import numpy as npimport pandas as pdimport rasterio as riofrom affine import Affinefrom rasterio.warp import calculate_default_transform, reproject, Resamplingimport datashader as dsfrom datashader.transfer_functions import shadefrom datashader.transfer_functions import stackfrom datashader.transfer_functions import dynspreadfrom datashader.transfer_functions import set_backgroundfrom datashader.colors import Elevationimport xrspatial# Convert the geometries of the input to x,y coordinates and create a new dataframedf = gdf_clipped.reset_index()[["index", "geometry"]]df["x"] = df.geometry.xdf["y"] = df.geometry.y# Calculate the canvas size based on the bounding box dimensions and pixel sizebbox_width = mask.total_bounds[2] - gdf_clipped.total_bounds[0]bbox_height = mask.total_bounds[3] - gdf_clipped.total_bounds[1]if resolution == "1":    pixel_size = 0.009 # decimal degrees (1km)elif resolution == "2":    pixel_size = 0.002245788210298804 # decimal degrees (250m)W = int(np.ceil(bbox_width / pixel_size))H = int(np.ceil(bbox_height / pixel_size))cvs = ds.Canvas(plot_width=W, plot_height=H,                 x_range=(gdf_clipped.total_bounds[0], gdf_clipped.total_bounds[2]),                 y_range=(gdf_clipped.total_bounds[1], gdf_clipped.total_bounds[3]))# Aggregate the points in the canvas using the minimum of their index valuepoints_agg = cvs.points(df, x="x", y="y", agg=ds.min("index"))# Set non-finite data to zeropoints_agg.data[~np.isfinite(points_agg.data)] = 0# Create a shaded version of the pointspoints_shaded = dynspread(shade(points_agg, cmap="salmon", min_alpha=0, span=(0,1), how="linear"),                          threshold=1,                          max_px=5)# Calculate the proximity of each point to its nearest neighbor using the Great Circle distance metricproximity_agg = xrspatial.proximity(points_agg, distance_metric="GREAT_CIRCLE")# Stack the shaded points and proximity map togetherstack(shade(proximity_agg, cmap=["#bbeb9e", "black"], how="eq_hist"),      points_shaded)