Sindbad~EG File Manager

"""
Additional tests for NumpyExtensionArray that aren't covered by
the interface tests.
"""
import numpy as np
import pytest

from pandas.core.dtypes.dtypes import NumpyEADtype

import pandas as pd
import pandas._testing as tm
from pandas.arrays import NumpyExtensionArray


@pytest.fixture(
    params=[
        np.array(["a", "b"], dtype=object),
        np.array([0, 1], dtype=float),
        np.array([0, 1], dtype=int),
        np.array([0, 1 + 2j], dtype=complex),
        np.array([True, False], dtype=bool),
        np.array([0, 1], dtype="datetime64[ns]"),
        np.array([0, 1], dtype="timedelta64[ns]"),
    ]
)
def any_numpy_array(request):
    """
    Parametrized fixture for NumPy arrays with different dtypes.

    This excludes string and bytes.
    """
    return request.param


# ----------------------------------------------------------------------------
# NumpyEADtype


@pytest.mark.parametrize(
    "dtype, expected",
    [
        ("bool", True),
        ("int", True),
        ("uint", True),
        ("float", True),
        ("complex", True),
        ("str", False),
        ("bytes", False),
        ("datetime64[ns]", False),
        ("object", False),
        ("void", False),
    ],
)
def test_is_numeric(dtype, expected):
    dtype = NumpyEADtype(dtype)
    assert dtype._is_numeric is expected


@pytest.mark.parametrize(
    "dtype, expected",
    [
        ("bool", True),
        ("int", False),
        ("uint", False),
        ("float", False),
        ("complex", False),
        ("str", False),
        ("bytes", False),
        ("datetime64[ns]", False),
        ("object", False),
        ("void", False),
    ],
)
def test_is_boolean(dtype, expected):
    dtype = NumpyEADtype(dtype)
    assert dtype._is_boolean is expected


def test_repr():
    dtype = NumpyEADtype(np.dtype("int64"))
    assert repr(dtype) == "NumpyEADtype('int64')"


def test_constructor_from_string():
    result = NumpyEADtype.construct_from_string("int64")
    expected = NumpyEADtype(np.dtype("int64"))
    assert result == expected


def test_dtype_idempotent(any_numpy_dtype):
    dtype = NumpyEADtype(any_numpy_dtype)

    result = NumpyEADtype(dtype)
    assert result == dtype


# ----------------------------------------------------------------------------
# Construction


def test_constructor_no_coercion():
    with pytest.raises(ValueError, match="NumPy array"):
        NumpyExtensionArray([1, 2, 3])


def test_series_constructor_with_copy():
    ndarray = np.array([1, 2, 3])
    ser = pd.Series(NumpyExtensionArray(ndarray), copy=True)

    assert ser.values is not ndarray


def test_series_constructor_with_astype():
    ndarray = np.array([1, 2, 3])
    result = pd.Series(NumpyExtensionArray(ndarray), dtype="float64")
    expected = pd.Series([1.0, 2.0, 3.0], dtype="float64")
    tm.assert_series_equal(result, expected)


def test_from_sequence_dtype():
    arr = np.array([1, 2, 3], dtype="int64")
    result = NumpyExtensionArray._from_sequence(arr, dtype="uint64")
    expected = NumpyExtensionArray(np.array([1, 2, 3], dtype="uint64"))
    tm.assert_extension_array_equal(result, expected)


def test_constructor_copy():
    arr = np.array([0, 1])
    result = NumpyExtensionArray(arr, copy=True)

    assert not tm.shares_memory(result, arr)


def test_constructor_with_data(any_numpy_array):
    nparr = any_numpy_array
    arr = NumpyExtensionArray(nparr)
    assert arr.dtype.numpy_dtype == nparr.dtype


# ----------------------------------------------------------------------------
# Conversion


def test_to_numpy():
    arr = NumpyExtensionArray(np.array([1, 2, 3]))
    result = arr.to_numpy()
    assert result is arr._ndarray

    result = arr.to_numpy(copy=True)
    assert result is not arr._ndarray

    result = arr.to_numpy(dtype="f8")
    expected = np.array([1, 2, 3], dtype="f8")
    tm.assert_numpy_array_equal(result, expected)


# ----------------------------------------------------------------------------
# Setitem


def test_setitem_series():
    ser = pd.Series([1, 2, 3])
    ser.array[0] = 10
    expected = pd.Series([10, 2, 3])
    tm.assert_series_equal(ser, expected)


def test_setitem(any_numpy_array):
    nparr = any_numpy_array
    arr = NumpyExtensionArray(nparr, copy=True)

    arr[0] = arr[1]
    nparr[0] = nparr[1]

    tm.assert_numpy_array_equal(arr.to_numpy(), nparr)


# ----------------------------------------------------------------------------
# Reductions


def test_bad_reduce_raises():
    arr = np.array([1, 2, 3], dtype="int64")
    arr = NumpyExtensionArray(arr)
    msg = "cannot perform not_a_method with type int"
    with pytest.raises(TypeError, match=msg):
        arr._reduce(msg)


def test_validate_reduction_keyword_args():
    arr = NumpyExtensionArray(np.array([1, 2, 3]))
    msg = "the 'keepdims' parameter is not supported .*all"
    with pytest.raises(ValueError, match=msg):
        arr.all(keepdims=True)


def test_np_max_nested_tuples():
    # case where checking in ufunc.nout works while checking for tuples
    #  does not
    vals = [
        (("j", "k"), ("l", "m")),
        (("l", "m"), ("o", "p")),
        (("o", "p"), ("j", "k")),
    ]
    ser = pd.Series(vals)
    arr = ser.array

    assert arr.max() is arr[2]
    assert ser.max() is arr[2]

    result = np.maximum.reduce(arr)
    assert result == arr[2]

    result = np.maximum.reduce(ser)
    assert result == arr[2]


def test_np_reduce_2d():
    raw = np.arange(12).reshape(4, 3)
    arr = NumpyExtensionArray(raw)

    res = np.maximum.reduce(arr, axis=0)
    tm.assert_extension_array_equal(res, arr[-1])

    alt = arr.max(axis=0)
    tm.assert_extension_array_equal(alt, arr[-1])


# ----------------------------------------------------------------------------
# Ops


@pytest.mark.parametrize("ufunc", [np.abs, np.negative, np.positive])
def test_ufunc_unary(ufunc):
    arr = NumpyExtensionArray(np.array([-1.0, 0.0, 1.0]))
    result = ufunc(arr)
    expected = NumpyExtensionArray(ufunc(arr._ndarray))
    tm.assert_extension_array_equal(result, expected)

    # same thing but with the 'out' keyword
    out = NumpyExtensionArray(np.array([-9.0, -9.0, -9.0]))
    ufunc(arr, out=out)
    tm.assert_extension_array_equal(out, expected)


def test_ufunc():
    arr = NumpyExtensionArray(np.array([-1.0, 0.0, 1.0]))

    r1, r2 = np.divmod(arr, np.add(arr, 2))
    e1, e2 = np.divmod(arr._ndarray, np.add(arr._ndarray, 2))
    e1 = NumpyExtensionArray(e1)
    e2 = NumpyExtensionArray(e2)
    tm.assert_extension_array_equal(r1, e1)
    tm.assert_extension_array_equal(r2, e2)


def test_basic_binop():
    # Just a basic smoke test. The EA interface tests exercise this
    # more thoroughly.
    x = NumpyExtensionArray(np.array([1, 2, 3]))
    result = x + x
    expected = NumpyExtensionArray(np.array([2, 4, 6]))
    tm.assert_extension_array_equal(result, expected)


@pytest.mark.parametrize("dtype", [None, object])
def test_setitem_object_typecode(dtype):
    arr = NumpyExtensionArray(np.array(["a", "b", "c"], dtype=dtype))
    arr[0] = "t"
    expected = NumpyExtensionArray(np.array(["t", "b", "c"], dtype=dtype))
    tm.assert_extension_array_equal(arr, expected)


def test_setitem_no_coercion():
    # https://github.com/pandas-dev/pandas/issues/28150
    arr = NumpyExtensionArray(np.array([1, 2, 3]))
    with pytest.raises(ValueError, match="int"):
        arr[0] = "a"

    # With a value that we do coerce, check that we coerce the value
    #  and not the underlying array.
    arr[0] = 2.5
    assert isinstance(arr[0], (int, np.integer)), type(arr[0])


def test_setitem_preserves_views():
    # GH#28150, see also extension test of the same name
    arr = NumpyExtensionArray(np.array([1, 2, 3]))
    view1 = arr.view()
    view2 = arr[:]
    view3 = np.asarray(arr)

    arr[0] = 9
    assert view1[0] == 9
    assert view2[0] == 9
    assert view3[0] == 9

    arr[-1] = 2.5
    view1[-1] = 5
    assert arr[-1] == 5


@pytest.mark.parametrize("dtype", [np.int64, np.uint64])
def test_quantile_empty(dtype):
    # we should get back np.nans, not -1s
    arr = NumpyExtensionArray(np.array([], dtype=dtype))
    idx = pd.Index([0.0, 0.5])

    result = arr._quantile(idx, interpolation="linear")
    expected = NumpyExtensionArray(np.array([np.nan, np.nan]))
    tm.assert_extension_array_equal(result, expected)


def test_factorize_unsigned():
    # don't raise when calling factorize on unsigned int NumpyExtensionArray
    arr = np.array([1, 2, 3], dtype=np.uint64)
    obj = NumpyExtensionArray(arr)

    res_codes, res_unique = obj.factorize()
    exp_codes, exp_unique = pd.factorize(arr)

    tm.assert_numpy_array_equal(res_codes, exp_codes)

    tm.assert_extension_array_equal(res_unique, NumpyExtensionArray(exp_unique))