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source: palm/trunk/SCRIPTS/palm_csd @ 3849

Last change on this file since 3849 was 3773, checked in by maronga, 6 years ago
added output of theta_2m_av and minor revisions in palm_csd
Property svn:executable set to ``* Property svn:keywords set to `Id`
File size: 55.9 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	#
	17	# Copyright 1997-2018 Leibniz Universitaet Hannover
	18	#--------------------------------------------------------------------------------#
	19	#
	20	# Current revisions:
	21	# -----------------
	22	#
	23	#
	24	# Former revisions:
	25	# -----------------
	26	# $Id: palm_csd 3773 2019-03-01 08:56:57Z knoop $
[3773]	27	# Unspecificed changes
	28	#
	29	# 3726 2019-02-07 18:22:49Z maronga
[3726]	30	# Removed some more bugs
	31	#
	32	# 3688 2019-01-22 10:44:20Z maronga
[3688]	33	# Some unspecified bugfixes
	34	#
	35	# 3668 2019-01-14 12:49:24Z maronga
[3668]	36	# Various improvements and bugfixes
	37	#
	38	# 3629 2018-12-13 12:18:54Z maronga
[3629]	39	# Added canopy generator calls. Some improvements
	40	#
	41	# 3567 2018-11-27 13:59:21Z maronga
[3567]	42	# Initial revisions
	43	#
	44	# Description:
	45	# ------------
	46	# Processing tool for creating PIDS conform static drivers from rastered NetCDF
	47	# input
	48	#
	49	# @Author Bjoern Maronga (maronga@muk.uni-hannover.de)
	50	#
	51	# @todo Make input files optional
	52	# @todo Allow for ASCII input of terrain height and building height
	53	# @todo Modularize reading config file
[3688]	54	# @todo Convert to object-oriented treatment (buidings, trees)
	55	# @todo Automatically shift child domains so that their origin lies intersects
	56	# a edge note of the parent grid
[3567]	57	#------------------------------------------------------------------------------#
	58
	59	from palm_csd_files.palm_csd_netcdf_interface import *
	60	from palm_csd_files.palm_csd_tools import *
[3629]	61	from palm_csd_files.palm_csd_canopy_generator import *
[3567]	62	import numpy as np
	63
	64
	65	def read_config_file():
	66
	67	import configparser
	68	from math import floor
	69	import numpy as np
	70	import os
	71	import subprocess as sub
	72	import sys
	73
	74	# Check if configuration files exists and quit otherwise
	75	input_config = ".csd.config"
	76	for i in range(1,len(sys.argv)):
	77	input_config = str(sys.argv[i])
	78
	79	config = configparser.RawConfigParser(allow_no_value=True)
	80
	81	if ( os.path.isfile(input_config) == False ):
	82	print ("Error. No configuration file " + input_config + " found.")
	83	raise SystemExit
	84
	85	config.read(input_config)
	86
	87
	88	# Definition of settings
[3668]	89
[3567]	90	global settings_bridge_width
[3629]	91	global settings_lai_roof_intensive
	92	global settings_lai_roof_extensive
	93	global settings_season
	94	global settings_lai_low_default
	95	global settings_lai_high_default
	96	global settings_patch_height_default
	97	global settings_lai_alpha
	98	global settings_lai_beta
[3567]	99	global ndomains
	100
	101	# Definition of global configuration parameters
	102	global global_acronym
	103	global global_angle
	104	global global_author
	105	global global_campaign
	106	global global_comment
	107	global global_contact
	108	global global_data_content
	109	global global_dependencies
	110	global global_institution
	111	global global_keywords
	112	global global_location
	113	global global_palm_version
	114	global global_references
	115	global global_site
	116	global global_source
	117
[3668]	118	global path_out
	119	global filename_out
	120	global version_out
[3567]	121
[3668]	122
[3567]	123	# Definition of domain parameters
	124	global domain_names
	125	global domain_px
	126	global domain_x0
	127	global domain_y0
	128	global domain_x1
	129	global domain_y1
	130	global domain_nx
	131	global domain_ny
	132	global domain_dz
	133	global domain_3d
[3629]	134	global domain_high_vegetation
[3567]	135	global domain_ip
	136	global domain_za
	137	global domain_parent
[3629]	138	global domain_green_roofs
	139	global domain_street_trees
	140	global domain_canopy_patches
[3567]	141
	142	# Definition of input data parameters
	143	global input_names
	144	global input_px
	145
	146
	147	global input_file_x
	148	global input_file_y
	149	global input_file_x_UTM
	150	global input_file_y_UTM
	151	global input_file_lat
	152	global input_file_lon
	153	global input_file_zt
	154	global input_file_buildings_2d
	155	global input_file_bridges_2d
	156	global input_file_building_id
	157	global input_file_bridges_id
	158	global input_file_building_type
	159	global input_file_building_type
[3629]	160	global input_file_lai
[3567]	161	global input_file_vegetation_type
	162	global input_file_vegetation_height
	163	global input_file_pavement_type
	164	global input_file_water_type
	165	global input_file_street_type
	166	global input_file_street_crossings
	167	global input_file_soil_type
[3629]	168	global input_file_vegetation_on_roofs
	169	global input_file_tree_crown_diameter
	170	global input_file_tree_height
	171	global input_file_tree_trunk_diameter
	172	global input_file_tree_type
	173	global input_file_patch_height
[3567]	174
	175	global zt_all
	176	global zt_min
	177
	178	settings_bridge_width = 3.0
[3629]	179	settings_lai_roof_intensive = 0.5
	180	settings_lai_roof_extensive = 1.0
	181	settings_season = "summer"
	182	settings_lai_high_default = 6.0
	183	settings_lai_low_default = 1.0
	184	settings_patch_height_default = 10.0
	185	settings_lai_alpha = 5.0
	186	settings_lai_beta = 3.0
[3567]	187	ndomains = 0
	188
	189	global_acronym = ""
	190	global_angle = ""
	191	global_author = ""
	192	global_campaign = ""
	193	global_comment = ""
	194	global_contact = ""
	195	global_data_content = ""
	196	global_dependencies = ""
	197	global_institution = ""
	198	global_keywords = ""
	199	global_location = ""
	200	global_palm_version = 6.0
	201	global_references = ""
	202	global_site = ""
	203	global_source = ""
	204
[3668]	205	path_out = ""
	206	version_out = 1
	207	filename_out = "default"
	208
[3567]	209	domain_names = []
	210	domain_px = []
	211	domain_x0 = []
	212	domain_y0 = []
	213	domain_x1 = []
	214	domain_y1 = []
	215	domain_nx = []
	216	domain_ny = []
	217	domain_dz = []
	218	domain_3d = []
[3629]	219	domain_high_vegetation = []
[3567]	220	domain_ip = []
	221	domain_za = []
	222	domain_parent = []
[3629]	223	domain_green_roofs = []
	224	domain_street_trees = []
	225	domain_canopy_patches = []
[3567]	226
	227	zt_min = 0.0
	228	zt_all = []
	229
	230	input_names = []
	231	input_px = []
	232
	233	input_file_x = []
	234	input_file_y = []
	235	input_file_x_UTM = []
	236	input_file_y_UTM = []
	237	input_file_lat = []
	238	input_file_lon = []
	239
	240	input_file_zt = []
	241	input_file_buildings_2d = []
	242	input_file_bridges_2d = []
	243	input_file_building_id = []
	244	input_file_bridges_id = []
	245	input_file_building_type = []
[3629]	246	input_file_lai = []
[3567]	247	input_file_vegetation_type = []
	248	input_file_vegetation_height = []
	249	input_file_pavement_type = []
	250	input_file_water_type = []
	251	input_file_street_type = []
	252	input_file_street_crossings = []
	253	input_file_soil_type = []
[3629]	254	input_file_vegetation_on_roofs = []
	255	input_file_tree_crown_diameter = []
	256	input_file_tree_height = []
	257	input_file_tree_trunk_diameter = []
	258	input_file_tree_type = []
	259	input_file_patch_height = []
[3567]	260
	261	# Load all user parameters from config file
	262	for i in range(0,len(config.sections())):
	263
	264	read_tmp = config.sections()[i]
	265
	266	if ( read_tmp == 'global' ):
	267	global_acronym = config.get(read_tmp, 'acronym')
	268	global_angle = float(config.get(read_tmp, 'rotation_angle'))
	269	global_author = config.get(read_tmp, 'author')
	270	global_campaign = config.get(read_tmp, 'campaign')
	271	global_comment = config.get(read_tmp, 'comment')
	272	global_contact = config.get(read_tmp, 'contact_person')
	273	global_data_content = config.get(read_tmp, 'data_content')
	274	global_dependencies = config.get(read_tmp, 'dependencies')
	275	global_institution = config.get(read_tmp, 'institution')
	276	global_keywords = config.get(read_tmp, 'keywords')
	277	global_location = config.get(read_tmp, 'location')
	278	global_palm_version = float(config.get(read_tmp, 'palm_version'))
	279	global_references = config.get(read_tmp, 'references')
	280	global_site = config.get(read_tmp, 'site')
	281	global_source = config.get(read_tmp, 'source')
	282
[3668]	283
[3567]	284	if ( read_tmp == 'settings' ):
[3629]	285	settings_lai_roof_intensive = config.get(read_tmp, 'lai_roof_intensive')
	286	settings_lai_roof_extensive = config.get(read_tmp, 'lai_roof_extensive')
	287	settings_bridge_width = float(config.get(read_tmp, 'bridge_width'))
	288	settings_season = config.get(read_tmp, 'season')
	289	settings_lai_high_default = float(config.get(read_tmp, 'lai_high_vegetation_default'))
	290	settings_lai_low_default = float(config.get(read_tmp, 'lai_low_vegetation_default'))
	291	settings_patch_height_default = float(config.get(read_tmp, 'patch_height_default'))
	292	settings_lai_alpha = float(config.get(read_tmp, 'lai_alpha'))
	293	settings_lai_beta = float(config.get(read_tmp, 'lai_beta'))
	294
[3668]	295	if ( read_tmp == 'output' ):
	296	path_out = config.get(read_tmp, 'path')
	297	filename_out = config.get(read_tmp, 'file_out')
	298	version_out = float(config.get(read_tmp, 'version'))
	299
[3567]	300	if ( read_tmp.split("_")[0] == 'domain' ):
	301	ndomains = ndomains + 1
	302	domain_names.append(read_tmp.split("_")[1])
	303	domain_px.append(float(config.get(read_tmp, 'pixel_size')))
	304	domain_nx.append(int(config.get(read_tmp, 'nx')))
	305	domain_ny.append(int(config.get(read_tmp, 'ny')))
	306	domain_dz.append(float(config.get(read_tmp, 'dz')))
	307	domain_3d.append(config.getboolean(read_tmp, 'buildings_3d'))
[3629]	308	domain_high_vegetation.append(config.getboolean(read_tmp, 'allow_high_vegetation'))
	309	domain_canopy_patches.append(config.getboolean(read_tmp, 'generate_vegetation_patches'))
[3567]	310	domain_ip.append(config.getboolean(read_tmp, 'interpolate_terrain'))
	311	domain_za.append(config.getboolean(read_tmp, 'use_palm_z_axis'))
	312	if domain_ip[ndomains-1] and not domain_za[ndomains-1]:
	313	domain_za[ndomains-1] = True
	314	print("+++ Overwrite user setting for use_palm_z_axis")
	315
	316	domain_parent.append(config.get(read_tmp, 'domain_parent'))
	317
	318	domain_x0.append(int(floor(float(config.get(read_tmp, 'origin_x'))/float(config.get(read_tmp, 'pixel_size')))))
	319	domain_y0.append(int(floor(float(config.get(read_tmp, 'origin_y'))/float(config.get(read_tmp, 'pixel_size')))))
	320	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'))))
	321	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]	322	domain_green_roofs.append(config.getboolean(read_tmp, 'vegetation_on_roofs'))
	323	domain_street_trees.append(config.getboolean(read_tmp, 'street_trees'))
	324
[3567]	325	if ( read_tmp.split("_")[0] == 'input' ):
	326	input_names.append(read_tmp.split("_")[1])
	327	input_px.append(float(config.get(read_tmp, 'pixel_size')))
	328	input_file_x.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_x'))
	329	input_file_y.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_y'))
	330	input_file_lat.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_lat'))
	331	input_file_lon.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_lon'))
	332	input_file_x_UTM.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_x_UTM'))
	333	input_file_y_UTM.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_y_UTM'))
	334	input_file_zt.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_zt'))
	335	input_file_buildings_2d.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_buildings_2d'))
	336	input_file_bridges_2d.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_bridges_2d'))
	337	input_file_building_id.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_building_id'))
	338	input_file_bridges_id.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_bridges_id'))
[3629]	339	input_file_building_type.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_building_type'))
	340	input_file_lai.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_lai'))
[3567]	341	input_file_vegetation_type.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_vegetation_type'))
	342	input_file_vegetation_height.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_vegetation_height'))
	343	input_file_pavement_type.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_pavement_type'))
	344	input_file_water_type.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_water_type'))
	345	input_file_street_type.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_street_type'))
[3629]	346	input_file_street_crossings.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_street_crossings'))
	347	input_file_patch_height.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_patch_height'))
[3668]	348
	349	tmp = config.get(read_tmp, 'file_tree_crown_diameter')
	350	if tmp is not None:
	351	input_file_tree_crown_diameter.append(config.get(read_tmp, 'path') + "/" + tmp)
	352	else:
	353	input_file_tree_crown_diameter.append(None)
[3629]	354	input_file_tree_height.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_tree_height'))
	355	input_file_tree_trunk_diameter.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_tree_trunk_diameter'))
	356	input_file_tree_type.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_tree_type'))
	357	input_file_vegetation_on_roofs.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_vegetation_on_roofs'))
[3567]	358	#input_file_soil_type.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_soil_type'))
	359	return 0
	360
	361
	362	############################################################
	363
	364	# Start of main program
	365	datatypes = {
	366	"x": "f4",
	367	"y": "f4",
	368	"z": "f4",
	369	"lat": "f4",
	370	"lon": "f4",
	371	"E_UTM": "f4",
	372	"N_UTM": "f4",
	373	"zt": "f4",
	374	"buildings_2d": "f4",
	375	"buildings_3d": "b",
	376	"bridges_2d": "f4",
	377	"building_id": "i",
	378	"bridges_id": "i",
	379	"building_type": "b",
	380	"nsurface_fraction": "i",
	381	"vegetation_type": "b",
	382	"vegetation_height": "f4",
	383	"pavement_type": "b",
	384	"water_type": "b",
	385	"street_type": "b",
	386	"street_crossings": "b",
	387	"soil_type": "b",
[3629]	388	"surface_fraction": "f4",
	389	"building_pars": "f4",
	390	"vegetation_pars": "f4",
	391	"tree_data": "f4",
	392	"tree_type": "b",
	393	"nbuilding_pars": "i",
	394	"nvegetation_pars": "i",
	395	"zlad": "f4"
[3567]	396	}
	397
	398	fillvalues = {
	399	"lat": float(-9999.0),
	400	"lon": float(-9999.0),
	401	"E_UTM": float(-9999.0),
	402	"N_UTM": float(-9999.0),
	403	"zt": float(-9999.0),
	404	"buildings_2d": float(-9999.0),
	405	"buildings_3d": np.byte(-127),
	406	"bridges_2d": float(-9999.0),
	407	"building_id": int(-9999),
	408	"bridges_id": int(-9999),
	409	"building_type": np.byte(-127),
	410	"nsurface_fraction": int(-9999),
	411	"vegetation_type": np.byte(-127),
	412	"vegetation_height": float(-9999.0),
	413	"pavement_type": np.byte(-127),
	414	"water_type": np.byte(-127),
	415	"street_type": np.byte(-127),
	416	"street_crossings": np.byte(-127),
	417	"soil_type": np.byte(-127),
[3629]	418	"surface_fraction": float(-9999.0),
	419	"building_pars": float(-9999.0),
	420	"vegetation_pars": float(-9999.0),
	421	"tree_data": float(-9999.0),
	422	"tree_type": np.byte(-127)
[3567]	423	}
	424
	425	defaultvalues = {
	426	"lat": float(-9999.0),
	427	"lon": float(-9999.0),
	428	"E_UTM": float(-9999.0),
	429	"N_UTM": float(-9999.0),
	430	"zt": float(0.0),
	431	"buildings_2d": float(0.0),
	432	"buildings_3d": 0,
	433	"bridges_2d": float(0.0),
	434	"building_id": int(0),
	435	"bridges_id": int(0),
	436	"building_type": 1,
	437	"nsurface_fraction": int(-9999),
	438	"vegetation_type": 3,
	439	"vegetation_height": float(-9999.0),
	440	"pavement_type": 1,
	441	"water_type": 1,
	442	"street_type": 1,
	443	"street_crossings": 0,
	444	"soil_type": 1,
[3629]	445	"surface_fraction": float(0.0),
	446	"buildings_pars": float(-9999.0),
	447	"tree_data": float(-9999.0),
	448	"tree_type": 0,
	449	"vegetation_pars": float(-9999.0)
[3567]	450	}
	451
	452	# Read configuration file and set parameters accordingly
	453	read_config_file()
	454
	455
	456	filename = []
	457	ii = []
	458	ii_parent = []
	459	# Define indices and filenames for all domains and create netCDF files
	460	for i in range(0,ndomains):
	461
	462	# Calculate indices and input files
	463	ii.append(input_px.index(domain_px[i]))
[3668]	464	filename.append(path_out + "/" + filename_out + "_" + domain_names[i])
[3567]	465	if domain_parent[i] is not None:
[3726]	466	ii_parent.append(input_px.index(domain_px[domain_names.index(domain_parent[i])]))
[3567]	467	else:
	468	ii_parent.append(None)
	469
	470
[3726]	471	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)
	472	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)
	473	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)
	474	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]	475
[3726]	476	# Calculate position of origin
	477	x_UTM_origin = float(x_UTM[0,0]) - 0.5 * (float(x_UTM[0,1]) - float(x_UTM[0,0]))
	478	y_UTM_origin = float(y_UTM[0,0]) - 0.5 * (float(y_UTM[1,0]) - float(y_UTM[0,0]))
	479	x_origin = float(lon[0,0]) - 0.5 * (float(lon[0,1]) - float(lon[0,0]))
	480	y_origin = float(lat[0,0]) - 0.5 * (float(lat[1,0]) - float(lat[0,0]))
	481
[3567]	482	# Create NetCDF output file and set global attributes
	483	nc_create_file(filename[i])
[3726]	484	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]	485
[3688]	486	del x_UTM, y_UTM, lat, lon
[3567]	487
	488	# Process terrain height
	489	for i in range(0,ndomains):
	490
	491	# Read and write terrain height (zt)
	492	zt = nc_read_from_file_2d(input_file_zt[ii[i]], 'Band1', domain_x0[i], domain_x1[i], domain_y0[i], domain_y1[i])
	493
	494	# Final step: add zt array to the global array
	495	zt_all.append(zt)
	496	del zt
	497
	498	# Calculate the global (all domains) minimum of the terrain height. This value will be substracted for all
	499	# data sets
	500	zt_min = min(zt_all[0].flatten())
	501	for i in range(0,ndomains):
	502	zt_min = min(zt_min,min(zt_all[i].flatten()))
	503
	504	del zt_all[:]
	505
[3726]	506	print( "Shift terrain heights by -" + str(zt_min))
[3567]	507	for i in range(0,ndomains):
	508
	509	# Read and write terrain height (zt)
	510	zt = nc_read_from_file_2d(input_file_zt[ii[i]], 'Band1', domain_x0[i], domain_x1[i], domain_y0[i], domain_y1[i])
	511	x = nc_read_from_file_1d(input_file_x[ii[i]], "x", domain_x0[i], domain_x1[i])
	512	y = nc_read_from_file_1d(input_file_y[ii[i]], "y", domain_y0[i], domain_y1[i])
	513
[3726]	514
[3567]	515	zt = zt - zt_min
[3668]	516
	517	nc_write_global_attribute(filename[i], 'origin_z', float(zt_min))
[3567]	518
	519	# If necessary, interpolate parent domain terrain height on child domain grid and blend the two
	520	if domain_ip[i]:
[3726]	521	parent_id = domain_names.index(domain_parent[i])
	522	tmp_x0 = int( domain_x0[i] * domain_px[i] / domain_px[parent_id] ) - 1
	523	tmp_y0 = int( domain_y0[i] * domain_px[i] / domain_px[parent_id] ) - 1
	524	tmp_x1 = int( domain_x1[i] * domain_px[i] / domain_px[parent_id] ) + 1
	525	tmp_y1 = int( domain_y1[i] * domain_px[i] / domain_px[parent_id] ) + 1
	526
[3567]	527	tmp_x = nc_read_from_file_1d(input_file_x[ii_parent[i]], "x", tmp_x0, tmp_x1)
	528	tmp_y = nc_read_from_file_1d(input_file_y[ii_parent[i]], "y", tmp_y0, tmp_y1)
	529
	530	zt_parent = nc_read_from_file_2d(input_file_zt[ii_parent[i]], 'Band1', tmp_x0, tmp_x1, tmp_y0, tmp_y1)
	531
	532	zt_parent = zt_parent - zt_min
	533
	534	# Interpolate array and bring to PALM grid of child domain
	535	zt_ip = interpolate_2d(zt_parent,tmp_x,tmp_y,x,y)
[3726]	536	zt_ip = bring_to_palm_grid(zt_ip,x,y,domain_dz[parent_id])
	537
	538
	539	# Shift the child terrain height according to the parent mean terrain height
[3773]	540	print("shifting: -" + str(np.mean(zt)) + " +" + str(np.mean(zt_ip)))
	541	#zt = zt - np.min(zt) + np.min(zt_ip)
[3726]	542	zt = zt - np.mean(zt) + np.mean(zt_ip)
[3567]	543
	544
	545	# Blend over the parent and child terrain height within a radius of 50 px
	546	zt = blend_array_2d(zt,zt_ip,50)
[3773]	547	# zt = zt_ip
[3567]	548
	549	# Final step: add zt array to the global array
[3726]	550
[3567]	551	zt_all.append(zt)
	552	del zt
	553
	554
	555	# Read and shift x and y coordinates, shift terrain height according to its minimum value and write all data
	556	# to file
	557	for i in range(0,ndomains):
	558	# Read horizontal grid variables from zt file and write them to output file
	559	x = nc_read_from_file_1d(input_file_x[ii[i]], "x", domain_x0[i], domain_x1[i])
	560	y = nc_read_from_file_1d(input_file_y[ii[i]], "y", domain_y0[i], domain_y1[i])
	561	x = x - min(x.flatten()) + domain_px[i]/2.0
	562	y = y - min(y.flatten()) + domain_px[i]/2.0
	563	nc_write_dimension(filename[i], 'x', x, datatypes["x"])
	564	nc_write_dimension(filename[i], 'y', y, datatypes["y"])
	565	nc_write_attribute(filename[i], 'x', 'long_name', 'x')
	566	nc_write_attribute(filename[i], 'x', 'standard_name','projection_x_coordinate')
	567	nc_write_attribute(filename[i], 'x', 'units', 'm')
	568	nc_write_attribute(filename[i], 'y', 'long_name', 'x')
	569	nc_write_attribute(filename[i], 'y', 'standard_name', 'projection_y_coordinate')
	570	nc_write_attribute(filename[i], 'y', 'units', 'm')
	571
	572	lat = nc_read_from_file_2d(input_file_lat[ii[i]], "Band1", domain_x0[i], domain_x1[i], domain_y0[i], domain_y1[i])
	573	lon = nc_read_from_file_2d(input_file_lon[ii[i]], "Band1", domain_x0[i], domain_x1[i], domain_y0[i], domain_y1[i])
	574
	575	nc_write_to_file_2d(filename[i], 'lat', lat, datatypes["lat"],'y','x',fillvalues["lat"])
	576	nc_write_to_file_2d(filename[i], 'lon', lon, datatypes["lon"],'y','x',fillvalues["lon"])
	577
	578	nc_write_attribute(filename[i], 'lat', 'long_name', 'latitude')
	579	nc_write_attribute(filename[i], 'lat', 'standard_name','latitude')
	580	nc_write_attribute(filename[i], 'lat', 'units', 'degrees_north')
	581
	582	nc_write_attribute(filename[i], 'lon', 'long_name', 'longitude')
	583	nc_write_attribute(filename[i], 'lon', 'standard_name','longitude')
	584	nc_write_attribute(filename[i], 'lon', 'units', 'degrees_east')
	585
	586	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])
	587	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]	588
[3567]	589
	590	nc_write_to_file_2d(filename[i], 'E_UTM', x_UTM, datatypes["E_UTM"],'y','x',fillvalues["E_UTM"])
	591	nc_write_to_file_2d(filename[i], 'N_UTM', y_UTM, datatypes["N_UTM"],'y','x',fillvalues["N_UTM"])
	592
	593	nc_write_attribute(filename[i], 'E_UTM', 'long_name', 'easting')
	594	nc_write_attribute(filename[i], 'E_UTM', 'standard_name','projection_x_coorindate')
	595	nc_write_attribute(filename[i], 'E_UTM', 'units', 'm')
	596
	597	nc_write_attribute(filename[i], 'N_UTM', 'long_name', 'northing')
	598	nc_write_attribute(filename[i], 'N_UTM', 'standard_name','projection_y_coorindate')
	599	nc_write_attribute(filename[i], 'N_UTM', 'units', 'm')
	600
	601	nc_write_crs(filename[i])
	602
	603
	604
	605	# If necessary, bring terrain height to PALM's vertical grid. This is either forced by the user or implicitly
	606	# by using interpolation for a child domain
	607	if domain_za[i]:
	608	zt_all[i] = bring_to_palm_grid(zt_all[i],x,y,domain_dz[i])
	609
	610	nc_write_to_file_2d(filename[i], 'zt', zt_all[i], datatypes["zt"],'y','x',fillvalues["zt"])
	611	nc_write_attribute(filename[i], 'zt', 'long_name', 'orography')
	612	nc_write_attribute(filename[i], 'zt', 'units', 'm')
	613	nc_write_attribute(filename[i], 'zt', 'res_orig', domain_px[i])
	614	nc_write_attribute(filename[i], 'zt', 'coordinates', 'E_UTM N_UTM lon lat')
	615	nc_write_attribute(filename[i], 'zt', 'grid_mapping', 'E_UTM N_UTM lon lat')
	616
	617	del zt_all
	618
	619
	620	# Process building height, id, and type
	621	for i in range(0,ndomains):
	622	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]	623
[3567]	624	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])
	625
	626	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]	627	building_type[building_type >= 254] = fillvalues["building_type"]
[3567]	628	building_type = np.where(building_type < 1,defaultvalues["building_type"],building_type)
	629
	630	check = check_arrays_2(buildings_2d,building_id,fillvalues["buildings_2d"],fillvalues["building_id"])
	631	if not check:
	632	buildings_2d = np.where(building_id != fillvalues["building_id"],buildings_2d,fillvalues["buildings_2d"])
	633	building_id = np.where(buildings_2d == fillvalues["buildings_2d"],fillvalues["building_id"],building_id)
	634	print("Data check #1 " + str(check_arrays_2(buildings_2d,building_id,fillvalues["buildings_2d"],fillvalues["building_id"])))
	635
	636	check = check_arrays_2(buildings_2d,building_type,fillvalues["buildings_2d"],fillvalues["building_type"])
	637	if not check:
	638	building_type = np.where(buildings_2d == fillvalues["buildings_2d"],fillvalues["building_type"],building_type)
	639	building_type = np.where((building_type == fillvalues["building_type"]) & (buildings_2d != fillvalues["buildings_2d"]),defaultvalues["building_type"],building_type)
	640	print("Data check #2 " + str(check_arrays_2(buildings_2d,building_type,fillvalues["buildings_2d"],fillvalues["building_type"])))
	641
[3688]	642
[3567]	643	nc_write_to_file_2d(filename[i], 'buildings_2d', buildings_2d, datatypes["buildings_2d"],'y','x',fillvalues["buildings_2d"])
	644	nc_write_attribute(filename[i], 'buildings_2d', 'long_name', 'buildings')
	645	nc_write_attribute(filename[i], 'buildings_2d', 'units', 'm')
	646	nc_write_attribute(filename[i], 'buildings_2d', 'res_orig', domain_px[i])
	647	nc_write_attribute(filename[i], 'buildings_2d', 'lod', 1)
	648	nc_write_attribute(filename[i], 'buildings_2d', 'coordinates', 'E_UTM N_UTM lon lat')
	649	nc_write_attribute(filename[i], 'buildings_2d', 'grid_mapping', 'E_UTM N_UTM lon lat')
	650
	651	nc_write_to_file_2d(filename[i], 'building_id', building_id, datatypes["building_id"],'y','x',fillvalues["building_id"])
	652	nc_write_attribute(filename[i], 'building_id', 'long_name', 'building id')
	653	nc_write_attribute(filename[i], 'building_id', 'units', '')
[3668]	654	nc_write_attribute(filename[i], 'building_id', 'res _orig', domain_px[i])
[3567]	655	nc_write_attribute(filename[i], 'building_id', 'coordinates', 'E_UTM N_UTM lon lat')
	656	nc_write_attribute(filename[i], 'building_id', 'grid_mapping', 'E_UTM N_UTM lon lat')
	657
	658	nc_write_to_file_2d(filename[i], 'building_type', building_type, datatypes["building_type"],'y','x',fillvalues["building_type"])
	659	nc_write_attribute(filename[i], 'building_type', 'long_name', 'building type')
	660	nc_write_attribute(filename[i], 'building_type', 'units', '')
	661	nc_write_attribute(filename[i], 'building_type', 'res_orig', domain_px[i])
	662	nc_write_attribute(filename[i], 'building_type', 'coordinates', 'E_UTM N_UTM lon lat')
	663	nc_write_attribute(filename[i], 'building_type', 'grid_mapping', 'E_UTM N_UTM lon lat')
	664
	665	del buildings_2d
	666	del building_id
	667	del building_type
	668
	669	# Create 3d buildings if necessary. In that course, read bridge objects and add them to building layer
	670	for i in range(0,ndomains):
	671
	672	if domain_3d[i]:
	673	x = nc_read_from_file_2d_all(filename[i], 'x')
	674	y = nc_read_from_file_2d_all(filename[i], 'y')
	675	buildings_2d = nc_read_from_file_2d_all(filename[i], 'buildings_2d')
	676	building_id = nc_read_from_file_2d_all(filename[i], 'building_id')
	677
	678	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])
	679	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])
	680
	681	bridges_2d = np.where(bridges_2d == 0.0,fillvalues["bridges_2d"],bridges_2d)
	682	building_id = np.where(bridges_2d == fillvalues["bridges_2d"],building_id,bridges_id)
	683
	684	if np.any(buildings_2d != fillvalues["buildings_2d"]):
	685	buildings_3d, z = make_3d_from_2d(buildings_2d,x,y,domain_dz[i])
	686	if np.any(bridges_2d != fillvalues["bridges_2d"]):
	687	buildings_3d = make_3d_from_bridges_2d(buildings_3d,bridges_2d,x,y,domain_dz[i],settings_bridge_width,fillvalues["bridges_2d"])
	688	else:
	689	print("Skipping creation of 3D bridges (no bridges in domain)")
	690
	691
	692	nc_write_dimension(filename[i], 'z', z, datatypes["z"])
	693	nc_write_attribute(filename[i], 'z', 'long_name', 'z')
	694	nc_write_attribute(filename[i], 'z', 'units', 'm')
[3726]	695
	696	nc_overwrite_to_file_2d(filename[i], 'building_id', building_id)
[3567]	697
	698	nc_write_to_file_3d(filename[i], 'buildings_3d', buildings_3d, datatypes["buildings_3d"],'z','y','x',fillvalues["buildings_3d"])
	699	nc_write_attribute(filename[i], 'buildings_3d', 'long_name', 'buildings 3d')
	700	nc_write_attribute(filename[i], 'buildings_3d', 'units', '')
	701	nc_write_attribute(filename[i], 'buildings_3d', 'res_orig', domain_px[i])
	702	nc_write_attribute(filename[i], 'buildings_3d', 'lod', 2)
	703
	704	del buildings_3d
	705
	706	else:
	707	print("Skipping creation of 3D buildings (no buildings in domain)")
	708
	709
[3629]	710	del bridges_2d, bridges_id, building_id, buildings_2d
[3567]	711
	712
	713
	714	# Read vegetation type, water_type, pavement_type, soil_type and make fields consistent
	715	for i in range(0,ndomains):
	716
	717	building_type = nc_read_from_file_2d_all(filename[i], 'building_type')
[3773]	718
[3567]	719	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])
	720	vegetation_type[vegetation_type == 255] = fillvalues["vegetation_type"]
	721	vegetation_type = np.where((vegetation_type < 1) & (vegetation_type != fillvalues["vegetation_type"]),defaultvalues["vegetation_type"],vegetation_type)
	722
	723	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])
	724	pavement_type[pavement_type == 255] = fillvalues["pavement_type"]
	725	pavement_type = np.where((pavement_type < 1) & (pavement_type != fillvalues["pavement_type"]),defaultvalues["pavement_type"],pavement_type)
	726
	727	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])
	728	water_type[water_type == 255] = fillvalues["water_type"]
	729	water_type = np.where((water_type < 1) & (water_type != fillvalues["water_type"]),defaultvalues["water_type"],water_type)
	730
	731	# to do: replace by real soil input data
	732	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])
	733	soil_type[soil_type == 255] = fillvalues["soil_type"]
	734	soil_type = np.where((soil_type < 1) & (soil_type != fillvalues["soil_type"]),defaultvalues["soil_type"],soil_type)
	735
	736	# Make arrays consistent
	737	# #1 Set vegetation type to missing for pixel where a pavement type is set
	738	vegetation_type = np.where((vegetation_type != fillvalues["vegetation_type"]) & (pavement_type != fillvalues["pavement_type"]),fillvalues["vegetation_type"],vegetation_type)
	739
	740	# #2 Set vegetation type to missing for pixel where a building type is set
	741	vegetation_type = np.where((vegetation_type != fillvalues["vegetation_type"]) & (building_type != fillvalues["building_type"]) ,fillvalues["vegetation_type"],vegetation_type)
	742
	743	# #3 Set vegetation type to missing for pixel where a building type is set
	744	vegetation_type = np.where((vegetation_type != fillvalues["vegetation_type"]) & (water_type != fillvalues["water_type"]),fillvalues["vegetation_type"],vegetation_type)
	745
	746	# #4 Remove pavement for pixels with buildings
	747	pavement_type = np.where((pavement_type != fillvalues["pavement_type"]) & (building_type != fillvalues["building_type"]),fillvalues["pavement_type"],pavement_type)
	748
[3773]	749	# #5 Remove pavement for pixels with water.
	750	pavement_type = np.where((pavement_type != fillvalues["pavement_type"]) & (water_type != fillvalues["water_type"]),fillvalues["pavement_type"],pavement_type)
	751
[3567]	752	# #6 Remove water for pixels with buildings
	753	water_type = np.where((water_type != fillvalues["water_type"]) & (building_type != fillvalues["building_type"]),fillvalues["water_type"],water_type)
[3773]	754
[3668]	755	# Correct vegetation_type when a vegetation height is available and is indicative of low vegeetation
	756	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])
	757
	758	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)
	759	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]	760
	761	# Check for consistency and fill empty fields with default vegetation type
	762	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"])
	763
	764	if test:
	765	vegetation_type = np.where(consistency_array == 0,defaultvalues["vegetation_type"],vegetation_type)
	766	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]	767
	768	# #7 to be removed: set default soil type everywhere
	769	soil_type = np.where((vegetation_type != fillvalues["vegetation_type"]) \| (pavement_type != fillvalues["pavement_type"]),defaultvalues["soil_type"],fillvalues["soil_type"])
	770
[3567]	771
[3668]	772	# Check for consistency and fill empty fields with default vegetation type
	773	consistency_array, test = check_consistency_3(vegetation_type,pavement_type,soil_type,fillvalues["vegetation_type"],fillvalues["pavement_type"],fillvalues["soil_type"])
	774
[3567]	775	# Create surface_fraction array
	776	x = nc_read_from_file_2d_all(filename[i], 'x')
	777	y = nc_read_from_file_2d_all(filename[i], 'y')
	778	nsurface_fraction = np.arange(0,3)
	779	surface_fraction = np.ones((len(nsurface_fraction),len(y),len(x)))
	780
	781	surface_fraction[0,:,:] = np.where(vegetation_type != fillvalues["vegetation_type"], 1.0, 0.0)
	782	surface_fraction[1,:,:] = np.where(pavement_type != fillvalues["pavement_type"], 1.0, 0.0)
	783	surface_fraction[2,:,:] = np.where(water_type != fillvalues["water_type"], 1.0, 0.0)
	784
	785	nc_write_dimension(filename[i], 'nsurface_fraction', nsurface_fraction, datatypes["nsurface_fraction"])
	786	nc_write_to_file_3d(filename[i], 'surface_fraction', surface_fraction, datatypes["surface_fraction"],'nsurface_fraction','y','x',fillvalues["surface_fraction"])
	787	nc_write_attribute(filename[i], 'surface_fraction', 'long_name', 'surface fraction')
	788	nc_write_attribute(filename[i], 'surface_fraction', 'units', '')
	789	nc_write_attribute(filename[i], 'surface_fraction', 'res_orig', domain_px[i])
	790	del surface_fraction
	791
	792	nc_write_to_file_2d(filename[i], 'vegetation_type', vegetation_type, datatypes["vegetation_type"],'y','x',fillvalues["vegetation_type"])
	793	nc_write_attribute(filename[i], 'vegetation_type', 'long_name', 'vegetation type')
	794	nc_write_attribute(filename[i], 'vegetation_type', 'units', '')
	795	nc_write_attribute(filename[i], 'vegetation_type', 'res_orig', domain_px[i])
	796	nc_write_attribute(filename[i], 'vegetation_type', 'coordinates', 'E_UTM N_UTM lon lat')
	797	nc_write_attribute(filename[i], 'vegetation_type', 'grid_mapping', 'E_UTM N_UTM lon lat')
	798	del vegetation_type
	799
	800	nc_write_to_file_2d(filename[i], 'pavement_type', pavement_type, datatypes["pavement_type"],'y','x',fillvalues["pavement_type"])
	801	nc_write_attribute(filename[i], 'pavement_type', 'long_name', 'pavement type')
	802	nc_write_attribute(filename[i], 'pavement_type', 'units', '')
	803	nc_write_attribute(filename[i], 'pavement_type', 'res_orig', domain_px[i])
	804	nc_write_attribute(filename[i], 'pavement_type', 'coordinates', 'E_UTM N_UTM lon lat')
	805	nc_write_attribute(filename[i], 'pavement_type', 'grid_mapping', 'E_UTM N_UTM lon lat')
	806	del pavement_type
	807
	808	nc_write_to_file_2d(filename[i], 'water_type', water_type, datatypes["water_type"],'y','x',fillvalues["water_type"])
	809	nc_write_attribute(filename[i], 'water_type', 'long_name', 'water type')
	810	nc_write_attribute(filename[i], 'water_type', 'units', '')
	811	nc_write_attribute(filename[i], 'water_type', 'res_orig', domain_px[i])
	812	nc_write_attribute(filename[i], 'water_type', 'coordinates', 'E_UTM N_UTM lon lat')
	813	nc_write_attribute(filename[i], 'water_type', 'grid_mapping', 'E_UTM N_UTM lon lat')
	814	del water_type
	815
	816	nc_write_to_file_2d(filename[i], 'soil_type', soil_type, datatypes["soil_type"],'y','x',fillvalues["soil_type"])
	817	nc_write_attribute(filename[i], 'soil_type', 'long_name', 'soil type')
	818	nc_write_attribute(filename[i], 'soil_type', 'units', '')
	819	nc_write_attribute(filename[i], 'soil_type', 'res_orig', domain_px[i])
	820	nc_write_attribute(filename[i], 'soil_type', 'coordinates', 'E_UTM N_UTM lon lat')
	821	nc_write_attribute(filename[i], 'soil_type', 'grid_mapping', 'E_UTM N_UTM lon lat')
	822	del soil_type
	823
[3629]	824	del x
	825	del y
[3567]	826
	827	# pixels with bridges get building_type = 7 = bridge. This does not change the _type setting for the under-bridge
[3668]	828	# area NOTE: when bridges are present the consistency check will fail at the moment
[3567]	829	if domain_3d[i]:
	830	if np.any(building_type != fillvalues["building_type"]):
	831
	832	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])
	833	bridges_2d = np.where(bridges_2d == 0.0,fillvalues["bridges_2d"],bridges_2d)
	834	building_type = np.where(bridges_2d != fillvalues["bridges_2d"],7,building_type)
	835	nc_overwrite_to_file_2d(filename[i], 'building_type', building_type)
	836
	837	del building_type
	838	del bridges_2d
	839
[3629]	840	# Read/write street type and street crossings
[3567]	841	for i in range(0,ndomains):
	842
	843	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])
	844	street_type[street_type == 255] = fillvalues["street_type"]
	845	street_type = np.where((street_type < 1) & (street_type != fillvalues["street_type"]),defaultvalues["street_type"],street_type)
[3773]	846
	847	pavement_type = nc_read_from_file_2d_all(filename[i], 'pavement_type')
	848	street_type = np.where((pavement_type == fillvalues["pavement_type"]),fillvalues["street_type"],street_type)
	849
[3567]	850	nc_write_to_file_2d(filename[i], 'street_type', street_type, datatypes["street_type"],'y','x',fillvalues["street_type"])
	851	nc_write_attribute(filename[i], 'street_type', 'long_name', 'street type')
	852	nc_write_attribute(filename[i], 'street_type', 'units', '')
	853	nc_write_attribute(filename[i], 'street_type', 'res_orig', domain_px[i])
	854	nc_write_attribute(filename[i], 'street_type', 'coordinates', 'E_UTM N_UTM lon lat')
	855	nc_write_attribute(filename[i], 'street_type', 'grid_mapping', 'E_UTM N_UTM lon lat')
	856	del street_type
	857
	858	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])
	859	street_crossings[street_crossings == 255] = fillvalues["street_crossings"]
	860	street_crossings = np.where((street_crossings < 1) & (street_crossings != fillvalues["street_crossings"]),defaultvalues["street_crossings"],street_crossings)
	861
[3726]	862	nc_write_to_file_2d(filename[i], 'street_crossing', street_crossings, datatypes["street_crossings"],'y','x',fillvalues["street_crossings"])
	863	nc_write_attribute(filename[i], 'street_crossing', 'long_name', 'street crossings')
	864	nc_write_attribute(filename[i], 'street_crossing', 'units', '')
	865	nc_write_attribute(filename[i], 'street_crossing', 'res_orig', domain_px[i])
	866	nc_write_attribute(filename[i], 'street_crossing', 'coordinates', 'E_UTM N_UTM lon lat')
	867	nc_write_attribute(filename[i], 'street_crossing', 'grid_mapping', 'E_UTM N_UTM lon lat')
[3567]	868	del street_crossings
[3629]	869
	870
	871	# Read/write vegetation on roofs
	872	for i in range(0,ndomains):
	873	if domain_green_roofs[i]:
	874	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])
	875	buildings_2d = nc_read_from_file_2d_all(filename[i], 'buildings_2d')
	876
	877
	878	x = nc_read_from_file_2d_all(filename[i], 'x')
	879	y = nc_read_from_file_2d_all(filename[i], 'y')
	880	nbuilding_pars = np.arange(0,46)
	881	building_pars = np.ones((len(nbuilding_pars),len(y),len(x)))
	882	building_pars[:,:,:] = fillvalues["building_pars"]
	883
	884	# assign green fraction on roofs
	885	building_pars[3,:,:] = np.where( (buildings_2d != fillvalues["buildings_2d"] ) & ( green_roofs != fillvalues["building_pars"] ),1.0,fillvalues["building_pars"])
	886
	887	# assign leaf area index for vegetation on roofs
	888	building_pars[4,:,:] = np.where( ( buildings_2d != fillvalues["buildings_2d"] ) & ( green_roofs == 1.0 ),settings_lai_roof_intensive,fillvalues["building_pars"])
	889	building_pars[4,:,:] = np.where( ( buildings_2d != fillvalues["buildings_2d"] ) & ( green_roofs == 2.0 ),settings_lai_roof_extensive,building_pars[4,:,:])
	890
	891
	892	nc_write_dimension(filename[i], 'nbuilding_pars', nbuilding_pars, datatypes["nbuilding_pars"])
	893	nc_write_to_file_3d(filename[i], 'building_pars', building_pars, datatypes["building_pars"],'nbuilding_pars','y','x',fillvalues["building_pars"])
	894	nc_write_attribute(filename[i], 'building_pars', 'long_name', 'building_pars')
	895	nc_write_attribute(filename[i], 'building_pars', 'units', '')
	896	nc_write_attribute(filename[i], 'building_pars', 'res_orig', domain_px[i])
	897	nc_write_attribute(filename[i], 'building_pars', 'coordinates', 'E_UTM N_UTM lon lat')
	898	nc_write_attribute(filename[i], 'building_pars', 'grid_mapping', 'E_UTM N_UTM lon lat')
	899
	900	del building_pars, buildings_2d, x, y
	901
	902
	903	# Read tree data and create LAD and BAD arrays using the canopy generator
	904	for i in range(0,ndomains):
[3668]	905	lai = nc_read_from_file_2d(input_file_lai[ii[i]], 'Band1', domain_x0[i], domain_x1[i], domain_y0[i], domain_y1[i])
	906
	907	vegetation_type = nc_read_from_file_2d_all(filename[i], 'vegetation_type')
	908
	909	lai = np.where(vegetation_type == fillvalues["vegetation_type"],fillvalues["vegetation_pars"],lai)
	910
[3629]	911
[3668]	912	x = nc_read_from_file_2d_all(filename[i], 'x')
	913	y = nc_read_from_file_2d_all(filename[i], 'y')
	914	nvegetation_pars = np.arange(0,12)
	915	vegetation_pars = np.ones((len(nvegetation_pars),len(y),len(x)))
	916	vegetation_pars[:,:,:] = fillvalues["vegetation_pars"]
[3629]	917
[3668]	918	vegetation_pars[1,:,:] = lai
[3629]	919
[3668]	920
	921	# Write out first version of LAI. Will later be overwritten.
	922	nc_write_dimension(filename[i], 'nvegetation_pars', nvegetation_pars, datatypes["nvegetation_pars"])
	923	nc_write_to_file_3d(filename[i], 'vegetation_pars', vegetation_pars, datatypes["vegetation_pars"],'nvegetation_pars','y','x',fillvalues["vegetation_pars"])
	924	nc_write_attribute(filename[i], 'vegetation_pars', 'long_name', 'vegetation_pars')
	925	nc_write_attribute(filename[i], 'vegetation_pars', 'units', '')
	926	nc_write_attribute(filename[i], 'vegetation_pars', 'res_orig', domain_px[i])
	927	nc_write_attribute(filename[i], 'vegetation_pars', 'coordinates', 'E_UTM N_UTM lon lat')
	928	nc_write_attribute(filename[i], 'vegetation_pars', 'grid_mapping', 'E_UTM N_UTM lon lat')
	929
	930	del lai, vegetation_pars, vegetation_type
	931
	932	# Read tree data and create LAD and BAD arrays using the canopy generator
	933	for i in range(0,ndomains):
	934	if domain_street_trees[i]:
	935
	936	vegetation_pars = nc_read_from_file_2d_all(filename[i], 'vegetation_pars')
	937
	938	lai = np.copy(vegetation_pars[1,:,:])
	939
[3629]	940	x = nc_read_from_file_2d_all(filename[i], 'x')
	941	y = nc_read_from_file_2d_all(filename[i], 'y')
	942
[3668]	943	# Save lai data as default for low and high vegetation
	944	lai_low = lai
	945	lai_high = lai
	946
	947	# Read all tree parameters from file
	948	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]	949
[3668]	950	if (input_file_tree_crown_diameter[ii[i]] is not None):
	951	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])
	952	tree_crown_diameter = np.where( (tree_crown_diameter == 0.0) \| (tree_crown_diameter == -1.0) ,fillvalues["tree_data"],tree_crown_diameter)
	953	else:
	954	tree_crown_diameter = np.ones((len(y),len(x)))
	955	tree_crown_diameter[:,:] = fillvalues["tree_data"]
[3629]	956
[3668]	957
[3629]	958	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])
	959	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])
	960	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])
	961
	962	# Remove missing values from the data. Reasonable values will be set by the tree generator
	963	tree_height = np.where( (tree_height == 0.0) \| (tree_height == -1.0) ,fillvalues["tree_data"],tree_height)
	964	tree_trunk_diameter = np.where( (tree_trunk_diameter == 0.0) \| (tree_trunk_diameter == -1.0) ,fillvalues["tree_data"],tree_trunk_diameter)
	965	tree_type = np.where( (tree_type == 0.0) \| (tree_type == -1.0) ,fillvalues["tree_data"],tree_type)
	966	patch_height = np.where( patch_height == -1.0 ,fillvalues["tree_data"],patch_height)
	967
	968	# Convert trunk diameter from cm to m
	969	tree_trunk_diameter = np.where(tree_trunk_diameter != fillvalues["tree_data"], tree_trunk_diameter * 0.01,tree_trunk_diameter)
	970
	971
	972	# Temporarily change missing value for tree_type
	973	tree_type = np.where( (tree_type == fillvalues["tree_type"]),fillvalues["tree_data"],tree_type)
	974
	975	# Compare patch height array with vegetation type and correct accordingly
	976	vegetation_type = nc_read_from_file_2d_all(filename[i], 'vegetation_type')
	977
	978
	979	# For zero-height patches, set vegetation_type to short grass and remove these pixels from the patch height array
	980	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)
[3668]	981	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)
	982
[3629]	983
	984	max_tree_height = max(tree_height.flatten())
	985	max_patch_height = max(patch_height.flatten())
	986
	987	if ( (max_tree_height != fillvalues["tree_data"]) \| (max_patch_height == fillvalues["tree_data"]) ):
[3668]	988
[3629]	989	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"])
	990
	991
	992	# Remove LAD volumes that are inside buildings
	993	buildings_2d = nc_read_from_file_2d_all(filename[i], 'buildings_2d')
	994	for k in range(0,len(zlad)-1):
	995
	996	lad[k,:,:] = np.where(buildings_2d == fillvalues["buildings_2d"],lad[k,:,:],fillvalues["tree_data"])
	997	bad[k,:,:] = np.where(buildings_2d == fillvalues["buildings_2d"],bad[k,:,:],fillvalues["tree_data"])
	998	tree_ids[k,:,:] = np.where(buildings_2d == fillvalues["buildings_2d"],tree_ids[k,:,:],fillvalues["tree_data"])
[3668]	999
	1000	del buildings_2d
[3629]	1001
	1002	nc_write_dimension(filename[i], 'zlad', zlad, datatypes["tree_data"])
	1003	nc_write_to_file_3d(filename[i], 'lad', lad, datatypes["tree_data"],'zlad','y','x',fillvalues["tree_data"])
	1004	nc_write_attribute(filename[i], 'lad', 'long_name', 'leaf area density')
	1005	nc_write_attribute(filename[i], 'lad', 'units', '')
	1006	nc_write_attribute(filename[i], 'lad', 'res_orig', domain_px[i])
	1007	nc_write_attribute(filename[i], 'lad', 'coordinates', 'E_UTM N_UTM lon lat')
	1008	nc_write_attribute(filename[i], 'lad', 'grid_mapping', 'E_UTM N_UTM lon lat')
	1009
	1010	nc_write_to_file_3d(filename[i], 'bad', bad, datatypes["tree_data"],'zlad','y','x',fillvalues["tree_data"])
	1011	nc_write_attribute(filename[i], 'bad', 'long_name', 'basal area density')
	1012	nc_write_attribute(filename[i], 'bad', 'units', '')
	1013	nc_write_attribute(filename[i], 'bad', 'res_orig', domain_px[i])
	1014	nc_write_attribute(filename[i], 'bad', 'coordinates', 'E_UTM N_UTM lon lat')
	1015	nc_write_attribute(filename[i], 'bad', 'grid_mapping', 'E_UTM N_UTM lon lat')
	1016
	1017	nc_write_to_file_3d(filename[i], 'tree_id', tree_ids, datatypes["tree_data"],'zlad','y','x',fillvalues["tree_data"])
	1018	nc_write_attribute(filename[i], 'tree_id', 'long_name', 'tree id')
	1019	nc_write_attribute(filename[i], 'tree_id', 'units', '')
	1020	nc_write_attribute(filename[i], 'tree_id', 'res_orig', domain_px[i])
	1021	nc_write_attribute(filename[i], 'tree_id', 'coordinates', 'E_UTM N_UTM lon lat')
	1022	nc_write_attribute(filename[i], 'tree_id', 'grid_mapping', 'E_UTM N_UTM lon lat')
	1023
[3668]	1024	del lai, lad, bad, tree_ids, zlad
[3629]	1025
[3668]	1026	del vegetation_pars, tree_height, tree_crown_diameter, tree_trunk_diameter, tree_type, patch_height, x, y
[3629]	1027
	1028
	1029	# Create vegetation patches for locations with high vegetation type
	1030	for i in range(0,ndomains):
	1031	if domain_canopy_patches[i]:
	1032
	1033	# Load vegetation_type and lad array (at level z = 0) for re-processing
	1034	vegetation_type = nc_read_from_file_2d_all(filename[i], 'vegetation_type')
	1035	lad = nc_read_from_file_3d_all(filename[i], 'lad')
[3668]	1036	zlad = nc_read_from_file_1d_all(filename[i], 'zlad')
[3629]	1037	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])
	1038	vegetation_pars = nc_read_from_file_3d_all(filename[i], 'vegetation_pars')
	1039	lai = vegetation_pars[1,:,:]
[3726]	1040
[3629]	1041
[3726]	1042	# 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
[3668]	1043	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"])
[3629]	1044
[3726]	1045	# Now, assign either the default LAI for high vegetation or keep 1.0 from the lai_high array.
	1046	lai_high = np.where( (lai_high != fillvalues["tree_data"]) & (lai == fillvalues["tree_data"]), settings_lai_high_default, lai_high)
[3668]	1047
[3726]	1048	# If lai values are available in the lai array, write them on the lai_high array
	1049	lai_high = np.where( (lai_high != fillvalues["tree_data"]) & (lai != fillvalues["tree_data"]), lai, lai_high)
	1050
[3668]	1051	# Define a patch height wherever it is missing, but where a high vegetation LAI was set
[3629]	1052	patch_height = np.where ( (lai_high != fillvalues["tree_data"]) & (patch_height == fillvalues["tree_data"]), settings_patch_height_default, patch_height)
	1053
	1054	# Remove pixels where street trees were already set
	1055	patch_height = np.where ( (lad[0,:,:] != fillvalues["tree_data"]), fillvalues["tree_data"], patch_height)
	1056
[3668]	1057	# Remove patch heights that have no lai_high value
	1058	patch_height = np.where ( (lai_high == fillvalues["tree_data"]), fillvalues["tree_data"], patch_height)
	1059
[3629]	1060	# For missing LAI values, set either the high vegetation default or the low vegetation default
[3668]	1061	lai_high = np.where( (patch_height > 2.0) & (patch_height != fillvalues["tree_data"]) & (lai_high == fillvalues["tree_data"]),settings_lai_high_default,lai_high)
	1062	lai_high = np.where( (patch_height <= 2.0) & (patch_height != fillvalues["tree_data"]) & (lai_high == fillvalues["tree_data"]),settings_lai_low_default,lai_high)
	1063
[3629]	1064	if ( max(patch_height.flatten()) >= (2.0 * domain_dz[i]) ):
[3726]	1065	print(" start calculating LAD (this might take some time)")
	1066
	1067
[3668]	1068	lad_patch, patch_nz, status = process_patch(domain_dz[i],patch_height,max(zlad),lai_high,settings_lai_alpha,settings_lai_beta)
[3629]	1069
	1070	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,:,:] )
	1071
	1072	# Remove high vegetation wherever it is replaced by a leaf area density. This should effectively remove all high vegetation pixels
	1073	vegetation_type = np.where((lad[0,:,:] != fillvalues["tree_data"]) & (vegetation_type != fillvalues["vegetation_type"]),3,vegetation_type)
[3668]	1074
[3629]	1075	# If desired, remove all high vegetation. TODO: check if this is still necessary
[3688]	1076	if not domain_high_vegetation[i]:
[3629]	1077	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)
	1078
	1079
[3668]	1080	# Set default low LAI for pixels with an LAD (short grass below trees)
	1081	lai_low = np.where( (lad[0,:,:] == fillvalues["tree_data"]), lai, settings_lai_low_default)
[3629]	1082
[3668]	1083	# Fill low vegetation pixels without LAI set or with LAI = 0 with default value
	1084	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]	1085
[3668]	1086	# Remove lai for pixels that have no vegetation_type
	1087	lai_low = np.where( vegetation_type != fillvalues["vegetation_type"], lai_low, fillvalues["tree_data"])
	1088
[3629]	1089	# Overwrite lai in vegetation_parameters
[3668]	1090	vegetation_pars[1,:,:] = np.copy(lai_low)
[3629]	1091	nc_overwrite_to_file_3d(filename[i], 'vegetation_pars', vegetation_pars)
	1092
	1093	# Overwrite lad array
	1094	nc_overwrite_to_file_3d(filename[i], 'lad', lad)
[3668]	1095
	1096	nc_overwrite_to_file_2d(filename[i], 'vegetation_type', vegetation_type)
	1097
[3629]	1098
[3668]	1099	del vegetation_type, lad, lai, patch_height, vegetation_pars, zlad
	1100
	1101	# Final consistency check
	1102	for i in range(0,ndomains):
	1103	vegetation_type = nc_read_from_file_2d_all(filename[i], 'vegetation_type')
	1104	pavement_type = nc_read_from_file_2d_all(filename[i], 'pavement_type')
	1105	building_type = nc_read_from_file_2d_all(filename[i], 'building_type')
	1106	water_type = nc_read_from_file_2d_all(filename[i], 'water_type')
	1107	soil_type = nc_read_from_file_2d_all(filename[i], 'soil_type')
	1108
	1109	# Check for consistency and fill empty fields with default vegetation type
	1110	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"])
	1111
	1112	# Check for consistency and fill empty fields with default vegetation type
	1113	consistency_array, test = check_consistency_3(vegetation_type,pavement_type,soil_type,fillvalues["vegetation_type"],fillvalues["pavement_type"],fillvalues["soil_type"])
	1114
	1115	surface_fraction = nc_read_from_file_3d_all(filename[i], 'surface_fraction')
	1116	surface_fraction[0,:,:] = np.where(vegetation_type != fillvalues["vegetation_type"], 1.0, 0.0)
	1117	surface_fraction[1,:,:] = np.where(pavement_type != fillvalues["pavement_type"], 1.0, 0.0)
	1118	surface_fraction[2,:,:] = np.where(water_type != fillvalues["water_type"], 1.0, 0.0)
	1119	nc_overwrite_to_file_3d(filename[i], 'surface_fraction', surface_fraction)
	1120
	1121	del vegetation_type, pavement_type, building_type, water_type, soil_type

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