#!/usr/bin/env python
# coding: utf-8

# #### seasonal extent change

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import ee
import geemap
import geopandas as gpd


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ee.Authenticate()
ee.Initialize()


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aoi = gpd.read_file('C:/Users/jtrum/Wash scan/data/aoiLuanda.geojson')


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# Define the extent (AOI) for Luanda
luanda_geometry = ee.Geometry.Point([13.2344, -8.8115]) 
luanda_extent = ee.Geometry.Rectangle([12.8, -9.1, 13.6, -8.5])  # Replace with the actual bounding box coordinates
# Set the location, AOI, and ID
pt = luanda_geometry
AOI = luanda_extent
id = "L1a"

# Create Water Mask
water_mask_id = 'JRC/GSW1_0/GlobalSurfaceWater'
WaterMask = ee.Image(water_mask_id)
water_mask = WaterMask.select('transition')
blank = ee.Image(0)
non_water = blank.addBands(water_mask).unmask().select('transition').eq(0).rename('non_water')

# Create Slope Mask
srtm_id = 'NASA/NASADEM_HGT/001'
srtm = ee.Image(srtm_id)
slope = ee.Terrain.slope(srtm)
grade_15 = slope.gt(15)
gt_grade_15 = grade_15.updateMask(grade_15.neq(0))
slope_mask = gt_grade_15.clip(AOI).unmask(0).subtract(1).multiply(-1)

# Load Sentinel-1 C-band SAR Ground Range collection (log scaling, VV co-polar)
collection = (ee.ImageCollection('COPERNICUS/S1_GRD')
              .filterBounds(pt)
              .filter(ee.Filter.listContains('transmitterReceiverPolarisation', 'VV'))
              .filter(ee.Filter.eq('orbitProperties_pass', 'DESCENDING'))
              .select('VV'))

# Filter by date

# Flood date: 03/13/2017 03/28/2017
during_flood_collection = (collection
                            .filterDate('2017-03-14', '2017-03-29'))
during_flood = (during_flood_collection
                .first()
                .clip(AOI)
                .updateMask(non_water)
                .updateMask(slope_mask))
print('During Flood Image Collection: ', during_flood_collection)

# Before Flood & in Dry Season: May - Sept
before_flood_collection = (collection
                            .filterDate('2016-08-01', '2016-09-30'))
before_flood = (before_flood_collection
                .median()
                .clip(AOI)
                .updateMask(non_water)
                .updateMask(slope_mask))
print('Before Flood Image Collection: ', before_flood_collection)

def ymd_list(image_collection):
    # Define a function to iterate over the image collection
    def iter_func(image, new_list):
        date = ee.Number.parse(ee.Date(image.date()).format("YYYYMMdd"))
        new_list = ee.List(new_list)
        return ee.List(new_list.add(date).sort())

    # Use list comprehension to iterate over the image collection
    return ee.List(image_collection.iterate(iter_func, ee.List([])))

# Print the image dates for Before Flood and During Flood collections
print('Before Flood Image Dates: ', ymd_list(before_flood_collection))
print('During Flood Image Dates: ', ymd_list(during_flood_collection))

# Set smoothing box (30m is 3x size of S1 pixel)
smoothing_box = 30



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# Create difference images (during flood - before flood)
difference_smoothed = (during_flood
                       .focal_median(radius=smoothing_box, kernelType='square', units='meters')
                       .subtract(before_flood.focal_median(radius=smoothing_box, kernelType='square', units='meters')))
difference_raw = during_flood.subtract(before_flood)


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# Check if difference_smoothed and AOI are defined and contain data
if difference_smoothed is not None and AOI is not None:
    # Check if the image has bands and AOI has coordinates
    if difference_smoothed.bandNames().size().getInfo() > 0 and AOI.coordinates().size().getInfo() > 0:
        # Clip the image
        difference_smoothed_clipped = difference_smoothed.clip(AOI)
        # Continue with further operations
    else:
        print("Either difference_smoothed or AOI is empty. Cannot clip image.")
else:
    print("Either difference_smoothed or AOI is not properly defined. Cannot clip image.")


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# Check if difference_smoothed is defined and print its information
if difference_smoothed is not None:
    print("difference_smoothed information:", difference_smoothed.getInfo())
else:
    print("difference_smoothed is not properly defined.")

# Check if AOI (luanda_extent) is defined and print its information
if AOI is not None:
    print("AOI (luanda_extent) information:", AOI.getInfo())
else:
    print("AOI (luanda_extent) is not properly defined.")

# Clip the image only if both difference_smoothed and AOI are properly defined
if difference_smoothed is not None and AOI is not None:
    difference_smoothed_clipped = difference_smoothed.clip(AOI)
    # Continue with further operations
else:
    print("Either difference_smoothed or AOI is not properly defined. Cannot clip image.")


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asset_id = 'NASA/NASADEM_HGT/001' # NASADEM elevation data

# Check asset existence
asset_info = ee.data.getInfo(asset_id)
if asset_info is not None:
    print(f"Asset '{asset_id}' exists.")
else:
    print(f"Asset '{asset_id}' does not exist or is not accessible.")

# Resample difference raster
band_VV = difference_smoothed.select('VV')
proj = band_VV.projection().getInfo()
crs = proj['crs']

# Ensure difference_smoothed is defined before attempting to resample
difference_smoothed_resampled = (difference_smoothed
                                  .resample('bilinear')
                                  .reproject({
                                      'crs': crs,
                                      'scale': 10.0
                                  })
                                  .clip(AOI))


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# Resample difference raster
band_VV = difference_smoothed.select('VV')
projection_info = band_VV.projection()
crs = projection_info.crs()

# Simplified resampling and reprojection
resampled_image = difference_smoothed.resample('bilinear').reproject(crs=crs)

# Update mask separately
resampled_image_with_mask = resampled_image.updateMask(AOI)

# Display the resampled image to check if it loads successfully
print(resampled_image_with_mask.getInfo())