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test_namedarray.py
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test_namedarray.py
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from __future__ import annotations
import copy
import warnings
from collections.abc import Mapping
from typing import TYPE_CHECKING, Any, Generic, cast, overload
import numpy as np
import pytest
from xarray.core.indexing import ExplicitlyIndexed
from xarray.namedarray._typing import _arrayfunction_or_api, _DType_co, _ShapeType_co
from xarray.namedarray.core import NamedArray, from_array
from xarray.namedarray.utils import _default
if TYPE_CHECKING:
from types import ModuleType
from numpy.typing import ArrayLike, DTypeLike, NDArray
from xarray.namedarray._typing import (
_AttrsLike,
_DimsLike,
_DType,
_Shape,
duckarray,
)
from xarray.namedarray.utils import Default
class CustomArrayBase(Generic[_ShapeType_co, _DType_co]):
def __init__(self, array: duckarray[Any, _DType_co]) -> None:
self.array: duckarray[Any, _DType_co] = array
@property
def dtype(self) -> _DType_co:
return self.array.dtype
@property
def shape(self) -> _Shape:
return self.array.shape
class CustomArray(
CustomArrayBase[_ShapeType_co, _DType_co], Generic[_ShapeType_co, _DType_co]
):
def __array__(self) -> np.ndarray[Any, np.dtype[np.generic]]:
return np.array(self.array)
class CustomArrayIndexable(
CustomArrayBase[_ShapeType_co, _DType_co],
ExplicitlyIndexed,
Generic[_ShapeType_co, _DType_co],
):
def __array_namespace__(self) -> ModuleType:
return np
@pytest.fixture
def random_inputs() -> np.ndarray[Any, np.dtype[np.float32]]:
return np.arange(3 * 4 * 5, dtype=np.float32).reshape((3, 4, 5))
def test_namedarray_init() -> None:
dtype = np.dtype(np.int8)
expected = np.array([1, 2], dtype=dtype)
actual: NamedArray[Any, np.dtype[np.int8]]
actual = NamedArray(("x",), expected)
assert np.array_equal(np.asarray(actual.data), expected)
with pytest.raises(AttributeError):
expected2 = [1, 2]
actual2: NamedArray[Any, Any]
actual2 = NamedArray(("x",), expected2) # type: ignore[arg-type]
assert np.array_equal(np.asarray(actual2.data), expected2)
@pytest.mark.parametrize(
"dims, data, expected, raise_error",
[
(("x",), [1, 2, 3], np.array([1, 2, 3]), False),
((1,), np.array([4, 5, 6]), np.array([4, 5, 6]), False),
((), 2, np.array(2), False),
# Fail:
(("x",), NamedArray("time", np.array([1, 2, 3])), np.array([1, 2, 3]), True),
],
)
def test_from_array(
dims: _DimsLike,
data: ArrayLike,
expected: np.ndarray[Any, Any],
raise_error: bool,
) -> None:
actual: NamedArray[Any, Any]
if raise_error:
with pytest.raises(TypeError, match="already a Named array"):
actual = from_array(dims, data)
# Named arrays are not allowed:
from_array(actual) # type: ignore[call-overload]
else:
actual = from_array(dims, data)
assert np.array_equal(np.asarray(actual.data), expected)
def test_from_array_with_masked_array() -> None:
masked_array: np.ndarray[Any, np.dtype[np.generic]]
masked_array = np.ma.array([1, 2, 3], mask=[False, True, False]) # type: ignore[no-untyped-call]
with pytest.raises(NotImplementedError):
from_array(("x",), masked_array)
def test_from_array_with_0d_object() -> None:
data = np.empty((), dtype=object)
data[()] = (10, 12, 12)
narr = from_array((), data)
np.array_equal(np.asarray(narr.data), data)
# TODO: Make xr.core.indexing.ExplicitlyIndexed pass as a subclass of_arrayfunction_or_api
# and remove this test.
def test_from_array_with_explicitly_indexed(
random_inputs: np.ndarray[Any, Any]
) -> None:
array: CustomArray[Any, Any]
array = CustomArray(random_inputs)
output: NamedArray[Any, Any]
output = from_array(("x", "y", "z"), array)
assert isinstance(output.data, np.ndarray)
array2: CustomArrayIndexable[Any, Any]
array2 = CustomArrayIndexable(random_inputs)
output2: NamedArray[Any, Any]
output2 = from_array(("x", "y", "z"), array2)
assert isinstance(output2.data, CustomArrayIndexable)
def test_properties() -> None:
data = 0.5 * np.arange(10).reshape(2, 5)
named_array: NamedArray[Any, Any]
named_array = NamedArray(["x", "y"], data, {"key": "value"})
assert named_array.dims == ("x", "y")
assert np.array_equal(np.asarray(named_array.data), data)
assert named_array.attrs == {"key": "value"}
assert named_array.ndim == 2
assert named_array.sizes == {"x": 2, "y": 5}
assert named_array.size == 10
assert named_array.nbytes == 80
assert len(named_array) == 2
def test_attrs() -> None:
named_array: NamedArray[Any, Any]
named_array = NamedArray(["x", "y"], np.arange(10).reshape(2, 5))
assert named_array.attrs == {}
named_array.attrs["key"] = "value"
assert named_array.attrs == {"key": "value"}
named_array.attrs = {"key": "value2"}
assert named_array.attrs == {"key": "value2"}
def test_data(random_inputs: np.ndarray[Any, Any]) -> None:
named_array: NamedArray[Any, Any]
named_array = NamedArray(["x", "y", "z"], random_inputs)
assert np.array_equal(np.asarray(named_array.data), random_inputs)
with pytest.raises(ValueError):
named_array.data = np.random.random((3, 4)).astype(np.float64)
def test_real_and_imag() -> None:
expected_real: np.ndarray[Any, np.dtype[np.float64]]
expected_real = np.arange(3, dtype=np.float64)
expected_imag: np.ndarray[Any, np.dtype[np.float64]]
expected_imag = -np.arange(3, dtype=np.float64)
arr: np.ndarray[Any, np.dtype[np.complex128]]
arr = expected_real + 1j * expected_imag
named_array: NamedArray[Any, np.dtype[np.complex128]]
named_array = NamedArray(["x"], arr)
actual_real: duckarray[Any, np.dtype[np.float64]] = named_array.real.data
assert np.array_equal(np.asarray(actual_real), expected_real)
assert actual_real.dtype == expected_real.dtype
actual_imag: duckarray[Any, np.dtype[np.float64]] = named_array.imag.data
assert np.array_equal(np.asarray(actual_imag), expected_imag)
assert actual_imag.dtype == expected_imag.dtype
# Additional tests as per your original class-based code
@pytest.mark.parametrize(
"data, dtype",
[
("foo", np.dtype("U3")),
(b"foo", np.dtype("S3")),
],
)
def test_0d_string(data: Any, dtype: DTypeLike) -> None:
named_array: NamedArray[Any, Any]
named_array = from_array([], data)
assert named_array.data == data
assert named_array.dims == ()
assert named_array.sizes == {}
assert named_array.attrs == {}
assert named_array.ndim == 0
assert named_array.size == 1
assert named_array.dtype == dtype
def test_0d_object() -> None:
named_array: NamedArray[Any, Any]
named_array = from_array([], (10, 12, 12))
expected_data = np.empty((), dtype=object)
expected_data[()] = (10, 12, 12)
assert np.array_equal(np.asarray(named_array.data), expected_data)
assert named_array.dims == ()
assert named_array.sizes == {}
assert named_array.attrs == {}
assert named_array.ndim == 0
assert named_array.size == 1
assert named_array.dtype == np.dtype("O")
def test_0d_datetime() -> None:
named_array: NamedArray[Any, Any]
named_array = from_array([], np.datetime64("2000-01-01"))
assert named_array.dtype == np.dtype("datetime64[D]")
@pytest.mark.parametrize(
"timedelta, expected_dtype",
[
(np.timedelta64(1, "D"), np.dtype("timedelta64[D]")),
(np.timedelta64(1, "s"), np.dtype("timedelta64[s]")),
(np.timedelta64(1, "m"), np.dtype("timedelta64[m]")),
(np.timedelta64(1, "h"), np.dtype("timedelta64[h]")),
(np.timedelta64(1, "us"), np.dtype("timedelta64[us]")),
(np.timedelta64(1, "ns"), np.dtype("timedelta64[ns]")),
(np.timedelta64(1, "ps"), np.dtype("timedelta64[ps]")),
(np.timedelta64(1, "fs"), np.dtype("timedelta64[fs]")),
(np.timedelta64(1, "as"), np.dtype("timedelta64[as]")),
],
)
def test_0d_timedelta(
timedelta: np.timedelta64, expected_dtype: np.dtype[np.timedelta64]
) -> None:
named_array: NamedArray[Any, Any]
named_array = from_array([], timedelta)
assert named_array.dtype == expected_dtype
assert named_array.data == timedelta
@pytest.mark.parametrize(
"dims, data_shape, new_dims, raises",
[
(["x", "y", "z"], (2, 3, 4), ["a", "b", "c"], False),
(["x", "y", "z"], (2, 3, 4), ["a", "b"], True),
(["x", "y", "z"], (2, 4, 5), ["a", "b", "c", "d"], True),
([], [], (), False),
([], [], ("x",), True),
],
)
def test_dims_setter(dims: Any, data_shape: Any, new_dims: Any, raises: bool) -> None:
named_array: NamedArray[Any, Any]
named_array = NamedArray(dims, np.asarray(np.random.random(data_shape)))
assert named_array.dims == tuple(dims)
if raises:
with pytest.raises(ValueError):
named_array.dims = new_dims
else:
named_array.dims = new_dims
assert named_array.dims == tuple(new_dims)
def test_duck_array_class() -> None:
def test_duck_array_typevar(a: duckarray[Any, _DType]) -> duckarray[Any, _DType]:
# Mypy checks a is valid:
b: duckarray[Any, _DType] = a
# Runtime check if valid:
if isinstance(b, _arrayfunction_or_api):
return b
else:
raise TypeError(f"a ({type(a)}) is not a valid _arrayfunction or _arrayapi")
numpy_a: NDArray[np.int64]
numpy_a = np.array([2.1, 4], dtype=np.dtype(np.int64))
test_duck_array_typevar(numpy_a)
masked_a: np.ma.MaskedArray[Any, np.dtype[np.int64]]
masked_a = np.ma.asarray([2.1, 4], dtype=np.dtype(np.int64)) # type: ignore[no-untyped-call]
test_duck_array_typevar(masked_a)
custom_a: CustomArrayIndexable[Any, np.dtype[np.int64]]
custom_a = CustomArrayIndexable(numpy_a)
test_duck_array_typevar(custom_a)
# Test numpy's array api:
with warnings.catch_warnings():
warnings.filterwarnings(
"ignore",
r"The numpy.array_api submodule is still experimental",
category=UserWarning,
)
import numpy.array_api as nxp
# TODO: nxp doesn't use dtype typevars, so can only use Any for the moment:
arrayapi_a: duckarray[Any, Any] # duckarray[Any, np.dtype[np.int64]]
arrayapi_a = nxp.asarray([2.1, 4], dtype=np.dtype(np.int64))
test_duck_array_typevar(arrayapi_a)
def test_new_namedarray() -> None:
dtype_float = np.dtype(np.float32)
narr_float: NamedArray[Any, np.dtype[np.float32]]
narr_float = NamedArray(("x",), np.array([1.5, 3.2], dtype=dtype_float))
assert narr_float.dtype == dtype_float
dtype_int = np.dtype(np.int8)
narr_int: NamedArray[Any, np.dtype[np.int8]]
narr_int = narr_float._new(("x",), np.array([1, 3], dtype=dtype_int))
assert narr_int.dtype == dtype_int
# Test with a subclass:
class Variable(
NamedArray[_ShapeType_co, _DType_co], Generic[_ShapeType_co, _DType_co]
):
@overload
def _new(
self,
dims: _DimsLike | Default = ...,
data: duckarray[Any, _DType] = ...,
attrs: _AttrsLike | Default = ...,
) -> Variable[Any, _DType]:
...
@overload
def _new(
self,
dims: _DimsLike | Default = ...,
data: Default = ...,
attrs: _AttrsLike | Default = ...,
) -> Variable[_ShapeType_co, _DType_co]:
...
def _new(
self,
dims: _DimsLike | Default = _default,
data: duckarray[Any, _DType] | Default = _default,
attrs: _AttrsLike | Default = _default,
) -> Variable[Any, _DType] | Variable[_ShapeType_co, _DType_co]:
dims_ = copy.copy(self._dims) if dims is _default else dims
attrs_: Mapping[Any, Any] | None
if attrs is _default:
attrs_ = None if self._attrs is None else self._attrs.copy()
else:
attrs_ = attrs
if data is _default:
return type(self)(dims_, copy.copy(self._data), attrs_)
else:
cls_ = cast("type[Variable[Any, _DType]]", type(self))
return cls_(dims_, data, attrs_)
var_float: Variable[Any, np.dtype[np.float32]]
var_float = Variable(("x",), np.array([1.5, 3.2], dtype=dtype_float))
assert var_float.dtype == dtype_float
var_int: Variable[Any, np.dtype[np.int8]]
var_int = var_float._new(("x",), np.array([1, 3], dtype=dtype_int))
assert var_int.dtype == dtype_int
def test_replace_namedarray() -> None:
dtype_float = np.dtype(np.float32)
np_val: np.ndarray[Any, np.dtype[np.float32]]
np_val = np.array([1.5, 3.2], dtype=dtype_float)
np_val2: np.ndarray[Any, np.dtype[np.float32]]
np_val2 = 2 * np_val
narr_float: NamedArray[Any, np.dtype[np.float32]]
narr_float = NamedArray(("x",), np_val)
assert narr_float.dtype == dtype_float
narr_float2: NamedArray[Any, np.dtype[np.float32]]
narr_float2 = NamedArray(("x",), np_val2)
assert narr_float2.dtype == dtype_float
# Test with a subclass:
class Variable(
NamedArray[_ShapeType_co, _DType_co], Generic[_ShapeType_co, _DType_co]
):
@overload
def _new(
self,
dims: _DimsLike | Default = ...,
data: duckarray[Any, _DType] = ...,
attrs: _AttrsLike | Default = ...,
) -> Variable[Any, _DType]:
...
@overload
def _new(
self,
dims: _DimsLike | Default = ...,
data: Default = ...,
attrs: _AttrsLike | Default = ...,
) -> Variable[_ShapeType_co, _DType_co]:
...
def _new(
self,
dims: _DimsLike | Default = _default,
data: duckarray[Any, _DType] | Default = _default,
attrs: _AttrsLike | Default = _default,
) -> Variable[Any, _DType] | Variable[_ShapeType_co, _DType_co]:
dims_ = copy.copy(self._dims) if dims is _default else dims
attrs_: Mapping[Any, Any] | None
if attrs is _default:
attrs_ = None if self._attrs is None else self._attrs.copy()
else:
attrs_ = attrs
if data is _default:
return type(self)(dims_, copy.copy(self._data), attrs_)
else:
cls_ = cast("type[Variable[Any, _DType]]", type(self))
return cls_(dims_, data, attrs_)
var_float: Variable[Any, np.dtype[np.float32]]
var_float = Variable(("x",), np_val)
assert var_float.dtype == dtype_float
var_float2: Variable[Any, np.dtype[np.float32]]
var_float2 = var_float._replace(("x",), np_val2)
assert var_float2.dtype == dtype_float