conversion 2D raster to 3D

RakhshindaBano — Sun, 15 Sep 2024 16:50:19 GMT

I am trying to generate a map with a 2D raster as basemap (observed landuse) and overlay it with 3D error bars (containing the incorrect land use categories that model predicted). So it involves conversion of one of the 2D files to 3D first

I have been trying both in arcgis pro and arc scene

In Arc Scene I first created a height column in attribute table as the raster doesn't have elevation information. when I tried this link and increased the value of vertical exaggeration, it also changed the 2D base map which I wanted to keep as it is.
I am looking to get something like the map at the bottom but the vertical bars come from a different raster with the same spatial boundary as base map
- The python code I have been using looks like below and it gives me a map skipping the basemap completely and very high error bars. I am trying to figure out what am I missing
  import arcpy
  import pandas as pd
  import numpy as np
  import matplotlib.pyplot as plt
  from matplotlib.colors import ListedColormap
  import os
  from mpl_toolkits.mplot3d import Axes3D # Import for 3D plotting
  
  # Switch to 'Agg' backend for non-interactive use, better for saving figures
  import matplotlib
  matplotlib.use('Agg')
  
  # Input file paths
  raster_a_path = r"C:\Users\xyz\ xyz \ xyz \ xyz \ xyz.tif"
  raster_c_path = r"C:\ Users\xyz\ xyz \ xyz \ xyz \ xyz.tif"
  csv_path = r"C:\ Users\xyz\ xyz \ xyz \ xyz \ xyz.csv"
  output_folder = r"C:\ Users\xyz\ xyz \ xyz \ xyz \ xyz”
  
  # Load the CSV file containing land use types and colors
  legend_df = pd.read_csv(csv_path)
  legend_df['LandUseType'] = legend_df['LandUseType'].str.strip() # Clean up data
  
  # Ensure all color codes are valid hexadecimal
  def validate_color(color):
      if isinstance(color, str) and color.startswith('#') and len(color) == 7:
          try:
              int(color[1:], 16) # Try converting from hex to RGB
              return True
          except ValueError:
              return False
      return False
  
  legend_df = legend_df[legend_df['Color'].apply(validate_color)]
  
  # Convert values and colors to a dictionary
  value_to_color = dict(zip(legend_df['Value'], legend_df['Color']))
  
  # Convert colors to RGB tuples for use with matplotlib, adjust brightness for 3D bars
  colors_rgb = [tuple(min(int(color.strip("#")[i:i+2], 16) / 255.0 * 1.5, 1.0) for i in (0, 2, 4)) for color in legend_df['Color']]
  cmap = ListedColormap(colors_rgb)
  
  # Load raster datasets
  raster_a = arcpy.Raster(raster_a_path)
  raster_c = arcpy.Raster(raster_c_path)
  
  # Convert raster_a and raster_c to NumPy arrays
  raster_a_array = arcpy.RasterToNumPyArray(raster_a)
  raster_c_array = arcpy.RasterToNumPyArray(raster_c)
  
  # Check if dimensions match
  if raster_a_array.shape != raster_c_array.shape:
      raise ValueError(f"Dimensions of raster_a ({raster_a_array.shape}) and raster_c ({raster_c_array.shape}) do not match")
  
  # Mask NoData values (-9999) in raster_a
  no_data_value = -9999
  raster_a_masked = np.ma.masked_equal(raster_a_array, no_data_value)
  
  # Adjust extent based on the raster's spatial reference
  extent = [raster_a.extent.XMin, raster_a.extent.XMax, raster_a.extent.YMin, raster_a.extent.YMax]
  
  # Plot Raster A in 2D
  fig = plt.figure(figsize=(10, 10))
  ax_2d = fig.add_subplot(111)
  img_2d = ax_2d.imshow(raster_a_masked, cmap=cmap, extent=extent, interpolation='none')
  
  # Overlay Raster C in 3D (vertical bars)
  ax_3d = fig.add_subplot(111, projection='3d')
  
  # Create grid for raster_c
  x = np.arange(raster_c_array.shape[1])
  y = np.arange(raster_c_array.shape[0])
  x, y = np.meshgrid(x, y)
  
  # Normalize height of 3D bars to a reasonable scale
  heights = raster_c_array / np.nanmax(raster_c_array) * 10 # Scale heights for visibility
  
  # Plot each bar as a vertical block
  for i in range(raster_c_array.shape[0]):
      for j in range(raster_c_array.shape[1]):
          if raster_c_array[i, j] != no_data_value:
              ax_3d.bar3d(x[i, j], y[i, j], 0, 1, 1, heights[i, j],
                          color=cmap(raster_c_array[i, j] % len(colors_rgb)), alpha=0.5)
  
  # Set aspect ratios and remove the 3D axis, background grids, and labels
  ax_3d.set_axis_off()
  
  # Set the view angle for 3D projection
  ax_3d.view_init(elev=50, azim=-60)
  
  # Pause to ensure rendering completes before saving
  plt.draw()
  plt.pause(0.1)
  
  # Construct the full path for saving the plot
  output_path = os.path.join(output_folder, "output_base_map_with_3D_overlay.png")
  # Save the plot to the specified folder
  plt.savefig(output_path, bbox_inches='tight', dpi=300)
  print(f"Plot saved successfully to: {output_path}")

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conversion 2D raster to 3D