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):
    # linecache is 1-indexed, which matches SLURM_ARRAY_TASK_ID perfectly
    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)