| [3567] | 1 | #!/usr/bin/env python3 |
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| 2 | # -*- coding: utf-8 -*- |
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| 3 | #--------------------------------------------------------------------------------# |
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| 4 | # This file is part of the PALM model system. |
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| 5 | # |
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| 6 | # PALM is free software: you can redistribute it and/or modify it under the terms |
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| 7 | # of the GNU General Public License as published by the Free Software Foundation, |
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| 8 | # either version 3 of the License, or (at your option) any later version. |
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| 9 | # |
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| 10 | # PALM is distributed in the hope that it will be useful, but WITHOUT ANY |
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| 11 | # WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR |
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| 12 | # A PARTICULAR PURPOSE. See the GNU General Public License for more details. |
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| 13 | # |
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| 14 | # You should have received a copy of the GNU General Public License along with |
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| 15 | # PALM. If not, see <http://www.gnu.org/licenses/>. |
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| 16 | # |
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| 17 | # Copyright 1997-2018 Leibniz Universitaet Hannover |
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| 18 | #--------------------------------------------------------------------------------# |
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| 19 | # |
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| 20 | # Current revisions: |
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| 21 | # ----------------- |
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| 22 | # |
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| 23 | # |
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| 24 | # Former revisions: |
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| 25 | # ----------------- |
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| 26 | # $Id: palm_csd 3629 2018-12-13 12:18:54Z maronga $ |
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| [3629] | 27 | # Added canopy generator calls. Some improvements |
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| 28 | # |
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| 29 | # 3567 2018-11-27 13:59:21Z maronga |
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| [3567] | 30 | # Initial revisions |
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| 31 | # |
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| 32 | # Description: |
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| 33 | # ------------ |
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| 34 | # Processing tool for creating PIDS conform static drivers from rastered NetCDF |
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| 35 | # input |
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| 36 | # |
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| 37 | # @Author Bjoern Maronga (maronga@muk.uni-hannover.de) |
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| 38 | # |
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| 39 | # @todo Make input files optional |
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| 40 | # @todo Allow for ASCII input of terrain height and building height |
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| 41 | # @todo Modularize reading config file |
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| 42 | #------------------------------------------------------------------------------# |
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| 43 | |
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| 44 | from palm_csd_files.palm_csd_netcdf_interface import * |
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| 45 | from palm_csd_files.palm_csd_tools import * |
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| [3629] | 46 | from palm_csd_files.palm_csd_canopy_generator import * |
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| [3567] | 47 | import numpy as np |
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| 48 | |
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| 49 | |
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| 50 | def read_config_file(): |
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| 51 | |
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| 52 | import configparser |
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| 53 | from math import floor |
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| 54 | import numpy as np |
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| 55 | import os |
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| 56 | import subprocess as sub |
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| 57 | import sys |
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| 58 | |
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| 59 | # Check if configuration files exists and quit otherwise |
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| 60 | input_config = ".csd.config" |
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| 61 | for i in range(1,len(sys.argv)): |
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| 62 | input_config = str(sys.argv[i]) |
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| 63 | |
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| 64 | config = configparser.RawConfigParser(allow_no_value=True) |
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| 65 | |
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| 66 | if ( os.path.isfile(input_config) == False ): |
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| 67 | print ("Error. No configuration file " + input_config + " found.") |
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| 68 | raise SystemExit |
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| 69 | |
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| 70 | config.read(input_config) |
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| 71 | |
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| 72 | |
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| 73 | # Definition of settings |
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| 74 | global settings_filename_out |
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| 75 | global settings_bridge_width |
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| [3629] | 76 | global settings_lai_roof_intensive |
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| 77 | global settings_lai_roof_extensive |
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| 78 | global settings_season |
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| 79 | global settings_lai_low_default |
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| 80 | global settings_lai_high_default |
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| 81 | global settings_patch_height_default |
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| 82 | global settings_lai_alpha |
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| 83 | global settings_lai_beta |
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| [3567] | 84 | global ndomains |
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| 85 | |
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| 86 | # Definition of global configuration parameters |
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| 87 | global global_acronym |
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| 88 | global global_angle |
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| 89 | global global_author |
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| 90 | global global_campaign |
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| 91 | global global_comment |
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| 92 | global global_contact |
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| 93 | global global_data_content |
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| 94 | global global_dependencies |
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| 95 | global global_institution |
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| 96 | global global_keywords |
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| 97 | global global_location |
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| 98 | global global_palm_version |
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| 99 | global global_references |
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| 100 | global global_site |
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| 101 | global global_source |
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| 102 | global global_version |
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| 103 | |
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| 104 | |
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| 105 | # Definition of domain parameters |
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| 106 | global domain_names |
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| 107 | global domain_px |
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| 108 | global domain_x0 |
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| 109 | global domain_y0 |
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| 110 | global domain_x1 |
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| 111 | global domain_y1 |
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| 112 | global domain_nx |
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| 113 | global domain_ny |
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| 114 | global domain_dz |
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| 115 | global domain_3d |
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| [3629] | 116 | global domain_high_vegetation |
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| [3567] | 117 | global domain_ip |
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| 118 | global domain_za |
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| 119 | global domain_parent |
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| [3629] | 120 | global domain_green_roofs |
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| 121 | global domain_street_trees |
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| 122 | global domain_canopy_patches |
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| [3567] | 123 | |
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| 124 | # Definition of input data parameters |
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| 125 | global input_names |
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| 126 | global input_px |
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| 127 | |
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| 128 | |
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| 129 | global input_file_x |
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| 130 | global input_file_y |
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| 131 | global input_file_x_UTM |
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| 132 | global input_file_y_UTM |
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| 133 | global input_file_lat |
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| 134 | global input_file_lon |
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| 135 | global input_file_zt |
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| 136 | global input_file_buildings_2d |
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| 137 | global input_file_bridges_2d |
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| 138 | global input_file_building_id |
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| 139 | global input_file_bridges_id |
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| 140 | global input_file_building_type |
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| 141 | global input_file_building_type |
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| [3629] | 142 | global input_file_lai |
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| [3567] | 143 | global input_file_vegetation_type |
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| 144 | global input_file_vegetation_height |
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| 145 | global input_file_pavement_type |
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| 146 | global input_file_water_type |
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| 147 | global input_file_street_type |
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| 148 | global input_file_street_crossings |
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| 149 | global input_file_soil_type |
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| [3629] | 150 | global input_file_vegetation_on_roofs |
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| 151 | global input_file_tree_crown_diameter |
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| 152 | global input_file_tree_height |
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| 153 | global input_file_tree_trunk_diameter |
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| 154 | global input_file_tree_type |
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| 155 | global input_file_patch_height |
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| [3567] | 156 | |
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| 157 | global zt_all |
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| 158 | global zt_min |
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| 159 | |
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| 160 | settings_filename_out = "default" |
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| 161 | settings_bridge_width = 3.0 |
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| [3629] | 162 | settings_lai_roof_intensive = 0.5 |
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| 163 | settings_lai_roof_extensive = 1.0 |
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| 164 | settings_season = "summer" |
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| 165 | settings_lai_high_default = 6.0 |
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| 166 | settings_lai_low_default = 1.0 |
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| 167 | settings_patch_height_default = 10.0 |
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| 168 | settings_lai_alpha = 5.0 |
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| 169 | settings_lai_beta = 3.0 |
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| [3567] | 170 | ndomains = 0 |
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| 171 | |
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| 172 | global_acronym = "" |
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| 173 | global_angle = "" |
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| 174 | global_author = "" |
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| 175 | global_campaign = "" |
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| 176 | global_comment = "" |
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| 177 | global_contact = "" |
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| 178 | global_data_content = "" |
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| 179 | global_dependencies = "" |
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| 180 | global_institution = "" |
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| 181 | global_keywords = "" |
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| 182 | global_location = "" |
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| 183 | global_palm_version = 6.0 |
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| 184 | global_references = "" |
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| 185 | global_site = "" |
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| 186 | global_source = "" |
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| 187 | global_version = 1 |
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| 188 | |
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| 189 | domain_names = [] |
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| 190 | domain_px = [] |
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| 191 | domain_x0 = [] |
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| 192 | domain_y0 = [] |
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| 193 | domain_x1 = [] |
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| 194 | domain_y1 = [] |
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| 195 | domain_nx = [] |
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| 196 | domain_ny = [] |
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| 197 | domain_dz = [] |
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| 198 | domain_3d = [] |
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| [3629] | 199 | domain_high_vegetation = [] |
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| [3567] | 200 | domain_ip = [] |
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| 201 | domain_za = [] |
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| 202 | domain_parent = [] |
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| [3629] | 203 | domain_green_roofs = [] |
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| 204 | domain_street_trees = [] |
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| 205 | domain_canopy_patches = [] |
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| [3567] | 206 | |
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| 207 | zt_min = 0.0 |
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| 208 | zt_all = [] |
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| 209 | |
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| 210 | input_names = [] |
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| 211 | input_px = [] |
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| 212 | |
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| 213 | input_file_x = [] |
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| 214 | input_file_y = [] |
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| 215 | input_file_x_UTM = [] |
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| 216 | input_file_y_UTM = [] |
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| 217 | input_file_lat = [] |
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| 218 | input_file_lon = [] |
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| 219 | |
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| 220 | input_file_zt = [] |
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| 221 | input_file_buildings_2d = [] |
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| 222 | input_file_bridges_2d = [] |
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| 223 | input_file_building_id = [] |
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| 224 | input_file_bridges_id = [] |
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| 225 | input_file_building_type = [] |
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| [3629] | 226 | input_file_lai = [] |
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| [3567] | 227 | input_file_vegetation_type = [] |
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| 228 | input_file_vegetation_height = [] |
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| 229 | input_file_pavement_type = [] |
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| 230 | input_file_water_type = [] |
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| 231 | input_file_street_type = [] |
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| 232 | input_file_street_crossings = [] |
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| 233 | input_file_soil_type = [] |
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| [3629] | 234 | input_file_vegetation_on_roofs = [] |
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| 235 | input_file_tree_crown_diameter = [] |
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| 236 | input_file_tree_height = [] |
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| 237 | input_file_tree_trunk_diameter = [] |
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| 238 | input_file_tree_type = [] |
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| 239 | input_file_patch_height = [] |
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| [3567] | 240 | |
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| 241 | # Load all user parameters from config file |
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| 242 | for i in range(0,len(config.sections())): |
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| 243 | |
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| 244 | read_tmp = config.sections()[i] |
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| 245 | |
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| 246 | if ( read_tmp == 'global' ): |
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| 247 | global_acronym = config.get(read_tmp, 'acronym') |
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| 248 | global_angle = float(config.get(read_tmp, 'rotation_angle')) |
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| 249 | global_author = config.get(read_tmp, 'author') |
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| 250 | global_campaign = config.get(read_tmp, 'campaign') |
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| 251 | global_comment = config.get(read_tmp, 'comment') |
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| 252 | global_contact = config.get(read_tmp, 'contact_person') |
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| 253 | global_data_content = config.get(read_tmp, 'data_content') |
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| 254 | global_dependencies = config.get(read_tmp, 'dependencies') |
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| 255 | global_institution = config.get(read_tmp, 'institution') |
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| 256 | global_keywords = config.get(read_tmp, 'keywords') |
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| 257 | global_location = config.get(read_tmp, 'location') |
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| 258 | global_palm_version = float(config.get(read_tmp, 'palm_version')) |
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| 259 | global_references = config.get(read_tmp, 'references') |
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| 260 | global_site = config.get(read_tmp, 'site') |
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| 261 | global_source = config.get(read_tmp, 'source') |
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| 262 | global_version = float(config.get(read_tmp, 'version')) |
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| 263 | |
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| 264 | if ( read_tmp == 'settings' ): |
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| 265 | settings_filename_out = config.get(read_tmp, 'filename_out') |
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| [3629] | 266 | settings_lai_roof_intensive = config.get(read_tmp, 'lai_roof_intensive') |
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| 267 | settings_lai_roof_extensive = config.get(read_tmp, 'lai_roof_extensive') |
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| 268 | settings_bridge_width = float(config.get(read_tmp, 'bridge_width')) |
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| 269 | settings_season = config.get(read_tmp, 'season') |
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| 270 | settings_lai_high_default = float(config.get(read_tmp, 'lai_high_vegetation_default')) |
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| 271 | settings_lai_low_default = float(config.get(read_tmp, 'lai_low_vegetation_default')) |
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| 272 | settings_patch_height_default = float(config.get(read_tmp, 'patch_height_default')) |
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| 273 | settings_lai_alpha = float(config.get(read_tmp, 'lai_alpha')) |
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| 274 | settings_lai_beta = float(config.get(read_tmp, 'lai_beta')) |
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| 275 | |
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| [3567] | 276 | if ( read_tmp.split("_")[0] == 'domain' ): |
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| 277 | ndomains = ndomains + 1 |
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| 278 | domain_names.append(read_tmp.split("_")[1]) |
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| 279 | domain_px.append(float(config.get(read_tmp, 'pixel_size'))) |
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| 280 | domain_nx.append(int(config.get(read_tmp, 'nx'))) |
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| 281 | domain_ny.append(int(config.get(read_tmp, 'ny'))) |
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| 282 | domain_dz.append(float(config.get(read_tmp, 'dz'))) |
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| 283 | domain_3d.append(config.getboolean(read_tmp, 'buildings_3d')) |
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| [3629] | 284 | domain_high_vegetation.append(config.getboolean(read_tmp, 'allow_high_vegetation')) |
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| 285 | domain_canopy_patches.append(config.getboolean(read_tmp, 'generate_vegetation_patches')) |
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| [3567] | 286 | domain_ip.append(config.getboolean(read_tmp, 'interpolate_terrain')) |
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| 287 | domain_za.append(config.getboolean(read_tmp, 'use_palm_z_axis')) |
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| 288 | if domain_ip[ndomains-1] and not domain_za[ndomains-1]: |
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| 289 | domain_za[ndomains-1] = True |
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| 290 | print("+++ Overwrite user setting for use_palm_z_axis") |
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| 291 | |
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| 292 | domain_parent.append(config.get(read_tmp, 'domain_parent')) |
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| 293 | |
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| 294 | domain_x0.append(int(floor(float(config.get(read_tmp, 'origin_x'))/float(config.get(read_tmp, 'pixel_size'))))) |
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| 295 | domain_y0.append(int(floor(float(config.get(read_tmp, 'origin_y'))/float(config.get(read_tmp, 'pixel_size'))))) |
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| 296 | 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')))) |
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| 297 | 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')))) |
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| [3629] | 298 | domain_green_roofs.append(config.getboolean(read_tmp, 'vegetation_on_roofs')) |
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| 299 | domain_street_trees.append(config.getboolean(read_tmp, 'street_trees')) |
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| 300 | |
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| [3567] | 301 | if ( read_tmp.split("_")[0] == 'input' ): |
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| 302 | input_names.append(read_tmp.split("_")[1]) |
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| 303 | input_px.append(float(config.get(read_tmp, 'pixel_size'))) |
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| 304 | input_file_x.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_x')) |
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| 305 | input_file_y.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_y')) |
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| 306 | input_file_lat.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_lat')) |
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| 307 | input_file_lon.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_lon')) |
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| 308 | input_file_x_UTM.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_x_UTM')) |
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| 309 | input_file_y_UTM.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_y_UTM')) |
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| 310 | input_file_zt.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_zt')) |
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| 311 | input_file_buildings_2d.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_buildings_2d')) |
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| 312 | input_file_bridges_2d.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_bridges_2d')) |
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| 313 | input_file_building_id.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_building_id')) |
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| 314 | input_file_bridges_id.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_bridges_id')) |
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| [3629] | 315 | input_file_building_type.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_building_type')) |
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| 316 | input_file_lai.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_lai')) |
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| [3567] | 317 | input_file_vegetation_type.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_vegetation_type')) |
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| 318 | input_file_vegetation_height.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_vegetation_height')) |
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| 319 | input_file_pavement_type.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_pavement_type')) |
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| 320 | input_file_water_type.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_water_type')) |
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| 321 | input_file_street_type.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_street_type')) |
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| [3629] | 322 | input_file_street_crossings.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_street_crossings')) |
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| 323 | input_file_patch_height.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_patch_height')) |
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| 324 | input_file_tree_crown_diameter.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_tree_crown_diameter')) |
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| 325 | input_file_tree_height.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_tree_height')) |
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| 326 | input_file_tree_trunk_diameter.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_tree_trunk_diameter')) |
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| 327 | input_file_tree_type.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_tree_type')) |
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| 328 | input_file_vegetation_on_roofs.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_vegetation_on_roofs')) |
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| [3567] | 329 | #input_file_soil_type.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_soil_type')) |
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| 330 | return 0 |
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| 331 | |
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| 332 | |
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| 333 | ############################################################ |
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| 334 | |
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| 335 | # Start of main program |
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| 336 | datatypes = { |
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| 337 | "x": "f4", |
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| 338 | "y": "f4", |
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| 339 | "z": "f4", |
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| 340 | "lat": "f4", |
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| 341 | "lon": "f4", |
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| 342 | "E_UTM": "f4", |
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| 343 | "N_UTM": "f4", |
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| 344 | "zt": "f4", |
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| 345 | "buildings_2d": "f4", |
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| 346 | "buildings_3d": "b", |
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| 347 | "bridges_2d": "f4", |
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| 348 | "building_id": "i", |
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| 349 | "bridges_id": "i", |
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| 350 | "building_type": "b", |
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| 351 | "nsurface_fraction": "i", |
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| 352 | "vegetation_type": "b", |
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| 353 | "vegetation_height": "f4", |
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| 354 | "pavement_type": "b", |
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| 355 | "water_type": "b", |
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| 356 | "street_type": "b", |
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| 357 | "street_crossings": "b", |
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| 358 | "soil_type": "b", |
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| [3629] | 359 | "surface_fraction": "f4", |
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| 360 | "building_pars": "f4", |
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| 361 | "vegetation_pars": "f4", |
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| 362 | "tree_data": "f4", |
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| 363 | "tree_type": "b", |
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| 364 | "nbuilding_pars": "i", |
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| 365 | "nvegetation_pars": "i", |
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| 366 | "zlad": "f4" |
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| [3567] | 367 | } |
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| 368 | |
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| 369 | fillvalues = { |
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| 370 | "lat": float(-9999.0), |
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| 371 | "lon": float(-9999.0), |
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| 372 | "E_UTM": float(-9999.0), |
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| 373 | "N_UTM": float(-9999.0), |
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| 374 | "zt": float(-9999.0), |
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| 375 | "buildings_2d": float(-9999.0), |
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| 376 | "buildings_3d": np.byte(-127), |
|---|
| 377 | "bridges_2d": float(-9999.0), |
|---|
| 378 | "building_id": int(-9999), |
|---|
| 379 | "bridges_id": int(-9999), |
|---|
| 380 | "building_type": np.byte(-127), |
|---|
| 381 | "nsurface_fraction": int(-9999), |
|---|
| 382 | "vegetation_type": np.byte(-127), |
|---|
| 383 | "vegetation_height": float(-9999.0), |
|---|
| 384 | "pavement_type": np.byte(-127), |
|---|
| 385 | "water_type": np.byte(-127), |
|---|
| 386 | "street_type": np.byte(-127), |
|---|
| 387 | "street_crossings": np.byte(-127), |
|---|
| 388 | "soil_type": np.byte(-127), |
|---|
| [3629] | 389 | "surface_fraction": float(-9999.0), |
|---|
| 390 | "building_pars": float(-9999.0), |
|---|
| 391 | "vegetation_pars": float(-9999.0), |
|---|
| 392 | "tree_data": float(-9999.0), |
|---|
| 393 | "tree_type": np.byte(-127) |
|---|
| [3567] | 394 | } |
|---|
| 395 | |
|---|
| 396 | defaultvalues = { |
|---|
| 397 | "lat": float(-9999.0), |
|---|
| 398 | "lon": float(-9999.0), |
|---|
| 399 | "E_UTM": float(-9999.0), |
|---|
| 400 | "N_UTM": float(-9999.0), |
|---|
| 401 | "zt": float(0.0), |
|---|
| 402 | "buildings_2d": float(0.0), |
|---|
| 403 | "buildings_3d": 0, |
|---|
| 404 | "bridges_2d": float(0.0), |
|---|
| 405 | "building_id": int(0), |
|---|
| 406 | "bridges_id": int(0), |
|---|
| 407 | "building_type": 1, |
|---|
| 408 | "nsurface_fraction": int(-9999), |
|---|
| 409 | "vegetation_type": 3, |
|---|
| 410 | "vegetation_height": float(-9999.0), |
|---|
| 411 | "pavement_type": 1, |
|---|
| 412 | "water_type": 1, |
|---|
| 413 | "street_type": 1, |
|---|
| 414 | "street_crossings": 0, |
|---|
| 415 | "soil_type": 1, |
|---|
| [3629] | 416 | "surface_fraction": float(0.0), |
|---|
| 417 | "buildings_pars": float(-9999.0), |
|---|
| 418 | "tree_data": float(-9999.0), |
|---|
| 419 | "tree_type": 0, |
|---|
| 420 | "vegetation_pars": float(-9999.0) |
|---|
| [3567] | 421 | } |
|---|
| 422 | |
|---|
| 423 | # Read configuration file and set parameters accordingly |
|---|
| 424 | read_config_file() |
|---|
| 425 | |
|---|
| 426 | |
|---|
| 427 | filename = [] |
|---|
| 428 | ii = [] |
|---|
| 429 | ii_parent = [] |
|---|
| 430 | # Define indices and filenames for all domains and create netCDF files |
|---|
| 431 | for i in range(0,ndomains): |
|---|
| 432 | |
|---|
| 433 | # Calculate indices and input files |
|---|
| 434 | ii.append(input_px.index(domain_px[i])) |
|---|
| 435 | filename.append(settings_filename_out + "_" + domain_names[i]) |
|---|
| 436 | if domain_parent[i] is not None: |
|---|
| 437 | ii_parent.append(domain_names.index(domain_parent[i])) |
|---|
| 438 | else: |
|---|
| 439 | ii_parent.append(None) |
|---|
| 440 | |
|---|
| 441 | |
|---|
| 442 | x_UTM = nc_read_from_file_2d(input_file_x[ii[i]], "Band1", domain_x0[i], domain_x0[i], domain_y0[i], domain_y0[i]) |
|---|
| 443 | y_UTM = nc_read_from_file_2d(input_file_y[ii[i]], "Band1", domain_x0[i], domain_x0[i], domain_y0[i], domain_y0[i]) |
|---|
| 444 | lat = nc_read_from_file_2d(input_file_lat[ii[i]], "Band1", domain_x0[i], domain_x0[i], domain_y0[i], domain_y0[i]) |
|---|
| 445 | lon = nc_read_from_file_2d(input_file_lon[ii[i]], "Band1", domain_x0[i], domain_x0[i], domain_y0[i], domain_y0[i]) |
|---|
| 446 | |
|---|
| 447 | # Create NetCDF output file and set global attributes |
|---|
| 448 | nc_create_file(filename[i]) |
|---|
| 449 | nc_write_global_attributes(filename[i],float(x_UTM[0,0]),float(y_UTM[0,0]),float(lat[0,0]),float(lon[0,0]),"",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,global_version) |
|---|
| 450 | |
|---|
| 451 | |
|---|
| 452 | # Process terrain height |
|---|
| 453 | for i in range(0,ndomains): |
|---|
| 454 | |
|---|
| 455 | # Read and write terrain height (zt) |
|---|
| 456 | zt = nc_read_from_file_2d(input_file_zt[ii[i]], 'Band1', domain_x0[i], domain_x1[i], domain_y0[i], domain_y1[i]) |
|---|
| 457 | |
|---|
| 458 | # Final step: add zt array to the global array |
|---|
| 459 | zt_all.append(zt) |
|---|
| 460 | del zt |
|---|
| 461 | |
|---|
| 462 | # Calculate the global (all domains) minimum of the terrain height. This value will be substracted for all |
|---|
| 463 | # data sets |
|---|
| 464 | zt_min = min(zt_all[0].flatten()) |
|---|
| 465 | for i in range(0,ndomains): |
|---|
| 466 | zt_min = min(zt_min,min(zt_all[i].flatten())) |
|---|
| 467 | |
|---|
| 468 | del zt_all[:] |
|---|
| 469 | |
|---|
| 470 | for i in range(0,ndomains): |
|---|
| 471 | |
|---|
| 472 | # Read and write terrain height (zt) |
|---|
| 473 | zt = nc_read_from_file_2d(input_file_zt[ii[i]], 'Band1', domain_x0[i], domain_x1[i], domain_y0[i], domain_y1[i]) |
|---|
| 474 | x = nc_read_from_file_1d(input_file_x[ii[i]], "x", domain_x0[i], domain_x1[i]) |
|---|
| 475 | y = nc_read_from_file_1d(input_file_y[ii[i]], "y", domain_y0[i], domain_y1[i]) |
|---|
| 476 | |
|---|
| 477 | print( "Shift terrain height by -" + str(zt_min)) |
|---|
| 478 | zt = zt - zt_min |
|---|
| 479 | |
|---|
| 480 | # If necessary, interpolate parent domain terrain height on child domain grid and blend the two |
|---|
| 481 | if domain_ip[i]: |
|---|
| 482 | tmp_x0 = int( domain_x0[i] * domain_px[i] / domain_px[ii_parent[i]] ) - 1 |
|---|
| 483 | tmp_y0 = int( domain_y0[i] * domain_px[i] / domain_px[ii_parent[i]] ) - 1 |
|---|
| 484 | tmp_x1 = int( domain_x1[i] * domain_px[i] / domain_px[ii_parent[i]] ) + 1 |
|---|
| 485 | tmp_y1 = int( domain_y1[i] * domain_px[i] / domain_px[ii_parent[i]] ) + 1 |
|---|
| 486 | |
|---|
| 487 | tmp_x = nc_read_from_file_1d(input_file_x[ii_parent[i]], "x", tmp_x0, tmp_x1) |
|---|
| 488 | tmp_y = nc_read_from_file_1d(input_file_y[ii_parent[i]], "y", tmp_y0, tmp_y1) |
|---|
| 489 | |
|---|
| 490 | zt_parent = nc_read_from_file_2d(input_file_zt[ii_parent[i]], 'Band1', tmp_x0, tmp_x1, tmp_y0, tmp_y1) |
|---|
| 491 | |
|---|
| 492 | print( "Shift terrain height by -" + str(zt_min)) |
|---|
| 493 | zt_parent = zt_parent - zt_min |
|---|
| 494 | |
|---|
| 495 | # Interpolate array and bring to PALM grid of child domain |
|---|
| 496 | zt_ip = interpolate_2d(zt_parent,tmp_x,tmp_y,x,y) |
|---|
| 497 | zt_ip = bring_to_palm_grid(zt_ip,x,y,domain_dz[ii_parent[i]]) |
|---|
| 498 | |
|---|
| 499 | # Shift the child terrain height according to the parent mean terrain height |
|---|
| 500 | zt = zt - np.mean(zt) + np.mean(zt_ip) |
|---|
| 501 | |
|---|
| 502 | |
|---|
| 503 | # Blend over the parent and child terrain height within a radius of 50 px |
|---|
| 504 | zt = blend_array_2d(zt,zt_ip,50) |
|---|
| 505 | |
|---|
| 506 | # Final step: add zt array to the global array |
|---|
| 507 | zt_all.append(zt) |
|---|
| 508 | del zt |
|---|
| 509 | |
|---|
| 510 | |
|---|
| 511 | # Read and shift x and y coordinates, shift terrain height according to its minimum value and write all data |
|---|
| 512 | # to file |
|---|
| 513 | for i in range(0,ndomains): |
|---|
| 514 | # Read horizontal grid variables from zt file and write them to output file |
|---|
| 515 | x = nc_read_from_file_1d(input_file_x[ii[i]], "x", domain_x0[i], domain_x1[i]) |
|---|
| 516 | y = nc_read_from_file_1d(input_file_y[ii[i]], "y", domain_y0[i], domain_y1[i]) |
|---|
| 517 | x = x - min(x.flatten()) + domain_px[i]/2.0 |
|---|
| 518 | y = y - min(y.flatten()) + domain_px[i]/2.0 |
|---|
| 519 | nc_write_dimension(filename[i], 'x', x, datatypes["x"]) |
|---|
| 520 | nc_write_dimension(filename[i], 'y', y, datatypes["y"]) |
|---|
| 521 | nc_write_attribute(filename[i], 'x', 'long_name', 'x') |
|---|
| 522 | nc_write_attribute(filename[i], 'x', 'standard_name','projection_x_coordinate') |
|---|
| 523 | nc_write_attribute(filename[i], 'x', 'units', 'm') |
|---|
| 524 | nc_write_attribute(filename[i], 'y', 'long_name', 'x') |
|---|
| 525 | nc_write_attribute(filename[i], 'y', 'standard_name', 'projection_y_coordinate') |
|---|
| 526 | nc_write_attribute(filename[i], 'y', 'units', 'm') |
|---|
| 527 | |
|---|
| 528 | lat = nc_read_from_file_2d(input_file_lat[ii[i]], "Band1", domain_x0[i], domain_x1[i], domain_y0[i], domain_y1[i]) |
|---|
| 529 | lon = nc_read_from_file_2d(input_file_lon[ii[i]], "Band1", domain_x0[i], domain_x1[i], domain_y0[i], domain_y1[i]) |
|---|
| 530 | |
|---|
| 531 | nc_write_to_file_2d(filename[i], 'lat', lat, datatypes["lat"],'y','x',fillvalues["lat"]) |
|---|
| 532 | nc_write_to_file_2d(filename[i], 'lon', lon, datatypes["lon"],'y','x',fillvalues["lon"]) |
|---|
| 533 | |
|---|
| 534 | nc_write_attribute(filename[i], 'lat', 'long_name', 'latitude') |
|---|
| 535 | nc_write_attribute(filename[i], 'lat', 'standard_name','latitude') |
|---|
| 536 | nc_write_attribute(filename[i], 'lat', 'units', 'degrees_north') |
|---|
| 537 | |
|---|
| 538 | nc_write_attribute(filename[i], 'lon', 'long_name', 'longitude') |
|---|
| 539 | nc_write_attribute(filename[i], 'lon', 'standard_name','longitude') |
|---|
| 540 | nc_write_attribute(filename[i], 'lon', 'units', 'degrees_east') |
|---|
| 541 | |
|---|
| 542 | 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]) |
|---|
| 543 | 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]) |
|---|
| 544 | |
|---|
| 545 | nc_write_to_file_2d(filename[i], 'E_UTM', x_UTM, datatypes["E_UTM"],'y','x',fillvalues["E_UTM"]) |
|---|
| 546 | nc_write_to_file_2d(filename[i], 'N_UTM', y_UTM, datatypes["N_UTM"],'y','x',fillvalues["N_UTM"]) |
|---|
| 547 | |
|---|
| 548 | nc_write_attribute(filename[i], 'E_UTM', 'long_name', 'easting') |
|---|
| 549 | nc_write_attribute(filename[i], 'E_UTM', 'standard_name','projection_x_coorindate') |
|---|
| 550 | nc_write_attribute(filename[i], 'E_UTM', 'units', 'm') |
|---|
| 551 | |
|---|
| 552 | nc_write_attribute(filename[i], 'N_UTM', 'long_name', 'northing') |
|---|
| 553 | nc_write_attribute(filename[i], 'N_UTM', 'standard_name','projection_y_coorindate') |
|---|
| 554 | nc_write_attribute(filename[i], 'N_UTM', 'units', 'm') |
|---|
| 555 | |
|---|
| 556 | nc_write_crs(filename[i]) |
|---|
| 557 | |
|---|
| 558 | |
|---|
| 559 | |
|---|
| 560 | # If necessary, bring terrain height to PALM's vertical grid. This is either forced by the user or implicitly |
|---|
| 561 | # by using interpolation for a child domain |
|---|
| 562 | if domain_za[i]: |
|---|
| 563 | zt_all[i] = bring_to_palm_grid(zt_all[i],x,y,domain_dz[i]) |
|---|
| 564 | |
|---|
| 565 | nc_write_to_file_2d(filename[i], 'zt', zt_all[i], datatypes["zt"],'y','x',fillvalues["zt"]) |
|---|
| 566 | nc_write_attribute(filename[i], 'zt', 'long_name', 'orography') |
|---|
| 567 | nc_write_attribute(filename[i], 'zt', 'units', 'm') |
|---|
| 568 | nc_write_attribute(filename[i], 'zt', 'res_orig', domain_px[i]) |
|---|
| 569 | nc_write_attribute(filename[i], 'zt', 'coordinates', 'E_UTM N_UTM lon lat') |
|---|
| 570 | nc_write_attribute(filename[i], 'zt', 'grid_mapping', 'E_UTM N_UTM lon lat') |
|---|
| 571 | |
|---|
| 572 | del zt_all |
|---|
| 573 | |
|---|
| 574 | |
|---|
| 575 | # Process building height, id, and type |
|---|
| 576 | for i in range(0,ndomains): |
|---|
| 577 | 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]) |
|---|
| 578 | |
|---|
| 579 | 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]) |
|---|
| 580 | |
|---|
| 581 | 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]) |
|---|
| 582 | building_type[building_type == 255] = fillvalues["building_type"] |
|---|
| 583 | building_type = np.where(building_type < 1,defaultvalues["building_type"],building_type) |
|---|
| 584 | |
|---|
| 585 | check = check_arrays_2(buildings_2d,building_id,fillvalues["buildings_2d"],fillvalues["building_id"]) |
|---|
| 586 | if not check: |
|---|
| 587 | buildings_2d = np.where(building_id != fillvalues["building_id"],buildings_2d,fillvalues["buildings_2d"]) |
|---|
| 588 | building_id = np.where(buildings_2d == fillvalues["buildings_2d"],fillvalues["building_id"],building_id) |
|---|
| 589 | print("Data check #1 " + str(check_arrays_2(buildings_2d,building_id,fillvalues["buildings_2d"],fillvalues["building_id"]))) |
|---|
| 590 | |
|---|
| 591 | check = check_arrays_2(buildings_2d,building_type,fillvalues["buildings_2d"],fillvalues["building_type"]) |
|---|
| 592 | if not check: |
|---|
| 593 | building_type = np.where(buildings_2d == fillvalues["buildings_2d"],fillvalues["building_type"],building_type) |
|---|
| 594 | building_type = np.where((building_type == fillvalues["building_type"]) & (buildings_2d != fillvalues["buildings_2d"]),defaultvalues["building_type"],building_type) |
|---|
| 595 | print("Data check #2 " + str(check_arrays_2(buildings_2d,building_type,fillvalues["buildings_2d"],fillvalues["building_type"]))) |
|---|
| 596 | |
|---|
| 597 | nc_write_to_file_2d(filename[i], 'buildings_2d', buildings_2d, datatypes["buildings_2d"],'y','x',fillvalues["buildings_2d"]) |
|---|
| 598 | nc_write_attribute(filename[i], 'buildings_2d', 'long_name', 'buildings') |
|---|
| 599 | nc_write_attribute(filename[i], 'buildings_2d', 'units', 'm') |
|---|
| 600 | nc_write_attribute(filename[i], 'buildings_2d', 'res_orig', domain_px[i]) |
|---|
| 601 | nc_write_attribute(filename[i], 'buildings_2d', 'lod', 1) |
|---|
| 602 | nc_write_attribute(filename[i], 'buildings_2d', 'coordinates', 'E_UTM N_UTM lon lat') |
|---|
| 603 | nc_write_attribute(filename[i], 'buildings_2d', 'grid_mapping', 'E_UTM N_UTM lon lat') |
|---|
| 604 | |
|---|
| 605 | nc_write_to_file_2d(filename[i], 'building_id', building_id, datatypes["building_id"],'y','x',fillvalues["building_id"]) |
|---|
| 606 | nc_write_attribute(filename[i], 'building_id', 'long_name', 'building id') |
|---|
| 607 | nc_write_attribute(filename[i], 'building_id', 'units', '') |
|---|
| 608 | nc_write_attribute(filename[i], 'building_id', 'res_orig', domain_px[i]) |
|---|
| 609 | nc_write_attribute(filename[i], 'building_id', 'coordinates', 'E_UTM N_UTM lon lat') |
|---|
| 610 | nc_write_attribute(filename[i], 'building_id', 'grid_mapping', 'E_UTM N_UTM lon lat') |
|---|
| 611 | |
|---|
| 612 | nc_write_to_file_2d(filename[i], 'building_type', building_type, datatypes["building_type"],'y','x',fillvalues["building_type"]) |
|---|
| 613 | nc_write_attribute(filename[i], 'building_type', 'long_name', 'building type') |
|---|
| 614 | nc_write_attribute(filename[i], 'building_type', 'units', '') |
|---|
| 615 | nc_write_attribute(filename[i], 'building_type', 'res_orig', domain_px[i]) |
|---|
| 616 | nc_write_attribute(filename[i], 'building_type', 'coordinates', 'E_UTM N_UTM lon lat') |
|---|
| 617 | nc_write_attribute(filename[i], 'building_type', 'grid_mapping', 'E_UTM N_UTM lon lat') |
|---|
| 618 | |
|---|
| 619 | del buildings_2d |
|---|
| 620 | del building_id |
|---|
| 621 | del building_type |
|---|
| 622 | |
|---|
| 623 | # Create 3d buildings if necessary. In that course, read bridge objects and add them to building layer |
|---|
| 624 | for i in range(0,ndomains): |
|---|
| 625 | |
|---|
| 626 | if domain_3d[i]: |
|---|
| 627 | x = nc_read_from_file_2d_all(filename[i], 'x') |
|---|
| 628 | y = nc_read_from_file_2d_all(filename[i], 'y') |
|---|
| 629 | buildings_2d = nc_read_from_file_2d_all(filename[i], 'buildings_2d') |
|---|
| 630 | building_id = nc_read_from_file_2d_all(filename[i], 'building_id') |
|---|
| 631 | |
|---|
| 632 | 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]) |
|---|
| 633 | 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]) |
|---|
| 634 | |
|---|
| 635 | bridges_2d = np.where(bridges_2d == 0.0,fillvalues["bridges_2d"],bridges_2d) |
|---|
| 636 | building_id = np.where(bridges_2d == fillvalues["bridges_2d"],building_id,bridges_id) |
|---|
| 637 | |
|---|
| 638 | |
|---|
| 639 | if np.any(buildings_2d != fillvalues["buildings_2d"]): |
|---|
| 640 | buildings_3d, z = make_3d_from_2d(buildings_2d,x,y,domain_dz[i]) |
|---|
| 641 | if np.any(bridges_2d != fillvalues["bridges_2d"]): |
|---|
| 642 | buildings_3d = make_3d_from_bridges_2d(buildings_3d,bridges_2d,x,y,domain_dz[i],settings_bridge_width,fillvalues["bridges_2d"]) |
|---|
| 643 | else: |
|---|
| 644 | print("Skipping creation of 3D bridges (no bridges in domain)") |
|---|
| 645 | |
|---|
| 646 | |
|---|
| 647 | nc_write_dimension(filename[i], 'z', z, datatypes["z"]) |
|---|
| 648 | nc_write_attribute(filename[i], 'z', 'long_name', 'z') |
|---|
| 649 | nc_write_attribute(filename[i], 'z', 'units', 'm') |
|---|
| 650 | |
|---|
| 651 | nc_write_to_file_3d(filename[i], 'buildings_3d', buildings_3d, datatypes["buildings_3d"],'z','y','x',fillvalues["buildings_3d"]) |
|---|
| 652 | nc_write_attribute(filename[i], 'buildings_3d', 'long_name', 'buildings 3d') |
|---|
| 653 | nc_write_attribute(filename[i], 'buildings_3d', 'units', '') |
|---|
| 654 | nc_write_attribute(filename[i], 'buildings_3d', 'res_orig', domain_px[i]) |
|---|
| 655 | nc_write_attribute(filename[i], 'buildings_3d', 'lod', 2) |
|---|
| 656 | |
|---|
| 657 | del buildings_3d |
|---|
| 658 | |
|---|
| 659 | else: |
|---|
| 660 | print("Skipping creation of 3D buildings (no buildings in domain)") |
|---|
| 661 | |
|---|
| 662 | |
|---|
| [3629] | 663 | del bridges_2d, bridges_id, building_id, buildings_2d |
|---|
| [3567] | 664 | |
|---|
| 665 | |
|---|
| 666 | |
|---|
| 667 | # Read vegetation type, water_type, pavement_type, soil_type and make fields consistent |
|---|
| 668 | for i in range(0,ndomains): |
|---|
| 669 | |
|---|
| 670 | building_type = nc_read_from_file_2d_all(filename[i], 'building_type') |
|---|
| 671 | |
|---|
| 672 | 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]) |
|---|
| 673 | vegetation_type[vegetation_type == 255] = fillvalues["vegetation_type"] |
|---|
| 674 | vegetation_type = np.where((vegetation_type < 1) & (vegetation_type != fillvalues["vegetation_type"]),defaultvalues["vegetation_type"],vegetation_type) |
|---|
| 675 | |
|---|
| 676 | 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]) |
|---|
| 677 | pavement_type[pavement_type == 255] = fillvalues["pavement_type"] |
|---|
| 678 | pavement_type = np.where((pavement_type < 1) & (pavement_type != fillvalues["pavement_type"]),defaultvalues["pavement_type"],pavement_type) |
|---|
| 679 | |
|---|
| 680 | 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]) |
|---|
| 681 | water_type[water_type == 255] = fillvalues["water_type"] |
|---|
| 682 | water_type = np.where((water_type < 1) & (water_type != fillvalues["water_type"]),defaultvalues["water_type"],water_type) |
|---|
| 683 | |
|---|
| 684 | # to do: replace by real soil input data |
|---|
| 685 | 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]) |
|---|
| 686 | soil_type[soil_type == 255] = fillvalues["soil_type"] |
|---|
| 687 | soil_type = np.where((soil_type < 1) & (soil_type != fillvalues["soil_type"]),defaultvalues["soil_type"],soil_type) |
|---|
| 688 | |
|---|
| 689 | # Make arrays consistent |
|---|
| 690 | # #1 Set vegetation type to missing for pixel where a pavement type is set |
|---|
| 691 | vegetation_type = np.where((vegetation_type != fillvalues["vegetation_type"]) & (pavement_type != fillvalues["pavement_type"]),fillvalues["vegetation_type"],vegetation_type) |
|---|
| 692 | |
|---|
| 693 | # #2 Set vegetation type to missing for pixel where a building type is set |
|---|
| 694 | vegetation_type = np.where((vegetation_type != fillvalues["vegetation_type"]) & (building_type != fillvalues["building_type"]) ,fillvalues["vegetation_type"],vegetation_type) |
|---|
| 695 | |
|---|
| 696 | # #3 Set vegetation type to missing for pixel where a building type is set |
|---|
| 697 | vegetation_type = np.where((vegetation_type != fillvalues["vegetation_type"]) & (water_type != fillvalues["water_type"]),fillvalues["vegetation_type"],vegetation_type) |
|---|
| 698 | |
|---|
| 699 | # #4 Remove pavement for pixels with buildings |
|---|
| 700 | pavement_type = np.where((pavement_type != fillvalues["pavement_type"]) & (building_type != fillvalues["building_type"]),fillvalues["pavement_type"],pavement_type) |
|---|
| 701 | |
|---|
| 702 | # #5 Remove pavement for pixels with water |
|---|
| 703 | pavement_type = np.where((pavement_type != fillvalues["pavement_type"]) & (water_type != fillvalues["water_type"]),fillvalues["pavement_type"],pavement_type) |
|---|
| 704 | |
|---|
| 705 | # #6 Remove water for pixels with buildings |
|---|
| 706 | water_type = np.where((water_type != fillvalues["water_type"]) & (building_type != fillvalues["building_type"]),fillvalues["water_type"],water_type) |
|---|
| 707 | |
|---|
| 708 | |
|---|
| 709 | # #7 to be removed: set default soil type everywhere |
|---|
| 710 | soil_type = np.where((vegetation_type != fillvalues["vegetation_type"]) | (pavement_type != fillvalues["pavement_type"]),defaultvalues["soil_type"],fillvalues["soil_type"]) |
|---|
| 711 | |
|---|
| 712 | |
|---|
| 713 | # Check for consistency and fill empty fields with default vegetation type |
|---|
| 714 | 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"]) |
|---|
| 715 | |
|---|
| 716 | if test: |
|---|
| 717 | vegetation_type = np.where(consistency_array == 0,defaultvalues["vegetation_type"],vegetation_type) |
|---|
| 718 | 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"]) |
|---|
| 719 | |
|---|
| 720 | # Create surface_fraction array |
|---|
| 721 | x = nc_read_from_file_2d_all(filename[i], 'x') |
|---|
| 722 | y = nc_read_from_file_2d_all(filename[i], 'y') |
|---|
| 723 | nsurface_fraction = np.arange(0,3) |
|---|
| 724 | surface_fraction = np.ones((len(nsurface_fraction),len(y),len(x))) |
|---|
| 725 | |
|---|
| 726 | surface_fraction[0,:,:] = np.where(vegetation_type != fillvalues["vegetation_type"], 1.0, 0.0) |
|---|
| 727 | surface_fraction[1,:,:] = np.where(pavement_type != fillvalues["pavement_type"], 1.0, 0.0) |
|---|
| 728 | surface_fraction[2,:,:] = np.where(water_type != fillvalues["water_type"], 1.0, 0.0) |
|---|
| 729 | |
|---|
| 730 | nc_write_dimension(filename[i], 'nsurface_fraction', nsurface_fraction, datatypes["nsurface_fraction"]) |
|---|
| 731 | nc_write_to_file_3d(filename[i], 'surface_fraction', surface_fraction, datatypes["surface_fraction"],'nsurface_fraction','y','x',fillvalues["surface_fraction"]) |
|---|
| 732 | nc_write_attribute(filename[i], 'surface_fraction', 'long_name', 'surface fraction') |
|---|
| 733 | nc_write_attribute(filename[i], 'surface_fraction', 'units', '') |
|---|
| 734 | nc_write_attribute(filename[i], 'surface_fraction', 'res_orig', domain_px[i]) |
|---|
| 735 | del surface_fraction |
|---|
| 736 | |
|---|
| 737 | |
|---|
| 738 | # Correct vegetation_type when a vegetation height is available and is indicative of low vegeetation |
|---|
| 739 | 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]) |
|---|
| 740 | |
|---|
| [3629] | 741 | 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) |
|---|
| 742 | 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] | 743 | |
|---|
| 744 | |
|---|
| 745 | nc_write_to_file_2d(filename[i], 'vegetation_type', vegetation_type, datatypes["vegetation_type"],'y','x',fillvalues["vegetation_type"]) |
|---|
| 746 | nc_write_attribute(filename[i], 'vegetation_type', 'long_name', 'vegetation type') |
|---|
| 747 | nc_write_attribute(filename[i], 'vegetation_type', 'units', '') |
|---|
| 748 | nc_write_attribute(filename[i], 'vegetation_type', 'res_orig', domain_px[i]) |
|---|
| 749 | nc_write_attribute(filename[i], 'vegetation_type', 'coordinates', 'E_UTM N_UTM lon lat') |
|---|
| 750 | nc_write_attribute(filename[i], 'vegetation_type', 'grid_mapping', 'E_UTM N_UTM lon lat') |
|---|
| 751 | del vegetation_type |
|---|
| 752 | |
|---|
| 753 | nc_write_to_file_2d(filename[i], 'pavement_type', pavement_type, datatypes["pavement_type"],'y','x',fillvalues["pavement_type"]) |
|---|
| 754 | nc_write_attribute(filename[i], 'pavement_type', 'long_name', 'pavement type') |
|---|
| 755 | nc_write_attribute(filename[i], 'pavement_type', 'units', '') |
|---|
| 756 | nc_write_attribute(filename[i], 'pavement_type', 'res_orig', domain_px[i]) |
|---|
| 757 | nc_write_attribute(filename[i], 'pavement_type', 'coordinates', 'E_UTM N_UTM lon lat') |
|---|
| 758 | nc_write_attribute(filename[i], 'pavement_type', 'grid_mapping', 'E_UTM N_UTM lon lat') |
|---|
| 759 | del pavement_type |
|---|
| 760 | |
|---|
| 761 | nc_write_to_file_2d(filename[i], 'water_type', water_type, datatypes["water_type"],'y','x',fillvalues["water_type"]) |
|---|
| 762 | nc_write_attribute(filename[i], 'water_type', 'long_name', 'water type') |
|---|
| 763 | nc_write_attribute(filename[i], 'water_type', 'units', '') |
|---|
| 764 | nc_write_attribute(filename[i], 'water_type', 'res_orig', domain_px[i]) |
|---|
| 765 | nc_write_attribute(filename[i], 'water_type', 'coordinates', 'E_UTM N_UTM lon lat') |
|---|
| 766 | nc_write_attribute(filename[i], 'water_type', 'grid_mapping', 'E_UTM N_UTM lon lat') |
|---|
| 767 | del water_type |
|---|
| 768 | |
|---|
| 769 | nc_write_to_file_2d(filename[i], 'soil_type', soil_type, datatypes["soil_type"],'y','x',fillvalues["soil_type"]) |
|---|
| 770 | nc_write_attribute(filename[i], 'soil_type', 'long_name', 'soil type') |
|---|
| 771 | nc_write_attribute(filename[i], 'soil_type', 'units', '') |
|---|
| 772 | nc_write_attribute(filename[i], 'soil_type', 'res_orig', domain_px[i]) |
|---|
| 773 | nc_write_attribute(filename[i], 'soil_type', 'coordinates', 'E_UTM N_UTM lon lat') |
|---|
| 774 | nc_write_attribute(filename[i], 'soil_type', 'grid_mapping', 'E_UTM N_UTM lon lat') |
|---|
| 775 | del soil_type |
|---|
| 776 | |
|---|
| [3629] | 777 | del x |
|---|
| 778 | del y |
|---|
| [3567] | 779 | |
|---|
| 780 | # pixels with bridges get building_type = 7 = bridge. This does not change the _type setting for the under-bridge |
|---|
| 781 | # area |
|---|
| 782 | if domain_3d[i]: |
|---|
| 783 | if np.any(building_type != fillvalues["building_type"]): |
|---|
| 784 | |
|---|
| 785 | 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]) |
|---|
| 786 | bridges_2d = np.where(bridges_2d == 0.0,fillvalues["bridges_2d"],bridges_2d) |
|---|
| 787 | building_type = np.where(bridges_2d != fillvalues["bridges_2d"],7,building_type) |
|---|
| 788 | nc_overwrite_to_file_2d(filename[i], 'building_type', building_type) |
|---|
| 789 | |
|---|
| 790 | del building_type |
|---|
| 791 | del bridges_2d |
|---|
| 792 | |
|---|
| [3629] | 793 | # Read/write street type and street crossings |
|---|
| [3567] | 794 | for i in range(0,ndomains): |
|---|
| 795 | |
|---|
| 796 | 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]) |
|---|
| 797 | street_type[street_type == 255] = fillvalues["street_type"] |
|---|
| 798 | street_type = np.where((street_type < 1) & (street_type != fillvalues["street_type"]),defaultvalues["street_type"],street_type) |
|---|
| 799 | |
|---|
| 800 | nc_write_to_file_2d(filename[i], 'street_type', street_type, datatypes["street_type"],'y','x',fillvalues["street_type"]) |
|---|
| 801 | nc_write_attribute(filename[i], 'street_type', 'long_name', 'street type') |
|---|
| 802 | nc_write_attribute(filename[i], 'street_type', 'units', '') |
|---|
| 803 | nc_write_attribute(filename[i], 'street_type', 'res_orig', domain_px[i]) |
|---|
| 804 | nc_write_attribute(filename[i], 'street_type', 'coordinates', 'E_UTM N_UTM lon lat') |
|---|
| 805 | nc_write_attribute(filename[i], 'street_type', 'grid_mapping', 'E_UTM N_UTM lon lat') |
|---|
| 806 | del street_type |
|---|
| 807 | |
|---|
| 808 | 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]) |
|---|
| 809 | street_crossings[street_crossings == 255] = fillvalues["street_crossings"] |
|---|
| 810 | street_crossings = np.where((street_crossings < 1) & (street_crossings != fillvalues["street_crossings"]),defaultvalues["street_crossings"],street_crossings) |
|---|
| 811 | |
|---|
| 812 | nc_write_to_file_2d(filename[i], 'street_crossings', street_crossings, datatypes["street_crossings"],'y','x',fillvalues["street_crossings"]) |
|---|
| 813 | nc_write_attribute(filename[i], 'street_crossings', 'long_name', 'street crossings') |
|---|
| 814 | nc_write_attribute(filename[i], 'street_crossings', 'units', '') |
|---|
| 815 | nc_write_attribute(filename[i], 'street_crossings', 'res_orig', domain_px[i]) |
|---|
| 816 | nc_write_attribute(filename[i], 'street_crossings', 'coordinates', 'E_UTM N_UTM lon lat') |
|---|
| 817 | nc_write_attribute(filename[i], 'street_crossings', 'grid_mapping', 'E_UTM N_UTM lon lat') |
|---|
| 818 | del street_crossings |
|---|
| [3629] | 819 | |
|---|
| 820 | |
|---|
| 821 | # Read/write vegetation on roofs |
|---|
| 822 | for i in range(0,ndomains): |
|---|
| 823 | if domain_green_roofs[i]: |
|---|
| 824 | 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]) |
|---|
| 825 | buildings_2d = nc_read_from_file_2d_all(filename[i], 'buildings_2d') |
|---|
| 826 | |
|---|
| 827 | |
|---|
| 828 | x = nc_read_from_file_2d_all(filename[i], 'x') |
|---|
| 829 | y = nc_read_from_file_2d_all(filename[i], 'y') |
|---|
| 830 | nbuilding_pars = np.arange(0,46) |
|---|
| 831 | building_pars = np.ones((len(nbuilding_pars),len(y),len(x))) |
|---|
| 832 | building_pars[:,:,:] = fillvalues["building_pars"] |
|---|
| 833 | |
|---|
| 834 | # assign green fraction on roofs |
|---|
| 835 | building_pars[3,:,:] = np.where( (buildings_2d != fillvalues["buildings_2d"] ) & ( green_roofs != fillvalues["building_pars"] ),1.0,fillvalues["building_pars"]) |
|---|
| 836 | |
|---|
| 837 | # assign leaf area index for vegetation on roofs |
|---|
| 838 | building_pars[4,:,:] = np.where( ( buildings_2d != fillvalues["buildings_2d"] ) & ( green_roofs == 1.0 ),settings_lai_roof_intensive,fillvalues["building_pars"]) |
|---|
| 839 | building_pars[4,:,:] = np.where( ( buildings_2d != fillvalues["buildings_2d"] ) & ( green_roofs == 2.0 ),settings_lai_roof_extensive,building_pars[4,:,:]) |
|---|
| 840 | |
|---|
| 841 | |
|---|
| 842 | nc_write_dimension(filename[i], 'nbuilding_pars', nbuilding_pars, datatypes["nbuilding_pars"]) |
|---|
| 843 | nc_write_to_file_3d(filename[i], 'building_pars', building_pars, datatypes["building_pars"],'nbuilding_pars','y','x',fillvalues["building_pars"]) |
|---|
| 844 | nc_write_attribute(filename[i], 'building_pars', 'long_name', 'building_pars') |
|---|
| 845 | nc_write_attribute(filename[i], 'building_pars', 'units', '') |
|---|
| 846 | nc_write_attribute(filename[i], 'building_pars', 'res_orig', domain_px[i]) |
|---|
| 847 | nc_write_attribute(filename[i], 'building_pars', 'coordinates', 'E_UTM N_UTM lon lat') |
|---|
| 848 | nc_write_attribute(filename[i], 'building_pars', 'grid_mapping', 'E_UTM N_UTM lon lat') |
|---|
| 849 | |
|---|
| 850 | del building_pars, buildings_2d, x, y |
|---|
| 851 | |
|---|
| 852 | |
|---|
| 853 | # Read tree data and create LAD and BAD arrays using the canopy generator |
|---|
| 854 | for i in range(0,ndomains): |
|---|
| 855 | if domain_street_trees[i]: |
|---|
| 856 | lai = nc_read_from_file_2d(input_file_lai[ii[i]], 'Band1', domain_x0[i], domain_x1[i], domain_y0[i], domain_y1[i]) |
|---|
| 857 | |
|---|
| 858 | |
|---|
| 859 | # Save lai data as default for low and high vegetation |
|---|
| 860 | lai_low = lai |
|---|
| 861 | lai_high = lai |
|---|
| 862 | |
|---|
| 863 | x = nc_read_from_file_2d_all(filename[i], 'x') |
|---|
| 864 | y = nc_read_from_file_2d_all(filename[i], 'y') |
|---|
| 865 | nvegetation_pars = np.arange(0,11) |
|---|
| 866 | vegetation_pars = np.ones((len(nvegetation_pars),len(y),len(x))) |
|---|
| 867 | vegetation_pars[:,:,:] = fillvalues["vegetation_pars"] |
|---|
| 868 | |
|---|
| 869 | vegetation_pars[1,:,:] = lai |
|---|
| 870 | |
|---|
| 871 | |
|---|
| 872 | # Write out first version of LAI. Will later be overwritten. |
|---|
| 873 | nc_write_dimension(filename[i], 'nvegetation_pars', nvegetation_pars, datatypes["nvegetation_pars"]) |
|---|
| 874 | nc_write_to_file_3d(filename[i], 'vegetation_pars', vegetation_pars, datatypes["vegetation_pars"],'nvegetation_pars','y','x',fillvalues["vegetation_pars"]) |
|---|
| 875 | nc_write_attribute(filename[i], 'vegetation_pars', 'long_name', 'vegetation_pars') |
|---|
| 876 | nc_write_attribute(filename[i], 'vegetation_pars', 'units', '') |
|---|
| 877 | nc_write_attribute(filename[i], 'vegetation_pars', 'res_orig', domain_px[i]) |
|---|
| 878 | nc_write_attribute(filename[i], 'vegetation_pars', 'coordinates', 'E_UTM N_UTM lon lat') |
|---|
| 879 | nc_write_attribute(filename[i], 'vegetation_pars', 'grid_mapping', 'E_UTM N_UTM lon lat') |
|---|
| 880 | |
|---|
| 881 | |
|---|
| 882 | # Read all tree parameters from file |
|---|
| 883 | 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]) |
|---|
| 884 | 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]) |
|---|
| 885 | 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]) |
|---|
| 886 | 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]) |
|---|
| 887 | 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]) |
|---|
| 888 | |
|---|
| 889 | # Remove missing values from the data. Reasonable values will be set by the tree generator |
|---|
| 890 | tree_height = np.where( (tree_height == 0.0) | (tree_height == -1.0) ,fillvalues["tree_data"],tree_height) |
|---|
| 891 | tree_crown_diameter = np.where( (tree_crown_diameter == 0.0) | (tree_crown_diameter == -1.0) ,fillvalues["tree_data"],tree_crown_diameter) |
|---|
| 892 | tree_trunk_diameter = np.where( (tree_trunk_diameter == 0.0) | (tree_trunk_diameter == -1.0) ,fillvalues["tree_data"],tree_trunk_diameter) |
|---|
| 893 | tree_type = np.where( (tree_type == 0.0) | (tree_type == -1.0) ,fillvalues["tree_data"],tree_type) |
|---|
| 894 | patch_height = np.where( patch_height == -1.0 ,fillvalues["tree_data"],patch_height) |
|---|
| 895 | |
|---|
| 896 | # Convert trunk diameter from cm to m |
|---|
| 897 | tree_trunk_diameter = np.where(tree_trunk_diameter != fillvalues["tree_data"], tree_trunk_diameter * 0.01,tree_trunk_diameter) |
|---|
| 898 | |
|---|
| 899 | |
|---|
| 900 | # Temporarily change missing value for tree_type |
|---|
| 901 | tree_type = np.where( (tree_type == fillvalues["tree_type"]),fillvalues["tree_data"],tree_type) |
|---|
| 902 | |
|---|
| 903 | # Compare patch height array with vegetation type and correct accordingly |
|---|
| 904 | vegetation_type = nc_read_from_file_2d_all(filename[i], 'vegetation_type') |
|---|
| 905 | |
|---|
| 906 | |
|---|
| 907 | # For zero-height patches, set vegetation_type to short grass and remove these pixels from the patch height array |
|---|
| 908 | 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) |
|---|
| 909 | 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) |
|---|
| 910 | nc_overwrite_to_file_2d(filename[i], 'vegetation_type', vegetation_type) |
|---|
| 911 | |
|---|
| 912 | max_tree_height = max(tree_height.flatten()) |
|---|
| 913 | max_patch_height = max(patch_height.flatten()) |
|---|
| 914 | |
|---|
| 915 | if ( (max_tree_height != fillvalues["tree_data"]) | (max_patch_height == fillvalues["tree_data"]) ): |
|---|
| 916 | |
|---|
| 917 | 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"]) |
|---|
| 918 | |
|---|
| 919 | |
|---|
| 920 | # Remove LAD volumes that are inside buildings |
|---|
| 921 | buildings_2d = nc_read_from_file_2d_all(filename[i], 'buildings_2d') |
|---|
| 922 | for k in range(0,len(zlad)-1): |
|---|
| 923 | |
|---|
| 924 | lad[k,:,:] = np.where(buildings_2d == fillvalues["buildings_2d"],lad[k,:,:],fillvalues["tree_data"]) |
|---|
| 925 | bad[k,:,:] = np.where(buildings_2d == fillvalues["buildings_2d"],bad[k,:,:],fillvalues["tree_data"]) |
|---|
| 926 | tree_ids[k,:,:] = np.where(buildings_2d == fillvalues["buildings_2d"],tree_ids[k,:,:],fillvalues["tree_data"]) |
|---|
| 927 | |
|---|
| 928 | |
|---|
| 929 | nc_write_dimension(filename[i], 'zlad', zlad, datatypes["tree_data"]) |
|---|
| 930 | nc_write_to_file_3d(filename[i], 'lad', lad, datatypes["tree_data"],'zlad','y','x',fillvalues["tree_data"]) |
|---|
| 931 | nc_write_attribute(filename[i], 'lad', 'long_name', 'leaf area density') |
|---|
| 932 | nc_write_attribute(filename[i], 'lad', 'units', '') |
|---|
| 933 | nc_write_attribute(filename[i], 'lad', 'res_orig', domain_px[i]) |
|---|
| 934 | nc_write_attribute(filename[i], 'lad', 'coordinates', 'E_UTM N_UTM lon lat') |
|---|
| 935 | nc_write_attribute(filename[i], 'lad', 'grid_mapping', 'E_UTM N_UTM lon lat') |
|---|
| 936 | |
|---|
| 937 | nc_write_to_file_3d(filename[i], 'bad', bad, datatypes["tree_data"],'zlad','y','x',fillvalues["tree_data"]) |
|---|
| 938 | nc_write_attribute(filename[i], 'bad', 'long_name', 'basal area density') |
|---|
| 939 | nc_write_attribute(filename[i], 'bad', 'units', '') |
|---|
| 940 | nc_write_attribute(filename[i], 'bad', 'res_orig', domain_px[i]) |
|---|
| 941 | nc_write_attribute(filename[i], 'bad', 'coordinates', 'E_UTM N_UTM lon lat') |
|---|
| 942 | nc_write_attribute(filename[i], 'bad', 'grid_mapping', 'E_UTM N_UTM lon lat') |
|---|
| 943 | |
|---|
| 944 | nc_write_to_file_3d(filename[i], 'tree_id', tree_ids, datatypes["tree_data"],'zlad','y','x',fillvalues["tree_data"]) |
|---|
| 945 | nc_write_attribute(filename[i], 'tree_id', 'long_name', 'tree id') |
|---|
| 946 | nc_write_attribute(filename[i], 'tree_id', 'units', '') |
|---|
| 947 | nc_write_attribute(filename[i], 'tree_id', 'res_orig', domain_px[i]) |
|---|
| 948 | nc_write_attribute(filename[i], 'tree_id', 'coordinates', 'E_UTM N_UTM lon lat') |
|---|
| 949 | nc_write_attribute(filename[i], 'tree_id', 'grid_mapping', 'E_UTM N_UTM lon lat') |
|---|
| 950 | |
|---|
| 951 | del buildings_2d, lai, lad, bad, tree_ids, zlad |
|---|
| 952 | |
|---|
| 953 | del vegetation_pars, tree_height, tree_crown_diameter, tree_trunk_diameter, tree_type, patch_height, vegetation_type, x, y |
|---|
| 954 | |
|---|
| 955 | |
|---|
| 956 | # Create vegetation patches for locations with high vegetation type |
|---|
| 957 | for i in range(0,ndomains): |
|---|
| 958 | if domain_canopy_patches[i]: |
|---|
| 959 | |
|---|
| 960 | # Load vegetation_type and lad array (at level z = 0) for re-processing |
|---|
| 961 | vegetation_type = nc_read_from_file_2d_all(filename[i], 'vegetation_type') |
|---|
| 962 | lad = nc_read_from_file_3d_all(filename[i], 'lad') |
|---|
| 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 | vegetation_pars = nc_read_from_file_3d_all(filename[i], 'vegetation_pars') |
|---|
| 965 | lai = vegetation_pars[1,:,:] |
|---|
| 966 | |
|---|
| 967 | # For all pixels where the LAD array does not contains a street tree and for which a high vegetation was set and for which a patch height is either missing of larger than one grid spacing |
|---|
| 968 | # the lai_high is reset to a low vegetation value |
|---|
| 969 | 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])) ),settings_lai_low_default,fillvalues["tree_data"]) |
|---|
| 970 | |
|---|
| 971 | # For all pixels where lai_high is set but no lai is available, set the default high vegetation lai TODO: check if this makes sense bacauce lai_high = lai anway?! |
|---|
| 972 | lai_high = np.where( (lai_high != fillvalues["tree_data"]) & (lai == fillvalues["tree_data"]), settings_lai_high_default, lai_high) |
|---|
| 973 | |
|---|
| 974 | # Define a patch height whereever it is missing, but where a high vegetation LAI was set |
|---|
| 975 | patch_height = np.where ( (lai_high != fillvalues["tree_data"]) & (patch_height == fillvalues["tree_data"]), settings_patch_height_default, patch_height) |
|---|
| 976 | |
|---|
| 977 | # Remove pixels where street trees were already set |
|---|
| 978 | patch_height = np.where ( (lad[0,:,:] != fillvalues["tree_data"]), fillvalues["tree_data"], patch_height) |
|---|
| 979 | |
|---|
| 980 | # For missing LAI values, set either the high vegetation default or the low vegetation default |
|---|
| 981 | lai_high = np.where( (patch_height > 2.0) & (lai_high == fillvalues["tree_data"]),settings_lai_high_default,lai_high) |
|---|
| 982 | lai_high = np.where( (patch_height <= 2.0) & (lai_high == fillvalues["tree_data"]),settings_lai_low_default,lai_high) |
|---|
| 983 | |
|---|
| 984 | |
|---|
| 985 | if ( max(patch_height.flatten()) >= (2.0 * domain_dz[i]) ): |
|---|
| 986 | print(" start calculating LAD (this might take some time)") |
|---|
| 987 | lad_patch, patch_nz, status = process_patch(domain_dz[i],patch_height,lai_high,settings_lai_alpha,settings_lai_beta) |
|---|
| 988 | |
|---|
| 989 | 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,:,:] ) |
|---|
| 990 | |
|---|
| 991 | # Remove high vegetation wherever it is replaced by a leaf area density. This should effectively remove all high vegetation pixels |
|---|
| 992 | vegetation_type = np.where((lad[0,:,:] != fillvalues["tree_data"]) & (vegetation_type != fillvalues["vegetation_type"]),3,vegetation_type) |
|---|
| 993 | |
|---|
| 994 | |
|---|
| 995 | # If desired, remove all high vegetation. TODO: check if this is still necessary |
|---|
| 996 | if domain_high_vegetation[i]: |
|---|
| 997 | 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) |
|---|
| 998 | |
|---|
| 999 | |
|---|
| 1000 | # Remove LAI from pixels where a LAD was set |
|---|
| 1001 | lai_low = np.where( (lad[0,:,:] == fillvalues["tree_data"]), lai,fillvalues["tree_data"]) |
|---|
| 1002 | |
|---|
| 1003 | # Fill low vegetation pixels without LAI set with default value |
|---|
| 1004 | lai_low = np.where( (lai == fillvalues["tree_data"]) & (vegetation_type != fillvalues["vegetation_type"] ), settings_lai_low_default, lai) |
|---|
| 1005 | |
|---|
| 1006 | |
|---|
| 1007 | # Overwrite lai in vegetation_parameters |
|---|
| 1008 | |
|---|
| 1009 | vegetation_pars[1,:,:] = lai_low |
|---|
| 1010 | nc_overwrite_to_file_3d(filename[i], 'vegetation_pars', vegetation_pars) |
|---|
| 1011 | |
|---|
| 1012 | # Overwrite lad array |
|---|
| 1013 | nc_overwrite_to_file_3d(filename[i], 'lad', lad) |
|---|
| 1014 | |
|---|
| 1015 | # Overwrite vegetation_type |
|---|
| 1016 | nc_overwrite_to_file_2d(filename[i], 'vegetation_type', vegetation_type) |
|---|
| 1017 | |
|---|
| 1018 | |
|---|
| 1019 | del vegetation_type, lad, lai, patch_height, vegetation_pars |
|---|
