Home


About us
PALM
PALM-4U
Installation
Release notes
Documentation
Getting Help
Gallery
Developers area

source: palm/trunk/SCRIPTS/palm_csd @ 3999

Last change on this file since 3999 was 3955, checked in by maronga, 6 years ago

bugfixes in palm_csd
  • Property svn:executable set to *
  • Property svn:keywords set to Id
File size: 57.1 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 3955 2019-05-07 09:55:25Z suehring $
27# Bugfix in preparaing of green roofs
28# Bugfix: remove LAI for bare soils
29#
30# 3859 2019-04-03 20:30:31Z maronga
31# Bugfix: wrong variable naming for 'y'
32#
33# 3773 2019-03-01 08:56:57Z maronga
34# Unspecificed changes
35#
36# 3726 2019-02-07 18:22:49Z maronga
37# Removed some more bugs
38#
39# 3688 2019-01-22 10:44:20Z maronga
40# Some unspecified bugfixes
41#
42# 3668 2019-01-14 12:49:24Z maronga
43# Various improvements and bugfixes
44#
45# 3629 2018-12-13 12:18:54Z maronga
46# Added canopy generator calls. Some improvements
47#
48# 3567 2018-11-27 13:59:21Z maronga
49# Initial revisions
50#
51# Description:
52# ------------
53# Processing tool for creating PIDS conform static drivers from rastered NetCDF
54# input
55#
56# @Author Bjoern Maronga (maronga@muk.uni-hannover.de)
57#
58# @todo Make input files optional
59# @todo Allow for ASCII input of terrain height and building height
60# @todo Modularize reading config file
61# @todo Convert to object-oriented treatment (buidings, trees)
62# @todo Automatically shift child domains so that their origin lies intersects
63#       a edge note of the parent grid
64#------------------------------------------------------------------------------#
65
66from palm_csd_files.palm_csd_netcdf_interface import *
67from palm_csd_files.palm_csd_tools import *
68from palm_csd_files.palm_csd_canopy_generator import *
69import numpy as np
70
71   
72def read_config_file():
73   
74   import configparser
75   from math import floor
76   import numpy as np
77   import os
78   import subprocess as sub
79   import sys
80
81#  Check if configuration files exists and quit otherwise
82   input_config = ".csd.config"
83   for i in range(1,len(sys.argv)): 
84      input_config = str(sys.argv[i])
85       
86   config = configparser.RawConfigParser(allow_no_value=True)
87
88   if ( os.path.isfile(input_config) == False ):
89      print ("Error. No configuration file " + input_config + " found.")
90      raise SystemExit   
91
92   config.read(input_config)
93
94
95#  Definition of settings
96
97   global settings_bridge_width
98   global settings_lai_roof_intensive
99   global settings_lai_roof_extensive
100   global settings_season
101   global settings_lai_low_default
102   global settings_lai_high_default
103   global settings_patch_height_default
104   global settings_lai_alpha
105   global settings_lai_beta
106   global settings_veg_type_below_trees
107   global ndomains
108
109#  Definition of global configuration parameters
110   global global_acronym
111   global global_angle
112   global global_author
113   global global_campaign
114   global global_comment
115   global global_contact
116   global global_data_content 
117   global global_dependencies
118   global global_institution
119   global global_keywords
120   global global_location
121   global global_palm_version
122   global global_references
123   global global_site
124   global global_source
125
126   global  path_out
127   global filename_out
128   global version_out
129
130
131#  Definition of domain parameters
132   global domain_names
133   global domain_px
134   global domain_x0
135   global domain_y0
136   global domain_x1
137   global domain_y1
138   global domain_nx
139   global domain_ny
140   global domain_dz
141   global domain_3d
142   global domain_high_vegetation
143   global domain_ip
144   global domain_za
145   global domain_parent
146   global domain_green_roofs
147   global domain_street_trees
148   global domain_canopy_patches
149
150#  Definition of input data parameters
151   global input_names
152   global input_px
153 
154       
155   global input_file_x
156   global input_file_y
157   global input_file_x_UTM
158   global input_file_y_UTM   
159   global input_file_lat
160   global input_file_lon 
161   global input_file_zt
162   global input_file_buildings_2d
163   global input_file_bridges_2d
164   global input_file_building_id
165   global input_file_bridges_id   
166   global input_file_building_type
167   global input_file_building_type
168   global input_file_lai 
169   global input_file_vegetation_type
170   global input_file_vegetation_height
171   global input_file_pavement_type
172   global input_file_water_type
173   global input_file_street_type
174   global input_file_street_crossings
175   global input_file_soil_type
176   global input_file_vegetation_on_roofs
177   global input_file_tree_crown_diameter
178   global input_file_tree_height
179   global input_file_tree_trunk_diameter
180   global input_file_tree_type
181   global input_file_patch_height
182
183   global zt_all
184   global zt_min
185
186   settings_bridge_width = 3.0
187   settings_lai_roof_intensive = 2.5
188   settings_lai_roof_extensive = 0.8
189   settings_season = "summer"
190   settings_lai_high_default = 6.0
191   settings_lai_low_default = 1.0
192   settings_patch_height_default = 10.0
193   settings_lai_alpha = 5.0
194   settings_lai_beta = 3.0
195   settings_veg_type_below_trees = 3
196   ndomains = 0
197
198   global_acronym = ""
199   global_angle = ""
200   global_author = ""
201   global_campaign = ""
202   global_comment = ""
203   global_contact = ""
204   global_data_content = ""
205   global_dependencies = ""
206   global_institution = ""
207   global_keywords = ""
208   global_location = ""
209   global_palm_version = 6.0
210   global_references = ""
211   global_site = ""
212   global_source = ""
213
214   path_out = ""
215   version_out = 1
216   filename_out = "default"
217   
218   domain_names = []
219   domain_px = []
220   domain_x0 = []
221   domain_y0 = []
222   domain_x1 = []
223   domain_y1 = []
224   domain_nx = []
225   domain_ny = []
226   domain_dz = []
227   domain_3d = []
228   domain_high_vegetation = []
229   domain_ip = []
230   domain_za = []
231   domain_parent = []
232   domain_green_roofs = []
233   domain_street_trees = []
234   domain_canopy_patches = []
235
236   zt_min = 0.0
237   zt_all = []
238
239   input_names = []
240   input_px = []
241
242   input_file_x = []
243   input_file_y = [] 
244   input_file_x_UTM = []
245   input_file_y_UTM = [] 
246   input_file_lat = []
247   input_file_lon = [] 
248   
249   input_file_zt = []
250   input_file_buildings_2d = []
251   input_file_bridges_2d = []
252   input_file_building_id = []
253   input_file_bridges_id = []   
254   input_file_building_type = []
255   input_file_lai = []
256   input_file_vegetation_type = []
257   input_file_vegetation_height = []   
258   input_file_pavement_type = []
259   input_file_water_type = []
260   input_file_street_type = [] 
261   input_file_street_crossings = []     
262   input_file_soil_type = []
263   input_file_vegetation_on_roofs = []
264   input_file_tree_crown_diameter = []
265   input_file_tree_height = []
266   input_file_tree_trunk_diameter = []
267   input_file_tree_type = []
268   input_file_patch_height = []
269   
270# Load all user parameters from config file
271   for i in range(0,len(config.sections())):
272
273      read_tmp = config.sections()[i]
274     
275      if ( read_tmp == 'global' ):
276         global_acronym = config.get(read_tmp, 'acronym')
277         global_angle = float(config.get(read_tmp, 'rotation_angle'))
278         global_author = config.get(read_tmp, 'author')
279         global_campaign = config.get(read_tmp, 'campaign')
280         global_comment = config.get(read_tmp, 'comment')
281         global_contact = config.get(read_tmp, 'contact_person')
282         global_data_content = config.get(read_tmp, 'data_content')   
283         global_dependencies = config.get(read_tmp, 'dependencies')
284         global_institution = config.get(read_tmp, 'institution')
285         global_keywords = config.get(read_tmp, 'keywords')
286         global_location = config.get(read_tmp, 'location')
287         global_palm_version = float(config.get(read_tmp, 'palm_version'))
288         global_references = config.get(read_tmp, 'references')
289         global_site = config.get(read_tmp, 'site')
290         global_source = config.get(read_tmp, 'source')
291
292
293      if ( read_tmp == 'settings' ):
294         settings_lai_roof_intensive = config.get(read_tmp, 'lai_roof_intensive')
295         settings_lai_roof_extensive = config.get(read_tmp, 'lai_roof_extensive') 
296         settings_bridge_width = float(config.get(read_tmp, 'bridge_width'))   
297         settings_season = config.get(read_tmp, 'season')   
298         settings_lai_high_default = float(config.get(read_tmp, 'lai_high_vegetation_default'))
299         settings_lai_low_default = float(config.get(read_tmp, 'lai_low_vegetation_default'))         
300         settings_patch_height_default = float(config.get(read_tmp, 'patch_height_default')) 
301         settings_lai_alpha = float(config.get(read_tmp, 'lai_alpha')) 
302         settings_lai_beta = float(config.get(read_tmp, 'lai_beta')) 
303         settings_veg_type_below_trees = config.get(read_tmp, 'vegetation_type_below_trees') 
304
305      if ( read_tmp == 'output' ):
306         path_out = config.get(read_tmp, 'path')         
307         filename_out = config.get(read_tmp, 'file_out')
308         version_out = float(config.get(read_tmp, 'version'))
309
310      if ( read_tmp.split("_")[0] == 'domain' ):
311         ndomains = ndomains + 1
312         domain_names.append(read_tmp.split("_")[1])
313         domain_px.append(float(config.get(read_tmp, 'pixel_size')))
314         domain_nx.append(int(config.get(read_tmp, 'nx')))
315         domain_ny.append(int(config.get(read_tmp, 'ny')))
316         domain_dz.append(float(config.get(read_tmp, 'dz')))       
317         domain_3d.append(config.getboolean(read_tmp, 'buildings_3d'))
318         domain_high_vegetation.append(config.getboolean(read_tmp, 'allow_high_vegetation'))
319         domain_canopy_patches.append(config.getboolean(read_tmp, 'generate_vegetation_patches'))       
320         domain_ip.append(config.getboolean(read_tmp, 'interpolate_terrain'))
321         domain_za.append(config.getboolean(read_tmp, 'use_palm_z_axis')) 
322         if domain_ip[ndomains-1] and not domain_za[ndomains-1]:
323            domain_za[ndomains-1] = True
324            print("+++ Overwrite user setting for use_palm_z_axis")
325         
326         domain_parent.append(config.get(read_tmp, 'domain_parent'))
327
328         domain_x0.append(int(floor(float(config.get(read_tmp, 'origin_x'))/float(config.get(read_tmp, 'pixel_size')))))
329         domain_y0.append(int(floor(float(config.get(read_tmp, 'origin_y'))/float(config.get(read_tmp, 'pixel_size')))))       
330         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'))))
331         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'))))
332         domain_green_roofs.append(config.getboolean(read_tmp, 'vegetation_on_roofs')) 
333         domain_street_trees.append(config.getboolean(read_tmp, 'street_trees')) 
334         
335      if ( read_tmp.split("_")[0] == 'input' ):
336         input_names.append(read_tmp.split("_")[1])
337         input_px.append(float(config.get(read_tmp, 'pixel_size')))
338         input_file_x.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_x'))
339         input_file_y.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_y')) 
340         input_file_lat.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_lat'))
341         input_file_lon.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_lon'))   
342         input_file_x_UTM.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_x_UTM'))
343         input_file_y_UTM.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_y_UTM')) 
344         input_file_zt.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_zt'))
345         input_file_buildings_2d.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_buildings_2d'))
346         input_file_bridges_2d.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_bridges_2d'))
347         input_file_building_id.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_building_id')) 
348         input_file_bridges_id.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_bridges_id')) 
349         input_file_building_type.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_building_type'))
350         input_file_lai.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_lai'))         
351         input_file_vegetation_type.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_vegetation_type')) 
352         input_file_vegetation_height.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_vegetation_height'))         
353         input_file_pavement_type.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_pavement_type'))   
354         input_file_water_type.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_water_type'))
355         input_file_street_type.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_street_type'))   
356         input_file_street_crossings.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_street_crossings')) 
357         input_file_patch_height.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_patch_height')) 
358         
359         tmp = config.get(read_tmp, 'file_tree_crown_diameter')
360         if tmp is not None:
361            input_file_tree_crown_diameter.append(config.get(read_tmp, 'path') + "/" + tmp) 
362         else:
363            input_file_tree_crown_diameter.append(None)
364         input_file_tree_height.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_tree_height')) 
365         input_file_tree_trunk_diameter.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_tree_trunk_diameter')) 
366         input_file_tree_type.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_tree_type')) 
367         input_file_vegetation_on_roofs.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_vegetation_on_roofs'))           
368         #input_file_soil_type.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_soil_type'))   
369   return 0
370
371
372############################################################
373
374# Start of main program
375datatypes = {
376   "x": "f4", 
377   "y": "f4", 
378   "z": "f4", 
379   "lat": "f4", 
380   "lon": "f4",   
381   "E_UTM": "f4", 
382   "N_UTM": "f4", 
383   "zt": "f4",
384   "buildings_2d": "f4",
385   "buildings_3d": "b",
386   "bridges_2d": "f4",
387   "building_id": "i",
388   "bridges_id": "i",
389   "building_type": "b",
390   "nsurface_fraction": "i",
391   "vegetation_type": "b",
392   "vegetation_height": "f4",
393   "pavement_type": "b", 
394   "water_type": "b", 
395   "street_type": "b", 
396   "street_crossings": "b",     
397   "soil_type": "b",
398   "surface_fraction": "f4",
399   "building_pars": "f4",
400   "vegetation_pars": "f4",
401   "tree_data": "f4",
402   "tree_type": "b",
403   "nbuilding_pars": "i",
404   "nvegetation_pars": "i",
405   "zlad": "f4"
406   }
407
408fillvalues = {
409   "lat": float(-9999.0),
410   "lon": float(-9999.0),
411   "E_UTM": float(-9999.0),
412   "N_UTM": float(-9999.0),   
413   "zt": float(-9999.0),
414   "buildings_2d": float(-9999.0),
415   "buildings_3d": np.byte(-127),
416   "bridges_2d": float(-9999.0),
417   "building_id": int(-9999),
418   "bridges_id": int(-9999),
419   "building_type": np.byte(-127),
420   "nsurface_fraction": int(-9999),
421   "vegetation_type": np.byte(-127),
422   "vegetation_height": float(-9999.0),
423   "pavement_type": np.byte(-127),
424   "water_type": np.byte(-127),
425   "street_type": np.byte(-127), 
426   "street_crossings": np.byte(-127),   
427   "soil_type": np.byte(-127),
428   "surface_fraction": float(-9999.0),
429   "building_pars": float(-9999.0),
430   "vegetation_pars": float(-9999.0),
431   "tree_data": float(-9999.0),
432   "tree_type": np.byte(-127)
433   }
434
435defaultvalues = {
436   "lat": float(-9999.0),
437   "lon": float(-9999.0),
438   "E_UTM": float(-9999.0),
439   "N_UTM": float(-9999.0), 
440   "zt": float(0.0),
441   "buildings_2d": float(0.0),
442   "buildings_3d": 0,   
443   "bridges_2d": float(0.0),
444   "building_id": int(0),
445   "bridges_id": int(0),
446   "building_type": 1,
447   "nsurface_fraction": int(-9999),
448   "vegetation_type": 3,
449   "vegetation_height": float(-9999.0),
450   "pavement_type": 1, 
451   "water_type": 1, 
452   "street_type": 1, 
453   "street_crossings": 0,   
454   "soil_type": 1,
455   "surface_fraction": float(0.0),
456   "buildings_pars": float(-9999.0),
457   "tree_data": float(-9999.0),
458   "tree_type": 0,
459   "vegetation_pars": float(-9999.0)   
460   }
461 
462# Read configuration file and set parameters accordingly
463read_config_file()
464
465
466filename = []
467ii = []
468ii_parent = []
469# Define indices and filenames for all domains and create netCDF files
470for i in range(0,ndomains):
471
472#  Calculate indices and input files
473   ii.append(input_px.index(domain_px[i]))
474   filename.append(path_out + "/" + filename_out + "_" + domain_names[i])
475   if domain_parent[i] is not None:
476      ii_parent.append(input_px.index(domain_px[domain_names.index(domain_parent[i])]))
477   else:
478      ii_parent.append(None)
479
480
481   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)
482   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)
483   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)
484   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) 
485   
486#  Calculate position of origin
487   x_UTM_origin = float(x_UTM[0,0]) - 0.5 * (float(x_UTM[0,1]) - float(x_UTM[0,0]))
488   y_UTM_origin = float(y_UTM[0,0]) - 0.5 * (float(y_UTM[1,0]) - float(y_UTM[0,0])) 
489   x_origin = float(lon[0,0]) - 0.5 * (float(lon[0,1]) - float(lon[0,0]))
490   y_origin = float(lat[0,0]) - 0.5 * (float(lat[1,0]) - float(lat[0,0]))   
491   
492#  Create NetCDF output file and set global attributes
493   nc_create_file(filename[i])
494   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)
495
496   del x_UTM, y_UTM, lat, lon
497   
498# Process terrain height
499for i in range(0,ndomains):
500
501#  Read and write terrain height (zt)
502   zt = nc_read_from_file_2d(input_file_zt[ii[i]], 'Band1', domain_x0[i], domain_x1[i], domain_y0[i], domain_y1[i])
503
504#  Final step: add zt array to the global array   
505   zt_all.append(zt)
506   del zt
507 
508#  Calculate the global (all domains) minimum of the terrain height. This value will be substracted for all
509#  data sets
510zt_min = min(zt_all[0].flatten()) 
511for i in range(0,ndomains):
512   zt_min = min(zt_min,min(zt_all[i].flatten()))
513   
514del zt_all[:]
515
516print( "Shift terrain heights by -" + str(zt_min))
517for i in range(0,ndomains):
518
519#  Read and write terrain height (zt)
520   zt = nc_read_from_file_2d(input_file_zt[ii[i]], 'Band1', domain_x0[i], domain_x1[i], domain_y0[i], domain_y1[i])
521   x = nc_read_from_file_1d(input_file_x[ii[i]], "x", domain_x0[i], domain_x1[i])
522   y = nc_read_from_file_1d(input_file_y[ii[i]], "y", domain_y0[i], domain_y1[i])
523
524 
525   zt = zt - zt_min
526   
527   nc_write_global_attribute(filename[i], 'origin_z', float(zt_min))
528
529#  If necessary, interpolate parent domain terrain height on child domain grid and blend the two
530   if domain_ip[i]:
531      parent_id = domain_names.index(domain_parent[i])
532      tmp_x0 = int( domain_x0[i] * domain_px[i] / domain_px[parent_id] ) - 1
533      tmp_y0 = int( domain_y0[i] * domain_px[i] / domain_px[parent_id] ) - 1
534      tmp_x1 = int( domain_x1[i] * domain_px[i] / domain_px[parent_id] ) + 1
535      tmp_y1 = int( domain_y1[i] * domain_px[i] / domain_px[parent_id] ) + 1
536
537      tmp_x = nc_read_from_file_1d(input_file_x[ii_parent[i]], "x", tmp_x0, tmp_x1)
538      tmp_y = nc_read_from_file_1d(input_file_y[ii_parent[i]], "y", tmp_y0, tmp_y1)
539
540      zt_parent = nc_read_from_file_2d(input_file_zt[ii_parent[i]], 'Band1', tmp_x0, tmp_x1, tmp_y0, tmp_y1)
541
542      zt_parent = zt_parent - zt_min
543
544#     Interpolate array and bring to PALM grid of child domain
545      zt_ip = interpolate_2d(zt_parent,tmp_x,tmp_y,x,y)
546      zt_ip = bring_to_palm_grid(zt_ip,x,y,domain_dz[parent_id])
547     
548     
549#     Shift the child terrain height according to the parent mean terrain height
550      print("shifting: -" + str(np.mean(zt)) + " +" + str(np.mean(zt_ip)))
551      #zt = zt - np.min(zt) + np.min(zt_ip)
552      zt = zt - np.mean(zt) + np.mean(zt_ip)
553 
554 
555#     Blend over the parent and child terrain height within a radius of 50 px 
556      zt = blend_array_2d(zt,zt_ip,50)
557     # zt = zt_ip
558   
559#  Final step: add zt array to the global array   
560
561   zt_all.append(zt)
562   del zt
563 
564
565# Read and shift x and y coordinates, shift terrain height according to its minimum value and write all data
566# to file 
567for i in range(0,ndomains):   
568#  Read horizontal grid variables from zt file and write them to output file
569   x = nc_read_from_file_1d(input_file_x[ii[i]], "x", domain_x0[i], domain_x1[i])
570   y = nc_read_from_file_1d(input_file_y[ii[i]], "y", domain_y0[i], domain_y1[i]) 
571   x = x - min(x.flatten()) + domain_px[i]/2.0
572   y = y - min(y.flatten()) + domain_px[i]/2.0 
573   nc_write_dimension(filename[i], 'x', x, datatypes["x"])
574   nc_write_dimension(filename[i], 'y', y, datatypes["y"]) 
575   nc_write_attribute(filename[i], 'x', 'long_name', 'x')
576   nc_write_attribute(filename[i], 'x', 'standard_name','projection_x_coordinate')   
577   nc_write_attribute(filename[i], 'x', 'units', 'm')
578   nc_write_attribute(filename[i], 'y', 'long_name', 'y')
579   nc_write_attribute(filename[i], 'y', 'standard_name', 'projection_y_coordinate')   
580   nc_write_attribute(filename[i], 'y', 'units', 'm')
581
582   lat = nc_read_from_file_2d(input_file_lat[ii[i]], "Band1", domain_x0[i], domain_x1[i], domain_y0[i], domain_y1[i])
583   lon = nc_read_from_file_2d(input_file_lon[ii[i]], "Band1", domain_x0[i], domain_x1[i], domain_y0[i], domain_y1[i]) 
584
585   nc_write_to_file_2d(filename[i], 'lat', lat, datatypes["lat"],'y','x',fillvalues["lat"])
586   nc_write_to_file_2d(filename[i], 'lon', lon, datatypes["lon"],'y','x',fillvalues["lon"]) 
587   
588   nc_write_attribute(filename[i], 'lat', 'long_name', 'latitude')
589   nc_write_attribute(filename[i], 'lat', 'standard_name','latitude')   
590   nc_write_attribute(filename[i], 'lat', 'units', 'degrees_north')
591 
592   nc_write_attribute(filename[i], 'lon', 'long_name', 'longitude')
593   nc_write_attribute(filename[i], 'lon', 'standard_name','longitude')   
594   nc_write_attribute(filename[i], 'lon', 'units', 'degrees_east') 
595   
596   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])
597   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]) 
598
599   
600   nc_write_to_file_2d(filename[i], 'E_UTM', x_UTM, datatypes["E_UTM"],'y','x',fillvalues["E_UTM"])
601   nc_write_to_file_2d(filename[i], 'N_UTM', y_UTM, datatypes["N_UTM"],'y','x',fillvalues["N_UTM"]) 
602   
603   nc_write_attribute(filename[i], 'E_UTM', 'long_name', 'easting')
604   nc_write_attribute(filename[i], 'E_UTM', 'standard_name','projection_x_coorindate')   
605   nc_write_attribute(filename[i], 'E_UTM', 'units', 'm')
606 
607   nc_write_attribute(filename[i], 'N_UTM', 'long_name', 'northing')
608   nc_write_attribute(filename[i], 'N_UTM', 'standard_name','projection_y_coorindate')   
609   nc_write_attribute(filename[i], 'N_UTM', 'units', 'm') 
610 
611   nc_write_crs(filename[i])
612   
613 
614 
615#  If necessary, bring terrain height to PALM's vertical grid. This is either forced by the user or implicitly
616#  by using interpolation for a child domain       
617   if domain_za[i]:
618      zt_all[i] = bring_to_palm_grid(zt_all[i],x,y,domain_dz[i])
619
620   nc_write_to_file_2d(filename[i], 'zt', zt_all[i], datatypes["zt"],'y','x',fillvalues["zt"])
621   nc_write_attribute(filename[i], 'zt', 'long_name', 'orography')
622   nc_write_attribute(filename[i], 'zt', 'units', 'm')
623   nc_write_attribute(filename[i], 'zt', 'res_orig', domain_px[i]) 
624   nc_write_attribute(filename[i], 'zt', 'coordinates', 'E_UTM N_UTM lon lat') 
625   nc_write_attribute(filename[i], 'zt', 'grid_mapping', 'E_UTM N_UTM lon lat')   
626
627del zt_all
628
629
630#  Process building height, id, and type
631for i in range(0,ndomains): 
632   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])
633
634   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]) 
635 
636   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]) 
637   building_type[building_type >= 254] = fillvalues["building_type"]
638   building_type = np.where(building_type < 1,defaultvalues["building_type"],building_type)
639 
640   check = check_arrays_2(buildings_2d,building_id,fillvalues["buildings_2d"],fillvalues["building_id"])
641   if not check:
642      buildings_2d = np.where(building_id != fillvalues["building_id"],buildings_2d,fillvalues["buildings_2d"])
643      building_id = np.where(buildings_2d == fillvalues["buildings_2d"],fillvalues["building_id"],building_id)
644      print("Data check #1 " + str(check_arrays_2(buildings_2d,building_id,fillvalues["buildings_2d"],fillvalues["building_id"])))
645
646   check = check_arrays_2(buildings_2d,building_type,fillvalues["buildings_2d"],fillvalues["building_type"])
647   if not check:
648      building_type = np.where(buildings_2d == fillvalues["buildings_2d"],fillvalues["building_type"],building_type)
649      building_type = np.where((building_type == fillvalues["building_type"]) & (buildings_2d != fillvalues["buildings_2d"]),defaultvalues["building_type"],building_type)
650      print("Data check #2 " + str(check_arrays_2(buildings_2d,building_type,fillvalues["buildings_2d"],fillvalues["building_type"])))
651 
652
653   nc_write_to_file_2d(filename[i], 'buildings_2d', buildings_2d, datatypes["buildings_2d"],'y','x',fillvalues["buildings_2d"])
654   nc_write_attribute(filename[i], 'buildings_2d', 'long_name', 'buildings')
655   nc_write_attribute(filename[i], 'buildings_2d', 'units', 'm')
656   nc_write_attribute(filename[i], 'buildings_2d', 'res_orig', domain_px[i]) 
657   nc_write_attribute(filename[i], 'buildings_2d', 'lod', 1) 
658   nc_write_attribute(filename[i], 'buildings_2d', 'coordinates', 'E_UTM N_UTM lon lat') 
659   nc_write_attribute(filename[i], 'buildings_2d', 'grid_mapping', 'E_UTM N_UTM lon lat')   
660   
661   nc_write_to_file_2d(filename[i], 'building_id', building_id, datatypes["building_id"],'y','x',fillvalues["building_id"])
662   nc_write_attribute(filename[i], 'building_id', 'long_name', 'building id')
663   nc_write_attribute(filename[i], 'building_id', 'units', '')
664   nc_write_attribute(filename[i], 'building_id', 'res _orig', domain_px[i]) 
665   nc_write_attribute(filename[i], 'building_id', 'coordinates', 'E_UTM N_UTM lon lat') 
666   nc_write_attribute(filename[i], 'building_id', 'grid_mapping', 'E_UTM N_UTM lon lat')   
667   
668   nc_write_to_file_2d(filename[i], 'building_type', building_type, datatypes["building_type"],'y','x',fillvalues["building_type"])
669   nc_write_attribute(filename[i], 'building_type', 'long_name', 'building type')
670   nc_write_attribute(filename[i], 'building_type', 'units', '')
671   nc_write_attribute(filename[i], 'building_type', 'res_orig', domain_px[i]) 
672   nc_write_attribute(filename[i], 'building_type', 'coordinates', 'E_UTM N_UTM lon lat') 
673   nc_write_attribute(filename[i], 'building_type', 'grid_mapping', 'E_UTM N_UTM lon lat')   
674 
675del buildings_2d
676del building_id
677del building_type
678
679# Create 3d buildings if necessary. In that course, read bridge objects and add them to building layer
680for i in range(0,ndomains): 
681   
682   if domain_3d[i]:
683      x = nc_read_from_file_2d_all(filename[i], 'x')
684      y = nc_read_from_file_2d_all(filename[i], 'y') 
685      buildings_2d = nc_read_from_file_2d_all(filename[i], 'buildings_2d') 
686      building_id = nc_read_from_file_2d_all(filename[i], 'building_id') 
687     
688      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])
689      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])
690     
691      bridges_2d = np.where(bridges_2d == 0.0,fillvalues["bridges_2d"],bridges_2d)
692      building_id = np.where(bridges_2d == fillvalues["bridges_2d"],building_id,bridges_id)
693     
694      if np.any(buildings_2d != fillvalues["buildings_2d"]):
695         buildings_3d, z = make_3d_from_2d(buildings_2d,x,y,domain_dz[i])
696         if np.any(bridges_2d != fillvalues["bridges_2d"]):
697            buildings_3d = make_3d_from_bridges_2d(buildings_3d,bridges_2d,x,y,domain_dz[i],settings_bridge_width,fillvalues["bridges_2d"])
698         else:
699            print("Skipping creation of 3D bridges (no bridges in domain)")
700           
701           
702         nc_write_dimension(filename[i], 'z', z, datatypes["z"]) 
703         nc_write_attribute(filename[i], 'z', 'long_name', 'z') 
704         nc_write_attribute(filename[i], 'z', 'units', 'm')
705         
706         nc_overwrite_to_file_2d(filename[i], 'building_id', building_id) 
707 
708         nc_write_to_file_3d(filename[i], 'buildings_3d', buildings_3d, datatypes["buildings_3d"],'z','y','x',fillvalues["buildings_3d"])   
709         nc_write_attribute(filename[i], 'buildings_3d', 'long_name', 'buildings 3d')
710         nc_write_attribute(filename[i], 'buildings_3d', 'units', '')
711         nc_write_attribute(filename[i], 'buildings_3d', 'res_orig', domain_px[i]) 
712         nc_write_attribute(filename[i], 'buildings_3d', 'lod', 2) 
713         
714         del buildings_3d
715         
716      else:
717         print("Skipping creation of 3D buildings (no buildings in domain)")
718
719
720      del bridges_2d, bridges_id, building_id, buildings_2d
721
722
723
724# Read vegetation type, water_type, pavement_type, soil_type and make fields consistent
725for i in range(0,ndomains): 
726
727   building_type = nc_read_from_file_2d_all(filename[i], 'building_type') 
728   
729   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])   
730   vegetation_type[vegetation_type == 255] = fillvalues["vegetation_type"]
731   vegetation_type = np.where((vegetation_type < 1) & (vegetation_type != fillvalues["vegetation_type"]),defaultvalues["vegetation_type"],vegetation_type)
732   
733   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])   
734   pavement_type[pavement_type == 255] = fillvalues["pavement_type"]
735   pavement_type = np.where((pavement_type < 1) & (pavement_type != fillvalues["pavement_type"]),defaultvalues["pavement_type"],pavement_type)
736
737   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])   
738   water_type[water_type == 255] = fillvalues["water_type"]
739   water_type = np.where((water_type < 1) & (water_type != fillvalues["water_type"]),defaultvalues["water_type"],water_type)
740 
741#  to do: replace by real soil input data
742   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]) 
743   soil_type[soil_type == 255] = fillvalues["soil_type"]
744   soil_type = np.where((soil_type < 1) & (soil_type != fillvalues["soil_type"]),defaultvalues["soil_type"],soil_type)
745
746#  Make arrays consistent
747#  #1 Set vegetation type to missing for pixel where a pavement type is set
748   vegetation_type = np.where((vegetation_type != fillvalues["vegetation_type"]) & (pavement_type != fillvalues["pavement_type"]),fillvalues["vegetation_type"],vegetation_type)
749
750#  #2 Set vegetation type to missing for pixel where a building type is set
751   vegetation_type = np.where((vegetation_type != fillvalues["vegetation_type"]) & (building_type != fillvalues["building_type"]) ,fillvalues["vegetation_type"],vegetation_type)
752
753#  #3 Set vegetation type to missing for pixel where a building type is set
754   vegetation_type = np.where((vegetation_type != fillvalues["vegetation_type"]) & (water_type != fillvalues["water_type"]),fillvalues["vegetation_type"],vegetation_type)   
755
756#  #4 Remove pavement for pixels with buildings
757   pavement_type = np.where((pavement_type != fillvalues["pavement_type"]) & (building_type != fillvalues["building_type"]),fillvalues["pavement_type"],pavement_type) 
758
759#  #5 Remove pavement for pixels with water.
760   pavement_type = np.where((pavement_type != fillvalues["pavement_type"]) & (water_type != fillvalues["water_type"]),fillvalues["pavement_type"],pavement_type) 
761   
762#  #6 Remove water for pixels with buildings
763   water_type = np.where((water_type != fillvalues["water_type"]) & (building_type != fillvalues["building_type"]),fillvalues["water_type"],water_type) 
764
765#  Correct vegetation_type when a vegetation height is available and is indicative of low vegeetation
766   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])   
767         
768   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)
769   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)
770 
771#  Check for consistency and fill empty fields with default vegetation type
772   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"])
773   
774   if test:
775      vegetation_type = np.where(consistency_array == 0,defaultvalues["vegetation_type"],vegetation_type)
776      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"])
777
778#  #7 to be removed: set default soil type everywhere
779   soil_type = np.where((vegetation_type != fillvalues["vegetation_type"]) | (pavement_type != fillvalues["pavement_type"]),defaultvalues["soil_type"],fillvalues["soil_type"]) 
780       
781   
782#  Check for consistency and fill empty fields with default vegetation type
783   consistency_array, test = check_consistency_3(vegetation_type,pavement_type,soil_type,fillvalues["vegetation_type"],fillvalues["pavement_type"],fillvalues["soil_type"])
784       
785#  Create surface_fraction array
786   x = nc_read_from_file_2d_all(filename[i], 'x')
787   y = nc_read_from_file_2d_all(filename[i], 'y') 
788   nsurface_fraction = np.arange(0,3)
789   surface_fraction = np.ones((len(nsurface_fraction),len(y),len(x)))
790   
791   surface_fraction[0,:,:] = np.where(vegetation_type != fillvalues["vegetation_type"], 1.0, 0.0)
792   surface_fraction[1,:,:] = np.where(pavement_type != fillvalues["pavement_type"], 1.0, 0.0)
793   surface_fraction[2,:,:] = np.where(water_type != fillvalues["water_type"], 1.0, 0.0)
794   
795   nc_write_dimension(filename[i], 'nsurface_fraction', nsurface_fraction, datatypes["nsurface_fraction"]) 
796   nc_write_to_file_3d(filename[i], 'surface_fraction', surface_fraction, datatypes["surface_fraction"],'nsurface_fraction','y','x',fillvalues["surface_fraction"])     
797   nc_write_attribute(filename[i], 'surface_fraction', 'long_name', 'surface fraction')
798   nc_write_attribute(filename[i], 'surface_fraction', 'units', '')
799   nc_write_attribute(filename[i], 'surface_fraction', 'res_orig', domain_px[i])     
800   del surface_fraction   
801
802   nc_write_to_file_2d(filename[i], 'vegetation_type', vegetation_type, datatypes["vegetation_type"],'y','x',fillvalues["vegetation_type"])     
803   nc_write_attribute(filename[i], 'vegetation_type', 'long_name', 'vegetation type')
804   nc_write_attribute(filename[i], 'vegetation_type', 'units', '')
805   nc_write_attribute(filename[i], 'vegetation_type', 'res_orig', domain_px[i]) 
806   nc_write_attribute(filename[i], 'vegetation_type', 'coordinates', 'E_UTM N_UTM lon lat') 
807   nc_write_attribute(filename[i], 'vegetation_type', 'grid_mapping', 'E_UTM N_UTM lon lat')   
808   del vegetation_type
809
810   nc_write_to_file_2d(filename[i], 'pavement_type', pavement_type, datatypes["pavement_type"],'y','x',fillvalues["pavement_type"])   
811   nc_write_attribute(filename[i], 'pavement_type', 'long_name', 'pavement type')
812   nc_write_attribute(filename[i], 'pavement_type', 'units', '')
813   nc_write_attribute(filename[i], 'pavement_type', 'res_orig', domain_px[i]) 
814   nc_write_attribute(filename[i], 'pavement_type', 'coordinates', 'E_UTM N_UTM lon lat') 
815   nc_write_attribute(filename[i], 'pavement_type', 'grid_mapping', 'E_UTM N_UTM lon lat')   
816   del pavement_type
817
818   nc_write_to_file_2d(filename[i], 'water_type', water_type, datatypes["water_type"],'y','x',fillvalues["water_type"]) 
819   nc_write_attribute(filename[i], 'water_type', 'long_name', 'water type')
820   nc_write_attribute(filename[i], 'water_type', 'units', '')
821   nc_write_attribute(filename[i], 'water_type', 'res_orig', domain_px[i]) 
822   nc_write_attribute(filename[i], 'water_type', 'coordinates', 'E_UTM N_UTM lon lat') 
823   nc_write_attribute(filename[i], 'water_type', 'grid_mapping', 'E_UTM N_UTM lon lat')   
824   del water_type
825
826   nc_write_to_file_2d(filename[i], 'soil_type', soil_type, datatypes["soil_type"],'y','x',fillvalues["soil_type"]) 
827   nc_write_attribute(filename[i], 'soil_type', 'long_name', 'soil type')
828   nc_write_attribute(filename[i], 'soil_type', 'units', '')
829   nc_write_attribute(filename[i], 'soil_type', 'res_orig', domain_px[i])   
830   nc_write_attribute(filename[i], 'soil_type', 'coordinates', 'E_UTM N_UTM lon lat') 
831   nc_write_attribute(filename[i], 'soil_type', 'grid_mapping', 'E_UTM N_UTM lon lat')   
832   del soil_type
833
834   del x
835   del y
836
837#  pixels with bridges get building_type = 7 = bridge. This does not change the _type setting for the under-bridge
838#  area NOTE: when bridges are present the consistency check will fail at the moment
839   if domain_3d[i]:
840      if np.any(building_type != fillvalues["building_type"]):
841 
842         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])         
843         bridges_2d = np.where(bridges_2d == 0.0,fillvalues["bridges_2d"],bridges_2d)
844         building_type = np.where(bridges_2d != fillvalues["bridges_2d"],7,building_type)
845         nc_overwrite_to_file_2d(filename[i], 'building_type', building_type) 
846 
847         del building_type 
848         del bridges_2d
849
850# Read/write street type and street crossings
851for i in range(0,ndomains): 
852
853   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])   
854   street_type[street_type == 255] = fillvalues["street_type"]
855   street_type = np.where((street_type < 1) & (street_type != fillvalues["street_type"]),defaultvalues["street_type"],street_type)
856
857   pavement_type = nc_read_from_file_2d_all(filename[i], 'pavement_type') 
858   street_type = np.where((pavement_type == fillvalues["pavement_type"]),fillvalues["street_type"],street_type) 
859
860   nc_write_to_file_2d(filename[i], 'street_type', street_type, datatypes["street_type"],'y','x',fillvalues["street_type"]) 
861   nc_write_attribute(filename[i], 'street_type', 'long_name', 'street type')
862   nc_write_attribute(filename[i], 'street_type', 'units', '')
863   nc_write_attribute(filename[i], 'street_type', 'res_orig', domain_px[i]) 
864   nc_write_attribute(filename[i], 'street_type', 'coordinates', 'E_UTM N_UTM lon lat') 
865   nc_write_attribute(filename[i], 'street_type', 'grid_mapping', 'E_UTM N_UTM lon lat')   
866   del street_type
867   
868   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])   
869   street_crossings[street_crossings == 255] = fillvalues["street_crossings"]
870   street_crossings = np.where((street_crossings < 1) & (street_crossings != fillvalues["street_crossings"]),defaultvalues["street_crossings"],street_crossings)
871   
872   nc_write_to_file_2d(filename[i], 'street_crossing', street_crossings, datatypes["street_crossings"],'y','x',fillvalues["street_crossings"]) 
873   nc_write_attribute(filename[i], 'street_crossing', 'long_name', 'street crossings')
874   nc_write_attribute(filename[i], 'street_crossing', 'units', '')
875   nc_write_attribute(filename[i], 'street_crossing', 'res_orig', domain_px[i]) 
876   nc_write_attribute(filename[i], 'street_crossing', 'coordinates', 'E_UTM N_UTM lon lat') 
877   nc_write_attribute(filename[i], 'street_crossing', 'grid_mapping', 'E_UTM N_UTM lon lat')   
878   del street_crossings
879
880
881# Read/write vegetation on roofs
882for i in range(0,ndomains): 
883   if domain_green_roofs[i]: 
884      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])   
885      buildings_2d = nc_read_from_file_2d_all(filename[i], 'buildings_2d') 
886
887
888      x = nc_read_from_file_2d_all(filename[i], 'x')
889      y = nc_read_from_file_2d_all(filename[i], 'y') 
890      nbuilding_pars = np.arange(0,46)
891      building_pars = np.ones((len(nbuilding_pars),len(y),len(x)))
892      building_pars[:,:,:] = fillvalues["building_pars"]
893
894#     assign green fraction on roofs
895      building_pars[3,:,:] = np.where( ( buildings_2d != fillvalues["buildings_2d"] ) & ( green_roofs != fillvalues["building_pars"] ) & ( green_roofs != 0.0 ),1,fillvalues["building_pars"] )
896
897#     assign leaf area index for vegetation on roofs
898      building_pars[4,:,:] = np.where( ( buildings_2d != fillvalues["buildings_2d"] ) & ( green_roofs >= 0.5 ),settings_lai_roof_intensive,fillvalues["building_pars"])
899      building_pars[4,:,:] = np.where( ( buildings_2d != fillvalues["buildings_2d"] ) & ( green_roofs < 0.5 ),settings_lai_roof_extensive,building_pars[4,:,:])
900   
901   
902      nc_write_dimension(filename[i], 'nbuilding_pars', nbuilding_pars, datatypes["nbuilding_pars"]) 
903      nc_write_to_file_3d(filename[i], 'building_pars', building_pars, datatypes["building_pars"],'nbuilding_pars','y','x',fillvalues["building_pars"]) 
904      nc_write_attribute(filename[i], 'building_pars', 'long_name', 'building_pars')
905      nc_write_attribute(filename[i], 'building_pars', 'units', '')
906      nc_write_attribute(filename[i], 'building_pars', 'res_orig', domain_px[i]) 
907      nc_write_attribute(filename[i], 'building_pars', 'coordinates', 'E_UTM N_UTM lon lat') 
908      nc_write_attribute(filename[i], 'building_pars', 'grid_mapping', 'E_UTM N_UTM lon lat')   
909         
910      del building_pars, buildings_2d, x, y
911
912
913# Read tree data and create LAD and BAD arrays using the canopy generator
914for i in range(0,ndomains): 
915   lai = nc_read_from_file_2d(input_file_lai[ii[i]], 'Band1', domain_x0[i], domain_x1[i], domain_y0[i], domain_y1[i])   
916     
917   vegetation_type = nc_read_from_file_2d_all(filename[i], 'vegetation_type')
918     
919   lai = np.where(vegetation_type == fillvalues["vegetation_type"],fillvalues["vegetation_pars"],lai)
920
921     
922   x = nc_read_from_file_2d_all(filename[i], 'x')
923   y = nc_read_from_file_2d_all(filename[i], 'y') 
924   nvegetation_pars = np.arange(0,12)
925   vegetation_pars = np.ones((len(nvegetation_pars),len(y),len(x)))
926   vegetation_pars[:,:,:] = fillvalues["vegetation_pars"] 
927     
928   vegetation_pars[1,:,:] = lai
929     
930
931#  Write out first version of LAI. Will later be overwritten.
932   nc_write_dimension(filename[i], 'nvegetation_pars', nvegetation_pars, datatypes["nvegetation_pars"]) 
933   nc_write_to_file_3d(filename[i], 'vegetation_pars', vegetation_pars, datatypes["vegetation_pars"],'nvegetation_pars','y','x',fillvalues["vegetation_pars"]) 
934   nc_write_attribute(filename[i], 'vegetation_pars', 'long_name', 'vegetation_pars')
935   nc_write_attribute(filename[i], 'vegetation_pars', 'units', '')
936   nc_write_attribute(filename[i], 'vegetation_pars', 'res_orig', domain_px[i]) 
937   nc_write_attribute(filename[i], 'vegetation_pars', 'coordinates', 'E_UTM N_UTM lon lat') 
938   nc_write_attribute(filename[i], 'vegetation_pars', 'grid_mapping', 'E_UTM N_UTM lon lat')   
939
940   del lai, vegetation_pars, vegetation_type
941
942# Read tree data and create LAD and BAD arrays using the canopy generator
943for i in range(0,ndomains): 
944   if domain_street_trees[i]: 
945
946      vegetation_pars =  nc_read_from_file_2d_all(filename[i], 'vegetation_pars')
947
948      lai = np.copy(vegetation_pars[1,:,:])
949
950      x = nc_read_from_file_2d_all(filename[i], 'x')
951      y = nc_read_from_file_2d_all(filename[i], 'y') 
952
953#     Save lai data as default for low and high vegetation
954      lai_low = lai
955      lai_high = lai
956   
957#     Read all tree parameters from file
958      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])   
959
960      if (input_file_tree_crown_diameter[ii[i]] is not None):
961         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])   
962         tree_crown_diameter = np.where( (tree_crown_diameter == 0.0) | (tree_crown_diameter == -1.0) ,fillvalues["tree_data"],tree_crown_diameter)
963      else:
964         tree_crown_diameter = np.ones((len(y),len(x)))
965         tree_crown_diameter[:,:] = fillvalues["tree_data"]
966
967
968      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])         
969      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]) 
970      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]) 
971     
972#     Remove missing values from the data. Reasonable values will be set by the tree generator   
973      tree_height = np.where( (tree_height == 0.0) | (tree_height == -1.0) ,fillvalues["tree_data"],tree_height)
974      tree_trunk_diameter = np.where( (tree_trunk_diameter == 0.0) | (tree_trunk_diameter == -1.0) ,fillvalues["tree_data"],tree_trunk_diameter)
975      tree_type = np.where( (tree_type == 0.0) | (tree_type == -1.0) ,fillvalues["tree_data"],tree_type)
976      patch_height = np.where( patch_height == -1.0 ,fillvalues["tree_data"],patch_height)     
977
978#     Convert trunk diameter from cm to m
979      tree_trunk_diameter = np.where(tree_trunk_diameter != fillvalues["tree_data"], tree_trunk_diameter * 0.01,tree_trunk_diameter)
980
981
982#     Temporarily change missing value for tree_type
983      tree_type = np.where( (tree_type == fillvalues["tree_type"]),fillvalues["tree_data"],tree_type)   
984
985#     Compare patch height array with vegetation type and correct accordingly
986      vegetation_type = nc_read_from_file_2d_all(filename[i], 'vegetation_type') 
987
988
989#     For zero-height patches, set vegetation_type to short grass and remove these pixels from the patch height array
990      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)
991      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)   
992     
993
994      max_tree_height = max(tree_height.flatten())
995      max_patch_height = max(patch_height.flatten())
996     
997      if ( (max_tree_height != fillvalues["tree_data"]) | (max_patch_height == fillvalues["tree_data"]) ):
998
999         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"])
1000 
1001 
1002#        Remove LAD volumes that are inside buildings
1003         buildings_2d = nc_read_from_file_2d_all(filename[i], 'buildings_2d') 
1004         for k in range(0,len(zlad)-1):
1005 
1006            lad[k,:,:] = np.where(buildings_2d == fillvalues["buildings_2d"],lad[k,:,:],fillvalues["tree_data"])
1007            bad[k,:,:] = np.where(buildings_2d == fillvalues["buildings_2d"],bad[k,:,:],fillvalues["tree_data"])
1008            tree_ids[k,:,:] = np.where(buildings_2d == fillvalues["buildings_2d"],tree_ids[k,:,:],fillvalues["tree_data"])     
1009
1010         del buildings_2d
1011 
1012         nc_write_dimension(filename[i], 'zlad', zlad, datatypes["tree_data"]) 
1013         nc_write_to_file_3d(filename[i], 'lad', lad, datatypes["tree_data"],'zlad','y','x',fillvalues["tree_data"]) 
1014         nc_write_attribute(filename[i], 'lad', 'long_name', 'leaf area density')
1015         nc_write_attribute(filename[i], 'lad', 'units', '')
1016         nc_write_attribute(filename[i], 'lad', 'res_orig', domain_px[i]) 
1017         nc_write_attribute(filename[i], 'lad', 'coordinates', 'E_UTM N_UTM lon lat') 
1018         nc_write_attribute(filename[i], 'lad', 'grid_mapping', 'E_UTM N_UTM lon lat') 
1019 
1020         nc_write_to_file_3d(filename[i], 'bad', bad, datatypes["tree_data"],'zlad','y','x',fillvalues["tree_data"]) 
1021         nc_write_attribute(filename[i], 'bad', 'long_name', 'basal area density')
1022         nc_write_attribute(filename[i], 'bad', 'units', '')
1023         nc_write_attribute(filename[i], 'bad', 'res_orig', domain_px[i]) 
1024         nc_write_attribute(filename[i], 'bad', 'coordinates', 'E_UTM N_UTM lon lat') 
1025         nc_write_attribute(filename[i], 'bad', 'grid_mapping', 'E_UTM N_UTM lon lat')
1026           
1027         nc_write_to_file_3d(filename[i], 'tree_id', tree_ids, datatypes["tree_data"],'zlad','y','x',fillvalues["tree_data"]) 
1028         nc_write_attribute(filename[i], 'tree_id', 'long_name', 'tree id')
1029         nc_write_attribute(filename[i], 'tree_id', 'units', '')
1030         nc_write_attribute(filename[i], 'tree_id', 'res_orig', domain_px[i]) 
1031         nc_write_attribute(filename[i], 'tree_id', 'coordinates', 'E_UTM N_UTM lon lat') 
1032         nc_write_attribute(filename[i], 'tree_id', 'grid_mapping', 'E_UTM N_UTM lon lat')           
1033           
1034         del lai, lad, bad, tree_ids, zlad
1035
1036      del vegetation_pars, tree_height, tree_crown_diameter, tree_trunk_diameter, tree_type, patch_height, x, y
1037
1038
1039# Create vegetation patches for locations with high vegetation type
1040for i in range(0,ndomains): 
1041   if domain_canopy_patches[i]: 
1042
1043#     Load vegetation_type and lad array (at level z = 0) for re-processing
1044      vegetation_type = nc_read_from_file_2d_all(filename[i], 'vegetation_type') 
1045      lad = nc_read_from_file_3d_all(filename[i], 'lad') 
1046      zlad = nc_read_from_file_1d_all(filename[i], 'zlad') 
1047      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]) 
1048      vegetation_pars = nc_read_from_file_3d_all(filename[i], 'vegetation_pars') 
1049      lai = vegetation_pars[1,:,:]
1050     
1051
1052#     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
1053      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"])
1054
1055#     Now, assign either the default LAI for high vegetation or keep 1.0 from the lai_high array.
1056      lai_high = np.where( (lai_high != fillvalues["tree_data"]) & (lai == fillvalues["tree_data"]), settings_lai_high_default, lai_high)
1057
1058#     If lai values are available in the lai array, write them on the lai_high array
1059      lai_high = np.where( (lai_high != fillvalues["tree_data"]) & (lai != fillvalues["tree_data"]), lai, lai_high)
1060
1061#     Define a patch height wherever it is missing, but where a high vegetation LAI was set
1062      patch_height = np.where ( (lai_high != fillvalues["tree_data"]) & (patch_height == fillvalues["tree_data"]), settings_patch_height_default, patch_height)
1063
1064#     Remove pixels where street trees were already set
1065      patch_height = np.where ( (lad[0,:,:] != fillvalues["tree_data"]), fillvalues["tree_data"], patch_height)
1066
1067#     Remove patch heights that have no lai_high value
1068      patch_height = np.where ( (lai_high == fillvalues["tree_data"]), fillvalues["tree_data"], patch_height)
1069
1070#     For missing LAI values, set either the high vegetation default or the low vegetation default   
1071      lai_high = np.where( (patch_height > 2.0) & (patch_height != fillvalues["tree_data"]) & (lai_high == fillvalues["tree_data"]),settings_lai_high_default,lai_high)
1072      lai_high = np.where( (patch_height <= 2.0) & (patch_height != fillvalues["tree_data"]) & (lai_high == fillvalues["tree_data"]),settings_lai_low_default,lai_high)
1073         
1074      if ( max(patch_height.flatten()) >= (2.0 * domain_dz[i]) ):
1075         print("    start calculating LAD (this might take some time)")       
1076
1077         
1078         lad_patch, patch_nz, status = process_patch(domain_dz[i],patch_height,max(zlad),lai_high,settings_lai_alpha,settings_lai_beta)
1079
1080         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,:,:] )
1081
1082#     Remove high vegetation wherever it is replaced by a leaf area density. This should effectively remove all high vegetation pixels
1083      vegetation_type = np.where((lad[0,:,:] != fillvalues["tree_data"]) & (vegetation_type != fillvalues["vegetation_type"]),settings_veg_type_below_trees,vegetation_type)
1084             
1085#     If desired, remove all high vegetation. TODO: check if this is still necessary
1086      if not domain_high_vegetation[i]:
1087         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)   
1088
1089 
1090#     Set default low LAI for pixels with an LAD (short grass below trees)
1091      lai_low = np.where( (lad[0,:,:] == fillvalues["tree_data"]), lai, settings_lai_low_default)
1092   
1093#     Fill low vegetation pixels without LAI set or with LAI = 0 with default value
1094      lai_low = np.where( ( (lai_low == fillvalues["tree_data"]) | (lai_low == 0.0) ) & (vegetation_type != fillvalues["vegetation_type"] ), settings_lai_low_default, lai_low)
1095
1096#     Remove lai for pixels that have no vegetation_type
1097      lai_low = np.where( ( (vegetation_type != fillvalues["vegetation_type"]) & (vegetation_type != 1) ), lai_low, fillvalues["tree_data"])
1098       
1099#     Overwrite lai in vegetation_parameters 
1100      vegetation_pars[1,:,:] = np.copy(lai_low)
1101      nc_overwrite_to_file_3d(filename[i], 'vegetation_pars', vegetation_pars) 
1102
1103#     Overwrite lad array
1104      nc_overwrite_to_file_3d(filename[i], 'lad', lad) 
1105           
1106      nc_overwrite_to_file_2d(filename[i], 'vegetation_type', vegetation_type) 
1107
1108     
1109      del vegetation_type, lad, lai, patch_height, vegetation_pars, zlad
1110
1111
1112# Final adjustment of vegetation parameters: remove LAI where a bare soil was set
1113for i in range(0,ndomains): 
1114   
1115      vegetation_type = nc_read_from_file_2d_all(filename[i], 'vegetation_type') 
1116      vegetation_pars = nc_read_from_file_3d_all(filename[i], 'vegetation_pars') 
1117      lai = vegetation_pars[1,:,:]   
1118     
1119     
1120#     Remove lai for pixels that have no vegetation_type
1121      lai = np.where( ( (vegetation_type != fillvalues["vegetation_type"]) & (vegetation_type != 1) ), lai, fillvalues["tree_data"])
1122       
1123#     Overwrite lai in vegetation_parameters 
1124      vegetation_pars[1,:,:] = np.copy(lai)
1125      nc_overwrite_to_file_3d(filename[i], 'vegetation_pars', vegetation_pars) 
1126     
1127      del vegetation_type, lai, vegetation_pars
1128
1129
1130# Final consistency check
1131for i in range(0,ndomains): 
1132   vegetation_type = nc_read_from_file_2d_all(filename[i], 'vegetation_type') 
1133   pavement_type = nc_read_from_file_2d_all(filename[i], 'pavement_type') 
1134   building_type = nc_read_from_file_2d_all(filename[i], 'building_type') 
1135   water_type = nc_read_from_file_2d_all(filename[i], 'water_type')     
1136   soil_type = nc_read_from_file_2d_all(filename[i], 'soil_type') 
1137
1138#  Check for consistency and fill empty fields with default vegetation type
1139   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"])
1140
1141#  Check for consistency and fill empty fields with default vegetation type
1142   consistency_array, test = check_consistency_3(vegetation_type,pavement_type,soil_type,fillvalues["vegetation_type"],fillvalues["pavement_type"],fillvalues["soil_type"])
1143
1144   surface_fraction = nc_read_from_file_3d_all(filename[i], 'surface_fraction')       
1145   surface_fraction[0,:,:] = np.where(vegetation_type != fillvalues["vegetation_type"], 1.0, 0.0)
1146   surface_fraction[1,:,:] = np.where(pavement_type != fillvalues["pavement_type"], 1.0, 0.0)
1147   surface_fraction[2,:,:] = np.where(water_type != fillvalues["water_type"], 1.0, 0.0)
1148   nc_overwrite_to_file_3d(filename[i], 'surface_fraction', surface_fraction) 
1149       
1150   del vegetation_type, pavement_type, building_type, water_type, soil_type
Note: See TracBrowser for help on using the repository browser.
                                                                                                                                                                                                                                                                                                                                                                               
  | Impressum | ©Leibniz Universität Hannover |