From d2785dae7567d0616efbf9022bb69fa4daa8705b Mon Sep 17 00:00:00 2001
From: Bert Palm <bert.palm@ufz.de>
Date: Thu, 4 Mar 2021 02:11:42 +0100
Subject: [PATCH] fixed flagger syntax in most tests
---
test/common.py | 23 +--
test/core/test_core.py | 52 +++---
.../{test_core_new.py => test_creation.py} | 0
test/core/test_masking.py | 3 +-
test/core/test_reader.py | 15 +-
test/flagger/test_dmpflagger.py | 4 +-
test/flagger/test_positionalflagger.py | 3 +-
test/funcs/conftest.py | 92 +++++-----
test/funcs/test_constants_detection.py | 24 ++-
test/funcs/test_functions.py | 165 ++++++++++--------
test/funcs/test_generic_api_functions.py | 46 ++---
test/funcs/test_generic_config_functions.py | 69 ++++----
test/funcs/test_pattern_rec.py | 34 ++--
test/funcs/test_spikes_detection.py | 61 +++----
14 files changed, 288 insertions(+), 303 deletions(-)
rename test/core/{test_core_new.py => test_creation.py} (100%)
diff --git a/test/common.py b/test/common.py
index e07cc5cfb..f774cd5ed 100644
--- a/test/common.py
+++ b/test/common.py
@@ -24,29 +24,22 @@ from hypothesis.strategies._internal.types import _global_type_lookup
from dios import DictOfSeries
+from saqc.common import *
from saqc.core.register import FUNC_MAP
from saqc.core.lib import SaQCFunction
from saqc.lib.types import FreqString, ColumnName, IntegerWindow
-from saqc.flagger import (
- CategoricalFlagger,
- SimpleFlagger,
- DmpFlagger,
-)
+from saqc.flagger import Flagger, initFlagsLike
TESTNODATA = (np.nan, -9999)
-
-
-TESTFLAGGER = (
- CategoricalFlagger(["NIL", "GOOD", "BAD"]),
- SimpleFlagger(),
- DmpFlagger(),
-)
+TESTFLAGGER = (Flagger(),)
def flagAll(data, field, flagger, **kwargs):
# NOTE: remember to rename flag -> flag_values
- return data, flagger.setFlags(field=field, flag=flagger.BAD)
+ flagger.copy()
+ flagger[:, field] = BAD
+ return data, flagger
def initData(cols=2, start_date="2017-01-01", end_date="2017-12-31", freq=None, rows=None):
@@ -125,10 +118,10 @@ def flaggers(draw, data):
initialize a flagger and set some flags
"""
# flagger = draw(sampled_from(TESTFLAGGER)).initFlags(data)
- flagger = draw(sampled_from([SimpleFlagger()])).initFlags(data)
+ flagger = initFlagsLike(data)
for col, srs in data.items():
loc_st = lists(sampled_from(sorted(srs.index)), unique=True, max_size=len(srs)-1)
- flagger = flagger.setFlags(field=col, loc=draw(loc_st))
+ flagger[draw(loc_st), col] = BAD
return flagger
diff --git a/test/core/test_core.py b/test/core/test_core.py
index d409eb6a8..5527f2ee2 100644
--- a/test/core/test_core.py
+++ b/test/core/test_core.py
@@ -8,10 +8,12 @@ import numpy as np
import pandas as pd
-from saqc import SaQC, register
+from saqc.common import *
+from saqc.flagger import Flagger, initFlagsLike
from saqc.funcs import flagRange
from saqc.lib import plotting as splot
from test.common import initData, TESTFLAGGER, flagAll
+from saqc import SaQC, register
# no logging output needed here
@@ -31,13 +33,12 @@ def data():
@pytest.fixture
-def flags(flagger, data, optional):
+def flags(data, optional):
if not optional:
- return flagger.initFlags(data[data.columns[::2]])._flags
+ return initFlagsLike(data[data.columns[::2]]).toDios()
-@pytest.mark.parametrize("flagger", TESTFLAGGER)
-def test_errorHandling(data, flagger):
+def test_errorHandling(data):
@register(masking='field')
def raisingFunc(data, field, flagger, **kwargs):
@@ -47,18 +48,17 @@ def test_errorHandling(data, flagger):
for policy in ["ignore", "warn"]:
# NOTE: should not fail, that's all we are testing here
- SaQC(flagger, data, error_policy=policy).raisingFunc(var1).getResult()
+ SaQC(data, error_policy=policy).raisingFunc(var1).getResult()
with pytest.raises(TypeError):
- SaQC(flagger, data, error_policy='raise').raisingFunc(var1).getResult()
+ SaQC(data, error_policy='raise').raisingFunc(var1).getResult()
-@pytest.mark.parametrize("flagger", TESTFLAGGER)
-def test_duplicatedVariable(flagger):
+def test_duplicatedVariable():
data = initData(1)
var1 = data.columns[0]
- pdata, pflags = SaQC(flagger, data).flagtools.flagDummy(var1).getResult()
+ pdata, pflags = SaQC(data).flagtools.flagDummy(var1).getResult()
if isinstance(pflags.columns, pd.MultiIndex):
cols = pflags.columns.get_level_values(0).drop_duplicates()
@@ -67,8 +67,7 @@ def test_duplicatedVariable(flagger):
assert (pflags.columns == [var1]).all()
-@pytest.mark.parametrize("flagger", TESTFLAGGER)
-def test_sourceTarget(flagger):
+def test_sourceTarget():
"""
test implicit assignments
"""
@@ -76,32 +75,29 @@ def test_sourceTarget(flagger):
var1 = data.columns[0]
target = "new"
- pdata, pflagger = SaQC(flagger, data).flagAll(field=var1, target=target).getResult(raw=True)
- pflags = pflagger.isFlagged()
+ pdata, pflagger = SaQC(data).flagAll(field=var1, target=target).getResult(raw=True)
assert (pdata[var1] == pdata[target]).all(axis=None)
- assert (pflags[var1] == False).all(axis=None)
- assert (pflags[target] == True).all(axis=None)
+ assert all(pflagger[var1] == UNFLAGGED)
+ assert all(pflagger[target] > UNFLAGGED)
-@pytest.mark.parametrize("flagger", TESTFLAGGER)
@pytest.mark.parametrize("optional", OPTIONAL)
-def test_dtypes(data, flagger, flags):
+def test_dtypes(data, flags):
"""
Test if the categorical dtype is preserved through the core functionality
"""
- flagger = flagger.initFlags(data)
- flags = flagger.getFlags()
+ flagger = initFlagsLike(data)
+ flags = flagger.toDios()
var1, var2 = data.columns[:2]
- pdata, pflagger = SaQC(flagger, data, flags=flags).flagAll(var1).flagAll(var2).getResult(raw=True)
+ pdata, pflagger = SaQC(data, flags=flags).flagAll(var1).flagAll(var2).getResult(raw=True)
- pflags = pflagger.getFlags()
- assert dict(flags.dtypes) == dict(pflags.dtypes)
+ for c in pflagger.columns:
+ assert pflagger[c].dtype == flagger[c].dtype
-@pytest.mark.parametrize("flagger", TESTFLAGGER)
-def test_plotting(data, flagger):
+def test_plotting(data):
"""
Test if the plotting code runs, does not show any plot.
@@ -110,9 +106,9 @@ def test_plotting(data, flagger):
"""
pytest.importorskip("matplotlib", reason="requires matplotlib")
field, *_ = data.columns
- flagger = flagger.initFlags(data)
- _, flagger_range = flagRange(data, field, flagger, min=10, max=90, flag=flagger.BAD)
- data_new, flagger_range = flagRange(data, field, flagger_range, min=40, max=60, flag=flagger.GOOD)
+ flagger = initFlagsLike(data)
+ _, flagger_range = flagRange(data, field, flagger, min=10, max=90, flag=BAD)
+ data_new, flagger_range = flagRange(data, field, flagger_range, min=40, max=60, flag=DOUBT)
splot._interactive = False
splot._plotSingleVariable(data, data_new, flagger, flagger_range, sources=[], targets=[data_new.columns[0]])
splot._plotMultipleVariables(data, data_new, flagger, flagger_range, targets=data_new.columns)
diff --git a/test/core/test_core_new.py b/test/core/test_creation.py
similarity index 100%
rename from test/core/test_core_new.py
rename to test/core/test_creation.py
diff --git a/test/core/test_masking.py b/test/core/test_masking.py
index 4d285eabf..48207621e 100644
--- a/test/core/test_masking.py
+++ b/test/core/test_masking.py
@@ -12,7 +12,8 @@ from hypothesis.strategies import (
sampled_from,
)
-from saqc.core.core import _maskData, _unmaskData
+from saqc.common import *
+from saqc.core.register import _maskData, _unmaskData
from test.common import dataFieldFlagger, MAX_EXAMPLES
diff --git a/test/core/test_reader.py b/test/core/test_reader.py
index ef5b1c841..ce8438eff 100644
--- a/test/core/test_reader.py
+++ b/test/core/test_reader.py
@@ -12,7 +12,6 @@ from saqc.core.config import Fields as F
from test.common import initData, writeIO
from saqc.core.core import SaQC
-from saqc.flagger import SimpleFlagger
from saqc.core.register import FUNC_MAP, register
@@ -29,7 +28,7 @@ def test_packagedConfig():
data_path = path / "data.csv"
data = pd.read_csv(data_path, index_col=0, parse_dates=True,)
- saqc = SaQC(SimpleFlagger(), dios.DictOfSeries(data)).readConfig(config_path)
+ saqc = SaQC(dios.DictOfSeries(data)).readConfig(config_path)
saqc.getResult()
@@ -46,7 +45,7 @@ def test_variableRegex(data):
for regex, expected in tests:
fobj = writeIO(header + "\n" + f"{regex} ; flagtools.flagDummy()")
- saqc = SaQC(SimpleFlagger(), data).readConfig(fobj)
+ saqc = SaQC(data).readConfig(fobj)
expansion = saqc._expandFields(saqc._to_call[0][0], saqc._to_call[0][2], data.columns)
result = [s.field for s, _ in expansion]
assert np.all(result == expected)
@@ -60,7 +59,7 @@ def test_inlineComments(data):
{F.VARNAME} ; {F.TEST} ; {F.PLOT}
pre2 ; flagtools.flagDummy() # test ; False # test
"""
- saqc = SaQC(SimpleFlagger(), data).readConfig(writeIO(config))
+ saqc = SaQC(data).readConfig(writeIO(config))
_, control, func = saqc._to_call[0]
assert control.plot is False
assert func.func == FUNC_MAP["flagtools.flagDummy"].func
@@ -78,7 +77,7 @@ def test_configReaderLineNumbers(data):
SM1 ; flagtools.flagDummy()
"""
- saqc = SaQC(SimpleFlagger(), data).readConfig(writeIO(config))
+ saqc = SaQC(data).readConfig(writeIO(config))
result = [c.lineno for _, c, _ in saqc._to_call]
expected = [3, 4, 5, 9]
assert result == expected
@@ -100,7 +99,7 @@ def test_configFile(data):
SM1;flagtools.flagDummy()
"""
- SaQC(SimpleFlagger(), data).readConfig(writeIO(config))
+ SaQC(data).readConfig(writeIO(config))
def test_configChecks(data):
@@ -122,7 +121,7 @@ def test_configChecks(data):
for test, expected in tests:
fobj = writeIO(header + "\n" + test)
with pytest.raises(expected):
- SaQC(SimpleFlagger(), data).readConfig(fobj).getResult()
+ SaQC(data).readConfig(fobj).getResult()
def test_supportedArguments(data):
@@ -151,4 +150,4 @@ def test_supportedArguments(data):
for test in tests:
fobj = writeIO(header + "\n" + test)
- SaQC(SimpleFlagger(), data).readConfig(fobj)
+ SaQC(data).readConfig(fobj)
diff --git a/test/flagger/test_dmpflagger.py b/test/flagger/test_dmpflagger.py
index b1a9c1b73..677f54cbe 100644
--- a/test/flagger/test_dmpflagger.py
+++ b/test/flagger/test_dmpflagger.py
@@ -8,7 +8,9 @@ import pandas as pd
import pytest
from test.common import initData
-from saqc.flagger import DmpFlagger
+
+DmpFlagger = NotImplemented
+pytest.skip("DmpFlagger is deprecated.", allow_module_level=True)
@pytest.fixture
diff --git a/test/flagger/test_positionalflagger.py b/test/flagger/test_positionalflagger.py
index 9875a7c74..45506a070 100644
--- a/test/flagger/test_positionalflagger.py
+++ b/test/flagger/test_positionalflagger.py
@@ -6,8 +6,9 @@ import pytest
import numpy as np
from test.common import initData
-from saqc.flagger import PositionalFlagger
+PositionalFlagger = NotImplemented
+pytest.skip("PositionalFlagger is deprecated.", allow_module_level=True)
@pytest.fixture
def data():
diff --git a/test/funcs/conftest.py b/test/funcs/conftest.py
index 1fd4685e6..abecdd3f2 100644
--- a/test/funcs/conftest.py
+++ b/test/funcs/conftest.py
@@ -16,7 +16,6 @@ def char_dict():
}
-
@pytest.fixture
def course_1(char_dict):
# MONOTONOUSLY ASCENDING/DESCENDING
@@ -24,23 +23,22 @@ def course_1(char_dict):
# the resulting drop/raise per value equals: (peak_level - initial_level) / (0.5*(periods-2))
# periods number better be even!
def fix_funk(
- freq="10min",
- periods=10,
- initial_level=0,
- peak_level=10,
- initial_index=pd.Timestamp(2000, 1, 1, 0, 0, 0),
- char_dict=char_dict,
- name='data'
+ freq="10min",
+ periods=10,
+ initial_level=0,
+ peak_level=10,
+ initial_index=pd.Timestamp(2000, 1, 1, 0, 0, 0),
+ char_dict=char_dict,
+ name='data'
):
-
t_index = pd.date_range(initial_index, freq=freq, periods=periods)
left = np.linspace(initial_level, peak_level, int(np.floor(len(t_index) / 2)))
right = np.linspace(peak_level, initial_level, int(np.ceil(len(t_index) / 2)))
s = pd.Series(np.append(left, right), index=t_index)
- char_dict["raise"] = s.index[1 : int(np.floor(len(t_index) / 2))]
- char_dict["drop"] = s.index[int(np.floor(len(t_index) / 2) + 1) :]
- char_dict["peak"] = s.index[int(np.floor(len(t_index) / 2)) - 1 : int(np.floor(len(t_index) / 2)) + 1]
+ char_dict["raise"] = s.index[1: int(np.floor(len(t_index) / 2))]
+ char_dict["drop"] = s.index[int(np.floor(len(t_index) / 2) + 1):]
+ char_dict["peak"] = s.index[int(np.floor(len(t_index) / 2)) - 1: int(np.floor(len(t_index) / 2)) + 1]
data = DictOfSeries(data=s, columns=[name])
return data, char_dict
@@ -55,13 +53,13 @@ def course_2(char_dict):
# one "anomalous" or "outlierish" value of magnitude "out_val" at position "periods/2"
# number of periods better be even!
def fix_funk(
- freq="10min",
- periods=10,
- initial_level=0,
- final_level=2,
- out_val=5,
- initial_index=pd.Timestamp(2000, 1, 1, 0, 0, 0),
- char_dict=char_dict,
+ freq="10min",
+ periods=10,
+ initial_level=0,
+ final_level=2,
+ out_val=5,
+ initial_index=pd.Timestamp(2000, 1, 1, 0, 0, 0),
+ char_dict=char_dict,
):
t_index = pd.date_range(initial_index, freq=freq, periods=periods)
data = np.linspace(initial_level, final_level, int(np.floor(len(t_index))))
@@ -88,21 +86,18 @@ def course_test(char_dict):
# Test function for pattern detection - same as test pattern for first three values, than constant function
def fix_funk(freq='1 D',
initial_index=pd.Timestamp(2000, 1, 1, 0, 0, 0), out_val=5, char_dict=char_dict):
-
t_index = pd.date_range(initial_index, freq=freq, periods=100)
data = pd.Series(data=0, index=t_index)
data.iloc[2] = out_val
data.iloc[3] = out_val
-
data = DictOfSeries(data=data, columns=['data'])
return data, char_dict
return fix_funk
-
@pytest.fixture
def course_3(char_dict):
# CROWD IN A PIT/CROWD ON A SUMMIT
@@ -113,15 +108,15 @@ def course_3(char_dict):
# number of periods better be even!
# chrowd_size * crowd_spacing better be less then freq[minutes].
def fix_funk(
- freq="10min",
- periods=10,
- initial_level=0,
- final_level=2,
- out_val=-5,
- initial_index=pd.Timestamp(2000, 1, 1, 0, 0, 0),
- char_dict=char_dict,
- crowd_size=5,
- crowd_spacing=1,
+ freq="10min",
+ periods=10,
+ initial_level=0,
+ final_level=2,
+ out_val=-5,
+ initial_index=pd.Timestamp(2000, 1, 1, 0, 0, 0),
+ char_dict=char_dict,
+ crowd_size=5,
+ crowd_spacing=1,
):
t_index = pd.date_range(initial_index, freq=freq, periods=periods)
@@ -158,19 +153,18 @@ def course_4(char_dict):
# of periods better be even!
def fix_funk(
- freq="10min",
- periods=10,
- base_level=0,
- out_val=5,
- initial_index=pd.Timestamp(2000, 1, 1, 0, 0, 0),
- char_dict=char_dict,
+ freq="10min",
+ periods=10,
+ base_level=0,
+ out_val=5,
+ initial_index=pd.Timestamp(2000, 1, 1, 0, 0, 0),
+ char_dict=char_dict,
):
-
t_index = pd.date_range(initial_index, freq=freq, periods=periods)
data = pd.Series(data=base_level, index=t_index)
- data[int(len(t_index) / 2) :: 2] = out_val
- char_dict["raise"] = t_index[int(len(t_index) / 2) :: 2]
- char_dict["return"] = t_index[int((len(t_index) / 2) + 1) :: 2]
+ data[int(len(t_index) / 2):: 2] = out_val
+ char_dict["raise"] = t_index[int(len(t_index) / 2):: 2]
+ char_dict["return"] = t_index[int((len(t_index) / 2) + 1):: 2]
data = DictOfSeries(data=data, columns=["data"])
return data, char_dict
@@ -187,13 +181,13 @@ def course_5(char_dict):
# periods better be greater 5
def fix_funk(
- freq="10min",
- periods=10,
- nan_slice=slice(0, None, 5),
- initial_level=0,
- final_level=10,
- initial_index=pd.Timestamp(2000, 1, 1, 0, 0, 0),
- char_dict=char_dict,
+ freq="10min",
+ periods=10,
+ nan_slice=slice(0, None, 5),
+ initial_level=0,
+ final_level=10,
+ initial_index=pd.Timestamp(2000, 1, 1, 0, 0, 0),
+ char_dict=char_dict,
):
t_index = pd.date_range(initial_index, freq=freq, periods=periods)
values = np.linspace(initial_level, final_level, periods)
@@ -205,5 +199,3 @@ def course_5(char_dict):
return data, char_dict
return fix_funk
-
-
diff --git a/test/funcs/test_constants_detection.py b/test/funcs/test_constants_detection.py
index 75dab02ae..b7cabb50e 100644
--- a/test/funcs/test_constants_detection.py
+++ b/test/funcs/test_constants_detection.py
@@ -6,7 +6,7 @@ import numpy as np
from saqc.funcs.constants import flagConstants, flagByVariance
-from test.common import TESTFLAGGER, initData
+from test.common import initData, initFlagsLike, BAD
@pytest.fixture
@@ -16,23 +16,21 @@ def data():
return constants_data
-@pytest.mark.parametrize("flagger", TESTFLAGGER)
-def test_constants_flagBasic(data, flagger):
+def test_constants_flagBasic(data):
expected = np.arange(5, 22)
field, *_ = data.columns
- flagger = flagger.initFlags(data)
- data, flagger_result = flagConstants(data, field, flagger, window="15Min", thresh=0.1, )
- flags = flagger_result.getFlags(field)
- assert np.all(flags[expected] == flagger.BAD)
+ flagger = initFlagsLike(data)
+ data, flagger_result = flagConstants(data, field, flagger, window="15Min", thresh=0.1, flag=BAD)
+ flags = flagger_result[field]
+ assert np.all(flags[expected] == BAD)
-@pytest.mark.parametrize("flagger", TESTFLAGGER)
-def test_constants_flagVarianceBased(data, flagger):
+def test_constants_flagVarianceBased(data):
expected = np.arange(5, 25)
field, *_ = data.columns
- flagger = flagger.initFlags(data)
- data, flagger_result1 = flagByVariance(data, field, flagger, window="1h")
+ flagger = initFlagsLike(data)
+ data, flagger_result1 = flagByVariance(data, field, flagger, window="1h", flag=BAD)
- flag_result1 = flagger_result1.getFlags(field)
- test_sum = (flag_result1[expected] == flagger.BAD).sum()
+ flag_result1 = flagger_result1[field]
+ test_sum = (flag_result1[expected] == BAD).sum()
assert test_sum == len(expected)
diff --git a/test/funcs/test_functions.py b/test/funcs/test_functions.py
index a47331cd0..2a466df14 100644
--- a/test/funcs/test_functions.py
+++ b/test/funcs/test_functions.py
@@ -6,6 +6,8 @@ import pandas as pd
import numpy as np
import dios
+from saqc.common import *
+from saqc.flagger import Flagger, initFlagsLike
from saqc.funcs.drift import flagDriftFromNorm, flagDriftFromReference, flagDriftFromScaledNorm
from saqc.funcs.outliers import flagCrossStatistic, flagRange
from saqc.funcs.flagtools import flagManual, forceFlags, clearFlags
@@ -15,8 +17,6 @@ from saqc.funcs.breaks import flagIsolated
from test.common import initData, TESTFLAGGER
-
-
@pytest.fixture
def data():
return initData(cols=1, start_date="2016-01-01", end_date="2018-12-31", freq="1D")
@@ -27,89 +27,88 @@ def field(data):
return data.columns[0]
-@pytest.mark.parametrize("flagger", TESTFLAGGER)
-def test_flagRange(data, field, flagger):
+def test_flagRange(data, field):
min, max = 10, 90
- flagger = flagger.initFlags(data)
- data, flagger = flagRange(data, field, flagger, min=min, max=max)
- flagged = flagger.isFlagged(field)
+ flagger = initFlagsLike(data)
+ data, flagger = flagRange(data, field, flagger, min=min, max=max, flag=BAD)
+ flagged = flagger[field] > UNFLAGGED
expected = (data[field] < min) | (data[field] > max)
- assert (flagged == expected).all()
+ assert all(flagged == expected)
-@pytest.mark.parametrize("flagger", TESTFLAGGER)
-def test_flagSesonalRange(data, field, flagger):
+def test_flagSesonalRange(data, field):
# prepare
data.iloc[::2] = 0
data.iloc[1::2] = 50
nyears = len(data[field].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,),
+ ({"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)
+ flagger = initFlagsLike(data)
newfield = f"{field}_masked"
start = f"{test['startmonth']:02}-{test['startday']:02}T00:00:00"
end = f"{test['endmonth']:02}-{test['endday']:02}T00:00:00"
data, flagger = copy(data, field, flagger, field + "_masked")
- data, flagger = mask(data, newfield, flagger, mode='periodic', period_start=start, period_end=end,
- include_bounds=True)
- data, flagger = flagRange(data, newfield, flagger, min=test['min'], max=test['max'])
- data, flagger = reindexFlags(data, field, flagger, method='match', source=newfield)
+ data, flagger = mask(
+ data, newfield, flagger,
+ mode='periodic', period_start=start, period_end=end, include_bounds=True, flag=BAD
+ )
+ data, flagger = flagRange(data, newfield, flagger, min=test['min'], max=test['max'], flag=BAD)
+ data, flagger = reindexFlags(data, field, flagger, method='match', source=newfield, flag=BAD)
data, flagger = drop(data, newfield, flagger)
- flagged = flagger.isFlagged(field)
+ flagged = flagger[field] > UNFLAGGED
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()
+def test_clearFlags(data, field):
+ flagger = initFlagsLike(data)
+ flagger[:, field] = BAD
+ assert all(flagger[field] == BAD)
+
_, flagger = clearFlags(data, field, flagger)
- flags_cleared = flagger.getFlags()
- assert (flags_orig != flags_set).all(None)
- assert (flags_orig == flags_cleared).all(None)
+ assert all(flagger[field] == UNFLAGGED)
-@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)
+def test_forceFlags(data, field):
+ flagger = initFlagsLike(data)
+ flagger[:, field] = BAD
+ assert all(flagger[field] == BAD)
+ _, flagger = forceFlags(data, field, flagger, flag=DOUBT)
+ assert all(flagger[field] == DOUBT)
+
+
+# todo: @luenensc: i dont get the test -- palmb
+def test_flagIsolated(data, field):
+ flagger = initFlagsLike(data)
-@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)
+
s = data[field].iloc[5:6]
- flagger = flagger.setFlags(field, loc=s)
+ flagger[s.index, field] = BAD
- _, flagger_result = flagIsolated(data, field, flagger, group_window="1D", gap_window="2.1D")
+ _, flagger_result = flagIsolated(data, field, flagger, group_window="1D", gap_window="2.1D", flag=BAD)
- assert flagger_result.isFlagged(field)[slice(3, 6, 2)].all()
+ assert flagger_result[field][slice(3, 6, 2)].all()
data, flagger_result = flagIsolated(
- data, field, flagger_result, group_window="2D", gap_window="2.1D", continuation_range="1.1D",
+ data, field, flagger_result,
+ group_window="2D", gap_window="2.1D", continuation_range="1.1D", flag=BAD
)
- assert flagger_result.isFlagged(field)[[3, 5, 13, 14]].all()
+ assert flagger_result[field][[3, 5, 13, 14]].all()
-@pytest.mark.parametrize("flagger", TESTFLAGGER)
@pytest.mark.parametrize("dat", [pytest.lazy_fixture("course_2")])
-def test_flagCrossScoring(dat, flagger):
+def test_flagCrossScoring(dat):
data1, characteristics = dat(initial_level=0, final_level=0, out_val=0)
data2, characteristics = dat(initial_level=0, final_level=0, out_val=10)
field = "dummy"
@@ -118,17 +117,15 @@ def test_flagCrossScoring(dat, flagger):
s1 = pd.Series(data=s1.values, index=s1.index)
s2 = pd.Series(data=s2.values, index=s1.index)
data = dios.DictOfSeries([s1, s2], columns=["data1", "data2"])
- flagger = flagger.initFlags(data)
- _, flagger_result = flagCrossStatistic(data, field, flagger, fields=fields, thresh=3, cross_stat=np.mean)
+ flagger = initFlagsLike(data)
+ _, flagger_result = flagCrossStatistic(data, field, flagger, fields=fields, thresh=3, cross_stat=np.mean, flag=BAD)
for field in fields:
- isflagged = flagger_result.isFlagged(field)
+ isflagged = flagger_result[field] > UNFLAGGED
assert isflagged[characteristics["raise"]].all()
-@pytest.mark.parametrize("flagger", TESTFLAGGER)
-def test_flagManual(data, flagger):
- field = data.columns[0]
- flagger = flagger.initFlags(data)
+def test_flagManual(data, field):
+ flagger = initFlagsLike(data)
args = data, field, flagger
dat = data[field]
@@ -139,20 +136,20 @@ def test_flagManual(data, flagger):
shrinked = mdata.loc[index_exp.union(mdata.iloc[[1, 2, 3, 4, 600, 601]].index)]
kwargs_list = [
- dict(mdata=mdata, mflag="a", method="plain"),
- dict(mdata=mdata.to_list(), mflag="a", method="plain"),
- dict(mdata=mdata, mflag="a", method="ontime"),
- dict(mdata=shrinked, mflag="a", method="ontime"),
+ dict(mdata=mdata, mflag="a", method="plain", flag=BAD),
+ dict(mdata=mdata.to_list(), mflag="a", method="plain", flag=BAD),
+ dict(mdata=mdata, mflag="a", method="ontime", flag=BAD),
+ dict(mdata=shrinked, mflag="a", method="ontime", flag=BAD),
]
for kw in kwargs_list:
_, fl = flagManual(*args, **kw)
- isflagged = fl.isFlagged(field)
+ isflagged = fl[field] > UNFLAGGED
assert isflagged[isflagged].index.equals(index_exp)
# flag not exist in mdata
- _, fl = flagManual(*args, mdata=mdata, mflag="i do not exist", method="ontime")
- isflagged = fl.isFlagged(field)
+ _, fl = flagManual(*args, mdata=mdata, mflag="i do not exist", method="ontime", flag=BAD)
+ isflagged = fl[field] > UNFLAGGED
assert isflagged[isflagged].index.equals(pd.DatetimeIndex([]))
# check right-open / ffill
@@ -179,9 +176,10 @@ def test_flagManual(data, flagger):
expected.loc[dat.index[-1]] = 1
expected = expected.astype(bool)
- _, fl = flagManual(*args, mdata=mdata, mflag=1, method="right-open")
- isflagged = fl.isFlagged(field)
+ _, fl = flagManual(*args, mdata=mdata, mflag=1, method="right-open", flag=BAD)
+ isflagged = fl[field] > UNFLAGGED
last = expected.index[0]
+
for curr in expected.index[1:]:
expected_value = mdata[last]
# datetime slicing is inclusive !
@@ -194,10 +192,11 @@ def test_flagManual(data, flagger):
# check left-open / bfill
expected.loc[dat.index[-1]] = 0 # this time the last is False
- _, fl = flagManual(*args, mdata=mdata, mflag=1, method="left-open")
- isflagged = fl.isFlagged(field)
+ _, fl = flagManual(*args, mdata=mdata, mflag=1, method="left-open", flag=BAD)
+ isflagged = fl[field] > UNFLAGGED
last = expected.index[0]
assert isflagged[last] == expected[last]
+
for curr in expected.index[1:]:
expected_value = mdata[curr]
# datetime slicing is inclusive !
@@ -206,24 +205,40 @@ def test_flagManual(data, flagger):
assert (chunk == expected_value).all()
last = curr
-@pytest.mark.parametrize("flagger", TESTFLAGGER)
+
@pytest.mark.parametrize("dat", [pytest.lazy_fixture("course_1")])
-def test_flagDriftFromNormal(dat, flagger):
+def test_flagDriftFromNormal(dat):
data = dat(periods=200, peak_level=5, name='d1')[0]
data['d2'] = dat(periods=200, peak_level=10, name='d2')[0]['d2']
data['d3'] = dat(periods=200, peak_level=100, name='d3')[0]['d3']
data['d4'] = 3 + 4 * data['d1']
data['d5'] = 3 + 4 * data['d1']
- flagger = flagger.initFlags(data)
- data_norm, flagger_norm = flagDriftFromNorm(data, 'dummy', flagger, ['d1', 'd2', 'd3'], segment_freq="200min",
- norm_spread=5)
+ flagger = initFlagsLike(data)
+ data_norm, flagger_norm = flagDriftFromNorm(
+ data, 'dummy', flagger,
+ ['d1', 'd2', 'd3'],
+ segment_freq="200min",
+ norm_spread=5,
+ flag=BAD,
+ )
- data_ref, flagger_ref = flagDriftFromReference(data, 'd1', flagger, ['d1', 'd2', 'd3'], segment_freq="3D",
- thresh=20)
+ data_ref, flagger_ref = flagDriftFromReference(
+ data, 'd1', flagger,
+ ['d1', 'd2', 'd3'],
+ segment_freq="3D",
+ thresh=20,
+ flag=BAD,
+ )
- data_scale, flagger_scale = flagDriftFromScaledNorm(data, 'dummy', flagger, ['d1', 'd3'], ['d4', 'd5'], segment_freq="3D",
- thresh=20, norm_spread=5)
- assert flagger_norm.isFlagged()['d3'].all()
- assert flagger_ref.isFlagged()['d3'].all()
- assert flagger_scale.isFlagged()['d3'].all()
+ data_scale, flagger_scale = flagDriftFromScaledNorm(
+ data, 'dummy', flagger,
+ ['d1', 'd3'], ['d4', 'd5'],
+ segment_freq="3D",
+ thresh=20,
+ norm_spread=5,
+ flag=BAD,
+ )
+ assert all(flagger_norm['d3'] > UNFLAGGED)
+ assert all(flagger_ref['d3'] > UNFLAGGED)
+ assert all(flagger_scale['d3'] > UNFLAGGED)
diff --git a/test/funcs/test_generic_api_functions.py b/test/funcs/test_generic_api_functions.py
index 8d200034f..950dbfd7f 100644
--- a/test/funcs/test_generic_api_functions.py
+++ b/test/funcs/test_generic_api_functions.py
@@ -6,16 +6,12 @@ import pytest
import numpy as np
import pandas as pd
-from dios import DictOfSeries
-
-from test.common import TESTFLAGGER, TESTNODATA, initData, writeIO, flagAll
-from saqc.core.visitor import ConfigFunctionParser
-from saqc.core.config import Fields as F
+from saqc.common import *
from saqc.core.register import register
-from saqc import SaQC, SimpleFlagger
-from saqc.funcs.generic import _execGeneric
+from saqc import SaQC
from saqc.funcs.tools import mask
+from test.common import initData, flagAll
register(masking='field')(flagAll)
@@ -25,38 +21,34 @@ def data():
return initData()
-@pytest.mark.parametrize("flagger", TESTFLAGGER)
-def test_addFieldFlagGeneric(data, flagger):
- saqc = SaQC(data=data, flagger=flagger)
+def test_addFieldFlagGeneric(data):
+ saqc = SaQC(data=data)
- data, flags = saqc.generic.flag(
- "tmp1",
- func=lambda var1: pd.Series(False, index=data[var1.name].index)
- ).getResult()
- assert "tmp1" in flags.columns and "tmp1" not in data
+ func = lambda var1: pd.Series(False, index=data[var1.name].index)
+ data, flagger = saqc.generic.flag("tmp1", func, flag=BAD).getResult()
+ assert "tmp1" in flagger.columns and "tmp1" not in data
-@pytest.mark.parametrize("flagger", TESTFLAGGER)
-def test_addFieldProcGeneric(data, flagger):
- saqc = SaQC(data=data, flagger=flagger)
+def test_addFieldProcGeneric(data):
+ saqc = SaQC(data=data)
- data, flagger = saqc.generic.process("tmp1", func=lambda: pd.Series([])).getResult(raw=True)
+ func = lambda: pd.Series([])
+ data, flagger = saqc.generic.process("tmp1", func, flag=BAD ).getResult(raw=True)
assert "tmp1" in data.columns and data["tmp1"].empty
- data, flagger = saqc.generic.process("tmp2", func=lambda var1, var2: var1 + var2).getResult()
+ func = lambda var1, var2: var1 + var2
+ data, flagger = saqc.generic.process("tmp2", func, flag=BAD).getResult()
assert "tmp2" in data.columns and (data["tmp2"] == data["var1"] + data["var2"]).all(axis=None)
-@pytest.mark.parametrize("flagger", TESTFLAGGER)
-def test_mask(data, flagger):
-
- saqc = SaQC(data=data, flagger=flagger)
+def test_mask(data):
+ saqc = SaQC(data=data)
data_org = data.copy(deep=True)
mean = data["var1"] / 2
- data, _ = saqc.generic.process("var1", lambda var1: mask(var1 < mean)).getResult()
+ data, _ = saqc.generic.process("var1", lambda var1: mask(var1 < mean), flag=BAD).getResult()
assert ((data["var1"].isna()) == (data_org["var1"] < 10) & data_org["var1"].isna()).all(axis=None)
- data, flags = saqc.generic.process("tmp", lambda var1: mask(var1 < mean)).getResult()
- assert ("tmp" in data.columns) and ("tmp" in flags.columns)
+ data, flagger = saqc.generic.process("tmp", lambda var1: mask(var1 < mean), flag=BAD).getResult()
+ assert ("tmp" in data.columns) and ("tmp" in flagger.columns)
assert ((data["tmp"].isna()) == (data_org["var1"] < 10) & data_org["var1"].isna()).all(axis=None)
diff --git a/test/funcs/test_generic_config_functions.py b/test/funcs/test_generic_config_functions.py
index 2a1e8a14c..7677c3c27 100644
--- a/test/funcs/test_generic_config_functions.py
+++ b/test/funcs/test_generic_config_functions.py
@@ -10,10 +10,13 @@ import pandas as pd
from dios import DictOfSeries
from test.common import TESTFLAGGER, TESTNODATA, initData, writeIO
+
+from saqc.common import *
+from saqc.flagger import Flagger, initFlagsLike
from saqc.core.visitor import ConfigFunctionParser
from saqc.core.config import Fields as F
from saqc.core.register import register
-from saqc import SaQC, SimpleFlagger
+from saqc import SaQC
from saqc.funcs.generic import _execGeneric
@@ -68,13 +71,12 @@ def test_syntaxError(flagger):
_compileGeneric(f"flag(func={test})", flagger)
-@pytest.mark.parametrize("flagger", TESTFLAGGER)
-def test_typeError(flagger):
-
+def test_typeError():
"""
test that forbidden constructs actually throw an error
TODO: find a few more cases or get rid of the test
"""
+ flagger = Flagger()
# : think about cases that should be forbidden
tests = ("lambda x: x * 2",)
@@ -84,9 +86,8 @@ def test_typeError(flagger):
_compileGeneric(f"generic.flag(func={test})", flagger)
-@pytest.mark.parametrize("flagger", TESTFLAGGER)
-def test_comparisonOperators(data, flagger):
- flagger = flagger.initFlags(data)
+def test_comparisonOperators(data):
+ flagger = initFlagsLike(data)
var1, var2, *_ = data.columns
this = var1
@@ -107,7 +108,7 @@ def test_comparisonOperators(data, flagger):
@pytest.mark.parametrize("flagger", TESTFLAGGER)
def test_arithmeticOperators(data, flagger):
- flagger = flagger.initFlags(data)
+ flagger = initFlagsLike(data)
var1, *_ = data.columns
this = data[var1]
@@ -128,7 +129,7 @@ def test_arithmeticOperators(data, flagger):
@pytest.mark.parametrize("flagger", TESTFLAGGER)
def test_nonReduncingBuiltins(data, flagger):
- flagger = flagger.initFlags(data)
+ flagger = initFlagsLike(data)
var1, *_ = data.columns
this = var1
mean = data[var1].mean()
@@ -151,7 +152,7 @@ def test_nonReduncingBuiltins(data, flagger):
def test_reduncingBuiltins(data, flagger, nodata):
data.loc[::4] = nodata
- flagger = flagger.initFlags(data)
+ flagger = initFlagsLike(data)
var1 = data.columns[0]
this = data.iloc[:, 0]
@@ -195,7 +196,7 @@ def test_bitOps(data, flagger, nodata):
var1, var2, *_ = data.columns
this = var1
- flagger = flagger.initFlags(data)
+ flagger = initFlagsLike(data)
tests = [
("~(this > mean(this))", ~(data[this] > np.nanmean(data[this]))),
@@ -209,19 +210,18 @@ def test_bitOps(data, flagger, nodata):
assert np.all(result == expected)
-@pytest.mark.parametrize("flagger", TESTFLAGGER)
-def test_isflagged(data, flagger):
+def test_isflagged(data):
var1, var2, *_ = data.columns
-
- flagger = flagger.initFlags(data).setFlags(var1, loc=data[var1].index[::2], flag=flagger.BAD)
+ flagger = initFlagsLike(data)
+ flagger[data[var1].index[::2], var1] = BAD
tests = [
- (f"isflagged({var1})", flagger.isFlagged(var1)),
- (f"isflagged({var1}, flag=BAD)", flagger.isFlagged(var1, flag=flagger.BAD, comparator=">=")),
- (f"isflagged({var1}, UNFLAGGED, '==')", flagger.isFlagged(var1, flag=flagger.UNFLAGGED, comparator="==")),
- (f"~isflagged({var2})", ~flagger.isFlagged(var2)),
- (f"~({var2}>999) & (~isflagged({var2}))", ~(data[var2] > 999) & (~flagger.isFlagged(var2))),
+ (f"isflagged({var1})", flagger[var1] > UNFLAGGED),
+ (f"isflagged({var1}, flag=BAD)", flagger[var1] >= BAD),
+ (f"isflagged({var1}, UNFLAGGED, '==')", flagger[var1] == UNFLAGGED),
+ (f"~isflagged({var2})", ~(flagger[var2] > UNFLAGGED)),
+ (f"~({var2}>999) & (~isflagged({var2}))", ~(data[var2] > 999) & ~(flagger[var2] > UNFLAGGED)),
]
for test, expected in tests:
@@ -230,8 +230,7 @@ def test_isflagged(data, flagger):
assert np.all(result == expected)
-@pytest.mark.parametrize("flagger", TESTFLAGGER)
-def test_variableAssignments(data, flagger):
+def test_variableAssignments(data):
var1, var2, *_ = data.columns
config = f"""
@@ -241,18 +240,17 @@ def test_variableAssignments(data, flagger):
"""
fobj = writeIO(config)
- saqc = SaQC(flagger, data).readConfig(fobj)
+ saqc = SaQC(data).readConfig(fobj)
result_data, result_flagger = saqc.getResult(raw=True)
assert set(result_data.columns) == set(data.columns) | {
"dummy1",
}
- assert set(result_flagger.getFlags().columns) == set(data.columns) | {"dummy1", "dummy2"}
+ assert set(result_flagger.columns) == set(data.columns) | {"dummy1", "dummy2"}
@pytest.mark.xfail(strict=True)
-@pytest.mark.parametrize("flagger", TESTFLAGGER)
-def test_processMultiple(data_diff, flagger):
+def test_processMultiple(data_diff):
var1, var2, *_ = data_diff.columns
config = f"""
@@ -262,9 +260,9 @@ def test_processMultiple(data_diff, flagger):
"""
fobj = writeIO(config)
- saqc = SaQC(flagger, data_diff).readConfig(fobj)
+ saqc = SaQC(data_diff).readConfig(fobj)
result_data, result_flagger = saqc.getResult()
- assert len(result_data["dummy"]) == len(result_flagger.getFlags("dummy"))
+ assert len(result_data["dummy"]) == len(result_flagger["dummy"])
def test_callableArgumentsUnary(data):
@@ -274,9 +272,8 @@ def test_callableArgumentsUnary(data):
@register(masking='field')
def testFuncUnary(data, field, flagger, func, **kwargs):
data[field] = data[field].rolling(window=window).apply(func)
- return data, flagger.initFlags(data=data)
+ return data, initFlagsLike(data)
- flagger = SimpleFlagger()
var = data.columns[0]
config = f"""
@@ -291,22 +288,20 @@ def test_callableArgumentsUnary(data):
for (name, func) in tests:
fobj = writeIO(config.format(name))
- result_config, _ = SaQC(flagger, data).readConfig(fobj).getResult()
- result_api, _ = SaQC(flagger, data).testFuncUnary(var, func=func).getResult()
+ result_config, _ = SaQC(data).readConfig(fobj).getResult()
+ result_api, _ = SaQC(data).testFuncUnary(var, func=func).getResult()
expected = data[var].rolling(window=window).apply(func)
assert (result_config[var].dropna() == expected.dropna()).all(axis=None)
assert (result_api[var].dropna() == expected.dropna()).all(axis=None)
def test_callableArgumentsBinary(data):
-
- flagger = SimpleFlagger()
var1, var2 = data.columns[:2]
@register(masking='field')
def testFuncBinary(data, field, flagger, func, **kwargs):
data[field] = func(data[var1], data[var2])
- return data, flagger.initFlags(data=data)
+ return data, initFlagsLike(data)
config = f"""
{F.VARNAME} ; {F.TEST}
@@ -320,8 +315,8 @@ def test_callableArgumentsBinary(data):
for (name, func) in tests:
fobj = writeIO(config.format(name))
- result_config, _ = SaQC(flagger, data).readConfig(fobj).getResult()
- result_api, _ = SaQC(flagger, data).testFuncBinary(var1, func=func).getResult()
+ result_config, _ = SaQC(data).readConfig(fobj).getResult()
+ result_api, _ = SaQC(data).testFuncBinary(var1, func=func).getResult()
expected = func(data[var1], data[var2])
assert (result_config[var1].dropna() == expected.dropna()).all(axis=None)
assert (result_api[var1].dropna() == expected.dropna()).all(axis=None)
diff --git a/test/funcs/test_pattern_rec.py b/test/funcs/test_pattern_rec.py
index 75f3f4e4c..0763a82f0 100644
--- a/test/funcs/test_pattern_rec.py
+++ b/test/funcs/test_pattern_rec.py
@@ -7,8 +7,10 @@ import pandas as pd
from dios import dios
+from saqc.common import *
+from saqc.flagger import Flagger, initFlagsLike
from saqc.funcs.pattern import *
-from test.common import initData, TESTFLAGGER
+from test.common import initData
@pytest.fixture
@@ -21,33 +23,31 @@ def field(data):
return data.columns[0]
-@pytest.mark.parametrize("flagger", TESTFLAGGER)
-def test_flagPattern_wavelet(flagger):
-
+@pytest.mark.skip(reason='faulty implementation - will get fixed by GL-MR191')
+def test_flagPattern_wavelet():
data = pd.Series(0, index=pd.date_range(start="2000", end='2001', freq='1d'))
data.iloc[2:4] = 7
pattern = data.iloc[1:6]
data = dios.DictOfSeries(dict(data=data, pattern_data=pattern))
+ flagger = initFlagsLike(data, name='data')
+ data, flagger = flagPatternByDTW(data, "data", flagger, ref_field="pattern_data", flag=BAD)
- flagger = flagger.initFlags(data)
- data, flagger = flagPatternByDTW(data, "data", flagger, ref_field="pattern_data")
- assert (flagger.isFlagged("data")[1:6]).all()
- assert (flagger.isFlagged("data")[:1]).any()
- assert (flagger.isFlagged("data")[7:]).any()
-
+ assert all(flagger["data"][1:6])
+ assert any(flagger["data"][:1])
+ assert any(flagger["data"][7:])
-@pytest.mark.parametrize("flagger", TESTFLAGGER)
-def test_flagPattern_dtw(flagger):
+@pytest.mark.skip(reason='faulty implementation - will get fixed by GL-MR191')
+def test_flagPattern_dtw():
data = pd.Series(0, index=pd.date_range(start="2000", end='2001', freq='1d'))
data.iloc[2:4] = 7
pattern = data.iloc[1:6]
data = dios.DictOfSeries(dict(data=data, pattern_data=pattern))
+ flagger = initFlagsLike(data, name='data')
+ data, flagger = flagPatternByWavelet(data, "data", flagger, ref_field="pattern_data", flag=BAD)
- flagger = flagger.initFlags(data)
- data, flagger = flagPatternByWavelet(data, "data", flagger, ref_field="pattern_data")
- assert (flagger.isFlagged("data")[1:6]).all()
- assert (flagger.isFlagged("data")[:1]).any()
- assert (flagger.isFlagged("data")[7:]).any()
+ assert all(flagger["data"][1:6])
+ assert any(flagger["data"][:1])
+ assert any(flagger["data"][7:])
diff --git a/test/funcs/test_spikes_detection.py b/test/funcs/test_spikes_detection.py
index da8683479..be38370e3 100644
--- a/test/funcs/test_spikes_detection.py
+++ b/test/funcs/test_spikes_detection.py
@@ -16,6 +16,8 @@ from saqc.funcs.outliers import (
)
from test.common import TESTFLAGGER
+from saqc.common import *
+from saqc.flagger import Flagger, initFlagsLike
@pytest.fixture(scope="module")
@@ -28,30 +30,27 @@ def spiky_data():
return dios.DictOfSeries(s), flag_assertion
-@pytest.mark.parametrize("flagger", TESTFLAGGER)
-def test_flagMad(spiky_data, flagger):
+def test_flagMad(spiky_data):
data = spiky_data[0]
field, *_ = data.columns
- flagger = flagger.initFlags(data)
- data, flagger_result = flagMAD(data, field, flagger, "1H")
- flag_result = flagger_result.getFlags(field)
- test_sum = (flag_result[spiky_data[1]] == flagger.BAD).sum()
+ flagger = initFlagsLike(data)
+ data, flagger_result = flagMAD(data, field, flagger, "1H", flag=BAD)
+ flag_result = flagger_result[field]
+ test_sum = (flag_result[spiky_data[1]] == BAD).sum()
assert test_sum == len(spiky_data[1])
-@pytest.mark.parametrize("flagger", TESTFLAGGER)
-def test_flagSpikesBasic(spiky_data, flagger):
+def test_flagSpikesBasic(spiky_data):
data = spiky_data[0]
field, *_ = data.columns
- flagger = flagger.initFlags(data)
- data, flagger_result = flagOffset(data, field, flagger, thresh=60, tolerance=10, window="20min")
- flag_result = flagger_result.getFlags(field)
- test_sum = (flag_result[spiky_data[1]] == flagger.BAD).sum()
+ flagger = initFlagsLike(data)
+ data, flagger_result = flagOffset(data, field, flagger, thresh=60, tolerance=10, window="20min", flag=BAD)
+ flag_result = flagger_result[field]
+ test_sum = (flag_result[spiky_data[1]] == BAD).sum()
assert test_sum == len(spiky_data[1])
# see test/functs/conftest.py for the 'course_N'
-@pytest.mark.parametrize("flagger", TESTFLAGGER)
@pytest.mark.parametrize(
"dat",
[
@@ -61,22 +60,22 @@ def test_flagSpikesBasic(spiky_data, flagger):
pytest.lazy_fixture("course_4"),
],
)
-def test_flagSpikesLimitRaise(dat, flagger):
+def test_flagSpikesLimitRaise(dat):
data, characteristics = dat()
field, *_ = data.columns
- flagger = flagger.initFlags(data)
+ flagger = initFlagsLike(data)
_, flagger_result = flagRaise(
- data, field, flagger, thresh=2, intended_freq="10min", raise_window="20min", numba_boost=False
+ data, field, flagger,
+ thresh=2, intended_freq="10min", raise_window="20min", numba_boost=False, flag=BAD
)
- assert flagger_result.isFlagged(field)[characteristics["raise"]].all()
- assert not flagger_result.isFlagged(field)[characteristics["return"]].any()
- assert not flagger_result.isFlagged(field)[characteristics["drop"]].any()
+ assert np.all(flagger_result[field][characteristics["raise"]] > UNFLAGGED)
+ assert not np.any(flagger_result[field][characteristics["return"]] > UNFLAGGED)
+ assert not np.any(flagger_result[field][characteristics["drop"]] > UNFLAGGED)
# see test/functs/conftest.py for the 'course_N'
-@pytest.mark.parametrize("flagger", TESTFLAGGER)
@pytest.mark.parametrize("dat", [pytest.lazy_fixture("course_3")])
-def test_flagMultivarScores(dat, flagger):
+def test_flagMultivarScores(dat):
data1, characteristics = dat(periods=1000, initial_level=5, final_level=15, out_val=50)
data2, characteristics = dat(periods=1000, initial_level=20, final_level=1, out_val=30)
field = "dummy"
@@ -85,24 +84,26 @@ def test_flagMultivarScores(dat, flagger):
s1 = pd.Series(data=s1.values, index=s1.index)
s2 = pd.Series(data=s2.values, index=s1.index)
data = dios.DictOfSeries([s1, s2], columns=["data1", "data2"])
- flagger = flagger.initFlags(data)
+ flagger = initFlagsLike(data)
_, flagger_result = flagMVScores(
- data, field, flagger, fields=fields, trafo=np.log, iter_start=0.95, n_neighbors=10
+ data, field, flagger, fields=fields, trafo=np.log, iter_start=0.95, n_neighbors=10, flag=BAD
)
for field in fields:
- isflagged = flagger_result.isFlagged(field)
+ isflagged = flagger_result[field] > UNFLAGGED
assert isflagged[characteristics["raise"]].all()
assert not isflagged[characteristics["return"]].any()
assert not isflagged[characteristics["drop"]].any()
-@pytest.mark.parametrize("flagger", TESTFLAGGER)
@pytest.mark.parametrize("dat", [pytest.lazy_fixture("course_3")])
-def test_grubbs(dat, flagger):
+def test_grubbs(dat):
data, char_dict = dat(
- freq="10min", periods=45, initial_level=0, final_level=0, crowd_size=1, crowd_spacing=3, out_val=-10
+ freq="10min", periods=45,
+ initial_level=0, final_level=0,
+ crowd_size=1, crowd_spacing=3,
+ out_val=-10,
)
- flagger = flagger.initFlags(data)
- data, result_flagger = flagByGrubbs(data, "data", flagger, winsz=20, min_periods=15)
- assert result_flagger.isFlagged("data")[char_dict["drop"]].all()
+ flagger = initFlagsLike(data)
+ data, result_flagger = flagByGrubbs(data, "data", flagger, winsz=20, min_periods=15, flag=BAD)
+ assert np.all(result_flagger["data"][char_dict["drop"]] > UNFLAGGED)
--
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