from torch.utils.data import DataLoader, Dataset
import cv2
import os
import rasterio
import torch
import numpy as np
from pyproj import Transformer
from datetime import date

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]

BIOMASS_MEAN = 93.8317
BIOMASS_STD = 110.5369


class S3OLCI_BiomassDataset(Dataset):
    '''
    4000/1000 train/test images 94x94x21 (full dataset is 25K)
    CCI biomass regression 282x282
    nodata: -inf
    time series: 1-4 images / location
    
    '''


    def __init__(self, root_dir, split='train', mode='static'):
        self.root_dir = root_dir
        self.split = split
        self.mode = mode
        self.img_dir = os.path.join(root_dir, split, 's3_olci')
        self.biomass_dir = os.path.join(root_dir, split, 'biomass')

        self.fnames = os.listdir(self.biomass_dir)

        if self.mode == 'static':
            self.static_csv = os.path.join(root_dir, split, 'static_fnames.csv')
            with open(self.static_csv, 'r') as f:
                lines = f.readlines()
                self.static_img = {}
                for line in lines:
                    dirname = line.strip().split(',')[0]
                    img_fname = line.strip().split(',')[1]
                    self.static_img[dirname] = img_fname
                


    def __len__(self):
        return len(self.fnames)
    
    def __getitem__(self, idx):
        fname = self.fnames[idx]
        biomass_path = os.path.join(self.biomass_dir, fname)
        s3_path = os.path.join(self.img_dir, fname.replace('.tif',''))
        
        if self.mode == 'static':
            img_fname = self.static_img[fname.replace('.tif','')]
            s3_paths = [os.path.join(s3_path, img_fname)]
        else:
            img_fnames = os.listdir(s3_path)
            s3_paths = []
            for img_fname in img_fnames:
                s3_paths.append(os.path.join(s3_path, img_fname))
            
        imgs = []
        img_paths = []
        meta_infos = []
        for img_path in s3_paths:
            with rasterio.open(img_path) as src:
                img = src.read()
                chs = []
                for b in range(21):
                    #ch = cv2.resize(img[b], (94,94), interpolation=cv2.INTER_CUBIC)
                    ch = cv2.resize(img[b], (282,282), interpolation=cv2.INTER_CUBIC)
                    chs.append(ch)
                img = np.stack(chs)
                img[np.isnan(img)] = 0
                for b in range(21):
                    img[b] = img[b]*S3_OLCI_SCALE[b]
                img = torch.from_numpy(img).float()

                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)
                    lon, lat = cx, cy
                    img_fname = os.path.basename(img_path)
                    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)


            imgs.append(img)
            img_paths.append(img_path)
        
        if self.mode == 'series':
            # pad to 4 images if less than 4
            while len(imgs) < 4:
                imgs.append(img)
                img_paths.append(img_path)
                meta_infos.append(meta_info)

        with rasterio.open(biomass_path) as src:
           biomass = src.read(1)
           biomass = cv2.resize(biomass, (282,282), interpolation=cv2.INTER_CUBIC) # 0-650
           biomass = torch.from_numpy(biomass.astype('float32'))
           biomass = (biomass - BIOMASS_MEAN) / BIOMASS_STD # 0-center normalized
        if self.mode == 'static':
            return imgs[0], meta_infos[0], biomass # 94x94x21, 282x282
        elif self.mode == 'series':
            return imgs[0], imgs[1], imgs[2], imgs[3], meta_infos[0], meta_infos[1], meta_infos[2], meta_infos[3], biomass # 94x94x21, 94x94x21, 94x94x21, 94x94x21, 282x282