Newer
Older
#! /usr/bin/env python
# -*- coding: utf-8 -*-
import pytest
import numpy as np
from saqc.funcs.functions import (
flagRange,
flagSesonalRange,
forceFlags,
clearFlags,
flagIsolated,
)
from test.common import initData, TESTFLAGGER
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_flagRange(data, field, flagger):
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)
@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)
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)
flagger = flagger.initFlags(data)
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)
@pytest.mark.parametrize("flagger", TESTFLAGGER)
def test_flagIsolated(data, flagger):
field = data.columns[0]
data.iloc[1:3, 0] = np.nan
data.iloc[4:5, 0] = np.nan
data.iloc[11:13, 0] = np.nan
data.iloc[15:17, 0] = np.nan
flagger = flagger.initFlags(data)
flagger = flagger.setFlags(field, iloc=slice(5, 6))
_, flagger_result = flagIsolated(data, field, flagger, group_window="1D", gap_window="2.1D")
assert flagger_result.isFlagged(field)[slice(3, 6, 2)].all()
flagger = flagger.setFlags(
field, iloc=slice(3, 4), flag=flagger.UNFLAGGED, force=True
)
data, flagger_result = flagIsolated(
flagger_result,
group_window="2D",
gap_window="2.1D",
assert flagger_result.isFlagged(field)[[3, 5, 13, 14]].all()