NumPy 源码解析（八十三）
.\numpy\numpy\_core\tests\test_mem_policy.py
import asyncio
import gc
import os
import sys
import threading
import warnings
import pytest
import numpy as np
from numpy.testing import extbuild, assert_warns, IS_WASM, IS_EDITABLE
from numpy._core.multiarray import get_handler_name
@pytest.fixture
def get_module(tmp_path):
"""添加一个内存策略，将一个虚假指针插入到实际分配的内存中间 64 字节处，并在前缀中填充一些文本。
然后在每次内存操作时检查前缀是否存在，以确保所有的分配/重新分配/释放/清零通过这里的函数进行。
"""
if sys.platform.startswith('cygwin'):
pytest.skip('link fails on cygwin')
if IS_WASM:
pytest.skip("Can't build module inside Wasm")
if IS_EDITABLE:
pytest.skip("Can't build module for editable install")
more_init = "import_array();"
try:
import mem_policy
return mem_policy
except ImportError:
pass
return extbuild.build_and_import_extension('mem_policy',
functions,
prologue=prologue,
include_dirs=[np.get_include()],
build_dir=tmp_path,
more_init=more_init)
@pytest.mark.skipif(sys.version_info >= (3, 12), reason="no numpy.distutils")
def test_set_policy(get_module):
get_handler_name = np._core.multiarray.get_handler_name
get_handler_version = np._core.multiarray.get_handler_version
orig_policy_name = get_handler_name()
a = np.arange(10).reshape((2, 5))
assert get_handler_name(a) is None
assert get_handler_version(a) is None
assert get_handler_name(a.base) == orig_policy_name
assert get_handler_version(a.base) == 1
orig_policy = get_module.set_secret_data_policy()
b = np.arange(10).reshape((2, 5))
assert get_handler_name(b) is None
assert get_handler_version(b) is None
assert get_handler_name(b.base) == 'secret_data_allocator'
assert get_handler_version(b.base) == 1
if orig_policy_name == 'default_allocator':
get_module.set_old_policy(None)
assert get_handler_name() == 'default_allocator'
else:
get_module.set_old_policy(orig_policy)
assert get_handler_name() == orig_policy_name
with pytest.raises(ValueError,
match="Capsule must be named 'mem_handler'"):
get_module.set_wrong_capsule_name_data_policy()
@pytest.mark.skipif(sys.version_info >= (3, 12), reason="no numpy.distutils")
def test_default_policy_singleton(get_module):
get_handler_name = np._core.multiarray.get_handler_name
orig_policy = get_module.set_old_policy(None)
assert get_handler_name() == 'default_allocator'
def_policy_1 = get_module.set_old_policy(None)
assert get_handler_name() == 'default_allocator'
def_policy_2 = get_module.set_old_policy(orig_policy)
assert def_policy_1 is def_policy_2 is get_module.get_default_policy()
@pytest.mark.skipif(sys.version_info >= (3, 12), reason="no numpy.distutils")
def test_policy_propagation(get_module):
class MyArr(np.ndarray):
pass
get_handler_name = np._core.multiarray.get_handler_name
orig_policy_name = get_handler_name()
a = np.arange(10).view(MyArr).reshape((2, 5))
assert get_handler_name(a) is None
assert a.flags.owndata is False
assert get_handler_name(a.base) is None
assert a.base.flags.owndata is False
assert get_handler_name(a.base.base) == orig_policy_name
assert a.base.base.flags.owndata is True
async def concurrent_context1(get_module, orig_policy_name, event):
if orig_policy_name == 'default_allocator':
get_module.set_secret_data_policy()
assert get_handler_name() == 'secret_data_allocator'
else:
get_module.set_old_policy(None)
assert get_handler_name() == 'default_allocator'
event.set()
async def concurrent_context2(get_module, orig_policy_name, event):
await event.wait()
assert get_handler_name() == orig_policy_name
if orig_policy_name == 'default_allocator':
get_module.set_secret_data_policy()
assert get_handler_name() == 'secret_data_allocator'
else:
get_module.set_old_policy(None)
assert get_handler_name() == 'default_allocator'
async def async_test_context_locality(get_module):
orig_policy_name = np._core.multiarray.get_handler_name()
event = asyncio.Event()
concurrent_task1 = asyncio.create_task(
concurrent_context1(get_module, orig_policy_name, event))
concurrent_task2 = asyncio.create_task(
concurrent_context2(get_module, orig_policy_name, event))
await concurrent_task1
await concurrent_task2
assert np._core.multiarray.get_handler_name() == orig_policy_name
@pytest.mark.skipif(sys.version_info >= (3, 12), reason="no numpy.distutils")
def test_context_locality(get_module):
if (sys.implementation.name == 'pypy'
and sys.pypy_version_info[:3] < (7, 3, 6)):
pytest.skip('no context-locality support in PyPy < 7.3.6')
asyncio.run(async_test_context_locality(get_module))
def concurrent_thread1(get_module, event):
get_module.set_secret_data_policy()
assert np._core.multiarray.get_handler_name() == 'secret_data_allocator'
event.set()
def concurrent_thread2(get_module, event):
event.wait()
assert np._core.multiarray.get_handler_name() == 'default_allocator'
get_module.set_secret_data_policy()
@pytest.mark.skipif(sys.version_info >= (3, 12), reason="no numpy.distutils")
def test_thread_locality(get_module):
orig_policy_name = np._core.multiarray.get_handler_name()
event = threading.Event()
concurrent_task1 = threading.Thread(target=concurrent_thread1,
args=(get_module, event))
concurrent_task2 = threading.Thread(target=concurrent_thread2,
args=(get_module, event))
concurrent_task1.start()
finally:
if oldval is not None:
np._core._multiarray_umath._set_numpy_warn_if_no_mem_policy(oldval)
@pytest.mark.skipif(sys.version_info >= (3, 12), reason="no numpy.distutils")
def test_owner_is_base(get_module):
a = get_module.get_array_with_base()
with pytest.warns(UserWarning, match='warn_on_free'):
del a
gc.collect()
gc.collect()
.\numpy\numpy\_core\tests\test_multiarray.py
from __future__ import annotations
import collections.abc
import tempfile
import sys
import warnings
import operator
import io
import itertools
import functools
import ctypes
import os
import gc
import re
import weakref
import pytest
from contextlib import contextmanager
import pickle
import pathlib
import builtins
from decimal import Decimal
import mmap
import numpy as np
import numpy._core._multiarray_tests as _multiarray_tests
from numpy._core._rational_tests import rational
from numpy.exceptions import AxisError, ComplexWarning
from numpy.testing import (
assert_, assert_raises, assert_warns, assert_equal, assert_almost_equal,
assert_array_equal, assert_raises_regex, assert_array_almost_equal,
assert_allclose, IS_PYPY, IS_WASM, IS_PYSTON, HAS_REFCOUNT,
assert_array_less, runstring, temppath, suppress_warnings, break_cycles,
_SUPPORTS_SVE, assert_array_compare,
)
from numpy.testing._private.utils import requires_memory, _no_tracing
from numpy._core.tests._locales import CommaDecimalPointLocale
from numpy.lib.recfunctions import repack_fields
from numpy._core.multiarray import _get_ndarray_c_version, dot
from datetime import timedelta, datetime
def assert_arg_sorted(arr, arg):
assert_equal(arr[arg], np.sort(arr))
assert_equal(np.sort(arg), np.arange(len(arg)))
def assert_arr_partitioned(kth, k, arr_part):
assert_equal(arr_part[k], kth)
assert_array_compare(operator.__le__, arr_part[:k], kth)
assert_array_compare(operator.__ge__, arr_part[k:], kth)
def _aligned_zeros(shape, dtype=float, order="C", align=None):
"""
Allocate a new ndarray with aligned memory.
The ndarray is guaranteed *not* aligned to twice the requested alignment.
Eg, if align=4, guarantees it is not aligned to 8. If align=None uses
dtype.alignment.
"""
dtype = np.dtype(dtype)
if dtype == np.dtype(object):
if align is not None:
raise ValueError("object array alignment not supported")
return np.zeros(shape, dtype=dtype, order=order)
if align is None:
align = dtype.alignment
if not hasattr(shape, '__len__'):
shape = (shape,)
size = functools.reduce(operator.mul, shape) * dtype.itemsize
buf = np.empty(size + 2*align + 1, np.uint8)
ptr = buf.__array_interface__['data'][0]
offset = ptr % align
if offset != 0:
offset = align - offset
if (ptr % (2*align)) == 0:
offset += align
buf = buf[offset:offset+size+1][:-1]
buf.fill(0)
data = np.ndarray(shape, dtype, buf, order=order)
return data
class TestFlags:
pass
def setup_method(self):
self.a = np.arange(10)
def test_writeable(self):
mydict = locals()
self.a.flags.writeable = False
assert_raises(ValueError, runstring, 'self.a[0] = 3', mydict)
self.a.flags.writeable = True
self.a[0] = 5
self.a[0] = 0
def test_writeable_any_base(self):
arr = np.arange(10)
class subclass(np.ndarray):
pass
view1 = arr.view(subclass)
view2 = view1[...]
arr.flags.writeable = False
view2.flags.writeable = False
view2.flags.writeable = True
arr = np.arange(10)
class frominterface:
def __init__(self, arr):
self.arr = arr
self.__array_interface__ = arr.__array_interface__
view1 = np.asarray(frominterface)
view2 = view1[...]
view2.flags.writeable = False
view2.flags.writeable = True
view1.flags.writeable = False
view2.flags.writeable = False
with assert_raises(ValueError):
view2.flags.writeable = True
def test_writeable_from_readonly(self):
data = b'\x00' * 100
vals = np.frombuffer(data, 'B')
assert_raises(ValueError, vals.setflags, write=True)
types = np.dtype( [('vals', 'u1'), ('res3', 'S4')] )
values = np._core.records.fromstring(data, types)
vals = values['vals']
assert_raises(ValueError, vals.setflags, write=True)
def test_writeable_from_buffer(self):
data = bytearray(b'\x00' * 100)
vals = np.frombuffer(data, 'B')
assert_(vals.flags.writeable)
vals.setflags(write=False)
assert_(vals.flags.writeable is False)
vals.setflags(write=True)
assert_(vals.flags.writeable)
types = np.dtype( [('vals', 'u1'), ('res3', 'S4')] )
values = np._core.records.fromstring(data, types)
vals = values['vals']
assert_(vals.flags.writeable)
vals.setflags(write=False)
assert_(vals.flags.writeable is False)
vals.setflags(write=True)
assert_(vals.flags.writeable)
@pytest.mark.skipif(IS_PYPY, reason="PyPy always copies")
def test_writeable_pickle(self):
import pickle
a = np.arange(1000)
for v in range(pickle.HIGHEST_PROTOCOL):
vals = pickle.loads(pickle.dumps(a, v))
assert_(vals.flags.writeable)
assert_(isinstance(vals.base, bytes))
def test_writeable_from_c_data(self):
from numpy._core._multiarray_tests import get_c_wrapping_array
arr_writeable = get_c_wrapping_array(True)
assert not arr_writeable.flags.owndata
assert arr_writeable.flags.writeable
view = arr_writeable[...]
view.flags.writeable = False
assert not view.flags.writeable
view.flags.writeable = True
assert view.flags.writeable
arr_writeable.flags.writeable = False
arr_readonly = get_c_wrapping_array(False)
assert not arr_readonly.flags.owndata
assert not arr_readonly.flags.writeable
for arr in [arr_writeable, arr_readonly]:
view = arr[...]
view.flags.writeable = False
arr.flags.writeable = False
assert not arr.flags.writeable
with assert_raises(ValueError):
view.flags.writeable = True
with warnings.catch_warnings():
warnings.simplefilter("error", DeprecationWarning)
with assert_raises(DeprecationWarning):
arr.flags.writeable = True
with assert_warns(DeprecationWarning):
arr.flags.writeable = True
def test_warnonwrite(self):
a = np.arange(10)
a.flags._warn_on_write = True
with warnings.catch_warnings(record=True) as w:
warnings.filterwarnings('always')
a[1] = 10
a[2] = 10
assert_(len(w) == 1)
@pytest.mark.parametrize(["flag", "flag_value", "writeable"],
[("writeable", True, True),
("_warn_on_write", True, False),
("writeable", False, False)])
def test_readonly_flag_protocols(self, flag, flag_value, writeable):
a = np.arange(10)
setattr(a.flags, flag, flag_value)
class MyArr():
__array_struct__ = a.__array_struct__
assert memoryview(a).readonly is not writeable
assert a.__array_interface__['data'][1] is not writeable
assert np.asarray(MyArr()).flags.writeable is writeable
def test_otherflags(self):
assert_equal(self.a.flags.carray, True)
assert_equal(self.a.flags['C'], True)
assert_equal(self.a.flags.farray, False)
assert_equal(self.a.flags.behaved, True)
assert_equal(self.a.flags.fnc, False)
assert_equal(self.a.flags.forc, True)
assert_equal(self.a.flags.owndata, True)
assert_equal(self.a.flags.writeable, True)
assert_equal(self.a.flags.aligned, True)
assert_equal(self.a.flags.writebackifcopy, False)
assert_equal(self.a.flags['X'], False)
assert_equal(self.a.flags['WRITEBACKIFCOPY'], False)
def test_string_align(self):
a = np.zeros(4, dtype=np.dtype('|S4'))
assert_(a.flags.aligned)
a = np.zeros(5, dtype=np.dtype('|S4'))
assert_(a.flags.aligned)
def test_void_align(self):
a = np.zeros(4, dtype=np.dtype([("a", "i4"), ("b", "i4")]))
assert_(a.flags.aligned)
class TestHash:
def test_int(self):
for st, ut, s in [(np.int8, np.uint8, 8),
(np.int16, np.uint16, 16),
(np.int32, np.uint32, 32),
(np.int64, np.uint64, 64)]:
for i in range(1, s):
assert_equal(hash(st(-2**i)), hash(-2**i),
err_msg="%r: -2**%d" % (st, i))
assert_equal(hash(st(2**(i - 1))), hash(2**(i - 1)),
err_msg="%r: 2**%d" % (st, i - 1))
assert_equal(hash(st(2**i - 1)), hash(2**i - 1),
err_msg="%r: 2**%d - 1" % (st, i))
i = max(i - 1, 1)
assert_equal(hash(ut(2**(i - 1))), hash(2**(i - 1)),
err_msg="%r: 2**%d" % (ut, i - 1))
assert_equal(hash(ut(2**i - 1)), hash(2**i - 1),
err_msg="%r: 2**%d - 1" % (ut, i))
class TestAttributes:
def setup_method(self):
self.one = np.arange(10)
self.two = np.arange(20).reshape(4, 5)
self.three = np.arange(60, dtype=np.float64).reshape(2, 5, 6)
def test_attributes(self):
assert_equal(self.one.shape, (10,))
assert_equal(self.two.shape, (4, 5))
assert_equal(self.three.shape, (2, 5, 6))
self.three.shape = (10, 3, 2)
assert_equal(self.three.shape, (10, 3, 2))
self.three.shape = (2, 5, 6)
assert_equal(self.one.strides, (self.one.itemsize,))
num = self.two.itemsize
assert_equal(self.two.strides, (5*num, num))
num = self.three.itemsize
assert_equal(self.three.strides, (30*num, 6*num, num))
assert_equal(self.one.ndim, 1)
assert_equal(self.two.ndim, 2)
assert_equal(self.three.ndim, 3)
num = self.two.itemsize
assert_equal(self.two.size, 20)
assert_equal(self.two.nbytes, 20*num)
assert_equal(self.two.itemsize, self.two.dtype.itemsize)
assert_equal(self.two.base, np.arange(20))
def test_dtypeattr(self):
assert_equal(self.one.dtype, np.dtype(np.int_))
assert_equal(self.three.dtype, np.dtype(np.float64))
assert_equal(self.one.dtype.char, np.dtype(int).char)
assert self.one.dtype.char in "lq"
assert_equal(self.three.dtype.char, 'd')
assert_(self.three.dtype.str[0] in '<>')
assert_equal(self.one.dtype.str[1], 'i')
assert_equal(self.three.dtype.str[1], 'f')
def test_int_subclassing(self):
numpy_int = np.int_(0)
assert_(not isinstance(numpy_int, int))
def test_stridesattr(self):
x = self.one
def make_array(size, offset, strides):
return np.ndarray(size, buffer=x, dtype=int,
offset=offset*x.itemsize,
strides=strides*x.itemsize)
assert_equal(make_array(4, 4, -1), np.array([4, 3, 2, 1]))
assert_raises(ValueError, make_array, 4, 4, -2)
assert_raises(ValueError, make_array, 4, 2, -1)
assert_raises(ValueError, make_array, 8, 3, 1)
assert_equal(make_array(8, 3, 0), np.array([3]*8))
assert_raises(ValueError, make_array, (2, 3), 5, np.array([-2, -3]))
def test_set_stridesattr(self):
x = self.one
def make_array(size, offset, strides):
try:
r = np.ndarray([size], dtype=int, buffer=x,
offset=offset*x.itemsize)
except Exception as e:
raise RuntimeError(e)
r.strides = strides = strides*x.itemsize
return r
assert_equal(make_array(4, 4, -1), np.array([4, 3, 2, 1]))
assert_equal(make_array(7, 3, 1), np.array([3, 4, 5, 6, 7, 8, 9]))
assert_raises(ValueError, make_array, 4, 4, -2)
assert_raises(ValueError, make_array, 4, 2, -1)
assert_raises(RuntimeError, make_array, 8, 3, 1)
x = np.lib.stride_tricks.as_strided(np.arange(1), (10, 10), (0, 0))
def set_strides(arr, strides):
arr.strides = strides
x = np.lib.stride_tricks.as_strided(np.arange(10, dtype=np.int8)[-1],
shape=(10,), strides=(-1,))
assert_raises(ValueError, set_strides, x[::-1], -1)
a = x[::-1]
a.strides = 1
arr_0d = np.array(0)
arr_0d.strides = ()
def test_fill(self):
for t in "?bhilqpBHILQPfdgFDGO":
x = np.empty((3, 2, 1), t)
y = np.empty((3, 2, 1), t)
x.fill(1)
y[...] = 1
assert_equal(x, y)
def test_fill_max_uint64(self):
x = np.empty((3, 2, 1), dtype=np.uint64)
y = np.empty((3, 2, 1), dtype=np.uint64)
value = 2**64 - 1
y[...] = value
x.fill(value)
assert_array_equal(x, y)
x = np.array([(0, 0.0), (1, 1.0)], dtype='i4,f8')
x.fill(x[0])
assert_equal(x['f1'][1], x['f1'][0])
x = np.zeros(2, dtype=[('a', 'f8'), ('b', 'i4')])
x.fill((3.5, -2))
assert_array_equal(x['a'], [3.5, 3.5])
assert_array_equal(x['b'], [-2, -2])
a = np.zeros(11)
a.setflags(write=False)
with pytest.raises(ValueError, match=".*read-only"):
a.fill(0)
class TestArrayConstruction:
def test_array(self):
d = np.ones(6)
r = np.array([d, d])
assert_equal(r, np.ones((2, 6)))
d = np.ones(6)
tgt = np.ones((2, 6))
r = np.array([d, d])
assert_equal(r, tgt)
tgt[1] = 2
r = np.array([d, d + 1])
assert_equal(r, tgt)
d = np.ones(6)
r = np.array([[d, d]])
assert_equal(r, np.ones((1, 2, 6)))
d = np.ones(6)
r = np.array([[d, d], [d, d]])
assert_equal(r, np.ones((2, 2, 6)))
d = np.ones((6, 6))
r = np.array([d, d])
assert_equal(r, np.ones((2, 6, 6)))
d = np.ones((6, ))
r = np.array([[d, d + 1], d + 2], dtype=object)
assert_equal(len(r), 2)
assert_equal(r[0], [d, d + 1])
assert_equal(r[1], d + 2)
tgt = np.ones((2, 3), dtype=bool)
tgt[0, 2] = False
tgt[1, 0:2] = False
r = np.array([[True, True, False], [False, False, True]])
assert_equal(r, tgt)
r = np.array([[True, False], [True, False], [False, True]])
assert_equal(r, tgt.T)
def test_array_empty(self):
assert_raises(TypeError, np.array)
def test_0d_array_shape(self):
assert np.ones(np.array(3)).shape == (3,)
def test_array_copy_false(self):
d = np.array([1, 2, 3])
e = np.array(d, copy=False)
d[1] = 3
assert_array_equal(e, [1, 3, 3])
np.array(d, copy=False, order='F')
def test_array_copy_if_needed(self):
d = np.array([1, 2, 3])
e = np.array(d, copy=None)
d[1] = 3
assert_array_equal(e, [1, 3, 3])
e = np.array(d, copy=None, order='F')
d[1] = 4
assert_array_equal(e, [1, 4, 3])
e[2] = 7
assert_array_equal(d, [1, 4, 7])
def test_array_copy_true(self):
d = np.array([[1,2,3], [1, 2, 3]])
e = np.array(d, copy=True)
d[0, 1] = 3
e[0, 2] = -7
assert_array_equal(e, [[1, 2, -7], [1, 2, 3]])
assert_array_equal(d, [[1, 3, 3], [1, 2, 3]])
e = np.array(d, copy=True, order='F')
d[0, 1] = 5
e[0, 2] = 7
assert_array_equal(e, [[1, 3, 7], [1, 2, 3]])
assert_array_equal(d, [[1, 5, 3], [1,2,3]])
def test_array_copy_str(self):
with pytest.raises(
ValueError,
match="strings are not allowed for 'copy' keyword. "
"Use True/False/None instead."
):
np.array([1, 2, 3], copy="always")
def test_array_cont(self):
d = np.ones(10)[::2]
assert_(np.ascontiguousarray(d).
@pytest.mark.parametrize("func",
[np.array,
np.asarray,
np.asanyarray,
np.ascontiguousarray,
np.asfortranarray])
def test_bad_arguments_error(self, func):
with pytest.raises(TypeError):
func(3, dtype="bad dtype")
with pytest.raises(TypeError):
func()
with pytest.raises(TypeError):
func(1, 2, 3, 4, 5, 6, 7, 8)
@pytest.mark.parametrize("func",
[np.array,
np.asarray,
np.asanyarray,
np.ascontiguousarray,
np.asfortranarray])
def test_array_as_keyword(self, func):
if func is np.array:
func(object=3)
else:
func(a=3)
class TestAssignment:
def test_assignment_broadcasting(self):
a = np.arange(6).reshape(2, 3)
a[...] = np.arange(3)
assert_equal(a, [[0, 1, 2], [0, 1, 2]])
a[...] = np.arange(2).reshape(2, 1)
assert_equal(a, [[0, 0, 0], [1, 1, 1]])
a[...] = np.arange(6)[::-1].reshape(1, 2, 3)
assert_equal(a, [[5, 4, 3], [2, 1, 0]])
def assign(a, b):
a[...] = b
assert_raises(ValueError, assign, a, np.arange(12).reshape(2, 2, 3))
def test_assignment_errors(self):
class C:
pass
a = np.zeros(1)
def assign(v):
a[0] = v
assert_raises((AttributeError, TypeError), assign, C())
assert_raises(ValueError, assign, [1])
@pytest.mark.filterwarnings(
"ignore:.*set_string_function.*:DeprecationWarning"
)
def test_unicode_assignment(self):
from numpy._core.arrayprint import set_printoptions
@contextmanager
def inject_str(s):
""" replace ndarray.__str__ temporarily """
set_printoptions(formatter={"all": lambda x: s})
try:
yield
finally:
set_printoptions()
a1d = np.array(['test'])
a0d = np.array('done')
with inject_str('bad'):
a1d[0] = a0d
assert_equal(a1d[0], 'done')
np.array([np.array('\xe5\xe4\xf6')])
def test_stringlike_empty_list(self):
u = np.array(['done'])
b = np.array([b'done'])
class bad_sequence:
def __getitem__(self): pass
def __len__(self): raise RuntimeError
assert_raises(ValueError, operator.setitem, u, 0, [])
assert_raises(ValueError, operator.setitem, b, 0, [])
assert_raises(ValueError, operator.setitem, u, 0, bad_sequence())
assert_raises(ValueError, operator.setitem, b, 0, bad_sequence())
def test_longdouble_assignment(self):
for dtype in (np.longdouble, np.clongdouble):
tinyb = np.nextafter(np.longdouble(0), 1).astype(dtype)
tinya = np.nextafter(np.longdouble(0), -1).astype(dtype)
tiny1d = np.array([tinya])
assert_equal(tiny1d[0], tinya)
tiny1d[0] = tinyb
assert_equal(tiny1d[0], tinyb)
tiny1d[0, ...] = tinya
assert_equal(tiny1d[0], tinya)
tiny1d[0, ...] = tinyb[...]
assert_equal(tiny1d[0], tinyb)
tiny1d[0] = tinyb[...]
assert_equal(tiny1d[0], tinyb)
arr = np.array([np.array(tinya)])
assert_equal(arr[0], tinya)
def test_cast_to_string(self):
a = np.zeros(1, dtype='S20')
a[:] = np.array(['1.12345678901234567890'], dtype='f8')
assert_equal(a[0], b"1.1234567890123457")
class TestDtypedescr:
def test_construction(self):
d1 = np.dtype('i4')
assert_equal(d1, np.dtype(np.int32))
d2 = np.dtype('f8')
assert_equal(d2, np.dtype(np.float64))
def test_byteorders(self):
assert_(np.dtype('<i4') != np.dtype('>i4'))
assert_(np.dtype([('a', '<i4')]) != np.dtype([('a', '>i4')]))
def test_structured_non_void(self):
fields = [('a', '<i2'), ('b', '<i2')]
dt_int = np.dtype(('i4', fields))
assert_equal(str(dt_int), "(numpy.int32, [('a', '<i2'), ('b', '<i2')])")
arr_int = np.zeros(4, dt_int)
assert_equal(repr(arr_int),
"array([0, 0, 0, 0], dtype=(numpy.int32, [('a', '<i2'), ('b', '<i2')]))")
class TestZeroRank:
def setup_method(self):
self.d = np.array(0), np.array('x', object)
def test_ellipsis_subscript(self):
a, b = self.d
assert_equal(a[...], 0)
assert_equal(b[...], 'x')
assert_(a[...].base is a)
assert_(b[...].base is b)
def test_empty_subscript(self):
a, b = self.d
assert_equal(a[()], 0)
assert_equal(b[()], 'x')
assert_(type(a[()]) is a.dtype.type)
assert_(type(b[()]) is str)
def test_invalid_subscript(self):
a, b = self.d
assert_raises(IndexError, lambda x: x[0], a)
assert_raises(IndexError, lambda x: x[0], b)
assert_raises(IndexError, lambda x: x[np.array([], int)], a)
assert_raises(IndexError, lambda x: x[np.array([], int)], b)
def test_ellipsis_subscript_assignment(self):
a, b = self.d
a[...] = 42
assert_equal(a, 42)
b[...] = ''
assert_equal(b.item(), '')
def test_empty_subscript_assignment(self):
a, b = self.d
a[()] = 42
assert_equal(a, 42)
b[()] = ''
assert_equal(b.item(), '')
def test_invalid_subscript_assignment(self):
a, b = self.d
def assign(x, i, v):
x[i] = v
assert_raises(IndexError, assign, a, 0, 42)
assert_raises(IndexError, assign, b, 0, '')
assert_raises(ValueError, assign, a, (), '')
def test_newaxis(self):
a, b = self.d
assert_equal(a[np.newaxis].shape, (1,))
assert_equal(a[..., np.newaxis].shape, (1,))
assert_equal(a[np.newaxis, ...].shape, (1,))
assert_equal(a[..., np.newaxis].shape, (1,))
assert_equal(a[np.newaxis, ..., np.newaxis].shape, (1, 1))
assert_equal(a[..., np.newaxis, np.newaxis].shape, (1, 1))
assert_equal(a[np.newaxis, np.newaxis, ...].shape, (1, 1))
assert_equal(a[(np.newaxis,)*10].shape, (1,)*10)
def test_invalid_newaxis(self):
a, b = self.d
def subscript(x, i):
x[i]
assert_raises(IndexError, subscript, a, (
def test_constructor(self):
x = np.ndarray(())
x[()] = 5
assert_equal(x[()], 5)
y = np.ndarray((), buffer=x)
y[()] = 6
assert_equal(x[()], 6)
with pytest.raises(ValueError):
np.ndarray((2,), strides=())
with pytest.raises(ValueError):
np.ndarray((), strides=(2,))
def test_output(self):
x = np.array(2)
assert_raises(ValueError, np.add, x, [1], x)
def test_real_imag(self):
x = np.array(1j)
xr = x.real
xi = x.imag
assert_equal(xr, np.array(0))
assert_(type(xr) is np.ndarray)
assert_equal(xr.flags.contiguous, True)
assert_equal(xr.flags.f_contiguous, True)
assert_equal(xi, np.array(1))
assert_(type(xi) is np.ndarray)
assert_equal(xi.flags.contiguous, True)
assert_equal(xi.flags.f_contiguous, True)
这段代码是针对 `numpy` 库中 `ndarray` 对象的几个功能进行了单元测试。每个方法都测试了不同的情况，通过断言来验证预期行为是否符合预期，同时包含了异常处理的测试。
class TestScalarIndexing:
def setup_method(self):
self.d = np.array([0, 1])[0]
def test_ellipsis_subscript(self):
a = self.d
assert_equal(a[...], 0)
assert_equal(a[...].shape, ())
def test_empty_subscript(self):
a = self.d
assert_equal(a[()], 0)
assert_equal(a[()].shape, ())
def test_invalid_subscript(self):
a = self.d
assert_raises(IndexError, lambda x: x[0], a)
assert_raises(IndexError, lambda x: x[np.array([], int)], a)
def test_invalid_subscript_assignment(self):
a = self.d
def assign(x, i, v):
x[i] = v
assert_raises(TypeError, assign, a, 0, 42)
def test_newaxis(self):
a = self.d
assert_equal(a[np.newaxis].shape, (1,))
assert_equal(a[..., np.newaxis].shape, (1,))
assert_equal(a[np.newaxis, ...].shape, (1,))
assert_equal(a[..., np.newaxis].shape, (1,))
assert_equal(a[np.newaxis, ..., np.newaxis].shape, (1, 1))
assert_equal(a[..., np.newaxis, np.newaxis].shape, (1, 1))
assert_equal(a[np.newaxis, np.newaxis, ...].shape, (1, 1))
assert_equal(a[(np.newaxis,)*10].shape, (1,)*10)
def test_invalid_newaxis(self):
a = self.d
def subscript(x, i):
x[i]
assert_raises(IndexError, subscript, a, (np.newaxis, 0))
assert_raises(IndexError, subscript, a, (np.newaxis,)*70)
def test_overlapping_assignment(self):
a = np.arange(4)
a[:-1] = a[1:]
assert_equal(a, [1, 2, 3, 3])
a = np.arange(4)
a[1:] = a[:-1]
assert_equal(a, [0, 0, 1, 2])
a = np.arange(4)
a[:] = a[::-1]
assert_equal(a, [3, 2, 1, 0])
a = np.arange(6).reshape(2, 3)
a[::-1,:] = a[:, ::-1]
assert_equal(a, [[5, 4, 3], [2, 1, 0]])
a = np.arange(6).reshape(2, 3)
a[::-1, ::-1] = a[:, ::-1]
assert_equal(a, [[3, 4, 5], [0, 1, 2]])
a = np.arange(5)
a[:3] = a[2:]
assert_equal(a, [2, 3, 4, 3, 4])
a = np.arange(5)
a[2:] = a[:3]
assert_equal(a, [0, 1, 0, 1, 2])
a = np.arange(5)
a[2::-1] = a[2:]
assert_equal(a, [4, 3, 2, 3, 4])
a = np.arange(5)
a[2:] = a[2::-1]
assert_equal(a, [0, 1, 2, 1, 0])
a = np.arange(5)
a[2::-1] = a[:1:-1]
assert_equal(a, [2, 3, 4, 3, 4])
a = np.arange(5)
a[:1:-1] = a[2::-1]
assert_equal(a, [0, 1, 0, 1, 2])
class TestCreation:
"""
Test the np.array constructor
"""
def test_from_attribute(self):
class x:
def __array__(self, dtype=None, copy=None):
pass
assert_raises(ValueError, np.array, x())
def test_from_string(self):
types = np.typecodes['AllInteger'] + np.typecodes['Float']
nstr = ['123', '123']
result = np.array([123, 123], dtype=int)
for type in types:
msg = 'String conversion for %s' % type
assert_equal(np.array(nstr, dtype=type), result, err_msg=msg)
def test_void(self):
arr = np.array([], dtype='V')
assert arr.dtype == 'V8'
arr = np.array([b"1234", b"1234"], dtype="V")
assert arr.dtype == "V4"
with pytest.raises(TypeError):
np.array([b"1234", b"12345"], dtype="V")
with pytest.raises(TypeError):
np.array([b"12345", b"1234"], dtype="V")
arr = np.array([b"1234", b"1234"], dtype="O").astype("V")
assert arr.dtype == "V4"
with pytest.raises(TypeError):
np.array([b"1234", b"12345"], dtype="O").astype("V")
@pytest.mark.parametrize("idx",
[pytest.param(Ellipsis, id="arr"), pytest.param((), id="scalar")])
def test_structured_void_promotion(self, idx):
arr = np.array(
[np.array(1, dtype="i,i")[idx], np.array(2, dtype='i,i')[idx]],
dtype="V")
assert_array_equal(arr, np.array([(1, 1), (2, 2)], dtype="i,i"))
with pytest.raises(TypeError):
np.array(
[np.array(1, dtype="i,i")[idx], np.array(2, dtype='i,i,i')[idx]],
dtype="V")
def test_too_big_error(self):
if np.iinfo('intp').max == 2**31 - 1:
shape = (46341, 46341)
elif np.iinfo('intp').max == 2**63 - 1:
shape = (3037000500, 3037000500)
else:
return
assert_raises(ValueError, np.empty, shape, dtype=np.int8)
assert_raises(ValueError, np.zeros, shape, dtype=np.int8)
assert_raises(ValueError, np.ones, shape, dtype=np.int8)
@pytest.mark.skipif(np.dtype(np.intp).itemsize != 8,
reason="malloc may not fail on 32 bit systems")
def test_malloc_fails(self):
with assert_raises(np._core._exceptions._ArrayMemoryError):
np.empty(np.iinfo(np.intp).max, dtype=np.uint8)
def test_zeros(self):
types = np.typecodes['AllInteger'] + np.typecodes['AllFloat']
for dt in types:
d = np.zeros((13,), dtype=dt)
assert_equal(np.count_nonzero(d), 0)
assert_equal(d.sum(), 0)
assert_(not d.any())
d = np.zeros(2, dtype='(2,4)i4')
assert_equal(np.count_nonzero(d), 0)
assert_equal(d.sum(), 0)
assert_(not d.any())
d = np.zeros(2, dtype='4i4')
assert_equal(np.count_nonzero(d), 0)
assert_equal(d.sum(), 0)
assert_(not d.any())
d = np.zeros(2, dtype='(2,4)i4, (2,4)i4')
assert_equal(np.count_nonzero(d), 0)
@pytest.mark.slow
def test_zeros_big(self):
types = np.typecodes['AllInteger'] + np.typecodes['AllFloat']
for dt in types:
d = np.zeros((30 * 1024**2,), dtype=dt)
assert_(not d.any())
del(d)
def test_zeros_obj(self):
d = np.zeros((13,), dtype=object)
assert_array_equal(d, [0] * 13)
assert_equal(np.count_nonzero(d), 0)
def test_zeros_obj_obj(self):
d = np.zeros(10, dtype=[('k', object, 2)])
assert_array_equal(d['k'], 0)
def test_zeros_like_like_zeros(self):
for c in np.typecodes['All']:
if c == 'V':
continue
d = np.zeros((3,3), dtype=c)
assert_array_equal(np.zeros_like(d), d)
assert_equal(np.zeros_like(d).dtype, d.dtype)
d = np.zeros((3,3), dtype='S5')
assert_array_equal(np.zeros_like(d), d)
assert_equal(np.zeros_like(d).dtype, d.dtype)
d = np.zeros((3,3), dtype='U5')
assert_array_equal(np.zeros_like(d), d)
assert_equal(np.zeros_like(d).dtype, d.dtype)
d = np.zeros((3,3), dtype='<i4')
assert_array_equal(np.zeros_like(d), d)
assert_equal(np.zeros_like(d).dtype, d.dtype)
d = np.zeros((3,3), dtype='>i4')
assert_array_equal(np.zeros_like(d), d)
assert_equal(np.zeros_like(d).dtype, d.dtype)
d = np.zeros((3,3), dtype='<M8[s]')
assert_array_equal(np.zeros_like(d), d)
assert_equal(np.zeros_like(d).dtype, d.dtype)
d = np.zeros((3,3), dtype='>M8[s]')
assert_array_equal(np.zeros_like(d), d)
assert_equal(np.zeros_like(d).dtype, d.dtype)
d = np.zeros((3,3), dtype='f4,f4')
assert_array_equal(np.zeros_like(d), d)
assert_equal(np.zeros_like(d).dtype, d.dtype)
def test_empty_unicode(self):
for i in range(5, 100, 5):
d = np.empty(i, dtype='U')
def test_sequence_non_homogeneous(self):
assert_equal(np.array([4, 2**80]).dtype, object)
assert_equal(np.array([4, 2**80, 4]).dtype, object)
assert_equal(np.array([2**80, 4]).dtype, object)
assert_equal(np.array([2**80] * 3).dtype, object)
assert_equal(np.array([[1, 1],[1j, 1j]]).dtype, complex)
assert_equal(np.array([[1j, 1j],[1, 1]]).dtype, complex)
assert_equal(np.array([[1, 1, 1],[1, 1j, 1.], [1, 1, 1]]).dtype, complex)
def test_non_sequence_sequence(self):
"""不应该导致段错误。
Class Fail 打破了新式类的序列协议，即继承自 object 的类。Class Map 是映射类型，通过引发 ValueError 表示。在 Fail 情况下，可能会在某些时候改为引发警告而不是错误。
"""
class Fail:
def __len__(self):
return 1
def __getitem__(self, index):
raise ValueError()
class Map:
def __len__(self):
return 1
def __getitem__(self, index):
raise KeyError()
a = np.array([Map()])
assert_(a.shape == (1,))
assert_(a.dtype == np.dtype(object))
assert_raises(ValueError, np.array, [Fail()])
def test_no_len_object_type(self):
class Point2:
def __init__(self):
pass
def __getitem__(self, ind):
if ind in [0, 1]:
return ind
else:
raise IndexError()
d = np.array([Point2(), Point2(), Point2()])
assert_equal(d.dtype, np.dtype(object))
def test_false_len_sequence(self):
class C:
def __getitem__(self, i):
raise IndexError
def __len__(self):
return 42
a = np.array(C())
assert_equal(len(a), 0)
def test_false_len_iterable(self):
class C:
def __getitem__(self, x):
raise Exception
def __iter__(self):
return iter(())
def __len__(self):
return 2
a = np.empty(2)
with assert_raises(ValueError):
a[:] = C()
np.array(C()) == list(C())
def test_failed_len_sequence(self):
class A:
def __init__(self, data):
self._data = data
def __getitem__(self, item):
return type(self)(self._data[item])
def __len__(self):
return len(self._data)
d = A([1,2,3])
assert_equal(len(np.array(d)), 3)
def test_array_too_big(self):
buf = np.zeros(100)
max_bytes = np.iinfo(np.intp).max
for dtype in ["intp", "S20", "b"]:
dtype = np.dtype(dtype)
itemsize = dtype.itemsize
np.ndarray(buffer=buf, strides=(0,),
shape=(max_bytes//itemsize,), dtype=dtype)
assert_raises(ValueError, np.ndarray, buffer=buf, strides=(0,),
shape=(max_bytes//itemsize + 1,), dtype=dtype)
def _ragged_creation(self, seq):
with pytest.raises(ValueError, match=".*detected shape was"):
a = np.array(seq)
return np.array(seq, dtype=object)
def test_ragged_ndim_object(self):
a = self._ragged_creation([[1], 2, 3])
assert_equal(a.shape, (3,))
assert_equal(a.dtype, object)
a = self._ragged_creation([1, [2], 3])
assert_equal(a.shape, (3,))
assert_equal(a.dtype, object)
a = self._ragged_creation([1, 2, [3]])
assert_equal(a.shape, (3,))
assert_equal(a.dtype, object)
def test_ragged_shape_object(self):
a = self._ragged_creation([[1, 1], [2], [3]])
assert_equal(a.shape, (3,))
assert_equal(a.dtype, object)
a = self._ragged_creation([[1], [2, 2], [3]])
assert_equal(a.shape, (3,))
assert_equal(a.dtype, object)
a = self._ragged_creation([[1], [2], [3, 3]])
assert a.shape == (3,)
assert a.dtype == object
def test_array_of_ragged_array(self):
outer = np.array([None, None])
outer[0] = outer[1] = np.array([1, 2, 3])
assert np.array(outer).shape == (2,)
assert np.array([outer]).shape == (1, 2)
outer_ragged = np.array([None, None])
outer_ragged[0] = np.array([1, 2, 3])
outer_ragged[1] = np.array([1, 2, 3, 4])
assert np.array(outer_ragged).shape == (2,)
assert np.array([outer_ragged]).shape == (1, 2,)
def test_deep_nonragged_object(self):
a = np.array([[[Decimal(1)]]])
a = np.array([1, Decimal(1)])
a = np.array([[1], [Decimal(1)]])
@pytest.mark.parametrize("dtype", [object, "O,O", "O,(3,)O", "(2,3)O"])
@pytest.mark.parametrize("function", [
np.ndarray, np.empty,
lambda shape, dtype: np.empty_like(np.empty(shape, dtype=dtype))])
def test_object_initialized_to_None(self, function, dtype):
arr = function(3, dtype=dtype)
expected = np.array(None).tobytes()
expected = expected * (arr.nbytes // len(expected))
assert arr.tobytes() == expected
@pytest.mark.parametrize("func", [
np.array, np.asarray, np.asanyarray, np.ascontiguousarray,
np.asfortranarray])
def test_creation_from_dtypemeta(self, func):
dtype = np.dtype('i')
arr1 = func([1, 2, 3], dtype=dtype)
arr2 = func([1, 2, 3], dtype=type(dtype))
assert_array_equal(arr1, arr2)
assert arr2.dtype == dtype
class TestStructured:
def test_subarray_field_access(self):
a = np.zeros((3, 5), dtype=[('a', ('i4', (2, 2)))])
a['a'] = np.arange(60).reshape(3, 5, 2, 2)
assert_array_equal(a.T['a'], a['a'].transpose(1, 0, 2, 3))
b = a.copy(order='F')
assert_equal(a['a'].shape, b['a'].shape)
assert_equal(a.T['a'].shape, a.T.copy()['a'].shape)
def test_subarray_comparison(self):
a = np.rec.fromrecords(
[([1, 2, 3], 'a', [[1, 2], [3, 4]]), ([3, 3, 3], 'b', [[0, 0], [0, 0]])],
dtype=[('a', ('f4', 3)), ('b', object), ('c', ('i4', (2, 2)))])
b = a.copy()
assert_equal(a == b, [True, True])
assert_equal(a != b, [False, False])
b[1].b = 'c'
assert_equal(a == b, [True, False])
assert_equal(a != b, [False, True])
for i in range(3):
b[0].a = a[0].a
b[0].a[i] = 5
assert_equal(a == b, [False, False])
assert_equal(a != b, [True, True])
for i in range(2):
for j in range(2):
b = a.copy()
b[0].c[i, j] = 10
assert_equal(a == b, [False, True])
assert_equal(a != b, [True, False])
a = np.array([[(0,)], [(1,)]], dtype=[('a', 'f8')])
b = np.array([(0,), (0,), (1,)], dtype=[('a', 'f8')])
assert_equal(a == b, [[True, True, False], [False, False, True]])
assert_equal(b == a, [[True, True, False], [False, False, True]])
a = np.array([[(0,)], [(1,)]], dtype=[('a', 'f8', (1,))])
b = np.array([(0,), (0,), (1,)], dtype=[('a', 'f8', (1,))])
assert_equal(a == b, [[True, True, False], [False, False, True]])
assert_equal(b == a, [[True, True, False], [False, False, True]])
a = np.array([[([0, 0],)], [([1, 1],)]], dtype=[('a', 'f8', (2,))])
b = np.array([([0, 0],), ([0, 1],), ([1, 1],)], dtype=[('a', 'f8', (2,))])
assert_equal(a == b, [[True, False, False], [False, False, True]])
assert_equal(b == a, [[True, False, False], [False, False, True]])
a = np.array([[([0, 0],)], [([1, 1],)]], dtype=[('a', 'f8', (2,))], order='F')
b = np.array([([0, 0],), ([0, 1],), ([1, 1],)], dtype=[('a', 'f8', (2,))])
assert_equal(a == b, [[True, False, False], [False, False, True]])
assert_equal(b == a, [[True, False, False], [False, False, True]])
x = np.zeros((1,), dtype=[('a', ('f4', (1, 2))), ('b', 'i1')])
y = np.zeros((1,), dtype=[('a', ('f4', (2,))), ('b', 'i1')])
with pytest.raises(TypeError):
x == y
x = np.zeros((1,), dtype=[('a', ('f4', (2, 1))), ('b', 'i1')])
y = np.zeros((1,), dtype=[('a', ('f4', (2,))), ('b', 'i1')])
with pytest.raises(TypeError):
x == y
def test_empty_structured_array_comparison(self):
a = np.zeros(0, [('a', '<f8', (1, 1))])
assert_equal(a, a)
a = np.zeros(0, [('a', '<f8', (1,))])
assert_equal(a, a)
a = np.zeros((0, 0), [('a', '<f8', (1, 1))])
assert_equal(a, a)
a = np.zeros((1, 0, 1), [('a', '<f8', (1, 1))])
assert_equal(a, a)
@pytest.mark.parametrize("op", [operator.eq, operator.ne])
def test_structured_array_comparison_bad_broadcasts(self, op):
a = np.zeros(3, dtype='i,i')
b = np.array([], dtype="i,i")
with pytest.raises(ValueError):
op(a, b)
def test_structured_comparisons_with_promotion(self):
a = np.array([(5, 42), (10, 1)], dtype=[('a', '>i8'), ('b', '<f8')])
b = np.array([(5, 43), (10, 1)], dtype=[('a', '<i8'), ('b', '>f8')])
assert_equal(a == b, [False, True])
assert_equal(a != b, [True, False])
a = np.array([(5, 42), (10, 1)], dtype=[('a', '>f8'), ('b', '<f8')])
b = np.array([(5, 43), (10, 1)], dtype=[('a', '<i8'), ('b', '>i8')])
assert_equal(a == b, [False, True])
assert_equal(a != b, [True, False])
a = np.array([(5, 42), (10, 1)], dtype=[('a', '10>f8'), ('b', '5<f8')])
b = np.array([(5, 43), (10, 1)], dtype=[('a', '10<i8'), ('b', '5>i8')])
assert_equal(a == b, [False, True])
assert_equal(a != b, [True, False])
@pytest.mark.parametrize("op", [
operator.eq, lambda x, y: operator.eq(y, x),
operator.ne, lambda x, y: operator.ne(y, x)])
def test_void_comparison_failures(self, op):
x = np.zeros(3, dtype=[('a', 'i1')])
y = np.zeros(3)
with pytest.raises(TypeError):
op(x, y)
y = np.zeros(3, dtype=[(('title', 'a'), 'i1')])
assert np.can_cast(y.dtype, x.dtype)
with pytest.raises(TypeError):
op(x, y)
x = np.zeros(3, dtype="V7")
y = np.zeros(3, dtype="V8")
with pytest.raises(TypeError):
op(x, y)
def test_casting(self):
a = np.array([(1,)], dtype=[('a', '<i4')])
assert_(np.can_cast(a.dtype, [('a', '>i4')], casting='unsafe'))
b = a.astype([('a', '>i4')])
a_tmp = a.byteswap()
a_tmp = a_tmp.view(a_tmp.dtype.newbyteorder())
assert_equal(b, a_tmp)
assert_equal(a['a'][0], b['a'][0])
a = np.array([(5, 42), (10, 1)], dtype=[('a', '>i4'), ('b', '<f8')])
b = np.array([(5, 42), (10, 1)], dtype=[('a', '<i4'), ('b', '>f8')])
assert_(np.can_cast(a.dtype, b.dtype, casting='equiv'))
assert_equal(a == b, [True, True])
assert_(np.can_cast(a.dtype, b.dtype, casting='equiv'))
c = a.astype(b.dtype, casting='equiv')
assert_equal(a == c, [True, True])
t = [('a', '<i8'), ('b', '>f8')]
assert_(np.can_cast(a.dtype, t, casting='safe'))
c = a.astype(t, casting='safe')
assert_equal((c == np.array([(5, 42), (10, 1)], dtype=t)),
[True, True])
t = [('a', '<i4'), ('b', '>f4')]
assert_(np.can_cast(a.dtype, t, casting='same_kind'))
c = a.astype(t, casting='same_kind')
assert_equal((c == np.array([(5, 42), (10, 1)], dtype=t)),
[True, True])
t = [('a', '>i8'), ('b', '<f4')]
assert_(not np.can_cast(a.dtype, t, casting='safe'))
assert_raises(TypeError, a.astype, t, casting='safe')
t = [('a', '>i2'), ('b', '<f8')]
assert_(not np.can_cast(a.dtype, t, casting='equiv'))
assert_raises(TypeError, a.astype, t, casting='equiv')
t = [('a', '>i8'), ('b', '<i2')]
assert_(not np.can_cast(a.dtype, t, casting='same_kind'))
assert_raises(TypeError, a.astype, t, casting='same_kind')
assert_(not np.can_cast(a.dtype, b.dtype, casting='no'))
assert_raises(TypeError, a.astype, b.dtype, casting='no')
for casting in ['no', 'safe', 'equiv', 'same_kind']:
t = [('a', '>i4')]
assert_(not np.can_cast(a.dtype, t, casting=casting))
t = [('a', '>i4'), ('b', '<f8'), ('c', 'i4')]
assert_(not np.can_cast(a.dtype, t, casting=casting))
def test_objview(self):
a = np.array([], dtype=[('a', 'f'), ('b', 'f'), ('c', 'O')])
a[['a', 'b']]
dat2 = np.zeros(3, [('A', 'i'), ('B', '|O')])
dat2[['B', 'A']]
def test_setfield(self):
struct_dt = np.dtype([('elem', 'i4', 5),])
dt = np.dtype([('field', 'i4', 10), ('struct', struct_dt)])
x = np.zeros(1, dt)
x[0]['field'] = np.ones(10, dtype='i4')
x[0]['struct'] = np.ones(1, dtype=struct_dt)
assert_equal(x[0]['field'], np.ones(10, dtype='i4'))
def test_setfield_object(self):
b = np.zeros(1, dtype=[('x', 'O')])
b[0]['x'] = np.arange(3)
assert_equal(b[0]['x'], np.arange(3))
c = np.zeros(1, dtype=[('x', 'O', 5)])
def testassign():
c[0]['x'] = np.arange(3)
assert_raises(ValueError, testassign)
def test_zero_width_string(self):
dt = np.dtype([('I', int), ('S', 'S0')])
x = np.zeros(4, dtype=dt)
assert_equal(x['S'], [b'', b'', b'', b''])
assert_equal(x['S'].itemsize, 0)
assert_equal(x['S'], [b'', b'', b'', b''])
assert_equal(x['I'], [0, 0, 0, 0])
x['S'][x['I'] == 0] = 'hello'
assert_equal(x['S'], [b'', b'', b'', b''])
assert_equal(x['I'], [0, 0, 0, 0])
x['S'] = 'A'
assert_equal(x['S'], [b'', b'', b'', b''])
assert_equal(x['I'], [0, 0, 0, 0])
y = np.ndarray(4, dtype=x['S'].dtype)
assert_equal(y.itemsize, 0)
assert_equal(x['S'], y)
assert_equal(np.zeros(4, dtype=[('a', 'S0,S0'), ('b', 'u1')])['a'].itemsize, 0)
assert_equal(np.empty(3, dtype='S0,S0').itemsize, 0)
assert_equal(np.zeros(4, dtype='S0,u1')['f0'].itemsize, 0)
xx = x['S'].reshape((2, 2))
assert_equal(xx.itemsize, 0)
assert_equal(xx, [[b'', b''], [b'', b'']])
assert_equal(xx[:].dtype, xx.dtype)
assert_array_equal(eval(repr(xx), dict(np=np, array=np.array)), xx)
b = io.BytesIO()
np.save(b, xx)
b.seek(0)
yy = np.load(b)
assert_equal(yy.itemsize, 0)
assert_equal(xx, yy)
with temppath(suffix='.npy') as tmp:
np.save(tmp, xx)
yy = np.load(tmp)
assert_equal(yy.itemsize, 0)
assert_equal(xx, yy)
def test_base_attr(self):
a = np.zeros(3, dtype='i4,f4')
b = a[0]
assert_(b.base is a)
def test_assignment(self):
def testassign(arr, v):
c = arr.copy()
c[0] = v
c[1:] = v
return c
dt = np.dtype([('foo', 'i8'), ('bar', 'i8')])
arr = np.ones(2, dt)
v1 = np.array([(2,3)], dtype=[('foo', 'i8'), ('bar', 'i8')])
v2 = np.array([(2,3)], dtype=[('bar', 'i8'), ('foo', 'i8')])
v3 = np.array([(2,3)], dtype=[('bar', 'i8'), ('baz', 'i8')])
v4 = np.array([(2,)], dtype=[('bar', 'i8')])
v5 = np.array([(2,3)], dtype=[('foo', 'f8'), ('bar', 'f8')])
w = arr.view({'names': ['bar'], 'formats': ['i8'], 'offsets': [8]})
ans = np.array([(2,3),(2,3)], dtype=dt)
assert_equal(testassign(arr, v1), ans)
assert_equal(testassign(arr, v2), ans)
assert_equal(testassign(arr, v3), ans)
assert_raises(TypeError, lambda: testassign(arr, v4))
assert_equal(testassign(arr, v5), ans)
w[:] = 4
assert_equal(arr, np.array([(1,4),(1,4)], dtype=dt))
a = np.array([(1,2,3)],
dtype=[('foo', 'i8'), ('bar', 'i8'), ('baz', 'f4')])
a[['foo', 'bar']] = a[['bar', 'foo']]
assert_equal(a[0].item(), (2,1,3))
a = np.array([(1,2)], dtype=[('a', 'i4'), ('b', 'i4')])
a[['a', 'b']] = a[['b', 'a']]
assert_equal(a[0].item(), (2,1))
def test_structuredscalar_indexing(self):
x = np.empty(shape=1, dtype="(2,)3S,(2,)3U")
assert_equal(x[["f0","f1"]][0], x[0][["f0","f1"]])
assert_equal(x[0], x[0][()])
def test_multiindex_titles(self):
a = np.zeros(4, dtype=[(('a', 'b'), 'i'), ('c', 'i'), ('d', 'i')])
assert_raises(KeyError, lambda : a[['a','c']])
assert_raises(KeyError, lambda : a[['a','a']])
assert_raises(ValueError, lambda : a[['b','b']])
a[['b','c']]
def test_structured_cast_promotion_fieldorder(self):
A = ("a", "<i8")
B = ("b", ">i8")
ab = np.array([(1, 2)], dtype=[A, B])
ba = np.array([(1, 2)], dtype=[B, A])
assert_raises(TypeError, np.concatenate, ab, ba)
assert_raises(TypeError, np.result_type, ab.dtype, ba.dtype)
assert_raises(TypeError, np.promote_types, ab.dtype, ba.dtype)
assert_equal(np.promote_types(ab.dtype, ba[['a', 'b']].dtype),
repack_fields(ab.dtype.newbyteorder('N')))
assert_equal(np.can_cast(ab.dtype, ba.dtype), True)
assert_equal(ab.astype(ba.dtype).dtype, ba.dtype)
assert_equal(np.can_cast('f8,i8', [('f0', 'f8'), ('f1', 'i8')]), True)
assert_equal(np.can_cast('f8,i8', [('f1', 'f8'), ('f0', 'i8')]), True)
assert_equal(np.can_cast('f8,i8', [('f1', 'i8'), ('f0', 'f8')]), False)
assert_equal(np.can_cast('f8,i8', [('f1', 'i8'), ('f0', 'f8')],
casting='unsafe'), True)
ab[:] = ba
dt1 = np.dtype([("", "i4")])
dt2 = np.dtype([("", "i8")])
assert_equal(np.promote_types(dt1, dt2), np.dtype([('f0', 'i8')]))
assert_equal(np.promote_types(dt2, dt1), np.dtype([('f0', 'i8')]))
assert_raises(TypeError, np.promote_types, dt1, np.dtype([("", "V3")]))
assert_equal(np.promote_types('i4,f8', 'i8,f4'),
np.dtype([('f0', 'i8'), ('f1', 'f8')]))
dt1nest = np.dtype([("", dt1)])
dt2nest = np.dtype([("", dt2)])
assert_equal(np.promote_types(dt1nest, dt2nest),
np.dtype([('f0', np.dtype([('f0', 'i8')]))]))
dt = np.dtype({'names': ['x'], 'formats': ['i4'], 'offsets': [8]})
a = np.ones(3, dtype=dt)
assert_equal(np.concatenate([a, a]).dtype, np.dtype([('x', 'i4')]))
@pytest.mark.parametrize("dtype_dict", [
dict(names=["a", "b"], formats=["i4", "f"], itemsize=100),
dict(names=["a", "b"], formats=["i4", "f"],
offsets=[0, 12])])
@pytest.mark.parametrize("align", [True, False])
def test_structured_promotion_packs(self, dtype_dict, align):
dtype = np.dtype(dtype_dict, align=align)
dtype_dict.pop("itemsize", None)
dtype_dict.pop("offsets", None)
expected = np.dtype(dtype_dict, align=align)
res = np.promote_types(dtype, dtype)
assert res.itemsize == expected.itemsize
assert res.fields == expected.fields
res = np.promote_types(expected, expected)
assert res is expected
def test_structured_asarray_is_view(self):
arr = np.array([1], dtype="i,i")
scalar = arr[0]
assert not scalar.flags.owndata
assert np.asarray(scalar).base is scalar
assert np.asarray(scalar, dtype=scalar.dtype).base is None
scalar = pickle.loads(pickle.dumps(scalar))
assert scalar.flags.owndata
assert np.asarray(scalar).base is None
class TestBool:
def test_test_interning(self):
a0 = np.bool(0)
b0 = np.bool(False)
assert_(a0 is b0)
a1 = np.bool(1)
b1 = np.bool(True)
assert_(a1 is b1)
assert_(np.array([True])[0] is a1)
assert_(np.array(True)[()] is a1)
def test_sum(self):
d = np.ones(101, dtype=bool)
assert_equal(d.sum(), d.size)
assert_equal(d[::2].sum(), d[::2].size)
assert_equal(d[::-2].sum(), d[::-2].size)
d = np.frombuffer(b'\xff\xff' * 100, dtype=bool)
assert_equal(d.sum(), d.size)
assert_equal(d[::2].sum(), d[::2].size)
assert_equal(d[::-2].sum(), d[::-2].size)
def check_count_nonzero(self, power, length):
powers = [2 ** i for i in range(length)]
for i in range(2**power):
l = [(i & x) != 0 for x in powers]
a = np.array(l, dtype=bool)
c = builtins.sum(l)
assert_equal(np.count_nonzero(a), c)
av = a.view(np.uint8)
av *= 3
assert_equal(np.count_nonzero(a), c)
av *= 4
assert_equal(np.count_nonzero(a), c)
av[av != 0] = 0xFF
assert_equal(np.count_nonzero(a), c)
def test_count_nonzero(self):
self.check_count_nonzero(12, 17)
@pytest.mark.slow
def test_count_nonzero_all(self):
self.check_count_nonzero(17, 17)
def test_count_nonzero_unaligned(self):
for o in range(7):
a = np.zeros((18,), dtype=bool)[o+1:]
a[:o] = True
assert_equal(np.count_nonzero(a), builtins.sum(a.tolist()))
a = np.ones((18,), dtype=bool)[o+1:]
a[:o] = False
assert_equal(np.count_nonzero(a), builtins.sum(a.tolist()))
def _test_cast_from_flexible(self, dtype):
for n in range(3):
v = np.array(b'', (dtype, n))
assert_equal(bool(v), False)
assert_equal(bool(v[()]), False)
assert_equal(v.astype(bool), False)
assert_(isinstance(v.astype(bool), np.ndarray))
assert_(v[()].astype(bool) is np.False_)
for n in range(1, 4):
for val in [b'a', b'0', b' ']:
v = np.array(val, (dtype, n))
assert_equal(bool(v), True)
assert_equal(bool(v[()]), True)
assert_equal(v.astype(bool), True)
assert_(isinstance(v.astype(bool), np.ndarray))
assert_(v[()].astype(bool) is np.True_)
def test_cast_from_void(self):
self._test_cast_from_flexible(np.void)
@pytest.mark.xfail(reason="See gh-9847")
def test_cast_from_unicode(self):
self._test_cast_from_flexible(np.str_)
def test_cast_from_bytes(self):
self._test_cast_from_flexible(np.bytes_)
class TestZeroSizeFlexible:
@staticmethod
def _zeros(shape, dtype=str):
dtype = np.dtype(dtype)
if dtype == np.void:
return np.zeros(shape, dtype=(dtype, 0))
dtype = np.dtype([('x', dtype, 0)])
return np.zeros(shape, dtype=dtype)['x']
def test_create(self):
zs = self._zeros(10, bytes)
assert_equal(zs.itemsize, 0)
zs = self._zeros(10, np.void)
assert_equal(zs.itemsize, 0)
zs = self._zeros(10, str)
assert_equal(zs.itemsize, 0)
def _test_sort_partition(self, name, kinds, **kwargs):
for dt in [bytes, np.void, str]:
zs = self._zeros(10, dt)
sort_method = getattr(zs, name)
sort_func = getattr(np, name)
for kind in kinds:
sort_method(kind=kind, **kwargs)
sort_func(zs, kind=kind, **kwargs)
def test_sort(self):
self._test_sort_partition('sort', kinds='qhs')
def test_argsort(self):
self._test_sort_partition('argsort', kinds='qhs')
def test_partition(self):
self._test_sort_partition('partition', kinds=['introselect'], kth=2)
def test_argpartition(self):
self._test_sort_partition('argpartition', kinds=['introselect'], kth=2)
def test_resize(self):
for dt in [bytes, np.void, str]:
zs = self._zeros(10, dt)
zs.resize(25)
zs.resize((10, 10))
def test_view(self):
for dt in [bytes, np.void, str]:
zs = self._zeros(10, dt)
assert_equal(zs.view(dt).dtype, np.dtype(dt))
assert_equal(zs.view((dt, 1)).shape, (0,))
def test_dumps(self):
zs = self._zeros(10, int)
assert_equal(zs, pickle.loads(zs.dumps()))
def test_pickle(self):
for proto in range(2, pickle.HIGHEST_PROTOCOL + 1):
for dt in [bytes, np.void, str]:
zs = self._zeros(10, dt)
p = pickle.dumps(zs, protocol=proto)
zs2 = pickle.loads(p)
assert_equal(zs.dtype, zs2.dtype)
def test_pickle_empty(self):
"""Checking if an empty array pickled and un-pickled will not cause a
segmentation fault"""
arr = np.array([]).reshape(999999, 0)
pk_dmp = pickle.dumps(arr)
pk_load = pickle.loads(pk_dmp)
assert pk_load.size == 0
@pytest.mark.skipif(pickle.HIGHEST_PROTOCOL < 5,
reason="requires pickle protocol 5")
def test_pickle_with_buffercallback(self):
array = np.arange(10)
buffers = []
bytes_string = pickle.dumps(array, buffer_callback=buffers.append,
protocol=5)
array_from_buffer = pickle.loads(bytes_string, buffers=buffers)
array[0] = -1
assert array_from_buffer[0] == -1, array_from_buffer[0]
class TestMethods:
sort_kinds = ['quicksort', 'heapsort', 'stable']
def test_all_where(self):
a = np.array([[True, False, True],
[False, False, False],
[True, True, True]])
wh_full = np.array([[True, False, True],
[False, False, False],
[True, False, True]])
wh_lower = np.array([[False],
[False],
[True]])
for _ax in [0, None]:
assert_equal(a.all(axis=_ax, where=wh_lower),
np.all(a[wh_lower[:,0],:], axis=_ax))
assert_equal(np.all(a, axis=_ax, where=wh_lower),
a[wh_lower[:,0],:].all(axis=_ax))
assert_equal(a.all(where=wh_full), True)
assert_equal(np.all(a, where=wh_full), True)
assert_equal(a.all(where=False), True)
assert_equal(np.all(a, where=False), True)
def test_any_where(self):
a = np.array([[True, False, True],
[False, False, False],
[True, True, True]])
wh_full = np.array([[False, True, False],
[True, True, True],
[False, False, False]])
wh_middle = np.array([[False],
[True],
[False]])
for _ax in [0, None]:
assert_equal(a.any(axis=_ax, where=wh_middle),
np.any(a[wh_middle[:,0],:], axis=_ax))
assert_equal(np.any(a, axis=_ax, where=wh_middle),
a[wh_middle[:,0],:].any(axis=_ax))
assert_equal(a.any(where=wh_full), False)
assert_equal(np.any(a, where=wh_full), False)
assert_equal(a.any(where=False), False)
assert_equal(np.any(a, where=False), False)
@pytest.mark.parametrize("dtype", ["i8", "U10", "object", "datetime64[ms]"])
def test_any_and_all_result_dtype(self, dtype):
arr = np.ones(3, dtype=dtype)
assert arr.any().dtype == np.bool
assert arr.all().dtype == np.bool
def test_any_and_all_object_dtype(self):
arr = np.ones(3, dtype=object)
assert arr.any(dtype=object, keepdims=True).dtype == object
assert arr.all(dtype=object, keepdims=True).dtype == object
def test_compress(self):
tgt = [[5, 6, 7, 8, 9]]
arr = np.arange(10).reshape(2, 5)
out = arr.compress([0, 1], axis=0)
assert_equal(out, tgt)
tgt = [[1, 3], [6, 8]]
out = arr.compress([0, 1, 0, 1, 0], axis=1)
assert_equal(out, tgt)
tgt = [[1], [6]]
arr = np.arange(10).reshape(2, 5)
out = arr.compress([0, 1], axis=1)
assert_equal(out, tgt)
arr = np.arange(10).reshape(2, 5)
out = arr.compress([0, 1])
assert_equal(out, 1)
def test_choose(self):
x = 2*np.ones((3,), dtype=int)
y = 3*np.ones((3,), dtype=int)
x2 = 2*np.ones((2, 3), dtype=int)
y2 = 3*np.ones((2, 3), dtype=int)
ind = np.array([0, 0, 1])
A = ind.choose((x, y))
assert_equal(A, [2, 2, 3])
A = ind.choose((x2, y2))
assert_equal(A, [[2, 2, 3], [2, 2, 3]])
A = ind.choose((x, y2))
assert_equal(A, [[2, 2, 3], [2, 2, 3]])
oned = np.ones(1)
assert_raises(TypeError, oned.choose, np.void(0), [oned])
out = np.array(0)
ret = np.choose(np.array(1), [10, 20, 30], out=out)
assert out is ret
assert_equal(out[()], 20)
x = np.arange(5)
y = np.choose([0, 0, 0], [x[:3], x[:3], x[:3]], out=x[1:4], mode='wrap')
assert_equal(y, np.array([0, 1, 2]))
def test_prod(self):
ba = [1, 2, 10, 11, 6, 5, 4]
ba2 = [[1, 2, 3, 4], [5, 6, 7, 9], [10, 3, 4, 5]]
for ctype in [np.int16, np.uint16, np.int32, np.uint32,
np.float32, np.float64, np.complex64, np.complex128]:
a = np.array(ba, ctype)
a2 = np.array(ba2, ctype)
if ctype in ['1', 'b']:
assert_raises(ArithmeticError, a.prod)
assert_raises(ArithmeticError, a2.prod, axis=1)
else:
assert_equal(a.prod(axis=0), 26400)
assert_array_equal(a2.prod(axis=0),
np.array([50, 36, 84, 180], ctype))
assert_array_equal(a2.prod(axis=-1),
np.array([24, 1890, 600], ctype))
@pytest.mark.parametrize('dtype', [None, object])
def test_repeat(self, dtype):
m = np.array([1, 2, 3, 4, 5, 6], dtype=dtype)
m_rect = m.reshape((2, 3))
A = m.repeat([1, 3, 2, 1, 1, 2])
assert_equal(A, [1, 2, 2, 2, 3,
3, 4, 5, 6, 6])
A = m.repeat(2)
assert_equal(A, [1, 1, 2, 2, 3, 3,
4, 4, 5, 5, 6, 6])
A = m_rect.repeat([2, 1], axis=0)
assert_equal(A, [[1, 2, 3],
[1, 2, 3],
[4, 5, 6]])
A = m_rect.repeat([1, 3, 2], axis=1)
assert_equal(A, [[1, 2, 2, 2, 3, 3],
[4, 5, 5, 5, 6, 6]])
A = m_rect.repeat(2, axis=0)
assert_equal(A, [[1, 2, 3],
[1, 2, 3],
[4, 5, 6],
[4, 5, 6]])
A = m_rect.repeat(2, axis=1)
assert_equal(A, [[1, 1, 2, 2, 3, 3],
[4, 4, 5, 5, 6, 6]])
def test_reshape(self):
arr = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]])
tgt = [[1, 2, 3, 4, 5, 6], [7, 8, 9, 10, 11, 12]]
assert_equal(arr.reshape(2, 6), tgt)
tgt = [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]
assert_equal(arr.reshape(3, 4), tgt)
tgt = [[1, 10, 8, 6], [4, 2, 11, 9], [7, 5, 3, 12]]
assert_equal(arr.reshape((3, 4), order='F'), tgt)
tgt = [[1, 4, 7, 10], [2, 5, 8, 11], [3, 6, 9, 12]]
assert_equal(arr.T.reshape((3, 4), order='C'), tgt)
def test_round(self):
def check_round(arr, expected, *round_args):
assert_equal(arr.round(*round_args), expected)
out = np.zeros_like(arr)
res = arr.round(*round_args, out=out)
assert_equal(out, expected)
assert out is res
check_round(np.array([1.2, 1.5]), [1, 2])
check_round(np.array(1.5), 2)
check_round(np.array([12.2, 15.5]), [10, 20], -1)
check_round(np.array([12.15, 15.51]), [12.2, 15.5], 1)
check_round(np.array([4.5 + 1.5j]), [4 + 2j])
check_round(np.array([12.5 + 15.5j]), [10 + 20j], -1)
def test_squeeze(self):
a = np.array([[[1], [2], [3]]])
assert_equal(a.squeeze(), [1, 2, 3])
assert_equal(a.squeeze(axis=(0,)), [[1], [2], [3]])
assert_raises(ValueError, a.squeeze, axis=(1,))
assert_equal(a.squeeze(axis=(2,)), [[1, 2, 3]])
def test_transpose(self):
a = np.array([[1, 2], [3, 4]])
assert_equal(a.transpose(), [[1, 3], [2, 4]])
assert_raises(ValueError, lambda: a.transpose(0))
def test_sort(self):
msg = "Test real sort order with nans"
a = np.array([np.nan, 1, 0])
b = np.sort(a)
assert_equal(b, a[::-1], msg)
msg = "Test complex sort order with nans"
a = np.zeros(9, dtype=np.complex128)
a.real += [np.nan, np.nan, np.nan, 1, 0, 1, 1, 0, 0]
a.imag += [np.nan, 1, 0, np.nan, np.nan, 1, 0, 1, 0]
b = np.sort(a)
assert_equal(b, a[::-1], msg)
with assert_raises_regex(
ValueError,
"kind` and `stable` parameters can't be provided at the same time"
):
np.sort(a, kind="stable", stable=True)
@pytest.mark.parametrize('dtype', [np.uint8, np.uint16, np.uint32, np.uint64,
np.float16, np.float32, np.float64,
np.longdouble])
def test_sort_unsigned(self, dtype):
a = np.arange(101, dtype=dtype)
b = a[::-1].copy()
for kind in self.sort_kinds:
msg = "scalar sort, kind=%s" % kind
c = a.copy()
c.sort(kind=kind)
assert_equal(c, a, msg)
c = b.copy()
c.sort(kind=kind)
assert_equal(c, a, msg)
@pytest.mark.parametrize('dtype',
[np.int8, np.int16, np.int32, np.int64, np.float16,
np.float32, np.float64, np.longdouble])
def test_sort_signed(self, dtype):
a = np.arange(-50, 51, dtype=dtype)
b = a[::-1].copy()
for kind in self.sort_kinds:
msg = "scalar sort, kind=%s" % (kind)
c = a.copy()
c.sort(kind=kind)
assert_equal(c, a, msg)
c = b.copy()
c.sort(kind=kind)
assert_equal(c, a, msg)
@pytest.mark.parametrize('dtype', [np.float32, np.float64, np.longdouble])
@pytest.mark.parametrize('part', ['real', 'imag'])
def test_sort_complex(self, part, dtype):
cdtype = {
np.single: np.csingle,
np.double: np.cdouble,
np.longdouble: np.clongdouble,
}[dtype]
a = np.arange(-50, 51, dtype=dtype)
b = a[::-1].copy()
ai = (a * (1+1j)).astype(cdtype)
bi = (b * (1+1j)).astype(cdtype)
setattr(ai, part, 1)
setattr(bi, part, 1)
for kind in self.sort_kinds:
msg = "complex sort, %s part == 1, kind=%s" % (part, kind)
c = ai.copy()
c.sort(kind=kind)
assert_equal(c, ai, msg)
c = bi.copy()
c.sort(kind=kind)
assert_equal(c, ai, msg)
def test_sort_complex_byte_swapping(self):
for endianness in '<>':
for dt in np.typecodes['Complex']:
arr = np.array([1+3.j, 2+2.j, 3+1.j], dtype=endianness + dt)
c = arr.copy()
c.sort()
msg = 'byte-swapped complex sort, dtype={0}'.format(dt)
assert_equal(c, arr, msg)
@pytest.mark.parametrize('dtype', [np.bytes_, np.str_])
def test_sort_string(self, dtype):
a = np.array(['aaaaaaaa' + chr(i) for i in range(101)], dtype=dtype)
b = a[::-1].copy()
for kind in self.sort_kinds:
msg = "kind=%s" % kind
c = a.copy()
c.sort(kind=kind)
assert_equal(c, a, msg)
c = b.copy()
c.sort(kind=kind)
assert_equal(c, a, msg)
def test_sort_object(self):
a = np.empty((101,), dtype=object)
a[:] = list(range(101))
b = a[::-1]
for kind in ['q', 'h', 'm']:
msg = "kind=%s" % kind
c = a.copy()
c.sort(kind=kind)
assert_equal(c, a, msg)
c = b.copy()
c.sort(kind=kind)
assert_equal(c, a, msg)
@pytest.mark.parametrize("dt", [
np.dtype([('f', float), ('i', int)]),
np.dtype([('f', float), ('i', object)])])
@pytest.mark.parametrize("step", [1, 2])
def test_sort_structured(self, dt, step):
a = np.array([(i, i) for i in range(101*step)], dtype=dt)
b = a[::-1]
for kind in ['q', 'h', 'm']:
msg = "kind=%s" % kind
c = a.copy()[::step]
indx = c.argsort(kind=kind)
c.sort(kind=kind)
assert_equal(c, a[::step], msg)
assert_equal(a[::step][indx], a[::step], msg)
c = b.copy()[::step]
indx = c.argsort(kind=kind)
c.sort(kind=kind)
assert_equal(c, a[step-1::step], msg)
assert_equal(b[::step][indx], a[step-1::step], msg)
@pytest.mark.parametrize('dtype', ['datetime64[D]', 'timedelta64[D]'])
def test_sort_time(self, dtype):
a = np.arange(0, 101, dtype=dtype)
b = a[::-1]
for kind in ['q', 'h', 'm']:
msg = "kind=%s" % kind
c = a.copy()
c.sort(kind=kind)
assert_equal(c, a, msg)
c = b.copy()
c.sort(kind=kind)
assert_equal(c, a, msg)
def test_sort_axis(self):
a = np.array([[3, 2], [1, 0]])
b = np.array([[1, 0], [3, 2]])
c = np.array([[2, 3], [0, 1]])
d = a.copy()
d.sort(axis=0)
assert_equal(d, b, "test sort with axis=0")
d = a.copy()
d.sort(axis=1)
assert_equal(d, c, "test sort with axis=1")
d = a.copy()
d.sort()
assert_equal(d, c, "test sort with default axis")
def test_sort_size_0(self):
a = np.array([])
a.shape = (3, 2, 1, 0)
for axis in range(-a.ndim, a.ndim):
msg = 'test empty array sort with axis={0}'.format(axis)
assert_equal(np.sort(a, axis=axis), a, msg)
msg = 'test empty array sort with axis=None'
assert_equal(np.sort(a, axis=None), a.ravel(), msg)
def test_sort_bad_ordering(self):
class Boom:
def __lt__(self, other):
return True
a = np.array([Boom()] * 100, dtype=object)
for kind in self.sort_kinds:
msg = "kind=%s" % kind
c = a.copy()
c.sort(kind=kind)
assert_equal(c, a, msg)
def test_void_sort(self):
for i in range(4):
rand = np.random.randint(256, size=4000, dtype=np.uint8)
arr = rand.view('V4')
arr[::-1].sort()
dt = np.dtype([('val', 'i4', (1,))])
for i in range(4):
rand = np.random.randint(256, size=4000, dtype=np.uint8)
arr = rand.view(dt)
arr[::-1].sort()
def test_sort_raises(self):
arr = np.array([0, datetime.now(), 1], dtype=object)
for kind in self.sort_kinds:
assert_raises(TypeError, arr.sort, kind=kind)
class Raiser:
def raises_anything(*args, **kwargs):
raise TypeError("SOMETHING ERRORED")
__eq__ = __ne__ = __lt__ = __gt__ = __ge__ = __le__ = raises_anything
arr = np.array([[Raiser(), n] for n in range(10)]).reshape(-1)
np.random.shuffle(arr)
for kind in self.sort_kinds:
assert_raises(TypeError, arr.sort, kind=kind)
def test_sort_degraded(self):
d = np.arange(1000000)
do = d.copy()
x = d
while x.size > 3:
mid = x.size // 2
x[mid], x[-2] = x[-2], x[mid]
x = x[:-2]
assert_equal(np.sort(d), do)
assert_equal(d[np.argsort(d)], do)
def test_copy(self):
def assert_fortran(arr):
assert_(arr.flags.fortran)
assert_(arr.flags.f_contiguous)
assert_(not arr.flags.c_contiguous)
def assert_c(arr):
assert_(not arr.flags.fortran)
assert_(not arr.flags.f_contiguous)
assert_(arr.flags.c_contiguous)
a = np.empty((2, 2), order='F')
assert_c(a.copy())
assert_c(a.copy('C'))
assert_fortran(a.copy('F'))
assert_fortran(a.copy('A'))
a = np.empty((2, 2), order='C')
assert_c(a.copy())
assert_c(a.copy('C'))
assert_fortran(a.copy('F'))
assert_c(a.copy('A'))
@pytest.mark.parametrize("dtype", ['O', np.int32, 'i,O'])
def test__deepcopy__(self, dtype):
a = np.empty(4, dtype=dtype)
ctypes.memset(a.ctypes.data, 0, a.nbytes)
b = a.__deepcopy__({})
a[0] = 42
with pytest.raises(AssertionError):
assert_array_equal(a, b)
def test__deepcopy__catches_failure(self):
class MyObj:
def __deepcopy__(self, *args, **kwargs):
raise RuntimeError
arr = np.array([1, MyObj(), 3], dtype='O')
with pytest.raises(RuntimeError):
arr.__deepcopy__({})
def test_sort_order(self):
x1 = np.array([21, 32, 14])
x2 = np.array(['my', 'first', 'name'])
x3 = np.array([3.1, 4.5, 6.2])
r = np.rec.fromarrays([x1, x2, x3], names='id,word,number')
r.sort(order=['id'])
assert_equal(r.id, np.array([14, 21, 32]))
assert_equal(r.word, np.array(['name', 'my', 'first']))
assert_equal(r.number, np.array([6.2, 3.1, 4.5]))
r.sort(order=['word'])
assert_equal(r.id, np.array([32, 21, 14]))
assert_equal(r.word, np.array(['first', 'my', 'name']))
assert_equal(r.number, np.array([4.5, 3.1, 6.2]))
r.sort(order=['number'])
assert_equal(r.id, np.array([21, 32, 14]))
assert_equal(r.word, np.array(['my', 'first', 'name']))
assert_equal(r.number, np.array([3.1, 4.5, 6.2]))
assert_raises_regex(ValueError, 'duplicate',
lambda: r.sort(order=['id', 'id']))
if sys.byteorder == 'little':
strtype = '>i2'
else:
strtype = '<i2'
mydtype = [('name', 'U5'), ('col2', strtype)]
r = np.array([('a', 1), ('b', 255), ('c', 3), ('d', 258)],
dtype=mydtype)
r.sort(order='col2')
assert_equal(r['col2'], [1, 3, 255, 258])
assert_equal(r, np.array([('a', 1), ('c', 3), ('b', 255), ('d', 258)],
dtype=mydtype))
def test_sort_unicode_kind(self):
d = np.arange(10)
k = b'\xc3\xa4'.decode("UTF8")
assert_raises(ValueError, d.sort, kind=k)
assert_raises(ValueError, d.argsort, kind=k)
@pytest.mark.parametrize('a', [
np.array([0, 1, np.nan], dtype=np.float16),
np.array([0, 1, np.nan], dtype=np.float32),
np.array([0, 1, np.nan]),
])
def test_searchsorted_floats(self, a):
msg = "Test real (%s) searchsorted with nans, side='l'" % a.dtype
b = a.searchsorted(a, side='left')
assert_equal(b, np.arange(3), msg)
msg = "Test real (%s) searchsorted with nans, side='r'" % a.dtype
b = a.searchsorted(a, side='right')
assert_equal(b, np.arange(1, 4), msg)
a.searchsorted(v=1)
x = np.array([0, 1, np.nan], dtype='float32')
y = np.searchsorted(x, x[-1])
assert_equal(y, 2)
def test_searchsorted_complex(self):
a = np.zeros(9, dtype=np.complex128)
a.real += [0, 0, 1, 1, 0, 1, np.nan, np.nan, np.nan]
a.imag += [0, 1, 0, 1, np.nan, np.nan, 0, 1, np.nan]
msg = "Test complex searchsorted with nans, side='l'"
b = a.searchsorted(a, side='left')
assert_equal(b, np.arange(9), msg)
msg = "Test complex searchsorted with nans, side='r'"
b = a.searchsorted(a, side='right')
assert_equal(b, np.arange(1, 10), msg)
msg = "Test searchsorted with little endian, side='l'"
a = np.array([0, 128], dtype='<i4')
b = a.searchsorted(np.array(128, dtype='<i4'))
assert_equal(b, 1, msg)
msg = "Test searchsorted with big endian, side='l'"
a = np.array([0, 128], dtype='>i4')
b = a.searchsorted(np.array(128, dtype='>i4'))
assert_equal(b, 1, msg)
def test_searchsorted_n_elements(self):
a = np.ones(0)
b = a.searchsorted([0, 1, 2], 'left')
assert_equal(b, [0, 0, 0])
b = a.searchsorted([0, 1, 2], 'right')
assert_equal(b, [0, 0, 0])
a = np.ones(1)
b = a.searchsorted([0, 1, 2], 'left')
assert_equal(b, [0, 0, 1])
b = a.searchsorted([0, 1, 2], 'right')
assert_equal(b, [0, 1, 1])
a = np.ones(2)
b = a.searchsorted([0, 1, 2], 'left')
assert_equal(b, [0, 0, 2])
b = a.searchsorted([0, 1, 2], 'right')
assert_equal(b, [0, 2, 2])
def test_searchsorted_unaligned_array(self):
a = np.arange(10)
aligned = np.empty(a.itemsize * a.size + 1, 'uint8')
unaligned = aligned[1:].view(a.dtype)
unaligned[:] = a
b = unaligned.searchsorted(a, 'left')
assert_equal(b, a)
b = unaligned.searchsorted(a, 'right')
assert_equal(b, a + 1)
b = a.searchsorted(unaligned, 'left')
assert_equal(b, a)
b = a.searchsorted(unaligned, 'right')
assert_equal(b, a + 1)
def test_searchsorted_resetting(self):
a = np.arange(5)
b = a.searchsorted([6, 5, 4], 'left')
assert_equal(b, [5, 5, 4])
b = a.searchsorted([6, 5, 4], 'right')
assert_equal(b, [5, 5, 5])
def test_searchsorted_type_specific(self):
types = ''.join((np.typecodes['AllInteger'], np.typecodes['AllFloat'],
np.typecodes['Datetime'], '?O'))
for dt in types:
if dt == 'M':
dt = 'M8[D]'
if dt == '?':
a = np.arange(2, dtype=dt)
out = np.arange(2)
else:
a = np.arange(0, 5, dtype=dt)
out = np.arange(5)
b = a.searchsorted(a, 'left')
assert_equal(b, out)
b = a.searchsorted(a, 'right')
assert_equal(b, out + 1)
e = np.ndarray(shape=0, buffer=b'', dtype=dt)
b = e.searchsorted(a, 'left')
assert_array_equal(b, np.zeros(len(a), dtype=np.intp))
b = a.searchsorted(e, 'left')
assert_array_equal(b, np.zeros(0, dtype=np.intp))
def test_searchsorted_unicode(self):
a = np.array(['P:\\20x_dapi_cy3\\20x_dapi_cy3_20100185_1',
'P:\\20x_dapi_cy3\\20x_dapi_cy3_20100186_1',
'P:\\20x_dapi_cy3\\20x_dapi_cy3_20100187_1',
'P:\\20x_dapi_cy3\\20x_dapi_cy3_20100189_1',
'P:\\20x_dapi_cy3\\20x_dapi_cy3_20100190_1',
'P:\\20x_dapi_cy3\\20x_dapi_cy3_20100191_1',
'P:\\20x_dapi_cy3\\20x_dapi_cy3_20100192_1',
'P:\\20x_dapi_cy3\\20x_dapi_cy3_20100193_1',
'P:\\20x_dapi_cy3\\20x_dapi_cy3_20100194_1',
'P:\\20x_dapi_cy3\\20x_dapi_cy3_20100195_1',
'P:\\20x_dapi_cy3\\20x_dapi_cy3_20100196_1',
'P:\\20x_dapi_cy3\\20x_dapi_cy3_20100197_1',
'P:\\20x_dapi_cy3\\20x_dapi_cy3_20100198_1',
'P:\\20x_dapi_cy3\\20x_dapi_cy3_20100199_1'],
dtype=np.str_)
ind = np.arange(len(a))
assert_equal([a.searchsorted(v, 'left') for v in a], ind)
assert_equal([a.searchsorted(v, 'right') for v in a], ind + 1)
assert_equal([a.searchsorted(a[i], 'left') for i in ind], ind)
assert_equal([a.searchsorted(a[i], 'right') for i in ind], ind + 1)
def test_searchsorted_with_invalid_sorter(self):
a = np.array([5, 2, 1, 3, 4])
s = np.argsort(a)
assert_raises(TypeError, np.searchsorted, a, 0,
sorter=np.array((1, (2, 3)), dtype=object))
assert_raises(TypeError, np.searchsorted, a, 0, sorter=[1.1])
assert_raises(ValueError, np.searchsorted, a, 0, sorter=[1, 2, 3, 4])
assert_raises(ValueError, np.searchsorted, a, 0, sorter=[1, 2, 3, 4, 5, 6])
assert_raises(ValueError, np.searchsorted, a, 4, sorter=[0, 1, 2, 3, 5])
assert_raises(ValueError, np.searchsorted, a, 0, sorter=[-1, 0, 1, 2, 3])
assert_raises(ValueError, np.searchsorted, a, 0, sorter=[4, 0, -1, 2, 3])
def test_searchsorted_return_type(self):
class A(np.ndarray):
pass
a = np.arange(5).view(A)
b = np.arange(1, 3).view(A)
s = np.arange(5).view(A)
assert_(not isinstance(a.searchsorted(b, 'left'), A))
assert_(not isinstance(a.searchsorted(b, 'right'), A))
assert_(not isinstance(a.searchsorted(b, 'left', s), A))
assert_(not isinstance(a.searchsorted(b, 'right', s), A))
@pytest.mark.parametrize("dtype", np.typecodes["All"])
def test_argpartition_out_of_range(self, dtype):
d = np.arange(10).astype(dtype=dtype)
assert_raises(ValueError, d.argpartition, 10)
assert_raises(ValueError, d.argpartition, -11)
@pytest.mark.parametrize("dtype", np.typecodes["All"])
def test_partition_out_of_range(self, dtype):
d = np.arange(10).astype(dtype=dtype)
assert_raises(ValueError, d.partition, 10)
assert_raises(ValueError, d.partition, -11)
def test_argpartition_integer(self):
d = np.arange(10)
assert_raises(TypeError, d.argpartition, 9.)
d_obj = np.arange(10, dtype=object)
assert_raises(TypeError, d_obj.argpartition, 9.)
def test_partition_integer(self):
d = np.arange(10)
assert_raises(TypeError, d.partition, 9.)
d_obj = np.arange(10, dtype=object)
assert_raises(TypeError, d_obj.partition, 9.)
@pytest.mark.parametrize("kth_dtype", np.typecodes["AllInteger"])
def test_partition_empty_array(self, kth_dtype):
kth = np.array(0, dtype=kth_dtype)[()]
a = np.array([])
a.shape = (3, 2, 1, 0)
for axis in range(-a.ndim, a.ndim):
msg = 'test empty array partition with axis={0}'.format(axis)
assert_equal(np.partition(a, kth, axis=axis), a, msg)
msg = 'test empty array partition with axis=None'
assert_equal(np.partition(a, kth, axis=None), a.ravel(), msg)
@pytest.mark.parametrize("kth_dtype", np.typecodes["AllInteger"])
def test_argpartition_empty_array(self, kth_dtype):
kth = np.array(0, dtype=kth_dtype)[()]
a = np.array([])
a.shape = (3, 2, 1, 0)
for axis in range(-a.ndim, a.ndim):
msg = 'test empty array argpartition with axis={0}'.format(axis)
assert_equal(np.partition(a, kth, axis=axis),
np.zeros_like(a, dtype=np.intp), msg)
msg = 'test empty array argpartition with axis=None'
assert_equal(np.partition(a, kth, axis=None),
np.zeros_like(a.ravel(), dtype=np.intp), msg)
def assert_partitioned(self, d, kth):
prev = 0
for k in np.sort(kth):
assert_array_compare(operator.__le__, d[prev:k], d[k],
err_msg='kth %d' % k)
assert_((d[k:] >= d[k]).all(),
msg="kth %d, %r not greater equal %r" % (k, d[k:], d[k]))
prev = k + 1
def test_partition_iterative(self):
d = np.arange(17)
kth = (0, 1, 2, 429, 231)
assert_raises(ValueError, d.partition, kth)
assert_raises(ValueError, d.argpartition, kth)
d = np.arange(10).reshape((2, 5))
assert_raises(ValueError, d.partition, kth, axis=0)
assert_raises(ValueError, d.partition, kth, axis=1)
assert_raises(ValueError, np.partition, d, kth, axis=1)
assert_raises(ValueError, np.partition, d, kth, axis=None)
d = np.array([3, 4, 2, 1])
p = np.partition(d, (0, 3))
self.assert_partitioned(p, (0, 3))
self.assert_partitioned(d[np.argpartition(d, (0, 3))], (0, 3))
assert_array_equal(p, np.partition(d, (-3, -1)))
assert_array_equal(p, d[np.argpartition(d, (-3, -1))])
d = np.arange(17)
np.random.shuffle(d)
d.partition(range(d.size))
assert_array_equal(np.arange(17), d)
np.random.shuffle(d)
assert_array_equal(np.arange(17), d[d.argpartition(range(d.size))])
d = np.arange(17)
np.random.shuffle(d)
keys = np.array([1, 3, 8, -2])
np.random.shuffle(d)
p = np.partition(d, keys)
self.assert_partitioned(p, keys)
p = d[np.argpartition(d, keys)]
self.assert_partitioned(p, keys)
np.random.shuffle(keys)
assert_array_equal(np.partition(d, keys), p)
assert_array_equal(d[np.argpartition(d, keys)], p)
d = np.arange(20)[::-1]
self.assert_partitioned(np.partition(d, [5]*4), [5])
self.assert_partitioned(np.partition(d, [5]*4 + [6, 13]),
[5]*4 + [6, 13])
self.assert_partitioned(d[np.argpartition(d, [5]*4)], [5])
self.assert_partitioned(d[np.argpartition(d, [5]*4 + [6, 13])],
[5]*4 + [6, 13])
d = np.arange(12)
np.random.shuffle(d)
d1 = np.tile(np.arange(12), (4, 1))
map(np.random.shuffle, d1)
d0 = np.transpose(d1)
kth = (1, 6, 7, -1)
p = np.partition(d1, kth, axis=1)
pa = d1[np.arange(d1.shape[0])[:, None],
d1.argpartition(kth, axis=1)]
assert_array_equal(p, pa)
for i in range(d1.shape[0]):
self.assert_partitioned(p[i,:], kth)
p = np.partition(d0, kth, axis=0)
pa = d0[np.argpartition(d0, kth, axis=0),
np.arange(d0.shape[1])[None,:]]
assert_array_equal(p, pa)
for i in range(d0.shape[1]):
self.assert_partitioned(p[:, i], kth)
def test_partition_cdtype(self):
d = np.array([('Galahad', 1.7, 38), ('Arthur', 1.8, 41),
('Lancelot', 1.9, 38)],
dtype=[('name', '|S10'), ('height', '<f8'), ('age', '<i4')])
tgt = np.sort(d, order=['age', 'height'])
assert_array_equal(np.partition(d, range(d.size),
order=['age', 'height']),
tgt)
assert_array_equal(d[np.argpartition(d, range(d.size),
order=['age', 'height'])],
tgt)
for k in range(d.size):
assert_equal(np.partition(d, k, order=['age', 'height'])[k],
tgt[k])
assert_equal(d[np.argpartition(d, k, order=['age', 'height'])][k],
tgt[k])
d = np.array(['Galahad', 'Arthur', 'zebra', 'Lancelot'])
tgt = np.sort(d)
assert_array_equal(np.partition(d, range(d.size)), tgt)
for k in range(d.size):
assert_equal(np.partition(d, k)[k], tgt[k])
assert_equal(d[np.argpartition(d, k)][k], tgt[k])
def test_partition_unicode_kind(self):
d = np.arange(10)
k = b'\xc3\xa4'.decode("UTF8")
assert_raises(ValueError, d.partition, 2, kind=k)
assert_raises(ValueError, d.argpartition, 2, kind=k)
def test_partition_fuzz(self):
for j in range(10, 30):
for i in range(1, j - 2):
d = np.arange(j)
np.random.shuffle(d)
d = d % np.random.randint(2, 30)
idx = np.random.randint(d.size)
kth = [0, idx, i, i + 1]
tgt = np.sort(d)[kth]
assert_array_equal(np.partition(d, kth)[kth], tgt,
err_msg="data: %r\n kth: %r" % (d, kth))
@pytest.mark.parametrize("kth_dtype", np.typecodes["AllInteger"])
def test_argpartition_gh5524(self, kth_dtype):
d = [6, 7, 3, 2, 9, 0]
p = np.argpartition(d, kth)
self.assert_partitioned(np.array(d)[p], [1])
def test_flatten(self):
x0 = np.array([[1, 2, 3], [4, 5, 6]], np.int32)
x1 = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]], np.int32)
y0 = np.array([1, 2, 3, 4, 5, 6], np.int32)
y0f = np.array([1, 4, 2, 5, 3, 6], np.int32)
y1 = np.array([1, 2, 3, 4, 5, 6, 7, 8], np.int32)
y1f = np.array([1, 5, 3, 7, 2, 6, 4, 8], np.int32)
assert_equal(x0.flatten(), y0)
assert_equal(x0.flatten('F'), y0f)
assert_equal(x0.flatten('F'), x0.T.flatten())
assert_equal(x1.flatten(), y1)
assert_equal(x1.flatten('F'), y1f)
assert_equal(x1.flatten('F'), x1.T.flatten())
def test_arr_mult(self, func):
a = np.array([[1, 0], [0, 1]])
b = np.array([[0, 1], [1, 0]])
c = np.array([[9, 1], [1, -9]])
d = np.arange(24).reshape(4, 6)
ddt = np.array(
[[ 55, 145, 235, 325],
[ 145, 451, 757, 1063],
[ 235, 757, 1279, 1801],
[ 325, 1063, 1801, 2539]]
)
dtd = np.array(
[[504, 540, 576, 612, 648, 684],
[540, 580, 620, 660, 700, 740],
[576, 620, 664, 708, 752, 796],
[612, 660, 708, 756, 804, 852],
[648, 700, 752, 804, 856, 908],
[684, 740, 796, 852, 908, 964]]
)
for et in [np.float32, np.float64, np.complex64, np.complex128]:
eaf = a.astype(et)
assert_equal(func(eaf, eaf), eaf)
assert_equal(func(eaf.T, eaf), eaf)
assert_equal(func(eaf, eaf.T), eaf)
assert_equal(func(eaf.T, eaf.T), eaf)
assert_equal(func(eaf.T.copy(), eaf), eaf)
assert_equal(func(eaf, eaf.T.copy()), eaf)
assert_equal(func(eaf.T.copy(), eaf.T.copy()), eaf)
for et in [np.float32, np.float64, np.complex64, np.complex128]:
eaf = a.astype(et)
ebf = b.astype(et)
assert_equal(func(ebf, ebf), eaf)
assert_equal(func(ebf.T, ebf), eaf)
assert_equal(func(ebf, ebf.T), eaf)
assert_equal(func(ebf.T, ebf.T), eaf)
for et in [np.float32, np.float64, np.complex64, np.complex128]:
edf = d.astype(et)
assert_equal(
func(edf[::-1, :], edf.T),
func(edf[::-1, :].copy(), edf.T.copy())
)
assert_equal(
func(edf[:, ::-1], edf.T),
func(edf[:, ::-1].copy(), edf.T.copy())
)
assert_equal(
func(edf, edf[::-1, :].T),
func(edf, edf[::-1, :].T.copy())
)
assert_equal(
func(edf, edf[:, ::-1].T),
func(edf, edf[:, ::-1].T.copy())
)
assert_equal(
func(edf[:edf.shape[0] // 2, :], edf[::2, :].T),
func(edf[:edf.shape[0] // 2, :].copy(), edf[::2, :].T.copy())
)
assert_equal(
func(edf[::2, :], edf[:edf.shape[0] // 2, :].T),
func(edf[::2, :].copy(), edf[:edf.shape[0] // 2, :].T.copy())
)
for et in [np.float32, np.float64, np.complex64, np.complex128]:
edf = d.astype(et)
eddtf = ddt.astype(et)
edtdf = dtd.astype(et)
assert_equal(func(edf, edf.T), eddtf)
assert_equal(func(edf.T, edf), edtdf)
@pytest.mark.parametrize('func', (np.dot, np.matmul))
@pytest.mark.parametrize('dtype', 'ifdFD')
def test_no_dgemv(self, func, dtype):
a = np.arange(8.0, dtype=dtype).reshape(2, 4)
b = np.broadcast_to(1., (4, 1))
ret1 = func(a, b)
ret2 = func(a, b.copy())
assert_equal(ret1, ret2)
ret1 = func(b.T, a.T)
ret2 = func(b.T.copy(), a.T)
assert_equal(ret1, ret2)
dt = np.dtype(dtype)
a = np.zeros(8 * dt.itemsize // 2 + 1, dtype='int16')[1:].view(dtype)
a = a.reshape(2, 4)
b = a[0]
assert_(a.__array_interface__['data'][0] % dt.itemsize != 0)
ret1 = func(a, b)
ret2 = func(a.copy(), b.copy())
assert_equal(ret1, ret2)
ret1 = func(b.T, a.T)
ret2 = func(b.T.copy(), a.T.copy())
assert_equal(ret1, ret2)
def test_dot(self):
a = np.array([[1, 0], [0, 1]])
b = np.array([[0, 1], [1, 0]])
c = np.array([[9, 1], [1, -9]])
assert_equal(np.dot(a, b), a.dot(b))
assert_equal(np.dot(np.dot(a, b), c), a.dot(b).dot(c))
c = np.zeros_like(a)
a.dot(b, c)
assert_equal(c, np.dot(a, b))
c = np.zeros_like(a)
a.dot(b=b, out=c)
assert_equal(c, np.dot(a, b))
def test_dot_type_mismatch(self):
c = 1.
A = np.array((1,1), dtype='i,i')
assert_raises(TypeError, np.dot, c, A)
assert_raises(TypeError, np.dot, A, c)
def test_dot_out_mem_overlap(self):
np.random.seed(1)
dtypes = [np.dtype(code) for code in np.typecodes['All']
if code not in 'USVM']
for dtype in dtypes:
a = np.random.rand(3, 3).astype(dtype)
b = _aligned_zeros((3, 3), dtype=dtype)
b[...] = np.random.rand(3, 3)
y = np.dot(a, b)
x = np.dot(a, b, out=b)
assert_equal(x, y, err_msg=repr(dtype))
assert_raises(ValueError, np.dot, a, b, out=b[::2])
assert_raises(ValueError, np.dot, a, b, out=b.T)
def test_dot_matmul_out(self):
class Sub(np.ndarray):
pass
a = np.ones((2, 2)).view(Sub)
b = np.ones((2, 2)).view(Sub)
out = np.ones((2, 2))
np.dot(a, b, out=out)
np.matmul(a, b, out=out)
def test_dot_matmul_inner_array_casting_fails(self):
class A:
def __array__(self, *args, **kwargs):
raise NotImplementedError
assert_raises(NotImplementedError, np.dot, A(), A())
assert_raises(NotImplementedError, np.matmul, A(), A())
assert_raises(NotImplementedError, np.inner, A(), A())
def test_matmul_out(self):
a = np.arange(18).reshape(2, 3, 3)
b = np.matmul(a, a)
c = np.matmul(a, a, out=a)
assert_(c is a)
assert_equal(c, b)
a = np.arange(18).reshape(2, 3, 3)
c = np.matmul(a, a, out=a[::-1, ...])
assert_(c.base is a.base)
assert_equal(c, b)
def test_diagonal(self):
a = np.arange(12).reshape((3, 4))
assert_equal(a.diagonal(), [0, 5, 10])
assert_equal(a.diagonal(0), [0, 5, 10])
assert_equal(a.diagonal(1), [1, 6, 11])
assert_equal(a.diagonal(-1), [4, 9])
assert_raises(AxisError, a.diagonal, axis1=0, axis2=5)
assert_raises(AxisError, a.diagonal, axis1=5, axis2=0)
assert_raises(AxisError, a.diagonal, axis1=5, axis2=5)
assert_raises(ValueError, a.diagonal, axis1=1, axis2=1)
b = np.arange(8).reshape((2, 2, 2))
assert_equal(b.diagonal(), [[0, 6], [1, 7]])
assert_equal(b.diagonal(0), [[0, 6], [1, 7]])
assert_equal(b.diagonal(1), [[2], [3]])
assert_equal(b.diagonal(-1), [[4], [5]])
assert_raises(ValueError, b.diagonal, axis1=0, axis2=0)
assert_equal(b.diagonal(0, 1, 2), [[0, 3], [4, 7]])
assert_equal(b.diagonal(0, 0, 1), [[0, 6], [1, 7]])
assert_equal(b.diagonal(offset=1, axis1=0, axis2=2), [[1], [3]])
assert_equal(b.diagonal(0, 2, 1), [[0, 3], [4, 7]])
def test_diagonal_view_notwriteable(self):
a = np.eye(3).diagonal()
assert_(not a.flags.writeable)
assert_(not a.flags.owndata)
a = np.diagonal(np.eye(3))
assert_(not a.flags.writeable)
assert_(not a.flags.owndata)
a = np.diag(np.eye(3))
assert_(not a.flags.writeable)
assert_(not a.flags.owndata)
def test_diagonal_memleak(self):
a = np.zeros((100, 100))
if HAS_REFCOUNT:
assert_(sys.getrefcount(a) < 50)
for i in range(100):
a.diagonal()
if HAS_REFCOUNT:
assert_(sys.getrefcount(a) < 50)
def test_size_zero_memleak(self):
a = np.array([], dtype=np.float64)
x = np.array(2.0)
for _ in range(100):
np.dot(a, a, out=x)
if HAS_REFCOUNT:
assert_(sys.getrefcount(x) < 50)
def test_trace(self):
a = np.arange(12).reshape((3, 4))
assert_equal(a.trace(), 15)
assert_equal(a.trace(0), 15)
assert_equal(a.trace(1), 18)
assert_equal(a.trace(-1), 13)
b = np.arange(8).reshape((2, 2, 2))
assert_equal(b.trace(), [6, 8])
assert_equal(b.trace(0), [6, 8])
assert_equal(b.trace(1), [2, 3])
assert_equal(b.trace(-1), [4, 5])
assert_equal(b.trace(0, 0, 1), [6, 8])
assert_equal(b.trace(0, 0, 2), [5, 9])
assert_equal(b.trace(0, 1, 2), [3, 11])
assert_equal(b.trace(offset=1, axis1=0, axis2=2), [1, 3])
b = np.arange(8).reshape((2, 2, 2)).view(MyArray)
t = b.trace()
assert_(isinstance(t, MyArray))
def test_put(self):
icodes = np.typecodes['AllInteger']
fcodes = np.typecodes['AllFloat']
for dt in icodes + fcodes + 'O':
tgt = np.array([0, 1, 0, 3, 0, 5], dtype=dt)
a = np.zeros(6, dtype=dt)
a.put([1, 3, 5], [1, 3, 5])
assert_equal(a, tgt)
a = np.zeros((2, 3), dtype=dt)
a.put([1, 3, 5], [1, 3, 5])
assert_equal(a, tgt.reshape(2, 3))
for dt in '?':
tgt = np.array([False, True, False, True, False, True], dtype=dt)
a = np.zeros(6, dtype=dt)
a.put([1, 3, 5], [True]*3)
assert_equal(a, tgt)
a = np.zeros((2, 3), dtype=dt)
a.put([1, 3, 5], [True]*3)
assert_equal(a, tgt.reshape(2, 3))
a = np.zeros(6)
a.flags.writeable = False
assert_raises(ValueError, a.put, [1, 3, 5], [1, 3, 5])
bad_array = [1, 2, 3]
assert_raises(TypeError, np.put, bad_array, [0, 2], 5)
empty_array = np.asarray(list())
with pytest.raises(IndexError, match="cannot replace elements of an empty array"):
np.put(empty_array, 1, 1, mode="wrap")
with pytest.raises(IndexError, match="cannot replace elements of an empty array"):
np.put(empty_array, 1, 1, mode="clip")
def test_ravel_subclass(self):
class ArraySubclass(np.ndarray):
pass
a = np.arange(10).view(ArraySubclass)
assert_(isinstance(a.ravel('C'), ArraySubclass))
assert_(isinstance(a.ravel('F'), ArraySubclass))
assert_(isinstance(a.ravel('A'), ArraySubclass))
assert_(isinstance(a.ravel('K'), ArraySubclass))
a = np.arange(10)[::2].view(ArraySubclass)
assert_(isinstance(a.ravel('C'), ArraySubclass))
assert_(isinstance(a.ravel('F'), ArraySubclass))
assert_(isinstance(a.ravel('A'), ArraySubclass))
assert_(isinstance(a.ravel('K'), ArraySubclass))
def test_swapaxes(self):
a = np.arange(1*2*3*4).reshape(1, 2, 3, 4).copy()
idx = np.indices(a.shape)
assert_(a.flags['OWNDATA'])
b = a.copy()
assert_raises(AxisError, a.swapaxes, -5, 0)
assert_raises(AxisError, a.swapaxes, 4, 0)
assert_raises(AxisError, a.swapaxes, 0, -5)
assert_raises(AxisError, a.swapaxes, 0, 4)
for i in range(-4, 4):
for j in range(-4, 4):
for k, src in enumerate((a, b)):
c = src.swapaxes(i, j)
shape = list(src.shape)
shape[i] = src.shape[j]
shape[j] = src.shape[i]
assert_equal(c.shape, shape, str((i, j, k)))
i0, i1, i2, i3 = [dim-1 for dim in c.shape]
j0, j1, j2, j3 = [dim-1 for dim in src.shape]
assert_equal(src[idx[j0], idx[j1], idx[j2], idx[j3]],
c[idx[i0], idx[i1], idx[i2], idx[i3]],
str((i, j, k)))
assert_(not c.flags['OWNDATA'], str((i, j, k)))
if k == 1:
b = c
def test_conjugate(self):
a = np.array([1-1j, 1+1j, 23+23.0j])
ac = a.conj()
assert_equal(a.real, ac.real)
assert_equal(a.imag, -ac.imag)
assert_equal(ac, a.conjugate())
assert_equal(ac, np.conjugate(a))
a = np.array([1-1j, 1+1j, 23+23.0j], 'F')
ac = a.conj()
assert_equal(a.real, ac.real)
assert_equal(a.imag, -ac.imag)
assert_equal(ac, a.conjugate())
assert_equal(ac, np.conjugate(a))
a = np.array([1, 2, 3])
ac = a.conj()
assert_equal(a, ac)
assert_equal(ac, a.conjugate())
assert_equal(ac, np.conjugate(a))
a = np.array([1.0, 2.0, 3.0])
ac = a.conj()
assert_equal(a, ac)
assert_equal(ac, a.conjugate())
assert_equal(ac, np.conjugate(a))
a = np.array([1-1j, 1, 2.0], object)
ac = a.conj()
assert_equal(ac, [k.conjugate() for k in a])
assert_equal(ac, a.conjugate())
assert_equal(ac, np.conjugate(a))
a = np.array([1-1j, 1, 2.0, 'f'], object)
assert_raises(TypeError, lambda: a.conj())
assert_raises(TypeError, lambda: a.conjugate())
def test_conjugate_out(self):
a = np.array([1-1j, 1+1j, 23+23.0j])
out = np.empty_like(a)
res = a.conjugate(out)
assert res is out
assert_array_equal(out, a.conjugate())
def test__complex__(self):
dtypes = ['i1', 'i2', 'i4', 'i8',
'u1', 'u2', 'u4', 'u8',
'f', 'd', 'g', 'F', 'D', 'G',
'?', 'O']
for dt in dtypes:
a = np.array(7, dtype=dt)
b = np.array([7], dtype=dt)
c = np.array([[[[[7]]]]], dtype=dt)
msg = 'dtype: {0}'.format(dt)
ap = complex(a)
assert_equal(ap, a, msg)
with assert_warns(DeprecationWarning):
bp = complex(b)
assert_equal(bp, b, msg)
with assert_warns(DeprecationWarning):
cp = complex(c)
assert_equal(cp, c, msg)
def test__complex__should_not_work(self):
dtypes = ['i1', 'i2', 'i4', 'i8',
'u1', 'u2', 'u4', 'u8',
'f', 'd', 'g', 'F', 'D', 'G',
'?', 'O']
for dt in dtypes:
a = np.array([1, 2, 3], dtype=dt)
assert_raises(TypeError, complex, a)
dt = np.dtype([('a', 'f8'), ('b', 'i1')])
b = np.array((1.0, 3), dtype=dt)
assert_raises(TypeError, complex, b)
c = np.array([(1.0, 3), (2e-3, 7)], dtype=dt)
assert_raises(TypeError, complex, c)
d = np.array('1+1j')
assert_raises(TypeError, complex, d)
e = np.array(['1+1j'], 'U')
with assert_warns(DeprecationWarning):
assert_raises(TypeError, complex, e)
classpython
class TestCequenceMethods:
def test_array_contains(self):
assert_(4.0 in np.arange(16.).reshape(4,4))
assert_(20.0 not in np.arange(16.).reshape(4,4))
class TestBinop:
def test_inplace(self):
assert_array_almost_equal(np.array([0.5]) * np.array([1.0, 2.0]), [0.5, 1.0])
d = np.array([0.5, 0.5])[::2]
assert_array_almost_equal(d * (d * np.array([1.0, 2.0])), [0.25, 0.5])
a = np.array([0.5])
b = np.array([0.5])
c = a + b
c = a - b
c = a * b
c = a / b
assert_equal(a, b)
assert_almost_equal(c, 1.)
c = a + b * 2. / b * a - a / b
assert_equal(a, b)
assert_equal(c, 0.5)
a = np.array([5])
b = np.array([3])
c = (a * a) / b
assert_almost_equal(c, 25 / 3)
assert_equal(a, 5)
assert_equal(b, 3)
@pytest.mark.xfail(IS_PYPY, reason="Bug in pypy3.{9, 10}-v7.3.13, #24862")
@pytest.mark.parametrize("priority", [None, "runtime error"])
def test_ufunc_binop_bad_array_priority(self, priority):
class BadPriority:
@property
def __array_priority__(self):
if priority == "runtime error":
raise RuntimeError("RuntimeError in __array_priority__!")
return priority
def __radd__(self, other):
return "result"
class LowPriority(np.ndarray):
__array_priority__ = -1000
res = np.arange(3).view(LowPriority) + BadPriority()
assert res.shape == (3,)
assert res[0] == 'result'
def test_ufunc_override_normalize_signature(self):
class SomeClass:
def __array_ufunc__(self, ufunc, method, *inputs, **kw):
return kw
a = SomeClass()
kw = np.add(a, [1])
assert_('sig' not in kw and 'signature' not in kw)
kw = np.add(a, [1], sig='ii->i')
assert_('sig' not in kw and 'signature' in kw)
assert_equal(kw['signature'], 'ii->i')
kw = np.add(a, [1], signature='ii->i')
assert_('sig' not in kw and 'signature' in kw)
assert_equal(kw['signature'], 'ii->i')
def test_array_ufunc_index(self):
class CheckIndex:
def __array_ufunc__(self, ufunc, method, *inputs, **kw):
for i, a in enumerate(inputs):
if a is self:
return i
for j, a in enumerate(kw['out']):
if a is self:
return (j,)
a = CheckIndex()
dummy = np.arange(2.)
assert_equal(np.sin(a), 0)
assert_equal(np.sin(dummy, a), (0,))
assert_equal(np.sin(dummy, out=a), (0,))
assert_equal(np.sin(dummy, out=(a,)), (0,))
assert_equal(np.sin(a, a), 0)
assert_equal(np.sin(a, out=a), 0)
assert_equal(np.sin(a, out=(a,)), 0)
assert_equal(np.modf(dummy, a), (0,))
assert_equal(np.modf(dummy, None, a), (1,))
assert_equal(np.modf(dummy, dummy, a), (1,))
assert_equal(np.modf(dummy, out=(a, None)), (0,))
assert_equal(np.modf(dummy, out=(a, dummy)), (0,))
assert_equal(np.modf(dummy, out=(None, a)), (1,))
assert_equal(np.modf(dummy, out=(dummy, a)), (1,))
assert_equal(np.modf(a, out=(dummy, a)), 0)
with assert_raises(TypeError):
np.modf(dummy, out=a)
assert_raises(ValueError, np.modf, dummy, out=(a,))
assert_equal(np.add(a, dummy), 0)
assert_equal(np.add(dummy, a), 1)
assert_equal(np.add(dummy, dummy, a), (0,))
assert_equal(np.add(dummy, a, a), 1)
assert_equal(np.add(dummy, dummy, out=a), (0,))
assert_equal(np.add(dummy, dummy, out=(a,)), (0,))
assert_equal(np.add(a, dummy, out=a), 0)
def test_out_override(self):
class OutClass(np.ndarray):
def __array_ufunc__(self, ufunc, method, *inputs, **kw):
if 'out' in kw:
tmp_kw = kw.copy()
tmp_kw.pop('out')
func = getattr(ufunc, method)
kw['out'][0][...] = func(*inputs, **tmp_kw)
A = np.array([0]).view(OutClass)
B = np.array([5])
C = np.array([6])
np.multiply(C, B, A)
assert_equal(A[0], 30)
assert_(isinstance(A, OutClass))
A[0] = 0
np.multiply(C, B, out=A)
assert_equal(A[0], 30)
assert_(isinstance(A, OutClass))
def test_pow_override_with_errors(self):
class PowerOnly(np.ndarray):
def __array_ufunc__(self, ufunc, method, *inputs, **kw):
if ufunc is not np.power:
raise NotImplementedError
return "POWER!"
a = np.array(5., dtype=np.float64).view(PowerOnly)
assert_equal(a ** 2.5, "POWER!")
with assert_raises(NotImplementedError):
a ** 0.5
with assert_raises(NotImplementedError):
a ** 0
with assert_raises(NotImplementedError):
a ** 1
with assert_raises(NotImplementedError):
a ** -1
with assert_raises(NotImplementedError):
a ** 2
def test_pow_array_object_dtype(self):
class SomeClass:
def __init__(self, num=None):
self.num = num
def __mul__(self, other):
raise AssertionError('__mul__ should not be called')
def __div__(self, other):
raise AssertionError('__div__ should not be called')
def __pow__(self, exp):
return SomeClass(num=self.num ** exp)
def __eq__(self, other):
if isinstance(other, SomeClass):
return self.num == other.num
__rpow__ = __pow__
def pow_for(exp, arr):
return np.array([x ** exp for x in arr])
obj_arr = np.array([SomeClass(1), SomeClass(2), SomeClass(3)])
assert_equal(obj_arr ** 0.5, pow_for(0.5, obj_arr))
assert_equal(obj_arr ** 0, pow_for(0, obj_arr))
assert_equal(obj_arr ** 1, pow_for(1, obj_arr))
assert_equal(obj_arr ** -1, pow_for(-1, obj_arr))
assert_equal(obj_arr ** 2, pow_for(2, obj_arr))
def test_pos_array_ufunc_override(self):
class A(np.ndarray):
def __array_ufunc__(self, ufunc, method, *inputs, **kwargs):
return getattr(ufunc, method)(*[i.view(np.ndarray) for
i in inputs], **kwargs)
tst = np.array('foo').view(A)
with assert_raises(TypeError):
+tst
class TestTemporaryElide:
def test_extension_incref_elide(self):
from numpy._core._multiarray_tests import incref_elide
d = np.ones(100000)
orig, res = incref_elide(d)
d + d
assert_array_equal(orig, d)
assert_array_equal(res, d + d)
def test_extension_incref_elide_stack(self):
from numpy._core._multiarray_tests import incref_elide_l
l = [1, 1, 1, 1, np.ones(100000)]
res = incref_elide_l(l)
assert_array_equal(l[4], np.ones(100000))
assert_array_equal(res, l[4] + l[4])
def test_temporary_with_cast(self):
d = np.ones(200000, dtype=np.int64)
r = ((d + d) + np.array(2**222, dtype='O'))
assert_equal(r.dtype, np.dtype('O'))
r = ((d + d) / 2)
assert_equal(r.dtype, np.dtype('f8'))
r = np.true_divide((d + d), 2)
assert_equal(r.dtype, np.dtype('f8'))
r = ((d + d) / 2.)
assert_equal(r.dtype, np.dtype('f8'))
r = ((d + d) // 2)
assert_equal(r.dtype, np.dtype(np.int64))
f = np.ones(100000, dtype=np.float32)
assert_equal(((f + f) + f.astype(np.float64)).dtype, np.dtype('f8'))
d = f.astype(np.float64)
assert_equal(((f + f) + d).dtype, d.dtype)
l = np.ones(100000, dtype=np.longdouble)
assert_equal(((d + d) + l).dtype, l.dtype)
for dt in (np.complex64, np.complex128, np.clongdouble):
c = np.ones(100000, dtype=dt)
r = abs(c * 2.0)
assert_equal(r.dtype, np.dtype('f%d' % (c.itemsize // 2)))
d = np.ones((2000, 1), dtype=int)
b = np.ones((2000), dtype=bool)
r = (1 - d) + b
assert_equal(r, 1)
assert_equal(r.shape, (2000, 2000))
a = np.bool()
assert_(type(~(a & a)) is np.bool)
a = np.empty(100000, dtype=np.float64)
a.imag ** 2
r = np.asarray(np.broadcast_to(np.zeros(1), 100000).flat) * 0.0
assert_equal(r, 0)
a = np.ones(2**20)[::2]
b = a.flat.__array__() + 1
del b
assert_equal(a, 1)
class TestCAPI:
def test_IsPythonScalar(self):
from numpy._core._multiarray_tests import IsPythonScalar
assert_(IsPythonScalar(b'foobar'))
assert_(IsPythonScalar(1))
assert_(IsPythonScalar(2**80))
assert_(IsPythonScalar(2.))
assert_(IsPythonScalar("a"))
@pytest.mark.parametrize("converter",
[_multiarray_tests.run_scalar_intp_converter,
_multiarray_tests.run_scalar_intp_from_sequence])
def test_intp_sequence_converters(self, converter):
assert converter(10) == (10,)
assert converter(-1) == (-1,)
assert converter(np.array(123)) == (123,)
assert converter((10,)) == (10,)
assert converter(np.array([11])) == (11,)
@pytest.mark.parametrize("converter",
[_multiarray_tests.run_scalar_intp_converter,
_multiarray_tests.run_scalar_intp_from_sequence])
@pytest.mark.skipif(IS_PYPY and sys.implementation.version <= (7, 3, 8),
reason="PyPy bug in error formatting")
def test_intp_sequence_converters_errors(self, converter):
with pytest.raises(TypeError,
match="expected a sequence of integers or a single integer, "):
converter(object())
with pytest.raises(TypeError,
match="expected a sequence of integers or a single integer, "
"got '32.0'"):
converter(32.)
with pytest.raises(TypeError,
match="'float' object cannot be interpreted as an integer"):
converter([32.])
with pytest.raises(ValueError,
match="Maximum allowed dimension"):
converter(2**64)
class TestSubscripting:
def test_test_zero_rank(self):
x = np.array([1, 2, 3])
assert_(isinstance(x[0], np.int_))
class TestPickling:
@pytest.mark.skipif(pickle.HIGHEST_PROTOCOL >= 5,
reason=('this tests the error messages when trying to'
'protocol 5 although it is not available'))
def test_correct_protocol5_error_message(self):
array = np.arange(10)
def test_record_array_with_object_dtype(self):
my_object = object()
arr_with_object = np.array(
[(my_object, 1, 2.0)],
dtype=[('a', object), ('b', int), ('c', float)])
arr_without_object = np.array(
[('xxx', 1, 2.0)],
dtype=[('a', str), ('b', int), ('c', float)])
for proto in range(2, pickle.HIGHEST_PROTOCOL + 1):
depickled_arr_with_object = pickle.loads(
pickle.dumps(arr_with_object, protocol=proto))
depickled_arr_without_object = pickle.loads(
pickle.dumps(arr_without_object, protocol=proto))
assert_equal(arr_with_object.dtype,
depickled_arr_with_object.dtype)
assert_equal(arr_without_object.dtype,
depickled_arr_without_object.dtype)
@pytest.mark.skipif(pickle.HIGHEST_PROTOCOL < 5,
reason="requires pickle protocol 5")
def test_f_contiguous_array(self):
f_contiguous_array = np.array([[1, 2, 3], [4, 5, 6]], order='F')
buffers = []
bytes_string = pickle.dumps(f_contiguous_array, protocol=5,
buffer_callback=buffers.append)
assert len(buffers) > 0
depickled_f_contiguous_array = pickle.loads(bytes_string,
buffers=buffers)
assert_equal(f_contiguous_array, depickled_f_contiguous_array)
def test_non_contiguous_array(self):
non_contiguous_array = np.arange(12).reshape(3, 4)[:, :2]
assert not non_contiguous_array.flags.c_contiguous
assert not non_contiguous_array.flags.f_contiguous
for proto in range(2, pickle.HIGHEST_PROTOCOL + 1):
depickled_non_contiguous_array = pickle.loads(
pickle.dumps(non_contiguous_array, protocol=proto))
assert_equal(non_contiguous_array, depickled_non_contiguous_array)
def test_roundtrip(self):
for proto in range(2, pickle.HIGHEST_PROTOCOL + 1):
carray = np.array([[2, 9], [7, 0], [3, 8]])
DATA = [
carray,
np.transpose(carray),
np.array([('xxx', 1, 2.0)], dtype=[('a', (str, 3)), ('b', int), ('c', float)])
]
refs = [weakref.ref(a) for a in DATA]
for a in DATA:
assert_equal(
a, pickle.loads(pickle.dumps(a, protocol=proto)),
err_msg="%r" % a)
del a, DATA, carray
break_cycles()
for ref in refs:
assert ref() is None
def _loads(self, obj):
return pickle.loads(obj, encoding='latin1')
def test_version0_int8(self):
s = b"\x80\x02cnumpy.core._internal\n_reconstruct\nq\x01cnumpy\nndarray\nq\x02K\x00\x85U\x01b\x87Rq\x03(K\x04\x85cnumpy\ndtype\nq\x04U\x02i1K\x00K\x01\x87Rq\x05(U\x01|NNJ\xff\xff\xff\xffJ\xff\xff\xff\xfftb\x89U\x04\x01\x02\x03\x04tb."
a = np.array([1, 2, 3, 4], dtype=np.int8)
p = self._loads(s)
assert_equal(a, p)
def test_version0_float32(self):
s = b"\x80\x02cnumpy.core._internal\n_reconstruct\nq\x01cnumpy\nndarray\nq\x02K\x00\x85U\x01b\x87Rq\x03(K\x04\x85cnumpy\ndtype\nq\x04U\x02f4K\x00K\x01\x87Rq\x05(U\x01<NNJ\xff\xff\xff\xffJ\xff\xff\xff\xfftb\x89U\x10\x00\x00\x80?\x00\x00\x00@\x00\x00@@\x00\x00\x80@tb."
a = np.array([1.0, 2.0, 3.0, 4.0], dtype=np.float32)
p = self._loads(s)
assert_equal(a, p)
def test_version0_object(self):
s = b"\x80\x02cnumpy.core._internal\n_reconstruct\nq\x01cnumpy\nndarray\nq\x02K\x00\x85U\x01b\x87Rq\x03(K\x02\x85cnumpy\ndtype\nq\x04U\x02O8K\x00K\x01\x87Rq\x05(U\x01|NNJ\xff\xff\xff\xffJ\xff\xff\xff\xfftb\x89]q\x06(}q\x07U\x01aK\x01s}q\x08U\x01bK\x02setb."
a = np.array([{'a': 1}, {'b': 2}])
p = self._loads(s)
assert_equal(a, p)
def test_version1_int8(self):
s = b"\x80\x02cnumpy.core._internal\n_reconstruct\nq\x01cnumpy\nndarray\nq\x02K\x00\x85U\x01b\x87Rq\x03(K\x01K\x04\x85cnumpy\ndtype\nq\x04U\x02i1K\x00K\x01\x87Rq\x05(K\x01U\x01|NNJ\xff\xff\xff\xffJ\xff\xff\xff\xfftb\x89U\x04\x01\x02\x03\x04tb."
a = np.array([1, 2, 3, 4], dtype=np.int8)
p = self._loads(s)
assert_equal(a, p)
def test_version1_float32(self):
s = b"\x80\x02cnumpy.core._internal\n_reconstruct\nq\x01cnumpy\nndarray\nq\x02K\x00\x85U\x01b\x87Rq\x03(K\x01K\x04\x85cnumpy\ndtype\nq\x04U\x02f4K\x00K\x01\x87Rq\x05(K\x01U\x01<NNJ\xff\xff\xff\xffJ\xff\xff\xff\xfftb\x89U\x10\x00\x00\x80?\x00\x00\x00@\x00\x00@@\x00\x00\x80@tb."
a = np.array([1.0, 2.0, 3.0, 4.0], dtype=np.float32)
p = self._loads(s)
assert_equal(a, p)
def test_version1_object(self):
s = b"\x80\x02cnumpy.core._internal\n_reconstruct\nq\x01cnumpy\nndarray\nq\x02K\x00\x85U\x01b\x87Rq\x03(K\x01K\x02\x85cnumpy\ndtype\nq\x04U\x02O8K\x00K\x01\x87Rq\x05(K\x01U\x01|NNJ\xff\xff\xff\xffJ\xff\xff\xff\xfftb\x89]q\x06(}q\x07U\x01aK\x01s}q\x08U\x01bK\x02setb."
a = np.array([{'a': 1}, {'b': 2}])
p = self._loads(s)
assert_equal(a, p)
def test_subarray_int_shape(self):
s = b"cnumpy.core.multiarray\n_reconstruct\np0\n(cnumpy\nndarray\np1\n(I0\ntp2\nS'b'\np3\ntp4\nRp5\n(I1\n(I1\ntp6\ncnumpy\ndtype\np7\n(S'V6'\np8\nI0\nI1\ntp9\nRp10\n(I3\nS'|'\np11\nN(S'a'\np12\ng3\ntp13\n(dp14\ng12\n(g7\n(S'V4'\np15\nI0\nI1\ntp16\nRp17\n(I3\nS'|'\np18\n(g7\n(S'i1'\np19\nI0\nI1\ntp20\nRp21\n(I3\nS'|'\np22\nNNNI-1\nI-1\nI0\ntp23\nb(I2\nI2\ntp24\ntp25\nNNI4\nI1\nI0\ntp26\nbI0\ntp27\nsg3\n(g7\n(S'V2'\np28\nI0\nI1\ntp29\nRp30\n(I3\nS'|'\np31\n(g21\nI2\ntp32\nNNI2\nI1\nI0\ntp33\nbI4\ntp34\nsI6\nI1\nI0\ntp35\nbI00\nS'\\x01\\x01\\x01\\x01\\x01\\x02'\np36\ntp37\nb."
a = np.array([(1, (1, 2))], dtype=[('a', 'i1', (2, 2)), ('b', 'i1', 2)])
p = self._loads(s)
assert_equal(a, p)
def test_datetime64_byteorder(self):
original = np.array([['2015-02-24T00:00:00.000000000']], dtype='datetime64[ns]')
original_byte_reversed = original.copy(order='K')
original_byte_reversed.dtype = original_byte_reversed.dtype.newbyteorder('S')
original_byte_reversed.byteswap(inplace=True)
new = pickle.loads(pickle.dumps(original_byte_reversed))
assert_equal(original.dtype, new.dtype)
class TestFancyIndexing:
def test_list(self):
x = np.ones((1, 1))
x[:, [0]] = 2.0
assert_array_equal(x, np.array([[2.0]]))
x = np.ones((1, 1, 1))
x[:, :, [0]] = 2.0
assert_array_equal(x, np.array([[[2.0]]]))
def test_tuple(self):
x = np.ones((1, 1))
x[:, (0,)] = 2.0
assert_array_equal(x, np.array([[2.0]]))
x = np.ones((1, 1, 1))
x[:, :, (0,)] = 2.0
assert_array_equal(x, np.array([[[2.0]]]))
def test_mask(self):
x = np.array([1, 2, 3, 4])
m = np.array([0, 1, 0, 0], bool)
assert_array_equal(x[m], np.array([2]))
def test_mask2(self):
x = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])
m = np.array([0, 1], bool)
m2 = np.array([[0, 1, 0, 0], [1, 0, 0, 0]], bool)
m3 = np.array([[0, 1, 0, 0], [0, 0, 0, 0]], bool)
assert_array_equal(x[m], np.array([[5, 6, 7, 8]]))
assert_array_equal(x[m2], np.array([2, 5]))
assert_array_equal(x[m3], np.array([2]))
def test_assign_mask(self):
x = np.array([1, 2, 3, 4])
m = np.array([0, 1, 0, 0], bool)
x[m] = 5
assert_array_equal(x, np.array([1, 5, 3, 4]))
def test_assign_mask2(self):
xorig = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])
m = np.array([0, 1], bool)
m2 = np.array([[0, 1, 0, 0], [1, 0, 0, 0]], bool)
m3 = np.array([[0, 1, 0, 0], [0, 0, 0, 0]], bool)
x = xorig.copy()
x[m] = 10
assert_array_equal(x, np.array([[1, 2, 3, 4], [10, 10, 10, 10]]))
x = xorig.copy()
x[m2] = 10
assert_array_equal(x, np.array([[1, 10, 3, 4], [10, 6, 7, 8]]))
x = xorig.copy()
x[m3] = 10
assert_array_equal(x, np.array([[1, 10, 3, 4], [5, 6, 7, 8]]))
class TestStringCompare:
def test_string(self):
g1 = np.array(["This", "is", "example"])
g2 = np.array(["This", "was", "example"])
assert_array_equal(g1 == g2, [g1[i] == g2[i] for i in [0, 1, 2]])
assert_array_equal(g1 != g2, [g1[i] != g2[i] for i in [0, 1, 2]])
assert_array_equal(g1 <= g2, [g1[i] <= g2[i] for i in [0, 1, 2]])
assert_array_equal(g1 >= g2, [g1[i] >= g2[i] for i in [0, 1, 2]])
assert_array_equal(g1 < g2, [g1[i] < g2[i] for i in [0, 1, 2]])
assert_array_equal(g1 > g2, [g1[i] > g2[i] for i in [0, 1, 2]])
def test_mixed(self):
g1 = np.array(["spam", "spa", "spammer", "and eggs"])
g2 = "spam"
def test_unicode(self):
g1 = np.array(["This", "is", "example"])
g2 = np.array(["This", "was", "example"])
assert_array_equal(g1 == g2, [g1[i] == g2[i] for i in [0, 1, 2]])
assert_array_equal(g1 != g2, [g1[i] != g2[i] for i in [0, 1, 2]])
assert_array_equal(g1 <= g2, [g1[i] <= g2[i] for i in [0, 1, 2]])
assert_array_equal(g1 >= g2, [g1[i] >= g2[i] for i in [0, 1, 2]])
assert_array_equal(g1 < g2, [g1[i] < g2[i] for i in [0, 1, 2]])
assert_array_equal(g1 > g2, [g1[i] > g2[i] for i in [0, 1, 2]])
class TestArgmaxArgminCommon:
sizes = [(), (3,), (3, 2), (2, 3),
(3, 3), (2, 3, 4), (4, 3, 2),
(1, 2, 3, 4), (2, 3, 4, 1),
(3, 4, 1, 2), (4, 1, 2, 3),
(64,), (128,), (256,)]
@pytest.mark.parametrize("size, axis", itertools.chain(*[[(size, axis)
for axis in list(range(-len(size), len(size))) + [None]]
for size in sizes]))
@pytest.mark.parametrize('method', [np.argmax, np.argmin])
def test_np_argmin_argmax_keepdims(self, size, axis, method):
arr = np.random.normal(size=size)
if axis is None:
new_shape = [1 for _ in range(len(size))]
else:
new_shape = list(size)
new_shape[axis] = 1
new_shape = tuple(new_shape)
_res_orig = method(arr, axis=axis)
res_orig = _res_orig.reshape(new_shape)
res = method(arr, axis=axis, keepdims=True)
assert_equal(res, res_orig)
assert_(res.shape == new_shape)
outarray = np.empty(res.shape, dtype=res.dtype)
res1 = method(arr, axis=axis, out=outarray, keepdims=True)
assert_(res1 is outarray)
assert_equal(res, outarray)
if len(size) > 0:
wrong_shape = list(new_shape)
if axis is not None:
wrong_shape[axis] = 2
else:
wrong_shape[0] = 2
wrong_outarray = np.empty(wrong_shape, dtype=res.dtype)
with pytest.raises(ValueError):
method(arr.T, axis=axis, out=wrong_outarray, keepdims=True)
if axis is None:
new_shape = [1 for _ in range(len(size))]
else:
new_shape = list(size)[::-1]
new_shape[axis] = 1
new_shape = tuple(new_shape)
_res_orig = method(arr.T, axis=axis)
res_orig = _res_orig.reshape(new_shape)
res = method(arr.T, axis=axis, keepdims=True)
assert_equal(res, res_orig)
assert_(res.shape == new_shape)
outarray = np.empty(new_shape[::-1], dtype=res.dtype)
outarray = outarray.T
res1 = method(arr.T, axis=axis, out=outarray, keepdims=True)
assert_(res1 is outarray)
assert_equal(res, outarray)
if len(size) > 0:
wrong_shape = list(new_shape)
if axis is not None:
wrong_shape[axis] = 2
else:
wrong_shape[0] = 2
wrong_outarray = np.empty(wrong_shape, dtype=res.dtype)
with pytest.raises(ValueError):
method(arr.T, axis=axis, out=wrong_outarray, keepdims=True)
def test_all(self, method):
a = np.random.normal(0, 1, (4, 5, 6, 7, 8))
arg_method = getattr(a, 'arg' + method)
val_method = getattr(a, method)
for i in range(a.ndim):
a_maxmin = val_method(i)
aarg_maxmin = arg_method(i)
axes = list(range(a.ndim))
axes.remove(i)
assert_(np.all(a_maxmin == aarg_maxmin.choose(
*a.transpose(i, *axes))))
@pytest.mark.parametrize('method', ['argmax', 'argmin'])
def test_output_shape(self, method):
a = np.ones((10, 5))
arg_method = getattr(a, method)
out = np.ones(11, dtype=np.int_)
assert_raises(ValueError, arg_method, -1, out)
out = np.ones((2, 5), dtype=np.int_)
assert_raises(ValueError, arg_method, -1, out)
out = np.ones((1, 10), dtype=np.int_)
assert_raises(ValueError, arg_method, -1, out)
out = np.ones(10, dtype=np.int_)
arg_method(-1, out=out)
assert_equal(out, arg_method(-1))
@pytest.mark.parametrize('ndim', [0, 1])
@pytest.mark.parametrize('method', ['argmax', 'argmin'])
def test_ret_is_out(self, ndim, method):
a = np.ones((4,) + (256,)*ndim)
arg_method = getattr(a, method)
out = np.empty((256,)*ndim, dtype=np.intp)
ret = arg_method(axis=0, out=out)
assert ret is out
@pytest.mark.parametrize('np_array, method, idx, val',
[(np.zeros, 'argmax', 5942, "as"),
(np.ones, 'argmin', 6001, "0")])
def test_unicode(self, np_array, method, idx, val):
d = np_array(6031, dtype='<U9')
arg_method = getattr(d, method)
d[idx] = val
assert_equal(arg_method(), idx)
@pytest.mark.parametrize('arr_method, np_method',
[('argmax', np.argmax),
('argmin', np.argmin)])
def test_np_vs_ndarray(self, arr_method, np_method):
a = np.random.normal(size=(2, 3))
arg_method = getattr(a, arr_method)
out1 = np.zeros(2, dtype=int)
out2 = np.zeros(2, dtype=int)
assert_equal(arg_method(1, out1), np_method(a, 1, out2))
assert_equal(out1, out2)
out1 = np.zeros(3, dtype=int)
out2 = np.zeros(3, dtype=int)
assert_equal(arg_method(out=out1, axis=0),
np_method(a, out=out2, axis=0))
assert_equal(out1, out2)
@pytest.mark.leaks_references(reason="replaces None with NULL.")
@pytest.mark.parametrize('method, vals',
[('argmax', (10, 30)),
('argmin', (30, 10))])
def test_object_with_NULLs(self, method, vals):
a = np.empty(4, dtype='O')
arg_method = getattr(a, method)
ctypes.memset(a.ctypes.data, 0, a.nbytes)
assert_equal(arg_method(), 0)
a[3] = vals[0]
assert_equal(arg_method(), 3)
a[1] = vals[1]
assert_equal(arg_method(), 1)
class TestArgmax:
usg_data = [
([1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0], 0),
([3, 3, 3, 3, 2, 2, 2, 2], 0),
([0, 1, 2, 3, 4, 5, 6, 7], 7),
([7, 6, 5, 4, 3, 2, 1, 0], 0)
]
sg_data = usg_data + [
([1, 2, 3, 4, -4, -3, -2, -1], 3),
([1, 2, 3, 4, -1, -2, -3, -4], 3)
]
darr = [(np.array(d[0], dtype=t), d[1]) for d, t in (
itertools.product(usg_data, (
np.uint8, np.uint16, np.uint32, np.uint64
))
)]
darr = darr + [(np.array(d[0], dtype=t), d[1]) for d, t in (
itertools.product(sg_data, (
np.int8, np.int16, np.int32, np.int64, np.float32, np.float64
))
)]
darr = darr + [(np.array(d[0], dtype=t), d[1]) for d, t in (
itertools.product((
([0, 1, 2, 3, np.nan], 4),
([0, 1, 2, np.nan, 3], 3),
([np.nan, 0, 1, 2, 3], 0),
([np.nan, 0, np.nan, 2, 3], 0),
([1] * (2*5-1) + [np.nan], 2*5-1),
([1] * (4*5-1) + [np.nan], 4*5-1),
([1] * (8*5-1) + [np.nan], 8*5-1),
([1] * (16*5-1) + [np.nan], 16*5-1),
([1] * (32*5-1) + [np.nan], 32*5-1)
), (
np.float32, np.float64
))
)]
nan_arr = darr + [
([0, 1, 2, 3, complex(0, np.nan)], 4),
([0, 1, 2, 3, complex(np.nan, 0)], 4),
([0, 1, 2, complex(np.nan, 0), 3], 3),
([0, 1, 2, complex(0, np.nan), 3], 3),
([complex(0, np.nan), 0, 1, 2, 3], 0),
([complex(np.nan, np.nan), 0, 1, 2, 3], 0),
([complex(np.nan, 0), complex(np.nan, 2), complex(np.nan, 1)], 0),
([complex(np.nan, np.nan), complex(np.nan, 2), complex(np.nan, 1)], 0),
([complex(np.nan, 0), complex(np.nan, 2), complex(np.nan, np.nan)], 0),
([complex(0, 0), complex(0, 2), complex(0, 1)], 1),
([complex(1, 0), complex(0, 2), complex(0, 1)], 0),
([complex(1, 0), complex(0, 2), complex(1, 1)], 2),
([np.datetime64('1923-04-14T12:43:12'),
np.datetime64('1994-06-21T14:43:15'),
np.datetime64('2001-10-15T04:10:32'),
np.datetime64('1995-11-25T16:02:16'),
np.datetime64('2005-01-04T03:14:12'),
np.datetime64('2041-12-03T14:05:03')], 5),
([np.datetime64('1935-09-14T04:40:11'),
np.datetime64('1949-10-12T12:32:11'),
np.datetime64('2010-01-03T05:14:12'),
np.datetime64('2015-11-20T12:20:59'),
np.datetime64('1932-09-23T10:10:13'),
np.datetime64('2014-10-10T03:50:30')], 3),
([np.datetime64('NaT'),
np.datetime64('NaT'),
np.datetime64('2010-01-03T05:14:12'),
np.datetime64('NaT'),
np.datetime64('2015-09-23T10:10:13'),
np.datetime64('1932-10-10T03:50:30')], 0),
([np.datetime64('2059-03-14T12:43:12'),
np.datetime64('1996-09-21T14:43:15'),
np.datetime64('NaT'),
np.datetime64('2022-12-25T16:02:16'),
np.datetime64('1963-10-04T03:14:12'),
np.datetime64('2013-05-08T18:15:23')], 2),
([np.timedelta64(2, 's'),
np.timedelta64(1, 's'),
np.timedelta64('NaT', 's'),
np.timedelta64(3, 's')], 2),
([np.timedelta64('NaT', 's')] * 3, 0),
([timedelta(days=5, seconds=14),
timedelta(days=2, seconds=35),
timedelta(days=-1, seconds=23)], 0),
([timedelta(days=1, seconds=43),
timedelta(days=10, seconds=5),
timedelta(days=5, seconds=14)], 1),
([timedelta(days=10, seconds=24),
timedelta(days=10, seconds=5),
timedelta(days=10, seconds=43)], 2),
([False, False, False, False, True], 4),
([False, False, False, True, False], 3),
([True, False, False, False, False], 0),
([True, False, True, False, False], 0),
]
@pytest.mark.parametrize('data', nan_arr)
def test_combinations(self, data):
arr, pos = data
with suppress_warnings() as sup:
sup.filter(RuntimeWarning,
"invalid value encountered in reduce")
val = np.max(arr)
assert_equal(np.argmax(arr), pos, err_msg="%r" % arr)
assert_equal(arr[np.argmax(arr)], val, err_msg="%r" % arr)
rarr = np.repeat(arr, 129)
rpos = pos * 129
assert_equal(np.argmax(rarr), rpos, err_msg="%r" % rarr)
assert_equal(rarr[np.argmax(rarr)], val, err_msg="%r" % rarr)
padd = np.repeat(np.min(arr), 513)
rarr = np.concatenate((arr, padd))
rpos = pos
assert_equal(np.argmax(rarr), rpos, err_msg="%r" % rarr)
assert_equal(rarr[np.argmax(rarr)], val, err_msg="%r" % rarr)
def test_maximum_signed_integers(self):
a = np.array([1, 2**7 - 1, -2**7], dtype=np.int8)
assert_equal(np.argmax(a), 1)
a = a.repeat(129)
assert_equal(np.argmax(a), 129)
a = np.array([1, 2**15 - 1, -2**15], dtype=np.int16)
assert_equal(np.argmax(a), 1)
a = a.repeat(129)
assert_equal(np.argmax(a), 129)
a = np.array([1, 2**31 - 1, -2**31], dtype=np.int32)
assert_equal(np.argmax(a), 1)
a = a.repeat(129)
assert_equal(np.argmax(a), 129)
a = np.array([1, 2**63 - 1, -2**63], dtype=np.int64)
assert_equal(np.argmax(a), 1)
a = a.repeat(129)
assert_equal(np.argmax(a), 129)
class TestArgmin:
usg_data = [
([1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0], 8),
([3, 3, 3, 3, 2, 2, 2, 2], 4),
([0, 1, 2, 3, 4, 5, 6, 7], 0),
([7, 6, 5, 4, 3, 2, 1, 0], 7)
]
sg_data = usg_data + [
([1, 2, 3, 4, -4, -3, -2, -1], 4),
([1, 2, 3, 4, -1, -2, -3, -4], 7)
]
darr = [(np.array(d[0], dtype=t), d[1]) for d, t in (
itertools.product(usg_data, (
np.uint8, np.uint16, np.uint32, np.uint64
))
)]
darr = darr + [(np.array(d[0], dtype=t), d[1]) for d, t in (
itertools.product(sg_data, (
np.int8, np.int16, np.int32, np.int64, np.float32, np.float64
))
)]
darr = darr + [(np.array(d[0], dtype=t), d[1]) for d, t in (
itertools.product((
([0, 1, 2, 3, np.nan], 4),
([0, 1, 2, np.nan, 3], 3),
([np.nan, 0, 1, 2, 3], 0),
([np.nan, 0, np.nan, 2, 3], 0),
([1] * (2*5-1) + [np.nan], 2*5-1),
([1] * (4*5-1) + [np.nan], 4*5-1),
([1] * (8*5-1) + [np.nan], 8*5-1),
([1] * (16*5-1) + [np.nan], 16*5-1),
([1] * (32*5-1) + [np.nan], 32*5-1)
), (
np.float32, np.float64
))
)]
nan_arr = darr + [
([0, 1, 2, 3, complex(0, np.nan)], 4),
([0, 1, 2, 3, complex(np.nan, 0)], 4),
([0, 1, 2, complex(np.nan, 0), 3], 3),
([0, 1, 2, complex(0, np.nan), 3], 3),
([complex(0, np.nan), 0, 1, 2, 3], 0),
([complex(np.nan, np.nan), 0, 1, 2, 3], 0),
([complex(np.nan, 0), complex(np.nan, 2), complex(np.nan, 1)], 0),
([complex(np.nan, np.nan), complex(np.nan, 2), complex(np.nan, 1)], 0),
([complex(np.nan, 0), complex(np.nan, 2), complex(np.nan, np.nan)], 0),
([complex(0, 0), complex(0, 2), complex(0, 1)], 0),
([complex(1, 0), complex(0, 2), complex(0, 1)], 2),
([complex(1, 0), complex(0, 2), complex(1, 1)], 1),
([np.datetime64('1923-04-14T12:43:12'),
np.datetime64('1994-06-21T14:43:15'),
np.datetime64('2001-10-15T04:10:32'),
np.datetime64('1995-11-25T16:02:16'),
np.datetime64('2005-01-04T03:14:12'),
np.datetime64('2041-12-03T14:05:03')], 0),
([np.datetime64('1935-09-14T04:40:11'),
np.datetime64('1949-10-12T12:32:11'),
np.datetime64('2010-01-03T05:14:12'),
np.datetime64('2014-11-20T12:20:59'),
np.datetime64('2015-09-23T10:10:13'),
np.datetime64('1932-10-10T03:50:30')], 5),
([np.datetime64('NaT'),
np.datetime64('NaT'),
np.datetime64('2010-01-03T05:14:12'),
np.datetime64('NaT'),
np.datetime64('2015-09-23T10:10:13'),
np.datetime64('1932-10-10T03:50:30')], 0),
([np.datetime64('2059-03-14T12:43:12'),
np.datetime64('1996-09-21T14:43:15'),
np.datetime64('NaT'),
np.datetime64('2022-12-25T16:02:16'),
np.datetime64('1963-10-04T03:14:12'),
np.datetime64('2013-05-08T18:15:23')], 2),
([np.timedelta64(2, 's'),
np.timedelta64(1, 's'),
np.timedelta64('NaT', 's'),
np.timedelta64(3, 's')], 2),
([np.timedelta64('NaT', 's')] * 3, 0),
([timedelta(days=5, seconds=14), timedelta(days=2, seconds=35),
timedelta(days=-1, seconds=23)], 2),
([timedelta(days=1, seconds=43), timedelta(days=10, seconds=5),
timedelta(days=5, seconds=14)], 0),
([timedelta(days=10, seconds=24), timedelta(days=10, seconds=5),
timedelta(days=10, seconds=43)], 1),
([True, True, True, True, False], 4),
([True, True, True, False, True], 3),
([False, True, True, True, True], 0),
([False, True, False, True, True], 0),
]
@pytest.mark.parametrize('data', nan_arr)
def test_combinations(self, data):
arr, pos = data
with suppress_warnings() as sup:
sup.filter(RuntimeWarning, "invalid value encountered in reduce")
min_val = np.min(arr)
assert_equal(np.argmin(arr), pos, err_msg="%r" % arr)
assert_equal(arr[np.argmin(arr)], min_val, err_msg="%r" % arr)
rarr = np.repeat(arr, 129)
rpos = pos * 129
assert_equal(np.argmin(rarr), rpos, err_msg="%r" % rarr)
assert_equal(rarr[np.argmin(rarr)], min_val, err_msg="%r" % rarr)
padd = np.repeat(np.max(arr), 513)
rarr = np.concatenate((arr, padd))
rpos = pos
assert_equal(np.argmin(rarr), rpos, err_msg="%r" % rarr)
assert_equal(rarr[np.argmin(rarr)], min_val, err_msg="%r" % rarr)
def test_minimum_signed_integers(self):
a = np.array([1, -2**7, -2**7 + 1, 2**7 - 1], dtype=np.int8)
assert_equal(np.argmin(a), 1)
a = a.repeat(129)
assert_equal(np.argmin(a), 129)
a = np.array([1, -2**15, -2**15 + 1, 2**15 - 1], dtype=np.int16)
assert_equal(np.argmin(a), 1)
a = a.repeat(129)
assert_equal(np.argmin(a), 129)
a = np.array([1, -2**31, -2**31 + 1, 2**31 - 1], dtype=np.int32)
assert_equal(np.argmin(a), 1)
a = a.repeat(129)
assert_equal(np.argmin(a), 129)
a = np.array([1, -2**63, -2**63 + 1, 2**63 - 1], dtype=np.int64)
assert_equal(np.argmin(a), 1)
a = a.repeat(129)
assert_equal(np.argmin(a), 129)
class TestMinMax:
def test_scalar(self):
assert_raises(AxisError, np.amax, 1, 1)
assert_raises(AxisError, np.amin, 1, 1)
assert_equal(np.amax(1, axis=0), 1)
assert_equal(np.amin(1, axis=0), 1)
assert_equal(np.amax(1, axis=None), 1)
assert_equal(np.amin(1, axis=None), 1)
def test_axis(self):
assert_raises(AxisError, np.amax, [1, 2, 3], 1000)
assert_equal(np.amax([[1, 2, 3]], axis=1), 3)
def test_datetime(self):
for dtype in ('m8[s]', 'm8[Y]'):
a = np.arange(10).astype(dtype)
assert_equal(np.amin(a), a[0])
assert_equal(np.amax(a), a[9])
a[3] = 'NaT'
assert_equal(np.amin(a), a[3])
assert_equal(np.amax(a), a[3])
class TestNewaxis:
def test_basic(self):
sk = np.array([0, -0.1, 0.1])
res = 250*sk[:, np.newaxis]
assert_almost_equal(res.ravel(), 250*sk)
class TestClip:
def _check_range(self, x, cmin, cmax):
assert_(np.all(x >= cmin))
assert_(np.all(x <= cmax))
def _clip_type(self, type_group, array_max,
clip_min, clip_max, inplace=False,
expected_min=None, expected_max=None):
if expected_min is None:
expected_min = clip_min
if expected_max is None:
expected_max = clip_max
for T in np._core.sctypes[type_group]:
if sys.byteorder == 'little':
byte_orders = ['=', '>']
else:
byte_orders = ['<', '=']
for byteorder in byte_orders:
dtype = np.dtype(T).newbyteorder(byteorder)
x = (np.random.random(1000) * array_max).astype(dtype)
if inplace:
x.clip(clip_min, clip_max, x, casting='unsafe')
else:
x = x.clip(clip_min, clip_max)
byteorder = '='
if x.dtype.byteorder == '|':
byteorder = '|'
assert_equal(x.dtype.byteorder, byteorder)
self._check_range(x, expected_min, expected_max)
return x
def test_basic(self):
for inplace in [False, True]:
self._clip_type('float', 1024, -12.8, 100.2, inplace=inplace)
self._clip_type('float', 1024, 0, 0, inplace=inplace)
self._clip_type('int', 1024, -120, 100, inplace=inplace)
self._clip_type('int', 1024, 0, 0, inplace=inplace)
self._clip_type('uint', 1024, 0, 0, inplace=inplace)
self._clip_type('uint', 1024, 10, 100, inplace=inplace)
@pytest.mark.parametrize("inplace", [False, True])
def test_int_range_error(self, inplace):
x = (np.random.random(1000) * 255).astype("uint8")
with pytest.raises(OverflowError):
x.clip(-1, 10, out=x if inplace else None)
with pytest.raises(OverflowError):
x.clip(0, 256, out=x if inplace else None)
def test_record_array(self):
rec = np.array([(-5, 2.0, 3.0), (5.0, 4.0, 3.0)],
dtype=[('x', '<f8'), ('y', '<f8'), ('z', '<f8')])
y = rec['x'].clip(-0.3, 0.5)
self._check_range(y, -0.3, 0.5)
def test_max_or_min(self):
val = np.array([0, 1, 2, 3, 4, 5, 6, 7])
x = val.clip(3)
assert_(np.all(x >= 3))
x = val.clip(min=3)
assert_(np.all(x >= 3))
x = val.clip(max=4)
assert_(np.all(x <= 4))
def test_nan(self):
input_arr = np.array([-2., np.nan, 0.5, 3., 0.25, np.nan])
result = input_arr.clip(-1, 1)
expected = np.array([-1., np.nan, 0.5, 1., 0.25, np.nan])
assert_array_equal(result, expected)
class TestCompress:
def test_axis(self):
tgt = [[5, 6, 7, 8, 9]]
arr = np.arange(10).reshape(2, 5)
out = np.compress([0, 1], arr, axis=0)
assert_equal(out, tgt)
tgt = [[1, 3], [6, 8]]
out = np.compress([0, 1, 0, 1, 0], arr, axis=1)
assert_equal(out, tgt)
def test_truncate(self):
tgt = [[1], [6]]
arr = np.arange(10).reshape(2, 5)
out = np.compress([0, 1], arr, axis=1)
assert_equal(out, tgt)
def test_flatten(self):
arr = np.arange(10).reshape(2, 5)
out = np.compress([0, 1], arr)
assert_equal(out, 1)
class TestPutmask:
def tst_basic(self, x, T, mask, val):
np.putmask(x, mask, val)
assert_equal(x[mask], np.array(val, T))
def test_ip_types(self):
unchecked_types = [bytes, str, np.void]
x = np.random.random(1000)*100
mask = x < 40
for val in [-100, 0, 15]:
for types in np._core.sctypes.values():
for T in types:
if T not in unchecked_types:
if val < 0 and np.dtype(T).kind == "u":
val = np.iinfo(T).max - 99
self.tst_basic(x.copy().astype(T), T, mask, val)
dt = np.dtype("S3")
self.tst_basic(x.astype(dt), dt.type, mask, dt.type(val)[:3])
def test_mask_size(self):
assert_raises(ValueError, np.putmask, np.array([1, 2, 3]), [True], 5)
@pytest.mark.parametrize('dtype', ('>i4', '<i4'))
def test_byteorder(self, dtype):
x = np.array([1, 2, 3], dtype)
np.putmask(x, [True, False, True], -1)
assert_array_equal(x, [-1, 2, -1])
def test_record_array(self):
rec = np.array([(-5, 2.0, 3.0), (5.0, 4.0, 3.0)],
dtype=[('x', '<f8'), ('y', '>f8'), ('z', '<f8')])
np.putmask(rec['x'], [True, False], 10)
assert_array_equal(rec['x'], [10, 5])
assert_array_equal(rec['y'], [2, 4])
assert_array_equal(rec['z'], [3, 3])
np.putmask(rec['y'], [True, False], 11)
assert_array_equal(rec['x'], [10, 5])
assert_array_equal(rec['y'], [11, 4])
assert_array_equal(rec['z'], [3, 3])
def test_overlaps(self):
x = np.array([True, False, True, False])
np.putmask(x[1:4], [True, True, True], x[:3])
assert_equal(x, np.array([True, True, False, True]))
x = np.array([True, False, True, False])
np.putmask(x[1:4], x[:3], [True, False, True])
assert_equal(x, np.array([True, True, True, True]))
def test_writeable(self):
a = np.arange(5)
a.flags.writeable = False
with pytest.raises(ValueError):
np.putmask(a, a >= 2, 3)
def test_kwargs(self):
x = np.array([0, 0])
np.putmask(x, [0, 1], [-1, -2])
assert_array_equal(x, [0, -2])
x = np.array([0, 0])
np.putmask(x, mask=[0, 1], values=[-1, -2])
assert_array_equal(x, [0, -2])
x = np.array([0, 0])
np.putmask(x, values=[-1, -2], mask=[0, 1])
assert_array_equal(x, [0, -2])
with pytest.raises(TypeError):
np.putmask(a=x, values=[-1, -2], mask=[0, 1])
class TestTake:
def tst_basic(self, x):
ind = list(range(x.shape[0]))
assert_array_equal(x.take(ind, axis=0), x)
def test_ip_types(self):
unchecked_types = [bytes, str, np.void]
x = np.random.random(24) * 100
x.shape = 2, 3, 4
for types in np._core.sctypes.values():
for T in types:
if T not in unchecked_types:
self.tst_basic(x.copy().astype(T))
self.tst_basic(x.astype("S3"))
def test_raise(self):
x = np.random.random(24) * 100
x.shape = 2, 3, 4
assert_raises(IndexError, x.take, [0, 1, 2], axis=0)
assert_raises(IndexError, x.take, [-3], axis=0)
assert_array_equal(x.take([-1], axis=0)[0], x[1])
def test_clip(self):
x = np.random.random(24) * 100
x.shape = 2, 3, 4
assert_array_equal(x.take([-1], axis=0, mode='clip')[0], x[0])
assert_array_equal(x.take([2], axis=0, mode='clip')[0], x[1])
def test_wrap(self):
x = np.random.random(24) * 100
x.shape = 2, 3, 4
assert_array_equal(x.take([-1], axis=0, mode='wrap')[0], x[1])
assert_array_equal(x.take([2], axis=0, mode='wrap')[0], x[0])
assert_array_equal(x.take([3], axis=0, mode='wrap')[0], x[1])
@pytest.mark.parametrize('dtype', ('>i4', '<i4'))
def test_byteorder(self, dtype):
x = np.array([1, 2, 3], dtype)
assert_array_equal(x.take([0, 2, 1]), [1, 3, 2])
def test_record_array(self):
rec = np.array([(-5, 2.0, 3.0), (5.0, 4.0, 3.0)],
dtype=[('x', '<f8'), ('y', '>f8'), ('z', '<f8')])
rec1 = rec.take([1])
assert_(rec1['x'] == 5.0 and rec1['y'] == 4.0)
def test_out_overlap(self):
x = np.arange(5)
y = np.take(x, [1, 2, 3], out=x[2:5], mode='wrap')
assert_equal(y, np.array([1, 2, 3]))
@pytest.mark.parametrize('shape', [(1, 2), (1,), ()])
def test_ret_is_out(self, shape):
x = np.arange(5)
inds = np.zeros(shape, dtype=np.intp)
out = np.zeros(shape, dtype=x.dtype)
ret = np.take(x, inds, out=out)
assert ret is out
class TestLexsort:
@pytest.mark.parametrize('dtype',[
np.uint8, np.uint16, np.uint32, np.uint64,
np.int8, np.int16, np.int32, np.int64,
np.float16, np.float32, np.float64
])
def test_basic(self, dtype):
a = np.array([1, 2, 1, 3, 1, 5], dtype=dtype)
b = np.array([0, 4, 5, 6, 2, 3], dtype=dtype)
idx = np.lexsort((b, a))
expected_idx = np.array([0, 4, 2, 1, 3, 5])
assert_array_equal(idx, expected_idx)
assert_array_equal(a[idx], np.sort(a))
def test_mixed(self):
a = np.array([1, 2, 1, 3, 1, 5])
b = np.array([0, 4, 5, 6, 2, 3], dtype='datetime64[D]')
idx = np.lexsort((b, a))
expected_idx = np.array([0, 4, 2, 1, 3, 5])
assert_array_equal(idx, expected_idx)
def test_datetime(self):
a = np.array([0,0,0], dtype='datetime64[D]')
b = np.array([2,1,0], dtype='datetime64[D]')
idx = np.lexsort((b, a))
expected_idx = np.array([2, 1, 0])
assert_array_equal(idx, expected_idx)
a = np.array([0,0,0], dtype='timedelta64[D]')
b = np.array([2,1,0], dtype='timedelta64[D]')
idx = np.lexsort((b, a))
expected_idx = np.array([2, 1, 0])
assert_array_equal(idx, expected_idx)
def test_object(self):
a = np.random.choice(10, 1000)
b = np.random.choice(['abc', 'xy', 'wz', 'efghi', 'qwst', 'x'], 1000)
for u in a, b:
left = np.lexsort((u.astype('O'),))
right = np.argsort(u, kind='mergesort')
assert_array_equal(left, right)
for u, v in (a, b), (b, a):
idx = np.lexsort((u, v))
assert_array_equal(idx, np.lexsort((u.astype('O'), v)))
assert_array_equal(idx, np.lexsort((u, v.astype('O'))))
u, v = np.array(u, dtype='object'), np.array(v, dtype='object')
assert_array_equal(idx, np.lexsort((u, v)))
def test_invalid_axis(self):
x = np.linspace(0., 1., 42*3).reshape(42, 3)
assert_raises(AxisError, np.lexsort, x, axis=2)
class TestIO:
"""Test tofile, fromfile, tobytes, and fromstring"""
@pytest.fixture()
def x(self):
shape = (2, 4, 3)
rand = np.random.random
x = rand(shape) + rand(shape).astype(complex) * 1j
x[0, :, 1] = [np.nan, np.inf, -np.inf, np.nan]
return x
@pytest.fixture(params=["string", "path_obj"])
def tmp_filename(self, tmp_path, request):
filename = tmp_path / "file"
if request.param == "string":
filename = str(filename)
yield filename
def test_nofile(self):
b = io.BytesIO()
assert_raises(OSError, np.fromfile, b, np.uint8, 80)
d = np.ones(7)
assert_raises(OSError, lambda x: x.tofile(b), d)
def test_bool_fromstring(self):
v = np.array([True, False, True, False], dtype=np.bool)
y = np.fromstring('1 0 -2.3 0.0', sep=' ', dtype=np.bool)
assert_array_equal(v, y)
def test_uint64_fromstring(self):
d = np.fromstring("9923372036854775807 104783749223640",
dtype=np.uint64, sep=' ')
e = np.array([9923372036854775807, 104783749223640], dtype=np.uint64)
assert_array_equal(d, e)
def test_int64_fromstring(self):
d = np.fromstring("-25041670086757 104783749223640",
dtype=np.int64, sep=' ')
e = np.array([-25041670086757, 104783749223640], dtype=np.int64)
assert_array_equal(d, e)
def test_fromstring_count0(self):
d = np.fromstring("1,2", sep=",", dtype=np.int64, count=0)
assert d.shape == (0,)
def test_empty_files_text(self, tmp_filename):
with open(tmp_filename, 'w') as f:
pass
y = np.fromfile(tmp_filename)
assert_(y.size == 0, "Array not empty")
def test_empty_files_binary(self, tmp_filename):
with open(tmp_filename, 'wb') as f:
pass
y = np.fromfile(tmp_filename, sep=" ")
assert_(y.size == 0, "Array not empty")
def test_roundtrip_file(self, x, tmp_filename):
with open(tmp_filename, 'wb') as f:
x.tofile(f)
with open(tmp_filename, 'rb') as f:
y = np.fromfile(f, dtype=x.dtype)
assert_array_equal(y, x.flat)
def test_roundtrip(self, x, tmp_filename):
x.tofile(tmp_filename)
y = np.fromfile(tmp_filename, dtype=x.dtype)
assert_array_equal(y, x.flat)
def test_roundtrip_dump_pathlib(self, x, tmp_filename):
p = pathlib.Path(tmp_filename)
x.dump(p)
y = np.load(p, allow_pickle=True)
assert_array_equal(y, x)
def test_roundtrip_binary_str(self, x):
s = x.tobytes()
y = np.frombuffer(s, dtype=x.dtype)
assert_array_equal(y, x.flat)
s = x.tobytes('F')
y = np.frombuffer(s, dtype=x.dtype)
assert_array_equal(y, x.flatten('F'))
def test_roundtrip_str(self, x):
x = x.real.ravel()
s = "@".join(map(str, x))
y = np.fromstring(s, sep="@")
nan_mask = ~np.isfinite(x)
assert_array_equal(x[nan_mask], y[nan_mask])
assert_array_equal(x[~nan_mask], y[~nan_mask])
def test_roundtrip_repr(self, x):
x = x.real.ravel()
s = "@".join(map(lambda x: repr(x)[11:-1], x))
y = np.fromstring(s, sep="@")
assert_array_equal(x, y)
def test_unseekable_fromfile(self, x, tmp_filename):
x.tofile(tmp_filename)
def fail(*args, **kwargs):
raise OSError('Can not tell or seek')
with open(tmp_filename, 'rb', buffering=0) as f:
f.seek = fail
f.tell = fail
assert_raises(OSError, np.fromfile, f, dtype=x.dtype)
def test_io_open_unbuffered_fromfile(self, x, tmp_filename):
x.tofile(tmp_filename)
with open(tmp_filename, 'rb', buffering=0) as f:
y = np.fromfile(f, dtype=x.dtype)
assert_array_equal(y, x.flat)
def test_io_open_buffered_fromfile(self, x, tmp_filename):
x.tofile(tmp_filename)
with open(tmp_filename, 'rb', buffering=-1) as f:
y = np.fromfile(f, dtype=x.dtype)
assert_array_equal(y, x.flat)
def test_largish_file(self, tmp_filename):
d = np.zeros(4 * 1024 ** 2)
d.tofile(tmp_filename)
assert_equal(os.path.getsize(tmp_filename), d.nbytes)
assert_array_equal(d, np.fromfile(tmp_filename))
with open(tmp_filename, "r+b") as f:
f.seek(d.nbytes)
d.tofile(f)
assert_equal(os.path.getsize(tmp_filename), d.nbytes * 2)
open(tmp_filename, "w").close()
with open(tmp_filename, "ab") as f:
d.tofile(f)
assert_array_equal(d, np.fromfile(tmp_filename))
with open(tmp_filename, "ab") as f:
d.tofile(f)
assert_equal(os.path.getsize(tmp_filename), d.nbytes * 2)
def test_file_position_after_fromfile(self, tmp_filename):
sizes = [io.DEFAULT_BUFFER_SIZE//8,
io.DEFAULT_BUFFER_SIZE,
io.DEFAULT_BUFFER_SIZE*8]
for size in sizes:
with open(tmp_filename, 'wb') as f:
f.seek(size-1)
f.write(b'\0')
for mode in ['rb', 'r+b']:
err_msg = "%d %s" % (size, mode)
with open(tmp_filename, mode) as f:
f.read(2)
np.fromfile(f, dtype=np.float64, count=1)
pos = f.tell()
assert_equal(pos, 10, err_msg=err_msg)
def test_file_position_after_tofile(self, tmp_filename):
sizes = [io.DEFAULT_BUFFER_SIZE//8,
io.DEFAULT_BUFFER_SIZE,
io.DEFAULT_BUFFER_SIZE*8]
for size in sizes:
err_msg = "%d" % (size,)
with open(tmp_filename, 'wb') as f:
f.seek(size-1)
f.write(b'\0')
f.seek(10)
f.write(b'12')
np.array([0], dtype=np.float64).tofile(f)
pos = f.tell()
assert_equal(pos, 10 + 2 + 8, err_msg=err_msg)
with open(tmp_filename, 'r+b') as f:
f.read(2)
f.seek(0, 1)
np.array([0], dtype=np.float64).tofile(f)
pos = f.tell()
assert_equal(pos, 10, err_msg=err_msg)
def test_load_object_array_fromfile(self, tmp_filename):
with open(tmp_filename, 'w') as f:
pass
with open(tmp_filename, 'rb') as f:
assert_raises_regex(ValueError, "Cannot read into object array",
np.fromfile, f, dtype=object)
assert_raises_regex(ValueError, "Cannot read into object array",
np.fromfile, tmp_filename, dtype=object)
def test_fromfile_offset(self, x, tmp_filename):
with open(tmp_filename, 'wb') as f:
x.tofile(f)
with open(tmp_filename, 'rb') as f:
y = np.fromfile(f, dtype=x.dtype, offset=0)
assert_array_equal(y, x.flat)
with open(tmp_filename, 'rb') as f:
count_items = len(x.flat) // 8
offset_items = len(x.flat) // 4
offset_bytes = x.dtype.itemsize * offset_items
y = np.fromfile(
f, dtype=x.dtype, count=count_items, offset=offset_bytes
)
assert_array_equal(
y, x.flat[offset_items:offset_items+count_items]
)
offset_bytes = x.dtype.itemsize
z = np.fromfile(f, dtype=x.dtype, offset=offset_bytes)
assert_array_equal(z, x.flat[offset_items+count_items+1:])
with open(tmp_filename, 'wb') as f:
x.tofile(f, sep=",")
with open(tmp_filename, 'rb') as f:
assert_raises_regex(
TypeError,
"'offset' argument only permitted for binary files",
np.fromfile, tmp_filename, dtype=x.dtype,
sep=",", offset=1)
@pytest.mark.skipif(IS_PYPY, reason="bug in PyPy's PyNumber_AsSsize_t")
def test_fromfile_bad_dup(self, x, tmp_filename):
def dup_str(fd):
return 'abc'
def dup_bigint(fd):
return 2**68
old_dup = os.dup
try:
with open(tmp_filename, 'wb') as f:
x.tofile(f)
for dup, exc in ((dup_str, TypeError), (dup_bigint, OSError)):
os.dup = dup
assert_raises(exc, np.fromfile, f)
finally:
os.dup = old_dup
def _check_from(self, s, value, filename, **kw):
if 'sep' not in kw:
y = np.frombuffer(s, **kw)
else:
y = np.fromstring(s, **kw)
assert_array_equal(y, value)
with open(filename, 'wb') as f:
f.write(s)
y = np.fromfile(filename, **kw)
assert_array_equal(y, value)
@pytest.fixture(params=["period", "comma"])
def decimal_sep_localization(self, request):
"""
包含此 fixture 到测试中，将会自动在两种小数分隔符下执行测试。
所以::
def test_decimal(decimal_sep_localization):
pass
相当于以下两个测试的结合::
def test_decimal_period_separator():
pass
def test_decimal_comma_separator():
with CommaDecimalPointLocale():
pass
"""
if request.param == "period":
yield
elif request.param == "comma":
with CommaDecimalPointLocale():
yield
else:
assert False, request.param
def test_nan(self, tmp_filename, decimal_sep_localization):
self._check_from(
b"nan +nan -nan NaN nan(foo) +NaN(BAR) -NAN(q_u_u_x_)",
[np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan],
tmp_filename,
sep=' ')
def test_inf(self, tmp_filename, decimal_sep_localization):
self._check_from(
b"inf +inf -inf infinity -Infinity iNfInItY -inF",
[np.inf, np.inf, -np.inf, np.inf, -np.inf, np.inf, -np.inf],
tmp_filename,
sep=' ')
def test_numbers(self, tmp_filename, decimal_sep_localization):
self._check_from(
b"1.234 -1.234 .3 .3e55 -123133.1231e+133",
[1.234, -1.234, .3, .3e55, -123133.1231e+133],
tmp_filename,
sep=' ')
def test_binary(self, tmp_filename):
self._check_from(
b'\x00\x00\x80?\x00\x00\x00@\x00\x00@@\x00\x00\x80@',
np.array([1, 2, 3, 4]),
tmp_filename,
dtype='<f4')
def test_string(self, tmp_filename):
self._check_from(b'1,2,3,4', [1., 2., 3., 4.], tmp_filename, sep=',')
def test_counted_string(self, tmp_filename, decimal_sep_localization):
self._check_from(
b'1,2,3,4', [1., 2., 3., 4.], tmp_filename, count=4, sep=',')
self._check_from(
b'1,2,3,4', [1., 2., 3.], tmp_filename, count=3, sep=',')
self._check_from(
b'1,2,3,4', [1., 2., 3., 4.], tmp_filename, count=-1, sep=',')
def test_string_with_ws(self, tmp_filename):
self._check_from(
b'1 2 3 4 ', [1, 2, 3, 4], tmp_filename, dtype=int, sep=' ')
def test_counted_string_with_ws(self, tmp_filename):
self._check_from(
b'1 2 3 4 ', [1, 2, 3], tmp_filename, count=3, dtype=int,
sep=' ')
def test_ascii(self, tmp_filename, decimal_sep_localization):
self._check_from(
b'1 , 2 , 3 , 4', [1., 2., 3., 4.], tmp_filename, sep=',')
self._check_from(
b'1,2,3,4', [1., 2., 3., 4.], tmp_filename, dtype=float, sep=',')
def test_malformed(self, tmp_filename, decimal_sep_localization):
with assert_warns(DeprecationWarning):
self._check_from(
b'1.234 1,234', [1.234, 1.], tmp_filename, sep=' ')
def test_long_sep(self, tmp_filename):
self._check_from(
b'1_x_3_x_4_x_5', [1, 3, 4, 5], tmp_filename, sep='_x_')
def test_dtype(self, tmp_filename):
v = np.array([1, 2, 3, 4], dtype=np.int_)
self._check_from(b'1,2,3,4', v, tmp_filename, sep=',', dtype=np.int_)
def test_dtype_bool(self, tmp_filename):
v = np.array([True, False, True, False], dtype=np.bool)
s = b'1,0,-2.3,0'
with open(tmp_filename, 'wb') as f:
f.write(s)
y = np.fromfile(tmp_filename, sep=',', dtype=np.bool)
assert_(y.dtype == '?')
assert_array_equal(y, v)
def test_tofile_sep(self, tmp_filename, decimal_sep_localization):
x = np.array([1.51, 2, 3.51, 4], dtype=float)
with open(tmp_filename, 'w') as f:
x.tofile(f, sep=',')
with open(tmp_filename, 'r') as f:
s = f.read()
y = np.array([float(p) for p in s.split(',')])
assert_array_equal(x, y)
def test_tofile_format(self, tmp_filename, decimal_sep_localization):
x = np.array([1.51, 2, 3.51, 4], dtype=float)
with open(tmp_filename, 'w') as f:
x.tofile(f, sep=',', format='%.2f')
with open(tmp_filename, 'r') as f:
s = f.read()
assert_equal(s, '1.51,2.00,3.51,4.00')
def test_tofile_cleanup(self, tmp_filename):
x = np.zeros((10), dtype=object)
with open(tmp_filename, 'wb') as f:
assert_raises(OSError, lambda: x.tofile(f, sep=''))
os.remove(tmp_filename)
assert_raises(OSError, lambda: x.tofile(tmp_filename))
os.remove(tmp_filename)
def test_fromfile_subarray_binary(self, tmp_filename):
x = np.arange(24, dtype="i4").reshape(2, 3, 4)
x.tofile(tmp_filename)
res = np.fromfile(tmp_filename, dtype="(3,4)i4")
assert_array_equal(x, res)
x_str = x.tobytes()
with assert_warns(DeprecationWarning):
res = np.fromstring(x_str, dtype="(3,4)i4")
assert_array_equal(x, res)
def test_parsing_subarray_unsupported(self, tmp_filename):
data = "12,42,13," * 50
with pytest.raises(ValueError):
expected = np.fromstring(data, dtype="(3,)i", sep=",")
with open(tmp_filename, "w") as f:
f.write(data)
with pytest.raises(ValueError):
np.fromfile(tmp_filename, dtype="(3,)i", sep=",")
def test_read_shorter_than_count_subarray(self, tmp_filename):
expected = np.arange(511 * 10, dtype="i").reshape(-1, 10)
binary = expected.tobytes()
with pytest.raises(ValueError):
with pytest.warns(DeprecationWarning):
np.fromstring(binary, dtype="(10,)i", count=10000)
expected.tofile(tmp_filename)
res = np.fromfile(tmp_filename, dtype="(10,)i", count=10000)
assert_array_equal(res, expected)
class TestFromBuffer:
@pytest.mark.parametrize('byteorder', ['<', '>'])
@pytest.mark.parametrize('dtype', [float, int, complex])
def test_basic(self, byteorder, dtype):
dt = np.dtype(dtype).newbyteorder(byteorder)
x = (np.random.random((4, 7)) * 5).astype(dt)
buf = x.tobytes()
assert_array_equal(np.frombuffer(buf, dtype=dt), x.flat)
def test_array_base(self, obj):
new = np.frombuffer(obj)
assert new.base is obj
def test_empty(self):
assert_array_equal(np.frombuffer(b''), np.array([]))
def test_mmap_close(self):
with tempfile.TemporaryFile(mode='wb') as tmp:
tmp.write(b"asdf")
tmp.flush()
mm = mmap.mmap(tmp.fileno(), 0)
arr = np.frombuffer(mm, dtype=np.uint8)
with pytest.raises(BufferError):
mm.close()
del arr
mm.close()
class TestFlat:
def setup_method(self):
a0 = np.arange(20.0)
a = a0.reshape(4, 5)
a0.shape = (4, 5)
a.flags.writeable = False
self.a = a
self.b = a[::2, ::2]
self.a0 = a0
self.b0 = a0[::2, ::2]
def test_contiguous(self):
testpassed = False
try:
self.a.flat[12] = 100.0
except ValueError:
testpassed = True
assert_(testpassed)
assert_(self.a.flat[12] == 12.0)
def test_discontiguous(self):
testpassed = False
try:
self.b.flat[4] = 100.0
except ValueError:
testpassed = True
assert_(testpassed)
assert_(self.b.flat[4] == 12.0)
def test___array__(self):
c = self.a.flat.__array__()
d = self.b.flat.__array__()
e = self.a0.flat.__array__()
f = self.b0.flat.__array__()
assert_(c.flags.writeable is False)
assert_(d.flags.writeable is False)
assert_(e.flags.writeable is True)
assert_(f.flags.writeable is False)
assert_(c.flags.writebackifcopy is False)
assert_(d.flags.writebackifcopy is False)
assert_(e.flags.writebackifcopy is False)
assert_(f.flags.writebackifcopy is False)
def test_refcount(self):
inds = [np.intp(0), np.array([True]*self.a.size), np.array([0]), None]
indtype = np.dtype(np.intp)
rc_indtype = sys.getrefcount(indtype)
for ind in inds:
rc_ind = sys.getrefcount(ind)
for _ in range(100):
try:
self.a.flat[ind]
except IndexError:
pass
assert_(abs(sys.getrefcount(ind) - rc_ind) < 50)
assert_(abs(sys.getrefcount(indtype) - rc_indtype) < 50)
def test_index_getset(self):
it = np.arange(10).reshape(2, 1, 5).flat
with pytest.raises(AttributeError):
it.index = 10
for _ in it:
pass
assert it.index == it.base.size
def test_maxdims(self):
a = np.ones((1,) * 64)
with pytest.raises(RuntimeError,
match=".*32 dimensions but the array has 64"):
a.flat
class TestResize:
@_no_tracing
def test_basic(self):
x = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
if IS_PYPY:
x.resize((5, 5), refcheck=False)
else:
x.resize((5, 5))
assert_array_equal(x.flat[:9],
np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]]).flat)
assert_array_equal(x[9:].flat, 0)
def test_check_reference(self):
x = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
y = x
assert_raises(ValueError, x.resize, (5, 1))
del y
@_no_tracing
def test_int_shape(self):
x = np.eye(3)
if IS_PYPY:
x.resize(3, refcheck=False)
else:
x.resize(3)
assert_array_equal(x, np.eye(3)[0,:])
def test_none_shape(self):
x = np.eye(3)
x.resize(None)
assert_array_equal(x, np.eye(3))
x.resize()
assert_array_equal(x, np.eye(3))
def test_0d_shape(self):
for i in range(10):
x = np.empty((1,))
x.resize(())
assert_equal(x.shape, ())
assert_equal(x.size, 1)
x = np.empty(())
x.resize((1,))
assert_equal(x.shape, (1,))
assert_equal(x.size, 1)
def test_invalid_arguments(self):
assert_raises(TypeError, np.eye(3).resize, 'hi')
assert_raises(ValueError, np.eye(3).resize, -1)
assert_raises(TypeError, np.eye(3).resize, order=1)
assert_raises(TypeError, np.eye(3).resize, refcheck='hi')
@_no_tracing
def test_freeform_shape(self):
x = np.eye(3)
if IS_PYPY:
x.resize(3, 2, 1, refcheck=False)
else:
x.resize(3, 2, 1)
assert_(x.shape == (3, 2, 1))
@_no_tracing
def test_zeros_appended(self):
x = np.eye(3)
if IS_PYPY:
x.resize(2, 3, 3, refcheck=False)
else:
x.resize(2, 3, 3)
assert_array_equal(x[0], np.eye(3))
assert_array_equal(x[1], np.zeros((3, 3)))
@_no_tracing
def test_obj_obj(self):
a = np.ones(10, dtype=[('k', object, 2)])
if IS_PYPY:
a.resize(15, refcheck=False)
else:
a.resize(15,)
assert_equal(a.shape, (15,))
assert_array_equal(a['k'][-5:], 0)
assert_array_equal(a['k'][:-5], 1)
def test_empty_view(self):
x = np.zeros((10, 0), int)
x_view = x[...]
x_view.resize((0, 10))
x_view.resize((0, 100))
def test_check_weakref(self):
x = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
xref = weakref.ref(x)
assert_raises(ValueError, x.resize, (5, 1))
del xref
class TestRecord:
def test_field_rename(self):
dt = np.dtype([('f', float), ('i', int)])
dt.names = ['p', 'q']
assert_equal(dt.names, ['p', 'q'])
def test_multiple_field_name_occurrence(self):
def test_dtype_init():
np.dtype([("A", "f8"), ("B", "f8"), ("A", "f8")])
assert_raises(ValueError, test_dtype_init)
def test_bytes_fields(self):
assert_raises(TypeError, np.dtype, [(b'a', int)])
assert_raises(TypeError, np.dtype, [(('b', b'a'), int)])
dt = np.dtype([((b'a', 'b'), int)])
assert_raises(TypeError, dt.__getitem__, b'a')
x = np.array([(1,), (2,), (3,)], dtype=dt)
assert_raises(IndexError, x.__getitem__, b'a')
y = x[0]
assert_raises(IndexError, y.__getitem__, b'a')
def test_multiple_field_name_unicode(self):
def test_dtype_unicode():
np.dtype([("\u20B9", "f8"), ("B", "f8"), ("\u20B9", "f8")])
assert_raises(ValueError, test_dtype_unicode)
def test_fromarrays_unicode(self):
x = np._core.records.fromarrays(
[[0], [1]], names='a,b', formats='i4,i4')
assert_equal(x['a'][0], 0)
assert_equal(x['b'][0], 1)
def test_unicode_order(self):
name = 'b'
x = np.array([1, 3, 2], dtype=[(name, int)])
x.sort(order=name)
assert_equal(x['b'], np.array([1, 2, 3]))
def test_field_names(self):
a = np.zeros((1,), dtype=[('f1', 'i4'),
('f2', 'i4'),
('f3', [('sf1', 'i4')])])
assert_raises(IndexError, a.__setitem__, b'f1', 1)
assert_raises(IndexError, a.__getitem__, b'f1')
assert_raises(IndexError, a['f1'].__setitem__, b'sf1', 1)
assert_raises(IndexError, a['f1'].__getitem__, b'sf1')
b = a.copy()
fn1 = str('f1')
b[fn1] = 1
assert_equal(b[fn1], 1)
fnn = str('not at all')
assert_raises(ValueError, b.__setitem__, fnn, 1)
assert_raises(ValueError, b.__getitem__, fnn)
b[0][fn1] = 2
assert_equal(b[fn1], 2)
fn3 = str('f3')
sfn1 = str('sf1')
b[fn3][sfn1] = 1
assert_equal(b[fn3][sfn1], 1)
assert_raises(ValueError, b[fn3].__setitem__, fnn, 1)
assert_raises(ValueError, b[fn3].__getitem__, fnn)
fn2 = str('f2')
b[fn2] = 3
assert_equal(b[['f1', 'f2']][0].tolist(), (2, 3))
assert_equal(b[['f2', 'f1']][0].tolist(), (3, 2))
assert_equal(b[['f1', 'f3']][0].tolist(), (2, (1,)))
assert_raises(ValueError, a.__setitem__, '\u03e0', 1)
assert_raises(ValueError, a.__getitem__, '\u03e0')
def test_record_hash(self):
a = np.array([(1, 2), (1, 2)], dtype='i1,i2')
a.flags.writeable = False
b = np.array([(1, 2), (3, 4)], dtype=[('num1', 'i1'), ('num2', 'i2')])
b.flags.writeable = False
c = np.array([(1, 2), (3, 4)], dtype='i1,i2')
c.flags.writeable = False
assert_(hash(a[0]) == hash(a[1]))
assert_(hash(a[0]) == hash(b[0]))
assert_(hash(a[0]) != hash(b[1]))
assert_(hash(c[0]) == hash(a[0]) and c[0] == a[0])
def test_record_no_hash(self):
a = np.array([(1, 2), (1, 2)], dtype='i1,i2')
assert_raises(TypeError, hash, a[0])
def test_empty_structure_creation(self):
np.array([()], dtype={'names': [], 'formats': [],
'offsets': [], 'itemsize': 12})
np.array([(), (), (), (), ()], dtype={'names': [], 'formats': [],
'offsets': [], 'itemsize': 12})
def test_multifield_indexing_view(self):
a = np.ones(3, dtype=[('a', 'i4'), ('b', 'f4'), ('c', 'u4')])
v = a[['a', 'c']]
assert_(v.base is a)
assert_(v.dtype == np.dtype({'names': ['a', 'c'],
'formats': ['i4', 'u4'],
'offsets': [0, 8]}))
v[:] = (4, 5)
assert_equal(a[0].item(), (4, 1, 5))
class TestView:
def test_basic(self):
x = np.array([(1, 2, 3, 4), (5, 6, 7, 8)],
dtype=[('r', np.int8), ('g', np.int8),
('b', np.int8), ('a', np.int8)])
y = x.view(dtype='<i4')
z = x.view('<i4')
assert_array_equal(y, z)
assert_array_equal(y, [67305985, 134678021])
def _mean(a, **args):
return a.mean(**args)
def _var(a, **args):
return a.var(**args)
def _std(a, **args):
return a.std(**args)
class TestStats:
funcs = [_mean, _var, _std]
def setup_method(self):
np.random.seed(range(3))
self.rmat = np.random.random((4, 5))
self.cmat = self.rmat + 1j * self.rmat
self.omat = np.array([Decimal(str(r)) for r in self.rmat.flat])
self.omat = self.omat.reshape(4, 5)
def test_python_type(self):
for x in (np.float16(1.), 1, 1., 1+0j):
assert_equal(np.mean([x]), 1.)
assert_equal(np.std([x]), 0.)
assert_equal(np.var([x]), 0.)
def test_keepdims(self):
mat = np.eye(3)
for f in self.funcs:
for axis in [0, 1]:
res = f(mat, axis=axis, keepdims=True)
assert_(res.ndim == mat.ndim)
assert_(res.shape[axis] == 1)
for axis in [None]:
res = f(mat, axis=axis, keepdims=True)
assert_(res.shape == (1, 1))
def test_out(self):
mat = np.eye(3)
for f in self.funcs:
out = np.zeros(3)
tgt = f(mat, axis=1)
res = f(mat, axis=1, out=out)
assert_almost_equal(res, out)
assert_almost_equal(res, tgt)
out = np.empty(2)
assert_raises(ValueError, f, mat, axis=1, out=out)
out = np.empty((2, 2))
assert_raises(ValueError, f, mat, axis=1, out=out)
def test_dtype_from_input(self):
icodes = np.typecodes['AllInteger']
fcodes = np.typecodes['AllFloat']
for f in self.funcs:
mat = np.array([[Decimal(1)]*3]*3)
tgt = mat.dtype.type
res = f(mat, axis=1).dtype.type
assert_(res is tgt)
res = type(f(mat, axis=None))
assert_(res is Decimal)
for f in self.funcs:
for c in icodes:
mat = np.eye(3, dtype=c)
tgt = np.float64
res = f(mat, axis=1).dtype.type
assert_(res is tgt)
res = f(mat, axis=None).dtype.type
assert_(res is tgt)
for f in [_mean]:
for c in fcodes:
mat = np.eye(3, dtype=c)
tgt = mat.dtype.type
res = f(mat, axis=1).dtype.type
assert_(res is tgt)
res = f(mat, axis=None).dtype.type
assert_(res is tgt)
for f in [_var, _std]:
for c in fcodes:
mat = np.eye(3, dtype=c)
tgt = mat.real.dtype.type
res = f(mat, axis=1).dtype.type
assert_(res is tgt)
res = f(mat, axis=None).dtype.type
assert_(res is tgt)
def test_dtype_from_dtype(self):
mat = np.eye(3)
for f in self.funcs:
for c in np.typecodes['AllFloat']:
tgt = np.dtype(c).type
res = f(mat, axis=1, dtype=c).dtype.type
assert_(res is tgt)
res = f(mat, axis=None, dtype=c).dtype.type
assert_(res is tgt)
def test_ddof(self):
for f in [_var]:
for ddof in range(3):
dim = self.rmat.shape[1]
tgt = f(self.rmat, axis=1) * dim
res = f(self.rmat, axis=1, ddof=ddof) * (dim - ddof)
for f in [_std]:
for ddof in range(3):
dim = self.rmat.shape[1]
tgt = f(self.rmat, axis=1) * np.sqrt(dim)
res = f(self.rmat, axis=1, ddof=ddof) * np.sqrt(dim - ddof)
assert_almost_equal(res, tgt)
assert_almost_equal(res, tgt)
def test_ddof_too_big(self):
dim = self.rmat.shape[1]
for f in [_var, _std]:
for ddof in range(dim, dim + 2):
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
res = f(self.rmat, axis=1, ddof=ddof)
assert_(not (res < 0).any())
assert_(len(w) > 0)
assert_(issubclass(w[0].category, RuntimeWarning))
def test_empty(self):
A = np.zeros((0, 3))
for f in self.funcs:
for axis in [0, None]:
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
assert_(np.isnan(f(A, axis=axis)).all())
assert_(len(w) > 0)
assert_(issubclass(w[0].category, RuntimeWarning))
for axis in [1]:
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
assert_equal(f(A, axis=axis), np.zeros([]))
def test_mean_values(self):
for mat in [self.rmat, self.cmat, self.omat]:
for axis in [0, 1]:
tgt = mat.sum(axis=axis)
res = _mean(mat, axis=axis) * mat.shape[axis]
assert_almost_equal(res, tgt)
for axis in [None]:
tgt = mat.sum(axis=axis)
res = _mean(mat, axis=axis) * np.prod(mat.shape)
assert_almost_equal(res, tgt)
def test_mean_float16(self):
assert_(_mean(np.ones(100000, dtype='float16')) == 1)
def test_mean_axis_error(self):
with assert_raises(np.exceptions.AxisError):
np.arange(10).mean(axis=2)
def test_mean_where(self):
a = np.arange(16).reshape((4, 4))
wh_full = np.array([[False, True, False, True],
[True, False, True, False],
[True, True, False, False],
[False, False, True, True]])
wh_partial = np.array([[False],
[True],
[True],
[False]])
_cases = [(1, True, [1.5, 5.5, 9.5, 13.5]),
(0, wh_full, [6., 5., 10., 9.]),
(1, wh_full, [2., 5., 8.5, 14.5]),
(0, wh_partial, [6., 7., 8., 9.])]
for _ax, _wh, _res in _cases:
assert_allclose(a.mean(axis=_ax, where=_wh),
np.array(_res))
assert_allclose(np.mean(a, axis=_ax, where=_wh),
np.array(_res))
a3d = np.arange(16).reshape((2, 2, 4))
_wh_partial = np.array([False, True, True, False])
_res = [[1.5, 5.5], [9.5, 13.5]]
assert_allclose(a3d.mean(axis=2, where=_wh_partial),
np.array(_res))
assert_allclose(np.mean(a3d, axis=2, where=_wh_partial),
np.array(_res))
with pytest.warns(RuntimeWarning) as w:
assert_allclose(a.mean(axis=1, where=wh_partial),
np.array([np.nan, 5.5, 9.5, np.nan]))
with pytest.warns(RuntimeWarning) as w:
assert_equal(a.mean(where=False), np.nan)
with pytest.warns(RuntimeWarning) as w:
assert_equal(np.mean(a, where=False), np.nan)
def test_var_values(self):
for mat in [self.rmat, self.cmat, self.omat]:
for axis in [0, 1, None]:
msqr = _mean(mat * mat.conj(), axis=axis)
mean = _mean(mat, axis=axis)
tgt = msqr - mean * mean.conjugate()
res = _var(mat, axis=axis)
assert_almost_equal(res, tgt)
@pytest.mark.parametrize(('complex_dtype', 'ndec'), (
('complex64', 6),
('complex128', 7),
('clongdouble', 7),
))
def test_var_complex_values(self, complex_dtype, ndec):
for axis in [0, 1, None]:
mat = self.cmat.copy().astype(complex_dtype)
msqr = _mean(mat * mat.conj(), axis=axis)
mean = _mean(mat, axis=axis)
tgt = msqr - mean * mean.conjugate()
res = _var(mat, axis=axis)
assert_almost_equal(res, tgt, decimal=ndec)
def test_var_dimensions(self):
mat = np.stack([self.cmat]*3)
for axis in [0, 1, 2, -1, None]:
msqr = _mean(mat * mat.conj(), axis=axis)
mean = _mean(mat, axis=axis)
tgt = msqr - mean * mean.conjugate()
res = _var(mat, axis=axis)
assert_almost_equal(res, tgt)
def test_var_complex_byteorder(self):
cmat = self.cmat.copy().astype('complex128')
cmat_swapped = cmat.astype(cmat.dtype.newbyteorder())
assert_almost_equal(cmat.var(), cmat_swapped.var())
def test_var_axis_error(self):
with assert_raises(np.exceptions.AxisError):
np.arange(10).var(axis=2)
def test_var_where(self):
a = np.arange(25).reshape((5, 5))
wh_full = np.array([[False, True, False, True, True],
[True, False, True, True, False],
[True, True, False, False, True],
[False, True, True, False, True],
[True, False, True, True, False]])
wh_partial = np.array([[False],
[True],
[True],
[False],
[True]])
_cases = [(0, True, [50., 50., 50., 50., 50.]),
(1, True, [2., 2., 2., 2., 2.])]
for _ax, _wh, _res in _cases:
assert_allclose(a.var(axis=_ax, where=_wh),
np.array(_res))
assert_allclose(np.var(a, axis=_ax, where=_wh),
np.array(_res))
a3d = np.arange(16).reshape((2, 2, 4))
_wh_partial = np.array([False, True, True, False])
_res = [[0.25, 0.25], [0.25, 0.25]]
assert_allclose(a3d.var(axis=2, where=_wh_partial),
np.array(_res))
assert_allclose(np.var(a3d, axis=2, where=_wh_partial),
np.array(_res))
assert_allclose(np.var(a, axis=1, where=wh_full),
np.var(a[wh_full].reshape((5, 3)), axis=1))
assert_allclose(np.var(a, axis=0, where=wh_partial),
np.var(a[wh_partial[:,0]], axis=0))
with pytest.warns(RuntimeWarning) as w:
assert_equal(a.var(where=False), np.nan)
with pytest.warns(RuntimeWarning) as w:
assert_equal(np.var(a, where=False), np.nan)
def test_std_values(self):
for mat in [self.rmat, self.cmat, self.omat]:
for axis in [0, 1, None]:
tgt = np.sqrt(_var(mat, axis=axis))
res = _std(mat, axis=axis)
assert_almost_equal(res, tgt)
def test_std_where(self):
a = np.arange(25).reshape((5,5))[::-1]
whf = np.array([[False, True, False, True, True],
[True, False, True, False, True],
[True, True, False, True, False],
[True, False, True, True, False],
[False, True, False, True, True]])
whp = np.array([[False],
[False],
[True],
[True],
[False]])
_cases = [
(0, True, 7.07106781*np.ones((5))),
(1, True, 1.41421356*np.ones((5))),
(0, whf,
np.array([4.0824829 , 8.16496581, 5., 7.39509973, 8.49836586])),
(0, whp, 2.5*np.ones((5)))
]
for _ax, _wh, _res in _cases:
assert_allclose(a.std(axis=_ax, where=_wh), _res)
assert_allclose(np.std(a, axis=_ax, where=_wh), _res)
a3d = np.arange(16).reshape((2, 2, 4))
_wh_partial = np.array([False, True, True, False])
_res = [[0.5, 0.5], [0.5, 0.5]]
assert_allclose(a3d.std(axis=2, where=_wh_partial),
np.array(_res))
assert_allclose(np.std(a3d, axis=2, where=_wh_partial),
np.array(_res))
assert_allclose(a.std(axis=1, where=whf),
np.std(a[whf].reshape((5,3)), axis=1))
assert_allclose(np.std(a, axis=1, where=whf),
(a[whf].reshape((5,3))).std(axis=1))
assert_allclose(a.std(axis=0, where=whp),
np.std(a[whp[:,0]], axis=0))
assert_allclose(np.std(a, axis=0, where=whp),
(a[whp[:,0]]).std(axis=0))
with pytest.warns(RuntimeWarning) as w:
assert_equal(a.std(where=False), np.nan)
with pytest.warns(RuntimeWarning) as w:
assert_equal(np.std(a, where=False), np.nan)
def test_subclass(self):
class TestArray(np.ndarray):
def __new__(cls, data, info):
result = np.array(data)
result = result.view(cls)
result.info = info
return result
def __array_finalize__(self, obj):
self.info = getattr(obj, "info", '')
dat = TestArray([[1, 2, 3, 4], [5, 6, 7, 8]], 'jubba')
res = dat.mean(1)
assert_(res.info == dat.info)
res = dat.std(1)
assert_(res.info == dat.info)
res = dat.var(1)
assert_(res.info == dat.info)
class TestVdot:
def test_basic(self):
dt_numeric = np.typecodes['AllFloat'] + np.typecodes['AllInteger']
dt_complex = np.typecodes['Complex']
a = np.eye(3)
for dt in dt_numeric + 'O':
b = a.astype(dt)
res = np.vdot(b, b)
assert_(np.isscalar(res))
assert_equal(np.vdot(b, b), 3)
a = np.eye(3) * 1j
for dt in dt_complex + 'O':
b = a.astype(dt)
res = np.vdot(b, b)
assert_(np.isscalar(res))
assert_equal(np.vdot(b, b), 3)
b = np.eye(3, dtype=bool)
res = np.vdot(b, b)
assert_(np.isscalar(res))
assert_equal(np.vdot(b, b), True)
def test_vdot_array_order(self):
a = np.array([[1, 2], [3, 4]], order='C')
b = np.array([[1, 2], [3, 4]], order='F')
res = np.vdot(a, a)
assert_equal(np.vdot(a, b), res)
assert_equal(np.vdot(b, a), res)
assert_equal(np.vdot(b, b), res)
def test_vdot_uncontiguous(self):
for size in [2, 1000]:
a = np.zeros((size, 2, 2))
b = np.zeros((size, 2, 2))
a[:, 0, 0] = np.arange(size)
b[:, 0, 0] = np.arange(size) + 1
a = a[..., 0]
b = b[..., 0]
assert_equal(np.vdot(a, b),
np.vdot(a.flatten(), b.flatten()))
assert_equal(np.vdot(a, b.copy()),
np.vdot(a.flatten(), b.flatten()))
assert_equal(np.vdot(a.copy(), b),
np.vdot(a.flatten(), b.flatten()))
assert_equal(np.vdot(a.copy('F'), b),
np.vdot(a.flatten(), b.flatten()))
assert_equal(np.vdot(a, b.copy('F')),
np.vdot(a.flatten(), b.flatten()))
def test_dotmatvec2(self):
A, b2 = self.A, self.b2
res = np.dot(A, b2)
tgt = np.array([0.29677940, 0.04518649, 0.14468333, 0.31039293])
assert_almost_equal(res, tgt, decimal=self.N)
def test_dotvecmat(self):
A, b4 = self.A, self.b4
res = np.dot(b4, A)
tgt = np.array([1.23495091, 1.12222648])
assert_almost_equal(res, tgt, decimal=self.N)
def test_dotvecmat2(self):
b3, A = self.b3, self.A
res = np.dot(b3, A.transpose())
tgt = np.array([[0.58793804, 0.08957460, 0.30605758, 0.62716383]])
assert_almost_equal(res, tgt, decimal=self.N)
def test_dotvecmat3(self):
A, b4 = self.A, self.b4
res = np.dot(A.transpose(), b4)
tgt = np.array([1.23495091, 1.12222648])
assert_almost_equal(res, tgt, decimal=self.N)
def test_dotvecvecouter(self):
b1, b3 = self.b1, self.b3
res = np.dot(b1, b3)
tgt = np.array([[0.20128610, 0.08400440], [0.07190947, 0.03001058]])
assert_almost_equal(res, tgt, decimal=self.N)
def test_dotvecvecinner(self):
b1, b3 = self.b1, self.b3
res = np.dot(b3, b1)
tgt = np.array([[0.23129668]])
assert_almost_equal(res, tgt, decimal=self.N)
def test_dotcolumnvect1(self):
b1 = np.ones((3, 1))
b2 = [5.3]
res = np.dot(b1, b2)
tgt = np.array([5.3, 5.3, 5.3])
assert_almost_equal(res, tgt, decimal=self.N)
def test_dotcolumnvect2(self):
b1 = np.ones((3, 1)).transpose()
b2 = [6.2]
res = np.dot(b2, b1)
tgt = np.array([6.2, 6.2, 6.2])
assert_almost_equal(res, tgt, decimal=self.N)
def test_dotvecscalar(self):
np.random.seed(100)
b1 = np.random.rand(1, 1)
b2 = np.random.rand(1, 4)
res = np.dot(b1, b2)
tgt = np.array([[0.15126730, 0.23068496, 0.45905553, 0.00256425]])
assert_almost_equal(res, tgt, decimal=self.N)
def test_dotvecscalar2(self):
np.random.seed(100)
b1 = np.random.rand(4, 1)
b2 = np.random.rand(1, 1)
res = np.dot(b1, b2)
tgt = np.array([[0.00256425],[0.00131359],[0.00200324],[ 0.00398638]])
assert_almost_equal(res, tgt, decimal=self.N)
def test_vecobject(self):
class Vec:
def __init__(self, sequence=None):
if sequence is None:
sequence = []
self.array = np.array(sequence)
def __add__(self, other):
out = Vec()
out.array = self.array + other.array
return out
def __sub__(self, other):
out = Vec()
out.array = self.array - other.array
return out
def __mul__(self, other):
out = Vec(self.array.copy())
out.array *= other
return out
def __rmul__(self, other):
return self * other
U_non_cont = np.transpose([[1., 1.], [1., 2.]])
U_cont = np.ascontiguousarray(U_non_cont)
x = np.array([Vec([1., 0.]), Vec([0., 1.])])
zeros = np.array([Vec([0., 0.]), Vec([0., 0.])])
zeros_test = np.dot(U_cont, x) - np.dot(U_non_cont, x)
assert_equal(zeros[0].array, zeros_test[0].array)
assert_equal(zeros[1].array, zeros_test[1].array)
def test_dot_2args(self):
a = np.array([[1, 2], [3, 4]], dtype=float)
b = np.array([[1, 0], [1, 1]], dtype=float)
c = np.array([[3, 2], [7, 4]], dtype=float)
d = dot(a, b)
assert_allclose(c, d)
def test_dot_3args(self):
np.random.seed(22)
f = np.random.random_sample((1024, 16))
v = np.random.random_sample((16, 32))
r = np.empty((1024, 32))
for i in range(12):
dot(f, v, r)
if HAS_REFCOUNT:
assert_equal(sys.getrefcount(r), 2)
r2 = dot(f, v, out=None)
assert_array_equal(r2, r)
assert_(r is dot(f, v, out=r))
v = v[:, 0].copy()
r = r[:, 0].copy()
r2 = dot(f, v)
assert_(r is dot(f, v, r))
assert_array_equal(r2, r)
def test_dot_3args_errors(self):
np.random.seed(22)
f = np.random.random_sample((1024, 16))
v = np.random.random_sample((16, 32))
r = np.empty((1024, 31))
assert_raises(ValueError, dot, f, v, r)
r = np.empty((1024,))
assert_raises(ValueError, dot, f, v, r)
r = np.empty((32,))
assert_raises(ValueError, dot, f, v, r)
r = np.empty((32, 1024))
assert_raises(ValueError, dot, f, v, r)
assert_raises(ValueError, dot, f, v, r.T)
r = np.empty((1024, 64))
assert_raises(ValueError, dot, f, v, r[:, ::2])
assert_raises(ValueError, dot, f, v, r[:, :32])
r = np.empty((1024, 32), dtype=np.float32)
assert_raises(ValueError, dot, f, v, r)
r = np.empty((1024, 32), dtype=int)
assert_raises(ValueError, dot, f, v, r)
def test_dot_out_result(self):
x = np.ones((), dtype=np.float16)
y = np.ones((5,), dtype=np.float16)
z = np.zeros((5,), dtype=np.float16)
res = x.dot(y, out=z)
assert np.array_equal(res, y)
assert np.array_equal(z, y)
def test_dot_out_aliasing(self):
x = np.ones((), dtype=np.float16)
y = np.ones((5,), dtype=np.float16)
z = np.zeros((5,), dtype=np.float16)
res = x.dot(y, out=z)
z[0] = 2
assert np.array_equal(res, z)
def test_dot_array_order(self):
a = np.array([[1, 2], [3, 4]], order='C')
b = np.array([[1, 2], [3, 4]], order='F')
res = np.dot(a, a)
assert_equal(np.dot(a, b), res)
assert_equal(np.dot(b, a), res)
assert_equal(np.dot(b, b), res)
def test_accelerate_framework_sgemv_fix(self):
def aligned_array(shape, align, dtype, order='C'):
d = dtype(0)
N = np.prod(shape)
tmp = np.zeros(N * d.nbytes + align, dtype=np.uint8)
address = tmp.__array_interface__["data"][0]
for offset in range(align):
if (address + offset) % align == 0:
break
tmp = tmp[offset:offset+N*d.nbytes].view(dtype=dtype)
return tmp.reshape(shape, order=order)
def as_aligned(arr, align, dtype, order='C'):
aligned = aligned_array(arr.shape, align, dtype, order)
aligned[:] = arr[:]
return aligned
def assert_dot_close(A, X, desired):
assert_allclose(np.dot(A, X), desired, rtol=1e-5, atol=1e-7)
m = aligned_array(100, 15, np.float32)
s = aligned_array((100, 100), 15, np.float32)
np.dot(s, m)
testdata = itertools.product((15, 32), (10000,), (200, 89), ('C', 'F'))
for align, m, n, a_order in testdata:
A_d = np.random.rand(m, n)
X_d = np.random.rand(n)
desired = np.dot(A_d, X_d)
A_f = as_aligned(A_d, align, np.float32, order=a_order)
X_f = as_aligned(X_d, align, np.float32)
assert_dot_close(A_f, X_f, desired)
A_d_2 = A_d[::2]
desired = np.dot(A_d_2, X_d)
A_f_2 = A_f[::2]
assert_dot_close(A_f_2, X_f, desired)
A_d_22 = A_d_2[:, ::2]
X_d_2 = X_d[::2]
desired = np.dot(A_d_22, X_d_2)
A_f_22 = A_f_2[:, ::2]
X_f_2 = X_f[::2]
assert_dot_close(A_f_22, X_f_2, desired)
if a_order == 'F':
assert_equal(A_f_22.strides, (8, 8 * m))
else:
assert_equal(A_f_22.strides, (8 * n, 8))
assert_equal(X_f_2.strides, (8,))
X_f_2c = as_aligned(X_f_2, align, np.float32)
assert_dot_close(A_f_22, X_f_2c, desired)
A_d_12 = A_d[:, ::2]
desired = np.dot(A_d_12, X_d_2)
A_f_12 = A_f[:, ::2]
assert_dot_close(A_f_12, X_f_2c, desired)
assert_dot_close(A_f_12, X_f_2, desired)
@pytest.mark.slow
@pytest.mark.parametrize("dtype", [np.float64, np.complex128])
@requires_memory(free_bytes=18e9)
def test_huge_vectordot(self, dtype):
data = np.ones(2**30+100, dtype=dtype)
res = np.dot(data, data)
assert res == 2**30+100
def test_dtype_discovery_fails(self):
class BadObject(object):
def __array__(self, dtype=None, copy=None):
raise TypeError("just this tiny mint leaf")
with pytest.raises(TypeError):
np.dot(BadObject(), BadObject())
with pytest.raises(TypeError):
np.dot(3.0, BadObject())
class MatmulCommon:
"""Common tests for '@' operator and numpy.matmul."""
types = "?bhilqBHILQefdgFDGO"
def test_exceptions(self):
dims = [
((1,), (2,)),
((2, 1,), (2,)),
((2,), (1, 2)),
((1, 2), (3, 1)),
((1,), ()),
((), (1)),
((1, 1), ()),
((), (1, 1)),
((2, 2, 1), (3, 1, 2)),
]
for dt, (dm1, dm2) in itertools.product(self.types, dims):
a = np.ones(dm1, dtype=dt)
b = np.ones(dm2, dtype=dt)
assert_raises(ValueError, self.matmul, a, b)
def test_shapes(self):
dims = [
((1, 1), (2, 1, 1)),
((2, 1, 1), (1, 1)),
((2, 1, 1), (2, 1, 1)),
]
for dt, (dm1, dm2) in itertools.product(self.types, dims):
a = np.ones(dm1, dtype=dt)
b = np.ones(dm2, dtype=dt)
res = self.matmul(a, b)
assert_(res.shape == (2, 1, 1))
for dt in self.types:
a = np.ones((2,), dtype=dt)
b = np.ones((2,), dtype=dt)
c = self.matmul(a, b)
assert_(np.array(c).shape == ())
def test_result_types(self):
mat = np.ones((1,1))
vec = np.ones((1,))
for dt in self.types:
m = mat.astype(dt)
v = vec.astype(dt)
for arg in [(m, v), (v, m), (m, m)]:
res = self.matmul(*arg)
assert_(res.dtype == dt)
if dt != "O":
res = self.matmul(v, v)
assert_(type(res) is np.dtype(dt).type)
def test_scalar_output(self):
vec1 = np.array([2])
vec2 = np.array([3, 4]).reshape(1, -1)
tgt = np.array([6, 8])
for dt in self.types[1:]:
v1 = vec1.astype(dt)
v2 = vec2.astype(dt)
res = self.matmul(v1, v2)
assert_equal(res, tgt)
res = self.matmul(v2.T, v1)
assert_equal(res, tgt)
vec = np.array([True, True], dtype='?').reshape(1, -1)
res = self.matmul(vec[:, 0], vec)
assert_equal(res, True)
def test_vector_vector_values(self):
vec1 = np.array([1, 2])
vec2 = np.array([3, 4]).reshape(-1, 1)
tgt1 = np.array([11])
tgt2 = np.array([[3, 6], [4, 8]])
for dt in self.types[1:]:
v1 = vec1.astype(dt)
v2 = vec2.astype(dt)
res = self.matmul(v1, v2)
assert_equal(res, tgt1)
res = self.matmul(v2, v1.reshape(1, -1))
assert_equal(res, tgt2)
vec = np.array([True, True], dtype='?')
res = self.matmul(vec, vec)
assert_equal(res, True)
def test_vector_matrix_values(self):
vec = np.array([1, 2])
mat1 = np.array([[1, 2], [3, 4]])
mat2 = np.stack([mat1]*2, axis=0)
tgt1 = np.array([7, 10])
tgt2 = np.stack([tgt1]*2, axis=0)
for dt in self.types[1:]:
v = vec.astype(dt)
m1 = mat1.astype(dt)
m2 = mat2.astype(dt)
res = self.matmul(v, m1)
assert_equal(res, tgt1)
res = self.matmul(v, m2)
assert_equal(res, tgt2)
vec = np.array([True, False])
mat1 = np.array([[True, False], [False, True]])
mat2 = np.stack([mat1]*2, axis=0)
tgt1 = np.array([True, False])
tgt2 = np.stack([tgt1]*2, axis=0)
res = self.matmul(vec, mat1)
assert_equal(res, tgt1)
res = self.matmul(vec, mat2)
assert_equal(res, tgt2)
def test_matrix_vector_values(self):
vec = np.array([1, 2])
mat1 = np.array([[1, 2], [3, 4]])
mat2 = np.stack([mat1]*2, axis=0)
tgt1 = np.array([5, 11])
tgt2 = np.stack([tgt1]*2, axis=0)
for dt in self.types[1:]:
v = vec.astype(dt)
m1 = mat1.astype(dt)
m2 = mat2.astype(dt)
res = self.matmul(m1, v)
assert_equal(res, tgt1)
res = self.matmul(m2, v)
assert_equal(res, tgt2)
vec = np.array([True, False])
mat1 = np.array([[True, False], [False, True]])
mat2 = np.stack([mat1]*2, axis=0)
tgt1 = np.array([True, False])
tgt2 = np.stack([tgt1]*2, axis=0)
res = self.matmul(vec, mat1)
assert_equal(res, tgt1)
res = self.matmul(vec, mat2)
assert_equal(res, tgt2)
def test_matrix_matrix_values(self):
mat1 = np.array([[1, 2], [3, 4]])
mat2 = np.array([[1, 0], [1, 1]])
mat12 = np.stack([mat1, mat2], axis=0)
mat21 = np.stack([mat2, mat1], axis=0)
tgt11 = np.array([[7, 10], [15, 22]])
tgt12 = np.array([[3, 2], [7, 4]])
tgt21 = np.array([[1, 2], [4, 6]])
tgt12_21 = np.stack([tgt12, tgt21], axis=0)
tgt11_12 = np.stack((tgt11, tgt12), axis=0)
tgt11_21 = np.stack((tgt11, tgt21), axis=0)
for dt in self.types[1:]:
m1 = mat1.astype(dt)
m2 = mat2.astype(dt)
m12 = mat12.astype(dt)
m21 = mat21.astype(dt)
res = self.matmul(m1, m2)
assert_equal(res, tgt12)
res = self.matmul(m2, m1)
assert_equal(res, tgt21)
res = self.matmul(m12, m1)
assert_equal(res, tgt11_21)
res = self.matmul(m1, m12)
assert_equal(res, tgt11_12)
res = self.matmul(m12, m21)
assert_equal(res, tgt12_21)
m1 = np.array([[1, 1], [0, 0]], dtype=np.bool)
m2 = np.array([[1, 0], [1, 1]], dtype=np.bool)
m12 = np.stack([m1, m2], axis=0)
m21 = np.stack([m2, m1], axis=0)
tgt11 = m1
tgt12 = m1
tgt21 = np.array([[1, 1], [1, 1]], dtype=np.bool)
tgt12_21 = np.stack([tgt12, tgt21], axis=0)
tgt11_12 = np.stack((tgt11, tgt12), axis=0)
tgt11_21 = np.stack((tgt11, tgt21), axis=0)
res = self.matmul(m1, m2)
assert_equal(res, tgt12)
res = self.matmul(m2, m1)
assert_equal(res, tgt21)
res = self.matmul(m12, m1)
assert_equal(res, tgt11_21)
res = self.matmul(m1, m12)
assert_equal(res, tgt11_12)
res = self.matmul(m12, m21)
assert_equal(res, tgt12_21)
class TestMatmul(MatmulCommon):
matmul = np.matmul
def test_out_arg(self):
a = np.ones((5, 2), dtype=float)
b = np.array([[1, 3], [5, 7]], dtype=float)
tgt = np.dot(a, b)
msg = "out positional argument"
out = np.zeros((5, 2), dtype=float)
self.matmul(a, b, out)
assert_array_equal(out, tgt, err_msg=msg)
msg = "out keyword argument"
out = np.zeros((5, 2), dtype=float)
self.matmul(a, b, out=out)
assert_array_equal(out, tgt, err_msg=msg)
msg = "Cannot cast ufunc .* output"
out = np.zeros((5, 2), dtype=np.int32)
assert_raises_regex(TypeError, msg, self.matmul, a, b, out=out)
out = np.zeros((5, 2), dtype=np.complex128)
c = self.matmul(a, b, out=out)
assert_(c is out)
with suppress_warnings() as sup:
sup.filter(ComplexWarning, '')
c = c.astype(tgt.dtype)
assert_array_equal(c, tgt)
def test_empty_out(self):
arr = np.ones((0, 1, 1))
out = np.ones((1, 1, 1))
assert self.matmul(arr, arr).shape == (0, 1, 1)
with pytest.raises(ValueError, match=r"non-broadcastable"):
self.matmul(arr, arr, out=out)
def test_out_contiguous(self):
a = np.ones((5, 2), dtype=float)
b = np.array([[1, 3], [5, 7]], dtype=float)
v = np.array([1, 3], dtype=float)
tgt = np.dot(a, b)
tgt_mv = np.dot(a, v)
out = np.ones((5, 2, 2), dtype=float)
c = self.matmul(a, b, out=out[..., 0])
assert c.base is out
assert_array_equal(c, tgt)
c = self.matmul(a, v, out=out[:, 0, 0])
assert_array_equal(c, tgt_mv)
c = self.matmul(v, a.T, out=out[:, 0, 0])
assert_array_equal(c, tgt_mv)
out = np.ones((10, 2), dtype=float)
c = self.matmul(a, b, out=out[::2, :])
assert_array_equal(c, tgt)
out = np.ones((5, 2), dtype=float)
c = self.matmul(b.T, a.T, out=out.T)
assert_array_equal(out, tgt)
m1 = np.arange(15.).reshape(5, 3)
m2 = np.arange(21.).reshape(3, 7)
m3 = np.arange(30.).reshape(5, 6)[:, ::2]
vc = np.arange(10.)
vr = np.arange(6.)
m0 = np.zeros((3, 0))
@pytest.mark.parametrize('args', (
(m1, m2), (m2.T, m1.T), (m2.T.copy(), m1.T), (m2.T, m1.T.copy()),
(m1, m1.T), (m1.T, m1), (m1, m3.T), (m3, m1.T),
(m3, m3.T), (m3.T, m3),
(m3, m2), (m2.T, m3.T), (m2.T.copy(), m3.T),
(m1, vr[:3]), (vc[:5], m1), (m1.T, vc[:5]), (vr[:3], m1.T),
(m1, vr[::2]), (vc[::2], m1), (m1.T, vc[::2]), (vr[::2], m1.T),
(m3, vr[:3]), (vc[:5], m3), (m3.T, vc[:5]), (vr[:3], m3.T),
(m3, vr[::2]), (vc[::2], m3), (m3.T, vc[::2]), (vr[::2], m3.T),
(m0, m0.T), (m0.T, m0), (m1, m0), (m0.T, m1.T),
))
def test_dot_equivalent(self, args):
r1 = np.matmul(*args)
r2 = np.dot(*args)
assert_equal(r1, r2)
r3 = np.matmul(args[0].copy(), args[1].copy())
assert_equal(r1, r3)
def test_matmul_object(self):
import fractions
f = np.vectorize(fractions.Fraction)
def random_ints():
return np.random.randint(1, 1000, size=(10, 3, 3))
M1 = f(random_ints(), random_ints())
M2 = f(random_ints(), random_ints())
M3 = self.matmul(M1, M2)
[N1, N2, N3] = [a.astype(float) for a in [M1, M2, M3]]
assert_allclose(N3, self.matmul(N1, N2))
def test_matmul_object_type_scalar(self):
from fractions import Fraction as F
v = np.array([F(2,3), F(5,7)])
res = self.matmul(v, v)
assert_(type(res) is F)
def test_matmul_empty(self):
a = np.empty((3, 0), dtype=object)
b = np.empty((0, 3), dtype=object)
c = np.zeros((3, 3))
assert_array_equal(np.matmul(a, b), c)
def test_matmul_exception_multiply(self):
class add_not_multiply():
def __add__(self, other):
return self
a = np.full((3,3), add_not_multiply())
with assert_raises(TypeError):
b = np.matmul(a, a)
def test_matmul_exception_add(self):
class multiply_not_add():
def __mul__(self, other):
return self
a = np.full((3,3), multiply_not_add())
with assert_raises(TypeError):
b = np.matmul(a, a)
def test_matmul_bool(self):
a = np.array([[1, 0],[1, 1]], dtype=bool)
assert np.max(a.view(np.uint8)) == 1
b = np.matmul(a, a)
assert np.max(b.view(np.uint8)) == 1
rg = np.random.default_rng(np.random.PCG64(43))
d = rg.integers(2, size=4*5, dtype=np.int8)
d = d.reshape(4, 5) > 0
out1 = np.matmul(d, d.reshape(5, 4))
out2 = np.dot(d, d.reshape(5, 4))
assert_equal(out1, out2)
c = np.matmul(np.zeros((2, 0), dtype=bool), np.zeros(0, dtype=bool))
assert not np.any(c)
class TestMatmulOperator(MatmulCommon):
import operator
matmul = operator.matmul
def test_array_priority_override(self):
class A:
__array_priority__ = 1000
def __matmul__(self, other):
return "A"
def __rmatmul__(self, other):
return "A"
a = A()
b = np.ones(2)
assert_equal(self.matmul(a, b), "A")
assert_equal(self.matmul(b, a), "A")
def test_matmul_raises(self):
assert_raises(TypeError, self.matmul, np.int8(5), np.int8(5))
assert_raises(TypeError, self.matmul, np.void(b'abc'), np.void(b'abc'))
assert_raises(TypeError, self.matmul, np.arange(10), np.void(b'abc'))
class TestMatmulInplace:
DTYPES = {}
for i in MatmulCommon.types:
for j in MatmulCommon.types:
if np.can_cast(j, i):
DTYPES[f"{i}-{j}"] = (np.dtype(i), np.dtype(j))
@pytest.mark.parametrize("dtype1,dtype2", DTYPES.values(), ids=DTYPES)
def test_basic(self, dtype1: np.dtype, dtype2: np.dtype) -> None:
a = np.arange(10).reshape(5, 2).astype(dtype1)
a_id = id(a)
b = np.ones((2, 2), dtype=dtype2)
ref = a @ b
a @= b
assert id(a) == a_id
assert a.dtype == dtype1
assert a.shape == (5, 2)
if dtype1.kind in "fc":
np.testing.assert_allclose(a, ref)
else:
np.testing.assert_array_equal(a, ref)
SHAPES = {
"2d_large": ((10**5, 10), (10, 10)),
"3d_large": ((10**4, 10, 10), (1, 10, 10)),
"1d": ((3,), (3,)),
"2d_1d": ((3, 3), (3,)),
"1d_2d": ((3,), (3, 3)),
"2d_broadcast": ((3, 3), (3, 1)),
"2d_broadcast_reverse": ((1, 3), (3, 3)),
"3d_broadcast1": ((3, 3, 3), (1, 3, 1)),
"3d_broadcast2": ((3, 3, 3), (1, 3, 3)),
"3d_broadcast3": ((3, 3, 3), (3, 3, 1)),
"3d_broadcast_reverse1": ((1, 3, 3), (3, 3, 3)),
"3d_broadcast_reverse2": ((3, 1, 3), (3, 3, 3)),
"3d_broadcast_reverse3": ((1, 1, 3), (3, 3, 3)),
}
@pytest.mark.parametrize("a_shape,b_shape", SHAPES.values(), ids=SHAPES)
def test_shapes(self, a_shape: tuple[int, ...], b_shape: tuple[int, ...]):
a_size = np.prod(a_shape)
a = np.arange(a_size).reshape(a_shape).astype(np.float64)
a_id = id(a)
b_size = np.prod(b_shape)
b = np.arange(b_size).reshape(b_shape)
ref = a @ b
if ref.shape != a_shape:
with pytest.raises(ValueError):
a @= b
return
else:
a @= b
assert id(a) == a_id
assert a.dtype.type == np.float64
assert a.shape == a_shape
np.testing.assert_allclose(a, ref)
def test_matmul_axes():
a = np.arange(3*4*5).reshape(3, 4, 5)
c = np.matmul(a, a, axes=[(-2, -1), (-1, -2), (1, 2)])
assert c.shape == (3, 4, 4)
d = np.matmul(a, a, axes=[(-2, -1), (-1, -2), (0, 1)])
assert d.shape == (4, 4, 3)
e = np.swapaxes(d, 0, 2)
assert_array_equal(e, c)
f = np.matmul(a, np.arange(3), axes=[(1, 0), (0), (0)])
assert f.shape == (4, 5)
class TestInner:
def test_inner_type_mismatch(self):
c = 1.
A = np.array((1, 1), dtype='i,i')
assert_raises(TypeError, np.inner, c, A)
assert_raises(TypeError, np.inner, A, c)
def test_inner_scalar_and_vector(self):
for dt in np.typecodes['AllInteger'] + np.typecodes['AllFloat'] + '?':
sca = np.array(3, dtype=dt)[()]
vec = np.array([1, 2], dtype=dt)
desired = np.array([3, 6], dtype=dt)
assert_equal(np.inner(vec, sca), desired)
assert_equal(np.inner(sca, vec), desired)
def test_vecself(self):
a = np.zeros(shape=(1, 80), dtype=np.float64)
p = np.inner(a, a)
assert_almost_equal(p, 0, decimal=14)
def test_inner_product_with_various_contiguities(self):
for dt in np.typecodes['AllInteger'] + np.typecodes['AllFloat'] + '?':
A = np.array([[1, 2], [3, 4]], dtype=dt)
B = np.array([[1, 3], [2, 4]], dtype=dt)
C = np.array([1, 1], dtype=dt)
desired = np.array([4, 6], dtype=dt)
assert_equal(np.inner(A.T, C), desired)
assert_equal(np.inner(C, A.T), desired)
assert_equal(np.inner(B, C), desired)
assert_equal(np.inner(C, B), desired)
desired = np.array([[7, 10], [15, 22]], dtype=dt)
assert_equal(np.inner(A, B), desired)
desired = np.array([[5, 11], [11, 25]], dtype=dt)
assert_equal(np.inner(A, A), desired)
assert_equal(np.inner(A, A.copy()), desired)
a = np.arange(5).astype(dt)
b = a[::-1]
desired = np.array(10, dtype=dt).item()
assert_equal(np.inner(b, a), desired)
def test_3d_tensor(self):
for dt in np.typecodes['AllInteger'] + np.typecodes['AllFloat'] + '?':
a = np.arange(24).reshape(2, 3, 4).astype(dt)
b = np.arange(24, 48).reshape(2, 3, 4).astype(dt)
desired = np.array(
[[[[ 158, 182, 206],
[ 230, 254, 278]],
[[ 566, 654, 742],
[ 830, 918, 1006]],
[[ 974, 1126, 1278],
[1430, 1582, 1734]]],
[[[1382, 1598, 1814],
[2030, 2246, 2462]],
[[1790, 2070, 2350],
[2630, 2910, 3190]],
[[2198, 2542, 2886],
[3230, 3574, 3918]]]]
).astype(dt)
assert_equal(np.inner(a, b), desired)
assert_equal(np.inner(b, a).transpose(2, 3, 0, 1), desired)
class TestChoose:
def setup_method(self):
self.x = 2*np.ones((3,), dtype=int)
self.y = 3*np.ones((3,), dtype=int)
self.x2 = 2*np.ones((2, 3), dtype=int)
self.y2 = 3*np.ones((2, 3), dtype=int)
def test_basic(self):
A = np.choose(self.ind, (self.x, self.y))
assert_equal(A, [2, 2, 3])
def test_broadcast1(self):
A = np.choose(self.ind, (self.x2, self.y2))
assert_equal(A, [[2, 2, 3], [2, 2, 3]])
def test_broadcast2(self):
A = np.choose(self.ind, (self.x, self.y2))
assert_equal(A, [[2, 2, 3], [2, 2, 3]])
@pytest.mark.parametrize("ops",
[(1000, np.array([1], dtype=np.uint8)),
(-1, np.array([1], dtype=np.uint8)),
(1., np.float32(3)),
(1., np.array([3], dtype=np.float32))],)
def test_output_dtype(self, ops):
expected_dt = np.result_type(*ops)
assert(np.choose([0], ops).dtype == expected_dt)
def test_dimension_and_args_limit(self):
a = np.ones((1,) * 32, dtype=np.intp)
res = a.choose([0, a] + [2] * 61)
with pytest.raises(ValueError,
match="Need at least 0 and at most 64 array objects"):
a.choose([0, a] + [2] * 62)
assert_array_equal(res, a)
a = np.ones((1,) * 60, dtype=np.intp)
with pytest.raises(RuntimeError,
match=".*32 dimensions but the array has 60"):
a.choose([a, a])
class TestRepeat:
def setup_method(self):
self.m = np.array([1, 2, 3, 4, 5, 6])
self.m_rect = self.m.reshape((2, 3))
def test_basic(self):
A = np.repeat(self.m, [1, 3, 2, 1, 1, 2])
assert_equal(A, [1, 2, 2, 2, 3,
3, 4, 5, 6, 6])
def test_broadcast1(self):
A = np.repeat(self.m, 2)
assert_equal(A, [1, 1, 2, 2, 3, 3,
4, 4, 5, 5, 6, 6])
def test_axis_spec(self):
A = np.repeat(self.m_rect, [2, 1], axis=0)
assert_equal(A, [[1, 2, 3],
[1, 2, 3],
[4, 5, 6]])
A = np.repeat(self.m_rect, [1, 3, 2], axis=1)
assert_equal(A, [[1, 2, 2, 2, 3, 3],
[4, 5, 5, 5, 6, 6]])
def test_broadcast2(self):
A = np.repeat(self.m_rect, 2, axis=0)
assert_equal(A, [[1, 2, 3],
[1, 2, 3],
[4, 5, 6],
[4, 5, 6]])
A = np.repeat(self.m_rect, 2, axis=1)
assert_equal(A, [[1, 1, 2, 2, 3, 3],
[4, 4, 5, 5, 6, 6]])
NEIGH_MODE = {'zero': 0, 'one': 1, 'constant': 2, 'circular': 3, 'mirror': 4}
@pytest.mark.parametrize('dt', [float, Decimal], ids=['float', 'object'])
class TestNeighborhoodIter:
def test_simple2d(self, dt):
x = np.array([[0, 1], [2, 3]], dtype=dt)
r = [np.array([[0, 0, 0], [0, 0, 1]], dtype=dt),
np.array([[0, 0, 0], [0, 1, 0]], dtype=dt),
np.array([[0, 0, 1], [0, 2, 3]], dtype=dt),
np.array([[0, 1, 0], [2, 3, 0]], dtype=dt)]
l = _multiarray_tests.test_neighborhood_iterator(
x, [-1, 0, -1, 1], x[0], NEIGH_MODE['zero'])
assert_array_equal(l, r)
r = [np.array([[1, 1, 1], [1, 0, 1]], dtype=dt),
np.array([[1, 1, 1], [0, 1, 1]], dtype=dt),
np.array([[1, 0, 1], [1, 2, 3]], dtype=dt),
np.array([[0, 1, 1], [2, 3, 1]], dtype=dt)]
l = _multiarray_tests.test_neighborhood_iterator(
x, [-1, 0, -1, 1], x[0], NEIGH_MODE['one'])
assert_array_equal(l, r)
r = [np.array([[4, 4, 4], [4, 0, 1]], dtype=dt),
np.array([[4, 4, 4], [0, 1, 4]], dtype=dt),
np.array([[4, 0, 1], [4, 2, 3]], dtype=dt),
np.array([[0, 1, 4], [2, 3, 4]], dtype=dt)]
l = _multiarray_tests.test_neighborhood_iterator(
x, [-1, 0, -1, 1], 4, NEIGH_MODE['constant'])
assert_array_equal(l, r)
r = [np.array([[4, 0, 1], [4, 2, 3]], dtype=dt),
np.array([[0, 1, 4], [2, 3, 4]], dtype=dt)]
l = _multiarray_tests.test_neighborhood_iterator(
x, [-1, 0, -1, 1], 4, NEIGH_MODE['constant'], 2)
assert_array_equal(l, r)
def test_mirror2d(self, dt):
x = np.array([[0, 1], [2, 3]], dtype=dt)
r = [np.array([[0, 0, 1], [0, 0, 1]], dtype=dt),
np.array([[0, 1, 1], [0, 1, 1]], dtype=dt),
np.array([[0, 0, 1], [2, 2, 3]], dtype=dt),
np.array([[0, 1, 1], [2, 3, 3]], dtype=dt)]
l = _multiarray_tests.test_neighborhood_iterator(
x, [-1, 0, -1, 1], x[0], NEIGH_MODE['mirror'])
assert_array_equal(l, r)
def test_simple(self, dt):
x = np.linspace(1, 5, 5).astype(dt)
r = [[0, 1, 2], [1, 2, 3], [2, 3, 4], [3, 4, 5], [4, 5, 0]]
l = _multiarray_tests.test_neighborhood_iterator(
x, [-1, 1], x[0], NEIGH_MODE['zero'])
assert_array_equal(l, r)
r = [[1, 1, 2], [1, 2, 3], [2, 3, 4], [3, 4, 5], [4, 5, 1]]
l = _multiarray_tests.test_neighborhood_iterator(
x, [-1, 1], x[0], NEIGH_MODE['one'])
assert_array_equal(l, r)
r = [[x[4], 1, 2], [1, 2, 3], [2, 3, 4], [3, 4, 5], [4, 5, x[4]]]
l = _multiarray_tests.test_neighborhood_iterator(
x, [-1, 1], x[4], NEIGH_MODE['constant'])
assert_array_equal(l, r)
def test_mirror(self, dt):
x = np.linspace(1, 5, 5).astype(dt)
r = np.array([[2, 1, 1, 2, 3], [1, 1, 2, 3, 4], [1, 2, 3, 4, 5],
[2, 3, 4, 5, 5], [3, 4, 5, 5, 4]], dtype=dt)
l = _multiarray_tests.test_neighborhood_iterator(
x, [-2, 2], x[1], NEIGH_MODE['mirror'])
assert_([i.dtype == dt for i in l])
assert_array_equal(l, r)
def test_circular(self, dt):
x = np.linspace(1, 5, 5).astype(dt)
r = np.array([[4, 5, 1, 2, 3], [5, 1, 2, 3, 4], [1, 2, 3, 4, 5],
[2, 3, 4, 5, 1], [3, 4, 5, 1, 2]], dtype=dt)
l = _multiarray_tests.test_neighborhood_iterator(
x, [-2, 2], x[0], NEIGH_MODE['circular'])
assert_array_equal(l, r)
class TestStackedNeighborhoodIter:
def test_simple_const(self):
dt = np.float64
x = np.array([1, 2, 3], dtype=dt)
r = [np.array([0], dtype=dt),
np.array([0], dtype=dt),
np.array([1], dtype=dt),
np.array([2], dtype=dt),
np.array([3], dtype=dt),
np.array([0], dtype=dt),
np.array([0], dtype=dt)]
l = _multiarray_tests.test_neighborhood_iterator_oob(
x, [-2, 4], NEIGH_MODE['zero'], [0, 0], NEIGH_MODE['zero'])
assert_array_equal(l, r)
r = [np.array([1, 0, 1], dtype=dt),
np.array([0, 1, 2], dtype=dt),
np.array([1, 2, 3], dtype=dt),
np.array([2, 3, 0], dtype=dt),
np.array([3, 0, 1], dtype=dt)]
l = _multiarray_tests.test_neighborhood_iterator_oob(
x, [-1, 3], NEIGH_MODE['zero'], [-1, 1], NEIGH_MODE['one'])
assert_array_equal(l, r)
def test_simple_mirror(self):
dt = np.float64
x = np.array([1, 2, 3], dtype=dt)
r = [np.array([0, 1, 1], dtype=dt),
np.array([1, 1, 2], dtype=dt),
np.array([1, 2, 3], dtype=dt),
np.array([2, 3, 3], dtype=dt),
np.array([3, 3, 0], dtype=dt)]
l = _multiarray_tests.test_neighborhood_iterator_oob(
x, [-1, 3], NEIGH_MODE['mirror'], [-1, 1], NEIGH_MODE['zero'])
assert_array_equal(l, r)
x = np.array([1, 2, 3], dtype=dt)
r = [np.array([1, 0, 0], dtype=dt),
np.array([0, 0, 1], dtype=dt),
np.array([0, 1, 2], dtype=dt),
np.array([1, 2, 3], dtype=dt),
np.array([2, 3, 0], dtype=dt)]
l = _multiarray_tests.test_neighborhood_iterator_oob(
x, [-1, 3], NEIGH_MODE['zero'], [-2, 0], NEIGH_MODE['mirror'])
assert_array_equal(l, r)
x = np.array([1, 2, 3], dtype=dt)
r = [np.array([0, 1, 2], dtype=dt),
np.array([1, 2, 3], dtype=dt),
np.array([2, 3, 0], dtype=dt),
np.array([3, 0, 0], dtype=dt),
np.array([0, 0, 3], dtype=dt)]
l = _multiarray_tests.test_neighborhood_iterator_oob(
x, [-1, 3], NEIGH_MODE['zero'], [0, 2], NEIGH_MODE['mirror'])
assert_array_equal(l, r)
x = np.array([1, 2, 3], dtype=dt)
r = [np.array([1, 0, 0, 1, 2], dtype=dt),
np.array([0, 0, 1, 2, 3], dtype=dt),
np.array([0, 1, 2, 3, 0], dtype=dt),
np.array([1, 2, 3, 0, 0], dtype=dt),
np.array([2, 3, 0, 0, 3], dtype=dt)]
l = _multiarray_tests.test_neighborhood_iterator_oob(
x, [-1, 3], NEIGH_MODE['zero'], [-2, 2], NEIGH_MODE['mirror'])
assert_array_equal(l, r)
def test_simple_circular(self):
dt = np.float64
x = np.array([1, 2, 3], dtype=dt)
r = [np.array([0, 3, 1], dtype=dt),
np.array([3, 1, 2], dtype=dt),
np.array([1, 2, 3], dtype=dt),
np.array([2, 3, 1], dtype=dt),
np.array([3, 1, 0], dtype=dt)]
l = _multiarray_tests.test_neighborhood_iterator_oob(
x, [-1, 3], NEIGH_MODE['circular'], [-1, 1], NEIGH_MODE['zero'])
assert_array_equal(l, r)
x = np.array([1, 2, 3], dtype=dt)
r = [np.array([3, 0, 0], dtype=dt),
np.array([0, 0, 1], dtype=dt),
np.array([0, 1, 2], dtype=dt),
np.array([1, 2, 3], dtype=dt),
np.array([2, 3, 0], dtype=dt)]
l = _multiarray_tests.test_neighborhood_iterator_oob(
x, [-1, 3], NEIGH_MODE['zero'], [-2, 0], NEIGH_MODE['circular'])
assert_array_equal(l, r)
x = np.array([1, 2, 3], dtype=dt)
r = [np.array([0, 1, 2], dtype=dt),
np.array([1, 2, 3], dtype=dt),
np.array([2, 3, 0], dtype=dt),
np.array([3, 0, 0], dtype=dt),
np.array([0, 0, 1], dtype=dt)]
l = _multiarray_tests.test_neighborhood_iterator_oob(
x, [-1, 3], NEIGH_MODE['zero'], [0, 2], NEIGH_MODE['circular'])
assert_array_equal(l, r)
x = np.array([1, 2, 3], dtype=dt)
r = [np.array([3, 0, 0, 1, 2], dtype=dt),
np.array([0, 0, 1, 2, 3], dtype=dt),
np.array([0, 1, 2, 3, 0], dtype=dt),
np.array([1, 2, 3, 0, 0], dtype=dt),
np.array([2, 3, 0, 0, 1], dtype=dt)]
l = _multiarray_tests.test_neighborhood_iterator_oob(
x, [-1, 3], NEIGH_MODE['zero'], [-2, 2], NEIGH_MODE['circular'])
assert_array_equal(l, r)
def test_simple_strict_within(self):
dt = np.float64
x = np.array([1, 2, 3], dtype=dt)
r = [np.array([1, 2, 3, 0], dtype=dt)]
l = _multiarray_tests.test_neighborhood_iterator_oob(
x, [1, 1], NEIGH_MODE['zero'], [-1, 2], NEIGH_MODE['zero'])
assert_array_equal(l, r)
x = np.array([1, 2, 3], dtype=dt)
r = [np.array([1, 2, 3, 3], dtype=dt)]
l = _multiarray_tests.test_neighborhood_iterator_oob(
x, [1, 1], NEIGH_MODE['zero'], [-1, 2], NEIGH_MODE['mirror'])
assert_array_equal(l, r)
x = np.array([1, 2, 3], dtype=dt)
r = [np.array([1, 2, 3, 1], dtype=dt)]
l = _multiarray_tests.test_neighborhood_iterator_oob(
x, [1, 1], NEIGH_MODE['zero'], [-1, 2], NEIGH_MODE['circular'])
assert_array_equal(l, r)
class TestWarnings:
def test_complex_warning(self):
x = np.array([1, 2])
y = np.array([1-2j, 1+2j])
with warnings.catch_warnings():
warnings.simplefilter("error", ComplexWarning)
assert_raises(ComplexWarning, x.__setitem__, slice(None), y)
assert_equal(x, [1, 2])
class TestMinScalarType:
def test_usigned_shortshort(self):
dt = np.min_scalar_type(2**8-1)
wanted = np.dtype('uint8')
assert_equal(wanted, dt)
def test_usigned_short(self):
dt = np.min_scalar_type(2**16-1)
wanted = np.dtype('uint16')
assert_equal(wanted, dt)
def test_usigned_int(self):
dt = np.min_scalar_type(2**32-1)
wanted = np.dtype('uint32')
assert_equal(wanted, dt)
def test_usigned_longlong(self):
dt = np.min_scalar_type(2**63-1)
wanted = np.dtype('uint64')
assert_equal(wanted, dt)
def test_object(self):
dt = np.min_scalar_type(2**64)
wanted = np.dtype('O')
assert_equal(wanted, dt)
from numpy._core._internal import _dtype_from_pep3118
class TestPEP3118Dtype:
def _check(self, spec, wanted):
dt = np.dtype(wanted)
actual = _dtype_from_pep3118(spec)
assert_equal(actual, dt,
err_msg="spec %r != dtype %r" % (spec, wanted))
def test_native_padding(self):
align = np.dtype('i').alignment
for j in range(8):
if j == 0:
s = 'bi'
else:
s = 'b%dxi' % j
self._check('@'+s, {'f0': ('i1', 0),
'f1': ('i', align*(1 + j//align))})
self._check('='+s, {'f0': ('i1', 0),
'f1': ('i', 1+j)})
def test_native_padding_2(self):
self._check('x3T{xi}', {'f0': (({'f0': ('i', 4)}, (3,)), 4)})
self._check('^x3T{xi}', {'f0': (({'f0': ('i', 1)}, (3,)), 1)})
def test_trailing_padding(self):
align = np.dtype('i').alignment
size = np.dtype('i').itemsize
def aligned(n):
return align*(1 + (n-1)//align)
base = dict(formats=['i'], names=['f0'])
self._check('ix', dict(itemsize=aligned(size + 1), **base))
self._check('ixx', dict(itemsize=aligned(size + 2), **base))
self._check('ixxx', dict(itemsize=aligned(size + 3), **base))
self._check('ixxxx', dict(itemsize=aligned(size + 4), **base))
self._check('i7x', dict(itemsize=aligned(size + 7), **base))
self._check('^ix', dict(itemsize=size + 1, **base))
self._check('^ixx', dict(itemsize=size + 2, **base))
self._check('^ixxx', dict(itemsize=size + 3, **base))
self._check('^ixxxx', dict(itemsize=size + 4, **base))
self._check('^i7x', dict(itemsize=size + 7, **base))
def test_native_padding_3(self):
dt = np.dtype(
[('a', 'b'), ('b', 'i'),
('sub', np.dtype('b,i')), ('c', 'i')],
align=True)
self._check("T{b:a:xxxi:b:T{b:f0:=i:f1:}:sub:xxxi:c:}", dt)
dt = np.dtype(
[('a', 'b'), ('b', 'i'), ('c', 'b'), ('d', 'b'),
('e', 'b'), ('sub', np.dtype('b,i', align=True))])
self._check("T{b:a:=i:b:b:c:b:d:b:e:T{b:f0:xxxi:f1:}:sub:}", dt)
def test_padding_with_array_inside_struct(self):
dt = np.dtype(
[('a', 'b'), ('b', 'i'), ('c', 'b', (3,)),
('d', 'i')],
align=True)
self._check("T{b:a:xxxi:b:3b:c:xi:d:}", dt)
def test_byteorder_inside_struct(self):
self._check('@T{^i}xi', {'f0': ({'f0': ('i', 0)}, 0),
'f1': ('i', 5)})
def test_intra_padding(self):
align = np.dtype('i').alignment
size = np.dtype('i').itemsize
def aligned(n):
return (align*(1 + (n-1)//align))
self._check('(3)T{ix}', (dict(
names=['f0'],
formats=['i'],
offsets=[0],
itemsize=aligned(size + 1)
), (3,)))
def test_char_vs_string(self):
dt = np.dtype('c')
self._check('c', dt)
dt = np.dtype([('f0', 'S1', (4,)), ('f1', 'S4')])
self._check('4c4s', dt)
def test_field_order(self):
self._check("(0)I:a:f:b:", [('a', 'I', (0,)), ('b', 'f')])
self._check("(0)I:b:f:a:", [('b', 'I', (0,)), ('a', 'f')])
def test_unnamed_fields(self):
self._check('ii', [('f0', 'i'), ('f1', 'i')])
self._check('ii:f0:', [('f1', 'i'), ('f0', 'i')])
self._check('i', 'i')
self._check('i:f0:', [('f0', 'i')])
class TestNewBufferProtocol:
""" Test PEP3118 buffers """
def _check_roundtrip(self, obj):
obj = np.asarray(obj)
x = memoryview(obj)
y = np.asarray(x)
y2 = np.array(x)
assert_(not y.flags.owndata)
assert_(y2.flags.owndata)
assert_equal(y.dtype, obj.dtype)
assert_equal(y.shape, obj.shape)
assert_array_equal(obj, y)
assert_equal(y2.dtype, obj.dtype)
assert_equal(y2.shape, obj.shape)
assert_array_equal(obj, y2)
def test_roundtrip(self):
x = np.array([1, 2, 3, 4, 5], dtype='i4')
self._check_roundtrip(x)
x = np.array([[1, 2], [3, 4]], dtype=np.float64)
self._check_roundtrip(x)
x = np.zeros((3, 3, 3), dtype=np.float32)[:, 0, :]
self._check_roundtrip(x)
dt = [('a', 'b'),
('b', 'h'),
('c', 'i'),
('d', 'l'),
('dx', 'q'),
('e', 'B'),
('f', 'H'),
('g', 'I'),
('h', 'L'),
('hx', 'Q'),
('i', np.single),
('j', np.double),
('k', np.longdouble),
('ix', np.csingle),
('jx', np.cdouble),
('kx', np.clongdouble),
('l', 'S4'),
('m', 'U4'),
('n', 'V3'),
('o', '?'),
('p', np.half),
]
x = np.array(
[(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
b'aaaa', 'bbbb', b'xxx', True, 1.0)],
dtype=dt)
self._check_roundtrip(x)
x = np.array(([[1, 2], [3, 4]],), dtype=[('a', (int, (2, 2)))])
self._check_roundtrip(x)
x = np.array([1, 2, 3], dtype='>i2')
self._check_roundtrip(x)
x = np.array([1, 2, 3], dtype='<i2')
self._check_roundtrip(x)
x = np.array([1, 2, 3], dtype='>i4')
self._check_roundtrip(x)
x = np.array([1, 2, 3], dtype='<i4')
self._check_roundtrip(x)
x = np.array([1, 2, 3], dtype='>q')
self._check_roundtrip(x)
if sys.byteorder == 'little':
x = np.array([1, 2, 3], dtype='>g')
assert_raises(ValueError, self._check_roundtrip, x)
x = np.array([1, 2, 3], dtype='<g')
self._check_roundtrip(x)
else:
x = np.array([1, 2, 3], dtype='>g')
self._check_roundtrip(x)
x = np.array([1, 2, 3], dtype='<g')
assert_raises(ValueError, self._check_roundtrip, x)
def test_roundtrip_half(self):
half_list = [
1.0,
-2.0,
6.5504 * 10**4,
2**-14,
2**-24,
0.0,
-0.0,
float('+inf'),
float('-inf'),
0.333251953125,
]
x = np.array(half_list, dtype='>e')
self._check_roundtrip(x)
x = np.array(half_list, dtype='<e')
self._check_roundtrip(x)
def test_roundtrip_single_types(self):
for typ in np._core.sctypeDict.values():
dtype = np.dtype(typ)
if dtype.char in 'Mm':
continue
if dtype.char == 'V':
continue
x = np.zeros(4, dtype=dtype)
self._check_roundtrip(x)
if dtype.char not in 'qQgG':
dt = dtype.newbyteorder('<')
x = np.zeros(4, dtype=dt)
self._check_roundtrip(x)
dt = dtype.newbyteorder('>')
x = np.zeros(4, dtype=dt)
self._check_roundtrip(x)
def test_roundtrip_scalar(self):
self._check_roundtrip(0)
def test_invalid_buffer_format(self):
dt = np.dtype([('a', 'uint16'), ('b', 'M8[s]')])
a = np.empty(3, dt)
assert_raises((ValueError, BufferError), memoryview, a)
assert_raises((ValueError, BufferError), memoryview, np.array((3), 'M8[D]'))
def test_export_simple_1d(self):
x = np.array([1, 2, 3, 4, 5], dtype='i')
y = memoryview(x)
assert_equal(y.format, 'i')
assert_equal(y.shape, (5,))
assert_equal(y.ndim, 1)
assert_equal(y.strides, (4,))
assert_equal(y.suboffsets, ())
assert_equal(y.itemsize, 4)
def test_export_simple_nd(self):
x = np.array([[1, 2], [3, 4]], dtype=np.float64)
y = memoryview(x)
assert_equal(y.format, 'd')
assert_equal(y.shape, (2, 2))
assert_equal(y.ndim, 2)
assert_equal(y.strides, (16, 8))
assert_equal(y.suboffsets, ())
assert_equal(y.itemsize, 8)
def test_export_discontiguous(self):
x = np.zeros((3, 3, 3), dtype=np.float32)[:, 0, :]
y = memoryview(x)
assert_equal(y.format, 'f')
assert_equal(y.shape, (3, 3))
assert_equal(y.ndim, 2)
assert_equal(y.strides, (36, 4))
assert_equal(y.suboffsets, ())
assert_equal(y.itemsize, 4)
def test_export_record(self):
dt = [('a', 'b'),
('b', 'h'),
('c', 'i'),
('d', 'l'),
('dx', 'q'),
('e', 'B'),
('f', 'H'),
('g', 'I'),
('h', 'L'),
('hx', 'Q'),
('i', np.single),
('j', np.double),
('k', np.longdouble),
('ix', np.csingle),
('jx', np.cdouble),
('kx', np.clongdouble),
('l', 'S4'),
('m', 'U4'),
('n', 'V3'),
('o', '?'),
('p', np.half),
]
x = np.array(
[(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
b'aaaa', 'bbbb', b' ', True, 1.0)],
dtype=dt)
y = memoryview(x)
assert_equal(y.shape, (1,))
assert_equal(y.ndim, 1)
assert_equal(y.suboffsets, ())
sz = sum([np.dtype(b).itemsize for a, b in dt])
if np.dtype('l').itemsize == 4:
assert_equal(y.format, 'T{b:a:=h:b:i:c:l:d:q:dx:B:e:@H:f:=I:g:L:h:Q:hx:f:i:d:j:^g:k:=Zf:ix:Zd:jx:^Zg:kx:4s:l:=4w:m:3x:n:?:o:@e:p:}')
else:
assert_equal(y.format, 'T{b:a:=h:b:i:c:q:d:q:dx:B:e:@H:f:=I:g:Q:h:Q:hx:f:i:d:j:^g:k:=Zf:ix:Zd:jx:^Zg:kx:4s:l:=4w:m:3x:n:?:o:@e:p:}')
assert_equal(y.strides, (sz,))
assert_equal(y.itemsize, sz)
def test_export_subarray(self):
x = np.array(([[1, 2], [3, 4]],), dtype=[('a', ('i', (2, 2)))])
y = memoryview(x)
assert_equal(y.format, 'T{(2,2)i:a:}')
assert_equal(y.shape, ())
assert_equal(y.ndim, 0)
assert_equal(y.strides, ())
assert_equal(y.suboffsets, ())
assert_equal(y.itemsize, 16)
def test_export_endian(self):
x = np.array([1, 2, 3], dtype='>i')
y = memoryview(x)
if sys.byteorder == 'little':
assert_equal(y.format, '>i')
else:
assert_equal(y.format, 'i')
x = np.array([1, 2, 3], dtype='<i')
y = memoryview(x)
if sys.byteorder == 'little':
assert_equal(y.format, 'i')
else:
assert_equal(y.format, '<i')
def test_export_flags(self):
assert_raises(ValueError,
_multiarray_tests.get_buffer_info,
np.arange(5)[::2], ('SIMPLE',))
@pytest.mark.parametrize(["obj", "error"], [
pytest.param(np.array([1, 2], dtype=rational), ValueError, id="array"),
pytest.param(rational(1, 2), TypeError, id="scalar")])
@pytest.mark.valgrind_error(reason="leaks buffer info cache temporarily.")
def test_relaxed_strides(self, c=np.ones((1, 10, 10), dtype='i8')):
c.strides = (-1, 80, 8)
assert_(memoryview(c).strides == (800, 80, 8))
fd = io.BytesIO()
fd.write(c.data)
fortran = c.T
assert_(memoryview(fortran).strides == (8, 80, 800))
arr = np.ones((1, 10))
if arr.flags.f_contiguous:
shape, strides = _multiarray_tests.get_buffer_info(
arr, ['F_CONTIGUOUS'])
assert_(strides[0] == 8)
arr = np.ones((10, 1), order='F')
shape, strides = _multiarray_tests.get_buffer_info(
arr, ['C_CONTIGUOUS'])
assert_(strides[-1] == 8)
def test_out_of_order_fields(self):
dt = np.dtype(dict(
formats=['<i4', '<i4'],
names=['one', 'two'],
offsets=[4, 0],
itemsize=8
))
arr = np.empty(1, dt)
with assert_raises(ValueError):
memoryview(arr)
def test_max_dims(self):
a = np.ones((1,) * 32)
self._check_roundtrip(a)
def test_error_pointer_type(self):
m = memoryview(ctypes.pointer(ctypes.c_uint8()))
assert_('&' in m.format)
assert_raises_regex(
ValueError, "format string",
np.array, m)
def test_error_message_unsupported(self):
t = ctypes.c_wchar * 4
with assert_raises(ValueError) as cm:
np.array(t())
exc = cm.exception
with assert_raises_regex(
NotImplementedError,
r"Unrepresentable .* 'u' \(UCS-2 strings\)"
):
raise exc.__cause__
def test_ctypes_integer_via_memoryview(self):
for c_integer in {ctypes.c_int, ctypes.c_long, ctypes.c_longlong}:
value = c_integer(42)
with warnings.catch_warnings(record=True):
warnings.filterwarnings('always', r'.*\bctypes\b', RuntimeWarning)
np.asarray(value)
def test_ctypes_struct_via_memoryview(self):
class foo(ctypes.Structure):
_fields_ = [('a', ctypes.c_uint8), ('b', ctypes.c_uint32)]
f = foo(a=1, b=2)
with warnings.catch_warnings(record=True):
warnings.filterwarnings('always', r'.*\bctypes\b', RuntimeWarning)
arr = np.asarray(f)
assert_equal(arr['a'], 1)
assert_equal(arr['b'], 2)
f.a = 3
assert_equal(arr['a'], 3)
@pytest.mark.parametrize("obj", [np.ones(3), np.ones(1, dtype="i,i")[()]])
def test_error_if_stored_buffer_info_is_corrupted(self, obj):
"""
If a user extends a NumPy array before 1.20 and then runs it
on NumPy 1.20+. A C-subclassed array might in theory modify
the new buffer-info field. This checks that an error is raised
if this happens (for buffer export), an error is written on delete.
This is a sanity check to help users transition to safe code, it
may be deleted at any point.
"""
_multiarray_tests.corrupt_or_fix_bufferinfo(obj)
name = type(obj)
with pytest.raises(RuntimeError,
match=f".*{name} appears to be C subclassed"):
memoryview(obj)
_multiarray_tests.corrupt_or_fix_bufferinfo(obj)
def test_no_suboffsets(self):
try:
import _testbuffer
except ImportError:
raise pytest.skip("_testbuffer is not available")
for shape in [(2, 3), (2, 3, 4)]:
data = list(range(np.prod(shape)))
buffer = _testbuffer.ndarray(data, shape, format='i',
flags=_testbuffer.ND_PIL)
msg = "NumPy currently does not support.*suboffsets"
with pytest.raises(BufferError, match=msg):
np.asarray(buffer)
with pytest.raises(BufferError, match=msg):
np.asarray([buffer])
with pytest.raises(BufferError):
np.frombuffer(buffer)
class TestArrayCreationCopyArgument(object):
class RaiseOnBool:
def __bool__(self):
raise ValueError
true_vals = [True, np._CopyMode.ALWAYS, np.True_]
if_needed_vals = [None, np._CopyMode.IF_NEEDED]
false_vals = [False, np._CopyMode.NEVER, np.False_]
def test_scalars(self):
for dtype in np.typecodes["All"]:
arr = np.zeros((), dtype=dtype)
scalar = arr[()]
pyscalar = arr.item(0)
assert_raises(ValueError, np.array, pyscalar,
copy=self.RaiseOnBool())
assert_raises(ValueError, _multiarray_tests.npy_ensurenocopy,
[1])
for copy in self.false_vals:
assert_raises(ValueError, np.array, scalar, copy=copy)
assert_raises(ValueError, np.array, pyscalar, copy=copy)
with pytest.raises(ValueError):
np.array(pyscalar, dtype=np.int64, copy=copy)
def test_compatible_cast(self):
def int_types(byteswap=False):
int_types = (np.typecodes["Integer"] +
np.typecodes["UnsignedInteger"])
for int_type in int_types:
yield np.dtype(int_type)
if byteswap:
yield np.dtype(int_type).newbyteorder()
for int1 in int_types():
for int2 in int_types(True):
arr = np.arange(10, dtype=int1)
for copy in self.true_vals:
res = np.array(arr, copy=copy, dtype=int2)
assert res is not arr and res.flags.owndata
assert_array_equal(res, arr)
if int1 == int2:
for copy in self.if_needed_vals:
res = np.array(arr, copy=copy, dtype=int2)
assert res is arr or res.base is arr
for copy in self.false_vals:
res = np.array(arr, copy=copy, dtype=int2)
assert res is arr or res.base is arr
else:
for copy in self.if_needed_vals:
res = np.array(arr, copy=copy, dtype=int2)
assert res is not arr and res.flags.owndata
assert_array_equal(res, arr)
assert_raises(ValueError, np.array,
arr, copy=False,
dtype=int2)
def test_buffer_interface(self):
arr = np.arange(10)
view = memoryview(arr)
for copy in self.true_vals:
res = np.array(view, copy=copy)
assert not np.may_share_memory(arr, res)
for copy in self.false_vals:
res = np.array(view, copy=copy)
assert np.may_share_memory(arr, res)
res = np.array(view, copy=np._CopyMode.NEVER)
assert np.may_share_memory(arr, res) == True
def test_array_interfaces(self):
base_arr = np.arange(10)
class ArrayLike:
__array_interface__ = base_arr.__array_interface__
arr = ArrayLike()
for copy, val in [(True, None), (np._CopyMode.ALWAYS, None),
(False, arr), (np._CopyMode.IF_NEEDED, arr),
(np._CopyMode.NEVER, arr)]:
res = np.array(arr, copy=copy)
assert res.base is val
def test___array__(self):
base_arr = np.arange(10)
class ArrayLike:
def __array__(self, dtype=None, copy=None):
return base_arr
arr = ArrayLike()
for copy in self.true_vals:
res = np.array(arr, copy=copy)
assert_array_equal(res, base_arr)
assert res is base_arr
for copy in self.if_needed_vals + self.false_vals:
res = np.array(arr, copy=copy)
assert_array_equal(res, base_arr)
assert res is base_arr
def test___array__copy_arg(self):
a = np.ones((10, 10), dtype=int)
assert np.shares_memory(a, a.__array__())
assert not np.shares_memory(a, a.__array__(float))
assert not np.shares_memory(a, a.__array__(float, copy=None))
assert not np.shares_memory(a, a.__array__(copy=True))
assert np.shares_memory(a, a.__array__(copy=None))
assert np.shares_memory(a, a.__array__(copy=False))
assert np.shares_memory(a, a.__array__(int, copy=False))
with pytest.raises(ValueError):
np.shares_memory(a, a.__array__(float, copy=False))
base_arr = np.arange(10)
class ArrayLikeNoCopy:
def __array__(self, dtype=None):
return base_arr
a = ArrayLikeNoCopy()
arr = np.array(a, copy=None)
assert_array_equal(arr, base_arr)
assert arr is base_arr
with pytest.warns(DeprecationWarning,
match="__array__.*must implement.*'copy'"):
arr = np.array(a, copy=True)
assert_array_equal(arr, base_arr)
assert arr is not base_arr
with pytest.warns(DeprecationWarning, match="__array__.*'copy'"):
with pytest.raises(ValueError,
match=r"Unable to avoid copy(.|\n)*numpy_2_0_migration_guide.html"):
np.array(a, copy=False)
def test___array__copy_once(self):
size = 100
base_arr = np.zeros((size, size))
copy_arr = np.zeros((size, size))
class ArrayRandom:
def __init__(self):
self.true_passed = False
def __array__(self, dtype=None, copy=None):
if copy:
self.true_passed = True
return copy_arr
else:
return base_arr
arr_random = ArrayRandom()
first_copy = np.array(arr_random, copy=True)
assert arr_random.true_passed
assert first_copy is copy_arr
arr_random = ArrayRandom()
no_copy = np.array(arr_random, copy=False)
assert not arr_random.true_passed
assert no_copy is base_arr
arr_random = ArrayRandom()
_ = np.array([arr_random], copy=True)
assert not arr_random.true_passed
arr_random = ArrayRandom()
second_copy = np.array(arr_random, copy=True, order="F")
assert arr_random.true_passed
assert second_copy is not copy_arr
def test__array__reference_leak(self):
class NotAnArray:
def __array__(self, dtype=None, copy=None):
raise NotImplementedError()
x = NotAnArray()
refcount = sys.getrefcount(x)
try:
np.array(x)
except NotImplementedError:
pass
gc.collect()
assert refcount == sys.getrefcount(x)
@pytest.mark.parametrize(
"arr", [np.ones(()), np.arange(81).reshape((9, 9))])
@pytest.mark.parametrize("order1", ["C", "F", None])
@pytest.mark.parametrize("order2", ["C", "F", "A", "K"])
def test_order_mismatch(self, arr, order1, order2):
arr = arr.copy(order1)
if order1 == "C":
assert arr.flags.c_contiguous
elif order1 == "F":
assert arr.flags.f_contiguous
elif arr.ndim != 0:
arr = arr[::2, ::2]
assert not arr.flags.forc
if order2 == "C":
no_copy_necessary = arr.flags.c_contiguous
elif order2 == "F":
no_copy_necessary = arr.flags.f_contiguous
else:
no_copy_necessary = True
for view in [arr, memoryview(arr)]:
for copy in self.true_vals:
res = np.array(view, copy=copy, order=order2)
assert res is not arr and res.flags.owndata
assert_array_equal(arr, res)
if no_copy_necessary:
for copy in self.if_needed_vals + self.false_vals:
res = np.array(view, copy=copy, order=order2)
if not IS_PYPY:
assert res is arr or res.base.obj is arr
else:
for copy in self.if_needed_vals:
res = np.array(arr, copy=copy, order=order2)
assert_array_equal(arr, res)
for copy in self.false_vals:
assert_raises(ValueError, np.array,
view, copy=copy, order=order2)
def test_striding_not_ok(self):
arr = np.array([[1, 2, 4], [3, 4, 5]])
assert_raises(ValueError, np.array,
arr.T, copy=np._CopyMode.NEVER,
order='C')
assert_raises(ValueError, np.array,
arr.T, copy=np._CopyMode.NEVER,
order='C', dtype=np.int64)
assert_raises(ValueError, np.array,
arr, copy=np._CopyMode.NEVER,
order='F')
assert_raises(ValueError, np.array,
arr, copy=np._CopyMode.NEVER,
order='F', dtype=np.int64)
class TestArrayAttributeDeletion:
def test_multiarray_writable_attributes_deletion(self):
a = np.ones(2)
attr = ['shape', 'strides', 'data', 'dtype', 'real', 'imag', 'flat']
with suppress_warnings() as sup:
sup.filter(DeprecationWarning, "Assigning the 'data' attribute")
for s in attr:
assert_raises(AttributeError, delattr, a, s)
def test_multiarray_not_writable_attributes_deletion(self):
a = np.ones(2)
attr = ["ndim", "flags", "itemsize", "size", "nbytes", "base",
"ctypes", "T", "__array_interface__", "__array_struct__",
"__array_priority__", "__array_finalize__"]
for s in attr:
assert_raises(AttributeError, delattr, a, s)
def test_multiarray_flags_writable_attribute_deletion(self):
a = np.ones(2).flags
attr = ['writebackifcopy', 'updateifcopy', 'aligned', 'writeable']
for s in attr:
assert_raises(AttributeError, delattr, a, s)
def test_multiarray_flags_not_writable_attribute_deletion(self):
a = np.ones(2).flags
attr = ["contiguous", "c_contiguous", "f_contiguous", "fortran",
"owndata", "fnc", "forc", "behaved", "carray", "farray",
"num"]
for s in attr:
assert_raises(AttributeError, delattr, a, s)
class TestArrayInterface():
class Foo:
def __init__(self, value):
self.value = value
self.iface = {'typestr': 'f8'}
def __float__(self):
return float(self.value)
@property
def __array_interface__(self):
return self.iface
f = Foo(0.5)
@pytest.mark.parametrize('val, iface, expected', [
(f, {}, 0.5),
([f], {}, [0.5]),
([f, f], {}, [0.5, 0.5]),
(f, {'shape': ()}, 0.5),
(f, {'shape': None}, TypeError),
(f, {'shape': (1, 1)}, [[0.5]]),
(f, {'shape': (2,)}, ValueError),
(f, {'strides': ()}, 0.5),
(f, {'strides': (2,)}, ValueError),
(f, {'strides': 16}, TypeError),
])
def test_scalar_interface(self, val, iface, expected):
self.f.iface = {'typestr': 'f8'}
self.f.iface.update(iface)
if HAS_REFCOUNT:
pre_cnt = sys.getrefcount(np.dtype('f8'))
if isinstance(expected, type):
assert_raises(expected, np.array, val)
else:
result = np.array(val)
assert_equal(np.array(val), expected)
assert result.dtype == 'f8'
del result
if HAS_REFCOUNT:
post_cnt = sys.getrefcount(np.dtype('f8'))
assert_equal(pre_cnt, post_cnt)
def test_interface_no_shape():
class ArrayLike:
array = np.array(1)
__array_interface__ = array.__array_interface__
assert_equal(np.array(ArrayLike()), 1)
def test_array_interface_itemsize():
my_dtype = np.dtype({'names': ['A', 'B'], 'formats': ['f4', 'f4'],
'offsets': [0, 8], 'itemsize': 16})
a = np.ones(10, dtype=my_dtype)
descr_t = np.dtype(a.__array_interface__['descr'])
typestr_t = np.dtype(a.__array_interface__['typestr'])
assert_equal(descr_t.itemsize, typestr_t.itemsize)
def test_array_interface_empty_shape():
arr = np.array([1, 2, 3])
interface1 = dict(arr.__array_interface__)
interface1['shape'] = ()
class DummyArray1:
__array_interface__ = interface1
interface2 = dict(interface1)
interface2['data'] = arr[0].tobytes()
class DummyArray2:
__array_interface__ = interface2
arr1 = np.asarray(DummyArray1())
arr2 = np.asarray(DummyArray2())
arr3 = arr[:1].reshape(())
assert_equal(arr1, arr2)
assert_equal(arr1, arr3)
def test_array_interface_offset():
arr = np.array([1, 2, 3], dtype='int32')
interface = dict(arr.__array_interface__)
interface['data'] = memoryview(arr)
interface['shape'] = (2,)
class DummyArray:
__array_interface__ = interface
arr1 = np.asarray(DummyArray())
assert_equal(arr1, arr[1:])
def test_array_interface_unicode_typestr():
arr = np.array([1, 2, 3], dtype='int32')
interface = dict(arr.__array_interface__)
class DummyArray:
__array_interface__ = interface
with pytest.raises(TypeError):
np.asarray(DummyArray())
def test_flat_element_deletion():
it = np.ones(3).flat
try:
del it[1]
del it[1:2]
except TypeError:
pass
except Exception:
raise AssertionError
def test_scalar_element_deletion():
a = np.zeros(2, dtype=[('x', 'int'), ('y', 'int')])
assert_raises(ValueError, a[0].__delitem__, 'x')
class TestAsCArray:
def test_1darray(self):
array = np.arange(24, dtype=np.double)
from_c = _multiarray_tests.test_as_c_array(array, 3)
assert_equal(array[3], from_c)
def test_2darray(self):
array = np.arange(24, dtype=np.double).reshape(3, 8)
from_c = _multiarray_tests.test_as_c_array(array, 2, 4)
assert_equal(array[2, 4], from_c)
def test_3darray(self):
array = np.arange(24, dtype=np.double).reshape(2, 3, 4)
from_c = _multiarray_tests.test_as_c_array(array, 1, 2, 3)
assert_equal(array[1, 2, 3], from_c)
def test_array_scalar_relational_operation(self):
for dt1 in np.typecodes['AllInteger']:
assert_(1 > np.array(0, dtype=dt1), "type %s failed" % (dt1,))
assert_(not 1 < np.array(0, dtype=dt1), "type %s failed" % (dt1,))
for dt2 in np.typecodes['AllInteger']:
assert_(np.array(1, dtype=dt1) > np.array(0, dtype=dt2),
"type %s and %s failed" % (dt1, dt2))
assert_(not np.array(1, dtype=dt1) < np.array(0, dtype=dt2),
"type %s and %s failed" % (dt1, dt2))
for dt1 in 'BHILQP':
assert_(-1 < np.array(1, dtype=dt1), "type %s failed" % (dt1,))
assert_(not -1 > np.array(1, dtype=dt1), "type %s failed" % (dt1,))
assert_(-1 != np.array(1, dtype=dt1), "type %s failed" % (dt1,))
for dt2 in 'bhilqp':
assert_(np.array(1, dtype=dt1) > np.array(-1, dtype=dt2),
"type %s and %s failed" % (dt1, dt2))
assert_(not np.array(1, dtype=dt1) < np.array(-1, dtype=dt2),
"type %s and %s failed" % (dt1, dt2))
assert_(np.array(1, dtype=dt1) != np.array(-1, dtype=dt2),
"type %s and %s failed" % (dt1, dt2))
for dt1 in 'bhlqp' + np.typecodes['Float']:
assert_(1 > np.array(-1, dtype=dt1), "type %s failed" % (dt1,))
assert_(not 1 < np.array(-1, dtype=dt1), "type %s failed" % (dt1,))
assert_(-1 == np.array(-1, dtype=dt1), "type %s failed" % (dt1,))
for dt2 in 'bhlqp' + np.typecodes['Float']:
assert_(np.array(1, dtype=dt1) > np.array(-1, dtype=dt2),
"type %s and %s failed" % (dt1, dt2))
assert_(not np.array(1, dtype=dt1) < np.array(-1, dtype=dt2),
"type %s and %s failed" % (dt1, dt2))
assert_(np.array(-1, dtype=dt1) == np.array(-1, dtype=dt2),
"type %s and %s failed" % (dt1, dt2))
def test_to_bool_scalar(self):
assert_equal(bool(np.array([False])), False)
assert_equal(bool(np.array([True])), True)
assert_equal(bool(np.array([[42]])), True)
assert_raises(ValueError, bool, np.array([1, 2]))
class NotConvertible:
def __bool__(self):
raise NotImplementedError
assert_raises(NotImplementedError, bool, np.array(NotConvertible()))
assert_raises(NotImplementedError, bool, np.array([NotConvertible()]))
if IS_PYSTON:
pytest.skip("Pyston disables recursion checking")
self_containing = np.array([None])
self_containing[0] = self_containing
Error = RecursionError
assert_raises(Error, bool, self_containing)
self_containing[0] = None
def test_to_int_scalar(self):
int_funcs = (int, lambda x: x.__int__())
for int_func in int_funcs:
assert_equal(int_func(np.array(0)), 0)
with assert_warns(DeprecationWarning):
assert_equal(int_func(np.array([1])), 1)
with assert_warns(DeprecationWarning):
assert_equal(int_func(np.array([[42]])), 42)
assert_raises(TypeError, int_func, np.array([1, 2]))
assert_equal(4, int_func(np.array('4')))
assert_equal(5, int_func(np.bytes_(b'5')))
assert_equal(6, int_func(np.str_('6')))
if sys.version_info < (3, 11):
class HasTrunc:
def __trunc__(self):
return 3
assert_equal(3, int_func(np.array(HasTrunc())))
with assert_warns(DeprecationWarning):
assert_equal(3, int_func(np.array([HasTrunc()])))
else:
pass
class NotConvertible:
def __int__(self):
raise NotImplementedError
assert_raises(NotImplementedError, int_func, np.array(NotConvertible()))
with assert_warns(DeprecationWarning):
assert_raises(NotImplementedError, int_func, np.array([NotConvertible()]))
class TestWhere:
def test_basic(self):
dts = [bool, np.int16, np.int32, np.int64, np.double, np.complex128,
np.longdouble, np.clongdouble]
for dt in dts:
c = np.ones(53, dtype=bool)
assert_equal(np.where( c, dt(0), dt(1)), dt(0))
assert_equal(np.where(~c, dt(0), dt(1)), dt(1))
assert_equal(np.where(True, dt(0), dt(1)), dt(0))
assert_equal(np.where(False, dt(0), dt(1)), dt(1))
d = np.ones_like(c).astype(dt)
e = np.zeros_like(d)
r = d.astype(dt)
c[7] = False
r[7] = e[7]
assert_equal(np.where(c, e, e), e)
assert_equal(np.where(c, d, e), r)
assert_equal(np.where(c, d, e[0]), r)
assert_equal(np.where(c, d[0], e), r)
assert_equal(np.where(c[::2], d[::2], e[::2]), r[::2])
assert_equal(np.where(c[1::2], d[1::2], e[1::2]), r[1::2])
assert_equal(np.where(c[::3], d[::3], e[::3]), r[::3])
assert_equal(np.where(c[1::3], d[1::3], e[1::3]), r[1::3])
assert_equal(np.where(c[::-2], d[::-2], e[::-2]), r[::-2])
assert_equal(np.where(c[::-3], d[::-3], e[::-3]), r[::-3])
assert_equal(np.where(c[1::-3], d[1::-3], e[1::-3]), r[1::-3])
@pytest.mark.skipif(IS_WASM, reason="no wasm fp exception support")
def test_exotic(self):
assert_array_equal(np.where(True, None, None), np.array(None))
m = np.array([], dtype=bool).reshape(0, 3)
b = np.array([], dtype=np.float64).reshape(0, 3)
assert_array_equal(np.where(m, 0, b), np.array([]).reshape(0, 3))
d = np.array([-1.34, -0.16, -0.54, -0.31, -0.08, -0.95, 0.000, 0.313,
0.547, -0.18, 0.876, 0.236, 1.969, 0.310, 0.699, 1.013,
1.267, 0.229, -1.39, 0.487])
nan = float('NaN')
e = np.array(['5z', '0l', nan, 'Wz', nan, nan, 'Xq', 'cs', nan, nan,
'QN', nan, nan, 'Fd', nan, nan, 'kp', nan, '36', 'i1'],
dtype=object)
m = np.array([0, 0, 1, 0, 1, 1, 0, 0, 1, 1,
0, 1, 1, 0, 1, 1, 0, 1, 0, 0], dtype=bool)
r = e[:]
r[np.where(m)] = d[np.where(m)]
assert_array_equal(np.where(m, d, e), r)
r = e[:]
r[np.where(~m)] = d[np.where(~m)]
assert_array_equal(np.where(m, e, d), r)
assert_array_equal(np.where(m, e, e), e)
d = np.array([1., 2.], dtype=np.float32)
e = float('NaN')
assert_equal(np.where(True, d, e).dtype, np.float32)
e = float('Infinity')
assert_equal(np.where(True, d, e).dtype, np.float32)
e = float('-Infinity')
assert_equal(np.where(True, d, e).dtype, np.float32)
e = float(1e150)
with pytest.warns(RuntimeWarning, match="overflow"):
res = np.where(True, d, e)
assert res.dtype == np.float32
def test_ndim(self):
c = [True, False]
a = np.zeros((2, 25))
b = np.ones((2, 25))
r = np.where(np.array(c)[:,np.newaxis], a, b)
assert_array_equal(r[0], a[0])
assert_array_equal(r[1], b[0])
r = np.where(c, a.T, b.T)
assert_array_equal(r[:,0], a[:,0])
assert_array_equal(r[:,1], b[:,0])
def test_dtype_mix(self):
c = np.array([False, True, False, False, False, False, True, False,
False, False, True, False])
a = np.uint32(1)
b = np.array([5., 0., 3., 2., -1., -4., 0., -10., 10., 1., 0., 3.],
dtype=np.float64)
r = np.array([5., 1., 3., 2., -1., -4., 1., -10., 10., 1., 1., 3.],
dtype=np.float64)
assert_equal(np.where(c, a, b), r)
a = a.astype(np.float32)
b = b.astype(np.int64)
assert_equal(np.where(c, a, b), r)
c = c.astype(int)
c[c != 0] = 34242324
assert_equal(np.where(c, a, b), r)
tmpmask = c != 0
c[c == 0] = 41247212
c[tmpmask] = 0
assert_equal(np.where(c, b, a), r)
def test_foreign(self):
c = np.array([False, True, False, False, False, False, True, False,
False, False, True, False])
r = np.array([5., 1., 3., 2., -1., -4., 1., -10., 10., 1., 1., 3.],
dtype=np.float64)
a = np.ones(1, dtype='>i4')
b = np.array([5., 0., 3., 2., -1., -4., 0., -10., 10., 1., 0., 3.],
dtype=np.float64)
assert_equal(np.where(c, a, b), r)
b = b.astype('>f8')
assert_equal(np.where(c, a, b), r)
a = a.astype('<i4')
assert_equal(np.where(c, a, b), r)
c = c.astype('>i4')
assert_equal(np.where(c, a, b), r)
def test_error(self):
c = [True, True]
a = np.ones((4, 5))
b = np.ones((5, 5))
assert_raises(ValueError, np.where, c, a, a)
assert_raises(ValueError, np.where, c[0], a, b)
def test_string(self):
a = np.array("abc")
b = np.array("x" * 753)
assert_equal(np.where(True, a, b), "abc")
assert_equal(np.where(False, b, a), "abc")
a = np.array("abcd")
b = np.array("x" * 8)
assert_equal(np.where(True, a, b), "abcd")
assert_equal(np.where(False, b, a), "abcd")
def test_empty_result(self):
x = np.zeros((1, 1))
ibad = np.vstack(np.where(x == 99.))
assert_array_equal(ibad,
np.atleast_2d(np.array([[],[]], dtype=np.intp)))
def test_largedim(self):
shape = [10, 2, 3, 4, 5, 6]
np.random.seed(2)
array = np.random.rand(*shape)
for i in range(10):
benchmark = array.nonzero()
result = array.nonzero()
assert_array_equal(benchmark, result)
def test_kwargs(self):
a = np.zeros(1)
with assert_raises(TypeError):
np.where(a, x=a, y=a)
if not IS_PYPY:
class TestSizeOf:
def test_empty_array(self):
x = np.array([])
assert_(sys.getsizeof(x) > 0)
def check_array(self, dtype):
elem_size = dtype(0).itemsize
for length in [10, 50, 100, 500]:
x = np.arange(length, dtype=dtype)
assert_(sys.getsizeof(x) > length * elem_size)
def test_array_int32(self):
self.check_array(np.int32)
def test_array_int64(self):
self.check_array(np.int64)
def test_array_float32(self):
self.check_array(np.float32)
def test_array_float64(self):
self.check_array(np.float64)
def test_view(self):
d = np.ones(100)
assert_(sys.getsizeof(d[...]) < sys.getsizeof(d))
def test_reshape(self):
d = np.ones(100)
assert_(sys.getsizeof(d) < sys.getsizeof(d.reshape(100, 1, 1).copy()))
@_no_tracing
def test_resize(self):
d = np.ones(100)
old = sys.getsizeof(d)
d.resize(50)
assert_(old > sys.getsizeof(d))
d.resize(150)
assert_(old < sys.getsizeof(d))
def test_error(self):
d = np.ones(100)
assert_raises(TypeError, d.__sizeof__, "a")
class TestHashing:
def test_arrays_not_hashable(self):
x = np.ones(3)
assert_raises(TypeError, hash, x)
def test_collections_hashable(self):
x = np.array([])
assert_(not isinstance(x, collections.abc.Hashable))
class TestArrayPriority:
op = operator
binary_ops = [
op.pow, op.add, op.sub, op.mul, op.floordiv, op.truediv, op.mod,
op.and_, op.or_, op.xor, op.lshift, op.rshift, op.mod, op.gt,
op.ge, op.lt, op.le, op.ne, op.eq
]
class Foo(np.ndarray):
__array_priority__ = 100.
def __new__(cls, *args, **kwargs):
return np.array(*args, **kwargs).view(cls)
class Bar(np.ndarray):
__array_priority__ = 101.
def __new__(cls, *args, **kwargs):
return np.array(*args, **kwargs).view(cls)
class Other:
__array_priority__ = 1000.
def _all(self, other):
return self.__class__()
__add__ = __radd__ = _all
__sub__ = __rsub__ = _all
__mul__ = __rmul__ = _all
__pow__ = __rpow__ = _all
__div__ = __rdiv__ = _all
__mod__ = __rmod__ = _all
__truediv__ = __rtruediv__ = _all
__floordiv__ = __rfloordiv__ = _all
__and__ = __rand__ = _all
__xor__ = __rxor__ = _all
__or__ = __ror__ = _all
__lshift__ = __rlshift__ = _all
__rshift__ = __rrshift__ = _all
__eq__ = _all
__ne__ = _all
__gt__ = _all
__ge__ = _all
__lt__ = _all
__le__ = _all
def test_ndarray_subclass(self):
a = np.array([1, 2])
b = self.Bar([1, 2])
for f in self.binary_ops:
msg = repr(f)
assert_(isinstance(f(a, b), self.Bar), msg)
assert_(isinstance(f(b, a), self.Bar), msg)
def test_ndarray_other(self):
a = np.array([1, 2])
b = self.Other()
for f in self.binary_ops:
msg = repr(f)
assert_(isinstance(f(a, b), self.Other), msg)
assert_(isinstance(f(b, a), self.Other), msg)
def test_subclass_subclass(self):
a = self.Foo([1, 2])
b = self.Bar([1, 2])
for f in self.binary_ops:
msg = repr(f)
assert_(isinstance(f(a, b), self.Bar), msg)
assert_(isinstance(f(b, a), self.Bar), msg)
def test_subclass_other(self):
a = self.Foo([1, 2])
b = self.Other()
for f in self.binary_ops:
msg = repr(f)
assert_(isinstance(f(a, b), self.Other), msg)
assert_(isinstance(f(b, a), self.Other), msg)
class TestBytestringArrayNonzero:
def test_empty_bstring_array_is_falsey(self):
assert_(not np.array([''], dtype=str))
def test_whitespace_bstring_array_is_truthy(self):
a = np.array(['spam'], dtype=str)
a[0] = ' \0\0'
assert_(a)
def test_all_null_bstring_array_is_falsey(self):
a = np.array(['spam'], dtype=str)
a[0] = '\0\0\0\0'
assert_(not a)
def test_null_inside_bstring_array_is_truthy(self):
a = np.array(['spam'], dtype=str)
a[0] = ' \0 \0'
assert_(a)
class TestUnicodeEncoding:
"""
Tests for encoding related bugs, such as UCS2 vs UCS4, round-tripping
issues, etc
"""
def test_round_trip(self):
""" Tests that GETITEM, SETITEM, and PyArray_Scalar roundtrip """
arr = np.zeros(shape=(), dtype="U1")
for i in range(1, sys.maxunicode + 1):
expected = chr(i)
arr[()] = expected
assert arr[()] == expected
assert arr.item() == expected
def test_assign_scalar(self):
l = np.array(['aa', 'bb'])
l[:] = np.str_('cc')
assert_equal(l, ['cc', 'cc'])
def test_fill_scalar(self):
l = np.array(['aa', 'bb'])
l.fill(np.str_('cc'))
assert_equal(l, ['cc', 'cc'])
class TestUnicodeArrayNonzero:
def test_empty_ustring_array_is_falsey(self):
assert_(not np.array([''], dtype=np.str_))
def test_whitespace_ustring_array_is_truthy(self):
a = np.array(['eggs'], dtype=np.str_)
a[0] = ' \0\0'
assert_(a)
def test_all_null_ustring_array_is_falsey(self):
a = np.array(['eggs'], dtype=np.str_)
a[0] = '\0\0\0\0'
assert_(not a)
def test_null_inside_ustring_array_is_truthy(self):
a = np.array(['eggs'], dtype=np.str_)
a[0] = ' \0 \0'
assert_(a)
class TestFormat:
def test_0d(self):
a = np.array(np.pi)
assert_equal('{:0.3g}'.format(a), '3.14')
assert_equal('{:0.3g}'.format(a[()]), '3.14')
def test_1d_no_format(self):
a = np.array([np.pi])
assert_equal('{}'.format(a), str(a))
def test_1d_format(self):
a = np.array([np.pi])
assert_raises(TypeError, '{:30}'.format, a)
from numpy.testing import IS_PYPY
class TestCTypes:
def test_ctypes_is_available(self):
test_arr = np.array([[1, 2, 3], [4, 5, 6]])
assert_equal(ctypes, test_arr.ctypes._ctypes)
assert_equal(tuple(test_arr.ctypes.shape), (2, 3))
def test_ctypes_is_not_available(self):
from numpy._core import _internal
_internal.ctypes = None
try:
test_arr = np.array([[1, 2, 3], [4, 5, 6]])
assert_(isinstance(test_arr.ctypes._ctypes,
_internal._missing_ctypes))
assert_equal(tuple(test_arr.ctypes.shape), (2, 3))
finally:
_internal.ctypes = ctypes
def _make_readonly(x):
x.flags.writeable = False
return x
@pytest.mark.parametrize('arr', [
np.array([1, 2, 3]),
np.array([['one', 'two'], ['three', 'four']]),
np.array((1, 2), dtype='i4,i4'),
np.zeros((2,), dtype=
np.dtype(dict(
formats=['<i4', '<i4'],
names=['a', 'b'],
offsets=[0, 2],
itemsize=6
))
),
np.array([None], dtype=object),
np.array([]),
np.empty((0, 0)),
_make_readonly(np.array([1, 2, 3])),
], ids=[
'1d',
'2d',
'structured',
'overlapping',
'object',
'empty',
'empty-2d',
'readonly'
])
def test_ctypes_data_as_holds_reference(self, arr):
arr = arr.copy()
arr_ref = weakref.ref(arr)
ctypes_ptr = arr.ctypes.data_as(ctypes.c_void_p)
del arr
break_cycles()
assert_(arr_ref() is not None, "ctypes pointer did not hold onto a reference")
del ctypes_ptr
if IS_PYPY:
break_cycles()
assert_(arr_ref() is None, "unknowable whether ctypes pointer holds a reference")
def test_ctypes_as_parameter_holds_reference(self):
arr = np.array([None]).copy()
arr_ref = weakref.ref(arr)
ctypes_ptr = arr.ctypes._as_parameter_
del arr
break_cycles()
assert_(arr_ref() is not None, "ctypes pointer did not hold onto a reference")
del ctypes_ptr
if IS_PYPY:
break_cycles()
assert_(arr_ref() is None, "unknowable whether ctypes pointer holds a reference")
class TestWritebackIfCopy:
def test_argmax_with_out(self):
mat = np.eye(5)
out = np.empty(5, dtype='i2')
res = np.argmax(mat, 0, out=out)
assert_equal(res, range(5))
def test_argmin_with_out(self):
mat = -np.eye(5)
out = np.empty(5, dtype='i2')
res = np.argmin(mat, 0, out=out)
assert_equal(res, range(5))
def test_insert_noncontiguous(self):
a = np.arange(6).reshape(2, 3).T
np.place(a, a > 2, [44, 55])
assert_equal(a, np.array([[0, 44], [1, 55], [2, 44]]))
assert_raises(ValueError, np.place, a, a > 20, [])
def test_put_noncontiguous(self):
a = np.arange(6).reshape(2, 3).T
np.put(a, [0, 2], [44, 55])
assert_equal(a, np.array([[44, 3], [55, 4], [2, 5]]))
def test_putmask_noncontiguous(self):
a = np.arange(6).reshape(2, 3).T
np.putmask(a, a > 2, a ** 2)
assert_equal(a, np.array([[0, 9], [1, 16], [2, 25]]))
def test_take_mode_raise(self):
a = np.arange(6, dtype='int')
out = np.empty(2, dtype='int')
np.take(a, [0, 2], out=out, mode='raise')
assert_equal(out, np.array([0, 2]))
def test_choose_mod_raise(self):
a = np.array([[1, 0, 1], [0, 1, 0], [1, 0, 1]])
out = np.empty((3, 3), dtype='int')
choices = [-10, 10]
np.choose(a, choices, out=out, mode='raise')
assert_equal(out, np.array([[10, -10, 10], [-10, 10, -10], [10, -10, 10]]))
def test_flatiter__array__(self):
a = np.arange(9).reshape(3, 3)
b = a.T.flat
c = b.__array__()
del c
def test_dot_out(self):
a = np.arange(9, dtype=float).reshape(3, 3)
b = np.dot(a, a, out=a)
assert_equal(b, np.array([[15, 18, 21], [42, 54, 66], [69, 90, 111]]))
def test_view_assign(self):
from numpy._core._multiarray_tests import (
npy_create_writebackifcopy, npy_resolve
)
arr = np.arange(9).reshape(3, 3).T
arr_wb = npy_create_writebackifcopy(arr)
assert_(arr_wb.flags.writebackifcopy)
assert_(arr_wb.base is arr)
arr_wb[...] = -100
npy_resolve(arr_wb)
assert_equal(arr, -100)
assert_(arr_wb.ctypes.data != 0)
assert_equal(arr_wb.base, None)
arr_wb[...] = 100
assert_equal(arr, -100)
@pytest.mark.leaks_references(
reason="increments self in dealloc; ignore since deprecated path.")
def test_dealloc_warning(self):
with suppress_warnings() as sup:
sup.record(RuntimeWarning)
arr = np.arange(9).reshape(3, 3)
v = arr.T
_multiarray_tests.npy_abuse_writebackifcopy(v)
assert len(sup.log) == 1
def test_view_discard_refcount(self):
from numpy._core._multiarray_tests import (
npy_create_writebackifcopy, npy_discard
)
arr = np.arange(9).reshape(3, 3).T
orig = arr.copy()
if HAS_REFCOUNT:
arr_cnt = sys.getrefcount(arr)
arr_wb = npy_create_writebackifcopy(arr)
assert_(arr_wb.flags.writebackifcopy)
assert_(arr_wb.base is arr)
arr_wb[...] = -100
npy_discard(arr_wb)
assert_equal(arr, orig)
assert_(arr_wb.ctypes.data != 0)
assert_equal(arr_wb.base, None)
if HAS_REFCOUNT:
assert_equal(arr_cnt, sys.getrefcount(arr))
arr_wb[...] = 100
assert_equal(arr, orig)
class TestArange:
def test_infinite(self):
assert_raises_regex(
ValueError, "size exceeded",
np.arange, 0, np.inf
)
def test_nan_step(self):
assert_raises_regex(
ValueError, "cannot compute length",
np.arange, 0, 1, np.nan
)
def test_zero_step(self):
assert_raises(ZeroDivisionError, np.arange, 0, 10, 0)
assert_raises(ZeroDivisionError, np.arange, 0.0, 10.0, 0.0)
assert_raises(ZeroDivisionError, np.arange, 0, 0, 0)
assert_raises(ZeroDivisionError, np.arange, 0.0, 0.0, 0.0)
def test_require_range(self):
assert_raises(TypeError, np.arange)
assert_raises(TypeError, np.arange, step=3)
assert_raises(TypeError, np.arange, dtype='int64')
assert_raises(TypeError, np.arange, start=4)
def test_start_stop_kwarg(self):
keyword_stop = np.arange(stop=3)
keyword_zerotostop = np.arange(0, stop=3)
keyword_start_stop = np.arange(start=3, stop=9)
assert len(keyword_stop) == 3
assert len(keyword_zerotostop) == 3
assert len(keyword_start_stop) == 6
assert_array_equal(keyword_stop, keyword_zerotostop)
def test_arange_booleans(self):
res = np.arange(False, dtype=bool)
assert_array_equal(res, np.array([], dtype="bool"))
res = np.arange(True, dtype="bool")
assert_array_equal(res, [False])
res = np.arange(2, dtype="bool")
assert_array_equal(res, [False, True])
res = np.arange(6, 8, dtype="bool")
assert_array_equal(res, [True, True])
with pytest.raises(TypeError):
np.arange(3, dtype="bool")
@pytest.mark.parametrize("dtype", ["S3", "U", "5i"])
def test_rejects_bad_dtypes(self, dtype):
dtype = np.dtype(dtype)
DType_name = re.escape(str(type(dtype)))
with pytest.raises(TypeError,
match=rf"arange\(\) not supported for inputs .* {DType_name}"):
np.arange(2, dtype=dtype)
def test_rejects_strings(self):
DType_name = re.escape(str(type(np.array("a").dtype)))
with pytest.raises(TypeError,
match=rf"arange\(\) not supported for inputs .* {DType_name}"):
np.arange("a", "b")
def test_byteswapped(self):
res_be = np.arange(1, 1000, dtype=">i4")
res_le = np.arange(1, 1000, dtype="<i4")
assert res_be.dtype == ">i4"
assert res_le.dtype == "<i4"
assert_array_equal(res_le, res_be)
@pytest.mark.parametrize("which", [0, 1, 2])
def test_error_paths_and_promotion(self, which):
args = [0, 1, 2]
args[which] = np.float64(2.)
assert np.arange(*args).dtype == np.float64
args[which] = [None, []]
with pytest.raises(ValueError):
np.arange(*args)
class TestArrayFinalize:
""" Tests __array_finalize__ """
def test_receives_base(self):
class SavesBase(np.ndarray):
def __array_finalize__(self, obj):
self.saved_base = self.base
a = np.array(1).view(SavesBase)
assert_(a.saved_base is a.base)
def test_bad_finalize1(self):
class BadAttributeArray(np.ndarray):
@property
def __array_finalize__(self):
raise RuntimeError("boohoo!")
with pytest.raises(TypeError, match="not callable"):
np.arange(10).view(BadAttributeArray)
def test_bad_finalize2(self):
class BadAttributeArray(np.ndarray):
def __array_finalize__(self):
raise RuntimeError("boohoo!")
with pytest.raises(TypeError, match="takes 1 positional"):
np.arange(10).view(BadAttributeArray)
def test_bad_finalize3(self):
class BadAttributeArray(np.ndarray):
def __array_finalize__(self, obj):
raise RuntimeError("boohoo!")
with pytest.raises(RuntimeError, match="boohoo!"):
np.arange(10).view(BadAttributeArray)
def test_lifetime_on_error(self):
class RaisesInFinalize(np.ndarray):
def __array_finalize__(self, obj):
raise Exception(self)
class Dummy: pass
obj_arr = np.array(Dummy())
obj_ref = weakref.ref(obj_arr[()])
with assert_raises(Exception) as e:
obj_arr.view(RaisesInFinalize)
obj_subarray = e.exception.args[0]
del e
assert_(isinstance(obj_subarray, RaisesInFinalize))
break_cycles()
assert_(obj_ref() is not None, "object should not already be dead")
del obj_arr
break_cycles()
assert_(obj_ref() is not None, "obj_arr should not hold the last reference")
del obj_subarray
break_cycles()
assert_(obj_ref() is None, "no references should remain")
def test_can_use_super(self):
class SuperFinalize(np.ndarray):
def __array_finalize__(self, obj):
self.saved_result = super().__array_finalize__(obj)
a = np.array(1).view(SuperFinalize)
assert_(a.saved_result is None)
def test_orderconverter_with_nonASCII_unicode_ordering():
a = np.arange(5)
assert_raises(ValueError, a.flatten, order='\xe2')
def test_equal_override():
class MyAlwaysEqual:
def __eq__(self, other):
return "eq"
def __ne__(self, other):
return "ne"
class MyAlwaysEqualOld(MyAlwaysEqual):
__array_priority__ = 10000
class MyAlwaysEqualNew(MyAlwaysEqual):
__array_ufunc__ = None
array = np.array([(0, 1), (2, 3)], dtype='i4,i4')
for my_always_equal_cls in MyAlwaysEqualOld, MyAlwaysEqualNew:
my_always_equal = my_always_equal_cls()
assert_equal(my_always_equal == array, 'eq')
assert_equal(array == my_always_equal, 'eq')
assert_equal(my_always_equal != array, 'ne')
assert_equal(array != my_always_equal, 'ne')
@pytest.mark.parametrize("op", [operator.eq, operator.ne])
@pytest.mark.parametrize(["dt1", "dt2"], [
([("f", "i")], [("f", "i")]),
("M8", "d"),
("d", "d"),
])
def test_equal_subclass_no_override(op, dt1, dt2):
class MyArr(np.ndarray):
called_wrap = 0
def __array_wrap__(self, new, context=None, return_scalar=False):
type(self).called_wrap += 1
return super().__array_wrap__(new)
numpy_arr = np.zeros(5, dtype=dt1)
my_arr = np.zeros(5, dtype=dt2).view(MyArr)
assert type(op(numpy_arr, my_arr)) is MyArr
assert type(op(my_arr, numpy_arr)) is MyArr
assert MyArr.called_wrap == 2
@pytest.mark.parametrize(["dt1", "dt2"], [
("M8[ns]", "d"),
("M8[s]", "l"),
("m8[ns]", "d"),
("M8[s]", "m8[s]"),
("S5", "U5"),
])
def test_no_loop_gives_all_true_or_false(dt1, dt2):
arr1 = np.random.randint(5, size=100).astype(dt1)
arr2 = np.random.randint(5, size=99)[:, np.newaxis].astype(dt2)
res = arr1 == arr2
assert res.shape == (99, 100)
assert res.dtype == bool
assert not res.any()
res = arr1 != arr2
assert res.shape == (99, 100)
assert res.dtype == bool
assert res.all()
arr2 = np.random.randint(5, size=99).astype(dt2)
with pytest.raises(ValueError):
arr1 == arr2
with pytest.raises(ValueError):
arr1 != arr2
with pytest.raises(np._core._exceptions._UFuncNoLoopError):
arr1 > arr2
@pytest.mark.parametrize("op", [
operator.eq, operator.ne, operator.le, operator.lt, operator.ge,
operator.gt])
def test_comparisons_forwards_error(op):
class NotArray:
def __array__(self, dtype=None, copy=None):
raise TypeError("run you fools")
with pytest.raises(TypeError, match="run you fools"):
op(np.arange(2), NotArray())
with pytest.raises(TypeError, match="run you fools"):
op(NotArray(), np.arange(2))
def test_richcompare_scalar_boolean_singleton_return():
assert (np.array(0) == "a") is False
assert (np.array(0) != "a") is True
assert (np.int16(0) == "a") is False
assert (np.int16(0) != "a") is True
@pytest.mark.parametrize("op", [
operator.eq, operator.ne, operator.le, operator.lt, operator.ge,
operator.gt])
def test_ragged_comparison_fails(op):
a = np.array([1, np.array([1, 2, 3])], dtype=object)
b = np.array([1, np.array([1, 2, 3])], dtype=object)
with pytest.raises(ValueError, match="The truth value.*ambiguous"):
op(a, b)
@pytest.mark.parametrize(
["fun", "npfun"],
[
(_multiarray_tests.npy_cabs, np.absolute),
(_multiarray_tests.npy_carg, np.angle)
]
)
@pytest.mark.parametrize("x", [1, np.inf, -np.inf, np.nan])
@pytest.mark.parametrize("y", [1, np.inf, -np.inf, np.nan])
@pytest.mark.parametrize("test_dtype", np.complexfloating.__subclasses__())
def test_npymath_complex(fun, npfun, x, y, test_dtype):
z = test_dtype(complex(x, y))
with np.errstate(invalid='ignore'):
got = fun(z)
expected = npfun(z)
assert_allclose(got, expected)
def test_npymath_real():
from numpy._core._multiarray_tests import (
npy_log10, npy_cosh, npy_sinh, npy_tan, npy_tanh)
funcs = {npy_log10: np.log10,
npy_cosh: np.cosh,
npy_sinh: np.sinh,
npy_tan: np.tan,
npy_tanh: np.tanh}
vals = (1, np.inf, -np.inf, np.nan)
types = (np.float32, np.float64, np.longdouble)
with np.errstate(all='ignore'):
for fun, npfun in funcs.items():
for x, t in itertools.product(vals, types):
z = t(x)
got = fun(z)
expected = npfun(z)
assert_allclose(got, expected)
def test_uintalignment_and_alignment():
d1 = np.dtype('u1,c8', align=True)
d2 = np.dtype('u4,c8', align=True)
d3 = np.dtype({'names': ['a', 'b'], 'formats': ['u1', d1]}, align=True)
assert_equal(np.zeros(1, dtype=d1)['f1'].flags['ALIGNED'], True)
assert_equal(np.zeros(1, dtype=d2)['f1'].flags['ALIGNED'], True)
assert_equal(np.zeros(1, dtype='u1,c8')['f1'].flags['ALIGNED'], False)
s = _multiarray_tests.get_struct_alignments()
for d, (alignment, size) in zip([d1,d2,d3], s):
assert_equal(d.alignment, alignment)
assert_equal(d.itemsize, size)
src = np.zeros((2,2), dtype=d1)['f1']
np.exp(src)
dst = np.zeros((2,2), dtype='c8')
dst[:,1] = src[:,1]
class TestAlignment:
def check(self, shape, dtype, order, align):
err_msg = repr((shape, dtype, order, align))
x = _aligned_zeros(shape, dtype, order, align=align)
if align is None:
align = np.dtype(dtype).alignment
assert_equal(x.__array_interface__['data'][0] % align, 0)
if hasattr(shape, '__len__'):
assert_equal(x.shape, shape, err_msg)
else:
assert_equal(x.shape, (shape,), err_msg)
assert_equal(x.dtype, dtype)
if order == "C":
assert_(x.flags.c_contiguous, err_msg)
elif order == "F":
if x.size > 0:
assert_(x.flags.f_contiguous, err_msg)
elif order is None:
assert_(x.flags.c_contiguous, err_msg)
else:
raise ValueError()
def test_various_alignments(self):
for align in [1, 2, 3, 4, 8, 12, 16, 32, 64, None]:
for n in [0, 1, 3, 11]:
for order in ["C", "F", None]:
for dtype in list(np.typecodes["All"]) + ['i4,i4,i4']:
if dtype == 'O':
continue
for shape in [n, (1, 2, 3, n)]:
self.check(shape, np.dtype(dtype), order, align)
def test_strided_loop_alignments(self):
for align in [1, 2, 4, 8, 12, 16, None]:
xf64 = _aligned_zeros(3, np.float64)
xc64 = _aligned_zeros(3, np.complex64, align=align)
xf128 = _aligned_zeros(3, np.longdouble, align=align)
with suppress_warnings() as sup:
sup.filter(ComplexWarning, "Casting complex values")
xc64.astype('f8')
xf64.astype(np.complex64)
test = xc64 + xf64
xf128.astype('f8')
xf64.astype(np.longdouble)
test = xf128 + xf64
test = xf128 + xc64
xf64[:] = xf64.copy()
xc64[:] = xc64.copy()
xf128[:] = xf128.copy()
xf64[::2] = xf64[::2].copy()
xc64[::2] = xc64[::2].copy()
xf128[::2] = xf128[::2].copy()
def test_getfield():
a = np.arange(32, dtype='uint16')
if sys.byteorder == 'little':
i = 0
j = 1
else:
i = 1
j = 0
b = a.getfield('int8', i)
assert_equal(b, a)
b = a.getfield('int8', j)
assert_equal(b, 0)
pytest.raises(ValueError, a.getfield, 'uint8', -1)
pytest.raises(ValueError, a.getfield, 'uint8', 16)
pytest.raises(ValueError, a.getfield, 'uint64', 0)
class TestViewDtype:
"""
Verify that making a view of a non-contiguous array works as expected.
"""
def test_smaller_dtype_multiple(self):
x = np.arange(10, dtype='<i4')[::2]
with pytest.raises(ValueError,
match='the last axis must be contiguous'):
x.view('<i2')
expected = [[0, 0], [2, 0], [4, 0], [6, 0], [8, 0]]
assert_array_equal(x[:, np.newaxis].view('<i2'), expected)
def test_smaller_dtype_not_multiple(self):
x = np.arange(5, dtype='<i4')[::2]
with pytest.raises(ValueError,
match='the last axis must be contiguous'):
x.view('S3')
with pytest.raises(ValueError,
match='When changing to a smaller dtype'):
x[:, np.newaxis].view('S3')
expected = [[b''], [b'\x02'], [b'\x04']]
assert_array_equal(x[:, np.newaxis].view('S4'), expected)
def test_larger_dtype_multiple(self):
x = np.arange(20, dtype='<i2').reshape(10, 2)[::2, :]
expected = np.array([[65536], [327684], [589832],
[851980], [1114128]], dtype='<i4')
assert_array_equal(x.view('<i4'), expected)
def test_larger_dtype_not_multiple(self):
x = np.arange(20, dtype='<i2').reshape(10, 2)[::2, :]
with pytest.raises(ValueError,
match='When changing to a larger dtype'):
x.view('S3')
expected = [[b'\x00\x00\x01'], [b'\x04\x00\x05'], [b'\x08\x00\t'],
[b'\x0c\x00\r'], [b'\x10\x00\x11']]
assert_array_equal(x.view('S4'), expected)
def test_f_contiguous(self):
x = np.arange(4 * 3, dtype='<i4').reshape(4, 3).T
with pytest.raises(ValueError,
match='the last axis must be contiguous'):
x.view('<i2')
def test_non_c_contiguous(self):
x = np.arange(2 * 3 * 4, dtype='i1').\
reshape(2, 3, 4).transpose(1, 0, 2)
expected = [[[256, 770], [3340, 3854]],
[[1284, 1798], [4368, 4882]],
[[2312, 2826], [5396, 5910]]]
assert_array_equal(x.view('<i2'), expected)
@pytest.mark.xfail(_SUPPORTS_SVE, reason="gh-22982")
@pytest.mark.parametrize("N", np.arange(1, 512))
@pytest.mark.parametrize("dtype", ['e', 'f', 'd'])
def test_sort_float(N, dtype):
np.random.seed(42)
arr = -0.5 + np.random.sample(N).astype(dtype)
arr[np.random.choice(arr.shape[0], 3)] = np.nan
assert_equal(np.sort(arr, kind='quick'), np.sort(arr, kind='heap'))
infarr = np.inf*np.ones(N, dtype=dtype)
infarr[np.random.choice(infarr.shape[0], 5)] = -1.0
assert_equal(np.sort(infarr, kind='quick'), np.sort(infarr, kind='heap'))
neginfarr = -np.inf*np.ones(N, dtype=dtype)
neginfarr[np.random.choice(neginfarr.shape[0], 5)] = 1.0
assert_equal(np.sort(neginfarr, kind='quick'),
np.sort(neginfarr, kind='heap'))
infarr = np.inf*np.ones(N, dtype=dtype)
infarr[np.random.choice(infarr.shape[0], (int)(N/2))] = -np.inf
assert_equal(np.sort(infarr, kind='quick'), np.sort(infarr, kind='heap'))
def test_sort_float16():
arr = np.arange(65536, dtype=np.int16)
temp = np.frombuffer(arr.tobytes(), dtype=np.float16)
data = np.copy(temp)
np.random.shuffle(data)
data_backup = data
assert_equal(np.sort(data, kind='quick'),
np.sort(data_backup, kind='heap'))
@pytest.mark.parametrize("N", np.arange(1, 512))
@pytest.mark.parametrize("dtype", ['h', 'H', 'i', 'I', 'l', 'L'])
def test_sort_int(N, dtype):
minv = np.iinfo(dtype).min
maxv = np.iinfo(dtype).max
arr = np.random.randint(low=minv, high=maxv-1, size=N, dtype=dtype)
arr[np.random.choice(arr.shape[0], 10)] = minv
arr[np.random.choice(arr.shape[0], 10)] = maxv
assert_equal(np.sort(arr, kind='quick'), np.sort(arr, kind='heap'))
def test_sort_uint():
rng = np.random.default_rng(42)
N = 2047
maxv = np.iinfo(np.uint32).max
arr = rng.integers(low=0, high=maxv, size=N).astype('uint32')
arr[np.random.choice(arr.shape[0], 10)] = maxv
assert_equal(np.sort(arr, kind='quick'), np.sort(arr, kind='heap'))
def test_private_get_ndarray_c_version():
assert isinstance(_get_ndarray_c_version(), int)
@pytest.mark.parametrize("N", np.arange(1, 512))
@pytest.mark.parametrize("dtype", [np.float32, np.float64])
def test_argsort_float(N, dtype):
rnd = np.random.RandomState(116112)
arr = -0.5 + rnd.random(N).astype(dtype)
arr[rnd.choice(arr.shape[0], 3)] = np.nan
assert_arg_sorted(arr, np.argsort(arr, kind='quick'))
arr = -0.5 + rnd.rand(N).astype(dtype)
arr[N-1] = np.inf
assert_arg_sorted(arr, np.argsort(arr, kind='quick'))
@pytest.mark.parametrize("N", np.arange(2, 512))
@pytest.mark.parametrize("dtype", [np.int32, np.uint32, np.int64, np.uint64])
def test_argsort_int(N, dtype):
rnd = np.random.RandomState(1100710816)
minv = np.iinfo(dtype).min
maxv = np.iinfo(dtype).max
arr = rnd.randint(low=minv, high=maxv, size=N, dtype=dtype)
i, j = rnd.choice(N, 2, replace=False)
arr[i] = minv
arr[j] = maxv
assert_arg_sorted(arr, np.argsort(arr, kind='quick'))
arr = rnd.randint(low=minv, high=maxv, size=N, dtype=dtype)
arr[N-1] = maxv
assert_arg_sorted(arr, np.argsort(arr, kind='quick'))
@pytest.mark.skipif(not HAS_REFCOUNT, reason="Python lacks refcounts")
def test_gh_22683():
b = 777.68760986
a = np.array([b] * 10000, dtype=object)
refc_start = sys.getrefcount(b)
np.choose(np.zeros(10000, dtype=int), [a], out=a)
np.choose(np.zeros(10000, dtype=int), [a], out=a)
refc_end = sys.getrefcount(b)
assert refc_end - refc_start < 10
def test_gh_24459():
a = np.zeros((50, 3), dtype=np.float64)
with pytest.raises(TypeError):
np.choose(a, [3, -1])
@pytest.mark.parametrize("N", np.arange(2, 512))
@pytest.mark.parametrize("dtype", [np.int16, np.uint16,
np.int32, np.uint32, np.int64, np.uint64])
def test_partition_int(N, dtype):
rnd = np.random.RandomState(1100710816)
minv = np.iinfo(dtype).min
maxv = np.iinfo(dtype).max
arr = rnd.randint(low=minv, high=maxv, size=N, dtype=dtype)
i, j = rnd.choice(N, 2, replace=False)
arr[i] = minv
arr[j] = maxv
k = rnd.choice(N, 1)[0]
assert_arr_partitioned(np.sort(arr)[k], k,
np.partition(arr, k, kind='introselect'))
assert_arr_partitioned(np.sort(arr)[k], k,
arr[np.argpartition(arr, k, kind='introselect')])
arr = rnd.randint(low=minv, high=maxv, size=N, dtype=dtype)
arr[N-1] = maxv
assert_arr_partitioned(np.sort(arr)[k], k,
np.partition(arr, k, kind='introselect'))
assert_arr_partitioned(np.sort(arr)[k], k,
arr[np.argpartition(arr, k, kind='introselect')])
@pytest.mark.parametrize("N", np.arange(2, 512))
@pytest.mark.parametrize("dtype", [np.float16, np.float32, np.float64])
def test_partition_fp(N, dtype):
rnd = np.random.RandomState(1100710816)
arr = -0.5 + rnd.random(N).astype(dtype)
k = rnd.choice(N, 1)[0]
assert_arr_partitioned(np.sort(arr)[k], k,
np.partition(arr, k, kind='introselect'))
assert_arr_partitioned(np.sort(arr)[k], k,
arr[np.argpartition(arr, k, kind='introselect')])
def test_cannot_assign_data():
a = np.arange(10)
b = np.linspace(0, 1, 10)
with pytest.raises(AttributeError):
a.data = b.data
def test_insufficient_width():
with pytest.raises(ValueError):
np.binary_repr(10, width=2)
with pytest.raises(ValueError):
np.binary_repr(-5, width=2)
def test_npy_char_raises():
from numpy._core._multiarray_tests import npy_char_deprecation
with pytest.raises(ValueError):
npy_char_deprecation()
class TestDevice:
"""
Test arr.device attribute and arr.to_device() method.
"""
@pytest.mark.parametrize("func, arg", [
(np.arange, 5),
(np.empty_like, []),
(np.zeros, 5),
(np.empty, (5, 5)),
(np.asarray, []),
(np.asanyarray, []),
])
def test_device(self, func, arg):
arr = func(arg)
assert arr.device == "cpu"
arr = func(arg, device=None)
assert arr.device == "cpu"
arr = func(arg, device="cpu")
assert arr.device == "cpu"
with assert_raises_regex(
ValueError,
r"Device not understood. Only \"cpu\" is allowed, "
r"but received: nonsense"
):
func(arg, device="nonsense")
with assert_raises_regex(
AttributeError,
r"attribute 'device' of '(numpy.|)ndarray' objects is "
r"not writable"
):
arr.device = "other"
def test_to_device(self):
arr = np.arange(5)
assert arr.to_device("cpu") is arr
with assert_raises_regex(
ValueError,
r"The stream argument in to_device\(\) is not supported"
):
arr.to_device("cpu", stream=1)
