# Licensed under a 3-clause BSD style license - see LICENSE.rst

import numpy as np
import pytest

from astropy import coordinates, time
from astropy import units as u
from astropy.table import Column, NdarrayMixin, QTable, Table, table_helpers, unique
from astropy.utils.compat import NUMPY_LT_1_22, NUMPY_LT_1_22_1
from astropy.utils.exceptions import AstropyUserWarning


def sort_eq(list1, list2):
    return sorted(list1) == sorted(list2)


def test_column_group_by(T1):
    for masked in (False, True):
        t1 = QTable(T1, masked=masked)
        t1a = t1["a"].copy()

        # Group by a Column (i.e. numpy array)
        t1ag = t1a.group_by(t1["a"])
        assert np.all(t1ag.groups.indices == np.array([0, 1, 4, 8]))

        # Group by a Table
        t1ag = t1a.group_by(t1["a", "b"])
        assert np.all(t1ag.groups.indices == np.array([0, 1, 3, 4, 5, 7, 8]))

        # Group by a numpy structured array
        t1ag = t1a.group_by(t1["a", "b"].as_array())
        assert np.all(t1ag.groups.indices == np.array([0, 1, 3, 4, 5, 7, 8]))


def test_table_group_by(T1):
    """
    Test basic table group_by functionality for possible key types and for
    masked/unmasked tables.
    """
    for masked in (False, True):
        t1 = QTable(T1, masked=masked)
        # Group by a single column key specified by name
        tg = t1.group_by("a")
        assert np.all(tg.groups.indices == np.array([0, 1, 4, 8]))
        assert str(tg.groups) == "<TableGroups indices=[0 1 4 8]>"
        assert str(tg["a"].groups) == "<ColumnGroups indices=[0 1 4 8]>"

        # Sorted by 'a' and in original order for rest
        assert tg.pformat() == [
            " a   b   c   d   q ",
            "                 m ",
            "--- --- --- --- ---",
            "  0   a 0.0   4 4.0",
            "  1   b 3.0   5 5.0",
            "  1   a 2.0   6 6.0",
            "  1   a 1.0   7 7.0",
            "  2   c 7.0   0 0.0",
            "  2   b 5.0   1 1.0",
            "  2   b 6.0   2 2.0",
            "  2   a 4.0   3 3.0",
        ]
        assert tg.meta["ta"] == 1
        assert tg["c"].meta["a"] == 1
        assert tg["c"].description == "column c"

        # Group by a table column
        tg2 = t1.group_by(t1["a"])
        assert tg.pformat() == tg2.pformat()

        # Group by two columns spec'd by name
        for keys in (["a", "b"], ("a", "b")):
            tg = t1.group_by(keys)
            assert np.all(tg.groups.indices == np.array([0, 1, 3, 4, 5, 7, 8]))
            # Sorted by 'a', 'b' and in original order for rest
            assert tg.pformat() == [
                " a   b   c   d   q ",
                "                 m ",
                "--- --- --- --- ---",
                "  0   a 0.0   4 4.0",
                "  1   a 2.0   6 6.0",
                "  1   a 1.0   7 7.0",
                "  1   b 3.0   5 5.0",
                "  2   a 4.0   3 3.0",
                "  2   b 5.0   1 1.0",
                "  2   b 6.0   2 2.0",
                "  2   c 7.0   0 0.0",
            ]

        # Group by a Table
        tg2 = t1.group_by(t1["a", "b"])
        assert tg.pformat() == tg2.pformat()

        # Group by a structured array
        tg2 = t1.group_by(t1["a", "b"].as_array())
        assert tg.pformat() == tg2.pformat()

        # Group by a simple ndarray
        tg = t1.group_by(np.array([0, 1, 0, 1, 2, 1, 0, 0]))
        assert np.all(tg.groups.indices == np.array([0, 4, 7, 8]))
        assert tg.pformat() == [
            " a   b   c   d   q ",
            "                 m ",
            "--- --- --- --- ---",
            "  2   c 7.0   0 0.0",
            "  2   b 6.0   2 2.0",
            "  1   a 2.0   6 6.0",
            "  1   a 1.0   7 7.0",
            "  2   b 5.0   1 1.0",
            "  2   a 4.0   3 3.0",
            "  1   b 3.0   5 5.0",
            "  0   a 0.0   4 4.0",
        ]


def test_groups_keys(T1):
    tg = T1.group_by("a")
    keys = tg.groups.keys
    assert keys.dtype.names == ("a",)
    assert np.all(keys["a"] == np.array([0, 1, 2]))

    tg = T1.group_by(["a", "b"])
    keys = tg.groups.keys
    assert keys.dtype.names == ("a", "b")
    assert np.all(keys["a"] == np.array([0, 1, 1, 2, 2, 2]))
    assert np.all(keys["b"] == np.array(["a", "a", "b", "a", "b", "c"]))

    # Grouping by Column ignores column name
    tg = T1.group_by(T1["b"])
    keys = tg.groups.keys
    assert keys.dtype.names is None


def test_groups_iterator(T1):
    tg = T1.group_by("a")
    for ii, group in enumerate(tg.groups):
        assert group.pformat() == tg.groups[ii].pformat()
        assert group["a"][0] == tg["a"][tg.groups.indices[ii]]


def test_grouped_copy(T1):
    """
    Test that copying a table or column copies the groups properly
    """
    for masked in (False, True):
        t1 = QTable(T1, masked=masked)
        tg = t1.group_by("a")
        tgc = tg.copy()
        assert np.all(tgc.groups.indices == tg.groups.indices)
        assert np.all(tgc.groups.keys == tg.groups.keys)

        tac = tg["a"].copy()
        assert np.all(tac.groups.indices == tg["a"].groups.indices)

        c1 = t1["a"].copy()
        gc1 = c1.group_by(t1["a"])
        gc1c = gc1.copy()
        assert np.all(gc1c.groups.indices == np.array([0, 1, 4, 8]))


def test_grouped_slicing(T1):
    """
    Test that slicing a table removes previous grouping
    """

    for masked in (False, True):
        t1 = QTable(T1, masked=masked)

        # Regular slice of a table
        tg = t1.group_by("a")
        tg2 = tg[3:5]
        assert np.all(tg2.groups.indices == np.array([0, len(tg2)]))
        assert tg2.groups.keys is None


def test_group_column_from_table(T1):
    """
    Group a column that is part of a table
    """
    cg = T1["c"].group_by(np.array(T1["a"]))
    assert np.all(cg.groups.keys == np.array([0, 1, 2]))
    assert np.all(cg.groups.indices == np.array([0, 1, 4, 8]))


def test_table_groups_mask_index(T1):
    """
    Use boolean mask as item in __getitem__ for groups
    """
    for masked in (False, True):
        t1 = Table(T1, masked=masked).group_by("a")

        t2 = t1.groups[np.array([True, False, True])]
        assert len(t2.groups) == 2
        assert t2.groups[0].pformat() == t1.groups[0].pformat()
        assert t2.groups[1].pformat() == t1.groups[2].pformat()
        assert np.all(t2.groups.keys["a"] == np.array([0, 2]))


def test_table_groups_array_index(T1):
    """
    Use numpy array as item in __getitem__ for groups
    """
    for masked in (False, True):
        t1 = Table(T1, masked=masked).group_by("a")

        t2 = t1.groups[np.array([0, 2])]
        assert len(t2.groups) == 2
        assert t2.groups[0].pformat() == t1.groups[0].pformat()
        assert t2.groups[1].pformat() == t1.groups[2].pformat()
        assert np.all(t2.groups.keys["a"] == np.array([0, 2]))


def test_table_groups_slicing(T1):
    """
    Test that slicing table groups works
    """

    for masked in (False, True):
        t1 = Table(T1, masked=masked).group_by("a")

        # slice(0, 2)
        t2 = t1.groups[0:2]
        assert len(t2.groups) == 2
        assert t2.groups[0].pformat() == t1.groups[0].pformat()
        assert t2.groups[1].pformat() == t1.groups[1].pformat()
        assert np.all(t2.groups.keys["a"] == np.array([0, 1]))

        # slice(1, 2)
        t2 = t1.groups[1:2]
        assert len(t2.groups) == 1
        assert t2.groups[0].pformat() == t1.groups[1].pformat()
        assert np.all(t2.groups.keys["a"] == np.array([1]))

        # slice(0, 3, 2)
        t2 = t1.groups[0:3:2]
        assert len(t2.groups) == 2
        assert t2.groups[0].pformat() == t1.groups[0].pformat()
        assert t2.groups[1].pformat() == t1.groups[2].pformat()
        assert np.all(t2.groups.keys["a"] == np.array([0, 2]))


def test_grouped_item_access(T1):
    """
    Test that column slicing preserves grouping
    """
    for masked in (False, True):
        t1 = Table(T1, masked=masked)

        # Regular slice of a table
        tg = t1.group_by("a")
        tgs = tg["a", "c", "d"]
        assert np.all(tgs.groups.keys == tg.groups.keys)
        assert np.all(tgs.groups.indices == tg.groups.indices)
        tgsa = tgs.groups.aggregate(np.sum)
        assert tgsa.pformat() == [
            " a   c    d ",
            "--- ---- ---",
            "  0  0.0   4",
            "  1  6.0  18",
            "  2 22.0   6",
        ]

        tgs = tg["c", "d"]
        assert np.all(tgs.groups.keys == tg.groups.keys)
        assert np.all(tgs.groups.indices == tg.groups.indices)
        tgsa = tgs.groups.aggregate(np.sum)
        assert tgsa.pformat() == [
            " c    d ",
            "---- ---",
            " 0.0   4",
            " 6.0  18",
            "22.0   6",
        ]


def test_mutable_operations(T1):
    """
    Operations like adding or deleting a row should removing grouping,
    but adding or removing or renaming a column should retain grouping.
    """
    for masked in (False, True):
        t1 = QTable(T1, masked=masked)

        # add row
        tg = t1.group_by("a")
        tg.add_row((0, "a", 3.0, 4, 4 * u.m))
        assert np.all(tg.groups.indices == np.array([0, len(tg)]))
        assert tg.groups.keys is None

        # remove row
        tg = t1.group_by("a")
        tg.remove_row(4)
        assert np.all(tg.groups.indices == np.array([0, len(tg)]))
        assert tg.groups.keys is None

        # add column
        tg = t1.group_by("a")
        indices = tg.groups.indices.copy()
        tg.add_column(Column(name="e", data=np.arange(len(tg))))
        assert np.all(tg.groups.indices == indices)
        assert np.all(tg["e"].groups.indices == indices)
        assert np.all(tg["e"].groups.keys == tg.groups.keys)

        # remove column (not key column)
        tg = t1.group_by("a")
        tg.remove_column("b")
        assert np.all(tg.groups.indices == indices)
        # Still has original key col names
        assert tg.groups.keys.dtype.names == ("a",)
        assert np.all(tg["a"].groups.indices == indices)

        # remove key column
        tg = t1.group_by("a")
        tg.remove_column("a")
        assert np.all(tg.groups.indices == indices)
        assert tg.groups.keys.dtype.names == ("a",)
        assert np.all(tg["b"].groups.indices == indices)

        # rename key column
        tg = t1.group_by("a")
        tg.rename_column("a", "aa")
        assert np.all(tg.groups.indices == indices)
        assert tg.groups.keys.dtype.names == ("a",)
        assert np.all(tg["aa"].groups.indices == indices)


def test_group_by_masked(T1):
    t1m = QTable(T1, masked=True)
    t1m["c"].mask[4] = True
    t1m["d"].mask[5] = True
    assert t1m.group_by("a").pformat() == [
        " a   b   c   d   q ",
        "                 m ",
        "--- --- --- --- ---",
        "  0   a  --   4 4.0",
        "  1   b 3.0  -- 5.0",
        "  1   a 2.0   6 6.0",
        "  1   a 1.0   7 7.0",
        "  2   c 7.0   0 0.0",
        "  2   b 5.0   1 1.0",
        "  2   b 6.0   2 2.0",
        "  2   a 4.0   3 3.0",
    ]


def test_group_by_errors(T1):
    """
    Appropriate errors get raised.
    """
    # Bad column name as string
    with pytest.raises(ValueError):
        T1.group_by("f")

    # Bad column names in list
    with pytest.raises(ValueError):
        T1.group_by(["f", "g"])

    # Wrong length array
    with pytest.raises(ValueError):
        T1.group_by(np.array([1, 2]))

    # Wrong type
    with pytest.raises(TypeError):
        T1.group_by(None)

    # Masked key column
    t1 = QTable(T1, masked=True)
    t1["a"].mask[4] = True
    with pytest.raises(ValueError):
        t1.group_by("a")


def test_groups_keys_meta(T1):
    """
    Make sure the keys meta['grouped_by_table_cols'] is working.
    """
    # Group by column in this table
    tg = T1.group_by("a")
    assert tg.groups.keys.meta["grouped_by_table_cols"] is True
    assert tg["c"].groups.keys.meta["grouped_by_table_cols"] is True
    assert tg.groups[1].groups.keys.meta["grouped_by_table_cols"] is True
    assert (
        tg["d"]
        .groups[np.array([False, True, True])]
        .groups.keys.meta["grouped_by_table_cols"]
        is True
    )

    # Group by external Table
    tg = T1.group_by(T1["a", "b"])
    assert tg.groups.keys.meta["grouped_by_table_cols"] is False
    assert tg["c"].groups.keys.meta["grouped_by_table_cols"] is False
    assert tg.groups[1].groups.keys.meta["grouped_by_table_cols"] is False

    # Group by external numpy array
    tg = T1.group_by(T1["a", "b"].as_array())
    assert not hasattr(tg.groups.keys, "meta")
    assert not hasattr(tg["c"].groups.keys, "meta")

    # Group by Column
    tg = T1.group_by(T1["a"])
    assert "grouped_by_table_cols" not in tg.groups.keys.meta
    assert "grouped_by_table_cols" not in tg["c"].groups.keys.meta


def test_table_aggregate(T1):
    """
    Aggregate a table
    """
    # Table with only summable cols
    t1 = T1["a", "c", "d"]
    tg = t1.group_by("a")
    tga = tg.groups.aggregate(np.sum)
    assert tga.pformat() == [
        " a   c    d ",
        "--- ---- ---",
        "  0  0.0   4",
        "  1  6.0  18",
        "  2 22.0   6",
    ]
    # Reverts to default groups
    assert np.all(tga.groups.indices == np.array([0, 3]))
    assert tga.groups.keys is None

    # metadata survives
    assert tga.meta["ta"] == 1
    assert tga["c"].meta["a"] == 1
    assert tga["c"].description == "column c"

    # Aggregate with np.sum with masked elements.  This results
    # in one group with no elements, hence a nan result and conversion
    # to float for the 'd' column.
    t1m = QTable(T1, masked=True)
    t1m["c"].mask[4:6] = True
    t1m["d"].mask[4:6] = True
    tg = t1m.group_by("a")
    with pytest.warns(UserWarning, match="converting a masked element to nan"):
        tga = tg.groups.aggregate(np.sum)

    assert tga.pformat() == [
        " a   c    d    q  ",
        "               m  ",
        "--- ---- ---- ----",
        "  0  nan  nan  4.0",
        "  1  3.0 13.0 18.0",
        "  2 22.0  6.0  6.0",
    ]

    # Aggregate with np.sum with masked elements, but where every
    # group has at least one remaining (unmasked) element.  Then
    # the int column stays as an int.
    t1m = QTable(t1, masked=True)
    t1m["c"].mask[5] = True
    t1m["d"].mask[5] = True
    tg = t1m.group_by("a")
    tga = tg.groups.aggregate(np.sum)
    assert tga.pformat() == [
        " a   c    d ",
        "--- ---- ---",
        "  0  0.0   4",
        "  1  3.0  13",
        "  2 22.0   6",
    ]

    # Aggregate with a column type that cannot by supplied to the aggregating
    # function.  This raises a warning but still works.
    tg = T1.group_by("a")
    with pytest.warns(AstropyUserWarning, match="Cannot aggregate column"):
        tga = tg.groups.aggregate(np.sum)
    assert tga.pformat() == [
        " a   c    d   q  ",
        "              m  ",
        "--- ---- --- ----",
        "  0  0.0   4  4.0",
        "  1  6.0  18 18.0",
        "  2 22.0   6  6.0",
    ]


def test_table_aggregate_reduceat(T1):
    """
    Aggregate table with functions which have a reduceat method
    """

    # Comparison functions without reduceat
    def np_mean(x):
        return np.mean(x)

    def np_sum(x):
        return np.sum(x)

    def np_add(x):
        return np.add(x)

    # Table with only summable cols
    t1 = T1["a", "c", "d"]
    tg = t1.group_by("a")
    # Comparison
    tga_r = tg.groups.aggregate(np.sum)
    tga_a = tg.groups.aggregate(np.add)
    tga_n = tg.groups.aggregate(np_sum)

    assert np.all(tga_r == tga_n)
    assert np.all(tga_a == tga_n)
    assert tga_n.pformat() == [
        " a   c    d ",
        "--- ---- ---",
        "  0  0.0   4",
        "  1  6.0  18",
        "  2 22.0   6",
    ]

    tga_r = tg.groups.aggregate(np.mean)
    tga_n = tg.groups.aggregate(np_mean)
    assert np.all(tga_r == tga_n)
    assert tga_n.pformat() == [
        " a   c   d ",
        "--- --- ---",
        "  0 0.0 4.0",
        "  1 2.0 6.0",
        "  2 5.5 1.5",
    ]

    # Binary ufunc np_add should raise warning without reduceat
    t2 = T1["a", "c"]
    tg = t2.group_by("a")

    with pytest.warns(AstropyUserWarning, match="Cannot aggregate column"):
        tga = tg.groups.aggregate(np_add)
    assert tga.pformat() == [" a ", "---", "  0", "  1", "  2"]


def test_column_aggregate(T1):
    """
    Aggregate a single table column
    """
    for masked in (False, True):
        tg = QTable(T1, masked=masked).group_by("a")
        tga = tg["c"].groups.aggregate(np.sum)
        assert tga.pformat() == [" c  ", "----", " 0.0", " 6.0", "22.0"]


@pytest.mark.skipif(
    not NUMPY_LT_1_22 and NUMPY_LT_1_22_1,
    reason="https://github.com/numpy/numpy/issues/20699",
)
def test_column_aggregate_f8():
    """https://github.com/astropy/astropy/issues/12706"""
    # Just want to make sure it does not crash again.
    for masked in (False, True):
        tg = Table({"a": np.arange(2, dtype=">f8")}, masked=masked).group_by("a")
        tga = tg["a"].groups.aggregate(np.sum)
        assert tga.pformat() == [" a ", "---", "0.0", "1.0"]


def test_table_filter():
    """
    Table groups filtering
    """

    def all_positive(table, key_colnames):
        return all(
            np.all(table[colname] >= 0)
            for colname in table.colnames
            if colname not in key_colnames
        )

    # Negative value in 'a' column should not filter because it is a key col
    t = Table.read(
        [
            " a c d",
            " -2 7.0 0",
            " -2 5.0 1",
            " 0 0.0 4",
            " 1 3.0 5",
            " 1 2.0 -6",
            " 1 1.0 7",
            " 3 3.0 5",
            " 3 -2.0 6",
            " 3 1.0 7",
        ],
        format="ascii",
    )
    tg = t.group_by("a")
    t2 = tg.groups.filter(all_positive)
    assert t2.groups[0].pformat() == [
        " a   c   d ",
        "--- --- ---",
        " -2 7.0   0",
        " -2 5.0   1",
    ]
    assert t2.groups[1].pformat() == [" a   c   d ", "--- --- ---", "  0 0.0   4"]


def test_column_filter():
    """
    Table groups filtering
    """

    def all_positive(column):
        if np.any(column < 0):
            return False
        return True

    # Negative value in 'a' column should not filter because it is a key col
    t = Table.read(
        [
            " a c d",
            " -2 7.0 0",
            " -2 5.0 1",
            " 0 0.0 4",
            " 1 3.0 5",
            " 1 2.0 -6",
            " 1 1.0 7",
            " 3 3.0 5",
            " 3 -2.0 6",
            " 3 1.0 7",
        ],
        format="ascii",
    )
    tg = t.group_by("a")
    c2 = tg["c"].groups.filter(all_positive)
    assert len(c2.groups) == 3
    assert c2.groups[0].pformat() == [" c ", "---", "7.0", "5.0"]
    assert c2.groups[1].pformat() == [" c ", "---", "0.0"]
    assert c2.groups[2].pformat() == [" c ", "---", "3.0", "2.0", "1.0"]


def test_group_mixins():
    """
    Test grouping a table with mixin columns
    """
    # Setup mixins
    idx = np.arange(4)
    x = np.array([3.0, 1.0, 2.0, 1.0])
    q = x * u.m
    lon = coordinates.Longitude(x * u.deg)
    lat = coordinates.Latitude(x * u.deg)
    # For Time do J2000.0 + few * 0.1 ns (this requires > 64 bit precision)
    tm = time.Time(2000, format="jyear") + time.TimeDelta(x * 1e-10, format="sec")
    sc = coordinates.SkyCoord(ra=lon, dec=lat)
    aw = table_helpers.ArrayWrapper(x)
    nd = np.array([(3, "c"), (1, "a"), (2, "b"), (1, "a")], dtype="<i4,|S1").view(
        NdarrayMixin
    )

    qt = QTable(
        [idx, x, q, lon, lat, tm, sc, aw, nd],
        names=["idx", "x", "q", "lon", "lat", "tm", "sc", "aw", "nd"],
    )

    # Test group_by with each supported mixin type
    mixin_keys = ["x", "q", "lon", "lat", "tm", "sc", "aw", "nd"]
    for key in mixin_keys:
        qtg = qt.group_by(key)

        # Test that it got the sort order correct
        assert np.all(qtg["idx"] == [1, 3, 2, 0])

        # Test that the groups are right
        # Note: skip testing SkyCoord column because that doesn't have equality
        for name in ["x", "q", "lon", "lat", "tm", "aw", "nd"]:
            assert np.all(qt[name][[1, 3]] == qtg.groups[0][name])
            assert np.all(qt[name][[2]] == qtg.groups[1][name])
            assert np.all(qt[name][[0]] == qtg.groups[2][name])

    # Test that unique also works with mixins since most of the work is
    # done with group_by().  This is using *every* mixin as key.
    uqt = unique(qt, keys=mixin_keys)
    assert len(uqt) == 3
    assert np.all(uqt["idx"] == [1, 2, 0])
    assert np.all(uqt["x"] == [1.0, 2.0, 3.0])

    # Column group_by() with mixins
    idxg = qt["idx"].group_by(qt[mixin_keys])
    assert np.all(idxg == [1, 3, 2, 0])


@pytest.mark.parametrize(
    "col",
    [
        time.TimeDelta([1, 2], format="sec"),
        time.Time([1, 2], format="cxcsec"),
        coordinates.SkyCoord([1, 2], [3, 4], unit="deg,deg"),
    ],
)
def test_group_mixins_unsupported(col):
    """Test that aggregating unsupported mixins produces a warning only"""

    t = Table([[1, 1], [3, 4], col], names=["a", "b", "mix"])
    tg = t.group_by("a")
    with pytest.warns(AstropyUserWarning, match="Cannot aggregate column 'mix'"):
        tg.groups.aggregate(np.sum)
