def exec(self, task_input, task_output):
labels_information_loc = task_input["labels_information_location"]
resources_path = task_output["res_loc"]
labels_info = jReader.parse_data(labels_information_loc)
os.makedirs(task_input["training_loc"], True)
resources = os.dir_res_list(resources_path)
training_path = f"{task_input['training_loc']}{os.get_path_seperator()}training.csv"
labels_path = f"{task_input['training_loc']}{os.get_path_seperator()}labels.csv"
with open(training_path, 'w') as training_file:
with open(labels_path, 'w') as labels_file:
labels_file.write("Sinus, AF\n")
index = 0
while index < task_input["readings"]:
training_file.write(f"x_{index}")
index += 1
if index < task_input["readings"]:
training_file.write(',')
training_file.write("\n")
self.prep_training_file(training_file, labels_file, labels_info, task_output["res_loc"], resources)
def exec(self, task_input, task_output):
target_labels_loc = task_input["labels_loc"]
#TODO - make sure folder exists
os.makedirs(target_labels_loc, True)
#TODO - join taining folder with name of label file
target_labels_loc = os.path_join(target_labels_loc, "labels.csv")
data_labels_mappings = jReader.parse_data(
task_input["data_labels_mappings"])
self.create_labels_file(target_labels_loc, data_labels_mappings)
def _training_loc(self, res_loc):
path_split = res_loc.split(os.get_path_seperator())
training_path = f"{path_split[0]}{os.get_path_seperator()}{path_split[1]}"
for x in range(2, len(path_split) - 1):
training_path = os.path_join(training_path, path_split[x])
training_path = os.path_join(training_path, "training")
os.makedirs(training_path, True)
return os.path_join(training_path,
f"{path_split[len(path_split) - 1]}.csv")
def _prep_split_folder(self):
os.makedirs(self.res_path.split('.')[0], True)