
|
y = array(ones(2), mask=[False, True])
|
|
z = concatenate((x, y))
|
|
assert_array_equal(z, [0, 0, 1, 1])
|
|
assert_array_equal(z.mask, [False, False, False, True])
|
|
z = concatenate((y, x))
|
|
assert_array_equal(z, [1, 1, 0, 0])
|
|
assert_array_equal(z.mask, [False, True, False, False])
|
|
|
|
def test_concatenate_flexible(self):
|
|
# Tests the concatenation on flexible arrays.
|
|
data = masked_array(list(zip(np.random.rand(10),
|
|
np.arange(10))),
|
|
dtype=[('a', float), ('b', int)])
|
|
|
|
test = concatenate([data[:5], data[5:]])
|
|
assert_equal_records(test, data)
|
|
|
|
def test_creation_ndmin(self):
|
|
# Check the use of ndmin
|
|
x = array([1, 2, 3], mask=[1, 0, 0], ndmin=2)
|
|
assert_equal(x.shape, (1, 3))
|
|
assert_equal(x._data, [[1, 2, 3]])
|
|
assert_equal(x._mask, [[1, 0, 0]])
|
|
|
|
def test_creation_ndmin_from_maskedarray(self):
|
|
# Make sure we're not losing the original mask w/ ndmin
|
|
x = array([1, 2, 3])
|
|
x[-1] = masked
|
|
xx = array(x, ndmin=2, dtype=float)
|
|
assert_equal(x.shape, x._mask.shape)
|
|
assert_equal(xx.shape, xx._mask.shape)
|
|
|
|
def test_creation_maskcreation(self):
|
|
# Tests how masks are initialized at the creation of Maskedarrays.
|
|
data = arange(24, dtype=float)
|
|
data[[3, 6, 15]] = masked
|
|
dma_1 = MaskedArray(data)
|
|
assert_equal(dma_1.mask, data.mask)
|
|
dma_2 = MaskedArray(dma_1)
|
|
assert_equal(dma_2.mask, dma_1.mask)
|
|
dma_3 = MaskedArray(dma_1, mask=[1, 0, 0, 0] * 6)
|
|
fail_if_equal(dma_3.mask, dma_1.mask)
|
|
|
|
x = array([1, 2, 3], mask=True)
|
|
assert_equal(x._mask, [True, True, True])
|
|
x = array([1, 2, 3], mask=False)
|
|
assert_equal(x._mask, [False, False, False])
|
|
y = array([1, 2, 3], mask=x._mask, copy=False)
|
|
assert_(np.may_share_memory(x.mask, y.mask))
|
|
y = array([1, 2, 3], mask=x._mask, copy=True)
|
|
assert_(not np.may_share_memory(x.mask, y.mask))
|
|
x = array([1, 2, 3], mask=None)
|
|
assert_equal(x._mask, [False, False, False])
|
|
|
|
def test_masked_singleton_array_creation_warns(self):
|
|
# The first works, but should not (ideally), there may be no way
|
|
# to solve this, however, as long as `np.ma.masked` is an ndarray.
|
|
np.array(np.ma.masked)
|
|
with pytest.warns(UserWarning):
|
|
# Tries to create a float array, using `float(np.ma.masked)`.
|
|
# We may want to define this is invalid behaviour in the future!
|
|
# (requiring np.ma.masked to be a known NumPy scalar probably
|
|
# with a DType.)
|
|
np.array([3., np.ma.masked])
|
|
|
|
def test_creation_with_list_of_maskedarrays(self):
|
|
# Tests creating a masked array from a list of masked arrays.
|
|
x = array(np.arange(5), mask=[1, 0, 0, 0, 0])
|
|
data = array((x, x[::-1]))
|
|
assert_equal(data, [[0, 1, 2, 3, 4], [4, 3, 2, 1, 0]])
|
|
assert_equal(data._mask, [[1, 0, 0, 0, 0], [0, 0, 0, 0, 1]])
|
|
|
|
x.mask = nomask
|
|
data = array((x, x[::-1]))
|
|
assert_equal(data, [[0, 1, 2, 3, 4], [4, 3, 2, 1, 0]])
|
|
assert_(data.mask is nomask)
|
|
|
|
def test_creation_with_list_of_maskedarrays_no_bool_cast(self):
|
|
# Tests the regression in gh-18551
|
|
masked_str = np.ma.masked_array(['a', 'b'], mask=[True, False])
|
|
normal_int = np.arange(2)
|
|
res = np.ma.asarray([masked_str, normal_int], dtype="U21")
|
|
assert_array_equal(res.mask, [[True, False], [False, False]])
|
|
|
|
# The above only failed due a long chain of oddity, try also with
|
|
# an object array that cannot be converted to bool always:
|
|
class NotBool():
|
|
def __bool__(self):
|
|
raise ValueError("not a bool!")
|
|
masked_obj = np.ma.masked_array([NotBool(), 'b'], mask=[True, False])
|
|
# Check that the NotBool actually fails like we would expect:
|
|
with pytest.raises(ValueError, match="not a bool!"):
|
|
np.asarray([masked_obj], dtype=bool)
|
|
|
|
res = np.ma.asarray([masked_obj, normal_int])
|
|
assert_array_equal(res.mask, [[True, False], [False, False]])
|
|
|
|
def test_creation_from_ndarray_with_padding(self):
|
|
x = np.array([('A', 0)], dtype={'names':['f0','f1'],
|
|
'formats':['S4','i8'],
|
|
'offsets':[0,8]})
|
|
array(x) # used to fail due to 'V' padding field in x.dtype.descr
|
|
|
|
def test_unknown_keyword_parameter(self):
|
|
with pytest.raises(TypeError, match="unexpected keyword argument"):
|
|
MaskedArray([1, 2, 3], maks=[0, 1, 0]) # `mask` is misspelled.
|
|
|
|
def test_asarray(self):
|
|
(x, y, a10, m1, m2, xm, ym, z, zm, xf) = self.d
|
|
xm.fill_value = -9999
|
|
xm._hardmask = True
|
|
xmm = asarray(xm)
|
|
assert_equal(xmm._data, xm._data)
|
|
assert_equal(xmm._mask, xm._mask)
|
|
assert_equal(xmm.fill_value, xm.fill_value)
|
|
assert_equal(xmm._hardmask, xm._hardmask)
|
|
|
|
def test_asarray_default_order(self):
|
|
# See Issue #6646
|
|
m = np.eye(3).T
|
|
assert_(not m.flags.c_contiguous)
|
|
|
|
new_m = asarray(m)
|
|
assert_(new_m.flags.c_contiguous)
|
|
|
|
def test_asarray_enforce_order(self):
|
|
# See Issue #6646
|
|
m = np.eye(3).T
|
|
assert_(not m.flags.c_contiguous)
|
|
|
|
new_m = asarray(m, order='C')
|
|
assert_(new_m.flags.c_contiguous)
|
|
|
|
def test_fix_invalid(self):
|
|
# Checks fix_invalid.
|
|
with np.errstate(invalid='ignore'):
|
|
data = masked_array([np.nan, 0., 1.], mask=[0, 0, 1])
|
|
data_fixed = fix_invalid(data)
|
|
assert_equal(data_fixed._data, [data.fill_value, 0., 1.])
|
|
assert_equal(data_fixed._mask, [1., 0., 1.])
|
|
|
|
def test_maskedelement(self):
|
|
# Test of masked element
|
|
x = arange(6)
|
|
x[1] = masked
|
|
assert_(str(masked) == '--')
|
|
assert_(x[1] is masked)
|
|
assert_equal(filled(x[1], 0), 0)
|
|
|
|
def test_set_element_as_object(self):
|
|
# Tests setting elements with object
|
|
a = empty(1, dtype=object)
|
|
x = (1, 2, 3, 4, 5)
|
|
a[0] = x
|
|
assert_equal(a[0], x)
|
|
assert_(a[0] is x)
|
|
|
|
import datetime
|
|
dt = datetime.datetime.now()
|
|
a[0] = dt
|
|
assert_(a[0] is dt)
|
|
|
|
def test_indexing(self):
|
|
# Tests conversions and indexing
|
|
x1 = np.array([1, 2, 4, 3])
|
|
x2 = array(x1, mask=[1, 0, 0, 0])
|
|
x3 = array(x1, mask=[0, 1, 0, 1])
|
|
x4 = array(x1)
|
|
# test conversion to strings
|
|
str(x2) # raises?
|
|
repr(x2) # raises?
|
|
assert_equal(np.sort(x1), sort(x2, endwith=False))
|
|
# tests of indexing
|
|
assert_(type(x2[1]) is type(x1[1]))
|
|
assert_(x1[1] == x2[1])
|
|
assert_(x2[0] is masked)
|
|
assert_equal(x1[2], x2[2])
|
|
assert_equal(x1[2:5], x2[2:5])
|
|
assert_equal(x1[:], x2[:])
|
|
assert_equal(x1[1:], x3[1:])
|
|
x1[2] = 9
|
|
x2[2] = 9
|
|
assert_equal(x1, x2)
|
|
x1[1:3] = 99
|
|
x2[1:3] = 99
|
|
assert_equal(x1, x2)
|
|
x2[1] = masked
|
|
assert_equal(x1, x2)
|
|
x2[1:3] = masked
|
|
assert_equal(x1, x2)
|
|
x2[:] = x1
|
|
x2[1] = masked
|
|
assert_(allequal(getmask(x2), array([0, 1, 0, 0])))
|
|
x3[:] = masked_array([1, 2, 3, 4], [0, 1, 1, 0])
|
|
assert_(allequal(getmask(x3), array([0, 1, 1, 0])))
|
|
x4[:] = masked_array([1, 2, 3, 4], [0, 1, 1, 0])
|
|
assert_(allequal(getmask(x4), array([0, 1, 1, 0])))
|
|
assert_(allequal(x4, array([1, 2, 3, 4])))
|
|
x1 = np.arange(5) * 1.0
|
|
x2 = masked_values(x1, 3.0)
|
|
assert_equal(x1, x2)
|
|
assert_(allequal(array([0, 0, 0, 1, 0], MaskType), x2.mask))
|
|
assert_equal(3.0, x2.fill_value)
|
|
x1 = array([1, 'hello', 2, 3], object)
|
|
x2 = np.array([1, 'hello', 2, 3], object)
|
|
s1 = x1[1]
|
|
s2 = x2[1]
|
|
assert_equal(type(s2), str)
|
|
assert_equal(type(s1), str)
|
|
assert_equal(s1, s2)
|
|
assert_(x1[1:1].shape == (0,))
|
|
|
|
def test_setitem_no_warning(self):
|
|
# Setitem shouldn't warn, because the assignment might be masked
|
|
# and warning for a masked assignment is weird (see gh-23000)
|
|
# (When the value is masked, otherwise a warning would be acceptable
|
|
# but is not given currently.)
|
|
x = np.ma.arange(60).reshape((6, 10))
|
|
index = (slice(1, 5, 2), [7, 5])
|
|
value = np.ma.masked_all((2, 2))
|
|
value._data[...] = np.inf # not a valid integer...
|
|
x[index] = value
|
|
# The masked scalar is special cased, but test anyway (it's NaN):
|
|
x[...] = np.ma.masked
|
|
# Finally, a large value that cannot be cast to the float32 `x`
|
|
x = np.ma.arange(3., dtype=np.float32)
|
|
value = np.ma.array([2e234, 1, 1], mask=[True, False, False])
|
|
x[...] = value
|
|
x[[0, 1, 2]] = value
|
|
|
|
@suppress_copy_mask_on_assignment
|
|
def test_copy(self):
|
|
# Tests of some subtle points of copying and sizing.
|
|
n = [0, 0, 1, 0, 0]
|
|
m = make_mask(n)
|
|
m2 = make_mask(m)
|
|
assert_(m is m2)
|
|
m3 = make_mask(m, copy=True)
|
|
assert_(m is not m3)
|
|
|
|
x1 = np.arange(5)
|
|
y1 = array(x1, mask=m)
|
|
assert_equal(y1._data.__array_interface__, x1.__array_interface__)
|
|
assert_(allequal(x1, y1.data))
|
|
assert_equal(y1._mask.__array_interface__, m.__array_interface__)
|
|
|
|
y1a = array(y1)
|
|
# Default for masked array is not to copy; see gh-10318.
|
|
assert_(y1a._data.__array_interface__ ==
|
|
y1._data.__array_interface__)
|
|
assert_(y1a._mask.__array_interface__ ==
|
|
y1._mask.__array_interface__)
|
|
|
|
y2 = array(x1, mask=m3)
|
|
assert_(y2._data.__array_interface__ == x1.__array_interface__)
|
|
assert_(y2._mask.__array_interface__ == m3.__array_interface__)
|
|
assert_(y2[2] is masked)
|
|
y2[2] = 9
|
|
assert_(y2[2] is not masked)
|
|
assert_(y2._mask.__array_interface__ == m3.__array_interface__)
|
|
assert_(allequal(y2.mask, 0))
|
|
|
|
y2a = array(x1, mask=m, copy=1)
|
|
assert_(y2a._data.__array_interface__ != x1.__array_interface__)
|
|
#assert_( y2a._mask is not m)
|
|
assert_(y2a._mask.__array_interface__ != m.__array_interface__)
|
|
assert_(y2a[2] is masked)
|
|
y2a[2] = 9
|
|
assert_(y2a[2] is not masked)
|
|
#assert_( y2a._mask is not m)
|
|
assert_(y2a._mask.__array_interface__ != m.__array_interface__)
|
|
assert_(allequal(y2a.mask, 0))
|
|
|
|
y3 = array(x1 * 1.0, mask=m)
|
|
assert_(filled(y3).dtype is (x1 * 1.0).dtype)
|
|
|
|
x4 = arange(4)
|
|
x4[2] = masked
|
|
y4 = resize(x4, (8,))
|
|
assert_equal(concatenate([x4, x4]), y4)
|
|
assert_equal(getmask(y4), [0, 0, 1, 0, 0, 0, 1, 0])
|
|
y5 = repeat(x4, (2, 2, 2, 2), axis=0)
|
|
assert_equal(y5, [0, 0, 1, 1, 2, 2, 3, 3])
|
|
y6 = repeat(x4, 2, axis=0)
|
|
assert_equal(y5, y6)
|
|
y7 = x4.repeat((2, 2, 2, 2), axis=0)
|
|
assert_equal(y5, y7)
|
|
y8 = x4.repeat(2, 0)
|
|
assert_equal(y5, y8)
|
|
|
|
y9 = x4.copy()
|
|
assert_equal(y9._data, x4._data)
|
|
assert_equal(y9._mask, x4._mask)
|
|
|
|
x = masked_array([1, 2, 3], mask=[0, 1, 0])
|
|
# Copy is False by default
|
|
y = masked_array(x)
|
|
assert_equal(y._data.ctypes.data, x._data.ctypes.data)
|
|
assert_equal(y._mask.ctypes.data, x._mask.ctypes.data)
|
|
y = masked_array(x, copy=True)
|
|
assert_not_equal(y._data.ctypes.data, x._data.ctypes.data)
|
|
assert_not_equal(y._mask.ctypes.data, x._mask.ctypes.data)
|
|
|
|
def test_copy_0d(self):
|
|
# gh-9430
|
|
x = np.ma.array(43, mask=True)
|
|
xc = x.copy()
|
|
assert_equal(xc.mask, True)
|
|
|
|
def test_copy_on_python_builtins(self):
|
|
# Tests copy works on python builtins (issue#8019)
|
|
assert_(isMaskedArray(np.ma.copy([1,2,3])))
|
|
assert_(isMaskedArray(np.ma.copy((1,2,3))))
|
|
|
|
def test_copy_immutable(self):
|
|
# Tests that the copy method is immutable, GitHub issue #5247
|
|
a = np.ma.array([1, 2, 3])
|
|
b = np.ma.array([4, 5, 6])
|
|
a_copy_method = a.copy
|
|
b.copy
|
|
assert_equal(a_copy_method(), [1, 2, 3])
|
|
|
|
def test_deepcopy(self):
|
|
from copy import deepcopy
|
|
a = array([0, 1, 2], mask=[False, True, False])
|
|
copied = deepcopy(a)
|
|
assert_equal(copied.mask, a.mask)
|
|
assert_not_equal(id(a._mask), id(copied._mask))
|
|
|
|
copied[1] = 1
|
|
assert_equal(copied.mask, [0, 0, 0])
|
|
assert_equal(a.mask, [0, 1, 0])
|
|
|
|
copied = deepcopy(a)
|
|
assert_equal(copied.mask, a.mask)
|
|
copied.mask[1] = False
|
|
assert_equal(copied.mask, [0, 0, 0])
|
|
assert_equal(a.mask, [0, 1, 0])
|
|
|
|
def test_format(self):
|
|
a = array([0, 1, 2], mask=[False, True, False])
|
|
assert_equal(format(a), "[0 -- 2]")
|
|
assert_equal(format(masked), "--")
|
|
assert_equal(format(masked, ""), "--")
|
|
|
|
# Postponed from PR #15410, perhaps address in the future.
|
|
# assert_equal(format(masked, " >5"), " --")
|
|
# assert_equal(format(masked, " <5"), "-- ")
|
|
|
|
# Expect a FutureWarning for using format_spec with MaskedElement
|
|
with assert_warns(FutureWarning):
|
|
with_format_string = format(masked, " >5")
|
|
assert_equal(with_format_string, "--")
|
|
|
|
def test_str_repr(self):
|
|
a = array([0, 1, 2], mask=[False, True, False])
|
|
assert_equal(str(a), '[0 -- 2]')
|
|
assert_equal(
|
|
repr(a),
|
|
textwrap.dedent('''\
|
|
masked_array(data=[0, --, 2],
|
|
mask=[False, True, False],
|
|
fill_value=999999)''')
|
|
)
|
|
|
|
# arrays with a continuation
|
|
a = np.ma.arange(2000)
|
|
a[1:50] = np.ma.masked
|
|
assert_equal(
|
|
repr(a),
|
|
textwrap.dedent('''\
|
|
masked_array(data=[0, --, --, ..., 1997, 1998, 1999],
|
|
mask=[False, True, True, ..., False, False, False],
|
|
fill_value=999999)''')
|
|
)
|
|
|
|
# line-wrapped 1d arrays are correctly aligned
|
|
a = np.ma.arange(20)
|
|
assert_equal(
|
|
repr(a),
|
|
textwrap.dedent('''\
|
|
masked_array(data=[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,
|
|
14, 15, 16, 17, 18, 19],
|
|
mask=False,
|
|
fill_value=999999)''')
|
|
)
|
|
|
|
# 2d arrays cause wrapping
|
|
a = array([[1, 2, 3], [4, 5, 6]], dtype=np.int8)
|
|
a[1,1] = np.ma.masked
|
|
assert_equal(
|
|
repr(a),
|
|
textwrap.dedent('''\
|
|
masked_array(
|
|
data=[[1, 2, 3],
|
|
[4, --, 6]],
|
|
mask=[[False, False, False],
|
|
[False, True, False]],
|
|
fill_value=999999,
|
|
dtype=int8)''')
|
|
)
|
|
|
|
# but not it they're a row vector
|
|
assert_equal(
|
|
repr(a[:1]),
|
|
textwrap.dedent('''\
|
|
masked_array(data=[[1, 2, 3]],
|
|
mask=[[False, False, False]],
|
|
fill_value=999999,
|
|
dtype=int8)''')
|
|
)
|
|
|
|
# dtype=int is implied, so not shown
|
|
assert_equal(
|
|
repr(a.astype(int)),
|
|
textwrap.dedent('''\
|
|
masked_array(
|
|
data=[[1, 2, 3],
|
|
[4, --, 6]],
|
|
mask=[[False, False, False],
|
|
[False, True, False]],
|
|
fill_value=999999)''')
|
|
)
|
|
|
|
def test_str_repr_legacy(self):
|
|
oldopts = np.get_printoptions()
|
|
np.set_printoptions(legacy='1.13')
|
|
try:
|
|
a = array([0, 1, 2], mask=[False, True, False])
|
|
assert_equal(str(a), '[0 -- 2]')
|
|
assert_equal(repr(a), 'masked_array(data = [0 -- 2],\n'
|
|
' mask = [False True False],\n'
|
|
' fill_value = 999999)\n')
|
|
|
|
a = np.ma.arange(2000)
|
|
a[1:50] = np.ma.masked
|
|
assert_equal(
|
|
repr(a),
|
|
'masked_array(data = [0 -- -- ..., 1997 1998 1999],\n'
|
|
' mask = [False True True ..., False False False],\n'
|
|
' fill_value = 999999)\n'
|
|
)
|
|
finally:
|
|
np.set_printoptions(**oldopts)
|
|
|
|
def test_0d_unicode(self):
|
|
u = 'caf\xe9'
|
|
utype = type(u)
|
|
|
|
arr_nomask = np.ma.array(u)
|
|
arr_masked = np.ma.array(u, mask=True)
|
|
|
|
assert_equal(utype(arr_nomask), u)
|
|
assert_equal(utype(arr_masked), '--')
|
|
|
|
def test_pickling(self):
|
|
# Tests pickling
|
|
for dtype in (int, float, str, object):
|
|
a = arange(10).astype(dtype)
|
|
a.fill_value = 999
|
|
|
|
masks = ([0, 0, 0, 1, 0, 1, 0, 1, 0, 1], # partially masked
|
|
True, # Fully masked
|
|
False) # Fully unmasked
|
|
|
|
for proto in range(2, pickle.HIGHEST_PROTOCOL + 1):
|
|
for mask in masks:
|
|
a.mask = mask
|
|
a_pickled = pickle.loads(pickle.dumps(a, protocol=proto))
|
|
assert_equal(a_pickled._mask, a._mask)
|
|
assert_equal(a_pickled._data, a._data)
|
|
if dtype in (object, int):
|
|
assert_equal(a_pickled.fill_value, 999)
|
|
else:
|
|
assert_equal(a_pickled.fill_value, dtype(999))
|
|
assert_array_equal(a_pickled.mask, mask)
|
|
|
|
def test_pickling_subbaseclass(self):
|
|
# Test pickling w/ a subclass of ndarray
|
|
x = np.array([(1.0, 2), (3.0, 4)],
|
|
dtype=[('x', float), ('y', int)]).view(np.recarray)
|
|
a = masked_array(x, mask=[(True, False), (False, True)])
|
|
for proto in range(2, pickle.HIGHEST_PROTOCOL + 1):
|
|
a_pickled = pickle.loads(pickle.dumps(a, protocol=proto))
|
|
assert_equal(a_pickled._mask, a._mask)
|
|
assert_equal(a_pickled, a)
|
|
assert_(isinstance(a_pickled._data, np.recarray))
|
|
|
|
def test_pickling_maskedconstant(self):
|
|
# Test pickling MaskedConstant
|
|
mc = np.ma.masked
|
|
for proto in range(2, pickle.HIGHEST_PROTOCOL + 1):
|
|
mc_pickled = pickle.loads(pickle.dumps(mc, protocol=proto))
|
|
assert_equal(mc_pickled._baseclass, mc._baseclass)
|
|
assert_equal(mc_pickled._mask, mc._mask)
|
|
assert_equal(mc_pickled._data, mc._data)
|
|
|
|
def test_pickling_wstructured(self):
|
|
# Tests pickling w/ structured array
|
|
a = array([(1, 1.), (2, 2.)], mask=[(0, 0), (0, 1)],
|
|
dtype=[('a', int), ('b', float)])
|
|
for proto in range(2, pickle.HIGHEST_PROTOCOL + 1):
|
|
a_pickled = pickle.loads(pickle.dumps(a, protocol=proto))
|
|
assert_equal(a_pickled._mask, a._mask)
|
|
assert_equal(a_pickled, a)
|
|
|
|
def test_pickling_keepalignment(self):
|
|
# Tests pickling w/ F_CONTIGUOUS arrays
|
|
a = arange(10)
|
|
a.shape = (-1, 2)
|
|
b = a.T
|
|
for proto in range(2, pickle.HIGHEST_PROTOCOL + 1):
|
|
test = pickle.loads(pickle.dumps(b, protocol=proto))
|
|
assert_equal(test, b)
|
|
|
|
def test_single_element_subscript(self):
|
|
# Tests single element subscripts of Maskedarrays.
|
|
a = array([1, 3, 2])
|
|
b = array([1, 3, 2], mask=[1, 0, 1])
|
|
assert_equal(a[0].shape, ())
|
|
assert_equal(b[0].shape, ())
|
|
assert_equal(b[1].shape, ())
|
|
|
|
def test_topython(self):
|
|
# Tests some communication issues with Python.
|
|
assert_equal(1, int(array(1)))
|
|
assert_equal(1.0, float(array(1)))
|
|
assert_equal(1, int(array([[[1]]])))
|
|
assert_equal(1.0, float(array([[1]])))
|
|
assert_raises(TypeError, float, array([1, 1]))
|
|
|
|
with suppress_warnings() as sup:
|
|
sup.filter(UserWarning, 'Warning: converting a masked element')
|
|
assert_(np.isnan(float(array([1], mask=[1]))))
|
|
|
|
a = array([1, 2, 3], mask=[1, 0, 0])
|
|
assert_raises(TypeError, lambda: float(a))
|
|
assert_equal(float(a[-1]), 3.)
|
|
assert_(np.isnan(float(a[0])))
|
|
assert_raises(TypeError, int, a)
|
|
assert_equal(int(a[-1]), 3)
|
|
assert_raises(MAError, lambda:int(a[0]))
|
|
|
|
def test_oddfeatures_1(self):
|
|
# Test of other odd features
|
|
x = arange(20)
|
|
x = x.reshape(4, 5)
|
|
x.flat[5] = 12
|
|
assert_(x[1, 0] == 12)
|
|
z = x + 10j * x
|
|
assert_equal(z.real, x)
|
|
assert_equal(z.imag, 10 * x)
|
|
assert_equal((z * conjugate(z)).real, 101 * x * x)
|
|
z.imag[...] = 0.0
|
|
|
|
x = arange(10)
|
|
x[3] = masked
|
|
assert_(str(x[3]) == str(masked))
|
|
c = x >= 8
|
|
assert_(count(where(c, masked, masked)) == 0)
|
|
assert_(shape(where(c, masked, masked)) == c.shape)
|
|
|
|
z = masked_where(c, x)
|
|
assert_(z.dtype is x.dtype)
|
|
assert_(z[3] is masked)
|
|
assert_(z[4] is not masked)
|
|
assert_(z[7] is not masked)
|
|
assert_(z[8] is masked)
|
|
assert_(z[9] is masked)
|
|
assert_equal(x, z)
|
|
|
|
def test_oddfeatures_2(self):
|
|
# Tests some more features.
|
|
x = array([1., 2., 3., 4., 5.])
|
|
c = array([1, 1, 1, 0, 0])
|
|
x[2] = masked
|
|
z = where(c, x, -x)
|
|
assert_equal(z, [1., 2., 0., -4., -5])
|
|
c[0] = masked
|
|
z = where(c, x, -x)
|
|
assert_equal(z, [1., 2., 0., -4., -5])
|
|
assert_(z[0] is masked)
|
|
assert_(z[1] is not masked)
|
|
assert_(z[2] is masked)
|
|
|
|
@suppress_copy_mask_on_assignment
|
|
def test_oddfeatures_3(self):
|
|
# Tests some generic features
|
|
atest = array([10], mask=True)
|
|
btest = array([20])
|
|
idx = atest.mask
|
|
atest[idx] = btest[idx]
|
|
assert_equal(atest, [20])
|
|
|
|
def test_filled_with_object_dtype(self):
|
|
a = np.ma.masked_all(1, dtype='O')
|
|
assert_equal(a.filled('x')[0], 'x')
|
|
|
|
def test_filled_with_flexible_dtype(self):
|
|
# Test filled w/ flexible dtype
|
|
flexi = array([(1, 1, 1)],
|
|
dtype=[('i', int), ('s', '|S8'), ('f', float)])
|
|
flexi[0] = masked
|
|
assert_equal(flexi.filled(),
|
|
np.array([(default_fill_value(0),
|
|
default_fill_value('0'),
|
|
default_fill_value(0.),)], dtype=flexi.dtype))
|
|
flexi[0] = masked
|
|
assert_equal(flexi.filled(1),
|
|
np.array([(1, '1', 1.)], dtype=flexi.dtype))
|
|
|
|
def test_filled_with_mvoid(self):
|
|
# Test filled w/ mvoid
|
|
ndtype = [('a', int), ('b', float)]
|
|
a = mvoid((1, 2.), mask=[(0, 1)], dtype=ndtype)
|
|
# Filled using default
|
|
test = a.filled()
|
|
assert_equal(tuple(test), (1, default_fill_value(1.)))
|
|
# Explicit fill_value
|
|
test = a.filled((-1, -1))
|
|
assert_equal(tuple(test), (1, -1))
|
|
# Using predefined filling values
|
|
a.fill_value = (-999, -999)
|
|
assert_equal(tuple(a.filled()), (1, -999))
|
|
|
|
def test_filled_with_nested_dtype(self):
|
|
# Test filled w/ nested dtype
|
|
ndtype = [('A', int), ('B', [('BA', int), ('BB', int)])]
|
|
a = array([(1, (1, 1)), (2, (2, 2))],
|
|
mask=[(0, (1, 0)), (0, (0, 1))], dtype=ndtype)
|
|
test = a.filled(0)
|
|
control = np.array([(1, (0, 1)), (2, (2, 0))], dtype=ndtype)
|
|
assert_equal(test, control)
|
|
|
|
test = a['B'].filled(0)
|
|
control = np.array([(0, 1), (2, 0)], dtype=a['B'].dtype)
|
|
assert_equal(test, control)
|
|
|
|
# test if mask gets set correctly (see #6760)
|
|
Z = numpy.ma.zeros(2, numpy.dtype([("A", "(2,2)i1,(2,2)i1", (2,2))]))
|
|
assert_equal(Z.data.dtype, numpy.dtype([('A', [('f0', 'i1', (2, 2)),
|
|
('f1', 'i1', (2, 2))], (2, 2))]))
|
|
assert_equal(Z.mask.dtype, numpy.dtype([('A', [('f0', '?', (2, 2)),
|
|
('f1', '?', (2, 2))], (2, 2))]))
|
|
|
|
def test_filled_with_f_order(self):
|
|
# Test filled w/ F-contiguous array
|
|
a = array(np.array([(0, 1, 2), (4, 5, 6)], order='F'),
|
|
mask=np.array([(0, 0, 1), (1, 0, 0)], order='F'),
|
|
order='F') # this is currently ignored
|
|
assert_(a.flags['F_CONTIGUOUS'])
|
|
assert_(a.filled(0).flags['F_CONTIGUOUS'])
|
|
|
|
def test_optinfo_propagation(self):
|
|
# Checks that _optinfo dictionary isn't back-propagated
|
|
x = array([1, 2, 3, ], dtype=float)
|
|
x._optinfo['info'] = '???'
|
|
y = x.copy()
|
|
assert_equal(y._optinfo['info'], '???')
|
|
y._optinfo['info'] = '!!!'
|
|
assert_equal(x._optinfo['info'], '???')
|
|
|
|
def test_optinfo_forward_propagation(self):
|
|
a = array([1,2,2,4])
|
|
a._optinfo["key"] = "value"
|
|
assert_equal(a._optinfo["key"], (a == 2)._optinfo["key"])
|
|
assert_equal(a._optinfo["key"], (a != 2)._optinfo["key"])
|
|
assert_equal(a._optinfo["key"], (a > 2)._optinfo["key"])
|
|
assert_equal(a._optinfo["key"], (a >= 2)._optinfo["key"])
|
|
assert_equal(a._optinfo["key"], (a <= 2)._optinfo["key"])
|
|
assert_equal(a._optinfo["key"], (a + 2)._optinfo["key"])
|
|
assert_equal(a._optinfo["key"], (a - 2)._optinfo["key"])
|
|
assert_equal(a._optinfo["key"], (a * 2)._optinfo["key"])
|
|
assert_equal(a._optinfo["key"], (a / 2)._optinfo["key"])
|
|
assert_equal(a._optinfo["key"], a[:2]._optinfo["key"])
|
|
assert_equal(a._optinfo["key"], a[[0,0,2]]._optinfo["key"])
|
|
assert_equal(a._optinfo["key"], np.exp(a)._optinfo["key"])
|
|
assert_equal(a._optinfo["key"], np.abs(a)._optinfo["key"])
|
|
assert_equal(a._optinfo["key"], array(a, copy=True)._optinfo["key"])
|
|
assert_equal(a._optinfo["key"], np.zeros_like(a)._optinfo["key"])
|
|
|
|
def test_fancy_printoptions(self):
|
|
# Test printing a masked array w/ fancy dtype.
|
|
fancydtype = np.dtype([('x', int), ('y', [('t', int), ('s', float)])])
|
|
test = array([(1, (2, 3.0)), (4, (5, 6.0))],
|
|
mask=[(1, (0, 1)), (0, (1, 0))],
|
|
dtype=fancydtype)
|
|
control = "[(--, (2, --)) (4, (--, 6.0))]"
|
|
assert_equal(str(test), control)
|
|
|
|
# Test 0-d array with multi-dimensional dtype
|
|
t_2d0 = masked_array(data = (0, [[0.0, 0.0, 0.0],
|
|
[0.0, 0.0, 0.0]],
|
|
0.0),
|
|
mask = (False, [[True, False, True],
|
|
[False, False, True]],
|
|
False),
|
|
dtype = "int, (2,3)float, float")
|
|
control = "(0, [[--, 0.0, --], [0.0, 0.0, --]], 0.0)"
|
|
assert_equal(str(t_2d0), control)
|
|
|
|
def test_flatten_structured_array(self):
|
|
# Test flatten_structured_array on arrays
|
|
# On ndarray
|
|
ndtype = [('a', int), ('b', float)]
|
|
a = np.array([(1, 1), (2, 2)], dtype=ndtype)
|
|
test = flatten_structured_array(a)
|
|
control = np.array([[1., 1.], [2., 2.]], dtype=float)
|
|
assert_equal(test, control)
|
|
assert_equal(test.dtype, control.dtype)
|
|
# On masked_array
|
|
a = array([(1, 1), (2, 2)], mask=[(0, 1), (1, 0)], dtype=ndtype)
|
|
test = flatten_structured_array(a)
|
|
control = array([[1., 1.], [2., 2.]],
|
|
mask=[[0, 1], [1, 0]], dtype=float)
|
|
assert_equal(test, control)
|
|
assert_equal(test.dtype, control.dtype)
|
|
assert_equal(test.mask, control.mask)
|
|
# On masked array with nested structure
|
|
ndtype = [('a', int), ('b', [('ba', int), ('bb', float)])]
|
|
a = array([(1, (1, 1.1)), (2, (2, 2.2))],
|
|
mask=[(0, (1, 0)), (1, (0, 1))], dtype=ndtype)
|
|
test = flatten_structured_array(a)
|
|
control = array([[1., 1., 1.1], [2., 2., 2.2]],
|
|
mask=[[0, 1, 0], [1, 0, 1]], dtype=float)
|
|
assert_equal(test, control)
|
|
assert_equal(test.dtype, control.dtype)
|
|
assert_equal(test.mask, control.mask)
|
|
# Keeping the initial shape
|
|
ndtype = [('a', int), ('b', float)]
|
|
a = np.array([[(1, 1), ], [(2, 2), ]], dtype=ndtype)
|
|
test = flatten_structured_array(a)
|
|
control = np.array([[[1., 1.], ], [[2., 2.], ]], dtype=float)
|
|
assert_equal(test, control)
|
|
assert_equal(test.dtype, control.dtype)
|
|
|
|
def test_void0d(self):
|
|
# Test creating a mvoid object
|
|
ndtype = [('a', int), ('b', int)]
|
|
a = np.array([(1, 2,)], dtype=ndtype)[0]
|
|
f = mvoid(a)
|
|
assert_(isinstance(f, mvoid))
|
|
|
|
a = masked_array([(1, 2)], mask=[(1, 0)], dtype=ndtype)[0]
|
|
assert_(isinstance(a, mvoid))
|
|
|
|
a = masked_array([(1, 2), (1, 2)], mask=[(1, 0), (0, 0)], dtype=ndtype)
|
|
f = mvoid(a._data[0], a._mask[0])
|
|
assert_(isinstance(f, mvoid))
|
|
|
|
def test_mvoid_getitem(self):
|
|
# Test mvoid.__getitem__
|
|
ndtype = [('a', int), ('b', int)]
|
|
a = masked_array([(1, 2,), (3, 4)], mask=[(0, 0), (1, 0)],
|
|
dtype=ndtype)
|
|
# w/o mask
|
|
f = a[0]
|
|
assert_(isinstance(f, mvoid))
|
|
assert_equal((f[0], f['a']), (1, 1))
|
|
assert_equal(f['b'], 2)
|
|
# w/ mask
|
|
f = a[1]
|
|
assert_(isinstance(f, mvoid))
|
|
assert_(f[0] is masked)
|
|
assert_(f['a'] is masked)
|
|
assert_equal(f[1], 4)
|
|
|
|
# exotic dtype
|
|
A = masked_array(data=[([0,1],)],
|
|
mask=[([True, False],)],
|
|
dtype=[("A", ">i2", (2,))])
|
|
assert_equal(A[0]["A"], A["A"][0])
|
|
assert_equal(A[0]["A"], masked_array(data=[0, 1],
|
|
mask=[True, False], dtype=">i2"))
|
|
|
|
def test_mvoid_iter(self):
|
|
# Test iteration on __getitem__
|
|
ndtype = [('a', int), ('b', int)]
|
|
a = masked_array([(1, 2,), (3, 4)], mask=[(0, 0), (1, 0)],
|
|
dtype=ndtype)
|
|
# w/o mask
|
|
assert_equal(list(a[0]), [1, 2])
|
|
# w/ mask
|
|
assert_equal(list(a[1]), [masked, 4])
|
|
|
|
def test_mvoid_print(self):
|
|
# Test printing a mvoid
|
|
mx = array([(1, 1), (2, 2)], dtype=[('a', int), ('b', int)])
|
|
assert_equal(str(mx[0]), "(1, 1)")
|
|
mx['b'][0] = masked
|
|
ini_display = masked_print_option._display
|
|
masked_print_option.set_display("-X-")
|
|
try:
|
|
assert_equal(str(mx[0]), "(1, -X-)")
|
|
assert_equal(repr(mx[0]), "(1, -X-)")
|
|
finally:
|
|
masked_print_option.set_display(ini_display)
|
|
|
|
# also check if there are object datatypes (see gh-7493)
|
|
mx = array([(1,), (2,)], dtype=[('a', 'O')])
|
|
assert_equal(str(mx[0]), "(1,)")
|
|
|
|
def test_mvoid_multidim_print(self):
|
|
|
|
# regression test for gh-6019
|
|
t_ma = masked_array(data = [([1, 2, 3],)],
|
|
mask = [([False, True, False],)],
|
|
fill_value = ([999999, 999999, 999999],),
|
|
dtype = [('a', '<i4', (3,))])
|
|
assert_(str(t_ma[0]) == "([1, --, 3],)")
|
|
assert_(repr(t_ma[0]) == "([1, --, 3],)")
|
|
|
|
# additional tests with structured arrays
|
|
|
|
t_2d = masked_array(data = [([[1, 2], [3,4]],)],
|
|
mask = [([[False, True], [True, False]],)],
|
|
dtype = [('a', '<i4', (2,2))])
|
|
assert_(str(t_2d[0]) == "([[1, --], [--, 4]],)")
|
|
assert_(repr(t_2d[0]) == "([[1, --], [--, 4]],)")
|
|
|
|
t_0d = masked_array(data = [(1,2)],
|
|
mask = [(True,False)],
|
|
dtype = [('a', '<i4'), ('b', '<i4')])
|
|
assert_(str(t_0d[0]) == "(--, 2)")
|
|
assert_(repr(t_0d[0]) == "(--, 2)")
|
|
|
|
t_2d = masked_array(data = [([[1, 2], [3,4]], 1)],
|
|
mask = [([[False, True], [True, False]], False)],
|
|
dtype = [('a', '<i4', (2,2)), ('b', float)])
|
|
assert_(str(t_2d[0]) == "([[1, --], [--, 4]], 1.0)")
|
|
assert_(repr(t_2d[0]) == "([[1, --], [--, 4]], 1.0)")
|
|
|
|
t_ne = masked_array(data=[(1, (1, 1))],
|
|
mask=[(True, (True, False))],
|
|
dtype = [('a', '<i4'), ('b', 'i4,i4')])
|
|
assert_(str(t_ne[0]) == "(--, (--, 1))")
|
|
assert_(repr(t_ne[0]) == "(--, (--, 1))")
|
|
|
|
def test_object_with_array(self):
|
|
mx1 = masked_array([1.], mask=[True])
|
|
mx2 = masked_array([1., 2.])
|
|
mx = masked_array([mx1, mx2], mask=[False, True], dtype=object)
|
|
assert_(mx[0] is mx1)
|
|
assert_(mx[1] is not mx2)
|
|
assert_(np.all(mx[1].data == mx2.data))
|
|
assert_(np.all(mx[1].mask))
|
|
# check that we return a view.
|
|
mx[1].data[0] = 0.
|
|
assert_(mx2[0] == 0.)
|
|
|
|
|
|
class TestMaskedArrayArithmetic:
|
|
# Base test class for MaskedArrays.
|
|
|
|
def setup_method(self):
|
|
# Base data definition.
|
|
x = np.array([1., 1., 1., -2., pi/2.0, 4., 5., -10., 10., 1., 2., 3.])
|
|
y = np.array([5., 0., 3., 2., -1., -4., 0., -10., 10., 1., 0., 3.])
|
|
a10 = 10.
|
|
m1 = [1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0]
|
|
m2 = [0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1]
|
|
xm = masked_array(x, mask=m1)
|
|
ym = masked_array(y, mask=m2)
|
|
z = np.array([-.5, 0., .5, .8])
|
|
zm = masked_array(z, mask=[0, 1, 0, 0])
|
|
xf = np.where(m1, 1e+20, x)
|
|
xm.set_fill_value(1e+20)
|
|
self.d = (x, y, a10, m1, m2, xm, ym, z, zm, xf)
|
|
self.err_status = np.geterr()
|
|
np.seterr(divide='ignore', invalid='ignore')
|
|
|
|
def teardown_method(self):
|
|
np.seterr(**self.err_status)
|
|
|
|
def test_basic_arithmetic(self):
|
|
# Test of basic arithmetic.
|
|
(x, y, a10, m1, m2, xm, ym, z, zm, xf) = self.d
|
|
a2d = array([[1, 2], [0, 4]])
|
|
a2dm = masked_array(a2d, [[0, 0], [1, 0]])
|
|
assert_equal(a2d * a2d, a2d * a2dm)
|
|
assert_equal(a2d + a2d, a2d + a2dm)
|
|
assert_equal(a2d - a2d, a2d - a2dm)
|
|
for s in [(12,), (4, 3), (2, 6)]:
|
|
x = x.reshape(s)
|
|
y = y.reshape(s)
|
|
xm = xm.reshape(s)
|
|
ym = ym.reshape(s)
|
|
xf = xf.reshape(s)
|
|
assert_equal(-x, -xm)
|
|
assert_equal(x + y, xm + ym)
|
|
assert_equal(x - y, xm - ym)
|
|
assert_equal(x * y, xm * ym)
|
|
assert_equal(x / y, xm / ym)
|
|
assert_equal(a10 + y, a10 + ym)
|
|
assert_equal(a10 - y, a10 - ym)
|
|
assert_equal(a10 * y, a10 * ym)
|
|
assert_equal(a10 / y, a10 / ym)
|
|
assert_equal(x + a10, xm + a10)
|
|
assert_equal(x - a10, xm - a10)
|
|
assert_equal(x * a10, xm * a10)
|
|
assert_equal(x / a10, xm / a10)
|
|
assert_equal(x ** 2, xm ** 2)
|
|
assert_equal(abs(x) ** 2.5, abs(xm) ** 2.5)
|
|
assert_equal(x ** y, xm ** ym)
|
|
assert_equal(np.add(x, y), add(xm, ym))
|
|
assert_equal(np.subtract(x, y), subtract(xm, ym))
|
|
assert_equal(np.multiply(x, y), multiply(xm, ym))
|
|
assert_equal(np.divide(x, y), divide(xm, ym))
|
|
|
|
def test_divide_on_different_shapes(self):
|
|
x = arange(6, dtype=float)
|
|
x.shape = (2, 3)
|
|
y = arange(3, dtype=float)
|
|
|
|
z = x / y
|
|
assert_equal(z, [[-1., 1., 1.], [-1., 4., 2.5]])
|
|
assert_equal(z.mask, [[1, 0, 0], [1, 0, 0]])
|
|
|
|
z = x / y[None,:]
|
|
assert_equal(z, [[-1., 1., 1.], [-1., 4., 2.5]])
|
|
assert_equal(z.mask, [[1, 0, 0], [1, 0, 0]])
|
|
|
|
y = arange(2, dtype=float)
|
|
z = x / y[:, None]
|
|
assert_equal(z, [[-1., -1., -1.], [3., 4., 5.]])
|
|
assert_equal(z.mask, [[1, 1, 1], [0, 0, 0]])
|
|
|
|
def test_mixed_arithmetic(self):
|
|
# Tests mixed arithmetic.
|
|
na = np.array([1])
|
|
ma = array([1])
|
|
assert_(isinstance(na + ma, MaskedArray))
|
|
assert_(isinstance(ma + na, MaskedArray))
|
|
|
|
def test_limits_arithmetic(self):
|
|
tiny = np.finfo(float).tiny
|
|
a = array([tiny, 1. / tiny, 0.])
|
|
assert_equal(getmaskarray(a / 2), [0, 0, 0])
|
|
assert_equal(getmaskarray(2 / a), [1, 0, 1])
|
|
|
|
def test_masked_singleton_arithmetic(self):
|
|
# Tests some scalar arithmetic on MaskedArrays.
|
|
# Masked singleton should remain masked no matter what
|
|
xm = array(0, mask=1)
|
|
assert_((1 / array(0)).mask)
|
|
assert_((1 + xm).mask)
|
|
assert_((-xm).mask)
|
|
assert_(maximum(xm, xm).mask)
|
|
assert_(minimum(xm, xm).mask)
|
|
|
|
def test_masked_singleton_equality(self):
|
|
# Tests (in)equality on masked singleton
|
|
a = array([1, 2, 3], mask=[1, 1, 0])
|
|
assert_((a[0] == 0) is masked)
|
|
assert_((a[0] != 0) is masked)
|
|
assert_equal((a[-1] == 0), False)
|
|
assert_equal((a[-1] != 0), True)
|
|
|
|
def test_arithmetic_with_masked_singleton(self):
|
|
# Checks that there's no collapsing to masked
|
|
x = masked_array([1, 2])
|
|
y = x * masked
|
|
assert_equal(y.shape, x.shape)
|
|
assert_equal(y._mask, [True, True])
|
|
y = x[0] * masked
|
|
assert_(y is masked)
|
|
y = x + masked
|
|
assert_equal(y.shape, x.shape)
|
|
assert_equal(y._mask, [True, True])
|
|
|
|
def test_arithmetic_with_masked_singleton_on_1d_singleton(self):
|
|
# Check that we're not losing the shape of a singleton
|
|
x = masked_array([1, ])
|
|
y = x + masked
|
|
assert_equal(y.shape, x.shape)
|
|
assert_equal(y.mask, [True, ])
|
|
|
|
def test_scalar_arithmetic(self):
|
|
x = array(0, mask=0)
|
|
assert_equal(x.filled().ctypes.data, x.ctypes.data)
|
|
# Make sure we don't lose the shape in some circumstances
|
|
xm = array((0, 0)) / 0.
|
|
assert_equal(xm.shape, (2,))
|
|
assert_equal(xm.mask, [1, 1])
|
|
|
|
def test_basic_ufuncs(self):
|
|
# Test various functions such as sin, cos.
|
|
(x, y, a10, m1, m2, xm, ym, z, zm, xf) = self.d
|
|
assert_equal(np.cos(x), cos(xm))
|
|
assert_equal(np.cosh(x), cosh(xm))
|
|
assert_equal(np.sin(x), sin(xm))
|
|
assert_equal(np.sinh(x), sinh(xm))
|
|
assert_equal(np.tan(x), tan(xm))
|
|
assert_equal(np.tanh(x), tanh(xm))
|
|
assert_equal(np.sqrt(abs(x)), sqrt(xm))
|
|
assert_equal(np.log(abs(x)), log(xm))
|
|
assert_equal(np.log10(abs(x)), log10(xm))
|
|
assert_equal(np.exp(x), exp(xm))
|
|
assert_equal(np.arcsin(z), arcsin(zm))
|
|
assert_equal(np.arccos(z), arccos(zm))
|
|
assert_equal(np.arctan(z), arctan(zm))
|
|
assert_equal(np.arctan2(x, y), arctan2(xm, ym))
|
|
assert_equal(np.absolute(x), absolute(xm))
|
|
assert_equal(np.angle(x + 1j*y), angle(xm + 1j*ym))
|
|
assert_equal(np.angle(x + 1j*y, deg=True), angle(xm + 1j*ym, deg=True))
|
|
assert_equal(np.equal(x, y), equal(xm, ym))
|
|
assert_equal(np.not_equal(x, y), not_equal(xm, ym))
|
|
assert_equal(np.less(x, y), less(xm, ym))
|
|
assert_equal(np.greater(x, y), greater(xm, ym))
|
|
assert_equal(np.less_equal(x, y), less_equal(xm, ym))
|
|
assert_equal(np.greater_equal(x, y), greater_equal(xm, ym))
|
|
assert_equal(np.conjugate(x), conjugate(xm))
|
|
|
|
def test_count_func(self):
|
|
# Tests count
|
|
assert_equal(1, count(1))
|
|
assert_equal(0, array(1, mask=[1]))
|
|
|
|
ott = array([0., 1., 2., 3.], mask=[1, 0, 0, 0])
|
|
res = count(ott)
|
|
assert_(res.dtype.type is np.intp)
|
|
assert_equal(3, res)
|
|
|
|
ott = ott.reshape((2, 2))
|
|
res = count(ott)
|
|
assert_(res.dtype.type is np.intp)
|
|
assert_equal(3, res)
|
|
res = count(ott, 0)
|
|
assert_(isinstance(res, ndarray))
|
|
assert_equal([1, 2], res)
|
|
assert_(getmask(res) is nomask)
|
|
|
|
ott = array([0., 1., 2., 3.])
|
|
res = count(ott, 0)
|
|
assert_(isinstance(res, ndarray))
|
|
assert_(res.dtype.type is np.intp)
|
|
assert_raises(np.AxisError, ott.count, axis=1)
|
|
|
|
def test_count_on_python_builtins(self):
|
|
# Tests count works on python builtins (issue#8019)
|
|
assert_equal(3, count([1,2,3]))
|
|
assert_equal(2, count((1,2)))
|
|
|
|
def test_minmax_func(self):
|
|
# Tests minimum and maximum.
|
|
(x, y, a10, m1, m2, xm, ym, z, zm, xf) = self.d
|
|
# max doesn't work if shaped
|
|
xr = np.ravel(x)
|
|
xmr = ravel(xm)
|
|
# following are true because of careful selection of data
|
|
assert_equal(max(xr), maximum.reduce(xmr))
|
|
assert_equal(min(xr), minimum.reduce(xmr))
|
|
|
|
assert_equal(minimum([1, 2, 3], [4, 0, 9]), [1, 0, 3])
|
|
assert_equal(maximum([1, 2, 3], [4, 0, 9]), [4, 2, 9])
|
|
x = arange(5)
|
|
y = arange(5) - 2
|
|
x[3] = masked
|
|
y[0] = masked
|
|
assert_equal(minimum(x, y), where(less(x, y), x, y))
|
|
assert_equal(maximum(x, y), where(greater(x, y), x, y))
|
|
assert_(minimum.reduce(x) == 0)
|
|
assert_(maximum.reduce(x) == 4)
|
|
|
|
x = arange(4).reshape(2, 2)
|
|
x[-1, -1] = masked
|
|
assert_equal(maximum.reduce(x, axis=None), 2)
|
|
|
|
def test_minimummaximum_func(self):
|
|
a = np.ones((2, 2))
|
|
aminimum = minimum(a, a)
|
|
assert_(isinstance(aminimum, MaskedArray))
|
|
assert_equal(aminimum, np.minimum(a, a))
|
|
|
|
aminimum = minimum.outer(a, a)
|
|
assert_(isinstance(aminimum, MaskedArray))
|
|
assert_equal(aminimum, np.minimum.outer(a, a))
|
|
|
|
amaximum = maximum(a, a)
|
|
assert_(isinstance(amaximum, MaskedArray))
|
|
assert_equal(amaximum, np.maximum(a, a))
|
|
|
|
amaximum = maximum.outer(a, a)
|
|
assert_(isinstance(amaximum, MaskedArray))
|
|
assert_equal(amaximum, np.maximum.outer(a, a))
|
|
|
|
def test_minmax_reduce(self):
|
|
# Test np.min/maximum.reduce on array w/ full False mask
|
|
a = array([1, 2, 3], mask=[False, False, False])
|
|
b = np.maximum.reduce(a)
|
|
assert_equal(b, 3)
|
|
|
|
def test_minmax_funcs_with_output(self):
|
|
# Tests the min/max functions with explicit outputs
|
|
mask = np.random.rand(12).round()
|
|
xm = array(np.random.uniform(0, 10, 12), mask=mask)
|
|
xm.shape = (3, 4)
|
|
for funcname in ('min', 'max'):
|
|
# Initialize
|
|
npfunc = getattr(np, funcname)
|
|
mafunc = getattr(numpy.ma.core, funcname)
|
|
# Use the np version
|
|
nout = np.empty((4,), dtype=int)
|
|
try:
|
|
result = npfunc(xm, axis=0, out=nout)
|
|
except MaskError:
|
|
pass
|
|
nout = np.empty((4,), dtype=float)
|
|
result = npfunc(xm, axis=0, out=nout)
|
|
assert_(result is nout)
|
|
# Use the ma version
|
|
nout.fill(-999)
|
|
result = mafunc(xm, axis=0, out=nout)
|
|
assert_(result is nout)
|
|
|
|
def test_minmax_methods(self):
|
|
# Additional tests on max/min
|
|
(_, _, _, _, _, xm, _, _, _, _) = self.d
|
|
xm.shape = (xm.size,)
|
|
assert_equal(xm.max(), 10)
|
|
assert_(xm[0].max() is masked)
|
|
assert_(xm[0].max(0) is masked)
|
|
assert_(xm[0].max(-1) is masked)
|
|
assert_equal(xm.min(), -10.)
|
|
assert_(xm[0].min() is masked)
|
|
assert_(xm[0].min(0) is masked)
|
|
assert_(xm[0].min(-1) is masked)
|
|
assert_equal(xm.ptp(), 20.)
|
|
assert_(xm[0].ptp() is masked)
|
|
assert_(xm[0].ptp(0) is masked)
|
|
assert_(xm[0].ptp(-1) is masked)
|
|
|
|
x = array([1, 2, 3], mask=True)
|
|
assert_(x.min() is masked)
|
|
assert_(x.max() is masked)
|
|
assert_(x.ptp() is masked)
|
|
|
|
def test_minmax_dtypes(self):
|
|
# Additional tests on max/min for non-standard float and complex dtypes
|
|
x = np.array([1., 1., 1., -2., pi/2.0, 4., 5., -10., 10., 1., 2., 3.])
|
|
a10 = 10.
|
|
an10 = -10.0
|
|
m1 = [1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0]
|
|
xm = masked_array(x, mask=m1)
|
|
xm.set_fill_value(1e+20)
|
|
float_dtypes = [np.float16, np.float32, np.float64, np.longdouble,
|
|
np.complex64, np.complex128, np.clongdouble]
|
|
for float_dtype in float_dtypes:
|
|
assert_equal(masked_array(x, mask=m1, dtype=float_dtype).max(),
|
|
float_dtype(a10))
|
|
assert_equal(masked_array(x, mask=m1, dtype=float_dtype).min(),
|
|
float_dtype(an10))
|
|
|
|
assert_equal(xm.min(), an10)
|
|
assert_equal(xm.max(), a10)
|
|
|
|
# Non-complex type only test
|
|
for float_dtype in float_dtypes[:4]:
|
|
assert_equal(masked_array(x, mask=m1, dtype=float_dtype).max(),
|
|
float_dtype(a10))
|
|
assert_equal(masked_array(x, mask=m1, dtype=float_dtype).min(),
|
|
float_dtype(an10))
|
|
|
|
# Complex types only test
|
|
for float_dtype in float_dtypes[-3:]:
|
|
ym = masked_array([1e20+1j, 1e20-2j, 1e20-1j], mask=[0, 1, 0],
|
|
dtype=float_dtype)
|
|
assert_equal(ym.min(), float_dtype(1e20-1j))
|
|
assert_equal(ym.max(), float_dtype(1e20+1j))
|
|
|
|
zm = masked_array([np.inf+2j, np.inf+3j, -np.inf-1j], mask=[0, 1, 0],
|
|
dtype=float_dtype)
|
|
assert_equal(zm.min(), float_dtype(-np.inf-1j))
|
|
assert_equal(zm.max(), float_dtype(np.inf+2j))
|
|
|
|
cmax = np.inf - 1j * np.finfo(np.float64).max
|
|
assert masked_array([-cmax, 0], mask=[0, 1]).max() == -cmax
|
|
assert masked_array([cmax, 0], mask=[0, 1]).min() == cmax
|
|
|
|
def test_addsumprod(self):
|
|
# Tests add, sum, product.
|
|
(x, y, a10, m1, m2, xm, ym, z, zm, xf) = self.d
|
|
assert_equal(np.add.reduce(x), add.reduce(x))
|
|
assert_equal(np.add.accumulate(x), add.accumulate(x))
|
|
assert_equal(4, sum(array(4), axis=0))
|
|
assert_equal(4, sum(array(4), axis=0))
|
|
assert_equal(np.sum(x, axis=0), sum(x, axis=0))
|
|
assert_equal(np.sum(filled(xm, 0), axis=0), sum(xm, axis=0))
|
|
assert_equal(np.sum(x, 0), sum(x, 0))
|
|
assert_equal(np.prod(x, axis=0), product(x, axis=0))
|
|
assert_equal(np.prod(x, 0), product(x, 0))
|
|
assert_equal(np.prod(filled(xm, 1), axis=0), product(xm, axis=0))
|
|
s = (3, 4)
|
|
x.shape = y.shape = xm.shape = ym.shape = s
|
|
if len(s) > 1:
|
|
assert_equal(np.concatenate((x, y), 1), concatenate((xm, ym), 1))
|
|
assert_equal(np.add.reduce(x, 1), add.reduce(x, 1))
|
|
assert_equal(np.sum(x, 1), sum(x, 1))
|
|
assert_equal(np.prod(x, 1), product(x, 1))
|
|
|
|
def test_binops_d2D(self):
|
|
# Test binary operations on 2D data
|
|
a = array([[1.], [2.], [3.]], mask=[[False], [True], [True]])
|
|
b = array([[2., 3.], [4., 5.], [6., 7.]])
|
|
|
|
test = a * b
|
|
control = array([[2., 3.], [2., 2.], [3., 3.]],
|
|
mask=[[0, 0], [1, 1], [1, 1]])
|
|
assert_equal(test, control)
|
|
assert_equal(test.data, control.data)
|
|
assert_equal(test.mask, control.mask)
|
|
|
|
test = b * a
|
|
control = array([[2., 3.], [4., 5.], [6., 7.]],
|
|
mask=[[0, 0], [1, 1], [1, 1]])
|
|
assert_equal(test, control)
|
|
assert_equal(test.data, control.data)
|
|
assert_equal(test.mask, control.mask)
|
|
|
|
a = array([[1.], [2.], [3.]])
|
|
b = array([[2., 3.], [4., 5.], [6., 7.]],
|
|
mask=[[0, 0], [0, 0], [0, 1]])
|
|
test = a * b
|
|
control = array([[2, 3], [8, 10], [18, 3]],
|
|
mask=[[0, 0], [0, 0], [0, 1]])
|
|
assert_equal(test, control)
|
|
assert_equal(test.data, control.data)
|
|
assert_equal(test.mask, control.mask)
|
|
|
|
test = b * a
|
|
control = array([[2, 3], [8, 10], [18, 7]],
|
|
mask=[[0, 0], [0, 0], [0, 1]])
|
|
assert_equal(test, control)
|
|
assert_equal(test.data, control.data)
|
|
assert_equal(test.mask, control.mask)
|
|
|
|
def test_domained_binops_d2D(self):
|
|
# Test domained binary operations on 2D data
|
|
a = array([[1.], [2.], [3.]], mask=[[False], [True], [True]])
|
|
b = array([[2., 3.], [4., 5.], [6., 7.]])
|
|
|
|
test = a / b
|
|
control = array([[1. / 2., 1. / 3.], [2., 2.], [3., 3.]],
|
|
mask=[[0, 0], [1, 1], [1, 1]])
|
|
assert_equal(test, control)
|
|
assert_equal(test.data, control.data)
|
|
assert_equal(test.mask, control.mask)
|
|
|
|
test = b / a
|
|
control = array([[2. / 1., 3. / 1.], [4., 5.], [6., 7.]],
|
|
mask=[[0, 0], [1, 1], [1, 1]])
|
|
assert_equal(test, control)
|
|
assert_equal(test.data, control.data)
|
|
assert_equal(test.mask, control.mask)
|
|
|
|
a = array([[1.], [2.], [3.]])
|
|
b = array([[2., 3.], [4., 5.], [6., 7.]],
|
|
mask=[[0, 0], [0, 0], [0, 1]])
|
|
test = a / b
|
|
control = array([[1. / 2, 1. / 3], [2. / 4, 2. / 5], [3. / 6, 3]],
|
|
mask=[[0, 0], [0, 0], [0, 1]])
|
|
assert_equal(test, control)
|
|
assert_equal(test.data, control.data)
|
|
assert_equal(test.mask, control.mask)
|
|
|
|
test = b / a
|
|
control = array([[2 / 1., 3 / 1.], [4 / 2., 5 / 2.], [6 / 3., 7]],
|
|
mask=[[0, 0], [0, 0], [0, 1]])
|
|
assert_equal(test, control)
|
|
assert_equal(test.data, control.data)
|
|
assert_equal(test.mask, control.mask)
|
|
|
|
def test_noshrinking(self):
|
|
# Check that we don't shrink a mask when not wanted
|
|
# Binary operations
|
|
a = masked_array([1., 2., 3.], mask=[False, False, False],
|
|
shrink=False)
|
|
b = a + 1
|
|
assert_equal(b.mask, [0, 0, 0])
|
|
# In place binary operation
|
|
a += 1
|
|
assert_equal(a.mask, [0, 0, 0])
|
|
# Domained binary operation
|
|
b = a / 1.
|
|
assert_equal(b.mask, [0, 0, 0])
|
|
# In place binary operation
|
|
a /= 1.
|
|
assert_equal(a.mask, [0, 0, 0])
|
|
|
|
def test_ufunc_nomask(self):
|
|
# check the case ufuncs should set the mask to false
|
|
m = np.ma.array([1])
|
|
# check we don't get array([False], dtype=bool)
|
|
assert_equal(np.true_divide(m, 5).mask.shape, ())
|
|
|
|
def test_noshink_on_creation(self):
|
|
# Check that the mask is not shrunk on array creation when not wanted
|
|
a = np.ma.masked_values([1., 2.5, 3.1], 1.5, shrink=False)
|
|
assert_equal(a.mask, [0, 0, 0])
|
|
|
|
def test_mod(self):
|
|
# Tests mod
|
|
(x, y, a10, m1, m2, xm, ym, z, zm, xf) = self.d
|
|
assert_equal(mod(x, y), mod(xm, ym))
|
|
test = mod(ym, xm)
|
|
assert_equal(test, np.mod(ym, xm))
|
|
assert_equal(test.mask, mask_or(xm.mask, ym.mask))
|
|
test = mod(xm, ym)
|
|
assert_equal(test, np.mod(xm, ym))
|
|
assert_equal(test.mask, mask_or(mask_or(xm.mask, ym.mask), (ym == 0)))
|
|
|
|
def test_TakeTransposeInnerOuter(self):
|
|
# Test of take, transpose, inner, outer products
|
|
x = arange(24)
|
|
y = np.arange(24)
|
|
x[5:6] = masked
|
|
x = x.reshape(2, 3, 4)
|
|
y = y.reshape(2, 3, 4)
|
|
assert_equal(np.transpose(y, (2, 0, 1)), transpose(x, (2, 0, 1)))
|
|
assert_equal(np.take(y, (2, 0, 1), 1), take(x, (2, 0, 1), 1))
|
|
assert_equal(np.inner(filled(x, 0), filled(y, 0)),
|
|
inner(x, y))
|
|
assert_equal(np.outer(filled(x, 0), filled(y, 0)),
|
|
outer(x, y))
|
|
y = array(['abc', 1, 'def', 2, 3], object)
|
|
y[2] = masked
|
|
t = take(y, [0, 3, 4])
|
|
assert_(t[0] == 'abc')
|
|
assert_(t[1] == 2)
|
|
assert_(t[2] == 3)
|
|
|
|
def test_imag_real(self):
|
|
# Check complex
|
|
xx = array([1 + 10j, 20 + 2j], mask=[1, 0])
|
|
assert_equal(xx.imag, [10, 2])
|
|
assert_equal(xx.imag.filled(), [1e+20, 2])
|
|
assert_equal(xx.imag.dtype, xx._data.imag.dtype)
|
|
assert_equal(xx.real, [1, 20])
|
|
assert_equal(xx.real.filled(), [1e+20, 20])
|
|
assert_equal(xx.real.dtype, xx._data.real.dtype)
|
|
|
|
def test_methods_with_output(self):
|
|
xm = array(np.random.uniform(0, 10, 12)).reshape(3, 4)
|
|
xm[:, 0] = xm[0] = xm[-1, -1] = masked
|
|
|
|
funclist = ('sum', 'prod', 'var', 'std', 'max', 'min', 'ptp', 'mean',)
|
|
|
|
for funcname in funclist:
|
|
npfunc = getattr(np, funcname)
|
|
xmmeth = getattr(xm, funcname)
|
|
# A ndarray as explicit input
|
|
output = np.empty(4, dtype=float)
|
|
output.fill(-9999)
|
|
result = npfunc(xm, axis=0, out=output)
|
|
# ... the result should be the given output
|
|
assert_(result is output)
|
|
assert_equal(result, xmmeth(axis=0, out=output))
|
|
|
|
output = empty(4, dtype=int)
|
|
result = xmmeth(axis=0, out=output)
|
|
assert_(result is output)
|
|
assert_(output[0] is masked)
|
|
|
|
def test_eq_on_structured(self):
|
|
# Test the equality of structured arrays
|
|
ndtype = [('A', int), ('B', int)]
|
|
a = array([(1, 1), (2, 2)], mask=[(0, 1), (0, 0)], dtype=ndtype)
|
|
|
|
test = (a == a)
|
|
assert_equal(test.data, [True, True])
|
|
assert_equal(test.mask, [False, False])
|
|
assert_(test.fill_value == True)
|
|
|
|
test = (a == a[0])
|
|
assert_equal(test.data, [True, False])
|
|
assert_equal(test.mask, [False, False])
|
|
assert_(test.fill_value == True)
|
|
|
|
b = array([(1, 1), (2, 2)], mask=[(1, 0), (0, 0)], dtype=ndtype)
|
|
test = (a == b)
|
|
assert_equal(test.data, [False, True])
|
|
assert_equal(test.mask, [True, False])
|
|
assert_(test.fill_value == True)
|
|
|
|
test = (a[0] == b)
|
|
assert_equal(test.data, [False, False])
|
|
assert_equal(test.mask, [True, False])
|
|
assert_(test.fill_value == True)
|
|
|
|
b = array([(1, 1), (2, 2)], mask=[(0, 1), (1, 0)], dtype=ndtype)
|
|
test = (a == b)
|
|
assert_equal(test.data, [True, True])
|
|
assert_equal(test.mask, [False, False])
|
|
assert_(test.fill_value == True)
|
|
|
|
# complicated dtype, 2-dimensional array.
|
|
ndtype = [('A', int), ('B', [('BA', int), ('BB', int)])]
|
|
a = array([[(1, (1, 1)), (2, (2, 2))],
|
|
[(3, (3, 3)), (4, (4, 4))]],
|
|
mask=[[(0, (1, 0)), (0, (0, 1))],
|
|
[(1, (0, 0)), (1, (1, 1))]], dtype=ndtype)
|
|
test = (a[0, 0] == a)
|
|
assert_equal(test.data, [[True, False], [False, False]])
|
|
assert_equal(test.mask, [[False, False], [False, True]])
|
|
assert_(test.fill_value == True)
|
|
|
|
def test_ne_on_structured(self):
|
|
# Test the equality of structured arrays
|
|
ndtype = [('A', int), ('B', int)]
|
|
a = array([(1, 1), (2, 2)], mask=[(0, 1), (0, 0)], dtype=ndtype)
|
|
|
|
test = (a != a)
|
|
assert_equal(test.data, [False, False])
|
|
assert_equal(test.mask, [False, False])
|
|
assert_(test.fill_value == True)
|
|
|
|
test = (a != a[0])
|
|
assert_equal(test.data, [False, True])
|
|
assert_equal(test.mask, [False, False])
|
|
assert_(test.fill_value == True)
|
|
|
|
b = array([(1, 1), (2, 2)], mask=[(1, 0), (0, 0)], dtype=ndtype)
|
|
test = (a != b)
|
|
assert_equal(test.data, [True, False])
|
|
assert_equal(test.mask, [True, False])
|
|
assert_(test.fill_value == True)
|
|
|
|
test = (a[0] != b)
|
|
assert_equal(test.data, [True, True])
|
|
assert_equal(test.mask, [True, False])
|
|
assert_(test.fill_value == True)
|
|
|
|
b = array([(1, 1), (2, 2)], mask=[(0, 1), (1, 0)], dtype=ndtype)
|
|
test = (a != b)
|
|
assert_equal(test.data, [False, False])
|
|
assert_equal(test.mask, [False, False])
|
|
assert_(test.fill_value == True)
|
|
|
|
# complicated dtype, 2-dimensional array.
|
|
ndtype = [('A', int), ('B', [('BA', int), ('BB', int)])]
|
|
a = array([[(1, (1, 1)), (2, (2, 2))],
|
|
[(3, (3, 3)), (4, (4, 4))]],
|
|
mask=[[(0, (1, 0)), (0, (0, 1))],
|
|
[(1, (0, 0)), (1, (1, 1))]], dtype=ndtype)
|
|
test = (a[0, 0] != a)
|
|
assert_equal(test.data, [[False, True], [True, True]])
|
|
assert_equal(test.mask, [[False, False], [False, True]])
|
|
assert_(test.fill_value == True)
|
|
|
|
def test_eq_ne_structured_with_non_masked(self):
|
|
a = array([(1, 1), (2, 2), (3, 4)],
|
|
mask=[(0, 1), (0, 0), (1, 1)], dtype='i4,i4')
|
|
eq = a == a.data
|
|
ne = a.data != a
|
|
# Test the obvious.
|
|
assert_(np.all(eq))
|
|
assert_(not np.any(ne))
|
|
# Expect the mask set only for items with all fields masked.
|
|
expected_mask = a.mask == np.ones((), a.mask.dtype)
|
|
assert_array_equal(eq.mask, expected_mask)
|
|
assert_array_equal(ne.mask, expected_mask)
|
|
# The masked element will indicated not equal, because the
|
|
# masks did not match.
|
|
assert_equal(eq.data, [True, True, False])
|
|
assert_array_equal(eq.data, ~ne.data)
|
|
|
|
def test_eq_ne_structured_extra(self):
|
|
# ensure simple examples are symmetric and make sense.
|
|
# from https://github.com/numpy/numpy/pull/8590#discussion_r101126465
|
|
dt = np.dtype('i4,i4')
|
|
for m1 in (mvoid((1, 2), mask=(0, 0), dtype=dt),
|
|
mvoid((1, 2), mask=(0, 1), dtype=dt),
|
|
mvoid((1, 2), mask=(1, 0), dtype=dt),
|
|
mvoid((1, 2), mask=(1, 1), dtype=dt)):
|
|
ma1 = m1.view(MaskedArray)
|
|
r1 = ma1.view('2i4')
|
|
for m2 in (np.array((1, 1), dtype=dt),
|
|
mvoid((1, 1), dtype=dt),
|
|
mvoid((1, 0), mask=(0, 1), dtype=dt),
|
|
mvoid((3, 2), mask=(0, 1), dtype=dt)):
|
|
ma2 = m2.view(MaskedArray)
|
|
r2 = ma2.view('2i4')
|
|
eq_expected = (r1 == r2).all()
|
|
assert_equal(m1 == m2, eq_expected)
|
|
assert_equal(m2 == m1, eq_expected)
|
|
assert_equal(ma1 == m2, eq_expected)
|
|
assert_equal(m1 == ma2, eq_expected)
|
|
assert_equal(ma1 == ma2, eq_expected)
|
|
# Also check it is the same if we do it element by element.
|
|
el_by_el = [m1[name] == m2[name] for name in dt.names]
|
|
assert_equal(array(el_by_el, dtype=bool).all(), eq_expected)
|
|
ne_expected = (r1 != r2).any()
|
|
assert_equal(m1 != m2, ne_expected)
|
|
assert_equal(m2 != m1, ne_expected)
|
|
assert_equal(ma1 != m2, ne_expected)
|
|
assert_equal(m1 != ma2, ne_expected)
|
|
assert_equal(ma1 != ma2, ne_expected)
|
|
el_by_el = [m1[name] != m2[name] for name in dt.names]
|
|
assert_equal(array(el_by_el, dtype=bool).any(), ne_expected)
|
|
|
|
@pytest.mark.parametrize('dt', ['S', 'U'])
|
|
@pytest.mark.parametrize('fill', [None, 'A'])
|
|
def test_eq_for_strings(self, dt, fill):
|
|
# Test the equality of structured arrays
|
|
a = array(['a', 'b'], dtype=dt, mask=[0, 1], fill_value=fill)
|
|
|
|
test = (a == a)
|
|
assert_equal(test.data, [True, True])
|
|
assert_equal(test.mask, [False, True])
|
|
assert_(test.fill_value == True)
|
|
|
|
test = (a == a[0])
|
|
assert_equal(test.data, [True, False])
|
|
assert_equal(test.mask, [False, True])
|
|
assert_(test.fill_value == True)
|
|
|
|
b = array(['a', 'b'], dtype=dt, mask=[1, 0], fill_value=fill)
|
|
test = (a == b)
|
|
assert_equal(test.data, [False, False])
|
|
assert_equal(test.mask, [True, True])
|
|
assert_(test.fill_value == True)
|
|
|
|
test = (a[0] == b)
|
|
assert_equal(test.data, [False, False])
|
|
assert_equal(test.mask, [True, False])
|
|
assert_(test.fill_value == True)
|
|
|
|
test = (b == a[0])
|
|
assert_equal(test.data, [False, False])
|
|
assert_equal(test.mask, [True, False])
|
|
assert_(test.fill_value == True)
|
|
|
|
@pytest.mark.parametrize('dt', ['S', 'U'])
|
|
@pytest.mark.parametrize('fill', [None, 'A'])
|
|
def test_ne_for_strings(self, dt, fill):
|
|
# Test the equality of structured arrays
|
|
a = array(['a', 'b'], dtype=dt, mask=[0, 1], fill_value=fill)
|
|
|
|
test = (a != a)
|
|
assert_equal(test.data, [False, False])
|
|
assert_equal(test.mask, [False, True])
|
|
assert_(test.fill_value == True)
|
|
|
|
test = (a != a[0])
|
|
assert_equal(test.data, [False, True])
|
|
assert_equal(test.mask, [False, True])
|
|
assert_(test.fill_value == True)
|
|
|
|
b = array(['a', 'b'], dtype=dt, mask=[1, 0], fill_value=fill)
|
|
test = (a != b)
|
|
assert_equal(test.data, [True, True])
|
|
assert_equal(test.mask, [True, True])
|
|
assert_(test.fill_value == True)
|
|
|
|
test = (a[0] != b)
|
|
assert_equal(test.data, [True, True])
|
|
assert_equal(test.mask, [True, False])
|
|
assert_(test.fill_value == True)
|
|
|
|
test = (b != a[0])
|
|
assert_equal(test.data, [True, True])
|
|
assert_equal(test.mask, [True, False])
|
|
assert_(test.fill_value == True)
|
|
|
|
@pytest.mark.parametrize('dt1', num_dts, ids=num_ids)
|
|
@pytest.mark.parametrize('dt2', num_dts, ids=num_ids)
|
|
@pytest.mark.parametrize('fill', [None, 1])
|
|
def test_eq_for_numeric(self, dt1, dt2, fill):
|
|
# Test the equality of structured arrays
|
|
a = array([0, 1], dtype=dt1, mask=[0, 1], fill_value=fill)
|
|
|
|
test = (a == a)
|
|
assert_equal(test.data, [True, True])
|
|
assert_equal(test.mask, [False, True])
|
|
assert_(test.fill_value == True)
|
|
|
|
test = (a == a[0])
|
|
assert_equal(test.data, [True, False])
|
|
assert_equal(test.mask, [False, True])
|
|
assert_(test.fill_value == True)
|
|
|
|
b = array([0, 1], dtype=dt2, mask=[1, 0], fill_value=fill)
|
|
test = (a == b)
|
|
assert_equal(test.data, [False, False])
|
|
assert_equal(test.mask, [True, True])
|
|
assert_(test.fill_value == True)
|
|
|
|
test = (a[0] == b)
|
|
assert_equal(test.data, [False, False])
|
|
assert_equal(test.mask, [True, False])
|
|
assert_(test.fill_value == True)
|
|
|
|
test = (b == a[0])
|
|
assert_equal(test.data, [False, False])
|
|
assert_equal(test.mask, [True, False])
|
|
assert_(test.fill_value == True)
|
|
|
|
@pytest.mark.parametrize("op", [operator.eq, operator.lt])
|
|
def test_eq_broadcast_with_unmasked(self, op):
|
|
a = array([0, 1], mask=[0, 1])
|
|
b = np.arange(10).reshape(5, 2)
|
|
result = op(a, b)
|
|
assert_(result.mask.shape == b.shape)
|
|
assert_equal(result.mask, np.zeros(b.shape, bool) | a.mask)
|
|
|
|
@pytest.mark.parametrize("op", [operator.eq, operator.gt])
|
|
def test_comp_no_mask_not_broadcast(self, op):
|
|
# Regression test for failing doctest in MaskedArray.nonzero
|
|
# after gh-24556.
|
|
a = array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
|
|
result = op(a, 3)
|
|
assert_(not result.mask.shape)
|
|
assert_(result.mask is nomask)
|
|
|
|
@pytest.mark.parametrize('dt1', num_dts, ids=num_ids)
|
|
@pytest.mark.parametrize('dt2', num_dts, ids=num_ids)
|
|
@pytest.mark.parametrize('fill', [None, 1])
|
|
def test_ne_for_numeric(self, dt1, dt2, fill):
|
|
# Test the equality of structured arrays
|
|
a = array([0, 1], dtype=dt1, mask=[0, 1], fill_value=fill)
|
|
|
|
test = (a != a)
|
|
assert_equal(test.data, [False, False])
|
|
assert_equal(test.mask, [False, True])
|
|
assert_(test.fill_value == True)
|
|
|
|
test = (a != a[0])
|
|
assert_equal(test.data, [False, True])
|
|
assert_equal(test.mask, [False, True])
|
|
assert_(test.fill_value == True)
|
|
|
|
b = array([0, 1], dtype=dt2, mask=[1, 0], fill_value=fill)
|
|
test = (a != b)
|
|
assert_equal(test.data, [True, True])
|
|
assert_equal(test.mask, [True, True])
|
|
assert_(test.fill_value == True)
|
|
|
|
test = (a[0] != b)
|
|
assert_equal(test.data, [True, True])
|
|
assert_equal(test.mask, [True, False])
|
|
assert_(test.fill_value == True)
|
|
|
|
test = (b != a[0])
|
|
assert_equal(test.data, [True, True])
|
|
assert_equal(test.mask, [True, False])
|
|
assert_(test.fill_value == True)
|
|
|
|
@pytest.mark.parametrize('dt1', num_dts, ids=num_ids)
|
|
@pytest.mark.parametrize('dt2', num_dts, ids=num_ids)
|
|
@pytest.mark.parametrize('fill', [None, 1])
|
|
@pytest.mark.parametrize('op',
|
|
[operator.le, operator.lt, operator.ge, operator.gt])
|
|
def test_comparisons_for_numeric(self, op, dt1, dt2, fill):
|
|
# Test the equality of structured arrays
|
|
a = array([0, 1], dtype=dt1, mask=[0, 1], fill_value=fill)
|
|
|
|
test = op(a, a)
|
|
assert_equal(test.data, op(a._data, a._data))
|
|
assert_equal(test.mask, [False, True])
|
|
assert_(test.fill_value == True)
|
|
|
|
test = op(a, a[0])
|
|
assert_equal(test.data, op(a._data, a._data[0]))
|
|
assert_equal(test.mask, [False, True])
|
|
assert_(test.fill_value == True)
|
|
|
|
b = array([0, 1], dtype=dt2, mask=[1, 0], fill_value=fill)
|
|
test = op(a, b)
|
|
assert_equal(test.data, op(a._data, b._data))
|
|
assert_equal(test.mask, [True, True])
|
|
assert_(test.fill_value == True)
|
|
|
|
test = op(a[0], b)
|
|
assert_equal(test.data, op(a._data[0], b._data))
|
|
assert_equal(test.mask, [True, False])
|
|
assert_(test.fill_value == True)
|
|
|
|
test = op(b, a[0])
|
|
assert_equal(test.data, op(b._data, a._data[0]))
|
|
assert_equal(test.mask, [True, False])
|
|
assert_(test.fill_value == True)
|
|
|
|
@pytest.mark.parametrize('op',
|
|
[operator.le, operator.lt, operator.ge, operator.gt])
|
|
@pytest.mark.parametrize('fill', [None, "N/A"])
|
|
def test_comparisons_strings(self, op, fill):
|
|
# See gh-21770, mask propagation is broken for strings (and some other
|
|
# cases) so we explicitly test strings here.
|
|
# In principle only == and != may need special handling...
|
|
ma1 = masked_array(["a", "b", "cde"], mask=[0, 1, 0], fill_value=fill)
|
|
ma2 = masked_array(["cde", "b", "a"], mask=[0, 1, 0], fill_value=fill)
|
|
assert_equal(op(ma1, ma2)._data, op(ma1._data, ma2._data))
|
|
|
|
def test_eq_with_None(self):
|
|
# Really, comparisons with None should not be done, but check them
|
|
# anyway. Note that pep8 will flag these tests.
|
|
# Deprecation is in place for arrays, and when it happens this
|
|
# test will fail (and have to be changed accordingly).
|
|
|
|
# With partial mask
|
|
with suppress_warnings() as sup:
|
|
sup.filter(FutureWarning, "Comparison to `None`")
|
|
a = array([None, 1], mask=[0, 1])
|
|
assert_equal(a == None, array([True, False], mask=[0, 1]))
|
|
assert_equal(a.data == None, [True, False])
|
|
assert_equal(a != None, array([False, True], mask=[0, 1]))
|
|
# With nomask
|
|
a = array([None, 1], mask=False)
|
|
assert_equal(a == None, [True, False])
|
|
assert_equal(a != None, [False, True])
|
|
# With complete mask
|
|
a = array([None, 2], mask=True)
|
|
assert_equal(a == None, array([False, True], mask=True))
|
|
assert_equal(a != None, array([True, False], mask=True))
|
|
# Fully masked, even comparison to None should return "masked"
|
|
a = masked
|
|
assert_equal(a == None, masked)
|
|
|
|
def test_eq_with_scalar(self):
|
|
a = array(1)
|
|
assert_equal(a == 1, True)
|
|
assert_equal(a == 0, False)
|
|
assert_equal(a != 1, False)
|
|
assert_equal(a != 0, True)
|
|
b = array(1, mask=True)
|
|
assert_equal(b == 0, masked)
|
|
assert_equal(b == 1, masked)
|
|
assert_equal(b != 0, masked)
|
|
assert_equal(b != 1, masked)
|
|
|
|
def test_eq_different_dimensions(self):
|
|
m1 = array([1, 1], mask=[0, 1])
|
|
# test comparison with both masked and regular arrays.
|
|
for m2 in (array([[0, 1], [1, 2]]),
|
|
np.array([[0, 1], [1, 2]])):
|
|
test = (m1 == m2)
|
|
assert_equal(test.data, [[False, False],
|
|
[True, False]])
|
|
assert_equal(test.mask, [[False, True],
|
|
[False, True]])
|
|
|
|
def test_numpyarithmetic(self):
|
|
# Check that the mask is not back-propagated when using numpy functions
|
|
a = masked_array([-1, 0, 1, 2, 3], mask=[0, 0, 0, 0, 1])
|
|
control = masked_array([np.nan, np.nan, 0, np.log(2), -1],
|
|
mask=[1, 1, 0, 0, 1])
|
|
|
|
test = log(a)
|
|
assert_equal(test, control)
|
|
assert_equal(test.mask, control.mask)
|
|
assert_equal(a.mask, [0, 0, 0, 0, 1])
|
|
|
|
test = np.log(a)
|
|
assert_equal(test, control)
|
|
assert_equal(test.mask, control.mask)
|
|
assert_equal(a.mask, [0, 0, 0, 0, 1])
|
|
|
|
|
|
class TestMaskedArrayAttributes:
|
|
|
|
def test_keepmask(self):
|
|
# Tests the keep mask flag
|
|
x = masked_array([1, 2, 3], mask=[1, 0, 0])
|
|
mx = masked_array(x)
|
|
assert_equal(mx.mask, x.mask)
|
|
mx = masked_array(x, mask=[0, 1, 0], keep_mask=False)
|
|
assert_equal(mx.mask, [0, 1, 0])
|
|
mx = masked_array(x, mask=[0, 1, 0], keep_mask=True)
|
|
assert_equal(mx.mask, [1, 1, 0])
|
|
# We default to true
|
|
mx = masked_array(x, mask=[0, 1, 0])
|
|
assert_equal(mx.mask, [1, 1, 0])
|
|
|
|
def test_hardmask(self):
|
|
# Test hard_mask
|
|
d = arange(5)
|
|
n = [0, 0, 0, 1, 1]
|
|
m = make_mask(n)
|
|
xh = array(d, mask=m, hard_mask=True)
|
|
# We need to copy, to avoid updating d in xh !
|
|
xs = array(d, mask=m, hard_mask=False, copy=True)
|
|
xh[[1, 4]] = [10, 40]
|
|
xs[[1, 4]] = [10, 40]
|
|
assert_equal(xh._data, [0, 10, 2, 3, 4])
|
|
assert_equal(xs._data, [0, 10, 2, 3, 40])
|
|
assert_equal(xs.mask, [0, 0, 0, 1, 0])
|
|
assert_(xh._hardmask)
|
|
assert_(not xs._hardmask)
|
|
xh[1:4] = [10, 20, 30]
|
|
xs[1:4] = [10, 20, 30]
|
|
assert_equal(xh._data, [0, 10, 20, 3, 4])
|
|
assert_equal(xs._data, [0, 10, 20, 30, 40])
|
|
assert_equal(xs.mask, nomask)
|
|
xh[0] = masked
|
|
xs[0] = masked
|
|
assert_equal(xh.mask, [1, 0, 0, 1, 1])
|
|
assert_equal(xs.mask, [1, 0, 0, 0, 0])
|
|
xh[:] = 1
|
|
xs[:] = 1
|
|
assert_equal(xh._data, [0, 1, 1, 3, 4])
|
|
assert_equal(xs._data, [1, 1, 1, 1, 1])
|
|
assert_equal(xh.mask, [1, 0, 0, 1, 1])
|
|
assert_equal(xs.mask, nomask)
|
|
# Switch to soft mask
|
|
xh.soften_mask()
|
|
xh[:] = arange(5)
|
|
assert_equal(xh._data, [0, 1, 2, 3, 4])
|
|
assert_equal(xh.mask, nomask)
|
|
# Switch back to hard mask
|
|
xh.harden_mask()
|
|
xh[xh < 3] = masked
|
|
assert_equal(xh._data, [0, 1, 2, 3, 4])
|
|
assert_equal(xh._mask, [1, 1, 1, 0, 0])
|
|
xh[filled(xh > 1, False)] = 5
|
|
assert_equal(xh._data, [0, 1, 2, 5, 5])
|
|
assert_equal(xh._mask, [1, 1, 1, 0, 0])
|
|
|
|
xh = array([[1, 2], [3, 4]], mask=[[1, 0], [0, 0]], hard_mask=True)
|
|
xh[0] = 0
|
|
assert_equal(xh._data, [[1, 0], [3, 4]])
|
|
assert_equal(xh._mask, [[1, 0], [0, 0]])
|
|
xh[-1, -1] = 5
|
|
assert_equal(xh._data, [[1, 0], [3, 5]])
|
|
assert_equal(xh._mask, [[1, 0], [0, 0]])
|
|
xh[filled(xh < 5, False)] = 2
|
|
assert_equal(xh._data, [[1, 2], [2, 5]])
|
|
assert_equal(xh._mask, [[1, 0], [0, 0]])
|
|
|
|
def test_hardmask_again(self):
|
|
# Another test of hardmask
|
|
d = arange(5)
|
|
n = [0, 0, 0, 1, 1]
|
|
m = make_mask(n)
|
|
xh = array(d, mask=m, hard_mask=True)
|
|
xh[4:5] = 999
|
|
xh[0:1] = 999
|
|
assert_equal(xh._data, [999, 1, 2, 3, 4])
|
|
|
|
def test_hardmask_oncemore_yay(self):
|
|
# OK, yet another test of hardmask
|
|
# Make sure that harden_mask/soften_mask//unshare_mask returns self
|
|
a = array([1, 2, 3], mask=[1, 0, 0])
|
|
b = a.harden_mask()
|
|
assert_equal(a, b)
|
|
b[0] = 0
|
|
assert_equal(a, b)
|
|
assert_equal(b, array([1, 2, 3], mask=[1, 0, 0]))
|
|
a = b.soften_mask()
|
|
a[0] = 0
|
|
assert_equal(a, b)
|
|
assert_equal(b, array([0, 2, 3], mask=[0, 0, 0]))
|
|
|
|
def test_smallmask(self):
|
|
# Checks the behaviour of _smallmask
|
|
a = arange(10)
|
|
a[1] = masked
|
|
a[1] = 1
|
|
assert_equal(a._mask, nomask)
|
|
a = arange(10)
|
|
a._smallmask = False
|
|
a[1] = masked
|
|
a[1] = 1
|
|
assert_equal(a._mask, zeros(10))
|
|
|
|
def test_shrink_mask(self):
|
|
# Tests .shrink_mask()
|
|
a = array([1, 2, 3], mask=[0, 0, 0])
|
|
b = a.shrink_mask()
|
|
assert_equal(a, b)
|
|
assert_equal(a.mask, nomask)
|
|
|
|
# Mask cannot be shrunk on structured types, so is a no-op
|
|
a = np.ma.array([(1, 2.0)], [('a', int), ('b', float)])
|
|
b = a.copy()
|
|
a.shrink_mask()
|
|
assert_equal(a.mask, b.mask)
|
|
|
|
def test_flat(self):
|
|
# Test that flat can return all types of items [#4585, #4615]
|
|
# test 2-D record array
|
|
# ... on structured array w/ masked records
|
|
x = array([[(1, 1.1, 'one'), (2, 2.2, 'two'), (3, 3.3, 'thr')],
|
|
[(4, 4.4, 'fou'), (5, 5.5, 'fiv'), (6, 6.6, 'six')]],
|
|
dtype=[('a', int), ('b', float), ('c', '|S8')])
|
|
x['a'][0, 1] = masked
|
|
x['b'][1, 0] = masked
|
|
x['c'][0, 2] = masked
|
|
x[-1, -1] = masked
|
|
xflat = x.flat
|
|
assert_equal(xflat[0], x[0, 0])
|
|
assert_equal(xflat[1], x[0, 1])
|
|
assert_equal(xflat[2], x[0, 2])
|
|
assert_equal(xflat[:3], x[0])
|
|
assert_equal(xflat[3], x[1, 0])
|
|
assert_equal(xflat[4], x[1, 1])
|
|
assert_equal(xflat[5], x[1, 2])
|
|
assert_equal(xflat[3:], x[1])
|
|
assert_equal(xflat[-1], x[-1, -1])
|
|
i = 0
|
|
j = 0
|
|
for xf in xflat:
|
|
assert_equal(xf, x[j, i])
|
|
i += 1
|
|
if i >= x.shape[-1]:
|
|
i = 0
|
|
j += 1
|
|
|
|
def test_assign_dtype(self):
|
|
# check that the mask's dtype is updated when dtype is changed
|
|
a = np.zeros(4, dtype='f4,i4')
|
|
|
|
m = np.ma.array(a)
|
|
m.dtype = np.dtype('f4')
|
|
repr(m) # raises?
|
|
assert_equal(m.dtype, np.dtype('f4'))
|
|
|
|
# check that dtype changes that change shape of mask too much
|
|
# are not allowed
|
|
def assign():
|
|
m = np.ma.array(a)
|
|
m.dtype = np.dtype('f8')
|
|
assert_raises(ValueError, assign)
|
|
|
|
b = a.view(dtype='f4', type=np.ma.MaskedArray) # raises?
|
|
assert_equal(b.dtype, np.dtype('f4'))
|
|
|
|
# check that nomask is preserved
|
|
a = np.zeros(4, dtype='f4')
|
|
m = np.ma.array(a)
|
|
m.dtype = np.dtype('f4,i4')
|
|
assert_equal(m.dtype, np.dtype('f4,i4'))
|
|
assert_equal(m._mask, np.ma.nomask)
|
|
|
|
|
|
class TestFillingValues:
|
|
|
|
def test_check_on_scalar(self):
|
|
# Test _check_fill_value set to valid and invalid values
|
|
_check_fill_value = np.ma.core._check_fill_value
|
|
|
|
fval = _check_fill_value(0, int)
|
|
assert_equal(fval, 0)
|
|
fval = _check_fill_value(None, int)
|
|
assert_equal(fval, default_fill_value(0))
|
|
|
|
fval = _check_fill_value(0, "|S3")
|
|
assert_equal(fval, b"0")
|
|
fval = _check_fill_value(None, "|S3")
|
|
assert_equal(fval, default_fill_value(b"camelot!"))
|
|
assert_raises(TypeError, _check_fill_value, 1e+20, int)
|
|
assert_raises(TypeError, _check_fill_value, 'stuff', int)
|
|
|
|
def test_check_on_fields(self):
|
|
# Tests _check_fill_value with records
|
|
_check_fill_value = np.ma.core._check_fill_value
|
|
ndtype = [('a', int), ('b', float), ('c', "|S3")]
|
|
# A check on a list should return a single record
|
|
fval = _check_fill_value([-999, -12345678.9, "???"], ndtype)
|
|
assert_(isinstance(fval, ndarray))
|
|
assert_equal(fval.item(), [-999, -12345678.9, b"???"])
|
|
# A check on None should output the defaults
|
|
fval = _check_fill_value(None, ndtype)
|
|
assert_(isinstance(fval, ndarray))
|
|
assert_equal(fval.item(), [default_fill_value(0),
|
|
default_fill_value(0.),
|
|
asbytes(default_fill_value("0"))])
|
|
#.....Using a structured type as fill_value should work
|
|
fill_val = np.array((-999, -12345678.9, "???"), dtype=ndtype)
|
|
fval = _check_fill_value(fill_val, ndtype)
|
|
assert_(isinstance(fval, ndarray))
|
|
assert_equal(fval.item(), [-999, -12345678.9, b"???"])
|
|
|
|
#.....Using a flexible type w/ a different type shouldn't matter
|
|
# BEHAVIOR in 1.5 and earlier, and 1.13 and later: match structured
|
|
# types by position
|
|
fill_val = np.array((-999, -12345678.9, "???"),
|
|
dtype=[("A", int), ("B", float), ("C", "|S3")])
|
|
fval = _check_fill_value(fill_val, ndtype)
|
|
assert_(isinstance(fval, ndarray))
|
|
assert_equal(fval.item(), [-999, -12345678.9, b"???"])
|
|
|
|
#.....Using an object-array shouldn't matter either
|
|
fill_val = np.ndarray(shape=(1,), dtype=object)
|
|
fill_val[0] = (-999, -12345678.9, b"???")
|
|
fval = _check_fill_value(fill_val, object)
|
|
assert_(isinstance(fval, ndarray))
|
|
assert_equal(fval.item(), [-999, -12345678.9, b"???"])
|
|
# NOTE: This test was never run properly as "fill_value" rather than
|
|
# "fill_val" was assigned. Written properly, it fails.
|
|
#fill_val = np.array((-999, -12345678.9, "???"))
|
|
#fval = _check_fill_value(fill_val, ndtype)
|
|
#assert_(isinstance(fval, ndarray))
|
|
#assert_equal(fval.item(), [-999, -12345678.9, b"???"])
|
|
#.....One-field-only flexible type should work as well
|
|
ndtype = [("a", int)]
|
|
fval = _check_fill_value(-999999999, ndtype)
|
|
assert_(isinstance(fval, ndarray))
|
|
assert_equal(fval.item(), (-999999999,))
|
|
|
|
def test_fillvalue_conversion(self):
|
|
# Tests the behavior of fill_value during conversion
|
|
# We had a tailored comment to make sure special attributes are
|
|
# properly dealt with
|
|
a = array([b'3', b'4', b'5'])
|
|
a._optinfo.update({'comment':"updated!"})
|
|
|
|
b = array(a, dtype=int)
|
|
assert_equal(b._data, [3, 4, 5])
|
|
assert_equal(b.fill_value, default_fill_value(0))
|
|
|
|
b = array(a, dtype=float)
|
|
assert_equal(b._data, [3, 4, 5])
|
|
assert_equal(b.fill_value, default_fill_value(0.))
|
|
|
|
b = a.astype(int)
|
|
assert_equal(b._data, [3, 4, 5])
|
|
assert_equal(b.fill_value, default_fill_value(0))
|
|
assert_equal(b._optinfo['comment'], "updated!")
|
|
|
|
b = a.astype([('a', '|S3')])
|
|
assert_equal(b['a']._data, a._data)
|
|
assert_equal(b['a'].fill_value, a.fill_value)
|
|
|
|
def test_default_fill_value(self):
|
|
# check all calling conventions
|
|
f1 = default_fill_value(1.)
|
|
f2 = default_fill_value(np.array(1.))
|
|
f3 = default_fill_value(np.array(1.).dtype)
|
|
assert_equal(f1, f2)
|
|
assert_equal(f1, f3)
|
|
|
|
def test_default_fill_value_structured(self):
|
|
fields = array([(1, 1, 1)],
|
|
dtype=[('i', int), ('s', '|S8'), ('f', float)])
|
|
|
|
f1 = default_fill_value(fields)
|
|
f2 = default_fill_value(fields.dtype)
|
|
expected = np.array((default_fill_value(0),
|
|
default_fill_value('0'),
|
|
default_fill_value(0.)), dtype=fields.dtype)
|
|
assert_equal(f1, expected)
|
|
assert_equal(f2, expected)
|
|
|
|
def test_default_fill_value_void(self):
|
|
dt = np.dtype([('v', 'V7')])
|
|
f = default_fill_value(dt)
|
|
assert_equal(f['v'], np.array(default_fill_value(dt['v']), dt['v']))
|
|
|
|
def test_fillvalue(self):
|
|
# Yet more fun with the fill_value
|
|
data = masked_array([1, 2, 3], fill_value=-999)
|
|
series = data[[0, 2, 1]]
|
|
assert_equal(series._fill_value, data._fill_value)
|
|
|
|
mtype = [('f', float), ('s', '|S3')]
|
|
x = array([(1, 'a'), (2, 'b'), (pi, 'pi')], dtype=mtype)
|
|
x.fill_value = 999
|
|
assert_equal(x.fill_value.item(), [999., b'999'])
|
|
assert_equal(x['f'].fill_value, 999)
|
|
assert_equal(x['s'].fill_value, b'999')
|
|
|
|
x.fill_value = (9, '???')
|
|
assert_equal(x.fill_value.item(), (9, b'???'))
|
|
assert_equal(x['f'].fill_value, 9)
|
|
assert_equal(x['s'].fill_value, b'???')
|
|
|
|
x = array([1, 2, 3.1])
|
|
x.fill_value = 999
|
|
assert_equal(np.asarray(x.fill_value).dtype, float)
|
|
assert_equal(x.fill_value, 999.)
|
|
assert_equal(x._fill_value, np.array(999.))
|
|
|
|
def test_subarray_fillvalue(self):
|
|
# gh-10483 test multi-field index fill value
|
|
fields = array([(1, 1, 1)],
|
|
dtype=[('i', int), ('s', '|S8'), ('f', float)])
|
|
with suppress_warnings() as sup:
|
|
sup.filter(FutureWarning, "Numpy has detected")
|
|
subfields = fields[['i', 'f']]
|
|
assert_equal(tuple(subfields.fill_value), (999999, 1.e+20))
|
|
# test comparison does not raise:
|
|
subfields[1:] == subfields[:-1]
|
|
|
|
def test_fillvalue_exotic_dtype(self):
|
|
# Tests yet more exotic flexible dtypes
|
|
_check_fill_value = np.ma.core._check_fill_value
|
|
ndtype = [('i', int), ('s', '|S8'), ('f', float)]
|
|
control = np.array((default_fill_value(0),
|
|
default_fill_value('0'),
|
|
default_fill_value(0.),),
|
|
dtype=ndtype)
|
|
assert_equal(_check_fill_value(None, ndtype), control)
|
|
# The shape shouldn't matter
|
|
ndtype = [('f0', float, (2, 2))]
|
|
control = np.array((default_fill_value(0.),),
|
|
dtype=[('f0', float)]).astype(ndtype)
|
|
assert_equal(_check_fill_value(None, ndtype), control)
|
|
control = np.array((0,), dtype=[('f0', float)]).astype(ndtype)
|
|
assert_equal(_check_fill_value(0, ndtype), control)
|
|
|
|
ndtype = np.dtype("int, (2,3)float, float")
|
|
control = np.array((default_fill_value(0),
|
|
default_fill_value(0.),
|
|
default_fill_value(0.),),
|
|
dtype="int, float, float").astype(ndtype)
|
|
test = _check_fill_value(None, ndtype)
|
|
assert_equal(test, control)
|
|
control = np.array((0, 0, 0), dtype="int, float, float").astype(ndtype)
|
|
assert_equal(_check_fill_value(0, ndtype), control)
|
|
# but when indexing, fill value should become scalar not tuple
|
|
# See issue #6723
|
|
M = masked_array(control)
|
|
assert_equal(M["f1"].fill_value.ndim, 0)
|
|
|
|
def test_fillvalue_datetime_timedelta(self):
|
|
# Test default fillvalue for datetime64 and timedelta64 types.
|
|
# See issue #4476, this would return '?' which would cause errors
|
|
# elsewhere
|
|
|
|
for timecode in ("as", "fs", "ps", "ns", "us", "ms", "s", "m",
|
|
"h", "D", "W", "M", "Y"):
|
|
control = numpy.datetime64("NaT", timecode)
|
|
test = default_fill_value(numpy.dtype("<M8[" + timecode + "]"))
|
|
np.testing.assert_equal(test, control)
|
|
|
|
control = numpy.timedelta64("NaT", timecode)
|
|
test = default_fill_value(numpy.dtype("<m8[" + timecode + "]"))
|
|
np.testing.assert_equal(test, control)
|
|
|
|
def test_extremum_fill_value(self):
|
|
# Tests extremum fill values for flexible type.
|
|
a = array([(1, (2, 3)), (4, (5, 6))],
|
|
dtype=[('A', int), ('B', [('BA', int), ('BB', int)])])
|
|
test = a.fill_value
|
|
assert_equal(test.dtype, a.dtype)
|
|
assert_equal(test['A'], default_fill_value(a['A']))
|
|
assert_equal(test['B']['BA'], default_fill_value(a['B']['BA']))
|
|
assert_equal(test['B']['BB'], default_fill_value(a['B']['BB']))
|
|
|
|
test = minimum_fill_value(a)
|
|
assert_equal(test.dtype, a.dtype)
|
|
assert_equal(test[0], minimum_fill_value(a['A']))
|
|
assert_equal(test[1][0], minimum_fill_value(a['B']['BA']))
|
|
assert_equal(test[1][1], minimum_fill_value(a['B']['BB']))
|
|
assert_equal(test[1], minimum_fill_value(a['B']))
|
|
|
|
test = maximum_fill_value(a)
|
|
assert_equal(test.dtype, a.dtype)
|
|
assert_equal(test[0], maximum_fill_value(a['A']))
|
|
assert_equal(test[1][0], maximum_fill_value(a['B']['BA']))
|
|
assert_equal(test[1][1], maximum_fill_value(a['B']['BB']))
|
|
assert_equal(test[1], maximum_fill_value(a['B']))
|
|
|
|
def test_extremum_fill_value_subdtype(self):
|
|
a = array(([2, 3, 4],), dtype=[('value', np.int8, 3)])
|
|
|
|
test = minimum_fill_value(a)
|
|
assert_equal(test.dtype, a.dtype)
|
|
assert_equal(test[0], np.full(3, minimum_fill_value(a['value'])))
|
|
|
|
test = maximum_fill_value(a)
|
|
assert_equal(test.dtype, a.dtype)
|
|
assert_equal(test[0], np.full(3, maximum_fill_value(a['value'])))
|
|
|
|
def test_fillvalue_individual_fields(self):
|
|
# Test setting fill_value on individual fields
|
|
ndtype = [('a', int), ('b', int)]
|
|
# Explicit fill_value
|
|
a = array(list(zip([1, 2, 3], [4, 5, 6])),
|
|
fill_value=(-999, -999), dtype=ndtype)
|
|
aa = a['a']
|
|
aa.set_fill_value(10)
|
|
assert_equal(aa._fill_value, np.array(10))
|
|
assert_equal(tuple(a.fill_value), (10, -999))
|
|
a.fill_value['b'] = -10
|
|
assert_equal(tuple(a.fill_value), (10, -10))
|
|
# Implicit fill_value
|
|
t = array(list(zip([1, 2, 3], [4, 5, 6])), dtype=ndtype)
|
|
tt = t['a']
|
|
tt.set_fill_value(10)
|
|
assert_equal(tt._fill_value, np.array(10))
|
|
assert_equal(tuple(t.fill_value), (10, default_fill_value(0)))
|
|
|
|
def test_fillvalue_implicit_structured_array(self):
|
|
# Check that fill_value is always defined for structured arrays
|
|
ndtype = ('b', float)
|
|
adtype = ('a', float)
|
|
a = array([(1.,), (2.,)], mask=[(False,), (False,)],
|
|
fill_value=(np.nan,), dtype=np.dtype([adtype]))
|
|
b = empty(a.shape, dtype=[adtype, ndtype])
|
|
b['a'] = a['a']
|
|
b['a'].set_fill_value(a['a'].fill_value)
|
|
f = b._fill_value[()]
|
|
assert_(np.isnan(f[0]))
|
|
assert_equal(f[-1], default_fill_value(1.))
|
|
|
|
def test_fillvalue_as_arguments(self):
|
|
# Test adding a fill_value parameter to empty/ones/zeros
|
|
a = empty(3, fill_value=999.)
|
|
assert_equal(a.fill_value, 999.)
|
|
|
|
a = ones(3, fill_value=999., dtype=float)
|
|
assert_equal(a.fill_value, 999.)
|
|
|
|
a = zeros(3, fill_value=0., dtype=complex)
|
|
assert_equal(a.fill_value, 0.)
|
|
|
|
a = identity(3, fill_value=0., dtype=complex)
|
|
assert_equal(a.fill_value, 0.)
|
|
|
|
def test_shape_argument(self):
|
|
# Test that shape can be provides as an argument
|
|
# GH issue 6106
|
|
a = empty(shape=(3, ))
|
|
assert_equal(a.shape, (3, ))
|
|
|
|
a = ones(shape=(3, ), dtype=float)
|
|
assert_equal(a.shape, (3, ))
|
|
|
|
a = zeros(shape=(3, ), dtype=complex)
|
|
assert_equal(a.shape, (3, ))
|
|
|
|
def test_fillvalue_in_view(self):
|
|
# Test the behavior of fill_value in view
|
|
|
|
# Create initial masked array
|
|
x = array([1, 2, 3], fill_value=1, dtype=np.int64)
|
|
|
|
# Check that fill_value is preserved by default
|
|
y = x.view()
|
|
assert_(y.fill_value == 1)
|
|
|
|
# Check that fill_value is preserved if dtype is specified and the
|
|
# dtype is an ndarray sub-class and has a _fill_value attribute
|
|
y = x.view(MaskedArray)
|
|
assert_(y.fill_value == 1)
|
|
|
|
# Check that fill_value is preserved if type is specified and the
|
|
# dtype is an ndarray sub-class and has a _fill_value attribute (by
|
|
# default, the first argument is dtype, not type)
|
|
y = x.view(type=MaskedArray)
|
|
assert_(y.fill_value == 1)
|
|
|
|
# Check that code does not crash if passed an ndarray sub-class that
|
|
# does not have a _fill_value attribute
|
|
y = x.view(np.ndarray)
|
|
y = x.view(type=np.ndarray)
|
|
|
|
# Check that fill_value can be overridden with view
|
|
y = x.view(MaskedArray, fill_value=2)
|
|
assert_(y.fill_value == 2)
|
|
|
|
# Check that fill_value can be overridden with view (using type=)
|
|
y = x.view(type=MaskedArray, fill_value=2)
|
|
assert_(y.fill_value == 2)
|
|
|
|
# Check that fill_value gets reset if passed a dtype but not a
|
|
# fill_value. This is because even though in some cases one can safely
|
|
# cast the fill_value, e.g. if taking an int64 view of an int32 array,
|
|
# in other cases, this cannot be done (e.g. int32 view of an int64
|
|
# array with a large fill_value).
|
|
y = x.view(dtype=np.int32)
|
|
assert_(y.fill_value == 999999)
|
|
|
|
def test_fillvalue_bytes_or_str(self):
|
|
# Test whether fill values work as expected for structured dtypes
|
|
# containing bytes or str. See issue #7259.
|
|
a = empty(shape=(3, ), dtype="(2)3S,(2)3U")
|
|
assert_equal(a["f0"].fill_value, default_fill_value(b"spam"))
|
|
assert_equal(a["f1"].fill_value, default_fill_value("eggs"))
|
|
|
|
|
|
class TestUfuncs:
|
|
# Test class for the application of ufuncs on MaskedArrays.
|
|
|
|
def setup_method(self):
|
|
# Base data definition.
|
|
self.d = (array([1.0, 0, -1, pi / 2] * 2, mask=[0, 1] + [0] * 6),
|
|
array([1.0, 0, -1, pi / 2] * 2, mask=[1, 0] + [0] * 6),)
|
|
self.err_status = np.geterr()
|
|
np.seterr(divide='ignore', invalid='ignore')
|
|
|
|
def teardown_method(self):
|
|
np.seterr(**self.err_status)
|
|
|
|
def test_testUfuncRegression(self):
|
|
# Tests new ufuncs on MaskedArrays.
|
|
for f in ['sqrt', 'log', 'log10', 'exp', 'conjugate',
|
|
'sin', 'cos', 'tan',
|
|
'arcsin', 'arccos', 'arctan',
|
|
'sinh', 'cosh', 'tanh',
|
|
'arcsinh',
|
|
'arccosh',
|
|
'arctanh',
|
|
'absolute', 'fabs', 'negative',
|
|
'floor', 'ceil',
|
|
'logical_not',
|
|
'add', 'subtract', 'multiply',
|
|
'divide', 'true_divide', 'floor_divide',
|
|
'remainder', 'fmod', 'hypot', 'arctan2',
|
|
'equal', 'not_equal', 'less_equal', 'greater_equal',
|
|
'less', 'greater',
|
|
'logical_and', 'logical_or', 'logical_xor',
|
|
]:
|
|
try:
|
|
uf = getattr(umath, f)
|
|
except AttributeError:
|
|
uf = getattr(fromnumeric, f)
|
|
mf = getattr(numpy.ma.core, f)
|
|
args = self.d[:uf.nin]
|
|
ur = uf(*args)
|
|
mr = mf(*args)
|
|
assert_equal(ur.filled(0), mr.filled(0), f)
|
|
assert_mask_equal(ur.mask, mr.mask, err_msg=f)
|
|
|
|
def test_reduce(self):
|
|
# Tests reduce on MaskedArrays.
|
|
a = self.d[0]
|
|
assert_(not alltrue(a, axis=0))
|
|
assert_(sometrue(a, axis=0))
|
|
assert_equal(sum(a[:3], axis=0), 0)
|
|
assert_equal(product(a, axis=0), 0)
|
|
assert_equal(add.reduce(a), pi)
|
|
|
|
def test_minmax(self):
|
|
# Tests extrema on MaskedArrays.
|
|
a = arange(1, 13).reshape(3, 4)
|
|
amask = masked_where(a < 5, a)
|
|
assert_equal(amask.max(), a.max())
|
|
assert_equal(amask.min(), 5)
|
|
assert_equal(amask.max(0), a.max(0))
|
|
assert_equal(amask.min(0), [5, 6, 7, 8])
|
|
assert_(amask.max(1)[0].mask)
|
|
assert_(amask.min(1)[0].mask)
|
|
|
|
def test_ndarray_mask(self):
|
|
# Check that the mask of the result is a ndarray (not a MaskedArray...)
|
|
a = masked_array([-1, 0, 1, 2, 3], mask=[0, 0, 0, 0, 1])
|
|
test = np.sqrt(a)
|
|
control = masked_array([-1, 0, 1, np.sqrt(2), -1],
|
|
mask=[1, 0, 0, 0, 1])
|
|
assert_equal(test, control)
|
|
assert_equal(test.mask, control.mask)
|
|
assert_(not isinstance(test.mask, MaskedArray))
|
|
|
|
def test_treatment_of_NotImplemented(self):
|
|
# Check that NotImplemented is returned at appropriate places
|
|
|
|
a = masked_array([1., 2.], mask=[1, 0])
|
|
assert_raises(TypeError, operator.mul, a, "abc")
|
|
assert_raises(TypeError, operator.truediv, a, "abc")
|
|
|
|
class MyClass:
|
|
__array_priority__ = a.__array_priority__ + 1
|
|
|
|
def __mul__(self, other):
|
|
return "My mul"
|
|
|
|
def __rmul__(self, other):
|
|
return "My rmul"
|
|
|
|
me = MyClass()
|
|
assert_(me * a == "My mul")
|
|
assert_(a * me == "My rmul")
|
|
|
|
# and that __array_priority__ is respected
|
|
class MyClass2:
|
|
__array_priority__ = 100
|
|
|
|
def __mul__(self, other):
|
|
return "Me2mul"
|
|
|
|
def __rmul__(self, other):
|
|
return "Me2rmul"
|
|
|
|
def __rdiv__(self, other):
|
|
return "Me2rdiv"
|
|
|
|
__rtruediv__ = __rdiv__
|
|
|
|
me_too = MyClass2()
|
|
assert_(a.__mul__(me_too) is NotImplemented)
|
|
assert_(all(multiply.outer(a, me_too) == "Me2rmul"))
|
|
assert_(a.__truediv__(me_too) is NotImplemented)
|
|
assert_(me_too * a == "Me2mul")
|
|
assert_(a * me_too == "Me2rmul")
|
|
assert_(a / me_too == "Me2rdiv")
|
|
|
|
def test_no_masked_nan_warnings(self):
|
|
# check that a nan in masked position does not
|
|
# cause ufunc warnings
|
|
|
|
m = np.ma.array([0.5, np.nan], mask=[0,1])
|
|
|
|
with warnings.catch_warnings():
|
|
warnings.filterwarnings("error")
|
|
|
|
# test unary and binary ufuncs
|
|
exp(m)
|
|
add(m, 1)
|
|
m > 0
|
|
|
|
# test different unary domains
|
|
sqrt(m)
|
|
log(m)
|
|
tan(m)
|
|
arcsin(m)
|
|
arccos(m)
|
|
arccosh(m)
|
|
|
|
# test binary domains
|
|
divide(m, 2)
|
|
|
|
# also check that allclose uses ma ufuncs, to avoid warning
|
|
allclose(m, 0.5)
|
|
|
|
class TestMaskedArrayInPlaceArithmetic:
|
|
# Test MaskedArray Arithmetic
|
|
|
|
def setup_method(self):
|
|
x = arange(10)
|
|
y = arange(10)
|
|
xm = arange(10)
|
|
xm[2] = masked
|
|
self.intdata = (x, y, xm)
|
|
self.floatdata = (x.astype(float), y.astype(float), xm.astype(float))
|
|
self.othertypes = np.typecodes['AllInteger'] + np.typecodes['AllFloat']
|
|
self.othertypes = [np.dtype(_).type for _ in self.othertypes]
|
|
self.uint8data = (
|
|
x.astype(np.uint8),
|
|
y.astype(np.uint8),
|
|
xm.astype(np.uint8)
|
|
)
|
|
|
|
def test_inplace_addition_scalar(self):
|
|
# Test of inplace additions
|
|
(x, y, xm) = self.intdata
|
|
xm[2] = masked
|
|
x += 1
|
|
assert_equal(x, y + 1)
|
|
xm += 1
|
|
assert_equal(xm, y + 1)
|
|
|
|
(x, _, xm) = self.floatdata
|
|
id1 = x.data.ctypes.data
|
|
x += 1.
|
|
assert_(id1 == x.data.ctypes.data)
|
|
assert_equal(x, y + 1.)
|
|
|
|
def test_inplace_addition_array(self):
|
|
# Test of inplace additions
|
|
(x, y, xm) = self.intdata
|
|
m = xm.mask
|
|
a = arange(10, dtype=np.int16)
|
|
a[-1] = masked
|
|
x += a
|
|
xm += a
|
|
assert_equal(x, y + a)
|
|
assert_equal(xm, y + a)
|
|
assert_equal(xm.mask, mask_or(m, a.mask))
|
|
|
|
def test_inplace_subtraction_scalar(self):
|
|
# Test of inplace subtractions
|
|
(x, y, xm) = self.intdata
|
|
x -= 1
|
|
assert_equal(x, y - 1)
|
|
xm -= 1
|
|
assert_equal(xm, y - 1)
|
|
|
|
def test_inplace_subtraction_array(self):
|
|
# Test of inplace subtractions
|
|
(x, y, xm) = self.floatdata
|
|
m = xm.mask
|
|
a = arange(10, dtype=float)
|
|
a[-1] = masked
|
|
x -= a
|
|
xm -= a
|
|
assert_equal(x, y - a)
|
|
assert_equal(xm, y - a)
|
|
assert_equal(xm.mask, mask_or(m, a.mask))
|
|
|
|
def test_inplace_multiplication_scalar(self):
|
|
# Test of inplace multiplication
|
|
(x, y, xm) = self.floatdata
|
|
x *= 2.0
|
|
assert_equal(x, y * 2)
|
|
xm *= 2.0
|
|
assert_equal(xm, y * 2)
|
|
|
|
def test_inplace_multiplication_array(self):
|
|
# Test of inplace multiplication
|
|
(x, y, xm) = self.floatdata
|
|
m = xm.mask
|
|
a = arange(10, dtype=float)
|
|
a[-1] = masked
|
|
x *= a
|
|
xm *= a
|
|
assert_equal(x, y * a)
|
|
assert_equal(xm, y * a)
|
|
assert_equal(xm.mask, mask_or(m, a.mask))
|
|
|
|
def test_inplace_division_scalar_int(self):
|
|
# Test of inplace division
|
|
(x, y, xm) = self.intdata
|
|
x = arange(10) * 2
|
|
xm = arange(10) * 2
|
|
xm[2] = masked
|
|
x //= 2
|
|
assert_equal(x, y)
|
|
xm //= 2
|
|
assert_equal(xm, y)
|
|
|
|
def test_inplace_division_scalar_float(self):
|
|
# Test of inplace division
|
|
(x, y, xm) = self.floatdata
|
|
x /= 2.0
|
|
assert_equal(x, y / 2.0)
|
|
xm /= arange(10)
|
|
assert_equal(xm, ones((10,)))
|
|
|
|
def test_inplace_division_array_float(self):
|
|
# Test of inplace division
|
|
(x, y, xm) = self.floatdata
|
|
m = xm.mask
|
|
a = arange(10, dtype=float)
|
|
a[-1] = masked
|
|
x /= a
|
|
xm /= a
|
|
assert_equal(x, y / a)
|
|
assert_equal(xm, y / a)
|
|
assert_equal(xm.mask, mask_or(mask_or(m, a.mask), (a == 0)))
|
|
|
|
def test_inplace_division_misc(self):
|
|
|
|
x = [1., 1., 1., -2., pi / 2., 4., 5., -10., 10., 1., 2., 3.]
|
|
y = [5., 0., 3., 2., -1., -4., 0., -10., 10., 1., 0., 3.]
|
|
m1 = [1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0]
|
|
m2 = [0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1]
|
|
xm = masked_array(x, mask=m1)
|
|
ym = masked_array(y, mask=m2)
|
|
|
|
z = xm / ym
|
|
assert_equal(z._mask, [1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1])
|
|
assert_equal(z._data,
|
|
[1., 1., 1., -1., -pi / 2., 4., 5., 1., 1., 1., 2., 3.])
|
|
|
|
xm = xm.copy()
|
|
xm /= ym
|
|
assert_equal(xm._mask, [1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1])
|
|
assert_equal(z._data,
|
|
[1., 1., 1., -1., -pi / 2., 4., 5., 1., 1., 1., 2., 3.])
|
|
|
|
def test_datafriendly_add(self):
|
|
# Test keeping data w/ (inplace) addition
|
|
x = array([1, 2, 3], mask=[0, 0, 1])
|
|
# Test add w/ scalar
|
|
xx = x + 1
|
|
assert_equal(xx.data, [2, 3, 3])
|
|
assert_equal(xx.mask, [0, 0, 1])
|
|
# Test iadd w/ scalar
|
|
x += 1
|
|
assert_equal(x.data, [2, 3, 3])
|
|
assert_equal(x.mask, [0, 0, 1])
|
|
# Test add w/ array
|
|
x = array([1, 2, 3], mask=[0, 0, 1])
|
|
xx = x + array([1, 2, 3], mask=[1, 0, 0])
|
|
assert_equal(xx.data, [1, 4, 3])
|
|
assert_equal(xx.mask, [1, 0, 1])
|
|
# Test iadd w/ array
|
|
x = array([1, 2, 3], mask=[0, 0, 1])
|
|
x += array([1, 2, 3], mask=[1, 0, 0])
|
|
assert_equal(x.data, [1, 4, 3])
|
|
assert_equal(x.mask, [1, 0, 1])
|
|
|
|
def test_datafriendly_sub(self):
|
|
# Test keeping data w/ (inplace) subtraction
|
|
# Test sub w/ scalar
|
|
x = array([1, 2, 3], mask=[0, 0, 1])
|
|
xx = x - 1
|
|
assert_equal(xx.data, [0, 1, 3])
|
|
assert_equal(xx.mask, [0, 0, 1])
|
|
# Test isub w/ scalar
|
|
x = array([1, 2, 3], mask=[0, 0, 1])
|
|
x -= 1
|
|
assert_equal(x.data, [0, 1, 3])
|
|
assert_equal(x.mask, [0, 0, 1])
|
|
# Test sub w/ array
|
|
x = array([1, 2, 3], mask=[0, 0, 1])
|
|
xx = x - array([1, 2, 3], mask=[1, 0, 0])
|
|
assert_equal(xx.data, [1, 0, 3])
|
|
assert_equal(xx.mask, [1, 0, 1])
|
|
# Test isub w/ array
|
|
x = array([1, 2, 3], mask=[0, 0, 1])
|
|
x -= array([1, 2, 3], mask=[1, 0, 0])
|
|
assert_equal(x.data, [1, 0, 3])
|
|
assert_equal(x.mask, [1, 0, 1])
|
|
|
|
def test_datafriendly_mul(self):
|
|
# Test keeping data w/ (inplace) multiplication
|
|
# Test mul w/ scalar
|
|
x = array([1, 2, 3], mask=[0, 0, 1])
|
|
xx = x * 2
|
|
assert_equal(xx.data, [2, 4, 3])
|
|
assert_equal(xx.mask, [0, 0, 1])
|
|
# Test imul w/ scalar
|
|
x = array([1, 2, 3], mask=[0, 0, 1])
|
|
x *= 2
|
|
assert_equal(x.data, [2, 4, 3])
|
|
assert_equal(x.mask, [0, 0, 1])
|
|
# Test mul w/ array
|
|
x = array([1, 2, 3], mask=[0, 0, 1])
|
|
xx = x * array([10, 20, 30], mask=[1, 0, 0])
|
|
assert_equal(xx.data, [1, 40, 3])
|
|
assert_equal(xx.mask, [1, 0, 1])
|
|
# Test imul w/ array
|
|
x = array([1, 2, 3], mask=[0, 0, 1])
|
|
x *= array([10, 20, 30], mask=[1, 0, 0])
|
|
assert_equal(x.data, [1, 40, 3])
|
|
assert_equal(x.mask, [1, 0, 1])
|
|
|
|
def test_datafriendly_div(self):
|
|
# Test keeping data w/ (inplace) division
|
|
# Test div on scalar
|
|
x = array([1, 2, 3], mask=[0, 0, 1])
|
|
xx = x / 2.
|
|
assert_equal(xx.data, [1 / 2., 2 / 2., 3])
|
|
assert_equal(xx.mask, [0, 0, 1])
|
|
# Test idiv on scalar
|
|
x = array([1., 2., 3.], mask=[0, 0, 1])
|
|
x /= 2.
|
|
assert_equal(x.data, [1 / 2., 2 / 2., 3])
|
|
assert_equal(x.mask, [0, 0, 1])
|
|
# Test div on array
|
|
x = array([1., 2., 3.], mask=[0, 0, 1])
|
|
xx = x / array([10., 20., 30.], mask=[1, 0, 0])
|
|
assert_equal(xx.data, [1., 2. / 20., 3.])
|
|
assert_equal(xx.mask, [1, 0, 1])
|
|
# Test idiv on array
|
|
x = array([1., 2., 3.], mask=[0, 0, 1])
|
|
x /= array([10., 20., 30.], mask=[1, 0, 0])
|
|
assert_equal(x.data, [1., 2 / 20., 3.])
|
|
assert_equal(x.mask, [1, 0, 1])
|
|
|
|
def test_datafriendly_pow(self):
|
|
# Test keeping data w/ (inplace) power
|
|
# Test pow on scalar
|
|
x = array([1., 2., 3.], mask=[0, 0, 1])
|
|
xx = x ** 2.5
|
|
assert_equal(xx.data, [1., 2. ** 2.5, 3.])
|
|
assert_equal(xx.mask, [0, 0, 1])
|
|
# Test ipow on scalar
|
|
x **= 2.5
|
|
assert_equal(x.data, [1., 2. ** 2.5, 3])
|
|
assert_equal(x.mask, [0, 0, 1])
|
|
|
|
def test_datafriendly_add_arrays(self):
|
|
a = array([[1, 1], [3, 3]])
|
|
b = array([1, 1], mask=[0, 0])
|
|
a += b
|
|
assert_equal(a, [[2, 2], [4, 4]])
|
|
if a.mask is not nomask:
|
|
assert_equal(a.mask, [[0, 0], [0, 0]])
|
|
|
|
a = array([[1, 1], [3, 3]])
|
|
b = array([1, 1], mask=[0, 1])
|
|
a += b
|
|
assert_equal(a, [[2, 2], [4, 4]])
|
|
assert_equal(a.mask, [[0, 1], [0, 1]])
|
|
|
|
def test_datafriendly_sub_arrays(self):
|
|
a = array([[1, 1], [3, 3]])
|
|
b = array([1, 1], mask=[0, 0])
|
|
a -= b
|
|
assert_equal(a, [[0, 0], [2, 2]])
|
|
if a.mask is not nomask:
|
|
assert_equal(a.mask, [[0, 0], [0, 0]])
|
|
|
|
a = array([[1, 1], [3, 3]])
|
|
b = array([1, 1], mask=[0, 1])
|
|
a -= b
|
|
assert_equal(a, [[0, 0], [2, 2]])
|
|
assert_equal(a.mask, [[0, 1], [0, 1]])
|
|
|
|
def test_datafriendly_mul_arrays(self):
|
|
a = array([[1, 1], [3, 3]])
|
|
b = array([1, 1], mask=[0, 0])
|
|
a *= b
|
|
assert_equal(a, [[1, 1], [3, 3]])
|
|
if a.mask is not nomask:
|
|
assert_equal(a.mask, [[0, 0], [0, 0]])
|
|
|
|
a = array([[1, 1], [3, 3]])
|
|
b = array([1, 1], mask=[0, 1])
|
|
a *= b
|
|
assert_equal(a, [[1, 1], [3, 3]])
|
|
assert_equal(a.mask, [[0, 1], [0, 1]])
|
|
|
|
def test_inplace_addition_scalar_type(self):
|
|
# Test of inplace additions
|
|
for t in self.othertypes:
|
|
with warnings.catch_warnings():
|
|
warnings.filterwarnings("error")
|
|
(x, y, xm) = (_.astype(t) for _ in self.uint8data)
|
|
xm[2] = masked
|
|
x += t(1)
|
|
assert_equal(x, y + t(1))
|
|
xm += t(1)
|
|
assert_equal(xm, y + t(1))
|
|
|
|
def test_inplace_addition_array_type(self):
|
|
# Test of inplace additions
|
|
for t in self.othertypes:
|
|
with warnings.catch_warnings():
|
|
warnings.filterwarnings("error")
|
|
(x, y, xm) = (_.astype(t) for _ in self.uint8data)
|
|
m = xm.mask
|
|
a = arange(10, dtype=t)
|
|
a[-1] = masked
|
|
x += a
|
|
xm += a
|
|
assert_equal(x, y + a)
|
|
assert_equal(xm, y + a)
|
|
assert_equal(xm.mask, mask_or(m, a.mask))
|
|
|
|
def test_inplace_subtraction_scalar_type(self):
|
|
# Test of inplace subtractions
|
|
for t in self.othertypes:
|
|
with warnings.catch_warnings():
|
|
warnings.filterwarnings("error")
|
|
(x, y, xm) = (_.astype(t) for _ in self.uint8data)
|
|
x -= t(1)
|
|
assert_equal(x, y - t(1))
|
|
xm -= t(1)
|
|
assert_equal(xm, y - t(1))
|
|
|
|
def test_inplace_subtraction_array_type(self):
|
|
# Test of inplace subtractions
|
|
for t in self.othertypes:
|
|
with warnings.catch_warnings():
|
|
warnings.filterwarnings("error")
|
|
(x, y, xm) = (_.astype(t) for _ in self.uint8data)
|
|
m = xm.mask
|
|
a = arange(10, dtype=t)
|
|
a[-1] = masked
|
|
x -= a
|
|
xm -= a
|
|
assert_equal(x, y - a)
|
|
assert_equal(xm, y - a)
|
|
assert_equal(xm.mask, mask_or(m, a.mask))
|
|
|
|
def test_inplace_multiplication_scalar_type(self):
|
|
# Test of inplace multiplication
|
|
for t in self.othertypes:
|
|
with warnings.catch_warnings():
|
|
warnings.filterwarnings("error")
|
|
(x, y, xm) = (_.astype(t) for _ in self.uint8data)
|
|
x *= t(2)
|
|
assert_equal(x, y * t(2))
|
|
xm *= t(2)
|
|
assert_equal(xm, y * t(2))
|
|
|
|
def test_inplace_multiplication_array_type(self):
|
|
# Test of inplace multiplication
|
|
for t in self.othertypes:
|
|
with warnings.catch_warnings():
|
|
warnings.filterwarnings("error")
|
|
(x, y, xm) = (_.astype(t) for _ in self.uint8data)
|
|
m = xm.mask
|
|
a = arange(10, dtype=t)
|
|
a[-1] = masked
|
|
x *= a
|
|
xm *= a
|
|
assert_equal(x, y * a)
|
|
assert_equal(xm, y * a)
|
|
assert_equal(xm.mask, mask_or(m, a.mask))
|
|
|
|
def test_inplace_floor_division_scalar_type(self):
|
|
# Test of inplace division
|
|
# Check for TypeError in case of unsupported types
|
|
unsupported = {np.dtype(t).type for t in np.typecodes["Complex"]}
|
|
for t in self.othertypes:
|
|
with warnings.catch_warnings():
|
|
warnings.filterwarnings("error")
|
|
(x, y, xm) = (_.astype(t) for _ in self.uint8data)
|
|
x = arange(10, dtype=t) * t(2)
|
|
xm = arange(10, dtype=t) * t(2)
|
|
xm[2] = masked
|
|
try:
|
|
x //= t(2)
|
|
xm //= t(2)
|
|
assert_equal(x, y)
|
|
assert_equal(xm, y)
|
|
except TypeError:
|
|
msg = f"Supported type {t} throwing TypeError"
|
|
assert t in unsupported, msg
|
|
|
|
def test_inplace_floor_division_array_type(self):
|
|
# Test of inplace division
|
|
# Check for TypeError in case of unsupported types
|
|
unsupported = {np.dtype(t).type for t in np.typecodes["Complex"]}
|
|
for t in self.othertypes:
|
|
with warnings.catch_warnings():
|
|
warnings.filterwarnings("error")
|
|
(x, y, xm) = (_.astype(t) for _ in self.uint8data)
|
|
m = xm.mask
|
|
a = arange(10, dtype=t)
|
|
a[-1] = masked
|
|
try:
|
|
x //= a
|
|
xm //= a
|
|
assert_equal(x, y // a)
|
|
assert_equal(xm, y // a)
|
|
assert_equal(
|
|
xm.mask,
|
|
mask_or(mask_or(m, a.mask), (a == t(0)))
|
|
)
|
|
except TypeError:
|
|
msg = f"Supported type {t} throwing TypeError"
|
|
assert t in unsupported, msg
|
|
|
|
def test_inplace_division_scalar_type(self):
|
|
# Test of inplace division
|
|
for t in self.othertypes:
|
|
with suppress_warnings() as sup:
|
|
sup.record(UserWarning)
|
|
|
|
(x, y, xm) = (_.astype(t) for _ in self.uint8data)
|
|
x = arange(10, dtype=t) * t(2)
|
|
xm = arange(10, dtype=t) * t(2)
|
|
xm[2] = masked
|
|
|
|
# May get a DeprecationWarning or a TypeError.
|
|
#
|
|
# This is a consequence of the fact that this is true divide
|
|
# and will require casting to float for calculation and
|
|
# casting back to the original type. This will only be raised
|
|
# with integers. Whether it is an error or warning is only
|
|
# dependent on how stringent the casting rules are.
|
|
#
|
|
# Will handle the same way.
|
|
try:
|
|
x /= t(2)
|
|
assert_equal(x, y)
|
|
except (DeprecationWarning, TypeError) as e:
|
|
warnings.warn(str(e), stacklevel=1)
|
|
try:
|
|
xm /= t(2)
|
|
assert_equal(xm, y)
|
|
except (DeprecationWarning, TypeError) as e:
|
|
warnings.warn(str(e), stacklevel=1)
|
|
|
|
if issubclass(t, np.integer):
|
|
assert_equal(len(sup.log), 2, f'Failed on type={t}.')
|
|
else:
|
|
assert_equal(len(sup.log), 0, f'Failed on type={t}.')
|
|
|
|
def test_inplace_division_array_type(self):
|
|
# Test of inplace division
|
|
for t in self.othertypes:
|
|
with suppress_warnings() as sup:
|
|
sup.record(UserWarning)
|
|
(x, y, xm) = (_.astype(t) for _ in self.uint8data)
|
|
m = xm.mask
|
|
a = arange(10, dtype=t)
|
|
a[-1] = masked
|
|
|
|
# May get a DeprecationWarning or a TypeError.
|
|
#
|
|
# This is a consequence of the fact that this is true divide
|
|
# and will require casting to float for calculation and
|
|
# casting back to the original type. This will only be raised
|
|
# with integers. Whether it is an error or warning is only
|
|
# dependent on how stringent the casting rules are.
|
|
#
|
|
# Will handle the same way.
|
|
try:
|
|
x /= a
|
|
assert_equal(x, y / a)
|
|
except (DeprecationWarning, TypeError) as e:
|
|
warnings.warn(str(e), stacklevel=1)
|
|
try:
|
|
xm /= a
|
|
assert_equal(xm, y / a)
|
|
assert_equal(
|
|
xm.mask,
|
|
mask_or(mask_or(m, a.mask), (a == t(0)))
|
|
)
|
|
except (DeprecationWarning, TypeError) as e:
|
|
warnings.warn(str(e), stacklevel=1)
|
|
|
|
if issubclass(t, np.integer):
|
|
assert_equal(len(sup.log), 2, f'Failed on type={t}.')
|
|
else:
|
|
assert_equal(len(sup.log), 0, f'Failed on type={t}.')
|
|
|
|
def test_inplace_pow_type(self):
|
|
# Test keeping data w/ (inplace) power
|
|
for t in self.othertypes:
|
|
with warnings.catch_warnings():
|
|
warnings.filterwarnings("error")
|
|
# Test pow on scalar
|
|
x = array([1, 2, 3], mask=[0, 0, 1], dtype=t)
|
|
xx = x ** t(2)
|
|
xx_r = array([1, 2 ** 2, 3], mask=[0, 0, 1], dtype=t)
|
|
assert_equal(xx.data, xx_r.data)
|
|
assert_equal(xx.mask, xx_r.mask)
|
|
# Test ipow on scalar
|
|
x **= t(2)
|
|
assert_equal(x.data, xx_r.data)
|
|
assert_equal(x.mask, xx_r.mask)
|
|
|
|
|
|
class TestMaskedArrayMethods:
|
|
# Test class for miscellaneous MaskedArrays methods.
|
|
def setup_method(self):
|
|
# Base data definition.
|
|
x = np.array([8.375, 7.545, 8.828, 8.5, 1.757, 5.928,
|
|
8.43, 7.78, 9.865, 5.878, 8.979, 4.732,
|
|
3.012, 6.022, 5.095, 3.116, 5.238, 3.957,
|
|
6.04, 9.63, 7.712, 3.382, 4.489, 6.479,
|
|
7.189, 9.645, 5.395, 4.961, 9.894, 2.893,
|
|
7.357, 9.828, 6.272, 3.758, 6.693, 0.993])
|
|
X = x.reshape(6, 6)
|
|
XX = x.reshape(3, 2, 2, 3)
|
|
|
|
m = np.array([0, 1, 0, 1, 0, 0,
|
|
1, 0, 1, 1, 0, 1,
|
|
0, 0, 0, 1, 0, 1,
|
|
0, 0, 0, 1, 1, 1,
|
|
1, 0, 0, 1, 0, 0,
|
|
0, 0, 1, 0, 1, 0])
|
|
mx = array(data=x, mask=m)
|
|
mX = array(data=X, mask=m.reshape(X.shape))
|
|
mXX = array(data=XX, mask=m.reshape(XX.shape))
|
|
|
|
m2 = np.array([1, 1, 0, 1, 0, 0,
|
|
1, 1, 1, 1, 0, 1,
|
|
0, 0, 1, 1, 0, 1,
|
|
0, 0, 0, 1, 1, 1,
|
|
1, 0, 0, 1, 1, 0,
|
|
0, 0, 1, 0, 1, 1])
|
|
m2x = array(data=x, mask=m2)
|
|
m2X = array(data=X, mask=m2.reshape(X.shape))
|
|
m2XX = array(data=XX, mask=m2.reshape(XX.shape))
|
|
self.d = (x, X, XX, m, mx, mX, mXX, m2x, m2X, m2XX)
|
|
|
|
def test_generic_methods(self):
|
|
# Tests some MaskedArray methods.
|
|
a = array([1, 3, 2])
|
|
assert_equal(a.any(), a._data.any())
|
|
assert_equal(a.all(), a._data.all())
|
|
assert_equal(a.argmax(), a._data.argmax())
|
|
assert_equal(a.argmin(), a._data.argmin())
|
|
assert_equal(a.choose(0, 1, 2, 3, 4), a._data.choose(0, 1, 2, 3, 4))
|
|
assert_equal(a.compress([1, 0, 1]), a._data.compress([1, 0, 1]))
|
|
assert_equal(a.conj(), a._data.conj())
|
|
assert_equal(a.conjugate(), a._data.conjugate())
|
|
|
|
m = array([[1, 2], [3, 4]])
|
|
assert_equal(m.diagonal(), m._data.diagonal())
|
|
assert_equal(a.sum(), a._data.sum())
|
|
assert_equal(a.take([1, 2]), a._data.take([1, 2]))
|
|
assert_equal(m.transpose(), m._data.transpose())
|
|
|
|
def test_allclose(self):
|
|
# Tests allclose on arrays
|
|
a = np.random.rand(10)
|
|
b = a + np.random.rand(10) * 1e-8
|
|
assert_(allclose(a, b))
|
|
# Test allclose w/ infs
|
|
a[0] = np.inf
|
|
assert_(not allclose(a, b))
|
|
b[0] = np.inf
|
|
assert_(allclose(a, b))
|
|
# Test allclose w/ masked
|
|
a = masked_array(a)
|
|
a[-1] = masked
|
|
assert_(allclose(a, b, masked_equal=True))
|
|
assert_(not allclose(a, b, masked_equal=False))
|
|
# Test comparison w/ scalar
|
|
a *= 1e-8
|
|
a[0] = 0
|
|
assert_(allclose(a, 0, masked_equal=True))
|
|
|
|
# Test that the function works for MIN_INT integer typed arrays
|
|
a = masked_array([np.iinfo(np.int_).min], dtype=np.int_)
|
|
assert_(allclose(a, a))
|
|
|
|
def test_allclose_timedelta(self):
|
|
# Allclose currently works for timedelta64 as long as `atol` is
|
|
# an integer or also a timedelta64
|
|
a = np.array([[1, 2, 3, 4]], dtype="m8[ns]")
|
|
assert allclose(a, a, atol=0)
|
|
assert allclose(a, a, atol=np.timedelta64(1, "ns"))
|
|
|
|
def test_allany(self):
|
|
# Checks the any/all methods/functions.
|
|
x = np.array([[0.13, 0.26, 0.90],
|
|
[0.28, 0.33, 0.63],
|
|
[0.31, 0.87, 0.70]])
|
|
m = np.array([[True, False, False],
|
|
[False, False, False],
|
|
[True, True, False]], dtype=np.bool_)
|
|
mx = masked_array(x, mask=m)
|
|
mxbig = (mx > 0.5)
|
|
mxsmall = (mx < 0.5)
|
|
|
|
assert_(not mxbig.all())
|
|
assert_(mxbig.any())
|
|
assert_equal(mxbig.all(0), [False, False, True])
|
|
assert_equal(mxbig.all(1), [False, False, True])
|
|
assert_equal(mxbig.any(0), [False, False, True])
|
|
assert_equal(mxbig.any(1), [True, True, True])
|
|
|
|
assert_(not mxsmall.all())
|
|
assert_(mxsmall.any())
|
|
assert_equal(mxsmall.all(0), [True, True, False])
|
|
assert_equal(mxsmall.all(1), [False, False, False])
|
|
assert_equal(mxsmall.any(0), [True, True, False])
|
|
assert_equal(mxsmall.any(1), [True, True, False])
|
|
|
|
def test_allany_oddities(self):
|
|
# Some fun with all and any
|
|
store = empty((), dtype=bool)
|
|
full = array([1, 2, 3], mask=True)
|
|
|
|
assert_(full.all() is masked)
|
|
full.all(out=store)
|
|
assert_(store)
|
|
assert_(store._mask, True)
|
|
assert_(store is not masked)
|
|
|
|
store = empty((), dtype=bool)
|
|
assert_(full.any() is masked)
|
|
full.any(out=store)
|
|
assert_(not store)
|
|
assert_(store._mask, True)
|
|
assert_(store is not masked)
|
|
|
|
def test_argmax_argmin(self):
|
|
# Tests argmin & argmax on MaskedArrays.
|
|
(x, X, XX, m, mx, mX, mXX, m2x, m2X, m2XX) = self.d
|
|
|
|
assert_equal(mx.argmin(), 35)
|
|
assert_equal(mX.argmin(), 35)
|
|
assert_equal(m2x.argmin(), 4)
|
|
assert_equal(m2X.argmin(), 4)
|
|
assert_equal(mx.argmax(), 28)
|
|
assert_equal(mX.argmax(), 28)
|
|
assert_equal(m2x.argmax(), 31)
|
|
assert_equal(m2X.argmax(), 31)
|
|
|
|
assert_equal(mX.argmin(0), [2, 2, 2, 5, 0, 5])
|
|
assert_equal(m2X.argmin(0), [2, 2, 4, 5, 0, 4])
|
|
assert_equal(mX.argmax(0), [0, 5, 0, 5, 4, 0])
|
|
assert_equal(m2X.argmax(0), [5, 5, 0, 5, 1, 0])
|
|
|
|
assert_equal(mX.argmin(1), [4, 1, 0, 0, 5, 5, ])
|
|
assert_equal(m2X.argmin(1), [4, 4, 0, 0, 5, 3])
|
|
assert_equal(mX.argmax(1), [2, 4, 1, 1, 4, 1])
|
|
assert_equal(m2X.argmax(1), [2, 4, 1, 1, 1, 1])
|
|
|
|
def test_clip(self):
|
|
# Tests clip on MaskedArrays.
|
|
x = np.array([8.375, 7.545, 8.828, 8.5, 1.757, 5.928,
|
|
8.43, 7.78, 9.865, 5.878, 8.979, 4.732,
|
|
3.012, 6.022, 5.095, 3.116, 5.238, 3.957,
|
|
6.04, 9.63, 7.712, 3.382, 4.489, 6.479,
|
|
7.189, 9.645, 5.395, 4.961, 9.894, 2.893,
|
|
7.357, 9.828, 6.272, 3.758, 6.693, 0.993])
|
|
m = np.array([0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1,
|
|
0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1,
|
|
1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0])
|
|
mx = array(x, mask=m)
|
|
clipped = mx.clip(2, 8)
|
|
assert_equal(clipped.mask, mx.mask)
|
|
assert_equal(clipped._data, x.clip(2, 8))
|
|
assert_equal(clipped._data, mx._data.clip(2, 8))
|
|
|
|
def test_clip_out(self):
|
|
# gh-14140
|
|
a = np.arange(10)
|
|
m = np.ma.MaskedArray(a, mask=[0, 1] * 5)
|
|
m.clip(0, 5, out=m)
|
|
assert_equal(m.mask, [0, 1] * 5)
|
|
|
|
def test_compress(self):
|
|
# test compress
|
|
a = masked_array([1., 2., 3., 4., 5.], fill_value=9999)
|
|
condition = (a > 1.5) & (a < 3.5)
|
|
assert_equal(a.compress(condition), [2., 3.])
|
|
|
|
a[[2, 3]] = masked
|
|
b = a.compress(condition)
|
|
assert_equal(b._data, [2., 3.])
|
|
assert_equal(b._mask, [0, 1])
|
|
assert_equal(b.fill_value, 9999)
|
|
assert_equal(b, a[condition])
|
|
|
|
condition = (a < 4.)
|
|
b = a.compress(condition)
|
|
assert_equal(b._data, [1., 2., 3.])
|
|
assert_equal(b._mask, [0, 0, 1])
|
|
assert_equal(b.fill_value, 9999)
|
|
assert_equal(b, a[condition])
|
|
|
|
a = masked_array([[10, 20, 30], [40, 50, 60]],
|
|
mask=[[0, 0, 1], [1, 0, 0]])
|
|
b = a.compress(a.ravel() >= 22)
|
|
assert_equal(b._data, [30, 40, 50, 60])
|
|
assert_equal(b._mask, [1, 1, 0, 0])
|
|
|
|
x = np.array([3, 1, 2])
|
|
b = a.compress(x >= 2, axis=1)
|
|
assert_equal(b._data, [[10, 30], [40, 60]])
|
|
assert_equal(b._mask, [[0, 1], [1, 0]])
|
|
|
|
def test_compressed(self):
|
|
# Tests compressed
|
|
a = array([1, 2, 3, 4], mask=[0, 0, 0, 0])
|
|
b = a.compressed()
|
|
assert_equal(b, a)
|
|
a[0] = masked
|
|
b = a.compressed()
|
|
assert_equal(b, [2, 3, 4])
|
|
|
|
def test_empty(self):
|
|
# Tests empty/like
|
|
datatype = [('a', int), ('b', float), ('c', '|S8')]
|
|
a = masked_array([(1, 1.1, '1.1'), (2, 2.2, '2.2'), (3, 3.3, '3.3')],
|
|
dtype=datatype)
|
|
assert_equal(len(a.fill_value.item()), len(datatype))
|
|
|
|
b = empty_like(a)
|
|
assert_equal(b.shape, a.shape)
|
|
assert_equal(b.fill_value, a.fill_value)
|
|
|
|
b = empty(len(a), dtype=datatype)
|
|
assert_equal(b.shape, a.shape)
|
|
assert_equal(b.fill_value, a.fill_value)
|
|
|
|
# check empty_like mask handling
|
|
a = masked_array([1, 2, 3], mask=[False, True, False])
|
|
b = empty_like(a)
|
|
assert_(not np.may_share_memory(a.mask, b.mask))
|
|
b = a.view(masked_array)
|
|
assert_(np.may_share_memory(a.mask, b.mask))
|
|
|
|
def test_zeros(self):
|
|
# Tests zeros/like
|
|
datatype = [('a', int), ('b', float), ('c', '|S8')]
|
|
a = masked_array([(1, 1.1, '1.1'), (2, 2.2, '2.2'), (3, 3.3, '3.3')],
|
|
dtype=datatype)
|
|
assert_equal(len(a.fill_value.item()), len(datatype))
|
|
|
|
b = zeros(len(a), dtype=datatype)
|
|
assert_equal(b.shape, a.shape)
|
|
assert_equal(b.fill_value, a.fill_value)
|
|
|
|
b = zeros_like(a)
|
|
assert_equal(b.shape, a.shape)
|
|
assert_equal(b.fill_value, a.fill_value)
|
|
|
|
# check zeros_like mask handling
|
|
a = masked_array([1, 2, 3], mask=[False, True, False])
|
|
b = zeros_like(a)
|
|
assert_(not np.may_share_memory(a.mask, b.mask))
|
|
b = a.view()
|
|
assert_(np.may_share_memory(a.mask, b.mask))
|
|
|
|
def test_ones(self):
|
|
# Tests ones/like
|
|
datatype = [('a', int), ('b', float), ('c', '|S8')]
|
|
a = masked_array([(1, 1.1, '1.1'), (2, 2.2, '2.2'), (3, 3.3, '3.3')],
|
|
dtype=datatype)
|
|
assert_equal(len(a.fill_value.item()), len(datatype))
|
|
|
|
b = ones(len(a), dtype=datatype)
|
|
assert_equal(b.shape, a.shape)
|
|
assert_equal(b.fill_value, a.fill_value)
|
|
|
|
b = ones_like(a)
|
|
assert_equal(b.shape, a.shape)
|
|
assert_equal(b.fill_value, a.fill_value)
|
|
|
|
# check ones_like mask handling
|
|
a = masked_array([1, 2, 3], mask=[False, True, False])
|
|
b = ones_like(a)
|
|
assert_(not np.may_share_memory(a.mask, b.mask))
|
|
b = a.view()
|
|
assert_(np.may_share_memory(a.mask, b.mask))
|
|
|
|
@suppress_copy_mask_on_assignment
|
|
def test_put(self):
|
|
# Tests put.
|
|
d = arange(5)
|
|
n = [0, 0, 0, 1, 1]
|
|
m = make_mask(n)
|
|
x = array(d, mask=m)
|
|
assert_(x[3] is masked)
|
|
assert_(x[4] is masked)
|
|
x[[1, 4]] = [10, 40]
|
|
assert_(x[3] is masked)
|
|
assert_(x[4] is not masked)
|
|
assert_equal(x, [0, 10, 2, -1, 40])
|
|
|
|
x = masked_array(arange(10), mask=[1, 0, 0, 0, 0] * 2)
|
|
i = [0, 2, 4, 6]
|
|
x.put(i, [6, 4, 2, 0])
|
|
assert_equal(x, asarray([6, 1, 4, 3, 2, 5, 0, 7, 8, 9, ]))
|
|
assert_equal(x.mask, [0, 0, 0, 0, 0, 1, 0, 0, 0, 0])
|
|
x.put(i, masked_array([0, 2, 4, 6], [1, 0, 1, 0]))
|
|
assert_array_equal(x, [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, ])
|
|
assert_equal(x.mask, [1, 0, 0, 0, 1, 1, 0, 0, 0, 0])
|
|
|
|
x = masked_array(arange(10), mask=[1, 0, 0, 0, 0] * 2)
|
|
put(x, i, [6, 4, 2, 0])
|
|
assert_equal(x, asarray([6, 1, 4, 3, 2, 5, 0, 7, 8, 9, ]))
|
|
assert_equal(x.mask, [0, 0, 0, 0, 0, 1, 0, 0, 0, 0])
|
|
put(x, i, masked_array([0, 2, 4, 6], [1, 0, 1, 0]))
|
|
assert_array_equal(x, [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, ])
|
|
assert_equal(x.mask, [1, 0, 0, 0, 1, 1, 0, 0, 0, 0])
|
|
|
|
def test_put_nomask(self):
|
|
# GitHub issue 6425
|
|
x = zeros(10)
|
|
z = array([3., -1.], mask=[False, True])
|
|
|
|
x.put([1, 2], z)
|
|
assert_(x[0] is not masked)
|
|
assert_equal(x[0], 0)
|
|
assert_(x[1] is not masked)
|
|
assert_equal(x[1], 3)
|
|
assert_(x[2] is masked)
|
|
assert_(x[3] is not masked)
|
|
assert_equal(x[3], 0)
|
|
|
|
def test_put_hardmask(self):
|
|
# Tests put on hardmask
|
|
d = arange(5)
|
|
n = [0, 0, 0, 1, 1]
|
|
m = make_mask(n)
|
|
xh = array(d + 1, mask=m, hard_mask=True, copy=True)
|
|
xh.put([4, 2, 0, 1, 3], [1, 2, 3, 4, 5])
|
|
assert_equal(xh._data, [3, 4, 2, 4, 5])
|
|
|
|
def test_putmask(self):
|
|
x = arange(6) + 1
|
|
mx = array(x, mask=[0, 0, 0, 1, 1, 1])
|
|
mask = [0, 0, 1, 0, 0, 1]
|
|
# w/o mask, w/o masked values
|
|
xx = x.copy()
|
|
putmask(xx, mask, 99)
|
|
assert_equal(xx, [1, 2, 99, 4, 5, 99])
|
|
# w/ mask, w/o masked values
|
|
mxx = mx.copy()
|
|
putmask(mxx, mask, 99)
|
|
assert_equal(mxx._data, [1, 2, 99, 4, 5, 99])
|
|
assert_equal(mxx._mask, [0, 0, 0, 1, 1, 0])
|
|
# w/o mask, w/ masked values
|
|
values = array([10, 20, 30, 40, 50, 60], mask=[1, 1, 1, 0, 0, 0])
|
|
xx = x.copy()
|
|
putmask(xx, mask, values)
|
|
assert_equal(xx._data, [1, 2, 30, 4, 5, 60])
|
|
assert_equal(xx._mask, [0, 0, 1, 0, 0, 0])
|
|
# w/ mask, w/ masked values
|
|
mxx = mx.copy()
|
|
putmask(mxx, mask, values)
|
|
assert_equal(mxx._data, [1, 2, 30, 4, 5, 60])
|
|
assert_equal(mxx._mask, [0, 0, 1, 1, 1, 0])
|
|
# w/ mask, w/ masked values + hardmask
|
|
mxx = mx.copy()
|
|
mxx.harden_mask()
|
|
putmask(mxx, mask, values)
|
|
assert_equal(mxx, [1, 2, 30, 4, 5, 60])
|
|
|
|
def test_ravel(self):
|
|
# Tests ravel
|
|
a = array([[1, 2, 3, 4, 5]], mask=[[0, 1, 0, 0, 0]])
|
|
aravel = a.ravel()
|
|
assert_equal(aravel._mask.shape, aravel.shape)
|
|
a = array([0, 0], mask=[1, 1])
|
|
aravel = a.ravel()
|
|
assert_equal(aravel._mask.shape, a.shape)
|
|
# Checks that small_mask is preserved
|
|
a = array([1, 2, 3, 4], mask=[0, 0, 0, 0], shrink=False)
|
|
assert_equal(a.ravel()._mask, [0, 0, 0, 0])
|
|
# Test that the fill_value is preserved
|
|
a.fill_value = -99
|
|
a.shape = (2, 2)
|
|
ar = a.ravel()
|
|
assert_equal(ar._mask, [0, 0, 0, 0])
|
|
assert_equal(ar._data, [1, 2, 3, 4])
|
|
assert_equal(ar.fill_value, -99)
|
|
# Test index ordering
|
|
assert_equal(a.ravel(order='C'), [1, 2, 3, 4])
|
|
assert_equal(a.ravel(order='F'), [1, 3, 2, 4])
|
|
|
|
@pytest.mark.parametrize("order", "AKCF")
|
|
@pytest.mark.parametrize("data_order", "CF")
|
|
def test_ravel_order(self, order, data_order):
|
|
# Ravelling must ravel mask and data in the same order always to avoid
|
|
# misaligning the two in the ravel result.
|
|
arr = np.ones((5, 10), order=data_order)
|
|
arr[0, :] = 0
|
|
mask = np.ones((10, 5), dtype=bool, order=data_order).T
|
|
mask[0, :] = False
|
|
x = array(arr, mask=mask)
|
|
assert x._data.flags.fnc != x._mask.flags.fnc
|
|
assert (x.filled(0) == 0).all()
|
|
raveled = x.ravel(order)
|
|
assert (raveled.filled(0) == 0).all()
|
|
|
|
# NOTE: Can be wrong if arr order is neither C nor F and `order="K"`
|
|
assert_array_equal(arr.ravel(order), x.ravel(order)._data)
|
|
|
|
def test_reshape(self):
|
|
# Tests reshape
|
|
x = arange(4)
|
|
x[0] = masked
|
|
y = x.reshape(2, 2)
|
|
assert_equal(y.shape, (2, 2,))
|
|
assert_equal(y._mask.shape, (2, 2,))
|
|
assert_equal(x.shape, (4,))
|
|
assert_equal(x._mask.shape, (4,))
|
|
|
|
def test_sort(self):
|
|
# Test sort
|
|
x = array([1, 4, 2, 3], mask=[0, 1, 0, 0], dtype=np.uint8)
|
|
|
|
sortedx = sort(x)
|
|
assert_equal(sortedx._data, [1, 2, 3, 4])
|
|
assert_equal(sortedx._mask, [0, 0, 0, 1])
|
|
|
|
sortedx = sort(x, endwith=False)
|
|
assert_equal(sortedx._data, [4, 1, 2, 3])
|
|
assert_equal(sortedx._mask, [1, 0, 0, 0])
|
|
|
|
x.sort()
|
|
assert_equal(x._data, [1, 2, 3, 4])
|
|
assert_equal(x._mask, [0, 0, 0, 1])
|
|
|
|
x = array([1, 4, 2, 3], mask=[0, 1, 0, 0], dtype=np.uint8)
|
|
x.sort(endwith=False)
|
|
assert_equal(x._data, [4, 1, 2, 3])
|
|
assert_equal(x._mask, [1, 0, 0, 0])
|
|
|
|
x = [1, 4, 2, 3]
|
|
sortedx = sort(x)
|
|
assert_(not isinstance(sorted, MaskedArray))
|
|
|
|
x = array([0, 1, -1, -2, 2], mask=nomask, dtype=np.int8)
|
|
sortedx = sort(x, endwith=False)
|
|
assert_equal(sortedx._data, [-2, -1, 0, 1, 2])
|
|
x = array([0, 1, -1, -2, 2], mask=[0, 1, 0, 0, 1], dtype=np.int8)
|
|
sortedx = sort(x, endwith=False)
|
|
assert_equal(sortedx._data, [1, 2, -2, -1, 0])
|
|
assert_equal(sortedx._mask, [1, 1, 0, 0, 0])
|
|
|
|
x = array([0, -1], dtype=np.int8)
|
|
sortedx = sort(x, kind="stable")
|
|
assert_equal(sortedx, array([-1, 0], dtype=np.int8))
|
|
|
|
def test_stable_sort(self):
|
|
x = array([1, 2, 3, 1, 2, 3], dtype=np.uint8)
|
|
expected = array([0, 3, 1, 4, 2, 5])
|
|
computed = argsort(x, kind='stable')
|
|
assert_equal(computed, expected)
|
|
|
|
def test_argsort_matches_sort(self):
|
|
x = array([1, 4, 2, 3], mask=[0, 1, 0, 0], dtype=np.uint8)
|
|
|
|
for kwargs in [dict(),
|
|
dict(endwith=True),
|
|
dict(endwith=False),
|
|
dict(fill_value=2),
|
|
dict(fill_value=2, endwith=True),
|
|
dict(fill_value=2, endwith=False)]:
|
|
sortedx = sort(x, **kwargs)
|
|
argsortedx = x[argsort(x, **kwargs)]
|
|
assert_equal(sortedx._data, argsortedx._data)
|
|
assert_equal(sortedx._mask, argsortedx._mask)
|
|
|
|
def test_sort_2d(self):
|
|
# Check sort of 2D array.
|
|
# 2D array w/o mask
|
|
a = masked_array([[8, 4, 1], [2, 0, 9]])
|
|
a.sort(0)
|
|
assert_equal(a, [[2, 0, 1], [8, 4, 9]])
|
|
a = masked_array([[8, 4, 1], [2, 0, 9]])
|
|
a.sort(1)
|
|
assert_equal(a, [[1, 4, 8], [0, 2, 9]])
|
|
# 2D array w/mask
|
|
a = masked_array([[8, 4, 1], [2, 0, 9]], mask=[[1, 0, 0], [0, 0, 1]])
|
|
a.sort(0)
|
|
assert_equal(a, [[2, 0, 1], [8, 4, 9]])
|
|
assert_equal(a._mask, [[0, 0, 0], [1, 0, 1]])
|
|
a = masked_array([[8, 4, 1], [2, 0, 9]], mask=[[1, 0, 0], [0, 0, 1]])
|
|
a.sort(1)
|
|
assert_equal(a, [[1, 4, 8], [0, 2, 9]])
|
|
assert_equal(a._mask, [[0, 0, 1], [0, 0, 1]])
|
|
# 3D
|
|
a = masked_array([[[7, 8, 9], [4, 5, 6], [1, 2, 3]],
|
|
[[1, 2, 3], [7, 8, 9], [4, 5, 6]],
|
|
[[7, 8, 9], [1, 2, 3], [4, 5, 6]],
|
|
[[4, 5, 6], [1, 2, 3], [7, 8, 9]]])
|
|
a[a % 4 == 0] = masked
|
|
am = a.copy()
|
|
an = a.filled(99)
|
|
am.sort(0)
|
|
an.sort(0)
|
|
assert_equal(am, an)
|
|
am = a.copy()
|
|
an = a.filled(99)
|
|
am.sort(1)
|
|
an.sort(1)
|
|
assert_equal(am, an)
|
|
am = a.copy()
|
|
an = a.filled(99)
|
|
am.sort(2)
|
|
an.sort(2)
|
|
assert_equal(am, an)
|
|
|
|
def test_sort_flexible(self):
|
|
# Test sort on structured dtype.
|
|
a = array(
|
|
data=[(3, 3), (3, 2), (2, 2), (2, 1), (1, 0), (1, 1), (1, 2)],
|
|
mask=[(0, 0), (0, 1), (0, 0), (0, 0), (1, 0), (0, 0), (0, 0)],
|
|
dtype=[('A', int), ('B', int)])
|
|
mask_last = array(
|
|
data=[(1, 1), (1, 2), (2, 1), (2, 2), (3, 3), (3, 2), (1, 0)],
|
|
mask=[(0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 1), (1, 0)],
|
|
dtype=[('A', int), ('B', int)])
|
|
mask_first = array(
|
|
data=[(1, 0), (1, 1), (1, 2), (2, 1), (2, 2), (3, 2), (3, 3)],
|
|
mask=[(1, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 1), (0, 0)],
|
|
dtype=[('A', int), ('B', int)])
|
|
|
|
test = sort(a)
|
|
assert_equal(test, mask_last)
|
|
assert_equal(test.mask, mask_last.mask)
|
|
|
|
test = sort(a, endwith=False)
|
|
assert_equal(test, mask_first)
|
|
assert_equal(test.mask, mask_first.mask)
|
|
|
|
# Test sort on dtype with subarray (gh-8069)
|
|
# Just check that the sort does not error, structured array subarrays
|
|
# are treated as byte strings and that leads to differing behavior
|
|
# depending on endianness and `endwith`.
|
|
dt = np.dtype([('v', int, 2)])
|
|
a = a.view(dt)
|
|
test = sort(a)
|
|
test = sort(a, endwith=False)
|
|
|
|
def test_argsort(self):
|
|
# Test argsort
|
|
a = array([1, 5, 2, 4, 3], mask=[1, 0, 0, 1, 0])
|
|
assert_equal(np.argsort(a), argsort(a))
|
|
|
|
def test_squeeze(self):
|
|
# Check squeeze
|
|
data = masked_array([[1, 2, 3]])
|
|
assert_equal(data.squeeze(), [1, 2, 3])
|
|
data = masked_array([[1, 2, 3]], mask=[[1, 1, 1]])
|
|
assert_equal(data.squeeze(), [1, 2, 3])
|
|
assert_equal(data.squeeze()._mask, [1, 1, 1])
|
|
|
|
# normal ndarrays return a view
|
|
arr = np.array([[1]])
|
|
arr_sq = arr.squeeze()
|
|
assert_equal(arr_sq, 1)
|
|
arr_sq[...] = 2
|
|
assert_equal(arr[0,0], 2)
|
|
|
|
# so maskedarrays should too
|
|
m_arr = masked_array([[1]], mask=True)
|
|
m_arr_sq = m_arr.squeeze()
|
|
assert_(m_arr_sq is not np.ma.masked)
|
|
assert_equal(m_arr_sq.mask, True)
|
|
m_arr_sq[...] = 2
|
|
assert_equal(m_arr[0,0], 2)
|
|
|
|
def test_swapaxes(self):
|
|
# Tests swapaxes on MaskedArrays.
|
|
x = np.array([8.375, 7.545, 8.828, 8.5, 1.757, 5.928,
|
|
8.43, 7.78, 9.865, 5.878, 8.979, 4.732,
|
|
3.012, 6.022, 5.095, 3.116, 5.238, 3.957,
|
|
6.04, 9.63, 7.712, 3.382, 4.489, 6.479,
|
|
7.189, 9.645, 5.395, 4.961, 9.894, 2.893,
|
|
7.357, 9.828, 6.272, 3.758, 6.693, 0.993])
|
|
m = np.array([0, 1, 0, 1, 0, 0,
|
|
1, 0, 1, 1, 0, 1,
|
|
0, 0, 0, 1, 0, 1,
|
|
0, 0, 0, 1, 1, 1,
|
|
1, 0, 0, 1, 0, 0,
|
|
0, 0, 1, 0, 1, 0])
|
|
mX = array(x, mask=m).reshape(6, 6)
|
|
mXX = mX.reshape(3, 2, 2, 3)
|
|
|
|
mXswapped = mX.swapaxes(0, 1)
|
|
assert_equal(mXswapped[-1], mX[:, -1])
|
|
|
|
mXXswapped = mXX.swapaxes(0, 2)
|
|
assert_equal(mXXswapped.shape, (2, 2, 3, 3))
|
|
|
|
def test_take(self):
|
|
# Tests take
|
|
x = masked_array([10, 20, 30, 40], [0, 1, 0, 1])
|
|
assert_equal(x.take([0, 0, 3]), masked_array([10, 10, 40], [0, 0, 1]))
|
|
assert_equal(x.take([0, 0, 3]), x[[0, 0, 3]])
|
|
assert_equal(x.take([[0, 1], [0, 1]]),
|
|
masked_array([[10, 20], [10, 20]], [[0, 1], [0, 1]]))
|
|
|
|
# assert_equal crashes when passed np.ma.mask
|
|
assert_(x[1] is np.ma.masked)
|
|
assert_(x.take(1) is np.ma.masked)
|
|
|
|
x = array([[10, 20, 30], [40, 50, 60]], mask=[[0, 0, 1], [1, 0, 0, ]])
|
|
assert_equal(x.take([0, 2], axis=1),
|
|
array([[10, 30], [40, 60]], mask=[[0, 1], [1, 0]]))
|
|
assert_equal(take(x, [0, 2], axis=1),
|
|
array([[10, 30], [40, 60]], mask=[[0, 1], [1, 0]]))
|
|
|
|
def test_take_masked_indices(self):
|
|
# Test take w/ masked indices
|
|
a = np.array((40, 18, 37, 9, 22))
|
|
indices = np.arange(3)[None,:] + np.arange(5)[:, None]
|
|
mindices = array(indices, mask=(indices >= len(a)))
|
|
# No mask
|
|
test = take(a, mindices, mode='clip')
|
|
ctrl = array([[40, 18, 37],
|
|
[18, 37, 9],
|
|
[37, 9, 22],
|
|
[9, 22, 22],
|
|
[22, 22, 22]])
|
|
assert_equal(test, ctrl)
|
|
# Masked indices
|
|
test = take(a, mindices)
|
|
ctrl = array([[40, 18, 37],
|
|
[18, 37, 9],
|
|
[37, 9, 22],
|
|
[9, 22, 40],
|
|
[22, 40, 40]])
|
|
ctrl[3, 2] = ctrl[4, 1] = ctrl[4, 2] = masked
|
|
assert_equal(test, ctrl)
|
|
assert_equal(test.mask, ctrl.mask)
|
|
# Masked input + masked indices
|
|
a = array((40, 18, 37, 9, 22), mask=(0, 1, 0, 0, 0))
|
|
test = take(a, mindices)
|
|
ctrl[0, 1] = ctrl[1, 0] = masked
|
|
assert_equal(test, ctrl)
|
|
assert_equal(test.mask, ctrl.mask)
|
|
|
|
def test_tolist(self):
|
|
# Tests to list
|
|
# ... on 1D
|
|
x = array(np.arange(12))
|
|
x[[1, -2]] = masked
|
|
xlist = x.tolist()
|
|
assert_(xlist[1] is None)
|
|
assert_(xlist[-2] is None)
|
|
# ... on 2D
|
|
x.shape = (3, 4)
|
|
xlist = x.tolist()
|
|
ctrl = [[0, None, 2, 3], [4, 5, 6, 7], [8, 9, None, 11]]
|
|
assert_equal(xlist[0], [0, None, 2, 3])
|
|
assert_equal(xlist[1], [4, 5, 6, 7])
|
|
assert_equal(xlist[2], [8, 9, None, 11])
|
|
assert_equal(xlist, ctrl)
|
|
# ... on structured array w/ masked records
|
|
x = array(list(zip([1, 2, 3],
|
|
[1.1, 2.2, 3.3],
|
|
['one', 'two', 'thr'])),
|
|
dtype=[('a', int), ('b', float), ('c', '|S8')])
|
|
x[-1] = masked
|
|
assert_equal(x.tolist(),
|
|
[(1, 1.1, b'one'),
|
|
(2, 2.2, b'two'),
|
|
(None, None, None)])
|
|
# ... on structured array w/ masked fields
|
|
a = array([(1, 2,), (3, 4)], mask=[(0, 1), (0, 0)],
|
|
dtype=[('a', int), ('b', int)])
|
|
test = a.tolist()
|
|
assert_equal(test, [[1, None], [3, 4]])
|
|
# ... on mvoid
|
|
a = a[0]
|
|
test = a.tolist()
|
|
assert_equal(test, [1, None])
|
|
|
|
def test_tolist_specialcase(self):
|
|
# Test mvoid.tolist: make sure we return a standard Python object
|
|
a = array([(0, 1), (2, 3)], dtype=[('a', int), ('b', int)])
|
|
# w/o mask: each entry is a np.void whose elements are standard Python
|
|
for entry in a:
|
|
for item in entry.tolist():
|
|
assert_(not isinstance(item, np.generic))
|
|
# w/ mask: each entry is a ma.void whose elements should be
|
|
# standard Python
|
|
a.mask[0] = (0, 1)
|
|
for entry in a:
|
|
for item in entry.tolist():
|
|
assert_(not isinstance(item, np.generic))
|
|
|
|
def test_toflex(self):
|
|
# Test the conversion to records
|
|
data = arange(10)
|
|
record = data.toflex()
|
|
assert_equal(record['_data'], data._data)
|
|
assert_equal(record['_mask'], data._mask)
|
|
|
|
data[[0, 1, 2, -1]] = masked
|
|
record = data.toflex()
|
|
assert_equal(record['_data'], data._data)
|
|
assert_equal(record['_mask'], data._mask)
|
|
|
|
ndtype = [('i', int), ('s', '|S3'), ('f', float)]
|
|
data = array([(i, s, f) for (i, s, f) in zip(np.arange(10),
|
|
'ABCDEFGHIJKLM',
|
|
np.random.rand(10))],
|
|
dtype=ndtype)
|
|
data[[0, 1, 2, -1]] = masked
|
|
record = data.toflex()
|
|
assert_equal(record['_data'], data._data)
|
|
assert_equal(record['_mask'], data._mask)
|
|
|
|
ndtype = np.dtype("int, (2,3)float, float")
|
|
data = array([(i, f, ff) for (i, f, ff) in zip(np.arange(10),
|
|
np.random.rand(10),
|
|
np.random.rand(10))],
|
|
dtype=ndtype)
|
|
data[[0, 1, 2, -1]] = masked
|
|
record = data.toflex()
|
|
assert_equal_records(record['_data'], data._data)
|
|
assert_equal_records(record['_mask'], data._mask)
|
|
|
|
def test_fromflex(self):
|
|
# Test the reconstruction of a masked_array from a record
|
|
a = array([1, 2, 3])
|
|
test = fromflex(a.toflex())
|
|
assert_equal(test, a)
|
|
assert_equal(test.mask, a.mask)
|
|
|
|
a = array([1, 2, 3], mask=[0, 0, 1])
|
|
test = fromflex(a.toflex())
|
|
assert_equal(test, a)
|
|
assert_equal(test.mask, a.mask)
|
|
|
|
a = array([(1, 1.), (2, 2.), (3, 3.)], mask=[(1, 0), (0, 0), (0, 1)],
|
|
dtype=[('A', int), ('B', float)])
|
|
test = fromflex(a.toflex())
|
|
assert_equal(test, a)
|
|
assert_equal(test.data, a.data)
|
|
|
|
def test_arraymethod(self):
|
|
# Test a _arraymethod w/ n argument
|
|
marray = masked_array([[1, 2, 3, 4, 5]], mask=[0, 0, 1, 0, 0])
|
|
control = masked_array([[1], [2], [3], [4], [5]],
|
|
mask=[0, 0, 1, 0, 0])
|
|
assert_equal(marray.T, control)
|
|
assert_equal(marray.transpose(), control)
|
|
|
|
assert_equal(MaskedArray.cumsum(marray.T, 0), control.cumsum(0))
|
|
|
|
def test_arraymethod_0d(self):
|
|
# gh-9430
|
|
x = np.ma.array(42, mask=True)
|
|
assert_equal(x.T.mask, x.mask)
|
|
assert_equal(x.T.data, x.data)
|
|
|
|
def test_transpose_view(self):
|
|
x = np.ma.array([[1, 2, 3], [4, 5, 6]])
|
|
x[0,1] = np.ma.masked
|
|
xt = x.T
|
|
|
|
xt[1,0] = 10
|
|
xt[0,1] = np.ma.masked
|
|
|
|
assert_equal(x.data, xt.T.data)
|
|
assert_equal(x.mask, xt.T.mask)
|
|
|
|
def test_diagonal_view(self):
|
|
x = np.ma.zeros((3,3))
|
|
x[0,0] = 10
|
|
x[1,1] = np.ma.masked
|
|
x[2,2] = 20
|
|
xd = x.diagonal()
|
|
x[1,1] = 15
|
|
assert_equal(xd.mask, x.diagonal().mask)
|
|
assert_equal(xd.data, x.diagonal().data)
|
|
|
|
|
|
class TestMaskedArrayMathMethods:
|
|
|
|
def setup_method(self):
|
|
# Base data definition.
|
|
x = np.array([8.375, 7.545, 8.828, 8.5, 1.757, 5.928,
|
|
8.43, 7.78, 9.865, 5.878, 8.979, 4.732,
|
|
3.012, 6.022, 5.095, 3.116, 5.238, 3.957,
|
|
6.04, 9.63, 7.712, 3.382, 4.489, 6.479,
|
|
7.189, 9.645, 5.395, 4.961, 9.894, 2.893,
|
|
7.357, 9.828, 6.272, 3.758, 6.693, 0.993])
|
|
X = x.reshape(6, 6)
|
|
XX = x.reshape(3, 2, 2, 3)
|
|
|
|
m = np.array([0, 1, 0, 1, 0, 0,
|
|
1, 0, 1, 1, 0, 1,
|
|
0, 0, 0, 1, 0, 1,
|
|
0, 0, 0, 1, 1, 1,
|
|
1, 0, 0, 1, 0, 0,
|
|
0, 0, 1, 0, 1, 0])
|
|
mx = array(data=x, mask=m)
|
|
mX = array(data=X, mask=m.reshape(X.shape))
|
|
mXX = array(data=XX, mask=m.reshape(XX.shape))
|
|
|
|
m2 = np.array([1, 1, 0, 1, 0, 0,
|
|
1, 1, 1, 1, 0, 1,
|
|
0, 0, 1, 1, 0, 1,
|
|
0, 0, 0, 1, 1, 1,
|
|
1, 0, 0, 1, 1, 0,
|
|
0, 0, 1, 0, 1, 1])
|
|
m2x = array(data=x, mask=m2)
|
|
m2X = array(data=X, mask=m2.reshape(X.shape))
|
|
m2XX = array(data=XX, mask=m2.reshape(XX.shape))
|
|
self.d = (x, X, XX, m, mx, mX, mXX, m2x, m2X, m2XX)
|
|
|
|
def test_cumsumprod(self):
|
|
# Tests cumsum & cumprod on MaskedArrays.
|
|
(x, X, XX, m, mx, mX, mXX, m2x, m2X, m2XX) = self.d
|
|
mXcp = mX.cumsum(0)
|
|
assert_equal(mXcp._data, mX.filled(0).cumsum(0))
|
|
mXcp = mX.cumsum(1)
|
|
assert_equal(mXcp._data, mX.filled(0).cumsum(1))
|
|
|
|
mXcp = mX.cumprod(0)
|
|
assert_equal(mXcp._data, mX.filled(1).cumprod(0))
|
|
mXcp = mX.cumprod(1)
|
|
assert_equal(mXcp._data, mX.filled(1).cumprod(1))
|
|
|
|
def test_cumsumprod_with_output(self):
|
|
# Tests cumsum/cumprod w/ output
|
|
xm = array(np.random.uniform(0, 10, 12)).reshape(3, 4)
|
|
xm[:, 0] = xm[0] = xm[-1, -1] = masked
|
|
|
|
for funcname in ('cumsum', 'cumprod'):
|
|
npfunc = getattr(np, funcname)
|
|
xmmeth = getattr(xm, funcname)
|
|
|
|
# A ndarray as explicit input
|
|
output = np.empty((3, 4), dtype=float)
|
|
output.fill(-9999)
|
|
result = npfunc(xm, axis=0, out=output)
|
|
# ... the result should be the given output
|
|
assert_(result is output)
|
|
assert_equal(result, xmmeth(axis=0, out=output))
|
|
|
|
output = empty((3, 4), dtype=int)
|
|
result = xmmeth(axis=0, out=output)
|
|
assert_(result is output)
|
|
|
|
def test_ptp(self):
|
|
# Tests ptp on MaskedArrays.
|
|
(x, X, XX, m, mx, mX, mXX, m2x, m2X, m2XX) = self.d
|
|
(n, m) = X.shape
|
|
assert_equal(mx.ptp(), mx.compressed().ptp())
|
|
rows = np.zeros(n, float)
|
|
cols = np.zeros(m, float)
|
|
for k in range(m):
|
|
cols[k] = mX[:, k].compressed().ptp()
|
|
for k in range(n):
|
|
rows[k] = mX[k].compressed().ptp()
|
|
assert_equal(mX.ptp(0), cols)
|
|
assert_equal(mX.ptp(1), rows)
|
|
|
|
def test_add_object(self):
|
|
x = masked_array(['a', 'b'], mask=[1, 0], dtype=object)
|
|
y = x + 'x'
|
|
assert_equal(y[1], 'bx')
|
|
assert_(y.mask[0])
|
|
|
|
def test_sum_object(self):
|
|
# Test sum on object dtype
|
|
a = masked_array([1, 2, 3], mask=[1, 0, 0], dtype=object)
|
|
assert_equal(a.sum(), 5)
|
|
a = masked_array([[1, 2, 3], [4, 5, 6]], dtype=object)
|
|
assert_equal(a.sum(axis=0), [5, 7, 9])
|
|
|
|
def test_prod_object(self):
|
|
# Test prod on object dtype
|
|
a = masked_array([1, 2, 3], mask=[1, 0, 0], dtype=object)
|
|
assert_equal(a.prod(), 2 * 3)
|
|
a = masked_array([[1, 2, 3], [4, 5, 6]], dtype=object)
|
|
assert_equal(a.prod(axis=0), [4, 10, 18])
|
|
|
|
def test_meananom_object(self):
|
|
# Test mean/anom on object dtype
|
|
a = masked_array([1, 2, 3], dtype=object)
|
|
assert_equal(a.mean(), 2)
|
|
assert_equal(a.anom(), [-1, 0, 1])
|
|
|
|
def test_anom_shape(self):
|
|
a = masked_array([1, 2, 3])
|
|
assert_equal(a.anom().shape, a.shape)
|
|
a.mask = True
|
|
assert_equal(a.anom().shape, a.shape)
|
|
assert_(np.ma.is_masked(a.anom()))
|
|
|
|
def test_anom(self):
|
|
a = masked_array(np.arange(1, 7).reshape(2, 3))
|
|
assert_almost_equal(a.anom(),
|
|
[[-2.5, -1.5, -0.5], [0.5, 1.5, 2.5]])
|
|
assert_almost_equal(a.anom(axis=0),
|
|
[[-1.5, -1.5, -1.5], [1.5, 1.5, 1.5]])
|
|
assert_almost_equal(a.anom(axis=1),
|
|
[[-1., 0., 1.], [-1., 0., 1.]])
|
|
a.mask = [[0, 0, 1], [0, 1, 0]]
|
|
mval = -99
|
|
assert_almost_equal(a.anom().filled(mval),
|
|
[[-2.25, -1.25, mval], [0.75, mval, 2.75]])
|
|
assert_almost_equal(a.anom(axis=0).filled(mval),
|
|
[[-1.5, 0.0, mval], [1.5, mval, 0.0]])
|
|
assert_almost_equal(a.anom(axis=1).filled(mval),
|
|
[[-0.5, 0.5, mval], [-1.0, mval, 1.0]])
|
|
|
|
def test_trace(self):
|
|
# Tests trace on MaskedArrays.
|
|
(x, X, XX, m, mx, mX, mXX, m2x, m2X, m2XX) = self.d
|
|
mXdiag = mX.diagonal()
|
|
assert_equal(mX.trace(), mX.diagonal().compressed().sum())
|
|
assert_almost_equal(mX.trace(),
|
|
X.trace() - sum(mXdiag.mask * X.diagonal(),
|
|
axis=0))
|
|
assert_equal(np.trace(mX), mX.trace())
|
|
|
|
# gh-5560
|
|
arr = np.arange(2*4*4).reshape(2,4,4)
|
|
m_arr = np.ma.masked_array(arr, False)
|
|
assert_equal(arr.trace(axis1=1, axis2=2), m_arr.trace(axis1=1, axis2=2))
|
|
|
|
def test_dot(self):
|
|
# Tests dot on MaskedArrays.
|
|
(x, X, XX, m, mx, mX, mXX, m2x, m2X, m2XX) = self.d
|
|
fx = mx.filled(0)
|
|
r = mx.dot(mx)
|
|
assert_almost_equal(r.filled(0), fx.dot(fx))
|
|
assert_(r.mask is nomask)
|
|
|
|
fX = mX.filled(0)
|
|
r = mX.dot(mX)
|
|
assert_almost_equal(r.filled(0), fX.dot(fX))
|
|
assert_(r.mask[1,3])
|
|
r1 = empty_like(r)
|
|
mX.dot(mX, out=r1)
|
|
assert_almost_equal(r, r1)
|
|
|
|
mYY = mXX.swapaxes(-1, -2)
|
|
fXX, fYY = mXX.filled(0), mYY.filled(0)
|
|
r = mXX.dot(mYY)
|
|
assert_almost_equal(r.filled(0), fXX.dot(fYY))
|
|
r1 = empty_like(r)
|
|
mXX.dot(mYY, out=r1)
|
|
assert_almost_equal(r, r1)
|
|
|
|
def test_dot_shape_mismatch(self):
|
|
# regression test
|
|
x = masked_array([[1,2],[3,4]], mask=[[0,1],[0,0]])
|
|
y = masked_array([[1,2],[3,4]], mask=[[0,1],[0,0]])
|
|
z = masked_array([[0,1],[3,3]])
|
|
x.dot(y, out=z)
|
|
assert_almost_equal(z.filled(0), [[1, 0], [15, 16]])
|
|
assert_almost_equal(z.mask, [[0, 1], [0, 0]])
|
|
|
|
def test_varmean_nomask(self):
|
|
# gh-5769
|
|
foo = array([1,2,3,4], dtype='f8')
|
|
bar = array([1,2,3,4], dtype='f8')
|
|
assert_equal(type(foo.mean()), np.float64)
|
|
assert_equal(type(foo.var()), np.float64)
|
|
assert((foo.mean() == bar.mean()) is np.bool_(True))
|
|
|
|
# check array type is preserved and out works
|
|
foo = array(np.arange(16).reshape((4,4)), dtype='f8')
|
|
bar = empty(4, dtype='f4')
|
|
assert_equal(type(foo.mean(axis=1)), MaskedArray)
|
|
assert_equal(type(foo.var(axis=1)), MaskedArray)
|
|
assert_(foo.mean(axis=1, out=bar) is bar)
|
|
assert_(foo.var(axis=1, out=bar) is bar)
|
|
|
|
def test_varstd(self):
|
|
# Tests var & std on MaskedArrays.
|
|
(x, X, XX, m, mx, mX, mXX, m2x, m2X, m2XX) = self.d
|
|
assert_almost_equal(mX.var(axis=None), mX.compressed().var())
|
|
assert_almost_equal(mX.std(axis=None), mX.compressed().std())
|
|
assert_almost_equal(mX.std(axis=None, ddof=1),
|
|
mX.compressed().std(ddof=1))
|
|
assert_almost_equal(mX.var(axis=None, ddof=1),
|
|
mX.compressed().var(ddof=1))
|
|
assert_equal(mXX.var(axis=3).shape, XX.var(axis=3).shape)
|
|
assert_equal(mX.var().shape, X.var().shape)
|
|
(mXvar0, mXvar1) = (mX.var(axis=0), mX.var(axis=1))
|
|
assert_almost_equal(mX.var(axis=None, ddof=2),
|
|
mX.compressed().var(ddof=2))
|
|
assert_almost_equal(mX.std(axis=None, ddof=2),
|
|
mX.compressed().std(ddof=2))
|
|
for k in range(6):
|
|
assert_almost_equal(mXvar1[k], mX[k].compressed().var())
|
|
assert_almost_equal(mXvar0[k], mX[:, k].compressed().var())
|
|
assert_almost_equal(np.sqrt(mXvar0[k]),
|
|
mX[:, k].compressed().std())
|
|
|
|
@suppress_copy_mask_on_assignment
|
|
def test_varstd_specialcases(self):
|
|
# Test a special case for var
|
|
nout = np.array(-1, dtype=float)
|
|
mout = array(-1, dtype=float)
|
|
|
|
x = array(arange(10), mask=True)
|
|
for methodname in ('var', 'std'):
|
|
method = getattr(x, methodname)
|
|
assert_(method() is masked)
|
|
assert_(method(0) is masked)
|
|
assert_(method(-1) is masked)
|
|
# Using a masked array as explicit output
|
|
method(out=mout)
|
|
assert_(mout is not masked)
|
|
assert_equal(mout.mask, True)
|
|
# Using a ndarray as explicit output
|
|
method(out=nout)
|
|
assert_(np.isnan(nout))
|
|
|
|
x = array(arange(10), mask=True)
|
|
x[-1] = 9
|
|
for methodname in ('var', 'std'):
|
|
method = getattr(x, methodname)
|
|
assert_(method(ddof=1) is masked)
|
|
assert_(method(0, ddof=1) is masked)
|
|
assert_(method(-1, ddof=1) is masked)
|
|
# Using a masked array as explicit output
|
|
method(out=mout, ddof=1)
|
|
assert_(mout is not masked)
|
|
assert_equal(mout.mask, True)
|
|
# Using a ndarray as explicit output
|
|
method(out=nout, ddof=1)
|
|
assert_(np.isnan(nout))
|
|
|
|
def test_varstd_ddof(self):
|
|
a = array([[1, 1, 0], [1, 1, 0]], mask=[[0, 0, 1], [0, 0, 1]])
|
|
test = a.std(axis=0, ddof=0)
|
|
assert_equal(test.filled(0), [0, 0, 0])
|
|
assert_equal(test.mask, [0, 0, 1])
|
|
test = a.std(axis=0, ddof=1)
|
|
assert_equal(test.filled(0), [0, 0, 0])
|
|
assert_equal(test.mask, [0, 0, 1])
|
|
test = a.std(axis=0, ddof=2)
|
|
assert_equal(test.filled(0), [0, 0, 0])
|
|
assert_equal(test.mask, [1, 1, 1])
|
|
|
|
def test_diag(self):
|
|
# Test diag
|
|
x = arange(9).reshape((3, 3))
|
|
x[1, 1] = masked
|
|
out = np.diag(x)
|
|
assert_equal(out, [0, 4, 8])
|
|
out = diag(x)
|
|
assert_equal(out, [0, 4, 8])
|
|
assert_equal(out.mask, [0, 1, 0])
|
|
out = diag(out)
|
|
control = array([[0, 0, 0], [0, 4, 0], [0, 0, 8]],
|
|
mask=[[0, 0, 0], [0, 1, 0], [0, 0, 0]])
|
|
assert_equal(out, control)
|
|
|
|
def test_axis_methods_nomask(self):
|
|
# Test the combination nomask & methods w/ axis
|
|
a = array([[1, 2, 3], [4, 5, 6]])
|
|
|
|
assert_equal(a.sum(0), [5, 7, 9])
|
|
assert_equal(a.sum(-1), [6, 15])
|
|
assert_equal(a.sum(1), [6, 15])
|
|
|
|
assert_equal(a.prod(0), [4, 10, 18])
|
|
assert_equal(a.prod(-1), [6, 120])
|
|
assert_equal(a.prod(1), [6, 120])
|
|
|
|
assert_equal(a.min(0), [1, 2, 3])
|
|
assert_equal(a.min(-1), [1, 4])
|
|
assert_equal(a.min(1), [1, 4])
|
|
|
|
assert_equal(a.max(0), [4, 5, 6])
|
|
assert_equal(a.max(-1), [3, 6])
|
|
assert_equal(a.max(1), [3, 6])
|
|
|
|
@requires_memory(free_bytes=2 * 10000 * 1000 * 2)
|
|
def test_mean_overflow(self):
|
|
# Test overflow in masked arrays
|
|
# gh-20272
|
|
a = masked_array(np.full((10000, 10000), 65535, dtype=np.uint16),
|
|
mask=np.zeros((10000, 10000)))
|
|
assert_equal(a.mean(), 65535.0)
|
|
|
|
def test_diff_with_prepend(self):
|
|
# GH 22465
|
|
x = np.array([1, 2, 2, 3, 4, 2, 1, 1])
|
|
|
|
a = np.ma.masked_equal(x[3:], value=2)
|
|
a_prep = np.ma.masked_equal(x[:3], value=2)
|
|
diff1 = np.ma.diff(a, prepend=a_prep, axis=0)
|
|
|
|
b = np.ma.masked_equal(x, value=2)
|
|
diff2 = np.ma.diff(b, axis=0)
|
|
|
|
assert_(np.ma.allequal(diff1, diff2))
|
|
|
|
def test_diff_with_append(self):
|
|
# GH 22465
|
|
x = np.array([1, 2, 2, 3, 4, 2, 1, 1])
|
|
|
|
a = np.ma.masked_equal(x[:3], value=2)
|
|
a_app = np.ma.masked_equal(x[3:], value=2)
|
|
diff1 = np.ma.diff(a, append=a_app, axis=0)
|
|
|
|
b = np.ma.masked_equal(x, value=2)
|
|
diff2 = np.ma.diff(b, axis=0)
|
|
|
|
assert_(np.ma.allequal(diff1, diff2))
|
|
|
|
def test_diff_with_dim_0(self):
|
|
with pytest.raises(
|
|
ValueError,
|
|
match="diff requires input that is at least one dimensional"
|
|
):
|
|
np.ma.diff(np.array(1))
|
|
|
|
def test_diff_with_n_0(self):
|
|
a = np.ma.masked_equal([1, 2, 2, 3, 4, 2, 1, 1], value=2)
|
|
diff = np.ma.diff(a, n=0, axis=0)
|
|
|
|
assert_(np.ma.allequal(a, diff))
|
|
|
|
|
|
class TestMaskedArrayMathMethodsComplex:
|
|
# Test class for miscellaneous MaskedArrays methods.
|
|
def setup_method(self):
|
|
# Base data definition.
|
|
x = np.array([8.375j, 7.545j, 8.828j, 8.5j, 1.757j, 5.928,
|
|
8.43, 7.78, 9.865, 5.878, 8.979, 4.732,
|
|
3.012, 6.022, 5.095, 3.116, 5.238, 3.957,
|
|
6.04, 9.63, 7.712, 3.382, 4.489, 6.479j,
|
|
7.189j, 9.645, 5.395, 4.961, 9.894, 2.893,
|
|
7.357, 9.828, 6.272, 3.758, 6.693, 0.993j])
|
|
X = x.reshape(6, 6)
|
|
XX = x.reshape(3, 2, 2, 3)
|
|
|
|
m = np.array([0, 1, 0, 1, 0, 0,
|
|
1, 0, 1, 1, 0, 1,
|
|
0, 0, 0, 1, 0, 1,
|
|
0, 0, 0, 1, 1, 1,
|
|
1, 0, 0, 1, 0, 0,
|
|
0, 0, 1, 0, 1, 0])
|
|
mx = array(data=x, mask=m)
|
|
mX = array(data=X, mask=m.reshape(X.shape))
|
|
mXX = array(data=XX, mask=m.reshape(XX.shape))
|
|
|
|
m2 = np.array([1, 1, 0, 1, 0, 0,
|
|
1, 1, 1, 1, 0, 1,
|
|
0, 0, 1, 1, 0, 1,
|
|
0, 0, 0, 1, 1, 1,
|
|
1, 0, 0, 1, 1, 0,
|
|
0, 0, 1, 0, 1, 1])
|
|
m2x = array(data=x, mask=m2)
|
|
m2X = array(data=X, mask=m2.reshape(X.shape))
|
|
m2XX = array(data=XX, mask=m2.reshape(XX.shape))
|
|
self.d = (x, X, XX, m, mx, mX, mXX, m2x, m2X, m2XX)
|
|
|
|
def test_varstd(self):
|
|
# Tests var & std on MaskedArrays.
|
|
(x, X, XX, m, mx, mX, mXX, m2x, m2X, m2XX) = self.d
|
|
assert_almost_equal(mX.var(axis=None), mX.compressed().var())
|
|
assert_almost_equal(mX.std(axis=None), mX.compressed().std())
|
|
assert_equal(mXX.var(axis=3).shape, XX.var(axis=3).shape)
|
|
assert_equal(mX.var().shape, X.var().shape)
|
|
(mXvar0, mXvar1) = (mX.var(axis=0), mX.var(axis=1))
|
|
assert_almost_equal(mX.var(axis=None, ddof=2),
|
|
mX.compressed().var(ddof=2))
|
|
assert_almost_equal(mX.std(axis=None, ddof=2),
|
|
mX.compressed().std(ddof=2))
|
|
for k in range(6):
|
|
assert_almost_equal(mXvar1[k], mX[k].compressed().var())
|
|
assert_almost_equal(mXvar0[k], mX[:, k].compressed().var())
|
|
assert_almost_equal(np.sqrt(mXvar0[k]),
|
|
mX[:, k].compressed().std())
|
|
|
|
|
|
class TestMaskedArrayFunctions:
|
|
# Test class for miscellaneous functions.
|
|
|
|
def setup_method(self):
|
|
x = np.array([1., 1., 1., -2., pi/2.0, 4., 5., -10., 10., 1., 2., 3.])
|
|
y = np.array([5., 0., 3., 2., -1., -4., 0., -10., 10., 1., 0., 3.])
|
|
m1 = [1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0]
|
|
m2 = [0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1]
|
|
xm = masked_array(x, mask=m1)
|
|
ym = masked_array(y, mask=m2)
|
|
xm.set_fill_value(1e+20)
|
|
self.info = (xm, ym)
|
|
|
|
def test_masked_where_bool(self):
|
|
x = [1, 2]
|
|
y = masked_where(False, x)
|
|
assert_equal(y, [1, 2])
|
|
assert_equal(y[1], 2)
|
|
|
|
def test_masked_equal_wlist(self):
|
|
x = [1, 2, 3]
|
|
mx = masked_equal(x, 3)
|
|
assert_equal(mx, x)
|
|
assert_equal(mx._mask, [0, 0, 1])
|
|
mx = masked_not_equal(x, 3)
|
|
assert_equal(mx, x)
|
|
assert_equal(mx._mask, [1, 1, 0])
|
|
|
|
def test_masked_equal_fill_value(self):
|
|
x = [1, 2, 3]
|
|
mx = masked_equal(x, 3)
|
|
assert_equal(mx._mask, [0, 0, 1])
|
|
assert_equal(mx.fill_value, 3)
|
|
|
|
def test_masked_where_condition(self):
|
|
# Tests masking functions.
|
|
x = array([1., 2., 3., 4., 5.])
|
|
x[2] = masked
|
|
assert_equal(masked_where(greater(x, 2), x), masked_greater(x, 2))
|
|
assert_equal(masked_where(greater_equal(x, 2), x),
|
|
masked_greater_equal(x, 2))
|
|
assert_equal(masked_where(less(x, 2), x), masked_less(x, 2))
|
|
assert_equal(masked_where(less_equal(x, 2), x),
|
|
masked_less_equal(x, 2))
|
|
assert_equal(masked_where(not_equal(x, 2), x), masked_not_equal(x, 2))
|
|
assert_equal(masked_where(equal(x, 2), x), masked_equal(x, 2))
|
|
assert_equal(masked_where(not_equal(x, 2), x), masked_not_equal(x, 2))
|
|
assert_equal(masked_where([1, 1, 0, 0, 0], [1, 2, 3, 4, 5]),
|
|
[99, 99, 3, 4, 5])
|
|
|
|
def test_masked_where_oddities(self):
|
|
# Tests some generic features.
|
|
atest = ones((10, 10, 10), dtype=float)
|
|
btest = zeros(atest.shape, MaskType)
|
|
ctest = masked_where(btest, atest)
|
|
assert_equal(atest, ctest)
|
|
|
|
def test_masked_where_shape_constraint(self):
|
|
a = arange(10)
|
|
with assert_raises(IndexError):
|
|
masked_equal(1, a)
|
|
test = masked_equal(a, 1)
|
|
assert_equal(test.mask, [0, 1, 0, 0, 0, 0, 0, 0, 0, 0])
|
|
|
|
def test_masked_where_structured(self):
|
|
# test that masked_where on a structured array sets a structured
|
|
# mask (see issue #2972)
|
|
a = np.zeros(10, dtype=[("A", "<f2"), ("B", "<f4")])
|
|
with np.errstate(over="ignore"):
|
|
# NOTE: The float16 "uses" 1e20 as mask, which overflows to inf
|
|
# and warns. Unrelated to this test, but probably undesired.
|
|
# But NumPy previously did not warn for this overflow.
|
|
am = np.ma.masked_where(a["A"] < 5, a)
|
|
assert_equal(am.mask.dtype.names, am.dtype.names)
|
|
assert_equal(am["A"],
|
|
np.ma.masked_array(np.zeros(10), np.ones(10)))
|
|
|
|
def test_masked_where_mismatch(self):
|
|
# gh-4520
|
|
x = np.arange(10)
|
|
y = np.arange(5)
|
|
assert_raises(IndexError, np.ma.masked_where, y > 6, x)
|
|
|
|
def test_masked_otherfunctions(self):
|
|
assert_equal(masked_inside(list(range(5)), 1, 3),
|
|
[0, 199, 199, 199, 4])
|
|
assert_equal(masked_outside(list(range(5)), 1, 3), [199, 1, 2, 3, 199])
|
|
assert_equal(masked_inside(array(list(range(5)),
|
|
mask=[1, 0, 0, 0, 0]), 1, 3).mask,
|
|
[1, 1, 1, 1, 0])
|
|
assert_equal(masked_outside(array(list(range(5)),
|
|
mask=[0, 1, 0, 0, 0]), 1, 3).mask,
|
|
[1, 1, 0, 0, 1])
|
|
assert_equal(masked_equal(array(list(range(5)),
|
|
mask=[1, 0, 0, 0, 0]), 2).mask,
|
|
[1, 0, 1, 0, 0])
|
|
assert_equal(masked_not_equal(array([2, 2, 1, 2, 1],
|
|
mask=[1, 0, 0, 0, 0]), 2).mask,
|
|
[1, 0, 1, 0, 1])
|
|
|
|
def test_round(self):
|
|
a = array([1.23456, 2.34567, 3.45678, 4.56789, 5.67890],
|
|
mask=[0, 1, 0, 0, 0])
|
|
assert_equal(a.round(), [1., 2., 3., 5., 6.])
|
|
assert_equal(a.round(1), [1.2, 2.3, 3.5, 4.6, 5.7])
|
|
assert_equal(a.round(3), [1.235, 2.346, 3.457, 4.568, 5.679])
|
|
b = empty_like(a)
|
|
a.round(out=b)
|
|
assert_equal(b, [1., 2., 3., 5., 6.])
|
|
|
|
x = array([1., 2., 3., 4., 5.])
|
|
c = array([1, 1, 1, 0, 0])
|
|
x[2] = masked
|
|
z = where(c, x, -x)
|
|
assert_equal(z, [1., 2., 0., -4., -5])
|
|
c[0] = masked
|
|
z = where(c, x, -x)
|
|
assert_equal(z, [1., 2., 0., -4., -5])
|
|
assert_(z[0] is masked)
|
|
assert_(z[1] is not masked)
|
|
assert_(z[2] is masked)
|
|
|
|
def test_round_with_output(self):
|
|
# Testing round with an explicit output
|
|
|
|
xm = array(np.random.uniform(0, 10, 12)).reshape(3, 4)
|
|
xm[:, 0] = xm[0] = xm[-1, -1] = masked
|
|
|
|
# A ndarray as explicit input
|
|
output = np.empty((3, 4), dtype=float)
|
|
output.fill(-9999)
|
|
result = np.round(xm, decimals=2, out=output)
|
|
# ... the result should be the given output
|
|
assert_(result is output)
|
|
assert_equal(result, xm.round(decimals=2, out=output))
|
|
|
|
output = empty((3, 4), dtype=float)
|
|
result = xm.round(decimals=2, out=output)
|
|
assert_(result is output)
|
|
|
|
def test_round_with_scalar(self):
|
|
# Testing round with scalar/zero dimension input
|
|
# GH issue 2244
|
|
a = array(1.1, mask=[False])
|
|
assert_equal(a.round(), 1)
|
|
|
|
a = array(1.1, mask=[True])
|
|
assert_(a.round() is masked)
|
|
|
|
a = array(1.1, mask=[False])
|
|
output = np.empty(1, dtype=float)
|
|
output.fill(-9999)
|
|
a.round(out=output)
|
|
assert_equal(output, 1)
|
|
|
|
a = array(1.1, mask=[False])
|
|
output = array(-9999., mask=[True])
|
|
a.round(out=output)
|
|
assert_equal(output[()], 1)
|
|
|
|
a = array(1.1, mask=[True])
|
|
output = array(-9999., mask=[False])
|
|
a.round(out=output)
|
|
assert_(output[()] is masked)
|
|
|
|
def test_identity(self):
|
|
a = identity(5)
|
|
assert_(isinstance(a, MaskedArray))
|
|
assert_equal(a, np.identity(5))
|
|
|
|
def test_power(self):
|
|
x = -1.1
|
|
assert_almost_equal(power(x, 2.), 1.21)
|
|
assert_(power(x, masked) is masked)
|
|
x = array([-1.1, -1.1, 1.1, 1.1, 0.])
|
|
b = array([0.5, 2., 0.5, 2., -1.], mask=[0, 0, 0, 0, 1])
|
|
y = power(x, b)
|
|
assert_almost_equal(y, [0, 1.21, 1.04880884817, 1.21, 0.])
|
|
assert_equal(y._mask, [1, 0, 0, 0, 1])
|
|
b.mask = nomask
|
|
y = power(x, b)
|
|
assert_equal(y._mask, [1, 0, 0, 0, 1])
|
|
z = x ** b
|
|
assert_equal(z._mask, y._mask)
|
|
assert_almost_equal(z, y)
|
|
assert_almost_equal(z._data, y._data)
|
|
x **= b
|
|
assert_equal(x._mask, y._mask)
|
|
assert_almost_equal(x, y)
|
|
assert_almost_equal(x._data, y._data)
|
|
|
|
def test_power_with_broadcasting(self):
|
|
# Test power w/ broadcasting
|
|
a2 = np.array([[1., 2., 3.], [4., 5., 6.]])
|
|
a2m = array(a2, mask=[[1, 0, 0], [0, 0, 1]])
|
|
b1 = np.array([2, 4, 3])
|
|
b2 = np.array([b1, b1])
|
|
b2m = array(b2, mask=[[0, 1, 0], [0, 1, 0]])
|
|
|
|
ctrl = array([[1 ** 2, 2 ** 4, 3 ** 3], [4 ** 2, 5 ** 4, 6 ** 3]],
|
|
mask=[[1, 1, 0], [0, 1, 1]])
|
|
# No broadcasting, base & exp w/ mask
|
|
test = a2m ** b2m
|
|
assert_equal(test, ctrl)
|
|
assert_equal(test.mask, ctrl.mask)
|
|
# No broadcasting, base w/ mask, exp w/o mask
|
|
test = a2m ** b2
|
|
assert_equal(test, ctrl)
|
|
assert_equal(test.mask, a2m.mask)
|
|
# No broadcasting, base w/o mask, exp w/ mask
|
|
test = a2 ** b2m
|
|
assert_equal(test, ctrl)
|
|
assert_equal(test.mask, b2m.mask)
|
|
|
|
ctrl = array([[2 ** 2, 4 ** 4, 3 ** 3], [2 ** 2, 4 ** 4, 3 ** 3]],
|
|
mask=[[0, 1, 0], [0, 1, 0]])
|
|
test = b1 ** b2m
|
|
assert_equal(test, ctrl)
|
|
assert_equal(test.mask, ctrl.mask)
|
|
test = b2m ** b1
|
|
assert_equal(test, ctrl)
|
|
assert_equal(test.mask, ctrl.mask)
|
|
|
|
@pytest.mark.skipif(IS_WASM, reason="fp errors don't work in wasm")
|
|
def test_where(self):
|
|
# Test the where function
|
|
x = np.array([1., 1., 1., -2., pi/2.0, 4., 5., -10., 10., 1., 2., 3.])
|
|
y = np.array([5., 0., 3., 2., -1., -4., 0., -10., 10., 1., 0., 3.])
|
|
m1 = [1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0]
|
|
m2 = [0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1]
|
|
xm = masked_array(x, mask=m1)
|
|
ym = masked_array(y, mask=m2)
|
|
xm.set_fill_value(1e+20)
|
|
|
|
d = where(xm > 2, xm, -9)
|
|
assert_equal(d, [-9., -9., -9., -9., -9., 4.,
|
|
-9., -9., 10., -9., -9., 3.])
|
|
assert_equal(d._mask, xm._mask)
|
|
d = where(xm > 2, -9, ym)
|
|
assert_equal(d, [5., 0., 3., 2., -1., -9.,
|
|
-9., -10., -9., 1., 0., -9.])
|
|
assert_equal(d._mask, [1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0])
|
|
d = where(xm > 2, xm, masked)
|
|
assert_equal(d, [-9., -9., -9., -9., -9., 4.,
|
|
-9., -9., 10., -9., -9., 3.])
|
|
tmp = xm._mask.copy()
|
|
tmp[(xm <= 2).filled(True)] = True
|
|
assert_equal(d._mask, tmp)
|
|
|
|
with np.errstate(invalid="warn"):
|
|
# The fill value is 1e20, it cannot be converted to `int`:
|
|
with pytest.warns(RuntimeWarning, match="invalid value"):
|
|
ixm = xm.astype(int)
|
|
d = where(ixm > 2, ixm, masked)
|
|
assert_equal(d, [-9, -9, -9, -9, -9, 4, -9, -9, 10, -9, -9, 3])
|
|
assert_equal(d.dtype, ixm.dtype)
|
|
|
|
def test_where_object(self):
|
|
a = np.array(None)
|
|
b = masked_array(None)
|
|
r = b.copy()
|
|
assert_equal(np.ma.where(True, a, a), r)
|
|
assert_equal(np.ma.where(True, b, b), r)
|
|
|
|
def test_where_with_masked_choice(self):
|
|
x = arange(10)
|
|
x[3] = masked
|
|
c = x >= 8
|
|
# Set False to masked
|
|
z = where(c, x, masked)
|
|
assert_(z.dtype is x.dtype)
|
|
assert_(z[3] is masked)
|
|
assert_(z[4] is masked)
|
|
assert_(z[7] is masked)
|
|
assert_(z[8] is not masked)
|
|
assert_(z[9] is not masked)
|
|
assert_equal(x, z)
|
|
# Set True to masked
|
|
z = where(c, masked, x)
|
|
assert_(z.dtype is x.dtype)
|
|
assert_(z[3] is masked)
|
|
assert_(z[4] is not masked)
|
|
assert_(z[7] is not masked)
|
|
assert_(z[8] is masked)
|
|
assert_(z[9] is masked)
|
|
|
|
def test_where_with_masked_condition(self):
|
|
x = array([1., 2., 3., 4., 5.])
|
|
c = array([1, 1, 1, 0, 0])
|
|
x[2] = masked
|
|
z = where(c, x, -x)
|
|
assert_equal(z, [1., 2., 0., -4., -5])
|
|
c[0] = masked
|
|
z = where(c, x, -x)
|
|
assert_equal(z, [1., 2., 0., -4., -5])
|
|
assert_(z[0] is masked)
|
|
assert_(z[1] is not masked)
|
|
assert_(z[2] is masked)
|
|
|
|
x = arange(1, 6)
|
|
x[-1] = masked
|
|
y = arange(1, 6) * 10
|
|
y[2] = masked
|
|
c = array([1, 1, 1, 0, 0], mask=[1, 0, 0, 0, 0])
|
|
cm = c.filled(1)
|
|
z = where(c, x, y)
|
|
zm = where(cm, x, y)
|
|
assert_equal(z, zm)
|
|
assert_(getmask(zm) is nomask)
|
|
assert_equal(zm, [1, 2, 3, 40, 50])
|
|
z = where(c, masked, 1)
|
|
assert_equal(z, [99, 99, 99, 1, 1])
|
|
z = where(c, 1, masked)
|
|
assert_equal(z, [99, 1, 1, 99, 99])
|
|
|
|
def test_where_type(self):
|
|
# Test the type conservation with where
|
|
x = np.arange(4, dtype=np.int32)
|
|
y = np.arange(4, dtype=np.float32) * 2.2
|
|
test = where(x > 1.5, y, x).dtype
|
|
control = np.result_type(np.int32, np.float32)
|
|
assert_equal(test, control)
|
|
|
|
def test_where_broadcast(self):
|
|
# Issue 8599
|
|
x = np.arange(9).reshape(3, 3)
|
|
y = np.zeros(3)
|
|
core = np.where([1, 0, 1], x, y)
|
|
ma = where([1, 0, 1], x, y)
|
|
|
|
assert_equal(core, ma)
|
|
assert_equal(core.dtype, ma.dtype)
|
|
|
|
def test_where_structured(self):
|
|
# Issue 8600
|
|
dt = np.dtype([('a', int), ('b', int)])
|
|
x = np.array([(1, 2), (3, 4), (5, 6)], dtype=dt)
|
|
y = np.array((10, 20), dtype=dt)
|
|
core = np.where([0, 1, 1], x, y)
|
|
ma = np.where([0, 1, 1], x, y)
|
|
|
|
assert_equal(core, ma)
|
|
assert_equal(core.dtype, ma.dtype)
|
|
|
|
def test_where_structured_masked(self):
|
|
dt = np.dtype([('a', int), ('b', int)])
|
|
x = np.array([(1, 2), (3, 4), (5, 6)], dtype=dt)
|
|
|
|
ma = where([0, 1, 1], x, masked)
|
|
expected = masked_where([1, 0, 0], x)
|
|
|
|
assert_equal(ma.dtype, expected.dtype)
|
|
assert_equal(ma, expected)
|
|
assert_equal(ma.mask, expected.mask)
|
|
|
|
def test_masked_invalid_error(self):
|
|
a = np.arange(5, dtype=object)
|
|
a[3] = np.PINF
|
|
a[2] = np.NaN
|
|
with pytest.raises(TypeError,
|
|
match="not supported for the input types"):
|
|
np.ma.masked_invalid(a)
|
|
|
|
def test_masked_invalid_pandas(self):
|
|
# getdata() used to be bad for pandas series due to its _data
|
|
# attribute. This test is a regression test mainly and may be
|
|
# removed if getdata() is adjusted.
|
|
class Series():
|
|
_data = "nonsense"
|
|
|
|
def __array__(self):
|
|
return np.array([5, np.nan, np.inf])
|
|
|
|
arr = np.ma.masked_invalid(Series())
|
|
assert_array_equal(arr._data, np.array(Series()))
|
|
assert_array_equal(arr._mask, [False, True, True])
|
|
|
|
@pytest.mark.parametrize("copy", [True, False])
|
|
def test_masked_invalid_full_mask(self, copy):
|
|
# Matplotlib relied on masked_invalid always returning a full mask
|
|
# (Also astropy projects, but were ok with it gh-22720 and gh-22842)
|
|
a = np.ma.array([1, 2, 3, 4])
|
|
assert a._mask is nomask
|
|
res = np.ma.masked_invalid(a, copy=copy)
|
|
assert res.mask is not nomask
|
|
# mask of a should not be mutated
|
|
assert a.mask is nomask
|
|
assert np.may_share_memory(a._data, res._data) != copy
|
|
|
|
def test_choose(self):
|
|
# Test choose
|
|
choices = [[0, 1, 2, 3], [10, 11, 12, 13],
|
|
[20, 21, 22, 23], [30, 31, 32, 33]]
|
|
chosen = choose([2, 3, 1, 0], choices)
|
|
assert_equal(chosen, array([20, 31, 12, 3]))
|
|
chosen = choose([2, 4, 1, 0], choices, mode='clip')
|
|
assert_equal(chosen, array([20, 31, 12, 3]))
|
|
chosen = choose([2, 4, 1, 0], choices, mode='wrap')
|
|
assert_equal(chosen, array([20, 1, 12, 3]))
|
|
# Check with some masked indices
|
|
indices_ = array([2, 4, 1, 0], mask=[1, 0, 0, 1])
|
|
chosen = choose(indices_, choices, mode='wrap')
|
|
assert_equal(chosen, array([99, 1, 12, 99]))
|
|
assert_equal(chosen.mask, [1, 0, 0, 1])
|
|
# Check with some masked choices
|
|
choices = array(choices, mask=[[0, 0, 0, 1], [1, 1, 0, 1],
|
|
[1, 0, 0, 0], [0, 0, 0, 0]])
|
|
indices_ = [2, 3, 1, 0]
|
|
chosen = choose(indices_, choices, mode='wrap')
|
|
assert_equal(chosen, array([20, 31, 12, 3]))
|
|
assert_equal(chosen.mask, [1, 0, 0, 1])
|
|
|
|
def test_choose_with_out(self):
|
|
# Test choose with an explicit out keyword
|
|
choices = [[0, 1, 2, 3], [10, 11, 12, 13],
|
|
[20, 21, 22, 23], [30, 31, 32, 33]]
|
|
store = empty(4, dtype=int)
|
|
chosen = choose([2, 3, 1, 0], choices, out=store)
|
|
assert_equal(store, array([20, 31, 12, 3]))
|
|
assert_(store is chosen)
|
|
# Check with some masked indices + out
|
|
store = empty(4, dtype=int)
|
|
indices_ = array([2, 3, 1, 0], mask=[1, 0, 0, 1])
|
|
chosen = choose(indices_, choices, mode='wrap', out=store)
|
|
assert_equal(store, array([99, 31, 12, 99]))
|
|
assert_equal(store.mask, [1, 0, 0, 1])
|
|
# Check with some masked choices + out ina ndarray !
|
|
choices = array(choices, mask=[[0, 0, 0, 1], [1, 1, 0, 1],
|
|
[1, 0, 0, 0], [0, 0, 0, 0]])
|
|
indices_ = [2, 3, 1, 0]
|
|
store = empty(4, dtype=int).view(ndarray)
|
|
chosen = choose(indices_, choices, mode='wrap', out=store)
|
|
assert_equal(store, array([999999, 31, 12, 999999]))
|
|
|
|
def test_reshape(self):
|
|
a = arange(10)
|
|
a[0] = masked
|
|
# Try the default
|
|
b = a.reshape((5, 2))
|
|
assert_equal(b.shape, (5, 2))
|
|
assert_(b.flags['C'])
|
|
# Try w/ arguments as list instead of tuple
|
|
b = a.reshape(5, 2)
|
|
assert_equal(b.shape, (5, 2))
|
|
assert_(b.flags['C'])
|
|
# Try w/ order
|
|
b = a.reshape((5, 2), order='F')
|
|
assert_equal(b.shape, (5, 2))
|
|
assert_(b.flags['F'])
|
|
# Try w/ order
|
|
b = a.reshape(5, 2, order='F')
|
|
assert_equal(b.shape, (5, 2))
|
|
assert_(b.flags['F'])
|
|
|
|
c = np.reshape(a, (2, 5))
|
|
assert_(isinstance(c, MaskedArray))
|
|
assert_equal(c.shape, (2, 5))
|
|
assert_(c[0, 0] is masked)
|
|
assert_(c.flags['C'])
|
|
|
|
def test_make_mask_descr(self):
|
|
# Flexible
|
|
ntype = [('a', float), ('b', float)]
|
|
test = make_mask_descr(ntype)
|
|
assert_equal(test, [('a', bool), ('b', bool)])
|
|
assert_(test is make_mask_descr(test))
|
|
|
|
# Standard w/ shape
|
|
ntype = (float, 2)
|
|
test = make_mask_descr(ntype)
|
|
assert_equal(test, (bool, 2))
|
|
assert_(test is make_mask_descr(test))
|
|
|
|
# Standard standard
|
|
ntype = float
|
|
test = make_mask_descr(ntype)
|
|
assert_equal(test, np.dtype(bool))
|
|
assert_(test is make_mask_descr(test))
|
|
|
|
# Nested
|
|
ntype = [('a', float), ('b', [('ba', float), ('bb', float)])]
|
|
test = make_mask_descr(ntype)
|
|
control = np.dtype([('a', 'b1'), ('b', [('ba', 'b1'), ('bb', 'b1')])])
|
|
assert_equal(test, control)
|
|
assert_(test is make_mask_descr(test))
|
|
|
|
# Named+ shape
|
|
ntype = [('a', (float, 2))]
|
|
test = make_mask_descr(ntype)
|
|
assert_equal(test, np.dtype([('a', (bool, 2))]))
|
|
assert_(test is make_mask_descr(test))
|
|
|
|
# 2 names
|
|
ntype = [(('A', 'a'), float)]
|
|
test = make_mask_descr(ntype)
|
|
assert_equal(test, np.dtype([(('A', 'a'), bool)]))
|
|
assert_(test is make_mask_descr(test))
|
|
|
|
# nested boolean types should preserve identity
|
|
base_type = np.dtype([('a', int, 3)])
|
|
base_mtype = make_mask_descr(base_type)
|
|
sub_type = np.dtype([('a', int), ('b', base_mtype)])
|
|
test = make_mask_descr(sub_type)
|
|
assert_equal(test, np.dtype([('a', bool), ('b', [('a', bool, 3)])]))
|
|
assert_(test.fields['b'][0] is base_mtype)
|
|
|
|
def test_make_mask(self):
|
|
# Test make_mask
|
|
# w/ a list as an input
|
|
mask = [0, 1]
|
|
test = make_mask(mask)
|
|
assert_equal(test.dtype, MaskType)
|
|
assert_equal(test, [0, 1])
|
|
# w/ a ndarray as an input
|
|
mask = np.array([0, 1], dtype=bool)
|
|
test = make_mask(mask)
|
|
assert_equal(test.dtype, MaskType)
|
|
assert_equal(test, [0, 1])
|
|
# w/ a flexible-type ndarray as an input - use default
|
|
mdtype = [('a', bool), ('b', bool)]
|
|
mask = np.array([(0, 0), (0, 1)], dtype=mdtype)
|
|
test = make_mask(mask)
|
|
assert_equal(test.dtype, MaskType)
|
|
assert_equal(test, [1, 1])
|
|
# w/ a flexible-type ndarray as an input - use input dtype
|
|
mdtype = [('a', bool), ('b', bool)]
|
|
mask = np.array([(0, 0), (0, 1)], dtype=mdtype)
|
|
test = make_mask(mask, dtype=mask.dtype)
|
|
assert_equal(test.dtype, mdtype)
|
|
assert_equal(test, mask)
|
|
# w/ a flexible-type ndarray as an input - use input dtype
|
|
mdtype = [('a', float), ('b', float)]
|
|
bdtype = [('a', bool), ('b', bool)]
|
|
mask = np.array([(0, 0), (0, 1)], dtype=mdtype)
|
|
test = make_mask(mask, dtype=mask.dtype)
|
|
assert_equal(test.dtype, bdtype)
|
|
assert_equal(test, np.array([(0, 0), (0, 1)], dtype=bdtype))
|
|
# Ensure this also works for void
|
|
mask = np.array((False, True), dtype='?,?')[()]
|
|
assert_(isinstance(mask, np.void))
|
|
test = make_mask(mask, dtype=mask.dtype)
|
|
assert_equal(test, mask)
|
|
assert_(test is not mask)
|
|
mask = np.array((0, 1), dtype='i4,i4')[()]
|
|
test2 = make_mask(mask, dtype=mask.dtype)
|
|
assert_equal(test2, test)
|
|
# test that nomask is returned when m is nomask.
|
|
bools = [True, False]
|
|
dtypes = [MaskType, float]
|
|
msgformat = 'copy=%s, shrink=%s, dtype=%s'
|
|
for cpy, shr, dt in itertools.product(bools, bools, dtypes):
|
|
res = make_mask(nomask, copy=cpy, shrink=shr, dtype=dt)
|
|
assert_(res is nomask, msgformat % (cpy, shr, dt))
|
|
|
|
def test_mask_or(self):
|
|
# Initialize
|
|
mtype = [('a', bool), ('b', bool)]
|
|
mask = np.array([(0, 0), (0, 1), (1, 0), (0, 0)], dtype=mtype)
|
|
# Test using nomask as input
|
|
test = mask_or(mask, nomask)
|
|
assert_equal(test, mask)
|
|
test = mask_or(nomask, mask)
|
|
assert_equal(test, mask)
|
|
# Using False as input
|
|
test = mask_or(mask, False)
|
|
assert_equal(test, mask)
|
|
# Using another array w / the same dtype
|
|
other = np.array([(0, 1), (0, 1), (0, 1), (0, 1)], dtype=mtype)
|
|
test = mask_or(mask, other)
|
|
control = np.array([(0, 1), (0, 1), (1, 1), (0, 1)], dtype=mtype)
|
|
assert_equal(test, control)
|
|
# Using another array w / a different dtype
|
|
othertype = [('A', bool), ('B', bool)]
|
|
other = np.array([(0, 1), (0, 1), (0, 1), (0, 1)], dtype=othertype)
|
|
try:
|
|
test = mask_or(mask, other)
|
|
except ValueError:
|
|
pass
|
|
# Using nested arrays
|
|
dtype = [('a', bool), ('b', [('ba', bool), ('bb', bool)])]
|
|
amask = np.array([(0, (1, 0)), (0, (1, 0))], dtype=dtype)
|
|
bmask = np.array([(1, (0, 1)), (0, (0, 0))], dtype=dtype)
|
|
cntrl = np.array([(1, (1, 1)), (0, (1, 0))], dtype=dtype)
|
|
assert_equal(mask_or(amask, bmask), cntrl)
|
|
|
|
def test_flatten_mask(self):
|
|
# Tests flatten mask
|
|
# Standard dtype
|
|
mask = np.array([0, 0, 1], dtype=bool)
|
|
assert_equal(flatten_mask(mask), mask)
|
|
# Flexible dtype
|
|
mask = np.array([(0, 0), (0, 1)], dtype=[('a', bool), ('b', bool)])
|
|
test = flatten_mask(mask)
|
|
control = np.array([0, 0, 0, 1], dtype=bool)
|
|
assert_equal(test, control)
|
|
|
|
mdtype = [('a', bool), ('b', [('ba', bool), ('bb', bool)])]
|
|
data = [(0, (0, 0)), (0, (0, 1))]
|
|
mask = np.array(data, dtype=mdtype)
|
|
test = flatten_mask(mask)
|
|
control = np.array([0, 0, 0, 0, 0, 1], dtype=bool)
|
|
assert_equal(test, control)
|
|
|
|
def test_on_ndarray(self):
|
|
# Test functions on ndarrays
|
|
a = np.array([1, 2, 3, 4])
|
|
m = array(a, mask=False)
|
|
test = anom(a)
|
|
assert_equal(test, m.anom())
|
|
test = reshape(a, (2, 2))
|
|
assert_equal(test, m.reshape(2, 2))
|
|
|
|
def test_compress(self):
|
|
# Test compress function on ndarray and masked array
|
|
# Address Github #2495.
|
|
arr = np.arange(8)
|
|
arr.shape = 4, 2
|
|
cond = np.array([True, False, True, True])
|
|
control = arr[[0, 2, 3]]
|
|
test = np.ma.compress(cond, arr, axis=0)
|
|
assert_equal(test, control)
|
|
marr = np.ma.array(arr)
|
|
test = np.ma.compress(cond, marr, axis=0)
|
|
assert_equal(test, control)
|
|
|
|
def test_compressed(self):
|
|
# Test ma.compressed function.
|
|
# Address gh-4026
|
|
a = np.ma.array([1, 2])
|
|
test = np.ma.compressed(a)
|
|
assert_(type(test) is np.ndarray)
|
|
|
|
# Test case when input data is ndarray subclass
|
|
class A(np.ndarray):
|
|
pass
|
|
|
|
a = np.ma.array(A(shape=0))
|
|
test = np.ma.compressed(a)
|
|
assert_(type(test) is A)
|
|
|
|
# Test that compress flattens
|
|
test = np.ma.compressed([[1],[2]])
|
|
assert_equal(test.ndim, 1)
|
|
test = np.ma.compressed([[[[[1]]]]])
|
|
assert_equal(test.ndim, 1)
|
|
|
|
# Test case when input is MaskedArray subclass
|
|
class M(MaskedArray):
|
|
pass
|
|
|
|
test = np.ma.compressed(M([[[]], [[]]]))
|
|
assert_equal(test.ndim, 1)
|
|
|
|
# with .compressed() overridden
|
|
class M(MaskedArray):
|
|
def compressed(self):
|
|
return 42
|
|
|
|
test = np.ma.compressed(M([[[]], [[]]]))
|
|
assert_equal(test, 42)
|
|
|
|
def test_convolve(self):
|
|
a = masked_equal(np.arange(5), 2)
|
|
b = np.array([1, 1])
|
|
test = np.ma.convolve(a, b)
|
|
assert_equal(test, masked_equal([0, 1, -1, -1, 7, 4], -1))
|
|
|
|
test = np.ma.convolve(a, b, propagate_mask=False)
|
|
assert_equal(test, masked_equal([0, 1, 1, 3, 7, 4], -1))
|
|
|
|
test = np.ma.convolve([1, 1], [1, 1, 1])
|
|
assert_equal(test, masked_equal([1, 2, 2, 1], -1))
|
|
|
|
a = [1, 1]
|
|
b = masked_equal([1, -1, -1, 1], -1)
|
|
test = np.ma.convolve(a, b, propagate_mask=False)
|
|
assert_equal(test, masked_equal([1, 1, -1, 1, 1], -1))
|
|
test = np.ma.convolve(a, b, propagate_mask=True)
|
|
assert_equal(test, masked_equal([-1, -1, -1, -1, -1], -1))
|
|
|
|
|
|
class TestMaskedFields:
|
|
|
|
def setup_method(self):
|
|
ilist = [1, 2, 3, 4, 5]
|
|
flist = [1.1, 2.2, 3.3, 4.4, 5.5]
|
|
slist = ['one', 'two', 'three', 'four', 'five']
|
|
ddtype = [('a', int), ('b', float), ('c', '|S8')]
|
|
mdtype = [('a', bool), ('b', bool), ('c', bool)]
|
|
mask = [0, 1, 0, 0, 1]
|
|
base = array(list(zip(ilist, flist, slist)), mask=mask, dtype=ddtype)
|
|
self.data = dict(base=base, mask=mask, ddtype=ddtype, mdtype=mdtype)
|
|
|
|
def test_set_records_masks(self):
|
|
base = self.data['base']
|
|
mdtype = self.data['mdtype']
|
|
# Set w/ nomask or masked
|
|
base.mask = nomask
|
|
assert_equal_records(base._mask, np.zeros(base.shape, dtype=mdtype))
|
|
base.mask = masked
|
|
assert_equal_records(base._mask, np.ones(base.shape, dtype=mdtype))
|
|
# Set w/ simple boolean
|
|
base.mask = False
|
|
assert_equal_records(base._mask, np.zeros(base.shape, dtype=mdtype))
|
|
base.mask = True
|
|
assert_equal_records(base._mask, np.ones(base.shape, dtype=mdtype))
|
|
# Set w/ list
|
|
base.mask = [0, 0, 0, 1, 1]
|
|
assert_equal_records(base._mask,
|
|
np.array([(x, x, x) for x in [0, 0, 0, 1, 1]],
|
|
dtype=mdtype))
|
|
|
|
def test_set_record_element(self):
|
|
# Check setting an element of a record)
|
|
base = self.data['base']
|
|
(base_a, base_b, base_c) = (base['a'], base['b'], base['c'])
|
|
base[0] = (pi, pi, 'pi')
|
|
|
|
assert_equal(base_a.dtype, int)
|
|
assert_equal(base_a._data, [3, 2, 3, 4, 5])
|
|
|
|
assert_equal(base_b.dtype, float)
|
|
assert_equal(base_b._data, [pi, 2.2, 3.3, 4.4, 5.5])
|
|
|
|
assert_equal(base_c.dtype, '|S8')
|
|
assert_equal(base_c._data,
|
|
[b'pi', b'two', b'three', b'four', b'five'])
|
|
|
|
def test_set_record_slice(self):
|
|
base = self.data['base']
|
|
(base_a, base_b, base_c) = (base['a'], base['b'], base['c'])
|
|
base[:3] = (pi, pi, 'pi')
|
|
|
|
assert_equal(base_a.dtype, int)
|
|
assert_equal(base_a._data, [3, 3, 3, 4, 5])
|
|
|
|
assert_equal(base_b.dtype, float)
|
|
assert_equal(base_b._data, [pi, pi, pi, 4.4, 5.5])
|
|
|
|
assert_equal(base_c.dtype, '|S8')
|
|
assert_equal(base_c._data,
|
|
[b'pi', b'pi', b'pi', b'four', b'five'])
|
|
|
|
def test_mask_element(self):
|
|
"Check record access"
|
|
base = self.data['base']
|
|
base[0] = masked
|
|
|
|
for n in ('a', 'b', 'c'):
|
|
assert_equal(base[n].mask, [1, 1, 0, 0, 1])
|
|
assert_equal(base[n]._data, base._data[n])
|
|
|
|
def test_getmaskarray(self):
|
|
# Test getmaskarray on flexible dtype
|
|
ndtype = [('a', int), ('b', float)]
|
|
test = empty(3, dtype=ndtype)
|
|
assert_equal(getmaskarray(test),
|
|
np.array([(0, 0), (0, 0), (0, 0)],
|
|
dtype=[('a', '|b1'), ('b', '|b1')]))
|
|
test[:] = masked
|
|
assert_equal(getmaskarray(test),
|
|
np.array([(1, 1), (1, 1), (1, 1)],
|
|
dtype=[('a', '|b1'), ('b', '|b1')]))
|
|
|
|
def test_view(self):
|
|
# Test view w/ flexible dtype
|
|
iterator = list(zip(np.arange(10), np.random.rand(10)))
|
|
data = np.array(iterator)
|
|
a = array(iterator, dtype=[('a', float), ('b', float)])
|
|
a.mask[0] = (1, 0)
|
|
controlmask = np.array([1] + 19 * [0], dtype=bool)
|
|
# Transform globally to simple dtype
|
|
test = a.view(float)
|
|
assert_equal(test, data.ravel())
|
|
assert_equal(test.mask, controlmask)
|
|
# Transform globally to dty
|
|
test = a.view((float, 2))
|
|
assert_equal(test, data)
|
|
assert_equal(test.mask, controlmask.reshape(-1, 2))
|
|
|
|
def test_getitem(self):
|
|
ndtype = [('a', float), ('b', float)]
|
|
a = array(list(zip(np.random.rand(10), np.arange(10))), dtype=ndtype)
|
|
a.mask = np.array(list(zip([0, 0, 0, 0, 0, 0, 0, 0, 1, 1],
|
|
[1, 0, 0, 0, 0, 0, 0, 0, 1, 0])),
|
|
dtype=[('a', bool), ('b', bool)])
|
|
|
|
def _test_index(i):
|
|
assert_equal(type(a[i]), mvoid)
|
|
assert_equal_records(a[i]._data, a._data[i])
|
|
assert_equal_records(a[i]._mask, a._mask[i])
|
|
|
|
assert_equal(type(a[i, ...]), MaskedArray)
|
|
assert_equal_records(a[i,...]._data, a._data[i,...])
|
|
assert_equal_records(a[i,...]._mask, a._mask[i,...])
|
|
|
|
_test_index(1) # No mask
|
|
_test_index(0) # One element masked
|
|
_test_index(-2) # All element masked
|
|
|
|
def test_setitem(self):
|
|
# Issue 4866: check that one can set individual items in [record][col]
|
|
# and [col][record] order
|
|
ndtype = np.dtype([('a', float), ('b', int)])
|
|
ma = np.ma.MaskedArray([(1.0, 1), (2.0, 2)], dtype=ndtype)
|
|
ma['a'][1] = 3.0
|
|
assert_equal(ma['a'], np.array([1.0, 3.0]))
|
|
ma[1]['a'] = 4.0
|
|
assert_equal(ma['a'], np.array([1.0, 4.0]))
|
|
# Issue 2403
|
|
mdtype = np.dtype([('a', bool), ('b', bool)])
|
|
# soft mask
|
|
control = np.array([(False, True), (True, True)], dtype=mdtype)
|
|
a = np.ma.masked_all((2,), dtype=ndtype)
|
|
a['a'][0] = 2
|
|
assert_equal(a.mask, control)
|
|
a = np.ma.masked_all((2,), dtype=ndtype)
|
|
a[0]['a'] = 2
|
|
assert_equal(a.mask, control)
|
|
# hard mask
|
|
control = np.array([(True, True), (True, True)], dtype=mdtype)
|
|
a = np.ma.masked_all((2,), dtype=ndtype)
|
|
a.harden_mask()
|
|
a['a'][0] = 2
|
|
assert_equal(a.mask, control)
|
|
a = np.ma.masked_all((2,), dtype=ndtype)
|
|
a.harden_mask()
|
|
a[0]['a'] = 2
|
|
assert_equal(a.mask, control)
|
|
|
|
def test_setitem_scalar(self):
|
|
# 8510
|
|
mask_0d = np.ma.masked_array(1, mask=True)
|
|
arr = np.ma.arange(3)
|
|
arr[0] = mask_0d
|
|
assert_array_equal(arr.mask, [True, False, False])
|
|
|
|
def test_element_len(self):
|
|
# check that len() works for mvoid (Github issue #576)
|
|
for rec in self.data['base']:
|
|
assert_equal(len(rec), len(self.data['ddtype']))
|
|
|
|
|
|
class TestMaskedObjectArray:
|
|
|
|
def test_getitem(self):
|
|
arr = np.ma.array([None, None])
|
|
for dt in [float, object]:
|
|
a0 = np.eye(2).astype(dt)
|
|
a1 = np.eye(3).astype(dt)
|
|
arr[0] = a0
|
|
arr[1] = a1
|
|
|
|
assert_(arr[0] is a0)
|
|
assert_(arr[1] is a1)
|
|
assert_(isinstance(arr[0,...], MaskedArray))
|
|
assert_(isinstance(arr[1,...], MaskedArray))
|
|
assert_(arr[0,...][()] is a0)
|
|
assert_(arr[1,...][()] is a1)
|
|
|
|
arr[0] = np.ma.masked
|
|
|
|
assert_(arr[1] is a1)
|
|
assert_(isinstance(arr[0,...], MaskedArray))
|
|
assert_(isinstance(arr[1,...], MaskedArray))
|
|
assert_equal(arr[0,...].mask, True)
|
|
assert_(arr[1,...][()] is a1)
|
|
|
|
# gh-5962 - object arrays of arrays do something special
|
|
assert_equal(arr[0].data, a0)
|
|
assert_equal(arr[0].mask, True)
|
|
assert_equal(arr[0,...][()].data, a0)
|
|
assert_equal(arr[0,...][()].mask, True)
|
|
|
|
def test_nested_ma(self):
|
|
|
|
arr = np.ma.array([None, None])
|
|
# set the first object to be an unmasked masked constant. A little fiddly
|
|
arr[0,...] = np.array([np.ma.masked], object)[0,...]
|
|
|
|
# check the above line did what we were aiming for
|
|
assert_(arr.data[0] is np.ma.masked)
|
|
|
|
# test that getitem returned the value by identity
|
|
assert_(arr[0] is np.ma.masked)
|
|
|
|
# now mask the masked value!
|
|
arr[0] = np.ma.masked
|
|
assert_(arr[0] is np.ma.masked)
|
|
|
|
|
|
class TestMaskedView:
|
|
|
|
def setup_method(self):
|
|
iterator = list(zip(np.arange(10), np.random.rand(10)))
|
|
data = np.array(iterator)
|
|
a = array(iterator, dtype=[('a', float), ('b', float)])
|
|
a.mask[0] = (1, 0)
|
|
controlmask = np.array([1] + 19 * [0], dtype=bool)
|
|
self.data = (data, a, controlmask)
|
|
|
|
def test_view_to_nothing(self):
|
|
(data, a, controlmask) = self.data
|
|
test = a.view()
|
|
assert_(isinstance(test, MaskedArray))
|
|
assert_equal(test._data, a._data)
|
|
assert_equal(test._mask, a._mask)
|
|
|
|
def test_view_to_type(self):
|
|
(data, a, controlmask) = self.data
|
|
test = a.view(np.ndarray)
|
|
assert_(not isinstance(test, MaskedArray))
|
|
assert_equal(test, a._data)
|
|
assert_equal_records(test, data.view(a.dtype).squeeze())
|
|
|
|
def test_view_to_simple_dtype(self):
|
|
(data, a, controlmask) = self.data
|
|
# View globally
|
|
test = a.view(float)
|
|
assert_(isinstance(test, MaskedArray))
|
|
assert_equal(test, data.ravel())
|
|
assert_equal(test.mask, controlmask)
|
|
|
|
def test_view_to_flexible_dtype(self):
|
|
(data, a, controlmask) = self.data
|
|
|
|
test = a.view([('A', float), ('B', float)])
|
|
assert_equal(test.mask.dtype.names, ('A', 'B'))
|
|
assert_equal(test['A'], a['a'])
|
|
assert_equal(test['B'], a['b'])
|
|
|
|
test = a[0].view([('A', float), ('B', float)])
|
|
assert_(isinstance(test, MaskedArray))
|
|
assert_equal(test.mask.dtype.names, ('A', 'B'))
|
|
assert_equal(test['A'], a['a'][0])
|
|
assert_equal(test['B'], a['b'][0])
|
|
|
|
test = a[-1].view([('A', float), ('B', float)])
|
|
assert_(isinstance(test, MaskedArray))
|
|
assert_equal(test.dtype.names, ('A', 'B'))
|
|
assert_equal(test['A'], a['a'][-1])
|
|
assert_equal(test['B'], a['b'][-1])
|
|
|
|
def test_view_to_subdtype(self):
|
|
(data, a, controlmask) = self.data
|
|
# View globally
|
|
test = a.view((float, 2))
|
|
assert_(isinstance(test, MaskedArray))
|
|
assert_equal(test, data)
|
|
assert_equal(test.mask, controlmask.reshape(-1, 2))
|
|
# View on 1 masked element
|
|
test = a[0].view((float, 2))
|
|
assert_(isinstance(test, MaskedArray))
|
|
assert_equal(test, data[0])
|
|
assert_equal(test.mask, (1, 0))
|
|
# View on 1 unmasked element
|
|
test = a[-1].view((float, 2))
|
|
assert_(isinstance(test, MaskedArray))
|
|
assert_equal(test, data[-1])
|
|
|
|
def test_view_to_dtype_and_type(self):
|
|
(data, a, controlmask) = self.data
|
|
|
|
test = a.view((float, 2), np.recarray)
|
|
assert_equal(test, data)
|
|
assert_(isinstance(test, np.recarray))
|
|
assert_(not isinstance(test, MaskedArray))
|
|
|
|
|
|
class TestOptionalArgs:
|
|
def test_ndarrayfuncs(self):
|
|
# test axis arg behaves the same as ndarray (including multiple axes)
|
|
|
|
d = np.arange(24.0).reshape((2,3,4))
|
|
m = np.zeros(24, dtype=bool).reshape((2,3,4))
|
|
# mask out last element of last dimension
|
|
m[:,:,-1] = True
|
|
a = np.ma.array(d, mask=m)
|
|
|
|
def testaxis(f, a, d):
|
|
numpy_f = numpy.__getattribute__(f)
|
|
ma_f = np.ma.__getattribute__(f)
|
|
|
|
# test axis arg
|
|
assert_equal(ma_f(a, axis=1)[...,:-1], numpy_f(d[...,:-1], axis=1))
|
|
assert_equal(ma_f(a, axis=(0,1))[...,:-1],
|
|
numpy_f(d[...,:-1], axis=(0,1)))
|
|
|
|
def testkeepdims(f, a, d):
|
|
numpy_f = numpy.__getattribute__(f)
|
|
ma_f = np.ma.__getattribute__(f)
|
|
|
|
# test keepdims arg
|
|
assert_equal(ma_f(a, keepdims=True).shape,
|
|
numpy_f(d, keepdims=True).shape)
|
|
assert_equal(ma_f(a, keepdims=False).shape,
|
|
numpy_f(d, keepdims=False).shape)
|
|
|
|
# test both at once
|
|
assert_equal(ma_f(a, axis=1, keepdims=True)[...,:-1],
|
|
numpy_f(d[...,:-1], axis=1, keepdims=True))
|
|
assert_equal(ma_f(a, axis=(0,1), keepdims=True)[...,:-1],
|
|
numpy_f(d[...,:-1], axis=(0,1), keepdims=True))
|
|
|
|
for f in ['sum', 'prod', 'mean', 'var', 'std']:
|
|
testaxis(f, a, d)
|
|
testkeepdims(f, a, d)
|
|
|
|
for f in ['min', 'max']:
|
|
testaxis(f, a, d)
|
|
|
|
d = (np.arange(24).reshape((2,3,4))%2 == 0)
|
|
a = np.ma.array(d, mask=m)
|
|
for f in ['all', 'any']:
|
|
testaxis(f, a, d)
|
|
testkeepdims(f, a, d)
|
|
|
|
def test_count(self):
|
|
# test np.ma.count specially
|
|
|
|
d = np.arange(24.0).reshape((2,3,4))
|
|
m = np.zeros(24, dtype=bool).reshape((2,3,4))
|
|
m[:,0,:] = True
|
|
a = np.ma.array(d, mask=m)
|
|
|
|
assert_equal(count(a), 16)
|
|
assert_equal(count(a, axis=1), 2*ones((2,4)))
|
|
assert_equal(count(a, axis=(0,1)), 4*ones((4,)))
|
|
assert_equal(count(a, keepdims=True), 16*ones((1,1,1)))
|
|
assert_equal(count(a, axis=1, keepdims=True), 2*ones((2,1,4)))
|
|
assert_equal(count(a, axis=(0,1), keepdims=True), 4*ones((1,1,4)))
|
|
assert_equal(count(a, axis=-2), 2*ones((2,4)))
|
|
assert_raises(ValueError, count, a, axis=(1,1))
|
|
assert_raises(np.AxisError, count, a, axis=3)
|
|
|
|
# check the 'nomask' path
|
|
a = np.ma.array(d, mask=nomask)
|
|
|
|
assert_equal(count(a), 24)
|
|
assert_equal(count(a, axis=1), 3*ones((2,4)))
|
|
assert_equal(count(a, axis=(0,1)), 6*ones((4,)))
|
|
assert_equal(count(a, keepdims=True), 24*ones((1,1,1)))
|
|
assert_equal(np.ndim(count(a, keepdims=True)), 3)
|
|
assert_equal(count(a, axis=1, keepdims=True), 3*ones((2,1,4)))
|
|
assert_equal(count(a, axis=(0,1), keepdims=True), 6*ones((1,1,4)))
|
|
assert_equal(count(a, axis=-2), 3*ones((2,4)))
|
|
assert_raises(ValueError, count, a, axis=(1,1))
|
|
assert_raises(np.AxisError, count, a, axis=3)
|
|
|
|
# check the 'masked' singleton
|
|
assert_equal(count(np.ma.masked), 0)
|
|
|
|
# check 0-d arrays do not allow axis > 0
|
|
assert_raises(np.AxisError, count, np.ma.array(1), axis=1)
|
|
|
|
|
|
class TestMaskedConstant:
|
|
def _do_add_test(self, add):
|
|
# sanity check
|
|
assert_(add(np.ma.masked, 1) is np.ma.masked)
|
|
|
|
# now try with a vector
|
|
vector = np.array([1, 2, 3])
|
|
result = add(np.ma.masked, vector)
|
|
|
|
# lots of things could go wrong here
|
|
assert_(result is not np.ma.masked)
|
|
assert_(not isinstance(result, np.ma.core.MaskedConstant))
|
|
assert_equal(result.shape, vector.shape)
|
|
assert_equal(np.ma.getmask(result), np.ones(vector.shape, dtype=bool))
|
|
|
|
def test_ufunc(self):
|
|
self._do_add_test(np.add)
|
|
|
|
def test_operator(self):
|
|
self._do_add_test(lambda a, b: a + b)
|
|
|
|
def test_ctor(self):
|
|
m = np.ma.array(np.ma.masked)
|
|
|
|
# most importantly, we do not want to create a new MaskedConstant
|
|
# instance
|
|
assert_(not isinstance(m, np.ma.core.MaskedConstant))
|
|
assert_(m is not np.ma.masked)
|
|
|
|
def test_repr(self):
|
|
# copies should not exist, but if they do, it should be obvious that
|
|
# something is wrong
|
|
assert_equal(repr(np.ma.masked), 'masked')
|
|
|
|
# create a new instance in a weird way
|
|
masked2 = np.ma.MaskedArray.__new__(np.ma.core.MaskedConstant)
|
|
assert_not_equal(repr(masked2), 'masked')
|
|
|
|
def test_pickle(self):
|
|
from io import BytesIO
|
|
|
|
for proto in range(2, pickle.HIGHEST_PROTOCOL + 1):
|
|
with BytesIO() as f:
|
|
pickle.dump(np.ma.masked, f, protocol=proto)
|
|
f.seek(0)
|
|
res = pickle.load(f)
|
|
assert_(res is np.ma.masked)
|
|
|
|
def test_copy(self):
|
|
# gh-9328
|
|
# copy is a no-op, like it is with np.True_
|
|
assert_equal(
|
|
np.ma.masked.copy() is np.ma.masked,
|
|
np.True_.copy() is np.True_)
|
|
|
|
def test__copy(self):
|
|
import copy
|
|
assert_(
|
|
copy.copy(np.ma.masked) is np.ma.masked)
|
|
|
|
def test_deepcopy(self):
|
|
import copy
|
|
assert_(
|
|
copy.deepcopy(np.ma.masked) is np.ma.masked)
|
|
|
|
def test_immutable(self):
|
|
orig = np.ma.masked
|
|
assert_raises(np.ma.core.MaskError, operator.setitem, orig, (), 1)
|
|
assert_raises(ValueError,operator.setitem, orig.data, (), 1)
|
|
assert_raises(ValueError, operator.setitem, orig.mask, (), False)
|
|
|
|
view = np.ma.masked.view(np.ma.MaskedArray)
|
|
assert_raises(ValueError, operator.setitem, view, (), 1)
|
|
assert_raises(ValueError, operator.setitem, view.data, (), 1)
|
|
assert_raises(ValueError, operator.setitem, view.mask, (), False)
|
|
|
|
def test_coercion_int(self):
|
|
a_i = np.zeros((), int)
|
|
assert_raises(MaskError, operator.setitem, a_i, (), np.ma.masked)
|
|
assert_raises(MaskError, int, np.ma.masked)
|
|
|
|
def test_coercion_float(self):
|
|
a_f = np.zeros((), float)
|
|
assert_warns(UserWarning, operator.setitem, a_f, (), np.ma.masked)
|
|
assert_(np.isnan(a_f[()]))
|
|
|
|
@pytest.mark.xfail(reason="See gh-9750")
|
|
def test_coercion_unicode(self):
|
|
a_u = np.zeros((), 'U10')
|
|
a_u[()] = np.ma.masked
|
|
assert_equal(a_u[()], '--')
|
|
|
|
@pytest.mark.xfail(reason="See gh-9750")
|
|
def test_coercion_bytes(self):
|
|
a_b = np.zeros((), 'S10')
|
|
a_b[()] = np.ma.masked
|
|
assert_equal(a_b[()], b'--')
|
|
|
|
def test_subclass(self):
|
|
# https://github.com/astropy/astropy/issues/6645
|
|
class Sub(type(np.ma.masked)): pass
|
|
|
|
a = Sub()
|
|
assert_(a is Sub())
|
|
assert_(a is not np.ma.masked)
|
|
assert_not_equal(repr(a), 'masked')
|
|
|
|
def test_attributes_readonly(self):
|
|
assert_raises(AttributeError, setattr, np.ma.masked, 'shape', (1,))
|
|
assert_raises(AttributeError, setattr, np.ma.masked, 'dtype', np.int64)
|
|
|
|
|
|
class TestMaskedWhereAliases:
|
|
|
|
# TODO: Test masked_object, masked_equal, ...
|
|
|
|
def test_masked_values(self):
|
|
res = masked_values(np.array([-32768.0]), np.int16(-32768))
|
|
assert_equal(res.mask, [True])
|
|
|
|
res = masked_values(np.inf, np.inf)
|
|
assert_equal(res.mask, True)
|
|
|
|
res = np.ma.masked_values(np.inf, -np.inf)
|
|
assert_equal(res.mask, False)
|
|
|
|
res = np.ma.masked_values([1, 2, 3, 4], 5, shrink=True)
|
|
assert_(res.mask is np.ma.nomask)
|
|
|
|
res = np.ma.masked_values([1, 2, 3, 4], 5, shrink=False)
|
|
assert_equal(res.mask, [False] * 4)
|
|
|
|
|
|
def test_masked_array():
|
|
a = np.ma.array([0, 1, 2, 3], mask=[0, 0, 1, 0])
|
|
assert_equal(np.argwhere(a), [[1], [3]])
|
|
|
|
def test_masked_array_no_copy():
|
|
# check nomask array is updated in place
|
|
a = np.ma.array([1, 2, 3, 4])
|
|
_ = np.ma.masked_where(a == 3, a, copy=False)
|
|
assert_array_equal(a.mask, [False, False, True, False])
|
|
# check masked array is updated in place
|
|
a = np.ma.array([1, 2, 3, 4], mask=[1, 0, 0, 0])
|
|
_ = np.ma.masked_where(a == 3, a, copy=False)
|
|
assert_array_equal(a.mask, [True, False, True, False])
|
|
# check masked array with masked_invalid is updated in place
|
|
a = np.ma.array([np.inf, 1, 2, 3, 4])
|
|
_ = np.ma.masked_invalid(a, copy=False)
|
|
assert_array_equal(a.mask, [True, False, False, False, False])
|
|
|
|
def test_append_masked_array():
|
|
a = np.ma.masked_equal([1,2,3], value=2)
|
|
b = np.ma.masked_equal([4,3,2], value=2)
|
|
|
|
result = np.ma.append(a, b)
|
|
expected_data = [1, 2, 3, 4, 3, 2]
|
|
expected_mask = [False, True, False, False, False, True]
|
|
assert_array_equal(result.data, expected_data)
|
|
assert_array_equal(result.mask, expected_mask)
|
|
|
|
a = np.ma.masked_all((2,2))
|
|
b = np.ma.ones((3,1))
|
|
|
|
result = np.ma.append(a, b)
|
|
expected_data = [1] * 3
|
|
expected_mask = [True] * 4 + [False] * 3
|
|
assert_array_equal(result.data[-3], expected_data)
|
|
assert_array_equal(result.mask, expected_mask)
|
|
|
|
result = np.ma.append(a, b, axis=None)
|
|
assert_array_equal(result.data[-3], expected_data)
|
|
assert_array_equal(result.mask, expected_mask)
|
|
|
|
|
|
def test_append_masked_array_along_axis():
|
|
a = np.ma.masked_equal([1,2,3], value=2)
|
|
b = np.ma.masked_values([[4, 5, 6], [7, 8, 9]], 7)
|
|
|
|
# When `axis` is specified, `values` must have the correct shape.
|
|
assert_raises(ValueError, np.ma.append, a, b, axis=0)
|
|
|
|
result = np.ma.append(a[np.newaxis,:], b, axis=0)
|
|
expected = np.ma.arange(1, 10)
|
|
expected[[1, 6]] = np.ma.masked
|
|
expected = expected.reshape((3,3))
|
|
assert_array_equal(result.data, expected.data)
|
|
assert_array_equal(result.mask, expected.mask)
|
|
|
|
def test_default_fill_value_complex():
|
|
# regression test for Python 3, where 'unicode' was not defined
|
|
assert_(default_fill_value(1 + 1j) == 1.e20 + 0.0j)
|
|
|
|
|
|
def test_ufunc_with_output():
|
|
# check that giving an output argument always returns that output.
|
|
# Regression test for gh-8416.
|
|
x = array([1., 2., 3.], mask=[0, 0, 1])
|
|
y = np.add(x, 1., out=x)
|
|
assert_(y is x)
|
|
|
|
|
|
def test_ufunc_with_out_varied():
|
|
""" Test that masked arrays are immune to gh-10459 """
|
|
# the mask of the output should not affect the result, however it is passed
|
|
a = array([ 1, 2, 3], mask=[1, 0, 0])
|
|
b = array([10, 20, 30], mask=[1, 0, 0])
|
|
out = array([ 0, 0, 0], mask=[0, 0, 1])
|
|
expected = array([11, 22, 33], mask=[1, 0, 0])
|
|
|
|
out_pos = out.copy()
|
|
res_pos = np.add(a, b, out_pos)
|
|
|
|
out_kw = out.copy()
|
|
res_kw = np.add(a, b, out=out_kw)
|
|
|
|
out_tup = out.copy()
|
|
res_tup = np.add(a, b, out=(out_tup,))
|
|
|
|
assert_equal(res_kw.mask, expected.mask)
|
|
assert_equal(res_kw.data, expected.data)
|
|
assert_equal(res_tup.mask, expected.mask)
|
|
assert_equal(res_tup.data, expected.data)
|
|
assert_equal(res_pos.mask, expected.mask)
|
|
assert_equal(res_pos.data, expected.data)
|
|
|
|
|
|
def test_astype_mask_ordering():
|
|
descr = np.dtype([('v', int, 3), ('x', [('y', float)])])
|
|
x = array([
|
|
[([1, 2, 3], (1.0,)), ([1, 2, 3], (2.0,))],
|
|
[([1, 2, 3], (3.0,)), ([1, 2, 3], (4.0,))]], dtype=descr)
|
|
x[0]['v'][0] = np.ma.masked
|
|
|
|
x_a = x.astype(descr)
|
|
assert x_a.dtype.names == np.dtype(descr).names
|
|
assert x_a.mask.dtype.names == np.dtype(descr).names
|
|
assert_equal(x, x_a)
|
|
|
|
assert_(x is x.astype(x.dtype, copy=False))
|
|
assert_equal(type(x.astype(x.dtype, subok=False)), np.ndarray)
|
|
|
|
x_f = x.astype(x.dtype, order='F')
|
|
assert_(x_f.flags.f_contiguous)
|
|
assert_(x_f.mask.flags.f_contiguous)
|
|
|
|
# Also test the same indirectly, via np.array
|
|
x_a2 = np.array(x, dtype=descr, subok=True)
|
|
assert x_a2.dtype.names == np.dtype(descr).names
|
|
assert x_a2.mask.dtype.names == np.dtype(descr).names
|
|
assert_equal(x, x_a2)
|
|
|
|
assert_(x is np.array(x, dtype=descr, copy=False, subok=True))
|
|
|
|
x_f2 = np.array(x, dtype=x.dtype, order='F', subok=True)
|
|
assert_(x_f2.flags.f_contiguous)
|
|
assert_(x_f2.mask.flags.f_contiguous)
|
|
|
|
|
|
@pytest.mark.parametrize('dt1', num_dts, ids=num_ids)
|
|
@pytest.mark.parametrize('dt2', num_dts, ids=num_ids)
|
|
@pytest.mark.filterwarnings('ignore::numpy.ComplexWarning')
|
|
def test_astype_basic(dt1, dt2):
|
|
# See gh-12070
|
|
src = np.ma.array(ones(3, dt1), fill_value=1)
|
|
dst = src.astype(dt2)
|
|
|
|
assert_(src.fill_value == 1)
|
|
assert_(src.dtype == dt1)
|
|
assert_(src.fill_value.dtype == dt1)
|
|
|
|
assert_(dst.fill_value == 1)
|
|
assert_(dst.dtype == dt2)
|
|
assert_(dst.fill_value.dtype == dt2)
|
|
|
|
assert_equal(src, dst)
|
|
|
|
|
|
def test_fieldless_void():
|
|
dt = np.dtype([]) # a void dtype with no fields
|
|
x = np.empty(4, dt)
|
|
|
|
# these arrays contain no values, so there's little to test - but this
|
|
# shouldn't crash
|
|
mx = np.ma.array(x)
|
|
assert_equal(mx.dtype, x.dtype)
|
|
assert_equal(mx.shape, x.shape)
|
|
|
|
mx = np.ma.array(x, mask=x)
|
|
assert_equal(mx.dtype, x.dtype)
|
|
assert_equal(mx.shape, x.shape)
|
|
|
|
|
|
def test_mask_shape_assignment_does_not_break_masked():
|
|
a = np.ma.masked
|
|
b = np.ma.array(1, mask=a.mask)
|
|
b.shape = (1,)
|
|
assert_equal(a.mask.shape, ())
|
|
|
|
@pytest.mark.skipif(sys.flags.optimize > 1,
|
|
reason="no docstrings present to inspect when PYTHONOPTIMIZE/Py_OptimizeFlag > 1")
|
|
def test_doc_note():
|
|
def method(self):
|
|
"""This docstring
|
|
|
|
Has multiple lines
|
|
|
|
And notes
|
|
|
|
Notes
|
|
-----
|
|
original note
|
|
"""
|
|
pass
|
|
|
|
expected_doc = """This docstring
|
|
|
|
Has multiple lines
|
|
|
|
And notes
|
|
|
|
Notes
|
|
-----
|
|
note
|
|
|
|
original note"""
|
|
|
|
assert_equal(np.ma.core.doc_note(method.__doc__, "note"), expected_doc)
|
|
|
|
|
|
def test_gh_22556():
|
|
source = np.ma.array([0, [0, 1, 2]], dtype=object)
|
|
deepcopy = copy.deepcopy(source)
|
|
deepcopy[1].append('this should not appear in source')
|
|
assert len(source[1]) == 3
|
|
|
|
|
|
def test_gh_21022():
|
|
# testing for absence of reported error
|
|
source = np.ma.masked_array(data=[-1, -1], mask=True, dtype=np.float64)
|
|
axis = np.array(0)
|
|
result = np.prod(source, axis=axis, keepdims=False)
|
|
result = np.ma.masked_array(result,
|
|
mask=np.ones(result.shape, dtype=np.bool_))
|
|
array = np.ma.masked_array(data=-1, mask=True, dtype=np.float64)
|
|
copy.deepcopy(array)
|
|
copy.deepcopy(result)
|
|
|
|
|
|
def test_deepcopy_2d_obj():
|
|
source = np.ma.array([[0, "dog"],
|
|
[1, 1],
|
|
[[1, 2], "cat"]],
|
|
mask=[[0, 1],
|
|
[0, 0],
|
|
[0, 0]],
|
|
dtype=object)
|
|
deepcopy = copy.deepcopy(source)
|
|
deepcopy[2, 0].extend(['this should not appear in source', 3])
|
|
assert len(source[2, 0]) == 2
|
|
assert len(deepcopy[2, 0]) == 4
|
|
assert_equal(deepcopy._mask, source._mask)
|
|
deepcopy._mask[0, 0] = 1
|
|
assert source._mask[0, 0] == 0
|
|
|
|
|
|
def test_deepcopy_0d_obj():
|
|
source = np.ma.array(0, mask=[0], dtype=object)
|
|
deepcopy = copy.deepcopy(source)
|
|
deepcopy[...] = 17
|
|
assert_equal(source, 0)
|
|
assert_equal(deepcopy, 17)
|