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source: palm/trunk/SCRIPTS/palm_csd @ 3755

Last change on this file since 3755 was 3726, checked in by maronga, 6 years ago
bugfixes in palm_csd
Property svn:executable set to ``* Property svn:keywords set to `Id`
File size: 55.6 KB

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

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