| 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 1997-2020 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 4749 2020-10-16 11:21:27Z maronga $ |
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| 27 | # Bugfix: processing of patch height faulty for multiple domains |
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| 28 | # |
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| 29 | # 4746 2020-10-15 17:16:30Z maronga |
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| 30 | # Add support for backyard high vegetation, which is commonly classified as |
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| 31 | # short grass |
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| 32 | # |
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| 33 | # 4490 2020-04-10 05:32:45Z maronga |
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| 34 | # Bugfix: call of canopy generator was not made if tree heights were only |
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| 35 | # available via patch_height, but not in tree_height |
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| 36 | # |
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| 37 | # 4481 2020-03-31 18:55:54Z maronga |
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| 38 | # Bugfix: green roofs were assigned to all available roofs when switched on |
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| 39 | # |
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| 40 | # 4149 2019-08-08 14:02:18Z suehring |
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| 41 | # Bugfix, nbuilding_pars was 1 element to small |
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| 42 | # |
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| 43 | # 3955 2019-05-07 09:55:25Z maronga |
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| 44 | # Bugfix in preparaing of green roofs |
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| 45 | # Bugfix: remove LAI for bare soils |
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| 46 | # |
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| 47 | # 3859 2019-04-03 20:30:31Z maronga |
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| 48 | # Bugfix: wrong variable naming for 'y' |
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| 49 | # |
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| 50 | # 3773 2019-03-01 08:56:57Z maronga |
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| 51 | # Unspecificed changes |
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| 52 | # |
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| 53 | # 3726 2019-02-07 18:22:49Z maronga |
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| 54 | # Removed some more bugs |
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| 55 | # |
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| 56 | # 3688 2019-01-22 10:44:20Z maronga |
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| 57 | # Some unspecified bugfixes |
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| 58 | # |
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| 59 | # 3668 2019-01-14 12:49:24Z maronga |
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| 60 | # Various improvements and bugfixes |
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| 61 | # |
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| 62 | # 3629 2018-12-13 12:18:54Z maronga |
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| 63 | # Added canopy generator calls. Some improvements |
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| 64 | # |
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| 65 | # 3567 2018-11-27 13:59:21Z maronga |
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| 66 | # Initial revisions |
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| 67 | # |
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| 68 | # Description: |
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| 69 | # ------------ |
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| 70 | # Processing tool for creating PIDS conform static drivers from rastered NetCDF |
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| 71 | # input |
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| 72 | # |
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| 73 | # @Author Bjoern Maronga (maronga@muk.uni-hannover.de) |
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| 74 | # |
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| 75 | # @todo Make input files optional |
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| 76 | # @todo Allow for ASCII input of terrain height and building height |
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| 77 | # @todo Modularize reading config file |
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| 78 | # @todo Convert to object-oriented treatment (buidings, trees) |
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| 79 | # @todo Automatically shift child domains so that their origin lies intersects |
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| 80 | # a edge note of the parent grid |
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| 81 | #------------------------------------------------------------------------------# |
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| 82 | |
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| 83 | from palm_csd_files.palm_csd_netcdf_interface import * |
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| 84 | from palm_csd_files.palm_csd_tools import * |
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| 85 | from palm_csd_files.palm_csd_canopy_generator import * |
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| 86 | import numpy as np |
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| 87 | |
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| 88 | |
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| 89 | def read_config_file(): |
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| 90 | |
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| 91 | import configparser |
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| 92 | from math import floor |
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| 93 | import numpy as np |
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| 94 | import os |
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| 95 | import subprocess as sub |
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| 96 | import sys |
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| 97 | |
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| 98 | # Check if configuration files exists and quit otherwise |
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| 99 | input_config = ".csd.config" |
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| 100 | for i in range(1,len(sys.argv)): |
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| 101 | input_config = str(sys.argv[i]) |
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| 102 | |
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| 103 | config = configparser.RawConfigParser(allow_no_value=True) |
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| 104 | |
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| 105 | if ( os.path.isfile(input_config) == False ): |
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| 106 | print ("Error. No configuration file " + input_config + " found.") |
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| 107 | raise SystemExit |
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| 108 | |
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| 109 | config.read(input_config) |
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| 110 | |
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| 111 | |
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| 112 | # Definition of settings |
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| 113 | |
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| 114 | global settings_bridge_width |
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| 115 | global settings_lai_roof_intensive |
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| 116 | global settings_lai_roof_extensive |
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| 117 | global settings_season |
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| 118 | global settings_lai_low_default |
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| 119 | global settings_lai_high_default |
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| 120 | global settings_patch_height_default |
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| 121 | global settings_lai_alpha |
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| 122 | global settings_lai_beta |
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| 123 | global settings_veg_type_below_trees |
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| 124 | global ndomains |
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| 125 | |
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| 126 | # Definition of global configuration parameters |
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| 127 | global global_acronym |
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| 128 | global global_angle |
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| 129 | global global_author |
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| 130 | global global_campaign |
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| 131 | global global_comment |
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| 132 | global global_contact |
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| 133 | global global_data_content |
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| 134 | global global_dependencies |
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| 135 | global global_institution |
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| 136 | global global_keywords |
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| 137 | global global_location |
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| 138 | global global_palm_version |
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| 139 | global global_references |
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| 140 | global global_site |
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| 141 | global global_source |
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| 142 | |
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| 143 | global path_out |
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| 144 | global filename_out |
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| 145 | global version_out |
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| 146 | |
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| 147 | |
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| 148 | # Definition of domain parameters |
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| 149 | global domain_names |
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| 150 | global domain_px |
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| 151 | global domain_x0 |
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| 152 | global domain_y0 |
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| 153 | global domain_x1 |
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| 154 | global domain_y1 |
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| 155 | global domain_nx |
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| 156 | global domain_ny |
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| 157 | global domain_dz |
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| 158 | global domain_3d |
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| 159 | global domain_high_vegetation |
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| 160 | global domain_ip |
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| 161 | global domain_za |
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| 162 | global domain_parent |
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| 163 | global domain_green_roofs |
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| 164 | global domain_street_trees |
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| 165 | global domain_canopy_patches |
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| 166 | |
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| 167 | # Definition of input data parameters |
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| 168 | global input_names |
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| 169 | global input_px |
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| 170 | |
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| 171 | |
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| 172 | global input_file_x |
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| 173 | global input_file_y |
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| 174 | global input_file_x_UTM |
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| 175 | global input_file_y_UTM |
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| 176 | global input_file_lat |
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| 177 | global input_file_lon |
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| 178 | global input_file_zt |
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| 179 | global input_file_buildings_2d |
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| 180 | global input_file_bridges_2d |
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| 181 | global input_file_building_id |
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| 182 | global input_file_bridges_id |
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| 183 | global input_file_building_type |
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| 184 | global input_file_building_type |
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| 185 | global input_file_lai |
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| 186 | global input_file_vegetation_type |
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| 187 | global input_file_vegetation_height |
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| 188 | global input_file_pavement_type |
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| 189 | global input_file_water_type |
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| 190 | global input_file_street_type |
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| 191 | global input_file_street_crossings |
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| 192 | global input_file_soil_type |
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| 193 | global input_file_vegetation_on_roofs |
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| 194 | global input_file_tree_crown_diameter |
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| 195 | global input_file_tree_height |
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| 196 | global input_file_tree_trunk_diameter |
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| 197 | global input_file_tree_type |
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| 198 | global input_file_patch_height |
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| 199 | |
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| 200 | global zt_all |
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| 201 | global zt_min |
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| 202 | |
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| 203 | settings_bridge_width = 3.0 |
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| 204 | settings_lai_roof_intensive = 2.5 |
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| 205 | settings_lai_roof_extensive = 0.8 |
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| 206 | settings_season = "summer" |
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| 207 | settings_lai_high_default = 6.0 |
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| 208 | settings_lai_low_default = 1.0 |
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| 209 | settings_patch_height_default = 10.0 |
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| 210 | settings_lai_alpha = 5.0 |
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| 211 | settings_lai_beta = 3.0 |
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| 212 | settings_veg_type_below_trees = 3 |
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| 213 | ndomains = 0 |
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| 214 | |
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| 215 | global_acronym = "" |
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| 216 | global_angle = "" |
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| 217 | global_author = "" |
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| 218 | global_campaign = "" |
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| 219 | global_comment = "" |
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| 220 | global_contact = "" |
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| 221 | global_data_content = "" |
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| 222 | global_dependencies = "" |
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| 223 | global_institution = "" |
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| 224 | global_keywords = "" |
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| 225 | global_location = "" |
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| 226 | global_palm_version = 6.0 |
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| 227 | global_references = "" |
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| 228 | global_site = "" |
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| 229 | global_source = "" |
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| 230 | |
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| 231 | path_out = "" |
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| 232 | version_out = 1 |
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| 233 | filename_out = "default" |
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| 234 | |
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| 235 | domain_names = [] |
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| 236 | domain_px = [] |
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| 237 | domain_x0 = [] |
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| 238 | domain_y0 = [] |
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| 239 | domain_x1 = [] |
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| 240 | domain_y1 = [] |
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| 241 | domain_nx = [] |
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| 242 | domain_ny = [] |
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| 243 | domain_dz = [] |
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| 244 | domain_3d = [] |
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| 245 | domain_high_vegetation = [] |
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| 246 | domain_ip = [] |
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| 247 | domain_za = [] |
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| 248 | domain_parent = [] |
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| 249 | domain_green_roofs = [] |
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| 250 | domain_street_trees = [] |
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| 251 | domain_canopy_patches = [] |
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| 252 | |
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| 253 | zt_min = 0.0 |
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| 254 | zt_all = [] |
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| 255 | |
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| 256 | input_names = [] |
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| 257 | input_px = [] |
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| 258 | |
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| 259 | input_file_x = [] |
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| 260 | input_file_y = [] |
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| 261 | input_file_x_UTM = [] |
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| 262 | input_file_y_UTM = [] |
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| 263 | input_file_lat = [] |
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| 264 | input_file_lon = [] |
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| 265 | |
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| 266 | input_file_zt = [] |
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| 267 | input_file_buildings_2d = [] |
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| 268 | input_file_bridges_2d = [] |
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| 269 | input_file_building_id = [] |
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| 270 | input_file_bridges_id = [] |
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| 271 | input_file_building_type = [] |
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| 272 | input_file_lai = [] |
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| 273 | input_file_vegetation_type = [] |
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| 274 | input_file_vegetation_height = [] |
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| 275 | input_file_pavement_type = [] |
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| 276 | input_file_water_type = [] |
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| 277 | input_file_street_type = [] |
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| 278 | input_file_street_crossings = [] |
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| 279 | input_file_soil_type = [] |
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| 280 | input_file_vegetation_on_roofs = [] |
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| 281 | input_file_tree_crown_diameter = [] |
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| 282 | input_file_tree_height = [] |
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| 283 | input_file_tree_trunk_diameter = [] |
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| 284 | input_file_tree_type = [] |
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| 285 | input_file_patch_height = [] |
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| 286 | |
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| 287 | # Load all user parameters from config file |
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| 288 | for i in range(0,len(config.sections())): |
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| 289 | |
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| 290 | read_tmp = config.sections()[i] |
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| 291 | |
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| 292 | if ( read_tmp == 'global' ): |
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| 293 | global_acronym = config.get(read_tmp, 'acronym') |
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| 294 | global_angle = float(config.get(read_tmp, 'rotation_angle')) |
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| 295 | global_author = config.get(read_tmp, 'author') |
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| 296 | global_campaign = config.get(read_tmp, 'campaign') |
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| 297 | global_comment = config.get(read_tmp, 'comment') |
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| 298 | global_contact = config.get(read_tmp, 'contact_person') |
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| 299 | global_data_content = config.get(read_tmp, 'data_content') |
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| 300 | global_dependencies = config.get(read_tmp, 'dependencies') |
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| 301 | global_institution = config.get(read_tmp, 'institution') |
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| 302 | global_keywords = config.get(read_tmp, 'keywords') |
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| 303 | global_location = config.get(read_tmp, 'location') |
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| 304 | global_palm_version = float(config.get(read_tmp, 'palm_version')) |
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| 305 | global_references = config.get(read_tmp, 'references') |
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| 306 | global_site = config.get(read_tmp, 'site') |
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| 307 | global_source = config.get(read_tmp, 'source') |
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| 308 | |
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| 309 | |
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| 310 | if ( read_tmp == 'settings' ): |
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| 311 | settings_lai_roof_intensive = config.get(read_tmp, 'lai_roof_intensive') |
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| 312 | settings_lai_roof_extensive = config.get(read_tmp, 'lai_roof_extensive') |
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| 313 | settings_bridge_width = float(config.get(read_tmp, 'bridge_width')) |
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| 314 | settings_season = config.get(read_tmp, 'season') |
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| 315 | settings_lai_high_default = float(config.get(read_tmp, 'lai_high_vegetation_default')) |
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| 316 | settings_lai_low_default = float(config.get(read_tmp, 'lai_low_vegetation_default')) |
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| 317 | settings_patch_height_default = float(config.get(read_tmp, 'patch_height_default')) |
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| 318 | settings_lai_alpha = float(config.get(read_tmp, 'lai_alpha')) |
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| 319 | settings_lai_beta = float(config.get(read_tmp, 'lai_beta')) |
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| 320 | settings_veg_type_below_trees = config.get(read_tmp, 'vegetation_type_below_trees') |
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| 321 | |
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| 322 | if ( read_tmp == 'output' ): |
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| 323 | path_out = config.get(read_tmp, 'path') |
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| 324 | filename_out = config.get(read_tmp, 'file_out') |
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| 325 | version_out = float(config.get(read_tmp, 'version')) |
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| 326 | |
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| 327 | if ( read_tmp.split("_")[0] == 'domain' ): |
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| 328 | ndomains = ndomains + 1 |
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| 329 | domain_names.append(read_tmp.split("_")[1]) |
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| 330 | domain_px.append(float(config.get(read_tmp, 'pixel_size'))) |
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| 331 | domain_nx.append(int(config.get(read_tmp, 'nx'))) |
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| 332 | domain_ny.append(int(config.get(read_tmp, 'ny'))) |
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| 333 | domain_dz.append(float(config.get(read_tmp, 'dz'))) |
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| 334 | domain_3d.append(config.getboolean(read_tmp, 'buildings_3d')) |
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| 335 | domain_high_vegetation.append(config.getboolean(read_tmp, 'allow_high_vegetation')) |
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| 336 | domain_canopy_patches.append(config.getboolean(read_tmp, 'generate_vegetation_patches')) |
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| 337 | domain_ip.append(config.getboolean(read_tmp, 'interpolate_terrain')) |
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| 338 | domain_za.append(config.getboolean(read_tmp, 'use_palm_z_axis')) |
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| 339 | if domain_ip[ndomains-1] and not domain_za[ndomains-1]: |
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| 340 | domain_za[ndomains-1] = True |
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| 341 | print("+++ Overwrite user setting for use_palm_z_axis") |
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| 342 | |
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| 343 | domain_parent.append(config.get(read_tmp, 'domain_parent')) |
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| 344 | |
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| 345 | domain_x0.append(int(floor(float(config.get(read_tmp, 'origin_x'))/float(config.get(read_tmp, 'pixel_size'))))) |
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| 346 | domain_y0.append(int(floor(float(config.get(read_tmp, 'origin_y'))/float(config.get(read_tmp, 'pixel_size'))))) |
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| 347 | 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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| 348 | 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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| 349 | domain_green_roofs.append(config.getboolean(read_tmp, 'vegetation_on_roofs')) |
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| 350 | domain_street_trees.append(config.getboolean(read_tmp, 'street_trees')) |
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| 351 | |
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| 352 | if ( read_tmp.split("_")[0] == 'input' ): |
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| 353 | input_names.append(read_tmp.split("_")[1]) |
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| 354 | input_px.append(float(config.get(read_tmp, 'pixel_size'))) |
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| 355 | input_file_x.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_x')) |
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| 356 | input_file_y.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_y')) |
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| 357 | input_file_lat.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_lat')) |
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| 358 | input_file_lon.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_lon')) |
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| 359 | input_file_x_UTM.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_x_UTM')) |
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| 360 | input_file_y_UTM.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_y_UTM')) |
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| 361 | input_file_zt.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_zt')) |
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| 362 | input_file_buildings_2d.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_buildings_2d')) |
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| 363 | input_file_bridges_2d.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_bridges_2d')) |
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| 364 | input_file_building_id.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_building_id')) |
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| 365 | input_file_bridges_id.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_bridges_id')) |
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| 366 | input_file_building_type.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_building_type')) |
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| 367 | input_file_lai.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_lai')) |
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| 368 | input_file_vegetation_type.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_vegetation_type')) |
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| 369 | input_file_vegetation_height.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_vegetation_height')) |
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| 370 | input_file_pavement_type.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_pavement_type')) |
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| 371 | input_file_water_type.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_water_type')) |
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| 372 | input_file_street_type.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_street_type')) |
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| 373 | input_file_street_crossings.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_street_crossings')) |
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| 374 | input_file_patch_height.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_patch_height')) |
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| 375 | |
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| 376 | tmp = config.get(read_tmp, 'file_tree_crown_diameter') |
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| 377 | if tmp is not None: |
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| 378 | input_file_tree_crown_diameter.append(config.get(read_tmp, 'path') + "/" + tmp) |
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| 379 | else: |
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| 380 | input_file_tree_crown_diameter.append(None) |
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| 381 | input_file_tree_height.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_tree_height')) |
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| 382 | input_file_tree_trunk_diameter.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_tree_trunk_diameter')) |
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| 383 | input_file_tree_type.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_tree_type')) |
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| 384 | input_file_vegetation_on_roofs.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_vegetation_on_roofs')) |
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| 385 | #input_file_soil_type.append(config.get(read_tmp, 'path') + "/" + config.get(read_tmp, 'file_soil_type')) |
|---|
| 386 | return 0 |
|---|
| 387 | |
|---|
| 388 | |
|---|
| 389 | ############################################################ |
|---|
| 390 | |
|---|
| 391 | # Start of main program |
|---|
| 392 | datatypes = { |
|---|
| 393 | "x": "f4", |
|---|
| 394 | "y": "f4", |
|---|
| 395 | "z": "f4", |
|---|
| 396 | "lat": "f4", |
|---|
| 397 | "lon": "f4", |
|---|
| 398 | "E_UTM": "f4", |
|---|
| 399 | "N_UTM": "f4", |
|---|
| 400 | "zt": "f4", |
|---|
| 401 | "buildings_2d": "f4", |
|---|
| 402 | "buildings_3d": "b", |
|---|
| 403 | "bridges_2d": "f4", |
|---|
| 404 | "building_id": "i", |
|---|
| 405 | "bridges_id": "i", |
|---|
| 406 | "building_type": "b", |
|---|
| 407 | "nsurface_fraction": "i", |
|---|
| 408 | "vegetation_type": "b", |
|---|
| 409 | "vegetation_height": "f4", |
|---|
| 410 | "pavement_type": "b", |
|---|
| 411 | "water_type": "b", |
|---|
| 412 | "street_type": "b", |
|---|
| 413 | "street_crossings": "b", |
|---|
| 414 | "soil_type": "b", |
|---|
| 415 | "surface_fraction": "f4", |
|---|
| 416 | "building_pars": "f4", |
|---|
| 417 | "vegetation_pars": "f4", |
|---|
| 418 | "tree_data": "f4", |
|---|
| 419 | "tree_type": "b", |
|---|
| 420 | "nbuilding_pars": "i", |
|---|
| 421 | "nvegetation_pars": "i", |
|---|
| 422 | "zlad": "f4" |
|---|
| 423 | } |
|---|
| 424 | |
|---|
| 425 | fillvalues = { |
|---|
| 426 | "lat": float(-9999.0), |
|---|
| 427 | "lon": float(-9999.0), |
|---|
| 428 | "E_UTM": float(-9999.0), |
|---|
| 429 | "N_UTM": float(-9999.0), |
|---|
| 430 | "zt": float(-9999.0), |
|---|
| 431 | "buildings_2d": float(-9999.0), |
|---|
| 432 | "buildings_3d": np.byte(-127), |
|---|
| 433 | "bridges_2d": float(-9999.0), |
|---|
| 434 | "building_id": int(-9999), |
|---|
| 435 | "bridges_id": int(-9999), |
|---|
| 436 | "building_type": np.byte(-127), |
|---|
| 437 | "nsurface_fraction": int(-9999), |
|---|
| 438 | "vegetation_type": np.byte(-127), |
|---|
| 439 | "vegetation_height": float(-9999.0), |
|---|
| 440 | "pavement_type": np.byte(-127), |
|---|
| 441 | "water_type": np.byte(-127), |
|---|
| 442 | "street_type": np.byte(-127), |
|---|
| 443 | "street_crossings": np.byte(-127), |
|---|
| 444 | "soil_type": np.byte(-127), |
|---|
| 445 | "surface_fraction": float(-9999.0), |
|---|
| 446 | "building_pars": float(-9999.0), |
|---|
| 447 | "vegetation_pars": float(-9999.0), |
|---|
| 448 | "tree_data": float(-9999.0), |
|---|
| 449 | "tree_type": np.byte(-127) |
|---|
| 450 | } |
|---|
| 451 | |
|---|
| 452 | defaultvalues = { |
|---|
| 453 | "lat": float(-9999.0), |
|---|
| 454 | "lon": float(-9999.0), |
|---|
| 455 | "E_UTM": float(-9999.0), |
|---|
| 456 | "N_UTM": float(-9999.0), |
|---|
| 457 | "zt": float(0.0), |
|---|
| 458 | "buildings_2d": float(0.0), |
|---|
| 459 | "buildings_3d": 0, |
|---|
| 460 | "bridges_2d": float(0.0), |
|---|
| 461 | "building_id": int(0), |
|---|
| 462 | "bridges_id": int(0), |
|---|
| 463 | "building_type": 1, |
|---|
| 464 | "nsurface_fraction": int(-9999), |
|---|
| 465 | "vegetation_type": 3, |
|---|
| 466 | "vegetation_height": float(-9999.0), |
|---|
| 467 | "pavement_type": 1, |
|---|
| 468 | "water_type": 1, |
|---|
| 469 | "street_type": 1, |
|---|
| 470 | "street_crossings": 0, |
|---|
| 471 | "soil_type": 1, |
|---|
| 472 | "surface_fraction": float(0.0), |
|---|
| 473 | "buildings_pars": float(-9999.0), |
|---|
| 474 | "tree_data": float(-9999.0), |
|---|
| 475 | "tree_type": 0, |
|---|
| 476 | "vegetation_pars": float(-9999.0) |
|---|
| 477 | } |
|---|
| 478 | |
|---|
| 479 | # Read configuration file and set parameters accordingly |
|---|
| 480 | read_config_file() |
|---|
| 481 | |
|---|
| 482 | |
|---|
| 483 | filename = [] |
|---|
| 484 | ii = [] |
|---|
| 485 | ii_parent = [] |
|---|
| 486 | # Define indices and filenames for all domains and create netCDF files |
|---|
| 487 | for i in range(0,ndomains): |
|---|
| 488 | |
|---|
| 489 | # Calculate indices and input files |
|---|
| 490 | ii.append(input_px.index(domain_px[i])) |
|---|
| 491 | filename.append(path_out + "/" + filename_out + "_" + domain_names[i]) |
|---|
| 492 | if domain_parent[i] is not None: |
|---|
| 493 | ii_parent.append(input_px.index(domain_px[domain_names.index(domain_parent[i])])) |
|---|
| 494 | else: |
|---|
| 495 | ii_parent.append(None) |
|---|
| 496 | |
|---|
| 497 | |
|---|
| 498 | 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) |
|---|
| 499 | 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) |
|---|
| 500 | 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) |
|---|
| 501 | 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) |
|---|
| 502 | |
|---|
| 503 | # Calculate position of origin |
|---|
| 504 | x_UTM_origin = float(x_UTM[0,0]) - 0.5 * (float(x_UTM[0,1]) - float(x_UTM[0,0])) |
|---|
| 505 | y_UTM_origin = float(y_UTM[0,0]) - 0.5 * (float(y_UTM[1,0]) - float(y_UTM[0,0])) |
|---|
| 506 | x_origin = float(lon[0,0]) - 0.5 * (float(lon[0,1]) - float(lon[0,0])) |
|---|
| 507 | y_origin = float(lat[0,0]) - 0.5 * (float(lat[1,0]) - float(lat[0,0])) |
|---|
| 508 | |
|---|
| 509 | # Create NetCDF output file and set global attributes |
|---|
| 510 | nc_create_file(filename[i]) |
|---|
| 511 | 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) |
|---|
| 512 | |
|---|
| 513 | del x_UTM, y_UTM, lat, lon |
|---|
| 514 | |
|---|
| 515 | # Process terrain height |
|---|
| 516 | for i in range(0,ndomains): |
|---|
| 517 | |
|---|
| 518 | # Read and write terrain height (zt) |
|---|
| 519 | zt = nc_read_from_file_2d(input_file_zt[ii[i]], 'Band1', domain_x0[i], domain_x1[i], domain_y0[i], domain_y1[i]) |
|---|
| 520 | |
|---|
| 521 | # Final step: add zt array to the global array |
|---|
| 522 | zt_all.append(zt) |
|---|
| 523 | del zt |
|---|
| 524 | |
|---|
| 525 | # Calculate the global (all domains) minimum of the terrain height. This value will be substracted for all |
|---|
| 526 | # data sets |
|---|
| 527 | zt_min = min(zt_all[0].flatten()) |
|---|
| 528 | for i in range(0,ndomains): |
|---|
| 529 | zt_min = min(zt_min,min(zt_all[i].flatten())) |
|---|
| 530 | |
|---|
| 531 | del zt_all[:] |
|---|
| 532 | |
|---|
| 533 | print( "Shift terrain heights by -" + str(zt_min)) |
|---|
| 534 | for i in range(0,ndomains): |
|---|
| 535 | |
|---|
| 536 | # Read and write terrain height (zt) |
|---|
| 537 | zt = nc_read_from_file_2d(input_file_zt[ii[i]], 'Band1', domain_x0[i], domain_x1[i], domain_y0[i], domain_y1[i]) |
|---|
| 538 | x = nc_read_from_file_1d(input_file_x[ii[i]], "x", domain_x0[i], domain_x1[i]) |
|---|
| 539 | y = nc_read_from_file_1d(input_file_y[ii[i]], "y", domain_y0[i], domain_y1[i]) |
|---|
| 540 | |
|---|
| 541 | |
|---|
| 542 | zt = zt - zt_min |
|---|
| 543 | |
|---|
| 544 | nc_write_global_attribute(filename[i], 'origin_z', float(zt_min)) |
|---|
| 545 | |
|---|
| 546 | # If necessary, interpolate parent domain terrain height on child domain grid and blend the two |
|---|
| 547 | if domain_ip[i]: |
|---|
| 548 | parent_id = domain_names.index(domain_parent[i]) |
|---|
| 549 | tmp_x0 = int( domain_x0[i] * domain_px[i] / domain_px[parent_id] ) - 1 |
|---|
| 550 | tmp_y0 = int( domain_y0[i] * domain_px[i] / domain_px[parent_id] ) - 1 |
|---|
| 551 | tmp_x1 = int( domain_x1[i] * domain_px[i] / domain_px[parent_id] ) + 1 |
|---|
| 552 | tmp_y1 = int( domain_y1[i] * domain_px[i] / domain_px[parent_id] ) + 1 |
|---|
| 553 | |
|---|
| 554 | tmp_x = nc_read_from_file_1d(input_file_x[ii_parent[i]], "x", tmp_x0, tmp_x1) |
|---|
| 555 | tmp_y = nc_read_from_file_1d(input_file_y[ii_parent[i]], "y", tmp_y0, tmp_y1) |
|---|
| 556 | |
|---|
| 557 | zt_parent = nc_read_from_file_2d(input_file_zt[ii_parent[i]], 'Band1', tmp_x0, tmp_x1, tmp_y0, tmp_y1) |
|---|
| 558 | |
|---|
| 559 | zt_parent = zt_parent - zt_min |
|---|
| 560 | |
|---|
| 561 | # Interpolate array and bring to PALM grid of child domain |
|---|
| 562 | zt_ip = interpolate_2d(zt_parent,tmp_x,tmp_y,x,y) |
|---|
| 563 | zt_ip = bring_to_palm_grid(zt_ip,x,y,domain_dz[parent_id]) |
|---|
| 564 | |
|---|
| 565 | |
|---|
| 566 | # Shift the child terrain height according to the parent mean terrain height |
|---|
| 567 | print("shifting: -" + str(np.mean(zt)) + " +" + str(np.mean(zt_ip))) |
|---|
| 568 | #zt = zt - np.min(zt) + np.min(zt_ip) |
|---|
| 569 | zt = zt - np.mean(zt) + np.mean(zt_ip) |
|---|
| 570 | |
|---|
| 571 | |
|---|
| 572 | # Blend over the parent and child terrain height within a radius of 50 px |
|---|
| 573 | zt = blend_array_2d(zt,zt_ip,50) |
|---|
| 574 | # zt = zt_ip |
|---|
| 575 | |
|---|
| 576 | # Final step: add zt array to the global array |
|---|
| 577 | |
|---|
| 578 | zt_all.append(zt) |
|---|
| 579 | del zt |
|---|
| 580 | |
|---|
| 581 | |
|---|
| 582 | # Read and shift x and y coordinates, shift terrain height according to its minimum value and write all data |
|---|
| 583 | # to file |
|---|
| 584 | for i in range(0,ndomains): |
|---|
| 585 | # Read horizontal grid variables from zt file and write them to output file |
|---|
| 586 | x = nc_read_from_file_1d(input_file_x[ii[i]], "x", domain_x0[i], domain_x1[i]) |
|---|
| 587 | y = nc_read_from_file_1d(input_file_y[ii[i]], "y", domain_y0[i], domain_y1[i]) |
|---|
| 588 | x = x - min(x.flatten()) + domain_px[i]/2.0 |
|---|
| 589 | y = y - min(y.flatten()) + domain_px[i]/2.0 |
|---|
| 590 | nc_write_dimension(filename[i], 'x', x, datatypes["x"]) |
|---|
| 591 | nc_write_dimension(filename[i], 'y', y, datatypes["y"]) |
|---|
| 592 | nc_write_attribute(filename[i], 'x', 'long_name', 'x') |
|---|
| 593 | nc_write_attribute(filename[i], 'x', 'standard_name','projection_x_coordinate') |
|---|
| 594 | nc_write_attribute(filename[i], 'x', 'units', 'm') |
|---|
| 595 | nc_write_attribute(filename[i], 'y', 'long_name', 'y') |
|---|
| 596 | nc_write_attribute(filename[i], 'y', 'standard_name', 'projection_y_coordinate') |
|---|
| 597 | nc_write_attribute(filename[i], 'y', 'units', 'm') |
|---|
| 598 | |
|---|
| 599 | lat = nc_read_from_file_2d(input_file_lat[ii[i]], "Band1", domain_x0[i], domain_x1[i], domain_y0[i], domain_y1[i]) |
|---|
| 600 | lon = nc_read_from_file_2d(input_file_lon[ii[i]], "Band1", domain_x0[i], domain_x1[i], domain_y0[i], domain_y1[i]) |
|---|
| 601 | |
|---|
| 602 | nc_write_to_file_2d(filename[i], 'lat', lat, datatypes["lat"],'y','x',fillvalues["lat"]) |
|---|
| 603 | nc_write_to_file_2d(filename[i], 'lon', lon, datatypes["lon"],'y','x',fillvalues["lon"]) |
|---|
| 604 | |
|---|
| 605 | nc_write_attribute(filename[i], 'lat', 'long_name', 'latitude') |
|---|
| 606 | nc_write_attribute(filename[i], 'lat', 'standard_name','latitude') |
|---|
| 607 | nc_write_attribute(filename[i], 'lat', 'units', 'degrees_north') |
|---|
| 608 | |
|---|
| 609 | nc_write_attribute(filename[i], 'lon', 'long_name', 'longitude') |
|---|
| 610 | nc_write_attribute(filename[i], 'lon', 'standard_name','longitude') |
|---|
| 611 | nc_write_attribute(filename[i], 'lon', 'units', 'degrees_east') |
|---|
| 612 | |
|---|
| 613 | 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]) |
|---|
| 614 | 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]) |
|---|
| 615 | |
|---|
| 616 | |
|---|
| 617 | nc_write_to_file_2d(filename[i], 'E_UTM', x_UTM, datatypes["E_UTM"],'y','x',fillvalues["E_UTM"]) |
|---|
| 618 | nc_write_to_file_2d(filename[i], 'N_UTM', y_UTM, datatypes["N_UTM"],'y','x',fillvalues["N_UTM"]) |
|---|
| 619 | |
|---|
| 620 | nc_write_attribute(filename[i], 'E_UTM', 'long_name', 'easting') |
|---|
| 621 | nc_write_attribute(filename[i], 'E_UTM', 'standard_name','projection_x_coorindate') |
|---|
| 622 | nc_write_attribute(filename[i], 'E_UTM', 'units', 'm') |
|---|
| 623 | |
|---|
| 624 | nc_write_attribute(filename[i], 'N_UTM', 'long_name', 'northing') |
|---|
| 625 | nc_write_attribute(filename[i], 'N_UTM', 'standard_name','projection_y_coorindate') |
|---|
| 626 | nc_write_attribute(filename[i], 'N_UTM', 'units', 'm') |
|---|
| 627 | |
|---|
| 628 | nc_write_crs(filename[i]) |
|---|
| 629 | |
|---|
| 630 | |
|---|
| 631 | |
|---|
| 632 | # If necessary, bring terrain height to PALM's vertical grid. This is either forced by the user or implicitly |
|---|
| 633 | # by using interpolation for a child domain |
|---|
| 634 | if domain_za[i]: |
|---|
| 635 | zt_all[i] = bring_to_palm_grid(zt_all[i],x,y,domain_dz[i]) |
|---|
| 636 | |
|---|
| 637 | nc_write_to_file_2d(filename[i], 'zt', zt_all[i], datatypes["zt"],'y','x',fillvalues["zt"]) |
|---|
| 638 | nc_write_attribute(filename[i], 'zt', 'long_name', 'orography') |
|---|
| 639 | nc_write_attribute(filename[i], 'zt', 'units', 'm') |
|---|
| 640 | nc_write_attribute(filename[i], 'zt', 'res_orig', domain_px[i]) |
|---|
| 641 | nc_write_attribute(filename[i], 'zt', 'coordinates', 'E_UTM N_UTM lon lat') |
|---|
| 642 | nc_write_attribute(filename[i], 'zt', 'grid_mapping', 'E_UTM N_UTM lon lat') |
|---|
| 643 | |
|---|
| 644 | del zt_all |
|---|
| 645 | |
|---|
| 646 | |
|---|
| 647 | # Process building height, id, and type |
|---|
| 648 | for i in range(0,ndomains): |
|---|
| 649 | 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]) |
|---|
| 650 | |
|---|
| 651 | 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]) |
|---|
| 652 | |
|---|
| 653 | 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]) |
|---|
| 654 | building_type[building_type >= 254] = fillvalues["building_type"] |
|---|
| 655 | building_type = np.where(building_type < 1,defaultvalues["building_type"],building_type) |
|---|
| 656 | |
|---|
| 657 | check = check_arrays_2(buildings_2d,building_id,fillvalues["buildings_2d"],fillvalues["building_id"]) |
|---|
| 658 | if not check: |
|---|
| 659 | buildings_2d = np.where(building_id != fillvalues["building_id"],buildings_2d,fillvalues["buildings_2d"]) |
|---|
| 660 | building_id = np.where(buildings_2d == fillvalues["buildings_2d"],fillvalues["building_id"],building_id) |
|---|
| 661 | print("Data check #1 " + str(check_arrays_2(buildings_2d,building_id,fillvalues["buildings_2d"],fillvalues["building_id"]))) |
|---|
| 662 | |
|---|
| 663 | check = check_arrays_2(buildings_2d,building_type,fillvalues["buildings_2d"],fillvalues["building_type"]) |
|---|
| 664 | if not check: |
|---|
| 665 | building_type = np.where(buildings_2d == fillvalues["buildings_2d"],fillvalues["building_type"],building_type) |
|---|
| 666 | building_type = np.where((building_type == fillvalues["building_type"]) & (buildings_2d != fillvalues["buildings_2d"]),defaultvalues["building_type"],building_type) |
|---|
| 667 | print("Data check #2 " + str(check_arrays_2(buildings_2d,building_type,fillvalues["buildings_2d"],fillvalues["building_type"]))) |
|---|
| 668 | |
|---|
| 669 | |
|---|
| 670 | nc_write_to_file_2d(filename[i], 'buildings_2d', buildings_2d, datatypes["buildings_2d"],'y','x',fillvalues["buildings_2d"]) |
|---|
| 671 | nc_write_attribute(filename[i], 'buildings_2d', 'long_name', 'buildings') |
|---|
| 672 | nc_write_attribute(filename[i], 'buildings_2d', 'units', 'm') |
|---|
| 673 | nc_write_attribute(filename[i], 'buildings_2d', 'res_orig', domain_px[i]) |
|---|
| 674 | nc_write_attribute(filename[i], 'buildings_2d', 'lod', 1) |
|---|
| 675 | nc_write_attribute(filename[i], 'buildings_2d', 'coordinates', 'E_UTM N_UTM lon lat') |
|---|
| 676 | nc_write_attribute(filename[i], 'buildings_2d', 'grid_mapping', 'E_UTM N_UTM lon lat') |
|---|
| 677 | |
|---|
| 678 | nc_write_to_file_2d(filename[i], 'building_id', building_id, datatypes["building_id"],'y','x',fillvalues["building_id"]) |
|---|
| 679 | nc_write_attribute(filename[i], 'building_id', 'long_name', 'building id') |
|---|
| 680 | nc_write_attribute(filename[i], 'building_id', 'units', '') |
|---|
| 681 | nc_write_attribute(filename[i], 'building_id', 'res _orig', domain_px[i]) |
|---|
| 682 | nc_write_attribute(filename[i], 'building_id', 'coordinates', 'E_UTM N_UTM lon lat') |
|---|
| 683 | nc_write_attribute(filename[i], 'building_id', 'grid_mapping', 'E_UTM N_UTM lon lat') |
|---|
| 684 | |
|---|
| 685 | nc_write_to_file_2d(filename[i], 'building_type', building_type, datatypes["building_type"],'y','x',fillvalues["building_type"]) |
|---|
| 686 | nc_write_attribute(filename[i], 'building_type', 'long_name', 'building type') |
|---|
| 687 | nc_write_attribute(filename[i], 'building_type', 'units', '') |
|---|
| 688 | nc_write_attribute(filename[i], 'building_type', 'res_orig', domain_px[i]) |
|---|
| 689 | nc_write_attribute(filename[i], 'building_type', 'coordinates', 'E_UTM N_UTM lon lat') |
|---|
| 690 | nc_write_attribute(filename[i], 'building_type', 'grid_mapping', 'E_UTM N_UTM lon lat') |
|---|
| 691 | |
|---|
| 692 | del buildings_2d |
|---|
| 693 | del building_id |
|---|
| 694 | del building_type |
|---|
| 695 | |
|---|
| 696 | # Create 3d buildings if necessary. In that course, read bridge objects and add them to building layer |
|---|
| 697 | for i in range(0,ndomains): |
|---|
| 698 | |
|---|
| 699 | if domain_3d[i]: |
|---|
| 700 | x = nc_read_from_file_2d_all(filename[i], 'x') |
|---|
| 701 | y = nc_read_from_file_2d_all(filename[i], 'y') |
|---|
| 702 | buildings_2d = nc_read_from_file_2d_all(filename[i], 'buildings_2d') |
|---|
| 703 | building_id = nc_read_from_file_2d_all(filename[i], 'building_id') |
|---|
| 704 | |
|---|
| 705 | 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]) |
|---|
| 706 | 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]) |
|---|
| 707 | |
|---|
| 708 | bridges_2d = np.where(bridges_2d == 0.0,fillvalues["bridges_2d"],bridges_2d) |
|---|
| 709 | building_id = np.where(bridges_2d == fillvalues["bridges_2d"],building_id,bridges_id) |
|---|
| 710 | |
|---|
| 711 | if np.any(buildings_2d != fillvalues["buildings_2d"]): |
|---|
| 712 | buildings_3d, z = make_3d_from_2d(buildings_2d,x,y,domain_dz[i]) |
|---|
| 713 | if np.any(bridges_2d != fillvalues["bridges_2d"]): |
|---|
| 714 | buildings_3d = make_3d_from_bridges_2d(buildings_3d,bridges_2d,x,y,domain_dz[i],settings_bridge_width,fillvalues["bridges_2d"]) |
|---|
| 715 | else: |
|---|
| 716 | print("Skipping creation of 3D bridges (no bridges in domain)") |
|---|
| 717 | |
|---|
| 718 | |
|---|
| 719 | nc_write_dimension(filename[i], 'z', z, datatypes["z"]) |
|---|
| 720 | nc_write_attribute(filename[i], 'z', 'long_name', 'z') |
|---|
| 721 | nc_write_attribute(filename[i], 'z', 'units', 'm') |
|---|
| 722 | |
|---|
| 723 | nc_overwrite_to_file_2d(filename[i], 'building_id', building_id) |
|---|
| 724 | |
|---|
| 725 | nc_write_to_file_3d(filename[i], 'buildings_3d', buildings_3d, datatypes["buildings_3d"],'z','y','x',fillvalues["buildings_3d"]) |
|---|
| 726 | nc_write_attribute(filename[i], 'buildings_3d', 'long_name', 'buildings 3d') |
|---|
| 727 | nc_write_attribute(filename[i], 'buildings_3d', 'units', '') |
|---|
| 728 | nc_write_attribute(filename[i], 'buildings_3d', 'res_orig', domain_px[i]) |
|---|
| 729 | nc_write_attribute(filename[i], 'buildings_3d', 'lod', 2) |
|---|
| 730 | |
|---|
| 731 | del buildings_3d |
|---|
| 732 | |
|---|
| 733 | else: |
|---|
| 734 | print("Skipping creation of 3D buildings (no buildings in domain)") |
|---|
| 735 | |
|---|
| 736 | |
|---|
| 737 | del bridges_2d, bridges_id, building_id, buildings_2d |
|---|
| 738 | |
|---|
| 739 | |
|---|
| 740 | |
|---|
| 741 | # Read vegetation type, water_type, pavement_type, soil_type and make fields consistent |
|---|
| 742 | for i in range(0,ndomains): |
|---|
| 743 | |
|---|
| 744 | building_type = nc_read_from_file_2d_all(filename[i], 'building_type') |
|---|
| 745 | |
|---|
| 746 | 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]) |
|---|
| 747 | vegetation_type[vegetation_type == 255] = fillvalues["vegetation_type"] |
|---|
| 748 | vegetation_type = np.where((vegetation_type < 1) & (vegetation_type != fillvalues["vegetation_type"]),defaultvalues["vegetation_type"],vegetation_type) |
|---|
| 749 | |
|---|
| 750 | 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]) |
|---|
| 751 | pavement_type[pavement_type == 255] = fillvalues["pavement_type"] |
|---|
| 752 | pavement_type = np.where((pavement_type < 1) & (pavement_type != fillvalues["pavement_type"]),defaultvalues["pavement_type"],pavement_type) |
|---|
| 753 | |
|---|
| 754 | 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]) |
|---|
| 755 | water_type[water_type == 255] = fillvalues["water_type"] |
|---|
| 756 | water_type = np.where((water_type < 1) & (water_type != fillvalues["water_type"]),defaultvalues["water_type"],water_type) |
|---|
| 757 | |
|---|
| 758 | # to do: replace by real soil input data |
|---|
| 759 | 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]) |
|---|
| 760 | soil_type[soil_type == 255] = fillvalues["soil_type"] |
|---|
| 761 | soil_type = np.where((soil_type < 1) & (soil_type != fillvalues["soil_type"]),defaultvalues["soil_type"],soil_type) |
|---|
| 762 | |
|---|
| 763 | # Make arrays consistent |
|---|
| 764 | # #1 Set vegetation type to missing for pixel where a pavement type is set |
|---|
| 765 | vegetation_type = np.where((vegetation_type != fillvalues["vegetation_type"]) & (pavement_type != fillvalues["pavement_type"]),fillvalues["vegetation_type"],vegetation_type) |
|---|
| 766 | |
|---|
| 767 | # #2 Set vegetation type to missing for pixel where a building type is set |
|---|
| 768 | vegetation_type = np.where((vegetation_type != fillvalues["vegetation_type"]) & (building_type != fillvalues["building_type"]) ,fillvalues["vegetation_type"],vegetation_type) |
|---|
| 769 | |
|---|
| 770 | # #3 Set vegetation type to missing for pixel where a building type is set |
|---|
| 771 | vegetation_type = np.where((vegetation_type != fillvalues["vegetation_type"]) & (water_type != fillvalues["water_type"]),fillvalues["vegetation_type"],vegetation_type) |
|---|
| 772 | |
|---|
| 773 | # #4 Remove pavement for pixels with buildings |
|---|
| 774 | pavement_type = np.where((pavement_type != fillvalues["pavement_type"]) & (building_type != fillvalues["building_type"]),fillvalues["pavement_type"],pavement_type) |
|---|
| 775 | |
|---|
| 776 | # #5 Remove pavement for pixels with water. |
|---|
| 777 | pavement_type = np.where((pavement_type != fillvalues["pavement_type"]) & (water_type != fillvalues["water_type"]),fillvalues["pavement_type"],pavement_type) |
|---|
| 778 | |
|---|
| 779 | # #6 Remove water for pixels with buildings |
|---|
| 780 | water_type = np.where((water_type != fillvalues["water_type"]) & (building_type != fillvalues["building_type"]),fillvalues["water_type"],water_type) |
|---|
| 781 | |
|---|
| 782 | # Correct vegetation_type when a vegetation height is available and is indicative of low vegeetation |
|---|
| 783 | 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]) |
|---|
| 784 | |
|---|
| 785 | 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) |
|---|
| 786 | 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) |
|---|
| 787 | |
|---|
| 788 | # Check for consistency and fill empty fields with default vegetation type |
|---|
| 789 | 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"]) |
|---|
| 790 | |
|---|
| 791 | if test: |
|---|
| 792 | vegetation_type = np.where(consistency_array == 0,defaultvalues["vegetation_type"],vegetation_type) |
|---|
| 793 | 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"]) |
|---|
| 794 | |
|---|
| 795 | # #7 to be removed: set default soil type everywhere |
|---|
| 796 | soil_type = np.where((vegetation_type != fillvalues["vegetation_type"]) | (pavement_type != fillvalues["pavement_type"]),defaultvalues["soil_type"],fillvalues["soil_type"]) |
|---|
| 797 | |
|---|
| 798 | |
|---|
| 799 | # Check for consistency and fill empty fields with default vegetation type |
|---|
| 800 | consistency_array, test = check_consistency_3(vegetation_type,pavement_type,soil_type,fillvalues["vegetation_type"],fillvalues["pavement_type"],fillvalues["soil_type"]) |
|---|
| 801 | |
|---|
| 802 | # Create surface_fraction array |
|---|
| 803 | x = nc_read_from_file_2d_all(filename[i], 'x') |
|---|
| 804 | y = nc_read_from_file_2d_all(filename[i], 'y') |
|---|
| 805 | nsurface_fraction = np.arange(0,3) |
|---|
| 806 | surface_fraction = np.ones((len(nsurface_fraction),len(y),len(x))) |
|---|
| 807 | |
|---|
| 808 | surface_fraction[0,:,:] = np.where(vegetation_type != fillvalues["vegetation_type"], 1.0, 0.0) |
|---|
| 809 | surface_fraction[1,:,:] = np.where(pavement_type != fillvalues["pavement_type"], 1.0, 0.0) |
|---|
| 810 | surface_fraction[2,:,:] = np.where(water_type != fillvalues["water_type"], 1.0, 0.0) |
|---|
| 811 | |
|---|
| 812 | nc_write_dimension(filename[i], 'nsurface_fraction', nsurface_fraction, datatypes["nsurface_fraction"]) |
|---|
| 813 | nc_write_to_file_3d(filename[i], 'surface_fraction', surface_fraction, datatypes["surface_fraction"],'nsurface_fraction','y','x',fillvalues["surface_fraction"]) |
|---|
| 814 | nc_write_attribute(filename[i], 'surface_fraction', 'long_name', 'surface fraction') |
|---|
| 815 | nc_write_attribute(filename[i], 'surface_fraction', 'units', '') |
|---|
| 816 | nc_write_attribute(filename[i], 'surface_fraction', 'res_orig', domain_px[i]) |
|---|
| 817 | del surface_fraction |
|---|
| 818 | |
|---|
| 819 | nc_write_to_file_2d(filename[i], 'vegetation_type', vegetation_type, datatypes["vegetation_type"],'y','x',fillvalues["vegetation_type"]) |
|---|
| 820 | nc_write_attribute(filename[i], 'vegetation_type', 'long_name', 'vegetation type') |
|---|
| 821 | nc_write_attribute(filename[i], 'vegetation_type', 'units', '') |
|---|
| 822 | nc_write_attribute(filename[i], 'vegetation_type', 'res_orig', domain_px[i]) |
|---|
| 823 | nc_write_attribute(filename[i], 'vegetation_type', 'coordinates', 'E_UTM N_UTM lon lat') |
|---|
| 824 | nc_write_attribute(filename[i], 'vegetation_type', 'grid_mapping', 'E_UTM N_UTM lon lat') |
|---|
| 825 | del vegetation_type |
|---|
| 826 | |
|---|
| 827 | nc_write_to_file_2d(filename[i], 'pavement_type', pavement_type, datatypes["pavement_type"],'y','x',fillvalues["pavement_type"]) |
|---|
| 828 | nc_write_attribute(filename[i], 'pavement_type', 'long_name', 'pavement type') |
|---|
| 829 | nc_write_attribute(filename[i], 'pavement_type', 'units', '') |
|---|
| 830 | nc_write_attribute(filename[i], 'pavement_type', 'res_orig', domain_px[i]) |
|---|
| 831 | nc_write_attribute(filename[i], 'pavement_type', 'coordinates', 'E_UTM N_UTM lon lat') |
|---|
| 832 | nc_write_attribute(filename[i], 'pavement_type', 'grid_mapping', 'E_UTM N_UTM lon lat') |
|---|
| 833 | del pavement_type |
|---|
| 834 | |
|---|
| 835 | nc_write_to_file_2d(filename[i], 'water_type', water_type, datatypes["water_type"],'y','x',fillvalues["water_type"]) |
|---|
| 836 | nc_write_attribute(filename[i], 'water_type', 'long_name', 'water type') |
|---|
| 837 | nc_write_attribute(filename[i], 'water_type', 'units', '') |
|---|
| 838 | nc_write_attribute(filename[i], 'water_type', 'res_orig', domain_px[i]) |
|---|
| 839 | nc_write_attribute(filename[i], 'water_type', 'coordinates', 'E_UTM N_UTM lon lat') |
|---|
| 840 | nc_write_attribute(filename[i], 'water_type', 'grid_mapping', 'E_UTM N_UTM lon lat') |
|---|
| 841 | del water_type |
|---|
| 842 | |
|---|
| 843 | nc_write_to_file_2d(filename[i], 'soil_type', soil_type, datatypes["soil_type"],'y','x',fillvalues["soil_type"]) |
|---|
| 844 | nc_write_attribute(filename[i], 'soil_type', 'long_name', 'soil type') |
|---|
| 845 | nc_write_attribute(filename[i], 'soil_type', 'units', '') |
|---|
| 846 | nc_write_attribute(filename[i], 'soil_type', 'res_orig', domain_px[i]) |
|---|
| 847 | nc_write_attribute(filename[i], 'soil_type', 'coordinates', 'E_UTM N_UTM lon lat') |
|---|
| 848 | nc_write_attribute(filename[i], 'soil_type', 'grid_mapping', 'E_UTM N_UTM lon lat') |
|---|
| 849 | del soil_type |
|---|
| 850 | |
|---|
| 851 | del x |
|---|
| 852 | del y |
|---|
| 853 | |
|---|
| 854 | # pixels with bridges get building_type = 7 = bridge. This does not change the _type setting for the under-bridge |
|---|
| 855 | # area NOTE: when bridges are present the consistency check will fail at the moment |
|---|
| 856 | if domain_3d[i]: |
|---|
| 857 | if np.any(building_type != fillvalues["building_type"]): |
|---|
| 858 | |
|---|
| 859 | 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]) |
|---|
| 860 | bridges_2d = np.where(bridges_2d == 0.0,fillvalues["bridges_2d"],bridges_2d) |
|---|
| 861 | building_type = np.where(bridges_2d != fillvalues["bridges_2d"],7,building_type) |
|---|
| 862 | nc_overwrite_to_file_2d(filename[i], 'building_type', building_type) |
|---|
| 863 | |
|---|
| 864 | del building_type |
|---|
| 865 | del bridges_2d |
|---|
| 866 | |
|---|
| 867 | # Read/write street type and street crossings |
|---|
| 868 | for i in range(0,ndomains): |
|---|
| 869 | |
|---|
| 870 | 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]) |
|---|
| 871 | street_type[street_type == 255] = fillvalues["street_type"] |
|---|
| 872 | street_type = np.where((street_type < 1) & (street_type != fillvalues["street_type"]),defaultvalues["street_type"],street_type) |
|---|
| 873 | |
|---|
| 874 | pavement_type = nc_read_from_file_2d_all(filename[i], 'pavement_type') |
|---|
| 875 | street_type = np.where((pavement_type == fillvalues["pavement_type"]),fillvalues["street_type"],street_type) |
|---|
| 876 | |
|---|
| 877 | nc_write_to_file_2d(filename[i], 'street_type', street_type, datatypes["street_type"],'y','x',fillvalues["street_type"]) |
|---|
| 878 | nc_write_attribute(filename[i], 'street_type', 'long_name', 'street type') |
|---|
| 879 | nc_write_attribute(filename[i], 'street_type', 'units', '') |
|---|
| 880 | nc_write_attribute(filename[i], 'street_type', 'res_orig', domain_px[i]) |
|---|
| 881 | nc_write_attribute(filename[i], 'street_type', 'coordinates', 'E_UTM N_UTM lon lat') |
|---|
| 882 | nc_write_attribute(filename[i], 'street_type', 'grid_mapping', 'E_UTM N_UTM lon lat') |
|---|
| 883 | del street_type |
|---|
| 884 | |
|---|
| 885 | 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]) |
|---|
| 886 | street_crossings[street_crossings == 255] = fillvalues["street_crossings"] |
|---|
| 887 | street_crossings = np.where((street_crossings < 1) & (street_crossings != fillvalues["street_crossings"]),defaultvalues["street_crossings"],street_crossings) |
|---|
| 888 | |
|---|
| 889 | nc_write_to_file_2d(filename[i], 'street_crossing', street_crossings, datatypes["street_crossings"],'y','x',fillvalues["street_crossings"]) |
|---|
| 890 | nc_write_attribute(filename[i], 'street_crossing', 'long_name', 'street crossings') |
|---|
| 891 | nc_write_attribute(filename[i], 'street_crossing', 'units', '') |
|---|
| 892 | nc_write_attribute(filename[i], 'street_crossing', 'res_orig', domain_px[i]) |
|---|
| 893 | nc_write_attribute(filename[i], 'street_crossing', 'coordinates', 'E_UTM N_UTM lon lat') |
|---|
| 894 | nc_write_attribute(filename[i], 'street_crossing', 'grid_mapping', 'E_UTM N_UTM lon lat') |
|---|
| 895 | del street_crossings |
|---|
| 896 | |
|---|
| 897 | |
|---|
| 898 | # Read/write vegetation on roofs |
|---|
| 899 | for i in range(0,ndomains): |
|---|
| 900 | if domain_green_roofs[i]: |
|---|
| 901 | 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]) |
|---|
| 902 | buildings_2d = nc_read_from_file_2d_all(filename[i], 'buildings_2d') |
|---|
| 903 | |
|---|
| 904 | |
|---|
| 905 | x = nc_read_from_file_2d_all(filename[i], 'x') |
|---|
| 906 | y = nc_read_from_file_2d_all(filename[i], 'y') |
|---|
| 907 | nbuilding_pars = np.arange(0,47) |
|---|
| 908 | building_pars = np.ones((len(nbuilding_pars),len(y),len(x))) |
|---|
| 909 | building_pars[:,:,:] = fillvalues["building_pars"] |
|---|
| 910 | |
|---|
| 911 | # assign green fraction on roofs |
|---|
| 912 | building_pars[3,:,:] = np.where( ( buildings_2d != fillvalues["buildings_2d"] ) & ( green_roofs != fillvalues["building_pars"] ) & ( green_roofs != 0.0 ),1,fillvalues["building_pars"] ) |
|---|
| 913 | |
|---|
| 914 | # assign leaf area index for vegetation on roofs |
|---|
| 915 | building_pars[4,:,:] = np.where( ( building_pars[3,:,:] != fillvalues["building_pars"] ) & ( green_roofs >= 0.5 ),settings_lai_roof_intensive,fillvalues["building_pars"]) |
|---|
| 916 | building_pars[4,:,:] = np.where( ( building_pars[3,:,:] != fillvalues["building_pars"] ) & ( green_roofs < 0.5 ),settings_lai_roof_extensive,building_pars[4,:,:]) |
|---|
| 917 | |
|---|
| 918 | |
|---|
| 919 | nc_write_dimension(filename[i], 'nbuilding_pars', nbuilding_pars, datatypes["nbuilding_pars"]) |
|---|
| 920 | nc_write_to_file_3d(filename[i], 'building_pars', building_pars, datatypes["building_pars"],'nbuilding_pars','y','x',fillvalues["building_pars"]) |
|---|
| 921 | nc_write_attribute(filename[i], 'building_pars', 'long_name', 'building_pars') |
|---|
| 922 | nc_write_attribute(filename[i], 'building_pars', 'units', '') |
|---|
| 923 | nc_write_attribute(filename[i], 'building_pars', 'res_orig', domain_px[i]) |
|---|
| 924 | nc_write_attribute(filename[i], 'building_pars', 'coordinates', 'E_UTM N_UTM lon lat') |
|---|
| 925 | nc_write_attribute(filename[i], 'building_pars', 'grid_mapping', 'E_UTM N_UTM lon lat') |
|---|
| 926 | |
|---|
| 927 | del building_pars, buildings_2d, x, y |
|---|
| 928 | |
|---|
| 929 | |
|---|
| 930 | # Read tree data and create LAD and BAD arrays using the canopy generator |
|---|
| 931 | for i in range(0,ndomains): |
|---|
| 932 | lai = nc_read_from_file_2d(input_file_lai[ii[i]], 'Band1', domain_x0[i], domain_x1[i], domain_y0[i], domain_y1[i]) |
|---|
| 933 | |
|---|
| 934 | vegetation_type = nc_read_from_file_2d_all(filename[i], 'vegetation_type') |
|---|
| 935 | |
|---|
| 936 | lai = np.where(vegetation_type == fillvalues["vegetation_type"],fillvalues["vegetation_pars"],lai) |
|---|
| 937 | |
|---|
| 938 | |
|---|
| 939 | x = nc_read_from_file_2d_all(filename[i], 'x') |
|---|
| 940 | y = nc_read_from_file_2d_all(filename[i], 'y') |
|---|
| 941 | nvegetation_pars = np.arange(0,12) |
|---|
| 942 | vegetation_pars = np.ones((len(nvegetation_pars),len(y),len(x))) |
|---|
| 943 | vegetation_pars[:,:,:] = fillvalues["vegetation_pars"] |
|---|
| 944 | |
|---|
| 945 | vegetation_pars[1,:,:] = lai |
|---|
| 946 | |
|---|
| 947 | |
|---|
| 948 | # Write out first version of LAI. Will later be overwritten. |
|---|
| 949 | nc_write_dimension(filename[i], 'nvegetation_pars', nvegetation_pars, datatypes["nvegetation_pars"]) |
|---|
| 950 | nc_write_to_file_3d(filename[i], 'vegetation_pars', vegetation_pars, datatypes["vegetation_pars"],'nvegetation_pars','y','x',fillvalues["vegetation_pars"]) |
|---|
| 951 | nc_write_attribute(filename[i], 'vegetation_pars', 'long_name', 'vegetation_pars') |
|---|
| 952 | nc_write_attribute(filename[i], 'vegetation_pars', 'units', '') |
|---|
| 953 | nc_write_attribute(filename[i], 'vegetation_pars', 'res_orig', domain_px[i]) |
|---|
| 954 | nc_write_attribute(filename[i], 'vegetation_pars', 'coordinates', 'E_UTM N_UTM lon lat') |
|---|
| 955 | nc_write_attribute(filename[i], 'vegetation_pars', 'grid_mapping', 'E_UTM N_UTM lon lat') |
|---|
| 956 | |
|---|
| 957 | del lai, vegetation_pars, vegetation_type |
|---|
| 958 | |
|---|
| 959 | # Read tree data and create LAD and BAD arrays using the canopy generator |
|---|
| 960 | for i in range(0,ndomains): |
|---|
| 961 | if domain_street_trees[i]: |
|---|
| 962 | |
|---|
| 963 | vegetation_pars = nc_read_from_file_2d_all(filename[i], 'vegetation_pars') |
|---|
| 964 | |
|---|
| 965 | lai = np.copy(vegetation_pars[1,:,:]) |
|---|
| 966 | |
|---|
| 967 | x = nc_read_from_file_2d_all(filename[i], 'x') |
|---|
| 968 | y = nc_read_from_file_2d_all(filename[i], 'y') |
|---|
| 969 | |
|---|
| 970 | # Save lai data as default for low and high vegetation |
|---|
| 971 | lai_low = lai |
|---|
| 972 | lai_high = lai |
|---|
| 973 | |
|---|
| 974 | # Read all tree parameters from file |
|---|
| 975 | 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]) |
|---|
| 976 | |
|---|
| 977 | if (input_file_tree_crown_diameter[ii[i]] is not None): |
|---|
| 978 | 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]) |
|---|
| 979 | tree_crown_diameter = np.where( (tree_crown_diameter == 0.0) | (tree_crown_diameter == -1.0) ,fillvalues["tree_data"],tree_crown_diameter) |
|---|
| 980 | else: |
|---|
| 981 | tree_crown_diameter = np.ones((len(y),len(x))) |
|---|
| 982 | tree_crown_diameter[:,:] = fillvalues["tree_data"] |
|---|
| 983 | |
|---|
| 984 | |
|---|
| 985 | 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]) |
|---|
| 986 | 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]) |
|---|
| 987 | 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]) |
|---|
| 988 | |
|---|
| 989 | # Remove missing values from the data. Reasonable values will be set by the tree generator |
|---|
| 990 | tree_height = np.where( (tree_height == 0.0) | (tree_height == -1.0) ,fillvalues["tree_data"],tree_height) |
|---|
| 991 | tree_trunk_diameter = np.where( (tree_trunk_diameter == 0.0) | (tree_trunk_diameter == -1.0) ,fillvalues["tree_data"],tree_trunk_diameter) |
|---|
| 992 | tree_type = np.where( (tree_type == 0.0) | (tree_type == -1.0) ,fillvalues["tree_data"],tree_type) |
|---|
| 993 | patch_height = np.where( patch_height == -1.0 ,fillvalues["tree_data"],patch_height) |
|---|
| 994 | |
|---|
| 995 | # Convert trunk diameter from cm to m |
|---|
| 996 | tree_trunk_diameter = np.where(tree_trunk_diameter != fillvalues["tree_data"], tree_trunk_diameter * 0.01,tree_trunk_diameter) |
|---|
| 997 | |
|---|
| 998 | |
|---|
| 999 | # Temporarily change missing value for tree_type |
|---|
| 1000 | tree_type = np.where( (tree_type == fillvalues["tree_type"]),fillvalues["tree_data"],tree_type) |
|---|
| 1001 | |
|---|
| 1002 | # Compare patch height array with vegetation type and correct accordingly |
|---|
| 1003 | vegetation_type = nc_read_from_file_2d_all(filename[i], 'vegetation_type') |
|---|
| 1004 | |
|---|
| 1005 | |
|---|
| 1006 | # For zero-height patches, set vegetation_type to short grass and remove these pixels from the patch height array |
|---|
| 1007 | 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) |
|---|
| 1008 | 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) |
|---|
| 1009 | |
|---|
| 1010 | |
|---|
| 1011 | max_tree_height = max(tree_height.flatten()) |
|---|
| 1012 | max_patch_height = max(patch_height.flatten()) |
|---|
| 1013 | |
|---|
| 1014 | # Call canopy generator for single trees only if there is any tree height available in the domain. |
|---|
| 1015 | # This does not guarantee that there are street trees that can be processed. This is checked in the |
|---|
| 1016 | # canopy generator. |
|---|
| 1017 | if ( (max_tree_height != fillvalues["tree_data"]) | (max_patch_height != fillvalues["tree_data"]) ): |
|---|
| 1018 | |
|---|
| 1019 | 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"]) |
|---|
| 1020 | |
|---|
| 1021 | |
|---|
| 1022 | # Remove LAD volumes that are inside buildings |
|---|
| 1023 | buildings_2d = nc_read_from_file_2d_all(filename[i], 'buildings_2d') |
|---|
| 1024 | for k in range(0,len(zlad)-1): |
|---|
| 1025 | |
|---|
| 1026 | lad[k,:,:] = np.where(buildings_2d == fillvalues["buildings_2d"],lad[k,:,:],fillvalues["tree_data"]) |
|---|
| 1027 | bad[k,:,:] = np.where(buildings_2d == fillvalues["buildings_2d"],bad[k,:,:],fillvalues["tree_data"]) |
|---|
| 1028 | tree_ids[k,:,:] = np.where(buildings_2d == fillvalues["buildings_2d"],tree_ids[k,:,:],fillvalues["tree_data"]) |
|---|
| 1029 | |
|---|
| 1030 | del buildings_2d |
|---|
| 1031 | |
|---|
| 1032 | nc_write_dimension(filename[i], 'zlad', zlad, datatypes["tree_data"]) |
|---|
| 1033 | nc_write_to_file_3d(filename[i], 'lad', lad, datatypes["tree_data"],'zlad','y','x',fillvalues["tree_data"]) |
|---|
| 1034 | nc_write_attribute(filename[i], 'lad', 'long_name', 'leaf area density') |
|---|
| 1035 | nc_write_attribute(filename[i], 'lad', 'units', '') |
|---|
| 1036 | nc_write_attribute(filename[i], 'lad', 'res_orig', domain_px[i]) |
|---|
| 1037 | nc_write_attribute(filename[i], 'lad', 'coordinates', 'E_UTM N_UTM lon lat') |
|---|
| 1038 | nc_write_attribute(filename[i], 'lad', 'grid_mapping', 'E_UTM N_UTM lon lat') |
|---|
| 1039 | |
|---|
| 1040 | nc_write_to_file_3d(filename[i], 'bad', bad, datatypes["tree_data"],'zlad','y','x',fillvalues["tree_data"]) |
|---|
| 1041 | nc_write_attribute(filename[i], 'bad', 'long_name', 'basal area density') |
|---|
| 1042 | nc_write_attribute(filename[i], 'bad', 'units', '') |
|---|
| 1043 | nc_write_attribute(filename[i], 'bad', 'res_orig', domain_px[i]) |
|---|
| 1044 | nc_write_attribute(filename[i], 'bad', 'coordinates', 'E_UTM N_UTM lon lat') |
|---|
| 1045 | nc_write_attribute(filename[i], 'bad', 'grid_mapping', 'E_UTM N_UTM lon lat') |
|---|
| 1046 | |
|---|
| 1047 | nc_write_to_file_3d(filename[i], 'tree_id', tree_ids, datatypes["tree_data"],'zlad','y','x',fillvalues["tree_data"]) |
|---|
| 1048 | nc_write_attribute(filename[i], 'tree_id', 'long_name', 'tree id') |
|---|
| 1049 | nc_write_attribute(filename[i], 'tree_id', 'units', '') |
|---|
| 1050 | nc_write_attribute(filename[i], 'tree_id', 'res_orig', domain_px[i]) |
|---|
| 1051 | nc_write_attribute(filename[i], 'tree_id', 'coordinates', 'E_UTM N_UTM lon lat') |
|---|
| 1052 | nc_write_attribute(filename[i], 'tree_id', 'grid_mapping', 'E_UTM N_UTM lon lat') |
|---|
| 1053 | |
|---|
| 1054 | del lai, lad, bad, tree_ids, zlad |
|---|
| 1055 | |
|---|
| 1056 | del vegetation_pars, tree_height, tree_crown_diameter, tree_trunk_diameter, tree_type, patch_height, x, y |
|---|
| 1057 | |
|---|
| 1058 | |
|---|
| 1059 | # Create vegetation patches for locations with high vegetation type |
|---|
| 1060 | for i in range(0,ndomains): |
|---|
| 1061 | if domain_canopy_patches[i]: |
|---|
| 1062 | |
|---|
| 1063 | # Load vegetation_type and lad array (at level z = 0) for re-processing |
|---|
| 1064 | vegetation_type = nc_read_from_file_2d_all(filename[i], 'vegetation_type') |
|---|
| 1065 | lad = nc_read_from_file_3d_all(filename[i], 'lad') |
|---|
| 1066 | zlad = nc_read_from_file_1d_all(filename[i], 'zlad') |
|---|
| 1067 | 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]) |
|---|
| 1068 | vegetation_pars = nc_read_from_file_3d_all(filename[i], 'vegetation_pars') |
|---|
| 1069 | lai = vegetation_pars[1,:,:] |
|---|
| 1070 | |
|---|
| 1071 | |
|---|
| 1072 | # 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 |
|---|
| 1073 | 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"]) |
|---|
| 1074 | |
|---|
| 1075 | |
|---|
| 1076 | # Treat all pixels where short grass is defined, but where a patch_height >= dz is found, as high vegetation (often the case in backyards) |
|---|
| 1077 | lai_high = np.where( (lai_high == fillvalues["tree_data"]) & (patch_height >= domain_dz[i]) & (vegetation_type == 3), 1.0, lai_high) |
|---|
| 1078 | |
|---|
| 1079 | # Now, assign either the default LAI for high vegetation or keep 1.0 from the lai_high array. |
|---|
| 1080 | lai_high = np.where( (lai_high != fillvalues["tree_data"]) & (lai == fillvalues["tree_data"]), settings_lai_high_default, lai_high) |
|---|
| 1081 | |
|---|
| 1082 | # If LAI values are available in the LAI array, write them on the lai_high array |
|---|
| 1083 | lai_high = np.where( (lai_high != fillvalues["tree_data"]) & (lai != fillvalues["tree_data"]), lai, lai_high) |
|---|
| 1084 | |
|---|
| 1085 | |
|---|
| 1086 | # Define a patch height wherever it is missing, but where a high vegetation LAI was set |
|---|
| 1087 | patch_height = np.where ( (lai_high != fillvalues["tree_data"]) & (patch_height == fillvalues["tree_data"]), settings_patch_height_default, patch_height) |
|---|
| 1088 | |
|---|
| 1089 | |
|---|
| 1090 | # Remove pixels where street trees were already set |
|---|
| 1091 | patch_height = np.where ( (lad[0,:,:] != fillvalues["tree_data"]), fillvalues["tree_data"], patch_height) |
|---|
| 1092 | |
|---|
| 1093 | # Remove patch heights that have no lai_high value |
|---|
| 1094 | patch_height = np.where ( (lai_high == fillvalues["tree_data"]), fillvalues["tree_data"], patch_height) |
|---|
| 1095 | |
|---|
| 1096 | # For missing LAI values, set either the high vegetation default or the low vegetation default |
|---|
| 1097 | lai_high = np.where( (patch_height > 2.0) & (patch_height != fillvalues["tree_data"]) & (lai_high == fillvalues["tree_data"]),settings_lai_high_default,lai_high) |
|---|
| 1098 | lai_high = np.where( (patch_height <= 2.0) & (patch_height != fillvalues["tree_data"]) & (lai_high == fillvalues["tree_data"]),settings_lai_low_default,lai_high) |
|---|
| 1099 | |
|---|
| 1100 | if ( max(patch_height.flatten()) >= (2.0 * domain_dz[i]) ): |
|---|
| 1101 | print(" start calculating LAD (this might take some time)") |
|---|
| 1102 | |
|---|
| 1103 | |
|---|
| 1104 | lad_patch, patch_nz, status = process_patch(domain_dz[i],patch_height,max(zlad),lai_high,settings_lai_alpha,settings_lai_beta) |
|---|
| 1105 | |
|---|
| 1106 | # 2D loop in order to avoid memory problems with large arrays |
|---|
| 1107 | for iii in range(0,domain_nx[i]): |
|---|
| 1108 | for jj in range(0,domain_ny[i]): |
|---|
| 1109 | |
|---|
| 1110 | lad[0:patch_nz+1,jj,iii] = np.where( (lad[0:patch_nz+1,jj,iii] == fillvalues["tree_data"]),lad_patch[0:patch_nz+1,jj,iii], lad[0:patch_nz+1,jj,iii] ) |
|---|
| 1111 | |
|---|
| 1112 | # Remove high vegetation wherever it is replaced by a leaf area density. This should effectively remove all high vegetation pixels |
|---|
| 1113 | vegetation_type = np.where((lad[0,:,:] != fillvalues["tree_data"]) & (vegetation_type != fillvalues["vegetation_type"]),settings_veg_type_below_trees,vegetation_type) |
|---|
| 1114 | |
|---|
| 1115 | # If desired, remove all high vegetation. TODO: check if this is still necessary |
|---|
| 1116 | if not domain_high_vegetation[i]: |
|---|
| 1117 | 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) |
|---|
| 1118 | |
|---|
| 1119 | |
|---|
| 1120 | # Set default low LAI for pixels with an LAD (short grass below trees) |
|---|
| 1121 | lai_low = np.where( (lad[0,:,:] == fillvalues["tree_data"]), lai, settings_lai_low_default) |
|---|
| 1122 | |
|---|
| 1123 | # Fill low vegetation pixels without LAI set or with LAI = 0 with default value |
|---|
| 1124 | lai_low = np.where( ( (lai_low == fillvalues["tree_data"]) | (lai_low == 0.0) ) & (vegetation_type != fillvalues["vegetation_type"] ), settings_lai_low_default, lai_low) |
|---|
| 1125 | |
|---|
| 1126 | # Remove lai for pixels that have no vegetation_type |
|---|
| 1127 | lai_low = np.where( ( (vegetation_type != fillvalues["vegetation_type"]) & (vegetation_type != 1) ), lai_low, fillvalues["tree_data"]) |
|---|
| 1128 | |
|---|
| 1129 | # Overwrite lai in vegetation_parameters |
|---|
| 1130 | vegetation_pars[1,:,:] = np.copy(lai_low) |
|---|
| 1131 | nc_overwrite_to_file_3d(filename[i], 'vegetation_pars', vegetation_pars) |
|---|
| 1132 | |
|---|
| 1133 | # Overwrite lad array |
|---|
| 1134 | nc_overwrite_to_file_3d(filename[i], 'lad', lad) |
|---|
| 1135 | |
|---|
| 1136 | nc_overwrite_to_file_2d(filename[i], 'vegetation_type', vegetation_type) |
|---|
| 1137 | |
|---|
| 1138 | |
|---|
| 1139 | del vegetation_type, lad, lai, patch_height, vegetation_pars, zlad |
|---|
| 1140 | |
|---|
| 1141 | |
|---|
| 1142 | # Final adjustment of vegetation parameters: remove LAI where a bare soil was set |
|---|
| 1143 | for i in range(0,ndomains): |
|---|
| 1144 | |
|---|
| 1145 | vegetation_type = nc_read_from_file_2d_all(filename[i], 'vegetation_type') |
|---|
| 1146 | vegetation_pars = nc_read_from_file_3d_all(filename[i], 'vegetation_pars') |
|---|
| 1147 | lai = vegetation_pars[1,:,:] |
|---|
| 1148 | |
|---|
| 1149 | |
|---|
| 1150 | # Remove lai for pixels that have no vegetation_type |
|---|
| 1151 | lai = np.where( ( (vegetation_type != fillvalues["vegetation_type"]) & (vegetation_type != 1) ), lai, fillvalues["tree_data"]) |
|---|
| 1152 | |
|---|
| 1153 | # Overwrite lai in vegetation_parameters |
|---|
| 1154 | vegetation_pars[1,:,:] = np.copy(lai) |
|---|
| 1155 | nc_overwrite_to_file_3d(filename[i], 'vegetation_pars', vegetation_pars) |
|---|
| 1156 | |
|---|
| 1157 | del vegetation_type, lai, vegetation_pars |
|---|
| 1158 | |
|---|
| 1159 | |
|---|
| 1160 | # Final consistency check |
|---|
| 1161 | for i in range(0,ndomains): |
|---|
| 1162 | vegetation_type = nc_read_from_file_2d_all(filename[i], 'vegetation_type') |
|---|
| 1163 | pavement_type = nc_read_from_file_2d_all(filename[i], 'pavement_type') |
|---|
| 1164 | building_type = nc_read_from_file_2d_all(filename[i], 'building_type') |
|---|
| 1165 | water_type = nc_read_from_file_2d_all(filename[i], 'water_type') |
|---|
| 1166 | soil_type = nc_read_from_file_2d_all(filename[i], 'soil_type') |
|---|
| 1167 | |
|---|
| 1168 | # Check for consistency and fill empty fields with default vegetation type |
|---|
| 1169 | 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"]) |
|---|
| 1170 | |
|---|
| 1171 | # Check for consistency and fill empty fields with default vegetation type |
|---|
| 1172 | consistency_array, test = check_consistency_3(vegetation_type,pavement_type,soil_type,fillvalues["vegetation_type"],fillvalues["pavement_type"],fillvalues["soil_type"]) |
|---|
| 1173 | |
|---|
| 1174 | surface_fraction = nc_read_from_file_3d_all(filename[i], 'surface_fraction') |
|---|
| 1175 | surface_fraction[0,:,:] = np.where(vegetation_type != fillvalues["vegetation_type"], 1.0, 0.0) |
|---|
| 1176 | surface_fraction[1,:,:] = np.where(pavement_type != fillvalues["pavement_type"], 1.0, 0.0) |
|---|
| 1177 | surface_fraction[2,:,:] = np.where(water_type != fillvalues["water_type"], 1.0, 0.0) |
|---|
| 1178 | nc_overwrite_to_file_3d(filename[i], 'surface_fraction', surface_fraction) |
|---|
| 1179 | |
|---|
| 1180 | del vegetation_type, pavement_type, building_type, water_type, soil_type |
|---|
