def test_calculate_percent_error_calculates_correct_values():
tpms = _get_test_tpms()
t.calculate_percent_error(tpms)
for index, row in tpms.iterrows():
val = 100 * ((CALC_TPMS_VALS[index] - REAL_TPMS_VALS[index])
/ float(REAL_TPMS_VALS[index]))
npt.assert_approx_equal(row[t.PERCENT_ERROR], val)
def test_calculate_percent_error_calculates_correct_values():
tpms = _get_test_tpms()
t.calculate_percent_error(tpms)
for index, row in tpms.iterrows():
val = 100 * ((CALC_TPMS_VALS[index] - REAL_TPMS_VALS[index]) /
float(REAL_TPMS_VALS[index]))
npt.assert_approx_equal(row[t.PERCENT_ERROR], val)
def _get_test_tpms():
tpms = pd.DataFrame.from_dict({
t.REAL_TPM: test_tpms.REAL_TPMS_VALS,
t.CALCULATED_TPM: test_tpms.CALC_TPMS_VALS,
test_tpms.GROUP_TEST_COL: test_tpms.GROUPS
})
t.calculate_log_ratios(tpms)
t.calculate_percent_error(tpms)
t.mark_positives_and_negatives(tpms)
return tpms, t.get_true_positives(tpms)