#!/usr/bin/env python3

# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.

""" Sample and download Satellite images with Google Earth Engine

#### run the script:

### Install and authenticate Google Earth Engine

### match and download pre-sampled locations
python download_ssl4eo.py \
    --save-path ./data \
    --collection COPERNICUS/S2_HARMONIZED \
    --meta-cloud-name CLOUDY_PIXEL_PERCENTAGE \
    --cloud-pct 20 \
    --dates 2021-12-21 2021-09-22 2021-06-21 2021-03-20 \
    --radius 1320 \
    --bands B1 B2 B3 B4 B5 B6 B7 B8 B8A B9 B10 B11 B12 \
    --dtype uint16 \
    --num-workers 8 \
    --log-freq 100 \
    --match-file ./data/sampled_locations.csv \
    --indices-range 0 250000

### resume from interruption
python download_ssl4eo.py \
    -- ... \
    --resume ./data/checked_locations.csv \
    --indices-range 0 250000

## Example1: download Landsat-8, match pre-sampled locations
python download_ssl4eo.py \
    --save-path ./data \
    --collection LANDSAT/LC08/C02/T1_TOA \
    --meta-cloud-name CLOUD_COVER \
    --cloud-pct 20 \
    --dates 2021-12-21 2021-09-22 2021-06-21 2021-03-20 \
    --radius 1980 \
    --bands B1 B2 B3 B4 B5 B6 B7 B8 B9 B10 B11 \
    --dtype float32 \
    --num-workers 8 \
    --log-freq 100 \
    --match-file ./data/sampled_locations.csv \
    --indices-range 0 250000

"""

import argparse
import csv
import json
import os
import time
import warnings
from collections import defaultdict
from datetime import date, timedelta
from multiprocessing.dummy import Lock, Pool
from typing import Any

import ee
import numpy as np
import rasterio
from rasterio.transform import Affine


def date2str(date: date) -> str:
    return date.strftime('%Y-%m-%d')


def get_period(date: date, days: int = 5) -> tuple[str, str, str, str]:
    date1 = date - timedelta(days=days / 2)
    date2 = date + timedelta(days=days / 2)
    date3 = date1 - timedelta(days=365)
    date4 = date2 - timedelta(days=365)
    return (
        date2str(date1),
        date2str(date2),
        date2str(date3),
        date2str(date4),
    )  # two-years buffer


"""get collection and remove clouds from ee"""


def mask_clouds(args: argparse.Namespace, image: ee.Image) -> ee.Image:
    qa = image.select(args.qa_band)
    cloudBitMask = 1 << args.qa_cloud_bit
    # Both flags should be set to zero, indicating clear conditions.
    mask = qa.bitwiseAnd(cloudBitMask).eq(0)
    return image.updateMask(mask)


def get_collection(
    collection_name: str, meta_cloud_name: str, cloud_pct: float
) -> ee.ImageCollection:
    collection = ee.ImageCollection(collection_name)
    collection = collection.filter(
        ee.Filter.And(
            ee.Filter.gte(meta_cloud_name, 0), ee.Filter.lte(meta_cloud_name, cloud_pct)
        )
    )
    # Uncomment the following line if you want to apply cloud masking.
    # collection = collection.map(mask_clouds, args)
    return collection


def filter_collection(
    collection: ee.ImageCollection,
    coords: tuple[float, float],
    period: tuple[str, str, str, str],
) -> ee.ImageCollection:
    filtered = collection
    if period is not None:
        # filtered = filtered.filterDate(*period)  # filter time, if there's one period
        filtered = filtered.filter(
            ee.Filter.Or(
                ee.Filter.date(period[0], period[1]),
                ee.Filter.date(period[2], period[3]),
            )
        )  # filter time, if there're two periods

    filtered = filtered.filterBounds(ee.Geometry.Point(coords))  # filter region

    if filtered.size().getInfo() == 0:
        raise ee.EEException(
            f'ImageCollection.filter: No suitable images found in ({coords[1]:.4f}, {coords[0]:.4f}) between {period[0]} and {period[1]}.'
        )
    return filtered


def center_crop(
    img: 'np.typing.NDArray[np.float32]', out_size: int
) -> 'np.typing.NDArray[np.float32]':
    image_height, image_width = img.shape[:2]
    crop_height = crop_width = out_size
    pad_height = max(crop_height - image_height, 0)
    pad_width = max(crop_width - image_width, 0)
    img = np.pad(img, ((pad_height, 0), (pad_width, 0), (0, 0)), mode='edge')
    crop_top = (image_height - crop_height + 1) // 2
    crop_left = (image_width - crop_width + 1) // 2
    return img[crop_top : crop_top + crop_height, crop_left : crop_left + crop_width]


def adjust_coords(
    coords: list[list[float]], old_size: tuple[int, int], new_size: tuple[int, int]
) -> list[list[float]]:
    xres = (coords[1][0] - coords[0][0]) / old_size[1]
    yres = (coords[0][1] - coords[1][1]) / old_size[0]
    xoff = int((old_size[1] - new_size[1] + 1) * 0.5)
    yoff = int((old_size[0] - new_size[0] + 1) * 0.5)
    return [
        [coords[0][0] + (xoff * xres), coords[0][1] - (yoff * yres)],
        [
            coords[0][0] + ((xoff + new_size[1]) * xres),
            coords[0][1] - ((yoff + new_size[0]) * yres),
        ],
    ]


def get_patch(
    collection: ee.ImageCollection,
    center_coord: tuple[float, float],
    radius: float,
    bands: list[str],
    original_resolutions: list[int],
    new_resolutions: list[int],
    dtype: str = 'float32',
    meta_cloud_name: str = 'CLOUD_COVER',
    default_value: float | None = None,
) -> dict[str, Any]:
    image = collection.sort(meta_cloud_name).first()
    region = ee.Geometry.Point(center_coord).buffer(radius).bounds()

    # Group by original and new resolution
    band_groups = defaultdict(list)
    for i in range(len(bands)):
        band_groups[(original_resolutions[i], new_resolutions[i])].append((i, bands[i]))

    # Reproject (if necessary) and download all bands
    raster = {}
    for (orig_res, new_res), value in band_groups.items():
        indices, bands_group = zip(*value)
        patch = image.select(*bands_group)
        if orig_res != new_res:
            patch = patch.reproject(patch.projection().crs(), scale=new_res)
        patch = patch.sampleRectangle(region, defaultValue=default_value)
        features = patch.getInfo()
        for i, band in zip(indices, bands_group):
            x = features['properties'][band]
            x = np.atleast_3d(x)
            x = center_crop(x, out_size=int(2 * radius // new_res))
            raster[i] = x.astype(dtype)

    # Compute coordinates after cropping
    coords0 = np.array(features['geometry']['coordinates'][0])
    coords = [
        [coords0[:, 0].min(), coords0[:, 1].max()],
        [coords0[:, 0].max(), coords0[:, 1].min()],
    ]
    old_size = (len(features['properties'][band]), len(features['properties'][band][0]))
    new_size = raster[0].shape[:2]
    coords = adjust_coords(coords, old_size, new_size)

    return {'raster': raster, 'coords': coords, 'metadata': image.getInfo()}


def get_random_patches_match(
    idx: int,
    collection: ee.ImageCollection,
    bands: list[str],
    original_resolutions: list[int],
    new_resolutions: list[int],
    dtype: str,
    meta_cloud_name: str,
    default_value: float | None,
    dates: list[date],
    radius: float,
    debug: bool = False,
    match_coords: dict[int, tuple[float, float]] = {},
) -> tuple[list[dict[str, Any]], tuple[float, float]]:
    # (lon,lat) of idx patch
    coords = match_coords[idx]

    # random +- 30 days of random days within 1 year from the reference dates
    periods = [get_period(date, days=60) for date in dates]

    try:
        filtered_collections = [
            filter_collection(collection, coords, p) for p in periods
        ]
        patches = [
            get_patch(
                c,
                coords,
                radius,
                bands,
                original_resolutions,
                new_resolutions,
                dtype,
                meta_cloud_name,
                default_value,
            )
            for c in filtered_collections
        ]
    except Exception as e:
        if debug:
            print(e)
        return [], coords

    return patches, coords


def save_geotiff(
    img: 'np.typing.NDArray[np.float32]',
    coords: list[tuple[float, float]],
    filename: str,
) -> None:
    height, width, channels = img.shape
    xres = (coords[1][0] - coords[0][0]) / width
    yres = (coords[0][1] - coords[1][1]) / height
    transform = Affine.translation(
        coords[0][0] - xres / 2, coords[0][1] + yres / 2
    ) * Affine.scale(xres, -yres)
    profile = {
        'driver': 'GTiff',
        'width': width,
        'height': height,
        'count': channels,
        'crs': '+proj=latlong',
        'transform': transform,
        'dtype': img.dtype,
        'compress': 'None',
    }
    with rasterio.open(filename, 'w', **profile) as f:
        f.write(img.transpose(2, 0, 1))


def save_patch(
    raster: dict[int, 'np.typing.NDArray[np.float32]'],
    coords: list[tuple[float, float]],
    metadata: dict[str, Any],
    bands: list[str],
    new_resolutions: list[int],
    path: str,
) -> None:
    patch_id = metadata['properties']['system:index']
    patch_path = os.path.join(path, patch_id)
    os.makedirs(patch_path, exist_ok=True)

    if len(set(new_resolutions)) == 1:
        img_all = np.concatenate([raster[i] for i in range(len(raster))], axis=2)
        save_geotiff(img_all, coords, os.path.join(patch_path, 'all_bands.tif'))
    else:
        for i, band in enumerate(bands):
            img = raster[i]
            save_geotiff(img, coords, os.path.join(patch_path, f'{band}.tif'))

    with open(os.path.join(patch_path, 'metadata.json'), 'w') as f:
        json.dump(metadata, f)


class Counter:
    def __init__(self, start: int = 0) -> None:
        self.value = start
        self.lock = Lock()

    def update(self, delta: int = 1) -> int:
        with self.lock:
            self.value += delta
            return self.value


if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument(
        '--save-path', type=str, default='./data/', help='dir to save data'
    )
    # collection properties
    parser.add_argument(
        '--collection',
        type=str,
        default='COPERNICUS/S2_HARMONIZED',
        help='GEE collection name',
    )
    parser.add_argument('--qa-band', type=str, default='QA60', help='qa band name')
    parser.add_argument(
        '--qa-cloud-bit', type=int, default=10, help='qa band cloud bit'
    )
    parser.add_argument(
        '--meta-cloud-name',
        type=str,
        default='CLOUDY_PIXEL_PERCENTAGE',
        help='meta data cloud percentage name',
    )
    parser.add_argument(
        '--cloud-pct', type=int, default=20, help='cloud percentage threshold'
    )
    # patch properties
    parser.add_argument(
        '--dates',
        type=str,
        nargs='+',
        # https://www.weather.gov/media/ind/seasons.pdf
        default=['2021-12-21', '2021-09-23', '2021-06-21', '2021-03-20'],
        help='reference dates',
    )
    parser.add_argument(
        '--radius', type=int, default=1320, help='patch radius in meters'
    )
    parser.add_argument(
        '--bands',
        type=str,
        nargs='+',
        default=[
            'B1',
            'B2',
            'B3',
            'B4',
            'B5',
            'B6',
            'B7',
            'B8',
            'B8A',
            'B9',
            'B10',
            'B11',
            'B12',
        ],
        help='bands to download',
    )
    # Reprojection options
    #
    # If the original resolutions differ between bands, you can reproject them to
    # new resolutions. Crop dimensions are the size of each patch you want to crop
    # to after reprojection. All of these options should either be a single value
    # or the same length as the bands flag.
    parser.add_argument(
        '--original-resolutions',
        type=int,
        nargs='+',
        default=[60, 10, 10, 10, 20, 20, 20, 10, 20, 60, 60, 20, 20],
        help='original band resolutions in meters',
    )
    parser.add_argument(
        '--new-resolutions',
        type=int,
        nargs='+',
        default=[10],
        help='new band resolutions in meters',
    )
    parser.add_argument('--dtype', type=str, default='float32', help='data type')
    # If None, don't download patches with nodata pixels
    parser.add_argument(
        '--default-value', type=float, default=None, help='default fill value'
    )
    # download settings
    parser.add_argument('--num-workers', type=int, default=8, help='number of workers')
    parser.add_argument('--log-freq', type=int, default=10, help='print frequency')
    parser.add_argument(
        '--resume', type=str, default=None, help='resume from a previous run'
    )
    # sampler options
    parser.add_argument(
        '--match-file',
        type=str,
        required=True,
        help='match pre-sampled coordinates and indexes',
    )
    # number of locations to download
    parser.add_argument(
        '--indices-range',
        type=int,
        nargs=2,
        default=[0, 250000],
        help='indices to download',
    )
    # debug
    parser.add_argument('--debug', action='store_true', help='debug mode')

    args = parser.parse_args()
    os.makedirs(args.save_path, exist_ok=True)

    # initialize ee
    ee.Initialize()

    # get data collection (remove clouds)
    collection = get_collection(args.collection, args.meta_cloud_name, args.cloud_pct)

    dates = []
    for d in args.dates:
        dates.append(date.fromisoformat(d))

    bands = args.bands
    original_resolutions = args.original_resolutions
    new_resolutions = args.new_resolutions
    dtype = args.dtype

    # Validate inputs
    num_bands = len(bands)
    if len(original_resolutions) == 1:
        original_resolutions *= num_bands
    if len(new_resolutions) == 1:
        new_resolutions *= num_bands

    for values in [original_resolutions, new_resolutions]:
        assert len(values) == num_bands

    # if resume
    ext_coords = {}
    ext_flags = {}
    if args.resume:
        ext_path = args.resume
        with open(ext_path) as csv_file:
            reader = csv.reader(csv_file)
            for row in reader:
                key = int(row[0])
                val1 = float(row[1])
                val2 = float(row[2])
                ext_coords[key] = (val1, val2)  # lon, lat
                ext_flags[key] = int(row[3])  # success or not
    else:
        ext_path = os.path.join(args.save_path, 'checked_locations.csv')

    # match from pre-sampled coords
    match_coords = {}
    with open(args.match_file) as csv_file:
        reader = csv.reader(csv_file)
        for row in reader:
            key = int(row[0])
            val1 = float(row[1])
            val2 = float(row[2])
            match_coords[key] = (val1, val2)  # lon, lat

    start_time = time.time()
    counter = Counter()

    def worker(idx: int) -> None:
        # Skip if idx has already been downloaded
        if idx in ext_coords.keys():
            return

        # Skip if idx is not in pre-sampled coordinates
        if idx not in match_coords.keys():
            warnings.warn(f'{idx} not found in {args.match_file}, skipping.')
            return

        worker_start = time.time()
        patches, center_coord = get_random_patches_match(
            idx,
            collection,
            bands,
            original_resolutions,
            new_resolutions,
            dtype,
            args.meta_cloud_name,
            args.default_value,
            dates,
            radius=args.radius,
            debug=args.debug,
            match_coords=match_coords,
        )

        if patches:
            location_path = os.path.join(args.save_path, 'imgs', f'{idx:07d}')
            os.makedirs(location_path, exist_ok=True)
            for patch in patches:
                save_patch(
                    patch['raster'],
                    patch['coords'],
                    patch['metadata'],
                    bands,
                    new_resolutions,
                    location_path,
                )

            count = counter.update(1)
            if count % args.log_freq == 0:
                print(f'Downloaded {count} images in {time.time() - start_time:.3f}s.')
        else:
            if args.debug:
                print(f'no suitable image for location {idx}.')

        # add to existing checked locations
        with open(ext_path, 'a') as f:
            writer = csv.writer(f)
            if patches:
                success = 1
            else:
                success = 0
            data = [idx, *center_coord, success]
            writer.writerow(data)

        # Throttle throughput to avoid exceeding GEE quota:
        # https://developers.google.com/earth-engine/guides/usage
        worker_end = time.time()
        elapsed = worker_end - worker_start
        num_workers = max(1, args.num_workers)
        time.sleep(max(0, num_workers / 100 - elapsed))

        return

    # set indices
    indices = list(range(args.indices_range[0], args.indices_range[1]))

    if args.num_workers == 0:
        for i in indices:
            worker(i)
    else:
        # parallelism data
        with Pool(processes=args.num_workers) as p:
            p.map(worker, indices)
