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test_array_coercion.py
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""" Tests for array coercion, mainly through testing `np.array` results directly. Note that other such tests exist, e.g., in `test_api.py` and many corner-cases are tested (sometimes indirectly) elsewhere. """ from itertools import permutations, product import pytest from pytest import param import numpy as np from numpy.core._rational_tests import rational from numpy.core._multiarray_umath import _discover_array_parameters from numpy.testing import ( assert_array_equal, assert_warns, IS_PYPY) def arraylikes(): """ Generator for functions converting an array into various array-likes. If full is True (default) it includes array-likes not capable of handling all dtypes. """ # base array: def ndarray(a): return a yield param(ndarray, id="ndarray") # subclass: class MyArr(np.ndarray): pass def subclass(a): return a.view(MyArr) yield subclass class _SequenceLike(): # Older NumPy versions, sometimes cared whether a protocol array was # also _SequenceLike. This shouldn't matter, but keep it for now # for __array__ and not the others. def __len__(self): raise TypeError def __getitem__(self): raise TypeError # Array-interface class ArrayDunder(_SequenceLike): def __init__(self, a): self.a = a def __array__(self, dtype=None): return self.a yield param(ArrayDunder, id="__array__") # memory-view yield param(memoryview, id="memoryview") # Array-interface class ArrayInterface: def __init__(self, a): self.a = a # need to hold on to keep interface valid self.__array_interface__ = a.__array_interface__ yield param(ArrayInterface, id="__array_interface__") # Array-Struct class ArrayStruct: def __init__(self, a): self.a = a # need to hold on to keep struct valid self.__array_struct__ = a.__array_struct__ yield param(ArrayStruct, id="__array_struct__") def scalar_instances(times=True, extended_precision=True, user_dtype=True): # Hard-coded list of scalar instances. # Floats: yield param(np.sqrt(np.float16(5)), id="float16") yield param(np.sqrt(np.float32(5)), id="float32") yield param(np.sqrt(np.float64(5)), id="float64") if extended_precision: yield param(np.sqrt(np.longdouble(5)), id="longdouble") # Complex: yield param(np.sqrt(np.complex64(2+3j)), id="complex64") yield param(np.sqrt(np.complex128(2+3j)), id="complex128") if extended_precision: yield param(np.sqrt(np.longcomplex(2+3j)), id="clongdouble") # Bool: # XFAIL: Bool should be added, but has some bad properties when it # comes to strings, see also gh-9875 # yield param(np.bool_(0), id="bool") # Integers: yield param(np.int8(2), id="int8") yield param(np.int16(2), id="int16") yield param(np.int32(2), id="int32") yield param(np.int64(2), id="int64") yield param(np.uint8(2), id="uint8") yield param(np.uint16(2), id="uint16") yield param(np.uint32(2), id="uint32") yield param(np.uint64(2), id="uint64") # Rational: if user_dtype: yield param(rational(1, 2), id="rational") # Cannot create a structured void scalar directly: structured = np.array([(1, 3)], "i,i")[0] assert isinstance(structured, np.void) assert structured.dtype == np.dtype("i,i") yield param(structured, id="structured") if times: # Datetimes and timedelta yield param(np.timedelta64(2), id="timedelta64[generic]") yield param(np.timedelta64(23, "s"), id="timedelta64[s]") yield param(np.timedelta64("NaT", "s"), id="timedelta64[s](NaT)") yield param(np.datetime64("NaT"), id="datetime64[generic](NaT)") yield param(np.datetime64("2020-06-07 12:43", "ms"), id="datetime64[ms]") # Strings and unstructured void: yield param(np.bytes_(b"1234"), id="bytes") yield param(np.str_("2345"), id="unicode") yield param(np.void(b"4321"), id="unstructured_void") def is_parametric_dtype(dtype): """Returns True if the dtype is a parametric legacy dtype (itemsize is 0, or a datetime without units) """ if dtype.itemsize == 0: return True if issubclass(dtype.type, (np.datetime64, np.timedelta64)): if dtype.name.endswith("64"): # Generic time units return True return False class TestStringDiscovery: @pytest.mark.parametrize("obj", [object(), 1.2, 10**43, None, "string"], ids=["object", "1.2", "10**43", "None", "string"]) def test_basic_stringlength(self, obj): length = len(str(obj)) expected = np.dtype(f"S{length}") assert np.array(obj, dtype="S").dtype == expected assert np.array([obj], dtype="S").dtype == expected # A nested array is also discovered correctly arr = np.array(obj, dtype="O") assert np.array(arr, dtype="S").dtype == expected # Also if we use the dtype class assert np.array(arr, dtype=type(expected)).dtype == expected # Check that .astype() behaves identical assert arr.astype("S").dtype == expected # The DType class is accepted by `.astype()` assert arr.astype(type(np.dtype("S"))).dtype == expected @pytest.mark.parametrize("obj", [object(), 1.2, 10**43, None, "string"], ids=["object", "1.2", "10**43", "None", "string"]) def test_nested_arrays_stringlength(self, obj): length = len(str(obj)) expected = np.dtype(f"S{length}") arr = np.array(obj, dtype="O") assert np.array([arr, arr], dtype="S").dtype == expected @pytest.mark.parametrize("arraylike", arraylikes()) def test_unpack_first_level(self, arraylike): # We unpack exactly one level of array likes obj = np.array([None]) obj[0] = np.array(1.2) # the length of the included item, not of the float dtype length = len(str(obj[0])) expected = np.dtype(f"S{length}") obj = arraylike(obj) # casting to string usually calls str(obj) arr = np.array([obj], dtype="S") assert arr.shape == (1, 1) assert arr.dtype == expected class TestScalarDiscovery: def test_void_special_case(self): # Void dtypes with structures discover tuples as elements arr = np.array((1, 2, 3), dtype="i,i,i") assert arr.shape == () arr = np.array([(1, 2, 3)], dtype="i,i,i") assert arr.shape == (1,) def test_char_special_case(self): arr = np.array("string", dtype="c") assert arr.shape == (6,) assert arr.dtype.char == "c" arr = np.array(["string"], dtype="c") assert arr.shape == (1, 6) assert arr.dtype.char == "c" def test_char_special_case_deep(self): # Check that the character special case errors correctly if the # array is too deep: nested = ["string"] # 2 dimensions (due to string being sequence) for i in range(np.MAXDIMS - 2): nested = [nested] arr = np.array(nested, dtype='c') assert arr.shape == (1,) * (np.MAXDIMS - 1) + (6,) with pytest.raises(ValueError): np.array([nested], dtype="c") def test_unknown_object(self): arr = np.array(object()) assert arr.shape == () assert arr.dtype == np.dtype("O") @pytest.mark.parametrize("scalar", scalar_instances()) def test_scalar(self, scalar): arr = np.array(scalar) assert arr.shape == () assert arr.dtype == scalar.dtype arr = np.array([[scalar, scalar]]) assert arr.shape == (1, 2) assert arr.dtype == scalar.dtype # Additionally to string this test also runs into a corner case # with datetime promotion (the difference is the promotion order). @pytest.mark.filterwarnings("ignore:Promotion of numbers:FutureWarning") def test_scalar_promotion(self): for sc1, sc2 in product(scalar_instances(), scalar_instances()): sc1, sc2 = sc1.values[0], sc2.values[0] # test all combinations: try: arr = np.array([sc1, sc2]) except (TypeError, ValueError): # The promotion between two times can fail # XFAIL (ValueError): Some object casts are currently undefined continue assert arr.shape == (2,) try: dt1, dt2 = sc1.dtype, sc2.dtype expected_dtype = np.promote_types(dt1, dt2) assert arr.dtype == expected_dtype except TypeError as e: # Will currently always go to object dtype assert arr.dtype == np.dtype("O") @pytest.mark.parametrize("scalar", scalar_instances()) def test_scalar_coercion(self, scalar): # This tests various scalar coercion paths, mainly for the numerical # types. It includes some paths not directly related to `np.array`. if isinstance(scalar, np.inexact): # Ensure we have a full-precision number if available scalar = type(scalar)((scalar * 2)**0.5) if type(scalar) is rational: # Rational generally fails due to a missing cast. In the future # object casts should automatically be defined based on `setitem`. pytest.xfail("Rational to object cast is undefined currently.") # Use casting from object: arr = np.array(scalar, dtype=object).astype(scalar.dtype) # Test various ways to create an array containing this scalar: arr1 = np.array(scalar).reshape(1) arr2 = np.array([scalar]) arr3 = np.empty(1, dtype=scalar.dtype) arr3[0] = scalar arr4 = np.empty(1, dtype=scalar.dtype) arr4[:] = [scalar] # All of these methods should yield the same results assert_array_equal(arr, arr1) assert_array_equal(arr, arr2) assert_array_equal(arr, arr3) assert_array_equal(arr, arr4) @pytest.mark.xfail(IS_PYPY, reason="`int(np.complex128(3))` fails on PyPy") @pytest.mark.filterwarnings("ignore::numpy.ComplexWarning") @pytest.mark.parametrize("cast_to", scalar_instances()) def test_scalar_coercion_same_as_cast_and_assignment(self, cast_to): """ Test that in most cases: * `np.array(scalar, dtype=dtype)` * `np.empty((), dtype=dtype)[()] = scalar` * `np.array(scalar).astype(dtype)` should behave the same. The only exceptions are parametric dtypes (mainly datetime/timedelta without unit) and void without fields. """ dtype = cast_to.dtype # use to parametrize only the target dtype for scalar in scalar_instances(times=False): scalar = scalar.values[0] if dtype.type == np.void: if scalar.dtype.fields is not None and dtype.fields is None: # Here, coercion to "V6" works, but the cast fails. # Since the types are identical, SETITEM takes care of # this, but has different rules than the cast. with pytest.raises(TypeError): np.array(scalar).astype(dtype) np.array(scalar, dtype=dtype) np.array([scalar], dtype=dtype) continue # The main test, we first try to use casting and if it succeeds # continue below testing that things are the same, otherwise # test that the alternative paths at least also fail. try: cast = np.array(scalar).astype(dtype) except (TypeError, ValueError, RuntimeError): # coercion should also raise (error type may change) with pytest.raises(Exception): np.array(scalar, dtype=dtype) if (isinstance(scalar, rational) and np.issubdtype(dtype, np.signedinteger)): return with pytest.raises(Exception): np.array([scalar], dtype=dtype) # assignment should also raise res = np.zeros((), dtype=dtype) with pytest.raises(Exception): res[()] = scalar return # Non error path: arr = np.array(scalar, dtype=dtype) assert_array_equal(arr, cast) # assignment behaves the same ass = np.zeros((), dtype=dtype) ass[()] = scalar assert_array_equal(ass, cast) @pytest.mark.parametrize("pyscalar", [10, 10.32, 10.14j, 10**100]) def test_pyscalar_subclasses(self, pyscalar): """NumPy arrays are read/write which means that anything but invariant behaviour is on thin ice. However, we currently are happy to discover subclasses of Python float, int, complex the same as the base classes. This should potentially be deprecated. """ class MyScalar(type(pyscalar)): pass res = np.array(MyScalar(pyscalar)) expected = np.array(pyscalar) assert_array_equal(res, expected) @pytest.mark.parametrize("dtype_char", np.typecodes["All"]) def test_default_dtype_instance(self, dtype_char): if dtype_char in "SU": dtype = np.dtype(dtype_char + "1") elif dtype_char == "V": # Legacy behaviour was to use V8. The reason was float64 being the # default dtype and that having 8 bytes. dtype = np.dtype("V8") else: dtype = np.dtype(dtype_char) discovered_dtype, _ = _discover_array_parameters([], type(dtype)) assert discovered_dtype == dtype assert discovered_dtype.itemsize == dtype.itemsize @pytest.mark.parametrize("dtype", np.typecodes["Integer"]) @pytest.mark.parametrize(["scalar", "error"], [(np.float64(np.nan), ValueError), (np.array(-1).astype(np.ulonglong)[()], OverflowError)]) def test_scalar_to_int_coerce_does_not_cast(self, dtype, scalar, error): """ Signed integers are currently different in that they do not cast other NumPy scalar, but instead use scalar.__int__(). The hardcoded exception to this rule is `np.array(scalar, dtype=integer)`. """ dtype = np.dtype(dtype) # This is a special case using casting logic. It warns for the NaN # but allows the cast (giving undefined behaviour). with np.errstate(invalid="ignore"): coerced = np.array(scalar, dtype=dtype) cast = np.array(scalar).astype(dtype) assert_array_equal(coerced, cast) # However these fail: with pytest.raises(error): np.array([scalar], dtype=dtype) with pytest.raises(error): cast[()] = scalar class TestTimeScalars: @pytest.mark.parametrize("dtype", [np.int64, np.float32]) @pytest.mark.parametrize("scalar", [param(np.timedelta64("NaT", "s"), id="timedelta64[s](NaT)"), param(np.timedelta64(123, "s"), id="timedelta64[s]"), param(np.datetime64("NaT", "generic"), id="datetime64[generic](NaT)"), param(np.datetime64(1, "D"), id="datetime64[D]")],) def test_coercion_basic(self, dtype, scalar): # Note the `[scalar]` is there because np.array(scalar) uses stricter # `scalar.__int__()` rules for backward compatibility right now. arr = np.array(scalar, dtype=dtype) cast = np.array(scalar).astype(dtype) assert_array_equal(arr, cast) ass = np.ones((), dtype=dtype) if issubclass(dtype, np.integer): with pytest.raises(TypeError): # raises, as would np.array([scalar], dtype=dtype), this is # conversion from times, but behaviour of integers. ass[()] = scalar else: ass[()] = scalar assert_array_equal(ass, cast) @pytest.mark.parametrize("dtype", [np.int64, np.float32]) @pytest.mark.parametrize("scalar", [param(np.timedelta64(123, "ns"), id="timedelta64[ns]"), param(np.timedelta64(12, "generic"), id="timedelta64[generic]")]) def test_coercion_timedelta_convert_to_number(self, dtype, scalar): # Only "ns" and "generic" timedeltas can be converted to numbers # so these are slightly special. arr = np.array(scalar, dtype=dtype) cast = np.array(scalar).astype(dtype) ass = np.ones((), dtype=dtype) ass[()] = scalar # raises, as would np.array([scalar], dtype=dtype) assert_array_equal(arr, cast) assert_array_equal(cast, cast) @pytest.mark.parametrize("dtype", ["S6", "U6"]) @pytest.mark.parametrize(["val", "unit"], [param(123, "s", id="[s]"), param(123, "D", id="[D]")]) def test_coercion_assignment_datetime(self, val, unit, dtype): # String from datetime64 assignment is currently special cased to # never use casting. This is because casting will error in this # case, and traditionally in most cases the behaviour is maintained # like this. (`np.array(scalar, dtype="U6")` would have failed before) # TODO: This discrepancy _should_ be resolved, either by relaxing the # cast, or by deprecating the first part. scalar = np.datetime64(val, unit) dtype = np.dtype(dtype) cut_string = dtype.type(str(scalar)[:6]) arr = np.array(scalar, dtype=dtype) assert arr[()] == cut_string ass = np.ones((), dtype=dtype) ass[()] = scalar assert ass[()] == cut_string with pytest.raises(RuntimeError): # However, unlike the above assignment using `str(scalar)[:6]` # due to being handled by the string DType and not be casting # the explicit cast fails: np.array(scalar).astype(dtype) @pytest.mark.parametrize(["val", "unit"], [param(123, "s", id="[s]"), param(123, "D", id="[D]")]) def test_coercion_assignment_timedelta(self, val, unit): scalar = np.timedelta64(val, unit) # Unlike datetime64, timedelta allows the unsafe cast: np.array(scalar, dtype="S6") cast = np.array(scalar).astype("S6") ass = np.ones((), dtype="S6") ass[()] = scalar expected = scalar.astype("S")[:6] assert cast[()] == expected assert ass[()] == expected class TestNested: def test_nested_simple(self): initial = [1.2] nested = initial for i in range(np.MAXDIMS - 1): nested = [nested] arr = np.array(nested, dtype="float64") assert arr.shape == (1,) * np.MAXDIMS with pytest.raises(ValueError): np.array([nested], dtype="float64") with pytest.raises(ValueError, match=".*would exceed the maximum"): np.array([nested]) # user must ask for `object` explicitly arr = np.array([nested], dtype=object) assert arr.dtype == np.dtype("O") assert arr.shape == (1,) * np.MAXDIMS assert arr.item() is initial def test_pathological_self_containing(self): # Test that this also works for two nested sequences l = [] l.append(l) arr = np.array([l, l, l], dtype=object) assert arr.shape == (3,) + (1,) * (np.MAXDIMS - 1) # Also check a ragged case: arr = np.array([l, [None], l], dtype=object) assert arr.shape == (3, 1) @pytest.mark.parametrize("arraylike", arraylikes()) def test_nested_arraylikes(self, arraylike): # We try storing an array like into an array, but the array-like # will have too many dimensions. This means the shape discovery # decides that the array-like must be treated as an object (a special # case of ragged discovery). The result will be an array with one # dimension less than the maximum dimensions, and the array being # assigned to it (which does work for object or if `float(arraylike)` # works). initial = arraylike(np.ones((1, 1))) nested = initial for i in range(np.MAXDIMS - 1): nested = [nested] with pytest.raises(ValueError, match=".*would exceed the maximum"): # It will refuse to assign the array into np.array(nested, dtype="float64") # If this is object, we end up assigning a (1, 1) array into (1,) # (due to running out of dimensions), this is currently supported but # a special case which is not ideal. arr = np.array(nested, dtype=object) assert arr.shape == (1,) * np.MAXDIMS assert arr.item() == np.array(initial).item() @pytest.mark.parametrize("arraylike", arraylikes()) def test_uneven_depth_ragged(self, arraylike): arr = np.arange(4).reshape((2, 2)) arr = arraylike(arr) # Array is ragged in the second dimension already: out = np.array([arr, [arr]], dtype=object) assert out.shape == (2,) assert out[0] is arr assert type(out[1]) is list # Array is ragged in the third dimension: with pytest.raises(ValueError): # This is a broadcast error during assignment, because # the array shape would be (2, 2, 2) but `arr[0, 0] = arr` fails. np.array([arr, [arr, arr]], dtype=object) def test_empty_sequence(self): arr = np.array([[], [1], [[1]]], dtype=object) assert arr.shape == (3,) # The empty sequence stops further dimension discovery, so the # result shape will be (0,) which leads to an error during: with pytest.raises(ValueError): np.array([[], np.empty((0, 1))], dtype=object) def test_array_of_different_depths(self): # When multiple arrays (or array-likes) are included in a # sequences and have different depth, we currently discover # as many dimensions as they share. (see also gh-17224) arr = np.zeros((3, 2)) mismatch_first_dim = np.zeros((1, 2)) mismatch_second_dim = np.zeros((3, 3)) dtype, shape = _discover_array_parameters( [arr, mismatch_second_dim], dtype=np.dtype("O")) assert shape == (2, 3) dtype, shape = _discover_array_parameters( [arr, mismatch_first_dim], dtype=np.dtype("O")) assert shape == (2,) # The second case is currently supported because the arrays # can be stored as objects: res = np.asarray([arr, mismatch_first_dim], dtype=np.dtype("O")) assert res[0] is arr assert res[1] is mismatch_first_dim class TestBadSequences: # These are tests for bad objects passed into `np.array`, in general # these have undefined behaviour. In the old code they partially worked # when now they will fail. We could (and maybe should) create a copy # of all sequences to be safe against bad-actors. def test_growing_list(self): # List to coerce, `mylist` will append to it during coercion obj = [] class mylist(list): def __len__(self): obj.append([1, 2]) return super().__len__() obj.append(mylist([1, 2])) with pytest.raises(RuntimeError): np.array(obj) # Note: We do not test a shrinking list. These do very evil things # and the only way to fix them would be to copy all sequences. # (which may be a real option in the future). def test_mutated_list(self): # List to coerce, `mylist` will mutate the first element obj = [] class mylist(list): def __len__(self): obj[0] = [2, 3] # replace with a different list. return super().__len__() obj.append([2, 3]) obj.append(mylist([1, 2])) # Does not crash: np.array(obj) def test_replace_0d_array(self): # List to coerce, `mylist` will mutate the first element obj = [] class baditem: def __len__(self): obj[0][0] = 2 # replace with a different list. raise ValueError("not actually a sequence!") def __getitem__(self): pass # Runs into a corner case in the new code, the `array(2)` is cached # so replacing it invalidates the cache. obj.append([np.array(2), baditem()]) with pytest.raises(RuntimeError): np.array(obj) class TestArrayLikes: @pytest.mark.parametrize("arraylike", arraylikes()) def test_0d_object_special_case(self, arraylike): arr = np.array(0.) obj = arraylike(arr) # A single array-like is always converted: res = np.array(obj, dtype=object) assert_array_equal(arr, res) # But a single 0-D nested array-like never: res = np.array([obj], dtype=object) assert res[0] is obj @pytest.mark.parametrize("arraylike", arraylikes()) @pytest.mark.parametrize("arr", [np.array(0.), np.arange(4)]) def test_object_assignment_special_case(self, arraylike, arr): obj = arraylike(arr) empty = np.arange(1, dtype=object) empty[:] = [obj] assert empty[0] is obj def test_0d_generic_special_case(self): class ArraySubclass(np.ndarray): def __float__(self): raise TypeError("e.g. quantities raise on this") arr = np.array(0.) obj = arr.view(ArraySubclass) res = np.array(obj) # The subclass is simply cast: assert_array_equal(arr, res) # If the 0-D array-like is included, __float__ is currently # guaranteed to be used. We may want to change that, quantities # and masked arrays half make use of this. with pytest.raises(TypeError): np.array([obj]) # The same holds for memoryview: obj = memoryview(arr) res = np.array(obj) assert_array_equal(arr, res) with pytest.raises(ValueError): # The error type does not matter much here. np.array([obj]) def test_arraylike_classes(self): # The classes of array-likes should generally be acceptable to be # stored inside a numpy (object) array. This tests all of the # special attributes (since all are checked during coercion). arr = np.array(np.int64) assert arr[()] is np.int64 arr = np.array([np.int64]) assert arr[0] is np.int64 # This also works for properties/unbound methods: class ArrayLike: @property def __array_interface__(self): pass @property def __array_struct__(self): pass def __array__(self): pass arr = np.array(ArrayLike) assert arr[()] is ArrayLike arr = np.array([ArrayLike]) assert arr[0] is ArrayLike @pytest.mark.skipif( np.dtype(np.intp).itemsize < 8, reason="Needs 64bit platform") def test_too_large_array_error_paths(self): """Test the error paths, including for memory leaks""" arr = np.array(0, dtype="uint8") # Guarantees that a contiguous copy won't work: arr = np.broadcast_to(arr, 2**62) for i in range(5): # repeat, to ensure caching cannot have an effect: with pytest.raises(MemoryError): np.array(arr) with pytest.raises(MemoryError): np.array([arr]) @pytest.mark.parametrize("attribute", ["__array_interface__", "__array__", "__array_struct__"]) @pytest.mark.parametrize("error", [RecursionError, MemoryError]) def test_bad_array_like_attributes(self, attribute, error): # RecursionError and MemoryError are considered fatal. All errors # (except AttributeError) should probably be raised in the future, # but shapely made use of it, so it will require a deprecation. class BadInterface: def __getattr__(self, attr): if attr == attribute: raise error super().__getattr__(attr) with pytest.raises(error): np.array(BadInterface()) @pytest.mark.parametrize("error", [RecursionError, MemoryError]) def test_bad_array_like_bad_length(self, error): # RecursionError and MemoryError are considered "critical" in # sequences. We could expand this more generally though. (NumPy 1.20) class BadSequence: def __len__(self): raise error def __getitem__(self): # must have getitem to be a Sequence return 1 with pytest.raises(error): np.array(BadSequence()) class TestAsArray: """Test expected behaviors of ``asarray``.""" def test_dtype_identity(self): """Confirm the intended behavior for *dtype* kwarg. The result of ``asarray()`` should have the dtype provided through the keyword argument, when used. This forces unique array handles to be produced for unique np.dtype objects, but (for equivalent dtypes), the underlying data (the base object) is shared with the original array object. Ref https://github.com/numpy/numpy/issues/1468 """ int_array = np.array([1, 2, 3], dtype='i') assert np.asarray(int_array) is int_array # The character code resolves to the singleton dtype object provided # by the numpy package. assert np.asarray(int_array, dtype='i') is int_array # Derive a dtype from n.dtype('i'), but add a metadata object to force # the dtype to be distinct. unequal_type = np.dtype('i', metadata={'spam': True}) annotated_int_array = np.asarray(int_array, dtype=unequal_type) assert annotated_int_array is not int_array assert annotated_int_array.base is int_array # Create an equivalent descriptor with a new and distinct dtype # instance. equivalent_requirement = np.dtype('i', metadata={'spam': True}) annotated_int_array_alt = np.asarray(annotated_int_array, dtype=equivalent_requirement) assert unequal_type == equivalent_requirement assert unequal_type is not equivalent_requirement assert annotated_int_array_alt is not annotated_int_array assert annotated_int_array_alt.dtype is equivalent_requirement # Check the same logic for a pair of C types whose equivalence may vary # between computing environments. # Find an equivalent pair. integer_type_codes = ('i', 'l', 'q') integer_dtypes = [np.dtype(code) for code in integer_type_codes] typeA = None typeB = None for typeA, typeB in permutations(integer_dtypes, r=2): if typeA == typeB: assert typeA is not typeB break assert isinstance(typeA, np.dtype) and isinstance(typeB, np.dtype) # These ``asarray()`` calls may produce a new view or a copy, # but never the same object. long_int_array = np.asarray(int_array, dtype='l') long_long_int_array = np.asarray(int_array, dtype='q') assert long_int_array is not int_array assert long_long_int_array is not int_array assert np.asarray(long_int_array, dtype='q') is not long_int_array array_a = np.asarray(int_array, dtype=typeA) assert typeA == typeB assert typeA is not typeB assert array_a.dtype is typeA assert array_a is not np.asarray(array_a, dtype=typeB) assert np.asarray(array_a, dtype=typeB).dtype is typeB assert array_a is np.asarray(array_a, dtype=typeB).base class TestSpecialAttributeLookupFailure: # An exception was raised while fetching the attribute class WeirdArrayLike: @property def __array__(self): raise RuntimeError("oops!") class WeirdArrayInterface: @property def __array_interface__(self): raise RuntimeError("oops!") def test_deprecated(self): with pytest.raises(RuntimeError): np.array(self.WeirdArrayLike()) with pytest.raises(RuntimeError): np.array(self.WeirdArrayInterface()) def test_subarray_from_array_construction(): # Arrays are more complex, since they "broadcast" on success: arr = np.array([1, 2]) res = arr.astype("(2)i,") assert_array_equal(res, [[1, 1], [2, 2]]) res = np.array(arr, dtype="(2)i,") assert_array_equal(res, [[1, 1], [2, 2]]) res = np.array([[(1,), (2,)], arr], dtype="(2)i,") assert_array_equal(res, [[[1, 1], [2, 2]], [[1, 1], [2, 2]]]) # Also try a multi-dimensional example: arr = np.arange(5 * 2).reshape(5, 2) expected = np.broadcast_to(arr[:, :, np.newaxis, np.newaxis], (5, 2, 2, 2)) res = arr.astype("(2,2)f") assert_array_equal(res, expected) res = np.array(arr, dtype="(2,2)f") assert_array_equal(res, expected) def test_empty_string(): # Empty strings are unfortunately often converted to S1 and we need to # make sure we are filling the S1 and not the (possibly) detected S0 # result. This should likely just return S0 and if not maybe the decision # to return S1 should be moved. res = np.array([""] * 10, dtype="S") assert_array_equal(res, np.array("\0", "S1")) assert res.dtype == "S1" arr = np.array([""] * 10, dtype=object) res = arr.astype("S") assert_array_equal(res, b"") assert res.dtype == "S1" res = np.array(arr, dtype="S") assert_array_equal(res, b"") # TODO: This is arguably weird/wrong, but seems old: assert res.dtype == f"S{np.dtype('O').itemsize}" res = np.array([[""] * 10, arr], dtype="S") assert_array_equal(res, b"") assert res.shape == (2, 10) assert res.dtype == "S1"