| 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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| 18 | # Copyright 1997-2020 Leibniz Universitaet Hannover |
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| 19 | #--------------------------------------------------------------------------------# |
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| 20 | # |
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| 21 | # Current revisions: |
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| 22 | # ----------------- |
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| 23 | # |
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| 24 | # |
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| 25 | # Former revisions: |
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| 26 | # ----------------- |
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| 27 | # $Id$ |
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| 28 | # In order to do not omit observations that are on the same site but have different |
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| 29 | # coordinates or feature-types, process all files rather than only one and omit |
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| 30 | # the rest. |
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| 31 | # |
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| 32 | # 4663 2020-09-02 14:54:09Z gronemeier |
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| 33 | # bugfix in non_measurable_vars; ignore station_h if featureType is trajectory |
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| 34 | # |
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| 35 | # 4400 2020-02-10 20:32:41Z suehring |
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| 36 | # Initial revision |
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| 37 | # |
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| 38 | # Description: |
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| 39 | # ------------ |
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| 40 | # Processing tool for creating PIDS conform virtual measurement setup file |
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| 41 | # from UC2 data-standard conform observational data or from prescribed input |
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| 42 | # coordinates. |
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| 43 | # |
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| 44 | # @Authors Matthias Suehring (suehring@muk.uni-hannover.de) |
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| 45 | # Tobias Gronemeier (gronemeier@muk.uni-hannover.de) |
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| 46 | # |
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| 47 | # @todo Add further feature tpyes for customized observations. At the moment only |
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| 48 | # timeSeries is possible. |
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| 49 | #--------------------------------------------------------------------------------# |
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| 50 | |
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| 51 | |
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| 52 | import netCDF4 |
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| 53 | from netCDF4 import Dataset, stringtochar |
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| 54 | import os |
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| 55 | import numpy as np |
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| 56 | |
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| 57 | |
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| 58 | # Function to read the config file |
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| 59 | def read_config_file(): |
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| 60 | |
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| 61 | import configparser |
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| 62 | import os |
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| 63 | import sys |
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| 64 | import json |
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| 65 | |
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| 66 | # Definition of global configuration parameters |
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| 67 | global global_acronym |
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| 68 | global global_author |
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| 69 | global global_campaign |
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| 70 | global global_comment |
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| 71 | global global_contact |
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| 72 | global global_data_content |
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| 73 | global global_dependencies |
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| 74 | global global_institution |
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| 75 | global global_keywords |
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| 76 | global global_location |
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| 77 | global global_references |
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| 78 | global global_site |
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| 79 | global global_source |
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| 80 | global global_palm_version |
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| 81 | global data_path |
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| 82 | global output_path |
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| 83 | global output_filename |
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| 84 | global number_positions |
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| 85 | global input_from_observations |
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| 86 | global coordinates |
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| 87 | global vars_to_be_measured |
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| 88 | global custom_coordinates |
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| 89 | |
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| 90 | global_acronym = " " |
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| 91 | global_author = " " |
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| 92 | global_campaign = " " |
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| 93 | global_comment = " " |
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| 94 | global_contact = " " |
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| 95 | global_data_content = " " |
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| 96 | global_dependencies = " " |
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| 97 | global_institution = " " |
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| 98 | global_keywords = " " |
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| 99 | global_location = " " |
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| 100 | global_references = " " |
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| 101 | global_site = " " |
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| 102 | global_source = " " |
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| 103 | global_palm_version = 6.0 |
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| 104 | data_path = " " |
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| 105 | output_path = " " |
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| 106 | output_filename = "none" |
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| 107 | number_positions = -999 |
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| 108 | input_from_observations = False |
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| 109 | coordinates = [] |
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| 110 | vars_to_be_measured = [] |
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| 111 | custom_coordinates = False |
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| 112 | |
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| 113 | # Check if configuration files exists and quit otherwise |
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| 114 | input_config = ".cvd.config.default" |
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| 115 | for i in range(1,len(sys.argv)): |
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| 116 | input_config = str(sys.argv[i]) |
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| 117 | |
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| 118 | # Allow empty settings |
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| 119 | config = configparser.RawConfigParser(allow_no_value=True) |
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| 120 | |
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| 121 | # Check if a config file exists. |
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| 122 | if ( os.path.isfile(input_config) == False ): |
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| 123 | print ("Error. No configuration file " + input_config + " found.") |
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| 124 | quit() |
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| 125 | |
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| 126 | config.read(input_config) |
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| 127 | |
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| 128 | for section in range( 0, len( config.sections() ) ): |
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| 129 | |
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| 130 | current_section = config.sections()[section] |
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| 131 | |
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| 132 | # read global attributes which are written into the output file header |
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| 133 | if ( current_section == 'global' ): |
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| 134 | |
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| 135 | global_acronym = config.get( current_section, 'acronym' ) |
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| 136 | global_author = config.get( current_section, 'author' ) |
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| 137 | global_campaign = config.get( current_section, 'campaign' ) |
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| 138 | global_comment = config.get( current_section, 'comment' ) |
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| 139 | global_contact = config.get( current_section, 'contact_person' ) |
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| 140 | global_data_content = config.get( current_section, 'data_content' ) |
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| 141 | global_dependencies = config.get( current_section, 'dependencies' ) |
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| 142 | global_institution = config.get( current_section, 'institution' ) |
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| 143 | global_keywords = config.get( current_section, 'keywords' ) |
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| 144 | global_location = config.get( current_section, 'location' ) |
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| 145 | global_references = config.get( current_section, 'references' ) |
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| 146 | global_site = config.get( current_section, 'site' ) |
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| 147 | global_source = config.get( current_section, 'source' ) |
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| 148 | global_palm_version = float( config.get( current_section, 'palm_version' ) ) |
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| 149 | |
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| 150 | # Read data input path for observational data |
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| 151 | elif ( current_section == 'input' ): |
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| 152 | |
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| 153 | data_path = config.get( current_section, 'data_path' ) |
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| 154 | input_from_observations = True |
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| 155 | |
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| 156 | # Read output path and filename for the VM driver |
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| 157 | elif ( current_section == 'output' ): |
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| 158 | |
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| 159 | output_path = config.get( current_section, 'output_path' ) |
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| 160 | output_filename = config.get( current_section, 'output_filename' ) |
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| 161 | |
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| 162 | # Read customized coordinates where virtual measurements shall be taken, |
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| 163 | # as well as the variables that should be sampled. |
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| 164 | elif ( current_section == 'custom_positions' ): |
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| 165 | |
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| 166 | number_positions = config.get( current_section, 'number_positions' ) |
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| 167 | |
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| 168 | for count in range( 0, int( number_positions ) ): |
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| 169 | coordinates.append( json.loads( config.get( current_section, \ |
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| 170 | "coordinates" + str( count + 1 ) ) ) ) |
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| 171 | # If coordinates are given, set a global flag. |
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| 172 | custom_coordinates = True |
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| 173 | |
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| 174 | for count in range( 0, int( number_positions ) ): |
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| 175 | vars_to_be_measured.append( json.loads( config.get( current_section, \ |
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| 176 | "vars_to_be_measured" + str( count + 1 ) ) ) ) |
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| 177 | |
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| 178 | |
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| 179 | return 0 |
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| 180 | |
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| 181 | #++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ |
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| 182 | # Main program: |
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| 183 | #++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ |
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| 184 | |
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| 185 | |
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| 186 | # Define strings |
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| 187 | name_featuretype = "featureType" |
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| 188 | name_ts = "timeSeries" |
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| 189 | name_traj = "trajectory" |
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| 190 | name_ntime = "ntime" |
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| 191 | name_time = "time" |
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| 192 | name_station = "station" |
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| 193 | name_traj_dim = "traj" |
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| 194 | name_nz = "nz" |
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| 195 | name_datacontent = "data_content" |
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| 196 | name_eutm = "E_UTM" |
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| 197 | name_nutm = "N_UTM" |
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| 198 | name_hao = "height_above_origin" |
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| 199 | name_station_h = "station_h" |
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| 200 | name_z = "z" |
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| 201 | name_soil_sampling = "soil_sample" |
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| 202 | name_num_stat = "number_of_stations" |
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| 203 | name_fill = "_FillValue" |
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| 204 | name_site = "site" |
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| 205 | name_acro = "acronym" |
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| 206 | name_content = "data_content" |
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| 207 | name_orig_x = "origin_x" |
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| 208 | name_orig_y = "origin_y" |
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| 209 | name_orig_z = "origin_z" |
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| 210 | |
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| 211 | max_string_len = 50 |
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| 212 | |
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| 213 | name_measvars = "measured_variables" |
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| 214 | |
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| 215 | non_measurable_vars = ['station_name', 'time', 'time_bounds', 'crs', \ |
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| 216 | 'vrs', 'x', 'y', 'z', 'lon', 'lat', 'ntime', 'station', 'traj', \ |
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| 217 | 'E_UTM', 'N_UTM', 'height_above_origin', 'station_h', \ |
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| 218 | 'traj_name', 'height', 'band_pm_size', 'bands_pm', 'bands_pm_size_bounds', \ |
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| 219 | 'bands_pm_size', 'ancillary_detected_layer' ] |
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| 220 | |
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| 221 | soil_vars = [ 't_soil', 'm_soil', 'lwc', 'lwcs', 'smp' ] |
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| 222 | |
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| 223 | dims_out = [ name_eutm, name_nutm, name_hao, name_z, name_station_h ] |
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| 224 | |
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| 225 | # Define list of attributes which need to be of type float. In the data set this is not |
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| 226 | # necessarily guranteed. |
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| 227 | atts_float = [ 'origin_x', 'origin_y', 'origin_z', 'origin_lon', 'origin_lat', 'rotation_angle' ] |
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| 228 | |
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| 229 | # Define list of default variables that shall be measured at each site |
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| 230 | vars_default = [ 'u', 'v', 'w', 'theta', 'hus' ] |
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| 231 | |
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| 232 | |
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| 233 | #Read config file |
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| 234 | read_config_file() |
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| 235 | |
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| 236 | # Initialize counter variable for the number of sites |
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| 237 | num_sites = 0 |
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| 238 | |
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| 239 | # Set the output path for the data |
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| 240 | output_filename = output_path + output_filename |
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| 241 | |
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| 242 | # Open output file |
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| 243 | ncfile_out = Dataset( output_filename, "w", format="NETCDF4" ) |
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| 244 | |
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| 245 | # First, add global attributes |
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| 246 | ncfile_out.setncattr( 'acronym', global_acronym ) |
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| 247 | ncfile_out.setncattr( 'author', global_author ) |
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| 248 | ncfile_out.setncattr( 'campaign', global_campaign ) |
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| 249 | ncfile_out.setncattr( 'comment', global_comment ) |
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| 250 | ncfile_out.setncattr( 'contact_person', global_contact ) |
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| 251 | ncfile_out.setncattr( 'data_content', global_data_content ) |
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| 252 | ncfile_out.setncattr( 'dependencies', global_dependencies ) |
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| 253 | ncfile_out.setncattr( 'institution', global_institution ) |
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| 254 | ncfile_out.setncattr( 'keywords', global_keywords ) |
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| 255 | ncfile_out.setncattr( 'location', global_location ) |
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| 256 | ncfile_out.setncattr( 'references', global_references ) |
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| 257 | ncfile_out.setncattr( 'site', global_site ) |
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| 258 | ncfile_out.setncattr( 'source', global_source ) |
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| 259 | ncfile_out.setncattr( 'palm_version', global_palm_version ) |
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| 260 | |
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| 261 | # Create universal dimension for the string length. |
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| 262 | ncfile_out.createDimension("string_len", max_string_len) |
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| 263 | |
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| 264 | |
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| 265 | # Check if observational data is available. This case, |
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| 266 | # obtain an alphabetically sorted list of input data. List is sorted |
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| 267 | # just for the sake of clarity in the resulting setup file. |
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| 268 | if ( input_from_observations == True ): |
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| 269 | list_input_data = sorted( os.listdir( data_path ) ) |
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| 270 | |
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| 271 | if ( input_from_observations ): |
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| 272 | |
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| 273 | # Run loop over all subdirectories, detect the files and extract a list of sites. |
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| 274 | # This is done to reduce the number of virtual measurements in the model. Each |
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| 275 | # virtual measurement has an overhead and consumes memory. |
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| 276 | sites = [] |
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| 277 | input_files = [] |
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| 278 | for dirname in list_input_data: |
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| 279 | data_file = data_path + dirname |
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| 280 | |
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| 281 | # Directory may contain various file versions. |
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| 282 | # Take the one with highest cycle number. |
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| 283 | highest_cycle_nr = 0 |
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| 284 | for filename in os.listdir(data_file): |
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| 285 | start_seq = len( filename ) - 6 |
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| 286 | end_seq = len( filename ) - 3 |
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| 287 | if int( filename[start_seq:end_seq] ) > highest_cycle_nr: |
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| 288 | highest_cycle_nr = int(filename[start_seq:end_seq]) |
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| 289 | latest_file = filename |
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| 290 | |
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| 291 | # Open the NetCDF file |
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| 292 | input_file = data_file + "/" + latest_file |
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| 293 | ncfile_in = Dataset( input_file, "r", format="NETCDF4", encoding='ascii') |
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| 294 | input_files.append(input_file) |
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| 295 | |
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| 296 | # Define a nested list of default variables that shall be measured. Based on this list, |
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| 297 | # the final number of measured variables is determined. |
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| 298 | measured_variables_all_sites = [ ['u', 'v', 'w', 'theta', 'hus'] for var in range(0, len(input_files))] |
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| 299 | |
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| 300 | # Run loop over all subdirectories that contain observational data |
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| 301 | for counter, file in enumerate(input_files): |
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| 302 | |
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| 303 | # Open the NetCDF input file |
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| 304 | input_file = input_files[counter] |
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| 305 | ncfile_in = Dataset( input_file, "r", format="NETCDF4", encoding='ascii' ) |
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| 306 | |
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| 307 | # Determine index for the treated site |
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| 308 | num_vmeas = input_files.index( input_file ) + 1 |
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| 309 | print( counter, num_vmeas ) |
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| 310 | |
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| 311 | # Read global attributes and write them immediately into the output file |
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| 312 | for att in ncfile_in.ncattrs(): |
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| 313 | if ( att == name_featuretype ): |
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| 314 | feature = ncfile_in.getncattr(att) |
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| 315 | if ( att == name_datacontent ): |
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| 316 | content = ncfile_in.getncattr(att) |
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| 317 | if ( att == name_site ): |
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| 318 | site = ncfile_in.getncattr(att) |
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| 319 | |
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| 320 | if ( att in atts_float ): |
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| 321 | ncfile_out.setncattr( att + str(num_vmeas), np.double(ncfile_in.getncattr(att)) ) |
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| 322 | else: |
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| 323 | ncfile_out.setncattr( att + str(num_vmeas), ncfile_in.getncattr(att) ) |
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| 324 | |
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| 325 | #timeSeries |
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| 326 | if ( feature == name_ts ): |
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| 327 | ntime = len( ncfile_in.dimensions[name_ntime] ) |
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| 328 | nstat = len( ncfile_in.dimensions[name_station] ) |
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| 329 | ncfile_out.createDimension( name_ntime + str(num_vmeas), ntime ) |
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| 330 | ncfile_out.createDimension( name_station + str(num_vmeas), nstat ) |
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| 331 | |
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| 332 | #trajectory |
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| 333 | elif ( feature == name_traj ): |
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| 334 | ntime = len( ncfile_in.dimensions[name_ntime] ) |
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| 335 | ntraj = len( ncfile_in.dimensions[name_traj_dim] ) |
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| 336 | ncfile_out.createDimension( name_ntime + str(num_vmeas), ntime ) |
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| 337 | ncfile_out.createDimension( name_traj_dim + str(num_vmeas), ntraj ) |
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| 338 | |
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| 339 | #timeseriesProfile |
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| 340 | else: |
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| 341 | ntime = len( ncfile_in.dimensions[name_ntime] ) |
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| 342 | nstat = len( ncfile_in.dimensions[name_station] ) |
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| 343 | nz = len( ncfile_in.dimensions[name_nz] ) |
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| 344 | ncfile_out.createDimension( name_ntime + str(num_vmeas), ntime ) |
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| 345 | ncfile_out.createDimension( name_station + str(num_vmeas), nstat ) |
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| 346 | ncfile_out.createDimension( name_nz + str(num_vmeas), nz ) |
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| 347 | |
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| 348 | for var in ncfile_in.variables.keys(): |
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| 349 | if ( var in dims_out ): |
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| 350 | # Create a variable and write it to file after it is read. In order to |
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| 351 | # avoid fill values in the dimensions, these are converted to zero |
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| 352 | # before written to file. Depending on the featureType of the measurement, |
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| 353 | # the array shape is different. For more informations, please see |
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| 354 | # [UC]2 data standard. |
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| 355 | # Timeseries |
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| 356 | if ( feature == name_ts ): |
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| 357 | temp_ts = ncfile_out.createVariable( var + str(num_vmeas), float, \ |
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| 358 | name_station + str(num_vmeas)) |
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| 359 | temp_ts[:] = np.nan_to_num( ncfile_in.variables[var][:] ) |
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| 360 | |
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| 361 | # Trajectories |
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| 362 | elif ( feature == name_traj ): |
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| 363 | # @note: If there are files where station_h is present although featureType is |
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| 364 | # trajectory, station_h must not be read. |
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| 365 | if var != name_station_h: |
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| 366 | temp_traj = ncfile_out.createVariable( var + str(num_vmeas), float, \ |
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| 367 | ( name_traj_dim + str(num_vmeas), \ |
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| 368 | name_ntime + str(num_vmeas) ) ) |
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| 369 | temp_traj[:,:] = np.nan_to_num( ncfile_in.variables[var][:,:] ) |
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| 370 | |
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| 371 | # TimeseriesProfiles |
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| 372 | else: |
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| 373 | if ( var == 'z' ): |
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| 374 | temp_pr = ncfile_out.createVariable( var + str(num_vmeas), float, \ |
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| 375 | ( name_station + str(num_vmeas), \ |
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| 376 | name_nz + str(num_vmeas) ) ) |
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| 377 | temp_pr[:] = np.nan_to_num( ncfile_in.variables[var][:,0,:] ) |
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| 378 | else: |
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| 379 | temp_pr = ncfile_out.createVariable( var + str(num_vmeas), float, \ |
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| 380 | name_station + str(num_vmeas)) |
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| 381 | temp_pr[:] = np.nan_to_num( ncfile_in.variables[var][:] ) |
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| 382 | |
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| 383 | # Search for variables to be measured. In case the variable isn't already defined, |
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| 384 | # append the variable to the list. |
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| 385 | for var in ncfile_in.variables.keys(): |
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| 386 | if ( var not in non_measurable_vars and \ |
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| 387 | var not in vars_default and \ |
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| 388 | var not in measured_variables_all_sites[input_files.index( input_file )] ): |
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| 389 | |
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| 390 | measured_variables_all_sites[input_files.index( input_file )].append(var) |
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| 391 | |
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| 392 | # Close the NetCDF input file |
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| 393 | ncfile_in.close() |
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| 394 | |
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| 395 | # After variables are gathered and dimensions / attributes are already written to file, |
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| 396 | # the list of measured variables is written to file. |
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| 397 | for site in input_files: |
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| 398 | |
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| 399 | num_vmeas = input_files.index( site ) + 1 |
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| 400 | |
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| 401 | ncfile_out.createDimension( "nvar"+ str(num_vmeas), \ |
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| 402 | len( measured_variables_all_sites[input_files.index( site )] ) ) |
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| 403 | |
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| 404 | measured = ncfile_out.createVariable( 'measured_variables' + str(num_vmeas), 'S1', \ |
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| 405 | ("nvar" + str(num_vmeas), "string_len")) # must be NC_CHAR |
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| 406 | |
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| 407 | for counter, meas in enumerate( measured_variables_all_sites[input_files.index( site )] ): |
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| 408 | measured[counter] = stringtochar( np.array( meas,"S%s"%(max_string_len) ) ) |
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| 409 | |
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| 410 | # Check if any of the measured variables is a soil variable. Set flag accordingly. |
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| 411 | soil = False |
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| 412 | for var in measured_variables_all_sites[input_files.index( site )]: |
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| 413 | if ( var in soil_vars ): |
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| 414 | soil = True |
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| 415 | |
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| 416 | # Write soil flag |
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| 417 | ncfile_out.setncattr( name_soil_sampling + str( num_vmeas), np.int8(soil) ) |
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| 418 | |
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| 419 | |
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| 420 | |
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| 421 | # Store the number of observational sites |
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| 422 | num_sites += len( input_files ) |
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| 423 | |
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| 424 | |
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| 425 | # Now process the customized input data. Please note, at the moment only timeseries are |
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| 426 | # are possible. |
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| 427 | if ( custom_coordinates ): |
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| 428 | |
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| 429 | count_site = num_sites + 1 |
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| 430 | for coord in coordinates: |
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| 431 | # Define mandatory attributes |
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| 432 | ncfile_out.setncattr( name_featuretype + str(count_site), \ |
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| 433 | name_ts ) |
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| 434 | ncfile_out.setncattr( name_site + str(count_site), \ |
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| 435 | "custom" + str(count_site - num_sites) ) |
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| 436 | ncfile_out.setncattr( name_orig_x + str(count_site), \ |
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| 437 | coord[0] ) |
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| 438 | ncfile_out.setncattr( name_orig_y + str(count_site), \ |
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| 439 | coord[1] ) |
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| 440 | ncfile_out.setncattr( name_orig_z + str(count_site), \ |
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| 441 | 0.0 ) |
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| 442 | |
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| 443 | # Define dimensions |
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| 444 | ntime = 1 |
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| 445 | nstat = 1 |
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| 446 | ncfile_out.createDimension( name_ntime + str(count_site), ntime ) |
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| 447 | ncfile_out.createDimension( name_station + str(count_site), nstat ) |
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| 448 | |
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| 449 | # Define coordinate variables |
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| 450 | temp_ts = ncfile_out.createVariable( name_eutm + str(count_site), \ |
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| 451 | float, \ |
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| 452 | name_station + str(count_site) ) |
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| 453 | temp_ts[:] = np.array( coord[0] ) |
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| 454 | |
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| 455 | temp_ts = ncfile_out.createVariable( name_nutm + str(count_site), \ |
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| 456 | float, \ |
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| 457 | name_station + str(count_site) ) |
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| 458 | temp_ts[:] = np.array( coord[1] ) |
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| 459 | |
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| 460 | temp_ts = ncfile_out.createVariable( name_z + str(count_site), \ |
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| 461 | float, \ |
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| 462 | name_station + str(count_site) ) |
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| 463 | temp_ts[:] = np.array( coord[2] ) |
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| 464 | |
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| 465 | temp_ts = ncfile_out.createVariable( name_station_h + str(count_site), \ |
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| 466 | float, \ |
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| 467 | name_station + str(count_site) ) |
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| 468 | temp_ts[:] = np.array( 0.0 ) |
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| 469 | |
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| 470 | |
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| 471 | count_site += 1 |
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| 472 | |
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| 473 | # Reset counter variable |
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| 474 | count_site = num_sites + 1 |
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| 475 | |
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| 476 | # check if variables are prescribed. If so, prepare final output string |
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| 477 | # stored in measured_variables. |
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| 478 | if ( vars_to_be_measured ): |
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| 479 | |
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| 480 | for custom_vars in vars_to_be_measured: |
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| 481 | |
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| 482 | measured_variables = [] |
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| 483 | for var in vars_default: |
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| 484 | measured_variables.append(var) |
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| 485 | |
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| 486 | # Check if given variables are already in the default variables. |
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| 487 | # If not, extend. |
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| 488 | for var in custom_vars: |
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| 489 | if ( var not in measured_variables ): |
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| 490 | |
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| 491 | measured_variables.append(var) |
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| 492 | |
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| 493 | ncfile_out.createDimension( "nvar"+ str(count_site), \ |
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| 494 | len( measured_variables ) ) |
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| 495 | |
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| 496 | measured_var = ncfile_out.createVariable( 'measured_variables' + str(count_site), 'S1', \ |
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| 497 | ("nvar" + str(count_site), "string_len") ) # must be NC_CHAR |
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| 498 | |
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| 499 | # Write the variables to the file |
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| 500 | for counter, meas in enumerate( measured_variables ): |
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| 501 | measured_var[counter] = stringtochar( np.array( meas,"S%s"%(max_string_len) ) ) |
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| 502 | |
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| 503 | # Add soil attribute for the current measurement. |
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| 504 | soil = False |
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| 505 | if ( any( var == soil_vars for var in measured_variables) ): |
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| 506 | soil = True |
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| 507 | |
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| 508 | # Write soil flag |
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| 509 | ncfile_out.setncattr( name_soil_sampling + str( count_site), np.int8(soil) ) |
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| 510 | |
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| 511 | # Increment counter variable |
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| 512 | count_site += 1 |
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| 513 | |
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| 514 | del ( measured_variables[:] ) |
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| 515 | |
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| 516 | # Add the number of customized sites. |
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| 517 | num_sites += int( number_positions ) |
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| 518 | |
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| 519 | |
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| 520 | # Finally, write the total number of sites to the output file |
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| 521 | ncfile_out.setncattr( name_num_stat, num_sites ) |
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| 522 | |
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| 523 | |
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| 524 | print( "*** palm_cvd has been finished. You can find the output file under: " ) |
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| 525 | print( " " + output_filename ) |
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| 526 | |
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| 527 | quit() |
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