import streamlit as st
import folium
import geemap.foliumap as geemap
import ee
import os
import pandas as pd
from folium import IFrame
import json
from folium.plugins import Draw
import datetime
import plotly.express as px
import numpy as np
from google.auth.transport.requests import Request
import google.auth.exceptions
import geopy
from geopy.geocoders import Nominatim

# Initialize Google Earth Engine
def initialize_gee():
    service = os.getenv('SA')
    file = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'gee', 'ee-muzzamil1-37ebc3dece52.json')
    credentials = ee.ServiceAccountCredentials(service, file)
    try:
        ee.Initialize(credentials)
        st.success("Google Earth Engine initialized successfully.")
    except google.auth.exceptions.RefreshError:
        try:
            request = Request()
            credentials.refresh(request)
            ee.Initialize(credentials)
            st.success("Google Earth Engine token refreshed and initialized successfully.")
        except Exception as e:
            st.error(f"Error refreshing Google Earth Engine token: {e}")
    except Exception as e:
        st.error(f"Error initializing Google Earth Engine: {e}")
# Load dataset information from CSV
def data_gee():
    file = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'gee','data_gee', 'gee_catalog.csv')
    data = pd.read_csv(file)
    data = data[~data['title'].str.contains('deprecated', na=False)].reset_index(drop=True)
    return data[['id', 'title']], data

# Display selected dataset
def type_map(Map, cod):
    if cod == 'AAFC/ACI':
        dataset = ee.ImageCollection('AAFC/ACI')
        crop2016 = dataset.filter(ee.Filter.date('2016-01-01', '2016-12-31')).first()
        Map.set_center(-103.8881, 53.0371, 10)
        

    elif cod == 'ACA/reef_habitat/v2_0':
        dataset = ee.Image('ACA/reef_habitat/v2_0')
        reefExtent = dataset.select('reef_mask').selfMask()
        geomorphicZonation = dataset.select('geomorphic').selfMask()
        benthicHabitat = dataset.select('benthic').selfMask()
        Map.set_center(-149.56194, -17.00872, 13)
        Map.set_options('SATELLITE')
        Map.add_ee_layer(reefExtent, {}, 'Global reef extent')
        Map.add_ee_layer(geomorphicZonation, {}, 'Geomorphic zonation')
        Map.add_ee_layer(benthicHabitat, {}, 'Benthic habitat')
        return "ACA/reef_habitat/v2_0 dataset loaded", None

    elif cod == 'AHN/AHN2_05M_INT':
        dataset = ee.Image('AHN/AHN2_05M_INT')
        elevation = dataset.select('elevation')
        elevationVis = {'min': -5.0,'max': 30.0}
        Map.setCenter(5.76583, 51.855276, 16)
        Map.addLayer(elevation, elevationVis, 'Elevation')

    elif cod == 'AHN/AHN2_05M_NON':
        dataset = ee.Image('AHN/AHN2_05M_NON')
        elevation = dataset.select('elevation')
        elevationVis = {'min': -5.0, 'max': 30.0}
        Map.setCenter(5.80258, 51.78547, 14)
        Map.addLayer(elevation, elevationVis, 'Elevation')

    elif cod == 'AHN/AHN2_05M_RUW':
        dataset = ee.Image('AHN/AHN2_05M_RUW')
        elevation = dataset.select('elevation')
        elevationVis = {'min': -5.0, 'max': 30.0}
        Map.setCenter(5.76583, 51.855276, 16)
        Map.addLayer(elevation, elevationVis, 'Elevation')

    elif cod == 'ASTER/AST_L1T_003':
        dataset = ee.ImageCollection('ASTER/AST_L1T_003').filter(ee.Filter.date('2018-01-01', '2018-08-15'))
        falseColor = dataset.select(['B3N', 'B02', 'B01'])
        falseColorVis = {'min': 0.0, 'max': 255.0}
        Map.setCenter(-122.0272, 39.6734, 11)
        Map.addLayer(falseColor.median(), falseColorVis, 'False Color')

    elif cod == 'AU/GA/AUSTRALIA_5M_DEM':
        dataset = ee.ImageCollection('AU/GA/AUSTRALIA_5M_DEM')
        elevation = dataset.select('elevation')
        elevationVis = {'min': 0.0, 'max': 150.0, 'palette': ['0000ff', '00ffff', 'ffff00', 'ff0000', 'ffffff']}
        Map.setCenter(140.1883, -35.9113, 8)
        Map.addLayer(elevation, elevationVis, 'Elevation')

    elif cod == 'NOAA/VIIRS/DNB/MONTHLY_V1/VCMCFG':
        dataset = ee.ImageCollection('NOAA/VIIRS/DNB/MONTHLY_V1/VCMCFG').filterDate('2022-01-01', '2023-12-31')
        nighttime = dataset.select('avg_rad')
        nighttimeVis = {"min": 0.0, "max": 60.0}
        Map.setCenter(67.1056, 24.8904, 8)
        Map.addLayer(nighttime, nighttimeVis, 'Nighttime')
    elif cod == 'ACA/reef_habitat/v2_0':
        dataset = ee.Image('ACA/reef_habitat/v2_0')
        reefExtent = dataset.select('reef_mask').selfMask()
        geomorphicZonation = dataset.select('geomorphic').selfMask()
        benthicHabitat = dataset.select('benthic').selfMask()
        Map.setCenter(-149.56194, -17.00872, 13);
        Map.setOptions('SATELLITE')
        Map.addLayer(reefExtent, {}, 'Global reef extent')
        Map.addLayer(geomorphicZonation, {}, 'Geomorphic zonation')
        Map.addLayer(benthicHabitat, {}, 'Benthic habitat')
        
    elif cod == 'AHN/AHN2_05M_INT':
        dataset = ee.Image('AHN/AHN2_05M_INT')
        elevation = dataset.select('elevation')
        elevationVis = {'min': -5.0,'max': 30.0}
        Map.setCenter(5.76583, 51.855276, 16)
        Map.addLayer(elevation, elevationVis, 'Elevation')
        
    elif cod == 'AHN/AHN2_05M_NON':
        dataset = ee.Image('AHN/AHN2_05M_NON')
        elevation = dataset.select('elevation')
        elevationVis = {
          'min': -5.0,
          'max': 30.0
        }
        Map.setCenter(5.80258, 51.78547, 14)
        Map.addLayer(elevation, elevationVis, 'Elevation')
        
    elif cod == 'AHN/AHN2_05M_RUW':
        dataset = ee.Image('AHN/AHN2_05M_RUW')
        elevation = dataset.select('elevation')
        elevationVis = {
          'min': -5.0,
          'max': 30.0
        }
        Map.setCenter(5.76583, 51.855276, 16)
        Map.addLayer(elevation, elevationVis, 'Elevation')
        
    elif cod == 'ASTER/AST_L1T_003':
        dataset = ee.ImageCollection('ASTER/AST_L1T_003').filter(ee.Filter.date('2018-01-01', '2018-08-15'))
        falseColor = dataset.select(['B3N', 'B02', 'B01'])
        falseColorVis = {
          'min': 0.0,
          'max': 255.0,
        }
        Map.setCenter(-122.0272, 39.6734, 11)
        Map.addLayer(falseColor.median(), falseColorVis, 'False Color')
        
    elif cod == 'AU/GA/AUSTRALIA_5M_DEM':
        dataset = ee.ImageCollection('AU/GA/AUSTRALIA_5M_DEM')
        elevation = dataset.select('elevation')
        elevationVis = {
          'min': 0.0,
          'max': 150.0,
          'palette': ['0000ff', '00ffff', 'ffff00', 'ff0000', 'ffffff']
        }
        Map.setCenter(140.1883, -35.9113, 8)
        Map.addLayer(elevation, elevationVis, 'Elevation')
        
    elif cod == 'AU/GA/DEM_1SEC/v10/DEM-H':
        dataset = ee.Image('AU/GA/DEM_1SEC/v10/DEM-H')
        elevation = dataset.select('elevation')
        elevationVis = {
          'min': -10.0,
          'max': 1300.0,
          'palette': [
            '3ae237', 'b5e22e', 'd6e21f', 'fff705', 'ffd611', 'ffb613', 'ff8b13',
            'ff6e08', 'ff500d', 'ff0000', 'de0101', 'c21301', '0602ff', '235cb1',
            '307ef3', '269db1', '30c8e2', '32d3ef', '3be285', '3ff38f', '86e26f'
          ],
        }
        Map.setCenter(133.95, -24.69, 5)
        Map.addLayer(elevation, elevationVis, 'Elevation')
        
    elif cod == 'AU/GA/DEM_1SEC/v10/DEM-S':
        dataset = ee.Image('AU/GA/DEM_1SEC/v10/DEM-S')
        elevation = dataset.select('elevation')
        elevationVis = {
          'min': -10.0,
          'max': 1300.0,
          'palette': [
            '3ae237', 'b5e22e', 'd6e21f', 'fff705', 'ffd611', 'ffb613', 'ff8b13',
            'ff6e08', 'ff500d', 'ff0000', 'de0101', 'c21301', '0602ff', '235cb1',
            '307ef3', '269db1', '30c8e2', '32d3ef', '3be285', '3ff38f', '86e26f'
          ],
        }
        Map.setCenter(133.95, -24.69, 5)
        Map.addLayer(elevation, elevationVis, 'Elevation')
        
    elif cod == 'BIOPAMA/GlobalOilPalm/v1':
        dataset = ee.ImageCollection('BIOPAMA/GlobalOilPalm/v1')
        opClass = dataset.select('classification')
        mosaic = opClass.mosaic()
        classificationVis = {
          'min': 1,
          'max': 3,
          'palette': ['ff0000','ef00ff', '696969']
        }
        mask = mosaic.neq(3)
        mask = mask.where(mask.eq(0), 0.6)

        Map.addLayer(mosaic.updateMask(mask), classificationVis, 'Oil palm plantation type', True)
        Map.setCenter(-73.628998,7.320244,8)
        
    elif cod == "BLM/AIM/v1/TerrADat/TerrestrialAIM":
        greens = ee.List(["#00441B", "#00682A", "#37A055", "#5DB96B", "#AEDEA7", "#E7F6E2", "#F7FCF5"])
        reds = ee.List(["#67000D", "#9E0D14", "#E32F27", "#F6553D", "#FCA082", "#FEE2D5", "#FFF5F0"])

        def normalize(value, min, max):
            return value.subtract(min).divide(ee.Number(max).subtract(min))

        def setColor(feature, property, min, max, palette):
            value = normalize(feature.getNumber(property), min, max).multiply(palette.size()).min(palette.size().subtract(1)).max(0)
            return feature.set({"style": {"color": palette.get(value.int())}})

        fc = ee.FeatureCollection("BLM/AIM/v1/TerrADat/TerrestrialAIM")
        woodyHeightStyle = lambda f:setColor(f, "WoodyHgt_Avg", 0, 100, greens)
        bareSoilStyle = lambda f: setColor(f, "BareSoilCover_FH", 0, 100, reds)

        treeHeight = fc.filter("WoodyHgt_Avg > 1").map(woodyHeightStyle)
        bareSoil = fc.filter("BareSoilCover_FH > 1").map(bareSoilStyle)
        Map.addLayer(bareSoil.style({"styleProperty": "style", "pointSize": 3}))
        Map.addLayer(treeHeight.style({"styleProperty": "style", "pointSize": 1}))
        Map.setCenter(-110, 40, 6)

    elif cod == "BNU/FGS/CCNL/v1":
        dataset = ee.ImageCollection("BNU/FGS/CCNL/v1").filter(ee.Filter.date("2010-01-01", "2010-12-31"))
        nighttimeLights = dataset.select("b1")
        nighttimeLightsVis = {
            "min": 3.0,
            "max": 60.0,
        }
        Map.setCenter(31.4, 30, 6)
        Map.addLayer(nighttimeLights, nighttimeLightsVis, "Nighttime Lights")

    elif cod == "CAS/IGSNRR/PML/V2_v017":
        dataset = ee.ImageCollection("CAS/IGSNRR/PML/V2_v017")
        visualization = {
            'bands': ["GPP"],
            "min": 0.0,
            "max": 9.0,
            "palette": ["a50026", "d73027", "f46d43", "fdae61", "fee08b", "ffffbf",
                  "d9ef8b", "a6d96a", "66bd63", "1a9850", "006837"]}
        Map.setCenter(0.0, 15.0, 2)
        Map.addLayer(dataset, visualization, "PML_V2 0.1.7 Gross Primary Product (GPP)")

    elif cod == "CGIAR/SRTM90_V4":
        dataset = ee.Image("CGIAR/SRTM90_V4")
        elevation = dataset.select("elevation")
        slope = ee.Terrain.slope(elevation)
        Map.setCenter(-112.8598, 36.2841, 10)
        Map.addLayer(slope, {"min": 0, "max": 60}, "slope")

    elif cod == "CIESIN/GPWv411/GPW_Basic_Demographic_Characteristics":
        dataset = ee.ImageCollection("CIESIN/GPWv411/GPW_Basic_Demographic_Characteristics").first()
        raster = dataset.select("basic_demographic_characteristics")
        raster_vis = {
            "max": 1000.0,
            "palette": ["ffffe7","86a192","509791","307296","2c4484","000066"],
            "min": 0.0
        }
        Map.setCenter(79.1, 19.81, 3)
        Map.addLayer(raster, raster_vis, "basic_demographic_characteristics")

    elif cod == "CIESIN/GPWv411/GPW_Data_Context":
        dataset = ee.Image("CIESIN/GPWv411/GPW_Data_Context")
        raster = dataset.select("data_context")
        raster_vis = {
            "min": 200.0,
            "palette": ["ffffff","099506","f04923","e62440","706984","a5a5a5","ffe152","d4cc11","000000"],
            "max": 207.0
        }
        Map.setCenter(-88.6, 26.4, 1)
        Map.addLayer(raster, raster_vis, "data_context")

    elif cod == "CIESIN/GPWv411/GPW_Land_Area":
        dataset = ee.Image("CIESIN/GPWv411/GPW_Land_Area")
        raster = dataset.select("land_area")
        raster_vis = {
            "min": 0.0,
            "palette": ["ecefb7","745638"],
            "max": 0.86
        }
        Map.setCenter(26.4, 19.81, 1)
        Map.addLayer(raster, raster_vis, "land_area")

    elif cod == "CIESIN/GPWv411/GPW_Mean_Administrative_Unit_Area":
        dataset = ee.Image("CIESIN/GPWv411/GPW_Mean_Administrative_Unit_Area")
        raster = dataset.select("mean_administrative_unit_area")
        raster_vis = {
            "min": 0.0,
            "palette": ["ffffff","747474","656565","3c3c3c","2f2f2f","000000"],
            "max": 40000.0
        }
        Map.setCenter(-88.6, 26.4, 1)
        Map.addLayer(raster, raster_vis, "mean_administrative_unit_area")

    elif cod == "CIESIN/GPWv411/GPW_National_Identifier_Grid":
        dataset = ee.Image("CIESIN/GPWv411/GPW_National_Identifier_Grid")
        raster = dataset.select("national_identifier_grid")
        raster_vis = {
        "min": 4.0,
        "palette": ["000000","ffffff"],
        "max": 999.0
        }
        Map.setCenter(-88.6, 26.4, 1)
        Map.addLayer(raster, raster_vis, "national_identifier_grid")
        
    elif cod == "CIESIN/GPWv411/GPW_Population_Count":
        dataset = ee.ImageCollection("CIESIN/GPWv411/GPW_Population_Count").first()
        raster = dataset.select("population_count")
        raster_vis = {
        "max": 1000.0, 
        "palette": ["ffffe7","86a192","509791","307296","2c4484","000066"],
        "min": 0.0}
        Map.setCenter(79.1, 19.81, 3)
        Map.addLayer(raster, raster_vis, "population_count")
        
    elif cod == "CIESIN/GPWv411/GPW_Population_Density":
        dataset = ee.ImageCollection("CIESIN/GPWv411/GPW_Population_Density").first()
        raster = dataset.select("population_density")
        raster_vis = {
            "max": 1000.0,
            "palette": ["ffffe7","FFc869","ffac1d","e17735","f2552c","9f0c21"],
            "min": 200.0
        }
        Map.setCenter(79.1, 19.81, 3)
        Map.addLayer(raster, raster_vis, "population_density")

    elif cod == "CIESIN/GPWv411/GPW_UNWPP-Adjusted_Population_Count":
        dataset = ee.ImageCollection("CIESIN/GPWv411/GPW_UNWPP-Adjusted_Population_Count").first()
        raster = dataset.select("unwpp-adjusted_population_count")
        raster_vis = {"max": 1000.0, "palette": ["ffffe7","86a192","509791","307296","2c4484","000066"], "min": 0.0}
        Map.setCenter(79.1, 19.81, 3)
        Map.addLayer(raster, raster_vis, "unwpp-adjusted_population_count")

    elif cod == "CIESIN/GPWv411/GPW_UNWPP-Adjusted_Population_Density":
        dataset = ee.ImageCollection("CIESIN/GPWv411/GPW_UNWPP-Adjusted_Population_Density").first()
        raster = dataset.select("unwpp-adjusted_population_density")
        raster_vis = {"max": 1000.0, "palette": ["ffffe7","FFc869","ffac1d","e17735","f2552c","9f0c21"], "min": 0.0}
        Map.setCenter(79.1, 19.81, 3)
        Map.addLayer(raster, raster_vis, "unwpp-adjusted_population_density")

    elif cod == "CIESIN/GPWv411/GPW_Water_Area":
        dataset = ee.Image("CIESIN/GPWv411/GPW_Water_Area")
        raster = dataset.select("water_area")
        raster_vis = {
            "min": 0.0,
            "palette": ["f5f6da","180d02"],
            "max": 0.860558}
        Map.setCenter(79.1, 19.81, 3)
        Map.addLayer(raster, raster_vis, "water_area")

    elif cod == "CIESIN/GPWv411/GPW_Water_Mask":
        dataset = ee.Image("CIESIN/GPWv411/GPW_Water_Mask")
        raster = dataset.select("water_mask")
        raster_vis = {
        "min": 0.0,
        "palette": ["005ce6","00ffc5","bed2ff","aed0f1"],
        "max": 3.0
        }
        Map.setCenter(-88.6, 26.4, 1)
        Map.addLayer(raster, raster_vis, "water_mask")

    elif cod == "COPERNICUS/CORINE/V20/100m/2012":
        dataset = ee.Image("COPERNICUS/CORINE/V20/100m/2012")
        landCover = dataset.select("landcover")
        Map.setCenter(16.436, 39.825, 6)
        Map.addLayer(landCover, {}, "Land Cover")


    elif cod == "COPERNICUS/DEM/GLO30":
        dataset = ee.ImageCollection("COPERNICUS/DEM/GLO30")
        elevation = dataset.select("DEM")
        elevationVis = {
        "min": 0.0,
        "max": 1000.0,
        'palette': ["0000ff","00ffff","ffff00","ff0000","ffffff"],
        }
        Map.setCenter(-73.388672,5.353521, 4)
        Map.addLayer(elevation, elevationVis, "DEM")

    elif cod == "COPERNICUS/Landcover/100m/Proba-V-C3/Global": 
        dataset = ee.Image("COPERNICUS/Landcover/100m/Proba-V-C3/Global/2019").select("discrete_classification")
        Map.setCenter(-88.6, 26.4, 1)
        Map.addLayer(dataset, {}, "Land Cover")

    elif cod == "COPERNICUS/S1_GRD":
        def ff(image):
            edge = image.lt(-30.0)
            maskedImage = image.mask()
            maskedImage = maskedImage.And(edge.Not())
            return image.updateMask(maskedImage)
        
        imgVV = ee.ImageCollection("COPERNICUS/S1_GRD") \
        .filter(ee.Filter.listContains("transmitterReceiverPolarisation", "VV")) \
        .filter(ee.Filter.eq("instrumentMode", "IW")) \
        .select("VV") \
        .map(lambda x:ff(x))
        
        desc = imgVV.filter(ee.Filter.eq("orbitProperties_pass", "DESCENDING"))
        asc = imgVV.filter(ee.Filter.eq("orbitProperties_pass", "ASCENDING"))
    
        spring = ee.Filter.date("2015-03-01", "2015-04-20")
        lateSpring = ee.Filter.date("2015-04-21", "2015-06-10")
        summer = ee.Filter.date("2015-06-11", "2015-08-31")

        descChange = ee.Image.cat(desc.filter(spring).mean(),desc.filter(lateSpring).mean(),desc.filter(summer).mean())
        ascChange = ee.Image.cat(asc.filter(spring).mean(),asc.filter(lateSpring).mean(),asc.filter(summer).mean())
        Map.setCenter(-73.388672,5.353521, 6)
        Map.addLayer(ascChange, {"min": -25, "max": 5}, "Multi-T Mean ASC", True)
        Map.addLayer(descChange, {"min": -25, "max": 5}, "Multi-T Mean DESC", True)

    elif cod == "COPERNICUS/S2":
        def maskS2clouds(image):
            qa = image.select("QA60")
            cloudBitMask = 1 << 10
            cirrusBitMask = 1 << 11
            mask = qa.bitwiseAnd(cloudBitMask).eq(0).And(qa.bitwiseAnd(cirrusBitMask).eq(0))
            return image.updateMask(mask).divide(10000)
        
        dataset = ee.ImageCollection("COPERNICUS/S2").filterDate("2018-01-01", "2018-01-31").filter(ee.Filter.lt("CLOUDY_PIXEL_PERCENTAGE", 20)).map(maskS2clouds)
        rgbVis = {"min": 0.0, "max": 0.3,"bands": ["B4", "B3", "B2"]
                 }
        Map.setCenter(-73.388672,5.353521, 6)
        Map.addLayer(dataset.median(), rgbVis, "RGB")
        
    elif cod == "COPERNICUS/S2_CLOUD_PROBABILITY":
        s2Sr = ee.ImageCollection("COPERNICUS/S2_SR")
        s2Clouds = ee.ImageCollection("COPERNICUS/S2_CLOUD_PROBABILITY")
        START_DATE = ee.Date("2019-01-01")
        END_DATE = ee.Date("2019-03-01")
        MAX_CLOUD_PROBABILITY = 65
        region = ee.Geometry.Rectangle([[-76.5, 2.0], [-74, 4.0]])
        Map.setCenter(-75, 3, 12)
        
        def maskClouds(img):
            clouds = ee.Image(img.get("cloud_mask")).select("probability")
            isNotCloud = clouds.lt(MAX_CLOUD_PROBABILITY)
            return img.updateMask(isNotCloud)

        def maskEdges(s2_img):
            return s2_img.updateMask(s2_img.select("B8A").mask().updateMask(s2_img.select("B9").mask()))
    
        criteria = ee.Filter.And(ee.Filter.bounds(region), ee.Filter.date(START_DATE, END_DATE))
        s2Sr = s2Sr.filter(criteria).map(maskEdges)
        s2Clouds = s2Clouds.filter(criteria)
        
        s2SrWithCloudMask = ee.Join.saveFirst("cloud_mask").apply({
            'primary': s2Sr,
            'secondary': s2Clouds,
            'condition': ee.Filter.equals(leftField= "system:index", rightField= "system:index")
        })
        
        s2CloudMasked = ee.ImageCollection(s2SrWithCloudMask).map(maskClouds).median()
        rgbVis = {"min": 0, "max": 3000, "bands": ["B4", "B3", "B2"]}
        
        Map.addLayer(s2CloudMasked, rgbVis, "S2 SR masked at " + MAX_CLOUD_PROBABILITY + "%", True)

    elif cod == "COPERNICUS/S2_HARMONIZED":
        def maskS2clouds(image):
            qa = image.select("QA60")
            cloudBitMask = 1 << 10
            cirrusBitMask = 1 << 11
            mask = qa.bitwiseAnd(cloudBitMask).eq(0).And(qa.bitwiseAnd(cirrusBitMask).eq(0))
            return image.updateMask(mask).divide(10000)

        dataset = ee.ImageCollection("COPERNICUS/S2_HARMONIZED") \
        .filterDate("2022-01-01", "2022-01-31") \
        .filter(ee.Filter.lt("CLOUDY_PIXEL_PERCENTAGE", 20)) \
        .map(maskS2clouds)
        rgbVis = {
            "min": 0.0,
            "max": 0.3,
            "bands": ["B4", "B3", "B2"],
        }

        Map.setCenter(-73.388672,5.353521, 6)
        Map.addLayer(dataset.median(), rgbVis, "RGB")

    elif cod == "COPERNICUS/S2_SR":
        def maskS2clouds(image):
            qa = image.select("QA60")
            cloudBitMask = 1 << 10
            cirrusBitMask = 1 << 11
            mask = qa.bitwiseAnd(cloudBitMask).eq(0).And(qa.bitwiseAnd(cirrusBitMask).eq(0))
            return image.updateMask(mask).divide(10000)
        dataset = ee.ImageCollection("COPERNICUS/S2_SR") \
        .filterDate("2020-01-01", "2020-01-30") \
        .filter(ee.Filter.lt("CLOUDY_PIXEL_PERCENTAGE",20)) \
        .map(maskS2clouds)
        visualization = {
            "min": 0.0,
            "max": 0.3,
            'bands': ["B4", "B3", "B2"],
        }
        Map.setCenter(83.277, 17.7009, 12)
        Map.addLayer(dataset.mean(), visualization, "RGB")
    

    elif cod == "COPERNICUS/S2_SR_HARMONIZED":
        def maskS2clouds(image):
            qa = image.select("QA60")
            cloudBitMask = 1 << 10
            cirrusBitMask = 1 << 11
            mask = qa.bitwiseAnd(cloudBitMask).eq(0).And(qa.bitwiseAnd(cirrusBitMask).eq(0))
            return image.updateMask(mask).divide(10000)
        dataset = ee.ImageCollection("COPERNICUS/S2_SR_HARMONIZED") \
        .filterDate("2020-01-01", "2020-01-30") \
        .filter(ee.Filter.lt("CLOUDY_PIXEL_PERCENTAGE",20)) \
        .map(maskS2clouds)
        visualization = {
          "min": 0.0,
          "max": 0.3,
          "bands": ["B4", "B3", "B2"],
        }
        Map.setCenter(83.277, 17.7009, 12)
        Map.addLayer(dataset.mean(), visualization, "RGB")

    elif cod == "COPERNICUS/S3/OLCI":
        dataset = ee.ImageCollection("COPERNICUS/S3/OLCI") \
        .filterDate("2018-04-01", "2018-04-04")
        rgb = dataset.select(["Oa08_radiance", "Oa06_radiance", "Oa04_radiance"]) \
        .median().multiply(ee.Image([0.00876539, 0.0123538, 0.0115198]))
        visParams = {"min": 0, "max": 6, "gamma": 1.5,}
        Map.setCenter(46.043, 1.45, 5)
        Map.addLayer(rgb, visParams, "RGB")

    elif cod == "COPERNICUS/S5P/NRTI/L3_AER_AI":
        collection = ee.ImageCollection("COPERNICUS/S5P/NRTI/L3_AER_AI") \
        .select("absorbing_aerosol_index") \
        .filterDate("2019-06-01", "2019-06-06")
        band_viz = {
          "min": -1,
          "max": 2.0,
          "palette": ["black", "blue", "purple", "cyan", "green", "yellow", "red"]
          }
        Map.addLayer(collection.mean(), band_viz, "S5P Aerosol")
        Map.setCenter(-118.82, 36.1, 5)

    elif cod == "COPERNICUS/S5P/NRTI/L3_AER_LH":
        collection = ee.ImageCollection("COPERNICUS/S5P/NRTI/L3_AER_LH") \
        .select("aerosol_height") \
        .filterDate("2019-06-01", "2019-06-06")
        band_viz = {
          "min": -81.17,
          "max": 67622.56,
          "palette": ["blue", "purple", "cyan", "green", "yellow", "red"]
          }
        Map.addLayer(collection.mean(), band_viz, "S5P Aerosol Height")
        Map.setCenter(44.09, 24.27, 4)

    elif cod == "COPERNICUS/S5P/NRTI/L3_CLOUD":
        collection = ee.ImageCollection("COPERNICUS/S5P/NRTI/L3_CLOUD") \
        .select("cloud_fraction") \
        .filterDate("2019-06-01", "2019-06-02")
        band_viz = {
          "min": 0,
          "max": 0.95,
          "palette": ["black", "blue", "purple", "cyan", "green", "yellow", "red"]
          }
        Map.addLayer(collection.mean(), band_viz, "S5P Cloud")
        Map.setCenter(-58.14, -10.47, 2)

    elif cod == "COPERNICUS/S5P/NRTI/L3_CO":
        collection = ee.ImageCollection("COPERNICUS/S5P/NRTI/L3_CO") \
        .select("CO_column_number_density") \
        .filterDate("2019-06-01", "2019-06-11")
        band_viz = {
          "min": 0,
          "max": 0.05,
          "palette": ["black", "blue", "purple", "cyan", "green", "yellow", "red"]
          }
        Map.addLayer(collection.mean(), band_viz, "S5P CO")
        Map.setCenter(-25.01, -4.28, 4)

    elif cod == "COPERNICUS/S5P/NRTI/L3_HCHO":
        collection = ee.ImageCollection("COPERNICUS/S5P/NRTI/L3_HCHO") \
        .select("tropospheric_HCHO_column_number_density") \
        .filterDate("2019-06-01", "2019-06-06")
        band_viz = {
          "min": 0.0,
          "max": 0.0003,
          "palette": ["black", "blue", "purple", "cyan", "green", "yellow", "red"]
        }
        Map.addLayer(collection.mean(), band_viz, "S5P HCHO")
        Map.setCenter(0.0, 0.0, 2)

    elif cod == "COPERNICUS/S5P/NRTI/L3_NO2":
        collection = ee.ImageCollection("COPERNICUS/S5P/NRTI/L3_NO2") \
        .select("NO2_column_number_density") \
        .filterDate("2019-06-01", "2019-06-06")
        band_viz = {
          "min": 0,
          "max": 0.0002,
          "palette": ["black", "blue", "purple", "cyan", "green", "yellow", "red"]
        }
        Map.addLayer(collection.mean(), band_viz, "S5P N02")
        Map.setCenter(65.27, 24.11, 4)

    elif cod == "COPERNICUS/S5P/NRTI/L3_O3":
        collection = ee.ImageCollection("COPERNICUS/S5P/NRTI/L3_O3") \
        .select("O3_column_number_density") \
        .filterDate("2019-06-01", "2019-06-05")
        band_viz = {
          "min": 0.12,
          "max": 0.15,
          "palette": ["black", "blue", "purple", "cyan", "green", "yellow", "red"]
        }
        Map.addLayer(collection.mean(), band_viz, "S5P O3")
        Map.setCenter(0.0, 0.0, 2)

    elif cod == "COPERNICUS/S5P/NRTI/L3_SO2":
        collection = ee.ImageCollection("COPERNICUS/S5P/NRTI/L3_SO2") \
        .select("SO2_column_number_density") \
        .filterDate("2019-06-01", "2019-06-11")
        band_viz = {
          "min": 0.0,
          "max": 0.0005,
          "palette": ["black", "blue", "purple", "cyan", "green", "yellow", "red"]
        }
        Map.addLayer(collection.mean(), band_viz, "S5P SO2")
        Map.setCenter(0.0, 0.0, 2)

    elif cod == "COPERNICUS/S5P/OFFL/L3_AER_AI":
        collection = ee.ImageCollection("COPERNICUS/S5P/OFFL/L3_AER_AI") \
        .select("absorbing_aerosol_index") \
        .filterDate("2019-06-01", "2019-06-06")
        band_viz = {
          "min": -1,
          "max": 2.0,
          "palette": ["black", "blue", "purple", "cyan", "green", "yellow", "red"]
        }
        Map.addLayer(collection.mean(), band_viz, "S5P Aerosol")
        Map.setCenter(-118.82, 36.1, 5)

    elif cod == "COPERNICUS/S5P/OFFL/L3_AER_LH":
        collection = ee.ImageCollection("COPERNICUS/S5P/OFFL/L3_AER_LH") \
        .select("aerosol_height") \
        .filterDate("2019-06-01", "2019-06-05")
        visualization = {
          "min": 0,
          "max": 6000,
          "palette": ["blue", "purple", "cyan", "green", "yellow", "red"]
        }
        Map.setCenter(44.09, 24.27, 4)
        Map.addLayer(collection.mean(), visualization, "S5P Aerosol Height")
    
    elif cod == "COPERNICUS/S5P/OFFL/L3_CH4":
        collection = ee.ImageCollection("COPERNICUS/S5P/OFFL/L3_CH4") \
        .select("CH4_column_volume_mixing_ratio_dry_air") \
        .filterDate("2019-06-01", "2019-07-16")
        band_viz = {
          "min": 1750,
          "max": 1900,
          "palette": ["black", "blue", "purple", "cyan", "green", "yellow", "red"]
        }
        Map.addLayer(collection.mean(), band_viz, "S5P CH4")
        Map.setCenter(0.0, 0.0, 2)

    elif cod == "COPERNICUS/S5P/OFFL/L3_CLOUD":
        collection = ee.ImageCollection("COPERNICUS/S5P/OFFL/L3_CLOUD") \
        .select("cloud_fraction") \
        .filterDate("2019-06-01", "2019-06-02")
        band_viz = {
          "min": 0,
          "max": 0.95,
          "palette": ["black", "blue", "purple", "cyan", "green", "yellow", "red"]
        }
        Map.addLayer(collection.mean(), band_viz, "S5P Cloud")
        Map.setCenter(-58.14, -10.47, 2)

    elif cod == "COPERNICUS/S5P/OFFL/L3_CO":
        collection = ee.ImageCollection("COPERNICUS/S5P/OFFL/L3_CO") \
        .select("CO_column_number_density") \
        .filterDate("2019-06-01", "2019-06-11")
        band_viz = {
          "min": 0,
          "max": 0.05,
          "palette": ["black", "blue", "purple", "cyan", "green", "yellow", "red"]
        }
        Map.addLayer(collection.mean(), band_viz, "S5P CO")
        Map.setCenter(-25.01, -4.28, 4)

    elif cod == "COPERNICUS/S5P/OFFL/L3_HCHO":
        collection = ee.ImageCollection("COPERNICUS/S5P/OFFL/L3_HCHO") \
        .select("tropospheric_HCHO_column_number_density") \
        .filterDate("2019-06-01", "2019-06-06")
        band_viz = {
          "min": 0.0,
          "max": 0.0003,
          "palette": ["black", "blue", "purple", "cyan", "green", "yellow", "red"]
        }
        Map.addLayer(collection.mean(), band_viz, "S5P HCHO")
        Map.setCenter(0.0, 0.0, 2)

    elif cod == "COPERNICUS/S5P/OFFL/L3_NO2":
        collection = ee.ImageCollection("COPERNICUS/S5P/OFFL/L3_NO2") \
        .select("tropospheric_NO2_column_number_density") \
        .filterDate("2019-06-01", "2019-06-06")
        band_viz = {
          "min": 0,
          "max": 0.0002,
          "palette": ["black", "blue", "purple", "cyan", "green", "yellow", "red"]
        }
        Map.addLayer(collection.mean(), band_viz, "S5P N02")
        Map.setCenter(65.27, 24.11, 4)

    elif cod == "COPERNICUS/S5P/OFFL/L3_O3":
        collection = ee.ImageCollection("COPERNICUS/S5P/OFFL/L3_O3") \
        .select("O3_column_number_density") \
        .filterDate("2019-06-01", "2019-06-05")
        band_viz = {
          "min": 0.12,
          "max": 0.15,
          "palette": ["black", "blue", "purple", "cyan", "green", "yellow", "red"]
        }
        Map.addLayer(collection.mean(), band_viz, "S5P O3")
        Map.setCenter(0.0, 0.0, 2)

    elif cod == "COPERNICUS/S5P/OFFL/L3_O3_TCL":
        collection = ee.ImageCollection("COPERNICUS/S5P/OFFL/L3_O3_TCL") \
        .select("ozone_tropospheric_vertical_column") \
        .filterDate("2019-06-01", "2019-07-01")
        band_viz = {
          "min": 0,
          "max": 0.02,
          "palette": ["black", "blue", "purple", "cyan", "green", "yellow", "red"]
        }
        Map.addLayer(collection.mean(), band_viz, "S5P O3")
        Map.setCenter(0.0, 0.0, 2)

    elif cod == "COPERNICUS/S5P/OFFL/L3_SO2":
        collection = ee.ImageCollection("COPERNICUS/S5P/OFFL/L3_SO2") \
        .select("SO2_column_number_density") \
        .filterDate("2019-06-01", "2019-06-11")
        band_viz = {
          "min": 0.0,
          "max": 0.0005,
          "palette": ["black", "blue", "purple", "cyan", "green", "yellow", "red"]
        }
        Map.addLayer(collection.mean(), band_viz, "S5P SO2")
        Map.setCenter(0.0, 0.0, 2)

    elif cod == "CPOM/CryoSat2/ANTARCTICA_DEM":
        dataset = ee.Image("CPOM/CryoSat2/ANTARCTICA_DEM")
        visualization = {
          "bands": ["elevation"],
          "min": 0.0,
          "max": 4000.0,
          "palette": ["001fff", "00ffff", "fbff00", "ff0000"]
        }
        Map.setCenter(17.0, -76.0, 3)
        Map.addLayer(dataset, visualization, "Elevation")

    elif cod == "CSIRO/SLGA":
        dataset = ee.ImageCollection("CSIRO/SLGA") \
        .filter(ee.Filter.eq("attribute_code", "DES"))
        soilDepth = dataset.select("DES_000_200_EV")
        soilDepthVis = {
          "min": 0.1,
          "max": 1.84,
          "palette": ["8d6738", "252525"],
        }
        Map.setCenter(132.495, -21.984, 5)
        Map.addLayer(soilDepth, soilDepthVis, "Soil Depth")

    elif cod == "CSP/ERGo/1_0/Global/ALOS_CHILI":
        dataset = ee.Image("CSP/ERGo/1_0/Global/ALOS_CHILI")
        alosChili = dataset.select("constant")
        alosChiliVis = {
            "min": 0.0,
            "max": 255.0,
        }
        Map.setCenter(-105.8636, 40.3439, 11)
        Map.addLayer(alosChili, alosChiliVis, "ALOS CHILI")
        
    elif cod == "CSP/ERGo/1_0/Global/ALOS_landforms":
        dataset = ee.Image("CSP/ERGo/1_0/Global/ALOS_landforms")
        landforms = dataset.select("constant")
        landformsVis = {
            "min": 11.0,
            "max": 42.0,
            "palette": [
            "141414", "383838", "808080", "EBEB8F", "F7D311", "AA0000", "D89382",
            "DDC9C9", "DCCDCE", "1C6330", "68AA63", "B5C98E", "E1F0E5", "a975ba",
            "6f198c"
            ],
        }
        Map.setCenter(-105.58, 40.5498, 11)
        Map.addLayer(landforms, landformsVis, "Landforms")

    elif cod == "CSP/ERGo/1_0/Global/ALOS_mTPI":
        dataset = ee.Image("CSP/ERGo/1_0/Global/ALOS_mTPI")
        alosMtpi = dataset.select("AVE")
        alosMtpiVis = {
            "min": -200.0,
            "max": 200.0,
            "palette": ["0b1eff", "4be450", "fffca4", "ffa011", "ff0000"],
        }
        Map.setCenter(-105.8636, 40.3439, 11)
        Map.addLayer(alosMtpi, alosMtpiVis, "ALOS mTPI")

    elif cod == "CSP/ERGo/1_0/Global/ALOS_topoDiversity":
        dataset = ee.Image("CSP/ERGo/1_0/Global/ALOS_topoDiversity")
        alosTopographicDiversity = dataset.select("constant")
        alosTopographicDiversityVis = {
            "min": 0.0,
            "max": 1.0,
        }
        Map.setCenter(-111.313, 39.724, 6)
        Map.addLayer(alosTopographicDiversity, alosTopographicDiversityVis, "ALOS Topographic Diversity")
            
    elif cod == "CSP/ERGo/1_0/Global/SRTM_CHILI":
        dataset = ee.Image("CSP/ERGo/1_0/Global/SRTM_CHILI")
        srtmChili = dataset.select("constant")
        srtmChiliVis = {
            "min": 0.0,
            "max": 255.0,
        }
        Map.setCenter(-105.8636, 40.3439, 11)
        Map.addLayer(srtmChili, srtmChiliVis, "SRTM CHILI")
        
    elif cod == "CSP/ERGo/1_0/Global/SRTM_landforms":
        dataset = ee.Image("CSP/ERGo/1_0/Global/SRTM_landforms")
        landforms = dataset.select("constant")
        landformsVis = {
            "min": 11.0,
            "max": 42.0,
            "palette": [
            "141414", "383838", "808080", "EBEB8F", "F7D311", "AA0000", "D89382",
            "DDC9C9", "DCCDCE", "1C6330", "68AA63", "B5C98E", "E1F0E5", "a975ba",
            "6f198c"
            ],
        }
        Map.setCenter(-105.58, 40.5498, 11)
        Map.addLayer(landforms, landformsVis, "Landforms")

    elif cod == "CSP/ERGo/1_0/Global/SRTM_mTPI":
        dataset = ee.Image("CSP/ERGo/1_0/Global/SRTM_mTPI")
        srtmMtpi = dataset.select("elevation")
        srtmMtpiVis = {
            "min": -200.0,
            "max": 200.0,
            "palette": ["0b1eff", "4be450", "fffca4", "ffa011", "ff0000"],
        }
        Map.setCenter(-105.8636, 40.3439, 11)
        Map.addLayer(srtmMtpi, srtmMtpiVis, "SRTM mTPI")

    elif cod == "CSP/ERGo/1_0/Global/SRTM_topoDiversity":
        dataset = ee.Image("CSP/ERGo/1_0/Global/SRTM_topoDiversity")
        srtmTopographicDiversity = dataset.select("constant")
        srtmTopographicDiversityVis = {
            "min": 0.0,
            "max": 1.0,
        }
        Map.setCenter(-111.313, 39.724, 6)
        Map.addLayer(srtmTopographicDiversity, srtmTopographicDiversityVis,"SRTM Topographic Diversity")

    elif cod == "CSP/ERGo/1_0/US/CHILI":
        dataset = ee.Image("CSP/ERGo/1_0/US/CHILI")
        usChili = dataset.select("constant")
        usChiliVis = {
            "min": 0.0,
            "max": 255.0,
        }
        Map.setCenter(-105.8636, 40.3439, 11)
        Map.addLayer(usChili, usChiliVis, "US CHILI")

    elif cod == "CSP/ERGo/1_0/US/landforms":
        dataset = ee.Image("CSP/ERGo/1_0/US/landforms")
        landforms = dataset.select("constant")
        landformsVis = {
            "min": 11.0,
            "max": 42.0,
            "palette": [
            "141414", "383838", "808080", "EBEB8F", "F7D311", "AA0000", "D89382",
            "DDC9C9", "DCCDCE", "1C6330", "68AA63", "B5C98E", "E1F0E5", "a975ba",
            "6f198c"
            ],
        }
        Map.setCenter(-105.58, 40.5498, 11)
        Map.addLayer(landforms, landformsVis, "Landforms")

    elif cod == "CSP/ERGo/1_0/US/lithology":
        dataset = ee.Image("CSP/ERGo/1_0/US/lithology")
        lithology = dataset.select("b1")
        lithologyVis = {
            "min": 0.0,
            "max": 20.0,
            "palette": [
            "356EFF", "ACB6DA", "D6B879", "313131", "EDA800", "616161", "D6D6D6",
            "D0DDAE", "B8D279", "D5D378", "141414", "6DB155", "9B6D55", "FEEEC9",
            "D6B879", "00B7EC", "FFDA90", "F8B28C"
            ],
        }
        Map.setCenter(-105.8636, 40.3439, 11)
        Map.addLayer(lithology, lithologyVis, "Lithology")

    elif cod == "CSP/ERGo/1_0/US/mTPI":
        dataset = ee.Image("CSP/ERGo/1_0/US/mTPI") 
        usMtpi = dataset.select("elevation")
        usMtpiVis = {
            "min": -200.0,
            "max": 200.0,
            "palette": ["0b1eff", "4be450", "fffca4", "ffa011", "ff0000"],
        }
        Map.setCenter(-105.8636, 40.3439, 11)
        Map.addLayer(usMtpi, usMtpiVis, "US mTPI")

    elif cod == "CSP/ERGo/1_0/US/physioDiversity":
        dataset = ee.Image("CSP/ERGo/1_0/US/physioDiversity")
        physiographicDiversity = dataset.select("b1")
        physiographicDiversityVis = {
            "min": 0.0,
            "max": 1.0,
        }
        Map.setCenter(-94.625, 39.825, 7)
        Map.addLayer(physiographicDiversity, physiographicDiversityVis,"Physiographic Diversity")

    elif cod == "CSP/ERGo/1_0/US/physiography":
        dataset = ee.Image("CSP/ERGo/1_0/US/physiography")
        physiography = dataset.select("constant")
        physiographyVis = {
            "min": 1100.0,
            "max": 4220.0,
        }
        Map.setCenter(-105.4248, 40.5242, 8)
        Map.addLayer(physiography, physiographyVis, "Physiography")
        
    elif cod == "CSP/ERGo/1_0/US/topoDiversity":
        dataset = ee.Image("CSP/ERGo/1_0/US/topoDiversity")
        usTopographicDiversity = dataset.select("constant")
        usTopographicDiversityVis = {
            "min": 0.0,
            "max": 1.0,
        }
        Map.setCenter(-111.313, 39.724, 6)
        Map.addLayer(usTopographicDiversity, usTopographicDiversityVis, "US Topographic Diversity")

    elif cod == "CSP/HM/GlobalHumanModification":
        dataset = ee.ImageCollection("CSP/HM/GlobalHumanModification")
        visualization = {"bands": ["gHM"],
                        "min": 0.0,
                        "max": 1.0,
                        "palette": ["0c0c0c", "071aff", "ff0000", "ffbd03", "fbff05", "fffdfd"]
        }
        Map.centerObject(dataset)
        Map.addLayer(dataset, visualization, "Human modification")

    elif cod == "DLR/WSF/WSF2015/v1":
        dataset = ee.Image("DLR/WSF/WSF2015/v1")
        opacity = 0.75
        blackBackground = ee.Image(0)
        Map.addLayer(blackBackground, None, "Black background", True, opacity)
        visualization = {
          "min": 0,
          "max": 255,
        }
        Map.addLayer(dataset, visualization, "Human settlement areas")
        Map.setCenter(90.45, 23.7, 7)

    elif cod == "DOE/ORNL/LandScan_HD/Ukraine_202201":
        dataset = ee.Image("DOE/ORNL/LandScan_HD/Ukraine_202201")
        vis = {
          "min": 0.0,
          "max": 10.0,
          "palette":["lemonchiffon", "khaki", "orange","orangered", "red", "maroon"],
        }
        Map.centerObject(dataset)
        Map.addLayer(dataset, vis, "Population Count")

    elif cod == "ECMWF/CAMS/NRT":
        dataset = ee.ImageCollection("ECMWF/CAMS/NRT").filter(ee.Filter.date("2019-01-01", "2019-01-31"))
        aod = dataset.select("total_aerosol_optical_depth_at_550nm_surface")
        visParams = {
          "min": 0.000096,
          "max": 3.582552,
          "palette": [
          "5E4FA2", "3288BD", "66C2A5", "ABE0A4",
          "E6F598", "FFFFBF", "FEE08B", "FDAE61",
          "F46D43", "D53E4F", "9E0142"
          ]
        }
        Map.setCenter(-94.18, 16.8, 1)
        Map.addLayer(aod, visParams, "Total Aerosal Optical Depth")
    
    elif cod == "ECMWF/ERA5/DAILY":
        era5_2mt = ee.ImageCollection("ECMWF/ERA5/DAILY").select("mean_2m_air_temperature").filter(ee.Filter.date("2019-07-01", "2019-07-31"))
        
        era5_tp = ee.ImageCollection("ECMWF/ERA5/DAILY").select("total_precipitation").filter(ee.Filter.date("2019-07-01", "2019-07-31"))
        era5_2d = ee.ImageCollection("ECMWF/ERA5/DAILY").select("dewpoint_2m_temperature").filter(ee.Filter.date("2019-07-01", "2019-07-31"))
        era5_mslp = ee.ImageCollection("ECMWF/ERA5/DAILY").select("mean_sea_level_pressure").filter(ee.Filter.date("2019-07-01", "2019-07-31")) 
        era5_sp = ee.ImageCollection("ECMWF/ERA5/DAILY").select("surface_pressure").filter(ee.Filter.date("2019-07-01", "2019-07-31"))
        era5_u_wind_10m = ee.ImageCollection("ECMWF/ERA5/DAILY").select("u_component_of_wind_10m").filter(ee.Filter.date("2019-07-01", "2019-07-31"))
        era5_sp = era5_sp.map(lambda image: image.divide(100).set("system:time_start", image.get("system:time_start")))
        visTp = {"min": 0, "max": 0.1, "palette": ["#FFFFFF", "#00FFFF", "#0080FF", "#DA00FF", "#FFA400", "#FF0000"]}
        vis2mt = {"min": 250, 
                  "max": 320, 
                  "palette": [
                          "#000080", "#0000D9", "#4000FF", "#8000FF", "#0080FF", "#00FFFF", "#00FF80",
                          "#80FF00", "#DAFF00", "#FFFF00", "#FFF500", "#FFDA00", "#FFB000", "#FFA400",
                          "#FF4F00", "#FF2500", "#FF0A00", "#FF00FF"]
        }
        visWind = {"min": 0, 
                   "max": 30, 
                   "palette": [
                           "#FFFFFF", "#FFFF71", "#DEFF00", "#9EFF00", "#77B038", "#007E55", "#005F51",
                           "#004B51", "#013A7B", "#023AAD"]
        }
        visPressure = {"min": 500, 
                       "max": 1150,
                       "palette": ["#01FFFF", "#058BFF", "#0600FF", "#DF00FF", "#FF00FF", "#FF8C00", "#FF8C00"]
        }
        Map.addLayer(era5_tp.filter(ee.Filter.date("2019-07-15")), visTp, "Daily total precipitation sums")
        Map.addLayer(era5_2d.filter(ee.Filter.date("2019-07-15")), vis2mt, "Daily mean 2m dewpoint temperature")
        Map.addLayer(era5_2mt.filter(ee.Filter.date("2019-07-15")), vis2mt, "Daily mean 2m air temperature")
        Map.addLayer(era5_u_wind_10m.filter(ee.Filter.date("2019-07-15")), visWind, "Daily mean 10m u-component of wind")
        Map.addLayer(era5_sp.filter(ee.Filter.date("2019-07-15")), visPressure, "Daily mean surface pressure")
        Map.setCenter(21.2, 22.2, 2)
                                
    elif cod == "ECMWF/ERA5/MONTHLY":
        dataset = ee.ImageCollection("ECMWF/ERA5/MONTHLY")
        visualization = {
             "bands": ["mean_2m_air_temperature"],
             "min": 250.0,
             "max": 320.0,
             "palette": ["#000080","#0000D9","#4000FF","#8000FF","#0080FF","#00FFFF","#00FF80","#80FF00","#DAFF00","#FFFF00","#FFF500","#FFDA00","#FFB000","#FFA400","#FF4F00","#FF2500","#FF0A00","#FF00FF"]
             }
        Map.setCenter(22.2, 21.2, 0)
        Map.addLayer(dataset, visualization, "Monthly average air temperature [K] at 2m height")

    elif cod == "ECMWF/ERA5_LAND/DAILY_RAW":
        dataset = ee.ImageCollection("ECMWF/ERA5_LAND/DAILY_RAW").filter(ee.Filter.date("2021-06-01", "2021-07-01"))
        visualization = {
             "bands": ["temperature_2m"],
             "min": 250.0,
             "max": 320.0,
             "palette": ["#000080","#0000D9","#4000FF","#8000FF","#0080FF","#00FFFF","#00FF80","#80FF00","#DAFF00","#FFFF00","#FFF500","#FFDA00","#FFB000","#FFA400","#FF4F00","#FF2500","#FF0A00","#FF00FF"]
             }
        Map.setCenter(-170.13, 45.62, 2)
        Map.addLayer(dataset, visualization, "Air temperature [K] at 2m height")

    elif cod == "ECMWF/ERA5_LAND/HOURLY":
        dataset = ee.ImageCollection("ECMWF/ERA5_LAND/HOURLY").filter(ee.Filter.date("2020-07-01", "2020-07-02"))
        visualization = {
             "bands": ["temperature_2m"],
             "min": 250.0,
             "max": 320.0,
             "palette": ["#000080","#0000D9","#4000FF","#8000FF","#0080FF","#00FFFF","#00FF80","#80FF00","#DAFF00","#FFFF00","#FFF500","#FFDA00","#FFB000","#FFA400","#FF4F00","#FF2500","#FF0A00","#FF00FF"]
             }
        Map.setCenter(22.2, 21.2, 0)
        Map.addLayer(dataset, visualization, "Air temperature [K] at 2m height")
                     
    elif cod == "ECMWF/ERA5_LAND/MONTHLY_AGGR":
        dataset = ee.ImageCollection("ECMWF/ERA5_LAND/MONTHLY_AGGR").filter(ee.Filter.date("2020-02-01", "2020-07-10"))
        visualization = {
             "bands": ["temperature_2m"],
             "min": 250.0,
             "max": 320.0,
             "palette": ["#000080","#0000D9","#4000FF","#8000FF","#0080FF","#00FFFF","#00FF80","#80FF00","#DAFF00","#FFFF00","#FFF500","#FFDA00","#FFB000","#FFA400","#FF4F00","#FF2500","#FF0A00","#FF00FF"]
             }
        Map.setCenter(-170.13, 45.62, 2)
        Map.addLayer(dataset, visualization, "Air temperature [K] at 2m height")
                     
    elif cod == "ECMWF/ERA5_LAND/MONTHLY_BY_HOUR": 
        dataset = ee.ImageCollection("ECMWF/ERA5_LAND/MONTHLY_BY_HOUR").filter(ee.Filter.date("2020-07-01", "2020-08-01"))
        visualization = {
             "bands": ["temperature_2m"],
             "min": 250.0,
             "max": 320.0,
             "palette": ["#000080","#0000D9","#4000FF","#8000FF","#0080FF","#00FFFF","#00FF80","#80FF00","#DAFF00","#FFFF00","#FFF500","#FFDA00","#FFB000","#FFA400","#FF4F00","#FF2500","#FF0A00","#FF00FF"]
             }
        Map.setCenter(22.2, 21.2, 0)
        Map.addLayer(dataset, visualization, "Air temperature [K] at 2m height")

    elif cod == "EO1/HYPERION":
        dataset = ee.ImageCollection("EO1/HYPERION").filter(ee.Filter.date("2016-01-01", "2017-03-01"))
        rgb = dataset.select(["B050", "B023", "B015"])
        rgbVis = {
             "min": 1000.0,
             "max": 14000.0,
             "gamma": 2.5,
             }
        Map.setCenter(162.0044, -77.3463, 9)
        Map.addLayer(rgb.median(), rgbVis, "RGB")

    elif cod == "EPA/Ecoregions/2013/L3":
        dataset = ee.FeatureCollection("EPA/Ecoregions/2013/L3")
        visParams = {
             "palette": ["0a3b04", "1a9924", "15d812"],
             "min": 23.0,
             "max": 3.57e+11,
             "opacity": 0.8,
             }
        image = ee.Image().float().paint(dataset, "shape_area")
        Map.setCenter(-99.814, 40.166, 5)
        Map.addLayer(image, visParams, "EPA/Ecoregions/2013/L3")
        Map.addLayer(dataset, None, "for Inspector", False)

    elif cod == "EPA/Ecoregions/2013/L4":
        dataset = ee.FeatureCollection("EPA/Ecoregions/2013/L4")
        visParams = {
             "palette": ["0a3b04", "1a9924", "15d812"],
             "min": 0.0,
             "max": 67800000000.0,
             "opacity": 0.8,
             }
        image = ee.Image().float().paint(dataset, "shape_area")
        Map.setCenter(-99.814, 40.166, 5)
        Map.addLayer(image, visParams, "EPA/Ecoregions/2013/L4")
        Map.addLayer(dataset, None, "for Inspector", False)

    elif cod == "ESA/CCI/FireCCI/5_1":
        dataset = ee.ImageCollection("ESA/CCI/FireCCI/5_1").filterDate("2020-01-01", "2020-12-31")
        burnedArea = dataset.select("BurnDate")
        baVis = {
             "min": 1,
             "max": 366,
             "palette": ["ff0000", "fd4100", "fb8200", "f9c400", "f2ff00", "b6ff05","7aff0a", "3eff0f", "02ff15", "00ff55", "00ff99", "00ffdd","00ddff", "0098ff", "0052ff", "0210ff", "3a0dfb", "7209f6","a905f1", "e102ed", "ff00cc", "ff0089", "ff0047", "ff0004"]
             }
        maxBA = burnedArea.max()
        Map.setCenter(0, 18, 2.1)
        Map.addLayer(maxBA, baVis, "Burned Area")

    elif cod == "ESA/GLOBCOVER_L4_200901_200912_V2_3":
        dataset = ee.Image("ESA/GLOBCOVER_L4_200901_200912_V2_3")
        landcover = dataset.select("landcover")
        Map.setCenter(-88.6, 26.4, 3)
        Map.addLayer(landcover, {}, "Landcover")

    elif cod == "ESA/WorldCover/v100":
        dataset = ee.ImageCollection("ESA/WorldCover/v100").first()
        visualization = {
             "bands": ["Map"],
             }
        Map.centerObject(dataset)
        Map.addLayer(dataset, visualization, "Landcover")
                     
    elif cod == "ESA/WorldCover/v200":
        dataset = ee.ImageCollection("ESA/WorldCover/v200").first()
        visualization = {
             "bands": ["Map"],
             }
        Map.centerObject(dataset)
        Map.addLayer(dataset, visualization, "Landcover")
                     
    elif cod == "FAO/GAUL/2015/level0":
        dataset = ee.FeatureCollection("FAO/GAUL/2015/level0")
        Map.setCenter(7.82, 49.1, 4)
        styleParams = {
             "fillColor": "#b5ffb4",
             "color": "#00909F",
             "width": 1.0,
             }
        dataset = dataset.style(styleParams)
        Map.addLayer(dataset, {}, "Country Boundaries")

    elif cod == "FAO/GAUL/2015/level1":
        dataset = ee.FeatureCollection("FAO/GAUL/2015/level1")
        Map.setCenter(7.82, 49.1, 4)
        styleParams = {
             "fillColor": "b5ffb4",
             "color": "#00909F",
             "width": 1.0,
             }
        dataset = dataset.style(styleParams)
        Map.addLayer(dataset, {}, "First Level Administrative Units")
                     
    elif cod == "FAO/GAUL/2015/level2":
        dataset = ee.FeatureCollection("FAO/GAUL/2015/level2")
        Map.setCenter(12.876, 42.682, 5)
        styleParams = {
             "fillColor": "#b5ffb4",
             "color": "#00909F",
             "width": 1.0,
             }
        dataset = dataset.style(styleParams)
        Map.addLayer(dataset, {}, "Second Level Administrative Units")

    elif cod == "FAO/GAUL_SIMPLIFIED_500m/2015/level0": 
        dataset = ee.FeatureCollection("FAO/GAUL_SIMPLIFIED_500m/2015/level0")
        Map.setCenter(7.82, 49.1, 4)
        styleParams = {
             "fillColor": "#b5ffb4",
             "color": "#00909F",
             "width": 1.0,
             }
        dataset = dataset.style(styleParams)
        Map.addLayer(dataset, {}, "Country Boundaries")
                     
    elif cod == "FAO/GAUL_SIMPLIFIED_500m/2015/level1":
        dataset = ee.FeatureCollection("FAO/GAUL_SIMPLIFIED_500m/2015/level1")
        Map.setCenter(7.82, 49.1, 4)
        styleParams = {
             "fillColor": "#b5ffb4",
             "color": "#00909F",
             "width": 1.0,
             }
        dataset = dataset.style(styleParams)
        Map.addLayer(dataset, {}, "First Level Administrative Units")

    elif cod == "FAO/GAUL_SIMPLIFIED_500m/2015/level2":
        dataset = ee.FeatureCollection("FAO/GAUL_SIMPLIFIED_500m/2015/level2")
        Map.setCenter(12.876, 42.682, 5)
        styleParams = {
             "fillColor": "b5ffb4",
             "color": "00909F",
             "width": 1.0,
             }
        dataset = dataset.style(styleParams)
        Map.addLayer(dataset, {}, "Second Level Administrative Units")
        
    elif cod == "FAO/GHG/1/DROSA_A":
        dataset = ee.ImageCollection("FAO/GHG/1/DROSA_A")
        visualization = {
        "bands": ["cropland"],
        "min": 1.0,
        "max": 60.0,
        "palette": ["white", "red"]
        }
        Map.setCenter(108.0, -0.4, 6)
        Map.addLayer(dataset, visualization, "Cropland area drained (Annual)")

    elif cod == "FAO/GHG/1/DROSE_A":
        dataset = ee.ImageCollection("FAO/GHG/1/DROSE_A")
        visualization = {
        "bands": ["croplandc"],
        "min": 0.1,
        "max": 0.1,
        "palette": ["yellow", "red"]
        }
        Map.setCenter(108.0, -0.4, 6)
        Map.addLayer(dataset, visualization, "Cropland C emissions (Annual)")

    elif cod == "FAO/SOFO/1/FPP":
        coll = ee.ImageCollection("FAO/SOFO/1/FPP")
        image = coll.first().select("FPP_1km")
        Map.setCenter(17.5, 20, 3)
        Map.addLayer(image, {"min": 0, "max": 12, "palette": ["blue", "yellow", "red"]},"Forest proximate people – 1km cutoff distance")

    elif cod == "FAO/SOFO/1/TPP":
        coll = ee.ImageCollection("FAO/SOFO/1/TPP")
        image = coll.first().select("TPP_1km")
        Map.setCenter(17.5, 20, 3)
        Map.addLayer(
        image, {"min": 0, "max": 12, "palette": ["blue", "yellow", "red"]}, "Tree proximate people – 1km cutoff distance")

    elif cod == "FAO/WAPOR/2/L1_AETI_D":
        coll = ee.ImageCollection("FAO/WAPOR/2/L1_AETI_D")
        image = coll.first()
        Map.setCenter(17.5, 20, 3)
        Map.addLayer(image, {"min": 0, "max": 50})

    elif cod == "FAO/WAPOR/2/L1_E_D":
        coll = ee.ImageCollection("FAO/WAPOR/2/L1_E_D")
        image = coll.first()
        Map.setCenter(17.5, 20, 3)
        Map.addLayer(image, {"min": 0, "max": 10})

    elif cod == "FAO/WAPOR/2/L1_I_D":
        coll = ee.ImageCollection("FAO/WAPOR/2/L1_I_D")
        image = coll.first()
        Map.setCenter(17.5, 20, 3)
        Map.addLayer(image, {"min": 0, "max": 50})
        
    elif cod == "FAO/WAPOR/2/L1_NPP_D":
        coll = ee.ImageCollection("FAO/WAPOR/2/L1_NPP_D")
        image = coll.first()
        Map.setCenter(17.5, 20, 3)
        Map.addLayer(image, {"min": 0, "max": 5000})

    elif cod == "FAO/WAPOR/2/L1_RET_D":
        coll = ee.ImageCollection("FAO/WAPOR/2/L1_RET_D")
        image = coll.first()
        Map.setCenter(17.5, 20, 3)
        Map.addLayer(image, {"min": 0, "max": 100})

    elif cod == "FAO/WAPOR/2/L1_RET_E":
        coll = ee.ImageCollection("FAO/WAPOR/2/L1_RET_E")
        image = coll.first()
        Map.setCenter(17.5, 20, 3)
        Map.addLayer(image, {"min": 0, "max": 100})

    elif cod == "FAO/WAPOR/2/L1_T_D":
        coll = ee.ImageCollection("FAO/WAPOR/2/L1_T_D")
        image = coll.first()
        Map.setCenter(17.5, 20, 3)
        Map.addLayer(image, {"min": 0, "max": 50})

    elif cod == "FIRMS":
        dataset = ee.ImageCollection("FIRMS").filter(
        ee.Filter.date("2018-08-01", "2018-08-10"))
        fires = dataset.select("T21")
        firesVis = {
        "min": 325.0,
        "max": 400.0,
        "palette": ["red", "orange", "yellow"],
        }
        Map.setCenter(-119.086, 47.295, 6)
        Map.addLayer(fires, firesVis, "Fires")

    elif cod == "Finland/MAVI/VV/50cm":
        dataset = ee.ImageCollection("Finland/MAVI/VV/50cm")
        Map.setCenter(25.7416, 62.2446, 16)
        Map.addLayer(dataset, None, "Finland 50 cm(False color)")

    elif cod == "GFW/GFF/V1/fishing_hours":
        dataset = ee.ImageCollection("GFW/GFF/V1/fishing_hours").filter(ee.Filter.date("2016-12-01", "2017-01-01"))
        trawlers = dataset.select("trawlers")
        trawlersVis = {
        "min": 0.0,
        "max": 5.0,
        }
        Map.setCenter(16.201, 36.316, 7)
        Map.addLayer(trawlers.max(), trawlersVis, "Trawlers")

    elif cod == "GFW/GFF/V1/vessel_hours":
        dataset = ee.ImageCollection("GFW/GFF/V1/vessel_hours").filter(ee.Filter.date("2016-12-01", "2017-01-01"))
        trawlers = dataset.select("trawlers")
        trawlersVis = {
        "min": 0.0,
        "max": 5.0,
        }
        Map.setCenter(130.61, 34.287, 8)
        Map.addLayer(trawlers.max(), trawlersVis, "Trawlers")

    elif cod == "GLCF/GLS_WATER":
        dataset = ee.ImageCollection("GLCF/GLS_WATER")
        water = dataset.select("water")
        waterVis = {
        "min": 1.0,
        "max": 4.0,
        "palette": ["FAFAFA", "00C5FF", "DF73FF", "828282", "CCCCCC"],
        }
        Map.setCenter(-79.3094, 44.5693, 8)
        Map.addLayer(water, waterVis, "Water")

    elif cod == "GLIMS/20210914":
        dataset = ee.FeatureCollection("GLIMS/20210914")
        visParams = {
        "palette": ["gray", "cyan", "blue"],
        "min": 0.0,
        "max": 10.0,
        "opacity": 0.8,
        }
        image = ee.Image().float().paint(dataset, "area")
        Map.setCenter(-35.618, 66.743, 7)
        Map.addLayer(image, visParams, "GLIMS/20210914")
        Map.addLayer(dataset, None, "for Inspector", False)

    elif cod == "Finland/SMK/VV/50cm":
        dataset = ee.ImageCollection("Finland/SMK/VV/50cm")
        Map.setCenter(25.7416, 62.2446, 16)
        Map.addLayer(dataset, None, "Finland 50 cm(False color)")

    elif cod == "Finland/SMK/V/50cm":
        dataset = ee.ImageCollection("Finland/SMK/V/50cm")
        Map.setCenter(24.9, 60.2, 17)
        Map.addLayer(dataset, None, "Finland 50 cm(color)")

    elif cod == "GLIMS/current":
        dataset = ee.FeatureCollection("GLIMS/current")
        visParams = {
        "palette": ["gray", "cyan", "blue"],
        "min": 0.0,
        "max": 10.0,
        "opacity": 0.8,
        }
        image = ee.Image().float().paint(dataset, "area")
        Map.setCenter(-35.618, 66.743, 7)
        Map.addLayer(image, visParams, "GLIMS/current")
        Map.addLayer(dataset, None, "for Inspector", False)

    elif cod == "GLOBAL_FLOOD_DB/MODIS_EVENTS/V1":
        gfd = ee.ImageCollection("GLOBAL_FLOOD_DB/MODIS_EVENTS/V1")
        hurricaneIsaacDartmouthId = 3977
        hurricaneIsaacUsa = ee.Image(gfd.filterMetadata("id", "equals", hurricaneIsaacDartmouthId).first())
        Map.setOptions("SATELLITE")
        Map.setCenter(-90.2922, 29.4064, 9)
        Map.addLayer(
        hurricaneIsaacUsa.select("flooded").selfMask(),{"min": 0, "max": 1, "palette": "001133"},"Hurricane Isaac - Inundation Extent")
        durationPalette = ["C3EFFE", "1341E8", "051CB0", "001133"]
        Map.addLayer(hurricaneIsaacUsa.select("duration").selfMask(),{"min": 0, "max": 4, "palette": durationPalette},"Hurricane Isaac - Duration")
        gfdFloodedSum = gfd.select("flooded").sum()
        Map.addLayer(gfdFloodedSum.selfMask(),{"min": 0, "max": 10, "palette": durationPalette},"GFD Satellite Observed Flood Plain")
        jrc = gfd.select("jrc_perm_water").sum().gte(1)
        Map.addLayer(jrc.selfMask(),{"min": 0, "max": 1, "palette": "C3EFFE"},"JRC Permanent Water")

    elif cod == "GOOGLE/DYNAMICWORLD/V1":
        COL_FILTER = ee.Filter.And(ee.Filter.bounds(ee.Geometry.Point(20.6729, 52.4305)),ee.Filter.date("2021-04-02", "2021-04-03"))
        dwCol = ee.ImageCollection("GOOGLE/DYNAMICWORLD/V1").filter(COL_FILTER)
        s2Col = ee.ImageCollection("COPERNICUS/S2").filter(COL_FILTER)
        DwS2Col = ee.Join.saveFirst("s2_img").apply(dwCol, s2Col,
        ee.Filter.equals({"leftField": "system:index", "rightField": "system:index"}))
        dwImage = ee.Image(DwS2Col.first())
        s2Image = ee.Image(dwImage.get("s2_img"))
        CLASS_NAMES = ["water", "trees", "grass", "flooded_vegetation", "crops","shrub_and_scrub", "built", "bare", "snow_and_ice"]
        VIS_PALETTE = ["419BDF", "397D49", "88B053", "7A87C6","E49635", "DFC35A", "C4281B", "A59B8F","B39FE1"]
        dwRgb = dwImage.select("label").visualize({"min": 0, "max": 8, "palette": VIS_PALETTE}).divide(255)
        top1Prob = dwImage.select(CLASS_NAMES).reduce(ee.Reducer.max())
        top1ProbHillshade = ee.Terrain.hillshade(top1Prob.multiply(100)).divide(255)
        dwRgbHillshade = dwRgb.multiply(top1ProbHillshade)
        Map.setCenter(20.6729, 52.4305, 12)
        Map.addLayer(s2Image,{"min": 0, "max": 3000, "bands": ["B4", "B3", "B2"]},"Sentinel-2 L1C")
        Map.addLayer(dwRgbHillshade,{"min": 0, "max": 0.65},"Dynamic World")

    elif cod == "GOOGLE/Research/open-buildings/v2/polygons":
        t = ee.FeatureCollection("GOOGLE/Research/open-buildings/v2/polygons")
        t_060_065 = t.filter("confidence >= 0.60 && confidence < 0.65")
        t_065_070 = t.filter("confidence >= 0.65 && confidence < 0.70")
        t_gte_070 = t.filter("confidence >= 0.70")
        Map.addLayer(t_060_065, {"color": "FF0000"}, "Buildings confidence [0.60 0.65)")
        Map.addLayer(t_065_070, {"color": "FFFF00"}, "Buildings confidence [0.65 0.70)")
        Map.addLayer(t_gte_070, {"color": "00FF00"}, "Buildings confidence >= 0.70")
        Map.setCenter(3.389, 6.492, 17)
        Map.setOptions("SATELLITE")

    elif cod == "GRIDMET/DROUGHT":
        collection = ee.ImageCollection("GRIDMET/DROUGHT")
        dS = "2020-03-30"
        dE = "2020-03-30"
        dSUTC = ee.Date(dS, "GMT")
        dEUTC = ee.Date(dE, "GMT")
        filtered = collection.filterDate(dSUTC, dEUTC.advance(1, "day"))
        PDSI = filtered.select("pdsi")
        Z = filtered.select("z")
        SPI2y = filtered.select("spi2y")
        SPEI2y = filtered.select("spei2y")
        EDDI2y = filtered.select("spei2y")
        usdmColors = ["0000aa","0000ff","00aaff","00ffff","aaff55","ffffff","ffff00","fcd37f","ffaa00","e60000","730000"]
        minColorbar= -2.5
        maxColorbar= 2.5
        colorbarOptions1 = {
            "min":minColorbar,
            "max":maxColorbar,
            "palette": usdmColors}
        minColorbar= -6
        maxColorbar= 6
        colorbarOptions2 = {
            "min":minColorbar,
            "max":maxColorbar,
            "palette": usdmColors}
        Map.addLayer(ee.Image(PDSI.first()), colorbarOptions2, "PDSI")
        Map.addLayer(ee.Image(Z.first()), colorbarOptions2, "Palmer-Z")
        Map.addLayer(ee.Image(SPI2y.first()), colorbarOptions1, "SPI-48months")
        Map.addLayer(ee.Image(SPEI2y.first()), colorbarOptions1, "SPEI-48months")
        Map.addLayer(ee.Image(EDDI2y.first()), colorbarOptions1, "EDDI-48months")

    elif cod == "Germany/Brandenburg/orthos/20cm":
        dataset = ee.Image("Germany/Brandenburg/orthos/20cm")
        Map.setCenter(13.386091, 52.507899, 18)
        Map.addLayer(dataset, None, "Brandenburg 20cm")

    elif cod == "HYCOM/sea_surface_elevation":
        dataset = ee.ImageCollection("HYCOM/sea_surface_elevation").filter(ee.Filter.date("2018-08-01", "2018-08-15"))
        surfaceElevation = dataset.select("surface_elevation")
        surfaceElevationVis = {
        "min": -2000.0,
        "max": 2000.0,
        "palette": ["blue", "cyan", "yellow", "red"],
        }
        Map.setCenter(-28.1, 28.3, 1)
        Map.addLayer(surfaceElevation, surfaceElevationVis, "Surface Elevation")

    elif cod == "HYCOM/sea_temp_salinity":
        dataset = ee.ImageCollection("HYCOM/sea_temp_salinity").filter(ee.Filter.date("2018-08-01", "2018-08-15"))
        seaWaterTemperature = dataset.select("water_temp_0").map(lambda image: ee.Image(image).multiply(0.001).add(20))
        visParams = {
        "min": -2.0,
        "max": 34.0,
        "palette": ["000000", "005aff", "43c8c8", "fff700", "ff0000"],
        }
        Map.setCenter(-88.6, 26.4, 1)
        Map.addLayer(seaWaterTemperature.mean(), visParams, "Sea Water Temperature")


    elif cod == "HYCOM/sea_water_velocity":
        velocity = ee.Image("HYCOM/sea_water_velocity/2014040700").divide(1000)
        Map.addLayer(velocity.select("velocity_u_0").hypot(velocity.select("velocity_v_0")))
        Map.setCenter(-60, 33, 5)

    elif cod == "IDAHO_EPSCOR/GRIDMET":
        dataset = ee.ImageCollection("IDAHO_EPSCOR/GRIDMET").filter(ee.Filter.date("2018-08-01", "2018-08-15"))
        maximumTemperature = dataset.select("tmmx")
        maximumTemperatureVis = {
        "min": 290.0,
        "max": 314.0,
        "palette": ["d8d8d8", "4addff", "5affa3", "f2ff89", "ff725c"],
        }
        Map.setCenter(-115.356, 38.686, 5)
        Map.addLayer(maximumTemperature, maximumTemperatureVis, "Maximum Temperature")

    elif cod == "IDAHO_EPSCOR/MACAv2_METDATA":
        dataset = ee.ImageCollection("IDAHO_EPSCOR/MACAv2_METDATA").filter(ee.Filter.date("2018-08-01", "2018-08-15"))
        maximumTemperature = dataset.select("tasmax")
        maximumTemperatureVis = {
        "min": 290.0,
        "max": 314.0,
        "palette": ["d8d8d8", "4addff", "5affa3", "f2ff89", "ff725c"],
        }
        Map.setCenter(-84.37, 33.5, 5)
        Map.addLayer(maximumTemperature, maximumTemperatureVis, "Maximum Temperature")

    elif cod == "IDAHO_EPSCOR/MACAv2_METDATA_MONTHLY":
        dataset = ee.ImageCollection("IDAHO_EPSCOR/MACAv2_METDATA_MONTHLY").filter(ee.Filter.date("2018-07-01", "2018-08-01"))
        maximumTemperature = dataset.select("tasmax")
        maximumTemperatureVis = {
        "min": 290.0,
        "max": 314.0,
        "palette": ["d8d8d8", "4addff", "5affa3", "f2ff89", "ff725c"],
        }
        Map.setCenter(-115.356, 38.686, 5)
        Map.addLayer(maximumTemperature, maximumTemperatureVis, "Maximum Temperature")

    elif cod == "IDAHO_EPSCOR/TERRACLIMATE":
        dataset = ee.ImageCollection("IDAHO_EPSCOR/TERRACLIMATE").filter(ee.Filter.date("2017-07-01", "2017-08-01"))
        maximumTemperature = dataset.select("tmmx")
        maximumTemperatureVis = {
        "min": -300.0,
        "max": 300.0,
        "palette": ["1a3678", "2955bc", "5699ff", "8dbae9", "acd1ff", "caebff", "e5f9ff",
                    "fdffb4", "ffe6a2", "ffc969", "ffa12d", "ff7c1f", "ca531a", "ff0000", "ab0000"]}
        Map.setCenter(71.72, 52.48, 3)
        Map.addLayer(maximumTemperature, maximumTemperatureVis, "Maximum Temperature")

    elif cod == "IGN/RGE_ALTI/1M/2_0/FXX":
        dataset = ee.Image("IGN/RGE_ALTI/1M/2_0/FXX")
        elevation = dataset.select("MNT")
        elevationVis = {
            "min": 0,
            "max": 1000,
            "palette": ["006600", "002200", "fff700", "ab7634", "c4d0ff", "ffffff"]}
        Map.addLayer(elevation, elevationVis, "Elevation")
        Map.setCenter(3, 47, 5)

    ###https://developers.google.com/earth-engine/datasets/catalog/ISDASOIL_Africa_v1_aluminium_extractable

    elif cod == "ISDASOIL/Africa/v1/aluminium_extractable":
        mean_0_20 = """
            <RasterSymbolizer>
            <ColorMap type="ramp">
            <ColorMapEntry color="#000004" label="0-21.2" opacity="1" quantity="31"/>
            <ColorMapEntry color="#0C0927" label="21.2-35.6" opacity="1" quantity="36"/>
            <ColorMapEntry color="#231151" label="35.6-53.6" opacity="1" quantity="40"/>
            <ColorMapEntry color="#410F75" label="53.6-65.7" opacity="1" quantity="42"/>
            <ColorMapEntry color="#5F187F" label="65.7-72.7" opacity="1" quantity="43"/>
            <ColorMapEntry color="#7B2382" label="72.7-80.5" opacity="1" quantity="44"/>
            <ColorMapEntry color="#982D80" label="80.5-89" opacity="1" quantity="45"/>
            <ColorMapEntry color="#B63679" label="89-98.5" opacity="1" quantity="46"/>
            <ColorMapEntry color="#D3436E" label="98.5-108.9" opacity="1" quantity="47"/>
            <ColorMapEntry color="#EB5760" label="108.9-120.5" opacity="1" quantity="48"/>
            <ColorMapEntry color="#F8765C" label="120.5-133.3" opacity="1" quantity="49"/>
            <ColorMapEntry color="#FD9969" label="133.3-147.4" opacity="1" quantity="50"/>
            <ColorMapEntry color="#FEBA80" label="147.4-163" opacity="1" quantity="51"/>
            <ColorMapEntry color="#FDDC9E" label="163-199.3" opacity="1" quantity="53"/>
            <ColorMapEntry color="#FCFDBF" label="199.3-1800" opacity="1" quantity="55"/>
            </ColorMap>
            <ContrastEnhancement/>
            </RasterSymbolizer> """
        mean_20_50 = """
            <RasterSymbolizer>
            <ColorMap type="ramp">
            <ColorMapEntry color="#000004" label="0-21.2" opacity="1" quantity="31"/>
            <ColorMapEntry color="#0C0927" label="21.2-35.6" opacity="1" quantity="36"/>
            <ColorMapEntry color="#231151" label="35.6-53.6" opacity="1" quantity="40"/>
            <ColorMapEntry color="#410F75" label="53.6-65.7" opacity="1" quantity="42"/>
            <ColorMapEntry color="#5F187F" label="65.7-72.7" opacity="1" quantity="43"/>
            <ColorMapEntry color="#7B2382" label="72.7-80.5" opacity="1" quantity="44"/>
            <ColorMapEntry color="#982D80" label="80.5-89" opacity="1" quantity="45"/>
            <ColorMapEntry color="#B63679" label="89-98.5" opacity="1" quantity="46"/>
            <ColorMapEntry color="#D3436E" label="98.5-108.9" opacity="1" quantity="47"/>
            <ColorMapEntry color="#EB5760" label="108.9-120.5" opacity="1" quantity="48"/>
            <ColorMapEntry color="#F8765C" label="120.5-133.3" opacity="1" quantity="49"/>
            <ColorMapEntry color="#FD9969" label="133.3-147.4" opacity="1" quantity="50"/>
            <ColorMapEntry color="#FEBA80" label="147.4-163" opacity="1" quantity="51"/>
            <ColorMapEntry color="#FDDC9E" label="163-199.3" opacity="1" quantity="53"/>
            <ColorMapEntry color="#FCFDBF" label="199.3-1800" opacity="1" quantity="55"/>
            </ColorMap>
            <ContrastEnhancement/>
            </RasterSymbolizer> """
        stdev_0_20 = """
            <RasterSymbolizer>
            <ColorMap type="ramp">
            <ColorMapEntry color="#fde725" label="low" opacity="1" quantity="5"/>
            <ColorMapEntry color="#5dc962" label=" " opacity="1" quantity="9"/>
            <ColorMapEntry color="#20908d" label=" " opacity="1" quantity="10"/>
            <ColorMapEntry color="#3a528b" label=" " opacity="1" quantity="12"/>
            <ColorMapEntry color="#440154" label="high" opacity="1" quantity="14"/>
            </ColorMap>
            <ContrastEnhancement/>
            </RasterSymbolizer> """
        stdev_20_50 = """
            <RasterSymbolizer>
            <ColorMap type="ramp">
            <ColorMapEntry color="#fde725" label="low" opacity="1" quantity="5"/>
            <ColorMapEntry color="#5dc962" label=" " opacity="1" quantity="9"/>
            <ColorMapEntry color="#20908d" label=" " opacity="1" quantity="10"/>
            <ColorMapEntry color="#3a528b" label=" " opacity="1" quantity="12"/>
            <ColorMapEntry color="#440154" label="high" opacity="1" quantity="14"/>
            </ColorMap>
            <ContrastEnhancement/>
            </RasterSymbolizer> """
        Map.setCenter(25, -3, 2)
        raw = ee.Image("ISDASOIL/Africa/v1/aluminium_extractable")
        Map.addLayer(
        raw.select(0).sldStyle(mean_0_20), {},"Aluminium, extractable, mean visualization, 0-20 cm")
        Map.addLayer(raw.select(1).sldStyle(mean_20_50), {},"Aluminium, extractable, mean visualization, 20-50 cm")
        Map.addLayer(raw.select(2).sldStyle(stdev_0_20), {},"Aluminium, extractable, stdev visualization, 0-20 cm")
        Map.addLayer(raw.select(3).sldStyle(stdev_20_50), {},"Aluminium, extractable, stdev visualization, 20-50 cm")
        converted = raw.divide(10).exp().subtract(1)
        Map.addLayer(converted.select(0), {"min": 0, "max": 100},"Aluminium, extractable, mean, 0-20 cm")

    elif cod == "ISDASOIL/Africa/v1/bedrock_depth":
        mean_0_200 = """
        <RasterSymbolizer>
        <ColorMap type="ramp">
        <ColorMapEntry color="#00204D" label="0-13" opacity="1" quantity="13"/>
        <ColorMapEntry color="#002D6C" label="13-26" opacity="1" quantity="26"/>
        <ColorMapEntry color="#16396D" label="26-39" opacity="1" quantity="39"/>
        <ColorMapEntry color="#36476B" label="39-52" opacity="1" quantity="52"/>
        <ColorMapEntry color="#4B546C" label="52-65" opacity="1" quantity="65"/>
        <ColorMapEntry color="#5C616E" label="65-78" opacity="1" quantity="78"/>
        <ColorMapEntry color="#6C6E72" label="78-91" opacity="1" quantity="91"/>
        <ColorMapEntry color="#7C7B78" label="91-104" opacity="1" quantity="104"/>
        <ColorMapEntry color="#8E8A79" label="104-117" opacity="1" quantity="117"/>
        <ColorMapEntry color="#A09877" label="117-130" opacity="1" quantity="130"/>
        <ColorMapEntry color="#B3A772" label="130-143" opacity="1" quantity="143"/>
        <ColorMapEntry color="#C6B66B" label="143-156" opacity="1" quantity="156"/>
        <ColorMapEntry color="#DBC761" label="156-169" opacity="1" quantity="169"/>
        <ColorMapEntry color="#F0D852" label="169-182" opacity="1" quantity="182"/>
        <ColorMapEntry color="#FFEA46" label="182-200" opacity="1" quantity="195"/>
        </ColorMap>
        <ContrastEnhancement/>
        </RasterSymbolizer> """
        stdev_0_200 = """
        <RasterSymbolizer>
        <ColorMap type="ramp">
        <ColorMapEntry color="#fde725" label="low" opacity="1" quantity="14"/>
        <ColorMapEntry color="#5dc962" label=" " opacity="1" quantity="18"/>
        <ColorMapEntry color="#20908d" label=" " opacity="1" quantity="21"/>
        <ColorMapEntry color="#3a528b" label=" " opacity="1" quantity="22"/>
        <ColorMapEntry color="#440154" label="high" opacity="1" quantity="25"/>
        </ColorMap>
        <ContrastEnhancement/>
        </RasterSymbolizer> """
        raw = ee.Image("ISDASOIL/Africa/v1/bedrock_depth")
        Map.addLayer(raw.select(0).sldStyle(mean_0_200), {},"Bedrock depth, mean visualization, 0-200 cm")
        Map.addLayer(raw.select(1).sldStyle(stdev_0_200), {},"Bedrock depth, stdev visualization, 0-200 cm")
        visualization = {"min": 27, "max": 200}
        Map.setCenter(25, -3, 2)
        Map.addLayer(raw.select(0), visualization, "Bedrock depth, mean, 0-200 cm")

    elif cod == "ISDASOIL/Africa/v1/bulk_density":
        mean_0_20 = """
        <RasterSymbolizer>
        <ColorMap type="ramp">
        <ColorMapEntry color="#00204D" label="0.8-1.05" opacity="1" quantity="105"/>
        <ColorMapEntry color="#002D6C" label="1.05-1.19" opacity="1" quantity="119"/>
        <ColorMapEntry color="#16396D" label="1.19-1.23" opacity="1" quantity="123"/>
        <ColorMapEntry color="#36476B" label="1.23-1.25" opacity="1" quantity="125"/>
        <ColorMapEntry color="#4B546C" label="1.25-1.28" opacity="1" quantity="128"/>
        <ColorMapEntry color="#5C616E" label="1.28-1.31" opacity="1" quantity="131"/>
        <ColorMapEntry color="#6C6E72" label="1.31-1.34" opacity="1" quantity="134"/>
        <ColorMapEntry color="#7C7B78" label="1.34-1.36" opacity="1" quantity="136"/>
        <ColorMapEntry color="#8E8A79" label="1.36-1.38" opacity="1" quantity="138"/>
        <ColorMapEntry color="#A09877" label="1.38-1.41" opacity="1" quantity="141"/>
        <ColorMapEntry color="#B3A772" label="1.41-1.43" opacity="1" quantity="143"/>
        <ColorMapEntry color="#C6B66B" label="1.43-1.45" opacity="1" quantity="145"/>
        <ColorMapEntry color="#DBC761" label="1.45-1.48" opacity="1" quantity="148"/>
        <ColorMapEntry color="#F0D852" label="1.48-1.51" opacity="1" quantity="151"/>
        <ColorMapEntry color="#FFEA46" label="1.51-1.85" opacity="1" quantity="154"/>
        </ColorMap>
        <ContrastEnhancement/>
        </RasterSymbolizer> """
        mean_20_50 = """
        <RasterSymbolizer>
        <ColorMap type="ramp">
        <ColorMapEntry color="#00204D" label="0.8-1.05" opacity="1" quantity="105"/>
        <ColorMapEntry color="#002D6C" label="1.05-1.19" opacity="1" quantity="119"/>
        <ColorMapEntry color="#16396D" label="1.19-1.23" opacity="1" quantity="123"/>
        <ColorMapEntry color="#36476B" label="1.23-1.25" opacity="1" quantity="125"/>
        <ColorMapEntry color="#4B546C" label="1.25-1.28" opacity="1" quantity="128"/>
        <ColorMapEntry color="#5C616E" label="1.28-1.31" opacity="1" quantity="131"/>
        <ColorMapEntry color="#6C6E72" label="1.31-1.34" opacity="1" quantity="134"/>
        <ColorMapEntry color="#7C7B78" label="1.34-1.36" opacity="1" quantity="136"/>
        <ColorMapEntry color="#8E8A79" label="1.36-1.38" opacity="1" quantity="138"/>
        <ColorMapEntry color="#A09877" label="1.38-1.41" opacity="1" quantity="141"/>
        <ColorMapEntry color="#B3A772" label="1.41-1.43" opacity="1" quantity="143"/>
        <ColorMapEntry color="#C6B66B" label="1.43-1.45" opacity="1" quantity="145"/>
        <ColorMapEntry color="#DBC761" label="1.45-1.48" opacity="1" quantity="148"/>
        <ColorMapEntry color="#F0D852" label="1.48-1.51" opacity="1" quantity="151"/>
        <ColorMapEntry color="#FFEA46" label="1.51-1.85" opacity="1" quantity="154"/>
        </ColorMap>
        <ContrastEnhancement/>
        </RasterSymbolizer> """
        stdev_0_20 = """
        <RasterSymbolizer>
        <ColorMap type="ramp">
        <ColorMapEntry color="#fde725" label="low" opacity="1" quantity="2"/>
        <ColorMapEntry color="#5dc962" label=" " opacity="1" quantity="4"/>
        <ColorMapEntry color="#20908d" label=" " opacity="1" quantity="5"/>
        <ColorMapEntry color="#3a528b" label=" " opacity="1" quantity="7"/>
        <ColorMapEntry color="#440154" label="high" opacity="1" quantity="9"/>
        </ColorMap>
        <ContrastEnhancement/>
        </RasterSymbolizer> """
        stdev_20_50 = """
        <RasterSymbolizer>
        <ColorMap type="ramp">
        <ColorMapEntry color="#fde725" label="low" opacity="1" quantity="2"/>
        <ColorMapEntry color="#5dc962" label=" " opacity="1" quantity="4"/>
        <ColorMapEntry color="#20908d" label=" " opacity="1" quantity="5"/>
        <ColorMapEntry color="#3a528b" label=" " opacity="1" quantity="7"/>
        <ColorMapEntry color="#440154" label="high" opacity="1" quantity="9"/>
        </ColorMap>
        <ContrastEnhancement/>
        </RasterSymbolizer> """
        raw = ee.Image("ISDASOIL/Africa/v1/bulk_density")
        Map.addLayer(raw.select(0).sldStyle(mean_0_20), {},"Bulk density, mean visualization, 0-20 cm")
        Map.addLayer(raw.select(1).sldStyle(mean_20_50), {},"Bulk density, mean visualization, 20-50 cm")
        Map.addLayer(raw.select(2).sldStyle(stdev_0_20), {},"Bulk density, stdev visualization, 0-20 cm")
        Map.addLayer(raw.select(3).sldStyle(stdev_20_50), {},"Bulk density, stdev visualization, 20-50 cm")
        converted = raw.divide(100)
        visualization = {"min": 1, "max": 1.5}
        Map.setCenter(25, -3, 2)
        Map.addLayer(converted.select(0), visualization, "Bulk density, mean, 0-20 cm")

    elif cod == "ISDASOIL/Africa/v1/calcium_extractable":
        mean_0_20 = """
        <RasterSymbolizer>
        <ColorMap type="ramp">
        <ColorMapEntry color="#0D0887" label="0-65.7" opacity="1" quantity="42"/>
        <ColorMapEntry color="#350498" label="65.7-120.5" opacity="1" quantity="48"/>
        <ColorMapEntry color="#5402A3" label="120.5-163" opacity="1" quantity="51"/>
        <ColorMapEntry color="#7000A8" label="163-199.3" opacity="1" quantity="53"/>
        <ColorMapEntry color="#8B0AA5" label="199.3-269.4" opacity="1" quantity="56"/>
        <ColorMapEntry color="#A31E9A" label="269.4-329.3" opacity="1" quantity="58"/>
        <ColorMapEntry color="#B93289" label="329.3-402.4" opacity="1" quantity="60"/>
        <ColorMapEntry color="#CC4678" label="402.4-491.7" opacity="1" quantity="62"/>
        <ColorMapEntry color="#DB5C68" label="491.7-600.8" opacity="1" quantity="64"/>
        <ColorMapEntry color="#E97158" label="600.8-664.1" opacity="1" quantity="65"/>
        <ColorMapEntry color="#F48849" label="664.1-811.4" opacity="1" quantity="67"/>
        <ColorMapEntry color="#FBA139" label="811.4-896.8" opacity="1" quantity="68"/>
        <ColorMapEntry color="#FEBC2A" label="896.8-1095.6" opacity="1" quantity="70"/>
        <ColorMapEntry color="#FADA24" label="1095.6-1479.3" opacity="1" quantity="73"/>
        <ColorMapEntry color="#F0F921" label="1479.3-12000" opacity="1" quantity="77"/>
        </ColorMap>
        <ContrastEnhancement/>
        </RasterSymbolizer> """
        mean_20_50 = """
        <RasterSymbolizer>
        <ColorMap type="ramp">
        <ColorMapEntry color="#0D0887" label="0-65.7" opacity="1" quantity="42"/>
        <ColorMapEntry color="#350498" label="65.7-120.5" opacity="1" quantity="48"/>
        <ColorMapEntry color="#5402A3" label="120.5-163" opacity="1" quantity="51"/>
        <ColorMapEntry color="#7000A8" label="163-199.3" opacity="1" quantity="53"/>
        <ColorMapEntry color="#8B0AA5" label="199.3-269.4" opacity="1" quantity="56"/>
        <ColorMapEntry color="#A31E9A" label="269.4-329.3" opacity="1" quantity="58"/>
        <ColorMapEntry color="#B93289" label="329.3-402.4" opacity="1" quantity="60"/>
        <ColorMapEntry color="#CC4678" label="402.4-491.7" opacity="1" quantity="62"/>
        <ColorMapEntry color="#DB5C68" label="491.7-600.8" opacity="1" quantity="64"/>
        <ColorMapEntry color="#E97158" label="600.8-664.1" opacity="1" quantity="65"/>
        <ColorMapEntry color="#F48849" label="664.1-811.4" opacity="1" quantity="67"/>
        <ColorMapEntry color="#FBA139" label="811.4-896.8" opacity="1" quantity="68"/>
        <ColorMapEntry color="#FEBC2A" label="896.8-1095.6" opacity="1" quantity="70"/>
        <ColorMapEntry color="#FADA24" label="1095.6-1479.3" opacity="1" quantity="73"/>
        <ColorMapEntry color="#F0F921" label="1479.3-12000" opacity="1" quantity="77"/>
        </ColorMap>
        <ContrastEnhancement/>
        </RasterSymbolizer> """
        stdev_0_20 = """
        <RasterSymbolizer>
        <ColorMap type="ramp">
        <ColorMapEntry color="#fde725" label="low" opacity="1" quantity="1"/>
        <ColorMapEntry color="#5dc962" label=" " opacity="1" quantity="2"/>
        <ColorMapEntry color="#20908d" label=" " opacity="1" quantity="3"/>
        <ColorMapEntry color="#3a528b" label=" " opacity="1" quantity="4"/>
        <ColorMapEntry color="#440154" label="high" opacity="1" quantity="5"/>
        </ColorMap>
        <ContrastEnhancement/>
        </RasterSymbolizer> """
        stdev_20_50 = """
        <RasterSymbolizer>
        <ColorMap type="ramp">
        <ColorMapEntry color="#fde725" label="low" opacity="1" quantity="1"/>
        <ColorMapEntry color="#5dc962" label=" " opacity="1" quantity="2"/>
        <ColorMapEntry color="#20908d" label=" " opacity="1" quantity="3"/>
        <ColorMapEntry color="#3a528b" label=" " opacity="1" quantity="4"/>
        <ColorMapEntry color="#440154" label="high" opacity="1" quantity="5"/>
        </ColorMap>
        <ContrastEnhancement/>
        </RasterSymbolizer> """
        raw = ee.Image("ISDASOIL/Africa/v1/calcium_extractable")
        Map.addLayer(raw.select(0).sldStyle(mean_0_20), {},"Calcium, extractable, mean visualization, 0-20 cm")
        Map.addLayer(raw.select(1).sldStyle(mean_20_50), {},"Calcium, extractable, mean visualization, 20-50 cm")
        Map.addLayer(raw.select(2).sldStyle(stdev_0_20), {},"Calcium, extractable, stdev visualization, 0-20 cm")
        Map.addLayer(raw.select(3).sldStyle(stdev_20_50), {},"Calcium, extractable, stdev visualization, 20-50 cm")
        converted = raw.divide(10).exp().subtract(1)
        visualization = {"min": 100, "max": 2000}
        Map.setCenter(25, -3, 2)
        Map.addLayer(converted.select(0), visualization, "Calcium, extractable, mean, 0-20 cm")

    elif cod == "ISDASOIL/Africa/v1/carbon_organic":
        mean_0_20 = """
        <RasterSymbolizer>
        <ColorMap type="ramp">
        <ColorMapEntry color="#000004" label="0-2.3" opacity="1" quantity="12"/>
        <ColorMapEntry color="#0C0927" label="2.3-3.5" opacity="1" quantity="15"/>
        <ColorMapEntry color="#231151" label="3.5-4" opacity="1" quantity="16"/>
        <ColorMapEntry color="#410F75" label="4-4.5" opacity="1" quantity="17"/>
        <ColorMapEntry color="#5F187F" label="4.5-5" opacity="1" quantity="18"/>
        <ColorMapEntry color="#7B2382" label="5-5.7" opacity="1" quantity="19"/>
        <ColorMapEntry color="#982D80" label="5.7-6.4" opacity="1" quantity="20"/>
        <ColorMapEntry color="#B63679" label="6.4-7.2" opacity="1" quantity="21"/>
        <ColorMapEntry color="#D3436E" label="7.2-8" opacity="1" quantity="22"/>
        <ColorMapEntry color="#EB5760" label="8-9" opacity="1" quantity="23"/>
        <ColorMapEntry color="#F8765C" label="9-10" opacity="1" quantity="24"/>
        <ColorMapEntry color="#FD9969" label="10-11.2" opacity="1" quantity="25"/>
        <ColorMapEntry color="#FEBA80" label="11.2-12.5" opacity="1" quantity="26"/>
        <ColorMapEntry color="#FDDC9E" label="12.5-13.9" opacity="1" quantity="27"/>
        <ColorMapEntry color="#FCFDBF" label="13.9-40" opacity="1" quantity="28"/>
        </ColorMap>
        <ContrastEnhancement/>
        </RasterSymbolizer> """
        mean_20_50 = """
        <RasterSymbolizer>
        <ColorMap type="ramp">
        <ColorMapEntry color="#000004" label="0-2.3" opacity="1" quantity="12"/>
        <ColorMapEntry color="#0C0927" label="2.3-3.5" opacity="1" quantity="15"/>
        <ColorMapEntry color="#231151" label="3.5-4" opacity="1" quantity="16"/>
        <ColorMapEntry color="#410F75" label="4-4.5" opacity="1" quantity="17"/>
        <ColorMapEntry color="#5F187F" label="4.5-5" opacity="1" quantity="18"/>
        <ColorMapEntry color="#7B2382" label="5-5.7" opacity="1" quantity="19"/>
        <ColorMapEntry color="#982D80" label="5.7-6.4" opacity="1" quantity="20"/>
        <ColorMapEntry color="#B63679" label="6.4-7.2" opacity="1" quantity="21"/>
        <ColorMapEntry color="#D3436E" label="7.2-8" opacity="1" quantity="22"/>
        <ColorMapEntry color="#EB5760" label="8-9" opacity="1" quantity="23"/>
        <ColorMapEntry color="#F8765C" label="9-10" opacity="1" quantity="24"/>
        <ColorMapEntry color="#FD9969" label="10-11.2" opacity="1" quantity="25"/>
        <ColorMapEntry color="#FEBA80" label="11.2-12.5" opacity="1" quantity="26"/>
        <ColorMapEntry color="#FDDC9E" label="12.5-13.9" opacity="1" quantity="27"/>
        <ColorMapEntry color="#FCFDBF" label="13.9-40" opacity="1" quantity="28"/>
        </ColorMap>
        <ContrastEnhancement/>
        </RasterSymbolizer> """
        stdev_0_20 = """
        <RasterSymbolizer>
        <ColorMap type="ramp">
        <ColorMapEntry color="#fde725" label="low" opacity="1" quantity="1"/>
        <ColorMapEntry color="#5dc962" label=" " opacity="1" quantity="2"/>
        <ColorMapEntry color="#20908d" label=" " opacity="1" quantity="3"/>
        <ColorMapEntry color="#3a528b" label=" " opacity="1" quantity="4"/>
        <ColorMapEntry color="#440154" label="high" opacity="1" quantity="5"/>
        </ColorMap>
        <ContrastEnhancement/>
        </RasterSymbolizer> """
        stdev_20_50 = """
        <RasterSymbolizer>
        <ColorMap type="ramp">
        <ColorMapEntry color="#fde725" label="low" opacity="1" quantity="1"/>
        <ColorMapEntry color="#5dc962" label=" " opacity="1" quantity="2"/>
        <ColorMapEntry color="#20908d" label=" " opacity="1" quantity="3"/>
        <ColorMapEntry color="#3a528b" label=" " opacity="1" quantity="4"/>
        <ColorMapEntry color="#440154" label="high" opacity="1" quantity="5"/>
        </ColorMap>
        <ContrastEnhancement/>
        </RasterSymbolizer> """
        raw = ee.Image("ISDASOIL/Africa/v1/carbon_organic")
        Map.addLayer(raw.select(0).sldStyle(mean_0_20), {},"Carbon, organic, mean visualization, 0-20 cm")
        Map.addLayer(raw.select(1).sldStyle(mean_20_50), {},"Carbon, organic, mean visualization, 20-50 cm")
        Map.addLayer(raw.select(2).sldStyle(stdev_0_20), {},"Carbon, organic, stdev visualization, 0-20 cm")
        Map.addLayer(raw.select(3).sldStyle(stdev_20_50), {},"Carbon, organic, stdev visualization, 20-50 cm")
        converted = raw.divide(10).exp().subtract(1)
        visualization = {"min": 0, "max": 20}
        Map.setCenter(25, -3, 2)
        Map.addLayer(converted.select(0), visualization, "Carbon, organic, mean, 0-20 cm")

    elif cod == "ISDASOIL/Africa/v1/carbon_total":
        mean_0_20 = """
        <RasterSymbolizer>
        <ColorMap type="ramp">
        <ColorMapEntry color="#000004" label="0-2" opacity="1" quantity="11"/>
        <ColorMapEntry color="#0C0927" label="2-5.7" opacity="1" quantity="19"/>
        <ColorMapEntry color="#231151" label="5.7-10" opacity="1" quantity="24"/>
        <ColorMapEntry color="#410F75" label="10-12.5" opacity="1" quantity="26"/>
        <ColorMapEntry color="#5F187F" label="12.5-13.9" opacity="1" quantity="27"/>
        <ColorMapEntry color="#7B2382" label="13.9-15.4" opacity="1" quantity="28"/>
        <ColorMapEntry color="#982D80" label="15.4-17.2" opacity="1" quantity="29"/>
        <ColorMapEntry color="#B63679" label="17.2-19.1" opacity="1" quantity="30"/>
        <ColorMapEntry color="#D3436E" label="19.1-21.2" opacity="1" quantity="31"/>
        <ColorMapEntry color="#EB5760" label="21.2-23.5" opacity="1" quantity="32"/>
        <ColorMapEntry color="#F8765C" label="23.5-26.1" opacity="1" quantity="33"/>
        <ColorMapEntry color="#FD9969" label="26.1-29" opacity="1" quantity="34"/>
        <ColorMapEntry color="#FEBA80" label="29-32.1" opacity="1" quantity="35"/>
        <ColorMapEntry color="#FDDC9E" label="32.1-35.6" opacity="1" quantity="36"/>
        <ColorMapEntry color="#FCFDBF" label="35.6-40" opacity="1" quantity="39"/>
        </ColorMap>
        <ContrastEnhancement/>
        </RasterSymbolizer> """
        mean_20_50 = """
        <RasterSymbolizer>
        <ColorMap type="ramp">
        <ColorMapEntry color="#000004" label="0-2" opacity="1" quantity="11"/>
        <ColorMapEntry color="#0C0927" label="2-5.7" opacity="1" quantity="19"/>
        <ColorMapEntry color="#231151" label="5.7-10" opacity="1" quantity="24"/>
        <ColorMapEntry color="#410F75" label="10-12.5" opacity="1" quantity="26"/>
        <ColorMapEntry color="#5F187F" label="12.5-13.9" opacity="1" quantity="27"/>
        <ColorMapEntry color="#7B2382" label="13.9-15.4" opacity="1" quantity="28"/>
        <ColorMapEntry color="#982D80" label="15.4-17.2" opacity="1" quantity="29"/>
        <ColorMapEntry color="#B63679" label="17.2-19.1" opacity="1" quantity="30"/>
        <ColorMapEntry color="#D3436E" label="19.1-21.2" opacity="1" quantity="31"/>
        <ColorMapEntry color="#EB5760" label="21.2-23.5" opacity="1" quantity="32"/>
        <ColorMapEntry color="#F8765C" label="23.5-26.1" opacity="1" quantity="33"/>
        <ColorMapEntry color="#FD9969" label="26.1-29" opacity="1" quantity="34"/>
        <ColorMapEntry color="#FEBA80" label="29-32.1" opacity="1" quantity="35"/>
        <ColorMapEntry color="#FDDC9E" label="32.1-35.6" opacity="1" quantity="36"/>
        <ColorMapEntry color="#FCFDBF" label="35.6-40" opacity="1" quantity="39"/>
        </ColorMap>
        <ContrastEnhancement/>
        </RasterSymbolizer> """
        stdev_0_20 = """
        <RasterSymbolizer>
        <ColorMap type="ramp">
        <ColorMapEntry color="#fde725" label="low" opacity="1" quantity="1"/>
        <ColorMapEntry color="#5dc962" label=" " opacity="1" quantity="3"/>
        <ColorMapEntry color="#20908d" label=" " opacity="1" quantity="4"/>
        <ColorMapEntry color="#3a528b" label=" " opacity="1" quantity="5"/>
        <ColorMapEntry color="#440154" label="high" opacity="1" quantity="6"/>
        </ColorMap>
        <ContrastEnhancement/>
        </RasterSymbolizer> """
        stdev_20_50 = """
        <RasterSymbolizer>
        <ColorMap type="ramp">
        <ColorMapEntry color="#fde725" label="low" opacity="1" quantity="1"/>
        <ColorMapEntry color="#5dc962" label=" " opacity="1" quantity="3"/>
        <ColorMapEntry color="#20908d" label=" " opacity="1" quantity="4"/>
        <ColorMapEntry color="#3a528b" label=" " opacity="1" quantity="5"/>
        <ColorMapEntry color="#440154" label="high" opacity="1" quantity="6"/>
        </ColorMap>
        <ContrastEnhancement/>
        </RasterSymbolizer> """
        raw = ee.Image("ISDASOIL/Africa/v1/carbon_total")
        Map.addLayer(raw.select(0).sldStyle(mean_0_20), {},"Carbon, total, mean visualization, 0-20 cm")
        Map.addLayer(raw.select(1).sldStyle(mean_20_50), {},"Carbon, total, mean visualization, 20-50 cm")
        Map.addLayer(raw.select(2).sldStyle(stdev_0_20), {},"Carbon, total, stdev visualization, 0-20 cm")
        Map.addLayer(raw.select(3).sldStyle(stdev_20_50), {},"Carbon, total, stdev visualization, 20-50 cm")
        converted = raw.divide(10).exp().subtract(1)
        visualization = {"min": 0, "max": 60}
        Map.setCenter(25, -3, 2)
        Map.addLayer(converted.select(0), visualization, "Carbon, total, mean, 0-20 cm")

    elif cod == "ISDASOIL/Africa/v1/cation_exchange_capacity":
        mean_0_20 = """
        <RasterSymbolizer>
        <ColorMap type="ramp">
        <ColorMapEntry color="#000004" label="0-3.5" opacity="1" quantity="15"/>
        <ColorMapEntry color="#0C0927" label="3.5-4.5" opacity="1" quantity="17"/>
        <ColorMapEntry color="#231151" label="4.5-5" opacity="1" quantity="18"/>
        <ColorMapEntry color="#410F75" label="5-6.4" opacity="1" quantity="20"/>
        <ColorMapEntry color="#5F187F" label="6.4-7.2" opacity="1" quantity="21"/>
        <ColorMapEntry color="#7B2382" label="7.2-8" opacity="1" quantity="22"/>
        <ColorMapEntry color="#982D80" label="8-9" opacity="1" quantity="23"/>
        <ColorMapEntry color="#B63679" label="9-10" opacity="1" quantity="24"/>
        <ColorMapEntry color="#D3436E" label="10-11.2" opacity="1" quantity="25"/>
        <ColorMapEntry color="#EB5760" label="11.2-12.5" opacity="1" quantity="26"/>
        <ColorMapEntry color="#F8765C" label="12.5-13.9" opacity="1" quantity="27"/>
        <ColorMapEntry color="#FD9969" label="13.9-15.4" opacity="1" quantity="28"/>
        <ColorMapEntry color="#FEBA80" label="15.4-17.2" opacity="1" quantity="29"/>
        <ColorMapEntry color="#FDDC9E" label="17.2-19.1" opacity="1" quantity="30"/>
        <ColorMapEntry color="#FCFDBF" label="19.1-130" opacity="1" quantity="31"/>
        </ColorMap>
        <ContrastEnhancement/>
        </RasterSymbolizer> """
        mean_20_50 = """
        <RasterSymbolizer>
        <ColorMap type="ramp">
        <ColorMapEntry color="#000004" label="0-3.5" opacity="1" quantity="15"/>
        <ColorMapEntry color="#0C0927" label="3.5-4.5" opacity="1" quantity="17"/>
        <ColorMapEntry color="#231151" label="4.5-5" opacity="1" quantity="18"/>
        <ColorMapEntry color="#410F75" label="5-6.4" opacity="1" quantity="20"/>
        <ColorMapEntry color="#5F187F" label="6.4-7.2" opacity="1" quantity="21"/>
        <ColorMapEntry color="#7B2382" label="7.2-8" opacity="1" quantity="22"/>
        <ColorMapEntry color="#982D80" label="8-9" opacity="1" quantity="23"/>
        <ColorMapEntry color="#B63679" label="9-10" opacity="1" quantity="24"/>
        <ColorMapEntry color="#D3436E" label="10-11.2" opacity="1" quantity="25"/>
        <ColorMapEntry color="#EB5760" label="11.2-12.5" opacity="1" quantity="26"/>
        <ColorMapEntry color="#F8765C" label="12.5-13.9" opacity="1" quantity="27"/>
        <ColorMapEntry color="#FD9969" label="13.9-15.4" opacity="1" quantity="28"/>
        <ColorMapEntry color="#FEBA80" label="15.4-17.2" opacity="1" quantity="29"/>
        <ColorMapEntry color="#FDDC9E" label="17.2-19.1" opacity="1" quantity="30"/>
        <ColorMapEntry color="#FCFDBF" label="19.1-130" opacity="1" quantity="31"/>
        </ColorMap>
        <ContrastEnhancement/>
        </RasterSymbolizer> """
        stdev_0_20 = """
        <RasterSymbolizer>
        <ColorMap type="ramp">
        <ColorMapEntry color="#fde725" label="low" opacity="1" quantity="1"/>
        <ColorMapEntry color="#5dc962" label=" " opacity="1" quantity="2"/>
        <ColorMapEntry color="#20908d" label=" " opacity="1" quantity="3"/>
        <ColorMapEntry color="#3a528b" label=" " opacity="1" quantity="4"/>
        <ColorMapEntry color="#440154" label="high" opacity="1" quantity="5"/>
        </ColorMap>
        <ContrastEnhancement/>
        </RasterSymbolizer> """
        stdev_20_50 = """
        <RasterSymbolizer>
        <ColorMap type="ramp">
        <ColorMapEntry color="#fde725" label="low" opacity="1" quantity="1"/>
        <ColorMapEntry color="#5dc962" label=" " opacity="1" quantity="2"/>
        <ColorMapEntry color="#20908d" label=" " opacity="1" quantity="3"/>
        <ColorMapEntry color="#3a528b" label=" " opacity="1" quantity="4"/>
        <ColorMapEntry color="#440154" label="high" opacity="1" quantity="5"/>
        </ColorMap>
        <ContrastEnhancement/>
        </RasterSymbolizer> """
        raw = ee.Image("ISDASOIL/Africa/v1/cation_exchange_capacity")
        Map.addLayer(raw.select(0).sldStyle(mean_0_20), {},"Cation exchange capacity, mean visualization, 0-20 cm")
        Map.addLayer(raw.select(1).sldStyle(mean_20_50), {},"Cation exchange capacity, mean visualization, 20-50 cm")
        Map.addLayer(raw.select(2).sldStyle(stdev_0_20), {},"Cation exchange capacity, stdev visualization, 0-20 cm")
        Map.addLayer(raw.select(3).sldStyle(stdev_20_50), {},"Cation exchange capacity, stdev visualization, 20-50 cm")
        converted = raw.divide(10).exp().subtract(1)
        visualization = {"min": 0, "max": 25}
        Map.setCenter(25, -3, 2)
        Map.addLayer(converted.select(0), visualization, "Cation exchange capacity, mean, 0-20 cm")

    elif cod == "ISDASOIL/Africa/v1/clay_content":
        mean_0_20 = """
        <RasterSymbolizer>
        <ColorMap type="ramp">
        <ColorMapEntry color="#00204D" label="0-8" opacity="1" quantity="8"/>
        <ColorMapEntry color="#002D6C" label="8-14" opacity="1" quantity="14"/>
        <ColorMapEntry color="#16396D" label="14-17" opacity="1" quantity="17"/>
        <ColorMapEntry color="#36476B" label="17-19" opacity="1" quantity="19"/>
        <ColorMapEntry color="#4B546C" label="19-21" opacity="1" quantity="21"/>
        <ColorMapEntry color="#5C616E" label="21-22" opacity="1" quantity="22"/>
        <ColorMapEntry color="#6C6E72" label="22-24" opacity="1" quantity="24"/>
        <ColorMapEntry color="#7C7B78" label="24-25" opacity="1" quantity="25"/>
        <ColorMapEntry color="#8E8A79" label="25-26" opacity="1" quantity="26"/>
        <ColorMapEntry color="#A09877" label="26-28" opacity="1" quantity="28"/>
        <ColorMapEntry color="#B3A772" label="28-30" opacity="1" quantity="30"/>
        <ColorMapEntry color="#C6B66B" label="30-31" opacity="1" quantity="31"/>
        <ColorMapEntry color="#DBC761" label="31-33" opacity="1" quantity="33"/>
        <ColorMapEntry color="#F0D852" label="33-36" opacity="1" quantity="36"/>
        <ColorMapEntry color="#FFEA46" label="36-70" opacity="1" quantity="40"/>
        </ColorMap>
        <ContrastEnhancement/>
        </RasterSymbolizer> """
        mean_20_50 = """
        <RasterSymbolizer>
        <ColorMap type="ramp">
        <ColorMapEntry color="#00204D" label="0-8" opacity="1" quantity="8"/>
        <ColorMapEntry color="#002D6C" label="8-14" opacity="1" quantity="14"/>
        <ColorMapEntry color="#16396D" label="14-17" opacity="1" quantity="17"/>
        <ColorMapEntry color="#36476B" label="17-19" opacity="1" quantity="19"/>
        <ColorMapEntry color="#4B546C" label="19-21" opacity="1" quantity="21"/>
        <ColorMapEntry color="#5C616E" label="21-22" opacity="1" quantity="22"/>
        <ColorMapEntry color="#6C6E72" label="22-24" opacity="1" quantity="24"/>
        <ColorMapEntry color="#7C7B78" label="24-25" opacity="1" quantity="25"/>
        <ColorMapEntry color="#8E8A79" label="25-26" opacity="1" quantity="26"/>
        <ColorMapEntry color="#A09877" label="26-28" opacity="1" quantity="28"/>
        <ColorMapEntry color="#B3A772" label="28-30" opacity="1" quantity="30"/>
        <ColorMapEntry color="#C6B66B" label="30-31" opacity="1" quantity="31"/>
        <ColorMapEntry color="#DBC761" label="31-33" opacity="1" quantity="33"/>
        <ColorMapEntry color="#F0D852" label="33-36" opacity="1" quantity="36"/>
        <ColorMapEntry color="#FFEA46" label="36-70" opacity="1" quantity="40"/>
        </ColorMap>
        <ContrastEnhancement/>
        </RasterSymbolizer> """
        stdev_0_20 = """
        <RasterSymbolizer>
        <ColorMap type="ramp">
        <ColorMapEntry color="#fde725" label="low" opacity="1" quantity="1"/>
        <ColorMapEntry color="#5dc962" label=" " opacity="1" quantity="2"/>
        <ColorMapEntry color="#20908d" label=" " opacity="1" quantity="3"/>
        <ColorMapEntry color="#3a528b" label=" " opacity="1" quantity="4"/>
        <ColorMapEntry color="#440154" label="high" opacity="1" quantity="6"/>
        </ColorMap>
        <ContrastEnhancement/>
        </RasterSymbolizer> """
        stdev_20_50 = """
        <RasterSymbolizer>
        <ColorMap type="ramp">
        <ColorMapEntry color="#fde725" label="low" opacity="1" quantity="1"/>
        <ColorMapEntry color="#5dc962" label=" " opacity="1" quantity="2"/>
        <ColorMapEntry color="#20908d" label=" " opacity="1" quantity="3"/>
        <ColorMapEntry color="#3a528b" label=" " opacity="1" quantity="4"/>
        <ColorMapEntry color="#440154" label="high" opacity="1" quantity="6"/>
        </ColorMap>
        <ContrastEnhancement/>
        </RasterSymbolizer> """
        raw = ee.Image("ISDASOIL/Africa/v1/clay_content")
        Map.addLayer(raw.select(0).sldStyle(mean_0_20), {},"Clay content, mean visualization, 0-20 cm")
        Map.addLayer(raw.select(1).sldStyle(mean_20_50), {},"Clay content, mean visualization, 20-50 cm")
        Map.addLayer(raw.select(2).sldStyle(stdev_0_20), {},"Clay content, stdev visualization, 0-20 cm")
        Map.addLayer(raw.select(3).sldStyle(stdev_20_50), {},"Clay content, stdev visualization, 20-50 cm")
        converted = raw.divide(10).exp().subtract(1)
        visualization = {"min": 0, "max": 50}
        Map.setCenter(25, -3, 2)
        Map.addLayer(converted.select(0), visualization, "Clay content, mean, 0-20 cm")

    elif cod == "ISDASOIL/Africa/v1/fcc":
        raw = ee.Image("ISDASOIL/Africa/v1/fcc").select(0)
        converted = ee.Image(raw.mod(3000).copyProperties(raw))
        Map.setCenter(25, -3, 2)
        Map.addLayer(converted, {}, "Fertility Capability Classification")

    elif cod == "ISDASOIL/Africa/v1/iron_extractable":
        mean_0_20 = """
        <RasterSymbolizer>
        <ColorMap type="ramp">
        <ColorMapEntry color="#0D0887" label="0-6.4" opacity="1" quantity="20"/>
        <ColorMapEntry color="#350498" label="6.4-13.9" opacity="1" quantity="27"/>
        <ColorMapEntry color="#5402A3" label="13.9-29" opacity="1" quantity="34"/>
        <ColorMapEntry color="#7000A8" label="29-35.6" opacity="1" quantity="36"/>
        <ColorMapEntry color="#8B0AA5" label="35.6-43.7" opacity="1" quantity="38"/>
        <ColorMapEntry color="#A31E9A" label="43.7-48.4" opacity="1" quantity="39"/>
        <ColorMapEntry color="#B93289" label="48.4-53.6" opacity="1" quantity="40"/>
        <ColorMapEntry color="#CC4678" label="53.6-59.3" opacity="1" quantity="41"/>
        <ColorMapEntry color="#DB5C68" label="59.3-65.7" opacity="1" quantity="42"/>
        <ColorMapEntry color="#E97158" label="65.7-72.7" opacity="1" quantity="43"/>
        <ColorMapEntry color="#F48849" label="72.7-80.5" opacity="1" quantity="44"/>
        <ColorMapEntry color="#FBA139" label="80.5-89" opacity="1" quantity="45"/>
        <ColorMapEntry color="#FEBC2A" label="89-98.5" opacity="1" quantity="46"/>
        <ColorMapEntry color="#FADA24" label="98.5-108.9" opacity="1" quantity="47"/>
        <ColorMapEntry color="#F0F921" label="108.9-1200" opacity="1" quantity="48"/>
        </ColorMap>
        <ContrastEnhancement/>
        </RasterSymbolizer> """
        mean_20_50 = """
        <RasterSymbolizer>
        <ColorMap type="ramp">
        <ColorMapEntry color="#0D0887" label="0-6.4" opacity="1" quantity="20"/>
        <ColorMapEntry color="#350498" label="6.4-13.9" opacity="1" quantity="27"/>
        <ColorMapEntry color="#5402A3" label="13.9-29" opacity="1" quantity="34"/>
        <ColorMapEntry color="#7000A8" label="29-35.6" opacity="1" quantity="36"/>
        <ColorMapEntry color="#8B0AA5" label="35.6-43.7" opacity="1" quantity="38"/>
        <ColorMapEntry color="#A31E9A" label="43.7-48.4" opacity="1" quantity="39"/>
        <ColorMapEntry color="#B93289" label="48.4-53.6" opacity="1" quantity="40"/>
        <ColorMapEntry color="#CC4678" label="53.6-59.3" opacity="1" quantity="41"/>
        <ColorMapEntry color="#DB5C68" label="59.3-65.7" opacity="1" quantity="42"/>
        <ColorMapEntry color="#E97158" label="65.7-72.7" opacity="1" quantity="43"/>
        <ColorMapEntry color="#F48849" label="72.7-80.5" opacity="1" quantity="44"/>
        <ColorMapEntry color="#FBA139" label="80.5-89" opacity="1" quantity="45"/>
        <ColorMapEntry color="#FEBC2A" label="89-98.5" opacity="1" quantity="46"/>
        <ColorMapEntry color="#FADA24" label="98.5-108.9" opacity="1" quantity="47"/>
        <ColorMapEntry color="#F0F921" label="108.9-1200" opacity="1" quantity="48"/>
        </ColorMap>
        <ContrastEnhancement/>
        </RasterSymbolizer> """
        stdev_0_20 = """
        <RasterSymbolizer>
        <ColorMap type="ramp">
        <ColorMapEntry color="#fde725" label="low" opacity="1" quantity="1"/>
        <ColorMapEntry color="#5dc962" label=" " opacity="1" quantity="2"/>
        <ColorMapEntry color="#20908d" label=" " opacity="1" quantity="3"/>
        <ColorMapEntry color="#3a528b" label=" " opacity="1" quantity="4"/>
        <ColorMapEntry color="#440154" label="high" opacity="1" quantity="6"/>
        </ColorMap>
        <ContrastEnhancement/>
        </RasterSymbolizer> """
        stdev_20_50 = """
        <RasterSymbolizer>
        <ColorMap type="ramp">
        <ColorMapEntry color="#fde725" label="low" opacity="1" quantity="1"/>
        <ColorMapEntry color="#5dc962" label=" " opacity="1" quantity="2"/>
        <ColorMapEntry color="#20908d" label=" " opacity="1" quantity="3"/>
        <ColorMapEntry color="#3a528b" label=" " opacity="1" quantity="4"/>
        <ColorMapEntry color="#440154" label="high" opacity="1" quantity="6"/>
        </ColorMap>
        <ContrastEnhancement/>
        </RasterSymbolizer> """
        raw = ee.Image("ISDASOIL/Africa/v1/iron_extractable")
        Map.addLayer(raw.select(0).sldStyle(mean_0_20), {},"Iron, extractable, mean visualization, 0-20 cm")
        Map.addLayer(raw.select(1).sldStyle(mean_20_50), {},"Iron, extractable, mean visualization, 20-50 cm")
        Map.addLayer(raw.select(2).sldStyle(stdev_0_20), {},"Iron, extractable, stdev visualization, 0-20 cm")
        Map.addLayer(raw.select(3).sldStyle(stdev_20_50), {},"Iron, extractable, stdev visualization, 20-50 cm")
        converted = raw.divide(10).exp().subtract(1)
        visualization = {"min": 0, "max": 140}
        Map.setCenter(25, -3, 2)
        Map.addLayer(converted.select(0), visualization, "Iron, extractable, mean, 0-20 cm")

    elif cod == "ISDASOIL/Africa/v1/magnesium_extractable":
        mean_0_20 = """
        <RasterSymbolizer>
        <ColorMap type="ramp">
        <ColorMapEntry color="#0D0887" label="0-19.1" opacity="1" quantity="30"/>
        <ColorMapEntry color="#350498" label="19.1-29" opacity="1" quantity="34"/>
        <ColorMapEntry color="#5402A3" label="29-39.4" opacity="1" quantity="37"/>
        <ColorMapEntry color="#7000A8" label="39.4-53.6" opacity="1" quantity="40"/>
        <ColorMapEntry color="#8B0AA5" label="53.6-72.7" opacity="1" quantity="43"/>
        <ColorMapEntry color="#A31E9A" label="72.7-89" opacity="1" quantity="45"/>
        <ColorMapEntry color="#B93289" label="89-108.9" opacity="1" quantity="47"/>
        <ColorMapEntry color="#CC4678" label="108.9-120.5" opacity="1" quantity="48"/>
        <ColorMapEntry color="#DB5C68" label="120.5-133.3" opacity="1" quantity="49"/>
        <ColorMapEntry color="#E97158" label="133.3-163" opacity="1" quantity="51"/>
        <ColorMapEntry color="#F48849" label="163-180.3" opacity="1" quantity="52"/>
        <ColorMapEntry color="#FBA139" label="180.3-220.4" opacity="1" quantity="54"/>
        <ColorMapEntry color="#FEBC2A" label="220.4-243.7" opacity="1" quantity="55"/>
        <ColorMapEntry color="#FADA24" label="243.7-297.9" opacity="1" quantity="57"/>
        <ColorMapEntry color="#F0F921" label="243.7-1200" opacity="1" quantity="60"/>
        </ColorMap>
        <ContrastEnhancement/>
        </RasterSymbolizer> """
        mean_20_50 = """
        <RasterSymbolizer>
        <ColorMap type="ramp">
        <ColorMapEntry color="#0D0887" label="0-19.1" opacity="1" quantity="30"/>
        <ColorMapEntry color="#350498" label="19.1-29" opacity="1" quantity="34"/>
        <ColorMapEntry color="#5402A3" label="29-39.4" opacity="1" quantity="37"/>
        <ColorMapEntry color="#7000A8" label="39.4-53.6" opacity="1" quantity="40"/>
        <ColorMapEntry color="#8B0AA5" label="53.6-72.7" opacity="1" quantity="43"/>
        <ColorMapEntry color="#A31E9A" label="72.7-89" opacity="1" quantity="45"/>
        <ColorMapEntry color="#B93289" label="89-108.9" opacity="1" quantity="47"/>
        <ColorMapEntry color="#CC4678" label="108.9-120.5" opacity="1" quantity="48"/>
        <ColorMapEntry color="#DB5C68" label="120.5-133.3" opacity="1" quantity="49"/>
        <ColorMapEntry color="#E97158" label="133.3-163" opacity="1" quantity="51"/>
        <ColorMapEntry color="#F48849" label="163-180.3" opacity="1" quantity="52"/>
        <ColorMapEntry color="#FBA139" label="180.3-220.4" opacity="1" quantity="54"/>
        <ColorMapEntry color="#FEBC2A" label="220.4-243.7" opacity="1" quantity="55"/>
        <ColorMapEntry color="#FADA24" label="243.7-297.9" opacity="1" quantity="57"/>
        <ColorMapEntry color="#F0F921" label="243.7-1200" opacity="1" quantity="60"/>
        </ColorMap>
        <ContrastEnhancement/>
        </RasterSymbolizer> """
        stdev_0_20 = """
        <RasterSymbolizer>
        <ColorMap type="ramp">
        <ColorMapEntry color="#fde725" label="low" opacity="1" quantity="1"/>
        <ColorMapEntry color="#5dc962" label=" " opacity="1" quantity="2"/>
        <ColorMapEntry color="#20908d" label=" " opacity="1" quantity="3"/>
        <ColorMapEntry color="#3a528b" label=" " opacity="1" quantity="4"/>
        <ColorMapEntry color="#440154" label="high" opacity="1" quantity="5"/>
        </ColorMap>
        <ContrastEnhancement/>
        </RasterSymbolizer> """
        stdev_20_50 = """
        <RasterSymbolizer>
        <ColorMap type="ramp">
        <ColorMapEntry color="#fde725" label="low" opacity="1" quantity="1"/>
        <ColorMapEntry color="#5dc962" label=" " opacity="1" quantity="2"/>
        <ColorMapEntry color="#20908d" label=" " opacity="1" quantity="3"/>
        <ColorMapEntry color="#3a528b" label=" " opacity="1" quantity="4"/>
        <ColorMapEntry color="#440154" label="high" opacity="1" quantity="5"/>
        </ColorMap>
        <ContrastEnhancement/>
        </RasterSymbolizer> """
        raw = ee.Image("ISDASOIL/Africa/v1/magnesium_extractable")
        Map.addLayer(raw.select(0).sldStyle(mean_0_20), {},"Magnesium, extractable, mean visualization, 0-20 cm")
        Map.addLayer(raw.select(1).sldStyle(mean_20_50), {},"Magnesium, extractable, mean visualization, 20-50 cm")
        Map.addLayer(raw.select(2).sldStyle(stdev_0_20), {},"Magnesium, extractable, stdev visualization, 0-20 cm")
        Map.addLayer(raw.select(3).sldStyle(stdev_20_50), {},"Magnesium, extractable, stdev visualization, 20-50 cm")
        converted = raw.divide(10).exp().subtract(1)
        visualization = {"min": 0, "max": 500}
        Map.setCenter(25, -3, 2)
        Map.addLayer(converted.select(0), visualization, "Magnesium, extractable, mean, 0-20 cm")

    elif cod == "ISDASOIL/Africa/v1/nitrogen_total":
        mean_0_20 = """
        <RasterSymbolizer>
        <ColorMap type="ramp">
        <ColorMapEntry color="#000004" label="0-0.2" opacity="1" quantity="20"/>
        <ColorMapEntry color="#0C0927" label="0.2-0.3" opacity="1" quantity="30"/>
        <ColorMapEntry color="#231151" label="0.3-0.4" opacity="1" quantity="36"/>
        <ColorMapEntry color="#410F75" label="0.4-0.5" opacity="1" quantity="43"/>
        <ColorMapEntry color="#5F187F" label="0.5-0.6" opacity="1" quantity="48"/>
        <ColorMapEntry color="#7B2382" label="0.6-0.7" opacity="1" quantity="52"/>
        <ColorMapEntry color="#982D80" label="0.7-0.8" opacity="1" quantity="58"/>
        <ColorMapEntry color="#B63679" label="0.8-0.9" opacity="1" quantity="64"/>
        <ColorMapEntry color="#D3436E" label="0.9-1" opacity="1" quantity="67"/>
        <ColorMapEntry color="#EB5760" label="1-1.1" opacity="1" quantity="75"/>
        <ColorMapEntry color="#F8765C" label="1.1-1.2" opacity="1" quantity="79"/>
        <ColorMapEntry color="#FD9969" label="1.2-1.3" opacity="1" quantity="83"/>
        <ColorMapEntry color="#FEBA80" label="1.3-1.4" opacity="1" quantity="89"/>
        <ColorMapEntry color="#FDDC9E" label="1.4-1.5" opacity="1" quantity="93"/>
        <ColorMapEntry color="#FCFDBF" label="1.5-10" opacity="1" quantity="99"/>
        </ColorMap>
        <ContrastEnhancement/>
        </RasterSymbolizer> """
        mean_20_50 = """
        <RasterSymbolizer>
        <ColorMap type="ramp">
        <ColorMapEntry color="#000004" label="0-0.2" opacity="1" quantity="20"/>
        <ColorMapEntry color="#0C0927" label="0.2-0.3" opacity="1" quantity="30"/>
        <ColorMapEntry color="#231151" label="0.3-0.4" opacity="1" quantity="36"/>
        <ColorMapEntry color="#410F75" label="0.4-0.5" opacity="1" quantity="43"/>
        <ColorMapEntry color="#5F187F" label="0.5-0.6" opacity="1" quantity="48"/>
        <ColorMapEntry color="#7B2382" label="0.6-0.7" opacity="1" quantity="52"/>
        <ColorMapEntry color="#982D80" label="0.7-0.8" opacity="1" quantity="58"/>
        <ColorMapEntry color="#B63679" label="0.8-0.9" opacity="1" quantity="64"/>
        <ColorMapEntry color="#D3436E" label="0.9-1" opacity="1" quantity="67"/>
        <ColorMapEntry color="#EB5760" label="1-1.1" opacity="1" quantity="75"/>
        <ColorMapEntry color="#F8765C" label="1.1-1.2" opacity="1" quantity="79"/>
        <ColorMapEntry color="#FD9969" label="1.2-1.3" opacity="1" quantity="83"/>
        <ColorMapEntry color="#FEBA80" label="1.3-1.4" opacity="1" quantity="89"/>
        <ColorMapEntry color="#FDDC9E" label="1.4-1.5" opacity="1" quantity="93"/>
        <ColorMapEntry color="#FCFDBF" label="1.5-10" opacity="1" quantity="99"/>
        </ColorMap>
        <ContrastEnhancement/>
        </RasterSymbolizer> """
        stdev_0_20 = """
        <RasterSymbolizer>
        <ColorMap type="ramp">
        <ColorMapEntry color="#fde725" label="low" opacity="1" quantity="1"/>
        <ColorMapEntry color="#5dc962" label=" " opacity="1" quantity="8"/>
        <ColorMapEntry color="#20908d" label=" " opacity="1" quantity="10"/>
        <ColorMapEntry color="#3a528b" label=" " opacity="1" quantity="14"/>
        <ColorMapEntry color="#440154" label="high" opacity="1" quantity="60"/>
        </ColorMap>
        <ContrastEnhancement/>
        </RasterSymbolizer> """
        stdev_20_50 = """
        <RasterSymbolizer>
        <ColorMap type="ramp">
        <ColorMapEntry color="#fde725" label="low" opacity="1" quantity="1"/>
        <ColorMapEntry color="#5dc962" label=" " opacity="1" quantity="8"/>
        <ColorMapEntry color="#20908d" label=" " opacity="1" quantity="10"/>
        <ColorMapEntry color="#3a528b" label=" " opacity="1" quantity="14"/>
        <ColorMapEntry color="#440154" label="high" opacity="1" quantity="60"/>
        </ColorMap>
        <ContrastEnhancement/>
        </RasterSymbolizer> """
        raw = ee.Image("ISDASOIL/Africa/v1/nitrogen_total")
        Map.addLayer(raw.select(0).sldStyle(mean_0_20), {},"Nitrogen, total, mean visualization, 0-20 cm")
        Map.addLayer(raw.select(1).sldStyle(mean_20_50), {},"Nitrogen, total, mean visualization, 20-50 cm")
        Map.addLayer(raw.select(2).sldStyle(stdev_0_20), {},"Nitrogen, total, stdev visualization, 0-20 cm")
        Map.addLayer(raw.select(3).sldStyle(stdev_20_50), {},"Nitrogen, total, stdev visualization, 20-50 cm")
        converted = raw.divide(100).exp().subtract(1)
        visualization = {"min": 0, "max": 10000}
        Map.setCenter(25, -3, 2)
        Map.addLayer(converted.select(0), visualization, "Nitrogen, total, mean, 0-20 cm")

    elif cod == "ISDASOIL/Africa/v1/ph":
        mean_0_20 = """
        <RasterSymbolizer>
        <ColorMap type="ramp">
        <ColorMapEntry color="#CC0000" label="3.5-4.6" opacity="1" quantity="46"/>
        <ColorMapEntry color="#FF0000" label="4.6-4.9" opacity="1" quantity="49"/>
        <ColorMapEntry color="#FF5500" label="4.9-5.2" opacity="1" quantity="52"/>
        <ColorMapEntry color="#FFAA00" label="5.2-5.4" opacity="1" quantity="54"/>
        <ColorMapEntry color="#FFFF00" label="5.4-5.5" opacity="1" quantity="55"/>
        <ColorMapEntry color="#D4FF2B" label="5.5-5.6" opacity="1" quantity="56"/>
        <ColorMapEntry color="#AAFF55" label="5.6-5.7" opacity="1" quantity="57"/>
        <ColorMapEntry color="#80FF80" label="5.7-5.9" opacity="1" quantity="59"/>
        <ColorMapEntry color="#55FFAA" label="5.9-6" opacity="1" quantity="60"/>
        <ColorMapEntry color="#2BFFD5" label="6-6.2" opacity="1" quantity="62"/>
        <ColorMapEntry color="#00FFFF" label="6.2-6.3" opacity="1" quantity="63"/>
        <ColorMapEntry color="#00AAFF" label="6.3-6.6" opacity="1" quantity="66"/>
        <ColorMapEntry color="#0055FF" label="6.6-6.8" opacity="1" quantity="68"/>
        <ColorMapEntry color="#0000FF" label="6.8-7.1" opacity="1" quantity="71"/>
        <ColorMapEntry color="#0000CC" label="7.1-10.5" opacity="1" quantity="76"/>
        </ColorMap>
        <ContrastEnhancement/>
        </RasterSymbolizer> """
        mean_20_50 = """
        <RasterSymbolizer>
        <ColorMap type="ramp">
        <ColorMapEntry color="#CC0000" label="3.5-4.6" opacity="1" quantity="46"/>
        <ColorMapEntry color="#FF0000" label="4.6-4.9" opacity="1" quantity="49"/>
        <ColorMapEntry color="#FF5500" label="4.9-5.2" opacity="1" quantity="52"/>
        <ColorMapEntry color="#FFAA00" label="5.2-5.4" opacity="1" quantity="54"/>
        <ColorMapEntry color="#FFFF00" label="5.4-5.5" opacity="1" quantity="55"/>
        <ColorMapEntry color="#D4FF2B" label="5.5-5.6" opacity="1" quantity="56"/>
        <ColorMapEntry color="#AAFF55" label="5.6-5.7" opacity="1" quantity="57"/>
        <ColorMapEntry color="#80FF80" label="5.7-5.9" opacity="1" quantity="59"/>
        <ColorMapEntry color="#55FFAA" label="5.9-6" opacity="1" quantity="60"/>
        <ColorMapEntry color="#2BFFD5" label="6-6.2" opacity="1" quantity="62"/>
        <ColorMapEntry color="#00FFFF" label="6.2-6.3" opacity="1" quantity="63"/>
        <ColorMapEntry color="#00AAFF" label="6.3-6.6" opacity="1" quantity="66"/>
        <ColorMapEntry color="#0055FF" label="6.6-6.8" opacity="1" quantity="68"/>
        <ColorMapEntry color="#0000FF" label="6.8-7.1" opacity="1" quantity="71"/>
        <ColorMapEntry color="#0000CC" label="7.1-10.5" opacity="1" quantity="76"/>
        </ColorMap>
        <ContrastEnhancement/>
        </RasterSymbolizer> """
        stdev_0_20 = """
        <RasterSymbolizer>
        <ColorMap type="ramp">
        <ColorMapEntry color="#fde725" label="low" opacity="1" quantity="1"/>
        <ColorMapEntry color="#5dc962" label=" " opacity="1" quantity="2"/>
        <ColorMapEntry color="#20908d" label=" " opacity="1" quantity="3"/>
        <ColorMapEntry color="#3a528b" label=" " opacity="1" quantity="4"/>
        <ColorMapEntry color="#440154" label="high" opacity="1" quantity="5"/>
        </ColorMap>
        <ContrastEnhancement/>
        </RasterSymbolizer> """
        stdev_20_50 = """
        <RasterSymbolizer>
        <ColorMap type="ramp">
        <ColorMapEntry color="#fde725" label="low" opacity="1" quantity="1"/>
        <ColorMapEntry color="#5dc962" label=" " opacity="1" quantity="2"/>
        <ColorMapEntry color="#20908d" label=" " opacity="1" quantity="3"/>
        <ColorMapEntry color="#3a528b" label=" " opacity="1" quantity="4"/>
        <ColorMapEntry color="#440154" label="high" opacity="1" quantity="5"/>
        </ColorMap>
        <ContrastEnhancement/>
        </RasterSymbolizer> """
        raw = ee.Image("ISDASOIL/Africa/v1/ph")
        Map.addLayer(raw.select(0).sldStyle(mean_0_20), {},"ph, mean visualization, 0-20 cm")
        Map.addLayer(raw.select(1).sldStyle(mean_20_50), {},"ph, mean visualization, 20-50 cm")
        Map.addLayer(raw.select(2).sldStyle(stdev_0_20), {},"ph, stdev visualization, 0-20 cm")
        Map.addLayer(raw.select(3).sldStyle(stdev_20_50), {},"ph, stdev visualization, 20-50 cm")
        converted = raw.divide(10)
        visualization = {"min": 4, "max": 8}
        Map.setCenter(25, -3, 2)
        Map.addLayer(converted.select(0), visualization, "ph, mean, 0-20 cm")

    elif cod == "ISDASOIL/Africa/v1/phosphorus_extractable":
        mean_0_20 = """
        <RasterSymbolizer>
        <ColorMap type="ramp">
        <ColorMapEntry color="#0D0887" label="0-2.7" opacity="1" quantity="13"/>
        <ColorMapEntry color="#350498" label="2.7-3" opacity="1" quantity="14"/>
        <ColorMapEntry color="#5402A3" label="3-3.5" opacity="1" quantity="15"/>
        <ColorMapEntry color="#7000A8" label="3.5-4" opacity="1" quantity="16"/>
        <ColorMapEntry color="#8B0AA5" label="4-4.5" opacity="1" quantity="17"/>
        <ColorMapEntry color="#A31E9A" label="4.5-5" opacity="1" quantity="18"/>
        <ColorMapEntry color="#B93289" label="5-5.7" opacity="1" quantity="19"/>
        <ColorMapEntry color="#CC4678" label="5.7-6.4" opacity="1" quantity="20"/>
        <ColorMapEntry color="#DB5C68" label="6.4-7.2" opacity="1" quantity="21"/>
        <ColorMapEntry color="#E97158" label="7.2-8" opacity="1" quantity="22"/>
        <ColorMapEntry color="#F48849" label="8-9" opacity="1" quantity="23"/>
        <ColorMapEntry color="#FBA139" label="9-10" opacity="1" quantity="24"/>
        <ColorMapEntry color="#FEBC2A" label="10-11.2" opacity="1" quantity="25"/>
        <ColorMapEntry color="#FADA24" label="11.2-12.5" opacity="1" quantity="26"/>
        <ColorMapEntry color="#F0F921" label="12.5-125" opacity="1" quantity="27"/>
        </ColorMap>
        <ContrastEnhancement/>
        </RasterSymbolizer> """
        mean_20_50 = """
        <RasterSymbolizer>
        <ColorMap type="ramp">
        <ColorMapEntry color="#0D0887" label="0-2.7" opacity="1" quantity="13"/>
        <ColorMapEntry color="#350498" label="2.7-3" opacity="1" quantity="14"/>
        <ColorMapEntry color="#5402A3" label="3-3.5" opacity="1" quantity="15"/>
        <ColorMapEntry color="#7000A8" label="3.5-4" opacity="1" quantity="16"/>
        <ColorMapEntry color="#8B0AA5" label="4-4.5" opacity="1" quantity="17"/>
        <ColorMapEntry color="#A31E9A" label="4.5-5" opacity="1" quantity="18"/>
        <ColorMapEntry color="#B93289" label="5-5.7" opacity="1" quantity="19"/>
        <ColorMapEntry color="#CC4678" label="5.7-6.4" opacity="1" quantity="20"/>
        <ColorMapEntry color="#DB5C68" label="6.4-7.2" opacity="1" quantity="21"/>
        <ColorMapEntry color="#E97158" label="7.2-8" opacity="1" quantity="22"/>
        <ColorMapEntry color="#F48849" label="8-9" opacity="1" quantity="23"/>
        <ColorMapEntry color="#FBA139" label="9-10" opacity="1" quantity="24"/>
        <ColorMapEntry color="#FEBC2A" label="10-11.2" opacity="1" quantity="25"/>
        <ColorMapEntry color="#FADA24" label="11.2-12.5" opacity="1" quantity="26"/>
        <ColorMapEntry color="#F0F921" label="12.5-125" opacity="1" quantity="27"/>
        </ColorMap>
        <ContrastEnhancement/>
        </RasterSymbolizer> """
        stdev_0_20 = """
        <RasterSymbolizer>
        <ColorMap type="ramp">
        <ColorMapEntry color="#fde725" label="low" opacity="1" quantity="1"/>
        <ColorMapEntry color="#5dc962" label=" " opacity="1" quantity="2"/>
        <ColorMapEntry color="#20908d" label=" " opacity="1" quantity="3"/>
        <ColorMapEntry color="#3a528b" label=" " opacity="1" quantity="4"/>
        <ColorMapEntry color="#440154" label="high" opacity="1" quantity="5"/>
        </ColorMap>
        <ContrastEnhancement/>
        </RasterSymbolizer> """
        stdev_20_50 = """
        <RasterSymbolizer>
        <ColorMap type="ramp">
        <ColorMapEntry color="#fde725" label="low" opacity="1" quantity="1"/>
        <ColorMapEntry color="#5dc962" label=" " opacity="1" quantity="2"/>
        <ColorMapEntry color="#20908d" label=" " opacity="1" quantity="3"/>
        <ColorMapEntry color="#3a528b" label=" " opacity="1" quantity="4"/>
        <ColorMapEntry color="#440154" label="high" opacity="1" quantity="5"/>
        </ColorMap>
        <ContrastEnhancement/>
        </RasterSymbolizer> """
        raw = ee.Image("ISDASOIL/Africa/v1/phosphorus_extractable")
        Map.addLayer(raw.select(0).sldStyle(mean_0_20), {},"Phosphorus extractable, mean visualization, 0-20 cm")
        Map.addLayer(raw.select(1).sldStyle(mean_20_50), {},"Phosphorus extractable, mean visualization, 20-50 cm")
        Map.addLayer(raw.select(2).sldStyle(stdev_0_20), {},"Phosphorus extractable, stdev visualization, 0-20 cm")
        Map.addLayer(raw.select(3).sldStyle(stdev_20_50), {},"Phosphorus extractable, stdev visualization, 20-50 cm")
        converted = raw.divide(10).exp().subtract(1)
        visualization = {"min": 0, "max": 15}
        Map.setCenter(25, -3, 2)
        Map.addLayer(converted.select(0), visualization, "Phosphorus extractable, mean, 0-20 cm")

    elif cod == "ISDASOIL/Africa/v1/potassium_extractable":
        mean_0_20 = """
        <RasterSymbolizer>
        <ColorMap type="ramp">
        <ColorMapEntry color="#0D0887" label="0-32.1" opacity="1" quantity="35"/>
        <ColorMapEntry color="#350498" label="32.1-43.7" opacity="1" quantity="38"/>
        <ColorMapEntry color="#5402A3" label="43.7-48.4" opacity="1" quantity="39"/>
        <ColorMapEntry color="#7000A8" label="48.4-53.6" opacity="1" quantity="40"/>
        <ColorMapEntry color="#8B0AA5" label="53.6-59.3" opacity="1" quantity="41"/>
        <ColorMapEntry color="#A31E9A" label="59.3-65.7" opacity="1" quantity="42"/>
        <ColorMapEntry color="#B93289" label="65.7-72.7" opacity="1" quantity="43"/>
        <ColorMapEntry color="#CC4678" label="72.7-89" opacity="1" quantity="45"/>
        <ColorMapEntry color="#DB5C68" label="89-98.5" opacity="1" quantity="46"/>
        <ColorMapEntry color="#E97158" label="98.5-108.9" opacity="1" quantity="47"/>
        <ColorMapEntry color="#F48849" label="108.9-120.5" opacity="1" quantity="48"/>
        <ColorMapEntry color="#FBA139" label="120.5-133.3" opacity="1" quantity="49"/>
        <ColorMapEntry color="#FEBC2A" label="133.3-163" opacity="1" quantity="51"/>
        <ColorMapEntry color="#FADA24" label="163-199.3" opacity="1" quantity="53"/>
        <ColorMapEntry color="#F0F921" label="163-1200" opacity="1" quantity="55"/>
        </ColorMap>
        <ContrastEnhancement/>
        </RasterSymbolizer> """
        mean_20_50 = """
        <RasterSymbolizer>
        <ColorMap type="ramp">
        <ColorMapEntry color="#0D0887" label="0-32.1" opacity="1" quantity="35"/>
        <ColorMapEntry color="#350498" label="32.1-43.7" opacity="1" quantity="38"/>
        <ColorMapEntry color="#5402A3" label="43.7-48.4" opacity="1" quantity="39"/>
        <ColorMapEntry color="#7000A8" label="48.4-53.6" opacity="1" quantity="40"/>
        <ColorMapEntry color="#8B0AA5" label="53.6-59.3" opacity="1" quantity="41"/>
        <ColorMapEntry color="#A31E9A" label="59.3-65.7" opacity="1" quantity="42"/>
        <ColorMapEntry color="#B93289" label="65.7-72.7" opacity="1" quantity="43"/>
        <ColorMapEntry color="#CC4678" label="72.7-89" opacity="1" quantity="45"/>
        <ColorMapEntry color="#DB5C68" label="89-98.5" opacity="1" quantity="46"/>
        <ColorMapEntry color="#E97158" label="98.5-108.9" opacity="1" quantity="47"/>
        <ColorMapEntry color="#F48849" label="108.9-120.5" opacity="1" quantity="48"/>
        <ColorMapEntry color="#FBA139" label="120.5-133.3" opacity="1" quantity="49"/>
        <ColorMapEntry color="#FEBC2A" label="133.3-163" opacity="1" quantity="51"/>
        <ColorMapEntry color="#FADA24" label="163-199.3" opacity="1" quantity="53"/>
        <ColorMapEntry color="#F0F921" label="163-1200" opacity="1" quantity="55"/>
        </ColorMap>
        <ContrastEnhancement/>
        </RasterSymbolizer> """
        stdev_0_20 = """
        <RasterSymbolizer>
        <ColorMap type="ramp">
        <ColorMapEntry color="#fde725" label="low" opacity="1" quantity="1"/>
        <ColorMapEntry color="#5dc962" label=" " opacity="1" quantity="2"/>
        <ColorMapEntry color="#20908d" label=" " opacity="1" quantity="3"/>
        <ColorMapEntry color="#3a528b" label=" " opacity="1" quantity="4"/>
        <ColorMapEntry color="#440154" label="high" opacity="1" quantity="5"/>
        </ColorMap>
        <ContrastEnhancement/>
        </RasterSymbolizer> """
        stdev_20_50 = """
        <RasterSymbolizer>
        <ColorMap type="ramp">
        <ColorMapEntry color="#fde725" label="low" opacity="1" quantity="1"/>
        <ColorMapEntry color="#5dc962" label=" " opacity="1" quantity="2"/>
        <ColorMapEntry color="#20908d" label=" " opacity="1" quantity="3"/>
        <ColorMapEntry color="#3a528b" label=" " opacity="1" quantity="4"/>
        <ColorMapEntry color="#440154" label="high" opacity="1" quantity="5"/>
        </ColorMap>
        <ContrastEnhancement/>
        </RasterSymbolizer> """
        raw = ee.Image("ISDASOIL/Africa/v1/potassium_extractable")
        Map.addLayer(raw.select(0).sldStyle(mean_0_20), {},"Potassium extractable, mean visualization, 0-20 cm")
        Map.addLayer(raw.select(1).sldStyle(mean_20_50), {},"Potassium extractable, mean visualization, 20-50 cm")
        Map.addLayer(raw.select(2).sldStyle(stdev_0_20), {},"Potassium extractable, stdev visualization, 0-20 cm")
        Map.addLayer(raw.select(3).sldStyle(stdev_20_50), {},"Potassium extractable, stdev visualization, 20-50 cm")
        converted = raw.divide(10).exp().subtract(1)
        visualization = {"min": 0, "max": 250}
        Map.setCenter(25, -3, 2)
        Map.addLayer(converted.select(0), visualization, "Potassium extractable, mean, 0-20 cm")

    elif cod == "ISDASOIL/Africa/v1/sand_content":
        mean_0_20 = """
        <RasterSymbolizer>
        <ColorMap type="ramp">
        <ColorMapEntry color="#00204D" label="0-31" opacity="1" quantity="31"/>
        <ColorMapEntry color="#002D6C" label="31-39" opacity="1" quantity="39"/>
        <ColorMapEntry color="#16396D" label="39-43" opacity="1" quantity="43"/>
        <ColorMapEntry color="#36476B" label="43-46" opacity="1" quantity="46"/>
        <ColorMapEntry color="#4B546C" label="46-49" opacity="1" quantity="49"/>
        <ColorMapEntry color="#5C616E" label="49-52" opacity="1" quantity="52"/>
        <ColorMapEntry color="#6C6E72" label="52-54" opacity="1" quantity="54"/>
        <ColorMapEntry color="#7C7B78" label="54-56" opacity="1" quantity="56"/>
        <ColorMapEntry color="#8E8A79" label="56-58" opacity="1" quantity="58"/>
        <ColorMapEntry color="#A09877" label="58-60" opacity="1" quantity="60"/>
        <ColorMapEntry color="#B3A772" label="60-63" opacity="1" quantity="63"/>
        <ColorMapEntry color="#C6B66B" label="63-65" opacity="1" quantity="65"/>
        <ColorMapEntry color="#DBC761" label="65-68" opacity="1" quantity="68"/>
        <ColorMapEntry color="#F0D852" label="68-71" opacity="1" quantity="71"/>
        <ColorMapEntry color="#FFEA46" label="71-100" opacity="1" quantity="75"/>
        </ColorMap>
        <ContrastEnhancement/>
        </RasterSymbolizer> """
        mean_20_50 = """
        <RasterSymbolizer>
        <ColorMap type="ramp">
        <ColorMapEntry color="#00204D" label="0-31" opacity="1" quantity="31"/>
        <ColorMapEntry color="#002D6C" label="31-39" opacity="1" quantity="39"/>
        <ColorMapEntry color="#16396D" label="39-43" opacity="1" quantity="43"/>
        <ColorMapEntry color="#36476B" label="43-46" opacity="1" quantity="46"/>
        <ColorMapEntry color="#4B546C" label="46-49" opacity="1" quantity="49"/>
        <ColorMapEntry color="#5C616E" label="49-52" opacity="1" quantity="52"/>
        <ColorMapEntry color="#6C6E72" label="52-54" opacity="1" quantity="54"/>
        <ColorMapEntry color="#7C7B78" label="54-56" opacity="1" quantity="56"/>
        <ColorMapEntry color="#8E8A79" label="56-58" opacity="1" quantity="58"/>
        <ColorMapEntry color="#A09877" label="58-60" opacity="1" quantity="60"/>
        <ColorMapEntry color="#B3A772" label="60-63" opacity="1" quantity="63"/>
        <ColorMapEntry color="#C6B66B" label="63-65" opacity="1" quantity="65"/>
        <ColorMapEntry color="#DBC761" label="65-68" opacity="1" quantity="68"/>
        <ColorMapEntry color="#F0D852" label="68-71" opacity="1" quantity="71"/>
        <ColorMapEntry color="#FFEA46" label="71-100" opacity="1" quantity="75"/>
        </ColorMap>
        <ContrastEnhancement/>
        </RasterSymbolizer> """
        stdev_0_20 = """
        <RasterSymbolizer>
        <ColorMap type="ramp">
        <ColorMapEntry color="#fde725" label="low" opacity="1" quantity="2"/>
        <ColorMapEntry color="#5dc962" label=" " opacity="1" quantity="3"/>
        <ColorMapEntry color="#20908d" label=" " opacity="1" quantity="4"/>
        <ColorMapEntry color="#3a528b" label=" " opacity="1" quantity="6"/>
        <ColorMapEntry color="#440154" label="high" opacity="1" quantity="7"/>
        </ColorMap>
        <ContrastEnhancement/>
        </RasterSymbolizer> """
        stdev_20_50 = """
        <RasterSymbolizer>
        <ColorMap type="ramp">
        <ColorMapEntry color="#fde725" label="low" opacity="1" quantity="2"/>
        <ColorMapEntry color="#5dc962" label=" " opacity="1" quantity="3"/>
        <ColorMapEntry color="#20908d" label=" " opacity="1" quantity="4"/>
        <ColorMapEntry color="#3a528b" label=" " opacity="1" quantity="6"/>
        <ColorMapEntry color="#440154" label="high" opacity="1" quantity="7"/>
        </ColorMap>
        <ContrastEnhancement/>
        </RasterSymbolizer> """
        raw = ee.Image("ISDASOIL/Africa/v1/sand_content")
        Map.addLayer(raw.select(0).sldStyle(mean_0_20), {},"Sand content, mean visualization, 0-20 cm")
        Map.addLayer(raw.select(1).sldStyle(mean_20_50), {},"Sand content, mean visualization, 20-50 cm")
        Map.addLayer(raw.select(2).sldStyle(stdev_0_20), {},"Sand content, stdev visualization, 0-20 cm")
        Map.addLayer(raw.select(3).sldStyle(stdev_20_50), {},"Sand content, stdev visualization, 20-50 cm")
        converted = raw.divide(10).exp().subtract(1)
        visualization = {"min": 0, "max": 3000}
        Map.setCenter(25, -3, 2)
        Map.addLayer(converted.select(0), visualization, "Sand content, mean, 0-20 cm")

    elif cod == "ISDASOIL/Africa/v1/silt_content":
        mean_0_20 = """
        <RasterSymbolizer>
        <ColorMap type="ramp">
        <ColorMapEntry color="#00204D" label="0-7" opacity="1" quantity="7"/>
        <ColorMapEntry color="#002D6C" label="7-9" opacity="1" quantity="9"/>
        <ColorMapEntry color="#16396D" label="9-10" opacity="1" quantity="10"/>
        <ColorMapEntry color="#36476B" label="10-11" opacity="1" quantity="11"/>
        <ColorMapEntry color="#4B546C" label="11-12" opacity="1" quantity="12"/>
        <ColorMapEntry color="#5C616E" label="12-13" opacity="1" quantity="13"/>
        <ColorMapEntry color="#6C6E72" label="13-14" opacity="1" quantity="14"/>
        <ColorMapEntry color="#7C7B78" label="14-15" opacity="1" quantity="15"/>
        <ColorMapEntry color="#8E8A79" label="15-16" opacity="1" quantity="16"/>
        <ColorMapEntry color="#A09877" label="16-17" opacity="1" quantity="17"/>
        <ColorMapEntry color="#B3A772" label="17-18" opacity="1" quantity="18"/>
        <ColorMapEntry color="#C6B66B" label="18-19" opacity="1" quantity="19"/>
        <ColorMapEntry color="#DBC761" label="19-20" opacity="1" quantity="20"/>
        <ColorMapEntry color="#F0D852" label="20-22" opacity="1" quantity="22"/>
        <ColorMapEntry color="#FFEA46" label="22-70" opacity="1" quantity="24"/>
        </ColorMap>
        <ContrastEnhancement/>
        </RasterSymbolizer> """
        mean_20_50 = """
        <RasterSymbolizer>
        <ColorMap type="ramp">
        <ColorMapEntry color="#00204D" label="0-7" opacity="1" quantity="7"/>
        <ColorMapEntry color="#002D6C" label="7-9" opacity="1" quantity="9"/>
        <ColorMapEntry color="#16396D" label="9-10" opacity="1" quantity="10"/>
        <ColorMapEntry color="#36476B" label="10-11" opacity="1" quantity="11"/>
        <ColorMapEntry color="#4B546C" label="11-12" opacity="1" quantity="12"/>
        <ColorMapEntry color="#5C616E" label="12-13" opacity="1" quantity="13"/>
        <ColorMapEntry color="#6C6E72" label="13-14" opacity="1" quantity="14"/>
        <ColorMapEntry color="#7C7B78" label="14-15" opacity="1" quantity="15"/>
        <ColorMapEntry color="#8E8A79" label="15-16" opacity="1" quantity="16"/>
        <ColorMapEntry color="#A09877" label="16-17" opacity="1" quantity="17"/>
        <ColorMapEntry color="#B3A772" label="17-18" opacity="1" quantity="18"/>
        <ColorMapEntry color="#C6B66B" label="18-19" opacity="1" quantity="19"/>
        <ColorMapEntry color="#DBC761" label="19-20" opacity="1" quantity="20"/>
        <ColorMapEntry color="#F0D852" label="20-22" opacity="1" quantity="22"/>
        <ColorMapEntry color="#FFEA46" label="22-70" opacity="1" quantity="24"/>
        </ColorMap>
        <ContrastEnhancement/>
        </RasterSymbolizer> """
        stdev_0_20 = """
        <RasterSymbolizer>
        <ColorMap type="ramp">
        <ColorMapEntry color="#fde725" label="low" opacity="1" quantity="1"/>
        <ColorMapEntry color="#5dc962" label=" " opacity="1" quantity="2"/>
        <ColorMapEntry color="#20908d" label=" " opacity="1" quantity="3"/>
        <ColorMapEntry color="#3a528b" label=" " opacity="1" quantity="4"/>
        <ColorMapEntry color="#440154" label="high" opacity="1" quantity="4.19000000000005"/>
        </ColorMap>
        <ContrastEnhancement/>
        </RasterSymbolizer> """
        stdev_20_50 = """
        <RasterSymbolizer>
        <ColorMap type="ramp">
        <ColorMapEntry color="#fde725" label="low" opacity="1" quantity="1"/>
        <ColorMapEntry color="#5dc962" label=" " opacity="1" quantity="2"/>
        <ColorMapEntry color="#20908d" label=" " opacity="1" quantity="3"/>
        <ColorMapEntry color="#3a528b" label=" " opacity="1" quantity="4"/>
        <ColorMapEntry color="#440154" label="high" opacity="1" quantity="4.19000000000005"/>
        </ColorMap>
        <ContrastEnhancement/>
        </RasterSymbolizer> """
        raw = ee.Image("ISDASOIL/Africa/v1/silt_content")
        Map.addLayer(raw.select(0).sldStyle(mean_0_20), {},"Silt content, mean visualization, 0-20 cm")
        Map.addLayer(raw.select(1).sldStyle(mean_20_50), {},"Silt content, mean visualization, 20-50 cm")
        Map.addLayer(raw.select(2).sldStyle(stdev_0_20), {},"Silt content, stdev visualization, 0-20 cm")
        Map.addLayer(raw.select(3).sldStyle(stdev_20_50), {},"Silt content, stdev visualization, 20-50 cm")
        converted = raw.divide(10).exp().subtract(1)
        visualization = {"min": 0, "max": 15}
        Map.setCenter(25, -3, 2)
        Map.addLayer(converted.select(0), visualization, "Silt content, mean, 0-20 cm")

    elif cod == "ISDASOIL/Africa/v1/stone_content":
        mean_0_20 = """
        <RasterSymbolizer>
        <ColorMap type="ramp">
        <ColorMapEntry color="#00204D" label="0-0.1" opacity="1" quantity="1"/>
        <ColorMapEntry color="#002D6C" label="0.1-0.3" opacity="1" quantity="3"/>
        <ColorMapEntry color="#16396D" label="0.3-0.5" opacity="1" quantity="4"/>
        <ColorMapEntry color="#36476B" label="0.5-0.6" opacity="1" quantity="5"/>
        <ColorMapEntry color="#4B546C" label="0.6-0.8" opacity="1" quantity="6"/>
        <ColorMapEntry color="#5C616E" label="0.8-1" opacity="1" quantity="7"/>
        <ColorMapEntry color="#6C6E72" label="1-1.2" opacity="1" quantity="8"/>
        <ColorMapEntry color="#7C7B78" label="1.2-1.5" opacity="1" quantity="9"/>
        <ColorMapEntry color="#8E8A79" label="1.5-1.7" opacity="1" quantity="10"/>
        <ColorMapEntry color="#A09877" label="1.7-2" opacity="1" quantity="11"/>
        <ColorMapEntry color="#B3A772" label="2-2.3" opacity="1" quantity="12"/>
        <ColorMapEntry color="#C6B66B" label="2.3-2.7" opacity="1" quantity="13"/>
        <ColorMapEntry color="#DBC761" label="2.7-3.1" opacity="1" quantity="14"/>
        <ColorMapEntry color="#F0D852" label="3.1-3.5" opacity="1" quantity="15"/>
        <ColorMapEntry color="#FFEA46" label="3.5-80" opacity="1" quantity="16"/>
        </ColorMap>
        <ContrastEnhancement/>
        </RasterSymbolizer> """
        mean_20_50 = """
        <RasterSymbolizer>
        <ColorMap type="ramp">
        <ColorMapEntry color="#00204D" label="0-0.1" opacity="1" quantity="1"/>
        <ColorMapEntry color="#002D6C" label="0.1-0.3" opacity="1" quantity="3"/>
        <ColorMapEntry color="#16396D" label="0.3-0.5" opacity="1" quantity="4"/>
        <ColorMapEntry color="#36476B" label="0.5-0.6" opacity="1" quantity="5"/>
        <ColorMapEntry color="#4B546C" label="0.6-0.8" opacity="1" quantity="6"/>
        <ColorMapEntry color="#5C616E" label="0.8-1" opacity="1" quantity="7"/>
        <ColorMapEntry color="#6C6E72" label="1-1.2" opacity="1" quantity="8"/>
        <ColorMapEntry color="#7C7B78" label="1.2-1.5" opacity="1" quantity="9"/>
        <ColorMapEntry color="#8E8A79" label="1.5-1.7" opacity="1" quantity="10"/>
        <ColorMapEntry color="#A09877" label="1.7-2" opacity="1" quantity="11"/>
        <ColorMapEntry color="#B3A772" label="2-2.3" opacity="1" quantity="12"/>
        <ColorMapEntry color="#C6B66B" label="2.3-2.7" opacity="1" quantity="13"/>
        <ColorMapEntry color="#DBC761" label="2.7-3.1" opacity="1" quantity="14"/>
        <ColorMapEntry color="#F0D852" label="3.1-3.5" opacity="1" quantity="15"/>
        <ColorMapEntry color="#FFEA46" label="3.5-80" opacity="1" quantity="16"/>
        </ColorMap>
        <ContrastEnhancement/>
        </RasterSymbolizer> """
        stdev_0_20 = """
        <RasterSymbolizer>
        <ColorMap type="ramp">
        <ColorMapEntry color="#fde725" label="low" opacity="1" quantity="1"/>
        <ColorMapEntry color="#5dc962" label=" " opacity="1" quantity="2"/>
        <ColorMapEntry color="#20908d" label=" " opacity="1" quantity="3"/>
        <ColorMapEntry color="#3a528b" label=" " opacity="1" quantity="4"/>
        <ColorMapEntry color="#440154" label="high" opacity="1" quantity="5"/>
        </ColorMap>
        <ContrastEnhancement/>
        </RasterSymbolizer> """
        stdev_20_50 = """
        <RasterSymbolizer>
        <ColorMap type="ramp">
        <ColorMapEntry color="#fde725" label="low" opacity="1" quantity="1"/>
        <ColorMapEntry color="#5dc962" label=" " opacity="1" quantity="2"/>
        <ColorMapEntry color="#20908d" label=" " opacity="1" quantity="3"/>
        <ColorMapEntry color="#3a528b" label=" " opacity="1" quantity="4"/>
        <ColorMapEntry color="#440154" label="high" opacity="1" quantity="5"/>
        </ColorMap>
        <ContrastEnhancement/>
        </RasterSymbolizer> """
        raw = ee.Image("ISDASOIL/Africa/v1/stone_content")
        Map.addLayer(raw.select(0).sldStyle(mean_0_20), {},"Stone content, mean visualization, 0-20 cm")
        Map.addLayer(raw.select(1).sldStyle(mean_20_50), {},"Stone content, mean visualization, 20-50 cm")
        Map.addLayer(raw.select(2).sldStyle(stdev_0_20), {},"Stone content, stdev visualization, 0-20 cm")
        Map.addLayer(raw.select(3).sldStyle(stdev_20_50), {},"Stone content, stdev visualization, 20-50 cm")
        converted = raw.divide(10).exp().subtract(1)
        visualization = {"min": 0, "max": 6}
        Map.setCenter(25, -3, 2)
        Map.addLayer(converted.select(0), visualization, "Stone content, mean, 0-20 cm")

    elif cod == "ISDASOIL/Africa/v1/sulphur_extractable":
        mean_0_20 = """
        <RasterSymbolizer>
        <ColorMap type="ramp">
        <ColorMapEntry color="#0D0887" label="0-2.3" opacity="1" quantity="12"/>
        <ColorMapEntry color="#350498" label="2.3-3.1" opacity="1" quantity="14"/>
        <ColorMapEntry color="#5402A3" label="3.1-3.5" opacity="1" quantity="15"/>
        <ColorMapEntry color="#7000A8" label="3.5-4" opacity="1" quantity="16"/>
        <ColorMapEntry color="#8B0AA5" label="4-5" opacity="1" quantity="18"/>
        <ColorMapEntry color="#A31E9A" label="5-5.7" opacity="1" quantity="19"/>
        <ColorMapEntry color="#B93289" label="5.7-6.4" opacity="1" quantity="20"/>
        <ColorMapEntry color="#CC4678" label="6.4-7.2" opacity="1" quantity="21"/>
        <ColorMapEntry color="#DB5C68" label="7.2-8" opacity="1" quantity="22"/>
        <ColorMapEntry color="#E97158" label="8-9" opacity="1" quantity="23"/>
        <ColorMapEntry color="#F48849" label="9-10" opacity="1" quantity="24"/>
        <ColorMapEntry color="#FBA139" label="10-11.2" opacity="1" quantity="25"/>
        <ColorMapEntry color="#FEBC2A" label="11.2-12.5" opacity="1" quantity="26"/>
        <ColorMapEntry color="#FADA24" label="12.5-15.4" opacity="1" quantity="28"/>
        <ColorMapEntry color="#F0F921" label="15.4-125" opacity="1" quantity="30"/>
        </ColorMap>
        <ContrastEnhancement/>
        </RasterSymbolizer> """
        mean_20_50 = """
        <RasterSymbolizer>
        <ColorMap type="ramp">
        <ColorMapEntry color="#0D0887" label="0-2.3" opacity="1" quantity="12"/>
        <ColorMapEntry color="#350498" label="2.3-3.1" opacity="1" quantity="14"/>
        <ColorMapEntry color="#5402A3" label="3.1-3.5" opacity="1" quantity="15"/>
        <ColorMapEntry color="#7000A8" label="3.5-4" opacity="1" quantity="16"/>
        <ColorMapEntry color="#8B0AA5" label="4-5" opacity="1" quantity="18"/>
        <ColorMapEntry color="#A31E9A" label="5-5.7" opacity="1" quantity="19"/>
        <ColorMapEntry color="#B93289" label="5.7-6.4" opacity="1" quantity="20"/>
        <ColorMapEntry color="#CC4678" label="6.4-7.2" opacity="1" quantity="21"/>
        <ColorMapEntry color="#DB5C68" label="7.2-8" opacity="1" quantity="22"/>
        <ColorMapEntry color="#E97158" label="8-9" opacity="1" quantity="23"/>
        <ColorMapEntry color="#F48849" label="9-10" opacity="1" quantity="24"/>
        <ColorMapEntry color="#FBA139" label="10-11.2" opacity="1" quantity="25"/>
        <ColorMapEntry color="#FEBC2A" label="11.2-12.5" opacity="1" quantity="26"/>
        <ColorMapEntry color="#FADA24" label="12.5-15.4" opacity="1" quantity="28"/>
        <ColorMapEntry color="#F0F921" label="15.4-125" opacity="1" quantity="30"/>
        </ColorMap>
        <ContrastEnhancement/>
        </RasterSymbolizer> """
        stdev_0_20 = """
        <RasterSymbolizer>
        <ColorMap type="ramp">
        <ColorMapEntry color="#fde725" label="low" opacity="1" quantity="1"/>
        <ColorMapEntry color="#5dc962" label=" " opacity="1" quantity="3"/>
        <ColorMapEntry color="#20908d" label=" " opacity="1" quantity="4"/>
        <ColorMapEntry color="#3a528b" label=" " opacity="1" quantity="6"/>
        <ColorMapEntry color="#440154" label="high" opacity="1" quantity="14"/>
        </ColorMap>
        <ContrastEnhancement/>
        </RasterSymbolizer> """
        stdev_20_50 = """
        <RasterSymbolizer>
        <ColorMap type="ramp">
        <ColorMapEntry color="#fde725" label="low" opacity="1" quantity="1"/>
        <ColorMapEntry color="#5dc962" label=" " opacity="1" quantity="3"/>
        <ColorMapEntry color="#20908d" label=" " opacity="1" quantity="4"/>
        <ColorMapEntry color="#3a528b" label=" " opacity="1" quantity="6"/>
        <ColorMapEntry color="#440154" label="high" opacity="1" quantity="14"/>
        </ColorMap>
        <ContrastEnhancement/>
        </RasterSymbolizer> """
        raw = ee.Image("ISDASOIL/Africa/v1/sulphur_extractable")
        Map.addLayer(raw.select(0).sldStyle(mean_0_20), {},"Sulphur extractable, mean visualization, 0-20 cm")
        Map.addLayer(raw.select(1).sldStyle(mean_20_50), {},"Sulphur extractable, mean visualization, 20-50 cm")
        Map.addLayer(raw.select(2).sldStyle(stdev_0_20), {},"Sulphur extractable, stdev visualization, 0-20 cm")
        Map.addLayer(raw.select(3).sldStyle(stdev_20_50), {},"Sulphur extractable, stdev visualization, 20-50 cm")
        converted = raw.divide(10).exp().subtract(1)
        visualization = {"min": 0, "max": 20}
        Map.setCenter(25, -3, 2)
        Map.addLayer(converted.select(0), visualization, "Sulphur extractable, mean, 0-20 cm")



    elif cod == "ISDASOIL/Africa/v1/texture_class":
        raw = ee.Image("ISDASOIL/Africa/v1/texture_class")
        Map.addLayer(raw.select(0), {}, "Texture class, 0-20 cm")
        Map.addLayer(raw.select(1), {}, "Texture class, 20-50 cm")
        Map.setCenter(25, -3, 2)

    elif cod == "ISDASOIL/Africa/v1/zinc_extractable":
        mean_0_20 = """
        <RasterSymbolizer>
        <ColorMap type="ramp">
        <ColorMapEntry color="#0D0887" label="0-0.6" opacity="1" quantity="5"/>
        <ColorMapEntry color="#350498" label="0.6-0.8" opacity="1" quantity="6"/>
        <ColorMapEntry color="#5402A3" label="0.8-1" opacity="1" quantity="7"/>
        <ColorMapEntry color="#7000A8" label="1-1.2" opacity="1" quantity="8"/>
        <ColorMapEntry color="#8B0AA5" label="1.2-1.5" opacity="1" quantity="9"/>
        <ColorMapEntry color="#A31E9A" label="1.5-1.7" opacity="1" quantity="10"/>
        <ColorMapEntry color="#B93289" label="1.7-2" opacity="1" quantity="11"/>
        <ColorMapEntry color="#CC4678" label="2-2.3" opacity="1" quantity="12"/>
        <ColorMapEntry color="#DB5C68" label="2.3-2.7" opacity="1" quantity="13"/>
        <ColorMapEntry color="#E97158" label="2.7-3.1" opacity="1" quantity="14"/>
        <ColorMapEntry color="#F48849" label="3.1-3.5" opacity="1" quantity="15"/>
        <ColorMapEntry color="#FBA139" label="3.5-4" opacity="1" quantity="16"/>
        <ColorMapEntry color="#FEBC2A" label="4-4.5" opacity="1" quantity="17"/>
        <ColorMapEntry color="#FADA24" label="4.5-5" opacity="1" quantity="18"/>
        <ColorMapEntry color="#F0F921" label="5-125" opacity="1" quantity="19"/>
        </ColorMap>
        <ContrastEnhancement/>
        </RasterSymbolizer> """
        mean_20_50 = """
        <RasterSymbolizer>
        <ColorMap type="ramp">
        <ColorMapEntry color="#0D0887" label="0-0.6" opacity="1" quantity="5"/>
        <ColorMapEntry color="#350498" label="0.6-0.8" opacity="1" quantity="6"/>
        <ColorMapEntry color="#5402A3" label="0.8-1" opacity="1" quantity="7"/>
        <ColorMapEntry color="#7000A8" label="1-1.2" opacity="1" quantity="8"/>
        <ColorMapEntry color="#8B0AA5" label="1.2-1.5" opacity="1" quantity="9"/>
        <ColorMapEntry color="#A31E9A" label="1.5-1.7" opacity="1" quantity="10"/>
        <ColorMapEntry color="#B93289" label="1.7-2" opacity="1" quantity="11"/>
        <ColorMapEntry color="#CC4678" label="2-2.3" opacity="1" quantity="12"/>
        <ColorMapEntry color="#DB5C68" label="2.3-2.7" opacity="1" quantity="13"/>
        <ColorMapEntry color="#E97158" label="2.7-3.1" opacity="1" quantity="14"/>
        <ColorMapEntry color="#F48849" label="3.1-3.5" opacity="1" quantity="15"/>
        <ColorMapEntry color="#FBA139" label="3.5-4" opacity="1" quantity="16"/>
        <ColorMapEntry color="#FEBC2A" label="4-4.5" opacity="1" quantity="17"/>
        <ColorMapEntry color="#FADA24" label="4.5-5" opacity="1" quantity="18"/>
        <ColorMapEntry color="#F0F921" label="5-125" opacity="1" quantity="19"/>
        </ColorMap>
        <ContrastEnhancement/>
        </RasterSymbolizer> """
        stdev_0_20 = """
        <RasterSymbolizer>
        <ColorMap type="ramp">
        <ColorMapEntry color="#fde725" label="low" opacity="1" quantity="1"/>
        <ColorMapEntry color="#5dc962" label=" " opacity="1" quantity="2"/>
        <ColorMapEntry color="#20908d" label=" " opacity="1" quantity="3"/>
        <ColorMapEntry color="#3a528b" label=" " opacity="1" quantity="4"/>
        <ColorMapEntry color="#440154" label="high" opacity="1" quantity="5"/>
        </ColorMap>
        <ContrastEnhancement/>
        </RasterSymbolizer> """
        stdev_20_50 = """
        <RasterSymbolizer>
        <ColorMap type="ramp">
        <ColorMapEntry color="#fde725" label="low" opacity="1" quantity="1"/>
        <ColorMapEntry color="#5dc962" label=" " opacity="1" quantity="2"/>
        <ColorMapEntry color="#20908d" label=" " opacity="1" quantity="3"/>
        <ColorMapEntry color="#3a528b" label=" " opacity="1" quantity="4"/>
        <ColorMapEntry color="#440154" label="high" opacity="1" quantity="5"/>
        </ColorMap>
        <ContrastEnhancement/>
        </RasterSymbolizer> """
        raw = ee.Image("ISDASOIL/Africa/v1/zinc_extractable")
        Map.addLayer(raw.select(0).sldStyle(mean_0_20), {},"Zinc, extractable, mean visualization, 0-20 cm")
        Map.addLayer(raw.select(1).sldStyle(mean_20_50), {},"Zinc, extractable, mean visualization, 20-50 cm")
        Map.addLayer(raw.select(2).sldStyle(stdev_0_20), {},"Zinc, extractable, stdev visualization, 0-20 cm")
        Map.addLayer(raw.select(3).sldStyle(stdev_20_50), {},"Zinc, extractable, stdev visualization, 20-50 cm")
        converted = raw.divide(10).exp().subtract(1)
        visualization = {"min": 0, "max": 10}
        Map.setCenter(25, -3, 2)
        Map.addLayer(converted.select(0), visualization, "Zinc, extractable, mean, 0-20 cm")
        
    elif cod == "JAXA/ALOS/AVNIR-2/ORI":
        dataset = ee.ImageCollection("JAXA/ALOS/AVNIR-2/ORI").filter(ee.Filter.date("2011-01-01", "2011-04-01"))
        avnir2OriRgb = dataset.select(["B3", "B2", "B1"])
        avnir2OriRgbVis = {
        "min": 0.0,
        "max": 255.0,
        }
        Map.setCenter(138.7302, 35.3641, 12)
        Map.addLayer(avnir2OriRgb, avnir2OriRgbVis, "AVNIR-2 ORI RGB")

    elif cod == "JAXA/ALOS/AW3D30/V3_2":
        dataset = ee.ImageCollection("JAXA/ALOS/AW3D30/V3_2")
        elevation = dataset.select("DSM")
        elevationVis = {
        "min": 0,
        "max": 5000,
        "palette": ["0000ff", "00ffff", "ffff00", "ff0000", "ffffff"]
        }
        Map.setCenter(138.73, 35.36, 11)
        Map.addLayer(elevation, elevationVis, "Elevation")
        proj = elevation.first().select(0).projection()
        slopeReprojected = ee.Terrain.slope(elevation.mosaic().setDefaultProjection(proj))
        Map.addLayer(slopeReprojected, {"min": 0, "max": 45}, "Slope")

    elif cod == "JAXA/ALOS/PALSAR-2/Level2_2/ScanSAR":
        collection = ee.ImageCollection("JAXA/ALOS/PALSAR-2/Level2_2/ScanSAR").filterBounds(ee.Geometry.Point(143, -5))
        image = collection.first()
        Map.addLayer(image.select(["HH"]), {"min": 0, "max": 8000}, "HH polarization")
        Map.centerObject(image)

    elif cod =="JAXA/ALOS/PALSAR/YEARLY/FNF":
        dataset = ee.ImageCollection("JAXA/ALOS/PALSAR/YEARLY/FNF").filterDate("2017-01-01", "2017-12-31")
        forestNonForest = dataset.select("fnf")
        forestNonForestVis = {
        "min": 1.0,
        "max": 3.0,
        "palette": ["006400", "FEFF99", "0000FF"],
        }
        Map.setCenter(136.85, 37.37, 4)
        Map.addLayer(forestNonForest, forestNonForestVis, "Forest/Non-Forest")

    elif cod == "JAXA/ALOS/PALSAR/YEARLY/FNF4":
        dataset = ee.ImageCollection("JAXA/ALOS/PALSAR/YEARLY/FNF4").filterDate("2018-01-01", "2018-12-31")
        forestNonForest = dataset.select("fnf")
        forestNonForestVis = {
        "min": 1.0,
        "max": 4.0,
        "palette": ["00B200","83EF62","FFFF99","0000FF"],
        }
        Map.setCenter(136.85, 37.37, 4)
        Map.addLayer(forestNonForest, forestNonForestVis, "Forest/Non-Forest")

    elif cod == "JAXA/ALOS/PALSAR/YEARLY/SAR":
        dataset = ee.ImageCollection("JAXA/ALOS/PALSAR/YEARLY/SAR").filter(ee.Filter.date("2017-01-01", "2018-01-01"))
        sarHh = dataset.select("HH")
        sarHhVis = {
        "min": 0.0,
        "max": 10000.0,
        }
        Map.setCenter(136.85, 37.37, 4)
        Map.addLayer(sarHh, sarHhVis, "SAR HH")

    elif cod == "JAXA/ALOS/PALSAR/YEARLY/SAR_EPOCH":
        dataset = ee.ImageCollection("JAXA/ALOS/PALSAR/YEARLY/SAR_EPOCH").filter(ee.Filter.date("2017-01-01", "2018-01-01"))
        sarHh = dataset.select("HH")
        sarHhVis = {
        "min": 0.0,
        "max": 10000.0,
        }
        Map.setCenter(136.85, 37.37, 4)
        Map.addLayer(sarHh, sarHhVis, "SAR HH")

    elif cod == "JAXA/GCOM-C/L3/LAND/LAI/V1":
        dataset = ee.ImageCollection("JAXA/GCOM-C/L3/LAND/LAI/V1").filterDate("2020-01-01", "2020-02-01").filter(ee.Filter.eq("SATELLITE_DIRECTION", "D"))
        dataset = dataset.mean().multiply(0.001)
        visualization = {
        "bands": ["LAI_AVE"],
        "min": -7,
        "max": 7,
        "palette": [
        "040274","040281","0502a3","0502b8","0502ce","0502e6",
        "0602ff","235cb1","307ef3","269db1","30c8e2","32d3ef",
        "3be285","3ff38f","86e26f","3ae237","b5e22e","d6e21f",
        "fff705","ffd611","ffb613","ff8b13","ff6e08","ff500d",
        "ff0000","de0101","c21301","a71001","911003",
        ]
        }
        Map.setCenter(128.45, 33.33, 5)
        Map.addLayer(dataset, visualization, "Leaf Area Index")

    elif cod == "JAXA/GCOM-C/L3/LAND/LAI/V2":
        dataset = ee.ImageCollection("JAXA/GCOM-C/L3/LAND/LAI/V2").filterDate("2020-01-01", "2020-02-01").filter(ee.Filter.eq("SATELLITE_DIRECTION", "D"))
        dataset = dataset.mean().multiply(0.001)
        visualization = {
        "bands": ["LAI_AVE"],
        "min": -7,
        "max": 7,
        "palette": [
        "040274","040281","0502a3","0502b8","0502ce","0502e6",
        "0602ff","235cb1","307ef3","269db1","30c8e2","32d3ef",
        "3be285","3ff38f","86e26f","3ae237","b5e22e","d6e21f",
        "fff705","ffd611","ffb613","ff8b13","ff6e08","ff500d",
        "ff0000","de0101","c21301","a71001","911003",
        ]
        }
        Map.setCenter(128.45, 33.33, 5)
        Map.addLayer(dataset, visualization, "Leaf Area Index")

    elif cod == "JAXA/GCOM-C/L3/LAND/LAI/V3":
        dataset = ee.ImageCollection("JAXA/GCOM-C/L3/LAND/LAI/V3").filterDate("2021-12-01", "2022-01-01").filter(ee.Filter.eq("SATELLITE_DIRECTION", "D"))
        dataset = dataset.mean().multiply(0.001)
        visualization = {
        "bands": ["LAI_AVE"],
        "min": -7,
        "max": 7,
        "palette": [
        "040274","040281","0502a3","0502b8","0502ce","0502e6",
        "0602ff","235cb1","307ef3","269db1","30c8e2","32d3ef",
        "3be285","3ff38f","86e26f","3ae237","b5e22e","d6e21f",
        "fff705","ffd611","ffb613","ff8b13","ff6e08","ff500d",
        "ff0000","de0101","c21301","a71001","911003",
        ]
        }
        Map.setCenter(128.45, 33.33, 5)
        Map.addLayer(dataset, visualization, "Leaf Area Index")

    elif cod == "JAXA/GCOM-C/L3/LAND/LST/V1":
        dataset = ee.ImageCollection("JAXA/GCOM-C/L3/LAND/LST/V1").filterDate("2020-01-01", "2020-02-01").filter(ee.Filter.eq("SATELLITE_DIRECTION", "D"))
        dataset = dataset.mean().multiply(0.02)
        visualization = {
        "bands": ["LST_AVE"],
        "min": 250,
        "max": 316,
        "palette": [
        "040274","040281","0502a3","0502b8","0502ce","0502e6",
        "0602ff","235cb1","307ef3","269db1","30c8e2","32d3ef",
        "3be285","3ff38f","86e26f","3ae237","b5e22e","d6e21f",
        "fff705","ffd611","ffb613","ff8b13","ff6e08","ff500d",
        "ff0000","de0101","c21301","a71001","911003",
        ]
        }
        Map.setCenter(128.45, 33.33, 5)
        Map.addLayer(dataset, visualization, "Land Surface Temperature")

    elif cod == "JAXA/GCOM-C/L3/LAND/LST/V2":
        dataset = ee.ImageCollection("JAXA/GCOM-C/L3/LAND/LST/V2").filterDate("2020-01-01", "2020-02-01").filter(ee.Filter.eq("SATELLITE_DIRECTION", "D"))
        dataset = dataset.mean().multiply(0.02)
        visualization = {
        "bands": ["LST_AVE"],
        "min": 250,
        "max": 316,
        "palette": [
        "040274","040281","0502a3","0502b8","0502ce","0502e6",
        "0602ff","235cb1","307ef3","269db1","30c8e2","32d3ef",
        "3be285","3ff38f","86e26f","3ae237","b5e22e","d6e21f",
        "fff705","ffd611","ffb613","ff8b13","ff6e08","ff500d",
        "ff0000","de0101","c21301","a71001","911003",
        ]
        }
        Map.setCenter(128.45, 33.33, 5)
        Map.addLayer(dataset, visualization, "Land Surface Temperature")

    elif cod == "JAXA/GCOM-C/L3/LAND/LST/V3":
        dataset = ee.ImageCollection("JAXA/GCOM-C/L3/LAND/LST/V3").filterDate("2021-12-01", "2022-01-01").filter(ee.Filter.eq("SATELLITE_DIRECTION", "D"))
        dataset = dataset.mean().multiply(0.02)
        visualization = {
        "bands": ["LST_AVE"],
        "min": 250,
        "max": 316,
        "palette": [
        "040274","040281","0502a3","0502b8","0502ce","0502e6",
        "0602ff","235cb1","307ef3","269db1","30c8e2","32d3ef",
        "3be285","3ff38f","86e26f","3ae237","b5e22e","d6e21f",
        "fff705","ffd611","ffb613","ff8b13","ff6e08","ff500d",
        "ff0000","de0101","c21301","a71001","911003",
        ]
        }
        Map.setCenter(128.45, 33.33, 5)
        Map.addLayer(dataset, visualization, "Land Surface Temperature")

    elif cod == "JAXA/GCOM-C/L3/OCEAN/CHLA/V1":
        dataset = ee.ImageCollection("JAXA/GCOM-C/L3/OCEAN/CHLA/V1").filterDate("2020-01-01", "2020-02-01").filter(ee.Filter.eq("SATELLITE_DIRECTION", "D"))
        image = dataset.mean().multiply(0.0016).log10()
        vis = {
        "bands": ["CHLA_AVE"],
        "min": -2,
        "max": 2,
        "palette": [
        "3500a8","0800ba","003fd6",
        "00aca9","77f800","ff8800",
        "b30000","920000","880000"
        ]
        }
        Map.addLayer(image, vis, "Chlorophyll-a concentration")
        Map.setCenter(128.45, 33.33, 5)

    elif cod == "JAXA/GCOM-C/L3/OCEAN/CHLA/V2":
        dataset = ee.ImageCollection("JAXA/GCOM-C/L3/OCEAN/CHLA/V2").filterDate("2020-01-01", "2020-02-01").filter(ee.Filter.eq("SATELLITE_DIRECTION", "D"))
        image = dataset.mean().multiply(0.0016).log10()
        vis = {
        "bands": ["CHLA_AVE"],
        "min": -2,
        "max": 2,
        "palette": [
        "3500a8","0800ba","003fd6",
        "00aca9","77f800","ff8800",
        "b30000","920000","880000"
        ]
        }
        Map.addLayer(image, vis, "Chlorophyll-a concentration")
        Map.setCenter(128.45, 33.33, 5)

    elif cod == "JAXA/GCOM-C/L3/OCEAN/CHLA/V3":
        dataset = ee.ImageCollection("JAXA/GCOM-C/L3/OCEAN/CHLA/V3").filterDate("2021-12-01", "2022-01-01").filter(ee.Filter.eq("SATELLITE_DIRECTION", "D"))
        image = dataset.mean().multiply(0.0016).log10()
        vis = {
        "bands": ["CHLA_AVE"],
        "min": -2,
        "max": 2,
        "palette": [
        "3500a8","0800ba","003fd6",
        "00aca9","77f800","ff8800",
        "b30000","920000","880000"
        ]
        }
        Map.addLayer(image, vis, "Chlorophyll-a concentration")
        Map.setCenter(128.45, 33.33, 5)

    elif cod == "JAXA/GCOM-C/L3/OCEAN/SST/V1":
        dataset = ee.ImageCollection("JAXA/GCOM-C/L3/OCEAN/SST/V1").filterDate("2020-01-01", "2020-02-01").filter(ee.Filter.eq("SATELLITE_DIRECTION", "D"))
        dataset = dataset.mean().multiply(0.0012).add(-10)
        vis = {
        "bands": ["SST_AVE"],
        "min": 0,
        "max": 30,
        "palette": ["000000", "005aff", "43c8c8", "fff700", "ff0000"],
        }
        Map.setCenter(128.45, 33.33, 5)
        Map.addLayer(dataset, vis, "Sea Surface Temperature")

    elif cod == "JAXA/GCOM-C/L3/OCEAN/SST/V2":
        dataset = ee.ImageCollection("JAXA/GCOM-C/L3/OCEAN/SST/V2").filterDate("2020-01-01", "2020-02-01").filter(ee.Filter.eq("SATELLITE_DIRECTION", "D"))
        dataset = dataset.mean().multiply(0.0012).add(-10)
        vis = {
        "bands": ["SST_AVE"],
        "min": 0,
        "max": 30,
        "palette": ["000000", "005aff", "43c8c8", "fff700", "ff0000"],
        }
        Map.setCenter(128.45, 33.33, 5)
        Map.addLayer(dataset, vis, "Sea Surface Temperature")

    elif cod == "JAXA/GCOM-C/L3/OCEAN/SST/V3":
        dataset = ee.ImageCollection("JAXA/GCOM-C/L3/OCEAN/SST/V3").filterDate("2021-12-01", "2022-01-01").filter(ee.Filter.eq("SATELLITE_DIRECTION", "D"))
        dataset = dataset.mean().multiply(0.0012).add(-10)
        vis = {
        "bands": ["SST_AVE"],
        "min": 0,
        "max": 30,
        "palette": ["000000", "005aff", "43c8c8", "fff700", "ff0000"],
        }
        Map.setCenter(128.45, 33.33, 5)
        Map.addLayer(dataset, vis, "Sea Surface Temperature")

    elif cod == "JAXA/GPM_L3/GSMaP/v6/operational":
        dataset = ee.ImageCollection("JAXA/GPM_L3/GSMaP/v6/operational").filter(ee.Filter.date("2018-08-06", "2018-08-07"))
        precipitation = dataset.select("hourlyPrecipRate")
        precipitationVis = {
        "min": 0.0,
        "max": 30.0,
        "palette":
        ["1621a2", "ffffff", "03ffff", "13ff03", "efff00", "ffb103", "ff2300"],
        }
        Map.setCenter(-90.7, 26.12, 2)
        Map.addLayer(precipitation, precipitationVis, "Precipitation")

    elif cod == "JAXA/GPM_L3/GSMaP/v6/reanalysis":
        dataset = ee.ImageCollection("JAXA/GPM_L3/GSMaP/v6/reanalysis").filter(ee.Filter.date("2014-02-01", "2014-02-02"))
        precipitation = dataset.select("hourlyPrecipRate")
        precipitationVis = {
        "min": 0.0,
        "max": 30.0,
        "palette":
        ["1621a2", "ffffff", "03ffff", "13ff03", "efff00", "ffb103", "ff2300"],
        }
        Map.setCenter(-90.7, 26.12, 2)
        Map.addLayer(precipitation, precipitationVis, "Precipitation")

    elif cod == "JCU/Murray/GIC/global_tidal_wetland_change/2019":
        dataset = ee.Image("JCU/Murray/GIC/global_tidal_wetland_change/2019")
        Map.setCenter(103.7, 1.3, 12)
        Map.setOptions("SATELLITE")
        plasma = [
        "0d0887", "3d049b", "6903a5", "8d0fa1", "ae2891", "cb4679", "df6363",
        "f0844c", "faa638", "fbcc27", "f0f921"
        ]
        Map.addLayer(dataset.select("twprobabilityStart"), {"palette": plasma, "min": 0, "max": 100},"twprobabilityStart", False, 1)
        Map.addLayer(dataset.select("twprobabilityEnd"), {"palette": plasma, "min": 0, "max": 100},"twprobabilityEnd", False, 1)
        lossPalette = ["FE4A49"]
        gainPalette = ["2AB7CA"]
        Map.addLayer(dataset.select("loss"), {"palette": lossPalette, "min": 1, "max": 1},"Tidal wetland loss", True, 1)
        Map.addLayer(dataset.select("gain"), {"palette": gainPalette, "min": 1, "max": 1},"Tidal wetland gain", True, 1)
        viridis = ["440154", "414487", "2a788e", "22a884", "7ad151", "fde725"]
        Map.addLayer(dataset.select("lossYear"), {"palette": viridis, "min": 4, "max": 19},"Year of loss", False, 0.9)
        Map.addLayer(dataset.select("gainYear"), {"palette": viridis, "min": 4, "max": 19},"Year of gain", False, 0.9)
        classPalette = ["9e9d9d", "ededed", "FF9900", "009966", "960000", "006699"]
        classNames = ["None", "None", "Tidal flat", "Mangrove", "None", "Tidal marsh"]
        Map.addLayer(dataset.select("lossType"), {"palette": classPalette, "min": 0, "max": 5}, "Loss type", False, 0.9)
        Map.addLayer(dataset.select("gainType"), {"palette": classPalette, "min": 0, "max": 5}, "Gain type", False, 0.9)

    elif cod == "JRC/D5/EUCROPMAP/V1":
        image = ee.Image("JRC/D5/EUCROPMAP/V1/2018")
        Map.addLayer(image, {}, "EUCROPMAP 2018")
        Map.setCenter(10, 48, 4)

    elif cod == "JRC/GHSL/P2016/BUILT_LDSMT_GLOBE_V1":
        dataset = ee.Image("JRC/GHSL/P2016/BUILT_LDSMT_GLOBE_V1")
        builtUpMultitemporal = dataset.select("built")
        visParams = {
        "min": 1.0,
        "max": 6.0,
        "palette": ["0c1d60", "000000", "448564", "70daa4", "83ffbf", "ffffff"],
        }
        Map.setCenter(8.9957, 45.5718, 12)
        Map.addLayer(builtUpMultitemporal, visParams, "Built-Up Multitemporal")

    elif cod == "JRC/GHSL/P2016/POP_GPW_GLOBE_V1":
        dataset = ee.ImageCollection("JRC/GHSL/P2016/POP_GPW_GLOBE_V1").filter(ee.Filter.date("2015-01-01", "2015-12-31"))
        populationCount = dataset.select("population_count")
        populationCountVis = {
        "min": 0.0,
        "max": 200.0,
        "palette": ["060606", "337663", "337663", "ffffff"],
        }
        Map.setCenter(78.22, 22.59, 3)
        Map.addLayer(populationCount, populationCountVis, "Population Count")

    elif cod == "JRC/GHSL/P2016/SMOD_POP_GLOBE_V1":
        dataset = ee.ImageCollection("JRC/GHSL/P2016/SMOD_POP_GLOBE_V1").filter(ee.Filter.date("2015-01-01", "2015-12-31"))
        degreeOfUrbanization = dataset.select("smod_code")
        visParams = {
        "min": 0.0,
        "max": 3.0,
        "palette": ["000000", "448564", "70daa4", "ffffff"],
        }
        Map.setCenter(114.96, 31.13, 4)
        Map.addLayer(degreeOfUrbanization, visParams, "Degree of Urbanization")

    elif cod == "JRC/GSW1_4/GlobalSurfaceWater":
        dataset = ee.Image("JRC/GSW1_4/GlobalSurfaceWater")
        visualization = {
        "bands": ["occurrence"],
        "min": 0.0,
        "max": 100.0,
        "palette": ["ffffff", "ffbbbb", "0000ff"]
        }
        Map.setCenter(59.414, 45.182, 6)
        Map.addLayer(dataset, visualization, "Occurrence")

    elif cod == "JRC/GSW1_4/Metadata":
        dataset = ee.Image("JRC/GSW1_4/Metadata")
        visualization = {
        "bands": ["detections", "valid_obs", "total_obs"],
        "min": 100.0,
        "max": 900.0,
        }
        Map.setCenter(71.72, 52.48, 0)
        Map.addLayer(dataset, visualization, "Detections/Observations")

    elif cod == "JRC/GSW1_4/MonthlyHistory":
        dataset = ee.Image("JRC/GSW1_4/MonthlyHistory/2020_06")
        visualization = {
        "bands": ["water"],
        "min": 0.0,
        "max": 2.0,
        "palette": ["ffffff", "fffcb8", "0905ff"]
        }
        Map.setCenter(-121.234, 38.109, 7)
        Map.addLayer(dataset, visualization, "Water")

    elif cod == "JRC/GSW1_4/MonthlyRecurrence":
        dataset = ee.ImageCollection("JRC/GSW1_4/MonthlyRecurrence")
        visualization = {
        "bands": ["monthly_recurrence"],
        "min": 0.0,
        "max": 100.0,
        "palette": ["ffffff", "ffbbbb", "0000ff"]
        }
        Map.setCenter(-51.482, -0.835, 6)
        Map.addLayer(dataset, visualization, "Monthly Recurrence")

    elif cod == "JRC/GSW1_4/YearlyHistory":
        dataset = ee.ImageCollection("JRC/GSW1_4/YearlyHistory")
        visualization = {
        "bands": ["waterClass"],
        "min": 0.0,
        "max": 3.0,
        "palette": ["cccccc", "ffffff", "99d9ea", "0000ff"]
        }
        Map.setCenter(59.414, 45.182, 7)
        Map.addLayer(dataset, visualization, "Water Class")

    elif cod == "JRC/GWIS/GlobFire/v2/DailyPerimeters":
        folder = "JRC/GWIS/GlobFire/v2/DailyPerimeters"
        def printAssetList(listAssetsOutput):
            print("Asset list:", listAssetsOutput["assets"])
            ee.data.listAssets(folder, {}, printAssetList)

        tableName = "JRC/GWIS/GlobFire/v2/DailyPerimeters/2020"
        computeArea = lambda f: f.set({"area": f.area()})
        
        features = ee.FeatureCollection(tableName).map(computeArea)
        visParams = {
        "palette": ["f5ff64", "b5ffb4", "beeaff", "ffc0e8", "8e8dff", "adadad"],
        "min": 0.0,
        "max": 600000000.0,
        "opacity": 0.8,
        }
        image = ee.Image().float().paint(features, "area")
        Map.addLayer(image, visParams, "GlobFire 2020")
        Map.addLayer(features, None, "For Inspector", False)
        Map.setCenter(-121.23, 39.7, 12)

    elif cod == "JRC/GWIS/GlobFire/v2/FinalPerimeters":
        dataset = ee.FeatureCollection("JRC/GWIS/GlobFire/v2/FinalPerimeters")
        visParams = {
        "palette": ["f5ff64", "b5ffb4", "beeaff", "ffc0e8", "8e8dff", "adadad"],
        "min": 0.0,
        "max": 600000000.0,
        "opacity": 0.8,
        }
        image = ee.Image().float().paint(dataset, "area")
        Map.addLayer(image, visParams, "GlobFire Final")
        Map.addLayer(dataset, None, "for Inspector", False)
        Map.setCenter(-122.121, 38.56, 12)

    elif cod == "JRC/LUCAS_HARMO/COPERNICUS_POLYGONS/V1/2018":
        dataset = ee.FeatureCollection("JRC/LUCAS_HARMO/COPERNICUS_POLYGONS/V1/2018")
        visParams = {
        "min": 35,
        "max": 60
        }
        dataset2 = dataset.map(lambda f: ee.Feature(f.buffer(5000)))
        image = ee.Image().float().paint(dataset2, "gps_lat").randomVisualizer()
        Map.addLayer(ee.Image(1), {"min":0, "max":1}, "background")
        Map.addLayer(image, visParams, "LUCAS Polygons")
        Map.addLayer(dataset, None, "for Inspector", False)
        Map.setCenter(19.514, 51.82, 8)

    elif cod == "JRC/LUCAS_HARMO/THLOC/V1":
        dataset = ee.FeatureCollection("JRC/LUCAS_HARMO/THLOC/V1")
        Map.addLayer(dataset, {}, "LUCAS Points (data)", False)
        dataset = dataset.style({
        "color": "489734",
        "pointSize": 3
        })
        Map.setCenter(-3.8233, 40.609, 10)
        Map.addLayer(dataset, {}, "LUCAS Points (styled green)")

    elif cod == "KNTU/LiDARLab/IranLandCover/V1":
        dataset = ee.Image("KNTU/LiDARLab/IranLandCover/V1")
        visualization = {
        "bands": ["classification"]
        }
        Map.setCenter(54.0, 33.0, 5)
        Map.addLayer(dataset, visualization, "Classification")

    elif cod == "LANDFIRE/Fire/FRG/v1_2_0":
        dataset = ee.ImageCollection("LANDFIRE/Fire/FRG/v1_2_0")
        visualization = {
        "bands": ["FRG"]
        }
        Map.setCenter(-121.671, 40.699, 5)
        Map.addLayer(dataset, visualization, "FRG")

    elif cod == "LANDFIRE/Fire/MFRI/v1_2_0":
        dataset = ee.ImageCollection("LANDFIRE/Fire/MFRI/v1_2_0")
        visualization = {
        "bands": ["MFRI"]
        }
        Map.setCenter(-121.671, 40.699, 5)
        Map.addLayer(dataset, visualization, "MFRI")

    elif cod == "LANDFIRE/Fire/PLS/v1_2_0":
        dataset = ee.ImageCollection("LANDFIRE/Fire/PLS/v1_2_0")
        visualization = {
        "bands": ["PLS"]
        }
        Map.setCenter(-121.671, 40.699, 5)
        Map.addLayer(dataset, visualization, "PLS")

    elif cod == "LANDFIRE/Fire/PMS/v1_2_0":
        dataset = ee.ImageCollection("LANDFIRE/Fire/PMS/v1_2_0")
        visualization = {
        "bands": ["PMS"]
        }
        Map.setCenter(-121.671, 40.699, 5)
        Map.addLayer(dataset, visualization, "PMS")

    elif cod == "LANDFIRE/Fire/PRS/v1_2_0":
        dataset = ee.ImageCollection("LANDFIRE/Fire/PRS/v1_2_0")
        visualization = {
        "bands": ["PRS"]
        }
        Map.setCenter(-121.671, 40.699, 5)
        Map.addLayer(dataset, visualization, "PRS")

    elif cod == "LANDFIRE/Fire/SClass/v1_4_0":
        dataset = ee.ImageCollection("LANDFIRE/Fire/SClass/v1_4_0")
        visualization = {
        "bands": ["SClass"]
        }
        Map.setCenter(-121.671, 40.699, 5)
        Map.addLayer(dataset, visualization, "SClass")

    elif cod == "LANDFIRE/Fire/VCC/v1_4_0":
        dataset = ee.ImageCollection("LANDFIRE/Fire/VCC/v1_4_0")
        visualization = {
        "bands": ["VCC"]
        }
        Map.setCenter(-121.671, 40.699, 5)
        Map.addLayer(dataset, visualization, "VCC")

    elif cod == "LANDFIRE/Fire/VDep/v1_4_0":
        dataset = ee.ImageCollection("LANDFIRE/Fire/VDep/v1_4_0")
        visualization = {
        "bands": ["VDep"]
        }
        Map.setCenter(-121.671, 40.699, 5)
        Map.addLayer(dataset, visualization, "VDep")

    elif cod == "LANDFIRE/Vegetation/BPS/v1_4_0":
        dataset = ee.ImageCollection("LANDFIRE/Vegetation/BPS/v1_4_0")
        visualization = {
            "bands": ["BPS"]
        }
        Map.setCenter(-121.671, 40.699, 5)
        Map.addLayer(dataset, visualization, "BPS")

    elif cod == "LANDFIRE/Vegetation/ESP/v1_2_0/AK":
        dataset = ee.Image("LANDFIRE/Vegetation/ESP/v1_2_0/AK")
        visualization = {
            "bands": ["ESP"]
        }
        Map.setCenter(-151.011, 63.427, 8)
        Map.addLayer(dataset, visualization, "ESP")

    elif cod == "LANDFIRE/Vegetation/ESP/v1_2_0/CONUS":
        dataset = ee.Image("LANDFIRE/Vegetation/ESP/v1_2_0/CONUS")
        visualization = {
            "bands": ["ESP"]
        }
        Map.setCenter(-121.671, 40.699, 5)
        Map.addLayer(dataset, visualization, "ESP")

    elif cod == "LANDFIRE/Vegetation/ESP/v1_2_0/HI":
        dataset = ee.Image("LANDFIRE/Vegetation/ESP/v1_2_0/HI")
        visualization = {
            "bands": ["ESP"]
        }
        Map.setCenter(-155.3, 19.627, 8)
        Map.addLayer(dataset, visualization, "ESP")

    elif cod == "LANDFIRE/Vegetation/EVC/v1_4_0":
        dataset = ee.ImageCollection("LANDFIRE/Vegetation/EVC/v1_4_0")
        visualization = {
            "bands": ["EVC"]
        }
        Map.setCenter(-121.671, 40.699, 5)
        Map.addLayer(dataset, visualization, "EVC")

    elif cod == "LANDFIRE/Vegetation/EVH/v1_4_0":
        dataset = ee.ImageCollection("LANDFIRE/Vegetation/EVH/v1_4_0")
        visualization = {
            "bands": ["EVH"]
        }
        Map.setCenter(-121.671, 40.699, 5)
        Map.addLayer(dataset, visualization, "EVH")

    elif cod == "LANDFIRE/Vegetation/EVT/v1_4_0":
        dataset = ee.ImageCollection("LANDFIRE/Vegetation/EVT/v1_4_0")
        visualization = {
            "bands": ["EVT"]
        }
        Map.setCenter(-121.671, 40.699, 5)
        Map.addLayer(dataset, visualization, "EVT")

    elif cod == "LANDSAT/GLS1975":
        dataset = ee.ImageCollection("LANDSAT/GLS1975")
        FalseColor = dataset.select(["30", "20", "10"])
        FalseColorVis = {
            "gamma": 1.6
        }
        Map.setCenter(44.517, 25.998, 5)
        Map.addLayer(FalseColor, FalseColorVis, "False Color")

    elif cod == "LANDSAT/GLS2005":
        dataset = ee.ImageCollection("LANDSAT/GLS2005")
        trueColor321 = dataset.select(["30", "20", "10"])
        Map.setCenter(6.746, 46.529, 6)
        Map.addLayer(trueColor321, {}, "True Color (321)")

    elif cod == "LANDSAT/GLS2005_L5":
        dataset = ee.ImageCollection("LANDSAT/GLS2005_L5")
        trueColor321 = dataset.select(["30", "20", "10"])
        Map.setCenter(6.746, 46.529, 6)
        Map.addLayer(trueColor321, {}, "True Color (321)")

    elif cod == "LANDSAT/GLS2005_L7":
        dataset = ee.ImageCollection("LANDSAT/GLS2005_L7")
        trueColor321 = dataset.select(["30", "20", "10"])
        Map.setCenter(6.746, 46.529, 6)
        Map.addLayer(trueColor321, {}, "True Color (321)")

    elif cod == "LANDSAT/LC08/C02/T1":
        dataset = ee.ImageCollection("LANDSAT/LC08/C02/T1").filterDate("2017-01-01", "2017-12-31")
        trueColor432 = dataset.select(["B4", "B3", "B2"])
        trueColor432Vis = {
        "min": 0.0,
        "max": 30000.0
        }
        Map.setCenter(6.746, 46.529, 6)
        Map.addLayer(trueColor432, trueColor432Vis, "True Color (432)")

    elif cod == "LANDSAT/LC08/C02/T1_L2":
        dataset = ee.ImageCollection("LANDSAT/LC08/C02/T1_L2").filterDate("2021-05-01", "2021-06-01")
        def applyScaleFactors(image):
            opticalBands = image.select("SR_B.").multiply(0.0000275).add(-0.2)
            thermalBands = image.select("ST_B.*").multiply(0.00341802).add(149.0)
            return image.addBands(opticalBands, None, True).addBands(thermalBands, None, True)
        dataset = dataset.map(applyScaleFactors)
        visualization = {
        "bands": ["SR_B4", "SR_B3", "SR_B2"],
        "min": 0.0,
        "max": 0.3
        }
        Map.setCenter(-114.2579, 38.9275, 8)
        Map.addLayer(dataset, visualization, "True Color (432)")

    elif cod == "LANDSAT/LC08/C02/T1_RT":
        dataset = ee.ImageCollection("LANDSAT/LC08/C02/T1_RT").filterDate("2017-01-01", "2017-12-31")
        trueColor432 = dataset.select(["B4", "B3", "B2"])
        trueColor432Vis = {
        "min": 0.0,
        "max": 30000.0
        }
        Map.setCenter(6.746, 46.529, 6)
        Map.addLayer(trueColor432, trueColor432Vis, "True Color (432)")

    elif cod == "LANDSAT/LC08/C02/T1_RT_TOA":
        dataset = ee.ImageCollection("LANDSAT/LC08/C02/T1_RT_TOA").filterDate("2017-01-01", "2017-12-31")
        trueColor432 = dataset.select(["B4", "B3", "B2"])
        trueColor432Vis = {
            "min": 0.0,
            "max": 0.4
            }
        Map.setCenter(6.746, 46.529, 6)
        Map.addLayer(trueColor432, trueColor432Vis, "True Color (432)")

    elif cod == "LANDSAT/LC08/C02/T1_TOA":
        dataset = ee.ImageCollection("LANDSAT/LC08/C02/T1_TOA").filterDate("2017-01-01", "2017-12-31")
        trueColor432 = dataset.select(["B4", "B3", "B2"])
        trueColor432Vis = {
            "min": 0.0,
            "max": 0.4}
        Map.setCenter(6.746, 46.529, 6)
        Map.addLayer(trueColor432, trueColor432Vis, "True Color (432)")

    elif cod == "LANDSAT/LC08/C02/T2":
        dataset = ee.ImageCollection("LANDSAT/LC08/C02/T2").filterDate("2017-01-01", "2017-12-31")
        trueColor432 = dataset.select(["B4", "B3", "B2"])
        trueColor432Vis = {
            "min": 0.0,
            "max": 30000.0
            }
        Map.setCenter(6.746, 46.529, 6)
        Map.addLayer(trueColor432, trueColor432Vis, "True Color (432)")

    elif cod == "LANDSAT/LC08/C02/T2_L2":
        dataset = ee.ImageCollection("LANDSAT/LC08/C02/T2_L2").filterDate("2021-05-01", "2021-06-01")
        def applyScaleFactors(image):
            opticalBands = image.select("SR_B.").multiply(0.0000275).add(-0.2)
            thermalBands = image.select("ST_B.*").multiply(0.00341802).add(149.0)
            return image.addBands(opticalBands, None, True) .addBands(thermalBands, None, True)
        dataset = dataset.map(applyScaleFactors)
        visualization = {
            "bands": ["SR_B4", "SR_B3", "SR_B2"],
            "min": 0.0,
            "max": 0.3
        }
        Map.setCenter(-83, 24, 8)
        Map.addLayer(dataset, visualization, "True Color (432)")

    elif cod == "LANDSAT/LC08/C02/T2_TOA":
        dataset = ee.ImageCollection("LANDSAT/LC08/C02/T2_TOA").filterDate("2017-01-01", "2017-12-31")
        trueColor432 = dataset.select(["B4", "B3", "B2"])
        trueColor432Vis = {
            "min": 0.0,
            "max": 0.4
        }
        Map.setCenter(6.746, 46.529, 6)
        Map.addLayer(trueColor432, trueColor432Vis, "True Color (432)")

    elif cod == "LANDSAT/LC09/C02/T1":
        dataset = ee.ImageCollection("LANDSAT/LC09/C02/T1").filterDate("2022-01-01", "2022-02-01")
        trueColor432 = dataset.select(["B4", "B3", "B2"])
        trueColor432Vis = {
            "min": 0.0,
            "max": 30000.0
            }
        Map.setCenter(6.746, 46.529, 6)
        Map.addLayer(trueColor432, trueColor432Vis, "True Color (432)")

    elif cod == "LANDSAT/LC09/C02/T1_L2":
        dataset = ee.ImageCollection("LANDSAT/LC09/C02/T1_L2").filterDate("2022-01-01", "2022-02-01")
        def applyScaleFactors(image):
            opticalBands = image.select("SR_B.").multiply(0.0000275).add(-0.2)
            thermalBands = image.select("ST_B.*").multiply(0.00341802).add(149.0)
            return image.addBands(opticalBands, None, True).addBands(thermalBands, None, True)
        dataset = dataset.map(applyScaleFactors)
        visualization = {
            "bands": ["SR_B4", "SR_B3", "SR_B2"],
            "min": 0.0,
            "max": 0.3
        }
        Map.setCenter(-114.2579, 38.9275, 8)
        Map.addLayer(dataset, visualization, "True Color (432)")

    elif cod == "LANDSAT/LC09/C02/T1_TOA":
        dataset = ee.ImageCollection("LANDSAT/LC09/C02/T1_TOA").filterDate("2022-01-01", "2022-02-01")
        trueColor432 = dataset.select(["B4", "B3", "B2"])
        trueColor432Vis = {
            "min": 0.0,
            "max": 0.4}
        Map.setCenter(6.746, 46.529, 6)
        Map.addLayer(trueColor432, trueColor432Vis, "True Color (432)")

    elif cod == "LANDSAT/LC09/C02/T2":
        dataset = ee.ImageCollection("LANDSAT/LC09/C02/T2").filterDate("2022-01-01", "2022-02-01")
        trueColor432 = dataset.select(["B4", "B3", "B2"])
        trueColor432Vis = {
            "min": 0.0,
            "max": 30000.0}
        Map.setCenter(6.746, 46.529, 6)
        Map.addLayer(trueColor432, trueColor432Vis, "True Color (432)")

    elif cod == "LANDSAT/LC09/C02/T2_L2":
        dataset = ee.ImageCollection("LANDSAT/LC09/C02/T2_L2").filterDate("2022-01-01", "2022-02-01")
        def applyScaleFactors(image):
            opticalBands = image.select("SR_B.").multiply(0.0000275).add(-0.2)
            thermalBands = image.select("ST_B.*").multiply(0.00341802).add(149.0)
            return image.addBands(opticalBands, None, True).addBands(thermalBands, None, True)
        dataset = dataset.map(applyScaleFactors)
        visualization = {
            "bands": ["SR_B4", "SR_B3", "SR_B2"],
            "min": 0.0,
            "max": 0.3}
        Map.setCenter(-83, 24, 8)
        Map.addLayer(dataset, visualization, "True Color (432)")

    elif cod == "LANDSAT/LC09/C02/T2_TOA":
        dataset = ee.ImageCollection("LANDSAT/LC09/C02/T2_TOA").filterDate("2022-01-01", "2022-02-01")
        trueColor432 = dataset.select(["B4", "B3", "B2"])
        trueColor432Vis = {
            "min": 0.0,
            "max": 0.4}
        Map.setCenter(6.746, 46.529, 6)
        Map.addLayer(trueColor432, trueColor432Vis, "True Color (432)")

    elif cod == "LANDSAT/LE07/C02/T1":
        dataset = ee.ImageCollection("LANDSAT/LE07/C02/T1").filterDate("1999-01-01", "2002-12-31")
        trueColor321 = dataset.select(["B3", "B2", "B1"])
        Map.setCenter(6.746, 46.529, 6)
        Map.addLayer(trueColor321, {}, "True Color (321)")

    elif cod == "LANDSAT/LE07/C02/T1_L2":
        dataset = ee.ImageCollection("LANDSAT/LE07/C02/T1_L2").filterDate("2017-06-01", "2017-07-01")
        def applyScaleFactors(image):
            opticalBands = image.select("SR_B.").multiply(0.0000275).add(-0.2)
            thermalBand = image.select("ST_B6").multiply(0.00341802).add(149.0)
            return image.addBands(opticalBands, None, True).addBands(thermalBand, None, True)
        dataset = dataset.map(applyScaleFactors)
        visualization = {
            "bands": ["SR_B3", "SR_B2", "SR_B1"],
            "min": 0.0,
            "max": 0.3}
        Map.setCenter(-114.2579, 38.9275, 8)
        Map.addLayer(dataset, visualization, "True Color (321)")

    elif cod == "LANDSAT/LE07/C02/T1_RT":
        dataset = ee.ImageCollection("LANDSAT/LE07/C02/T1_RT").filterDate("1999-01-01", "2002-12-31")
        trueColor321 = dataset.select(["B3", "B2", "B1"])
        Map.setCenter(6.746, 46.529, 6)
        Map.addLayer(trueColor321, {}, "True Color (321)")

    elif cod == "LANDSAT/LE07/C02/T1_RT_TOA":
        dataset = ee.ImageCollection("LANDSAT/LE07/C02/T1_RT_TOA").filterDate("1999-01-01", "2002-12-31")
        trueColor321 = dataset.select(["B3", "B2", "B1"])
        trueColor321Vis = {
            "min": 0.0,
            "max": 0.4,
            "gamma": 1.2}
        Map.setCenter(6.746, 46.529, 6)
        Map.addLayer(trueColor321, trueColor321Vis, "True Color (321)")

    elif cod == "LANDSAT/LE07/C02/T1_TOA":
        dataset = ee.ImageCollection("LANDSAT/LE07/C02/T1_TOA").filterDate("1999-01-01", "2002-12-31")
        trueColor321 = dataset.select(["B3", "B2", "B1"])
        trueColor321Vis = {
            "min": 0.0,
            "max": 0.4,
            "gamma": 1.2}
        Map.setCenter(6.746, 46.529, 6)
        Map.addLayer(trueColor321, trueColor321Vis, "True Color (321)")

    elif cod == "LANDSAT/LE07/C02/T2":
        dataset = ee.ImageCollection("LANDSAT/LE07/C02/T2").filterDate("1999-01-01", "2002-12-31")
        trueColor321 = dataset.select(["B3", "B2", "B1"])
        Map.setCenter(6.746, 46.529, 6)
        Map.addLayer(trueColor321, {}, "True Color (321)")

    elif cod == "LANDSAT/LE07/C02/T2_L2":
        dataset = ee.ImageCollection("LANDSAT/LE07/C02/T2_L2").filterDate("2017-06-01", "2017-07-01")
        def applyScaleFactors(image):
            opticalBands = image.select("SR_B.").multiply(0.0000275).add(-0.2)
            thermalBand = image.select("ST_B6").multiply(0.00341802).add(149.0)
            return image.addBands(opticalBands, None, True).addBands(thermalBand, None, True)
        dataset = dataset.map(applyScaleFactors)
        visualization = {
            "bands": ["SR_B3", "SR_B2", "SR_B1"],
            "min": 0.0,
            "max": 0.3}
        Map.setCenter(-83, 24, 8)
        Map.addLayer(dataset, visualization, "True Color (321)")

    elif cod == "LANDSAT/LE07/C02/T2_TOA":
        dataset = ee.ImageCollection("LANDSAT/LE07/C02/T2_TOA").filterDate("1999-01-01", "2002-12-31")
        trueColor321 = dataset.select(["B3", "B2", "B1"])
        trueColor321Vis = {
            "min": 0.0,
            "max": 0.4,
            "gamma": 1.2}
        Map.setCenter(6.746, 46.529, 6)
        Map.addLayer(trueColor321, trueColor321Vis, "True Color (321)")

    elif cod == "LANDSAT/LM01/C02/T1":
        dataset = ee.ImageCollection("LANDSAT/LM01/C02/T1").filterDate("1974-01-01", "1978-12-31")
        nearInfrared321 = dataset.select(["B6", "B5", "B4"])
        Map.setCenter(6.746, 46.529, 6)
        Map.addLayer(nearInfrared321, {}, "Near Infrared (321)")

    elif cod == "LANDSAT/LM01/C02/T2":
        dataset = ee.ImageCollection("LANDSAT/LM01/C02/T2").filterDate("1974-01-01", "1978-12-31")
        nearInfrared321 = dataset.select(["B6", "B5", "B4"])
        Map.setCenter(6.746, 46.529, 6)
        Map.addLayer(nearInfrared321, {}, "Near Infrared (321)")

    elif cod == "LANDSAT/LM02/C02/T1":
        dataset = ee.ImageCollection("LANDSAT/LM02/C02/T1").filterDate("1978-01-01", "1980-12-31")
        nearInfrared321 = dataset.select(["B6", "B5", "B4"])
        Map.setCenter(6.746, 46.529, 6)
        Map.addLayer(nearInfrared321, {}, "Near Infrared (321)")

    elif cod == "LANDSAT/LM02/C02/T2":
        dataset = ee.ImageCollection("LANDSAT/LM02/C02/T2").filterDate("1978-01-01", "1980-12-31")
        nearInfrared321 = dataset.select(["B6", "B5", "B4"])
        Map.setCenter(6.746, 46.529, 6)
        Map.addLayer(nearInfrared321, {}, "Near Infrared (321)")

    elif cod == "LANDSAT/LM03/C02/T1":
        dataset = ee.ImageCollection("LANDSAT/LM03/C02/T1").filterDate("1978-01-01", "1980-12-31")
        nearInfrared321 = dataset.select(["B6", "B5", "B4"])
        Map.setCenter(6.746, 46.529, 6)
        Map.addLayer(nearInfrared321, {}, "Near Infrared (321)")

    elif cod == "LANDSAT/LM03/C02/T2":
        dataset = ee.ImageCollection("LANDSAT/LM03/C02/T2").filterDate("1978-01-01", "1980-12-31")
        nearInfrared321 = dataset.select(["B6", "B5", "B4"])
        Map.setCenter(6.746, 46.529, 6)
        Map.addLayer(nearInfrared321, {}, "Near Infrared (321)")

    elif cod == "LANDSAT/LM04/C02/T1":
        dataset = ee.ImageCollection("LANDSAT/LM04/C02/T1").filterDate("1989-01-01", "1992-12-31")
        nearInfrared321 = dataset.select(["B3", "B2", "B1"])
        Map.setCenter(6.746, 46.529, 6)
        Map.addLayer(nearInfrared321, {}, "Near Infrared (321)")

    elif cod == "LANDSAT/LM04/C02/T2":
        dataset = ee.ImageCollection("LANDSAT/LM04/C02/T2").filterDate("1989-01-01", "1992-12-31")
        nearInfrared321 = dataset.select(["B3", "B2", "B1"])
        Map.setCenter(6.746, 46.529, 6)
        Map.addLayer(nearInfrared321, {}, "Near Infrared (321)")

    elif cod == "LANDSAT/LM05/C02/T1":
        dataset = ee.ImageCollection("LANDSAT/LM05/C02/T1").filterDate("1985-01-01", "1989-12-31")
        nearInfrared321 = dataset.select(["B3", "B2", "B1"])
        Map.setCenter(6.746, 46.529, 6)
        Map.addLayer(nearInfrared321, {}, "Near Infrared (321)")

    elif cod == "LANDSAT/LM05/C02/T2":
        dataset = ee.ImageCollection("LANDSAT/LM05/C02/T2").filterDate("1985-01-01", "1989-12-31")
        nearInfrared321 = dataset.select(["B3", "B2", "B1"])
        nearInfrared321Vis = {}
        Map.setCenter(6.746, 46.529, 6)
        Map.addLayer(nearInfrared321, nearInfrared321Vis, "Near Infrared (321)")

    elif cod == "LANDSAT/LT04/C02/T1":
        dataset = ee.ImageCollection("LANDSAT/LT04/C02/T1").filterDate("1989-01-01", "1992-12-31")
        trueColor321 = dataset.select(["B3", "B2", "B1"])
        trueColor321Vis = {}
        Map.setCenter(6.746, 46.529, 6)
        Map.addLayer(trueColor321, trueColor321Vis, "True Color (321)")

    elif cod == "LANDSAT/LT04/C02/T1_L2":
        dataset = ee.ImageCollection("LANDSAT/LT04/C02/T1_L2").filterDate("1990-04-01", "1990-05-01")
        def applyScaleFactors(image):
                     opticalBands = image.select("SR_B.").multiply(0.0000275).add(-0.2)
                     thermalBand = image.select("ST_B6").multiply(0.00341802).add(149.0)
                     return image.addBands(opticalBands, None, True).addBands(thermalBand, None, True)
        dataset = dataset.map(applyScaleFactors)
        visualization = {
        "bands": ["SR_B3", "SR_B2", "SR_B1"],
        "min": 0.0,
        "max": 0.3,
        }
        Map.setCenter(15, 53, 8)
        Map.addLayer(dataset, visualization, "True Color (321)")

    elif cod == "LANDSAT/LT04/C02/T1_TOA":
        dataset = ee.ImageCollection("LANDSAT/LT04/C02/T1_TOA").filterDate("1989-01-01", "1992-12-31")
        trueColor321 = dataset.select(["B3", "B2", "B1"])
        trueColor321Vis = {
        "min": 0.0,
        "max": 0.4,
        "gamma": 1.2,
        }
        Map.setCenter(6.746, 46.529, 6)
        Map.addLayer(trueColor321, trueColor321Vis, "True Color (321)")

    elif cod == "LANDSAT/LT04/C02/T2":
        dataset = ee.ImageCollection("LANDSAT/LT04/C02/T2").filterDate("1989-01-01", "1992-12-31")
        trueColor321 = dataset.select(["B3", "B2", "B1"])
        trueColor321Vis = {}
        Map.setCenter(6.746, 46.529, 6)
        Map.addLayer(trueColor321, trueColor321Vis, "True Color (321)")

    elif cod == "LANDSAT/LT04/C02/T2_L2":
        dataset = ee.ImageCollection("LANDSAT/LT04/C02/T2_L2").filterDate("1990-04-01", "1990-05-01")
        def applyScaleFactors(image):
                     opticalBands = image.select("SR_B.").multiply(0.0000275).add(-0.2)
                     thermalBand = image.select("ST_B6").multiply(0.00341802).add(149.0)
                     return image.addBands(opticalBands, None, True).addBands(thermalBand, None, True)
        dataset = dataset.map(applyScaleFactors)
        visualization = {
        "bands": ["SR_B3", "SR_B2", "SR_B1"],
        "min": 0.0,
        "max": 0.3,
        }
        Map.setCenter(-83, 24, 8)
        Map.addLayer(dataset, visualization, "True Color (321)")

    elif cod == "LANDSAT/LT04/C02/T2_TOA":
        dataset = ee.ImageCollection("LANDSAT/LT04/C02/T2_TOA").filterDate("1989-01-01", "1992-12-31")
        trueColor321 = dataset.select(["B3", "B2", "B1"])
        trueColor321Vis = {
        "min": 0.0,
        "max": 0.4,
        "gamma": 1.2,
        }
        Map.setCenter(6.746, 46.529, 6)
        Map.addLayer(trueColor321, trueColor321Vis, "True Color (321)")

    elif cod == "LANDSAT/LT05/C02/T1":
        dataset = ee.ImageCollection("LANDSAT/LT05/C02/T1").filterDate("2011-01-01", "2011-12-31")
        trueColor321 = dataset.select(["B3", "B2", "B1"])
        trueColor321Vis = {}
        Map.setCenter(6.746, 46.529, 6)
        Map.addLayer(trueColor321, trueColor321Vis, "True Color (321)")

    elif cod == "LANDSAT/LT05/C02/T1_L2":
        dataset = ee.ImageCollection("LANDSAT/LT05/C02/T1_L2").filterDate("2000-06-01", "2000-07-01")
        def applyScaleFactors(image):
                     opticalBands = image.select("SR_B.").multiply(0.0000275).add(-0.2)
                     thermalBand = image.select("ST_B6").multiply(0.00341802).add(149.0)
                     return image.addBands(opticalBands, None, True).addBands(thermalBand, None, True)
        dataset = dataset.map(applyScaleFactors)
        visualization = {
        "bands": ["SR_B3", "SR_B2", "SR_B1"],
        "min": 0.0,
        "max": 0.3,
        }
        Map.setCenter(-114.2579, 38.9275, 8)
        Map.addLayer(dataset, visualization, "True Color (321)")

    elif cod == "LANDSAT/LT05/C02/T1_TOA":
        dataset = ee.ImageCollection("LANDSAT/LT05/C02/T1_TOA").filterDate("2011-01-01", "2011-12-31")
        trueColor321 = dataset.select(["B3", "B2", "B1"])
        trueColor321Vis = {
        "min": 0.0,
        "max": 0.4,
        "gamma": 1.2,
        }
        Map.setCenter(6.746, 46.529, 6)
        Map.addLayer(trueColor321, trueColor321Vis, "True Color (321)")

    elif cod == "LANDSAT/LT05/C02/T2":
        dataset = ee.ImageCollection("LANDSAT/LT05/C02/T2").filterDate("2011-01-01", "2011-12-31")
        trueColor321 = dataset.select(["B3", "B2", "B1"])
        trueColor321Vis = {}
        Map.setCenter(6.746, 46.529, 6)
        Map.addLayer(trueColor321, trueColor321Vis, "True Color (321)")

    elif cod == "LANDSAT/LT05/C02/T2_L2":
        dataset = ee.ImageCollection("LANDSAT/LT05/C02/T2_L2").filterDate("2000-06-01", "2000-07-01")
        def applyScaleFactors(image):
                     opticalBands = image.select("SR_B.").multiply(0.0000275).add(-0.2)
                     thermalBand = image.select("ST_B6").multiply(0.00341802).add(149.0)
                     return image.addBands(opticalBands, None, True).addBands(thermalBand, None, True)
        dataset = dataset.map(applyScaleFactors)
        visualization = {
        "bands": ["SR_B3", "SR_B2", "SR_B1"],
        "min": 0.0,
        "max": 0.3,
        }
        Map.setCenter(-83, 24, 8)
        Map.addLayer(dataset, visualization, "True Color (321)")

    elif cod == "LANDSAT/LT05/C02/T2_TOA":
        dataset = ee.ImageCollection("LANDSAT/LT05/C02/T2_TOA").filterDate("2011-01-01", "2011-12-31")
        trueColor321 = dataset.select(["B3", "B2", "B1"])
        trueColor321Vis = {
        "min": 0.0,
        "max": 0.4,
        "gamma": 1.2,
        }
        Map.setCenter(6.746, 46.529, 6)
        Map.addLayer(trueColor321, trueColor321Vis, "True Color (321)")

    elif cod == "LANDSAT/MANGROVE_FORESTS":
        dataset = ee.ImageCollection("LANDSAT/MANGROVE_FORESTS")
        mangrovesVis = {
        "min": 0,
        "max": 1.0,
        "palette": ["d40115"],
        }
        Map.setCenter(-44.5626, -2.0164, 9)
        Map.addLayer(dataset, mangrovesVis, "Mangroves")

    elif cod == "LARSE/GEDI/GEDI02_A_002/GEDI02_A_2021244154857_O15413_04_T05622_02_003_02_V002":
        dataset = ee.FeatureCollection("LARSE/GEDI/GEDI02_A_002/GEDI02_A_2021244154857_O15413_04_T05622_02_003_02_V002")
        dataset = dataset.style({"color": "black", "pointSize": 1})
        Map.setCenter(-64.88, -31.77, 15)
        Map.addLayer(dataset)

    elif cod == "LARSE/GEDI/GEDI02_A_002_INDEX":
        rectangle = ee.Geometry.Rectangle([-111.22, 24.06, -6.54, 51.9])
        filter_index = ee.FeatureCollection("LARSE/GEDI/GEDI02_A_002_INDEX").filter("time_start > 2020-10-10T15:57:18Z" & "time_end < 2020-10-11T01:20:45Z").filterBounds(rectangle)
        Map.addLayer(filter_index)

    elif cod == "LARSE/GEDI/GEDI02_A_002_MONTHLY":
        qualityMask = lambda im: im.updateMask(im.select("quality_flag").eq(1)).updateMask(im.select("degrade_flag").eq(0))
        dataset = ee.ImageCollection("LARSE/GEDI/GEDI02_A_002_MONTHLY").map(qualityMask).select("rh98")
        gediVis = {
        "min": 1,
        "max": 60,
        "palette": "darkred,red,orange,green,darkgreen",
        }
        Map.setCenter(-74.803466, -9.342209, 10)
        Map.addLayer(dataset, gediVis, "rh98")

    elif cod == "LARSE/GEDI/GEDI02_B_002/GEDI02_B_2021043114136_O12295_03_T07619_02_003_01_V002":
        dataset = ee.FeatureCollection("LARSE/GEDI/GEDI02_B_002/GEDI02_B_2021043114136_O12295_03_T07619_02_003_01_V002")
        Map.setCenter(12.60033, 51.01051, 14)
        Map.addLayer(dataset)
            
    elif cod == "LARSE/GEDI/GEDI02_B_002_INDEX":
        rectangle = ee.Geometry.Rectangle([-111.22, 24.06, -6.54, 51.9])
        filter_index = ee.FeatureCollection("LARSE/GEDI/GEDI02_B_002_INDEX").filter("time_start > 2020-10-10T15:57:18Z" & "time_end < 2020-10-11T01:20:45Z").filterBounds(rectangle)
        Map.addLayer(filter_index)

    elif cod == "LARSE/GEDI/GEDI02_B_002_MONTHLY":
        qualityMask = lambda im: im.updateMask(im.select("l2b_quality_flag").eq(1)).updateMask(im.select("degrade_flag").eq(0))
        dataset = ee.ImageCollection("LARSE/GEDI/GEDI02_B_002_MONTHLY").map(qualityMask).select("solar_elevation")
        gediVis = {
        "min": 1,
        "max": 60,
        "palette": "red, green, blue",
        }
        Map.setCenter(12.60033, 51.01051, 12)
        Map.addLayer(dataset, gediVis, "Solar Elevation")

    elif cod == "LARSE/GEDI/GEDI04_A_002/GEDI04_A_2022157233128_O19728_03_T11129_02_003_01_V002":
        dataset = ee.FeatureCollection("LARSE/GEDI/GEDI04_A_002/GEDI04_A_2022157233128_O19728_03_T11129_02_003_01_V002")
        Map.setCenter(-94.77616, 38.9587, 14)
        Map.addLayer(dataset)

    elif cod == "LARSE/GEDI/GEDI04_A_002_INDEX":
        rectangle = ee.Geometry.Rectangle([-111.22, 24.06, -6.54, 51.9])
        filter_index = ee.FeatureCollection("LARSE/GEDI/GEDI04_A_002_INDEX").filter("time_start > 2020-10-10T15:57:18Z" & "time_end < 2020-10-11T01:20:45Z").filterBounds(rectangle)
        Map.addLayer(filter_index)

    elif cod == "LARSE/GEDI/GEDI04_A_002_MONTHLY":
        qualityMask = lambda im : im.updateMask(im.select("l4_quality_flag").eq(1)).updateMask(im.select("degrade_flag").eq(0))
        dataset = ee.ImageCollection("LARSE/GEDI/GEDI04_A_002_MONTHLY").map(qualityMask).select("solar_elevation")
        gediVis = {
        "min": 1,
        "max": 60,
        "palette": "red, green, blue",
        }
        Map.setCenter(5.0198, 51.7564, 12)
        Map.addLayer(dataset, gediVis, "Solar Elevation")

    elif cod == "LARSE/GEDI/GEDI04_B_002":
        l4b = ee.Image("LARSE/GEDI/GEDI04_B_002")
        Map.addLayer(
            l4b.select("MU"), {"min": 10, "max": 250, "palette": "440154,414387,2a788e,23a884,7ad151,fde725"}, "Mean Biomass"
        )
        Map.addLayer( l4b.select("SE"), {"min": 10, "max": 50, "palette": "000004,3b0f6f,8c2981,dd4a69,fe9f6d,fcfdbf"}, "Standard Error"
                    )

    elif cod == "MERIT/DEM/v1_0_3":
        dataset = ee.Image("MERIT/DEM/v1_0_3")
        visualization = {
        "bands": ["dem"],
        "min": -3,
        "max": 18,
        "palette": ["000000", "478FCD", "86C58E", "AFC35E", "8F7131",
                    "B78D4F", "E2B8A6", "FFFFFF"]
        }
        Map.setCenter(90.301, 23.052, 10)
        Map.addLayer(dataset, visualization, "Elevation")

    elif cod == "MERIT/Hydro/v1_0_1":
        dataset = ee.Image("MERIT/Hydro/v1_0_1")
        visualization = {
        "bands": ["viswth"],
        }
        Map.setCenter(90.301, 23.052, 10)
        Map.addLayer(dataset, visualization, "River width")

    elif cod == "MERIT/Hydro_reduced/v1_0_1":
        dataset = ee.Image("MERIT/Hydro_reduced/v1_0_1")
        visualization = {
        "bands": "wth",
        "min": 0,
        "max": 400
        }
        Map.setCenter(90.301, 23.052, 10)
        Map.addLayer(dataset, visualization, "River width")

    elif cod == "MODIS/006/MCD19A2_GRANULES":
        collection = ee.ImageCollection("MODIS/006/MCD19A2_GRANULES").select("Optical_Depth_047").filterDate("2019-01-01", "2019-01-15")
        band_viz = {
        "min": 0,
        "max": 500,
        "palette": ["black", "blue", "purple", "cyan", "green", "yellow", "red"]
        }
        Map.addLayer(collection.mean(), band_viz, "Optical Depth 047")
        Map.setCenter(76, 13, 6)

    elif cod == "MODIS/006/MOD10A1":
        dataset = ee.ImageCollection("MODIS/006/MOD10A1").filter(ee.Filter.date("2018-01-01", "2018-05-01"))
        snowCover = dataset.select("NDSI_Snow_Cover")
        snowCoverVis = {
        "min": 0.0,
        "max": 100.0,
        "palette": ["black", "0dffff", "0524ff", "ffffff"],
        }
        Map.setCenter(-41.13, 76.35, 2)
        Map.addLayer(snowCover, snowCoverVis, "Snow Cover")

    elif cod == "MODIS/006/MOD16A2":
        dataset = ee.ImageCollection("MODIS/006/MOD16A2").filter(ee.Filter.date("2018-01-01", "2018-05-01"))
        evapotranspiration = dataset.select("ET")
        evapotranspirationVis = {
        "min": 0.0,
        "max": 300.0,
        "palette": [
            "ffffff", "fcd163", "99b718", "66a000", "3e8601", "207401", "056201",
            "004c00", "011301"
        ],
        }
        Map.setCenter(6.746, 46.529, 2)
        Map.addLayer(evapotranspiration, evapotranspirationVis, "Evapotranspiration")

    elif cod == "MODIS/006/MOD17A2H":
        dataset = ee.ImageCollection("MODIS/006/MOD17A2H").filter(ee.Filter.date("2018-01-01", "2018-05-01"))
        gpp = dataset.select("Gpp")
        gppVis = {
        "min": 0.0,
        "max": 600.0,
        "palette": ["bbe029", "0a9501", "074b03"],
        }
        Map.setCenter(6.746, 46.529, 2)
        Map.addLayer(gpp, gppVis, "GPP")

    elif cod == "MODIS/006/MOD44B":
        dataset = ee.ImageCollection("MODIS/006/MOD44B")
        visualization = {
        "bands": ["Percent_Tree_Cover"],
        "min": 0.0,
        "max": 100.0,
        "palette": ["bbe029", "0a9501", "074b03"]
        }
        Map.centerObject(dataset)
        Map.addLayer(dataset, visualization, "Percent Tree Cover")

    elif cod == "MODIS/006/MOD44W":
        dataset = ee.ImageCollection("MODIS/006/MOD44W").filter(ee.Filter.date("2015-01-01", "2015-05-01"))
        waterMask = dataset.select("water_mask")
        waterMaskVis = {
        "min": 0.0,
        "max": 1.0,
        "palette": ["bcba99", "2d0491"],
        }
        Map.setCenter(6.746, 46.529, 2)
        Map.addLayer(waterMask, waterMaskVis, "Water Mask")

    elif cod == "MODIS/006/MOD44W":
        dataset = ee.ImageCollection("MODIS/006/MOD44W").filter(ee.Filter.date("2015-01-01", "2015-05-01"))
        waterMask = dataset.select("water_mask")
        waterMaskVis = {
        "min": 0.0,
        "max": 1.0,
        "palette": ["bcba99", "2d0491"],
        }
        Map.setCenter(6.746, 46.529, 2)
        Map.addLayer(waterMask, waterMaskVis, "Water Mask")

    elif cod == "MODIS/006/MODOCGA":
        dataset = ee.ImageCollection("MODIS/006/MODOCGA").filter(ee.Filter.date("2018-01-01", "2018-05-01"))
        FalseColor = dataset.select(["sur_refl_b11", "sur_refl_b10", "sur_refl_b09"])
        FalseColorVis = {
        "min": 0.0,
        "max": 2000.0}
        Map.setCenter(6.746, 46.529, 2)
        Map.addLayer(FalseColor, FalseColorVis, "False Color")

    elif cod == "MODIS/006/MYD10A1":
        dataset = ee.ImageCollection("MODIS/006/MYD10A1").filter(ee.Filter.date("2018-01-01", "2018-05-01"))
        snowCover = dataset.select("NDSI_Snow_Cover")
        snowCoverVis = {
        "min": 0.0,
        "max": 100.0,
        "palette": ["black", "0dffff", "0524ff", "ffffff"],
        }
        Map.setCenter(-38.13, 40, 2)
        Map.addLayer(snowCover, snowCoverVis, "Snow Cover")

    elif cod == "MODIS/006/MYD17A2H":
        dataset = ee.ImageCollection("MODIS/006/MYD17A2H").filter(ee.Filter.date("2018-01-01", "2018-05-01"))
        gpp = dataset.select("Gpp")
        gppVis = {
        "min": 0.0,
        "max": 600.0,
        "palette": ["bbe029", "0a9501", "074b03"],
        }
        Map.setCenter(6.746, 46.529, 2)
        Map.addLayer(gpp, gppVis, "GPP")

    elif cod == "MODIS/006/MYDOCGA":
        dataset = ee.ImageCollection("MODIS/006/MYDOCGA").filter(ee.Filter.date("2018-01-01", "2018-05-01"))
        FalseColor = dataset.select(["sur_refl_b11", "sur_refl_b10", "sur_refl_b09"])
        FalseColorVis = {
        "min": 0.0,
        "max": 2000.0,
        }
        Map.setCenter(6.746, 46.529, 2)
        Map.addLayer(FalseColor, FalseColorVis, "False Color")

    elif cod == "MODIS/061/MCD12Q1":
        dataset = ee.ImageCollection("MODIS/061/MCD12Q1")
        igbpLandCover = dataset.select("LC_Type1")
        igbpLandCoverVis = {
        "min": 1.0,
        "max": 17.0,
        "palette": [
        "05450a", "086a10", "54a708", "78d203", "009900", "c6b044", "dcd159",
        "dade48", "fbff13", "b6ff05", "27ff87", "c24f44", "a5a5a5", "ff6d4c",
        "69fff8", "f9ffa4", "1c0dff"
        ],
        }
        Map.setCenter(6.746, 46.529, 6)
        Map.addLayer(igbpLandCover, igbpLandCoverVis, "IGBP Land Cover")

    elif cod == "MODIS/061/MCD12Q2":
        dataset = ee.ImageCollection("MODIS/061/MCD12Q2").filter(ee.Filter.date("2001-01-01", "2002-01-01"))
        vegetationPeak = dataset.select("Peak_1")
        vegetationPeakVis = {
        "min": 11400,
        "max": 11868,
        "palette": ["0f17ff", "b11406", "f1ff23"],
        }
        Map.setCenter(6.746, 46.529, 2)
        Map.addLayer(vegetationPeak, vegetationPeakVis, "Vegetation Peak 2001")

    elif cod == "MODIS/061/MCD15A3H":
        dataset = ee.ImageCollection("MODIS/061/MCD15A3H")
        defaultVisualization = dataset.first().select("Fpar")
        defaultVisualizationVis = {
        "min": 0.0,
        "max": 100.0,
        "palette": ["e1e4b4", "999d60", "2ec409", "0a4b06"],
        }
        Map.setCenter(6.746, 46.529, 6)
        Map.addLayer(
        defaultVisualization, defaultVisualizationVis, "Default visualization")

    elif cod == "MODIS/061/MCD18C2":
        dataset = ee.ImageCollection("MODIS/061/MCD18C2").filter(ee.Filter.date("2001-01-01", "2001-02-01"))
        gmt_1200_par = dataset.select("GMT_1200_PAR")
        gmt_1200_par_vis = {
        "min": -236,
        "max": 316,
        "palette": ["0f17ff", "b11406", "f1ff23"],
        }
        Map.setCenter(6.746, 46.529, 2)
        Map.addLayer(gmt_1200_par, gmt_1200_par_vis,"Total PAR at GMT 12:00")

    elif cod == "COPERNICUS/CORINE/V20/100m":
        dataset = ee.Image('COPERNICUS/CORINE/V20/100m/2012')
        landCover = dataset.select('landcover')
        Map.setCenter(16.436, 39.825, 6)
        Map.addLayer(landCover, {}, 'Land Cover')
    
    elif cod == "LANDSAT/GLS1975_MOSAIC":
        dataset = ee.ImageCollection('LANDSAT/GLS1975_MOSAIC')
        falseColor = dataset.select(['30', '20', '10'])
        falseColorVis = {
        "gamma": 1.6,
        }
        Map.setCenter(-72.882406,5.181746, 5);
        Map.addLayer(falseColor, falseColorVis, 'False Color')
    
    elif cod == "LANDSAT/LC08/C01/T1_8DAY_BAI":
        dataset = ee.ImageCollection('LANDSAT/LC08/C01/T1_8DAY_BAI').filterDate('2017-01-01', '2017-12-31')
        scaled = dataset.select('BAI')
        scaledVis = {
        "min": 0.0,
        "max": 100.0,
        }
        Map.setCenter(-72.882406,5.181746, 6)
        Map.addLayer(scaled, scaledVis, 'Scaled')
    
    elif cod == "LANDSAT/LC08/C01/T1_8DAY_EVI":
        dataset = ee.ImageCollection('LANDSAT/LC08/C01/T1_8DAY_EVI').filterDate('2017-01-01', '2017-12-31')
        colorized = dataset.select('EVI')
        colorizedVis = {
        "min": 0.0,
        "max": 1.0,
        "palette": [
            'FFFFFF', 'CE7E45', 'DF923D', 'F1B555', 'FCD163', '99B718', '74A901',
            '66A000', '529400', '3E8601', '207401', '056201', '004C00', '023B01',
            '012E01', '011D01', '011301']
        }
        Map.setCenter(-72.882406,5.181746, 6)
        Map.addLayer(colorized, colorizedVis, 'Colorized')
    
    elif cod == "LANDSAT/LC08/C01/T1_8DAY_NDSI":
        dataset = ee.ImageCollection('LANDSAT/LC08/C01/T1_8DAY_NDSI').filterDate('2017-01-01', '2017-12-31')
        colorized = dataset.select('NDSI')
        colorizedVis = {
        "palette": ['000088', '0000FF', '8888FF', 'FFFFFF'],
        }
        Map.setCenter(-72.882406,5.181746, 6);
        Map.addLayer(colorized, colorizedVis, 'Colorized')
    
    elif cod == "LANDSAT/LC08/C01/T1_8DAY_NDVI":
        dataset = ee.ImageCollection('LANDSAT/LC08/C01/T1_8DAY_NDVI').filterDate('2017-01-01', '2017-12-31')
        colorized = dataset.select('NDVI')
        colorizedVis = {
        "min": 0.0,
        "max": 1.0,
        "palette": [
            'FFFFFF', 'CE7E45', 'DF923D', 'F1B555', 'FCD163', '99B718', '74A901',
            '66A000', '529400', '3E8601', '207401', '056201', '004C00', '023B01',
            '012E01', '011D01', '011301']}
        Map.setCenter(-72.882406,5.181746, 6)
        Map.addLayer(colorized, colorizedVis, 'Colorized')
    
    elif cod == "LANDSAT/LC08/C01/T1_8DAY_NDWI":
        dataset = ee.ImageCollection('LANDSAT/LC08/C01/T1_8DAY_NDWI').filterDate('2017-01-01', '2017-12-31')
        colorized = dataset.select('NDWI')
        colorizedVis = {
        "min": 0.0,
        "max": 1.0,
        "palette": ['0000ff', '00ffff', 'ffff00', 'ff0000', 'ffffff'],
        };
        Map.setCenter(-72.882406,5.181746, 6)
        Map.addLayer(colorized, colorizedVis, 'Colorized')
    
    elif cod == "LANDSAT/LC08/C01/T1_8DAY_RAW":
        dataset = ee.ImageCollection('LANDSAT/LC08/C01/T1_8DAY_RAW').filterDate('2017-01-01', '2017-12-31')
        visParams = {
        "min": 0,
        "max": 20000,
        "gamma": 1.2,
        "bands": ['B4', 'B3', 'B2'],
        }
        Map.setCenter(-72.882406,5.181746, 6)
        Map.addLayer(dataset, visParams, 'LANDSAT/LC08/C01/T1_8DAY_RAW')
    
    elif cod == "LANDSAT/LC08/C01/T1_8DAY_TOA":
        dataset = ee.ImageCollection('LANDSAT/LC08/C01/T1_8DAY_TOA').filterDate('2017-01-01', '2017-12-31')
        trueColor = dataset.select(['B4', 'B3', 'B2'])
        trueColorVis = {
        "min": 0,
        "max": 0.4,
        "gamma": 1.2,
        }
        Map.setCenter(-72.882406,5.181746, 6)
        Map.addLayer(trueColor, trueColorVis, 'True Color (432)')
    
    elif cod == "LANDSAT/LC08/C01/T1_32DAY_BAI":
        dataset = ee.ImageCollection('LANDSAT/LC08/C01/T1_32DAY_BAI').filterDate('2017-01-01', '2017-12-31')
        scaled = dataset.select('BAI')
        scaledVis = {
        "min": 0.0,
        "max": 100.0,
        }
        Map.setCenter(-72.882406,5.181746, 6)
        Map.addLayer(scaled, scaledVis, 'Scaled')
    
    elif cod == "LANDSAT/LC08/C01/T1_32DAY_EVI":
        dataset = ee.ImageCollection('LANDSAT/LC08/C01/T1_32DAY_EVI').filterDate('2017-01-01', '2017-12-31')
        colorized = dataset.select('EVI')
        colorizedVis = {
        "min": 0.0,
        "max": 1.0,
        "palette": [
            'FFFFFF', 'CE7E45', 'DF923D', 'F1B555', 'FCD163', '99B718', '74A901',
            '66A000', '529400', '3E8601', '207401', '056201', '004C00', '023B01',
            '012E01', '011D01', '011301']
        }
        Map.setCenter(-72.882406,5.181746, 6)
        Map.addLayer(colorized, colorizedVis, 'Colorized')
    
    elif cod == "LANDSAT/LC08/C01/T1_32DAY_NBRT":
        dataset = ee.ImageCollection('LANDSAT/LC08/C01/T1_32DAY_NBRT').filterDate('2017-01-01', '2017-12-31')
        colorized = dataset.select('NBRT')
        colorizedVis = {
        "min": 0.9,
        "max": 1.0,
        "palette": ['000000', 'FFFFFF'],
        }
        Map.setCenter(-72.882406,5.181746, 6)
        Map.addLayer(colorized, colorizedVis, 'Colorized')
    
    elif cod == "LANDSAT/LC08/C01/T1_32DAY_NDSI":
        dataset = ee.ImageCollection('LANDSAT/LC08/C01/T1_32DAY_NDSI').filterDate('2017-01-01', '2017-12-31')
        colorized = dataset.select('NDSI')
        colorizedVis = {
        "palette": ['000088', '0000FF', '8888FF', 'FFFFFF'],
        };
        Map.setCenter(-72.882406,5.181746, 6)
        Map.addLayer(colorized, colorizedVis, 'Colorized')
    
    elif cod == "LANDSAT/LC08/C01/T1_32DAY_NDVI":
        dataset = ee.ImageCollection('LANDSAT/LC08/C01/T1_32DAY_NDVI').filterDate('2017-01-01', '2017-12-31')
        colorized = dataset.select('NDVI')
        colorizedVis = {
        "min": 0.0,
        "max": 1.0,
        "palette": [
            'FFFFFF', 'CE7E45', 'DF923D', 'F1B555', 'FCD163', '99B718', '74A901',
            '66A000', '529400', '3E8601', '207401', '056201', '004C00', '023B01',
            '012E01', '011D01', '011301']
        }
        Map.setCenter(-72.882406,5.181746, 6)
        Map.addLayer(colorized, colorizedVis, 'Colorized')
    
    elif cod == "LANDSAT/LC08/C01/T1_32DAY_NDWI":
        dataset = ee.ImageCollection('LANDSAT/LC08/C01/T1_32DAY_NDWI').filterDate('2017-01-01', '2017-12-31')        
        colorized = dataset.select('NDWI');
        colorizedVis = {
        "min": 0.0,
        "max": 1.0,
        "palette": ['0000ff', '00ffff', 'ffff00', 'ff0000', 'ffffff']
        }
        Map.setCenter(-72.882406,5.181746, 6)
        Map.addLayer(colorized, colorizedVis, 'Colorized')
    
    elif cod == "LANDSAT/LC08/C01/T1_32DAY_RAW":
        dataset = ee.ImageCollection('LANDSAT/LC08/C01/T1_32DAY_RAW').filterDate('2017-01-01', '2017-12-31')
        visParams = {
        "min": 0,
        "max": 20000,
        "gamma": 1.2,
        "bands": ['B4', 'B3', 'B2']
        }
        Map.setCenter(-72.882406,5.181746, 6)
        Map.addLayer(dataset, visParams, 'LANDSAT/LC08/C01/T1_32DAY_RAW')
    
    elif cod == "LANDSAT/LC08/C01/T1_32DAY_TOA":
        dataset = ee.ImageCollection('LANDSAT/LC08/C01/T1_32DAY_TOA').filterDate('2017-01-01', '2017-12-31')
        trueColor = dataset.select(['B4', 'B3', 'B2'])
        trueColorVis = {
        "min": 0,
        "max": 0.4,
        "gamma": 1.2,
        }
        Map.setCenter(-72.882406,5.181746, 6)
        Map.addLayer(trueColor, trueColorVis, 'True Color (432)')
    
    elif cod == "LANDSAT/LC08/C01/T1_ANNUAL_BAI":
        dataset = ee.ImageCollection('LANDSAT/LC08/C01/T1_ANNUAL_BAI') \
                  .filterDate('2017-01-01', '2017-12-31')
        scaled = dataset.select('BAI')
        scaledVis = {
        "min": 0.0,
        "max": 100.0,
        }
        Map.setCenter(-72.882406,5.181746, 6)
        Map.addLayer(scaled, scaledVis, 'Scaled')

    elif cod == "NOAA/DMSP-OLS/CALIBRATED_LIGHTS_V4":
        dataset = ee.ImageCollection('NOAA/DMSP-OLS/CALIBRATED_LIGHTS_V4')\
                  .filterDate('2010-01-01', '2010-12-31')
        nighttimeLights = dataset.select('avg_vis')
        nighttimeLightsVis = {
        "min": 3.0,
        "max": 60.0,
        }
        Map.setCenter(7.82, 49.1, 4)
        Map.addLayer(nighttimeLights, nighttimeLightsVis, 'Nighttime Lights')

    elif cod == "NOAA/VIIRS/DNB/MONTHLY_V1/VCMCFG":
        dataset = ee.ImageCollection('NOAA/VIIRS/DNB/MONTHLY_V1/VCMCFG').filterDate('2022-01-01', '2023-12-31')
        nighttime = dataset.select('avg_rad')
        nighttimeVis = {
            "min": 0.0,
            "max": 60.0,
        }
        Map.setCenter(67.1056, 24.8904, 8)
        Map.addLayer(nighttime, nighttimeVis, 'Nighttime')


def get_coordinates(location_name):
    try:
        geolocator = Nominatim(user_agent="geoapiExercises")
        location = geolocator.geocode(location_name, exactly_one=True)
        if location:
            return location.latitude, location.longitude
        else:
            raise ValueError("Location not found")
    except Exception as e:
        raise ValueError(f"Error getting coordinates: {e}")

def create_ndvi_time_series(coordinates):
    try:
        # Define the area as a buffer around the coordinates
        center = ee.Geometry.Point(coordinates)
        drawn_area = center.buffer(10000).bounds()  # Buffer of 10 km for example
        
        # Initialize the ImageCollection
        collection = ee.ImageCollection('AAFC/ACI') \
            .filterBounds(drawn_area) \
            .filterDate('2009-01-01', '2024-12-31')  # Extended date range
        
        # Check the number of images in the collection
        count = collection.size().getInfo()
        st.write(f"Number of images found: {count}")  # Debug output
        
        if count == 0:
            st.error("No images found for the specified date range and area.")
            return px.scatter(title="No NDVI Data Available")
        
        # Define a function to calculate NDVI and extract data from each image
        def extract_data(image):
            date = ee.Date(image.get('system:time_start')).format('YYYY-MM-dd').getInfo()
            # Ensure correct band names
            ndvi = image.normalizedDifference(['B5', 'B4']).rename('NDVI')  # Common bands for NDVI
            data = ndvi.reduceRegion(
                reducer=ee.Reducer.mean(),
                geometry=drawn_area,
                scale=30,
                maxPixels=1e8,  # Increase maxPixels if necessary
                bestEffort=True
            ).getInfo()
            return pd.DataFrame({'Date': [date], 'NDVI': [data.get('NDVI', None)]})

        # Map the extraction function over the collection and convert to a DataFrame
        ndvi_df = pd.DataFrame()
        image_list = collection.toList(count).getInfo()
        
        if not image_list:
            st.error("Image list is empty. Check your filter parameters.")
            return px.scatter(title="No NDVI Data Available")

        for img_info in image_list:
            img = ee.Image(img_info['id'])
            img_data = extract_data(img)
            ndvi_df = pd.concat([ndvi_df, img_data], ignore_index=True)
        
        if ndvi_df.empty:
            st.error("No NDVI data was extracted.")
            return px.scatter(title="No NDVI Data Available")

        fig = px.line(ndvi_df, x='Date', y='NDVI', title='NDVI Time Series')
        return fig

    except Exception as e:
        st.error(f"Error creating NDVI time series: {e}")
        return px.scatter(title="Error")
    
geolocator = Nominatim(user_agent="geoapiExercises")
def get_coordinates(location_name):
    try:
        geolocator = Nominatim(user_agent="my_geocoder_app")
        location = geolocator.geocode(location_name, exactly_one=True)
        if location:
            return location.latitude, location.longitude
        else:
            raise ValueError("Location not found")
    except Exception as e:
        raise ValueError(f"Error getting coordinates: {e}")

def create_nighttime_lights_time_series(coordinates):
    try:
        # Define the area as a buffer around the coordinates
        center = ee.Geometry.Point(coordinates)
        drawn_area = center.buffer(10000).bounds()  # Buffer of 10 km for example

        # Initialize the ImageCollection
        collection = ee.ImageCollection('NOAA/VIIRS/DNB/MONTHLY_V1/VCMCFG') \
            .filterBounds(drawn_area) \
            .filterDate('2021-01-01', '2021-12-31')

        # Define a function to extract data from each image
        def extract_data(image):
            date = ee.Date(image.get('system:time_start')).format('YYYY-MM-dd').getInfo()
            data = image.reduceRegion(
                reducer=ee.Reducer.mean(),
                geometry=drawn_area,
                scale=500
            ).getInfo()
            return pd.DataFrame({'Date': [date], 'Radiance': [data.get('avg_rad', 0)]})

        # Map the extraction function over the collection and convert to a DataFrame
        radiance_df = pd.DataFrame()
        image_list = collection.toList(collection.size()).getInfo()
        
        for img_info in image_list:
            img = ee.Image(img_info['id'])
            img_data = extract_data(img)
            radiance_df = pd.concat([radiance_df, img_data], ignore_index=True)
        
        fig = px.line(radiance_df, x='Date', y='Radiance', title='Nighttime Lights Time Series')
        return fig

    except Exception as e:
        st.error(f"Error creating nighttime lights time series: {e}")
        return px.scatter(title="Error")



# Streamlit application
st.sidebar.title("Navigation")
pages = {
    "Home": lambda: st.write("Welcome to the Home Page!"),
    "About": lambda: st.write("The main purpose is to provide navigation to other pages/maps."),
    "Interactive Map": lambda: st.write("Use the form below to create your interactive map.")
}
selection = st.sidebar.radio("Go to", list(pages.keys()))
pages[selection]()

if selection == "Interactive Map":
    # Initialize GEE
    initialize_gee()

    # Load dataset information
    dataset_info, _ = data_gee()
    

    # Inputs
def create_folium_map(selected_dataset):
    folium_map = geemap.Map(location=[25.5973518, 65.54495724], zoom_start=7)
    try:
        type_map(folium_map, selected_dataset)
    except Exception as e:
        st.error(f"Error processing dataset: {e}")
    folium_map_html = folium_map.to_html()
    return folium_map_html

def app():
    st.title("Google Earth Engine Maps and Graphs")
    dataset_info, _ = data_gee()
    available_datasets = dataset_info['id'].tolist()

    selected_dataset = st.selectbox(
        "Select Dataset",
        ['AAFC/ACI', 'ACA/reef_habitat/v2_0', 'AHN/AHN2_05M_INT', 'AHN/AHN2_05M_NON', 'AHN/AHN2_05M_RUW', 'ASTER/AST_L1T_003', 'AU/GA/AUSTRALIA_5M_DEM', 'NOAA/VIIRS/DNB/MONTHLY_V1/VCMCFG',"AU/GA/DEM_1SEC/v10/DEM-H", "AU/GA/DEM_1SEC/v10/DEM-S", "BIOPAMA/GlobalOilPalm/v1", "BLM/AIM/v1/TerrADat/TerrestrialAIM", "BNU/FGS/CCNL/v1", "CAS/IGSNRR/PML/V2_v017", "CGIAR/SRTM90_V4", "CIESIN/GPWv411/GPW_Basic_Demographic_Characteristics", "CIESIN/GPWv411/GPW_Data_Context", "CIESIN/GPWv411/GPW_Land_Area", "CIESIN/GPWv411/GPW_Mean_Administrative_Unit_Area", "CIESIN/GPWv411/GPW_National_Identifier_Grid", "CIESIN/GPWv411/GPW_Population_Count", "CIESIN/GPWv411/GPW_Population_Density", "CIESIN/GPWv411/GPW_UNWPP-Adjusted_Population_Count", "CIESIN/GPWv411/GPW_UNWPP-Adjusted_Population_Density", "CIESIN/GPWv411/GPW_Water_Area", "CIESIN/GPWv411/GPW_Water_Mask", "COPERNICUS/CORINE/V20/100m/2012", "COPERNICUS/DEM/GLO30", "COPERNICUS/Landcover/100m/Proba-V-C3/Global", "COPERNICUS/S1_GRD"
'AAFC/ACI', 'ACA/reef_habitat/v2_0', 'AHN/AHN2_05M_INT', 'AHN/AHN2_05M_NON', 
        'AHN/AHN2_05M_RUW', 'ASTER/AST_L1T_003', 'AU/GA/AUSTRALIA_5M_DEM', 
        'NOAA/VIIRS/DNB/MONTHLY_V1/VCMCFG', 'AU/GA/DEM_1SEC/v10/DEM-H', 
        'AU/GA/DEM_1SEC/v10/DEM-S', 'BIOPAMA/GlobalOilPalm/v1', 
        'BLM/AIM/v1/TerrADat/TerrestrialAIM', 'BNU/FGS/CCNL/v1', 
        'CAS/IGSNRR/PML/V2_v017', 'CGIAR/SRTM90_V4', 
        'CIESIN/GPWv411/GPW_Basic_Demographic_Characteristics', 
        'CIESIN/GPWv411/GPW_Data_Context', 'CIESIN/GPWv411/GPW_Land_Area', 
        'CIESIN/GPWv411/GPW_Mean_Administrative_Unit_Area', 
        'CIESIN/GPWv411/GPW_National_Identifier_Grid', 'CIESIN/GPWv411/GPW_Population_Count', 
        'CIESIN/GPWv411/GPW_Population_Density', 
        'CIESIN/GPWv411/GPW_UNWPP-Adjusted_Population_Count', 
        'CIESIN/GPWv411/GPW_UNWPP-Adjusted_Population_Density', 
        'CIESIN/GPWv411/GPW_Water_Area', 'CIESIN/GPWv411/GPW_Water_Mask', 
        'COPERNICUS/CORINE/V20/100m/2012', 'COPERNICUS/DEM/GLO30', 
        'COPERNICUS/Landcover/100m/Proba-V-C3/Global', 'COPERNICUS/S1_GRD', 
        'COPERNICUS/S2', 'COPERNICUS/S2_CLOUD_PROBABILITY', 'COPERNICUS/S2_HARMONIZED', 
        'COPERNICUS/S2_SR', 'COPERNICUS/S2_SR_HARMONIZED', 'COPERNICUS/S3/OLCI', 
        'COPERNICUS/S5P/NRTI/L3_AER_AI', 'COPERNICUS/S5P/NRTI/L3_AER_LH', 
        'COPERNICUS/S5P/NRTI/L3_CLOUD', 'COPERNICUS/S5P/NRTI/L3_CO', 
        'COPERNICUS/S5P/NRTI/L3_HCHO', 'COPERNICUS/S5P/NRTI/L3_NO2', 
        'COPERNICUS/S5P/NRTI/L3_O3', 'COPERNICUS/S5P/NRTI/L3_SO2', 
        'COPERNICUS/S5P/OFFL/L3_AER_AI', 'COPERNICUS/S5P/OFFL/L3_AER_LH', 
        'COPERNICUS/S5P/OFFL/L3_CH4', 'COPERNICUS/S5P/OFFL/L3_CLOUD', 
        'COPERNICUS/S5P/OFFL/L3_CO', 'COPERNICUS/S5P/OFFL/L3_HCHO', 
        'COPERNICUS/S5P/OFFL/L3_NO2', 'COPERNICUS/S5P/OFFL/L3_O3', 
        'COPERNICUS/S5P/OFFL/L3_O3_TCL', 'COPERNICUS/S5P/OFFL/L3_SO2', 
        'CPOM/CryoSat2/ANTARCTICA_DEM', 'CSIRO/SLGA', 
        'CSP/ERGo/1_0/Global/ALOS_CHILI', 'CSP/ERGo/1_0/Global/ALOS_landforms', 
        'CSP/ERGo/1_0/Global/ALOS_mTPI', 'CSP/ERGo/1_0/Global/ALOS_topoDiversity', 
        'CSP/ERGo/1_0/Global/SRTM_CHILI', 'CSP/ERGo/1_0/Global/SRTM_landforms', 
        'CSP/ERGo/1_0/Global/SRTM_mTPI', 'CSP/ERGo/1_0/Global/SRTM_topoDiversity', 
        'CSP/ERGo/1_0/US/CHILI', 'CSP/ERGo/1_0/US/landforms', 
        'CSP/ERGo/1_0/US/lithology', 'CSP/ERGo/1_0/US/mTPI', 
        'CSP/ERGo/1_0/US/physioDiversity', 'CSP/ERGo/1_0/US/physiography', 
        'CSP/ERGo/1_0/US/topoDiversity', 'CSP/HM/GlobalHumanModification', 
        'DLR/WSF/WSF2015/v1', 'DOE/ORNL/LandScan_HD/Ukraine_202201', 'ECMWF/CAMS/NRT', 
        'ECMWF/ERA5/DAILY', 'ECMWF/ERA5/MONTHLY', 'ECMWF/ERA5_LAND/DAILY_RAW', 
        'ECMWF/ERA5_LAND/HOURLY', 'ECMWF/ERA5_LAND/MONTHLY_AGGR', 
        'ECMWF/ERA5_LAND/MONTHLY_BY_HOUR', 'EO1/HYPERION', 
        'EPA/Ecoregions/2013/L3', 'EPA/Ecoregions/2013/L4', 'ESA/CCI/FireCCI/5_1', 
        'ESA/GLOBCOVER_L4_200901_200912_V2_3', 'ESA/WorldCover/v100', 
        'ESA/WorldCover/v200', 'FAO/GAUL/2015/level0', 'FAO/GAUL/2015/level1', 
        'FAO/GAUL/2015/level2', 'FAO/GAUL_SIMPLIFIED_500m/2015/level0', 
        'FAO/GAUL_SIMPLIFIED_500m/2015/level1', 'FAO/GAUL_SIMPLIFIED_500m/2015/level2',
        'FAO/GHG/1/DROSA_A', 'FAO/GHG/1/DROSE_A', 'FAO/SOFO/1/FPP', 
        'FAO/SOFO/1/TPP', 'FAO/WAPOR/2/L1_AETI_D', 'FAO/WAPOR/2/L1_E_D', 
        'FAO/WAPOR/2/L1_I_D', 'FAO/WAPOR/2/L1_NPP_D', 'FAO/WAPOR/2/L1_RET_D', 
        'FAO/WAPOR/2/L1_RET_E', 'FAO/WAPOR/2/L1_T_D', 'FIRMS', 
        'Finland/MAVI/VV/50cm', 'GFW/GFF/V1/fishing_hours', 'GFW/GFF/V1/vessel_hours', 
        'GLCF/GLS_WATER', 'GLIMS/20210914', 'Finland/SMK/VV/50cm', 
        'Finland/SMK/V/50cm', 'GLIMS/current', 'GLOBAL_FLOOD_DB/MODIS_EVENTS/V1', 
        'GOOGLE/DYNAMICWORLD/V1', 'GOOGLE/Research/open-buildings/v2/polygons', 
        'GRIDMET/DROUGHT', 'Germany/Brandenburg/orthos/20cm', 'HYCOM/sea_surface_elevation', 
        'HYCOM/sea_temp_salinity', 'HYCOM/sea_water_velocity', 
        'IDAHO_EPSCOR/GRIDMET', 'IDAHO_EPSCOR/MACAv2_METDATA', 
        'IDAHO_EPSCOR/MACAv2_METDATA_MONTHLY', 'IDAHO_EPSCOR/TERRACLIMATE','NOAA/DMSP-OLS/CALIBRATED_LIGHTS_V4','NOAA/VIIRS/DNB/MONTHLY_V1/VCMCFG']
    )
    
    map_html = create_folium_map(selected_dataset)
    st.components.v1.html(map_html, height=600, width=800)

    area_option = st.sidebar.selectbox("Choose Area Definition Method", ["Draw on Map", "Upload GeoJSON"])
    
    drawn_area = None

    if area_option == "Draw on Map":
        st.sidebar.text("Draw a rectangle on the map to define the area.")
      

    elif area_option == "Upload GeoJSON":
        uploaded_file = st.file_uploader("Upload GeoJSON file", type="geojson")
        if uploaded_file:
            geojson_data = json.load(uploaded_file)
            drawn_area = geojson_data

    st.title('Nighttime Lights Time Series')

    # Input for location name
    location_name = st.text_input("Enter a location (e.g., Karachi, New York)")

    if location_name:
        try:
            # Get coordinates for the location
            coordinates = get_coordinates(location_name)
            # Generate the graph
            fig = create_nighttime_lights_time_series(coordinates)
            st.plotly_chart(fig)
        except Exception as e:
            st.error(f"Error processing location data: {e}")
    st.title('NDVI Time Series')

    # Input for location name with unique key
    location_name = st.text_input("Enter a location (e.g., Karachi, New York)", key="location_input")

    if location_name:
        try:
            # Get coordinates for the location
            coordinates = get_coordinates(location_name)
            # Generate the NDVI graph
            fig = create_ndvi_time_series(coordinates)
            st.plotly_chart(fig)
        except Exception as e:
            st.error(f"Error processing location data: {e}")

if __name__ == "__main__":
    app()