#! /usr/bin/env python
# -*- coding: utf-8 -*-

import pytest
import numpy as np
import pandas as pd

from saqc.core.evaluator import evalExpression
from saqc.funcs.functions import flagRange, flagSesonalRange, forceFlags, clearFlags

from test.common import initData, TESTFLAGGER, initMetaDict


@pytest.fixture
def data():
    return initData(
        cols=1,
        start_date="2016-01-01", end_date="2018-12-31",
        freq="1D")


@pytest.fixture
def field(data):
    return data.columns[0]


@pytest.mark.parametrize('flagger', TESTFLAGGER)
def test_flagAfter(data, field, flagger):

    flagger = flagger.initFlags(data)

    min = data.iloc[int(len(data)*.3), 0]
    max = data.iloc[int(len(data)*.6), 0]
    _, flagger_range = flagRange(data, field, flagger, min, max)
    flagged_range = flagger_range.isFlagged(field,
                                          loc=flagger_range.isFlagged(field))

    tests = [
        (f"flagWindowAfterFlag(window='3D', func=range(min={min}, max={max}))", "3D"),
        (f"flagNextAfterFlag(n=4, func=range(min={min}, max={max}))", 4),
    ]

    for expr, window in tests:
        _, flagger_range_repeated = evalExpression(expr, data, field, flagger)

        check = (flagged_range
                 .rolling(window=window)
                 .apply(
                     lambda df: (flagger_range_repeated
                                 .isFlagged(field, loc=df.index)
                                 .all()),
                     raw=False))
        assert check.all()


@pytest.mark.parametrize('flagger', TESTFLAGGER)
def test_range(data, field, flagger):
    min, max = 10, 90
    flagger = flagger.initFlags(data)
    data, flagger = flagRange(data, field, flagger, min=min, max=max)
    flagged = flagger.isFlagged(field)
    expected = (data[field] < min) | (data[field] >= max)
    assert np.all(flagged == expected)


# @pytest.mark.parametrize('flagger', TESTFLAGGER)
# def test_missing(data, field, flagger):
#     pass


@pytest.mark.parametrize('flagger', TESTFLAGGER)
def test_flagSesonalRange(data, field, flagger):
    # prepare
    data.loc[::2] = 0
    data.loc[1::2] = 50
    nyears = len(data.index.year.unique())

    tests = [
        ({"min": 1, "max": 100, "startmonth": 7, "startday": 1, "endmonth": 8, "endday": 31},
         31*2*nyears//2),
        ({"min": 1, "max": 100, "startmonth": 12, "startday": 16, "endmonth": 1, "endday": 15},
         31*nyears//2 + 1)
    ]

    for test, expected in tests:
        flagger = flagger.initFlags(data)
        data, flagger = flagSesonalRange(data, field, flagger, **test)
        flagged = flagger.isFlagged(field)
        assert flagged.sum() == expected


@pytest.mark.parametrize('flagger', TESTFLAGGER)
def test_clearFlags(data, field, flagger):
    flagger = flagger.initFlags(data)
    flags_orig = flagger.getFlags()
    flags_set = flagger.setFlags(field, flag=flagger.BAD).getFlags()
    _, flagger = clearFlags(data, field, flagger)
    flags_cleared = flagger.getFlags()
    assert np.all(flags_orig != flags_set)
    assert np.all(flags_orig == flags_cleared)


@pytest.mark.parametrize('flagger', TESTFLAGGER)
def test_forceFlags(data, flagger):
    flagger = flagger.initFlags(data)
    field, *_ = data.columns
    flags_orig = flagger.setFlags(field).getFlags(field)
    _, flagger = forceFlags(data, field, flagger, flag=flagger.GOOD)
    flags_forced = flagger.getFlags(field)
    assert np.all(flags_orig != flags_forced)