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source: palm/trunk/SCRIPTS/palm_csd @ 4732

Last change on this file since 4732 was 4490, checked in by maronga, 5 years ago

bugfix in palm_csd
  • Property svn:executable set to *
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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 1997-2020  Leibniz Universitaet Hannover
18#--------------------------------------------------------------------------------#
19#
20# Current revisions:
21# -----------------
22#
23#
24# Former revisions:
25# -----------------
26# $Id: palm_csd 4490 2020-04-10 05:32:45Z schwenkel $
27# Bugfix: call of canopy generator was not made if tree heights were only
28# available via patch_height, but not in tree_height
29#
30# 4481 2020-03-31 18:55:54Z maronga
31# Bugfix: green roofs were assigned to all available roofs when switched on
32#
33# 4149 2019-08-08 14:02:18Z suehring
34# Bugfix, nbuilding_pars was 1 element to small
35#
36# 3955 2019-05-07 09:55:25Z maronga
37# Bugfix in preparaing of green roofs
38# Bugfix: remove LAI for bare soils
39#
40# 3859 2019-04-03 20:30:31Z maronga
41# Bugfix: wrong variable naming for 'y'
42#
43# 3773 2019-03-01 08:56:57Z maronga
44# Unspecificed changes
45#
46# 3726 2019-02-07 18:22:49Z maronga
47# Removed some more bugs
48#
49# 3688 2019-01-22 10:44:20Z maronga
50# Some unspecified bugfixes
51#
52# 3668 2019-01-14 12:49:24Z maronga
53# Various improvements and bugfixes
54#
55# 3629 2018-12-13 12:18:54Z maronga
56# Added canopy generator calls. Some improvements
57#
58# 3567 2018-11-27 13:59:21Z maronga
59# Initial revisions
60#
61# Description:
62# ------------
63# Processing tool for creating PIDS conform static drivers from rastered NetCDF
64# input
65#
66# @Author Bjoern Maronga (maronga@muk.uni-hannover.de)
67#
68# @todo Make input files optional
69# @todo Allow for ASCII input of terrain height and building height
70# @todo Modularize reading config file
71# @todo Convert to object-oriented treatment (buidings, trees)
72# @todo Automatically shift child domains so that their origin lies intersects
73#       a edge note of the parent grid
74#------------------------------------------------------------------------------#
75
76from palm_csd_files.palm_csd_netcdf_interface import *
77from palm_csd_files.palm_csd_tools import *
78from palm_csd_files.palm_csd_canopy_generator import *
79import numpy as np
80
81   
82def read_config_file():
83   
84   import configparser
85   from math import floor
86   import numpy as np
87   import os
88   import subprocess as sub
89   import sys
90
91#  Check if configuration files exists and quit otherwise
92   input_config = ".csd.config"
93   for i in range(1,len(sys.argv)): 
94      input_config = str(sys.argv[i])
95       
96   config = configparser.RawConfigParser(allow_no_value=True)
97
98   if ( os.path.isfile(input_config) == False ):
99      print ("Error. No configuration file " + input_config + " found.")
100      raise SystemExit   
101
102   config.read(input_config)
103
104
105#  Definition of settings
106
107   global settings_bridge_width
108   global settings_lai_roof_intensive
109   global settings_lai_roof_extensive
110   global settings_season
111   global settings_lai_low_default
112   global settings_lai_high_default
113   global settings_patch_height_default
114   global settings_lai_alpha
115   global settings_lai_beta
116   global settings_veg_type_below_trees
117   global ndomains
118
119#  Definition of global configuration parameters
120   global global_acronym
121   global global_angle
122   global global_author
123   global global_campaign
124   global global_comment
125   global global_contact
126   global global_data_content 
127   global global_dependencies
128   global global_institution
129   global global_keywords
130   global global_location
131   global global_palm_version
132   global global_references
133   global global_site
134   global global_source
135
136   global  path_out
137   global filename_out
138   global version_out
139
140
141#  Definition of domain parameters
142   global domain_names
143   global domain_px
144   global domain_x0
145   global domain_y0
146   global domain_x1
147   global domain_y1
148   global domain_nx
149   global domain_ny
150   global domain_dz
151   global domain_3d
152   global domain_high_vegetation
153   global domain_ip
154   global domain_za
155   global domain_parent
156   global domain_green_roofs
157   global domain_street_trees
158   global domain_canopy_patches
159
160#  Definition of input data parameters
161   global input_names
162   global input_px
163 
164       
165   global input_file_x
166   global input_file_y
167   global input_file_x_UTM
168   global input_file_y_UTM   
169   global input_file_lat
170   global input_file_lon 
171   global input_file_zt
172   global input_file_buildings_2d
173   global input_file_bridges_2d
174   global input_file_building_id
175   global input_file_bridges_id   
176   global input_file_building_type
177   global input_file_building_type
178   global input_file_lai 
179   global input_file_vegetation_type
180   global input_file_vegetation_height
181   global input_file_pavement_type
182   global input_file_water_type
183   global input_file_street_type
184   global input_file_street_crossings
185   global input_file_soil_type
186   global input_file_vegetation_on_roofs
187   global input_file_tree_crown_diameter
188   global input_file_tree_height
189   global input_file_tree_trunk_diameter
190   global input_file_tree_type
191   global input_file_patch_height
192
193   global zt_all
194   global zt_min
195
196   settings_bridge_width = 3.0
197   settings_lai_roof_intensive = 2.5
198   settings_lai_roof_extensive = 0.8
199   settings_season = "summer"
200   settings_lai_high_default = 6.0
201   settings_lai_low_default = 1.0
202   settings_patch_height_default = 10.0
203   settings_lai_alpha = 5.0
204   settings_lai_beta = 3.0
205   settings_veg_type_below_trees = 3
206   ndomains = 0
207
208   global_acronym = ""
209   global_angle = ""
210   global_author = ""
211   global_campaign = ""
212   global_comment = ""
213   global_contact = ""
214   global_data_content = ""
215   global_dependencies = ""
216   global_institution = ""
217   global_keywords = ""
218   global_location = ""
219   global_palm_version = 6.0
220   global_references = ""
221   global_site = ""
222   global_source = ""
223
224   path_out = ""
225   version_out = 1
226   filename_out = "default"
227   
228   domain_names = []
229   domain_px = []
230   domain_x0 = []
231   domain_y0 = []
232   domain_x1 = []
233   domain_y1 = []
234   domain_nx = []
235   domain_ny = []
236   domain_dz = []
237   domain_3d = []
238   domain_high_vegetation = []
239   domain_ip = []
240   domain_za = []
241   domain_parent = []
242   domain_green_roofs = []
243   domain_street_trees = []
244   domain_canopy_patches = []
245
246   zt_min = 0.0
247   zt_all = []
248
249   input_names = []
250   input_px = []
251
252   input_file_x = []
253   input_file_y = [] 
254   input_file_x_UTM = []
255   input_file_y_UTM = [] 
256   input_file_lat = []
257   input_file_lon = [] 
258   
259   input_file_zt = []
260   input_file_buildings_2d = []
261   input_file_bridges_2d = []
262   input_file_building_id = []
263   input_file_bridges_id = []   
264   input_file_building_type = []
265   input_file_lai = []
266   input_file_vegetation_type = []
267   input_file_vegetation_height = []   
268   input_file_pavement_type = []
269   input_file_water_type = []
270   input_file_street_type = [] 
271   input_file_street_crossings = []     
272   input_file_soil_type = []
273   input_file_vegetation_on_roofs = []
274   input_file_tree_crown_diameter = []
275   input_file_tree_height = []
276   input_file_tree_trunk_diameter = []
277   input_file_tree_type = []
278   input_file_patch_height = []
279   
280# Load all user parameters from config file
281   for i in range(0,len(config.sections())):
282
283      read_tmp = config.sections()[i]
284     
285      if ( read_tmp == 'global' ):
286         global_acronym = config.get(read_tmp, 'acronym')
287         global_angle = float(config.get(read_tmp, 'rotation_angle'))
288         global_author = config.get(read_tmp, 'author')
289         global_campaign = config.get(read_tmp, 'campaign')
290         global_comment = config.get(read_tmp, 'comment')
291         global_contact = config.get(read_tmp, 'contact_person')
292         global_data_content = config.get(read_tmp, 'data_content')   
293         global_dependencies = config.get(read_tmp, 'dependencies')
294         global_institution = config.get(read_tmp, 'institution')
295         global_keywords = config.get(read_tmp, 'keywords')
296         global_location = config.get(read_tmp, 'location')
297         global_palm_version = float(config.get(read_tmp, 'palm_version'))
298         global_references = config.get(read_tmp, 'references')
299         global_site = config.get(read_tmp, 'site')
300         global_source = config.get(read_tmp, 'source')
301
302
303      if ( read_tmp == 'settings' ):
304         settings_lai_roof_intensive = config.get(read_tmp, 'lai_roof_intensive')
305         settings_lai_roof_extensive = config.get(read_tmp, 'lai_roof_extensive') 
306         settings_bridge_width = float(config.get(read_tmp, 'bridge_width'))   
307         settings_season = config.get(read_tmp, 'season')   
308         settings_lai_high_default = float(config.get(read_tmp, 'lai_high_vegetation_default'))
309         settings_lai_low_default = float(config.get(read_tmp, 'lai_low_vegetation_default'))         
310         settings_patch_height_default = float(config.get(read_tmp, 'patch_height_default')) 
311         settings_lai_alpha = float(config.get(read_tmp, 'lai_alpha')) 
312         settings_lai_beta = float(config.get(read_tmp, 'lai_beta')) 
313         settings_veg_type_below_trees = config.get(read_tmp, 'vegetation_type_below_trees') 
314
315      if ( read_tmp == 'output' ):
316         path_out = config.get(read_tmp, 'path')         
317         filename_out = config.get(read_tmp, 'file_out')
318         version_out = float(config.get(read_tmp, 'version'))
319
320      if ( read_tmp.split("_")[0] == 'domain' ):
321         ndomains = ndomains + 1
322         domain_names.append(read_tmp.split("_")[1])
323         domain_px.append(float(config.get(read_tmp, 'pixel_size')))
324         domain_nx.append(int(config.get(read_tmp, 'nx')))
325         domain_ny.append(int(config.get(read_tmp, 'ny')))
326         domain_dz.append(float(config.get(read_tmp, 'dz')))       
327         domain_3d.append(config.getboolean(read_tmp, 'buildings_3d'))
328         domain_high_vegetation.append(config.getboolean(read_tmp, 'allow_high_vegetation'))
329         domain_canopy_patches.append(config.getboolean(read_tmp, 'generate_vegetation_patches'))       
330         domain_ip.append(config.getboolean(read_tmp, 'interpolate_terrain'))
331         domain_za.append(config.getboolean(read_tmp, 'use_palm_z_axis')) 
332         if domain_ip[ndomains-1] and not domain_za[ndomains-1]:
333            domain_za[ndomains-1] = True
334            print("+++ Overwrite user setting for use_palm_z_axis")
335         
336         domain_parent.append(config.get(read_tmp, 'domain_parent'))
337
338         domain_x0.append(int(floor(float(config.get(read_tmp, 'origin_x'))/float(config.get(read_tmp, 'pixel_size')))))
339         domain_y0.append(int(floor(float(config.get(read_tmp, 'origin_y'))/float(config.get(read_tmp, 'pixel_size')))))       
340         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'))))
341         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'))))
342         domain_green_roofs.append(config.getboolean(read_tmp, 'vegetation_on_roofs')) 
343         domain_street_trees.append(config.getboolean(read_tmp, 'street_trees')) 
344         
345      if ( read_tmp.split("_")[0] == 'input' ):
346         input_names.append(read_tmp.split("_")[1])
347         input_px.append(float(config.get(read_tmp, 'pixel_size')))
348         input_file_x.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_x'))
349         input_file_y.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_y')) 
350         input_file_lat.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_lat'))
351         input_file_lon.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_lon'))   
352         input_file_x_UTM.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_x_UTM'))
353         input_file_y_UTM.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_y_UTM')) 
354         input_file_zt.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_zt'))
355         input_file_buildings_2d.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_buildings_2d'))
356         input_file_bridges_2d.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_bridges_2d'))
357         input_file_building_id.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_building_id')) 
358         input_file_bridges_id.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_bridges_id')) 
359         input_file_building_type.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_building_type'))
360         input_file_lai.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_lai'))         
361         input_file_vegetation_type.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_vegetation_type')) 
362         input_file_vegetation_height.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_vegetation_height'))         
363         input_file_pavement_type.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_pavement_type'))   
364         input_file_water_type.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_water_type'))
365         input_file_street_type.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_street_type'))   
366         input_file_street_crossings.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_street_crossings')) 
367         input_file_patch_height.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_patch_height')) 
368         
369         tmp = config.get(read_tmp, 'file_tree_crown_diameter')
370         if tmp is not None:
371            input_file_tree_crown_diameter.append(config.get(read_tmp, 'path') + "/" + tmp) 
372         else:
373            input_file_tree_crown_diameter.append(None)
374         input_file_tree_height.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_tree_height')) 
375         input_file_tree_trunk_diameter.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_tree_trunk_diameter')) 
376         input_file_tree_type.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_tree_type')) 
377         input_file_vegetation_on_roofs.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_vegetation_on_roofs'))           
378         #input_file_soil_type.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_soil_type'))   
379   return 0
380
381
382############################################################
383
384# Start of main program
385datatypes = {
386   "x": "f4", 
387   "y": "f4", 
388   "z": "f4", 
389   "lat": "f4", 
390   "lon": "f4",   
391   "E_UTM": "f4", 
392   "N_UTM": "f4", 
393   "zt": "f4",
394   "buildings_2d": "f4",
395   "buildings_3d": "b",
396   "bridges_2d": "f4",
397   "building_id": "i",
398   "bridges_id": "i",
399   "building_type": "b",
400   "nsurface_fraction": "i",
401   "vegetation_type": "b",
402   "vegetation_height": "f4",
403   "pavement_type": "b", 
404   "water_type": "b", 
405   "street_type": "b", 
406   "street_crossings": "b",     
407   "soil_type": "b",
408   "surface_fraction": "f4",
409   "building_pars": "f4",
410   "vegetation_pars": "f4",
411   "tree_data": "f4",
412   "tree_type": "b",
413   "nbuilding_pars": "i",
414   "nvegetation_pars": "i",
415   "zlad": "f4"
416   }
417
418fillvalues = {
419   "lat": float(-9999.0),
420   "lon": float(-9999.0),
421   "E_UTM": float(-9999.0),
422   "N_UTM": float(-9999.0),   
423   "zt": float(-9999.0),
424   "buildings_2d": float(-9999.0),
425   "buildings_3d": np.byte(-127),
426   "bridges_2d": float(-9999.0),
427   "building_id": int(-9999),
428   "bridges_id": int(-9999),
429   "building_type": np.byte(-127),
430   "nsurface_fraction": int(-9999),
431   "vegetation_type": np.byte(-127),
432   "vegetation_height": float(-9999.0),
433   "pavement_type": np.byte(-127),
434   "water_type": np.byte(-127),
435   "street_type": np.byte(-127), 
436   "street_crossings": np.byte(-127),   
437   "soil_type": np.byte(-127),
438   "surface_fraction": float(-9999.0),
439   "building_pars": float(-9999.0),
440   "vegetation_pars": float(-9999.0),
441   "tree_data": float(-9999.0),
442   "tree_type": np.byte(-127)
443   }
444
445defaultvalues = {
446   "lat": float(-9999.0),
447   "lon": float(-9999.0),
448   "E_UTM": float(-9999.0),
449   "N_UTM": float(-9999.0), 
450   "zt": float(0.0),
451   "buildings_2d": float(0.0),
452   "buildings_3d": 0,   
453   "bridges_2d": float(0.0),
454   "building_id": int(0),
455   "bridges_id": int(0),
456   "building_type": 1,
457   "nsurface_fraction": int(-9999),
458   "vegetation_type": 3,
459   "vegetation_height": float(-9999.0),
460   "pavement_type": 1, 
461   "water_type": 1, 
462   "street_type": 1, 
463   "street_crossings": 0,   
464   "soil_type": 1,
465   "surface_fraction": float(0.0),
466   "buildings_pars": float(-9999.0),
467   "tree_data": float(-9999.0),
468   "tree_type": 0,
469   "vegetation_pars": float(-9999.0)   
470   }
471 
472# Read configuration file and set parameters accordingly
473read_config_file()
474
475
476filename = []
477ii = []
478ii_parent = []
479# Define indices and filenames for all domains and create netCDF files
480for i in range(0,ndomains):
481
482#  Calculate indices and input files
483   ii.append(input_px.index(domain_px[i]))
484   filename.append(path_out + "/" + filename_out + "_" + domain_names[i])
485   if domain_parent[i] is not None:
486      ii_parent.append(input_px.index(domain_px[domain_names.index(domain_parent[i])]))
487   else:
488      ii_parent.append(None)
489
490
491   x_UTM = nc_read_from_file_2d(input_file_x[ii[i]], "Band1", domain_x0[i], domain_x0[i]+1, domain_y0[i], domain_y0[i]+1)
492   y_UTM = nc_read_from_file_2d(input_file_y[ii[i]], "Band1", domain_x0[i], domain_x0[i]+1, domain_y0[i], domain_y0[i]+1)
493   lat = nc_read_from_file_2d(input_file_lat[ii[i]], "Band1", domain_x0[i], domain_x0[i]+1, domain_y0[i], domain_y0[i]+1)
494   lon = nc_read_from_file_2d(input_file_lon[ii[i]], "Band1", domain_x0[i], domain_x0[i]+1, domain_y0[i], domain_y0[i]+1) 
495   
496#  Calculate position of origin
497   x_UTM_origin = float(x_UTM[0,0]) - 0.5 * (float(x_UTM[0,1]) - float(x_UTM[0,0]))
498   y_UTM_origin = float(y_UTM[0,0]) - 0.5 * (float(y_UTM[1,0]) - float(y_UTM[0,0])) 
499   x_origin = float(lon[0,0]) - 0.5 * (float(lon[0,1]) - float(lon[0,0]))
500   y_origin = float(lat[0,0]) - 0.5 * (float(lat[1,0]) - float(lat[0,0]))   
501   
502#  Create NetCDF output file and set global attributes
503   nc_create_file(filename[i])
504   nc_write_global_attributes(filename[i],x_UTM_origin,y_UTM_origin,y_origin,x_origin,"",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)
505
506   del x_UTM, y_UTM, lat, lon
507   
508# Process terrain height
509for i in range(0,ndomains):
510
511#  Read and write terrain height (zt)
512   zt = nc_read_from_file_2d(input_file_zt[ii[i]], 'Band1', domain_x0[i], domain_x1[i], domain_y0[i], domain_y1[i])
513
514#  Final step: add zt array to the global array   
515   zt_all.append(zt)
516   del zt
517 
518#  Calculate the global (all domains) minimum of the terrain height. This value will be substracted for all
519#  data sets
520zt_min = min(zt_all[0].flatten()) 
521for i in range(0,ndomains):
522   zt_min = min(zt_min,min(zt_all[i].flatten()))
523   
524del zt_all[:]
525
526print( "Shift terrain heights by -" + str(zt_min))
527for i in range(0,ndomains):
528
529#  Read and write terrain height (zt)
530   zt = nc_read_from_file_2d(input_file_zt[ii[i]], 'Band1', domain_x0[i], domain_x1[i], domain_y0[i], domain_y1[i])
531   x = nc_read_from_file_1d(input_file_x[ii[i]], "x", domain_x0[i], domain_x1[i])
532   y = nc_read_from_file_1d(input_file_y[ii[i]], "y", domain_y0[i], domain_y1[i])
533
534 
535   zt = zt - zt_min
536   
537   nc_write_global_attribute(filename[i], 'origin_z', float(zt_min))
538
539#  If necessary, interpolate parent domain terrain height on child domain grid and blend the two
540   if domain_ip[i]:
541      parent_id = domain_names.index(domain_parent[i])
542      tmp_x0 = int( domain_x0[i] * domain_px[i] / domain_px[parent_id] ) - 1
543      tmp_y0 = int( domain_y0[i] * domain_px[i] / domain_px[parent_id] ) - 1
544      tmp_x1 = int( domain_x1[i] * domain_px[i] / domain_px[parent_id] ) + 1
545      tmp_y1 = int( domain_y1[i] * domain_px[i] / domain_px[parent_id] ) + 1
546
547      tmp_x = nc_read_from_file_1d(input_file_x[ii_parent[i]], "x", tmp_x0, tmp_x1)
548      tmp_y = nc_read_from_file_1d(input_file_y[ii_parent[i]], "y", tmp_y0, tmp_y1)
549
550      zt_parent = nc_read_from_file_2d(input_file_zt[ii_parent[i]], 'Band1', tmp_x0, tmp_x1, tmp_y0, tmp_y1)
551
552      zt_parent = zt_parent - zt_min
553
554#     Interpolate array and bring to PALM grid of child domain
555      zt_ip = interpolate_2d(zt_parent,tmp_x,tmp_y,x,y)
556      zt_ip = bring_to_palm_grid(zt_ip,x,y,domain_dz[parent_id])
557     
558     
559#     Shift the child terrain height according to the parent mean terrain height
560      print("shifting: -" + str(np.mean(zt)) + " +" + str(np.mean(zt_ip)))
561      #zt = zt - np.min(zt) + np.min(zt_ip)
562      zt = zt - np.mean(zt) + np.mean(zt_ip)
563 
564 
565#     Blend over the parent and child terrain height within a radius of 50 px 
566      zt = blend_array_2d(zt,zt_ip,50)
567     # zt = zt_ip
568   
569#  Final step: add zt array to the global array   
570
571   zt_all.append(zt)
572   del zt
573 
574
575# Read and shift x and y coordinates, shift terrain height according to its minimum value and write all data
576# to file 
577for i in range(0,ndomains):   
578#  Read horizontal grid variables from zt file and write them to output file
579   x = nc_read_from_file_1d(input_file_x[ii[i]], "x", domain_x0[i], domain_x1[i])
580   y = nc_read_from_file_1d(input_file_y[ii[i]], "y", domain_y0[i], domain_y1[i]) 
581   x = x - min(x.flatten()) + domain_px[i]/2.0
582   y = y - min(y.flatten()) + domain_px[i]/2.0 
583   nc_write_dimension(filename[i], 'x', x, datatypes["x"])
584   nc_write_dimension(filename[i], 'y', y, datatypes["y"]) 
585   nc_write_attribute(filename[i], 'x', 'long_name', 'x')
586   nc_write_attribute(filename[i], 'x', 'standard_name','projection_x_coordinate')   
587   nc_write_attribute(filename[i], 'x', 'units', 'm')
588   nc_write_attribute(filename[i], 'y', 'long_name', 'y')
589   nc_write_attribute(filename[i], 'y', 'standard_name', 'projection_y_coordinate')   
590   nc_write_attribute(filename[i], 'y', 'units', 'm')
591
592   lat = nc_read_from_file_2d(input_file_lat[ii[i]], "Band1", domain_x0[i], domain_x1[i], domain_y0[i], domain_y1[i])
593   lon = nc_read_from_file_2d(input_file_lon[ii[i]], "Band1", domain_x0[i], domain_x1[i], domain_y0[i], domain_y1[i]) 
594
595   nc_write_to_file_2d(filename[i], 'lat', lat, datatypes["lat"],'y','x',fillvalues["lat"])
596   nc_write_to_file_2d(filename[i], 'lon', lon, datatypes["lon"],'y','x',fillvalues["lon"]) 
597   
598   nc_write_attribute(filename[i], 'lat', 'long_name', 'latitude')
599   nc_write_attribute(filename[i], 'lat', 'standard_name','latitude')   
600   nc_write_attribute(filename[i], 'lat', 'units', 'degrees_north')
601 
602   nc_write_attribute(filename[i], 'lon', 'long_name', 'longitude')
603   nc_write_attribute(filename[i], 'lon', 'standard_name','longitude')   
604   nc_write_attribute(filename[i], 'lon', 'units', 'degrees_east') 
605   
606   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])
607   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]) 
608
609   
610   nc_write_to_file_2d(filename[i], 'E_UTM', x_UTM, datatypes["E_UTM"],'y','x',fillvalues["E_UTM"])
611   nc_write_to_file_2d(filename[i], 'N_UTM', y_UTM, datatypes["N_UTM"],'y','x',fillvalues["N_UTM"]) 
612   
613   nc_write_attribute(filename[i], 'E_UTM', 'long_name', 'easting')
614   nc_write_attribute(filename[i], 'E_UTM', 'standard_name','projection_x_coorindate')   
615   nc_write_attribute(filename[i], 'E_UTM', 'units', 'm')
616 
617   nc_write_attribute(filename[i], 'N_UTM', 'long_name', 'northing')
618   nc_write_attribute(filename[i], 'N_UTM', 'standard_name','projection_y_coorindate')   
619   nc_write_attribute(filename[i], 'N_UTM', 'units', 'm') 
620 
621   nc_write_crs(filename[i])
622   
623 
624 
625#  If necessary, bring terrain height to PALM's vertical grid. This is either forced by the user or implicitly
626#  by using interpolation for a child domain       
627   if domain_za[i]:
628      zt_all[i] = bring_to_palm_grid(zt_all[i],x,y,domain_dz[i])
629
630   nc_write_to_file_2d(filename[i], 'zt', zt_all[i], datatypes["zt"],'y','x',fillvalues["zt"])
631   nc_write_attribute(filename[i], 'zt', 'long_name', 'orography')
632   nc_write_attribute(filename[i], 'zt', 'units', 'm')
633   nc_write_attribute(filename[i], 'zt', 'res_orig', domain_px[i]) 
634   nc_write_attribute(filename[i], 'zt', 'coordinates', 'E_UTM N_UTM lon lat') 
635   nc_write_attribute(filename[i], 'zt', 'grid_mapping', 'E_UTM N_UTM lon lat')   
636
637del zt_all
638
639
640#  Process building height, id, and type
641for i in range(0,ndomains): 
642   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])
643
644   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]) 
645 
646   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]) 
647   building_type[building_type >= 254] = fillvalues["building_type"]
648   building_type = np.where(building_type < 1,defaultvalues["building_type"],building_type)
649 
650   check = check_arrays_2(buildings_2d,building_id,fillvalues["buildings_2d"],fillvalues["building_id"])
651   if not check:
652      buildings_2d = np.where(building_id != fillvalues["building_id"],buildings_2d,fillvalues["buildings_2d"])
653      building_id = np.where(buildings_2d == fillvalues["buildings_2d"],fillvalues["building_id"],building_id)
654      print("Data check #1 " + str(check_arrays_2(buildings_2d,building_id,fillvalues["buildings_2d"],fillvalues["building_id"])))
655
656   check = check_arrays_2(buildings_2d,building_type,fillvalues["buildings_2d"],fillvalues["building_type"])
657   if not check:
658      building_type = np.where(buildings_2d == fillvalues["buildings_2d"],fillvalues["building_type"],building_type)
659      building_type = np.where((building_type == fillvalues["building_type"]) & (buildings_2d != fillvalues["buildings_2d"]),defaultvalues["building_type"],building_type)
660      print("Data check #2 " + str(check_arrays_2(buildings_2d,building_type,fillvalues["buildings_2d"],fillvalues["building_type"])))
661 
662
663   nc_write_to_file_2d(filename[i], 'buildings_2d', buildings_2d, datatypes["buildings_2d"],'y','x',fillvalues["buildings_2d"])
664   nc_write_attribute(filename[i], 'buildings_2d', 'long_name', 'buildings')
665   nc_write_attribute(filename[i], 'buildings_2d', 'units', 'm')
666   nc_write_attribute(filename[i], 'buildings_2d', 'res_orig', domain_px[i]) 
667   nc_write_attribute(filename[i], 'buildings_2d', 'lod', 1) 
668   nc_write_attribute(filename[i], 'buildings_2d', 'coordinates', 'E_UTM N_UTM lon lat') 
669   nc_write_attribute(filename[i], 'buildings_2d', 'grid_mapping', 'E_UTM N_UTM lon lat')   
670   
671   nc_write_to_file_2d(filename[i], 'building_id', building_id, datatypes["building_id"],'y','x',fillvalues["building_id"])
672   nc_write_attribute(filename[i], 'building_id', 'long_name', 'building id')
673   nc_write_attribute(filename[i], 'building_id', 'units', '')
674   nc_write_attribute(filename[i], 'building_id', 'res _orig', domain_px[i]) 
675   nc_write_attribute(filename[i], 'building_id', 'coordinates', 'E_UTM N_UTM lon lat') 
676   nc_write_attribute(filename[i], 'building_id', 'grid_mapping', 'E_UTM N_UTM lon lat')   
677   
678   nc_write_to_file_2d(filename[i], 'building_type', building_type, datatypes["building_type"],'y','x',fillvalues["building_type"])
679   nc_write_attribute(filename[i], 'building_type', 'long_name', 'building type')
680   nc_write_attribute(filename[i], 'building_type', 'units', '')
681   nc_write_attribute(filename[i], 'building_type', 'res_orig', domain_px[i]) 
682   nc_write_attribute(filename[i], 'building_type', 'coordinates', 'E_UTM N_UTM lon lat') 
683   nc_write_attribute(filename[i], 'building_type', 'grid_mapping', 'E_UTM N_UTM lon lat')   
684 
685del buildings_2d
686del building_id
687del building_type
688
689# Create 3d buildings if necessary. In that course, read bridge objects and add them to building layer
690for i in range(0,ndomains): 
691   
692   if domain_3d[i]:
693      x = nc_read_from_file_2d_all(filename[i], 'x')
694      y = nc_read_from_file_2d_all(filename[i], 'y') 
695      buildings_2d = nc_read_from_file_2d_all(filename[i], 'buildings_2d') 
696      building_id = nc_read_from_file_2d_all(filename[i], 'building_id') 
697     
698      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])
699      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])
700     
701      bridges_2d = np.where(bridges_2d == 0.0,fillvalues["bridges_2d"],bridges_2d)
702      building_id = np.where(bridges_2d == fillvalues["bridges_2d"],building_id,bridges_id)
703     
704      if np.any(buildings_2d != fillvalues["buildings_2d"]):
705         buildings_3d, z = make_3d_from_2d(buildings_2d,x,y,domain_dz[i])
706         if np.any(bridges_2d != fillvalues["bridges_2d"]):
707            buildings_3d = make_3d_from_bridges_2d(buildings_3d,bridges_2d,x,y,domain_dz[i],settings_bridge_width,fillvalues["bridges_2d"])
708         else:
709            print("Skipping creation of 3D bridges (no bridges in domain)")
710           
711           
712         nc_write_dimension(filename[i], 'z', z, datatypes["z"]) 
713         nc_write_attribute(filename[i], 'z', 'long_name', 'z') 
714         nc_write_attribute(filename[i], 'z', 'units', 'm')
715         
716         nc_overwrite_to_file_2d(filename[i], 'building_id', building_id) 
717 
718         nc_write_to_file_3d(filename[i], 'buildings_3d', buildings_3d, datatypes["buildings_3d"],'z','y','x',fillvalues["buildings_3d"])   
719         nc_write_attribute(filename[i], 'buildings_3d', 'long_name', 'buildings 3d')
720         nc_write_attribute(filename[i], 'buildings_3d', 'units', '')
721         nc_write_attribute(filename[i], 'buildings_3d', 'res_orig', domain_px[i]) 
722         nc_write_attribute(filename[i], 'buildings_3d', 'lod', 2) 
723         
724         del buildings_3d
725         
726      else:
727         print("Skipping creation of 3D buildings (no buildings in domain)")
728
729
730      del bridges_2d, bridges_id, building_id, buildings_2d
731
732
733
734# Read vegetation type, water_type, pavement_type, soil_type and make fields consistent
735for i in range(0,ndomains): 
736
737   building_type = nc_read_from_file_2d_all(filename[i], 'building_type') 
738   
739   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])   
740   vegetation_type[vegetation_type == 255] = fillvalues["vegetation_type"]
741   vegetation_type = np.where((vegetation_type < 1) & (vegetation_type != fillvalues["vegetation_type"]),defaultvalues["vegetation_type"],vegetation_type)
742   
743   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])   
744   pavement_type[pavement_type == 255] = fillvalues["pavement_type"]
745   pavement_type = np.where((pavement_type < 1) & (pavement_type != fillvalues["pavement_type"]),defaultvalues["pavement_type"],pavement_type)
746
747   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])   
748   water_type[water_type == 255] = fillvalues["water_type"]
749   water_type = np.where((water_type < 1) & (water_type != fillvalues["water_type"]),defaultvalues["water_type"],water_type)
750 
751#  to do: replace by real soil input data
752   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]) 
753   soil_type[soil_type == 255] = fillvalues["soil_type"]
754   soil_type = np.where((soil_type < 1) & (soil_type != fillvalues["soil_type"]),defaultvalues["soil_type"],soil_type)
755
756#  Make arrays consistent
757#  #1 Set vegetation type to missing for pixel where a pavement type is set
758   vegetation_type = np.where((vegetation_type != fillvalues["vegetation_type"]) & (pavement_type != fillvalues["pavement_type"]),fillvalues["vegetation_type"],vegetation_type)
759
760#  #2 Set vegetation type to missing for pixel where a building type is set
761   vegetation_type = np.where((vegetation_type != fillvalues["vegetation_type"]) & (building_type != fillvalues["building_type"]) ,fillvalues["vegetation_type"],vegetation_type)
762
763#  #3 Set vegetation type to missing for pixel where a building type is set
764   vegetation_type = np.where((vegetation_type != fillvalues["vegetation_type"]) & (water_type != fillvalues["water_type"]),fillvalues["vegetation_type"],vegetation_type)   
765
766#  #4 Remove pavement for pixels with buildings
767   pavement_type = np.where((pavement_type != fillvalues["pavement_type"]) & (building_type != fillvalues["building_type"]),fillvalues["pavement_type"],pavement_type) 
768
769#  #5 Remove pavement for pixels with water.
770   pavement_type = np.where((pavement_type != fillvalues["pavement_type"]) & (water_type != fillvalues["water_type"]),fillvalues["pavement_type"],pavement_type) 
771   
772#  #6 Remove water for pixels with buildings
773   water_type = np.where((water_type != fillvalues["water_type"]) & (building_type != fillvalues["building_type"]),fillvalues["water_type"],water_type) 
774
775#  Correct vegetation_type when a vegetation height is available and is indicative of low vegeetation
776   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])   
777         
778   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)
779   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)
780 
781#  Check for consistency and fill empty fields with default vegetation type
782   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"])
783   
784   if test:
785      vegetation_type = np.where(consistency_array == 0,defaultvalues["vegetation_type"],vegetation_type)
786      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"])
787
788#  #7 to be removed: set default soil type everywhere
789   soil_type = np.where((vegetation_type != fillvalues["vegetation_type"]) | (pavement_type != fillvalues["pavement_type"]),defaultvalues["soil_type"],fillvalues["soil_type"]) 
790       
791   
792#  Check for consistency and fill empty fields with default vegetation type
793   consistency_array, test = check_consistency_3(vegetation_type,pavement_type,soil_type,fillvalues["vegetation_type"],fillvalues["pavement_type"],fillvalues["soil_type"])
794       
795#  Create surface_fraction array
796   x = nc_read_from_file_2d_all(filename[i], 'x')
797   y = nc_read_from_file_2d_all(filename[i], 'y') 
798   nsurface_fraction = np.arange(0,3)
799   surface_fraction = np.ones((len(nsurface_fraction),len(y),len(x)))
800   
801   surface_fraction[0,:,:] = np.where(vegetation_type != fillvalues["vegetation_type"], 1.0, 0.0)
802   surface_fraction[1,:,:] = np.where(pavement_type != fillvalues["pavement_type"], 1.0, 0.0)
803   surface_fraction[2,:,:] = np.where(water_type != fillvalues["water_type"], 1.0, 0.0)
804   
805   nc_write_dimension(filename[i], 'nsurface_fraction', nsurface_fraction, datatypes["nsurface_fraction"]) 
806   nc_write_to_file_3d(filename[i], 'surface_fraction', surface_fraction, datatypes["surface_fraction"],'nsurface_fraction','y','x',fillvalues["surface_fraction"])     
807   nc_write_attribute(filename[i], 'surface_fraction', 'long_name', 'surface fraction')
808   nc_write_attribute(filename[i], 'surface_fraction', 'units', '')
809   nc_write_attribute(filename[i], 'surface_fraction', 'res_orig', domain_px[i])     
810   del surface_fraction   
811
812   nc_write_to_file_2d(filename[i], 'vegetation_type', vegetation_type, datatypes["vegetation_type"],'y','x',fillvalues["vegetation_type"])     
813   nc_write_attribute(filename[i], 'vegetation_type', 'long_name', 'vegetation type')
814   nc_write_attribute(filename[i], 'vegetation_type', 'units', '')
815   nc_write_attribute(filename[i], 'vegetation_type', 'res_orig', domain_px[i]) 
816   nc_write_attribute(filename[i], 'vegetation_type', 'coordinates', 'E_UTM N_UTM lon lat') 
817   nc_write_attribute(filename[i], 'vegetation_type', 'grid_mapping', 'E_UTM N_UTM lon lat')   
818   del vegetation_type
819
820   nc_write_to_file_2d(filename[i], 'pavement_type', pavement_type, datatypes["pavement_type"],'y','x',fillvalues["pavement_type"])   
821   nc_write_attribute(filename[i], 'pavement_type', 'long_name', 'pavement type')
822   nc_write_attribute(filename[i], 'pavement_type', 'units', '')
823   nc_write_attribute(filename[i], 'pavement_type', 'res_orig', domain_px[i]) 
824   nc_write_attribute(filename[i], 'pavement_type', 'coordinates', 'E_UTM N_UTM lon lat') 
825   nc_write_attribute(filename[i], 'pavement_type', 'grid_mapping', 'E_UTM N_UTM lon lat')   
826   del pavement_type
827
828   nc_write_to_file_2d(filename[i], 'water_type', water_type, datatypes["water_type"],'y','x',fillvalues["water_type"]) 
829   nc_write_attribute(filename[i], 'water_type', 'long_name', 'water type')
830   nc_write_attribute(filename[i], 'water_type', 'units', '')
831   nc_write_attribute(filename[i], 'water_type', 'res_orig', domain_px[i]) 
832   nc_write_attribute(filename[i], 'water_type', 'coordinates', 'E_UTM N_UTM lon lat') 
833   nc_write_attribute(filename[i], 'water_type', 'grid_mapping', 'E_UTM N_UTM lon lat')   
834   del water_type
835
836   nc_write_to_file_2d(filename[i], 'soil_type', soil_type, datatypes["soil_type"],'y','x',fillvalues["soil_type"]) 
837   nc_write_attribute(filename[i], 'soil_type', 'long_name', 'soil type')
838   nc_write_attribute(filename[i], 'soil_type', 'units', '')
839   nc_write_attribute(filename[i], 'soil_type', 'res_orig', domain_px[i])   
840   nc_write_attribute(filename[i], 'soil_type', 'coordinates', 'E_UTM N_UTM lon lat') 
841   nc_write_attribute(filename[i], 'soil_type', 'grid_mapping', 'E_UTM N_UTM lon lat')   
842   del soil_type
843
844   del x
845   del y
846
847#  pixels with bridges get building_type = 7 = bridge. This does not change the _type setting for the under-bridge
848#  area NOTE: when bridges are present the consistency check will fail at the moment
849   if domain_3d[i]:
850      if np.any(building_type != fillvalues["building_type"]):
851 
852         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])         
853         bridges_2d = np.where(bridges_2d == 0.0,fillvalues["bridges_2d"],bridges_2d)
854         building_type = np.where(bridges_2d != fillvalues["bridges_2d"],7,building_type)
855         nc_overwrite_to_file_2d(filename[i], 'building_type', building_type) 
856 
857         del building_type 
858         del bridges_2d
859
860# Read/write street type and street crossings
861for i in range(0,ndomains): 
862
863   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])   
864   street_type[street_type == 255] = fillvalues["street_type"]
865   street_type = np.where((street_type < 1) & (street_type != fillvalues["street_type"]),defaultvalues["street_type"],street_type)
866
867   pavement_type = nc_read_from_file_2d_all(filename[i], 'pavement_type') 
868   street_type = np.where((pavement_type == fillvalues["pavement_type"]),fillvalues["street_type"],street_type) 
869
870   nc_write_to_file_2d(filename[i], 'street_type', street_type, datatypes["street_type"],'y','x',fillvalues["street_type"]) 
871   nc_write_attribute(filename[i], 'street_type', 'long_name', 'street type')
872   nc_write_attribute(filename[i], 'street_type', 'units', '')
873   nc_write_attribute(filename[i], 'street_type', 'res_orig', domain_px[i]) 
874   nc_write_attribute(filename[i], 'street_type', 'coordinates', 'E_UTM N_UTM lon lat') 
875   nc_write_attribute(filename[i], 'street_type', 'grid_mapping', 'E_UTM N_UTM lon lat')   
876   del street_type
877   
878   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])   
879   street_crossings[street_crossings == 255] = fillvalues["street_crossings"]
880   street_crossings = np.where((street_crossings < 1) & (street_crossings != fillvalues["street_crossings"]),defaultvalues["street_crossings"],street_crossings)
881   
882   nc_write_to_file_2d(filename[i], 'street_crossing', street_crossings, datatypes["street_crossings"],'y','x',fillvalues["street_crossings"]) 
883   nc_write_attribute(filename[i], 'street_crossing', 'long_name', 'street crossings')
884   nc_write_attribute(filename[i], 'street_crossing', 'units', '')
885   nc_write_attribute(filename[i], 'street_crossing', 'res_orig', domain_px[i]) 
886   nc_write_attribute(filename[i], 'street_crossing', 'coordinates', 'E_UTM N_UTM lon lat') 
887   nc_write_attribute(filename[i], 'street_crossing', 'grid_mapping', 'E_UTM N_UTM lon lat')   
888   del street_crossings
889
890
891# Read/write vegetation on roofs
892for i in range(0,ndomains): 
893   if domain_green_roofs[i]: 
894      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])   
895      buildings_2d = nc_read_from_file_2d_all(filename[i], 'buildings_2d') 
896
897
898      x = nc_read_from_file_2d_all(filename[i], 'x')
899      y = nc_read_from_file_2d_all(filename[i], 'y') 
900      nbuilding_pars = np.arange(0,47)
901      building_pars = np.ones((len(nbuilding_pars),len(y),len(x)))
902      building_pars[:,:,:] = fillvalues["building_pars"]
903
904#     assign green fraction on roofs
905      building_pars[3,:,:] = np.where( ( buildings_2d != fillvalues["buildings_2d"] ) & ( green_roofs != fillvalues["building_pars"] ) & ( green_roofs != 0.0 ),1,fillvalues["building_pars"] )
906
907#     assign leaf area index for vegetation on roofs
908      building_pars[4,:,:] = np.where( ( building_pars[3,:,:] != fillvalues["building_pars"] ) & ( green_roofs >= 0.5 ),settings_lai_roof_intensive,fillvalues["building_pars"])
909      building_pars[4,:,:] = np.where( ( building_pars[3,:,:] != fillvalues["building_pars"] ) & ( green_roofs < 0.5 ),settings_lai_roof_extensive,building_pars[4,:,:])
910   
911   
912      nc_write_dimension(filename[i], 'nbuilding_pars', nbuilding_pars, datatypes["nbuilding_pars"]) 
913      nc_write_to_file_3d(filename[i], 'building_pars', building_pars, datatypes["building_pars"],'nbuilding_pars','y','x',fillvalues["building_pars"]) 
914      nc_write_attribute(filename[i], 'building_pars', 'long_name', 'building_pars')
915      nc_write_attribute(filename[i], 'building_pars', 'units', '')
916      nc_write_attribute(filename[i], 'building_pars', 'res_orig', domain_px[i]) 
917      nc_write_attribute(filename[i], 'building_pars', 'coordinates', 'E_UTM N_UTM lon lat') 
918      nc_write_attribute(filename[i], 'building_pars', 'grid_mapping', 'E_UTM N_UTM lon lat')   
919         
920      del building_pars, buildings_2d, x, y
921
922
923# Read tree data and create LAD and BAD arrays using the canopy generator
924for i in range(0,ndomains): 
925   lai = nc_read_from_file_2d(input_file_lai[ii[i]], 'Band1', domain_x0[i], domain_x1[i], domain_y0[i], domain_y1[i])   
926     
927   vegetation_type = nc_read_from_file_2d_all(filename[i], 'vegetation_type')
928     
929   lai = np.where(vegetation_type == fillvalues["vegetation_type"],fillvalues["vegetation_pars"],lai)
930
931     
932   x = nc_read_from_file_2d_all(filename[i], 'x')
933   y = nc_read_from_file_2d_all(filename[i], 'y') 
934   nvegetation_pars = np.arange(0,12)
935   vegetation_pars = np.ones((len(nvegetation_pars),len(y),len(x)))
936   vegetation_pars[:,:,:] = fillvalues["vegetation_pars"] 
937     
938   vegetation_pars[1,:,:] = lai
939     
940
941#  Write out first version of LAI. Will later be overwritten.
942   nc_write_dimension(filename[i], 'nvegetation_pars', nvegetation_pars, datatypes["nvegetation_pars"]) 
943   nc_write_to_file_3d(filename[i], 'vegetation_pars', vegetation_pars, datatypes["vegetation_pars"],'nvegetation_pars','y','x',fillvalues["vegetation_pars"]) 
944   nc_write_attribute(filename[i], 'vegetation_pars', 'long_name', 'vegetation_pars')
945   nc_write_attribute(filename[i], 'vegetation_pars', 'units', '')
946   nc_write_attribute(filename[i], 'vegetation_pars', 'res_orig', domain_px[i]) 
947   nc_write_attribute(filename[i], 'vegetation_pars', 'coordinates', 'E_UTM N_UTM lon lat') 
948   nc_write_attribute(filename[i], 'vegetation_pars', 'grid_mapping', 'E_UTM N_UTM lon lat')   
949
950   del lai, vegetation_pars, vegetation_type
951
952# Read tree data and create LAD and BAD arrays using the canopy generator
953for i in range(0,ndomains): 
954   if domain_street_trees[i]: 
955
956      vegetation_pars =  nc_read_from_file_2d_all(filename[i], 'vegetation_pars')
957
958      lai = np.copy(vegetation_pars[1,:,:])
959
960      x = nc_read_from_file_2d_all(filename[i], 'x')
961      y = nc_read_from_file_2d_all(filename[i], 'y') 
962
963#     Save lai data as default for low and high vegetation
964      lai_low = lai
965      lai_high = lai
966   
967#     Read all tree parameters from file
968      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])   
969
970      if (input_file_tree_crown_diameter[ii[i]] is not None):
971         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])   
972         tree_crown_diameter = np.where( (tree_crown_diameter == 0.0) | (tree_crown_diameter == -1.0) ,fillvalues["tree_data"],tree_crown_diameter)
973      else:
974         tree_crown_diameter = np.ones((len(y),len(x)))
975         tree_crown_diameter[:,:] = fillvalues["tree_data"]
976
977
978      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])         
979      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]) 
980      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]) 
981     
982#     Remove missing values from the data. Reasonable values will be set by the tree generator   
983      tree_height = np.where( (tree_height == 0.0) | (tree_height == -1.0) ,fillvalues["tree_data"],tree_height)
984      tree_trunk_diameter = np.where( (tree_trunk_diameter == 0.0) | (tree_trunk_diameter == -1.0) ,fillvalues["tree_data"],tree_trunk_diameter)
985      tree_type = np.where( (tree_type == 0.0) | (tree_type == -1.0) ,fillvalues["tree_data"],tree_type)
986      patch_height = np.where( patch_height == -1.0 ,fillvalues["tree_data"],patch_height)     
987
988#     Convert trunk diameter from cm to m
989      tree_trunk_diameter = np.where(tree_trunk_diameter != fillvalues["tree_data"], tree_trunk_diameter * 0.01,tree_trunk_diameter)
990
991
992#     Temporarily change missing value for tree_type
993      tree_type = np.where( (tree_type == fillvalues["tree_type"]),fillvalues["tree_data"],tree_type)   
994
995#     Compare patch height array with vegetation type and correct accordingly
996      vegetation_type = nc_read_from_file_2d_all(filename[i], 'vegetation_type') 
997
998
999#     For zero-height patches, set vegetation_type to short grass and remove these pixels from the patch height array
1000      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)
1001      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)   
1002     
1003
1004      max_tree_height = max(tree_height.flatten())
1005      max_patch_height = max(patch_height.flatten())
1006     
1007#     Call canopy generator for single trees only if there is any tree height available in the domain.
1008#     This does not guarantee that there are street trees that can be processed. This is checked in the
1009#     canopy generator.
1010      if ( (max_tree_height != fillvalues["tree_data"]) | (max_patch_height != fillvalues["tree_data"]) ):
1011
1012         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"])
1013 
1014 
1015#        Remove LAD volumes that are inside buildings
1016         buildings_2d = nc_read_from_file_2d_all(filename[i], 'buildings_2d') 
1017         for k in range(0,len(zlad)-1):
1018 
1019            lad[k,:,:] = np.where(buildings_2d == fillvalues["buildings_2d"],lad[k,:,:],fillvalues["tree_data"])
1020            bad[k,:,:] = np.where(buildings_2d == fillvalues["buildings_2d"],bad[k,:,:],fillvalues["tree_data"])
1021            tree_ids[k,:,:] = np.where(buildings_2d == fillvalues["buildings_2d"],tree_ids[k,:,:],fillvalues["tree_data"])     
1022
1023         del buildings_2d
1024 
1025         nc_write_dimension(filename[i], 'zlad', zlad, datatypes["tree_data"]) 
1026         nc_write_to_file_3d(filename[i], 'lad', lad, datatypes["tree_data"],'zlad','y','x',fillvalues["tree_data"]) 
1027         nc_write_attribute(filename[i], 'lad', 'long_name', 'leaf area density')
1028         nc_write_attribute(filename[i], 'lad', 'units', '')
1029         nc_write_attribute(filename[i], 'lad', 'res_orig', domain_px[i]) 
1030         nc_write_attribute(filename[i], 'lad', 'coordinates', 'E_UTM N_UTM lon lat') 
1031         nc_write_attribute(filename[i], 'lad', 'grid_mapping', 'E_UTM N_UTM lon lat') 
1032 
1033         nc_write_to_file_3d(filename[i], 'bad', bad, datatypes["tree_data"],'zlad','y','x',fillvalues["tree_data"]) 
1034         nc_write_attribute(filename[i], 'bad', 'long_name', 'basal area density')
1035         nc_write_attribute(filename[i], 'bad', 'units', '')
1036         nc_write_attribute(filename[i], 'bad', 'res_orig', domain_px[i]) 
1037         nc_write_attribute(filename[i], 'bad', 'coordinates', 'E_UTM N_UTM lon lat') 
1038         nc_write_attribute(filename[i], 'bad', 'grid_mapping', 'E_UTM N_UTM lon lat')
1039           
1040         nc_write_to_file_3d(filename[i], 'tree_id', tree_ids, datatypes["tree_data"],'zlad','y','x',fillvalues["tree_data"]) 
1041         nc_write_attribute(filename[i], 'tree_id', 'long_name', 'tree id')
1042         nc_write_attribute(filename[i], 'tree_id', 'units', '')
1043         nc_write_attribute(filename[i], 'tree_id', 'res_orig', domain_px[i]) 
1044         nc_write_attribute(filename[i], 'tree_id', 'coordinates', 'E_UTM N_UTM lon lat') 
1045         nc_write_attribute(filename[i], 'tree_id', 'grid_mapping', 'E_UTM N_UTM lon lat')           
1046           
1047         del lai, lad, bad, tree_ids, zlad
1048
1049      del vegetation_pars, tree_height, tree_crown_diameter, tree_trunk_diameter, tree_type, patch_height, x, y
1050
1051
1052# Create vegetation patches for locations with high vegetation type
1053for i in range(0,ndomains): 
1054   if domain_canopy_patches[i]: 
1055
1056#     Load vegetation_type and lad array (at level z = 0) for re-processing
1057      vegetation_type = nc_read_from_file_2d_all(filename[i], 'vegetation_type') 
1058      lad = nc_read_from_file_3d_all(filename[i], 'lad') 
1059      zlad = nc_read_from_file_1d_all(filename[i], 'zlad') 
1060      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]) 
1061      vegetation_pars = nc_read_from_file_3d_all(filename[i], 'vegetation_pars') 
1062      lai = vegetation_pars[1,:,:]
1063     
1064
1065#     Determine all pixels that do not already have an LAD but which are high vegetation to a dummy value of 1.0 and remove all other pixels
1066      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"])
1067
1068#     Now, assign either the default LAI for high vegetation or keep 1.0 from the lai_high array.
1069      lai_high = np.where( (lai_high != fillvalues["tree_data"]) & (lai == fillvalues["tree_data"]), settings_lai_high_default, lai_high)
1070
1071#     If lai values are available in the lai array, write them on the lai_high array
1072      lai_high = np.where( (lai_high != fillvalues["tree_data"]) & (lai != fillvalues["tree_data"]), lai, lai_high)
1073
1074#     Define a patch height wherever it is missing, but where a high vegetation LAI was set
1075      patch_height = np.where ( (lai_high != fillvalues["tree_data"]) & (patch_height == fillvalues["tree_data"]), settings_patch_height_default, patch_height)
1076
1077#     Remove pixels where street trees were already set
1078      patch_height = np.where ( (lad[0,:,:] != fillvalues["tree_data"]), fillvalues["tree_data"], patch_height)
1079
1080#     Remove patch heights that have no lai_high value
1081      patch_height = np.where ( (lai_high == fillvalues["tree_data"]), fillvalues["tree_data"], patch_height)
1082
1083#     For missing LAI values, set either the high vegetation default or the low vegetation default   
1084      lai_high = np.where( (patch_height > 2.0) & (patch_height != fillvalues["tree_data"]) & (lai_high == fillvalues["tree_data"]),settings_lai_high_default,lai_high)
1085      lai_high = np.where( (patch_height <= 2.0) & (patch_height != fillvalues["tree_data"]) & (lai_high == fillvalues["tree_data"]),settings_lai_low_default,lai_high)
1086         
1087      if ( max(patch_height.flatten()) >= (2.0 * domain_dz[i]) ):
1088         print("    start calculating LAD (this might take some time)")       
1089
1090         
1091         lad_patch, patch_nz, status = process_patch(domain_dz[i],patch_height,max(zlad),lai_high,settings_lai_alpha,settings_lai_beta)
1092
1093         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,:,:] )
1094
1095#     Remove high vegetation wherever it is replaced by a leaf area density. This should effectively remove all high vegetation pixels
1096      vegetation_type = np.where((lad[0,:,:] != fillvalues["tree_data"]) & (vegetation_type != fillvalues["vegetation_type"]),settings_veg_type_below_trees,vegetation_type)
1097             
1098#     If desired, remove all high vegetation. TODO: check if this is still necessary
1099      if not domain_high_vegetation[i]:
1100         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)   
1101
1102 
1103#     Set default low LAI for pixels with an LAD (short grass below trees)
1104      lai_low = np.where( (lad[0,:,:] == fillvalues["tree_data"]), lai, settings_lai_low_default)
1105   
1106#     Fill low vegetation pixels without LAI set or with LAI = 0 with default value
1107      lai_low = np.where( ( (lai_low == fillvalues["tree_data"]) | (lai_low == 0.0) ) & (vegetation_type != fillvalues["vegetation_type"] ), settings_lai_low_default, lai_low)
1108
1109#     Remove lai for pixels that have no vegetation_type
1110      lai_low = np.where( ( (vegetation_type != fillvalues["vegetation_type"]) & (vegetation_type != 1) ), lai_low, fillvalues["tree_data"])
1111       
1112#     Overwrite lai in vegetation_parameters 
1113      vegetation_pars[1,:,:] = np.copy(lai_low)
1114      nc_overwrite_to_file_3d(filename[i], 'vegetation_pars', vegetation_pars) 
1115
1116#     Overwrite lad array
1117      nc_overwrite_to_file_3d(filename[i], 'lad', lad) 
1118           
1119      nc_overwrite_to_file_2d(filename[i], 'vegetation_type', vegetation_type) 
1120
1121     
1122      del vegetation_type, lad, lai, patch_height, vegetation_pars, zlad
1123
1124
1125# Final adjustment of vegetation parameters: remove LAI where a bare soil was set
1126for i in range(0,ndomains): 
1127   
1128      vegetation_type = nc_read_from_file_2d_all(filename[i], 'vegetation_type') 
1129      vegetation_pars = nc_read_from_file_3d_all(filename[i], 'vegetation_pars') 
1130      lai = vegetation_pars[1,:,:]   
1131     
1132     
1133#     Remove lai for pixels that have no vegetation_type
1134      lai = np.where( ( (vegetation_type != fillvalues["vegetation_type"]) & (vegetation_type != 1) ), lai, fillvalues["tree_data"])
1135       
1136#     Overwrite lai in vegetation_parameters 
1137      vegetation_pars[1,:,:] = np.copy(lai)
1138      nc_overwrite_to_file_3d(filename[i], 'vegetation_pars', vegetation_pars) 
1139     
1140      del vegetation_type, lai, vegetation_pars
1141
1142
1143# Final consistency check
1144for i in range(0,ndomains): 
1145   vegetation_type = nc_read_from_file_2d_all(filename[i], 'vegetation_type') 
1146   pavement_type = nc_read_from_file_2d_all(filename[i], 'pavement_type') 
1147   building_type = nc_read_from_file_2d_all(filename[i], 'building_type') 
1148   water_type = nc_read_from_file_2d_all(filename[i], 'water_type')     
1149   soil_type = nc_read_from_file_2d_all(filename[i], 'soil_type') 
1150
1151#  Check for consistency and fill empty fields with default vegetation type
1152   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"])
1153
1154#  Check for consistency and fill empty fields with default vegetation type
1155   consistency_array, test = check_consistency_3(vegetation_type,pavement_type,soil_type,fillvalues["vegetation_type"],fillvalues["pavement_type"],fillvalues["soil_type"])
1156
1157   surface_fraction = nc_read_from_file_3d_all(filename[i], 'surface_fraction')       
1158   surface_fraction[0,:,:] = np.where(vegetation_type != fillvalues["vegetation_type"], 1.0, 0.0)
1159   surface_fraction[1,:,:] = np.where(pavement_type != fillvalues["pavement_type"], 1.0, 0.0)
1160   surface_fraction[2,:,:] = np.where(water_type != fillvalues["water_type"], 1.0, 0.0)
1161   nc_overwrite_to_file_3d(filename[i], 'surface_fraction', surface_fraction) 
1162       
1163   del vegetation_type, pavement_type, building_type, water_type, soil_type
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