source: palm/trunk/SCRIPTS/palm_csd_files/palm_csd_canopy_generator.py @ 3668

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
#--------------------------------------------------------------------------------#
# This file is part of the PALM model system.
#
# PALM is free software: you can redistribute it and/or modify it under the terms
# of the GNU General Public License as published by the Free Software Foundation,
# either version 3 of the License, or (at your option) any later version.
#
# PALM is distributed in the hope that it will be useful, but WITHOUT ANY
# WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR
# A PARTICULAR PURPOSE.  See the GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License along with
# PALM. If not, see <http://www.gnu.org/licenses/>.
#
# Copyright 1997-2018  Leibniz Universitaet Hannover
#--------------------------------------------------------------------------------#
#
# Current revisions:
# -----------------
# 
# 
# Former revisions:
# -----------------
# $Id: palm_csd_canopy_generator.py 3668 2019-01-14 12:49:24Z maronga $
# Various improvements and bugfixes
# 
# 3629 2018-12-13 12:18:54Z maronga
# Initial revision
# 
# Description:
# ------------
# Canopy generator routines for creating 3D leaf and basal area densities for 
# single trees and tree patches
#
# @Author Bjoern Maronga (maronga@muk.uni-hannover.de)
#------------------------------------------------------------------------------#
import numpy as np
import math
import scipy.integrate as integrate

def generate_single_tree_lad(x,y,dz,max_tree_height,max_patch_height,tree_type,tree_height,tree_dia,trunk_dia,tree_lai,season,fill):


#  Step 1: Create arrays for storing the data
   max_canopy_height = max( max_tree_height,max_patch_height )
 
   zlad = np.arange(0,math.floor(max_canopy_height/dz)*dz+2*dz,dz)
   zlad[1:] = zlad[1:] - 0.5 * dz

   lad = np.ones((len(zlad),len(y),len(x)))
   lad[:,:,:] = fill

   bad = np.ones((len(zlad),len(y),len(x)))
   bad[:,:,:] = fill
   
   ids = np.ones((len(zlad),len(y),len(x)))
   ids[:,:,:] = fill 
   

#  Calculating the number of trees in the arrays and a boolean array storing the location of trees which is used for convenience in the following loop environment   
   number_of_trees_array = np.where( (tree_type.flatten() != fill) | (tree_dia.flatten() != fill) | (trunk_dia.flatten() != fill),1.0,fill)
   number_of_trees = len( number_of_trees_array[number_of_trees_array == 1.0] )
   dx = x[1] - x[0]
       
   valid_pixels = np.where( (tree_type[:,:] != fill) | (tree_dia[:,:] != fill) | (trunk_dia[:,:] != fill),True,False)
   
#  For each tree, create a small 3d array containing the LAD field for the individual tree
   print("Start generating " +  str(number_of_trees) + " trees...")
   tree_id_counter = 0
   if (number_of_trees > 0):
      for i in range(0,len(x)-1):
         for j in range(0,len(y)-1):
            if valid_pixels[j,i]:
               tree_id_counter = tree_id_counter + 1
#               print("   Processing tree No " +  str(tree_id_counter) + " ...", end="")
               lad_loc, bad_loc, x_loc, y_loc, z_loc, status = process_single_tree(dx,dz,tree_type[j,i],tree_height[j,i],tree_lai[j,i],tree_dia[j,i],trunk_dia[j,i],season)
               if ( np.any(lad_loc) != fill ):
 
#                 Calculate the position of the local 3d tree array within the full domain in order to achieve correct mapping and cutting off at the edges of the full domain
                  lad_loc_nx = int(len(x_loc) / 2)
                  lad_loc_ny = int(len(y_loc) / 2)
                  lad_loc_nz = int(len(z_loc))
                             
                  odd_x = int(len(x_loc) % 2)
                  odd_y = int(len(y_loc) % 2)
               
                  ind_l_x  = max(0,(i-lad_loc_nx))
                  ind_l_y  = max(0,(j-lad_loc_ny))
                  ind_r_x  = min(len(x)-1,i+lad_loc_nx-1+odd_x)
                  ind_r_y  = min(len(y)-1,j+lad_loc_ny-1+odd_y)
               
                  out_l_x = ind_l_x - (i-lad_loc_nx)
                  out_l_y = ind_l_y - (j-lad_loc_ny)
                  out_r_x = len(x_loc)-1 + ind_r_x - (i+lad_loc_nx-1+odd_x)
                  out_r_y = len(y_loc)-1 + ind_r_y - (j+lad_loc_ny-1+odd_y)               
   

                  lad[0:lad_loc_nz,ind_l_y:ind_r_y+1,ind_l_x:ind_r_x+1] = np.where(lad_loc[0:lad_loc_nz,out_l_y:out_r_y+1,out_l_x:out_r_x+1] != fill,lad_loc[0:lad_loc_nz,out_l_y:out_r_y+1,out_l_x:out_r_x+1],lad[0:lad_loc_nz,ind_l_y:ind_r_y+1,ind_l_x:ind_r_x+1]) 
                  bad[0:lad_loc_nz,ind_l_y:ind_r_y+1,ind_l_x:ind_r_x+1] = np.where(bad_loc[0:lad_loc_nz,out_l_y:out_r_y+1,out_l_x:out_r_x+1] != fill,bad_loc[0:lad_loc_nz,out_l_y:out_r_y+1,out_l_x:out_r_x+1],bad[0:lad_loc_nz,ind_l_y:ind_r_y+1,ind_l_x:ind_r_x+1]) 
                  ids[0:lad_loc_nz,ind_l_y:ind_r_y+1,ind_l_x:ind_r_x+1] = np.where(lad_loc[0:lad_loc_nz,out_l_y:out_r_y+1,out_l_x:out_r_x+1] != fill,tree_id_counter,ids[0:lad_loc_nz,ind_l_y:ind_r_y+1,ind_l_x:ind_r_x+1]) 


#                  if ( status == 0 ):
#                     status_char = " ok."
#                  else:
#                     status_char = " skipped."
#                  print(status_char)

               del lad_loc, x_loc, y_loc, z_loc, status
   return lad, bad, ids, zlad


def process_single_tree(dx,dz,tree_type,tree_height,tree_lai,tree_dia,trunk_dia,season):

#  Set some parameters
   sphere_extinction = 0.6
   cone_extinction = 0.2
   ml_n_low = 0.5
   ml_n_high = 6.0
    
#  Populate look up table for tree species and their properties
#  #1 Tree shapes were manually lookep up.
#  #2 Crown h/w ratio - missing
#  #3 Crown diameter based on Berlin tree statistics
#  #4 Tree height based on Berlin tree statistics
#  #5 Tree LAI summer - missing
#  #6 Tree LAI winter - missing
#  #7 Height of lad maximum - missing
#  #8 Ratio LAD/BAD - missing
#  #9 Trunk diameter at breast height from Berlin 
   default_trees = []
                                                #1   #2   #3   #4    #5   #6   #7   #8     #9
   default_trees.append(tree("Default",         1.0, 1.0, 4.0, 12.0, 3.0, 0.8, 0.6, 0.025, 0.35)) 
   default_trees.append(tree("Abies",           3.0, 1.0, 4.0, 12.0, 3.0, 0.8, 0.6, 0.025, 0.80)) 
   default_trees.append(tree("Acer",            1.0, 1.0, 7.0, 12.0, 3.0, 0.8, 0.6, 0.025, 0.80))
   default_trees.append(tree("Aesculus",        1.0, 1.0, 7.0, 12.0, 3.0, 0.8, 0.6, 0.025, 1.00))
   default_trees.append(tree("Ailanthus",       1.0, 1.0, 8.5, 13.5, 3.0, 0.8, 0.6, 0.025, 1.30))
   default_trees.append(tree("Alnus",           3.0, 1.0, 6.0, 16.0, 3.0, 0.8, 0.6, 0.025, 1.20))
   default_trees.append(tree("Amelanchier",     1.0, 1.0, 3.0,  4.0, 3.0, 0.8, 0.6, 0.025, 1.20))
   default_trees.append(tree("Betula",          1.0, 1.0, 6.0, 14.0, 3.0, 0.8, 0.6, 0.025, 0.30))
   default_trees.append(tree("Buxus",           1.0, 1.0, 4.0,  4.0, 3.0, 0.8, 0.6, 0.025, 0.90))
   default_trees.append(tree("Calocedrus",      3.0, 1.0, 5.0, 10.0, 3.0, 0.8, 0.6, 0.025, 0.50))
   default_trees.append(tree("Caragana",        1.0, 1.0, 3.5,  6.0, 3.0, 0.8, 0.6, 0.025, 0.90))
   default_trees.append(tree("Carpinus",        1.0, 1.0, 6.0, 10.0, 3.0, 0.8, 0.6, 0.025, 0.70))
   default_trees.append(tree("Carya",           1.0, 1.0, 5.0, 17.0, 3.0, 0.8, 0.6, 0.025, 0.80))
   default_trees.append(tree("Castanea",        1.0, 1.0, 4.5,  7.0, 3.0, 0.8, 0.6, 0.025, 0.80))
   default_trees.append(tree("Catalpa",         1.0, 1.0, 5.5,  6.5, 3.0, 0.8, 0.6, 0.025, 0.70))
   default_trees.append(tree("Cedrus",          1.0, 1.0, 8.0, 13.0, 3.0, 0.8, 0.6, 0.025, 0.80))
   default_trees.append(tree("Celtis",          1.0, 1.0, 6.0,  9.0, 3.0, 0.8, 0.6, 0.025, 0.80))
   default_trees.append(tree("Cercidiphyllum",  1.0, 1.0, 3.0,  6.5, 3.0, 0.8, 0.6, 0.025, 0.80))
   default_trees.append(tree("Cercis",          1.0, 1.0, 2.5,  7.5, 3.0, 0.8, 0.6, 0.025, 0.90))
   default_trees.append(tree("Chamaecyparis",   5.0, 1.0, 3.5,  9.0, 3.0, 0.8, 0.6, 0.025, 0.70))
   default_trees.append(tree("Cladrastis",      1.0, 1.0, 5.0, 10.0, 3.0, 0.8, 0.6, 0.025, 0.80))
   default_trees.append(tree("Cornus",          1.0, 1.0, 4.5,  6.5, 3.0, 0.8, 0.6, 0.025, 1.20))
   default_trees.append(tree("Corylus",         1.0, 1.0, 5.0,  9.0, 3.0, 0.8, 0.6, 0.025, 0.40))
   default_trees.append(tree("Cotinus",         1.0, 1.0, 4.0,  4.0, 3.0, 0.8, 0.6, 0.025, 0.70))
   default_trees.append(tree("Crataegus",       3.0, 1.0, 3.5,  6.0, 3.0, 0.8, 0.6, 0.025, 1.40))
   default_trees.append(tree("Cryptomeria",     3.0, 1.0, 5.0, 10.0, 3.0, 0.8, 0.6, 0.025, 0.50))
   default_trees.append(tree("Cupressocyparis", 3.0, 1.0, 3.0,  8.0, 3.0, 0.8, 0.6, 0.025, 0.40))
   default_trees.append(tree("Cupressus",       3.0, 1.0, 5.0,  7.0, 3.0, 0.8, 0.6, 0.025, 0.40))
   default_trees.append(tree("Cydonia",         1.0, 1.0, 2.0,  3.0, 3.0, 0.8, 0.6, 0.025, 0.90))
   default_trees.append(tree("Davidia",         1.0, 1.0,10.0, 14.0, 3.0, 0.8, 0.6, 0.025, 0.40))
   default_trees.append(tree("Elaeagnus",       1.0, 1.0, 6.5,  6.0, 3.0, 0.8, 0.6, 0.025, 1.20))
   default_trees.append(tree("Euodia",          1.0, 1.0, 4.5,  6.0, 3.0, 0.8, 0.6, 0.025, 0.90))
   default_trees.append(tree("Euonymus",        1.0, 1.0, 4.5,  6.0, 3.0, 0.8, 0.6, 0.025, 0.60))
   default_trees.append(tree("Fagus",           1.0, 1.0,10.0, 12.5, 3.0, 0.8, 0.6, 0.025, 0.50))
   default_trees.append(tree("Fraxinus",        1.0, 1.0, 5.5, 10.5, 3.0, 0.8, 0.6, 0.025, 1.60))
   default_trees.append(tree("Ginkgo",          3.0, 1.0, 4.0,  8.5, 3.0, 0.8, 0.6, 0.025, 0.80))
   default_trees.append(tree("Gleditsia",       1.0, 1.0, 6.5, 10.5, 3.0, 0.8, 0.6, 0.025, 0.60))
   default_trees.append(tree("Gymnocladus",     1.0, 1.0, 5.5, 10.0, 3.0, 0.8, 0.6, 0.025, 0.80))
   default_trees.append(tree("Hippophae",       1.0, 1.0, 9.5,  8.5, 3.0, 0.8, 0.6, 0.025, 0.80))
   default_trees.append(tree("Ilex",            1.0, 1.0, 4.0,  7.5, 3.0, 0.8, 0.6, 0.025, 0.80))
   default_trees.append(tree("Juglans",         1.0, 1.0, 7.0,  9.0, 3.0, 0.8, 0.6, 0.025, 0.50))
   default_trees.append(tree("Juniperus",       5.0, 1.0, 3.0,  7.0, 3.0, 0.8, 0.6, 0.025, 0.90))
   default_trees.append(tree("Koelreuteria",    1.0, 1.0, 3.5,  5.5, 3.0, 0.8, 0.6, 0.025, 0.50))
   default_trees.append(tree("Laburnum",        1.0, 1.0, 3.0,  6.0, 3.0, 0.8, 0.6, 0.025, 0.60))
   default_trees.append(tree("Larix",           3.0, 1.0, 7.0, 16.5, 3.0, 0.8, 0.6, 0.025, 0.60))
   default_trees.append(tree("Ligustrum",       1.0, 1.0, 3.0,  6.0, 3.0, 0.8, 0.6, 0.025, 1.10))
   default_trees.append(tree("Liquidambar",     3.0, 1.0, 3.0,  7.0, 3.0, 0.8, 0.6, 0.025, 0.30))
   default_trees.append(tree("Liriodendron",    3.0, 1.0, 4.5,  9.5, 3.0, 0.8, 0.6, 0.025, 0.50))
   default_trees.append(tree("Lonicera",        1.0, 1.0, 7.0,  9.0, 3.0, 0.8, 0.6, 0.025, 0.70))
   default_trees.append(tree("Magnolia",        1.0, 1.0, 3.0,  5.0, 3.0, 0.8, 0.6, 0.025, 0.60))
   default_trees.append(tree("Malus",           1.0, 1.0, 4.5,  5.0, 3.0, 0.8, 0.6, 0.025, 0.30))
   default_trees.append(tree("Metasequoia",     5.0, 1.0, 4.5, 12.0, 3.0, 0.8, 0.6, 0.025, 0.50))
   default_trees.append(tree("Morus",           1.0, 1.0, 7.5, 11.5, 3.0, 0.8, 0.6, 0.025, 1.00))
   default_trees.append(tree("Ostrya",          1.0, 1.0, 2.0,  6.0, 3.0, 0.8, 0.6, 0.025, 1.00))
   default_trees.append(tree("Parrotia",        1.0, 1.0, 7.0,  7.0, 3.0, 0.8, 0.6, 0.025, 0.30))
   default_trees.append(tree("Paulownia",       1.0, 1.0, 4.0,  8.0, 3.0, 0.8, 0.6, 0.025, 0.40))
   default_trees.append(tree("Phellodendron",   1.0, 1.0,13.5, 13.5, 3.0, 0.8, 0.6, 0.025, 0.50))
   default_trees.append(tree("Picea",           3.0, 1.0, 3.0, 13.0, 3.0, 0.8, 0.6, 0.025, 0.90))
   default_trees.append(tree("Pinus",           3.0, 1.0, 6.0, 16.0, 3.0, 0.8, 0.6, 0.025, 0.80))
   default_trees.append(tree("Platanus",        1.0, 1.0,10.0, 14.5, 3.0, 0.8, 0.6, 0.025, 1.10))
   default_trees.append(tree("Populus",         1.0, 1.0, 9.0, 20.0, 3.0, 0.8, 0.6, 0.025, 1.40))
   default_trees.append(tree("Prunus",          1.0, 1.0, 5.0,  7.0, 3.0, 0.8, 0.6, 0.025, 1.60))
   default_trees.append(tree("Pseudotsuga",     3.0, 1.0, 6.0, 17.5, 3.0, 0.8, 0.6, 0.025, 0.70))
   default_trees.append(tree("Ptelea",          1.0, 1.0, 5.0,  4.0, 3.0, 0.8, 0.6, 0.025, 1.10))
   default_trees.append(tree("Pterocaria",      1.0, 1.0,10.0, 12.0, 3.0, 0.8, 0.6, 0.025, 0.50))
   default_trees.append(tree("Pterocarya",      1.0, 1.0,11.5, 14.5, 3.0, 0.8, 0.6, 0.025, 1.60))
   default_trees.append(tree("Pyrus",           3.0, 1.0, 3.0,  6.0, 3.0, 0.8, 0.6, 0.025, 1.80))
   default_trees.append(tree("Quercus",         1.0, 1.0, 8.0, 14.0, 3.1, 0.1, 0.6, 0.025, 0.40)) #
   default_trees.append(tree("Rhamnus",         1.0, 1.0, 4.5,  4.5, 3.0, 0.8, 0.6, 0.025, 1.30))
   default_trees.append(tree("Rhus",            1.0, 1.0, 7.0,  5.5, 3.0, 0.8, 0.6, 0.025, 0.50))
   default_trees.append(tree("Robinia",         1.0, 1.0, 4.5, 13.5, 3.0, 0.8, 0.6, 0.025, 0.50))
   default_trees.append(tree("Salix",           1.0, 1.0, 7.0, 14.0, 3.0, 0.8, 0.6, 0.025, 1.10))
   default_trees.append(tree("Sambucus",        1.0, 1.0, 8.0,  6.0, 3.0, 0.8, 0.6, 0.025, 1.40))
   default_trees.append(tree("Sasa",            1.0, 1.0,10.0, 25.0, 3.0, 0.8, 0.6, 0.025, 0.60))
   default_trees.append(tree("Sequoiadendron",  5.0, 1.0, 5.5, 10.5, 3.0, 0.8, 0.6, 0.025, 1.60))
   default_trees.append(tree("Sophora",         1.0, 1.0, 7.5, 10.0, 3.0, 0.8, 0.6, 0.025, 1.40))
   default_trees.append(tree("Sorbus",          1.0, 1.0, 4.0,  7.0, 3.0, 0.8, 0.6, 0.025, 1.10))
   default_trees.append(tree("Syringa",         1.0, 1.0, 4.5,  5.0, 3.0, 0.8, 0.6, 0.025, 0.60))
   default_trees.append(tree("Tamarix",         1.0, 1.0, 6.0,  7.0, 3.0, 0.8, 0.6, 0.025, 0.50))
   default_trees.append(tree("Taxodium",        5.0, 1.0, 6.0, 16.5, 3.0, 0.8, 0.6, 0.025, 0.60))
   default_trees.append(tree("Taxus",           2.0, 1.0, 5.0,  7.5, 3.0, 0.8, 0.6, 0.025, 1.50))
   default_trees.append(tree("Thuja",           3.0, 1.0, 3.5,  9.0, 3.0, 0.8, 0.6, 0.025, 0.70))
   default_trees.append(tree("Tilia",           3.0, 1.0, 7.0, 12.5, 3.0, 0.8, 0.6, 0.025, 0.70))
   default_trees.append(tree("Tsuga",           3.0, 1.0, 6.0, 10.5, 3.0, 0.8, 0.6, 0.025, 1.10))
   default_trees.append(tree("Ulmus",           1.0, 1.0, 7.5, 14.0, 3.0, 0.8, 0.6, 0.025, 0.80))
   default_trees.append(tree("Zelkova",         1.0, 1.0, 4.0,  5.5, 3.0, 0.8, 0.6, 0.025, 1.20))
   default_trees.append(tree("Zenobia",         1.0, 1.0, 5.0,  5.0, 3.0, 0.8, 0.6, 0.025, 0.40))
   
#  Define fill values
   fillvalues = {
   "tree_data": float(-9999.0),
   }
   
   
#  Check for missing data in the input and set default values if needed
   if ( tree_type == fillvalues["tree_data"] ):
      tree_type = int(0)
   else:
      tree_type = int(tree_type)

   if ( tree_height == fillvalues["tree_data"] ):
      tree_height = default_trees[tree_type].height
      
   if ( tree_lai == fillvalues["tree_data"] ):
      if (season == "summer"):
         tree_lai = default_trees[tree_type].lai_summer

      else:
         tree_lai = default_trees[tree_type].lai_winter      

   if ( tree_dia == fillvalues["tree_data"] ):
      tree_dia = default_trees[tree_type].diameter
      
   if ( trunk_dia == fillvalues["tree_data"] ):
      trunk_dia = default_trees[tree_type].dbh   
 
 
#  Assign values that are not defined as user input from lookup table
   tree_shape          = default_trees[tree_type].shape
   tree_ratio          = default_trees[tree_type].ratio
   lad_max_height      = default_trees[tree_type].lad_max_height
   bad_scale           = default_trees[tree_type].bad_scale

   #print("Tree input parameters:")
   #print("----------------------")
   #print("type:           " + str(default_trees[tree_type].species) )
   #print("height:         " + str(tree_height))
   #print("lai:            " + str(tree_lai))
   #print("crown diameter: " + str(tree_dia))
   #print("trunk diameter: " + str(trunk_dia))
   #print("shape: " + str(tree_shape))
   #print("height/width: " + str(tree_ratio))

#  Calculate crown height and height of the crown center
   crown_height   = tree_ratio * tree_dia
   if ( crown_height > tree_height ):
      crown_height = tree_height

   crown_center   = tree_height - crown_height * 0.5


#  Calculate height of maximum LAD
   z_lad_max = lad_max_height * tree_height
   
#  Calculate the maximum LAD after Lalic and Mihailovic (2004)
   lad_max_part_1 = integrate.quad(lambda z: ( ( tree_height - z_lad_max ) / ( tree_height - z ) ) ** (ml_n_high) * np.exp( ml_n_high * (1.0 - ( tree_height - z_lad_max ) / ( tree_height - z ) ) ), 0.0, z_lad_max)
   lad_max_part_2 = integrate.quad(lambda z: ( ( tree_height - z_lad_max ) / ( tree_height - z ) ) ** (ml_n_low) * np.exp( ml_n_low * (1.0 - ( tree_height - z_lad_max ) / ( tree_height - z ) ) ), z_lad_max, tree_height)
   
   lad_max = tree_lai / (lad_max_part_1[0] + lad_max_part_2[0])
   
   
#  Define position of tree and its output domain
   nx = int(tree_dia / dx) + 2
   nz = int(tree_height / dz) + 2
    
#  Add one grid point if diameter is an odd value    
   if ( (tree_dia % 2.0) != 0.0 ):
      nx = nx + 1


#  Create local domain of the tree's LAD
   x = np.arange(0,nx*dx,dx)
   x[:] = x[:] - 0.5 * dx
   y = x

   z = np.arange(0,nz*dz,dz)
   z[1:] = z[1:] - 0.5 * dz

#  Define center of the tree position inside the local LAD domain
   tree_location_x = x[nx/2]
   tree_location_y = y[nx/2]


#  Calculate LAD profile after Lalic and Mihailovic (2004). Will be later used for normalization
   lad_profile     =  np.arange(0,nz,1.0)
   lad_profile[:]  = 0.0

   for k in range(1,nz-1):
       if ( (z[k] > 0.0) & (z[k] < z_lad_max) ):
          n = ml_n_high
       else:
          n = ml_n_low
     
       lad_profile[k] =  lad_max * ( ( tree_height - z_lad_max ) / ( tree_height - z[k] ) ) ** (n) * np.exp( n * (1.0 - ( tree_height - z_lad_max ) / ( tree_height - z[k] ) ) )

#  Create lad array and populate according to the specific tree shape. This is still experimental
   lad_loc = np.ones((nz,nx,nx))
   lad_loc[:,:,:] = fillvalues["tree_data"]
   bad_loc = np.copy(lad_loc)


#  For very small trees, no LAD is calculated
   if ( tree_height <=  (0.5*dz) ):
        print("    Shallow tree found. Action: ignore.")
        return lad_loc, bad_loc, x, y, z, 1
   
   
#  Branch for spheres and ellipsoids. A symmetric LAD sphere is created assuming an LAD extinction towards the center of the tree, representing the effect of sunlight extinction and thus
#  less leaf mass inside the tree crown. Extinction coefficients are experimental.
   if ( tree_shape == 1 ):
      for i in range(0,nx-1):
         for j in range(0,nx-1):
            for k in range(0,nz):
               r_test = np.sqrt( (x[i] - tree_location_x)**(2)/(tree_dia * 0.5)**(2) + (y[j] - tree_location_y)**(2)/(tree_dia * 0.5)**2 + (z[k] - crown_center)**(2)/(crown_height * 0.5)**(2))
               if ( r_test <= 1.0 ):
                  lad_loc[k,j,i] = lad_max * np.exp( - sphere_extinction * (1.0 - r_test) )  
               else:
                  lad_loc[k,j,i] = fillvalues["tree_data"]

            if ( np.any( lad_loc[:,j,i] != fillvalues["tree_data"]) ):
               lad_loc[0,j,i] = 0.0


#  Branch for cylinder shapes
   if ( tree_shape == 2 ):
      k_min = int((crown_center - crown_height * 0.5) / dz)
      k_max = int((crown_center + crown_height * 0.5) / dz)
      for i in range(0,nx-1):
         for j in range(0,nx-1):
            for k in range(k_min,k_max):
               r_test = np.sqrt( (x[i] - tree_location_x)**(2)/(tree_dia * 0.5)**(2) + (y[j] - tree_location_y)**(2)/(tree_dia * 0.5)**(2))
               if ( r_test <= 1.0 ):
                  r_test3 = np.sqrt( (z[k] - crown_center)**(2)/(crown_height * 0.5)**(2))
                  lad_loc[k,j,i] = lad_max * np.exp ( - sphere_extinction * (1.0 - max(r_test,r_test3) ) )                 
               else:
                  lad_loc[k,j,i] = fillvalues["tree_data"]

            if ( np.any( lad_loc[:,j,i] != fillvalues["tree_data"]) ):
               lad_loc[0,j,i] = 0.0

#  Branch for cone shapes
   if ( tree_shape == 3 ):
      k_min = int((crown_center - crown_height * 0.5) / dz)
      k_max = int((crown_center + crown_height * 0.5) / dz)
      for i in range(0,nx-1):
         for j in range(0,nx-1):
            for k in range(k_min,k_max):
               k_rel = k - k_min
               r_test = (x[i] - tree_location_x)**(2) + (y[j] - tree_location_y)**(2) - ( (tree_dia * 0.5)**(2) / crown_height**(2) ) * ( z[k_rel] - crown_height)**(2)
               if ( r_test <= 0.0 ):
                  r_test2 = np.sqrt( (x[i] - tree_location_x)**(2)/(tree_dia * 0.5)**(2) + (y[j] - tree_location_y)**(2)/(tree_dia * 0.5)**(2))
                  r_test3 = np.sqrt( (z[k] - crown_center)**(2)/(crown_height * 0.5)**(2))
                  lad_loc[k,j,i] = lad_max * np.exp ( - cone_extinction * (1.0 - max((r_test+1.0),r_test2,r_test3)) )         
               else:
                  lad_loc[k,j,i] = fillvalues["tree_data"]

            if ( np.any( lad_loc[:,j,i] != fillvalues["tree_data"]) ):
               lad_loc[0,j,i] = 0.0
 
#  Branch for inverted cone shapes. TODO: what is r_test2 and r_test3 used for? Debugging needed!
   if ( tree_shape == 4 ):
      k_min = int((crown_center - crown_height * 0.5) / dz)
      k_max = int((crown_center + crown_height * 0.5) / dz)
      for i in range(0,nx-1):
         for j in range(0,nx-1):
            for k in range(k_min,k_max):
               k_rel = k_max - k
               r_test = (x[i] - tree_location_x)**(2) + (y[j] - tree_location_y)**(2) - ( (tree_dia * 0.5)**(2) / crown_height**(2) ) * ( z[k_rel] - crown_height)**(2)
               if ( r_test <= 0.0 ):
                  r_test2 = np.sqrt( (x[i] - tree_location_x)**(2)/(tree_dia * 0.5)**(2) + (y[j] - tree_location_y)**(2)/(tree_dia * 0.5)**(2))
                  r_test3 = np.sqrt( (z[k] - crown_center)**(2)/(crown_height * 0.5)**(2))
                  lad_loc[k,j,i] = lad_max * np.exp ( - cone_extinction * ( - r_test ) )         
               else:
                  lad_loc[k,j,i] = fillvalues["tree_data"]

            if ( np.any( lad_loc[:,j,i] != fillvalues["tree_data"]) ):
               lad_loc[0,j,i] = 0.0
 
 
#  Branch for paraboloid shapes
   if ( tree_shape == 5 ):
      k_min = int((crown_center - crown_height * 0.5) / dz)
      k_max = int((crown_center + crown_height * 0.5) / dz)
      for i in range(0,nx-1):
         for j in range(0,nx-1):
            for k in range(k_min,k_max):
               k_rel = k - k_min
               r_test = ((x[i] - tree_location_x)**(2) + (y[j] - tree_location_y)**(2)) * crown_height / (tree_dia * 0.5)**(2) - z[k_rel]
               if ( r_test <= 0.0 ):
                   lad_loc[k,j,i] = lad_max * np.exp ( - cone_extinction * (- r_test) )                
               else:
                  lad_loc[k,j,i] = fillvalues["tree_data"]

            if ( np.any( lad_loc[:,j,i] != fillvalues["tree_data"]) ):
               lad_loc[0,j,i] = 0.0
 
 
 
#  Branch for inverted paraboloid shapes
   if ( tree_shape == 6 ):
      k_min = int((crown_center - crown_height * 0.5) / dz)
      k_max = int((crown_center + crown_height * 0.5) / dz)
      for i in range(0,nx-1):
         for j in range(0,nx-1):
            for k in range(k_min,k_max):
               k_rel = k_max - k
               r_test = ((x[i] - tree_location_x)**(2) + (y[j] - tree_location_y)**(2)) * crown_height / (tree_dia * 0.5)**(2) - z[k_rel]
               if ( r_test <= 0.0 ):
                   lad_loc[k,j,i] = lad_max * np.exp ( - cone_extinction * (- r_test) )                
               else:
                  lad_loc[k,j,i] = fillvalues["tree_data"]

            if ( np.any( lad_loc[:,j,i] != fillvalues["tree_data"]) ):
               lad_loc[0,j,i] = 0.0 
 
 
#  Normalize the LAD profile so that the vertically integrated Lalic and Mihailovic (2004) is reproduced by the LAD array. Deactivated for now.
   #for i in range(0,nx-1):
      #for j in range(0,nx-1):
         #lad_clean = np.where(lad_loc[:,j,i] == fillvalues["tree_data"],0.0,lad_loc[:,j,i])
         #lai_from_int = integrate.simps (lad_clean, z)
         #print(lai_from_int)
         #for k in range(0,nz):
            #if ( np.any(lad_loc[k,j,i] > 0.0) ):  
               #lad_loc[k,j,i] = np.where((lad_loc[k,j,i] != fillvalues["tree_data"]),lad_loc[k,j,i] / lai_from_int * lad_profile[k],lad_loc[k,j,i])


#  Dreate BAD array and populate
   bad_loc = np.where(lad_loc != fillvalues["tree_data"],lad_loc*0.01,lad_loc)
   

#  Overwrite grid cells that are occupied by the tree trunk
   radius = trunk_dia * 0.5
   for i in range(0,nx-1):
      for j in range(0,nx-1):
         for k in range(0,nz):
            if ( z[k] <= crown_center ):
               r_test = np.sqrt( (x[i] - tree_location_x)**(2) + (y[j] - tree_location_y)**(2)  )
               if ( r_test <= radius ):
                  bad_loc[k,j,i] = 1.0
               if ( (r_test == 0.0) & (trunk_dia <= dx) ):
                   bad_loc[k,j,i] = radius**(2) * 3.14159265359

   return lad_loc, bad_loc, x, y, z, 0


def process_patch(dz,patch_height,max_height_lad,patch_lai,alpha,beta):
   
#  Define fill values
   fillvalues = {
   "tree_data": float(-9999.0),
   "pch_index": int(-9999),
   }

   phdz = patch_height[:,:] / dz
   pch_index = np.where( (patch_height[:,:] != fillvalues["tree_data"]),phdz.astype(int)+1,int(-1))    
   pch_index = np.where( (pch_index[:,:] == 0) ,fillvalues["pch_index"],pch_index[:,:])
   pch_index = np.where( (pch_index[:,:] == -1) ,0,pch_index[:,:])

   max_canopy_height = max(max(patch_height.flatten()),max_height_lad)

   z = np.arange(0,math.floor(max_canopy_height/dz)*dz+2*dz,dz)
   
   z[1:] = z[1:] - 0.5 * dz

   nz = len(z)
   ny = len(patch_height[:,0])
   nx = len(patch_height[0,:])

   pre_lad = np.ones((nz))
   pre_lad[:] = 0.0
   lad_loc = np.ones( (nz,ny,nx) )
   lad_loc[:,:,:] = fillvalues["tree_data"]
    
   for i in range(0,nx-1):
      for j in range(0,ny-1):
          int_bpdf = 0.0
          if ( (patch_height[j,i] != fillvalues["tree_data"]) & (patch_height[j,i] >= (0.5*dz)) ):
             for k in range(1,pch_index[j,i]):
                int_bpdf = int_bpdf + ( ( ( z[k] / patch_height[j,i] )**( alpha - 1 ) ) * ( ( 1.0 - ( z[k] / patch_height[j,i] ) )**(beta - 1 ) ) * ( dz / patch_height[j,i] ) )

             for k in range(1,pch_index[j,i]):
                pre_lad[k] =  patch_lai[j,i] * ( ( ( dz*k / patch_height[j,i] )**( alpha - 1.0 ) ) * ( ( 1.0 - ( dz*k / patch_height[j,i] ) )**(beta - 1.0 ) ) / int_bpdf ) / patch_height[j,i]

             lad_loc[0,j,i] = pre_lad[0]
             
             for k in range(0,pch_index[j,i]):            
                lad_loc[k,j,i] = 0.5 * ( pre_lad[k-1] + pre_lad[k] )

   return lad_loc, nz, 0


# CLASS TREE
#
# Default tree geometrical parameters:
#
# species: name of the tree type 
#
# shape: defines the general shape of the tree and can be one of the following types:
# 1.0 sphere or ellipsoid
# 2.0 cylinder
# 3.0 cone
# 4.0 inverted cone
# 5.0 paraboloid (rounded cone)
# 6.0 inverted paraboloid (invertes rounded cone)
#  
# ratio:  ratio of maximum crown height to the maximum crown diameter
# diameter: default crown diameter (m)
# height:   default total height of the tree including trunk (m)
# lai_summer: default leaf area index fully leafed
# lai_winter: default winter-teim leaf area index
# lad_max: default maximum leaf area density (m2/m3)
# lad_max_height: default height where the leaf area density is maximum relative to total tree height
# bad_scale: ratio of basal area in the crown area to the leaf area
# dbh: default trunk diameter at breast height (1.4 m) (m)
#
class tree:
    def __init__(self, species, shape, ratio, diameter, height, lai_summer, lai_winter, lad_max_height, bad_scale, dbh): 
       self.species = species
       self.shape = shape
       self.ratio = ratio
       self.diameter = diameter
       self.height = height
       self.lai_summer = lai_summer
       self.lai_winter = lai_winter
       self.lad_max_height = lad_max_height
       self.bad_scale = bad_scale
       self.dbh = dbh