From 98881ef6144db531fbc01f5eb842db5b882a1759 Mon Sep 17 00:00:00 2001
From: PennyHow <pho@geus.dk>
Date: Wed, 29 May 2024 16:48:43 -0100
Subject: [PATCH] sentinelhub module removed

---
 .../sentinelhub_addon/detectIceMargin.py      | 194 -----------
 src/griml/sentinelhub_addon/loadURL.py        | 138 --------
 .../sentinelhub_addon/retrieveSentinelHub.py  |  67 ----
 .../sentinelhub_addon/sentinelhub_test.py     | 311 ------------------
 4 files changed, 710 deletions(-)
 delete mode 100644 src/griml/sentinelhub_addon/detectIceMargin.py
 delete mode 100755 src/griml/sentinelhub_addon/loadURL.py
 delete mode 100644 src/griml/sentinelhub_addon/retrieveSentinelHub.py
 delete mode 100755 src/griml/sentinelhub_addon/sentinelhub_test.py

diff --git a/src/griml/sentinelhub_addon/detectIceMargin.py b/src/griml/sentinelhub_addon/detectIceMargin.py
deleted file mode 100644
index 0647f48..0000000
--- a/src/griml/sentinelhub_addon/detectIceMargin.py
+++ /dev/null
@@ -1,194 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-"""
-Created on Thu Sep 16 15:35:05 2021
-
-@author: pho
-"""
-
-from sentinelhub import SHConfig
-import datetime
-import matplotlib.pyplot as plt
-import rasterio as rio
-from rasterio.features import shapes
-from sentinelhub import MimeType, CRS, BBox, SentinelHubRequest, BBoxSplitter,\
-    DataCollection, bbox_to_dimensions, read_data
-import geopandas as gp
-from shapely.geometry import shape
-
-
-#------------------------   Input parameters   --------------------------------
-
-#Input parameters
-# INPUT_FILE = './icemargin_mask.json'                                          #Input mask
-INPUT_FILE = './test_mask_json_pstereo.json'                                    #Input mask
-OUTPUT_FILE = './icemargin_2020_mask_nomosaic.shp'
-epsg = 3413                                                                    #EPSG of mask (and output)
-date = (datetime.datetime(2020,7,1), datetime.datetime(2020,9,1))              #Start/end dates
-resolution = 10                                                                #Output spatial resolution
-bbox_col = 20                                                                  #Bbox width splitter 
-bbox_row = 50                                                                  #Bbox length splitter
-
-#---------------------   Scripts for classification   -------------------------
-
-#SentinelHub script for classifying snow/ice 
-evalscript = """
-//VERSION=3
-function setup() {
-  return {
-    input: {bands: ["B03","B11","CLM","dataMask"]},
-    output: { bands: 1, 
-             nodataValue: 0}
-  }
-}
-
-function spectralIndices(cloud, mask, green, SWIR) {     
-    if (cloud == 0) {                                 // Exclude cloudy pixels
-        if (mask == 1) {                              // Exclude nodata pixels
-            if (index(green, SWIR) >= 0.41) {         // NDSI threshold    
-                return [1]
-            }}}}
-
-function evaluatePixel(sample) {   
-    var ndsi = spectralIndices(sample.CLM, sample.dataMask, sample.B03, sample.B11)
-    if (ndsi == 1) {
-        return[1]
-        }}             
-"""
-
-
-#Vectorisation function
-def getGeodata(img, trans):
-    '''Get vector features from binary raster image. Raster data is loaded 
-    from array as a rasterio inmemory object, and returns features as a 
-    geopandas dataframe
-
-    Variables
-    img (arr)                           Binary raster array
-    trans (Affine):                     Raster transformation (computed using
-                                        affine package, or 
-                                        rasterio.transform) 
-    Returns
-    feats (Geodataframe):               Vector features geodataframe
-    '''
-    #Open array as rasterio memory object
-    with rio.io.MemoryFile() as memfile:
-        with memfile.open(
-            driver='GTiff',
-            height=img.shape[0],
-            width=img.shape[1],
-            count=1,
-            dtype=img.dtype,
-            transform=trans
-        ) as dataset:
-            dataset.write(img, 1)
-        
-        #Vectorize array
-        with memfile.open() as dataset:
-                image = dataset.read(1)
-                mask = image==255
-                results = (
-                {'properties': {'raster_val': v}, 'geometry': s}
-                for i, (s, v) 
-                in enumerate(
-                    shapes(image, 
-                           mask=mask, 
-                           transform=rio.transform.from_origin(transf[0], 
-                                                               transf[3], 
-                                                               transf[1], 
-                                                               transf[5]))))
-                
-                ##Transform geometries to geodataframe
-                geoms = list(results)
-                feats = gp.GeoDataFrame.from_features(geoms)
-                
-    return feats
-
-#--------------------   Configure SentinelHub client   ------------------------
-
-#Configure Sentinelhub connection client    
-config = SHConfig()
-# config.instance_id = 'e4ac5ac3-ca5e-4624-ab3a-8214df144373'                  #Instance ID needed
-# config.sh_client_id = '23032a7f-66c1-447e-99ed-44c10d13072f'                 #Client ID needed
-# config.sh_client_secret = 'WZ|ufGd;yp|YW];2<qO;,]Yr>[aZ!Oh7uvr~r<g*'         #Client password needed
-# config.save()                                                                #Save client login so not needed again
-
-#-------------------------   SentinelHub request   ----------------------------
-
-#Load mask and split into processing chunks
-geo_json = read_data(INPUT_FILE)
-gl_area = shape(geo_json["features"][0]["geometry"])
-bbox_splitter = BBoxSplitter([gl_area], CRS(epsg), (bbox_col, bbox_row), reduce_bbox_sizes=True)  
-geometry_list = bbox_splitter.get_geometry_list()
-print('Area bounding box: {}'.format(bbox_splitter.get_area_bbox().__repr__()))
-print(f'Number of bboxes: {len(geometry_list)}')
-
-#Create empty geodataframe for outputs to be appended to
-ice = gp.GeoDataFrame(crs='EPSG:'+str(epsg))
-
-#Iterate through bbox list
-count=1
-for g in geometry_list[100:110]:
-    
-    #Construct bbox
-    bbox_obj = BBox(bbox=g, crs=CRS(epsg))
-    bbox_obj = bbox_obj.buffer(1)
-    bbox_size = bbox_to_dimensions(bbox_obj, resolution=resolution)
-    print(f'\nBbox {count} at {resolution} m resolution, {bbox_size} pixels') 
-    
-    #Fetch transform 
-    transf = bbox_obj.get_transform_vector(resx=resolution, resy=resolution)
-                
-    #Construct SentinelHub request   
-    request = SentinelHubRequest(evalscript=evalscript,                                
-                                input_data=[
-                                SentinelHubRequest.input_data(
-                                data_collection=DataCollection.SENTINEL2_L1C,
-                                time_interval=date,
-                                maxcc=0.1                                      #Max cloud cover (0-1)
-                                # mosaicking_order = 'ORBIT',                  #leastCC/mostRecent/leastRecent/Orbit
-                                # other_args={'orbit_direction': 'ascending'}  #Not sure if this works so check it!
-                                ),
-                                SentinelHubRequest.input_data(
-                                data_collection=DataCollection.DEM_COPERNICUS_30,
-                                upsampling='BILINEAR',
-                                # time_interval=date
-                                )],
-                                responses=[
-                                SentinelHubRequest.output_response('default', MimeType.TIFF)],
-                                bbox=bbox_obj,
-                                size=bbox_size,
-                                config=config)
-
-    #Perform request 
-    img = request.get_data()
-    print(f'Single element returned of type {type(img[-1])} ({img[0].dtype})')
-
-    # plt.imshow(img[0])
-    # plt.show()
-        
-    #Vectorise array
-    features = getGeodata(img[0], rio.transform.from_origin(transf[0], transf[3], transf[1],transf[5]))
-    
-    #Check if no features detected
-    if features.empty:
-        print('No features detected. Moving to next request.')
-
-    #Append to final geodatabase
-    else:
-        ice = ice.append(features)
-    count=count+1
-
-
-#Dissolve all detected features
-print('\nDissolving features...')
-ice['dissolve']=1    
-ice = ice.dissolve(by='dissolve')
-ice['area'] = ice['geometry'].area/10**6
-
-#Write to file
-print(f'Saving to {OUTPUT_FILE}')
-ice.to_file(OUTPUT_FILE)
-
-#------------------------------------------------------------------------------
-print('Finished')
\ No newline at end of file
diff --git a/src/griml/sentinelhub_addon/loadURL.py b/src/griml/sentinelhub_addon/loadURL.py
deleted file mode 100755
index 214c31f..0000000
--- a/src/griml/sentinelhub_addon/loadURL.py
+++ /dev/null
@@ -1,138 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-"""
-Created on Tue Sep 14 10:15:32 2021
-
-@author: pho
-"""
-
-from sentinelsat import SentinelAPI, read_geojson, geojson_to_wkt
-from urllib.request import urlopen, HTTPError, URLError
-from collections import OrderedDict
-import matplotlib.pyplot as plt
-# import xarray as xr
-import os, sys
-
-
-from io import BytesIO
-from zipfile import ZipFile
-from urllib.request import urlopen
-import requests
-# or: requests.get(url).content
-        
-
-weblink1 = 'https://storage.googleapis.com/gcp-public-data-sentinel-2/tiles/22/W/ES/S2A_MSIL1C_20210801T150911_N0301_R025_T22WES_20210801T171130.SAFE/GRANULE/L1C_T22WES_A031911_20210801T150914/IMG_DATA/T22WES_20210801T150911_B02.jp2'
-weblink2 = 'https://storage.googleapis.com/gcp-public-data-sentinel-2/tiles/22/W/ES/S2A_MSIL1C_20210801T150911_N0301_R025_T22WES_20210801T171130.SAFE/GRANULE/L1C_T22WES_A031911_20210801T150914/IMG_DATA/T22WES_20210801T150911_B04.jp2'
-
-
-# def openURL(weblink):
-# with rasterio.open(weblink1) as dataset1:
-#     rplot.show(dataset1)
-#     band1 = dataset1.read(1)
-#     bounds1 = dataset1.bounds
-#     transform1 = dataset1.transform
-#     crs1 = dataset1.crs
-#     idx = dataset1.res
-#     # crs=dataset.read_crs()
-#     print(sys.getsizeof(dataset1))
-#     dataset1.close()
-
-# with xr.open_rasterio(weblink2, chunks={'band':1, 'x':1000, 'y':1000}) as dataset1:
-#     # rplot.show(dataset1)
-#     # band1 = dataset1.read(1)
-#     transform1 = dataset1.transform
-#     crs1 = dataset1.crs
-#     # crs=dataset.read_crs()
-#     print(sys.getsizeof(dataset1))
-#     dataset1.close()    
-
-  
-# membytes = os.sysconf('SC_PAGE_SIZE') * os.sysconf('SC_PHYS_PAGES')
-# mem_gib = membytes/(1024. **3)
-# print(membytes)
-
-# dataset1 = dataset1.squeeze()
-
-# im = dataset1.compute().plot.imshow()
-# plt.show()
-
-
-from io import BytesIO
-from zipfile import ZipFile
-
-
-def get_zip(file_url):
-    url = requests.get(file_url)
-    zipfile = ZipFile(BytesIO(url.content))
-    files = [zipfile.open(file_name) for file_name in zipfile.namelist()]
-    return files.pop() if en(files) == 1 else files
-
-
-
-# resp = urlopen("http://www.test.com/file.zip")
-# zipfile = ZipFile(BytesIO(resp.read()))
-# files = zipfile.namelist()
-# file = files[0]
-# for line in zipfile.open(file).readlines():
-#     print(line.decode('utf-8'))
-    
-    
-
-#Log on to SciHub
-def getSentinelLinks(user, password, tilenames, start, end, cloud, product):
-    print('Logging onto SciHub...')
-    api = SentinelAPI(user, password, 'https://scihub.copernicus.eu/dhus')
-    print('Login successful\n\n')
-    
-    # #Gather products to download from aoi
-    # if tiles is None:
-        
-    #     #Load geojson file
-    #     print('Loading AOI from geojson file: ' + str(json))
-    #     footprint = geojson_to_wkt(read_geojson(json))
-    #     print('AOI loaded\n\n')
-        
-    #     print('Retrieving Sentinel products from geojson file ...')
-    #     if cloud is not None:
-    #         products = api.query(footprint, date=(start, end), platformname="Sentinel-2",
-    #                              producttype= product, cloudcoverpercentage = cloud)
-    #     else:
-    #         print('Cloud cover percentage not specified')
-    #         products = api.query(footprint, date=(start, end), platformname="Sentinel-2",
-    #                              producttype= product)
-    
-    # #Gather products to download from tiles
-    # elif json is None:
-    print('Retrieving Sentinel products from tile identifiers: ' + str(tilenames)) 
-    products = OrderedDict()
-    for t in tilenames:
-        if cloud is not None:
-            p = api.query(tileid=t, date=(start, end), platformname="Sentinel-2",
-                          producttype= product, cloudcoverpercentage = cloud)
-        else:
-            print('Cloud cover percentage not specified')
-            p = api.query(tileid=t, date=(start, end), platformname="Sentinel-2",
-                          producttype= product)
-        products.update(p)
-            
-    
-    #Exit program if no products retrieved
-    if len((list(products.keys())))==0:
-        sys.exit('No products retrieved. Check input parameters')
-    
-    #Compile keys and file titles
-    keys = list(products.keys())
-    titles=[]
-    idx = list(products.values())
-    for i in idx:
-        titles.append(i['title'])
-    print(str(len(titles)) + ' products retrieved')
-    
-    return keys, idx 
-
-# k,i = getSentinelLinks('penelopehow', 'CoreyFeldman1', ['22WES', '22WET'], '20210801', '20210901', [0,0.5], 'S2MSI1C')
-# print(k[0])
-# print(i[0])
-
-# i1 = i[0]
-# weblink = i1['link_alternative']
diff --git a/src/griml/sentinelhub_addon/retrieveSentinelHub.py b/src/griml/sentinelhub_addon/retrieveSentinelHub.py
deleted file mode 100644
index a7e6cf1..0000000
--- a/src/griml/sentinelhub_addon/retrieveSentinelHub.py
+++ /dev/null
@@ -1,67 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-"""
-Created on Wed Jan  5 12:55:07 2022
-
-@author: pho
-"""
-
-import rasterio as rio
-import geopandas as gp
-from rasterio.features import shapes
-
-
-def fromSHRequest():
-    pass
-
-
-def fromSHBatch():
-    pass
-
-
-#Vectorisation function
-def getGeodata(img, trans):
-    '''Get vector features from binary raster image. Raster data is loaded 
-    from array as a rasterio inmemory object, and returns features as a 
-    geopandas dataframe.
-
-    Variables
-    img (arr)                           Binary raster array
-    trans (Affine):                     Raster transformation (computed using
-                                        affine package, or 
-                                        rasterio.transform) 
-    Returns
-    feats (Geodataframe):               Vector features geodataframe
-    '''
-    #Open array as rasterio memory object
-    with rio.io.MemoryFile() as memfile:
-        with memfile.open(
-            driver='GTiff',
-            height=img.shape[0],
-            width=img.shape[1],
-            count=1,
-            dtype=img.dtype,
-            transform=trans
-        ) as dataset:
-            dataset.write(img, 1)
-        
-        #Vectorize array
-        with memfile.open() as dataset:
-                image = dataset.read(1)
-                mask = image==255
-                results = (
-                {'properties': {'raster_val': v}, 'geometry': s}
-                for i, (s, v) 
-                in enumerate(
-                    shapes(image, 
-                           mask=mask, 
-                           transform=rio.transform.from_origin(trans[0], 
-                                                               trans[3], 
-                                                               trans[1], 
-                                                               trans[5]))))
-                
-                ##Transform geometries to geodataframe
-                geoms = list(results)
-                feats = gp.GeoDataFrame.from_features(geoms)
-                
-    return feats
\ No newline at end of file
diff --git a/src/griml/sentinelhub_addon/sentinelhub_test.py b/src/griml/sentinelhub_addon/sentinelhub_test.py
deleted file mode 100755
index d77beb7..0000000
--- a/src/griml/sentinelhub_addon/sentinelhub_test.py
+++ /dev/null
@@ -1,311 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-"""
-Created on Thu Sep 16 15:35:05 2021
-
-@author: pho
-"""
-
-
-
-import datetime
-import matplotlib.pyplot as plt
-import rasterio as rio
-from rasterio.features import shapes
-import geopandas as gp
-from shapely.geometry import shape
-from sentinelhub import SHConfig, MimeType, CRS, BBox, SentinelHubRequest, \
-    DataCollection, bbox_to_dimensions, read_data, BBoxSplitter
-    
-import xarray as xr
-from sentinelhub import OsmSplitter, TileSplitter, CustomGridSplitter, UtmZoneSplitter, UtmGridSplitter
-import matplotlib.pyplot as plt
-
-#------------------------   Input parameters   --------------------------------
-
-#Input parameters
-INPUT_FILE = './test_mask_json_pstereo.json'                                   #Input mask
-OUTPUT_FILE = './IMLs.shp'
-epsg = 3413                                                                    #EPSG of mask (and output)
-
-date = (datetime.datetime(2020,7,1), datetime.datetime(2020,7,10))             #Start/end dates
-maxcloud = 0.4                                                                 #Maximum cloud cover (0-1)
-# n_chunks = 2
-
-resolution = 10                                                                #Output spatial resolution
-
-bbox_col = 20                                                                  #Bbox width splitter 
-bbox_row = 50                                                                  #Bbox length splitter
-
-#---------------------------   Functions   ------------------------------------
-
-evalscript = """
-//VERSION=3
-function setup() {
-  return {
-    input: [
-        {bands: ["B02", "B03", "B04", "B08", "B11", "B12", "CLM", "dataMask"]},
-        {bands: ["VH", "VV", "dataMask"]}  //"shadowMask"]} 
-        ],
-    output: { bands: 1, 
-//             sampleType: "UNIT8",
-             nodataValue: 0}
-  }
-}
-
-function classMultiSpectral(cloud, nodata, blue, green, red, nir, swir11, swir12) {
-    
-    // Exclude cloudy pixels
-    if (cloud== 0) {
-            
-    // Exclude nodata pixels
-    if (nodata == 1) { 
-            
-    // NDWI threshold    
-    if (index(green, nir) >= 0.3) {
-                    
-    // MNDWI threshold
-    if (index(green, swir11 <= 0.1)) {
-                           
-    // AWEIsh threshold
-    if (50 <= (blue + 2.5 * green - 
-               1.5 * (nir + swir11) - 
-               0.25 * swir12) <= 200) {
-                                                  
-    // AWEInsh threshold
-    if (50 <= (4 * (green - swir11) - 
-               (0.25 * nir + 2.75 * swir12)) <= 254) {
-                                                                                    
-    // Brightness threshold
-    if (((red + green + blue)/3) <= 70) {
-                                            
-        return [1]
-}}}}}}}}
-
-                   
-function classBackScatter(sigmaHH, sigmaHV) {
-    // Convert sigma0 to Decibels
-    let vh_Db = toDb(sigmaVH)
-    let vv_Db = toDb(sigmaVV)
-  
-    // Calculate NRPB (Filgueiras et al. (2019), eq. 4)
-    let NRPB = (vh_Db - vv_Db) / (vh_Db + vv_Db)
-  
-    // Calculate NDVI_nc with approach A3 (Filgueiras et al. (2019), eq. 14)
-    let NDVInc = 2.572 - 0.05047 * vh_Db + 0.176 * vv_Db + 3.422 * NRPB
-    return NDVInc
-}
- 
-                    
-function evaluatePixel(sample) {
-    optical = sample["0"][0]
-    var opt = classMultiSpectral(optical.CLM, optical.dataMask, 
-                                 optical.B02, optical.B03, optical.B04, 
-                                 optical.B08, optical.B11, optical.B12) 
-    return [opt]
-}
-"""
-
-#Vectorisation function
-def getGeodata(img, trans):
-    '''Get vector features from binary raster image. Raster data is loaded 
-    from array as a rasterio inmemory object, and returns features as a 
-    geopandas dataframe.
-
-    Variables
-    img (arr)                           Binary raster array
-    trans (Affine):                     Raster transformation (computed using
-                                        affine package, or 
-                                        rasterio.transform) 
-    Returns
-    feats (Geodataframe):               Vector features geodataframe
-    '''
-    #Open array as rasterio memory object
-    with rio.io.MemoryFile() as memfile:
-        with memfile.open(
-            driver='GTiff',
-            height=img.shape[0],
-            width=img.shape[1],
-            count=1,
-            dtype=img.dtype,
-            transform=trans
-        ) as dataset:
-            dataset.write(img, 1)
-        
-        #Vectorize array
-        with memfile.open() as dataset:
-                image = dataset.read(1)
-                mask = image==255
-                results = (
-                {'properties': {'raster_val': v}, 'geometry': s}
-                for i, (s, v) 
-                in enumerate(
-                    shapes(image, 
-                           mask=mask, 
-                           transform=rio.transform.from_origin(transf[0], 
-                                                               transf[3], 
-                                                               transf[1], 
-                                                               transf[5]))))
-                
-                ##Transform geometries to geodataframe
-                geoms = list(results)
-                feats = gp.GeoDataFrame.from_features(geoms)
-                
-    return feats
-
-#--------------------   Configure SentinelHub client   ------------------------
-
-#Configure Sentinelhub connection client    
-config = SHConfig()
-# config.instance_id = 'e4ac5ac3-ca5e-4624-ab3a-8214df144373'                  #Instance ID needed
-# config.sh_client_id = '23032a7f-66c1-447e-99ed-44c10d13072f'                 #Client ID needed
-# config.sh_client_secret = 'WZ|ufGd;yp|YW];2<qO;,]Yr>[aZ!Oh7uvr~r<g*'         #Client password needed
-# config.save()                                                                #Save client login so not needed again
-
-#-----------------------   Sentinelhub request   ------------------------------
-
-#Load mask and split into processing chunks
-geo_json = read_data(INPUT_FILE)
-
-
-#Split bbox into processing pieces
-gl_area = shape(geo_json["features"][0]["geometry"])
-bbox_splitter = BBoxSplitter([gl_area], CRS(epsg), 
-                             (bbox_col, bbox_row), 
-                             reduce_bbox_sizes=True)  
-geometry_list = bbox_splitter.get_geometry_list()
-# bbox_list = bbox_splitter.get_bbox_list()
-# info_list = bbox_splitter.get_info_list()
-print('Area bounding box: {}'.format(bbox_splitter.get_area_bbox().__repr__()))
-print(f'Number of bboxes: {len(geometry_list)}')
-
-
-##Split datetime range into equal chunks
-# tdelta = (end - start) / n_chunks
-# edges = [(start + i*tdelta).date().isoformat() for i in range(n_chunks)]
-# slots = [(edges[i], edges[i+1]) for i in range(len(edges)-1)]
-# print('Monthly time windows:')
-# for slot in slots:
-#     print(slot)
-
-
-#Create empty geodataframe for outputs to be appended to
-lakes = gp.GeoDataFrame(crs='EPSG:'+str(epsg))
-
-
-#Iterate through bbox list
-count=1
-for g in geometry_list[100:110]:
-
-    #Construct bbox
-    bbox_obj = BBox(bbox=g, crs=CRS(epsg))
-    bbox_obj = bbox_obj.buffer(1)
-    bbox_size = bbox_to_dimensions(bbox_obj, resolution=resolution)
-    print(f'\nBbox {count} at {resolution} m resolution, {bbox_size} pixels') 
-    
-    #Fetch transform 
-    transf = bbox_obj.get_transform_vector(resx=resolution, resy=resolution)
-
-    # bb_utm = geo_utils.to_utm_bbox(bbox_obj)                                   #Convert bounding box to UTM coordinates 
-    # transf = bb_utm.get_transform_vector(resx=resolution, resy=resolution)     #Fetch transform 
-    # pix_lat = np.array(np.arange(0, bbox_size[1]))                             #Generate latitude grid
-    # lats = np.array([pix_lat] * bbox_size[0]).transpose()
-    # pix_lon = np.array(np.arange(0, bbox_size[0]))                             #Generate longitude grid
-    # lons = np.array([pix_lon] * bbox_size[1])
-    # lon, lat = geo_utils.pixel_to_utm(lats, lons, transf)                      #Convert px pos to UTM
-    # lon_degrees, lat_degrees = geo_utils.to_wgs84(lon, lat, bb_utm.crs)        #Convert UTM to WGS84
-    # da=[]
-
-
-                
-    #Construct SentinelHub request  
-    # for s in slots:
-    request = SentinelHubRequest(evalscript=evalscript,                                
-                                input_data=[
-                                SentinelHubRequest.input_data(
-                                data_collection=DataCollection.SENTINEL2_L1C,
-                                time_interval=date,
-                                maxcc=maxcloud                                 
-                                #mosaicking_order = 'leastCC'                  #leastCC/mostRecent/leastRecent
-                                ),
-                                SentinelHubRequest.input_data(
-                                data_collection=DataCollection.SENTINEL1_IW_DES,
-                                time_interval=date,
-                                #mosaicking_order = 'leastCC'                  #leastCC/mostRecent/leastRecent
-                                other_args={'type': 'sentinel-1-grd', 
-                                            'orthorectify': True,
-                                            'demInstance': 'COPERNICUS',       #MAPZEN/COPERNICUS/COPERNICUS_30/COPERNICUS_90
-                                            'backCoeff': 'GAMMA0_TERRAIN',     #BETA0/SIGMA0_ELLIPSOID/GAMMA0_ELLIPSOID/GAMMA0_TERRAIN
-                                            'speckleFilter': {'type': 'LEE',
-                                            'windowSizeX': 5, 'windowSizeY': 5}}
-                                )],                                
-                                # SentinelHubRequest.input_data(
-                                # data_collection=DataCollection.DEM_COPERNICUS_30,
-                                # upsampling='BILINEAR',
-                                # # time_interval=date
-                                # )],
-                                responses=[
-                                SentinelHubRequest.output_response('default', MimeType.TIFF)],
-                                bbox=bbox_obj,
-                                size=bbox_size,
-                                config=config)
-
-    #Perform request 
-    img = request.get_data()
-    # plt.imshow(img[0])
-    # plt.show()
-    print(f'Single element returned of type {type(img[-1])} ({img[0].dtype})')
-
-    # #Construct array
-    # xout = xr.DataArray(
-    #     data=img[0].reshape(img[0].shape[0], img[0].shape[1]),
-    #     dims=["x", "y"],
-    #     coords=dict(
-    #     lon=(["x", "y"], lon),
-    #     lat=(["x", "y"], lat),
-    #     time=s[1],
-    #     reference_time=s[0],
-    #     ),
-    #     attrs=dict(
-    #     description="Binary",
-    #     units="0=no water; 1=water"
-    #     ),
-    #     ) 
-    # da.append(xout)                                                          #Need np.dstack but for xarray
-        
-    #Vectorise array
-    features = getGeodata(img[0], rio.transform.from_origin(transf[0], transf[3], transf[1],transf[5]))
-    
-    #Check if no features detected
-    if features.empty:
-        print('No features detected. Moving to next request.')
-
-    #Append to final geodatabase
-    else:
-        
-        #Filter features by area
-        print(f'{len(features.index)} features detected. Filtering by area...')
-        features['area'] = features['geometry'].area/10**6
-        # features.drop(features['raster_val']) 
-        features.drop(features.index[features['area'] < 0.05], inplace=True) 
-        
-        #Check features
-        if features.empty:
-            print('No features left after filtering. Moving to next request.')
-        
-        #Populate metadata and append to final geodatabase
-        else:
-            print(f'{len(features.index)} features remaining')
-            #Populate metadata
-            features['startdate'] = date[0].strftime('%Y%m%d')
-            features['enddate'] = date[1].strftime('%Y%m%d')
-            lakes = lakes.append(features)
-    count=count+1
-
- 
-#Write to file
-print(f'\nSaving to {OUTPUT_FILE}')
-lakes.to_file(OUTPUT_FILE)
-
-#------------------------------------------------------------------------------
-print('Finished)')
\ No newline at end of file