| import numpy as np |
| import torch |
| from torch.utils.data import Dataset |
| import pickle |
| import h5py |
| import json |
| import linecache |
|
|
| output_path = '/data/beegfs/darcy/sionna_simulation/BigSim/' |
|
|
| def get_args(config_file, task_id): |
| |
| line = linecache.getline(config_file, task_id) |
| return json.loads(line) |
|
|
| class SICDFDataset(Dataset): |
| """ |
| """ |
| def __init__(self, task_id, mode = "train", angle_mode = "deg"): |
| args = get_args("config.jsonl", task_id) |
|
|
| print(f"Starting run with task_id {task_id} and arguments {args}...") |
|
|
| jammer_sig_file = args['jammer_file'] |
| jammer_file = jammer_sig_file.split('/')[-1][:-4] |
| trajectory = args['trajectory'] |
| ant_dist = args['ant_dist'] |
| antenna_gain = args['antenna_gain'] |
| layout = args['layout'] |
| snr = args['snr'] |
| reflection_depth = args['reflection_depth'] |
| refraction = args['refraction'] |
| data_file_name = output_path + f"{jammer_file}_{trajectory}_{ant_dist}_{antenna_gain}_{layout}_{snr}_{reflection_depth}_{refraction}" |
|
|
| self.angle_mode = angle_mode |
| self.h5_file = None |
| PATH_IQ_TRAIN = data_file_name + "_train.h5" |
| PATH_IQ_TEST = data_file_name + "_test.h5" |
| DATA_FILE_TRAIN = data_file_name + "_train.txt" |
| DATA_FILE_TEST = data_file_name + "_test.txt" |
| if mode == "train": |
| with open(DATA_FILE_TRAIN, "r") as f: |
| data_info = f.readlines() |
| elif mode == "test": |
| with open(DATA_FILE_TEST, "r") as f: |
| data_info = f.readlines() |
| else: |
| raise ValueError("Mode must be 'train', 'test', 'outside' or 'shifted'") |
| data_info = np.array([x.split("\n")[0].split("\t") for x in data_info]) |
| data_info = data_info[1:] |
| if mode == "train": |
| if self.h5_file is None: |
| self.h5_file = h5py.File(PATH_IQ_TRAIN, 'r') |
| elif mode == "test": |
| if self.h5_file is None: |
| self.h5_file = h5py.File(PATH_IQ_TEST, 'r') |
| else: |
| raise ValueError("Mode must be 'train', 'test', 'outside' or 'shifted'") |
|
|
| self.x_files = [] |
| self.positions = [] |
| self.az_angles = [] |
| self.el_angles = [] |
| for x in data_info: |
| self.positions.append(np.array([float(x[1]), float(x[2]), float(x[3])])) |
| if self.angle_mode == "deg": |
| self.az_angles.append(float(x[4])) |
| self.el_angles.append(float(x[5])) |
| elif self.angle_mode == "rad": |
| self.az_angles.append(np.deg2rad(float(x[4]))) |
| self.el_angles.append(np.deg2rad(float(x[5]))) |
| elif self.angle_mode == "sincos": |
| self.az_angles.append([np.sin(np.deg2rad(float(x[4]))), np.cos(np.deg2rad(float(x[4])))]) |
| self.el_angles.append([np.sin(np.deg2rad(float(x[5]))), np.cos(np.deg2rad(float(x[5])))]) |
| else: |
| raise ValueError("angle_mode must be 'deg', 'rad' or 'sincos'") |
| self.x_files.append(float(x[0])) |
| self.az_angles = np.array(self.az_angles) |
| self.el_angles = np.array(self.el_angles) |
| self.positions = np.array(self.positions) |
| self.x_files = np.array(self.x_files) |
|
|
| def __getitem__(self, item: int): |
| az_angle = torch.tensor(self.az_angles[item], dtype=torch.float) |
| el_angle = torch.tensor(self.el_angles[item], dtype=torch.float) |
| position = torch.tensor(self.positions[item]) |
| index = int(self.x_files[item]) |
| return torch.tensor(self.h5_file["received_signals"][index], dtype=torch.complex64), (position, az_angle, el_angle) |
|
|
| def __len__(self) -> int: |
| return len(self.az_angles) |
