Home

Context Navigation

source: palm/trunk/SCRIPTS/palm_csd @ 3954

Last change on this file since 3954 was 3859, checked in by maronga, 6 years ago
comments in radiation model updated, minor bugfix in palm_csd
Property svn:executable set to ``* Property svn:keywords set to `Id`
File size: 56.0 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 3859 2019-04-03 20:30:31Z gronemeier $
[3859]	27	# Bugfix: wrong variable naming for 'y'
	28	#
	29	# 3773 2019-03-01 08:56:57Z maronga
[3773]	30	# Unspecificed changes
	31	#
	32	# 3726 2019-02-07 18:22:49Z maronga
[3726]	33	# Removed some more bugs
	34	#
	35	# 3688 2019-01-22 10:44:20Z maronga
[3688]	36	# Some unspecified bugfixes
	37	#
	38	# 3668 2019-01-14 12:49:24Z maronga
[3668]	39	# Various improvements and bugfixes
	40	#
	41	# 3629 2018-12-13 12:18:54Z maronga
[3629]	42	# Added canopy generator calls. Some improvements
	43	#
	44	# 3567 2018-11-27 13:59:21Z maronga
[3567]	45	# Initial revisions
	46	#
	47	# Description:
	48	# ------------
	49	# Processing tool for creating PIDS conform static drivers from rastered NetCDF
	50	# input
	51	#
	52	# @Author Bjoern Maronga (maronga@muk.uni-hannover.de)
	53	#
	54	# @todo Make input files optional
	55	# @todo Allow for ASCII input of terrain height and building height
	56	# @todo Modularize reading config file
[3688]	57	# @todo Convert to object-oriented treatment (buidings, trees)
	58	# @todo Automatically shift child domains so that their origin lies intersects
	59	# a edge note of the parent grid
[3567]	60	#------------------------------------------------------------------------------#
	61
	62	from palm_csd_files.palm_csd_netcdf_interface import *
	63	from palm_csd_files.palm_csd_tools import *
[3629]	64	from palm_csd_files.palm_csd_canopy_generator import *
[3567]	65	import numpy as np
	66
	67
	68	def read_config_file():
	69
	70	import configparser
	71	from math import floor
	72	import numpy as np
	73	import os
	74	import subprocess as sub
	75	import sys
	76
	77	# Check if configuration files exists and quit otherwise
	78	input_config = ".csd.config"
	79	for i in range(1,len(sys.argv)):
	80	input_config = str(sys.argv[i])
	81
	82	config = configparser.RawConfigParser(allow_no_value=True)
	83
	84	if ( os.path.isfile(input_config) == False ):
	85	print ("Error. No configuration file " + input_config + " found.")
	86	raise SystemExit
	87
	88	config.read(input_config)
	89
	90
	91	# Definition of settings
[3668]	92
[3567]	93	global settings_bridge_width
[3629]	94	global settings_lai_roof_intensive
	95	global settings_lai_roof_extensive
	96	global settings_season
	97	global settings_lai_low_default
	98	global settings_lai_high_default
	99	global settings_patch_height_default
	100	global settings_lai_alpha
	101	global settings_lai_beta
[3567]	102	global ndomains
	103
	104	# Definition of global configuration parameters
	105	global global_acronym
	106	global global_angle
	107	global global_author
	108	global global_campaign
	109	global global_comment
	110	global global_contact
	111	global global_data_content
	112	global global_dependencies
	113	global global_institution
	114	global global_keywords
	115	global global_location
	116	global global_palm_version
	117	global global_references
	118	global global_site
	119	global global_source
	120
[3668]	121	global path_out
	122	global filename_out
	123	global version_out
[3567]	124
[3668]	125
[3567]	126	# Definition of domain parameters
	127	global domain_names
	128	global domain_px
	129	global domain_x0
	130	global domain_y0
	131	global domain_x1
	132	global domain_y1
	133	global domain_nx
	134	global domain_ny
	135	global domain_dz
	136	global domain_3d
[3629]	137	global domain_high_vegetation
[3567]	138	global domain_ip
	139	global domain_za
	140	global domain_parent
[3629]	141	global domain_green_roofs
	142	global domain_street_trees
	143	global domain_canopy_patches
[3567]	144
	145	# Definition of input data parameters
	146	global input_names
	147	global input_px
	148
	149
	150	global input_file_x
	151	global input_file_y
	152	global input_file_x_UTM
	153	global input_file_y_UTM
	154	global input_file_lat
	155	global input_file_lon
	156	global input_file_zt
	157	global input_file_buildings_2d
	158	global input_file_bridges_2d
	159	global input_file_building_id
	160	global input_file_bridges_id
	161	global input_file_building_type
	162	global input_file_building_type
[3629]	163	global input_file_lai
[3567]	164	global input_file_vegetation_type
	165	global input_file_vegetation_height
	166	global input_file_pavement_type
	167	global input_file_water_type
	168	global input_file_street_type
	169	global input_file_street_crossings
	170	global input_file_soil_type
[3629]	171	global input_file_vegetation_on_roofs
	172	global input_file_tree_crown_diameter
	173	global input_file_tree_height
	174	global input_file_tree_trunk_diameter
	175	global input_file_tree_type
	176	global input_file_patch_height
[3567]	177
	178	global zt_all
	179	global zt_min
	180
	181	settings_bridge_width = 3.0
[3629]	182	settings_lai_roof_intensive = 0.5
	183	settings_lai_roof_extensive = 1.0
	184	settings_season = "summer"
	185	settings_lai_high_default = 6.0
	186	settings_lai_low_default = 1.0
	187	settings_patch_height_default = 10.0
	188	settings_lai_alpha = 5.0
	189	settings_lai_beta = 3.0
[3567]	190	ndomains = 0
	191
	192	global_acronym = ""
	193	global_angle = ""
	194	global_author = ""
	195	global_campaign = ""
	196	global_comment = ""
	197	global_contact = ""
	198	global_data_content = ""
	199	global_dependencies = ""
	200	global_institution = ""
	201	global_keywords = ""
	202	global_location = ""
	203	global_palm_version = 6.0
	204	global_references = ""
	205	global_site = ""
	206	global_source = ""
	207
[3668]	208	path_out = ""
	209	version_out = 1
	210	filename_out = "default"
	211
[3567]	212	domain_names = []
	213	domain_px = []
	214	domain_x0 = []
	215	domain_y0 = []
	216	domain_x1 = []
	217	domain_y1 = []
	218	domain_nx = []
	219	domain_ny = []
	220	domain_dz = []
	221	domain_3d = []
[3629]	222	domain_high_vegetation = []
[3567]	223	domain_ip = []
	224	domain_za = []
	225	domain_parent = []
[3629]	226	domain_green_roofs = []
	227	domain_street_trees = []
	228	domain_canopy_patches = []
[3567]	229
	230	zt_min = 0.0
	231	zt_all = []
	232
	233	input_names = []
	234	input_px = []
	235
	236	input_file_x = []
	237	input_file_y = []
	238	input_file_x_UTM = []
	239	input_file_y_UTM = []
	240	input_file_lat = []
	241	input_file_lon = []
	242
	243	input_file_zt = []
	244	input_file_buildings_2d = []
	245	input_file_bridges_2d = []
	246	input_file_building_id = []
	247	input_file_bridges_id = []
	248	input_file_building_type = []
[3629]	249	input_file_lai = []
[3567]	250	input_file_vegetation_type = []
	251	input_file_vegetation_height = []
	252	input_file_pavement_type = []
	253	input_file_water_type = []
	254	input_file_street_type = []
	255	input_file_street_crossings = []
	256	input_file_soil_type = []
[3629]	257	input_file_vegetation_on_roofs = []
	258	input_file_tree_crown_diameter = []
	259	input_file_tree_height = []
	260	input_file_tree_trunk_diameter = []
	261	input_file_tree_type = []
	262	input_file_patch_height = []
[3567]	263
	264	# Load all user parameters from config file
	265	for i in range(0,len(config.sections())):
	266
	267	read_tmp = config.sections()[i]
	268
	269	if ( read_tmp == 'global' ):
	270	global_acronym = config.get(read_tmp, 'acronym')
	271	global_angle = float(config.get(read_tmp, 'rotation_angle'))
	272	global_author = config.get(read_tmp, 'author')
	273	global_campaign = config.get(read_tmp, 'campaign')
	274	global_comment = config.get(read_tmp, 'comment')
	275	global_contact = config.get(read_tmp, 'contact_person')
	276	global_data_content = config.get(read_tmp, 'data_content')
	277	global_dependencies = config.get(read_tmp, 'dependencies')
	278	global_institution = config.get(read_tmp, 'institution')
	279	global_keywords = config.get(read_tmp, 'keywords')
	280	global_location = config.get(read_tmp, 'location')
	281	global_palm_version = float(config.get(read_tmp, 'palm_version'))
	282	global_references = config.get(read_tmp, 'references')
	283	global_site = config.get(read_tmp, 'site')
	284	global_source = config.get(read_tmp, 'source')
	285
[3668]	286
[3567]	287	if ( read_tmp == 'settings' ):
[3629]	288	settings_lai_roof_intensive = config.get(read_tmp, 'lai_roof_intensive')
	289	settings_lai_roof_extensive = config.get(read_tmp, 'lai_roof_extensive')
	290	settings_bridge_width = float(config.get(read_tmp, 'bridge_width'))
	291	settings_season = config.get(read_tmp, 'season')
	292	settings_lai_high_default = float(config.get(read_tmp, 'lai_high_vegetation_default'))
	293	settings_lai_low_default = float(config.get(read_tmp, 'lai_low_vegetation_default'))
	294	settings_patch_height_default = float(config.get(read_tmp, 'patch_height_default'))
	295	settings_lai_alpha = float(config.get(read_tmp, 'lai_alpha'))
	296	settings_lai_beta = float(config.get(read_tmp, 'lai_beta'))
	297
[3668]	298	if ( read_tmp == 'output' ):
	299	path_out = config.get(read_tmp, 'path')
	300	filename_out = config.get(read_tmp, 'file_out')
	301	version_out = float(config.get(read_tmp, 'version'))
	302
[3567]	303	if ( read_tmp.split("_")[0] == 'domain' ):
	304	ndomains = ndomains + 1
	305	domain_names.append(read_tmp.split("_")[1])
	306	domain_px.append(float(config.get(read_tmp, 'pixel_size')))
	307	domain_nx.append(int(config.get(read_tmp, 'nx')))
	308	domain_ny.append(int(config.get(read_tmp, 'ny')))
	309	domain_dz.append(float(config.get(read_tmp, 'dz')))
	310	domain_3d.append(config.getboolean(read_tmp, 'buildings_3d'))
[3629]	311	domain_high_vegetation.append(config.getboolean(read_tmp, 'allow_high_vegetation'))
	312	domain_canopy_patches.append(config.getboolean(read_tmp, 'generate_vegetation_patches'))
[3567]	313	domain_ip.append(config.getboolean(read_tmp, 'interpolate_terrain'))
	314	domain_za.append(config.getboolean(read_tmp, 'use_palm_z_axis'))
	315	if domain_ip[ndomains-1] and not domain_za[ndomains-1]:
	316	domain_za[ndomains-1] = True
	317	print("+++ Overwrite user setting for use_palm_z_axis")
	318
	319	domain_parent.append(config.get(read_tmp, 'domain_parent'))
	320
	321	domain_x0.append(int(floor(float(config.get(read_tmp, 'origin_x'))/float(config.get(read_tmp, 'pixel_size')))))
	322	domain_y0.append(int(floor(float(config.get(read_tmp, 'origin_y'))/float(config.get(read_tmp, 'pixel_size')))))
	323	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'))))
	324	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]	325	domain_green_roofs.append(config.getboolean(read_tmp, 'vegetation_on_roofs'))
	326	domain_street_trees.append(config.getboolean(read_tmp, 'street_trees'))
	327
[3567]	328	if ( read_tmp.split("_")[0] == 'input' ):
	329	input_names.append(read_tmp.split("_")[1])
	330	input_px.append(float(config.get(read_tmp, 'pixel_size')))
	331	input_file_x.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_x'))
	332	input_file_y.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_y'))
	333	input_file_lat.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_lat'))
	334	input_file_lon.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_lon'))
	335	input_file_x_UTM.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_x_UTM'))
	336	input_file_y_UTM.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_y_UTM'))
	337	input_file_zt.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_zt'))
	338	input_file_buildings_2d.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_buildings_2d'))
	339	input_file_bridges_2d.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_bridges_2d'))
	340	input_file_building_id.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_building_id'))
	341	input_file_bridges_id.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_bridges_id'))
[3629]	342	input_file_building_type.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_building_type'))
	343	input_file_lai.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_lai'))
[3567]	344	input_file_vegetation_type.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_vegetation_type'))
	345	input_file_vegetation_height.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_vegetation_height'))
	346	input_file_pavement_type.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_pavement_type'))
	347	input_file_water_type.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_water_type'))
	348	input_file_street_type.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_street_type'))
[3629]	349	input_file_street_crossings.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_street_crossings'))
	350	input_file_patch_height.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_patch_height'))
[3668]	351
	352	tmp = config.get(read_tmp, 'file_tree_crown_diameter')
	353	if tmp is not None:
	354	input_file_tree_crown_diameter.append(config.get(read_tmp, 'path') + "/" + tmp)
	355	else:
	356	input_file_tree_crown_diameter.append(None)
[3629]	357	input_file_tree_height.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_tree_height'))
	358	input_file_tree_trunk_diameter.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_tree_trunk_diameter'))
	359	input_file_tree_type.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_tree_type'))
	360	input_file_vegetation_on_roofs.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_vegetation_on_roofs'))
[3567]	361	#input_file_soil_type.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_soil_type'))
	362	return 0
	363
	364
	365	############################################################
	366
	367	# Start of main program
	368	datatypes = {
	369	"x": "f4",
	370	"y": "f4",
	371	"z": "f4",
	372	"lat": "f4",
	373	"lon": "f4",
	374	"E_UTM": "f4",
	375	"N_UTM": "f4",
	376	"zt": "f4",
	377	"buildings_2d": "f4",
	378	"buildings_3d": "b",
	379	"bridges_2d": "f4",
	380	"building_id": "i",
	381	"bridges_id": "i",
	382	"building_type": "b",
	383	"nsurface_fraction": "i",
	384	"vegetation_type": "b",
	385	"vegetation_height": "f4",
	386	"pavement_type": "b",
	387	"water_type": "b",
	388	"street_type": "b",
	389	"street_crossings": "b",
	390	"soil_type": "b",
[3629]	391	"surface_fraction": "f4",
	392	"building_pars": "f4",
	393	"vegetation_pars": "f4",
	394	"tree_data": "f4",
	395	"tree_type": "b",
	396	"nbuilding_pars": "i",
	397	"nvegetation_pars": "i",
	398	"zlad": "f4"
[3567]	399	}
	400
	401	fillvalues = {
	402	"lat": float(-9999.0),
	403	"lon": float(-9999.0),
	404	"E_UTM": float(-9999.0),
	405	"N_UTM": float(-9999.0),
	406	"zt": float(-9999.0),
	407	"buildings_2d": float(-9999.0),
	408	"buildings_3d": np.byte(-127),
	409	"bridges_2d": float(-9999.0),
	410	"building_id": int(-9999),
	411	"bridges_id": int(-9999),
	412	"building_type": np.byte(-127),
	413	"nsurface_fraction": int(-9999),
	414	"vegetation_type": np.byte(-127),
	415	"vegetation_height": float(-9999.0),
	416	"pavement_type": np.byte(-127),
	417	"water_type": np.byte(-127),
	418	"street_type": np.byte(-127),
	419	"street_crossings": np.byte(-127),
	420	"soil_type": np.byte(-127),
[3629]	421	"surface_fraction": float(-9999.0),
	422	"building_pars": float(-9999.0),
	423	"vegetation_pars": float(-9999.0),
	424	"tree_data": float(-9999.0),
	425	"tree_type": np.byte(-127)
[3567]	426	}
	427
	428	defaultvalues = {
	429	"lat": float(-9999.0),
	430	"lon": float(-9999.0),
	431	"E_UTM": float(-9999.0),
	432	"N_UTM": float(-9999.0),
	433	"zt": float(0.0),
	434	"buildings_2d": float(0.0),
	435	"buildings_3d": 0,
	436	"bridges_2d": float(0.0),
	437	"building_id": int(0),
	438	"bridges_id": int(0),
	439	"building_type": 1,
	440	"nsurface_fraction": int(-9999),
	441	"vegetation_type": 3,
	442	"vegetation_height": float(-9999.0),
	443	"pavement_type": 1,
	444	"water_type": 1,
	445	"street_type": 1,
	446	"street_crossings": 0,
	447	"soil_type": 1,
[3629]	448	"surface_fraction": float(0.0),
	449	"buildings_pars": float(-9999.0),
	450	"tree_data": float(-9999.0),
	451	"tree_type": 0,
	452	"vegetation_pars": float(-9999.0)
[3567]	453	}
	454
	455	# Read configuration file and set parameters accordingly
	456	read_config_file()
	457
	458
	459	filename = []
	460	ii = []
	461	ii_parent = []
	462	# Define indices and filenames for all domains and create netCDF files
	463	for i in range(0,ndomains):
	464
	465	# Calculate indices and input files
	466	ii.append(input_px.index(domain_px[i]))
[3668]	467	filename.append(path_out + "/" + filename_out + "_" + domain_names[i])
[3567]	468	if domain_parent[i] is not None:
[3726]	469	ii_parent.append(input_px.index(domain_px[domain_names.index(domain_parent[i])]))
[3567]	470	else:
	471	ii_parent.append(None)
	472
	473
[3726]	474	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)
	475	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)
	476	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)
	477	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]	478
[3726]	479	# Calculate position of origin
	480	x_UTM_origin = float(x_UTM[0,0]) - 0.5 * (float(x_UTM[0,1]) - float(x_UTM[0,0]))
	481	y_UTM_origin = float(y_UTM[0,0]) - 0.5 * (float(y_UTM[1,0]) - float(y_UTM[0,0]))
	482	x_origin = float(lon[0,0]) - 0.5 * (float(lon[0,1]) - float(lon[0,0]))
	483	y_origin = float(lat[0,0]) - 0.5 * (float(lat[1,0]) - float(lat[0,0]))
	484
[3567]	485	# Create NetCDF output file and set global attributes
	486	nc_create_file(filename[i])
[3726]	487	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]	488
[3688]	489	del x_UTM, y_UTM, lat, lon
[3567]	490
	491	# Process terrain height
	492	for i in range(0,ndomains):
	493
	494	# Read and write terrain height (zt)
	495	zt = nc_read_from_file_2d(input_file_zt[ii[i]], 'Band1', domain_x0[i], domain_x1[i], domain_y0[i], domain_y1[i])
	496
	497	# Final step: add zt array to the global array
	498	zt_all.append(zt)
	499	del zt
	500
	501	# Calculate the global (all domains) minimum of the terrain height. This value will be substracted for all
	502	# data sets
	503	zt_min = min(zt_all[0].flatten())
	504	for i in range(0,ndomains):
	505	zt_min = min(zt_min,min(zt_all[i].flatten()))
	506
	507	del zt_all[:]
	508
[3726]	509	print( "Shift terrain heights by -" + str(zt_min))
[3567]	510	for i in range(0,ndomains):
	511
	512	# Read and write terrain height (zt)
	513	zt = nc_read_from_file_2d(input_file_zt[ii[i]], 'Band1', domain_x0[i], domain_x1[i], domain_y0[i], domain_y1[i])
	514	x = nc_read_from_file_1d(input_file_x[ii[i]], "x", domain_x0[i], domain_x1[i])
	515	y = nc_read_from_file_1d(input_file_y[ii[i]], "y", domain_y0[i], domain_y1[i])
	516
[3726]	517
[3567]	518	zt = zt - zt_min
[3668]	519
	520	nc_write_global_attribute(filename[i], 'origin_z', float(zt_min))
[3567]	521
	522	# If necessary, interpolate parent domain terrain height on child domain grid and blend the two
	523	if domain_ip[i]:
[3726]	524	parent_id = domain_names.index(domain_parent[i])
	525	tmp_x0 = int( domain_x0[i] * domain_px[i] / domain_px[parent_id] ) - 1
	526	tmp_y0 = int( domain_y0[i] * domain_px[i] / domain_px[parent_id] ) - 1
	527	tmp_x1 = int( domain_x1[i] * domain_px[i] / domain_px[parent_id] ) + 1
	528	tmp_y1 = int( domain_y1[i] * domain_px[i] / domain_px[parent_id] ) + 1
	529
[3567]	530	tmp_x = nc_read_from_file_1d(input_file_x[ii_parent[i]], "x", tmp_x0, tmp_x1)
	531	tmp_y = nc_read_from_file_1d(input_file_y[ii_parent[i]], "y", tmp_y0, tmp_y1)
	532
	533	zt_parent = nc_read_from_file_2d(input_file_zt[ii_parent[i]], 'Band1', tmp_x0, tmp_x1, tmp_y0, tmp_y1)
	534
	535	zt_parent = zt_parent - zt_min
	536
	537	# Interpolate array and bring to PALM grid of child domain
	538	zt_ip = interpolate_2d(zt_parent,tmp_x,tmp_y,x,y)
[3726]	539	zt_ip = bring_to_palm_grid(zt_ip,x,y,domain_dz[parent_id])
	540
	541
	542	# Shift the child terrain height according to the parent mean terrain height
[3773]	543	print("shifting: -" + str(np.mean(zt)) + " +" + str(np.mean(zt_ip)))
	544	#zt = zt - np.min(zt) + np.min(zt_ip)
[3726]	545	zt = zt - np.mean(zt) + np.mean(zt_ip)
[3567]	546
	547
	548	# Blend over the parent and child terrain height within a radius of 50 px
	549	zt = blend_array_2d(zt,zt_ip,50)
[3773]	550	# zt = zt_ip
[3567]	551
	552	# Final step: add zt array to the global array
[3726]	553
[3567]	554	zt_all.append(zt)
	555	del zt
	556
	557
	558	# Read and shift x and y coordinates, shift terrain height according to its minimum value and write all data
	559	# to file
	560	for i in range(0,ndomains):
	561	# Read horizontal grid variables from zt file and write them to output file
	562	x = nc_read_from_file_1d(input_file_x[ii[i]], "x", domain_x0[i], domain_x1[i])
	563	y = nc_read_from_file_1d(input_file_y[ii[i]], "y", domain_y0[i], domain_y1[i])
	564	x = x - min(x.flatten()) + domain_px[i]/2.0
	565	y = y - min(y.flatten()) + domain_px[i]/2.0
	566	nc_write_dimension(filename[i], 'x', x, datatypes["x"])
	567	nc_write_dimension(filename[i], 'y', y, datatypes["y"])
	568	nc_write_attribute(filename[i], 'x', 'long_name', 'x')
	569	nc_write_attribute(filename[i], 'x', 'standard_name','projection_x_coordinate')
	570	nc_write_attribute(filename[i], 'x', 'units', 'm')
[3859]	571	nc_write_attribute(filename[i], 'y', 'long_name', 'y')
[3567]	572	nc_write_attribute(filename[i], 'y', 'standard_name', 'projection_y_coordinate')
	573	nc_write_attribute(filename[i], 'y', 'units', 'm')
	574
	575	lat = nc_read_from_file_2d(input_file_lat[ii[i]], "Band1", domain_x0[i], domain_x1[i], domain_y0[i], domain_y1[i])
	576	lon = nc_read_from_file_2d(input_file_lon[ii[i]], "Band1", domain_x0[i], domain_x1[i], domain_y0[i], domain_y1[i])
	577
	578	nc_write_to_file_2d(filename[i], 'lat', lat, datatypes["lat"],'y','x',fillvalues["lat"])
	579	nc_write_to_file_2d(filename[i], 'lon', lon, datatypes["lon"],'y','x',fillvalues["lon"])
	580
	581	nc_write_attribute(filename[i], 'lat', 'long_name', 'latitude')
	582	nc_write_attribute(filename[i], 'lat', 'standard_name','latitude')
	583	nc_write_attribute(filename[i], 'lat', 'units', 'degrees_north')
	584
	585	nc_write_attribute(filename[i], 'lon', 'long_name', 'longitude')
	586	nc_write_attribute(filename[i], 'lon', 'standard_name','longitude')
	587	nc_write_attribute(filename[i], 'lon', 'units', 'degrees_east')
	588
	589	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])
	590	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]	591
[3567]	592
	593	nc_write_to_file_2d(filename[i], 'E_UTM', x_UTM, datatypes["E_UTM"],'y','x',fillvalues["E_UTM"])
	594	nc_write_to_file_2d(filename[i], 'N_UTM', y_UTM, datatypes["N_UTM"],'y','x',fillvalues["N_UTM"])
	595
	596	nc_write_attribute(filename[i], 'E_UTM', 'long_name', 'easting')
	597	nc_write_attribute(filename[i], 'E_UTM', 'standard_name','projection_x_coorindate')
	598	nc_write_attribute(filename[i], 'E_UTM', 'units', 'm')
	599
	600	nc_write_attribute(filename[i], 'N_UTM', 'long_name', 'northing')
	601	nc_write_attribute(filename[i], 'N_UTM', 'standard_name','projection_y_coorindate')
	602	nc_write_attribute(filename[i], 'N_UTM', 'units', 'm')
	603
	604	nc_write_crs(filename[i])
	605
	606
	607
	608	# If necessary, bring terrain height to PALM's vertical grid. This is either forced by the user or implicitly
	609	# by using interpolation for a child domain
	610	if domain_za[i]:
	611	zt_all[i] = bring_to_palm_grid(zt_all[i],x,y,domain_dz[i])
	612
	613	nc_write_to_file_2d(filename[i], 'zt', zt_all[i], datatypes["zt"],'y','x',fillvalues["zt"])
	614	nc_write_attribute(filename[i], 'zt', 'long_name', 'orography')
	615	nc_write_attribute(filename[i], 'zt', 'units', 'm')
	616	nc_write_attribute(filename[i], 'zt', 'res_orig', domain_px[i])
	617	nc_write_attribute(filename[i], 'zt', 'coordinates', 'E_UTM N_UTM lon lat')
	618	nc_write_attribute(filename[i], 'zt', 'grid_mapping', 'E_UTM N_UTM lon lat')
	619
	620	del zt_all
	621
	622
	623	# Process building height, id, and type
	624	for i in range(0,ndomains):
	625	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]	626
[3567]	627	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])
	628
	629	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]	630	building_type[building_type >= 254] = fillvalues["building_type"]
[3567]	631	building_type = np.where(building_type < 1,defaultvalues["building_type"],building_type)
	632
	633	check = check_arrays_2(buildings_2d,building_id,fillvalues["buildings_2d"],fillvalues["building_id"])
	634	if not check:
	635	buildings_2d = np.where(building_id != fillvalues["building_id"],buildings_2d,fillvalues["buildings_2d"])
	636	building_id = np.where(buildings_2d == fillvalues["buildings_2d"],fillvalues["building_id"],building_id)
	637	print("Data check #1 " + str(check_arrays_2(buildings_2d,building_id,fillvalues["buildings_2d"],fillvalues["building_id"])))
	638
	639	check = check_arrays_2(buildings_2d,building_type,fillvalues["buildings_2d"],fillvalues["building_type"])
	640	if not check:
	641	building_type = np.where(buildings_2d == fillvalues["buildings_2d"],fillvalues["building_type"],building_type)
	642	building_type = np.where((building_type == fillvalues["building_type"]) & (buildings_2d != fillvalues["buildings_2d"]),defaultvalues["building_type"],building_type)
	643	print("Data check #2 " + str(check_arrays_2(buildings_2d,building_type,fillvalues["buildings_2d"],fillvalues["building_type"])))
	644
[3688]	645
[3567]	646	nc_write_to_file_2d(filename[i], 'buildings_2d', buildings_2d, datatypes["buildings_2d"],'y','x',fillvalues["buildings_2d"])
	647	nc_write_attribute(filename[i], 'buildings_2d', 'long_name', 'buildings')
	648	nc_write_attribute(filename[i], 'buildings_2d', 'units', 'm')
	649	nc_write_attribute(filename[i], 'buildings_2d', 'res_orig', domain_px[i])
	650	nc_write_attribute(filename[i], 'buildings_2d', 'lod', 1)
	651	nc_write_attribute(filename[i], 'buildings_2d', 'coordinates', 'E_UTM N_UTM lon lat')
	652	nc_write_attribute(filename[i], 'buildings_2d', 'grid_mapping', 'E_UTM N_UTM lon lat')
	653
	654	nc_write_to_file_2d(filename[i], 'building_id', building_id, datatypes["building_id"],'y','x',fillvalues["building_id"])
	655	nc_write_attribute(filename[i], 'building_id', 'long_name', 'building id')
	656	nc_write_attribute(filename[i], 'building_id', 'units', '')
[3668]	657	nc_write_attribute(filename[i], 'building_id', 'res _orig', domain_px[i])
[3567]	658	nc_write_attribute(filename[i], 'building_id', 'coordinates', 'E_UTM N_UTM lon lat')
	659	nc_write_attribute(filename[i], 'building_id', 'grid_mapping', 'E_UTM N_UTM lon lat')
	660
	661	nc_write_to_file_2d(filename[i], 'building_type', building_type, datatypes["building_type"],'y','x',fillvalues["building_type"])
	662	nc_write_attribute(filename[i], 'building_type', 'long_name', 'building type')
	663	nc_write_attribute(filename[i], 'building_type', 'units', '')
	664	nc_write_attribute(filename[i], 'building_type', 'res_orig', domain_px[i])
	665	nc_write_attribute(filename[i], 'building_type', 'coordinates', 'E_UTM N_UTM lon lat')
	666	nc_write_attribute(filename[i], 'building_type', 'grid_mapping', 'E_UTM N_UTM lon lat')
	667
	668	del buildings_2d
	669	del building_id
	670	del building_type
	671
	672	# Create 3d buildings if necessary. In that course, read bridge objects and add them to building layer
	673	for i in range(0,ndomains):
	674
	675	if domain_3d[i]:
	676	x = nc_read_from_file_2d_all(filename[i], 'x')
	677	y = nc_read_from_file_2d_all(filename[i], 'y')
	678	buildings_2d = nc_read_from_file_2d_all(filename[i], 'buildings_2d')
	679	building_id = nc_read_from_file_2d_all(filename[i], 'building_id')
	680
	681	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])
	682	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])
	683
	684	bridges_2d = np.where(bridges_2d == 0.0,fillvalues["bridges_2d"],bridges_2d)
	685	building_id = np.where(bridges_2d == fillvalues["bridges_2d"],building_id,bridges_id)
	686
	687	if np.any(buildings_2d != fillvalues["buildings_2d"]):
	688	buildings_3d, z = make_3d_from_2d(buildings_2d,x,y,domain_dz[i])
	689	if np.any(bridges_2d != fillvalues["bridges_2d"]):
	690	buildings_3d = make_3d_from_bridges_2d(buildings_3d,bridges_2d,x,y,domain_dz[i],settings_bridge_width,fillvalues["bridges_2d"])
	691	else:
	692	print("Skipping creation of 3D bridges (no bridges in domain)")
	693
	694
	695	nc_write_dimension(filename[i], 'z', z, datatypes["z"])
	696	nc_write_attribute(filename[i], 'z', 'long_name', 'z')
	697	nc_write_attribute(filename[i], 'z', 'units', 'm')
[3726]	698
	699	nc_overwrite_to_file_2d(filename[i], 'building_id', building_id)
[3567]	700
	701	nc_write_to_file_3d(filename[i], 'buildings_3d', buildings_3d, datatypes["buildings_3d"],'z','y','x',fillvalues["buildings_3d"])
	702	nc_write_attribute(filename[i], 'buildings_3d', 'long_name', 'buildings 3d')
	703	nc_write_attribute(filename[i], 'buildings_3d', 'units', '')
	704	nc_write_attribute(filename[i], 'buildings_3d', 'res_orig', domain_px[i])
	705	nc_write_attribute(filename[i], 'buildings_3d', 'lod', 2)
	706
	707	del buildings_3d
	708
	709	else:
	710	print("Skipping creation of 3D buildings (no buildings in domain)")
	711
	712
[3629]	713	del bridges_2d, bridges_id, building_id, buildings_2d
[3567]	714
	715
	716
	717	# Read vegetation type, water_type, pavement_type, soil_type and make fields consistent
	718	for i in range(0,ndomains):
	719
	720	building_type = nc_read_from_file_2d_all(filename[i], 'building_type')
[3773]	721
[3567]	722	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])
	723	vegetation_type[vegetation_type == 255] = fillvalues["vegetation_type"]
	724	vegetation_type = np.where((vegetation_type < 1) & (vegetation_type != fillvalues["vegetation_type"]),defaultvalues["vegetation_type"],vegetation_type)
	725
	726	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])
	727	pavement_type[pavement_type == 255] = fillvalues["pavement_type"]
	728	pavement_type = np.where((pavement_type < 1) & (pavement_type != fillvalues["pavement_type"]),defaultvalues["pavement_type"],pavement_type)
	729
	730	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])
	731	water_type[water_type == 255] = fillvalues["water_type"]
	732	water_type = np.where((water_type < 1) & (water_type != fillvalues["water_type"]),defaultvalues["water_type"],water_type)
	733
	734	# to do: replace by real soil input data
	735	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])
	736	soil_type[soil_type == 255] = fillvalues["soil_type"]
	737	soil_type = np.where((soil_type < 1) & (soil_type != fillvalues["soil_type"]),defaultvalues["soil_type"],soil_type)
	738
	739	# Make arrays consistent
	740	# #1 Set vegetation type to missing for pixel where a pavement type is set
	741	vegetation_type = np.where((vegetation_type != fillvalues["vegetation_type"]) & (pavement_type != fillvalues["pavement_type"]),fillvalues["vegetation_type"],vegetation_type)
	742
	743	# #2 Set vegetation type to missing for pixel where a building type is set
	744	vegetation_type = np.where((vegetation_type != fillvalues["vegetation_type"]) & (building_type != fillvalues["building_type"]) ,fillvalues["vegetation_type"],vegetation_type)
	745
	746	# #3 Set vegetation type to missing for pixel where a building type is set
	747	vegetation_type = np.where((vegetation_type != fillvalues["vegetation_type"]) & (water_type != fillvalues["water_type"]),fillvalues["vegetation_type"],vegetation_type)
	748
	749	# #4 Remove pavement for pixels with buildings
	750	pavement_type = np.where((pavement_type != fillvalues["pavement_type"]) & (building_type != fillvalues["building_type"]),fillvalues["pavement_type"],pavement_type)
	751
[3773]	752	# #5 Remove pavement for pixels with water.
	753	pavement_type = np.where((pavement_type != fillvalues["pavement_type"]) & (water_type != fillvalues["water_type"]),fillvalues["pavement_type"],pavement_type)
	754
[3567]	755	# #6 Remove water for pixels with buildings
	756	water_type = np.where((water_type != fillvalues["water_type"]) & (building_type != fillvalues["building_type"]),fillvalues["water_type"],water_type)
[3773]	757
[3668]	758	# Correct vegetation_type when a vegetation height is available and is indicative of low vegeetation
	759	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])
	760
	761	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)
	762	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]	763
	764	# Check for consistency and fill empty fields with default vegetation type
	765	consistency_array, test = check_consistency_4(vegetation_type,building_type,pavement_type,water_type,fillvalues["vegetation_type"],fillvalues["building_type"],fillvalues["pavement_type"],fillvalues["water_type"])
	766
	767	if test:
	768	vegetation_type = np.where(consistency_array == 0,defaultvalues["vegetation_type"],vegetation_type)
	769	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]	770
	771	# #7 to be removed: set default soil type everywhere
	772	soil_type = np.where((vegetation_type != fillvalues["vegetation_type"]) \| (pavement_type != fillvalues["pavement_type"]),defaultvalues["soil_type"],fillvalues["soil_type"])
	773
[3567]	774
[3668]	775	# Check for consistency and fill empty fields with default vegetation type
	776	consistency_array, test = check_consistency_3(vegetation_type,pavement_type,soil_type,fillvalues["vegetation_type"],fillvalues["pavement_type"],fillvalues["soil_type"])
	777
[3567]	778	# Create surface_fraction array
	779	x = nc_read_from_file_2d_all(filename[i], 'x')
	780	y = nc_read_from_file_2d_all(filename[i], 'y')
	781	nsurface_fraction = np.arange(0,3)
	782	surface_fraction = np.ones((len(nsurface_fraction),len(y),len(x)))
	783
	784	surface_fraction[0,:,:] = np.where(vegetation_type != fillvalues["vegetation_type"], 1.0, 0.0)
	785	surface_fraction[1,:,:] = np.where(pavement_type != fillvalues["pavement_type"], 1.0, 0.0)
	786	surface_fraction[2,:,:] = np.where(water_type != fillvalues["water_type"], 1.0, 0.0)
	787
	788	nc_write_dimension(filename[i], 'nsurface_fraction', nsurface_fraction, datatypes["nsurface_fraction"])
	789	nc_write_to_file_3d(filename[i], 'surface_fraction', surface_fraction, datatypes["surface_fraction"],'nsurface_fraction','y','x',fillvalues["surface_fraction"])
	790	nc_write_attribute(filename[i], 'surface_fraction', 'long_name', 'surface fraction')
	791	nc_write_attribute(filename[i], 'surface_fraction', 'units', '')
	792	nc_write_attribute(filename[i], 'surface_fraction', 'res_orig', domain_px[i])
	793	del surface_fraction
	794
	795	nc_write_to_file_2d(filename[i], 'vegetation_type', vegetation_type, datatypes["vegetation_type"],'y','x',fillvalues["vegetation_type"])
	796	nc_write_attribute(filename[i], 'vegetation_type', 'long_name', 'vegetation type')
	797	nc_write_attribute(filename[i], 'vegetation_type', 'units', '')
	798	nc_write_attribute(filename[i], 'vegetation_type', 'res_orig', domain_px[i])
	799	nc_write_attribute(filename[i], 'vegetation_type', 'coordinates', 'E_UTM N_UTM lon lat')
	800	nc_write_attribute(filename[i], 'vegetation_type', 'grid_mapping', 'E_UTM N_UTM lon lat')
	801	del vegetation_type
	802
	803	nc_write_to_file_2d(filename[i], 'pavement_type', pavement_type, datatypes["pavement_type"],'y','x',fillvalues["pavement_type"])
	804	nc_write_attribute(filename[i], 'pavement_type', 'long_name', 'pavement type')
	805	nc_write_attribute(filename[i], 'pavement_type', 'units', '')
	806	nc_write_attribute(filename[i], 'pavement_type', 'res_orig', domain_px[i])
	807	nc_write_attribute(filename[i], 'pavement_type', 'coordinates', 'E_UTM N_UTM lon lat')
	808	nc_write_attribute(filename[i], 'pavement_type', 'grid_mapping', 'E_UTM N_UTM lon lat')
	809	del pavement_type
	810
	811	nc_write_to_file_2d(filename[i], 'water_type', water_type, datatypes["water_type"],'y','x',fillvalues["water_type"])
	812	nc_write_attribute(filename[i], 'water_type', 'long_name', 'water type')
	813	nc_write_attribute(filename[i], 'water_type', 'units', '')
	814	nc_write_attribute(filename[i], 'water_type', 'res_orig', domain_px[i])
	815	nc_write_attribute(filename[i], 'water_type', 'coordinates', 'E_UTM N_UTM lon lat')
	816	nc_write_attribute(filename[i], 'water_type', 'grid_mapping', 'E_UTM N_UTM lon lat')
	817	del water_type
	818
	819	nc_write_to_file_2d(filename[i], 'soil_type', soil_type, datatypes["soil_type"],'y','x',fillvalues["soil_type"])
	820	nc_write_attribute(filename[i], 'soil_type', 'long_name', 'soil type')
	821	nc_write_attribute(filename[i], 'soil_type', 'units', '')
	822	nc_write_attribute(filename[i], 'soil_type', 'res_orig', domain_px[i])
	823	nc_write_attribute(filename[i], 'soil_type', 'coordinates', 'E_UTM N_UTM lon lat')
	824	nc_write_attribute(filename[i], 'soil_type', 'grid_mapping', 'E_UTM N_UTM lon lat')
	825	del soil_type
	826
[3629]	827	del x
	828	del y
[3567]	829
	830	# pixels with bridges get building_type = 7 = bridge. This does not change the _type setting for the under-bridge
[3668]	831	# area NOTE: when bridges are present the consistency check will fail at the moment
[3567]	832	if domain_3d[i]:
	833	if np.any(building_type != fillvalues["building_type"]):
	834
	835	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])
	836	bridges_2d = np.where(bridges_2d == 0.0,fillvalues["bridges_2d"],bridges_2d)
	837	building_type = np.where(bridges_2d != fillvalues["bridges_2d"],7,building_type)
	838	nc_overwrite_to_file_2d(filename[i], 'building_type', building_type)
	839
	840	del building_type
	841	del bridges_2d
	842
[3629]	843	# Read/write street type and street crossings
[3567]	844	for i in range(0,ndomains):
	845
	846	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])
	847	street_type[street_type == 255] = fillvalues["street_type"]
	848	street_type = np.where((street_type < 1) & (street_type != fillvalues["street_type"]),defaultvalues["street_type"],street_type)
[3773]	849
	850	pavement_type = nc_read_from_file_2d_all(filename[i], 'pavement_type')
	851	street_type = np.where((pavement_type == fillvalues["pavement_type"]),fillvalues["street_type"],street_type)
	852
[3567]	853	nc_write_to_file_2d(filename[i], 'street_type', street_type, datatypes["street_type"],'y','x',fillvalues["street_type"])
	854	nc_write_attribute(filename[i], 'street_type', 'long_name', 'street type')
	855	nc_write_attribute(filename[i], 'street_type', 'units', '')
	856	nc_write_attribute(filename[i], 'street_type', 'res_orig', domain_px[i])
	857	nc_write_attribute(filename[i], 'street_type', 'coordinates', 'E_UTM N_UTM lon lat')
	858	nc_write_attribute(filename[i], 'street_type', 'grid_mapping', 'E_UTM N_UTM lon lat')
	859	del street_type
	860
	861	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])
	862	street_crossings[street_crossings == 255] = fillvalues["street_crossings"]
	863	street_crossings = np.where((street_crossings < 1) & (street_crossings != fillvalues["street_crossings"]),defaultvalues["street_crossings"],street_crossings)
	864
[3726]	865	nc_write_to_file_2d(filename[i], 'street_crossing', street_crossings, datatypes["street_crossings"],'y','x',fillvalues["street_crossings"])
	866	nc_write_attribute(filename[i], 'street_crossing', 'long_name', 'street crossings')
	867	nc_write_attribute(filename[i], 'street_crossing', 'units', '')
	868	nc_write_attribute(filename[i], 'street_crossing', 'res_orig', domain_px[i])
	869	nc_write_attribute(filename[i], 'street_crossing', 'coordinates', 'E_UTM N_UTM lon lat')
	870	nc_write_attribute(filename[i], 'street_crossing', 'grid_mapping', 'E_UTM N_UTM lon lat')
[3567]	871	del street_crossings
[3629]	872
	873
	874	# Read/write vegetation on roofs
	875	for i in range(0,ndomains):
	876	if domain_green_roofs[i]:
	877	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])
	878	buildings_2d = nc_read_from_file_2d_all(filename[i], 'buildings_2d')
	879
	880
	881	x = nc_read_from_file_2d_all(filename[i], 'x')
	882	y = nc_read_from_file_2d_all(filename[i], 'y')
	883	nbuilding_pars = np.arange(0,46)
	884	building_pars = np.ones((len(nbuilding_pars),len(y),len(x)))
	885	building_pars[:,:,:] = fillvalues["building_pars"]
	886
	887	# assign green fraction on roofs
	888	building_pars[3,:,:] = np.where( (buildings_2d != fillvalues["buildings_2d"] ) & ( green_roofs != fillvalues["building_pars"] ),1.0,fillvalues["building_pars"])
	889
	890	# assign leaf area index for vegetation on roofs
	891	building_pars[4,:,:] = np.where( ( buildings_2d != fillvalues["buildings_2d"] ) & ( green_roofs == 1.0 ),settings_lai_roof_intensive,fillvalues["building_pars"])
	892	building_pars[4,:,:] = np.where( ( buildings_2d != fillvalues["buildings_2d"] ) & ( green_roofs == 2.0 ),settings_lai_roof_extensive,building_pars[4,:,:])
	893
	894
	895	nc_write_dimension(filename[i], 'nbuilding_pars', nbuilding_pars, datatypes["nbuilding_pars"])
	896	nc_write_to_file_3d(filename[i], 'building_pars', building_pars, datatypes["building_pars"],'nbuilding_pars','y','x',fillvalues["building_pars"])
	897	nc_write_attribute(filename[i], 'building_pars', 'long_name', 'building_pars')
	898	nc_write_attribute(filename[i], 'building_pars', 'units', '')
	899	nc_write_attribute(filename[i], 'building_pars', 'res_orig', domain_px[i])
	900	nc_write_attribute(filename[i], 'building_pars', 'coordinates', 'E_UTM N_UTM lon lat')
	901	nc_write_attribute(filename[i], 'building_pars', 'grid_mapping', 'E_UTM N_UTM lon lat')
	902
	903	del building_pars, buildings_2d, x, y
	904
	905
	906	# Read tree data and create LAD and BAD arrays using the canopy generator
	907	for i in range(0,ndomains):
[3668]	908	lai = nc_read_from_file_2d(input_file_lai[ii[i]], 'Band1', domain_x0[i], domain_x1[i], domain_y0[i], domain_y1[i])
	909
	910	vegetation_type = nc_read_from_file_2d_all(filename[i], 'vegetation_type')
	911
	912	lai = np.where(vegetation_type == fillvalues["vegetation_type"],fillvalues["vegetation_pars"],lai)
	913
[3629]	914
[3668]	915	x = nc_read_from_file_2d_all(filename[i], 'x')
	916	y = nc_read_from_file_2d_all(filename[i], 'y')
	917	nvegetation_pars = np.arange(0,12)
	918	vegetation_pars = np.ones((len(nvegetation_pars),len(y),len(x)))
	919	vegetation_pars[:,:,:] = fillvalues["vegetation_pars"]
[3629]	920
[3668]	921	vegetation_pars[1,:,:] = lai
[3629]	922
[3668]	923
	924	# Write out first version of LAI. Will later be overwritten.
	925	nc_write_dimension(filename[i], 'nvegetation_pars', nvegetation_pars, datatypes["nvegetation_pars"])
	926	nc_write_to_file_3d(filename[i], 'vegetation_pars', vegetation_pars, datatypes["vegetation_pars"],'nvegetation_pars','y','x',fillvalues["vegetation_pars"])
	927	nc_write_attribute(filename[i], 'vegetation_pars', 'long_name', 'vegetation_pars')
	928	nc_write_attribute(filename[i], 'vegetation_pars', 'units', '')
	929	nc_write_attribute(filename[i], 'vegetation_pars', 'res_orig', domain_px[i])
	930	nc_write_attribute(filename[i], 'vegetation_pars', 'coordinates', 'E_UTM N_UTM lon lat')
	931	nc_write_attribute(filename[i], 'vegetation_pars', 'grid_mapping', 'E_UTM N_UTM lon lat')
	932
	933	del lai, vegetation_pars, vegetation_type
	934
	935	# Read tree data and create LAD and BAD arrays using the canopy generator
	936	for i in range(0,ndomains):
	937	if domain_street_trees[i]:
	938
	939	vegetation_pars = nc_read_from_file_2d_all(filename[i], 'vegetation_pars')
	940
	941	lai = np.copy(vegetation_pars[1,:,:])
	942
[3629]	943	x = nc_read_from_file_2d_all(filename[i], 'x')
	944	y = nc_read_from_file_2d_all(filename[i], 'y')
	945
[3668]	946	# Save lai data as default for low and high vegetation
	947	lai_low = lai
	948	lai_high = lai
	949
	950	# Read all tree parameters from file
	951	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]	952
[3668]	953	if (input_file_tree_crown_diameter[ii[i]] is not None):
	954	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])
	955	tree_crown_diameter = np.where( (tree_crown_diameter == 0.0) \| (tree_crown_diameter == -1.0) ,fillvalues["tree_data"],tree_crown_diameter)
	956	else:
	957	tree_crown_diameter = np.ones((len(y),len(x)))
	958	tree_crown_diameter[:,:] = fillvalues["tree_data"]
[3629]	959
[3668]	960
[3629]	961	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])
	962	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])
	963	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])
	964
	965	# Remove missing values from the data. Reasonable values will be set by the tree generator
	966	tree_height = np.where( (tree_height == 0.0) \| (tree_height == -1.0) ,fillvalues["tree_data"],tree_height)
	967	tree_trunk_diameter = np.where( (tree_trunk_diameter == 0.0) \| (tree_trunk_diameter == -1.0) ,fillvalues["tree_data"],tree_trunk_diameter)
	968	tree_type = np.where( (tree_type == 0.0) \| (tree_type == -1.0) ,fillvalues["tree_data"],tree_type)
	969	patch_height = np.where( patch_height == -1.0 ,fillvalues["tree_data"],patch_height)
	970
	971	# Convert trunk diameter from cm to m
	972	tree_trunk_diameter = np.where(tree_trunk_diameter != fillvalues["tree_data"], tree_trunk_diameter * 0.01,tree_trunk_diameter)
	973
	974
	975	# Temporarily change missing value for tree_type
	976	tree_type = np.where( (tree_type == fillvalues["tree_type"]),fillvalues["tree_data"],tree_type)
	977
	978	# Compare patch height array with vegetation type and correct accordingly
	979	vegetation_type = nc_read_from_file_2d_all(filename[i], 'vegetation_type')
	980
	981
	982	# For zero-height patches, set vegetation_type to short grass and remove these pixels from the patch height array
	983	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]	984	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)
	985
[3629]	986
	987	max_tree_height = max(tree_height.flatten())
	988	max_patch_height = max(patch_height.flatten())
	989
	990	if ( (max_tree_height != fillvalues["tree_data"]) \| (max_patch_height == fillvalues["tree_data"]) ):
[3668]	991
[3629]	992	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"])
	993
	994
	995	# Remove LAD volumes that are inside buildings
	996	buildings_2d = nc_read_from_file_2d_all(filename[i], 'buildings_2d')
	997	for k in range(0,len(zlad)-1):
	998
	999	lad[k,:,:] = np.where(buildings_2d == fillvalues["buildings_2d"],lad[k,:,:],fillvalues["tree_data"])
	1000	bad[k,:,:] = np.where(buildings_2d == fillvalues["buildings_2d"],bad[k,:,:],fillvalues["tree_data"])
	1001	tree_ids[k,:,:] = np.where(buildings_2d == fillvalues["buildings_2d"],tree_ids[k,:,:],fillvalues["tree_data"])
[3668]	1002
	1003	del buildings_2d
[3629]	1004
	1005	nc_write_dimension(filename[i], 'zlad', zlad, datatypes["tree_data"])
	1006	nc_write_to_file_3d(filename[i], 'lad', lad, datatypes["tree_data"],'zlad','y','x',fillvalues["tree_data"])
	1007	nc_write_attribute(filename[i], 'lad', 'long_name', 'leaf area density')
	1008	nc_write_attribute(filename[i], 'lad', 'units', '')
	1009	nc_write_attribute(filename[i], 'lad', 'res_orig', domain_px[i])
	1010	nc_write_attribute(filename[i], 'lad', 'coordinates', 'E_UTM N_UTM lon lat')
	1011	nc_write_attribute(filename[i], 'lad', 'grid_mapping', 'E_UTM N_UTM lon lat')
	1012
	1013	nc_write_to_file_3d(filename[i], 'bad', bad, datatypes["tree_data"],'zlad','y','x',fillvalues["tree_data"])
	1014	nc_write_attribute(filename[i], 'bad', 'long_name', 'basal area density')
	1015	nc_write_attribute(filename[i], 'bad', 'units', '')
	1016	nc_write_attribute(filename[i], 'bad', 'res_orig', domain_px[i])
	1017	nc_write_attribute(filename[i], 'bad', 'coordinates', 'E_UTM N_UTM lon lat')
	1018	nc_write_attribute(filename[i], 'bad', 'grid_mapping', 'E_UTM N_UTM lon lat')
	1019
	1020	nc_write_to_file_3d(filename[i], 'tree_id', tree_ids, datatypes["tree_data"],'zlad','y','x',fillvalues["tree_data"])
	1021	nc_write_attribute(filename[i], 'tree_id', 'long_name', 'tree id')
	1022	nc_write_attribute(filename[i], 'tree_id', 'units', '')
	1023	nc_write_attribute(filename[i], 'tree_id', 'res_orig', domain_px[i])
	1024	nc_write_attribute(filename[i], 'tree_id', 'coordinates', 'E_UTM N_UTM lon lat')
	1025	nc_write_attribute(filename[i], 'tree_id', 'grid_mapping', 'E_UTM N_UTM lon lat')
	1026
[3668]	1027	del lai, lad, bad, tree_ids, zlad
[3629]	1028
[3668]	1029	del vegetation_pars, tree_height, tree_crown_diameter, tree_trunk_diameter, tree_type, patch_height, x, y
[3629]	1030
	1031
	1032	# Create vegetation patches for locations with high vegetation type
	1033	for i in range(0,ndomains):
	1034	if domain_canopy_patches[i]:
	1035
	1036	# Load vegetation_type and lad array (at level z = 0) for re-processing
	1037	vegetation_type = nc_read_from_file_2d_all(filename[i], 'vegetation_type')
	1038	lad = nc_read_from_file_3d_all(filename[i], 'lad')
[3668]	1039	zlad = nc_read_from_file_1d_all(filename[i], 'zlad')
[3629]	1040	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])
	1041	vegetation_pars = nc_read_from_file_3d_all(filename[i], 'vegetation_pars')
	1042	lai = vegetation_pars[1,:,:]
[3726]	1043
[3629]	1044
[3726]	1045	# 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]	1046	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]	1047
[3726]	1048	# Now, assign either the default LAI for high vegetation or keep 1.0 from the lai_high array.
	1049	lai_high = np.where( (lai_high != fillvalues["tree_data"]) & (lai == fillvalues["tree_data"]), settings_lai_high_default, lai_high)
[3668]	1050
[3726]	1051	# If lai values are available in the lai array, write them on the lai_high array
	1052	lai_high = np.where( (lai_high != fillvalues["tree_data"]) & (lai != fillvalues["tree_data"]), lai, lai_high)
	1053
[3668]	1054	# Define a patch height wherever it is missing, but where a high vegetation LAI was set
[3629]	1055	patch_height = np.where ( (lai_high != fillvalues["tree_data"]) & (patch_height == fillvalues["tree_data"]), settings_patch_height_default, patch_height)
	1056
	1057	# Remove pixels where street trees were already set
	1058	patch_height = np.where ( (lad[0,:,:] != fillvalues["tree_data"]), fillvalues["tree_data"], patch_height)
	1059
[3668]	1060	# Remove patch heights that have no lai_high value
	1061	patch_height = np.where ( (lai_high == fillvalues["tree_data"]), fillvalues["tree_data"], patch_height)
	1062
[3629]	1063	# For missing LAI values, set either the high vegetation default or the low vegetation default
[3668]	1064	lai_high = np.where( (patch_height > 2.0) & (patch_height != fillvalues["tree_data"]) & (lai_high == fillvalues["tree_data"]),settings_lai_high_default,lai_high)
	1065	lai_high = np.where( (patch_height <= 2.0) & (patch_height != fillvalues["tree_data"]) & (lai_high == fillvalues["tree_data"]),settings_lai_low_default,lai_high)
	1066
[3629]	1067	if ( max(patch_height.flatten()) >= (2.0 * domain_dz[i]) ):
[3726]	1068	print(" start calculating LAD (this might take some time)")
	1069
	1070
[3668]	1071	lad_patch, patch_nz, status = process_patch(domain_dz[i],patch_height,max(zlad),lai_high,settings_lai_alpha,settings_lai_beta)
[3629]	1072
	1073	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,:,:] )
	1074
	1075	# Remove high vegetation wherever it is replaced by a leaf area density. This should effectively remove all high vegetation pixels
	1076	vegetation_type = np.where((lad[0,:,:] != fillvalues["tree_data"]) & (vegetation_type != fillvalues["vegetation_type"]),3,vegetation_type)
[3668]	1077
[3629]	1078	# If desired, remove all high vegetation. TODO: check if this is still necessary
[3688]	1079	if not domain_high_vegetation[i]:
[3629]	1080	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)
	1081
	1082
[3668]	1083	# Set default low LAI for pixels with an LAD (short grass below trees)
	1084	lai_low = np.where( (lad[0,:,:] == fillvalues["tree_data"]), lai, settings_lai_low_default)
[3629]	1085
[3668]	1086	# Fill low vegetation pixels without LAI set or with LAI = 0 with default value
	1087	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]	1088
[3668]	1089	# Remove lai for pixels that have no vegetation_type
	1090	lai_low = np.where( vegetation_type != fillvalues["vegetation_type"], lai_low, fillvalues["tree_data"])
	1091
[3629]	1092	# Overwrite lai in vegetation_parameters
[3668]	1093	vegetation_pars[1,:,:] = np.copy(lai_low)
[3629]	1094	nc_overwrite_to_file_3d(filename[i], 'vegetation_pars', vegetation_pars)
	1095
	1096	# Overwrite lad array
	1097	nc_overwrite_to_file_3d(filename[i], 'lad', lad)
[3668]	1098
	1099	nc_overwrite_to_file_2d(filename[i], 'vegetation_type', vegetation_type)
	1100
[3629]	1101
[3668]	1102	del vegetation_type, lad, lai, patch_height, vegetation_pars, zlad
	1103
	1104	# Final consistency check
	1105	for i in range(0,ndomains):
	1106	vegetation_type = nc_read_from_file_2d_all(filename[i], 'vegetation_type')
	1107	pavement_type = nc_read_from_file_2d_all(filename[i], 'pavement_type')
	1108	building_type = nc_read_from_file_2d_all(filename[i], 'building_type')
	1109	water_type = nc_read_from_file_2d_all(filename[i], 'water_type')
	1110	soil_type = nc_read_from_file_2d_all(filename[i], 'soil_type')
	1111
	1112	# Check for consistency and fill empty fields with default vegetation type
	1113	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"])
	1114
	1115	# Check for consistency and fill empty fields with default vegetation type
	1116	consistency_array, test = check_consistency_3(vegetation_type,pavement_type,soil_type,fillvalues["vegetation_type"],fillvalues["pavement_type"],fillvalues["soil_type"])
	1117
	1118	surface_fraction = nc_read_from_file_3d_all(filename[i], 'surface_fraction')
	1119	surface_fraction[0,:,:] = np.where(vegetation_type != fillvalues["vegetation_type"], 1.0, 0.0)
	1120	surface_fraction[1,:,:] = np.where(pavement_type != fillvalues["pavement_type"], 1.0, 0.0)
	1121	surface_fraction[2,:,:] = np.where(water_type != fillvalues["water_type"], 1.0, 0.0)
	1122	nc_overwrite_to_file_3d(filename[i], 'surface_fraction', surface_fraction)
	1123
	1124	del vegetation_type, pavement_type, building_type, water_type, soil_type

Note: See TracBrowser for help on using the repository browser.

Download in other formats:

| Impressum | ©Leibniz Universität Hannover |