Upload 3 files

y_gee_ee-muzzamil1-37ebc3dece52.json

@@ -0,0 +1,13 @@
+{
+  "type": "service_account",
+  "project_id": "ee-muzzamil1",
+  "private_key_id": "37ebc3dece52ae603066e6e3e6b614e8d4ba10cb",
+  "private_key": "-----BEGIN PRIVATE KEY-----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\nsWmCRdg+vzGkP88aqRqxCJ/N\n-----END PRIVATE KEY-----\n",
+  "client_email": "geo-spatial-app@ee-muzzamil1.iam.gserviceaccount.com",
+  "client_id": "101116468635843693944",
+  "auth_uri": "https://accounts.google.com/o/oauth2/auth",
+  "token_uri": "https://oauth2.googleapis.com/token",
+  "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
+  "client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/geo-spatial-app%40ee-muzzamil1.iam.gserviceaccount.com",
+  "universe_domain": "googleapis.com"
+}

y_gee_views.py

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+from django.shortcuts import render,redirect
+import geopandas as gpd
+from folium import GeoJson
+import json
+import geemap
+import os
+# generic base view
+from django.views.generic import TemplateView 
+
+# folium
+import folium
+from folium import plugins
+
+# gee
+import ee
+
+#---
+from .forms import *
+from django.http import HttpResponse
+from django.shortcuts import render
+from django.http import JsonResponse
+from .gee import type_map, data_gee
+from django.contrib.auth.decorators import login_required
+import geemap.foliumap as geemap
+from django.views.decorators.csrf import csrf_exempt
+from django.contrib.auth import login as auth_login
+from django.urls import reverse_lazy
+from django.views.generic import UpdateView
+from django.shortcuts import render
+import ee
+import pandas as pd
+import numpy as np
+from scipy import optimize
+import matplotlib.pyplot as plt
+import matplotlib.pyplot as plt
+import numpy as np
+from scipy import optimize
+
+
+from django.shortcuts import render
+import ee
+import pandas as pd
+import numpy as np
+from scipy import optimize
+from django.http import JsonResponse
+import plotly.graph_objs as go
+from datetime import datetime
+
+
+
+
+
+
+#e.Authenticate()
+ ## Credenciales de EE
+
+
+# D:\Desktop\Django_app_12_sep-2023\gee\ee-muzzamil.json
+
+def index(request):
+    
+    print("I am in index")
+    return render (request, "index.html")
+
+# ee.Initialize()
+@csrf_exempt
+
+@login_required
+def home(request):
+    template_name = 'home.html'
+    
+    
+
+    if request.method == 'GET':
+        selected_dataset = request.GET.get('dataset')
+        selected_shapefile = request.GET.get('shapefile')
+        selected_date_range_From = request.GET.get('dateRangeFrom')
+        selected_date_range_To = request.GET.get('dateRangeTo')
+
+        print(f'Selected Dataset: {selected_dataset}')
+        print(f'Selected Dataset: {selected_shapefile}')
+        
+    figure = folium.Figure()
+
+    m = folium.Map(
+        location=[25.5973518, 65.54495724],
+        zoom_start=7,
+    )
+    m.add_to(figure)
+
+#----------------------------------------------------------------------------------------------------------------------#
+    if selected_dataset == "Modis":
+        if selected_shapefile != None:
+
+            shapefile_path = ('C:\\Users\\piv\\Desktop\\y\\media\\shp')
+           
+
+
+            roi_gdf = gpd.read_file(shapefile_path)
+            roi_geojson = roi_gdf.to_crs("EPSG:4326").to_json()
+            
+            # Create a folium GeoJson layer for visualization
+            roi_geojson_layer = folium.GeoJson(roi_geojson, name='ROI GeoJSON')
+            roi_geojson_layer.add_to(m)
+            
+            # Convert the GeoJSON content to Earth Engine object
+            ee_object = geemap.geojson_to_ee(json.loads(roi_geojson))
+            if selected_date_range_From != None:
+                if selected_date_range_To != None:
+                    print("I am here")
+                    F = selected_date_range_From
+                    T = selected_date_range_To
+                    print(F,"==>",T)
+
+
+                    dataset = ee.ImageCollection('MODIS/006/MOD13Q1').filter(ee.Filter.date(F, T)).filterBounds(ee_object)
+            
+                    modisndvi = dataset.select('NDVI')
+
+                    modisndvi = modisndvi.clip(ee_object)
+                    
+                    
+
+                    vis_paramsNDVI = {
+                        'min': 0,
+                        'max': 9000,
+                        'palette': ['FE8374', 'C0E5DE', '3A837C', '034B48']}
+
+                    map_id_dict = ee.Image(modisndvi).getMapId(vis_paramsNDVI)
+                    folium.raster_layers.TileLayer(
+                        tiles=map_id_dict['tile_fetcher'].url_format,
+                        attr='Google Earth Engine',
+                        name='NDVI',
+                        overlay=True,
+                        control=True
+                    ).add_to(m)
+                    
+                
+                    
+                    m.add_child(folium.LayerControl())
+                    figure.render()
+
+                else:
+                    F = "2015-07-01"
+                    T = "2019-11-30"
+                    print("Date TO is Missing")
+            else:
+                F = "2015-07-01"
+                T = "2019-11-30"
+                print("Date From is Missing")
+
+        else:
+            pass
+
+        
+
+
+
+
+
+
+#--------------------------------------------------------------------------------------------------------------------------------#   
+    elif selected_dataset == "dataset_nighttime":
+        if selected_shapefile != None:
+
+            shapefile_path = ('C:\\Users\\piv\\Desktop\\y\\media\\shp')
+           
+                                # D:\Desktop\final_working1-New-2023\final\media
+
+            roi_gdf = gpd.read_file(shapefile_path)
+            roi_geojson = roi_gdf.to_crs("EPSG:4326").to_json()
+            
+            # Create a folium GeoJson layer for visualization
+            roi_geojson_layer = folium.GeoJson(roi_geojson, name='ROI GeoJSON')
+            roi_geojson_layer.add_to(m)
+            
+            # Convert the GeoJSON content to Earth Engine object
+            ee_object = geemap.geojson_to_ee(json.loads(roi_geojson))
+            if selected_date_range_From != None:
+                if selected_date_range_To != None:
+                    print("I am here")
+                    F = selected_date_range_From
+                    T = selected_date_range_To
+                    print(F,"==>",T)
+
+
+                    dataset_nighttime = ee.ImageCollection('NOAA/VIIRS/DNB/MONTHLY_V1/VCMCFG').filter(ee.Filter.date(F, T))
+              
+
+
+                    # Mosaic the image collection to a single image
+                    nighttime = dataset_nighttime.select('avg_rad').mosaic()
+                    
+                    # Clip the nighttime lights image to the defined region
+                    nighttime_clipped = nighttime.clip(ee_object)
+                    
+                    nighttimeVis = {'min': 0.0, 'max': 60.0,'palette': ['1a3678', '2955bc', '5699ff', '8dbae9', 'acd1ff', 'caebff', 'e5f9ff',
+                    'fdffb4', 'ffe6a2', 'ffc969', 'ffa12d', 'ff7c1f', 'ca531a', 'ff0000',
+                    'ab0000']}
+                    nighttime_layer = folium.TileLayer(
+                        tiles=nighttime_clipped.getMapId(nighttimeVis)['tile_fetcher'].url_format,
+                        attr='Google Earth Engine',
+                        name='Nighttime Lights',
+                        overlay=True,
+                        control=True
+                    ).add_to(m)
+                                
+                    m.add_child(folium.LayerControl())
+                    figure.render()
+
+                else:
+                    F = "2015-07-01"
+                    T = "2023-09-30"
+                    print("Date TO is Missing")
+            else:
+                F = "2015-07-01"
+                T = "2023-09-30"
+                print("Date From is Missing")
+
+        else:
+            pass
+#------------------------------------------------------------------------------------------------------------------------------------#
+
+#------------------------------------------------------------------------------------------------------------------------------------#
+    elif selected_dataset == "precipitation":
+        if selected_shapefile != None:
+
+            shapefile_path = ('C:\\Users\\piv\\Desktop\\y\\media\\shp')
+            
+
+
+            roi_gdf = gpd.read_file(shapefile_path)
+            roi_geojson = roi_gdf.to_crs("EPSG:4326").to_json()
+            
+            # Create a folium GeoJson layer for visualization
+            roi_geojson_layer = folium.GeoJson(roi_geojson, name='ROI GeoJSON')
+            roi_geojson_layer.add_to(m)
+            
+            # Convert the GeoJSON content to Earth Engine object
+            ee_object = geemap.geojson_to_ee(json.loads(roi_geojson))
+            if selected_date_range_From != None:
+                if selected_date_range_To != None:
+                    print("I am here")
+                    F = selected_date_range_From
+                    T = selected_date_range_To
+                    print(F,"==>",T)
+
+
+                  
+                    # Load the dataset
+                    dataset = (ee.ImageCollection('UCSB-CHG/CHIRPS/DAILY').filterBounds(ee_object).filter(ee.Filter.date(F, T)))
+
+                  
+                 
+                  
+                    # Calculate the sum of the dataset
+                    dataset1 = dataset.sum()
+             
+                    # Clip the summed dataset to the defined region
+                    dataset2 = dataset1.clip(ee_object)
+             
+                    # Select the 'precipitation' band
+                    precipitation = dataset2.select('precipitation')
+             
+                    # Define visualization parameters
+                    imageVisParam = {
+                        'min': 80,
+                        'max': 460,
+                        'palette': ["001137","0aab1e","e7eb05","ff4a2d","e90000"]
+                    }
+             
+                    # Clip the precipitation data to the region
+                    precipitation_clipped = precipitation.clip(ee_object)
+             
+                    # Add precipitation layer to the map
+                    folium.TileLayer(
+                        tiles=precipitation_clipped.getMapId(imageVisParam)['tile_fetcher'].url_format,
+                        attr='Google Earth Engine',
+                        name='Precipitation',
+                        overlay=True,
+                        control=True
+                    ).add_to(m)
+                    
+                    m.add_child(folium.LayerControl())
+                    figure.render()
+
+            else:
+                    F = "2015-07-01"
+                    T = "2023-09-30"
+                    print("Date TO is Missing")
+        else:
+                F = "2015-07-01"
+                T = "2023-09-30"
+                print("Date From is Missing")
+
+    else:
+            pass
+
+#------------------------------------------------------------------------------------------------------------------------------------#
+                
+
+#------------------------------------------------------------------------------------------------------------------------------------#
+
+
+
+#to be rendered 
+    dataset_options = ['Modis',  
+                    'dataset_nighttime',
+                    'precipitation',
+                    'GlobalSurfaceWater',
+                    'WorldPop',
+                    'COPERNICUS']
+
+
+
+
+    shapes_options = ['District_Boundary',
+                    'hydro_basins',
+                    'karachi',
+                    'National_Constituency_with_Projected_2010_Population',
+                    'Provincial_Boundary',
+                    'Provincial_Constituency',
+                    'Tehsil_Boundary',
+                    'Union_Council']
+    # print(figure)
+    # map_html = m._repr_html_()
+    m.save('ndvi_map.html')
+    
+    
+    
+
+    context = {"map": figure,"dataset_options":dataset_options,"shapes_options": shapes_options}
+    return render(request, template_name , context)
+@login_required
+def generate_ndvi_map(request):
+    # Create a response object for the HTML file
+    response = HttpResponse(content_type='text/html')
+    # Open and read the HTML file
+    with open('ndvi_map.html', 'rb') as html_file:
+        response.write(html_file.read())
+    
+    # Set the Content-Disposition header to suggest a filename for download
+    response['Content-Disposition'] = 'attachment; filename="ndvi_map.html"'
+    
+    return response
+@login_required
+def generate_chart(request):
+    template_name = 'results.html'
+
+    water_threshold=0.2
+    if request.method == 'GET':
+        selected_dataset = request.GET.get('dataset')
+        selected_shapefile = request.GET.get('shapefile')
+        selected_date_range_From = request.GET.get('dateRangeFrom')
+        selected_date_range_To = request.GET.get('dateRangeTo')
+
+        print(f'Selected Dataset: {selected_dataset}')
+        print(f'Selected Dataset: {selected_shapefile}')
+        
+    figure = folium.Figure()
+
+    m = folium.Map(
+        location=[25.5973518, 65.54495724],
+        zoom_start=7,
+    )
+    m.add_to(figure)
+
+
+#----------------------------------------------------------------------------------------------------------------------#
+    if selected_dataset == "Modis":
+        if selected_shapefile != None:
+
+            shapefile_path = ('C:\\Users\\piv\\Desktop\\y\\media\\shp')
+           
+
+            roi_gdf = gpd.read_file(shapefile_path)
+            roi_geojson = roi_gdf.to_crs("EPSG:4326").to_json()
+            
+            # Create a folium GeoJson layer for visualization
+            roi_geojson_layer = folium.GeoJson(roi_geojson, name='ROI GeoJSON')
+            roi_geojson_layer.add_to(m)
+            
+            # Convert the GeoJSON content to Earth Engine object
+            ee_object = geemap.geojson_to_ee(json.loads(roi_geojson))
+            if selected_date_range_From != None:
+                if selected_date_range_To != None:
+                    print("I am here")
+                    F = selected_date_range_From
+                    T = selected_date_range_To
+                    print(F,"==>",T)
+
+
+                    dataset = ee.ImageCollection('MODIS/006/MOD13Q1').filter(ee.Filter.date(F, T)).filterBounds(ee_object).first()
+            
+                    modisndvi = dataset.select('NDVI')
+
+                    def water_function(image):
+                        ndwi = image.normalizedDifference(['B3', 'B5']).rename('NDWI')
+                        ndwi1 = ndwi.select('NDWI')
+                        water01 = ndwi1.gt(water_threshold)
+                        image = image.updateMask(water01).addBands(ndwi1)
+                        area = ee.Image.pixelArea()
+                        water_area = water01.multiply(area).rename('waterArea')
+                        image = image.addBands(water_area)
+                        stats = water_area.reduceRegion({
+                            'reducer': ee.Reducer.sum(),
+                            'geometry': shapefile_path,
+                            'scale': 30,
+                        })
+                        return image.set(stats)
+                    
+                    
+
+                    modisndvi = modisndvi.clip(ee_object)
+
+                    vis_paramsNDVI = {
+                        'min': 0,
+                        'max': 9000,
+                        'palette': ['FE8374', 'C0E5DE', '3A837C', '034B48']}
+
+                    map_id_dict = ee.Image(modisndvi).getMapId(vis_paramsNDVI)
+                    folium.raster_layers.TileLayer(
+                        tiles=map_id_dict['tile_fetcher'].url_format,
+                        attr='Google Earth Engine',
+                        name='NDVI',
+                        overlay=True,
+                        control=True
+                    ).add_to(m)
+                    
+                    m.add_child(folium.LayerControl())
+                    figure.render()
+
+                else:
+                    F = "2015-07-01"
+                    T = "2019-11-30"
+                    print("Date TO is Missing")
+            else:
+                F = "2015-07-01"
+                T = "2019-11-30"
+                print("Date From is Missing")
+
+        else:
+            pass
+
+
+
+
+#--------------------------------------------------------------------------------------------------------------------------------#   
+    elif selected_dataset == "dataset_nighttime":
+        if selected_shapefile != None:
+
+            shapefile_path =('C:\\Users\\piv\\Desktop\\y\\media\\shp')
+                                # D:\Desktop\final_working1-New-2023\final\media
+
+            roi_gdf = gpd.read_file(shapefile_path)
+            roi_geojson = roi_gdf.to_crs("EPSG:4326").to_json()
+            
+            # Create a folium GeoJson layer for visualization
+            roi_geojson_layer = folium.GeoJson(roi_geojson, name='ROI GeoJSON')
+            roi_geojson_layer.add_to(m)
+            
+            # Convert the GeoJSON content to Earth Engine object
+            ee_object = geemap.geojson_to_ee(json.loads(roi_geojson))
+            if selected_date_range_From != None:
+                if selected_date_range_To != None:
+                    print("I am here")
+                    F = selected_date_range_From
+                    T = selected_date_range_To
+                    print(F,"==>",T)
+
+
+                    dataset_nighttime = ee.ImageCollection('NOAA/VIIRS/DNB/MONTHLY_V1/VCMCFG').filter(ee.Filter.date(F, T))
+              
+
+
+                    # Mosaic the image collection to a single image
+                    nighttime = dataset_nighttime.select('avg_rad').mosaic()
+                    
+                    # Clip the nighttime lights image to the defined region
+                    nighttime_clipped = nighttime.clip(ee_object)
+                    
+                    nighttimeVis = {'min': 0.0, 'max': 60.0,'palette': ['1a3678', '2955bc', '5699ff', '8dbae9', 'acd1ff', 'caebff', 'e5f9ff',
+                    'fdffb4', 'ffe6a2', 'ffc969', 'ffa12d', 'ff7c1f', 'ca531a', 'ff0000',
+                    'ab0000']}
+                    nighttime_layer = folium.TileLayer(
+                        tiles=nighttime_clipped.getMapId(nighttimeVis)['tile_fetcher'].url_format,
+                        attr='Google Earth Engine',
+                        name='Nighttime Lights',
+                        overlay=True,
+                        control=True
+                    ).add_to(m)
+                                
+                    m.add_child(folium.LayerControl())
+                    figure.render()
+
+                else:
+                    F = "2015-07-01"
+                    T = "2023-09-30"
+                    print("Date TO is Missing")
+            else:
+                F = "2015-07-01"
+                T = "2023-09-30"
+                print("Date From is Missing")
+
+        else:
+            pass
+
+    elif selected_dataset == "precipitation":
+        if selected_shapefile != None:
+            shapefile_path = ('C:\\Users\\piv\\Desktop\\y\\media\\shp')
+           
+            roi_gdf = gpd.read_file(shapefile_path)
+            roi_geojson = roi_gdf.to_crs("EPSG:4326").to_json()
+
+            # Create a folium GeoJson layer for visualization
+            roi_geojson_layer = folium.GeoJson(roi_geojson, name='ROI GeoJSON')
+            roi_geojson_layer.add_to(m)
+
+            # Convert the GeoJSON content to Earth Engine object
+            ee_object = geemap.geojson_to_ee(json.loads(roi_geojson))
+            if selected_date_range_From != None:
+                if selected_date_range_To != None:
+                    print("I am here")
+                    F = selected_date_range_From
+                    T = selected_date_range_To
+                    print(F, "=>", T)
+
+                    # Load the dataset
+                    dataset = (ee.ImageCollection('UCSB-CHG/CHIRPS/DAILY').filterBounds(ee_object).filter(ee.Filter.date(F, T)))
+
+                    # Calculate the sum of the dataset
+                    dataset1 = dataset.sum()
+
+                    # Clip the summed dataset to the defined region
+                    dataset2 = dataset1.clip(ee_object)
+
+                    # Select the 'precipitation' band
+                    precipitation = dataset2.select('precipitation')
+
+                    # Define visualization parameters
+                    imageVisParam = {
+                        'min': 80,
+                        'max': 460,
+                        'palette': ["001137", "0aab1e", "e7eb05", "ff4a2d", "e90000"]
+                    }
+
+                    # Clip the precipitation data to the region
+                    precipitation_clipped = precipitation.clip(ee_object)
+
+                    # Add precipitation layer to the map
+                    folium.TileLayer(
+                        tiles=precipitation_clipped.getMapId(imageVisParam)['tile_fetcher'].url_format,
+                        attr='Google Earth Engine',
+                        name='Precipitation',
+                        overlay=True,
+                        control=True
+                    ).add_to(m)
+
+                    m.add_child(folium.LayerControl())
+                    figure.render()
+            else:
+                F = "2015-07-01"
+                T = "2023-09-30"
+                print("Date TO is Missing")
+        else:
+            F = "2015-07-01"
+            T = "2023-09-30"
+            print("Date From is Missing")
+    
+
+
+    elif selected_dataset == "WorldPop": 
+        shapefile_path = ('C:\\Users\\piv\\Desktop\\y\\media\\shp')
+
+        roi_gdf = gpd.read_file(shapefile_path)
+        roi_geojson = roi_gdf.to_crs("EPSG:4326").to_json()
+
+        # Create a folium GeoJson layer for visualization
+        m = folium.Map(location=[25.5, 61], zoom_start=6)
+        roi_geojson_layer = folium.GeoJson(roi_geojson, name='ROI GeoJSON')
+        roi_geojson_layer.add_to(m)
+
+        # Convert the GeoJSON content to Earth Engine object
+        ee_object = geemap.geojson_to_ee(json.loads(roi_geojson))
+
+        if selected_date_range_From and selected_date_range_To:
+            F = selected_date_range_From
+            T = selected_date_range_To
+
+            # Load the image collection
+            collection = (ee.ImageCollection("WorldPop/GP/100m/pop")
+                        .filterBounds(ee_object)
+                        .filter(ee.Filter.date(F, T)))
+
+            # Calculate the sum of population for the specified region and time range
+            s2median = collection.sum()
+
+            # Clip the result to the ROI
+            roi = s2median.clip(ee_object)
+
+            # Create an image time series chart
+            chart = (ee.Image.cat(collection)
+                    .reduceRegion(ee.Reducer.sum(), roi, 200)
+                    .getInfo())
+
+            # Return the chart as JSON and map HTML as a response
+            clipped_image_url = roi.getThumbUrl({
+                'min': 0,
+                'max': 2000,
+                'dimensions': 512,
+                'palette': ['000000', 'ffffff']
+            })
+
+            # Add the clipped population image as a layer to the map
+            folium.TileLayer(
+                tiles=clipped_image_url,
+                attr="Population year17",
+                overlay=True,
+                control=True,
+            ).add_to(m)
+
+            # Return the folium map as HTML in the JSON response
+            map_html = m.get_root().render()
+            response_data = {'chart': chart, 'map_html': map_html}
+            return JsonResponse(response_data)
+
+
+
+#to be rendered 
+    dataset_options = ['Modis',  
+                    'dataset_nighttime',
+                    'precipitation',
+                    'GlobalSurfaceWater',
+                    'WorldPop',
+                    'COPERNICUS']
+
+
+
+
+    shapes_options = ['District_Boundary',
+                    'hydro_basins',
+                    'karachi',
+                    'National_Constituency_with_Projected_2010_Population',
+                    'Provincial_Boundary',
+                    'Provincial_Constituency',
+                    'Tehsil_Boundary',
+                    'Union_Council']
+    # print(figure)
+    # map_html = m._repr_html_()
+    m.save('ndvi_map.html')
+    
+    
+    
+
+    context = {"map": figure,"dataset_options":dataset_options,"shapes_options": shapes_options}
+    return render(request, template_name , context)
+    # You can continue with the existing code or add more logic as needed
+@login_required
+def map (request):
+    template_name='map.html'
+
+
+    return render(request,template_name)
+
+@login_required
+def GEE(request):
+    if request.method == 'POST':
+        formulario = dataset_geemap(data=request.POST)
+        if formulario.is_valid():
+            option = formulario.cleaned_data['option']
+            
+            # Apply custom styles to the form fields or widgets
+            formulario.fields['option'].widget.attrs['class'] = 'custom-select'
+            
+            figure = folium.Figure()
+            Map = geemap.Map(
+                        plugin_Draw = True, 
+                        Draw_export = False,
+                        plugin_LayerControl = False,
+                        location = [25, 67],
+                        zoom_start = 10,
+                        plugin_LatLngPopup = False)
+            Map.add_basemap('HYBRID')
+            type_map(Map, option)
+            file, url_d = data_gee()
+            Map.add_layer_control()
+            url = url_d[url_d['id'] == option].reset_index()
+            url = url['asset_url'].iloc[0]
+            form = dataset_geemap(data=request.POST)
+    else:
+        form = dataset_geemap()
+
+        figure = folium.Figure()
+        Map = geemap.Map(
+                    plugin_Draw = True, 
+                    Draw_export = False,
+                    plugin_LayerControl = False,
+                    location = [25, 67],
+                    zoom_start = 10,
+                    plugin_LatLngPopup = False)
+        Map.add_basemap('HYBRID')
+        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(25,67,8)
+
+        url = 'https://developers.google.com/earth-engine/datasets/catalog/BIOPAMA_GlobalOilPalm_v1#terms-of-use'
+    
+
+    Map.add_to(figure)
+    figure = figure._repr_html_() #updated
+
+    return render(request, 'gee.html', {'form':form, 'map':figure, 'url':url})
+
+
+
+
+
+
+
+# Define your ee_array_to_df, t_modis_to_celsius, and fit_func functions here
+
+
+def result_options(request):
+
+    return render (request, "result_options.html" )
+
+def temp_result(request):
+
+    if request.method == 'GET':
+        selected_shapefile = request.GET.get('shapefile')
+        selected_date_range_From = request.GET.get('dateRangeFrom')
+        selected_date_range_To = request.GET.get('dateRangeTo')
+
+        print(selected_shapefile)
+        print(selected_date_range_From)
+        print(selected_date_range_To)
+
+        if selected_date_range_From == None or selected_date_range_To == None:
+            i_date ='2022-06-24'
+            f_date ='2023-09-19'
+        else:
+            i_date = selected_date_range_From
+            f_date = selected_date_range_To
+
+        # Import the MODIS land surface temperature collection.
+        lst = ee.ImageCollection('MODIS/006/MOD11A1')
+
+        # Selection of appropriate bands and dates for LST.
+        lst = lst.select('LST_Day_1km', 'QC_Day').filterDate(i_date, f_date)
+
+        if selected_shapefile == None:
+            u_lon = 4.8148
+            u_lat = 45.7758
+            u_poi = ee.Geometry.Point(u_lon, u_lat)
+        else:
+            shapefile_path = ('C:\\Users\\piv\\Desktop\\y\\media\\shp')
+            
+
+            roi_gdf = gpd.read_file(shapefile_path)
+            roi_geojson = roi_gdf.to_crs("EPSG:4326").to_json()
+            
+            # Create a folium GeoJson layer for visualization
+            ee_object = geemap.geojson_to_ee(json.loads(roi_geojson))
+
+            u_poi = ee_object
+
+        # Get the data for the pixel intersecting the point in the urban area.
+        scale = 1000  # scale in meters
+        lst_u_poi = lst.getRegion(u_poi, scale).getInfo()
+
+        # Convert the Earth Engine data to a DataFrame using the provided function.
+        lst_df_urban = ee_array_to_df(lst_u_poi, ['LST_Day_1km'])
+
+        # Apply the function to convert temperature units to Celsius.
+        lst_df_urban['LST_Day_1km'] = lst_df_urban['LST_Day_1km'].apply(t_modis_to_celsius)
+
+        # Fitting curves.
+        ## First, extract x values (times) from the df.
+        x_data_u = np.asanyarray(lst_df_urban['time'].apply(float))
+
+        ## Then, extract y values (LST) from the df.
+        y_data_u = np.asanyarray(lst_df_urban['LST_Day_1km'].apply(float))
+
+        ## Define the fitting function with parameters.
+        def fit_func(t, lst0, delta_lst, tau, phi):
+            return lst0 + (delta_lst/2)*np.sin(2*np.pi*t/tau + phi)
+
+        ## Optimize the parameters using a good start p0.
+        lst0 = 20
+        delta_lst = 40
+        tau = 365*24*3600*1000   # milliseconds in a year
+        phi = 2*np.pi*4*30.5*3600*1000/tau  # offset regarding when we expect LST(t)=LST0
+
+        params_u, params_covariance_u = optimize.curve_fit(
+            fit_func, x_data_u, y_data_u, p0=[lst0, delta_lst, tau, phi])
+
+
+        x_data_u_formatted = [datetime.utcfromtimestamp(ts / 1000).strftime('%d %m %Y') for ts in x_data_u]
+        # x_data_r_formatted = [datetime.utcfromtimestamp(ts / 1000).strftime('%d %m %Y') for ts in x_data_r]
+
+        # return render(request, 'chart.html', {'chart_data': chart_data})
+        urban_trace = go.Scatter(
+            x=x_data_u_formatted,  # Use the formatted dates
+            y=fit_func(x_data_u, *params_u),  # Use your fit_func to generate y values
+            mode='lines',
+            name='Urban Area'
+        )
+
+        # Create a Plotly figure for the rural data
+
+
+        data = [urban_trace]
+
+        layout = go.Layout(
+            title='Land Surface Temperature over Time',
+            xaxis=dict(title='Time'),
+            yaxis=dict(title='LST (°C)'),
+            showlegend=True
+        )
+
+        fig = go.Figure(data=data, layout=layout)
+
+        # Convert the Plotly figure to HTML
+        plot_div = fig.to_html(full_html=False, default_height=500, default_width=700)
+
+        shapes_options = ['District_Boundary',
+        'hydro_basins',
+        'karachi',
+        'National_Constituency_with_Projected_2010_Population',
+        'Provincial_Boundary',
+        'Provincial_Constituency',
+        'Tehsil_Boundary',
+        'Union_Council']
+        
+
+        context={
+            "shapes_options":shapes_options,
+            "plot_div":plot_div
+
+        }
+
+
+
+        return render(request, "temp_result.html",context )
+
+
+    else:
+
+
+
+
+
+        shapes_options = ['District_Boundary',
+                'hydro_basins',
+                'karachi',
+                'National_Constituency_with_Projected_2010_Population',
+                'Provincial_Boundary',
+                'Provincial_Constituency',
+                'Tehsil_Boundary',
+                'Union_Council']
+        
+
+        context={
+            "shapes_options":shapes_options
+
+        }
+
+
+
+        return render(request, "temp_result.html",context )
+
+
+def chart(request):
+# Define the date range of interest.
+
+
+    #replaceble with dates 
+    i_date = '2017-01-01'
+    f_date = '2020-01-01'
+
+    # Import the MODIS land surface temperature collection.
+    lst = ee.ImageCollection('MODIS/006/MOD11A1')
+
+    # Selection of appropriate bands and dates for LST.
+    lst = lst.select('LST_Day_1km', 'QC_Day').filterDate(i_date, f_date)
+
+    # Define the urban location of interest as a point near Lyon, France.
+    #replaceble with shapefile
+    u_lon = 4.8148
+    u_lat = 45.7758
+    u_poi = ee.Geometry.Point(u_lon, u_lat)
+
+    # Get the data for the pixel intersecting the point in the urban area.
+    scale = 1000  # scale in meters
+    lst_u_poi = lst.getRegion(u_poi, scale).getInfo()
+
+    # Convert the Earth Engine data to a DataFrame using the provided function.
+    lst_df_urban = ee_array_to_df(lst_u_poi, ['LST_Day_1km'])
+
+    # Apply the function to convert temperature units to Celsius.
+    lst_df_urban['LST_Day_1km'] = lst_df_urban['LST_Day_1km'].apply(t_modis_to_celsius)
+
+    # Fitting curves.
+    ## First, extract x values (times) from the df.
+    x_data_u = np.asanyarray(lst_df_urban['time'].apply(float))
+
+    ## Then, extract y values (LST) from the df.
+    y_data_u = np.asanyarray(lst_df_urban['LST_Day_1km'].apply(float))
+
+    ## Define the fitting function with parameters.
+    def fit_func(t, lst0, delta_lst, tau, phi):
+        return lst0 + (delta_lst/2)*np.sin(2*np.pi*t/tau + phi)
+
+    ## Optimize the parameters using a good start p0.
+    lst0 = 20
+    delta_lst = 40
+    tau = 365*24*3600*1000   # milliseconds in a year
+    phi = 2*np.pi*4*30.5*3600*1000/tau  # offset regarding when we expect LST(t)=LST0
+
+    params_u, params_covariance_u = optimize.curve_fit(
+        fit_func, x_data_u, y_data_u, p0=[lst0, delta_lst, tau, phi])
+
+
+    x_data_u_formatted = [datetime.utcfromtimestamp(ts / 1000).strftime('%d %m %Y') for ts in x_data_u]
+    # x_data_r_formatted = [datetime.utcfromtimestamp(ts / 1000).strftime('%d %m %Y') for ts in x_data_r]
+
+    # return render(request, 'chart.html', {'chart_data': chart_data})
+    urban_trace = go.Scatter(
+        x=x_data_u_formatted,  # Use the formatted dates
+        y=fit_func(x_data_u, *params_u),  # Use your fit_func to generate y values
+        mode='lines',
+        name='Urban Area'
+    )
+
+    # Create a Plotly figure for the rural data
+
+
+    data = [urban_trace]
+
+    layout = go.Layout(
+        title='Land Surface Temperature over Time',
+        xaxis=dict(title='Time'),
+        yaxis=dict(title='LST (°C)'),
+        showlegend=True
+    )
+
+    fig = go.Figure(data=data, layout=layout)
+
+    # Convert the Plotly figure to HTML
+    plot_div = fig.to_html(full_html=False, default_height=500, default_width=700)
+
+    return render(request, 'chart.html', {'plot_div': plot_div})
+
+
+
+
+
+
+
+
+
+
+#Auth
+def signup (request):
+    form = SignUpForm()
+    if request.method == "POST":
+        
+        form = SignUpForm(request.POST)
+        if form.is_valid():
+            user = form.save()
+            auth_login(request,user)
+            return redirect('index')
+           
+           
+
+    return render(request, 'signup.html', {'form':form})
+
+
+class UserUpdateView(UpdateView):
+    model=User
+    fields =('first_name','last_name', 'email',)
+    template_name = 'my_account.html'
+    success_url = reverse_lazy('my_account')
+
+    def get_object(self):
+        return self.request.user
+    
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+def ee_array_to_df(arr, list_of_bands):
+    """Transforms client-side ee.Image.getRegion array to pandas.DataFrame."""
+    df = pd.DataFrame(arr)
+
+    # Rearrange the header.
+    headers = df.iloc[0]
+    df = pd.DataFrame(df.values[1:], columns=headers)
+
+    # Remove rows without data inside.
+    df = df[['longitude', 'latitude', 'time', *list_of_bands]].dropna()
+
+    # Convert the data to numeric values.
+    for band in list_of_bands:
+        df[band] = pd.to_numeric(df[band], errors='coerce')
+
+    # Convert the time field into a datetime.
+    df['datetime'] = pd.to_datetime(df['time'], unit='ms')
+
+    # Keep the columns of interest.
+    df = df[['time', 'datetime', *list_of_bands]]
+    print(df)
+
+    return df
+
+def t_modis_to_celsius(t_modis):
+    """Converts MODIS LST units to degrees Celsius."""
+    t_celsius = 0.02 * t_modis - 273.15
+    return t_celsius
+
+def fit_func(t, lst0, delta_lst, tau, phi):
+    """Fitting function for the curve."""
+    return lst0 + (delta_lst / 2) * np.sin(2 * np.pi * t / tau + phi)