def main():
"""
Main function of this module, takes in arguments of an input
and output filename corresponding to featurized run data
and creates a predictor object output for analysis/ML processing
"""
# Parse args and construct initial cycler run
logger.info("starting", extra=s)
logger.info("Running version=%s", __version__, extra=s)
try:
args = docopt(__doc__)
input_json = args["INPUT_JSON"]
if args["--fit"]:
print(
process_file_list_from_json(
input_json, predict_only=False, model_dir=MODEL_DIR
),
end="",
)
else:
print(process_file_list_from_json(input_json, model_dir=MODEL_DIR), end="")
except Exception as e:
logger.error(str(e), extra=s)
raise e
logger.info("finish", extra=s)
return None
def get_protocol_parameters(filepath,
parameters_path="data-share/raw/parameters"):
"""
Helper function to get the project parameters for a file given the filename
Args:
filepath (str): full path to the file
parameters_path (str): location to look for parameter files
Returns:
pandas.DataFrame: single row DataFrame corresponding to the parameters for this file
pandas.DataFrame: DataFrame with all of the parameter for the project
"""
project_name_list = get_project_sequence(filepath)
project_name = project_name_list[0]
path = os.path.join(os.environ.get("BEEP_PROCESSING_DIR", "/"),
parameters_path)
project_parameter_files = glob(os.path.join(path, project_name + "*"))
assert len(project_parameter_files) <= 1, (
"Found too many parameter files for: " + project_name)
if len(project_parameter_files) == 1:
df = pd.read_csv(project_parameter_files[0])
parameter_row = df[df.seq_num == int(project_name_list[1])]
if parameter_row.empty:
logger.error("Unable to get project parameters for: %s",
filepath,
extra=s)
parameter_row = None
df = None
else:
parameter_row = None
df = None
return parameter_row, df
def dummy(ctx, test):
logger.debug(f"Test msg: {test}")
logger.info("Info msg")
logger.warning("Warning message")
logger.error("Error message")
logger.critical("CRITICAL MESSAGE!")
if test == "throw_error":
raise ValueError("Some error!")
dumpfn({"example": "status"}, ctx.obj.output_status_json)
def main():
logger.info('starting', extra=s)
logger.info('Running version=%s', __version__, extra=s)
try:
args = docopt(__doc__)
input_json = args['INPUT_JSON']
print(validate_file_list_from_json(input_json), end="")
except Exception as e:
logger.error(str(e), extra=s)
raise e
logger.info('finish', extra=s)
return None
def main():
"""Main function for the script"""
logger.info("starting", extra=s)
logger.info("Running version=%s", __version__, extra=s)
try:
args = docopt(__doc__)
input_json = args["INPUT_JSON"]
print(process_csv_file_list_from_json(input_json), end="")
except Exception as e:
logger.error(str(e), extra=s)
raise e
logger.info("finish", extra=s)
return None
def main():
"""Main function of this module, takes in arguments of an input
and output filename and uses the input file to create a
structured data output for analysis/ML processing.
"""
logger.info("starting", extra=SERVICE_CONFIG)
logger.info("Running version=%s", __version__, extra=SERVICE_CONFIG)
try:
args = docopt(__doc__)
input_json = args["INPUT_JSON"]
print(process_file_list_from_json(input_json))
except Exception as e:
logger.error(str(e), extra=SERVICE_CONFIG)
raise e
logger.info("finish", extra=SERVICE_CONFIG)
return None
def main():
"""
Main function for running of this module as a script
Returns:
(None)
"""
logger.info("starting", extra=s)
logger.info("Running version=%s", __version__, extra=s)
try:
args = docopt(__doc__)
input_json = args["INPUT_JSON"]
print(validate_file_list_from_json(input_json), end="")
except Exception as e:
logger.error(str(e), extra=s)
raise e
logger.info("finish", extra=s)
return None
def main():
"""
Main function of this module, takes in arguments of an input
and output filename corresponding to structured cycler run data
and creates a predictor object output for analysis/ML processing
Returns:
None
"""
# Parse args and construct initial cycler run
logger.info('starting', extra=s)
logger.info('Running version=%s', __version__, extra=s)
try:
args = docopt(__doc__)
input_json = args['INPUT_JSON']
print(process_file_list_from_json(input_json), end="")
except Exception as e:
logger.error(str(e), extra=s)
raise e
logger.info('finish', extra=s)
return None
def generate_protocol_files_from_csv(csv_filename, output_directory=None):
"""
Generates a set of protocol files from csv filename input by
reading protocol file input corresponding to each line of
the csv file. Writes a csv file that.
Args:
csv_filename (str): CSV containing protocol file parameters.
output_directory (str): directory in which to place the output files
"""
# Read csv file
protocol_params_df = pd.read_csv(csv_filename)
successfully_generated_files = []
file_generation_failures = []
names = []
result = ""
message = {"comment": "", "error": ""}
if output_directory is None:
output_directory = PROCEDURE_TEMPLATE_DIR
for index, protocol_params in protocol_params_df.iterrows():
template = protocol_params["template"]
protocol = None
# Filename for the output
filename_prefix = "_".join(
[
protocol_params["project_name"],
"{:06d}".format(protocol_params["seq_num"]),
]
)
if ".000" in template: # Extension for maccor procedure files
template_fullpath = os.path.join(PROCEDURE_TEMPLATE_DIR, template)
template_length = template_detection(template_fullpath)
if "diagnostic_parameter_set" in protocol_params: # For parameters include diagnostics load those values
diag_params_df = pd.read_csv(
os.path.join(PROCEDURE_TEMPLATE_DIR, "PreDiag_parameters - DP.csv")
)
diagnostic_params = diag_params_df[
diag_params_df["diagnostic_parameter_set"]
== protocol_params["diagnostic_parameter_set"]
].squeeze()
if template_length == 23 and template == "EXP.000": # length and name for initial procedure files
protocol = Procedure.from_exp(
**protocol_params[["cutoff_voltage", "charge_rate", "discharge_rate"]]
)
elif template_length == 72: # length for V1 and V1 diagnostic templates without ending diagnostics
protocol = Procedure.from_regcyclev2(protocol_params)
protocol.add_procedure_diagcyclev2(
protocol_params["capacity_nominal"], diagnostic_params
)
elif template_length == 96: # template length for diagnostic type cycling
mwf_dir = os.path.join(output_directory, "mwf_files")
if protocol_params["project_name"] == "RapidC": # Project with charging waveform
waveform_name = insert_charging_parametersv1(protocol_params,
waveform_directory=mwf_dir)
protocol = Procedure.generate_procedure_chargingv1(index,
protocol_params,
waveform_name,
template=template_fullpath)
elif protocol_params["project_name"] == "Drive": # Project with discharging waveform
waveform_name = insert_driving_parametersv1(protocol_params,
waveform_directory=mwf_dir)
protocol = Procedure.generate_procedure_drivingv1(index,
protocol_params,
waveform_name,
template=template_fullpath)
else: # Use the default parameterization for PreDiag/Prediction Diagnostic projects
protocol = Procedure.generate_procedure_regcyclev3(index,
protocol_params,
template=template_fullpath)
protocol.generate_procedure_diagcyclev3(
protocol_params["capacity_nominal"], diagnostic_params
)
else: # Case where its not possible to match the procedure template
failure = {
"comment": "Unable to find template: " + template,
"error": "Not Found",
}
file_generation_failures.append(failure)
warnings.warn("Unsupported file template {}, skipping.".format(template))
result = "error"
continue
filename = "{}.000".format(filename_prefix)
filename = os.path.join(output_directory, "procedures", filename)
elif ".mps" in template and template == "formationV1.mps": # biologic settings template and formation project
protocol = Settings.from_file(os.path.join(BIOLOGIC_TEMPLATE_DIR, template))
protocol = protocol.formation_protocol_bcs(protocol_params)
filename = "{}.mps".format(filename_prefix)
filename = os.path.join(output_directory, "settings", filename)
elif ".sdu" in template: # No schedule file templates implemented
failure = {
"comment": "Schedule file generation is not yet implemented",
"error": "Not Implemented"
}
file_generation_failures.append(failure)
logger.warning("Schedule file generation not yet implemented", extra=s)
result = "error"
continue
else: # Unable to match to any known template format
failure = {
"comment": "Unable to find template: " + template,
"error": "Not Found",
}
file_generation_failures.append(failure)
warnings.warn("Unsupported file template {}, skipping.".format(template))
result = "error"
continue
logger.info(filename, extra=s)
protocol.to_file(filename)
successfully_generated_files.append(filename)
names.append(filename_prefix + "_")
# This block of code produces the file containing all of the run file
# names produced in this function call. This is to make starting tests easier
_, namefile = os.path.split(csv_filename)
namefile = namefile.split("_")[0] + "_names_"
namefile = namefile + datetime.datetime.now().strftime("%Y%m%d_%H%M") + ".csv"
names_dir = os.path.join(output_directory, "names")
os.makedirs(names_dir, exist_ok=True)
with open(os.path.join(names_dir, namefile), "w", newline="") as outputfile:
wr = csv.writer(outputfile)
for name in names:
wr.writerow([name])
outputfile.close()
num_generated_files = len(successfully_generated_files)
num_generation_failures = len(file_generation_failures)
num_files = num_generated_files + num_generation_failures
message = {
"comment": "Generated {} of {} protocols".format(num_generated_files, num_files),
"error": ""
}
if not result:
result = "success"
else:
message["error"] = "Failed to generate {} of {} protocols".format(num_generation_failures, num_files)
logger.error(message["error"])
return successfully_generated_files, file_generation_failures, result, message
