def test_no_flatliner(self):
df = df_no_flatliner
threshold = 0.25
expected = None
result = find_nonzero_flatliner(df, threshold)
self.assertEqual(result, expected)
def test_no_flatliner(self):
df = df_no_flatliner
suspicious_moments = validation.find_nonzero_flatliner(df, 0.25)
expected = df
result = preprocessing.replace_invalid_data(df, suspicious_moments)
self.assertDataframeEqual(expected, result)
def test_flatliner_start_end(self):
"""Data: zero value flatliner for complete station
Expected: empty list, since the function detects only non-zero station flatliners
"""
df = df_flatliner_start_end
threshold = 0.25
result = find_nonzero_flatliner(df, threshold)
self.assertEqual(1, len(result))
def test_zero_flatliner(self):
"""Data: zero value flatliner for complete station
Expected: empty list, since the function detects only non-zero station flatliners
"""
df = df_zero_flatliner
threshold = 0.25
expected = None
result = find_nonzero_flatliner(df, threshold)
self.assertEqual(result, expected)
def test_no_data(self):
"""Data: empty database
Expected: empty list, since there is no data to check
"""
df = df_zero_file
threshold = 0.25
expected = None
result = find_nonzero_flatliner(df, threshold)
self.assertEqual(result, expected)
def test_trafo_flatliner(self):
"""Data: only one trafo shows a flatliner
Expected: empty list, since the function only detects a complete station flatliner (all trafo's)
"""
df = df_trafo_flatliner
threshold = 0.25
expected = None
result = find_nonzero_flatliner(df, threshold)
self.assertEqual(result, expected)
def test_no_data(self):
"""Data: empty database
Expected: empty list, since there is no data to check
"""
df = df_zero_file
suspicious_moments = validation.find_nonzero_flatliner(df, 0.25)
expected = df
result = preprocessing.replace_invalid_data(df, suspicious_moments)
self.assertDataframeEqual(expected, result)
def test_station_flatliner(self):
"""Data: all trafo's containing non zero-value flatliners
Expected: list containing all trafo values and timestamps of the
stationsflatliners + the generates diff_columns to detect the flatliners.
"""
df = df_flatliner.copy()
suspicious_moments = validation.find_nonzero_flatliner(df, 0.25)
# create expected output with diff_columns
df_flatliner_output = pd.DataFrame({
"date":
date_range2,
"LC_trafo": [0, np.nan, np.nan, np.nan, 0],
"col1": [8.0, np.nan, np.nan, np.nan, 8.0],
"col2": [8.0, np.nan, np.nan, np.nan, 8.0],
})
df_flatliner_output = df_flatliner_output.set_index("date")
expected = df_flatliner_output
result = preprocessing.replace_invalid_data(df, suspicious_moments)
self.assertDataframeEqual(expected, result)
def test_flatliner_zerovalues(self):
"""Data: one trafo showing a non zero-value flatliner, one trafo showing a zero-value flatliner
Expected: list containing all trafo values and timestamps of the stationsflatliners + the generates diff_columns to detect the flatliners.
"""
df = df_zerovalues_flatliner
threshold = 0.25
# create expected output with diff_columns
df_zerovalues_flatliner_output = pd.DataFrame({
"from_time": [now - timedelta(minutes=45)],
"to_time": [now - timedelta(minutes=0)],
"duration_h":
[(now - timedelta(minutes=0)) - (now - timedelta(minutes=45))],
})
expected = list()
expected.append(df_zerovalues_flatliner_output)
result = find_nonzero_flatliner(df, threshold)
expected_output = expected[0]
self.assertDataframeEqual(expected_output,
result,
check_index_type=False,
check_dtype=False)