def exec(self, task_input, task_output): path = task_output["res_loc"] path = os.path_join(path, "apple_health_export", "electrocardiograms") resources = os.dir_res_list(path) workers = [] for elem in resources: res_path = os.path_join(path, elem) if os.is_path_file(res_path): splitted_elem = elem.split('.') backup_name = f"{splitted_elem[0]}.bak" worker = DataPreparerWorker(res_path, backup_name) worker.start() workers.append(worker) for worker in workers: worker.join() task_output["res_loc"] = path
def exec(self, task_input, task_output): task_output["readings"] = self.data_readings resources = os.dir_res_list(task_output["res_loc"]) splitter_threads = [] for elem in resources: res_path = os.path_join(task_output["res_loc"], elem) if not os.is_path_file(res_path): continue splitter = _Splitter(elem, res_path, self.data_readings) splitter.start() splitter_threads.append(splitter) if len(splitter_threads) > 5: for thread in splitter_threads: thread.join() splitter_threads = [] for thread in splitter_threads: thread.join()
def exec(self, task_input, task_output): ressources, training_data, backup = self._setup(task_output) with open(training_data, 'w') as training: if backup is not None and os.is_path_file(backup): self._merge_training(training, backup) else: headders = self._get_headders(task_output["readings"]) training.write(headders) for split_folder in ressources: split_dir = os.path_join(task_output["res_loc"], split_folder) if os.is_path_file(split_dir) and not os.is_dir(split_dir): continue self.append_training_set(training, split_dir, os.dir_res_list(split_dir)) os.remove_dir(split_dir)
def _bakup_saved(self, training_loc): if not os.is_path_file(training_loc): return return os.copy_file(training_loc, "temp.bak")
def exec(self, task_input, task_output): sampling_frequency = task_input["sampled_frequency"] target_frequency = task_input["target_frequency"] doubling_rate = math.ceil(sampling_frequency / (((target_frequency / sampling_frequency) - 1) * sampling_frequency)) res_elems = os.dir_res_list(task_output["res_loc"]) for res_elem in res_elems: origin_path = os.path_join(task_output["res_loc"], res_elem) if os.is_path_file(origin_path): backup_path = os.copy_file(origin_path, f"{task_input['name']}_bakup.bak") with open(origin_path, 'w') as file: with open(backup_path) as backup_file: temp_reading = backup_file.readline() sampling_nr = 1 while temp_reading != "": temp_reading = backup_file.readline() if temp_reading != "": splitted = temp_reading.split(',') file.write(f"{sampling_nr},{splitted[1]},{splitted[2]}") if "f2" in res_elem: file.write("\n") sampling_nr += 1 for _ in range(1, doubling_rate): temp_reading = backup_file.readline() if temp_reading != "": splitted = temp_reading.split(',') file.write(f"{sampling_nr},{splitted[1]},{splitted[2]}") if "f2" in res_elem: file.write("\n") sampling_nr += 1 if temp_reading != "": splitted = temp_reading.split(',') file.write(f"{sampling_nr},{splitted[1]},{splitted[2]}") if "f2" in res_elem: file.write("\n") sampling_nr += 1 splitted = temp_reading.split(',') file.write(f"{sampling_nr},{splitted[1]},{splitted[2]}") if "f2" in res_elem: file.write("\n") sampling_nr += 1 temp_reading = backup_file.readline() os.remove_file(backup_path)