from warnings import catch_warnings, simplefilter

import numpy as np

from pandas.core.dtypes import generic as gt

import pandas as pd
from pandas.util import testing as tm


class TestABCClasses:
    tuples = [[1, 2, 2], ["red", "blue", "red"]]
    multi_index = pd.MultiIndex.from_arrays(tuples, names=("number", "color"))
    datetime_index = pd.to_datetime(["2000/1/1", "2010/1/1"])
    timedelta_index = pd.to_timedelta(np.arange(5), unit="s")
    period_index = pd.period_range("2000/1/1", "2010/1/1/", freq="M")
    categorical = pd.Categorical([1, 2, 3], categories=[2, 3, 1])
    categorical_df = pd.DataFrame({"values": [1, 2, 3]}, index=categorical)
    df = pd.DataFrame({"names": ["a", "b", "c"]}, index=multi_index)
    with catch_warnings():
        simplefilter("ignore", FutureWarning)
        sparse_series = pd.Series([1, 2, 3]).to_sparse()
        sparse_frame = pd.SparseDataFrame({"a": [1, -1, None]})

    sparse_array = pd.SparseArray(np.random.randn(10))
    datetime_array = pd.core.arrays.DatetimeArray(datetime_index)
    timedelta_array = pd.core.arrays.TimedeltaArray(timedelta_index)

    def test_abc_types(self):
        assert isinstance(pd.Index(["a", "b", "c"]), gt.ABCIndex)
        assert isinstance(pd.Int64Index([1, 2, 3]), gt.ABCInt64Index)
        assert isinstance(pd.UInt64Index([1, 2, 3]), gt.ABCUInt64Index)
        assert isinstance(pd.Float64Index([1, 2, 3]), gt.ABCFloat64Index)
        assert isinstance(self.multi_index, gt.ABCMultiIndex)
        assert isinstance(self.datetime_index, gt.ABCDatetimeIndex)
        assert isinstance(self.timedelta_index, gt.ABCTimedeltaIndex)
        assert isinstance(self.period_index, gt.ABCPeriodIndex)
        assert isinstance(self.categorical_df.index, gt.ABCCategoricalIndex)
        assert isinstance(pd.Index(["a", "b", "c"]), gt.ABCIndexClass)
        assert isinstance(pd.Int64Index([1, 2, 3]), gt.ABCIndexClass)
        assert isinstance(pd.Series([1, 2, 3]), gt.ABCSeries)
        assert isinstance(self.df, gt.ABCDataFrame)
        assert isinstance(self.sparse_series, gt.ABCSparseSeries)
        assert isinstance(self.sparse_array, gt.ABCSparseArray)
        assert isinstance(self.sparse_frame, gt.ABCSparseDataFrame)
        assert isinstance(self.categorical, gt.ABCCategorical)
        assert isinstance(pd.Period("2012", freq="A-DEC"), gt.ABCPeriod)

        assert isinstance(pd.DateOffset(), gt.ABCDateOffset)
        assert isinstance(pd.Period("2012", freq="A-DEC").freq, gt.ABCDateOffset)
        assert not isinstance(pd.Period("2012", freq="A-DEC"), gt.ABCDateOffset)
        assert isinstance(pd.Interval(0, 1.5), gt.ABCInterval)
        assert not isinstance(pd.Period("2012", freq="A-DEC"), gt.ABCInterval)

        assert isinstance(self.datetime_array, gt.ABCDatetimeArray)
        assert not isinstance(self.datetime_index, gt.ABCDatetimeArray)

        assert isinstance(self.timedelta_array, gt.ABCTimedeltaArray)
        assert not isinstance(self.timedelta_index, gt.ABCTimedeltaArray)


def test_setattr_warnings():
    # GH7175 - GOTCHA: You can't use dot notation to add a column...
    d = {
        "one": pd.Series([1.0, 2.0, 3.0], index=["a", "b", "c"]),
        "two": pd.Series([1.0, 2.0, 3.0, 4.0], index=["a", "b", "c", "d"]),
    }
    df = pd.DataFrame(d)

    with catch_warnings(record=True) as w:
        #  successfully add new column
        #  this should not raise a warning
        df["three"] = df.two + 1
        assert len(w) == 0
        assert df.three.sum() > df.two.sum()

    with catch_warnings(record=True) as w:
        #  successfully modify column in place
        #  this should not raise a warning
        df.one += 1
        assert len(w) == 0
        assert df.one.iloc[0] == 2

    with catch_warnings(record=True) as w:
        #  successfully add an attribute to a series
        #  this should not raise a warning
        df.two.not_an_index = [1, 2]
        assert len(w) == 0

    with tm.assert_produces_warning(UserWarning):
        #  warn when setting column to nonexistent name
        df.four = df.two + 2
        assert df.four.sum() > df.two.sum()