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source: palm/trunk/SCRIPTS/palm_csd @ 3715

Last change on this file since 3715 was 3688, checked in by maronga, 6 years ago

bugfixes in palm_csd
  • Property svn:executable set to *
  • Property svn:keywords set to Id
File size: 54.9 KB
Line 
1#!/usr/bin/env python3
2# -*- coding: utf-8 -*-
3#--------------------------------------------------------------------------------#
4# This file is part of the PALM model system.
5#
6# PALM is free software: you can redistribute it and/or modify it under the terms
7# of the GNU General Public License as published by the Free Software Foundation,
8# either version 3 of the License, or (at your option) any later version.
9#
10# PALM is distributed in the hope that it will be useful, but WITHOUT ANY
11# WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR
12# A PARTICULAR PURPOSE.  See the GNU General Public License for more details.
13#
14# You should have received a copy of the GNU General Public License along with
15# PALM. If not, see <http://www.gnu.org/licenses/>.
16#
17# Copyright 1997-2018  Leibniz Universitaet Hannover
18#--------------------------------------------------------------------------------#
19#
20# Current revisions:
21# -----------------
22#
23#
24# Former revisions:
25# -----------------
26# $Id: palm_csd 3688 2019-01-22 10:44:20Z suehring $
27# Some unspecified bugfixes
28#
29# 3668 2019-01-14 12:49:24Z maronga
30# Various improvements and bugfixes
31#
32# 3629 2018-12-13 12:18:54Z maronga
33# Added canopy generator calls. Some improvements
34#
35# 3567 2018-11-27 13:59:21Z maronga
36# Initial revisions
37#
38# Description:
39# ------------
40# Processing tool for creating PIDS conform static drivers from rastered NetCDF
41# input
42#
43# @Author Bjoern Maronga (maronga@muk.uni-hannover.de)
44#
45# @todo Make input files optional
46# @todo Allow for ASCII input of terrain height and building height
47# @todo Modularize reading config file
48# @todo Convert to object-oriented treatment (buidings, trees)
49# @todo Automatically shift child domains so that their origin lies intersects
50#       a edge note of the parent grid
51#------------------------------------------------------------------------------#
52
53from palm_csd_files.palm_csd_netcdf_interface import *
54from palm_csd_files.palm_csd_tools import *
55from palm_csd_files.palm_csd_canopy_generator import *
56import numpy as np
57
58   
59def read_config_file():
60   
61   import configparser
62   from math import floor
63   import numpy as np
64   import os
65   import subprocess as sub
66   import sys
67
68#  Check if configuration files exists and quit otherwise
69   input_config = ".csd.config"
70   for i in range(1,len(sys.argv)): 
71      input_config = str(sys.argv[i])
72       
73   config = configparser.RawConfigParser(allow_no_value=True)
74
75   if ( os.path.isfile(input_config) == False ):
76      print ("Error. No configuration file " + input_config + " found.")
77      raise SystemExit   
78
79   config.read(input_config)
80
81
82#  Definition of settings
83
84   global settings_bridge_width
85   global settings_lai_roof_intensive
86   global settings_lai_roof_extensive
87   global settings_season
88   global settings_lai_low_default
89   global settings_lai_high_default
90   global settings_patch_height_default
91   global settings_lai_alpha
92   global settings_lai_beta
93   global ndomains
94
95#  Definition of global configuration parameters
96   global global_acronym
97   global global_angle
98   global global_author
99   global global_campaign
100   global global_comment
101   global global_contact
102   global global_data_content 
103   global global_dependencies
104   global global_institution
105   global global_keywords
106   global global_location
107   global global_palm_version
108   global global_references
109   global global_site
110   global global_source
111
112   global  path_out
113   global filename_out
114   global version_out
115
116
117#  Definition of domain parameters
118   global domain_names
119   global domain_px
120   global domain_x0
121   global domain_y0
122   global domain_x1
123   global domain_y1
124   global domain_nx
125   global domain_ny
126   global domain_dz
127   global domain_3d
128   global domain_high_vegetation
129   global domain_ip
130   global domain_za
131   global domain_parent
132   global domain_green_roofs
133   global domain_street_trees
134   global domain_canopy_patches
135
136#  Definition of input data parameters
137   global input_names
138   global input_px
139 
140       
141   global input_file_x
142   global input_file_y
143   global input_file_x_UTM
144   global input_file_y_UTM   
145   global input_file_lat
146   global input_file_lon 
147   global input_file_zt
148   global input_file_buildings_2d
149   global input_file_bridges_2d
150   global input_file_building_id
151   global input_file_bridges_id   
152   global input_file_building_type
153   global input_file_building_type
154   global input_file_lai 
155   global input_file_vegetation_type
156   global input_file_vegetation_height
157   global input_file_pavement_type
158   global input_file_water_type
159   global input_file_street_type
160   global input_file_street_crossings
161   global input_file_soil_type
162   global input_file_vegetation_on_roofs
163   global input_file_tree_crown_diameter
164   global input_file_tree_height
165   global input_file_tree_trunk_diameter
166   global input_file_tree_type
167   global input_file_patch_height
168
169   global zt_all
170   global zt_min
171
172   settings_bridge_width = 3.0
173   settings_lai_roof_intensive = 0.5
174   settings_lai_roof_extensive = 1.0
175   settings_season = "summer"
176   settings_lai_high_default = 6.0
177   settings_lai_low_default = 1.0
178   settings_patch_height_default = 10.0
179   settings_lai_alpha = 5.0
180   settings_lai_beta = 3.0
181   ndomains = 0
182
183   global_acronym = ""
184   global_angle = ""
185   global_author = ""
186   global_campaign = ""
187   global_comment = ""
188   global_contact = ""
189   global_data_content = ""
190   global_dependencies = ""
191   global_institution = ""
192   global_keywords = ""
193   global_location = ""
194   global_palm_version = 6.0
195   global_references = ""
196   global_site = ""
197   global_source = ""
198
199   path_out = ""
200   version_out = 1
201   filename_out = "default"
202   
203   domain_names = []
204   domain_px = []
205   domain_x0 = []
206   domain_y0 = []
207   domain_x1 = []
208   domain_y1 = []
209   domain_nx = []
210   domain_ny = []
211   domain_dz = []
212   domain_3d = []
213   domain_high_vegetation = []
214   domain_ip = []
215   domain_za = []
216   domain_parent = []
217   domain_green_roofs = []
218   domain_street_trees = []
219   domain_canopy_patches = []
220
221   zt_min = 0.0
222   zt_all = []
223
224   input_names = []
225   input_px = []
226
227   input_file_x = []
228   input_file_y = [] 
229   input_file_x_UTM = []
230   input_file_y_UTM = [] 
231   input_file_lat = []
232   input_file_lon = [] 
233   
234   input_file_zt = []
235   input_file_buildings_2d = []
236   input_file_bridges_2d = []
237   input_file_building_id = []
238   input_file_bridges_id = []   
239   input_file_building_type = []
240   input_file_lai = []
241   input_file_vegetation_type = []
242   input_file_vegetation_height = []   
243   input_file_pavement_type = []
244   input_file_water_type = []
245   input_file_street_type = [] 
246   input_file_street_crossings = []     
247   input_file_soil_type = []
248   input_file_vegetation_on_roofs = []
249   input_file_tree_crown_diameter = []
250   input_file_tree_height = []
251   input_file_tree_trunk_diameter = []
252   input_file_tree_type = []
253   input_file_patch_height = []
254   
255# Load all user parameters from config file
256   for i in range(0,len(config.sections())):
257
258      read_tmp = config.sections()[i]
259     
260      if ( read_tmp == 'global' ):
261         global_acronym = config.get(read_tmp, 'acronym')
262         global_angle = float(config.get(read_tmp, 'rotation_angle'))
263         global_author = config.get(read_tmp, 'author')
264         global_campaign = config.get(read_tmp, 'campaign')
265         global_comment = config.get(read_tmp, 'comment')
266         global_contact = config.get(read_tmp, 'contact_person')
267         global_data_content = config.get(read_tmp, 'data_content')   
268         global_dependencies = config.get(read_tmp, 'dependencies')
269         global_institution = config.get(read_tmp, 'institution')
270         global_keywords = config.get(read_tmp, 'keywords')
271         global_location = config.get(read_tmp, 'location')
272         global_palm_version = float(config.get(read_tmp, 'palm_version'))
273         global_references = config.get(read_tmp, 'references')
274         global_site = config.get(read_tmp, 'site')
275         global_source = config.get(read_tmp, 'source')
276
277
278      if ( read_tmp == 'settings' ):
279         settings_lai_roof_intensive = config.get(read_tmp, 'lai_roof_intensive')
280         settings_lai_roof_extensive = config.get(read_tmp, 'lai_roof_extensive') 
281         settings_bridge_width = float(config.get(read_tmp, 'bridge_width'))   
282         settings_season = config.get(read_tmp, 'season')   
283         settings_lai_high_default = float(config.get(read_tmp, 'lai_high_vegetation_default'))
284         settings_lai_low_default = float(config.get(read_tmp, 'lai_low_vegetation_default'))         
285         settings_patch_height_default = float(config.get(read_tmp, 'patch_height_default')) 
286         settings_lai_alpha = float(config.get(read_tmp, 'lai_alpha')) 
287         settings_lai_beta = float(config.get(read_tmp, 'lai_beta')) 
288
289      if ( read_tmp == 'output' ):
290         path_out = config.get(read_tmp, 'path')         
291         filename_out = config.get(read_tmp, 'file_out')
292         version_out = float(config.get(read_tmp, 'version'))
293
294      if ( read_tmp.split("_")[0] == 'domain' ):
295         ndomains = ndomains + 1
296         domain_names.append(read_tmp.split("_")[1])
297         domain_px.append(float(config.get(read_tmp, 'pixel_size')))
298         domain_nx.append(int(config.get(read_tmp, 'nx')))
299         domain_ny.append(int(config.get(read_tmp, 'ny')))
300         domain_dz.append(float(config.get(read_tmp, 'dz')))       
301         domain_3d.append(config.getboolean(read_tmp, 'buildings_3d'))
302         domain_high_vegetation.append(config.getboolean(read_tmp, 'allow_high_vegetation'))
303         domain_canopy_patches.append(config.getboolean(read_tmp, 'generate_vegetation_patches'))       
304         domain_ip.append(config.getboolean(read_tmp, 'interpolate_terrain'))
305         domain_za.append(config.getboolean(read_tmp, 'use_palm_z_axis')) 
306         if domain_ip[ndomains-1] and not domain_za[ndomains-1]:
307            domain_za[ndomains-1] = True
308            print("+++ Overwrite user setting for use_palm_z_axis")
309         
310         domain_parent.append(config.get(read_tmp, 'domain_parent'))
311
312         domain_x0.append(int(floor(float(config.get(read_tmp, 'origin_x'))/float(config.get(read_tmp, 'pixel_size')))))
313         domain_y0.append(int(floor(float(config.get(read_tmp, 'origin_y'))/float(config.get(read_tmp, 'pixel_size')))))       
314         domain_x1.append(int(floor(float(config.get(read_tmp, 'origin_x'))/float(config.get(read_tmp, 'pixel_size'))) + int(config.get(read_tmp, 'nx'))))
315         domain_y1.append(int(floor(float(config.get(read_tmp, 'origin_y'))/float(config.get(read_tmp, 'pixel_size'))) + int(config.get(read_tmp, 'ny'))))
316         domain_green_roofs.append(config.getboolean(read_tmp, 'vegetation_on_roofs')) 
317         domain_street_trees.append(config.getboolean(read_tmp, 'street_trees')) 
318         
319      if ( read_tmp.split("_")[0] == 'input' ):
320         input_names.append(read_tmp.split("_")[1])
321         input_px.append(float(config.get(read_tmp, 'pixel_size')))
322         input_file_x.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_x'))
323         input_file_y.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_y')) 
324         input_file_lat.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_lat'))
325         input_file_lon.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_lon'))   
326         input_file_x_UTM.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_x_UTM'))
327         input_file_y_UTM.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_y_UTM')) 
328         input_file_zt.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_zt'))
329         input_file_buildings_2d.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_buildings_2d'))
330         input_file_bridges_2d.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_bridges_2d'))
331         input_file_building_id.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_building_id')) 
332         input_file_bridges_id.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_bridges_id')) 
333         input_file_building_type.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_building_type'))
334         input_file_lai.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_lai'))         
335         input_file_vegetation_type.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_vegetation_type')) 
336         input_file_vegetation_height.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_vegetation_height'))         
337         input_file_pavement_type.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_pavement_type'))   
338         input_file_water_type.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_water_type'))
339         input_file_street_type.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_street_type'))   
340         input_file_street_crossings.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_street_crossings')) 
341         input_file_patch_height.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_patch_height')) 
342         
343         tmp = config.get(read_tmp, 'file_tree_crown_diameter')
344         if tmp is not None:
345            input_file_tree_crown_diameter.append(config.get(read_tmp, 'path') + "/" + tmp) 
346         else:
347            input_file_tree_crown_diameter.append(None)
348         input_file_tree_height.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_tree_height')) 
349         input_file_tree_trunk_diameter.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_tree_trunk_diameter')) 
350         input_file_tree_type.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_tree_type')) 
351         input_file_vegetation_on_roofs.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_vegetation_on_roofs'))           
352         #input_file_soil_type.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_soil_type'))   
353   return 0
354
355
356############################################################
357
358# Start of main program
359datatypes = {
360   "x": "f4", 
361   "y": "f4", 
362   "z": "f4", 
363   "lat": "f4", 
364   "lon": "f4",   
365   "E_UTM": "f4", 
366   "N_UTM": "f4", 
367   "zt": "f4",
368   "buildings_2d": "f4",
369   "buildings_3d": "b",
370   "bridges_2d": "f4",
371   "building_id": "i",
372   "bridges_id": "i",
373   "building_type": "b",
374   "nsurface_fraction": "i",
375   "vegetation_type": "b",
376   "vegetation_height": "f4",
377   "pavement_type": "b", 
378   "water_type": "b", 
379   "street_type": "b", 
380   "street_crossings": "b",     
381   "soil_type": "b",
382   "surface_fraction": "f4",
383   "building_pars": "f4",
384   "vegetation_pars": "f4",
385   "tree_data": "f4",
386   "tree_type": "b",
387   "nbuilding_pars": "i",
388   "nvegetation_pars": "i",
389   "zlad": "f4"
390   }
391
392fillvalues = {
393   "lat": float(-9999.0),
394   "lon": float(-9999.0),
395   "E_UTM": float(-9999.0),
396   "N_UTM": float(-9999.0),   
397   "zt": float(-9999.0),
398   "buildings_2d": float(-9999.0),
399   "buildings_3d": np.byte(-127),
400   "bridges_2d": float(-9999.0),
401   "building_id": int(-9999),
402   "bridges_id": int(-9999),
403   "building_type": np.byte(-127),
404   "nsurface_fraction": int(-9999),
405   "vegetation_type": np.byte(-127),
406   "vegetation_height": float(-9999.0),
407   "pavement_type": np.byte(-127),
408   "water_type": np.byte(-127),
409   "street_type": np.byte(-127), 
410   "street_crossings": np.byte(-127),   
411   "soil_type": np.byte(-127),
412   "surface_fraction": float(-9999.0),
413   "building_pars": float(-9999.0),
414   "vegetation_pars": float(-9999.0),
415   "tree_data": float(-9999.0),
416   "tree_type": np.byte(-127)
417   }
418
419defaultvalues = {
420   "lat": float(-9999.0),
421   "lon": float(-9999.0),
422   "E_UTM": float(-9999.0),
423   "N_UTM": float(-9999.0), 
424   "zt": float(0.0),
425   "buildings_2d": float(0.0),
426   "buildings_3d": 0,   
427   "bridges_2d": float(0.0),
428   "building_id": int(0),
429   "bridges_id": int(0),
430   "building_type": 1,
431   "nsurface_fraction": int(-9999),
432   "vegetation_type": 3,
433   "vegetation_height": float(-9999.0),
434   "pavement_type": 1, 
435   "water_type": 1, 
436   "street_type": 1, 
437   "street_crossings": 0,   
438   "soil_type": 1,
439   "surface_fraction": float(0.0),
440   "buildings_pars": float(-9999.0),
441   "tree_data": float(-9999.0),
442   "tree_type": 0,
443   "vegetation_pars": float(-9999.0)   
444   }
445 
446# Read configuration file and set parameters accordingly
447read_config_file()
448
449
450filename = []
451ii = []
452ii_parent = []
453# Define indices and filenames for all domains and create netCDF files
454for i in range(0,ndomains):
455
456#  Calculate indices and input files
457   ii.append(input_px.index(domain_px[i]))
458   filename.append(path_out + "/" + filename_out + "_" + domain_names[i])
459   if domain_parent[i] is not None:
460      ii_parent.append(domain_names.index(domain_parent[i]))
461   else:
462      ii_parent.append(None)
463
464
465   x_UTM = nc_read_from_file_2d(input_file_x[ii[i]], "Band1", domain_x0[i], domain_x0[i], domain_y0[i], domain_y0[i]) - 0.5 * domain_px[i]
466   y_UTM = nc_read_from_file_2d(input_file_y[ii[i]], "Band1", domain_x0[i], domain_x0[i], domain_y0[i], domain_y0[i]) - 0.5 * domain_px[i]
467   lat = nc_read_from_file_2d(input_file_lat[ii[i]], "Band1", domain_x0[i], domain_x0[i], domain_y0[i], domain_y0[i])
468   lon = nc_read_from_file_2d(input_file_lon[ii[i]], "Band1", domain_x0[i], domain_x0[i], domain_y0[i], domain_y0[i]) 
469   
470#  Create NetCDF output file and set global attributes
471   nc_create_file(filename[i])
472   nc_write_global_attributes(filename[i],float(x_UTM[0,0]),float(y_UTM[0,0]),float(lat[0,0]),float(lon[0,0]),"",global_acronym,global_angle,global_author,global_campaign,global_comment,global_contact,global_data_content,global_dependencies,global_institution,global_keywords,global_location,global_palm_version,global_references,global_site,global_source,version_out)
473
474   del x_UTM, y_UTM, lat, lon
475   
476# Process terrain height
477for i in range(0,ndomains):
478
479#  Read and write terrain height (zt)
480   zt = nc_read_from_file_2d(input_file_zt[ii[i]], 'Band1', domain_x0[i], domain_x1[i], domain_y0[i], domain_y1[i])
481
482#  Final step: add zt array to the global array   
483   zt_all.append(zt)
484   del zt
485 
486#  Calculate the global (all domains) minimum of the terrain height. This value will be substracted for all
487#  data sets
488zt_min = min(zt_all[0].flatten()) 
489for i in range(0,ndomains):
490   zt_min = min(zt_min,min(zt_all[i].flatten()))
491   
492del zt_all[:]
493
494print( "Shift terrain height by -" + str(zt_min))
495for i in range(0,ndomains):
496
497#  Read and write terrain height (zt)
498   zt = nc_read_from_file_2d(input_file_zt[ii[i]], 'Band1', domain_x0[i], domain_x1[i], domain_y0[i], domain_y1[i])
499   x = nc_read_from_file_1d(input_file_x[ii[i]], "x", domain_x0[i], domain_x1[i])
500   y = nc_read_from_file_1d(input_file_y[ii[i]], "y", domain_y0[i], domain_y1[i])
501
502   zt = zt - zt_min
503   
504   nc_write_global_attribute(filename[i], 'origin_z', float(zt_min))
505
506#  If necessary, interpolate parent domain terrain height on child domain grid and blend the two
507   if domain_ip[i]:
508      tmp_x0 = int( domain_x0[i] * domain_px[i] / domain_px[ii_parent[i]] ) - 1
509      tmp_y0 = int( domain_y0[i] * domain_px[i] / domain_px[ii_parent[i]] ) - 1
510      tmp_x1 = int( domain_x1[i] * domain_px[i] / domain_px[ii_parent[i]] ) + 1
511      tmp_y1 = int( domain_y1[i] * domain_px[i] / domain_px[ii_parent[i]] ) + 1
512               
513      tmp_x = nc_read_from_file_1d(input_file_x[ii_parent[i]], "x", tmp_x0, tmp_x1)
514      tmp_y = nc_read_from_file_1d(input_file_y[ii_parent[i]], "y", tmp_y0, tmp_y1)
515
516      zt_parent = nc_read_from_file_2d(input_file_zt[ii_parent[i]], 'Band1', tmp_x0, tmp_x1, tmp_y0, tmp_y1)
517
518      print( "Shift terrain height by -" + str(zt_min))
519      zt_parent = zt_parent - zt_min
520
521#     Interpolate array and bring to PALM grid of child domain
522
523      zt_ip = interpolate_2d(zt_parent,tmp_x,tmp_y,x,y)
524      zt_ip = bring_to_palm_grid(zt_ip,x,y,domain_dz[ii_parent[i]])
525
526#     Shift the child terrain height according to the parent mean terrain height       
527      zt = zt - np.mean(zt)  + np.mean(zt_ip)
528 
529 
530#     Blend over the parent and child terrain height within a radius of 50 px 
531      zt = blend_array_2d(zt,zt_ip,50)
532   
533#  Final step: add zt array to the global array   
534   zt_all.append(zt)
535   del zt
536 
537
538# Read and shift x and y coordinates, shift terrain height according to its minimum value and write all data
539# to file 
540for i in range(0,ndomains):   
541#  Read horizontal grid variables from zt file and write them to output file
542   x = nc_read_from_file_1d(input_file_x[ii[i]], "x", domain_x0[i], domain_x1[i])
543   y = nc_read_from_file_1d(input_file_y[ii[i]], "y", domain_y0[i], domain_y1[i]) 
544   x = x - min(x.flatten()) + domain_px[i]/2.0
545   y = y - min(y.flatten()) + domain_px[i]/2.0 
546   nc_write_dimension(filename[i], 'x', x, datatypes["x"])
547   nc_write_dimension(filename[i], 'y', y, datatypes["y"]) 
548   nc_write_attribute(filename[i], 'x', 'long_name', 'x')
549   nc_write_attribute(filename[i], 'x', 'standard_name','projection_x_coordinate')   
550   nc_write_attribute(filename[i], 'x', 'units', 'm')
551   nc_write_attribute(filename[i], 'y', 'long_name', 'x')
552   nc_write_attribute(filename[i], 'y', 'standard_name', 'projection_y_coordinate')   
553   nc_write_attribute(filename[i], 'y', 'units', 'm')
554
555   lat = nc_read_from_file_2d(input_file_lat[ii[i]], "Band1", domain_x0[i], domain_x1[i], domain_y0[i], domain_y1[i])
556   lon = nc_read_from_file_2d(input_file_lon[ii[i]], "Band1", domain_x0[i], domain_x1[i], domain_y0[i], domain_y1[i]) 
557
558   nc_write_to_file_2d(filename[i], 'lat', lat, datatypes["lat"],'y','x',fillvalues["lat"])
559   nc_write_to_file_2d(filename[i], 'lon', lon, datatypes["lon"],'y','x',fillvalues["lon"]) 
560   
561   nc_write_attribute(filename[i], 'lat', 'long_name', 'latitude')
562   nc_write_attribute(filename[i], 'lat', 'standard_name','latitude')   
563   nc_write_attribute(filename[i], 'lat', 'units', 'degrees_north')
564 
565   nc_write_attribute(filename[i], 'lon', 'long_name', 'longitude')
566   nc_write_attribute(filename[i], 'lon', 'standard_name','longitude')   
567   nc_write_attribute(filename[i], 'lon', 'units', 'degrees_east') 
568   
569   x_UTM = nc_read_from_file_2d(input_file_x_UTM[ii[i]], "Band1", domain_x0[i], domain_x1[i], domain_y0[i], domain_y1[i])
570   y_UTM = nc_read_from_file_2d(input_file_y_UTM[ii[i]], "Band1", domain_x0[i], domain_x1[i], domain_y0[i], domain_y1[i]) 
571   
572   nc_write_to_file_2d(filename[i], 'E_UTM', x_UTM, datatypes["E_UTM"],'y','x',fillvalues["E_UTM"])
573   nc_write_to_file_2d(filename[i], 'N_UTM', y_UTM, datatypes["N_UTM"],'y','x',fillvalues["N_UTM"]) 
574   
575   nc_write_attribute(filename[i], 'E_UTM', 'long_name', 'easting')
576   nc_write_attribute(filename[i], 'E_UTM', 'standard_name','projection_x_coorindate')   
577   nc_write_attribute(filename[i], 'E_UTM', 'units', 'm')
578 
579   nc_write_attribute(filename[i], 'N_UTM', 'long_name', 'northing')
580   nc_write_attribute(filename[i], 'N_UTM', 'standard_name','projection_y_coorindate')   
581   nc_write_attribute(filename[i], 'N_UTM', 'units', 'm') 
582 
583   nc_write_crs(filename[i])
584   
585 
586 
587#  If necessary, bring terrain height to PALM's vertical grid. This is either forced by the user or implicitly
588#  by using interpolation for a child domain       
589   if domain_za[i]:
590      zt_all[i] = bring_to_palm_grid(zt_all[i],x,y,domain_dz[i])
591
592   nc_write_to_file_2d(filename[i], 'zt', zt_all[i], datatypes["zt"],'y','x',fillvalues["zt"])
593   nc_write_attribute(filename[i], 'zt', 'long_name', 'orography')
594   nc_write_attribute(filename[i], 'zt', 'units', 'm')
595   nc_write_attribute(filename[i], 'zt', 'res_orig', domain_px[i]) 
596   nc_write_attribute(filename[i], 'zt', 'coordinates', 'E_UTM N_UTM lon lat') 
597   nc_write_attribute(filename[i], 'zt', 'grid_mapping', 'E_UTM N_UTM lon lat')   
598
599del zt_all
600
601
602#  Process building height, id, and type
603for i in range(0,ndomains): 
604   buildings_2d = nc_read_from_file_2d(input_file_buildings_2d[ii[i]], 'Band1', domain_x0[i], domain_x1[i], domain_y0[i], domain_y1[i])
605
606   building_id = nc_read_from_file_2d(input_file_building_id[ii[i]], 'Band1', domain_x0[i], domain_x1[i], domain_y0[i], domain_y1[i]) 
607 
608   building_type = nc_read_from_file_2d(input_file_building_type[ii[i]], 'Band1', domain_x0[i], domain_x1[i], domain_y0[i], domain_y1[i]) 
609   building_type[building_type >= 254] = fillvalues["building_type"]
610   building_type = np.where(building_type < 1,defaultvalues["building_type"],building_type)
611 
612   check = check_arrays_2(buildings_2d,building_id,fillvalues["buildings_2d"],fillvalues["building_id"])
613   if not check:
614      buildings_2d = np.where(building_id != fillvalues["building_id"],buildings_2d,fillvalues["buildings_2d"])
615      building_id = np.where(buildings_2d == fillvalues["buildings_2d"],fillvalues["building_id"],building_id)
616      print("Data check #1 " + str(check_arrays_2(buildings_2d,building_id,fillvalues["buildings_2d"],fillvalues["building_id"])))
617
618   check = check_arrays_2(buildings_2d,building_type,fillvalues["buildings_2d"],fillvalues["building_type"])
619   if not check:
620      building_type = np.where(buildings_2d == fillvalues["buildings_2d"],fillvalues["building_type"],building_type)
621      building_type = np.where((building_type == fillvalues["building_type"]) & (buildings_2d != fillvalues["buildings_2d"]),defaultvalues["building_type"],building_type)
622      print("Data check #2 " + str(check_arrays_2(buildings_2d,building_type,fillvalues["buildings_2d"],fillvalues["building_type"])))
623 
624
625   nc_write_to_file_2d(filename[i], 'buildings_2d', buildings_2d, datatypes["buildings_2d"],'y','x',fillvalues["buildings_2d"])
626   nc_write_attribute(filename[i], 'buildings_2d', 'long_name', 'buildings')
627   nc_write_attribute(filename[i], 'buildings_2d', 'units', 'm')
628   nc_write_attribute(filename[i], 'buildings_2d', 'res_orig', domain_px[i]) 
629   nc_write_attribute(filename[i], 'buildings_2d', 'lod', 1) 
630   nc_write_attribute(filename[i], 'buildings_2d', 'coordinates', 'E_UTM N_UTM lon lat') 
631   nc_write_attribute(filename[i], 'buildings_2d', 'grid_mapping', 'E_UTM N_UTM lon lat')   
632   
633   nc_write_to_file_2d(filename[i], 'building_id', building_id, datatypes["building_id"],'y','x',fillvalues["building_id"])
634   nc_write_attribute(filename[i], 'building_id', 'long_name', 'building id')
635   nc_write_attribute(filename[i], 'building_id', 'units', '')
636   nc_write_attribute(filename[i], 'building_id', 'res _orig', domain_px[i]) 
637   nc_write_attribute(filename[i], 'building_id', 'coordinates', 'E_UTM N_UTM lon lat') 
638   nc_write_attribute(filename[i], 'building_id', 'grid_mapping', 'E_UTM N_UTM lon lat')   
639   
640   nc_write_to_file_2d(filename[i], 'building_type', building_type, datatypes["building_type"],'y','x',fillvalues["building_type"])
641   nc_write_attribute(filename[i], 'building_type', 'long_name', 'building type')
642   nc_write_attribute(filename[i], 'building_type', 'units', '')
643   nc_write_attribute(filename[i], 'building_type', 'res_orig', domain_px[i]) 
644   nc_write_attribute(filename[i], 'building_type', 'coordinates', 'E_UTM N_UTM lon lat') 
645   nc_write_attribute(filename[i], 'building_type', 'grid_mapping', 'E_UTM N_UTM lon lat')   
646 
647del buildings_2d
648del building_id
649del building_type
650
651# Create 3d buildings if necessary. In that course, read bridge objects and add them to building layer
652for i in range(0,ndomains): 
653   
654   if domain_3d[i]:
655      x = nc_read_from_file_2d_all(filename[i], 'x')
656      y = nc_read_from_file_2d_all(filename[i], 'y') 
657      buildings_2d = nc_read_from_file_2d_all(filename[i], 'buildings_2d') 
658      building_id = nc_read_from_file_2d_all(filename[i], 'building_id') 
659     
660      bridges_2d = nc_read_from_file_2d(input_file_bridges_2d[ii[i]], 'Band1', domain_x0[i], domain_x1[i], domain_y0[i], domain_y1[i])
661      bridges_id = nc_read_from_file_2d(input_file_bridges_id[ii[i]], 'Band1', domain_x0[i], domain_x1[i], domain_y0[i], domain_y1[i])
662     
663      bridges_2d = np.where(bridges_2d == 0.0,fillvalues["bridges_2d"],bridges_2d)
664      building_id = np.where(bridges_2d == fillvalues["bridges_2d"],building_id,bridges_id)
665     
666     
667      if np.any(buildings_2d != fillvalues["buildings_2d"]):
668         buildings_3d, z = make_3d_from_2d(buildings_2d,x,y,domain_dz[i])
669         if np.any(bridges_2d != fillvalues["bridges_2d"]):
670            buildings_3d = make_3d_from_bridges_2d(buildings_3d,bridges_2d,x,y,domain_dz[i],settings_bridge_width,fillvalues["bridges_2d"])
671         else:
672            print("Skipping creation of 3D bridges (no bridges in domain)")
673           
674           
675         nc_write_dimension(filename[i], 'z', z, datatypes["z"]) 
676         nc_write_attribute(filename[i], 'z', 'long_name', 'z') 
677         nc_write_attribute(filename[i], 'z', 'units', 'm')
678 
679         nc_write_to_file_3d(filename[i], 'buildings_3d', buildings_3d, datatypes["buildings_3d"],'z','y','x',fillvalues["buildings_3d"])   
680         nc_write_attribute(filename[i], 'buildings_3d', 'long_name', 'buildings 3d')
681         nc_write_attribute(filename[i], 'buildings_3d', 'units', '')
682         nc_write_attribute(filename[i], 'buildings_3d', 'res_orig', domain_px[i]) 
683         nc_write_attribute(filename[i], 'buildings_3d', 'lod', 2) 
684         
685         del buildings_3d
686         
687      else:
688         print("Skipping creation of 3D buildings (no buildings in domain)")
689
690
691      del bridges_2d, bridges_id, building_id, buildings_2d
692
693
694
695# Read vegetation type, water_type, pavement_type, soil_type and make fields consistent
696for i in range(0,ndomains): 
697
698   building_type = nc_read_from_file_2d_all(filename[i], 'building_type') 
699     
700   vegetation_type = nc_read_from_file_2d(input_file_vegetation_type[ii[i]], 'Band1', domain_x0[i], domain_x1[i], domain_y0[i], domain_y1[i])   
701   vegetation_type[vegetation_type == 255] = fillvalues["vegetation_type"]
702   vegetation_type = np.where((vegetation_type < 1) & (vegetation_type != fillvalues["vegetation_type"]),defaultvalues["vegetation_type"],vegetation_type)
703   
704   pavement_type = nc_read_from_file_2d(input_file_pavement_type[ii[i]], 'Band1', domain_x0[i], domain_x1[i], domain_y0[i], domain_y1[i])   
705   pavement_type[pavement_type == 255] = fillvalues["pavement_type"]
706   pavement_type = np.where((pavement_type < 1) & (pavement_type != fillvalues["pavement_type"]),defaultvalues["pavement_type"],pavement_type)
707
708   water_type = nc_read_from_file_2d(input_file_water_type[ii[i]], 'Band1', domain_x0[i], domain_x1[i], domain_y0[i], domain_y1[i])   
709   water_type[water_type == 255] = fillvalues["water_type"]
710   water_type = np.where((water_type < 1) & (water_type != fillvalues["water_type"]),defaultvalues["water_type"],water_type)
711 
712#  to do: replace by real soil input data
713   soil_type = nc_read_from_file_2d(input_file_vegetation_type[ii[i]], 'Band1', domain_x0[i], domain_x1[i], domain_y0[i], domain_y1[i]) 
714   soil_type[soil_type == 255] = fillvalues["soil_type"]
715   soil_type = np.where((soil_type < 1) & (soil_type != fillvalues["soil_type"]),defaultvalues["soil_type"],soil_type)
716
717#  Make arrays consistent
718#  #1 Set vegetation type to missing for pixel where a pavement type is set
719   vegetation_type = np.where((vegetation_type != fillvalues["vegetation_type"]) & (pavement_type != fillvalues["pavement_type"]),fillvalues["vegetation_type"],vegetation_type)
720
721#  #2 Set vegetation type to missing for pixel where a building type is set
722   vegetation_type = np.where((vegetation_type != fillvalues["vegetation_type"]) & (building_type != fillvalues["building_type"]) ,fillvalues["vegetation_type"],vegetation_type)
723
724#  #3 Set vegetation type to missing for pixel where a building type is set
725   vegetation_type = np.where((vegetation_type != fillvalues["vegetation_type"]) & (water_type != fillvalues["water_type"]),fillvalues["vegetation_type"],vegetation_type)   
726
727#  #4 Remove pavement for pixels with buildings
728   pavement_type = np.where((pavement_type != fillvalues["pavement_type"]) & (building_type != fillvalues["building_type"]),fillvalues["pavement_type"],pavement_type) 
729
730#  #5 Remove pavement for pixels with water
731   pavement_type = np.where((pavement_type != fillvalues["pavement_type"]) & (water_type != fillvalues["water_type"]),fillvalues["pavement_type"],pavement_type)   
732
733#  #6 Remove water for pixels with buildings
734   water_type = np.where((water_type != fillvalues["water_type"]) & (building_type != fillvalues["building_type"]),fillvalues["water_type"],water_type) 
735 
736   
737#  Correct vegetation_type when a vegetation height is available and is indicative of low vegeetation
738   vegetation_height = nc_read_from_file_2d(input_file_vegetation_height[ii[i]], 'Band1', domain_x0[i], domain_x1[i], domain_y0[i], domain_y1[i])   
739         
740   vegetation_type = np.where((vegetation_height != fillvalues["vegetation_height"]) & (vegetation_height == 0.0) & ((vegetation_type == 4) | (vegetation_type == 5) | (vegetation_type == 6) |(vegetation_type == 7) | (vegetation_type == 17) | (vegetation_type == 18)), 3, vegetation_type)
741   vegetation_height = np.where((vegetation_height != fillvalues["vegetation_height"]) & (vegetation_height == 0.0) & ((vegetation_type == 4) | (vegetation_type == 5) | (vegetation_type == 6) |(vegetation_type == 7) | (vegetation_type == 17) | (vegetation_type == 18)), fillvalues["vegetation_height"],vegetation_height)
742 
743#  Check for consistency and fill empty fields with default vegetation type
744   consistency_array, test = check_consistency_4(vegetation_type,building_type,pavement_type,water_type,fillvalues["vegetation_type"],fillvalues["building_type"],fillvalues["pavement_type"],fillvalues["water_type"])
745   
746   if test:
747      vegetation_type = np.where(consistency_array == 0,defaultvalues["vegetation_type"],vegetation_type)
748      consistency_array, test = check_consistency_4(vegetation_type,building_type,pavement_type,water_type,fillvalues["vegetation_type"],fillvalues["building_type"],fillvalues["pavement_type"],fillvalues["water_type"])
749
750#  #7 to be removed: set default soil type everywhere
751   soil_type = np.where((vegetation_type != fillvalues["vegetation_type"]) | (pavement_type != fillvalues["pavement_type"]),defaultvalues["soil_type"],fillvalues["soil_type"]) 
752       
753   
754#  Check for consistency and fill empty fields with default vegetation type
755   consistency_array, test = check_consistency_3(vegetation_type,pavement_type,soil_type,fillvalues["vegetation_type"],fillvalues["pavement_type"],fillvalues["soil_type"])
756       
757#  Create surface_fraction array
758   x = nc_read_from_file_2d_all(filename[i], 'x')
759   y = nc_read_from_file_2d_all(filename[i], 'y') 
760   nsurface_fraction = np.arange(0,3)
761   surface_fraction = np.ones((len(nsurface_fraction),len(y),len(x)))
762   
763   surface_fraction[0,:,:] = np.where(vegetation_type != fillvalues["vegetation_type"], 1.0, 0.0)
764   surface_fraction[1,:,:] = np.where(pavement_type != fillvalues["pavement_type"], 1.0, 0.0)
765   surface_fraction[2,:,:] = np.where(water_type != fillvalues["water_type"], 1.0, 0.0)
766   
767   nc_write_dimension(filename[i], 'nsurface_fraction', nsurface_fraction, datatypes["nsurface_fraction"]) 
768   nc_write_to_file_3d(filename[i], 'surface_fraction', surface_fraction, datatypes["surface_fraction"],'nsurface_fraction','y','x',fillvalues["surface_fraction"])     
769   nc_write_attribute(filename[i], 'surface_fraction', 'long_name', 'surface fraction')
770   nc_write_attribute(filename[i], 'surface_fraction', 'units', '')
771   nc_write_attribute(filename[i], 'surface_fraction', 'res_orig', domain_px[i])     
772   del surface_fraction   
773
774   nc_write_to_file_2d(filename[i], 'vegetation_type', vegetation_type, datatypes["vegetation_type"],'y','x',fillvalues["vegetation_type"])     
775   nc_write_attribute(filename[i], 'vegetation_type', 'long_name', 'vegetation type')
776   nc_write_attribute(filename[i], 'vegetation_type', 'units', '')
777   nc_write_attribute(filename[i], 'vegetation_type', 'res_orig', domain_px[i]) 
778   nc_write_attribute(filename[i], 'vegetation_type', 'coordinates', 'E_UTM N_UTM lon lat') 
779   nc_write_attribute(filename[i], 'vegetation_type', 'grid_mapping', 'E_UTM N_UTM lon lat')   
780   del vegetation_type
781
782   nc_write_to_file_2d(filename[i], 'pavement_type', pavement_type, datatypes["pavement_type"],'y','x',fillvalues["pavement_type"])   
783   nc_write_attribute(filename[i], 'pavement_type', 'long_name', 'pavement type')
784   nc_write_attribute(filename[i], 'pavement_type', 'units', '')
785   nc_write_attribute(filename[i], 'pavement_type', 'res_orig', domain_px[i]) 
786   nc_write_attribute(filename[i], 'pavement_type', 'coordinates', 'E_UTM N_UTM lon lat') 
787   nc_write_attribute(filename[i], 'pavement_type', 'grid_mapping', 'E_UTM N_UTM lon lat')   
788   del pavement_type
789
790   nc_write_to_file_2d(filename[i], 'water_type', water_type, datatypes["water_type"],'y','x',fillvalues["water_type"]) 
791   nc_write_attribute(filename[i], 'water_type', 'long_name', 'water type')
792   nc_write_attribute(filename[i], 'water_type', 'units', '')
793   nc_write_attribute(filename[i], 'water_type', 'res_orig', domain_px[i]) 
794   nc_write_attribute(filename[i], 'water_type', 'coordinates', 'E_UTM N_UTM lon lat') 
795   nc_write_attribute(filename[i], 'water_type', 'grid_mapping', 'E_UTM N_UTM lon lat')   
796   del water_type
797
798   nc_write_to_file_2d(filename[i], 'soil_type', soil_type, datatypes["soil_type"],'y','x',fillvalues["soil_type"]) 
799   nc_write_attribute(filename[i], 'soil_type', 'long_name', 'soil type')
800   nc_write_attribute(filename[i], 'soil_type', 'units', '')
801   nc_write_attribute(filename[i], 'soil_type', 'res_orig', domain_px[i])   
802   nc_write_attribute(filename[i], 'soil_type', 'coordinates', 'E_UTM N_UTM lon lat') 
803   nc_write_attribute(filename[i], 'soil_type', 'grid_mapping', 'E_UTM N_UTM lon lat')   
804   del soil_type
805
806   del x
807   del y
808
809#  pixels with bridges get building_type = 7 = bridge. This does not change the _type setting for the under-bridge
810#  area NOTE: when bridges are present the consistency check will fail at the moment
811   if domain_3d[i]:
812      if np.any(building_type != fillvalues["building_type"]):
813 
814         bridges_2d = nc_read_from_file_2d(input_file_bridges_2d[ii[i]], 'Band1', domain_x0[i], domain_x1[i], domain_y0[i], domain_y1[i])         
815         bridges_2d = np.where(bridges_2d == 0.0,fillvalues["bridges_2d"],bridges_2d)
816         building_type = np.where(bridges_2d != fillvalues["bridges_2d"],7,building_type)
817         nc_overwrite_to_file_2d(filename[i], 'building_type', building_type) 
818 
819         del building_type 
820         del bridges_2d
821
822# Read/write street type and street crossings
823for i in range(0,ndomains): 
824
825   street_type = nc_read_from_file_2d(input_file_street_type[ii[i]], 'Band1', domain_x0[i], domain_x1[i], domain_y0[i], domain_y1[i])   
826   street_type[street_type == 255] = fillvalues["street_type"]
827   street_type = np.where((street_type < 1) & (street_type != fillvalues["street_type"]),defaultvalues["street_type"],street_type)
828   
829   nc_write_to_file_2d(filename[i], 'street_type', street_type, datatypes["street_type"],'y','x',fillvalues["street_type"]) 
830   nc_write_attribute(filename[i], 'street_type', 'long_name', 'street type')
831   nc_write_attribute(filename[i], 'street_type', 'units', '')
832   nc_write_attribute(filename[i], 'street_type', 'res_orig', domain_px[i]) 
833   nc_write_attribute(filename[i], 'street_type', 'coordinates', 'E_UTM N_UTM lon lat') 
834   nc_write_attribute(filename[i], 'street_type', 'grid_mapping', 'E_UTM N_UTM lon lat')   
835   del street_type
836   
837   street_crossings = nc_read_from_file_2d(input_file_street_crossings[ii[i]], 'Band1', domain_x0[i], domain_x1[i], domain_y0[i], domain_y1[i])   
838   street_crossings[street_crossings == 255] = fillvalues["street_crossings"]
839   street_crossings = np.where((street_crossings < 1) & (street_crossings != fillvalues["street_crossings"]),defaultvalues["street_crossings"],street_crossings)
840   
841   nc_write_to_file_2d(filename[i], 'street_crossings', street_crossings, datatypes["street_crossings"],'y','x',fillvalues["street_crossings"]) 
842   nc_write_attribute(filename[i], 'street_crossings', 'long_name', 'street crossings')
843   nc_write_attribute(filename[i], 'street_crossings', 'units', '')
844   nc_write_attribute(filename[i], 'street_crossings', 'res_orig', domain_px[i]) 
845   nc_write_attribute(filename[i], 'street_crossings', 'coordinates', 'E_UTM N_UTM lon lat') 
846   nc_write_attribute(filename[i], 'street_crossings', 'grid_mapping', 'E_UTM N_UTM lon lat')   
847   del street_crossings
848
849
850# Read/write vegetation on roofs
851for i in range(0,ndomains): 
852   if domain_green_roofs[i]: 
853      green_roofs = nc_read_from_file_2d(input_file_vegetation_on_roofs[ii[i]], 'Band1', domain_x0[i], domain_x1[i], domain_y0[i], domain_y1[i])   
854      buildings_2d = nc_read_from_file_2d_all(filename[i], 'buildings_2d') 
855
856
857      x = nc_read_from_file_2d_all(filename[i], 'x')
858      y = nc_read_from_file_2d_all(filename[i], 'y') 
859      nbuilding_pars = np.arange(0,46)
860      building_pars = np.ones((len(nbuilding_pars),len(y),len(x)))
861      building_pars[:,:,:] = fillvalues["building_pars"]
862
863#     assign green fraction on roofs
864      building_pars[3,:,:] = np.where( (buildings_2d != fillvalues["buildings_2d"] ) & ( green_roofs != fillvalues["building_pars"] ),1.0,fillvalues["building_pars"])
865
866#     assign leaf area index for vegetation on roofs
867      building_pars[4,:,:] = np.where( ( buildings_2d != fillvalues["buildings_2d"] ) & ( green_roofs == 1.0 ),settings_lai_roof_intensive,fillvalues["building_pars"])
868      building_pars[4,:,:] = np.where( ( buildings_2d != fillvalues["buildings_2d"] ) & ( green_roofs == 2.0 ),settings_lai_roof_extensive,building_pars[4,:,:])
869   
870   
871      nc_write_dimension(filename[i], 'nbuilding_pars', nbuilding_pars, datatypes["nbuilding_pars"]) 
872      nc_write_to_file_3d(filename[i], 'building_pars', building_pars, datatypes["building_pars"],'nbuilding_pars','y','x',fillvalues["building_pars"]) 
873      nc_write_attribute(filename[i], 'building_pars', 'long_name', 'building_pars')
874      nc_write_attribute(filename[i], 'building_pars', 'units', '')
875      nc_write_attribute(filename[i], 'building_pars', 'res_orig', domain_px[i]) 
876      nc_write_attribute(filename[i], 'building_pars', 'coordinates', 'E_UTM N_UTM lon lat') 
877      nc_write_attribute(filename[i], 'building_pars', 'grid_mapping', 'E_UTM N_UTM lon lat')   
878         
879      del building_pars, buildings_2d, x, y
880
881
882# Read tree data and create LAD and BAD arrays using the canopy generator
883for i in range(0,ndomains): 
884   lai = nc_read_from_file_2d(input_file_lai[ii[i]], 'Band1', domain_x0[i], domain_x1[i], domain_y0[i], domain_y1[i])   
885     
886   vegetation_type = nc_read_from_file_2d_all(filename[i], 'vegetation_type')
887     
888   lai = np.where(vegetation_type == fillvalues["vegetation_type"],fillvalues["vegetation_pars"],lai)
889
890     
891   x = nc_read_from_file_2d_all(filename[i], 'x')
892   y = nc_read_from_file_2d_all(filename[i], 'y') 
893   nvegetation_pars = np.arange(0,12)
894   vegetation_pars = np.ones((len(nvegetation_pars),len(y),len(x)))
895   vegetation_pars[:,:,:] = fillvalues["vegetation_pars"] 
896     
897   vegetation_pars[1,:,:] = lai
898     
899
900#  Write out first version of LAI. Will later be overwritten.
901   nc_write_dimension(filename[i], 'nvegetation_pars', nvegetation_pars, datatypes["nvegetation_pars"]) 
902   nc_write_to_file_3d(filename[i], 'vegetation_pars', vegetation_pars, datatypes["vegetation_pars"],'nvegetation_pars','y','x',fillvalues["vegetation_pars"]) 
903   nc_write_attribute(filename[i], 'vegetation_pars', 'long_name', 'vegetation_pars')
904   nc_write_attribute(filename[i], 'vegetation_pars', 'units', '')
905   nc_write_attribute(filename[i], 'vegetation_pars', 'res_orig', domain_px[i]) 
906   nc_write_attribute(filename[i], 'vegetation_pars', 'coordinates', 'E_UTM N_UTM lon lat') 
907   nc_write_attribute(filename[i], 'vegetation_pars', 'grid_mapping', 'E_UTM N_UTM lon lat')   
908
909   del lai, vegetation_pars, vegetation_type
910
911# Read tree data and create LAD and BAD arrays using the canopy generator
912for i in range(0,ndomains): 
913   if domain_street_trees[i]: 
914
915      vegetation_pars =  nc_read_from_file_2d_all(filename[i], 'vegetation_pars')
916
917      lai = np.copy(vegetation_pars[1,:,:])
918
919      x = nc_read_from_file_2d_all(filename[i], 'x')
920      y = nc_read_from_file_2d_all(filename[i], 'y') 
921
922#     Save lai data as default for low and high vegetation
923      lai_low = lai
924      lai_high = lai
925   
926#     Read all tree parameters from file
927      tree_height = nc_read_from_file_2d(input_file_tree_height[ii[i]], 'Band1', domain_x0[i], domain_x1[i], domain_y0[i], domain_y1[i])   
928
929      if (input_file_tree_crown_diameter[ii[i]] is not None):
930         tree_crown_diameter = nc_read_from_file_2d(input_file_tree_crown_diameter[ii[i]], 'Band1', domain_x0[i], domain_x1[i], domain_y0[i], domain_y1[i])   
931         tree_crown_diameter = np.where( (tree_crown_diameter == 0.0) | (tree_crown_diameter == -1.0) ,fillvalues["tree_data"],tree_crown_diameter)
932      else:
933         tree_crown_diameter = np.ones((len(y),len(x)))
934         tree_crown_diameter[:,:] = fillvalues["tree_data"]
935
936
937      tree_trunk_diameter = nc_read_from_file_2d(input_file_tree_trunk_diameter[ii[i]], 'Band1', domain_x0[i], domain_x1[i], domain_y0[i], domain_y1[i])         
938      tree_type = nc_read_from_file_2d(input_file_tree_type[ii[i]], 'Band1', domain_x0[i], domain_x1[i], domain_y0[i], domain_y1[i]) 
939      patch_height = nc_read_from_file_2d(input_file_patch_height[ii[i]], 'Band1', domain_x0[i], domain_x1[i], domain_y0[i], domain_y1[i]) 
940     
941#     Remove missing values from the data. Reasonable values will be set by the tree generator   
942      tree_height = np.where( (tree_height == 0.0) | (tree_height == -1.0) ,fillvalues["tree_data"],tree_height)
943      tree_trunk_diameter = np.where( (tree_trunk_diameter == 0.0) | (tree_trunk_diameter == -1.0) ,fillvalues["tree_data"],tree_trunk_diameter)
944      tree_type = np.where( (tree_type == 0.0) | (tree_type == -1.0) ,fillvalues["tree_data"],tree_type)
945      patch_height = np.where( patch_height == -1.0 ,fillvalues["tree_data"],patch_height)     
946
947#     Convert trunk diameter from cm to m
948      tree_trunk_diameter = np.where(tree_trunk_diameter != fillvalues["tree_data"], tree_trunk_diameter * 0.01,tree_trunk_diameter)
949
950
951#     Temporarily change missing value for tree_type
952      tree_type = np.where( (tree_type == fillvalues["tree_type"]),fillvalues["tree_data"],tree_type)   
953
954#     Compare patch height array with vegetation type and correct accordingly
955      vegetation_type = nc_read_from_file_2d_all(filename[i], 'vegetation_type') 
956
957
958#     For zero-height patches, set vegetation_type to short grass and remove these pixels from the patch height array
959      vegetation_type = np.where( (patch_height == 0.0) & ( (vegetation_type == 4) | (vegetation_type == 5) | (vegetation_type == 6) |(vegetation_type == 7) | (vegetation_type == 17) | (vegetation_type == 18) ),3,vegetation_type)
960      patch_type = np.where( (patch_height == 0.0) & ( (vegetation_type == 4) | (vegetation_type == 5) | (vegetation_type == 6) |(vegetation_type == 7) | (vegetation_type == 17) | (vegetation_type == 18) ),fillvalues["tree_data"],patch_height)   
961     
962
963      max_tree_height = max(tree_height.flatten())
964      max_patch_height = max(patch_height.flatten())
965     
966      if ( (max_tree_height != fillvalues["tree_data"]) | (max_patch_height == fillvalues["tree_data"]) ):
967
968         lad, bad, tree_ids, zlad = generate_single_tree_lad(x,y,domain_dz[i],max_tree_height,max_patch_height,tree_type,tree_height,tree_crown_diameter,tree_trunk_diameter,lai,settings_season,fillvalues["tree_data"])
969 
970 
971#        Remove LAD volumes that are inside buildings
972         buildings_2d = nc_read_from_file_2d_all(filename[i], 'buildings_2d') 
973         for k in range(0,len(zlad)-1):
974 
975            lad[k,:,:] = np.where(buildings_2d == fillvalues["buildings_2d"],lad[k,:,:],fillvalues["tree_data"])
976            bad[k,:,:] = np.where(buildings_2d == fillvalues["buildings_2d"],bad[k,:,:],fillvalues["tree_data"])
977            tree_ids[k,:,:] = np.where(buildings_2d == fillvalues["buildings_2d"],tree_ids[k,:,:],fillvalues["tree_data"])     
978
979         del buildings_2d
980 
981         nc_write_dimension(filename[i], 'zlad', zlad, datatypes["tree_data"]) 
982         nc_write_to_file_3d(filename[i], 'lad', lad, datatypes["tree_data"],'zlad','y','x',fillvalues["tree_data"]) 
983         nc_write_attribute(filename[i], 'lad', 'long_name', 'leaf area density')
984         nc_write_attribute(filename[i], 'lad', 'units', '')
985         nc_write_attribute(filename[i], 'lad', 'res_orig', domain_px[i]) 
986         nc_write_attribute(filename[i], 'lad', 'coordinates', 'E_UTM N_UTM lon lat') 
987         nc_write_attribute(filename[i], 'lad', 'grid_mapping', 'E_UTM N_UTM lon lat') 
988 
989         nc_write_to_file_3d(filename[i], 'bad', bad, datatypes["tree_data"],'zlad','y','x',fillvalues["tree_data"]) 
990         nc_write_attribute(filename[i], 'bad', 'long_name', 'basal area density')
991         nc_write_attribute(filename[i], 'bad', 'units', '')
992         nc_write_attribute(filename[i], 'bad', 'res_orig', domain_px[i]) 
993         nc_write_attribute(filename[i], 'bad', 'coordinates', 'E_UTM N_UTM lon lat') 
994         nc_write_attribute(filename[i], 'bad', 'grid_mapping', 'E_UTM N_UTM lon lat')
995           
996         nc_write_to_file_3d(filename[i], 'tree_id', tree_ids, datatypes["tree_data"],'zlad','y','x',fillvalues["tree_data"]) 
997         nc_write_attribute(filename[i], 'tree_id', 'long_name', 'tree id')
998         nc_write_attribute(filename[i], 'tree_id', 'units', '')
999         nc_write_attribute(filename[i], 'tree_id', 'res_orig', domain_px[i]) 
1000         nc_write_attribute(filename[i], 'tree_id', 'coordinates', 'E_UTM N_UTM lon lat') 
1001         nc_write_attribute(filename[i], 'tree_id', 'grid_mapping', 'E_UTM N_UTM lon lat')           
1002           
1003         del lai, lad, bad, tree_ids, zlad
1004
1005      del vegetation_pars, tree_height, tree_crown_diameter, tree_trunk_diameter, tree_type, patch_height, x, y
1006
1007
1008# Create vegetation patches for locations with high vegetation type
1009for i in range(0,ndomains): 
1010   if domain_canopy_patches[i]: 
1011
1012#     Load vegetation_type and lad array (at level z = 0) for re-processing
1013      vegetation_type = nc_read_from_file_2d_all(filename[i], 'vegetation_type') 
1014      lad = nc_read_from_file_3d_all(filename[i], 'lad') 
1015      zlad = nc_read_from_file_1d_all(filename[i], 'zlad') 
1016      patch_height = nc_read_from_file_2d(input_file_patch_height[ii[i]], 'Band1', domain_x0[i], domain_x1[i], domain_y0[i], domain_y1[i]) 
1017      vegetation_pars = nc_read_from_file_3d_all(filename[i], 'vegetation_pars') 
1018      lai = vegetation_pars[1,:,:]
1019
1020#     Determine all pixels that do not already have and LAD but which are high vegetation to a dummy value of 1.0 and remove all other pixels
1021      lai_high = np.where( (lad[0,:,:] == fillvalues["tree_data"]) & ( ( (vegetation_type == 4) | (vegetation_type == 5) | (vegetation_type == 6) |(vegetation_type == 7) | (vegetation_type == 17) | (vegetation_type == 18) ) & ( (patch_height == fillvalues["tree_data"]) | (patch_height >= domain_dz[i])) ),1.0,fillvalues["tree_data"])
1022
1023#     Now, assign either the default LAI for high vegetation or the value stored in the LAI array.
1024      lai_high = np.where( (lai_high != fillvalues["tree_data"]) & (lai == fillvalues["tree_data"]), settings_lai_high_default, lai)
1025
1026#     Define a patch height wherever it is missing, but where a high vegetation LAI was set
1027      patch_height = np.where ( (lai_high != fillvalues["tree_data"]) & (patch_height == fillvalues["tree_data"]), settings_patch_height_default, patch_height)
1028
1029#     Remove pixels where street trees were already set
1030      patch_height = np.where ( (lad[0,:,:] != fillvalues["tree_data"]), fillvalues["tree_data"], patch_height)
1031
1032#     Remove patch heights that have no lai_high value
1033      patch_height = np.where ( (lai_high == fillvalues["tree_data"]), fillvalues["tree_data"], patch_height)
1034
1035#     For missing LAI values, set either the high vegetation default or the low vegetation default   
1036      lai_high = np.where( (patch_height > 2.0) & (patch_height != fillvalues["tree_data"]) & (lai_high == fillvalues["tree_data"]),settings_lai_high_default,lai_high)
1037      lai_high = np.where( (patch_height <= 2.0) & (patch_height != fillvalues["tree_data"]) & (lai_high == fillvalues["tree_data"]),settings_lai_low_default,lai_high)
1038         
1039      if ( max(patch_height.flatten()) >= (2.0 * domain_dz[i]) ):
1040         print("    start calculating LAD (this might take some time)") 
1041         lad_patch, patch_nz, status = process_patch(domain_dz[i],patch_height,max(zlad),lai_high,settings_lai_alpha,settings_lai_beta)
1042
1043         lad[0:patch_nz+1,:,:] = np.where( (lad[0:patch_nz+1,:,:] == fillvalues["tree_data"]),lad_patch[0:patch_nz+1,:,:], lad[0:patch_nz+1,:,:] )
1044
1045#     Remove high vegetation wherever it is replaced by a leaf area density. This should effectively remove all high vegetation pixels
1046      vegetation_type = np.where((lad[0,:,:] != fillvalues["tree_data"]) & (vegetation_type != fillvalues["vegetation_type"]),3,vegetation_type)
1047             
1048#     If desired, remove all high vegetation. TODO: check if this is still necessary
1049      if not domain_high_vegetation[i]:
1050         vegetation_type = np.where((vegetation_type != fillvalues["vegetation_type"]) & ( (vegetation_type == 4) | (vegetation_type == 5) | (vegetation_type == 6) |(vegetation_type == 7) | (vegetation_type == 17) | (vegetation_type == 18) ),3,vegetation_type)   
1051
1052 
1053#     Set default low LAI for pixels with an LAD (short grass below trees)
1054      lai_low = np.where( (lad[0,:,:] == fillvalues["tree_data"]), lai, settings_lai_low_default)
1055   
1056#     Fill low vegetation pixels without LAI set or with LAI = 0 with default value
1057      lai_low = np.where( ( (lai_low == fillvalues["tree_data"]) | (lai_low == 0.0) ) & (vegetation_type != fillvalues["vegetation_type"] ), settings_lai_low_default, lai_low)
1058
1059#     Remove lai for pixels that have no vegetation_type
1060      lai_low = np.where( vegetation_type != fillvalues["vegetation_type"], lai_low, fillvalues["tree_data"])
1061       
1062#     Overwrite lai in vegetation_parameters 
1063      vegetation_pars[1,:,:] = np.copy(lai_low)
1064      nc_overwrite_to_file_3d(filename[i], 'vegetation_pars', vegetation_pars) 
1065
1066#     Overwrite lad array
1067      nc_overwrite_to_file_3d(filename[i], 'lad', lad) 
1068           
1069      nc_overwrite_to_file_2d(filename[i], 'vegetation_type', vegetation_type) 
1070
1071     
1072      del vegetation_type, lad, lai, patch_height, vegetation_pars, zlad
1073
1074# Final consistency check
1075for i in range(0,ndomains): 
1076   vegetation_type = nc_read_from_file_2d_all(filename[i], 'vegetation_type') 
1077   pavement_type = nc_read_from_file_2d_all(filename[i], 'pavement_type') 
1078   building_type = nc_read_from_file_2d_all(filename[i], 'building_type') 
1079   water_type = nc_read_from_file_2d_all(filename[i], 'water_type')     
1080   soil_type = nc_read_from_file_2d_all(filename[i], 'soil_type') 
1081
1082#  Check for consistency and fill empty fields with default vegetation type
1083   consistency_array, test = check_consistency_4(vegetation_type,building_type,pavement_type,water_type,fillvalues["vegetation_type"],fillvalues["building_type"],fillvalues["pavement_type"],fillvalues["water_type"])
1084
1085#  Check for consistency and fill empty fields with default vegetation type
1086   consistency_array, test = check_consistency_3(vegetation_type,pavement_type,soil_type,fillvalues["vegetation_type"],fillvalues["pavement_type"],fillvalues["soil_type"])
1087
1088   surface_fraction = nc_read_from_file_3d_all(filename[i], 'surface_fraction')       
1089   surface_fraction[0,:,:] = np.where(vegetation_type != fillvalues["vegetation_type"], 1.0, 0.0)
1090   surface_fraction[1,:,:] = np.where(pavement_type != fillvalues["pavement_type"], 1.0, 0.0)
1091   surface_fraction[2,:,:] = np.where(water_type != fillvalues["water_type"], 1.0, 0.0)
1092   nc_overwrite_to_file_3d(filename[i], 'surface_fraction', surface_fraction) 
1093       
1094   del vegetation_type, pavement_type, building_type, water_type, soil_type
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