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source: palm/trunk/SCRIPTS/palm_csd @ 4793

Last change on this file since 4793 was 4793, checked in by maronga, 4 years ago
allow for overhanging vegetation in palm_csd
Property svn:executable set to ``* Property svn:keywords set to `Id`
File size: 62.3 KB

Rev	Line
[3567]	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	#
[4481]	17	# Copyright 1997-2020 Leibniz Universitaet Hannover
[3567]	18	#--------------------------------------------------------------------------------#
	19	#
	20	# Current revisions:
	21	# -----------------
[4746]	22	#
	23	#
[3567]	24	# Former revisions:
	25	# -----------------
	26	# $Id: palm_csd 4793 2020-11-24 09:48:21Z maronga $
[4793]	27	# Added output of tree_type (species or classification). Added tree_id for
	28	# vegetation patches. Allow for overlapping vegetation with buildings
	29	#
	30	# 4749 2020-10-16 11:21:27Z maronga
[4749]	31	# Bugfix: processing of patch height faulty for multiple domains
	32	#
	33	# 4746 2020-10-15 17:16:30Z maronga
[4746]	34	# Add support for backyard high vegetation, which is commonly classified as
	35	# short grass
	36	#
	37	# 4490 2020-04-10 05:32:45Z maronga
[4490]	38	# Bugfix: call of canopy generator was not made if tree heights were only
	39	# available via patch_height, but not in tree_height
	40	#
	41	# 4481 2020-03-31 18:55:54Z maronga
[4311]	42	# Bugfix: green roofs were assigned to all available roofs when switched on
	43	#
	44	# 4149 2019-08-08 14:02:18Z suehring
[4149]	45	# Bugfix, nbuilding_pars was 1 element to small
	46	#
	47	# 3955 2019-05-07 09:55:25Z maronga
[3955]	48	# Bugfix in preparaing of green roofs
	49	# Bugfix: remove LAI for bare soils
	50	#
	51	# 3859 2019-04-03 20:30:31Z maronga
[3859]	52	# Bugfix: wrong variable naming for 'y'
	53	#
	54	# 3773 2019-03-01 08:56:57Z maronga
[3773]	55	# Unspecificed changes
	56	#
	57	# 3726 2019-02-07 18:22:49Z maronga
[3726]	58	# Removed some more bugs
	59	#
	60	# 3688 2019-01-22 10:44:20Z maronga
[3688]	61	# Some unspecified bugfixes
	62	#
	63	# 3668 2019-01-14 12:49:24Z maronga
[3668]	64	# Various improvements and bugfixes
	65	#
	66	# 3629 2018-12-13 12:18:54Z maronga
[3629]	67	# Added canopy generator calls. Some improvements
	68	#
	69	# 3567 2018-11-27 13:59:21Z maronga
[3567]	70	# Initial revisions
	71	#
	72	# Description:
	73	# ------------
	74	# Processing tool for creating PIDS conform static drivers from rastered NetCDF
	75	# input
	76	#
	77	# @Author Bjoern Maronga (maronga@muk.uni-hannover.de)
	78	#
	79	# @todo Make input files optional
	80	# @todo Allow for ASCII input of terrain height and building height
	81	# @todo Modularize reading config file
[3688]	82	# @todo Convert to object-oriented treatment (buidings, trees)
	83	# @todo Automatically shift child domains so that their origin lies intersects
	84	# a edge note of the parent grid
[3567]	85	#------------------------------------------------------------------------------#
	86
	87	from palm_csd_files.palm_csd_netcdf_interface import *
	88	from palm_csd_files.palm_csd_tools import *
[3629]	89	from palm_csd_files.palm_csd_canopy_generator import *
[3567]	90	import numpy as np
	91
	92
	93	def read_config_file():
	94
	95	import configparser
	96	from math import floor
	97	import numpy as np
	98	import os
	99	import subprocess as sub
	100	import sys
	101
	102	# Check if configuration files exists and quit otherwise
	103	input_config = ".csd.config"
	104	for i in range(1,len(sys.argv)):
	105	input_config = str(sys.argv[i])
	106
	107	config = configparser.RawConfigParser(allow_no_value=True)
	108
	109	if ( os.path.isfile(input_config) == False ):
	110	print ("Error. No configuration file " + input_config + " found.")
	111	raise SystemExit
	112
	113	config.read(input_config)
	114
	115
	116	# Definition of settings
[3668]	117
[3567]	118	global settings_bridge_width
[3629]	119	global settings_lai_roof_intensive
	120	global settings_lai_roof_extensive
	121	global settings_season
	122	global settings_lai_low_default
	123	global settings_lai_high_default
	124	global settings_patch_height_default
	125	global settings_lai_alpha
	126	global settings_lai_beta
[3955]	127	global settings_veg_type_below_trees
[3567]	128	global ndomains
	129
	130	# Definition of global configuration parameters
	131	global global_acronym
	132	global global_angle
	133	global global_author
	134	global global_campaign
	135	global global_comment
	136	global global_contact
	137	global global_data_content
	138	global global_dependencies
	139	global global_institution
	140	global global_keywords
	141	global global_location
	142	global global_palm_version
	143	global global_references
	144	global global_site
	145	global global_source
	146
[3668]	147	global path_out
	148	global filename_out
	149	global version_out
[3567]	150
[3668]	151
[3567]	152	# Definition of domain parameters
	153	global domain_names
	154	global domain_px
	155	global domain_x0
	156	global domain_y0
	157	global domain_x1
	158	global domain_y1
	159	global domain_nx
	160	global domain_ny
	161	global domain_dz
	162	global domain_3d
[3629]	163	global domain_high_vegetation
[3567]	164	global domain_ip
	165	global domain_za
	166	global domain_parent
[3629]	167	global domain_green_roofs
	168	global domain_street_trees
	169	global domain_canopy_patches
[4793]	170	global domain_overhanging_trees
[3567]	171
	172	# Definition of input data parameters
	173	global input_names
	174	global input_px
	175
	176
	177	global input_file_x
	178	global input_file_y
	179	global input_file_x_UTM
	180	global input_file_y_UTM
	181	global input_file_lat
	182	global input_file_lon
	183	global input_file_zt
	184	global input_file_buildings_2d
	185	global input_file_bridges_2d
	186	global input_file_building_id
	187	global input_file_bridges_id
	188	global input_file_building_type
	189	global input_file_building_type
[3629]	190	global input_file_lai
[3567]	191	global input_file_vegetation_type
	192	global input_file_vegetation_height
	193	global input_file_pavement_type
	194	global input_file_water_type
	195	global input_file_street_type
	196	global input_file_street_crossings
	197	global input_file_soil_type
[3629]	198	global input_file_vegetation_on_roofs
	199	global input_file_tree_crown_diameter
	200	global input_file_tree_height
	201	global input_file_tree_trunk_diameter
	202	global input_file_tree_type
	203	global input_file_patch_height
[3567]	204
	205	global zt_all
	206	global zt_min
	207
	208	settings_bridge_width = 3.0
[3955]	209	settings_lai_roof_intensive = 2.5
	210	settings_lai_roof_extensive = 0.8
[3629]	211	settings_season = "summer"
	212	settings_lai_high_default = 6.0
	213	settings_lai_low_default = 1.0
	214	settings_patch_height_default = 10.0
	215	settings_lai_alpha = 5.0
	216	settings_lai_beta = 3.0
[3955]	217	settings_veg_type_below_trees = 3
[3567]	218	ndomains = 0
	219
	220	global_acronym = ""
	221	global_angle = ""
	222	global_author = ""
	223	global_campaign = ""
	224	global_comment = ""
	225	global_contact = ""
	226	global_data_content = ""
	227	global_dependencies = ""
	228	global_institution = ""
	229	global_keywords = ""
	230	global_location = ""
	231	global_palm_version = 6.0
	232	global_references = ""
	233	global_site = ""
	234	global_source = ""
	235
[3668]	236	path_out = ""
	237	version_out = 1
	238	filename_out = "default"
	239
[3567]	240	domain_names = []
	241	domain_px = []
	242	domain_x0 = []
	243	domain_y0 = []
	244	domain_x1 = []
	245	domain_y1 = []
	246	domain_nx = []
	247	domain_ny = []
	248	domain_dz = []
	249	domain_3d = []
[3629]	250	domain_high_vegetation = []
[3567]	251	domain_ip = []
	252	domain_za = []
	253	domain_parent = []
[3629]	254	domain_green_roofs = []
	255	domain_street_trees = []
	256	domain_canopy_patches = []
[4793]	257	domain_overhanging_trees = []
[3567]	258
	259	zt_min = 0.0
	260	zt_all = []
	261
	262	input_names = []
	263	input_px = []
	264
	265	input_file_x = []
	266	input_file_y = []
	267	input_file_x_UTM = []
	268	input_file_y_UTM = []
	269	input_file_lat = []
	270	input_file_lon = []
	271
	272	input_file_zt = []
	273	input_file_buildings_2d = []
	274	input_file_bridges_2d = []
	275	input_file_building_id = []
	276	input_file_bridges_id = []
	277	input_file_building_type = []
[3629]	278	input_file_lai = []
[3567]	279	input_file_vegetation_type = []
	280	input_file_vegetation_height = []
	281	input_file_pavement_type = []
	282	input_file_water_type = []
	283	input_file_street_type = []
	284	input_file_street_crossings = []
	285	input_file_soil_type = []
[3629]	286	input_file_vegetation_on_roofs = []
	287	input_file_tree_crown_diameter = []
	288	input_file_tree_height = []
	289	input_file_tree_trunk_diameter = []
	290	input_file_tree_type = []
	291	input_file_patch_height = []
[3567]	292
	293	# Load all user parameters from config file
	294	for i in range(0,len(config.sections())):
	295
	296	read_tmp = config.sections()[i]
	297
	298	if ( read_tmp == 'global' ):
	299	global_acronym = config.get(read_tmp, 'acronym')
	300	global_angle = float(config.get(read_tmp, 'rotation_angle'))
	301	global_author = config.get(read_tmp, 'author')
	302	global_campaign = config.get(read_tmp, 'campaign')
	303	global_comment = config.get(read_tmp, 'comment')
	304	global_contact = config.get(read_tmp, 'contact_person')
	305	global_data_content = config.get(read_tmp, 'data_content')
	306	global_dependencies = config.get(read_tmp, 'dependencies')
	307	global_institution = config.get(read_tmp, 'institution')
	308	global_keywords = config.get(read_tmp, 'keywords')
	309	global_location = config.get(read_tmp, 'location')
	310	global_palm_version = float(config.get(read_tmp, 'palm_version'))
	311	global_references = config.get(read_tmp, 'references')
	312	global_site = config.get(read_tmp, 'site')
	313	global_source = config.get(read_tmp, 'source')
	314
[3668]	315
[3567]	316	if ( read_tmp == 'settings' ):
[3629]	317	settings_lai_roof_intensive = config.get(read_tmp, 'lai_roof_intensive')
	318	settings_lai_roof_extensive = config.get(read_tmp, 'lai_roof_extensive')
	319	settings_bridge_width = float(config.get(read_tmp, 'bridge_width'))
	320	settings_season = config.get(read_tmp, 'season')
	321	settings_lai_high_default = float(config.get(read_tmp, 'lai_high_vegetation_default'))
	322	settings_lai_low_default = float(config.get(read_tmp, 'lai_low_vegetation_default'))
	323	settings_patch_height_default = float(config.get(read_tmp, 'patch_height_default'))
	324	settings_lai_alpha = float(config.get(read_tmp, 'lai_alpha'))
	325	settings_lai_beta = float(config.get(read_tmp, 'lai_beta'))
[3955]	326	settings_veg_type_below_trees = config.get(read_tmp, 'vegetation_type_below_trees')
[3629]	327
[3668]	328	if ( read_tmp == 'output' ):
	329	path_out = config.get(read_tmp, 'path')
	330	filename_out = config.get(read_tmp, 'file_out')
	331	version_out = float(config.get(read_tmp, 'version'))
	332
[3567]	333	if ( read_tmp.split("_")[0] == 'domain' ):
	334	ndomains = ndomains + 1
	335	domain_names.append(read_tmp.split("_")[1])
	336	domain_px.append(float(config.get(read_tmp, 'pixel_size')))
	337	domain_nx.append(int(config.get(read_tmp, 'nx')))
	338	domain_ny.append(int(config.get(read_tmp, 'ny')))
	339	domain_dz.append(float(config.get(read_tmp, 'dz')))
	340	domain_3d.append(config.getboolean(read_tmp, 'buildings_3d'))
[3629]	341	domain_high_vegetation.append(config.getboolean(read_tmp, 'allow_high_vegetation'))
	342	domain_canopy_patches.append(config.getboolean(read_tmp, 'generate_vegetation_patches'))
[3567]	343	domain_ip.append(config.getboolean(read_tmp, 'interpolate_terrain'))
	344	domain_za.append(config.getboolean(read_tmp, 'use_palm_z_axis'))
	345	if domain_ip[ndomains-1] and not domain_za[ndomains-1]:
	346	domain_za[ndomains-1] = True
	347	print("+++ Overwrite user setting for use_palm_z_axis")
	348
	349	domain_parent.append(config.get(read_tmp, 'domain_parent'))
	350
	351	domain_x0.append(int(floor(float(config.get(read_tmp, 'origin_x'))/float(config.get(read_tmp, 'pixel_size')))))
	352	domain_y0.append(int(floor(float(config.get(read_tmp, 'origin_y'))/float(config.get(read_tmp, 'pixel_size')))))
	353	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'))))
	354	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'))))
[3629]	355	domain_green_roofs.append(config.getboolean(read_tmp, 'vegetation_on_roofs'))
	356	domain_street_trees.append(config.getboolean(read_tmp, 'street_trees'))
[4793]	357	domain_overhanging_trees.append(config.getboolean(read_tmp, 'overhanging_trees'))
	358
[3567]	359	if ( read_tmp.split("_")[0] == 'input' ):
	360	input_names.append(read_tmp.split("_")[1])
	361	input_px.append(float(config.get(read_tmp, 'pixel_size')))
	362	input_file_x.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_x'))
	363	input_file_y.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_y'))
	364	input_file_lat.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_lat'))
	365	input_file_lon.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_lon'))
	366	input_file_x_UTM.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_x_UTM'))
	367	input_file_y_UTM.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_y_UTM'))
	368	input_file_zt.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_zt'))
	369	input_file_buildings_2d.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_buildings_2d'))
	370	input_file_bridges_2d.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_bridges_2d'))
	371	input_file_building_id.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_building_id'))
	372	input_file_bridges_id.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_bridges_id'))
[3629]	373	input_file_building_type.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_building_type'))
	374	input_file_lai.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_lai'))
[3567]	375	input_file_vegetation_type.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_vegetation_type'))
	376	input_file_vegetation_height.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_vegetation_height'))
	377	input_file_pavement_type.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_pavement_type'))
	378	input_file_water_type.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_water_type'))
	379	input_file_street_type.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_street_type'))
[3629]	380	input_file_street_crossings.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_street_crossings'))
	381	input_file_patch_height.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_patch_height'))
[3668]	382
	383	tmp = config.get(read_tmp, 'file_tree_crown_diameter')
	384	if tmp is not None:
	385	input_file_tree_crown_diameter.append(config.get(read_tmp, 'path') + "/" + tmp)
	386	else:
	387	input_file_tree_crown_diameter.append(None)
[3629]	388	input_file_tree_height.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_tree_height'))
	389	input_file_tree_trunk_diameter.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_tree_trunk_diameter'))
	390	input_file_tree_type.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_tree_type'))
	391	input_file_vegetation_on_roofs.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_vegetation_on_roofs'))
[3567]	392	#input_file_soil_type.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_soil_type'))
	393	return 0
	394
	395
	396	############################################################
	397
	398	# Start of main program
	399	datatypes = {
	400	"x": "f4",
	401	"y": "f4",
	402	"z": "f4",
	403	"lat": "f4",
	404	"lon": "f4",
	405	"E_UTM": "f4",
	406	"N_UTM": "f4",
	407	"zt": "f4",
	408	"buildings_2d": "f4",
	409	"buildings_3d": "b",
	410	"bridges_2d": "f4",
	411	"building_id": "i",
	412	"bridges_id": "i",
	413	"building_type": "b",
	414	"nsurface_fraction": "i",
	415	"vegetation_type": "b",
	416	"vegetation_height": "f4",
	417	"pavement_type": "b",
	418	"water_type": "b",
	419	"street_type": "b",
	420	"street_crossings": "b",
	421	"soil_type": "b",
[3629]	422	"surface_fraction": "f4",
	423	"building_pars": "f4",
	424	"vegetation_pars": "f4",
	425	"tree_data": "f4",
[4793]	426	"tree_id": "i",
	427	"tree_type": "f4",
	428	"tree_types": "b",
[3629]	429	"nbuilding_pars": "i",
	430	"nvegetation_pars": "i",
	431	"zlad": "f4"
[3567]	432	}
	433
	434	fillvalues = {
	435	"lat": float(-9999.0),
	436	"lon": float(-9999.0),
	437	"E_UTM": float(-9999.0),
	438	"N_UTM": float(-9999.0),
	439	"zt": float(-9999.0),
	440	"buildings_2d": float(-9999.0),
	441	"buildings_3d": np.byte(-127),
	442	"bridges_2d": float(-9999.0),
	443	"building_id": int(-9999),
	444	"bridges_id": int(-9999),
	445	"building_type": np.byte(-127),
	446	"nsurface_fraction": int(-9999),
	447	"vegetation_type": np.byte(-127),
	448	"vegetation_height": float(-9999.0),
	449	"pavement_type": np.byte(-127),
	450	"water_type": np.byte(-127),
	451	"street_type": np.byte(-127),
	452	"street_crossings": np.byte(-127),
	453	"soil_type": np.byte(-127),
[3629]	454	"surface_fraction": float(-9999.0),
	455	"building_pars": float(-9999.0),
	456	"vegetation_pars": float(-9999.0),
	457	"tree_data": float(-9999.0),
[4793]	458	"tree_id": int(-9999),
	459	"tree_type": float(-9999.0),
	460	"tree_types": np.byte(-127)
[3567]	461	}
	462
	463	defaultvalues = {
	464	"lat": float(-9999.0),
	465	"lon": float(-9999.0),
	466	"E_UTM": float(-9999.0),
	467	"N_UTM": float(-9999.0),
	468	"zt": float(0.0),
	469	"buildings_2d": float(0.0),
	470	"buildings_3d": 0,
	471	"bridges_2d": float(0.0),
	472	"building_id": int(0),
	473	"bridges_id": int(0),
	474	"building_type": 1,
	475	"nsurface_fraction": int(-9999),
	476	"vegetation_type": 3,
	477	"vegetation_height": float(-9999.0),
	478	"pavement_type": 1,
	479	"water_type": 1,
	480	"street_type": 1,
	481	"street_crossings": 0,
	482	"soil_type": 1,
[3629]	483	"surface_fraction": float(0.0),
	484	"buildings_pars": float(-9999.0),
	485	"tree_data": float(-9999.0),
[4793]	486	"tree_type": np.byte(-127),
[3629]	487	"vegetation_pars": float(-9999.0)
[3567]	488	}
	489
	490	# Read configuration file and set parameters accordingly
	491	read_config_file()
	492
	493
	494	filename = []
	495	ii = []
	496	ii_parent = []
	497	# Define indices and filenames for all domains and create netCDF files
	498	for i in range(0,ndomains):
	499
	500	# Calculate indices and input files
	501	ii.append(input_px.index(domain_px[i]))
[3668]	502	filename.append(path_out + "/" + filename_out + "_" + domain_names[i])
[3567]	503	if domain_parent[i] is not None:
[3726]	504	ii_parent.append(input_px.index(domain_px[domain_names.index(domain_parent[i])]))
[3567]	505	else:
	506	ii_parent.append(None)
	507
	508
[3726]	509	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)
	510	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)
	511	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)
	512	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)
[3567]	513
[3726]	514	# Calculate position of origin
	515	x_UTM_origin = float(x_UTM[0,0]) - 0.5 * (float(x_UTM[0,1]) - float(x_UTM[0,0]))
	516	y_UTM_origin = float(y_UTM[0,0]) - 0.5 * (float(y_UTM[1,0]) - float(y_UTM[0,0]))
	517	x_origin = float(lon[0,0]) - 0.5 * (float(lon[0,1]) - float(lon[0,0]))
	518	y_origin = float(lat[0,0]) - 0.5 * (float(lat[1,0]) - float(lat[0,0]))
	519
[3567]	520	# Create NetCDF output file and set global attributes
	521	nc_create_file(filename[i])
[3726]	522	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)
[3567]	523
[3688]	524	del x_UTM, y_UTM, lat, lon
[3567]	525
	526	# Process terrain height
	527	for 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
	532	# Final step: add zt array to the global array
	533	zt_all.append(zt)
	534	del zt
	535
	536	# Calculate the global (all domains) minimum of the terrain height. This value will be substracted for all
	537	# data sets
	538	zt_min = min(zt_all[0].flatten())
	539	for i in range(0,ndomains):
	540	zt_min = min(zt_min,min(zt_all[i].flatten()))
	541
	542	del zt_all[:]
	543
[3726]	544	print( "Shift terrain heights by -" + str(zt_min))
[3567]	545	for i in range(0,ndomains):
	546
	547	# Read and write terrain height (zt)
	548	zt = nc_read_from_file_2d(input_file_zt[ii[i]], 'Band1', domain_x0[i], domain_x1[i], domain_y0[i], domain_y1[i])
	549	x = nc_read_from_file_1d(input_file_x[ii[i]], "x", domain_x0[i], domain_x1[i])
	550	y = nc_read_from_file_1d(input_file_y[ii[i]], "y", domain_y0[i], domain_y1[i])
	551
[3726]	552
[3567]	553	zt = zt - zt_min
[3668]	554
	555	nc_write_global_attribute(filename[i], 'origin_z', float(zt_min))
[3567]	556
	557	# If necessary, interpolate parent domain terrain height on child domain grid and blend the two
	558	if domain_ip[i]:
[3726]	559	parent_id = domain_names.index(domain_parent[i])
	560	tmp_x0 = int( domain_x0[i] * domain_px[i] / domain_px[parent_id] ) - 1
	561	tmp_y0 = int( domain_y0[i] * domain_px[i] / domain_px[parent_id] ) - 1
	562	tmp_x1 = int( domain_x1[i] * domain_px[i] / domain_px[parent_id] ) + 1
	563	tmp_y1 = int( domain_y1[i] * domain_px[i] / domain_px[parent_id] ) + 1
	564
[3567]	565	tmp_x = nc_read_from_file_1d(input_file_x[ii_parent[i]], "x", tmp_x0, tmp_x1)
	566	tmp_y = nc_read_from_file_1d(input_file_y[ii_parent[i]], "y", tmp_y0, tmp_y1)
	567
	568	zt_parent = nc_read_from_file_2d(input_file_zt[ii_parent[i]], 'Band1', tmp_x0, tmp_x1, tmp_y0, tmp_y1)
	569
	570	zt_parent = zt_parent - zt_min
	571
	572	# Interpolate array and bring to PALM grid of child domain
	573	zt_ip = interpolate_2d(zt_parent,tmp_x,tmp_y,x,y)
[3726]	574	zt_ip = bring_to_palm_grid(zt_ip,x,y,domain_dz[parent_id])
	575
	576
	577	# Shift the child terrain height according to the parent mean terrain height
[3773]	578	print("shifting: -" + str(np.mean(zt)) + " +" + str(np.mean(zt_ip)))
	579	#zt = zt - np.min(zt) + np.min(zt_ip)
[3726]	580	zt = zt - np.mean(zt) + np.mean(zt_ip)
[3567]	581
	582
	583	# Blend over the parent and child terrain height within a radius of 50 px
	584	zt = blend_array_2d(zt,zt_ip,50)
[3773]	585	# zt = zt_ip
[3567]	586
	587	# Final step: add zt array to the global array
[3726]	588
[3567]	589	zt_all.append(zt)
	590	del zt
	591
	592
	593	# Read and shift x and y coordinates, shift terrain height according to its minimum value and write all data
	594	# to file
	595	for i in range(0,ndomains):
	596	# Read horizontal grid variables from zt file and write them to output file
	597	x = nc_read_from_file_1d(input_file_x[ii[i]], "x", domain_x0[i], domain_x1[i])
	598	y = nc_read_from_file_1d(input_file_y[ii[i]], "y", domain_y0[i], domain_y1[i])
	599	x = x - min(x.flatten()) + domain_px[i]/2.0
	600	y = y - min(y.flatten()) + domain_px[i]/2.0
	601	nc_write_dimension(filename[i], 'x', x, datatypes["x"])
	602	nc_write_dimension(filename[i], 'y', y, datatypes["y"])
	603	nc_write_attribute(filename[i], 'x', 'long_name', 'x')
	604	nc_write_attribute(filename[i], 'x', 'standard_name','projection_x_coordinate')
	605	nc_write_attribute(filename[i], 'x', 'units', 'm')
[3859]	606	nc_write_attribute(filename[i], 'y', 'long_name', 'y')
[3567]	607	nc_write_attribute(filename[i], 'y', 'standard_name', 'projection_y_coordinate')
	608	nc_write_attribute(filename[i], 'y', 'units', 'm')
	609
	610	lat = nc_read_from_file_2d(input_file_lat[ii[i]], "Band1", domain_x0[i], domain_x1[i], domain_y0[i], domain_y1[i])
	611	lon = nc_read_from_file_2d(input_file_lon[ii[i]], "Band1", domain_x0[i], domain_x1[i], domain_y0[i], domain_y1[i])
	612
	613	nc_write_to_file_2d(filename[i], 'lat', lat, datatypes["lat"],'y','x',fillvalues["lat"])
	614	nc_write_to_file_2d(filename[i], 'lon', lon, datatypes["lon"],'y','x',fillvalues["lon"])
	615
	616	nc_write_attribute(filename[i], 'lat', 'long_name', 'latitude')
	617	nc_write_attribute(filename[i], 'lat', 'standard_name','latitude')
	618	nc_write_attribute(filename[i], 'lat', 'units', 'degrees_north')
	619
	620	nc_write_attribute(filename[i], 'lon', 'long_name', 'longitude')
	621	nc_write_attribute(filename[i], 'lon', 'standard_name','longitude')
	622	nc_write_attribute(filename[i], 'lon', 'units', 'degrees_east')
	623
	624	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])
	625	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])
[3726]	626
[3567]	627
	628	nc_write_to_file_2d(filename[i], 'E_UTM', x_UTM, datatypes["E_UTM"],'y','x',fillvalues["E_UTM"])
	629	nc_write_to_file_2d(filename[i], 'N_UTM', y_UTM, datatypes["N_UTM"],'y','x',fillvalues["N_UTM"])
	630
	631	nc_write_attribute(filename[i], 'E_UTM', 'long_name', 'easting')
	632	nc_write_attribute(filename[i], 'E_UTM', 'standard_name','projection_x_coorindate')
	633	nc_write_attribute(filename[i], 'E_UTM', 'units', 'm')
	634
	635	nc_write_attribute(filename[i], 'N_UTM', 'long_name', 'northing')
	636	nc_write_attribute(filename[i], 'N_UTM', 'standard_name','projection_y_coorindate')
	637	nc_write_attribute(filename[i], 'N_UTM', 'units', 'm')
	638
	639	nc_write_crs(filename[i])
	640
	641
	642
	643	# If necessary, bring terrain height to PALM's vertical grid. This is either forced by the user or implicitly
	644	# by using interpolation for a child domain
	645	if domain_za[i]:
	646	zt_all[i] = bring_to_palm_grid(zt_all[i],x,y,domain_dz[i])
	647
	648	nc_write_to_file_2d(filename[i], 'zt', zt_all[i], datatypes["zt"],'y','x',fillvalues["zt"])
	649	nc_write_attribute(filename[i], 'zt', 'long_name', 'orography')
	650	nc_write_attribute(filename[i], 'zt', 'units', 'm')
	651	nc_write_attribute(filename[i], 'zt', 'res_orig', domain_px[i])
	652	nc_write_attribute(filename[i], 'zt', 'coordinates', 'E_UTM N_UTM lon lat')
	653	nc_write_attribute(filename[i], 'zt', 'grid_mapping', 'E_UTM N_UTM lon lat')
	654
	655	del zt_all
	656
	657
	658	# Process building height, id, and type
	659	for i in range(0,ndomains):
	660	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])
[3688]	661
[3567]	662	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])
	663
	664	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])
[3668]	665	building_type[building_type >= 254] = fillvalues["building_type"]
[3567]	666	building_type = np.where(building_type < 1,defaultvalues["building_type"],building_type)
	667
	668	check = check_arrays_2(buildings_2d,building_id,fillvalues["buildings_2d"],fillvalues["building_id"])
	669	if not check:
	670	buildings_2d = np.where(building_id != fillvalues["building_id"],buildings_2d,fillvalues["buildings_2d"])
	671	building_id = np.where(buildings_2d == fillvalues["buildings_2d"],fillvalues["building_id"],building_id)
	672	print("Data check #1 " + str(check_arrays_2(buildings_2d,building_id,fillvalues["buildings_2d"],fillvalues["building_id"])))
	673
	674	check = check_arrays_2(buildings_2d,building_type,fillvalues["buildings_2d"],fillvalues["building_type"])
	675	if not check:
	676	building_type = np.where(buildings_2d == fillvalues["buildings_2d"],fillvalues["building_type"],building_type)
	677	building_type = np.where((building_type == fillvalues["building_type"]) & (buildings_2d != fillvalues["buildings_2d"]),defaultvalues["building_type"],building_type)
	678	print("Data check #2 " + str(check_arrays_2(buildings_2d,building_type,fillvalues["buildings_2d"],fillvalues["building_type"])))
	679
[3688]	680
[3567]	681	nc_write_to_file_2d(filename[i], 'buildings_2d', buildings_2d, datatypes["buildings_2d"],'y','x',fillvalues["buildings_2d"])
	682	nc_write_attribute(filename[i], 'buildings_2d', 'long_name', 'buildings')
	683	nc_write_attribute(filename[i], 'buildings_2d', 'units', 'm')
	684	nc_write_attribute(filename[i], 'buildings_2d', 'res_orig', domain_px[i])
	685	nc_write_attribute(filename[i], 'buildings_2d', 'lod', 1)
	686	nc_write_attribute(filename[i], 'buildings_2d', 'coordinates', 'E_UTM N_UTM lon lat')
	687	nc_write_attribute(filename[i], 'buildings_2d', 'grid_mapping', 'E_UTM N_UTM lon lat')
	688
	689	nc_write_to_file_2d(filename[i], 'building_id', building_id, datatypes["building_id"],'y','x',fillvalues["building_id"])
	690	nc_write_attribute(filename[i], 'building_id', 'long_name', 'building id')
	691	nc_write_attribute(filename[i], 'building_id', 'units', '')
[3668]	692	nc_write_attribute(filename[i], 'building_id', 'res _orig', domain_px[i])
[3567]	693	nc_write_attribute(filename[i], 'building_id', 'coordinates', 'E_UTM N_UTM lon lat')
	694	nc_write_attribute(filename[i], 'building_id', 'grid_mapping', 'E_UTM N_UTM lon lat')
	695
	696	nc_write_to_file_2d(filename[i], 'building_type', building_type, datatypes["building_type"],'y','x',fillvalues["building_type"])
	697	nc_write_attribute(filename[i], 'building_type', 'long_name', 'building type')
	698	nc_write_attribute(filename[i], 'building_type', 'units', '')
	699	nc_write_attribute(filename[i], 'building_type', 'res_orig', domain_px[i])
	700	nc_write_attribute(filename[i], 'building_type', 'coordinates', 'E_UTM N_UTM lon lat')
	701	nc_write_attribute(filename[i], 'building_type', 'grid_mapping', 'E_UTM N_UTM lon lat')
	702
	703	del buildings_2d
	704	del building_id
	705	del building_type
	706
	707	# Create 3d buildings if necessary. In that course, read bridge objects and add them to building layer
	708	for i in range(0,ndomains):
	709
	710	if domain_3d[i]:
	711	x = nc_read_from_file_2d_all(filename[i], 'x')
	712	y = nc_read_from_file_2d_all(filename[i], 'y')
	713	buildings_2d = nc_read_from_file_2d_all(filename[i], 'buildings_2d')
	714	building_id = nc_read_from_file_2d_all(filename[i], 'building_id')
	715
	716	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])
	717	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])
	718
	719	bridges_2d = np.where(bridges_2d == 0.0,fillvalues["bridges_2d"],bridges_2d)
	720	building_id = np.where(bridges_2d == fillvalues["bridges_2d"],building_id,bridges_id)
	721
	722	if np.any(buildings_2d != fillvalues["buildings_2d"]):
	723	buildings_3d, z = make_3d_from_2d(buildings_2d,x,y,domain_dz[i])
	724	if np.any(bridges_2d != fillvalues["bridges_2d"]):
	725	buildings_3d = make_3d_from_bridges_2d(buildings_3d,bridges_2d,x,y,domain_dz[i],settings_bridge_width,fillvalues["bridges_2d"])
	726	else:
	727	print("Skipping creation of 3D bridges (no bridges in domain)")
	728
	729
	730	nc_write_dimension(filename[i], 'z', z, datatypes["z"])
	731	nc_write_attribute(filename[i], 'z', 'long_name', 'z')
	732	nc_write_attribute(filename[i], 'z', 'units', 'm')
[3726]	733
	734	nc_overwrite_to_file_2d(filename[i], 'building_id', building_id)
[3567]	735
	736	nc_write_to_file_3d(filename[i], 'buildings_3d', buildings_3d, datatypes["buildings_3d"],'z','y','x',fillvalues["buildings_3d"])
	737	nc_write_attribute(filename[i], 'buildings_3d', 'long_name', 'buildings 3d')
	738	nc_write_attribute(filename[i], 'buildings_3d', 'units', '')
	739	nc_write_attribute(filename[i], 'buildings_3d', 'res_orig', domain_px[i])
	740	nc_write_attribute(filename[i], 'buildings_3d', 'lod', 2)
	741
	742	del buildings_3d
	743
	744	else:
	745	print("Skipping creation of 3D buildings (no buildings in domain)")
	746
	747
[3629]	748	del bridges_2d, bridges_id, building_id, buildings_2d
[3567]	749
	750
	751
	752	# Read vegetation type, water_type, pavement_type, soil_type and make fields consistent
	753	for i in range(0,ndomains):
	754
	755	building_type = nc_read_from_file_2d_all(filename[i], 'building_type')
[3773]	756
[3567]	757	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])
	758	vegetation_type[vegetation_type == 255] = fillvalues["vegetation_type"]
	759	vegetation_type = np.where((vegetation_type < 1) & (vegetation_type != fillvalues["vegetation_type"]),defaultvalues["vegetation_type"],vegetation_type)
	760
	761	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])
	762	pavement_type[pavement_type == 255] = fillvalues["pavement_type"]
	763	pavement_type = np.where((pavement_type < 1) & (pavement_type != fillvalues["pavement_type"]),defaultvalues["pavement_type"],pavement_type)
	764
	765	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])
	766	water_type[water_type == 255] = fillvalues["water_type"]
	767	water_type = np.where((water_type < 1) & (water_type != fillvalues["water_type"]),defaultvalues["water_type"],water_type)
	768
	769	# to do: replace by real soil input data
	770	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])
	771	soil_type[soil_type == 255] = fillvalues["soil_type"]
	772	soil_type = np.where((soil_type < 1) & (soil_type != fillvalues["soil_type"]),defaultvalues["soil_type"],soil_type)
	773
	774	# Make arrays consistent
	775	# #1 Set vegetation type to missing for pixel where a pavement type is set
	776	vegetation_type = np.where((vegetation_type != fillvalues["vegetation_type"]) & (pavement_type != fillvalues["pavement_type"]),fillvalues["vegetation_type"],vegetation_type)
	777
	778	# #2 Set vegetation type to missing for pixel where a building type is set
	779	vegetation_type = np.where((vegetation_type != fillvalues["vegetation_type"]) & (building_type != fillvalues["building_type"]) ,fillvalues["vegetation_type"],vegetation_type)
	780
	781	# #3 Set vegetation type to missing for pixel where a building type is set
	782	vegetation_type = np.where((vegetation_type != fillvalues["vegetation_type"]) & (water_type != fillvalues["water_type"]),fillvalues["vegetation_type"],vegetation_type)
	783
	784	# #4 Remove pavement for pixels with buildings
	785	pavement_type = np.where((pavement_type != fillvalues["pavement_type"]) & (building_type != fillvalues["building_type"]),fillvalues["pavement_type"],pavement_type)
	786
[3773]	787	# #5 Remove pavement for pixels with water.
	788	pavement_type = np.where((pavement_type != fillvalues["pavement_type"]) & (water_type != fillvalues["water_type"]),fillvalues["pavement_type"],pavement_type)
	789
[3567]	790	# #6 Remove water for pixels with buildings
	791	water_type = np.where((water_type != fillvalues["water_type"]) & (building_type != fillvalues["building_type"]),fillvalues["water_type"],water_type)
[3773]	792
[3668]	793	# Correct vegetation_type when a vegetation height is available and is indicative of low vegeetation
	794	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])
	795
	796	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)
	797	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)
[3567]	798
	799	# Check for consistency and fill empty fields with default vegetation type
	800	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"])
	801
	802	if test:
	803	vegetation_type = np.where(consistency_array == 0,defaultvalues["vegetation_type"],vegetation_type)
	804	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"])
[3668]	805
	806	# #7 to be removed: set default soil type everywhere
	807	soil_type = np.where((vegetation_type != fillvalues["vegetation_type"]) \| (pavement_type != fillvalues["pavement_type"]),defaultvalues["soil_type"],fillvalues["soil_type"])
	808
[3567]	809
[3668]	810	# Check for consistency and fill empty fields with default vegetation type
	811	consistency_array, test = check_consistency_3(vegetation_type,pavement_type,soil_type,fillvalues["vegetation_type"],fillvalues["pavement_type"],fillvalues["soil_type"])
	812
[3567]	813	# Create surface_fraction array
	814	x = nc_read_from_file_2d_all(filename[i], 'x')
	815	y = nc_read_from_file_2d_all(filename[i], 'y')
	816	nsurface_fraction = np.arange(0,3)
	817	surface_fraction = np.ones((len(nsurface_fraction),len(y),len(x)))
	818
	819	surface_fraction[0,:,:] = np.where(vegetation_type != fillvalues["vegetation_type"], 1.0, 0.0)
	820	surface_fraction[1,:,:] = np.where(pavement_type != fillvalues["pavement_type"], 1.0, 0.0)
	821	surface_fraction[2,:,:] = np.where(water_type != fillvalues["water_type"], 1.0, 0.0)
	822
	823	nc_write_dimension(filename[i], 'nsurface_fraction', nsurface_fraction, datatypes["nsurface_fraction"])
	824	nc_write_to_file_3d(filename[i], 'surface_fraction', surface_fraction, datatypes["surface_fraction"],'nsurface_fraction','y','x',fillvalues["surface_fraction"])
	825	nc_write_attribute(filename[i], 'surface_fraction', 'long_name', 'surface fraction')
	826	nc_write_attribute(filename[i], 'surface_fraction', 'units', '')
	827	nc_write_attribute(filename[i], 'surface_fraction', 'res_orig', domain_px[i])
	828	del surface_fraction
	829
	830	nc_write_to_file_2d(filename[i], 'vegetation_type', vegetation_type, datatypes["vegetation_type"],'y','x',fillvalues["vegetation_type"])
	831	nc_write_attribute(filename[i], 'vegetation_type', 'long_name', 'vegetation type')
	832	nc_write_attribute(filename[i], 'vegetation_type', 'units', '')
	833	nc_write_attribute(filename[i], 'vegetation_type', 'res_orig', domain_px[i])
	834	nc_write_attribute(filename[i], 'vegetation_type', 'coordinates', 'E_UTM N_UTM lon lat')
	835	nc_write_attribute(filename[i], 'vegetation_type', 'grid_mapping', 'E_UTM N_UTM lon lat')
	836	del vegetation_type
	837
	838	nc_write_to_file_2d(filename[i], 'pavement_type', pavement_type, datatypes["pavement_type"],'y','x',fillvalues["pavement_type"])
	839	nc_write_attribute(filename[i], 'pavement_type', 'long_name', 'pavement type')
	840	nc_write_attribute(filename[i], 'pavement_type', 'units', '')
	841	nc_write_attribute(filename[i], 'pavement_type', 'res_orig', domain_px[i])
	842	nc_write_attribute(filename[i], 'pavement_type', 'coordinates', 'E_UTM N_UTM lon lat')
	843	nc_write_attribute(filename[i], 'pavement_type', 'grid_mapping', 'E_UTM N_UTM lon lat')
	844	del pavement_type
	845
	846	nc_write_to_file_2d(filename[i], 'water_type', water_type, datatypes["water_type"],'y','x',fillvalues["water_type"])
	847	nc_write_attribute(filename[i], 'water_type', 'long_name', 'water type')
	848	nc_write_attribute(filename[i], 'water_type', 'units', '')
	849	nc_write_attribute(filename[i], 'water_type', 'res_orig', domain_px[i])
	850	nc_write_attribute(filename[i], 'water_type', 'coordinates', 'E_UTM N_UTM lon lat')
	851	nc_write_attribute(filename[i], 'water_type', 'grid_mapping', 'E_UTM N_UTM lon lat')
	852	del water_type
	853
	854	nc_write_to_file_2d(filename[i], 'soil_type', soil_type, datatypes["soil_type"],'y','x',fillvalues["soil_type"])
	855	nc_write_attribute(filename[i], 'soil_type', 'long_name', 'soil type')
	856	nc_write_attribute(filename[i], 'soil_type', 'units', '')
	857	nc_write_attribute(filename[i], 'soil_type', 'res_orig', domain_px[i])
	858	nc_write_attribute(filename[i], 'soil_type', 'coordinates', 'E_UTM N_UTM lon lat')
	859	nc_write_attribute(filename[i], 'soil_type', 'grid_mapping', 'E_UTM N_UTM lon lat')
	860	del soil_type
	861
[3629]	862	del x
	863	del y
[3567]	864
	865	# pixels with bridges get building_type = 7 = bridge. This does not change the _type setting for the under-bridge
[3668]	866	# area NOTE: when bridges are present the consistency check will fail at the moment
[3567]	867	if domain_3d[i]:
	868	if np.any(building_type != fillvalues["building_type"]):
	869
	870	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])
	871	bridges_2d = np.where(bridges_2d == 0.0,fillvalues["bridges_2d"],bridges_2d)
	872	building_type = np.where(bridges_2d != fillvalues["bridges_2d"],7,building_type)
	873	nc_overwrite_to_file_2d(filename[i], 'building_type', building_type)
	874
	875	del building_type
	876	del bridges_2d
	877
[3629]	878	# Read/write street type and street crossings
[3567]	879	for i in range(0,ndomains):
	880
	881	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])
	882	street_type[street_type == 255] = fillvalues["street_type"]
	883	street_type = np.where((street_type < 1) & (street_type != fillvalues["street_type"]),defaultvalues["street_type"],street_type)
[3773]	884
	885	pavement_type = nc_read_from_file_2d_all(filename[i], 'pavement_type')
	886	street_type = np.where((pavement_type == fillvalues["pavement_type"]),fillvalues["street_type"],street_type)
	887
[3567]	888	nc_write_to_file_2d(filename[i], 'street_type', street_type, datatypes["street_type"],'y','x',fillvalues["street_type"])
	889	nc_write_attribute(filename[i], 'street_type', 'long_name', 'street type')
	890	nc_write_attribute(filename[i], 'street_type', 'units', '')
	891	nc_write_attribute(filename[i], 'street_type', 'res_orig', domain_px[i])
	892	nc_write_attribute(filename[i], 'street_type', 'coordinates', 'E_UTM N_UTM lon lat')
	893	nc_write_attribute(filename[i], 'street_type', 'grid_mapping', 'E_UTM N_UTM lon lat')
	894	del street_type
	895
	896	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])
	897	street_crossings[street_crossings == 255] = fillvalues["street_crossings"]
	898	street_crossings = np.where((street_crossings < 1) & (street_crossings != fillvalues["street_crossings"]),defaultvalues["street_crossings"],street_crossings)
	899
[3726]	900	nc_write_to_file_2d(filename[i], 'street_crossing', street_crossings, datatypes["street_crossings"],'y','x',fillvalues["street_crossings"])
	901	nc_write_attribute(filename[i], 'street_crossing', 'long_name', 'street crossings')
	902	nc_write_attribute(filename[i], 'street_crossing', 'units', '')
	903	nc_write_attribute(filename[i], 'street_crossing', 'res_orig', domain_px[i])
	904	nc_write_attribute(filename[i], 'street_crossing', 'coordinates', 'E_UTM N_UTM lon lat')
	905	nc_write_attribute(filename[i], 'street_crossing', 'grid_mapping', 'E_UTM N_UTM lon lat')
[3567]	906	del street_crossings
[3629]	907
	908
	909	# Read/write vegetation on roofs
	910	for i in range(0,ndomains):
	911	if domain_green_roofs[i]:
	912	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])
	913	buildings_2d = nc_read_from_file_2d_all(filename[i], 'buildings_2d')
	914
	915
	916	x = nc_read_from_file_2d_all(filename[i], 'x')
	917	y = nc_read_from_file_2d_all(filename[i], 'y')
[4149]	918	nbuilding_pars = np.arange(0,47)
[3629]	919	building_pars = np.ones((len(nbuilding_pars),len(y),len(x)))
	920	building_pars[:,:,:] = fillvalues["building_pars"]
	921
	922	# assign green fraction on roofs
[3955]	923	building_pars[3,:,:] = np.where( ( buildings_2d != fillvalues["buildings_2d"] ) & ( green_roofs != fillvalues["building_pars"] ) & ( green_roofs != 0.0 ),1,fillvalues["building_pars"] )
[3629]	924
	925	# assign leaf area index for vegetation on roofs
[4311]	926	building_pars[4,:,:] = np.where( ( building_pars[3,:,:] != fillvalues["building_pars"] ) & ( green_roofs >= 0.5 ),settings_lai_roof_intensive,fillvalues["building_pars"])
	927	building_pars[4,:,:] = np.where( ( building_pars[3,:,:] != fillvalues["building_pars"] ) & ( green_roofs < 0.5 ),settings_lai_roof_extensive,building_pars[4,:,:])
[3629]	928
	929
	930	nc_write_dimension(filename[i], 'nbuilding_pars', nbuilding_pars, datatypes["nbuilding_pars"])
	931	nc_write_to_file_3d(filename[i], 'building_pars', building_pars, datatypes["building_pars"],'nbuilding_pars','y','x',fillvalues["building_pars"])
	932	nc_write_attribute(filename[i], 'building_pars', 'long_name', 'building_pars')
	933	nc_write_attribute(filename[i], 'building_pars', 'units', '')
	934	nc_write_attribute(filename[i], 'building_pars', 'res_orig', domain_px[i])
	935	nc_write_attribute(filename[i], 'building_pars', 'coordinates', 'E_UTM N_UTM lon lat')
	936	nc_write_attribute(filename[i], 'building_pars', 'grid_mapping', 'E_UTM N_UTM lon lat')
	937
	938	del building_pars, buildings_2d, x, y
	939
	940
	941	# Read tree data and create LAD and BAD arrays using the canopy generator
	942	for i in range(0,ndomains):
[3668]	943	lai = nc_read_from_file_2d(input_file_lai[ii[i]], 'Band1', domain_x0[i], domain_x1[i], domain_y0[i], domain_y1[i])
	944
	945	vegetation_type = nc_read_from_file_2d_all(filename[i], 'vegetation_type')
	946
	947	lai = np.where(vegetation_type == fillvalues["vegetation_type"],fillvalues["vegetation_pars"],lai)
	948
[3629]	949
[3668]	950	x = nc_read_from_file_2d_all(filename[i], 'x')
	951	y = nc_read_from_file_2d_all(filename[i], 'y')
	952	nvegetation_pars = np.arange(0,12)
	953	vegetation_pars = np.ones((len(nvegetation_pars),len(y),len(x)))
	954	vegetation_pars[:,:,:] = fillvalues["vegetation_pars"]
[3629]	955
[3668]	956	vegetation_pars[1,:,:] = lai
[3629]	957
[3668]	958
	959	# Write out first version of LAI. Will later be overwritten.
	960	nc_write_dimension(filename[i], 'nvegetation_pars', nvegetation_pars, datatypes["nvegetation_pars"])
	961	nc_write_to_file_3d(filename[i], 'vegetation_pars', vegetation_pars, datatypes["vegetation_pars"],'nvegetation_pars','y','x',fillvalues["vegetation_pars"])
	962	nc_write_attribute(filename[i], 'vegetation_pars', 'long_name', 'vegetation_pars')
	963	nc_write_attribute(filename[i], 'vegetation_pars', 'units', '')
	964	nc_write_attribute(filename[i], 'vegetation_pars', 'res_orig', domain_px[i])
	965	nc_write_attribute(filename[i], 'vegetation_pars', 'coordinates', 'E_UTM N_UTM lon lat')
	966	nc_write_attribute(filename[i], 'vegetation_pars', 'grid_mapping', 'E_UTM N_UTM lon lat')
	967
	968	del lai, vegetation_pars, vegetation_type
	969
	970	# Read tree data and create LAD and BAD arrays using the canopy generator
	971	for i in range(0,ndomains):
	972	if domain_street_trees[i]:
	973
	974	vegetation_pars = nc_read_from_file_2d_all(filename[i], 'vegetation_pars')
	975
[4793]	976	lai = nc_read_from_file_2d(input_file_lai[ii[i]], 'Band1', domain_x0[i], domain_x1[i], domain_y0[i], domain_y1[i])
[3668]	977
[3629]	978	x = nc_read_from_file_2d_all(filename[i], 'x')
	979	y = nc_read_from_file_2d_all(filename[i], 'y')
	980
[3668]	981	# Save lai data as default for low and high vegetation
	982	lai_low = lai
	983	lai_high = lai
	984
	985	# Read all tree parameters from file
	986	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])
[3629]	987
[3668]	988	if (input_file_tree_crown_diameter[ii[i]] is not None):
	989	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])
	990	tree_crown_diameter = np.where( (tree_crown_diameter == 0.0) \| (tree_crown_diameter == -1.0) ,fillvalues["tree_data"],tree_crown_diameter)
	991	else:
	992	tree_crown_diameter = np.ones((len(y),len(x)))
	993	tree_crown_diameter[:,:] = fillvalues["tree_data"]
[3629]	994
[3668]	995
[3629]	996	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])
	997	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])
	998	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])
	999
	1000	# Remove missing values from the data. Reasonable values will be set by the tree generator
	1001	tree_height = np.where( (tree_height == 0.0) \| (tree_height == -1.0) ,fillvalues["tree_data"],tree_height)
	1002	tree_trunk_diameter = np.where( (tree_trunk_diameter == 0.0) \| (tree_trunk_diameter == -1.0) ,fillvalues["tree_data"],tree_trunk_diameter)
	1003	tree_type = np.where( (tree_type == 0.0) \| (tree_type == -1.0) ,fillvalues["tree_data"],tree_type)
[4793]	1004
	1005	# For vegetation pixel with missing height information (-1), set default patch height
	1006	patch_height = np.where( patch_height == -1.0 ,settings_patch_height_default,patch_height)
[3629]	1007
	1008	# Convert trunk diameter from cm to m
	1009	tree_trunk_diameter = np.where(tree_trunk_diameter != fillvalues["tree_data"], tree_trunk_diameter * 0.01,tree_trunk_diameter)
	1010
	1011
	1012	# Temporarily change missing value for tree_type
	1013	tree_type = np.where( (tree_type == fillvalues["tree_type"]),fillvalues["tree_data"],tree_type)
	1014
	1015	# Compare patch height array with vegetation type and correct accordingly
	1016	vegetation_type = nc_read_from_file_2d_all(filename[i], 'vegetation_type')
	1017
	1018
	1019	# For zero-height patches, set vegetation_type to short grass and remove these pixels from the patch height array
[4793]	1020	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)
	1021	patch_height = 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)
[3629]	1022
	1023	max_tree_height = max(tree_height.flatten())
	1024	max_patch_height = max(patch_height.flatten())
	1025
[4490]	1026	# Call canopy generator for single trees only if there is any tree height available in the domain.
	1027	# This does not guarantee that there are street trees that can be processed. This is checked in the
	1028	# canopy generator.
	1029	if ( (max_tree_height != fillvalues["tree_data"]) \| (max_patch_height != fillvalues["tree_data"]) ):
[3668]	1030
[4793]	1031	lad, bad, tree_ids, tree_types, 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"])
[3629]	1032
	1033	# Remove LAD volumes that are inside buildings
[4793]	1034	if not domain_overhanging_trees[i]:
	1035	buildings_2d = nc_read_from_file_2d_all(filename[i], 'buildings_2d')
	1036	for k in range(0,len(zlad)-1):
[3629]	1037
[4793]	1038	lad[k,:,:] = np.where(buildings_2d == fillvalues["buildings_2d"],lad[k,:,:],fillvalues["tree_data"])
	1039	bad[k,:,:] = np.where(buildings_2d == fillvalues["buildings_2d"],bad[k,:,:],fillvalues["tree_data"])
	1040	tree_ids[k,:,:] = np.where(buildings_2d == fillvalues["buildings_2d"],tree_ids[k,:,:],fillvalues["tree_id"])
[3668]	1041
[4793]	1042	del buildings_2d
[3629]	1043
[4793]	1044
[3629]	1045	nc_write_dimension(filename[i], 'zlad', zlad, datatypes["tree_data"])
[4793]	1046
[3629]	1047	nc_write_to_file_3d(filename[i], 'lad', lad, datatypes["tree_data"],'zlad','y','x',fillvalues["tree_data"])
	1048	nc_write_attribute(filename[i], 'lad', 'long_name', 'leaf area density')
	1049	nc_write_attribute(filename[i], 'lad', 'units', '')
	1050	nc_write_attribute(filename[i], 'lad', 'res_orig', domain_px[i])
	1051	nc_write_attribute(filename[i], 'lad', 'coordinates', 'E_UTM N_UTM lon lat')
	1052	nc_write_attribute(filename[i], 'lad', 'grid_mapping', 'E_UTM N_UTM lon lat')
[4793]	1053
[3629]	1054	nc_write_to_file_3d(filename[i], 'bad', bad, datatypes["tree_data"],'zlad','y','x',fillvalues["tree_data"])
	1055	nc_write_attribute(filename[i], 'bad', 'long_name', 'basal area density')
	1056	nc_write_attribute(filename[i], 'bad', 'units', '')
	1057	nc_write_attribute(filename[i], 'bad', 'res_orig', domain_px[i])
	1058	nc_write_attribute(filename[i], 'bad', 'coordinates', 'E_UTM N_UTM lon lat')
	1059	nc_write_attribute(filename[i], 'bad', 'grid_mapping', 'E_UTM N_UTM lon lat')
	1060
[4793]	1061	nc_write_to_file_3d(filename[i], 'tree_id', tree_ids, datatypes["tree_id"],'zlad','y','x',fillvalues["tree_id"])
[3629]	1062	nc_write_attribute(filename[i], 'tree_id', 'long_name', 'tree id')
	1063	nc_write_attribute(filename[i], 'tree_id', 'units', '')
	1064	nc_write_attribute(filename[i], 'tree_id', 'res_orig', domain_px[i])
	1065	nc_write_attribute(filename[i], 'tree_id', 'coordinates', 'E_UTM N_UTM lon lat')
	1066	nc_write_attribute(filename[i], 'tree_id', 'grid_mapping', 'E_UTM N_UTM lon lat')
[4793]	1067
	1068	nc_write_to_file_3d(filename[i], 'tree_type', tree_types, datatypes["tree_types"],'zlad','y','x',fillvalues["tree_types"])
	1069	nc_write_attribute(filename[i], 'tree_type', 'long_name', 'tree type')
	1070	nc_write_attribute(filename[i], 'tree_type', 'units', '')
	1071	nc_write_attribute(filename[i], 'tree_type', 'res_orig', domain_px[i])
	1072	nc_write_attribute(filename[i], 'tree_type', 'coordinates', 'E_UTM N_UTM lon lat')
	1073	nc_write_attribute(filename[i], 'tree_type', 'grid_mapping', 'E_UTM N_UTM lon lat')
[3629]	1074
[4793]	1075	del lai, lad, bad, tree_ids, tree_types, zlad
[3629]	1076
[4793]	1077	else:
	1078	print('No street trees generated in domain ' + str(i))
	1079
[3668]	1080	del vegetation_pars, tree_height, tree_crown_diameter, tree_trunk_diameter, tree_type, patch_height, x, y
[3629]	1081
	1082
	1083	# Create vegetation patches for locations with high vegetation type
	1084	for i in range(0,ndomains):
	1085	if domain_canopy_patches[i]:
	1086
	1087	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])
	1088
[4793]	1089	# For vegetation pixel with missing height information (-1), set default patch height
	1090	patch_height = np.where( patch_height == -1.0 ,settings_patch_height_default,patch_height)
	1091
	1092	max_patch_height = max(patch_height.flatten())
	1093	if ( max_patch_height != fillvalues["tree_data"] ):
	1094	# Call canopy generator for single trees only if there is any tree height available in the domain.
	1095	# This does not guarantee that there are street trees that can be processed. This is checked in the
	1096	# canopy generator.
[3629]	1097
[4793]	1098	# Load vegetation_type and lad array (at level z = 0) for re-processing
	1099	vegetation_type = nc_read_from_file_2d_all(filename[i], 'vegetation_type')
	1100	lad = nc_read_from_file_3d_all(filename[i], 'lad')
	1101	tree_id = nc_read_from_file_3d_all(filename[i], 'tree_id')
	1102	tree_types = nc_read_from_file_3d_all(filename[i], 'tree_type')
	1103	zlad = nc_read_from_file_1d_all(filename[i], 'zlad')
	1104	vegetation_pars = nc_read_from_file_3d_all(filename[i], 'vegetation_pars')
	1105	lai = nc_read_from_file_2d(input_file_lai[ii[i]], 'Band1', domain_x0[i], domain_x1[i], domain_y0[i], domain_y1[i])
	1106
	1107	nc_write_to_file_2d(filename[i], 'patch_height', patch_height, datatypes["tree_data"],'y','x',fillvalues["tree_data"])
	1108	nc_write_attribute(filename[i], 'patch_height', 'long_name', 'tree type')
	1109	nc_write_attribute(filename[i], 'patch_height', 'units', '')
	1110	nc_write_attribute(filename[i], 'patch_height', 'res_orig', domain_px[i])
	1111	nc_write_attribute(filename[i], 'patch_height', 'coordinates', 'E_UTM N_UTM lon lat')
	1112	nc_write_attribute(filename[i], 'patch_height', 'grid_mapping', 'E_UTM N_UTM lon lat')
[4746]	1113
[4793]	1114	# 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
	1115	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"])
	1116
	1117
	1118	# Treat all pixels where short grass is defined, but where a patch_height >= dz is found, as high vegetation (often the case in backyards)
	1119	lai_high = np.where( (lai_high == fillvalues["tree_data"]) & (patch_height >= domain_dz[i]) & (vegetation_type == 3), 1.0, lai_high)
[4746]	1120
[4793]	1121	# If overhanging trees are allowed, assign pixels with patch_height > dz that are not included in vegetation_type to high
	1122	if domain_overhanging_trees[i]:
	1123	lai_high = np.where( (lai_high == fillvalues["tree_data"]) & (patch_height >= domain_dz[i]), 1.0, lai_high)
[3668]	1124
[4793]	1125	# Now, assign either the default LAI for high vegetation or keep 1.0 from the lai_high array.
	1126	lai_high = np.where( (lai_high != fillvalues["tree_data"]) & (lai == fillvalues["tree_data"]), settings_lai_high_default, lai_high)
[3726]	1127
[4793]	1128	# If LAI values are available in the LAI array, write them on the lai_high array
	1129	lai_high = np.where( (lai_high != fillvalues["tree_data"]) & (lai != fillvalues["tree_data"]), lai, lai_high)
[4746]	1130
[3629]	1131
[4793]	1132	# Define a patch height wherever it is missing, but where a high vegetation LAI was set
	1133	patch_height = np.where ( (lai_high != fillvalues["tree_data"]) & (patch_height == fillvalues["tree_data"]), settings_patch_height_default, patch_height)
[4746]	1134
[3629]	1135
[4793]	1136	# Remove pixels where street trees were already se
	1137	patch_height = np.where ( (lad[0,:,:] != fillvalues["tree_data"]), fillvalues["tree_data"], patch_height)
[3668]	1138
[4793]	1139	# Remove patch heights that have no lai_high value
	1140	patch_height = np.where ( (lai_high == fillvalues["tree_data"]), fillvalues["tree_data"], patch_height)
	1141
	1142
	1143
	1144
	1145	# For missing LAI values, set either the high vegetation default or the low vegetation default
	1146	lai_high = np.where( (patch_height > 2.0) & (patch_height != fillvalues["tree_data"]) & (lai_high == fillvalues["tree_data"]),settings_lai_high_default,lai_high)
	1147	lai_high = np.where( (patch_height <= 2.0) & (patch_height != fillvalues["tree_data"]) & (lai_high == fillvalues["tree_data"]),settings_lai_low_default,lai_high)
[3668]	1148
[4793]	1149	if ( max(patch_height.flatten()) >= (2.0 * domain_dz[i]) ):
	1150	print(" start calculating LAD (this might take some time)")
[3726]	1151
	1152
[4793]	1153	lad_patch, patch_id, patch_types, patch_nz, status = process_patch(domain_dz[i],patch_height,vegetation_type,max(zlad),lai_high,settings_lai_alpha,settings_lai_beta)
[3629]	1154
[4793]	1155	# Increase the patch ids by the maximum id for street trees to avoid double ids
	1156	max_id = max(tree_id.flatten())
	1157	patch_id = np.where( patch_id != fillvalues["tree_id"], patch_id+max_id, patch_id)
[3629]	1158
[4793]	1159	# 2D loop in order to avoid memory problems with large arrays
	1160	for iii in range(0,domain_nx[i]):
	1161	for jj in range(0,domain_ny[i]):
[4746]	1162
[4793]	1163	tree_id[0:patch_nz+1,jj,iii] = np.where( (lad[0:patch_nz+1,jj,iii] == fillvalues["tree_data"]),patch_id[0:patch_nz+1,jj,iii], tree_id[0:patch_nz+1,jj,iii] )
	1164	tree_types[0:patch_nz+1,jj,iii] = np.where( (lad[0:patch_nz+1,jj,iii] == fillvalues["tree_data"]),patch_types[0:patch_nz+1,jj,iii], tree_types[0:patch_nz+1,jj,iii] )
	1165	lad[0:patch_nz+1,jj,iii] = np.where( (lad[0:patch_nz+1,jj,iii] == fillvalues["tree_data"]),lad_patch[0:patch_nz+1,jj,iii], lad[0:patch_nz+1,jj,iii] )
	1166
	1167	# Remove high vegetation wherever it is replaced by a leaf area density. This should effectively remove all high vegetation pixels
	1168	vegetation_type = np.where((lad[0,:,:] != fillvalues["tree_data"]) & (vegetation_type != fillvalues["vegetation_type"]),settings_veg_type_below_trees,vegetation_type)
[3668]	1169
[4793]	1170	# If desired, remove all high vegetation. TODO: check if this is still necessary
	1171	if not domain_high_vegetation[i]:
	1172	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)
[3629]	1173
	1174
[4793]	1175	# Set default low LAI for pixels with an LAD (short grass below trees)
	1176	lai_low = np.where( (lad[0,:,:] == fillvalues["tree_data"]), lai, settings_lai_low_default)
[3629]	1177
[4793]	1178	# Fill low vegetation pixels without LAI set or with LAI = 0 with default value
	1179	lai_low = np.where( ( (lai_low == fillvalues["tree_data"]) \| (lai_low == 0.0) ) & (vegetation_type != fillvalues["vegetation_type"] ), settings_lai_low_default, lai_low)
[3629]	1180
[4793]	1181	# Remove lai for pixels that have no vegetation_type
	1182	lai_low = np.where( ( (vegetation_type != fillvalues["vegetation_type"]) & (vegetation_type != 1) ), lai_low, fillvalues["tree_data"])
[3668]	1183
[4793]	1184	# Overwrite lai in vegetation_parameters
	1185	vegetation_pars[1,:,:] = np.copy(lai_low)
	1186	nc_overwrite_to_file_3d(filename[i], 'vegetation_pars', vegetation_pars)
[3629]	1187
[4793]	1188	# Overwrite lad and id arrays
	1189	nc_overwrite_to_file_3d(filename[i], 'lad', lad)
	1190	nc_overwrite_to_file_3d(filename[i], 'tree_id', tree_id)
	1191	nc_overwrite_to_file_3d(filename[i], 'tree_type', tree_types)
[3668]	1192
[4793]	1193	nc_overwrite_to_file_2d(filename[i], 'vegetation_type', vegetation_type)
[3668]	1194
[3955]	1195
[4793]	1196	del vegetation_type, lad, lai, patch_height, vegetation_pars, zlad
	1197	else:
	1198	print('No tree patches found in domain ' + str(i))
	1199
[3955]	1200	# Final adjustment of vegetation parameters: remove LAI where a bare soil was set
	1201	for i in range(0,ndomains):
	1202
	1203	vegetation_type = nc_read_from_file_2d_all(filename[i], 'vegetation_type')
	1204	vegetation_pars = nc_read_from_file_3d_all(filename[i], 'vegetation_pars')
	1205	lai = vegetation_pars[1,:,:]
	1206
	1207
	1208	# Remove lai for pixels that have no vegetation_type
	1209	lai = np.where( ( (vegetation_type != fillvalues["vegetation_type"]) & (vegetation_type != 1) ), lai, fillvalues["tree_data"])
	1210
	1211	# Overwrite lai in vegetation_parameters
	1212	vegetation_pars[1,:,:] = np.copy(lai)
	1213	nc_overwrite_to_file_3d(filename[i], 'vegetation_pars', vegetation_pars)
	1214
	1215	del vegetation_type, lai, vegetation_pars
	1216
	1217
[3668]	1218	# Final consistency check
	1219	for i in range(0,ndomains):
	1220	vegetation_type = nc_read_from_file_2d_all(filename[i], 'vegetation_type')
	1221	pavement_type = nc_read_from_file_2d_all(filename[i], 'pavement_type')
	1222	building_type = nc_read_from_file_2d_all(filename[i], 'building_type')
	1223	water_type = nc_read_from_file_2d_all(filename[i], 'water_type')
	1224	soil_type = nc_read_from_file_2d_all(filename[i], 'soil_type')
	1225
	1226	# Check for consistency and fill empty fields with default vegetation type
	1227	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"])
	1228
	1229	# Check for consistency and fill empty fields with default vegetation type
	1230	consistency_array, test = check_consistency_3(vegetation_type,pavement_type,soil_type,fillvalues["vegetation_type"],fillvalues["pavement_type"],fillvalues["soil_type"])
	1231
	1232	surface_fraction = nc_read_from_file_3d_all(filename[i], 'surface_fraction')
	1233	surface_fraction[0,:,:] = np.where(vegetation_type != fillvalues["vegetation_type"], 1.0, 0.0)
	1234	surface_fraction[1,:,:] = np.where(pavement_type != fillvalues["pavement_type"], 1.0, 0.0)
	1235	surface_fraction[2,:,:] = np.where(water_type != fillvalues["water_type"], 1.0, 0.0)
	1236	nc_overwrite_to_file_3d(filename[i], 'surface_fraction', surface_fraction)
	1237
	1238	del vegetation_type, pavement_type, building_type, water_type, soil_type

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