import torch
from torch.utils.data import Dataset, DataLoader
import os
import rasterio
import numpy as np
from datetime import date
from pyproj import Transformer

S3_OLCI_SCALE = [0.0139465,0.0133873,0.0121481,0.0115198,0.0100953,0.0123538,0.00879161,0.00876539,
                    0.0095103,0.00773378,0.00675523,0.0071996,0.00749684,0.0086512,0.00526779,0.00530267,
                    0.00493004,0.00549962,0.00502847,0.00326378,0.00324118]

Cls_index_binary = {
    'invalid': 0,
    'clear': 1,
    'cloud': 2,
}

Cls_index_multi = {
    'invalid': 0,
    'clear': 1,
    'cloud-sure': 2,
    'cloud-ambiguous': 3,
    'cloud shadow': 4,
    'snow and ice': 5,
}



class S3OLCI_CloudDataset(Dataset):
    '''
    1596/399 train/test images 256x256
    21 bands
    nodata: nan

    '''
    def __init__(self, root_dir, split='train', mode='multi', meta=True):
        self.root_dir = root_dir
        self.meta = meta

        self.img_dir = os.path.join(root_dir, split, 's3_olci')
        self.fpaths = os.listdir(self.img_dir)
        self.fpaths = [f for f in self.fpaths if f.endswith('.tif')]

        if mode == 'multi':
            self.cloud_dir = os.path.join(root_dir, split, 'cloud_multi')
        elif mode == 'binary':
            self.cloud_dir = os.path.join(root_dir, split, 'cloud_binary')

        if self.meta:
            self.reference_date = date(1970, 1, 1)


    def __len__(self):
        return len(self.fpaths)
    
    def __getitem__(self, idx):
        fpath = self.fpaths[idx]
        fpath_img = os.path.join(self.img_dir, fpath)
        fpath_cloud = os.path.join(self.cloud_dir, fpath)

        with rasterio.open(fpath_img) as src:
            img = src.read()
            # convert nan pixels to 0
            img[np.isnan(img)] = 0

            for b in range(21):
                img[b] = img[b] * S3_OLCI_SCALE[b]

            if self.meta:
                cx,cy = src.xy(src.height // 2, src.width // 2)
                crs_transformer = Transformer.from_crs(src.crs, 'epsg:4326')
                lon, lat = crs_transformer.transform(cx,cy)
                img_fname = os.path.basename(fpath_img)
                date_str = img_fname.split('____')[1][:8]  
                date_obj = date(int(date_str[:4]), int(date_str[4:6]), int(date_str[6:8]))
                delta = (date_obj - self.reference_date).days
                meta_info = np.array([lon, lat, delta, np.nan]).astype(np.float32)
            else:
                meta_info = np.array([np.nan,np.nan,np.nan,np.nan]).astype(np.float32)

        img = torch.from_numpy(img).float()

        with rasterio.open(fpath_cloud) as src:
            cloud = src.read(1)
        cloud = torch.from_numpy(cloud).long()

        return img, cloud, meta_info


    
if __name__ == '__main__':
    dataset = S3OLCI_CloudDataset(root_dir='./cloud_s3olci', split='train', mode='multi')
    dataloader = DataLoader(dataset, batch_size=2, shuffle=False)
    for img, cloud, meta in dataloader:
        print(img.shape, cloud.shape, meta.shape)
        break