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source: palm/trunk/SCRIPTS/palm_csd @ 4084

Last change on this file since 4084 was 3955, checked in by maronga, 6 years ago
bugfixes in palm_csd
Property svn:executable set to ``* Property svn:keywords set to `Id`
File size: 57.1 KB

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 3955 2019-05-07 09:55:25Z knoop $
[3955]	27	# Bugfix in preparaing of green roofs
	28	# Bugfix: remove LAI for bare soils
	29	#
	30	# 3859 2019-04-03 20:30:31Z maronga
[3859]	31	# Bugfix: wrong variable naming for 'y'
	32	#
	33	# 3773 2019-03-01 08:56:57Z maronga
[3773]	34	# Unspecificed changes
	35	#
	36	# 3726 2019-02-07 18:22:49Z maronga
[3726]	37	# Removed some more bugs
	38	#
	39	# 3688 2019-01-22 10:44:20Z maronga
[3688]	40	# Some unspecified bugfixes
	41	#
	42	# 3668 2019-01-14 12:49:24Z maronga
[3668]	43	# Various improvements and bugfixes
	44	#
	45	# 3629 2018-12-13 12:18:54Z maronga
[3629]	46	# Added canopy generator calls. Some improvements
	47	#
	48	# 3567 2018-11-27 13:59:21Z maronga
[3567]	49	# Initial revisions
	50	#
	51	# Description:
	52	# ------------
	53	# Processing tool for creating PIDS conform static drivers from rastered NetCDF
	54	# input
	55	#
	56	# @Author Bjoern Maronga (maronga@muk.uni-hannover.de)
	57	#
	58	# @todo Make input files optional
	59	# @todo Allow for ASCII input of terrain height and building height
	60	# @todo Modularize reading config file
[3688]	61	# @todo Convert to object-oriented treatment (buidings, trees)
	62	# @todo Automatically shift child domains so that their origin lies intersects
	63	# a edge note of the parent grid
[3567]	64	#------------------------------------------------------------------------------#
	65
	66	from palm_csd_files.palm_csd_netcdf_interface import *
	67	from palm_csd_files.palm_csd_tools import *
[3629]	68	from palm_csd_files.palm_csd_canopy_generator import *
[3567]	69	import numpy as np
	70
	71
	72	def read_config_file():
	73
	74	import configparser
	75	from math import floor
	76	import numpy as np
	77	import os
	78	import subprocess as sub
	79	import sys
	80
	81	# Check if configuration files exists and quit otherwise
	82	input_config = ".csd.config"
	83	for i in range(1,len(sys.argv)):
	84	input_config = str(sys.argv[i])
	85
	86	config = configparser.RawConfigParser(allow_no_value=True)
	87
	88	if ( os.path.isfile(input_config) == False ):
	89	print ("Error. No configuration file " + input_config + " found.")
	90	raise SystemExit
	91
	92	config.read(input_config)
	93
	94
	95	# Definition of settings
[3668]	96
[3567]	97	global settings_bridge_width
[3629]	98	global settings_lai_roof_intensive
	99	global settings_lai_roof_extensive
	100	global settings_season
	101	global settings_lai_low_default
	102	global settings_lai_high_default
	103	global settings_patch_height_default
	104	global settings_lai_alpha
	105	global settings_lai_beta
[3955]	106	global settings_veg_type_below_trees
[3567]	107	global ndomains
	108
	109	# Definition of global configuration parameters
	110	global global_acronym
	111	global global_angle
	112	global global_author
	113	global global_campaign
	114	global global_comment
	115	global global_contact
	116	global global_data_content
	117	global global_dependencies
	118	global global_institution
	119	global global_keywords
	120	global global_location
	121	global global_palm_version
	122	global global_references
	123	global global_site
	124	global global_source
	125
[3668]	126	global path_out
	127	global filename_out
	128	global version_out
[3567]	129
[3668]	130
[3567]	131	# Definition of domain parameters
	132	global domain_names
	133	global domain_px
	134	global domain_x0
	135	global domain_y0
	136	global domain_x1
	137	global domain_y1
	138	global domain_nx
	139	global domain_ny
	140	global domain_dz
	141	global domain_3d
[3629]	142	global domain_high_vegetation
[3567]	143	global domain_ip
	144	global domain_za
	145	global domain_parent
[3629]	146	global domain_green_roofs
	147	global domain_street_trees
	148	global domain_canopy_patches
[3567]	149
	150	# Definition of input data parameters
	151	global input_names
	152	global input_px
	153
	154
	155	global input_file_x
	156	global input_file_y
	157	global input_file_x_UTM
	158	global input_file_y_UTM
	159	global input_file_lat
	160	global input_file_lon
	161	global input_file_zt
	162	global input_file_buildings_2d
	163	global input_file_bridges_2d
	164	global input_file_building_id
	165	global input_file_bridges_id
	166	global input_file_building_type
	167	global input_file_building_type
[3629]	168	global input_file_lai
[3567]	169	global input_file_vegetation_type
	170	global input_file_vegetation_height
	171	global input_file_pavement_type
	172	global input_file_water_type
	173	global input_file_street_type
	174	global input_file_street_crossings
	175	global input_file_soil_type
[3629]	176	global input_file_vegetation_on_roofs
	177	global input_file_tree_crown_diameter
	178	global input_file_tree_height
	179	global input_file_tree_trunk_diameter
	180	global input_file_tree_type
	181	global input_file_patch_height
[3567]	182
	183	global zt_all
	184	global zt_min
	185
	186	settings_bridge_width = 3.0
[3955]	187	settings_lai_roof_intensive = 2.5
	188	settings_lai_roof_extensive = 0.8
[3629]	189	settings_season = "summer"
	190	settings_lai_high_default = 6.0
	191	settings_lai_low_default = 1.0
	192	settings_patch_height_default = 10.0
	193	settings_lai_alpha = 5.0
	194	settings_lai_beta = 3.0
[3955]	195	settings_veg_type_below_trees = 3
[3567]	196	ndomains = 0
	197
	198	global_acronym = ""
	199	global_angle = ""
	200	global_author = ""
	201	global_campaign = ""
	202	global_comment = ""
	203	global_contact = ""
	204	global_data_content = ""
	205	global_dependencies = ""
	206	global_institution = ""
	207	global_keywords = ""
	208	global_location = ""
	209	global_palm_version = 6.0
	210	global_references = ""
	211	global_site = ""
	212	global_source = ""
	213
[3668]	214	path_out = ""
	215	version_out = 1
	216	filename_out = "default"
	217
[3567]	218	domain_names = []
	219	domain_px = []
	220	domain_x0 = []
	221	domain_y0 = []
	222	domain_x1 = []
	223	domain_y1 = []
	224	domain_nx = []
	225	domain_ny = []
	226	domain_dz = []
	227	domain_3d = []
[3629]	228	domain_high_vegetation = []
[3567]	229	domain_ip = []
	230	domain_za = []
	231	domain_parent = []
[3629]	232	domain_green_roofs = []
	233	domain_street_trees = []
	234	domain_canopy_patches = []
[3567]	235
	236	zt_min = 0.0
	237	zt_all = []
	238
	239	input_names = []
	240	input_px = []
	241
	242	input_file_x = []
	243	input_file_y = []
	244	input_file_x_UTM = []
	245	input_file_y_UTM = []
	246	input_file_lat = []
	247	input_file_lon = []
	248
	249	input_file_zt = []
	250	input_file_buildings_2d = []
	251	input_file_bridges_2d = []
	252	input_file_building_id = []
	253	input_file_bridges_id = []
	254	input_file_building_type = []
[3629]	255	input_file_lai = []
[3567]	256	input_file_vegetation_type = []
	257	input_file_vegetation_height = []
	258	input_file_pavement_type = []
	259	input_file_water_type = []
	260	input_file_street_type = []
	261	input_file_street_crossings = []
	262	input_file_soil_type = []
[3629]	263	input_file_vegetation_on_roofs = []
	264	input_file_tree_crown_diameter = []
	265	input_file_tree_height = []
	266	input_file_tree_trunk_diameter = []
	267	input_file_tree_type = []
	268	input_file_patch_height = []
[3567]	269
	270	# Load all user parameters from config file
	271	for i in range(0,len(config.sections())):
	272
	273	read_tmp = config.sections()[i]
	274
	275	if ( read_tmp == 'global' ):
	276	global_acronym = config.get(read_tmp, 'acronym')
	277	global_angle = float(config.get(read_tmp, 'rotation_angle'))
	278	global_author = config.get(read_tmp, 'author')
	279	global_campaign = config.get(read_tmp, 'campaign')
	280	global_comment = config.get(read_tmp, 'comment')
	281	global_contact = config.get(read_tmp, 'contact_person')
	282	global_data_content = config.get(read_tmp, 'data_content')
	283	global_dependencies = config.get(read_tmp, 'dependencies')
	284	global_institution = config.get(read_tmp, 'institution')
	285	global_keywords = config.get(read_tmp, 'keywords')
	286	global_location = config.get(read_tmp, 'location')
	287	global_palm_version = float(config.get(read_tmp, 'palm_version'))
	288	global_references = config.get(read_tmp, 'references')
	289	global_site = config.get(read_tmp, 'site')
	290	global_source = config.get(read_tmp, 'source')
	291
[3668]	292
[3567]	293	if ( read_tmp == 'settings' ):
[3629]	294	settings_lai_roof_intensive = config.get(read_tmp, 'lai_roof_intensive')
	295	settings_lai_roof_extensive = config.get(read_tmp, 'lai_roof_extensive')
	296	settings_bridge_width = float(config.get(read_tmp, 'bridge_width'))
	297	settings_season = config.get(read_tmp, 'season')
	298	settings_lai_high_default = float(config.get(read_tmp, 'lai_high_vegetation_default'))
	299	settings_lai_low_default = float(config.get(read_tmp, 'lai_low_vegetation_default'))
	300	settings_patch_height_default = float(config.get(read_tmp, 'patch_height_default'))
	301	settings_lai_alpha = float(config.get(read_tmp, 'lai_alpha'))
	302	settings_lai_beta = float(config.get(read_tmp, 'lai_beta'))
[3955]	303	settings_veg_type_below_trees = config.get(read_tmp, 'vegetation_type_below_trees')
[3629]	304
[3668]	305	if ( read_tmp == 'output' ):
	306	path_out = config.get(read_tmp, 'path')
	307	filename_out = config.get(read_tmp, 'file_out')
	308	version_out = float(config.get(read_tmp, 'version'))
	309
[3567]	310	if ( read_tmp.split("_")[0] == 'domain' ):
	311	ndomains = ndomains + 1
	312	domain_names.append(read_tmp.split("_")[1])
	313	domain_px.append(float(config.get(read_tmp, 'pixel_size')))
	314	domain_nx.append(int(config.get(read_tmp, 'nx')))
	315	domain_ny.append(int(config.get(read_tmp, 'ny')))
	316	domain_dz.append(float(config.get(read_tmp, 'dz')))
	317	domain_3d.append(config.getboolean(read_tmp, 'buildings_3d'))
[3629]	318	domain_high_vegetation.append(config.getboolean(read_tmp, 'allow_high_vegetation'))
	319	domain_canopy_patches.append(config.getboolean(read_tmp, 'generate_vegetation_patches'))
[3567]	320	domain_ip.append(config.getboolean(read_tmp, 'interpolate_terrain'))
	321	domain_za.append(config.getboolean(read_tmp, 'use_palm_z_axis'))
	322	if domain_ip[ndomains-1] and not domain_za[ndomains-1]:
	323	domain_za[ndomains-1] = True
	324	print("+++ Overwrite user setting for use_palm_z_axis")
	325
	326	domain_parent.append(config.get(read_tmp, 'domain_parent'))
	327
	328	domain_x0.append(int(floor(float(config.get(read_tmp, 'origin_x'))/float(config.get(read_tmp, 'pixel_size')))))
	329	domain_y0.append(int(floor(float(config.get(read_tmp, 'origin_y'))/float(config.get(read_tmp, 'pixel_size')))))
	330	domain_x1.append(int(floor(float(config.get(read_tmp, 'origin_x'))/float(config.get(read_tmp, 'pixel_size'))) + int(config.get(read_tmp, 'nx'))))
	331	domain_y1.append(int(floor(float(config.get(read_tmp, 'origin_y'))/float(config.get(read_tmp, 'pixel_size'))) + int(config.get(read_tmp, 'ny'))))
[3629]	332	domain_green_roofs.append(config.getboolean(read_tmp, 'vegetation_on_roofs'))
	333	domain_street_trees.append(config.getboolean(read_tmp, 'street_trees'))
	334
[3567]	335	if ( read_tmp.split("_")[0] == 'input' ):
	336	input_names.append(read_tmp.split("_")[1])
	337	input_px.append(float(config.get(read_tmp, 'pixel_size')))
	338	input_file_x.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_x'))
	339	input_file_y.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_y'))
	340	input_file_lat.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_lat'))
	341	input_file_lon.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_lon'))
	342	input_file_x_UTM.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_x_UTM'))
	343	input_file_y_UTM.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_y_UTM'))
	344	input_file_zt.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_zt'))
	345	input_file_buildings_2d.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_buildings_2d'))
	346	input_file_bridges_2d.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_bridges_2d'))
	347	input_file_building_id.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_building_id'))
	348	input_file_bridges_id.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_bridges_id'))
[3629]	349	input_file_building_type.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_building_type'))
	350	input_file_lai.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_lai'))
[3567]	351	input_file_vegetation_type.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_vegetation_type'))
	352	input_file_vegetation_height.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_vegetation_height'))
	353	input_file_pavement_type.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_pavement_type'))
	354	input_file_water_type.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_water_type'))
	355	input_file_street_type.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_street_type'))
[3629]	356	input_file_street_crossings.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_street_crossings'))
	357	input_file_patch_height.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_patch_height'))
[3668]	358
	359	tmp = config.get(read_tmp, 'file_tree_crown_diameter')
	360	if tmp is not None:
	361	input_file_tree_crown_diameter.append(config.get(read_tmp, 'path') + "/" + tmp)
	362	else:
	363	input_file_tree_crown_diameter.append(None)
[3629]	364	input_file_tree_height.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_tree_height'))
	365	input_file_tree_trunk_diameter.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_tree_trunk_diameter'))
	366	input_file_tree_type.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_tree_type'))
	367	input_file_vegetation_on_roofs.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_vegetation_on_roofs'))
[3567]	368	#input_file_soil_type.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_soil_type'))
	369	return 0
	370
	371
	372	############################################################
	373
	374	# Start of main program
	375	datatypes = {
	376	"x": "f4",
	377	"y": "f4",
	378	"z": "f4",
	379	"lat": "f4",
	380	"lon": "f4",
	381	"E_UTM": "f4",
	382	"N_UTM": "f4",
	383	"zt": "f4",
	384	"buildings_2d": "f4",
	385	"buildings_3d": "b",
	386	"bridges_2d": "f4",
	387	"building_id": "i",
	388	"bridges_id": "i",
	389	"building_type": "b",
	390	"nsurface_fraction": "i",
	391	"vegetation_type": "b",
	392	"vegetation_height": "f4",
	393	"pavement_type": "b",
	394	"water_type": "b",
	395	"street_type": "b",
	396	"street_crossings": "b",
	397	"soil_type": "b",
[3629]	398	"surface_fraction": "f4",
	399	"building_pars": "f4",
	400	"vegetation_pars": "f4",
	401	"tree_data": "f4",
	402	"tree_type": "b",
	403	"nbuilding_pars": "i",
	404	"nvegetation_pars": "i",
	405	"zlad": "f4"
[3567]	406	}
	407
	408	fillvalues = {
	409	"lat": float(-9999.0),
	410	"lon": float(-9999.0),
	411	"E_UTM": float(-9999.0),
	412	"N_UTM": float(-9999.0),
	413	"zt": float(-9999.0),
	414	"buildings_2d": float(-9999.0),
	415	"buildings_3d": np.byte(-127),
	416	"bridges_2d": float(-9999.0),
	417	"building_id": int(-9999),
	418	"bridges_id": int(-9999),
	419	"building_type": np.byte(-127),
	420	"nsurface_fraction": int(-9999),
	421	"vegetation_type": np.byte(-127),
	422	"vegetation_height": float(-9999.0),
	423	"pavement_type": np.byte(-127),
	424	"water_type": np.byte(-127),
	425	"street_type": np.byte(-127),
	426	"street_crossings": np.byte(-127),
	427	"soil_type": np.byte(-127),
[3629]	428	"surface_fraction": float(-9999.0),
	429	"building_pars": float(-9999.0),
	430	"vegetation_pars": float(-9999.0),
	431	"tree_data": float(-9999.0),
	432	"tree_type": np.byte(-127)
[3567]	433	}
	434
	435	defaultvalues = {
	436	"lat": float(-9999.0),
	437	"lon": float(-9999.0),
	438	"E_UTM": float(-9999.0),
	439	"N_UTM": float(-9999.0),
	440	"zt": float(0.0),
	441	"buildings_2d": float(0.0),
	442	"buildings_3d": 0,
	443	"bridges_2d": float(0.0),
	444	"building_id": int(0),
	445	"bridges_id": int(0),
	446	"building_type": 1,
	447	"nsurface_fraction": int(-9999),
	448	"vegetation_type": 3,
	449	"vegetation_height": float(-9999.0),
	450	"pavement_type": 1,
	451	"water_type": 1,
	452	"street_type": 1,
	453	"street_crossings": 0,
	454	"soil_type": 1,
[3629]	455	"surface_fraction": float(0.0),
	456	"buildings_pars": float(-9999.0),
	457	"tree_data": float(-9999.0),
	458	"tree_type": 0,
	459	"vegetation_pars": float(-9999.0)
[3567]	460	}
	461
	462	# Read configuration file and set parameters accordingly
	463	read_config_file()
	464
	465
	466	filename = []
	467	ii = []
	468	ii_parent = []
	469	# Define indices and filenames for all domains and create netCDF files
	470	for i in range(0,ndomains):
	471
	472	# Calculate indices and input files
	473	ii.append(input_px.index(domain_px[i]))
[3668]	474	filename.append(path_out + "/" + filename_out + "_" + domain_names[i])
[3567]	475	if domain_parent[i] is not None:
[3726]	476	ii_parent.append(input_px.index(domain_px[domain_names.index(domain_parent[i])]))
[3567]	477	else:
	478	ii_parent.append(None)
	479
	480
[3726]	481	x_UTM = nc_read_from_file_2d(input_file_x[ii[i]], "Band1", domain_x0[i], domain_x0[i]+1, domain_y0[i], domain_y0[i]+1)
	482	y_UTM = nc_read_from_file_2d(input_file_y[ii[i]], "Band1", domain_x0[i], domain_x0[i]+1, domain_y0[i], domain_y0[i]+1)
	483	lat = nc_read_from_file_2d(input_file_lat[ii[i]], "Band1", domain_x0[i], domain_x0[i]+1, domain_y0[i], domain_y0[i]+1)
	484	lon = nc_read_from_file_2d(input_file_lon[ii[i]], "Band1", domain_x0[i], domain_x0[i]+1, domain_y0[i], domain_y0[i]+1)
[3567]	485
[3726]	486	# Calculate position of origin
	487	x_UTM_origin = float(x_UTM[0,0]) - 0.5 * (float(x_UTM[0,1]) - float(x_UTM[0,0]))
	488	y_UTM_origin = float(y_UTM[0,0]) - 0.5 * (float(y_UTM[1,0]) - float(y_UTM[0,0]))
	489	x_origin = float(lon[0,0]) - 0.5 * (float(lon[0,1]) - float(lon[0,0]))
	490	y_origin = float(lat[0,0]) - 0.5 * (float(lat[1,0]) - float(lat[0,0]))
	491
[3567]	492	# Create NetCDF output file and set global attributes
	493	nc_create_file(filename[i])
[3726]	494	nc_write_global_attributes(filename[i],x_UTM_origin,y_UTM_origin,y_origin,x_origin,"",global_acronym,global_angle,global_author,global_campaign,global_comment,global_contact,global_data_content,global_dependencies,global_institution,global_keywords,global_location,global_palm_version,global_references,global_site,global_source,version_out)
[3567]	495
[3688]	496	del x_UTM, y_UTM, lat, lon
[3567]	497
	498	# Process terrain height
	499	for i in range(0,ndomains):
	500
	501	# Read and write terrain height (zt)
	502	zt = nc_read_from_file_2d(input_file_zt[ii[i]], 'Band1', domain_x0[i], domain_x1[i], domain_y0[i], domain_y1[i])
	503
	504	# Final step: add zt array to the global array
	505	zt_all.append(zt)
	506	del zt
	507
	508	# Calculate the global (all domains) minimum of the terrain height. This value will be substracted for all
	509	# data sets
	510	zt_min = min(zt_all[0].flatten())
	511	for i in range(0,ndomains):
	512	zt_min = min(zt_min,min(zt_all[i].flatten()))
	513
	514	del zt_all[:]
	515
[3726]	516	print( "Shift terrain heights by -" + str(zt_min))
[3567]	517	for i in range(0,ndomains):
	518
	519	# Read and write terrain height (zt)
	520	zt = nc_read_from_file_2d(input_file_zt[ii[i]], 'Band1', domain_x0[i], domain_x1[i], domain_y0[i], domain_y1[i])
	521	x = nc_read_from_file_1d(input_file_x[ii[i]], "x", domain_x0[i], domain_x1[i])
	522	y = nc_read_from_file_1d(input_file_y[ii[i]], "y", domain_y0[i], domain_y1[i])
	523
[3726]	524
[3567]	525	zt = zt - zt_min
[3668]	526
	527	nc_write_global_attribute(filename[i], 'origin_z', float(zt_min))
[3567]	528
	529	# If necessary, interpolate parent domain terrain height on child domain grid and blend the two
	530	if domain_ip[i]:
[3726]	531	parent_id = domain_names.index(domain_parent[i])
	532	tmp_x0 = int( domain_x0[i] * domain_px[i] / domain_px[parent_id] ) - 1
	533	tmp_y0 = int( domain_y0[i] * domain_px[i] / domain_px[parent_id] ) - 1
	534	tmp_x1 = int( domain_x1[i] * domain_px[i] / domain_px[parent_id] ) + 1
	535	tmp_y1 = int( domain_y1[i] * domain_px[i] / domain_px[parent_id] ) + 1
	536
[3567]	537	tmp_x = nc_read_from_file_1d(input_file_x[ii_parent[i]], "x", tmp_x0, tmp_x1)
	538	tmp_y = nc_read_from_file_1d(input_file_y[ii_parent[i]], "y", tmp_y0, tmp_y1)
	539
	540	zt_parent = nc_read_from_file_2d(input_file_zt[ii_parent[i]], 'Band1', tmp_x0, tmp_x1, tmp_y0, tmp_y1)
	541
	542	zt_parent = zt_parent - zt_min
	543
	544	# Interpolate array and bring to PALM grid of child domain
	545	zt_ip = interpolate_2d(zt_parent,tmp_x,tmp_y,x,y)
[3726]	546	zt_ip = bring_to_palm_grid(zt_ip,x,y,domain_dz[parent_id])
	547
	548
	549	# Shift the child terrain height according to the parent mean terrain height
[3773]	550	print("shifting: -" + str(np.mean(zt)) + " +" + str(np.mean(zt_ip)))
	551	#zt = zt - np.min(zt) + np.min(zt_ip)
[3726]	552	zt = zt - np.mean(zt) + np.mean(zt_ip)
[3567]	553
	554
	555	# Blend over the parent and child terrain height within a radius of 50 px
	556	zt = blend_array_2d(zt,zt_ip,50)
[3773]	557	# zt = zt_ip
[3567]	558
	559	# Final step: add zt array to the global array
[3726]	560
[3567]	561	zt_all.append(zt)
	562	del zt
	563
	564
	565	# Read and shift x and y coordinates, shift terrain height according to its minimum value and write all data
	566	# to file
	567	for i in range(0,ndomains):
	568	# Read horizontal grid variables from zt file and write them to output file
	569	x = nc_read_from_file_1d(input_file_x[ii[i]], "x", domain_x0[i], domain_x1[i])
	570	y = nc_read_from_file_1d(input_file_y[ii[i]], "y", domain_y0[i], domain_y1[i])
	571	x = x - min(x.flatten()) + domain_px[i]/2.0
	572	y = y - min(y.flatten()) + domain_px[i]/2.0
	573	nc_write_dimension(filename[i], 'x', x, datatypes["x"])
	574	nc_write_dimension(filename[i], 'y', y, datatypes["y"])
	575	nc_write_attribute(filename[i], 'x', 'long_name', 'x')
	576	nc_write_attribute(filename[i], 'x', 'standard_name','projection_x_coordinate')
	577	nc_write_attribute(filename[i], 'x', 'units', 'm')
[3859]	578	nc_write_attribute(filename[i], 'y', 'long_name', 'y')
[3567]	579	nc_write_attribute(filename[i], 'y', 'standard_name', 'projection_y_coordinate')
	580	nc_write_attribute(filename[i], 'y', 'units', 'm')
	581
	582	lat = nc_read_from_file_2d(input_file_lat[ii[i]], "Band1", domain_x0[i], domain_x1[i], domain_y0[i], domain_y1[i])
	583	lon = nc_read_from_file_2d(input_file_lon[ii[i]], "Band1", domain_x0[i], domain_x1[i], domain_y0[i], domain_y1[i])
	584
	585	nc_write_to_file_2d(filename[i], 'lat', lat, datatypes["lat"],'y','x',fillvalues["lat"])
	586	nc_write_to_file_2d(filename[i], 'lon', lon, datatypes["lon"],'y','x',fillvalues["lon"])
	587
	588	nc_write_attribute(filename[i], 'lat', 'long_name', 'latitude')
	589	nc_write_attribute(filename[i], 'lat', 'standard_name','latitude')
	590	nc_write_attribute(filename[i], 'lat', 'units', 'degrees_north')
	591
	592	nc_write_attribute(filename[i], 'lon', 'long_name', 'longitude')
	593	nc_write_attribute(filename[i], 'lon', 'standard_name','longitude')
	594	nc_write_attribute(filename[i], 'lon', 'units', 'degrees_east')
	595
	596	x_UTM = nc_read_from_file_2d(input_file_x_UTM[ii[i]], "Band1", domain_x0[i], domain_x1[i], domain_y0[i], domain_y1[i])
	597	y_UTM = nc_read_from_file_2d(input_file_y_UTM[ii[i]], "Band1", domain_x0[i], domain_x1[i], domain_y0[i], domain_y1[i])
[3726]	598
[3567]	599
	600	nc_write_to_file_2d(filename[i], 'E_UTM', x_UTM, datatypes["E_UTM"],'y','x',fillvalues["E_UTM"])
	601	nc_write_to_file_2d(filename[i], 'N_UTM', y_UTM, datatypes["N_UTM"],'y','x',fillvalues["N_UTM"])
	602
	603	nc_write_attribute(filename[i], 'E_UTM', 'long_name', 'easting')
	604	nc_write_attribute(filename[i], 'E_UTM', 'standard_name','projection_x_coorindate')
	605	nc_write_attribute(filename[i], 'E_UTM', 'units', 'm')
	606
	607	nc_write_attribute(filename[i], 'N_UTM', 'long_name', 'northing')
	608	nc_write_attribute(filename[i], 'N_UTM', 'standard_name','projection_y_coorindate')
	609	nc_write_attribute(filename[i], 'N_UTM', 'units', 'm')
	610
	611	nc_write_crs(filename[i])
	612
	613
	614
	615	# If necessary, bring terrain height to PALM's vertical grid. This is either forced by the user or implicitly
	616	# by using interpolation for a child domain
	617	if domain_za[i]:
	618	zt_all[i] = bring_to_palm_grid(zt_all[i],x,y,domain_dz[i])
	619
	620	nc_write_to_file_2d(filename[i], 'zt', zt_all[i], datatypes["zt"],'y','x',fillvalues["zt"])
	621	nc_write_attribute(filename[i], 'zt', 'long_name', 'orography')
	622	nc_write_attribute(filename[i], 'zt', 'units', 'm')
	623	nc_write_attribute(filename[i], 'zt', 'res_orig', domain_px[i])
	624	nc_write_attribute(filename[i], 'zt', 'coordinates', 'E_UTM N_UTM lon lat')
	625	nc_write_attribute(filename[i], 'zt', 'grid_mapping', 'E_UTM N_UTM lon lat')
	626
	627	del zt_all
	628
	629
	630	# Process building height, id, and type
	631	for i in range(0,ndomains):
	632	buildings_2d = nc_read_from_file_2d(input_file_buildings_2d[ii[i]], 'Band1', domain_x0[i], domain_x1[i], domain_y0[i], domain_y1[i])
[3688]	633
[3567]	634	building_id = nc_read_from_file_2d(input_file_building_id[ii[i]], 'Band1', domain_x0[i], domain_x1[i], domain_y0[i], domain_y1[i])
	635
	636	building_type = nc_read_from_file_2d(input_file_building_type[ii[i]], 'Band1', domain_x0[i], domain_x1[i], domain_y0[i], domain_y1[i])
[3668]	637	building_type[building_type >= 254] = fillvalues["building_type"]
[3567]	638	building_type = np.where(building_type < 1,defaultvalues["building_type"],building_type)
	639
	640	check = check_arrays_2(buildings_2d,building_id,fillvalues["buildings_2d"],fillvalues["building_id"])
	641	if not check:
	642	buildings_2d = np.where(building_id != fillvalues["building_id"],buildings_2d,fillvalues["buildings_2d"])
	643	building_id = np.where(buildings_2d == fillvalues["buildings_2d"],fillvalues["building_id"],building_id)
	644	print("Data check #1 " + str(check_arrays_2(buildings_2d,building_id,fillvalues["buildings_2d"],fillvalues["building_id"])))
	645
	646	check = check_arrays_2(buildings_2d,building_type,fillvalues["buildings_2d"],fillvalues["building_type"])
	647	if not check:
	648	building_type = np.where(buildings_2d == fillvalues["buildings_2d"],fillvalues["building_type"],building_type)
	649	building_type = np.where((building_type == fillvalues["building_type"]) & (buildings_2d != fillvalues["buildings_2d"]),defaultvalues["building_type"],building_type)
	650	print("Data check #2 " + str(check_arrays_2(buildings_2d,building_type,fillvalues["buildings_2d"],fillvalues["building_type"])))
	651
[3688]	652
[3567]	653	nc_write_to_file_2d(filename[i], 'buildings_2d', buildings_2d, datatypes["buildings_2d"],'y','x',fillvalues["buildings_2d"])
	654	nc_write_attribute(filename[i], 'buildings_2d', 'long_name', 'buildings')
	655	nc_write_attribute(filename[i], 'buildings_2d', 'units', 'm')
	656	nc_write_attribute(filename[i], 'buildings_2d', 'res_orig', domain_px[i])
	657	nc_write_attribute(filename[i], 'buildings_2d', 'lod', 1)
	658	nc_write_attribute(filename[i], 'buildings_2d', 'coordinates', 'E_UTM N_UTM lon lat')
	659	nc_write_attribute(filename[i], 'buildings_2d', 'grid_mapping', 'E_UTM N_UTM lon lat')
	660
	661	nc_write_to_file_2d(filename[i], 'building_id', building_id, datatypes["building_id"],'y','x',fillvalues["building_id"])
	662	nc_write_attribute(filename[i], 'building_id', 'long_name', 'building id')
	663	nc_write_attribute(filename[i], 'building_id', 'units', '')
[3668]	664	nc_write_attribute(filename[i], 'building_id', 'res _orig', domain_px[i])
[3567]	665	nc_write_attribute(filename[i], 'building_id', 'coordinates', 'E_UTM N_UTM lon lat')
	666	nc_write_attribute(filename[i], 'building_id', 'grid_mapping', 'E_UTM N_UTM lon lat')
	667
	668	nc_write_to_file_2d(filename[i], 'building_type', building_type, datatypes["building_type"],'y','x',fillvalues["building_type"])
	669	nc_write_attribute(filename[i], 'building_type', 'long_name', 'building type')
	670	nc_write_attribute(filename[i], 'building_type', 'units', '')
	671	nc_write_attribute(filename[i], 'building_type', 'res_orig', domain_px[i])
	672	nc_write_attribute(filename[i], 'building_type', 'coordinates', 'E_UTM N_UTM lon lat')
	673	nc_write_attribute(filename[i], 'building_type', 'grid_mapping', 'E_UTM N_UTM lon lat')
	674
	675	del buildings_2d
	676	del building_id
	677	del building_type
	678
	679	# Create 3d buildings if necessary. In that course, read bridge objects and add them to building layer
	680	for i in range(0,ndomains):
	681
	682	if domain_3d[i]:
	683	x = nc_read_from_file_2d_all(filename[i], 'x')
	684	y = nc_read_from_file_2d_all(filename[i], 'y')
	685	buildings_2d = nc_read_from_file_2d_all(filename[i], 'buildings_2d')
	686	building_id = nc_read_from_file_2d_all(filename[i], 'building_id')
	687
	688	bridges_2d = nc_read_from_file_2d(input_file_bridges_2d[ii[i]], 'Band1', domain_x0[i], domain_x1[i], domain_y0[i], domain_y1[i])
	689	bridges_id = nc_read_from_file_2d(input_file_bridges_id[ii[i]], 'Band1', domain_x0[i], domain_x1[i], domain_y0[i], domain_y1[i])
	690
	691	bridges_2d = np.where(bridges_2d == 0.0,fillvalues["bridges_2d"],bridges_2d)
	692	building_id = np.where(bridges_2d == fillvalues["bridges_2d"],building_id,bridges_id)
	693
	694	if np.any(buildings_2d != fillvalues["buildings_2d"]):
	695	buildings_3d, z = make_3d_from_2d(buildings_2d,x,y,domain_dz[i])
	696	if np.any(bridges_2d != fillvalues["bridges_2d"]):
	697	buildings_3d = make_3d_from_bridges_2d(buildings_3d,bridges_2d,x,y,domain_dz[i],settings_bridge_width,fillvalues["bridges_2d"])
	698	else:
	699	print("Skipping creation of 3D bridges (no bridges in domain)")
	700
	701
	702	nc_write_dimension(filename[i], 'z', z, datatypes["z"])
	703	nc_write_attribute(filename[i], 'z', 'long_name', 'z')
	704	nc_write_attribute(filename[i], 'z', 'units', 'm')
[3726]	705
	706	nc_overwrite_to_file_2d(filename[i], 'building_id', building_id)
[3567]	707
	708	nc_write_to_file_3d(filename[i], 'buildings_3d', buildings_3d, datatypes["buildings_3d"],'z','y','x',fillvalues["buildings_3d"])
	709	nc_write_attribute(filename[i], 'buildings_3d', 'long_name', 'buildings 3d')
	710	nc_write_attribute(filename[i], 'buildings_3d', 'units', '')
	711	nc_write_attribute(filename[i], 'buildings_3d', 'res_orig', domain_px[i])
	712	nc_write_attribute(filename[i], 'buildings_3d', 'lod', 2)
	713
	714	del buildings_3d
	715
	716	else:
	717	print("Skipping creation of 3D buildings (no buildings in domain)")
	718
	719
[3629]	720	del bridges_2d, bridges_id, building_id, buildings_2d
[3567]	721
	722
	723
	724	# Read vegetation type, water_type, pavement_type, soil_type and make fields consistent
	725	for i in range(0,ndomains):
	726
	727	building_type = nc_read_from_file_2d_all(filename[i], 'building_type')
[3773]	728
[3567]	729	vegetation_type = nc_read_from_file_2d(input_file_vegetation_type[ii[i]], 'Band1', domain_x0[i], domain_x1[i], domain_y0[i], domain_y1[i])
	730	vegetation_type[vegetation_type == 255] = fillvalues["vegetation_type"]
	731	vegetation_type = np.where((vegetation_type < 1) & (vegetation_type != fillvalues["vegetation_type"]),defaultvalues["vegetation_type"],vegetation_type)
	732
	733	pavement_type = nc_read_from_file_2d(input_file_pavement_type[ii[i]], 'Band1', domain_x0[i], domain_x1[i], domain_y0[i], domain_y1[i])
	734	pavement_type[pavement_type == 255] = fillvalues["pavement_type"]
	735	pavement_type = np.where((pavement_type < 1) & (pavement_type != fillvalues["pavement_type"]),defaultvalues["pavement_type"],pavement_type)
	736
	737	water_type = nc_read_from_file_2d(input_file_water_type[ii[i]], 'Band1', domain_x0[i], domain_x1[i], domain_y0[i], domain_y1[i])
	738	water_type[water_type == 255] = fillvalues["water_type"]
	739	water_type = np.where((water_type < 1) & (water_type != fillvalues["water_type"]),defaultvalues["water_type"],water_type)
	740
	741	# to do: replace by real soil input data
	742	soil_type = nc_read_from_file_2d(input_file_vegetation_type[ii[i]], 'Band1', domain_x0[i], domain_x1[i], domain_y0[i], domain_y1[i])
	743	soil_type[soil_type == 255] = fillvalues["soil_type"]
	744	soil_type = np.where((soil_type < 1) & (soil_type != fillvalues["soil_type"]),defaultvalues["soil_type"],soil_type)
	745
	746	# Make arrays consistent
	747	# #1 Set vegetation type to missing for pixel where a pavement type is set
	748	vegetation_type = np.where((vegetation_type != fillvalues["vegetation_type"]) & (pavement_type != fillvalues["pavement_type"]),fillvalues["vegetation_type"],vegetation_type)
	749
	750	# #2 Set vegetation type to missing for pixel where a building type is set
	751	vegetation_type = np.where((vegetation_type != fillvalues["vegetation_type"]) & (building_type != fillvalues["building_type"]) ,fillvalues["vegetation_type"],vegetation_type)
	752
	753	# #3 Set vegetation type to missing for pixel where a building type is set
	754	vegetation_type = np.where((vegetation_type != fillvalues["vegetation_type"]) & (water_type != fillvalues["water_type"]),fillvalues["vegetation_type"],vegetation_type)
	755
	756	# #4 Remove pavement for pixels with buildings
	757	pavement_type = np.where((pavement_type != fillvalues["pavement_type"]) & (building_type != fillvalues["building_type"]),fillvalues["pavement_type"],pavement_type)
	758
[3773]	759	# #5 Remove pavement for pixels with water.
	760	pavement_type = np.where((pavement_type != fillvalues["pavement_type"]) & (water_type != fillvalues["water_type"]),fillvalues["pavement_type"],pavement_type)
	761
[3567]	762	# #6 Remove water for pixels with buildings
	763	water_type = np.where((water_type != fillvalues["water_type"]) & (building_type != fillvalues["building_type"]),fillvalues["water_type"],water_type)
[3773]	764
[3668]	765	# Correct vegetation_type when a vegetation height is available and is indicative of low vegeetation
	766	vegetation_height = nc_read_from_file_2d(input_file_vegetation_height[ii[i]], 'Band1', domain_x0[i], domain_x1[i], domain_y0[i], domain_y1[i])
	767
	768	vegetation_type = np.where((vegetation_height != fillvalues["vegetation_height"]) & (vegetation_height == 0.0) & ((vegetation_type == 4) \| (vegetation_type == 5) \| (vegetation_type == 6) \|(vegetation_type == 7) \| (vegetation_type == 17) \| (vegetation_type == 18)), 3, vegetation_type)
	769	vegetation_height = np.where((vegetation_height != fillvalues["vegetation_height"]) & (vegetation_height == 0.0) & ((vegetation_type == 4) \| (vegetation_type == 5) \| (vegetation_type == 6) \|(vegetation_type == 7) \| (vegetation_type == 17) \| (vegetation_type == 18)), fillvalues["vegetation_height"],vegetation_height)
[3567]	770
	771	# Check for consistency and fill empty fields with default vegetation type
	772	consistency_array, test = check_consistency_4(vegetation_type,building_type,pavement_type,water_type,fillvalues["vegetation_type"],fillvalues["building_type"],fillvalues["pavement_type"],fillvalues["water_type"])
	773
	774	if test:
	775	vegetation_type = np.where(consistency_array == 0,defaultvalues["vegetation_type"],vegetation_type)
	776	consistency_array, test = check_consistency_4(vegetation_type,building_type,pavement_type,water_type,fillvalues["vegetation_type"],fillvalues["building_type"],fillvalues["pavement_type"],fillvalues["water_type"])
[3668]	777
	778	# #7 to be removed: set default soil type everywhere
	779	soil_type = np.where((vegetation_type != fillvalues["vegetation_type"]) \| (pavement_type != fillvalues["pavement_type"]),defaultvalues["soil_type"],fillvalues["soil_type"])
	780
[3567]	781
[3668]	782	# Check for consistency and fill empty fields with default vegetation type
	783	consistency_array, test = check_consistency_3(vegetation_type,pavement_type,soil_type,fillvalues["vegetation_type"],fillvalues["pavement_type"],fillvalues["soil_type"])
	784
[3567]	785	# Create surface_fraction array
	786	x = nc_read_from_file_2d_all(filename[i], 'x')
	787	y = nc_read_from_file_2d_all(filename[i], 'y')
	788	nsurface_fraction = np.arange(0,3)
	789	surface_fraction = np.ones((len(nsurface_fraction),len(y),len(x)))
	790
	791	surface_fraction[0,:,:] = np.where(vegetation_type != fillvalues["vegetation_type"], 1.0, 0.0)
	792	surface_fraction[1,:,:] = np.where(pavement_type != fillvalues["pavement_type"], 1.0, 0.0)
	793	surface_fraction[2,:,:] = np.where(water_type != fillvalues["water_type"], 1.0, 0.0)
	794
	795	nc_write_dimension(filename[i], 'nsurface_fraction', nsurface_fraction, datatypes["nsurface_fraction"])
	796	nc_write_to_file_3d(filename[i], 'surface_fraction', surface_fraction, datatypes["surface_fraction"],'nsurface_fraction','y','x',fillvalues["surface_fraction"])
	797	nc_write_attribute(filename[i], 'surface_fraction', 'long_name', 'surface fraction')
	798	nc_write_attribute(filename[i], 'surface_fraction', 'units', '')
	799	nc_write_attribute(filename[i], 'surface_fraction', 'res_orig', domain_px[i])
	800	del surface_fraction
	801
	802	nc_write_to_file_2d(filename[i], 'vegetation_type', vegetation_type, datatypes["vegetation_type"],'y','x',fillvalues["vegetation_type"])
	803	nc_write_attribute(filename[i], 'vegetation_type', 'long_name', 'vegetation type')
	804	nc_write_attribute(filename[i], 'vegetation_type', 'units', '')
	805	nc_write_attribute(filename[i], 'vegetation_type', 'res_orig', domain_px[i])
	806	nc_write_attribute(filename[i], 'vegetation_type', 'coordinates', 'E_UTM N_UTM lon lat')
	807	nc_write_attribute(filename[i], 'vegetation_type', 'grid_mapping', 'E_UTM N_UTM lon lat')
	808	del vegetation_type
	809
	810	nc_write_to_file_2d(filename[i], 'pavement_type', pavement_type, datatypes["pavement_type"],'y','x',fillvalues["pavement_type"])
	811	nc_write_attribute(filename[i], 'pavement_type', 'long_name', 'pavement type')
	812	nc_write_attribute(filename[i], 'pavement_type', 'units', '')
	813	nc_write_attribute(filename[i], 'pavement_type', 'res_orig', domain_px[i])
	814	nc_write_attribute(filename[i], 'pavement_type', 'coordinates', 'E_UTM N_UTM lon lat')
	815	nc_write_attribute(filename[i], 'pavement_type', 'grid_mapping', 'E_UTM N_UTM lon lat')
	816	del pavement_type
	817
	818	nc_write_to_file_2d(filename[i], 'water_type', water_type, datatypes["water_type"],'y','x',fillvalues["water_type"])
	819	nc_write_attribute(filename[i], 'water_type', 'long_name', 'water type')
	820	nc_write_attribute(filename[i], 'water_type', 'units', '')
	821	nc_write_attribute(filename[i], 'water_type', 'res_orig', domain_px[i])
	822	nc_write_attribute(filename[i], 'water_type', 'coordinates', 'E_UTM N_UTM lon lat')
	823	nc_write_attribute(filename[i], 'water_type', 'grid_mapping', 'E_UTM N_UTM lon lat')
	824	del water_type
	825
	826	nc_write_to_file_2d(filename[i], 'soil_type', soil_type, datatypes["soil_type"],'y','x',fillvalues["soil_type"])
	827	nc_write_attribute(filename[i], 'soil_type', 'long_name', 'soil type')
	828	nc_write_attribute(filename[i], 'soil_type', 'units', '')
	829	nc_write_attribute(filename[i], 'soil_type', 'res_orig', domain_px[i])
	830	nc_write_attribute(filename[i], 'soil_type', 'coordinates', 'E_UTM N_UTM lon lat')
	831	nc_write_attribute(filename[i], 'soil_type', 'grid_mapping', 'E_UTM N_UTM lon lat')
	832	del soil_type
	833
[3629]	834	del x
	835	del y
[3567]	836
	837	# pixels with bridges get building_type = 7 = bridge. This does not change the _type setting for the under-bridge
[3668]	838	# area NOTE: when bridges are present the consistency check will fail at the moment
[3567]	839	if domain_3d[i]:
	840	if np.any(building_type != fillvalues["building_type"]):
	841
	842	bridges_2d = nc_read_from_file_2d(input_file_bridges_2d[ii[i]], 'Band1', domain_x0[i], domain_x1[i], domain_y0[i], domain_y1[i])
	843	bridges_2d = np.where(bridges_2d == 0.0,fillvalues["bridges_2d"],bridges_2d)
	844	building_type = np.where(bridges_2d != fillvalues["bridges_2d"],7,building_type)
	845	nc_overwrite_to_file_2d(filename[i], 'building_type', building_type)
	846
	847	del building_type
	848	del bridges_2d
	849
[3629]	850	# Read/write street type and street crossings
[3567]	851	for i in range(0,ndomains):
	852
	853	street_type = nc_read_from_file_2d(input_file_street_type[ii[i]], 'Band1', domain_x0[i], domain_x1[i], domain_y0[i], domain_y1[i])
	854	street_type[street_type == 255] = fillvalues["street_type"]
	855	street_type = np.where((street_type < 1) & (street_type != fillvalues["street_type"]),defaultvalues["street_type"],street_type)
[3773]	856
	857	pavement_type = nc_read_from_file_2d_all(filename[i], 'pavement_type')
	858	street_type = np.where((pavement_type == fillvalues["pavement_type"]),fillvalues["street_type"],street_type)
	859
[3567]	860	nc_write_to_file_2d(filename[i], 'street_type', street_type, datatypes["street_type"],'y','x',fillvalues["street_type"])
	861	nc_write_attribute(filename[i], 'street_type', 'long_name', 'street type')
	862	nc_write_attribute(filename[i], 'street_type', 'units', '')
	863	nc_write_attribute(filename[i], 'street_type', 'res_orig', domain_px[i])
	864	nc_write_attribute(filename[i], 'street_type', 'coordinates', 'E_UTM N_UTM lon lat')
	865	nc_write_attribute(filename[i], 'street_type', 'grid_mapping', 'E_UTM N_UTM lon lat')
	866	del street_type
	867
	868	street_crossings = nc_read_from_file_2d(input_file_street_crossings[ii[i]], 'Band1', domain_x0[i], domain_x1[i], domain_y0[i], domain_y1[i])
	869	street_crossings[street_crossings == 255] = fillvalues["street_crossings"]
	870	street_crossings = np.where((street_crossings < 1) & (street_crossings != fillvalues["street_crossings"]),defaultvalues["street_crossings"],street_crossings)
	871
[3726]	872	nc_write_to_file_2d(filename[i], 'street_crossing', street_crossings, datatypes["street_crossings"],'y','x',fillvalues["street_crossings"])
	873	nc_write_attribute(filename[i], 'street_crossing', 'long_name', 'street crossings')
	874	nc_write_attribute(filename[i], 'street_crossing', 'units', '')
	875	nc_write_attribute(filename[i], 'street_crossing', 'res_orig', domain_px[i])
	876	nc_write_attribute(filename[i], 'street_crossing', 'coordinates', 'E_UTM N_UTM lon lat')
	877	nc_write_attribute(filename[i], 'street_crossing', 'grid_mapping', 'E_UTM N_UTM lon lat')
[3567]	878	del street_crossings
[3629]	879
	880
	881	# Read/write vegetation on roofs
	882	for i in range(0,ndomains):
	883	if domain_green_roofs[i]:
	884	green_roofs = nc_read_from_file_2d(input_file_vegetation_on_roofs[ii[i]], 'Band1', domain_x0[i], domain_x1[i], domain_y0[i], domain_y1[i])
	885	buildings_2d = nc_read_from_file_2d_all(filename[i], 'buildings_2d')
	886
	887
	888	x = nc_read_from_file_2d_all(filename[i], 'x')
	889	y = nc_read_from_file_2d_all(filename[i], 'y')
	890	nbuilding_pars = np.arange(0,46)
	891	building_pars = np.ones((len(nbuilding_pars),len(y),len(x)))
	892	building_pars[:,:,:] = fillvalues["building_pars"]
	893
	894	# assign green fraction on roofs
[3955]	895	building_pars[3,:,:] = np.where( ( buildings_2d != fillvalues["buildings_2d"] ) & ( green_roofs != fillvalues["building_pars"] ) & ( green_roofs != 0.0 ),1,fillvalues["building_pars"] )
[3629]	896
	897	# assign leaf area index for vegetation on roofs
[3955]	898	building_pars[4,:,:] = np.where( ( buildings_2d != fillvalues["buildings_2d"] ) & ( green_roofs >= 0.5 ),settings_lai_roof_intensive,fillvalues["building_pars"])
	899	building_pars[4,:,:] = np.where( ( buildings_2d != fillvalues["buildings_2d"] ) & ( green_roofs < 0.5 ),settings_lai_roof_extensive,building_pars[4,:,:])
[3629]	900
	901
	902	nc_write_dimension(filename[i], 'nbuilding_pars', nbuilding_pars, datatypes["nbuilding_pars"])
	903	nc_write_to_file_3d(filename[i], 'building_pars', building_pars, datatypes["building_pars"],'nbuilding_pars','y','x',fillvalues["building_pars"])
	904	nc_write_attribute(filename[i], 'building_pars', 'long_name', 'building_pars')
	905	nc_write_attribute(filename[i], 'building_pars', 'units', '')
	906	nc_write_attribute(filename[i], 'building_pars', 'res_orig', domain_px[i])
	907	nc_write_attribute(filename[i], 'building_pars', 'coordinates', 'E_UTM N_UTM lon lat')
	908	nc_write_attribute(filename[i], 'building_pars', 'grid_mapping', 'E_UTM N_UTM lon lat')
	909
	910	del building_pars, buildings_2d, x, y
	911
	912
	913	# Read tree data and create LAD and BAD arrays using the canopy generator
	914	for i in range(0,ndomains):
[3668]	915	lai = nc_read_from_file_2d(input_file_lai[ii[i]], 'Band1', domain_x0[i], domain_x1[i], domain_y0[i], domain_y1[i])
	916
	917	vegetation_type = nc_read_from_file_2d_all(filename[i], 'vegetation_type')
	918
	919	lai = np.where(vegetation_type == fillvalues["vegetation_type"],fillvalues["vegetation_pars"],lai)
	920
[3629]	921
[3668]	922	x = nc_read_from_file_2d_all(filename[i], 'x')
	923	y = nc_read_from_file_2d_all(filename[i], 'y')
	924	nvegetation_pars = np.arange(0,12)
	925	vegetation_pars = np.ones((len(nvegetation_pars),len(y),len(x)))
	926	vegetation_pars[:,:,:] = fillvalues["vegetation_pars"]
[3629]	927
[3668]	928	vegetation_pars[1,:,:] = lai
[3629]	929
[3668]	930
	931	# Write out first version of LAI. Will later be overwritten.
	932	nc_write_dimension(filename[i], 'nvegetation_pars', nvegetation_pars, datatypes["nvegetation_pars"])
	933	nc_write_to_file_3d(filename[i], 'vegetation_pars', vegetation_pars, datatypes["vegetation_pars"],'nvegetation_pars','y','x',fillvalues["vegetation_pars"])
	934	nc_write_attribute(filename[i], 'vegetation_pars', 'long_name', 'vegetation_pars')
	935	nc_write_attribute(filename[i], 'vegetation_pars', 'units', '')
	936	nc_write_attribute(filename[i], 'vegetation_pars', 'res_orig', domain_px[i])
	937	nc_write_attribute(filename[i], 'vegetation_pars', 'coordinates', 'E_UTM N_UTM lon lat')
	938	nc_write_attribute(filename[i], 'vegetation_pars', 'grid_mapping', 'E_UTM N_UTM lon lat')
	939
	940	del lai, vegetation_pars, vegetation_type
	941
	942	# Read tree data and create LAD and BAD arrays using the canopy generator
	943	for i in range(0,ndomains):
	944	if domain_street_trees[i]:
	945
	946	vegetation_pars = nc_read_from_file_2d_all(filename[i], 'vegetation_pars')
	947
	948	lai = np.copy(vegetation_pars[1,:,:])
	949
[3629]	950	x = nc_read_from_file_2d_all(filename[i], 'x')
	951	y = nc_read_from_file_2d_all(filename[i], 'y')
	952
[3668]	953	# Save lai data as default for low and high vegetation
	954	lai_low = lai
	955	lai_high = lai
	956
	957	# Read all tree parameters from file
	958	tree_height = nc_read_from_file_2d(input_file_tree_height[ii[i]], 'Band1', domain_x0[i], domain_x1[i], domain_y0[i], domain_y1[i])
[3629]	959
[3668]	960	if (input_file_tree_crown_diameter[ii[i]] is not None):
	961	tree_crown_diameter = nc_read_from_file_2d(input_file_tree_crown_diameter[ii[i]], 'Band1', domain_x0[i], domain_x1[i], domain_y0[i], domain_y1[i])
	962	tree_crown_diameter = np.where( (tree_crown_diameter == 0.0) \| (tree_crown_diameter == -1.0) ,fillvalues["tree_data"],tree_crown_diameter)
	963	else:
	964	tree_crown_diameter = np.ones((len(y),len(x)))
	965	tree_crown_diameter[:,:] = fillvalues["tree_data"]
[3629]	966
[3668]	967
[3629]	968	tree_trunk_diameter = nc_read_from_file_2d(input_file_tree_trunk_diameter[ii[i]], 'Band1', domain_x0[i], domain_x1[i], domain_y0[i], domain_y1[i])
	969	tree_type = nc_read_from_file_2d(input_file_tree_type[ii[i]], 'Band1', domain_x0[i], domain_x1[i], domain_y0[i], domain_y1[i])
	970	patch_height = nc_read_from_file_2d(input_file_patch_height[ii[i]], 'Band1', domain_x0[i], domain_x1[i], domain_y0[i], domain_y1[i])
	971
	972	# Remove missing values from the data. Reasonable values will be set by the tree generator
	973	tree_height = np.where( (tree_height == 0.0) \| (tree_height == -1.0) ,fillvalues["tree_data"],tree_height)
	974	tree_trunk_diameter = np.where( (tree_trunk_diameter == 0.0) \| (tree_trunk_diameter == -1.0) ,fillvalues["tree_data"],tree_trunk_diameter)
	975	tree_type = np.where( (tree_type == 0.0) \| (tree_type == -1.0) ,fillvalues["tree_data"],tree_type)
	976	patch_height = np.where( patch_height == -1.0 ,fillvalues["tree_data"],patch_height)
	977
	978	# Convert trunk diameter from cm to m
	979	tree_trunk_diameter = np.where(tree_trunk_diameter != fillvalues["tree_data"], tree_trunk_diameter * 0.01,tree_trunk_diameter)
	980
	981
	982	# Temporarily change missing value for tree_type
	983	tree_type = np.where( (tree_type == fillvalues["tree_type"]),fillvalues["tree_data"],tree_type)
	984
	985	# Compare patch height array with vegetation type and correct accordingly
	986	vegetation_type = nc_read_from_file_2d_all(filename[i], 'vegetation_type')
	987
	988
	989	# For zero-height patches, set vegetation_type to short grass and remove these pixels from the patch height array
	990	vegetation_type = np.where( (patch_height == 0.0) & ( (vegetation_type == 4) \| (vegetation_type == 5) \| (vegetation_type == 6) \|(vegetation_type == 7) \| (vegetation_type == 17) \| (vegetation_type == 18) ),3,vegetation_type)
[3668]	991	patch_type = np.where( (patch_height == 0.0) & ( (vegetation_type == 4) \| (vegetation_type == 5) \| (vegetation_type == 6) \|(vegetation_type == 7) \| (vegetation_type == 17) \| (vegetation_type == 18) ),fillvalues["tree_data"],patch_height)
	992
[3629]	993
	994	max_tree_height = max(tree_height.flatten())
	995	max_patch_height = max(patch_height.flatten())
	996
	997	if ( (max_tree_height != fillvalues["tree_data"]) \| (max_patch_height == fillvalues["tree_data"]) ):
[3668]	998
[3629]	999	lad, bad, tree_ids, zlad = generate_single_tree_lad(x,y,domain_dz[i],max_tree_height,max_patch_height,tree_type,tree_height,tree_crown_diameter,tree_trunk_diameter,lai,settings_season,fillvalues["tree_data"])
	1000
	1001
	1002	# Remove LAD volumes that are inside buildings
	1003	buildings_2d = nc_read_from_file_2d_all(filename[i], 'buildings_2d')
	1004	for k in range(0,len(zlad)-1):
	1005
	1006	lad[k,:,:] = np.where(buildings_2d == fillvalues["buildings_2d"],lad[k,:,:],fillvalues["tree_data"])
	1007	bad[k,:,:] = np.where(buildings_2d == fillvalues["buildings_2d"],bad[k,:,:],fillvalues["tree_data"])
	1008	tree_ids[k,:,:] = np.where(buildings_2d == fillvalues["buildings_2d"],tree_ids[k,:,:],fillvalues["tree_data"])
[3668]	1009
	1010	del buildings_2d
[3629]	1011
	1012	nc_write_dimension(filename[i], 'zlad', zlad, datatypes["tree_data"])
	1013	nc_write_to_file_3d(filename[i], 'lad', lad, datatypes["tree_data"],'zlad','y','x',fillvalues["tree_data"])
	1014	nc_write_attribute(filename[i], 'lad', 'long_name', 'leaf area density')
	1015	nc_write_attribute(filename[i], 'lad', 'units', '')
	1016	nc_write_attribute(filename[i], 'lad', 'res_orig', domain_px[i])
	1017	nc_write_attribute(filename[i], 'lad', 'coordinates', 'E_UTM N_UTM lon lat')
	1018	nc_write_attribute(filename[i], 'lad', 'grid_mapping', 'E_UTM N_UTM lon lat')
	1019
	1020	nc_write_to_file_3d(filename[i], 'bad', bad, datatypes["tree_data"],'zlad','y','x',fillvalues["tree_data"])
	1021	nc_write_attribute(filename[i], 'bad', 'long_name', 'basal area density')
	1022	nc_write_attribute(filename[i], 'bad', 'units', '')
	1023	nc_write_attribute(filename[i], 'bad', 'res_orig', domain_px[i])
	1024	nc_write_attribute(filename[i], 'bad', 'coordinates', 'E_UTM N_UTM lon lat')
	1025	nc_write_attribute(filename[i], 'bad', 'grid_mapping', 'E_UTM N_UTM lon lat')
	1026
	1027	nc_write_to_file_3d(filename[i], 'tree_id', tree_ids, datatypes["tree_data"],'zlad','y','x',fillvalues["tree_data"])
	1028	nc_write_attribute(filename[i], 'tree_id', 'long_name', 'tree id')
	1029	nc_write_attribute(filename[i], 'tree_id', 'units', '')
	1030	nc_write_attribute(filename[i], 'tree_id', 'res_orig', domain_px[i])
	1031	nc_write_attribute(filename[i], 'tree_id', 'coordinates', 'E_UTM N_UTM lon lat')
	1032	nc_write_attribute(filename[i], 'tree_id', 'grid_mapping', 'E_UTM N_UTM lon lat')
	1033
[3668]	1034	del lai, lad, bad, tree_ids, zlad
[3629]	1035
[3668]	1036	del vegetation_pars, tree_height, tree_crown_diameter, tree_trunk_diameter, tree_type, patch_height, x, y
[3629]	1037
	1038
	1039	# Create vegetation patches for locations with high vegetation type
	1040	for i in range(0,ndomains):
	1041	if domain_canopy_patches[i]:
	1042
	1043	# Load vegetation_type and lad array (at level z = 0) for re-processing
	1044	vegetation_type = nc_read_from_file_2d_all(filename[i], 'vegetation_type')
	1045	lad = nc_read_from_file_3d_all(filename[i], 'lad')
[3668]	1046	zlad = nc_read_from_file_1d_all(filename[i], 'zlad')
[3629]	1047	patch_height = nc_read_from_file_2d(input_file_patch_height[ii[i]], 'Band1', domain_x0[i], domain_x1[i], domain_y0[i], domain_y1[i])
	1048	vegetation_pars = nc_read_from_file_3d_all(filename[i], 'vegetation_pars')
	1049	lai = vegetation_pars[1,:,:]
[3726]	1050
[3629]	1051
[3726]	1052	# Determine all pixels that do not already have an LAD but which are high vegetation to a dummy value of 1.0 and remove all other pixels
[3668]	1053	lai_high = np.where( (lad[0,:,:] == fillvalues["tree_data"]) & ( ( (vegetation_type == 4) \| (vegetation_type == 5) \| (vegetation_type == 6) \|(vegetation_type == 7) \| (vegetation_type == 17) \| (vegetation_type == 18) ) & ( (patch_height == fillvalues["tree_data"]) \| (patch_height >= domain_dz[i])) ),1.0,fillvalues["tree_data"])
[3629]	1054
[3726]	1055	# Now, assign either the default LAI for high vegetation or keep 1.0 from the lai_high array.
	1056	lai_high = np.where( (lai_high != fillvalues["tree_data"]) & (lai == fillvalues["tree_data"]), settings_lai_high_default, lai_high)
[3668]	1057
[3726]	1058	# If lai values are available in the lai array, write them on the lai_high array
	1059	lai_high = np.where( (lai_high != fillvalues["tree_data"]) & (lai != fillvalues["tree_data"]), lai, lai_high)
	1060
[3668]	1061	# Define a patch height wherever it is missing, but where a high vegetation LAI was set
[3629]	1062	patch_height = np.where ( (lai_high != fillvalues["tree_data"]) & (patch_height == fillvalues["tree_data"]), settings_patch_height_default, patch_height)
	1063
	1064	# Remove pixels where street trees were already set
	1065	patch_height = np.where ( (lad[0,:,:] != fillvalues["tree_data"]), fillvalues["tree_data"], patch_height)
	1066
[3668]	1067	# Remove patch heights that have no lai_high value
	1068	patch_height = np.where ( (lai_high == fillvalues["tree_data"]), fillvalues["tree_data"], patch_height)
	1069
[3629]	1070	# For missing LAI values, set either the high vegetation default or the low vegetation default
[3668]	1071	lai_high = np.where( (patch_height > 2.0) & (patch_height != fillvalues["tree_data"]) & (lai_high == fillvalues["tree_data"]),settings_lai_high_default,lai_high)
	1072	lai_high = np.where( (patch_height <= 2.0) & (patch_height != fillvalues["tree_data"]) & (lai_high == fillvalues["tree_data"]),settings_lai_low_default,lai_high)
	1073
[3629]	1074	if ( max(patch_height.flatten()) >= (2.0 * domain_dz[i]) ):
[3726]	1075	print(" start calculating LAD (this might take some time)")
	1076
	1077
[3668]	1078	lad_patch, patch_nz, status = process_patch(domain_dz[i],patch_height,max(zlad),lai_high,settings_lai_alpha,settings_lai_beta)
[3629]	1079
	1080	lad[0:patch_nz+1,:,:] = np.where( (lad[0:patch_nz+1,:,:] == fillvalues["tree_data"]),lad_patch[0:patch_nz+1,:,:], lad[0:patch_nz+1,:,:] )
	1081
	1082	# Remove high vegetation wherever it is replaced by a leaf area density. This should effectively remove all high vegetation pixels
[3955]	1083	vegetation_type = np.where((lad[0,:,:] != fillvalues["tree_data"]) & (vegetation_type != fillvalues["vegetation_type"]),settings_veg_type_below_trees,vegetation_type)
[3668]	1084
[3629]	1085	# If desired, remove all high vegetation. TODO: check if this is still necessary
[3688]	1086	if not domain_high_vegetation[i]:
[3629]	1087	vegetation_type = np.where((vegetation_type != fillvalues["vegetation_type"]) & ( (vegetation_type == 4) \| (vegetation_type == 5) \| (vegetation_type == 6) \|(vegetation_type == 7) \| (vegetation_type == 17) \| (vegetation_type == 18) ),3,vegetation_type)
	1088
	1089
[3668]	1090	# Set default low LAI for pixels with an LAD (short grass below trees)
	1091	lai_low = np.where( (lad[0,:,:] == fillvalues["tree_data"]), lai, settings_lai_low_default)
[3629]	1092
[3668]	1093	# Fill low vegetation pixels without LAI set or with LAI = 0 with default value
	1094	lai_low = np.where( ( (lai_low == fillvalues["tree_data"]) \| (lai_low == 0.0) ) & (vegetation_type != fillvalues["vegetation_type"] ), settings_lai_low_default, lai_low)
[3629]	1095
[3668]	1096	# Remove lai for pixels that have no vegetation_type
[3955]	1097	lai_low = np.where( ( (vegetation_type != fillvalues["vegetation_type"]) & (vegetation_type != 1) ), lai_low, fillvalues["tree_data"])
[3668]	1098
[3629]	1099	# Overwrite lai in vegetation_parameters
[3668]	1100	vegetation_pars[1,:,:] = np.copy(lai_low)
[3629]	1101	nc_overwrite_to_file_3d(filename[i], 'vegetation_pars', vegetation_pars)
	1102
	1103	# Overwrite lad array
	1104	nc_overwrite_to_file_3d(filename[i], 'lad', lad)
[3668]	1105
	1106	nc_overwrite_to_file_2d(filename[i], 'vegetation_type', vegetation_type)
	1107
[3629]	1108
[3668]	1109	del vegetation_type, lad, lai, patch_height, vegetation_pars, zlad
	1110
[3955]	1111
	1112	# Final adjustment of vegetation parameters: remove LAI where a bare soil was set
	1113	for i in range(0,ndomains):
	1114
	1115	vegetation_type = nc_read_from_file_2d_all(filename[i], 'vegetation_type')
	1116	vegetation_pars = nc_read_from_file_3d_all(filename[i], 'vegetation_pars')
	1117	lai = vegetation_pars[1,:,:]
	1118
	1119
	1120	# Remove lai for pixels that have no vegetation_type
	1121	lai = np.where( ( (vegetation_type != fillvalues["vegetation_type"]) & (vegetation_type != 1) ), lai, fillvalues["tree_data"])
	1122
	1123	# Overwrite lai in vegetation_parameters
	1124	vegetation_pars[1,:,:] = np.copy(lai)
	1125	nc_overwrite_to_file_3d(filename[i], 'vegetation_pars', vegetation_pars)
	1126
	1127	del vegetation_type, lai, vegetation_pars
	1128
	1129
[3668]	1130	# Final consistency check
	1131	for i in range(0,ndomains):
	1132	vegetation_type = nc_read_from_file_2d_all(filename[i], 'vegetation_type')
	1133	pavement_type = nc_read_from_file_2d_all(filename[i], 'pavement_type')
	1134	building_type = nc_read_from_file_2d_all(filename[i], 'building_type')
	1135	water_type = nc_read_from_file_2d_all(filename[i], 'water_type')
	1136	soil_type = nc_read_from_file_2d_all(filename[i], 'soil_type')
	1137
	1138	# Check for consistency and fill empty fields with default vegetation type
	1139	consistency_array, test = check_consistency_4(vegetation_type,building_type,pavement_type,water_type,fillvalues["vegetation_type"],fillvalues["building_type"],fillvalues["pavement_type"],fillvalues["water_type"])
	1140
	1141	# Check for consistency and fill empty fields with default vegetation type
	1142	consistency_array, test = check_consistency_3(vegetation_type,pavement_type,soil_type,fillvalues["vegetation_type"],fillvalues["pavement_type"],fillvalues["soil_type"])
	1143
	1144	surface_fraction = nc_read_from_file_3d_all(filename[i], 'surface_fraction')
	1145	surface_fraction[0,:,:] = np.where(vegetation_type != fillvalues["vegetation_type"], 1.0, 0.0)
	1146	surface_fraction[1,:,:] = np.where(pavement_type != fillvalues["pavement_type"], 1.0, 0.0)
	1147	surface_fraction[2,:,:] = np.where(water_type != fillvalues["water_type"], 1.0, 0.0)
	1148	nc_overwrite_to_file_3d(filename[i], 'surface_fraction', surface_fraction)
	1149
	1150	del vegetation_type, pavement_type, building_type, water_type, soil_type

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