hanover_bands

import ee
import numpy as np
import pandas as pd
import shapefile

# Initialize the Earth Engine module
ee.Initialize()

# Load the shapefile
sf = shapefile.Reader("/Users/hydro3/Documents/USGSSites/shape_files/virginia/hanover.shp")
shapes = sf.shapes()
points = shapes[0].points
aoi = ee.Geometry.Polygon(points)

# Create date ranges
start = pd.date_range(start='2017-08-08', end='2024-06-22', freq='5d')
end = pd.date_range(start='2017-08-12', end='2024-06-26', freq='5d')
dates = pd.DataFrame({'start': start, 'end': end})

def pulldata(startDate, endDate):
    # Define source data
    image = (ee.ImageCollection('COPERNICUS/S2_SR_HARMONIZED')
              .filterDate(startDate, endDate)
              .filterMetadata('CLOUDY_PIXEL_PERCENTAGE', 'less_than', 10)
              .select('B2', 'B3', 'B4', 'B5', 'B8', 'B11'))

    # Create the mosaic
    mosaic = image.median().reproject(crs='EPSG:32736', scale=10)
    band_arrs = mosaic.sampleRectangle(region=aoi, defaultValue=0)

    # Get individual band arrays
    band_arr_b2 = band_arrs.get('B2')
    band_arr_b3 = band_arrs.get('B3')
    band_arr_b4 = band_arrs.get('B4')
    band_arr_b5 = band_arrs.get('B5')  # Corrected to B5
    band_arr_b8 = band_arrs.get('B8')
    band_arr_b11 = band_arrs.get('B11')

    # Transfer the arrays from server to client and cast as np array
    b2 = np.array(band_arr_b2.getInfo())
    b3 = np.array(band_arr_b3.getInfo())
    b4 = np.array(band_arr_b4.getInfo())
    b5 = np.array(band_arr_b5.getInfo())
    b8 = np.array(band_arr_b8.getInfo())
    b11 = np.array(band_arr_b11.getInfo())

    # Calculate NDWI
    ndwiG = (b3 - b8) / (b3 + b8)  # Gao
    water = ndwiG > -0.02

    # Initialize variables for calculations
    results = {}

    for band, arr in zip(['B2', 'B3', 'B4', 'B5', 'B8', 'B11'], [b2, b3, b4, b5, b8, b11]):
        arr = arr * water  # Apply water mask
        results[band] = np.sum(arr) / np.sum(arr > 0) if np.sum(arr > 0) > 0 else np.nan

    # Write results to file
    with open("HanoverBands.txt", "a") as f:
        f.write(f"{startDate}, {endDate}, {results['B2']}, {results['B3']}, "
                f"{results['B4']}, {results['B5']}, {results['B8']}, {results['B11']}\n")

# Loop through each date range and pull data
for i in range(len(dates)):
    pulldata(dates["start"][i], dates["end"][i])

#TESTING
ndwiG = (b3 - b8) / (b3 + b8)
plt.imshow(ndwiG, cmap='gray')  # Use a colormap to visualize
plt.colorbar()
plt.show()


print(f"B2: {b2}")
print(f"B3: {b3}")
print(f"B8: {b8}")


def pulldata(startDate, endDate):
    # Define source data
    image = (ee.ImageCollection('COPERNICUS/S2_SR_HARMONIZED')
              .filterDate(startDate, endDate)
              .select('B2', 'B3', 'B4', 'B5', 'B8', 'B11'))

    # Create the mosaic
    mosaic = image.median().reproject(crs='EPSG:32736', scale=10)
    band_arrs = mosaic.sampleRectangle(region=aoi, defaultValue=0)

    # Get individual band arrays
    band_arr_b2 = band_arrs.get('B2')   # Blue
    band_arr_b3 = band_arrs.get('B3')   # Green
    band_arr_b4 = band_arrs.get('B4')   # Red
    band_arr_b5 = band_arrs.get('B5')   # Correctly fetching B5
    band_arr_b8 = band_arrs.get('B8')   # NIR
    band_arr_b11 = band_arrs.get('B11') # SWIR

    # Transfer the arrays from server to client and cast as np array
    b2 = np.array(band_arr_b2.getInfo())
    b3 = np.array(band_arr_b3.getInfo())
    b4 = np.array(band_arr_b4.getInfo())
    b5 = np.array(band_arr_b5.getInfo())  # Ensure this is defined
    b8 = np.array(band_arr_b8.getInfo())
    b11 = np.array(band_arr_b11.getInfo())

    # Continue with the rest of your code...