| [4400] | 1 | #!/usr/bin/env python3 |
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| 2 | # -*- coding: utf-8 -*- |
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| 3 | # |
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| 4 | #--------------------------------------------------------------------------------# |
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| 5 | # This file is part of the PALM model system. |
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| 6 | # |
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| 7 | # PALM is free software: you can redistribute it and/or modify it under the terms |
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| 8 | # of the GNU General Public License as published by the Free Software Foundation, |
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| 9 | # either version 3 of the License, or (at your option) any later version. |
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| 10 | # |
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| 11 | # PALM is distributed in the hope that it will be useful, but WITHOUT ANY |
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| 12 | # WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR |
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| 13 | # A PARTICULAR PURPOSE. See the GNU General Public License for more details. |
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| 14 | # |
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| 15 | # You should have received a copy of the GNU General Public License along with |
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| 16 | # PALM. If not, see <http://www.gnu.org/licenses/>. |
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| 17 | # |
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| [4843] | 18 | # Copyright 1997-2021 Leibniz Universitaet Hannover |
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| [4400] | 19 | #--------------------------------------------------------------------------------# |
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| 20 | # |
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| 21 | # Current revisions: |
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| 22 | # ----------------- |
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| [4663] | 23 | # |
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| 24 | # |
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| [4400] | 25 | # Former revisions: |
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| 26 | # ----------------- |
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| 27 | # $Id$ |
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| [4763] | 28 | # - Check automatically for data organization (stored in subdirectories or not) |
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| 29 | # - Convert trajectory and timeseriesProfile coordinates into 1-D coordinates |
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| 30 | # equivalent to timeseries coordiates. This simplifies processing in PALM and |
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| 31 | # makes the virtual-measurement module also applicable to other campaigns. |
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| 32 | # |
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| 33 | # 4758 2020-10-26 13:03:52Z suehring |
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| [4758] | 34 | # In order to do not omit observations that are on the same site but have different |
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| 35 | # coordinates or feature-types, process all files rather than only one and omit |
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| 36 | # the rest. |
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| 37 | # |
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| 38 | # 4663 2020-09-02 14:54:09Z gronemeier |
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| [4663] | 39 | # bugfix in non_measurable_vars; ignore station_h if featureType is trajectory |
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| 40 | # |
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| 41 | # 4400 2020-02-10 20:32:41Z suehring |
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| [4400] | 42 | # Initial revision |
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| 43 | # |
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| 44 | # Description: |
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| 45 | # ------------ |
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| 46 | # Processing tool for creating PIDS conform virtual measurement setup file |
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| 47 | # from UC2 data-standard conform observational data or from prescribed input |
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| 48 | # coordinates. |
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| 49 | # |
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| 50 | # @Authors Matthias Suehring (suehring@muk.uni-hannover.de) |
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| 51 | # Tobias Gronemeier (gronemeier@muk.uni-hannover.de) |
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| 52 | # |
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| 53 | # @todo Add further feature tpyes for customized observations. At the moment only |
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| 54 | # timeSeries is possible. |
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| 55 | #--------------------------------------------------------------------------------# |
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| 56 | |
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| 57 | |
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| 58 | import netCDF4 |
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| 59 | from netCDF4 import Dataset, stringtochar |
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| 60 | import os |
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| 61 | import numpy as np |
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| [4763] | 62 | import time |
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| [4400] | 63 | |
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| 64 | |
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| 65 | # Function to read the config file |
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| 66 | def read_config_file(): |
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| 67 | |
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| 68 | import configparser |
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| 69 | import os |
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| 70 | import sys |
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| 71 | import json |
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| 72 | |
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| 73 | # Definition of global configuration parameters |
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| [4663] | 74 | global global_acronym |
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| 75 | global global_author |
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| 76 | global global_campaign |
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| 77 | global global_comment |
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| 78 | global global_contact |
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| [4400] | 79 | global global_data_content |
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| 80 | global global_dependencies |
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| [4663] | 81 | global global_institution |
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| 82 | global global_keywords |
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| 83 | global global_location |
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| 84 | global global_references |
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| 85 | global global_site |
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| 86 | global global_source |
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| [4400] | 87 | global global_palm_version |
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| [4663] | 88 | global data_path |
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| 89 | global output_path |
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| 90 | global output_filename |
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| [4400] | 91 | global number_positions |
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| 92 | global input_from_observations |
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| 93 | global coordinates |
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| 94 | global vars_to_be_measured |
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| 95 | global custom_coordinates |
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| 96 | |
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| 97 | global_acronym = " " |
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| 98 | global_author = " " |
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| 99 | global_campaign = " " |
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| 100 | global_comment = " " |
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| 101 | global_contact = " " |
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| 102 | global_data_content = " " |
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| 103 | global_dependencies = " " |
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| 104 | global_institution = " " |
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| 105 | global_keywords = " " |
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| 106 | global_location = " " |
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| 107 | global_references = " " |
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| 108 | global_site = " " |
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| 109 | global_source = " " |
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| 110 | global_palm_version = 6.0 |
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| 111 | data_path = " " |
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| 112 | output_path = " " |
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| 113 | output_filename = "none" |
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| 114 | number_positions = -999 |
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| 115 | input_from_observations = False |
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| 116 | coordinates = [] |
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| 117 | vars_to_be_measured = [] |
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| 118 | custom_coordinates = False |
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| 119 | |
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| 120 | # Check if configuration files exists and quit otherwise |
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| 121 | input_config = ".cvd.config.default" |
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| [4663] | 122 | for i in range(1,len(sys.argv)): |
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| [4400] | 123 | input_config = str(sys.argv[i]) |
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| 124 | |
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| 125 | # Allow empty settings |
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| 126 | config = configparser.RawConfigParser(allow_no_value=True) |
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| 127 | |
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| 128 | # Check if a config file exists. |
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| 129 | if ( os.path.isfile(input_config) == False ): |
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| 130 | print ("Error. No configuration file " + input_config + " found.") |
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| 131 | quit() |
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| 132 | |
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| 133 | config.read(input_config) |
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| [4663] | 134 | |
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| [4400] | 135 | for section in range( 0, len( config.sections() ) ): |
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| 136 | |
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| 137 | current_section = config.sections()[section] |
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| 138 | |
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| 139 | # read global attributes which are written into the output file header |
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| 140 | if ( current_section == 'global' ): |
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| 141 | |
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| 142 | global_acronym = config.get( current_section, 'acronym' ) |
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| 143 | global_author = config.get( current_section, 'author' ) |
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| 144 | global_campaign = config.get( current_section, 'campaign' ) |
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| 145 | global_comment = config.get( current_section, 'comment' ) |
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| 146 | global_contact = config.get( current_section, 'contact_person' ) |
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| 147 | global_data_content = config.get( current_section, 'data_content' ) |
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| 148 | global_dependencies = config.get( current_section, 'dependencies' ) |
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| 149 | global_institution = config.get( current_section, 'institution' ) |
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| 150 | global_keywords = config.get( current_section, 'keywords' ) |
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| 151 | global_location = config.get( current_section, 'location' ) |
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| 152 | global_references = config.get( current_section, 'references' ) |
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| 153 | global_site = config.get( current_section, 'site' ) |
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| 154 | global_source = config.get( current_section, 'source' ) |
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| 155 | global_palm_version = float( config.get( current_section, 'palm_version' ) ) |
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| 156 | |
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| 157 | # Read data input path for observational data |
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| 158 | elif ( current_section == 'input' ): |
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| 159 | |
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| 160 | data_path = config.get( current_section, 'data_path' ) |
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| 161 | input_from_observations = True |
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| 162 | |
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| 163 | # Read output path and filename for the VM driver |
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| 164 | elif ( current_section == 'output' ): |
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| 165 | |
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| 166 | output_path = config.get( current_section, 'output_path' ) |
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| 167 | output_filename = config.get( current_section, 'output_filename' ) |
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| 168 | |
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| [4663] | 169 | # Read customized coordinates where virtual measurements shall be taken, |
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| [4400] | 170 | # as well as the variables that should be sampled. |
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| 171 | elif ( current_section == 'custom_positions' ): |
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| 172 | |
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| 173 | number_positions = config.get( current_section, 'number_positions' ) |
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| 174 | |
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| 175 | for count in range( 0, int( number_positions ) ): |
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| 176 | coordinates.append( json.loads( config.get( current_section, \ |
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| 177 | "coordinates" + str( count + 1 ) ) ) ) |
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| 178 | # If coordinates are given, set a global flag. |
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| 179 | custom_coordinates = True |
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| 180 | |
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| 181 | for count in range( 0, int( number_positions ) ): |
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| 182 | vars_to_be_measured.append( json.loads( config.get( current_section, \ |
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| 183 | "vars_to_be_measured" + str( count + 1 ) ) ) ) |
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| 184 | |
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| 185 | |
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| 186 | return 0 |
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| 187 | |
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| 188 | #++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ |
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| 189 | # Main program: |
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| 190 | #++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ |
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| 191 | |
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| 192 | |
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| 193 | # Define strings |
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| 194 | name_featuretype = "featureType" |
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| 195 | name_ts = "timeSeries" |
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| [4763] | 196 | name_tspr = "timeSeriesProfile" |
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| [4400] | 197 | name_traj = "trajectory" |
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| 198 | name_ntime = "ntime" |
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| 199 | name_time = "time" |
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| 200 | name_station = "station" |
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| 201 | name_traj_dim = "traj" |
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| 202 | name_nz = "nz" |
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| 203 | name_datacontent = "data_content" |
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| 204 | name_eutm = "E_UTM" |
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| 205 | name_nutm = "N_UTM" |
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| [4763] | 206 | name_hao = "height" |
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| [4400] | 207 | name_station_h = "station_h" |
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| 208 | name_z = "z" |
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| 209 | name_soil_sampling = "soil_sample" |
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| 210 | name_num_stat = "number_of_stations" |
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| 211 | name_fill = "_FillValue" |
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| 212 | name_site = "site" |
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| [4758] | 213 | name_acro = "acronym" |
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| 214 | name_content = "data_content" |
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| [4400] | 215 | name_orig_x = "origin_x" |
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| 216 | name_orig_y = "origin_y" |
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| 217 | name_orig_z = "origin_z" |
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| 218 | |
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| 219 | max_string_len = 50 |
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| 220 | |
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| 221 | name_measvars = "measured_variables" |
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| 222 | |
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| 223 | non_measurable_vars = ['station_name', 'time', 'time_bounds', 'crs', \ |
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| 224 | 'vrs', 'x', 'y', 'z', 'lon', 'lat', 'ntime', 'station', 'traj', \ |
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| 225 | 'E_UTM', 'N_UTM', 'height_above_origin', 'station_h', \ |
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| [4663] | 226 | 'traj_name', 'height', 'band_pm_size', 'bands_pm', 'bands_pm_size_bounds', \ |
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| [4400] | 227 | 'bands_pm_size', 'ancillary_detected_layer' ] |
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| 228 | |
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| 229 | soil_vars = [ 't_soil', 'm_soil', 'lwc', 'lwcs', 'smp' ] |
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| 230 | |
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| 231 | dims_out = [ name_eutm, name_nutm, name_hao, name_z, name_station_h ] |
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| 232 | |
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| [4663] | 233 | # Define list of attributes which need to be of type float. In the data set this is not |
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| [4400] | 234 | # necessarily guranteed. |
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| 235 | atts_float = [ 'origin_x', 'origin_y', 'origin_z', 'origin_lon', 'origin_lat', 'rotation_angle' ] |
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| 236 | |
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| 237 | # Define list of default variables that shall be measured at each site |
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| 238 | vars_default = [ 'u', 'v', 'w', 'theta', 'hus' ] |
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| 239 | |
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| 240 | |
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| 241 | #Read config file |
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| 242 | read_config_file() |
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| 243 | |
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| 244 | # Initialize counter variable for the number of sites |
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| 245 | num_sites = 0 |
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| 246 | |
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| 247 | # Set the output path for the data |
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| 248 | output_filename = output_path + output_filename |
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| 249 | |
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| 250 | # Open output file |
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| 251 | ncfile_out = Dataset( output_filename, "w", format="NETCDF4" ) |
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| 252 | |
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| 253 | # First, add global attributes |
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| 254 | ncfile_out.setncattr( 'acronym', global_acronym ) |
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| 255 | ncfile_out.setncattr( 'author', global_author ) |
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| 256 | ncfile_out.setncattr( 'campaign', global_campaign ) |
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| 257 | ncfile_out.setncattr( 'comment', global_comment ) |
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| 258 | ncfile_out.setncattr( 'contact_person', global_contact ) |
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| 259 | ncfile_out.setncattr( 'data_content', global_data_content ) |
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| 260 | ncfile_out.setncattr( 'dependencies', global_dependencies ) |
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| 261 | ncfile_out.setncattr( 'institution', global_institution ) |
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| 262 | ncfile_out.setncattr( 'keywords', global_keywords ) |
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| 263 | ncfile_out.setncattr( 'location', global_location ) |
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| 264 | ncfile_out.setncattr( 'references', global_references ) |
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| 265 | ncfile_out.setncattr( 'site', global_site ) |
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| 266 | ncfile_out.setncattr( 'source', global_source ) |
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| 267 | ncfile_out.setncattr( 'palm_version', global_palm_version ) |
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| 268 | |
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| 269 | # Create universal dimension for the string length. |
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| 270 | ncfile_out.createDimension("string_len", max_string_len) |
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| 271 | |
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| 272 | |
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| [4663] | 273 | # Check if observational data is available. This case, |
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| 274 | # obtain an alphabetically sorted list of input data. List is sorted |
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| [4400] | 275 | # just for the sake of clarity in the resulting setup file. |
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| 276 | if ( input_from_observations == True ): |
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| 277 | list_input_data = sorted( os.listdir( data_path ) ) |
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| 278 | |
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| 279 | if ( input_from_observations ): |
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| 280 | |
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| [4763] | 281 | # Run loop over all listed input data. Depending on the data set, this could be |
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| 282 | # a list of files or a list of subdirectories. |
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| [4400] | 283 | # This is done to reduce the number of virtual measurements in the model. Each |
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| 284 | # virtual measurement has an overhead and consumes memory. |
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| 285 | sites = [] |
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| [4758] | 286 | input_files = [] |
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| [4763] | 287 | input_files_orig = [] |
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| [4400] | 288 | for dirname in list_input_data: |
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| 289 | data_file = data_path + dirname |
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| 290 | |
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| [4763] | 291 | if ( os.path.isdir(data_file) == True ): |
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| 292 | # Directory may contain various file versions. |
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| 293 | # Take the one with highest cycle number. |
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| 294 | highest_cycle_nr = 0 |
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| 295 | for filename in os.listdir(data_file): |
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| 296 | start_seq = len( filename ) - 6 |
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| 297 | end_seq = len( filename ) - 3 |
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| 298 | if int( filename[start_seq:end_seq] ) > highest_cycle_nr: |
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| 299 | highest_cycle_nr = int(filename[start_seq:end_seq]) |
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| 300 | latest_file = filename |
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| 301 | input_file = data_file + "/" + latest_file |
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| 302 | input_file_orig = latest_file |
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| 303 | else: |
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| 304 | input_file = data_file |
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| 305 | input_file_orig = dirname |
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| [4400] | 306 | |
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| 307 | # Open the NetCDF file |
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| 308 | ncfile_in = Dataset( input_file, "r", format="NETCDF4", encoding='ascii') |
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| [4758] | 309 | input_files.append(input_file) |
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| [4763] | 310 | input_files_orig.append(input_file_orig) |
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| [4400] | 311 | |
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| [4763] | 312 | # Gather all files according to their feature type and all sites for the respective feature type |
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| 313 | files_traj = [] |
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| 314 | files_ts = [] |
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| 315 | files_tspr = [] |
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| 316 | sites_traj = [] |
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| 317 | sites_ts = [] |
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| 318 | sites_tspr = [] |
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| 319 | for input_file in input_files: |
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| [4400] | 320 | ncfile_in = Dataset( input_file, "r", format="NETCDF4", encoding='ascii' ) |
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| 321 | |
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| 322 | for att in ncfile_in.ncattrs(): |
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| [4758] | 323 | if ( att == name_featuretype ): |
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| 324 | feature = ncfile_in.getncattr(att) |
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| [4400] | 325 | if ( att == name_site ): |
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| 326 | site = ncfile_in.getncattr(att) |
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| 327 | |
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| [4763] | 328 | if ( feature == name_traj ): |
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| 329 | files_traj.append(input_file) |
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| 330 | elif ( feature == name_ts ): |
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| 331 | files_ts.append(input_file) |
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| 332 | else: |
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| 333 | files_tspr.append(input_file) |
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| [4400] | 334 | |
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| [4763] | 335 | if ( feature == name_traj and site not in sites_traj ): |
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| 336 | sites_traj.append(site) |
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| 337 | if ( feature == name_ts and site not in sites_ts ): |
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| 338 | sites_ts.append(site) |
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| 339 | if ( feature == name_tspr and site not in sites_tspr ): |
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| 340 | sites_tspr.append(site) |
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| [4400] | 341 | |
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| [4763] | 342 | ncfile_in.close() |
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| [4400] | 343 | |
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| [4763] | 344 | for input_file in files_traj: |
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| 345 | print( "traj", input_file ) |
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| 346 | for site in sites_traj: |
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| 347 | print( "traj", site ) |
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| [4400] | 348 | |
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| [4763] | 349 | for site in sites_ts: |
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| 350 | print( "ts", site ) |
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| 351 | for site in sites_tspr: |
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| 352 | print( "tspr", site ) |
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| 353 | |
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| 354 | for file in files_tspr: |
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| 355 | print( "tspr", file ) |
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| 356 | counter_id = 1 |
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| 357 | for site_traj in sites_traj: |
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| 358 | # For the given site already define the featureTpye and site |
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| 359 | ncfile_out.setncattr( name_featuretype + str(counter_id), name_traj ) |
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| 360 | ncfile_out.setncattr( name_site + str(counter_id), site_traj ) |
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| [4400] | 361 | |
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| [4763] | 362 | # Define the number of coordinates for the site |
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| 363 | num_coords = 0 |
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| [4400] | 364 | |
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| [4763] | 365 | e_utm_traj = np.array([]) |
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| 366 | n_utm_traj = np.array([]) |
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| 367 | h_traj = np.array([]) |
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| 368 | measured_variables = ['u', 'v', 'w', 'theta', 'hus'] |
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| 369 | for input_file in files_traj: |
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| 370 | ncfile_in = Dataset( input_file, "r", format="NETCDF4", encoding='ascii' ) |
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| 371 | for att in ncfile_in.ncattrs(): |
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| 372 | if ( att == name_site ): |
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| 373 | site = ncfile_in.getncattr(att) |
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| [4400] | 374 | |
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| [4763] | 375 | if ( site == site_traj ): |
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| [4400] | 376 | |
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| [4763] | 377 | for att in ncfile_in.ncattrs(): |
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| 378 | if ( att == name_orig_x ): |
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| 379 | orig_x = ncfile_in.getncattr(att) |
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| 380 | if ( att == name_orig_y ): |
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| 381 | orig_y = ncfile_in.getncattr(att) |
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| 382 | if ( att == name_orig_z ): |
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| 383 | orig_z = ncfile_in.getncattr(att) |
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| [4400] | 384 | |
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| [4763] | 385 | ntime = len( ncfile_in.dimensions[name_ntime] ) |
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| 386 | ntraj = len( ncfile_in.dimensions[name_traj_dim] ) |
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| [4400] | 387 | |
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| [4763] | 388 | num_coords += ntime * ntraj |
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| 389 | # Gather UTM and height coordinates and merge them into one array. Further, gather |
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| 390 | # the variables that shall be sampled. Coordinates are checked to for NaN values and |
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| 391 | # are tranformed to arithmetric numbers. Further, 2D input array is transformed into |
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| 392 | # a 1D array. |
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| 393 | for var in ncfile_in.variables.keys(): |
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| 394 | if ( var in dims_out and var == name_eutm ): |
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| 395 | e_utm_traj = np.append(e_utm_traj, np.nan_to_num( ncfile_in.variables[var][:,:] ).flatten()) |
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| 396 | #e_utm_traj.append( np.nan_to_num( ncfile_in.variables[var][:,:] ).flatten() ) |
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| 397 | if ( var in dims_out and var == name_nutm ): |
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| 398 | n_utm_traj = np.append(n_utm_traj, np.nan_to_num( ncfile_in.variables[var][:,:] ).flatten()) |
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| 399 | if ( var in dims_out and var == name_hao ): |
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| 400 | h_traj = np.append(h_traj, np.nan_to_num( ncfile_in.variables[var][:,:] ).flatten()) |
|---|
| [4400] | 401 | |
|---|
| [4763] | 402 | if ( var not in non_measurable_vars and \ |
|---|
| 403 | var not in vars_default and \ |
|---|
| 404 | var not in measured_variables ): |
|---|
| 405 | measured_variables.append(var) |
|---|
| [4400] | 406 | |
|---|
| [4763] | 407 | ncfile_in.close() |
|---|
| [4400] | 408 | |
|---|
| [4763] | 409 | # After all files for the current site are processed, write the origin-coordinates for x,y,z |
|---|
| 410 | ncfile_out.setncattr( name_orig_x + str(counter_id), orig_x ) |
|---|
| 411 | ncfile_out.setncattr( name_orig_y + str(counter_id), orig_y ) |
|---|
| 412 | ncfile_out.setncattr( name_orig_z + str(counter_id), orig_z ) |
|---|
| 413 | # Create the dimensions |
|---|
| 414 | ncfile_out.createDimension( name_station + str(counter_id), num_coords ) |
|---|
| [4400] | 415 | |
|---|
| [4763] | 416 | temp_traj = ncfile_out.createVariable( name_eutm + str(counter_id), float, name_station + str(counter_id) ) |
|---|
| 417 | temp_traj[:] = e_utm_traj |
|---|
| [4400] | 418 | |
|---|
| [4763] | 419 | temp_traj = ncfile_out.createVariable( name_nutm + str(counter_id), float, name_station + str(counter_id) ) |
|---|
| 420 | temp_traj[:] = n_utm_traj |
|---|
| 421 | |
|---|
| 422 | temp_traj = ncfile_out.createVariable( name_hao + str(counter_id), float, name_station + str(counter_id) ) |
|---|
| 423 | temp_traj[:] = h_traj |
|---|
| 424 | |
|---|
| 425 | # Check if any of the measured variables is a soil variable. Set flag accordingly. |
|---|
| [4400] | 426 | soil = False |
|---|
| [4763] | 427 | for var in measured_variables: |
|---|
| [4400] | 428 | if ( var in soil_vars ): |
|---|
| 429 | soil = True |
|---|
| [4763] | 430 | # Write soil flag |
|---|
| 431 | ncfile_out.setncattr( name_soil_sampling + str( counter_id), np.int8(soil) ) |
|---|
| [4400] | 432 | |
|---|
| [4763] | 433 | # Create dimension for sample-variable string |
|---|
| 434 | ncfile_out.createDimension( "nvar"+ str(counter_id), len( measured_variables ) ) |
|---|
| [4400] | 435 | |
|---|
| [4763] | 436 | measured_var = ncfile_out.createVariable( 'measured_variables' + str(counter_id), 'S1', \ |
|---|
| 437 | ("nvar" + str(counter_id), "string_len") ) # must be NC_CHAR |
|---|
| [4400] | 438 | |
|---|
| [4763] | 439 | # Write the variables to the file |
|---|
| 440 | for counter, meas in enumerate( measured_variables ): |
|---|
| 441 | measured_var[counter] = stringtochar( np.array( meas,"S%s"%(max_string_len) ) ) |
|---|
| [4400] | 442 | |
|---|
| [4763] | 443 | # Increment the counter |
|---|
| 444 | counter_id += 1 |
|---|
| 445 | |
|---|
| 446 | |
|---|
| 447 | for site_tspr in sites_tspr: |
|---|
| 448 | # For the given site already define the featureTpye and site |
|---|
| 449 | ncfile_out.setncattr( name_featuretype + str(counter_id), name_tspr ) |
|---|
| 450 | ncfile_out.setncattr( name_site + str(counter_id), site_tspr ) |
|---|
| [4400] | 451 | |
|---|
| [4763] | 452 | # Define the number of coordinates for the site |
|---|
| 453 | num_coords = 0 |
|---|
| 454 | e_utm_tspr = np.array([]) |
|---|
| 455 | n_utm_tspr = np.array([]) |
|---|
| 456 | station_h_tspr = np.array([]) |
|---|
| 457 | z_tspr = np.array([]) |
|---|
| [4400] | 458 | |
|---|
| [4763] | 459 | measured_variables = ['u', 'v', 'w', 'theta', 'hus'] |
|---|
| 460 | for input_file in files_tspr: |
|---|
| 461 | ncfile_in = Dataset( input_file, "r", format="NETCDF4", encoding='ascii' ) |
|---|
| 462 | for att in ncfile_in.ncattrs(): |
|---|
| 463 | if ( att == name_site ): |
|---|
| 464 | site = ncfile_in.getncattr(att) |
|---|
| 465 | |
|---|
| 466 | if ( site == site_tspr ): |
|---|
| 467 | for att in ncfile_in.ncattrs(): |
|---|
| 468 | if ( att == name_orig_x ): |
|---|
| 469 | orig_x = ncfile_in.getncattr(att) |
|---|
| 470 | if ( att == name_orig_y ): |
|---|
| 471 | orig_y = ncfile_in.getncattr(att) |
|---|
| 472 | if ( att == name_orig_z ): |
|---|
| 473 | orig_z = ncfile_in.getncattr(att) |
|---|
| 474 | |
|---|
| 475 | nstation = len( ncfile_in.dimensions[name_station] ) |
|---|
| 476 | ntime = len( ncfile_in.dimensions[name_ntime] ) |
|---|
| 477 | nz = len( ncfile_in.dimensions[name_nz] ) |
|---|
| 478 | |
|---|
| 479 | num_coords += nstation * ntime * nz |
|---|
| 480 | # Gather UTM and height coordinates and merge them into one array. Further, gather |
|---|
| 481 | # the variables that shall be sampled. Coordinates are checked to for NaN values and |
|---|
| 482 | # are tranformed to arithmetric numbers. Further, 2D input array is transformed into |
|---|
| 483 | # a 1D array. |
|---|
| 484 | for var in ncfile_in.variables.keys(): |
|---|
| 485 | tspr_tmp1 = np.zeros((nstation)) |
|---|
| 486 | tspr_tmp2 = np.zeros((ntime*nz)) |
|---|
| 487 | if ( var in dims_out and var == name_eutm ): |
|---|
| 488 | tspr_tmp1 = np.nan_to_num( ncfile_in.variables[var][:] ) |
|---|
| 489 | for ns in range(0,int(nstation)): |
|---|
| 490 | tspr_tmp2[:] = tspr_tmp1[ns] |
|---|
| 491 | e_utm_tspr = np.append(e_utm_tspr, tspr_tmp2) |
|---|
| 492 | if ( var in dims_out and var == name_nutm ): |
|---|
| 493 | tspr_tmp1 = np.nan_to_num( ncfile_in.variables[var][:] ) |
|---|
| 494 | for ns in range(0,int(nstation)): |
|---|
| 495 | tspr_tmp2[:] = tspr_tmp1[ns] |
|---|
| 496 | n_utm_tspr = np.append(n_utm_tspr, tspr_tmp2) |
|---|
| 497 | if ( var in dims_out and var == name_z ): |
|---|
| 498 | z_tspr_tmp = np.nan_to_num( ncfile_in.variables[var][:,:,:] ) |
|---|
| 499 | z_tspr = np.append(z_tspr, np.concatenate( z_tspr_tmp )) |
|---|
| 500 | if ( var in dims_out and var == name_station_h ): |
|---|
| 501 | tspr_tmp1 = np.nan_to_num( ncfile_in.variables[var][:] ) |
|---|
| 502 | for ns in range(0,int(nstation)): |
|---|
| 503 | tspr_tmp2[:] = tspr_tmp1[ns] |
|---|
| 504 | station_h_tspr = np.append(station_h_tspr, tspr_tmp2) |
|---|
| 505 | |
|---|
| 506 | if ( var not in non_measurable_vars and \ |
|---|
| 507 | var not in vars_default and \ |
|---|
| 508 | var not in measured_variables ): |
|---|
| 509 | measured_variables.append(var) |
|---|
| 510 | |
|---|
| 511 | ncfile_in.close() |
|---|
| 512 | |
|---|
| 513 | # After all files for the current site are processed, write the origin-coordinates for x,y,z |
|---|
| 514 | ncfile_out.setncattr( name_orig_x + str(counter_id), orig_x ) |
|---|
| 515 | ncfile_out.setncattr( name_orig_y + str(counter_id), orig_y ) |
|---|
| 516 | ncfile_out.setncattr( name_orig_z + str(counter_id), orig_z ) |
|---|
| 517 | # Create the dimensions |
|---|
| 518 | ncfile_out.createDimension( name_station + str(counter_id), num_coords ) |
|---|
| 519 | |
|---|
| 520 | temp_tspr = ncfile_out.createVariable( name_eutm + str(counter_id), float, name_station + str(counter_id) ) |
|---|
| 521 | temp_tspr[:] = e_utm_tspr |
|---|
| 522 | |
|---|
| 523 | temp_tspr = ncfile_out.createVariable( name_nutm + str(counter_id), float, name_station + str(counter_id) ) |
|---|
| 524 | temp_tspr[:] = n_utm_tspr |
|---|
| 525 | |
|---|
| 526 | temp_tspr = ncfile_out.createVariable( name_z + str(counter_id), float, name_station + str(counter_id) ) |
|---|
| 527 | temp_tspr[:] = z_tspr |
|---|
| 528 | |
|---|
| 529 | temp_tspr = ncfile_out.createVariable( name_station_h + str(counter_id), float, name_station + str(counter_id) ) |
|---|
| 530 | temp_tspr[:] = station_h_tspr |
|---|
| 531 | |
|---|
| 532 | # Check if any of the measured variables is a soil variable. Set flag accordingly. |
|---|
| 533 | soil = False |
|---|
| 534 | for var in measured_variables: |
|---|
| 535 | if ( var in soil_vars ): |
|---|
| 536 | soil = True |
|---|
| 537 | # Write soil flag |
|---|
| 538 | ncfile_out.setncattr( name_soil_sampling + str( counter_id), np.int8(soil) ) |
|---|
| 539 | |
|---|
| 540 | # Create dimension for sample-variable string |
|---|
| 541 | ncfile_out.createDimension( "nvar"+ str(counter_id), len( measured_variables ) ) |
|---|
| 542 | |
|---|
| 543 | measured_var = ncfile_out.createVariable( 'measured_variables' + str(counter_id), 'S1', \ |
|---|
| 544 | ("nvar" + str(counter_id), "string_len") ) # must be NC_CHAR |
|---|
| 545 | |
|---|
| 546 | # Write the variables to the file |
|---|
| 547 | for counter, meas in enumerate( measured_variables ): |
|---|
| 548 | measured_var[counter] = stringtochar( np.array( meas,"S%s"%(max_string_len) ) ) |
|---|
| 549 | |
|---|
| 550 | # Increment the counter |
|---|
| 551 | counter_id += 1 |
|---|
| 552 | |
|---|
| 553 | |
|---|
| 554 | for site_ts in sites_ts: |
|---|
| 555 | # For the given site already define the featureTpye and site |
|---|
| 556 | ncfile_out.setncattr( name_featuretype + str(counter_id), name_ts ) |
|---|
| 557 | ncfile_out.setncattr( name_site + str(counter_id), site_ts ) |
|---|
| 558 | |
|---|
| 559 | # Define the number of coordinates for the site |
|---|
| 560 | num_coords = 0 |
|---|
| 561 | e_utm_ts = np.array([]) |
|---|
| 562 | n_utm_ts = np.array([]) |
|---|
| 563 | station_h_ts = np.array([]) |
|---|
| 564 | z_ts = np.array([]) |
|---|
| 565 | |
|---|
| 566 | measured_variables = ['u', 'v', 'w', 'theta', 'hus'] |
|---|
| 567 | for input_file in files_ts: |
|---|
| 568 | ncfile_in = Dataset( input_file, "r", format="NETCDF4", encoding='ascii' ) |
|---|
| 569 | for att in ncfile_in.ncattrs(): |
|---|
| 570 | if ( att == name_site ): |
|---|
| 571 | site = ncfile_in.getncattr(att) |
|---|
| 572 | |
|---|
| 573 | if ( site == site_ts ): |
|---|
| 574 | |
|---|
| 575 | for att in ncfile_in.ncattrs(): |
|---|
| 576 | if ( att == name_orig_x ): |
|---|
| 577 | orig_x = ncfile_in.getncattr(att) |
|---|
| 578 | if ( att == name_orig_y ): |
|---|
| 579 | orig_y = ncfile_in.getncattr(att) |
|---|
| 580 | if ( att == name_orig_z ): |
|---|
| 581 | orig_z = ncfile_in.getncattr(att) |
|---|
| 582 | |
|---|
| 583 | nstation = len( ncfile_in.dimensions[name_station] ) |
|---|
| 584 | num_coords += nstation |
|---|
| 585 | # Gather UTM and height coordinates and merge them into one array. Further, gather |
|---|
| 586 | # the variables that shall be sampled. Coordinates are checked to for NaN values and |
|---|
| 587 | # are tranformed to arithmetric numbers. |
|---|
| 588 | for var in ncfile_in.variables.keys(): |
|---|
| 589 | if ( var in dims_out and var == name_eutm ): |
|---|
| 590 | e_utm_ts = np.append(e_utm_ts, np.nan_to_num( ncfile_in.variables[var][:] )) |
|---|
| 591 | if ( var in dims_out and var == name_nutm ): |
|---|
| 592 | n_utm_ts = np.append(n_utm_ts, np.nan_to_num( ncfile_in.variables[var][:] )) |
|---|
| 593 | if ( var in dims_out and var == name_z ): |
|---|
| 594 | z_ts = np.append(z_ts, np.nan_to_num( ncfile_in.variables[var][:] )) |
|---|
| 595 | if ( var in dims_out and var == name_station_h ): |
|---|
| 596 | station_h_ts = np.append(station_h_ts, np.nan_to_num( ncfile_in.variables[var][:] )) |
|---|
| 597 | |
|---|
| 598 | if ( var not in non_measurable_vars and \ |
|---|
| 599 | var not in vars_default and \ |
|---|
| 600 | var not in measured_variables ): |
|---|
| 601 | measured_variables.append(var) |
|---|
| 602 | |
|---|
| 603 | ncfile_in.close() |
|---|
| 604 | |
|---|
| 605 | # After all files for the current site are processed, write the origin-coordinates for x,y,z |
|---|
| 606 | ncfile_out.setncattr( name_orig_x + str(counter_id), orig_x ) |
|---|
| 607 | ncfile_out.setncattr( name_orig_y + str(counter_id), orig_y ) |
|---|
| 608 | ncfile_out.setncattr( name_orig_z + str(counter_id), orig_z ) |
|---|
| 609 | # Create the dimensions |
|---|
| 610 | ncfile_out.createDimension( name_station + str(counter_id), num_coords ) |
|---|
| 611 | |
|---|
| 612 | temp_ts = ncfile_out.createVariable( name_eutm + str(counter_id), float, name_station + str(counter_id) ) |
|---|
| 613 | temp_ts[:] = e_utm_ts |
|---|
| 614 | |
|---|
| 615 | temp_ts = ncfile_out.createVariable( name_nutm + str(counter_id), float, name_station + str(counter_id) ) |
|---|
| 616 | temp_ts[:] = n_utm_ts |
|---|
| 617 | |
|---|
| 618 | temp_ts = ncfile_out.createVariable( name_z + str(counter_id), float, name_station + str(counter_id) ) |
|---|
| 619 | temp_ts[:] = z_ts |
|---|
| 620 | |
|---|
| 621 | temp_ts = ncfile_out.createVariable( name_station_h + str(counter_id), float, name_station + str(counter_id) ) |
|---|
| 622 | temp_ts[:] = station_h_ts |
|---|
| 623 | |
|---|
| 624 | # Check if any of the measured variables is a soil variable. Set flag accordingly. |
|---|
| 625 | soil = False |
|---|
| 626 | for var in measured_variables: |
|---|
| 627 | if ( var in soil_vars ): |
|---|
| 628 | soil = True |
|---|
| 629 | # Write soil flag |
|---|
| 630 | ncfile_out.setncattr( name_soil_sampling + str( counter_id), np.int8(soil) ) |
|---|
| 631 | |
|---|
| 632 | # Create dimension for sample-variable string |
|---|
| 633 | ncfile_out.createDimension( "nvar"+ str(counter_id), len( measured_variables ) ) |
|---|
| 634 | |
|---|
| 635 | measured_var = ncfile_out.createVariable( 'measured_variables' + str(counter_id), 'S1', \ |
|---|
| 636 | ("nvar" + str(counter_id), "string_len") ) # must be NC_CHAR |
|---|
| 637 | |
|---|
| 638 | # Write the variables to the file |
|---|
| 639 | for counter, meas in enumerate( measured_variables ): |
|---|
| 640 | measured_var[counter] = stringtochar( np.array( meas,"S%s"%(max_string_len) ) ) |
|---|
| 641 | |
|---|
| 642 | # Increment the counter |
|---|
| 643 | counter_id += 1 |
|---|
| 644 | |
|---|
| 645 | # Store the number of observational sites |
|---|
| 646 | num_sites = len( sites_traj ) + len( sites_ts ) + len( sites_tspr ) |
|---|
| 647 | |
|---|
| 648 | |
|---|
| [4400] | 649 | # Now process the customized input data. Please note, at the moment only timeseries are |
|---|
| 650 | # are possible. |
|---|
| 651 | if ( custom_coordinates ): |
|---|
| [4763] | 652 | num_sites = counter_id - 1 |
|---|
| [4400] | 653 | for coord in coordinates: |
|---|
| 654 | # Define mandatory attributes |
|---|
| [4763] | 655 | ncfile_out.setncattr( name_featuretype + str(counter_id), \ |
|---|
| [4400] | 656 | name_ts ) |
|---|
| [4763] | 657 | ncfile_out.setncattr( name_site + str(counter_id), \ |
|---|
| 658 | "custom" + str(counter_id - num_sites) ) |
|---|
| 659 | ncfile_out.setncattr( name_orig_x + str(counter_id), \ |
|---|
| [4400] | 660 | coord[0] ) |
|---|
| [4763] | 661 | ncfile_out.setncattr( name_orig_y + str(counter_id), \ |
|---|
| [4400] | 662 | coord[1] ) |
|---|
| [4763] | 663 | ncfile_out.setncattr( name_orig_z + str(counter_id), \ |
|---|
| [4400] | 664 | 0.0 ) |
|---|
| 665 | |
|---|
| 666 | # Define dimensions |
|---|
| 667 | ntime = 1 |
|---|
| 668 | nstat = 1 |
|---|
| [4763] | 669 | ncfile_out.createDimension( name_ntime + str(counter_id), ntime ) |
|---|
| 670 | ncfile_out.createDimension( name_station + str(counter_id), nstat ) |
|---|
| [4400] | 671 | |
|---|
| 672 | # Define coordinate variables |
|---|
| [4763] | 673 | temp_ts = ncfile_out.createVariable( name_eutm + str(counter_id), \ |
|---|
| [4400] | 674 | float, \ |
|---|
| [4763] | 675 | name_station + str(counter_id) ) |
|---|
| [4400] | 676 | temp_ts[:] = np.array( coord[0] ) |
|---|
| 677 | |
|---|
| [4763] | 678 | temp_ts = ncfile_out.createVariable( name_nutm + str(counter_id), \ |
|---|
| [4400] | 679 | float, \ |
|---|
| [4763] | 680 | name_station + str(counter_id) ) |
|---|
| [4400] | 681 | temp_ts[:] = np.array( coord[1] ) |
|---|
| 682 | |
|---|
| [4763] | 683 | temp_ts = ncfile_out.createVariable( name_z + str(counter_id), \ |
|---|
| [4400] | 684 | float, \ |
|---|
| [4763] | 685 | name_station + str(counter_id) ) |
|---|
| [4400] | 686 | temp_ts[:] = np.array( coord[2] ) |
|---|
| 687 | |
|---|
| [4763] | 688 | temp_ts = ncfile_out.createVariable( name_station_h + str(counter_id), \ |
|---|
| [4400] | 689 | float, \ |
|---|
| [4763] | 690 | name_station + str(counter_id) ) |
|---|
| [4400] | 691 | temp_ts[:] = np.array( 0.0 ) |
|---|
| 692 | |
|---|
| 693 | |
|---|
| [4763] | 694 | counter_id += 1 |
|---|
| [4400] | 695 | |
|---|
| 696 | # Reset counter variable |
|---|
| [4763] | 697 | counter_id = num_sites + 1 |
|---|
| [4400] | 698 | |
|---|
| 699 | # check if variables are prescribed. If so, prepare final output string |
|---|
| 700 | # stored in measured_variables. |
|---|
| 701 | if ( vars_to_be_measured ): |
|---|
| 702 | |
|---|
| 703 | for custom_vars in vars_to_be_measured: |
|---|
| 704 | |
|---|
| 705 | measured_variables = [] |
|---|
| 706 | for var in vars_default: |
|---|
| 707 | measured_variables.append(var) |
|---|
| 708 | |
|---|
| [4663] | 709 | # Check if given variables are already in the default variables. |
|---|
| [4400] | 710 | # If not, extend. |
|---|
| 711 | for var in custom_vars: |
|---|
| 712 | if ( var not in measured_variables ): |
|---|
| 713 | |
|---|
| 714 | measured_variables.append(var) |
|---|
| 715 | |
|---|
| [4763] | 716 | ncfile_out.createDimension( "nvar"+ str(counter_id), \ |
|---|
| [4400] | 717 | len( measured_variables ) ) |
|---|
| 718 | |
|---|
| [4763] | 719 | measured_var = ncfile_out.createVariable( 'measured_variables' + str(counter_id), 'S1', \ |
|---|
| 720 | ("nvar" + str(counter_id), "string_len") ) # must be NC_CHAR |
|---|
| [4400] | 721 | |
|---|
| 722 | # Write the variables to the file |
|---|
| 723 | for counter, meas in enumerate( measured_variables ): |
|---|
| 724 | measured_var[counter] = stringtochar( np.array( meas,"S%s"%(max_string_len) ) ) |
|---|
| 725 | |
|---|
| 726 | # Add soil attribute for the current measurement. |
|---|
| 727 | soil = False |
|---|
| 728 | if ( any( var == soil_vars for var in measured_variables) ): |
|---|
| 729 | soil = True |
|---|
| 730 | |
|---|
| 731 | # Write soil flag |
|---|
| [4763] | 732 | ncfile_out.setncattr( name_soil_sampling + str( counter_id), np.int8(soil) ) |
|---|
| [4400] | 733 | |
|---|
| 734 | # Increment counter variable |
|---|
| [4763] | 735 | counter_id += 1 |
|---|
| [4400] | 736 | |
|---|
| 737 | del ( measured_variables[:] ) |
|---|
| 738 | |
|---|
| 739 | # Add the number of customized sites. |
|---|
| [4763] | 740 | num_sites = counter_id - 1 |
|---|
| [4400] | 741 | |
|---|
| 742 | |
|---|
| 743 | # Finally, write the total number of sites to the output file |
|---|
| 744 | ncfile_out.setncattr( name_num_stat, num_sites ) |
|---|
| 745 | |
|---|
| 746 | |
|---|
| 747 | print( "*** palm_cvd has been finished. You can find the output file under: " ) |
|---|
| 748 | print( " " + output_filename ) |
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| 749 | |
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| 750 | quit() |
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