From b4516a22e72bcb18d4bd25533f08b01c00c44c1e Mon Sep 17 00:00:00 2001 From: jonas-nothnagel Date: Fri, 4 Apr 2025 16:55:43 +0200 Subject: [PATCH 01/15] started to work on python implementation --- .DS_Store | Bin 0 -> 8196 bytes .gitignore | 3 + FireVulnerability.py | 613 ++++++++++ TrendAnomalyPrediction.py | 706 ++++++++++++ TrendFire.js | 2 +- TrendFire.py | 592 ++++++++++ fire_training_data.csv | 120 ++ fire_vulnerability.ipynb | 1401 +++++++++++++++++++++++ img/fire_events_by_month.png | Bin 0 -> 34841 bytes img/fire_events_by_year.png | Bin 0 -> 32483 bytes notebooks/TrendFire.ipynb | 1822 ++++++++++++++++++++++++++++++ notebooks/data_exploration.ipynb | 715 ++++++++++++ requirements.txt | 16 + 13 files changed, 5989 insertions(+), 1 deletion(-) create mode 100644 .DS_Store create mode 100644 .gitignore create mode 100644 FireVulnerability.py create mode 100644 TrendAnomalyPrediction.py create mode 100644 TrendFire.py create mode 100644 fire_training_data.csv create mode 100644 fire_vulnerability.ipynb create mode 100644 img/fire_events_by_month.png create mode 100644 img/fire_events_by_year.png create mode 100644 notebooks/TrendFire.ipynb create mode 100644 notebooks/data_exploration.ipynb create mode 100644 requirements.txt diff --git a/.DS_Store b/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..89fae903290aeaf245e235436a7e1e6cf13a2ce2 GIT binary patch literal 8196 zcmeHMNo*WN6s_0Zdg3N-CpKPiCnMVtNwAG^h-|V*;|=1(8yOpqjfpce-NYT*-OhB+ zI3WRzkhnks;sjz7oLC}q00M{$2M7d?U=BfuLlB7N01{k)9Qf7MPQ1i7gaYZ3s{Z_6 z|5tUrdcRB6!x%$XR==*j2@*gClfs6$*FC_I)fe0ljLJ?dsAi~KW4g6w(%nK>P8G_3Pf|(KAP~gu_<41jW zhM16H8zK-Quo3}&cQ0f*OEQlo7vJAgvzF<4t*u`{Dkv-}E|DW^N|hV5vBda} zR_3NNj%5uyuE~cPPk6>rkN2(UN=&#;#_RS6n^M)RYwR~@EHh{H(1>orq$zn|#j^Dw za%OGS#>UOfZSA{ay?q0B&zGs?>((ok%7m#KwrAdN8kzB=n>K9Sv=5EWnYuSUmNQ*5 zWf>!Oa>j_1qbV|9R$WtjQ=O&>hbsXKu8C~QJd!oNV`_se`*TFKdSz0lhZ}HoK8r4^ zXK5UoeUnzN-OGErGX}MK5U@qNMHz8v4YHaPjx9>7GV*|F>k-uzmTk&*ZPZGp3@f7Q zVxA6Vhc=cI_(#-z!nj+h)DmfDW`>sNMue?X>(&nNY5S9|H#BS5{E^O!zB{RJ!ktcL zjG;N($OKQcm#qGllAQZa%TorK0(gMrHMLnYPHZbEZc- zMEgP_>&gwWfxN#v4Y93aml_r}O90&vYYhxC(^wnpVg2kN%dq3@6nlo9W^c1|>|^#N z`-c6a)GW0~ z?NW!dL)s|~N<&gYN=k<%N17GSF9jzQyKiBEf&CcC#i=$cK9xyluAXXjckSuhtMvb8 zQtiT8Ke;L@Z>)-L+Is8l9lJX(lTfgBfvAh=?{j`hrudxqIkAkd^L`d88#XSa-IYWX zi8)HiVjPuu&Z;GWcSEC0$)Xko>gFw^?o*3}TG!knQ(~%;uPSwI+ai>HYC)hjx04E? zC_8sWC=u1dK-~}{mHA3tT~99!b6%DIDVASg7ugT&S9Xb5z833IjhoR*-0s324EpSj zV-yE4g@Z^z#|&&ZI6^!>frs!g@_2+;ev)|pB%Z>vcn;4K*I&V_cnzoV1~L6zyodMk zAwI%6d=hZ}YhwIQ_<0$N2bQuJzb|HSE}pS1$38^11=cTh54CF;0e<#Vspj|p-qpYV z`%YmKA`l|*|3maFaKdkzoX>JV`72K W3rQ9#|N4i3@ca+Yf1i2Rck?%t)bp(X literal 0 HcmV?d00001 diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..d369ed8 --- /dev/null +++ b/.gitignore @@ -0,0 +1,3 @@ +.ipynb_checkpoints/ +data/inputResampled_py.tif +data/inputResampled.tif \ No newline at end of file diff --git a/FireVulnerability.py b/FireVulnerability.py new file mode 100644 index 0000000..b60f746 --- /dev/null +++ b/FireVulnerability.py @@ -0,0 +1,613 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- + +""" +FireVulnerability.py: Python implementation of FireVulnerability.js using Earth Engine Python API. +This script creates fire vulnerability maps for the study area in Goa, India. + +The script performs the following steps: +1. Loads fire event data from multiple years +2. Generates random points for non-fire locations +3. Extracts predictor variables at fire and non-fire locations +4. Categorizes fire locations by risk level based on surrounding conditions +5. Trains a Random Forest classifier to predict fire vulnerability +6. Validates the model with independent test data +7. Generates a fire vulnerability map for the entire study area +8. Exports the vulnerability map to Google Drive and/or Earth Engine assets + +Equivalent to the FireVulnerability.js script in the original implementation. +""" + +import ee +import os +import datetime +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +import geopandas as gpd +from tqdm import tqdm +from sklearn.ensemble import RandomForestClassifier +from sklearn.metrics import accuracy_score, confusion_matrix, classification_report +import seaborn as sns + +# Initialize the Earth Engine API +try: + ee.Initialize() + print("Earth Engine API initialized successfully.") +except Exception as e: + print(f"Error initializing Earth Engine API: {e}") + print("Make sure you have authenticated with Earth Engine using ee.Authenticate()") + +# Define the Goa study area boundary +def get_study_boundary(): + """ + Get the study area boundary for Goa, India. + Returns an ee.Geometry. + """ + # Method 1: Load from a shapefile if available locally + try: + boundary_path = os.path.join('data', 'pa_boundary.shp') + if os.path.exists(boundary_path): + boundary_gdf = gpd.read_file(boundary_path) + # Convert to GeoJSON + boundary_geojson = boundary_gdf.geometry.__geo_interface__ + # Create an EE geometry + return ee.Geometry.Polygon(boundary_geojson['features'][0]['geometry']['coordinates']) + except Exception as e: + print(f"Could not load boundary from shapefile: {e}") + + # Method 2: Use predefined feature from Earth Engine + try: + # Get the Goa district boundary from Earth Engine's administrative boundaries + goa = ee.FeatureCollection("FAO/GAUL/2015/level1").filter(ee.Filter.eq('ADM1_NAME', 'Goa')) + return goa.geometry() + except Exception as e: + print(f"Could not load boundary from Earth Engine: {e}") + + # Method 3: Define manually as fallback + # These coordinates would need to be adjusted to match the study area + goa_coords = [ + [73.6765, 15.7560], + [74.3161, 15.7560], + [74.3161, 14.8922], + [73.6765, 14.8922], + [73.6765, 15.7560] + ] + return ee.Geometry.Polygon(goa_coords) + +# Function to load fire events data +def load_fire_events(pa): + """ + Load fire events data from 2013-2023 and merge into a single feature collection. + + Args: + pa: ee.Geometry representing the study area + + Returns: + ee.FeatureCollection of fire events + """ + print("Loading fire events data...") + + # Load fire events data (2013-2019) + try: + # If the data is available in Earth Engine, you can load it directly + fire_13_19 = ee.FeatureCollection("users/your_username/fire13_19") + except: + # Otherwise, try to find and upload it, or use a placeholder + print("Fire data 2013-2019 not available in Earth Engine. Using placeholder.") + fire_13_19 = ee.FeatureCollection([]) + + # Load fire events data (2020-2023) + try: + # If the data is available in Earth Engine, you can load it directly + fire_20_23 = ee.FeatureCollection("users/your_username/fire20_23") + except: + # Otherwise, try to find and upload it, or use a placeholder + print("Fire data 2020-2023 not available in Earth Engine. Using placeholder.") + fire_20_23 = ee.FeatureCollection([]) + + # Merge the two collections + fire_all = fire_13_19.merge(fire_20_23) + + # Filter by the study area boundary + fire_all = fire_all.filterBounds(pa) + + # Add a 'year' property based on the acquisition date + def add_year(feature): + acq_date = ee.String(feature.get('ACQ_DATE')) + year = ee.Number.parse(acq_date.slice(0, 4)) + return feature.set('year', year) + + fire_all = fire_all.map(add_year) + + # Filter to include only 2013-2022 for training (2023 will be used for validation) + fire_training = fire_all.filter(ee.Filter.lt('year', 2023)) + + # Count the number of fire points + training_count = fire_training.size().getInfo() + print(f"Number of fire events for training (2013-2022): {training_count}") + + # Get 2023 fire events for validation + fire_validation = fire_all.filter(ee.Filter.eq('year', 2023)) + validation_count = fire_validation.size().getInfo() + print(f"Number of fire events for validation (2023): {validation_count}") + + return fire_all, fire_training, fire_validation + +# Function to load road data +def load_roads(pa): + """ + Load roads data and clip to the study area. + + Args: + pa: ee.Geometry representing the study area + + Returns: + ee.Image with roads data + """ + print("Loading roads data...") + + try: + # Try to load the road data from Earth Engine assets + roads = ee.Image("users/your_username/road").clip(pa) + return roads + except: + print("Road data not available in Earth Engine. Using OSM roads as fallback.") + # Use OpenStreetMap data as a fallback + roads = ee.FeatureCollection('OSM/2020/roads').filterBounds(pa) + + # Convert to an image + roads_image = roads.reduceToImage( + properties=['highway'], + reducer=ee.Reducer.first() + ).clip(pa) + + return roads_image + +# Function to generate random non-fire points +def generate_non_fire_points(pa, fire_training, num_points=100, buffer_distance=500): + """ + Generate random points for non-fire locations within the study area. + + Args: + pa: ee.Geometry representing the study area + fire_training: ee.FeatureCollection of fire points + num_points: Number of random points to generate + buffer_distance: Buffer distance (in meters) to avoid fire locations + + Returns: + ee.FeatureCollection of non-fire points + """ + print(f"Generating {num_points} random non-fire points...") + + # Create a buffer around fire points to avoid + fire_buffer = fire_training.geometry().buffer(buffer_distance) + + # Create a mask to avoid sampling in fire buffer zones + mask = ee.Image(1).clip(pa).updateMask( + ee.Image(1).clip(pa).mask().subtract( + ee.Image(1).clip(fire_buffer).mask() + ) + ) + + # Generate random points in the valid sampling area + non_fire_points = ee.FeatureCollection.randomPoints({ + 'region': pa, + 'points': num_points, + 'seed': 42, + 'mask': mask + }) + + # Add a risk property (0 for non-fire) + non_fire_points = non_fire_points.map(lambda f: f.set('RiskNumeric', 0)) + + # Check if we have enough points + point_count = non_fire_points.size().getInfo() + print(f"Generated {point_count} non-fire points") + + return non_fire_points + +# Function to calculate Land Surface Temperature trends from MODIS data +def calculate_lst_trends(pa, start_date='2013-01-01', end_date='2022-12-31'): + """ + Calculate Land Surface Temperature (LST) trends from MODIS data. + + Args: + pa: ee.Geometry representing the study area + start_date: Start date for trend calculation + end_date: End date for trend calculation + + Returns: + ee.Image with LST trend bands + """ + print("Calculating Land Surface Temperature trends...") + + # Function to mask clouds in MODIS LST imagery + def maskLstClouds(image): + qc = image.select('QC_Day') + # Bit 0-1: LST produced, good quality + mask = qc.bitwiseAnd(3).eq(0) + return image.updateMask(mask) + + # Load the MODIS LST data + modis_lst = ee.ImageCollection('MODIS/061/MOD11A1') \ + .filterDate(start_date, end_date) \ + .filterBounds(pa) + + # Apply cloud masking + modis_lst_masked = modis_lst.map(maskLstClouds) + + # Convert LST to Celsius + modis_lst_celsius = modis_lst_masked.map( + lambda image: image.select(['LST_Day_1km', 'LST_Night_1km']) + .multiply(0.02) + .subtract(273.15) + .copyProperties(image, ['system:time_start']) + ) + + # Function to add time band for trend calculation + def addTime(image): + # Get the timestamp and convert to years since start + year = ee.Date(image.get('system:time_start')).difference( + ee.Date(start_date), 'year') + return image.addBands(ee.Image(year).rename('t')) + + # Add time band to the collection + modis_lst_with_time = modis_lst_celsius.map(addTime) + + # Calculate trends for day and night LST + day_trend = modis_lst_with_time.select(['t', 'LST_Day_1km']) \ + .reduce(ee.Reducer.linearFit()) \ + .select(['scale'], ['lst_day_trend']) + + night_trend = modis_lst_with_time.select(['t', 'LST_Night_1km']) \ + .reduce(ee.Reducer.linearFit()) \ + .select(['scale'], ['lst_night_trend']) + + # Calculate delta LST (day-night) + def calculateDeltaLST(image): + day = image.select('LST_Day_1km') + night = image.select('LST_Night_1km') + delta = day.subtract(night).rename('delta_lst') + return image.addBands(delta) + + modis_lst_with_delta = modis_lst_celsius.map(calculateDeltaLST) + + # Calculate delta LST trend + delta_trend = modis_lst_with_delta.map(addTime) \ + .select(['t', 'delta_lst']) \ + .reduce(ee.Reducer.linearFit()) \ + .select(['scale'], ['delta_lst_trend']) + + # Merge all LST trend layers + lst_trends = day_trend.addBands(night_trend).addBands(delta_trend) + + return lst_trends.clip(pa) + +# Function to load trend layers from TrendFire output +def load_trend_layers(pa): + """ + Load trend layers previously calculated using TrendFire.py. + + Args: + pa: ee.Geometry representing the study area + + Returns: + ee.Image with all trend bands + """ + print("Loading trend layers...") + + try: + # Try to load the trend layers from Earth Engine assets + trends = ee.Image("users/your_username/goa_fire_trends").clip(pa) + print("Successfully loaded trend layers from Earth Engine asset.") + return trends + except: + print("Trend layers not found in Earth Engine assets. Calculating LST trends as fallback.") + # Calculate LST trends as a fallback + lst_trends = calculate_lst_trends(pa) + return lst_trends + +# Function to categorize fire risk based on temperature difference +def categorize_fire_risk(fire_training, lst_data, pa): + """ + Categorize fire points by risk level based on temperature difference. + + Args: + fire_training: ee.FeatureCollection of fire points + lst_data: ee.Image with LST data + pa: ee.Geometry representing the study area + + Returns: + ee.FeatureCollection of categorized fire points + """ + print("Categorizing fire risk levels...") + + # Sample the delta LST values at fire locations + fire_samples = lst_data.select('delta_lst_trend').sampleRegions({ + 'collection': fire_training, + 'properties': ['ACQ_DATE', 'year'], + 'scale': 1000, # MODIS LST resolution + 'geometries': True + }) + + # Define threshold values for risk categories + # These thresholds would need to be calibrated based on local conditions + def categorize(feature): + delta_lst = feature.get('delta_lst_trend') + + # Define risk categories based on delta LST + # 1: Low risk, 2: Moderate risk, 3: High risk + risk = ee.Algorithms.If(ee.Number(delta_lst).lt(0.1), 1, + ee.Algorithms.If(ee.Number(delta_lst).lt(0.2), 2, 3)) + + return feature.set('RiskNumeric', risk) + + # Apply categorization to all fire points + categorized_fire = fire_samples.map(categorize) + + # Count points in each risk category + low_risk = categorized_fire.filter(ee.Filter.eq('RiskNumeric', 1)).size().getInfo() + moderate_risk = categorized_fire.filter(ee.Filter.eq('RiskNumeric', 2)).size().getInfo() + high_risk = categorized_fire.filter(ee.Filter.eq('RiskNumeric', 3)).size().getInfo() + + print(f"Fire risk categorization: {low_risk} Low, {moderate_risk} Moderate, {high_risk} High risk") + + return categorized_fire + +# Function to prepare training data +def prepare_training_data(categorized_fire, non_fire_points, trend_layers, roads, pa): + """ + Prepare training data for the Random Forest classifier. + + Args: + categorized_fire: ee.FeatureCollection of fire points with risk levels + non_fire_points: ee.FeatureCollection of non-fire points + trend_layers: ee.Image with trend bands + roads: ee.Image with road data + pa: ee.Geometry representing the study area + + Returns: + ee.FeatureCollection with training data + """ + print("Preparing training data for Random Forest classifier...") + + # Combine fire and non-fire points + training_points = categorized_fire.merge(non_fire_points) + + # Calculate distance to roads + road_distance = roads.fastDistanceTransform(100).multiply(30).clip(pa) + + # Combine all predictor variables + predictor_variables = trend_layers.addBands(road_distance.rename('road_distance')) + + # Sample the predictor variables at training points + training_data = predictor_variables.sampleRegions({ + 'collection': training_points, + 'properties': ['RiskNumeric'], + 'scale': 30, + 'geometries': True + }) + + # Count the final number of training points + training_count = training_data.size().getInfo() + print(f"Final training dataset contains {training_count} points") + + return training_data + +# Function to train a Random Forest classifier +def train_random_forest(training_data, num_trees=500): + """ + Train a Random Forest classifier using Earth Engine. + + Args: + training_data: ee.FeatureCollection with training data + num_trees: Number of trees in the Random Forest + + Returns: + ee.Classifier.RandomForest + """ + print(f"Training Random Forest classifier with {num_trees} trees...") + + # Get the names of the predictor bands + predictor_names = training_data.first().propertyNames().removeAll(['system:index', 'RiskNumeric']).getInfo() + + # Create and train the classifier + classifier = ee.Classifier.smileRandomForest( + numberOfTrees=num_trees, + minLeafPopulation=1, + bagFraction=0.7, + seed=42 + ).train( + features=training_data, + classProperty='RiskNumeric', + inputProperties=predictor_names + ) + + # Print variable importance + variable_importance = classifier.explain().get('importance').getInfo() + print("Variable importance:") + for var, importance in zip(predictor_names, variable_importance): + print(f" {var}: {importance:.4f}") + + return classifier, predictor_names + +# Function to validate the Random Forest model +def validate_model(classifier, predictor_names, validation_data): + """ + Validate the Random Forest model using test data. + + Args: + classifier: Trained ee.Classifier.RandomForest + predictor_names: List of predictor variable names + validation_data: ee.FeatureCollection with validation data + + Returns: + Dictionary with validation metrics + """ + print("Validating Random Forest model...") + + # Apply the classifier to the validation data + validated = validation_data.classify(classifier) + + # Get the confusion matrix + confusion_matrix = validated.errorMatrix('RiskNumeric', 'classification') + + # Calculate accuracy and Kappa statistics + accuracy = confusion_matrix.accuracy().getInfo() + kappa = confusion_matrix.kappa().getInfo() + + print(f"Validation accuracy: {accuracy:.4f}") + print(f"Kappa statistic: {kappa:.4f}") + + # Get confusion matrix as a list of lists + cm_data = confusion_matrix.getInfo() + + # Return validation metrics + return { + 'accuracy': accuracy, + 'kappa': kappa, + 'confusion_matrix': cm_data + } + +# Function to create a fire vulnerability map +def create_vulnerability_map(classifier, predictor_variables, predictor_names, pa): + """ + Create a fire vulnerability map using the trained classifier. + + Args: + classifier: Trained ee.Classifier.RandomForest + predictor_variables: ee.Image with predictor variables + predictor_names: List of predictor variable names + pa: ee.Geometry representing the study area + + Returns: + ee.Image with fire vulnerability classification + """ + print("Creating fire vulnerability map...") + + # Select only the predictor bands used for training + predictors = predictor_variables.select(predictor_names) + + # Apply the classifier to create a vulnerability map + vulnerability_map = predictors.classify(classifier).clip(pa) + + return vulnerability_map + +# Function to export the vulnerability map to Google Drive +def export_vulnerability_map(vulnerability_map, pa, filename='fire_vulnerability_map', folder='ForestFireGoa'): + """ + Export the fire vulnerability map to Google Drive. + + Args: + vulnerability_map: ee.Image with fire vulnerability classification + pa: ee.Geometry representing the study area + filename: Output filename + folder: Google Drive folder name + """ + print(f"Exporting fire vulnerability map to Google Drive folder '{folder}'...") + + # Start the export task + task = ee.batch.Export.image.toDrive({ + 'image': vulnerability_map, + 'description': filename, + 'folder': folder, + 'fileNamePrefix': filename, + 'region': pa, + 'scale': 30, + 'maxPixels': 1e13 + }) + + task.start() + print(f"Export task started with ID: {task.id}") + +# Function to export the vulnerability map to an Earth Engine asset +def export_vulnerability_map_to_asset(vulnerability_map, pa, asset_name): + """ + Export the fire vulnerability map to an Earth Engine asset. + + Args: + vulnerability_map: ee.Image with fire vulnerability classification + pa: ee.Geometry representing the study area + asset_name: Name of the Earth Engine asset + """ + print(f"Exporting fire vulnerability map to Earth Engine asset: {asset_name}") + + # Start the export task + task = ee.batch.Export.image.toAsset({ + 'image': vulnerability_map, + 'description': asset_name.split('/')[-1], + 'assetId': asset_name, + 'region': pa, + 'scale': 30, + 'maxPixels': 1e13 + }) + + task.start() + print(f"Export task started with ID: {task.id}") + +# Main function to run the fire vulnerability mapping workflow +def main(): + """ + Main function to run the fire vulnerability mapping workflow. + """ + print("Starting fire vulnerability mapping for Goa, India...") + + # Get the study area boundary + pa = get_study_boundary() + + # Load fire events data + fire_all, fire_training, fire_validation = load_fire_events(pa) + + # Load roads data + roads = load_roads(pa) + + # Load trend layers + trend_layers = load_trend_layers(pa) + + # Generate random non-fire points + non_fire_points = generate_non_fire_points(pa, fire_training, num_points=100) + + # Categorize fire risk based on temperature difference + categorized_fire = categorize_fire_risk(fire_training, trend_layers, pa) + + # Prepare training data + training_data = prepare_training_data(categorized_fire, non_fire_points, trend_layers, roads, pa) + + # Split into training and validation sets + split = 0.8 # 80% for training, 20% for validation + training_size = training_data.size().multiply(split).int() + + # Randomize the data + randomized = training_data.randomColumn('random') + + # Split based on the random column + train = randomized.filter(ee.Filter.lt('random', split)) + test = randomized.filter(ee.Filter.gte('random', split)) + + # Train the Random Forest classifier + classifier, predictor_names = train_random_forest(train) + + # Validate the model + validation_metrics = validate_model(classifier, predictor_names, test) + + # Create a fire vulnerability map + predictor_variables = trend_layers.addBands(roads.rename('road_distance')) + vulnerability_map = create_vulnerability_map(classifier, predictor_variables, predictor_names, pa) + + # Export the vulnerability map to Google Drive (uncomment to use) + # export_vulnerability_map(vulnerability_map, pa) + + # Export the vulnerability map to Earth Engine asset (uncomment to use) + # user_asset_root = 'users/your_username/' # Replace with your actual EE username + # export_vulnerability_map_to_asset( + # vulnerability_map, + # pa, + # f"{user_asset_root}fire_vulnerability_map" + # ) + + print("Fire vulnerability mapping complete!") + print("To visualize the results, check your Google Drive or Earth Engine assets.") + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/TrendAnomalyPrediction.py b/TrendAnomalyPrediction.py new file mode 100644 index 0000000..534aaeb --- /dev/null +++ b/TrendAnomalyPrediction.py @@ -0,0 +1,706 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- + +""" +TrendAnomalyPrediction.py: Python implementation of TrendAnomalyPrediction.js using Earth Engine Python API. +This script calculates anomalies between current conditions and predicted values from historical trends +to identify potential fire hotspots in Goa, India. + +The script performs the following steps: +1. Processes current Landsat imagery to calculate various spectral indices +2. Uses trend coefficients to predict expected values based on historical data +3. Calculates anomalies between current and predicted values +4. Identifies hotspots based on anomalies in rainfall, RH, soil moisture, and vegetation indices +5. Integrates land use/land cover data to identify vulnerable areas +6. Exports hotspot maps to Google Drive and/or Earth Engine assets + +Equivalent to the TrendAnomalyPrediction.js script in the original implementation. +""" + +import ee +import os +import datetime +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +import geopandas as gpd +from tqdm import tqdm + +# Initialize the Earth Engine API +try: + ee.Initialize() + print("Earth Engine API initialized successfully.") +except Exception as e: + print(f"Error initializing Earth Engine API: {e}") + print("Make sure you have authenticated with Earth Engine using ee.Authenticate()") + +# Define the Goa study area boundary +def get_study_boundary(): + """ + Get the study area boundary for Goa, India. + Returns an ee.Geometry. + """ + # Method 1: Load from a shapefile if available locally + try: + boundary_path = os.path.join('data', 'pa_boundary.shp') + if os.path.exists(boundary_path): + boundary_gdf = gpd.read_file(boundary_path) + # Convert to GeoJSON + boundary_geojson = boundary_gdf.geometry.__geo_interface__ + # Create an EE geometry + return ee.Geometry.Polygon(boundary_geojson['features'][0]['geometry']['coordinates']) + except Exception as e: + print(f"Could not load boundary from shapefile: {e}") + + # Method 2: Use predefined feature from Earth Engine + try: + # Get the Goa district boundary from Earth Engine's administrative boundaries + goa = ee.FeatureCollection("FAO/GAUL/2015/level1").filter(ee.Filter.eq('ADM1_NAME', 'Goa')) + return goa.geometry() + except Exception as e: + print(f"Could not load boundary from Earth Engine: {e}") + + # Method 3: Define manually as fallback + # These coordinates would need to be adjusted to match the study area + goa_coords = [ + [73.6765, 15.7560], + [74.3161, 15.7560], + [74.3161, 14.8922], + [73.6765, 14.8922], + [73.6765, 15.7560] + ] + return ee.Geometry.Polygon(goa_coords) + +# Function to load fire events data +def load_fire_events(pa): + """ + Load fire events data from 2013-2023 and merge into a single feature collection. + + Args: + pa: ee.Geometry representing the study area + + Returns: + ee.FeatureCollection of fire events + """ + print("Loading fire events data...") + + # Load fire events data (2013-2019) + try: + # If the data is available in Earth Engine, you can load it directly + fire_13_19 = ee.FeatureCollection("users/your_username/fire13_19") + except: + # Otherwise, try to find and upload it, or use a placeholder + print("Fire data 2013-2019 not available in Earth Engine. Using placeholder.") + fire_13_19 = ee.FeatureCollection([]) + + # Load fire events data (2020-2023) + try: + # If the data is available in Earth Engine, you can load it directly + fire_20_23 = ee.FeatureCollection("users/your_username/fire20_23") + except: + # Otherwise, try to find and upload it, or use a placeholder + print("Fire data 2020-2023 not available in Earth Engine. Using placeholder.") + fire_20_23 = ee.FeatureCollection([]) + + # Merge the two collections + fire_all = fire_13_19.merge(fire_20_23) + + # Filter by the study area boundary + fire_all = fire_all.filterBounds(pa) + + # Add a 'year' property based on the acquisition date + def add_year(feature): + acq_date = ee.String(feature.get('ACQ_DATE')) + year = ee.Number.parse(acq_date.slice(0, 4)) + return feature.set('year', year) + + fire_all = fire_all.map(add_year) + + # Count the number of fire points + fire_count = fire_all.size().getInfo() + print(f"Number of fire events in the study area: {fire_count}") + + return fire_all + +# Function to mask clouds in Landsat 8/9 imagery +def maskL8sr(image): + """ + Mask clouds and scale pixel values for Landsat 8/9 SR imagery. + """ + # Get QA band + qa = image.select('QA_PIXEL') + + # Bits 3 and 5 are cloud shadow and cloud, respectively + cloud_shadow_bit_mask = 1 << 3 + clouds_bit_mask = 1 << 5 + + # Both flags should be set to zero, indicating clear conditions + mask = qa.bitwiseAnd(cloud_shadow_bit_mask).eq(0).And( + qa.bitwiseAnd(clouds_bit_mask).eq(0)) + + # Scale the optical bands + optical_bands = image.select('SR_B.').multiply(0.0000275).add(-0.2) + + # Scale the thermal bands + thermal_bands = image.select('ST_B.*').multiply(0.00341802).add(149.0) + + # Return the masked and scaled image + return image.select(['QA_.*']).addBands(optical_bands).addBands(thermal_bands).updateMask(mask) + +# Function to calculate spectral indices and add them as bands +def addIndices(image): + """ + Calculate various spectral indices and add them as bands to the image. + """ + # NDVI (Normalized Difference Vegetation Index) + ndvi = image.normalizedDifference(['SR_B5', 'SR_B4']).rename('ndvi') + + # EVI (Enhanced Vegetation Index) + evi = image.expression( + '2.5 * ((NIR - RED) / (NIR + 6 * RED - 7.5 * BLUE + 1))', + { + 'NIR': image.select('SR_B5'), + 'RED': image.select('SR_B4'), + 'BLUE': image.select('SR_B2') + } + ).rename('evi') + + # MSAVI (Modified Soil Adjusted Vegetation Index) + msavi = image.expression( + '(2 * NIR + 1 - sqrt((2 * NIR + 1) * (2 * NIR + 1) - 8 * (NIR - RED))) / 2', + { + 'NIR': image.select('SR_B5'), + 'RED': image.select('SR_B4') + } + ).rename('msavi') + + # MIRBI (Mid-Infrared Burn Index) + mirbi = image.expression( + '10 * SWIR2 - 9.8 * SWIR1 + 2', + { + 'SWIR1': image.select('SR_B6'), + 'SWIR2': image.select('SR_B7') + } + ).rename('mirbi') + + # NDMI (Normalized Difference Moisture Index) + ndmi = image.normalizedDifference(['SR_B5', 'SR_B6']).rename('ndmi') + + # NDFI (Normalized Difference Fraction Index) + ndfi = image.normalizedDifference(['SR_B7', 'SR_B5']).rename('ndfi') + + # NBR (Normalized Burn Ratio) + nbr = image.normalizedDifference(['SR_B5', 'SR_B7']).rename('nbr') + + # NBR2 (Normalized Burn Ratio 2) + nbr2 = image.normalizedDifference(['SR_B6', 'SR_B7']).rename('nbr2') + + # BSI (Bare Soil Index) + bsi = image.expression( + '((SWIR1 + RED) - (NIR + BLUE)) / ((SWIR1 + RED) + (NIR + BLUE))', + { + 'SWIR1': image.select('SR_B6'), + 'RED': image.select('SR_B4'), + 'NIR': image.select('SR_B5'), + 'BLUE': image.select('SR_B2') + } + ).rename('bsi') + + # Add all indices as bands + return image.addBands([ndvi, evi, msavi, mirbi, ndmi, ndfi, nbr, nbr2, bsi]) + +# Function to calculate SMI (Soil Moisture Index) +def calculateSMI(image): + """ + Calculate Soil Moisture Index (SMI) from Landsat data. + """ + Ts = image.select('ST_B10') + ndvi = image.select('ndvi') + + # Apply the SMI calculation + smi = image.expression( + '(Ts_max - Ts) / (Ts_max - Ts_min)', + { + 'Ts': Ts, + 'Ts_max': Ts.reduceRegion( + reducer=ee.Reducer.max(), + geometry=image.geometry(), + scale=30, + maxPixels=1e9 + ).get('ST_B10'), + 'Ts_min': Ts.reduceRegion( + reducer=ee.Reducer.min(), + geometry=image.geometry(), + scale=30, + maxPixels=1e9 + ).get('ST_B10') + } + ).rename('smi') + + return image.addBands(smi) + +# Function to get current Landsat data +def get_current_landsat(pa, date_range): + """ + Get the most recent Landsat 8/9 imagery for a specified date range. + + Args: + pa: ee.Geometry representing the study area + date_range: Dictionary with 'start' and 'end' dates + + Returns: + ee.Image with all spectral indices and bands + """ + print(f"Getting current Landsat data for {date_range['start']} to {date_range['end']}...") + + # Load Landsat collection and filter by date and location + ls_collection = ee.ImageCollection('LANDSAT/LC08/C02/T1_L2') \ + .merge(ee.ImageCollection('LANDSAT/LC09/C02/T1_L2')) \ + .filterDate(date_range['start'], date_range['end']) \ + .filterBounds(pa) \ + .filter(ee.Filter.lt('CLOUD_COVER', 20)) + + # Apply cloud masking + ls_masked = ls_collection.map(maskL8sr) + + # Add indices to all images in collection + ls_indices = ls_masked.map(addIndices) + + # Calculate SMI for each image + ls_with_smi = ls_indices.map(calculateSMI) + + # Create mosaic of the collection + current_ls = ls_with_smi.mosaic().clip(pa) + + return current_ls + +# Function to calculate vapor pressure from temperature +def calcVaporPressure(temp): + """ + Calculate vapor pressure from temperature using the Clausius-Clapeyron equation. + + Args: + temp: ee.Image with temperature in Celsius + + Returns: + ee.Image with vapor pressure (hPa) + """ + denom = temp.add(243.5) + num = temp.multiply(17.67) + exponent = num.divide(denom).exp() + return exponent.multiply(6.112) + +# Function to get current ERA5 relative humidity data +def get_current_relative_humidity(pa, date_range): + """ + Get the most recent ERA5 relative humidity data. + + Args: + pa: ee.Geometry representing the study area + date_range: Dictionary with 'start' and 'end' dates + + Returns: + ee.Image with relative humidity + """ + print(f"Getting current relative humidity data for {date_range['start']} to {date_range['end']}...") + + # Load ERA5 data + era5 = ee.ImageCollection('ECMWF/ERA5/DAILY') \ + .filterDate(date_range['start'], date_range['end']) \ + .filterBounds(pa) \ + .select(['dewpoint_temperature_2m', 'temperature_2m']) + + # Calculate relative humidity + def calculateRH(image): + dewPoint = image.select('dewpoint_temperature_2m') + temperature = image.select('temperature_2m') + + # Calculate vapor pressures + eT = calcVaporPressure(temperature) + eTd = calcVaporPressure(dewPoint) + + # Compute relative humidity (%) + RH = eTd.divide(eT).multiply(100).rename('rh') + + # Add time property + return RH.set('system:time_start', image.get('system:time_start')) + + # Apply RH calculation to the collection + rh_collection = era5.map(calculateRH) + + # Create mosaic of the collection + current_rh = rh_collection.mosaic().clip(pa) + + return current_rh + +# Function to get current SMAP soil moisture data +def get_current_soil_moisture(pa, date_range): + """ + Get the most recent SMAP soil moisture data. + + Args: + pa: ee.Geometry representing the study area + date_range: Dictionary with 'start' and 'end' dates + + Returns: + ee.Image with soil moisture + """ + print(f"Getting current soil moisture data for {date_range['start']} to {date_range['end']}...") + + # Load SMAP data + smap = ee.ImageCollection('NASA/SMAP/SPL3SMP_E/005') \ + .filterDate(date_range['start'], date_range['end']) \ + .filterBounds(pa) \ + .select(['soil_moisture_am']) + + # Create mosaic of the collection + current_sm = smap.mosaic().clip(pa).rename('sm') + + return current_sm + +# Function to get current CHIRPS precipitation data +def get_current_precipitation(pa, date_range): + """ + Get the most recent CHIRPS precipitation data. + + Args: + pa: ee.Geometry representing the study area + date_range: Dictionary with 'start' and 'end' dates + + Returns: + ee.Image with precipitation + """ + print(f"Getting current precipitation data for {date_range['start']} to {date_range['end']}...") + + # Load CHIRPS data + chirps = ee.ImageCollection('UCSB-CHG/CHIRPS/DAILY') \ + .filterDate(date_range['start'], date_range['end']) \ + .filterBounds(pa) + + # Create sum of the collection + current_rain = chirps.sum().clip(pa).rename('rain') + + return current_rain + +# Function to load trend coefficients from the TrendFire output +def load_trend_coefficients(pa): + """ + Load trend coefficients previously calculated using TrendFire.py. + + Args: + pa: ee.Geometry representing the study area + + Returns: + ee.Image with all trend coefficients + """ + print("Loading trend coefficients...") + + try: + # Try to load the trend layers from Earth Engine assets + trends = ee.Image("users/your_username/goa_fire_trends").clip(pa) + print("Successfully loaded trend coefficients from Earth Engine asset.") + return trends + except: + print("Trend coefficients not found in Earth Engine assets. Using placeholder.") + # Create a placeholder image with zeros for all bands + # This should be replaced with actual trend calculations + placeholder = ee.Image(0).rename('ndvi_trend') + return placeholder + +# Function to predict index values based on trend coefficients +def predict_from_trends(trend_coefficients, current_time, reference_time): + """ + Predict index values for the current time based on historical trends. + + Args: + trend_coefficients: ee.Image with trend coefficients + current_time: Current time in decimal years since start + reference_time: Reference time in decimal years for the intercept + + Returns: + ee.Image with predicted values + """ + print("Predicting index values from trend coefficients...") + + # Construct regression expressions + # Predicted value = intercept + slope * (current_time - reference_time) + time_diff = ee.Number(current_time).subtract(reference_time) + + # Create prediction expressions for each index + indices = [ + 'ndvi', 'evi', 'msavi', 'mirbi', 'ndmi', 'ndfi', + 'nbr', 'nbr2', 'bsi', 'smi', 'ST_B10', 'rain', 'rh', 'sm' + ] + + predicted_indices = ee.Image.constant(0) + + for index in indices: + # Try to get trend coefficient for this index + try: + trend = trend_coefficients.select(f'{index}_trend') + + # Calculate predicted value + # Here we're assuming the intercept is the current average value + # This is a simplification - in a full implementation we'd have both slope and intercept + predicted = trend.multiply(time_diff).rename(index) + + # Add to the predicted indices image + predicted_indices = predicted_indices.addBands(predicted) + except: + print(f"Warning: Could not find trend coefficient for {index}") + + return predicted_indices + +# Function to calculate anomalies between current and predicted values +def calculate_anomalies(current, predicted): + """ + Calculate anomalies between current observations and predicted values. + + Args: + current: ee.Image with current observations + predicted: ee.Image with predicted values + + Returns: + ee.Image with anomalies for each band + """ + print("Calculating anomalies...") + + # Calculate anomalies (current - predicted) + anomaly_image = current.subtract(predicted) + + # Rename bands to indicate they are anomalies + band_names = anomaly_image.bandNames() + new_names = band_names.map(lambda name: ee.String(name).cat('_Anomaly')) + + return anomaly_image.rename(new_names) + +# Function to identify hotspots based on anomalies +def identify_hotspots(anomaly_image, thresholds): + """ + Identify potential fire hotspots based on anomalies. + + Args: + anomaly_image: ee.Image with anomalies for each band + thresholds: Dictionary with threshold values for each anomaly + + Returns: + Dictionary with hotspot layers for different indices + """ + print("Identifying potential fire hotspots...") + + # Rainfall hotspot (deficit) + rain_anomaly = anomaly_image.select('rain_Anomaly') + rain_hotspot = rain_anomaly.lt(thresholds['rain']).selfMask().rename('rain_hotspot') + + # Relative humidity hotspot (deficit) + rh_anomaly = anomaly_image.select('rh_Anomaly') + rh_hotspot = rh_anomaly.lt(thresholds['rh']).selfMask().rename('rh_hotspot') + + # Soil moisture hotspot (deficit) + sm_anomaly = anomaly_image.select('sm_Anomaly') + sm_hotspot = sm_anomaly.lt(thresholds['sm']).selfMask().rename('sm_hotspot') + + # Vegetation indices hotspots (deficit indicating stress) + veg_indices = anomaly_image.select(['ndvi_Anomaly', 'evi_Anomaly', 'msavi_Anomaly', 'ndmi_Anomaly']) + veg_hotspot = veg_indices.lt(thresholds['veg']).selfMask().rename('veg_hotspot') + + # Burn indices hotspots (excess indicating fire potential) + burn_indices = anomaly_image.select(['mirbi_Anomaly', 'ST_B10_Anomaly', 'bsi_Anomaly']) + burn_hotspot = burn_indices.gt(thresholds['burn']).selfMask().rename('burn_hotspot') + + # Combine all hotspots + all_hotspots = ee.Image.cat([ + rain_hotspot, rh_hotspot, sm_hotspot, veg_hotspot, burn_hotspot + ]) + + return { + 'rain': rain_hotspot, + 'rh': rh_hotspot, + 'sm': sm_hotspot, + 'veg': veg_hotspot, + 'burn': burn_hotspot, + 'all': all_hotspots + } + +# Function to get land use/land cover data +def get_land_cover(pa, date_range): + """ + Get land use/land cover data for the study area. + + Args: + pa: ee.Geometry representing the study area + date_range: Dictionary with 'start' and 'end' dates + + Returns: + ee.Image with land cover classification + """ + print("Getting land use/land cover data...") + + # Load Dynamic World land cover data + dw_collection = ee.ImageCollection('GOOGLE/DYNAMICWORLD/V1') \ + .filterDate(date_range['start'], date_range['end']) \ + .filterBounds(pa) + + # Create a cloud-masked composite + dw_composite = dw_collection.map(lambda img: img.clip(pa)).mosaic() + + # Get the label band (classification) + lulc = dw_composite.select('label') + + return lulc + +# Function to analyze hotspots by land cover type +def analyze_hotspots_by_land_cover(hotspots, lulc, pa): + """ + Analyze hotspots by land cover type. + + Args: + hotspots: Dictionary with hotspot layers + lulc: ee.Image with land cover classification + pa: ee.Geometry representing the study area + + Returns: + Dictionary with hotspot statistics by land cover type + """ + print("Analyzing hotspots by land cover type...") + + # Define land cover classes + land_cover_classes = { + 0: 'water', + 1: 'trees', + 2: 'grass', + 3: 'flooded_vegetation', + 4: 'crops', + 5: 'shrub_and_scrub', + 6: 'built', + 7: 'bare', + 8: 'snow_and_ice' + } + + # Analyze each hotspot type by land cover + results = {} + + for hotspot_name, hotspot_image in hotspots.items(): + if hotspot_name == 'all': + continue + + # Create a binary mask of the hotspot + hotspot_mask = hotspot_image.gt(0) + + # Calculate area of hotspot by land cover class + hotspot_by_lulc = hotspot_mask.multiply(ee.Image.pixelArea()).addBands(lulc) \ + .reduceRegion( + reducer=ee.Reducer.sum().group(1), + geometry=pa, + scale=30, + maxPixels=1e13 + ) + + # Extract results + groups = ee.List(hotspot_by_lulc.get('groups')) + + # Store results + results[hotspot_name] = groups + + return results + +# Function to export hotspot maps to Google Drive +def export_hotspots(hotspots, pa, output_folder='ForestFireGoa'): + """ + Export hotspot maps to Google Drive. + + Args: + hotspots: Dictionary with hotspot layers + pa: ee.Geometry representing the study area + output_folder: Google Drive folder name + """ + print(f"Exporting hotspot maps to Google Drive folder '{output_folder}'...") + + # Export each hotspot type + for hotspot_name, hotspot_image in hotspots.items(): + task = ee.batch.Export.image.toDrive({ + 'image': hotspot_image, + 'description': f'fire_hotspot_{hotspot_name}', + 'folder': output_folder, + 'fileNamePrefix': f'fire_hotspot_{hotspot_name}', + 'region': pa, + 'scale': 30, + 'maxPixels': 1e13 + }) + + task.start() + print(f"Started export task for {hotspot_name} hotspot with ID: {task.id}") + +# Main function to run the entire workflow +def main(): + """ + Main function to run the trend anomaly prediction workflow. + """ + print("Starting forest fire trend anomaly prediction for Goa, India...") + + # Get the study area boundary + pa = get_study_boundary() + + # Define date range for current conditions (e.g., latest month) + # These dates should be adjusted based on your specific analysis period + current_date_range = { + 'start': '2023-02-01', + 'end': '2023-02-28' + } + + # Define reference time for trend calculations (e.g., start of historical period) + reference_time = 2013.0 # Start of analysis in decimal years + + # Calculate current time in decimal years + current_year = 2023 + current_month = 2 + current_time = current_year + (current_month - 1) / 12 + + # Load fire events data + fire_events = load_fire_events(pa) + + # Get current observational data + current_ls = get_current_landsat(pa, current_date_range) + current_rh = get_current_relative_humidity(pa, current_date_range) + current_sm = get_current_soil_moisture(pa, current_date_range) + current_rain = get_current_precipitation(pa, current_date_range) + + # Combine all current data + current_all = current_ls.addBands([current_rain, current_sm, current_rh]) + + # Load trend coefficients from TrendFire output + trend_coefficients = load_trend_coefficients(pa) + + # Predict values based on trend coefficients + predicted_values = predict_from_trends(trend_coefficients, current_time, reference_time) + + # Calculate anomalies + anomaly_image = calculate_anomalies(current_all, predicted_values) + + # Define thresholds for hotspot identification + # These thresholds would need to be calibrated for your specific study area + thresholds = { + 'rain': 0, # Rainfall below predicted value + 'rh': 60, # RH below 60% of predicted value + 'sm': -50, # Soil moisture 50% below predicted value + 'veg': -0.15, # Vegetation indices 0.15 below predicted value + 'burn': 3.2 # Burn indices 3.2 above predicted value + } + + # Identify hotspots + hotspots = identify_hotspots(anomaly_image, thresholds) + + # Get land cover data + lulc = get_land_cover(pa, current_date_range) + + # Analyze hotspots by land cover + hotspot_analysis = analyze_hotspots_by_land_cover(hotspots, lulc, pa) + + # Export hotspot maps to Google Drive (uncomment to use) + # export_hotspots(hotspots, pa) + + print("Forest fire trend anomaly prediction complete!") + print("To visualize the results, check your Google Drive for the exported hotspot maps.") + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/TrendFire.js b/TrendFire.js index 8d6feae..b2e6fa6 100644 --- a/TrendFire.js +++ b/TrendFire.js @@ -2,7 +2,7 @@ // and vulnerability mapping. // The required input is - 'region of interest' geometry - +// the goa object here is not defined? JN Map.centerObject(goa,8) //Mask to filter clouds and to scale the DN values to surface reflectance diff --git a/TrendFire.py b/TrendFire.py new file mode 100644 index 0000000..f299273 --- /dev/null +++ b/TrendFire.py @@ -0,0 +1,592 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- + +""" +TrendFire.py: Python implementation of TrendFire.js using Earth Engine Python API. +This script calculates various vegetation, burn, and climate trends for a study area in Goa, India. + +The script performs the following steps: +1. Processes Landsat 8 imagery for various spectral indices (NDVI, EVI, MSAVI, etc.) +2. Calculates long-term trends using linear regression for each index +3. Processes CHIRPS precipitation data and calculates rainfall trends +4. Processes SMAP soil moisture data and calculates soil moisture trends +5. Processes ERA5 relative humidity data and calculates humidity trends +6. Exports trend layers as GEE assets and local GeoTIFF files + +Equivalent to the TrendFire.js script in the original implementation. +""" + +import ee +import os +import datetime +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +import geopandas as gpd +from tqdm import tqdm +from shapely.geometry import Polygon + +# Initialize the Earth Engine API +def initialize_ee(): + try: + ee.Authenticate() + ee.Initialize() + print("Earth Engine API initialized successfully.") + except Exception as e: + print(f"Error initializing Earth Engine API: {e}") + print("Make sure you have authenticated with Earth Engine using ee.Authenticate()") + +# Function to debug the shapefile +def debug_shapefile(boundary_path): + """ + Debug function to check shapefile contents and geometry validity. + """ + try: + print("\nDebugging shapefile...") + gdf = gpd.read_file(boundary_path) + print(f"\nShapefile contains {len(gdf)} features") + print("\nFirst few rows of the data:") + print(gdf.head()) + print("\nGeometry type:", gdf.geometry.type.unique()) + print("\nCRS:", gdf.crs) + + # Check if geometries are valid + invalid_geoms = gdf[~gdf.geometry.is_valid] + if len(invalid_geoms) > 0: + print("\nWarning: Found invalid geometries!") + print("Number of invalid geometries:", len(invalid_geoms)) + print("\nInvalid geometries:") + print(invalid_geoms) + + return gdf + except Exception as e: + print(f"Error debugging shapefile: {e}") + return None + +# Define the Goa study area boundary +def get_goa_boundary(): + """ + Get the boundary of the study area from the pa_boundary shapefile. + Returns an ee.Geometry object. + """ + try: + # Try to load the boundary from the shapefile + boundary_path = os.path.join('data', 'pa_boundary.shp') + if os.path.exists(boundary_path): + print("Loading study area boundary from shapefile...") + + # Debug the shapefile + gdf = debug_shapefile(boundary_path) + if gdf is None: + return None + + # Ensure we have at least one valid geometry + if len(gdf) == 0: + print("Error: Shapefile contains no features") + return None + + # Get the first geometry + geometry = gdf.geometry.iloc[0] + + # Check if geometry is valid + if not geometry.is_valid: + print("Error: Geometry is invalid") + return None + + # Convert 3D polygon to 2D by dropping Z coordinates + if geometry.has_z: + print("Converting 3D polygon to 2D...") + # Get the coordinates and drop Z values + coords = list(geometry.exterior.coords) + coords_2d = [(x, y) for x, y, z in coords] + # Create new 2D polygon + geometry = Polygon(coords_2d) + + # Convert to GeoJSON + gdf.geometry = gpd.GeoSeries([geometry]) + boundary_geojson = gdf.__geo_interface__ + + # Create an EE geometry + return ee.Geometry.Polygon(boundary_geojson['features'][0]['geometry']['coordinates']) + else: + print(f"Error: Could not find pa_boundary.shp at {boundary_path}") + print("Please ensure the shapefile exists in the data directory") + return None + except Exception as e: + print(f"Error loading boundary from shapefile: {e}") + return None + +# Function to mask clouds in Landsat 8 imagery +def maskL8sr(image): + """ + Mask clouds and scale pixel values for Landsat 8 SR imagery. + """ + # Get QA band + qa = image.select('QA_PIXEL') + + # Bits 3 and 5 are cloud shadow and cloud, respectively + cloud_shadow_bit_mask = 1 << 3 + clouds_bit_mask = 1 << 5 + + # Both flags should be set to zero, indicating clear conditions + mask = qa.bitwiseAnd(cloud_shadow_bit_mask).eq(0).And( + qa.bitwiseAnd(clouds_bit_mask).eq(0)) + + # Scale the optical bands + optical_bands = image.select('SR_B.').multiply(0.0000275).add(-0.2) + + # Scale the thermal bands + thermal_bands = image.select('ST_B.*').multiply(0.00341802).add(149.0) + + # Return the masked and scaled image + return image.select(['QA_.*']).addBands(optical_bands).addBands(thermal_bands).updateMask(mask) + +# Function to calculate spectral indices and add them as bands +def addIndices(image): + """ + Calculate various spectral indices and add them as bands to the image. + """ + # NDVI (Normalized Difference Vegetation Index) + ndvi = image.normalizedDifference(['SR_B5', 'SR_B4']).rename('ndvi') + + # EVI (Enhanced Vegetation Index) + evi = image.expression( + '2.5 * ((NIR - RED) / (NIR + 6 * RED - 7.5 * BLUE + 1))', + { + 'NIR': image.select('SR_B5'), + 'RED': image.select('SR_B4'), + 'BLUE': image.select('SR_B2') + } + ).rename('evi') + + # MSAVI (Modified Soil Adjusted Vegetation Index) + msavi = image.expression( + '(2 * NIR + 1 - sqrt((2 * NIR + 1) * (2 * NIR + 1) - 8 * (NIR - RED))) / 2', + { + 'NIR': image.select('SR_B5'), + 'RED': image.select('SR_B4') + } + ).rename('msavi') + + # MIRBI (Mid-Infrared Burn Index) + mirbi = image.expression( + '10 * SWIR2 - 9.8 * SWIR1 + 2', + { + 'SWIR1': image.select('SR_B6'), + 'SWIR2': image.select('SR_B7') + } + ).rename('mirbi') + + # NDMI (Normalized Difference Moisture Index) + ndmi = image.normalizedDifference(['SR_B5', 'SR_B6']).rename('ndmi') + + # NDFI (Normalized Difference Fraction Index) + ndfi = image.normalizedDifference(['SR_B7', 'SR_B5']).rename('ndfi') + + # NBR (Normalized Burn Ratio) + nbr = image.normalizedDifference(['SR_B5', 'SR_B7']).rename('nbr') + + # NBR2 (Normalized Burn Ratio 2) + nbr2 = image.normalizedDifference(['SR_B6', 'SR_B7']).rename('nbr2') + + # BSI (Bare Soil Index) + bsi = image.expression( + '((SWIR1 + RED) - (NIR + BLUE)) / ((SWIR1 + RED) + (NIR + BLUE))', + { + 'SWIR1': image.select('SR_B6'), + 'RED': image.select('SR_B4'), + 'NIR': image.select('SR_B5'), + 'BLUE': image.select('SR_B2') + } + ).rename('bsi') + + # Add all indices as bands + return image.addBands([ndvi, evi, msavi, mirbi, ndmi, ndfi, nbr, nbr2, bsi]) + +# Function to calculate SMI (Soil Moisture Index) +def calculateSMI(image): + """ + Calculate Soil Moisture Index (SMI) from Landsat data. + """ + Ts = image.select('ST_B10') + ndvi = image.select('ndvi') + + # Apply the SMI calculation + smi = image.expression( + '(Ts_max - Ts) / (Ts_max - Ts_min)', + { + 'Ts': Ts, + 'Ts_max': Ts.reduceRegion( + reducer=ee.Reducer.max(), + geometry=image.geometry(), + scale=30, + maxPixels=1e9 + ).get('ST_B10'), + 'Ts_min': Ts.reduceRegion( + reducer=ee.Reducer.min(), + geometry=image.geometry(), + scale=30, + maxPixels=1e9 + ).get('ST_B10') + } + ).rename('smi') + + return image.addBands(smi) + +# Main function to process Landsat 8 data and calculate trends +def process_landsat_trends(goa, start_date='2013-03-20', end_date='2023-02-28'): + """ + Process Landsat 8 imagery, calculate vegetation indices and trends. + """ + print("Processing Landsat 8 data and calculating trends...") + + # Load Landsat 8 collection and filter by date and location + ls8_collection = ee.ImageCollection('LANDSAT/LC08/C02/T1_L2') \ + .filterDate(start_date, end_date) \ + .filterBounds(goa) \ + .filterMetadata('CLOUD_COVER', 'less_than', 10) \ + .map(maskL8sr) \ + .map(lambda image: image.clip(goa)) \ + .map(addIndices) + + # Calculate SWIR min/max for SMI calculation + swir_stats = ls8_collection.mean().select('SR_B7').reduceRegion( + reducer=ee.Reducer.minMax(), + geometry=goa, + scale=30 + ) + + swir_min = ee.Number(swir_stats.get('SR_B7_min')) + swir_max = ee.Number(swir_stats.get('SR_B7_max')) + + # Function to calculate SMI using SWIR bands + def addSMI(image): + smi = image.select('SR_B7').subtract(swir_min) \ + .divide(swir_max.subtract(swir_min)) \ + .multiply(-1).add(1).rename('smi') + return image.addBands(smi) + + # Add SMI to collection + ls8_with_smi = ls8_collection.map(addSMI) + + # Select bands for trend analysis + trend_bands = ['ndvi', 'evi', 'mirbi', 'ndfi', 'bsi', 'ndmi', 'nbr', 'nbr2', 'msavi', 'smi', 'ST_B10'] + ls8_with_smi = ls8_with_smi.select(trend_bands).sort('system:time_start') + + # Add a time band (in years) + def addTimeBand(image): + years = ee.Date(image.get('system:time_start')).difference(ee.Date(start_date), 'year') + return image.addBands(ee.Image.constant(years).float().rename('time')) + + collection_with_time = ls8_with_smi.map(addTimeBand) + + # Calculate trends for each band and combine into a single image + all_trends = None + + for band in trend_bands: + print(f"Calculating trend for {band}...") + + # Select the index and time bands + stacked = collection_with_time.select(['time', band]) + + # Perform linear regression + regression = stacked.reduce(ee.Reducer.linearFit()) + + # Extract slope and intercept + slope = regression.select('scale').rename(f'{band}_Slope') + intercept = regression.select('offset').rename(f'{band}_Intercept') + + # Combine with previous trends + if all_trends is None: + all_trends = slope.addBands(intercept) + else: + all_trends = all_trends.addBands(slope).addBands(intercept) + + # Export the combined trends + export_to_asset( + all_trends, + 'users/jonasnothnagel/Trend2023_landsat', + goa, + 'Landsat_Trends_2023', + scale=30 + ) + + return all_trends, ls8_with_smi + +# Function to process CHIRPS precipitation data +def process_chirps_trends(goa, start_date='1982-01-01', end_date='2022-12-31'): + """ + Process CHIRPS precipitation data and calculate rainfall trends. + """ + print("Processing CHIRPS precipitation data and calculating trends...") + + # Load CHIRPS data + chirps = ee.ImageCollection('UCSB-CHG/CHIRPS/DAILY') \ + .filterDate(start_date, end_date) \ + .filterBounds(goa) + + # Function to compute the yearly sum + def createYearlySum(year): + # Filter for the specific year + year_filter = chirps.filter(ee.Filter.calendarRange(year, year, 'year')) + + # Create the yearly sum + yearly_sum = year_filter.sum().clip(goa) + + # Add the year as a property + return yearly_sum.set('year', year) \ + .set('system:time_start', ee.Date.fromYMD(year, 1, 1).millis()) \ + .rename('precipitation') + + # Create a list of yearly sums + years = ee.List.sequence(1982, 2021) # Match JS implementation + yearly_sums = years.map(createYearlySum) + + # Convert to ImageCollection + yearly_sums_collection = ee.ImageCollection.fromImages(yearly_sums) + + # Add time band + def addTimeBand(image): + time = ee.Date(image.get('system:time_start')).difference(ee.Date('1982-01-01'), 'year') + return image.addBands(ee.Image.constant(time).rename('time').float()) + + collection_with_time = yearly_sums_collection.map(addTimeBand) + + # Calculate trend + stacked = collection_with_time.select(['time', 'precipitation']) + regression = stacked.reduce(ee.Reducer.linearFit()) + slope = regression.select('scale').rename('rain_Slope') + intercept = regression.select('offset').rename('rain_Intercept') + trend = slope.addBands(intercept) + + # Export the trend + export_to_asset( + trend, + 'users/jonasnothnagel/Trend2022_rain_new', + goa, + 'Precipitation_Trend_1982_2022', + scale=30 + ) + + return trend, yearly_sums_collection + +# Function to process SMAP soil moisture data +def process_smap_trends(goa, start_date='2015-04-01', end_date='2023-02-28'): + """ + Process SMAP soil moisture data and calculate trends. + Note: SMAP data starts from April 2015. + """ + print("Processing SMAP soil moisture data and calculating trends...") + + # Load SMAP data + smap = ee.ImageCollection('NASA/SMAP/SPL3SMP_E/005') \ + .filterDate(start_date, end_date) \ + .filterBounds(goa) \ + .select(['soil_moisture_am']) \ + .map(lambda image: image.clip(goa)) + + # Add time band + def addTimeBand(image): + time = ee.Date(image.get('system:time_start')).difference(ee.Date('2015-04-01'), 'year') + return image.addBands(ee.Image.constant(time).rename('time').float()) + + # Apply time band to each image in the collection + soil_moisture_with_time = smap.map(addTimeBand) + + # Calculate trend + regression = soil_moisture_with_time.reduce(ee.Reducer.linearFit()) + slope = regression.select('scale').rename('sm_surface_Slope') + intercept = regression.select('offset').rename('sm_surface_Intercept') + trend = slope.addBands(intercept) + + # Export the trend + export_to_asset( + trend, + 'users/jonasnothnagel/Trend2023_SM_new', + goa, + 'SM_Trend2015_2023', + scale=30 + ) + + return trend, smap + +# Function to process ERA5 relative humidity data +def process_era5_trends(goa, start_date='1980-01-01', end_date='2023-02-28'): + """ + Process ERA5 data and calculate relative humidity trends. + """ + print("Processing ERA5 relative humidity data and calculating trends...") + + # Load ERA5 data + era5 = ee.ImageCollection("ECMWF/ERA5_LAND/MONTHLY_AGGR") \ + .filterDate(start_date, end_date) \ + .filterBounds(goa) \ + .map(lambda image: image.clip(goa)) + + # Function to calculate vapor pressure + def calcVaporPressure(temp): + denom = temp.add(243.5) + num = temp.multiply(19.67) # Note: JS uses 19.67 instead of 17.67 + exponent = num.divide(denom).exp() + return exponent.multiply(6.112) + + # Calculate RH for each time step + def calculateRH(image): + dewPoint = image.select('dewpoint_temperature_2m') + temperature = image.select('temperature_2m') + + # Calculate vapor pressures + eT = calcVaporPressure(temperature) + eTd = calcVaporPressure(dewPoint) + + # Compute relative humidity (%) + RH = eTd.divide(eT).multiply(100).rename('RH') + + # Add time property + return RH.set('system:time_start', image.get('system:time_start')) + + # Apply RH calculation to the collection + rh_collection = era5.map(calculateRH) + + # Add time band + def addTimeBand(image): + time = ee.Date(image.get('system:time_start')).difference(ee.Date('1980-01-01'), 'year') + return image.addBands(ee.Image.constant(time).rename('time').float()) + + rh_with_time = rh_collection.map(addTimeBand) + + # Calculate trend + regression = rh_with_time.reduce(ee.Reducer.linearFit()) + slope = regression.select('scale').rename('rh_Slope') + intercept = regression.select('offset').rename('rh_Intercept') + trend = slope.addBands(intercept) + + # Export the trend + export_to_asset( + trend, + 'users/jonasnothnagel/Trend2024_RH_new', + goa, + 'RH_Trend1980_2023', + scale=30 + ) + + return trend, rh_collection + +# Function to merge all trend layers +def merge_trend_layers(ls_trends, rain_trend, sm_trend, rh_trend): + """ + Merge all trend layers into a single multi-band image. + """ + # Combine all trend layers + all_trends = ls_trends.addBands(rain_trend).addBands(sm_trend).addBands(rh_trend) + + return all_trends + +# Function to export the trends to Google Earth Engine assets +def export_to_asset(image, asset_name, region, description=None, scale=30): + """ + Export an image to a Google Earth Engine asset. + """ + if description is None: + description = asset_name.split('/')[-1] + + task = ee.batch.Export.image.toAsset( + image=image, + description=description, + assetId=asset_name, + region=region, + scale=scale, + maxPixels=1e13 + ) + + task.start() + print(f"Started export task: {description}") + return task + +# Function to export the trends to Google Drive +def export_to_drive(image, filename, region, description=None, folder='GEE_Exports', scale=30): + """ + Export an image to Google Drive. + """ + if description is None: + description = filename + + task = ee.batch.Export.image.toDrive( + image=image, + description=description, + folder=folder, + fileNamePrefix=filename, + region=region, + scale=scale, + maxPixels=1e13 + ) + + task.start() + print(f"Started export task to Drive: {description}") + return task + +def main(): + """ + Main function to process all trends and export results. + """ + # Initialize Earth Engine + initialize_ee() + + # Get Goa boundary + print("Retrieving Goa boundary...") + goa = get_goa_boundary() + if goa is None: + print("Error: Could not retrieve Goa boundary") + return + + print("Successfully retrieved Goa boundary") + + # Process Landsat trends + print("\nProcessing Landsat trends...") + landsat_trends, landsat_collection = process_landsat_trends(goa) + print("Landsat trends processed successfully") + + # Process CHIRPS precipitation trends + print("\nProcessing CHIRPS precipitation trends...") + rain_trends, rain_collection = process_chirps_trends(goa) + print("CHIRPS precipitation trends processed successfully") + + # Process SMAP soil moisture trends + print("\nProcessing SMAP soil moisture trends...") + sm_trends, sm_collection = process_smap_trends(goa) + print("SMAP soil moisture trends processed successfully") + + # Process ERA5 relative humidity trends + print("\nProcessing ERA5 relative humidity trends...") + rh_trends, rh_collection = process_era5_trends(goa) + print("ERA5 relative humidity trends processed successfully") + + # Merge all trend layers + print("\nMerging all trend layers...") + all_trends = landsat_trends.addBands(rain_trends).addBands(sm_trends).addBands(rh_trends) + + # Export the merged trends + print("Exporting merged trends...") + export_to_asset( + all_trends, + 'users/jonasnothnagel/Trend2024_all_new', + goa, + 'All_Trends_2024', + scale=30 + ) + + print("\nAnalysis completed successfully!") + print("Results exported to Earth Engine assets") + + return { + 'trends': all_trends, + 'collections': { + 'landsat': landsat_collection, + 'rain': rain_collection, + 'sm': sm_collection, + 'rh': rh_collection + } + } + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/fire_training_data.csv b/fire_training_data.csv new file mode 100644 index 0000000..50ba51a --- /dev/null +++ b/fire_training_data.csv @@ -0,0 +1,120 @@ +geometry,Shape_Leng,Shape_Area,Name,RiskNumeric,bright_ti5,bright_ti4,acq_time,frp,Delta T,acq_date,confidence,scan,instrument,type,version,RiskNumeri,daynight,satellite,track,RiskCatego,latitude,longitude,system_ind,RiskCategory,band_0,band_1,band_2,band_3,band_4,band_5,band_6,band_7,band_8,band_9,band_10,band_11,band_12,band_13,band_14,band_15,band_16,band_17,band_18,band_19,band_20,band_21,band_22,band_23,band_24,band_25,band_26,band_27,band_28,band_29,band_30,band_31,band_32,band_33,band_34,band_35,band_36,band_37,band_38,band_39 +POINT 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WLS,0,,,,,,,,,,,,,,,,,,,,,19.382850646972656,2113.504638671875,0.04423021897673607,17.48790740966797,0.051474228501319885,302.9670715332031,-0.00160194409545511,0.4932066798210144,-0.0017336014425382018,0.46702298521995544,-0.0026895334012806416,2.913046360015869,0.0006098828162066638,-0.23620019853115082,2.3598642349243164,2.984099864959717,-0.0005892888875678182,0.2963998019695282,-0.00045201831380836666,0.5998298525810242,-0.0002218000590801239,0.3759107291698456,25.976356506347656,0.0006646952824667096,0.8039725422859192,154.8152618408203,68.13226318359375,129.23353576660156,248.87908935546875,129.8836669921875,-49.919857025146484,189.59017944335938,2406.3642578125,-0.011352026835083961,0.7726869583129883,-0.06790860742330551,29.711496353149414,0.04321415722370148,21.72971534729004,2078.54443359375 +POINT (8261181.633496349 1737151.5131185204),0.0,0.0,Bhagwan Mahavir WLS,0,,,,,,,,,,,,,,,,,,,,,16.12369728088379,2153.84423828125,0.05328503996133804,16.63265609741211,0.07660961151123047,303.3529357910156,0.001785141066648066,0.41765549778938293,0.0017335254233330488,0.388749897480011,0.003004734870046377,2.742203712463379,-0.0005311852437444031,-0.2359922230243683,3.2316770553588867,2.59718656539917,0.0004947457346133888,0.2924010157585144,0.0006432573427446187,0.6031634211540222,0.0003137508756481111,0.38240352272987366,5.393972396850586,-0.0003394868108443916,0.8513416647911072,126.53034210205078,-40.749820709228516,107.3917465209961,156.82940673828125,113.51212310791016,-109.016845703125,147.71905517578125,2045.8905029296875,-0.012822668068110943,0.8014622330665588,-0.09826058894395828,31.831863403320312,0.02201393060386181,21.478727340698242,1122.7733154296875 +POINT (8261552.063117143 1719890.3674817118),0.0,0.0,Bhagwan Mahavir WLS,0,,,,,,,,,,,,,,,,,,,,,16.300752639770508,2173.091796875,0.05328503996133804,16.63265609741211,0.08311744034290314,307.2726135253906,1.598159724380821e-05,0.35455119609832764,-0.0003264515253249556,0.32171350717544556,-0.001953464699909091,2.927485227584839,-0.0010137929348275065,-0.037232089787721634,3.298588752746582,2.5584447383880615,0.0003755649668164551,0.08805225044488907,0.001480843173339963,0.35683029890060425,0.001387083437293768,0.2852901816368103,5.180638313293457,0.0026825314853340387,0.6797186732292175,126.26046752929688,25.516143798828125,113.95465087890625,161.3827667236328,117.74287414550781,-107.84561920166016,155.04196166992188,1944.988037109375,-0.008643073961138725,0.7303717136383057,-0.07374374568462372,30.790075302124023,0.027237525209784508,21.205472946166992,880.5986328125 +POINT (8261353.889461433 1748217.5277047132),1.73538633992,0.01879725637,Mhadei WLS,0,,,,,,,,,,,,,,,,,,,,,14.928257942199707,2073.187744140625,0.04621821641921997,17.30975914001465,0.17526425421237946,305.9162902832031,-0.0008680293103680015,0.4052399694919586,-0.000991616165265441,0.37123364210128784,0.0040992246940732,2.878767728805542,0.0030624577775597572,-0.14735622704029083,2.976552724838257,2.7044215202331543,-0.003201441140845418,0.19851438701152802,-0.0032892562448978424,0.4987478256225586,-0.0008780166390351951,0.3456137180328369,11.499750137329102,-0.0024313784670084715,0.7734515070915222,93.22777557373047,141.59207153320312,87.63451385498047,248.69747924804688,104.98493957519531,-42.82523727416992,113.294677734375,2204.594482421875,-0.004330268129706383,0.7205092310905457,-0.026762770488858223,31.050888061523438,0.022190114483237267,21.29987907409668,691.5078125 +POINT (8262066.107653767 1763827.2445334455),1.73538633992,0.01879725637,Mhadei WLS,0,,,,,,,,,,,,,,,,,,,,,12.756263732910156,1992.9703369140625,0.0439036525785923,17.53630256652832,0.19830422103405,301.5170593261719,-0.0070106107741594315,0.6894866824150085,-0.007769692223519087,0.6708515286445618,-0.00040294864447787404,3.00681209564209,0.008339139632880688,-0.40163829922676086,2.306614875793457,2.9972360134124756,-0.0078113144263625145,0.45161622762680054,-0.008176960982382298,0.7456730604171753,-0.005086902994662523,0.4415399134159088,29.39374542236328,-0.004566200077533722,0.8459840416908264,176.56198120117188,-160.8659210205078,154.5343017578125,48.6362419128418,156.1862335205078,-224.57986450195312,187.99110412597656,2886.087890625,-0.023310748860239983,0.8660500049591064,-0.038287628442049026,28.582712173461914,0.05084999278187752,21.16116714477539,1119.0450439453125 +POINT (8252966.42941239 1685295.5393211427),0.0,0.0,Cotigaon WLS,0,,,,,,,,,,,,,,,,,,,,,23.504873275756836,2569.070068359375,0.09081510454416275,13.04919147491455,-0.018681438639760017,305.7333984375,0.003071489278227091,0.4161742627620697,0.0028597842901945114,0.3936383128166199,0.003299175063148141,2.8843441009521484,-0.002601898740977049,-0.15189096331596375,2.7865242958068848,2.740579843521118,0.002226866316050291,0.20705026388168335,0.0035243737511336803,0.5112313628196716,0.001653237733989954,0.35217246413230896,2.962559700012207,0.0014740470796823502,0.782730221748352,79.71941375732422,97.75496673583984,79.07776641845703,238.92477416992188,89.12870788574219,-51.9503288269043,133.95274353027344,2195.6513671875,-0.006646840833127499,0.7720575928688049,-0.1395138055086136,30.897869110107422,0.012419543229043484,21.54703712463379,199.9700164794922 +POINT (8266493.973501359 1702502.6333610571),0.0,0.0,Netravali WLS,0,,,,,,,,,,,,,,,,,,,,,17.637788772583008,2430.905517578125,0.06117723137140274,15.88566780090332,0.056207891553640366,301.68170166015625,0.0006864207680337131,0.39570796489715576,0.0006284348201006651,0.3671559691429138,-0.0005949529586359859,2.5686933994293213,-0.0008131762151606381,-0.32097843289375305,5.493683815002441,1.311958909034729,0.0004567378491628915,0.38393571972846985,0.000377143471268937,0.6751805543899536,0.00017674225091468543,0.3934765160083771,21.581520080566406,0.0002755316672846675,0.9023265838623047,171.00454711914062,-152.9395294189453,140.9563446044922,35.191158294677734,132.32679748535156,-160.06948852539062,164.83139038085938,1639.147705078125,-0.015814851969480515,0.8103013634681702,-0.04471790790557861,28.525503158569336,0.031126869842410088,21.374767303466797,2110.1572265625 +POINT (8264219.0261052 1692366.3916520006),0.0,0.0,Netravali WLS,0,,,,,,,,,,,,,,,,,,,,,23.021440505981445,2472.308349609375,0.07669062167406082,14.396920204162598,0.05937211588025093,301.96502685546875,0.0014667330542579293,0.4448661208152771,0.0016351545928046107,0.41852545738220215,-0.00018382012785878032,2.664808511734009,-0.0028333121445029974,-0.3063919246196747,2.8360955715179443,2.6934564113616943,0.0022741924040019512,0.3700748085975647,0.0028053384739905596,0.6585643887519836,0.0021715781185775995,0.3816904127597809,27.656890869140625,0.0013535136822611094,0.8734142184257507,170.21180725097656,-18.4632625579834,147.85638427734375,132.1824188232422,162.1001434326172,-190.08726501464844,142.36245727539062,2138.1376953125,-0.019585730507969856,0.810249388217926,-0.05248381569981575,29.330591201782227,0.029538217931985855,21.792821884155273,1851.169677734375 +POINT (8263441.534239668 1755058.9681129523),1.73538633992,0.01879725637,Mhadei WLS,0,,,,,,,,,,,,,,,,,,,,,14.390942573547363,2018.94677734375,0.0439036525785923,17.53630256652832,0.0963275134563446,302.47332763671875,-0.0026545505970716476,0.40231403708457947,-0.0025840953458100557,0.36753368377685547,-0.0018266853876411915,2.740663766860962,0.0029578227549791336,-0.20001842081546783,2.8782966136932373,2.7207159996032715,-0.002690128516405821,0.25594624876976013,-0.0032235905528068542,0.5495625734329224,-0.0015781554393470287,0.3538074791431427,19.21757698059082,-0.001017609378322959,0.8242944478988647,94.40872192382812,-17.333995819091797,76.29496765136719,170.0533905029297,99.69710540771484,-126.63162994384766,82.61861419677734,1996.3101806640625,-0.010750885121524334,0.7795593738555908,-0.03604848310351372,29.62470817565918,0.03766153007745743,21.4419002532959,1507.030517578125 +POINT (8263953.761028758 1720732.4310992796),0.0,0.0,Bhagwan Mahavir WLS,0,,,,,,,,,,,,,,,,,,,,,18.488798141479492,2254.447021484375,0.06798024475574493,15.237241744995117,0.07837918400764465,302.2860412597656,-0.00012828508624807,0.4345840811729431,-0.0002606911875773221,0.40570545196533203,-0.00028381001902744174,2.6609930992126465,0.0003336509398650378,-0.30299118161201477,3.1579742431640625,2.646435260772705,-0.0005240152822807431,0.36477136611938477,-0.00043382972944527864,0.6589313745498657,-7.871473644627258e-05,0.3883114755153656,17.684913635253906,-2.5615909180487506e-05,0.8786348104476929,113.67413330078125,31.870874404907227,99.77638244628906,212.45851135253906,98.50244140625,-33.61927032470703,115.49274444580078,2219.52587890625,-0.01280528586357832,0.8009641766548157,-0.05736849829554558,29.00293731689453,0.043110255151987076,21.762653350830078,1963.4893798828125 +POINT (8273554.2578175375 1723842.1766357203),0.0,0.0,Mollem NP,0,,,,,,,,,,,,,,,,,,,,,16.178546905517578,2108.369873046875,0.06290382146835327,15.734601974487305,0.03720001503825188,300.8758544921875,0.001645763055421412,0.3853974938392639,0.0017168783815577626,0.35314232110977173,-0.001241031801328063,2.5749945640563965,-0.0035668006166815758,-0.286088764667511,5.83398962020874,1.2709428071975708,0.003770690644159913,0.3430604636669159,0.003462736727669835,0.6341547966003418,0.0013582200044766068,0.3730332553386688,40.55246353149414,0.0011008295696228743,0.8899705410003662,166.3529510498047,-238.62579345703125,124.54154205322266,-10.568404197692871,131.48287963867188,-234.65650939941406,147.0846405029297,1388.1617431640625,-0.016398150473833084,0.8131328225135803,-0.04270009323954582,28.151073455810547,0.042068544775247574,20.384599685668945,1261.284423828125 +POINT (8268235.0026916 1725959.7999124222),0.0,0.0,Mollem NP,0,,,,,,,,,,,,,,,,,,,,,18.239013671875,2170.728271484375,0.06290382146835327,15.734601974487305,0.07582572102546692,303.1377258300781,0.0016410751268267632,0.5610355734825134,0.0016956148901954293,0.531405508518219,0.0037005303893238306,2.9535088539123535,-0.00041632409556768835,-0.29349038004875183,4.112709045410156,2.169313669204712,0.0006662926753051579,0.3425050973892212,0.0007240552804432809,0.6542083621025085,0.0004185580473858863,0.4028489887714386,22.534149169921875,-0.00044012913713231683,0.8238675594329834,131.92637634277344,-48.12061309814453,112.44924926757812,169.68392944335938,63.932857513427734,105.38275146484375,187.93646240234375,2600.82275390625,-0.00838171225041151,0.8250625729560852,-0.05506659299135208,29.576372146606445,0.038734763860702515,21.40384292602539,803.92529296875 +POINT (8266388.990074024 1694551.274522168),0.0,0.0,Netravali WLS,0,,,,,,,,,,,,,,,,,,,,,21.10080909729004,2478.678955078125,0.06601659208536148,15.423352241516113,-0.1493058204650879,306.282470703125,0.0070447055622935295,0.37799072265625,0.005783691070973873,0.3583005666732788,-0.009871874935925007,3.0098838806152344,-0.011458190158009529,-0.04249124228954315,5.493683815002441,1.311958909034729,0.01018204353749752,0.11473958194255829,0.012063218280673027,0.38115671277046204,0.004262955859303474,0.2807195484638214,20.695884704589844,0.009791607968509197,0.6552827954292297,112.58413696289062,110.73049926757812,108.11543273925781,227.6793670654297,122.49010467529297,-67.68344116210938,137.37542724609375,2473.015625,-0.00875935424119234,0.7617656588554382,-0.09916195273399353,29.665870666503906,0.03187447041273117,20.558530807495117,1064.2706298828125 +POINT (8261219.219836153 1684957.6648397886),0.0,0.0,Cotigaon WLS,0,,,,,,,,,,,,,,,,,,,,,22.6862735748291,2552.69091796875,0.07669062167406082,14.396920204162598,0.030689295381307602,303.5072326660156,0.0038372769486159086,0.4495801031589508,0.004071256145834923,0.42837098240852356,0.0024717499036341906,2.752889394760132,-0.0035445140674710274,-0.26724880933761597,2.0055959224700928,3.0989205837249756,0.0030275858007371426,0.3272094130516052,0.003130686469376087,0.6301913261413574,0.001734824967570603,0.38191744685173035,6.609221935272217,0.0006786038284189999,0.8526982069015503,63.1298713684082,94.74947357177734,59.43310546875,263.9061584472656,71.206787109375,-37.44039535522461,105.55801391601562,2340.80322265625,-0.004466347396373749,0.8006084561347961,-0.06534507125616074,29.380664825439453,0.03097464330494404,21.76676368713379,1248.38525390625 +POINT (8250397.364717637 1755368.375313728),1.73538633992,0.01879725637,Mhadei WLS,0,,,,,,,,,,,,,,,,,,,,,20.66444969177246,2120.4052734375,0.04423021897673607,17.48790740966797,0.10646850615739822,302.9615783691406,0.005014218855649233,0.4862692356109619,0.0050658248364925385,0.46310389041900635,0.003656828310340643,2.8269917964935303,-0.003927069716155529,-0.27665600180625916,3.006338357925415,2.7003719806671143,0.0034251627512276173,0.33918625116348267,0.003379173344001174,0.6446586847305298,0.0017296969890594482,0.39197438955307007,13.754830360412598,0.0005045743891969323,0.8430717587471008,78.96881103515625,198.7745361328125,54.847686767578125,393.4236145019531,48.36432647705078,140.8699951171875,98.94148254394531,2569.42578125,-0.007934837602078915,0.7957490086555481,-0.06146763637661934,30.92523956298828,0.02991015464067459,21.787553787231445,1128.42919921875 +POINT (8261456.143758265 1739123.713541585),0.0,0.0,Bhagwan Mahavir WLS,0,,,,,,,,,,,,,,,,,,,,,17.40591049194336,2074.81591796875,0.0439036525785923,17.53630256652832,0.14065667986869812,308.1080017089844,-0.001716605038382113,0.4141395390033722,-0.002478592097759247,0.38111451268196106,-0.013009009882807732,3.0417320728302,-0.0028097883332520723,-0.07306268066167831,3.006338357925415,2.7003719806671143,0.002877117833122611,0.12042617797851562,0.004383511375635862,0.399059534072876,0.0009993977146223187,0.3100510835647583,3.617776393890381,0.006949680391699076,0.6737844944000244,89.58692169189453,98.87937927246094,80.56713104248047,264.61767578125,98.05528259277344,-64.78831481933594,94.866943359375,2557.598876953125,-0.0073540392331779,0.7604257464408875,-0.10466708987951279,32.834415435791016,0.03671526536345482,20.826221466064453,211.67398071289062 +POINT (8262969.966020776 1736118.2703905904),0.0,0.0,Bhagwan Mahavir WLS,0,,,,,,,,,,,,,,,,,,,,,18.47934341430664,2241.210693359375,0.06798024475574493,15.237241744995117,0.05792616680264473,307.1551208496094,-0.0008305389201268554,0.39923617243766785,-0.0009881769074127078,0.3714723587036133,-0.0008701516198925674,2.8861184120178223,0.0003406304167583585,-0.12789414823055267,3.0465664863586426,2.6813604831695557,-0.00045663610217161477,0.18644283711910248,0.00031854267581366,0.4777415096759796,0.000665009138174355,0.33057400584220886,7.686184883117676,0.001145554124377668,0.7569004893302917,118.54546356201172,88.72157287597656,92.86561584472656,268.12969970703125,93.51020812988281,-9.483024597167969,150.03648376464844,2096.291259765625,-0.008658132515847683,0.7385255694389343,-0.15413472056388855,32.54884338378906,0.040253520011901855,21.723073959350586,564.7798461914062 +POINT (8266019.501779899 1705007.3604806927),0.0,0.0,Netravali WLS,0,,,,,,,,,,,,,,,,,,,,,17.637788772583008,2430.905517578125,0.06117723137140274,15.88566780090332,0.10756994038820267,302.3316650390625,-0.00041710553341545165,0.545075535774231,-0.0004507893754635006,0.5191692113876343,0.0019531315192580223,2.870347499847412,0.0014157391851767898,-0.3151480555534363,5.316018104553223,1.480040192604065,-0.001618637004867196,0.3739423453807831,-0.0012433043448254466,0.673336386680603,-0.0002833984908647835,0.40094754099845886,24.242244720458984,-0.0006605049129575491,0.8407925963401794,154.27691650390625,-94.75945281982422,115.11351776123047,127.18408203125,113.91524505615234,-134.88214111328125,182.7025909423828,2393.664794921875,-0.012259970419108868,0.8340820670127869,-0.03704620152711868,28.62795639038086,0.04611867293715477,21.419403076171875,3166.17919921875 +POINT (8264263.179335943 1717601.9789250183),0.0,0.0,Netravali 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NP,0,,,,,,,,,,,,,,,,,,,,,18.488798141479492,2254.447021484375,0.06798024475574493,15.237241744995117,0.11204791814088821,302.43511962890625,0.0010569439036771655,0.43030887842178345,0.001025176141411066,0.40188440680503845,-8.804628305369988e-05,2.6517224311828613,-0.0013249411713331938,-0.29616597294807434,4.112709045410156,2.169313669204712,0.000981425866484642,0.3594789505004883,0.0010734850075095892,0.6493462324142456,0.0007092743762768805,0.37973397970199585,12.84316349029541,0.00045232949196361005,0.8748148679733276,106.37291717529297,62.94661331176758,90.36048126220703,248.5903778076172,87.9568862915039,38.229251861572266,103.19638061523438,2216.14990234375,-0.009315443225204945,0.7879675626754761,-0.06045638024806976,29.376924514770508,0.05381540209054947,21.469715118408203,3440.5966796875 +POINT (8262991.047038437 1757646.8486342842),1.73538633992,0.01879725637,Mhadei WLS,0,,,,,,,,,,,,,,,,,,,,,18.551830291748047,2328.795654296875,0.07300230115652084,14.763771057128906,-0.011170260608196259,305.1404113769531,-0.0014035082422196865,0.388773649930954,-0.0017161160940304399,0.35939082503318787,-0.0011446651769801974,2.856858253479004,0.0011644067708402872,-0.12658992409706116,3.2029500007629395,2.5971996784210205,-0.001108364900574088,0.1771734654903412,-0.00042219413444399834,0.4652484059333801,-0.0002588187635410577,0.3351559638977051,15.364158630371094,0.0008344775415025651,0.7585928440093994,100.55213928222656,41.313560485839844,83.56103515625,225.8474884033203,108.73534393310547,-92.76527404785156,87.28367614746094,2216.256103515625,-0.010062336921691895,0.7610608339309692,-0.1258036345243454,31.084882736206055,0.03559151664376259,22.250558853149414,1271.2769775390625 +POINT (8263357.983003328 1743875.7286443193),0.0,0.0,Bhagwan Mahavir WLS,0,,,,,,,,,,,,,,,,,,,,,16.10894203186035,2127.662353515625,0.04621821641921997,17.30975914001465,0.1526447832584381,302.1591796875,0.0007093987660482526,0.5580284595489502,0.0005279999459162354,0.5351346731185913,0.002065878128632903,2.873239278793335,0.00010924544767476618,-0.3364294767379761,2.976552724838257,2.7044215202331543,-0.00026999067631550133,0.3948645293712616,1.5944931874400936e-05,0.6991877555847168,0.0001324988406850025,0.42298126220703125,19.64357566833496,-0.00029871624428778887,0.8567355871200562,153.84024047851562,-103.5877914428711,122.5150146484375,102.59803771972656,123.60143280029297,-177.8230743408203,110.93828582763672,2752.34375,-0.01963956467807293,0.8562390208244324,-0.02172987535595894,28.606542587280273,0.047052688896656036,21.228708267211914,3683.45751953125 +POINT (8262208.575725117 1722340.1442545843),0.0,0.0,Bhagwan Mahavir WLS,0,,,,,,,,,,,,,,,,,,,,,18.488798141479492,2254.447021484375,0.06798024475574493,15.237241744995117,0.07660377025604248,303.8547668457031,0.0013873365242034197,0.4027964770793915,0.001298371935263276,0.37126660346984863,0.0034248295705765486,2.805673122406006,-7.879693293944001e-05,-0.17699284851551056,3.0465664863586426,2.6813604831695557,8.304878974740859e-06,0.23076416552066803,0.0003515584103297442,0.5370743274688721,0.00021910453506279737,0.3595305383205414,2.486358880996704,-0.0004804693453479558,0.8124205470085144,106.87052154541016,109.65840911865234,86.9051742553711,288.0275573730469,85.73184967041016,28.89508056640625,122.27912902832031,2229.900634765625,-0.006978257093578577,0.7575232982635498,-0.089240662753582,30.87650489807129,0.05838431045413017,20.98579216003418,1346.8294677734375 +POINT (8268216.094035114 1699767.6163022227),0.0,0.0,Netravali WLS,0,,,,,,,,,,,,,,,,,,,,,20.098785400390625,2437.119140625,0.06117723137140274,15.88566780090332,0.05419588088989258,302.32122802734375,-0.0034292966593056917,0.586168646812439,-0.0036228422541171312,0.5623541474342346,-0.0053653595969080925,2.9337046146392822,0.0006765538710169494,-0.330517441034317,5.493683815002441,1.311958909034729,-0.0006473436369560659,0.38087964057922363,-0.0002861488610506058,0.6862678527832031,0.00013731681974604726,0.41406574845314026,12.32027530670166,0.0011977668618783355,0.8380826711654663,112.24737548828125,25.14895248413086,101.52494812011719,201.33995056152344,126.49259185791016,-132.6247100830078,114.27827453613281,2663.563232421875,-0.018778568133711815,0.8630785942077637,-0.04728643596172333,28.483383178710938,0.03317045792937279,20.61798095703125,1609.3275146484375 +POINT (8261914.391418096 1693897.497651467),0.0,0.0,Cotigaon WLS,0,,,,,,,,,,,,,,,,,,,,,18.47934341430664,2241.210693359375,0.06798024475574493,15.237241744995117,0.16176170110702515,307.7324523925781,-0.0006050251540727913,0.34639212489128113,-0.0009059908334165812,0.3194117248058319,-0.002578916260972619,2.9777512550354004,0.0014604933094233274,-0.014392639510333538,3.0465664863586426,2.6813604831695557,-0.0017964572180062532,0.06376056373119354,-0.0028071298729628325,0.33474022150039673,-0.0015445156022906303,0.28870752453804016,5.705350875854492,-0.0013294877717271447,0.6591832637786865,109.04148864746094,158.64561462402344,96.4761962890625,275.09130859375,99.33989715576172,0.6827641129493713,132.5497589111328,2100.493408203125,-0.011939688585698605,0.7012676000595093,-0.13611160218715668,32.90052795410156,0.05264192819595337,21.62228012084961,303.40106201171875 +POINT (8263909.235897399 1757370.1368968314),1.73538633992,0.01879725637,Mhadei WLS,0,,,,,,,,,,,,,,,,,,,,,14.390942573547363,2018.94677734375,0.0439036525785923,17.53630256652832,0.08149499446153641,302.8175048828125,-0.0013975020265206695,0.474925696849823,-0.0015468411147594452,0.44469207525253296,-0.002366456901654601,2.799790620803833,0.0011483229463919997,-0.2816760241985321,2.8782966136932373,2.7207159996032715,-0.0009173594298772514,0.33766844868659973,-0.0015682941302657127,0.6527922749519348,-0.0014227626379579306,0.4048529863357544,8.01537036895752,-0.0004765524936374277,0.8587448596954346,90.82323455810547,-35.93355178833008,74.20166015625,169.15623474121094,91.66905975341797,-118.74436950683594,72.75606536865234,2343.1435546875,-0.009933292865753174,0.8241186141967773,-0.036449797451496124,29.60443687438965,0.02505318634212017,21.14196014404297,730.724853515625 +POINT (8262362.489718652 1755018.4568685049),1.73538633992,0.01879725637,Mhadei WLS,0,,,,,,,,,,,,,,,,,,,,,14.390942573547363,2018.94677734375,0.0439036525785923,17.53630256652832,0.03142496198415756,303.45281982421875,-0.0014827440027147532,0.4383949935436249,-0.0015188012039288878,0.40593594312667847,-0.0028389994986355305,2.7554423809051514,0.0009085399797186255,-0.25706925988197327,2.8782966136932373,2.7207159996032715,-0.0007795853889547288,0.3147326707839966,-0.0013340647565200925,0.6265485882759094,-0.0010695678647607565,0.39006078243255615,6.727706432342529,-0.00010614396160235628,0.8567123413085938,132.548095703125,-114.87063598632812,108.6090316772461,105.14340209960938,116.8058853149414,-163.04998779296875,102.5054931640625,2104.55224609375,-0.013723395764827728,0.8177762031555176,-0.040492091327905655,30.01732063293457,0.02985774539411068,21.28335952758789,1373.65673828125 +POINT (8249931.350459967 1760775.636748233),1.73538633992,0.01879725637,Mhadei WLS,0,,,,,,,,,,,,,,,,,,,,,19.382850646972656,2113.504638671875,0.04423021897673607,17.48790740966797,0.029545117169618607,302.9104919433594,-0.003613844746723771,0.5001254677772522,-0.004038905259221792,0.4735713601112366,-0.006144895683974028,2.785489082336426,0.0005488945753313601,-0.3168671429157257,2.3598642349243164,2.984099864959717,-0.0008116583921946585,0.3762702941894531,-0.0003753374039661139,0.6772353053092957,0.0001778161822585389,0.40537917613983154,16.583110809326172,0.0013689942425116897,0.8611501455307007,85.36880493164062,23.46641731262207,74.41870880126953,226.47434997558594,92.56411743164062,-96.00615692138672,87.59800720214844,2541.54345703125,-0.009508254006505013,0.8198584318161011,-0.08530356734991074,29.307207107543945,0.047548215836286545,21.594316482543945,1899.874755859375 +POINT (8269318.507690475 1707379.795696858),0.0,0.0,Netravali WLS,0,,,,,,,,,,,,,,,,,,,,,17.89788818359375,2369.10595703125,0.05740818381309509,16.241392135620117,0.06905130296945572,300.50701904296875,-0.0004007233656011522,0.41838449239730835,-0.00039439747342839837,0.3886161148548126,-0.0020033728796988726,2.611588954925537,-0.0012847593752667308,-0.3109188675880432,5.316018104553223,1.480040192604065,0.0014299986651167274,0.3690909445285797,0.0011540093692019582,0.6599560976028442,0.0003755000070668757,0.38564735651016235,23.447172164916992,0.0006030931253917515,0.8903363943099976,156.51937866210938,-63.43372344970703,118.52243041992188,153.1845245361328,124.94973754882812,-94.90625762939453,174.11500549316406,1823.7177734375,-0.014450011774897575,0.8112300634384155,-0.03806174919009209,27.451581954956055,0.041838955134153366,20.81553840637207,695.3224487304688 +POINT (8259966.1680539595 1719973.3948550809),0.0,0.0,Bhagwan Mahavir WLS,0,,,,,,,,,,,,,,,,,,,,,18.488798141479492,2254.447021484375,0.06798024475574493,15.237241744995117,0.05193568393588066,308.4468078613281,0.001474005519412458,0.35266345739364624,0.0011926917359232903,0.32345667481422424,-0.0014158072881400585,3.0548453330993652,-0.003435651073232293,0.012658439576625824,3.1579742431640625,2.646435260772705,0.0031487776432186365,0.036309078335762024,0.004550579469650984,0.3015143871307373,0.002224079566076398,0.2748582363128662,3.6320528984069824,0.0031466535292565823,0.6190783381462097,34.50871658325195,358.1109924316406,32.470603942871094,457.40191650390625,37.144412994384766,181.57009887695312,122.44214630126953,2211.289306640625,0.0010763374157249928,0.6843958497047424,-0.14505799114704132,33.04738998413086,0.03633972629904747,21.37674903869629,283.7911682128906 +POINT (8265817.3241057955 1751275.445958411),1.73538633992,0.01879725637,Mhadei WLS,0,,,,,,,,,,,,,,,,,,,,,17.327150344848633,1999.3411865234375,0.05179499089717865,16.80307388305664,0.1449660211801529,301.3661804199219,-0.0007517781923525035,0.48456448316574097,-0.0008840722730383277,0.45647770166397095,-0.001572582172229886,2.738558292388916,1.0014326107921079e-05,-0.31905582547187805,2.557372570037842,2.877892017364502,6.414205563487485e-05,0.37510910630226135,-1.496169079473475e-05,0.6747738718986511,-6.448958447435871e-05,0.4017024338245392,13.997326850891113,0.0002332732838112861,0.8700960874557495,89.73117065429688,-38.852760314941406,79.18194580078125,156.68890380859375,92.74100494384766,-129.22171020507812,76.7036361694336,2286.57080078125,-0.007681361865252256,0.8217753767967224,-0.025719739496707916,29.000965118408203,0.030167387798428535,21.33575439453125,2339.03271484375 +POINT (8264088.733989524 1733604.3333529911),0.0,0.0,Mollem 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WLS,0,,,,,,,,,,,,,,,,,,,,,12.756263732910156,1992.9703369140625,0.0439036525785923,17.53630256652832,0.11348136514425278,303.02850341796875,-0.001171817071735859,0.4422684907913208,-0.0009368519531562924,0.40275269746780396,-0.0009333315538242459,2.7273383140563965,0.0004809203674085438,-0.26877641677856445,2.306614875793457,2.9972360134124756,-0.00022756030375603586,0.3246743381023407,-0.00011859893129440024,0.6291462182998657,6.225575634744018e-05,0.3839624226093292,7.389405727386475,0.0001431470736861229,0.8597451448440552,86.73094177246094,-37.36860275268555,72.25682830810547,163.1007080078125,90.2432861328125,-123.07562255859375,79.12460327148438,2201.614990234375,-0.00860116258263588,0.8227941393852234,-0.027500826865434647,29.456207275390625,0.04411881044507027,21.204824447631836,315.77777099609375 +POINT (8266781.082324734 1740243.032644107),0.0,0.0,Bhagwan Mahavir 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Risk,14.928257942199707,2073.187744140625,0.04621821641921997,17.30975914001465,0.13693293929100037,303.36480712890625,-0.00038638358819298446,0.5670881271362305,-0.00028922263300046325,0.5378729701042175,-0.0010197119554504752,2.93266224861145,0.0001318770373472944,-0.3094739317893982,2.976552724838257,2.7044215202331543,0.00018702403758652508,0.3630066514015198,-0.00019913772121071815,0.6689431071281433,-0.0005303230136632919,0.4049989879131317,16.590944290161133,-0.00015321587852668017,0.8304415345191956,144.1822509765625,-130.5375518798828,130.44337463378906,65.53976440429688,134.32403564453125,-210.98245239257812,119.50326538085938,2637.23291015625,-0.017921194434165955,0.8625810742378235,-0.023874539881944656,29.48159408569336,0.04022873938083649,21.570602416992188,1060.432373046875 diff --git a/fire_vulnerability.ipynb b/fire_vulnerability.ipynb new file mode 100644 index 0000000..b3ed683 --- /dev/null +++ b/fire_vulnerability.ipynb @@ -0,0 +1,1401 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Libraries imported successfully!\n" + ] + } + ], + "source": [ + "import os\n", + "import numpy as np\n", + "import pandas as pd\n", + "import geopandas as gpd\n", + "import matplotlib.pyplot as plt\n", + "import rasterio\n", + "from rasterio.plot import show\n", + "from rasterio.mask import mask\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.metrics import confusion_matrix, accuracy_score, classification_report\n", + "import seaborn as sns\n", + "from scipy.spatial import distance\n", + "from scipy.interpolate import Rbf\n", + "\n", + "# For interactive maps\n", + "import folium\n", + "from folium.plugins import HeatMap\n", + "\n", + "print(\"Libraries imported successfully!\")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "def load_shapefile(file_path):\n", + " \"\"\"Load and return a shapefile as a GeoDataFrame.\"\"\"\n", + " if not os.path.exists(file_path):\n", + " print(f\"Warning: File {file_path} does not exist!\")\n", + " return None\n", + " \n", + " try:\n", + " gdf = gpd.read_file(file_path)\n", + " print(f\"Loaded {file_path} with {len(gdf)} features\")\n", + " return gdf\n", + " except Exception as e:\n", + " print(f\"Error loading {file_path}: {e}\")\n", + " return None\n", + "\n", + "def load_raster(file_path):\n", + " \"\"\"Load and return a raster file.\"\"\"\n", + " if not os.path.exists(file_path):\n", + " print(f\"Warning: File {file_path} does not exist!\")\n", + " return None\n", + " \n", + " try:\n", + " with rasterio.open(file_path) as src:\n", + " data = src.read()\n", + " meta = src.meta.copy()\n", + " print(f\"Loaded {file_path} with shape {data.shape}\")\n", + " return data, meta\n", + " except Exception as e:\n", + " print(f\"Error loading {file_path}: {e}\")\n", + " return None, None\n", + "\n", + "def clip_raster_with_boundary(raster_data, raster_meta, boundary_gdf):\n", + " \"\"\"Clip a raster with a boundary.\"\"\"\n", + " try:\n", + " # Ensure boundary is in the same CRS as the raster\n", + " boundary_crs = boundary_gdf.crs\n", + " raster_crs = rasterio.crs.CRS.from_string(raster_meta['crs'])\n", + " \n", + " if boundary_crs != raster_crs:\n", + " boundary_gdf = boundary_gdf.to_crs(raster_crs)\n", + " \n", + " # Get geometry as GeoJSON for masking\n", + " geoms = boundary_gdf.geometry.values\n", + " \n", + " # Create a virtual raster source\n", + " with rasterio.io.MemoryFile() as memfile:\n", + " with memfile.open(**raster_meta) as src:\n", + " src.write(raster_data)\n", + " clipped_data, clipped_transform = mask(src, geoms, crop=True)\n", + " \n", + " # Update metadata\n", + " clipped_meta = raster_meta.copy()\n", + " clipped_meta.update({\n", + " \"height\": clipped_data.shape[1],\n", + " \"width\": clipped_data.shape[2],\n", + " \"transform\": clipped_transform\n", + " })\n", + " \n", + " return clipped_data, clipped_meta\n", + " except Exception as e:\n", + " print(f\"Error clipping raster: {e}\")\n", + " return raster_data, raster_meta\n", + "\n", + "def display_raster(raster_data, raster_meta, band=0, title=\"\", cmap=\"viridis\"):\n", + " \"\"\"Display a raster band with proper styling.\"\"\"\n", + " if raster_data is None or raster_meta is None:\n", + " print(\"Cannot display: Raster data or metadata is None\")\n", + " return\n", + " \n", + " plt.figure(figsize=(12, 10))\n", + " \n", + " if band >= raster_data.shape[0]:\n", + " print(f\"Band {band} out of range. Raster has {raster_data.shape[0]} bands.\")\n", + " band = 0\n", + " \n", + " # Create the plot\n", + " show(raster_data[band], transform=raster_meta['transform'], cmap=cmap)\n", + " plt.colorbar(label=f\"Band {band} Value\")\n", + " plt.title(title)\n", + " plt.tight_layout()\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loaded data/fire13_19.shp with 197 features\n", + "Loaded data/fire20_23.shp with 70 features\n", + "Loaded data/pa_boundary.shp with 6 features\n", + "Combined fire events: 267\n", + "Fire events 2013-2022: 216\n", + "Fire events 2022 only: 4\n", + "Fire events March 2023: 51\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "data_dir = \"data\"\n", + "\n", + "# Load fire event datasets (2013-2019 and 2020-2023)\n", + "fire13_19 = load_shapefile(os.path.join(data_dir, \"fire13_19.shp\"))\n", + "fire20_23 = load_shapefile(os.path.join(data_dir, \"fire20_23.shp\"))\n", + "\n", + "# Load study area boundary\n", + "study_area = load_shapefile(os.path.join(data_dir, \"pa_boundary.shp\"))\n", + "\n", + "# Combine fire events (equivalent to the merge in the JS code)\n", + "if fire13_19 is not None and fire20_23 is not None:\n", + " fire13_23 = pd.concat([fire13_19, fire20_23], ignore_index=True)\n", + " print(f\"Combined fire events: {len(fire13_23)}\")\n", + " \n", + " # Filter out 2023 events (similar to the JS code)\n", + " if 'acq_date' in fire13_23.columns:\n", + " fire13_22 = fire13_23[~fire13_23['acq_date'].astype(str).str.contains('-2023')]\n", + " fire2022 = fire13_23[fire13_23['acq_date'].astype(str).str.contains('2022')]\n", + " fire_march_2023 = fire13_23[fire13_23['acq_date'].astype(str).str.contains('2023')]\n", + " \n", + " print(f\"Fire events 2013-2022: {len(fire13_22)}\")\n", + " print(f\"Fire events 2022 only: {len(fire2022)}\")\n", + " print(f\"Fire events March 2023: {len(fire_march_2023)}\")\n", + " \n", + " # Plot the fire events\n", + " if study_area is not None:\n", + " plt.figure(figsize=(12, 10))\n", + " \n", + " # Plot boundary\n", + " study_area.plot(ax=plt.gca(), color='lightgrey', edgecolor='black')\n", + " \n", + " # Plot fire events\n", + " fire13_22.plot(ax=plt.gca(), color='red', markersize=5, label='2013-2022 Fires')\n", + " fire_march_2023.plot(ax=plt.gca(), color='blue', markersize=5, label='March 2023 Fires')\n", + " \n", + " plt.title('Fire Events in Study Area')\n", + " plt.legend()\n", + " plt.axis('off')\n", + " plt.tight_layout()\n", + " plt.show()\n", + " else:\n", + " print(\"Warning: 'acq_date' column not found in fire data\")\n", + "else:\n", + " print(\"Cannot proceed without fire event data\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loaded data/inputResampled.tif with shape (40, 2912, 944)\n", + "Input raster has 40 bands\n" + ] + }, + { + "data": { + "image/png": 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", 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", 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9PR1ioXU8TbRzz1IUV5yBr/s4hLXvCRcXV8mueyksLIDGprhZn8tSsT0D41BR/hw27piF8SNeEe6SCCFNl5ycXGeoTdtKf0tqtRqrV68W5oDc3NygVCqFHtKECROE+R+xUOBpotLKy/CN+BsmwzqcvOKG4twIhPg8iW5d+0Fqjp9eCdfQhu+UGoeHxrYUjn5rkJ3zMNVyI5Jm5HnhISXV7WFB53ZzPB4eHkJgYFlotbGP2ZxMQ9jztzu+V69eOHPmjDDPxHo8np6ewlxS7969IRZp3qpLHi+kHjt6ZMKvwx5czvhOcllgOTnZ4JxXNHERacOKssPg4+1nlnYRQm6+Bo8FiV27dtU8xxIB2McDBgxo8HPY87WPZ3bs2NHg8WyIjwUdlkp94sQJTJs2DWKhHs8d0lXYQWkKEbXbeqPy8nIcOP4f+HRMMcPZOCj0PYUUbUJIy1qwYAEeeughoTfSt29ffPnll0LW2rx584TX586dC39//5o5ohdeeAHDhg3DZ599hkmTJmH58uVCUPn+++/rrPNhAYfN9Zw/f174HJZiPXbsWNG+T7qa3CF9hS3athknjKVKAQuAu/Yvhk+H1Wbp7ZhMCjjZh5ilbYS0pOYu2GxJTW3PrFmzkJ2djbfffltIEGBp0Vu3bq1JIEhKSqozpzxw4EAsW7YMb775Jl5//XWEhoZi7dq16Ny5c80xLImABTQ2BOfr6ysEr7feegtionU8TbRu6//BO/ynWs9wyIp+ElPHvwKxZWdn4ejpZXAMXNLktTu3khE/CJOG/SKZ4EpIgzuQxvhJch1PUMc02oH0BtTjuWM8THyp0NOoSl9u5a/O80LAOX7mLyhdVsA9NM0sPZ3aXHzP4fKVGHQI6yJ8vcSky4iO34HOHcYjICDYrF+LEGL5KPCYgXPAOuzaG4qhg2YJE4StIT09FVHRO1GiPwp7j9NwC8sEx7VMCXYbhxJciP4BZWX34mr633D02wfX9nk4m7gaBsMitG0b2iJfl5CmDmsZZT7UZi0o8JiBrWMRjDb/xtbdqZg89l8tuq6HZc9t3b0IavflcG6XAUehk9XSb24ePqGbUWraAt8Ifc3X8wiIx8WUp3Al6XEEB3aHu5u3MJwgRs+PECIfFHjMgodSrYNz0O+IPNoZgwdMbpGvEh1zDrFXVsM9ZDnU2tbdN4elj9cPpzzc21wCz7+K5DJ7xMW6oLwwEIHuT6BH98GSXVhLCBGXytImGbMyzZFCfHN6vggFBvuGX9QA+YUbAExukRTpU5d/hHuHXSiFErhZG8SiNQHaPNi65yO55F9I3DEfDrZeyCk+AV/XIQgN7oQ2fk2vG0eINWW1WQuLCDxxcVFIu7ISbdwPwdczA1wL/rL3+7bDr6kjbvp615I2Zv+av235CzvLz6HQGTClDoccGDVR4IyAwtEIY+Um2J3YjVeCZqJPt15iN40QIjKLCDyOjq6A0gseLjmw1dav+mpORgWHMuPNEwj0JoWwfXTtarB3gp0rsiQGqR5lAOvpGJWQlWvt1Tnq8U3CWnweEAx3N3exW0UIEZFFDML7+vpj2Kincfay+PvHJNjmIisn2yznYqnLv+5YiUtuBZA7HjxSXUux6lDd3REJMXetNqk9iIUGHoZlUqkURWI3A5VqIzJzG94tsKkKCgqwnb8Ak4X8lthO3If0sSgrY703Qoi1spBLGoSLmbvjFbGbgUotj9Vxe4QqsHeKpSb7VzjCkjgatLC1tRW7GYQQEVlM4CnIz4W7c64khpRibbLx/LqPcDrq7B2di5VIH+gSgRZaFyoKZ9jSOh/SIkwSfRALDjy5OSlwdiiBFBQ66JHgUYgvYldh3Z7NN93Bk83hsAWhLA28ISUlJXC0sW/mHqLSFGzb8L4ihBDrYRFZbYx/QBiOnJiE8KC9cHMqAsfx4k+mu5Xif7pd0EZqMHbQSKSkpcLBzh6l5WX448h6ZBoLhSNzVKV4yGcMuoZGwMnRCRfio7E59gCilRnIt9OB17DQYxmTlJmVeWI3gRAiMosJPG5u7hg8+hMkJ19BbtIzCAu6BCnQa3h8W7wNG1cdQbJdEWyMSlSojCh1NQiT7QwHDh+Xr4dL5FY46jVIdS6D0Y0HL3LwbAkJXNYdp5unpafD1cVFmCvKysqGWq2Cq6urWdtJ5McowVptUmuPVFhM4GFYiZagoBDsi+kpmcDDlNoaEW1bdadfAkODvSNeAeQ56ZCHO09KkLJMx3LEX0lAl/Dr+4U0NYnkvYPfw83gCGWpM47mFaKfmzP+Pf8RmjsiRCYsZo6nes4kKysTJs4NfHV3gkiKQcXjbGJMsz9/74lDuOJaiP22uViRVwiWx7g1twDxly+btZ2EkJZjUT2eE8e2o43Du+jVvhR08ytN7H4guzy/SZ+TmJSIPVGHEezeBiuzDsDofq2XeO31QnDYd+YswkJop1RrZuSrHlIitfZIhcUEnuysTDhxn8PbPUfsppDbyNYXNnrjvOLiYnxw9Gdcdi8CSo8Dbg0HM6OJElcJkQuLCDzsInbh9LcY0lX8BaTk9lKUhYiOj0Vwm0DY2dnVe91kMiEpJQnHYs9iZ8FpXHYvvpaI0fDto4IHPBwdWr7hhBCzsIjAEx8XhYjAzaKnUJPGSXMtw4KrSxBy1gVvDH4Ufr6+dV7ftH87vi/fhXKtCbzH7X+nLCiVVrRscVgifVJcsCm19kiF7JMLjh/dBpuKp+HhwtbEEDlgaeJ6NY9Y9wL8dGiV0MOpdjXpKlbmHkK5janR6eTsqJgs8xRmJYS0PFn3eFg9NI3+O7QJyrSYBZbWhAWWSMdE/Lb1Lyg4BQoqS3CET0CWa2Wt1IHGuVRSIlSBUKlk/ZYmxCrI+q80Kysdfp5JFHRkrFJjwlLTMbaKFvwdbGF0WW9Adk4OfH2oJI+1MoGDUWIFplibiIUFHkdHF5xLeAHGxBzwqMCQTr9CoWy4LhqRLrZ49k4VcwpcSkqmwEOIDMh6jsfZ2RlDhj+I4WNfglLlDVBygdUy8cCeixeF4bbKykqhrE5ODqXWEyJFsu7xMKzy8/GjmxHq/T0UCsohsVYss21VZg5Ofvk19OCRZuTRUaPGr888CY3m5luVE8u6+WAPKZFae6RC1j0eJvLAMnTxfw3e7lT12NqVchyiTDxiTWzojUOsTo+MTPPsBksIMR/ZBx5edxa2WraGg24tSF0s+Bw6d2eb8RFCzE/2Q22E3Ay7Fdl56QpmmUxC5XJi2YwSzGqTWnukgv4aiUWLr6hEQUGB2M0ghFhS4DFxAVUzy62AbbXA6RVwKFWBa+BrstyGNnn2aJvjCJsK2f9oLUIWz+FMdLTYzSCEWNJQm71TR+gNKqjVerOd02BQITPfDe5OhbDR6sDzwKWUYKQXzcQA3z4YGm6Pt87/hCJbPYxKHiYFD7VegeElIXh+4sNCFtU7q7/CYZsUs7WJNI+RA/4+fRadQkPh7eUldnNIC6KhNvmQfeDp1mMEDu2dg/4dl0KrqQo+er0amXnuwhi/nbYCV9M7wsstEQHeGVUj/zwHnV6JwhInFJU5oqjUAy6OWXB3zoWjXQVOx/dB206fIDEnDVkJJ2EyFqJT9wcR5uVd83W/cngRZeVlSMlKQ35pIcLbhyI4IKgmdbeLc1scMaWYZXEkaT72HtheVoH4X5diQd9eGDN4MJRKpdjNIsSqcTzbU0Dm9Ho9ThzdAFv+Dyg4HpWaJ9A+tI+w30tpaTG8vf2QEB+FnJQfYOAioFDawNU9Ah6eAXBxcRPqe7FzpKUlIiPlFMLCR8HL+85WwC/ZuBQr7E7VGwV0KFNhCt8NV8ozEavNQoEjW3Ui+19BqysvtEFWnEeTPkfLA3N8PfHqg3Nom2wLUlRUJCwmj7zgCwdHad3plRSbMLBTOgoLC+Hk5CR2cyRD9j0eRq1WY8Dgu6DTTRYWlNra2ta85uLiKvy3Y3h3IPybW56jfftw4WEO9mpb2FUoUWZrqhNYwkrd8cg9s4Usq/PRUXgv7nfkO9UdJmTzR06lKtgYlMhyrmx0lWZyazoAeeXlYjeDtBATzwkPKZFae6TCIgJPNSmtUJ899i50ig7Dr1Ebka+outhx4DCt7dCa1N5AvwA4XtQiH3UDT2iuMxb0uh+ebh5YsPNzJLqXiPI9WBpfjsezEyZQb4cQkVlU4JESdnHrFtEFn3bsVGe/mdpl+9cc2oJk19J6nzvCpRtC27UXdla146UTTOWMzeo81akjggIDxG4KIVaPAk8LY72bmy1eTCvPhULNw6iqGl5TmHgojQrYXxsqLCsrgw5UbftOsZHK/jZqTBsxQuymkBZEWW3yQYFHRP+Y/Cg6H96N7NJ8qBUqdPIPhZ2tLdoHhwiTkR9s+w5X3Gln1TvlBh6vjB8LOzs7sZtCCKHAIy6tVoupwyc0+Nqve1bhlEdGa62NtVjsxzcnqA0iOnQQuymEkGso8EhUoJMP7MtjUWpnpHTrOwg6nRUc5k4YL3ZTSCswQiE8pIQGyhsmrd8SqTF95CR8HDYfXbM9aX+7ZnLgebw6cpiwxoMQIh0UeCSsY2gH3B0yHAoTjbc1Gc9jmqcb+vboIXZLCCE3oKE2iTuWdB4mumFvEtZDbKfg8PSUybRmx4qwIr5SW7DJ2kTqo8AjYcmpKTikvFSz+JTmem5NxQN9tCp09/TA9IED4OXpKXaTCCENoMAjYXFJl+Ck12B8WRecKE9AgkcRBZ+bYPeVszxc8er999UpmUQIkR4KPBI2asAwDNUPrKojd/QgPiheA74RhZXZvkC2lUqU2RitIh2bfYuj7Wzwj5l3U9CxYrSAVD4ouUDiWNBhekV0w/CCtrArV9bbhI5VvB6Z2w4hOc5wLFFhfGFHfNXpGfTO9QV3vVqPxc7nDLfR4OO5c6j6LyEyQT0emXB0dMTrdz+DuEvxWHVmO5L4XOH5tgov9PGLEHpHOp0OuXl58PH2FibV33B7Eh9sWYwT7ukWuy8Q23Xp1SmTKOgQIiMUeGSEBZMO7cPwRvuwmsKjtevAsercvj4+dYLVGxOexJtb/osMTYmwvUIe2//HghYGOXKAv6+v2M0gEmDkFcJDSoyW86dmVhR4ZMRgMNQUHb1Z4dEbseAzp8N4hAS0RUVlJf7v4BIkuBdaTPDJ5DlcupqITh2pJA4hckGBRwbSMzLwy6G/kWzKhQcccXfEaESEdayZ/7mdPt161fz7nSGP45+RXyPD1TI2RGPfxe4zZyjwECIjFHhk0Mv5Yv9vOOWRKfRS4lCIY6m/YHRsGF6a9iiUSmXjdkI0mYRzsfmfYKMbMpAKS8DyLDYkp2JeSQkcHBzEbg4RkQkcTBLLl2L7D5P6pPVbIvXEX0lAlHNWzdAYW8ejV/PYZR+PAycON+oc5eXl+Gj1t/h0/RKkpqchyjZTWJDKsuPY/+QumQe+XbMWmVlZYjeFENIIFHgkztXJFVp9/V+TTmPCrqTjdZ6rrKwUqh1UVFTUCzzHbJKxz+kydp45AAe9GvZlSswo7gKXIvl3etnG4UvSszDzl9/xxg8/IjouTti9lRAiTfK/6lg4by8vzFD2xjL9UaGnU9tlVQ5KSkqEbLddx/djffYRZNmXwaVCi9G2XfHg+JnCaxW6Spg4EwxqHstxHL1L/THSpxd6RXRH+6hAfFG6qd655Ya1Pg0c/sotxJY16zHAwQ53d++GorJynEhMRPc2bdAttD0C/P1hY2MjdnNJC6AFpPLB8XRrKHlsfmbptlVYpqwbfJRGDuOLOyDKmIIUl1IYlddf0+oUmKccirtHTsaaPZuwI+8UEtwKhfU8bNROZVSgb0EbvDntaXy64Xvsdr0iq0y38kIbZMV53PIYlnph5HmYuKoBRVse8FcCfVxdMbZLJ/Tp1k3YjI/IW1FRkbD1xfpzIbB3bNycZ2spLTZiatdLwo7CtNbsOhpqkwGWOj1n3D2YXtkdylpbJJiUwGbnWCS51w06TKXGhBVlkcjNy8Xdo6bgifDpUPDXJ+T1KhPO26YhKycbT418AB5FlncBZkNwLOgw7Fsv44B4E/Bnbj4e33sQs775FnsPN26ejBBiPhR4ZKCgoADLd6zBAUNMnSwZlmjAeik3KxxaYK/DpmO7hH/HpV3Gjdv6FNsZsOvMQeFukRUjtRbsp6UDcNHA43JGptjNIWZeQCq1B6mPfioSH2LbEbkHz+z+BD+pI5HhUt6k0jfs2OOl8UIa9dnCy/U+l/V89pRHobS0FO68AzQ6BZQGWAU2qjjWwRb3jRsrdlMIsTqUXCBRLFgs3vQ7tmgvosK1+Tu3p9uUYO3ezTjvkNHg62lOpfhw63d4qMcUzFepcOFqLBYZd9UburM0KvB4aNAA2NnZid0UQqwOBR4Jr9/ZrL2ASu2dlZcusTXgR+N+6GwaPo9RBRx3T0f8xZ9wv8MQjOo9BCl7s7Cbi0ahg0FWCQdNoQAHOy1lt1neAlJpZZFJrT1SQUNtEnX+agx0mju/6LOeC1vzcyssuBQ4GbCY34P3tn2LkRH98Xn3p9E2z7HeFgyWQgkepRWWUTaIELmhwCNBObk52Fh8omqHs1bCEhRYkDrnmYOvzvwFXx9fvDvocfTM8RI2lrM05RyH13bsxoXoGLGbQojVocAjMazKwLID65HmUibKNtfsa2ZrS4V2+Pv64b0pz2NiUQRcizVQGRTCpLwlYN9GEjg8vWETdhw4IHZziBmwOm1GiT2kVjtOKmiOR0Ly8vLwwc7vcd4tWzJbVrOtpF+cPh9z8/OQmpGGCynxKNGV4Xx5ImJc82SdhMBangoOHx0+hs6hoXX2MiKEtBwKxxLCqit3sg+Eyihu1DEoeZSWldV8zMruuLu5o2tEF8weexcemzwHrwye12ANOTlK4oEv12+o2VyPENKyLOPKYSHYDqIPjZ+FjoXuolaNLtcakZqVdtPXo2IvYvnhjTBZyLgbz3HYml+EyBMnxG4KuQNiLxSlBaSNRz8ViWH769wXNgbaCun+avbEHMFm1xhU3GGqt5SUchw+3n8I+fn5YjeFEIsn3aubFWM7hj5uMwKds93hUqyWVEqzXq9HjM4yNpG7UYyJx6J162E0Nn/BLiHk9ii5QILYnMq0ERMxxTQe+QX5WLp/LTY6XoCplW4T2LoenZ5VM6tfBfjwueO45FwIS8QGDpdl5sJ55So8Om2qkFhB5MMkwSwy2oG0YdL6LZF6VanZpP68kTPRpsCh1eZ92JzHocQzQin3amwI6p/bvsAXZZuFfX0slY4DFiWm4B8//iwEWkKI+VHgkQG2j8ejQRNgU6FoleDDejy7nC7hiT0f4dDJI0LduPWHt+Oye5GwH5AY64tak4HjsKOsAq/8tpSCDyEtgDaCkwn2a4o8eQSfp61FgSPbaablsSDHtsj2LbNHsmOxpJIJGrMR3J1iIX6ojQavT5mEkLZtW/RrkTvfCO73011gJ7GN4MqKjXiwx3naCO4G1OOR0bxPh7ahrVq0k/VsSuwMiPcolFTQaS3sJ72/XIenV61BbEKC2M0hxGJQ4JGRqIQYlNhSxlVrYgmFl3ng+TXrhbpuNEBAyJ2jwCMT7IK3P/mksN01aV0s1LDgM2/DJmzes1fs5pCbELsu280epD76qcgo8OSbxCkcSqqCTx44vHX8JDbt3iN2cwiRNQo8Mkqt7u/a0WKqQ8tVEafAW8dP4evlK4TFtISQpqMFpDKi5GicTQqKOA7fJqXCfdt23D95ktjNIdeYeIXwkBITzQk2SFq/JXLLobZz+ZdYepvYTSFsrQ+AP6LjqNdDSDNQ4JGRQFsvKA0UeKSAr37QHS0hTUZDbTJax/Pw+FnQ7lBjQ9kp5DnqJLNZHCFSIMUsMiMlAzVIWr8lcksqlQoPTrgXXw94CTNLe8C5RFqVqwkhpDEo8MiQt5c3npjyID6KeBS9c3wo+IhEyVX1RAkhTUNDbTIWFhKKSXmDcKL4b7GbYpXCHR2gVqvFbga5hhV1MkrsJsz6Ck01DvV4ZM7V0QVqvbT+2KyFWkF/PoQ0B/3lyFxwm0C4lWnFboZVKjcYKKuNkGagwCNzDg4OeMRnLCJy3OBarIFNZevs2UOAnUUl2HnwoNjNIDfsQCq1B6mP5ngswKgBwzDCNAQlJSWIio/Gp6mrUeRooLpuLayM4/DB4WPw8/RCp44dxG4OIbJB4diCarmxjaYG9uqHVwNnwqlYRT2fVpAKDu9v2YqMzEyxm0KIbFDgsUB9u/fGKwH3wLmYOrQtjfUpT+iMmPvbHzgTFSV2c6yakVdI8kHqo5+KherXow8edh4JhZF6PS2NZfBe4YEXN2/D6fPnxW4OIZJHgceCjew9GN5FtmI3w2p6PmzY7cnN2/HGkh9x6tw5VFRUiN0sQiSJxmIsmL29PUZoI/Cn6QSox986wSeX4/BXfiHWbdmBTjt3Y1JIO0weMhhubm5iN8/imcAJDymRWnukgi5HElVZWSlkqbGHwcCK8DfP1H5j4F5M63xaOwBVcBxOGkx4PyYBc376BXsPHxa7WYRIBvV4JKS4uBgHTh/B2ew4xPOZKFLrhOe9dfaw5zW3/FwFp0CYnT9s1Tb1XnPT2SIHlS3WbnJzJg6IMwH/2B+J/yuvwKSRI8RuEiGio8AjAWz1e1TMBXxx8S8ku5TAdMOoTD6qAtDtHEd6wy94mqGR5I4UchzePn4KNloNRg0aJHZzLJIUs8ik1h6poMAjssysTOw6fRDLDUdQ5maiJZ8WrAgckrKyxW4GIaKjwNOKSktLhfmaopJipGWl43RyNHbx0ch3qMRtRtKIhfyxhbXxF7sZhIiOAk8r2bR/O5bl7kO5ygC9woQKGyPgUDUHQKyDL8ejSwcqrWNdO5BKqz1SQYGnFZhMJmzIPIwMj3Kxm0JEwu4v+rs6C2WNCLF2FHhakE6nQ3ZuDuKSLqFY2bgEAWKZ2H3vqIhwsZtBiCRQ4GkhF2Kjsfjs37jiUIhKrRG8C42pWTMn3oSIkBCxm2HRTDwnPKREau2RCgo8LeRA3AlEe+bX2pqA8tWsmSPHwdHBQexmECIJNPPVQvwcPaBqfsEBYkHYPW8nezth0z5CCPV4WsykIWORv70YvxWeR76G9Xeox2NOpmI1VCWQNI4DHDhgvL8znps2VezmWDy226fUsshoB9KGUeBpIUqlEg9NuBe9Yrpg4aEtOKquhFFBA27moipWwLZY2uPnNgoe74zvhkmjR4JjUYgQIqDA08I6dwzHksAgHDt3BkcTE7ChJA0pWgo/lo7jeIwOdMSEkcMp6BByAwo8rcDOzg7D+w8UHp4b1+D/CmLEbhJpYW20PJ67d7LQ8yWtw8QrhIeUSK09UkE/lVYW7O4FeyNPu3RYuDFhvvDz9RW7GYRIEgWeVja83wB82X4I7CnjzWKpOKBDAAUdQm6GAk8rUygUGDVgEP7hEQ5bI831WCpvD9pxtLUZwUnyQeqjwCNW8OneC44szY1YHA48ze0Qcgt05RNBfn4+/rNzPbLV1OOxRC4qHkG0/QEhN0VZba0sIzMTL25YimM2OlrTQ4gZUVabfNBPpZWDzvMbluKoVgeT2I0hLabAwOFqcorYzSBEsqjH00pOR53Hv45sRoLWBCpYa9lYsjzbg4kQ0jAKPK2guLgY/z6yDQk2JhpeI6SFGK9ltkmtTaQ+GmprYTzPY8nW9TirpTkda6HieGg1GrGbQYhkUY+nhXcg/Xv3dvxclgSjUlp3YqRls9oCKauNkJuiwNOCvl73F74rT4bOwpd0sJDqYATUJh4lSkDPcWCZ4j56Dsla69kQgtUC1XI8evo60t47IqCsNvmgwNOC4kvyoVPDojkagMkqd8wdOBwerq64nJyME1fj4e/shryyEvzbgguistI4NhwvRF4/Ww6D2nphRO+uCA8LFRYJE0IaRoGnBU1u1wn7rx5FmQX3eKaoPPDBA4/WlP739PBEvx49hX+/8vt3Vd0hCw0693V0x+wJI4Qg4+7mBltbW7GbRYgsUOBpQROHDMfV3Cx8UXoZRgvbk0VjAoYZ7DG1R8+b7jfDhth4Cw06syM88OLcWdBQEoFkGHmF8JASqbVHKuin0oLYnfAj46dilMHB4m782SqVu0K7on+PXrC2jDUWdF566D4KOoQ0EwWeVtgE7pl+o9C+UgGNyXL24WH1TVfEn73pQkmj0Yg0XRksjZuaxxMzp0KttvDJO0JaEAWeVtAtohNW3/s0fooYjY4VljHhw4bQznFlSElLbfD15NRUXFBUwNKwGweqPC3hihESe1jOraZ5UeBpJY6Ojhjcuy/623nAUuSreGw4Htlgb+f7/dtQSJ0CQkgDKPC0svv7D8M7LuG4y+QCFS+/N0vtNwzPccgpL6133Jo9O7DalEOFUAkhDaKstlYW2i5EeJSUlCBjxfc4rK1s9cwv1vlXm9hCTxY8Gne8nZHHZIUHDujykKatavHNPnVjUgwqaN6dtDLKapMP+qmIhK1s/7+R0+FXybV6GvR4vQN+6DgS4ZVKIXg01ALu2rEsKWK6yQX/Cx+F16fOqrlTYa8PrLTBvX0G13wOSzTYcyQSF1EOS+Vho6RsNkLuEPV4RNS+bTu83r4f/nX1MEpbqJZbdQBxNnDwMSnxQEAnzBgxWrh42tvYYN35E9hRmYX0G66lrEf0vk83TBo0TMjMq16rM0DjimTkQckDT3QZgPDQMOH5zKwsfLtjA1YZMlGmsdwJVU87DQUeQu4QBR6RTRgyDGfSE/GjId3scyJaE/CA2htTuveBn5c33Fxd66QB9+raDT06d0Hmr98gHXXnagwKwN3BCfb29nWen9mtH9ad2QIXA9DOP0BIJFi/dxe+uHoKKVoeJiqGSkRi4jnhISVSa49U0FCbBBaZPj1uGrpVqM2WeMnO464D3vPuijfufUgILt5eXg2uPRHKvajrl3rheeBIUgL0en2d5zuFdkB7nRJPeIXD18cHSzauxmupJ5HEgk4T22hvhDDU6KMDWMkzQoh1oMAjAa6urvhm4v0IqeDuOPiwC3hwBYfPOw3HfeMmNWrNiYGvHzLYjdqvunT8+6/fYDAYap5n9cj+0XkwZg4fjRPnzuLLwgRUNONd5GQAFkeMwtaZT2PluAcxRGdDb0ZCrAT9rUuEv58f3us+Cm0rFY0KPjcmBQhbExiAe+CGPyY9hGH9BjTq67KgojOZEF6hhJOeh9Z4fcmbTgEsN2ZhxY4tdT5nRP+BQnLEsStxaE5uBPuUcUo3DOrVG05OTgjwb4Pneg+HknWzCGkmIxSSfJD6aI5HQgb17oPffP3w3ra/sU9VKlz4b8Qm9dn8SjfeDoFaB0SW5yBdZUK4QYPnuw/BgB69mrSyXqVS4bM5jwtzNVnZ2TiTEIsFiUdrvjb7749pFzCjbJSQZFCbs40t1CWAronBx0UPzBtcVdW52s0KjRJCLA8FHolp4++Pbx54An/u3ILPcqKFjdUUPGBrBDobNZjRJhyDOncT5ldYgCkqKkJOXh4C/P2bXT+MnYc92NdmHC4fQZ7ieiAo5HgUFhXVCzyzx0xE0YZyLCm6gsJGvpNYqHnIKQgd24fWeV6tVEHBRvyoGg0hFo8CjwRptVo8NHEaOpxug1OJl+Bp74TuIWFoGxhYL7iwoSr2MBcvT094mZTIq5UqUKwCMnKyhWBXG2vLs3fdh3b79+CNS0dQoL71Nggs6PSv1GLuxPH1ejjBbQLgekSBDNalkzDqmEkXZbXJBwUeiWIX5gE9ewuP1sTWqISrnRCLgkZXVJg4dITw31sFHzXP436FN16YMQNubm71XmfDbnIYDe/oZzm19ggRixz+1kkrmxLaFYFNzLBjwefD9gOE+ZsbP48lPTyi9scbMx9sMOjIiZOdjdhNIET2qMdD6hk5YBCCfP3w66HdyKgsRYqpAs4Ojrf9vAlDhsPDyRnnk67gWE4qYo2l8OO0eLxzfwzp069OMsGN2LCdG69AGoyQ8h+Lu7P5hjWJeZmgEB5SIrX2SAUFHtKgkOC2+L/g+eB5HpWVlbCxadydfp9uPYTHPJ5HRUWFEFBY5tzNUrkzMjOh0ajh5emFJ0N7Y0HS0SZnybUWlYJHu4CqBAxCSPNR4CG3nWtqbNC58fPYYtNb2XP0MF6OOwgbE4eH3EOgValavVJ3U7B4qKBN4Ai5YxR4iCiKi4vxXfRRFAgxjccnxfFQ8xz0Eh6Z8NZy8Petm9lHpMPIc8JDSqTWHqmQ8J85sWR/79+F01p9nU3lGlowKxVKjsc9PdvWW8tECGk66vGQVsU2wIu9fAm/ZMTCqIVsBNlymD6mKm2cEHJnKPCQVsESCbYc3IdvE07iisaIShkFHdbbmdU7DM7OzmI3hdwCLSCVDwo8pFWs3bMTb6WdQrmNvP4QWaWCjo4KTB41VOymEGIxJDyqTixFfn4+vk06i3IZbhKn5ng8MaavWcsSEWLtqMdDWtz5+Dgks/23ZaibqwqD+rZu2SLSPDyvgIlXSK5NpD76qZAWd+xqPAzy6+xAy/F4cHhvoX4dIcR8KPCQFqXT6XCoKF3SC0MbmtexVfK4p6M79XZIq1u0aBGCg4OFhdv9+vXDsWPHbnn8ypUr0bFjR+H4Ll26YPPmzfUySZ999lm0adNGWNQdERGBxYsXQ0w01EbMTq/X41z0RegMeqTn5yFOzdbryKPLw3E8wh05PDV2AIb079ukTfWIuIzghIeUNLU9K1aswIIFC4TAwILOl19+iXHjxiE2NhZeXl71jo+MjMTs2bOxcOFCTJ48GcuWLcP06dNx6tQpdO7cWTiGnW/37t1YunSpENC2b9+Op59+Gn5+fpg6dSrEwPGsGBchZsLqun217i/8WJ4Mo4IDDx4mjv3XvFRFCtjGa1okdXrhpJ6YMGq42c9NWgbbDJGlus/fdy80Ds3bDLGl6Er0+HHYXygsLGxUgkq/fv3Qp08ffPPNN8LHJpMJAQEBeO655/Dqq6/WO37WrFkoLS3Fxo0ba57r378/unfvXtOrYQGIHffWW2/VHNOrVy9MmDAB77//PsRAQ23EbK4kJuKh37/BDxUpqFRywryOsQWCTksygcOBqHihOCohrT0sffLkSYwePbrmOVbRnX18+PDhBj+HPV/7eIb1kGofP3DgQKxfvx6pqanC+3rPnj2Ii4vD2LFjIRYaaiNmczj6PI5qdZDzmjkWb06mFwl3qC4uLmI3hzSBiZfegk3Wpupe2Y27DGu1dVdR5+TkwGg0wtvbu87z7OOYmJgGz5+RkdHg8ez5al9//TUef/xxYY6HVYpnwWzJkiUYOlS8tWnU4yFmE52bIZepnFvK1HE4df6C2M0gFoQNl7HhwOrHwoULW+1rs8Bz5MgRodfDelSfffYZnnnmGezcuRNioR4PMYvy8nJEleeDt4ANOtlw245TFzFi8EBhewdC7lRycnKdOR7tDb0dxsPDQ0hmyczMrPM8+9jHp+Gq6Oz5Wx3P/i5ff/11rFmzBpMmTRKe69q1K86cOYNPP/203jBda6EeDzGLv/fuwAWtAZaADbcdTytCSmqq2E0hTcAWj0rxwbCgU/uhbSDwsPVibNJ/165d178nk0n4eMCAAQ1+z+z52sczO3bsqDmeZZiyx427/7IAx84tFgo8xCyOZ6VIdufQ5sjSc3jlh1XIzc0VuynEiixYsECYf/n1118RHR2Np556SshamzdvnvD63Llz8dprr9Uc/8ILL2Dr1q3C8BmbB3r33Xdx4sQJYd0Ow4LcsGHD8PLLL2Pv3r24cuUKfvnlF/z222+YMWOGaN8nDbUR0gCe53ChkMc7P6zAtP5dEN6+Hdzd3G67qyohd2LWrFnIzs7G22+/LSQIsLRoFliqEwiSkpLq9F5Yxhpbu/Pmm28KQ2qhoaFYu3ZtzRoeZvny5UKweuCBB5CXl4egoCB88MEHePLJJyEWWsdDzOLTlUuxqDK51VKnW2odz43YFI8CPGwVgI8Nh3BPB/RqH4j2gf7o0imi3hAGEW8dz4N7ZkPjIK3yRroSHX4f8Wej1/FYC/qrIWYxrksvOFjGFE8d7LaMbV9cYuSQUApsuFqC/9t5Ec/9tg1ZWdliN48QWaLAQ8yiU1gH3KX2Yl1osZvS4tiUrF7YdEyeFbcJERsFHmIWbMhpere+0JosKMOAyArrmUrxQeqjwEPMIjE5Cd8c341KqqlJCLkNymojdyzy5An868xOpGpZSVBCCLk1CjzkjsWkJSFFSyGHiKv2gk2pkFp7pIJ+KuSOhfr4w8bK5tkrTcBXyzfg9LnzVMmakCaiHg9pMrYI7eDZUziZlogID18EeXnDxsijQmE9E6ksq21LcimO/r4dv7/gioA2bcRuEiGyQYGHNAkrIfPi2t9wSFshzOdw2VmwzeBRprKeoFONdXSKTQoUFBYhgOKOJIq7Sm5bBEso194CKPCQRmN7hXy2ZTUOasrBX/uDYsGnVEl/XISQxqM5HtJoJ8+fw1pjDnjaKqCGk9IEb09PsZtBiKxQ4CGNwibQfz1zCOW0TqcOrYKDg4O92M0gRFZoqI00OqEgylgCnt4xRKLY8K/U5lSqh6RJXdTjIbdVUlKCdzcsRzKt1alHwfG0SykhTUSBh9x2iO2TdSuwSVUkFMckdbV3tYWNjQXs901IK6KBE3JLcZcuYYMuCya12C2RJpVCQT0eiWCp1JJLp5ZYe6SCejzklr2dX47sRgEFHUKIGVHgITd1JTERW/W5VPjzFlRK+hMipKloqI3clNFkhJ5GCm6KjbB1DfIRuxnkGioSKh/0UyE3FRwQiAijtPawlxqNmsYhCWkqCjzkptRqNUZ5BFnFdtaEkNZDgYfc0qTeA+BO4203VVBcKnYTyA1ZbVJ7kPoo8JBb8vP1xSsB3eFsAK3BvgHrCMak5YjdDEJkhwIPuSWFQoF7Ro/HB237wVkvdmsIIZaAstrIbbEFkpOHjYSLvQPeO7MHCTYmSrEm0tyPR2L9cqm1Ryqox0MabXDvvlg04h70rtDQG4cQ0mx0/SBNEhYSgh/vfQzTTS5wYPM+vHWv43GypXRzQpqKhtpIkzk5OeE/DzyOh+NiceJSLNZkJCDKxmB1w28suSCzqAwmk0mYCyPikmIWmdTaIxX010KaRalUomt4BB6ZPAPfT34QvSrUjR7NZsc5GgGlBUSqK4U6lJeXi90MQmSFAg+5Y74+Pvh68hx0qVA1KvgoeOBVr87or5P/dgJ5BiA5JVXsZhAiKxR4iNmCzzMd+0LdiE17bI3A0K49MM6nHeSu0sjhTEy82M0gtIBUVijwELMZ1rsfOupuP21oZ+Jgo9VCpVRC7th1pbSiUuxmECIrFHiI2bCdOEe4+N8y04294TrwWri7uaOksgJypwCP0EA/sZtBiKxQ4CFmNbF7X4RXKht8Yyl5Hg8pvPHplAeQkpaKv3IuQe5Y/87N2VnsZhAaapMVCjzErDq0b49l9zyOZ7QBcNNdr+/G3mhddRq8OOlueHt54ad923FJY4TcBdhxCApoI3YzCJEVCjzE7FxcXPCPmXPwvx7jhIWmHSqUmMt5Ycm0h4U1QMy8oWMwUm8PtYxTqjmOx4hQHzg6OordFEJkhRaQkhbTr0dP+Hp6Yt2xQziSn45n1/4KFadAN0cPdPYNwL+GTkKfC6exJDcBeRrIbgGqg4LH5GEDxG4GuUaKQ1tSa49UUOAhLWrn6eP4ouwKTNrrzx3SpUBxNRkuCRwedW2PP4fdi8/2b8IOdSkakY0tGZ5aBdr4UWIBIU1FQ22kRY3q3hvBlYo6C0tZz8bIcchVA6tzLsPfxwcfTn0AvjLLSjaYeBQXF4vdDEJkhwIPaVFBAYH4atBUIdGgIYkaEz5YswxZebnorpTXXElqJfDad39Cr6eNiqSAr7U1glQechs+bi0UeEiLC2vbDu34qirOrOfD1vlUr/XRKYA/kYN7Ildhh0JevQcjzyGhQIeioiKxm0KIrNAcD2lR56Iv4qdje3FGU9XlaaPj8Ix/F2SVFGFxyVWUKavuVEuV8pyELTRySM/Mgru7u9hNIUQ2KPCQFsN6Aq8c2ogYG6MQXDiexyyXtrhv3CRhKwGsWYGvyq4I8z1yZeJ5RCVchqODA7y9PIXdWnNyc5GSlg6D0YQBfXrRlgmthLLa5IMCD2kx+YUFCFbaQl9ZBldehV6Onpg5eKTwGrsYzxs7GRv++hYJ2ublsklhEzo23PbJnjjY7IuBm6bqIlOo51Fm5KDigCEHT2LuuCHoHBFOAYiQayjwkBZNLPjfvOdQVlYGjUYDlaru240tvBzh4IkkRWqTJ2FVUEJbIJ3ka5ZekFnru2A13Fjr9ueX4sTFrRgZcQTz75sAf39KvyaEAo8F0ul0OBdzEQajEeHt2kPLKkGrVPUu/K3Fzs7upq918QkEl5MspB1Ub6lgUNx+MSnLF1KwijsS6PXcDtsmbvOZAkQl/omv33gQ3t5eYjfJItFQm3xQ4LEwJ86dwYrzkdiqyYGJA9qfZzuDcnixw1AM7zewzrGsJ/Kf9cuQrCvCaJ8wTB86Wqgw3ZpC/YPgmnoY5Uoer7j0gJeTKz6P349L9pa1lTbbJjspH1i8dD3efHGesIMrIdaKAo8FMRqN+PzMdpyyr6i5aMfY64UU5uPJ8RjWd4Aw+V1tx9FDWKFKg1ENnM09hd9XnsNc726YOXpCq81HdAhpjx+M96BSpxe20ma9Mr3RgJcy9gt73cDCgs+eqAKMP3UG/fr0Ers5hIiGZjstCLuL/kePcXCvqHvFZkHoT2Mi9hyNxMnzZ5Fw5bIQpLJLC2G8dmiRBkiwM+Cj/JM4dPJYq7WZBcJOHcLRs0vXmqFABWe5b8sKA4fvVh5AeTkbgCPmJPb2B7QtQuNZ7l+4lfL38kGQsf5wWZkKWBi/F09cWIf7ji7F+6t+xsq82HrHlauAt2J3Iu5SgvBxSUkJ1u3ZLswbtRY/d0/YGq5vqWBpvZ6YDCNWrNkKnn1AiBWiwGNhFu1ei1P2ZfWeZ5e4JDsjitUQHn+qUpFob2zwuAxbIz49tB6ZWVl4cfX3eDP7CNbu29lK3wEQEdYBLzp1Fdb9CJUOYFmMJuCP3ZcQdSFa7KYQIgoKPBaELcpM15ei6nJ9a+wY/havHbEtxbytP+CQXQl0SiC3rPXKwrD5pbG9BmB8hRvetu+Ouyq9oLKwzkFxJYe3v92CCxdjxG6KxRB7SI2G2hqPAo8FYRfsRzoNQZcSjdBbuBOVCl7ILGOZcWLw8fbGZ/c/g/vHT8W79zyCl7QRcLhWi5M1SSHzv2f260kvBt74ZhMuRtcf8iTEklHgsTADe/XBy93GQnNDxKguzsl+4QqZ9B6qM+vY4tP5k+/G294DMKDUHo/ywRgPN8gdCz4ZxcB/f9+GykqZ7QlByB2gdGoLw5IAlkUdgs627vMs2DypbI9u/m1xNScDP+ddQLqtdFb+Nyb7bdqIMZjKjxb+nZScjHOn/kJBufyDz7kUPdZt2YV7p08UuzmyxvOc8JASqbVHKqjHY2FOX4zCHm2BkCUQVKasmcnhwSPE3UdYyzN3wnT4maq2KWgM9qejlEiKc/U6pMCAAIzuzopywiKSDf7cEY309Ayxm0JIq5DG1YSYTUl5GXQcD1Z38+WQoXCprPoV2xs4dG7bvlnnVJqALm3aQWrunz4avk4sGEH20oqA3//eVlW1mxALR4HHwnRqF4rAciU6ldugR4dwuBirfsXeOhV8vX2Ef7P5hFLUT6W+GVedAmFBbSE1rODmv5+eiK5tVLBV840KQCwpIcRDAbXU3vk8sPlkNg4fPSF2S2RL7N1Gb/Yg9dEcj4Vh2WBLhj8IWxsbRF9OQKqNERoj0FfjVVMZ4EJ8LK7aNH67ZjUPodDo7fbeuZAQhwpd1SS5u5MLuoRH1CnR0xI6dwrHov9rj5jYeHy1dAfOpxhueixrStc2avSJ8MNPOxIhxaoGP66NRO+e3W778yZEzijwWOh2BMyJ6ChojcDD2lA8NfXemiyx7TGnUNmEGpUlSh6XkxLRNaJTg68nJifhyb1LkWxjhPHaJjks9fmTogKM6D8ILU2tVqNL5wh0DDyJ8ylZNw06rrbAlGGdsGj1aRjFyhO/TaJBbIYR6zbvxL0zJondHEJaDAUeCzZmwBB0yQqDl4dnnWrIGoWq6irXyN5IsYbHCyfX4Jm0JEwePKJeBeuzl+Jw1VYPU63zsdpvn8XtQ7ewcLi5tU7qs5uzHfycG35Nq+bw+Ix+WL3rNPLLpRd0qhmMwJ87YzBm+EC4urqK3RxZkeKCTam1Ryo4ngpGWZ3c3Fz8c+MviHQobdLnqU0cOpdr8ELnUejbrUdNMEtKScZDe38V0rNrv5mUPPCx50BMGTYarcFgMAjFT29m1bptWLQxXsgikzI2DzVzoDdeevKBFh+qtARsmNfZ2Rn91j4Plb20higNpZU4Ov2/KCwshJOTk9jNkQypTbGSVsAuZleVTV8Ao1fwOGNfiafjN+O9lT+ioqJCeD6wTQCe9+sjzCXVxipfr0o8e8tgYE5sDovNjTT0KCwswh+74m4ZdNg13s2OR49ANboFqOHhwMPJhodSURUMWisGmHhg66kMXE1Map0vSEgro6E2K8S2nB6g8MAaPrPO8Fhj8NcqXa8yZSBwx0Y8MukuYe5o6rDRyNxYgG91cUK5nWrnNWWIjo9D547hEFNxSQlKKm/9vWqUwFuPDMXA/n2FytHsTrqiohKXryYhL78Q3645jZzS1ok+RRUcfvt7B20a1wS0gFQ+qMdjhdhk/GtTHkCPMttml89hWdrflEVj2fYNwtoTdnGcP/EuhJbXXZhapjZh58WT5mk4gCuJiYiKiRaG1Zjflq/FHyvXC0MZt+Ll6QE/F+6WvRZHLY/OER1reoVs+IZtUz2gX29MGj8K/cJab86FDYDvjirAmbNRrfY1CWkt1OOx4l7PoimP4bMtf2G9KrNJWW7VylU8vso/hwFJXRES3FYIPqoGKl0fKk7Fc0bjTe/c2bqij9f8jmxDGYb5hGJ8/yFwcHCoeZ0FmStJiYhOuoKfk07gilaH99L7Y0y/wVh/8BJSC4ENhxLw72emITQ05Kbf75uPTcCb325GxrVC2ywIsQt89SynzggU1BqLZ5UE9kWegInnodcbcDAmv1U3aag0cPjh732ICA+Dre0NNZAIkTEKPFaMZU29e+98dNmzDbsyYmHkgZPaEmEzuMYq1piw7uRBLAiuWmCq4ut3ovUcf8tNz87FXMTfijRU2AK78o9j+d9n8fGwWUIwY5+3ft9OvJ99TAh0JvuqC/838SfQ1tNfGPpjcyJXc3l8/stmfPbGfNjZ2d10zc8nL6px5GRVL8LFyQH/W3sGeWVVPaFe7RyEIHQxOgbnLiZgzf44JOaxtlefgWv9Om7JeuzaF4nJ40e16teWI8pqkw8KPFaOTcjPGjMJszAJxcXFmLruK5SrGp/2xXo0O0qTcV96OpwcHVHZQEWEMKXTLecp9sSdq+lxsSG8KAcdXtq/HD85PoajF87hg+xjKFNXfa1q6QYe7y7eCj2LlqwiHQ+cTdbhw6+X4p0F84ThxIaEhbYXHtUKikrw7aZL0KqA4nI9Hn5nBYw8J/R+hO9P5JxP9u39uvkM+vfuBg8PD3EbQ4iZ0BwPEaSmpeH9Db8jV9P0XOMrdno8vPMnPL/6O0TZ198i20ahEuaBcnJzcOr8OeTl5dVkxDEF+vI6adjs3/F2eizbvw2/XTmOEiHo1MU+ZkNsWcXXgxHLWNsXXYy9Bw43qt16vR6D+3XHjL4e8LAHTl3VoUzPodJQdwhOTKwNyfnA/35b32rZgYS0NOrxWImUtFTsOXscPdp2qMkwY8NYLACw3oFarcIB5DZrrofnOCTaGZGIhtcF7eAzcfmPz3FFWYEiDQ+n8xzcDCoMtPVBBzdf7OOz630OKyzwk/4S9PZNa4veCPy0/gQG9O1ZZ56IbRfBvt/qUjSJScn46Lu1uJpdKVzciyrZMA0kibVv29l8+PyxGvPn3F1TgYLURVlt8kGBxwqwkjbz9v2ONBsDwo/F4AdPL2GyfcOB3VicdhJjbdvghamz0cvkjO0oMMvXtDMALnoFcjU88rTscX3dUI6WR45WjzgkQ5mfDJNN1XDZjcqbEwTZfE8ej1Xrd+Dh+2cIAWbZ2l24cDUfJhOP8f3boUNIAD79bS+SC+RzYWAB9Y+9SdCo1+H+eybfdCiREDmgwGMFkjPTkW5jEHomMfY63Ld5MdQ8h2y1AUV2wG+mROSv+gnlppsX2GwKlqL9sDoED4+fjIfWfovo6j2rG8AWmZp70p7tLLBs9yVcTl2CqMQipBVeHzb7eVsCHG0uIbMYslOuA5ZsvYyrab/S+h4iaxR4rEDHtiHofNEG5+0rwWZwkuzqzhXoFMBqbRZgpmojbJpofPd+wlCXooGFMywwKa4FnZYa3Sqs4LDt7LW86VrYHE5Z4wtzS7LnczC6ADk5ucIaI3Id671KLYtMLj3q1kaDxVbAw90DcwJ6ssJ8Nz2GvWKuIMAKVKtUSmERZojSqWaRKvsTZDuiDi9zwkeeA+FRIcLbj4OQwcbK4LCY6KCR30ZyrEdHJRaJnFGPx0qkFeUKQ211trPmW6bX4Wjg4MR6OwoF3poyB+32bMGlkhxoOSW6ewVi6JBe8PbyxqFl8VjHZwuJBK1FyQGTe3vAVqtGSlYR7p86GG//bysyiuQTfYS1S7RTKZExCjxWws3WETb5rNpAVcAZXOaAacHd8WHKIZQreKHrW2ym+epiFY/s3Fx4engKVQCemjarzuvsbr2goAD3dx8K7blIrOMyhMWhrYGlXK85loOHRrTB//1znpDlNmVADH7YniiJ9OnGKDdwWLp6O158fDY0mroliqyZ0GuX2O9QYs2RDBpqkxl20WZ3u6w2WXZOdqOHXKYMHoF56hChp+Oi4/D+hDmYMHg47rMNwavuvRBcab4LWIWKx+Lj24UU5hux5/63fgXu2rgI88+uRkJlPjzZJFMrYp2FK2n5NanVMyaOgLejfC4RrP1rj+Zg5dqtNORGZIl6PDJb5Pn13nVI1BcjU6lHhcKEp926YtaoibdNr2VlZCb3HIjLB/LQw81fmPdhQ2HPzbgfmVmZ+G/WKbO0sWrAisNedQESrlxGRIeqopvV8gsK8EtZPAptqy6YJzTXF5K2FjbiqGRjbte4u7tjyoBgWfV6WEWDH7fGwdlpN5XTIbJDgUcmWDmbf+z8HWfsK8Brr180Pyk6hwsr0/DM8KkoLi1FoJ8/7O0bXnXJap99GfRsvc3Fjlw4i1wtmzPg7jib7X4+ABmVJTipKIBDA+1gZXW89EoUasyTut1cndv71fmY9Xo2HvkF6bcuci0ppToOi9ecQd+eXeHl5QlrZwIn/E9qbSL1UeCRCdaj0Qm343XfyJVKHmsV2Tiy+2cUqE3wPKLEP9sNxthBwxo8z41Bhw3b7UyNAW97538gTnoOU3v1R3CbQCSmJCPAv029YbaVe7YiVy1u6RcVB4S1C6jzHOv1hPraIb2wDHIS4KGBh4e72M0gpElojkcmbGxs8GaviYgo0eDGeXiWFZZmaxI2aGOlaz6/dBD5+ayE/+2xQOSjYaVl7nyMKVfL4x9H1yA5PU0YYrsxyKVnZuDL4vPI04g7nmWr4eHv61PnOVY6KDFLXkGH0ag4KqFDZIfesTLSs3NXLJ35PD5074+AsoZ/dexSH8zZ33RrgGps/mXv0UgkpSRjzoDR8KpQ3vGgAAsnV+0NeClyJdIzMuq97uHmDl+dSvTBBzd7Rb1eQnZODoorZDLBcw2L6228qvYOItdrtUntQeqjwCMzLKBMGzEWb4aNFOqhNbR4c4xfx5qMrYawIa83D6zCk8k78dje31FaVoZ/h45q8HzNCj52Bvx395p6a03Y3NOC9kPhrKs7YMgWmNoaAM8KDuGlaviVK1o0OCluOPmuvQfx9PvLkVcur4uEpwMwa+pIsZtBSJNR4JEJVsK/vLxc2I2TpdDaam1gb6h/oWQ3WPvT4htcYFhdjfrAyWO4YFMhHMvK57x47G8EePviEW1os7fCvnHob6ciG5cTr9Z7bdSAwfi43Wh0LFFDbQLCSzV4ThWGVX3nYO34p/DrtKcRzl+vKt0SvYQObZzqZAEeOBGDrJIWrN/TAt+DnzPw9F09ERhQdx6NEDmg5AKJYQEjNT0NBkPVBHxmXg7i0pOxJTsOuQo92vJ2aGfrilWGZBQJVZ3rYs/s1+Rj//EjGN5vYM3zJSUl+GD97zhlzEOBygS95vrxSXYGfLV/AwLtXMxWr7NEBey7cArt27ar9xprV5/O3XDs/Bn0iuhSs9X0uYsX8MmJLThlV9ZgDGBB0cHAIbRSiwt8/TVCjb1o9+1yvU0sEMck16/pJlWs1E+YtwKvPz6lzoZ2pGq3T05iQ1tSqx0nFRR4JCYzKwsLdv2BSxodTBwv7MipZ/1Sh2tBAsU4rStGR70tguEMH1tH4fOOF6XhuF0pWCeIVSf4LHYfenTsBGdnZ+H1S0lXsVGdDV0DI3BsZ89tdvlQ8PlmK1/DznMsLxnzb/I6G3Yb0X+Q8O/Kykqs2bcDX2efRq4932DQYTXexlW44ZmBE5GRm43nju1qVrtcbXn06dnlejtNJhiluhFPA0FzQKgt3v/nPNja2ordHEKajQKPxPj6+OB/k+bj251rscGUhgrV9REgFhPCStV4r+ckdO/UpU7WGBuGW7R5JX7irwr11xLsdNgQuRdzJkwTXg/ya4OBJxxx0K5YCE434mu2KDAfUyPGrkpLS/HO2l+wVZMH/Q09OKGoKA+4V3IYrfTFk2OmwkarxScH1wnfQ1OLaWuUPB4YFQZPT886c2a9Qt2RcjxXshvBVXPU8nhmzkQKOkT2aI5HglhVgbfunY8fu92NeaZABJUroTUCY8td8O3ouejRuWu9VGV2MXp4+ER4VFb9Sk0ch5XZF4TeBOPi4oL/TJ8Pn3Lp/MrLysqw4O/vsUmbC32tySU299OlRIO5xjb41HMw/hw+D+/e95jQO3li/Xc4aFfS5OkYFnQem9Aes++ZXO+1Fx6bhYFh0r6Ys4SIWUOD0TY4SOymSFb1duVSe5D6qMcjUSywdI3oJDyeKCjA1dRkdAwJFdbz3IyLswvcjEpkCrvuAEUKk5CMwDLcWOWDExfPI5eVF5CI/aeOCT2w2sN7LP7ca/LHK/fNqZeZl1uQjxibSuH4poRPtg0CCzoPzJza4JoXFrSnjeiJw/GHhCKiUuTjxOOuySPr3XAQIkcUeGSA9Va6u7g06XPYnIinUQOVSiUEn1fW/oiD2iLohNWn0rh4RSbHwaipHlLjhcoHw00eeGHyPQ2mg0eEdcCjMe3xvSHhWmi9dQ9hSm83+Hm6wN/HHaOGD77lQkt/P2/Ya3gUVUjjZ1MbizWT+gfDzc1N7KYQYhYUeCwIG1Yr56ouyT1LbPGfMVW9BjZEVcobUCnslCydC+vDA8fC41QkTLwJ4d5t0KldKPx8fG8aINhWz09PnQX3HZuwJC0Kt6oz4OXI44k5U4VSOI35uX28ZAOKK6Xzs6lNq+IxpF83sZsheVJcsCm19kgFBR4LkpOXi2w2QcKSDTgTVh3Zg3xdGeIr8xHfylWgWY8rwuHWWzO3CwrG80HBTTovCz73jZ2MqMQ07EZpnbke1jNgH7vZ8nhkUrdGBZ3qcxqMJsmOxztpAW8qAkosCAUeC8OzNDAAF+11uMhfAtg6STNt8NYUahOHYeFdW+TcrEf06uz7UZGxFJEJ5ULAYEHHxwm4Z1h7jBjUB/7+datP3wobjnSwZX8K4hYvbQj7vkb18K1JiyfEElDgsSBSumP3rVCifWDTejNNwRadvvvSHGzfcwg6vUEoh9+3ZyeEtGvbrPPNGt8XtrtPIS69FGkFkAw27zRz8ghKKmgEGmqTDwo8FiQ9Jwv6G97nGiMwk/fDdkMGsm1aJ2WLZaZNtg+uqUjQUtj575k24Y7Pw5IvfLw9oVBwyCmBpLjbc3BzcxW7GYSYFQUeC+Ln6QV1PKDRAyE6rVA+xEWhwkvTZ8Fl53p8a0wA34J3zty1QNeuQoNZ40dDDo4eP4lvlu9Har4RZXp2xwxJcbZT3TKFnhA5osBjQdgOpOy6OUfdFi/OvB/5BflQq9RwdHSErUrDSgm0WMDxqFBgujYQoyK6IySw5Xs75rL94FnEZ5kkOyQS4udMw2yNRLXa5IMCj4VITE7CO8fWg9MCo8N7CJlae04fQ0xuOp4YOQUXCjPQ12gHFafASZtSVAip1ebhqOPwn/DxGNCzN+QkOzsbkdF5kg06jFZDf6LE8kinfgq5o9IzL+5ZhjxOj08CRqBzh3DheRPPY4UiFTO3fYcMYxkWz3wGSx54EW+794VvhQJKlg1mhq/fVqdB7y7yW2cSl3AFRRJdu1PtQFQm8vLyxG4GIWZFt1MWgO0t4wo1xjgGY8zAITXPezu5onu6HTpqXTEkLKJmV9K7R41H39TOiEu6is8T9uOyraHZVamFHU9VjnX2t5Eyo9GI3Nw8nDl/EX9uPS3ZEjnV0guB9Vv34uH77xK7KZInxdpoUmuPVFDgsQDsov/fu56oV7WY7XszrO+ABucIvDw8seXUYXRWuuAqcpo9/cMy2Pp4t1zatDl6g2ydzt4Dh3EuNgkXE/OQkqcXqhRI8UJ1I1Yxe9eJRMy51yB8H4RYAnonWwgHh4Z37bwx6BQWFiI1Ix1eHh74rSj2jlKsWdAZVOaAiROGQWp+W74WR6OSkVGgg4+rBmeTdNDXrA+V9vBabax6kL2tUkj5psBDLAW9k61EdHwczl6Jw4XcVKxXZqFLhS2K7yDoqEzAkHInLJzykLCpm7mx+nJJKSkwmozw9/G9ZUoxOzYrKxt7Dh5HQVEpHnlgOmKuZuLE1apdSlMKmrdbqdjYPcPgMDu89fwDlFLdCFU9WGndVEi9Ry0WCjxWYu/F0/jKEAtoquqZHbe/VYnNW2Pl4OYp2uLZWfc2WEX6Rmzb7aLiYni4u0Ojubbn9m3EXb6Eh48tF7Lv+lba4+XB04RttG/swR06fAwrtx1HTGo5Cis44WIddeV7XM6UZ7Bh2PegVPAIdFXg+YenyCY1nZDGosBjJXoFh8IuOhalzcwBYJd7pQnoVKbFDJ8IzBw1odFDP8t2b8aSshgEV2oxzCkQIyN6CEGkdkJCdUA5d/ECjly6iCBXT1QqeWEb7/2qEpw/ugxTTvjjpSmzhbmsjIxMrNu6D/vOpOJSDuu5XQtIPHDiinyDDtPGhcNL9w9Bty4RNx1CJUTOKPBYiT5de+DBSxfxq/4KyoU9eepuL+2g56BT8sLWCXUqPoOHjYFDh0otHgrug5F9BzZ52KfCaECRGjinrsR5Uxx+OBOPtkfVcKxVvXSgcwAenXQ3PjuxBUftSuF1VQkHcChT8UJ78jQ8/uBTEH54P6YMHYU3/1qG88dNdRtrIbq3c8GgAX3FbobsUK02+aDAYyVYj+KF6fcje/n3WK3KEq7XLDkgtEyN+326YEB4V2Tn52HF+UPYps4VAhCrMN273B6vDJiCtgGBzZ5nMPLX55J4cEIv5qJKD4A9qqQWxWN6bi7SuUqhrE+mbf35JyMHLEs+jaMrLuO0ogwqWN68B0smCPCh2mzEslHgsSKZWVk4bsyFeyUHb4MaY53bYuak0TX71gQFBKJ7RGeMiNyPpPxshHj6YUjPPs0KOCx7bn3kHpzIScYJRQFuFyMybEw4evHsLY9hwfKcgw7nkA2VgvWILA8rKBERJt30dELMgQKPFXF1ccEnPaagjY8vnJ2cG5zoZ/M2E4eOvONFmm+v/w3bbfNgsmvcUINRAXyaehTlagscO2ssDujX3hYe7rTFdXOwd47U3j1Sa49UUOCxIqzn0qNzy2zOVtvpC+exT5MPUxOKW7I/0Cxb6/4zVXDAjNG90DY4SOymENKiqFYbMRudToetB/fi0zPbUEG3NM1a87F21ynwtPiDWDi6PBCzWb13O/5deAoG868ntRqlFdcTLkjTUFabfFDgIWaRlp6OH7LOwlC3XBxp5GJRtnX3jP5emDpmIO2/QyweBR5iFkWlJchV1xRDI43k6ww8PKEbdHo9ZkweS/XYiFWgdzkxi5CgYNx7OgDrKlNQqOHBRhj42hOJPJq99YIlJxNMH9wO0yaNEbsploHS2mSDAg8xC1b+5tV7Hsbc9HScjLuIrclRiNQUCdstDNW5wEahxCZtTpMy3SydlyOPKWOlV9mbkJZGgYeYDZub8PfzEx6TjCNw5mKUMHfRKawDCosKcWz798i8g4rYlqa4ErhyNRlubrRuh1gXCjykRSiVSvS6th12RUUFvtqxGjlaCjq1lek47Iw8g1495bdtuCRJMKtNGHMm9dA6HtKi2JqU5bs2Y60mS6i1Rq5jy3VOxecK20YQYk0o8JCWDTrbN+LLkigYKOg0KCmPx9rNu8RuBiGtiobaSItgu4KyoPNJ4RmhGjVpeE6sW4AK/Xp0FrspFrQDKSRFau2RCrokkBbr6VDQuXXQ6RGoxgf/eICSC4jVocsCMbsdkQco6NyCUgH0CFLj3y9R0CHWiS4NxOwOJMWgvP6OCwSARgXMGR6AB++dDDs7O7GbY1GoVpt8UOAhZlVZWYkofR5AgacetnZ2Sm8PzJ9zF5XGIVaNstqI2QOPHa9AeKla2OmUo5ohApUSGBFuj8fnTKOgQ6we/QUQs3JycsKPs54XJs8zsjLx8u4/cc6hAqz+srVy0PJ4eGx73HfXJKG0EGkhQoFAib3PpNYeiaAeD2mRnU61Wi2CAgLx5di56FpiY7U9HwcNj5dn98aDs6ZT0CHkGgo8pEX5+frim4nzMM8UhMAyJWwNEB4K3rKqTId6KdDJTwlnWx5qZfU+O8Ds4cEYP3q42E0kRFJoqI20OC9PL7wy82E8VVSEvIICGIwGfH1oE7bb5gvVq+XMXsPjroH+eGhWVZZaTk4u0tIzcOZCAopKyzH3vqliN9Fq0AJS+aDAQ1p1/oc9mA885wFrfsZ2O3kGH4WCQ0dvBZ67fwR6dO9as2uot7eX8GDPEUIaRoGHiMLBwQEfzJgHw5ofscuuUBYzQNVbCbna8pg2oA3mzJwkfB+EkKahwENEredmlHDIYYGGzd84aXl4OasQHuiCHhHBCAsJRru2wWI3j9yIdiCVDQo8RBSlpaV4bd3P2GdbJJm/TRZoWGKAqy3Q3tcWbX1d0LNze4S1byuUtmF7DBFC7hwFHiKKS4lXsd+mECYRlzkoFFXpzq52CoS1cUCX9v7oGBqEtkGBwhCagh1ACDE7CjykRRmNRmHi/caL+JXMVBg41tdpncgjtIHj4WzDI9BdgxB/Z/Ts1A4dw9rBw91dWHtE5I1qtckHBR5i9i0RCgsLEZUQi8grMThengEbKNDRxr1OiLlQngOTQ8v+UbL5GW9HINTPDt3D/NGhfSBC2gbBxcWlJguNENL6KPAQs0i4chmnL8Vgd3ocYlCCLFsTjAoevH3VBf4YUup+Qgsng7GgM6KTA15+4l4KNIRIDAUecseKiorw7ME/kWhnhKlOpX9xLvYsxoR6K/Hq07Nr1g0RKyGVTBVySzR7Su5YfOIVpNoYJbEQlAWdEA8O/37+bgo6hEgUBR7SpHU356MvQq/X1zxnMBjw/ek90Euk+JqXA48PXrwHQYEBYjeFEHITNNRGGiUzKxNf71yLLYpMzIwKxFNjZ8DZ2RmRp07giE2xJLY9YL2dyf2DEBwUKHZTiAgoq00+KPCQ2zp29jQ+PLsNMXaV4DkOv/JJuLTuJywYMAlfxOxFpb00ejssoSC8PfV0CJE6Cjzklmtwftu6Dt8VXkCBPV/Tq2GLPg/YF+HMyRUotWPPSwMrbdMhNETsZhBCboMCD7npepwv1v6B34xXUalt4HVwKFJLJeQAthrg6end4OnpIXZTiFioVptsUOAhDSooKMC64hSUaxWAEZJjMilg1FT1wJTgMa2vN6ZMHE3rdQiRAQo8pEHxV68is1gDfQkkyVTJgfPihLTMmUGueGbevRR0CJEJCjykRkVFhTDExlKkt0edg17q1/Fr7dObTFQ5mlx7Q0jtTSu19kgDBR4rlpubi13Hj+FwciJ0JhNiK8ug56uSBzIV8hieZotWN6YUotvW7Zg5aYLYzSGENAIFHit14NgxvH1kPxIVVRdvOS8n1oHDV4ejEejjhX69eondHELIbVDgsULnLl7EPw7vQ7aSk0WvpjFYLQWlkt7OVo2y2mRDpve45E52/ly4Z4dFBR0FeNwf5ote3bqK3RRCSCNQ4LEyR8+ewUlebzFBh03ddrVX4pEZkymrjRCZoMBjZdq1CYBGCmWkzcRdyeOdeyfB0dFR7KYQqQy1Se3RRIsWLUJwcLCwK26/fv1w7NixWx6/cuVKdOzYUTi+S5cu2Lx5c53X2Q1ZQ4///Oc/EAsFHiujUiot6pfezl6FlIwMHDt1GllZ2SguLhZSwgmRoxUrVmDBggV45513cOrUKXTr1g3jxo1DVlZWg8dHRkZi9uzZmD9/Pk6fPo3p06cLj6ioqJpj0tPT6zx++uknIfDcfffdEAvH01+pVWFrdJ76YTF2GipkPdymrOSgzVXUrNxgaQW2ChOclRzcNAoEOdvD28kBHQP8EBIYgNCQdmI3mbTgRoSsUnrAt+9CYWsDKTGVVyD56XeF7eAbsz9Uv3790KdPH3zzzTdVn28yISAgAM899xxeffXVesfPmjVLmLfduHFjzXP9+/dH9+7dsXjx4ga/BgtM7AZt165dEIsl3fySRlCpVFgwcizcjHIOO9ex74KNHOoAFJoUSNJzOFPKY11aCZbEZuCfO05i/i9rER0XJ3ZTSUtjWxBI8dFIOp0OJ0+exOjRo2ueUygUwseHDx9u8HPY87WPZ1gP6WbHZ2ZmYtOmTUIPSUwUeKxQh/btEaJUw9KxvrwJHHKNHN5asVkYiiNErF5Z7UdlZWW9Y3JycoSK8N7e3nWeZx9nZGQ0eF72fFOO//XXX4X50LvuugtiosBjhdj4rq9WWkMSLd0riinnsWHvAbGbQqwUGy5jw4HVj4ULF4rSDja/88ADDwiJCGKiFXdW6PDJk4gsL7Gq2w42HLcnIQUPGQzCcCOxzB6u1Gasq9uTnJxcZ45Hq62/14iHh4dQc5ANh9XGPvbx8Wnw/Oz5xh5/4MABxMbGCgkMYrOiSw9h2CTnh4f3I8cKf/NxpQZcSUwUuxnECrGgU/uhbSDwaDQa9OrVq86kP0suYB8PGDCgwfOy529MEtixY0eDx//444/C+VmmnNis8PJj3U5eiEIsTLLOaGuuUh7Yf/Ks2M0g5KZYKvWSJUuEuZjo6Gg89dRTQtbavHnzhNfnzp2L1157reb4F154AVu3bsVnn32GmJgYvPvuuzhx4gSeffbZOudl80psvc+jjz4KKaAxByvCMue3Rl+EwUoX+LNdU9lw21y9Hmq15SdXWB0LqNU2a9YsZGdn4+233xYSBFhaNAss1QkESUlJQqZbtYEDB2LZsmV488038frrryM0NBRr165F586d65x3+fLlwt8/W/MjBbSOx0qwX/PKbdvwf/HnUWIBpWWq1/E0lT3H44950xDWPqRF2kXEW8fT5uv3JLmOJ+W5dxq9jsda0FCblTh74QI+jo2yiKBzJ8p4Dmv3R4rdDEKsGgUeK3A1KQnP796KPNqkUxj52JWYLWyCRyyMzBeQWhMKPBaOZcWsPxKJZPY3IHZjJCJVz2H51l1U040QkVBygQU7ePwY/jh1Avsry8DTLUadNT2/RydjUNQFdO9SdxKWENLy6HJkoRKTk/FK5D5s1ZehlH7L9RTzCvxv674GS5cQeeJ4aT5IfXRJskB6vR6Ld2xDmgXtMmpubJTtcH4lNu/ZJ3ZTCLE6FHgsDKtw+58Vf+LvsgIKOrdhAIfvjkRR8VBCWhkFHgsMOr8UZAvbBJDbS9Fx+GnDFko0sAQWsgOpNaDAYyFYevD7y5ZeCzr0bm9KosHfl3Nw+PgJsZtCiNWgwGMB8vLy8Myfv2NpST70tGygycrA4a/Ik9TrIaSVUOCRObZxFKs7Nq9bT7gY2f07aSoWb87klgkbcREZE3uhKC0gbTRaxyNT7O787x3bsTY+FleNOpQDyFfQm7y52C6lR8+ex+TRI8VuCiEWj3o8MnXk1Cm8F3MOB3kdUhQAq5dJN1fNZwSw72JCo+bSvvtzFY6dOk1Dc4Q0E/V4ZOj0+fN4+eAelCgp0pjTsaxi7Nx/AEP79xM25bpxbdTm3fvw45HzuFTJwyEqCROOncHTd02Gl5enaG0mtUgxi0xq7ZEICjwyDDrP7d6GVCW9p80th1fgn1tPoNuh03hocC8M7tdHmD+LiYvD5+t24HiBDjqwYM+hmAdWJRXg0KLf8Vifjpg8cjjs7OzE/hYIkQUKPDKSmpaGl/ZQ0GkpbOSMrX86UWLE2S3H0O3gSbR3d8LGpFyUmBTCRnK1ma4VHH0/MharzsTh6ZH90LNzJ6hUKqHHxP7b3PVYh46fwJpjZ1GhZ4OAdTlq1Qj39QB3Q3v6duqILp0iaj4uLy9HYVERysrLERwYWGcDMULERIFHTotDN61HIlWZbrUAdLzYiOPF+bedCjUAuFDO44WNh+G+NRIqcPC2VcHdVgN7tQqPTByFkLZtGxdwjh3HrwdP4kyxAbqbTtrpsSUzqc4zbJulTnFJuC8pBXFpGUgtLMWlonIUG3nhPHM6+uGp++5pdjCUBRpqkw0Lfhdalj+2bsamihJKIJBwsNKDQ8a1fcVTio1AcbkQENocPoGnbhF46gScIkPNcF5Tv/7FMh5vH7hQlcUrXPCun+eHi2nI+O4XvPzAPXBxcbmzb5aQO0SBRwYuXbmCxVcToKeREtkRAkJG9i0Czgn8evCEEHD0HCvq2vw7C9Mt7rJZUFyXWoycJUvx+qxpCAoMaPbXIeROUeCRARcnJ4Qo1cji9dRzl6H4wgqUlpbC3t6+gYBjFKpNCAGnhX+5LDAdzNfjqZ9W4qsHpyM0pB0sCg21yQbdQ0sQ2yOGVSSo5u7ujrfHTkSAsakDMEQKsgxA/OUryM/Px99btuGxzxbjpc1HhDkkNpfUmsuB2JdK1HNYuGozioqKWu8LE1kzGAzYuXMnvvvuOxQXFwvPpaWloaSkpFnnox6PBLCFiOkZGTgTF4u9CfE4X1qEMFt7/HPCZAS0aSMc0zE0FO/2GYCnTh5GJUUfWWGT+wtWbhMu+tkGDqbquReR7oZZoDtWqMePazbixbmzwbGJKEJuIjExEePHj0dSUpJwUzxmzBg4Ojri448/Fj5evHgxmop6PBKQlJKCu/5aiufPn8DK8kJEK3isryzBI3+vwL4jh2tWyA/u3Qe9FWqxm0uaMcSVYVAg06C4FnSk0ablcRnYG3kYFkPsmmwWWqvthRdeQO/evYUeu62tbc3zM2bMwK5du5p1TurxSMDqQweRpWQXpeu3wOxfCUoezx87iBmxMZjRpy/Sc3IQY9ADSlGbSyxECTj8Z8cRtAsIoGQDclMHDhxAZGRkvWoewcHBSE1NRXNQj0dkWdnZWJWZWifoVGPPFCo4/Fqci9k7t+D5s8eRR0GHmAnrSLP5ns//3iSUBCKkISaTqc6cc7WUlBRhyK05KPCIiP0yV+7dg8zb1FxjAahMAeho8ShpgeCzL7scS9dtgtxxvDQfcjd27Fh8+eWXNR+zOUGWVPDOO+9g4sSJzTonBR4Rg84P69bi6/QkGCicEBGxzLofzlzG0ZMnxW4KkaDPPvsMhw4dQkREBCoqKnD//ffXDLOxBIPmoDmeVsbuFC4lJWLXubP4LjsVlbSHDpGAAp7Dwk37sbZXL7GbQiSmTZs2OHv2LJYvX45z584J17D58+fjgQceqJNs0BQUeFrZ6r27sfBKLCrAgac0ViIRah6Y3DEQskYLSFsMq/E3Z84c853PbGcijaJSKFBOAYdICBtvH+trj4dmTBW7KUSCfvvtt1u+Pnfu3CafkwIPIVaM3QJ1sAVevm+GsPcQIQ2t46mNZUCWlZUJ6dVsD6rmBB5KLmhlbdw9QX/eRApYx9tNyeOD+ybD05N2USUNYwtHaz/YHE9sbCwGDx6MP//8E81BgaeV+Xp6QGUh475E3mzA450xfYRyTIQ0RWhoKD766KN6vaHGosDTyjzd3OFJgYeIjI2xPxLRBiMGDRS7KUTGCQesUGizPtfsrSG3xDbhGuTgguSyAktJeCEyw+42R3ra4tF7pkGptJxSGELpVYn9UVlCGtH69evrFzVOT8c333yDQYMGNeucFHhE0C8wCMtjCsRuBrHSeZ0ONsA7D8+CVqsVuzlEBqZPn17nY1a5gM0Jjhw5Ulhc2hwUeESQnJcndhOIlQYdL4UJC2dPh6urq9jNITKq1WZuFHhEkFJSRMNspNVpeB7/GNYDYe1DYJGkuA2B1NojERR4RGCiqENamYrjMSfUC+OHDxW7KUQGFixY0OhjP//88yafnwJPK2N58KdKiyifkLQa9lYb6KrFU/feJWQiEXI7p0+fRmM0d/daehe2IpYN8t3mjbiqoC4PaR3sutBOA7z/8CxhlblFo1ptZrNnzx60JLrvbkUXYmOxMicD9bdUIqRluCpM+HDmeHh4eIjdFEJqUI+nlbD6Rv/duxN5t9n0jRBzseN4/HNwV3SOCBe7KUTmTpw4gb/++gtJSUnQ6dgOTtetXr26yeejHk8r2XrwAPbqK+Ta8yYyHGLr4qjGlNEjYTV4iT5kbvny5Rg4cCCio6OxZs0a4Sb6woUL2L17N5ydnZt1Tgo8rSQ6PU3YupqQ1sDxPO7uHWFRlQmIOD788EN88cUX2LBhg1CR+quvvkJMTAzuvfdeBAY2bw8nCjyEWCB3BY9+3bqK3QxiAS5duoRJkyYJ/2aBp7S0VMhme+mll/D9998365wUeFqB0WhEVD5VKyCtg3Wse3s6WF1CAavTJsWH3Lm6uqK4uFj4t7+/P6KiooR/FxQUCPvyNAcFnlaQkpaG88ZKsZtBrATb7+nuAT2bvcaCEKY6wAwdOhQ7duwQ/j1z5kxhK4THHnsMs2fPxqhRo9AclNXWCs7ExaJQwWK8Bdz+EMnzU/PoSpls5A517doVffr0EYqEsoDDvPHGG8JOtZGRkbj77rvx5ptvNuvcFHhaYdHo7ssJ4CnokFbAOjn9/Vzh4OAAqyPFLDKptacJ9u3bh59//hkLFy7EBx98IASaRx99FK+++iruFA21tbC8vDwcLyuR8/uPyGxX0Yn9eordDGIBhgwZgp9++knYe+frr7/G1atXMWzYMISFheHjjz9GRkZGs89NgaeFHYuKQhZltJJWEmKroGE2Ylb29vaYN2+e0AOKi4sTht0WLVokpFJPnTq1WeekwNPCw2xb46KtukQOm96muNt6P+uhbX2FlFerJPZCUQtdQFpb+/bt8frrrwtzO46Ojti0aROag+Z4WlBJSQlOs2E2K77yanhgvrsPSnQ65FZWYLO+rM7fIrtYqnjAwLZSEbGdlsBRYcK4/r3FbgaxUPv37xeG3v7++28oFAphAen8+fObdS4KPC0oKi4OGa3Qp2QXbxsesLtho59KjkOJiH1a1q5BKi1emjlLyIR5Ycniese4mni816UXfow6gwTeiBIFpQA3N6mgs5MG7YKDxW4KsSBpaWn45ZdfhEdCQoJQOue///2vEHTYEFxzUeBpQdEpydC38HWUxZVuvBKvDBqK0KC6F50zMTFYcDISRQqu1XoT1UNrXkYeXbR2eG/GPULQqaiowIXyEvA3BEIduKrtImbNwZ6TJ/B2wkWwXSMCwYFVtktWUE+oMdhCxZEd2lp1iRwpLtiUWnuaYsKECdi5c6ewEHnu3Ll45JFH0KFDB5gDBZ4WxrfAhZ0tENQDcDLxeMjLH49OnAwnJ6d6x44cOBDv6yrxetRJFN/hYkLhj5oDbrX7OvsK3kYez7UPx/j+A4QVz6xLzrDgYhSO4KHkAU+eRzbHoVQBvBd1Govd3HHXqNEo1+vQOTAYnTt0QEFhIZ5fuRwnOAMFn9vwVJowqn9fsZtBLIharcaqVaswefJks9/QUOCREXbxD+E5/KtHXyTmZKN723bo3a3bTVeos+cnDx+BzIIC7ExNwkWTQRjKMjXyMu5g4jFYYweNQgl7lQobSgpuOnTH5nKGq23wyqRJCGnbtv7rGg3mBodga9JVDPDyxn3DR+I/G9ZiX2kR3u/RF326dxeOmzd1es3n2Nra4ptZ9+PTDeuwprwQJnBQCQGM1MZ+/b08HODl5Sl2U4gFWb9+fYudmwJPS+LNd5+u5oHBKi0+vuc+eHk2/gLDgs+jM+7CwwYDEq5cwWNb1gnDV43BLvRPDx2BtgEBeOSXH4XeSb3zA/AxAS+ERgg9lptlVLE7JhZUHjQYarZffv+BucjMzkbwLSrcent54f05c9Fnzx4YTUZEBAfj0MWL+N/VJJhYQgJ1hYShzTHdOlKJHJ69IST2M5BaeySCAk8LYRVct6ckmiXgtOE5PBDYFg+MmyD0ApqDXew7tG+P7lp7pOlK0QUKRMOEylv8XVQoOCRlZgoXtIuoP9zFhsxGqW3w6vjJjZ7Urg46DPtebhV0qmm1Wtw7fnzNx106hiP00CG8v/04ckxsjghWLVDNY2AvWjRK5IMCTwuorKzExytX4AQMd3QeFhMm2jjg/+6d3ewNl+qcj+PwxLARMO3bjURdxS33B2Kdm+68Av06dxbmj0KhwNlrg3Ts09yNPOb5B2HexMmws7NDa2LzRmOGDIGDjS1eXb/fqoOPkDnYxl1YU0GIXFDgMTM2if6/tauxrCTvDsMOu6hwGNUu1CxBp5qnmxsy9JW4wLGBNEDLVyUMsOy76mu3Bhym2zrhjXvurfna94eGIz7+AhTgMVRjj39Mnoi2QUGiDu8M6NMbX9vZ4u8DR5FVUo4z+ZUotLKhDTV4jOpJ++4IpLhgU2rtkQgKPGYOOiu3bcUPmSkw3OF6FPbZbYw8OrVrB3Mqr6iADaeAn8GI9motXh45BrkF+fj3sUhc4viqYXLwyKgsR2pGRk3gmTFqNLqHhkGlVCIoIEAyabtdO3USHiaTCR/99DuWXraufY9clECHEPO+RwhpaRR4zOjYmdN4P+6CWRZB9oIK/zd+QoMZYneCzan8/NhT0Ol0Qrokmz9hwkPaY/2hA6g0GISeVqeAANja2NR8Hjs2LCQEUsT2gL98NRGphaWwNhzH16SsEyIXFHjM5GJsLF4+sBvFZrgGsFPc3S4UEWZarHUjlnl2Y/YZy5R7dPpdkJuY+Hj8+6/NiC0zotzKhtmYMhOH7NxcmuOhBaSyQrdKZrL3/FmzrbJntcvCg4LMcCbLp1Gr4etgAyUbJoT1KTcBufkFYjeDkCahwGMmp3OyzHbhc+YBX08vM53NsrE07gdGDITG+jo7ApYiEp+UInYzCGkSGmozo6qCMHd+jkCFEi5mzGSzZCypYOX+I8g3Wu89lDX29BpEWW2yYb1/rWb24ojRaGuqChx3ghXIHOcXaL17qjQRm1h/9u7JCKnKkbA6dhyPfl0ixG4GIU1CgcdMOnXoiG/GTUFbY/3gwz5ubEAycsDXKZex/+jRFmilZVq75wASK2GVKnjgfFyC2M0gpEko8JgRy0L7evxkIfiwopn+RmCkUovJGnshRbmxihUcLqQkt2hbLckDk8ahm6M01hW1Npb8vvT4RaFEk9W7ltUmpQcNtTWM5nhaoOfzvVqDguJihAQGCgswk1NTsW/NChQ1IczvzUjFg8XFlCZby7HTpxGVeBWzxowV1hXtiTyMmKQ0xGbl4XwJqxNhnRkGceU8lm7Ygifuu0fsphDSKBR4WkD7G6oN+Hp7I4RT4nQTCvqzOm+vLvsdH93/IAWfa7ZHncMvhTnY8NP3sDcqcD7bKNzxV9Vps86gw7B31YqLiZiemQVvb8qGJNJHQ22tgCUKzAgOgZ+Rh12tndQU4NDexKErr4CLqe4vgx22RV+Glbt2itFkSZrepx88TTxOc0YcNhqg5623OOiNsgwKrNy5B1aNl+iD1EOBp5U8OHkKNs19FM/5X98+wMfI49d7ZuPvJ5/DhrvuQy9eVee+nQWfqwX5orRXijp37IjOmutlfEjd98rfsSm4mpgkdlMIuS0KPK2EVXFmW0H7u7rC5lqvJ1MBbDl6RJivCGjTBm+MGA032l7zpioqKpCis9L0tUb2er7fsE0oVkuIlFHgaWUThw7D623DYM9XbU5YUFFe81r3zp3xUkgHISOuehM4x2tFPElVMdBCvtZYJamDvW12ZxQL9euskthDajTU1mgUeFoZ24FzzqTJeDMkHEMUWjwybkKd1+8bPwHT7JwRYALeadsBT0+dLlpbifyUmjgkp2WI3QxCbomy2kRabT974iTco9cLw2w3Bqa3771P2MXUw8NDtDYSefJQmdCzM1UyINJGgUdENwadaix9mlKoSXP0dHeAu7s7rBFtiyAfNNRGZKOgsBAV1rtc57ZY7YbRXTqIuh05IY1BgYfIpgr10n17kE8X1VsOs/Xp2lnsZhByWxR4iCycj4nGH7mZQiaggIYw6mDxuIebvdUOsxF5oTkeIgu7I09DE8/D9VrAETJVaQC9hooDxozsSMNsRBYo8BB5KDbCOfr66lqjDYdyD+qwV/Ow49G3exexm0FIo1DgIbIQFOQFhSIdJlo/Wg/r5PRq6wQ3NzdYNSku2JRaeySCbhmJLHh6utBf8U2oOB5ThvekYTYiGxR4iCx4eLjhJsuerJ6PE4fOER3FbgYhjUZDbUQWvLw8oFEDlVQjtA7Wyenb3h329vawdrSAVD6ox0NkgVVyePSRXhg62AX+fgpoNeyiS3/VKgWPkf0pqYDIC/V4iGw205s6dSymTq3aHuHylav4Yul2nE0zWPVmcL4OQKfwDmI3g5AmoR4PkR0bGxtEhHfEf16eixFhdrDmYbY+oR40zFab2Fsg0JYIjUKBh8iWi4sLhvUKg0JhvdlsI/pRiRwiP1b6J0ssRYC/DxRWOtfj6wjKZiOyRHM8RNZS0jKtdo7HVqMQ5r7INVIc3pJaeySCejxEtrKysrFk02kYTda5cLK40gSDwSB2MwhpMgo8RFbi4y/h089+xsXoGHz1yxqkFFrvLWVJJY/Y+EvgrbXLR2SLhtqIbOj1evxv8SacOVeK3UfWosBJDb5mnwTrU6xT4B+LtuKJ8YmYPmmM1Q+70QJS+aDAQyRFp9Nhw8adOHHiCgwGE2xsVZg/byKCg4Nw9WoSYmJKhWBTUs5Bb2+9QYdhHZ2iSgW+2BCDw1GJeP7BSWgbHCR2swi5LQo8RFIuXozFkiVnUannataqXLq8AkMHByA2NqvmeXKdwcTh0OVyxP5nFR4d3wnTJo6BUsk2wiZEmijwEMlgcxUbNx2BzsDVuavPyDDhr1WJNR+T+lgvMLsU+HztBZyNS8EzD06Dl5cnrApltckGJRcQyczfrFmzFUeO5tYLLuzj6ge5tUojh61Rhfj17y1iN4WQm6IeD5FE0Plm0TJs3pIGg5GG0u6kYGgbZw4T+rTF9PHDxW4OITdFgYeIKicnB198sQLHThZT0GkmlZJHqIcSs0d3w7BB/WBnZ5316yirTT4o8BDRZGdn471/L8XF6AqrTotuDpZ0oVTwCPNkAac7hg/uLxRPJUQOKPAQUZw7F4VPP9+ClFS2rQEFnaYEHBslj75t7TBjVG/06t6FAg6RHQo8RJSezsKPNyEzi6eg04yA88DkwejaOYJSpm9EWW2yQYGHtHrK9Nq1u5CVzYKO2K2RPrbLqosNj9FdvDBuSE90jgingENkjwIPaVUJCZexftNVmExit0T6bFU8BoU64Kn7JyIwIEDs5hBiNhR4SKthlZT/XL4LpaVit0T6w2rOWh7/uKcXxo4cCoW17nTXVDTUJhsUeEirOXr0FCIj2QJRmte5VdDp4KnAG/MnomOHMLGbQ0iLoMBDWkVJSQl++mV/nXI4pC61gseQUHu88cz9cHJyErs5hLQYCjykVRw7dhpJySx1WuyWSJOLjQlzhrfH/XdPglqtFrs5skQLSOWDAg9pFX5+3lAo2F8h9XhuzFrzdwL+MWsIBg/sJ3ZzCGkVFHhIqwgMbAM3VwUyMukWsJpWyWNImAOefmASAtq0Ebs5hLQaCjykVbD6YSHtnJCZVWi1w20scaCaRsnjyXGhuP/uyZS1Zi6U1SYbFHhIq+naJQCRRwosariNVYRWcNcDi5sNq6FW9bGdRoEgL/ua79bf0xn+3m7Cv91dnTGwX28KOsQqUeAhraZr1zCo1eeh00H2XG1NuG9IO3Ro1wYebq7CcyyI+Pn6QKWq+rPiOA4ajUbklhIiPRR4SIu4fPkKEhISERISCD8/X9ja2iIgwB/urgqky3yeh1UUeGJCZ9w9dbzYTSG10VCbbFDgIWaXn5+Pt95dKWxZrVUfhoeHEu1CnGFro0JBkbz/EpUcj8fHheKuKePEbgohskWBh5hdcnIqsjKNMJk4lFdySE41ITk1H5aQ+jwy3BEzp44ThtEIIc1DgYeYncnEVvLBorA4E+6lxPNzp0Gr1YrdHNIAWkAqH5RSQ8xu9+6TQm/HkrjZmPDyQ+Pg4+MtdlMIkT0KPMTsHBxthH13bLU8XJyrhqjkvtDzsQmdhb1wCCF3jobaiNn5eLth8oQKTJo0CFqtBv/3/kpcTdTLsiq1guMxtrMLZkweK3ZTyO1QVptsUOAhd6y4uBh5efnw9/cT1rBMmTJGeL56An7BS5Pw2utrUSLDfXi6+qnxzydm00JPQsyIAg+5I+Xl5fjXq0uQnKxDQIAGPXv4IzklD/6+LujWLRSurs7Ysf0Y9AbIUv8If6HcDyHEfCjwkCZj8zenT59DVnYe0tNzcelyJfR6DjGxOsTGXRFqsXFcIVatviqUj9EbOdkmGxSVlovdBNJIlNUmHxR4SJOUlZVh06bd+OW38yivqH72elCpLgDK/mswcjAYIWsJqay2HCHEnCjwkCZZtGg5tu3MhNEozx5MU6XlVwrBlobbCDEfmjElTdK9e4gss9Oaq0QHlJbKMCvCmrPapPYg9VDgIQ0qKSnBr7/9jZycHBgMBuFRWFiIXbvPC3M81qK4ErialCJ2MwixKDTURuowGo3IyclF5OFTWLY8Hps2x8PWtur+pFLHIzubt6oejwkcrqako0+vHmI3hRCLQYGH1HH1aiL++a+/hDU3RiOQncOeNcFasc5dRk6h2M0gjSHFoS2ptUciaKiN1MH2zTGaAIPheoaatbuUlm9Vw4uEtDQKPKQOVuLm2gaa5JqcokphCJIQYh4UeEgdaWkZKCmhu/vaMopNwuZ2RNo4iT5IfRR4SI2oC9H45D+bhGE2cl2FgUNGVrbYzSDEYlDgIQI2lPTLrzuQkWWCkxOHtsEqtA1S0rAbAJ0RyMgUsiwIIWZAlxVSU0l62pQ+mD6VQ1hYCNzcXKHT6fDPlxcjOlYHa3c+LgljRordCnJLlNUmGxR4rFhFRQX27z+CQ5ExCAnxwqCB3RES0k54jWVxXbwYi5RUCjrs6uFgT9tdE2IuFHisVFpaOj5cuByx8RVC3bUDhwqwZm0c+vf1RLfu7XDqZAIOH8tBaal1T4+y3VM7+6hw90Tq7hBiLhR4rNT+/ccRHVtZZ7uCgkJg285sbNuZJeTjWFOFgpuxVwOvzJsId3d3sZtCboO2RZAPSi6wUuPGDYG7W/3AwtZJsoBjjeslr22YWsegUCeEhbYXozmEWCwKPFbK1dUVo0cGQaMRuyXSCDg2Kh6h7lyd4KNS8Bg7sEvNFt6EEPOgwGPF5s27G2+9Phzdu9rC3r5qPsPasJjiasNjWi8v5JTUrUnnZQ906xwuWttIE4m9/QFti9BoFHisGMtcO3U6HnqDCa++PBxTJ/vD2QlWxUZlQpiPFutPZSG/8voQIwtIPdu6wNnZWewmEmJxKLnASmVmZuGzz1fg9NlSmEw8vvhqH4KDbDFmdADWrksStq22BpVGDseuVsJ0QyKFiuMxqn9n0dpFiCWjwGOlQeefL/+C1HTjtcw1Dnn5PPILypBfoENAGxWuJDa9KCbrJWi1VV2Gigp5BK7aWX3V1EpgdLgT+vamPXhkh4a2ZIECjxW6cDEOaRmmeunSbJgpv8AgrOUpKMpGUVHVnjyNDTreXgq8/I9xyMjIwRf/PQaDQR7Bp5qC4xHsqsBjU3tj2KD+UKvVYjeJEItEgcfKmEwm7Nx59tr+Mg0EBh4wGkwIaWsPJyctDh3KRaWeqxNglEoeCgUHvb5qN1L2nI8Xh7ffmo6OHcOQmJgEW9tjKC6GbNiqedw3sA0euGs8zesQ0sIo8FgZFnDyCypvuji0sAjYuSdHSCHu3ZPD3LkRWPFXtLAjKftcB3vg+WcHwcPDFQs/3oycXB6+Pgq8/upU4fV9+yKx7M/DKCmBbLDAObyDE558aCYUCsq3kStaQCofFHisjFKpRHCQM2JiGy7zX5XVVZXddeJUCRSKZHz6yUwkJaUKSQje3u7o2rUzioqK4O2txaCB7ph17zhs3LgXq9Zchk5X1QuSU9UDezWPB6YOp6BDSCuhwGOFhUFLSxtX+NNkAo6fLMawoYkYP75urTInJyd8/umzQiA7ePAIVq+7dC2hQD4Bp7q3MySMqhMQ0proFs9KpKSk4sOFP+C557/B4SMFjf48VkB02/bzMDSwO5xKpRLO+7/v9qOsTF4Bpxprdef2/lSdwBJYyALSRYsWITg4GDY2NujXrx+OHTt2y+NXrlyJjh07Csd36dIFmzdvrndMdHQ0pk6dKsxf2tvbo0+fPkhKSoJYqMdjBcrLy/HJpysRdaG8Jn26KaKjy7By1WZ4e7vVe23T5lPIyJTvQDZr+eUU2l2USMOKFSuwYMECLF68WAg6X375JcaNG4fY2Fh4eXnVOz4yMhKzZ8/GwoULMXnyZCxbtgzTp0/HqVOn0Llz1Tq0S5cuYfDgwZg/fz7ee+89YbTiwoULQqASC8dXpTcRC7Z69RYsWnxWGDprDtYZuFmHgL19xJjPMdpwKPcwT7rzoBAbfPHmU9TrkSk238ju5Ds/9iGUGvEupg0x6ioQteR1FBYWChf82+nXr5/QG/nmm29qslADAgLw3HPP4dVXX613/KxZs1BaWoqNGzfWPNe/f390795dCF7MfffdJywN+P333yEVNNRmBZKSs5sddBh2a8I+v6GHnJIIbiajoLLBoUQiz6w2qT2qg2PtR2VlZb32sx1/T548idGjR9c8xxJe2MeHDx9u8Htmz9c+nmE9pOrjWeDatGkTwsLChOdZr4kFt7Vr10JMFHiI1csqMSEnJ1fsZhALxnotrFdW/Vi4cGG9Y3JycmA0GuHt7V3nefZxRkZGg+dlz9/q+KysLJSUlOCjjz7C+PHjsX37dsyYMQN33XUX9u3bB7HQHI8VUHAcWKawRs1Dp+fuqPdjiXQGIDc/H76+PmI3hVio5OTkOkNtWm3rbKXOejzMtGnT8NJLLwn/ZsNwbG6IDcUNGzYMYqDAYwWmTx+OPn0y4OrqjP98tg6XrzS9Dpsl05mAuMtJ6BxBWyDImhS3IbjWHhZ0bjfH4+HhISxPyMzMrPM8+9jHp+GbIvb8rY5n52TZpxEREXWOCQ8Px8GDByEWGmqzAoGBARgwoA/s7GyRnUNzGfVxWLH7olA8lRCxaDQa9OrVC7t27arTY2EfDxgwoMHPYc/XPp7ZsWNHzfHsnCxZgWXF1RYXF4egoCCIhQKPFWFvQmdH5U0z1Bg2JGdtC/hZ8sSVfB6f/bhamOAlRCwLFizAkiVL8Ouvvwprb5566ikha23evHnC63PnzsVrr71Wc/wLL7yArVu34rPPPkNMTAzeffddnDhxAs8++2zNMS+//LKQps3Om5CQIGTMbdiwAU8//TTEYmWXGOvm4+ONu+/qygri3PQYJwceQQG3Dk6WGnwOXCrFui07xW4KaSYpZ7U11qxZs/Dpp5/i7bffFuZizpw5IwSW6gQCtugzPT295viBAwcKa3e+//57dOvWDatWrRIy1qrX8DAsmYDN53zyySfCAtMffvgBf//9t7C2Ryy0jsfKsAyXx59chPSMhn/tbD+de+9pj79WJaCyUrrRx5zreGpzseHxxfMTab5Hhut4uj4izXU8535q/Doea0E9HitTWFiEkpKbp7XpdJyQBTdquPcdDbmxvW0cHfg7OocYCis5fPTzNhQUNL6sECGkaWR2WSDNodfrhf/m5+fjq//+jdJb1FVj/V+FUoH586fDy7Pxbw+FggfHVT38/Dg8/mhnvPbKaKjV8upQs+8/PteIL35cJaypIDJiIbXarAEFHgt37lwUvvjyd6G0TUxMAk6fLam3jof1SljAqP63jVYNNzc3jB8X0ugey8jhHnjmqW6YOycMX30+H7NmTUFeXiHkOFfPtsPeebEQf6/fKnZTCLFIFHgsWHJyCj5YuAlqNUsW4GBrawNFrbprLNh4eXKYPy8c/n5K2NlV9VwiIkKE14cP6wNbm+u3bOzzPD04jBzuVicgaTTAiOHdcPddE/HwQ3cLaweYM2evyG6bhGo6I4cft8fg7PkLYjeFEItDC0gtFMv//+779cjOMYE3VQWPDh3a4+03R+JQ5AUkJhagSxdfTJk8DP7+fpg2tRQJCVfw48870aaNn3C8q6sLNBoOpWVVczbh4TZ4+skJCAjwx5Wr3+FqYtWaoNAQLfr06VHn67O05NjY/Gsby8lTfgWHL//YgW/eChZKyROJk+LQltTaIxEUeCzUgQNHcOJUoVDEM+pijlCU0NbWFoMG9RMebOitdjVmdmHt1q0zFn7QtuYia2dnh4hwJ1y4WIR+fb3w/HOzheeYN167C199vR4xseUYNKidUP22NvZxv77+SF2XLOMSPTyySwzIzcujwEOIGVHgsUCs2OAPPx6sSYdOTjFg3brtuPfeKTXH3GwLgNoXWFZq460356O4uBguLi51jgsJaYcP/j0fsXEJiAjvUO887PwPPzwdly7/iLPny2QZfHwdgM9fvAuBAQFiN4UQi0JzPBaGpQEv/GgZ0jKuX+lZxf8//ryAI0dONLn8Pzv+p5/X4+V/LcJrb/wPaWnXF685Ojqid68eNb2ghoLYW28+iPAO2lZZkKpR8vC05+GgMU+UyykD/ty4D2VlZWY5H2lZlrCA1FpQ4LEgLEh8/c1fOHOufg+juAR479878NwL/8W27Xsa3A/kRuyYRd+uwLYdWThzrhwnT5Vgwcu/ISoq+vp5i4vrfV7tNcmurq545umJQhKDkyNabG2PrZrHS9MisHzhY5jYvf5Ojc1NMNh4Ng+Hjpwwy/kIIVUo8FhQ0Pl+yQrsP5Db4LAWiwUVlRxi43T49LMjWLVqyy3PFxeXgKee+Ro7dmbWnI/NF2Vl8fj3B+sRFXURp0+fw3MvLMaGDdtrelIpKal4/4MfsHbt1pr1Q+HhHfDd/57Ezz8+jm+/mYu2wSqhQoKzs3nqwmmVPJ6fEoG7p4zHoaMnseGU+bayNvEcLiQkm+18hBCa47EYO3bsx7oNyTAYbz2mxQIQO2bl6hjY2W/BtKnjhF0Ob5Sbm4/kZH2987HPz87h8ebb66HTAxUVwH8XncKBg3Gwt1fj3Pl85Bfw2H8gBwcj4zB6VBcMG9pfKGnCuLkB7//fg0IFBUdHByz9Ywt27MoShgNrrykyGrma4bmGMuOqt+NWK3gM7eCIGZPGCsOMv249jXKD+cb12NeOTc6rl4xBJIiy2mSDAo8F2LVrP/733bEmLdYsLAS+W3IGfr5sK9xedV7LysrGb0sPwGi6+cW4sOj6xyxoHD9ZUusIDgYjcPJUGc6eO4pVf5/EnPsHYPjwQTXFStmDeenFB6HV/IntO1Ph66vBwAGByM0pFjLxevXwxZ59qSgqrtpmmwUlW1sgOFCL8E7e8PFzQ7sgf0R0DBMCw459h3E5D2bFgmCnYA8KOoSYEQUemYuPv4Rvvo0U5nCaSq/ncPx4NPr27Vnnwrrszy2IjasUhtbuFAtKly4b8POvhzBwYB9ha4baWG/rqafuw9SpacJ6IvY6G6Jj65DYLo3Tpyfj4sV4ZGUXwMvLFRHh7YXj2IZZDAs4S1eux94zV3ElVw+eN+/osbMWmDy6KmASQsyDAo/MXbqchPIK1p9vWpBgcYZN+Ht5OeOVV7/B9Gn9MHBgX+G1Af07YfPWVFybojGL0hITCgoK4eXlWVNV4dy5GGzafA7t2rli3Ni+NcGk9pogtokde9xMRUUFVh1MQFoR1yJTlu09NQi6xdcn0sGx4VCJrViWWnukggKPzI0dMwyFBSX4+beoJm1jwK7toaHO+HVpFMrLOcQl7MZrSgX69esNX18vKJWcWQNPXgGPr/67Em++MU9YyLpz1xEsXZYgDNvFxGVg5+71GDH0CEaN7omQdsFCqjZbR3Q7rMfTkmuEOga60TAbIWZGgUfm2FDVzJmToNcb8NsfMcI9v40dB5blfKsLsoszh/NRBSi7Vqm6qAj49rs9CA1tBw8Pd7QL1iA6Vme2kjds2O7o8UK8/sb3mPvgCORkF9VqHweW3b1tZxZ27t4KBwcObYNtMffBkUI1hVtd+FlwcrZVIuMWWz00F/uyPh51F84SQu4cBR4LCT733DMBQUE+8PHxglarwauv/4n0WotIb5STy9cJKuzfKSlGbNi4Fw/NvQvPPTsZb7y1Gnn55msny1RjVQz+9eqma8k+N2bMVSUlFBRCWDcU984mDOx/FA/OGY82bfwbDEAs8Lg6qIBs85fBVimA8NBgs5+XtBDKapMNCjwWwsbGBkOGDKj5eN5D/ZCYmIkjx5KEyf0bNdQbYs+dOpWEB+eY0LFjGO6/rysWLzlXk+psDtXB5fbHAaWlwM7d2Thxaik6hjmibbA72rdvg9DQtkKwzczKRuSpizidzBbDmn84zF7Nw9Pd3eznJcTaUeCxUGPGDBPmPy5dXoRLlxuf8paRWVFTUHTChOHYsOkCEpPE2xCNBar8fODw0WIcOVYEjrsKrfYgTFoOxc5q6E2s59YyczC2asDFpWr9ESHEfCjwWLCEhMs4f6GpedbXL+KsBpubqxaJSdKoVcYCDOsJlZcDRh7QObBnW27iX6uqGsYk8iDF2mhSa49UUOCxEA2trC8vKwcyi6EqbfzEu29bl5q1Nmw9TWlKIVSZ0gg8tSlsVTApHFru/Apg0phQYS0RIcS8KPBYiF3b9uJKTBK823jA2c0JQ0cMRqfO4ejW1QGnVl+tU7jzZljg6tSpY816mqKiIuRGZUKZIb39qxX2NuBU2hbr8Dg5cxgzvH/LnJwQK0eBx0Jkp+di9fuRwr+1TkoYFxsxcuxw3PXYeJzb/B305bcPPCpbDgNG9q75eOPKbSjLMeNiHplgZXL6D/GBn5+v2E0hTUFZbbJBA9gyw3ouN9vSgL0mvF5kxPLPtwrH2dvbglM2rltgMvKIjUoQKk3HxcZjw1eRMBms7y/HwZHDzFmjaeEoIS2EejwSwy76SYlJOH7wNFRqFTr3DEdgUICQZbZv9wGs/3E3CjPL0H10KO5+aBJ8favuykuLrs/DsOCTebEQCXGXsG3VXugbOcdjrOTx88tbUJBbBIPeiNJs6+ztDBzmh+C2QWI3hRCLRYFHQgEn8sARrPluB64cz4auyCD00tW2W+EaYIf+0zth//JzyL9aFWBSzxzDyS0xWPDfh3Hi4Bls+fZonfPpyky4eDYW7bsEYSd3ptEVCAwVJmxdfBTuwY6wRra2HO6+dyT1dmSIstrkgwKPBOTn5+Pr937E8VUJ0Jdf752obBTC8FdmTDHW/+coUKvjwgJJxoVivHXXt9CVGOsNiVUPuw0c3g+FHxajtLAcW789jorC268G1RUb0X1EGJJOHAFvxsWjUsdiTceujggKChS7KYRYNJrjERkr///TF3/i8NK4mqDD7rbd29njyW+mYdRj3aBQceCNVYGkNvZxRYHhlvMwHh4eePCx+zBwdG8YKhs35GbjosawCf1h42Rd9yUsi3zW7KE1WX2EkJZhXVcWCYqNicOBpVE1Q2EaByV6zwjBI/+cLdQnKx1biosHryL59J0VTUu6kgKjvnH9fl2ZEQe2H0NFkXgVC8SY2+nS0xnde3QVuymkuSirTTaoxyMydw83+HV2EXo5bKhnzBM98ObnC4Sgw9jb28OtzZ0tlCwvL8emH/YLvabGYL2oDZ8fbvTxlsDRSYEnnp5KvR1CWgH1eETm5eWFd5a8gF//+xeSL2Zh9uN31SvT0tytCdKuZAlp0X8uWoekk43fE5oN4enLeOvKZBtOmWyEtBYKPBLg7e2Nl99/Vlh3w6pM15aVlYXkqNxmnXfHt6ex+4ez0JcZzbavjiXy8lbigQcnUiabzFFWm3xQ4JEIdtGrHXRYryPmYgx+/3INCptZpJPN6Rj11jNP0xysczlhWkf4+vqI3RRCrAYFHonKzc3FB/O+R+7lMuqttCD/QBUmTRkhdjMIsSqUXCARrBL0mVNnsXv7XmFdj7u7O+5/YwK0jjTZ3VJUKh4PPDwArq6uYjeFmDOrTWoPUg/1eCTi9IkzeP++n2CsNMGj3Xo8/+UcnNofJSwgJS0jsK0WQ4ZSBWpCWhsFHgnIzMzE6cgo8MaqeZmsuFJ8+OCPKM/TN7hFNTHP3M7Qke1pvx1CRECBRwJ2bdyHtQsP18zlsMSCUivcjqA1OTgCw0f2E7sZxMwoi0weaI5HZGxx55FN51t0C2dSF8ua7trTDf7+fmI3hRCrRIFH5Dpty39ejUuRWY3aIZSYBytOMHREF1q3Q4hIaKhNxKCzdMkKrP7wQKNrqBHz8PZTof+AXmI3g5gbu3mT2g2c1NojEdTjEYlOp0NRXgncgu2F7Q/o7rv1yuMMGdkWdnZ2YjeFEKtFPR6RsCoFz772GEqfL8XF89HYseoAzm67gqL0Shp2a0EODhxGjuordjMIsWoUeETGqk/36d8bvfv1wuUnL+ODRxcjPaqIgk8LUHDAqIlBaNs2WOymkBZAtdrkg4baJIINtYW0D8GLX869abUCdoyTrw3ChntDqaWhuaby9lNi9gNUDJQQsVHgkZjwTh3h37VuCRelRoE2PVzh1cEBr/z0MP6z7E0E9/GAUs2hw0gfzHh9AEY83ln4mDRMpQQemNdPKEVECBEXDbVJjFqtxpAZPXH58HZhIzZ2d95rRlu8/vkLQj03R0dH4bhnFz6IgrwC9OjdXdi8bNHCH6nKwU2wDk5YJ1sMGz5A7KaQliTF2mhSa49EUOCRoB79u+AP7U5hHx2m14hOQjJC7W0TwiM6CvNA0Rei8dvna3B+S7JV7RjaFDa2wLzHx8DW1lbsphBCKPBIU+yFBBgrjMLQmWuQHdqGBjVY8eCvX9dgw1eRKM3WUzLCLdKnR4xtg+7du4jdFELINRR4JMjHzxNO/rboPTUMj//zwZrhtWqsl/Pde8sRty8DJgMFnFtxdFbg7ntHUUKBFeBMVQ8pkVp7pIICjwSx1Orv9oUJqdYqVd1f0dUrV/HeA4tRmFJBvZzbUCh4TJoRiqCgQLGbQgiphbLaJIgNo7GV9TcGHeb86YsoTKOg0xhevircfc84sZtBCLkBBR4JBp2XZ3+AH778rcHX27YPgr27utXbJcfezsSp4XBxcRG7KaS1iL3TKO1A2mgUeCSGbUxm52SDoxuiUVZWVu/1zl074Zn/zoSNE22JfTNsOqd9RxtMnT5a7KYQQhpAgUdiFatX/bEWV45nojS3Uigk2pBho4ZgxCPdhV00Sf2g4+zK4cWXZ8DBwUHs5hBCGkCXLolgcza/fLsMf7yx49ruozyMxqp1PDdSKBSYPncCPELpwnojNi0274l+CAtrL3ZTiEi12qT2IPVR4JFQ4Dmx7SIqS6uCTVmeARfORd/0+KVf/43shNJWbKE8ioCOnuiHceOHi90UQsgtUOCRCDafU5xTUfOxSc8jPurKTY9Nj8+hSgU3DLGFRthg/uN3NZgNSAiRDvoLlQhWzsXB3QbZKBE+Vqg5dOndsd5xrF7bp699i0uHc0RopXS5unF4bsFUymKzZrQDqWxQ4JEINm8T2qsNkk/nwVBRtdw5+mw8evXtiWOHT+DIrlPgTTxKCspwbFUC9XZq0WiBuY/1QceOYWI3hRDSCBR4JIKVdHnuzccALMH2b87AqDNh1Qf7obXRYsX7u1BRYKRFozdZrzNyvD8mTBwldlMIIY1EczwS2xIhrHtbcMqqumKs5xN/9ioqiijo3KwAaGi4LR574m5hawhi3cTOXqOstsajwCMhpaWlyEjOqVmfY+eugVFvomG1m3BwqprXcXZ2FrsphJAmoMAjIRfOXcSaDw/CZKj62MFDiwt7ksRulmS5uqnRrl2w2M0ghDQRBR4JcfP4//bOA6yps4vj/7ARkCWCMgX3HghuxFFnHa2tWuuqVqt2aG3tVGuHq/WrWm1tXa1176114kJFFERAUVD23hsSyPe8L4WqgDKS3Jtwfs9zWwhJ7klM3v895z3DArqGWmVhteTQbGQnVNy9gACSE6VITk4R2gxCLAjdk416tVUZEh4REeAbiIKc/wZ4FEnltLfzAmRSICEhSWgzCIKoJiQ8IiEtLQ0nNl6l/ZxqIJXKEROdILQZBEFUExIekbBv6xHE3ct4aco1y3ijaZr/8SCY9sAIQt2gOh4REB8fj4vb/VD8gjG5TGxM7Qww8ZsheBwcCa+/A5CbJC0LxXFRksihpacFfeOS1GJZQTEK/+39VoqOvhbvLyPNrbgBqTohl0uQlJTD3wMSY0KM6ctis0cskPCIgCM7TiEjKu+F9zEw18H8DRPRtZsr5CPl8BwejD+W7EZ8SDoaOJugWWd7tOrSFFbWlnBoYs8X4vS0DMRExT7zPCb1TXB8+zl4//0QmkBcVA5vI6Snpye0KQRBVBESHoGJCI+A13b/F3s72hIMntUVru5dSn6XSPhAuJ/3foPU1FTen4wVnz5PgwYN0LSZyzPTTQ9sP4qAf8KhKWSkFyEiIhLNmtEYBIJQF2iPR+DBb9vW7Ed61H9dqZ+HiYxLdytMmDGmXDiJVetbWVlVKDoVFaeu+HQddi28gOxEzUnRzs0B/tx0mosqUccpbRIqtoMoBwmPgLBBb3lZBZUm+zOhsXc1x+frZ9VqmiYTuK1rd+Lm3lCeoq1JsO+17/V0nD7lJbQpBEFUERIeAWGeyuxvJqN+I4MKRcfcyRBf/DoTdna2tRKdHZv34p9f72hsqjYb1Lpj6x1cOH+Fv16CIMQN7fEIDBOVyd8Pwx8fH0F+uowLTot+Nmjp5oShbw6Ag4N9tb2oRw9DkZOVg+C7D5GelIlzf/hpRBbbi8hIk+OnHy4hNjYJb00YxcdMEHULympTH0h4RMCQEa9AV1cH107egZW9OcZNHw1LS8tqPQdLKX7y+Al2rDuEWwdC+ViFYhnrfIA6AXudhQXAzi3ByMzIxcTJI2FiYiK0WQRBVAAJjwhgXs7Aof35URNYeGnPXwexf+VF5CbJ6nSbncJC4PCeJ7jt8yventoTvXt3o1HYBCEy6Bup5jCR2fPnAez65kK5YtG6SnGxBJGPpfjxWy+cc/PHkOGuaNHShXuRFILTYMTYlFNs9ogEEh4157bPHez54SKJTkWht0LA51o6fK+fhZHxOTg3M8bQEV3Q1a0jheEIQkBIeNSYzMxMbFqyDwUZJDovEqCiIgkyM4C7t7Nxz88LjeyuYdjINujt4QYbG2uhTSSIOgcJjxqH2P7+bS8ifNPq9J5OTUQoOkKGjev8sXdHADwGOGDo8F5wcnKkMJyaQ1lt6gMJj5oS4H8PZ//Q3NocVewDpaXIcWRPOM4cj0CnruZ4Y5wnWrdpSQJEEEqGhEdNCbgVjIKMf2dkEzVGDglvu+N9KQ23bx5Ez74NMWnqcNjaNhbaNILQWOjSTk3pN6wP6jcu3/GAqHkYLj8PuHA6AfNmb8Whgyd512tCjSiWi/MgykHCo6aYm5vBxIqERxkzflKT5fhjzR1s3riXWvAQhBKgUJuakZ6ejg3LtyEtPhNxwZlCm6OxSGXA0f1P0KKVNzw9ewltDkFoFCQ8agTrw/bn2t24tDkQcroQVzrSQmDDmktwcXGsds88QgCogFRtoFCbmnDq6Bl8PWM5zv3hT6Kjwn2f1ORi/G/FPpr3QxAKhIRHDUhOTsa2JSfhdzgCsjxSHVXv+dwPyMXan7fTfg9BKAgSHjWAtXdp5+nIR2ATqqeoGLh5LZFfABDiRfJUEaloDqHfFJFCwqMG6OvrY/bXU+HkZllu/DWhGszMdGBubi60GQShEZDwqAlmZmaY9f146JtoC21K3USrZHwFQRC1h4RHjWjbvg3cxjQX2ow6iYOjCbS1SfRFnw0ixoMoBwmPGsF6iJlZUTt/ITA20SePhyAUBAkPQVSB9LQ8ymojCAVBwkMQVcD/VjpOnbxAIyhEjOAZbJUcRHlIeAiiChQUAL+tvoFvFv6GwHvBQptDEGoNCQ9BVKN7tbdXKlb/eAy5ublCm0QQagsJj5pRkFsotAmo6wIUFV6I8+euCm0KUVmvNrEdRDlIeNQIVjl/++RDoc2o87Dx2f+cCKBkA4KoISQ8asSxPf8g5UmO0GYQAAoLiinRgCBqCI1FUBNu+9zB8V+uU2dqkVBcLCfhERkSuZwfYkJs9ogF8njUgJSUFPz2xW7kJtMoZrGQnChFcnKK0GYQhFpCwqMG3Lzqi9jADLrCFhHFRczrIfeTIGoChdrUAJ+zASiWPSs6WlqAoaUeCrOLIMun/QZV06ChLiwtLYQ2g3gadh0gtmsBsdkjEsjjUQOadyqZxcN6hekaasHSuR76zmiHNee+wPSfh0Fbn3qIqRLWsq1lW0s+roIgiOpDHo8a0H+4B+IikmFtb4l2rq3QxMUJ9evX539rONIKAd4huLH7EYoK6fJKFehoA737dhDaDIJQW0h41ABra2vM/3Z2hX/T09PDguXvY3+bIzj44yXkpsgo7KZkmjTXR+fO7YU2g3gOympTH0h4NAAW8nlr2hto37U1Am/f57dlZ+bixNqbyM+QCW2eRqElATwHtKQwG0HUAhIeDYHt/7Tr0JYfDJZxFfckCdd3PCIPSIEYm0rQvWdnoc0gCLWGkgs0eGjcqKmDoGNIiQeKpF1HM9jZ2QptBlERQvdko15tVYaER4Np1qIpLByMhDZDw5IKSjxKgiBqDgmPBqOtrQ0tbfJ4FEXDxjro3sNVaDMIQu2hPR4N5n7QA6RGUlNRRdXudHK1hpEReZCihe1lim0/U2z2iATyeDSYEzsvoDCbansURbMWtLdDEIqAhEeDMTarJ7QJGoPk34QNgiBqD32TNJiO3VtDS09oKzQDXT2gRUtnoc0gXoBELs6DKA8Jjwbj1sMVjl0seY0PUTssG+qgYUMroc0gCI2AhEeDMTAwwMTPRkLPuPw/M2s6ampngK5vOsO2gxmJUyWwt4U1KRgzvhNMTEyENocgNALKatNw3Lq7YvjcB7iy9y5Swksy3JzdG6LP653hObg3LC0t+XTTJWM2QppLcYHnRcfMQoL35/dFnz49hDaHeBmU1aY2kPDUgVqed+dOxvjp2bh90w9Zmdl4ZVh/3ly0lMA7IZDl0RfkaSQSOZxc9DFn3hB07NhOaHMIQqNQivCwPmEsdEPhG/FgbGwMj/69y93uc/0WTq6/rpb93CS8H4niP2Na2nJ0djfF3E/G8s7gBEGIXHjy8vKwcv5q6BnoYvjkQUhLSUe3Xu7PXGET4oCJzo8Tf0dGdC7UDokcknyZwnWHd58eZYu5n06mDtRqhqS45BATYrNHI4WHXTUf3XMC17feRbFUjiub/FAsK8aIhfcxYfZYmJqaKvJ0RC24deMWVr61HpmROeoZhi6UQktWpHCPp4GdDqa8O5JEhyDELjxMcEIePMS+9YdwY/u9skmY0hwp//+x7y/i0p8+6D3RFbO/nEGFeALD/r0O/XZCfUVHSegbAhPm9KTwGkEoGYUogN9tf3ze7wdc2eCHwqwSsXkaWX4RUkMzcWbNNTx6GKqIUxK1gO29vffdNNi50QL7tOi8PbcrBg/tL7QpRG2z2sR2EMoRHtZ+v0kPW2jplCQUaOloVZhYUJBeiJiImEqfRyaT4czxs7h84Qoehz1GXFwcT1QgFI+Dgz1mr54CcxcTnjZc10Vn4jw3jBk7nBJiCEJdQm2ssG7p9kXYuvpvRN6PgZW9Jf5Zca3az5OcnIzfZvyN/LRCaOlpQc9ED808HDB4kic6uXWEmRkVOioSV3dXrDyzCN+9vQpRN+Lr3MUZ+yzVt5Rg3Cw3jB4zlD5bBKFuyQWGhoaY/cUMSKVSfPve8ho9BytmtGhiisiEBKCwGIXZMvjvCUHAgYcwdTRGh6EtMeTtAejYuQMtEgrCqYkTFu38FJ+9sgQpjzJRV2CfH3MbbSxYNgqdOncQ2hxCEYhx4qfY7BEJCt/l/3Pt37i1M6hGdSFpaWmQ5smeuY09T5G0mO8Ref1yC4sHrYL3lesKtJhwdHRA1zHtlVESI0pIdAhCg4QnOjoap9deKctqex7zpiawsbOp9PF/rtqBhHuplf6diVB+eiEO/3pSLQsexcygsf2ha1Q3GlkY1APmLhlGokMQAqHQlSY+Jh45CXmV/r3XBFe0btOq3O0FBQWICI9EVFAsiotfLCgswta+b2sKtSkYW3tbGJjqQZr9rMepabCPjefoJnBz7yK0KYSCkcjl/BATYrNHLKj0ElfCysIr4NShf7Bp1t4KU7GfR99UDz0GuCvBuroNSxBp2NwCWTFq2MWgGphYauG18QPpwoUgNCXUZmllCX2z8q1xWHp1484NYG1f8TyT+uYmkOXJqhQ+M7I2hLWNZtSfFBUV8Rqom94+SEhIEDR8qKOjA7s2NtAz1lH79OqSPoEl3s3zAtPAxgC2to0Fs40gCAV7PPYO9rDtYIVH56KeEZ0Riz3xzrxJfD5MRVzYcxXFspcvumyGTN8p7rzhpSaQm5uL5eN/QXp4Fowb1cP0teMxZOQgwez59Me5eDA1BAd/O4onvtFlt2fG5iA3qUAt9tW0tCVoaK+DV9/qhNzcfOzbEIDC/P/+rmeoLaR5hDIRY8Gm2OzRNOHJz8/H3q0HEOkTz3/X1teGg7s1+ox3x5hJoysVnfAn4Yh/lPTSRY2JTouBDhg/8w1oEsVFci66mVE5+GvBPtg6NkZ7gdrws/5kHTq1R7sNbXkxbym3bvhi2ch1KMh4eShUKJhnY1BPjv5vOOPtqSNhbm7OX0PgnUgEXMngny/Wqal1J1tq2UQQAqOwb6CP9y3s/PgYCjKl0DfVhccsV3y5ZS7enjG+UtFhi8HWZTuQcD+10pi7joE2HLpZY9K6UVi6exHq168PTSU1NANLx61GgP89Qe1gCzPrJl56dO/VDZ6z3EUbgmOfnQa22vhqzSi8P28yF53S8OHYyf1gYMR+Brr0a4A33hoitLkEUedRiPCwK8vjm8/yjtQMliRwaf0tLH5jJTIyMl64YCz4eS7m7ZgGbd2KVzV7N2v87+T3XMA0bfRwvXr14DndHQbmenxRZ05f6qNMLB27Gnf9AiAWmBANeMMTOvXEl27N3jfWUfrLH99EV7cu5S5gOrt2xNzvB2LqAjcsXjEbFhYWgtlKKBm2/BSL7KBIm/KE58rFawg+EVYWLpMXAw1bm+OVmX0q9XZKMTIyQmf3TnDxtK/Q64m8mYDFU5bjfvADaOJ00Fmfz8DXxz6CVSszfhsXn1AmPmtw+ug/yMrKEsXeSvOWzWDVosSTECpZoKLbW7mZ4LtfJlWYpl96n779emPMuFehq6urfGMJgngptb6EvXbZG+umbeXtbZ4mKz4Hbh5dqjTXhPVg+2bbZ9i47C9c23oHhZn/ZbgVFRQh+NhjfOW7DB7T3GDjZI22rq3h0tT5paKmDrCF0a17VzTrdQ6JwX7PiM/at7Zii81uNG5rDV19bZja1Mf7S2by90vVsJZIzXo6Ic6P7cep+OSsduu15kiPzURsQDJkOTJItIE23U3x1dJ3yIshiLokPFFR0fh93nZk/lv7oaXFOlOXDCTuNqEjmjg3qfJzNWjQAAtWzkXQxGDsXXsY/oce8P0iBhOhrNg8HP/uEl+oWcjn1S/7Ytbn70IIWIeG/Lx8NG3WVCHPd/GMF3x3B5a7XZpbhLTH2fworYMyNP4LH34zW5Cr90bODYWJHMjlMGtkiu+3LUTow1BcPX0D5lamGDpmsMaFX4maQwWkdUB42OK75K2ViL+bUnabY69GmLliIrS0tdG0uQvf3K1u6IlldLn84oyvM75H4OGwcvdhIiTLlfFxCexnVRcCslENi8YsR25GPr45+ClatmpRq+dLTEzE1s93V6l4Vl4sx9lV3tDV18WsL95VuPiwDhI3r/kgKyMbwTdDUCQrQo+hbujTrzevOQq5GSZIzJp9d333BSLuozi0bd+WHwRB1DHhKSwsxLovNiLy+rNFj83cndDFrUutF/YVs9fi/sknL7zfjX1+SIpchrnLZ/PQE9sLSU/P4MWBykyXDQl6iGjfRL4antp5Bi2/q53w7Fi3B/EBqVVe0FkfvJNLL8HASB8z5k+DIjm65zg2z9jLm7KWyLkEDy6FoeWJFoiKiEbwyfIXAqqCtWLa9/thfLp8LnUdIIi6KDxeZy/D/8BDpWx6H9h8hO/pvOi52d/YYp0YmIbIu1+j87C2yErNxp3Dwfjx/CLe6l9ZcFFjGWjFQNyjhFo/n7aONi96rEoB7TPi86MXBozyhLOLMxTBvbv3sPeb42UNXv9NE0GsXzLmen7FN/er4pUpDTng/bcfwmeGVyuES9S1sQgiC22JzByxUG3XIDY2Ftu+2o/iSjpQ1xZpgbTKgsYaikb5JOLI4gu4sMYHOckFzxQ+KoPmrZvBxMaQ/xwTmMC7D9SE0tf47qdT0XtWF743Vh3yMwqRnVWy91MbmB2PHj7CsglrkRGRXWF4L+l+OhKD0wX/TjOvZ++GQ6LI8iMIQkXCw+blfD/lJ+5pVPTlr+2CwB6fkZRV7ceVjtvW0dfi+0TKxNraGp7vdYe2nhYyYnKwYu7/8O2cZTh78ny1Xv+B7Ydw5sQ57kGxTDVb14ZQNazGattvO/D5gB+4uIh+PZcDN3b4Izq68vHpBEFoWKjtzJHzCLsYXeECxRb/wrwSb6WmMfiw0DD4H6l+vY5JY0O8v3kqLK0s4ODoAGXCXtvMBdNQkFuAf1Zeg/emu/w23z1BKNxYiKGjBpe9/lIhioyMwtUz3ug1sDscnRyRl5eHM5suIfp2Ana2OghDMwPE3kmCKmG2/TR/LW7+dQ/yIrErzn/kJOTj/NGLmDJnotCmEGKDerWpDTrVWah8T/ujuJIIG0txHjJxQI1Fh43M3rpsJ29GWV3y0qWIi4iH50APqAKWTTZq6nBc+sMHeSmF/L3JSy3Ab9P+RnhwBKZ/MhWXzl7GyY3nuBjHBycjKy4PB21PodcUV2Sn5SDiRjyKpcWIua1awSklOzsbIZeeqJXolIb+zvx6Ga+OG8pHpRMEocHCExkRibBr/3Wdfh7rVmZo0651jQ257XMHfvtDahSuk+ZIcezn84h8EIPJ88ejUaNGUDZsU3/iqteweeZeFBWUqHFBuhTHvruIexdCEH0noVxTzczoXJz8/jLEQFRkNHISKx/aJ2aSQzJwbPdJ8noIQtP3eHau3YfsuKf6yz+HnpEewh6FYeemPdiy5i+kplY+wroiTm8/zwWkpiQ/SMeF1Tex/qtNNd7wrw7Msxs57lX0neP2TGIAywpj4Uhld3I2tNCHlXXF842qQkpiCmR5RVBHuNfzhzdSUv6rISMIwfuyVXYQNRcer/W+L/RGonwT8VnvpfjzvYO4+Od1PibheVjRZ2WwTXbW+aA2MPt8tgdi+6+7oApY5+aPvpsNh27K97Cep56FQY1b57BC0aMbTqldmO1pctILkZ5WeQNagiA0QHg6vN6cZ45VBvNWCrKk0NLTwnurJ6Fx48Zlewnbf9/Js6c+GPYZ/7lUgJhncu7Uef7/Od++iz5zXF94jqrAFtOrO30RER7xQqFTZA+zgdN6VzsduraY25tWOYMvOOg+vK9c51mJrAPBjt93496RUPXd95QAXYY3hbML1fMQhEbv8SzZ/AVWNfgFV36/g2JZ5Qs6q+85tP4E7Bxt0cCqAZKTkrF/8WnkpbAJlkDUrTjcPOYHu1aNUJBXCO/N/mjW7zTmr5uDBrYWzC2q9YtixaVzey5Cc09HfLHuY6U31Rw8+hWc3XwFEddiVbaYh3lF4fSRMxjxxvCX3jctORX/m7QRBqZ6aNrdET7b75UViqojhhZ6ePP916iDAfEM1KtNfdCqzpX9/BUf4M2VQ2BsbVDpl56FuwIOhuLDbgsxzfUj7F53ALLCIl7sybO/0grx4FQEzv3vBq78docvgCH/ROLLV5fixEovRegOP092fB4Cj4UiIT4RyoYNp/t4wyw4dLfhng+br8PGHOibKa+Rpyy/CDu/PoSQBw8rfP2srVEpPT164v2NU5ARmQ3vjf5qu7dTir6JHrS1aYoooZmsX78eTk5OvPu+u7s7fHx8Xnj/ffv2oWXLlvz+7dq1w8mTJ5/5+5QpU/4dLfLfMXjwYAiJRF7NNDJ29+DAYPh53+W/378ZilvbgkRZTW7SuB5+vvotHJVc21NKcnIy/G/dhUsLZ9g0ssGNqz749b2/kB6epZTWGUz7G7azxMLdH6NFy+Zlt585fha7lx6BQ4fGaOnWFPoG+riy/yafmaTOns7Tr7uRWyMs+nsuH49B1G0yMzNhamqK/m0XQEf75WNYVImsqADnA0sGYlZlevKePXswadIkbNiwgYvO6tWrubCEhISgYcPyRebe3t7o06cPli1bhuHDh2Pnzp1YsWIF7ty5g7Zt25YJT0JCArZu3Vr2ODaupnRSr1oIz/P43fbHl31W8CtwMcFUvdd7nbDwl88EDcmw/ZWFQ1cgMypHKc/PxaetBRbumc/Fx+f6LayavAFpYZnl7ivCa4MaIzExhvOwZli54X2NHodOVEN42nwqTuEJ+rHKwuPu7o6uXbti3bp1/He2T21vb48PPvgAn3/+ebn7jx07Fjk5OTh+/HjZbd26dUPHjh25eJUKT3p6Og4fPgyxUOt4hZGxEZ8TI0Z09YQd1cwSK/78bgey/p1XpAyYmCQGpuK7cat4Hz2vw1e56JQWcT99aBLseikiOA/LF27mCw9BiBn2GX36KCgoXyjPwuO3b9/GgAEDnh07P2AArl+/XuHzstufvj9j0KBB5e7v5eXFPaYWLVpg1qxZgpciaGygnC1M/iceVLueSJHs//Mg7h58yOtOlAkTlYR7qVg+czWCLzzSOJGpDLZv6HcplcSHED3Ma2FeWemxbNmyCkP1LOuU9YN8GvZ7fHx8hc/Lbn/Z/dl+zrZt23D+/Hkehrt06RKGDBnCzyUUtXYJLBtYwMjaAOlPat8pWdGkPc7EoW3HMG3uZEHCbc07NIOxTT3u8Sh9D0wO3D/x4hlGmghLRmHis2LxFixaPqtKo9YJDUWMrv2/9kRFRT0TatNX4ed03LhxZT+z5IP27dvDxcWFe0H9+/eHWno8LFW5dX8XUaa2spk5J366iCePhVmQe/TujtFfD6KhHCoQH/9LKfh7ywFBr+IIojKY6Dx96FcgPA0aNOC1eSwR4GnY7zY2NhU+L7u9OvdnODs783OFhoZCKGotPOyNenfhZNh1tRKl+LBuxgc3H1PZ+dhm4I1rN7Fr8x5exMr2mSTa4ntfNA02hunQ5vvYunEviQ+hlujp6aFLly48JPb0esJ+7969e4WPYbc/fX/G2bNnK70/Izo6mu/xqKKnZWUoZPfdzs4OH/06A9+8+j/kxOeJKrWa2ZIYrrqNtOtXrmP56PUoyJRhT8MTKCoogpzWQZUgKwQObQqGBHvxzsxxorwQIupmqK2qfPzxx5g8eTJcXV3h5ubG06lZ1trUqVP531mqta2tbdke0UcffQQPDw+sWrUKw4YNw+7du+Hr64s//vijLMFpyZIleP3117kXFBYWhgULFqBp06Y8CUHtkws6dGqPj/58B/qmyiuafBFmjsbQMahYR1u6u6jMjsz0bORnSHlCARPh/LSSsQmE6sTn2F/BuOx1TWhTCKLajB07Fj/99BMWLVrEU6L9/f1x+vTpsgSCyMhIxMXFld2/R48evHaHCU2HDh2wf/9+njZdWsPDIlIBAQEYMWIEmjdvjmnTpnGv6sqVK4Luh9a6judpmFu4Zc02HFz4D6Qqro5v0MIUnUe0wYVffLiXYdnCFFkxOSjMleHDXZMw/PVhSjs3C+3437kLv2t3EXT5IYKPh6FYRmKjVIyNoOVkV9K4rQJMzLXwwZJX4NG3p8pNIwSq42k1X5x1PPdXVbmOp66g0EIXlnM+9cOJyM3MxYnvL/F0V1WRGpYF++aNMWX9a/A7fw/vL3sXsVFxOLbpNNx6d1XKOZnQsmPbrztwYOFpSLNlovP06yrZ6XKsXfQP9JbpontPN6HNIVQBa8ohtuiq+jcKUQoKr7Bkrt2UeW8jLSEDMUElueQ5qXlIup+h1JAT60p98IfT+HTHLLz+9ijo6OjwvSe37ooXHVb8tXbxrwi7FYkiaTEfYa3u/c80DfZZy8mQYPcWL7h3d+UXRQRBiAOllPYzl3LR+s/LxhKEPwnHgn7fIVOpFfxypEfkYMng1Wgz3AXTF01Es+bNlHIuJq769fQR5ZuAwizlDnwjaveZyM2S0h4bQYgMpV0GsowitkD7+fphxfS1yIpX/phltsCwmUB+u0NwZt+zKYaKhHlT73/9Hj7a+Q6cPWyhY6DNe6YR4qORowl5O3VsLILYDqI8Sm1mdun8ZayetJmPKFAlZk2M0ca9lVLPwRazV4YNQO9+PRHgdw+XjlzDrf33+OgBdZ7sqUmwiwFHF3HWlxFEXUZpl4IymQy7lh7iBZyqgi0wTHRm/ToJfQd4qOScbE6Rew83LFgxDxt8foTnh27k/YgE9u/QtKVqRmIQBCECjyfoXjCi/RJVGl/X1pXgnVVj0XdAHwiBhYUF8jJVJ7TEy8nOVM44CkKEaEABaV1BKR5PTEwM1n+8BYWZMqiSIqkcm+fuxsUzXhAC1tY8/HY0fdZEglwuwXWv+2VJLgRBaKjwsPYOS6f/jPArcSrPJmLny4jMwbHfz0AIWKhPR+AZQMRz03J9UxAf/2wTRYIgNEh42Bd9x4Y9CL3ArvqFu+xX9vybFzX5m/T9G+gzpwvsXK1gYK7Hh+TR5rZw5GXLEXJfuC68hAph33sxHkQ5FHp5HuB/Dyd+9EKxTNjQRuTtOBzecxTDXhsCXV3V9o5jSQ3syM/PR2xMLIL8gvHQ7zFu7b+L1NCSyaCE6mANWvNyad+NIDRSePLy8vD38r2CeRtPkxWbh9/f2Y1bp/3w3rfTYG/PenqpFgMDAzi7OPMDY4ALfS7ip9d/hzSXOhyoEh19wMlZ9f/+BEGoINTGrvANjfVRXCiOjVxZvgy3/g7Giplr+Ka/0HgM6IMxS4dAx1BbaFPqFDraQH1Tas5Yp7LaxHYQyhEeqVSKW963EXojincOEANs+JqWjgTFReIQQtbFYfKctzFm6WASHxVibqOHBg0shTaDIAhFhtrYmNXVn/6Gu4ceiqZRJtvMbzfSBc4dHdB7WE++6S8GeAPV9yfySdh7PzuFYukLRFECWLUyg5ltfb5BGX4zjne/JqqHjq6Wyvf5CIJQovCw8NrCN5ciwju+whEIpdlcpRluPN24ng5keWxQGpRK4OEw3DsShtvHArHuwkrUq1cPYhGft2aOxa1jd/HY69maH/Z2aelpwdjGEG0HNcfMxVP51ED2Pk93+wiJQWlCmq6WGNTTpiahdQYxhrbEZo8GCA8bgJYZn1Oh6OjoaWHAvO4wtaoP7z23kRKeiTaDXTB+3mvY+b8D8N0eDGXBFprSz1/C/VQ8CglFPSNDFOQXwNbeFubm5hAS1mZn+tIJWPbGLzC0MIREIoe2jjZa92uG3q92R/NWzXgXhFLhLvk/fYBrwpPgHOzZcQRjJ4zkzV0JghAepX0TmWfzyth+aNehHSbOfguJiUmwsbHmYY8R7+bg7qFHMG9iDFmeDGmPs5V2VSrLLcIPY9YgNzUfRYVy9PvADZ8unwuh6eTaCWu8f4CVlVWZuIglJKhJSPOBXev8UVRUjEnvvCG0OQRBKLNJaEGGFD9OXY+I8Ag+25ulNJfG2l3du6DvLFcsPfIlVp5dDIduJfPElQHzxtLCs1CQKeWZbtmpOaIJubFBdey9YQeJjvKQFgDXzj4URXYjoUSEzl6jrDbVCA8fiGZS8YLJPJj4gDScO1K+bxoToI9/+ACOTo5ISUqBtEB1m+b3zz9GbGws1AkWIjK3NxPaDLUmLaGQz70nCELNhYcVSfYa7woDM71n9iPYwWZvtRrmhIGj+1V8Yi0tbFn7F354bQ3i/FKgKtIjsnHDywfqJjwj5gyCoYW+0KaoLVlpclw46y20GQRBKGKPZ9q8KWjcxAa/TNiGYing+WFXyKRFsLAxxaQPJ8DExKTSxISru24pdRx2ZZ4Ys0/dGDi0P2SbZNjw7nbkpRQIbY7aUVwkx+1roRj7ltCWEEqDJzmJLLQlgk4uGik8SUlJOPPXZf7FZp5P14Gd0G+QZ5nnUxks3Db8gwHYMmcf339RFVpaEujpq19dB3svh4wcBB0dbWz5ZDdSH1Hft+oSF5mD9PR0mJlR2JIg1DbU5nfbn9fx3D/xBMbWBvjq6AfoO9CDh9Gq0pG5zyu9YNdZeYkFFaFrpIPWnZQ7FltZsPf0leED8f2xL9BmZFNo6SotN0QjSY6W4sypy0KbQRB1Hq3adCxYOelXhF+L5793G9+Rj4Cuaq1EZmYmFk1YhieXo6FoSr2tisRPXiTHrjX7cT/4AdSVps2aYtnOxZj4yyjUt61Ho7arSHERcPl0MAoKKFSpkbCqdDEehOJCbVfPeSPlUQbvicYWvrY9WlVrn+X3pVsQeiFK4f8upg5GcOlpD5eOjri66/Yz6YzsvGkRWbi+5S6iA+Px49Elaht2YYkdb88Yj659umDDl1sRdCyMiypROexCpEhWTJ0MCEJdhWfgq/1xdbgPHNvZIj0xE8E3QzB01OAqP56nUCuhIn/MwiF4c8oYvshM+XDiM39jI5Dj4+IRFvIYAd7BCA0Jhau7K9SZFi2b46s/5uM910/49FWicqwddPDx4je5aBMEoYbCU79+fTi1t8PwtwdDR1cX237axa8kq7K3w+4z55t3EXwxFAn3UmtqQrnnrGelD6vGDfgeE6OiokynJk786D+431PtdeRlj1FHWAGqRFt97VcF9epLMP2TAWjWvKnQphDKQowFm2KzRxOy2rr274wmzk34ov3pjx9Va8Qza9qpq6+48QC6RtpYsHcWuvV0r9L9mfcTdC8I+9cfRVJEKvq+1QP9hnmgQYMGL30sE6qQBw/h6OTA+64xWCNPlqnHimpVDfPiCjILuANJn/PyGBhLMOsrT/T26CG0KQRB1FZ4evTuXvZzdcMXycnJSI+pPDT0fGfrF8Hu2+WN1nDr3hVpaWl4EBiCHn26VyqEj8Me489lO+F36AFv7cPOEXZhL7w8r+HbnV++UHxycnKw8/c9OLb8AmxaW6LXWDc8uReJcN8oDJzhgbemj4WqYeK/YN9s7Fx6AGFeMYKPHhcDPLlEWw4zKx0MHdcGAwZ5CG0SQRD/Ili7XuZxmNsbo56lAYoKZEh9nMU3x3UMtNF6aBM0dW2Cu2eC8eRq3MsXUokc/cb15ovNxqV/IvD8I3S42h7Gxsbl7sq8nG9Gr0JGeM4zosZ6uoVeiMH/Pl2P1t2bV3ia9MQM3DkRiJjbiSiWyRF+JRbhV4/wcd8G5npo2sYZQsAyCXv17YmOrh2w8/e9OL78PPJS6nZfMtbxe+CbTpgyYzTv9E3UAaiAVG0QTHgaN26M37xW8cU/Ly8P30xZjsTQNLz93Si88upAHrbK/SAXm378E2d+vvbiyaZyCS4fuo6wwCe4suk2mnrYw8jIqNzdWBrt+k+3lhOdsqeRy3FrWxA/Kj/X01lyJf9hk05fmdsLXbsJm6jAhHbG/Hdg39QW6yb9hUKRTIMVguJioJ6RPokOQYgQwYSHeSdsU7w0TPflHx/D1/sOz4675/0ALbs2xdDRg/H+wveQHJ2K61sCKn0uJhhXf/f777m1Kq7hYckGeoa6Ck+nbdrPHtM/mVKtPS5l0mdAL+xteRTRtxKhSbC318Bcnw/Ly8158XvNckVMzcpffBAEITyimYzFkg12fnMESUFpXBgu6fnA3ysQHqN7IDoo4aWPf1pMtCup6GfCYONihSCEKdR2FiIUi+gwfLx9EeufDE2Cvb02HS3x0YYZMDYxxs/ztyAiRsI9m+fR1gHcBjTE4OF9hTCVEArKalMbRCM8ubm5PDOrVEBkhcXw3ngX3pvvAlXYK2eFrLqG2tDW14bnuJ6VNiZl+zSK5vHVGFw6exmDXn0FYuCOlz/kGpRgwDzYjm+2wPvLZ8DBwZ7ftnLXl/ht9S5cPR6Nwvx/7ycBLBvrYPTkznh19CtlHjVBEOJCNMIT6Bf0bxcECazbmfMOAyzjDFVsJN2ogyW+3jEP+gYGsLOzrfA+/nfuIvCYYr0dRnFhMTbP243mbZrxDDMhYftl986EaMyFFvtMDPm8N6YvmPJMp3NWR/bJV9Ph1tMbf6y8gMxkGTxfc8RbU4ehceNGgtpMEIQaCA+bDLln2RGkP8nmwiPNL4K5owniA6peXKqjr8MX/cp6xbE6m01fblfKhjtb5NOeZOG7Caswe/UUtO3QtqSoU4Dw2/2gB0i8r5iiXLF4O5082lc4XoPVTHn27w3npo64cc0Pr785rMq9AgkN5N9kH1EhMnPEgii+pWyxMGpQj//MQm2s5X91SQlLR/iTcN5AsyLOnTiP8KuxyuvTJQeifRKwfNw6uPRwQAt3F0ye87bKC0q9/7kJaZ76zRuqDBYyDLgWiL4DKq/DcXR04AdBEOqBQvqssMV8//ZD2LBiI86cOMdnnlTLCC0tuA/rVJYUUNrGpjrkJhXg6F8n+eMe3A/BhX8ulj1HgP89bP5oH4qkyr38YKfLjMzBnd33sffzUzi27wSys7OhKmQyGYIvP9Koqyz2nt6/GsZfG0EQmoFCPB7WhWD3wqPIjMrlMXmrVvvRd3I3TJw9noecHoY8QkxEDDxfqTzLaNRbI3DnfCACDjxEkbT6G+NMZK5suY2o+18iwicOhdkyBHwYhGETBiHqcRRy4vNU0pW49BTFBUXY9N5uHHQ5hWVHv4atbcX7ToqEeXxRviVjKjSJGP8EREVGCb5/RogcymqrW8JTOvuGeyoyOW/8eeDL0wi5GYq3P3sTy976hceiXH268Fj9vbv3cGTLKQwa3w9a2lqob2oCPX19tHR3QWpkBiKvx9dIJLIT8hB4+L/kgZM/XEFafAamfTUJFk1NkBqapbKW+Ow0bLJqYlAqEuISVSI8V89cR3665nUsyE+XItj/PgkPQWgIChEec3NzmDY2RnrEf2Ellg59d/8jhFz4EfmphXDxtOUFnLGxsfhp2m+I809B0NlHyEstgJ6JLk+ZZo9nHpOiYCITcOIRtBZKsGD7HPzw2hpkxuQq7PmrZEOxHImxyi/kzMjIwPUDbP4QNA5WJ+V/ORDDXhsqtCkEQYg9uYC3w0kpgI6eFt768jXeBmfTD9u46LC/JYeU1NTkJOU/tZksh7aeFgwtDJAdn1drG5jXkZ2Vg86uneA+vgPO/nQdql40N36wC3k5+Rg59lWFj19ISkrC3o0HcX2vH/euNJXQ6xG85RHV5hCVwquJRVa/VlGFM6GarDb23ifHp/DO0XePsxqTF1+WM/HJT1XMeGIzByPY2dvyRSs9ofrZcgpJOIjKwcYZu1EkK8KYia8p1MtZOG4pwryiNdLTeZrk0HTExcbxWUoEQag3KpkexrpL/zl3P/5euwsFWS/fg2CdolmoTlELf2xMHNYs/JVnmwmFNEeGY2vOKSzLLS4uDqs++QWPL8dovOgwWDHxTa9bQptBEISYhEfH4MXOU25yAU58f7mkG4EKSQnJwGf9vsf5NTdrlC2nSOIDkrHm6195MWttSE1NxTcTVuD6lrs8lFcXYHtldy8FqSw5hFDjrDaxHYRyQm2sSLL3WDc89ooR3cLA7FHEXpEiYDN8Lv/qi9SYb9GsqzPGvDOqShNPI8IjYGBogPy8fOzfeBjxYUmIupVQJzydpwn3jeGD+Cqas0QQRB3c4zGzrE+jl6soPgEHHuHeoVDYOFhh1LiR5e6TlZXFB+UlJSYhMT4JW7/chfTYbMjyZSUiWkffY5b1GBkeidZtWwttCkEQYhCekDuhJDrVJDMtG4EBgdDR1UXTZi68dVDoo1AsGrUCBdlSvh8mzZahmIXT6L2FLFeGAJ9AEh6iYsQY2hKbPZq2x9OhV1uYNDYU1VwaUSOXY8fco/jE/Tt80n0Jzp08z2++eOwykh6k80y4gnQp95BIdEqRwOeEPx9vQRCE+qIw4ek/uB++PbkATr0bQevfCaBsPo6Lpx08P3SDVSszEqXnLoSKpcUoKijmtUZ/f7mf7+Vo62iD3qXK9+ui/OKq3QuQIAgNruNp07Y1Vh76BptW/IW87HwMnTgA7Tu154Wj8fHx+HXhJvjsCEJRAV2xPk/S/XSs/ngDer7WFRIdLcgFzsATK1lxuQgNCYNlD0uhTSHERjGfiwDx2UQ8j0SupDQ09rTPezisiHPfnwdwYMk/yEnMF10GXJVQps0SQM9Yhzc4Fdv3Ryywj9SYlUMwY/47QptCiITMzEyYmppigMVU6GjpQUzIigtxLnUrL/ZmwwsJJReQVhRWY+1OJswYj7l/T4OusShGAYkLOVCYRaLzMt0PvhzCs/4IglBPVNK54HlB6u3ZCwM+6AZDC32+H0QQ1SHCN553biCIp5HLi0V5ECIQHn5SLS18sHgWVt9YgteXD4JtFyveSJSSD4iqkJuUhwf3QoQ2gyAIdRIeBqtZcXZx5rH6X71+xGfHZqHXe51Q37YeT8tuNtAB/ee6w9CcuhETz8LaBN29Gii0GQRB1BBRbLTUq1cPfQd4wKN/HyQvTuZJB5aWJVlL8598jaAj/w13Iwi2z/PA+zEfh80uYAjivxoFkW2QqmMClSZ7PBXBQm1WVlZo2LAh7//Gjn7je8LYxlBhA+LYnhLLHKtvZwSJqF49UR2SHmcjMTFJaDMIgqgBor9cHP76UHTp0QkXT1zG/sWnkJNc/dHObOvIyEofth0aotvITujSuzMsLM1xfNcpHF95ETnJBVW/MHmuLUfJtlTJ2G9CdeRmFsPXJwAjRjUS2hSCIDRNeFgigq2tLd6eMR7WDjZYx4Qiu6RO6GlKExNK64e0dQALSy04Na2Pnh6t4dqtI+8E/XRo5p2PJqNbfzdsWH0U4WE5yMuTMAl55nnl8udEhf9Ycg5TcwmGjGqBEwceIDNDvYWHvXKGXE1yuWVGOrgVEoERQhtCiAf+PRXZ55cuSNVTeJ5m4GBPXtW/ZrU3cp+adMC0pG1bI9jZ1YdUWsy9kI4dXdCtW2feQr+ybDl2e5t2rbFyXROkpKQiLCzimfoQJjhBQeHw949HQoIUhQUSFBXJoacLNG9hiBkzB6N165aA3l7s3hmm1p+xzq7GcHaxQlJiJp48yUBmZhHycoHCQjkPm7N3UEyvr8hQC/cTsvlsIwMDA6HNIQhCU4WHMWCAB/davLzuwdLSCE5O1mjUyApt2rSEoaFhjZ6TPc7OzpYfz9OvX29IpVK+nxAeHoXU1Ey4uNijWTMX3gqI8cYbg+F97Q9EhKt2yJ2iaNhQC3PnvYHGjUvCVmzTnlWDZ2fnIDw8GgkJKbgfzP6fi8dhBZDJ5NDSBmQCv9yEbDliYmLh4uIsrCEEQWi28DD69u3JD1XBBMbWtjE/KoK165g4qTuWL70MmazkNuZksUNbGzA2LgngsXCc2Arumbc4eYpbmeiU3KYDCwsLfjg42JfdXlhYiAcPHiI+PpmL/ZHD13DtajIKq7/tphDyi4BrvgEkPEQJ7MslEdkXjApINUd4xEjv3t1wtXcQ7txOgVVDPbRq1QAdOrigoXUDODjY8rDdggUbEfpQoFW6ApgwunUzw4CBfap0fz09PbRv3xbt25f8zsKMXl7XsHXzdcTHsypt1cfiIuJTVX5OgiBqBwmPgmBewmefT+WjmU1MTMrVl7Cpounp4grF1TeVYPLkQTWuhWHp7v3790G7di2xZcsxXLqYoDLvh2XX93UxwvQ3h6rmhARBKAyqZFEgzCMwNzevcCFnYarCfIiKNm1N0bSpS62fh4XkRo/uAzMz1X2cLIyBz2aMQaNGNio7JyFySksdxHYQ5SCPR0WwxITcXIiKnOxCnsXHUtarC3tcUNB9nD/viwf3kxEVWYiCAig9NMgKgLW15TCpX1JgTBCE+kHCoyJsbRvBwBDIzoJoCAvLQ1xcfKVJEy8SnbVr/8bZf2K42Cj7ok7fABg6zAGm9evBqUkjNGzYAPb2trzVEkEQ6gcJj4rQ19dDDRwLpZKTLYfPzbsY/Vr1hCcwMJiLTr6KQoceHtaYM2cCdS8nXoi8uBhykWW10ViEihHZUqi5sEVTbKOHmKdy82b1GrCyGp/du7yUHlYrRV8fGP5qDxIdgtAgSHhUBBt7a9NIfCMeHj/OQlpaWpXvz9Knb/tmqmzPtE1bY7Ro0Uw1JyMIQiWQ8KgItoGvoyO+q/a0VDn8/YKq7O0cPXIHrGGDsYnk3wapSkLCCncleGVQBxp9QFQNobPXKKutytA3ug7CBMOwngQ21jookstx/fp9ePbr9dLHsSyy18e4w87OBsHBoVjzs69C7WKNXV2c9eHoZIpGjczQtl1TtG3bSqHnIAhCeEh4VIi2UB6PBNDRZq19JGjdxhzt2zvAzb0DrK0b8gy12Ni4qj2NRAIPjx5l/e1MTW8jI0P+4os61jZIq6Sbycsu/vT1gA8+GlbSeJUgCI2FhEeFtG7dGHf9Q5Xeub00BMaaNjs46KNVm4bo2bM9XFyceF+552nSxKna52C93b5eNAxrV59GZJSs4tckAezsdDBkaEv8tTXwpV0NGjXWo9ocoubwNuoiC21RqK1CSHhUSIcOTbF3dyiKipTz/GzNNjXTQpMmRujdpwU6dGjFBUJZeySdOrXHN9+a4Yfv9+JxWGHZd6yeEdChoxnMzQ3x5psDeZ1QdHQKbt9KQEGhnHtJTwsVSzPv178hZswcXTbynCAIzYWER4XY2TWGYT3FFpGyjf7Gtrro1NkGHTs2Rfv2rXmvuJp0I6gJjo4OWPW/mVi54m9c907n4mNupoNFi6bzFkKlzJs3iY+XePIkAosXHkRq6n8hOptG2vjgw3F8dhJBEJoPCY8KsbAwh5mZDrKz/p2doAA8+jbEx/MnQp8VvAgEE7rPv5iMzZsP4uSJKD6r5/m6GxZCY0erVi2weMkoHD50BRERmYiLLUS79lYkOkTt4VcyIivYpFBbhZDwqBAmDh07WSMmOkZhn0djE31BRacUIyMjvP/+BDg5nYWujk7ZkLyKaNOmFT+KiooQFRVNxaEEUccg4VEx/fp1xrkzscjP17wrIRbeGzFiUJXvzzwgJydHpdpEEIT4oAJSFdOuXRu0aWukkOdinoKlhYlCnosg1B15sVyUB1EeEh4B0NXT5u39awuLUDVxtlWITQRBEKqChEeAcNTnn0/CuzPbwdZeGzzTmbY4CIKoQ9Aej0BZYG++ORzDhnkiKOgBjh/3wQ3vdBQVVc8tl8vlCA+PRcuWaTAzM6NNeqJuw0cQiC2rTWT2iASJnK1ehKCwsdjr1+/CqROx1RKfkomcgEl9CRzsDdHHowVGjHyFqv+JOkVmZibvyOGp/Rp0JJVnUwqBTC7FxaKDyMjI4B3qiRLI4xEBrNByzpzxiIvdgNu3s6vcUoddMrAuCOlpcqSn5SI42A9SWRH3pgiCIMQK7fGISHxmzR4BM7Oah8tkMuDy5Yc8BEcQdQ2hs9coq63qkPCICNas09HJsFZzbiikTBCE2CHhERmvv94dRsY1Vx4J/YsSBCFyaJkSGT16uOHrhYNgYSnhiQPVgXlK3bs7U3YbUTdh7r4YD6IclFwgMphodO3aGT+vtsbVq7exd08QMp8ftiYB9P7tSm1q+l8WTxNnS4wcOUAQuwmCIKoKCY9IsbOzxdixjdGvXzfcvHkXBw/4ISpSBtYPtGNnU4wZ0xtt2rR8ZvQAQdRlZJAqfchijWwiykF1PARBqDX5+flo0qQJ4uPjIUZsbGzw5MkTGLCRwASHhIcgCI0QH1aILUZYVIJE51lIeAiCIAiVQlltBEEQhEoh4SEIgiBUCgkPQRAEoVJIeAiCIAiVQsJDEARBqBQSHoIgCEKlkPAQBEEQUCX/B2UNjTiTXIo9AAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Load the inputResampled.tif file (trend layers from TrendFire.js)\n", + "input_resampled, input_meta = load_raster(os.path.join(data_dir, \"inputResampled.tif\"))\n", + "\n", + "if input_resampled is not None and input_meta is not None:\n", + " print(f\"Input raster has {input_resampled.shape[0]} bands\")\n", + " \n", + " # Display the first few bands to understand the data\n", + " for band_idx in range(min(3, input_resampled.shape[0])):\n", + " plt.figure(figsize=(12, 10))\n", + " plt.imshow(input_resampled[band_idx], cmap=\"viridis\")\n", + " plt.colorbar(label='Value')\n", + " plt.title(f\"Trend Layer - Band {band_idx+1}\")\n", + " plt.axis('off')\n", + " plt.show()\n", + "else:\n", + " print(\"Input raster (inputResampled.tif) not found.\")\n", + " print(\"This file contains 14 trend layers representing vegetation, burn indices, and meteorological parameters.\")\n", + " print(\"Download from: https://www.dropbox.com/scl/fi/ena1qfeqv5ppshluop8kt/inputResampled.tif\")" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Error loading data/road.tif: 'data/road.tif' not recognized as being in a supported file format.\n", + "Road data (road.tif) not found\n", + "This layer was developed separately using QGIS and python\n" + ] + } + ], + "source": [ + "# Load the road.tif file\n", + "road_data, road_meta = load_raster(os.path.join(data_dir, \"road.tif\"))\n", + "\n", + "if road_data is not None and road_meta is not None:\n", + " print(\"Road data loaded successfully\")\n", + " \n", + " # Clip to study area if available\n", + " if study_area is not None:\n", + " road_data, road_meta = clip_raster_with_boundary(road_data, road_meta, study_area)\n", + " print(\"Road data clipped to study area\")\n", + " \n", + " # Display the road data\n", + " display_raster(road_data, road_meta, band=0, title=\"Road Network\", cmap=\"binary\")\n", + "else:\n", + " print(\"Road data (road.tif) not found\")\n", + " print(\"This layer was developed separately using QGIS and python\")" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Generating random points for non-fire locations...\n", + "Generated 62 random points within study area\n", + "After filtering: 62 non-fire points\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Low risk fire points: 31\n", + "Moderate risk fire points: 70\n", + "High risk fire points: 64\n", + "Total training points: 126\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# CELL 15: Generate random points and prepare training data\n", + "\n", + "# This cell corresponds to the random points generation section in FireVulnerability.js\n", + "try:\n", + " if study_area is not None and 'fire13_22' in locals():\n", + " # Generate random points across the entire study area\n", + " print(\"Generating random points for non-fire locations...\")\n", + " \n", + " # Create a bounding box from the study area\n", + " bounds = study_area.total_bounds\n", + " \n", + " # Generate 200 random points within the bounding box\n", + " random_x = np.random.uniform(bounds[0], bounds[2], 200)\n", + " random_y = np.random.uniform(bounds[1], bounds[3], 200)\n", + " \n", + " # Create a GeoDataFrame with these points\n", + " from shapely.geometry import Point\n", + " random_pts = gpd.GeoDataFrame(\n", + " geometry=[Point(x, y) for x, y in zip(random_x, random_y)],\n", + " crs=study_area.crs\n", + " )\n", + " \n", + " # Filter to ensure they're within the study area boundary\n", + " random_pts = gpd.sjoin(random_pts, study_area, predicate='within', how='inner')\n", + " \n", + " # Remove problematic columns that might cause join issues later\n", + " cols_to_drop = [col for col in random_pts.columns if col.startswith('index_')]\n", + " if cols_to_drop:\n", + " random_pts = random_pts.drop(columns=cols_to_drop)\n", + " \n", + " print(f\"Generated {len(random_pts)} random points within study area\")\n", + " \n", + " # Now filter out points that are too close to fire points\n", + " \n", + " # Convert to a projected CRS if the current one is geographic\n", + " if study_area.crs.is_geographic:\n", + " # Choose an appropriate projected CRS for your study area\n", + " # UTM is a good choice - this is approximately UTM Zone 43N for Goa, India\n", + " projected_crs = 'EPSG:32643' # UTM Zone 43N\n", + " random_pts_proj = random_pts.to_crs(projected_crs)\n", + " fire13_22_proj = fire13_22.to_crs(projected_crs)\n", + " \n", + " # Now create the buffer in the projected CRS (100 meters)\n", + " buffer_size = 100 # meters\n", + " \n", + " # Create a buffer around each fire point\n", + " fire_buffers = fire13_22_proj.copy()\n", + " fire_buffers['geometry'] = fire13_22_proj.geometry.buffer(buffer_size)\n", + " \n", + " # Use a different approach to find non-fire points\n", + " # Create a spatial index for faster operations\n", + " sindex = fire_buffers.sindex\n", + " \n", + " # For each random point, check if it intersects with any fire buffer\n", + " non_fire_indices = []\n", + " for idx, point in random_pts_proj.iterrows():\n", + " # Query spatial index to find potential fire buffers this point might intersect with\n", + " possible_matches_index = list(sindex.intersection(point.geometry.bounds))\n", + " possible_matches = fire_buffers.iloc[possible_matches_index]\n", + " \n", + " # Check if this point intersects with any fire buffer\n", + " if not any(possible_matches.geometry.intersects(point.geometry)):\n", + " non_fire_indices.append(idx)\n", + " \n", + " # Get the non-fire points\n", + " non_fire_pts = random_pts_proj.loc[non_fire_indices].copy()\n", + " \n", + " # Convert back to original CRS\n", + " non_fire_pts = non_fire_pts.to_crs(study_area.crs)\n", + " \n", + " else:\n", + " # If already in projected CRS, proceed with buffer\n", + " buffer_size = 100 # meters\n", + " \n", + " # Create a buffer around each fire point\n", + " fire_buffers = fire13_22.copy()\n", + " fire_buffers['geometry'] = fire13_22.geometry.buffer(buffer_size)\n", + " \n", + " # Use a different approach to find non-fire points\n", + " # Create a spatial index for faster operations\n", + " sindex = fire_buffers.sindex\n", + " \n", + " # For each random point, check if it intersects with any fire buffer\n", + " non_fire_indices = []\n", + " for idx, point in random_pts.iterrows():\n", + " # Query spatial index to find potential fire buffers this point might intersect with\n", + " possible_matches_index = list(sindex.intersection(point.geometry.bounds))\n", + " possible_matches = fire_buffers.iloc[possible_matches_index]\n", + " \n", + " # Check if this point intersects with any fire buffer\n", + " if not any(possible_matches.geometry.intersects(point.geometry)):\n", + " non_fire_indices.append(idx)\n", + " \n", + " # Get the non-fire points\n", + " non_fire_pts = random_pts.loc[non_fire_indices].copy()\n", + " \n", + " # Limit to 90 points and add risk label\n", + " non_fire_pts = non_fire_pts.head(90)\n", + " non_fire_pts['RiskNumeric'] = 0 # Label as non-fire (low risk)\n", + " \n", + " print(f\"After filtering: {len(non_fire_pts)} non-fire points\")\n", + " \n", + " # Plot the sampled points\n", + " plt.figure(figsize=(12, 10))\n", + " study_area.plot(ax=plt.gca(), color='lightgrey', edgecolor='black')\n", + " non_fire_pts.plot(ax=plt.gca(), color='blue', markersize=5, label='Non-Fire Points')\n", + " fire13_22.plot(ax=plt.gca(), color='red', markersize=5, label='Fire Points')\n", + " \n", + " plt.title('Training Data: Fire and Non-Fire Points')\n", + " plt.legend()\n", + " plt.axis('off')\n", + " plt.tight_layout()\n", + " plt.show()\n", + " \n", + " # Now categorize fire points based on Delta T as in the JS code\n", + " if 'Delta T' in fire13_22.columns:\n", + " # Create low, moderate, and high risk categories\n", + " low_risk = fire13_22[fire13_22['Delta T'].between(0, 25)].copy()\n", + " low_risk['RiskCategory'] = 'Low Risk'\n", + " low_risk['RiskNumeric'] = 1\n", + " \n", + " moderate_risk = fire13_22[fire13_22['Delta T'].between(25, 35)].copy()\n", + " moderate_risk['RiskCategory'] = 'Moderate Risk'\n", + " moderate_risk['RiskNumeric'] = 2\n", + " moderate_risk = moderate_risk.head(70) # Limit as in JS\n", + " \n", + " high_risk = fire13_22[fire13_22['Delta T'] >= 35].copy()\n", + " high_risk['RiskCategory'] = 'High Risk' \n", + " high_risk['RiskNumeric'] = 3\n", + " \n", + " print(f\"Low risk fire points: {len(low_risk)}\")\n", + " print(f\"Moderate risk fire points: {len(moderate_risk)}\")\n", + " print(f\"High risk fire points: {len(high_risk)}\")\n", + " \n", + " # Merge just non-fire and high risk for binary classification\n", + " # This matches the JS code which only uses noFire.merge(highRisk)\n", + " categorized_points = pd.concat([non_fire_pts, high_risk])\n", + " \n", + " print(f\"Total training points: {len(categorized_points)}\")\n", + " \n", + " # Plot the categorized points\n", + " plt.figure(figsize=(12, 10))\n", + " study_area.plot(ax=plt.gca(), color='lightgrey', edgecolor='black')\n", + " \n", + " # Plot each category\n", + " for category, color, label in zip(\n", + " [0, 3], \n", + " ['blue', 'red'],\n", + " ['Non-Fire (0)', 'High Risk (3)']\n", + " ):\n", + " subset = categorized_points[categorized_points['RiskNumeric'] == category]\n", + " if len(subset) > 0:\n", + " subset.plot(ax=plt.gca(), color=color, markersize=5, label=label)\n", + " \n", + " plt.title('Categorized Points for Training')\n", + " plt.legend()\n", + " plt.axis('off')\n", + " plt.tight_layout()\n", + " plt.show()\n", + " else:\n", + " print(\"Warning: 'Delta T' column not found in fire data\")\n", + " categorized_points = pd.concat([non_fire_pts, fire13_22])\n", + " categorized_points.loc[categorized_points['RiskNumeric'].isna(), 'RiskNumeric'] = 1\n", + " else:\n", + " print(\"Cannot generate random points without study area boundary and fire data\")\n", + "except Exception as e:\n", + " print(f\"Error generating training points: {e}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracting raster values at training points...\n", + "Input raster shape: (40, 2912, 944), CRS: EPSG:3857\n", + "Points count: 126, CRS: EPSG:4326\n", + "Raster CRS: EPSG:3857\n", + "Points CRS: EPSG:4326\n", + "Reprojecting points from EPSG:4326 to EPSG:3857\n", + "Successfully sampled 126 out of 126 points\n", + "\n", + "NaN counts per column:\n", + "geometry 0\n", + "Shape_Leng 64\n", + "Shape_Area 64\n", + "Name 64\n", + "RiskNumeric 0\n", + " ..\n", + "band_35 0\n", + "band_36 0\n", + "band_37 0\n", + "band_38 0\n", + "band_39 0\n", + "Length: 65, dtype: int64\n", + "After removing NaN values: 119 of 126 points remain\n", + "\n", + "Sample of the training data:\n" + ] + }, + { + "data": { + "text/html": [ + "
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11POINT (8262691.529 1742415.806)0.00.0Bhagwan Mahavir WLS0NaNNaNNaNNaNNaN...-9.98116491.5356672283.200439-0.0143550.806202-0.02580629.1335910.03865221.5011392455.502686
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" + ], + "text/plain": [ + " geometry Shape_Leng Shape_Area \\\n", + "1 POINT (8261046.148 1687053.48) 0.0 0.0 \n", + "3 POINT (8269121.273 1703992.482) 0.0 0.0 \n", + "8 POINT (8273698.954 1723359.797) 0.0 0.0 \n", + "11 POINT (8262691.529 1742415.806) 0.0 0.0 \n", + "21 POINT (8266441.378 1724849.769) 0.0 0.0 \n", + "\n", + " Name RiskNumeric bright_ti5 bright_ti4 acq_time frp \\\n", + "1 Cotigaon WLS 0 NaN NaN NaN NaN \n", + "3 Netravali WLS 0 NaN NaN NaN NaN \n", + "8 Mollem NP 0 NaN NaN NaN NaN \n", + "11 Bhagwan Mahavir WLS 0 NaN NaN NaN NaN \n", + "21 Bhagwan Mahavir WLS 0 NaN NaN NaN NaN \n", + "\n", + " Delta T ... band_30 band_31 band_32 band_33 band_34 \\\n", + "1 NaN ... 2.158347 116.887054 2020.793701 -0.010756 0.675415 \n", + "3 NaN ... -32.076859 89.234467 2545.909912 -0.008131 0.735141 \n", + "8 NaN ... -100.272911 154.442795 1719.764526 0.001684 0.686330 \n", + "11 NaN ... -9.981164 91.535667 2283.200439 -0.014355 0.806202 \n", + "21 NaN ... -216.587692 170.010757 2016.565430 -0.017417 0.842181 \n", + "\n", + " band_35 band_36 band_37 band_38 band_39 \n", + "1 -0.136112 32.900528 0.052642 21.622280 325.735016 \n", + "3 -0.104667 32.834415 0.036715 20.826221 57.634197 \n", + "8 -0.009360 30.074121 0.033624 21.575224 1130.676025 \n", + "11 -0.025806 29.133591 0.038652 21.501139 2455.502686 \n", + "21 -0.071792 30.308001 0.043228 21.482294 1875.385132 \n", + "\n", + "[5 rows x 65 columns]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Correlation of predictor bands with fire risk:\n", + "band_13 0.349020\n", + "band_5 0.342209\n", + "band_26 0.218241\n", + "band_36 0.195580\n", + "band_12 0.187006\n", + "band_28 0.185220\n", + "band_15 0.177950\n", + "band_11 0.160622\n", + "band_30 0.147343\n", + "band_2 0.119539\n", + "band_4 0.108818\n", + "band_38 0.087956\n", + "band_25 0.070305\n", + "band_0 0.050785\n", + "band_10 0.050171\n", + "band_27 0.045494\n", + "band_37 0.043963\n", + "band_29 0.042347\n", + "band_1 0.029640\n", + "band_32 -0.026380\n", + "band_39 -0.043690\n", + "band_31 -0.048724\n", + "band_33 -0.113953\n", + "band_3 -0.119704\n", + "band_23 -0.133185\n", + "band_20 -0.159907\n", + "band_35 -0.164105\n", + "band_22 -0.170958\n", + "band_14 -0.182354\n", + "band_18 -0.185606\n", + "band_16 -0.197270\n", + "band_6 -0.207323\n", + "band_8 -0.225793\n", + "band_9 -0.253226\n", + "band_7 -0.253393\n", + "band_24 -0.307073\n", + "band_17 -0.343829\n", + "band_19 -0.346002\n", + "band_21 -0.354162\n", + "band_34 -0.381846\n", + "Name: RiskNumeric, dtype: float64\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved training data to 'fire_training_data.csv'\n" + ] + } + ], + "source": [ + "## CELL 7: Sample raster values at training points\n", + "\n", + "# This mimics the \"sample\" operation in GEE where values from the raster are extracted at point locations\n", + "try:\n", + " if 'categorized_points' in locals() and input_resampled is not None and input_meta is not None:\n", + " print(\"Extracting raster values at training points...\")\n", + " print(f\"Input raster shape: {input_resampled.shape}, CRS: {input_meta['crs']}\")\n", + " print(f\"Points count: {len(categorized_points)}, CRS: {categorized_points.crs}\")\n", + " \n", + " # Create a point sampler function with improved error handling\n", + " def sample_raster_at_points(raster_data, raster_meta, points_gdf):\n", + " \"\"\"Extract raster values at specified points with better error handling.\"\"\"\n", + " # Make sure we're in the same CRS\n", + " if isinstance(raster_meta['crs'], str):\n", + " raster_crs = rasterio.crs.CRS.from_string(raster_meta['crs'])\n", + " else:\n", + " # If it's already a CRS object, use it directly\n", + " raster_crs = raster_meta['crs']\n", + " \n", + " print(f\"Raster CRS: {raster_crs}\")\n", + " print(f\"Points CRS: {points_gdf.crs}\")\n", + " \n", + " if points_gdf.crs != raster_crs:\n", + " print(f\"Reprojecting points from {points_gdf.crs} to {raster_crs}\")\n", + " points_gdf = points_gdf.to_crs(raster_crs)\n", + " \n", + " # Create a copy to avoid modifying the original\n", + " points_with_values = points_gdf.copy()\n", + " \n", + " # Initialize columns for each band\n", + " for i in range(raster_data.shape[0]):\n", + " points_with_values[f\"band_{i}\"] = np.nan\n", + " \n", + " # Open the raster directly for sampling\n", + " with rasterio.io.MemoryFile() as memfile:\n", + " with memfile.open(**raster_meta) as src:\n", + " # Write the data\n", + " src.write(raster_data)\n", + " \n", + " # Track successful samples\n", + " successful_samples = 0\n", + " \n", + " # Sample at each point\n", + " for idx, point in points_with_values.iterrows():\n", + " try:\n", + " # Get point coordinates\n", + " x, y = point.geometry.x, point.geometry.y\n", + " \n", + " # Check if point is within raster bounds\n", + " row, col = src.index(x, y)\n", + " if 0 <= row < src.height and 0 <= col < src.width:\n", + " # Sample raster at point\n", + " sample = list(src.sample([(x, y)]))[0]\n", + " \n", + " # Add to results\n", + " for i in range(len(sample)):\n", + " points_with_values.at[idx, f\"band_{i}\"] = float(sample[i])\n", + " successful_samples += 1\n", + " else:\n", + " print(f\"Point at ({x}, {y}) is outside raster bounds\")\n", + " except Exception as e:\n", + " print(f\"Error sampling at point {idx}: {e}\")\n", + " \n", + " print(f\"Successfully sampled {successful_samples} out of {len(points_with_values)} points\")\n", + " \n", + " return points_with_values\n", + " \n", + " # Sample the points\n", + " sampled_points = sample_raster_at_points(input_resampled, input_meta, categorized_points)\n", + " \n", + " # Check for NaN values\n", + " nan_counts = sampled_points.isna().sum()\n", + " print(\"\\nNaN counts per column:\")\n", + " print(nan_counts)\n", + " \n", + " # Drop points with NaN values (outside raster or masked)\n", + " sampled_points_clean = sampled_points.dropna(subset=[f\"band_{i}\" for i in range(input_resampled.shape[0])])\n", + " print(f\"After removing NaN values: {len(sampled_points_clean)} of {len(sampled_points)} points remain\")\n", + " \n", + " if len(sampled_points_clean) == 0:\n", + " # Attempt recovery: Use nearest-pixel approach instead of exact location\n", + " print(\"\\nAttempting recovery using nearest pixel approach...\")\n", + " \n", + " # Create a more tolerant sampling approach\n", + " from scipy.spatial import cKDTree\n", + " \n", + " def sample_nearest_valid_pixel(raster_data, raster_meta, points_gdf, max_distance=5):\n", + " \"\"\"Sample nearest valid pixel if direct sampling fails\"\"\"\n", + " # Convert raster to array of coordinates and values\n", + " with rasterio.io.MemoryFile() as memfile:\n", + " with memfile.open(**raster_meta) as src:\n", + " # Write the data\n", + " src.write(raster_data)\n", + " \n", + " # Get all valid pixel coordinates\n", + " mask = ~np.isnan(raster_data[0])\n", + " indices = np.where(mask)\n", + " rows, cols = indices[0], indices[1]\n", + " \n", + " # Convert to x,y coordinates\n", + " xs, ys = rasterio.transform.xy(src.transform, rows, cols, offset='center')\n", + " coords = np.array(list(zip(xs, ys)))\n", + " \n", + " if len(coords) == 0:\n", + " print(\"No valid pixels found in raster\")\n", + " return points_gdf\n", + " \n", + " # Build KD-tree for efficient nearest-neighbor search\n", + " tree = cKDTree(coords)\n", + " \n", + " # Create output DataFrame\n", + " points_with_values = points_gdf.copy()\n", + " \n", + " # Initialize band columns\n", + " for i in range(raster_data.shape[0]):\n", + " points_with_values[f\"band_{i}\"] = np.nan\n", + " \n", + " # For each point, find nearest valid pixel\n", + " successful = 0\n", + " for idx, point in points_with_values.iterrows():\n", + " try:\n", + " x, y = point.geometry.x, point.geometry.y\n", + " distance, idx_nearest = tree.query([x, y], k=1)\n", + " \n", + " if distance <= max_distance:\n", + " # Get nearest pixel coordinates\n", + " x_nearest, y_nearest = coords[idx_nearest]\n", + " \n", + " # Sample at nearest valid pixel\n", + " sample = list(src.sample([(x_nearest, y_nearest)]))[0]\n", + " \n", + " # Store values\n", + " for i in range(len(sample)):\n", + " points_with_values.at[idx, f\"band_{i}\"] = float(sample[i])\n", + " successful += 1\n", + " except Exception as e:\n", + " print(f\"Error in nearest pixel sampling at point {idx}: {e}\")\n", + " \n", + " print(f\"Nearest-pixel approach: {successful} successful samples out of {len(points_with_values)}\")\n", + " return points_with_values\n", + " \n", + " # Try nearest pixel approach\n", + " sampled_points = sample_nearest_valid_pixel(input_resampled, input_meta, categorized_points)\n", + " sampled_points_clean = sampled_points.dropna(subset=[f\"band_{i}\" for i in range(input_resampled.shape[0])])\n", + " print(f\"After recovery: {len(sampled_points_clean)} of {len(sampled_points)} points have valid values\")\n", + " \n", + " if len(sampled_points_clean) > 0:\n", + " # Print sample of the data\n", + " print(\"\\nSample of the training data:\")\n", + " display(sampled_points_clean.head())\n", + " \n", + " # Calculate correlation between predictor bands and risk\n", + " predictor_cols = [f\"band_{i}\" for i in range(input_resampled.shape[0])]\n", + " \n", + " # Create a correlation DataFrame\n", + " correlation_data = sampled_points_clean[predictor_cols + ['RiskNumeric']]\n", + " corr = correlation_data.corr()['RiskNumeric'].drop('RiskNumeric').sort_values(ascending=False)\n", + " \n", + " print(\"\\nCorrelation of predictor bands with fire risk:\")\n", + " print(corr)\n", + " \n", + " # Plot correlation heatmap\n", + " plt.figure(figsize=(12, 10))\n", + " sns.heatmap(correlation_data.corr(), annot=True, cmap='coolwarm', center=0)\n", + " plt.title('Correlation Matrix of Predictor Bands')\n", + " plt.tight_layout()\n", + " plt.show()\n", + " \n", + " # Save the sampled points as a CSV for later use\n", + " sampled_points_clean.to_csv('fire_training_data.csv', index=False)\n", + " print(\"Saved training data to 'fire_training_data.csv'\")\n", + " else:\n", + " print(\"\\nWARNING: No valid points remain after sampling. Check that:\")\n", + " print(\"1. The raster and points overlap spatially\")\n", + " print(\"2. The CRS definitions are compatible\")\n", + " print(\"3. The raster contains valid (non-NaN) values\")\n", + " \n", + " # Print bounds for debugging\n", + " print(f\"\\nRaster bounds: {[input_meta['transform'] * (0, 0), input_meta['transform'] * (input_meta['width'], input_meta['height'])]}\")\n", + " print(f\"Points bounds: {categorized_points.total_bounds}\")\n", + " else:\n", + " print(\"Cannot sample raster without training points and input raster\")\n", + "except Exception as e:\n", + " import traceback\n", + " print(f\"Error sampling raster at training points: {e}\")\n", + " print(traceback.format_exc())" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training Random Forest classifier...\n", + "Training set: 96 points\n", + "Validation set: 23 points\n", + "Error training model: The 'min_samples_split' parameter of RandomForestClassifier must be an int in the range [2, inf) or a float in the range (0.0, 1.0]. Got 1 instead.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonas/miniconda3/envs/ee/lib/python3.13/site-packages/geopandas/geodataframe.py:1819: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " super().__setitem__(key, value)\n", + "/Users/jonas/miniconda3/envs/ee/lib/python3.13/site-packages/geopandas/geodataframe.py:1819: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " super().__setitem__(key, value)\n" + ] + } + ], + "source": [ + "# CELL 8: Train and validate the Random Forest model\n", + "\n", + "# This mimics the Random Forest classifier training in GEE\n", + "try:\n", + " if 'sampled_points_clean' in locals() and len(sampled_points_clean) > 0:\n", + " print(\"Training Random Forest classifier...\")\n", + " \n", + " # Define predictors and label\n", + " predictor_cols = [f\"band_{i}\" for i in range(input_resampled.shape[0])]\n", + " label_col = 'RiskNumeric'\n", + " \n", + " # Create a binary label (0: no fire, 1: fire)\n", + " sampled_points_clean['binary_risk'] = (sampled_points_clean[label_col] > 0).astype(int)\n", + " \n", + " # Split into training and validation sets (80/20 split)\n", + " sampled_points_clean['random'] = np.random.random(len(sampled_points_clean))\n", + " training_data = sampled_points_clean[sampled_points_clean['random'] <= 0.8]\n", + " validation_data = sampled_points_clean[sampled_points_clean['random'] > 0.8]\n", + " \n", + " print(f\"Training set: {len(training_data)} points\")\n", + " print(f\"Validation set: {len(validation_data)} points\")\n", + " \n", + " # Train the Random Forest classifier\n", + " rf_model = RandomForestClassifier(\n", + " n_estimators=1000, # Similar to the JS code\n", + " min_samples_split=1,\n", + " random_state=42\n", + " )\n", + " \n", + " # Fit the model on binary risk for simplicity\n", + " rf_model.fit(\n", + " training_data[predictor_cols], \n", + " training_data['binary_risk']\n", + " )\n", + " \n", + " # Print feature importances\n", + " importances = rf_model.feature_importances_\n", + " indices = np.argsort(importances)[::-1]\n", + " \n", + " print(\"\\nFeature ranking:\")\n", + " for i, idx in enumerate(indices):\n", + " print(f\"{i+1}. Band {idx} - Importance: {importances[idx]:.4f}\")\n", + " \n", + " # Plot feature importances\n", + " plt.figure(figsize=(12, 6))\n", + " plt.bar(range(len(importances)), importances[indices], align='center')\n", + " plt.xticks(range(len(importances)), [f\"Band {i}\" for i in indices], rotation=90)\n", + " plt.xlabel('Predictor Band')\n", + " plt.ylabel('Importance')\n", + " plt.title('Feature Importances from Random Forest')\n", + " plt.tight_layout()\n", + " plt.show()\n", + " \n", + " # Validate the model\n", + " validation_pred = rf_model.predict(validation_data[predictor_cols])\n", + " \n", + " # Calculate accuracy and other metrics\n", + " accuracy = accuracy_score(validation_data['binary_risk'], validation_pred)\n", + " conf_matrix = confusion_matrix(validation_data['binary_risk'], validation_pred)\n", + " \n", + " print(f\"\\nValidation accuracy: {accuracy:.4f}\")\n", + " print(\"\\nConfusion matrix:\")\n", + " print(conf_matrix)\n", + " \n", + " # Plot confusion matrix\n", + " plt.figure(figsize=(8, 6))\n", + " sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues',\n", + " xticklabels=['No Fire', 'Fire'],\n", + " yticklabels=['No Fire', 'Fire'])\n", + " plt.xlabel('Predicted')\n", + " plt.ylabel('Actual')\n", + " plt.title('Confusion Matrix')\n", + " plt.tight_layout()\n", + " plt.show()\n", + " \n", + " # Calculate classification report\n", + " print(\"\\nClassification Report:\")\n", + " print(classification_report(validation_data['binary_risk'], validation_pred, \n", + " target_names=['No Fire', 'Fire']))\n", + " \n", + " # Calculate Kappa statistic (mimics the JS code)\n", + " observed_accuracy = accuracy\n", + " \n", + " # Calculate expected accuracy (what would be expected by chance)\n", + " n_samples = len(validation_pred)\n", + " n_fire_actual = np.sum(validation_data['binary_risk'])\n", + " n_fire_pred = np.sum(validation_pred)\n", + " \n", + " expected_accuracy = (((n_fire_actual * n_fire_pred) + \n", + " ((n_samples - n_fire_actual) * (n_samples - n_fire_pred))) / \n", + " (n_samples ** 2))\n", + " \n", + " kappa = (observed_accuracy - expected_accuracy) / (1 - expected_accuracy)\n", + " print(f\"\\nKappa Statistic: {kappa:.4f}\")\n", + " \n", + " else:\n", + " print(\"Cannot train model without sampled points\")\n", + "except Exception as e:\n", + " print(f\"Error training model: {e}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training Random Forest classifier...\n", + "Training set: 96 points\n", + "Validation set: 23 points\n", + "Error training model: The 'min_samples_split' parameter of RandomForestClassifier must be an int in the range [2, inf) or a float in the range (0.0, 1.0]. Got 1 instead.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonas/miniconda3/envs/ee/lib/python3.13/site-packages/geopandas/geodataframe.py:1819: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " super().__setitem__(key, value)\n", + "/Users/jonas/miniconda3/envs/ee/lib/python3.13/site-packages/geopandas/geodataframe.py:1819: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " super().__setitem__(key, value)\n" + ] + } + ], + "source": [ + "# CELL 8: Train and validate the Random Forest model\n", + "\n", + "# This mimics the Random Forest classifier training in GEE\n", + "try:\n", + " if 'sampled_points_clean' in locals() and len(sampled_points_clean) > 0:\n", + " print(\"Training Random Forest classifier...\")\n", + " \n", + " # Define predictors and label\n", + " predictor_cols = [f\"band_{i}\" for i in range(input_resampled.shape[0])]\n", + " label_col = 'RiskNumeric'\n", + " \n", + " # Create a binary label (0: no fire, 1: fire)\n", + " sampled_points_clean['binary_risk'] = (sampled_points_clean[label_col] > 0).astype(int)\n", + " \n", + " # Split into training and validation sets (80/20 split)\n", + " sampled_points_clean['random'] = np.random.random(len(sampled_points_clean))\n", + " training_data = sampled_points_clean[sampled_points_clean['random'] <= 0.8]\n", + " validation_data = sampled_points_clean[sampled_points_clean['random'] > 0.8]\n", + " \n", + " print(f\"Training set: {len(training_data)} points\")\n", + " print(f\"Validation set: {len(validation_data)} points\")\n", + " \n", + " # Train the Random Forest classifier\n", + " rf_model = RandomForestClassifier(\n", + " n_estimators=1000, # Similar to the JS code\n", + " min_samples_split=1,\n", + " random_state=42\n", + " )\n", + " \n", + " # Fit the model on binary risk for simplicity\n", + " rf_model.fit(\n", + " training_data[predictor_cols], \n", + " training_data['binary_risk']\n", + " )\n", + " \n", + " # Print feature importances\n", + " importances = rf_model.feature_importances_\n", + " indices = np.argsort(importances)[::-1]\n", + " \n", + " print(\"\\nFeature ranking:\")\n", + " for i, idx in enumerate(indices):\n", + " print(f\"{i+1}. Band {idx} - Importance: {importances[idx]:.4f}\")\n", + " \n", + " # Plot feature importances\n", + " plt.figure(figsize=(12, 6))\n", + " plt.bar(range(len(importances)), importances[indices], align='center')\n", + " plt.xticks(range(len(importances)), [f\"Band {i}\" for i in indices], rotation=90)\n", + " plt.xlabel('Predictor Band')\n", + " plt.ylabel('Importance')\n", + " plt.title('Feature Importances from Random Forest')\n", + " plt.tight_layout()\n", + " plt.show()\n", + " \n", + " # Validate the model\n", + " validation_pred = rf_model.predict(validation_data[predictor_cols])\n", + " \n", + " # Calculate accuracy and other metrics\n", + " accuracy = accuracy_score(validation_data['binary_risk'], validation_pred)\n", + " conf_matrix = confusion_matrix(validation_data['binary_risk'], validation_pred)\n", + " \n", + " print(f\"\\nValidation accuracy: {accuracy:.4f}\")\n", + " print(\"\\nConfusion matrix:\")\n", + " print(conf_matrix)\n", + " \n", + " # Plot confusion matrix\n", + " plt.figure(figsize=(8, 6))\n", + " sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues',\n", + " xticklabels=['No Fire', 'Fire'],\n", + " yticklabels=['No Fire', 'Fire'])\n", + " plt.xlabel('Predicted')\n", + " plt.ylabel('Actual')\n", + " plt.title('Confusion Matrix')\n", + " plt.tight_layout()\n", + " plt.show()\n", + " \n", + " # Calculate classification report\n", + " print(\"\\nClassification Report:\")\n", + " print(classification_report(validation_data['binary_risk'], validation_pred, \n", + " target_names=['No Fire', 'Fire']))\n", + " \n", + " # Calculate Kappa statistic (mimics the JS code)\n", + " observed_accuracy = accuracy\n", + " \n", + " # Calculate expected accuracy (what would be expected by chance)\n", + " n_samples = len(validation_pred)\n", + " n_fire_actual = np.sum(validation_data['binary_risk'])\n", + " n_fire_pred = np.sum(validation_pred)\n", + " \n", + " expected_accuracy = (((n_fire_actual * n_fire_pred) + \n", + " ((n_samples - n_fire_actual) * (n_samples - n_fire_pred))) / \n", + " (n_samples ** 2))\n", + " \n", + " kappa = (observed_accuracy - expected_accuracy) / (1 - expected_accuracy)\n", + " print(f\"\\nKappa Statistic: {kappa:.4f}\")\n", + " \n", + " else:\n", + " print(\"Cannot train model without sampled points\")\n", + "except Exception as e:\n", + " print(f\"Error training model: {e}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"\\n\" + \"=\"*50)\n", + "print(\"SUMMARY OF FINDINGS AND RECOMMENDATIONS\")\n", + "print(\"=\"*50)\n", + "\n", + "findings = []\n", + "recommendations = []\n", + "\n", + "# Check if we have the key datasets\n", + "if 'fire_all' in locals() and len(fire_all) > 0:\n", + " findings.append(f\"- Found {len(fire_all)} fire events.\")\n", + " if 'year' in fire_all.columns:\n", + " years = sorted(fire_all['year'].unique())\n", + " findings.append(f\"- Fire events cover {len(years)} years from {min(years)} to {max(years)}.\")\n", + "else:\n", + " findings.append(\"- Fire event data is missing or could not be loaded.\")\n", + " recommendations.append(\"- Acquire or fix the fire event datasets (fire13_19.shp and fire20_23.shp).\")\n", + "\n", + "if 'boundary' in locals():\n", + " findings.append(\"- Study area boundary data is available.\")\n", + "else:\n", + " findings.append(\"- Study area boundary data is missing or could not be loaded.\")\n", + " recommendations.append(\"- Ensure the pa_boundary.shp file is available and properly formatted.\")\n", + "\n", + "# Check for the trend layer file\n", + "if os.path.exists(os.path.join(data_dir, \"inputResampled.tif\")):\n", + " findings.append(\"- The trend layer file (inputResampled.tif) is available locally.\")\n", + "else:\n", + " findings.append(\"- The trend layer file (inputResampled.tif) is not available locally.\")\n", + " recommendations.append(\"- Download the trend layer file from the Dropbox link provided in the README.\")\n", + "\n", + "# Check for the road data\n", + "if os.path.exists(os.path.join(data_dir, \"road.tif\")):\n", + " findings.append(\"- The road layer file (road.tif) is available.\")\n", + "else:\n", + " findings.append(\"- The road layer file (road.tif) is not available or could not be loaded.\")\n", + " recommendations.append(\"- Ensure the road.tif file is available for fire vulnerability mapping.\")\n", + "\n", + "# Model training/testing dataset assessment\n", + "if 'train_sufficient' in locals() and 'test_sufficient' in locals():\n", + " if train_sufficient and test_sufficient:\n", + " findings.append(\"- The dataset appears to have sufficient fire events for both training and testing.\")\n", + " else:\n", + " if not train_sufficient:\n", + " findings.append(\"- The training dataset may not have enough fire events.\")\n", + " recommendations.append(\"- Consider acquiring more historical fire data for training.\")\n", + " if not test_sufficient:\n", + " findings.append(\"- The testing dataset may not have enough fire events.\")\n", + " recommendations.append(\"- Consider using cross-validation techniques to maximize use of available data.\")\n", + "\n", + "# Display findings and recommendations\n", + "print(\"\\nFINDINGS:\")\n", + "for finding in findings:\n", + " print(finding)\n", + "\n", + "print(\"\\nRECOMMENDATIONS:\")\n", + "if recommendations:\n", + " for recommendation in recommendations:\n", + " print(recommendation)\n", + "else:\n", + " print(\"- All necessary data appears to be available and sufficient for the analysis.\")\n", + "\n", + "print(\"\\nNext Steps:\")\n", + "print(\"1. Ensure all required datasets are downloaded and properly formatted\")\n", + "print(\"2. Prepare training and testing datasets based on appropriate temporal splits\")\n", + "print(\"3. Apply the methodology described in the README for fire vulnerability mapping\")\n", + "print(\"4. Evaluate model performance using metrics like precision, recall, and F1-score\")\n", + "print(\"5. Generate predictions for future fire-prone areas\")\n", + "\n", + "print(\"\\nThis script generated several visualization files. Check your directory for the PNG files.\")\n", + "\n", + "# Print final message about notebook conversion\n", + "print(\"\\nNote: This script is structured in cells (marked by # %%) that can be\")\n", + "print(\"directly copied into a Jupyter notebook when your notebook environment is working.\") " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "ee", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/img/fire_events_by_month.png b/img/fire_events_by_month.png new file mode 100644 index 0000000000000000000000000000000000000000..7b99829a3a514f521f630a15d5a9ce5146720ce5 GIT binary patch literal 34841 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"source": [ + "import ee\n", + "import geemap\n", + "import os\n", + "import datetime\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "import geopandas as gpd\n", + "from tqdm import tqdm" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Earth Engine API initialized successfully.\n" + ] + } + ], + "source": [ + "# Initialize the Earth Engine API\n", + "try:\n", + " ee.Authenticate()\n", + " ee.Initialize()\n", + " print(\"Earth Engine API initialized successfully.\")\n", + "except Exception as e:\n", + " print(f\"Error initializing Earth Engine API: {e}\")\n", + " print(\"Make sure you have authenticated with Earth Engine using ee.Authenticate()\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def get_goa_boundary():\n", + " \"\"\"Get the administrative boundary for Goa, India.\"\"\"\n", + " try:\n", + " # Get the Goa district boundary from Earth Engine's administrative boundaries\n", + " goa = ee.FeatureCollection(\"FAO/GAUL/2015/level1\").filter(ee.Filter.eq('ADM1_NAME', 'Goa'))\n", + " return goa.geometry()\n", + " except Exception as e:\n", + " print(f\"Could not load Goa boundary from Earth Engine: {e}\")\n", + " \n", + " # Try to load from shapefile as fallback\n", + " try:\n", + " boundary_path = os.path.join('data', 'pa_boundary.shp')\n", + " if os.path.exists(boundary_path):\n", + " boundary_gdf = gpd.read_file(boundary_path)\n", + " # Convert to GeoJSON\n", + " boundary_geojson = boundary_gdf.geometry.__geo_interface__\n", + " # Create an EE geometry\n", + " return ee.Geometry.Polygon(boundary_geojson['features'][0]['geometry']['coordinates'])\n", + " except Exception as e:\n", + " print(f\"Could not load boundary from shapefile: {e}\")\n", + " \n", + " # Manual definition as last resort\n", + " goa_coords = [\n", + " [73.6765, 15.7560],\n", + " [74.3161, 15.7560],\n", + " [74.3161, 14.8922],\n", + " [73.6765, 14.8922],\n", + " [73.6765, 15.7560]\n", + " ]\n", + " return ee.Geometry.Polygon(goa_coords)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def maskL8sr(image):\n", + " \"\"\"\n", + " Mask clouds and scale pixel values for Landsat 8 SR imagery.\n", + " \"\"\"\n", + " # Get QA band\n", + " qa = image.select('QA_PIXEL')\n", + " \n", + " # Bits 3 and 5 are cloud shadow and cloud, respectively\n", + " cloud_shadow_bit_mask = 1 << 3\n", + " clouds_bit_mask = 1 << 5\n", + " \n", + " # Both flags should be set to zero, indicating clear conditions\n", + " mask = qa.bitwiseAnd(cloud_shadow_bit_mask).eq(0).And(\n", + " qa.bitwiseAnd(clouds_bit_mask).eq(0))\n", + " \n", + " # Scale the optical bands\n", + " optical_bands = image.select('SR_B.').multiply(0.0000275).add(-0.2)\n", + " \n", + " # Scale the thermal bands\n", + " thermal_bands = image.select('ST_B.*').multiply(0.00341802).add(149.0)\n", + " \n", + " # Return the masked and scaled image\n", + " return image.select(['QA_.*']).addBands(optical_bands).addBands(thermal_bands).updateMask(mask)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def addIndices(image):\n", + " \"\"\"\n", + " Calculate various spectral indices and add them as bands to the image.\n", + " Match JavaScript version exactly.\n", + " \"\"\"\n", + " # NDVI (Normalized Difference Vegetation Index)\n", + " ndvi = image.normalizedDifference(['SR_B5', 'SR_B4']).rename('ndvi')\n", + " \n", + " # MIRBI (Mid-Infrared Burn Index)\n", + " mirbi = image.expression(\n", + " '10 * swir1 - 9.8 * swir2 + 2',\n", + " {\n", + " 'swir1': image.select('SR_B6'),\n", + " 'swir2': image.select('SR_B7')\n", + " }\n", + " ).rename('mirbi')\n", + " \n", + " # NDFI (Normalized Difference Fire Index) - Corrected to match JS\n", + " ndfi = image.normalizedDifference(['ST_B10', 'SR_B6']).rename('ndfi')\n", + " \n", + " # EVI (Enhanced Vegetation Index)\n", + " evi = image.expression(\n", + " '2.5 * ((NIR - RED) / (NIR + 6 * RED - 7.5 * BLUE + 1))',\n", + " {\n", + " 'NIR': image.select('SR_B5'),\n", + " 'RED': image.select('SR_B4'),\n", + " 'BLUE': image.select('SR_B2')\n", + " }\n", + " ).rename('evi')\n", + " \n", + " # BSI (Bare Soil Index)\n", + " bsi = image.expression(\n", + " '((X + Y) - (A + B)) / ((X + Y) + (A + B))',\n", + " {\n", + " 'X': image.select('SR_B6'),\n", + " 'Y': image.select('SR_B4'),\n", + " 'A': image.select('SR_B5'),\n", + " 'B': image.select('SR_B2')\n", + " }\n", + " ).rename('bsi')\n", + " \n", + " # NDMI (Normalized Difference Moisture Index)\n", + " ndmi = image.normalizedDifference(['SR_B5', 'SR_B6']).rename('ndmi')\n", + " \n", + " # NBR (Normalized Burn Ratio)\n", + " nbr = image.normalizedDifference(['SR_B5', 'SR_B7']).rename('nbr')\n", + " \n", + " # NBR2 (Normalized Burn Ratio 2)\n", + " nbr2 = image.normalizedDifference(['SR_B6', 'SR_B7']).rename('nbr2')\n", + " \n", + " # MSAVI (Modified Soil Adjusted Vegetation Index)\n", + " msavi = image.expression(\n", + " '(2 * NIR + 1 - sqrt((2 * NIR + 1) ** 2 - 8 * (NIR - RED))) / 2',\n", + " {\n", + " 'NIR': image.select('SR_B5'),\n", + " 'RED': image.select('SR_B4')\n", + " }\n", + " ).rename('msavi')\n", + " \n", + " return image.addBands([ndvi, evi, msavi, mirbi, ndmi, ndfi, nbr, nbr2, bsi])" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Function to calculate SMI (Soil Moisture Index)\n", + "def calculateSMI(image):\n", + " \"\"\"\n", + " Calculate Soil Moisture Index (SMI) from Landsat data.\n", + " \"\"\"\n", + " Ts = image.select('ST_B10')\n", + " ndvi = image.select('ndvi')\n", + " \n", + " # Apply the SMI calculation\n", + " smi = image.expression(\n", + " '(Ts_max - Ts) / (Ts_max - Ts_min)',\n", + " {\n", + " 'Ts': Ts,\n", + " 'Ts_max': Ts.reduceRegion(\n", + " reducer=ee.Reducer.max(),\n", + " geometry=image.geometry(),\n", + " scale=30,\n", + " maxPixels=1e9\n", + " ).get('ST_B10'),\n", + " 'Ts_min': Ts.reduceRegion(\n", + " reducer=ee.Reducer.min(),\n", + " geometry=image.geometry(),\n", + " scale=30,\n", + " maxPixels=1e9\n", + " ).get('ST_B10')\n", + " }\n", + " ).rename('smi')\n", + " \n", + " return image.addBands(smi)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def process_landsat_trends(goa, start_date='2013-03-20', end_date='2023-02-28'):\n", + " \"\"\"\n", + " Process Landsat 8 data to calculate vegetation and burn index trends.\n", + " Fixed to handle date calculations properly.\n", + " \n", + " Args:\n", + " goa: Earth Engine geometry for Goa\n", + " start_date: Start date for analysis\n", + " end_date: End date for analysis\n", + " \n", + " Returns:\n", + " Tuple of (trend image, Landsat collection)\n", + " \"\"\"\n", + " print(f\"Processing Landsat 8 data for {start_date} to {end_date}...\")\n", + " \n", + " # Convert dates to ee.Date objects\n", + " ee_start_date = ee.Date(start_date)\n", + " ee_end_date = ee.Date(end_date)\n", + " \n", + " # Load Landsat 8 collection\n", + " ls8 = ee.ImageCollection(\"LANDSAT/LC08/C02/T1_L2\") \\\n", + " .filterDate(start_date, end_date) \\\n", + " .filterBounds(goa)\n", + " \n", + " # Apply cloud masking and add indices\n", + " ls8_masked = ls8.map(maskL8sr).map(addIndices)\n", + " \n", + " # Filter out images with too many clouds\n", + " ls8_filtered = ls8_masked.filter(ee.Filter.lt('CLOUD_COVER', 80))\n", + " \n", + " # Add a time band (fixed version)\n", + " def add_time_band(image):\n", + " # Get image timestamp\n", + " date = ee.Date(image.get('system:time_start'))\n", + " \n", + " # Get the year (server-side)\n", + " year = date.get('year')\n", + " \n", + " # Calculate days since the start of the analysis (safer approach)\n", + " days_since_start = date.difference(ee_start_date, 'day')\n", + " \n", + " # Normalize to years (for better coefficient interpretation)\n", + " years_since_start = days_since_start.divide(365)\n", + " \n", + " # Create an image with this value\n", + " time_band = ee.Image.constant(years_since_start).float().rename('t')\n", + " \n", + " # Add date info as properties (for debugging)\n", + " return image.addBands(time_band).set({\n", + " 'year': year,\n", + " 'days_since_start': days_since_start,\n", + " 'years_since_start': years_since_start\n", + " })\n", + " \n", + " # Add time bands\n", + " ls8_time = ls8_filtered.map(add_time_band)\n", + " \n", + " # Define the bands to calculate trends for\n", + " trend_bands = ['ndvi', 'evi', 'msavi', 'mirbi', 'ndmi', 'ndfi', 'nbr', 'nbr2', 'bsi']\n", + " \n", + " # Calculate trends for each band\n", + " trends = {}\n", + " \n", + " for band in trend_bands:\n", + " print(f\"Calculating trend for {band}...\")\n", + " \n", + " # Define the dependent and independent variables\n", + " trend_x = 't'\n", + " trend_y = band\n", + " \n", + " # Calculate trend (slope and intercept)\n", + " trend = ls8_time.select([trend_x, trend_y]).reduce(\n", + " ee.Reducer.linearFit()\n", + " )\n", + " \n", + " # Rename the bands\n", + " trend = trend.select(['scale'], [f'{band}_Slope'])\n", + " \n", + " # Store in dictionary\n", + " trends[band] = trend\n", + " \n", + " # Merge all trend images\n", + " all_trends = ee.Image.cat(list(trends.values()))\n", + " \n", + " print(\"Landsat trend calculation complete.\")\n", + " \n", + " return all_trends, ls8_filtered" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Function to process CHIRPS precipitation data\n", + "def process_chirps_trends(goa, start_date='2013-01-01', end_date='2022-12-31'):\n", + " \"\"\"\n", + " Process CHIRPS precipitation data and calculate rainfall trends.\n", + " \"\"\"\n", + " print(\"Processing CHIRPS precipitation data and calculating trends...\")\n", + " \n", + " # Load CHIRPS data\n", + " chirps = ee.ImageCollection('UCSB-CHG/CHIRPS/DAILY') \\\n", + " .filterDate(start_date, end_date) \\\n", + " .filterBounds(goa)\n", + " \n", + " # Function to compute the yearly sum\n", + " def createYearlySum(year):\n", + " # Filter for the specific year\n", + " year_filter = chirps.filter(ee.Filter.calendarRange(year, year, 'year'))\n", + " \n", + " # Create the yearly sum\n", + " yearly_sum = year_filter.sum().clip(goa)\n", + " \n", + " # Add the year as a property\n", + " return yearly_sum.set('year', year) \\\n", + " .set('system:time_start', ee.Date.fromYMD(year, 1, 1).millis()) \\\n", + " .rename('precipitation')\n", + " \n", + " # Create a list of yearly sums\n", + " years = ee.List.sequence(int(start_date[:4]), int(end_date[:4]))\n", + " yearly_sums = years.map(createYearlySum)\n", + " \n", + " # Convert to ImageCollection\n", + " yearly_sums_collection = ee.ImageCollection.fromImages(yearly_sums)\n", + " \n", + " # Calculate trend\n", + " def addTime(image):\n", + " return image.addBands(image.metadata('system:time_start').divide(1000 * 60 * 60 * 24 * 365).rename('t'))\n", + " \n", + " collection_with_time = yearly_sums_collection.map(addTime)\n", + " \n", + " # Calculate linear regression\n", + " trend = collection_with_time.select(['t', 'precipitation']).reduce(ee.Reducer.linearFit())\n", + " \n", + " # Rename bands\n", + " rain_trend = trend.select(['scale'], ['rain_trend'])\n", + " \n", + " return rain_trend, yearly_sums_collection\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Function to process SMAP soil moisture data\n", + "def process_smap_trends(goa, start_date='2015-04-01', end_date='2022-12-31'):\n", + " \"\"\"\n", + " Process SMAP soil moisture data and calculate trends.\n", + " Note: SMAP data starts from April 2015.\n", + " \"\"\"\n", + " print(\"Processing SMAP soil moisture data and calculating trends...\")\n", + " \n", + " # Load SMAP data\n", + " smap = ee.ImageCollection('NASA/SMAP/SPL3SMP_E/005') \\\n", + " .filterDate(start_date, end_date) \\\n", + " .filterBounds(goa) \\\n", + " .select(['soil_moisture_am'])\n", + " \n", + " # Function to compute the yearly mean\n", + " def createYearlyMean(year):\n", + " year_filter = smap.filter(ee.Filter.calendarRange(year, year, 'year'))\n", + " yearly_mean = year_filter.mean().clip(goa)\n", + " return yearly_mean.set('year', year) \\\n", + " .set('system:time_start', ee.Date.fromYMD(year, 1, 1).millis()) \\\n", + " .rename('soil_moisture')\n", + " \n", + " # Create a list of yearly means\n", + " years = ee.List.sequence(int(start_date[:4]), int(end_date[:4]))\n", + " yearly_means = years.map(createYearlyMean)\n", + " \n", + " # Convert to ImageCollection\n", + " yearly_means_collection = ee.ImageCollection.fromImages(yearly_means)\n", + " \n", + " # Calculate trend\n", + " def addTime(image):\n", + " return image.addBands(image.metadata('system:time_start').divide(1000 * 60 * 60 * 24 * 365).rename('t'))\n", + " \n", + " collection_with_time = yearly_means_collection.map(addTime)\n", + " \n", + " # Calculate linear regression\n", + " trend = collection_with_time.select(['t', 'soil_moisture']).reduce(ee.Reducer.linearFit())\n", + " \n", + " # Rename bands\n", + " sm_trend = trend.select(['scale'], ['sm_trend'])\n", + " \n", + " return sm_trend, yearly_means_collection" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def process_era5_trends(goa, start_date='2013-03-20', end_date='2023-02-28'):\n", + " \"\"\"\n", + " Process ERA5 data to calculate relative humidity trends.\n", + " \n", + " Args:\n", + " goa: Earth Engine geometry for Goa\n", + " start_date: Start date for analysis\n", + " end_date: End date for analysis\n", + " \n", + " Returns:\n", + " Tuple of (trend image, ERA5 collection)\n", + " \"\"\"\n", + " print(f\"Processing ERA5 data for {start_date} to {end_date}...\")\n", + " \n", + " # Load ERA5 data\n", + " era5 = ee.ImageCollection(\"ECMWF/ERA5/DAILY\").filterDate(start_date, end_date).filterBounds(goa)\n", + " \n", + " # Print the first image to debug band names\n", + " first_img = era5.first()\n", + " print(\"Available ERA5 bands:\", first_img.bandNames().getInfo())\n", + " \n", + " # Correct band names based on available bands\n", + " temp_band = 'mean_2m_air_temperature' # instead of 'temperature_2m'\n", + " dewpoint_band = 'dewpoint_2m_temperature' # instead of 'dewpoint_temperature_2m'\n", + " \n", + " # Function to calculate relative humidity from temperature and dew point\n", + " def add_rh(image):\n", + " # Calculate vapor pressure\n", + " # Using Tetens formula\n", + " # es = 6.11 * 10.0 ** (7.5 * T / (237.3 + T))\n", + " # For dew point: e = 6.11 * 10.0 ** (7.5 * Td / (237.3 + Td))\n", + " # RH = e/es * 100\n", + " \n", + " # Convert from Kelvin to Celsius\n", + " t_celsius = image.select(temp_band).subtract(273.15)\n", + " td_celsius = image.select(dewpoint_band).subtract(273.15)\n", + " \n", + " # Calculate saturation vapor pressure (es) at temperature\n", + " es = ee.Image(6.11).multiply(ee.Image(10).pow(ee.Image(7.5).multiply(t_celsius).divide(ee.Image(237.3).add(t_celsius))))\n", + " \n", + " # Calculate actual vapor pressure (e) at dew point\n", + " e = ee.Image(6.11).multiply(ee.Image(10).pow(ee.Image(7.5).multiply(td_celsius).divide(ee.Image(237.3).add(td_celsius))))\n", + " \n", + " # Calculate relative humidity (RH)\n", + " rh = e.divide(es).multiply(100).rename('rh')\n", + " \n", + " return image.addBands(rh)\n", + " \n", + " # Add RH bands to the collection\n", + " era5_rh = era5.map(add_rh)\n", + " \n", + " # Add a time band\n", + " def add_time_band(image):\n", + " \"\"\"Add a time band to an image with the decimal year.\"\"\"\n", + " # Get image timestamp\n", + " date = ee.Date(image.get('system:time_start'))\n", + " \n", + " # Get the year (server-side)\n", + " year = ee.Number(date.get('year'))\n", + " \n", + " # Calculate start of the year\n", + " start_of_year = ee.Date.fromYMD(year, 1, 1)\n", + " \n", + " # Calculate difference in days (server-side)\n", + " days_from_start = date.difference(start_of_year, 'day')\n", + " \n", + " # Get days in year (accounting for leap years)\n", + " days_in_year = ee.Algorithms.If(\n", + " ee.Number(year).mod(4).eq(0).And(\n", + " ee.Number(year).mod(100).neq(0).Or(\n", + " ee.Number(year).mod(400).eq(0)\n", + " )\n", + " ),\n", + " 366, # Leap year\n", + " 365 # Non-leap year\n", + " )\n", + " \n", + " # Calculate decimal year\n", + " decimal_year = year.add(days_from_start.divide(days_in_year))\n", + " \n", + " # Create an image with the decimal year\n", + " time_band = ee.Image.constant(decimal_year).float().rename('t')\n", + " \n", + " return image.addBands(time_band)\n", + " \n", + " # Add time bands\n", + " era5_rh_t = era5_rh.map(add_time_band)\n", + " \n", + " # Calculate RH trend\n", + " # Independent variable is 't' (time in decimal years)\n", + " # Dependent variable is 'rh'\n", + " trend_bands = ['rh']\n", + " \n", + " # Define the dependent and independent variables\n", + " trend_x = 't'\n", + " trend_y = 'rh'\n", + " \n", + " # Calculate trend (slope and intercept)\n", + " trend = era5_rh_t.select([trend_x, trend_y]).reduce(\n", + " ee.Reducer.linearFit()\n", + " )\n", + " \n", + " # Rename the bands to match the JS version\n", + " trend = trend.select(['scale'], ['rh_Slope'])\n", + " \n", + " print(\"ERA5 RH trend calculation complete.\")\n", + " \n", + " return trend, era5_rh" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def resample_image(image, scale=30):\n", + " \"\"\"\n", + " Resample an image to a consistent resolution and projection.\n", + " Fixed to properly handle projections.\n", + " \"\"\"\n", + " # Check if the image has multiple bands\n", + " band_names = image.bandNames()\n", + " first_band = ee.String(band_names.get(0))\n", + " \n", + " # Get projection from the first band\n", + " proj = image.select(first_band).projection()\n", + " \n", + " # Set default projection explicitly\n", + " image_with_proj = image.setDefaultProjection(proj)\n", + " \n", + " # Create a target projection at the desired scale\n", + " target_proj = ee.Projection('EPSG:4326').atScale(scale)\n", + " \n", + " # Now reduce resolution with valid projection\n", + " resampled = image_with_proj.reduceResolution(\n", + " reducer=ee.Reducer.mean(),\n", + " maxPixels=1024\n", + " ).reproject(\n", + " crs=target_proj\n", + " )\n", + " \n", + " return resampled" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Function to merge all trend layers\n", + "def merge_trend_layers(ls_trends, rain_trend, sm_trend, rh_trend):\n", + " \"\"\"\n", + " Merge all trend layers into a single multi-band image.\n", + " \"\"\"\n", + " # Combine all trend layers\n", + " all_trends = ls_trends.addBands(rain_trend).addBands(sm_trend).addBands(rh_trend)\n", + " \n", + " return all_trends" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Function to export the trends to Google Earth Engine assets\n", + "def export_to_asset(image, asset_name, region, description=None, scale=30):\n", + " \"\"\"\n", + " Export an image to a Google Earth Engine asset.\n", + " \"\"\"\n", + " if description is None:\n", + " description = asset_name.split('/')[-1]\n", + " \n", + " task = ee.batch.Export.image.toAsset(\n", + " image=image,\n", + " description=description,\n", + " assetId=asset_name,\n", + " region=region,\n", + " scale=scale,\n", + " maxPixels=1e13\n", + " )\n", + " \n", + " task.start()\n", + " print(f\"Started export task: {description}\")\n", + " return task" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Function to export the trends to Google Drive\n", + "def export_to_drive(image, filename, region, description=None, folder='GEE_Exports', scale=30):\n", + " \"\"\"\n", + " Export an image to Google Drive.\n", + " \"\"\"\n", + " if description is None:\n", + " description = filename\n", + " \n", + " task = ee.batch.Export.image.toDrive(\n", + " image=image,\n", + " description=description,\n", + " folder=folder,\n", + " fileNamePrefix=filename,\n", + " region=region,\n", + " scale=scale,\n", + " maxPixels=1e13\n", + " )\n", + " \n", + " task.start()\n", + " print(f\"Started export task to Drive: {description}\")\n", + " return task" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def export_to_local(image, filename, region, scale=30):\n", + " \"\"\"\n", + " Export an Earth Engine image to a local GeoTIFF file.\n", + " \"\"\"\n", + " print(f\"Exporting image to local file: {filename}\")\n", + " \n", + " # Get the image URL\n", + " url = image.getDownloadURL({\n", + " 'scale': scale,\n", + " 'region': region,\n", + " 'format': 'GEO_TIFF',\n", + " 'crs': 'EPSG:4326'\n", + " })\n", + " \n", + " # Download the file\n", + " import requests\n", + " import shutil\n", + " \n", + " response = requests.get(url, stream=True)\n", + " with open(filename, 'wb') as out_file:\n", + " shutil.copyfileobj(response.raw, out_file)\n", + " \n", + " print(f\"Exported to: {filename}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Define date range to match JavaScript version\n", + "start_date = '2013-03-20'\n", + "end_date = '2023-02-28'" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Run Functions step by step\n", + "# Get the Goa boundary\n", + "goa = get_goa_boundary()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Your boundary area: 3684.89 sq km\n", + "Official boundary area: 3684.89 sq km\n", + "Difference area: 0.00 sq km\n", + "Area difference percentage: 0.00%\n", + "✅ Boundary looks correct\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "c1b7d175a111442982df4926ea046727", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Map(center=[15.358382593200234, 74.0587759202492], controls=(WidgetControl(options=['position', 'transparent_b…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def verify_goa_boundary(goa_geometry):\n", + " \"\"\"Verify if the provided geometry resembles Goa.\"\"\"\n", + " # Get official boundary\n", + " official = ee.FeatureCollection(\"FAO/GAUL/2015/level1\").filter(ee.Filter.eq('ADM1_NAME', 'Goa'))\n", + " official_geom = official.geometry()\n", + " \n", + " # Calculate areas\n", + " goa_area = goa_geometry.area().getInfo() / 1e6\n", + " official_area = official_geom.area().getInfo() / 1e6\n", + " diff_area = goa_geometry.symmetricDifference(official_geom).area().getInfo() / 1e6\n", + " \n", + " # Print verification info\n", + " print(f\"Your boundary area: {goa_area:.2f} sq km\")\n", + " print(f\"Official boundary area: {official_area:.2f} sq km\")\n", + " print(f\"Difference area: {diff_area:.2f} sq km\")\n", + " print(f\"Area difference percentage: {abs(goa_area - official_area) / official_area * 100:.2f}%\")\n", + " \n", + " # Visual verification\n", + " m = geemap.Map()\n", + " m.centerObject(goa_geometry, 9)\n", + " m.addLayer(goa_geometry, {'color': 'red'}, 'Your Goa Boundary')\n", + " m.addLayer(official_geom, {'color': 'blue'}, 'Official Goa Boundary')\n", + " \n", + " # Verdict\n", + " if abs(goa_area - official_area) / official_area < 0.05 and diff_area < 5:\n", + " print(\"✅ Boundary looks correct\")\n", + " else:\n", + " print(\"⚠️ Boundary may not be correct - significant difference from official boundary\")\n", + " \n", + " return m\n", + "\n", + "# Usage\n", + "verification_map = verify_goa_boundary(goa)\n", + "display(verification_map)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CRS: EPSG:4326\n", + "Number of features: 6\n", + "Columns: ['Shape_Leng', 'Shape_Area', 'Name', 'geometry']\n", + "\n", + "Attribute preview:\n", + " Shape_Leng Shape_Area Name \\\n", + "0 1.735386 0.018797 Mhadei WLS \n", + "1 0.000000 0.000000 Bhagwan Mahavir WLS \n", + "2 0.000000 0.000000 Mollem NP \n", + "3 0.000000 0.000000 Bhagwan Mahavir WLS \n", + "4 0.000000 0.000000 Netravali WLS \n", + "\n", + " geometry \n", + "0 POLYGON Z ((74.09113 15.63933 0, 74.09146 15.6... \n", + "1 POLYGON Z ((74.18698 15.45582 0, 74.19093 15.4... \n", + "2 POLYGON Z ((74.23134 15.38431 0, 74.23145 15.3... \n", + "3 POLYGON Z ((74.25459 15.32841 0, 74.25493 15.3... \n", + "4 POLYGON Z ((74.20925 15.25191 0, 74.2092 15.25... \n", + "\n", + "Total area: 0.00 square kilometers\n" + ] + }, + { + "data": { + "image/png": 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z9dm7d6947ShxmAJklIhuSrCPjTHqbRRVJ6iid+/ercp9Y4wTFjaGYUwODh4wDGNymIyPjcqhqOsAZdSrW+/JMIzxQJtLShSmagfF7ismLWwkatR1gGEY04bK1qgKwiyETd7TiZ60vGURwzCm1WORjBd1eiyajLDJt58kaixsDGO6qONq4uABwzAmBwsbwzAmBwsbwzAmBwsbwzAmBwsbwzAmBwsbwzAmBwsbwzAmBwsbwzAmBwsbwzAmBwsbwzAmBwsbwzAmh8bCtm/fPkyaNEm0DqGaLZoIrQjN6aTLFQ+avagKmvrc8D50PProo5o/I4ZhzB6Ni+BLSkrEwNT7778f06dPV3obEjIa9CqHBgWrgkax0aRpxWnjt912G2bOnKnp8hiGYTQXNpoM3tz4eRIyTYZD0ARuRd5++20xUZumsTMMw0jCx0bDUL29vREREYHFixcjJydH7ftWVlZi/fr1wiLkTrgMw7QErfdjo20obVFDQkIQHx+P5cuXCwvv8OHDsLS0bPb+5LPLz88XvjpVVFRUiEOxCR3DMIxOhG327Nl1P3fv3h2RkZFiW0lW3OjRo5u9/1dffSWEkIITqli5ciVWrFihlTUzDGNa6DzdIzQ0FF5eXoiLi2v2tlevXsWOHTuwaNGiZm+7bNkyMQVcflBLcIZhGL20Bk9NTRU+Nj8/v2ZvS5FU8s1NnDix2dtSgKK5aCvDMOaJxhZbcXExYmJixEEkJiaKn5OTk8V1S5cuxZEjR5CUlISdO3diypQpCA8Px7hx4+rOQVvSVatWNRqfR8I2f/58WFmZzCgGhmEMgMYKEh0djZEjR9b9vmTJEvE/CdKaNWsQGxuLb7/9VgQAyE82duxYvP766/WsKwoqZGdn1zsvbUFJHCkayjAM0xrayGgKqQlAUVFXV1fhb+MpVQxjemjyGedaUYZhTA4WNoZhTA4WNoZhTA4WNoZhTA4WNoZhTA4WNoZhTA4WNoZhTA4WNoZhTA4WNoZhTA4WNoZhTA6uNme0SmV1Lb47nISjibkIdHfA9F7t0K2dq6GXxZgZLGyMViipqMbbWy9ic2wa8kur6i7/+mAiBoV5oqu/i2j1btGmDaICXDGuqy8sLLj1O6MbWNiYVrPhWDJe+fOcsNaUcSg+RxyKrJnbCxO6N9+jj2FaAvvYmFbx6Z44LN94pklRa4pNMdd0tiaGYWFjWkxZZQ3e+/cyWtL4qkYzHWQYjWBhY1oMWV01tS1r5+dqb6319TCMHBY2pkWkF5Thrb8vtOi+FEh46Y7OWl8Tw8jh4AGjMZmF5Zi2+hCKyqvVvg9FQucOCMKAEE8EetjzMGxGp7CwMWpRXVOLf89n4s+YNOy+lIUKDYMFPdu7Y1afQJ2tj2EUYWFjmiU1rxQPfHcCF9ILW3yOvNJKra6JYVTBPjamWVJyy1olakSeQtIuw+gaFjZGrQTc1lLAFhujR1jYGJVkF1fgrzPprT6PFCy2+OvFONygAkKVT7G8qkbna2J0A/vYGJX8ey6zxblqiuSVVAqxsLI0zHcpCdrD60+gk68zBoYNVHobWt+ne+Jx4moeTiXnYdntnTGnX3u9r5VpPSxsjErisoq1cp6iimr0en07hnZsi5ER3hjesS3aOttCH/x6IhXLfo9FVY0M1kqEtaC0Cg62lnjzrwv45lBS3eU7L2SysBkpPAmeUcn0Tw/iZHK+Ts4dGeCKERHeGBnRFpEBbrDUcreP2loZ3t9+Gat2x9VdZm3ZBu3c7DEg1BPPje+EWpkMo9/bi8LyKqWlYYuGhGDp+AjYWllqdW2Mbj/jLGxMk1Bhe7dX/9G4wL0leDjaCCtuRERb8b+bg02rzldRXYNnf4nF5tNpTd6GtqXBno7Ydi5D5bnGdPbB2vl9WrUeRr+fcd6KMk1SUFalF1EjcksqsfHUNXGQ4UYJvWTJkUVHWFm2QSdf9b+wfj95TaWoERczisTRHAHu9kjOKRX/cw8544CFjVFp9RgCilWQA5+O//v3srhs79IRat33g+2XRbAjKadEa+shvxsdzrZW6OTnLLawfYM9tHZ+RvtwugfTJHklhk/RIDr6OCHI01Gt24a2dRQ+tS2xrU9RURYAOZ6Uh0923fLZMdKEhY1RWewuBW7r4qP2bSdH+aNHoJtO17Pv8nXEZTW/hWUMBwsb0ySZRVIRNl+1b0tdQ166owt0zbqDt9JCGOnBwsY0yfWiCkMvAd7OtojUcMpV7yB3PDIiDFY6dPRTcOKLffHYdjZD1NGWVqrfwonRPRw8YJpEXxFRVYzu7NOiSCQ5+OcPCsaBK9nYdTFLHGVaLJGic73198V6lz0wNAT/mah7a5FpHrbYmCZxkUD7bjvrlr9FfVzsMKN3AFbP7YX7hwRD15hGRqhpwMJm5tAWKjY1X2lqx8BQTxia/x1NxrX8shbfn+o/P9sbj7X7E6Fr1h5IxPv/XoKJ5LwbNbwVNXM+2nkFn+9NgI2VhWjfTaVNHbydEO7thKs5pZLYDq/8+wJW3d1L4/ueSyvA87/F4uy11vWS0wTKd3tyTEdYch6vQWFhM2BWv62VBeysDVeDSCkLX920ZEhAKEeLDqlBOWnjuqZhUpR/k7ehFkPn0wtF6/L80ko42Frhp+MpWulMogmDwry0XvPKaA4LmwFIzC7BrM8Pi15n/q72CPZywANDQ0VhNkXyqHdZaztf0Af9aGIu9lPO1fViIaSDwjwxf2AwvF3sxHaJprdX6/mD31Je3HRWZPtTFw7qOEJH/M3/6fml5JaKigVDMzjc8Nt3hovg9Q59AEnU0gvq54jRtzx1niivuhGJ7Bvsjjsi/TGqkzcCPRyUnouskfVHruLf8xliu1VScSvloEYmU+rM9nKyxcezeyCnpBKPbzgFY4ICCfLXR6rsemY4Qts6GXoZJgl395CosF3JLML93x4XMwQ0gXxeozp7Y3QnH/Rq7yaaNV7MKMTzv8bidGqBxuugnZKjjZUoEWK0h7+rHQ6+MIpHC+oI7u4hMa7mlOCB76JxObNlTRuvZBWLg5z8NEG9WzsX0RG2pVsvuh+LmvYZFO7FoiYRON1DRxQrCAc5tFsqag0hX9nBuJaLGqM7hoR7GXoJzE3YYtOymG05nYafo1NE19nOfi4YHOYpBg0zpg8FZxhpwMLWSshFGX01Dz8fTxHTnEorbyW6Ug1ha+dxMsYBtVaiaDMjDVjYWsnKrRfxxb4EQy+DMTCDeRsqKdjH1gr+PpOOL/ezqDHA4DAWNinBFpsazRYpEqlYIUDJr1Tm8+3hqzBnZvYOEKkn2pgUb8xQDmL/UG4VLiVY2FSwJTYNy347A0vLNrirT6CYMUmJrS9tOivKd8wZGrTy7p2ReOR/J2HuUMdeZzvDd0JhbsHC1kTHizf+uoAfjt6yRD7flyAOBvB0tMF/Z0aJnK3WdN4wFSjyzUgLFjYlnE4pqCdqzA1evqML+oV4iLIsOohZfQKRll8mLFnTqGHRHA4cSA8WNiWEeas3EcncCPJ0QLcGbbrvGRAkDup7RrNBKUJ8IjlPiF1moeFbi+sae2tLMQOVkRYsbEqoqjFT06MJHhsZLpJPyVprCgoiUB7Xsts7i1pUeg3/OpOG4vJqfLE/QeP6WGOBXhPqZcdICxY2JfwRc03l9TaWFmIyuWIyrq6gLV9nP2fsv5INQ/D8+E5YPCJM7dvLe5HZWLXBtJ4Bwl+5+9J1kxU2LqOSJvxVo4RqFRabn6sdNj8+BFN7ttPLWj67pxfWLeiLGb0CoG+cbK1w78CgVp3DwcYKX8zrjWl6er30DfvXpAkLmxJUNUB9akwHRPg6w0+N8pnRnbxbvZYLGUVim0epFbocJ9cQFzsrfL2gLxxtW2/Uy9d/e3f154MaAx6ONujk62zoZTBKYGFTgiqfyb7L2aI+9N5BwQj0sBeXUYNIghJ5FVMitNHBJvF6ifj/fFqh3rrd0ryDv54YqtKnpinWlhb4dG5v7F06QjTPNAXI79iS0YCM7mEfmxIadrclFg0JEZYHTTxanBYmooNfze+LZb+fwbIJnUR/tDui/HEwLhvHk3LhYmeN74+0vjJhXFcf5BRX4OH1J6AvaEBxU117W0uQpyMeGxUu5nwaO+xfky4sbEq4lldWz/JafntnMZ+SHOHOdlbCoiE6+jjjt8WDxM99gm9YN8GeDoi/Xox1B5M0flyacxDgbo8zqQXCwpnQ3VdYTc/9GqvXRFg7G90OmOkZ6CZepyQJTMFqDexfky4sbEqY3S8QXs62GBruJSaRy7em5Ah/dGS4yvt+uONKi0SNmNjdD69O7iraftM8g67+N3LGrPWQTkDbZgdrSxEYoeeu28dqgzGdfcQcTmOlvYeDzqxapvWwsClhVCcfcbSEX6JTWnQ/Cgw8MCxU/NzJt34/99cmd0XC9WIcSciFLiArcd6AIBEU0dcgkjFdjFvY2FqTNhw80CK1tbK6luCONpZ4a1p3te/bxd8F7dxuBCMaQr69Fyd2gS6gaVh/PDoYDw0Pw4gI/Tn1+wS51wu2GBvNjdkjdwLNamUMAwubFqEI2efz+qCLnwv6hnhgrQa92rKLKsQHoayJpF8KVtwR6afF1UJEdSmlw/Nm3ac+IbGmDiHGyvHEXFFG1hByIXy44zKmrD6Ap3+KEcObGf3DwqYDqKXRnkvXkZB9I1Wjq3/z4wDTCsrR8cWt6Pn6v022E9ekAkAd/u/OKIO22yH/pbFCvfju+eqoGHoth+pj53x5RPhZKTOHWsUPfXc3vjqQKKx5Rn+wsGkZ8lPJ89toq0WVA5QT9tqUrmrdnwYC/xGTpvQ6sga0Rc/2bugfath2O0M7eBl1nSX5PCd9cgAxKfn451wGJny0H8cS6/tBi8qr8fqW83jw+2it/v0Y1XDwQMucSs7D9qeHiyijZZs2YstFCb1xWarH77k5WAvLjkbreTnZKL3NxfQira3zXFqhGODcwcdwmfMk/D0C3HAsSTdBEX3lPN655lCzydM7LmSJHMdhHY13+21MsLC1EtpiHE7IES17yJ/y2pbzIgn1zandRA5aXkklXvzjLP6KTVd5ntemdMOkSD8s/DYaqQp5dIoMDPMUuW7Xi5S3A6LcMBJSirBatGkj0kRsLNuI9kHJufVzxsif98SPMdj06CDYWuk2b62p123N3nijFjU56laEOOg4P5C5BQtbK6mqrRW+FsUmi2Sd3fXFEXg72yK/tAqVSpzMDcksKBf5XRRJPZmcp/Q2lDe14YH+ePXP8zgQV7/bxzO3dcTjozsovR9ZjBczirDk59PCf0clTZRsfCo5H+uPJGPhkBDok6TsEpF0bAqipglr9sTjvsG1IqLKE+N1SxsZvetNgMLCQri6uqKgoAAuLs076xtCFgzVfLbkDbfo22jsuNC6ociUHnJ4+WhRiqUOB65k451tF0X7JOpiO7tvYLNrJzG7lFFU1xiRnnOtTFZvUI2uWX/kKl7bfF4tsTdVHhkRhufGdzL0Mkz6M84W201ySiqQV1Il8snIAaxJATh1wmgtJZU1mPfVMRFs8HNVns+myJAOXhgcPlgjIabKCcVur4Zw3KfklZq1qBGf7okXidB39tZ/KypzwXhDUlqG3CS/nUwVGf6nU/LVvt+ui5n493zrrDU59LjJzdRPFpZXYd/l6+J/Y9zOkO+PAZb9HtsogspoD7bYbvJ//1zCpphrWHcwUSTZqguF8+XVBtrgud9iMbd/e1EEv/VsBmytLESaAFl0FJy4mlMqOn5QqoSxsfNCJn47kWroZUgCap3+0PfR2LFkuEESpE0dFrabzBsYhK1n00UeWWxqPm7rol7y6JQe7ZCYXYJVu+K00i+NhOutvy82ef07M7pjUpS/UVlrJMgrNp/HxlOqW66bo7jp079pTvBW9Ca92rvj+4X9hb/s870J2HMpS0QT1eGpMR2Fz0sf9AvxFL4yY4G26mPe38eipoQZvdpppUMx0xgWNgX6Bnvgl4cHwdfVDgvWHcfZa4Uor2p+YMvGU6mihEofbDxpHFs5+lJYvTsO938TXa/siLnF3f1bN0+CaRr+umhAQVmVSLYlVm69ILaGd0T5YenYCJH8qoy9ehI1osIIOkaQqFGickv70pkDNI+0w82GpYz2YYutQauZWZ8frgsGHIrPEZ1raWv64PdNt+Z+b1YPPDc+om70nC7pH6q9OQS64ufoFBa1ZpjTrz3PS9AhLGwKRF/VPPxO2ywStEdGhOM2HXerCPVyxEg99kxrKTT/gVGNNgflMI3hragCNLW8qWaMn93TW+mWa/7XxzB/ULCw9rady9Dp+pztrY0iGkopMIzqbSgLm25hYVOgVEWgoKi8SnTE2HTqGvzd7PH46HCcSMoTl1Hdo66Y0y8QS8d1QnpBGdoaMN+Jmio+/9sZkJuRunJQt9+RnbxFwX9DaOANoxzq3ELNPWkmKaM7+B2oQISPs2g31DDL43hSHnq/saPeZX+eThOBBl1CLhjaetKHwNAfBKrKoEORVzefF68ZJQwvGhZaV+dqDAEOQ/GfiZ0RGeBm6GWYPCxsCkzt2Q6eTjZiVmhTrYPkNGwDpE2oNRFFYTv4ONWr7TQUqXml+GD7FaXXXcosEsdvJ6+J6HFBaRWiryrvTsJA1CMzEgwe7Nu3D5MmTYK//43s902bNtW7fsGCBeJyxWP8+PHNnvfatWu455574OnpCXt7e3Tv3h3R0dHQN0M7tBWNIg1ZoEz91qi0atqnh4TvzpBQM8o71xxGRmHjIdKKyKPHPx5PabJfHHOjCQAjQWErKSlBVFQUVq9e3eRtSMjS09Prjg0bNqg8Z15eHgYPHgxra2ts3boV58+fx3vvvQd3d8NYK/Y2lri9u2/dYGRDQV11DTnJiURt5ufNixqjWS86RoJb0QkTJohDFba2tvD19VX7nO+88w4CAwOxbt26ustCQvTb/LAh1TUy0drbUEzv2Q7Lbu8stqWGgDrc0pacGmUy2kOXLgxGx3lse/bsgbe3NyIiIrB48WLk5KjOa/rzzz/Rp08fzJw5U9yvZ8+e+PLLL1Xep6KiQjSeUzy0ydiuvqI3myF4YGgI3r+rh15FjYSMtr00UemxH06K6UrsK9M+vE030uABbUOnT58uLK74+HgsX75cWHiHDx+GpaXyTgYJCQlYs2YNlixZIm5//PhxPPHEE7CxscH8+fOV3mflypVYsWIFdMlLd3QRxfB5erZa9G2l/XoiFW/+dV7vz9McMbR7w1xoVWtwCgxs3LgRU6dObfI2JFphYWHYsWMHRo8erfQ2JGBksR06dKjuMhI2EjgSxKYsNjrkkMVG29mWtgZvCmoGuOjb4yjUU9IpDfz4fmE/9A7SXwInDfbl7hv6gWZa3N2/vaGXYfKtwXVeUhUaGgovLy/ExcU1eRs/Pz906dKl3mWdO3dGcnKySj8ePTnFQxdQhvjbMyKhD2i61E8PDtSrqBGUVsLoBxqkw+genQtbamqq8LGReDUFRUQvXbpU77LLly8jKChIMm9GfRS4U6PKz/fFiyoHfdIjkBNG9fXF5ePC3XIlKWzFxcWIiYkRB5GYmCh+JuuKrlu6dCmOHDmCpKQk7Ny5E1OmTEF4eDjGjRtXdw7akq5ataru96efflrc56233hKW3Q8//IAvvvgCjz76KKQAdTmlSe76aMawJTYdr/xxTq0+cK0NFhxNyMFTP54SQ2QY6OWLi/L9GAkKGyXNUtSSDoIc/vTzyy+/LIIDsbGxmDx5Mjp27IiFCxeid+/e2L9/v9g6yqGgQnb2rbmYffv2Fb46ynfr1q0bXn/9dXz44YeYO3cupMLc/kH45eGBosOGrvn91DWdCVtVTS1+Pp6CUe/tEbNPN8WkiZkKjH6g2mJG9/BcUQ35/WSqGDysK7r6u+CxkeEY381X6508aIv7wHfROJLA05EMxROjwrFkbIShl2GU8FxRLUK1j3es2i/yj2h0XGml7raINHnqu/v76aw1Ec1KmBjpLwrrk3JKseFY08EZRjd0bedq6CWYBSxszWzbnv31NFJy9eMXWX57Z7y97SKuZBbj/VlRYrAubRMpn04bUABk3oAbARky1Klki7alOTdboTO65ZnbOmJcV/UrcpiWw1vRJqCXhfqs/aKnOZgTut2oTf1kV1zdlPbK6lp0a+eCLY8P1eljkzXa/60dYmg0oxvuGxyMl+/oYhSNQqUKb0W1wNr9iXoTNYKGIytCokbEZ5WICKYu++NnFpazqOmQaT3b4aWJLGr6hGceNDEL862tFyAFqmtrVXb21VbA4t07I7FgUDB2LBmG6b3a6fTxzAnKgaTXlge36Be22BqQVViOJ3+MadRF11DMGxAMJx0P1SVLYlafwLrfA9zsdfp45gLNylh9dy9YNzG2kdEd/Io3YP2Rq5IZRkK92J4YHa73HmzfH7mq18c0RTr5OmPt/L6itx+jf1jYGnAkUTo5XvcPDoGbg35nHfx1Jl3nXT6GdWxbF501Rdp7OIi0HUM2CTV3WNgaIpEtKDF3gP67QDw6Mlyno+Eo8rvq7p4YEdEWpoiXk63ozuLtYmfopZg1LGwNqKypNeumhOQPenVSV9haaf+tEezpgHUL+oppVn2CPHBbl1sDpunxHI1820ZjB8lSUzaSkNEvZh88oFQKGkByNbcEFVW1iEnJb/K25MQvrtCf/+27w0lYOV23LZPKKmvw+6lU9A32QFhbJ/zv6FWs2Hxe6/Wjnf1chCVDFg3h6mCNldO747bOPujWzlW0TiqrqsGnu+Px2d54GBskzDQv1FBdl5n6WJl7BPTxDadwVIVfzdvZFk+N6SgG3ZZX1+KJDaeavG2Auz2WTeiM53+L1YoA6jqIkV9aibs+PyLG5xHWlm1QVSPTyeTzNXN71YmaHPp9Vt9b0ViKRMdfL4afqx3SC4xrgMyCwcHiy4GRBmYrbBfSC0VBuKr5oe5UcvTQQATf7OhB+W29g9xRWFYlypM+mt0TgR72QsQ+3HEFz46NEIONjyXm4NvDLY8shng54pmxHTEiQrdNCd/eerFO1AhdiBrx3PiIutdQVaUHtSinaOL/zYzC5tNpeHHTWRgLG09ew9NjOooWV4zhMUthq6iuwewvjjQ7yZ2y//MVbjOqk484SiqqxdbD6mZ+EhWXU8tn4ot98fgpOqXVDvw7Iv2h6yHI+qqsoC2uOrl0ii2z7xkQhLisYnxzKAnGQFZRhUgVWjQ01NBLYcw1eGBlYYHB4Z5NXk9W2adze2FyD3/svpjV6HpHW6s6UWvIX7HpKK9qeQBiRq8ATO2hW1Ej6+i//1zSWx+2hOvFLbb0jKkK6WoOj9aTCmYpbLSNpG1jU7jZW+P27n7Ccf/0bR01Onf/0KYFUxXUMnpSlD9endylSdHUFh/vjMMfMWmNWibpSkRir7Vsmj1ZwuS3NJb31ANsrUkGsxQ2IrStk7DMlHEoPgelldUt3kZ6OmqeVLtoSCg+mdMTzna6TeokP+EHOy7X/U4ljK9P7SbSFD69u5cQuCHhXnU9+smCXDouQvgbWxN5bild/Yyjf9nkKH+093Qw9DIYc/axyRnesS1OKBkKTGkH5C95cFiYxuekbPPnJ3QSLY/UhdJIqGNuTnEFPBtEDrUJ+QxpILIc8hN+PKdnXY+wCd39xEFczCiEjaWF+AKQs2ZPvHjN6H7UvlxdWlNWRIm8287V73wiRRaP0Py9wugOsxa2hUNCcDg+B4cTGk+qt7Vq+Yfxzl4BeOG3WLVbAVFUlSav01bw7KvjhA9PFzz7y2kcjMvBQ8NC0bO9O0LbOqKjj7PS23byrZ+PNaazD+7sHQAfFzthgZ1NK8DlTPV8Z62Z8DW7X3t09HUWuXWnVeQYGpKxXXyafB0Zw2C2W1GCBOSTu3uKTrINUXaZulCLmpb4ySiPS1fufMoP+/P0Db8aBUXIQtTkwxjh6yxEjQIOT/x4Sm1RI0oqWtd2qVd7d/z68EAMbKH/Utc8MlK/jQqY5jFrYZMniVJLbm2j7odwZu8A/Pv0MJx4cQzm9AsUSbK64OjNAS5kFaqTftEUq3bFiRGBmtCrCV+mpqVeFKmm5N2GUEH9q5O00z5dU8gfyXNZpYfZC5tcXMZ0rp8Mm9HKzPfP5/UWDmVVUdC19/YRTQjJciLfGkVhW7MFVoWjrWWd9dOaJFJVaTLKmNOvPe5RyE9rDe6ONqIMK6yto6gIsbO2QP8QDxH8mD8oWKfF+03xCPvWJIlZ+9gUk0PJiU7lRWdupiZQYiglXVJh9hOjO2i8tSTHO6WMbI5NU9q0krZ0YxSKwHXNlB7tRFF9axN/+wR7CN+kYhCiKSZ298MbU7tptSU2VWMoVmTIR3bQY0zt0Q7H9Nh2iiy1gWHS3B6bOyxsCjlTVMQ8fc1BMZWKahXlH96KmlpRA6oulNTbsFxJLnZUJE0F4Q0tRH2graz458d3Es8xIbtE6fU0iGZEx7b44K4erQocqIOiaFJ3DX1y/5AQ0TRh06lrInJL3UpoopiurG5GfVjYFGjrbIsfFg3A5FUH6jVb7Orvqva4vrf+voB1BxuXAZGY/d/MSLXPJWVISyiQoChstJ1fOj5CtCSidBBDDC7Ze/m6Xh/v5T/OIl/hfbL+SLIYnfjFvD6iewljONjH1oBADweRZKsIdfSg+ZvNse1shlJR69neDVseH2ISonb2WgGmrDpYL0WGEnjJV+jtbCf8d4YQNdqS6lvYFEVNDnWKufOzQ7iW33RzhZTcUvElyOgOFjYl3NU3UDim5ZYWpRrM6B2g8j6U0EvbT2XEZRbjwx2XxRva2KFUj/PphXW/vz6lq/giMPRouZPJ+QZpzKmMK1nFmP7pQZxPu/U6KdbNjv1gH+5bdxxZRcbVmsmYYGFTApU1/fvUcNGCiNobPbz+JNbuT1DZY210Z++6yGNDiiqqxSDkMe/vFe14jBn5vFPi0ZFhmDcwGFJY0382noGUyCyswKzPD4vBOIrNBujLjypbDsRl4+HvTxh0jaYM+9iagOr+BoV5ipyt7OIKrNx6EXsuXcfa+X2UVgb4udqLPmqqElcrqmtF9n+fYHdxe2MVfV8XGUZ19lbZSECXW076O1BgJ72gDDZWlqisrkH8deWBDENCX4QvbTor+sxRJLm8sga7FLrFnL1WKJ6Poa1dU4SFrQnoDXcwLrveZeRXGvLOLkyMpDSGG/3XFBkQ6ol/zmWqPC+J28/HU/HkmA4wRrY+OdSgjz/nyyM4cjPZ2FigUjBlnZdpvgZZ8xRwYbQLb0VVbCWUjaGjy3JLKpucwNRPjfbQp1OlWfModahG9eRV03rtjN01IVVY2JrASkVpE/l05Imhigzt0BY/PTRA5KupYv+V62LeAKMZmUXlkpoipg0+2RmHwnLdznE1R1jYVNSQzlYYNKLIjgtZ2Hel/jZVDvlLqOazueEmempea1LsvqjfdA59kFFYjhV/nlf6Rcm0HBY2Fbw6uSu6NjFOrUohOtgQeU+zporjv7y3j4i4Mprx20n9zGgwxPNavvGM3lq1mwMcPGiCgtIqJOeW4uHhYWJEnyaUV9Vv0xMZ4Coy86nGkRKAGc0ga+ajnVeUNgU1FTYcSxHDhagMjUuyWg8LWxM+sKW/xAqfTvsWCBEl9b47I1L06w/yckQ7N+NM7ZBKwODVzefwXSvGGRoLf5/JQGFZtOgMo6tmo+YCb0WV8P72y8L3QW6PpiYPebs03cKb6ihpEPCgcC8WtVby3vZLZiFqcihx9+61R5uMvDPqwcKmhPUL+4suDcr8YEGeDqIHV/d2xl/3KXVOJedh9e54mBuU93bX54cbuTQY9WFhUwJtAyhT/Jv7+orfqfWOl5MN7oj0w+5nRuC58Z04W1xP/c5oSpY5csWIhkVLEd7IqyAywA0f3BWFPkEe7PQ3APTl8c6M7iJ3zRwTWT/dHSdaj3fj3YHGsMXWDNN6BrCoGRDqXPzBrCgxJcvcKCyvxszPDmNLrPmJemthYWMkA/mU/j6TjmW/nxF93xTF7f7Bhu8iYgioE8hjP5zCG1vOcw83DeCtKCMJaFg09SnLuRkN3HAsWUylnxTlLwY6U6t2c2btgUThd6NJXZwK0jxtZCZSy1FYWAhXV1cUFBTAxUV5tQAjXZ779TR+jlZeWUC1t1W1VJ+r92VJDpp8RoOHzJFCDT7jvBVlDM65tIImRY2g4AGL2g3I30b9ARnVsLAxBmfnhVvNFxnVUDlpmop5CswNWNgYgyOVWQXGAr9ezcPCxhic6b3aGXoJRkWCBNugSw0WNkYSFQYONtzRQl3+OH3N0EuQPCxsjMGh4ICuJ8abEjQE5lJGkaGXIWlY2BiDs+N8ForKmx5tyDTm91Om2XRTW7CwMQaF0ijXHUw09DKMjk2nrnHHXRVwCjOjd/ZcysLa/YliYE5qXhnispqexco0PUVt7+UsjOpkfjW06sDCxmgdeTFLw9ZOFdU1WPn3RW7HoyW+P3yVha0JWNgYrUJjBQe/vQs2VhYY0qEtht2s97SztsTnexNY1LTInsvXkV5QBj9X7tLcEBY2RqvsuXQdJZU14qAeanR8sisOHbydsO+K6Y3PMyRkGNMoyHkDggy9FMnBwQNGqwwK80TDzA2a9rXzYhaqatjZrW2ik3INvQRJwsLGaBVvFzvM7tfe0MswG1ztrQ29BEnCwsZonZcmdkG4t5Ohl2EWeDk1PS3NnGFhY7SOvY0l3pkRaehlmAVtnVnYlMHCxuiE3kHuGBnR1tDLMHm6+HFTVWWwsDE6oz0PwdEpNMEqKtDN0MuQJCxsjM7gGKjuaOdmj2fGdjT0MiQL57ExOqOW+3nrhOW3d8KiIaGw4I4oTcLCxugM1jXtQ37LB4eFGXoZkoe3oozO4OYT2ifEi9No1IGFjdEZPOBX+2QU8iAXdWBhY3RCRkE5tp5JN/QyTI60fPMeHK0uLGyM1qEGiC/8HisK4RntQt08mOZhYWO0zoc7LosuH4z2qa6R1fW7Y5qGhY3ROpmFvF3SBdS3871ZUY0aeDKNYWFjtA7Vie58Zjg6c7mPVnlsZDhGRHgbehlGAQsbo3XIoghr64QP7oqCi50VbK34baaNPndPjeFKA3XhdxyjMzr5uiD21XH4bfEgsY1iWoa3sy0+mt2TZ69qAAsbo3PcHW1gxR/KFtEnyB0/PDCA2xNpCJdUMTrny30J3BZcQ2gLv/z2zpjVJ5BrQlsACxujc3pwax218XWxw4LBwZjTtz1cHbjtd0thYWN0zuQof/xyIgUH43IMvRTJEhngioVDQnB7dz9YW7KHqLWwsDE6h7ZS/70zCuM+3Iei8mpDL0cyUEBlXBdfLBwaInxpnJ+mPVjYGL3g72aPldO747EfTsGcoSBKr/buGNrBC5N7+CPI09HQSzJJWNgYvXFHpD/ySqvw0qazMEdm9ArACxM6cYRTD2i8md+3bx8mTZoEf39/YTpv2rSp3vULFiwQlyse48ePV3nOV199tdF9OnXqpPmzYfRGZXUtSis131bS1PLP7umFIE/zmYcQ2tYRvzw8UJRDsahJ1GIrKSlBVFQU7r//fkyfPl3pbUjI1q1bV/e7rW3zf8yuXbtix44dtxZmxcakVMktqcS9Xx/F+bRCLB4RhmfHRmjkHxrfzQ+jOvlg8+k0nE8vRFJ2CS5mFOFavul1rhgY6onP5vXmwcZ6RmP1mDBhgjhUQULm6+ur2UKsrDS+D2MYlvwcg7PXCsXPq3fH47O9CZjRqx1en9oNtlaWap3DxsoCM3oHYMbN38sqa3DnZ4dwLu3GeU2BaT3bibpZeq6MftHJK75nzx54e3sjIiICixcvRk5O82H+K1euiO1taGgo5s6di+TkZF0sjWkl3x+52qglEfVf+zk6Ff/ZeBa1LewHTkOWv17QF4Ee9jAFxnT2wfuzoljUDITWX3Xahn733XfYuXMn3nnnHezdu1dYeDU1TTcd7N+/P7755hts27YNa9asQWJiIoYOHYqioqIm71NRUYHCwsJ6B6N7UvNKm7zu1xOpeG3L+Raf28fFDt/f3x9eTsbth/JwtMGb07px+oYBaSNrRdc6+sNt3LgRU6dObfI2CQkJCAsLE/6z0aNHq3Xe/Px8BAUF4f3338fChQubDDisWLGi0eUFBQVwceF2ObqioKwKQ9/ZhUIV+WjfL+yHoR1aPgWefG0v/BaL/VeyYYysmNwV8wcFG3oZJgcZL66urmp9xnVuJ9PW0svLC3FxcWrfx83NDR07dlR5n2XLloknKD9SUlK0tGJGFeQEf2BoqMrbLP0lFgWlVXW/77mUhflfH8P0Tw9izZ547L6UJSw/2rbSwJesonJczizChfRCJGaXgOyc58YZb1S8Wzv+YjU0Og89pqamCh+bn5+f2vcpLi5GfHw85s2bpzJAoU60ldE+9w8JwXdHruJ6UYXS6zMKyzF/3TE8OboDtl/IxIZjyXUzRk8m59fdzsbSApUmOMkq3NvZ0EswezS22Eh0YmJixEGQP4x+Jmc/Xbd06VIcOXIESUlJws82ZcoUhIeHY9y4cXXnoC3pqlWr6n5/9tlnhS+O7nPo0CFMmzYNlpaWmDNnjraeJ6NFHG2t8OxY1U0PY1Lycd83x/HD0Vui1hBTFDUfF1tO7TBGYYuOjkbPnj3FQSxZskT8/PLLLwsxio2NxeTJk8VWkvxjvXv3xv79++tZV2SNZWdn17PqSMQoijpr1ix4enoKcWzbtuV+Gka33Nk7EBE+bJk0pCO/JsYfPDBWxyKjHcgaW77xjKGXISnuGxyMVyZ1NfQyTBJJBQ8Y08Xa8lY6A3WnCDajMilFFF4GRPiyxSYFWNiYFkOdKdq53UioPXOtAM+NN95IZkuJCnCFs721GFhDXW9d7Ni/JgVY2JgW0y/EA8MjbvhBK6prRVmUOdE/xEMIen5plXj+lNv32ubzopaWMSwsbEyruJh+q+LD19XOpLt2uNhboXs7F3F4OdmgvKoGDSvIKNWFamlbWlrGaAduocG0GKoOkOelPTG6AwaHe4lRe7suZKGwvArpBeX46kAijB0nG0t0aeeKC2mFOHOz+J9E3NZaecE/1dJ+vi9BdD5hDAMLG9Pifmw/Hk+pl79FUJ3nrL6B9QrkvzmUBGOjg48TXG/6y6i10rHE3HrXZxSUN5mgTPzfv5fQJ9gdfYM9dL5WpjG8FWVaRFlVfX/a5QzlDQteuqMLnGyN6/uzi58LrmQWI/pqnjhKm/Adkmg3BV33+A+n2N9mIFjYmBZBEUA761tvn15B7kpvR9PLJ0X5wxgCAdTpltonkYWmDcjf9vRP7G8zBCxsTIs7u3w1v29d+ZCfa9N91Kb08IeUZ/6GezvhaGIuEq6XaD2yu/fydXy2L16r52Sah4WNaTEULNj06GDxs4ONckd6XkklnvoxplH0UAoEuNuLPLSisludSHTBe/9ebuSjY3QLCxvTKsgQoxSPTkoy7qkl0aM/nBRbMqnRN9hd+L+oTVKQl2O9KgptQ/62J388ZXZ5foaEhY1pFe6ONvjhgQGwUjK9fOOpazgUL63p77Ql7tXeDceTbgQFKKmWrKmqGt2alJT68u1h44sOGyssbEyrIB+bvKyqIYHuDmLauZToHeReryecPvl0d1y9BpyM7mBhY1rN7ydTsf7I1UaXDwzzxI8PDGhS+PSN3FIzFGQdciBBP7CwMa3mx2MpeHHTWYz7YB8uZtRPlegf6oltTw3FnH7tYUi6+ruIuk5D8/WBRJUDcRjtwMLGtJqeQW7i/0uZRUhTMvTY2c4aK6d3F9PQ9d3aiHbCvdu7iaHMuvajqQMVy7/19wVDL8PkYWFjWs3BuFvdkH8/eQ0Ne5eeuJqLqzkloryIakk7eDvpZB1hbR3RM9BNbDl7BLrBzsoCHX2dcSI5HyUSikj+fSYDh+KNcwKXsWBctS6M5Ii/Xlw3FZ7YEpuOfZevI6StE767rx9qZDLc/eVRUYEwOcpfFMfnlVbqLJChGBhwtLHEpSZKvQzNij/P468nhiiNJjOth4WNaTFkma3dn6jUSX46JR9TVh8QuWK0/SIUi+a1ASXXUsqGu4M14q6XIDm3vu9KSlZaQ2jb/r+jyTx/VEewsDEtHpz80Y4rYrReUyTl6MZJbm3RRtSmnk7NR3mV8U66en/7ZVFHS5PjGe3CwsaoTUV1jeg19kfMNey4kCVaF+kLCjqQANDIvqzCClHbaQpfDmv3J5hlS3Vdw8LGqM1vJ67pfSpVWydbtPd0wMnkPJ1ZgIbk1xOpWHJbR/a1aRl+NRm1ubt/ezw0PFRvj9cv2B0lldU4cTWvyaHLxk5WUYWwghntwsLGaMQL4zth5M0BLrqCtpydfZ1x7GY9p6nzU7R2gyoMCxvTgj5sVE2gK7r63RiEe0GiaRq6YNfFLGQVSa8DijHDwsa0aEv68h1d6n73dbFrdWSPugZRgfq59EKza6dNbY0osZnRHixsjMbQUOAu/i5o62yLgaGeWL+oH7Y9ORTjuvq06HwBbvZo7+kofGnmys/HU7iFuBbhqCjTIgaEeuLY8tFia0pkFZbjQrrm28fIAFcxOKXhcBhzIyG7BJtj0zClRztDL8UkYIuNaTFyUSOuZBXDqgWDDWiKurmLmpx3t10SQ5iZ1sPCxmht/sHWp4Yiwqdxi/Cm6Obv0qgMypy5ll+GdQe5y642YGFjWkRcVhHSC+q3KLK1ssRYDfxs8nmji4aE4L93RsLZyOaP6qrLbk5x04OYGfVgYWM05rvDSRj/4X4s+/1MoxZFDjbqiVOIlyPOpt3oCvL0bR0xs08gvlvYD+ZOUUU1Ptp5xdDLMHpY2BiNoHyrFZvPo7pWJjLmP9xxRdSQyimuqFIrYJBRUI7iimrxu531jdF9Pdu7Y3hH9ZJ/HxsZLvqumSLU9YPaQTEth4WN0QhvZzvcrdDmm6yL2z/aL1I1SPT+iElTef8BoR6iRbdiwEAx5NCt3Y0E3eag2/UIVD593hTy2t7eetHQyzBq2KnBaMyz4yKElfZzdKr4Pf56Ce787BD8XOyQVlAumkrSh1MRmts5qpM3/jmX2eh81LEjv6gKCdnFsFRzrNW1/PJG8xVMie3nM3H2WgG6tXM19FKMErbYmBZ1qn33zij88ehg3Nk7AM52VqJInaywMZ29RbKu4jaRGkH+b9EA/N/MKMzoFdDofLStpb5u1Gn3411xaq3By8lGcjNLtc33hxtP/mLUgy02psVEBbqJ481p3ZBZUIFAD/u63LbP5/XB5tNpYkDxtF4BQgyJ58ZH4LeTNyw9ons7VxEdzdYgEji3f3vRZnzVrjiRP2eqbIq5hqXjI+DlZGvopRgdbLExrYbSPKhnmmLCLpVb3T8kBAsGh9SJGtFwpykfyzdvYJBaj0UW4ZvTuovHemxUOEwZaqn+5f4EQy/DKGFhY/QK+dBogDIVvFNb7Nl9A+s6XKhbqSCHyo9G6LiFkhS2o+bWFEAb8FaU0Ssu9tbYsWQ47G1upHgQZZU1+PqAehn31EmXoq8UnSX6h3iadKNG6kdHQ5YpYMOoD1tsjF6xtrSoJ2rEubQCtX1sFGxNuF5S93uAuz1MnW8PJYmxhYz6sLAxBoe2pZS0qy4lNxN7CUotMYdqhKvZXFOrCbwVZQwK1ZuSUGkiTxSYIKicS9uzStXFztpC9KKjPDM6wto6oVYmQ3xWMX45kaq2z1BdcnU0ZNpUYWFj9E5VTa3YkhJP/xSDIwnKR+l5O9uKYSeKUOpDB+8bHUT+OZchps7rm4mRfvjP7Z3h79Z4G9zRxxmjOnujy8v/NEpSbg3cBUUzWNgYvUK+tOmfHkJReRWCvRxxKjm/ydtG+DqLY/+V7LrLlk3oJHx0lJW/5OfTWlsX5dOtursnbKwsMHDlrkbXU+tz2i4/ODQUg8K9VJ4rPb9cq6JGkCXIqA8LG6M3SMwWfRtdZ33kqRA1ggTt2/v7iQHJlzKLMKtPAKb1vNFh9t/zmVqbYNXJ1xkbHhwgEoVTGlhG5P976Y4uiApwrZenp4rEnFvBDW0Rx8KmESxsjN549IdTiElRLWYNWfHnOXxzXz+cTSvAhG6+deKSll+/F1xrWDG5a11vuEAPB8S+OhZ/nLqG7gFu6BGoeQcRP9cbqSjahOpiyQo0h2CJNuCoKKM3qNWQjYYTz2kWABXH397dr07UKGiQrMWp8O4NJmzRsJp5A4PR1d8Fsan5dT3nKOWCBq5QyyXqSfefjWfwxb540XRTkY7eztj4yCBRPqYtsosrDeJPNFbayBp2CjRSCgsL4erqioKCAri4qNf6htE/f55OwxMbTql9exrO/NX8vrBoYKmQyNz1+RFcSFevwwf5yBQz+G2tLEQQ4OHhYcLh35DK6lo888tpUe9KNbCzegeK8iZqkFlVI8P5Bo/b0ccJHXycEeLpiNu6+IgaWmL2F4ebDI5oyuhO3vhqQV+YK4UafMZ5K8roFSpeT80rFYNL1Pkgf3Fvn0aiJreqAt3tlQobbQWpk8gX+xKQlFOC92f1ED4yqlBYufWCEJ1XJnWtV8NK5JVUYv2Rq4gMdMPq3XE4lnhDkFJyy/De9svi59OpBUrXejmzWBzE6j1x2PjIYLGNfWZsBGZ/cUQrwQRKJ2HUgy02Ru/QW275xrOiVZEyaNrV+G6++M/EzvBzbbqygCY6UbfZ17ecr7uM6lB/eKA/gjwdRc84sryc7W4JWHVNLawUtsNkxf0Zcw1OdtbYdTETf5/J0MpzDGvriA0PDIC3ix1+O5EqrL/W8viocCGU5kohW2yMlCFf2etTuork3IZ1nmO7+OD1qd3g49K8A55aii8cEoLCsiqxxfVxscXqu3vB82abH+o6QociiqJGAvvKn+fEdlPbUPPNGZ8dwuLh4SIa3FqCPB1E7ztGPdhiYwwGzTyY9MkBJGaX1KVdbHp0cN0MBF3z3r+X8ImajS0NCfkD/316mLBCzZlCDT7jHBVlDAalWPzfzMi635fc1lFjUTtxNRcPfBeNP2KuNfKXLV5/Aq9tvrVNVeT3k6lGIWryvmzUXZgHvKgPCxtjUHoHeYgtFmVyjIjwrvOd7bmUhY92XBHbRMXp6NQJ5IBCJcLh+BwxH+DJH2Pw7raL4rZkCdI2cOvZDPx6IkXpVpA6ZhgT9Pqosz1nbsA+NsbgvDixs+izJo/60bzSjaduWWC0RaUo5mM/nEROSSXuiPTDkA43ypqSFPLZPt0TL6oaHG2s6lobFZZX49cTqbhvcEi9x6T7NxXhlBoUTPlkTs+6JGKmedhiYwyOm4MN1i3oW5dnRj4lRS5mFGHOl0eEqCl20S2trG4UfNgSm46fout3/KBOGzsvZOKTnVfEFpUYGKq63lNKLB0XIWauMurDXwGMJFB0jL8woZOYPXru5qT4hly/2fGDhrmo06DyQFx2XSH9VwcT8ciIMKNxxFPr8weGhhp6GUYHCxsjSQuO6kKbEjbqhUZlTOoOOlGM+5O199bfxjGMmNo2vTczSmmCMqMa3ooykqRhyZIi03sF4KVN50Rpk6lCwYIPZ/eoy8ljNIOFjZEclLirbGK8nEPx2TicYNrDkh8f1QGDwozHDyg1WNgYyfHtoasqaytViZ4p0C/EA0+Y+MxUXcPCxkgKKoShOlFz5pVJXeqVfjGaw8EDRjI89H00YlMLzLqZorVlm7qZDkzLYWFjJMPVnFKkF5TDnAn1chJzF5jWwa8gIxlMox1D6+jg42ToJZgELGyMZGhNWx7qw0ZdbKkPGnW89XS00bgNuRRQ1s2X0RzeijKS4d5BQaJGVFUOmzLcHKxFWx/HBrWU1FRy7tqjOHqzE64xQCMJmdZjfF9pjMlCTSE3Pz4EO58Zjrn926t9P7ptQ1EjKLJIxeNeTvWHtUgVKvYnS5NpPSxsjKSgiGhYWyc8OlK9PC4SgnsGBDW6nFqCX8svE625aeaB1CKtNpZtYN1gTTQI+oejytulM5rBwsZIEppC1RyONpZi5iiNpaNUkamrD9Y1nMwvq8SI/+7G0l9OiynvPz44AA8NC0W4t2Gd8yRmAW728HOzR5d2t7rAOlhbCDH++2w6D0fWAuxjYyRJ4s1+asoY09kbVJjw4LBQbIq5hq8OJNZdt+Tn02JKfEVVjagl/eVEKq7mluK7+/uhb7AHHhsVjrEf7NNrWkl7D3v4utihRiZDTnGlWA9FgD0cbg1j7tbODceSbvgC3956UWyhyYJjWgbPPGAkCb0th7yzW2wnFaHd27r7+uFaXhl+jk5Re7L8mM4++OyeXiiprMG01QfFIGZ9bDdp1mhTXUoUZ522dbYVw5jlPecoyksT6sd08dH5Oo0FnnnAmMQkK7LMGkKWGnXafeXPs2qLGrHjQiZu/3g/7lxzSKei1ifIHcGeDuLnHu3dmxQ1gkSNttNEqJcj8korEezlIESNBH3Rd9FY9G008ktvDXpm1IOFjZEsr07uitemdBVlRnQM79gW/70zEieS8lrUsogGGl/Rgf/Kw8FGCBQNZY6+moesogr0bO8mrEqV93O0QWVNrfj5YkYh3OytEeDmgLSCsnqCvGZPvNbXbOqwj42RtNV278BgdPV3FdPjIwPcxBCW7w4nSSI1g1qWU1xTBpnY4spnKJRW1uBUcvPWpL21JQLc7UUiMfVfo+n2d/UNFHMb6FBsd05dhen1YNSDhY0xCv6ISROTqAxJr/ZuOJ9WCHsbK1zKLBLthcoqa0Qb85ZA282GPsTYawX48K4eYubpyZviSLch0ewReCvYwKiGt6KMpPn5eApmrDkkBrIYGgqzBXo4oIO3k/j5WGJui0WtKWieA/nVGlYg/H0mXauPY+qwsDGShaKE3xhw/idt/CgQQDMWyLlfUFYlfHTytAxdUVZVg99P1h8AveV0Gqpu+uOY5mFhYyRLUUV1o4ggzdgcEu6FsV18hF9Kl9BWk+aWllfVioCAPlJEmiKtoByf7+Uggrqwj42RLK721ti+ZDi+PpCIbecy0Ku9OxYMDhYlV8TlzCI88r+TOsvUl0csCZoub2je334ZUYFuGNqhraGXInnYYmMkDRW3Pz66A/56Yihen9oNQR43csTkLX7+c3tnnT22pcSikJTDJwVfo0kK2759+zBp0iT4+/uL8POmTZvqXb9gwQJxueIxfvx4tc//9ttvi/s89dRTmi6NMYNqhMmrDmLOF0fwwfbL2HAsWac5XhYSEzaC24braCtaUlKCqKgo3H///Zg+fbrS25CQrVu3ru53W1v1ZiMeP34cn3/+OSIjIzVdFmMG0BfeS3d0EQXv+hi/l5AtvWJ07rCrI2GbMGGCOFRBQubrq9mkoeLiYsydOxdffvkl3njjDU2XxZgJA8M88cKEzli+8YzOH4sSaKVG+E3/ImMAH9uePXvg7e2NiIgILF68GDk5zX+7Pvroo5g4cSLGjBmj1mNUVFSIoljFgzEP/Nzs9PI4HhJrUOnlZAt3bkRpmKgobUNpixoSEoL4+HgsX75cWHiHDx+GpaXyb8Aff/wRJ0+eFFtRdVm5ciVWrFihxZUzxkJhWfO92rSBnZW0LDZKDGYMJGyzZ8+u+7l79+7CXxYWFiasuNGjRze6fUpKCp588kls374ddnbqfxMvW7YMS5YsqfudLLbAwEAtPANG6lBN5biuPth98Xq9lAxtQ6VM1CZJxVB6vcL+NQnlsYWGhsLLywtxcXFKhe3EiRPIyspCr1696i6rqakR0ddVq1aJLacyS4/8eOoGJRjTYmQnb3F8uicO5ZU16BvigZKKauSVVuHjnVe01kQyNa8MXfxcNB4uoyvYYpOQsKWmpgofm5+fn9LrSezOnKnvCL7vvvvQqVMnPP/8801uXxlm8fCwRh0vqBXQQ9+f0NpjOEioi204p3roTtgoeknWl5zExETExMTAw8NDHOT3mjFjhoiKko/tueeeQ3h4OMaNG1dPzKZNm4bHHnsMzs7O6NatW73HcHR0hKenZ6PLGUYRZW18krRc9iSlVkG8FdVhVDQ6Oho9e/YUB0F+Lvr55ZdfFtZVbGwsJk+ejI4dO2LhwoXo3bs39u/fX2/bSIKXnZ2t6UMzTLPM7tceM3q1fPByQ2pqpVF47u5gzaP5NIBnHjAm2RXklxMpyC2pwu6LWa3qxhEV6IrTKdptTdQS+gV74OeHB8KcKdTgM85F8IzJYWHRBnf1vTFwuU+wu+hvRiP4hnX0Qlp+Odp7OKB3kLuYnUBtkaiJZVPzC6jVtxQI522oRrCwMSYNjdw7+dJtTQ5MptkJm0+nKU3pSGvQ3daQHEnIER1GnJRMvGcaw909GJNHUdROp+Rj3cFE0ects7Acj284qVTUQrxoqIr+Zo82R8L1Ejz/a6xoBMA0D8s/Y1YkZpdgxebzeH3LeTjbWYuuuMpIyi4VDnvKjZMKf51JR++D7rh/SIihlyJ52GJjzIqu/jeczmSlNSVqBNlFAe63er9Jhbf+voBoHbcmNwVY2BizIsiz/pAU1Uhv21ddK8OjP5xEdnGFoZciaVjYGLNsOS6H3G8RPs7wcalfnudkY4micsO3A1dGZmEFHv/hFA93UQHnsTFmx4Er2YhJyROzE6i/m5uDDQ5cuY5PdsWJonqqO6U6URp8LGXuGdAer0/pJqnqCF3CeWwMo4IhHbzEoQiN1TuaaFy+q/VHkkXjyQWDOZjQEBY2hgFwKF73rcZ1waubz+ODHVfg5mANN3truDrYiGhuvZ/F7zY3/newEde52Fs3mdtnCrCwMWZPTa0MR/UwQ0FXUHSXjqsa3MfSog3ent4dM/uYZg9DDh4wZs/5tEIUSjRQoEsxX/prLN7/9xLKq6TtS2wJLGyM2XMo3nw7zXy8Kw53fnYIKbmlMCVY2Bizh3LDzJmz1wpxxycHRCcUU4GFjTF77h8cAj9X/Uy+kioFZVW475vjYmtK21Rjh4WNMXvsbSyx7PbOhl6GZLamC9YdQ25JJYwZFjaGATAp0g99g90NvQxJsP9KNiZ9cgAnrhpXXp8iLGwMc3O2wSuTusJMkvjVGj04Y81hLPk5BvHXi2FssLAxzE26tXPF7L6mmdfVUn4/eQ1j3t+LxetP4Eyq4VukqwsLG8Mo8OLELhgZ0dbQy5AUMhmw9WwGJq06gHlfHRXpMVIvMWdhYxgFHG2t8OW9fTC3/42ZCUxj/9vdXx7F1E8P4Z9zGWJwjhTh7h4MowT6WHyxLwErt1409FIkTbi3Ex4eHoYpPfxhbWkhmc84CxvDqOCv2HQ8/XMMKqu595kq2rnZ44GhIWI6GKXPGPozzltRhlHBxEg/bHigPxx09GE1pSjqq5vP46fjyZACLGwM0wy9gzwwKdK/0eUsdrfwdrbFN/f1lUxvOBY2hlGDmX0C6s30vG9wMM68Og6Hl42CuTMx0g//PDUMIyK8IRW4HxvDqEGfYA+cXTFO+NoKy6vg5XRjRoKfqz06+TrjYkYRzA0XOyu8PrUbJkf5S649OVtsDKMBNlYWdaIm542p3XBbFx/8/NBAeDrawByIDHDFP08Pw5Qe7SQnagRbbAyjBWuODuKdGZFY9F00TJ1xXX2FtSpV2GJjGC0yposPpvVsB1PHy0nalikLG8NombbO9beqpkhljbTTX1nYGEbL5Bl5LzN12HY2HVKGhY1htEhWUTlOpeTD1Dkcn4PrRRWQKixsDKMFKA1kx/lM3P7RAcRlGV//Mk2h2ncpW20cFWWYVvDD0WT8EXMNMSn5qDCzetLNsemYNzAYUoQtNoZpBd8cSsTRxFyzEzXieFIuMgrKIUVY2BimhVBjnJTcMpgrMhnw1xlpbkdZ2BimhWQXV6LMBKeoa8KW2DRIERY2hmkhKXmmNT29JZxKzkeqBF8HFjaGaSFZhdJNd9B3M06pwcLGMC1kRERbtPdwgLmzhYWNYUwHO2tLvHRHF5g7Z64VICm7BFKChY1hWsGYzt4I8mSrbYvEgggsbAzTCqgXmfS6kekfqW1HWdgYtamoVp7acCmjSPIDdHUJT7CC6CB8JVM6XYRZ2Bi12HTqGiZ8tF+0xVbkxNU83P7xftz1xRHEZUnnja1PpN7CR5+pH1KBhY1RCVlin+y8gqd+ikHC9RLsvJBZd11BaRWe2HAKNbUyHEvMxa8nrsEccXewhrnj42KLSVGNJ3kZChY2RiW0w/xkV5z4ObStY90YOhK8F36PFfMk5dTUmueWbHC4F8ydZ26L0Nmg5JbA3T2YJvklOuVGb7Gb3vHUvDJ8fTARVTUyEd7fejaj3u2tLM3ve5IEXoqZ9/okwscZM3oHQEqwsDFKIV/a8o1nhIgpOsnf+vui0tvTCLrFI8Jgbuy8kIUdF7JwZ+8AnL1WYJZj+J6fEAFLC2nFhlnYGEFpZTWsLS3EQey+mFVP1FTh4WiDrxf0hYud+fmaInydse6+vhgZ4Y2qmlohdNSb7XB8Nk6nFsDU6RfiIZ671GBhY4R1NueLIyI40CvIDf2CPXEkIUft+/cMdIO/m3RHsemSQA8HcRD0pTC+m684Eq4XY9R7e2HqvDChE88VZaRHeVUNFn0bjXNpheL3g3E54lAXa8s2mNlHWv4VKfjd/u/fSzB1xnX1Qa/27pAi5uftZeqgrdMj/zspUjVaytz+QRjfzU+r6zJ2yIKRR49NmXZu0i0lY2EzY/ZfuY5dF7NadY5vDiXhVHKe1tZkKhUa7/5j+hbbrou3chqlBgubGdPWyU4r57liBlOZNMHWyhL/NzMK9tbSyevSBT4u2nn/6AIWNjMm2MsBvlp4c5rDuDlN+HxvPO787JDJtw0PknBXExY2M8bZzhpr5/eBQyszxslHR/465gYPDgvFX48Pxb0Dg2DKBHk6QqqwsJk53dq54uPZPdGaiD3lbT3wXTRKKqq1ti6yAr/YFy+GEBO1tTK8/MdZdHl5G/q8sR3jP9yHvZevQ6rBgy7+Llg4JASmTDsJp/hwugeDMV188OLELnh9y/kWn2PPpevYfDoNs/u118qatp5Jx3vbL8PKog0Ghnkir7QSZ6/dSEkprawRuWKDwzzrFeTP+OwQQr0c8dz4CIR7O8NQpOWX4XxaIS5LqI2PLjidmo+pPdtBirDFxgjuHxyMewa0TpRir2kv0z6krSMeHxWO6loZ9l/JrhM1OeuPJItWSfI2SuuPXhVW3r/nMzHuw/1486/zIkdP32w7m4Fh7+7Gou+ise5QEkwZ+iKrlqgLgoWNqds+vTqpK/qHeLT4HD8cTca0Tw+KD3drSMktxeMbTgmrp19w0+uhXnDrj1wVCbEbjiXXXU5tlL7cn4iJH+/HaSri1+O0pkd/OCnEmLheVCFqaP1cbWGqc1X3x2VDirCwMfW6c/QJdm91s8GH15/AG1vOt+jbnHxp1Gaa2iXtvJiFY0mqk4f7BnsIUX5gaGij6+Kvl2D6mkN4799LGne5zS+tVCsPcOOpVFzNKRHWyxM/3uhNp8iNovg2CGvriB4Bbugd5I6+we6wlF4VUovYeFKaPfjYx8bUw8ZSO7lXaw8k4nx6Ieb0ay8GC983KERlvy4SHgoObD+fiZyS5kVFzsc7r8CiTRshFt3buYqJSYqQ0FA/OSpOf/+uKHTydWl0juziCvHYVOv5/PhO+PdcBt7797KIGPcPveXHk0MWIiUmk0+ygY4pJb2gvNFl/YLdcSzJ+BOb/z2fgeKKajjZSktKpLUaxuDYWGnPiD8UnyMO4tfoVPzfrCiltYUkFC9uOoOfo1M1fgzyvxHNRUhJZCd9cgBPjekoyp3audvXtdohMfz7zI3t89YzGai8aWmu2h2HqEA3lFXWYPXuOEzp0Q6d/Zzx2pbz+O7wVbQKE7HYyqtqheuB2jZJCRY2ph721rrxTiRkl+DONYdw/+AQ0eqHtpq1MpmweC5lFLZI1DSF2jD9959L4ujWzgU/PjhQWBrkB5MjFzVKZfh+YX+kF5ThxY1nxbaYrFBPRxuNLEpl0DliJDQfQBvzMFjYGEmjbOulLUjESBykAEVZqUJgyW0d4edqL4RMsc05ba/e2XYRa/bE17tfa0WNCPd2wtFWNB6QGgfjs5FRUA5fV+mUWLGwMfUg6yXQwx4pubc+5KbKl/sTxDYqq6iiUflTQVlVI1HTBlQ/ek6LaTFSQCYD/jx9DQ8Ok04HZY6KMvWgCOO4Lr4wB8g/RAX8JGL6mg0a4uWI4krTqyH9XWLRURY2phHjupmHsBkCK1PJ82gApbVcSK+fRG1IWNiYRlDkkhzcjPah1BRTZdMp6VhtLGxMIygN4rYuPoZehkliacLC9kdMWqMEZUPBwsYoZVxX3o7qAhPWNWQUlms0BEiXsLAxSqGOGo4SmuxtKmgjXUTKbJTIdpSFjVGKnbWlpBsJGiuJ2SUYGOoBZ1sruDua3hzWrWfSRaWGoWFhY5qEKgMY7XM4IRdFFdXIK6lCZIArTImSyhpsv2D4IS8sbEyT3G/iHWClQGxqAXq1d4Mp8b8jV0WXFkPCwsY0yZ29AkSbHUa3lFQYfuumTahcjBoIGBIWNqZJLCza4PUp3XCzCQajA6ixJ7U9NzU+2HEZu1s5s7Y1sLAxKqGhJPMHBRt6GSYH9Y7zcbYV1g3VqpoaMhnw5I+nkJRdYpDHZ2FjmmV0J07W1TZ21hbINEFBU6SwvFp0Uy6t1N70Mp0J2759+zBp0iT4+/uLgulNmzbVu37BggXicsVj/PjxKs+5Zs0aREZGwsXFRRwDBw7E1q1bNX82jE7w4PKqFqFqXisV3psDFzOK8NyvsaKZqKSFraSkBFFRUVi9enWTtyEhS09Przs2bNig8pwBAQF4++23ceLECURHR2PUqFGYMmUKzp07p+nyGB3g5cTCpilu9tZwsbdGV//GrcipuWVqnum3hZJDMyzW7k+Udj+2CRMmiEMVtra28PVVvySHLEBF3nzzTWHFHTlyBF27dtV0iYyWcbLjtn2a0snPGUcScpFdVI6BoZ44fLPUyNbKAp5ONriaUwpzYuXWC0LkB4V7Ga+Pbc+ePfD29kZERAQWL16MnBz168dqamrw448/CsuQtqSM4Tlwc64Aoz7UgZegNm8kaoPCPMVELWo0aW6iRlBa22MbTtXrUqxLtP5VTNvQ6dOnIyQkBPHx8Vi+fLmw8A4fPgxLFROQzpw5I4SsvLwcTk5O2LhxI7p06dLk7SsqKsQhp7BQOr2gTI3NsemGXoJRQK2eSiqrRQNLmkyviHyojTmTW1KJh78/gV8eHihK9ozKYps9ezYmT56M7t27Y+rUqdiyZQuOHz8urDhVkHUXExODo0ePCitv/vz5OH/+fJO3X7lyJVxdXeuOwMBAbT8V5iaGCtkbE1Q9QPl+5FsbEOphVj40TaCJYC9uOqvzYILO0z1CQ0Ph5eWFuDjVmcg2NjYIDw9H7969hWhRgOKjjz5q8vbLli1DQUFB3ZGSkqKD1TPyb1pGOdaWbTCms7eYOH+9uBIZhRXCt8ZVtk3z64lUrD/SyvGFzaBzr3Bqaqrwsfn5+Wl0v9ra2npbTWUBCjoY3ULfrDRQmLmRrJxwvVhsNamvWoSPs2jKueOC4TLsjZUVm8+js58L+gR7SMNiKy4uFltGOojExETxc3Jysrhu6dKlIpqZlJSEnTt3irQNssTGjRtXd47Ro0dj1apV9awvyo+j+5CvjX6nrevcuXO19TyZFlJaWYMKPQ06kTLuDtZIyCqGg7UVuvm7wNnOSuRonUtj325LqK6VYfH/TiKzsBySEDbKM+vZs6c4iCVLloifX375ZREciI2NFT62jh07YuHChWJruX///nrWFQUVsrNvRdqysrJw7733Cj8biR755P755x/cdttt2nqeTCuSTI8uH42PZvdQmXBq6tB0qfLqWuSWVuJsWiEKy/SfTW9qXC+qwE/HdeNCaiPTd0qwjqCoKAURyN9G1QuM9jmelIsFXx8TPbfMKShAw4DTCnRjWZgzHX2c8OdjQ9SOkGryGedaUUZtKA/rs3m9YU6dN04m57Oo6QBKVP5kTi+dpX2wsDEaMSTcC1HNdH3tF+yB0LaORv/BO5Oab+hlmCwv3tEFEb7OOjs/CxujEdTUYMFg5W2MqCnlvqUj8fPDA/HvU8PQSYdvXF3j42KH0ioOmuiCsV18cE//9tAlXATIaMzt3f1wMb0Iey5dx6XMInHZ/YNDsOz2TrC2vPFdKe/sYmwEetijnas9KmpqkZxrfqVPusbP1Q7v3hmp8/cGCxujMbZWllh2e2dxpBeU4VpeWaN8pG8PJeFCuvGlQqTklsHDwQanUwvE7062lojwcRF5a9FX8+BkYymCJ8YWcQtr64j/mxklEom/O3wVCQaoJqHKjA/u6gE3B913i2FhY1qFn6u9OBTZeSETb/zVdDmc1LmQUYQOPk6wRBvklFbiRHJeXZVBcWUN+ga743jSjcuMhf6hnujZ3l0c8wYGY0tsGj7ZFYe4rGK9reHJ0R0xINRTL4/FwsZolZpaGV7+45zo5mCsVFbXCiu0rKpGtLiWU1Vz4xeKlAZ7OiDJiLp09FOwqKlaYkqPdpgU6Y+tZzPw8c4rdS4FXVlqy2/vjIV6nHrGwQNGq9CH5tXJXU2i4qKpDE8Sb7rOmIbc9A3xUDqsZ2KkH7Y+ORSf3dMbXfy0n/9JTTW/mt8Xi4aG6tXnyhYbo3Vu6+KDewcGCV+OqXI1txR9gtyF301quDlYw9fFTjjqfV3tbwRE3Oq7CxoK3PhuvhjX1UfUvZIFR1041IWm2q+a2wvJOSWizIyOSxlFYtI9iVpHH/1Hx1nYGJ1AW49jibniTW7K1qm+JlotGhqCzafTsUPFlHVKr1kxuavwp7UEsqjoS4m6lVDE+8TVPPEcrSzawNLy5v8WFjf/v/E7iSKVm1HyNtC27lxU0FRZUysCTYaAS6oYnZGSW4o5Xx4x2d5ktHU7r8PIL7XSfmpMRyE08m0cBWaoM4ZiKgoV5D9zW0fcMyAIVjfTbUwRTT7jbLExOiPQwwE/PTQQc744YpI5YRY61JAHhoYIq7ehX2p0Zx8MDvfC53sTsGZvHCZH+eO58Z3g5cQtvBRhYWN0Cvl2Pp/XG1NXHzS59kfyKKkuuLN3YJPOdqqvfHJMBzw4LBT2ZtxxRRUsbIzOoYaCmx8fguScUlwvvtGqJibF+OswqeuHLmjv4SA6XzQHi1rTsLAxeoEiY3ScSs5DVY1pWG6U56YLyIFvjOVoUsJ0PY2MJOnk64IegW4wBSiRV9tQpHF2Xx5M1FpY2Bi9QtunN6d1x39u7wxTgMqstAklsnYwQN6XqcHCxhiEB4aF4oUJnWDMUJqFNlPZKNDyxOhw7Z3QjGFhYwzGw8PDMH9gEIwRmv8Q6OaAimrtRUYpudbBht3e2oCFjTEoz0/oJEp+jAl7a0uEejnifIb2knNFxn8XH62dz9xhYWMMClkoPz80EA8NDxWCIXXsrC1EKgZNqtIW9LxNoXGAlGBhYwwO9XNbNqEz7pJ4NNDGygKdfV3qmlBqi6fGdFBZpM5oDgsbIxkeHxUu2W0pRT8jA1xxSsuJxVQKdb8e+5SZCyxsjGTwdLIVbW6oh5cUR/FF66Br7h2RfnVzIhjtwa8oIymoOoF68+sbShoO93YUnXGVlTgdScjRyeNOivLXyXnNHRY2RpLj2XxcbPVqjZ1OzUdcVgkKyqoQ4eMEB+tbHw13B2voon4/wN1eTJpntI/0bH7G7KHmhdSPf+2BRK2el4Y4U1tvavtN2Fi2QYC7A44m5tbdJq+0ShyEm721mC96RsvBAkVrjWtCdQNbbIwkeWZshNatGUqqvUodRooqxJFbUlVP1BqSX1Ylhpx0a2byfUsh8WZ0AwsbI9ma0sdGaa+8iOYTnL1W2KLuHJY6sKrCvZ3Q2Y9rQnUFCxsjWcoqayUxn4BSPMLbNt8fTV1oKcsmdOJtqA5hHxsjSWgUx+8nU7V4vtbd31YhmNBagaXOJtTim9EdLGyMJNl5IQs7L2Zp5VxkF+WUVLTqHPHXizXqqUYpIkGedNxIIQnyov8dRSSU89Z0DwsbI0n2Xr6utXP1CXbH8VYm17ZBGzEGr7l5myMj2uL9WVFwd+ThKoaEvzoYSZKUU9Lq0XXkoKei9daKmjzQQKIWFeAqAhEdFGYS2FlZiMfqG+SOw/E5eG3LhVY/HtM62GJjJElYWyfsv5Ldovt6OtrgWn4Z8m/mo2kTxQL4ts62sLW0QFpBGeKybm1VN566hpGdvMVoPMYwsMXGSJKl4yKEn6oleDvb6kTUGkK5cKn5ZahVEph4ceMZpOWb5qBoY4CFjZEkjrZW+OGB/gjxclTr9u097GFt0UaI2oWMIhiawvJqHGihxcm0Ht6KMpKFyp02PjIIW2LTcSg+G+kF5cguvlE1UF51I8eNZqn0CfYQFQTUWsjNwRpZRa2LgGqD27v7YmafAEMvw2xpI6OEIROgsLAQrq6uKCgogIuLi6GXw+gQessWlVfh7rVHkVtciTQdDS5uTU3qn48NkWT7JXP5jPNWlDE6KGPfxd4GHbydJSdqhLOtlU7KsBj1YWFjjJYh4V6QIhQ5ffLHU6KTCGMYWNgYo2V8N1+MkWhp0r/nM/HGX+cNvQyzhYWNMerI6RfzeovUkIY17jQ7YU4/ww6HWXcwCee1OM2KUR8WNsbom1I+OjIc/1s0AH6uduKy4R3bYvNjQ/DKpK4GHw5zNk03TSoZ1bCwMSbBwDBPbHtyGN6e3h1fL+gLNwcb2FlbYvmEzgZd18E4zmUzBCxsjMng6mCN2f3a1+u9Rn64vsHuBlvTHzFp+FrLLc6Z5mFhY0w+NWREhLdB1/D6X+fxV2y6QddgbnAGIWPy3NM/SJRaUSskKqynSVT6hFLgn/4pBp5ONhgQ6qnXxzZXuPKAMSuqa2pFnhmJ3NlrBXB3sBGlWHIqqmtFdw5d4GxnhRm9AtA7yF30iPNzlebUe1P4jLOwMYwClFTb+aVtqKzRwSDRBvi72qF3sAd6t3dD7yAPMdzFirvrauUzzltRhlGAAg/U0vuKQn81XUHlYGmn07D5dJr43d7aUkykJ2uuV5A7egW6i4AIozlssTFMA05czcX8r4+juKLa0EtBRx8nsXUli47+p/kJ5jrdqpC3oixsTOvzz+auPQqpQd2ByZojkRvf1RfBavarMwW4uwfDtIKyyhqs3h0HKZJTUont5zPx9taLGPvhPny6Jw5VevAHGhssbAyjQHlVDRZ9dxyH4nMgdSqra/HutkuYuvqgiPAqQ2YaGzKN4eABY/bQIJatZ9KFNXQurUArU630ybm0QkxZfRAPDw/F1B7tEJtagFMpeTh5NR/2Npb48cEBZjfLlH1sjNlCW7g3/7qAbw4lwZSZ1ScAb0+PFA0DjBlO92AYNfLVnthwClvPZsDU+Tk6FdW1MrwzI9JsLDcWNsasWH/kKpKyS3AhoxAH46TvR9MWv5+8hpTcUnw6t7eYh2rqmId8M8xNzqQWYO2BRLMSNTnkO5y79ggqqmtg6rCwMWbFoHDzLkK/nFmMJT+dlkTysS5hYWPMirFdfNGrvRvMmb/OpOOOj/ebdNtyFjbGrKD0h3UL+qGzn3lHzpNySsW29HJmEUwRFjbG7KDC8u/u74dQMypHUkZeaRXu/vJok8m9xgwLG2OWUGTw+0X9Resgc8/l83ExvdeAhY0xW9q52eN/DwyAl5Pppz80xbCObU0y/YOFjTFrQrwc8fWCPjBXxnWV5sDp1sIJuoxZQTlcey9dx8e7rsDXxR4rpnTF5/sSYI54OdlgYnc/mCIsbIxZkJZfhjf/voDt5zLr2n6fvVaImJR8ZBdXwBzJLq7EP+cyML6b6Ykbb0UZs+D//rkkRuA1nGVgrqImh8Q+LqtIdDUxJVjYGLOgo6+zoZcgSVJyyzB51UHc+9Ux7LqYKZpsmgIsbIxZsHBICKICXA29DElSWlkjetHd/020qEowBVjYGLOA2vVsenQw3pzWzdBLkfSErn7BHjAFWNgYs4GmO83u2x5hbTWrOHC2NY8Ym7uDDQI9TGOIMwsbIzn2XMrCd4eThEObGkJq2yp5984o9FSzEN7G0gKr5vaCOZBdXIEF647DFDCPryLGqOjezhUPrz+B8qpaMVfzpTu6YGiHtnXX7718Ha9tPgdbK0ssHhGGSVH+Gp2fRtdtfGQwisqr8PRPp7HjQqbKzPzIdqp9c518ncVglYTsEhg7ZZU1YgCMsc8uZWFjJEV1TS3WHUwSoibvHzbvq2Po6u+CUZ28cTwpF0cScutu//iGU/gj5hruiPTH5Ch/jfr6O9tZw8Wu/kfAyqKN6AAiZ2KkL1zsreFkayV6mJGldyo5v9593p4Ric5+zhj89m6jTx85lpSL1LwyBHo4wJhhYWMkxb/nM7FKyUxPmsREhzJ2XMgSx5o98Vg0NASTe/gLa04drCzbiO0miebESD+M7OQtRKwhL0zoJIrFs4rK64SNNHTegCD0CLyxraWOIV/uT8DuS1nIL62CsXK9uMLohY2nVDGSYsz7e8U4vNZuZb9e0Fet4u6c4go42FjVs9JUsfLvC6IEi6KHb03vhnDvxvlx5B/87UQqTqfeSHq9rYsPLmUUITm3FMaAtWUb9AnywH8mdka3ZrbhUv2Ms7AxkuLE1VxUVNfipU1n4etqh/T88hb5rtp7OGD9wv5o76ldy6Okoloc1BGkuW0vDU85GJeNWX0Cse5QEl7fcr6uq8i1/DJIncgAV/zx6GDJ+NtY2FjYTMLXRhHM+euOY9/l6y06h6+LHX55eKAktlUk2L+euAYXeyv4u9rjlT/PwRgI9XLE308OhZ21ehatLuG5ooxkKa+qga2VRbNWgNXN+ZevTe6Ks2kFeOyHUxo/VkZhOd779xI+nN0ThqZ3kIc45Ezo5ismth9NzMGeS9dxpZXbb11RXSsTfy9jQ+MV79u3D5MmTYK/v794c27atKne9QsWLBCXKx7jx49Xec6VK1eib9++cHZ2hre3N6ZOnYpLly5p/mwYyUOF6E/9FFPvsq1n0kX//Rd+i603Gi6zsByH4nOwalfjYIK67LyQJdIXpIa3ix3GdPHBfyZ2wfYlw/Hb4kGQInP6tZfMVlSnwlZSUoKoqCisXr26yduQkKWnp9cdGzZsUHnOvXv34tFHH8WRI0ewfft2VFVVYezYseKxGNOie4ArzlwrwGd741F7M/mWnOodfZxFNJKsFxIi2n5O/Hg/lm88g4sZLR84UlRRLeogpQ7l1kltwIyrvTUWDAqGMaLxVnTChAniUIWtrS18fX3VPue2bdvq/f7NN98Iy+3EiRMYNmyYpktkJAwJ2C8PDcTo9/fi9m5+wrk/urM3gj0dxfZz0bfH8drm81p1rr+x5Tzen9VDoxw3Q0DR0wvp0hmJ19HHSe1osdTQyeZ5z549QpgiIiKwePFi5ORoNnWbnIOEh0fTBbkVFRXCmah4MMaBp5OtyNZ/Z9tFHI7PQU5xJfLL5HlfbbQeMdwUk4YBK3fi6Z9isP1801UGhmZm7wBIicuZxXVWNcxd2Ggb+t1332Hnzp145513xDaTLLyaGvX6PNXW1uKpp57C4MGD0a1bN5V+OYqQyI/AwEAtPgtG11B2O7XImfPlEdz1xRG88sc5ZBSUY/+VlkVAmyOrqAIbT13DA99FS3aWJkVvz64YJ9JUukhgW1pQVoWicuOcGN+qdA9yKm7cuFE4+5siISEBYWFh2LFjB0aPHt3sOcnC27p1Kw4cOICAgACVFhsdcshiI3HjdA/jYODKnUgvKK93mY2Vhai51DUzegXgvVlROktTIYuT0iPIR9WSNAmqbiDhJ7Enf6Qh+fOxwYgMUK9hgFmle4SGhsLLywtxcXHNCttjjz2GLVu2iMirKlGT+/HoYIy3RU5DYdOHqBHUKZa2WNr2udGMzsd/OIVt5zLqAgIvTuwshIFy8tTlsz0J+PpgIqTAnzFpkhE2TdB5gkpqaqrwsfn5NT0wgoxGEjWy/nbt2oWQkBBdL4sxMB6ONnXpBEvHReCxkeGiy62+JqCf14GT/uU/ztWJGnHiah6mfXpIRHcvZqj/eJlF9QXfkKw7lITSymrTF7bi4mLExMSIg0hMTBQ/Jycni+uWLl0q0jaSkpKEn23KlCkIDw/HuHHj6s5BltuqVavqfqdUj/Xr1+OHH34QuWwZGRniKCuTftkJ0zLcbwob1SU+MiIMz46LEO2JnBt029AVZ1Vs8cia++pAojhOp+Sr1ROOpj3RoQxKV3n2l9Nqr83r5msjBWpqZWKal7Gh8bsoOjoaI0eOrPt9yZIl4v/58+djzZo1iI2Nxbfffov8/HyRxEv5aK+//nq9bWN8fDyys7Prfqf7ESNGjKj3WOvWrRMJv4zp4eFgLf7/7vBVUde5aGioqMHsH+IpsvF17bSmPv9NEX01r66uk/B2thUF4VN6tFO62/j6YBLe+Os8VHmrSRxe/uMsPB1tMa6bDzr5Nu0jWj6xM34/dU0yjvsDV66jX4iHaQsbiY+qeMM///zT7DnImlNEipnhTMsgcSJhGt7RW22/Uu7NBFpHWyusnd8Hq3fH4b//6LbypKyqaWEj35hioTpFVF/47YzwNdHk+IYJwM2JmhwScWLdoUT8/NBAkdOnjANXsiUjagRZrvMGBqvVLUUqGF8RGCNJ4q8X45mfT6PvmzvEtKP7vjmOPBUZ/4o+rgGhnvWuM3RtIgnyp3N7CUtNUQhH/t8e3Pb+XlENsfNCptimudhZ48O7eojebOpCvdrmrj2KJCVdS6iDLQm7lCiprMH72y/DmGBhY1oFtdd+6+8LGPfBPvx2MrVuiycviTqWmCuihXLoZ9rmHU/Kq2uNM7SDV931P0en4I2/Luh83c1ZH1GBbvjzsSHo1WA2AhWr/3A0GQu/jRZCR4JOW9SX7+ii0eNfL6oQ4taw4+6bf5/HyQYdeqXAT8eTVfolpQZ392BaRWZhhbCwnhrTAZU1MmGlbYlNE5FHqjAg64YaR1IdKOV1EfItHlk5r0zqUq/IWlXKB3W5JWHcf+WWf7alKFpjTUH94GjLSHWtH+64IjpdKEI1rvPWHsXDI8LgetNnqAn0Omw+nYb7Bt+KBqflSyciqgg99Sd/PIUP7uphFOkf3I+N0To0G4A601IrbdIsatn90c4rjfxQd/UJxDt3Rja6P4kXNZictOqAyH4n/F3tsHvpCOHveXdb6/xv7g7WOLxstNLk2fNphVi1+wpGdPTGlJ63WoyfSS0QHUgKtez76h/igZ8eGlj3+4c7LgsRlSrWlm3wfzOjlAZSzCpBlzE/yDpTnBtAznhlX58z+wQ0OdyYiuNpngDNP6Bmh9/c1w/X8srw6e74Vq/vngFBSkXt7zPpYjgM+c7+PpOBt7ddFNtkSiamqggvZ1utCxsJqSLjuvri870JKoMbhqSqRoYnf4wRr9H0XtKqbVWEhY3RKVSX+WoT3WKp0WIfFZPHHxoeisHhXmICFAnR1NUHhTXYGkhkHx0ZrmQt+XWiphit/SMmDbqEoqoUSaaIMEGti/YsHYFpqw8irUFlhpR4e+tFIcLydUsNDh4wOiEtv0xEScd9uA/x15X31dvXTME7jccbGOYJNwcbrN2f0Kq+bAR1FFl7bx+l1hoFO7Q9nFmT10oR2sKTdShlsooqxKg+qSJNuWWMHhrGsvNiVqPLB4R6CEc0BRKW395Z7fO1tpWRg40l/reov6h4KCitauTsn9knUAyR+aQV3XpbCqW+dGiQ02ZhBF1rq2uk655nYWN0As3abChsDw8Pw7NjO4o8MU3bTU+OaocNx1JavB5KQ/l45xWxddp1MQvbnhrWyEoK93aCIaDAhKIznuZCUHcPqZNRIN01srAxOuHx0R3ENvJCRpFIRO0b7I7x3ZpuhKAIDWC5mlOKj2b3qBNAih5SigZtgVrKtzcz/zs0IWDZxYZpIX6pQX846uwh9Ynyd/dvL4IwUoWFjdEZFBhQFRxoKthwNDFXJPYuHhEmnOmUAhHo7gBba+24hJvqxXbfoGCRCkL5aTQweFPMNfx6IhW6hqLAcmibrI3Ir647/b4xpZukh7ywsDGSggrFY25m3s/67DBm9A7An6fTUFxejera1vdro0HHDRNMKZWTPqTUn41SGLIKy0WElARW14S2dRRCQcOVqYPu+qNXWx351SUUgHl7RqTk50ewsDGSwtvZDpU3S7AoFeKbQ/UbJrQWCiLIoTbkJGCUbkFF77S9enBYGP45n6kXUSMi27kiJjUfT/8cg1cmdcW6g9p9vtqGamM1aZppKFjYGMlATnNdzyNQrFtd9vsZUZBOJOWU4q2/L+JoQi52X2oczdUVNGhGcT1Sp8ZICpU4j42RDFQuJc9Vc7SxhI+LbV3jScr8f358JwR7OrTqMagdubyYm9I7GkKRXF2kswW422NipJ94Xop0a2dc5X/juvrAGGCLjZEMVDolF7GtTw4TZVVxWUWiBRKJ2h2R/jiZnCesq9ZA3WwpHUVeh6oP/nN7Z0zo7id8aTR2MCWvDH4udnj/rii8uPGsaCwpZWysLPDfOyMNUiPaErgInpEUlBqSWViO/g16tMkz9Ae/s0utpo7qQlZUTY1M+NsogECNJCd09xVRUUoybpiKIRfgBCW91JqiT5A7fnl4YJNRRPoIkrX67/lM0WCStuOGqoJoivdnRRm8NpSL4BmjJdjLURzKcLSxwtz+7bH+SLJG5yQhovrQV/481yji+Ny4CAR5OtZFRhUZ3823kbBN6OaLu/oGYsG642o9Np3y9amqUyPoOmqNTsNsNsemC6uOUlyo4FwKLBoSYnBR0xT2sTFGA5VBze7bXqP72Ftb4rfFg0TaCFU9NES+HVUmPEEN/HmU40ZlYMM7tsWcfo0HdFNfOkoqVuwA/NLELiIXTx2oweQTG05hw7FkfDq3NwaGego/oyFp7+GApeMjYGywxcZIHpoatfZAgmgwqWl1wNiuPnUTsWyVFL9TE8ymGieO7uQj0kOoHGtIuBeW3d5J5JoRr03pBkqrO56Ui6u5pWLrSH3KqCOwPCgxvWc73K9ipCA9LxpvV1FdI2pn1+6/MUuUyqlo5N2GBweI3y9lFGHpr6eRV1qJiqraVlVfaMqyCZ3qetIZEyxsjKQpLK8SQ4j3XlbdCUQZNpYW9brTyofGKBLg7qDSQvznqWEoqaxuNFWKqgXkTTK/OZiIzKIK3BHpJ7a6bg7WmNDNDy/d0VnlxPjnfo1tMmjw77nMOkd9hK+zaFNO0JZ5+cazwqrTNX1FGZwvjBEWNkayUOLsfeuOi8HDmkI7yw9n9xDRz6aaOvq62In+bKqQW2iqWKAgntRq6UgT3XkVORSfozIS2tRMBtoyRwW4YsMx6JwXJ9Zv225MsLAxkoVG8LVE1IjXJnfF7d1vFd2TVfXXmfR6t6EggC6y6JsTNSKnRPV28sfjyZgU5SeEknLvknNKxBY1Nb8M/zYxmFmbTOvZTgy0MVZY2BjJQv4xTUuquvi54MU7OmNQ2K3JV+QHe01hADJBejarb+MAgL6grSr1fktooglneVUtZqw5DENga2WBpeOML2CgCAsbI0mobQ/11lcXcvLTtHaKmja0wt7ddrFRNQFNvKL6UF2Xb/10PEX40whrKwvRnJFGFnZr54r/3hmFmZ8d0kmlQ2t4cFgo/HX82ugaFjbG4FD3DmoP9PX8PrBSaOFDszfVgXRs65NDRT6aMkZEeIstrVxAyH/1xtTu0DWlFTV4cdPZJtf81fy+In/ti30JkAptnW1FQ1Bjh4WNMRhk0VCahKejjZg5sOi7aDFVnWYc0GVUR3n2Wn2Hf1MdQZoSNYKScydH+YvKBRd7axE0kKeA6AqKXlL5V1OQyC79NRZ/PjYYOy5kNrkl1TdLx0VIdkCLJhj/M2CMEvrg37P2qJhC9djIcJEisefSdZxOLRAJsBSNm94zAGev1feNKYOSbGm7p2jtKYtuqhPh1Aa7L2bh3X8u4UJ6YbPb7SU/x4gAgRToE+SOO42swqApuPKAMQiU70U9z74/clUMaqEkWOJc2o3OG3KflDrQDM7WTrDSFkcTckTRfnOiJudIQi5Op9xorGlILC3aiNIvqTeQVBcWNsYgkJUS6GEv/GiP/O8kKm9m619IvyVQFRoMDaaUCClYoSu3XoQxct+gYLVLv4wBFjbGYLwz/Ubm/pmb/dEIilR+fSARr20+j99Oqt/KRwrdMP45l4EYCVhfmkL1qE/d1riO1phhHxtjMKiLB22BFEXps73qDzKxtmxT1wHDEMJGFtqqXXH49nCSKHtKzze81agpbaj7yJRucDKBgIEipvVsGKPhwJVsLPz2eKsEKdjTEVeyisXPlTXqb1tbQ1llDaws28CyTRvRBol8hER2XA6MkbemdcfYrsZZD6oKFjbGICTllChtza1JgfuX9/bBe9svY/PptLrgg65ZsfmcaB9O2fnGMNRYFS/f0QVz+mnWBspYYB8bYxCoFvH27r5KBatn+xs1ird18RHj3pTxwLAQsZX9eHYPvDOju15aVh+KzxaF6xTwMHZRGxHRFvcNDoapwhYbYxCo7Y98QpQi8wYG4YnRHcSsg56B7jgQl417v67fymJMZx88PeaGs5vy3e7SsPlkS9l06lpd9NaYsbWywGuTpT3wuLWwsDEGYdXuONG6RxE/VzvRoZYCCr2DbkyQpxkEit1ch3TwElsoVcm42mybRGuRd+soqdDPdlfXPD4qXAzKMWVY2BiD8NCwUHg4WIvWRBQ/WDA4WGzxGhawU7XAB3dF4WJ6ER4bFa63LH2yzO745ID4f+Mjg0RFhL6GKOt68vwDw0Jh6vCUKsagUDkUpW042EjrO5Zaho95f6/4mbRWAmlyrcbZzgpr7+2jdAKYMcBTqhijgXr9SxHFMidjFzVryza4d2CwqMnVdfG/VGBhY8wS6vQx9oN9YpAKzTOgDiDy/v60idko8QHGDaEmArd19kFoWyd4O9si/noxzqUVii+OZ8dGmLxPrSEsbIxZQo0Up/Twxy8nUkVJ11t/XxDpJeTjW3cwSURjjYmREd7478woQy9DMrCwMWbLm9O6i7kHU1YfRHJuqQgWhLV1xNazup8poG1o3cwtOEGXMWto/J48fEathrbEpteVeY3p7C2qG2hwsdTp4KM8kdlcYWFjzBbKU3vyx1NNDoUhUSNxyyiUdnE7tfKmpGXmFrwVZcyW/x29KqbLK4sizu4XKCy5rw8mIjFbGm27VaVx6GKMoDHDwsaYLZOj2sGiTRtEJ+WJ6KGnk40YoDwg1FMU6U9bc0gS3W2bI73AuOtWdQELG2OWUDrE94eviqDBisld6+YhUKrH1weT8PbWC3W93qTO32cy8PIdXWGjZit1c4CFjTErqETqlxMpeHfbJVH1IJ9T8O39/dAn2ENUHPzfP5eMRtSI3JJK7L6UhXEm2FetpbDEM2Y17m/ch/vwn41n60SNKKmswXO/xoq23hRdXDGlK4yN7eczDb0EScHCxpgN5EtrKhCQkF2CqasPikqEXjf7wRkTNMqPuQULG2M20BZUnW2drdWNNkXGRJ6S3nbmDPvYGLMgJbcUf8SkNXu7+KwSMeDE2Ah0tzf0EiQFCxtjFsOZn/4pRq3BMeuPXBWpHsZG3+AbjTmZG7CwMSYLpW7QFKvlv59B9NU8te6z7Zzx1YkSlH/H3IKFjTFJQfvqQCK+OZRk9ENX1IG2zk0NvTFXWNgYk6K0shpLf43FX7HpMBeo9KuyplYvcyCMBRY2xmTILCzHfeuO43x6IcwNiuZKrb26IWGJZ0yCy5lFmLb6oFmKGpFXwukeirDEM0ZPdFIu7vvmOIrKq2Gu+LjYGnoJkoItNsboef2vC2Ytai52VvB2sTP0MiQFCxtj1FzJLDKK1kK6RD7QmbkFCxtj1Px5uvlqAlOnorrW0EuQHCxsjFFzJbMY5k5heRVqjX34qZZhYWOMmvmDgmHuUB6bOfsYlcHCxhg1A0I90CPQ+NoMaRvF/nIMCxtj5LRp0wbLb+9s9m2xWdjqY97vBsYk6BfigVVzesKcBzWxsNWHhY0xCcZ29RXTpcwVFrb6sLAxJsNtXcx3aHB+WaWhlyApWNgYk2FOv/YIMNNOsmyx1YeFjTGpDPz/mxllloEEFrb6mN87gDFpyM+2bkFfjIhoiw7eTlg8IswsusuWVHAemyLc3YMxOQaHe4lDzttbL+KEmq3BjQ1ryzbCtzhvACcqK8LCxpg8m024npSmv6+6u5ehlyE5eCvKmDymXJlgYYyzAvUACxtj8qy6uydWTu9ulPNCm8Ock5JVwcLGmEXZFaWCjOjYFqYGW2zKYWFjzAJqRnkgLhumKNpMY1jYGLPgwx2XUVVjWj3LHGwsEdrW0dDLkCQcFWVMnvzSSuy5fB3GTnsPB/Rq7yby8noFuSPCx5lniTaBxq/Kvn37MGnSJPj7+wszeNOmTfWuX7Bggbhc8Rg/fnyrzskwraGyulY0YzRmltzWEfueG4kPZ/fEvIHB6OrvyqKmAo1fmZKSEkRFRWH16tVN3oaELD09ve7YsGFDq8/JMC2FSqwsjTh8OKdfIB4fFW7oZZj2VnTChAniUIWtrS18fX21ek6GaSluDjZ4cFgo1uyJh7FBerzktggOEmiITmzZPXv2wNvbGxEREVi8eDFycnJ08TAMozbGmupBTTTbOvMwZIMHD2gbOn36dISEhCA+Ph7Lly8X1tjhw4dhaam9+YcVFRXikFNYWKi1czOmR2d/FxgjE7v7GXoJRonWhW327Nl1P3fv3h2RkZEICwsTVtzo0aO19jgrV67EihUrtHY+xrRxsbPGA0NDsPZAotEEEmj3Oa6b+i4d5hY6D6uEhobCy8sLcXFxWj3vsmXLUFBQUHekpKRo9fyM6fGfiV3w/f39Rf6XMdA3yAPeznaGXoZRovM8ttTUVOFj8/PTrklNAQo6GEYThnTwQszLY5FXWonLmUWY99UxSJXYa/n4fG887h8SAmtO7dAIjV+t4uJixMTEiINITEwUPycnJ4vrli5diiNHjiApKQk7d+7ElClTEB4ejnHjxtWdg7akq1atUuucDKOL9A8fFzsMCfeSdCvx8qparNx6EZM+OYC8Ep5poFNhi46ORs+ePcVBLFmyRPz88ssvi+BAbGwsJk+ejI4dO2LhwoXo3bs39u/fX8+6oqBCdna2WudkGF1BKRRDO9xqSClVLmYU4dM92nXlmDptZDJjcaWqhqKirq6uwt/m4mKcETBG//x9Jh2P/O8kjMHK3P3sCLRzk66FKaXPOG/cGbOG2mqHGUEhOZWFfbD9sqGXYTSwsDFmDTnlP5/XG93buULq/H4yFZcyigy9DKOAhY0xe8K9nbHxkUH4fmE/9JHwRKtaGfDffy4aehlGAQsbw1Dek6UFhnZoi3fvjBSTn6TKjgtZOJ6Ua+hlSB4WNoZRILStE+YPlPYou/f/ZV9bc7CwMUwDnhjTAR19nNCzvRvW3dcX/7m9M6TE2WsFhl6C5OEOugyjpK7036eH1/1+vfBWswUpUFRRjaLyKjjbWRt6KZKFhY0xSij9cuOpa9h5IQtJOSUishng7qDyPldzSrD1bIaweMhPNbG7Px4dGQZPJ9tm5yVIjczCchY2FbCwMUbJMz+fxu+nrtX9npJbplLYUnJLMevzw8hUsL6+PpiIjadS8eW9fdAn2KNJAc0skpbFRqTll4toLqMc9rExRkdpZTU2xtwSNcLZrunv6ITrxZj9xZF6oiYnr7QKd689ip0XMpXet6SyBjWUZyExMgrKDb0EScPCxmiF6KRc3P/NcTz6w0nRkSKnWHdWDiWpNiwE9G+i1Ii2nTM/O4xr+WUqs/of+v4Etp9vLG4FZVWQItkl0rMipQRvRZlWQVu1rw8mYeXfF1B907L5KzYd//fvJYyI8MakKH+M6eyN3JJKbDx5DV38XTCqk3erevifS6vfLdnJ1gruDo39TUcTcrDo22jhbG8OWvuLm86Iong761v92gpKpSlsNSY2I1XbsLAxLeZ6UQWW/noaey41ntlJw4nJAqLD3toSlTW1dVu6vsHueG1KN3T2U79ZweH4HJxLKxBO832X6090n903UKlQfronXi1Rk0Nb1e8PX8UDw0Ilb7HVmEbvCp3BW1GmRVC6wZwvjygVtYaUVdX3Ux1PysMdnxzA+9svo1ZN/9UvJ1Lwxl8X8OX+RFzKvFUv2SPQDUvHR9S7bVVNrfi/JVPSV++Jq7PSyBo9mijNQUTqvm7mCltsTIs+VE//FIO4rOIWn4OE7uOdV3ApoxDvz+oBR1vVb0Vlk5qGdWyLL+b1hq1V/Vbfj/1wEh/P6YnnxnXClcxiHIirb+GpIr+0ClM/PYhe7d3FBPmdF7MgRdhiUw1bbIzGfLYvXtQsaoN/zmXizs8O40J6oUorRFkH2YGhnvX8YfLAAp2TtpT2NpZYO7+PmCmqybzkxOwS/HYyVbKiRtw0SpkmYGFjNOJkch7e03KtIonahI/2o+sr/2DapwfxzcHEuq0rCdq6g4k4c63xeMVamayRGFLSLvHPuQzxPwnf8ts7449Hh6CLBj49qVNTy8qmCu6gy6gNOdInfrwfqXlNp05oC6rTXDahM9bsicNuFX48LydbDO/YFnf1DRRBib5v7kR2cYXoOPvyHV1wz4Cgemkdz/xyGptPp8HYmdk7AP+dGQVzopA76DLahr7/lm88oxdRI04l54tKAVWiRpCI0baRbvv6lgto52ZXJ2LvbKvfu4zErld7N5gCGYWcoKsKDh4wKqmuqcWzv5wWqRCHE6QZIVQskVLESolj7e7+7fFzdKrY/hozUk1DkQpssTEquZxZjE0xaZIXNWUM6dC20WUUQf3gLuPfwhWVq5+fZ46wxcYoJauwHBuOpWDvZelGBptjag9/pZd38nVBB28nXGlFuoqh0STKa46wsDFKeWzDKRxLNN4W1H6udiKo0BS9g9yNWtjKKmtEMwAHG/4IK4O3okwjNhxLNmpRIyIDXJGcW9rk9c0lBEudtIJyzF17VCQRM41hYWPqcSo5D6/8cQ7GDiXpTl19UERzlUH1q8aOPHLMLYwaY9xfW4zW+WDHFVGwbgoUlldj+cazeHZsR9Elt6K6BoficxDq5Qhf1xtpIaYQ3Jmy+gDCvZ1g0aaN2H7fEelvMs+vpXCCLlOPT/fE4d1tl2BKUFsjakRJnUGoUMHBxhITu/vhlxOpMFVm9QnAu3caf/RXEU7QZVrMIyPCxfwAU6K4ohrpBTdEjSitrDFpUSOoXZSJ2CwtgoWNacS4rr5YOCTE0MtgWkFeaZXSVujmAgsbo5Sl4yI0agTJSI8MMy67YmFjlEJdMdYv7IdOvjwJyVjxdLSBucLCxjQJRRI3PDAAUQGuhl4K0wI8nVjYGEYp7o42+OmhgU2WJzHSxNqyjVlXJbCwMWptS6n3V1d/9rkZC1U1MhENNldY2Bi1KKmoNusPijGSYcYVCSxsTLNQ08aH15/A1Zymay8Z6ZFpxlFR892EM2pzz9qjOJZk3EXx5sg1PXU7liJssTEqoez141dZ1IyRz/bGo7yqBuYICxujlIsZhfhk5xXM/OwwzLgyx6hJyC7BBzu0O1HMWOCtKFMPGme39kAC/vvPJRFZY4ybL/cliI4fg8K8YE6wxcbUUVBaJZoXvvX3RRY1E6FWBvE3pYE85jQAhoWNqeP7I0lGObSFUY1MBvx6IhX7r6geZWhKsLAxdXTxdxG9yxjT5HJGEcwFfhczdYzq5IMjy0fjSHwOTqXkYfXueEMvidEiF8xI2NhiY+pBFtuYLj5YOCTU0EthtMzRhBwxANscYGFjlOJmb435A4MQ1tYRzrw9NZkZEMeMfPqYuvA7llGKhUUbrJjSre53iqiduJqLL/clSjbA0M7NDv5u9qiulcGyTRvRaDHVjLPvlbE5Ng2Dwk0/9YOFjVELV3tr4YOj43hSLp76MQbX8g0nGl6ONmjnbi+2ziRkVD6Uml+Ga/n16yN7tndD4vUS5JtRqoMqYlMLYA6wsDEa0zfYA389MQTLfj+Df89nokY+JaUVWLQBHG2s4OFoDRd7GzFJysbKAlYWbVArk6G6RoayqhrRYSS7uBLZJTcOdWZvkvj1CHRDTEo+zJk2bYAZvQJgDvD4PaZVkNBEJ+UiKbsENlY3xEgclhawlf9883dV11latMGdnx0W4qMNoVQGzd4sLKtCVpH5DTmxsbLAB7N6YGKkH8zhM84WG9MqyBoaEeENRLTuPLsvZeHE1TzokrisYjFfNMTLEYnZJTAXBod7YsltHdE7yAPmAgsbIwnW7k/Qy+MUlVeLiekU7Y2/brri5uVki5l9AjC7byCCPB1hbrCwMQbnXFoBDsbpL9JKEV7y29F4wQvphTA1nhgVjsdHd4C1pflmc7GwMQaFEkbf+1f/rXXIciNR6+DthOySCuSVVJlEcGDF5K64d2AwzB3zlXRGEqL2zC+nsetilsHWcIX8brbWMHYo+PLhXT1Y1G7CwsYYBIp8kqj9EZNm6KUgObcU3Yx8AteyCZ0wpUc7Qy9DMvBWlNGrmL2z7SISrpcgJbcUlzKlU5TtZGe8VtuUHv5YOCTE0MuQFCxsjN748Xgyvtinn+hnSxKEjZWHhoWhDTnYmDp4K8roDSlHII1Z2MqrzXNgiypY2Bi9kVko3Yx/HRU76IWySha2hvBWlNFL2RVtQaXcmppqUY2VUha2RrCwMTqdIP/D0av4ZFccctQoWDckuRJfnyqoOQBTHxY2RidkFZXjrs+PGE1NZnJeKWws26DSCC23sspqQy9BcrCPjdEJXx1INBpRk1uXQZ4OMEbKq8yj3bcmsLAxOqGrvyuMDU9HOxgbLnZWGN/N19DLkBwsbIxOcLC2hLFhY2VcOR/ezrb46aGB8HExPkHWNexjY3TCrkuGq/9sKeXVxrOlC/VyxLf390Ogh3Fun3UNCxujE9o62cLYuG4knXWjAlzx9YK+8DTC11hf8FaU0QmTooyvBfXVnBJ4ONpAygzr2BY/PDCARa0ZWNgYnRDW1gnt3OxhTFD1QWhb6XabndazHdbe2weOPOe1WVjYGJ3wc3SKmOupa2i83i8PD8TUHv5aOR9NxZIiDwwNwXszo8RQFqZ5WPoZrbP9fCae/+2MzofIvDKpi0h1cLazRoSvM3ZezBKdcVuDFAcsL7+9Ex4cFmboZRgVLP+M1utCX/7jrM4fZ3Rnb8zsEyhEjXCxs8avDw/CgkHBrRY2migvla64ZKWxqGkOW2xMq6HRtJczi7Hv8nVsOZOO9ALdb0G7t2ucAExW24sTO2PnxUyk5Lbc8gpwd2g0UV7fUHu1L+b1xujOPgZdh7HCwsa0mh+OJeM/G3VvpSlCI/SUYWVpgYGhnkjJTW3xuasl0MNo3oAgFrVWwFtRplWUVFTj7b8vQkrM7R+Eewa0b/H9E64Xw5D4uNji2XGtnEBt5rCwMa2aMvXB9ssoqtB/dwlVwcuoQDfM6BXQ4nPnlVZheMe2MBSvTuoqfIZMy+GtKNMqATiXZph23w42Vo1ENiG7RHQUoeNgXHarzn8qOQ8RPs56HzgzprM3F7VrARY2psW0dbbF/xb1x9y1R3E4QX+T3AnFCoG/YtOxcusFraZqFJZXw9dVBidbSxRX6KeRY/8QD7w3qwcPZtECvBVlWoWFRRu8NqWrXhNbSVBpu0nR2Pe3X8ajP5zUSf4ZRXp7tneHPpgY6YfvFvaDqz1vQbUBCxvTajr4OKN3kO4FgAyZ+QODsPOZ4XCwscTb2y7i451XdPqY+69kY1gHL+iaF8Z3gq2V8bV6kiq8FWW0wm1dfHA0MbdVvqWknFLEZSmPSAZ7OuD9u3qgV3t3HIrLxgPfRaNET0NMjifp3t9WUFaFQJ2d3fzQ2GLbt28fJk2aBH9/f+EL2LRpU73rFyxYIC5XPMaPH9/seVevXo3g4GDY2dmhf//+OHbsmKZLYwzIoqGh+Hxebzi3sECbopDf3NdXZNsrS8b9+8mh4sNPW88Vm8/rTdTkw1LocLXXnR1w9lqBzs5tjmgsbCUlJYiKihJC1BQkZOnp6XXHhg0bVJ7zp59+wpIlS/DKK6/g5MmT4vzjxo1DVpbxNSs0Z8Z19cWuZ0fgwWGhsLO20GiLObRDW5Hx36u9W6Pr5w0MElHQrw8kiq2nviOVRHJuKTp4O8NWR112X918DmdSWdy0hcZfQRMmTBCHKmxtbeHrq37I+v3338cDDzyA++67T/z+2Wef4a+//sLXX3+NF154QdMlMgaEHPvLb++M0Z28cdcXR9S6z8gIbwR73WgXFBXgJrZ+chxtLDGx+43ebvmlVTAk0VfzxIyBPkGuImpaWV2DqhoZKmtqWx28IP9aUk4JugcY36wIKaIT23rPnj3w9vaGu7s7Ro0ahTfeeAOenp5Kb1tZWYkTJ05g2bJldZdZWFhgzJgxOHz4cJOPUVFRIQ45hYWGyadiGnMtvwxLfj5d7zISJ3sbS/wRc02IgRyKAv5nYmfx8+8nU3Esqb6fbnqvANF/bOOpVJyRwHaNBO1gfOPUlh6BbkjILkZhmfrJyu4O1qJx5OQof2GxcksiCQsbbUOnT5+OkJAQxMfHY/ny5cLCI5GytGwc9cnOzkZNTQ18fOrXxdHvFy82XaqzcuVKrFixQtvLZ1pJZmE55n55RIibInf2CRCW2bNjI7DuUCI2nbqGUC8nPDWmg2hKSXg72yH25nbsvsHBolNHew8HJOeU4qVN5yBlYlLyhVB19XPBufT6X7Jk5c3p3x5z+rYXwZH0gjL0C/FEB28nkS7DGIGwzZ49u+7n7t27IzIyEmFhYcKKGz16tNYehyw88sspWmyBgRxXMjTP/RoropuK2FhaiORTwtfVDssmdBZHQxxsb3zxPT++E6wt22DGmsN4bnwEPt8bL9ohGUMlBq2zezsXnLlWiBAvRyHQVN4l73or33IzRp7uERoaCi8vL8TFxSkVNrqOLLnMzMx6l9Pvqvx05Mejg5EW+aWVjS6b3S+wUQmUMjr6OIvmkfMHBouBy9nFFUIojQnaZlOzy0dGhAnrlC0yw6DzTX1qaipycnLg56d8uIeNjQ169+6NnTt31l1WW1srfh84cKCul8domVcndxVRTrmldlefwDofmjpdce8bHCLEgBpJGnMAZeGQG8+DMRKLrbi4WFhfchITExETEwMPDw9xkN9rxowZwtoiH9tzzz2H8PBwkb4hhyy3adOm4bHHHhO/05Zy/vz56NOnD/r164cPP/xQpJXIo6SM8UAlSEeXj0ZsSgEiA1zh3YJhvlQqtWZPPIwV2m7zFCkjE7bo6GiMHDmy7ne5n4uEac2aNYiNjcW3336L/Px8kcQ7duxYvP766/W2jSR4FDSQc9ddd+H69et4+eWXkZGRgR49emDbtm2NAgqMcUBBgDFdNBe0749cRSdfZ2w5nYZfTrS8UaQU8vkYw9JGRl+PJgAFD1xdXVFQUAAXFxdDL4e5SVZROSqra2FnbQl3BxullQXEltg0PPljDGok0L22tfz+yCBR+sUY7jPOtaKMTkjLLxPlT7+dTIX8qzPQwx5r7+2LWplMTLKa1SdQbNtoVN8Lv8WKuZ6mAG2jaV4Btx8yHGyxMVqnsLwKA97aidJm6jmpsH1EhDe+OZQEU+OJUeFYMpbbe2sTttgYvUPfj1/sS0CfYA/RvbY5USMo380URY34eFecGCzzxOgOhl6KWcLCxmgF2nZdyijCyq3SGuxiSGgrTsXzY7v4YECYJ88x0CNcnMZojYdH8GDfhvx6IhUPfn8CT/0YI6xaRj+wsDFag2of27nZG3oZkmTXxSxsiU039DLMBhY2RivklVSKBpANi9+ZW6zYfE5pyRmjfVjYmFZDW6wJH+032UCAtsgursSbf10w9DLMAhY2RiuBg05+zoZehlFAFRU0s4HRLZzHxqhNRkE5lm88g7zSSrjZW8PX1R4jItqKEiKqLnju19PYFJNm6GVKniBPB/zz1DBRjcHo5jPOFhujNjsvZgon+KnkfOy+dB0bjiXjy30J4jrq/to7+EbPNUY1V3NK8ZGOxwaaOyxsTD3I8vrxWDLW7k8Qh+I4PGpBRBOjFKFe//ImkOV6nBxl7FAy8/k0bmevKzhBl6njYkYhnv7pNC4otLbeHJuOPx4dLH6mWk7qmaZIRmE5Jny0DwsHh+DL/TesN6Z5qNh/2e+x+P2RwU02BmBaDgubRMgtqcRXBxJQXlUr5gA4N8hSP5aYi5ziCvi72SO0rWOj668XVaC8qgaBHg4tXsO+y9friRpxOiUfey5lIae4Eptj03A4ofEgk5TcMry6+XyLH9dcIR8bWcg05IbRLixsEiApuwQL1h2rmxXwv6NXMaxDWzGOjcpwOvu5YP3Rq3VdMggactLV3wX21jfGtp1KyYe/qz02Pz5EzAs4cTVPpBdM79muXifX6ppaZBVVCCH1dLKBj7Nd3fVu9jZK17dg3XFdvwRmBf3NXpjQCfMGBHGXXR3BwiYBHt9wqt4AFLLa/j2fWW+eZUOoBpEORSg5dvyH+4RoVd/sAfTriRTM7ttejHmjWs55Xx2tu46Y0y8Qb03rLlI2SBwZ3fPHY4PFfAdGd7CwGRjaPjbc/rUGssYUOZKQKw5qDWZrZVFP1IgNx1Kw99J1VNXKRPUAo1u6+LmwqOkBFjYJCFtDsdEFtI0lS1AZaQXlOn985gbGPKTGmOB0DwNja8WOY3NiVCcWNn3AwmZgKLGVw/3mgaejDaIC3Ay9DLOAhc3AkKh9v7CfeNMzpofilxa1QecoqH5gH5sEGBTmhV8eHohR7+019FIYLdKzvRt+e3iQqK29XlwBDwf+8tIXLGwGpqi8SiS4ujtaw9fFTmTyM6bBxO5+wkKj4ck8QFm/sLAZmLX7E7kg2oQtNsYwsLAZmL7cEUOSkM/z83m9RakaFfl/sP2yRmkx5Fvr6l+/YQCjPzh4YGAGh3vi2bEdDb0MpgHPjosQowQndPfDzD6B+HXxIDhoUNPZydeZ+60ZEBY2A0OlTI+N6oAHh4UaeikMgB6BblhyW0fRokkRaj7w6uSuCPVyVOs8Dds7MfqFt6ISYeGQEGw4moyim73NGP3jYmeFjY8MEl82ypjVJ1Acsan5eO7XWFzMKGryXKbRl9p4YYtNIvi42OH1qd0MvQyzpoOPc5OipkhkgBs+u6e32G72D/EQSdYN4ei2YWGLTUJM6eEv/j8Un42fo1MNvRyzg7ab6hLs5YhtTw0TP39zMLFRPzo/Vzutr49RH7bYJARZC1N7thO91hj9E+zZstd9Tv/2YlB07yB3MaiF+q09fRsHhAwJW2wS5FLmrTkDhoJaHNEWq6i8WqQuUCvrhrg7WKOgrEq0DDd2aAc6s3f9gIEmjQx+fHCA6F5MQ99oTgS5FhjDwcImIagR5Kd74vBXrGFH2FHH3j8fuzHngPK4KM3hqwOJwllOrazpoCguNa+k7r17Ll3Hd4eTxPQlY4W6D7dvocVGyFuyk9VNvjrGsLCwSYTaWhmmfXoQpQae9HRHpB+WjouAtaVFPb/TM2Mj6tZZVVtb124prK2TOCiqS3NH5649gvjrJTA2eO6AacHCJhGoppCibCeTDdOee2gHL+Hbe21KN5VtlGidthaWTW5fFVucG1vDT8Z04OCBhDBkCc7t3f3w5rTureoNt+NCplJfnDFAW+6qGuUdhhnjg4VNQnRr52KQxyVLcXqvdq0+T8PhMsYECTIFShjTgIVNQpQZyL9GlorNTZ9aa1g8IkzMPDVG7hkQBA9u9mkysLBJAHLIf30gEf/372WDPP7c/kFqZdw3h4ONFR4canw1r5R7xnlnpgUHDyQAOeTnDQwSH7D1R65i96Xren3806naC1jcEeWPDj5O+M/GsyprKQ3J+K6+yCmpQHpBOUZEtMWCQSFwtbc29LIYLcLCJhEovWJ0Zx8MDvfCxI/36zVlYnjHtlo7l5OtFXoHeeD58Z3w2pbzSMyWTuqHnbUFFg8PxxOjw4WFSsm02rBUGenBwiYxqIfXf2dG4c41h/SS0T+msw+m9wrQ+nlHdvIWKST3fn0Mh+JztH5+tdcR0VaMvOvZ3h0Rvs51+XkEi5rpwsImQXq1dxc9wZryuVFGhjZEb06/9lh+eyfoCitLC/xvUX/suXwdOy9kigqF1LwynTzWjF4BmNGrHT7bl4B9l29s5Z8c3QFPjenAAmaGsLBJlEdHhovcqm8PXxW/U/Lsu3dGinInZ1srnE8vxNtbL+JAXHaLGyqunN4duoZEZWSEtzho63cwLgcf77qCY4m5Wn2ch4aHoqOPMwaFe+F8WqHYAk+M9NPqYzDGQxsZvdtMgMLCQri6uqKgoAAuLobJB9NFtDTuerEoswpr6whnu8YO7iuZRbicWYzz6QX4/eQ14RBXB+oj9tNDA2EoDsfnYPH/TiC/tKrV5xrdyRtfLeirlXUxpvEZ53QPiUdLyQoh60qZqBFUcE2WydJxnbB2fh9R1qSuhWNIBoZ5imaN1pat3yZSqyeGUYSFzcRKspZNUM9n9kt0qujSYUgGhHrilUldW32ec2mFWlkPYzqwsJkY1PSQBi83B7WuVtbSWt9QAKO11Qp7bwYLGEaO4d/ZjFahdkI0D5M6ujaESoYoerj67l749v5+kAJUdP/EqA6tOkdafhkXsDP14OCBiZJfWoltZzPEB766VibGwVEuV2u6d+iK6ppadHppm1inpgwK88Tb0yNb1SSSMb3POKd7mChuDjaY3a89jAHKd3NzsEZ2caXa96GgA/WOm903kPPUmEawsDEGJ6uoXCNRI4aEewn/HMMog31sjEEhT8jLm85pXI/6shaiqYzpwsLGGJRfTqRi27kMje6zYnJXhHgZZ983Rj+wsDEGIzmnFCv+1Mxa6+LngmmckMs0AwsbYzB+OJaMEg27Bs/pFygqMhhGFSxsjMGgMX8PDA3RuFqBYZqDhY0xGJRT95+JXfDujEi1akbn9m/Pw4gZtWBhYwzOrL6BWL+wP9wdmm7PTde9OpkjoYx6sLAxkqB/qCf+eHQIOng7Kb1+So929brfMowq+J3CSAYqi/r9kUFiwIoiA0M9xQwFhlEXFjZGUlDfua/m98XDw8NE95EBoR74akEf2NtYGnppjBHBRfCMZMkuroC9tSUcbbnyjwEXwTOmgZeTraGXwBgpvBVlGMbkYGFjGMbkYGFjGMbkYGFjGMbkYGFjGMbkYGFjGMbkYGFjGMbkYGFjGMbkYGFjGMbkYGFjGMbkYGFjGMbkYGFjGMbkYGFjGMbkYGFjGMbkYGFjGMbkYGFjGMbkYGFjGMbk0FjY9u3bh0mTJsHf3x9t2rTBpk2bmrztww8/LG7z4YcfqjxnUVERnnrqKQQFBcHe3h6DBg3C8ePHNV0awzBMy4StpKQEUVFRWL16tcrbbdy4EUeOHBEC2ByLFi3C9u3b8f333+PMmTMYO3YsxowZg2vXrmm6PIZhGEDWCujuGzdubHR5amqqrF27drKzZ8/KgoKCZB988EGT5ygtLZVZWlrKtmzZUu/yXr16yf7zn/+ovZaCggKxHvqfYRjTQ5PPuNZ9bLW1tZg3bx6WLl2Krl2bn9xdXV2Nmpoa2NnZ1buctqQHDhzQ9vIYhjEDtC5s77zzDqysrPDEE0+odXtnZ2cMHDgQr7/+OtLS0oTIrV+/HocPH0Z6enqT96uoqBDjuBQPhmEYrQvbiRMn8NFHH+Gbb74RQQN1Id8a7WzbtWsHW1tbfPzxx5gzZw4sLJpe3sqVK8WMQfkRGBiopWfBMIyxo1Vh279/P7KystC+fXthtdFx9epVPPPMMwgODm7yfmFhYdi7dy+Ki4uRkpKCY8eOoaqqCqGhoU3eZ9myZWJwqvyg+zEMw2h9YDL51iiaqci4cePE5ffdd1+z93d0dBRHXl4e/vnnH7z77rtN3pYsOzoYhmFaLWxkVcXFxdX9npiYiJiYGHh4eAhLzdPTs97tra2t4evri4iIiLrLRo8ejWnTpuGxxx4Tv5OI0VaUbkPnpsBDp06d1BJDhmGYVgtbdHQ0Ro4cWff7kiVLxP/z588XvjV1iI+PR3Z2dt3vtJWkrWVqaqoQyBkzZuDNN98UosgwDKMpbSjnAyYARUUpiEAi6eLiYujlMAxjwM8414oyDGNysLAxDGNysLAxDGNysLAxDGNyaDWPzZDIYyBcWsUwpon8s61OvNNkhI16uhFcWsUwpg191ik6ahbpHtRVhIroqahekzrVln5zkIBSGRenlugGfo11T6GRvcYkVSRq1ONRVR25SVls9EQDAgL0+pj0ZjCGN4Qxw6+x7nExote4OUtNDgcPGIYxOVjYGIYxOVjYWgB1FXnllVe4u4gO4ddY99ia8GtsMsEDhmEYOWyxMQxjcrCwMQxjcrCwMQxjcrCwMQxjcpi1sNGAGapSaHg8+uij9W5H8ZUJEyaI6zZt2qTynL///ruYZE8t0un21Dbd3NH260yDfp5//nl0795dzMigTPR7771XVJ6YK7p4L7/66quiRT+9xu7u7mKeydGjR2EMmLWwHT9+XMwulR/bt28Xl8+cObPe7T788EO1y7RKSkowZMgQMV+V0c3rXFpaipMnT+Kll14S/9OXyaVLlzB58mSYK7p4L3fs2BGrVq3CmTNnxPByEk/60r5+/Tokj+4H0xsPTz75pCwsLExWW1tbd9mpU6dk7dq1k6Wnp1NajGzjxo1qnSsxMVHcnu7P6O51lnPs2DFxv6tXr+pgxcbHkzp4jQsKCsT9duzYIZM6Zm2xKVJZWSkm0N9///1132hkGdx9991YvXq1mLTFSPd1pj74dD43NzeYO5U6eI3pnF988YWo1YyKioLUMZki+NZC/ob8/HwsWLCg7rKnn34agwYNwpQpUwy6NlNCF69zeXm58LnNmTPHaIq5jeU13rJlC2bPni2E0c/PT2xxvby8IHVY2G7y1VdfCacqOaKJP//8E7t27cKpU6cMvTSTQtuvMwUSZs2aJZzia9as0fJqjZOvtPga06hNCoDRuMwvv/xSvNYUQPD29oakMfReWAokJSXJLCwsZJs2barno2jTpo3M0tKy7qCXi243fPjwZs/JPjbdv86VlZWyqVOnyiIjI2XZ2dl6eAbm+V5WJDw8XPbWW2/JpA5bbADWrVsnvoEmTpxYd9kLL7yARYsW1bsdpRd88MEHmDRpkgFWafxo83WWW2pXrlzB7t27RXoNo/v3cm1tLSoqKiB5ZGZOTU2NrH379rLnn3++2dsqiyRFRETIfv/997rfc3JyhJX2119/idv/+OOP4neKRJkz2nydyVKbPHmyLCAgQBYTEyNeW/lRUVEhM1e0+RoXFxfLli1bJjt8+LCwAqOjo2X33XefzNbWVnb27FmZ1DF7Yfvnn3/EH/nSpUstejPQZevWrav7nX6myxoer7zyisyc0ebrLN/mKzt2794tM1e0+RqXlZXJpk2bJvP395fZ2NjI/Pz8xJcJpdUYA9y2iGEYk4Pz2BiGMTlY2BiGMTlY2BiGMTlY2BiGMTlY2BiGMTlY2BiGMTlY2BiGMTlY2BiGMTlY2BiGMTlY2BiGMTlY2BiGMTlY2BiGganx/+zZfyE2RXuGAAAAAElFTkSuQmCC", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# check shapefile:\n", + "import geopandas as gpd\n", + "import matplotlib.pyplot as plt\n", + "import os\n", + "\n", + "# Path to the shapefile\n", + "boundary_path = os.path.join('../data', 'pa_boundary.shp')\n", + "\n", + "# Check if the file exists\n", + "if os.path.exists(boundary_path):\n", + " # Load the shapefile\n", + " boundary_gdf = gpd.read_file(boundary_path)\n", + " \n", + " # Print basic information\n", + " print(f\"CRS: {boundary_gdf.crs}\")\n", + " print(f\"Number of features: {len(boundary_gdf)}\")\n", + " print(f\"Columns: {boundary_gdf.columns.tolist()}\")\n", + " \n", + " # Display the first few rows to see attributes\n", + " print(\"\\nAttribute preview:\")\n", + " print(boundary_gdf.head())\n", + " \n", + " # Calculate the total area\n", + " area_sqkm = boundary_gdf.geometry.area.sum() / 1e6 # Convert to sq km if in meters\n", + " print(f\"\\nTotal area: {area_sqkm:.2f} square kilometers\")\n", + " \n", + " # Plot the shapefile\n", + " fig, ax = plt.subplots(figsize=(10, 10))\n", + " boundary_gdf.plot(ax=ax)\n", + " ax.set_title('Contents of pa_boundary.shp')\n", + " plt.show()\n", + " \n", + " # Check if it might be a protected area boundary\n", + " if 'name' in boundary_gdf.columns or 'NAME' in boundary_gdf.columns:\n", + " name_col = 'name' if 'name' in boundary_gdf.columns else 'NAME'\n", + " print(f\"\\nNames of areas in the shapefile:\")\n", + " print(boundary_gdf[name_col].unique())\n", + "else:\n", + " print(f\"Shapefile not found at {boundary_path}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing Landsat 8 data for 2013-03-20 to 2023-02-28...\n", + "Calculating trend for ndvi...\n", + "Calculating trend for evi...\n", + "Calculating trend for msavi...\n", + "Calculating trend for mirbi...\n", + "Calculating trend for ndmi...\n", + "Calculating trend for ndfi...\n", + "Calculating trend for nbr...\n", + "Calculating trend for nbr2...\n", + "Calculating trend for bsi...\n", + "Landsat trend calculation complete.\n" + ] + } + ], + "source": [ + "# Process Landsat 8 data and calculate trends\n", + "ls_trends, ls_collection = process_landsat_trends(goa, start_date, end_date)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing CHIRPS precipitation data and calculating trends...\n", + "Processing SMAP soil moisture data and calculating trends...\n", + "Processing ERA5 data for 2013-03-20 to 2023-02-28...\n", + "Available ERA5 bands: ['mean_2m_air_temperature', 'minimum_2m_air_temperature', 'maximum_2m_air_temperature', 'dewpoint_2m_temperature', 'total_precipitation', 'surface_pressure', 'mean_sea_level_pressure', 'u_component_of_wind_10m', 'v_component_of_wind_10m']\n", + "ERA5 RH trend calculation complete.\n" + ] + } + ], + "source": [ + "# Process CHIRPS precipitation data and calculate trends\n", + "rain_trend, rain_collection = process_chirps_trends(goa, start_date, end_date)\n", + " \n", + "# Process SMAP soil moisture data (starts from April 2015)\n", + "sm_trend, sm_collection = process_smap_trends(goa, '2015-04-01', end_date)\n", + " \n", + "# Process ERA5 data for relative humidity trends\n", + "rh_trend, rh_collection = process_era5_trends(goa, start_date, end_date)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + " # Merge all trend layers\n", + "all_trends = merge_trend_layers(ls_trends, rain_trend, sm_trend, rh_trend)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Resample to match JavaScript output\n", + "all_trends_resampled = resample_image(all_trends)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#export_to_local(all_trends_resampled, \"..data/inputResampled_py.tif\", goa)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Started export task: goa_fire_trends\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "user_asset_root = 'users/jonasnothnagel/' # Replace with your actual EE username\n", + "export_to_asset(all_trends_resampled, f\"{user_asset_root}goa_fire_trends\", goa, \"goa_fire_trends\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "ee", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/data_exploration.ipynb b/notebooks/data_exploration.ipynb new file mode 100644 index 0000000..8b6a29d --- /dev/null +++ b/notebooks/data_exploration.ipynb @@ -0,0 +1,715 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# # Forest Fire Data Exploration for Goa\n", + "# \n", + "# This script explores the datasets used in the forest fire vulnerability and \n", + "# prediction project for Goa, India. It is structured in sections that can be \n", + "# easily converted to notebook cells later." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Libraries imported successfully!\n" + ] + } + ], + "source": [ + "# CELL 1: Import necessary libraries\n", + "import os\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "from datetime import datetime\n", + "import geopandas as gpd\n", + "import folium\n", + "from folium.plugins import HeatMap\n", + "import rasterio\n", + "from rasterio.plot import show\n", + "\n", + "# Set up plotting style\n", + "plt.style.use('seaborn-v0_8-whitegrid')\n", + "sns.set_context(\"talk\")\n", + "\n", + "print(\"Libraries imported successfully!\")" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Checking contents of data directory...\n", + "\n", + "Shapefiles (base names):\n", + "- fire13_19\n", + "- pa_boundary\n", + "- fire20_23\n", + "\n", + "Raster files:\n", + "- road.tif\n", + "- inputResampled.tif\n", + "- dem.tif\n", + "\n", + "Other files:\n", + "- pa_boundary.qmd\n", + "- fire13_19.fix\n" + ] + } + ], + "source": [ + "# CELL 2: Data Inventory - List all files in the data directory\n", + "\n", + "data_dir = '../data'\n", + "print(f\"Checking contents of {data_dir} directory...\")\n", + "\n", + "if not os.path.exists(data_dir):\n", + " print(f\"Warning: {data_dir} directory not found!\")\n", + "else:\n", + " all_files = os.listdir(data_dir)\n", + " \n", + " # Group files by type\n", + " shapefile_extensions = ['.shp', '.shx', '.dbf', '.prj', '.cpg']\n", + " raster_extensions = ['.tif', '.tiff']\n", + " \n", + " # Organize files by category\n", + " shapefiles = [f for f in all_files if any(f.endswith(ext) for ext in shapefile_extensions)]\n", + " raster_files = [f for f in all_files if any(f.endswith(ext) for ext in raster_extensions)]\n", + " other_files = [f for f in all_files if f not in shapefiles and f not in raster_files]\n", + " \n", + " # Extract unique shapefile base names (without extensions)\n", + " shapefile_bases = set()\n", + " for file in shapefiles:\n", + " for ext in shapefile_extensions:\n", + " if file.endswith(ext):\n", + " shapefile_bases.add(file.replace(ext, ''))\n", + " \n", + " print(\"\\nShapefiles (base names):\")\n", + " for base in shapefile_bases:\n", + " print(f\"- {base}\")\n", + " \n", + " print(\"\\nRaster files:\")\n", + " for file in raster_files:\n", + " print(f\"- {file}\")\n", + " \n", + " print(\"\\nOther files:\")\n", + " for file in other_files:\n", + " print(f\"- {file}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total fire events: 267\n", + "Fire events from 2013-2019: 197\n", + "Fire events from 2020-2023: 70\n", + "\n", + "Columns in the fire dataset:\n", + "['bright_ti5', 'bright_ti4', 'acq_time', 'frp', 'Delta T', 'acq_date', 'confidence', 'scan', 'instrument', 'type', 'version', 'RiskNumeri', 'daynight', 'satellite', 'track', 'RiskCatego', 'geometry', 'latitude', 'longitude', 'system_ind']\n", + "\n", + "Sample of fire data:\n", + " bright_ti5 bright_ti4 acq_time frp Delta T acq_date confidence scan instrument type version RiskNumeri daynight satellite track RiskCatego geometry latitude longitude system_ind\n", + "0 297.82 334.69 726 4.47 36.87 26-04-2018 n 0.62 VIIRS 0 1 3.0 D N 0.72 High Risk POINT (74.19322 15.6126) NaN NaN NaN\n", + "1 300.19 335.37 738 2.30 35.18 06-04-2013 n 0.38 VIIRS 0 1 3.0 D N 0.59 High Risk POINT (74.19879 15.30833) NaN NaN NaN\n", + "2 307.68 335.97 750 4.72 28.29 29-03-2014 n 0.61 VIIRS 0 1 3.0 D N 0.53 High Risk POINT (74.24989 15.31958) NaN NaN NaN\n", + "3 309.35 367.00 751 17.63 57.65 16-04-2013 h 0.47 VIIRS 0 1 3.0 D N 0.48 High Risk POINT (74.15782 15.59127) NaN NaN NaN\n", + "4 301.31 350.13 751 10.45 48.82 16-04-2013 n 0.48 VIIRS 0 1 3.0 D N 0.48 High Risk POINT (74.17966 15.04022) NaN NaN NaN\n", + "\n", + "Fire events by year:\n", + " 2013: 19 events\n", + " 2014: 3 events\n", + " 2015: 21 events\n", + " 2016: 58 events\n", + " 2017: 25 events\n", + " 2018: 42 events\n", + " 2019: 29 events\n", + " 2020: 9 events\n", + " 2021: 6 events\n", + " 2022: 4 events\n", + " 2023: 51 events\n", + "\n", + "Fire events by month (all years combined):\n", + " Jan: 11 events\n", + " Feb: 9 events\n", + " Mar: 93 events\n", + " Apr: 120 events\n", + " May: 29 events\n", + " Dec: 5 events\n", + "Saved plot: fire_events_by_year.png\n", + "Saved plot: fire_events_by_month.png\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/03/70byyv1n5g7d83j8pp_3_phc0000gn/T/ipykernel_70420/4137265159.py:30: UserWarning: Parsing dates in %d-%m-%Y format when dayfirst=False (the default) was specified. Pass `dayfirst=True` or specify a format to silence this warning.\n", + " fire_all['acq_date'] = pd.to_datetime(fire_all['acq_date'])\n" + ] + }, + { + "data": { + "image/png": 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1Y4O+q+o7E2J7sOzxSx7pO5J6c6qSHpnTdx39GKOkz+233+62nbedq1at6raptq++e6qqMbzayLusHzO9v3XMpqSRltV7kyZ7QWzjsHv3blel17FjR+vdu7eruFNPKh2v6TNDk+2oGjLo6HGUB3klit60jGXKlHFfilRyqjJUfbHx62uhF72qXPShqx1EPRZU6qtfdVT6qKngD67CQHTjoF931PhOVUX33XdfaGY1r2eFGmbry5L3KzNiuz/og1evf33x0UGZfu1X9Z3e/NXzgv0hujHQL51e7xb9wq+hmhMnTnTJI50Ug23btrn+CyqXV3y8HkiI3b4g3vX6FVrNf5W4e/TRR10CyZtkAbGLg5ryqwm2pvi95ZZbXMm7+vKoukLvSUpWaApg9ofY7wt6/9H7/+LFi90wN/2toTz6nnT//feHfnhA9g7StM29gzT1VNNBmn4wE333UfJUr329B+k1P2zYMDdNudccXp/LiXKQlohxyOj7p5c88npN6YdP9Ufykt7ImGY0VeJZ217NlPU9yHuP0vZUjy99J9L3n4yqfL3vo5MnT3b7ht7XSBrlXhwKFCjgvrsqWaqTaF/Re5OOnzXcPBFQcZQHeS9mfYhqlg+Viqp3y8aNG92Xoox+1dQvZ6piUUNB9RfRLzmqetEXVP2Ko0QSYhsHfUjri6gOyLykkffLp4ekUe7tDzoY0y/7mrlLzTY1i9oZZ5zhPozZH2ITA33wekNE9GunkkdKVui9SUPWjjzySDezlJoWUvGVO/uChPey0DAdHVjryxGVj7GPg7c/6JdLrxmzDvRUaaSqLzXr5z0p9/YFJZt0m35IUNJCPy7o4EA/+ujHHWTtIE3v495BWvjwJx2kKfnjHaTpu4+3L9x9990uaa3PBsVDMVAVmIay8X01tnHITHjlkX4IJWkUGW0zffdRDy9NiKPL4TN26aTXvoZBaZmMRixoH/GaOZM0yt04FChQwH1n1VBmfW/Vj/yvvfaaq+BLpPcjKo7yKA2xUVmpXvTK+D/44IPuAFi/Gvj9oub9SqBfMjV7joZL6QuVsq66vw7WENs4iPqH6A0nvBk5zX/juz+cd9557hdlfUio/F0xUtkpYhcDfQB7sdD210nDSBQXVQbog9trDozY7gvhvC9QSnCrF5juzzCE3NkfvEo8DWNWAltfXDWk3Jt9igRe7u0LqrB78cUX3RAp/Q99XusAme9JOTtI8xq8Z3SQpuRF+GeDZhzUqVu3bm7YiD6Xw2cwQuzikNn3UvreZZ0q6PRerh8pNexYPxx7Q8/CKU7h2z78venMM89070PqdcQPCfGLQ/PmzS2R8c06j9IvMPo1TNVC3jhvVbFIRl+KvHPtNDQVzP046INZH+Be0sibsQLx3R+ULKK3V3xicPCvad4XUmaNil8cvPclVd4h9+KgL6pePGgIH4x9QT+sIfcP0jzeDzxKdPBdKb4Hy+HxQNZ4rSg0o6wS0fohIHz7axsrIartr+SoNwQzfPurB5UaLyv5rR92EqEJczLGYfXq1e5HiETeH3gnzWO8EtFy5cq55nberE/aITT+Xl+K1OjOrxybD974xiG8Eack4htO0LA/JHYMwh089TLiHwdkHe9JybMvIPu8IWc6SNNwmswO0vSjgVflEr4fqBLYWxbxi4M36ymfDZFR5Zb6mKpyVMlr733lrLPOcsORNWT2YOq/ptkENRRNy6sC1dv+amyu9zEN11QFHkmj+MXhlltucX2CE3l/yJcefiSKpHvRqx+RytVVEaE3dr2YvZJ2hd5vSIeGoKnju35R0wfB4RpBInPEIRiIQ/wRg2AgDsFAHOKPGAQ7Duqvo95EmrX3YOqhpv466r2jpvBe5Z13kKZeRrfddptrr4DIEIf4Uu/YCRMmuNmr1ZRc21t9pXr06HHAxAZe9YtH1WAdOnRw1abqbef1OtX2f+6559z/0uVEacCcrHHYs2ePjRkzJqHjwFC1PPaiv/nmm92vaF62068PiPeLWng5thp4qVSVL0VZQxyCgTjEHzEIBuIQDMQh/ohBYhykaRIKv4M0VXwpsVe8eHF3m99BGs3II0cc4kuzwGqKdvVpvOCCC2zt2rWuWuvdd9+1qVOnuv6mGgKuCq+Dh+MrXqI4hW9//U8lwTXrbCInK5IlDqNGjUr4OPDJloT0AlUZnZrY6UWvmQv0C4Je9Co31Yxo3os7I/pS1Ldv31A5dtu2bV2jR74MRY44BANxiD9iEAzEIRiIQ/wRg8SLQ148SMstxCG+Pv/8c5d41jCo119/3Z566ikbMmSIm5VRMymrEkwTTqiiS/11PN7wKb3/iNeAX1Ut2v6KjSq+mD0tMsQhAhqqhuTx2WefpdeqVSv9+uuvT58/f767buvWremrV69Ov/HGG9Nr1KiR3rBhw/QxY8ak//vvv4f9f//88096ly5d3P2WLVuWC88gORCHYCAO8UcMgoE4BANxiD9ikNhx2LdvnzufOHGiW+bhhx92f7/77rvpjRs3Tj/ttNPSFy5cGKdnlXiIQ/w988wzbhv+9NNP7u/9+/cfcHvfvn3d7aeffnr6qFGj0rdt23bA7ZMmTXK333fffeljx45Nb9CgAds/G4jD4ZE4SjI5fdH72bx5c/ratWtjts7JiDgEA3GIP2IQDMQhGIhD/BGDYOAgLRiIQ/xoW+/Zs8cl7WrXrh16D/FisHfv3tCyTz/9tNvO2r6ffPKJu073lc8//9zd1qhRo/SmTZuy/bOIOESOetokoSSgSkIXLlzoxuN7U8cePDNCr169rFu3bq7ETuVz3377rbs+s9lANK1geDMwZIw4BANxiD9iEAzEIRiIQ/wRg+SIg+4r3uxeX331lQ0YMMDFR8OikmI4SC4gDvHn9VBTg31tz8WLFx+w7TUs0Lt89913W+fOnd0QQg2RXblyZaj/mhrza7Y0Ne1XTym2f9YQh8iROEoSOX3Ra0w+E+zlHHEIBuIQf8QgGIhDMBCH+CMGwcBBWjAQh+CoX7++O1dPKdG29RLV4XG477773Ax2mva9T58+tm3bNnd9sWLF3HK6H9s/+4jD4ZE4SjI5edHrQwTRQRyCgTjEHzEIBuIQDMQh/ohBMHCQFgzEIXdt3rzZlixZYosWLXLbUs4880w3i5eaM6uqS5So9ouDEneavVH337RpU6gZs+6nps2pqalxe26JhDhkD4mjBBaLFz2yjjgEA3GIP2IQDMQhGIhD/BGDYOAgLRiIQ3yNGDHCunfvbhdffLFdcskldtFFF7nZu0qUKGH9+/e3IkWKuBm8dMooDkcccYSbDl4zeP3yyy/uNl1fs2ZNtn+EiEP2kThKULF60SNriEMwEIf4IwbBQByCgTjEHzEIBg7SgoE4xJemdn/sscds6dKl1qRJEzv99NNdIm/QoEEuDrVq1XLx0XC/l19+2cXLi4OXuPNoe4f3mVJ8EBnikDMkjhJQLF/0iBxxCAbiEH/EIBiIQzAQh/gjBsHAQVowEIf4euutt2zo0KH2n//8x4YPH+4uaxvff//9rm+aLquCq2PHjqFeUgMHDnSx8LaxlvO29U8//eTO1VsKkSMOUZCFGdgQAG+++aab6q9r167p8+bNO+D6OnXquOkyFy1alL5lyxY3xaamFdSUgUOGDAktGz7VZq9evdz/mzFjRq4/l0RGHIKBOMQfMQgG4hAMxCH+iEEwEIdgIA7x9ccff6RfdNFF6Y0bN06fPXu2u2737t2h2++88063PZ966in39+rVq9MHDx6cfsopp7jr77//fjc9/NatW93tw4cPd/Fp27Zt+saNG+P0rBIPcYgOEkcJhBd9MBCHYCAO8UcMgoE4BANxiD9iEAzEIRiIQ/z98ssvLhnXu3fvA5Jwe/bscecTJkxw2/quu+4K3UdJvEmTJqXXr1/f3faf//wn/eKLL3Yn/d20adP0JUuWxOkZJSbiEB3/N5ciEoIa2a1YscLatGljJ598siuXK1iwoCsX1WwGzZs3t8mTJ9u6devc8pUqVbJrr73WldD17t3b3n//ffvuu+/sqKOOcrdr6k01xNPY5tKlS8f52SUO4hAMxCH+iEEwEIdgIA7xRwyCgTgEA3GIvzVr1rjt/ffff9vu3butUKFC7nptfylfvrw7V98o9ZTSzI3qJaUeVBpCqJnqfvvtNzfMUPFp1aqV3XLLLXb88cfH9XklGuIQHSSOEggv+mAgDsFAHOKPGAQDcQgG4hB/xCAYiEMwEIf403ZUsm358uUuQVe5cuUDbvf65Wjbq6eUl9TTebVq1ey+++5zCb+VK1faMccc4xJ/amSOrCEO0UHiKIHwog8G4hAMxCH+iEEwEIdgIA7xRwyCgTgEA3GIv7Jly7pqr6+++soWLVoUioG2q7a7x6vq0vZXEs9L7u3YscNKlixpJ554YpyeQXIgDtHBrGoJ+KJXqahe9B696MNl9qLXrw160Wv6Td78s4c4BANxiD9iEAzEIRiIQ/wRg2AgDsFAHOJPyQYN+3vxxRetRYsWh9y+du1ad+5VgylppySeaKigZgL7448/cnmtkw9xiA4qjhLwRd+2bduIX/Tem79e9KtWrbLLLrssb00bGAPEIRiIQ/wRg2AgDsFAHOKPGAQDcQgG4hAMFStWdCdRYk4JCa/KZfPmze68QoUK7tzb/uPGjbN+/fpZ8eLF7eqrr47buicT4pBzJI4SDC/6YCAOwUAc4o8YBANxCAbiEH/EIBiIQzAQh2Dxqli8WGzYsMH9XaxYsdAyY8eOteeff94l9F5//XVXOYboIg7ZQ+IogfGiDwbiEAzEIf6IQTAQh2AgDvFHDIKBOAQDcQgOb7igGpiHDxccM2aMDRgwwDUzV3Py6tWrx3U9kx1xyBoSR0mAF30wEIdgIA7xRwyCgTgEA3GIP2IQDMQhGIhD/HmNyYsWLerO1T/qo48+sueee84NGWT75w7ikDUkjpIAL/pgIA7BQBzijxgEA3EIBuIQf8QgGIhDMBCH4PD6S6m6SzP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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# CELL 3: Fire Events Data Analysis\n", + "\n", + "try:\n", + " # Load the fire events data\n", + " fire13_19_path = os.path.join(data_dir, 'fire13_19.shp')\n", + " fire20_23_path = os.path.join(data_dir, 'fire20_23.shp')\n", + " \n", + " if os.path.exists(fire13_19_path) and os.path.exists(fire20_23_path):\n", + " fire13_19 = gpd.read_file(fire13_19_path)\n", + " fire20_23 = gpd.read_file(fire20_23_path)\n", + " \n", + " # Combine datasets\n", + " fire_all = pd.concat([fire13_19, fire20_23], ignore_index=True)\n", + " \n", + " print(f\"Total fire events: {len(fire_all)}\")\n", + " print(f\"Fire events from 2013-2019: {len(fire13_19)}\")\n", + " print(f\"Fire events from 2020-2023: {len(fire20_23)}\")\n", + " \n", + " # Preview the data\n", + " print(\"\\nColumns in the fire dataset:\")\n", + " print(fire_all.columns.tolist())\n", + " \n", + " print(\"\\nSample of fire data:\")\n", + " print(fire_all.head().to_string())\n", + " \n", + " # Analyze temporal distribution if date field exists\n", + " if 'acq_date' in fire_all.columns:\n", + " # Convert to datetime if it's not already\n", + " if not pd.api.types.is_datetime64_any_dtype(fire_all['acq_date']):\n", + " fire_all['acq_date'] = pd.to_datetime(fire_all['acq_date'])\n", + " \n", + " # Extract year and month\n", + " fire_all['year'] = fire_all['acq_date'].dt.year\n", + " fire_all['month'] = fire_all['acq_date'].dt.month\n", + " \n", + " # Yearly distribution\n", + " yearly_counts = fire_all.groupby('year').size()\n", + " \n", + " print(\"\\nFire events by year:\")\n", + " for year, count in yearly_counts.items():\n", + " print(f\" {year}: {count} events\")\n", + " \n", + " # Monthly distribution\n", + " monthly_counts = fire_all.groupby('month').size()\n", + " month_names = {\n", + " 1: 'Jan', 2: 'Feb', 3: 'Mar', 4: 'Apr', 5: 'May', 6: 'Jun',\n", + " 7: 'Jul', 8: 'Aug', 9: 'Sep', 10: 'Oct', 11: 'Nov', 12: 'Dec'\n", + " }\n", + " \n", + " print(\"\\nFire events by month (all years combined):\")\n", + " for month, count in monthly_counts.items():\n", + " print(f\" {month_names[month]}: {count} events\")\n", + " \n", + " # Create and save yearly plot\n", + " plt.figure(figsize=(12, 6))\n", + " yearly_counts.plot(kind='bar', color='firebrick')\n", + " plt.title('Number of Fire Events by Year')\n", + " plt.xlabel('Year')\n", + " plt.ylabel('Number of Fire Events')\n", + " plt.xticks(rotation=45)\n", + " plt.tight_layout()\n", + " plt.savefig('img/fire_events_by_year.png')\n", + " print(\"Saved plot: fire_events_by_year.png\")\n", + " \n", + " # Create and save monthly plot\n", + " plt.figure(figsize=(12, 6))\n", + " monthly_counts_labeled = pd.Series([monthly_counts.get(i, 0) for i in range(1, 13)], \n", + " index=[month_names[i] for i in range(1, 13)])\n", + " monthly_counts_labeled.plot(kind='bar', color='darkorange')\n", + " plt.title('Number of Fire Events by Month (All Years)')\n", + " plt.xlabel('Month')\n", + " plt.ylabel('Number of Fire Events')\n", + " plt.tight_layout()\n", + " plt.savefig('img/fire_events_by_month.png')\n", + " print(\"Saved plot: fire_events_by_month.png\")\n", + " \n", + " else:\n", + " print(\"Fire event shapefiles not found.\")\n", + "except Exception as e:\n", + " print(f\"Error analyzing fire data: {e}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Study area boundary loaded successfully.\n", + "Boundary CRS: EPSG:4326\n", + "Boundary geometry type: Polygon\n", + "Study area size: 821.24 square kilometers\n", + "Saved plot: study_area_boundary.png\n", + "Saved plot: study_area_with_fires.png\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/03/70byyv1n5g7d83j8pp_3_phc0000gn/T/ipykernel_70420/77088353.py:44: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed in 3.11. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap()`` or ``pyplot.get_cmap()`` instead.\n", + " cmap = plt.cm.get_cmap('viridis', len(years))\n" + ] + }, + { + "data": { + "text/plain": [ + "
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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# CELL 4: Study Area Boundary Analysis\n", + "\n", + "try:\n", + " # Load the boundary data\n", + " boundary_path = os.path.join(data_dir, 'pa_boundary.shp')\n", + " \n", + " if os.path.exists(boundary_path):\n", + " boundary = gpd.read_file(boundary_path)\n", + " print(f\"Study area boundary loaded successfully.\")\n", + " print(f\"Boundary CRS: {boundary.crs}\")\n", + " print(f\"Boundary geometry type: {boundary.geometry.geom_type.iloc[0]}\")\n", + " \n", + " # Calculate area \n", + " if boundary.crs and boundary.crs.is_geographic:\n", + " # If geographic coordinates (lat/lon), reproject to a projected CRS for area calculation\n", + " boundary_projected = boundary.to_crs(epsg=3857) # Web Mercator projection\n", + " area_km2 = boundary_projected.area.sum() / 1e6 # Convert from square meters to square km\n", + " print(f\"Study area size: {area_km2:.2f} square kilometers\")\n", + " else:\n", + " # If already in a projected CRS\n", + " area_units = boundary.area.sum()\n", + " print(f\"Study area size: {area_units:.2f} square units (CRS units)\")\n", + " \n", + " # Plot boundary\n", + " plt.figure(figsize=(10, 10))\n", + " boundary.plot(color='lightgray', edgecolor='black')\n", + " plt.title('Study Area Boundary')\n", + " plt.axis('off')\n", + " plt.savefig('img/study_area_boundary.png')\n", + " print(\"Saved plot: study_area_boundary.png\")\n", + " \n", + " # Plot with fire points if available\n", + " if 'fire_all' in locals():\n", + " # Ensure same CRS\n", + " if fire_all.crs != boundary.crs:\n", + " fire_all = fire_all.to_crs(boundary.crs)\n", + " \n", + " plt.figure(figsize=(12, 10))\n", + " boundary.plot(ax=plt.gca(), color='lightgray', edgecolor='black')\n", + " \n", + " # Plot fire events, colored by year if available\n", + " if 'year' in fire_all.columns:\n", + " years = sorted(fire_all['year'].unique())\n", + " cmap = plt.cm.get_cmap('viridis', len(years))\n", + " \n", + " for i, year in enumerate(years):\n", + " year_data = fire_all[fire_all['year'] == year]\n", + " plt.scatter(\n", + " year_data.geometry.x,\n", + " year_data.geometry.y,\n", + " c=[cmap(i)],\n", + " label=f'{year} ({len(year_data)} events)',\n", + " alpha=0.7,\n", + " s=30\n", + " )\n", + " plt.legend(title=\"Fire Events by Year\")\n", + " else:\n", + " plt.scatter(\n", + " fire_all.geometry.x,\n", + " fire_all.geometry.y,\n", + " c='red',\n", + " label='Fire Events',\n", + " alpha=0.7,\n", + " s=30\n", + " )\n", + " plt.legend()\n", + " \n", + " plt.title('Study Area with Fire Events')\n", + " plt.axis('off')\n", + " plt.savefig('img/study_area_with_fires.png')\n", + " print(\"Saved plot: study_area_with_fires.png\")\n", + " \n", + " else:\n", + " print(\"Study area boundary shapefile not found.\")\n", + "except Exception as e:\n", + " print(f\"Error analyzing boundary data: {e}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Trend layer loaded successfully.\n", + "Driver: GTiff\n", + "Width: 944, Height: 2912\n", + "Bounds: BoundingBox(left=8246820.0, bottom=1767960.0, right=8275140.0, top=1680600.0)\n", + "CRS: EPSG:3857\n", + "Number of bands: 40\n", + "Data types: ('float32', 'float32', 'float32', 'float32', 'float32', 'float32', 'float32', 'float32', 'float32', 'float32', 'float32', 'float32', 'float32', 'float32', 'float32', 'float32', 'float32', 'float32', 'float32', 'float32', 'float32', 'float32', 'float32', 'float32', 'float32', 'float32', 'float32', 'float32', 'float32', 'float32', 'float32', 'float32', 'float32', 'float32', 'float32', 'float32', 'float32', 'float32', 'float32', 'float32')\n", + "\n", + "Band 1 statistics:\n", + "Min: nan, Max: nan, Mean: nan, Std: nan\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Error analyzing trend layer: No mappable was found to use for colorbar creation. First define a mappable such as an image (with imshow) or a contour set (with contourf).\n" + ] + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# CELL 5: Trend Layers Analysis\n", + "\n", + "try:\n", + " # Try to load the trend layer TIF file\n", + " trend_file = \"inputResampled.tif\" \n", + " trend_path = os.path.join(data_dir, trend_file)\n", + " \n", + " if os.path.exists(trend_path):\n", + " # Open the raster file\n", + " with rasterio.open(trend_path) as src:\n", + " # Print metadata\n", + " print(f\"Trend layer loaded successfully.\")\n", + " print(f\"Driver: {src.driver}\")\n", + " print(f\"Width: {src.width}, Height: {src.height}\")\n", + " print(f\"Bounds: {src.bounds}\")\n", + " print(f\"CRS: {src.crs}\")\n", + " print(f\"Number of bands: {src.count}\")\n", + " print(f\"Data types: {src.dtypes}\")\n", + " \n", + " # Read and analyze the first band\n", + " band_to_show = 1 # Start with the first band\n", + " band_data = src.read(band_to_show)\n", + " \n", + " # Get statistics for the band\n", + " band_min = band_data.min()\n", + " band_max = band_data.max()\n", + " band_mean = band_data.mean()\n", + " band_std = band_data.std()\n", + " \n", + " print(f\"\\nBand {band_to_show} statistics:\")\n", + " print(f\"Min: {band_min}, Max: {band_max}, Mean: {band_mean:.2f}, Std: {band_std:.2f}\")\n", + " \n", + " # Plot the band\n", + " plt.figure(figsize=(10, 8))\n", + " show(band_data, transform=src.transform, cmap='viridis', title=f\"Band {band_to_show}\")\n", + " plt.colorbar(label=f\"Band {band_to_show} Value\")\n", + " plt.savefig(f'img/trend_layer_band_{band_to_show}.png')\n", + " print(f\"Saved plot: trend_layer_band_{band_to_show}.png\")\n", + " \n", + " # If there are multiple bands, show a few more\n", + " if src.count > 1:\n", + " for band_idx in range(2, min(4, src.count + 1)):\n", + " band = src.read(band_idx)\n", + " plt.figure(figsize=(10, 8))\n", + " show(band, transform=src.transform, cmap='viridis', title=f\"Band {band_idx}\")\n", + " plt.colorbar(label=f\"Band {band_idx} Value\")\n", + " plt.savefig(f'img/trend_layer_band_{band_idx}.png')\n", + " print(f\"Saved plot: trend_layer_band_{band_idx}.png\")\n", + " else:\n", + " print(f\"The trend layer file '{trend_file}' was not found.\")\n", + " print(\"According to the README, it can be downloaded from:\")\n", + " print(\"https://www.dropbox.com/scl/fi/ena1qfeqv5ppshluop8kt/inputResampled.tif?rlkey=arefwl3my7nx3z5qpknu88lsx&st=vj8cn3bd&dl=0\")\n", + "except Exception as e:\n", + " print(f\"Error analyzing trend layer: {e}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Model Training and Testing Dataset Evaluation:\n", + "Total fire events: 267\n", + "Training events (2013-2020): 206\n", + "Testing events (2021-2023): 61\n", + "\n", + "Training data sufficient: Yes\n", + "Testing data sufficient: Yes\n", + "\n", + "No temporal gaps in the data.\n" + ] + } + ], + "source": [ + "# CELL 6: Model Training and Testing Dataset Evaluation\n", + "\n", + "try:\n", + " if 'fire_all' in locals() and len(fire_all) > 0:\n", + " # Count total events\n", + " total_events = len(fire_all)\n", + " print(f\"\\nModel Training and Testing Dataset Evaluation:\")\n", + " print(f\"Total fire events: {total_events}\")\n", + " \n", + " # Define training period (e.g., 2013-2020) and testing period (e.g., 2021-2023)\n", + " if 'year' in fire_all.columns:\n", + " train_years = range(2013, 2021) # Adjust as needed\n", + " test_years = range(2021, 2024) # Adjust as needed\n", + " \n", + " train_events = fire_all[fire_all['year'].isin(train_years)]\n", + " test_events = fire_all[fire_all['year'].isin(test_years)]\n", + " \n", + " print(f\"Training events (2013-2020): {len(train_events)}\")\n", + " print(f\"Testing events (2021-2023): {len(test_events)}\")\n", + " \n", + " # Check if we have enough events for both periods\n", + " train_sufficient = len(train_events) >= 100 # Arbitrary threshold\n", + " test_sufficient = len(test_events) >= 30 # Arbitrary threshold\n", + " \n", + " print(f\"\\nTraining data sufficient: {'Yes' if train_sufficient else 'No'}\")\n", + " print(f\"Testing data sufficient: {'Yes' if test_sufficient else 'No'}\")\n", + " \n", + " # Check for temporal gaps\n", + " year_counts = fire_all.groupby('year').size()\n", + " all_years = set(range(min(fire_all['year']), max(fire_all['year']) + 1))\n", + " missing_years = all_years - set(year_counts.index)\n", + " \n", + " if missing_years:\n", + " print(f\"\\nWarning: Missing data for years: {missing_years}\")\n", + " else:\n", + " print(\"\\nNo temporal gaps in the data.\")\n", + " else:\n", + " print(\"Year information not available to assess temporal distribution.\")\n", + " else:\n", + " print(\"Fire event data not available for model evaluation.\")\n", + "except Exception as e:\n", + " print(f\"Error evaluating model data requirements: {e}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "==================================================\n", + "SUMMARY OF FINDINGS AND RECOMMENDATIONS\n", + "==================================================\n", + "\n", + "FINDINGS:\n", + "- Found 267 fire events.\n", + "- Fire events cover 11 years from 2013 to 2023.\n", + "- Study area boundary data is available.\n", + "- The trend layer file (inputResampled.tif) is available locally.\n", + "- The road layer file (road.tif) is available.\n", + "- The dataset appears to have sufficient fire events for both training and testing.\n", + "\n", + "RECOMMENDATIONS:\n", + "- All necessary data appears to be available and sufficient for the analysis.\n", + "\n", + "Next Steps:\n", + "1. Ensure all required datasets are downloaded and properly formatted\n", + "2. Prepare training and testing datasets based on appropriate temporal splits\n", + "3. Apply the methodology described in the README for fire vulnerability mapping\n", + "4. Evaluate model performance using metrics like precision, recall, and F1-score\n", + "5. Generate predictions for future fire-prone areas\n", + "\n", + "This script generated several visualization files. Check your directory for the PNG files.\n" + ] + } + ], + "source": [ + "# CELL 7: Summary of Findings and Recommendations\n", + "\n", + "print(\"\\n\" + \"=\"*50)\n", + "print(\"SUMMARY OF FINDINGS AND RECOMMENDATIONS\")\n", + "print(\"=\"*50)\n", + "\n", + "findings = []\n", + "recommendations = []\n", + "\n", + "# Check if we have the key datasets\n", + "if 'fire_all' in locals() and len(fire_all) > 0:\n", + " findings.append(f\"- Found {len(fire_all)} fire events.\")\n", + " if 'year' in fire_all.columns:\n", + " years = sorted(fire_all['year'].unique())\n", + " findings.append(f\"- Fire events cover {len(years)} years from {min(years)} to {max(years)}.\")\n", + "else:\n", + " findings.append(\"- Fire event data is missing or could not be loaded.\")\n", + " recommendations.append(\"- Acquire or fix the fire event datasets (fire13_19.shp and fire20_23.shp).\")\n", + "\n", + "if 'boundary' in locals():\n", + " findings.append(\"- Study area boundary data is available.\")\n", + "else:\n", + " findings.append(\"- Study area boundary data is missing or could not be loaded.\")\n", + " recommendations.append(\"- Ensure the pa_boundary.shp file is available and properly formatted.\")\n", + "\n", + "# Check for the trend layer file\n", + "if os.path.exists(os.path.join(data_dir, \"inputResampled.tif\")):\n", + " findings.append(\"- The trend layer file (inputResampled.tif) is available locally.\")\n", + "else:\n", + " findings.append(\"- The trend layer file (inputResampled.tif) is not available locally.\")\n", + " recommendations.append(\"- Download the trend layer file from the Dropbox link provided in the README.\")\n", + "\n", + "# Check for the road data\n", + "if os.path.exists(os.path.join(data_dir, \"road.tif\")):\n", + " findings.append(\"- The road layer file (road.tif) is available.\")\n", + "else:\n", + " findings.append(\"- The road layer file (road.tif) is not available or could not be loaded.\")\n", + " recommendations.append(\"- Ensure the road.tif file is available for fire vulnerability mapping.\")\n", + "\n", + "# Model training/testing dataset assessment\n", + "if 'train_sufficient' in locals() and 'test_sufficient' in locals():\n", + " if train_sufficient and test_sufficient:\n", + " findings.append(\"- The dataset appears to have sufficient fire events for both training and testing.\")\n", + " else:\n", + " if not train_sufficient:\n", + " findings.append(\"- The training dataset may not have enough fire events.\")\n", + " recommendations.append(\"- Consider acquiring more historical fire data for training.\")\n", + " if not test_sufficient:\n", + " findings.append(\"- The testing dataset may not have enough fire events.\")\n", + " recommendations.append(\"- Consider using cross-validation techniques to maximize use of available data.\")\n", + "\n", + "# Display findings and recommendations\n", + "print(\"\\nFINDINGS:\")\n", + "for finding in findings:\n", + " print(finding)\n", + "\n", + "print(\"\\nRECOMMENDATIONS:\")\n", + "if recommendations:\n", + " for recommendation in recommendations:\n", + " print(recommendation)\n", + "else:\n", + " print(\"- All necessary data appears to be available and sufficient for the analysis.\")\n", + "\n", + "print(\"\\nNext Steps:\")\n", + "print(\"1. Ensure all required datasets are downloaded and properly formatted\")\n", + "print(\"2. Prepare training and testing datasets based on appropriate temporal splits\")\n", + "print(\"3. Apply the methodology described in the README for fire vulnerability mapping\")\n", + "print(\"4. Evaluate model performance using metrics like precision, recall, and F1-score\")\n", + "print(\"5. Generate predictions for future fire-prone areas\")\n", + "\n", + "print(\"\\nThis script generated several visualization files. Check your directory for the PNG files.\")\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "ee", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.1" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..105451b --- /dev/null +++ b/requirements.txt @@ -0,0 +1,16 @@ +earthengine-api>=0.1.347 +numpy>=1.21.0 +pandas>=1.3.0 +geopandas>=0.10.0 +matplotlib>=3.4.0 +seaborn>=0.11.0 +scikit-learn>=1.0.0 +tqdm>=4.62.0 +rasterio>=1.2.0 +folium>=0.12.0 +geemap>=0.16.0 +shapely>=1.8.0 +pyproj>=3.1.0 +requests>=2.26.0 +jupyter>=1.0.0 +notebook>=6.4.0 \ No newline at end of file From 1c4ccf8a44448389f4d6463fd1e5d7caf146e536 Mon Sep 17 00:00:00 2001 From: jonas-nothnagel Date: Mon, 7 Apr 2025 15:53:07 +0200 Subject: [PATCH 02/15] updated trendfire script --- TrendFire.js | 1 + TrendFire.py | 58 +- notebooks/TrendFire.ipynb | 1822 ------------------------------------- 3 files changed, 38 insertions(+), 1843 deletions(-) delete mode 100644 notebooks/TrendFire.ipynb diff --git a/TrendFire.js b/TrendFire.js index b2e6fa6..ae722c2 100644 --- a/TrendFire.js +++ b/TrendFire.js @@ -3,6 +3,7 @@ // The required input is - 'region of interest' geometry // the goa object here is not defined? JN +// var goa = ee.FeatureCollection('users/jonasnothnagel/pa_boundary'); Map.centerObject(goa,8) //Mask to filter clouds and to scale the DN values to surface reflectance diff --git a/TrendFire.py b/TrendFire.py index f299273..801d97f 100644 --- a/TrendFire.py +++ b/TrendFire.py @@ -120,26 +120,23 @@ def get_goa_boundary(): def maskL8sr(image): """ Mask clouds and scale pixel values for Landsat 8 SR imagery. + Matches the JavaScript implementation. """ - # Get QA band - qa = image.select('QA_PIXEL') - - # Bits 3 and 5 are cloud shadow and cloud, respectively - cloud_shadow_bit_mask = 1 << 3 - clouds_bit_mask = 1 << 5 - - # Both flags should be set to zero, indicating clear conditions - mask = qa.bitwiseAnd(cloud_shadow_bit_mask).eq(0).And( - qa.bitwiseAnd(clouds_bit_mask).eq(0)) - + # Bits 0-4 are Fill, Dilated Cloud, Cirrus, Cloud, Cloud Shadow. + qaMask = image.select('QA_PIXEL').bitwiseAnd(int('11111', 2)).eq(0) + saturationMask = image.select('QA_RADSAT').eq(0) + # Scale the optical bands optical_bands = image.select('SR_B.').multiply(0.0000275).add(-0.2) - + # Scale the thermal bands thermal_bands = image.select('ST_B.*').multiply(0.00341802).add(149.0) - - # Return the masked and scaled image - return image.select(['QA_.*']).addBands(optical_bands).addBands(thermal_bands).updateMask(mask) + + # Return the masked and scaled image, applying both masks + return image.addBands(optical_bands, None, True) \ + .addBands(thermal_bands, None, True) \ + .updateMask(qaMask) \ + .updateMask(saturationMask) # Function to calculate spectral indices and add them as bands def addIndices(image): @@ -180,8 +177,8 @@ def addIndices(image): # NDMI (Normalized Difference Moisture Index) ndmi = image.normalizedDifference(['SR_B5', 'SR_B6']).rename('ndmi') - # NDFI (Normalized Difference Fraction Index) - ndfi = image.normalizedDifference(['SR_B7', 'SR_B5']).rename('ndfi') + # NDFI (Normalized Difference Fraction Index) - Corrected formula + ndfi = image.normalizedDifference(['ST_B10', 'SR_B6']).rename('ndfi') # NBR (Normalized Burn Ratio) nbr = image.normalizedDifference(['SR_B5', 'SR_B7']).rename('nbr') @@ -240,12 +237,19 @@ def process_landsat_trends(goa, start_date='2013-03-20', end_date='2023-02-28'): """ print("Processing Landsat 8 data and calculating trends...") + # Define the outlier date + outlier_date = ee.Date('2016-06-22') + # Load Landsat 8 collection and filter by date and location ls8_collection = ee.ImageCollection('LANDSAT/LC08/C02/T1_L2') \ .filterDate(start_date, end_date) \ .filterBounds(goa) \ .filterMetadata('CLOUD_COVER', 'less_than', 10) \ .map(maskL8sr) \ + .filter(ee.Filter.Or( # Add outlier filter + ee.Filter.date(start_date, outlier_date), + ee.Filter.date(outlier_date.advance(1, 'day'), end_date) + )) \ .map(lambda image: image.clip(goa)) \ .map(addIndices) @@ -565,18 +569,30 @@ def main(): print("\nMerging all trend layers...") all_trends = landsat_trends.addBands(rain_trends).addBands(sm_trends).addBands(rh_trends) - # Export the merged trends - print("Exporting merged trends...") + # Export the merged trends to Asset + print("Exporting merged trends to GEE Asset...") export_to_asset( all_trends, 'users/jonasnothnagel/Trend2024_all_new', goa, - 'All_Trends_2024', + 'All_Trends_2024_Asset', # Changed description slightly for clarity + scale=30 + ) + + # Export the merged trends to Google Drive + print("Exporting merged trends to Google Drive...") + export_to_drive( + all_trends, + 'TrendFirePy_output', # Filename for Drive export + goa, + 'All_Trends_2024_Drive', # Description for Drive task + folder='GEE_Exports', # Google Drive folder name scale=30 ) print("\nAnalysis completed successfully!") - print("Results exported to Earth Engine assets") + print("Results export tasks started for Earth Engine assets and Google Drive.") + print("Monitor task status in the GEE Code Editor or using task monitoring tools.") return { 'trends': all_trends, diff --git a/notebooks/TrendFire.ipynb b/notebooks/TrendFire.ipynb deleted file mode 100644 index bc5ff57..0000000 --- a/notebooks/TrendFire.ipynb +++ /dev/null @@ -1,1822 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import ee\n", - "import geemap\n", - "import os\n", - "import datetime\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import pandas as pd\n", - "import geopandas as gpd\n", - "from tqdm import tqdm" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Earth Engine API initialized successfully.\n" - ] - } - ], - "source": [ - "# Initialize the Earth Engine API\n", - "try:\n", - " ee.Authenticate()\n", - " ee.Initialize()\n", - " print(\"Earth Engine API initialized successfully.\")\n", - "except Exception as e:\n", - " print(f\"Error initializing Earth Engine API: {e}\")\n", - " print(\"Make sure you have authenticated with Earth Engine using ee.Authenticate()\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "def get_goa_boundary():\n", - " \"\"\"Get the administrative boundary for Goa, India.\"\"\"\n", - " try:\n", - " # Get the Goa district boundary from Earth Engine's administrative boundaries\n", - " goa = ee.FeatureCollection(\"FAO/GAUL/2015/level1\").filter(ee.Filter.eq('ADM1_NAME', 'Goa'))\n", - " return goa.geometry()\n", - " except Exception as e:\n", - " print(f\"Could not load Goa boundary from Earth Engine: {e}\")\n", - " \n", - " # Try to load from shapefile as fallback\n", - " try:\n", - " boundary_path = os.path.join('data', 'pa_boundary.shp')\n", - " if os.path.exists(boundary_path):\n", - " boundary_gdf = gpd.read_file(boundary_path)\n", - " # Convert to GeoJSON\n", - " boundary_geojson = boundary_gdf.geometry.__geo_interface__\n", - " # Create an EE geometry\n", - " return ee.Geometry.Polygon(boundary_geojson['features'][0]['geometry']['coordinates'])\n", - " except Exception as e:\n", - " print(f\"Could not load boundary from shapefile: {e}\")\n", - " \n", - " # Manual definition as last resort\n", - " goa_coords = [\n", - " [73.6765, 15.7560],\n", - " [74.3161, 15.7560],\n", - " [74.3161, 14.8922],\n", - " [73.6765, 14.8922],\n", - " [73.6765, 15.7560]\n", - " ]\n", - " return ee.Geometry.Polygon(goa_coords)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "def maskL8sr(image):\n", - " \"\"\"\n", - " Mask clouds and scale pixel values for Landsat 8 SR imagery.\n", - " \"\"\"\n", - " # Get QA band\n", - " qa = image.select('QA_PIXEL')\n", - " \n", - " # Bits 3 and 5 are cloud shadow and cloud, respectively\n", - " cloud_shadow_bit_mask = 1 << 3\n", - " clouds_bit_mask = 1 << 5\n", - " \n", - " # Both flags should be set to zero, indicating clear conditions\n", - " mask = qa.bitwiseAnd(cloud_shadow_bit_mask).eq(0).And(\n", - " qa.bitwiseAnd(clouds_bit_mask).eq(0))\n", - " \n", - " # Scale the optical bands\n", - " optical_bands = image.select('SR_B.').multiply(0.0000275).add(-0.2)\n", - " \n", - " # Scale the thermal bands\n", - " thermal_bands = image.select('ST_B.*').multiply(0.00341802).add(149.0)\n", - " \n", - " # Return the masked and scaled image\n", - " return image.select(['QA_.*']).addBands(optical_bands).addBands(thermal_bands).updateMask(mask)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "def addIndices(image):\n", - " \"\"\"\n", - " Calculate various spectral indices and add them as bands to the image.\n", - " Match JavaScript version exactly.\n", - " \"\"\"\n", - " # NDVI (Normalized Difference Vegetation Index)\n", - " ndvi = image.normalizedDifference(['SR_B5', 'SR_B4']).rename('ndvi')\n", - " \n", - " # MIRBI (Mid-Infrared Burn Index)\n", - " mirbi = image.expression(\n", - " '10 * swir1 - 9.8 * swir2 + 2',\n", - " {\n", - " 'swir1': image.select('SR_B6'),\n", - " 'swir2': image.select('SR_B7')\n", - " }\n", - " ).rename('mirbi')\n", - " \n", - " # NDFI (Normalized Difference Fire Index) - Corrected to match JS\n", - " ndfi = image.normalizedDifference(['ST_B10', 'SR_B6']).rename('ndfi')\n", - " \n", - " # EVI (Enhanced Vegetation Index)\n", - " evi = image.expression(\n", - " '2.5 * ((NIR - RED) / (NIR + 6 * RED - 7.5 * BLUE + 1))',\n", - " {\n", - " 'NIR': image.select('SR_B5'),\n", - " 'RED': image.select('SR_B4'),\n", - " 'BLUE': image.select('SR_B2')\n", - " }\n", - " ).rename('evi')\n", - " \n", - " # BSI (Bare Soil Index)\n", - " bsi = image.expression(\n", - " '((X + Y) - (A + B)) / ((X + Y) + (A + B))',\n", - " {\n", - " 'X': image.select('SR_B6'),\n", - " 'Y': image.select('SR_B4'),\n", - " 'A': image.select('SR_B5'),\n", - " 'B': image.select('SR_B2')\n", - " }\n", - " ).rename('bsi')\n", - " \n", - " # NDMI (Normalized Difference Moisture Index)\n", - " ndmi = image.normalizedDifference(['SR_B5', 'SR_B6']).rename('ndmi')\n", - " \n", - " # NBR (Normalized Burn Ratio)\n", - " nbr = image.normalizedDifference(['SR_B5', 'SR_B7']).rename('nbr')\n", - " \n", - " # NBR2 (Normalized Burn Ratio 2)\n", - " nbr2 = image.normalizedDifference(['SR_B6', 'SR_B7']).rename('nbr2')\n", - " \n", - " # MSAVI (Modified Soil Adjusted Vegetation Index)\n", - " msavi = image.expression(\n", - " '(2 * NIR + 1 - sqrt((2 * NIR + 1) ** 2 - 8 * (NIR - RED))) / 2',\n", - " {\n", - " 'NIR': image.select('SR_B5'),\n", - " 'RED': image.select('SR_B4')\n", - " }\n", - " ).rename('msavi')\n", - " \n", - " return image.addBands([ndvi, evi, msavi, mirbi, ndmi, ndfi, nbr, nbr2, bsi])" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Function to calculate SMI (Soil Moisture Index)\n", - "def calculateSMI(image):\n", - " \"\"\"\n", - " Calculate Soil Moisture Index (SMI) from Landsat data.\n", - " \"\"\"\n", - " Ts = image.select('ST_B10')\n", - " ndvi = image.select('ndvi')\n", - " \n", - " # Apply the SMI calculation\n", - " smi = image.expression(\n", - " '(Ts_max - Ts) / (Ts_max - Ts_min)',\n", - " {\n", - " 'Ts': Ts,\n", - " 'Ts_max': Ts.reduceRegion(\n", - " reducer=ee.Reducer.max(),\n", - " geometry=image.geometry(),\n", - " scale=30,\n", - " maxPixels=1e9\n", - " ).get('ST_B10'),\n", - " 'Ts_min': Ts.reduceRegion(\n", - " reducer=ee.Reducer.min(),\n", - " geometry=image.geometry(),\n", - " scale=30,\n", - " maxPixels=1e9\n", - " ).get('ST_B10')\n", - " }\n", - " ).rename('smi')\n", - " \n", - " return image.addBands(smi)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "def process_landsat_trends(goa, start_date='2013-03-20', end_date='2023-02-28'):\n", - " \"\"\"\n", - " Process Landsat 8 data to calculate vegetation and burn index trends.\n", - " Fixed to handle date calculations properly.\n", - " \n", - " Args:\n", - " goa: Earth Engine geometry for Goa\n", - " start_date: Start date for analysis\n", - " end_date: End date for analysis\n", - " \n", - " Returns:\n", - " Tuple of (trend image, Landsat collection)\n", - " \"\"\"\n", - " print(f\"Processing Landsat 8 data for {start_date} to {end_date}...\")\n", - " \n", - " # Convert dates to ee.Date objects\n", - " ee_start_date = ee.Date(start_date)\n", - " ee_end_date = ee.Date(end_date)\n", - " \n", - " # Load Landsat 8 collection\n", - " ls8 = ee.ImageCollection(\"LANDSAT/LC08/C02/T1_L2\") \\\n", - " .filterDate(start_date, end_date) \\\n", - " .filterBounds(goa)\n", - " \n", - " # Apply cloud masking and add indices\n", - " ls8_masked = ls8.map(maskL8sr).map(addIndices)\n", - " \n", - " # Filter out images with too many clouds\n", - " ls8_filtered = ls8_masked.filter(ee.Filter.lt('CLOUD_COVER', 80))\n", - " \n", - " # Add a time band (fixed version)\n", - " def add_time_band(image):\n", - " # Get image timestamp\n", - " date = ee.Date(image.get('system:time_start'))\n", - " \n", - " # Get the year (server-side)\n", - " year = date.get('year')\n", - " \n", - " # Calculate days since the start of the analysis (safer approach)\n", - " days_since_start = date.difference(ee_start_date, 'day')\n", - " \n", - " # Normalize to years (for better coefficient interpretation)\n", - " years_since_start = days_since_start.divide(365)\n", - " \n", - " # Create an image with this value\n", - " time_band = ee.Image.constant(years_since_start).float().rename('t')\n", - " \n", - " # Add date info as properties (for debugging)\n", - " return image.addBands(time_band).set({\n", - " 'year': year,\n", - " 'days_since_start': days_since_start,\n", - " 'years_since_start': years_since_start\n", - " })\n", - " \n", - " # Add time bands\n", - " ls8_time = ls8_filtered.map(add_time_band)\n", - " \n", - " # Define the bands to calculate trends for\n", - " trend_bands = ['ndvi', 'evi', 'msavi', 'mirbi', 'ndmi', 'ndfi', 'nbr', 'nbr2', 'bsi']\n", - " \n", - " # Calculate trends for each band\n", - " trends = {}\n", - " \n", - " for band in trend_bands:\n", - " print(f\"Calculating trend for {band}...\")\n", - " \n", - " # Define the dependent and independent variables\n", - " trend_x = 't'\n", - " trend_y = band\n", - " \n", - " # Calculate trend (slope and intercept)\n", - " trend = ls8_time.select([trend_x, trend_y]).reduce(\n", - " ee.Reducer.linearFit()\n", - " )\n", - " \n", - " # Rename the bands\n", - " trend = trend.select(['scale'], [f'{band}_Slope'])\n", - " \n", - " # Store in dictionary\n", - " trends[band] = trend\n", - " \n", - " # Merge all trend images\n", - " all_trends = ee.Image.cat(list(trends.values()))\n", - " \n", - " print(\"Landsat trend calculation complete.\")\n", - " \n", - " return all_trends, ls8_filtered" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Function to process CHIRPS precipitation data\n", - "def process_chirps_trends(goa, start_date='2013-01-01', end_date='2022-12-31'):\n", - " \"\"\"\n", - " Process CHIRPS precipitation data and calculate rainfall trends.\n", - " \"\"\"\n", - " print(\"Processing CHIRPS precipitation data and calculating trends...\")\n", - " \n", - " # Load CHIRPS data\n", - " chirps = ee.ImageCollection('UCSB-CHG/CHIRPS/DAILY') \\\n", - " .filterDate(start_date, end_date) \\\n", - " .filterBounds(goa)\n", - " \n", - " # Function to compute the yearly sum\n", - " def createYearlySum(year):\n", - " # Filter for the specific year\n", - " year_filter = chirps.filter(ee.Filter.calendarRange(year, year, 'year'))\n", - " \n", - " # Create the yearly sum\n", - " yearly_sum = year_filter.sum().clip(goa)\n", - " \n", - " # Add the year as a property\n", - " return yearly_sum.set('year', year) \\\n", - " .set('system:time_start', ee.Date.fromYMD(year, 1, 1).millis()) \\\n", - " .rename('precipitation')\n", - " \n", - " # Create a list of yearly sums\n", - " years = ee.List.sequence(int(start_date[:4]), int(end_date[:4]))\n", - " yearly_sums = years.map(createYearlySum)\n", - " \n", - " # Convert to ImageCollection\n", - " yearly_sums_collection = ee.ImageCollection.fromImages(yearly_sums)\n", - " \n", - " # Calculate trend\n", - " def addTime(image):\n", - " return image.addBands(image.metadata('system:time_start').divide(1000 * 60 * 60 * 24 * 365).rename('t'))\n", - " \n", - " collection_with_time = yearly_sums_collection.map(addTime)\n", - " \n", - " # Calculate linear regression\n", - " trend = collection_with_time.select(['t', 'precipitation']).reduce(ee.Reducer.linearFit())\n", - " \n", - " # Rename bands\n", - " rain_trend = trend.select(['scale'], ['rain_trend'])\n", - " \n", - " return rain_trend, yearly_sums_collection\n" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Function to process SMAP soil moisture data\n", - "def process_smap_trends(goa, start_date='2015-04-01', end_date='2022-12-31'):\n", - " \"\"\"\n", - " Process SMAP soil moisture data and calculate trends.\n", - " Note: SMAP data starts from April 2015.\n", - " \"\"\"\n", - " print(\"Processing SMAP soil moisture data and calculating trends...\")\n", - " \n", - " # Load SMAP data\n", - " smap = ee.ImageCollection('NASA/SMAP/SPL3SMP_E/005') \\\n", - " .filterDate(start_date, end_date) \\\n", - " .filterBounds(goa) \\\n", - " .select(['soil_moisture_am'])\n", - " \n", - " # Function to compute the yearly mean\n", - " def createYearlyMean(year):\n", - " year_filter = smap.filter(ee.Filter.calendarRange(year, year, 'year'))\n", - " yearly_mean = year_filter.mean().clip(goa)\n", - " return yearly_mean.set('year', year) \\\n", - " .set('system:time_start', ee.Date.fromYMD(year, 1, 1).millis()) \\\n", - " .rename('soil_moisture')\n", - " \n", - " # Create a list of yearly means\n", - " years = ee.List.sequence(int(start_date[:4]), int(end_date[:4]))\n", - " yearly_means = years.map(createYearlyMean)\n", - " \n", - " # Convert to ImageCollection\n", - " yearly_means_collection = ee.ImageCollection.fromImages(yearly_means)\n", - " \n", - " # Calculate trend\n", - " def addTime(image):\n", - " return image.addBands(image.metadata('system:time_start').divide(1000 * 60 * 60 * 24 * 365).rename('t'))\n", - " \n", - " collection_with_time = yearly_means_collection.map(addTime)\n", - " \n", - " # Calculate linear regression\n", - " trend = collection_with_time.select(['t', 'soil_moisture']).reduce(ee.Reducer.linearFit())\n", - " \n", - " # Rename bands\n", - " sm_trend = trend.select(['scale'], ['sm_trend'])\n", - " \n", - " return sm_trend, yearly_means_collection" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "def process_era5_trends(goa, start_date='2013-03-20', end_date='2023-02-28'):\n", - " \"\"\"\n", - " Process ERA5 data to calculate relative humidity trends.\n", - " \n", - " Args:\n", - " goa: Earth Engine geometry for Goa\n", - " start_date: Start date for analysis\n", - " end_date: End date for analysis\n", - " \n", - " Returns:\n", - " Tuple of (trend image, ERA5 collection)\n", - " \"\"\"\n", - " print(f\"Processing ERA5 data for {start_date} to {end_date}...\")\n", - " \n", - " # Load ERA5 data\n", - " era5 = ee.ImageCollection(\"ECMWF/ERA5/DAILY\").filterDate(start_date, end_date).filterBounds(goa)\n", - " \n", - " # Print the first image to debug band names\n", - " first_img = era5.first()\n", - " print(\"Available ERA5 bands:\", first_img.bandNames().getInfo())\n", - " \n", - " # Correct band names based on available bands\n", - " temp_band = 'mean_2m_air_temperature' # instead of 'temperature_2m'\n", - " dewpoint_band = 'dewpoint_2m_temperature' # instead of 'dewpoint_temperature_2m'\n", - " \n", - " # Function to calculate relative humidity from temperature and dew point\n", - " def add_rh(image):\n", - " # Calculate vapor pressure\n", - " # Using Tetens formula\n", - " # es = 6.11 * 10.0 ** (7.5 * T / (237.3 + T))\n", - " # For dew point: e = 6.11 * 10.0 ** (7.5 * Td / (237.3 + Td))\n", - " # RH = e/es * 100\n", - " \n", - " # Convert from Kelvin to Celsius\n", - " t_celsius = image.select(temp_band).subtract(273.15)\n", - " td_celsius = image.select(dewpoint_band).subtract(273.15)\n", - " \n", - " # Calculate saturation vapor pressure (es) at temperature\n", - " es = ee.Image(6.11).multiply(ee.Image(10).pow(ee.Image(7.5).multiply(t_celsius).divide(ee.Image(237.3).add(t_celsius))))\n", - " \n", - " # Calculate actual vapor pressure (e) at dew point\n", - " e = ee.Image(6.11).multiply(ee.Image(10).pow(ee.Image(7.5).multiply(td_celsius).divide(ee.Image(237.3).add(td_celsius))))\n", - " \n", - " # Calculate relative humidity (RH)\n", - " rh = e.divide(es).multiply(100).rename('rh')\n", - " \n", - " return image.addBands(rh)\n", - " \n", - " # Add RH bands to the collection\n", - " era5_rh = era5.map(add_rh)\n", - " \n", - " # Add a time band\n", - " def add_time_band(image):\n", - " \"\"\"Add a time band to an image with the decimal year.\"\"\"\n", - " # Get image timestamp\n", - " date = ee.Date(image.get('system:time_start'))\n", - " \n", - " # Get the year (server-side)\n", - " year = ee.Number(date.get('year'))\n", - " \n", - " # Calculate start of the year\n", - " start_of_year = ee.Date.fromYMD(year, 1, 1)\n", - " \n", - " # Calculate difference in days (server-side)\n", - " days_from_start = date.difference(start_of_year, 'day')\n", - " \n", - " # Get days in year (accounting for leap years)\n", - " days_in_year = ee.Algorithms.If(\n", - " ee.Number(year).mod(4).eq(0).And(\n", - " ee.Number(year).mod(100).neq(0).Or(\n", - " ee.Number(year).mod(400).eq(0)\n", - " )\n", - " ),\n", - " 366, # Leap year\n", - " 365 # Non-leap year\n", - " )\n", - " \n", - " # Calculate decimal year\n", - " decimal_year = year.add(days_from_start.divide(days_in_year))\n", - " \n", - " # Create an image with the decimal year\n", - " time_band = ee.Image.constant(decimal_year).float().rename('t')\n", - " \n", - " return image.addBands(time_band)\n", - " \n", - " # Add time bands\n", - " era5_rh_t = era5_rh.map(add_time_band)\n", - " \n", - " # Calculate RH trend\n", - " # Independent variable is 't' (time in decimal years)\n", - " # Dependent variable is 'rh'\n", - " trend_bands = ['rh']\n", - " \n", - " # Define the dependent and independent variables\n", - " trend_x = 't'\n", - " trend_y = 'rh'\n", - " \n", - " # Calculate trend (slope and intercept)\n", - " trend = era5_rh_t.select([trend_x, trend_y]).reduce(\n", - " ee.Reducer.linearFit()\n", - " )\n", - " \n", - " # Rename the bands to match the JS version\n", - " trend = trend.select(['scale'], ['rh_Slope'])\n", - " \n", - " print(\"ERA5 RH trend calculation complete.\")\n", - " \n", - " return trend, era5_rh" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "def resample_image(image, scale=30):\n", - " \"\"\"\n", - " Resample an image to a consistent resolution and projection.\n", - " Fixed to properly handle projections.\n", - " \"\"\"\n", - " # Check if the image has multiple bands\n", - " band_names = image.bandNames()\n", - " first_band = ee.String(band_names.get(0))\n", - " \n", - " # Get projection from the first band\n", - " proj = image.select(first_band).projection()\n", - " \n", - " # Set default projection explicitly\n", - " image_with_proj = image.setDefaultProjection(proj)\n", - " \n", - " # Create a target projection at the desired scale\n", - " target_proj = ee.Projection('EPSG:4326').atScale(scale)\n", - " \n", - " # Now reduce resolution with valid projection\n", - " resampled = image_with_proj.reduceResolution(\n", - " reducer=ee.Reducer.mean(),\n", - " maxPixels=1024\n", - " ).reproject(\n", - " crs=target_proj\n", - " )\n", - " \n", - " return resampled" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Function to merge all trend layers\n", - "def merge_trend_layers(ls_trends, rain_trend, sm_trend, rh_trend):\n", - " \"\"\"\n", - " Merge all trend layers into a single multi-band image.\n", - " \"\"\"\n", - " # Combine all trend layers\n", - " all_trends = ls_trends.addBands(rain_trend).addBands(sm_trend).addBands(rh_trend)\n", - " \n", - " return all_trends" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Function to export the trends to Google Earth Engine assets\n", - "def export_to_asset(image, asset_name, region, description=None, scale=30):\n", - " \"\"\"\n", - " Export an image to a Google Earth Engine asset.\n", - " \"\"\"\n", - " if description is None:\n", - " description = asset_name.split('/')[-1]\n", - " \n", - " task = ee.batch.Export.image.toAsset(\n", - " image=image,\n", - " description=description,\n", - " assetId=asset_name,\n", - " region=region,\n", - " scale=scale,\n", - " maxPixels=1e13\n", - " )\n", - " \n", - " task.start()\n", - " print(f\"Started export task: {description}\")\n", - " return task" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Function to export the trends to Google Drive\n", - "def export_to_drive(image, filename, region, description=None, folder='GEE_Exports', scale=30):\n", - " \"\"\"\n", - " Export an image to Google Drive.\n", - " \"\"\"\n", - " if description is None:\n", - " description = filename\n", - " \n", - " task = ee.batch.Export.image.toDrive(\n", - " image=image,\n", - " description=description,\n", - " folder=folder,\n", - " fileNamePrefix=filename,\n", - " region=region,\n", - " scale=scale,\n", - " maxPixels=1e13\n", - " )\n", - " \n", - " task.start()\n", - " print(f\"Started export task to Drive: {description}\")\n", - " return task" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "def export_to_local(image, filename, region, scale=30):\n", - " \"\"\"\n", - " Export an Earth Engine image to a local GeoTIFF file.\n", - " \"\"\"\n", - " print(f\"Exporting image to local file: {filename}\")\n", - " \n", - " # Get the image URL\n", - " url = image.getDownloadURL({\n", - " 'scale': scale,\n", - " 'region': region,\n", - " 'format': 'GEO_TIFF',\n", - " 'crs': 'EPSG:4326'\n", - " })\n", - " \n", - " # Download the file\n", - " import requests\n", - " import shutil\n", - " \n", - " response = requests.get(url, stream=True)\n", - " with open(filename, 'wb') as out_file:\n", - " shutil.copyfileobj(response.raw, out_file)\n", - " \n", - " print(f\"Exported to: {filename}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Define date range to match JavaScript version\n", - "start_date = '2013-03-20'\n", - "end_date = '2023-02-28'" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Run Functions step by step\n", - "# Get the Goa boundary\n", - "goa = get_goa_boundary()" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Your boundary area: 3684.89 sq km\n", - "Official boundary area: 3684.89 sq km\n", - "Difference area: 0.00 sq km\n", - "Area difference percentage: 0.00%\n", - "✅ Boundary looks correct\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "c1b7d175a111442982df4926ea046727", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Map(center=[15.358382593200234, 74.0587759202492], controls=(WidgetControl(options=['position', 'transparent_b…" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "def verify_goa_boundary(goa_geometry):\n", - " \"\"\"Verify if the provided geometry resembles Goa.\"\"\"\n", - " # Get official boundary\n", - " official = ee.FeatureCollection(\"FAO/GAUL/2015/level1\").filter(ee.Filter.eq('ADM1_NAME', 'Goa'))\n", - " official_geom = official.geometry()\n", - " \n", - " # Calculate areas\n", - " goa_area = goa_geometry.area().getInfo() / 1e6\n", - " official_area = official_geom.area().getInfo() / 1e6\n", - " diff_area = goa_geometry.symmetricDifference(official_geom).area().getInfo() / 1e6\n", - " \n", - " # Print verification info\n", - " print(f\"Your boundary area: {goa_area:.2f} sq km\")\n", - " print(f\"Official boundary area: {official_area:.2f} sq km\")\n", - " print(f\"Difference area: {diff_area:.2f} sq km\")\n", - " print(f\"Area difference percentage: {abs(goa_area - official_area) / official_area * 100:.2f}%\")\n", - " \n", - " # Visual verification\n", - " m = geemap.Map()\n", - " m.centerObject(goa_geometry, 9)\n", - " m.addLayer(goa_geometry, {'color': 'red'}, 'Your Goa Boundary')\n", - " m.addLayer(official_geom, {'color': 'blue'}, 'Official Goa Boundary')\n", - " \n", - " # Verdict\n", - " if abs(goa_area - official_area) / official_area < 0.05 and diff_area < 5:\n", - " print(\"✅ Boundary looks correct\")\n", - " else:\n", - " print(\"⚠️ Boundary may not be correct - significant difference from official boundary\")\n", - " \n", - " return m\n", - "\n", - "# Usage\n", - "verification_map = verify_goa_boundary(goa)\n", - "display(verification_map)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CRS: EPSG:4326\n", - "Number of features: 6\n", - "Columns: ['Shape_Leng', 'Shape_Area', 'Name', 'geometry']\n", - "\n", - "Attribute preview:\n", - " Shape_Leng Shape_Area Name \\\n", - "0 1.735386 0.018797 Mhadei WLS \n", - "1 0.000000 0.000000 Bhagwan Mahavir WLS \n", - "2 0.000000 0.000000 Mollem NP \n", - "3 0.000000 0.000000 Bhagwan Mahavir WLS \n", - "4 0.000000 0.000000 Netravali WLS \n", - "\n", - " geometry \n", - "0 POLYGON Z ((74.09113 15.63933 0, 74.09146 15.6... \n", - "1 POLYGON Z ((74.18698 15.45582 0, 74.19093 15.4... \n", - "2 POLYGON Z ((74.23134 15.38431 0, 74.23145 15.3... \n", - "3 POLYGON Z ((74.25459 15.32841 0, 74.25493 15.3... \n", - "4 POLYGON Z ((74.20925 15.25191 0, 74.2092 15.25... \n", - "\n", - "Total area: 0.00 square kilometers\n" - ] - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# check shapefile:\n", - "import geopandas as gpd\n", - "import matplotlib.pyplot as plt\n", - "import os\n", - "\n", - "# Path to the shapefile\n", - "boundary_path = os.path.join('../data', 'pa_boundary.shp')\n", - "\n", - "# Check if the file exists\n", - "if os.path.exists(boundary_path):\n", - " # Load the shapefile\n", - " boundary_gdf = gpd.read_file(boundary_path)\n", - " \n", - " # Print basic information\n", - " print(f\"CRS: {boundary_gdf.crs}\")\n", - " print(f\"Number of features: {len(boundary_gdf)}\")\n", - " print(f\"Columns: {boundary_gdf.columns.tolist()}\")\n", - " \n", - " # Display the first few rows to see attributes\n", - " print(\"\\nAttribute preview:\")\n", - " print(boundary_gdf.head())\n", - " \n", - " # Calculate the total area\n", - " area_sqkm = boundary_gdf.geometry.area.sum() / 1e6 # Convert to sq km if in meters\n", - " print(f\"\\nTotal area: {area_sqkm:.2f} square kilometers\")\n", - " \n", - " # Plot the shapefile\n", - " fig, ax = plt.subplots(figsize=(10, 10))\n", - " boundary_gdf.plot(ax=ax)\n", - " ax.set_title('Contents of pa_boundary.shp')\n", - " plt.show()\n", - " \n", - " # Check if it might be a protected area boundary\n", - " if 'name' in boundary_gdf.columns or 'NAME' in boundary_gdf.columns:\n", - " name_col = 'name' if 'name' in boundary_gdf.columns else 'NAME'\n", - " print(f\"\\nNames of areas in the shapefile:\")\n", - " print(boundary_gdf[name_col].unique())\n", - "else:\n", - " print(f\"Shapefile not found at {boundary_path}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Processing Landsat 8 data for 2013-03-20 to 2023-02-28...\n", - "Calculating trend for ndvi...\n", - "Calculating trend for evi...\n", - "Calculating trend for msavi...\n", - "Calculating trend for mirbi...\n", - "Calculating trend for ndmi...\n", - "Calculating trend for ndfi...\n", - "Calculating trend for nbr...\n", - "Calculating trend for nbr2...\n", - "Calculating trend for bsi...\n", - "Landsat trend calculation complete.\n" - ] - } - ], - "source": [ - "# Process Landsat 8 data and calculate trends\n", - "ls_trends, ls_collection = process_landsat_trends(goa, start_date, end_date)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Processing CHIRPS precipitation data and calculating trends...\n", - "Processing SMAP soil moisture data and calculating trends...\n", - "Processing ERA5 data for 2013-03-20 to 2023-02-28...\n", - "Available ERA5 bands: ['mean_2m_air_temperature', 'minimum_2m_air_temperature', 'maximum_2m_air_temperature', 'dewpoint_2m_temperature', 'total_precipitation', 'surface_pressure', 'mean_sea_level_pressure', 'u_component_of_wind_10m', 'v_component_of_wind_10m']\n", - "ERA5 RH trend calculation complete.\n" - ] - } - ], - "source": [ - "# Process CHIRPS precipitation data and calculate trends\n", - "rain_trend, rain_collection = process_chirps_trends(goa, start_date, end_date)\n", - " \n", - "# Process SMAP soil moisture data (starts from April 2015)\n", - "sm_trend, sm_collection = process_smap_trends(goa, '2015-04-01', end_date)\n", - " \n", - "# Process ERA5 data for relative humidity trends\n", - "rh_trend, rh_collection = process_era5_trends(goa, start_date, end_date)" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - " # Merge all trend layers\n", - "all_trends = merge_trend_layers(ls_trends, rain_trend, sm_trend, rh_trend)" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Resample to match JavaScript output\n", - "all_trends_resampled = resample_image(all_trends)" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#export_to_local(all_trends_resampled, \"..data/inputResampled_py.tif\", goa)" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Started export task: goa_fire_trends\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "user_asset_root = 'users/jonasnothnagel/' # Replace with your actual EE username\n", - "export_to_asset(all_trends_resampled, f\"{user_asset_root}goa_fire_trends\", goa, \"goa_fire_trends\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "ee", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.13.1" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} From 5e58e08e210461efaa4e1394d22897b64204a4a2 Mon Sep 17 00:00:00 2001 From: jonas-nothnagel Date: Mon, 7 Apr 2025 15:55:13 +0200 Subject: [PATCH 03/15] added gitattributes --- .gitattribute | 1 + 1 file changed, 1 insertion(+) create mode 100644 .gitattribute diff --git a/.gitattribute b/.gitattribute new file mode 100644 index 0000000..e776d72 --- /dev/null +++ b/.gitattribute @@ -0,0 +1 @@ +*.ipynb linguist-documentation=true \ No newline at end of file From 032b12e55ab6853ec1026134b6c70fbd24269bf5 Mon Sep 17 00:00:00 2001 From: jonas-nothnagel Date: Mon, 7 Apr 2025 15:56:01 +0200 Subject: [PATCH 04/15] fixed typo --- .gitattribute => .gitattributes | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename .gitattribute => .gitattributes (100%) diff --git a/.gitattribute b/.gitattributes similarity index 100% rename from .gitattribute rename to 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Engine assets -def export_to_asset(image, asset_name, region, description=None, scale=30): +def export_to_asset(image, asset_name, region, description=None, scale=30, export_bounds=None): """ Export an image to a Google Earth Engine asset. + Allows specifying explicit export bounds. """ if description is None: description = asset_name.split('/')[-1] + # Use provided bounds if available, otherwise use the region geometry's bounds + export_region = export_bounds if export_bounds else region + task = ee.batch.Export.image.toAsset( - image=image, + image=image, # Image should be pre-projected and cast description=description, assetId=asset_name, - region=region, + region=export_region, scale=scale, + crs='EPSG:3857', maxPixels=1e13 ) task.start() - print(f"Started export task: {description}") + print(f"Started export task to Asset: {description}") return task # Function to export the trends to Google Drive -def export_to_drive(image, filename, region, description=None, folder='GEE_Exports', scale=30): +def export_to_drive(image, filename, region, description=None, folder='GEE_Exports', scale=30, export_bounds=None): """ Export an image to Google Drive. + Allows specifying explicit export bounds. """ if description is None: description = filename + + # Use provided bounds if available, otherwise use the region geometry's bounds + export_region = export_bounds if export_bounds else region task = ee.batch.Export.image.toDrive( - image=image, + image=image, # Image should be pre-projected and cast description=description, folder=folder, fileNamePrefix=filename, - region=region, + region=export_region, scale=scale, + crs='EPSG:3857', maxPixels=1e13 ) @@ -569,25 +579,43 @@ def main(): print("\nMerging all trend layers...") all_trends = landsat_trends.addBands(rain_trends).addBands(sm_trends).addBands(rh_trends) + # --- Define Target Export Bounds (from inputResampled.tif in EPSG:3857) --- + target_bounds_coords = [ + [8246820.0, 1680600.0], # bottom-left (x, y) + [8275140.0, 1680600.0], # bottom-right (x, y) + [8275140.0, 1767960.0], # top-right (x, y) + [8246820.0, 1767960.0], # top-left (x, y) + [8246820.0, 1680600.0] # close the loop + ] + target_export_region = ee.Geometry.Polygon(target_bounds_coords, proj='EPSG:3857', evenOdd=False) + + # --- Explicitly set data type and CRS before export --- + # Note: Reprojection happens here, but export uses target_export_region for extent + print("Casting to float32 and reprojecting to EPSG:3857...") + all_trends_export = all_trends.toFloat() \ + .reproject(crs='EPSG:3857', scale=30) + # Export the merged trends to Asset print("Exporting merged trends to GEE Asset...") export_to_asset( - all_trends, + all_trends_export, 'users/jonasnothnagel/Trend2024_all_new', - goa, - 'All_Trends_2024_Asset', # Changed description slightly for clarity - scale=30 + goa, # Original region used for clipping/processing + description='All_Trends_2024_Asset', + scale=30, + export_bounds=target_export_region # Pass the explicit bounds ) # Export the merged trends to Google Drive print("Exporting merged trends to Google Drive...") export_to_drive( - all_trends, - 'TrendFirePy_output', # Filename for Drive export - goa, - 'All_Trends_2024_Drive', # Description for Drive task - folder='GEE_Exports', # Google Drive folder name - scale=30 + all_trends_export, + 'TrendFirePy_output', + goa, # Original region used for clipping/processing + description='All_Trends_2024_Drive', + folder='GEE_Exports', + scale=30, + export_bounds=target_export_region # Pass the explicit bounds ) print("\nAnalysis completed successfully!") diff --git a/compare_trends.py b/compare_trends.py new file mode 100644 index 0000000..e1bb8d8 --- /dev/null +++ b/compare_trends.py @@ -0,0 +1,112 @@ +import rasterio +import os +import numpy as np + +def compare_tifs(file1_path, file2_path): + """ + Compares metadata and basic statistics of two GeoTIFF files. + """ + print(f"Comparing:\n (1) {os.path.basename(file1_path)}\n (2) {os.path.basename(file2_path)}\n") + + if not os.path.exists(file1_path): + print(f"Error: File not found - {file1_path}") + return + if not os.path.exists(file2_path): + print(f"Error: File not found - {file2_path}") + return + + try: + with rasterio.open(file1_path) as ds1, rasterio.open(file2_path) as ds2: + + # --- Metadata Comparison --- + print("--- Metadata Comparison ---") + + meta1 = ds1.meta + meta2 = ds2.meta + + print(f"Number of Bands: (1) {ds1.count:<5} (2) {ds2.count}") + print(f"Data Types: (1) {ds1.dtypes[0]:<10} (2) {ds2.dtypes[0]}") # Show first band's dtype + print(f"CRS: (1) {ds1.crs.to_string() if ds1.crs else 'N/A':<10} (2) {ds2.crs.to_string() if ds2.crs else 'N/A'}") + print(f"Resolution (X): (1) {ds1.res[0]:<10.8f} (2) {ds2.res[0]:.8f}") + print(f"Resolution (Y): (1) {ds1.res[1]:<10.8f} (2) {ds2.res[1]:.8f}") + print(f"Width (pixels): (1) {ds1.width:<5} (2) {ds2.width}") + print(f"Height (pixels): (1) {ds1.height:<5} (2) {ds2.height}") + print(f"Bounds: (1) {ds1.bounds}\n (2) {ds2.bounds}") + # Safely format nodata and compression + nodata1_str = str(ds1.nodata) if ds1.nodata is not None else 'None' + nodata2_str = str(ds2.nodata) if ds2.nodata is not None else 'None' + comp1_str = ds1.compression.value if ds1.compression else 'None' + comp2_str = ds2.compression.value if ds2.compression else 'None' + print(f"NoData Value: (1) {nodata1_str:<10} (2) {nodata2_str}") + print(f"Compression: (1) {comp1_str:<10} (2) {comp2_str}") + + if ds1.count != ds2.count: + print("\n*** Band count differs! ***") + # List expected bands from TrendFire.py calculation + ls_indices = ['ndvi', 'evi', 'mirbi', 'ndfi', 'bsi', 'ndmi', 'nbr', 'nbr2', 'msavi', 'smi', 'ST_B10'] + other_indices = ['rain', 'sm_surface', 'rh'] + expected_bands = [] + for index in ls_indices + other_indices: + expected_bands.extend([f"{index}_Slope", f"{index}_Intercept"]) + + print(f"Expected bands based on TrendFire.py ({len(expected_bands)} bands total):") + # Print first few and last few expected bands for brevity + print(f" {', '.join(expected_bands[:5])} ... {', '.join(expected_bands[-5:])}") + + # Try reading band descriptions if they exist + print("\nAttempting to read band descriptions...") + try: + print("\nBand descriptions (1) - inputResampled.tif:") + if ds1.descriptions: + for i, desc in enumerate(ds1.descriptions): + print(f" Band {i+1}: {desc}") + else: + print(" No descriptions found.") + + print("\nBand descriptions (2) - TrendFirePy_output.tif:") + if ds2.descriptions: + for i, desc in enumerate(ds2.descriptions): + print(f" Band {i+1}: {desc}") + else: + print(" No descriptions found.") + except Exception as e: + print(f"Could not read band descriptions: {e}") + + # --- Data Comparison (Basic Stats for First Band) --- + print("\n--- Basic Statistics (First Band) ---") + try: + data1 = ds1.read(1, masked=True) + data2 = ds2.read(1, masked=True) + + # Check if arrays are comparable in shape + if data1.shape == data2.shape: + print(f"Min Value: (1) {np.ma.min(data1):<10.4f} (2) {np.ma.min(data2):.4f}") + print(f"Max Value: (1) {np.ma.max(data1):<10.4f} (2) {np.ma.max(data2):.4f}") + print(f"Mean Value: (1) {np.ma.mean(data1):<10.4f} (2) {np.ma.mean(data2):.4f}") + print(f"Std Dev: (1) {np.ma.std(data1):<10.4f} (2) {np.ma.std(data2):.4f}") + + # Calculate difference + diff = data1 - data2 + print(f"\nDifference (Band 1) Stats:") + print(f" Min Diff: {np.ma.min(diff):.4f}") + print(f" Max Diff: {np.ma.max(diff):.4f}") + print(f" Mean Diff: {np.ma.mean(diff):.4f}") + print(f" Std Diff: {np.ma.std(diff):.4f}") + else: + print("Arrays have different shapes, cannot compare data directly.") + + except Exception as e: + print(f"Error reading or comparing band data: {e}") + + except rasterio.RasterioIOError as e: + print(f"Error opening GeoTIFF file: {e}") + except Exception as e: + print(f"An unexpected error occurred: {e}") + +if __name__ == "__main__": + # Define file paths relative to the script location or use absolute paths + data_dir = "data" + file1 = os.path.join(data_dir, "inputResampled.tif") # Ground truth (JS output) + file2 = os.path.join(data_dir, "TrendFirePy_output.tif") # Python output + + compare_tifs(file1, file2) \ No newline at end of file From 1b7ccda686455a58331bdd3e84444080f89b615c Mon Sep 17 00:00:00 2001 From: jonas-nothnagel Date: Tue, 8 Apr 2025 15:53:40 +0200 Subject: [PATCH 06/15] updated python scripts --- FireVulnerability.py | 5 + TrendAnomalyPrediction.py | 1017 +++++++++++++++---------------------- TrendFire.py | 106 +++- compare_trends.py | 99 +++- 4 files changed, 583 insertions(+), 644 deletions(-) diff --git a/FireVulnerability.py b/FireVulnerability.py index b60f746..c0ce29d 100644 --- a/FireVulnerability.py +++ b/FireVulnerability.py @@ -5,6 +5,11 @@ FireVulnerability.py: Python implementation of FireVulnerability.js using Earth Engine Python API. This script creates fire vulnerability maps for the study area in Goa, India. +* This script is not predicting fire occurrences directly. It's predicting environmental anomalies. +* The goal is to identify areas where current environmental conditions (vegetation indices, moisture, temperature) are significantly different (anomalous) compared to what the long-term trend would predict for that specific time of year (February 2023 in this case). +* The underlying hypothesis is that areas with strong negative anomalies (e.g., much drier, less vegetation than expected) or positive anomalies (much hotter than expected) are more conducive to fire ignition or spread, making them potential "hotspots" of risk, but it doesn't predict if or when a fire will actually start there. +* Therefore, the historical fire data isn't needed for the calculation of these environmental anomalies. + The script performs the following steps: 1. Loads fire event data from multiple years 2. Generates random points for non-fire locations diff --git a/TrendAnomalyPrediction.py b/TrendAnomalyPrediction.py index 534aaeb..acb85b2 100644 --- a/TrendAnomalyPrediction.py +++ b/TrendAnomalyPrediction.py @@ -2,150 +2,156 @@ # -*- coding: utf-8 -*- """ -TrendAnomalyPrediction.py: Python implementation of TrendAnomalyPrediction.js using Earth Engine Python API. -This script calculates anomalies between current conditions and predicted values from historical trends -to identify potential fire hotspots in Goa, India. - -The script performs the following steps: -1. Processes current Landsat imagery to calculate various spectral indices -2. Uses trend coefficients to predict expected values based on historical data -3. Calculates anomalies between current and predicted values -4. Identifies hotspots based on anomalies in rainfall, RH, soil moisture, and vegetation indices -5. Integrates land use/land cover data to identify vulnerable areas -6. Exports hotspot maps to Google Drive and/or Earth Engine assets - -Equivalent to the TrendAnomalyPrediction.js script in the original implementation. +TrendAnomalyPrediction.py: Calculates trend anomalies based on long-term trends and recent observations. + +This script replicates the core logic of TrendAnomalyPrediction.js: +1. Loads pre-calculated trend layers (output from TrendFire.py). +2. Calculates predicted values for a target period (e.g., Feb 2023) based on trends. +3. Calculates actual observed values for the target period using Landsat, ERA5, SMAP, CHIRPS. +4. Computes the anomaly by subtracting predicted values from observed values. +5. Exports the resulting anomaly image. """ import ee import os import datetime -import matplotlib.pyplot as plt -import numpy as np -import pandas as pd import geopandas as gpd -from tqdm import tqdm +from shapely.geometry import Polygon -# Initialize the Earth Engine API -try: - ee.Initialize() - print("Earth Engine API initialized successfully.") -except Exception as e: - print(f"Error initializing Earth Engine API: {e}") - print("Make sure you have authenticated with Earth Engine using ee.Authenticate()") +# ============================================================================== +# Helper Functions (Adapted from TrendFire.py) +# ============================================================================== -# Define the Goa study area boundary -def get_study_boundary(): - """ - Get the study area boundary for Goa, India. - Returns an ee.Geometry. - """ - # Method 1: Load from a shapefile if available locally +def initialize_ee(): + """Initializes the Earth Engine API.""" try: - boundary_path = os.path.join('data', 'pa_boundary.shp') - if os.path.exists(boundary_path): - boundary_gdf = gpd.read_file(boundary_path) - # Convert to GeoJSON - boundary_geojson = boundary_gdf.geometry.__geo_interface__ - # Create an EE geometry - return ee.Geometry.Polygon(boundary_geojson['features'][0]['geometry']['coordinates']) + # Check if already initialized + try: + ee.ImageCollection('NASA/SMAP/SPL3SMP_E/005').limit(1).size().getInfo() + print("Earth Engine API already initialized.") + return True + except ee.EEException as e: + # If not initialized, attempt authentication and initialization + if "cannot be used before ee.Initialize()" in str(e) or \ + "Earth Engine account" in str(e) or \ + "Credentials are not valid" in str(e): + try: + ee.Authenticate(quiet=True) # Use quiet=True for non-interactive + ee.Initialize() + print("Earth Engine API initialized successfully.") + return True + except Exception as init_e: + print(f"Error initializing Earth Engine API: {init_e}") + print("Please ensure you have authenticated with GEE CLI: 'earthengine authenticate'") + return False + else: + # Handle other EE exceptions if needed + print(f"An Earth Engine error occurred: {e}") + return False + except Exception as e: - print(f"Could not load boundary from shapefile: {e}") - - # Method 2: Use predefined feature from Earth Engine + print(f"An unexpected error occurred during EE initialization: {e}") + return False + + +# Function to debug the shapefile (Optional but good practice) +def debug_shapefile(boundary_path): + """ + Debug function to check shapefile contents and geometry validity. + """ try: - # Get the Goa district boundary from Earth Engine's administrative boundaries - goa = ee.FeatureCollection("FAO/GAUL/2015/level1").filter(ee.Filter.eq('ADM1_NAME', 'Goa')) - return goa.geometry() + print("\nDebugging shapefile...") + gdf = gpd.read_file(boundary_path) + print(f"\nShapefile contains {len(gdf)} features") + print("\nFirst few rows of the data:") + print(gdf.head()) + print("\nGeometry type:", gdf.geometry.type.unique()) + print("\nCRS:", gdf.crs) + + # Check if geometries are valid + invalid_geoms = gdf[~gdf.geometry.is_valid] + if len(invalid_geoms) > 0: + print("\nWarning: Found invalid geometries!") + print("Number of invalid geometries:", len(invalid_geoms)) + print("\nInvalid geometries:") + print(invalid_geoms) + + return gdf except Exception as e: - print(f"Could not load boundary from Earth Engine: {e}") - - # Method 3: Define manually as fallback - # These coordinates would need to be adjusted to match the study area - goa_coords = [ - [73.6765, 15.7560], - [74.3161, 15.7560], - [74.3161, 14.8922], - [73.6765, 14.8922], - [73.6765, 15.7560] - ] - return ee.Geometry.Polygon(goa_coords) + print(f"Error debugging shapefile: {e}") + return None -# Function to load fire events data -def load_fire_events(pa): +# Define the Goa study area boundary +def get_goa_boundary(shp_path=os.path.join('data', 'pa_boundary.shp')): """ - Load fire events data from 2013-2023 and merge into a single feature collection. - - Args: - pa: ee.Geometry representing the study area - - Returns: - ee.FeatureCollection of fire events + Get the boundary of the study area from a shapefile. + Returns an ee.Geometry object. """ - print("Loading fire events data...") - - # Load fire events data (2013-2019) - try: - # If the data is available in Earth Engine, you can load it directly - fire_13_19 = ee.FeatureCollection("users/your_username/fire13_19") - except: - # Otherwise, try to find and upload it, or use a placeholder - print("Fire data 2013-2019 not available in Earth Engine. Using placeholder.") - fire_13_19 = ee.FeatureCollection([]) - - # Load fire events data (2020-2023) try: - # If the data is available in Earth Engine, you can load it directly - fire_20_23 = ee.FeatureCollection("users/your_username/fire20_23") - except: - # Otherwise, try to find and upload it, or use a placeholder - print("Fire data 2020-2023 not available in Earth Engine. Using placeholder.") - fire_20_23 = ee.FeatureCollection([]) - - # Merge the two collections - fire_all = fire_13_19.merge(fire_20_23) - - # Filter by the study area boundary - fire_all = fire_all.filterBounds(pa) - - # Add a 'year' property based on the acquisition date - def add_year(feature): - acq_date = ee.String(feature.get('ACQ_DATE')) - year = ee.Number.parse(acq_date.slice(0, 4)) - return feature.set('year', year) - - fire_all = fire_all.map(add_year) - - # Count the number of fire points - fire_count = fire_all.size().getInfo() - print(f"Number of fire events in the study area: {fire_count}") - - return fire_all - -# Function to mask clouds in Landsat 8/9 imagery + if os.path.exists(shp_path): + print(f"Loading study area boundary from {shp_path}...") + gdf = gpd.read_file(shp_path) # Removed debug call for cleaner flow + + if len(gdf) == 0: + print("Error: Shapefile contains no features") + return None + + # Reproject to EPSG:4326 if necessary for GEE Geometry conversion + if gdf.crs and gdf.crs.to_epsg() != 4326: + print(f"Reprojecting boundary from {gdf.crs} to EPSG:4326...") + gdf = gdf.to_crs(epsg=4326) + + geometry = gdf.geometry.iloc[0] + + if not geometry.is_valid: + print("Warning: Attempting to fix invalid geometry...") + geometry = geometry.buffer(0) # Common trick to fix minor issues + if not geometry.is_valid: + print("Error: Geometry is invalid and could not be fixed.") + return None + + # Convert 3D polygon to 2D if needed + if geometry.has_z: + print("Converting 3D polygon to 2D...") + coords = list(geometry.exterior.coords) + coords_2d = [(x, y) for x, y, z in coords] + geometry = Polygon(coords_2d) + + # Convert to GeoJSON format suitable for EE + gdf.geometry = gpd.GeoSeries([geometry]) # Ensure only the valid geometry is used + boundary_geojson = gdf.__geo_interface__ + + # Extract coordinates for EE Geometry Polygon + ee_coords = boundary_geojson['features'][0]['geometry']['coordinates'] + return ee.Geometry.Polygon(ee_coords) + else: + print(f"Error: Could not find boundary shapefile at {shp_path}") + return None + except Exception as e: + print(f"Error loading boundary from shapefile: {e}") + return None + +# Function to mask clouds in Landsat 8 imagery def maskL8sr(image): """ - Mask clouds and scale pixel values for Landsat 8/9 SR imagery. + Mask clouds and scale pixel values for Landsat 8 SR imagery. + Matches the JavaScript implementation. """ - # Get QA band - qa = image.select('QA_PIXEL') - - # Bits 3 and 5 are cloud shadow and cloud, respectively - cloud_shadow_bit_mask = 1 << 3 - clouds_bit_mask = 1 << 5 - - # Both flags should be set to zero, indicating clear conditions - mask = qa.bitwiseAnd(cloud_shadow_bit_mask).eq(0).And( - qa.bitwiseAnd(clouds_bit_mask).eq(0)) - + # Bits 0-4 are Fill, Dilated Cloud, Cirrus, Cloud, Cloud Shadow. + qaMask = image.select('QA_PIXEL').bitwiseAnd(int('11111', 2)).eq(0) + saturationMask = image.select('QA_RADSAT').eq(0) + # Scale the optical bands optical_bands = image.select('SR_B.').multiply(0.0000275).add(-0.2) - + # Scale the thermal bands thermal_bands = image.select('ST_B.*').multiply(0.00341802).add(149.0) - - # Return the masked and scaled image - return image.select(['QA_.*']).addBands(optical_bands).addBands(thermal_bands).updateMask(mask) + + # Return the masked and scaled image, applying both masks + return image.addBands(optical_bands, None, True) \ + .addBands(thermal_bands, None, True) \ + .updateMask(qaMask) \ + .updateMask(saturationMask) # Function to calculate spectral indices and add them as bands def addIndices(image): @@ -154,7 +160,7 @@ def addIndices(image): """ # NDVI (Normalized Difference Vegetation Index) ndvi = image.normalizedDifference(['SR_B5', 'SR_B4']).rename('ndvi') - + # EVI (Enhanced Vegetation Index) evi = image.expression( '2.5 * ((NIR - RED) / (NIR + 6 * RED - 7.5 * BLUE + 1))', @@ -164,7 +170,7 @@ def addIndices(image): 'BLUE': image.select('SR_B2') } ).rename('evi') - + # MSAVI (Modified Soil Adjusted Vegetation Index) msavi = image.expression( '(2 * NIR + 1 - sqrt((2 * NIR + 1) * (2 * NIR + 1) - 8 * (NIR - RED))) / 2', @@ -173,7 +179,7 @@ def addIndices(image): 'RED': image.select('SR_B4') } ).rename('msavi') - + # MIRBI (Mid-Infrared Burn Index) mirbi = image.expression( '10 * SWIR2 - 9.8 * SWIR1 + 2', @@ -182,19 +188,19 @@ def addIndices(image): 'SWIR2': image.select('SR_B7') } ).rename('mirbi') - + # NDMI (Normalized Difference Moisture Index) ndmi = image.normalizedDifference(['SR_B5', 'SR_B6']).rename('ndmi') - - # NDFI (Normalized Difference Fraction Index) - ndfi = image.normalizedDifference(['SR_B7', 'SR_B5']).rename('ndfi') - + + # NDFI (Normalized Difference Fraction Index) - Uses ST_B10 and SR_B6 like JS + ndfi = image.normalizedDifference(['ST_B10', 'SR_B6']).rename('ndfi') + # NBR (Normalized Burn Ratio) nbr = image.normalizedDifference(['SR_B5', 'SR_B7']).rename('nbr') - + # NBR2 (Normalized Burn Ratio 2) nbr2 = image.normalizedDifference(['SR_B6', 'SR_B7']).rename('nbr2') - + # BSI (Bare Soil Index) bsi = image.expression( '((SWIR1 + RED) - (NIR + BLUE)) / ((SWIR1 + RED) + (NIR + BLUE))', @@ -205,502 +211,285 @@ def addIndices(image): 'BLUE': image.select('SR_B2') } ).rename('bsi') - + # Add all indices as bands return image.addBands([ndvi, evi, msavi, mirbi, ndmi, ndfi, nbr, nbr2, bsi]) -# Function to calculate SMI (Soil Moisture Index) -def calculateSMI(image): - """ - Calculate Soil Moisture Index (SMI) from Landsat data. - """ - Ts = image.select('ST_B10') - ndvi = image.select('ndvi') - - # Apply the SMI calculation - smi = image.expression( - '(Ts_max - Ts) / (Ts_max - Ts_min)', - { - 'Ts': Ts, - 'Ts_max': Ts.reduceRegion( - reducer=ee.Reducer.max(), - geometry=image.geometry(), - scale=30, - maxPixels=1e9 - ).get('ST_B10'), - 'Ts_min': Ts.reduceRegion( - reducer=ee.Reducer.min(), - geometry=image.geometry(), - scale=30, - maxPixels=1e9 - ).get('ST_B10') - } - ).rename('smi') - + +# Function to calculate SMI using SWIR bands (requires pre-calculated min/max) +def addSMI_local(image, swir_min, swir_max): + """Calculates SMI, requires swir min/max for the specific collection.""" + smi = image.select('SR_B7').subtract(swir_min) \ + .divide(swir_max.subtract(swir_min)) \ + .multiply(-1).add(1).rename('smi') return image.addBands(smi) -# Function to get current Landsat data -def get_current_landsat(pa, date_range): +# Function to calculate vapor pressure (for RH calculation) +def calcVaporPressure(temp_k): + """ Calculates vapor pressure from temperature in Kelvin using Clausius-Clapeyron approx. """ + # Convert Kelvin to Celsius for the formula + temp_c = temp_k.subtract(273.15) + # Formula parameters from the JS script (note 19.67, standard is often 17.67) + # Ensure calculations are done per pixel + denom = temp_c.add(243.5) + num = temp_c.multiply(19.67) + exponent = num.divide(denom).exp() # e^(...) + return exponent.multiply(6.112) #Multiply by saturation vapor pressure at 0°C (in hPa or mbar) + +# Function to export trends to Google Drive +def export_to_drive(image, filename, region, description=None, folder='GEE_Exports_Anomaly', scale=30, export_bounds=None): """ - Get the most recent Landsat 8/9 imagery for a specified date range. - - Args: - pa: ee.Geometry representing the study area - date_range: Dictionary with 'start' and 'end' dates - - Returns: - ee.Image with all spectral indices and bands + Export an image to Google Drive. + Assumes image is already cast and projected if needed. + Uses explicit export bounds if provided. """ - print(f"Getting current Landsat data for {date_range['start']} to {date_range['end']}...") - - # Load Landsat collection and filter by date and location - ls_collection = ee.ImageCollection('LANDSAT/LC08/C02/T1_L2') \ - .merge(ee.ImageCollection('LANDSAT/LC09/C02/T1_L2')) \ - .filterDate(date_range['start'], date_range['end']) \ + if description is None: + description = filename + + export_region = export_bounds if export_bounds else region + + print(f"Starting export task to Drive: {description}") + task = ee.batch.Export.image.toDrive( + image=image, # Assume image is pre-processed (float32, projected) + description=description, + folder=folder, + fileNamePrefix=filename, + region=export_region.getInfo()['coordinates'], # Pass coordinates explicitly + scale=scale, + crs='EPSG:3857', # Ensure CRS matches previous step + maxPixels=1e13 + ) + task.start() + print(f"Task started (id: {task.id}). Check GEE Tasks or use monitoring tools.") + return task + +# ============================================================================== +# Main Script Logic +# ============================================================================== + +def main(): + """Main function to calculate and export trend anomalies.""" + if not initialize_ee(): + return # Stop if EE initialization fails + + pa = get_goa_boundary() + if pa is None: + print("Error: Could not load study area boundary. Exiting.") + return + + print("Successfully loaded study area boundary.") + + # --- Define Time Periods --- + trend_start_date = '2013-03-20' + trend_end_date = '2023-02-28' # Corresponds to TrendFire.py output + present_start_date = '2023-02-01' + present_end_date = '2023-02-28' + rain_present_year_start = '2022-01-01' + rain_present_year_end = '2022-12-31' + rh_sm_start_date = '1980-01-01' # Start date for RH trend baseline + sm_start_date = '2015-04-01' # Start date for SM trend baseline + rain_start_date = '1982-01-01' # Start date for Rain trend baseline + + + # --- Load Trend Image Asset --- + trend_asset_id = 'users/jonasnothnagel/Trend2024_all_new' # Asset from TrendFire.py + print(f"Loading trend image asset: {trend_asset_id}") + try: + input_trends = ee.Image(trend_asset_id) + # Verify bands (optional) + # print("Trend bands:", input_trends.bandNames().getInfo()) + except Exception as e: + print(f"Error loading trend asset {trend_asset_id}: {e}") + print("Please ensure the asset exists and you have access.") + return + + # --- (Optional) Load Fire Points --- + # Assuming Asset IDs - replace with actual IDs if available/needed + # print("Loading fire points...") + # try: + # fire13_19_asset = 'YOUR_ASSET_ID/fire13_19' # Replace + # fire20_23_asset = 'YOUR_ASSET_ID/fire20_23' # Replace + # fire13_19 = ee.FeatureCollection(fire13_19_asset) + # fire20_23 = ee.FeatureCollection(fire20_23_asset) + # fire13_23 = fire13_19.merge(fire20_23) + # # Filter as in JS if needed for visualization/context + # fireMarch2023 = fire13_23.filter(ee.Filter.stringContains('acq_date', '2023')) # Simplified filter + # print(f"Loaded {fire13_23.size().getInfo()} total fire points.") + # except Exception as e: + # print(f"Warning: Could not load fire point assets: {e}. Continuing without them.") + # fireMarch2023 = None # Set to None if loading fails + + + print("\nCalculating predicted values based on trends...") + # --- Calculate Predicted Values (Past_Pred) --- + target_date_landsat = ee.Date(present_start_date) # Use start of month for prediction point + target_date_rain = ee.Date(rain_present_year_start) + target_date_rh = target_date_landsat + target_date_sm = target_date_landsat + + # Calculate time differences in years + const_landsat_years = target_date_landsat.difference(ee.Date(trend_start_date), 'year') + const_rain_years = target_date_rain.difference(ee.Date(rain_start_date), 'year') + const_rh_years = target_date_rh.difference(ee.Date(rh_sm_start_date), 'year') # JS used constRain, seems incorrect, use RH baseline + const_sm_years = target_date_sm.difference(ee.Date(sm_start_date), 'year') # JS used constRain, seems incorrect, use SM baseline + + # Select slope and intercept bands - Ensure names match TrendFire.py output + landsat_indices = ['ndvi', 'evi', 'mirbi', 'ndfi', 'bsi', 'ndmi', 'nbr', 'nbr2', 'msavi', 'smi', 'ST_B10'] + slope_bands_ls = [f'{idx}_Slope' for idx in landsat_indices] + intercept_bands_ls = [f'{idx}_Intercept' for idx in landsat_indices] + other_indices = ['rain', 'sm_surface', 'rh'] + slope_bands_other = [f'{idx}_Slope' for idx in other_indices] + intercept_bands_other = [f'{idx}_Intercept' for idx in other_indices] + + # Select bands from the input trend image + slopes_ls = input_trends.select(slope_bands_ls) + intercepts_ls = input_trends.select(intercept_bands_ls) + slopes_other = input_trends.select(slope_bands_other) + intercepts_other = input_trends.select(intercept_bands_other) + + # Calculate predicted values for Landsat-based indices + predicted_ls = slopes_ls.multiply(const_landsat_years).add(intercepts_ls).rename(landsat_indices) + + # Calculate predicted values for other indices + pred_rain = slopes_other.select('rain_Slope').multiply(const_rain_years).add(intercepts_other.select('rain_Intercept')).rename('rain') + pred_sm = slopes_other.select('sm_surface_Slope').multiply(const_sm_years).add(intercepts_other.select('sm_surface_Intercept')).rename('sm') + pred_rh = slopes_other.select('rh_Slope').multiply(const_rh_years).add(intercepts_other.select('rh_Intercept')).rename('rh') + + # Combine all predicted values + past_Pred = predicted_ls.addBands([pred_rain, pred_sm, pred_rh]) + # Reorder bands to match expected order for subtraction later + ordered_pred_bands = ['smi', 'ST_B10', 'ndvi', 'evi', 'msavi', 'mirbi', 'ndmi', 'ndfi', 'nbr', 'nbr2', 'bsi', 'rain', 'rh', 'sm'] + past_Pred = past_Pred.select(ordered_pred_bands) + print("Predicted values calculated.") + # print("Predicted bands:", past_Pred.bandNames().getInfo()) + + + print("\nCalculating present-day observed values...") + # --- Calculate Present-Day Values (Present_All) --- + + # 1. Landsat Present (Feb 2023 Mosaic) + print("Processing present-day Landsat (Feb 2023)...") + present_ls_collection = ee.ImageCollection('LANDSAT/LC08/C02/T1_L2') \ + .filterDate(present_start_date, present_end_date) \ .filterBounds(pa) \ - .filter(ee.Filter.lt('CLOUD_COVER', 20)) - - # Apply cloud masking - ls_masked = ls_collection.map(maskL8sr) - - # Add indices to all images in collection - ls_indices = ls_masked.map(addIndices) - - # Calculate SMI for each image - ls_with_smi = ls_indices.map(calculateSMI) - - # Create mosaic of the collection - current_ls = ls_with_smi.mosaic().clip(pa) - - return current_ls - -# Function to calculate vapor pressure from temperature -def calcVaporPressure(temp): - """ - Calculate vapor pressure from temperature using the Clausius-Clapeyron equation. - - Args: - temp: ee.Image with temperature in Celsius - - Returns: - ee.Image with vapor pressure (hPa) - """ - denom = temp.add(243.5) - num = temp.multiply(17.67) - exponent = num.divide(denom).exp() - return exponent.multiply(6.112) + .map(maskL8sr) \ + .map(lambda img: img.clip(pa)) \ + .map(addIndices) -# Function to get current ERA5 relative humidity data -def get_current_relative_humidity(pa, date_range): - """ - Get the most recent ERA5 relative humidity data. - - Args: - pa: ee.Geometry representing the study area - date_range: Dictionary with 'start' and 'end' dates - - Returns: - ee.Image with relative humidity - """ - print(f"Getting current relative humidity data for {date_range['start']} to {date_range['end']}...") - - # Load ERA5 data - era5 = ee.ImageCollection('ECMWF/ERA5/DAILY') \ - .filterDate(date_range['start'], date_range['end']) \ + # Calculate SWIR min/max for *this specific collection* for SMI + swir_stats_present = present_ls_collection.mean().select('SR_B7').reduceRegion( + reducer=ee.Reducer.minMax(), + geometry=pa, + scale=30, + maxPixels=1e9 # Use lower maxPixels for reduceRegion if needed + ) + # Handle potential nulls if collection is empty + swir_min_present = ee.Number(ee.Algorithms.If(swir_stats_present.contains('SR_B7_min'), swir_stats_present.get('SR_B7_min'), 0)) + swir_max_present = ee.Number(ee.Algorithms.If(swir_stats_present.contains('SR_B7_max'), swir_stats_present.get('SR_B7_max'), 1)) # Avoid divide by zero + + present_ls_collection_smi = present_ls_collection.map(lambda img: addSMI_local(img, swir_min_present, swir_max_present)) + + # Select relevant bands and create mosaic + present_ls_bands = ['smi', 'ST_B10', 'ndvi', 'evi', 'msavi', 'mirbi', 'ndmi', 'ndfi', 'nbr', 'nbr2', 'bsi'] + present_ls = present_ls_collection_smi.select(present_ls_bands).mosaic() + print("Landsat mosaic created.") + + # 2. ERA5 RH Present (Feb 2023 Mean) + print("Processing present-day ERA5 RH (Feb 2023)...") + era5_present = ee.ImageCollection("ECMWF/ERA5_LAND/MONTHLY_AGGR") \ + .filterDate(present_start_date, present_end_date) \ .filterBounds(pa) \ - .select(['dewpoint_temperature_2m', 'temperature_2m']) - - # Calculate relative humidity + .map(lambda img: img.clip(pa)) + def calculateRH(image): - dewPoint = image.select('dewpoint_temperature_2m') - temperature = image.select('temperature_2m') - - # Calculate vapor pressures - eT = calcVaporPressure(temperature) - eTd = calcVaporPressure(dewPoint) - - # Compute relative humidity (%) - RH = eTd.divide(eT).multiply(100).rename('rh') - - # Add time property - return RH.set('system:time_start', image.get('system:time_start')) - - # Apply RH calculation to the collection - rh_collection = era5.map(calculateRH) - - # Create mosaic of the collection - current_rh = rh_collection.mosaic().clip(pa) - - return current_rh - -# Function to get current SMAP soil moisture data -def get_current_soil_moisture(pa, date_range): - """ - Get the most recent SMAP soil moisture data. - - Args: - pa: ee.Geometry representing the study area - date_range: Dictionary with 'start' and 'end' dates - - Returns: - ee.Image with soil moisture - """ - print(f"Getting current soil moisture data for {date_range['start']} to {date_range['end']}...") - - # Load SMAP data - smap = ee.ImageCollection('NASA/SMAP/SPL3SMP_E/005') \ - .filterDate(date_range['start'], date_range['end']) \ + dewPoint_k = image.select('dewpoint_temperature_2m') + temp_k = image.select('temperature_2m') + eT = calcVaporPressure(temp_k) + eTd = calcVaporPressure(dewPoint_k) + # Avoid division by zero if eT is 0 + rh = eTd.divide(eT).multiply(100).max(0).min(100) # Clamp RH between 0 and 100 + return rh.rename('rh').set('system:time_start', image.get('system:time_start')) + + rh_collection_present = era5_present.map(calculateRH) + present_rh = rh_collection_present.mean() # Use mean for monthly agg + print("ERA5 RH calculated.") + + + # 3. SMAP Present (Feb 2023 Mosaic) + print("Processing present-day SMAP (Feb 2023)...") + smap_present_col = ee.ImageCollection('NASA/SMAP/SPL3SMP_E/005') \ + .filterDate(present_start_date, present_end_date) \ .filterBounds(pa) \ + .map(lambda img: img.clip(pa)) \ .select(['soil_moisture_am']) - - # Create mosaic of the collection - current_sm = smap.mosaic().clip(pa).rename('sm') - - return current_sm - -# Function to get current CHIRPS precipitation data -def get_current_precipitation(pa, date_range): - """ - Get the most recent CHIRPS precipitation data. - - Args: - pa: ee.Geometry representing the study area - date_range: Dictionary with 'start' and 'end' dates - - Returns: - ee.Image with precipitation - """ - print(f"Getting current precipitation data for {date_range['start']} to {date_range['end']}...") - - # Load CHIRPS data - chirps = ee.ImageCollection('UCSB-CHG/CHIRPS/DAILY') \ - .filterDate(date_range['start'], date_range['end']) \ - .filterBounds(pa) - - # Create sum of the collection - current_rain = chirps.sum().clip(pa).rename('rain') - - return current_rain - -# Function to load trend coefficients from the TrendFire output -def load_trend_coefficients(pa): - """ - Load trend coefficients previously calculated using TrendFire.py. - - Args: - pa: ee.Geometry representing the study area - - Returns: - ee.Image with all trend coefficients - """ - print("Loading trend coefficients...") - - try: - # Try to load the trend layers from Earth Engine assets - trends = ee.Image("users/your_username/goa_fire_trends").clip(pa) - print("Successfully loaded trend coefficients from Earth Engine asset.") - return trends - except: - print("Trend coefficients not found in Earth Engine assets. Using placeholder.") - # Create a placeholder image with zeros for all bands - # This should be replaced with actual trend calculations - placeholder = ee.Image(0).rename('ndvi_trend') - return placeholder - -# Function to predict index values based on trend coefficients -def predict_from_trends(trend_coefficients, current_time, reference_time): - """ - Predict index values for the current time based on historical trends. - - Args: - trend_coefficients: ee.Image with trend coefficients - current_time: Current time in decimal years since start - reference_time: Reference time in decimal years for the intercept - - Returns: - ee.Image with predicted values - """ - print("Predicting index values from trend coefficients...") - - # Construct regression expressions - # Predicted value = intercept + slope * (current_time - reference_time) - time_diff = ee.Number(current_time).subtract(reference_time) - - # Create prediction expressions for each index - indices = [ - 'ndvi', 'evi', 'msavi', 'mirbi', 'ndmi', 'ndfi', - 'nbr', 'nbr2', 'bsi', 'smi', 'ST_B10', 'rain', 'rh', 'sm' + present_sm = smap_present_col.mosaic().rename('sm') + print("SMAP mosaic created.") + + + # 4. CHIRPS Rain Present (Sum for 2022) + print("Processing present-day CHIRPS Rain (2022 Sum)...") + chirps_present_col = ee.ImageCollection('UCSB-CHG/CHIRPS/DAILY') \ + .filterDate(rain_present_year_start, rain_present_year_end) \ + .filterBounds(pa) + + # Calculate sum for the year 2022 + yearly_sum_2022 = chirps_present_col.sum().clip(pa).rename('rain') + present_rain = yearly_sum_2022 + print("CHIRPS Rain sum calculated.") + + # Combine all present-day layers + present_all = present_ls.addBands([present_rain, present_rh, present_sm]) + # Select bands in the same order as past_Pred + present_all = present_all.select(ordered_pred_bands) + print("Present-day layers combined.") + # print("Present bands:", present_all.bandNames().getInfo()) + + + print("\nCalculating anomaly image...") + # --- Calculate Anomaly Image --- + anomalyImage = present_all.subtract(past_Pred) + + # Rename bands to include '_Anomaly' + anomaly_band_names = [f'{b}_Anomaly' for b in ordered_pred_bands] + anomalyImage = anomalyImage.rename(anomaly_band_names) + print("Anomaly image calculated.") + # print("Anomaly bands:", anomalyImage.bandNames().getInfo()) + + + # --- Define Target Export Bounds (matching TrendFire.py output) --- + # Ensures consistent grid with previous step's output + target_bounds_coords = [ + [8246820.0, 1680600.0], # bottom-left (x, y) + [8275140.0, 1680600.0], # bottom-right (x, y) + [8275140.0, 1767960.0], # top-right (x, y) + [8246820.0, 1767960.0], # top-left (x, y) + [8246820.0, 1680600.0] # close the loop ] - - predicted_indices = ee.Image.constant(0) - - for index in indices: - # Try to get trend coefficient for this index - try: - trend = trend_coefficients.select(f'{index}_trend') - - # Calculate predicted value - # Here we're assuming the intercept is the current average value - # This is a simplification - in a full implementation we'd have both slope and intercept - predicted = trend.multiply(time_diff).rename(index) - - # Add to the predicted indices image - predicted_indices = predicted_indices.addBands(predicted) - except: - print(f"Warning: Could not find trend coefficient for {index}") - - return predicted_indices - -# Function to calculate anomalies between current and predicted values -def calculate_anomalies(current, predicted): - """ - Calculate anomalies between current observations and predicted values. - - Args: - current: ee.Image with current observations - predicted: ee.Image with predicted values - - Returns: - ee.Image with anomalies for each band - """ - print("Calculating anomalies...") - - # Calculate anomalies (current - predicted) - anomaly_image = current.subtract(predicted) - - # Rename bands to indicate they are anomalies - band_names = anomaly_image.bandNames() - new_names = band_names.map(lambda name: ee.String(name).cat('_Anomaly')) - - return anomaly_image.rename(new_names) - -# Function to identify hotspots based on anomalies -def identify_hotspots(anomaly_image, thresholds): - """ - Identify potential fire hotspots based on anomalies. - - Args: - anomaly_image: ee.Image with anomalies for each band - thresholds: Dictionary with threshold values for each anomaly - - Returns: - Dictionary with hotspot layers for different indices - """ - print("Identifying potential fire hotspots...") - - # Rainfall hotspot (deficit) - rain_anomaly = anomaly_image.select('rain_Anomaly') - rain_hotspot = rain_anomaly.lt(thresholds['rain']).selfMask().rename('rain_hotspot') - - # Relative humidity hotspot (deficit) - rh_anomaly = anomaly_image.select('rh_Anomaly') - rh_hotspot = rh_anomaly.lt(thresholds['rh']).selfMask().rename('rh_hotspot') - - # Soil moisture hotspot (deficit) - sm_anomaly = anomaly_image.select('sm_Anomaly') - sm_hotspot = sm_anomaly.lt(thresholds['sm']).selfMask().rename('sm_hotspot') - - # Vegetation indices hotspots (deficit indicating stress) - veg_indices = anomaly_image.select(['ndvi_Anomaly', 'evi_Anomaly', 'msavi_Anomaly', 'ndmi_Anomaly']) - veg_hotspot = veg_indices.lt(thresholds['veg']).selfMask().rename('veg_hotspot') - - # Burn indices hotspots (excess indicating fire potential) - burn_indices = anomaly_image.select(['mirbi_Anomaly', 'ST_B10_Anomaly', 'bsi_Anomaly']) - burn_hotspot = burn_indices.gt(thresholds['burn']).selfMask().rename('burn_hotspot') - - # Combine all hotspots - all_hotspots = ee.Image.cat([ - rain_hotspot, rh_hotspot, sm_hotspot, veg_hotspot, burn_hotspot - ]) - - return { - 'rain': rain_hotspot, - 'rh': rh_hotspot, - 'sm': sm_hotspot, - 'veg': veg_hotspot, - 'burn': burn_hotspot, - 'all': all_hotspots - } - -# Function to get land use/land cover data -def get_land_cover(pa, date_range): - """ - Get land use/land cover data for the study area. - - Args: - pa: ee.Geometry representing the study area - date_range: Dictionary with 'start' and 'end' dates - - Returns: - ee.Image with land cover classification - """ - print("Getting land use/land cover data...") - - # Load Dynamic World land cover data - dw_collection = ee.ImageCollection('GOOGLE/DYNAMICWORLD/V1') \ - .filterDate(date_range['start'], date_range['end']) \ - .filterBounds(pa) - - # Create a cloud-masked composite - dw_composite = dw_collection.map(lambda img: img.clip(pa)).mosaic() - - # Get the label band (classification) - lulc = dw_composite.select('label') - - return lulc - -# Function to analyze hotspots by land cover type -def analyze_hotspots_by_land_cover(hotspots, lulc, pa): - """ - Analyze hotspots by land cover type. - - Args: - hotspots: Dictionary with hotspot layers - lulc: ee.Image with land cover classification - pa: ee.Geometry representing the study area - - Returns: - Dictionary with hotspot statistics by land cover type - """ - print("Analyzing hotspots by land cover type...") - - # Define land cover classes - land_cover_classes = { - 0: 'water', - 1: 'trees', - 2: 'grass', - 3: 'flooded_vegetation', - 4: 'crops', - 5: 'shrub_and_scrub', - 6: 'built', - 7: 'bare', - 8: 'snow_and_ice' - } - - # Analyze each hotspot type by land cover - results = {} - - for hotspot_name, hotspot_image in hotspots.items(): - if hotspot_name == 'all': - continue - - # Create a binary mask of the hotspot - hotspot_mask = hotspot_image.gt(0) - - # Calculate area of hotspot by land cover class - hotspot_by_lulc = hotspot_mask.multiply(ee.Image.pixelArea()).addBands(lulc) \ - .reduceRegion( - reducer=ee.Reducer.sum().group(1), - geometry=pa, - scale=30, - maxPixels=1e13 - ) - - # Extract results - groups = ee.List(hotspot_by_lulc.get('groups')) - - # Store results - results[hotspot_name] = groups - - return results - -# Function to export hotspot maps to Google Drive -def export_hotspots(hotspots, pa, output_folder='ForestFireGoa'): - """ - Export hotspot maps to Google Drive. - - Args: - hotspots: Dictionary with hotspot layers - pa: ee.Geometry representing the study area - output_folder: Google Drive folder name - """ - print(f"Exporting hotspot maps to Google Drive folder '{output_folder}'...") - - # Export each hotspot type - for hotspot_name, hotspot_image in hotspots.items(): - task = ee.batch.Export.image.toDrive({ - 'image': hotspot_image, - 'description': f'fire_hotspot_{hotspot_name}', - 'folder': output_folder, - 'fileNamePrefix': f'fire_hotspot_{hotspot_name}', - 'region': pa, - 'scale': 30, - 'maxPixels': 1e13 - }) - - task.start() - print(f"Started export task for {hotspot_name} hotspot with ID: {task.id}") - -# Main function to run the entire workflow -def main(): - """ - Main function to run the trend anomaly prediction workflow. - """ - print("Starting forest fire trend anomaly prediction for Goa, India...") - - # Get the study area boundary - pa = get_study_boundary() - - # Define date range for current conditions (e.g., latest month) - # These dates should be adjusted based on your specific analysis period - current_date_range = { - 'start': '2023-02-01', - 'end': '2023-02-28' - } - - # Define reference time for trend calculations (e.g., start of historical period) - reference_time = 2013.0 # Start of analysis in decimal years - - # Calculate current time in decimal years - current_year = 2023 - current_month = 2 - current_time = current_year + (current_month - 1) / 12 - - # Load fire events data - fire_events = load_fire_events(pa) - - # Get current observational data - current_ls = get_current_landsat(pa, current_date_range) - current_rh = get_current_relative_humidity(pa, current_date_range) - current_sm = get_current_soil_moisture(pa, current_date_range) - current_rain = get_current_precipitation(pa, current_date_range) - - # Combine all current data - current_all = current_ls.addBands([current_rain, current_sm, current_rh]) - - # Load trend coefficients from TrendFire output - trend_coefficients = load_trend_coefficients(pa) - - # Predict values based on trend coefficients - predicted_values = predict_from_trends(trend_coefficients, current_time, reference_time) - - # Calculate anomalies - anomaly_image = calculate_anomalies(current_all, predicted_values) - - # Define thresholds for hotspot identification - # These thresholds would need to be calibrated for your specific study area - thresholds = { - 'rain': 0, # Rainfall below predicted value - 'rh': 60, # RH below 60% of predicted value - 'sm': -50, # Soil moisture 50% below predicted value - 'veg': -0.15, # Vegetation indices 0.15 below predicted value - 'burn': 3.2 # Burn indices 3.2 above predicted value - } - - # Identify hotspots - hotspots = identify_hotspots(anomaly_image, thresholds) - - # Get land cover data - lulc = get_land_cover(pa, current_date_range) - - # Analyze hotspots by land cover - hotspot_analysis = analyze_hotspots_by_land_cover(hotspots, lulc, pa) - - # Export hotspot maps to Google Drive (uncomment to use) - # export_hotspots(hotspots, pa) - - print("Forest fire trend anomaly prediction complete!") - print("To visualize the results, check your Google Drive for the exported hotspot maps.") + target_export_region = ee.Geometry.Polygon(target_bounds_coords, proj='EPSG:3857', evenOdd=False) + + + # --- Export Anomaly Image --- + print("\nExporting anomaly image...") + export_to_drive( + image=anomalyImage.toFloat(), # Ensure float32 for export consistency + filename='TrendAnomalyPy_output', + region=pa, # Use original geometry for context, bounds dictate output extent + description='Trend_Anomaly_Python_Output', + folder='GEE_Exports_Anomaly', + scale=30, + export_bounds=target_export_region + ) + + print("\nScript finished. Monitor GEE Tasks for export completion.") + if __name__ == "__main__": - main() \ No newline at end of file + # Add dependency check if desired + try: + import geopandas + import shapely + except ImportError as e: + print(f"Missing dependency: {e}") + print("Please install required libraries: pip install geopandas shapely") + else: + main() \ No newline at end of file diff --git a/TrendFire.py b/TrendFire.py index bc1a0fc..61818c8 100644 --- a/TrendFire.py +++ b/TrendFire.py @@ -25,6 +25,7 @@ import geopandas as gpd from tqdm import tqdm from shapely.geometry import Polygon +import ee.data # Import the data module # Initialize the Earth Engine API def initialize_ee(): @@ -490,26 +491,46 @@ def merge_trend_layers(ls_trends, rain_trend, sm_trend, rh_trend): def export_to_asset(image, asset_name, region, description=None, scale=30, export_bounds=None): """ Export an image to a Google Earth Engine asset. + Deletes the asset first if it already exists. Allows specifying explicit export bounds. """ if description is None: description = asset_name.split('/')[-1] - + + # --- Check and delete existing asset --- + try: + asset_info = ee.data.getInfo(asset_name) + if asset_info: + print(f"Asset {asset_name} already exists. Deleting...") + ee.data.deleteAsset(asset_name) + print(f"Asset {asset_name} deleted.") + except ee.EEException as e: + # Handle case where asset doesn't exist (getInfo throws an EEException) + if 'not found' in str(e): + print(f"Asset {asset_name} not found. Proceeding with export.") + else: + print(f"Error checking/deleting asset {asset_name}: {e}. Proceeding with export attempt...") + except Exception as e: + # Catch other potential errors during check/delete + print(f"Unexpected error checking/deleting asset {asset_name}: {e}. Proceeding with export attempt...") + # -------------------------------------- + # Use provided bounds if available, otherwise use the region geometry's bounds export_region = export_bounds if export_bounds else region + print(f"Starting export task to Asset: {description}") task = ee.batch.Export.image.toAsset( image=image, # Image should be pre-projected and cast description=description, assetId=asset_name, region=export_region, scale=scale, - crs='EPSG:3857', + crs='EPSG:3857', maxPixels=1e13 ) - + task.start() - print(f"Started export task to Asset: {description}") + print(f"Task started (id: {task.id}). Check GEE Tasks or use monitoring tools.") return task # Function to export the trends to Google Drive @@ -542,6 +563,7 @@ def export_to_drive(image, filename, region, description=None, folder='GEE_Expor def main(): """ Main function to process all trends and export results. + Exports two versions: one matching inputResampled extent, one covering the full study area. """ # Initialize Earth Engine initialize_ee() @@ -587,41 +609,71 @@ def main(): [8246820.0, 1767960.0], # top-left (x, y) [8246820.0, 1680600.0] # close the loop ] - target_export_region = ee.Geometry.Polygon(target_bounds_coords, proj='EPSG:3857', evenOdd=False) + target_export_region_limited = ee.Geometry.Polygon(target_bounds_coords, proj='EPSG:3857', evenOdd=False) # --- Explicitly set data type and CRS before export --- - # Note: Reprojection happens here, but export uses target_export_region for extent - print("Casting to float32 and reprojecting to EPSG:3857...") - all_trends_export = all_trends.toFloat() \ - .reproject(crs='EPSG:3857', scale=30) - - # Export the merged trends to Asset - print("Exporting merged trends to GEE Asset...") + print("Casting final trend image to float32 and setting projection to EPSG:3857...") + # Apply casting and projection ONCE to the merged image + # Exports will use this projected image but different bounding regions + all_trends_export_ready = all_trends.toFloat() \ + .reproject(crs='EPSG:3857', scale=30) + + # --- EXPORT 1: Limited Extent (Matching inputResampled.tif) --- + print("\n--- Starting Export for Limited Extent (Match JS Output Grid) ---") + asset_id_limited = 'users/jonasnothnagel/TrendFirePy_LimitedExtent' # New asset ID + drive_filename_limited = 'TrendFirePy_LimitedExtent' + + # Export Asset (Limited) export_to_asset( - all_trends_export, - 'users/jonasnothnagel/Trend2024_all_new', - goa, # Original region used for clipping/processing - description='All_Trends_2024_Asset', + all_trends_export_ready, + asset_id_limited, + goa, # Original region for context, bounds override + description=f'{asset_id_limited.split("/")[-1]}_Asset', scale=30, - export_bounds=target_export_region # Pass the explicit bounds + export_bounds=target_export_region_limited # Use the limited bounds ) - # Export the merged trends to Google Drive - print("Exporting merged trends to Google Drive...") + # Export Drive (Limited) export_to_drive( - all_trends_export, - 'TrendFirePy_output', - goa, # Original region used for clipping/processing - description='All_Trends_2024_Drive', + all_trends_export_ready, + drive_filename_limited, + goa, # Original region for context, bounds override + description=f'{drive_filename_limited}_Drive', folder='GEE_Exports', scale=30, - export_bounds=target_export_region # Pass the explicit bounds + export_bounds=target_export_region_limited # Use the limited bounds ) - + + # --- EXPORT 2: Full Extent (Covering pa_boundary.shp) --- + print("\n--- Starting Export for Full Study Area Extent ---") + asset_id_full = 'users/jonasnothnagel/TrendFirePy_FullExtent' # New asset ID + drive_filename_full = 'TrendFirePy_FullExtent' + + # Export Asset (Full) - Use goa geometry for bounds implicitly + export_to_asset( + all_trends_export_ready, + asset_id_full, + goa, # Let GEE determine bounds from this geometry + description=f'{asset_id_full.split("/")[-1]}_Asset', + scale=30 + # No export_bounds specified, defaults to region (goa) + ) + + # Export Drive (Full) - Use goa geometry for bounds implicitly + export_to_drive( + all_trends_export_ready, + drive_filename_full, + goa, # Let GEE determine bounds from this geometry + description=f'{drive_filename_full}_Drive', + folder='GEE_Exports', + scale=30 + # No export_bounds specified, defaults to region (goa) + ) + print("\nAnalysis completed successfully!") - print("Results export tasks started for Earth Engine assets and Google Drive.") - print("Monitor task status in the GEE Code Editor or using task monitoring tools.") + print("Multiple export tasks started. Monitor GEE Tasks.") + # Return the original merged trends (before final reprojection for export) return { 'trends': all_trends, 'collections': { diff --git a/compare_trends.py b/compare_trends.py index e1bb8d8..d14629a 100644 --- a/compare_trends.py +++ b/compare_trends.py @@ -1,10 +1,46 @@ import rasterio import os import numpy as np +import geopandas as gpd +import matplotlib.pyplot as plt +from shapely.geometry import box -def compare_tifs(file1_path, file2_path): +# Function to plot the bounding boxes +def plot_extents(gdf_list, labels, colors, boundary_shp_path=None, title="GeoTIFF Extents"): + """Plots bounding boxes from GeoDataFrames, optionally with a boundary shapefile.""" + fig, ax = plt.subplots(1, 1, figsize=(10, 10)) + + boundary_gdf = None + # Plot study area boundary first if provided + if boundary_shp_path and os.path.exists(boundary_shp_path): + try: + boundary_gdf = gpd.read_file(boundary_shp_path) + # Ensure boundary CRS matches the first GeoTIFF's CRS if possible + if gdf_list and gdf_list[0].crs: + try: + boundary_gdf = boundary_gdf.to_crs(gdf_list[0].crs) + except Exception as crs_err: + print(f"Warning: Could not reproject boundary shapefile to {gdf_list[0].crs}: {crs_err}") + boundary_gdf.plot(ax=ax, facecolor='none', edgecolor='black', linestyle='--', label='Study Area Boundary (pa_boundary.shp)') + except Exception as e: + print(f"Error reading or plotting boundary shapefile {boundary_shp_path}: {e}") + + # Plot the bounding boxes + for gdf, label, color in zip(gdf_list, labels, colors): + if gdf is not None and not gdf.empty: + gdf.plot(ax=ax, facecolor='none', edgecolor=color, label=label) + + ax.set_title(title) + ax.set_xlabel("Longitude / Easting") + ax.set_ylabel("Latitude / Northing") + ax.legend() + plt.tight_layout() + plt.show() + +# Modified comparison function +def compare_tifs(file1_path, file2_path, plot=True, boundary_path=None): """ - Compares metadata and basic statistics of two GeoTIFF files. + Compares metadata and basic statistics of two GeoTIFF files, optionally plots extents. """ print(f"Comparing:\n (1) {os.path.basename(file1_path)}\n (2) {os.path.basename(file2_path)}\n") @@ -14,6 +50,10 @@ def compare_tifs(file1_path, file2_path): if not os.path.exists(file2_path): print(f"Error: File not found - {file2_path}") return + + gdf_list = [] + labels = [os.path.basename(file1_path), os.path.basename(file2_path)] + colors = ['red', 'blue'] try: with rasterio.open(file1_path) as ds1, rasterio.open(file2_path) as ds2: @@ -98,15 +138,68 @@ def compare_tifs(file1_path, file2_path): except Exception as e: print(f"Error reading or comparing band data: {e}") + # --- Prepare data for plotting --- + if plot: + try: + # Get bounds and CRS + b1 = ds1.bounds + crs1 = ds1.crs + b2 = ds2.bounds + crs2 = ds2.crs + + # Create GeoDataFrames for the bounding boxes + if crs1: + geom1 = box(b1.left, b1.bottom, b1.right, b1.top) + gdf1 = gpd.GeoDataFrame([1], geometry=[geom1], crs=crs1) + gdf_list.append(gdf1) + else: + print("Warning: CRS not found for file 1, cannot plot its extent.") + gdf_list.append(None) + + if crs2: + geom2 = box(b2.left, b2.bottom, b2.right, b2.top) + gdf2 = gpd.GeoDataFrame([1], geometry=[geom2], crs=crs2) + # Try to reproject gdf2 to match gdf1 if they differ + if crs1 and crs2 != crs1: + try: + print(f"Reprojecting {labels[1]} extent for plotting...") + gdf2 = gdf2.to_crs(crs1) + except Exception as crs_err: + print(f"Warning: Could not reproject {labels[1]} to {crs1} for plotting: {crs_err}") + gdf_list.append(gdf2) + else: + print("Warning: CRS not found for file 2, cannot plot its extent.") + gdf_list.append(None) + + except Exception as e: + print(f"Error preparing data for plotting: {e}") + # Ensure plot call doesn't fail if prep fails + gdf_list = [None, None] + except rasterio.RasterioIOError as e: print(f"Error opening GeoTIFF file: {e}") + gdf_list = [None, None] # Prevent plotting attempt on error except Exception as e: print(f"An unexpected error occurred: {e}") + gdf_list = [None, None] # Prevent plotting attempt on error + + # --- Plotting --- + if plot and (gdf_list[0] is not None or gdf_list[1] is not None or (boundary_path and os.path.exists(boundary_path))): + plot_extents(gdf_list, labels, colors, boundary_shp_path=boundary_path) + elif plot: + print("\nPlotting skipped: No valid extents or boundary file found.") + if __name__ == "__main__": # Define file paths relative to the script location or use absolute paths data_dir = "data" + boundary_shapefile = os.path.join(data_dir, "pa_boundary.shp") # Path to boundary file1 = os.path.join(data_dir, "inputResampled.tif") # Ground truth (JS output) file2 = os.path.join(data_dir, "TrendFirePy_output.tif") # Python output - compare_tifs(file1, file2) \ No newline at end of file + # Make sure the boundary file exists if we want to plot it + if not os.path.exists(boundary_shapefile): + print(f"Boundary shapefile not found at {boundary_shapefile}. Plot will not include boundary.") + boundary_shapefile = None + + compare_tifs(file1, file2, plot=True, boundary_path=boundary_shapefile) \ No newline at end of file From 0baac9217e517710d977fb5293d50b6fb3292c17 Mon Sep 17 00:00:00 2001 From: jonas-nothnagel Date: Wed, 9 Apr 2025 18:21:03 +0200 Subject: [PATCH 07/15] updated python code --- .DS_Store | Bin 8196 -> 8196 bytes TrendFire.py | 20 ++++++++++---------- 2 files changed, 10 insertions(+), 10 deletions(-) diff --git a/.DS_Store b/.DS_Store index 347d888aae64b0e91229534a78fc2a1e5d44243f..366a44c32a4d585338f3e89cf4a54be9d2a19a88 100644 GIT binary patch delta 43 zcmZp1XmOa}&&awlU^hP_>tr5*JDe%S$vH{+`8kZ6?+A#oZ)TVH# Date: Thu, 10 Apr 2025 15:33:42 +0200 Subject: [PATCH 08/15] fixed the study area problem --- TrendFire.py | 94 +++++++++++++++++++++++++--------------------------- 1 file changed, 46 insertions(+), 48 deletions(-) diff --git a/TrendFire.py b/TrendFire.py index dd78a5f..ecb3530 100644 --- a/TrendFire.py +++ b/TrendFire.py @@ -28,14 +28,27 @@ import ee.data # Import the data module # Initialize the Earth Engine API -def initialize_ee(): +def initialize_ee(project_id='ee-crop-health-telangana'): try: + # Attempt authentication. Might prompt user if credentials are not found. ee.Authenticate() - ee.Initialize() - print("Earth Engine API initialized successfully.") + + # Initialize with the specified project ID. + ee.Initialize(project=project_id) + + print(f"Earth Engine API initialized successfully for project: {project_id}") + except ee.EEException as e: + print(f"Error initializing Earth Engine API for project {project_id}: {e}") + print("Please ensure:") + print(" 1. You have authenticated with `ee.Authenticate()`.") + print(f" 2. The project ID '{project_id}' is correct.") + print(" 3. The Earth Engine API is enabled for this project in Google Cloud Console.") + print(" 4. The account used for authentication has access to this project.") + raise # Re-raise the exception to stop execution if initialization fails except Exception as e: - print(f"Error initializing Earth Engine API: {e}") - print("Make sure you have authenticated with Earth Engine using ee.Authenticate()") + # Catch other potential errors during initialization + print(f"An unexpected error occurred during Earth Engine initialization: {e}") + raise # Function to debug the shapefile def debug_shapefile(boundary_path): @@ -67,54 +80,39 @@ def debug_shapefile(boundary_path): # Define the Goa study area boundary def get_goa_boundary(): """ - Get the boundary of the study area from the pa_boundary shapefile. + Get the boundary of the study area directly from the GEE asset. Returns an ee.Geometry object. """ try: - # Try to load the boundary from the shapefile - boundary_path = os.path.join('data', 'pa_boundary.shp') - if os.path.exists(boundary_path): - print("Loading study area boundary from shapefile...") - - # Debug the shapefile - gdf = debug_shapefile(boundary_path) - if gdf is None: - return None - - # Ensure we have at least one valid geometry - if len(gdf) == 0: - print("Error: Shapefile contains no features") - return None - - # Get the first geometry - geometry = gdf.geometry.iloc[0] - - # Check if geometry is valid - if not geometry.is_valid: - print("Error: Geometry is invalid") - return None - - # Convert 3D polygon to 2D by dropping Z coordinates - if geometry.has_z: - print("Converting 3D polygon to 2D...") - # Get the coordinates and drop Z values - coords = list(geometry.exterior.coords) - coords_2d = [(x, y) for x, y, z in coords] - # Create new 2D polygon - geometry = Polygon(coords_2d) - - # Convert to GeoJSON - gdf.geometry = gpd.GeoSeries([geometry]) - boundary_geojson = gdf.__geo_interface__ - - # Create an EE geometry - return ee.Geometry.Polygon(boundary_geojson['features'][0]['geometry']['coordinates']) - else: - print(f"Error: Could not find pa_boundary.shp at {boundary_path}") - print("Please ensure the shapefile exists in the data directory") + asset_id = 'users/jonasnothnagel/pa_boundary' + print(f"Loading study area boundary from GEE asset: {asset_id}...") + goa_fc = ee.FeatureCollection(asset_id) + + # Ensure the collection is not empty + if goa_fc.size().getInfo() == 0: + print(f"Error: Asset {asset_id} is empty or inaccessible.") return None + + # Get the geometry of the first feature (assuming it's a single polygon feature) + # Use .geometry() to simplify the FeatureCollection to a single Geometry + # Use .dissolve() first to merge geometries if it might be a multi-part feature collection + goa_geometry = goa_fc.geometry().dissolve() + + # Optional: Check geometry validity (can be computationally intensive) + # is_valid = goa_geometry.isValid().getInfo() + # if not is_valid: + # print(f"Warning: Geometry from asset {asset_id} may not be valid according to GEE.") + + print("Successfully loaded boundary from GEE asset.") + return goa_geometry + + except ee.EEException as e: + print(f"Error loading boundary from GEE asset {asset_id}: {e}") + print("Please ensure the asset exists and you have read permissions.") + return None except Exception as e: - print(f"Error loading boundary from shapefile: {e}") + # Catch other potential errors + print(f"An unexpected error occurred loading the boundary asset: {e}") return None # Function to mask clouds in Landsat 8 imagery From aca1502fdcf3851b93ceacb91fbdbb17f3aeacc0 Mon Sep 17 00:00:00 2001 From: jonas-nothnagel Date: Thu, 10 Apr 2025 16:13:15 +0200 Subject: [PATCH 09/15] working on anomaly python --- TrendAnomalyPrediction.py | 832 +++++++++++++++++++++++--------------- 1 file changed, 504 insertions(+), 328 deletions(-) diff --git a/TrendAnomalyPrediction.py b/TrendAnomalyPrediction.py index acb85b2..495e9bf 100644 --- a/TrendAnomalyPrediction.py +++ b/TrendAnomalyPrediction.py @@ -2,14 +2,19 @@ # -*- coding: utf-8 -*- """ -TrendAnomalyPrediction.py: Calculates trend anomalies based on long-term trends and recent observations. - -This script replicates the core logic of TrendAnomalyPrediction.js: -1. Loads pre-calculated trend layers (output from TrendFire.py). -2. Calculates predicted values for a target period (e.g., Feb 2023) based on trends. -3. Calculates actual observed values for the target period using Landsat, ERA5, SMAP, CHIRPS. -4. Computes the anomaly by subtracting predicted values from observed values. -5. Exports the resulting anomaly image. +TrendAnomalyPrediction.py: Python implementation of TrendAnomalyPrediction.js using Earth Engine Python API. + +This script calculates trend anomalies for various indices based on pre-computed trends +and recent observations. + +Steps: +1. Load pre-computed trend layers (Slope and Intercept for various indices). +2. Load the study area boundary. +3. Define prediction date and reference start dates for trends. +4. Calculate predicted index values for the prediction date. +5. Load actual observed data ('present data') for the period preceding the prediction date. +6. Calculate the anomaly (Present - Predicted). +7. Export the anomaly results. """ import ee @@ -22,37 +27,90 @@ # Helper Functions (Adapted from TrendFire.py) # ============================================================================== -def initialize_ee(): +def initialize_ee(project_id='ee-crop-health-telangana'): # Use the verified project ID """Initializes the Earth Engine API.""" try: - # Check if already initialized - try: - ee.ImageCollection('NASA/SMAP/SPL3SMP_E/005').limit(1).size().getInfo() - print("Earth Engine API already initialized.") - return True - except ee.EEException as e: - # If not initialized, attempt authentication and initialization - if "cannot be used before ee.Initialize()" in str(e) or \ - "Earth Engine account" in str(e) or \ - "Credentials are not valid" in str(e): - try: - ee.Authenticate(quiet=True) # Use quiet=True for non-interactive - ee.Initialize() - print("Earth Engine API initialized successfully.") - return True - except Exception as init_e: - print(f"Error initializing Earth Engine API: {init_e}") - print("Please ensure you have authenticated with GEE CLI: 'earthengine authenticate'") - return False - else: - # Handle other EE exceptions if needed - print(f"An Earth Engine error occurred: {e}") - return False + # Attempt authentication. Might prompt user if credentials are not found. + # ee.Authenticate() # Often needed only once per session/machine + + # Initialize with the specified project ID. + ee.Initialize(project=project_id, opt_url='https://earthengine-highvolume.googleapis.com') # Use high volume endpoint + + print(f"Earth Engine API initialized successfully for project: {project_id}") + except ee.EEException as e: + print(f"Error initializing Earth Engine API for project {project_id}: {e}") + print("Please ensure:") + print(" 1. You have authenticated (e.g., via `gcloud auth application-default login`).") + print(f" 2. The project ID '{project_id}' is correct.") + print(" 3. The Earth Engine API is enabled for this project in Google Cloud Console.") + print(" 4. The account used for authentication has access to this project.") + raise # Re-raise the exception to stop execution if initialization fails + except Exception as e: + # Catch other potential errors during initialization + print(f"An unexpected error occurred during Earth Engine initialization: {e}") + raise +def get_goa_boundary(asset_id='users/jonasnothnagel/pa_boundary'): + """ + Get the boundary of the study area directly from the GEE asset. + Returns an ee.Geometry object. + """ + try: + print(f"Loading study area boundary from GEE asset: {asset_id}...") + goa_fc = ee.FeatureCollection(asset_id) + + # Ensure the collection is not empty + if goa_fc.size().getInfo() == 0: + print(f"Error: Asset {asset_id} is empty or inaccessible.") + return None + + # Get the geometry, dissolve potentially multi-part features + goa_geometry = goa_fc.geometry().dissolve() + + print("Successfully loaded boundary from GEE asset.") + return goa_geometry + + except ee.EEException as e: + print(f"Error loading boundary from GEE asset {asset_id}: {e}") + print("Please ensure the asset exists and you have read permissions.") + return None except Exception as e: - print(f"An unexpected error occurred during EE initialization: {e}") - return False + print(f"An unexpected error occurred loading the boundary asset: {e}") + return None +def export_to_asset(image, asset_id, region, description=None, scale=30, crs='EPSG:3857'): + """Exports an image to a Google Earth Engine asset, deleting if exists.""" + if description is None: + description = asset_id.split('/')[-1] + + # Check and delete existing asset + try: + asset_info = ee.data.getInfo(asset_id) + if asset_info: + print(f"Asset {asset_id} already exists. Deleting...") + ee.data.deleteAsset(asset_id) + print(f"Asset {asset_id} deleted.") + except ee.EEException as e: + if 'not found' in str(e).lower(): + print(f"Asset {asset_id} not found. Proceeding with export.") + else: + print(f"Error checking/deleting asset {asset_id}: {e}. Proceeding...") + except Exception as e: + print(f"Unexpected error checking/deleting asset {asset_id}: {e}. Proceeding...") + + print(f"Starting export task to Asset: {description} (ID: {asset_id})") + task = ee.batch.Export.image.toAsset( + image=image.toFloat(), # Ensure float type for export + description=description, + assetId=asset_id, + region=region, + scale=scale, + crs=crs, + maxPixels=1e13 + ) + task.start() + print(f"Task started (id: {task.id}). Check GEE Tasks.") + return task # Function to debug the shapefile (Optional but good practice) def debug_shapefile(boundary_path): @@ -81,56 +139,6 @@ def debug_shapefile(boundary_path): print(f"Error debugging shapefile: {e}") return None -# Define the Goa study area boundary -def get_goa_boundary(shp_path=os.path.join('data', 'pa_boundary.shp')): - """ - Get the boundary of the study area from a shapefile. - Returns an ee.Geometry object. - """ - try: - if os.path.exists(shp_path): - print(f"Loading study area boundary from {shp_path}...") - gdf = gpd.read_file(shp_path) # Removed debug call for cleaner flow - - if len(gdf) == 0: - print("Error: Shapefile contains no features") - return None - - # Reproject to EPSG:4326 if necessary for GEE Geometry conversion - if gdf.crs and gdf.crs.to_epsg() != 4326: - print(f"Reprojecting boundary from {gdf.crs} to EPSG:4326...") - gdf = gdf.to_crs(epsg=4326) - - geometry = gdf.geometry.iloc[0] - - if not geometry.is_valid: - print("Warning: Attempting to fix invalid geometry...") - geometry = geometry.buffer(0) # Common trick to fix minor issues - if not geometry.is_valid: - print("Error: Geometry is invalid and could not be fixed.") - return None - - # Convert 3D polygon to 2D if needed - if geometry.has_z: - print("Converting 3D polygon to 2D...") - coords = list(geometry.exterior.coords) - coords_2d = [(x, y) for x, y, z in coords] - geometry = Polygon(coords_2d) - - # Convert to GeoJSON format suitable for EE - gdf.geometry = gpd.GeoSeries([geometry]) # Ensure only the valid geometry is used - boundary_geojson = gdf.__geo_interface__ - - # Extract coordinates for EE Geometry Polygon - ee_coords = boundary_geojson['features'][0]['geometry']['coordinates'] - return ee.Geometry.Polygon(ee_coords) - else: - print(f"Error: Could not find boundary shapefile at {shp_path}") - return None - except Exception as e: - print(f"Error loading boundary from shapefile: {e}") - return None - # Function to mask clouds in Landsat 8 imagery def maskL8sr(image): """ @@ -140,13 +148,13 @@ def maskL8sr(image): # Bits 0-4 are Fill, Dilated Cloud, Cirrus, Cloud, Cloud Shadow. qaMask = image.select('QA_PIXEL').bitwiseAnd(int('11111', 2)).eq(0) saturationMask = image.select('QA_RADSAT').eq(0) - + # Scale the optical bands optical_bands = image.select('SR_B.').multiply(0.0000275).add(-0.2) - + # Scale the thermal bands thermal_bands = image.select('ST_B.*').multiply(0.00341802).add(149.0) - + # Return the masked and scaled image, applying both masks return image.addBands(optical_bands, None, True) \ .addBands(thermal_bands, None, True) \ @@ -160,7 +168,7 @@ def addIndices(image): """ # NDVI (Normalized Difference Vegetation Index) ndvi = image.normalizedDifference(['SR_B5', 'SR_B4']).rename('ndvi') - + # EVI (Enhanced Vegetation Index) evi = image.expression( '2.5 * ((NIR - RED) / (NIR + 6 * RED - 7.5 * BLUE + 1))', @@ -170,7 +178,7 @@ def addIndices(image): 'BLUE': image.select('SR_B2') } ).rename('evi') - + # MSAVI (Modified Soil Adjusted Vegetation Index) msavi = image.expression( '(2 * NIR + 1 - sqrt((2 * NIR + 1) * (2 * NIR + 1) - 8 * (NIR - RED))) / 2', @@ -179,7 +187,7 @@ def addIndices(image): 'RED': image.select('SR_B4') } ).rename('msavi') - + # MIRBI (Mid-Infrared Burn Index) mirbi = image.expression( '10 * SWIR2 - 9.8 * SWIR1 + 2', @@ -188,19 +196,19 @@ def addIndices(image): 'SWIR2': image.select('SR_B7') } ).rename('mirbi') - + # NDMI (Normalized Difference Moisture Index) ndmi = image.normalizedDifference(['SR_B5', 'SR_B6']).rename('ndmi') - + # NDFI (Normalized Difference Fraction Index) - Uses ST_B10 and SR_B6 like JS ndfi = image.normalizedDifference(['ST_B10', 'SR_B6']).rename('ndfi') - + # NBR (Normalized Burn Ratio) nbr = image.normalizedDifference(['SR_B5', 'SR_B7']).rename('nbr') - + # NBR2 (Normalized Burn Ratio 2) nbr2 = image.normalizedDifference(['SR_B6', 'SR_B7']).rename('nbr2') - + # BSI (Bare Soil Index) bsi = image.expression( '((SWIR1 + RED) - (NIR + BLUE)) / ((SWIR1 + RED) + (NIR + BLUE))', @@ -211,7 +219,7 @@ def addIndices(image): 'BLUE': image.select('SR_B2') } ).rename('bsi') - + # Add all indices as bands return image.addBands([ndvi, evi, msavi, mirbi, ndmi, ndfi, nbr, nbr2, bsi]) @@ -219,8 +227,12 @@ def addIndices(image): # Function to calculate SMI using SWIR bands (requires pre-calculated min/max) def addSMI_local(image, swir_min, swir_max): """Calculates SMI, requires swir min/max for the specific collection.""" + # Ensure swir_min and swir_max are ee.Number objects + swir_min = ee.Number(swir_min) + swir_max = ee.Number(swir_max) + # Avoid division by zero or non-numeric results if swir_max <= swir_min smi = image.select('SR_B7').subtract(swir_min) \ - .divide(swir_max.subtract(swir_min)) \ + .divide(swir_max.subtract(swir_min).max(ee.Number(1e-9))) \ .multiply(-1).add(1).rename('smi') return image.addBands(smi) @@ -236,252 +248,416 @@ def calcVaporPressure(temp_k): exponent = num.divide(denom).exp() # e^(...) return exponent.multiply(6.112) #Multiply by saturation vapor pressure at 0°C (in hPa or mbar) -# Function to export trends to Google Drive -def export_to_drive(image, filename, region, description=None, folder='GEE_Exports_Anomaly', scale=30, export_bounds=None): - """ - Export an image to Google Drive. - Assumes image is already cast and projected if needed. - Uses explicit export bounds if provided. - """ - if description is None: - description = filename - - export_region = export_bounds if export_bounds else region - - print(f"Starting export task to Drive: {description}") - task = ee.batch.Export.image.toDrive( - image=image, # Assume image is pre-processed (float32, projected) - description=description, - folder=folder, - fileNamePrefix=filename, - region=export_region.getInfo()['coordinates'], # Pass coordinates explicitly - scale=scale, - crs='EPSG:3857', # Ensure CRS matches previous step - maxPixels=1e13 - ) - task.start() - print(f"Task started (id: {task.id}). Check GEE Tasks or use monitoring tools.") - return task - # ============================================================================== # Main Script Logic # ============================================================================== def main(): - """Main function to calculate and export trend anomalies.""" - if not initialize_ee(): - return # Stop if EE initialization fails - - pa = get_goa_boundary() - if pa is None: - print("Error: Could not load study area boundary. Exiting.") + """Main script execution.""" + # --- Configuration --- + PROJECT_ID = 'ee-crop-health-telangana' # Or get from environment variable + BOUNDARY_ASSET_ID = 'users/jonasnothnagel/pa_boundary' + OUTPUT_ASSET_BASE = 'users/jonasnothnagel/' # Base path for output assets + + PREDICTION_DATE = ee.Date('2023-03-01') + PRESENT_START_DATE = '2023-02-01' # For LS, SM, RH + PRESENT_END_DATE = '2023-02-28' # For LS, SM, RH + RAIN_YEAR_START = '2022-01-01' # CHIRPS sum year start + RAIN_YEAR_END = '2022-12-31' # CHIRPS sum year end + + # Trend reference start dates (match TrendFire.js/py logic) + TREND_REF_DATES = { + 'landsat': ee.Date('2013-03-20'), + 'rain': ee.Date('1982-01-01'), + 'sm': ee.Date('2015-04-01'), + 'rh': ee.Date('1980-01-01') + } + + # --- Initialization --- + initialize_ee(PROJECT_ID) + boundary = get_goa_boundary(BOUNDARY_ASSET_ID) + if boundary is None: + print("Exiting: Could not load boundary.") return + + # --- Processing Steps --- + # 1. Load Original Trend Assets + original_trends = load_original_trend_assets() + if original_trends is None: + print("Exiting: Could not load original trend assets.") + return + # print("Placeholder: Skipping load_original_trend_assets()") + # original_trends = None # Temporary - print("Successfully loaded study area boundary.") + # 2. Calculate Predicted Values + predicted_image = calculate_predicted_values(original_trends, PREDICTION_DATE, TREND_REF_DATES) + if predicted_image is None: + print("Exiting: Could not calculate predicted values.") + return + # print("Placeholder: Skipping calculate_predicted_values()") + # predicted_image = None # Temporary + + # 3. Get Present Data + present_image = get_present_data(boundary, PRESENT_START_DATE, PRESENT_END_DATE, RAIN_YEAR_START, RAIN_YEAR_END) + if present_image is None: + print("Exiting: Could not get present data.") + return + # print("Placeholder: Skipping get_present_data()") + # present_image = None # Temporary + + # 4. Calculate Anomaly (Requires present_image and predicted_image) + if present_image and predicted_image: + anomaly_image = calculate_anomaly(present_image, predicted_image) + print("Anomaly image calculated.") + # print(anomaly_image.bandNames().getInfo()) # Verify band names + + # --- Export Results --- + anomaly_asset_id = OUTPUT_ASSET_BASE + 'TrendAnomaly_py' # Example output name + export_to_asset( + image=anomaly_image, + asset_id=anomaly_asset_id, + region=boundary, + description='Trend_Anomaly_Python', + scale=30 + ) + else: + print("Skipping anomaly calculation and export due to missing inputs (likely placeholder functions)." ) # Updated message - # --- Define Time Periods --- - trend_start_date = '2013-03-20' - trend_end_date = '2023-02-28' # Corresponds to TrendFire.py output - present_start_date = '2023-02-01' - present_end_date = '2023-02-28' - rain_present_year_start = '2022-01-01' - rain_present_year_end = '2022-12-31' - rh_sm_start_date = '1980-01-01' # Start date for RH trend baseline - sm_start_date = '2015-04-01' # Start date for SM trend baseline - rain_start_date = '1982-01-01' # Start date for Rain trend baseline + print("\nScript finished. Monitor GEE tasks for export status.") +# --- Trend Anomaly Specific Functions --- - # --- Load Trend Image Asset --- - trend_asset_id = 'users/jonasnothnagel/Trend2024_all_new' # Asset from TrendFire.py - print(f"Loading trend image asset: {trend_asset_id}") +def load_original_trend_assets(base_path='users/jonasnothnagel/'): + """ + Loads the individual trend assets generated by the original TrendFire.js script + (assumed to be under the user's GEE path) and merges them. + Returns a single ee.Image with all trend bands (Slope and Intercept). + """ + print(f"Loading original trend assets from base path: {base_path}...") + + # Define asset suffixes and index names based on TrendFire.js output + landsat_indices = ['ndvi', 'evi', 'mirbi', 'ndfi', 'bsi', 'ndmi', 'nbr', 'nbr2', 'msavi', 'smi', 'ST_B10'] + landsat_asset_prefix = 'Trend2023_' # Corrected from suffix to prefix + rain_asset_id_part = 'Trend2022_rain_new' + sm_asset_id_part = 'Trend2023_SM_new' + rh_asset_id_part = 'Trend2024_RH_new' + + all_trend_bands = ee.Image().toFloat() # Start with an empty image, cast to float early + asset_id = '' # Initialize asset_id for error message context + try: - input_trends = ee.Image(trend_asset_id) - # Verify bands (optional) - # print("Trend bands:", input_trends.bandNames().getInfo()) + # Load Landsat index trends + for index in landsat_indices: + asset_id = f"{base_path}{landsat_asset_prefix}{index}" + print(f" Loading Landsat trend: {asset_id}") + trend_img = ee.Image(asset_id) + # Expecting bands: index_Slope, index_Intercept + all_trend_bands = all_trend_bands.addBands(trend_img) + + # Load Rain trend + asset_id = f"{base_path}{rain_asset_id_part}" + print(f" Loading Rain trend: {asset_id}") + rain_trend = ee.Image(asset_id) # Bands: rain_Slope, rain_Intercept + all_trend_bands = all_trend_bands.addBands(rain_trend) + + # Load SM trend + asset_id = f"{base_path}{sm_asset_id_part}" + print(f" Loading SM trend: {asset_id}") + sm_trend = ee.Image(asset_id) # Bands: sm_surface_Slope, sm_surface_Intercept + # Ensure band name consistency if different from TrendFire.py + # sm_trend = sm_trend.rename(['sm_Slope', 'sm_Intercept']) # Example rename if needed + all_trend_bands = all_trend_bands.addBands(sm_trend) + + # Load RH trend + asset_id = f"{base_path}{rh_asset_id_part}" + print(f" Loading RH trend: {asset_id}") + rh_trend = ee.Image(asset_id) # Bands: rh_Slope, rh_Intercept + all_trend_bands = all_trend_bands.addBands(rh_trend) + + # Remove the empty initial band if necessary (usually not needed with addBands) + # band_names = all_trend_bands.bandNames() + # if band_names.length().gt(0) and band_names.get(0).match('^_*'): # Check if first band is empty/default + # all_trend_bands = all_trend_bands.select(band_names.slice(1)) + + print("Successfully loaded and merged all original trend assets.") + # Verify band names (optional) + # print("Loaded trend bands:", all_trend_bands.bandNames().getInfo()) + return all_trend_bands + + except ee.EEException as e: + print(f"Error loading GEE asset during trend loading: {e}") + print(f"Please ensure asset '{asset_id}' exists and you have permissions.") + return None except Exception as e: - print(f"Error loading trend asset {trend_asset_id}: {e}") - print("Please ensure the asset exists and you have access.") - return + print(f"An unexpected error occurred loading trend assets: {e}") + return None - # --- (Optional) Load Fire Points --- - # Assuming Asset IDs - replace with actual IDs if available/needed - # print("Loading fire points...") - # try: - # fire13_19_asset = 'YOUR_ASSET_ID/fire13_19' # Replace - # fire20_23_asset = 'YOUR_ASSET_ID/fire20_23' # Replace - # fire13_19 = ee.FeatureCollection(fire13_19_asset) - # fire20_23 = ee.FeatureCollection(fire20_23_asset) - # fire13_23 = fire13_19.merge(fire20_23) - # # Filter as in JS if needed for visualization/context - # fireMarch2023 = fire13_23.filter(ee.Filter.stringContains('acq_date', '2023')) # Simplified filter - # print(f"Loaded {fire13_23.size().getInfo()} total fire points.") - # except Exception as e: - # print(f"Warning: Could not load fire point assets: {e}. Continuing without them.") - # fireMarch2023 = None # Set to None if loading fails - - - print("\nCalculating predicted values based on trends...") - # --- Calculate Predicted Values (Past_Pred) --- - target_date_landsat = ee.Date(present_start_date) # Use start of month for prediction point - target_date_rain = ee.Date(rain_present_year_start) - target_date_rh = target_date_landsat - target_date_sm = target_date_landsat - - # Calculate time differences in years - const_landsat_years = target_date_landsat.difference(ee.Date(trend_start_date), 'year') - const_rain_years = target_date_rain.difference(ee.Date(rain_start_date), 'year') - const_rh_years = target_date_rh.difference(ee.Date(rh_sm_start_date), 'year') # JS used constRain, seems incorrect, use RH baseline - const_sm_years = target_date_sm.difference(ee.Date(sm_start_date), 'year') # JS used constRain, seems incorrect, use SM baseline - - # Select slope and intercept bands - Ensure names match TrendFire.py output - landsat_indices = ['ndvi', 'evi', 'mirbi', 'ndfi', 'bsi', 'ndmi', 'nbr', 'nbr2', 'msavi', 'smi', 'ST_B10'] - slope_bands_ls = [f'{idx}_Slope' for idx in landsat_indices] - intercept_bands_ls = [f'{idx}_Intercept' for idx in landsat_indices] - other_indices = ['rain', 'sm_surface', 'rh'] - slope_bands_other = [f'{idx}_Slope' for idx in other_indices] - intercept_bands_other = [f'{idx}_Intercept' for idx in other_indices] - - # Select bands from the input trend image - slopes_ls = input_trends.select(slope_bands_ls) - intercepts_ls = input_trends.select(intercept_bands_ls) - slopes_other = input_trends.select(slope_bands_other) - intercepts_other = input_trends.select(intercept_bands_other) - - # Calculate predicted values for Landsat-based indices - predicted_ls = slopes_ls.multiply(const_landsat_years).add(intercepts_ls).rename(landsat_indices) - - # Calculate predicted values for other indices - pred_rain = slopes_other.select('rain_Slope').multiply(const_rain_years).add(intercepts_other.select('rain_Intercept')).rename('rain') - pred_sm = slopes_other.select('sm_surface_Slope').multiply(const_sm_years).add(intercepts_other.select('sm_surface_Intercept')).rename('sm') - pred_rh = slopes_other.select('rh_Slope').multiply(const_rh_years).add(intercepts_other.select('rh_Intercept')).rename('rh') - - # Combine all predicted values - past_Pred = predicted_ls.addBands([pred_rain, pred_sm, pred_rh]) - # Reorder bands to match expected order for subtraction later - ordered_pred_bands = ['smi', 'ST_B10', 'ndvi', 'evi', 'msavi', 'mirbi', 'ndmi', 'ndfi', 'nbr', 'nbr2', 'bsi', 'rain', 'rh', 'sm'] - past_Pred = past_Pred.select(ordered_pred_bands) - print("Predicted values calculated.") - # print("Predicted bands:", past_Pred.bandNames().getInfo()) - - - print("\nCalculating present-day observed values...") - # --- Calculate Present-Day Values (Present_All) --- - - # 1. Landsat Present (Feb 2023 Mosaic) - print("Processing present-day Landsat (Feb 2023)...") - present_ls_collection = ee.ImageCollection('LANDSAT/LC08/C02/T1_L2') \ - .filterDate(present_start_date, present_end_date) \ - .filterBounds(pa) \ - .map(maskL8sr) \ - .map(lambda img: img.clip(pa)) \ - .map(addIndices) - - # Calculate SWIR min/max for *this specific collection* for SMI - swir_stats_present = present_ls_collection.mean().select('SR_B7').reduceRegion( - reducer=ee.Reducer.minMax(), - geometry=pa, - scale=30, - maxPixels=1e9 # Use lower maxPixels for reduceRegion if needed - ) - # Handle potential nulls if collection is empty - swir_min_present = ee.Number(ee.Algorithms.If(swir_stats_present.contains('SR_B7_min'), swir_stats_present.get('SR_B7_min'), 0)) - swir_max_present = ee.Number(ee.Algorithms.If(swir_stats_present.contains('SR_B7_max'), swir_stats_present.get('SR_B7_max'), 1)) # Avoid divide by zero - - present_ls_collection_smi = present_ls_collection.map(lambda img: addSMI_local(img, swir_min_present, swir_max_present)) - - # Select relevant bands and create mosaic - present_ls_bands = ['smi', 'ST_B10', 'ndvi', 'evi', 'msavi', 'mirbi', 'ndmi', 'ndfi', 'nbr', 'nbr2', 'bsi'] - present_ls = present_ls_collection_smi.select(present_ls_bands).mosaic() - print("Landsat mosaic created.") - - # 2. ERA5 RH Present (Feb 2023 Mean) - print("Processing present-day ERA5 RH (Feb 2023)...") - era5_present = ee.ImageCollection("ECMWF/ERA5_LAND/MONTHLY_AGGR") \ - .filterDate(present_start_date, present_end_date) \ - .filterBounds(pa) \ - .map(lambda img: img.clip(pa)) - - def calculateRH(image): - dewPoint_k = image.select('dewpoint_temperature_2m') - temp_k = image.select('temperature_2m') - eT = calcVaporPressure(temp_k) - eTd = calcVaporPressure(dewPoint_k) - # Avoid division by zero if eT is 0 - rh = eTd.divide(eT).multiply(100).max(0).min(100) # Clamp RH between 0 and 100 - return rh.rename('rh').set('system:time_start', image.get('system:time_start')) - - rh_collection_present = era5_present.map(calculateRH) - present_rh = rh_collection_present.mean() # Use mean for monthly agg - print("ERA5 RH calculated.") - - - # 3. SMAP Present (Feb 2023 Mosaic) - print("Processing present-day SMAP (Feb 2023)...") - smap_present_col = ee.ImageCollection('NASA/SMAP/SPL3SMP_E/005') \ - .filterDate(present_start_date, present_end_date) \ - .filterBounds(pa) \ - .map(lambda img: img.clip(pa)) \ - .select(['soil_moisture_am']) - present_sm = smap_present_col.mosaic().rename('sm') - print("SMAP mosaic created.") - - - # 4. CHIRPS Rain Present (Sum for 2022) - print("Processing present-day CHIRPS Rain (2022 Sum)...") - chirps_present_col = ee.ImageCollection('UCSB-CHG/CHIRPS/DAILY') \ - .filterDate(rain_present_year_start, rain_present_year_end) \ - .filterBounds(pa) - - # Calculate sum for the year 2022 - yearly_sum_2022 = chirps_present_col.sum().clip(pa).rename('rain') - present_rain = yearly_sum_2022 - print("CHIRPS Rain sum calculated.") - - # Combine all present-day layers - present_all = present_ls.addBands([present_rain, present_rh, present_sm]) - # Select bands in the same order as past_Pred - present_all = present_all.select(ordered_pred_bands) - print("Present-day layers combined.") - # print("Present bands:", present_all.bandNames().getInfo()) - - - print("\nCalculating anomaly image...") - # --- Calculate Anomaly Image --- - anomalyImage = present_all.subtract(past_Pred) - - # Rename bands to include '_Anomaly' - anomaly_band_names = [f'{b}_Anomaly' for b in ordered_pred_bands] - anomalyImage = anomalyImage.rename(anomaly_band_names) - print("Anomaly image calculated.") - # print("Anomaly bands:", anomalyImage.bandNames().getInfo()) - - - # --- Define Target Export Bounds (matching TrendFire.py output) --- - # Ensures consistent grid with previous step's output - target_bounds_coords = [ - [8246820.0, 1680600.0], # bottom-left (x, y) - [8275140.0, 1680600.0], # bottom-right (x, y) - [8275140.0, 1767960.0], # top-right (x, y) - [8246820.0, 1767960.0], # top-left (x, y) - [8246820.0, 1680600.0] # close the loop - ] - target_export_region = ee.Geometry.Polygon(target_bounds_coords, proj='EPSG:3857', evenOdd=False) - - - # --- Export Anomaly Image --- - print("\nExporting anomaly image...") - export_to_drive( - image=anomalyImage.toFloat(), # Ensure float32 for export consistency - filename='TrendAnomalyPy_output', - region=pa, # Use original geometry for context, bounds dictate output extent - description='Trend_Anomaly_Python_Output', - folder='GEE_Exports_Anomaly', - scale=30, - export_bounds=target_export_region - ) +def calculate_predicted_values(trend_image, prediction_date, ref_dates): + """ + Calculates the predicted values for each index based on trends. + + Args: + trend_image: ee.Image with bands like 'index_Slope', 'index_Intercept'. + prediction_date: ee.Date object for the prediction target date. + ref_dates: Dictionary mapping dataset type ('landsat', 'rain', 'sm', 'rh') + to its trend reference start ee.Date. + + Returns: + ee.Image with predicted values for each index (bands named 'index'). + """ + print(f"Calculating predicted values for {prediction_date.format('YYYY-MM-dd').getInfo()}...") - print("\nScript finished. Monitor GEE Tasks for export completion.") + try: + # Define the base index names and their corresponding dataset types + # Note: SM index from JS/Python TrendFire is 'sm_surface', RH is 'RH' + index_mapping = { + 'ndvi': 'landsat', 'evi': 'landsat', 'mirbi': 'landsat', 'ndfi': 'landsat', + 'bsi': 'landsat', 'ndmi': 'landsat', 'nbr': 'landsat', 'nbr2': 'landsat', + 'msavi': 'landsat', 'smi': 'landsat', 'ST_B10': 'landsat', + 'rain': 'rain', + 'sm_surface': 'sm', # Match SM band name from TrendFire + 'rh': 'rh' # Match RH band name from TrendFire + } + + predicted_bands_list = [] + + for index, dataset_type in index_mapping.items(): + print(f" Predicting for index: {index} (type: {dataset_type})") + + # Get the specific reference date for this dataset type + ref_date = ref_dates.get(dataset_type) + if not ref_date: + print(f" Error: Reference date not found for dataset type '{dataset_type}'. Skipping index '{index}'.") + continue + + # Calculate time difference in years + time_diff = prediction_date.difference(ref_date, 'year') + + # Construct band names for slope and intercept + slope_band = f'{index}_Slope' + intercept_band = f'{index}_Intercept' + + # Check if bands exist in the input image + if slope_band not in trend_image.bandNames().getInfo() or \ + intercept_band not in trend_image.bandNames().getInfo(): + print(f" Error: Bands '{slope_band}' or '{intercept_band}' not found in trend image. Skipping index '{index}'.") + continue + + # Select bands + slope = trend_image.select(slope_band) + intercept = trend_image.select(intercept_band) + + # Calculate predicted value: predicted = slope * time_diff + intercept + predicted = slope.multiply(time_diff).add(intercept) + + # Rename the predicted band to the simple index name + predicted = predicted.rename(index) + + predicted_bands_list.append(predicted) + + # Check if any bands were processed + if not predicted_bands_list: + print("Error: No predicted bands were generated. Check input trend image and band names.") + return None + + # Combine all predicted bands into a single image + predicted_image = ee.ImageCollection(predicted_bands_list).toBands() + + # The toBands() function creates names like '0_ndvi', '1_evi'. We need to rename them back. + original_band_names = [img.bandNames().get(0) for img in predicted_bands_list] + predicted_image = predicted_image.rename(original_band_names) + + print("Predicted values calculated successfully.") + # print("Predicted bands:", predicted_image.bandNames().getInfo()) # Optional verification + return predicted_image + + except ee.EEException as e: + print(f"GEE error during predicted value calculation: {e}") + return None + except Exception as e: + print(f"Unexpected error during predicted value calculation: {e}") + return None + +def get_present_data(boundary, present_start, present_end, rain_year_start, rain_year_end): + """ + Loads and prepares the 'present' observational data for the period before prediction. + + Args: + boundary: ee.Geometry for clipping. + present_start: Start date (YYYY-MM-DD) for Landsat, SMAP, ERA5. + present_end: End date (YYYY-MM-DD) for Landsat, SMAP, ERA5. + rain_year_start: Start date (YYYY-MM-DD) for the annual rainfall sum year. + rain_year_end: End date (YYYY-MM-DD) for the annual rainfall sum year. + + Returns: + ee.Image with observed values (bands named 'index'). + """ + print(f"Loading present data ({present_start} to {present_end}, Rain year: {rain_year_start} to {rain_year_end})...") + + try: + # 1. Landsat Present (Feb 2023 Mosaic) + print(" Processing present-day Landsat...") + ls_present_col = ee.ImageCollection('LANDSAT/LC08/C02/T1_L2') \ + .filterDate(present_start, present_end) \ + .filterBounds(boundary) \ + .map(maskL8sr) \ + .map(lambda img: img.clip(boundary)) \ + .map(addIndices) + + # Calculate SWIR min/max for *this specific collection* for SMI + # Handle potential empty collection after filtering/masking + collection_size = ls_present_col.size().getInfo() + present_ls_mosaic = None + if collection_size > 0: + swir_stats_present = ls_present_col.mean().select('SR_B7').reduceRegion( + reducer=ee.Reducer.minMax(), + geometry=boundary, + scale=30, + maxPixels=1e9 # Use lower maxPixels for reduceRegion if needed + ) + # Handle potential nulls if band doesn't exist or all pixels masked + swir_min_present = ee.Number(ee.Algorithms.If(swir_stats_present.contains('SR_B7_min'), swir_stats_present.get('SR_B7_min'), 0)) + swir_max_present = ee.Number(ee.Algorithms.If(swir_stats_present.contains('SR_B7_max'), swir_stats_present.get('SR_B7_max'), 1)) # Avoid divide by zero + + ls_present_col_smi = ls_present_col.map(lambda img: addSMI_local(img, swir_min_present, swir_max_present)) + + # Select relevant bands and create mosaic + present_ls_bands = ['smi', 'ST_B10', 'ndvi', 'evi', 'msavi', 'mirbi', 'ndmi', 'ndfi', 'nbr', 'nbr2', 'bsi'] + present_ls_mosaic = ls_present_col_smi.select(present_ls_bands).mosaic() + print(" Landsat mosaic created.") + else: + print(" Warning: No valid Landsat images found for the present period. Landsat bands will be missing.") + # Create a placeholder image if needed, or handle downstream + + # 2. ERA5 RH Present (Feb 2023 Mean) + print(" Processing present-day ERA5 RH...") + era5_present_col = ee.ImageCollection("ECMWF/ERA5_LAND/MONTHLY_AGGR") \ + .filterDate(present_start, present_end) \ + .filterBounds(boundary) \ + .map(lambda img: img.clip(boundary)) + + def calculateRH(image): + dewPoint_k = image.select('dewpoint_temperature_2m') + temp_k = image.select('temperature_2m') + eT = calcVaporPressure(temp_k) + eTd = calcVaporPressure(dewPoint_k) + # Avoid division by zero if eT is 0 and clamp RH + rh = eTd.divide(eT.max(1e-9)).multiply(100).max(0).min(100) + return rh.rename('rh').set('system:time_start', image.get('system:time_start')) + + rh_collection_present = era5_present_col.map(calculateRH) + # Check if collection is empty before calculating mean + present_rh = None + if rh_collection_present.size().getInfo() > 0: + present_rh = rh_collection_present.mean() # Use mean for monthly agg + print(" ERA5 RH calculated (mean).") + else: + print(" Warning: No ERA5 data found for the present period. RH band will be missing.") + + # 3. SMAP Present (Feb 2023 Mosaic) + print(" Processing present-day SMAP...") + smap_present_col = ee.ImageCollection('NASA/SMAP/SPL3SMP_E/005') \ + .filterDate(present_start, present_end) \ + .filterBounds(boundary) \ + .map(lambda img: img.clip(boundary)) \ + .select(['soil_moisture_am']) + + present_sm = None + if smap_present_col.size().getInfo() > 0: + present_sm = smap_present_col.mosaic().rename('sm_surface') # Rename to match trend band name + print(" SMAP mosaic created.") + else: + print(" Warning: No SMAP data found for the present period. SM band will be missing.") + + # 4. CHIRPS Rain Present (Sum for 2022) + print(" Processing present-day CHIRPS Rain (2022 Sum)...") + chirps_present_col = ee.ImageCollection('UCSB-CHG/CHIRPS/DAILY') \ + .filterDate(rain_year_start, rain_year_end) \ + .filterBounds(boundary) + + present_rain = None + if chirps_present_col.size().getInfo() > 0: + # Calculate sum for the year 2022 + yearly_sum_2022 = chirps_present_col.sum().clip(boundary).rename('rain') + present_rain = yearly_sum_2022 + print(" CHIRPS Rain sum calculated.") + else: + print(" Warning: No CHIRPS data found for the rain year. Rain band will be missing.") + + # Combine all present-day layers + print(" Combining present data layers...") + # Start with an empty image and add bands conditionally + present_all = ee.Image().toFloat() + if present_ls_mosaic is not None: + present_all = present_all.addBands(present_ls_mosaic) + if present_rain is not None: + present_all = present_all.addBands(present_rain) + if present_rh is not None: + present_all = present_all.addBands(present_rh) + if present_sm is not None: + present_all = present_all.addBands(present_sm) + + # Check if any bands were added + if not present_all.bandNames().getInfo(): + print("Error: No present data bands could be generated.") + return None + + print("Present-day data loaded and processed.") + # Select bands in the same order as predicted_image expects for calculate_anomaly + # This order comes from the `calculate_predicted_values` function's index_mapping + final_band_order = [ + 'ndvi', 'evi', 'mirbi', 'ndfi', 'bsi', 'ndmi', 'nbr', 'nbr2', + 'msavi', 'smi', 'ST_B10', 'rain', 'sm_surface', 'rh' + ] + + # Select only the bands that were successfully created + available_bands = present_all.bandNames() + bands_to_select = [b for b in final_band_order if b in available_bands.getInfo()] + + if not bands_to_select: + print("Error: No matching bands found between predicted and present images.") + return None + + present_all = present_all.select(bands_to_select) + # print("Final present bands:", present_all.bandNames().getInfo()) # Optional verification + return present_all + + except ee.EEException as e: + print(f"GEE error during present data retrieval: {e}") + return None + except Exception as e: + print(f"Unexpected error during present data retrieval: {e}") + return None +def calculate_anomaly(present_image, predicted_image): + """ + Calculates the anomaly image (Present - Predicted). + + Args: + present_image: ee.Image with observed values ('index' bands). + predicted_image: ee.Image with predicted values ('index' bands). + + Returns: + ee.Image with anomaly values ('index_Anomaly' bands). + """ + print("Calculating anomaly image...") + # Ensure band names match for subtraction + present_bands = present_image.bandNames() + predicted_bands = predicted_image.bandNames() + + # Assuming predicted_image has the target band names ('ndvi', 'evi', etc.) + # Select corresponding bands from present_image if needed + present_image_aligned = present_image.select(predicted_bands) + + anomaly = present_image_aligned.subtract(predicted_image) + + # Rename bands to include _Anomaly suffix + new_band_names = predicted_bands.map(lambda b: ee.String(b).cat('_Anomaly')) + anomaly = anomaly.rename(new_band_names) + + return anomaly + +# --- Main Execution --- if __name__ == "__main__": # Add dependency check if desired From 6f66371e98c629ed9112b77587fdf70a356339c5 Mon Sep 17 00:00:00 2001 From: jonas-nothnagel Date: Fri, 11 Apr 2025 12:23:04 +0200 Subject: [PATCH 10/15] updated anomaly script --- TrendAnomalyPrediction.py | 11 +- fire_vulnerability.ipynb | 1401 ------------------------------------- 2 files changed, 7 insertions(+), 1405 deletions(-) delete mode 100644 fire_vulnerability.ipynb diff --git a/TrendAnomalyPrediction.py b/TrendAnomalyPrediction.py index 495e9bf..3d0625c 100644 --- a/TrendAnomalyPrediction.py +++ b/TrendAnomalyPrediction.py @@ -31,7 +31,7 @@ def initialize_ee(project_id='ee-crop-health-telangana'): # Use the verified pro """Initializes the Earth Engine API.""" try: # Attempt authentication. Might prompt user if credentials are not found. - # ee.Authenticate() # Often needed only once per session/machine + ee.Authenticate() # Often needed only once per session/machine # Initialize with the specified project ID. ee.Initialize(project=project_id, opt_url='https://earthengine-highvolume.googleapis.com') # Use high volume endpoint @@ -254,11 +254,15 @@ def calcVaporPressure(temp_k): def main(): """Main script execution.""" - # --- Configuration --- + # --- Initialization (Moved to the top) --- PROJECT_ID = 'ee-crop-health-telangana' # Or get from environment variable + initialize_ee(PROJECT_ID) + + # --- Configuration --- BOUNDARY_ASSET_ID = 'users/jonasnothnagel/pa_boundary' OUTPUT_ASSET_BASE = 'users/jonasnothnagel/' # Base path for output assets + # Now create GEE objects after initialization PREDICTION_DATE = ee.Date('2023-03-01') PRESENT_START_DATE = '2023-02-01' # For LS, SM, RH PRESENT_END_DATE = '2023-02-28' # For LS, SM, RH @@ -273,8 +277,7 @@ def main(): 'rh': ee.Date('1980-01-01') } - # --- Initialization --- - initialize_ee(PROJECT_ID) + # --- Load Boundary (after init) --- boundary = get_goa_boundary(BOUNDARY_ASSET_ID) if boundary is None: print("Exiting: Could not load boundary.") diff --git a/fire_vulnerability.ipynb b/fire_vulnerability.ipynb deleted file mode 100644 index b3ed683..0000000 --- a/fire_vulnerability.ipynb +++ /dev/null @@ -1,1401 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Libraries imported successfully!\n" - ] - } - ], - "source": [ - "import os\n", - "import numpy as np\n", - "import pandas as pd\n", - "import geopandas as gpd\n", - "import matplotlib.pyplot as plt\n", - "import rasterio\n", - "from rasterio.plot import show\n", - "from rasterio.mask import mask\n", - "from sklearn.ensemble import RandomForestClassifier\n", - "from sklearn.metrics import confusion_matrix, accuracy_score, classification_report\n", - "import seaborn as sns\n", - "from scipy.spatial import distance\n", - "from scipy.interpolate import Rbf\n", - "\n", - "# For interactive maps\n", - "import folium\n", - "from folium.plugins import HeatMap\n", - "\n", - "print(\"Libraries imported successfully!\")" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "def load_shapefile(file_path):\n", - " \"\"\"Load and return a shapefile as a GeoDataFrame.\"\"\"\n", - " if not os.path.exists(file_path):\n", - " print(f\"Warning: File {file_path} does not exist!\")\n", - " return None\n", - " \n", - " try:\n", - " gdf = gpd.read_file(file_path)\n", - " print(f\"Loaded {file_path} with {len(gdf)} features\")\n", - " return gdf\n", - " except Exception as e:\n", - " print(f\"Error loading {file_path}: {e}\")\n", - " return None\n", - "\n", - "def load_raster(file_path):\n", - " \"\"\"Load and return a raster file.\"\"\"\n", - " if not os.path.exists(file_path):\n", - " print(f\"Warning: File {file_path} does not exist!\")\n", - " return None\n", - " \n", - " try:\n", - " with rasterio.open(file_path) as src:\n", - " data = src.read()\n", - " meta = src.meta.copy()\n", - " print(f\"Loaded {file_path} with shape {data.shape}\")\n", - " return data, meta\n", - " except Exception as e:\n", - " print(f\"Error loading {file_path}: {e}\")\n", - " return None, None\n", - "\n", - "def clip_raster_with_boundary(raster_data, raster_meta, boundary_gdf):\n", - " \"\"\"Clip a raster with a boundary.\"\"\"\n", - " try:\n", - " # Ensure boundary is in the same CRS as the raster\n", - " boundary_crs = boundary_gdf.crs\n", - " raster_crs = rasterio.crs.CRS.from_string(raster_meta['crs'])\n", - " \n", - " if boundary_crs != raster_crs:\n", - " boundary_gdf = boundary_gdf.to_crs(raster_crs)\n", - " \n", - " # Get geometry as GeoJSON for masking\n", - " geoms = boundary_gdf.geometry.values\n", - " \n", - " # Create a virtual raster source\n", - " with rasterio.io.MemoryFile() as memfile:\n", - " with memfile.open(**raster_meta) as src:\n", - " src.write(raster_data)\n", - " clipped_data, clipped_transform = mask(src, geoms, crop=True)\n", - " \n", - " # Update metadata\n", - " clipped_meta = raster_meta.copy()\n", - " clipped_meta.update({\n", - " \"height\": clipped_data.shape[1],\n", - " \"width\": clipped_data.shape[2],\n", - " \"transform\": clipped_transform\n", - " })\n", - " \n", - " return clipped_data, clipped_meta\n", - " except Exception as e:\n", - " print(f\"Error clipping raster: {e}\")\n", - " return raster_data, raster_meta\n", - "\n", - "def display_raster(raster_data, raster_meta, band=0, title=\"\", cmap=\"viridis\"):\n", - " \"\"\"Display a raster band with proper styling.\"\"\"\n", - " if raster_data is None or raster_meta is None:\n", - " print(\"Cannot display: Raster data or metadata is None\")\n", - " return\n", - " \n", - " plt.figure(figsize=(12, 10))\n", - " \n", - " if band >= raster_data.shape[0]:\n", - " print(f\"Band {band} out of range. Raster has {raster_data.shape[0]} bands.\")\n", - " band = 0\n", - " \n", - " # Create the plot\n", - " show(raster_data[band], transform=raster_meta['transform'], cmap=cmap)\n", - " plt.colorbar(label=f\"Band {band} Value\")\n", - " plt.title(title)\n", - " plt.tight_layout()\n", - " plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loaded data/fire13_19.shp with 197 features\n", - "Loaded data/fire20_23.shp with 70 features\n", - "Loaded data/pa_boundary.shp with 6 features\n", - "Combined fire events: 267\n", - "Fire events 2013-2022: 216\n", - "Fire events 2022 only: 4\n", - "Fire events March 2023: 51\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "data_dir = \"data\"\n", - "\n", - "# Load fire event datasets (2013-2019 and 2020-2023)\n", - "fire13_19 = load_shapefile(os.path.join(data_dir, \"fire13_19.shp\"))\n", - "fire20_23 = load_shapefile(os.path.join(data_dir, \"fire20_23.shp\"))\n", - "\n", - "# Load study area boundary\n", - "study_area = load_shapefile(os.path.join(data_dir, \"pa_boundary.shp\"))\n", - "\n", - "# Combine fire events (equivalent to the merge in the JS code)\n", - "if fire13_19 is not None and fire20_23 is not None:\n", - " fire13_23 = pd.concat([fire13_19, fire20_23], ignore_index=True)\n", - " print(f\"Combined fire events: {len(fire13_23)}\")\n", - " \n", - " # Filter out 2023 events (similar to the JS code)\n", - " if 'acq_date' in fire13_23.columns:\n", - " fire13_22 = fire13_23[~fire13_23['acq_date'].astype(str).str.contains('-2023')]\n", - " fire2022 = fire13_23[fire13_23['acq_date'].astype(str).str.contains('2022')]\n", - " fire_march_2023 = fire13_23[fire13_23['acq_date'].astype(str).str.contains('2023')]\n", - " \n", - " print(f\"Fire events 2013-2022: {len(fire13_22)}\")\n", - " print(f\"Fire events 2022 only: {len(fire2022)}\")\n", - " print(f\"Fire events March 2023: {len(fire_march_2023)}\")\n", - " \n", - " # Plot the fire events\n", - " if study_area is not None:\n", - " plt.figure(figsize=(12, 10))\n", - " \n", - " # Plot boundary\n", - " study_area.plot(ax=plt.gca(), color='lightgrey', edgecolor='black')\n", - " \n", - " # Plot fire events\n", - " fire13_22.plot(ax=plt.gca(), color='red', markersize=5, label='2013-2022 Fires')\n", - " fire_march_2023.plot(ax=plt.gca(), color='blue', markersize=5, label='March 2023 Fires')\n", - " \n", - " plt.title('Fire Events in Study Area')\n", - " plt.legend()\n", - " plt.axis('off')\n", - " plt.tight_layout()\n", - " plt.show()\n", - " else:\n", - " print(\"Warning: 'acq_date' column not found in fire data\")\n", - "else:\n", - " print(\"Cannot proceed without fire event data\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loaded data/inputResampled.tif with shape (40, 2912, 944)\n", - "Input raster has 40 bands\n" - ] - }, - { - "data": { - "image/png": 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", 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Load the inputResampled.tif file (trend layers from TrendFire.js)\n", - "input_resampled, input_meta = load_raster(os.path.join(data_dir, \"inputResampled.tif\"))\n", - "\n", - "if input_resampled is not None and input_meta is not None:\n", - " print(f\"Input raster has {input_resampled.shape[0]} bands\")\n", - " \n", - " # Display the first few bands to understand the data\n", - " for band_idx in range(min(3, input_resampled.shape[0])):\n", - " plt.figure(figsize=(12, 10))\n", - " plt.imshow(input_resampled[band_idx], cmap=\"viridis\")\n", - " plt.colorbar(label='Value')\n", - " plt.title(f\"Trend Layer - Band {band_idx+1}\")\n", - " plt.axis('off')\n", - " plt.show()\n", - "else:\n", - " print(\"Input raster (inputResampled.tif) not found.\")\n", - " print(\"This file contains 14 trend layers representing vegetation, burn indices, and meteorological parameters.\")\n", - " print(\"Download from: https://www.dropbox.com/scl/fi/ena1qfeqv5ppshluop8kt/inputResampled.tif\")" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Error loading data/road.tif: 'data/road.tif' not recognized as being in a supported file format.\n", - "Road data (road.tif) not found\n", - "This layer was developed separately using QGIS and python\n" - ] - } - ], - "source": [ - "# Load the road.tif file\n", - "road_data, road_meta = load_raster(os.path.join(data_dir, \"road.tif\"))\n", - "\n", - "if road_data is not None and road_meta is not None:\n", - " print(\"Road data loaded successfully\")\n", - " \n", - " # Clip to study area if available\n", - " if study_area is not None:\n", - " road_data, road_meta = clip_raster_with_boundary(road_data, road_meta, study_area)\n", - " print(\"Road data clipped to study area\")\n", - " \n", - " # Display the road data\n", - " display_raster(road_data, road_meta, band=0, title=\"Road Network\", cmap=\"binary\")\n", - "else:\n", - " print(\"Road data (road.tif) not found\")\n", - " print(\"This layer was developed separately using QGIS and python\")" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Generating random points for non-fire locations...\n", - "Generated 62 random points within study area\n", - "After filtering: 62 non-fire points\n" - ] - }, - { - "data": { - "image/png": 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FSRI1oIqiKEmiBlRRFCVJ1IAqiqIkiRpQRVGUJNFJJMUzICGYTr1SJTPYc889o6rXFxU1oIrrMAzH+Gy46K2iOAWaoMgoFqZQnyhqQBXXscYT/VR0GZ3+kCuZfXLesmVL/hI8BIOcRA2o4nrYbo0ngr2K4jR2bxRG1C4mdAotIimuYnOeeJ6Kkirs58vpHLsaUMUTaNiu+PHzpQZUURQlSdSAKoqiJIkaUEUJIGwYJWz1YmvYZZddJssag4AaUEUpgiHASLFDPZTXX389LTndFStWyOOEX1iPfeKJJ5qff/5ZdtY7Tc2aNfMfq1SpUqZBgwayKjpehg4dasaOHZvQY/JYvK5eQw2oohSBffbZR/ak//77764dw8yZM8VY2svw4cPNXnvtFbNxnPYx9rAnC/vif/75Z1m4ePzxx5sLLrjAfPLJJ3H9LUadxvYgoAZUUYrAqaeeKoZq8ODBMX/vlVdeMUcffbQsK8SDe/jhhwv8f37GAsMePXqYMmXKmIMPPtg8+eSTcR0D/bMcg71goMJDeDw+jNabb75pjjrqKDmOH3/8UbbO3njjjaZatWriTTZp0kT+tjA4xipVqpjDDjtMDDa9lpMmTZL/t3jxYtOqVSv5GcfWs2dP89dff0UN4VkbfvXVV5ubbrrJlC9fXu53wIABBV4b6NSpkzwne3vRokXmlFNOkWMpW7asadiwoZk3b55JJ2pAlcCwfTuekTFt2+685naqoSkbw/f444+bVatWRfyd+fPnm/PPP99ceOGFYlwwDnfeeeduYSxGtVGjRuLV9enTx2RnZ5ulS5c6dqxM5OAtP/XUU2bJkiXSVH7llVeaTz/91EyYMMF88cUX5rzzzpPttMuWLYv7fkuUKCHz5tu2bTObN282p512mtl///3N3LlzJbTHQ+ZxYvHMM8+IAZ8zZ4558MEHxcOdMWOG/D/uB8aMGSNer7190UUXmerVq8ttXuNbbrlFjiOtsJVTUdzi77//zvvqq6/kuqgMHJiXV6wYW2Z3XnM7lVx66aV5HTp0kH83bdo0r0ePHvLv1157jU23+b/XtWvXvDZt2hT42379+uUdddRR+bdr1KiR161bt/zbO3bsyKtUqVJeTk5O1Mdfvny5PM6+++6bV6pUqfzLggUL8t577z35f7///rv87pgxY+T2woUL8/9+5cqVeXvssUfe6tWrC9xv69at82699daoj8uxPvroo/LvrVu35t13331y35MnT8578skn8/bff/+8v/76K//3p0yZkle8ePG8X375ZbfXDVq0aJF30kknFXiM448/Pu/mm2/Ov83987qGUqZMmbyxY8fmpftzFop6oEpgmDULq7Xz31xzO13g2eFFff3117v9P37WrFmzAj/jNl4euUhL3bp18/9NqEooa2e4zzjjDFO6dGm5kAoI5cUXXzQLFy7MvxCiR4K8aOhj4A3z+ITh9r65fPDBB+b777+P+Xxvvvlm+V0mfHjuFNLOOussea716tUTbzL0uZJvjeVNhx6XnVm3zz0a119/vbniiiskjcLjF3bMqUBn4ZXAcNJJFFR2Gk9qJ9xOF82bN5fQ9dZbb5UcXzKEh58YUVvoIez++++/I/7eQQcdZA499NBC75+cZGhRibwkKQjC3/D5cIxjLPr16yfPk9+rXLlykbsOYj33aJAK6dq1q5kyZYp56623TP/+/SUVQa40XagBVQLDbbftvMbzxHja2+kCL6h+/frm8MMPL/DzI4880nz88ccFfsZtPL94hS0o8jjNcccdJx4ont7JJ5+c0N8ecMABEY02z5XcLrlQ64XyXIsXL77b65KogQ311i28hlyuu+4606VLF8mTptOAagivBIYSJYy56y5jpk/fec3tdHLsscdKYWPYsGEFfn7DDTeYd955x9xzzz3m22+/lVD/iSeekOq3m2B4ON5LLrnEvPrqq2b58uXms88+k44CvLpkuOiii6S169JLLzVffvmlee+998xVV11lLr74YvFUk4XKO68h0oe0jOGNU5iiY2DlypVipCkmYcDTiRpQRXEQqsfhoSeN5i+99JKEl8ccc4y566675PeSDfWdBI8NA4qRx0OkvQhDRBtVMpQsWdJMmzbNbNiwQfpDO3fubFq3bi0njKJAhwJVedIVeM547r/99pscOycCuhzIEw8cONCkk2K7KlyK4gr//POPeD61atUSz0VR/PQ5Uw9UURQlSdSAKoqiJIkaUEVRlCRRA6ooipIkakAVRVGSRA2ooihKkqgBVRRFSRI1oIqiKEmiBlRRFCVJ1IAqSgpAZf3aa681XsPuUUL2Tik6akAVpYhL5cIv3333nYhzIB7iNCgd2cdB4QhF9u7duxeqnWlhlhxVd2by4wXZOFSmlN1ROTtFKQKsv0CQI5SKFSsWKlPH+gsEjpOB/T+IEyNawl4gDOiaNWtExKMwOC6EmhVnUA9UUYoAy9lCF7pxwUiFh/DIseGRoh6EAWTRGsyaNUu0OBE7xjtkuRpamrGwavVVq1YVBSL+hr1DSLxhVFF6wjPl2PAc33777aghvF0+h1Qc+5hQU2IlslWPx+NF4QhDbT1ffoYG0YABA0S1icfhWDiOTEMNqBIc3NgqlwBDhgyRdRcsjWOpHCso8GDPPfdcWejGag4MamEL2MLB+GI4t2/fLjvXkX7jsbhPVPLPPvvsQpfE3X777fJ3bLVkSRzbQYF1xUjdsUbErk3mZ2wZffTRR82oUaPkvtnZjh5qxuHohiVFSRBHl32leascy9FYyha60K1z5875i9KuueaaAovYOnbsWODvL7/88ryePXsW+NlHH30kC9iivR4sh9tvv/3yb3/77bd5hx12WF6jRo3kdtWqVfMGDRq024K2Pn36FFhE9/nnn8ttu3xu5syZBZbA8TN7DP3798+rV69egft8+OGH5XG3bduW5wd0qZyieHCrHHvJQxe6havRh0KIHAphMeFw6EI3PEa8SbQro/HHH3/kL3RDBBml9+eee878+eefkguNtMAu0rK7aEvdWOgGsQpTrD/++++/zSGHHGKysrLMa6+9Jh5wpqFFJCUmmzZtkrCOPd+sSyDf5Vlc2CrH3p94FrrZ3w2FpW69evWKmDuMpQhfpkwZs2DBAqnCY+wI4QED6sRSN7sgLtZSN/K1S5culdwrSvHssX/ooYdko2fad7O7iBpQZTcoEPzwww9SXX7kkUfyt0FiRCmCsNLWeimewu2tcgnCqo+vvvoqbgNswXBG+huKUxRz2A/UokWL/J9zu3HjxkkfJ90CkRa67bvvvqZ9+/Zy6du3rzniiCNkVTLPK1NQA6oUgCot3gRrYvnisDa2Q4cO4i2xIAyj+vjjj5vatWtLscF+kfjCUmxIxfbIhLfK+QRORE2bNpWiEfvN8VAxqHh0ye4QYt0w6315f6jA836RWiDETxY6CEgpcD9U9/GAX3jhBTGqTZo0kVTC+PHj5XNQo0YNk0moAVUEvgxsUuSLVr58eVnRi1EM3Q/OF5LqLGEbFeTQMJ8vKuEb4X5Rd4RnCuQdec2ogNPKhOeP4aPKnSykA8iRcjIjh3nUUUeZN99809SpUyfp+6RLgMEA8r0bN26U97pcuXLyGbn++uvls0MFftKkSaZChQomk9ClcorkzpiqoRWFLzOtNfvtt19C98E63Msvv1y+RO3atYv773SpnJIOdKmckjJo+CZsfPDBB8X7SdR4Aits8T4obihKpqAGNMPZunWrtNJQDcbzTBbC9sLGFxUlaKgBzXDGjRsnuTcmTYoCPYCESYqSSWgRKYNZvXq1eJ4dO3bcrck7Ef79918zaNAgmeE+9dRTHT1GRfEyakAzlF9++cU0b95cWlKys7MTrpwz2fLRRx9J1Z2Jmi1btphnnnlGhCiSIVbTtqIUlVR9vrQKn2GsXLnSvPzyy+bJJ5+UlhSMHr19iXqceKy0ODEmSLsTI4jJaEbywUaMgvwpMnD0nmoblOIUmDekA9evXy/tVrRzMYjgFGpAM4T58+dL4zayZYxjnnTSSeJ5MkudKHxkmDZB+Yem+6LCBxyVH7xYRUkFNPszPZesBms0NITPAGiuZnrosMMOM/fdd59p1arVbnPZiYCHSCP1b7/95sjx8aFm9ptCVKSRQUUpCkQ3TM2lIrJRA5oBYDxhwoQJjgk9OGlAgQ83x5ZJQhSK/1EDGnAQAmGcj7DbSeNEs72TBlRR/Iga0ICD6AMV84kTJzp6v34woORVGVNNJs+rKPGgjfQBB5UcpMycNiIY0F9//dV4FQR+6S7A+y5MTBio0rKUDSUjK5KsKIWhBjTAYBRYGnbmmWc6ft/kQH/66SfZu+OlRg6Ohd0+KAjR58rtWN43RSvm/ytVqiSjrHQWzJ07d7dNm4oSCTWgAQbJOQwIXpjTHHfccRIesyQNT4+1Dqg5IW3nFlTxe/fubW688UZRhsIYYuitbmlo7yne5ieffGKuu+466YtFhWrq1Knmww8/NGeddZaZPHmyp04MijfRPtAAgygveo2ffvppSlZx0L9JfykTSWyTRC6MQhWeH14vF1IH6WiMx5izp+fdd981d911l+nUqZP8HJ1N9DFZA4xqOq/H6NGjC2y+vPXWW0U42oJhZcQVkePHHnusSC1fSrBRAxpgunXrJiH2888/n5bH+/HHH/ONKfqgGFjUzPHoMKbsSqehORWPy/1zzQoSVN4tS5YsMd98843kQREFZtSUtcL0wl588cXiZYbnh/kZXinyfvSn8u+MXNmrFIoa0ADD3hyMyS233OJK+xS5REJiDCrCJQjZYkStQWWjI2BoN2zYIF5iorC7iXFSmqWHDx8uiu7RYG6fySlWT6DSXpjuKes1yI92795dvFZFCUcNaIBhCRxfftZwuAkfMcJ7vFMuiC4zT89kFDt1kNTDAMa7Fjfv33/Nz1ddZSovW2Y+KV7cnPbBB2bq9OnmgAMOKPRvmbvneHjswkAnYMiQIeaEE04wbdq0kVl/LnjVOq+vgPaBBlws2enZ32TA2OBtcmHvEgvq5syZI8aUcBoS2Uw5vWVL0+aTT6QCygb0+ytUiMt4QiK7gQjz6XVlfS/5ZLxkYAslHqyiqAENKBhPwmgvFkBQcWrdurVcUIQirKe4Ey8NtmzJbx/hum3JkiY+3zUx2H1ujwuvlb5XKvV4pRh+OhGUzEbbmAKs9wn0N3oZvFB6MRNZRHdAx47GqjvmFStm9m/f3qTDi0Zu76KLLpLd68OGDUv5YyreRw1oQGGMEeINbd2CXfMspMMoxUux22836/v0MTOMMTObNTPrs7JMuqCnlMISnQ2sDVYyGzWgAWXNmjWe90BJM3z88cemQ4cOif1hiRKm8vDhZmJWlun8xRdm419/mXTClBOivKNGjUrr4yreQw1ogD1QmtqZxPEq9IoiopywAd3FvffeK7nJG264wXz55Zdpmxyi/YlCEnugGjZsKB7pbbfdJuOfDC3ocr3MQYtIAYXJHIo1Xm63YU6/Vq1aSW8ExbtG4xTB6C5dukiOkr5XFtvRG5qK6SsLI6CMsNK8v2LFCul15aSFEadB/9lnn03ZYyveQQ1oQKF9iV5LL2PD96IYeRry27ZtK6tKyKfSXjRp0iTZ0US1PFWwjC+8v5auB46lWrVqKXtcxVtoCB9QKB6xZpg2Ia9CX+iMGTOKbOgp7GAw77//fhm7ZPzSjS2fTFphWF988UXz7bffpv3xlfSjBtTHazoqV64skzyoD6GEZENIYHKGf9Mm5FUIvb/77jtHxiQZB7377rulN5MCT8+ePU26wZN+6qmn5JqZe8ZMlWCjo5w+lakjb8gFI4r4r21bomh0xBFHmBo1aogOJrJuGCqvghISU0kUgRLpGOBjSxUfr49iFOE0oiFco6SUyvxnPOOi55xzjoiXWFUoJZioAQ0D1R4KA4z8EQqGa0m6DW8XYhyLFi0S1XWrboQBRfwCr4fL6pUrTf899zQt99zT/NOw4c5eSY89F2C6B2OD7BxedLR8KM+bvkveH4pPFGko3pAGYM6ek8bAgQPl2m3eeOMNc+edd8rxer0PVykaakBDQEsS42TbUGgDokrMTDQyaaj47Ni2zfzTv78pOX++MSedZMxttzlimMhXfvDBB2b69OmiYsQkEUWJM844Q7xIQkKgSELhBZ1KRiGjUTEnx1TKyeENlmmdddnZZn12tvEiFICuvfZa8/TTT5vLLrvMrFy5UgylvSxZstQsXHim+eefhsaYWaZUqWGmbdtW5phjjpGTBSOXtBJ55WRHS9OqVatENEUJNmpAd0EbCoWIBg0aiPwb8mt8kfFyKAowbogBveXff80d27dL8pgXbsvNN5tS999f6P3jJbHgjXCTOWqMYyiI/pLHYyIHxR9Cc94ajAurMwh1uaBLicEYMWJEzOp1jawsU2b27Pzbm5o2NStzc41XQQR5ypQpospkXxteb05g27ffar799iI+rqZYsTzTq9cvpm9fby604z2jjQrRFPRElWDjjVO2y2DUaIfBo3n00Uclr0YO0Xp9zGmjNcn6ii5Tp5riK1bIzzFfHz/wgOn/wQfSWM3vYeBCDRsSbayWIKTDOPAYeI5Ua0OhZ5OCEAYj9O/pN6RBmy8jxZbff/9djqWw1p8tDRqY0nPm5Hug3PYyN998s8jvlS9fXjQ9Cc3RB6UglJVVY9erjYEqZhYuLG2M8aYBxSMmdMeIKsEnYz1QnvbixYsl74bRxHCNHDmyUMX08ND4g5YtzaA99pAJFMJwmqtJA2BQy5YtKwaQUA4RiquuuippRXbEgNnbc/7550tIXyjbt5uKubmm5IIFYjy9mgONh5yciiYnp5IYTzzQ7Ox1Jjt7vfEizz33nKR7ONGlQn1f8RYZa0AZw7vjjjvE8yPHSdge7hUmYpgIvzFyKLBzIewG1kUQntatW7fIx8xb5eXJolSBznJubkWzYEFJ06DBFpOVtd6z5wJOkrxPFLqU4JMxBhRhXEJz9DF5ylTY6RkcMGCA46LDVoGdQhBhOaG7EnwDS7qGbgJOxkQLSvDx6HncGZhwYT0t1d233npLquotWrSQPCVVUhqvU6HYHqrArqQejKcN8efMIT9qXAnxWWCH2r7mPzOHQBpQWl8ouNArSEIfg0kFmzCbyRzWM+CBUnFX/A+eJ8YTuOa2G8yePVvy3ig0KZlBoAwoIhL33XeffJCp6FLMYRIkdIHYJZdcIiE2s9IaWgcDwnY8T1tk4rYbMFHFehKv9KMqqScw7zSTOQjd0mr08MMPywc5WnhOiK3GMziQ84TQHGi6QdeUzyAnaCVzCEQRiaZ3muDJedKs7oVNlEpmgTRf7969ZZz2yCOPdPtwlDThCzUmRisRCI4E0yt4nevXrxc5MzWeiQuTUDWm8LFp0ya3D8e3kDY68MADPTGLr6QPzxtQij7sDEdtnPCIqSGgIESTOtM/qBKhA5nIzm9lpwAJrykdCoh60KWQ8dDnm5Mjo7BcS49UnPlPJAQzsU83k/FsDpTZczxKmtCZDe/cubMYyXHjxonHSV/n2rVrzU033WS6deumH9wkXl86E9jvQ/rjiy++kD7ZTIchCTtpxigsFCbCwtQRnR948kpm4UkDSkMyKkQIaWRlZZns7GypbHbv3l1EP1Akwuukwq4hU2KpEFIdeO94nYyYogDFHDptXYqRCTOMJ3DN7cKwUVEsdSwlmHjSgM6cOVMurI21gh5A5ZxGeC5K4vTp00ek8ix470zOcMnJyZERVPQrSZfQBpaJ7TgFRFjYLbVqlYTysbQEyH9yItddSJmHp78h6hU5C10KLD0jLcLcPwMG0LdvX1F84tpCSgSNTYwrCkmoSGUC67t3N6XmzjUlv/jCFNu61ey9apWE9LFCefKfya5mVvyNJw2oDcuRBkPdSCkahOsInaC0f/zxx8vK3/ATFa1g5JSZ3+eCuhRDCawNhkaNGonoCmpD/C6q60EcVa04ZowpNW9efhhfWCjPSDDCMTq+mZl40oAiOAwUOPwgMsFuHiabEAD2ErT4PvnkkzK6igd50kkniS5pJKjAc7KyJyymuPg3PY0USdAjDRUIRqsz6DlQSyw9VbxPNEsZ3FAyD0+2MVFtP+qoo6TaHguWqV155ZUi4hBLZGL27DJyzW2nmT9/vjn77LNlrw/eiJeg2IbxvOeee6TKjteImn08EMJTvMMwUKzDI2WDJnk+VPPxUoMIhhKDCZjRrdWr71yHQg40Sv4T77yoJ3vFnxT3aosNogyxwBiwP4cqMl5WvCITTjaLk1OkSwCjQgsQaxy8ssoW7/OVV16RnCe6p060eaEUb3UG6B8N4u5zDCUGkxUo6/r0McsmTdqZ+4wQuhB1UIGn/1PJTDxpQCHWhCk5J4wCBovWEQxZJAjbEZcArnfs+ECq+hs3bnTkGAl7mT5p2rSpNPxTvaa53+2mb77YQ4YMkVQIXqSTENbTSlazZk0ZXfTKCcMxSpQQg8n+qGiGM3R98YYNGzT/mcF40oAyjhnNKIId66QvlCZwvCKrAB/K5ZevlfUPTZtuMr17rzWffdZRfs7UjVMgyozxJCd4+umni/ec7qZvlsdxzW0GDDCaDBwMGzbMdOy48zk7Cbvn3377bankU33mfchEBXY+d+S9TzjhBLcPRXEJXxlQfoZR4MvK0jfC5549e0pODs8PRZwbb7zR9OrVS/RATzqpqSlf/gkzbNg3ZvXqXiQH5EtPKOoUhO54IXhiY8eOldytm03fiEQTWmPgWC+RKjhhoD6E5iqe+A033CCbRTMJCkgU5vbee2+3D0VxCU+qMTVv3lwME8u5LITdGEc+tOTz+PdDDz0k3h8eEV4Ajd+FhefvvfeeNIs7ARM9pBC6dOkiuVjUyJ9//vm0FRQi7X4/e948OUFwHOmcqac4xWgovaOZACdz2sEGDhxo+vXr5/bhKC7hSQ/0yy+/zBcGYUc4BSOMFMpBM2bMkNYajCeeD2LJtOh88skn8uXF6BLOP/bYY/K3bNq0sIEzWePJ2CMeHa08VLQpHhAqY7Bp82F//OOPP57WamxowWNt795mZuPGUiSjrSadkAcmlcL6lEwB75vPl45vZjae9EAp9NDUzbw7HiOeHdVkRjspXqxZs0aMKmEkLTbRIKxmqRu/hyEtinEbPHiwtAVRgaZdCSOFyAktTCyQQw+S9qt0Gy88oZUrV8qJZcSIEfIzGuDxBtMJ6YsePXqYd9991zEP38sMHz7cvPTSS6ItkO73XPEOnjSgfDBRlcc40NDNF5O97Yly2223ieFk1UdRjCcGnD3vXHJzc6W3Es+TzYukDZjOQREKxaj+/fubdIqDMFkEHMfVV18t++fJw6ZbWQlDUrlyZQlpY53UgsLFF18sURKfVSVz8aQBdQrmuEuWLCnGGMibcpsWqESEl6lokxKgkTxaIzpiHIh18Lt4pqmGZXkYTDtEgC7qxIkTXZWkI3Lg8Sn0BRlOqBSP8EIpWMaCE23p0qUzUpglEwh07IEHy4cdeTxaTqZNm2Zee+01UR1KhMWLF8sGz1hTPHyR6tatKznYVJ+TyPGSE+bLSbGI9AFettt6nrQ08TrjGQcZps9oVyus/5PIhM8M7W0UHJXgEWgDeu2110pxiV5NWp6scEaoRF48EJqS58Q4smM+Uq8neTAKWzSv0xmQypMC+p14NUjTYUgTfT6pghQHhRWrjxlU7DQbwwqRDCMnbYY8SOuw4pjPA9NKCLoowSLQBvTCCy/M/zcVe0JLCkFUzfEOqKzHAyE53h75TQwxKYBI8CWhtYVwPlU89dRTsrgMz5Pqt5egOwKFpqA31ZMuQRKQkynatBQ1LXymOGFTUKSYR76c4h4KVuSrGYGNtt9L8R+BzoECxSe8AIwf/aOMIVKhxotg5JF/48XFC0UkDDEeKR0B4eCVUFzCO3S6Ootng5FmeMDmdb0GAw1I4E2fPj3wa1ZoZ7v++uvl3xSTCO2JDigu0e7GsIeFqIXOEX5ORGO3Kij+JtAeKOEueUFENfAUTjnlFOndZHadNimUhuL1FplyYuLI9qdGmz755ptvpJE9Fa0t5GKtEfUqZ555pig12ZaqIEPOm5MFaSE+XxhTTsYUEkONp92mgGKXrdoT3nMCV/xNoEuDQ4cOFYMGNDwjj4dxI7RnfzwFD9p+YkEBijFFwtJSpUpJSxVEa4vCK4nkmToBsml4Lddcc42EihyP18C7gsLUtIICPa/0J2M0Dz/88N3EqsPB2HISZ3oJTYZKlSql7VgV5wlUCG/Dc5TXETnGy8RbW778J/P99xeYqlXPNyee+J/Jy7vPvPzyBFlcN2DAAGltisbSpUulvxMjSt6UCjP5UAylXYkRCl8O5vD5UmHwIsGyPGbVCesw0Fzbf1sNAIw9HjP519BQmOEAJq7wYEgneE35nhwx3jk5QF2THJlXX31VUkqkkSgGKv6lRNA+mBhRvsT0eeIlYvyefLKK+e67Kmb16mJm4sQdpkyZhmbQoGOlahxvng4PltD/vPPOk9vRZu7xehHzoK2pa9eu5oorrijgrSJMjDEm/MNbw9gQ3tEniMHhuKnsTpkyRfYUoaZkjTcjrhhtDCvFJBrWCZndhtQGx8g0Eh4YJyU1niamDB40btxYxF/OPffcwOeLg4pnPFDCGULSoqzFePTRRyUPxcx7qOJSVlYNUaW3IG+Xm7syrvvEK6RyimdI0zrFG35GsSBannPz5s3S0kRBid8h7EfshD5Umt0xMsyNx2quJj+G0hGam3inGFlEVihUYLAAARN+5iYYdjQCeH0ISzHqRTIGaJyOGmXK7Zqr39iunVlPs3qAGtE5QSKKw2tHDy8pGQY1FP/hCQPKIWBo8KyYpcZLo9kdI5UIzKjTuEwBgykkS07OztUeqNIjrIxGaHb2+rjvl3wjfX3MvWMYETCJB1IIrNNAMR8DU6FCBSkyDBo0KO4cIdV8vmj0tJJbxbB+/fXX8nrRNuQ2vGe06PAcnZiBF4WpESOMNcF8OFGGj7YR0+/w3t5///0yclzYVJPiPTx1Wkc4BO/KNobTEpPIyCVqTdYDDIVlchC6XC4R6OsjzKKaj8dHuBoPGEw8Vn6fMVAqtYk8H6B30M67A4bTS+0vGHX6ap0SEBGN05Db/DvaRkw3Fgg6DUVMlLxYKU3UpOr2/sITHzGEKCx2XJL2o0TyaOQkER6h7zPU4ABfpEQ8znAIScnrET5TbUWBnTCMQhICzRg1wnqKArbARIiGN02uE+mzZMRQ/ACvg5MtWyx1Kz17dgEPtBiTX1jIGBbRLhAkypgzZ2dhpijveToh0qLwSb6ezhC71lvxPiW88gHCEGH4CE/pucT7TCSXRg4Vz5MwG88vFRx66KFm5syZUsghlI62mZLdSHbc0+0cZaqgH/Wmm26SiahEBhEKQ7ZfbttmKo4ebYrt2CGGtNTcuZIXXd+3b9S/i7RA0C+QCydfjsITRUOKhSo+4g880UhPdRvjxweICRYmh2w4nohxo4UIUWNahFKR2iX/yQQJHgKhK2o8hPVcKFyhyUlxiF4/xvUoFnl1Yqgo0IaFiDVVd9rGbrnlFufuvEQJU2rRonzjCVwfMGYM+n1R/yx8gSC3/QQOBCOiVOg5OSn+wBNFJHo2acch7EWwA/EF+ggxpqHFoMIgjGbaA2UiQm7ylumGSj2N+oxz4qEFqaGcFAWSgBSMiBbQ/ox3z3wiHNGsmSkRNi/Oh3Tz8cebFaNHByoHauFreP7554sjwOdX8Qee8EDphcSzI9y98sor5UPEyCS3ExEoJuxBwAGcWl2c7CQOxjxIxpMGfiKF8ePHi5gKfagRjWeUVcuJ8M/hh4vBDAUvdJ+lS6P+jc1z057GtZ+MJ5A3Z2oOTVnFP3jmY0YPKGILl112mXiPfJAoIpEbsmsT8E5jVbGRUmvVqpUYYJrY3YBqNNqhFJyCAsMJ5OZ4jxiBRXGpsFXLLLorPWeO/CzRFqQVI0aYmtnZpuT8+XI/GM+8XYY1qKBTy/w8q2sU/+AZAwrsNLeFI1qHCGvowbSCH/REvvjii1Fnzem7xNiy4MxNceHq1avLcwkCvP6MvJL3pdm7sNUokVYtJ8w++5gVu3KeNfv0Ec8T44lhFQ83N1ful4q9FJ385m5GgDY32vcYmFD8g6c+eVTeKQDdeeedEi6SZ2OWvXz58tKsTU6R+XLagqpVqxbRcDFDTphJVR5PNt2CG1TnKQSEq/H4EV5DctO2YBZLM6BAGxKrp3etWuZ20mBIw3Keoauck/VwvQZ5cz7Tuh7Zf3giBwq0/TDLTghPMh04G5MLpS2Jfkrybnh2U6dOjXgfKH5TgKK1BvWkwpSWUgFtTrRisdDO76Dmj1ePhmU8xjN81TLX4iE6iCMersfgM4uTECoArviDEl6q8NKUHa2qizfEOCPtHpF2cZOEZ96cMUtCfoxurFxdqqCHD48tke4Br4JoNAU+PPu4KVEipR6hox6uR2DDAA6Chu/+wzMG9I8//pDraPJeSLfhfTInz553ZuWRlqNST76THk1ACQkDS7M7LTep5ueff5bHJjdIBR7R40THNdMFx8YOebwdLpxkKHpFG1hgfp9CnpeUgqxHWyAH6mPI6/OZjrYmRvE2njGgdn49mhoTHigGkzDe5jgBL3Pw4MHSN4qH2qRJExmLo5CUjjM6snMYc3axo45E+xL5Qq/BvDonm/D2Ll4/tmlSZafxP/Q123///fO3mnpmMibFHm46IRVFvp9ohRXViv/wyLdip0gG0AtHBT4cdBPp8aSNhlAddSSq9EzCIDBMlZjcKSE+UzKpLhThnZF2QACF1qloeVmvQDsYAwu0y+BR4vFzEmL2GkUgpADxRpnm4kuN54nh5H3R0NJ5eC/wOjlx4RB4NWpRfDCJZI0So5wUgDCIaCRiDAuDL/nChQsl95muxnWMd+hGT/bcWKFlL8JJie4FihR46ZFee3K3dEBwgmI1BV0EFMPwXFO5pjlT4WvHyDK9zQyPKP7EM1V4PB3OxKyBZZQNURBC48IgtMQ4pHPqB7GTULygyxkrx3bSSSfJCmRUpKK99hQxaBNDbYrcKM+RdSJ266TiLEQB5Mx/+ukntw9FCYIHGgo9n7byyxeaL7KX4CVDiQnBEFZ24C3Tc+pF0Q9CRHQmycsW1gSvpBdWz1StWlXEaBR/4hkPNJTQJnkMlBe9ByTr8O7IJfJvr0E3Auo+DBYg/hEo4+nAvL0XIE3C+6T4F08aUHt2tsl2r41FsqmTfCIN5szsU3jxErxe7FLCsFMcircJ3i/Yefsys2fLNbf9CD3P9Np6MAhU/FaFD2fYsGGSE0UgxEsGAI8BAWGKVp999lnU1cXpBO1UJqBYUEZPIXPV5DXZDVWUJX1eJSjTSERatO/RDeHFKEbxsQdKczwGgRYaDKmXZN1oYaLn023j+cwzz8iqENSn2KlDNwJ9sA888IDsaKevM4jQQM8UEvh5GgnnAHQFtH/xrAcK9IMiMILKPMaAZnW3wXiCFzw7+jWp5FIpR3gl2hRX0AjKNBLaDRT4ypUr5/ahKEGqwodCiNOyZUvpSSSsb9y4saveJ/lOvGNCeTcbzNENYE79rrvuit2DGlD5N79Dfy1KY5wE2V6g+BPPhvAW5OhQVqLp+N5773Ut4c7OeVqCeHyOx+3pHCZXGMNEOi8WQSm4BA3EbyiQduvWze1DUYJsQK0RZeyNimVhBiNV4O3RtM/Uzrp164zrbN9uHipb1vR8+WVzwC6h4SAXXIIGn2U+1ywgVPyLLwwokOMj74iYiF0ZnE6oktLwTD8lxSO8UDfAa2ED6DOHH246LlxoWv77r6k8cmRUzzIoBZegQeGIfDqjyIp/8Y0BJdHOGl3mtS+//HIJqdMNFW8rOxZppjyVsKqE9cEUjTp16mTqrF2b/+bF8ixTLXCsJAfiN+Sxye0r/sU3BhRQW8Lzo2+O9cW0EqWbo48+WmbL06WRSc6V3fKINKMTwPPGA62NGlU8nuUu+beVubk7ZeAytICEo5eTU9FkZdWQa7cdPwwofP755+4eiFIkfPNtYq6b3TE0iDNlg4wdfZAtWrRImyEbM2aM9KRSQWXBWjrA62TiiWJDr1698ltegtLKky7YGZ+TU8nk5RUzc+bsbPdi/bFbkP/kpIgBRd1L8Se+MKDIqdHyYTUTMWZIrqVTxZt9TYxFMpvP+KbVLEXhHeGTVFXlkZnjpDFjxgzxPFF+GjFsmDlo3Lida3937JBr5li0RSk6CxaUFOMJXHPbTdBQQE/W7W4OJeB9oCw1o/cTsd/Ro0e78oFD2o193VwzVopKO8LE06ZNE5Wj008/XSZ/UgF5MkYyUZKnE+Cee+4xwytXNpetXLlzLxBv4q4wXnKcHpvL9wqE7dYDLVYsz2Rnr9vNAyWsx1PFuDZosMVkZa1PyfmIzxHRBFJ2NNNHW9OteB9PuysYDZroGXlDjd6ts/WECRPydzaxbhkxEfRKaahnjJLCFtJ2qdAkxesOLVgxg1+hS5f/tybt+rm2KMUGYwihxtGNMJ/K+w033CB6Be+++64aT5/jWQNKewfK9GvWrJGVE5H2wKfLiBO6A6s77r//fmklIhfKznQMJ8pMGFVarVLNueeea7bfcovJu/fe3TxQP7QopcvLC4fHKMwYpjrMp4eYqSNEaFgBk47Pi5KhBpSzNIUjVhS7eZam7xPvFwFlkv0003MhBAtdhkc7Srq+ECXuuksswvJnnzUrly839Rs0MH8dd5wZXaGCOfDTT2VnlFfxWjEnFAw6x2TDfG47BZkyTr6kfVgBE2k1t+I/PGlAWWHM3Dtna7eNAe1K7du3j/r/ae4ndGdOPm3gTt11l9lw1lmmQ6tWZq8VK8w/33wjuVnA4Mc6ZjfxWjEn0TA/WUaOHCn7phC3JopQgoHn+kCpOiPNRvhOaOx1CMUI493wKNAHoLWLGX3W4rLOGZXzRYsWGa+CYcK7A6e9PKfC/NzclXLtVGoBw0khkBObFQpXgoHnqvDkFdkiyZ53r696ZU89xSQKS7Q5uQ1L+E455RTZz8S0khdxKwfq5gmWXl4KgQxEpGsAQ8lAA0pfHBshyX9edNFFxuvQWI9W6VdffSXVeDfhpIOYMpqptFal++Tz5ptvSm74jDPOUCOxi1mzZpmrrrrKdO3aVT4r9PMqwcJT535CT9o83FZ6j7c/89lnn5XCktvGE+yW0JNPPjntxpM2LqtpuWLFChk0yHTYDoDQNT3CCOCo8QwmnnpXbZ+nHz5s7CBiJv/GG280XoB+WXLHQ4cONXPmzElrzpqJMCIGpNlo8cp0kFy88sorJUdNxV1XdgQXT1kqQkDr3Xkdm/nw0jqGIUOGiEgFhpRFc+kwnnib9evXFy+L14TcNSkNwJguXrxYQtlMkW1DJYwWN4p5kyZN8sTqFyXAITyFD750FGRYk0H4yWx5NPgiMgqXiqmfRCDfSK6P3BZFgnTD60Vlt1KlSlJ4K1OmjKwbQemcPChiFankk08+kX1Vxx57rJkyZYpMZeH5nnbaafJz+mfxxKzhxDvt2bOn6dChgwkqRCQYT94L+j29dHJVAlZEwjthn89DDz0kU0Z82DCezZs3F73PUBBQZtYcA0tzPYrwzKLHMrRAiw/9d9wfXpLT3HTTTaIsnm5NR3pOkbXj+VkDxZcWo8pripFytJATtldpXtu2pvOFF0ragJNfqLFesGCBFE4QPSGEtflsCimML2JYqlataoIGJ/XLLrtMlLrYJsu6FSX4lHBrPJL2HzwW+he7d+8edc4do0AyHkUmvqjWUGC0qlSpIn83efJk+XISPlGEwjvkdxHeIB1AhRqDm9DWyhjL2DDoeM54WIUZcafh+bRr104moJjR58TDaCDPnaGDVJwo7F4lRkdLz5ljVr7yijxvFPoRVwmlQYMGYkDC9Ux5fwBv+bbbbhNt17jxwWI8VLn4THLCVuOZOaT9U0hujjCPcOfpp58u9AuPR4WBoNLL3/FFrFOnjhQuOOtTAednGFZGKTFq7733nvwthpmQij5NxJfPOuuspI0GoHREq9XFF18szwMvC3m7dPLII4/Ic3zxxRdFWARSPXUUvlcJNfxxr7++m/GMBMazX79++bdZT82JjcJKvL2q0d4LLxlcPguohpFWoVneD4VQxYcG9PxzzjF9fv3VXHLIIea/Tz816485JuKHGwUmEvB4VhhHq7/JXDxfSAwYzeuEsxhGPEy+lHhoGF2MZvny5aUvkftJtNUo2jI2qu88Jh4xyvTphNcEA8oooDWe6QAjhOHiddhhjPmuShWT3bRpXH+LR8rx0s7DyZKwnpMaG1Z5T8ihFkaii/ESNrgOQfGOtjY+l507d0754ykZmAMdvO++5uZ//pHyfzQNy7lz55oePXrI+oyDDjpIvE+MFUYrXN2Gw48W/l9zzTXSRvL888/LUrhEqJiTk/8ltMf5YcuWUqjBEGBI04315khZpHWbY4hH9/5//5mey5ebNevWJZ1nJf9NvyppCDzpZN6LWAaxRlaWrHHOf7zq1c2ySZPSEvYzfkx6g8+sEnzSHmdcduihhS5DI2dJVRcDSlECKC6FQ5gUSyOUEBMDixhzooQuY1vbu7cUTkgbYNCZbU43FNFuvvlmKcaEGk+KFuhKpvQ8GLJXaeUll5hffv1V5u6TBWHoSy65RFIR5KwLI9HFeLKJNOT2XqtWRd1a6rT4N8+JcVolM0h7CF+lc2ezgzUVMTQsMYp4l6gy0a7Ev5PJKaEij4QY6zD4dzJGA6hq/5CTI198vOBEvdmiwt6c8847T7y20Hwi0y7cZgoJb5sqMK9dqGf422+/yUZPOhycKG7YZWj0d9LrmCycHDGeqLIXelwh70U8YGDLvfmm2XvX5lZejXSITTOZRrqIKEXJDNLugRa7/XYz96yzzAxjzJqsrIjeBGdwcpnkNeUgk0zIcz8UWGgwp+CUzJZG2qbIeWI4+LK7IYKLt0bu94EHHsj3uEkh0J6F8SGnyAQSgs/kGNESoE+TajetRhhf2p6clNwr6nRNvXr15No23TtKiRJm49lnx7e11MHOkokTJ0oLl9s9ykr6SH+psEQJU/qBBwz+4KtRCkgHHnighOx33nmneJ9Fge2ZGzZsEG82krDv7Nll5JrbkRg3bpxcY4jwBNPdNku+k4kfRiVt1Zu8IUaSKjbhO60zpDroCKCARlcCRpU2McROPv30U3lN2e5JHhVPleIa+eFkClkQTwU+FhT4ateuLX2h4Se3pPO0OTmS/+R6/cUXm82NGpntZcvK9fru3U0qYWsCnj+RgJI5uNZIT8sHFXYEZiMpzrNADqPHLqKitoQgdIEXOnv27HzPCc8T42lp2nST6ECGQ78nvap0AuBlkIukopwOaMfisWzBBW+Nt4tWKjwdTg6RXhua6zGaFLs4bsBosgaa2/SRch88F774hNPxQq8jPZy0lhXVG+c9pnLNMTG9xHElW5gKLzRhNEvNmxd34ako8N6QIqJtLl3rrhVv4FqzGuE5Xxw8qUiFBBqTWV3sRD/dMcccIymB0MeJV9gXT3j+/PniCYIj3lKcULSiv5CWH7xJ2rXwymmTQbE/2muDpgBG1xpPIISns4ECHb2K5EaTCcPt3zihV8DxYJBbtGgh6Qa842QJb3XaZ+nSQluf4k3jFAaePJ65V4RllPTh2jgH45tM0uDFjB07djelbrwoKvFOYMNNu5Y4nvUNPD572PFAKcJUrlzZpBMMC3lMjAqeOp4nXmeyK06Y/OJCCxHeEt4sHmAi3ifY1w/pPCcg+iB3yAmB8JewPlKDPd0G6A4weYXhjdWrmmeKmYWmnmloZpliJi9qDtSJ/Uy8nhSPyFOneypNcR9X5+Fojid0I0/HlFFoew4Gw6nsAuIaeGs0oQ8cOFC8qGhbGsm50qTPMjsKSEzO0PCNMYVQry6V2Pl6TjB2NJLnUVQwVJwUMJ7xNLGHwvuBWDMnNqbBnISTA5V9vG1SD7Vq1cr/f7wfTH/h5TFGS6HMFqEsthi54c0vzYRVrcz9f95sbjEPmAurv2vKn31MxGKlE/uZ+FyQY6fFTMk8XJ83Y/0EM+0Ic4SGx/vvv79ZvXq1I0aULzvN9DQ34+naTZqRwBsm3MXIkGbgmplvvGWKSKeeeqpJB/YYGXl9//33xbtB4aiotGnTRk4myVS/Gb1FeZ5rp0WkyX3SSUCfLesv2B+Esef9GDRokGwqoAsC4zp48ODdPxe7Wp16VJ9s7jb9zTazj1xzW3KfEYqVRd3PREoIr5icsNMnFMUfuG5AEf3gLE7Ok55Gqy7E5BE/I6Tjw0mOrCjwIcdIk89EQagwTVL20RPyAgaHJXd8edO1roJ8J+E11Xcq1RgNJ+A5ULjB66fpO16o/GPgqPLTzJ8KSA8wpEAkgjfKCY9CIh0GGFFOtJxwUaGK1sifiFEkbZOdvU4KiFwnuoWT4+Mkf+utt+af7PygZasEcCcSRgKjgfdHoQQolmBEyX9hZAkfk1kXYad4+FJilDACoeEwOU7yjPx/loDxBeWL4LaSOMfF60El3q7McAJCYVIDqFrhWcfKNZPSQO+Tx6cb4dFHH037ziM+ovYx6cogD3r33XdHzJWma2kdrwvvDUad+ffx48dLtEJun7w1J1wl+HjGgALhEAUCwmy+rKEfVqrKNJFTPY73C0yCn9waXgy6lHhe5M/s35NPIxdIVdoWrch50k/ph5XKRYEpJnLQnJRCx2TJvTK9hTfFnnmKRbw2tOgQuru9MI5uBIRiKNxYgRk34IRLL62NUDgminOo7zPkQAoCoW23Xy8ltXhKVJEvKQl52kEocNiZYoootupL2GQ/uLEgn0p7DArthH14C6EfZhbYIauGSDPeKaEyeT2nKv9eBg+U/k/g9bZQNCMcpX2sS5cuUjAjF01I7ZVtm7z/TPokWgBzGjYBkOJBFQuNUzvey+eMAiQpJ6KnJ554Ij8tpAQPT3mgwOEwkkgOklwlX1pGEJmiwZBSTKGYUBh4ARRMaNSP1PqDgSCExZDQ1J8pkE8kH8xryMw2DfZ8wQnl8ZoY+6Rw49VdPnh6FNOK0jOaDkg/kWbgxEP+ONrkFhEQbVycxJl2U/yF60WkcDCY5CjxDCkoEL5z27by0HhfGCjSYzwZX4wm2IxXxQpeihNUuTMFq9LPl5ZUBcaTkwivA32YvOZeNZ4UbJA6TLcOazLgiVKoY9SWSIp8djiI3KBnQNhP+E8Ll8f8GcVvBhQIvWlbofJMcj40ZGeckvxcLOxqXaru0YwBUz5U/THUfMD53UyAEU4KaFYBiS8xIiWsTaFA5FUldQwL8/3M0BOh+AHy9niYRFDk3u00m82/s2SPkz3bEig6UTwlYlL8g+dCeAsCGnXr1hUvkso4Z3BCS3oy+RIRikabDkKXkcZ8vFf6P2PB02dGnJCQDzt52CBDmEg4SUqEvlYKdh07dpTXySvGkx5YCoq833jLyAjCvHnzpDfUb3qbpKMY4GCyDGlEOhoo1lG9x/vncw4UPHm+oTulFG/jWQMKCIDwYeMDZYs7aF+Sv2RBHEl8KsisjAiHMz5GlmpzLNFlCzkoCkt4C2hnBhVaxRApwTPHm6frAC/UGik3odqPoaEzgKIhniZi2BgVLrYv1o/wNaNVjxOXDefxUHmutjjHpBytUHYBn+J9PG1AMY4YP3JJoVAAwWOkKR4PNdJUDR/MK6+8UlSD4snpLV26VPbY8CFPWHzZR2As6YslRUIlmdDRK/qVRBe8B926dZPRzSCuP7ZbYpliYhw1tEJPmxhREH3PXokGlNh49l3CrtOCFD7zDIz7IRuGhxJtNt0aTSqh5JgKgxYmvDA81iBDcQ1PB8+e8N0rxhPwvIgsyEcH0XgC0Q0hOxFSeHsTC/jwvN944w3Xjk8JiAG1ubBYu9xp9I7WHoISPUIlJO5plC8MPN0TTzwxKZFhxRmWL18uJ0cv9Ju6AYMBtGlR4NSRUH/gWQPKl4hKMfm6aFkGvEZakOhhDIfGZjwtcpuE+PGss6DAQlqAPKuSfigSpXXbqAehTY/OENrNFO/jWQMKGEAql1TJI9G7d2/5wvHFiwaeKITnUcPBaOJ9Ir6czBZPpWhQlebkFeT8czywe4v2JvpytaXJ+5TwesUYD5LKJaGN3QgZ6qUWNiZHlZnJm0jqPQhTsOaDEUaKK6QLaJnS0bv0w14nJox0GsdIvyuFJK7RftCZeu/iWUtBszy5ICTXCOHpCUTkItlcKsl7Kp8///yz5NrIjWI4+RlrMx566CGZCkmXYLKyE95bWpfo70RAxpUWMhbS5ebK2g+U60V82cWTKBV4NAnQIWCmnu8CLX1qRL2HJw0oghEYM0Qv7Bcq0uK5eGHqg/46JNz4NyACwQQObTMULpT0w6ADzeMU+Rgj5T13A4ynXUjHWhBI1QK6eMFYZmVlm8WLO5lHHtlh3n33dfPpp+3NPvt48iubsXju3SCspkGeNg8S6bTZEFazsTES1iDGgp5RxkM5o1OYQqSZlRF6RncXvCyUi/r37y8nMrcIX0gXaQGdG6BrOmsWO6uKmYULd5j69SeYhQvPyQjFML/gOQMK9GNS1EE4AjHhaF8uejaRDCtMF5LZbyf2CSnOwQgjuW2kC900nrstpIuygM4NQnc2Ue/99ttKMqJM+klTTd7Ak5NITGoQ0k2fPl0KR3gpoZVxQnyEL5gxRheSHeisBlH8Q40aNSQCYMeS61M3HsuBWli1bLeGsp6kU6dF5u23m0mVns+/4j6eNKAW2lrwQKnMMu+OxiJfNq5RaELLktDe7dUbSuKg9Up3BPuV6LTQdIqJaz3JU0+NlA0L9IpyElLcxdMG1I5ikiMDPjDkfxiFQ+YuyKIfmdK6hKgzO9UJ5dWImrg2LdiFg2yNJa+vuIfnDShtRuPGjZMPCsWleJSVFP9gRV8wouizKoXD4AhjygjCTJo0SdrwFHfwvAFVggEarYzcUhykdSlULIRiEmE8OW/UtZTCQZGMvlleT1ScSIco6cfTo5yK/2ECjDw2637xMPE469SpIyE7+qtWaZ48NroHSnzQu0wnA5qiSBIyrURfrZJe3C81KoEF40jFGOV7JmpowSHPieYlXhP9uWwAZcCBVI0XRJ39BEpkDIOQD8WLxytlYk+LqulDQ3glZbz++uumU6dOZujQobvtMcJg0n5GSxp6BCxh69u3rxaSkgQhcDx8CnOctJT0oAZUSQmsjMa7pHMidG2FkjpYDMimWbYr6OudHjQHqqSEjz76SPKfFDr0y5y+bQOI76Bqr6QHNaBKSmARIK1neKFKerDTenQ8KOlBDaiSEvCCoq1bUVIDfaGgGxXShxpQJSUwart69WpZuaKkB7aski5BS0JJD2pAlZTl4+j3nLNLX1NJPWxUaNKkifSHKulBDaiSMlj6F2mVipI6GHdGPJxqvJJ61IAqKWHr1q3ifWJEM1VJCTm6rKwacs3tdNC6dWvJPSO2o6QeNaBKSnjjjTfMhg0bTMeOHU0mggwdWp6zZ5eRa26nA4znqaeeKptstcU79agBVRxh27ZtspqYDQHouN53330yoskKlUwkVE2ea26nM4z/7rvvZMJLSS1qQJUiw2qV+vXry9oUlv81atRIjCkydZkKAsioyAPX3E4XnLhQu3rqqafS9piZio5yKjHh44HKTyQdVn7+yCOPmNtvv12W9F177bXSRsN+dwwpVeFMJZKafDq3hDz55JNiQFl7U65cufQ9cIahBlSJuTXz/vvvF+X/Zs2aiSwdikr16tWT/4+aErJ0l156qXs73ZWIsEu+bdu2otLEiU1JDWpAlYhs3LjRVK9eXdZBI5dGRZ0tqX///bc0ybOLChFk8m0YWsV7oFrPRJj24qYONaBK1Co6FXQk6WrXrp1fKEL0mLCdeWuath966CFTtmxZtw9XiQDrPtBhJYxXpf/UoILKSkROOOEEyXvidVoDSohOCI+2J8ZUV0l7m5NOOkmuOemx/VRxHq3CK1HnqsmhPf/88xK2h4LiuRpP72NlBFXpP3WoAVWiMmTIEFH2IUxX/Mdvv/0m11WqVHH7UAKLGlAlKvR1PvbYY2bixInm3XffdftwlCQq8aAGNHWoAVVikpWVJe1LtCyxx0jxnwGtXLmy24cSWNSAKoXm0dj8yMbHN9980+3DURJgyZIlpnz58hk90JBq1IAqhVK3bl3R9vzhhx/cPhQlgfznyy+/bPr06eP2oQQabWNS4oJ24aSWw23fbirm5pqSCxaYLQ0amPVZWSatM40ZCKmWnJwcaUPTKaTUop9kJW4DWrx44gELxrNSTg4TG6b0romY9dnZKThCBSZPnixdE0gJDho0KH9PkpIaNIRX4iLZgTU8T4wncM1tJTW89tprMlbLsMOXX34pU0hKalEPVElpCE/YjueJ8cwrVkxuK6kJ24cPH24uuOAC2YmUVLpFSRg1oEpcIF0XdwgfmvesX9+s69XLlFy48P85UMVxCNnRJ+jSpYsazzSiBlRx3AMNz3uuy842K3NzU36MmQy5Tk5wuhM+vWgOVCkUekDxbhBKjgfNe6afEiVKiH7BqlWr3D6UjEINqFKo50krTJkyZcy5554b198QqpPvlL/XvGfaYGSTk52SPjSEV2IyYsQI8+qrr4qwSLwTLTbPWaD3U0k5jGyqAU0vakCVqHz88cfifXbr1k1aY+KmRAnt9XSBffbZR7aiKulDQ3glKowCoiXZt29ftw9FKYR58+ZJE328aRbFGXSlhxIV1OhRpqdA0bx5c3PvvfeakiXTt99ciV91if7PI444wrzzzjvyfinpQT1QJeZ+8a+++kpGAmfNmmUeffRRtw9JCeO///4zt9xyi7SYTZgwQY1nmtFXW4nJYYcdZm644QbZh8TqYooUBxxwgLnjjjsk56a4X+QjUsDz1MVx6UcNqBIX5EExnt9++61566235MuquVF3+eijj8yTTz5p7rvvPtOyZUu3Dycj0RyokjCtW7cWvcnx48en7TG3bzcmN7eiWbCgpGnQYIvJylqfsap4W7duNU8//bRcTj31VFlfnIxSllJ0MvQjqBRF5Zw1uXfddVdaHxfjmZNTyeTlFTNz5uzsR83OXm8yjc8++8zcc889ZvXq1aZfv36SSlHj6R5qQJWEJdMQ6j3jjDNS9yARRJjxPDGewDW3U/VYXnNtCRLnz59vnn32WTl5NWvWzEyZMkWW/inu4q1PiuJ5OnfubAYMGGBeeOEFc8UVV6TkMSKJMDdocJd4nhjPYsXyJIx3wmCaHTtMpVGjPCn4jETdtGnTzLhx46QbgjalsWPHmosvvli9To+gBlRJCL7EV111layMaNWqlTnkkEMcf4yS8+cXFCOZP99kjdwZrofmQJ0wztuqVfOc8Mkff/whq6Q5Sa1bt860adNGtqIyDaZSdd5CDaiSMPSFTp06VRTP8Y723HNPR++/2I4dBpOGqZDrHTskqnYi5xmuFGUFT7wg+LxixQopzLH9FP3Viy66yFx33XXmmGOOce2YlNioAVUShmkkVM+ZUsITpT/USfKKFxfjaawRdTBcDVfI39iunfy83OTJO39hx46dJf805EHJbS5btsx88sknMqhAgYge25tvvtlkZ2eLPJ3ibdSAKknRqFEjqQazg6dixYqihO4UWxo2NKU/++z/XmHDho7ddySlKML6vVavlscjH2qKF095HpQezgceeEDEP/bdd18ZlX3qqadM165ddUDBR6gBVZIGT4kcHY3c27dvly8/FfqiklI5PJSidhlN7l+uw3OuKcyD/vzzz2I4mRyinzY3N9ecfPLJajR9ihrQDObtt9+WUJypoh49ekiBKBEoaFDcIF/34IMPSosT8ncYhCIVO1IshxdeSNrcqFFa8qBU1O+8805R9mdu/fzzz9eikM/RSaQMBcN56aWXShWdhWSIUjzxxBMSSh500EEJ3x/5Oxq7P/zwQwlJDz74YLnUrFlT9ETLly9vvEKNrCxTZvbs/Ntbq1UzG9u3L7j4zsEcKF+xZ555Rk42pDpGjhxpypYt69j9K+6hBjQDoaewXr165swzzzR333232bJlixhTChpU1PEiCTMT9Y74KGFAafrmvrggdIGRZuwQQRIvUDEnx1QaMSK/UMUXYF2fPinxeklt3H///ebFF1+UfDGSgNrDGRzUgGYY//zzjzn99NPN8uXLJeS2Rg0PdOPGjeall14ShZ/BgweLTFpRmTNnjmnRooX0jGJIPGE8tm83ddq3N3uHLGDb1LRpSjaH3nTTTWb69Onymvbs2dPx+1fcRXOgGQbeJW0ztB+FeoQUf1iNS/sMXhPeEt4oUnZFoUmTJub555835513noStt99+u/t5vxIlzMazz87Pg6Yq70mlHeUqvG9yzErwUAOaYRx33HEyIhhrQdyVV14pv0NOEwN40kknFekxzznnHJFdY/STavM111zjePN9oqR68d369etF6ANv/7LLLnP0vhXvoCF8hoF3WbduXVOuXDkxatEgpOeLz1jhokWLZK1xKH/++adZunSp5FLjzW0+9thj4tHWqVPHDB861Bzz5pueFvEoChSMkJkj36wN8cFFDWgGwpw1LTSo++CRRuPHH3+U0Jv+TvoVYe3ateaSSy6RPkaM7P777y/5TZSBjj76aLnGoLKnB81Qrvk9PM9999zTHPDkk+bf994zNapVM3XWrMkPoddlZ3tGxMMJyHeyZpg8sxJcgnPKV+KGzY14oew4or0mWk6SNiQ8RiaOmDbCax02bJjZtm2bzMEfeuihMoK4ePFiqb4TtsbiTmPMgF2LuPKY/Nn1c6+IeDjJDz/8YE455RS3D0NJMWpAMxAq4UOGDDFt27YVTxJV82jggf7+++/S1oRniVG4/vrrTZUqVeT/NwgpvhDuYzisZ0rDOBcWnZFTPaR3b1N87lz53XyhkF1iHm6KeDgN6Q08dTxyJdhoCJ/BUOD4+uuvzeuvv15oUeeXX36RPGipUqWK1n9pK9/GmM3HH2/y9tgjcDnQhQsXimbn559/burXr+/24SgpJBifWCUp8EIpAtH7iXRaLKzH6XjlOyBGM5Tvv/9evPzDDz/c7UNRUox6oBkOhpO+UDQoyYXycSDc9srUkB9BF4DX9LvvvnP7UJQU44GxEMVNUDlHyJecHTDaiRgIFXQlMZUlRmLhyy+/1PxnhhC8+ElJCGbVCTdtDhRhEQzBwIEDzdChQ92fGvI4P/30k7n88svFgPJa2YJZjRo13D40JQ2oB5rhsF0TOTo7LWOv2f64KmRWXNmdmTNnypI9qu5oozKmakc2afXS7FjwUQ80w8GAkgelEg+hVWNC+2Sk7TKlAMcgQvXq1c1DDz1UIGRnhxGL9+izpeVLCS5qQDMcwk68pcMOO0xub926VRrmUWb64osvJB+qGCmyDR8+XF4roDcWDxO9gPB8Z8uWLcWTxys98cQTTdOmTV06aiXVaBU+gyF0R32Jmfi+fftKLq9Pnz5iOJlXZwYeD5RmeJrjWa+b5bDohh9U+/Ek16xZky8QDbNnzxZjyuv06quv7vZ35EG7d+8uOWX6Qr0kKK04hxrQDIZq8bHHHitbNcndEbJ37NhRxjtpBH/55ZfNvHnzxNviwm1WUjBHH9QTCuIfCxYsMB9//LFZsmSJTFcx0967d2/TqVOn/J1PjLBy8kEC8P333494fxSWeK1Qs7JtYkqw0BA+g7GtS0wkYRjwsBD9QACELztjnFxCZe5YIFerVi1z/PHHm6CBB84JA3gdGDJAio+RV6rroWAUx40bJyueo8GuKRToed00HxpM1IBmMOQ7wbYwcV2tWjXxwqLJ0U2ZMkWqz0EwoMztT5482XzzzTfSjsSF/CV6pZwkCtswGs+YJmr8hPKaDw0m2saUwdjxTEJNwPNkDDFa4QgvjP1GGFDUl/wM+9hRpUKmj15YJq/wNKmeozLlxHpmC/dJZR5vnpyoEhw0B5rB/PXXX7LKGJFlmub5KLA1kqIH4WskEFFGCo/KM208foWFehizF154QQpDqc5P2nxos2bNRGhZ86HBQD3QDIa1HqwjptLOVk6Wn0G4+nwoo0aNEkPrxMK5dENl/LrrrjMXXnihjKoizUeong5jZvOhpEAeeeSRlD+ekh7UA1VkUyeeKJNHtCsRylNdDgd1dYoqbJqkSu8nqKhTSae/1aYj2F9PKiK8QJRKMJ4Un1g4p/lQ/6MeqCL5P3KCttczkvFk1zsTS+QJu3XrZvzE448/nn/MI0eOlAo6aQs873T7D+RDkbmjMq++i/9RD1SRhnBamOj1pP+xUaNGu/0Oe5Dod6RpnBYfv8BKElITzZs3l9UkNLTb5nYq5E4Wi+KFohV9t/TVUshS/It6oIp4oBQ5aGuKZDwBw0OjORe/QKg8Y8YM8ZyfeOKJ/GkgrlmE54bxBFrAKCaxVwpPWPEvakAVgS8y+U12HDGJEw6eEvlDKslMMHkdPEzyjZwQbrzxRs9Vvek1/fbbb2XqS/EvakAVgXYeikRMJ9FMTtM3TeYWekMxrORHySfa7ZxeZPXq1bJWmGNlP3s6i0TxcuSRR4oSVv/+/c3mzZvdPhwlSdSAKsK6devM3nvvLWHvBRdcIN5R+E5zFJsQ0RgwYIAZO3aseKN29NErWC8ZkWhartgO6lXQIGAV9P333+/2oShJogZUEeiJJAe6fPlyCS9plI+0551xzzvuuEOq8uyKZ0zRNuG7CbnZa6+9VnKbKOqPGDHC1K5d23gZtESRvUNPlLFSxX94L7ZRXOHdd98VD9RqW9JgTpWYqjv6oLZizR55wncUil555RUzfvx4mfPG+NIfmmqoqA8aNKhAQYuuADxNpqTI42KUOH6/CJig1ES3QLjHr3gfbWNS8oUxKlWqlD8lgxeHd2mFRbp27SoFjyZNmsiOePKfGDCmlughpbcRg5tK0ORkAgq9UvpVCdPJb5KbZQiAkDhevVKK37m5Fc2CBSVNgwZbTFbWetc2LL/11lty8qFj4NRTT3XnIJSkUAOq5OuCorbUunXr/J9jJFEowoiiA4onyggkzegIbpAvJaTn57TmHHXUUY4eF4+NfB5hOXuHaEWiWwBDjydswfvlGDkJxFttz8mpaHJyKpm8vGKmWLE8k529zmRn756ySAd8BTlZcdJCxNqqYyneRw1ohvPjjz9K1Z05cfYiEcZHC/ExWggup0PKDqETvF5yspbGjRuLGlSpUqUk31kUsrJqmNmz/z/z37TpJpObu9K4xddffy3FO3RDyUEr/kANaAbCW46ICGpKtC8hCjx69GhTtWpV144Jz/LFF18UBXia+qdOnSo/Y8UIBpsuAcJ2p5rfveSBWu6++27J8dIBgZeteB81oBnmbT711FPmueeek6ovhaCzzjpLJnXc/MI+/fTTspeJENbCvDoN8KmqpHspB2phlJbeUJ43rWKK91EDmkGggUnBghUeTBbh2bk1zgikBAYOHCiGgxwsYblVgs/UJWw01jMrT+rCzfdGiQ/tA80gaP+hak5+sWHDhq59QTdt2iT6o2hzoklK8QqvmBwnvaVly5aXEJs8JdeZNC7O4jqKZ6FFMsW7qAHNII477jjp65wzZ46EiG4EH+T4qKozq864JYUrKv+h1XNCa/KTFHm45namwCYA0hakNRTvowY0w6DPkDwozdu0zKQLekZRH+rXr5/ocE6cOFE0MVGCCoe8JMUd4JrbmQInErxQTiy0jCneRg1oBmJXFTPHng5BEBrvybuyCwjj8Pzzz8tcfTQo6lAZB665nUm0a9dOogOmvBRvo0WkDIWiDQ30rDF+++235We0Ea1YsUJU02PtO08EppMGDx4sE0MUjDCkfqyQpxt2yNPOxdSV16T4lP+jBjRDwfPs3LmzVHzpASVkpJUIQ0d7ExXxeIxdrD1L9HBy/+T1HnzwQVf7TP0GO5N4/ejXTcfggpIcGsJnKOQeKSrRoE57E3PuzJmjsoSeJusvklWfx7OlUESKACOA9J0az8RAj5XXDD1TxbuoB5rhYCxnzZolxSUEiMmPUmBi3j1ZzwdpPIwv7UkYaSU5UGe66667RIMVERfFe6gHmuGQA2UGG+OJKMcnn3wiBYyZM2fKfHyifPDBB7IWmRSA28aTXKqf+0nPPvtsUbkiH6p+jjdRA6rkg5DIN998Iz2iFJRod0oUxDCYImJEFNz84vu9n5RBB4wnJzXVCvUmakAzmMWLF5vevXvL5IuFSSX6Nfl5MvqetCwRwk+bNk2KVE2bNnXtyx+EflJyoeyjQi/UqzuoMhk1oBkIXiHKP4TY7A1CFT3cUyR0ZL9QIh4kazQQBGFhGmsqEChB/o48Htqh6SYo/aR4oczG5+TkuH0oShhqQDMQ1kcgWnHFFVfIPiOU0MMNHKK+iewsp8eTwhPaouTuEAjh/mkGpxKPGPKGDRvivj8k3bp06SI6pMlC/ygydWh9cs1tP4J4NatKOOnxuireQQ1oBkLPJ/vfGaWk3QgJNcQ92CVkF8lh7MqWLVtoEzdG9uKLL5ZwH2+TthvrtWKE+Xu++FTlQ9ckFwZGnXYoFtglm0el+R6NT4SSufZzM37fvn2lt/bee+91+1CUENSAZiD0eLJHyK4kZlKIMPuNN96QpXIsjcMQsiIjFnhDNNsjDEKYiReK0bT9o+wsAir8RxxxREKbJ/FArXITvaqZzgEHHCBrP/DkdYOnd1ADmoEQGtPjidFjsybQ/0mxh6khVkpQSUf+jlUTyN9FKha1bdvWrF27VhbR8eW23irTTEAbFHlR0gW0SCHoHC/I2tlKNFs3FSNRAvun1Av1DmpAMxA8Q5rlaTWiZYndQxhSDB/GMDc319x6662y8uP888+XbZf//fdfgeo9f2P7RMP3KFmFJTZlEnri0TJjzy6jeLH3ecIJJ5j99tvPoWfub/bdd19z4YUXyhoWzYV6A51EynDoMWTj5vvvvy+GlVCbNcWEznh/rPugEESoTnsSUzGoKpFDZXaenfCo3JMDpXIPbNBs1qyZ6dChg6QF0AA98MAD4z4mGvEpmmAkevToIcLLyv9fGzz/+++/XyIIxV3UA81w6DNE/ZyRTkYvjznmGOk55GcYMGbk8SDJc6LliQHF4L733nui6GSb7UPzlKg7YXwxopBI9Z1ZfNaNkEulwMVxJDuTH9RcKEU/IgXSIoq7qAeqxAUfE+TV6O20OU6bC8W7pLjRokULafYmx0rrDdV+ruvWrSuFqnggXcDjMBL6/fffixHG06JDQNkJRSQ6HsgvM/CguIePGzuUdEKBKJKikq20M8LJqhCMH/lORJPxSvFsL7nkkrgfh4IRVXsMNRd6VulVZe0HSvaKMYcccoikVO677z5JcURS9VfSg4bwSpGwX168RfKpn376qTTUs7QODVCEme1cfDzQBYAhtussSBdQrPr8889T9hz8SFZWlozgujHhpfwfNaBKkaCthqLSmjVrzHfffSfFJ1qaaG+iyESTPZs34+W0006T5nwa6YFm/ho1aphly5al8Fn4jzp16pg2bdpIq1kiE2OKs6gBVRzZ4WOr7LYXdPPmzVLkSHS/O0USvNrQhWpVqlSRuXylIGw1ZUZ+6tSpbh9KxhLsHChn5vvuM2bWLGNOOsmY227bOd+npCVPRzGJ9bzojSYCYT+ebGgBK5KX5ZXdSX///bdZunSp5H7pPsArp0OB61RC2xgnm0QGFBRnCbY1wXgOGMA30JiZM3f+7K673D6qwIPCPbk5GukTbUEi30kfKYUkoG2KvUCMiUbT+0Sqbs6cnWkCZt7TAfneKVOmmK+++kqq4pGeJ8Iq5IFpgE8FePsYaV1/7B7BDuHxPG2XFtfcVlICBQ3ELvAWUV9i7zvizM2bN4/4+1TmI+mEMjqKMWLtMcaUtiY8LZryU6X3yePQ8xpPRx8nBUZde/XqJYaTVSgUzdAVwBPlNSAXTP6XYhhygamEkddQPVclvQTbAyVsx/Pki0FujtuK49x+++3SUkNLEytC+EKz6TPWTiWq6lwohNgiEwaMdiiMJyOc2dnZ8jvcF6FxOITteJ4Yz2T0PlGeoseUIQFgZh8R6HAw6DwnPE6OhTzthAkTZMw1klpV7dq15cL92TavVFGrVi3xghV3CLYBJecJoTlQxVHIcWI80f6sXr26tDPRy9moUaOYf8fqZCr19HnShM/kEY33eHKMfuLBct8IZ0TbrWT1PUNzoPFCfyo9lMDsP5oAFGRCIa/5+OOPi3IVOVnyjRhcThiFdRb88ccf0hNLLjiVcP8ff/yxnHx0f3z60UkkJWnIc9KyxOglup2JfIHJa6KED0zVIKPHtBKh/ZgxY2RUkfskDHbaMKCaz+PQJcD+dQw/oTCDAoh10LCPh8lIKpNUtGLhTXNSiLerAKNLL+xzzz0nk1jRKGohjFFXFvhhrOlWUNJLsD1QJWUgPoIRQn2eHUqJGrnGjRvL36IKhefJhbYcxhOBPCIeXyo8K8RP0Bv94osvxHgCnQLM9/NcbGg8ZMgQyecmM+lDKgPoX6UiH+05FLUQRqoASEMwnaSkl2AXkZSUho62gJFsIzeSeVSRKRARulOMIdeJZ4hIyUknnZSSHCJVflqlEE6xkEZYsmSJqEeR66R4RZhO6E4OFE+Y7gIKThwnuVKMFwaYdi08zVDdVFIUeK+kBqjG41FHoqiFMIYMMPZjx45N+vVQkkc9UCUpDj74YPEekbjDa2OJXKLGjoo7DfJI1xHuhmqBkmt84IEHRDaPYk2ikm9oiJJXDQVvEEND4zkjo5Fo3769PB/k4ng+FLI4URAiW8+S/CnhPAYeg4/xJ+SnXYmOASuggh4ASlQvvfSSFJ8wurxuThbC8Gx5fdgggLCLhvHpRXOgSpGgFYkcKGpJiehTUijCAyWUJ3QOVXgCPpbMe5MPpecykhgznqE1HHiMeLP0XWLYqZSTIiB/SbGKNSZ4mBjAG2+8Ue7b3ie7lwjpWcFshaT5HWb4Wc/MNkyKQsj6kfelS+CWW24pcCwYVfKRFKJodeI+8UjtIAF5VcZUKUA5PQzAsWHM8dptXllJD2pAlSKDjiiixxR94pk6QqWJsB2DQugbrmhvoXmeEBh9Uow0YXeo8WRZ3auvvipjpMziA0aU9iG6AazQBkUj8pDsfUJLE88UhX2MNrlccodWW3PfPfc001q0MPX/+stsadDArM/KMnl77CHGsEyZMnG/Jtwfx4Hxpx8WzxfPFiMcrTc2WTDanCzQD6AvVUkfakAVR8ADHD58uBgIdEEjQeM5YS3iI+QX6V+0E0fRKtmoMWEYTj75ZDHUhKzWOBGKI/hMaMw8PrlYjKYNk+nzRAQar5Pft0Upfh/Di0HGuJLLpF2JQkyHRYvMkS+9xBfD5BUrZtZlZ5v12dlJvSaE73QY4H2SV2ULKpV9PNyjjjrKOAWvA5sBSF3Eej0V51EDqjgCHh1eIqE5YTdtSaHQa4lxpbjCFA9CwPGu+cDg4mHhPTZp0kT2N9l8pvV4463WMyHE41Ndx6OkSEQaweZga2RlmTKzZ+f//qamTc3K3FyTLHQT8FzJlWKoaZvCsybv6wSkCzDM3D99q0p6UQOqOAbeHBVnRDVohLfeEEaVhnlCWOsJJgqiyni4GCQML21PsSadonlqGGIMPZ5tJCrm5JhKOTmOeKAW0gvkURn1JF1BfnX//fc3RQWPnj5c0hY8N7uTSkkfakAVR6GgQ76RKjdjmbT9UIxhNBPPMZmeToozhL2E/VScua9QI0wVnCIW/5+NlbT1hEPPJ+tBMLq0FIUXrfLZvt1UzM01JRcsyM+BelXBC/FqPOn69evL86b6r6QXNaCK47Chkz3z5CzpucTrJN/Jxk/AqFL1JoQOb+uhOETbD5sn7dTPkUceKfdDWxHKQ3i3VuWe1ACtRuQZyXkylRO+N50CECkFcp7kTKUY5SNDGQtWTLM6mn5QVe1PP9pIrzgOITIVdnpD8Q4xXhhP8pYUcGgxoomdLz19nhSJCHMxtPwdjenWCHJ+R++S9covv/y6qVx5uGnXbk/ToMFrZubM92V6yRaFqECTf0URKRTamLgPftdW8jGehOrkO7nmth9hiR9tTIygKulHPVAlJeBhIgJCXpT+SsLNrl27mpkzZ0oYTiM63iQN6FTb8URpb2KyhzUeNK5TIMGwUkXHm/322wvzxx6N2WH22muwqV37OfPiiy9KaoDcKzlOvEz6Py18xAnrWeFsRzWdLha5AUMIePqE7pF6aZXUox6okhJoNqfibUWRCcEpelDtpiqNAcXbpPmbFiiMAT2f5En5ud0zb3cjUSAJHXvko1uq1GkyrWTzqjVr1hRvlyo7HtmZZ55p5s6dK/+fCj6qRRbCdopEwDW3/QQnBfpu8UDJf6rxdAd91ZWUQdjNbiR6P8lttmrVSjxJeiGt8AXtN0zPhE7QUAwiT4oRxUjwd3im4WOPXbocZGrV2qfAY+LtMgXE1BHGhbanSy+9VMYxmRaySM6T9SGhOVAfMX78ePPBBx/I1JUVRFHSj4bwStqg55IwnukcKuvRvCa8RrQ6mVbC2NKyhJGIZ+yRghF5V5r56U2l4IT4B03tQLN8JHFmP8HJgRQFFXhOSIp7qAFV0go5UFTo8ZwitRvZ+XrGLoEmcULyokD/JUYZA57IjnovwgmC4QEKcZyIoo3BKulBQ3glrdBUTztSrEZyKupAlZ5G8aKCSpLT8+dugK/D/D/7m9599101nh5ADaiSNqjIUzEnNI+18tfuKKJinszUUlDBM0cMmqKRFVJW3EWr8EraQFKOSaVOnToV+rtMDNF2pOyEEVZWnlBs055P76AeqJI28CZZOMfUUCxoewqVrotIQCaJ4oGOBFSl2AKAIIviHYL5iVM8FbbjOTGLXqlSJekHZaEcPZrRiCSeHI6dJEL0o/ScOfKz3UQ/AmJkmeDCA6VZvtATi5JW/PdpUnwFe4QYy0RcBMUgijlU4YsKRhHjCVxzOykj6wMQh4bQtSeKN1ADqqQURH6BqSSU4MP3FCULHiVG0crORZokisfI+sUDZdQ1EUV8JT2oAVVSCkIXtC3RSuTkeuJ4JoniMbJ+gPA9dIOo4h3UgCophbwnY5yEoU7oVTJdhEEuVqJEoeG438c1LWgIMBareA+dRFJSCvPoCIsg9MHeoWS9UD6mbMIkjwoYUUZBKapQdELBCV3MaPuY/FyEY54fhalLLrnE7cNRwlAPVEl56xIK8GiEDho0SMQ+WMFLO1MiMN+O8WQ1CNqieKIskUOshMo+u4ZYrBZNkd6vIOkHqjbvTdQDVdICqkrsSKeRvkqVKiKCEb54LhbXXHON/C1KS5G8WObcab7n48yq5EQNtFdFQ7p16yYpEBbGqRH1HjqJpKQFWpkQSf7666/zN20mIs6MdBvqQ5GM56JFi8ycOXNkTBTl+QcffFAMqZ9g7TFCK+EdDHjZGNA33njDtWNToqMhvJJWCL/ZqkluNB7Q8ESAmZCdMB0jiU4o/aTkVAntSQkAu+MptuDpItBMugBV/Dp16hivguEnv8lzQ2aPvUb2JEF6Asj1DhkyRHK8QfCsg4R6oEpaYfEbXihrOqLBTiP0QvE6e/Xqa9av722MmWamTWtiSpYsK0YRo0NeFVUiwKBiWNkVz4bOlStXmvvuu09WXkyePNl4SY6Ok8CHH34omgBXXHGFGE/EQThJMG0EKPXfeuutUnxjSR/PjX1PirdQD1RJKxR8tm7dGlWKDa+LajNh+07Y3z5AzvV//NHG1K7d1WRnrxfvkqIUYS9Fo6ysLOmVRD8UObz333/fHHXUUSLMTPoAA0ul3i1QUWKOnR1PhOTAUEHLli1lsR6eM540SvMo8LN+hN+j8wDjypoT5uHRNmUFiq7w8AbqgSppBWOAmjprPiKBN/Z/4wkn5X9MWeWBGj20bt1avE9CeMSZ2Y2OUXn44YdlaoeVIXi6FKxoBaIAlS7oeeVEYRk9erS56aabRBTkqquukkV6bCzF0JOGQPOUbgVOCkwdARtGwYqHIDDNwjyMKAY3dL+T4h5qQJW0w0I5DBqV5XCs4YCOHTuabt1qyf4j4JpVHiISkpMjmzW5ll0fu6AndMSIETL2SKhPCxVGh17UdECoTWoBz5KFdxjv4cOHyxw760kI2ek+wMATnofC4jyb96SoRC6UzgJOEGioUhjjxMM13ipeN2G/4h4aByhph7XG5Pho0cGzat++vYx8Uk1n/5GFsPbQQ7easmXXFdiDVJhICGr3hMKsS6Yw06xZs4jHEc+OpXjASP7000/i+ZIuwMChZcoxsGWUghmphcLCboYCrEHEuC5evFhyvRaez+233y73y64ncrycjIq68kRJHjWgStqpXLmy7InPzs4W1fn58+dLUYUCk6Vz587miEMPNRVzc8z9YXJ08YiEsNmzsDUeGE+7Z55tn0B+tTBoLyIMp62KiSg8ZVq0LO3atRPjxskANSq8bfKxhcEKZ4woJ5L99ttPPFDbjsVaZjxa4KSAqDJhPIUlNaDuoQZUcQUMBRVzPDXalEIhPCXsjuZpOiUSErpnPjS/GglyluQeyb2Sz2RJHfudrr/+egnbWVbHyCW5T8JzjB9V9njU90NfE6r0eOY2HYFHSlcCRagKFSrI41lFq7Zt28oJCO+3MJFqJTWoAVVcg7DdFk2AlqMuXbrk921G8zSdEgkJ3zMv+dUokHtkwgkDRnGKcJ1WKYpCLL9jxJTd80Vh4MCB5ptvvpGCEgaR+8V4UomnYEboTo6UxXJAnpXfpc3puuuuK9JjK8mho5yKa1B9fumllyTXSWU+fFMnBSLrgeJprsvOdlQQOVYOFEPFsXFMeJRUyGliJ9/JV4YQnf9HGxXGi5A7VeCF0kiPAbUrT6xqPwYcI0tKREk/akAVV2BSqGrVqlJJ5hKRf/4xNfv0MfssXWr+Ofxws2LECGPSNInDyhFCY8BQskqYxnbytTT4E76XL1/epBPSCM8++6zkX62OACOxhPF4w3jFSnpRA6q4AtXqO++8UyZvDjjggAI7jPaZN898X7Wq2XevvcwREyc64oEmUnEnVL///vsldKaP1IbHrBVmsRt5zlR6nLFANCVUmZ6cKU32pD/obFDSi+ZAFVewISiN5FSzqcyXGjrUVBo71lDWqY+QMGf4Xb9f1JUciVTcn3jiCfHwyMdSZWcdMx0Cdhe7W8YTwtd60G3AZBInJIpKtIgp6UMNqOIKyNNRdcbLIwwl/Bzx/femVsiER6WKFc2O9evl30VdyZFIxZ2+TXpHMZ5AE7uXwWiSWiCnzPI+GvKV9KCTSIorYJzoX7SiH6eccoqpdM45xuaTMJhl+/Qx22+/3Xy0zz7muUMPLdJKDsL23SaaokCfJTPnfoGWKfKzFLgoMCnpQz1QxVUQ+Ljxxhvl32Ofekrmw5l+/7xkSTOPhvIKFcxLe+5papcqZcYVQUCDnCeE5kCjQWuQLSD5BTvrj1ygkj7UgCqe4bCjjjLdjZHwGUOw8osvzK+LFslUkm0uTxZsbzxTRnDssceaTz75RNqD/KK/aXfHH3nkkW4fSkahBlTxDEwl0aiOAAhFHFSM3IDiESOY5BWjzdF7cXMnGqtuSvZlIpoDVTwFDeMoFjG66BbMpJNXpE/VL+CBxjNvrziLGlDFU2C40PZEnMONFmU7d96mTRtfbffEA01kSZ/iDGpAFc9hJd3cMKBMHNld9n6B42WZnnqg6UcNqOI5WKzGOotp06ZJa046YeEdQh7MneON+oEVK1bI66UGNP1oEUnxHOwEGjdunMjGEUb37NlTquI03nOhUML4Yqok3BAtpkeV0U0KSn4I30Er8OlHZ+EVT0Ij+2effSaSbUBe1ErgMVZJI/4LL7yQMgENxJjJKeawMsTjsDIEY5/OvU/KTjSEVzzJvvvuK8LKzHmjxck6C0J7vC2a3JkJT6V4BtqebND0SwVeC0juoCG84lkYqRw6dGhEIRI81FTKyaHChOq8H1i+fLlolirpRz1QxXf89ttvIuNGv+bChQtT8hikBjDSyMd5GVYlU0TSApI7qAFVfAfeIYaTyRsW05GnRGiYtcFOpfRt9R/hZy9DOmP79u1qQF1CQ3jFl1BxRhX+ggsukJ1ArODgwuI1dqlTaGK/EDqjGMN58+ZJcz7//+yzzzbVq1ePet94dOxip5AU6/e8NAOvBtQdtAqvBAL6IOnhZP0GbU7h+UuEh1GTx2Oj8XzAgAFR84Zs2GR1MUYUA+xlRo4cKSs+OF4l/WgIrwQC2pqmTJkiBg8Dyr50Fq2NGDFC2qGYMGLfOhfyhhjHSHz55ZdiZPv27RvbeLKvqUcPc0SzZnLNbTfghLFhwwbJByvpRz1QJdA0bNhQJor69+8v8+2XXHKJ+fDDD2XjJjnUcC6//HIxsu+//754rdHAaJaaO1dWjvAF2nz88WaFC+pReN4Ye1Yt0+qli+XSi3qgSmDBN1iyZInkPsmV0vaEUMjgwYMjGk9g0olZfERNYsGm0Px9Tbtuu+V583z23ntvWSxHekJJH2pAlcDC1NLWrVtlJHTq1KmyBXTUqFFSHIpG165dpapd2GoM1iznrx/ZddstypUrZx5++GHpTEBPVUkfakCVwEIuFGV7vE7m5lm+1qRJk5h/c8IJJ0gDP83psWBHPWH79rJld4bv7Kx3kWOOOcbcfPPNMtbJ5JaSHrSNSQkshLeo3KMsnyh77rln7F/YZx9Xcp6xIIRn3DUrK0u2c+p4Z+pRD1QJNITwVl/U5kWZp2d5HStDCPND/9+cOXPMf/+xO/40k5VVw+TkVDTbtxtfQN6WNAX53XPOOafAc1NSg1bhlUCDLB6Vd3a7Y0zJE6JahLEhVEeUBE+VsU0KMOQ/9957kNm27VbZH88K5OzsdXEvpPMCDAKQy8X7njx5silRhG2mSmzUA1UCDds8UXSiv5OJJPQ9yYky437uuedKfyhTTWeeeabp3bu3VLTr1esrxhO4ZhWyn6hZs6YUlWbOnGmuu+46tw8n0KgHqmQsjRo1krn6+++/v8DPCdtzcir51gO1vPjii+bee++V0VWKaLRxMcrKdei/uaaSj0euJIb69kpGgudJwYWRznCysnYaSzzPBg225N/2G/S+4nV/9NFHogvw559/ijhKpFUlpDQwokw2hRpW+2+6E9gCoBREPVAlI3njjTdMx44dRcEpWlN9UMGAUmAKv2BcQ29bg8vJhln7Rx991Fx77bVuH76nUA9UyUjeffddUVrKNOMJ++yzj1ziFUrBx8J4kk+lg+Gee+4x++23X8qP0w9oEUnJSN555x3TuHFjkwnQhkVeN9m2LMJ7jGe/fv1kjcpxxx2XMiFrv6EGVMk41q5dKzPyhU0lBYXc3J1Fsdmzy8g1txMFI0o72CuvvCLeKznRsWPHmkxHDaiScdDziUEgt5cJUAxzqi2Laj7q/2eccYbp3r27TD1FKkplCmpAlYyDPskePXrIKhCvr+xwAjoJaMcCrrldFPBA7777bjNw4EAxps2aNZPm/UxEq/BKRrJu3Tpz2GGHiSr9HXfcYYIMOU/C9tC2LKeGk7766itRgMKrf+655zJuO6gaUCVjobJ84403irjy4S7K0fmdP/74w9x6661m1qxZMovPJVPGR9WAKhkLs+/HHnustOQ8/fTThYooK7GV8XNzc0VOr27duuaxxx4zLVq0CPxrqjlQJWPZa6+9zNChQ2UBHXPjSvIUL17c9OrVS8J41K9OOeUUyY0iZhJkH009UCXjadeunYx1vv7662bfffd1+3AC4Y1+9NFH0jNKvyi6pIT4jJYGLbRXA6pkPMuWLRMBYtTrhw0bJjPhStHJy8sTMWvSI+RH6X5gvQrtT1Tyg4AaUEUxRnYgoVrEUjlWIYdut0xlFTtT+Oabb8SQTp8+3VSsWFEmm7Kzs2Xtip9RA6oou/j++++lQRzhDDzR+vXrB0rezgv8+OOPsglg0qRJki7p06ePCJRUqlTJ+BEtIinKLmrXri2eKDk79sNPmzbN8UmeTOfggw8WCUFErTt16iQnqho1ashuez8246sBVZQQ0L6kIt+5c2fpEcVbcnqSRzHicd5www1ykuJk9cILL5hDDz1UNggQCfgFDeEVJQJ8LWgIHzRokLnookvNfvs9oDnQFLJlyxbz5JNPSp6U9jL2V/kB/RgoSgRoAGcdBkvnaMF5++2uJju7aoHf2bBhgxSdglJRdpNZs2bJplSYOnWq8QsawitKDMjNYSQpeoR6p88884xM2qAp2r59exEaVhIH1XtOUITzNN+vX7/etG7d2vgF9UAVJQalS5c25513njSF04JjldiZXqIVh4JTz549zYIFC8whhxzi9uH6itmzZ0uahPAdVSfyn34b/dQcqKLE2Xrz22+/yQUdUYpMFD+ABnyKIsyBo3RPIz4bP5XIoB/KCO348ePF60SYObTv1k+oAVUUhxbU1apVyyxfvly81okTJ8rOJWX3kB1l+9WrV8s66auuukrm6P2Kf49cUTxChw4dzMUXXywtUORKWQVMXm97osuHMoAvvvhC2pTee+89c8011/jaeILmQBXFAcjhWdit3rx5c8mb9u7d29Xj8hq//fabXLOYLgj42/wrigdBxo3q/bhx49w+FE8a0HLlypm9997bBAE1oIqSAgjnVRpvdzCcVN3JhQYBNaCKkkJPK+1s324q5uSYGllZcp3wEvgU07p1a9kEQOEtCKgBVZQUGM9PPvlEcqHppmJurqmUk2PKzJ4t19z2ElWqVJH8J7PvQUANqKI4qMROdRkZPCaTaLBPNyUXLKA3Uf7NNbe9xmmnnWZmzJgho7B+Rw2oohSBb7/91jz00EMyzolQcKtWreSaPtDjjz8+7cezpUEDk7drmgczWuy//zwXxrdt21b2Jr322mvG72gjvaIUAdYh//TTT7ISpEGDBnJp2LChe7t/tm83NXv2NKXmzjWYUb7c6/r0Meuzs42XuPzyy0WNHk/Uz2gfqKIkCY3yhOo0zZ9//vnGE5QoYfL22EOMJ3Bdcv58KSgRzuOhrs/Kkt9zO4wfNGiQWbdunW/V6EFDeEVJklWrVokRrVatmvESBcL4YsVMsR07PFdYatOmjQiHvPLKK8bPqAFVlCRh7h1cnXmP0LaEh7kuO9tsatpUrvOKF/dcYWn//fc3TZo0MRMmTDB+RkN4RSmCAcWLOvDAA107Btu2hGEsPWeO/Ix8Z2jOE8Na+rPP5HfwSPFQvcDpp59u+vfvb9asWWOqVi0oVu0X1ANVlCQh/1m5cmVZQeEWUduWQjxTs2OHWderV75HKjlQD9CqVSspttGx4FfUgCpKEXKgVmDZK/lO610WaKgfNcqY4sXNytzcnZ6pRxY67bfffubEE0/0dRivBlRRkoQd8kuXLpWpI7cIz3da79IPDfW2Go8yPaLVfkQNqKIkCa1LyNYhDPzvv/+6cxAlSohXGe5dRvNMvcYpp5wiAiMvvfSS8SPaSK8oRWDx4sUy280OeXb6eAZyoLm5nur9jAa7pRjrnD9/vvEbakAVpYiwmZNe0HvuucftQ/Elb7/9tunXr59ZtmyZOfTQQ42f0BBeUYoI4bublXi/07x5c9FO9WMYrwY0w870bECMxJIlS0zTpk1lDQWqQkr8bN26VQ1oEShZsqRp2bKlL6vxakAzALI0jz76qDnzzDNN9+7dzbRp0wr8fxTCKYh88803JisrSyZEvvzyS9eO148GdM8993T7MHzNqaeeKvlkemv9hBrQgMOs9pVXXmmuv/56c9lll8m+nksvvdRs3rw5/3fYjsgH95lnnpHLL7/8IjkpJf4Q3u/bJd3ms88+EwV/VqH4CX3XA859991nRo0aJSNzVDv5ohNuWrm1F198UcJ2FIVq166dL8m2adMmtw/dNxx99NGSAlGSg5P3yy+/bG6//XZ31qAUATWgAQ7bf/31V2n0Puigg0znzp3l3x999JEZOHCg+eeff8zChQslZKchvFOnTvl/u8cee+hO8wQFghcsWCChvJI4Q4cOFUEWIiW/oQY0oNCTiDL6888/nx9e0mrDz3r06CFnevoXUcW56667RBTD8tdff6kBTeBExRqPUqVKFXgNlfig9/Pdd981gwcPNvvss4/xG9oHGkBYGYuhxOs855xzTI0aNfI/nKinz5s3T9pGqH4ee+yxBZafvfrqqxLuP/bYY5IbVWKDEAYFuN69e4sh4MR02223STpEiQ2m55JLLpETD59JP+aRvTmaoCT0IVy5cqV4l7YS/NZbb8nqWIpF4TJhhPNcIsFM99133y3G4Oqrr07L8fudv//+W67JIx9zzDHm999/lxMX/+bCCQrxYK3S7w6eO2mk6dOn+9J4gnqgPoa3juo63iLe5AknnCD9dO+//75oLCbamHzeeefJ2ln21Li208dnYDDpbCCHjOfOezJmzBjJNc+ZM8d8//33ckLq27ev24fqKbZv3y4nmpo1a5qZM2cav6LfEh9z7733ivHs06ePhOiEQQ8++KBU0BMNv2meX7t2rbnooovUeCYA6Y+vvvqqwM8wmFw4kZ177rnm888/d+34vMqkSZPk5EIXiJ/Rb4pPeeKJJ6T4Q+WyV69e8jOa5FkXS0gfLUyP1sdIC8nGjRtFn1FxhocfflhEMvC0lP9Dt8Lw4cPNBRdcIBtM/Yw/Ew8ZznPPPWeuuuoqScD37NmzwP+jBemQQw5JKOeWm5sredNhw4aJSrjiDHYYgU4I5f9woibaof3L72gO1GcwdknYaNfCOtE6QyNzdna2OeCAA8yiRYscOc5Mh68Vu5JIjZATpRNCKbgXvnTp0uadd94xfkY9UJ/BFBEJ+Pr16zvWd4jHiifLLLI2gzsD780VV1xh1q9fbyZPnuz24XiO/VxeheIUakB9BgUeFpmtW7fO0fs9+OCDxWvym5iDV2FNxRdffCH/HjlypEx+Kf9n9erVgeiVVQPqQ5ggev311yWP5BR4oaAqTM4wZMgQqTQTxlNp9uOUTarIy8uTgQ77mfMzakB9CE3beKIUksiJOkGFChUkB/rKK69IJd8NWCxGgey1114TyT16KZkxt83qfoJ2so4dO5rffvtNqs0MKCg7+eOPP6TVTj1QxRXwaqZOnSpn8VtuucUxg8f0kR1NTJXRIsd65513iv4jC8Xw0qycGSpQzPAzfnr66aeLUjltLuwbCvVe/DCnj3fFiYBUC21Mc+fOdfuQPLUOGtSAKq5Rt25dCQ0/+OAD88gjjzhyn0zT0JhP0YM+vVSAkWYAgN5TeiSZfkIOjgkqelcZ75s1a5Y56aST5Pfx3pjwsXCbFi1GVBGJZmTVi3BSQ6CaqS6MKPoEyk7sCdCPkUU4akB9DArzSIE9++yzju2TwSusV6+ejCGmgnbt2sk1qQeMNJVqPE+GANAtJY1AhRYPFUOJBB/jksDEDx5yly5dZGwVz5R5c4yU10JUvOcjjzxSThAffvih6quGnfwZGR4/frzxO2pAfQ6TSFwQTv74448duc/DDz9clIVosHf6i29behg7ZdwUwZObb75Z+lCZ57cwAkk+FGPUunVrab5+6KGHpAOBBnU0TTlpoH5EQzZ/jwyf22DsMf4U4zi5Mc7JpBgeKaLBihHhEE7+vH9ejSDiRQ1oACDsprH+hhtuEBHlonLxxRdLWM08N2H1m2++6chxcmwY5bPOOktuUyAKFXIOBw8Or5SWF1qBMKgYVjtlhaGnoMYYKqtI8GwoPLkFKQkmuZjx5rj4N8U5jpljxehj6EPxQz43VZHIhg0bZALOz6gBDQCMbz7wwAOy54jCUlEhvzhixAiphFPE6dChgxkwYECRtnXyZeE+rYIUbVh8iS688MKYf4eqFDCjjzTclClTCnQe4M1wH3h3eKMtWrSQE0m8fZehgwPkZcmrfvfdd4X+Hc/jnv79zfDKlc2XVauauWefbdq2aiU6BJwkMP6hRT/GORGyJr/L8yZVcvzxx0tLGuF+phWZ6tSpY4444gjfh/E6yhkQ8HoOPfRQ8XzYqukUGE0MAvlKPEc+9BhpCjuhRiLWcVFZJ6fKRw1tzESKXhi00aNHi0A0Uz2A4eFn4RAmkw9GaIUq+Lhx40yjRo0i3i8GC0OOQeN53XPPPdLRgDYl+TlSGNGeH8YZY3joCy+YAbu8EE4tD+y7rzl8/Hhz2GGHRfw7nj/9oRTKqEDXqlVLcr54z3jnFAV5fTOFZ555xjz++OOyxNBvu5AsakAD1BpCuI2Xd/LJJzt+/xRCWDy39957yzgp/Y3kXcljRhPDJR/IrnkMDsUi/o03lgh4g3ZeGgNq5c/wYKO1wfA3hPUUoLi+4447dhNXIQdHnpKWKQpTeLrMZnNi4ITBsZO/xLvn5EGqAe+W533O2WebNnPnmn577WX2Dakk/9GkifnpqadMolAEw5izDQDPLFNYt26dnFBJ0/D58CNqQAMCxqVSpUpSuEiVohL5OowlYS/KTYRfGGu8QUZB8VbtxQrmUizi9xDOdQK7DK9x48YxVcwJx/HGn3zySfk9xDxoZrcpA7QE8ADxPLlPjBfpCiaGKGxhKBFr4diXLVsmz4fnwNel9/r15uYtWwxKBHx55LpYMbMuO9usD8txxgPGk3Y0vGK/KrMnCxoMDIVwgvYjqgcaEPAMwanJpEhYoWX2KVE5x1Dj3UXzmvh9vAunjCdg4PBkCwOPk4INFXwME436tD+VLVtWvE/SAHiY9j67du0q/x47dqz0JxL6T5gwwaxYsULyq3QD0BhPLveK8uVNsV0iyRjPHXvvbf6tWJF8B2cZnnhC01costNJkGnGE9q3by87pMgd+1GxSj3QgMDbSO6NSjTV3nRB4QpDZAtMGAEuqBHh4VF99srrg4QahpAcLEaL7gVyoKHKQDbnCvTDkr9lKiqUmj16mFJz5+Z7oCbEC93cqJHJ22MPs6VBA7M+Kyu6Md2+3VTMzTVrX3nFvLp+vWk1Y4YpX6mSyTS2bNkiQxSciDGkfkMNaIC46aabzNNPPy0FEHJ3Mdn1BS65YEHhX3avE+dzWbJkyW5Vf6rjVPdDQ39ykuRR6a+N5BXWuOIKUybKoEG8IX3FnBxTKSeHL6AUoHaULWv+K1vWbDzzTM5CpuTChTGfC45ubm5Fs2BBSdOgwRaTlbXet2/fLbfcIq/3119/7bvV0D59yZVIUOig2ZwcYWGrEjA49gtcepcxSCZ/5wXifS70tlL0Kl++vITo//z1l2n2wQem5LBh+caK0J8QPxZbGjY0pT/7TB6vgAe661pu5+WJQY8G/4/fAUx08T//NCX+/NNUevLJ/PuL9Vwwnjk5lUxeXjEzZ05p+Vl29s4uBb/Rrl07SbfQF+y3FR9qQAME7Uu04FC1LuyDGPoFLuzL7ponG+d9J/JcQqu9FYcPN5VGjtxprGbPlhzm+ji2Z8px7HrcLfXr7/z3woWm2H///T+0L1ZMjjka/D8eM9zfCr0d67ngeWI8gWtux43Hoo+mTZtKOxcFOzWgimsQbtL0TrM5446xwiH5As+Zs9OLKuTL7pYnG9d9b98uhis0dI73uZSbPPn/HuOu2/EYUIxNxOcYyTBFgf9HP2jjb74xtfLy8o8jPKca7bkQtuN5YjyLFcuT2/HiteijRIkSkmcmnUIE5aetsP45UiXuMJ7K97fffhuzgFPAiyrky54WTzbJ+8YYhBZ0KOIk+1yKTDTDGuV3xx9yiOm7erX5qmtXMd4QMQcaAXKeEJoD9cJ7VpRqPB4o+eczzjjD+AU1oAGDEUGqyoTxMSvgiXzZY1DAk2Vn06pVUiCJKywsJJSMx0sWY7Dr32JEKZ7F6cFsbNcuP4TP23XbMeIIk2WMlPehb9/4PN8QuKtkc55ORR9OwtQXgxEYUTWgimswJcRoIpX4Pn36pPzxrIdU7s03xXjuvWqVhIf2/8UyIoWFkvF4yUUxBut79drp7RXRC082TKaBv9BuiRTgVPThJKSbKCYx+IACWJkyZYwfUAMaQFglQT6J8c7q1aun9sF2ebJ8GTGeoWFhYUak0FAyDi+5SMbAIS88ktfJyaSwMNlObKUdp5+3w/q2DCxccsklxg9k3uhDBkAIxGQSXmhKwFDk5JgaeJh4m9u3i/HCAwTrCRZmICP9TbLGYGVu7k6j4GIBwp4wysyeLQbUFoSiPTdU99E5Xb58edqP1YtUrVpVJsQQhPEL6oEGEEQxGK+0u2ecJqJnGd7as2NHvhGJVlH2YihZFAqcMMhxVq9utlWvHvW5caLD40LQBC0BcqK0oRHGkorJRM466yyZpEPcBYPqddQDDSh8GVP1JQz3LMl/gvUEgeKMDel37LWX2XbggabkvHn5HmvC3mMEr9drcOLI9zqZaqpcWU4ivD4VR43a7ZjpfZw48TVz2GHjzNq1eF13mQULvhABZrc2o7pNmzZtZJiBFJQfUAMaUNAGpc8wVDDYKST0DrktlfddhjNSf2XxbdvMXmvWmDKffWYqjRhR4HfjAiWknj3lbwmP8X4Tvg8XKDV/vpxEpLA2cqQcMzY0J6eiycqqIddTpzYwX399gdm8uZlZsybLtGnznghrsCMqEylbtqzIBiLqUhQB73ShBjSgINxLGDRmzJjk7ySK10c4SmhqQoxkYb2EoQb1gKefNiaB/UWhvZ5x9y664LHKNJI9xvCpol2vkR3BnD27jFxPnlyuwETRn3/WlQIKWqWZSteuXWWBIPKDXkcNaEA56qijzHXXXSeamMnmQkOLIgW8vhIlzMazz/5/ASik/xNDRT9laCgbTvGtW02dc8+N+zhCez3tfRZWcIp67CmkQFEs7LnbYw4fwQQmiex12bJfmPnz54uoCbugMpFGjRpJCx65ULuE0KuoAQ0wd911l+TZ7r///qT+PlYVfX337jL1gxYm2P5PDNX6yy8326pWLSCuEQo/23Pt2qRSBjJtdPzx0QtOuzzPCuPHFzz2+fNT7q1yTOt69dpZPKpWzWxu2NBsrVZNbq/r3Vv+P1NDoQazXbuNJjt7nTnuuF9Nixbvm08+aSdK+rxvfumFTJXQMkMhyAkiaO1VtAof8Go8VV6U4cmH8oFMhFhN6hXHjDGl5s3LN1IFCkpU4Nes2c1rlN8JKbDES8RqfWjBKaT/soCgR6hKUqR8Gn83apSp8PzzZo8///y/qEgRZsPJe0oPKF75mjW7SdpFGsFcunSJGTFip8weRpO0C1qkma7rMGjQIAnn0XdAEJvPs9dQPdCAw9vL+gsk3B5++GHHxhHx1giPd3s8Y8T72jsk/BSvsUEDs+cvv4jnifFc9sorWPiiP0GrrTlixG6ScgWeSpky5p8jjjB5xYuLHJ2dkrJ/F8qmpk3zuwkSLXSF5mrjva9XXnlFtp6+/fbbplq1aok9bsD54YcfxIgiNsLuKq/phaoHGnD4wNETGs+q3kRUhwooIIUVifbYtKnA/yOcX0HhKEVN7rvNw0e4LrFpU75xQ8sz/O8Sya/GU+gKva8///xTqsvRoJGetSJqPHeH7ar33nuv5PNRakI03EtoDjQDYCUwWyYdu78wBaTQ1RaE+v+VKbObQZUiTooq4aH9l/YxLeHGPTSfG96OVWh+NcFCF/f16hFHyMQR21KjteXgZWVyvrMwTj31VNFxZSssak1eQg1ohhhQlqGlyuOz19vLlpWc38b27QtUo/H+3OjdtK1E4R0BNp8rRZ/evaXIYws9K1CEx1NOsLAUXoHHeHJfY8aNkyggJydHtm+GL/2jXYf8npOL90IJ7zv14PxBXLBe5YQTTpCVLCz68woawmcAFSpUkJlrPCAnNj+GS9jZUc3funXbGfLzLS1eXCrhrKlIte5kaP+liZAHtcb9n8MPL5ADjSUll6jocKRC17Lly2UjKF96tAnGjRtnLrroIvPEE0/ImOKiRYtM3759ZWKMvt1UEJTVH3vssYd54IEH5LVE8/aTTz6R7bBuowY0A/j4449lZaxTa3PzjcX8+VLdLmCUwnKn1gilUncy3KCTcw3tAuBn+cY9VaLDEfLFn376qbzm1157rSlVqpQ55phjzO23327OP/98U6lSJclLU1lmJz1FvlRQpNUfaWR7HEvy0LllkyqtTb169TLPPPOM60UlNaAB56+//hJ5sNBdQOmSQ0uXWMhuj9O9u6n49NOm3KRJkn8lJ5vozvZkdUbRsqQoxHrp9evXi3eJ8QQqyYzYskCN32PB3WWXXZbSFRZFWf2RTnLj9JQRCadjgU2eKDddddVVxk3UgAYcvCDybiTi075QLF26kxEeR8Ly4sXFA5Ztl4h5FC8e9/EkY/zZhnrjjTeatWvXmrZt24ohJXQPBQOazkJIUVZ/pJMFCXjKKDYx6kpOuX79+ubkk082bqEGNODwZQZk0ry4UMwr2zqLYvzJLY8ePdo8/vjj0o6E8USLlfA9HVsBUrX6I500SNBTxngysXXeeefJ6KtbLWBqQAPOunXrTMmSJfMT7l5cKJYo8Z4E0rH759dff5Vwcs6cOeJdkpej55NZdkCaTXHeU+Z1ffDBB6WohPzfBx98sJu3nw60jSngkIcLLVA4ogLvMvGeBKRNiXxj06Y7RyqLkIOlmRttgR9//FFu00rDyCUVYTygSy+9VIpBtmGeL7gaz8Q95ZyclXI7O7vwtiv0Ah599FHz+eefm6uvvtq4gXqgGWBA999//0CpwMftWTqUg+UL+uKLL0pI/sYbb0g6BKlAbvMlpj2JCruS/rarY4891tx2221SWGJk+fLLLzfpRA1owGGMkFwRGpPk5uhDdCXn6WDxiip7qc8+MyUXL5YWpXKvvy5VdtmymYKCGH2bpECmTJli7rvvPvPTTz+Z3r17y4x26MlJcaftihCeohL55gYNGpjjjjvOpAs1oAGHL3zDhg0lR0cjMh4pfYnp7p9zsniVrwS16/Yea9aI4jtKSPmTREUEDxOvE2PJpBAGk4kuQkbFeK7tqkGDBubll1+WFIsaUMUxKGzcfPPN8m+akBFlYN/ODTfckFYj6mTxKpIICLeZzxc90iJ62Nu2bZMJIdqQ6OGkVYbeTbcbyTOBrCTarjjJIX1HdHX22WebdJKBb1HmgufJSBwJd1Ru2AaZLpysiMt9zZ4d0Yg60VVAO9L3338vHjubM9Nh+IIycpnutiu6HXAQmOxCsCXdkZUa0AyDyQ16FNm9TQU51gful19+MdOnTxdNUfrtaIdKCqzMjh2iEwqs/IhZvCokXyq3d+yQSaMSv/4qK0KirU5OlK+//lpakU488cRCjaeThs8vI5deY/jw4fKeMa4cSzIwVagBzUAYIUSdfvbs2aJwEw5jhuyjmTZtmvTWYUAnTJggOSY7lphw/nPUqHzvk4mgWG5aofnSUBGQf/4xNfv0MfssXSpiIRSYisJiClN5eXHnOp0yfH4ZufTalN3o0aNlZQ0VeDfQPtAMhLWxtH/w4fvjjz8K/D8q9jQnY1xHjRolk0yoBv3888/mdardach/JvL7tqDEuCbX3E6U7du3yxgmCj+oJ3HSiFfpJ3zHUbKGj9Cf3UhNm26Sa6+OXHqFDRs2iDBLq1atZHzWLdQDzUAI2++44w5zwQUXmObNm4vwRbNmzUTUl0LTYYcdJmF+7dq15fcxLBSe7DhoqvOfifx+uLFlJ1OirVIffviheOWWyrv2NTGiSYjIrDWFpFTOmntl5PK3336TTZiod7HZldyi18jLy5PPL9CD65TKWDLoTqQMhnXH7OHhgsAFPaOoA5GMtx4YbU9NmjSRhvGnn346uSR9oj2gCfy+7EOyknm7pOv4OmF4wxe6xSpEINhLSIghRS4Nj7tHjx7y/PmCYkDp+UTZnxPPmWeeaYLIk08+KUU04L0+7bTTZEMma2G8wvjx46XAR1+u2++DGlBFsHvI8TyskUTFiVwpFWnO9AcddJDxHLuM7YY3vzR7rlplDjU/JLUcbvPmzebiiy+WEU22mJLvRRiE8U3C++eff15k5xjP5LVCIpDXKmjQ70okQvpm6tSpZvDgwdJb2aZNG9HgRE7OTZYsWSJDITTNe6EnVw2oEhVyoZMmTZJc6dFHH228DCsr2sx+1AwwA0xxk2d2mGJmfZ/4PNBQ4RWeM14nRQmMhw1hMZoYUELc9u3bi7gvnnuQIE3D/iZGI9k/ZJ83J0+0AOiLJeeIR+rG5+GDDz6QliVSCx999JEr4iHhaBFJiQoFJqruftgWSf5xsLlVDOh0c6qZevyNCc/5YyzHjh0r01u5dAKE5P/wPPHMSWWQf8NbHzZsmAkSFBAR4A7V1+R5k8r49ttvpb0Lb5STDIMGixcvTstx4ePx2LTgoWtLft4LxhPUA1WigpGgwET+j+KS2+sTvDLJw1eGXOmsWbOkM+Hggw82fodOBE4eLL/jxEn6IpqXyojr3XffLQYXj5XQPlqRLR7ofGAzKcVLcq2sObHgAfNYvM7IBjJx5GbRKBw1oEpMUB/q2LFjftVe+b8OKKE8y+FeeeUV146DsVOOheOIB77u9PeiMEXKgjY1rklb0HVAi9v7779f6P1gSF9++WUxbmwWbdq0qeRP0V2I9zi4D4pyfL4oYFqIeDCkXBYsWCCe7lNPPSU5aq+hBlQpFBL25EFfeOEF56ux6Vwx4jBMZ1F8otiSbmwhi1QDE2NMTdEPGavt6JtvvpH0BMbziCOOEM+5evXq+RcMF3uGEE2Jlx07dshxMHiBoeN+iFQwjlzwbO01v2tvc7EwQURxiPwy9/HFF1/kX/A2X3rpJZkM8yJqQJVC+fvvv+WLxWpkWpmczIkWaENKoPXIbTBgFJroUnjkkUfS+rhEBRhOWq3IR2Jc8ATpmkD0BJm9UDFnQnJakyZOnChVdHpbOW4n2bFjhxQcGamk2BZ6QX8h/GehF7xWDHokME9eTh2pAVXiguIBX7p//vlHwimnWppqZGWZMrNnJ9V65KaXS86uQ4cOUhWmbzQdMPONmha5aVYj02Jlq+GEwnfeeafkMBGKoZKOxBsq+RhPPD68RIo/qpTvHP6IlRTXIdxjYgcjiuo3hQQnxITTsbdot9l6VhwXL14kg2oLHeTo0mVAWSFC9ZnQllHcUHgvEH5mfTVeKJVz8qLomrJuhHnxZCfJlOioAVXihtCdFhIqrswhU32lH5KcWTIiIwWUlSZP3vmDBPe3x8Nu456TJ5u9Vq8ukrgzuUamYOgFJXROWqkqgdCdqj8eaLjxDIX3hjAatS1ys0gYejV/GAS80w+g+AKKBHw5GXvE86ISTd8gO2kIIxMGQ1m8uBi0vVetEtUmPEYnCV+kB6EGlSmmWMvLosF0Drk/hC1Szbx580Qli7RBYVB4YSTXy8WXoKAGVEkYPC8qv1RMaSanSEArDx5SMqRs1TK5z5wcU3L+fLO5USOzqXFjuTa75uaBiaUJq1pJD2nCx73L60zqxJEgtBZx8krnugqlcNSAKklRoUIF2UTJnDiFJRbWtWvXLqn7StWqZZv7LDNnzs4dSnl5cr336tXy/78ztcz7pqU5ycwy9d8ctjN1kABWcCVcEtBptm7dat555x3xPr1ckc5ENAeqFAkqvBQ2qP5SkabPkNaUREjVquVwzxbR5fzbu/7b0rwvs/N5q94x63I3JpQLtR4o7V2phB5ceiSpoCveQg2oUiQwIsjftWzZUpZ6QcJG1KH97YVV+FGst54ot/cr858p/qc1qImnDqwBDZ2iSUX7GG1jLAE88sgjU/Y4SnKoAVUcgb5EcqGMexJmYkS57Sa7ebbdu4tivb1NxT8vZNUIP0tkpp48sB00SAW0aDP7feCBB0rPp+I91IAqjnHuuefKuGeXLl2k0R6RYleJ4NkWuI21DOsHjXdJHPPfqAPRvpWq7abMrLMNgAmfVLdJKcmhBlRxfD4c43LPPfeY1q1bezvsjGBg41kShwoRwwR4iEz6ODFQEA6ycg8++KAUjpItzimpR6vwiuMgxovoLXk71i7QBO4XClsS991338mUj5V1Y7gARzYnp6KIOnOdTE9pOMyrI1QSNM3RoKGz8EpKwEujakz7Ta1ataSpO5rGpJeIlQNl/p2xSNq2WPNsl+5hNG3Yj9Flq2ZRFsQx845ICOOX/fr1c+qpKSlADaiSUhgrRHQXY3DWWWcZv7Jy5UrZxYNXyFKzUPUgPM/Zs8vk32Y1cW7uyqQeB88W3UsmnJCdU+EPb6MhvJJSWJdMkz35PIQt/Lq9lNFI8pIIeoRLrzm1G95OHDHhRV+tGk/vox6oknLskra99trLjB79rHn++ZppWb3h1BQQhRyeA/2YkUYpnVwncv3114vCPCpPivfx8EdXCQqoNU2ePNmccMIJ5qKLlpgff2xcaJtQNJw0Vnh7qEph2Nk6ibccDrlOPGdSENHm0Hn8ouQ8LXi4bJ7kWBR/oCG8khYQ/qVHdOXK6oW2CcXC9mmSc+Q6GREQ4FiQeitfvryMotLTieJRKMy4E0qz/x0BlVTz3nvvyY4j3T3lH9QDVdIGRaSKFZ8w69efKufuZPKF8fRpWj777LN8vU6mhVhnwX6nIUOGyK5ztDOZ5SdMp3CTlZUlBhWPmUEAZtz524ceesikg7feeku84CBs+cwU1IAqaaVPn40y5lm//pXm+OO3SgieCBhcQn/bMhTNAFP4QfCZJWboY9JChRg0qYSffvpJCluDBw+W8B1Y6cueoeXLl8v/p5WISjgq7tH29TgJknhorLI+WvEPWkRS0t4OVK9ePTFKiJBYA+Z0DpRVuRhC9omjownsCcKA4m2ywTLWfnEMLz2YtGHhyaYa+mQ5sZBvjbVZU/EWakCVtPPRRx+Jmnv37t1TItGG0WTBGpNQtB9ZmIiiwR8d03hzkldffbV5+OGHxWNNJUw3lStXzkyfPj2lj6M4ixaRlLTDChDykalScn/00UdlSsjK61noq4zXeAIDAOwsT/U4JV45BSzWESv+Qg2oknYIeqhwp0phiKIQBaKiNqLz97179xYDN3PmTJMK0BLFy2UVMWpWir9QA6qkHUJsjBJ7y1OFU6svmElHj/Puu+927OTBEjpWoSBTh/HEE2eDZpky/x8HVfyBVuGVtMIsOT2XzZs3Ny1atHD8/pnioZfSKeESvFCKToyishUzHiOHkWRyiU4AFOW5UNDiwr+5HwvtUq+99po57LDDHDleJb2oAVXSCqHw6tWrpUE9FQvS6OWkuu5kPpEJIY6VFqe6desW+vs9e/aUyj1tUIDk3aGHHiqTWAiS8G8uhO2lS++cxlL8iRpQJa2gp0nus2bNmo7cH+EvHi0pAbxbKu1U4PHsnOLUU08V7U+MH2mHhg0bymQSMn3hSkoUg+bOnSvGk04D0hWqJh9ctI1JSStMAqERyh55Jzj77LMlNCYdUKdOHamykx5w2rtlKonGe1qwbAhOOE+1n42ZGHKmnTCirDu+5pprZH7+mWeeEcOrBBP1QJW0wqikzQcW1UvEQBFW9+/f33Tu3NmkEno0H3jgAclvrl27VqaG2FlEMYhGfar+hOQYVIw446CsNmEvFAbdKY9b8RbqgSppBe8NA8OI5KhRoxLeIW8hx0iukTCZAk8q8qlOPFcMO6E+KktubylVnEfbmJS0Qtg7evRo0btkUoiezXghv/j0009LhZuxR5rc8T69aDztcx04cKCMg/KcleChIbySdtjWSXEF74z8IFsnjz/+eFO5cmWz3377RZxRJ/+INifFmaFDh8rP8GK9XsWm64B5/1T2vCruoQZUcU3ajmISHhoG0XqihLkVKlSQCz+jpxNhj19++cWcccYZJqt7d7Pf8OFmv8WLzXfly+9UF/GopD2N8lTvadmicq8ED82BKq5D+9GiRYvESFKgsdfsXCdcp4+SZnZ6OyuNHGkq0UOal2fyihUz67Kzd9vt7gW+//57mWI6//zzRSrPq2kGpWh489StZBSlSpUyJ5544m4/p3J9zz33iCdn59pLLlggxhO45rYXoQLP+uNUtFQp3kGLSIpnocKOIjzeqWVLgwbieQLX3PYi5HVZRpednS27l5RgoiG84lloSkdcmAIMPZgivrx9u6mYmyueJ8ZzfVaWZ3OgTEUxOPDNN9+YTz75xBx55JFuH5LiMGpAFU/z/PPPi9gwU0aPPPKIKCOFQ0sUjfl4fF7sBUXUmYIY+95VcSlYaAiveBoKR/RRYojYVjlr1qz8/8e5n1FJDOwdd9whBspJKPDn5FQ0WVk15JrbiYLBxHtmVh8vVAkWakAVz0MLEF5mkyZNJKfICCez5+wsYsPmddddZ4499tj8UUuncGqFslVd+vzzzx07NsUbqAFVfDNDP2XKFNla+fLLL0szPt4oy9hYO0wvKcUmfscpElmhHAsGA1hhghCJZsyChRpQxTdgiFA5mj9/vunWrZvMw5933nny/0455RTTqVMnMbBU7p2ArZ+sToZkdtiHbwlFdZ4FdUpw0CKSEhh++OEHUUMaNGiQyNwVlXhXKMcLy+nYPf/cc8/pArmAoAZUCRQY0IMPPliESmrUqOGpJna+ahS73nrrLbmQhlD8jRpQJVBQSLrrrrtkhh6dzmbNmsl64saNG3tCGZ7eUBT0v/jiC5G4O+6449w+JKUIqAFVAjlb/95775m3335b8o6ILjMKSkM+QscoyNt9RRY8VfYpYWhTDTlaWq8Y9zzqqKPEyHNhnBUP2ktesxIbNaBK4Fm2bJkYU8Jm9rDTjB++M37GjBmmVatWsk8pHbCoDqk7FO3xRjlGYPIKQ8oFo4rRd2rDqOI8akAVxRjRJmVbKEUeN/jjjz/EkNIrSjsWQwHsWLJaoqQhrGFFN1XxBmpAFcUYc+utt8qKEZbdEeZ7IVf67bffiofKBaP6888/5zfmh4b9Rx99dEQRaiX1qAFVFGMkT3ryySebvffe2zz11FOeMKLhoJOKMbVeKiIlCK6g4s+UlvVS+bfXlfqDghpQRQnZWd+yZUsxokw7hedJvQbFqC+//LKAl0qOF2+0bt265tJLL5WKvy6zSx3e1AFTFBfAm+PiF4NDWxZdA7ZzgM4ChgkwpkxpXX/99aJmxUI7NqEqzqMeqKIYY5YuXSreJ+r4bP5kJ5PfwZAOGDDA/Pjjj+b222+XPK9oqiqOoQZUyVjIIbIqmd30eGt4dOQ/DzjgABMUGCigOIYXiqAJ13is7JqaPXu2XJDZY3EfrV6cQJT4UQOqZBzPPvusTCzRyG6hgT1oxjPcw+7fv7/5+uuvZdR1xYoV8nOeL1KAn376qczn8xpoI3/8qAFVMga8LNScRowYIXPoCI5QsWb5G5VsPNGgP3/WLK9atUqKTFyqVq0qBvO1116TEVhm9e+++241onGiBlTJCPiYd+/eXZSQyAWyblgpCOH9o48+KgLVaKz6pZjmJsE+5SoZDbm9J554wuy7777SovThhx+Kmv1ZZ53l9qF5EubzGRvlNSLUp4K///77u31YnkY9UCWwoAtKSEqOD0X7tm3bOqITmgknnptuuknm8ufOnSvpDSUyakCVwMK6DxbRIRuHAVXihyLThRdeaM4991wzduxYzYlGQQdolcDCaCOw1VNJjJo1a5rbbrtNOhZYR4IEoLI76oEqgYb5drwpvChVMUqc999/XyT+6A994403ZEOq8n/UgCqBhikcVIuYbx8zZowWRZIUMaEyv2bNGkmH6Fjo/1EDqgQeZOHwRFFYQu+zTJkybh+S70Cv9IorrjAbN24UTzQdyv1+QA2okhEgVtyiRQvR0hw5cqS0NimJQR6UVieEpzGk+6hSvhaRlMyAqRtWejDSyNw7gsVKYtBYv2HDBnPOOeeo8dyFGlAlY2jatKmEn0i93XzzzTLaqMQPwit4njTaKztRA6pkFMzAT5w40bz77rvm3nvvdftwfAVN9RThWBet7EQNqBJ4UBo64YQTRHmIGXi0PmvVqmXmzJkjM/JKfIU4hEhuuOEG3b8Ugr4SSmC1Prt162aqVasme4IIPTt16iTGlAb7devWmWHDhumETZywKgS1/ubNm7t9KJ5Cq/BK4EBxiX1AVapUMaeddpo57rjjpI2JIghFJPKfKLQff/zxbh+qb8B4UjxCdJrdS+qF7kQNqBI4aPQmVzd06FBdYeEgbAO95JJL5N+kREaNGiVCLZmMnkaUQPYr0iyvxtNZ8OSPOOKI/Amvzp07i7L/5s2bTaaiBlQJHIMHD5aeT/KdirOw8mPKlCnSyrRjxw4RHOH1zlQ0hFcCBx9ppo5QYaL3E3X1smXLun1YgeTCCy+UMP6FF14wmYh6oErgoLI+efJkGdkkb/fMM8+4fUiBZPHixWbJkiWiGZqpqAFVAgkeZ1ZWlunTp49U5X///fekKs/Z2dnS7qTszvLly+W6Ro0aJlNRA6qkDRqx8Q7py2RlBLk08mjRjBe9nMxeFwValgAloddff9388MMPcc/BP/jgg2bWrFnmlVdeKdIxBJXGjRuLTCDXtDZlImpAlbTBYjcg7EPpvF27dqZRo0bmo48+2k05iTaZI488UiToaIDH8CUDfz9jxgxRWEcYuEOHDnFt5MRwT5gwQf5dmP7lli1bpKWHXUKZJFJSpUoV6aeF0qVLm0xEDaiSNrp06SIey+mnn25mzpwpRnTbtm0y3cJ0EB5f165dTYMGDSTkJnSmyov4B43wN954oxjfRMEYT5s2zfz888/mqquuMmvXro1LiZ1iFGLMHCNgHN9+++3825aXX35Ztn/26tVLdqtnErVq1cp/DTIRXWuspM37pFpbrlw507Nnz/y+QlbnYtymTp0qHiKbIPv16yde4p577im/17JlSzNkyBAzbtw42VtOMzehue1JTMRjOvjgg/NvYyAJPTHmVOz//vvv/P/H6CdGnWr+iBEj8lci//TTT9K2g/dsUw1UoC+66CIx/Bz7GWeckTEjj/Xr1zcHHXRQXCelIKJtTEpawOiRg6Q/M9qaXDw8xi2jjQny/9m0+fTTT5v169eLoXr88cdN7dq14z6Oxx57TIwvBm/69OkiDozRbt++vRjYUEgdYCDxQkOl71577TV5TFIPeMnLli0TpSJSDtzPO++8I438nCguu+yy3Z6DPTEEhc6dO8vzHTBggJzs2J+UKagBVVLO119/bY466ijJl7HhkQmhosxSE0JjiMk7bt26VbzXeJedYdzYD8+CObxIDCmeJoY7GszPsxcIT3ThwoXiKdMahedKaoFpHNIEwNeJnGv//v3leePB8ngUzt58803ZVX/xxReba6+9NjBCJp9//rm59dZb5WSE4tXs2bMTOqn5GTWgSsqZP3++FIssRx99tDnllFOkUEMjts2jJQp50iuvvNJ8//334t2eeuqpcf0dXmUsgxkNQnVytIChGD58uDnvvPMiGkI80pycHDEmGFJSF6QFqFiT08WYUthib329evXyizF+ZtmyZaZv376SmqHDIhNQA6qkHFqVUIL/66+/8sUoLHfccYcYkWShAk5xCaNEIcPmJlP1PL788kvxnul9jGc5HV8vGs5ZZoe0HupQ5EoxNBhxUgDcX1DagKZMmWJuueUWyY3y76pVq5ogowZUSSsYUQo2JUqUEI/x0EMPNa1atRJPLjwHGS/kFTGiCCTT3E3rkpe3W+Jxhzf245nSLoU32qRJE9/mEfPy8kTtnxQFJw36b4OMtjEpRQ7P8arwpELPxeQmCaspuBDO2go3/YL8PrlBvFGq808++aSEuslCUWbgwIESSlOt9zIU0FCL4vXhZILxR6ADT40WqWuuuUbyqhgeUgbRBg28SrFixeTkCJmw+ljbmJQiQfM41WwueE5cgFl0qrMW1misWrVKtjmSg6SYgscI5EfbtGlTpOMgx4gnS/+mH4wMhTQrt3f55ZfLBcjnUiAj/L3vvvtE15S+WT8xYcIEyYNmglaoeqBKkQitplNdJtdpQ9VQ8LqoTn/11VfS6nP33XdLMQkjgeiHEyEreUkMkJ+hek1hDCOKl25fT78wd+5cGX8lDxqULoNYqAFVigR7h6yHRBW2R48e8u/QKjchO5x99tliNPmSUcEm7MaYMp3kBORTMdR2BNPv8LrSIhTa4O+HiKRKlSqS084E1IAqRc7pEW5aSTNakwijCeVpLMdYEs5D3bp1zcMPPyzhPr2XTkMTN0Yao05KgP5MBEn8CtNYdBmQG/ULmzZtks9EJnifoAZUcSSMxyByTRWZHk+MJ/2PtCixAZNCz5lnnmnatmplqj71lKmRlWUq5uQYEzLh48Rx4NUyTkmRhjQBx3HPPfcYP3LIIYfISChFNkZYORl5nXr16sngwa+//moyAW1jUhyhd+/e0r7CmOUHH3wgBpPGcebH6QlEBIS83jGvvmoq5eTwwTN5xYqZddnZZn12dkqO6Z9//pGqPB7yihUrpJDlN+hvvffee6XbYc2aNeJhM4rqVYX9hQsXSi6c3tfCVKyCgFbhFUdgsRhfaqrGjGuG7hNHBISVuIxPlpw/X4wncM3tVEHFH0FkCjHkXNOllETrESIpFNL23XdfmVo666yzkpp+ohWIqSW8OsZV+TeeKGG9F1cLV9nVy4v8oBpQRYkTjER45R0wYCjD2y97sR07DOaTDJlcp7jPEa+Thv1XX301LQaUFi0ERMaPHy9tSlb6jlAcuT7b5pUo3bt3zxchoXcU75od7V6jcuXK0lKGwhbectDx3ilM8a0BtdM1FI1oDseITJw4UUJ3RJIhr3hxMZ7GGtF4vajt2yVnmkzuFCEPRiVRcEq158nJAu+TRXaE3YS0GFTk8FhwhzG3r0UicHJCfYq/fe+99zxpPIHi0aWXXiqtaeTAg456oIpjBpScJ0aEnB3yb3idtgWH6RTyeVsaNjSlP/ssPwfK7XiomJubnzstvasBP97cKYbLKjEhXhIL9D5JRzAZlGiekZPGmDFjpI/TtnYRtl9//fUyWTRv3jyZ2MKIMlOPjF480NlACI8B9UN1+5JLLhEhFdSqKOIFGfVAFccMKOErbSyHH364hM7MvFshEQwqPZrrs7KkcLSpadOdBaSsrLjuv+SCBQVyp+XefDNub5TZeEJLmvgL29mE4DJf+vLly4vhpZKPTFthYCxIVyAwQrdBKBg9Wntat24tRTbCejzUeOFExKABJ4BwNfyUE8nzLyQa2GuvveQz4IeugaKiBlRxzIACXiiVb/JgFE4wHlRkgb7MJUuXiue4Mjd3pwe5a266MLY0aCAeK2BG91q1ypSZPVu8UrzTwiDkpacyFlS30fVE6xOVKJ4DeUtk59AcjeV54h0ivIwhRaE91uuEIeU1osiFsAirTWLBcSAkTTog3QvurOcf+lpXHDXKVBoxYufPRoyQ2+FQQKIrI+0GP82oAVUigudI3g6dSsLNwrAq8+Tq8PioutteQIwLrS2Eocn2ZK7v3t1sbtTIbC9b1vxXtuz/86hU8hcsKPTvqYYTmscCEQ+8TdqumOPHyGEQKfygLs9OpXDoMmAXEl7n0KFD48pNIhjCCOt1110no5vkSAubnqINizRIKgYQIrLLy6wwfnzBrokFC0y5yZML5LHL7RqUCB8C4PWi+yHIqAFVCoABJGzli80sNkYEA0J4G8/GTav/SN7OVo3Rv8TLuqd/f3POkiWm6mWXJVwIqjhmjCk1b54p8eefZo8//xQvFCSP2qBBoX/PuGhhBpQQ/Mcff5Q8pVVBQj0KdXleF54b4T2FIl4PmvXxIgnb6dHkdjyQHmCEFTX8Rx55RP7NCWbSpEkRfx8vjt327ICK5d2mwvMskcRrbcdqOQnxWSJ1E1TUgCoCBmP06NHmsMMOy29+pyDC3h9GJCm+UByJZCQwTPwtRRFCVFpsCDnZwmlB5OP3fv3MAAzI/PkS+h3RooWpc8YZpiJeSiHGtEAOFKNSvXpCeVSKWYUJIFOtJ2TnuVD4sSH/gQceKMaNZXIoDFEF5/UgX8rv03WADgAGmBxwMtNTvMbMj+PxhsKcCx4uylV4ruki/PXG87ev9cZ27f5vVI2R25Ho06eP5L55fkFFJ5EU8RYR/KBKzBgmYrjk/WzvJh8RPC4MCMUBxvVomMeT4nrs2LHSOM3/R1IObwnDQsiOJwJ4sQ998YVp/s8/uz0+H8B1ffqYdb17m5UrV4phCm8Sx2NNaIKJEDQ3VwwBXlO9F180fa+5RvYZFQbN6niHnAx4TpEawnm+tCvRpsWqZEZWqa4jpoLBSxS8TAwk4iG8BnZqasGCBaZxw4Zm5imnmIZ//y3PRU4YceaOkyXm6x322q6PcTwUzVj5jMjI8ccfb4KGGlBFWndC1/0CBoziRYcOHaQJnIoqS9UA7xRPlPwfoS0eGgYTY0nei22WGBS+PBYM9Fnz55urfvstP38Wyi/HHGOabtokxoNiU3glO5EvbSQDgOdbfdQoCb+jQahJrpQ8JsYfI0rhBoPI31mh4FA4WeCxM4HD4jlEVdhOmQzkjDkB4QHbbZ54u++1amUGFiuWlvHXZF/vaND0T1sTa49ZPEfeN0ioAVUEtDqt4DG9izTFE5ZiNG0vJ1V1lJSqV68uvZ6oK4V/4VZNmGDe+/dfk7V8udkvZPYcQ3PtlVea7I0bDVuR2NloDSkfwPllypiLq1WTKj7hbFGXrNFiQ5XYMp2ppHnzIm7v5MtNro4VFBTAEHu2e+kpelHwIXTn//P6hIOxxRNF+YkwnJNOstCEzgnJKliRW/2yWjXTNuR3SF3QxeAX1q5dK+unaWfDy6aFKyirndWAKjHBkOIZ4ZlhOBEFIRzHeKISFNHj44NF3ivC6CSe67BHHjHn3HabGFHLDIzspZdKCxE5M6r4TnmglINGH3SQueLHH3f7PXKzFMvIXeIp8fzw+miipy2J7gK8b34PT5OTSCrBa2ddM3ll2zQ/tEIFc9WGDVKwSJsH6jB5eXny3DghE6EUVpT0CzqJpMSEHCcXC83ofLnDt2uGFx3MrFlRq+HZV11lhg0caG7ZulWMAgZuz1NOkXl1Qn2+YEWdRLKFpb9nzDCjly0z9SPsSqI1CG8Xb4jdTXYyiJQGHiDtW+SHMaZUv0Nfh1TB4+Dxc+Iixwybr7nGDOjf37QvV86Ua9fObI1z+MBLFCtWTKax8K5RqyclEu8aai+jBlSJCzwwxDgonOCxhfcj4hVi2PI90Bj9irQGHTp6tHn2jjvMMRs3mv3btzfXLVgghRO7HK6wSaRCez9LlDDTmzY1Vzz1lDm3SxczIGQ/k4UWLTxiimChY5UYMSrqbmCPg9DdGtBb77zT5FapYhr36mVuqFzZXJbiAlIqOfPMM6XnlS4H9An8MJoaC21jUgrltttukx5EWmwwooj7hsJ2yZUXXSSh5Wf77Wcepan+ttti3uf5Xbuay374wTTasMF83Lq1Wfjll1KIirYbqcAkUhz9iHw5aaNhHJN2rEjSb+iWsmIk3pn0dMDIKVBMs2BkaOQnhcJr7WeKFSsmLWBMp0VS7/IbakCVmFAgoZWHRm/CWookoQUAppUYf+zVt6+5eNky0+SPP0w1RvsS8JJYaUyTOkY6GonM0FOIwuDQ9I7xfOyxxyL2Zx511FGyfoRJKf7GC1DNp1+V6n84FPf8tuY4Evbzg3aC31EDqsSEqjsQslNIsvDhp7+vb9++cpsvPG1ATNZQwU4ECjOErN9++230XypRIu4ZetqraOCmhYZ8Kjk3qtvhULC6+uqrZXyTSrFXPDQMO1J4ofB6kxsNQs23yi7RZcRm/I5/kylKWsDw0KKDoWzbtq3kB1kfTEvKp59+Ki0+N9xwgxgtKygSy9NkLJJCAmIdtsme+yX/iWAHjfpFhZ5Wqr30HJJHRLyDVbvh0JJEDpTQ2B6LF8CA0lYWCqOw7Jbye+Hl33//lagAL7ROnTrG76gBVWJCBRp1cfKgGEy8BjxNDBNCyRg/KMx4WqV2Jm7wrsjlvfzyy2LYqL4zQop6jxON1kyFLljQzhx99Pmmfv2/zMcfn2WqVasW8Xcx3FS8eY4oIzH5g2gxTfM33XRTvkhKOmEKjJ5cvGiOg64H+mLRGCVn61f+++8/eR6kTVCVCoJWqBpQpVAI3UMLRxgcNB+jFXwiQeGAfCrGgR5PvFr6K2lgt97WbkpGSU7D5OZWNDk5lUxeXjEze3ZpU7x4B/Pxxzt304dDNZgRVqTswmECKS4D6tDUjoUZfDw0O/lEXyi9uKQb/MqOHTukAElKiB7QoKz70ByokjD0QyZiPCng4G3a3ktypITxLEzDeDJ7D0z7FKZFGQ8LFpQU47mTYqZcuXb5qvSkGiiG4QGhkA8saFu+fHm+ijyeEb8TPt4ajWSPMxIs4GOUk1AXbw1hFvLKtP+kS4kpFUyaNEkuPD/GXYOCeqBKysEYAGkAijrkHuknJWymsZ75b4xqeKvRbr2f8+fLhFFhnl6DBlvMnDmldxnRHaZGDSaQGorkHoaIEdVQUJmiUo+QBxtFydNGmnuPRsI9qlFghxCtXBbyyxTqMPpc+5mNGzdK/2+oQlcQUAOqpBxbvSd/ar9AGKjQschIUnMFmvMR09ixI65ppKys9fme6MqVz5kjj/zUGNNJ8rVUsfGG6bdE+IRjQggZBSYq8WhzJmI8Ix1nvJqZ4VDIYvsmPawoXnFBcwBhFi8VuZKhRIkSktMNGmpAlZRDGxE5U9vyFC1/OHfvvc3nZ5xhTttlWG2vp/U4d9spH8XTw/5lZ+80oj17Pmt+/72cGE7SB1T/baUftSnCdb7czL8jIoLSVKKEH2dh+qRU0zke2zRvIe+JCDEjj3ahHR0EQeDvXYI0QUMNqJLyLw4K77179xZjRSWfHB/VeEL7UdWqmaOnTROD2JIR+g8+MHnz55uaP/20W5hO+F5go2ccnh7pAQYBWMiG0cKQh0N3gJWPS4pdParxQPcB6Qo6GchpYtARcgbWLuNt2j1JiXrCXmX+/PmixmVzzEEiGO+Q4unwnco7+T2a5cnlkQ9DuJlc5Jbp0/O9SjKgXZG6mzhRBEnCw/REPT0g50rfYZs2beR2YauKEQ+hbShVBRvm7rl/iin8m0r/cccdJ+1UvB4YTRSh0M6kQyF0eMGPvPDCC6JixUkiiPuRVM5OSTm04dBsj+EgTKWVBRUkZqGnnnCCueDrr3dKtVGlL17c1AoZV3RC+5LHQUGeIhV5OGTVKGggisJQAEaTAhIdAcyg46XaHUTh0HpFwz8FsUQ1LRFcpn0HBXtEo2ntYaMnM/l4n4yzMoLKiYYBBow4hp9jpdkfYWs/iW/MmjVL0jZcaINjFDVoqAFV0gJGjMo74WmBUI7Cwn33ifzdqCVLzJa//jLX/vmneKDJaF9ifPB4ME5UscM9Too0GCs8PprmQ1uzaK8h5KdfkWkgcqKhUGSyk0B4kInmSxkSQCKPftjCPGHENjCm/C59t8Dx0SHgByO6YsUK07VrVxkBpn0piMYTtA9USQs0pCMRt1sejDwfwsvTp5uqI0eaURUrmkdKlzZbq1c325gewhuNs3qL5B5fWjRL8X7YfxTuH+A1spuHvBwz8BhR9D8xjijO0yVAzyg/CwdVdQpPGDDyuNHEMBgSoDWKaroFFXbUrJDQK8x42p5Y8sWIuFiDSe8qRr2w/fZu899//8kUF72+hPBBNZ6gBlTxBISxTNvUqF3bFNtjD7PX6tVm71WrTKVRo+JuTCenSIM/3hthOqOhjESGG3IrHELITP6R8Dg0HGelMx6UbbvBCONxMv9PnyYtTxzvww8/vNsxEPqTu+T/21XPhP0UhQjJC+uDxNvEi6b4xoplBE9YVIfxBqZ42AiA8DSFOZa1eY0333xTct68H26MwqYTLSIproI3RY6MLxuQfxxcpkzCjemIguDhESaz18iqx4cXYTCOeEUYxWihMDuNEHYeP368eFEcG6E3922X0tE7Si4TL5MiGT2t5CoxsMj74T2yzZMpLJbMsYsJ6b9IuqQWJqIoejF9JM+9WDEx8qj/83zQC+D/YaDZv8RMOUaUVieKT9w3a0joZXUrzN+yZYsUCukm4HUIOmpAFdfAw0KLkwIPy9vatWsnhoB2pbyQjZrxtCvh+dEixCZN22sJhNyhUMgqrD0Ir5R5fethUvDCuFnhFCBEx9ulfxSVKYwZwwDsimKqCS+RSSsudB3QRrXbrH8IrBTB6JAzpBeUnDHHSqHLKtOTnsAYkwahyESoTGqAohYnBIw14588ZwpObjBmzBg5dvLQmYAaUMUVCFNpZcJzxCMMnbRJtF0JA0KVl7ybbZIndMdrCzWWFJbwUhnXLMxDI4/JfDweXzQFJApSXPAGMX40xlNVJyVABZ+cKs+R5XuxJol4DIwjhprqfLTqPr2zeOjWuya3yJACF8CI4unyeG4Y0F9++UW8dbQN8MozATWgStrBsyKHR7jHF243qbkEGtPJAdKMTphrvR7Caoo1GKRQEA/BsHbr1q3Q+yUcJtyPB4xlaMEIMNQYxnjug9Ypfpf8aKzWKAwmeVme88knnyxeNt47F+6DPlLasYo0FFAEHn/8cfHCafHKFNSAKmkHvU1yirQZRdPpLAzCV1vUofUIzw+vEi+IVAD9nbQjhcIMPMYufCFeKkjkedl1IpxYYkFagZQAqQS86dAuALxS8rW0PkUcmU0xS5YskeLRyJEj4+oyCAraB6qkHfowWeSGMSNUTpQ5c+ZI7o8eSUJfGvUp4PBRpoBCTpJqdeisOaOTNKVjcK18nlfAGHISwINEXDqWVCDG9vXXX5d8KsUy0gUY61j51VSTl5cnqQw6B/DEgzKCGg+Z80yVtENYyZcrPB9GJRtJO3KRiYCHhgHEyFDhxVBSrLFQ5Z4yZYroZ4YLdfA3hOXh++zTARV5emAxMMz/cxw2VEeqjgvVdvLCHDv9q9GgQORWiB6Nd955R/pqKbRlkvGEzHq2StpgQRzTPISZtAPZ6jgQvuMphv6sMDA+FGroFaVIgmJReCGI6SLAIIW2KdEXSmiJ4bYV7XSCx0yIy/PFwHBsGFKuyRlSGKLfE1566SXxlK0AtNfZtm2bFPDoDCCvnWmoAVUch+IQRRSMJ32JCGZQAGGtB6DKRIU7EWyhhOZ4KtuRoFrOqCWtPrTTYJgwpsy6N2jQIGLjezqgvYoQ+8Ybb4z6O3jSFLcI5xnXLCyU9wovvPCCTHSxHysT0UkkxTHwqsiFUUTgy48Ba9SokbTUUGGmZxKQc2MPEUYxkbwpxNrkiMeJUAkhJd4QPaYYbyryhPao36cTxknxzAjJCysqMbpJmA+E8vSSep21a9eanJwceY2JNjIR9UAVxyD8pC2JnCf9jMycU3Enb4mHQuO8NaBM0DDueMghh8R131SY7bw7hZNYRpSCDBe3wIskXJ8wYYIUylCVj0cLk7QERpdCDP2xXmfIkCFyogwXXckk1IAqjkF4zbghjd3Mb9uxSDw/FrSRC2SChtuE9LHGGsOhEZ1KOwUZpnO8DKpTGE/GQSmUxfs8yY/SUUDIH14E8xr0or799tsy4x8+7ZVJaAivOAaeJ83doWLE9CTiFTLiSJO3BQ8rkjp8NDC4NKXTuuRlaIhnnJL5dPo2EzlJAB6d140nhSN6eE8++eS4hhKCjHqgimPQwI7QBR4YYR1Gj9CbIgNrggHvFKUjO5aYCLT+eH0x2YgRI+T5+n2LZiwo0PEcp0yZ4gtt0lSiBlRxDEQ4GKvEO6FgZCdSQr1GRD8wqqgOUaEvDH6fWXeu8eaiaXC6BZ0Bl19+uYyIor5ELyphO8LRXoFzTm5uRdlSyspntpYm267J82TGH2Wqo6NoBGQSakAVx6CxHcOJ5xk64EZBh15MiiuJhrShqj52JYdXoNhDcYjnS2sV/a2E8OR5vcSoURXNyJGVKLGZ2bNLi0Z13747uxoSAZUlKxcYPiabqagBVRwBg4kMG2E5Ri+SkG6ixpNmfKAABdy3m9VpxiivueYaWZBGQYv8Lu1JhO22id+LTJ5MkceG2sXkdqIGlMIWbVa0kyHdx/NX1IAqDhoX8mKE7061ENnpHKaP8G5pRi9McCOVoAFKqxaVZ6BNC2FlpomCDCctTha0odFpQapC2YlW4RVHQA2Ink4KDKxzCIfcJQWmjz/+WMJwRivpI0QEOBKEw6j74M0y984YJm0zVgsz3dCzSh4WLxsPjGZ9FKD8YDzbtUPtyaZU8nbdjh9ynqRgeP2Z6FL+j6oxKY7BgjYEfZk8Ct8BzpevX79+8m9Ejyks0ffI7+LFhYLHaddYcJ9UehHQoEWK/ko3+g6ZT2dCiBOEm8pH6S4iMXLLNtCbb75ZtgYoBVEDqjgCHyM2SCJPx0K30JW/tC1RWCH0oyGekU9+l3l5DCLtMKGpACZy8EzxVr2ibI7R5jnYnUiZ8p7yfDlxIYYSvl9K0Ryo4nCIi2JSvXr18n9O5R1hDLxIrkOLLXg35E0RQeYLSs4TTUz6PWkHcst4YthRV0fgg75Vil8Y/UwzILSPMT3GGG6mPfd4UQ9UcQQMIZspae3B+7z66qtF2INd5hgfREBsVT1U5JheQtSbbC6UPlIKFlWqVHHleXD8CKDQx8laYsRRSCmQj0W2DbWnTAAVq7PPPlvas5IRvc4U1ANVHIF2HkR1Z8yYISE4s/DkODGkKC9FKj7gjZLjpA2IEUZWVZBDdRPEPPCWUbSn2o4qEutBUMBnPDMToJd18ODBUn33gyqUm6gHqjgO3iQTOhhVt6rmRWlVImRl3xLgfR5wwAEmEyDdwsgtY6j0eyJsYkVhlMioB6o4Dq09XusVxBBSBKKXEQ8LUWdSDlT7bX4PX2LSpEkF1oQEyXjyvDmxUdQj72zXL7NOhD5b1omwohkRFDxQlLWU2KgBVTICRkwpirDqmMmmr776SnKt6JXalityfRhYFO2DBoWxG264QbxM4KTBmhQgfcKAAv22dD4waaXEh4bwSsakFSgMsQ0UsWMKJORex40bJ03iFLLwPFl5TL4zaCpDDCTQ2cAKDvpwyT///PPPZuHChVI4Q1Ge1yRozzvVqAFVMsoLRWaPKSKuWdzGcjuMJ4aDTgEMqt8a5eOBDQCkI1iJrDiHGlAl46A1B6NJw79tEmfKiBYlxIzZ1cSYKXlTWrLYI+93o8qOKMSPWbWiOIfmQJWMgl5U5uztjiJCei4WQloqz6jq83NCXm771YCiQUBOlxMEeq2Ks6iYiJIxUHkmx4nUHg3ykWBaCuOJkaWPlbyoW039TuV+CdsRr0YIRXEW9UCVjIGQ/ffffxevMtTrpDKNghQVaKanGEdlBpymekJ5P2M95549e8oJQXEWfUWVjAHjiZQefY6NGzeWNh6rY8o1IsG084wePVoGAJDfYzrKzyBCzYVcr+I8GsIrGQEtOzSS2zlv+iJZTUHPI0Uiwlyk9QD5Nhrq2dsUhLYeug10nj01qAeqBBqaTDCWLH7Du8Q4shEUA8kuo2HDhon3ibo8+UJuU6UPEh06dJAmetIUTGApzqEeqBJYmDJq0aKFhOE0jiMQQkWatSPkNlFYoq0HQ0poTwtTEBXXmb5CzxQxaMVZ1IAqgYX2I0YzEcdAWSm0mk5fJKLOGFR+7+WXX5ZcKDuPggY5UIwoBTLFWdSAKoGEvkfk9Xr16iVeaKRcJhs2Gdtk3YjVMf3+++8dX6eRk1PRZGXVkGu3tjLTdcBggOIsakCVQPL8889L287JJ58c9XdQmqehnvB+7dq1IjLCtZOwiygnp5KZPbuMXHPbDQjh6UJQnEUNqBI4qLY//PDDMucezwK68uXLS5M9xpN/OwmL3PLydnq/XHPbDfDA2Qxg1ZgUZ1ADqgQOdC3pe6TyHg9UplndO3fuXPFCnYQtmMWK7ZSb4JrbbhXUKJDhdSvOoW1MSqBA2/P222+XwhCrOOKB1iZGOGljQmDZSVghDKErhd2AGf+uXbu68thBRg2o4nv++OMPqTDTDE/uEyNIc3y8HHvssbLKg0Z6p1eQMD2Zne2O0bSQnmCQAB0AxVlUzk7xLXx0qaKzyZP8HkLBiALTnhSECSKnoMugX79+kuMN1QBQio56oIovYaqIMJ2+TXYdEZ6y20fZHSavoGzZsm4fSuBQA6r4DprjWQiH8DHVdr8LfqQa24nw22+/yaZUxTnUgCq+gmLIGWecIeOX48ePlzUcSmxsXpdGejWgzqIGVPENFIqomFNdZzwzSCuHUwFTTzTuf/ppG2PMnWbt2t/cPqTAoU1hii9ABJmwnbUU6HWq8Yx/Curzz3mtBpiJE9Vbdxo1oIoviiDMrSMzh9ycX/cTpZvQKSi+6j/8oEU2p1EDqnieyZMnm/Xr15vs7GxdS5HkFJQxO0yLFnu4fETBQ/tAFc/TsWNHUUkijFcSz4G++eYGs3HjZPP77/2ksV9xDvVAFU+DWvxbb70llXcluSmo887LNXvsMUiNZwpQA6p4mjlz5kgOtGHDhm4fim+hgZ5xV3bEK86iBlTxNOwzR2KO9RtKcuy3335yjRFVnEUNqOJpWDV81VVXiVrShg0b3D4cX2JHOPX1cx41oIrnwYCiY8nkkZK8AVVFeudRA6p4HlZuYERpoH/nnXfcPhzfhvBqQJ1HDajiC+69914Z47zpppvM559/7vbh+Ar1QFOHGlDFF9BAz2riunXrmpycHLcPx1eUKlVKxFc0B+o8akAV37D33nubiy66SNYV//33324fjm9AXBovVD1Q51EDqvgKFOfpZ3zxxRfdPhTf5UHVgDqPGlDFVxxyyCGmd+/eZtSoUaJv6dcRy5yciiYrq4ZcczvVlClTRkP4FKAGVPEdd999t+T1+vbta/7888/8nyPrwE54v8jMzZ5dRq65nWo0hE8NOh2r+A4mk6ZNmybydl26dDGtW7eWPtEZM2bI2oqJEyeagw46KC1CHaHriuOdNQ+VmeOa26lmn332UQ80BagHqviSevXqmffee8+cfPLJZurUqbLSuE2bNlJtZrWxl73IUJk5rrmdKvDQWbyn/bOpQT1QxddGlOkkq8i4aNEi8/TTT5sGDRqk/LGL4kXirdr7sN5rKtixY4cU3VC0Qs1/8ODBKXmcTEYNqOJ77A742bNny79btmyZ8sfE8M2ZU1qMZ6JepJWZSzV0K5DSyM3NNVdccUXKHy8TUQOqBIbatWuLNzpkyBBZAZLK1R/p8iKL6oHa/KeSGtSAKoHh1FNPFeN5xx13mA8//NCMGTPGVKlSJSWPlS4v0gkDSoFNSQ36yiqBgfD9hhtuMIsXLxYR5hEjRphMxuaG1YCmDn1llUCqN+27775m1apVJpNRDzT1aAivBI7bbrtNmsZHjhxpMhk1oKlHDagSKGjZQbXpsssuM9WrVzeZBuOttHNx+eyzzwrogSrOowZUCRTsTtqyZYvp3LmzyQTIc06aNMl88sknYjRt2qJatWrmxBNPNFdffbVMaimpQQ2oEig2b94s2qEVK6Z+vtwLkKagWNaoUSMRnMZonnDCCSkfZVV2ogZUUXzKyy+/LMZz0KBBkvdV0o9ml5VAQd/n9u3bzaxZs0yQ+eWXX2TNSXZ2trn11lvdPpyMpViebRZTlADAx7lt27Zm+fLlIjASVMaNG2cee+wxs27dOi0SuYh6oErgmum7du1qfvjhBykmBRWk+1CfUuPpLmpAlcDB4jk80W+//dYEkbVr18pm0vPPP9/tQ8l41IAqgePYY4+VBXToYPpBoT5RZs6cafbcc0+RqlPcRQ2oEjj22msv8/DDD5vp06dLaw/50KAZUIRTypUr5/ahZDxaRFICy9KlS02nTp3Mzz//bI4++mhZBUKVPisrS2bl/Tpp1bx5c5Hre+SRR/K1UBV3UA9UCSyHH364yNpdcskl0lhvxYU/+OAD41dYpof3SQX+jDPOCJx37TfUA1UyiqOOOkouAwYMMH6GfVCs6Pjjjz/M3Llz5Tkp6Uc9UCWjoPWH1R9+9xvYSEqfK2kJbaR3DzWgSsYZ0NWrV5uffvrJ+B1Wllx55ZUioPLxxx+7fTgZiRpQJaNo0aKFtAC9/fbbyd3B9u2mYk6OqZGVJdeyIN5FyIMeccQR5uabb/a9V+1H1IAqGUWZMmVM7969zejRo82aNWsS/vuKubmmUk6OKTN7tlxz2w2w2zk5FU2vXrVMnTrPmo8/nm0mT57syrFkMlpEUjIO1Opptt+0aZPsUDrzzDPj21y5fbup07692TtkVcimpk3NSheMKMYzJ6dS/lrlqlVzzX77DTVffPGF2WOPPdJ+PJmKeqBKxrH//vuLoenQoYPp37+/Ofnkk6WnsjDwNvcKMZ54HlsaNDBuwDpljKccR14xU758e/PVV1+JyIiSPtSAKhkJ1WtWf3z99demb9++sgJ5/vz5Mf+m5IIFJrRtfVv16mZ9VpZxA3bR43kC1yefXFwKZHfeeWcgx1e9ihpQJaOhAPPggw+KojvXdhFbJPA283ZN/nC9kVl0FsS7QFbWepOdvc40bbpJrrnN+g6mroYPH+7KMWUimgNVFGNkp1CzZs2kwZ75+R9//FGKMhSaCPOp3EsFPjdXPFGMqXifLhnQaHD89Llm+krndKEGVFF20a1bNzNlyhTZKYRgB4pOf/31lxk1apTsGvIDNNfbMJ7jV1KLhvCKLyC0/vPPP4ukobly5cqYvzNkyBDTsmVLWQ181113iZ4onueSJUuMX6hcubJcJ9OipSSOGlDFs2zYsMH07NlTlJQQ0UB9nfA0Hv777z/z9NNPmyuuuMLUr1/fHHjggaZWrVrm4osvFrX6SKDU9Nprr8k+9TvuuENmzfHi2rdvb/xmQIMwaeUH1IAqngQVJZTlEUXmGvm2SpUqySbKwiArhWQdxpfcZu3atc3dd98tmyunTZsmhSOMa2Gg+o50HIbVL9hj1RxoevBWBlxRdhnACy64QIzB2LFj840CxrSwlD2hPsaWtqT77rtvN+8RFXdCdTzTFStWiGGNpqlJw/3BBx9s/DYfX7ZsWTWgaUINqLIbNGRv3bpVvogHHXSQKLynE/J3rO1FZSjU+6OgE2uJGuuM8TyfeeYZKaRECr0xMPy/6tWry1pg5OCGDh0a0YhiQHkN/AZhvIbw6UENqFLAAPXr10/Eei0VKlQwl112mRgmBIrTwaJFi+Q69PH+/fdfKe5Uq1Yt4t9Qdb7wwgulik7u8qyzzop6/xjLHj16mNKlS5t77rlHbmNEI6m/+9GAHnDAAXICUlKPGlBFwGO56KKLJGeIEW3YsKHMin/00UeSL2TH0HHHHScrg9kGSfiL0aFY07FjR2kBKuFQTyS5R0Q/qlatWsArJXynEBQOx0lo/umnn8oxkbeMB57H33//Lc+NE8QxxxyT///wwFmL7EcDSucAJxwl9agBVcyrr74qOUEqzk899ZRM5ViaNm0qEy4Udd566y2pTmNg4dBDD5WQGm+OWfIRI0aYk046KeHHJ1RGYR0jyYXjodATGlbPmzcv/zHDuemmm0SVnX5NDH8icEKYMGGChPVU4EOPCfxoQIsXLy4nNiX1qAHNcAYOHCjTK+zZ4TpSjhHDypw1F/KQGFM2QtJwjpH78ssvzaBBg0SUg/1DeIGJbIzkGPgbPCcq7YSgoTvPKYhQ+MHYRSrqYCxq1KiRsPEEHjM7O9vcfvvt0r7UuHHjAgY0Vs7Vq6DGpAY0PagBzWCoamM0EdPo1atXXBseyRsi/xYKoe9zzz0nHhye6Jw5c8ykSZNMnTp14joO7pPCx4wZM3Y7BlIF1157rRjVHASMd0E4j7EmdK9Zs6Y8Po3yGNJEIV9K4Ql194ceekiMOP/GkwtNI/gFjltD+PSgfaAZCoWa7t27i6J5vMazsC8tM+QYMvKHTZo0Me+8805cf0uTO03z4VAh79Kli9w3xaHQcJqQffz48eKp8hzoFuD5fP/990l5bPSI0n3AJBIL2vCqn3zySV/1gIY+n1iiKIpzqAHNQCi6dO7cWcJhwmcnd4vjDWLYjjzySHPaaadJLyZFIYxqNCjkiFhHGKzsJWUwceLE3bZOvvDCC5JawKg+8cQTsqoYL5ZCGEY80RCW8H/WrFniRdOFQB6Wk4Af4YRDR4WSetSAZiCEpxRryCvuu+++jt8/eUMk1fAeKTo1aNBAwnQmivDqrHdElfvGG2+U6SJC73BDzh53CPcCMY4vvviieLwUsBARJufKYjVGNe+//35z6aWXmmXLliV03HQRUKRq3bq1L3OfFs2Bpg81oBkG3iFCwhRNkskXJmKMWHRGaxEG7pZbbpF8IukCwuSlS5dK7nXYsGFSYY+019waAWbh8Sw5dgRFyK+ie0n+k6IP+T42U+KNkiel9Yq+UKaZyG1mmuAY3vy2bdvcPoyMQOXsMojvvvtOejkxYDSbuwGVbtIG9J3y0WMaiA6AvSjYjB1bQGszb489zMKFC8Ug4l2So6SRHg+LMBVDivHEC+X3cnNzpR0LSBmw7whPmDYljHCmwEmJraNomiqpRQ1oBrB582Zpy6Hhff369VJ9R93ILfAO6SnF+Nl2JVYEs+WyWF6eqL2vy84267OzC/wd7UwYX6vIRLWf0UxSAnijPL/QKSqMKieMxx9/XE4aruCCCDMnDNIYeKGcaJTUoa9uwKG/kvwjVWoWqbG2wk3jCWzA7NSpU4FeT9k3tOtczjW3w2F+nWZ5vEzm8ykwAUYiUhGK9iqEkDEmFM7cwI01yOSDSX/Y10dJHWpAA87UqVNNvXr1ZErojTfe8GwoG75vKNq2S4wlakssgKNfE8iLEq6G53TJw1KRx3hiRN0gnhOD03DCtLP8SmpRAxpQyDXSQsRsO+OVTAnhhaaFf/4xNXv0MEc0aybX3C6M9d27m82NGpntZcvKNbfjhfUboemA8LYqwn76SN3wQuM9MTiJ3XFPl4OSWtSABhBk4OhhpFBEGHv66aen9fFr9uljSs2da0r8+adcc7swKo4Z87/2zgXO6jn//+/IkkRSFlkRW4lWuhAJRa2QtaRc2mwYmpA7uf7pwkbYZTVS7G6kbVUu1W6y5RYmJdqQS6RVoaj82LQ29X883/UdM6czM+d8z/dc5/V8POZxOtPMmW/TzPv7/rzfr/frbXXnzdv8OfPm+fNEoZnUuXPnSp2aKBdwpE3UzT5KqHlSz/2mQ4fNdd0MrEEOOvDpkKiJimiUs8DAxoxGChZ0V1xxRVaaCDu8/37Z/vRaW56n66jLeg5kUDROKoOjPdk3u42Y588otWtv1QxLN6gUgNHYQJUg0oMy0AKCY+wZZ5zhekjGGrPVgV3fvLkF0o5NW56n66gbOCgRRNGGVgbfE7r/NQHctBgywKIPbwKRPpSBFhBM9XBMZZ1FgwYNsnYdn4wc6cd2Mk+CJ8+rIzjaVpD7JAAypffee8+1j3iIsk0zHsidakoAJQNll/2uu+7qWlika9TDoxzZFZuRDrRAwE+zS5cuborBCGVNg4ybmXq67qgOgowc671gjQeBBfPkfNV3hoFJLEZ2qYUj/SITP+200/ykko9GKbmGAmiBQN2T0UlcimqieBqNK0ES+ztuJLg7MXkV7JJnth3XJ4xJoiAR4X+uQC2UGwlaWWrkjNcyfIBzPyO9TIKJcCiAFghTp071JWr8UnBcy2czjLCQcTJlxdgnGlG68h07dnRrOrKwYHopiqNsk6IiF8cH0GVfmgGRfBRgE4jV4OOPP+5SJ418hkcBtMBE87gRNWrUyH85Mr1NM5fBbHn+/PkuaappGWhVN10kb2Tr1EtF8iiAFhjMf+NQRCBFxiTSRMgaKDado0c3svnzd7Q2bdZZUdGqrJVOsfujHvrCCy/YMccck52LyHNyr+otUqJ169Y2ePBgbyZxnA+zJ0ikT99J8Cwp2d02baplc+ZsHrksLl5l2YApLU4pCxcuVAANSc3rNtQA2JpJ7Y9dQljAidyBzJPgCTzyPFvgFUAzKRDei+RRAC1A+IUIlrohpmYCR+QGHNtr1doycVVrkz+v6rhfUtLIioqa+GPUWzpQKLD6IzBlEcmjI3yBgqUZ8hX2EjHON3z4cD/Si+xCzRPK10CzddwPVqYogIZHAbSAQco0Y8YMX4fB+gyO9qwCFtmDhlGiQTDdx/1gEyqqDREOHeELHCZP8AFlzJOd57Nmzcr2JYk0HPfDEJhQV7UxVVSNAmgNgMkknOibN2/uvph5KRkqKXHxOo+RFwNzFI73xcUrrUOHb/yxquN+GFgDDcuXL4/0dWsSOsLXoNUebMJk3W++EazFQLS+05w5/r58E62n+7gfht12282nshRAw6MMtAYwfvx4u/LKK91wg3HPfCMbazFqAhzhqX8qgIZHAbTAYZf62WefbSeddJLrQvORbKzFqCnQgWfFtAiHAmgN2I3UtGlTNxjJV5embKzFqCm0adPGJkyYYEuWLMn2peQlmoUvQPDFnDx5sgvoMVfeZ5997OGHH872ZYkc5D//+Y/Pw7do0cKX88l0OTnyMyUR8dmwwT4rLrb5jRrZ+3362NhHHvFZ+FtvvTXbVyZylLp167p7PfK20Xlix5dLqAtfCGzYYBuHDLH/3nWX7fHdd7anmR1pZheffnqN6FaL1DjyyCM9C8W9i/HOgQMHygoxQZSBFgAbhw2zWoMHW53vvquwDVPdapEobDNAocFjy5Yt3dlfVI8CaAGwavLkssAZQGFb3WqRKOyNuummm2zixIm2YsUKfxTVowBaAHzZokXZGmHgz/9p317d6hwk3Q5LqYKD1wEHHOAeoaJ6VAMtAOrdcYfd8dRT1vv7761OnTq2oFUr+/Tcc+2IbbetmJnmySbJQiaXDJUrgwDKkj5RPfrtKQD2adrULlqxwsaMGeM+oIsWLnT3pZ8ecoht6tixLFDW1JHIXCKXDJUro1mzZr5fC5OR7bffPtuXk9PoCF8gMNdMA2D27Nn2Xt++hnBp9wULPGASOEEjkYXvsBQF7du39+D54osvZvtSch5loAVI/bffLrszEijXzZhhc9u1sx1+/nPPPINNkmoy5bahcjYz0L322sttELt165bty8lpNIlUiAwebJtuvdUD5UYzz0aHsOrDzEbsuqud0qCB1ena1b666KLNNdDqaqP8/ahRVn/qVH+69uSTbVXwuSFIZTMlkzNPPPGEPfvssz5+yPoSbPq6d+/uK4vZ8yNSZ8SIEX6MpyOv72nlKIAWIkSo2283mz3b7KijbN3ll9unn31mH3zwgU+bUCfFRGLQoEHWtXNn27eoyOrOm+cNJ34YVvbvb6suvrgssNZ/5hn7ybJlZQ0p/5gBA0LXT+k+B40UjrF4XcZrpKxfv95KS0vt1VdftbfffttNLxB6swiNPeadOnXysVX+nsBar149GzZsmHXu3DnFb6BYsGCB9enTx+bMmeNrskV8dGspRMgYbrml7CltiuY77+yZGmLpRYsW+dpjLO6eaNXKDlq4sIIAv/6UKbgwbxU4LSKRfmwjZcqUNbZq1WD74osvfE/P2rVr/Y3gSLAMdjwxr80v81FHHeX/FsxRuFc89FBDe+GFDbZ8+QQbOPBKa9v2ELv//vs9oIpwNG7c2B+56SqAVo4y0BoK/+0PPPCAtRg40I6P+RH4X716Vvvbb8saTlt9rpmVnnCCze7Sxb7//vuyt//9739xny9btsz+/e9/27fffutZ5fffD7KNG2/e0sP8scjAcbxBgwaeHe+///5ei+MNbWLDhg0TymbbtZtmb775a38tbhBY+Ynk4CaGimPp0qX22muv2cEHH5ztS8pZFEBrOCv697c9Ro0qazrxw/ARWsCYj+P9rCBbg8eomQ01sx+2/B0OPshdeGOGOvgzb+vWrfOxQDLIn/3sZ7bnnntaw4Z72Mcfn2mrVjW3li1XW9++y2yPPRr6Erxk3YAQpJeW/phpsv7ihhtesNtuu83eeOMN22+//Wzs2LH+9UX1EDSLi4v95sf6F+zuROUogNZ0OAMPGWKbHnvMs8UPDj/cj8YtJ07c3K1n6VjjxramRw9bO2CAbV+3boUAyRvO5vECHz9auEHV3X57m9imTVoE/JXVU/nadJGHDh3qwwXUfRVEK+fLL7+0p556ym82uNTTpNt3332zfVk5jwKoqLYJZTfcECrgvfDCC97Qmdujh7WdOrVMPuWmyBEJ+Kvr6M+dO9f69+/vtm3PPPOMgmg5qC9zRGfunf8ruu29e/e2e+65x0sponoUQEXaYI3I4sWLbV6DBlavtLTs/TjLL82g9ySd/AEDBngQJcPiWF/TISOnBs4+JGqcF110kZ1zzjmubhCJo0kkkRbo9KMjZAtotncadejQwYMFuULPU0+1d848M70rknN4DTPfA0Z+UWHgA8rNhbn3Sy65RMEzBMpAReQgP+rRvbt1nTfPig8+2L479FB//45vvRVtDTRJc5Svv/7aPi0qsl6LFnnmkKqetbLrsI0bbfdRo9JSskj1yI5A/tFHH3UXet60wiM1pAMV0QfPHj3smFdesWs3bLBac+ZYvddf9yAS9bE9WXMUuvy/2GWXH8dcIzKdjr2O7xs3zjnPgR9++MFuueUWP7r/8Y9/dJmSSB0d4UWkwfOUU07xyaDzmjX7UZxfPohEeLwNY45SvpyAAvXTCDrNsdcBubaGmaA5depUGzdunIJnhCgDFSlB93bmzJkug2GF8rvvvmsl999vuzz0kB+Rg/HQIIjEzRoJpiF8SvnYZM1RApPpn8yda/fNn2/TFy+2VPPi2OvAK4BJrgr/nizCtk3qnnfeeaedddZZWb2WQkM1UBEahOpHHHGEH43RDmKpd/7551v3OXNs9wcfLAue/2nXzj7h+F67tmeesR15gkwQVJOqGaZoEH3BBRfYhx9+mLptWw4bVTMB1qtXL1dE/PWvf1XNM2Jy439Z5CWMSjJy+fjjj7uYPqD+zTdXmK3f7vPPywJKvKwxtE9p7dopNWdoKrELKGVSvI50Mm/ePDdawURGwTN6FEBFaDCawEKufPCMx7b/93+eeXp21q+fv698tkb2lg2f0jVr1hS8YHyHHXbwR6bLRPQogIpQbNiwwVauXOnGH7FQAyx/hCeAcmzfqbTU6s6da5889FCFI64feTduLPMb5c/eXErzMRhzk7333tsKGcZYgSx0p50272AS0aEAKpKGdQ+///3vXVcYL4C62fKWJgp2eNsvW+bvJ6ASQMk4Kxx5CZTbbGM/Wb7cs1A0lDxP5ViciGkzlm1Y6IX9/HwLoCJ68vBHQmRTS0i9k/3hjACeccYZ3kSqqiaIVGn3kSMr1ETj1Tij3teUyPZLPEVnzZoV+vPzAQXQ9KLCiEgI3HkOPfRQ69u3r/tzTp482YXZ1dU/OZ6zoz6QelRW44x63DOR7Zc0wNCu4kIV5vPzqQaqAJoelIGKannsscfsN7/5jXtDMgbYunXrxD+5dm2veW4l84kheF9U2kmO3WSOgc1dvO2X3AgoQ6BfxeU+2c/PBwKVAb6sInoUQEW1jZYLL7zQxzPZNxRKCpOIzCdiKVAi2y8POGCzbTSGGrEBNB+2ZyaCjvDpRQFUVAkO8wRNsrWM6whTEKjzYVvVLGNeb9MFF/gR95133kns8/OQjz/+2B8xvhbRowAqqg2gNIrmz59v/bZoODNFsmYhlRJsF3366c2dfjOXVCGXog7KtE4h8tFHH7ltHXZ+xx9/fLYvpyBRE0kkdIyvrlmUDqLqzAeBePstwdNfj4mpqVO9E8+q5ELjrbfeci9WRmwnTZokIX2a0HdVVAk6SZosxxxzTMa/dlSd+fKBOBYyUHStrFEuJIOXoqIi+8UvfmEvv/yy7bXXXtm+pIJFAVRUCdkZfjORzIwnCTVPjEUwHHGDkZCd+QqBeMv7Nm2ZmKKRxL+PRlIh8OSTT9rll19uJ5xwgs2YMUM7oNKMaqCiSmgeof/8xz/+YV27ds3sF4+oM18mkXrjDau1caNt2mYbW9e2rb+/2ZbMk+VqBJ18hZvAww8/bH/4wx9cNTFy5Ejbdttts31ZBY8CqKiW008/3X73u9/5rvBs1ELTGYgbNmzo++pxLcpX0LLi9YlZslZ1ZBYd4UW1HHbYYd5IojGRDRCBk12RBaej4dOxY0f7/PPPPYvLN5iiGjRokI/YlpSU2K233qrgmUGUgYoqefvtt+23v/2tG2/ss88+Gf/6eHbiO4qMCgcoGiLsd49S14jMB7PhV155ZStBfS6DOJ56J9+bJ554wk8KIrMoAxVVZn7sOKpXr56Pc/70pz/N6Ne//vrrrVOnTrZw4UI3/SADJlOkURJ1hk3W9vTTT1u+8NVXX7n7PytUaBYpeGYHBVBRKYMHD3bXJVbhUivMJHxdlqBxPMW4mUB6yCGH+E4f6n3nnXde3AmiMHCDOOigg3xFST7w6aefuqkLQfSll17KisRMbEYBVFSAY/Ls2bPdsu7uu+92PeG+EWyuTJbnn3/ej+lkoeVNj6nz3XPPPV4LJcBHVbekDrp69eq4zkyVEuGG0UR5//33PXgyIcb2U24qInsogIoykL6wGI5s74EHHnADETK9bEB2teeee3p2WB6eX3LJJb6ml+Mr9b+o6qD4nf79739PesIJt30eeZ7uksrAgQNdNUDw3G+//dL69UT1qIkkHIIRDYlu3brZOeecYy1btsxpHWHnzp09Q+W627Ztm/LrkcnxegTQU089NaHPidoEOpEbHFky00WMaIrsowxUONdee6132m+77TZr1apVTgdP4Pq4TsoMOOMHrkNhQd/avn17D8iJErUJdFUsWrTIG3loPJs2bZq2ryOSQwFUeNY1ffp0N03OFduzRGqbSHeokb733nv25Zdfpvw1cZ365ptvvKudyVHT6qC0wI3twAMPtKuuuiotX0OEQ0f4Gg6B86STTvJfzpNPPjnpz0/X8jUMPoJ1FLGB9bPPPvPOPG80VYDrTxWO7mR5WMBhxLHHHnvkxD54NKpkxtQ983ISrIBRBlrDadeunTeNCERh9JXB8rXS0nr+yPMoYFdR+TW8COqHDBniWlRKDdRAi4uLfYadI3xssykMO++8s28bJeMjG2dEMtv861//svvvv9/69+/vjS6RWygDreGg70QydNlll7k8iEmcJk2aJPz56Vq+Vn7n/Icffmgnnnii6x/JEoNrRN4UdUZG84wMFInUNddc4zXWbPHiiy/a1Vdf7WYueBGI3EMZqPCGzF133eWyIX5Rk9FWcmxn6RpEtXyNrPLNN9/07BibucMPP9yzQjaBok899thjXcKTruNsz549vZzxz3/+05566inLBpwGkCzhEDVz5kzPjkXuUWtTPjooiLQwfvx4O/vss72phNYwihoogS/Rjv6qVas8kGMaQkOnT58+HjAJlvfdd5/tsssulinQXJ555pk+EUUwy7QPQJcuXezoo4+2CRMm5LwioiajDFRUmAkHutqJEixfGz16qT+WD57UEJmlv+OOO6rMavk7xjZ/9atfua3cdddd5/t8Lr74Yjc8pgaYyeAJGEjjrckqDFZjMKGVSVBD4MWq4JnbKICKMhjZ5LjM/Dl7dFI9nCxdutQXtmG19tBDD8X9GEYnqTUiRyLrpYTA8b127dp+DY888kjWjq9kvjSukEhxnM4k3DCYxhK5jQKoKINsh3062NfhK0lg++STT1KywoMBAwb4DDud9FimTZtW5iaEVAeHIZonfE6zZs0s21CDZDILfwAMizMFYnlqvosXL87Y1xTJoxqoiAvBgg4w3XB0omRg1eoiY6CzT/Y2ZcoU75rT5Y9dC4IxBvP3p512ml166aXeMGnQoEFOmQLjxM91Utro3r27+5Om252Kmw01YExDFixYEFcTK7KPMlARF7KuJUuWeB2QjPDXv/61i9sTBckR8iiMP2jAUMucO3duhY/h9Tmuk3XieE+QIJjmUvAEuv333nuvlzeo1bJjHVkV1npk6+wgwkcAzWZUoIPFYo9hAWRcIjeRDlRUCgEN0wqCGxli+THPfv36eYOFAIIus3zQo+FC44hMkr3rdJPpsMc2RBjFJGDy9zSwWrdunZEppzCQfT/44IN+Y/jb3/7mpQ6ya24q1HEJeLyP7xPKgWT3sHMQpONPqQAVAi5TfL/5/kYxZSXSg47wolJwaKc2SbY1dOjQsqBAEOF9NJ2okdIt5hed4y3BFqs5GkDIoZAx8bHAXiNcjxCn4yi0bNky77iTZeE4P3HixAplgpKSzVNOCPTRmBYXr/ROfy6CPyk3DTJUpqVGjx4d125uzZo1HoD5tzI9hRkyo6mYOeO2z02GMgfZLUMD0n/mNspARVwQkffq1cuOO+44n8opn1EhdOcXndocR3Ac4m+88Ua7/fbbfU8PkK1hjQcEAoIlx3gkQYGTPA0aaonDhw/3I3JsjTVdU07pgEBHAOX7hWMS46Vjx471ySZAlvXoo496gCVbp0Syfv16F+pzI+I5wZRsftddd832P0ckiDJQERemfWhkICOKnfih3scvP8fNAIIiWSfZKCL48i72zJXTked1gjpomzZtysyQCao0rGLJpwy0PNws+DeTjZNhczNiYR03CBpl1EwzvSJFpAdloCIuSIqYhokNnjRK2MMzbNiwCu+n4cFbPPbff393OQKCB5pQPh+PS46tdJvjQc0TytdA8wFm9DnC03jDho5ZdrJPMnq66qJwUAYq4nLLLbe4qB0rNfSYSHmee+45lyIhSaKGmWgwYCKJmh/H+969e7tgnqkj5ECFCuWN4MZAHTjZppLID5SBirhQ06RLTsCjk07zY8WKFS7h4VifTCZF8CD7CqRLmBbTfS9kAp9SnP4VPAsX/c+KuCChQZbDHDo7h+iws5edLDRRo5GgNsqxnXoqtUEaS9T/aLIUMoFmtrLyhCgMdIQXaQWD4iuuuMJn26mn0mEeM2ZMUkE4H8EAGco32kThoSO8SKqOyVQSwTAQ2eOb2aJFi7ifg8QJMw6O6xdccIF37vn4ZEdC8xFMVH75y19m+zJEmlEGKqqF+XRqeciO6KhT0+OISpDAhJksk+M9AZbOM0Yk2NIRLMk0R40alXE7umyDfynKA/SffI9EYaIAKqoEydFFF13kE0QcxcvvYKepxPuQPOGfiQicDBXBPIGDxWwEkrp161pNg8mkHj16+DQSOtD69etn+5JEGlAAFXE7yMxjFxUVWceOHX2mHVOReCYfwQw344vUOKn5jRgxwjWhSKFyZU1yNsC9idIFKgaabxrLLDwUQMVW+kWyTLSLzGojOeIIfuSRR2b70vISVAjciFq1auWrQYJFeaIwUAAVFUDvyTgmYvn27dv7cZyZdRGehQsXukE0x3qclZh7x4Ff5D8KoGIrOIIjpMcpvtAF75mC9RzoaufMmePlEZyq0NaK/EYBVGzF6tWrrXHjxlZcXGznnXdeti+noODXjcECGnAc73faaadsX5JIAU0iia1+wV999VXXfWojZAg2bLBGJSXWpKjIH90Vuhw04miusSrl5ptvztplimhQABUVjpkcK5Hf4A5f6OOW6aDR6NG2e0mJ1Sst9Ueex4I2lpooEq/YNSciv1AAFRU0n+wxYv6dcUu0nSI5dpw/n7qY/5lHnscDv1DkTciccLoS+YkCaA3kiy++8H0+7C4qD9NCHN3Rc+baYrd8YV2bNrZpy/eOR57Hg3FYnOsxmsYiUOQnCqA1CJaf4cHJLDpz7IG5BwvdOFKy/4j5bTZpjh8/PtuX6+VDXOmLipr4Y0y8z0lWFRXZyuJi+6ZDB3/keWUwbEAmyuir9r/nJ+rC1yBYxUHdjR3vjBhiMUfHnaVuSGvY14PdXM+ePd15nn3u2SRfV3okw7p163yTJ8d5Rj6V+ecXcmMqMJYuXerNoACE8MEIIdNEjGSS+bC3KIAaHAGU1cNkpexxDwyBs0k+LZULC3VmuvL4DfzlL3/x7aYif9ARvkBgL/lVV13lmSWjmMEbwZKaJ9BV79Spk2/Z5OMDCJqYHNM4YmUxxsd16tSxbMMeJDJP4JHnhQg3Npyr+P9D3iTyBwXQJGAunBnxXDStYHEZ+9g5prN3nDcyGsYHyTqBBhGuQDSQeH952H9EHa5p06a+apcVu9mGJXIc2zt0+MYf82WpXBiuueYa/9ni/0/kD6qBJgBel8wuU6NilpmNizRegGBEQ4YVtjfddJOtXbvWj8Cs9SX7w0SivByIuiNjfNQdec0otjQGru8sgIvdjIlYGx9PlriVlpb6n6mDHn300Vu9DtcW/Nvi/b1IHyzc69ChQ9mfJSHLD1QDrQZqgxx9McW97LLLfNEaP+gYbpDFcTzGdIMAisEw758+fbofxbg3YT7MVksCKg0bXNqpOfJ+VvqWlJTYJ5984jZwZICM+e21115xs1+O17i6I8TmyB00HIJskYZELIjiCdjTpk1zM+R777230uCIjCnefnaRXl577TXvxBM0+f9R8MwflIFWEzwJQNQNhw8f7j/YHLMCQwgkQNQcn332WXdoJwMlSAHPCYg0YzhiE2ixhyODPe644zxgsjOcbJXAC9QdMR++++67/evy+g8//LAf73BJKg8Gx3hMkv1y5MYJngwZX85Y+C9Wdzf34P+Fn6tx48bZscce6//XlFBE/qAAWgXnnnuuH3upJ6ZjLhyfTTJVmghtEGBv2uTialx7yFA5SnPk5perS5curt/EoJgmDzVLnvN3BHSyUwKoyB8+/vhjL60MHTrUyzlaf5x/KIBWQcuWLX0tBZllJuFoT4bKGxkvGWlsrZQaKsc+ygFkrl27dnVRtsgf/vznP9vIkSNddpYLqgeRPAqgVcDxGPNb5pWFiBqsAnGoZ4BB5Cc6M1QBWR/jj0JEDYoHyjPUukX+ogBaBew+pxkkRNTguUp55qSTTsr2pYgUUACtAtzCyRKQEAkRJax/Rkmx9957Z/tSRApIB1oFd911l/Xq1cvOPvtsF6sHEiUhkgHVBGOyeBKwYwp5GrrcTDcnRfSoiVQNaDlPPfVUF8qr2C/CgFoCrTBNSfTADDwgWWLCjUk1kb/oCF8JyIOoU7FygTUXuBwxtilEMgROV0x4oftkTJOSEIFUwTP/0RG+EoYNG+aTQuVZvny5j1AKkSgfffSRZ5zHH398hdq6tnEWBgqglRAU95nuIYsgc2DOPRlwUB89upH7WGLFhptQbX3HcxoqWrjxU65p0aKF+xJQwgl741y4cKFPseGWJQoP/TpXQseOHf2RMclTTjkl1GsQPANH9TlzNmccheaoXmjgRsVSPRqGGMegBf7Tn/7kTURMY5KFnUcHH3ywDEIKFNVAK4Hsgzll7OCwiQtDTXBULyTwJcDQA2NjmoeYTlO2adeunb8v1kM10QB6+OGHp+V6RfZRAK0E3IsmTZrkC9aoh2Iqkiw1xVE935fLzZw5041dMI/B0u/GG28sG6Tg6P7oo4/6RNp1113nHxvPNjAefByBOPCOFYWHZEzVwLeH+hVmD+xNT8YWTjXQ3F8uxzQQq094ZCPpAw88EFfv+/jjj7sc6cMPP/Qg269fP59lr+rnAZtCJo2oo2NhKAoP/TpXA78gdONpJDz99NP+mCgES9U8UyedpRCO6ATPGTNmuKNVZTBMwRsZJfVQ3ho0aGAnnHCC75Riz3ssQeMJdy1RmOgInwDUQtmWiAcn2tBFixZ5ZioyQzpLIWwDAIyuE4Etp+yewqGLbZpsJyCDZbNALDSOMNEmSIvCRBlogrCY7aWXXvL1s8BRT3uDMkOwTK58KSQqyA6RGbGyJRkImAjhWdSHQJ7OfefOnb35WD4404iS5rNwUQ00CdCC0lii2TBhwgQ3XBb5zT333OOrUXD0Dws6YQInGSdSJ36lWO7HLiqE9Kx1oSElCg9loEnAviK6scwxJ7y7ZsMGazR6tO04f76ta9PGVhUVbS6OpkoVr4sFHwvsqMthXEE3GCOLynwp+VgGB6jl1SQ2bNjgM+qpNnj4vrGKhRsrsiX2ZOE2HzSfFDwLFwXQJH7ZEFbfeeed/guS6C8FQW73khJSfdtpzhx/36ri4pSvp7LX5TjJao9As8gvN9dOrY6NnwRT6nJANk2HmMya5XYMD+DA36RJE4uUdN1EUuT111+3FStW2MCBA1N+LRpMLA8866yz/GjPps2XX37ZzjzzzEiuVeQm2f8pzgPIKmgkEZyOOuooX2+cKAQNghzwyPMoiPe6ZJJYpLGZk0V4HB8JEAREusZIbwKYsGHVMlKck08+2SdwWGbHGCONEhojp512WiRlinTdRFIFQw9o3bp1yq9Ftn/77beXPT/iiCP8TRQ2CqAJ8O6773rwRGzNBs1kIOMiaBA8NtWq5c+jIPZ136xb1zd0UmYgq4ydfiF44izF31PzYyndfffd51s+oVu3bp6dTp482QMp4nFcg1i7myrpuomkyvr16/1GEk+CJEQi6CcnAVLJUPy4uiWIlB1fIyB4nc8nTrRJK1fa7TNn2tHHHONjp6w7joUgEWREbBqNB02QPn36eE3wkUceiSyDStdNJFUow1DTRgeajrXVovBRAE0AjrRIUdB/JpuBUutLy3F1y+ve/OmnNmXKFK97ki3GC57JwsAAjTIy2ihI100kVQJ5EWuF2Y4pRLIogCYAwYT9NR+8+641KinZKhBkq0HCqCBmvcxoI/KPiieffNJroPXr14/mBdN1E0mRZs2a+SPO8JQwhEgWBdAEIaA0GTvWdn/uuQrNEKi0QZLm7jM7m+iwX3/99RYldOnffPNNW7NmjcugChX0mSChuwiLRjkTAK0kI5xHbNiwVTOkqgZJ0H2uV1rqj2BMt64AAAffSURBVDyPklWrNk/kJOoOlCg0l5iwwTyjUOcs0MqiTGBdS9JlGSG2oACawAgnR9l58+ZZna5dvQkCQTOEt9j3ZaL7TNB85513rEePHkmPIVYHLuzUU2fNmuWZKFluoYHkiBvQ3Xffne1LEXmMjvDVUH5vd/eXX7YH27SxLtttt1UzJF6DJJ3dZzrmGP0yRpgOAjE90zXYu6ErRfKTbyBVGjx4sH322Wd2xhlnuC4Wgfv8+fPdaT5RExEh4qFZ+ATgW8Rqj3HjxrkTz5AhQxKztUtjDRTpDUL3xo0bu8lJOiDoMFFz+umn26WXXupTSuhI8wWE8ldeeaV7eDKbzimC6+/SpYv17t3b/z1CpIICaJKQkdGl5o2JnygIY7xMMKfzziNjhOmkb9++LqynlIHOlKCdi+DVSWa5YMECX+a2ZMkSl3Uh86IJyDAEJ4pgeECIVFEATRI604w3sqETS7tkHOqjclyn9okfZa9evXyHT7rhR4QpJhouCPL79+/va3oz3aGn8cOOKmbNyYqDRW1c38iRI+3BBx90QTyDAjSGmP/H8FirqEW6UAANARkNmzqLioq2MqJAGkOAW7t2rXfvsTljVQRa0spg109p6WaDD+jQ4RsbPXrpVh/HfxW+kwRNpqOef/75Sl2W0sGyZct8uRr1UG4cbJvs1KmT1xYzEaQwc6F8QpDkKM4NBLMOgifGKOyuuvzyy7UBU2QMBdCQjBgxwq655hq7+uqrPYDgejRmzBjPgpAAsTeH4IbPJEGUX2wyogojg1tqpKufedv+uqyLDbMbbWOtbSvNQDmCYmoyaNAgb4xky36ORgwrMKZPn27Tpk3zRlbPnj3dtT+KSajKar782/HbxMPz3nvvdQOUb7/91r+n3FQorwiRSRRAU4AASiANIMtkzQPC9qBjPXv2bLv22mu9GRM4IBFQydx+s2RJmQh/k9WyMXtfb2+dMrDSGiiuUNilMTnDZFSulDTQjSL3Ipjh2B+49kcJBig0hLCgC/az87WZ2Scb19I2kQ0UQFOAbx3ORayFIHiwvRNpUbyPQzpD4MPZiY4+f/74gANsv8WLyz7umw4dbGms2L5cJ3+2mfUoLbX3Fy+Ouzkym+A/irYS/SjmwmSkUcH3j475brvt5mULIXIFBdAswLec4+bPx4+3m374oUwnupLMbZttKsieyntpbjSzYdttZzf997+RNK/S8e+6+OKL/WjNziBKFlGAkJ+skwBN1i9ErqAAmiWYJDqifXvrv3q19dl3380i+40bbfdRo34MqMXFHkwZBQ2YYWbzhg2zG264wXIRgh270DlqY3QS1Zw5xs84Jk2dOjWS1xMiCjTKmSXoFA8bPtwGfP65TbzwQjcg2fGtt7Ya/awwKkpN1SxndZiAzIm6JDcI9gFFBZsAOL4zWSRErqAAmkXI1KibYkeHzjJ2rv7b1q3t2bZt7alDDrHZderY/0NAv88+PkWTyxDg0akivmffUhTQeCMov/LKK5G8nhBRoCN8lqEBhZaRJlO3Ll2s7/LldtDq1bagXj27YtUq++rrr/3oeuKJJ/obwvBgKVwug2aURhf7o5hkSgWmihglxfiYSSNuOkLkAgqgOVI3pEGCVAdNJWsmkDwx7YPbEg2UqoT4uQp7mXB2Qvwehu+++85F8mPHjnXZFuYfuSLfEgIUQEXaYPSTmiXTU2GlSyy/u+2223wCSsvfRK6hn0iRtrl1tK4E0TCwY52jO+ObUWpKhYiS/DsXiryA/fTULGmUhQm+iPExbMFzQIhcRRmoiByms3B6Z/6/efPmSX8+n8vcf2lpaV6aOIuag2qgIlJwocJujjUjTCQlu2+dmidHdsxC6OALkcsoAxWRwignNn6I6ZMNnoDHKoYrxTm4BlmIWFQDFZE6wjN9xNRQGFs7ap8c/3Fd0tFd5AMKoCIyEM7jGM8MPLP6BMRkQCSPl2rHjh3Tdo1CRIlqoCJy2JvUr18/byCxez0Rt3qC7fnnn+81VBpI+Tg4IGoe+ikVkcNo6osvvuhbMVl4x5qT6qBpxMcxdaTgKfIF/aSKtHDYYYe5QQqbS5mFnzVrVqUfy/gqq0GQPR199NEZvU4hUkEBVKQNVgiz0gQDFIIje4viVYyYNuLonusuU0LEogAq0grbMwmQNJWohzKhhFlKAMvp7rzzTq+ZsttIiHxCTSSRMZA44Sx/4IEHereejaXBVlO8Q8PoRoXIJgqgIqOwUI9JI/xCaRbx53HjxslpSeQlCqAi49A0mjRpko9tsgI6W/vthUgVBVAhhAiJmkhCCBESBVAhhAiJAqgQQoREAVQIIUKiACqEECFRABVCiJAogAohREgUQIUQIiQKoEIIERIFUCGECIkCqBBChEQBVAghQqIAKoQQIVEAFUKIkCiACiFESBRAhRAiJAqgQggREgVQIYQIiQKoEEKERAFUCCFCogAqhBAhUQAVQoiQKIAKIURIFECFECIkCqBCCBESBVAhhAiJAqgQQoREAVQIIUKiACqEECFRABVCiJAogAohREgUQIUQIiQKoEIIERIFUCGECIkCqBBChEQBVAghQqIAKoQQIVEAFUKIkCiACiFESBRAhRAiJAqgQggREgVQIYQIiQKoEEKERAFUCCFCogAqhBAhUQAVQoiQKIAKIURIFECFECIkCqBCCBESBVAhhAiJAqgQQlg4/j+SK9JQpGRlQQAAAABJRU5ErkJggg==", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Low risk fire points: 31\n", - "Moderate risk fire points: 70\n", - "High risk fire points: 64\n", - "Total training points: 126\n" - ] - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# CELL 15: Generate random points and prepare training data\n", - "\n", - "# This cell corresponds to the random points generation section in FireVulnerability.js\n", - "try:\n", - " if study_area is not None and 'fire13_22' in locals():\n", - " # Generate random points across the entire study area\n", - " print(\"Generating random points for non-fire locations...\")\n", - " \n", - " # Create a bounding box from the study area\n", - " bounds = study_area.total_bounds\n", - " \n", - " # Generate 200 random points within the bounding box\n", - " random_x = np.random.uniform(bounds[0], bounds[2], 200)\n", - " random_y = np.random.uniform(bounds[1], bounds[3], 200)\n", - " \n", - " # Create a GeoDataFrame with these points\n", - " from shapely.geometry import Point\n", - " random_pts = gpd.GeoDataFrame(\n", - " geometry=[Point(x, y) for x, y in zip(random_x, random_y)],\n", - " crs=study_area.crs\n", - " )\n", - " \n", - " # Filter to ensure they're within the study area boundary\n", - " random_pts = gpd.sjoin(random_pts, study_area, predicate='within', how='inner')\n", - " \n", - " # Remove problematic columns that might cause join issues later\n", - " cols_to_drop = [col for col in random_pts.columns if col.startswith('index_')]\n", - " if cols_to_drop:\n", - " random_pts = random_pts.drop(columns=cols_to_drop)\n", - " \n", - " print(f\"Generated {len(random_pts)} random points within study area\")\n", - " \n", - " # Now filter out points that are too close to fire points\n", - " \n", - " # Convert to a projected CRS if the current one is geographic\n", - " if study_area.crs.is_geographic:\n", - " # Choose an appropriate projected CRS for your study area\n", - " # UTM is a good choice - this is approximately UTM Zone 43N for Goa, India\n", - " projected_crs = 'EPSG:32643' # UTM Zone 43N\n", - " random_pts_proj = random_pts.to_crs(projected_crs)\n", - " fire13_22_proj = fire13_22.to_crs(projected_crs)\n", - " \n", - " # Now create the buffer in the projected CRS (100 meters)\n", - " buffer_size = 100 # meters\n", - " \n", - " # Create a buffer around each fire point\n", - " fire_buffers = fire13_22_proj.copy()\n", - " fire_buffers['geometry'] = fire13_22_proj.geometry.buffer(buffer_size)\n", - " \n", - " # Use a different approach to find non-fire points\n", - " # Create a spatial index for faster operations\n", - " sindex = fire_buffers.sindex\n", - " \n", - " # For each random point, check if it intersects with any fire buffer\n", - " non_fire_indices = []\n", - " for idx, point in random_pts_proj.iterrows():\n", - " # Query spatial index to find potential fire buffers this point might intersect with\n", - " possible_matches_index = list(sindex.intersection(point.geometry.bounds))\n", - " possible_matches = fire_buffers.iloc[possible_matches_index]\n", - " \n", - " # Check if this point intersects with any fire buffer\n", - " if not any(possible_matches.geometry.intersects(point.geometry)):\n", - " non_fire_indices.append(idx)\n", - " \n", - " # Get the non-fire points\n", - " non_fire_pts = random_pts_proj.loc[non_fire_indices].copy()\n", - " \n", - " # Convert back to original CRS\n", - " non_fire_pts = non_fire_pts.to_crs(study_area.crs)\n", - " \n", - " else:\n", - " # If already in projected CRS, proceed with buffer\n", - " buffer_size = 100 # meters\n", - " \n", - " # Create a buffer around each fire point\n", - " fire_buffers = fire13_22.copy()\n", - " fire_buffers['geometry'] = fire13_22.geometry.buffer(buffer_size)\n", - " \n", - " # Use a different approach to find non-fire points\n", - " # Create a spatial index for faster operations\n", - " sindex = fire_buffers.sindex\n", - " \n", - " # For each random point, check if it intersects with any fire buffer\n", - " non_fire_indices = []\n", - " for idx, point in random_pts.iterrows():\n", - " # Query spatial index to find potential fire buffers this point might intersect with\n", - " possible_matches_index = list(sindex.intersection(point.geometry.bounds))\n", - " possible_matches = fire_buffers.iloc[possible_matches_index]\n", - " \n", - " # Check if this point intersects with any fire buffer\n", - " if not any(possible_matches.geometry.intersects(point.geometry)):\n", - " non_fire_indices.append(idx)\n", - " \n", - " # Get the non-fire points\n", - " non_fire_pts = random_pts.loc[non_fire_indices].copy()\n", - " \n", - " # Limit to 90 points and add risk label\n", - " non_fire_pts = non_fire_pts.head(90)\n", - " non_fire_pts['RiskNumeric'] = 0 # Label as non-fire (low risk)\n", - " \n", - " print(f\"After filtering: {len(non_fire_pts)} non-fire points\")\n", - " \n", - " # Plot the sampled points\n", - " plt.figure(figsize=(12, 10))\n", - " study_area.plot(ax=plt.gca(), color='lightgrey', edgecolor='black')\n", - " non_fire_pts.plot(ax=plt.gca(), color='blue', markersize=5, label='Non-Fire Points')\n", - " fire13_22.plot(ax=plt.gca(), color='red', markersize=5, label='Fire Points')\n", - " \n", - " plt.title('Training Data: Fire and Non-Fire Points')\n", - " plt.legend()\n", - " plt.axis('off')\n", - " plt.tight_layout()\n", - " plt.show()\n", - " \n", - " # Now categorize fire points based on Delta T as in the JS code\n", - " if 'Delta T' in fire13_22.columns:\n", - " # Create low, moderate, and high risk categories\n", - " low_risk = fire13_22[fire13_22['Delta T'].between(0, 25)].copy()\n", - " low_risk['RiskCategory'] = 'Low Risk'\n", - " low_risk['RiskNumeric'] = 1\n", - " \n", - " moderate_risk = fire13_22[fire13_22['Delta T'].between(25, 35)].copy()\n", - " moderate_risk['RiskCategory'] = 'Moderate Risk'\n", - " moderate_risk['RiskNumeric'] = 2\n", - " moderate_risk = moderate_risk.head(70) # Limit as in JS\n", - " \n", - " high_risk = fire13_22[fire13_22['Delta T'] >= 35].copy()\n", - " high_risk['RiskCategory'] = 'High Risk' \n", - " high_risk['RiskNumeric'] = 3\n", - " \n", - " print(f\"Low risk fire points: {len(low_risk)}\")\n", - " print(f\"Moderate risk fire points: {len(moderate_risk)}\")\n", - " print(f\"High risk fire points: {len(high_risk)}\")\n", - " \n", - " # Merge just non-fire and high risk for binary classification\n", - " # This matches the JS code which only uses noFire.merge(highRisk)\n", - " categorized_points = pd.concat([non_fire_pts, high_risk])\n", - " \n", - " print(f\"Total training points: {len(categorized_points)}\")\n", - " \n", - " # Plot the categorized points\n", - " plt.figure(figsize=(12, 10))\n", - " study_area.plot(ax=plt.gca(), color='lightgrey', edgecolor='black')\n", - " \n", - " # Plot each category\n", - " for category, color, label in zip(\n", - " [0, 3], \n", - " ['blue', 'red'],\n", - " ['Non-Fire (0)', 'High Risk (3)']\n", - " ):\n", - " subset = categorized_points[categorized_points['RiskNumeric'] == category]\n", - " if len(subset) > 0:\n", - " subset.plot(ax=plt.gca(), color=color, markersize=5, label=label)\n", - " \n", - " plt.title('Categorized Points for Training')\n", - " plt.legend()\n", - " plt.axis('off')\n", - " plt.tight_layout()\n", - " plt.show()\n", - " else:\n", - " print(\"Warning: 'Delta T' column not found in fire data\")\n", - " categorized_points = pd.concat([non_fire_pts, fire13_22])\n", - " categorized_points.loc[categorized_points['RiskNumeric'].isna(), 'RiskNumeric'] = 1\n", - " else:\n", - " print(\"Cannot generate random points without study area boundary and fire data\")\n", - "except Exception as e:\n", - " print(f\"Error generating training points: {e}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Extracting raster values at training points...\n", - "Input raster shape: (40, 2912, 944), CRS: EPSG:3857\n", - "Points count: 126, CRS: EPSG:4326\n", - "Raster CRS: EPSG:3857\n", - "Points CRS: EPSG:4326\n", - "Reprojecting points from EPSG:4326 to EPSG:3857\n", - "Successfully sampled 126 out of 126 points\n", - "\n", - "NaN counts per column:\n", - "geometry 0\n", - "Shape_Leng 64\n", - "Shape_Area 64\n", - "Name 64\n", - "RiskNumeric 0\n", - " ..\n", - "band_35 0\n", - "band_36 0\n", - "band_37 0\n", - "band_38 0\n", - "band_39 0\n", - "Length: 65, dtype: int64\n", - "After removing NaN values: 119 of 126 points remain\n", - "\n", - "Sample of the training data:\n" - ] - }, - { - "data": { - "text/html": [ - "
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3POINT (8269121.273 1703992.482)0.00.0Netravali WLS0NaNNaNNaNNaNNaN...-32.07685989.2344672545.909912-0.0081310.735141-0.10466732.8344150.03671520.82622157.634197
8POINT (8273698.954 1723359.797)0.00.0Mollem NP0NaNNaNNaNNaNNaN...-100.272911154.4427951719.7645260.0016840.686330-0.00936030.0741210.03362421.5752241130.676025
11POINT (8262691.529 1742415.806)0.00.0Bhagwan Mahavir WLS0NaNNaNNaNNaNNaN...-9.98116491.5356672283.200439-0.0143550.806202-0.02580629.1335910.03865221.5011392455.502686
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5 rows × 65 columns

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" - ], - "text/plain": [ - " geometry Shape_Leng Shape_Area \\\n", - "1 POINT (8261046.148 1687053.48) 0.0 0.0 \n", - "3 POINT (8269121.273 1703992.482) 0.0 0.0 \n", - "8 POINT (8273698.954 1723359.797) 0.0 0.0 \n", - "11 POINT (8262691.529 1742415.806) 0.0 0.0 \n", - "21 POINT (8266441.378 1724849.769) 0.0 0.0 \n", - "\n", - " Name RiskNumeric bright_ti5 bright_ti4 acq_time frp \\\n", - "1 Cotigaon WLS 0 NaN NaN NaN NaN \n", - "3 Netravali WLS 0 NaN NaN NaN NaN \n", - "8 Mollem NP 0 NaN NaN NaN NaN \n", - "11 Bhagwan Mahavir WLS 0 NaN NaN NaN NaN \n", - "21 Bhagwan Mahavir WLS 0 NaN NaN NaN NaN \n", - "\n", - " Delta T ... band_30 band_31 band_32 band_33 band_34 \\\n", - "1 NaN ... 2.158347 116.887054 2020.793701 -0.010756 0.675415 \n", - "3 NaN ... -32.076859 89.234467 2545.909912 -0.008131 0.735141 \n", - "8 NaN ... -100.272911 154.442795 1719.764526 0.001684 0.686330 \n", - "11 NaN ... -9.981164 91.535667 2283.200439 -0.014355 0.806202 \n", - "21 NaN ... -216.587692 170.010757 2016.565430 -0.017417 0.842181 \n", - "\n", - " band_35 band_36 band_37 band_38 band_39 \n", - "1 -0.136112 32.900528 0.052642 21.622280 325.735016 \n", - "3 -0.104667 32.834415 0.036715 20.826221 57.634197 \n", - "8 -0.009360 30.074121 0.033624 21.575224 1130.676025 \n", - "11 -0.025806 29.133591 0.038652 21.501139 2455.502686 \n", - "21 -0.071792 30.308001 0.043228 21.482294 1875.385132 \n", - "\n", - "[5 rows x 65 columns]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Correlation of predictor bands with fire risk:\n", - "band_13 0.349020\n", - "band_5 0.342209\n", - "band_26 0.218241\n", - "band_36 0.195580\n", - "band_12 0.187006\n", - "band_28 0.185220\n", - "band_15 0.177950\n", - "band_11 0.160622\n", - "band_30 0.147343\n", - "band_2 0.119539\n", - "band_4 0.108818\n", - "band_38 0.087956\n", - "band_25 0.070305\n", - "band_0 0.050785\n", - "band_10 0.050171\n", - "band_27 0.045494\n", - "band_37 0.043963\n", - "band_29 0.042347\n", - "band_1 0.029640\n", - "band_32 -0.026380\n", - "band_39 -0.043690\n", - "band_31 -0.048724\n", - "band_33 -0.113953\n", - "band_3 -0.119704\n", - "band_23 -0.133185\n", - "band_20 -0.159907\n", - "band_35 -0.164105\n", - "band_22 -0.170958\n", - "band_14 -0.182354\n", - "band_18 -0.185606\n", - "band_16 -0.197270\n", - "band_6 -0.207323\n", - "band_8 -0.225793\n", - "band_9 -0.253226\n", - "band_7 -0.253393\n", - "band_24 -0.307073\n", - "band_17 -0.343829\n", - "band_19 -0.346002\n", - "band_21 -0.354162\n", - "band_34 -0.381846\n", - "Name: RiskNumeric, dtype: float64\n" - ] - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Saved training data to 'fire_training_data.csv'\n" - ] - } - ], - "source": [ - "## CELL 7: Sample raster values at training points\n", - "\n", - "# This mimics the \"sample\" operation in GEE where values from the raster are extracted at point locations\n", - "try:\n", - " if 'categorized_points' in locals() and input_resampled is not None and input_meta is not None:\n", - " print(\"Extracting raster values at training points...\")\n", - " print(f\"Input raster shape: {input_resampled.shape}, CRS: {input_meta['crs']}\")\n", - " print(f\"Points count: {len(categorized_points)}, CRS: {categorized_points.crs}\")\n", - " \n", - " # Create a point sampler function with improved error handling\n", - " def sample_raster_at_points(raster_data, raster_meta, points_gdf):\n", - " \"\"\"Extract raster values at specified points with better error handling.\"\"\"\n", - " # Make sure we're in the same CRS\n", - " if isinstance(raster_meta['crs'], str):\n", - " raster_crs = rasterio.crs.CRS.from_string(raster_meta['crs'])\n", - " else:\n", - " # If it's already a CRS object, use it directly\n", - " raster_crs = raster_meta['crs']\n", - " \n", - " print(f\"Raster CRS: {raster_crs}\")\n", - " print(f\"Points CRS: {points_gdf.crs}\")\n", - " \n", - " if points_gdf.crs != raster_crs:\n", - " print(f\"Reprojecting points from {points_gdf.crs} to {raster_crs}\")\n", - " points_gdf = points_gdf.to_crs(raster_crs)\n", - " \n", - " # Create a copy to avoid modifying the original\n", - " points_with_values = points_gdf.copy()\n", - " \n", - " # Initialize columns for each band\n", - " for i in range(raster_data.shape[0]):\n", - " points_with_values[f\"band_{i}\"] = np.nan\n", - " \n", - " # Open the raster directly for sampling\n", - " with rasterio.io.MemoryFile() as memfile:\n", - " with memfile.open(**raster_meta) as src:\n", - " # Write the data\n", - " src.write(raster_data)\n", - " \n", - " # Track successful samples\n", - " successful_samples = 0\n", - " \n", - " # Sample at each point\n", - " for idx, point in points_with_values.iterrows():\n", - " try:\n", - " # Get point coordinates\n", - " x, y = point.geometry.x, point.geometry.y\n", - " \n", - " # Check if point is within raster bounds\n", - " row, col = src.index(x, y)\n", - " if 0 <= row < src.height and 0 <= col < src.width:\n", - " # Sample raster at point\n", - " sample = list(src.sample([(x, y)]))[0]\n", - " \n", - " # Add to results\n", - " for i in range(len(sample)):\n", - " points_with_values.at[idx, f\"band_{i}\"] = float(sample[i])\n", - " successful_samples += 1\n", - " else:\n", - " print(f\"Point at ({x}, {y}) is outside raster bounds\")\n", - " except Exception as e:\n", - " print(f\"Error sampling at point {idx}: {e}\")\n", - " \n", - " print(f\"Successfully sampled {successful_samples} out of {len(points_with_values)} points\")\n", - " \n", - " return points_with_values\n", - " \n", - " # Sample the points\n", - " sampled_points = sample_raster_at_points(input_resampled, input_meta, categorized_points)\n", - " \n", - " # Check for NaN values\n", - " nan_counts = sampled_points.isna().sum()\n", - " print(\"\\nNaN counts per column:\")\n", - " print(nan_counts)\n", - " \n", - " # Drop points with NaN values (outside raster or masked)\n", - " sampled_points_clean = sampled_points.dropna(subset=[f\"band_{i}\" for i in range(input_resampled.shape[0])])\n", - " print(f\"After removing NaN values: {len(sampled_points_clean)} of {len(sampled_points)} points remain\")\n", - " \n", - " if len(sampled_points_clean) == 0:\n", - " # Attempt recovery: Use nearest-pixel approach instead of exact location\n", - " print(\"\\nAttempting recovery using nearest pixel approach...\")\n", - " \n", - " # Create a more tolerant sampling approach\n", - " from scipy.spatial import cKDTree\n", - " \n", - " def sample_nearest_valid_pixel(raster_data, raster_meta, points_gdf, max_distance=5):\n", - " \"\"\"Sample nearest valid pixel if direct sampling fails\"\"\"\n", - " # Convert raster to array of coordinates and values\n", - " with rasterio.io.MemoryFile() as memfile:\n", - " with memfile.open(**raster_meta) as src:\n", - " # Write the data\n", - " src.write(raster_data)\n", - " \n", - " # Get all valid pixel coordinates\n", - " mask = ~np.isnan(raster_data[0])\n", - " indices = np.where(mask)\n", - " rows, cols = indices[0], indices[1]\n", - " \n", - " # Convert to x,y coordinates\n", - " xs, ys = rasterio.transform.xy(src.transform, rows, cols, offset='center')\n", - " coords = np.array(list(zip(xs, ys)))\n", - " \n", - " if len(coords) == 0:\n", - " print(\"No valid pixels found in raster\")\n", - " return points_gdf\n", - " \n", - " # Build KD-tree for efficient nearest-neighbor search\n", - " tree = cKDTree(coords)\n", - " \n", - " # Create output DataFrame\n", - " points_with_values = points_gdf.copy()\n", - " \n", - " # Initialize band columns\n", - " for i in range(raster_data.shape[0]):\n", - " points_with_values[f\"band_{i}\"] = np.nan\n", - " \n", - " # For each point, find nearest valid pixel\n", - " successful = 0\n", - " for idx, point in points_with_values.iterrows():\n", - " try:\n", - " x, y = point.geometry.x, point.geometry.y\n", - " distance, idx_nearest = tree.query([x, y], k=1)\n", - " \n", - " if distance <= max_distance:\n", - " # Get nearest pixel coordinates\n", - " x_nearest, y_nearest = coords[idx_nearest]\n", - " \n", - " # Sample at nearest valid pixel\n", - " sample = list(src.sample([(x_nearest, y_nearest)]))[0]\n", - " \n", - " # Store values\n", - " for i in range(len(sample)):\n", - " points_with_values.at[idx, f\"band_{i}\"] = float(sample[i])\n", - " successful += 1\n", - " except Exception as e:\n", - " print(f\"Error in nearest pixel sampling at point {idx}: {e}\")\n", - " \n", - " print(f\"Nearest-pixel approach: {successful} successful samples out of {len(points_with_values)}\")\n", - " return points_with_values\n", - " \n", - " # Try nearest pixel approach\n", - " sampled_points = sample_nearest_valid_pixel(input_resampled, input_meta, categorized_points)\n", - " sampled_points_clean = sampled_points.dropna(subset=[f\"band_{i}\" for i in range(input_resampled.shape[0])])\n", - " print(f\"After recovery: {len(sampled_points_clean)} of {len(sampled_points)} points have valid values\")\n", - " \n", - " if len(sampled_points_clean) > 0:\n", - " # Print sample of the data\n", - " print(\"\\nSample of the training data:\")\n", - " display(sampled_points_clean.head())\n", - " \n", - " # Calculate correlation between predictor bands and risk\n", - " predictor_cols = [f\"band_{i}\" for i in range(input_resampled.shape[0])]\n", - " \n", - " # Create a correlation DataFrame\n", - " correlation_data = sampled_points_clean[predictor_cols + ['RiskNumeric']]\n", - " corr = correlation_data.corr()['RiskNumeric'].drop('RiskNumeric').sort_values(ascending=False)\n", - " \n", - " print(\"\\nCorrelation of predictor bands with fire risk:\")\n", - " print(corr)\n", - " \n", - " # Plot correlation heatmap\n", - " plt.figure(figsize=(12, 10))\n", - " sns.heatmap(correlation_data.corr(), annot=True, cmap='coolwarm', center=0)\n", - " plt.title('Correlation Matrix of Predictor Bands')\n", - " plt.tight_layout()\n", - " plt.show()\n", - " \n", - " # Save the sampled points as a CSV for later use\n", - " sampled_points_clean.to_csv('fire_training_data.csv', index=False)\n", - " print(\"Saved training data to 'fire_training_data.csv'\")\n", - " else:\n", - " print(\"\\nWARNING: No valid points remain after sampling. Check that:\")\n", - " print(\"1. The raster and points overlap spatially\")\n", - " print(\"2. The CRS definitions are compatible\")\n", - " print(\"3. The raster contains valid (non-NaN) values\")\n", - " \n", - " # Print bounds for debugging\n", - " print(f\"\\nRaster bounds: {[input_meta['transform'] * (0, 0), input_meta['transform'] * (input_meta['width'], input_meta['height'])]}\")\n", - " print(f\"Points bounds: {categorized_points.total_bounds}\")\n", - " else:\n", - " print(\"Cannot sample raster without training points and input raster\")\n", - "except Exception as e:\n", - " import traceback\n", - " print(f\"Error sampling raster at training points: {e}\")\n", - " print(traceback.format_exc())" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training Random Forest classifier...\n", - "Training set: 96 points\n", - "Validation set: 23 points\n", - "Error training model: The 'min_samples_split' parameter of RandomForestClassifier must be an int in the range [2, inf) or a float in the range (0.0, 1.0]. Got 1 instead.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jonas/miniconda3/envs/ee/lib/python3.13/site-packages/geopandas/geodataframe.py:1819: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " super().__setitem__(key, value)\n", - "/Users/jonas/miniconda3/envs/ee/lib/python3.13/site-packages/geopandas/geodataframe.py:1819: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " super().__setitem__(key, value)\n" - ] - } - ], - "source": [ - "# CELL 8: Train and validate the Random Forest model\n", - "\n", - "# This mimics the Random Forest classifier training in GEE\n", - "try:\n", - " if 'sampled_points_clean' in locals() and len(sampled_points_clean) > 0:\n", - " print(\"Training Random Forest classifier...\")\n", - " \n", - " # Define predictors and label\n", - " predictor_cols = [f\"band_{i}\" for i in range(input_resampled.shape[0])]\n", - " label_col = 'RiskNumeric'\n", - " \n", - " # Create a binary label (0: no fire, 1: fire)\n", - " sampled_points_clean['binary_risk'] = (sampled_points_clean[label_col] > 0).astype(int)\n", - " \n", - " # Split into training and validation sets (80/20 split)\n", - " sampled_points_clean['random'] = np.random.random(len(sampled_points_clean))\n", - " training_data = sampled_points_clean[sampled_points_clean['random'] <= 0.8]\n", - " validation_data = sampled_points_clean[sampled_points_clean['random'] > 0.8]\n", - " \n", - " print(f\"Training set: {len(training_data)} points\")\n", - " print(f\"Validation set: {len(validation_data)} points\")\n", - " \n", - " # Train the Random Forest classifier\n", - " rf_model = RandomForestClassifier(\n", - " n_estimators=1000, # Similar to the JS code\n", - " min_samples_split=1,\n", - " random_state=42\n", - " )\n", - " \n", - " # Fit the model on binary risk for simplicity\n", - " rf_model.fit(\n", - " training_data[predictor_cols], \n", - " training_data['binary_risk']\n", - " )\n", - " \n", - " # Print feature importances\n", - " importances = rf_model.feature_importances_\n", - " indices = np.argsort(importances)[::-1]\n", - " \n", - " print(\"\\nFeature ranking:\")\n", - " for i, idx in enumerate(indices):\n", - " print(f\"{i+1}. Band {idx} - Importance: {importances[idx]:.4f}\")\n", - " \n", - " # Plot feature importances\n", - " plt.figure(figsize=(12, 6))\n", - " plt.bar(range(len(importances)), importances[indices], align='center')\n", - " plt.xticks(range(len(importances)), [f\"Band {i}\" for i in indices], rotation=90)\n", - " plt.xlabel('Predictor Band')\n", - " plt.ylabel('Importance')\n", - " plt.title('Feature Importances from Random Forest')\n", - " plt.tight_layout()\n", - " plt.show()\n", - " \n", - " # Validate the model\n", - " validation_pred = rf_model.predict(validation_data[predictor_cols])\n", - " \n", - " # Calculate accuracy and other metrics\n", - " accuracy = accuracy_score(validation_data['binary_risk'], validation_pred)\n", - " conf_matrix = confusion_matrix(validation_data['binary_risk'], validation_pred)\n", - " \n", - " print(f\"\\nValidation accuracy: {accuracy:.4f}\")\n", - " print(\"\\nConfusion matrix:\")\n", - " print(conf_matrix)\n", - " \n", - " # Plot confusion matrix\n", - " plt.figure(figsize=(8, 6))\n", - " sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues',\n", - " xticklabels=['No Fire', 'Fire'],\n", - " yticklabels=['No Fire', 'Fire'])\n", - " plt.xlabel('Predicted')\n", - " plt.ylabel('Actual')\n", - " plt.title('Confusion Matrix')\n", - " plt.tight_layout()\n", - " plt.show()\n", - " \n", - " # Calculate classification report\n", - " print(\"\\nClassification Report:\")\n", - " print(classification_report(validation_data['binary_risk'], validation_pred, \n", - " target_names=['No Fire', 'Fire']))\n", - " \n", - " # Calculate Kappa statistic (mimics the JS code)\n", - " observed_accuracy = accuracy\n", - " \n", - " # Calculate expected accuracy (what would be expected by chance)\n", - " n_samples = len(validation_pred)\n", - " n_fire_actual = np.sum(validation_data['binary_risk'])\n", - " n_fire_pred = np.sum(validation_pred)\n", - " \n", - " expected_accuracy = (((n_fire_actual * n_fire_pred) + \n", - " ((n_samples - n_fire_actual) * (n_samples - n_fire_pred))) / \n", - " (n_samples ** 2))\n", - " \n", - " kappa = (observed_accuracy - expected_accuracy) / (1 - expected_accuracy)\n", - " print(f\"\\nKappa Statistic: {kappa:.4f}\")\n", - " \n", - " else:\n", - " print(\"Cannot train model without sampled points\")\n", - "except Exception as e:\n", - " print(f\"Error training model: {e}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training Random Forest classifier...\n", - "Training set: 96 points\n", - "Validation set: 23 points\n", - "Error training model: The 'min_samples_split' parameter of RandomForestClassifier must be an int in the range [2, inf) or a float in the range (0.0, 1.0]. Got 1 instead.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jonas/miniconda3/envs/ee/lib/python3.13/site-packages/geopandas/geodataframe.py:1819: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " super().__setitem__(key, value)\n", - "/Users/jonas/miniconda3/envs/ee/lib/python3.13/site-packages/geopandas/geodataframe.py:1819: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " super().__setitem__(key, value)\n" - ] - } - ], - "source": [ - "# CELL 8: Train and validate the Random Forest model\n", - "\n", - "# This mimics the Random Forest classifier training in GEE\n", - "try:\n", - " if 'sampled_points_clean' in locals() and len(sampled_points_clean) > 0:\n", - " print(\"Training Random Forest classifier...\")\n", - " \n", - " # Define predictors and label\n", - " predictor_cols = [f\"band_{i}\" for i in range(input_resampled.shape[0])]\n", - " label_col = 'RiskNumeric'\n", - " \n", - " # Create a binary label (0: no fire, 1: fire)\n", - " sampled_points_clean['binary_risk'] = (sampled_points_clean[label_col] > 0).astype(int)\n", - " \n", - " # Split into training and validation sets (80/20 split)\n", - " sampled_points_clean['random'] = np.random.random(len(sampled_points_clean))\n", - " training_data = sampled_points_clean[sampled_points_clean['random'] <= 0.8]\n", - " validation_data = sampled_points_clean[sampled_points_clean['random'] > 0.8]\n", - " \n", - " print(f\"Training set: {len(training_data)} points\")\n", - " print(f\"Validation set: {len(validation_data)} points\")\n", - " \n", - " # Train the Random Forest classifier\n", - " rf_model = RandomForestClassifier(\n", - " n_estimators=1000, # Similar to the JS code\n", - " min_samples_split=1,\n", - " random_state=42\n", - " )\n", - " \n", - " # Fit the model on binary risk for simplicity\n", - " rf_model.fit(\n", - " training_data[predictor_cols], \n", - " training_data['binary_risk']\n", - " )\n", - " \n", - " # Print feature importances\n", - " importances = rf_model.feature_importances_\n", - " indices = np.argsort(importances)[::-1]\n", - " \n", - " print(\"\\nFeature ranking:\")\n", - " for i, idx in enumerate(indices):\n", - " print(f\"{i+1}. Band {idx} - Importance: {importances[idx]:.4f}\")\n", - " \n", - " # Plot feature importances\n", - " plt.figure(figsize=(12, 6))\n", - " plt.bar(range(len(importances)), importances[indices], align='center')\n", - " plt.xticks(range(len(importances)), [f\"Band {i}\" for i in indices], rotation=90)\n", - " plt.xlabel('Predictor Band')\n", - " plt.ylabel('Importance')\n", - " plt.title('Feature Importances from Random Forest')\n", - " plt.tight_layout()\n", - " plt.show()\n", - " \n", - " # Validate the model\n", - " validation_pred = rf_model.predict(validation_data[predictor_cols])\n", - " \n", - " # Calculate accuracy and other metrics\n", - " accuracy = accuracy_score(validation_data['binary_risk'], validation_pred)\n", - " conf_matrix = confusion_matrix(validation_data['binary_risk'], validation_pred)\n", - " \n", - " print(f\"\\nValidation accuracy: {accuracy:.4f}\")\n", - " print(\"\\nConfusion matrix:\")\n", - " print(conf_matrix)\n", - " \n", - " # Plot confusion matrix\n", - " plt.figure(figsize=(8, 6))\n", - " sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues',\n", - " xticklabels=['No Fire', 'Fire'],\n", - " yticklabels=['No Fire', 'Fire'])\n", - " plt.xlabel('Predicted')\n", - " plt.ylabel('Actual')\n", - " plt.title('Confusion Matrix')\n", - " plt.tight_layout()\n", - " plt.show()\n", - " \n", - " # Calculate classification report\n", - " print(\"\\nClassification Report:\")\n", - " print(classification_report(validation_data['binary_risk'], validation_pred, \n", - " target_names=['No Fire', 'Fire']))\n", - " \n", - " # Calculate Kappa statistic (mimics the JS code)\n", - " observed_accuracy = accuracy\n", - " \n", - " # Calculate expected accuracy (what would be expected by chance)\n", - " n_samples = len(validation_pred)\n", - " n_fire_actual = np.sum(validation_data['binary_risk'])\n", - " n_fire_pred = np.sum(validation_pred)\n", - " \n", - " expected_accuracy = (((n_fire_actual * n_fire_pred) + \n", - " ((n_samples - n_fire_actual) * (n_samples - n_fire_pred))) / \n", - " (n_samples ** 2))\n", - " \n", - " kappa = (observed_accuracy - expected_accuracy) / (1 - expected_accuracy)\n", - " print(f\"\\nKappa Statistic: {kappa:.4f}\")\n", - " \n", - " else:\n", - " print(\"Cannot train model without sampled points\")\n", - "except Exception as e:\n", - " print(f\"Error training model: {e}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(\"\\n\" + \"=\"*50)\n", - "print(\"SUMMARY OF FINDINGS AND RECOMMENDATIONS\")\n", - "print(\"=\"*50)\n", - "\n", - "findings = []\n", - "recommendations = []\n", - "\n", - "# Check if we have the key datasets\n", - "if 'fire_all' in locals() and len(fire_all) > 0:\n", - " findings.append(f\"- Found {len(fire_all)} fire events.\")\n", - " if 'year' in fire_all.columns:\n", - " years = sorted(fire_all['year'].unique())\n", - " findings.append(f\"- Fire events cover {len(years)} years from {min(years)} to {max(years)}.\")\n", - "else:\n", - " findings.append(\"- Fire event data is missing or could not be loaded.\")\n", - " recommendations.append(\"- Acquire or fix the fire event datasets (fire13_19.shp and fire20_23.shp).\")\n", - "\n", - "if 'boundary' in locals():\n", - " findings.append(\"- Study area boundary data is available.\")\n", - "else:\n", - " findings.append(\"- Study area boundary data is missing or could not be loaded.\")\n", - " recommendations.append(\"- Ensure the pa_boundary.shp file is available and properly formatted.\")\n", - "\n", - "# Check for the trend layer file\n", - "if os.path.exists(os.path.join(data_dir, \"inputResampled.tif\")):\n", - " findings.append(\"- The trend layer file (inputResampled.tif) is available locally.\")\n", - "else:\n", - " findings.append(\"- The trend layer file (inputResampled.tif) is not available locally.\")\n", - " recommendations.append(\"- Download the trend layer file from the Dropbox link provided in the README.\")\n", - "\n", - "# Check for the road data\n", - "if os.path.exists(os.path.join(data_dir, \"road.tif\")):\n", - " findings.append(\"- The road layer file (road.tif) is available.\")\n", - "else:\n", - " findings.append(\"- The road layer file (road.tif) is not available or could not be loaded.\")\n", - " recommendations.append(\"- Ensure the road.tif file is available for fire vulnerability mapping.\")\n", - "\n", - "# Model training/testing dataset assessment\n", - "if 'train_sufficient' in locals() and 'test_sufficient' in locals():\n", - " if train_sufficient and test_sufficient:\n", - " findings.append(\"- The dataset appears to have sufficient fire events for both training and testing.\")\n", - " else:\n", - " if not train_sufficient:\n", - " findings.append(\"- The training dataset may not have enough fire events.\")\n", - " recommendations.append(\"- Consider acquiring more historical fire data for training.\")\n", - " if not test_sufficient:\n", - " findings.append(\"- The testing dataset may not have enough fire events.\")\n", - " recommendations.append(\"- Consider using cross-validation techniques to maximize use of available data.\")\n", - "\n", - "# Display findings and recommendations\n", - "print(\"\\nFINDINGS:\")\n", - "for finding in findings:\n", - " print(finding)\n", - "\n", - "print(\"\\nRECOMMENDATIONS:\")\n", - "if recommendations:\n", - " for recommendation in recommendations:\n", - " print(recommendation)\n", - "else:\n", - " print(\"- All necessary data appears to be available and sufficient for the analysis.\")\n", - "\n", - "print(\"\\nNext Steps:\")\n", - "print(\"1. Ensure all required datasets are downloaded and properly formatted\")\n", - "print(\"2. Prepare training and testing datasets based on appropriate temporal splits\")\n", - "print(\"3. Apply the methodology described in the README for fire vulnerability mapping\")\n", - "print(\"4. Evaluate model performance using metrics like precision, recall, and F1-score\")\n", - "print(\"5. Generate predictions for future fire-prone areas\")\n", - "\n", - "print(\"\\nThis script generated several visualization files. Check your directory for the PNG files.\")\n", - "\n", - "# Print final message about notebook conversion\n", - "print(\"\\nNote: This script is structured in cells (marked by # %%) that can be\")\n", - "print(\"directly copied into a Jupyter notebook when your notebook environment is working.\") " - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "ee", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.13.1" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} From 53b35e6a22023d97f46490b1f507b510fd6f348f Mon Sep 17 00:00:00 2001 From: jonas-nothnagel Date: Mon, 14 Apr 2025 14:43:42 +0200 Subject: [PATCH 11/15] updated python and readme --- README.md | 60 ++++++++++++++++++++++++++------------- TrendAnomalyPrediction.py | 41 ++++++++++++++++++++++++-- 2 files changed, 80 insertions(+), 21 deletions(-) diff --git a/README.md b/README.md index 6f165ef..cc85323 100644 --- a/README.md +++ b/README.md @@ -16,28 +16,50 @@ This project analyzes forest fire vulnerability and predicts potential fire-pron - Utilize trend layers to predict high-risk fire zones in the near future. ## Execution Sequence -### 1. Trend Calculation (`Trendfire.js`) - - **Input:** `pa_boundary` - - **Output:** 14 trend layers representing long-term changes in vegetation, burn indices, and meteorological parameters - download the trend layer from here - https://www.dropbox.com/scl/fi/ena1qfeqv5ppshluop8kt/inputResampled.tif?rlkey=arefwl3my7nx3z5qpknu88lsx&st=vj8cn3bd&dl=0 +### 1. Trend Calculation (`Trendfire.js` / `TrendFire.py`) + - Calculates long-term (decadal) linear trends (Slope and Intercept) for various environmental indicators within the study area (`pa_boundary`). + - **Datasets Used:** Landsat 8 (Vegetation/Burn Indices, LST), CHIRPS Daily (Precipitation), SMAP (Soil Moisture), ERA5-Land Monthly (Relative Humidity). + - **Method:** Filters datasets by date and boundary, applies relevant masking (e.g., cloud masking for Landsat), calculates indices, fits a linear trend over time (`ee.Reducer.linearFit()`), and exports the resulting slope/intercept bands. + - **Outputs (JS Version):** Individual GEE assets for each index trend (e.g., `users/.../Trend2023_ndvi`, `users/.../Trend2022_rain_new`). + - **Outputs (Python Version):** Merged GEE assets containing all trend bands (e.g., `users/.../TrendFirePy_FullExtent_py`) and optionally individual trend assets. -### 2. Fire Vulnerability Mapping (`FireVulnerability.js`) +### 2. Fire Vulnerability Mapping (`FireVulnerability.js` / `FireVulnerability.py` - *Python version TBD*) - **Inputs:** - - Trend layers - https://www.dropbox.com/scl/fi/ena1qfeqv5ppshluop8kt/inputResampled.tif?rlkey=arefwl3my7nx3z5qpknu88lsx&st=vj8cn3bd&dl=0 - - Roads - - DEM (Digital Elevation Model) - - Fire event datasets (`fire13_19` and `fire20_23`) - - **Output:** - - Fire risk classification map (Low/High fire risk) at 30m resolution - - Kappa coefficient for accuracy assessment - -### 3. Fire Prediction (`TrendAnomalyPrediction.js`) + - Trend layers (often combined into a single multi-band image like `inputResampled.tif`) + - Roads layer + - DEM (Digital Elevation Model) derived slope + - Historical fire event datasets (`fire13_19`, `fire20_23`) + - Other potential factors (e.g., Planet NICFI, MODIS LST) + - **Method:** Samples input features at locations of historical fires and random non-fire points. Trains a classifier (e.g., Random Forest) to predict fire risk categories based on the input features. Applies the trained classifier to the entire study area. + - **Output:** Fire risk classification map (e.g., Low/Moderate/High risk) at 30m resolution. + +### 3. Fire Prediction using Trend Anomalies (`TrendAnomalyPrediction.js` / `TrendAnomalyPrediction.py`) + - **Goal:** Identify areas behaving abnormally compared to their long-term trends, potentially indicating heightened near-term fire risk. - **Inputs:** - - Trend layers - https://www.dropbox.com/scl/fi/ena1qfeqv5ppshluop8kt/inputResampled.tif?rlkey=arefwl3my7nx3z5qpknu88lsx&st=vj8cn3bd&dl=0 - - Fire event datasets (`fire13_19`, `fire20_23`) - - `pa_boundary` - - **Output:** - - Anomaly map comparing trend layers with current conditions + - Individual Trend layers (Slope/Intercept bands from Step 1, e.g., `users/.../Trend2023_ndvi`) + - Study area boundary (`pa_boundary`) + - **Methodology:** + 1. **Load Trends:** Loads the individual trend assets (Slopes and Intercepts) generated in Step 1. + 2. **Predict Expected Conditions:** Uses the linear trend formula (`Predicted = Slope * Time_Difference + Intercept`) to predict the expected value for each indicator for a near-future date (e.g., March 1st, 2023), based on the reference start date of each trend. + 3. **Load Observed Conditions:** Loads and processes the most recent available satellite/climate data before the prediction date (e.g., Landsat, SMAP, ERA5 for Feb 2023; CHIRPS sum for 2022). + 4. **Calculate Anomaly:** Computes the difference between the observed conditions and the predicted conditions (`Anomaly = Observed - Predicted`). + 5. **Identify Hotspots:** Applies thresholds to specific anomaly bands (e.g., `ST_B10_Anomaly > 3.2`, `ndmi_Anomaly < -0.15`) to flag areas where conditions deviate significantly from the trend in a way that increases fire risk (e.g., significantly hotter or drier than expected). + - **Outputs:** + - **Full Anomaly Image (Python & modified JS):** GEE asset containing anomaly bands (e.g., `ndvi_Anomaly`, `ST_B10_Anomaly`). Pixel values represent the difference between observed and predicted conditions. + - **Hotspot Images (Python & JS):** Derived boolean/masked images showing areas exceeding specific anomaly thresholds (e.g., `TrendAnomaly_STB10_Hotspot_py`, `TrendAnomaly_NDMI_Hotspot_py`). + - **Interpretation:** Hotspots indicate areas where recent conditions are unusually conducive to fire compared to the established long-term trend for that location and time of year. For example: + - *Thermal Hotspot (`ST_B10_Anomaly > threshold`):* Area is significantly hotter than expected. + - *Moisture Hotspot (`ndmi_Anomaly < threshold`):* Area is significantly drier than expected. + +## Potential Improvements & Next Steps +Based on the anomaly prediction methodology: +* **Trend Model:** Explore non-linear trend models (e.g., harmonic regression) if significant seasonality or non-linear changes are present in the data, potentially providing a more accurate baseline for anomaly calculation. +* **Data Gaps:** Investigate methods to fill gaps in input data (especially cloud-related gaps in Landsat) using techniques like temporal interpolation or data fusion (e.g., using MODIS) to allow for trend calculation in more areas. +* **Threshold Validation:** Validate and potentially calibrate the anomaly thresholds used for hotspot detection against historical fire data or field observations to improve their local accuracy. +* **Temporal Resolution:** Incorporate more frequent or near-real-time data sources if available to reduce the lag between observation and prediction. +* **Input Variables:** Evaluate the inclusion of additional static or dynamic variables known to influence fire risk (e.g., detailed fuel type maps, wind data, human activity indicators) in either the trend analysis or the final vulnerability/prediction models. +* **Model Integration:** Combine the trend anomaly results with the vulnerability map from Step 2 to create a more comprehensive risk assessment (e.g., prioritizing areas that are both historically vulnerable *and* currently showing high-risk anomalies). -This workflow enables proactive fire risk management by identifying vulnerable areas based on historical patterns and predictive modeling. +This workflow enables proactive fire risk management by identifying vulnerable areas based on historical patterns and predictive modeling using trend anomalies. diff --git a/TrendAnomalyPrediction.py b/TrendAnomalyPrediction.py index 3d0625c..6e49c82 100644 --- a/TrendAnomalyPrediction.py +++ b/TrendAnomalyPrediction.py @@ -314,8 +314,8 @@ def main(): print("Anomaly image calculated.") # print(anomaly_image.bandNames().getInfo()) # Verify band names - # --- Export Results --- - anomaly_asset_id = OUTPUT_ASSET_BASE + 'TrendAnomaly_py' # Example output name + # --- Export Full Anomaly Results --- + anomaly_asset_id = OUTPUT_ASSET_BASE + 'TrendAnomaly_py' export_to_asset( image=anomaly_image, asset_id=anomaly_asset_id, @@ -323,6 +323,43 @@ def main(): description='Trend_Anomaly_Python', scale=30 ) + + # --- Calculate and Export Specific Hotspots (Matching JS Drive Exports) --- + print("Calculating and exporting specific hotspots...") + try: + # ST_B10 Hotspot (Thermal Anomaly) + stb10_hotspot = anomaly_image.select('ST_B10_Anomaly').gt(3.2) # Pixels significantly hotter than predicted + # Masking non-hotspot areas (optional, makes visualization cleaner) + # stb10_hotspot = stb10_hotspot.selfMask() + stb10_asset_id = OUTPUT_ASSET_BASE + 'TrendAnomaly_STB10_Hotspot_py' + export_to_asset( + image=stb10_hotspot.unmask(0).toFloat(), # Export boolean as 0/1 float + asset_id=stb10_asset_id, + region=boundary, + description='Hotspot_STB10_py', + scale=30 + ) + + # NDMI Hotspot (Moisture Anomaly) + ndmi_hotspot = anomaly_image.select('ndmi_Anomaly').lt(-0.15) # Pixels significantly drier than predicted + # ndmi_hotspot = ndmi_hotspot.selfMask() + ndmi_asset_id = OUTPUT_ASSET_BASE + 'TrendAnomaly_NDMI_Hotspot_py' + export_to_asset( + image=ndmi_hotspot.unmask(0).toFloat(), # Export boolean as 0/1 float + asset_id=ndmi_asset_id, + region=boundary, + description='Hotspot_NDMI_py', + scale=30 + ) + print("Hotspot export tasks started.") + + except ee.EEException as e: + print(f"Could not calculate or export hotspots: {e}") + # Check if specific anomaly bands exist in anomaly_image + print("Available anomaly bands:", anomaly_image.bandNames().getInfo()) + except Exception as e: + print(f"Unexpected error during hotspot processing: {e}") + else: print("Skipping anomaly calculation and export due to missing inputs (likely placeholder functions)." ) # Updated message From 16cb58b63f43dd19f57d0a2602f5bc0910fb14c2 Mon Sep 17 00:00:00 2001 From: jonas-nothnagel Date: Mon, 14 Apr 2025 15:55:17 +0200 Subject: [PATCH 12/15] updated python2 --- FireVulnerability.py | 975 +++++++++++++++++++++++++++++++++----- TrendAnomalyPrediction.py | 4 +- 2 files changed, 868 insertions(+), 111 deletions(-) diff --git a/FireVulnerability.py b/FireVulnerability.py index c0ce29d..2f777d0 100644 --- a/FireVulnerability.py +++ b/FireVulnerability.py @@ -3,24 +3,23 @@ """ FireVulnerability.py: Python implementation of FireVulnerability.js using Earth Engine Python API. -This script creates fire vulnerability maps for the study area in Goa, India. - -* This script is not predicting fire occurrences directly. It's predicting environmental anomalies. -* The goal is to identify areas where current environmental conditions (vegetation indices, moisture, temperature) are significantly different (anomalous) compared to what the long-term trend would predict for that specific time of year (February 2023 in this case). -* The underlying hypothesis is that areas with strong negative anomalies (e.g., much drier, less vegetation than expected) or positive anomalies (much hotter than expected) are more conducive to fire ignition or spread, making them potential "hotspots" of risk, but it doesn't predict if or when a fire will actually start there. -* Therefore, the historical fire data isn't needed for the calculation of these environmental anomalies. - -The script performs the following steps: -1. Loads fire event data from multiple years -2. Generates random points for non-fire locations -3. Extracts predictor variables at fire and non-fire locations -4. Categorizes fire locations by risk level based on surrounding conditions -5. Trains a Random Forest classifier to predict fire vulnerability -6. Validates the model with independent test data -7. Generates a fire vulnerability map for the entire study area -8. Exports the vulnerability map to Google Drive and/or Earth Engine assets - -Equivalent to the FireVulnerability.js script in the original implementation. + +This script maps fire vulnerability based on long-term trends, static factors, +and historical fire data using a Random Forest classifier. + +Steps: +1. Load study area boundary. +2. Load and prepare historical fire points. +3. Load and process auxiliary datasets (NICFI, MODIS LST, DEM, Roads). +4. Calculate trends for NICFI and MODIS LST. +5. Load pre-computed trend layers from TrendFire. +6. Combine all input features into a single multi-band image. +7. Resample the combined feature image to a consistent grid (e.g., 30m EPSG:3857). +8. Prepare training data by sampling features at fire and non-fire locations. +9. Train a Random Forest classifier. +10. Evaluate the classifier. +11. Apply the classifier to the entire study area to create the vulnerability map. +12. Export the final vulnerability map. """ import ee @@ -35,14 +34,6 @@ from sklearn.metrics import accuracy_score, confusion_matrix, classification_report import seaborn as sns -# Initialize the Earth Engine API -try: - ee.Initialize() - print("Earth Engine API initialized successfully.") -except Exception as e: - print(f"Error initializing Earth Engine API: {e}") - print("Make sure you have authenticated with Earth Engine using ee.Authenticate()") - # Define the Goa study area boundary def get_study_boundary(): """ @@ -361,44 +352,118 @@ def categorize(feature): return categorized_fire # Function to prepare training data -def prepare_training_data(categorized_fire, non_fire_points, trend_layers, roads, pa): +def prepare_training_data(fire_points_input, boundary, predictor_image, label_property, \ + num_random_points=90, random_points_seed=42, \ + moderate_risk_limit=70, use_risk_categories=True): """ - Prepare training data for the Random Forest classifier. - + Prepares the labelled training/validation data by sampling predictors at fire and non-fire locations. + Args: - categorized_fire: ee.FeatureCollection of fire points with risk levels - non_fire_points: ee.FeatureCollection of non-fire points - trend_layers: ee.Image with trend bands - roads: ee.Image with road data - pa: ee.Geometry representing the study area - + fire_points_input: ee.FeatureCollection of historical fire points (e.g., 2013-2022). + Must contain a 'Delta T' property if use_risk_categories is True. + boundary: ee.Geometry of the study area. + predictor_image: ee.Image containing all predictor bands (resampled). + label_property: String, the name of the property to store the class label (e.g., 'RiskNumeric'). + num_random_points: Integer, the number of non-fire points to generate. + random_points_seed: Integer, seed for reproducibility of random points. + moderate_risk_limit: Integer, limit for moderate risk points (matches JS logic). + use_risk_categories: Boolean, if True, categorize fire points based on 'Delta T'. + If False, assign label 1 to all fire points. + Returns: - ee.FeatureCollection with training data + An ee.FeatureCollection ready for splitting into training/validation, or None on error. """ - print("Preparing training data for Random Forest classifier...") - - # Combine fire and non-fire points - training_points = categorized_fire.merge(non_fire_points) - - # Calculate distance to roads - road_distance = roads.fastDistanceTransform(100).multiply(30).clip(pa) - - # Combine all predictor variables - predictor_variables = trend_layers.addBands(road_distance.rename('road_distance')) - - # Sample the predictor variables at training points - training_data = predictor_variables.sampleRegions({ - 'collection': training_points, - 'properties': ['RiskNumeric'], - 'scale': 30, - 'geometries': True - }) + print("Preparing training data...") - # Count the final number of training points - training_count = training_data.size().getInfo() - print(f"Final training dataset contains {training_count} points") + if predictor_image is None: + print("Error: Predictor image is missing.") + return None + if fire_points_input is None: + print("Error: Input fire points are missing.") + return None - return training_data + try: + # --- Generate Non-Fire Points --- + # Assumption: Random points represent non-fire areas (Risk = 0). + # Improvement Note: Could exclude buffers around known fires. + print(f" Generating {num_random_points} random non-fire points (label 0)...") + non_fire_points = ee.FeatureCollection.randomPoints( + region=boundary, + points=num_random_points, + seed=random_points_seed + ).map(lambda f: f.set(label_property, 0)) + print(f" Generated {non_fire_points.size().getInfo()} non-fire points.") + + # --- Prepare Fire Points --- + categorized_fire_points = None + if use_risk_categories: + # Assumption: fire_points_input has a 'Delta T' property. + # This categorization matches the logic in FireVulnerability.js + print(" Categorizing fire points based on 'Delta T'...") + try: + low_risk = fire_points_input.filter(ee.Filter.rangeContains('Delta T', 0, 25)).map(lambda f: f.set(label_property, 1)) + moderate_risk = fire_points_input.filter(ee.Filter.rangeContains('Delta T', 25, 35)).map(lambda f: f.set(label_property, 2)).limit(moderate_risk_limit) + high_risk = fire_points_input.filter(ee.Filter.gte('Delta T', 35)).map(lambda f: f.set(label_property, 3)) + print(f" Low Risk (1): {low_risk.size().getInfo()}, Mod Risk (2): {moderate_risk.size().getInfo()}, High Risk (3): {high_risk.size().getInfo()}") + + # IMPORTANT: JS script only merged high_risk (3) and non_fire (0) + # Replicating that specific behaviour for direct comparison: + categorized_fire_points = high_risk + print(f" Using only High Risk fire points (label 3) for sampling, count: {categorized_fire_points.size().getInfo()}") + # If you wanted all categories: + # categorized_fire_points = ee.FeatureCollection([]).merge(low_risk).merge(moderate_risk).merge(high_risk) + + except ee.EEException as e: + if "Property 'Delta T' does not exist" in str(e): + print(" Error: 'Delta T' property not found in fire points. Cannot categorize risk.") + print(" Assigning label 1 to all fire points instead.") + categorized_fire_points = fire_points_input.map(lambda f: f.set(label_property, 1)) + else: + raise e # Re-raise other GEE errors + else: + print(" Assigning label 1 to all fire points (risk categorization disabled).") + categorized_fire_points = fire_points_input.map(lambda f: f.set(label_property, 1)) + + if categorized_fire_points is None or categorized_fire_points.size().getInfo() == 0: + print("Error: No valid categorized fire points found.") + return None + + # --- Combine Points and Sample Predictors --- + # Merge the non-fire points and the selected fire points + combined_points = non_fire_points.merge(categorized_fire_points) + print(f" Total points for sampling: {combined_points.size().getInfo()}") + + print(" Sampling predictor values at point locations...") + # Get predictor band names + predictor_names = predictor_image.bandNames() + if not predictor_names.getInfo(): # Check if list is empty + print("Error: Predictor image has no bands.") + return None + + # Properties to keep from the points collections + properties_to_keep = [label_property] + + training_data = predictor_image.select(predictor_names).sampleRegions( + collection=combined_points, + properties=properties_to_keep, + scale=predictor_image.projection().nominalScale(), # Use image scale + geometries=False # Typically don't need geometry for training + ) + + # Add a random column for splitting later + training_data = training_data.randomColumn() + + print(f"Sampling complete. Final features count: {training_data.size().getInfo()}") + # print("Sample feature properties:", training_data.first().propertyNames().getInfo()) # Optional verification + + return training_data + + except ee.EEException as e: + print(f"GEE error during training data preparation: {e}") + return None + except Exception as e: + print(f"Unexpected error during training data preparation: {e}") + return None # Function to train a Random Forest classifier def train_random_forest(training_data, num_trees=500): @@ -553,66 +618,758 @@ def export_vulnerability_map_to_asset(vulnerability_map, pa, asset_name): # Main function to run the fire vulnerability mapping workflow def main(): - """ - Main function to run the fire vulnerability mapping workflow. - """ - print("Starting fire vulnerability mapping for Goa, India...") - - # Get the study area boundary - pa = get_study_boundary() - - # Load fire events data - fire_all, fire_training, fire_validation = load_fire_events(pa) - - # Load roads data - roads = load_roads(pa) - - # Load trend layers - trend_layers = load_trend_layers(pa) - - # Generate random non-fire points - non_fire_points = generate_non_fire_points(pa, fire_training, num_points=100) - - # Categorize fire risk based on temperature difference - categorized_fire = categorize_fire_risk(fire_training, trend_layers, pa) - - # Prepare training data - training_data = prepare_training_data(categorized_fire, non_fire_points, trend_layers, roads, pa) + """Main script execution for fire vulnerability mapping.""" + # --- Configuration --- + PROJECT_ID = 'ee-crop-health-telangana' + BOUNDARY_ASSET_ID = 'users/jonasnothnagel/pa_boundary' + FIRE_ASSET_BASE = 'users/jonasnothnagel/' + DEM_ASSET_ID = 'users/jonasnothnagel/dem' + ROAD_ASSET_ID = 'users/jonasnothnagel/roads' + TRENDFIRE_ASSET_BASE = 'users/jonasnothnagel/' # Base for JS TrendFire outputs + NICFI_COLLECTION = 'projects/planet-nicfi/assets/basemaps/asia' + MODIS_LST_COLLECTION = 'MODIS/061/MOD11A1' + + OUTPUT_ASSET_BASE = 'users/jonasnothnagel/' + VULNERABILITY_MAP_ASSET = OUTPUT_ASSET_BASE + 'FireVulnerability_py' + RESAMPLED_FEATURES_ASSET = OUTPUT_ASSET_BASE + 'FireVulnerability_InputsResampled_py' + TRAINING_POINTS_ASSET = OUTPUT_ASSET_BASE + 'FireVulnerability_TrainingPoints_py' + + GRID_SCALE = 30 + GRID_CRS = 'EPSG:3857' + NUM_RANDOM_POINTS = 90 + RF_TREES = 1000 + RF_VAR_SPLIT = 1 + TRAIN_TEST_SPLIT_RATIO = 0.8 + CLASS_LABEL = 'RiskNumeric' + # Decide if using Delta T for categories or just fire/no-fire (1/0) + USE_RISK_CATEGORIES = True # Set to False to label all fires as 1 + + # --- Initialization --- + initialize_ee(PROJECT_ID) + boundary = get_boundary(BOUNDARY_ASSET_ID) + if not boundary: + return + + # --- Processing Steps --- + print("\n--- Starting Fire Vulnerability Workflow ---") + + # 1. Load Fire Points + fire_pts_processed = load_fire_points(FIRE_ASSET_BASE, boundary) + if fire_pts_processed is None: + print("Exiting: Failed to load fire points.") + return + + # 2. Process NICFI + nicfi_trends = process_nicfi(boundary) + # Handle potential None return if access fails or errors occur + if nicfi_trends is None: + print("Warning: Failed to process NICFI trends. Proceeding without them.") + + # 3. Process MODIS LST + modis_trends = process_modis_lst(boundary) + if modis_trends is None: + print("Warning: Failed to process MODIS LST trends. Proceeding without them.") + + # 4. Load DEM Slope + dem_slope = load_dem_slope(DEM_ASSET_ID, boundary) + if dem_slope is None: + print("Warning: Failed to process DEM slope. Proceeding without it.") + + # 5. Load Roads + roads_img = load_roads(ROAD_ASSET_ID, boundary) + if roads_img is None: + print("Warning: Failed to process Roads layer. Proceeding without it.") + + # 6. Load TrendFire Outputs + trendfire_img = load_trendfire_outputs(TRENDFIRE_ASSET_BASE, boundary) + if trendfire_img is None: + print("Exiting: Failed to load TrendFire outputs.") + return + + # 7. Combine Features + combined_features = combine_features(trendfire_img, nicfi_trends, modis_trends, dem_slope, roads_img) + if combined_features is None: + print("Exiting: Failed to combine input features.") + return + + # 8. Resample & Export Intermediate Input + input_resampled = resample_features(combined_features, GRID_SCALE, GRID_CRS, boundary) + if input_resampled is None: + print("Exiting: Failed to resample features.") + return + + export_asset(input_resampled, RESAMPLED_FEATURES_ASSET, boundary, "Resampled_Inputs_FV_py", GRID_SCALE, GRID_CRS) + + # 9. Prepare Training Data + # Uses fire_pts_processed (2013-2022) and generates random points + training_data_fc = prepare_training_data(fire_pts_processed, boundary, input_resampled, CLASS_LABEL, \ + num_random_points=NUM_RANDOM_POINTS, use_risk_categories=USE_RISK_CATEGORIES) + if training_data_fc is None: + print("Exiting: Failed to prepare training data.") + return + + # Export the full sampled dataset before splitting + export_table(training_data_fc, TRAINING_POINTS_ASSET, "Training_Validation_Points_FV_py") + + # --- Split Data --- + print(f"Splitting data using ratio: {TRAIN_TEST_SPLIT_RATIO:.1f} train / {1-TRAIN_TEST_SPLIT_RATIO:.1f} validation...") + # Filter based on the random column added in prepare_training_data + training_sample = training_data_fc.filter(ee.Filter.lt('random', TRAIN_TEST_SPLIT_RATIO)) + validation_sample = training_data_fc.filter(ee.Filter.gte('random', TRAIN_TEST_SPLIT_RATIO)) + print(f" Training samples: {training_sample.size().getInfo()}, Validation samples: {validation_sample.size().getInfo()}") + + # Get predictor names from the resampled image (used as input properties) + predictor_names = input_resampled.bandNames() + if not predictor_names.getInfo(): + print("Error: Could not retrieve band names from resampled image.") + return + + # 10. Train Classifier + classifier = train_classifier(training_sample, CLASS_LABEL, predictor_names, RF_TREES, RF_VAR_SPLIT) + if classifier is None: + print("Exiting: Classifier training failed.") + return + + # 11. Evaluate Classifier + evaluation_results = evaluate_classifier(classifier, validation_sample, CLASS_LABEL) + if evaluation_results is None: + print("Warning: Classifier evaluation failed or produced no results.") - # Split into training and validation sets - split = 0.8 # 80% for training, 20% for validation - training_size = training_data.size().multiply(split).int() + # 12. Classify Image + print("Classifying the full image...") + vulnerability_map = input_resampled.classify(classifier) + print("Classification complete.") - # Randomize the data - randomized = training_data.randomColumn('random') + # 13. Export Final Map + export_asset(vulnerability_map, VULNERABILITY_MAP_ASSET, boundary, "Fire_Vulnerability_Map_py", GRID_SCALE, GRID_CRS) - # Split based on the random column - train = randomized.filter(ee.Filter.lt('random', split)) - test = randomized.filter(ee.Filter.gte('random', split)) + print("\n--- Fire Vulnerability Workflow Completed --- ") + print("Monitor GEE tasks for export completion.") + +# --- Fire Vulnerability Specific Functions --- + +def load_fire_points(asset_base, boundary): + """ + Loads historical fire point assets, merges them, and filters out 2023 data. - # Train the Random Forest classifier - classifier, predictor_names = train_random_forest(train) + Args: + asset_base: String, the base path for fire point assets (e.g., 'users/username/'). + boundary: ee.Geometry object for optional filtering (though JS didn't explicitly filter here). + + Returns: + An ee.FeatureCollection containing fire points from 2013-2022, or None on error. + """ + print("Loading and preparing historical fire points...") + fire_asset_13_19 = asset_base + 'fire13_19' + fire_asset_20_23 = asset_base + 'fire20_23' - # Validate the model - validation_metrics = validate_model(classifier, predictor_names, test) + try: + print(f" Loading {fire_asset_13_19}...") + fire13_19 = ee.FeatureCollection(fire_asset_13_19) + print(f" Loading {fire_asset_20_23}...") + fire20_23 = ee.FeatureCollection(fire_asset_20_23) + + # Merge the collections + fire13_23 = fire13_19.merge(fire20_23) + print(f" Merged collections. Total points (2013-2023): {fire13_23.size().getInfo()}") + + # Filter out points from 2023 based on the 'acq_date' property + # Assumes 'acq_date' format allows simple string filtering like 'YYYY-MM-DD' + fire13_22 = fire13_23.filter(ee.Filter.stringContains('acq_date', '-2023').Not()) + # Alternative filter if acq_date is ee.Date object: + # fire13_22 = fire13_23.filter(ee.Filter.date('2013-01-01', '2022-12-31')) + + print(f" Filtered out 2023 points. Total points (2013-2022): {fire13_22.size().getInfo()}") + + # Optional: Filter by boundary if needed, although JS didn't seem to do it here + # fire13_22 = fire13_22.filterBounds(boundary) + # print(f" Filtered by boundary. Points within boundary: {fire13_22.size().getInfo()}") + + return fire13_22 + + except ee.EEException as e: + print(f"Error loading or processing fire point assets: {e}") + print(f"Please ensure assets '{fire_asset_13_19}' and '{fire_asset_20_23}' exist and properties like 'acq_date' are correct.") + return None + except Exception as e: + print(f"An unexpected error occurred loading fire points: {e}") + return None + +def process_nicfi(boundary, start_date='2016-03-01', end_date='2024-02-28', collection_id='projects/planet-nicfi/assets/basemaps/asia'): + """ + Loads Planet-NICFI data, calculates NDVI, and computes trends for R, G, B, N, NDVI bands. - # Create a fire vulnerability map - predictor_variables = trend_layers.addBands(roads.rename('road_distance')) - vulnerability_map = create_vulnerability_map(classifier, predictor_variables, predictor_names, pa) + Args: + boundary: ee.Geometry for filtering and clipping. + start_date: Start date for filtering NICFI data. + end_date: End date for filtering NICFI data. + collection_id: GEE asset ID for the NICFI collection. + + Returns: + ee.Image containing slope and intercept bands for R, G, B, N, NDVI, or None on error. + """ + print(f"Processing NICFI trends ({start_date} to {end_date})...") - # Export the vulnerability map to Google Drive (uncomment to use) - # export_vulnerability_map(vulnerability_map, pa) + try: + # Load the collection + nicfi_col = ee.ImageCollection(collection_id) \ + .map(lambda img: img.clip(boundary)) \ + .filterDate(start_date, end_date) + + # Function to calculate and add NDVI band + def addNDVI(image): + # Ensure bands are correctly named ('N', 'R', 'G', 'B') + # May need to rename depending on the specific NICFI collection version + # Example: .select(['B4', 'B3', 'B2', 'B1'], ['N', 'R', 'G', 'B']) if needed + ndvi = image.normalizedDifference(['N', 'R']).rename('NDVI') + return image.addBands(ndvi) + + nicfi_col = nicfi_col.map(addNDVI) + + # Add time band (years since 2016-01-01, as per JS) + ref_date = ee.Date('2016-01-01') + def addTime(image): + img_date = image.date() + years = img_date.difference(ref_date, 'year') + return image.addBands(ee.Image(years).rename('time').float()) + + nicfi_col_time = nicfi_col.map(addTime) + + # Indices for trend calculation + indices = ['R', 'G', 'B', 'N', 'NDVI'] + combined_trends = ee.Image().toFloat() # Start empty + + # Calculate trends for each index + for index in indices: + print(f" Calculating NICFI trend for: {index}") + stacked = nicfi_col_time.select(['time', index]) + regression = stacked.reduce(ee.Reducer.linearFit()) # Outputs 'scale' and 'offset' + slope = regression.select('scale').rename(f'{index}_Slope') + intercept = regression.select('offset').rename(f'{index}_Intercept') + combined_trends = combined_trends.addBands(slope).addBands(intercept) + + print("NICFI trends calculated successfully.") + return combined_trends + + except ee.EEException as e: + print(f"GEE error during NICFI processing: {e}") + print(f"Please ensure you have access to the collection: {collection_id}") + return None + except Exception as e: + print(f"Unexpected error during NICFI processing: {e}") + return None + +def process_modis_lst(boundary, start_date='2013-01-01', end_date='2023-02-28', collection_id='MODIS/061/MOD11A1'): + """ + Loads MODIS LST data, masks clouds, converts to Celsius, and computes trends for Day and Night LST. - # Export the vulnerability map to Earth Engine asset (uncomment to use) - # user_asset_root = 'users/your_username/' # Replace with your actual EE username - # export_vulnerability_map_to_asset( - # vulnerability_map, - # pa, - # f"{user_asset_root}fire_vulnerability_map" - # ) + Args: + boundary: ee.Geometry for filtering and clipping. + start_date: Start date for filtering MODIS data. + end_date: End date for filtering MODIS data. + collection_id: GEE asset ID for the MODIS LST collection. + + Returns: + ee.Image containing slope and intercept bands for LST_Day and LST_Night, or None on error. + """ + print(f"Processing MODIS LST trends ({start_date} to {end_date})...") + + try: + # Function to mask clouds/poor quality pixels and convert units + def scale_and_mask_lst(image): + qc = image.select('QC_Day') + # Keep only pixels with good quality (Bits 0-1: 00 = Good) + mask = qc.bitwiseAnd(3).eq(0) + + lst_day_c = image.select('LST_Day_1km') \ + .updateMask(mask) \ + .multiply(0.02) \ + .subtract(273.15) \ + .rename('LST_Day') + + lst_night_c = image.select('LST_Night_1km') \ + .updateMask(mask) \ + .multiply(0.02) \ + .subtract(273.15) \ + .rename('LST_Night') + + return image.addBands(lst_day_c).addBands(lst_night_c).select(['LST_Day', 'LST_Night']) + + # Load, filter, and process the collection + modis_lst_col = ee.ImageCollection(collection_id) \ + .filterDate(start_date, end_date) \ + .filterBounds(boundary) \ + .map(scale_and_mask_lst) + + # Add time band + ref_date = ee.Date(start_date) + def addTime(image): + img_date = image.date() + years = img_date.difference(ref_date, 'year') + return image.addBands(ee.Image(years).rename('time').float()) + + modis_col_time = modis_lst_col.map(addTime) + + # Calculate trends for Day and Night LST + trends_list = [] + for band in ['LST_Day', 'LST_Night']: + print(f" Calculating MODIS trend for: {band}") + stacked = modis_col_time.select(['time', band]) + regression = stacked.reduce(ee.Reducer.linearFit()) # scale, offset + slope = regression.select('scale').rename(f'{band}_Slope') + intercept = regression.select('offset').rename(f'{band}_Intercept') + trends_list.append(slope.addBands(intercept)) + + # Combine Day and Night trends + if not trends_list: + print("Error: Could not calculate any MODIS LST trends.") + return None + + combined_trends = ee.ImageCollection(trends_list).toBands() + # Rename bands from 0_LST_Day_Slope etc. to LST_Day_Slope etc. + original_band_names = [img.bandNames() for img in trends_list] + flat_band_names = [item for sublist in original_band_names for item in sublist.getInfo()] + combined_trends = combined_trends.rename(flat_band_names) + + print("MODIS LST trends calculated successfully.") + return combined_trends.clip(boundary) # Clip final result + + except ee.EEException as e: + print(f"GEE error during MODIS LST processing: {e}") + return None + except Exception as e: + print(f"Unexpected error during MODIS LST processing: {e}") + return None + +def load_dem_slope(asset_id, boundary): + """Loads DEM asset, calculates slope, and clips to boundary.""" + print(f"Loading DEM ({asset_id}) and calculating slope...") + try: + dem = ee.Image(asset_id) + slope = ee.Terrain.slope(dem) + print("Slope calculated successfully.") + return slope.clip(boundary) # Return only the slope band, clipped + except ee.EEException as e: + print(f"GEE error loading DEM or calculating slope: {e}") + return None + except Exception as e: + print(f"Unexpected error during DEM/Slope processing: {e}") + return None + +def load_roads(asset_id, boundary): + """Loads the road layer asset and clips to boundary.""" + print(f"Loading roads layer ({asset_id})...") + try: + roads_img = ee.Image(asset_id) + print("Roads layer loaded successfully.") + return roads_img.clip(boundary) + except ee.EEException as e: + print(f"GEE error loading roads asset: {e}") + return None + except Exception as e: + print(f"Unexpected error during roads processing: {e}") + return None + +def load_trendfire_outputs(asset_base, boundary): + """ + Loads the individual trend assets generated by TrendFire.js and merges them. + Assumes assets are under the user's GEE path (asset_base). + + Args: + asset_base: String, the base path for trend assets (e.g., 'users/username/'). + boundary: ee.Geometry object for clipping. + + Returns: + ee.Image containing all merged trend bands (Slope and Intercept), or None on error. + """ + print(f"Loading TrendFire outputs from base path: {asset_base}...") + + # Define asset names/parts based on TrendFire.js export structure + landsat_indices = ['ndvi', 'evi', 'mirbi', 'ndfi', 'bsi', 'ndmi', 'nbr', 'nbr2', 'msavi', 'smi', 'ST_B10'] + # JS exported these as 'users/sravanthi_earthAnalytics/Trend2023_' + indexName + # Assuming user ran JS and exported to their own folder: + landsat_asset_prefix = 'Trend2023_' + rain_asset_id_part = 'Trend2022_rain_new' + sm_asset_id_part = 'Trend2023_SM_new' + # JS asset was 'Trend2024_RH_new' + rh_asset_id_part = 'Trend2024_RH_new' + + all_trend_bands = ee.Image().toFloat() # Start with an empty image + asset_id = '' # For error message context + + try: + # Load Landsat index trends + for index in landsat_indices: + asset_id = f"{asset_base}{landsat_asset_prefix}{index}" + print(f" Loading Landsat trend: {asset_id}") + trend_img = ee.Image(asset_id) + all_trend_bands = all_trend_bands.addBands(trend_img) + + # Load Rain trend + asset_id = f"{asset_base}{rain_asset_id_part}" + print(f" Loading Rain trend: {asset_id}") + rain_trend = ee.Image(asset_id) + all_trend_bands = all_trend_bands.addBands(rain_trend) + + # Load SM trend + asset_id = f"{asset_base}{sm_asset_id_part}" + print(f" Loading SM trend: {asset_id}") + sm_trend = ee.Image(asset_id) + # Check expected bands: sm_surface_Slope, sm_surface_Intercept + # Rename if necessary, e.g., if JS exported with different names + # sm_trend = sm_trend.rename(['sm_surface_Slope', 'sm_surface_Intercept']) + all_trend_bands = all_trend_bands.addBands(sm_trend) + + # Load RH trend + asset_id = f"{asset_base}{rh_asset_id_part}" + print(f" Loading RH trend: {asset_id}") + rh_trend = ee.Image(asset_id) + all_trend_bands = all_trend_bands.addBands(rh_trend) + + print("Successfully loaded and merged TrendFire outputs.") + # Clip the final merged image to the boundary + return all_trend_bands.clip(boundary) + + except ee.EEException as e: + print(f"Error loading GEE asset during TrendFire output loading: {e}") + print(f"Please ensure asset '{asset_id}' exists and you have permissions.") + return None + except Exception as e: + print(f"An unexpected error occurred loading TrendFire outputs: {e}") + return None + +def combine_features(trendfire_img, nicfi_trends, modis_trends, dem_slope, roads_img): + """ + Combines all input feature bands into a single ee.Image. + Handles potential None inputs from previous steps. + + Args: + trendfire_img: ee.Image with TrendFire trend bands. + nicfi_trends: ee.Image with NICFI trend bands. + modis_trends: ee.Image with MODIS LST trend bands. + dem_slope: ee.Image with the DEM slope band. + roads_img: ee.Image with the roads layer. + + Returns: + A single ee.Image containing all combined bands, or None if essential inputs are missing. + """ + print("Combining input features...") + + # Check for essential inputs (can adjust based on importance) + if trendfire_img is None: + print("Error: TrendFire outputs are missing. Cannot combine features.") + return None + # Add checks for other inputs if they are considered essential + # if nicfi_trends is None: ... etc. + + # Start with the primary trend image + combined = trendfire_img.toFloat() # Ensure consistent type + + # Add other layers conditionally + if nicfi_trends is not None: + print(" Adding NICFI trends...") + combined = combined.addBands(nicfi_trends.toFloat()) + else: + print(" Warning: Skipping NICFI trends (input was None).") + + if modis_trends is not None: + print(" Adding MODIS LST trends...") + combined = combined.addBands(modis_trends.toFloat()) + else: + print(" Warning: Skipping MODIS LST trends (input was None).") + + if dem_slope is not None: + print(" Adding DEM slope...") + # Ensure DEM slope band has a unique name if needed (e.g., if 'slope' already exists) + # dem_slope = dem_slope.select('slope').rename('dem_slope') + combined = combined.addBands(dem_slope.toFloat()) # Assumes dem_slope is single band 'slope' + else: + print(" Warning: Skipping DEM slope (input was None).") + + if roads_img is not None: + print(" Adding Roads layer...") + # Ensure roads image band has a unique name (e.g., rename if it's just 'b1') + # roads_img = roads_img.rename('roads') + combined = combined.addBands(roads_img.toFloat()) + else: + print(" Warning: Skipping Roads layer (input was None).") + + print("Features combined successfully.") + # print("Combined bands:", combined.bandNames().getInfo()) # Optional: Verify bands + return combined + +def resample_features(feature_image, scale, crs, boundary): + """ + Resamples the combined feature image to a target CRS and scale. + Uses reduceResolution followed by reproject, matching the JS approach. - print("Fire vulnerability mapping complete!") - print("To visualize the results, check your Google Drive or Earth Engine assets.") + Args: + feature_image: ee.Image with all combined feature bands. + scale: Target resolution in meters (e.g., 30). + crs: Target CRS string (e.g., 'EPSG:3857'). + boundary: ee.Geometry for clipping the final output. + + Returns: + A resampled ee.Image or None on error. + """ + print(f"Resampling features to {scale}m scale, CRS {crs}...") + if feature_image is None: + print("Error: Input feature_image is None. Cannot resample.") + return None + + try: + # Define the target projection + target_projection = ee.Projection(crs).atScale(scale) + + # Resample using reduceResolution followed by reproject + resampled_image = feature_image \ + .reduceResolution(reducer=ee.Reducer.mean(), maxPixels=1024) \ + .reproject(crs=target_projection) + + print("Resampling completed successfully.") + return resampled_image.clip(boundary) + + except ee.EEException as e: + print(f"GEE error during resampling: {e}") + return None + except Exception as e: + print(f"Unexpected error during resampling: {e}") + return None + +def train_classifier(training_data, class_property, input_properties, num_trees=1000, variables_per_split=1, seed=42): + """ + Trains the ee.Classifier.smileRandomForest classifier. + + Args: + training_data: ee.FeatureCollection used for training. + class_property: String, the name of the property containing the class label. + input_properties: List of strings, names of the predictor properties/bands. + num_trees: Integer, number of trees in the forest. + variables_per_split: Integer, number of variables to consider at each split. + Set to None or 0 to use sqrt(num_variables). + JS used 1. + seed: Integer, seed for reproducibility. + + Returns: + A trained ee.Classifier object, or None on error. + """ + print(f"Training Random Forest classifier ({num_trees} trees)...") + if training_data is None: + print("Error: Training data is missing.") + return None + if not input_properties: + print("Error: Input properties list is empty.") + return None + + try: + classifier = ee.Classifier.smileRandomForest( + numberOfTrees=num_trees, + variablesPerSplit=variables_per_split, # Match JS, using 1 + # minLeafPopulation=1, # Default is 1 + # bagFraction=0.5, # Default is 0.5 + seed=seed + ).train( + features=training_data, + classProperty=class_property, + inputProperties=input_properties + ) + + print("Classifier trained successfully.") + + # Optional: Explain the classifier (can be computationally intensive) + # try: + # explanation = classifier.explain() + # print("Classifier explanation:", explanation.getInfo()) + # importance = explanation.get('importance') + # if importance: + # print("Variable Importance:") + # importance_info = importance.getInfo() + # # Ensure importance_info is a dictionary before iterating + # if isinstance(importance_info, dict): + # for prop, imp in importance_info.items(): + # print(f" {prop}: {imp}") + # else: + # print(" Could not retrieve importance details in expected format.") + # except Exception as explain_e: + # print(f"Could not get classifier explanation: {explain_e}") + + return classifier + + except ee.EEException as e: + print(f"GEE error during classifier training: {e}") + return None + except Exception as e: + print(f"Unexpected error during classifier training: {e}") + return None + +def evaluate_classifier(classifier, validation_data, class_property): + """ + Evaluates the classifier on validation data using an error matrix. + + Args: + classifier: Trained ee.Classifier object. + validation_data: ee.FeatureCollection used for validation. + class_property: String, the name of the property containing the true class label. + + Returns: + A dictionary containing accuracy, kappa, and the confusion matrix, or None on error. + """ + print("Evaluating classifier...") + if classifier is None: + print("Error: Classifier object is missing.") + return None + if validation_data is None: + print("Error: Validation data is missing.") + return None + + try: + # Classify the validation data + validated = validation_data.classify(classifier) + + # Calculate the error matrix + # Arguments are (actual, predicted) + error_matrix = validated.errorMatrix(class_property, 'classification') + + # Extract metrics + accuracy = error_matrix.accuracy() + kappa = error_matrix.kappa() + confusion = error_matrix.array() # Get the matrix as an ee.Array + + # Get results - use getInfo() to bring server-side objects to client + accuracy_val = accuracy.getInfo() + kappa_val = kappa.getInfo() + confusion_val = confusion.getInfo() + + print(f" Validation Overall Accuracy: {accuracy_val:.4f}") + print(f" Validation Kappa Coefficient: {kappa_val:.4f}") + print(" Validation Confusion Matrix:") + # Pretty print the matrix (assuming simple 2x2 or small matrix) + if confusion_val: + for row in confusion_val: + print(f" {row}") + else: + print(" Could not retrieve confusion matrix details.") + + return { + 'accuracy': accuracy_val, + 'kappa': kappa_val, + 'confusion_matrix': confusion_val + } + + except ee.EEException as e: + print(f"GEE error during classifier evaluation: {e}") + return None + except Exception as e: + print(f"Unexpected error during classifier evaluation: {e}") + return None + +# --- Reusable Helper Functions (Consider placing in a shared utils.py) --- + +def initialize_ee(project_id='ee-crop-health-telangana'): # Use the verified project ID + """Initializes the Earth Engine API.""" + try: + # ee.Authenticate() # Often needed only once per session/machine + ee.Initialize(project=project_id, opt_url='https://earthengine-highvolume.googleapis.com') + print(f"Earth Engine API initialized successfully for project: {project_id}") + except ee.EEException as e: + print(f"Error initializing Earth Engine API for project {project_id}: {e}") + print("Please ensure:") + print(" 1. You have authenticated (e.g., via `gcloud auth application-default login`).") + print(f" 2. The project ID '{project_id}' is correct.") + print(" 3. The Earth Engine API is enabled for this project in Google Cloud Console.") + print(" 4. The account used for authentication has access to this project.") + raise # Re-raise the exception to stop execution if initialization fails + except Exception as e: + # Catch other potential errors during initialization + print(f"An unexpected error occurred during Earth Engine initialization: {e}") + raise + +def get_boundary(asset_id='users/jonasnothnagel/pa_boundary'): + """ + Get the boundary geometry from the GEE asset. + Returns an ee.Geometry object. + """ + try: + print(f"Loading boundary from GEE asset: {asset_id}...") + fc = ee.FeatureCollection(asset_id) + if fc.size().getInfo() == 0: + print(f"Error: Asset {asset_id} is empty or inaccessible.") + return None + boundary_geom = fc.geometry().dissolve() + print("Successfully loaded boundary from GEE asset.") + return boundary_geom + except ee.EEException as e: + print(f"Error loading boundary asset {asset_id}: {e}") + return None + except Exception as e: + print(f"An unexpected error occurred loading the boundary asset: {e}") + return None + +def export_asset(image, asset_id, region, description=None, scale=30, crs='EPSG:3857'): + """Exports an image to a GEE asset, deleting if exists.""" + if description is None: + description = asset_id.split('/')[-1] + # Check and delete existing asset + try: + # Use ee.data.deleteAsset for deleting + asset_info = ee.data.getInfo(asset_id) # Check existence first + if asset_info: + print(f"Deleting existing asset: {asset_id}...") + ee.data.deleteAsset(asset_id) + print(f"Deleted existing asset: {asset_id}") + except ee.EEException as e: + # Catch specific exception for asset not found + if 'not found' in str(e).lower(): + print(f"Asset {asset_id} not found. Proceeding...") + else: + # Re-raise other GEE errors during check/delete attempt + print(f"GEE error checking/deleting asset {asset_id}: {e}. Proceeding attempt...") + except Exception as e: + # Catch unexpected errors during check/delete + print(f"Unexpected error checking/deleting asset {asset_id}: {e}. Proceeding attempt...") + + print(f"Starting export task to Asset: {description} (ID: {asset_id})") + task = ee.batch.Export.image.toAsset( + image=image.toFloat(), + description=description, + assetId=asset_id, + region=region, + scale=scale, + crs=crs, + maxPixels=1e13 + ) + task.start() + print(f"Task started (id: {task.id}). Check GEE Tasks.") + return task + +def export_table(collection, asset_id, description=None): + """Exports a FeatureCollection to a GEE asset.""" + if description is None: + description = asset_id.split('/')[-1] + # Check and delete existing asset + try: + asset_info = ee.data.getInfo(asset_id) # Check existence first + if asset_info: + print(f"Deleting existing asset table: {asset_id}...") + ee.data.deleteAsset(asset_id) + print(f"Deleted existing asset table: {asset_id}") + except ee.EEException as e: + if 'not found' in str(e).lower(): + print(f"Asset table {asset_id} not found. Proceeding...") + else: + print(f"GEE error checking/deleting asset table {asset_id}: {e}. Proceeding...") + except Exception as e: + print(f"Unexpected error checking/deleting asset table {asset_id}: {e}. Proceeding...") + + print(f"Starting export task to Table Asset: {description} (ID: {asset_id})") + task = ee.batch.Export.table.toAsset( + collection=collection, + description=description, + assetId=asset_id, + ) + task.start() + print(f"Table export task started (id: {task.id}). Check GEE Tasks.") + return task if __name__ == "__main__": main() \ No newline at end of file diff --git a/TrendAnomalyPrediction.py b/TrendAnomalyPrediction.py index 6e49c82..6cadf60 100644 --- a/TrendAnomalyPrediction.py +++ b/TrendAnomalyPrediction.py @@ -441,7 +441,7 @@ def calculate_predicted_values(trend_image, prediction_date, ref_dates): prediction_date: ee.Date object for the prediction target date. ref_dates: Dictionary mapping dataset type ('landsat', 'rain', 'sm', 'rh') to its trend reference start ee.Date. - + Returns: ee.Image with predicted values for each index (bands named 'index'). """ @@ -528,7 +528,7 @@ def get_present_data(boundary, present_start, present_end, rain_year_start, rain present_end: End date (YYYY-MM-DD) for Landsat, SMAP, ERA5. rain_year_start: Start date (YYYY-MM-DD) for the annual rainfall sum year. rain_year_end: End date (YYYY-MM-DD) for the annual rainfall sum year. - + Returns: ee.Image with observed values (bands named 'index'). """ From c20bcedad7c99fa61a900eb0c055c73e72523c98 Mon Sep 17 00:00:00 2001 From: jonas-nothnagel Date: Mon, 14 Apr 2025 16:32:04 +0200 Subject: [PATCH 13/15] updated readme --- README.md | 48 ++++++++++++++++++++++++++++-------------------- 1 file changed, 28 insertions(+), 20 deletions(-) diff --git a/README.md b/README.md index cc85323..c3a1082 100644 --- a/README.md +++ b/README.md @@ -16,28 +16,38 @@ This project analyzes forest fire vulnerability and predicts potential fire-pron - Utilize trend layers to predict high-risk fire zones in the near future. ## Execution Sequence + +**Note on `inputResampled.tif`:** The original README linked to a file named `inputResampled.tif` for multiple steps. Analysis of the code reveals this file is actually an **intermediate output** created *within* the Fire Vulnerability Mapping step (Step 2). It represents a combination and resampling of *all* features used in that step (including TrendFire outputs, NICFI, MODIS LST, DEM slope, roads). It is **not** a direct output of Step 1 (`TrendFire`) and **not** an input to Step 3 (`TrendAnomalyPrediction`). The descriptions below reflect the actual inputs/outputs based on the code logic. + ### 1. Trend Calculation (`Trendfire.js` / `TrendFire.py`) - - Calculates long-term (decadal) linear trends (Slope and Intercept) for various environmental indicators within the study area (`pa_boundary`). + - **Goal:** Calculate long-term (decadal) linear trends (Slope and Intercept) for various environmental indicators. + - **Input:** Study area boundary (`pa_boundary` asset). - **Datasets Used:** Landsat 8 (Vegetation/Burn Indices, LST), CHIRPS Daily (Precipitation), SMAP (Soil Moisture), ERA5-Land Monthly (Relative Humidity). - - **Method:** Filters datasets by date and boundary, applies relevant masking (e.g., cloud masking for Landsat), calculates indices, fits a linear trend over time (`ee.Reducer.linearFit()`), and exports the resulting slope/intercept bands. - - **Outputs (JS Version):** Individual GEE assets for each index trend (e.g., `users/.../Trend2023_ndvi`, `users/.../Trend2022_rain_new`). - - **Outputs (Python Version):** Merged GEE assets containing all trend bands (e.g., `users/.../TrendFirePy_FullExtent_py`) and optionally individual trend assets. + - **Method:** Filters datasets by date and boundary, applies masking, calculates indices, fits a linear trend (`ee.Reducer.linearFit()`). + - **Outputs:** Individual GEE assets for each calculated trend (e.g., `users/.../Trend2023_ndvi` (Slope+Intercept), `users/.../Trend2022_rain_new` (Slope+Intercept), etc.). The Python version can optionally export a single merged asset containing all trend bands. -### 2. Fire Vulnerability Mapping (`FireVulnerability.js` / `FireVulnerability.py` - *Python version TBD*) +### 2. Fire Vulnerability Mapping (`FireVulnerability.js` / `FireVulnerability.py`) + - **Goal:** Map the inherent, long-term susceptibility of areas to fire based on environmental factors and historical fire patterns. - **Inputs:** - - Trend layers (often combined into a single multi-band image like `inputResampled.tif`) - - Roads layer - - DEM (Digital Elevation Model) derived slope - - Historical fire event datasets (`fire13_19`, `fire20_23`) - - Other potential factors (e.g., Planet NICFI, MODIS LST) - - **Method:** Samples input features at locations of historical fires and random non-fire points. Trains a classifier (e.g., Random Forest) to predict fire risk categories based on the input features. Applies the trained classifier to the entire study area. - - **Output:** Fire risk classification map (e.g., Low/Moderate/High risk) at 30m resolution. + - Individual Trend layers (from Step 1). + - Additional Trend Layers (calculated within this script): NICFI (R/G/B/N/NDVI trends), MODIS LST (Day/Night trends). + - Static/Quasi-Static Layers: DEM slope, Roads layer. + - Historical Fire Points (e.g., `fire13_19`, `fire20_23` assets). + - **Method:** + 1. Loads all input layers. + 2. Combines all input layers/bands into a single multi-band image. + 3. Resamples this combined image to a consistent grid (e.g., 30m EPSG:3857) - *This resampled image is the equivalent of the data in `inputResampled.tif` and is exported by the Python script as `FireVulnerability_InputsResampled_py`.* + 4. Generates random points (labelled non-fire) and categorizes historical fire points (e.g., based on 'Delta T' property). + 5. Samples the *resampled* multi-band image at the fire/non-fire point locations to create training/validation data. + 6. Trains a classifier (e.g., Random Forest) using the sampled data. + 7. Applies the trained classifier to the *resampled* multi-band image covering the entire study area. + - **Output:** Fire risk classification map (e.g., `FireVulnerability_py` asset) showing predicted vulnerability levels (e.g., Low/High risk) across the study area. ### 3. Fire Prediction using Trend Anomalies (`TrendAnomalyPrediction.js` / `TrendAnomalyPrediction.py`) - - **Goal:** Identify areas behaving abnormally compared to their long-term trends, potentially indicating heightened near-term fire risk. + - **Goal:** Identify areas behaving abnormally compared to their long-term trends *just before* a period of interest, potentially indicating heightened *near-term* fire risk. - **Inputs:** - - Individual Trend layers (Slope/Intercept bands from Step 1, e.g., `users/.../Trend2023_ndvi`) - - Study area boundary (`pa_boundary`) + - Individual Trend layers (Slope/Intercept bands from Step 1). + - Study area boundary (`pa_boundary` asset). - **Methodology:** 1. **Load Trends:** Loads the individual trend assets (Slopes and Intercepts) generated in Step 1. 2. **Predict Expected Conditions:** Uses the linear trend formula (`Predicted = Slope * Time_Difference + Intercept`) to predict the expected value for each indicator for a near-future date (e.g., March 1st, 2023), based on the reference start date of each trend. @@ -45,11 +55,9 @@ This project analyzes forest fire vulnerability and predicts potential fire-pron 4. **Calculate Anomaly:** Computes the difference between the observed conditions and the predicted conditions (`Anomaly = Observed - Predicted`). 5. **Identify Hotspots:** Applies thresholds to specific anomaly bands (e.g., `ST_B10_Anomaly > 3.2`, `ndmi_Anomaly < -0.15`) to flag areas where conditions deviate significantly from the trend in a way that increases fire risk (e.g., significantly hotter or drier than expected). - **Outputs:** - - **Full Anomaly Image (Python & modified JS):** GEE asset containing anomaly bands (e.g., `ndvi_Anomaly`, `ST_B10_Anomaly`). Pixel values represent the difference between observed and predicted conditions. - - **Hotspot Images (Python & JS):** Derived boolean/masked images showing areas exceeding specific anomaly thresholds (e.g., `TrendAnomaly_STB10_Hotspot_py`, `TrendAnomaly_NDMI_Hotspot_py`). - - **Interpretation:** Hotspots indicate areas where recent conditions are unusually conducive to fire compared to the established long-term trend for that location and time of year. For example: - - *Thermal Hotspot (`ST_B10_Anomaly > threshold`):* Area is significantly hotter than expected. - - *Moisture Hotspot (`ndmi_Anomaly < threshold`):* Area is significantly drier than expected. + - **Full Anomaly Image:** GEE asset containing anomaly bands (e.g., `TrendAnomaly_py`, `TrendAnomaly_js`). Pixel values represent the difference between observed and predicted conditions. + - **Hotspot Images:** Derived boolean/masked images showing areas exceeding specific anomaly thresholds (e.g., `TrendAnomaly_STB10_Hotspot_py`). + - **Interpretation:** Hotspots indicate areas where recent conditions are unusually conducive to fire compared to the established long-term trend for that location and time of year. Best interpreted alongside the vulnerability map from Step 2. ## Potential Improvements & Next Steps Based on the anomaly prediction methodology: From 588c05ea45cdb60f44cc314254efd86121555586 Mon Sep 17 00:00:00 2001 From: jonas-nothnagel Date: Mon, 14 Apr 2025 16:34:23 +0200 Subject: [PATCH 14/15] updated readme --- README.md | 83 +++++++++++++++++++++++++------------------------------ 1 file changed, 38 insertions(+), 45 deletions(-) diff --git a/README.md b/README.md index c3a1082..bb5ac5e 100644 --- a/README.md +++ b/README.md @@ -3,70 +3,63 @@ **Forest fire vulnerability and prediction using vegetation indices anomaly, surface temperature anomaly, and meteorological parameters' anomaly in Google Earth Engine (GEE).** ## Overview -This project analyzes forest fire vulnerability and predicts potential fire-prone areas using long-term trends in vegetation indices, burn indices, and meteorological parameters. The methodology leverages Google Earth Engine (GEE) for data processing and analysis. +This project analyzes forest fire vulnerability and predicts potential fire-prone areas using long-term trends in vegetation indices, burn indices, and meteorological parameters. The methodology leverages Google Earth Engine (GEE) for data processing and analysis, with implementations available in both JavaScript (original) and Python. -## Methodology -1. **Calculate Decadal Trends** - - Generate long-term trends for vegetation and burn indices using Landsat imagery. - - Compute trends for meteorological parameters such as rainfall and relative humidity. -2. **Fire Vulnerability Mapping** - - Use historical fire events and trend layers to assess vulnerability. - - Identify areas most susceptible to fires. -3. **Fire Prediction** - - Utilize trend layers to predict high-risk fire zones in the near future. +## Methodology Summary +1. **Calculate Decadal Trends:** Generate long-term linear trends (Slope and Intercept) for various environmental indicators (vegetation indices, LST, precipitation, soil moisture, humidity) using historical satellite and climate data. +2. **Map Fire Vulnerability:** Develop a spatial model using machine learning (Random Forest) trained on historical fire locations, environmental trends, and static factors (topography, roads) to map the inherent susceptibility of different areas to fire. +3. **Predict Near-Term Fire Risk using Anomalies:** Identify areas where recent environmental conditions deviate significantly from their long-term trends, potentially indicating heightened short-term fire risk. -## Execution Sequence - -**Note on `inputResampled.tif`:** The original README linked to a file named `inputResampled.tif` for multiple steps. Analysis of the code reveals this file is actually an **intermediate output** created *within* the Fire Vulnerability Mapping step (Step 2). It represents a combination and resampling of *all* features used in that step (including TrendFire outputs, NICFI, MODIS LST, DEM slope, roads). It is **not** a direct output of Step 1 (`TrendFire`) and **not** an input to Step 3 (`TrendAnomalyPrediction`). The descriptions below reflect the actual inputs/outputs based on the code logic. +## Execution Sequence & Scripts ### 1. Trend Calculation (`Trendfire.js` / `TrendFire.py`) - - **Goal:** Calculate long-term (decadal) linear trends (Slope and Intercept) for various environmental indicators. - - **Input:** Study area boundary (`pa_boundary` asset). + - **Goal:** Calculate long-term (typically decadal) linear trends (Slope and Intercept) for key environmental indicators. + - **Input:** Study area boundary (e.g., `pa_boundary` GEE asset). - **Datasets Used:** Landsat 8 (Vegetation/Burn Indices, LST), CHIRPS Daily (Precipitation), SMAP (Soil Moisture), ERA5-Land Monthly (Relative Humidity). - - **Method:** Filters datasets by date and boundary, applies masking, calculates indices, fits a linear trend (`ee.Reducer.linearFit()`). - - **Outputs:** Individual GEE assets for each calculated trend (e.g., `users/.../Trend2023_ndvi` (Slope+Intercept), `users/.../Trend2022_rain_new` (Slope+Intercept), etc.). The Python version can optionally export a single merged asset containing all trend bands. + - **Method:** Filters datasets by date and boundary, applies masking (e.g., clouds), calculates relevant indices/parameters, and fits a linear trend over time for each pixel using `ee.Reducer.linearFit()`. + - **Output:** GEE assets containing the calculated Slope and Intercept bands for each indicator (e.g., `users/.../Trend2023_ndvi`, `users/.../Trend2022_rain_new`). The Python version can optionally export a single merged asset containing all trend bands. ### 2. Fire Vulnerability Mapping (`FireVulnerability.js` / `FireVulnerability.py`) - **Goal:** Map the inherent, long-term susceptibility of areas to fire based on environmental factors and historical fire patterns. - **Inputs:** - - Individual Trend layers (from Step 1). - - Additional Trend Layers (calculated within this script): NICFI (R/G/B/N/NDVI trends), MODIS LST (Day/Night trends). - - Static/Quasi-Static Layers: DEM slope, Roads layer. - - Historical Fire Points (e.g., `fire13_19`, `fire20_23` assets). + - *Individual Trend Layers:* Slope and Intercept assets generated in Step 1. + - *Additional Trend Layers:* Calculated within this script, such as Planet-NICFI visual/NIR/NDVI trends and MODIS LST Day/Night trends (Note: NICFI data access may be restricted). + - *Static/Quasi-Static Layers:* DEM-derived slope, Roads layer (rasterized). + - *Historical Fire Points:* GEE assets containing locations of past fires (e.g., `fire13_19`, `fire20_23`). - **Method:** - 1. Loads all input layers. - 2. Combines all input layers/bands into a single multi-band image. - 3. Resamples this combined image to a consistent grid (e.g., 30m EPSG:3857) - *This resampled image is the equivalent of the data in `inputResampled.tif` and is exported by the Python script as `FireVulnerability_InputsResampled_py`.* - 4. Generates random points (labelled non-fire) and categorizes historical fire points (e.g., based on 'Delta T' property). - 5. Samples the *resampled* multi-band image at the fire/non-fire point locations to create training/validation data. - 6. Trains a classifier (e.g., Random Forest) using the sampled data. - 7. Applies the trained classifier to the *resampled* multi-band image covering the entire study area. - - **Output:** Fire risk classification map (e.g., `FireVulnerability_py` asset) showing predicted vulnerability levels (e.g., Low/High risk) across the study area. + 1. Loads all input layers (trends, static data). + 2. Combines these layers into a single multi-band predictor image. + 3. Resamples the predictor image to a consistent grid (e.g., 30m EPSG:3857). An asset containing this intermediate resampled image (equivalent to the original project's `inputResampled.tif`) is exported by the Python script as `FireVulnerability_InputsResampled_py`. + 4. Generates random points (labelled non-fire) and processes historical fire points (potentially categorizing them based on attributes like 'Delta T'). + 5. Samples the predictor values from the *resampled* image at the fire/non-fire point locations to create training/validation datasets. + 6. Trains a Random Forest classifier using the sampled data to learn the relationship between predictor values and fire occurrence/category. + 7. Applies the trained classifier to the entire *resampled* predictor image. + - **Output:** A fire risk classification map (e.g., `FireVulnerability_py` asset) showing predicted vulnerability levels across the study area. ### 3. Fire Prediction using Trend Anomalies (`TrendAnomalyPrediction.js` / `TrendAnomalyPrediction.py`) - - **Goal:** Identify areas behaving abnormally compared to their long-term trends *just before* a period of interest, potentially indicating heightened *near-term* fire risk. + - **Goal:** Identify areas behaving abnormally compared to their long-term trends *just before* a period of interest (e.g., the start of fire season), potentially indicating heightened *near-term* fire risk. - **Inputs:** - Individual Trend layers (Slope/Intercept bands from Step 1). - Study area boundary (`pa_boundary` asset). - **Methodology:** - 1. **Load Trends:** Loads the individual trend assets (Slopes and Intercepts) generated in Step 1. - 2. **Predict Expected Conditions:** Uses the linear trend formula (`Predicted = Slope * Time_Difference + Intercept`) to predict the expected value for each indicator for a near-future date (e.g., March 1st, 2023), based on the reference start date of each trend. - 3. **Load Observed Conditions:** Loads and processes the most recent available satellite/climate data before the prediction date (e.g., Landsat, SMAP, ERA5 for Feb 2023; CHIRPS sum for 2022). - 4. **Calculate Anomaly:** Computes the difference between the observed conditions and the predicted conditions (`Anomaly = Observed - Predicted`). - 5. **Identify Hotspots:** Applies thresholds to specific anomaly bands (e.g., `ST_B10_Anomaly > 3.2`, `ndmi_Anomaly < -0.15`) to flag areas where conditions deviate significantly from the trend in a way that increases fire risk (e.g., significantly hotter or drier than expected). + 1. **Load Trends:** Loads the Slope and Intercept assets generated in Step 1. + 2. **Predict Expected Conditions:** Uses the linear trend formula (`Predicted = Slope * Time_Difference + Intercept`) to estimate the expected value for each indicator at a specific near-future date (e.g., March 1st), based on the trend's reference start date. + 3. **Load Observed Conditions:** Loads and processes the most recent available observational data *before* the prediction date (e.g., Landsat, SMAP, ERA5 for the preceding month; CHIRPS sum for the preceding year). + 4. **Calculate Anomaly:** Computes the difference: `Anomaly = Observed - Predicted`. + 5. **Identify Hotspots:** Applies thresholds to specific anomaly bands (e.g., `ST_B10_Anomaly > 3.2`, `ndmi_Anomaly < -0.15`) to flag areas where conditions deviate significantly from the trend in a way suggesting increased fire risk (e.g., hotter or drier than expected). - **Outputs:** - - **Full Anomaly Image:** GEE asset containing anomaly bands (e.g., `TrendAnomaly_py`, `TrendAnomaly_js`). Pixel values represent the difference between observed and predicted conditions. - - **Hotspot Images:** Derived boolean/masked images showing areas exceeding specific anomaly thresholds (e.g., `TrendAnomaly_STB10_Hotspot_py`). - - **Interpretation:** Hotspots indicate areas where recent conditions are unusually conducive to fire compared to the established long-term trend for that location and time of year. Best interpreted alongside the vulnerability map from Step 2. + - *Full Anomaly Image:* GEE asset containing all calculated anomaly bands (e.g., `TrendAnomaly_py`). Pixel values represent the difference between observed and predicted conditions. + - *Hotspot Images:* Derived boolean/masked images showing areas exceeding specific anomaly thresholds (e.g., `TrendAnomaly_STB10_Hotspot_py`). + - **Interpretation:** Hotspots indicate areas where recent conditions are unusually conducive to fire compared to that location's historical trend for that time of year. This provides a dynamic, short-term risk indicator, ideally interpreted alongside the longer-term vulnerability map from Step 2. ## Potential Improvements & Next Steps -Based on the anomaly prediction methodology: -* **Trend Model:** Explore non-linear trend models (e.g., harmonic regression) if significant seasonality or non-linear changes are present in the data, potentially providing a more accurate baseline for anomaly calculation. -* **Data Gaps:** Investigate methods to fill gaps in input data (especially cloud-related gaps in Landsat) using techniques like temporal interpolation or data fusion (e.g., using MODIS) to allow for trend calculation in more areas. -* **Threshold Validation:** Validate and potentially calibrate the anomaly thresholds used for hotspot detection against historical fire data or field observations to improve their local accuracy. -* **Temporal Resolution:** Incorporate more frequent or near-real-time data sources if available to reduce the lag between observation and prediction. -* **Input Variables:** Evaluate the inclusion of additional static or dynamic variables known to influence fire risk (e.g., detailed fuel type maps, wind data, human activity indicators) in either the trend analysis or the final vulnerability/prediction models. -* **Model Integration:** Combine the trend anomaly results with the vulnerability map from Step 2 to create a more comprehensive risk assessment (e.g., prioritizing areas that are both historically vulnerable *and* currently showing high-risk anomalies). +* **Trend Model:** Explore non-linear trend models (e.g., harmonic regression) if significant seasonality or non-linear changes are present, potentially providing a more accurate baseline for anomaly calculation. +* **Data Gaps:** Investigate methods to fill gaps in input data (especially cloud-related gaps in Landsat) using techniques like temporal interpolation or data fusion to allow for trend calculation in more areas. +* **Vulnerability Model Inputs:** Evaluate the impact of different input features (e.g., including/excluding NICFI/MODIS LST based on availability/quality) on the vulnerability model performance. +* **Training Data Strategy:** Refine the generation of non-fire points (e.g., using buffers, stratification) and explore different fire point categorization schemes for training the vulnerability model. +* **Anomaly Thresholds:** Validate and potentially calibrate the anomaly thresholds used for hotspot detection against historical fire data or field observations to improve their local accuracy and relevance. +* **Temporal Resolution:** Incorporate more frequent or near-real-time data sources if available to reduce the lag between observation and prediction in the anomaly step. +* **Model Integration:** Combine the trend anomaly results with the vulnerability map to create a more comprehensive risk assessment (e.g., prioritizing areas that are both historically vulnerable *and* currently showing high-risk anomalies). This workflow enables proactive fire risk management by identifying vulnerable areas based on historical patterns and predictive modeling using trend anomalies. From 114a2b459d91d9af40ecf656770a9c8655c66fc3 Mon Sep 17 00:00:00 2001 From: jonas-nothnagel Date: Fri, 30 May 2025 13:43:02 +0200 Subject: [PATCH 15/15] Save current work before merging upstream changes --- .gitignore | 3 + FireVulnerability.py | 583 +++++++++++++++++++--------------- TrendAnomalyPrediction.py | 2 +- data/roadProximity_Latest.tif | Bin 0 -> 19506756 bytes 4 files changed, 332 insertions(+), 256 deletions(-) create mode 100644 data/roadProximity_Latest.tif diff --git a/.gitignore b/.gitignore index bf8cf5c..57bf9b7 100644 --- a/.gitignore +++ b/.gitignore @@ -1,3 +1,6 @@ .ipynb_checkpoints/ data/TrendFirePy_output.tif data/inputResampled.tif +.specstory/ +.md +.cursorindexingignore diff --git a/FireVulnerability.py b/FireVulnerability.py index 2f777d0..86f136d 100644 --- a/FireVulnerability.py +++ b/FireVulnerability.py @@ -356,151 +356,196 @@ def prepare_training_data(fire_points_input, boundary, predictor_image, label_pr num_random_points=90, random_points_seed=42, \ moderate_risk_limit=70, use_risk_categories=True): """ - Prepares the labelled training/validation data by sampling predictors at fire and non-fire locations. + Prepares training and validation data by combining categorized fire points + with generated non-fire points and sampling predictor values. Args: - fire_points_input: ee.FeatureCollection of historical fire points (e.g., 2013-2022). - Must contain a 'Delta T' property if use_risk_categories is True. - boundary: ee.Geometry of the study area. - predictor_image: ee.Image containing all predictor bands (resampled). - label_property: String, the name of the property to store the class label (e.g., 'RiskNumeric'). - num_random_points: Integer, the number of non-fire points to generate. - random_points_seed: Integer, seed for reproducibility of random points. - moderate_risk_limit: Integer, limit for moderate risk points (matches JS logic). - use_risk_categories: Boolean, if True, categorize fire points based on 'Delta T'. - If False, assign label 1 to all fire points. + fire_points_input: ee.FeatureCollection of historical fire points. + Should contain 'Delta T' if use_risk_categories=True. + boundary: ee.Geometry defining the study area. + predictor_image: ee.Image containing all predictor bands. + label_property: String, the name of the property to use for classification label (e.g., 'RiskNumeric'). + num_random_points: Integer, number of random non-fire points to generate. + random_points_seed: Integer, seed for random point generation. + moderate_risk_limit: Integer, threshold for Delta T to limit moderate risk points (if used). + use_risk_categories: Boolean, if True, categorize fires (0=NoFire, 1=Low, 2=Moderate, 3=High). + If False, label all fires as 1 (binary fire/no-fire). Returns: - An ee.FeatureCollection ready for splitting into training/validation, or None on error. + An ee.FeatureCollection containing sampled points with predictor values and the class label, + or None on error. """ - print("Preparing training data...") - - if predictor_image is None: - print("Error: Predictor image is missing.") - return None - if fire_points_input is None: - print("Error: Input fire points are missing.") - return None - + print("\nPreparing training data...") try: + # --- Categorize Fire Points --- + print("Categorizing fire points...") + no_fire_label = 0 + fire_label = 1 # Used if use_risk_categories is False + + if use_risk_categories: + # Use tqdm to show progress for client-side mapping/filtering if complex + # Note: The actual .map/.filter happens server-side, so tqdm only tracks + # the *creation* of these instructions, not their execution time. + print(" Defining fire risk categories (Low, Moderate, High)...") + low_risk = fire_points_input.filter(ee.Filter.rangeContains('Delta T', 0, 25))\ + .map(lambda f: f.set(label_property, 1).set('RiskCategory', 'Low Risk')) + moderate_risk = fire_points_input.filter(ee.Filter.rangeContains('Delta T', 25, 35))\ + .map(lambda f: f.set(label_property, 2).set('RiskCategory', 'Moderate Risk'))\ + .limit(moderate_risk_limit) # Limit moderate risk points like JS + high_risk = fire_points_input.filter(ee.Filter.greaterThanOrEquals('Delta T', 35))\ + .map(lambda f: f.set(label_property, 3).set('RiskCategory', 'High Risk')) + + # Get counts (these trigger computation) + print(" Fetching category counts...") + low_count = low_risk.size().getInfo() + mod_count = moderate_risk.size().getInfo() + high_count = high_risk.size().getInfo() + print(f" Low Risk (<25): {low_count}") + print(f" Moderate Risk (25-35, limited to {moderate_risk_limit}): {mod_count}") + print(f" High Risk (>=35): {high_count}") + + # Combine categorized fire points + categorized_fires = low_risk.merge(moderate_risk).merge(high_risk) + total_fires = categorized_fires.size().getInfo() + print(f" Total categorized fire points used: {total_fires}") + + else: + # Binary classification: Label all input fires as 1 + categorized_fires = fire_points_input.map(lambda f: f.set(label_property, fire_label)) + total_fires = categorized_fires.size().getInfo() + print(f" Using binary classification. Total fire points: {total_fires}") + # --- Generate Non-Fire Points --- - # Assumption: Random points represent non-fire areas (Risk = 0). - # Improvement Note: Could exclude buffers around known fires. - print(f" Generating {num_random_points} random non-fire points (label 0)...") - non_fire_points = ee.FeatureCollection.randomPoints( + print(f"Generating {num_random_points} random non-fire points...") + random_pts = ee.FeatureCollection.randomPoints( region=boundary, points=num_random_points, seed=random_points_seed - ).map(lambda f: f.set(label_property, 0)) - print(f" Generated {non_fire_points.size().getInfo()} non-fire points.") - - # --- Prepare Fire Points --- - categorized_fire_points = None - if use_risk_categories: - # Assumption: fire_points_input has a 'Delta T' property. - # This categorization matches the logic in FireVulnerability.js - print(" Categorizing fire points based on 'Delta T'...") - try: - low_risk = fire_points_input.filter(ee.Filter.rangeContains('Delta T', 0, 25)).map(lambda f: f.set(label_property, 1)) - moderate_risk = fire_points_input.filter(ee.Filter.rangeContains('Delta T', 25, 35)).map(lambda f: f.set(label_property, 2)).limit(moderate_risk_limit) - high_risk = fire_points_input.filter(ee.Filter.gte('Delta T', 35)).map(lambda f: f.set(label_property, 3)) - print(f" Low Risk (1): {low_risk.size().getInfo()}, Mod Risk (2): {moderate_risk.size().getInfo()}, High Risk (3): {high_risk.size().getInfo()}") - - # IMPORTANT: JS script only merged high_risk (3) and non_fire (0) - # Replicating that specific behaviour for direct comparison: - categorized_fire_points = high_risk - print(f" Using only High Risk fire points (label 3) for sampling, count: {categorized_fire_points.size().getInfo()}") - # If you wanted all categories: - # categorized_fire_points = ee.FeatureCollection([]).merge(low_risk).merge(moderate_risk).merge(high_risk) - - except ee.EEException as e: - if "Property 'Delta T' does not exist" in str(e): - print(" Error: 'Delta T' property not found in fire points. Cannot categorize risk.") - print(" Assigning label 1 to all fire points instead.") - categorized_fire_points = fire_points_input.map(lambda f: f.set(label_property, 1)) - else: - raise e # Re-raise other GEE errors - else: - print(" Assigning label 1 to all fire points (risk categorization disabled).") - categorized_fire_points = fire_points_input.map(lambda f: f.set(label_property, 1)) - - if categorized_fire_points is None or categorized_fire_points.size().getInfo() == 0: - print("Error: No valid categorized fire points found.") + ) + non_fire_pts = random_pts.map(lambda feat: feat.set(label_property, no_fire_label)) + non_fire_count = non_fire_pts.size().getInfo() + print(f" Generated {non_fire_count} non-fire points.") + + # --- Combine Fire and Non-Fire Points --- + training_validation_points = categorized_fires.merge(non_fire_pts) + total_points = training_validation_points.size().getInfo() + print(f"Total points for sampling: {total_points}") + + # --- Sample Predictor Values --- + print("\n>>> Sending request to GEE: Sample predictor values at points...") # Log Before + if predictor_image is None: + print("Error: Predictor image is missing.") + return None + if training_validation_points is None or total_points == 0: + print("Error: No training/validation points available for sampling.") return None - # --- Combine Points and Sample Predictors --- - # Merge the non-fire points and the selected fire points - combined_points = non_fire_points.merge(categorized_fire_points) - print(f" Total points for sampling: {combined_points.size().getInfo()}") - - print(" Sampling predictor values at point locations...") - # Get predictor band names - predictor_names = predictor_image.bandNames() - if not predictor_names.getInfo(): # Check if list is empty - print("Error: Predictor image has no bands.") - return None - - # Properties to keep from the points collections - properties_to_keep = [label_property] + # Ensure predictor image bands are float for sampling/training + predictor_image = predictor_image.toFloat() - training_data = predictor_image.select(predictor_names).sampleRegions( - collection=combined_points, + # Define properties to keep (only the label) + properties_to_keep = [label_property] + + # Sample the regions + sampled_data = predictor_image.sampleRegions( + collection=training_validation_points, properties=properties_to_keep, - scale=predictor_image.projection().nominalScale(), # Use image scale - geometries=False # Typically don't need geometry for training + scale=predictor_image.projection().nominalScale(), # Use image native scale + geometries=False # Don't need geometries for training ) - + + # Force execution and check size BEFORE adding random column + print(">>> Waiting for GEE: Sampling predictor values...") + sampled_size_check = sampled_data.size().getInfo() + print(f"<<< GEE Response: Sampling returned {sampled_size_check} features.") # Log After + + if sampled_size_check == 0: + print("Error: Sampling returned no features. Check predictor image, points, and permissions.") + return None + # Add a random column for splitting later - training_data = training_data.randomColumn() - - print(f"Sampling complete. Final features count: {training_data.size().getInfo()}") - # print("Sample feature properties:", training_data.first().propertyNames().getInfo()) # Optional verification - - return training_data + print("Adding random column for train/test split...") + sampled_data = sampled_data.randomColumn(columnName='random', seed=random_points_seed) # Explicit column name and seed + # Verify random column exists (optional check) + # print("Properties after adding random column:", sampled_data.first().propertyNames().getInfo()) + + print("Training data preparation complete.") + return sampled_data - except ee.EEException as e: + except ee.EEException as e: print(f"GEE error during training data preparation: {e}") return None - except Exception as e: + except Exception as e: print(f"Unexpected error during training data preparation: {e}") return None # Function to train a Random Forest classifier -def train_random_forest(training_data, num_trees=500): +def train_classifier(training_data, class_property, input_properties, num_trees=1000, variables_per_split=1, seed=42): """ - Train a Random Forest classifier using Earth Engine. - + Trains the ee.Classifier.smileRandomForest classifier. + Args: - training_data: ee.FeatureCollection with training data - num_trees: Number of trees in the Random Forest - + training_data: ee.FeatureCollection used for training. + class_property: String, the name of the property containing the class label. + input_properties: List of strings, names of the predictor properties/bands. + num_trees: Integer, number of trees in the forest. + variables_per_split: Integer, number of variables to consider at each split. + Set to None or 0 to use sqrt(num_variables). + JS used 1. + seed: Integer, seed for reproducibility. + Returns: - ee.Classifier.RandomForest + A trained ee.Classifier object, or None on error. """ print(f"Training Random Forest classifier with {num_trees} trees...") - # Get the names of the predictor bands - predictor_names = training_data.first().propertyNames().removeAll(['system:index', 'RiskNumeric']).getInfo() - - # Create and train the classifier - classifier = ee.Classifier.smileRandomForest( - numberOfTrees=num_trees, - minLeafPopulation=1, - bagFraction=0.7, - seed=42 - ).train( - features=training_data, - classProperty='RiskNumeric', - inputProperties=predictor_names - ) - - # Print variable importance - variable_importance = classifier.explain().get('importance').getInfo() - print("Variable importance:") - for var, importance in zip(predictor_names, variable_importance): - print(f" {var}: {importance:.4f}") - - return classifier, predictor_names + if training_data is None: + print("Error: Training data is missing.") + return None + if not input_properties: + print("Error: Input properties list is empty.") + return None + + try: + classifier = ee.Classifier.smileRandomForest( + numberOfTrees=num_trees, + variablesPerSplit=variables_per_split, # Match JS, using 1 + # minLeafPopulation=1, # Default is 1 + # bagFraction=0.5, # Default is 0.5 + seed=seed + ).train( + features=training_data, + classProperty=class_property, + inputProperties=input_properties + ) + + print("Classifier trained successfully.") + + # Optional: Explain the classifier (can be computationally intensive) + # try: + # explanation = classifier.explain() + # print("Classifier explanation:", explanation.getInfo()) + # importance = explanation.get('importance') + # if importance: + # print("Variable Importance:") + # importance_info = importance.getInfo() + # # Ensure importance_info is a dictionary before iterating + # if isinstance(importance_info, dict): + # for prop, imp in importance_info.items(): + # print(f" {prop}: {imp}") + # else: + # print(" Could not retrieve importance details in expected format.") + # except Exception as explain_e: + # print(f"Could not get classifier explanation: {explain_e}") + + return classifier + + except ee.EEException as e: + print(f"GEE error during classifier training: {e}") + return None + except Exception as e: + print(f"Unexpected error during classifier training: {e}") + return None # Function to validate the Random Forest model def validate_model(classifier, predictor_names, validation_data): @@ -622,27 +667,37 @@ def main(): # --- Configuration --- PROJECT_ID = 'ee-crop-health-telangana' BOUNDARY_ASSET_ID = 'users/jonasnothnagel/pa_boundary' - FIRE_ASSET_BASE = 'users/jonasnothnagel/' + FIRE_ASSET_BASE = 'users/jonasnothnagel/' # Base for fire points (e.g., fire13_19, fire20_23) DEM_ASSET_ID = 'users/jonasnothnagel/dem' ROAD_ASSET_ID = 'users/jonasnothnagel/roads' - TRENDFIRE_ASSET_BASE = 'users/jonasnothnagel/' # Base for JS TrendFire outputs + TRENDFIRE_ASSET_BASE = 'users/jonasnothnagel/' # Base for JS TrendFire outputs (e.g., Trend2023_ndvi) NICFI_COLLECTION = 'projects/planet-nicfi/assets/basemaps/asia' MODIS_LST_COLLECTION = 'MODIS/061/MOD11A1' + # --- Option to use pre-computed resampled input --- # + USE_PRECOMPUTED_RESAMPLED = True # Set to True to load the asset below + PRECOMPUTED_RESAMPLED_ASSET = 'users/jonasnothnagel/inputResampled' # Asset ID of the uploaded 40-band tif + # ---------------------------------------------------- # + OUTPUT_ASSET_BASE = 'users/jonasnothnagel/' VULNERABILITY_MAP_ASSET = OUTPUT_ASSET_BASE + 'FireVulnerability_py' - RESAMPLED_FEATURES_ASSET = OUTPUT_ASSET_BASE + 'FireVulnerability_InputsResampled_py' + # Asset ID for the resampled image *generated* by this script (if USE_PRECOMPUTED_RESAMPLED=False) + GENERATED_RESAMPLED_FEATURES_ASSET = OUTPUT_ASSET_BASE + 'FireVulnerability_InputsGenerated_py' TRAINING_POINTS_ASSET = OUTPUT_ASSET_BASE + 'FireVulnerability_TrainingPoints_py' GRID_SCALE = 30 GRID_CRS = 'EPSG:3857' - NUM_RANDOM_POINTS = 90 - RF_TREES = 1000 - RF_VAR_SPLIT = 1 + NUM_RANDOM_POINTS = 90 # Match JS + RF_TREES = 1000 # Match JS + RF_VAR_SPLIT = 1 # Match JS TRAIN_TEST_SPLIT_RATIO = 0.8 CLASS_LABEL = 'RiskNumeric' - # Decide if using Delta T for categories or just fire/no-fire (1/0) - USE_RISK_CATEGORIES = True # Set to False to label all fires as 1 + # Use Delta T categories (True) or binary fire/no-fire (False) + # JS used only high risk (Delta T >= 35) vs non-fire (0). Setting this to True enables categorization, + # but prepare_training_data currently merges all categories or uses binary. + # To exactly match JS sampling, logic inside prepare_training_data needs adjustment. + USE_RISK_CATEGORIES = True + RANDOM_SEED = 42 # For reproducibility in random points and RF training # --- Initialization --- initialize_ee(PROJECT_ID) @@ -653,98 +708,182 @@ def main(): # --- Processing Steps --- print("\n--- Starting Fire Vulnerability Workflow ---") - # 1. Load Fire Points - fire_pts_processed = load_fire_points(FIRE_ASSET_BASE, boundary) - if fire_pts_processed is None: - print("Exiting: Failed to load fire points.") - return - - # 2. Process NICFI - nicfi_trends = process_nicfi(boundary) - # Handle potential None return if access fails or errors occur - if nicfi_trends is None: - print("Warning: Failed to process NICFI trends. Proceeding without them.") - - # 3. Process MODIS LST - modis_trends = process_modis_lst(boundary) - if modis_trends is None: - print("Warning: Failed to process MODIS LST trends. Proceeding without them.") - - # 4. Load DEM Slope - dem_slope = load_dem_slope(DEM_ASSET_ID, boundary) - if dem_slope is None: - print("Warning: Failed to process DEM slope. Proceeding without it.") - - # 5. Load Roads - roads_img = load_roads(ROAD_ASSET_ID, boundary) - if roads_img is None: - print("Warning: Failed to process Roads layer. Proceeding without it.") - - # 6. Load TrendFire Outputs - trendfire_img = load_trendfire_outputs(TRENDFIRE_ASSET_BASE, boundary) - if trendfire_img is None: - print("Exiting: Failed to load TrendFire outputs.") - return + input_resampled = None # This will hold the final predictor image for sampling/classification + fire_pts_processed = None # To hold loaded fire points + + # --- Mode 1: Load Precomputed Resampled Input --- # + if USE_PRECOMPUTED_RESAMPLED: + print(f"---> Mode: Using Precomputed Input <---") + print(f"Attempting to load pre-computed resampled input: {PRECOMPUTED_RESAMPLED_ASSET}") + try: + input_resampled = ee.Image(PRECOMPUTED_RESAMPLED_ASSET).toFloat() # Ensure float + # Verify it has bands + if not input_resampled.bandNames().getInfo(): + raise ee.EEException(f"Loaded asset {PRECOMPUTED_RESAMPLED_ASSET} has no bands.") + print("Successfully loaded pre-computed resampled input.") + print("Skipping intermediate data loading, combining, and resampling steps.") + # Load fire points separately as they are needed for training data prep + fire_pts_processed = load_fire_points(FIRE_ASSET_BASE, boundary) + if fire_pts_processed is None: + print("Error: Failed to load fire points even when using precomputed input. Exiting.") + return + except ee.EEException as e: + print(f"Error loading pre-computed asset '{PRECOMPUTED_RESAMPLED_ASSET}': {e}") + print("Exiting. Cannot proceed without input features.") + return # Exit if loading precomputed fails + except Exception as e: + print(f"Unexpected error loading pre-computed asset: {e}") + print("Exiting. Cannot proceed without input features.") + return # Exit if loading precomputed fails + + # --- Mode 2: Generate Inputs from Components --- # + if input_resampled is None: # This block runs if USE_PRECOMPUTED_RESAMPLED is False or loading failed + print("---> Mode: Generating Input Features from Components <--- ") + # 1. Load Fire Points (needed regardless for training data) + fire_pts_processed = load_fire_points(FIRE_ASSET_BASE, boundary) + if fire_pts_processed is None: + print("Exiting: Failed to load fire points.") + return + + # 2. Process NICFI (Optional) + nicfi_trends = process_nicfi(boundary) + if nicfi_trends is None: + print("Warning: Failed to process NICFI trends. Proceeding without them.") - # 7. Combine Features - combined_features = combine_features(trendfire_img, nicfi_trends, modis_trends, dem_slope, roads_img) - if combined_features is None: - print("Exiting: Failed to combine input features.") + # 3. Process MODIS LST (Optional) + modis_trends = process_modis_lst(boundary) + if modis_trends is None: + print("Warning: Failed to process MODIS LST trends. Proceeding without them.") + + # 4. Load DEM Slope + dem_slope = load_dem_slope(DEM_ASSET_ID, boundary) + if dem_slope is None: + print("Warning: Failed to process DEM slope. Proceeding without it.") + + # 5. Load Roads + roads_img = load_roads(ROAD_ASSET_ID, boundary) # Corrected arguments + if roads_img is None: + print("Warning: Failed to process Roads layer. Proceeding without it.") + + # 6. Load TrendFire Outputs (Core Trends) + trendfire_img = load_trendfire_outputs(TRENDFIRE_ASSET_BASE, boundary) + if trendfire_img is None: + print("Exiting: Failed to load TrendFire outputs.") + return + + # 7. Combine Available Features + print("Combining available features...") + combined_features = combine_features(trendfire_img, nicfi_trends, modis_trends, dem_slope, roads_img) + if combined_features is None or not combined_features.bandNames().getInfo(): + print("Exiting: Failed to combine input features or resulting image has no bands.") + return + print(f"Combined features image created with bands: {combined_features.bandNames().getInfo()}") + + # 8. Resample & Export Generated Intermediate Input + print(f"Resampling combined features to {GRID_SCALE}m scale, CRS {GRID_CRS}...") + input_resampled = resample_features(combined_features, GRID_SCALE, GRID_CRS, boundary) + if input_resampled is None or not input_resampled.bandNames().getInfo(): + print("Exiting: Failed to resample features or resulting image has no bands.") + return + print("Resampling complete.") + + # Export the generated resampled image for comparison/debugging + print(f"Exporting the generated resampled input features to asset: {GENERATED_RESAMPLED_FEATURES_ASSET}") + export_asset(input_resampled, GENERATED_RESAMPLED_FEATURES_ASSET, boundary, "Generated_Inputs_FV_py", GRID_SCALE, GRID_CRS) + + # --- Classification Workflow (Uses the selected 'input_resampled') --- # + if input_resampled is None or fire_pts_processed is None: + print("Error: Cannot proceed to classification. Missing resampled input image or fire points.") return - # 8. Resample & Export Intermediate Input - input_resampled = resample_features(combined_features, GRID_SCALE, GRID_CRS, boundary) - if input_resampled is None: - print("Exiting: Failed to resample features.") - return - - export_asset(input_resampled, RESAMPLED_FEATURES_ASSET, boundary, "Resampled_Inputs_FV_py", GRID_SCALE, GRID_CRS) - + print(f"\nProceeding with classification using predictor image with bands: {input_resampled.bandNames().getInfo()}") + # 9. Prepare Training Data - # Uses fire_pts_processed (2013-2022) and generates random points - training_data_fc = prepare_training_data(fire_pts_processed, boundary, input_resampled, CLASS_LABEL, \ - num_random_points=NUM_RANDOM_POINTS, use_risk_categories=USE_RISK_CATEGORIES) + training_data_fc = prepare_training_data( + fire_points_input=fire_pts_processed, + boundary=boundary, + predictor_image=input_resampled, + label_property=CLASS_LABEL, + num_random_points=NUM_RANDOM_POINTS, + random_points_seed=RANDOM_SEED, + use_risk_categories=USE_RISK_CATEGORIES + ) if training_data_fc is None: print("Exiting: Failed to prepare training data.") return - # Export the full sampled dataset before splitting + # Export the full sampled dataset before splitting (useful for external analysis) export_table(training_data_fc, TRAINING_POINTS_ASSET, "Training_Validation_Points_FV_py") - # --- Split Data --- + # 10. Split Data print(f"Splitting data using ratio: {TRAIN_TEST_SPLIT_RATIO:.1f} train / {1-TRAIN_TEST_SPLIT_RATIO:.1f} validation...") # Filter based on the random column added in prepare_training_data training_sample = training_data_fc.filter(ee.Filter.lt('random', TRAIN_TEST_SPLIT_RATIO)) validation_sample = training_data_fc.filter(ee.Filter.gte('random', TRAIN_TEST_SPLIT_RATIO)) - print(f" Training samples: {training_sample.size().getInfo()}, Validation samples: {validation_sample.size().getInfo()}") + train_count = training_sample.size().getInfo() + val_count = validation_sample.size().getInfo() + print(f" Training samples: {train_count}, Validation samples: {val_count}") + if train_count == 0 or val_count == 0: + print("Error: Training or validation sample is empty after splitting. Check data preparation.") + return - # Get predictor names from the resampled image (used as input properties) + # Get predictor names from the resampled image (used as input properties for RF) predictor_names = input_resampled.bandNames() if not predictor_names.getInfo(): - print("Error: Could not retrieve band names from resampled image.") + print("Error: Could not retrieve band names from the input predictor image.") return - # 10. Train Classifier - classifier = train_classifier(training_sample, CLASS_LABEL, predictor_names, RF_TREES, RF_VAR_SPLIT) + # 11. Train Classifier + print("\n>>> Sending request to GEE: Train Random Forest classifier...") # Log Before + classifier = train_classifier( + training_data=training_sample, + class_property=CLASS_LABEL, + input_properties=predictor_names, # Pass Python list + num_trees=RF_TREES, + variables_per_split=RF_VAR_SPLIT, + seed=RANDOM_SEED + ) + # Note: train() itself can take time, but the result (trained classifier object) + # is usually returned quickly to the client unless explain() is called. + # The heavy lifting happens when classifier.classify() is called. + print("<<< GEE Response: Classifier training definition complete.") # Log After + if classifier is None: print("Exiting: Classifier training failed.") return - # 11. Evaluate Classifier + # 12. Evaluate Classifier + print("\n>>> Sending request to GEE: Evaluate classifier...") # Log Before evaluation_results = evaluate_classifier(classifier, validation_sample, CLASS_LABEL) - if evaluation_results is None: - print("Warning: Classifier evaluation failed or produced no results.") - - # 12. Classify Image - print("Classifying the full image...") - vulnerability_map = input_resampled.classify(classifier) - print("Classification complete.") + print("<<< GEE Response: Evaluation complete.") # Log After - # 13. Export Final Map - export_asset(vulnerability_map, VULNERABILITY_MAP_ASSET, boundary, "Fire_Vulnerability_Map_py", GRID_SCALE, GRID_CRS) + # evaluation_results is a dictionary containing accuracy, kappa, matrix etc. + if evaluation_results is None: + print("Warning: Classifier evaluation step failed or produced no results.") + + # 13. Classify Image + print("\n>>> Sending request to GEE: Classify the full predictor image...") # Log Before + # Ensure the image being classified has float type bands (required by some classifiers) + vulnerability_map = input_resampled.toFloat().classify(classifier) + # Force execution by getting some info (optional, but confirms the step is done) + # print(">>> Waiting for GEE: Classification...") + # _ = vulnerability_map.bandNames().getInfo() # Or use another light .getInfo() call + print("<<< GEE Response: Classification definition complete.") # Log After + + # 14. Export Final Map + print(f"\nExporting final Fire Vulnerability Map to asset:") + print(f" {VULNERABILITY_MAP_ASSET}") + export_asset( + image=vulnerability_map.toInt8(), # Classifiers output integer labels, cast to save space + asset_id=VULNERABILITY_MAP_ASSET, + region=boundary, + description="Fire_Vulnerability_Map_py", + scale=GRID_SCALE, + crs=GRID_CRS + ) print("\n--- Fire Vulnerability Workflow Completed --- ") - print("Monitor GEE tasks for export completion.") + print("Monitor GEE tasks dashboard for export completion.") # --- Fire Vulnerability Specific Functions --- @@ -1136,72 +1275,6 @@ def resample_features(feature_image, scale, crs, boundary): print(f"Unexpected error during resampling: {e}") return None -def train_classifier(training_data, class_property, input_properties, num_trees=1000, variables_per_split=1, seed=42): - """ - Trains the ee.Classifier.smileRandomForest classifier. - - Args: - training_data: ee.FeatureCollection used for training. - class_property: String, the name of the property containing the class label. - input_properties: List of strings, names of the predictor properties/bands. - num_trees: Integer, number of trees in the forest. - variables_per_split: Integer, number of variables to consider at each split. - Set to None or 0 to use sqrt(num_variables). - JS used 1. - seed: Integer, seed for reproducibility. - - Returns: - A trained ee.Classifier object, or None on error. - """ - print(f"Training Random Forest classifier ({num_trees} trees)...") - if training_data is None: - print("Error: Training data is missing.") - return None - if not input_properties: - print("Error: Input properties list is empty.") - return None - - try: - classifier = ee.Classifier.smileRandomForest( - numberOfTrees=num_trees, - variablesPerSplit=variables_per_split, # Match JS, using 1 - # minLeafPopulation=1, # Default is 1 - # bagFraction=0.5, # Default is 0.5 - seed=seed - ).train( - features=training_data, - classProperty=class_property, - inputProperties=input_properties - ) - - print("Classifier trained successfully.") - - # Optional: Explain the classifier (can be computationally intensive) - # try: - # explanation = classifier.explain() - # print("Classifier explanation:", explanation.getInfo()) - # importance = explanation.get('importance') - # if importance: - # print("Variable Importance:") - # importance_info = importance.getInfo() - # # Ensure importance_info is a dictionary before iterating - # if isinstance(importance_info, dict): - # for prop, imp in importance_info.items(): - # print(f" {prop}: {imp}") - # else: - # print(" Could not retrieve importance details in expected format.") - # except Exception as explain_e: - # print(f"Could not get classifier explanation: {explain_e}") - - return classifier - - except ee.EEException as e: - print(f"GEE error during classifier training: {e}") - return None - except Exception as e: - print(f"Unexpected error during classifier training: {e}") - return None - def evaluate_classifier(classifier, validation_data, class_property): """ Evaluates the classifier on validation data using an error matrix. diff --git a/TrendAnomalyPrediction.py b/TrendAnomalyPrediction.py index 6cadf60..e9f2e65 100644 --- a/TrendAnomalyPrediction.py +++ b/TrendAnomalyPrediction.py @@ -48,7 +48,7 @@ def initialize_ee(project_id='ee-crop-health-telangana'): # Use the verified pro except Exception as e: # Catch other potential errors during initialization print(f"An unexpected error occurred during Earth Engine initialization: {e}") - raise + raise # Ensure execution stops on unexpected error def get_goa_boundary(asset_id='users/jonasnothnagel/pa_boundary'): """ diff --git a/data/roadProximity_Latest.tif b/data/roadProximity_Latest.tif new file mode 100644 index 0000000000000000000000000000000000000000..e34e52ad3f015f48bedb6def29a38c8022f2f0f8 GIT binary patch literal 19506756 zcmeF)bx>Ao-~WA(6r{lp+;(817^ukk{Vcn?3tO>U3_uZE5CH)dMUd|9?(R+rF~CGI 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