def test_from_raw_cycler_run_maccor(self):
rcycler_run = RawCyclerRun.from_file(self.maccor_file_w_diagnostics)
pcycler_run = ProcessedCyclerRun.from_raw_cycler_run(rcycler_run)
self.assertIsInstance(pcycler_run, ProcessedCyclerRun)
# Ensure barcode/protocol are passed
self.assertEqual(pcycler_run.barcode, "EXP")
self.assertEqual(pcycler_run.protocol, "xTESLADIAG_000020_CH71.000")
steps = pcycler_run.cycles_interpolated.step_type.unique().tolist()
# Ensure that charge and discharge steps are processed
self.assertEqual(steps, ['discharge', 'charge'])
min_index = pcycler_run.cycles_interpolated.cycle_index.min()
if 'step_type' in pcycler_run.cycles_interpolated.columns:
discharge_interpolated = pcycler_run.cycles_interpolated[(
pcycler_run.cycles_interpolated.step_type == 'discharge')]
min_index_df = pcycler_run.cycles_interpolated[
(pcycler_run.cycles_interpolated.cycle_index == min_index)
& (pcycler_run.cycles_interpolated.step_type == 'discharge')]
else:
discharge_interpolated = pcycler_run.cycles_interpolated
min_index_df = pcycler_run.cycles_interpolated[(
pcycler_run.cycles_interpolated.cycle_index == min_index)]
matches = discharge_interpolated.groupby(
"cycle_index").apply(lambda x: np.allclose(
x.voltage.values, min_index_df.voltage.values))
if not np.all(matches):
raise ValueError("cycles_interpolated are not uniform")
def test_from_raw_cycler_run_maccor(self):
rcycler_run = RawCyclerRun.from_file(self.maccor_file_w_diagnostics)
pcycler_run = ProcessedCyclerRun.from_raw_cycler_run(rcycler_run)
self.assertIsInstance(pcycler_run, ProcessedCyclerRun)
# Ensure barcode/protocol are passed
self.assertEqual(pcycler_run.barcode, "EXP")
self.assertEqual(pcycler_run.protocol, "xTESLADIAG_000020_CH71.000")
def test_from_raw_cycler_run_arbin(self):
rcycler_run = RawCyclerRun.from_file(self.arbin_file)
pcycler_run = ProcessedCyclerRun.from_raw_cycler_run(rcycler_run)
self.assertIsInstance(pcycler_run, ProcessedCyclerRun)
# Ensure barcode/protocol are passed
self.assertEqual(pcycler_run.barcode, "EL151000429559")
self.assertEqual(pcycler_run.protocol,
r"2017-12-04_tests\20170630-4_65C_69per_6C.sdu")
all_summary = pcycler_run.summary
reg_dtypes = all_summary.dtypes.tolist()
reg_columns = all_summary.columns.tolist()
reg_dtypes = [str(dtyp) for dtyp in reg_dtypes]
for indx, col in enumerate(reg_columns):
self.assertEqual(reg_dtypes[indx],
STRUCTURE_DTYPES['summary'][col])
all_interpolated = pcycler_run.cycles_interpolated
cycles_interpolated_dyptes = all_interpolated.dtypes.tolist()
cycles_interpolated_columns = all_interpolated.columns.tolist()
cycles_interpolated_dyptes = [
str(dtyp) for dtyp in cycles_interpolated_dyptes
]
for indx, col in enumerate(cycles_interpolated_columns):
self.assertEqual(cycles_interpolated_dyptes[indx],
STRUCTURE_DTYPES['cycles_interpolated'][col])
def test_from_raw_cycler_run_arbin(self):
rcycler_run = RawCyclerRun.from_file(self.arbin_file)
pcycler_run = ProcessedCyclerRun.from_raw_cycler_run(rcycler_run)
self.assertIsInstance(pcycler_run, ProcessedCyclerRun)
# Ensure barcode/protocol are passed
self.assertEqual(pcycler_run.barcode, "EL151000429559")
self.assertEqual(pcycler_run.protocol,
r"2017-12-04_tests\20170630-4_65C_69per_6C.sdu")
def test_from_raw_cycler_run_parameters(self):
rcycler_run = RawCyclerRun.from_file(self.maccor_file_w_parameters)
pcycler_run = ProcessedCyclerRun.from_raw_cycler_run(rcycler_run)
self.assertIsInstance(pcycler_run, ProcessedCyclerRun)
# Ensure barcode/protocol are passed
self.assertEqual(pcycler_run.barcode, "0001BC")
self.assertEqual(pcycler_run.protocol, "PredictionDiagnostics_000109.000")
self.assertEqual(pcycler_run.channel_id, 10)
def memory_profile(s3_objs=None):
"""
Function for memory profiling pipelines with s3_objs.
Args:
s3_objs ([str]): list of s3_objs in the kitware d3batt
publication s3 bucket.
"""
s3_objs = s3_objs or MEMORY_PROFILE_S3_OBJS
# Cache s3 objects
cache_s3_objs(s3_objs, filter_existing_files=True)
with ScratchDir("."):
# Copy all pre-defined s3 objects
for obj_name in s3_objs:
shutil.copy(os.path.join(S3_CACHE, obj_name), ".")
# Snip data path prefix
data_paths = [
os.path.basename(obj) for obj in s3_objs if "Metadata" not in obj
]
# Validation
# validator = ValidatorBeep()
# validator.validate_from_paths(data_paths)
# Data structuring
raw_cycler_runs = [
RawCyclerRun.from_file(data_path) for data_path in data_paths
]
processed_cycler_runs = [
ProcessedCyclerRun.from_raw_cycler_run(raw_cycler_run)
for raw_cycler_run in raw_cycler_runs
]
# Featurization
predictors = [
DegradationPredictor.init_full_model(processed_cycler_run)
for processed_cycler_run in processed_cycler_runs
]
# Prediction
[
DegradationModel.init_full_model().predict(predictor)
for predictor in predictors
]
def test_diagnostic_feature_generation(self):
os.environ['BEEP_ROOT'] = TEST_FILE_DIR
maccor_file_w_parameters = os.path.join(
TEST_FILE_DIR, "PredictionDiagnostics_000136_00002D.037")
raw_run = RawCyclerRun.from_file(maccor_file_w_parameters)
v_range, resolution, nominal_capacity, full_fast_charge, diagnostic_available = \
raw_run.determine_structuring_parameters()
pcycler_run = ProcessedCyclerRun.from_raw_cycler_run(
raw_run, diagnostic_available=diagnostic_available)
predictor = DegradationPredictor.init_full_model(
pcycler_run,
predict_only=False,
mid_pred_cycle=11,
final_pred_cycle=12,
diagnostic_features=True)
diagnostic_feature_label = predictor.feature_labels[-1]
self.assertEqual(diagnostic_feature_label,
"median_diagnostic_cycles_discharge_capacity")
np.testing.assert_almost_equal(
predictor.X[diagnostic_feature_label][0], 4.2327690032, decimal=8)
def test_from_raw_cycler_run_neware(self):
rcycler_run = RawCyclerRun.from_file(self.neware_file)
pcycler_run = ProcessedCyclerRun.from_raw_cycler_run(rcycler_run)
self.assertIsInstance(pcycler_run, ProcessedCyclerRun)