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source: palm/trunk/SCRIPTS/palm_csd @ 4332

Last change on this file since 4332 was 4311, checked in by maronga, 5 years ago
bugfix in palm_csd regarding green roofs
Property svn:executable set to ``* Property svn:keywords set to `Id`
File size: 57.3 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 4311 2019-11-27 11:51:43Z suehring $
[4311]	27	# Bugfix: green roofs were assigned to all available roofs when switched on
	28	#
	29	# 4149 2019-08-08 14:02:18Z suehring
[4149]	30	# Bugfix, nbuilding_pars was 1 element to small
	31	#
	32	# 3955 2019-05-07 09:55:25Z maronga
[3955]	33	# Bugfix in preparaing of green roofs
	34	# Bugfix: remove LAI for bare soils
	35	#
	36	# 3859 2019-04-03 20:30:31Z maronga
[3859]	37	# Bugfix: wrong variable naming for 'y'
	38	#
	39	# 3773 2019-03-01 08:56:57Z maronga
[3773]	40	# Unspecificed changes
	41	#
	42	# 3726 2019-02-07 18:22:49Z maronga
[3726]	43	# Removed some more bugs
	44	#
	45	# 3688 2019-01-22 10:44:20Z maronga
[3688]	46	# Some unspecified bugfixes
	47	#
	48	# 3668 2019-01-14 12:49:24Z maronga
[3668]	49	# Various improvements and bugfixes
	50	#
	51	# 3629 2018-12-13 12:18:54Z maronga
[3629]	52	# Added canopy generator calls. Some improvements
	53	#
	54	# 3567 2018-11-27 13:59:21Z maronga
[3567]	55	# Initial revisions
	56	#
	57	# Description:
	58	# ------------
	59	# Processing tool for creating PIDS conform static drivers from rastered NetCDF
	60	# input
	61	#
	62	# @Author Bjoern Maronga (maronga@muk.uni-hannover.de)
	63	#
	64	# @todo Make input files optional
	65	# @todo Allow for ASCII input of terrain height and building height
	66	# @todo Modularize reading config file
[3688]	67	# @todo Convert to object-oriented treatment (buidings, trees)
	68	# @todo Automatically shift child domains so that their origin lies intersects
	69	# a edge note of the parent grid
[3567]	70	#------------------------------------------------------------------------------#
	71
	72	from palm_csd_files.palm_csd_netcdf_interface import *
	73	from palm_csd_files.palm_csd_tools import *
[3629]	74	from palm_csd_files.palm_csd_canopy_generator import *
[3567]	75	import numpy as np
	76
	77
	78	def read_config_file():
	79
	80	import configparser
	81	from math import floor
	82	import numpy as np
	83	import os
	84	import subprocess as sub
	85	import sys
	86
	87	# Check if configuration files exists and quit otherwise
	88	input_config = ".csd.config"
	89	for i in range(1,len(sys.argv)):
	90	input_config = str(sys.argv[i])
	91
	92	config = configparser.RawConfigParser(allow_no_value=True)
	93
	94	if ( os.path.isfile(input_config) == False ):
	95	print ("Error. No configuration file " + input_config + " found.")
	96	raise SystemExit
	97
	98	config.read(input_config)
	99
	100
	101	# Definition of settings
[3668]	102
[3567]	103	global settings_bridge_width
[3629]	104	global settings_lai_roof_intensive
	105	global settings_lai_roof_extensive
	106	global settings_season
	107	global settings_lai_low_default
	108	global settings_lai_high_default
	109	global settings_patch_height_default
	110	global settings_lai_alpha
	111	global settings_lai_beta
[3955]	112	global settings_veg_type_below_trees
[3567]	113	global ndomains
	114
	115	# Definition of global configuration parameters
	116	global global_acronym
	117	global global_angle
	118	global global_author
	119	global global_campaign
	120	global global_comment
	121	global global_contact
	122	global global_data_content
	123	global global_dependencies
	124	global global_institution
	125	global global_keywords
	126	global global_location
	127	global global_palm_version
	128	global global_references
	129	global global_site
	130	global global_source
	131
[3668]	132	global path_out
	133	global filename_out
	134	global version_out
[3567]	135
[3668]	136
[3567]	137	# Definition of domain parameters
	138	global domain_names
	139	global domain_px
	140	global domain_x0
	141	global domain_y0
	142	global domain_x1
	143	global domain_y1
	144	global domain_nx
	145	global domain_ny
	146	global domain_dz
	147	global domain_3d
[3629]	148	global domain_high_vegetation
[3567]	149	global domain_ip
	150	global domain_za
	151	global domain_parent
[3629]	152	global domain_green_roofs
	153	global domain_street_trees
	154	global domain_canopy_patches
[3567]	155
	156	# Definition of input data parameters
	157	global input_names
	158	global input_px
	159
	160
	161	global input_file_x
	162	global input_file_y
	163	global input_file_x_UTM
	164	global input_file_y_UTM
	165	global input_file_lat
	166	global input_file_lon
	167	global input_file_zt
	168	global input_file_buildings_2d
	169	global input_file_bridges_2d
	170	global input_file_building_id
	171	global input_file_bridges_id
	172	global input_file_building_type
	173	global input_file_building_type
[3629]	174	global input_file_lai
[3567]	175	global input_file_vegetation_type
	176	global input_file_vegetation_height
	177	global input_file_pavement_type
	178	global input_file_water_type
	179	global input_file_street_type
	180	global input_file_street_crossings
	181	global input_file_soil_type
[3629]	182	global input_file_vegetation_on_roofs
	183	global input_file_tree_crown_diameter
	184	global input_file_tree_height
	185	global input_file_tree_trunk_diameter
	186	global input_file_tree_type
	187	global input_file_patch_height
[3567]	188
	189	global zt_all
	190	global zt_min
	191
	192	settings_bridge_width = 3.0
[3955]	193	settings_lai_roof_intensive = 2.5
	194	settings_lai_roof_extensive = 0.8
[3629]	195	settings_season = "summer"
	196	settings_lai_high_default = 6.0
	197	settings_lai_low_default = 1.0
	198	settings_patch_height_default = 10.0
	199	settings_lai_alpha = 5.0
	200	settings_lai_beta = 3.0
[3955]	201	settings_veg_type_below_trees = 3
[3567]	202	ndomains = 0
	203
	204	global_acronym = ""
	205	global_angle = ""
	206	global_author = ""
	207	global_campaign = ""
	208	global_comment = ""
	209	global_contact = ""
	210	global_data_content = ""
	211	global_dependencies = ""
	212	global_institution = ""
	213	global_keywords = ""
	214	global_location = ""
	215	global_palm_version = 6.0
	216	global_references = ""
	217	global_site = ""
	218	global_source = ""
	219
[3668]	220	path_out = ""
	221	version_out = 1
	222	filename_out = "default"
	223
[3567]	224	domain_names = []
	225	domain_px = []
	226	domain_x0 = []
	227	domain_y0 = []
	228	domain_x1 = []
	229	domain_y1 = []
	230	domain_nx = []
	231	domain_ny = []
	232	domain_dz = []
	233	domain_3d = []
[3629]	234	domain_high_vegetation = []
[3567]	235	domain_ip = []
	236	domain_za = []
	237	domain_parent = []
[3629]	238	domain_green_roofs = []
	239	domain_street_trees = []
	240	domain_canopy_patches = []
[3567]	241
	242	zt_min = 0.0
	243	zt_all = []
	244
	245	input_names = []
	246	input_px = []
	247
	248	input_file_x = []
	249	input_file_y = []
	250	input_file_x_UTM = []
	251	input_file_y_UTM = []
	252	input_file_lat = []
	253	input_file_lon = []
	254
	255	input_file_zt = []
	256	input_file_buildings_2d = []
	257	input_file_bridges_2d = []
	258	input_file_building_id = []
	259	input_file_bridges_id = []
	260	input_file_building_type = []
[3629]	261	input_file_lai = []
[3567]	262	input_file_vegetation_type = []
	263	input_file_vegetation_height = []
	264	input_file_pavement_type = []
	265	input_file_water_type = []
	266	input_file_street_type = []
	267	input_file_street_crossings = []
	268	input_file_soil_type = []
[3629]	269	input_file_vegetation_on_roofs = []
	270	input_file_tree_crown_diameter = []
	271	input_file_tree_height = []
	272	input_file_tree_trunk_diameter = []
	273	input_file_tree_type = []
	274	input_file_patch_height = []
[3567]	275
	276	# Load all user parameters from config file
	277	for i in range(0,len(config.sections())):
	278
	279	read_tmp = config.sections()[i]
	280
	281	if ( read_tmp == 'global' ):
	282	global_acronym = config.get(read_tmp, 'acronym')
	283	global_angle = float(config.get(read_tmp, 'rotation_angle'))
	284	global_author = config.get(read_tmp, 'author')
	285	global_campaign = config.get(read_tmp, 'campaign')
	286	global_comment = config.get(read_tmp, 'comment')
	287	global_contact = config.get(read_tmp, 'contact_person')
	288	global_data_content = config.get(read_tmp, 'data_content')
	289	global_dependencies = config.get(read_tmp, 'dependencies')
	290	global_institution = config.get(read_tmp, 'institution')
	291	global_keywords = config.get(read_tmp, 'keywords')
	292	global_location = config.get(read_tmp, 'location')
	293	global_palm_version = float(config.get(read_tmp, 'palm_version'))
	294	global_references = config.get(read_tmp, 'references')
	295	global_site = config.get(read_tmp, 'site')
	296	global_source = config.get(read_tmp, 'source')
	297
[3668]	298
[3567]	299	if ( read_tmp == 'settings' ):
[3629]	300	settings_lai_roof_intensive = config.get(read_tmp, 'lai_roof_intensive')
	301	settings_lai_roof_extensive = config.get(read_tmp, 'lai_roof_extensive')
	302	settings_bridge_width = float(config.get(read_tmp, 'bridge_width'))
	303	settings_season = config.get(read_tmp, 'season')
	304	settings_lai_high_default = float(config.get(read_tmp, 'lai_high_vegetation_default'))
	305	settings_lai_low_default = float(config.get(read_tmp, 'lai_low_vegetation_default'))
	306	settings_patch_height_default = float(config.get(read_tmp, 'patch_height_default'))
	307	settings_lai_alpha = float(config.get(read_tmp, 'lai_alpha'))
	308	settings_lai_beta = float(config.get(read_tmp, 'lai_beta'))
[3955]	309	settings_veg_type_below_trees = config.get(read_tmp, 'vegetation_type_below_trees')
[3629]	310
[3668]	311	if ( read_tmp == 'output' ):
	312	path_out = config.get(read_tmp, 'path')
	313	filename_out = config.get(read_tmp, 'file_out')
	314	version_out = float(config.get(read_tmp, 'version'))
	315
[3567]	316	if ( read_tmp.split("_")[0] == 'domain' ):
	317	ndomains = ndomains + 1
	318	domain_names.append(read_tmp.split("_")[1])
	319	domain_px.append(float(config.get(read_tmp, 'pixel_size')))
	320	domain_nx.append(int(config.get(read_tmp, 'nx')))
	321	domain_ny.append(int(config.get(read_tmp, 'ny')))
	322	domain_dz.append(float(config.get(read_tmp, 'dz')))
	323	domain_3d.append(config.getboolean(read_tmp, 'buildings_3d'))
[3629]	324	domain_high_vegetation.append(config.getboolean(read_tmp, 'allow_high_vegetation'))
	325	domain_canopy_patches.append(config.getboolean(read_tmp, 'generate_vegetation_patches'))
[3567]	326	domain_ip.append(config.getboolean(read_tmp, 'interpolate_terrain'))
	327	domain_za.append(config.getboolean(read_tmp, 'use_palm_z_axis'))
	328	if domain_ip[ndomains-1] and not domain_za[ndomains-1]:
	329	domain_za[ndomains-1] = True
	330	print("+++ Overwrite user setting for use_palm_z_axis")
	331
	332	domain_parent.append(config.get(read_tmp, 'domain_parent'))
	333
	334	domain_x0.append(int(floor(float(config.get(read_tmp, 'origin_x'))/float(config.get(read_tmp, 'pixel_size')))))
	335	domain_y0.append(int(floor(float(config.get(read_tmp, 'origin_y'))/float(config.get(read_tmp, 'pixel_size')))))
	336	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'))))
	337	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]	338	domain_green_roofs.append(config.getboolean(read_tmp, 'vegetation_on_roofs'))
	339	domain_street_trees.append(config.getboolean(read_tmp, 'street_trees'))
	340
[3567]	341	if ( read_tmp.split("_")[0] == 'input' ):
	342	input_names.append(read_tmp.split("_")[1])
	343	input_px.append(float(config.get(read_tmp, 'pixel_size')))
	344	input_file_x.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_x'))
	345	input_file_y.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_y'))
	346	input_file_lat.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_lat'))
	347	input_file_lon.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_lon'))
	348	input_file_x_UTM.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_x_UTM'))
	349	input_file_y_UTM.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_y_UTM'))
	350	input_file_zt.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_zt'))
	351	input_file_buildings_2d.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_buildings_2d'))
	352	input_file_bridges_2d.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_bridges_2d'))
	353	input_file_building_id.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_building_id'))
	354	input_file_bridges_id.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_bridges_id'))
[3629]	355	input_file_building_type.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_building_type'))
	356	input_file_lai.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_lai'))
[3567]	357	input_file_vegetation_type.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_vegetation_type'))
	358	input_file_vegetation_height.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_vegetation_height'))
	359	input_file_pavement_type.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_pavement_type'))
	360	input_file_water_type.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_water_type'))
	361	input_file_street_type.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_street_type'))
[3629]	362	input_file_street_crossings.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_street_crossings'))
	363	input_file_patch_height.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_patch_height'))
[3668]	364
	365	tmp = config.get(read_tmp, 'file_tree_crown_diameter')
	366	if tmp is not None:
	367	input_file_tree_crown_diameter.append(config.get(read_tmp, 'path') + "/" + tmp)
	368	else:
	369	input_file_tree_crown_diameter.append(None)
[3629]	370	input_file_tree_height.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_tree_height'))
	371	input_file_tree_trunk_diameter.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_tree_trunk_diameter'))
	372	input_file_tree_type.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_tree_type'))
	373	input_file_vegetation_on_roofs.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_vegetation_on_roofs'))
[3567]	374	#input_file_soil_type.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_soil_type'))
	375	return 0
	376
	377
	378	############################################################
	379
	380	# Start of main program
	381	datatypes = {
	382	"x": "f4",
	383	"y": "f4",
	384	"z": "f4",
	385	"lat": "f4",
	386	"lon": "f4",
	387	"E_UTM": "f4",
	388	"N_UTM": "f4",
	389	"zt": "f4",
	390	"buildings_2d": "f4",
	391	"buildings_3d": "b",
	392	"bridges_2d": "f4",
	393	"building_id": "i",
	394	"bridges_id": "i",
	395	"building_type": "b",
	396	"nsurface_fraction": "i",
	397	"vegetation_type": "b",
	398	"vegetation_height": "f4",
	399	"pavement_type": "b",
	400	"water_type": "b",
	401	"street_type": "b",
	402	"street_crossings": "b",
	403	"soil_type": "b",
[3629]	404	"surface_fraction": "f4",
	405	"building_pars": "f4",
	406	"vegetation_pars": "f4",
	407	"tree_data": "f4",
	408	"tree_type": "b",
	409	"nbuilding_pars": "i",
	410	"nvegetation_pars": "i",
	411	"zlad": "f4"
[3567]	412	}
	413
	414	fillvalues = {
	415	"lat": float(-9999.0),
	416	"lon": float(-9999.0),
	417	"E_UTM": float(-9999.0),
	418	"N_UTM": float(-9999.0),
	419	"zt": float(-9999.0),
	420	"buildings_2d": float(-9999.0),
	421	"buildings_3d": np.byte(-127),
	422	"bridges_2d": float(-9999.0),
	423	"building_id": int(-9999),
	424	"bridges_id": int(-9999),
	425	"building_type": np.byte(-127),
	426	"nsurface_fraction": int(-9999),
	427	"vegetation_type": np.byte(-127),
	428	"vegetation_height": float(-9999.0),
	429	"pavement_type": np.byte(-127),
	430	"water_type": np.byte(-127),
	431	"street_type": np.byte(-127),
	432	"street_crossings": np.byte(-127),
	433	"soil_type": np.byte(-127),
[3629]	434	"surface_fraction": float(-9999.0),
	435	"building_pars": float(-9999.0),
	436	"vegetation_pars": float(-9999.0),
	437	"tree_data": float(-9999.0),
	438	"tree_type": np.byte(-127)
[3567]	439	}
	440
	441	defaultvalues = {
	442	"lat": float(-9999.0),
	443	"lon": float(-9999.0),
	444	"E_UTM": float(-9999.0),
	445	"N_UTM": float(-9999.0),
	446	"zt": float(0.0),
	447	"buildings_2d": float(0.0),
	448	"buildings_3d": 0,
	449	"bridges_2d": float(0.0),
	450	"building_id": int(0),
	451	"bridges_id": int(0),
	452	"building_type": 1,
	453	"nsurface_fraction": int(-9999),
	454	"vegetation_type": 3,
	455	"vegetation_height": float(-9999.0),
	456	"pavement_type": 1,
	457	"water_type": 1,
	458	"street_type": 1,
	459	"street_crossings": 0,
	460	"soil_type": 1,
[3629]	461	"surface_fraction": float(0.0),
	462	"buildings_pars": float(-9999.0),
	463	"tree_data": float(-9999.0),
	464	"tree_type": 0,
	465	"vegetation_pars": float(-9999.0)
[3567]	466	}
	467
	468	# Read configuration file and set parameters accordingly
	469	read_config_file()
	470
	471
	472	filename = []
	473	ii = []
	474	ii_parent = []
	475	# Define indices and filenames for all domains and create netCDF files
	476	for i in range(0,ndomains):
	477
	478	# Calculate indices and input files
	479	ii.append(input_px.index(domain_px[i]))
[3668]	480	filename.append(path_out + "/" + filename_out + "_" + domain_names[i])
[3567]	481	if domain_parent[i] is not None:
[3726]	482	ii_parent.append(input_px.index(domain_px[domain_names.index(domain_parent[i])]))
[3567]	483	else:
	484	ii_parent.append(None)
	485
	486
[3726]	487	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)
	488	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)
	489	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)
	490	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]	491
[3726]	492	# Calculate position of origin
	493	x_UTM_origin = float(x_UTM[0,0]) - 0.5 * (float(x_UTM[0,1]) - float(x_UTM[0,0]))
	494	y_UTM_origin = float(y_UTM[0,0]) - 0.5 * (float(y_UTM[1,0]) - float(y_UTM[0,0]))
	495	x_origin = float(lon[0,0]) - 0.5 * (float(lon[0,1]) - float(lon[0,0]))
	496	y_origin = float(lat[0,0]) - 0.5 * (float(lat[1,0]) - float(lat[0,0]))
	497
[3567]	498	# Create NetCDF output file and set global attributes
	499	nc_create_file(filename[i])
[3726]	500	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]	501
[3688]	502	del x_UTM, y_UTM, lat, lon
[3567]	503
	504	# Process terrain height
	505	for i in range(0,ndomains):
	506
	507	# Read and write terrain height (zt)
	508	zt = nc_read_from_file_2d(input_file_zt[ii[i]], 'Band1', domain_x0[i], domain_x1[i], domain_y0[i], domain_y1[i])
	509
	510	# Final step: add zt array to the global array
	511	zt_all.append(zt)
	512	del zt
	513
	514	# Calculate the global (all domains) minimum of the terrain height. This value will be substracted for all
	515	# data sets
	516	zt_min = min(zt_all[0].flatten())
	517	for i in range(0,ndomains):
	518	zt_min = min(zt_min,min(zt_all[i].flatten()))
	519
	520	del zt_all[:]
	521
[3726]	522	print( "Shift terrain heights by -" + str(zt_min))
[3567]	523	for i in range(0,ndomains):
	524
	525	# Read and write terrain height (zt)
	526	zt = nc_read_from_file_2d(input_file_zt[ii[i]], 'Band1', domain_x0[i], domain_x1[i], domain_y0[i], domain_y1[i])
	527	x = nc_read_from_file_1d(input_file_x[ii[i]], "x", domain_x0[i], domain_x1[i])
	528	y = nc_read_from_file_1d(input_file_y[ii[i]], "y", domain_y0[i], domain_y1[i])
	529
[3726]	530
[3567]	531	zt = zt - zt_min
[3668]	532
	533	nc_write_global_attribute(filename[i], 'origin_z', float(zt_min))
[3567]	534
	535	# If necessary, interpolate parent domain terrain height on child domain grid and blend the two
	536	if domain_ip[i]:
[3726]	537	parent_id = domain_names.index(domain_parent[i])
	538	tmp_x0 = int( domain_x0[i] * domain_px[i] / domain_px[parent_id] ) - 1
	539	tmp_y0 = int( domain_y0[i] * domain_px[i] / domain_px[parent_id] ) - 1
	540	tmp_x1 = int( domain_x1[i] * domain_px[i] / domain_px[parent_id] ) + 1
	541	tmp_y1 = int( domain_y1[i] * domain_px[i] / domain_px[parent_id] ) + 1
	542
[3567]	543	tmp_x = nc_read_from_file_1d(input_file_x[ii_parent[i]], "x", tmp_x0, tmp_x1)
	544	tmp_y = nc_read_from_file_1d(input_file_y[ii_parent[i]], "y", tmp_y0, tmp_y1)
	545
	546	zt_parent = nc_read_from_file_2d(input_file_zt[ii_parent[i]], 'Band1', tmp_x0, tmp_x1, tmp_y0, tmp_y1)
	547
	548	zt_parent = zt_parent - zt_min
	549
	550	# Interpolate array and bring to PALM grid of child domain
	551	zt_ip = interpolate_2d(zt_parent,tmp_x,tmp_y,x,y)
[3726]	552	zt_ip = bring_to_palm_grid(zt_ip,x,y,domain_dz[parent_id])
	553
	554
	555	# Shift the child terrain height according to the parent mean terrain height
[3773]	556	print("shifting: -" + str(np.mean(zt)) + " +" + str(np.mean(zt_ip)))
	557	#zt = zt - np.min(zt) + np.min(zt_ip)
[3726]	558	zt = zt - np.mean(zt) + np.mean(zt_ip)
[3567]	559
	560
	561	# Blend over the parent and child terrain height within a radius of 50 px
	562	zt = blend_array_2d(zt,zt_ip,50)
[3773]	563	# zt = zt_ip
[3567]	564
	565	# Final step: add zt array to the global array
[3726]	566
[3567]	567	zt_all.append(zt)
	568	del zt
	569
	570
	571	# Read and shift x and y coordinates, shift terrain height according to its minimum value and write all data
	572	# to file
	573	for i in range(0,ndomains):
	574	# Read horizontal grid variables from zt file and write them to output file
	575	x = nc_read_from_file_1d(input_file_x[ii[i]], "x", domain_x0[i], domain_x1[i])
	576	y = nc_read_from_file_1d(input_file_y[ii[i]], "y", domain_y0[i], domain_y1[i])
	577	x = x - min(x.flatten()) + domain_px[i]/2.0
	578	y = y - min(y.flatten()) + domain_px[i]/2.0
	579	nc_write_dimension(filename[i], 'x', x, datatypes["x"])
	580	nc_write_dimension(filename[i], 'y', y, datatypes["y"])
	581	nc_write_attribute(filename[i], 'x', 'long_name', 'x')
	582	nc_write_attribute(filename[i], 'x', 'standard_name','projection_x_coordinate')
	583	nc_write_attribute(filename[i], 'x', 'units', 'm')
[3859]	584	nc_write_attribute(filename[i], 'y', 'long_name', 'y')
[3567]	585	nc_write_attribute(filename[i], 'y', 'standard_name', 'projection_y_coordinate')
	586	nc_write_attribute(filename[i], 'y', 'units', 'm')
	587
	588	lat = nc_read_from_file_2d(input_file_lat[ii[i]], "Band1", domain_x0[i], domain_x1[i], domain_y0[i], domain_y1[i])
	589	lon = nc_read_from_file_2d(input_file_lon[ii[i]], "Band1", domain_x0[i], domain_x1[i], domain_y0[i], domain_y1[i])
	590
	591	nc_write_to_file_2d(filename[i], 'lat', lat, datatypes["lat"],'y','x',fillvalues["lat"])
	592	nc_write_to_file_2d(filename[i], 'lon', lon, datatypes["lon"],'y','x',fillvalues["lon"])
	593
	594	nc_write_attribute(filename[i], 'lat', 'long_name', 'latitude')
	595	nc_write_attribute(filename[i], 'lat', 'standard_name','latitude')
	596	nc_write_attribute(filename[i], 'lat', 'units', 'degrees_north')
	597
	598	nc_write_attribute(filename[i], 'lon', 'long_name', 'longitude')
	599	nc_write_attribute(filename[i], 'lon', 'standard_name','longitude')
	600	nc_write_attribute(filename[i], 'lon', 'units', 'degrees_east')
	601
	602	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])
	603	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]	604
[3567]	605
	606	nc_write_to_file_2d(filename[i], 'E_UTM', x_UTM, datatypes["E_UTM"],'y','x',fillvalues["E_UTM"])
	607	nc_write_to_file_2d(filename[i], 'N_UTM', y_UTM, datatypes["N_UTM"],'y','x',fillvalues["N_UTM"])
	608
	609	nc_write_attribute(filename[i], 'E_UTM', 'long_name', 'easting')
	610	nc_write_attribute(filename[i], 'E_UTM', 'standard_name','projection_x_coorindate')
	611	nc_write_attribute(filename[i], 'E_UTM', 'units', 'm')
	612
	613	nc_write_attribute(filename[i], 'N_UTM', 'long_name', 'northing')
	614	nc_write_attribute(filename[i], 'N_UTM', 'standard_name','projection_y_coorindate')
	615	nc_write_attribute(filename[i], 'N_UTM', 'units', 'm')
	616
	617	nc_write_crs(filename[i])
	618
	619
	620
	621	# If necessary, bring terrain height to PALM's vertical grid. This is either forced by the user or implicitly
	622	# by using interpolation for a child domain
	623	if domain_za[i]:
	624	zt_all[i] = bring_to_palm_grid(zt_all[i],x,y,domain_dz[i])
	625
	626	nc_write_to_file_2d(filename[i], 'zt', zt_all[i], datatypes["zt"],'y','x',fillvalues["zt"])
	627	nc_write_attribute(filename[i], 'zt', 'long_name', 'orography')
	628	nc_write_attribute(filename[i], 'zt', 'units', 'm')
	629	nc_write_attribute(filename[i], 'zt', 'res_orig', domain_px[i])
	630	nc_write_attribute(filename[i], 'zt', 'coordinates', 'E_UTM N_UTM lon lat')
	631	nc_write_attribute(filename[i], 'zt', 'grid_mapping', 'E_UTM N_UTM lon lat')
	632
	633	del zt_all
	634
	635
	636	# Process building height, id, and type
	637	for i in range(0,ndomains):
	638	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]	639
[3567]	640	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])
	641
	642	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]	643	building_type[building_type >= 254] = fillvalues["building_type"]
[3567]	644	building_type = np.where(building_type < 1,defaultvalues["building_type"],building_type)
	645
	646	check = check_arrays_2(buildings_2d,building_id,fillvalues["buildings_2d"],fillvalues["building_id"])
	647	if not check:
	648	buildings_2d = np.where(building_id != fillvalues["building_id"],buildings_2d,fillvalues["buildings_2d"])
	649	building_id = np.where(buildings_2d == fillvalues["buildings_2d"],fillvalues["building_id"],building_id)
	650	print("Data check #1 " + str(check_arrays_2(buildings_2d,building_id,fillvalues["buildings_2d"],fillvalues["building_id"])))
	651
	652	check = check_arrays_2(buildings_2d,building_type,fillvalues["buildings_2d"],fillvalues["building_type"])
	653	if not check:
	654	building_type = np.where(buildings_2d == fillvalues["buildings_2d"],fillvalues["building_type"],building_type)
	655	building_type = np.where((building_type == fillvalues["building_type"]) & (buildings_2d != fillvalues["buildings_2d"]),defaultvalues["building_type"],building_type)
	656	print("Data check #2 " + str(check_arrays_2(buildings_2d,building_type,fillvalues["buildings_2d"],fillvalues["building_type"])))
	657
[3688]	658
[3567]	659	nc_write_to_file_2d(filename[i], 'buildings_2d', buildings_2d, datatypes["buildings_2d"],'y','x',fillvalues["buildings_2d"])
	660	nc_write_attribute(filename[i], 'buildings_2d', 'long_name', 'buildings')
	661	nc_write_attribute(filename[i], 'buildings_2d', 'units', 'm')
	662	nc_write_attribute(filename[i], 'buildings_2d', 'res_orig', domain_px[i])
	663	nc_write_attribute(filename[i], 'buildings_2d', 'lod', 1)
	664	nc_write_attribute(filename[i], 'buildings_2d', 'coordinates', 'E_UTM N_UTM lon lat')
	665	nc_write_attribute(filename[i], 'buildings_2d', 'grid_mapping', 'E_UTM N_UTM lon lat')
	666
	667	nc_write_to_file_2d(filename[i], 'building_id', building_id, datatypes["building_id"],'y','x',fillvalues["building_id"])
	668	nc_write_attribute(filename[i], 'building_id', 'long_name', 'building id')
	669	nc_write_attribute(filename[i], 'building_id', 'units', '')
[3668]	670	nc_write_attribute(filename[i], 'building_id', 'res _orig', domain_px[i])
[3567]	671	nc_write_attribute(filename[i], 'building_id', 'coordinates', 'E_UTM N_UTM lon lat')
	672	nc_write_attribute(filename[i], 'building_id', 'grid_mapping', 'E_UTM N_UTM lon lat')
	673
	674	nc_write_to_file_2d(filename[i], 'building_type', building_type, datatypes["building_type"],'y','x',fillvalues["building_type"])
	675	nc_write_attribute(filename[i], 'building_type', 'long_name', 'building type')
	676	nc_write_attribute(filename[i], 'building_type', 'units', '')
	677	nc_write_attribute(filename[i], 'building_type', 'res_orig', domain_px[i])
	678	nc_write_attribute(filename[i], 'building_type', 'coordinates', 'E_UTM N_UTM lon lat')
	679	nc_write_attribute(filename[i], 'building_type', 'grid_mapping', 'E_UTM N_UTM lon lat')
	680
	681	del buildings_2d
	682	del building_id
	683	del building_type
	684
	685	# Create 3d buildings if necessary. In that course, read bridge objects and add them to building layer
	686	for i in range(0,ndomains):
	687
	688	if domain_3d[i]:
	689	x = nc_read_from_file_2d_all(filename[i], 'x')
	690	y = nc_read_from_file_2d_all(filename[i], 'y')
	691	buildings_2d = nc_read_from_file_2d_all(filename[i], 'buildings_2d')
	692	building_id = nc_read_from_file_2d_all(filename[i], 'building_id')
	693
	694	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])
	695	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])
	696
	697	bridges_2d = np.where(bridges_2d == 0.0,fillvalues["bridges_2d"],bridges_2d)
	698	building_id = np.where(bridges_2d == fillvalues["bridges_2d"],building_id,bridges_id)
	699
	700	if np.any(buildings_2d != fillvalues["buildings_2d"]):
	701	buildings_3d, z = make_3d_from_2d(buildings_2d,x,y,domain_dz[i])
	702	if np.any(bridges_2d != fillvalues["bridges_2d"]):
	703	buildings_3d = make_3d_from_bridges_2d(buildings_3d,bridges_2d,x,y,domain_dz[i],settings_bridge_width,fillvalues["bridges_2d"])
	704	else:
	705	print("Skipping creation of 3D bridges (no bridges in domain)")
	706
	707
	708	nc_write_dimension(filename[i], 'z', z, datatypes["z"])
	709	nc_write_attribute(filename[i], 'z', 'long_name', 'z')
	710	nc_write_attribute(filename[i], 'z', 'units', 'm')
[3726]	711
	712	nc_overwrite_to_file_2d(filename[i], 'building_id', building_id)
[3567]	713
	714	nc_write_to_file_3d(filename[i], 'buildings_3d', buildings_3d, datatypes["buildings_3d"],'z','y','x',fillvalues["buildings_3d"])
	715	nc_write_attribute(filename[i], 'buildings_3d', 'long_name', 'buildings 3d')
	716	nc_write_attribute(filename[i], 'buildings_3d', 'units', '')
	717	nc_write_attribute(filename[i], 'buildings_3d', 'res_orig', domain_px[i])
	718	nc_write_attribute(filename[i], 'buildings_3d', 'lod', 2)
	719
	720	del buildings_3d
	721
	722	else:
	723	print("Skipping creation of 3D buildings (no buildings in domain)")
	724
	725
[3629]	726	del bridges_2d, bridges_id, building_id, buildings_2d
[3567]	727
	728
	729
	730	# Read vegetation type, water_type, pavement_type, soil_type and make fields consistent
	731	for i in range(0,ndomains):
	732
	733	building_type = nc_read_from_file_2d_all(filename[i], 'building_type')
[3773]	734
[3567]	735	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])
	736	vegetation_type[vegetation_type == 255] = fillvalues["vegetation_type"]
	737	vegetation_type = np.where((vegetation_type < 1) & (vegetation_type != fillvalues["vegetation_type"]),defaultvalues["vegetation_type"],vegetation_type)
	738
	739	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])
	740	pavement_type[pavement_type == 255] = fillvalues["pavement_type"]
	741	pavement_type = np.where((pavement_type < 1) & (pavement_type != fillvalues["pavement_type"]),defaultvalues["pavement_type"],pavement_type)
	742
	743	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])
	744	water_type[water_type == 255] = fillvalues["water_type"]
	745	water_type = np.where((water_type < 1) & (water_type != fillvalues["water_type"]),defaultvalues["water_type"],water_type)
	746
	747	# to do: replace by real soil input data
	748	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])
	749	soil_type[soil_type == 255] = fillvalues["soil_type"]
	750	soil_type = np.where((soil_type < 1) & (soil_type != fillvalues["soil_type"]),defaultvalues["soil_type"],soil_type)
	751
	752	# Make arrays consistent
	753	# #1 Set vegetation type to missing for pixel where a pavement type is set
	754	vegetation_type = np.where((vegetation_type != fillvalues["vegetation_type"]) & (pavement_type != fillvalues["pavement_type"]),fillvalues["vegetation_type"],vegetation_type)
	755
	756	# #2 Set vegetation type to missing for pixel where a building type is set
	757	vegetation_type = np.where((vegetation_type != fillvalues["vegetation_type"]) & (building_type != fillvalues["building_type"]) ,fillvalues["vegetation_type"],vegetation_type)
	758
	759	# #3 Set vegetation type to missing for pixel where a building type is set
	760	vegetation_type = np.where((vegetation_type != fillvalues["vegetation_type"]) & (water_type != fillvalues["water_type"]),fillvalues["vegetation_type"],vegetation_type)
	761
	762	# #4 Remove pavement for pixels with buildings
	763	pavement_type = np.where((pavement_type != fillvalues["pavement_type"]) & (building_type != fillvalues["building_type"]),fillvalues["pavement_type"],pavement_type)
	764
[3773]	765	# #5 Remove pavement for pixels with water.
	766	pavement_type = np.where((pavement_type != fillvalues["pavement_type"]) & (water_type != fillvalues["water_type"]),fillvalues["pavement_type"],pavement_type)
	767
[3567]	768	# #6 Remove water for pixels with buildings
	769	water_type = np.where((water_type != fillvalues["water_type"]) & (building_type != fillvalues["building_type"]),fillvalues["water_type"],water_type)
[3773]	770
[3668]	771	# Correct vegetation_type when a vegetation height is available and is indicative of low vegeetation
	772	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])
	773
	774	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)
	775	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]	776
	777	# Check for consistency and fill empty fields with default vegetation type
	778	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"])
	779
	780	if test:
	781	vegetation_type = np.where(consistency_array == 0,defaultvalues["vegetation_type"],vegetation_type)
	782	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]	783
	784	# #7 to be removed: set default soil type everywhere
	785	soil_type = np.where((vegetation_type != fillvalues["vegetation_type"]) \| (pavement_type != fillvalues["pavement_type"]),defaultvalues["soil_type"],fillvalues["soil_type"])
	786
[3567]	787
[3668]	788	# Check for consistency and fill empty fields with default vegetation type
	789	consistency_array, test = check_consistency_3(vegetation_type,pavement_type,soil_type,fillvalues["vegetation_type"],fillvalues["pavement_type"],fillvalues["soil_type"])
	790
[3567]	791	# Create surface_fraction array
	792	x = nc_read_from_file_2d_all(filename[i], 'x')
	793	y = nc_read_from_file_2d_all(filename[i], 'y')
	794	nsurface_fraction = np.arange(0,3)
	795	surface_fraction = np.ones((len(nsurface_fraction),len(y),len(x)))
	796
	797	surface_fraction[0,:,:] = np.where(vegetation_type != fillvalues["vegetation_type"], 1.0, 0.0)
	798	surface_fraction[1,:,:] = np.where(pavement_type != fillvalues["pavement_type"], 1.0, 0.0)
	799	surface_fraction[2,:,:] = np.where(water_type != fillvalues["water_type"], 1.0, 0.0)
	800
	801	nc_write_dimension(filename[i], 'nsurface_fraction', nsurface_fraction, datatypes["nsurface_fraction"])
	802	nc_write_to_file_3d(filename[i], 'surface_fraction', surface_fraction, datatypes["surface_fraction"],'nsurface_fraction','y','x',fillvalues["surface_fraction"])
	803	nc_write_attribute(filename[i], 'surface_fraction', 'long_name', 'surface fraction')
	804	nc_write_attribute(filename[i], 'surface_fraction', 'units', '')
	805	nc_write_attribute(filename[i], 'surface_fraction', 'res_orig', domain_px[i])
	806	del surface_fraction
	807
	808	nc_write_to_file_2d(filename[i], 'vegetation_type', vegetation_type, datatypes["vegetation_type"],'y','x',fillvalues["vegetation_type"])
	809	nc_write_attribute(filename[i], 'vegetation_type', 'long_name', 'vegetation type')
	810	nc_write_attribute(filename[i], 'vegetation_type', 'units', '')
	811	nc_write_attribute(filename[i], 'vegetation_type', 'res_orig', domain_px[i])
	812	nc_write_attribute(filename[i], 'vegetation_type', 'coordinates', 'E_UTM N_UTM lon lat')
	813	nc_write_attribute(filename[i], 'vegetation_type', 'grid_mapping', 'E_UTM N_UTM lon lat')
	814	del vegetation_type
	815
	816	nc_write_to_file_2d(filename[i], 'pavement_type', pavement_type, datatypes["pavement_type"],'y','x',fillvalues["pavement_type"])
	817	nc_write_attribute(filename[i], 'pavement_type', 'long_name', 'pavement type')
	818	nc_write_attribute(filename[i], 'pavement_type', 'units', '')
	819	nc_write_attribute(filename[i], 'pavement_type', 'res_orig', domain_px[i])
	820	nc_write_attribute(filename[i], 'pavement_type', 'coordinates', 'E_UTM N_UTM lon lat')
	821	nc_write_attribute(filename[i], 'pavement_type', 'grid_mapping', 'E_UTM N_UTM lon lat')
	822	del pavement_type
	823
	824	nc_write_to_file_2d(filename[i], 'water_type', water_type, datatypes["water_type"],'y','x',fillvalues["water_type"])
	825	nc_write_attribute(filename[i], 'water_type', 'long_name', 'water type')
	826	nc_write_attribute(filename[i], 'water_type', 'units', '')
	827	nc_write_attribute(filename[i], 'water_type', 'res_orig', domain_px[i])
	828	nc_write_attribute(filename[i], 'water_type', 'coordinates', 'E_UTM N_UTM lon lat')
	829	nc_write_attribute(filename[i], 'water_type', 'grid_mapping', 'E_UTM N_UTM lon lat')
	830	del water_type
	831
	832	nc_write_to_file_2d(filename[i], 'soil_type', soil_type, datatypes["soil_type"],'y','x',fillvalues["soil_type"])
	833	nc_write_attribute(filename[i], 'soil_type', 'long_name', 'soil type')
	834	nc_write_attribute(filename[i], 'soil_type', 'units', '')
	835	nc_write_attribute(filename[i], 'soil_type', 'res_orig', domain_px[i])
	836	nc_write_attribute(filename[i], 'soil_type', 'coordinates', 'E_UTM N_UTM lon lat')
	837	nc_write_attribute(filename[i], 'soil_type', 'grid_mapping', 'E_UTM N_UTM lon lat')
	838	del soil_type
	839
[3629]	840	del x
	841	del y
[3567]	842
	843	# pixels with bridges get building_type = 7 = bridge. This does not change the _type setting for the under-bridge
[3668]	844	# area NOTE: when bridges are present the consistency check will fail at the moment
[3567]	845	if domain_3d[i]:
	846	if np.any(building_type != fillvalues["building_type"]):
	847
	848	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])
	849	bridges_2d = np.where(bridges_2d == 0.0,fillvalues["bridges_2d"],bridges_2d)
	850	building_type = np.where(bridges_2d != fillvalues["bridges_2d"],7,building_type)
	851	nc_overwrite_to_file_2d(filename[i], 'building_type', building_type)
	852
	853	del building_type
	854	del bridges_2d
	855
[3629]	856	# Read/write street type and street crossings
[3567]	857	for i in range(0,ndomains):
	858
	859	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])
	860	street_type[street_type == 255] = fillvalues["street_type"]
	861	street_type = np.where((street_type < 1) & (street_type != fillvalues["street_type"]),defaultvalues["street_type"],street_type)
[3773]	862
	863	pavement_type = nc_read_from_file_2d_all(filename[i], 'pavement_type')
	864	street_type = np.where((pavement_type == fillvalues["pavement_type"]),fillvalues["street_type"],street_type)
	865
[3567]	866	nc_write_to_file_2d(filename[i], 'street_type', street_type, datatypes["street_type"],'y','x',fillvalues["street_type"])
	867	nc_write_attribute(filename[i], 'street_type', 'long_name', 'street type')
	868	nc_write_attribute(filename[i], 'street_type', 'units', '')
	869	nc_write_attribute(filename[i], 'street_type', 'res_orig', domain_px[i])
	870	nc_write_attribute(filename[i], 'street_type', 'coordinates', 'E_UTM N_UTM lon lat')
	871	nc_write_attribute(filename[i], 'street_type', 'grid_mapping', 'E_UTM N_UTM lon lat')
	872	del street_type
	873
	874	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])
	875	street_crossings[street_crossings == 255] = fillvalues["street_crossings"]
	876	street_crossings = np.where((street_crossings < 1) & (street_crossings != fillvalues["street_crossings"]),defaultvalues["street_crossings"],street_crossings)
	877
[3726]	878	nc_write_to_file_2d(filename[i], 'street_crossing', street_crossings, datatypes["street_crossings"],'y','x',fillvalues["street_crossings"])
	879	nc_write_attribute(filename[i], 'street_crossing', 'long_name', 'street crossings')
	880	nc_write_attribute(filename[i], 'street_crossing', 'units', '')
	881	nc_write_attribute(filename[i], 'street_crossing', 'res_orig', domain_px[i])
	882	nc_write_attribute(filename[i], 'street_crossing', 'coordinates', 'E_UTM N_UTM lon lat')
	883	nc_write_attribute(filename[i], 'street_crossing', 'grid_mapping', 'E_UTM N_UTM lon lat')
[3567]	884	del street_crossings
[3629]	885
	886
	887	# Read/write vegetation on roofs
	888	for i in range(0,ndomains):
	889	if domain_green_roofs[i]:
	890	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])
	891	buildings_2d = nc_read_from_file_2d_all(filename[i], 'buildings_2d')
	892
	893
	894	x = nc_read_from_file_2d_all(filename[i], 'x')
	895	y = nc_read_from_file_2d_all(filename[i], 'y')
[4149]	896	nbuilding_pars = np.arange(0,47)
[3629]	897	building_pars = np.ones((len(nbuilding_pars),len(y),len(x)))
	898	building_pars[:,:,:] = fillvalues["building_pars"]
	899
	900	# assign green fraction on roofs
[3955]	901	building_pars[3,:,:] = np.where( ( buildings_2d != fillvalues["buildings_2d"] ) & ( green_roofs != fillvalues["building_pars"] ) & ( green_roofs != 0.0 ),1,fillvalues["building_pars"] )
[3629]	902
	903	# assign leaf area index for vegetation on roofs
[4311]	904	building_pars[4,:,:] = np.where( ( building_pars[3,:,:] != fillvalues["building_pars"] ) & ( green_roofs >= 0.5 ),settings_lai_roof_intensive,fillvalues["building_pars"])
	905	building_pars[4,:,:] = np.where( ( building_pars[3,:,:] != fillvalues["building_pars"] ) & ( green_roofs < 0.5 ),settings_lai_roof_extensive,building_pars[4,:,:])
[3629]	906
	907
	908	nc_write_dimension(filename[i], 'nbuilding_pars', nbuilding_pars, datatypes["nbuilding_pars"])
	909	nc_write_to_file_3d(filename[i], 'building_pars', building_pars, datatypes["building_pars"],'nbuilding_pars','y','x',fillvalues["building_pars"])
	910	nc_write_attribute(filename[i], 'building_pars', 'long_name', 'building_pars')
	911	nc_write_attribute(filename[i], 'building_pars', 'units', '')
	912	nc_write_attribute(filename[i], 'building_pars', 'res_orig', domain_px[i])
	913	nc_write_attribute(filename[i], 'building_pars', 'coordinates', 'E_UTM N_UTM lon lat')
	914	nc_write_attribute(filename[i], 'building_pars', 'grid_mapping', 'E_UTM N_UTM lon lat')
	915
	916	del building_pars, buildings_2d, x, y
	917
	918
	919	# Read tree data and create LAD and BAD arrays using the canopy generator
	920	for i in range(0,ndomains):
[3668]	921	lai = nc_read_from_file_2d(input_file_lai[ii[i]], 'Band1', domain_x0[i], domain_x1[i], domain_y0[i], domain_y1[i])
	922
	923	vegetation_type = nc_read_from_file_2d_all(filename[i], 'vegetation_type')
	924
	925	lai = np.where(vegetation_type == fillvalues["vegetation_type"],fillvalues["vegetation_pars"],lai)
	926
[3629]	927
[3668]	928	x = nc_read_from_file_2d_all(filename[i], 'x')
	929	y = nc_read_from_file_2d_all(filename[i], 'y')
	930	nvegetation_pars = np.arange(0,12)
	931	vegetation_pars = np.ones((len(nvegetation_pars),len(y),len(x)))
	932	vegetation_pars[:,:,:] = fillvalues["vegetation_pars"]
[3629]	933
[3668]	934	vegetation_pars[1,:,:] = lai
[3629]	935
[3668]	936
	937	# Write out first version of LAI. Will later be overwritten.
	938	nc_write_dimension(filename[i], 'nvegetation_pars', nvegetation_pars, datatypes["nvegetation_pars"])
	939	nc_write_to_file_3d(filename[i], 'vegetation_pars', vegetation_pars, datatypes["vegetation_pars"],'nvegetation_pars','y','x',fillvalues["vegetation_pars"])
	940	nc_write_attribute(filename[i], 'vegetation_pars', 'long_name', 'vegetation_pars')
	941	nc_write_attribute(filename[i], 'vegetation_pars', 'units', '')
	942	nc_write_attribute(filename[i], 'vegetation_pars', 'res_orig', domain_px[i])
	943	nc_write_attribute(filename[i], 'vegetation_pars', 'coordinates', 'E_UTM N_UTM lon lat')
	944	nc_write_attribute(filename[i], 'vegetation_pars', 'grid_mapping', 'E_UTM N_UTM lon lat')
	945
	946	del lai, vegetation_pars, vegetation_type
	947
	948	# Read tree data and create LAD and BAD arrays using the canopy generator
	949	for i in range(0,ndomains):
	950	if domain_street_trees[i]:
	951
	952	vegetation_pars = nc_read_from_file_2d_all(filename[i], 'vegetation_pars')
	953
	954	lai = np.copy(vegetation_pars[1,:,:])
	955
[3629]	956	x = nc_read_from_file_2d_all(filename[i], 'x')
	957	y = nc_read_from_file_2d_all(filename[i], 'y')
	958
[3668]	959	# Save lai data as default for low and high vegetation
	960	lai_low = lai
	961	lai_high = lai
	962
	963	# Read all tree parameters from file
	964	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]	965
[3668]	966	if (input_file_tree_crown_diameter[ii[i]] is not None):
	967	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])
	968	tree_crown_diameter = np.where( (tree_crown_diameter == 0.0) \| (tree_crown_diameter == -1.0) ,fillvalues["tree_data"],tree_crown_diameter)
	969	else:
	970	tree_crown_diameter = np.ones((len(y),len(x)))
	971	tree_crown_diameter[:,:] = fillvalues["tree_data"]
[3629]	972
[3668]	973
[3629]	974	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])
	975	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])
	976	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])
	977
	978	# Remove missing values from the data. Reasonable values will be set by the tree generator
	979	tree_height = np.where( (tree_height == 0.0) \| (tree_height == -1.0) ,fillvalues["tree_data"],tree_height)
	980	tree_trunk_diameter = np.where( (tree_trunk_diameter == 0.0) \| (tree_trunk_diameter == -1.0) ,fillvalues["tree_data"],tree_trunk_diameter)
	981	tree_type = np.where( (tree_type == 0.0) \| (tree_type == -1.0) ,fillvalues["tree_data"],tree_type)
	982	patch_height = np.where( patch_height == -1.0 ,fillvalues["tree_data"],patch_height)
	983
	984	# Convert trunk diameter from cm to m
	985	tree_trunk_diameter = np.where(tree_trunk_diameter != fillvalues["tree_data"], tree_trunk_diameter * 0.01,tree_trunk_diameter)
	986
	987
	988	# Temporarily change missing value for tree_type
	989	tree_type = np.where( (tree_type == fillvalues["tree_type"]),fillvalues["tree_data"],tree_type)
	990
	991	# Compare patch height array with vegetation type and correct accordingly
	992	vegetation_type = nc_read_from_file_2d_all(filename[i], 'vegetation_type')
	993
	994
	995	# For zero-height patches, set vegetation_type to short grass and remove these pixels from the patch height array
	996	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]	997	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)
	998
[3629]	999
	1000	max_tree_height = max(tree_height.flatten())
	1001	max_patch_height = max(patch_height.flatten())
	1002
	1003	if ( (max_tree_height != fillvalues["tree_data"]) \| (max_patch_height == fillvalues["tree_data"]) ):
[3668]	1004
[3629]	1005	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"])
	1006
	1007
	1008	# Remove LAD volumes that are inside buildings
	1009	buildings_2d = nc_read_from_file_2d_all(filename[i], 'buildings_2d')
	1010	for k in range(0,len(zlad)-1):
	1011
	1012	lad[k,:,:] = np.where(buildings_2d == fillvalues["buildings_2d"],lad[k,:,:],fillvalues["tree_data"])
	1013	bad[k,:,:] = np.where(buildings_2d == fillvalues["buildings_2d"],bad[k,:,:],fillvalues["tree_data"])
	1014	tree_ids[k,:,:] = np.where(buildings_2d == fillvalues["buildings_2d"],tree_ids[k,:,:],fillvalues["tree_data"])
[3668]	1015
	1016	del buildings_2d
[3629]	1017
	1018	nc_write_dimension(filename[i], 'zlad', zlad, datatypes["tree_data"])
	1019	nc_write_to_file_3d(filename[i], 'lad', lad, datatypes["tree_data"],'zlad','y','x',fillvalues["tree_data"])
	1020	nc_write_attribute(filename[i], 'lad', 'long_name', 'leaf area density')
	1021	nc_write_attribute(filename[i], 'lad', 'units', '')
	1022	nc_write_attribute(filename[i], 'lad', 'res_orig', domain_px[i])
	1023	nc_write_attribute(filename[i], 'lad', 'coordinates', 'E_UTM N_UTM lon lat')
	1024	nc_write_attribute(filename[i], 'lad', 'grid_mapping', 'E_UTM N_UTM lon lat')
	1025
	1026	nc_write_to_file_3d(filename[i], 'bad', bad, datatypes["tree_data"],'zlad','y','x',fillvalues["tree_data"])
	1027	nc_write_attribute(filename[i], 'bad', 'long_name', 'basal area density')
	1028	nc_write_attribute(filename[i], 'bad', 'units', '')
	1029	nc_write_attribute(filename[i], 'bad', 'res_orig', domain_px[i])
	1030	nc_write_attribute(filename[i], 'bad', 'coordinates', 'E_UTM N_UTM lon lat')
	1031	nc_write_attribute(filename[i], 'bad', 'grid_mapping', 'E_UTM N_UTM lon lat')
	1032
	1033	nc_write_to_file_3d(filename[i], 'tree_id', tree_ids, datatypes["tree_data"],'zlad','y','x',fillvalues["tree_data"])
	1034	nc_write_attribute(filename[i], 'tree_id', 'long_name', 'tree id')
	1035	nc_write_attribute(filename[i], 'tree_id', 'units', '')
	1036	nc_write_attribute(filename[i], 'tree_id', 'res_orig', domain_px[i])
	1037	nc_write_attribute(filename[i], 'tree_id', 'coordinates', 'E_UTM N_UTM lon lat')
	1038	nc_write_attribute(filename[i], 'tree_id', 'grid_mapping', 'E_UTM N_UTM lon lat')
	1039
[3668]	1040	del lai, lad, bad, tree_ids, zlad
[3629]	1041
[3668]	1042	del vegetation_pars, tree_height, tree_crown_diameter, tree_trunk_diameter, tree_type, patch_height, x, y
[3629]	1043
	1044
	1045	# Create vegetation patches for locations with high vegetation type
	1046	for i in range(0,ndomains):
	1047	if domain_canopy_patches[i]:
	1048
	1049	# Load vegetation_type and lad array (at level z = 0) for re-processing
	1050	vegetation_type = nc_read_from_file_2d_all(filename[i], 'vegetation_type')
	1051	lad = nc_read_from_file_3d_all(filename[i], 'lad')
[3668]	1052	zlad = nc_read_from_file_1d_all(filename[i], 'zlad')
[3629]	1053	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])
	1054	vegetation_pars = nc_read_from_file_3d_all(filename[i], 'vegetation_pars')
	1055	lai = vegetation_pars[1,:,:]
[3726]	1056
[3629]	1057
[3726]	1058	# 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]	1059	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]	1060
[3726]	1061	# Now, assign either the default LAI for high vegetation or keep 1.0 from the lai_high array.
	1062	lai_high = np.where( (lai_high != fillvalues["tree_data"]) & (lai == fillvalues["tree_data"]), settings_lai_high_default, lai_high)
[3668]	1063
[3726]	1064	# If lai values are available in the lai array, write them on the lai_high array
	1065	lai_high = np.where( (lai_high != fillvalues["tree_data"]) & (lai != fillvalues["tree_data"]), lai, lai_high)
	1066
[3668]	1067	# Define a patch height wherever it is missing, but where a high vegetation LAI was set
[3629]	1068	patch_height = np.where ( (lai_high != fillvalues["tree_data"]) & (patch_height == fillvalues["tree_data"]), settings_patch_height_default, patch_height)
	1069
	1070	# Remove pixels where street trees were already set
	1071	patch_height = np.where ( (lad[0,:,:] != fillvalues["tree_data"]), fillvalues["tree_data"], patch_height)
	1072
[3668]	1073	# Remove patch heights that have no lai_high value
	1074	patch_height = np.where ( (lai_high == fillvalues["tree_data"]), fillvalues["tree_data"], patch_height)
	1075
[3629]	1076	# For missing LAI values, set either the high vegetation default or the low vegetation default
[3668]	1077	lai_high = np.where( (patch_height > 2.0) & (patch_height != fillvalues["tree_data"]) & (lai_high == fillvalues["tree_data"]),settings_lai_high_default,lai_high)
	1078	lai_high = np.where( (patch_height <= 2.0) & (patch_height != fillvalues["tree_data"]) & (lai_high == fillvalues["tree_data"]),settings_lai_low_default,lai_high)
	1079
[3629]	1080	if ( max(patch_height.flatten()) >= (2.0 * domain_dz[i]) ):
[3726]	1081	print(" start calculating LAD (this might take some time)")
	1082
	1083
[3668]	1084	lad_patch, patch_nz, status = process_patch(domain_dz[i],patch_height,max(zlad),lai_high,settings_lai_alpha,settings_lai_beta)
[3629]	1085
	1086	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,:,:] )
	1087
	1088	# Remove high vegetation wherever it is replaced by a leaf area density. This should effectively remove all high vegetation pixels
[3955]	1089	vegetation_type = np.where((lad[0,:,:] != fillvalues["tree_data"]) & (vegetation_type != fillvalues["vegetation_type"]),settings_veg_type_below_trees,vegetation_type)
[3668]	1090
[3629]	1091	# If desired, remove all high vegetation. TODO: check if this is still necessary
[3688]	1092	if not domain_high_vegetation[i]:
[3629]	1093	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)
	1094
	1095
[3668]	1096	# Set default low LAI for pixels with an LAD (short grass below trees)
	1097	lai_low = np.where( (lad[0,:,:] == fillvalues["tree_data"]), lai, settings_lai_low_default)
[3629]	1098
[3668]	1099	# Fill low vegetation pixels without LAI set or with LAI = 0 with default value
	1100	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]	1101
[3668]	1102	# Remove lai for pixels that have no vegetation_type
[3955]	1103	lai_low = np.where( ( (vegetation_type != fillvalues["vegetation_type"]) & (vegetation_type != 1) ), lai_low, fillvalues["tree_data"])
[3668]	1104
[3629]	1105	# Overwrite lai in vegetation_parameters
[3668]	1106	vegetation_pars[1,:,:] = np.copy(lai_low)
[3629]	1107	nc_overwrite_to_file_3d(filename[i], 'vegetation_pars', vegetation_pars)
	1108
	1109	# Overwrite lad array
	1110	nc_overwrite_to_file_3d(filename[i], 'lad', lad)
[3668]	1111
	1112	nc_overwrite_to_file_2d(filename[i], 'vegetation_type', vegetation_type)
	1113
[3629]	1114
[3668]	1115	del vegetation_type, lad, lai, patch_height, vegetation_pars, zlad
	1116
[3955]	1117
	1118	# Final adjustment of vegetation parameters: remove LAI where a bare soil was set
	1119	for i in range(0,ndomains):
	1120
	1121	vegetation_type = nc_read_from_file_2d_all(filename[i], 'vegetation_type')
	1122	vegetation_pars = nc_read_from_file_3d_all(filename[i], 'vegetation_pars')
	1123	lai = vegetation_pars[1,:,:]
	1124
	1125
	1126	# Remove lai for pixels that have no vegetation_type
	1127	lai = np.where( ( (vegetation_type != fillvalues["vegetation_type"]) & (vegetation_type != 1) ), lai, fillvalues["tree_data"])
	1128
	1129	# Overwrite lai in vegetation_parameters
	1130	vegetation_pars[1,:,:] = np.copy(lai)
	1131	nc_overwrite_to_file_3d(filename[i], 'vegetation_pars', vegetation_pars)
	1132
	1133	del vegetation_type, lai, vegetation_pars
	1134
	1135
[3668]	1136	# Final consistency check
	1137	for i in range(0,ndomains):
	1138	vegetation_type = nc_read_from_file_2d_all(filename[i], 'vegetation_type')
	1139	pavement_type = nc_read_from_file_2d_all(filename[i], 'pavement_type')
	1140	building_type = nc_read_from_file_2d_all(filename[i], 'building_type')
	1141	water_type = nc_read_from_file_2d_all(filename[i], 'water_type')
	1142	soil_type = nc_read_from_file_2d_all(filename[i], 'soil_type')
	1143
	1144	# Check for consistency and fill empty fields with default vegetation type
	1145	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"])
	1146
	1147	# Check for consistency and fill empty fields with default vegetation type
	1148	consistency_array, test = check_consistency_3(vegetation_type,pavement_type,soil_type,fillvalues["vegetation_type"],fillvalues["pavement_type"],fillvalues["soil_type"])
	1149
	1150	surface_fraction = nc_read_from_file_3d_all(filename[i], 'surface_fraction')
	1151	surface_fraction[0,:,:] = np.where(vegetation_type != fillvalues["vegetation_type"], 1.0, 0.0)
	1152	surface_fraction[1,:,:] = np.where(pavement_type != fillvalues["pavement_type"], 1.0, 0.0)
	1153	surface_fraction[2,:,:] = np.where(water_type != fillvalues["water_type"], 1.0, 0.0)
	1154	nc_overwrite_to_file_3d(filename[i], 'surface_fraction', surface_fraction)
	1155
	1156	del vegetation_type, pavement_type, building_type, water_type, soil_type

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