def test_generate_maccor_waveform_file_default(self):
velocity_waveform_file = os.path.join(TEST_FILE_DIR,
"LA4_velocity_waveform.txt")
with ScratchDir('.') as scratch_dir:
df_power = convert_velocity_to_power_waveform(
velocity_waveform_file, 'mph')
df_MWF = pd.read_csv(generate_maccor_waveform_file(
df_power, "test_LA4_waveform", scratch_dir),
sep='\t',
header=None)
# Reference mwf file generated by the cycler for the same power waveform.
df_MWF_ref = pd.read_csv(os.path.join(
TEST_FILE_DIR, "LA4_reference_default_settings.mwf"),
sep="\t",
header=None)
self.assertEqual(df_MWF.shape, df_MWF_ref.shape)
# Check that the fourth column for charge/discharge limit is empty (default setting)
self.assertTrue(df_MWF.iloc[:, 3].isnull().all())
# Check that sum of durations equals length of the power timeseries
self.assertEqual(df_MWF.iloc[:, 5].sum(), len(df_power))
# Check that charge/discharge steps are identical
self.assertTrue((df_MWF.iloc[:, 0] == df_MWF_ref.iloc[:, 0]).all())
# Check that power values are close to each other (col 2)
relative_differences = np.abs(
(df_MWF.iloc[:, 2] - df_MWF_ref.iloc[:, 2]) /
df_MWF_ref.iloc[:, 2])
self.assertLessEqual(np.mean(relative_differences) * 100,
0.01) # mean percentage error < 0.01%
def test_generate_maccor_waveform_from_power(self):
power_waveform_file = os.path.join(TEST_FILE_DIR,
"LA4_power_profile.csv")
mwf_config = {
"control_mode": "P",
"value_scale": 30,
"charge_limit_mode": "V",
"charge_limit_value": 4.2,
"discharge_limit_mode": "V",
"discharge_limit_value": 2.7,
"charge_end_mode": "V",
"charge_end_operation": ">=",
"charge_end_mode_value": 4.25,
"discharge_end_mode": "V",
"discharge_end_operation": "<=",
"discharge_end_mode_value": 2.5,
"report_mode": "T",
"report_value": 3.0000,
"range": "A",
}
with ScratchDir(".") as scratch_dir:
waveform_name = "LA4_8rep_lim"
df_power = pd.read_csv(power_waveform_file)
df_power = pd.concat([
df_power,
df_power,
df_power,
df_power,
df_power,
df_power,
df_power,
df_power,
])
df_power.drop(columns=["power"], inplace=True)
df_power.rename(columns={"power_scaled": "power"}, inplace=True)
df_MWF = pd.read_csv(
generate_maccor_waveform_file(df_power,
waveform_name,
scratch_dir,
mwf_config=mwf_config),
sep="\t",
header=None,
)
sign = df_MWF[0].apply(lambda x: -1 if x is "D" else 1)
energy = df_MWF[2] * df_MWF[5] * sign
self.assertLess(df_MWF[5].sum(), 10961)
self.assertLess(energy.sum(), -20000)
self.assertGreater(40, df_MWF[2].max())
self.assertLess(-40, df_MWF[2].min())
# Uncomment line below to keep the output in the test file directory
shutil.copyfile(
os.path.join(scratch_dir, waveform_name + ".MWF"),
os.path.join(TEST_FILE_DIR, waveform_name + ".MWF"),
)
def test_generate_maccor_waveform_file_custom(self):
velocity_waveform_file = os.path.join(TEST_FILE_DIR,
"US06_velocity_waveform.txt")
mwf_config = {
"control_mode": "I",
"value_scale": 1,
"charge_limit_mode": "R",
"charge_limit_value": 2,
"discharge_limit_mode": "P",
"discharge_limit_value": 3,
"charge_end_mode": "V",
"charge_end_operation": ">=",
"charge_end_mode_value": 4.2,
"discharge_end_mode": "V",
"discharge_end_operation": "<=",
"discharge_end_mode_value": 3,
"report_mode": "T",
"report_value": 10,
"range": "A",
}
with ScratchDir(".") as scratch_dir:
df_power = convert_velocity_to_power_waveform(
velocity_waveform_file, "mph")
df_MWF = pd.read_csv(
generate_maccor_waveform_file(df_power,
"test_US06_waveform",
scratch_dir,
mwf_config=mwf_config),
sep="\t",
header=None,
)
df_MWF_ref = pd.read_csv(
os.path.join(TEST_FILE_DIR,
"US06_reference_custom_settings.mwf"),
sep="\t",
header=None,
)
# Check dimensions with the reference mwf file
self.assertEqual(df_MWF.shape, df_MWF_ref.shape)
# Check that control_mode, charge/discharge state, limit and limit_value columns are identical.
self.assertTrue(
(df_MWF.iloc[:, [0, 1, 3, 4]] == df_MWF_ref.iloc[:,
[0, 1, 3, 4]]
).all().all())
# Check that power values are close to each other (col 2)
relative_differences = np.abs(
(df_MWF.iloc[:, 2] - df_MWF_ref.iloc[:, 2]) /
df_MWF_ref.iloc[:, 2])
self.assertLessEqual(np.mean(relative_differences) * 100,
0.01) # mean percentage error < 0.01%
def test_generate_maccor_waveform_file_custom(self):
velocity_waveform_file = os.path.join(TEST_FILE_DIR,
"US06_velocity_waveform.txt")
mwf_config = {
'control_mode': 'I',
'value_scale': 1,
'charge_limit_mode': 'R',
'charge_limit_value': 2,
'discharge_limit_mode': 'P',
'discharge_limit_value': 3,
'charge_end_mode': 'V',
'charge_end_operation': '>=',
'charge_end_mode_value': 4.2,
'discharge_end_mode': 'V',
'discharge_end_operation': '<=',
'discharge_end_mode_value': 3,
'report_mode': 'T',
'report_value': 10,
'range': 'A',
}
with ScratchDir('.') as scratch_dir:
df_power = convert_velocity_to_power_waveform(
velocity_waveform_file, 'mph')
df_MWF = pd.read_csv(generate_maccor_waveform_file(
df_power,
"test_US06_waveform",
scratch_dir,
mwf_config=mwf_config),
sep='\t',
header=None)
df_MWF_ref = pd.read_csv(os.path.join(
TEST_FILE_DIR, "US06_reference_custom_settings.mwf"),
sep="\t",
header=None)
# Check dimensions with the reference mwf file
self.assertEqual(df_MWF.shape, df_MWF_ref.shape)
# Check that control_mode, charge/discharge state, limit and limit_value columns are identical.
self.assertTrue(
(df_MWF.iloc[:, [0, 1, 3, 4]] == df_MWF_ref.iloc[:,
[0, 1, 3, 4]]
).all().all())
# Check that power values are close to each other (col 2)
relative_differences = np.abs(
(df_MWF.iloc[:, 2] - df_MWF_ref.iloc[:, 2]) /
df_MWF_ref.iloc[:, 2])
self.assertLessEqual(np.mean(relative_differences) * 100,
0.01) # mean percentage error < 0.01%