1 | """datamanager.py - input output for AnuGA |
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2 | |
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3 | |
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4 | This module takes care of reading and writing datafiles such as topograhies, |
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5 | model output, etc |
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6 | |
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7 | |
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8 | Formats used within AnuGA: |
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9 | |
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10 | .sww: Netcdf format for storing model output f(t,x,y) |
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11 | .tms: Netcdf format for storing time series f(t) |
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12 | |
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13 | .csv: ASCII format for storing arbitrary points and associated attributes |
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14 | .pts: NetCDF format for storing arbitrary points and associated attributes |
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15 | |
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16 | .asc: ASCII format of regular DEMs as output from ArcView |
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17 | .prj: Associated ArcView file giving more meta data for asc format |
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18 | .ers: ERMapper header format of regular DEMs for ArcView |
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19 | |
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20 | .dem: NetCDF representation of regular DEM data |
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21 | |
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22 | .tsh: ASCII format for storing meshes and associated boundary and region info |
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23 | .msh: NetCDF format for storing meshes and associated boundary and region info |
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24 | |
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25 | .nc: Native ferret NetCDF format |
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26 | .geo: Houdinis ascii geometry format (?) |
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27 | |
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28 | |
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29 | A typical dataflow can be described as follows |
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30 | |
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31 | Manually created files: |
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32 | ASC, PRJ: Digital elevation models (gridded) |
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33 | TSH: Triangular meshes (e.g. created from anuga.pmesh) |
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34 | NC Model outputs for use as boundary conditions (e.g from MOST) |
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35 | |
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36 | |
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37 | AUTOMATICALLY CREATED FILES: |
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38 | |
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39 | ASC, PRJ -> DEM -> PTS: Conversion of DEM's to native pts file |
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40 | |
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41 | NC -> SWW: Conversion of MOST bundary files to boundary sww |
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42 | |
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43 | PTS + TSH -> TSH with elevation: Least squares fit |
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44 | |
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45 | TSH -> SWW: Conversion of TSH to sww viewable using Swollen |
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46 | |
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47 | TSH + Boundary SWW -> SWW: Simluation using abstract_2d_finite_volumes |
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48 | |
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49 | """ |
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50 | |
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51 | # This file was reverted from changeset:5484 to changeset:5470 on 10th July |
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52 | # by Ole. |
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53 | |
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54 | import os, sys |
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55 | import csv |
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56 | import exceptions |
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57 | import string |
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58 | import shutil |
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59 | from struct import unpack |
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60 | import array as p_array |
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61 | from os import sep, path, remove, mkdir, access, F_OK, W_OK, getcwd |
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62 | |
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63 | import numpy as num |
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64 | |
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65 | from Scientific.IO.NetCDF import NetCDFFile |
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66 | from os.path import exists, basename, join |
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67 | from os import getcwd |
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68 | |
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69 | from anuga.coordinate_transforms.redfearn import redfearn, \ |
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70 | convert_from_latlon_to_utm |
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71 | from anuga.coordinate_transforms.geo_reference import Geo_reference, \ |
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72 | write_NetCDF_georeference, ensure_geo_reference |
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73 | from anuga.geospatial_data.geospatial_data import Geospatial_data,\ |
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74 | ensure_absolute |
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75 | from anuga.config import minimum_storable_height as \ |
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76 | default_minimum_storable_height |
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77 | from anuga.config import netcdf_mode_r, netcdf_mode_w, netcdf_mode_a |
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78 | from anuga.config import netcdf_float, netcdf_float32, netcdf_int |
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79 | from anuga.config import max_float |
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80 | from anuga.utilities.numerical_tools import ensure_numeric, mean |
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81 | from anuga.caching.caching import myhash |
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82 | from anuga.utilities.anuga_exceptions import ANUGAError |
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83 | from anuga.shallow_water import Domain |
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84 | from anuga.abstract_2d_finite_volumes.pmesh2domain import \ |
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85 | pmesh_to_domain_instance |
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86 | from anuga.abstract_2d_finite_volumes.util import get_revision_number, \ |
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87 | remove_lone_verts, sww2timeseries, get_centroid_values |
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88 | |
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89 | from anuga.abstract_2d_finite_volumes.neighbour_mesh import segment_midpoints |
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90 | from anuga.load_mesh.loadASCII import export_mesh_file |
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91 | from anuga.utilities.polygon import intersection |
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92 | |
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93 | from anuga.utilities.system_tools import get_vars_in_expression |
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94 | |
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95 | |
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96 | # Default block size for sww2dem() |
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97 | DEFAULT_BLOCK_SIZE = 10000 |
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98 | |
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99 | ###### |
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100 | # Exception classes |
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101 | ###### |
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102 | |
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103 | class TitleValueError(exceptions.Exception): pass |
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104 | class DataMissingValuesError(exceptions.Exception): pass |
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105 | class DataFileNotOpenError(exceptions.Exception): pass |
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106 | class DataTimeError(exceptions.Exception): pass |
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107 | class DataDomainError(exceptions.Exception): pass |
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108 | class NewQuantity(exceptions.Exception): pass |
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109 | |
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110 | |
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111 | ###### |
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112 | # formula mappings |
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113 | ###### |
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114 | |
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115 | quantity_formula = {'momentum':'(xmomentum**2 + ymomentum**2)**0.5', |
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116 | 'depth':'stage-elevation', |
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117 | 'speed': \ |
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118 | '(xmomentum**2 + ymomentum**2)**0.5/(stage-elevation+1.e-6/(stage-elevation))'} |
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119 | |
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120 | |
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121 | ## |
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122 | # @brief Convert a possible filename into a standard form. |
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123 | # @param s Filename to process. |
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124 | # @return The new filename string. |
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125 | def make_filename(s): |
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126 | """Transform argument string into a Sexsuitable filename |
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127 | """ |
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128 | |
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129 | s = s.strip() |
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130 | s = s.replace(' ', '_') |
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131 | s = s.replace('(', '') |
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132 | s = s.replace(')', '') |
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133 | s = s.replace('__', '_') |
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134 | |
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135 | return s |
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136 | |
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137 | |
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138 | ## |
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139 | # @brief Check that a specified filesystem directory path exists. |
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140 | # @param path The dirstory path to check. |
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141 | # @param verbose True if this function is to be verbose. |
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142 | # @note If directory path doesn't exist, it will be created. |
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143 | def check_dir(path, verbose=None): |
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144 | """Check that specified path exists. |
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145 | If path does not exist it will be created if possible |
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146 | |
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147 | USAGE: |
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148 | checkdir(path, verbose): |
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149 | |
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150 | ARGUMENTS: |
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151 | path -- Directory |
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152 | verbose -- Flag verbose output (default: None) |
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153 | |
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154 | RETURN VALUE: |
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155 | Verified path including trailing separator |
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156 | """ |
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157 | |
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158 | import os.path |
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159 | |
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160 | if sys.platform in ['nt', 'dos', 'win32', 'what else?']: |
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161 | unix = 0 |
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162 | else: |
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163 | unix = 1 |
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164 | |
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165 | # add terminal separator, if it's not already there |
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166 | if path[-1] != os.sep: |
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167 | path = path + os.sep |
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168 | |
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169 | # expand ~ or ~username in path |
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170 | path = os.path.expanduser(path) |
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171 | |
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172 | # create directory if required |
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173 | if not (os.access(path, os.R_OK and os.W_OK) or path == ''): |
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174 | try: |
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175 | exitcode = os.mkdir(path) |
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176 | |
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177 | # Change access rights if possible |
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178 | if unix: |
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179 | exitcode = os.system('chmod 775 ' + path) |
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180 | else: |
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181 | pass # FIXME: What about access rights under Windows? |
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182 | |
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183 | if verbose: print 'MESSAGE: Directory', path, 'created.' |
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184 | except: |
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185 | print 'WARNING: Directory', path, 'could not be created.' |
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186 | if unix: |
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187 | path = '/tmp/' |
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188 | else: |
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189 | path = 'C:' + os.sep |
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190 | |
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191 | print "Using directory '%s' instead" % path |
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192 | |
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193 | return path |
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194 | |
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195 | |
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196 | ## |
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197 | # @brief Delete directory and all sub-directories. |
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198 | # @param path Path to the directory to delete. |
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199 | def del_dir(path): |
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200 | """Recursively delete directory path and all its contents |
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201 | """ |
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202 | |
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203 | if os.path.isdir(path): |
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204 | for file in os.listdir(path): |
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205 | X = os.path.join(path, file) |
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206 | |
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207 | if os.path.isdir(X) and not os.path.islink(X): |
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208 | del_dir(X) |
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209 | else: |
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210 | try: |
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211 | os.remove(X) |
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212 | except: |
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213 | print "Could not remove file %s" % X |
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214 | |
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215 | os.rmdir(path) |
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216 | |
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217 | |
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218 | ## |
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219 | # @brief ?? |
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220 | # @param path |
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221 | # @param __func__ |
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222 | # @param verbose True if this function is to be verbose. |
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223 | # @note ANOTHER OPTION, IF NEED IN THE FUTURE, Nick B 7/2007 |
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224 | def rmgeneric(path, func, verbose=False): |
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225 | ERROR_STR= """Error removing %(path)s, %(error)s """ |
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226 | |
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227 | try: |
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228 | func(path) |
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229 | if verbose: print 'Removed ', path |
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230 | except OSError, (errno, strerror): |
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231 | print ERROR_STR % {'path' : path, 'error': strerror } |
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232 | |
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233 | |
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234 | ## |
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235 | # @brief Remove directory and all sub-directories. |
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236 | # @param path Filesystem path to directory to remove. |
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237 | # @param verbose True if this function is to be verbose. |
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238 | def removeall(path, verbose=False): |
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239 | if not os.path.isdir(path): |
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240 | return |
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241 | |
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242 | for x in os.listdir(path): |
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243 | fullpath = os.path.join(path, x) |
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244 | if os.path.isfile(fullpath): |
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245 | f = os.remove |
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246 | rmgeneric(fullpath, f) |
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247 | elif os.path.isdir(fullpath): |
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248 | removeall(fullpath) |
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249 | f = os.rmdir |
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250 | rmgeneric(fullpath, f, verbose) |
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251 | |
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252 | |
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253 | ## |
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254 | # @brief Create a standard filename. |
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255 | # @param datadir Directory where file is to be created. |
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256 | # @param filename Filename 'stem'. |
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257 | # @param format Format of the file, becomes filename extension. |
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258 | # @param size Size of file, becomes part of filename. |
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259 | # @param time Time (float), becomes part of filename. |
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260 | # @return The complete filename path, including directory. |
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261 | # @note The containing directory is created, if necessary. |
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262 | def create_filename(datadir, filename, format, size=None, time=None): |
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263 | FN = check_dir(datadir) + filename |
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264 | |
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265 | if size is not None: |
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266 | FN += '_size%d' % size |
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267 | |
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268 | if time is not None: |
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269 | FN += '_time%.2f' % time |
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270 | |
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271 | FN += '.' + format |
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272 | |
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273 | return FN |
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274 | |
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275 | |
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276 | ## |
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277 | # @brief Get all files with a standard name and a given set of attributes. |
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278 | # @param datadir Directory files must be in. |
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279 | # @param filename Filename stem. |
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280 | # @param format Filename extension. |
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281 | # @param size Filename size. |
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282 | # @return A list of fielnames (including directory) that match the attributes. |
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283 | def get_files(datadir, filename, format, size): |
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284 | """Get all file (names) with given name, size and format |
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285 | """ |
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286 | |
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287 | import glob |
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288 | |
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289 | dir = check_dir(datadir) |
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290 | pattern = dir + os.sep + filename + '_size=%d*.%s' % (size, format) |
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291 | |
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292 | return glob.glob(pattern) |
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293 | |
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294 | |
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295 | ## |
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296 | # @brief Generic class for storing output to e.g. visualisation or checkpointing |
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297 | class Data_format: |
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298 | """Generic interface to data formats |
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299 | """ |
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300 | |
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301 | ## |
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302 | # @brief Instantiate this instance. |
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303 | # @param domain |
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304 | # @param extension |
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305 | # @param mode The mode of the underlying file. |
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306 | def __init__(self, domain, extension, mode=netcdf_mode_w): |
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307 | assert mode[0] in ['r', 'w', 'a'], \ |
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308 | "Mode %s must be either:\n" % mode + \ |
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309 | " 'w' (write)\n" + \ |
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310 | " 'r' (read)\n" + \ |
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311 | " 'a' (append)" |
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312 | |
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313 | #Create filename |
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314 | self.filename = create_filename(domain.get_datadir(), |
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315 | domain.get_name(), extension) |
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316 | |
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317 | self.timestep = 0 |
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318 | self.domain = domain |
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319 | |
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320 | # Exclude ghosts in case this is a parallel domain |
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321 | self.number_of_nodes = domain.number_of_full_nodes |
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322 | self.number_of_volumes = domain.number_of_full_triangles |
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323 | #self.number_of_volumes = len(domain) |
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324 | |
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325 | #FIXME: Should we have a general set_precision function? |
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326 | |
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327 | |
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328 | ## |
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329 | # @brief Class for storing output to e.g. visualisation |
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330 | class Data_format_sww(Data_format): |
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331 | """Interface to native NetCDF format (.sww) for storing model output |
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332 | |
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333 | There are two kinds of data |
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334 | |
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335 | 1: Constant data: Vertex coordinates and field values. Stored once |
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336 | 2: Variable data: Conserved quantities. Stored once per timestep. |
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337 | |
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338 | All data is assumed to reside at vertex locations. |
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339 | """ |
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340 | |
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341 | ## |
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342 | # @brief Instantiate this instance. |
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343 | # @param domain ?? |
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344 | # @param mode Mode of the underlying data file. |
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345 | # @param max_size ?? |
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346 | # @param recursion ?? |
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347 | # @note Prepare the underlying data file if mode starts with 'w'. |
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348 | def __init__(self, domain, mode=netcdf_mode_w, max_size=2000000000, recursion=False): |
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349 | from Scientific.IO.NetCDF import NetCDFFile |
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350 | |
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351 | self.precision = netcdf_float32 #Use single precision for quantities |
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352 | self.recursion = recursion |
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353 | self.mode = mode |
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354 | if hasattr(domain, 'max_size'): |
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355 | self.max_size = domain.max_size #file size max is 2Gig |
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356 | else: |
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357 | self.max_size = max_size |
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358 | if hasattr(domain, 'minimum_storable_height'): |
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359 | self.minimum_storable_height = domain.minimum_storable_height |
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360 | else: |
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361 | self.minimum_storable_height = default_minimum_storable_height |
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362 | |
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363 | # call owning constructor |
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364 | Data_format.__init__(self, domain, 'sww', mode) |
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365 | |
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366 | # NetCDF file definition |
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367 | fid = NetCDFFile(self.filename, mode) |
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368 | if mode[0] == 'w': |
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369 | description = 'Output from anuga.abstract_2d_finite_volumes ' \ |
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370 | 'suitable for plotting' |
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371 | self.writer = Write_sww() |
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372 | self.writer.store_header(fid, |
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373 | domain.starttime, |
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374 | self.number_of_volumes, |
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375 | self.domain.number_of_full_nodes, |
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376 | description=description, |
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377 | smoothing=domain.smooth, |
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378 | order=domain.default_order, |
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379 | sww_precision=self.precision) |
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380 | |
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381 | # Extra optional information |
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382 | if hasattr(domain, 'texture'): |
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383 | fid.texture = domain.texture |
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384 | |
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385 | if domain.quantities_to_be_monitored is not None: |
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386 | fid.createDimension('singleton', 1) |
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387 | fid.createDimension('two', 2) |
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388 | |
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389 | poly = domain.monitor_polygon |
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390 | if poly is not None: |
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391 | N = len(poly) |
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392 | fid.createDimension('polygon_length', N) |
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393 | fid.createVariable('extrema.polygon', |
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394 | self.precision, |
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395 | ('polygon_length', 'two')) |
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396 | fid.variables['extrema.polygon'][:] = poly |
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397 | |
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398 | interval = domain.monitor_time_interval |
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399 | if interval is not None: |
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400 | fid.createVariable('extrema.time_interval', |
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401 | self.precision, |
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402 | ('two',)) |
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403 | fid.variables['extrema.time_interval'][:] = interval |
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404 | |
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405 | for q in domain.quantities_to_be_monitored: |
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406 | fid.createVariable(q + '.extrema', self.precision, |
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407 | ('numbers_in_range',)) |
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408 | fid.createVariable(q + '.min_location', self.precision, |
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409 | ('numbers_in_range',)) |
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410 | fid.createVariable(q + '.max_location', self.precision, |
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411 | ('numbers_in_range',)) |
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412 | fid.createVariable(q + '.min_time', self.precision, |
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413 | ('singleton',)) |
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414 | fid.createVariable(q + '.max_time', self.precision, |
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415 | ('singleton',)) |
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416 | |
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417 | fid.close() |
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418 | |
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419 | ## |
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420 | # @brief Store connectivity data into the underlying data file. |
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421 | def store_connectivity(self): |
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422 | """Specialisation of store_connectivity for net CDF format |
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423 | |
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424 | Writes x,y,z coordinates of triangles constituting |
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425 | the bed elevation. |
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426 | """ |
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427 | |
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428 | from Scientific.IO.NetCDF import NetCDFFile |
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429 | |
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430 | domain = self.domain |
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431 | |
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432 | # append to the NetCDF file |
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433 | fid = NetCDFFile(self.filename, netcdf_mode_a) |
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434 | |
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435 | # # Get the variables |
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436 | # x = fid.variables['x'] |
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437 | # y = fid.variables['y'] |
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438 | # z = fid.variables['elevation'] |
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439 | # volumes = fid.variables['volumes'] |
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440 | |
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441 | # Get X, Y and bed elevation Z |
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442 | Q = domain.quantities['elevation'] |
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443 | X,Y,Z,V = Q.get_vertex_values(xy=True, precision=self.precision) |
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444 | |
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445 | # store the connectivity data |
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446 | points = num.concatenate( (X[:,num.newaxis],Y[:,num.newaxis]), axis=1 ) |
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447 | self.writer.store_triangulation(fid, |
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448 | points, |
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449 | V.astype(num.float32), |
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450 | Z, |
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451 | points_georeference=\ |
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452 | domain.geo_reference) |
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453 | |
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454 | fid.close() |
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455 | |
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456 | ## |
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457 | # @brief Store time and named quantities to the underlying data file. |
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458 | # @param names The names of the quantities to store. |
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459 | # @note If 'names' not supplied, store a standard set of quantities. |
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460 | def store_timestep(self, names=None): |
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461 | """Store time and named quantities to file |
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462 | """ |
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463 | |
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464 | from Scientific.IO.NetCDF import NetCDFFile |
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465 | import types |
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466 | from time import sleep |
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467 | from os import stat |
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468 | |
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469 | if names is None: |
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470 | # Standard shallow water wave equation quantitites in ANUGA |
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471 | names = ['stage', 'xmomentum', 'ymomentum'] |
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472 | |
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473 | # Get NetCDF |
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474 | retries = 0 |
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475 | file_open = False |
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476 | while not file_open and retries < 10: |
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477 | try: |
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478 | fid = NetCDFFile(self.filename, netcdf_mode_a) # Open existing file |
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479 | except IOError: |
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480 | # This could happen if someone was reading the file. |
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481 | # In that case, wait a while and try again |
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482 | msg = 'Warning (store_timestep): File %s could not be opened' \ |
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483 | % self.filename |
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484 | msg += ' - trying step %s again' % self.domain.time |
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485 | print msg |
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486 | retries += 1 |
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487 | sleep(1) |
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488 | else: |
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489 | file_open = True |
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490 | |
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491 | if not file_open: |
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492 | msg = 'File %s could not be opened for append' % self.filename |
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493 | raise DataFileNotOpenError, msg |
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494 | |
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495 | # Check to see if the file is already too big: |
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496 | time = fid.variables['time'] |
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497 | i = len(time) + 1 |
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498 | file_size = stat(self.filename)[6] |
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499 | file_size_increase = file_size / i |
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500 | if file_size + file_size_increase > self.max_size * 2**self.recursion: |
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501 | # In order to get the file name and start time correct, |
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502 | # I change the domain.filename and domain.starttime. |
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503 | # This is the only way to do this without changing |
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504 | # other modules (I think). |
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505 | |
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506 | # Write a filename addon that won't break swollens reader |
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507 | # (10.sww is bad) |
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508 | filename_ext = '_time_%s' % self.domain.time |
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509 | filename_ext = filename_ext.replace('.', '_') |
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510 | |
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511 | # Remember the old filename, then give domain a |
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512 | # name with the extension |
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513 | old_domain_filename = self.domain.get_name() |
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514 | if not self.recursion: |
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515 | self.domain.set_name(old_domain_filename + filename_ext) |
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516 | |
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517 | # Change the domain starttime to the current time |
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518 | old_domain_starttime = self.domain.starttime |
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519 | self.domain.starttime = self.domain.time |
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520 | |
---|
521 | # Build a new data_structure. |
---|
522 | next_data_structure = Data_format_sww(self.domain, mode=self.mode, |
---|
523 | max_size=self.max_size, |
---|
524 | recursion=self.recursion+1) |
---|
525 | if not self.recursion: |
---|
526 | print ' file_size = %s' % file_size |
---|
527 | print ' saving file to %s' % next_data_structure.filename |
---|
528 | |
---|
529 | #set up the new data_structure |
---|
530 | self.domain.writer = next_data_structure |
---|
531 | |
---|
532 | #FIXME - could be cleaner to use domain.store_timestep etc. |
---|
533 | next_data_structure.store_connectivity() |
---|
534 | next_data_structure.store_timestep(names) |
---|
535 | fid.sync() |
---|
536 | fid.close() |
---|
537 | |
---|
538 | #restore the old starttime and filename |
---|
539 | self.domain.starttime = old_domain_starttime |
---|
540 | self.domain.set_name(old_domain_filename) |
---|
541 | else: |
---|
542 | self.recursion = False |
---|
543 | domain = self.domain |
---|
544 | |
---|
545 | # Get the variables |
---|
546 | time = fid.variables['time'] |
---|
547 | stage = fid.variables['stage'] |
---|
548 | xmomentum = fid.variables['xmomentum'] |
---|
549 | ymomentum = fid.variables['ymomentum'] |
---|
550 | i = len(time) |
---|
551 | if type(names) not in [types.ListType, types.TupleType]: |
---|
552 | names = [names] |
---|
553 | |
---|
554 | if 'stage' in names \ |
---|
555 | and 'xmomentum' in names \ |
---|
556 | and 'ymomentum' in names: |
---|
557 | # Get stage, elevation, depth and select only those |
---|
558 | # values where minimum_storable_height is exceeded |
---|
559 | Q = domain.quantities['stage'] |
---|
560 | A, _ = Q.get_vertex_values(xy=False, precision=self.precision) |
---|
561 | z = fid.variables['elevation'] |
---|
562 | |
---|
563 | storable_indices = (A-z[:] >= self.minimum_storable_height) |
---|
564 | stage = num.choose(storable_indices, (z[:], A)) |
---|
565 | |
---|
566 | # Define a zero vector of same size and type as A |
---|
567 | # for use with momenta |
---|
568 | null = num.zeros(num.size(A), A.dtype.char) #??# |
---|
569 | |
---|
570 | # Get xmomentum where depth exceeds minimum_storable_height |
---|
571 | Q = domain.quantities['xmomentum'] |
---|
572 | xmom, _ = Q.get_vertex_values(xy=False, |
---|
573 | precision=self.precision) |
---|
574 | xmomentum = num.choose(storable_indices, (null, xmom)) |
---|
575 | |
---|
576 | |
---|
577 | # Get ymomentum where depth exceeds minimum_storable_height |
---|
578 | Q = domain.quantities['ymomentum'] |
---|
579 | ymom, _ = Q.get_vertex_values(xy=False, |
---|
580 | precision=self.precision) |
---|
581 | ymomentum = num.choose(storable_indices, (null, ymom)) |
---|
582 | |
---|
583 | # Write quantities to underlying data file |
---|
584 | self.writer.store_quantities(fid, |
---|
585 | time=self.domain.time, |
---|
586 | sww_precision=self.precision, |
---|
587 | stage=stage, |
---|
588 | xmomentum=xmomentum, |
---|
589 | ymomentum=ymomentum) |
---|
590 | else: |
---|
591 | msg = 'Quantities stored must be: stage, xmomentum, ymomentum, ' |
---|
592 | msg += 'but I got: ' + str(names) |
---|
593 | raise Exception, msg |
---|
594 | |
---|
595 | # Update extrema if requested |
---|
596 | domain = self.domain |
---|
597 | if domain.quantities_to_be_monitored is not None: |
---|
598 | for q, info in domain.quantities_to_be_monitored.items(): |
---|
599 | if info['min'] is not None: |
---|
600 | fid.variables[q + '.extrema'][0] = info['min'] |
---|
601 | fid.variables[q + '.min_location'][:] = \ |
---|
602 | info['min_location'] |
---|
603 | fid.variables[q + '.min_time'][0] = info['min_time'] |
---|
604 | |
---|
605 | if info['max'] is not None: |
---|
606 | fid.variables[q + '.extrema'][1] = info['max'] |
---|
607 | fid.variables[q + '.max_location'][:] = \ |
---|
608 | info['max_location'] |
---|
609 | fid.variables[q + '.max_time'][0] = info['max_time'] |
---|
610 | |
---|
611 | # Flush and close |
---|
612 | fid.sync() |
---|
613 | fid.close() |
---|
614 | |
---|
615 | |
---|
616 | ## |
---|
617 | # @brief Class for handling checkpoints data |
---|
618 | class Data_format_cpt(Data_format): |
---|
619 | """Interface to native NetCDF format (.cpt) |
---|
620 | """ |
---|
621 | |
---|
622 | ## |
---|
623 | # @brief Initialize this instantiation. |
---|
624 | # @param domain ?? |
---|
625 | # @param mode Mode of underlying data file (default WRITE). |
---|
626 | def __init__(self, domain, mode=netcdf_mode_w): |
---|
627 | from Scientific.IO.NetCDF import NetCDFFile |
---|
628 | |
---|
629 | self.precision = netcdf_float #Use full precision |
---|
630 | |
---|
631 | Data_format.__init__(self, domain, 'sww', mode) |
---|
632 | |
---|
633 | # NetCDF file definition |
---|
634 | fid = NetCDFFile(self.filename, mode) |
---|
635 | if mode[0] == 'w': |
---|
636 | #Create new file |
---|
637 | fid.institution = 'Geoscience Australia' |
---|
638 | fid.description = 'Checkpoint data' |
---|
639 | #fid.smooth = domain.smooth |
---|
640 | fid.order = domain.default_order |
---|
641 | |
---|
642 | # dimension definitions |
---|
643 | fid.createDimension('number_of_volumes', self.number_of_volumes) |
---|
644 | fid.createDimension('number_of_vertices', 3) |
---|
645 | |
---|
646 | #Store info at all vertices (no smoothing) |
---|
647 | fid.createDimension('number_of_points', 3*self.number_of_volumes) |
---|
648 | fid.createDimension('number_of_timesteps', None) #extensible |
---|
649 | |
---|
650 | # variable definitions |
---|
651 | |
---|
652 | #Mesh |
---|
653 | fid.createVariable('x', self.precision, ('number_of_points',)) |
---|
654 | fid.createVariable('y', self.precision, ('number_of_points',)) |
---|
655 | |
---|
656 | |
---|
657 | fid.createVariable('volumes', netcdf_int, ('number_of_volumes', |
---|
658 | 'number_of_vertices')) |
---|
659 | |
---|
660 | fid.createVariable('time', self.precision, ('number_of_timesteps',)) |
---|
661 | |
---|
662 | #Allocate space for all quantities |
---|
663 | for name in domain.quantities.keys(): |
---|
664 | fid.createVariable(name, self.precision, |
---|
665 | ('number_of_timesteps', |
---|
666 | 'number_of_points')) |
---|
667 | |
---|
668 | fid.close() |
---|
669 | |
---|
670 | ## |
---|
671 | # @brief Store connectivity data to underlying data file. |
---|
672 | def store_checkpoint(self): |
---|
673 | """Write x,y coordinates of triangles. |
---|
674 | Write connectivity ( |
---|
675 | constituting |
---|
676 | the bed elevation. |
---|
677 | """ |
---|
678 | |
---|
679 | from Scientific.IO.NetCDF import NetCDFFile |
---|
680 | |
---|
681 | domain = self.domain |
---|
682 | |
---|
683 | #Get NetCDF |
---|
684 | fid = NetCDFFile(self.filename, netcdf_mode_a) |
---|
685 | |
---|
686 | # Get the variables |
---|
687 | x = fid.variables['x'] |
---|
688 | y = fid.variables['y'] |
---|
689 | |
---|
690 | volumes = fid.variables['volumes'] |
---|
691 | |
---|
692 | # Get X, Y and bed elevation Z |
---|
693 | Q = domain.quantities['elevation'] |
---|
694 | X,Y,Z,V = Q.get_vertex_values(xy=True, precision=self.precision) |
---|
695 | |
---|
696 | x[:] = X.astype(self.precision) |
---|
697 | y[:] = Y.astype(self.precision) |
---|
698 | z[:] = Z.astype(self.precision) |
---|
699 | |
---|
700 | volumes[:] = V |
---|
701 | |
---|
702 | fid.close() |
---|
703 | |
---|
704 | ## |
---|
705 | # @brief Store tiem and named quantities to underlying data file. |
---|
706 | # @param name |
---|
707 | def store_timestep(self, name): |
---|
708 | """Store time and named quantity to file |
---|
709 | """ |
---|
710 | |
---|
711 | from Scientific.IO.NetCDF import NetCDFFile |
---|
712 | from time import sleep |
---|
713 | |
---|
714 | #Get NetCDF |
---|
715 | retries = 0 |
---|
716 | file_open = False |
---|
717 | while not file_open and retries < 10: |
---|
718 | try: |
---|
719 | fid = NetCDFFile(self.filename, netcdf_mode_a) |
---|
720 | except IOError: |
---|
721 | #This could happen if someone was reading the file. |
---|
722 | #In that case, wait a while and try again |
---|
723 | msg = 'Warning (store_timestep): File %s could not be opened' \ |
---|
724 | ' - trying again' % self.filename |
---|
725 | print msg |
---|
726 | retries += 1 |
---|
727 | sleep(1) |
---|
728 | else: |
---|
729 | file_open = True |
---|
730 | |
---|
731 | if not file_open: |
---|
732 | msg = 'File %s could not be opened for append' % self.filename |
---|
733 | raise DataFileNotOpenError, msg |
---|
734 | |
---|
735 | domain = self.domain |
---|
736 | |
---|
737 | # Get the variables |
---|
738 | time = fid.variables['time'] |
---|
739 | stage = fid.variables['stage'] |
---|
740 | i = len(time) |
---|
741 | |
---|
742 | #Store stage |
---|
743 | time[i] = self.domain.time |
---|
744 | |
---|
745 | # Get quantity |
---|
746 | Q = domain.quantities[name] |
---|
747 | A,V = Q.get_vertex_values(xy=False, precision=self.precision) |
---|
748 | |
---|
749 | stage[i,:] = A.astype(self.precision) |
---|
750 | |
---|
751 | #Flush and close |
---|
752 | fid.sync() |
---|
753 | fid.close() |
---|
754 | |
---|
755 | |
---|
756 | ## |
---|
757 | # @brief Class for National Exposure Database storage (NEXIS). |
---|
758 | |
---|
759 | LAT_TITLE = 'LATITUDE' |
---|
760 | LONG_TITLE = 'LONGITUDE' |
---|
761 | X_TITLE = 'x' |
---|
762 | Y_TITLE = 'y' |
---|
763 | |
---|
764 | class Exposure_csv: |
---|
765 | |
---|
766 | ## |
---|
767 | # @brief Instantiate this instance. |
---|
768 | # @param file_name Name of underlying data file. |
---|
769 | # @param latitude_title ?? |
---|
770 | # @param longitude_title ?? |
---|
771 | # @param is_x_y_locations ?? |
---|
772 | # @param x_title ?? |
---|
773 | # @param y_title ?? |
---|
774 | # @param refine_polygon ?? |
---|
775 | # @param title_check_list ?? |
---|
776 | def __init__(self,file_name, latitude_title=LAT_TITLE, |
---|
777 | longitude_title=LONG_TITLE, is_x_y_locations=None, |
---|
778 | x_title=X_TITLE, y_title=Y_TITLE, |
---|
779 | refine_polygon=None, title_check_list=None): |
---|
780 | """ |
---|
781 | This class is for handling the exposure csv file. |
---|
782 | It reads the file in and converts the lats and longs to a geospatial |
---|
783 | data object. |
---|
784 | Use the methods to read and write columns. |
---|
785 | |
---|
786 | The format of the csv files it reads is; |
---|
787 | The first row is a title row. |
---|
788 | comma's are the delimiters |
---|
789 | each column is a 'set' of data |
---|
790 | |
---|
791 | Feel free to use/expand it to read other csv files. |
---|
792 | |
---|
793 | It is not for adding and deleting rows |
---|
794 | |
---|
795 | Can geospatial handle string attributes? It's not made for them. |
---|
796 | Currently it can't load and save string att's. |
---|
797 | |
---|
798 | So just use geospatial to hold the x, y and georef? Bad, since |
---|
799 | different att's are in diferent structures. Not so bad, the info |
---|
800 | to write if the .csv file is saved is in attribute_dic |
---|
801 | |
---|
802 | The location info is in the geospatial attribute. |
---|
803 | """ |
---|
804 | |
---|
805 | self._file_name = file_name |
---|
806 | self._geospatial = None # |
---|
807 | |
---|
808 | # self._attribute_dic is a dictionary. |
---|
809 | #The keys are the column titles. |
---|
810 | #The values are lists of column data |
---|
811 | |
---|
812 | # self._title_index_dic is a dictionary. |
---|
813 | #The keys are the column titles. |
---|
814 | #The values are the index positions of file columns. |
---|
815 | self._attribute_dic, self._title_index_dic = \ |
---|
816 | csv2dict(self._file_name, title_check_list=title_check_list) |
---|
817 | try: |
---|
818 | #Have code here that handles caps or lower |
---|
819 | lats = self._attribute_dic[latitude_title] |
---|
820 | longs = self._attribute_dic[longitude_title] |
---|
821 | except KeyError: |
---|
822 | # maybe a warning.. |
---|
823 | #Let's see if this works.. |
---|
824 | if False != is_x_y_locations: |
---|
825 | is_x_y_locations = True |
---|
826 | pass |
---|
827 | else: |
---|
828 | self._geospatial = Geospatial_data(latitudes=lats, |
---|
829 | longitudes=longs) |
---|
830 | |
---|
831 | if is_x_y_locations is True: |
---|
832 | if self._geospatial is not None: |
---|
833 | pass #fixme throw an error |
---|
834 | try: |
---|
835 | xs = self._attribute_dic[x_title] |
---|
836 | ys = self._attribute_dic[y_title] |
---|
837 | points = [[float(i),float(j)] for i,j in map(None,xs,ys)] |
---|
838 | except KeyError: |
---|
839 | # maybe a warning.. |
---|
840 | msg = "Could not find location information." |
---|
841 | raise TitleValueError, msg |
---|
842 | else: |
---|
843 | self._geospatial = Geospatial_data(data_points=points) |
---|
844 | |
---|
845 | # create a list of points that are in the refining_polygon |
---|
846 | # described by a list of indexes representing the points |
---|
847 | |
---|
848 | ## |
---|
849 | # @brief Create a comparison method. |
---|
850 | # @param self This object. |
---|
851 | # @param other The other object. |
---|
852 | # @return True if objects are 'same'. |
---|
853 | def __cmp__(self, other): |
---|
854 | #check that 'other' is an instance of this class |
---|
855 | if isinstance(self, type(other)): |
---|
856 | result = cmp(self._attribute_dic, other._attribute_dic) |
---|
857 | if result <> 0: |
---|
858 | return result |
---|
859 | |
---|
860 | # The order of the columns is important. Therefore.. |
---|
861 | result = cmp(self._title_index_dic, other._title_index_dic) |
---|
862 | if result <> 0: |
---|
863 | return result |
---|
864 | for self_ls, other_ls in map(None, self._attribute_dic, |
---|
865 | other._attribute_dic): |
---|
866 | result = cmp(self._attribute_dic[self_ls], |
---|
867 | other._attribute_dic[other_ls]) |
---|
868 | if result <> 0: |
---|
869 | return result |
---|
870 | return 0 |
---|
871 | else: |
---|
872 | return 1 |
---|
873 | |
---|
874 | ## |
---|
875 | # @brief Get a list of column values given a column name. |
---|
876 | # @param column_name The name of the column to get values from. |
---|
877 | # @param use_refind_polygon Unused?? |
---|
878 | def get_column(self, column_name, use_refind_polygon=False): |
---|
879 | """ |
---|
880 | Given a column name return a list of the column values |
---|
881 | |
---|
882 | Note, the type of the values will be String! |
---|
883 | do this to change a list of strings to a list of floats |
---|
884 | time = [float(x) for x in time] |
---|
885 | |
---|
886 | Not implemented: |
---|
887 | if use_refind_polygon is True, only return values in the |
---|
888 | refined polygon |
---|
889 | """ |
---|
890 | |
---|
891 | if not self._attribute_dic.has_key(column_name): |
---|
892 | msg = 'There is no column called %s!' % column_name |
---|
893 | raise TitleValueError, msg |
---|
894 | |
---|
895 | return self._attribute_dic[column_name] |
---|
896 | |
---|
897 | ## |
---|
898 | # @brief ?? |
---|
899 | # @param value_column_name ?? |
---|
900 | # @param known_column_name ?? |
---|
901 | # @param known_values ?? |
---|
902 | # @param use_refind_polygon ?? |
---|
903 | def get_value(self, value_column_name, known_column_name, |
---|
904 | known_values, use_refind_polygon=False): |
---|
905 | """ |
---|
906 | Do linear interpolation on the known_colum, using the known_value, |
---|
907 | to return a value of the column_value_name. |
---|
908 | """ |
---|
909 | |
---|
910 | pass |
---|
911 | |
---|
912 | ## |
---|
913 | # @brief Get a geospatial object that describes the locations. |
---|
914 | # @param use_refind_polygon Unused?? |
---|
915 | def get_location(self, use_refind_polygon=False): |
---|
916 | """ |
---|
917 | Return a geospatial object which describes the |
---|
918 | locations of the location file. |
---|
919 | |
---|
920 | Note, if there is not location info, this returns None. |
---|
921 | |
---|
922 | Not implemented: |
---|
923 | if use_refind_polygon is True, only return values in the |
---|
924 | refined polygon |
---|
925 | """ |
---|
926 | |
---|
927 | return self._geospatial |
---|
928 | |
---|
929 | ## |
---|
930 | # @brief Add column to 'end' of CSV data. |
---|
931 | # @param column_name The new column name. |
---|
932 | # @param column_values The new column values. |
---|
933 | # @param overwrite If True, overwrites last column, doesn't add at end. |
---|
934 | def set_column(self, column_name, column_values, overwrite=False): |
---|
935 | """ |
---|
936 | Add a column to the 'end' (with the right most column being the end) |
---|
937 | of the csv file. |
---|
938 | |
---|
939 | Set overwrite to True if you want to overwrite a column. |
---|
940 | |
---|
941 | Note, in column_name white space is removed and case is not checked. |
---|
942 | Precondition |
---|
943 | The column_name and column_values cannot have comma's in it. |
---|
944 | """ |
---|
945 | |
---|
946 | # sanity checks |
---|
947 | value_row_count = \ |
---|
948 | len(self._attribute_dic[self._title_index_dic.keys()[0]]) |
---|
949 | if len(column_values) <> value_row_count: |
---|
950 | msg = 'The number of column values must equal the number of rows.' |
---|
951 | raise DataMissingValuesError, msg |
---|
952 | |
---|
953 | # check new column name isn't already used, and we aren't overwriting |
---|
954 | if self._attribute_dic.has_key(column_name): |
---|
955 | if not overwrite: |
---|
956 | msg = 'Column name %s already in use!' % column_name |
---|
957 | raise TitleValueError, msg |
---|
958 | else: |
---|
959 | # New title. Add it to the title index. |
---|
960 | self._title_index_dic[column_name] = len(self._title_index_dic) |
---|
961 | |
---|
962 | self._attribute_dic[column_name] = column_values |
---|
963 | |
---|
964 | ## |
---|
965 | # @brief Save the exposure CSV file. |
---|
966 | # @param file_name If supplied, use this filename, not original. |
---|
967 | def save(self, file_name=None): |
---|
968 | """ |
---|
969 | Save the exposure csv file |
---|
970 | """ |
---|
971 | |
---|
972 | if file_name is None: |
---|
973 | file_name = self._file_name |
---|
974 | |
---|
975 | fd = open(file_name, 'wb') |
---|
976 | writer = csv.writer(fd) |
---|
977 | |
---|
978 | #Write the title to a cvs file |
---|
979 | line = [None] * len(self._title_index_dic) |
---|
980 | for title in self._title_index_dic.iterkeys(): |
---|
981 | line[self._title_index_dic[title]] = title |
---|
982 | writer.writerow(line) |
---|
983 | |
---|
984 | # Write the values to a cvs file |
---|
985 | value_row_count = \ |
---|
986 | len(self._attribute_dic[self._title_index_dic.keys()[0]]) |
---|
987 | for row_i in range(value_row_count): |
---|
988 | line = [None] * len(self._title_index_dic) |
---|
989 | for title in self._title_index_dic.iterkeys(): |
---|
990 | line[self._title_index_dic[title]] = \ |
---|
991 | self._attribute_dic[title][row_i] |
---|
992 | writer.writerow(line) |
---|
993 | |
---|
994 | |
---|
995 | def csv2building_polygons(file_name, |
---|
996 | floor_height=3, |
---|
997 | clipping_polygons=None): |
---|
998 | """ |
---|
999 | Convert CSV files of the form: |
---|
1000 | |
---|
1001 | easting,northing,id,floors |
---|
1002 | 422664.22,870785.46,2,0 |
---|
1003 | 422672.48,870780.14,2,0 |
---|
1004 | 422668.17,870772.62,2,0 |
---|
1005 | 422660.35,870777.17,2,0 |
---|
1006 | 422664.22,870785.46,2,0 |
---|
1007 | 422661.30,871215.06,3,1 |
---|
1008 | 422667.50,871215.70,3,1 |
---|
1009 | 422668.30,871204.86,3,1 |
---|
1010 | 422662.21,871204.33,3,1 |
---|
1011 | 422661.30,871215.06,3,1 |
---|
1012 | |
---|
1013 | to a dictionary of polygons with id as key. |
---|
1014 | The associated number of floors are converted to m above MSL and |
---|
1015 | returned as a separate dictionary also keyed by id. |
---|
1016 | |
---|
1017 | Optional parameter floor_height is the height of each building story. |
---|
1018 | Optional parameter clipping_olygons is a list of polygons selecting |
---|
1019 | buildings. Any building not in these polygons will be omitted. |
---|
1020 | |
---|
1021 | See csv2polygons for more details |
---|
1022 | """ |
---|
1023 | |
---|
1024 | polygons, values = csv2polygons(file_name, |
---|
1025 | value_name='floors', |
---|
1026 | clipping_polygons=None) |
---|
1027 | |
---|
1028 | |
---|
1029 | heights = {} |
---|
1030 | for key in values.keys(): |
---|
1031 | v = float(values[key]) |
---|
1032 | heights[key] = v*floor_height |
---|
1033 | |
---|
1034 | return polygons, heights |
---|
1035 | |
---|
1036 | |
---|
1037 | ## |
---|
1038 | # @brief Convert CSV file into a dictionary of polygons and associated values. |
---|
1039 | # @param filename The path to the file to read, value_name name for the 4th column |
---|
1040 | def csv2polygons(file_name, |
---|
1041 | value_name='value', |
---|
1042 | clipping_polygons=None): |
---|
1043 | """ |
---|
1044 | Convert CSV files of the form: |
---|
1045 | |
---|
1046 | easting,northing,id,value |
---|
1047 | 422664.22,870785.46,2,0 |
---|
1048 | 422672.48,870780.14,2,0 |
---|
1049 | 422668.17,870772.62,2,0 |
---|
1050 | 422660.35,870777.17,2,0 |
---|
1051 | 422664.22,870785.46,2,0 |
---|
1052 | 422661.30,871215.06,3,1 |
---|
1053 | 422667.50,871215.70,3,1 |
---|
1054 | 422668.30,871204.86,3,1 |
---|
1055 | 422662.21,871204.33,3,1 |
---|
1056 | 422661.30,871215.06,3,1 |
---|
1057 | |
---|
1058 | to a dictionary of polygons with id as key. |
---|
1059 | The associated values are returned as a separate dictionary also keyed by id. |
---|
1060 | |
---|
1061 | |
---|
1062 | easting: x coordinate relative to zone implied by the model |
---|
1063 | northing: y coordinate relative to zone implied by the model |
---|
1064 | id: tag for polygon comprising points with this tag |
---|
1065 | value: numeral associated with each polygon. These must be the same for all points in each polygon. |
---|
1066 | |
---|
1067 | The last header, value, can take on other names such as roughness, floors, etc - or it can be omitted |
---|
1068 | in which case the returned values will be None |
---|
1069 | |
---|
1070 | Eastings and Northings will be returned as floating point values while |
---|
1071 | id and values will be returned as strings. |
---|
1072 | |
---|
1073 | Optional argument: clipping_polygons will select only those polygons that are |
---|
1074 | fully within one or more of the clipping_polygons. In other words any polygon from |
---|
1075 | the csv file which has at least one point not inside one of the clipping polygons |
---|
1076 | will be excluded |
---|
1077 | |
---|
1078 | See underlying function csv2dict for more details. |
---|
1079 | """ |
---|
1080 | |
---|
1081 | X, _ = csv2dict(file_name) |
---|
1082 | |
---|
1083 | msg = 'Polygon csv file must have 3 or 4 columns' |
---|
1084 | assert len(X.keys()) in [3, 4], msg |
---|
1085 | |
---|
1086 | msg = 'Did not find expected column header: easting' |
---|
1087 | assert 'easting' in X.keys(), msg |
---|
1088 | |
---|
1089 | msg = 'Did not find expected column header: northing' |
---|
1090 | assert 'northing' in X.keys(), northing |
---|
1091 | |
---|
1092 | msg = 'Did not find expected column header: northing' |
---|
1093 | assert 'id' in X.keys(), msg |
---|
1094 | |
---|
1095 | if value_name is not None: |
---|
1096 | msg = 'Did not find expected column header: %s' % value_name |
---|
1097 | assert value_name in X.keys(), msg |
---|
1098 | |
---|
1099 | polygons = {} |
---|
1100 | if len(X.keys()) == 4: |
---|
1101 | values = {} |
---|
1102 | else: |
---|
1103 | values = None |
---|
1104 | |
---|
1105 | # Loop through entries and compose polygons |
---|
1106 | excluded_polygons={} |
---|
1107 | past_ids = {} |
---|
1108 | last_id = None |
---|
1109 | for i, id in enumerate(X['id']): |
---|
1110 | |
---|
1111 | # Check for duplicate polygons |
---|
1112 | if id in past_ids: |
---|
1113 | msg = 'Polygon %s was duplicated in line %d' % (id, i) |
---|
1114 | raise Exception, msg |
---|
1115 | |
---|
1116 | if id not in polygons: |
---|
1117 | # Start new polygon |
---|
1118 | polygons[id] = [] |
---|
1119 | if values is not None: |
---|
1120 | values[id] = X[value_name][i] |
---|
1121 | |
---|
1122 | # Keep track of previous polygon ids |
---|
1123 | if last_id is not None: |
---|
1124 | past_ids[last_id] = i |
---|
1125 | |
---|
1126 | # Append this point to current polygon |
---|
1127 | point = [float(X['easting'][i]), float(X['northing'][i])] |
---|
1128 | |
---|
1129 | if clipping_polygons is not None: |
---|
1130 | exclude=True |
---|
1131 | for clipping_polygon in clipping_polygons: |
---|
1132 | if inside_polygon(point, clipping_polygon): |
---|
1133 | exclude=False |
---|
1134 | break |
---|
1135 | |
---|
1136 | if exclude is True: |
---|
1137 | excluded_polygons[id]=True |
---|
1138 | |
---|
1139 | polygons[id].append(point) |
---|
1140 | |
---|
1141 | # Check that value is the same across each polygon |
---|
1142 | msg = 'Values must be the same across each polygon.' |
---|
1143 | msg += 'I got %s in line %d but it should have been %s' % (X[value_name][i], i, values[id]) |
---|
1144 | assert values[id] == X[value_name][i], msg |
---|
1145 | |
---|
1146 | last_id = id |
---|
1147 | |
---|
1148 | # Weed out polygons that were not wholly inside clipping polygons |
---|
1149 | for id in excluded_polygons: |
---|
1150 | del polygons[id] |
---|
1151 | |
---|
1152 | return polygons, values |
---|
1153 | |
---|
1154 | |
---|
1155 | |
---|
1156 | |
---|
1157 | ## |
---|
1158 | # @brief Convert CSV file to a dictionary of arrays. |
---|
1159 | # @param file_name The path to the file to read. |
---|
1160 | def csv2array(file_name): |
---|
1161 | """ |
---|
1162 | Convert CSV files of the form: |
---|
1163 | |
---|
1164 | time, discharge, velocity |
---|
1165 | 0.0, 1.2, 0.0 |
---|
1166 | 0.1, 3.2, 1.1 |
---|
1167 | ... |
---|
1168 | |
---|
1169 | to a dictionary of numeric arrays. |
---|
1170 | |
---|
1171 | |
---|
1172 | See underlying function csv2dict for more details. |
---|
1173 | """ |
---|
1174 | |
---|
1175 | X, _ = csv2dict(file_name) |
---|
1176 | |
---|
1177 | Y = {} |
---|
1178 | for key in X.keys(): |
---|
1179 | Y[key] = num.array([float(x) for x in X[key]]) |
---|
1180 | |
---|
1181 | return Y |
---|
1182 | |
---|
1183 | |
---|
1184 | ## |
---|
1185 | # @brief Read a CSV file and convert to a dictionary of {key: column}. |
---|
1186 | # @param file_name The path to the file to read. |
---|
1187 | # @param title_check_list List of titles that *must* be columns in the file. |
---|
1188 | # @return Two dicts: ({key:column}, {title:index}). |
---|
1189 | # @note WARNING: Values are returned as strings. |
---|
1190 | def csv2dict(file_name, title_check_list=None): |
---|
1191 | """ |
---|
1192 | Load in the csv as a dictionary, title as key and column info as value. |
---|
1193 | Also, create a dictionary, title as key and column index as value, |
---|
1194 | to keep track of the column order. |
---|
1195 | |
---|
1196 | Two dictionaries are returned. |
---|
1197 | |
---|
1198 | WARNING: Values are returned as strings. |
---|
1199 | Do this to change a list of strings to a list of floats |
---|
1200 | time = [float(x) for x in time] |
---|
1201 | """ |
---|
1202 | |
---|
1203 | # FIXME(Ole): Consider dealing with files without headers |
---|
1204 | # FIXME(Ole): Consider a wrapper automatically converting text fields |
---|
1205 | # to the right type by trying for: int, float, string |
---|
1206 | |
---|
1207 | attribute_dic = {} |
---|
1208 | title_index_dic = {} |
---|
1209 | titles_stripped = [] # List of titles |
---|
1210 | |
---|
1211 | reader = csv.reader(file(file_name)) |
---|
1212 | |
---|
1213 | # Read in and manipulate the title info |
---|
1214 | titles = reader.next() |
---|
1215 | for i, title in enumerate(titles): |
---|
1216 | header = title.strip() |
---|
1217 | titles_stripped.append(header) |
---|
1218 | title_index_dic[header] = i |
---|
1219 | title_count = len(titles_stripped) |
---|
1220 | |
---|
1221 | # Check required columns |
---|
1222 | if title_check_list is not None: |
---|
1223 | for title_check in title_check_list: |
---|
1224 | if not title_index_dic.has_key(title_check): |
---|
1225 | msg = 'Reading error. This row is not present %s' % title_check |
---|
1226 | raise IOError, msg |
---|
1227 | |
---|
1228 | # Create a dictionary of column values, indexed by column title |
---|
1229 | for line in reader: |
---|
1230 | n = len(line) # Number of entries |
---|
1231 | if n != title_count: |
---|
1232 | msg = 'Entry in file %s had %d columns ' % (file_name, n) |
---|
1233 | msg += 'although there were %d headers' % title_count |
---|
1234 | raise IOError, msg |
---|
1235 | for i, value in enumerate(line): |
---|
1236 | attribute_dic.setdefault(titles_stripped[i], []).append(value) |
---|
1237 | |
---|
1238 | return attribute_dic, title_index_dic |
---|
1239 | |
---|
1240 | |
---|
1241 | ## |
---|
1242 | # @brief |
---|
1243 | # @param filename |
---|
1244 | # @param x |
---|
1245 | # @param y |
---|
1246 | # @param z |
---|
1247 | def write_obj(filename, x, y, z): |
---|
1248 | """Store x,y,z vectors into filename (obj format). |
---|
1249 | |
---|
1250 | Vectors are assumed to have dimension (M,3) where |
---|
1251 | M corresponds to the number elements. |
---|
1252 | triangles are assumed to be disconnected |
---|
1253 | |
---|
1254 | The three numbers in each vector correspond to three vertices, |
---|
1255 | |
---|
1256 | e.g. the x coordinate of vertex 1 of element i is in x[i,1] |
---|
1257 | """ |
---|
1258 | |
---|
1259 | import os.path |
---|
1260 | |
---|
1261 | root, ext = os.path.splitext(filename) |
---|
1262 | if ext == '.obj': |
---|
1263 | FN = filename |
---|
1264 | else: |
---|
1265 | FN = filename + '.obj' |
---|
1266 | |
---|
1267 | outfile = open(FN, 'wb') |
---|
1268 | outfile.write("# Triangulation as an obj file\n") |
---|
1269 | |
---|
1270 | M, N = x.shape |
---|
1271 | assert N == 3 #Assuming three vertices per element |
---|
1272 | |
---|
1273 | for i in range(M): |
---|
1274 | for j in range(N): |
---|
1275 | outfile.write("v %f %f %f\n" % (x[i,j], y[i,j], z[i,j])) |
---|
1276 | |
---|
1277 | for i in range(M): |
---|
1278 | base = i * N |
---|
1279 | outfile.write("f %d %d %d\n" % (base+1, base+2, base+3)) |
---|
1280 | |
---|
1281 | outfile.close() |
---|
1282 | |
---|
1283 | |
---|
1284 | ######################################################### |
---|
1285 | #Conversion routines |
---|
1286 | ######################################################## |
---|
1287 | |
---|
1288 | ## |
---|
1289 | # @brief Convert SWW data to OBJ data. |
---|
1290 | # @param basefilename Stem of filename, needs size and extension added. |
---|
1291 | # @param size The number of lines to write. |
---|
1292 | def sww2obj(basefilename, size): |
---|
1293 | """Convert netcdf based data output to obj |
---|
1294 | """ |
---|
1295 | |
---|
1296 | from Scientific.IO.NetCDF import NetCDFFile |
---|
1297 | |
---|
1298 | # Get NetCDF |
---|
1299 | FN = create_filename('.', basefilename, 'sww', size) |
---|
1300 | print 'Reading from ', FN |
---|
1301 | fid = NetCDFFile(FN, netcdf_mode_r) #Open existing file for read |
---|
1302 | |
---|
1303 | # Get the variables |
---|
1304 | x = fid.variables['x'] |
---|
1305 | y = fid.variables['y'] |
---|
1306 | z = fid.variables['elevation'] |
---|
1307 | time = fid.variables['time'] |
---|
1308 | stage = fid.variables['stage'] |
---|
1309 | |
---|
1310 | M = size #Number of lines |
---|
1311 | xx = num.zeros((M,3), num.float) |
---|
1312 | yy = num.zeros((M,3), num.float) |
---|
1313 | zz = num.zeros((M,3), num.float) |
---|
1314 | |
---|
1315 | for i in range(M): |
---|
1316 | for j in range(3): |
---|
1317 | xx[i,j] = x[i+j*M] |
---|
1318 | yy[i,j] = y[i+j*M] |
---|
1319 | zz[i,j] = z[i+j*M] |
---|
1320 | |
---|
1321 | # Write obj for bathymetry |
---|
1322 | FN = create_filename('.', basefilename, 'obj', size) |
---|
1323 | write_obj(FN,xx,yy,zz) |
---|
1324 | |
---|
1325 | # Now read all the data with variable information, combine with |
---|
1326 | # x,y info and store as obj |
---|
1327 | for k in range(len(time)): |
---|
1328 | t = time[k] |
---|
1329 | print 'Processing timestep %f' %t |
---|
1330 | |
---|
1331 | for i in range(M): |
---|
1332 | for j in range(3): |
---|
1333 | zz[i,j] = stage[k,i+j*M] |
---|
1334 | |
---|
1335 | #Write obj for variable data |
---|
1336 | #FN = create_filename(basefilename, 'obj', size, time=t) |
---|
1337 | FN = create_filename('.', basefilename[:5], 'obj', size, time=t) |
---|
1338 | write_obj(FN, xx, yy, zz) |
---|
1339 | |
---|
1340 | |
---|
1341 | ## |
---|
1342 | # @brief |
---|
1343 | # @param basefilename Stem of filename, needs size and extension added. |
---|
1344 | def dat2obj(basefilename): |
---|
1345 | """Convert line based data output to obj |
---|
1346 | FIXME: Obsolete? |
---|
1347 | """ |
---|
1348 | |
---|
1349 | import glob, os |
---|
1350 | from anuga.config import data_dir |
---|
1351 | |
---|
1352 | # Get bathymetry and x,y's |
---|
1353 | lines = open(data_dir+os.sep+basefilename+'_geometry.dat', 'r').readlines() |
---|
1354 | |
---|
1355 | M = len(lines) #Number of lines |
---|
1356 | x = num.zeros((M,3), num.float) |
---|
1357 | y = num.zeros((M,3), num.float) |
---|
1358 | z = num.zeros((M,3), num.float) |
---|
1359 | |
---|
1360 | for i, line in enumerate(lines): |
---|
1361 | tokens = line.split() |
---|
1362 | values = map(float, tokens) |
---|
1363 | |
---|
1364 | for j in range(3): |
---|
1365 | x[i,j] = values[j*3] |
---|
1366 | y[i,j] = values[j*3+1] |
---|
1367 | z[i,j] = values[j*3+2] |
---|
1368 | |
---|
1369 | # Write obj for bathymetry |
---|
1370 | write_obj(data_dir + os.sep + basefilename + '_geometry', x, y, z) |
---|
1371 | |
---|
1372 | # Now read all the data files with variable information, combine with |
---|
1373 | # x,y info and store as obj. |
---|
1374 | |
---|
1375 | files = glob.glob(data_dir + os.sep + basefilename + '*.dat') |
---|
1376 | for filename in files: |
---|
1377 | print 'Processing %s' % filename |
---|
1378 | |
---|
1379 | lines = open(data_dir + os.sep + filename, 'r').readlines() |
---|
1380 | assert len(lines) == M |
---|
1381 | root, ext = os.path.splitext(filename) |
---|
1382 | |
---|
1383 | # Get time from filename |
---|
1384 | i0 = filename.find('_time=') |
---|
1385 | if i0 == -1: |
---|
1386 | #Skip bathymetry file |
---|
1387 | continue |
---|
1388 | |
---|
1389 | i0 += 6 #Position where time starts |
---|
1390 | i1 = filename.find('.dat') |
---|
1391 | |
---|
1392 | if i1 > i0: |
---|
1393 | t = float(filename[i0:i1]) |
---|
1394 | else: |
---|
1395 | raise DataTimeError, 'Hmmmm' |
---|
1396 | |
---|
1397 | for i, line in enumerate(lines): |
---|
1398 | tokens = line.split() |
---|
1399 | values = map(float,tokens) |
---|
1400 | |
---|
1401 | for j in range(3): |
---|
1402 | z[i,j] = values[j] |
---|
1403 | |
---|
1404 | # Write obj for variable data |
---|
1405 | write_obj(data_dir + os.sep + basefilename + '_time=%.4f' % t, x, y, z) |
---|
1406 | |
---|
1407 | |
---|
1408 | ## |
---|
1409 | # @brief Filter data file, selecting timesteps first:step:last. |
---|
1410 | # @param filename1 Data file to filter. |
---|
1411 | # @param filename2 File to write filtered timesteps to. |
---|
1412 | # @param first First timestep. |
---|
1413 | # @param last Last timestep. |
---|
1414 | # @param step Timestep stride. |
---|
1415 | def filter_netcdf(filename1, filename2, first=0, last=None, step=1): |
---|
1416 | """Filter data file, selecting timesteps first:step:last. |
---|
1417 | |
---|
1418 | Read netcdf filename1, pick timesteps first:step:last and save to |
---|
1419 | nettcdf file filename2 |
---|
1420 | """ |
---|
1421 | |
---|
1422 | from Scientific.IO.NetCDF import NetCDFFile |
---|
1423 | |
---|
1424 | # Get NetCDF |
---|
1425 | infile = NetCDFFile(filename1, netcdf_mode_r) #Open existing file for read |
---|
1426 | outfile = NetCDFFile(filename2, netcdf_mode_w) #Open new file |
---|
1427 | |
---|
1428 | # Copy dimensions |
---|
1429 | for d in infile.dimensions: |
---|
1430 | outfile.createDimension(d, infile.dimensions[d]) |
---|
1431 | |
---|
1432 | # Copy variable definitions |
---|
1433 | for name in infile.variables: |
---|
1434 | var = infile.variables[name] |
---|
1435 | outfile.createVariable(name, var.dtype.char, var.dimensions) #??# |
---|
1436 | |
---|
1437 | # Copy the static variables |
---|
1438 | for name in infile.variables: |
---|
1439 | if name == 'time' or name == 'stage': |
---|
1440 | pass |
---|
1441 | else: |
---|
1442 | outfile.variables[name][:] = infile.variables[name][:] |
---|
1443 | |
---|
1444 | # Copy selected timesteps |
---|
1445 | time = infile.variables['time'] |
---|
1446 | stage = infile.variables['stage'] |
---|
1447 | |
---|
1448 | newtime = outfile.variables['time'] |
---|
1449 | newstage = outfile.variables['stage'] |
---|
1450 | |
---|
1451 | if last is None: |
---|
1452 | last = len(time) |
---|
1453 | |
---|
1454 | selection = range(first, last, step) |
---|
1455 | for i, j in enumerate(selection): |
---|
1456 | print 'Copying timestep %d of %d (%f)' % (j, last-first, time[j]) |
---|
1457 | newtime[i] = time[j] |
---|
1458 | newstage[i,:] = stage[j,:] |
---|
1459 | |
---|
1460 | # Close |
---|
1461 | infile.close() |
---|
1462 | outfile.close() |
---|
1463 | |
---|
1464 | |
---|
1465 | ## |
---|
1466 | # @brief Return instance of class of given format using filename. |
---|
1467 | # @param domain Data domain (eg, 'sww', etc). |
---|
1468 | # @param mode The mode to open domain in. |
---|
1469 | # @return A class instance of required domain and mode. |
---|
1470 | #Get data objects |
---|
1471 | def get_dataobject(domain, mode=netcdf_mode_w): |
---|
1472 | """Return instance of class of given format using filename |
---|
1473 | """ |
---|
1474 | |
---|
1475 | cls = eval('Data_format_%s' % domain.format) |
---|
1476 | return cls(domain, mode) |
---|
1477 | |
---|
1478 | |
---|
1479 | ## |
---|
1480 | # @brief Convert DEM data to PTS data. |
---|
1481 | # @param basename_in Stem of input filename. |
---|
1482 | # @param basename_out Stem of output filename. |
---|
1483 | # @param easting_min |
---|
1484 | # @param easting_max |
---|
1485 | # @param northing_min |
---|
1486 | # @param northing_max |
---|
1487 | # @param use_cache |
---|
1488 | # @param verbose |
---|
1489 | # @return |
---|
1490 | def dem2pts(basename_in, basename_out=None, |
---|
1491 | easting_min=None, easting_max=None, |
---|
1492 | northing_min=None, northing_max=None, |
---|
1493 | use_cache=False, verbose=False,): |
---|
1494 | """Read Digitial Elevation model from the following NetCDF format (.dem) |
---|
1495 | |
---|
1496 | Example: |
---|
1497 | |
---|
1498 | ncols 3121 |
---|
1499 | nrows 1800 |
---|
1500 | xllcorner 722000 |
---|
1501 | yllcorner 5893000 |
---|
1502 | cellsize 25 |
---|
1503 | NODATA_value -9999 |
---|
1504 | 138.3698 137.4194 136.5062 135.5558 .......... |
---|
1505 | |
---|
1506 | Convert to NetCDF pts format which is |
---|
1507 | |
---|
1508 | points: (Nx2) float array |
---|
1509 | elevation: N float array |
---|
1510 | """ |
---|
1511 | |
---|
1512 | kwargs = {'basename_out': basename_out, |
---|
1513 | 'easting_min': easting_min, |
---|
1514 | 'easting_max': easting_max, |
---|
1515 | 'northing_min': northing_min, |
---|
1516 | 'northing_max': northing_max, |
---|
1517 | 'verbose': verbose} |
---|
1518 | |
---|
1519 | if use_cache is True: |
---|
1520 | from caching import cache |
---|
1521 | result = cache(_dem2pts, basename_in, kwargs, |
---|
1522 | dependencies = [basename_in + '.dem'], |
---|
1523 | verbose = verbose) |
---|
1524 | |
---|
1525 | else: |
---|
1526 | result = apply(_dem2pts, [basename_in], kwargs) |
---|
1527 | |
---|
1528 | return result |
---|
1529 | |
---|
1530 | |
---|
1531 | ## |
---|
1532 | # @brief |
---|
1533 | # @param basename_in |
---|
1534 | # @param basename_out |
---|
1535 | # @param verbose |
---|
1536 | # @param easting_min |
---|
1537 | # @param easting_max |
---|
1538 | # @param northing_min |
---|
1539 | # @param northing_max |
---|
1540 | def _dem2pts(basename_in, basename_out=None, verbose=False, |
---|
1541 | easting_min=None, easting_max=None, |
---|
1542 | northing_min=None, northing_max=None): |
---|
1543 | """Read Digitial Elevation model from the following NetCDF format (.dem) |
---|
1544 | |
---|
1545 | Internal function. See public function dem2pts for details. |
---|
1546 | """ |
---|
1547 | |
---|
1548 | # FIXME: Can this be written feasibly using write_pts? |
---|
1549 | |
---|
1550 | import os |
---|
1551 | from Scientific.IO.NetCDF import NetCDFFile |
---|
1552 | |
---|
1553 | root = basename_in |
---|
1554 | |
---|
1555 | # Get NetCDF |
---|
1556 | infile = NetCDFFile(root + '.dem', netcdf_mode_r) |
---|
1557 | |
---|
1558 | if verbose: print 'Reading DEM from %s' %(root + '.dem') |
---|
1559 | |
---|
1560 | ncols = infile.ncols[0] |
---|
1561 | nrows = infile.nrows[0] |
---|
1562 | xllcorner = infile.xllcorner[0] # Easting of lower left corner |
---|
1563 | yllcorner = infile.yllcorner[0] # Northing of lower left corner |
---|
1564 | cellsize = infile.cellsize[0] |
---|
1565 | NODATA_value = infile.NODATA_value[0] |
---|
1566 | dem_elevation = infile.variables['elevation'] |
---|
1567 | |
---|
1568 | zone = infile.zone[0] |
---|
1569 | false_easting = infile.false_easting[0] |
---|
1570 | false_northing = infile.false_northing[0] |
---|
1571 | |
---|
1572 | # Text strings |
---|
1573 | projection = infile.projection |
---|
1574 | datum = infile.datum |
---|
1575 | units = infile.units |
---|
1576 | |
---|
1577 | # Get output file |
---|
1578 | if basename_out == None: |
---|
1579 | ptsname = root + '.pts' |
---|
1580 | else: |
---|
1581 | ptsname = basename_out + '.pts' |
---|
1582 | |
---|
1583 | if verbose: print 'Store to NetCDF file %s' %ptsname |
---|
1584 | |
---|
1585 | # NetCDF file definition |
---|
1586 | outfile = NetCDFFile(ptsname, netcdf_mode_w) |
---|
1587 | |
---|
1588 | # Create new file |
---|
1589 | outfile.institution = 'Geoscience Australia' |
---|
1590 | outfile.description = 'NetCDF pts format for compact and portable ' \ |
---|
1591 | 'storage of spatial point data' |
---|
1592 | |
---|
1593 | # Assign default values |
---|
1594 | if easting_min is None: easting_min = xllcorner |
---|
1595 | if easting_max is None: easting_max = xllcorner + ncols*cellsize |
---|
1596 | if northing_min is None: northing_min = yllcorner |
---|
1597 | if northing_max is None: northing_max = yllcorner + nrows*cellsize |
---|
1598 | |
---|
1599 | # Compute offsets to update georeferencing |
---|
1600 | easting_offset = xllcorner - easting_min |
---|
1601 | northing_offset = yllcorner - northing_min |
---|
1602 | |
---|
1603 | # Georeferencing |
---|
1604 | outfile.zone = zone |
---|
1605 | outfile.xllcorner = easting_min # Easting of lower left corner |
---|
1606 | outfile.yllcorner = northing_min # Northing of lower left corner |
---|
1607 | outfile.false_easting = false_easting |
---|
1608 | outfile.false_northing = false_northing |
---|
1609 | |
---|
1610 | outfile.projection = projection |
---|
1611 | outfile.datum = datum |
---|
1612 | outfile.units = units |
---|
1613 | |
---|
1614 | # Grid info (FIXME: probably not going to be used, but heck) |
---|
1615 | outfile.ncols = ncols |
---|
1616 | outfile.nrows = nrows |
---|
1617 | |
---|
1618 | dem_elevation_r = num.reshape(dem_elevation, (nrows, ncols)) |
---|
1619 | totalnopoints = nrows*ncols |
---|
1620 | |
---|
1621 | # Calculating number of NODATA_values for each row in clipped region |
---|
1622 | # FIXME: use array operations to do faster |
---|
1623 | nn = 0 |
---|
1624 | k = 0 |
---|
1625 | i1_0 = 0 |
---|
1626 | j1_0 = 0 |
---|
1627 | thisj = 0 |
---|
1628 | thisi = 0 |
---|
1629 | for i in range(nrows): |
---|
1630 | y = (nrows-i-1)*cellsize + yllcorner |
---|
1631 | for j in range(ncols): |
---|
1632 | x = j*cellsize + xllcorner |
---|
1633 | if easting_min <= x <= easting_max \ |
---|
1634 | and northing_min <= y <= northing_max: |
---|
1635 | thisj = j |
---|
1636 | thisi = i |
---|
1637 | if dem_elevation_r[i,j] == NODATA_value: |
---|
1638 | nn += 1 |
---|
1639 | |
---|
1640 | if k == 0: |
---|
1641 | i1_0 = i |
---|
1642 | j1_0 = j |
---|
1643 | |
---|
1644 | k += 1 |
---|
1645 | |
---|
1646 | index1 = j1_0 |
---|
1647 | index2 = thisj |
---|
1648 | |
---|
1649 | # Dimension definitions |
---|
1650 | nrows_in_bounding_box = int(round((northing_max-northing_min)/cellsize)) |
---|
1651 | ncols_in_bounding_box = int(round((easting_max-easting_min)/cellsize)) |
---|
1652 | |
---|
1653 | clippednopoints = (thisi+1-i1_0)*(thisj+1-j1_0) |
---|
1654 | nopoints = clippednopoints-nn |
---|
1655 | |
---|
1656 | clipped_dem_elev = dem_elevation_r[i1_0:thisi+1,j1_0:thisj+1] |
---|
1657 | |
---|
1658 | if verbose: |
---|
1659 | print 'There are %d values in the elevation' %totalnopoints |
---|
1660 | print 'There are %d values in the clipped elevation' %clippednopoints |
---|
1661 | print 'There are %d NODATA_values in the clipped elevation' %nn |
---|
1662 | |
---|
1663 | outfile.createDimension('number_of_points', nopoints) |
---|
1664 | outfile.createDimension('number_of_dimensions', 2) #This is 2d data |
---|
1665 | |
---|
1666 | # Variable definitions |
---|
1667 | outfile.createVariable('points', netcdf_float, ('number_of_points', |
---|
1668 | 'number_of_dimensions')) |
---|
1669 | outfile.createVariable('elevation', netcdf_float, ('number_of_points',)) |
---|
1670 | |
---|
1671 | # Get handles to the variables |
---|
1672 | points = outfile.variables['points'] |
---|
1673 | elevation = outfile.variables['elevation'] |
---|
1674 | |
---|
1675 | lenv = index2-index1+1 |
---|
1676 | |
---|
1677 | # Store data |
---|
1678 | global_index = 0 |
---|
1679 | # for i in range(nrows): |
---|
1680 | for i in range(i1_0, thisi+1, 1): |
---|
1681 | if verbose and i % ((nrows+10)/10) == 0: |
---|
1682 | print 'Processing row %d of %d' % (i, nrows) |
---|
1683 | |
---|
1684 | lower_index = global_index |
---|
1685 | |
---|
1686 | v = dem_elevation_r[i,index1:index2+1] |
---|
1687 | no_NODATA = num.sum(v == NODATA_value) |
---|
1688 | if no_NODATA > 0: |
---|
1689 | newcols = lenv - no_NODATA # ncols_in_bounding_box - no_NODATA |
---|
1690 | else: |
---|
1691 | newcols = lenv # ncols_in_bounding_box |
---|
1692 | |
---|
1693 | telev = num.zeros(newcols, num.float) |
---|
1694 | tpoints = num.zeros((newcols, 2), num.float) |
---|
1695 | |
---|
1696 | local_index = 0 |
---|
1697 | |
---|
1698 | y = (nrows-i-1)*cellsize + yllcorner |
---|
1699 | #for j in range(ncols): |
---|
1700 | for j in range(j1_0,index2+1,1): |
---|
1701 | x = j*cellsize + xllcorner |
---|
1702 | if easting_min <= x <= easting_max \ |
---|
1703 | and northing_min <= y <= northing_max \ |
---|
1704 | and dem_elevation_r[i,j] <> NODATA_value: |
---|
1705 | tpoints[local_index, :] = [x-easting_min, y-northing_min] |
---|
1706 | telev[local_index] = dem_elevation_r[i, j] |
---|
1707 | global_index += 1 |
---|
1708 | local_index += 1 |
---|
1709 | |
---|
1710 | upper_index = global_index |
---|
1711 | |
---|
1712 | if upper_index == lower_index + newcols: |
---|
1713 | points[lower_index:upper_index, :] = tpoints |
---|
1714 | elevation[lower_index:upper_index] = telev |
---|
1715 | |
---|
1716 | assert global_index == nopoints, 'index not equal to number of points' |
---|
1717 | |
---|
1718 | infile.close() |
---|
1719 | outfile.close() |
---|
1720 | |
---|
1721 | |
---|
1722 | ## |
---|
1723 | # @brief Return block of surface lines for each cross section |
---|
1724 | # @param lines Iterble of text lines to process. |
---|
1725 | # @note BROKEN? UNUSED? |
---|
1726 | def _read_hecras_cross_sections(lines): |
---|
1727 | """Return block of surface lines for each cross section |
---|
1728 | Starts with SURFACE LINE, |
---|
1729 | Ends with END CROSS-SECTION |
---|
1730 | """ |
---|
1731 | |
---|
1732 | points = [] |
---|
1733 | |
---|
1734 | reading_surface = False |
---|
1735 | for i, line in enumerate(lines): |
---|
1736 | if len(line.strip()) == 0: #Ignore blanks |
---|
1737 | continue |
---|
1738 | |
---|
1739 | if lines[i].strip().startswith('SURFACE LINE'): |
---|
1740 | reading_surface = True |
---|
1741 | continue |
---|
1742 | |
---|
1743 | if lines[i].strip().startswith('END') and reading_surface: |
---|
1744 | yield points |
---|
1745 | reading_surface = False |
---|
1746 | points = [] |
---|
1747 | |
---|
1748 | if reading_surface: |
---|
1749 | fields = line.strip().split(',') |
---|
1750 | easting = float(fields[0]) |
---|
1751 | northing = float(fields[1]) |
---|
1752 | elevation = float(fields[2]) |
---|
1753 | points.append([easting, northing, elevation]) |
---|
1754 | |
---|
1755 | |
---|
1756 | ## |
---|
1757 | # @brief Convert HECRAS (.sdf) file to PTS file. |
---|
1758 | # @param basename_in Sterm of input filename. |
---|
1759 | # @param basename_out Sterm of output filename. |
---|
1760 | # @param verbose True if this function is to be verbose. |
---|
1761 | def hecras_cross_sections2pts(basename_in, |
---|
1762 | basename_out=None, |
---|
1763 | verbose=False): |
---|
1764 | """Read HEC-RAS Elevation datal from the following ASCII format (.sdf) |
---|
1765 | |
---|
1766 | Example: |
---|
1767 | |
---|
1768 | # RAS export file created on Mon 15Aug2005 11:42 |
---|
1769 | # by HEC-RAS Version 3.1.1 |
---|
1770 | |
---|
1771 | BEGIN HEADER: |
---|
1772 | UNITS: METRIC |
---|
1773 | DTM TYPE: TIN |
---|
1774 | DTM: v:\1\cit\perth_topo\river_tin |
---|
1775 | STREAM LAYER: c:\local\hecras\21_02_03\up_canning_cent3d.shp |
---|
1776 | CROSS-SECTION LAYER: c:\local\hecras\21_02_03\up_can_xs3d.shp |
---|
1777 | MAP PROJECTION: UTM |
---|
1778 | PROJECTION ZONE: 50 |
---|
1779 | DATUM: AGD66 |
---|
1780 | VERTICAL DATUM: |
---|
1781 | NUMBER OF REACHES: 19 |
---|
1782 | NUMBER OF CROSS-SECTIONS: 14206 |
---|
1783 | END HEADER: |
---|
1784 | |
---|
1785 | Only the SURFACE LINE data of the following form will be utilised |
---|
1786 | CROSS-SECTION: |
---|
1787 | STREAM ID:Southern-Wungong |
---|
1788 | REACH ID:Southern-Wungong |
---|
1789 | STATION:19040.* |
---|
1790 | CUT LINE: |
---|
1791 | 405548.671603161 , 6438142.7594925 |
---|
1792 | 405734.536092045 , 6438326.10404912 |
---|
1793 | 405745.130459356 , 6438331.48627354 |
---|
1794 | 405813.89633823 , 6438368.6272789 |
---|
1795 | SURFACE LINE: |
---|
1796 | 405548.67, 6438142.76, 35.37 |
---|
1797 | 405552.24, 6438146.28, 35.41 |
---|
1798 | 405554.78, 6438148.78, 35.44 |
---|
1799 | 405555.80, 6438149.79, 35.44 |
---|
1800 | 405559.37, 6438153.31, 35.45 |
---|
1801 | 405560.88, 6438154.81, 35.44 |
---|
1802 | 405562.93, 6438156.83, 35.42 |
---|
1803 | 405566.50, 6438160.35, 35.38 |
---|
1804 | 405566.99, 6438160.83, 35.37 |
---|
1805 | ... |
---|
1806 | END CROSS-SECTION |
---|
1807 | |
---|
1808 | Convert to NetCDF pts format which is |
---|
1809 | |
---|
1810 | points: (Nx2) float array |
---|
1811 | elevation: N float array |
---|
1812 | """ |
---|
1813 | |
---|
1814 | import os |
---|
1815 | from Scientific.IO.NetCDF import NetCDFFile |
---|
1816 | |
---|
1817 | root = basename_in |
---|
1818 | |
---|
1819 | # Get ASCII file |
---|
1820 | infile = open(root + '.sdf', 'r') |
---|
1821 | |
---|
1822 | if verbose: print 'Reading DEM from %s' %(root + '.sdf') |
---|
1823 | |
---|
1824 | lines = infile.readlines() |
---|
1825 | infile.close() |
---|
1826 | |
---|
1827 | if verbose: print 'Converting to pts format' |
---|
1828 | |
---|
1829 | # Scan through the header, picking up stuff we need. |
---|
1830 | i = 0 |
---|
1831 | while lines[i].strip() == '' or lines[i].strip().startswith('#'): |
---|
1832 | i += 1 |
---|
1833 | |
---|
1834 | assert lines[i].strip().upper() == 'BEGIN HEADER:' |
---|
1835 | i += 1 |
---|
1836 | |
---|
1837 | assert lines[i].strip().upper().startswith('UNITS:') |
---|
1838 | units = lines[i].strip().split()[1] |
---|
1839 | i += 1 |
---|
1840 | |
---|
1841 | assert lines[i].strip().upper().startswith('DTM TYPE:') |
---|
1842 | i += 1 |
---|
1843 | |
---|
1844 | assert lines[i].strip().upper().startswith('DTM:') |
---|
1845 | i += 1 |
---|
1846 | |
---|
1847 | assert lines[i].strip().upper().startswith('STREAM') |
---|
1848 | i += 1 |
---|
1849 | |
---|
1850 | assert lines[i].strip().upper().startswith('CROSS') |
---|
1851 | i += 1 |
---|
1852 | |
---|
1853 | assert lines[i].strip().upper().startswith('MAP PROJECTION:') |
---|
1854 | projection = lines[i].strip().split(':')[1] |
---|
1855 | i += 1 |
---|
1856 | |
---|
1857 | assert lines[i].strip().upper().startswith('PROJECTION ZONE:') |
---|
1858 | zone = int(lines[i].strip().split(':')[1]) |
---|
1859 | i += 1 |
---|
1860 | |
---|
1861 | assert lines[i].strip().upper().startswith('DATUM:') |
---|
1862 | datum = lines[i].strip().split(':')[1] |
---|
1863 | i += 1 |
---|
1864 | |
---|
1865 | assert lines[i].strip().upper().startswith('VERTICAL DATUM:') |
---|
1866 | i += 1 |
---|
1867 | |
---|
1868 | assert lines[i].strip().upper().startswith('NUMBER OF REACHES:') |
---|
1869 | i += 1 |
---|
1870 | |
---|
1871 | assert lines[i].strip().upper().startswith('NUMBER OF CROSS-SECTIONS:') |
---|
1872 | number_of_cross_sections = int(lines[i].strip().split(':')[1]) |
---|
1873 | i += 1 |
---|
1874 | |
---|
1875 | # Now read all points |
---|
1876 | points = [] |
---|
1877 | elevation = [] |
---|
1878 | for j, entries in enumerate(_read_hecras_cross_sections(lines[i:])): |
---|
1879 | for k, entry in enumerate(entries): |
---|
1880 | points.append(entry[:2]) |
---|
1881 | elevation.append(entry[2]) |
---|
1882 | |
---|
1883 | msg = 'Actual #number_of_cross_sections == %d, Reported as %d'\ |
---|
1884 | %(j+1, number_of_cross_sections) |
---|
1885 | assert j+1 == number_of_cross_sections, msg |
---|
1886 | |
---|
1887 | # Get output file, write PTS data |
---|
1888 | if basename_out == None: |
---|
1889 | ptsname = root + '.pts' |
---|
1890 | else: |
---|
1891 | ptsname = basename_out + '.pts' |
---|
1892 | |
---|
1893 | geo_ref = Geo_reference(zone, 0, 0, datum, projection, units) |
---|
1894 | geo = Geospatial_data(points, {"elevation":elevation}, |
---|
1895 | verbose=verbose, geo_reference=geo_ref) |
---|
1896 | geo.export_points_file(ptsname) |
---|
1897 | |
---|
1898 | |
---|
1899 | ## |
---|
1900 | # @brief |
---|
1901 | # @param basename_in |
---|
1902 | # @param extra_name_out |
---|
1903 | # @param quantities |
---|
1904 | # @param timestep |
---|
1905 | # @param reduction |
---|
1906 | # @param cellsize |
---|
1907 | # @param number_of_decimal_places |
---|
1908 | # @param NODATA_value |
---|
1909 | # @param easting_min |
---|
1910 | # @param easting_max |
---|
1911 | # @param northing_min |
---|
1912 | # @param northing_max |
---|
1913 | # @param verbose |
---|
1914 | # @param origin |
---|
1915 | # @param datum |
---|
1916 | # @param format |
---|
1917 | # @return |
---|
1918 | def export_grid(basename_in, extra_name_out=None, |
---|
1919 | quantities=None, # defaults to elevation |
---|
1920 | timestep=None, |
---|
1921 | reduction=None, |
---|
1922 | cellsize=10, |
---|
1923 | number_of_decimal_places=None, |
---|
1924 | NODATA_value=-9999, |
---|
1925 | easting_min=None, |
---|
1926 | easting_max=None, |
---|
1927 | northing_min=None, |
---|
1928 | northing_max=None, |
---|
1929 | verbose=False, |
---|
1930 | origin=None, |
---|
1931 | datum='WGS84', |
---|
1932 | format='ers'): |
---|
1933 | """Wrapper for sww2dem. |
---|
1934 | See sww2dem to find out what most of the parameters do. |
---|
1935 | |
---|
1936 | Quantities is a list of quantities. Each quantity will be |
---|
1937 | calculated for each sww file. |
---|
1938 | |
---|
1939 | This returns the basenames of the files returned, which is made up |
---|
1940 | of the dir and all of the file name, except the extension. |
---|
1941 | |
---|
1942 | This function returns the names of the files produced. |
---|
1943 | |
---|
1944 | It will also produce as many output files as there are input sww files. |
---|
1945 | """ |
---|
1946 | |
---|
1947 | if quantities is None: |
---|
1948 | quantities = ['elevation'] |
---|
1949 | |
---|
1950 | if type(quantities) is str: |
---|
1951 | quantities = [quantities] |
---|
1952 | |
---|
1953 | # How many sww files are there? |
---|
1954 | dir, base = os.path.split(basename_in) |
---|
1955 | |
---|
1956 | iterate_over = get_all_swwfiles(dir, base, verbose) |
---|
1957 | |
---|
1958 | if dir == "": |
---|
1959 | dir = "." # Unix compatibility |
---|
1960 | |
---|
1961 | files_out = [] |
---|
1962 | for sww_file in iterate_over: |
---|
1963 | for quantity in quantities: |
---|
1964 | if extra_name_out is None: |
---|
1965 | basename_out = sww_file + '_' + quantity |
---|
1966 | else: |
---|
1967 | basename_out = sww_file + '_' + quantity + '_' + extra_name_out |
---|
1968 | |
---|
1969 | file_out = sww2dem(dir+sep+sww_file, dir+sep+basename_out, |
---|
1970 | quantity, |
---|
1971 | timestep, |
---|
1972 | reduction, |
---|
1973 | cellsize, |
---|
1974 | number_of_decimal_places, |
---|
1975 | NODATA_value, |
---|
1976 | easting_min, |
---|
1977 | easting_max, |
---|
1978 | northing_min, |
---|
1979 | northing_max, |
---|
1980 | verbose, |
---|
1981 | origin, |
---|
1982 | datum, |
---|
1983 | format) |
---|
1984 | files_out.append(file_out) |
---|
1985 | return files_out |
---|
1986 | |
---|
1987 | |
---|
1988 | ## |
---|
1989 | # @brief |
---|
1990 | # @param production_dirs |
---|
1991 | # @param output_dir |
---|
1992 | # @param scenario_name |
---|
1993 | # @param gauges_dir_name |
---|
1994 | # @param plot_quantity |
---|
1995 | # @param generate_fig |
---|
1996 | # @param reportname |
---|
1997 | # @param surface |
---|
1998 | # @param time_min |
---|
1999 | # @param time_max |
---|
2000 | # @param title_on |
---|
2001 | # @param verbose |
---|
2002 | # @param nodes |
---|
2003 | def get_timeseries(production_dirs, output_dir, scenario_name, gauges_dir_name, |
---|
2004 | plot_quantity, generate_fig=False, |
---|
2005 | reportname=None, surface=False, time_min=None, |
---|
2006 | time_max=None, title_on=False, verbose=True, |
---|
2007 | nodes=None): |
---|
2008 | """ |
---|
2009 | nodes - number of processes used. |
---|
2010 | |
---|
2011 | warning - this function has no tests |
---|
2012 | """ |
---|
2013 | |
---|
2014 | if reportname == None: |
---|
2015 | report = False |
---|
2016 | else: |
---|
2017 | report = True |
---|
2018 | |
---|
2019 | if nodes is None: |
---|
2020 | is_parallel = False |
---|
2021 | else: |
---|
2022 | is_parallel = True |
---|
2023 | |
---|
2024 | # Generate figures |
---|
2025 | swwfiles = {} |
---|
2026 | if is_parallel is True: |
---|
2027 | for i in range(nodes): |
---|
2028 | print 'Sending node %d of %d' % (i, nodes) |
---|
2029 | swwfiles = {} |
---|
2030 | if not reportname == None: |
---|
2031 | reportname = report_name + '_%s' % i |
---|
2032 | for label_id in production_dirs.keys(): |
---|
2033 | if label_id == 'boundaries': |
---|
2034 | swwfile = best_boundary_sww |
---|
2035 | else: |
---|
2036 | file_loc = output_dir + label_id + sep |
---|
2037 | sww_extra = '_P%s_%s' % (i, nodes) |
---|
2038 | swwfile = file_loc + scenario_name + sww_extra + '.sww' |
---|
2039 | print 'swwfile', swwfile |
---|
2040 | swwfiles[swwfile] = label_id |
---|
2041 | |
---|
2042 | texname, elev_output = sww2timeseries(swwfiles, |
---|
2043 | gauges_dir_name, |
---|
2044 | production_dirs, |
---|
2045 | report=report, |
---|
2046 | reportname=reportname, |
---|
2047 | plot_quantity=plot_quantity, |
---|
2048 | generate_fig=generate_fig, |
---|
2049 | surface=surface, |
---|
2050 | time_min=time_min, |
---|
2051 | time_max=time_max, |
---|
2052 | title_on=title_on, |
---|
2053 | verbose=verbose) |
---|
2054 | else: |
---|
2055 | for label_id in production_dirs.keys(): |
---|
2056 | if label_id == 'boundaries': |
---|
2057 | print 'boundaries' |
---|
2058 | file_loc = project.boundaries_in_dir |
---|
2059 | swwfile = project.boundaries_dir_name3 + '.sww' |
---|
2060 | # swwfile = boundary_dir_filename |
---|
2061 | else: |
---|
2062 | file_loc = output_dir + label_id + sep |
---|
2063 | swwfile = file_loc + scenario_name + '.sww' |
---|
2064 | swwfiles[swwfile] = label_id |
---|
2065 | |
---|
2066 | texname, elev_output = sww2timeseries(swwfiles, |
---|
2067 | gauges_dir_name, |
---|
2068 | production_dirs, |
---|
2069 | report=report, |
---|
2070 | reportname=reportname, |
---|
2071 | plot_quantity=plot_quantity, |
---|
2072 | generate_fig=generate_fig, |
---|
2073 | surface=surface, |
---|
2074 | time_min=time_min, |
---|
2075 | time_max=time_max, |
---|
2076 | title_on=title_on, |
---|
2077 | verbose=verbose) |
---|
2078 | |
---|
2079 | |
---|
2080 | ## |
---|
2081 | # @brief Convert SWW file to DEM file (.asc or .ers). |
---|
2082 | # @param basename_in |
---|
2083 | # @param basename_out |
---|
2084 | # @param quantity |
---|
2085 | # @param timestep |
---|
2086 | # @param reduction |
---|
2087 | # @param cellsize |
---|
2088 | # @param number_of_decimal_places |
---|
2089 | # @param NODATA_value |
---|
2090 | # @param easting_min |
---|
2091 | # @param easting_max |
---|
2092 | # @param northing_min |
---|
2093 | # @param northing_max |
---|
2094 | # @param verbose |
---|
2095 | # @param origin |
---|
2096 | # @param datum |
---|
2097 | # @param format |
---|
2098 | # @return |
---|
2099 | def sww2dem(basename_in, basename_out=None, |
---|
2100 | quantity=None, # defaults to elevation |
---|
2101 | timestep=None, |
---|
2102 | reduction=None, |
---|
2103 | cellsize=10, |
---|
2104 | number_of_decimal_places=None, |
---|
2105 | NODATA_value=-9999, |
---|
2106 | easting_min=None, |
---|
2107 | easting_max=None, |
---|
2108 | northing_min=None, |
---|
2109 | northing_max=None, |
---|
2110 | verbose=False, |
---|
2111 | origin=None, |
---|
2112 | datum='WGS84', |
---|
2113 | format='ers', |
---|
2114 | block_size=None): |
---|
2115 | """Read SWW file and convert to Digitial Elevation model format |
---|
2116 | (.asc or .ers) |
---|
2117 | |
---|
2118 | Example (ASC): |
---|
2119 | ncols 3121 |
---|
2120 | nrows 1800 |
---|
2121 | xllcorner 722000 |
---|
2122 | yllcorner 5893000 |
---|
2123 | cellsize 25 |
---|
2124 | NODATA_value -9999 |
---|
2125 | 138.3698 137.4194 136.5062 135.5558 .......... |
---|
2126 | |
---|
2127 | The number of decimal places can be specified by the user to save |
---|
2128 | on disk space requirements by specifying in the call to sww2dem. |
---|
2129 | |
---|
2130 | Also write accompanying file with same basename_in but extension .prj |
---|
2131 | used to fix the UTM zone, datum, false northings and eastings. |
---|
2132 | |
---|
2133 | The prj format is assumed to be as |
---|
2134 | |
---|
2135 | Projection UTM |
---|
2136 | Zone 56 |
---|
2137 | Datum WGS84 |
---|
2138 | Zunits NO |
---|
2139 | Units METERS |
---|
2140 | Spheroid WGS84 |
---|
2141 | Xshift 0.0000000000 |
---|
2142 | Yshift 10000000.0000000000 |
---|
2143 | Parameters |
---|
2144 | |
---|
2145 | |
---|
2146 | The parameter quantity must be the name of an existing quantity or |
---|
2147 | an expression involving existing quantities. The default is |
---|
2148 | 'elevation'. Quantity is not a list of quantities. |
---|
2149 | |
---|
2150 | if timestep (an index) is given, output quantity at that timestep |
---|
2151 | |
---|
2152 | if reduction is given use that to reduce quantity over all timesteps. |
---|
2153 | |
---|
2154 | datum |
---|
2155 | |
---|
2156 | format can be either 'asc' or 'ers' |
---|
2157 | block_size - sets the number of slices along the non-time axis to |
---|
2158 | process in one block. |
---|
2159 | """ |
---|
2160 | |
---|
2161 | import sys |
---|
2162 | |
---|
2163 | from anuga.utilities.polygon import inside_polygon, outside_polygon, \ |
---|
2164 | separate_points_by_polygon |
---|
2165 | from anuga.abstract_2d_finite_volumes.util import \ |
---|
2166 | apply_expression_to_dictionary |
---|
2167 | |
---|
2168 | msg = 'Format must be either asc or ers' |
---|
2169 | assert format.lower() in ['asc', 'ers'], msg |
---|
2170 | |
---|
2171 | false_easting = 500000 |
---|
2172 | false_northing = 10000000 |
---|
2173 | |
---|
2174 | if quantity is None: |
---|
2175 | quantity = 'elevation' |
---|
2176 | |
---|
2177 | if reduction is None: |
---|
2178 | reduction = max |
---|
2179 | |
---|
2180 | if basename_out is None: |
---|
2181 | basename_out = basename_in + '_%s' % quantity |
---|
2182 | |
---|
2183 | if quantity_formula.has_key(quantity): |
---|
2184 | quantity = quantity_formula[quantity] |
---|
2185 | |
---|
2186 | if number_of_decimal_places is None: |
---|
2187 | number_of_decimal_places = 3 |
---|
2188 | |
---|
2189 | if block_size is None: |
---|
2190 | block_size = DEFAULT_BLOCK_SIZE |
---|
2191 | |
---|
2192 | # Read SWW file |
---|
2193 | swwfile = basename_in + '.sww' |
---|
2194 | demfile = basename_out + '.' + format |
---|
2195 | # Note the use of a .ers extension is optional (write_ermapper_grid will |
---|
2196 | # deal with either option |
---|
2197 | |
---|
2198 | # Read sww file |
---|
2199 | if verbose: |
---|
2200 | print 'Reading from %s' % swwfile |
---|
2201 | print 'Output directory is %s' % basename_out |
---|
2202 | |
---|
2203 | from Scientific.IO.NetCDF import NetCDFFile |
---|
2204 | fid = NetCDFFile(swwfile) |
---|
2205 | |
---|
2206 | #Get extent and reference |
---|
2207 | x = fid.variables['x'][:] |
---|
2208 | y = fid.variables['y'][:] |
---|
2209 | volumes = fid.variables['volumes'][:] |
---|
2210 | if timestep is not None: |
---|
2211 | times = fid.variables['time'][timestep] |
---|
2212 | else: |
---|
2213 | times = fid.variables['time'][:] |
---|
2214 | |
---|
2215 | number_of_timesteps = fid.dimensions['number_of_timesteps'] |
---|
2216 | number_of_points = fid.dimensions['number_of_points'] |
---|
2217 | |
---|
2218 | if origin is None: |
---|
2219 | # Get geo_reference |
---|
2220 | # sww files don't have to have a geo_ref |
---|
2221 | try: |
---|
2222 | geo_reference = Geo_reference(NetCDFObject=fid) |
---|
2223 | except AttributeError, e: |
---|
2224 | geo_reference = Geo_reference() # Default georef object |
---|
2225 | |
---|
2226 | xllcorner = geo_reference.get_xllcorner() |
---|
2227 | yllcorner = geo_reference.get_yllcorner() |
---|
2228 | zone = geo_reference.get_zone() |
---|
2229 | else: |
---|
2230 | zone = origin[0] |
---|
2231 | xllcorner = origin[1] |
---|
2232 | yllcorner = origin[2] |
---|
2233 | |
---|
2234 | # FIXME: Refactor using code from Interpolation_function.statistics |
---|
2235 | # (in interpolate.py) |
---|
2236 | # Something like print swwstats(swwname) |
---|
2237 | if verbose: |
---|
2238 | print '------------------------------------------------' |
---|
2239 | print 'Statistics of SWW file:' |
---|
2240 | print ' Name: %s' %swwfile |
---|
2241 | print ' Reference:' |
---|
2242 | print ' Lower left corner: [%f, %f]'\ |
---|
2243 | %(xllcorner, yllcorner) |
---|
2244 | if timestep is not None: |
---|
2245 | print ' Time: %f' %(times) |
---|
2246 | else: |
---|
2247 | print ' Start time: %f' %fid.starttime[0] |
---|
2248 | print ' Extent:' |
---|
2249 | print ' x [m] in [%f, %f], len(x) == %d'\ |
---|
2250 | %(num.min(x), num.max(x), len(x.flat)) |
---|
2251 | print ' y [m] in [%f, %f], len(y) == %d'\ |
---|
2252 | %(num.min(y), num.max(y), len(y.flat)) |
---|
2253 | if timestep is not None: |
---|
2254 | print ' t [s] = %f, len(t) == %d' %(times, 1) |
---|
2255 | else: |
---|
2256 | print ' t [s] in [%f, %f], len(t) == %d'\ |
---|
2257 | %(min(times), max(times), len(times)) |
---|
2258 | print ' Quantities [SI units]:' |
---|
2259 | # Comment out for reduced memory consumption |
---|
2260 | for name in ['stage', 'xmomentum', 'ymomentum']: |
---|
2261 | q = fid.variables[name][:].flatten() |
---|
2262 | if timestep is not None: |
---|
2263 | q = q[timestep*len(x):(timestep+1)*len(x)] |
---|
2264 | if verbose: print ' %s in [%f, %f]' %(name, min(q), max(q)) |
---|
2265 | for name in ['elevation']: |
---|
2266 | q = fid.variables[name][:].flatten() |
---|
2267 | if verbose: print ' %s in [%f, %f]' %(name, min(q), max(q)) |
---|
2268 | |
---|
2269 | # Get the variables in the supplied expression. |
---|
2270 | # This may throw a SyntaxError exception. |
---|
2271 | var_list = get_vars_in_expression(quantity) |
---|
2272 | |
---|
2273 | # Check that we have the required variables in the SWW file. |
---|
2274 | missing_vars = [] |
---|
2275 | for name in var_list: |
---|
2276 | try: |
---|
2277 | _ = fid.variables[name] |
---|
2278 | except: |
---|
2279 | missing_vars.append(name) |
---|
2280 | if missing_vars: |
---|
2281 | msg = ("In expression '%s', variables %s are not in the SWW file '%s'" |
---|
2282 | % (quantity, swwfile)) |
---|
2283 | raise Exception, msg |
---|
2284 | |
---|
2285 | # Create result array and start filling, block by block. |
---|
2286 | result = num.zeros(number_of_points, num.float) |
---|
2287 | |
---|
2288 | for start_slice in xrange(0, number_of_points, block_size): |
---|
2289 | # limit slice size to array end if at last block |
---|
2290 | end_slice = min(start_slice + block_size, number_of_points) |
---|
2291 | |
---|
2292 | # get slices of all required variables |
---|
2293 | q_dict = {} |
---|
2294 | for name in var_list: |
---|
2295 | # check if variable has time axis |
---|
2296 | if len(fid.variables[name].shape) == 2: |
---|
2297 | q_dict[name] = fid.variables[name][:,start_slice:end_slice] |
---|
2298 | else: # no time axis |
---|
2299 | q_dict[name] = fid.variables[name][start_slice:end_slice] |
---|
2300 | |
---|
2301 | # Evaluate expression with quantities found in SWW file |
---|
2302 | res = apply_expression_to_dictionary(quantity, q_dict) |
---|
2303 | |
---|
2304 | if len(res.shape) == 2: |
---|
2305 | new_res = num.zeros(res.shape[1], num.float) |
---|
2306 | for k in xrange(res.shape[1]): |
---|
2307 | new_res[k] = reduction(res[:,k]) |
---|
2308 | res = new_res |
---|
2309 | |
---|
2310 | result[start_slice:end_slice] = res |
---|
2311 | |
---|
2312 | #Post condition: Now q has dimension: number_of_points |
---|
2313 | assert len(result.shape) == 1 |
---|
2314 | assert result.shape[0] == number_of_points |
---|
2315 | |
---|
2316 | if verbose: |
---|
2317 | print 'Processed values for %s are in [%f, %f]' % \ |
---|
2318 | (quantity, min(result), max(result)) |
---|
2319 | |
---|
2320 | #Create grid and update xll/yll corner and x,y |
---|
2321 | #Relative extent |
---|
2322 | if easting_min is None: |
---|
2323 | xmin = min(x) |
---|
2324 | else: |
---|
2325 | xmin = easting_min - xllcorner |
---|
2326 | |
---|
2327 | if easting_max is None: |
---|
2328 | xmax = max(x) |
---|
2329 | else: |
---|
2330 | xmax = easting_max - xllcorner |
---|
2331 | |
---|
2332 | if northing_min is None: |
---|
2333 | ymin = min(y) |
---|
2334 | else: |
---|
2335 | ymin = northing_min - yllcorner |
---|
2336 | |
---|
2337 | if northing_max is None: |
---|
2338 | ymax = max(y) |
---|
2339 | else: |
---|
2340 | ymax = northing_max - yllcorner |
---|
2341 | |
---|
2342 | msg = 'xmax must be greater than or equal to xmin.\n' |
---|
2343 | msg += 'I got xmin = %f, xmax = %f' %(xmin, xmax) |
---|
2344 | assert xmax >= xmin, msg |
---|
2345 | |
---|
2346 | msg = 'ymax must be greater than or equal to xmin.\n' |
---|
2347 | msg += 'I got ymin = %f, ymax = %f' %(ymin, ymax) |
---|
2348 | assert ymax >= ymin, msg |
---|
2349 | |
---|
2350 | if verbose: print 'Creating grid' |
---|
2351 | ncols = int((xmax-xmin)/cellsize) + 1 |
---|
2352 | nrows = int((ymax-ymin)/cellsize) + 1 |
---|
2353 | |
---|
2354 | #New absolute reference and coordinates |
---|
2355 | newxllcorner = xmin + xllcorner |
---|
2356 | newyllcorner = ymin + yllcorner |
---|
2357 | |
---|
2358 | x = x + xllcorner - newxllcorner |
---|
2359 | y = y + yllcorner - newyllcorner |
---|
2360 | |
---|
2361 | vertex_points = num.concatenate ((x[:,num.newaxis], y[:,num.newaxis]), axis=1) |
---|
2362 | assert len(vertex_points.shape) == 2 |
---|
2363 | |
---|
2364 | grid_points = num.zeros ((ncols*nrows, 2), num.float) |
---|
2365 | |
---|
2366 | for i in xrange(nrows): |
---|
2367 | if format.lower() == 'asc': |
---|
2368 | yg = i * cellsize |
---|
2369 | else: |
---|
2370 | #this will flip the order of the y values for ers |
---|
2371 | yg = (nrows-i) * cellsize |
---|
2372 | |
---|
2373 | for j in xrange(ncols): |
---|
2374 | xg = j * cellsize |
---|
2375 | k = i*ncols + j |
---|
2376 | |
---|
2377 | grid_points[k, 0] = xg |
---|
2378 | grid_points[k, 1] = yg |
---|
2379 | |
---|
2380 | #Interpolate |
---|
2381 | from anuga.fit_interpolate.interpolate import Interpolate |
---|
2382 | |
---|
2383 | # Remove loners from vertex_points, volumes here |
---|
2384 | vertex_points, volumes = remove_lone_verts(vertex_points, volumes) |
---|
2385 | #export_mesh_file('monkey.tsh',{'vertices':vertex_points, 'triangles':volumes}) |
---|
2386 | #import sys; sys.exit() |
---|
2387 | interp = Interpolate(vertex_points, volumes, verbose = verbose) |
---|
2388 | |
---|
2389 | #Interpolate using quantity values |
---|
2390 | if verbose: print 'Interpolating' |
---|
2391 | grid_values = interp.interpolate(result, grid_points).flatten() |
---|
2392 | |
---|
2393 | if verbose: |
---|
2394 | print 'Interpolated values are in [%f, %f]' %(num.min(grid_values), |
---|
2395 | num.max(grid_values)) |
---|
2396 | |
---|
2397 | #Assign NODATA_value to all points outside bounding polygon (from interpolation mesh) |
---|
2398 | P = interp.mesh.get_boundary_polygon() |
---|
2399 | outside_indices = outside_polygon(grid_points, P, closed=True) |
---|
2400 | |
---|
2401 | for i in outside_indices: |
---|
2402 | grid_values[i] = NODATA_value |
---|
2403 | |
---|
2404 | if format.lower() == 'ers': |
---|
2405 | # setup ERS header information |
---|
2406 | grid_values = num.reshape(grid_values, (nrows, ncols)) |
---|
2407 | header = {} |
---|
2408 | header['datum'] = '"' + datum + '"' |
---|
2409 | # FIXME The use of hardwired UTM and zone number needs to be made optional |
---|
2410 | # FIXME Also need an automatic test for coordinate type (i.e. EN or LL) |
---|
2411 | header['projection'] = '"UTM-' + str(zone) + '"' |
---|
2412 | header['coordinatetype'] = 'EN' |
---|
2413 | if header['coordinatetype'] == 'LL': |
---|
2414 | header['longitude'] = str(newxllcorner) |
---|
2415 | header['latitude'] = str(newyllcorner) |
---|
2416 | elif header['coordinatetype'] == 'EN': |
---|
2417 | header['eastings'] = str(newxllcorner) |
---|
2418 | header['northings'] = str(newyllcorner) |
---|
2419 | header['nullcellvalue'] = str(NODATA_value) |
---|
2420 | header['xdimension'] = str(cellsize) |
---|
2421 | header['ydimension'] = str(cellsize) |
---|
2422 | header['value'] = '"' + quantity + '"' |
---|
2423 | #header['celltype'] = 'IEEE8ByteReal' #FIXME: Breaks unit test |
---|
2424 | |
---|
2425 | #Write |
---|
2426 | if verbose: print 'Writing %s' %demfile |
---|
2427 | |
---|
2428 | import ermapper_grids |
---|
2429 | |
---|
2430 | ermapper_grids.write_ermapper_grid(demfile, grid_values, header) |
---|
2431 | |
---|
2432 | fid.close() |
---|
2433 | else: |
---|
2434 | #Write to Ascii format |
---|
2435 | #Write prj file |
---|
2436 | prjfile = basename_out + '.prj' |
---|
2437 | |
---|
2438 | if verbose: print 'Writing %s' %prjfile |
---|
2439 | prjid = open(prjfile, 'w') |
---|
2440 | prjid.write('Projection %s\n' %'UTM') |
---|
2441 | prjid.write('Zone %d\n' %zone) |
---|
2442 | prjid.write('Datum %s\n' %datum) |
---|
2443 | prjid.write('Zunits NO\n') |
---|
2444 | prjid.write('Units METERS\n') |
---|
2445 | prjid.write('Spheroid %s\n' %datum) |
---|
2446 | prjid.write('Xshift %d\n' %false_easting) |
---|
2447 | prjid.write('Yshift %d\n' %false_northing) |
---|
2448 | prjid.write('Parameters\n') |
---|
2449 | prjid.close() |
---|
2450 | |
---|
2451 | if verbose: print 'Writing %s' %demfile |
---|
2452 | |
---|
2453 | ascid = open(demfile, 'w') |
---|
2454 | |
---|
2455 | ascid.write('ncols %d\n' %ncols) |
---|
2456 | ascid.write('nrows %d\n' %nrows) |
---|
2457 | ascid.write('xllcorner %d\n' %newxllcorner) |
---|
2458 | ascid.write('yllcorner %d\n' %newyllcorner) |
---|
2459 | ascid.write('cellsize %f\n' %cellsize) |
---|
2460 | ascid.write('NODATA_value %d\n' %NODATA_value) |
---|
2461 | |
---|
2462 | #Get bounding polygon from mesh |
---|
2463 | #P = interp.mesh.get_boundary_polygon() |
---|
2464 | #inside_indices = inside_polygon(grid_points, P) |
---|
2465 | for i in range(nrows): |
---|
2466 | if verbose and i % ((nrows+10)/10) == 0: |
---|
2467 | print 'Doing row %d of %d' %(i, nrows) |
---|
2468 | |
---|
2469 | base_index = (nrows-i-1)*ncols |
---|
2470 | |
---|
2471 | slice = grid_values[base_index:base_index+ncols] |
---|
2472 | #s = array2string(slice, max_line_width=sys.maxint) |
---|
2473 | s = num.array2string(slice, max_line_width=sys.maxint, |
---|
2474 | precision=number_of_decimal_places) |
---|
2475 | ascid.write(s[1:-1] + '\n') |
---|
2476 | |
---|
2477 | #Close |
---|
2478 | ascid.close() |
---|
2479 | fid.close() |
---|
2480 | |
---|
2481 | return basename_out |
---|
2482 | |
---|
2483 | ################################################################################ |
---|
2484 | # Obsolete functions - mainatined for backwards compatibility |
---|
2485 | ################################################################################ |
---|
2486 | |
---|
2487 | ## |
---|
2488 | # @brief |
---|
2489 | # @param basename_in |
---|
2490 | # @param basename_out |
---|
2491 | # @param quantity |
---|
2492 | # @param timestep |
---|
2493 | # @param reduction |
---|
2494 | # @param cellsize |
---|
2495 | # @param verbose |
---|
2496 | # @param origin |
---|
2497 | # @note OBSOLETE - use sww2dem() instead. |
---|
2498 | def sww2asc(basename_in, basename_out = None, |
---|
2499 | quantity = None, |
---|
2500 | timestep = None, |
---|
2501 | reduction = None, |
---|
2502 | cellsize = 10, |
---|
2503 | verbose = False, |
---|
2504 | origin = None): |
---|
2505 | print 'sww2asc will soon be obsoleted - please use sww2dem' |
---|
2506 | sww2dem(basename_in, |
---|
2507 | basename_out = basename_out, |
---|
2508 | quantity = quantity, |
---|
2509 | timestep = timestep, |
---|
2510 | reduction = reduction, |
---|
2511 | cellsize = cellsize, |
---|
2512 | number_of_decimal_places = number_of_decimal_places, |
---|
2513 | verbose = verbose, |
---|
2514 | origin = origin, |
---|
2515 | datum = 'WGS84', |
---|
2516 | format = 'asc') |
---|
2517 | |
---|
2518 | |
---|
2519 | ## |
---|
2520 | # @brief |
---|
2521 | # @param basename_in |
---|
2522 | # @param basename_out |
---|
2523 | # @param quantity |
---|
2524 | # @param timestep |
---|
2525 | # @param reduction |
---|
2526 | # @param cellsize |
---|
2527 | # @param verbose |
---|
2528 | # @param origin |
---|
2529 | # @param datum |
---|
2530 | # @note OBSOLETE - use sww2dem() instead. |
---|
2531 | def sww2ers(basename_in, basename_out=None, |
---|
2532 | quantity=None, |
---|
2533 | timestep=None, |
---|
2534 | reduction=None, |
---|
2535 | cellsize=10, |
---|
2536 | verbose=False, |
---|
2537 | origin=None, |
---|
2538 | datum='WGS84'): |
---|
2539 | print 'sww2ers will soon be obsoleted - please use sww2dem' |
---|
2540 | sww2dem(basename_in, |
---|
2541 | basename_out=basename_out, |
---|
2542 | quantity=quantity, |
---|
2543 | timestep=timestep, |
---|
2544 | reduction=reduction, |
---|
2545 | cellsize=cellsize, |
---|
2546 | number_of_decimal_places=number_of_decimal_places, |
---|
2547 | verbose=verbose, |
---|
2548 | origin=origin, |
---|
2549 | datum=datum, |
---|
2550 | format='ers') |
---|
2551 | |
---|
2552 | ################################################################################ |
---|
2553 | # End of obsolete functions |
---|
2554 | ################################################################################ |
---|
2555 | |
---|
2556 | |
---|
2557 | ## |
---|
2558 | # @brief Convert SWW file to PTS file (at selected points). |
---|
2559 | # @param basename_in Stem name of input SWW file. |
---|
2560 | # @param basename_out Stem name of output file. |
---|
2561 | # @param data_points If given, points where quantity is to be computed. |
---|
2562 | # @param quantity Name (or expression) of existing quantity(s) (def: elevation). |
---|
2563 | # @param timestep If given, output quantity at that timestep. |
---|
2564 | # @param reduction If given, reduce quantity by this factor. |
---|
2565 | # @param NODATA_value The NODATA value (default -9999). |
---|
2566 | # @param verbose True if this function is to be verbose. |
---|
2567 | # @param origin ?? |
---|
2568 | def sww2pts(basename_in, basename_out=None, |
---|
2569 | data_points=None, |
---|
2570 | quantity=None, |
---|
2571 | timestep=None, |
---|
2572 | reduction=None, |
---|
2573 | NODATA_value=-9999, |
---|
2574 | verbose=False, |
---|
2575 | origin=None): |
---|
2576 | """Read SWW file and convert to interpolated values at selected points |
---|
2577 | |
---|
2578 | The parameter 'quantity' must be the name of an existing quantity or |
---|
2579 | an expression involving existing quantities. The default is 'elevation'. |
---|
2580 | |
---|
2581 | if timestep (an index) is given, output quantity at that timestep. |
---|
2582 | |
---|
2583 | if reduction is given use that to reduce quantity over all timesteps. |
---|
2584 | |
---|
2585 | data_points (Nx2 array) give locations of points where quantity is to |
---|
2586 | be computed. |
---|
2587 | """ |
---|
2588 | |
---|
2589 | import sys |
---|
2590 | from anuga.utilities.polygon import inside_polygon, outside_polygon, \ |
---|
2591 | separate_points_by_polygon |
---|
2592 | from anuga.abstract_2d_finite_volumes.util import \ |
---|
2593 | apply_expression_to_dictionary |
---|
2594 | from anuga.geospatial_data.geospatial_data import Geospatial_data |
---|
2595 | |
---|
2596 | if quantity is None: |
---|
2597 | quantity = 'elevation' |
---|
2598 | |
---|
2599 | if reduction is None: |
---|
2600 | reduction = max |
---|
2601 | |
---|
2602 | if basename_out is None: |
---|
2603 | basename_out = basename_in + '_%s' % quantity |
---|
2604 | |
---|
2605 | swwfile = basename_in + '.sww' |
---|
2606 | ptsfile = basename_out + '.pts' |
---|
2607 | |
---|
2608 | # Read sww file |
---|
2609 | if verbose: print 'Reading from %s' % swwfile |
---|
2610 | from Scientific.IO.NetCDF import NetCDFFile |
---|
2611 | fid = NetCDFFile(swwfile) |
---|
2612 | |
---|
2613 | # Get extent and reference |
---|
2614 | x = fid.variables['x'][:] |
---|
2615 | y = fid.variables['y'][:] |
---|
2616 | volumes = fid.variables['volumes'][:] |
---|
2617 | |
---|
2618 | number_of_timesteps = fid.dimensions['number_of_timesteps'] |
---|
2619 | number_of_points = fid.dimensions['number_of_points'] |
---|
2620 | if origin is None: |
---|
2621 | # Get geo_reference |
---|
2622 | # sww files don't have to have a geo_ref |
---|
2623 | try: |
---|
2624 | geo_reference = Geo_reference(NetCDFObject=fid) |
---|
2625 | except AttributeError, e: |
---|
2626 | geo_reference = Geo_reference() # Default georef object |
---|
2627 | |
---|
2628 | xllcorner = geo_reference.get_xllcorner() |
---|
2629 | yllcorner = geo_reference.get_yllcorner() |
---|
2630 | zone = geo_reference.get_zone() |
---|
2631 | else: |
---|
2632 | zone = origin[0] |
---|
2633 | xllcorner = origin[1] |
---|
2634 | yllcorner = origin[2] |
---|
2635 | |
---|
2636 | # FIXME: Refactor using code from file_function.statistics |
---|
2637 | # Something like print swwstats(swwname) |
---|
2638 | if verbose: |
---|
2639 | x = fid.variables['x'][:] |
---|
2640 | y = fid.variables['y'][:] |
---|
2641 | times = fid.variables['time'][:] |
---|
2642 | print '------------------------------------------------' |
---|
2643 | print 'Statistics of SWW file:' |
---|
2644 | print ' Name: %s' % swwfile |
---|
2645 | print ' Reference:' |
---|
2646 | print ' Lower left corner: [%f, %f]' % (xllcorner, yllcorner) |
---|
2647 | print ' Start time: %f' % fid.starttime[0] |
---|
2648 | print ' Extent:' |
---|
2649 | print ' x [m] in [%f, %f], len(x) == %d' \ |
---|
2650 | % (num.min(x), num.max(x), len(x.flat)) |
---|
2651 | print ' y [m] in [%f, %f], len(y) == %d' \ |
---|
2652 | % (num.min(y), num.max(y), len(y.flat)) |
---|
2653 | print ' t [s] in [%f, %f], len(t) == %d' \ |
---|
2654 | % (min(times), max(times), len(times)) |
---|
2655 | print ' Quantities [SI units]:' |
---|
2656 | for name in ['stage', 'xmomentum', 'ymomentum', 'elevation']: |
---|
2657 | q = fid.variables[name][:].flat |
---|
2658 | print ' %s in [%f, %f]' % (name, min(q), max(q)) |
---|
2659 | |
---|
2660 | # Get quantity and reduce if applicable |
---|
2661 | if verbose: print 'Processing quantity %s' % quantity |
---|
2662 | |
---|
2663 | # Turn NetCDF objects into numeric arrays |
---|
2664 | quantity_dict = {} |
---|
2665 | for name in fid.variables.keys(): |
---|
2666 | quantity_dict[name] = fid.variables[name][:] |
---|
2667 | |
---|
2668 | # Convert quantity expression to quantities found in sww file |
---|
2669 | q = apply_expression_to_dictionary(quantity, quantity_dict) |
---|
2670 | |
---|
2671 | if len(q.shape) == 2: |
---|
2672 | # q has a time component and needs to be reduced along |
---|
2673 | # the temporal dimension |
---|
2674 | if verbose: print 'Reducing quantity %s' % quantity |
---|
2675 | |
---|
2676 | q_reduced = num.zeros(number_of_points, num.float) |
---|
2677 | for k in range(number_of_points): |
---|
2678 | q_reduced[k] = reduction(q[:,k]) |
---|
2679 | q = q_reduced |
---|
2680 | |
---|
2681 | # Post condition: Now q has dimension: number_of_points |
---|
2682 | assert len(q.shape) == 1 |
---|
2683 | assert q.shape[0] == number_of_points |
---|
2684 | |
---|
2685 | if verbose: |
---|
2686 | print 'Processed values for %s are in [%f, %f]' \ |
---|
2687 | % (quantity, min(q), max(q)) |
---|
2688 | |
---|
2689 | # Create grid and update xll/yll corner and x,y |
---|
2690 | vertex_points = num.concatenate((x[:, num.newaxis], y[:, num.newaxis]), axis=1) |
---|
2691 | assert len(vertex_points.shape) == 2 |
---|
2692 | |
---|
2693 | # Interpolate |
---|
2694 | from anuga.fit_interpolate.interpolate import Interpolate |
---|
2695 | interp = Interpolate(vertex_points, volumes, verbose=verbose) |
---|
2696 | |
---|
2697 | # Interpolate using quantity values |
---|
2698 | if verbose: print 'Interpolating' |
---|
2699 | interpolated_values = interp.interpolate(q, data_points).flatten() #????# |
---|
2700 | |
---|
2701 | if verbose: |
---|
2702 | print ('Interpolated values are in [%f, %f]' |
---|
2703 | % (num.min(interpolated_values), num.max(interpolated_values))) |
---|
2704 | |
---|
2705 | # Assign NODATA_value to all points outside bounding polygon |
---|
2706 | # (from interpolation mesh) |
---|
2707 | P = interp.mesh.get_boundary_polygon() |
---|
2708 | outside_indices = outside_polygon(data_points, P, closed=True) |
---|
2709 | |
---|
2710 | for i in outside_indices: |
---|
2711 | interpolated_values[i] = NODATA_value |
---|
2712 | |
---|
2713 | # Store results |
---|
2714 | G = Geospatial_data(data_points=data_points, attributes=interpolated_values) |
---|
2715 | |
---|
2716 | G.export_points_file(ptsfile, absolute = True) |
---|
2717 | |
---|
2718 | fid.close() |
---|
2719 | |
---|
2720 | |
---|
2721 | ## |
---|
2722 | # @brief Convert ASC file to DEM file. |
---|
2723 | # @param basename_in Stem of input filename. |
---|
2724 | # @param basename_out Stem of output filename. |
---|
2725 | # @param use_cache ?? |
---|
2726 | # @param verbose True if this function is to be verbose. |
---|
2727 | # @return |
---|
2728 | # @note A PRJ file with same stem basename must exist and is used to fix the |
---|
2729 | # UTM zone, datum, false northings and eastings. |
---|
2730 | def convert_dem_from_ascii2netcdf(basename_in, basename_out=None, |
---|
2731 | use_cache=False, |
---|
2732 | verbose=False): |
---|
2733 | """Read Digital Elevation model from the following ASCII format (.asc) |
---|
2734 | |
---|
2735 | Example: |
---|
2736 | ncols 3121 |
---|
2737 | nrows 1800 |
---|
2738 | xllcorner 722000 |
---|
2739 | yllcorner 5893000 |
---|
2740 | cellsize 25 |
---|
2741 | NODATA_value -9999 |
---|
2742 | 138.3698 137.4194 136.5062 135.5558 .......... |
---|
2743 | |
---|
2744 | Convert basename_in + '.asc' to NetCDF format (.dem) |
---|
2745 | mimicking the ASCII format closely. |
---|
2746 | |
---|
2747 | An accompanying file with same basename_in but extension .prj must exist |
---|
2748 | and is used to fix the UTM zone, datum, false northings and eastings. |
---|
2749 | |
---|
2750 | The prj format is assumed to be as |
---|
2751 | |
---|
2752 | Projection UTM |
---|
2753 | Zone 56 |
---|
2754 | Datum WGS84 |
---|
2755 | Zunits NO |
---|
2756 | Units METERS |
---|
2757 | Spheroid WGS84 |
---|
2758 | Xshift 0.0000000000 |
---|
2759 | Yshift 10000000.0000000000 |
---|
2760 | Parameters |
---|
2761 | """ |
---|
2762 | |
---|
2763 | kwargs = {'basename_out': basename_out, 'verbose': verbose} |
---|
2764 | |
---|
2765 | if use_cache is True: |
---|
2766 | from caching import cache |
---|
2767 | result = cache(_convert_dem_from_ascii2netcdf, basename_in, kwargs, |
---|
2768 | dependencies=[basename_in + '.asc', |
---|
2769 | basename_in + '.prj'], |
---|
2770 | verbose=verbose) |
---|
2771 | |
---|
2772 | else: |
---|
2773 | result = apply(_convert_dem_from_ascii2netcdf, [basename_in], kwargs) |
---|
2774 | |
---|
2775 | return result |
---|
2776 | |
---|
2777 | |
---|
2778 | ## |
---|
2779 | # @brief Convert an ASC file to a DEM file. |
---|
2780 | # @param basename_in Stem of input filename. |
---|
2781 | # @param basename_out Stem of output filename. |
---|
2782 | # @param verbose True if this function is to be verbose. |
---|
2783 | def _convert_dem_from_ascii2netcdf(basename_in, basename_out = None, |
---|
2784 | verbose = False): |
---|
2785 | """Read Digital Elevation model from the following ASCII format (.asc) |
---|
2786 | |
---|
2787 | Internal function. See public function convert_dem_from_ascii2netcdf |
---|
2788 | for details. |
---|
2789 | """ |
---|
2790 | |
---|
2791 | import os |
---|
2792 | from Scientific.IO.NetCDF import NetCDFFile |
---|
2793 | |
---|
2794 | root = basename_in |
---|
2795 | |
---|
2796 | # Read Meta data |
---|
2797 | if verbose: print 'Reading METADATA from %s' % root + '.prj' |
---|
2798 | |
---|
2799 | metadatafile = open(root + '.prj') |
---|
2800 | metalines = metadatafile.readlines() |
---|
2801 | metadatafile.close() |
---|
2802 | |
---|
2803 | L = metalines[0].strip().split() |
---|
2804 | assert L[0].strip().lower() == 'projection' |
---|
2805 | projection = L[1].strip() #TEXT |
---|
2806 | |
---|
2807 | L = metalines[1].strip().split() |
---|
2808 | assert L[0].strip().lower() == 'zone' |
---|
2809 | zone = int(L[1].strip()) |
---|
2810 | |
---|
2811 | L = metalines[2].strip().split() |
---|
2812 | assert L[0].strip().lower() == 'datum' |
---|
2813 | datum = L[1].strip() #TEXT |
---|
2814 | |
---|
2815 | L = metalines[3].strip().split() |
---|
2816 | assert L[0].strip().lower() == 'zunits' #IGNORE |
---|
2817 | zunits = L[1].strip() #TEXT |
---|
2818 | |
---|
2819 | L = metalines[4].strip().split() |
---|
2820 | assert L[0].strip().lower() == 'units' |
---|
2821 | units = L[1].strip() #TEXT |
---|
2822 | |
---|
2823 | L = metalines[5].strip().split() |
---|
2824 | assert L[0].strip().lower() == 'spheroid' #IGNORE |
---|
2825 | spheroid = L[1].strip() #TEXT |
---|
2826 | |
---|
2827 | L = metalines[6].strip().split() |
---|
2828 | assert L[0].strip().lower() == 'xshift' |
---|
2829 | false_easting = float(L[1].strip()) |
---|
2830 | |
---|
2831 | L = metalines[7].strip().split() |
---|
2832 | assert L[0].strip().lower() == 'yshift' |
---|
2833 | false_northing = float(L[1].strip()) |
---|
2834 | |
---|
2835 | #Read DEM data |
---|
2836 | datafile = open(basename_in + '.asc') |
---|
2837 | |
---|
2838 | if verbose: print 'Reading DEM from %s' % basename_in + '.asc' |
---|
2839 | |
---|
2840 | lines = datafile.readlines() |
---|
2841 | datafile.close() |
---|
2842 | |
---|
2843 | if verbose: print 'Got', len(lines), ' lines' |
---|
2844 | |
---|
2845 | ncols = int(lines[0].split()[1].strip()) |
---|
2846 | nrows = int(lines[1].split()[1].strip()) |
---|
2847 | |
---|
2848 | # Do cellsize (line 4) before line 2 and 3 |
---|
2849 | cellsize = float(lines[4].split()[1].strip()) |
---|
2850 | |
---|
2851 | # Checks suggested by Joaquim Luis |
---|
2852 | # Our internal representation of xllcorner |
---|
2853 | # and yllcorner is non-standard. |
---|
2854 | xref = lines[2].split() |
---|
2855 | if xref[0].strip() == 'xllcorner': |
---|
2856 | xllcorner = float(xref[1].strip()) # + 0.5*cellsize # Correct offset |
---|
2857 | elif xref[0].strip() == 'xllcenter': |
---|
2858 | xllcorner = float(xref[1].strip()) |
---|
2859 | else: |
---|
2860 | msg = 'Unknown keyword: %s' % xref[0].strip() |
---|
2861 | raise Exception, msg |
---|
2862 | |
---|
2863 | yref = lines[3].split() |
---|
2864 | if yref[0].strip() == 'yllcorner': |
---|
2865 | yllcorner = float(yref[1].strip()) # + 0.5*cellsize # Correct offset |
---|
2866 | elif yref[0].strip() == 'yllcenter': |
---|
2867 | yllcorner = float(yref[1].strip()) |
---|
2868 | else: |
---|
2869 | msg = 'Unknown keyword: %s' % yref[0].strip() |
---|
2870 | raise Exception, msg |
---|
2871 | |
---|
2872 | NODATA_value = int(lines[5].split()[1].strip()) |
---|
2873 | |
---|
2874 | assert len(lines) == nrows + 6 |
---|
2875 | |
---|
2876 | if basename_out == None: |
---|
2877 | netcdfname = root + '.dem' |
---|
2878 | else: |
---|
2879 | netcdfname = basename_out + '.dem' |
---|
2880 | |
---|
2881 | if verbose: print 'Store to NetCDF file %s' % netcdfname |
---|
2882 | |
---|
2883 | # NetCDF file definition |
---|
2884 | fid = NetCDFFile(netcdfname, netcdf_mode_w) |
---|
2885 | |
---|
2886 | #Create new file |
---|
2887 | fid.institution = 'Geoscience Australia' |
---|
2888 | fid.description = 'NetCDF DEM format for compact and portable storage ' \ |
---|
2889 | 'of spatial point data' |
---|
2890 | |
---|
2891 | fid.ncols = ncols |
---|
2892 | fid.nrows = nrows |
---|
2893 | fid.xllcorner = xllcorner |
---|
2894 | fid.yllcorner = yllcorner |
---|
2895 | fid.cellsize = cellsize |
---|
2896 | fid.NODATA_value = NODATA_value |
---|
2897 | |
---|
2898 | fid.zone = zone |
---|
2899 | fid.false_easting = false_easting |
---|
2900 | fid.false_northing = false_northing |
---|
2901 | fid.projection = projection |
---|
2902 | fid.datum = datum |
---|
2903 | fid.units = units |
---|
2904 | |
---|
2905 | # dimension definitions |
---|
2906 | fid.createDimension('number_of_rows', nrows) |
---|
2907 | fid.createDimension('number_of_columns', ncols) |
---|
2908 | |
---|
2909 | # variable definitions |
---|
2910 | fid.createVariable('elevation', netcdf_float, ('number_of_rows', |
---|
2911 | 'number_of_columns')) |
---|
2912 | |
---|
2913 | # Get handles to the variables |
---|
2914 | elevation = fid.variables['elevation'] |
---|
2915 | |
---|
2916 | #Store data |
---|
2917 | n = len(lines[6:]) |
---|
2918 | for i, line in enumerate(lines[6:]): |
---|
2919 | fields = line.split() |
---|
2920 | if verbose and i % ((n+10)/10) == 0: |
---|
2921 | print 'Processing row %d of %d' % (i, nrows) |
---|
2922 | elevation[i, :] = num.array([float(x) for x in fields]) |
---|
2923 | |
---|
2924 | fid.close() |
---|
2925 | |
---|
2926 | |
---|
2927 | ## |
---|
2928 | # @brief Convert 'ferret' file to SWW file. |
---|
2929 | # @param basename_in Stem of input filename. |
---|
2930 | # @param basename_out Stem of output filename. |
---|
2931 | # @param verbose True if this function is to be verbose. |
---|
2932 | # @param minlat |
---|
2933 | # @param maxlat |
---|
2934 | # @param minlon |
---|
2935 | # @param maxlon |
---|
2936 | # @param mint |
---|
2937 | # @param maxt |
---|
2938 | # @param mean_stage |
---|
2939 | # @param origin |
---|
2940 | # @param zscale |
---|
2941 | # @param fail_on_NaN |
---|
2942 | # @param NaN_filler |
---|
2943 | # @param elevation |
---|
2944 | # @param inverted_bathymetry |
---|
2945 | def ferret2sww(basename_in, basename_out=None, |
---|
2946 | verbose=False, |
---|
2947 | minlat=None, maxlat=None, |
---|
2948 | minlon=None, maxlon=None, |
---|
2949 | mint=None, maxt=None, mean_stage=0, |
---|
2950 | origin=None, zscale=1, |
---|
2951 | fail_on_NaN=True, |
---|
2952 | NaN_filler=0, |
---|
2953 | elevation=None, |
---|
2954 | inverted_bathymetry=True |
---|
2955 | ): #FIXME: Bathymetry should be obtained |
---|
2956 | #from MOST somehow. |
---|
2957 | #Alternatively from elsewhere |
---|
2958 | #or, as a last resort, |
---|
2959 | #specified here. |
---|
2960 | #The value of -100 will work |
---|
2961 | #for the Wollongong tsunami |
---|
2962 | #scenario but is very hacky |
---|
2963 | """Convert MOST and 'Ferret' NetCDF format for wave propagation to |
---|
2964 | sww format native to abstract_2d_finite_volumes. |
---|
2965 | |
---|
2966 | Specify only basename_in and read files of the form |
---|
2967 | basefilename_ha.nc, basefilename_ua.nc, basefilename_va.nc containing |
---|
2968 | relative height, x-velocity and y-velocity, respectively. |
---|
2969 | |
---|
2970 | Also convert latitude and longitude to UTM. All coordinates are |
---|
2971 | assumed to be given in the GDA94 datum. |
---|
2972 | |
---|
2973 | min's and max's: If omitted - full extend is used. |
---|
2974 | To include a value min may equal it, while max must exceed it. |
---|
2975 | Lat and lon are assuemd to be in decimal degrees |
---|
2976 | |
---|
2977 | origin is a 3-tuple with geo referenced |
---|
2978 | UTM coordinates (zone, easting, northing) |
---|
2979 | |
---|
2980 | nc format has values organised as HA[TIME, LATITUDE, LONGITUDE] |
---|
2981 | which means that longitude is the fastest |
---|
2982 | varying dimension (row major order, so to speak) |
---|
2983 | |
---|
2984 | ferret2sww uses grid points as vertices in a triangular grid |
---|
2985 | counting vertices from lower left corner upwards, then right |
---|
2986 | """ |
---|
2987 | |
---|
2988 | import os |
---|
2989 | from Scientific.IO.NetCDF import NetCDFFile |
---|
2990 | |
---|
2991 | precision = num.float |
---|
2992 | |
---|
2993 | msg = 'Must use latitudes and longitudes for minlat, maxlon etc' |
---|
2994 | |
---|
2995 | if minlat != None: |
---|
2996 | assert -90 < minlat < 90 , msg |
---|
2997 | if maxlat != None: |
---|
2998 | assert -90 < maxlat < 90 , msg |
---|
2999 | if minlat != None: |
---|
3000 | assert maxlat > minlat |
---|
3001 | if minlon != None: |
---|
3002 | assert -180 < minlon < 180 , msg |
---|
3003 | if maxlon != None: |
---|
3004 | assert -180 < maxlon < 180 , msg |
---|
3005 | if minlon != None: |
---|
3006 | assert maxlon > minlon |
---|
3007 | |
---|
3008 | # Get NetCDF data |
---|
3009 | if verbose: print 'Reading files %s_*.nc' % basename_in |
---|
3010 | |
---|
3011 | file_h = NetCDFFile(basename_in + '_ha.nc', netcdf_mode_r) # Wave amplitude (cm) |
---|
3012 | file_u = NetCDFFile(basename_in + '_ua.nc', netcdf_mode_r) # Velocity (x) (cm/s) |
---|
3013 | file_v = NetCDFFile(basename_in + '_va.nc', netcdf_mode_r) # Velocity (y) (cm/s) |
---|
3014 | file_e = NetCDFFile(basename_in + '_e.nc', netcdf_mode_r) # Elevation (z) (m) |
---|
3015 | |
---|
3016 | if basename_out is None: |
---|
3017 | swwname = basename_in + '.sww' |
---|
3018 | else: |
---|
3019 | swwname = basename_out + '.sww' |
---|
3020 | |
---|
3021 | # Get dimensions of file_h |
---|
3022 | for dimension in file_h.dimensions.keys(): |
---|
3023 | if dimension[:3] == 'LON': |
---|
3024 | dim_h_longitude = dimension |
---|
3025 | if dimension[:3] == 'LAT': |
---|
3026 | dim_h_latitude = dimension |
---|
3027 | if dimension[:4] == 'TIME': |
---|
3028 | dim_h_time = dimension |
---|
3029 | |
---|
3030 | times = file_h.variables[dim_h_time] |
---|
3031 | latitudes = file_h.variables[dim_h_latitude] |
---|
3032 | longitudes = file_h.variables[dim_h_longitude] |
---|
3033 | |
---|
3034 | kmin, kmax, lmin, lmax = _get_min_max_indexes(latitudes[:], |
---|
3035 | longitudes[:], |
---|
3036 | minlat, maxlat, |
---|
3037 | minlon, maxlon) |
---|
3038 | # get dimensions for file_e |
---|
3039 | for dimension in file_e.dimensions.keys(): |
---|
3040 | if dimension[:3] == 'LON': |
---|
3041 | dim_e_longitude = dimension |
---|
3042 | if dimension[:3] == 'LAT': |
---|
3043 | dim_e_latitude = dimension |
---|
3044 | |
---|
3045 | # get dimensions for file_u |
---|
3046 | for dimension in file_u.dimensions.keys(): |
---|
3047 | if dimension[:3] == 'LON': |
---|
3048 | dim_u_longitude = dimension |
---|
3049 | if dimension[:3] == 'LAT': |
---|
3050 | dim_u_latitude = dimension |
---|
3051 | if dimension[:4] == 'TIME': |
---|
3052 | dim_u_time = dimension |
---|
3053 | |
---|
3054 | # get dimensions for file_v |
---|
3055 | for dimension in file_v.dimensions.keys(): |
---|
3056 | if dimension[:3] == 'LON': |
---|
3057 | dim_v_longitude = dimension |
---|
3058 | if dimension[:3] == 'LAT': |
---|
3059 | dim_v_latitude = dimension |
---|
3060 | if dimension[:4] == 'TIME': |
---|
3061 | dim_v_time = dimension |
---|
3062 | |
---|
3063 | # Precision used by most for lat/lon is 4 or 5 decimals |
---|
3064 | e_lat = num.around(file_e.variables[dim_e_latitude][:], 5) |
---|
3065 | e_lon = num.around(file_e.variables[dim_e_longitude][:], 5) |
---|
3066 | |
---|
3067 | # Check that files are compatible |
---|
3068 | assert num.allclose(latitudes, file_u.variables[dim_u_latitude]) |
---|
3069 | assert num.allclose(latitudes, file_v.variables[dim_v_latitude]) |
---|
3070 | assert num.allclose(latitudes, e_lat) |
---|
3071 | |
---|
3072 | assert num.allclose(longitudes, file_u.variables[dim_u_longitude]) |
---|
3073 | assert num.allclose(longitudes, file_v.variables[dim_v_longitude]) |
---|
3074 | assert num.allclose(longitudes, e_lon) |
---|
3075 | |
---|
3076 | if mint is None: |
---|
3077 | jmin = 0 |
---|
3078 | mint = times[0] |
---|
3079 | else: |
---|
3080 | jmin = num.searchsorted(times, mint) |
---|
3081 | |
---|
3082 | if maxt is None: |
---|
3083 | jmax = len(times) |
---|
3084 | maxt = times[-1] |
---|
3085 | else: |
---|
3086 | jmax = num.searchsorted(times, maxt) |
---|
3087 | |
---|
3088 | kmin, kmax, lmin, lmax = _get_min_max_indexes(latitudes[:], |
---|
3089 | longitudes[:], |
---|
3090 | minlat, maxlat, |
---|
3091 | minlon, maxlon) |
---|
3092 | |
---|
3093 | |
---|
3094 | times = times[jmin:jmax] |
---|
3095 | latitudes = latitudes[kmin:kmax] |
---|
3096 | longitudes = longitudes[lmin:lmax] |
---|
3097 | |
---|
3098 | if verbose: print 'cropping' |
---|
3099 | |
---|
3100 | zname = 'ELEVATION' |
---|
3101 | |
---|
3102 | amplitudes = file_h.variables['HA'][jmin:jmax, kmin:kmax, lmin:lmax] |
---|
3103 | uspeed = file_u.variables['UA'][jmin:jmax, kmin:kmax, lmin:lmax] #Lon |
---|
3104 | vspeed = file_v.variables['VA'][jmin:jmax, kmin:kmax, lmin:lmax] #Lat |
---|
3105 | elevations = file_e.variables[zname][kmin:kmax, lmin:lmax] |
---|
3106 | |
---|
3107 | # if latitudes2[0]==latitudes[0] and latitudes2[-1]==latitudes[-1]: |
---|
3108 | # elevations = file_e.variables['ELEVATION'][kmin:kmax, lmin:lmax] |
---|
3109 | # elif latitudes2[0]==latitudes[-1] and latitudes2[-1]==latitudes[0]: |
---|
3110 | # from numpy import asarray |
---|
3111 | # elevations=elevations.tolist() |
---|
3112 | # elevations.reverse() |
---|
3113 | # elevations=asarray(elevations) |
---|
3114 | # else: |
---|
3115 | # from numpy import asarray |
---|
3116 | # elevations=elevations.tolist() |
---|
3117 | # elevations.reverse() |
---|
3118 | # elevations=asarray(elevations) |
---|
3119 | # 'print hmmm' |
---|
3120 | |
---|
3121 | #Get missing values |
---|
3122 | nan_ha = file_h.variables['HA'].missing_value[0] |
---|
3123 | nan_ua = file_u.variables['UA'].missing_value[0] |
---|
3124 | nan_va = file_v.variables['VA'].missing_value[0] |
---|
3125 | if hasattr(file_e.variables[zname],'missing_value'): |
---|
3126 | nan_e = file_e.variables[zname].missing_value[0] |
---|
3127 | else: |
---|
3128 | nan_e = None |
---|
3129 | |
---|
3130 | #Cleanup |
---|
3131 | missing = (amplitudes == nan_ha) |
---|
3132 | if num.sometrue (missing): |
---|
3133 | if fail_on_NaN: |
---|
3134 | msg = 'NetCDFFile %s contains missing values' \ |
---|
3135 | % basename_in + '_ha.nc' |
---|
3136 | raise DataMissingValuesError, msg |
---|
3137 | else: |
---|
3138 | amplitudes = amplitudes*(missing==0) + missing*NaN_filler |
---|
3139 | |
---|
3140 | missing = (uspeed == nan_ua) |
---|
3141 | if num.sometrue (missing): |
---|
3142 | if fail_on_NaN: |
---|
3143 | msg = 'NetCDFFile %s contains missing values' \ |
---|
3144 | % basename_in + '_ua.nc' |
---|
3145 | raise DataMissingValuesError, msg |
---|
3146 | else: |
---|
3147 | uspeed = uspeed*(missing==0) + missing*NaN_filler |
---|
3148 | |
---|
3149 | missing = (vspeed == nan_va) |
---|
3150 | if num.sometrue (missing): |
---|
3151 | if fail_on_NaN: |
---|
3152 | msg = 'NetCDFFile %s contains missing values' \ |
---|
3153 | % basename_in + '_va.nc' |
---|
3154 | raise DataMissingValuesError, msg |
---|
3155 | else: |
---|
3156 | vspeed = vspeed*(missing==0) + missing*NaN_filler |
---|
3157 | |
---|
3158 | missing = (elevations == nan_e) |
---|
3159 | if num.sometrue (missing): |
---|
3160 | if fail_on_NaN: |
---|
3161 | msg = 'NetCDFFile %s contains missing values' \ |
---|
3162 | % basename_in + '_e.nc' |
---|
3163 | raise DataMissingValuesError, msg |
---|
3164 | else: |
---|
3165 | elevations = elevations*(missing==0) + missing*NaN_filler |
---|
3166 | |
---|
3167 | number_of_times = times.shape[0] |
---|
3168 | number_of_latitudes = latitudes.shape[0] |
---|
3169 | number_of_longitudes = longitudes.shape[0] |
---|
3170 | |
---|
3171 | assert amplitudes.shape[0] == number_of_times |
---|
3172 | assert amplitudes.shape[1] == number_of_latitudes |
---|
3173 | assert amplitudes.shape[2] == number_of_longitudes |
---|
3174 | |
---|
3175 | if verbose: |
---|
3176 | print '------------------------------------------------' |
---|
3177 | print 'Statistics:' |
---|
3178 | print ' Extent (lat/lon):' |
---|
3179 | print ' lat in [%f, %f], len(lat) == %d' \ |
---|
3180 | % (num.min(latitudes), num.max(latitudes), len(latitudes.flat)) |
---|
3181 | print ' lon in [%f, %f], len(lon) == %d' \ |
---|
3182 | % (num.min(longitudes), num.max(longitudes), |
---|
3183 | len(longitudes.flat)) |
---|
3184 | print ' t in [%f, %f], len(t) == %d' \ |
---|
3185 | % (num.min(times), num.max(times), len(times.flat)) |
---|
3186 | |
---|
3187 | q = amplitudes.flatten() |
---|
3188 | name = 'Amplitudes (ha) [cm]' |
---|
3189 | print ' %s in [%f, %f]' % (name, min(q), max(q)) |
---|
3190 | |
---|
3191 | q = uspeed.flatten() |
---|
3192 | name = 'Speeds (ua) [cm/s]' |
---|
3193 | print ' %s in [%f, %f]' % (name, min(q), max(q)) |
---|
3194 | |
---|
3195 | q = vspeed.flatten() |
---|
3196 | name = 'Speeds (va) [cm/s]' |
---|
3197 | print ' %s in [%f, %f]' % (name, min(q), max(q)) |
---|
3198 | |
---|
3199 | q = elevations.flatten() |
---|
3200 | name = 'Elevations (e) [m]' |
---|
3201 | print ' %s in [%f, %f]' % (name, min(q), max(q)) |
---|
3202 | |
---|
3203 | # print number_of_latitudes, number_of_longitudes |
---|
3204 | number_of_points = number_of_latitudes * number_of_longitudes |
---|
3205 | number_of_volumes = (number_of_latitudes-1) * (number_of_longitudes-1) * 2 |
---|
3206 | |
---|
3207 | file_h.close() |
---|
3208 | file_u.close() |
---|
3209 | file_v.close() |
---|
3210 | file_e.close() |
---|
3211 | |
---|
3212 | # NetCDF file definition |
---|
3213 | outfile = NetCDFFile(swwname, netcdf_mode_w) |
---|
3214 | |
---|
3215 | description = 'Converted from Ferret files: %s, %s, %s, %s' \ |
---|
3216 | % (basename_in + '_ha.nc', |
---|
3217 | basename_in + '_ua.nc', |
---|
3218 | basename_in + '_va.nc', |
---|
3219 | basename_in + '_e.nc') |
---|
3220 | |
---|
3221 | # Create new file |
---|
3222 | starttime = times[0] |
---|
3223 | |
---|
3224 | sww = Write_sww() |
---|
3225 | sww.store_header(outfile, times, number_of_volumes, |
---|
3226 | number_of_points, description=description, |
---|
3227 | verbose=verbose, sww_precision=netcdf_float) |
---|
3228 | |
---|
3229 | # Store |
---|
3230 | from anuga.coordinate_transforms.redfearn import redfearn |
---|
3231 | x = num.zeros(number_of_points, num.float) #Easting |
---|
3232 | y = num.zeros(number_of_points, num.float) #Northing |
---|
3233 | |
---|
3234 | if verbose: print 'Making triangular grid' |
---|
3235 | |
---|
3236 | # Check zone boundaries |
---|
3237 | refzone, _, _ = redfearn(latitudes[0], longitudes[0]) |
---|
3238 | |
---|
3239 | vertices = {} |
---|
3240 | i = 0 |
---|
3241 | for k, lat in enumerate(latitudes): # Y direction |
---|
3242 | for l, lon in enumerate(longitudes): # X direction |
---|
3243 | vertices[l,k] = i |
---|
3244 | |
---|
3245 | zone, easting, northing = redfearn(lat,lon) |
---|
3246 | |
---|
3247 | #msg = 'Zone boundary crossed at longitude =', lon |
---|
3248 | #assert zone == refzone, msg |
---|
3249 | #print '%7.2f %7.2f %8.2f %8.2f' %(lon, lat, easting, northing) |
---|
3250 | x[i] = easting |
---|
3251 | y[i] = northing |
---|
3252 | i += 1 |
---|
3253 | |
---|
3254 | #Construct 2 triangles per 'rectangular' element |
---|
3255 | volumes = [] |
---|
3256 | for l in range(number_of_longitudes-1): # X direction |
---|
3257 | for k in range(number_of_latitudes-1): # Y direction |
---|
3258 | v1 = vertices[l,k+1] |
---|
3259 | v2 = vertices[l,k] |
---|
3260 | v3 = vertices[l+1,k+1] |
---|
3261 | v4 = vertices[l+1,k] |
---|
3262 | |
---|
3263 | volumes.append([v1,v2,v3]) #Upper element |
---|
3264 | volumes.append([v4,v3,v2]) #Lower element |
---|
3265 | |
---|
3266 | volumes = num.array(volumes, num.int) #array default# |
---|
3267 | |
---|
3268 | if origin is None: |
---|
3269 | origin = Geo_reference(refzone, min(x), min(y)) |
---|
3270 | geo_ref = write_NetCDF_georeference(origin, outfile) |
---|
3271 | |
---|
3272 | if elevation is not None: |
---|
3273 | z = elevation |
---|
3274 | else: |
---|
3275 | if inverted_bathymetry: |
---|
3276 | z = -1 * elevations |
---|
3277 | else: |
---|
3278 | z = elevations |
---|
3279 | #FIXME: z should be obtained from MOST and passed in here |
---|
3280 | |
---|
3281 | #FIXME use the Write_sww instance(sww) to write this info |
---|
3282 | z = num.resize(z, outfile.variables['z'][:].shape) |
---|
3283 | outfile.variables['x'][:] = x - geo_ref.get_xllcorner() |
---|
3284 | outfile.variables['y'][:] = y - geo_ref.get_yllcorner() |
---|
3285 | outfile.variables['z'][:] = z #FIXME HACK for bacwards compat. |
---|
3286 | outfile.variables['elevation'][:] = z |
---|
3287 | outfile.variables['volumes'][:] = volumes.astype(num.int32) #For Opteron 64 |
---|
3288 | |
---|
3289 | #Time stepping |
---|
3290 | stage = outfile.variables['stage'] |
---|
3291 | xmomentum = outfile.variables['xmomentum'] |
---|
3292 | ymomentum = outfile.variables['ymomentum'] |
---|
3293 | |
---|
3294 | if verbose: print 'Converting quantities' |
---|
3295 | |
---|
3296 | n = len(times) |
---|
3297 | for j in range(n): |
---|
3298 | if verbose and j % ((n+10)/10) == 0: print ' Doing %d of %d' %(j, n) |
---|
3299 | |
---|
3300 | i = 0 |
---|
3301 | for k in range(number_of_latitudes): # Y direction |
---|
3302 | for l in range(number_of_longitudes): # X direction |
---|
3303 | w = zscale * amplitudes[j,k,l] / 100 + mean_stage |
---|
3304 | stage[j,i] = w |
---|
3305 | h = w - z[i] |
---|
3306 | xmomentum[j,i] = uspeed[j,k,l]/100*h |
---|
3307 | ymomentum[j,i] = vspeed[j,k,l]/100*h |
---|
3308 | i += 1 |
---|
3309 | |
---|
3310 | #outfile.close() |
---|
3311 | |
---|
3312 | #FIXME: Refactor using code from file_function.statistics |
---|
3313 | #Something like print swwstats(swwname) |
---|
3314 | if verbose: |
---|
3315 | x = outfile.variables['x'][:] |
---|
3316 | y = outfile.variables['y'][:] |
---|
3317 | print '------------------------------------------------' |
---|
3318 | print 'Statistics of output file:' |
---|
3319 | print ' Name: %s' %swwname |
---|
3320 | print ' Reference:' |
---|
3321 | print ' Lower left corner: [%f, %f]' \ |
---|
3322 | % (geo_ref.get_xllcorner(), geo_ref.get_yllcorner()) |
---|
3323 | print ' Start time: %f' %starttime |
---|
3324 | print ' Min time: %f' %mint |
---|
3325 | print ' Max time: %f' %maxt |
---|
3326 | print ' Extent:' |
---|
3327 | print ' x [m] in [%f, %f], len(x) == %d' \ |
---|
3328 | % (num.min(x), num.max(x), len(x.flat)) |
---|
3329 | print ' y [m] in [%f, %f], len(y) == %d' \ |
---|
3330 | % (num.min(y), num.max(y), len(y.flat)) |
---|
3331 | print ' t [s] in [%f, %f], len(t) == %d' \ |
---|
3332 | % (min(times), max(times), len(times)) |
---|
3333 | print ' Quantities [SI units]:' |
---|
3334 | for name in ['stage', 'xmomentum', 'ymomentum', 'elevation']: |
---|
3335 | q = outfile.variables[name][:].flatten() |
---|
3336 | print ' %s in [%f, %f]' % (name, min(q), max(q)) |
---|
3337 | |
---|
3338 | outfile.close() |
---|
3339 | |
---|
3340 | |
---|
3341 | ## |
---|
3342 | # @brief Convert time-series text file to TMS file. |
---|
3343 | # @param filename |
---|
3344 | # @param quantity_names |
---|
3345 | # @param time_as_seconds |
---|
3346 | def timefile2netcdf(filename, quantity_names=None, time_as_seconds=False): |
---|
3347 | """Template for converting typical text files with time series to |
---|
3348 | NetCDF tms file. |
---|
3349 | |
---|
3350 | The file format is assumed to be either two fields separated by a comma: |
---|
3351 | |
---|
3352 | time [DD/MM/YY hh:mm:ss], value0 value1 value2 ... |
---|
3353 | |
---|
3354 | E.g |
---|
3355 | |
---|
3356 | 31/08/04 00:00:00, 1.328223 0 0 |
---|
3357 | 31/08/04 00:15:00, 1.292912 0 0 |
---|
3358 | |
---|
3359 | or time (seconds), value0 value1 value2 ... |
---|
3360 | |
---|
3361 | 0.0, 1.328223 0 0 |
---|
3362 | 0.1, 1.292912 0 0 |
---|
3363 | |
---|
3364 | will provide a time dependent function f(t) with three attributes |
---|
3365 | |
---|
3366 | filename is assumed to be the rootname with extenisons .txt and .sww |
---|
3367 | """ |
---|
3368 | |
---|
3369 | import time, calendar |
---|
3370 | from anuga.config import time_format |
---|
3371 | from anuga.utilities.numerical_tools import ensure_numeric |
---|
3372 | |
---|
3373 | file_text = filename + '.txt' |
---|
3374 | fid = open(file_text) |
---|
3375 | line = fid.readline() |
---|
3376 | fid.close() |
---|
3377 | |
---|
3378 | fields = line.split(',') |
---|
3379 | msg = "File %s must have the format 'datetime, value0 value1 value2 ...'" \ |
---|
3380 | % file_text |
---|
3381 | assert len(fields) == 2, msg |
---|
3382 | |
---|
3383 | if not time_as_seconds: |
---|
3384 | try: |
---|
3385 | starttime = calendar.timegm(time.strptime(fields[0], time_format)) |
---|
3386 | except ValueError: |
---|
3387 | msg = 'First field in file %s must be' % file_text |
---|
3388 | msg += ' date-time with format %s.\n' % time_format |
---|
3389 | msg += 'I got %s instead.' % fields[0] |
---|
3390 | raise DataTimeError, msg |
---|
3391 | else: |
---|
3392 | try: |
---|
3393 | starttime = float(fields[0]) |
---|
3394 | except Error: |
---|
3395 | msg = "Bad time format" |
---|
3396 | raise DataTimeError, msg |
---|
3397 | |
---|
3398 | # Split values |
---|
3399 | values = [] |
---|
3400 | for value in fields[1].split(): |
---|
3401 | values.append(float(value)) |
---|
3402 | |
---|
3403 | q = ensure_numeric(values) |
---|
3404 | |
---|
3405 | msg = 'ERROR: File must contain at least one independent value' |
---|
3406 | assert len(q.shape) == 1, msg |
---|
3407 | |
---|
3408 | # Read times proper |
---|
3409 | from anuga.config import time_format |
---|
3410 | import time, calendar |
---|
3411 | |
---|
3412 | fid = open(file_text) |
---|
3413 | lines = fid.readlines() |
---|
3414 | fid.close() |
---|
3415 | |
---|
3416 | N = len(lines) |
---|
3417 | d = len(q) |
---|
3418 | |
---|
3419 | T = num.zeros(N, num.float) # Time |
---|
3420 | Q = num.zeros((N, d), num.float) # Values |
---|
3421 | |
---|
3422 | for i, line in enumerate(lines): |
---|
3423 | fields = line.split(',') |
---|
3424 | if not time_as_seconds: |
---|
3425 | realtime = calendar.timegm(time.strptime(fields[0], time_format)) |
---|
3426 | else: |
---|
3427 | realtime = float(fields[0]) |
---|
3428 | T[i] = realtime - starttime |
---|
3429 | |
---|
3430 | for j, value in enumerate(fields[1].split()): |
---|
3431 | Q[i, j] = float(value) |
---|
3432 | |
---|
3433 | msg = 'File %s must list time as a monotonuosly ' % filename |
---|
3434 | msg += 'increasing sequence' |
---|
3435 | assert num.alltrue(T[1:] - T[:-1] > 0), msg |
---|
3436 | |
---|
3437 | #Create NetCDF file |
---|
3438 | from Scientific.IO.NetCDF import NetCDFFile |
---|
3439 | |
---|
3440 | fid = NetCDFFile(filename + '.tms', netcdf_mode_w) |
---|
3441 | |
---|
3442 | fid.institution = 'Geoscience Australia' |
---|
3443 | fid.description = 'Time series' |
---|
3444 | |
---|
3445 | #Reference point |
---|
3446 | #Start time in seconds since the epoch (midnight 1/1/1970) |
---|
3447 | #FIXME: Use Georef |
---|
3448 | fid.starttime = starttime |
---|
3449 | |
---|
3450 | # dimension definitions |
---|
3451 | #fid.createDimension('number_of_volumes', self.number_of_volumes) |
---|
3452 | #fid.createDimension('number_of_vertices', 3) |
---|
3453 | |
---|
3454 | fid.createDimension('number_of_timesteps', len(T)) |
---|
3455 | |
---|
3456 | fid.createVariable('time', netcdf_float, ('number_of_timesteps',)) |
---|
3457 | |
---|
3458 | fid.variables['time'][:] = T |
---|
3459 | |
---|
3460 | for i in range(Q.shape[1]): |
---|
3461 | try: |
---|
3462 | name = quantity_names[i] |
---|
3463 | except: |
---|
3464 | name = 'Attribute%d' % i |
---|
3465 | |
---|
3466 | fid.createVariable(name, netcdf_float, ('number_of_timesteps',)) |
---|
3467 | fid.variables[name][:] = Q[:,i] |
---|
3468 | |
---|
3469 | fid.close() |
---|
3470 | |
---|
3471 | |
---|
3472 | ## |
---|
3473 | # @brief Get the extents of a NetCDF data file. |
---|
3474 | # @param file_name The path to the SWW file. |
---|
3475 | # @return A list of x, y, z and stage limits (min, max). |
---|
3476 | def extent_sww(file_name): |
---|
3477 | """Read in an sww file. |
---|
3478 | |
---|
3479 | Input: |
---|
3480 | file_name - the sww file |
---|
3481 | |
---|
3482 | Output: |
---|
3483 | A list: [min(x),max(x),min(y),max(y),min(stage.flat),max(stage.flat)] |
---|
3484 | """ |
---|
3485 | |
---|
3486 | from Scientific.IO.NetCDF import NetCDFFile |
---|
3487 | |
---|
3488 | #Get NetCDF |
---|
3489 | fid = NetCDFFile(file_name, netcdf_mode_r) |
---|
3490 | |
---|
3491 | # Get the variables |
---|
3492 | x = fid.variables['x'][:] |
---|
3493 | y = fid.variables['y'][:] |
---|
3494 | stage = fid.variables['stage'][:] |
---|
3495 | |
---|
3496 | fid.close() |
---|
3497 | |
---|
3498 | return [min(x), max(x), min(y), max(y), num.min(stage), num.max(stage)] |
---|
3499 | |
---|
3500 | |
---|
3501 | ## |
---|
3502 | # @brief |
---|
3503 | # @param filename |
---|
3504 | # @param boundary |
---|
3505 | # @param t |
---|
3506 | # @param fail_if_NaN |
---|
3507 | # @param NaN_filler |
---|
3508 | # @param verbose |
---|
3509 | # @param very_verbose |
---|
3510 | # @return |
---|
3511 | def sww2domain(filename, boundary=None, t=None, |
---|
3512 | fail_if_NaN=True, NaN_filler=0, |
---|
3513 | verbose=False, very_verbose=False): |
---|
3514 | """ |
---|
3515 | Usage: domain = sww2domain('file.sww',t=time (default = last time in file)) |
---|
3516 | |
---|
3517 | Boundary is not recommended if domain.smooth is not selected, as it |
---|
3518 | uses unique coordinates, but not unique boundaries. This means that |
---|
3519 | the boundary file will not be compatable with the coordinates, and will |
---|
3520 | give a different final boundary, or crash. |
---|
3521 | """ |
---|
3522 | |
---|
3523 | from Scientific.IO.NetCDF import NetCDFFile |
---|
3524 | from shallow_water import Domain |
---|
3525 | |
---|
3526 | # initialise NaN. |
---|
3527 | NaN = 9.969209968386869e+036 |
---|
3528 | |
---|
3529 | if verbose: print 'Reading from ', filename |
---|
3530 | |
---|
3531 | fid = NetCDFFile(filename, netcdf_mode_r) # Open existing file for read |
---|
3532 | time = fid.variables['time'] # Timesteps |
---|
3533 | if t is None: |
---|
3534 | t = time[-1] |
---|
3535 | time_interp = get_time_interp(time,t) |
---|
3536 | |
---|
3537 | # Get the variables as numeric arrays |
---|
3538 | x = fid.variables['x'][:] # x-coordinates of vertices |
---|
3539 | y = fid.variables['y'][:] # y-coordinates of vertices |
---|
3540 | elevation = fid.variables['elevation'] # Elevation |
---|
3541 | stage = fid.variables['stage'] # Water level |
---|
3542 | xmomentum = fid.variables['xmomentum'] # Momentum in the x-direction |
---|
3543 | ymomentum = fid.variables['ymomentum'] # Momentum in the y-direction |
---|
3544 | |
---|
3545 | starttime = fid.starttime[0] |
---|
3546 | volumes = fid.variables['volumes'][:] # Connectivity |
---|
3547 | coordinates = num.transpose(num.asarray([x.tolist(), y.tolist()])) |
---|
3548 | # FIXME (Ole): Something like this might be better: |
---|
3549 | # concatenate((x, y), axis=1) |
---|
3550 | # or concatenate((x[:,num.newaxis], x[:,num.newaxis]), axis=1) |
---|
3551 | |
---|
3552 | conserved_quantities = [] |
---|
3553 | interpolated_quantities = {} |
---|
3554 | other_quantities = [] |
---|
3555 | |
---|
3556 | # get geo_reference |
---|
3557 | try: # sww files don't have to have a geo_ref |
---|
3558 | geo_reference = Geo_reference(NetCDFObject=fid) |
---|
3559 | except: # AttributeError, e: |
---|
3560 | geo_reference = None |
---|
3561 | |
---|
3562 | if verbose: print ' getting quantities' |
---|
3563 | |
---|
3564 | for quantity in fid.variables.keys(): |
---|
3565 | dimensions = fid.variables[quantity].dimensions |
---|
3566 | if 'number_of_timesteps' in dimensions: |
---|
3567 | conserved_quantities.append(quantity) |
---|
3568 | interpolated_quantities[quantity] = \ |
---|
3569 | interpolated_quantity(fid.variables[quantity][:], time_interp) |
---|
3570 | else: |
---|
3571 | other_quantities.append(quantity) |
---|
3572 | |
---|
3573 | other_quantities.remove('x') |
---|
3574 | other_quantities.remove('y') |
---|
3575 | other_quantities.remove('z') |
---|
3576 | other_quantities.remove('volumes') |
---|
3577 | try: |
---|
3578 | other_quantities.remove('stage_range') |
---|
3579 | other_quantities.remove('xmomentum_range') |
---|
3580 | other_quantities.remove('ymomentum_range') |
---|
3581 | other_quantities.remove('elevation_range') |
---|
3582 | except: |
---|
3583 | pass |
---|
3584 | |
---|
3585 | conserved_quantities.remove('time') |
---|
3586 | |
---|
3587 | if verbose: print ' building domain' |
---|
3588 | |
---|
3589 | # From domain.Domain: |
---|
3590 | # domain = Domain(coordinates, volumes,\ |
---|
3591 | # conserved_quantities = conserved_quantities,\ |
---|
3592 | # other_quantities = other_quantities,zone=zone,\ |
---|
3593 | # xllcorner=xllcorner, yllcorner=yllcorner) |
---|
3594 | |
---|
3595 | # From shallow_water.Domain: |
---|
3596 | coordinates = coordinates.tolist() |
---|
3597 | volumes = volumes.tolist() |
---|
3598 | # FIXME:should this be in mesh? (peter row) |
---|
3599 | if fid.smoothing == 'Yes': |
---|
3600 | unique = False |
---|
3601 | else: |
---|
3602 | unique = True |
---|
3603 | if unique: |
---|
3604 | coordinates, volumes, boundary = weed(coordinates, volumes,boundary) |
---|
3605 | |
---|
3606 | try: |
---|
3607 | domain = Domain(coordinates, volumes, boundary) |
---|
3608 | except AssertionError, e: |
---|
3609 | fid.close() |
---|
3610 | msg = 'Domain could not be created: %s. ' \ |
---|
3611 | 'Perhaps use "fail_if_NaN=False and NaN_filler = ..."' % e |
---|
3612 | raise DataDomainError, msg |
---|
3613 | |
---|
3614 | if not boundary is None: |
---|
3615 | domain.boundary = boundary |
---|
3616 | |
---|
3617 | domain.geo_reference = geo_reference |
---|
3618 | |
---|
3619 | domain.starttime = float(starttime) + float(t) |
---|
3620 | domain.time = 0.0 |
---|
3621 | |
---|
3622 | for quantity in other_quantities: |
---|
3623 | try: |
---|
3624 | NaN = fid.variables[quantity].missing_value |
---|
3625 | except: |
---|
3626 | pass # quantity has no missing_value number |
---|
3627 | X = fid.variables[quantity][:] |
---|
3628 | if very_verbose: |
---|
3629 | print ' ', quantity |
---|
3630 | print ' NaN =', NaN |
---|
3631 | print ' max(X)' |
---|
3632 | print ' ', max(X) |
---|
3633 | print ' max(X)==NaN' |
---|
3634 | print ' ', max(X)==NaN |
---|
3635 | print '' |
---|
3636 | if max(X) == NaN or min(X) == NaN: |
---|
3637 | if fail_if_NaN: |
---|
3638 | msg = 'quantity "%s" contains no_data entry' % quantity |
---|
3639 | raise DataMissingValuesError, msg |
---|
3640 | else: |
---|
3641 | data = (X != NaN) |
---|
3642 | X = (X*data) + (data==0)*NaN_filler |
---|
3643 | if unique: |
---|
3644 | X = num.resize(X, (len(X)/3, 3)) |
---|
3645 | domain.set_quantity(quantity, X) |
---|
3646 | # |
---|
3647 | for quantity in conserved_quantities: |
---|
3648 | try: |
---|
3649 | NaN = fid.variables[quantity].missing_value |
---|
3650 | except: |
---|
3651 | pass # quantity has no missing_value number |
---|
3652 | X = interpolated_quantities[quantity] |
---|
3653 | if very_verbose: |
---|
3654 | print ' ',quantity |
---|
3655 | print ' NaN =', NaN |
---|
3656 | print ' max(X)' |
---|
3657 | print ' ', max(X) |
---|
3658 | print ' max(X)==NaN' |
---|
3659 | print ' ', max(X)==NaN |
---|
3660 | print '' |
---|
3661 | if max(X) == NaN or min(X) == NaN: |
---|
3662 | if fail_if_NaN: |
---|
3663 | msg = 'quantity "%s" contains no_data entry' % quantity |
---|
3664 | raise DataMissingValuesError, msg |
---|
3665 | else: |
---|
3666 | data = (X != NaN) |
---|
3667 | X = (X*data) + (data==0)*NaN_filler |
---|
3668 | if unique: |
---|
3669 | X = num.resize(X, (X.shape[0]/3, 3)) |
---|
3670 | domain.set_quantity(quantity, X) |
---|
3671 | |
---|
3672 | fid.close() |
---|
3673 | |
---|
3674 | return domain |
---|
3675 | |
---|
3676 | |
---|
3677 | ## |
---|
3678 | # @brief Interpolate a quantity wrt time. |
---|
3679 | # @param saved_quantity The quantity to interpolate. |
---|
3680 | # @param time_interp (index, ratio) |
---|
3681 | # @return A vector of interpolated values. |
---|
3682 | def interpolated_quantity(saved_quantity, time_interp): |
---|
3683 | '''Given an index and ratio, interpolate quantity with respect to time.''' |
---|
3684 | |
---|
3685 | index, ratio = time_interp |
---|
3686 | |
---|
3687 | Q = saved_quantity |
---|
3688 | |
---|
3689 | if ratio > 0: |
---|
3690 | q = (1-ratio)*Q[index] + ratio*Q[index+1] |
---|
3691 | else: |
---|
3692 | q = Q[index] |
---|
3693 | |
---|
3694 | #Return vector of interpolated values |
---|
3695 | return q |
---|
3696 | |
---|
3697 | |
---|
3698 | ## |
---|
3699 | # @brief |
---|
3700 | # @parm time |
---|
3701 | # @param t |
---|
3702 | # @return An (index, ration) tuple. |
---|
3703 | def get_time_interp(time, t=None): |
---|
3704 | #Finds the ratio and index for time interpolation. |
---|
3705 | #It is borrowed from previous abstract_2d_finite_volumes code. |
---|
3706 | if t is None: |
---|
3707 | t=time[-1] |
---|
3708 | index = -1 |
---|
3709 | ratio = 0. |
---|
3710 | else: |
---|
3711 | T = time |
---|
3712 | tau = t |
---|
3713 | index=0 |
---|
3714 | msg = 'Time interval derived from file %s [%s:%s]' \ |
---|
3715 | % ('FIXMEfilename', T[0], T[-1]) |
---|
3716 | msg += ' does not match model time: %s' % tau |
---|
3717 | if tau < time[0]: raise DataTimeError, msg |
---|
3718 | if tau > time[-1]: raise DataTimeError, msg |
---|
3719 | while tau > time[index]: index += 1 |
---|
3720 | while tau < time[index]: index -= 1 |
---|
3721 | if tau == time[index]: |
---|
3722 | #Protect against case where tau == time[-1] (last time) |
---|
3723 | # - also works in general when tau == time[i] |
---|
3724 | ratio = 0 |
---|
3725 | else: |
---|
3726 | #t is now between index and index+1 |
---|
3727 | ratio = (tau - time[index])/(time[index+1] - time[index]) |
---|
3728 | |
---|
3729 | return (index, ratio) |
---|
3730 | |
---|
3731 | |
---|
3732 | ## |
---|
3733 | # @brief |
---|
3734 | # @param coordinates |
---|
3735 | # @param volumes |
---|
3736 | # @param boundary |
---|
3737 | def weed(coordinates, volumes, boundary=None): |
---|
3738 | if isinstance(coordinates, num.ndarray): |
---|
3739 | coordinates = coordinates.tolist() |
---|
3740 | if isinstance(volumes, num.ndarray): |
---|
3741 | volumes = volumes.tolist() |
---|
3742 | |
---|
3743 | unique = False |
---|
3744 | point_dict = {} |
---|
3745 | same_point = {} |
---|
3746 | for i in range(len(coordinates)): |
---|
3747 | point = tuple(coordinates[i]) |
---|
3748 | if point_dict.has_key(point): |
---|
3749 | unique = True |
---|
3750 | same_point[i] = point |
---|
3751 | #to change all point i references to point j |
---|
3752 | else: |
---|
3753 | point_dict[point] = i |
---|
3754 | same_point[i] = point |
---|
3755 | |
---|
3756 | coordinates = [] |
---|
3757 | i = 0 |
---|
3758 | for point in point_dict.keys(): |
---|
3759 | point = tuple(point) |
---|
3760 | coordinates.append(list(point)) |
---|
3761 | point_dict[point] = i |
---|
3762 | i += 1 |
---|
3763 | |
---|
3764 | for volume in volumes: |
---|
3765 | for i in range(len(volume)): |
---|
3766 | index = volume[i] |
---|
3767 | if index > -1: |
---|
3768 | volume[i] = point_dict[same_point[index]] |
---|
3769 | |
---|
3770 | new_boundary = {} |
---|
3771 | if not boundary is None: |
---|
3772 | for segment in boundary.keys(): |
---|
3773 | point0 = point_dict[same_point[segment[0]]] |
---|
3774 | point1 = point_dict[same_point[segment[1]]] |
---|
3775 | label = boundary[segment] |
---|
3776 | #FIXME should the bounday attributes be concaterated |
---|
3777 | #('exterior, pond') or replaced ('pond')(peter row) |
---|
3778 | |
---|
3779 | if new_boundary.has_key((point0, point1)): |
---|
3780 | new_boundary[(point0,point1)] = new_boundary[(point0, point1)] |
---|
3781 | |
---|
3782 | elif new_boundary.has_key((point1, point0)): |
---|
3783 | new_boundary[(point1,point0)] = new_boundary[(point1, point0)] |
---|
3784 | else: new_boundary[(point0, point1)] = label |
---|
3785 | |
---|
3786 | boundary = new_boundary |
---|
3787 | |
---|
3788 | return coordinates, volumes, boundary |
---|
3789 | |
---|
3790 | |
---|
3791 | ## |
---|
3792 | # @brief Read DEM file, decimate data, write new DEM file. |
---|
3793 | # @param basename_in Stem of the input filename. |
---|
3794 | # @param stencil |
---|
3795 | # @param cellsize_new New cell size to resample on. |
---|
3796 | # @param basename_out Stem of the output filename. |
---|
3797 | # @param verbose True if this function is to be verbose. |
---|
3798 | def decimate_dem(basename_in, stencil, cellsize_new, basename_out=None, |
---|
3799 | verbose=False): |
---|
3800 | """Read Digitial Elevation model from the following NetCDF format (.dem) |
---|
3801 | |
---|
3802 | Example: |
---|
3803 | |
---|
3804 | ncols 3121 |
---|
3805 | nrows 1800 |
---|
3806 | xllcorner 722000 |
---|
3807 | yllcorner 5893000 |
---|
3808 | cellsize 25 |
---|
3809 | NODATA_value -9999 |
---|
3810 | 138.3698 137.4194 136.5062 135.5558 .......... |
---|
3811 | |
---|
3812 | Decimate data to cellsize_new using stencil and write to NetCDF dem format. |
---|
3813 | """ |
---|
3814 | |
---|
3815 | import os |
---|
3816 | from Scientific.IO.NetCDF import NetCDFFile |
---|
3817 | |
---|
3818 | root = basename_in |
---|
3819 | inname = root + '.dem' |
---|
3820 | |
---|
3821 | #Open existing netcdf file to read |
---|
3822 | infile = NetCDFFile(inname, netcdf_mode_r) |
---|
3823 | |
---|
3824 | if verbose: print 'Reading DEM from %s' % inname |
---|
3825 | |
---|
3826 | #Read metadata |
---|
3827 | ncols = infile.ncols[0] |
---|
3828 | nrows = infile.nrows[0] |
---|
3829 | xllcorner = infile.xllcorner[0] |
---|
3830 | yllcorner = infile.yllcorner[0] |
---|
3831 | cellsize = infile.cellsize[0] |
---|
3832 | NODATA_value = infile.NODATA_value[0] |
---|
3833 | zone = infile.zone[0] |
---|
3834 | false_easting = infile.false_easting[0] |
---|
3835 | false_northing = infile.false_northing[0] |
---|
3836 | projection = infile.projection |
---|
3837 | datum = infile.datum |
---|
3838 | units = infile.units |
---|
3839 | |
---|
3840 | dem_elevation = infile.variables['elevation'] |
---|
3841 | |
---|
3842 | #Get output file name |
---|
3843 | if basename_out == None: |
---|
3844 | outname = root + '_' + repr(cellsize_new) + '.dem' |
---|
3845 | else: |
---|
3846 | outname = basename_out + '.dem' |
---|
3847 | |
---|
3848 | if verbose: print 'Write decimated NetCDF file to %s' % outname |
---|
3849 | |
---|
3850 | #Determine some dimensions for decimated grid |
---|
3851 | (nrows_stencil, ncols_stencil) = stencil.shape |
---|
3852 | x_offset = ncols_stencil / 2 |
---|
3853 | y_offset = nrows_stencil / 2 |
---|
3854 | cellsize_ratio = int(cellsize_new / cellsize) |
---|
3855 | ncols_new = 1 + (ncols - ncols_stencil) / cellsize_ratio |
---|
3856 | nrows_new = 1 + (nrows - nrows_stencil) / cellsize_ratio |
---|
3857 | |
---|
3858 | #Open netcdf file for output |
---|
3859 | outfile = NetCDFFile(outname, netcdf_mode_w) |
---|
3860 | |
---|
3861 | #Create new file |
---|
3862 | outfile.institution = 'Geoscience Australia' |
---|
3863 | outfile.description = 'NetCDF DEM format for compact and portable ' \ |
---|
3864 | 'storage of spatial point data' |
---|
3865 | |
---|
3866 | #Georeferencing |
---|
3867 | outfile.zone = zone |
---|
3868 | outfile.projection = projection |
---|
3869 | outfile.datum = datum |
---|
3870 | outfile.units = units |
---|
3871 | |
---|
3872 | outfile.cellsize = cellsize_new |
---|
3873 | outfile.NODATA_value = NODATA_value |
---|
3874 | outfile.false_easting = false_easting |
---|
3875 | outfile.false_northing = false_northing |
---|
3876 | |
---|
3877 | outfile.xllcorner = xllcorner + (x_offset * cellsize) |
---|
3878 | outfile.yllcorner = yllcorner + (y_offset * cellsize) |
---|
3879 | outfile.ncols = ncols_new |
---|
3880 | outfile.nrows = nrows_new |
---|
3881 | |
---|
3882 | # dimension definition |
---|
3883 | outfile.createDimension('number_of_points', nrows_new*ncols_new) |
---|
3884 | |
---|
3885 | # variable definition |
---|
3886 | outfile.createVariable('elevation', netcdf_float, ('number_of_points',)) |
---|
3887 | |
---|
3888 | # Get handle to the variable |
---|
3889 | elevation = outfile.variables['elevation'] |
---|
3890 | |
---|
3891 | dem_elevation_r = num.reshape(dem_elevation, (nrows, ncols)) |
---|
3892 | |
---|
3893 | #Store data |
---|
3894 | global_index = 0 |
---|
3895 | for i in range(nrows_new): |
---|
3896 | if verbose: print 'Processing row %d of %d' %(i, nrows_new) |
---|
3897 | |
---|
3898 | lower_index = global_index |
---|
3899 | telev = num.zeros(ncols_new, num.float) |
---|
3900 | local_index = 0 |
---|
3901 | trow = i * cellsize_ratio |
---|
3902 | |
---|
3903 | for j in range(ncols_new): |
---|
3904 | tcol = j * cellsize_ratio |
---|
3905 | tmp = dem_elevation_r[trow:trow+nrows_stencil, |
---|
3906 | tcol:tcol+ncols_stencil] |
---|
3907 | |
---|
3908 | #if dem contains 1 or more NODATA_values set value in |
---|
3909 | #decimated dem to NODATA_value, else compute decimated |
---|
3910 | #value using stencil |
---|
3911 | if num.sum(num.sum(num.equal(tmp, NODATA_value))) > 0: |
---|
3912 | telev[local_index] = NODATA_value |
---|
3913 | else: |
---|
3914 | telev[local_index] = num.sum(num.sum(tmp * stencil)) |
---|
3915 | |
---|
3916 | global_index += 1 |
---|
3917 | local_index += 1 |
---|
3918 | |
---|
3919 | upper_index = global_index |
---|
3920 | |
---|
3921 | elevation[lower_index:upper_index] = telev |
---|
3922 | |
---|
3923 | assert global_index == nrows_new*ncols_new, \ |
---|
3924 | 'index not equal to number of points' |
---|
3925 | |
---|
3926 | infile.close() |
---|
3927 | outfile.close() |
---|
3928 | |
---|
3929 | |
---|
3930 | ## |
---|
3931 | # @brief |
---|
3932 | # @param filename |
---|
3933 | # @param verbose |
---|
3934 | def tsh2sww(filename, verbose=False): |
---|
3935 | """ |
---|
3936 | to check if a tsh/msh file 'looks' good. |
---|
3937 | """ |
---|
3938 | |
---|
3939 | if verbose == True:print 'Creating domain from', filename |
---|
3940 | |
---|
3941 | domain = pmesh_to_domain_instance(filename, Domain) |
---|
3942 | |
---|
3943 | if verbose == True:print "Number of triangles = ", len(domain) |
---|
3944 | |
---|
3945 | domain.smooth = True |
---|
3946 | domain.format = 'sww' #Native netcdf visualisation format |
---|
3947 | file_path, filename = path.split(filename) |
---|
3948 | filename, ext = path.splitext(filename) |
---|
3949 | domain.set_name(filename) |
---|
3950 | domain.reduction = mean |
---|
3951 | |
---|
3952 | if verbose == True:print "file_path",file_path |
---|
3953 | |
---|
3954 | if file_path == "": |
---|
3955 | file_path = "." |
---|
3956 | domain.set_datadir(file_path) |
---|
3957 | |
---|
3958 | if verbose == True: |
---|
3959 | print "Output written to " + domain.get_datadir() + sep + \ |
---|
3960 | domain.get_name() + "." + domain.format |
---|
3961 | |
---|
3962 | sww = get_dataobject(domain) |
---|
3963 | sww.store_connectivity() |
---|
3964 | sww.store_timestep() |
---|
3965 | |
---|
3966 | |
---|
3967 | ## |
---|
3968 | # @brief Convert CSIRO ESRI file to an SWW boundary file. |
---|
3969 | # @param bath_dir |
---|
3970 | # @param elevation_dir |
---|
3971 | # @param ucur_dir |
---|
3972 | # @param vcur_dir |
---|
3973 | # @param sww_file |
---|
3974 | # @param minlat |
---|
3975 | # @param maxlat |
---|
3976 | # @param minlon |
---|
3977 | # @param maxlon |
---|
3978 | # @param zscale |
---|
3979 | # @param mean_stage |
---|
3980 | # @param fail_on_NaN |
---|
3981 | # @param elevation_NaN_filler |
---|
3982 | # @param bath_prefix |
---|
3983 | # @param elevation_prefix |
---|
3984 | # @param verbose |
---|
3985 | # @note Also convert latitude and longitude to UTM. All coordinates are |
---|
3986 | # assumed to be given in the GDA94 datum. |
---|
3987 | def asc_csiro2sww(bath_dir, |
---|
3988 | elevation_dir, |
---|
3989 | ucur_dir, |
---|
3990 | vcur_dir, |
---|
3991 | sww_file, |
---|
3992 | minlat=None, maxlat=None, |
---|
3993 | minlon=None, maxlon=None, |
---|
3994 | zscale=1, |
---|
3995 | mean_stage=0, |
---|
3996 | fail_on_NaN=True, |
---|
3997 | elevation_NaN_filler=0, |
---|
3998 | bath_prefix='ba', |
---|
3999 | elevation_prefix='el', |
---|
4000 | verbose=False): |
---|
4001 | """ |
---|
4002 | Produce an sww boundary file, from esri ascii data from CSIRO. |
---|
4003 | |
---|
4004 | Also convert latitude and longitude to UTM. All coordinates are |
---|
4005 | assumed to be given in the GDA94 datum. |
---|
4006 | |
---|
4007 | assume: |
---|
4008 | All files are in esri ascii format |
---|
4009 | |
---|
4010 | 4 types of information |
---|
4011 | bathymetry |
---|
4012 | elevation |
---|
4013 | u velocity |
---|
4014 | v velocity |
---|
4015 | |
---|
4016 | Assumptions |
---|
4017 | The metadata of all the files is the same |
---|
4018 | Each type is in a seperate directory |
---|
4019 | One bath file with extention .000 |
---|
4020 | The time period is less than 24hrs and uniform. |
---|
4021 | """ |
---|
4022 | |
---|
4023 | from Scientific.IO.NetCDF import NetCDFFile |
---|
4024 | |
---|
4025 | from anuga.coordinate_transforms.redfearn import redfearn |
---|
4026 | |
---|
4027 | precision = netcdf_float # So if we want to change the precision its done here |
---|
4028 | |
---|
4029 | # go in to the bath dir and load the only file, |
---|
4030 | bath_files = os.listdir(bath_dir) |
---|
4031 | bath_file = bath_files[0] |
---|
4032 | bath_dir_file = bath_dir + os.sep + bath_file |
---|
4033 | bath_metadata, bath_grid = _read_asc(bath_dir_file) |
---|
4034 | |
---|
4035 | #Use the date.time of the bath file as a basis for |
---|
4036 | #the start time for other files |
---|
4037 | base_start = bath_file[-12:] |
---|
4038 | |
---|
4039 | #go into the elevation dir and load the 000 file |
---|
4040 | elevation_dir_file = elevation_dir + os.sep + elevation_prefix \ |
---|
4041 | + base_start |
---|
4042 | |
---|
4043 | elevation_files = os.listdir(elevation_dir) |
---|
4044 | ucur_files = os.listdir(ucur_dir) |
---|
4045 | vcur_files = os.listdir(vcur_dir) |
---|
4046 | elevation_files.sort() |
---|
4047 | |
---|
4048 | # the first elevation file should be the |
---|
4049 | # file with the same base name as the bath data |
---|
4050 | assert elevation_files[0] == 'el' + base_start |
---|
4051 | |
---|
4052 | number_of_latitudes = bath_grid.shape[0] |
---|
4053 | number_of_longitudes = bath_grid.shape[1] |
---|
4054 | number_of_volumes = (number_of_latitudes-1) * (number_of_longitudes-1) * 2 |
---|
4055 | |
---|
4056 | longitudes = [bath_metadata['xllcorner'] + x*bath_metadata['cellsize'] \ |
---|
4057 | for x in range(number_of_longitudes)] |
---|
4058 | latitudes = [bath_metadata['yllcorner'] + y*bath_metadata['cellsize'] \ |
---|
4059 | for y in range(number_of_latitudes)] |
---|
4060 | |
---|
4061 | # reverse order of lat, so the first lat represents the first grid row |
---|
4062 | latitudes.reverse() |
---|
4063 | |
---|
4064 | kmin, kmax, lmin, lmax = _get_min_max_indexes(latitudes[:],longitudes[:], |
---|
4065 | minlat=minlat, maxlat=maxlat, |
---|
4066 | minlon=minlon, maxlon=maxlon) |
---|
4067 | |
---|
4068 | bath_grid = bath_grid[kmin:kmax,lmin:lmax] |
---|
4069 | latitudes = latitudes[kmin:kmax] |
---|
4070 | longitudes = longitudes[lmin:lmax] |
---|
4071 | number_of_latitudes = len(latitudes) |
---|
4072 | number_of_longitudes = len(longitudes) |
---|
4073 | number_of_times = len(os.listdir(elevation_dir)) |
---|
4074 | number_of_points = number_of_latitudes * number_of_longitudes |
---|
4075 | number_of_volumes = (number_of_latitudes-1) * (number_of_longitudes-1) * 2 |
---|
4076 | |
---|
4077 | #Work out the times |
---|
4078 | if len(elevation_files) > 1: |
---|
4079 | # Assume: The time period is less than 24hrs. |
---|
4080 | time_period = (int(elevation_files[1][-3:]) \ |
---|
4081 | - int(elevation_files[0][-3:])) * 60*60 |
---|
4082 | times = [x*time_period for x in range(len(elevation_files))] |
---|
4083 | else: |
---|
4084 | times = [0.0] |
---|
4085 | |
---|
4086 | if verbose: |
---|
4087 | print '------------------------------------------------' |
---|
4088 | print 'Statistics:' |
---|
4089 | print ' Extent (lat/lon):' |
---|
4090 | print ' lat in [%f, %f], len(lat) == %d' \ |
---|
4091 | % (min(latitudes), max(latitudes), len(latitudes)) |
---|
4092 | print ' lon in [%f, %f], len(lon) == %d' \ |
---|
4093 | % (min(longitudes), max(longitudes), len(longitudes)) |
---|
4094 | print ' t in [%f, %f], len(t) == %d' \ |
---|
4095 | % (min(times), max(times), len(times)) |
---|
4096 | |
---|
4097 | ######### WRITE THE SWW FILE ############# |
---|
4098 | |
---|
4099 | # NetCDF file definition |
---|
4100 | outfile = NetCDFFile(sww_file, netcdf_mode_w) |
---|
4101 | |
---|
4102 | #Create new file |
---|
4103 | outfile.institution = 'Geoscience Australia' |
---|
4104 | outfile.description = 'Converted from XXX' |
---|
4105 | |
---|
4106 | #For sww compatibility |
---|
4107 | outfile.smoothing = 'Yes' |
---|
4108 | outfile.order = 1 |
---|
4109 | |
---|
4110 | #Start time in seconds since the epoch (midnight 1/1/1970) |
---|
4111 | outfile.starttime = starttime = times[0] |
---|
4112 | |
---|
4113 | # dimension definitions |
---|
4114 | outfile.createDimension('number_of_volumes', number_of_volumes) |
---|
4115 | outfile.createDimension('number_of_vertices', 3) |
---|
4116 | outfile.createDimension('number_of_points', number_of_points) |
---|
4117 | outfile.createDimension('number_of_timesteps', number_of_times) |
---|
4118 | |
---|
4119 | # variable definitions |
---|
4120 | outfile.createVariable('x', precision, ('number_of_points',)) |
---|
4121 | outfile.createVariable('y', precision, ('number_of_points',)) |
---|
4122 | outfile.createVariable('elevation', precision, ('number_of_points',)) |
---|
4123 | |
---|
4124 | #FIXME: Backwards compatibility |
---|
4125 | outfile.createVariable('z', precision, ('number_of_points',)) |
---|
4126 | ################################# |
---|
4127 | |
---|
4128 | outfile.createVariable('volumes', netcdf_int, ('number_of_volumes', |
---|
4129 | 'number_of_vertices')) |
---|
4130 | |
---|
4131 | outfile.createVariable('time', precision, ('number_of_timesteps',)) |
---|
4132 | |
---|
4133 | outfile.createVariable('stage', precision, ('number_of_timesteps', |
---|
4134 | 'number_of_points')) |
---|
4135 | |
---|
4136 | outfile.createVariable('xmomentum', precision, ('number_of_timesteps', |
---|
4137 | 'number_of_points')) |
---|
4138 | |
---|
4139 | outfile.createVariable('ymomentum', precision, ('number_of_timesteps', |
---|
4140 | 'number_of_points')) |
---|
4141 | |
---|
4142 | #Store |
---|
4143 | from anuga.coordinate_transforms.redfearn import redfearn |
---|
4144 | |
---|
4145 | x = num.zeros(number_of_points, num.float) #Easting |
---|
4146 | y = num.zeros(number_of_points, num.float) #Northing |
---|
4147 | |
---|
4148 | if verbose: print 'Making triangular grid' |
---|
4149 | |
---|
4150 | #Get zone of 1st point. |
---|
4151 | refzone, _, _ = redfearn(latitudes[0], longitudes[0]) |
---|
4152 | |
---|
4153 | vertices = {} |
---|
4154 | i = 0 |
---|
4155 | for k, lat in enumerate(latitudes): |
---|
4156 | for l, lon in enumerate(longitudes): |
---|
4157 | vertices[l,k] = i |
---|
4158 | |
---|
4159 | zone, easting, northing = redfearn(lat, lon) |
---|
4160 | |
---|
4161 | #msg = 'Zone boundary crossed at longitude =', lon |
---|
4162 | #assert zone == refzone, msg |
---|
4163 | #print '%7.2f %7.2f %8.2f %8.2f' %(lon, lat, easting, northing) |
---|
4164 | x[i] = easting |
---|
4165 | y[i] = northing |
---|
4166 | i += 1 |
---|
4167 | |
---|
4168 | #Construct 2 triangles per 'rectangular' element |
---|
4169 | volumes = [] |
---|
4170 | for l in range(number_of_longitudes-1): #X direction |
---|
4171 | for k in range(number_of_latitudes-1): #Y direction |
---|
4172 | v1 = vertices[l,k+1] |
---|
4173 | v2 = vertices[l,k] |
---|
4174 | v3 = vertices[l+1,k+1] |
---|
4175 | v4 = vertices[l+1,k] |
---|
4176 | |
---|
4177 | #Note, this is different to the ferrit2sww code |
---|
4178 | #since the order of the lats is reversed. |
---|
4179 | volumes.append([v1,v3,v2]) #Upper element |
---|
4180 | volumes.append([v4,v2,v3]) #Lower element |
---|
4181 | |
---|
4182 | volumes = num.array(volumes, num.int) #array default# |
---|
4183 | |
---|
4184 | geo_ref = Geo_reference(refzone, min(x), min(y)) |
---|
4185 | geo_ref.write_NetCDF(outfile) |
---|
4186 | |
---|
4187 | # This will put the geo ref in the middle |
---|
4188 | #geo_ref = Geo_reference(refzone, (max(x)+min(x))/2., (max(x)+min(y))/2.) |
---|
4189 | |
---|
4190 | if verbose: |
---|
4191 | print '------------------------------------------------' |
---|
4192 | print 'More Statistics:' |
---|
4193 | print ' Extent (/lon):' |
---|
4194 | print ' x in [%f, %f], len(lat) == %d' \ |
---|
4195 | % (min(x), max(x), len(x)) |
---|
4196 | print ' y in [%f, %f], len(lon) == %d' \ |
---|
4197 | % (min(y), max(y), len(y)) |
---|
4198 | print 'geo_ref: ', geo_ref |
---|
4199 | |
---|
4200 | z = num.resize(bath_grid,outfile.variables['z'][:].shape) |
---|
4201 | outfile.variables['x'][:] = x - geo_ref.get_xllcorner() |
---|
4202 | outfile.variables['y'][:] = y - geo_ref.get_yllcorner() |
---|
4203 | # FIXME (Ole): Remove once viewer has been recompiled and changed |
---|
4204 | # to use elevation instead of z |
---|
4205 | outfile.variables['z'][:] = z |
---|
4206 | outfile.variables['elevation'][:] = z |
---|
4207 | outfile.variables['volumes'][:] = volumes.astype(num.int32) # On Opteron 64 |
---|
4208 | |
---|
4209 | stage = outfile.variables['stage'] |
---|
4210 | xmomentum = outfile.variables['xmomentum'] |
---|
4211 | ymomentum = outfile.variables['ymomentum'] |
---|
4212 | |
---|
4213 | outfile.variables['time'][:] = times #Store time relative |
---|
4214 | |
---|
4215 | if verbose: print 'Converting quantities' |
---|
4216 | |
---|
4217 | n = number_of_times |
---|
4218 | for j in range(number_of_times): |
---|
4219 | # load in files |
---|
4220 | elevation_meta, elevation_grid = \ |
---|
4221 | _read_asc(elevation_dir + os.sep + elevation_files[j]) |
---|
4222 | |
---|
4223 | _, u_momentum_grid = _read_asc(ucur_dir + os.sep + ucur_files[j]) |
---|
4224 | _, v_momentum_grid = _read_asc(vcur_dir + os.sep + vcur_files[j]) |
---|
4225 | |
---|
4226 | #cut matrix to desired size |
---|
4227 | elevation_grid = elevation_grid[kmin:kmax,lmin:lmax] |
---|
4228 | u_momentum_grid = u_momentum_grid[kmin:kmax,lmin:lmax] |
---|
4229 | v_momentum_grid = v_momentum_grid[kmin:kmax,lmin:lmax] |
---|
4230 | |
---|
4231 | # handle missing values |
---|
4232 | missing = (elevation_grid == elevation_meta['NODATA_value']) |
---|
4233 | if num.sometrue (missing): |
---|
4234 | if fail_on_NaN: |
---|
4235 | msg = 'File %s contains missing values' \ |
---|
4236 | % (elevation_files[j]) |
---|
4237 | raise DataMissingValuesError, msg |
---|
4238 | else: |
---|
4239 | elevation_grid = elevation_grid*(missing==0) \ |
---|
4240 | + missing*elevation_NaN_filler |
---|
4241 | |
---|
4242 | if verbose and j % ((n+10)/10) == 0: print ' Doing %d of %d' % (j, n) |
---|
4243 | |
---|
4244 | i = 0 |
---|
4245 | for k in range(number_of_latitudes): #Y direction |
---|
4246 | for l in range(number_of_longitudes): #X direction |
---|
4247 | w = zscale*elevation_grid[k,l] + mean_stage |
---|
4248 | stage[j,i] = w |
---|
4249 | h = w - z[i] |
---|
4250 | xmomentum[j,i] = u_momentum_grid[k,l]*h |
---|
4251 | ymomentum[j,i] = v_momentum_grid[k,l]*h |
---|
4252 | i += 1 |
---|
4253 | |
---|
4254 | outfile.close() |
---|
4255 | |
---|
4256 | |
---|
4257 | ## |
---|
4258 | # @brief Return max&min indexes (for slicing) of area specified. |
---|
4259 | # @param latitudes_ref ?? |
---|
4260 | # @param longitudes_ref ?? |
---|
4261 | # @param minlat Minimum latitude of specified area. |
---|
4262 | # @param maxlat Maximum latitude of specified area. |
---|
4263 | # @param minlon Minimum longitude of specified area. |
---|
4264 | # @param maxlon Maximum longitude of specified area. |
---|
4265 | # @return Tuple (lat_min_index, lat_max_index, lon_min_index, lon_max_index) |
---|
4266 | def _get_min_max_indexes(latitudes_ref,longitudes_ref, |
---|
4267 | minlat=None, maxlat=None, |
---|
4268 | minlon=None, maxlon=None): |
---|
4269 | """ |
---|
4270 | Return max, min indexes (for slicing) of the lat and long arrays to cover |
---|
4271 | the area specified with min/max lat/long. |
---|
4272 | |
---|
4273 | Think of the latitudes and longitudes describing a 2d surface. |
---|
4274 | The area returned is, if possible, just big enough to cover the |
---|
4275 | inputed max/min area. (This will not be possible if the max/min area |
---|
4276 | has a section outside of the latitudes/longitudes area.) |
---|
4277 | |
---|
4278 | asset longitudes are sorted, |
---|
4279 | long - from low to high (west to east, eg 148 - 151) |
---|
4280 | assert latitudes are sorted, ascending or decending |
---|
4281 | """ |
---|
4282 | |
---|
4283 | latitudes = latitudes_ref[:] |
---|
4284 | longitudes = longitudes_ref[:] |
---|
4285 | |
---|
4286 | latitudes = ensure_numeric(latitudes) |
---|
4287 | longitudes = ensure_numeric(longitudes) |
---|
4288 | |
---|
4289 | assert num.allclose(num.sort(longitudes), longitudes) |
---|
4290 | |
---|
4291 | #print latitudes[0],longitudes[0] |
---|
4292 | #print len(latitudes),len(longitudes) |
---|
4293 | #print latitudes[len(latitudes)-1],longitudes[len(longitudes)-1] |
---|
4294 | |
---|
4295 | lat_ascending = True |
---|
4296 | if not num.allclose(num.sort(latitudes), latitudes): |
---|
4297 | lat_ascending = False |
---|
4298 | # reverse order of lat, so it's in ascending order |
---|
4299 | latitudes = latitudes[::-1] |
---|
4300 | assert num.allclose(num.sort(latitudes), latitudes) |
---|
4301 | |
---|
4302 | largest_lat_index = len(latitudes)-1 |
---|
4303 | |
---|
4304 | #Cut out a smaller extent. |
---|
4305 | if minlat == None: |
---|
4306 | lat_min_index = 0 |
---|
4307 | else: |
---|
4308 | lat_min_index = num.searchsorted(latitudes, minlat)-1 |
---|
4309 | if lat_min_index <0: |
---|
4310 | lat_min_index = 0 |
---|
4311 | |
---|
4312 | if maxlat == None: |
---|
4313 | lat_max_index = largest_lat_index #len(latitudes) |
---|
4314 | else: |
---|
4315 | lat_max_index = num.searchsorted(latitudes, maxlat) |
---|
4316 | if lat_max_index > largest_lat_index: |
---|
4317 | lat_max_index = largest_lat_index |
---|
4318 | |
---|
4319 | if minlon == None: |
---|
4320 | lon_min_index = 0 |
---|
4321 | else: |
---|
4322 | lon_min_index = num.searchsorted(longitudes, minlon)-1 |
---|
4323 | if lon_min_index <0: |
---|
4324 | lon_min_index = 0 |
---|
4325 | |
---|
4326 | if maxlon == None: |
---|
4327 | lon_max_index = len(longitudes) |
---|
4328 | else: |
---|
4329 | lon_max_index = num.searchsorted(longitudes, maxlon) |
---|
4330 | |
---|
4331 | # Reversing the indexes, if the lat array is decending |
---|
4332 | if lat_ascending is False: |
---|
4333 | lat_min_index, lat_max_index = largest_lat_index - lat_max_index, \ |
---|
4334 | largest_lat_index - lat_min_index |
---|
4335 | lat_max_index = lat_max_index + 1 # taking into account how slicing works |
---|
4336 | lon_max_index = lon_max_index + 1 # taking into account how slicing works |
---|
4337 | |
---|
4338 | return lat_min_index, lat_max_index, lon_min_index, lon_max_index |
---|
4339 | |
---|
4340 | |
---|
4341 | ## |
---|
4342 | # @brief Read an ASC file. |
---|
4343 | # @parem filename Path to the file to read. |
---|
4344 | # @param verbose True if this function is to be verbose. |
---|
4345 | def _read_asc(filename, verbose=False): |
---|
4346 | """Read esri file from the following ASCII format (.asc) |
---|
4347 | |
---|
4348 | Example: |
---|
4349 | |
---|
4350 | ncols 3121 |
---|
4351 | nrows 1800 |
---|
4352 | xllcorner 722000 |
---|
4353 | yllcorner 5893000 |
---|
4354 | cellsize 25 |
---|
4355 | NODATA_value -9999 |
---|
4356 | 138.3698 137.4194 136.5062 135.5558 .......... |
---|
4357 | """ |
---|
4358 | |
---|
4359 | datafile = open(filename) |
---|
4360 | |
---|
4361 | if verbose: print 'Reading DEM from %s' % filename |
---|
4362 | |
---|
4363 | lines = datafile.readlines() |
---|
4364 | datafile.close() |
---|
4365 | |
---|
4366 | if verbose: print 'Got', len(lines), ' lines' |
---|
4367 | |
---|
4368 | ncols = int(lines.pop(0).split()[1].strip()) |
---|
4369 | nrows = int(lines.pop(0).split()[1].strip()) |
---|
4370 | xllcorner = float(lines.pop(0).split()[1].strip()) |
---|
4371 | yllcorner = float(lines.pop(0).split()[1].strip()) |
---|
4372 | cellsize = float(lines.pop(0).split()[1].strip()) |
---|
4373 | NODATA_value = float(lines.pop(0).split()[1].strip()) |
---|
4374 | |
---|
4375 | assert len(lines) == nrows |
---|
4376 | |
---|
4377 | #Store data |
---|
4378 | grid = [] |
---|
4379 | |
---|
4380 | n = len(lines) |
---|
4381 | for i, line in enumerate(lines): |
---|
4382 | cells = line.split() |
---|
4383 | assert len(cells) == ncols |
---|
4384 | grid.append(num.array([float(x) for x in cells])) |
---|
4385 | grid = num.array(grid) |
---|
4386 | |
---|
4387 | return {'xllcorner':xllcorner, |
---|
4388 | 'yllcorner':yllcorner, |
---|
4389 | 'cellsize':cellsize, |
---|
4390 | 'NODATA_value':NODATA_value}, grid |
---|
4391 | |
---|
4392 | |
---|
4393 | #### URS 2 SWW ### |
---|
4394 | |
---|
4395 | # Definitions of various NetCDF dimension names, etc. |
---|
4396 | lon_name = 'LON' |
---|
4397 | lat_name = 'LAT' |
---|
4398 | time_name = 'TIME' |
---|
4399 | precision = netcdf_float # So if we want to change the precision its done here |
---|
4400 | |
---|
4401 | ## |
---|
4402 | # @brief Clas for a NetCDF data file writer. |
---|
4403 | class Write_nc: |
---|
4404 | """Write an nc file. |
---|
4405 | |
---|
4406 | Note, this should be checked to meet cdc netcdf conventions for gridded |
---|
4407 | data. http://www.cdc.noaa.gov/cdc/conventions/cdc_netcdf_standard.shtml |
---|
4408 | """ |
---|
4409 | |
---|
4410 | ## |
---|
4411 | # @brief Instantiate a Write_nc instance. |
---|
4412 | # @param quantity_name |
---|
4413 | # @param file_name |
---|
4414 | # @param time_step_count The number of time steps. |
---|
4415 | # @param time_step The time_step size. |
---|
4416 | # @param lon |
---|
4417 | # @param lat |
---|
4418 | def __init__(self, |
---|
4419 | quantity_name, |
---|
4420 | file_name, |
---|
4421 | time_step_count, |
---|
4422 | time_step, |
---|
4423 | lon, |
---|
4424 | lat): |
---|
4425 | """Instantiate a Write_nc instance (NetCDF file writer). |
---|
4426 | |
---|
4427 | time_step_count is the number of time steps. |
---|
4428 | time_step is the time step size |
---|
4429 | |
---|
4430 | pre-condition: quantity_name must be 'HA', 'UA'or 'VA'. |
---|
4431 | """ |
---|
4432 | |
---|
4433 | self.quantity_name = quantity_name |
---|
4434 | quantity_units = {'HA':'CENTIMETERS', |
---|
4435 | 'UA':'CENTIMETERS/SECOND', |
---|
4436 | 'VA':'CENTIMETERS/SECOND'} |
---|
4437 | |
---|
4438 | multiplier_dic = {'HA':100.0, # To convert from m to cm |
---|
4439 | 'UA':100.0, # and m/s to cm/sec |
---|
4440 | 'VA':-100.0} # MUX files have positive x in the |
---|
4441 | # Southern direction. This corrects |
---|
4442 | # for it, when writing nc files. |
---|
4443 | |
---|
4444 | self.quantity_multiplier = multiplier_dic[self.quantity_name] |
---|
4445 | |
---|
4446 | #self.file_name = file_name |
---|
4447 | self.time_step_count = time_step_count |
---|
4448 | self.time_step = time_step |
---|
4449 | |
---|
4450 | # NetCDF file definition |
---|
4451 | self.outfile = NetCDFFile(file_name, netcdf_mode_w) |
---|
4452 | outfile = self.outfile |
---|
4453 | |
---|
4454 | #Create new file |
---|
4455 | nc_lon_lat_header(outfile, lon, lat) |
---|
4456 | |
---|
4457 | # TIME |
---|
4458 | outfile.createDimension(time_name, None) |
---|
4459 | outfile.createVariable(time_name, precision, (time_name,)) |
---|
4460 | |
---|
4461 | #QUANTITY |
---|
4462 | outfile.createVariable(self.quantity_name, precision, |
---|
4463 | (time_name, lat_name, lon_name)) |
---|
4464 | outfile.variables[self.quantity_name].missing_value = -1.e+034 |
---|
4465 | outfile.variables[self.quantity_name].units = \ |
---|
4466 | quantity_units[self.quantity_name] |
---|
4467 | outfile.variables[lon_name][:]= ensure_numeric(lon) |
---|
4468 | outfile.variables[lat_name][:]= ensure_numeric(lat) |
---|
4469 | |
---|
4470 | #Assume no one will be wanting to read this, while we are writing |
---|
4471 | #outfile.close() |
---|
4472 | |
---|
4473 | ## |
---|
4474 | # @brief Write a time-step of quantity data. |
---|
4475 | # @param quantity_slice The data to be stored for this time-step. |
---|
4476 | def store_timestep(self, quantity_slice): |
---|
4477 | """Write a time slice of quantity info |
---|
4478 | |
---|
4479 | quantity_slice is the data to be stored at this time step |
---|
4480 | """ |
---|
4481 | |
---|
4482 | # Get the variables |
---|
4483 | time = self.outfile.variables[time_name] |
---|
4484 | quantity = self.outfile.variables[self.quantity_name] |
---|
4485 | |
---|
4486 | # get index oflice to write |
---|
4487 | i = len(time) |
---|
4488 | |
---|
4489 | #Store time |
---|
4490 | time[i] = i * self.time_step #self.domain.time |
---|
4491 | quantity[i,:] = quantity_slice * self.quantity_multiplier |
---|
4492 | |
---|
4493 | ## |
---|
4494 | # @brief Close file underlying the class instance. |
---|
4495 | def close(self): |
---|
4496 | self.outfile.close() |
---|
4497 | |
---|
4498 | |
---|
4499 | ## |
---|
4500 | # @brief Convert URS file to SWW file. |
---|
4501 | # @param basename_in Stem of the input filename. |
---|
4502 | # @param basename_out Stem of the output filename. |
---|
4503 | # @param verbose True if this function is to be verbose. |
---|
4504 | # @param remove_nc_files |
---|
4505 | # @param minlat Sets extent of area to be used. If not supplied, full extent. |
---|
4506 | # @param maxlat Sets extent of area to be used. If not supplied, full extent. |
---|
4507 | # @param minlon Sets extent of area to be used. If not supplied, full extent. |
---|
4508 | # @param maxlon Sets extent of area to be used. If not supplied, full extent. |
---|
4509 | # @param mint |
---|
4510 | # @param maxt |
---|
4511 | # @param mean_stage |
---|
4512 | # @param origin A 3-tuple with geo referenced UTM coordinates |
---|
4513 | # @param zscale |
---|
4514 | # @param fail_on_NaN |
---|
4515 | # @param NaN_filler |
---|
4516 | # @param elevation |
---|
4517 | # @note Also convert latitude and longitude to UTM. All coordinates are |
---|
4518 | # assumed to be given in the GDA94 datum. |
---|
4519 | def urs2sww(basename_in='o', basename_out=None, verbose=False, |
---|
4520 | remove_nc_files=True, |
---|
4521 | minlat=None, maxlat=None, |
---|
4522 | minlon=None, maxlon=None, |
---|
4523 | mint=None, maxt=None, |
---|
4524 | mean_stage=0, |
---|
4525 | origin=None, |
---|
4526 | zscale=1, |
---|
4527 | fail_on_NaN=True, |
---|
4528 | NaN_filler=0, |
---|
4529 | elevation=None): |
---|
4530 | """Convert a URS file to an SWW file. |
---|
4531 | Convert URS C binary format for wave propagation to |
---|
4532 | sww format native to abstract_2d_finite_volumes. |
---|
4533 | |
---|
4534 | Specify only basename_in and read files of the form |
---|
4535 | basefilename-z-mux2, basefilename-e-mux2 and |
---|
4536 | basefilename-n-mux2 containing relative height, |
---|
4537 | x-velocity and y-velocity, respectively. |
---|
4538 | |
---|
4539 | Also convert latitude and longitude to UTM. All coordinates are |
---|
4540 | assumed to be given in the GDA94 datum. The latitude and longitude |
---|
4541 | information is for a grid. |
---|
4542 | |
---|
4543 | min's and max's: If omitted - full extend is used. |
---|
4544 | To include a value min may equal it, while max must exceed it. |
---|
4545 | Lat and lon are assumed to be in decimal degrees. |
---|
4546 | NOTE: minlon is the most east boundary. |
---|
4547 | |
---|
4548 | origin is a 3-tuple with geo referenced |
---|
4549 | UTM coordinates (zone, easting, northing) |
---|
4550 | It will be the origin of the sww file. This shouldn't be used, |
---|
4551 | since all of anuga should be able to handle an arbitary origin. |
---|
4552 | |
---|
4553 | URS C binary format has data orgainised as TIME, LONGITUDE, LATITUDE |
---|
4554 | which means that latitude is the fastest |
---|
4555 | varying dimension (row major order, so to speak) |
---|
4556 | |
---|
4557 | In URS C binary the latitudes and longitudes are in assending order. |
---|
4558 | """ |
---|
4559 | |
---|
4560 | if basename_out == None: |
---|
4561 | basename_out = basename_in |
---|
4562 | |
---|
4563 | files_out = urs2nc(basename_in, basename_out) |
---|
4564 | |
---|
4565 | ferret2sww(basename_out, |
---|
4566 | minlat=minlat, |
---|
4567 | maxlat=maxlat, |
---|
4568 | minlon=minlon, |
---|
4569 | maxlon=maxlon, |
---|
4570 | mint=mint, |
---|
4571 | maxt=maxt, |
---|
4572 | mean_stage=mean_stage, |
---|
4573 | origin=origin, |
---|
4574 | zscale=zscale, |
---|
4575 | fail_on_NaN=fail_on_NaN, |
---|
4576 | NaN_filler=NaN_filler, |
---|
4577 | inverted_bathymetry=True, |
---|
4578 | verbose=verbose) |
---|
4579 | |
---|
4580 | if remove_nc_files: |
---|
4581 | for file_out in files_out: |
---|
4582 | os.remove(file_out) |
---|
4583 | |
---|
4584 | |
---|
4585 | ## |
---|
4586 | # @brief Convert 3 URS files back to 4 NC files. |
---|
4587 | # @param basename_in Stem of the input filenames. |
---|
4588 | # @param basename_outStem of the output filenames. |
---|
4589 | # @note The name of the urs file names must be: |
---|
4590 | # [basename_in]-z-mux |
---|
4591 | # [basename_in]-e-mux |
---|
4592 | # [basename_in]-n-mux |
---|
4593 | def urs2nc(basename_in='o', basename_out='urs'): |
---|
4594 | """Convert the 3 urs files to 4 nc files. |
---|
4595 | |
---|
4596 | The name of the urs file names must be; |
---|
4597 | [basename_in]-z-mux |
---|
4598 | [basename_in]-e-mux |
---|
4599 | [basename_in]-n-mux |
---|
4600 | """ |
---|
4601 | |
---|
4602 | files_in = [basename_in + WAVEHEIGHT_MUX_LABEL, |
---|
4603 | basename_in + EAST_VELOCITY_LABEL, |
---|
4604 | basename_in + NORTH_VELOCITY_LABEL] |
---|
4605 | files_out = [basename_out + '_ha.nc', |
---|
4606 | basename_out + '_ua.nc', |
---|
4607 | basename_out + '_va.nc'] |
---|
4608 | quantities = ['HA', 'UA', 'VA'] |
---|
4609 | |
---|
4610 | #if os.access(files_in[0]+'.mux', os.F_OK) == 0 : |
---|
4611 | for i, file_name in enumerate(files_in): |
---|
4612 | if os.access(file_name, os.F_OK) == 0: |
---|
4613 | if os.access(file_name + '.mux', os.F_OK) == 0 : |
---|
4614 | msg = 'File %s does not exist or is not accessible' % file_name |
---|
4615 | raise IOError, msg |
---|
4616 | else: |
---|
4617 | files_in[i] += '.mux' |
---|
4618 | print "file_name", file_name |
---|
4619 | |
---|
4620 | hashed_elevation = None |
---|
4621 | for file_in, file_out, quantity in map(None, files_in, |
---|
4622 | files_out, |
---|
4623 | quantities): |
---|
4624 | lonlatdep, lon, lat, depth = _binary_c2nc(file_in, |
---|
4625 | file_out, |
---|
4626 | quantity) |
---|
4627 | if hashed_elevation == None: |
---|
4628 | elevation_file = basename_out + '_e.nc' |
---|
4629 | write_elevation_nc(elevation_file, |
---|
4630 | lon, |
---|
4631 | lat, |
---|
4632 | depth) |
---|
4633 | hashed_elevation = myhash(lonlatdep) |
---|
4634 | else: |
---|
4635 | msg = "The elevation information in the mux files is inconsistent" |
---|
4636 | assert hashed_elevation == myhash(lonlatdep), msg |
---|
4637 | |
---|
4638 | files_out.append(elevation_file) |
---|
4639 | |
---|
4640 | return files_out |
---|
4641 | |
---|
4642 | |
---|
4643 | ## |
---|
4644 | # @brief Convert a quantity URS file to a NetCDF file. |
---|
4645 | # @param file_in Path to input URS file. |
---|
4646 | # @param file_out Path to the output file. |
---|
4647 | # @param quantity Name of the quantity to be written to the output file. |
---|
4648 | # @return A tuple (lonlatdep, lon, lat, depth). |
---|
4649 | def _binary_c2nc(file_in, file_out, quantity): |
---|
4650 | """Reads in a quantity urs file and writes a quantity nc file. |
---|
4651 | Additionally, returns the depth and lat, long info, |
---|
4652 | so it can be written to a file. |
---|
4653 | """ |
---|
4654 | |
---|
4655 | columns = 3 # long, lat , depth |
---|
4656 | mux_file = open(file_in, 'rb') |
---|
4657 | |
---|
4658 | # Number of points/stations |
---|
4659 | (points_num,) = unpack('i', mux_file.read(4)) |
---|
4660 | |
---|
4661 | # nt, int - Number of time steps |
---|
4662 | (time_step_count,) = unpack('i', mux_file.read(4)) |
---|
4663 | |
---|
4664 | #dt, float - time step, seconds |
---|
4665 | (time_step,) = unpack('f', mux_file.read(4)) |
---|
4666 | |
---|
4667 | msg = "Bad data in the mux file." |
---|
4668 | if points_num < 0: |
---|
4669 | mux_file.close() |
---|
4670 | raise ANUGAError, msg |
---|
4671 | if time_step_count < 0: |
---|
4672 | mux_file.close() |
---|
4673 | raise ANUGAError, msg |
---|
4674 | if time_step < 0: |
---|
4675 | mux_file.close() |
---|
4676 | raise ANUGAError, msg |
---|
4677 | |
---|
4678 | lonlatdep = p_array.array('f') |
---|
4679 | lonlatdep.read(mux_file, columns * points_num) |
---|
4680 | lonlatdep = num.array(lonlatdep, dtype=num.float) |
---|
4681 | lonlatdep = num.reshape(lonlatdep, (points_num, columns)) |
---|
4682 | |
---|
4683 | lon, lat, depth = lon_lat2grid(lonlatdep) |
---|
4684 | lon_sorted = list(lon) |
---|
4685 | lon_sorted.sort() |
---|
4686 | |
---|
4687 | if not num.alltrue(lon == lon_sorted): |
---|
4688 | msg = "Longitudes in mux file are not in ascending order" |
---|
4689 | raise IOError, msg |
---|
4690 | |
---|
4691 | lat_sorted = list(lat) |
---|
4692 | lat_sorted.sort() |
---|
4693 | |
---|
4694 | nc_file = Write_nc(quantity, |
---|
4695 | file_out, |
---|
4696 | time_step_count, |
---|
4697 | time_step, |
---|
4698 | lon, |
---|
4699 | lat) |
---|
4700 | |
---|
4701 | for i in range(time_step_count): |
---|
4702 | #Read in a time slice from mux file |
---|
4703 | hz_p_array = p_array.array('f') |
---|
4704 | hz_p_array.read(mux_file, points_num) |
---|
4705 | hz_p = num.array(hz_p_array, dtype=num.float) |
---|
4706 | hz_p = num.reshape(hz_p, (len(lon), len(lat))) |
---|
4707 | hz_p = num.transpose(hz_p) # mux has lat varying fastest, nc has long v.f. |
---|
4708 | |
---|
4709 | #write time slice to nc file |
---|
4710 | nc_file.store_timestep(hz_p) |
---|
4711 | |
---|
4712 | mux_file.close() |
---|
4713 | nc_file.close() |
---|
4714 | |
---|
4715 | return lonlatdep, lon, lat, depth |
---|
4716 | |
---|
4717 | |
---|
4718 | ## |
---|
4719 | # @brief Write an NC elevation file. |
---|
4720 | # @param file_out Path to the output file. |
---|
4721 | # @param lon ?? |
---|
4722 | # @param lat ?? |
---|
4723 | # @param depth_vector The elevation data to write. |
---|
4724 | def write_elevation_nc(file_out, lon, lat, depth_vector): |
---|
4725 | """Write an nc elevation file.""" |
---|
4726 | |
---|
4727 | # NetCDF file definition |
---|
4728 | outfile = NetCDFFile(file_out, netcdf_mode_w) |
---|
4729 | |
---|
4730 | #Create new file |
---|
4731 | nc_lon_lat_header(outfile, lon, lat) |
---|
4732 | |
---|
4733 | # ELEVATION |
---|
4734 | zname = 'ELEVATION' |
---|
4735 | outfile.createVariable(zname, precision, (lat_name, lon_name)) |
---|
4736 | outfile.variables[zname].units = 'CENTIMETERS' |
---|
4737 | outfile.variables[zname].missing_value = -1.e+034 |
---|
4738 | |
---|
4739 | outfile.variables[lon_name][:] = ensure_numeric(lon) |
---|
4740 | outfile.variables[lat_name][:] = ensure_numeric(lat) |
---|
4741 | |
---|
4742 | depth = num.reshape(depth_vector, (len(lat), len(lon))) |
---|
4743 | outfile.variables[zname][:] = depth |
---|
4744 | |
---|
4745 | outfile.close() |
---|
4746 | |
---|
4747 | |
---|
4748 | ## |
---|
4749 | # @brief Write lat/lon headers to a NetCDF file. |
---|
4750 | # @param outfile Handle to open file to write to. |
---|
4751 | # @param lon An iterable of the longitudes. |
---|
4752 | # @param lat An iterable of the latitudes. |
---|
4753 | # @note Defines lat/long dimensions and variables. Sets various attributes: |
---|
4754 | # .point_spacing and .units |
---|
4755 | # and writes lat/lon data. |
---|
4756 | |
---|
4757 | def nc_lon_lat_header(outfile, lon, lat): |
---|
4758 | """Write lat/lon headers to a NetCDF file. |
---|
4759 | |
---|
4760 | outfile is the netcdf file handle. |
---|
4761 | lon - a list/array of the longitudes |
---|
4762 | lat - a list/array of the latitudes |
---|
4763 | """ |
---|
4764 | |
---|
4765 | outfile.institution = 'Geoscience Australia' |
---|
4766 | outfile.description = 'Converted from URS binary C' |
---|
4767 | |
---|
4768 | # Longitude |
---|
4769 | outfile.createDimension(lon_name, len(lon)) |
---|
4770 | outfile.createVariable(lon_name, precision, (lon_name,)) |
---|
4771 | outfile.variables[lon_name].point_spacing = 'uneven' |
---|
4772 | outfile.variables[lon_name].units = 'degrees_east' |
---|
4773 | outfile.variables[lon_name].assignValue(lon) |
---|
4774 | |
---|
4775 | # Latitude |
---|
4776 | outfile.createDimension(lat_name, len(lat)) |
---|
4777 | outfile.createVariable(lat_name, precision, (lat_name,)) |
---|
4778 | outfile.variables[lat_name].point_spacing = 'uneven' |
---|
4779 | outfile.variables[lat_name].units = 'degrees_north' |
---|
4780 | outfile.variables[lat_name].assignValue(lat) |
---|
4781 | |
---|
4782 | |
---|
4783 | ## |
---|
4784 | # @brief |
---|
4785 | # @param long_lat_dep |
---|
4786 | # @return A tuple (long, lat, quantity). |
---|
4787 | # @note The latitude is the fastest varying dimension - in mux files. |
---|
4788 | def lon_lat2grid(long_lat_dep): |
---|
4789 | """ |
---|
4790 | given a list of points that are assumed to be an a grid, |
---|
4791 | return the long's and lat's of the grid. |
---|
4792 | long_lat_dep is an array where each row is a position. |
---|
4793 | The first column is longitudes. |
---|
4794 | The second column is latitudes. |
---|
4795 | |
---|
4796 | The latitude is the fastest varying dimension - in mux files |
---|
4797 | """ |
---|
4798 | |
---|
4799 | LONG = 0 |
---|
4800 | LAT = 1 |
---|
4801 | QUANTITY = 2 |
---|
4802 | |
---|
4803 | long_lat_dep = ensure_numeric(long_lat_dep, num.float) |
---|
4804 | |
---|
4805 | num_points = long_lat_dep.shape[0] |
---|
4806 | this_rows_long = long_lat_dep[0,LONG] |
---|
4807 | |
---|
4808 | # Count the length of unique latitudes |
---|
4809 | i = 0 |
---|
4810 | while long_lat_dep[i,LONG] == this_rows_long and i < num_points: |
---|
4811 | i += 1 |
---|
4812 | |
---|
4813 | # determine the lats and longsfrom the grid |
---|
4814 | lat = long_lat_dep[:i, LAT] |
---|
4815 | long = long_lat_dep[::i, LONG] |
---|
4816 | |
---|
4817 | lenlong = len(long) |
---|
4818 | lenlat = len(lat) |
---|
4819 | |
---|
4820 | msg = 'Input data is not gridded' |
---|
4821 | assert num_points % lenlat == 0, msg |
---|
4822 | assert num_points % lenlong == 0, msg |
---|
4823 | |
---|
4824 | # Test that data is gridded |
---|
4825 | for i in range(lenlong): |
---|
4826 | msg = 'Data is not gridded. It must be for this operation' |
---|
4827 | first = i * lenlat |
---|
4828 | last = first + lenlat |
---|
4829 | |
---|
4830 | assert num.allclose(long_lat_dep[first:last,LAT], lat), msg |
---|
4831 | assert num.allclose(long_lat_dep[first:last,LONG], long[i]), msg |
---|
4832 | |
---|
4833 | msg = 'Out of range latitudes/longitudes' |
---|
4834 | for l in lat:assert -90 < l < 90 , msg |
---|
4835 | for l in long:assert -180 < l < 180 , msg |
---|
4836 | |
---|
4837 | # Changing quantity from lat being the fastest varying dimension to |
---|
4838 | # long being the fastest varying dimension |
---|
4839 | # FIXME - make this faster/do this a better way |
---|
4840 | # use numeric transpose, after reshaping the quantity vector |
---|
4841 | quantity = num.zeros(num_points, num.float) |
---|
4842 | |
---|
4843 | for lat_i, _ in enumerate(lat): |
---|
4844 | for long_i, _ in enumerate(long): |
---|
4845 | q_index = lat_i*lenlong + long_i |
---|
4846 | lld_index = long_i*lenlat + lat_i |
---|
4847 | temp = long_lat_dep[lld_index, QUANTITY] |
---|
4848 | quantity[q_index] = temp |
---|
4849 | |
---|
4850 | return long, lat, quantity |
---|
4851 | |
---|
4852 | ################################################################################ |
---|
4853 | # END URS 2 SWW |
---|
4854 | ################################################################################ |
---|
4855 | |
---|
4856 | ################################################################################ |
---|
4857 | # URS UNGRIDDED 2 SWW |
---|
4858 | ################################################################################ |
---|
4859 | |
---|
4860 | ### PRODUCING THE POINTS NEEDED FILE ### |
---|
4861 | |
---|
4862 | # Ones used for FESA 2007 results |
---|
4863 | #LL_LAT = -50.0 |
---|
4864 | #LL_LONG = 80.0 |
---|
4865 | #GRID_SPACING = 1.0/60.0 |
---|
4866 | #LAT_AMOUNT = 4800 |
---|
4867 | #LONG_AMOUNT = 3600 |
---|
4868 | |
---|
4869 | |
---|
4870 | ## |
---|
4871 | # @brief |
---|
4872 | # @param file_name |
---|
4873 | # @param boundary_polygon |
---|
4874 | # @param zone |
---|
4875 | # @param ll_lat |
---|
4876 | # @param ll_long |
---|
4877 | # @param grid_spacing |
---|
4878 | # @param lat_amount |
---|
4879 | # @param long_amount |
---|
4880 | # @param isSouthernHemisphere |
---|
4881 | # @param export_csv |
---|
4882 | # @param use_cache |
---|
4883 | # @param verbose True if this function is to be verbose. |
---|
4884 | # @return |
---|
4885 | def URS_points_needed_to_file(file_name, boundary_polygon, zone, |
---|
4886 | ll_lat, ll_long, |
---|
4887 | grid_spacing, |
---|
4888 | lat_amount, long_amount, |
---|
4889 | isSouthernHemisphere=True, |
---|
4890 | export_csv=False, use_cache=False, |
---|
4891 | verbose=False): |
---|
4892 | """ |
---|
4893 | Given the info to replicate the URS grid and a polygon output |
---|
4894 | a file that specifies the cloud of boundary points for URS. |
---|
4895 | |
---|
4896 | This creates a .urs file. This is in the format used by URS; |
---|
4897 | 1st line is the number of points, |
---|
4898 | each line after represents a point,in lats and longs. |
---|
4899 | |
---|
4900 | Note: The polygon cannot cross zones or hemispheres. |
---|
4901 | |
---|
4902 | A work-a-round for different zones or hemispheres is to run this twice, |
---|
4903 | once for each zone, and then combine the output. |
---|
4904 | |
---|
4905 | file_name - name of the urs file produced for David. |
---|
4906 | boundary_polygon - a list of points that describes a polygon. |
---|
4907 | The last point is assumed ot join the first point. |
---|
4908 | This is in UTM (lat long would be better though) |
---|
4909 | |
---|
4910 | This is info about the URS model that needs to be inputted. |
---|
4911 | |
---|
4912 | ll_lat - lower left latitude, in decimal degrees |
---|
4913 | ll-long - lower left longitude, in decimal degrees |
---|
4914 | grid_spacing - in deciamal degrees |
---|
4915 | lat_amount - number of latitudes |
---|
4916 | long_amount- number of longs |
---|
4917 | |
---|
4918 | Don't add the file extension. It will be added. |
---|
4919 | """ |
---|
4920 | |
---|
4921 | geo = URS_points_needed(boundary_polygon, zone, ll_lat, ll_long, |
---|
4922 | grid_spacing, |
---|
4923 | lat_amount, long_amount, isSouthernHemisphere, |
---|
4924 | use_cache, verbose) |
---|
4925 | |
---|
4926 | if not file_name[-4:] == ".urs": |
---|
4927 | file_name += ".urs" |
---|
4928 | |
---|
4929 | geo.export_points_file(file_name, isSouthHemisphere=isSouthernHemisphere) |
---|
4930 | |
---|
4931 | if export_csv: |
---|
4932 | if file_name[-4:] == ".urs": |
---|
4933 | file_name = file_name[:-4] + ".csv" |
---|
4934 | geo.export_points_file(file_name) |
---|
4935 | |
---|
4936 | return geo |
---|
4937 | |
---|
4938 | |
---|
4939 | ## |
---|
4940 | # @brief |
---|
4941 | # @param boundary_polygon |
---|
4942 | # @param zone |
---|
4943 | # @param ll_lat |
---|
4944 | # @param ll_long |
---|
4945 | # @param grid_spacing |
---|
4946 | # @param lat_amount |
---|
4947 | # @param long_amount |
---|
4948 | # @param isSouthHemisphere |
---|
4949 | # @param use_cache |
---|
4950 | # @param verbose |
---|
4951 | def URS_points_needed(boundary_polygon, zone, ll_lat, |
---|
4952 | ll_long, grid_spacing, |
---|
4953 | lat_amount, long_amount, isSouthHemisphere=True, |
---|
4954 | use_cache=False, verbose=False): |
---|
4955 | args = (boundary_polygon, |
---|
4956 | zone, ll_lat, |
---|
4957 | ll_long, grid_spacing, |
---|
4958 | lat_amount, long_amount, isSouthHemisphere) |
---|
4959 | kwargs = {} |
---|
4960 | |
---|
4961 | if use_cache is True: |
---|
4962 | try: |
---|
4963 | from anuga.caching import cache |
---|
4964 | except: |
---|
4965 | msg = 'Caching was requested, but caching module' \ |
---|
4966 | 'could not be imported' |
---|
4967 | raise msg |
---|
4968 | |
---|
4969 | geo = cache(_URS_points_needed, |
---|
4970 | args, kwargs, |
---|
4971 | verbose=verbose, |
---|
4972 | compression=False) |
---|
4973 | else: |
---|
4974 | geo = apply(_URS_points_needed, args, kwargs) |
---|
4975 | |
---|
4976 | return geo |
---|
4977 | |
---|
4978 | |
---|
4979 | ## |
---|
4980 | # @brief |
---|
4981 | # @param boundary_polygon An iterable of points that describe a polygon. |
---|
4982 | # @param zone |
---|
4983 | # @param ll_lat Lower left latitude, in decimal degrees |
---|
4984 | # @param ll_long Lower left longitude, in decimal degrees |
---|
4985 | # @param grid_spacing Grid spacing in decimal degrees. |
---|
4986 | # @param lat_amount |
---|
4987 | # @param long_amount |
---|
4988 | # @param isSouthHemisphere |
---|
4989 | def _URS_points_needed(boundary_polygon, |
---|
4990 | zone, ll_lat, |
---|
4991 | ll_long, grid_spacing, |
---|
4992 | lat_amount, long_amount, |
---|
4993 | isSouthHemisphere): |
---|
4994 | """ |
---|
4995 | boundary_polygon - a list of points that describes a polygon. |
---|
4996 | The last point is assumed ot join the first point. |
---|
4997 | This is in UTM (lat long would b better though) |
---|
4998 | |
---|
4999 | ll_lat - lower left latitude, in decimal degrees |
---|
5000 | ll-long - lower left longitude, in decimal degrees |
---|
5001 | grid_spacing - in decimal degrees |
---|
5002 | |
---|
5003 | """ |
---|
5004 | |
---|
5005 | msg = "grid_spacing can not be zero" |
---|
5006 | assert not grid_spacing == 0, msg |
---|
5007 | |
---|
5008 | a = boundary_polygon |
---|
5009 | |
---|
5010 | # List of segments. Each segment is two points. |
---|
5011 | segs = [i and [a[i-1], a[i]] or [a[len(a)-1], a[0]] for i in range(len(a))] |
---|
5012 | |
---|
5013 | # convert the segs to Lat's and longs. |
---|
5014 | # Don't assume the zone of the segments is the same as the lower left |
---|
5015 | # corner of the lat long data!! They can easily be in different zones |
---|
5016 | lat_long_set = frozenset() |
---|
5017 | for seg in segs: |
---|
5018 | points_lat_long = points_needed(seg, ll_lat, ll_long, grid_spacing, |
---|
5019 | lat_amount, long_amount, zone, |
---|
5020 | isSouthHemisphere) |
---|
5021 | lat_long_set |= frozenset(points_lat_long) |
---|
5022 | |
---|
5023 | if lat_long_set == frozenset([]): |
---|
5024 | msg = "URS region specified and polygon does not overlap." |
---|
5025 | raise ValueError, msg |
---|
5026 | |
---|
5027 | # Warning there is no info in geospatial saying the hemisphere of |
---|
5028 | # these points. There should be. |
---|
5029 | geo = Geospatial_data(data_points=list(lat_long_set), |
---|
5030 | points_are_lats_longs=True) |
---|
5031 | |
---|
5032 | return geo |
---|
5033 | |
---|
5034 | |
---|
5035 | ## |
---|
5036 | # @brief Get the points that are needed to interpolate any point a a segment. |
---|
5037 | # @param seg Two points in the UTM. |
---|
5038 | # @param ll_lat Lower left latitude, in decimal degrees |
---|
5039 | # @param ll_long Lower left longitude, in decimal degrees |
---|
5040 | # @param grid_spacing |
---|
5041 | # @param lat_amount |
---|
5042 | # @param long_amount |
---|
5043 | # @param zone |
---|
5044 | # @param isSouthHemisphere |
---|
5045 | # @return A list of points. |
---|
5046 | def points_needed(seg, ll_lat, ll_long, grid_spacing, |
---|
5047 | lat_amount, long_amount, zone, |
---|
5048 | isSouthHemisphere): |
---|
5049 | """ |
---|
5050 | seg is two points, in UTM |
---|
5051 | return a list of the points, in lats and longs that are needed to |
---|
5052 | interpolate any point on the segment. |
---|
5053 | """ |
---|
5054 | |
---|
5055 | from math import sqrt |
---|
5056 | |
---|
5057 | geo_reference = Geo_reference(zone=zone) |
---|
5058 | geo = Geospatial_data(seg, geo_reference=geo_reference) |
---|
5059 | seg_lat_long = geo.get_data_points(as_lat_long=True, |
---|
5060 | isSouthHemisphere=isSouthHemisphere) |
---|
5061 | |
---|
5062 | # 1.415 = 2^0.5, rounded up.... |
---|
5063 | sqrt_2_rounded_up = 1.415 |
---|
5064 | buffer = sqrt_2_rounded_up * grid_spacing |
---|
5065 | |
---|
5066 | max_lat = max(seg_lat_long[0][0], seg_lat_long[1][0]) + buffer |
---|
5067 | max_long = max(seg_lat_long[0][1], seg_lat_long[1][1]) + buffer |
---|
5068 | min_lat = min(seg_lat_long[0][0], seg_lat_long[1][0]) - buffer |
---|
5069 | min_long = min(seg_lat_long[0][1], seg_lat_long[1][1]) - buffer |
---|
5070 | |
---|
5071 | first_row = (min_long - ll_long) / grid_spacing |
---|
5072 | |
---|
5073 | # To round up |
---|
5074 | first_row_long = int(round(first_row + 0.5)) |
---|
5075 | |
---|
5076 | last_row = (max_long - ll_long) / grid_spacing # round down |
---|
5077 | last_row_long = int(round(last_row)) |
---|
5078 | |
---|
5079 | first_row = (min_lat - ll_lat) / grid_spacing |
---|
5080 | # To round up |
---|
5081 | first_row_lat = int(round(first_row + 0.5)) |
---|
5082 | |
---|
5083 | last_row = (max_lat - ll_lat) / grid_spacing # round down |
---|
5084 | last_row_lat = int(round(last_row)) |
---|
5085 | |
---|
5086 | max_distance = 157147.4112 * grid_spacing |
---|
5087 | points_lat_long = [] |
---|
5088 | |
---|
5089 | # Create a list of the lat long points to include. |
---|
5090 | for index_lat in range(first_row_lat, last_row_lat + 1): |
---|
5091 | for index_long in range(first_row_long, last_row_long + 1): |
---|
5092 | lat = ll_lat + index_lat*grid_spacing |
---|
5093 | long = ll_long + index_long*grid_spacing |
---|
5094 | |
---|
5095 | #filter here to keep good points |
---|
5096 | if keep_point(lat, long, seg, max_distance): |
---|
5097 | points_lat_long.append((lat, long)) #must be hashable |
---|
5098 | |
---|
5099 | # Now that we have these points, lets throw ones out that are too far away |
---|
5100 | return points_lat_long |
---|
5101 | |
---|
5102 | |
---|
5103 | ## |
---|
5104 | # @brief |
---|
5105 | # @param lat |
---|
5106 | # @param long |
---|
5107 | # @param seg Two points in UTM. |
---|
5108 | # @param max_distance |
---|
5109 | def keep_point(lat, long, seg, max_distance): |
---|
5110 | """ |
---|
5111 | seg is two points, UTM |
---|
5112 | """ |
---|
5113 | |
---|
5114 | from math import sqrt |
---|
5115 | |
---|
5116 | _ , x0, y0 = redfearn(lat, long) |
---|
5117 | x1 = seg[0][0] |
---|
5118 | y1 = seg[0][1] |
---|
5119 | x2 = seg[1][0] |
---|
5120 | y2 = seg[1][1] |
---|
5121 | x2_1 = x2-x1 |
---|
5122 | y2_1 = y2-y1 |
---|
5123 | try: |
---|
5124 | d = abs((x2_1)*(y1-y0)-(x1-x0)*(y2_1))/sqrt( \ |
---|
5125 | (x2_1)*(x2_1)+(y2_1)*(y2_1)) |
---|
5126 | except ZeroDivisionError: |
---|
5127 | if sqrt((x2_1)*(x2_1)+(y2_1)*(y2_1)) == 0 \ |
---|
5128 | and abs((x2_1)*(y1-y0)-(x1-x0)*(y2_1)) == 0: |
---|
5129 | return True |
---|
5130 | else: |
---|
5131 | return False |
---|
5132 | |
---|
5133 | return d <= max_distance |
---|
5134 | |
---|
5135 | ################################################################################ |
---|
5136 | # CONVERTING UNGRIDDED URS DATA TO AN SWW FILE |
---|
5137 | ################################################################################ |
---|
5138 | |
---|
5139 | WAVEHEIGHT_MUX_LABEL = '-z-mux' |
---|
5140 | EAST_VELOCITY_LABEL = '-e-mux' |
---|
5141 | NORTH_VELOCITY_LABEL = '-n-mux' |
---|
5142 | |
---|
5143 | ## |
---|
5144 | # @brief Convert URS file(s) (wave prop) to an SWW file. |
---|
5145 | # @param basename_in Stem of the input filenames. |
---|
5146 | # @param basename_out Path to the output SWW file. |
---|
5147 | # @param verbose True if this function is to be verbose. |
---|
5148 | # @param mint |
---|
5149 | # @param maxt |
---|
5150 | # @param mean_stage |
---|
5151 | # @param origin Tuple with geo-ref UTM coordinates (zone, easting, northing). |
---|
5152 | # @param hole_points_UTM |
---|
5153 | # @param zscale |
---|
5154 | # @note Also convert latitude and longitude to UTM. All coordinates are |
---|
5155 | # assumed to be given in the GDA94 datum. |
---|
5156 | # @note Input filename stem has suffixes '-z-mux', '-e-mux' and '-n-mux' |
---|
5157 | # added for relative height, x-velocity and y-velocity respectively. |
---|
5158 | def urs_ungridded2sww(basename_in='o', basename_out=None, verbose=False, |
---|
5159 | mint=None, maxt=None, |
---|
5160 | mean_stage=0, |
---|
5161 | origin=None, |
---|
5162 | hole_points_UTM=None, |
---|
5163 | zscale=1): |
---|
5164 | """ |
---|
5165 | Convert URS C binary format for wave propagation to |
---|
5166 | sww format native to abstract_2d_finite_volumes. |
---|
5167 | |
---|
5168 | Specify only basename_in and read files of the form |
---|
5169 | basefilename-z-mux, basefilename-e-mux and |
---|
5170 | basefilename-n-mux containing relative height, |
---|
5171 | x-velocity and y-velocity, respectively. |
---|
5172 | |
---|
5173 | Also convert latitude and longitude to UTM. All coordinates are |
---|
5174 | assumed to be given in the GDA94 datum. The latitude and longitude |
---|
5175 | information is assumed ungridded grid. |
---|
5176 | |
---|
5177 | min's and max's: If omitted - full extend is used. |
---|
5178 | To include a value min ans max may equal it. |
---|
5179 | Lat and lon are assumed to be in decimal degrees. |
---|
5180 | |
---|
5181 | origin is a 3-tuple with geo referenced |
---|
5182 | UTM coordinates (zone, easting, northing) |
---|
5183 | It will be the origin of the sww file. This shouldn't be used, |
---|
5184 | since all of anuga should be able to handle an arbitary origin. |
---|
5185 | The mux point info is NOT relative to this origin. |
---|
5186 | |
---|
5187 | URS C binary format has data organised as TIME, LONGITUDE, LATITUDE |
---|
5188 | which means that latitude is the fastest |
---|
5189 | varying dimension (row major order, so to speak) |
---|
5190 | |
---|
5191 | In URS C binary the latitudes and longitudes are in assending order. |
---|
5192 | |
---|
5193 | Note, interpolations of the resulting sww file will be different |
---|
5194 | from results of urs2sww. This is due to the interpolation |
---|
5195 | function used, and the different grid structure between urs2sww |
---|
5196 | and this function. |
---|
5197 | |
---|
5198 | Interpolating data that has an underlying gridded source can |
---|
5199 | easily end up with different values, depending on the underlying |
---|
5200 | mesh. |
---|
5201 | |
---|
5202 | consider these 4 points |
---|
5203 | 50 -50 |
---|
5204 | |
---|
5205 | 0 0 |
---|
5206 | |
---|
5207 | The grid can be |
---|
5208 | - |
---|
5209 | |\| A |
---|
5210 | - |
---|
5211 | or; |
---|
5212 | - |
---|
5213 | |/| B |
---|
5214 | - |
---|
5215 | |
---|
5216 | If a point is just below the center of the midpoint, it will have a |
---|
5217 | +ve value in grid A and a -ve value in grid B. |
---|
5218 | """ |
---|
5219 | |
---|
5220 | from anuga.mesh_engine.mesh_engine import NoTrianglesError |
---|
5221 | from anuga.pmesh.mesh import Mesh |
---|
5222 | |
---|
5223 | files_in = [basename_in + WAVEHEIGHT_MUX_LABEL, |
---|
5224 | basename_in + EAST_VELOCITY_LABEL, |
---|
5225 | basename_in + NORTH_VELOCITY_LABEL] |
---|
5226 | quantities = ['HA','UA','VA'] |
---|
5227 | |
---|
5228 | # instantiate urs_points of the three mux files. |
---|
5229 | mux = {} |
---|
5230 | for quantity, file in map(None, quantities, files_in): |
---|
5231 | mux[quantity] = Urs_points(file) |
---|
5232 | |
---|
5233 | # Could check that the depth is the same. (hashing) |
---|
5234 | |
---|
5235 | # handle to a mux file to do depth stuff |
---|
5236 | a_mux = mux[quantities[0]] |
---|
5237 | |
---|
5238 | # Convert to utm |
---|
5239 | lat = a_mux.lonlatdep[:,1] |
---|
5240 | long = a_mux.lonlatdep[:,0] |
---|
5241 | points_utm, zone = convert_from_latlon_to_utm(latitudes=lat, |
---|
5242 | longitudes=long) |
---|
5243 | |
---|
5244 | elevation = a_mux.lonlatdep[:,2] * -1 |
---|
5245 | |
---|
5246 | # grid (create a mesh from the selected points) |
---|
5247 | # This mesh has a problem. Triangles are streched over ungridded areas. |
---|
5248 | # If these areas could be described as holes in pmesh, that would be great. |
---|
5249 | |
---|
5250 | # I can't just get the user to selection a point in the middle. |
---|
5251 | # A boundary is needed around these points. |
---|
5252 | # But if the zone of points is obvious enough auto-segment should do |
---|
5253 | # a good boundary. |
---|
5254 | mesh = Mesh() |
---|
5255 | mesh.add_vertices(points_utm) |
---|
5256 | mesh.auto_segment(smooth_indents=True, expand_pinch=True) |
---|
5257 | |
---|
5258 | # To try and avoid alpha shape 'hugging' too much |
---|
5259 | mesh.auto_segment(mesh.shape.get_alpha() * 1.1) |
---|
5260 | if hole_points_UTM is not None: |
---|
5261 | point = ensure_absolute(hole_points_UTM) |
---|
5262 | mesh.add_hole(point[0], point[1]) |
---|
5263 | |
---|
5264 | try: |
---|
5265 | mesh.generate_mesh(minimum_triangle_angle=0.0, verbose=False) |
---|
5266 | except NoTrianglesError: |
---|
5267 | # This is a bit of a hack, going in and changing the data structure. |
---|
5268 | mesh.holes = [] |
---|
5269 | mesh.generate_mesh(minimum_triangle_angle=0.0, verbose=False) |
---|
5270 | |
---|
5271 | mesh_dic = mesh.Mesh2MeshList() |
---|
5272 | |
---|
5273 | #mesh.export_mesh_file(basename_in + '_168.tsh') |
---|
5274 | #import sys; sys.exit() |
---|
5275 | # These are the times of the mux file |
---|
5276 | mux_times = [] |
---|
5277 | for i in range(a_mux.time_step_count): |
---|
5278 | mux_times.append(a_mux.time_step * i) |
---|
5279 | (mux_times_start_i, mux_times_fin_i) = mux2sww_time(mux_times, mint, maxt) |
---|
5280 | times = mux_times[mux_times_start_i:mux_times_fin_i] |
---|
5281 | |
---|
5282 | if mux_times_start_i == mux_times_fin_i: |
---|
5283 | # Close the mux files |
---|
5284 | for quantity, file in map(None, quantities, files_in): |
---|
5285 | mux[quantity].close() |
---|
5286 | msg = "Due to mint and maxt there's no time info in the boundary SWW." |
---|
5287 | raise Exception, msg |
---|
5288 | |
---|
5289 | # If this raise is removed there is currently no downstream errors |
---|
5290 | |
---|
5291 | points_utm=ensure_numeric(points_utm) |
---|
5292 | assert num.alltrue(ensure_numeric(mesh_dic['generatedpointlist']) |
---|
5293 | == ensure_numeric(points_utm)) |
---|
5294 | |
---|
5295 | volumes = mesh_dic['generatedtrianglelist'] |
---|
5296 | |
---|
5297 | # write sww intro and grid stuff. |
---|
5298 | if basename_out is None: |
---|
5299 | swwname = basename_in + '.sww' |
---|
5300 | else: |
---|
5301 | swwname = basename_out + '.sww' |
---|
5302 | |
---|
5303 | if verbose: print 'Output to ', swwname |
---|
5304 | |
---|
5305 | outfile = NetCDFFile(swwname, netcdf_mode_w) |
---|
5306 | |
---|
5307 | # For a different way of doing this, check out tsh2sww |
---|
5308 | # work out sww_times and the index range this covers |
---|
5309 | sww = Write_sww() |
---|
5310 | sww.store_header(outfile, times, len(volumes), len(points_utm), |
---|
5311 | verbose=verbose, sww_precision=netcdf_float) |
---|
5312 | outfile.mean_stage = mean_stage |
---|
5313 | outfile.zscale = zscale |
---|
5314 | |
---|
5315 | sww.store_triangulation(outfile, points_utm, volumes, |
---|
5316 | elevation, zone, new_origin=origin, |
---|
5317 | verbose=verbose) |
---|
5318 | |
---|
5319 | if verbose: print 'Converting quantities' |
---|
5320 | |
---|
5321 | # Read in a time slice from each mux file and write it to the SWW file |
---|
5322 | j = 0 |
---|
5323 | for ha, ua, va in map(None, mux['HA'], mux['UA'], mux['VA']): |
---|
5324 | if j >= mux_times_start_i and j < mux_times_fin_i: |
---|
5325 | stage = zscale*ha + mean_stage |
---|
5326 | h = stage - elevation |
---|
5327 | xmomentum = ua*h |
---|
5328 | ymomentum = -1 * va * h # -1 since in mux files south is positive. |
---|
5329 | sww.store_quantities(outfile, |
---|
5330 | slice_index=j-mux_times_start_i, |
---|
5331 | verbose=verbose, |
---|
5332 | stage=stage, |
---|
5333 | xmomentum=xmomentum, |
---|
5334 | ymomentum=ymomentum, |
---|
5335 | sww_precision=num.float) |
---|
5336 | j += 1 |
---|
5337 | |
---|
5338 | if verbose: sww.verbose_quantities(outfile) |
---|
5339 | |
---|
5340 | outfile.close() |
---|
5341 | |
---|
5342 | # Do some conversions while writing the sww file |
---|
5343 | |
---|
5344 | |
---|
5345 | ################################################################################ |
---|
5346 | # READ MUX2 FILES line of points |
---|
5347 | ################################################################################ |
---|
5348 | |
---|
5349 | WAVEHEIGHT_MUX2_LABEL = '-z-mux2' |
---|
5350 | EAST_VELOCITY_MUX2_LABEL = '-e-mux2' |
---|
5351 | NORTH_VELOCITY_MUX2_LABEL = '-n-mux2' |
---|
5352 | |
---|
5353 | ## |
---|
5354 | # @brief |
---|
5355 | # @param filenames List of mux2 format input filenames. |
---|
5356 | # @param weights Weights associated with each source file. |
---|
5357 | # @param permutation The gauge numbers for which data is extracted. |
---|
5358 | # @param verbose True if this function is to be verbose. |
---|
5359 | # @return (times, latitudes, longitudes, elevation, quantity, starttime) |
---|
5360 | def read_mux2_py(filenames, |
---|
5361 | weights=None, |
---|
5362 | permutation=None, |
---|
5363 | verbose=False): |
---|
5364 | """Access the mux files specified in the filenames list. Combine the |
---|
5365 | data found therin as a weighted linear sum as specifed by the weights. |
---|
5366 | If permutation is None or empty extract timeseries data for all gauges |
---|
5367 | within the files. |
---|
5368 | |
---|
5369 | Input: |
---|
5370 | filenames: List of filenames specifiying the file containing the |
---|
5371 | timeseries data (mux2 format) for each source |
---|
5372 | weights: Weighs associated with each source |
---|
5373 | (defaults to 1 for each source) |
---|
5374 | permutation: Specifies the gauge numbers that for which data is to be |
---|
5375 | extracted |
---|
5376 | """ |
---|
5377 | |
---|
5378 | from urs_ext import read_mux2 |
---|
5379 | |
---|
5380 | numSrc = len(filenames) |
---|
5381 | |
---|
5382 | file_params = -1 * num.ones(3, num.float) # [nsta,dt,nt] |
---|
5383 | |
---|
5384 | # Convert verbose to int C flag |
---|
5385 | if verbose: |
---|
5386 | verbose=1 |
---|
5387 | else: |
---|
5388 | verbose=0 |
---|
5389 | |
---|
5390 | if weights is None: |
---|
5391 | weights = num.ones(numSrc, num.int) #array default# |
---|
5392 | |
---|
5393 | if permutation is None: |
---|
5394 | permutation = ensure_numeric([], num.float) |
---|
5395 | |
---|
5396 | # Call underlying C implementation urs2sts_ext.c |
---|
5397 | data = read_mux2(numSrc, filenames, weights, file_params, |
---|
5398 | permutation, verbose) |
---|
5399 | |
---|
5400 | msg = 'File parameter values were not read in correctly from c file' |
---|
5401 | assert len(num.compress(file_params > 0, file_params)) != 0, msg |
---|
5402 | |
---|
5403 | msg = 'The number of stations specifed in the c array and in the file ' \ |
---|
5404 | 'are inconsistent' |
---|
5405 | assert file_params[0] >= len(permutation), msg |
---|
5406 | |
---|
5407 | msg = 'The number of stations returned is inconsistent with ' \ |
---|
5408 | 'the requested number' |
---|
5409 | assert len(permutation) == 0 or len(permutation) == data.shape[0], msg |
---|
5410 | |
---|
5411 | nsta = int(file_params[0]) |
---|
5412 | msg = 'Must have at least one station' |
---|
5413 | assert nsta > 0, msg |
---|
5414 | |
---|
5415 | dt = file_params[1] |
---|
5416 | msg = 'Must have a postive timestep' |
---|
5417 | assert dt > 0, msg |
---|
5418 | |
---|
5419 | nt = int(file_params[2]) |
---|
5420 | msg = 'Must have at least one gauge value' |
---|
5421 | assert nt > 0, msg |
---|
5422 | |
---|
5423 | OFFSET = 5 # Number of site parameters p passed back with data |
---|
5424 | # p = [geolat,geolon,depth,start_tstep,finish_tstep] |
---|
5425 | |
---|
5426 | # FIXME (Ole): What is the relationship with params and data.shape ? |
---|
5427 | # It looks as if the following asserts should pass but they don't always |
---|
5428 | # |
---|
5429 | #msg = 'nt = %d, data.shape[1] == %d' %(nt, data.shape[1]) |
---|
5430 | #assert nt == data.shape[1] - OFFSET, msg |
---|
5431 | # |
---|
5432 | #msg = 'nsta = %d, data.shape[0] == %d' %(nsta, data.shape[0]) |
---|
5433 | #assert nsta == data.shape[0], msg |
---|
5434 | |
---|
5435 | # Number of stations in ordering file |
---|
5436 | number_of_selected_stations = data.shape[0] |
---|
5437 | |
---|
5438 | # Index where data ends and parameters begin |
---|
5439 | parameters_index = data.shape[1] - OFFSET |
---|
5440 | |
---|
5441 | times = dt * num.arange(parameters_index) |
---|
5442 | latitudes = num.zeros(number_of_selected_stations, num.float) |
---|
5443 | longitudes = num.zeros(number_of_selected_stations, num.float) |
---|
5444 | elevation = num.zeros(number_of_selected_stations, num.float) |
---|
5445 | quantity = num.zeros((number_of_selected_stations, parameters_index), num.float) |
---|
5446 | |
---|
5447 | starttime = 1e16 |
---|
5448 | for i in range(number_of_selected_stations): |
---|
5449 | quantity[i][:] = data[i][:parameters_index] |
---|
5450 | latitudes[i] = data[i][parameters_index] |
---|
5451 | longitudes[i] = data[i][parameters_index+1] |
---|
5452 | elevation[i] = -data[i][parameters_index+2] |
---|
5453 | first_time_step = data[i][parameters_index+3] |
---|
5454 | starttime = min(dt*first_time_step, starttime) |
---|
5455 | |
---|
5456 | return times, latitudes, longitudes, elevation, quantity, starttime |
---|
5457 | |
---|
5458 | |
---|
5459 | ## |
---|
5460 | # @brief ?? |
---|
5461 | # @param mux_times ?? |
---|
5462 | # @param mint ?? |
---|
5463 | # @param maxt ?? |
---|
5464 | # @return ?? |
---|
5465 | def mux2sww_time(mux_times, mint, maxt): |
---|
5466 | """ |
---|
5467 | """ |
---|
5468 | |
---|
5469 | if mint == None: |
---|
5470 | mux_times_start_i = 0 |
---|
5471 | else: |
---|
5472 | mux_times_start_i = num.searchsorted(mux_times, mint) |
---|
5473 | |
---|
5474 | if maxt == None: |
---|
5475 | mux_times_fin_i = len(mux_times) |
---|
5476 | else: |
---|
5477 | maxt += 0.5 # so if you specify a time where there is |
---|
5478 | # data that time will be included |
---|
5479 | mux_times_fin_i = num.searchsorted(mux_times, maxt) |
---|
5480 | |
---|
5481 | return mux_times_start_i, mux_times_fin_i |
---|
5482 | |
---|
5483 | |
---|
5484 | ## |
---|
5485 | # @brief Convert a URS (mux2, wave propagation) file to an STS file. |
---|
5486 | # @param basename_in String (or list) of source file stems. |
---|
5487 | # @param basename_out Stem of output STS file path. |
---|
5488 | # @param weights |
---|
5489 | # @param verbose True if this function is to be verbose. |
---|
5490 | # @param origin Tuple with with geo-ref UTM coords (zone, easting, northing). |
---|
5491 | # @param zone |
---|
5492 | # @param mean_stage |
---|
5493 | # @param zscale |
---|
5494 | # @param ordering_filename Path of a file specifying which mux2 gauge points are |
---|
5495 | # to be stored. |
---|
5496 | # @note Also convert latitude and longitude to UTM. All coordinates are |
---|
5497 | # assumed to be given in the GDA94 datum. |
---|
5498 | def urs2sts(basename_in, basename_out=None, |
---|
5499 | weights=None, |
---|
5500 | verbose=False, |
---|
5501 | origin=None, |
---|
5502 | zone=None, |
---|
5503 | central_meridian=None, |
---|
5504 | mean_stage=0.0, |
---|
5505 | zscale=1.0, |
---|
5506 | ordering_filename=None): |
---|
5507 | """Convert URS mux2 format for wave propagation to sts format |
---|
5508 | |
---|
5509 | Also convert latitude and longitude to UTM. All coordinates are |
---|
5510 | assumed to be given in the GDA94 datum |
---|
5511 | |
---|
5512 | origin is a 3-tuple with geo referenced |
---|
5513 | UTM coordinates (zone, easting, northing) |
---|
5514 | |
---|
5515 | inputs: |
---|
5516 | |
---|
5517 | basename_in: list of source file prefixes |
---|
5518 | |
---|
5519 | These are combined with the extensions: |
---|
5520 | WAVEHEIGHT_MUX2_LABEL = '-z-mux2' for stage |
---|
5521 | EAST_VELOCITY_MUX2_LABEL = '-e-mux2' xmomentum |
---|
5522 | NORTH_VELOCITY_MUX2_LABEL = '-n-mux2' and ymomentum |
---|
5523 | |
---|
5524 | to create a 2D list of mux2 file. The rows are associated with each |
---|
5525 | quantity and must have the above extensions |
---|
5526 | the columns are the list of file prefixes. |
---|
5527 | |
---|
5528 | ordering: a .txt file name specifying which mux2 gauge points are |
---|
5529 | to be stored. This is indicated by the index of the gauge |
---|
5530 | in the ordering file. |
---|
5531 | |
---|
5532 | ordering file format: |
---|
5533 | 1st line: 'index,longitude,latitude\n' |
---|
5534 | other lines: index,longitude,latitude |
---|
5535 | |
---|
5536 | If ordering is None or ordering file is empty then |
---|
5537 | all points are taken in the order they |
---|
5538 | appear in the mux2 file. |
---|
5539 | |
---|
5540 | |
---|
5541 | output: |
---|
5542 | basename_out: name of sts file in which mux2 data is stored. |
---|
5543 | |
---|
5544 | |
---|
5545 | |
---|
5546 | NOTE: South is positive in mux files so sign of y-component of velocity is reverted |
---|
5547 | """ |
---|
5548 | |
---|
5549 | import os |
---|
5550 | from Scientific.IO.NetCDF import NetCDFFile |
---|
5551 | from types import ListType,StringType |
---|
5552 | from operator import __and__ |
---|
5553 | |
---|
5554 | if not isinstance(basename_in, ListType): |
---|
5555 | if verbose: print 'Reading single source' |
---|
5556 | basename_in = [basename_in] |
---|
5557 | |
---|
5558 | # This is the value used in the mux file format to indicate NAN data |
---|
5559 | # FIXME (Ole): This should be changed everywhere to IEEE NAN when |
---|
5560 | # we upgrade to Numpy |
---|
5561 | NODATA = 99 |
---|
5562 | |
---|
5563 | # Check that basename is a list of strings |
---|
5564 | if not reduce(__and__, map(lambda z:isinstance(z,StringType), basename_in)): |
---|
5565 | msg= 'basename_in must be a string or list of strings' |
---|
5566 | raise Exception, msg |
---|
5567 | |
---|
5568 | # Find the number of sources to be used |
---|
5569 | numSrc = len(basename_in) |
---|
5570 | |
---|
5571 | # A weight must be specified for each source |
---|
5572 | if weights is None: |
---|
5573 | # Default is equal weighting |
---|
5574 | weights = num.ones(numSrc, num.float) / numSrc |
---|
5575 | else: |
---|
5576 | weights = ensure_numeric(weights) |
---|
5577 | msg = 'When combining multiple sources must specify a weight for ' \ |
---|
5578 | 'mux2 source file' |
---|
5579 | assert len(weights) == numSrc, msg |
---|
5580 | |
---|
5581 | if verbose: print 'Weights used in urs2sts:', weights |
---|
5582 | |
---|
5583 | # Check output filename |
---|
5584 | if basename_out is None: |
---|
5585 | msg = 'STS filename must be specified as basename_out ' \ |
---|
5586 | 'in function urs2sts' |
---|
5587 | raise Exception, msg |
---|
5588 | |
---|
5589 | if basename_out.endswith('.sts'): |
---|
5590 | stsname = basename_out |
---|
5591 | else: |
---|
5592 | stsname = basename_out + '.sts' |
---|
5593 | |
---|
5594 | # Create input filenames from basenames and check their existence |
---|
5595 | files_in = [[], [], []] |
---|
5596 | for files in basename_in: |
---|
5597 | files_in[0].append(files + WAVEHEIGHT_MUX2_LABEL), |
---|
5598 | files_in[1].append(files + EAST_VELOCITY_MUX2_LABEL) |
---|
5599 | files_in[2].append(files + NORTH_VELOCITY_MUX2_LABEL) |
---|
5600 | |
---|
5601 | quantities = ['HA','UA','VA'] # Quantity names used in the MUX2 format |
---|
5602 | for i in range(len(quantities)): |
---|
5603 | for file_in in files_in[i]: |
---|
5604 | if (os.access(file_in, os.R_OK) == 0): |
---|
5605 | msg = 'File %s does not exist or is not accessible' % file_in |
---|
5606 | raise IOError, msg |
---|
5607 | |
---|
5608 | # Establish permutation array |
---|
5609 | if ordering_filename is not None: |
---|
5610 | if verbose is True: print 'Reading ordering file', ordering_filename |
---|
5611 | |
---|
5612 | # Read ordering file |
---|
5613 | try: |
---|
5614 | fid = open(ordering_filename, 'r') |
---|
5615 | file_header = fid.readline().split(',') |
---|
5616 | ordering_lines = fid.readlines() |
---|
5617 | fid.close() |
---|
5618 | except: |
---|
5619 | msg = 'Cannot open %s' % ordering_filename |
---|
5620 | raise Exception, msg |
---|
5621 | |
---|
5622 | reference_header = 'index, longitude, latitude\n' |
---|
5623 | reference_header_split = reference_header.split(',') |
---|
5624 | for i in range(3): |
---|
5625 | if not file_header[i].strip() == reference_header_split[i].strip(): |
---|
5626 | msg = 'File must contain header: ' + reference_header |
---|
5627 | raise Exception, msg |
---|
5628 | |
---|
5629 | if len(ordering_lines) < 2: |
---|
5630 | msg = 'File must contain at least two points' |
---|
5631 | raise Exception, msg |
---|
5632 | |
---|
5633 | permutation = [int(line.split(',')[0]) for line in ordering_lines] |
---|
5634 | permutation = ensure_numeric(permutation) |
---|
5635 | else: |
---|
5636 | permutation = None |
---|
5637 | |
---|
5638 | # Read MUX2 files |
---|
5639 | if (verbose): print 'reading mux2 file' |
---|
5640 | |
---|
5641 | mux={} |
---|
5642 | for i, quantity in enumerate(quantities): |
---|
5643 | # For each quantity read the associated list of source mux2 file with |
---|
5644 | # extention associated with that quantity |
---|
5645 | |
---|
5646 | times, latitudes, longitudes, elevation, mux[quantity], starttime \ |
---|
5647 | = read_mux2_py(files_in[i], weights, permutation, verbose=verbose) |
---|
5648 | |
---|
5649 | # Check that all quantities have consistent time and space information |
---|
5650 | if quantity != quantities[0]: |
---|
5651 | msg = '%s, %s and %s have inconsistent gauge data' \ |
---|
5652 | % (files_in[0], files_in[1], files_in[2]) |
---|
5653 | assert num.allclose(times, times_old), msg |
---|
5654 | assert num.allclose(latitudes, latitudes_old), msg |
---|
5655 | assert num.allclose(longitudes, longitudes_old), msg |
---|
5656 | assert num.allclose(elevation, elevation_old), msg |
---|
5657 | assert num.allclose(starttime, starttime_old), msg |
---|
5658 | times_old = times |
---|
5659 | latitudes_old = latitudes |
---|
5660 | longitudes_old = longitudes |
---|
5661 | elevation_old = elevation |
---|
5662 | starttime_old = starttime |
---|
5663 | |
---|
5664 | # Self check - can be removed to improve speed |
---|
5665 | #ref_longitudes = [float(line.split(',')[1]) for line in ordering_lines] |
---|
5666 | #ref_latitudes = [float(line.split(',')[2]) for line in ordering_lines] |
---|
5667 | # |
---|
5668 | #msg = 'Longitudes specified in ordering file do not match those ' \ |
---|
5669 | # 'found in mux files. ' \ |
---|
5670 | # 'I got %s instead of %s (only beginning shown)' \ |
---|
5671 | # % (str(longitudes[:10]) + '...', |
---|
5672 | # str(ref_longitudes[:10]) + '...') |
---|
5673 | #assert allclose(longitudes, ref_longitudes), msg |
---|
5674 | # |
---|
5675 | #msg = 'Latitudes specified in ordering file do not match those ' \ |
---|
5676 | # 'found in mux files. ' |
---|
5677 | # 'I got %s instead of %s (only beginning shown)' \ |
---|
5678 | # % (str(latitudes[:10]) + '...', |
---|
5679 | # str(ref_latitudes[:10]) + '...') |
---|
5680 | #assert allclose(latitudes, ref_latitudes), msg |
---|
5681 | |
---|
5682 | # Store timeseries in STS file |
---|
5683 | msg = 'File is empty and or clipped region not in file region' |
---|
5684 | assert len(latitudes > 0), msg |
---|
5685 | |
---|
5686 | number_of_points = latitudes.shape[0] # Number of stations retrieved |
---|
5687 | number_of_times = times.shape[0] # Number of timesteps |
---|
5688 | number_of_latitudes = latitudes.shape[0] # Number latitudes |
---|
5689 | number_of_longitudes = longitudes.shape[0] # Number longitudes |
---|
5690 | |
---|
5691 | # The permutation vector of contains original indices |
---|
5692 | # as given in ordering file or None in which case points |
---|
5693 | # are assigned the trivial indices enumerating them from |
---|
5694 | # 0 to number_of_points-1 |
---|
5695 | if permutation is None: |
---|
5696 | permutation = num.arange(number_of_points, dtype=num.int) |
---|
5697 | |
---|
5698 | # NetCDF file definition |
---|
5699 | outfile = NetCDFFile(stsname, netcdf_mode_w) |
---|
5700 | |
---|
5701 | description = 'Converted from URS mux2 files: %s' % basename_in |
---|
5702 | |
---|
5703 | # Create new file |
---|
5704 | sts = Write_sts() |
---|
5705 | sts.store_header(outfile, |
---|
5706 | times+starttime, |
---|
5707 | number_of_points, |
---|
5708 | description=description, |
---|
5709 | verbose=verbose, |
---|
5710 | sts_precision=netcdf_float) |
---|
5711 | |
---|
5712 | # Store |
---|
5713 | from anuga.coordinate_transforms.redfearn import redfearn |
---|
5714 | |
---|
5715 | x = num.zeros(number_of_points, num.float) # Easting |
---|
5716 | y = num.zeros(number_of_points, num.float) # Northing |
---|
5717 | |
---|
5718 | # Check zone boundaries |
---|
5719 | if zone is None: |
---|
5720 | refzone, _, _ = redfearn(latitudes[0], longitudes[0], |
---|
5721 | central_meridian=central_meridian) |
---|
5722 | else: |
---|
5723 | refzone = zone |
---|
5724 | |
---|
5725 | old_zone = refzone |
---|
5726 | |
---|
5727 | for i in range(number_of_points): |
---|
5728 | computed_zone, easting, northing = redfearn(latitudes[i], longitudes[i], |
---|
5729 | zone=zone, |
---|
5730 | central_meridian=central_meridian) |
---|
5731 | x[i] = easting |
---|
5732 | y[i] = northing |
---|
5733 | if computed_zone != refzone: |
---|
5734 | msg = 'All sts gauges need to be in the same zone. \n' |
---|
5735 | msg += 'offending gauge:Zone %d,%.4f, %4f\n' \ |
---|
5736 | % (computed_zone, easting, northing) |
---|
5737 | msg += 'previous gauge:Zone %d,%.4f, %4f' \ |
---|
5738 | % (old_zone, old_easting, old_northing) |
---|
5739 | raise Exception, msg |
---|
5740 | old_zone = computed_zone |
---|
5741 | old_easting = easting |
---|
5742 | old_northing = northing |
---|
5743 | |
---|
5744 | if origin is None: |
---|
5745 | origin = Geo_reference(refzone, min(x), min(y)) |
---|
5746 | geo_ref = write_NetCDF_georeference(origin, outfile) |
---|
5747 | |
---|
5748 | elevation = num.resize(elevation, outfile.variables['elevation'][:].shape) |
---|
5749 | outfile.variables['permutation'][:] = permutation.astype(num.int32) # Opteron 64 |
---|
5750 | outfile.variables['x'][:] = x - geo_ref.get_xllcorner() |
---|
5751 | outfile.variables['y'][:] = y - geo_ref.get_yllcorner() |
---|
5752 | outfile.variables['elevation'][:] = elevation |
---|
5753 | |
---|
5754 | stage = outfile.variables['stage'] |
---|
5755 | xmomentum = outfile.variables['xmomentum'] |
---|
5756 | ymomentum = outfile.variables['ymomentum'] |
---|
5757 | |
---|
5758 | if verbose: print 'Converting quantities' |
---|
5759 | |
---|
5760 | for j in range(len(times)): |
---|
5761 | for i in range(number_of_points): |
---|
5762 | ha = mux['HA'][i,j] |
---|
5763 | ua = mux['UA'][i,j] |
---|
5764 | va = mux['VA'][i,j] |
---|
5765 | if ha == NODATA: |
---|
5766 | if verbose: |
---|
5767 | msg = 'Setting nodata value %d to 0 at time = %f, ' \ |
---|
5768 | 'point = %d' % (ha, times[j], i) |
---|
5769 | print msg |
---|
5770 | ha = 0.0 |
---|
5771 | ua = 0.0 |
---|
5772 | va = 0.0 |
---|
5773 | |
---|
5774 | w = zscale*ha + mean_stage |
---|
5775 | h = w - elevation[i] |
---|
5776 | stage[j,i] = w |
---|
5777 | |
---|
5778 | xmomentum[j,i] = ua * h |
---|
5779 | ymomentum[j,i] = -va * h # South is positive in mux files |
---|
5780 | |
---|
5781 | |
---|
5782 | outfile.close() |
---|
5783 | |
---|
5784 | |
---|
5785 | ## |
---|
5786 | # @brief Create a list of points defining a boundary from an STS file. |
---|
5787 | # @param stsname Stem of path to the STS file to read. |
---|
5788 | # @return A list of boundary points. |
---|
5789 | def create_sts_boundary(stsname): |
---|
5790 | """Create a list of points defining a boundary from an STS file. |
---|
5791 | |
---|
5792 | Create boundary segments from .sts file. Points can be stored in |
---|
5793 | arbitrary order within the .sts file. The order in which the .sts points |
---|
5794 | make up the boundary are given in order.txt file |
---|
5795 | |
---|
5796 | FIXME: |
---|
5797 | Point coordinates are stored in relative eastings and northings. |
---|
5798 | But boundary is produced in absolute coordinates |
---|
5799 | """ |
---|
5800 | |
---|
5801 | try: |
---|
5802 | fid = NetCDFFile(stsname + '.sts', netcdf_mode_r) |
---|
5803 | except: |
---|
5804 | msg = 'Cannot open %s' % stsname + '.sts' |
---|
5805 | raise msg |
---|
5806 | |
---|
5807 | xllcorner = fid.xllcorner[0] |
---|
5808 | yllcorner = fid.yllcorner[0] |
---|
5809 | |
---|
5810 | #Points stored in sts file are normalised to [xllcorner,yllcorner] but |
---|
5811 | #we cannot assume that boundary polygon will be. At least the |
---|
5812 | #additional points specified by the user after this function is called |
---|
5813 | x = fid.variables['x'][:] + xllcorner |
---|
5814 | y = fid.variables['y'][:] + yllcorner |
---|
5815 | |
---|
5816 | x = num.reshape(x, (len(x),1)) |
---|
5817 | y = num.reshape(y, (len(y),1)) |
---|
5818 | sts_points = num.concatenate((x,y), axis=1) |
---|
5819 | |
---|
5820 | return sts_points.tolist() |
---|
5821 | |
---|
5822 | |
---|
5823 | ## |
---|
5824 | # @brief A class to write an SWW file. |
---|
5825 | class Write_sww: |
---|
5826 | from anuga.shallow_water.shallow_water_domain import Domain |
---|
5827 | |
---|
5828 | # FIXME (Ole): Hardwiring the conserved quantities like |
---|
5829 | # this could be a problem. I would prefer taking them from |
---|
5830 | # the instantiation of Domain. |
---|
5831 | # |
---|
5832 | # (DSG) There is not always a Domain instance when Write_sww is used. |
---|
5833 | # Check to see if this is the same level of hardwiring as is in |
---|
5834 | # shallow water doamain. |
---|
5835 | |
---|
5836 | sww_quantities = Domain.conserved_quantities |
---|
5837 | |
---|
5838 | RANGE = '_range' |
---|
5839 | EXTREMA = ':extrema' |
---|
5840 | |
---|
5841 | ## |
---|
5842 | # brief Instantiate the SWW writer class. |
---|
5843 | def __init__(self): |
---|
5844 | pass |
---|
5845 | |
---|
5846 | ## |
---|
5847 | # @brief Store a header in the SWW file. |
---|
5848 | # @param outfile Open handle to the file that will be written. |
---|
5849 | # @param times A list of time slices *or* a start time. |
---|
5850 | # @param number_of_volumes The number of triangles. |
---|
5851 | # @param number_of_points The number of points. |
---|
5852 | # @param description The internal file description string. |
---|
5853 | # @param smoothing True if smoothing is to be used. |
---|
5854 | # @param order |
---|
5855 | # @param sww_precision Data type of the quantity written (netcdf constant) |
---|
5856 | # @param verbose True if this function is to be verbose. |
---|
5857 | # @note If 'times' is a list, the info will be made relative. |
---|
5858 | def store_header(self, |
---|
5859 | outfile, |
---|
5860 | times, |
---|
5861 | number_of_volumes, |
---|
5862 | number_of_points, |
---|
5863 | description='Converted from XXX', |
---|
5864 | smoothing=True, |
---|
5865 | order=1, |
---|
5866 | sww_precision=netcdf_float32, |
---|
5867 | verbose=False): |
---|
5868 | """Write an SWW file header. |
---|
5869 | |
---|
5870 | outfile - the open file that will be written |
---|
5871 | times - A list of the time slice times OR a start time |
---|
5872 | Note, if a list is given the info will be made relative. |
---|
5873 | number_of_volumes - the number of triangles |
---|
5874 | """ |
---|
5875 | |
---|
5876 | outfile.institution = 'Geoscience Australia' |
---|
5877 | outfile.description = description |
---|
5878 | |
---|
5879 | # For sww compatibility |
---|
5880 | if smoothing is True: |
---|
5881 | # Smoothing to be depreciated |
---|
5882 | outfile.smoothing = 'Yes' |
---|
5883 | outfile.vertices_are_stored_uniquely = 'False' |
---|
5884 | else: |
---|
5885 | # Smoothing to be depreciated |
---|
5886 | outfile.smoothing = 'No' |
---|
5887 | outfile.vertices_are_stored_uniquely = 'True' |
---|
5888 | outfile.order = order |
---|
5889 | |
---|
5890 | try: |
---|
5891 | revision_number = get_revision_number() |
---|
5892 | except: |
---|
5893 | revision_number = None |
---|
5894 | # Allow None to be stored as a string |
---|
5895 | outfile.revision_number = str(revision_number) |
---|
5896 | |
---|
5897 | # This is being used to seperate one number from a list. |
---|
5898 | # what it is actually doing is sorting lists from numeric arrays. |
---|
5899 | if isinstance(times, (list, num.ndarray)): |
---|
5900 | number_of_times = len(times) |
---|
5901 | times = ensure_numeric(times) |
---|
5902 | if number_of_times == 0: |
---|
5903 | starttime = 0 |
---|
5904 | else: |
---|
5905 | starttime = times[0] |
---|
5906 | times = times - starttime #Store relative times |
---|
5907 | else: |
---|
5908 | number_of_times = 0 |
---|
5909 | starttime = times |
---|
5910 | #times = ensure_numeric([]) |
---|
5911 | |
---|
5912 | outfile.starttime = starttime |
---|
5913 | |
---|
5914 | # dimension definitions |
---|
5915 | outfile.createDimension('number_of_volumes', number_of_volumes) |
---|
5916 | outfile.createDimension('number_of_vertices', 3) |
---|
5917 | outfile.createDimension('numbers_in_range', 2) |
---|
5918 | |
---|
5919 | if smoothing is True: |
---|
5920 | outfile.createDimension('number_of_points', number_of_points) |
---|
5921 | # FIXME(Ole): This will cause sww files for parallel domains to |
---|
5922 | # have ghost nodes stored (but not used by triangles). |
---|
5923 | # To clean this up, we have to change get_vertex_values and |
---|
5924 | # friends in quantity.py (but I can't be bothered right now) |
---|
5925 | else: |
---|
5926 | outfile.createDimension('number_of_points', 3*number_of_volumes) |
---|
5927 | |
---|
5928 | outfile.createDimension('number_of_timesteps', number_of_times) |
---|
5929 | |
---|
5930 | # variable definitions |
---|
5931 | outfile.createVariable('x', sww_precision, ('number_of_points',)) |
---|
5932 | outfile.createVariable('y', sww_precision, ('number_of_points',)) |
---|
5933 | outfile.createVariable('elevation', sww_precision, |
---|
5934 | ('number_of_points',)) |
---|
5935 | q = 'elevation' |
---|
5936 | outfile.createVariable(q + Write_sww.RANGE, sww_precision, |
---|
5937 | ('numbers_in_range',)) |
---|
5938 | |
---|
5939 | # Initialise ranges with small and large sentinels. |
---|
5940 | # If this was in pure Python we could have used None sensibly |
---|
5941 | outfile.variables[q+Write_sww.RANGE][0] = max_float # Min |
---|
5942 | outfile.variables[q+Write_sww.RANGE][1] = -max_float # Max |
---|
5943 | |
---|
5944 | # FIXME: Backwards compatibility |
---|
5945 | outfile.createVariable('z', sww_precision, ('number_of_points',)) |
---|
5946 | |
---|
5947 | outfile.createVariable('volumes', netcdf_int, ('number_of_volumes', |
---|
5948 | 'number_of_vertices')) |
---|
5949 | |
---|
5950 | # Doing sww_precision instead of Float gives cast errors. |
---|
5951 | outfile.createVariable('time', netcdf_float, |
---|
5952 | ('number_of_timesteps',)) |
---|
5953 | |
---|
5954 | for q in Write_sww.sww_quantities: |
---|
5955 | outfile.createVariable(q, sww_precision, ('number_of_timesteps', |
---|
5956 | 'number_of_points')) |
---|
5957 | outfile.createVariable(q + Write_sww.RANGE, sww_precision, |
---|
5958 | ('numbers_in_range',)) |
---|
5959 | |
---|
5960 | # Initialise ranges with small and large sentinels. |
---|
5961 | # If this was in pure Python we could have used None sensibly |
---|
5962 | outfile.variables[q+Write_sww.RANGE][0] = max_float # Min |
---|
5963 | outfile.variables[q+Write_sww.RANGE][1] = -max_float # Max |
---|
5964 | |
---|
5965 | if isinstance(times, (list, num.ndarray)): |
---|
5966 | outfile.variables['time'][:] = times #Store time relative |
---|
5967 | |
---|
5968 | if verbose: |
---|
5969 | print '------------------------------------------------' |
---|
5970 | print 'Statistics:' |
---|
5971 | print ' t in [%f, %f], len(t) == %d' \ |
---|
5972 | % (num.min(times), num.max(times), len(times.flat)) |
---|
5973 | |
---|
5974 | ## |
---|
5975 | # @brief Store triangulation data in the underlying file. |
---|
5976 | # @param outfile Open handle to underlying file. |
---|
5977 | # @param points_utm List or array of points in UTM. |
---|
5978 | # @param volumes |
---|
5979 | # @param elevation |
---|
5980 | # @param zone |
---|
5981 | # @param new_origin georeference that the points can be set to. |
---|
5982 | # @param points_georeference The georeference of the points_utm. |
---|
5983 | # @param verbose True if this function is to be verbose. |
---|
5984 | def store_triangulation(self, |
---|
5985 | outfile, |
---|
5986 | points_utm, |
---|
5987 | volumes, |
---|
5988 | elevation, zone=None, new_origin=None, |
---|
5989 | points_georeference=None, verbose=False): |
---|
5990 | """ |
---|
5991 | new_origin - qa georeference that the points can be set to. (Maybe |
---|
5992 | do this before calling this function.) |
---|
5993 | |
---|
5994 | points_utm - currently a list or array of the points in UTM. |
---|
5995 | points_georeference - the georeference of the points_utm |
---|
5996 | |
---|
5997 | How about passing new_origin and current_origin. |
---|
5998 | If you get both, do a convertion from the old to the new. |
---|
5999 | |
---|
6000 | If you only get new_origin, the points are absolute, |
---|
6001 | convert to relative |
---|
6002 | |
---|
6003 | if you only get the current_origin the points are relative, store |
---|
6004 | as relative. |
---|
6005 | |
---|
6006 | if you get no georefs create a new georef based on the minimums of |
---|
6007 | points_utm. (Another option would be to default to absolute) |
---|
6008 | |
---|
6009 | Yes, and this is done in another part of the code. |
---|
6010 | Probably geospatial. |
---|
6011 | |
---|
6012 | If you don't supply either geo_refs, then supply a zone. If not |
---|
6013 | the default zone will be used. |
---|
6014 | |
---|
6015 | precon: |
---|
6016 | header has been called. |
---|
6017 | """ |
---|
6018 | |
---|
6019 | number_of_points = len(points_utm) |
---|
6020 | volumes = num.array(volumes) |
---|
6021 | points_utm = num.array(points_utm) |
---|
6022 | |
---|
6023 | # given the two geo_refs and the points, do the stuff |
---|
6024 | # described in the method header |
---|
6025 | # if this is needed else where, pull out as a function |
---|
6026 | points_georeference = ensure_geo_reference(points_georeference) |
---|
6027 | new_origin = ensure_geo_reference(new_origin) |
---|
6028 | if new_origin is None and points_georeference is not None: |
---|
6029 | points = points_utm |
---|
6030 | geo_ref = points_georeference |
---|
6031 | else: |
---|
6032 | if new_origin is None: |
---|
6033 | new_origin = Geo_reference(zone, min(points_utm[:,0]), |
---|
6034 | min(points_utm[:,1])) |
---|
6035 | points = new_origin.change_points_geo_ref(points_utm, |
---|
6036 | points_georeference) |
---|
6037 | geo_ref = new_origin |
---|
6038 | |
---|
6039 | # At this stage I need a georef and points |
---|
6040 | # the points are relative to the georef |
---|
6041 | geo_ref.write_NetCDF(outfile) |
---|
6042 | |
---|
6043 | # This will put the geo ref in the middle |
---|
6044 | #geo_ref = Geo_reference(refzone,(max(x)+min(x))/2.0,(max(x)+min(y))/2.) |
---|
6045 | |
---|
6046 | x = points[:,0] |
---|
6047 | y = points[:,1] |
---|
6048 | z = outfile.variables['z'][:] |
---|
6049 | |
---|
6050 | if verbose: |
---|
6051 | print '------------------------------------------------' |
---|
6052 | print 'More Statistics:' |
---|
6053 | print ' Extent (/lon):' |
---|
6054 | print ' x in [%f, %f], len(lat) == %d' \ |
---|
6055 | % (min(x), max(x), len(x)) |
---|
6056 | print ' y in [%f, %f], len(lon) == %d' \ |
---|
6057 | % (min(y), max(y), len(y)) |
---|
6058 | print ' z in [%f, %f], len(z) == %d' \ |
---|
6059 | % (min(elevation), max(elevation), len(elevation)) |
---|
6060 | print 'geo_ref: ',geo_ref |
---|
6061 | print '------------------------------------------------' |
---|
6062 | |
---|
6063 | #z = resize(bath_grid, outfile.variables['z'][:].shape) |
---|
6064 | outfile.variables['x'][:] = points[:,0] #- geo_ref.get_xllcorner() |
---|
6065 | outfile.variables['y'][:] = points[:,1] #- geo_ref.get_yllcorner() |
---|
6066 | outfile.variables['z'][:] = elevation |
---|
6067 | outfile.variables['elevation'][:] = elevation #FIXME HACK |
---|
6068 | outfile.variables['volumes'][:] = volumes.astype(num.int32) #On Opteron 64 |
---|
6069 | |
---|
6070 | q = 'elevation' |
---|
6071 | # This updates the _range values |
---|
6072 | outfile.variables[q + Write_sww.RANGE][0] = num.min(elevation) |
---|
6073 | outfile.variables[q + Write_sww.RANGE][1] = num.max(elevation) |
---|
6074 | |
---|
6075 | |
---|
6076 | ## |
---|
6077 | # @brief Write the quantity data to the underlying file. |
---|
6078 | # @param outfile Handle to open underlying file. |
---|
6079 | # @param sww_precision Format of quantity data to write (default Float32). |
---|
6080 | # @param slice_index |
---|
6081 | # @param time |
---|
6082 | # @param verbose True if this function is to be verbose. |
---|
6083 | # @param **quant |
---|
6084 | def store_quantities(self, outfile, sww_precision=num.float32, |
---|
6085 | slice_index=None, time=None, |
---|
6086 | verbose=False, **quant): |
---|
6087 | """ |
---|
6088 | Write the quantity info. |
---|
6089 | |
---|
6090 | **quant is extra keyword arguments passed in. These must be |
---|
6091 | the sww quantities, currently; stage, xmomentum, ymomentum. |
---|
6092 | |
---|
6093 | if the time array is already been built, use the slice_index |
---|
6094 | to specify the index. |
---|
6095 | |
---|
6096 | Otherwise, use time to increase the time dimension |
---|
6097 | |
---|
6098 | Maybe make this general, but the viewer assumes these quantities, |
---|
6099 | so maybe we don't want it general - unless the viewer is general |
---|
6100 | |
---|
6101 | precon |
---|
6102 | triangulation and |
---|
6103 | header have been called. |
---|
6104 | """ |
---|
6105 | |
---|
6106 | if time is not None: |
---|
6107 | file_time = outfile.variables['time'] |
---|
6108 | slice_index = len(file_time) |
---|
6109 | file_time[slice_index] = time |
---|
6110 | |
---|
6111 | # Write the conserved quantities from Domain. |
---|
6112 | # Typically stage, xmomentum, ymomentum |
---|
6113 | # other quantities will be ignored, silently. |
---|
6114 | # Also write the ranges: stage_range, |
---|
6115 | # xmomentum_range and ymomentum_range |
---|
6116 | for q in Write_sww.sww_quantities: |
---|
6117 | if not quant.has_key(q): |
---|
6118 | msg = 'SWW file can not write quantity %s' % q |
---|
6119 | raise NewQuantity, msg |
---|
6120 | else: |
---|
6121 | q_values = quant[q] |
---|
6122 | outfile.variables[q][slice_index] = \ |
---|
6123 | q_values.astype(sww_precision) |
---|
6124 | |
---|
6125 | # This updates the _range values |
---|
6126 | q_range = outfile.variables[q + Write_sww.RANGE][:] |
---|
6127 | q_values_min = num.min(q_values) |
---|
6128 | if q_values_min < q_range[0]: |
---|
6129 | outfile.variables[q + Write_sww.RANGE][0] = q_values_min |
---|
6130 | q_values_max = num.max(q_values) |
---|
6131 | if q_values_max > q_range[1]: |
---|
6132 | outfile.variables[q + Write_sww.RANGE][1] = q_values_max |
---|
6133 | |
---|
6134 | ## |
---|
6135 | # @brief Print the quantities in the underlying file. |
---|
6136 | # @param outfile UNUSED. |
---|
6137 | def verbose_quantities(self, outfile): |
---|
6138 | print '------------------------------------------------' |
---|
6139 | print 'More Statistics:' |
---|
6140 | for q in Write_sww.sww_quantities: |
---|
6141 | print ' %s in [%f, %f]' % (q, |
---|
6142 | outfile.variables[q+Write_sww.RANGE][0], |
---|
6143 | outfile.variables[q+Write_sww.RANGE][1]) |
---|
6144 | print '------------------------------------------------' |
---|
6145 | |
---|
6146 | |
---|
6147 | ## |
---|
6148 | # @brief Obsolete? |
---|
6149 | # @param outfile |
---|
6150 | # @param has |
---|
6151 | # @param uas |
---|
6152 | # @param vas |
---|
6153 | # @param elevation |
---|
6154 | # @param mean_stage |
---|
6155 | # @param zscale |
---|
6156 | # @param verbose |
---|
6157 | def obsolete_write_sww_time_slices(outfile, has, uas, vas, elevation, |
---|
6158 | mean_stage=0, zscale=1, |
---|
6159 | verbose=False): |
---|
6160 | #Time stepping |
---|
6161 | stage = outfile.variables['stage'] |
---|
6162 | xmomentum = outfile.variables['xmomentum'] |
---|
6163 | ymomentum = outfile.variables['ymomentum'] |
---|
6164 | |
---|
6165 | n = len(has) |
---|
6166 | j = 0 |
---|
6167 | for ha, ua, va in map(None, has, uas, vas): |
---|
6168 | if verbose and j % ((n+10)/10) == 0: print ' Doing %d of %d' % (j, n) |
---|
6169 | w = zscale*ha + mean_stage |
---|
6170 | stage[j] = w |
---|
6171 | h = w - elevation |
---|
6172 | xmomentum[j] = ua * h |
---|
6173 | ymomentum[j] = -1 * va * h # -1 since in mux files south is positive. |
---|
6174 | j += 1 |
---|
6175 | |
---|
6176 | |
---|
6177 | ## |
---|
6178 | # @brief Convert a set of URS files to a text file. |
---|
6179 | # @param basename_in Stem path to the 3 URS files. |
---|
6180 | # @param location_index ?? |
---|
6181 | def urs2txt(basename_in, location_index=None): |
---|
6182 | """ |
---|
6183 | Not finished or tested |
---|
6184 | """ |
---|
6185 | |
---|
6186 | files_in = [basename_in + WAVEHEIGHT_MUX_LABEL, |
---|
6187 | basename_in + EAST_VELOCITY_LABEL, |
---|
6188 | basename_in + NORTH_VELOCITY_LABEL] |
---|
6189 | quantities = ['HA','UA','VA'] |
---|
6190 | |
---|
6191 | d = "," |
---|
6192 | |
---|
6193 | # instantiate urs_points of the three mux files. |
---|
6194 | mux = {} |
---|
6195 | for quantity, file in map(None, quantities, files_in): |
---|
6196 | mux[quantity] = Urs_points(file) |
---|
6197 | |
---|
6198 | # Could check that the depth is the same. (hashing) |
---|
6199 | |
---|
6200 | # handle to a mux file to do depth stuff |
---|
6201 | a_mux = mux[quantities[0]] |
---|
6202 | |
---|
6203 | # Convert to utm |
---|
6204 | latitudes = a_mux.lonlatdep[:,1] |
---|
6205 | longitudes = a_mux.lonlatdep[:,0] |
---|
6206 | points_utm, zone = \ |
---|
6207 | convert_from_latlon_to_utm(latitudes=latitudes, longitudes=longitudes) |
---|
6208 | depths = a_mux.lonlatdep[:,2] |
---|
6209 | |
---|
6210 | # open the output text file, start writing. |
---|
6211 | fid = open(basename_in + '.txt', 'w') |
---|
6212 | |
---|
6213 | fid.write("zone: " + str(zone) + "\n") |
---|
6214 | |
---|
6215 | if location_index is not None: |
---|
6216 | #Title |
---|
6217 | li = location_index |
---|
6218 | fid.write('location_index' + d + 'lat' + d + 'long' + d + |
---|
6219 | 'Easting' + d + 'Northing' + '\n') |
---|
6220 | fid.write(str(li) + d + str(latitudes[li]) + d + |
---|
6221 | str(longitudes[li]) + d + str(points_utm[li][0]) + d + |
---|
6222 | str(points_utm[li][01]) + '\n') |
---|
6223 | |
---|
6224 | # the non-time dependent stuff |
---|
6225 | #Title |
---|
6226 | fid.write('location_index' + d + 'lat' + d + 'long' + d + |
---|
6227 | 'Easting' + d + 'Northing' + d + 'depth m' + '\n') |
---|
6228 | i = 0 |
---|
6229 | for depth, point_utm, lat, long in map(None, depths, points_utm, |
---|
6230 | latitudes, longitudes): |
---|
6231 | |
---|
6232 | fid.write(str(i) + d + str(lat) + d + str(long) + d + |
---|
6233 | str(point_utm[0]) + d + str(point_utm[01]) + d + |
---|
6234 | str(depth) + '\n') |
---|
6235 | i += 1 |
---|
6236 | |
---|
6237 | #Time dependent |
---|
6238 | if location_index is not None: |
---|
6239 | time_step = a_mux.time_step |
---|
6240 | i = 0 |
---|
6241 | #Title |
---|
6242 | fid.write('time' + d + 'HA depth m' + d + 'UA momentum East x m/sec' + |
---|
6243 | d + 'VA momentum North y m/sec' + '\n') |
---|
6244 | for HA, UA, VA in map(None, mux['HA'], mux['UA'], mux['VA']): |
---|
6245 | fid.write(str(i*time_step) + d + str(HA[location_index]) + d + |
---|
6246 | str(UA[location_index]) + d + |
---|
6247 | str(VA[location_index]) + '\n') |
---|
6248 | i += 1 |
---|
6249 | |
---|
6250 | |
---|
6251 | ## |
---|
6252 | # @brief A class to write STS files. |
---|
6253 | class Write_sts: |
---|
6254 | sts_quantities = ['stage','xmomentum','ymomentum'] |
---|
6255 | RANGE = '_range' |
---|
6256 | EXTREMA = ':extrema' |
---|
6257 | |
---|
6258 | ## |
---|
6259 | # @brief Instantiate this STS writer class. |
---|
6260 | def __init__(self): |
---|
6261 | pass |
---|
6262 | |
---|
6263 | ## |
---|
6264 | # @brief Write a header to the underlying data file. |
---|
6265 | # @param outfile Handle to open file to write. |
---|
6266 | # @param times A list of the time slice times *or* a start time. |
---|
6267 | # @param number_of_points The number of URS gauge sites. |
---|
6268 | # @param description Description string to write into the STS file. |
---|
6269 | # @param sts_precision Format of data to write (netcdf constant ONLY). |
---|
6270 | # @param verbose True if this function is to be verbose. |
---|
6271 | # @note If 'times' is a list, the info will be made relative. |
---|
6272 | def store_header(self, |
---|
6273 | outfile, |
---|
6274 | times, |
---|
6275 | number_of_points, |
---|
6276 | description='Converted from URS mux2 format', |
---|
6277 | sts_precision=netcdf_float32, |
---|
6278 | verbose=False): |
---|
6279 | """ |
---|
6280 | outfile - the name of the file that will be written |
---|
6281 | times - A list of the time slice times OR a start time |
---|
6282 | Note, if a list is given the info will be made relative. |
---|
6283 | number_of_points - the number of urs gauges sites |
---|
6284 | """ |
---|
6285 | |
---|
6286 | outfile.institution = 'Geoscience Australia' |
---|
6287 | outfile.description = description |
---|
6288 | |
---|
6289 | try: |
---|
6290 | revision_number = get_revision_number() |
---|
6291 | except: |
---|
6292 | revision_number = None |
---|
6293 | |
---|
6294 | # Allow None to be stored as a string |
---|
6295 | outfile.revision_number = str(revision_number) |
---|
6296 | |
---|
6297 | # Start time in seconds since the epoch (midnight 1/1/1970) |
---|
6298 | # This is being used to seperate one number from a list. |
---|
6299 | # what it is actually doing is sorting lists from numeric arrays. |
---|
6300 | if isinstance(times, (list, num.ndarray)): |
---|
6301 | number_of_times = len(times) |
---|
6302 | times = ensure_numeric(times) |
---|
6303 | if number_of_times == 0: |
---|
6304 | starttime = 0 |
---|
6305 | else: |
---|
6306 | starttime = times[0] |
---|
6307 | times = times - starttime #Store relative times |
---|
6308 | else: |
---|
6309 | number_of_times = 0 |
---|
6310 | starttime = times |
---|
6311 | |
---|
6312 | outfile.starttime = starttime |
---|
6313 | |
---|
6314 | # Dimension definitions |
---|
6315 | outfile.createDimension('number_of_points', number_of_points) |
---|
6316 | outfile.createDimension('number_of_timesteps', number_of_times) |
---|
6317 | outfile.createDimension('numbers_in_range', 2) |
---|
6318 | |
---|
6319 | # Variable definitions |
---|
6320 | outfile.createVariable('permutation', netcdf_int, ('number_of_points',)) |
---|
6321 | outfile.createVariable('x', sts_precision, ('number_of_points',)) |
---|
6322 | outfile.createVariable('y', sts_precision, ('number_of_points',)) |
---|
6323 | outfile.createVariable('elevation',sts_precision, ('number_of_points',)) |
---|
6324 | |
---|
6325 | q = 'elevation' |
---|
6326 | outfile.createVariable(q + Write_sts.RANGE, sts_precision, |
---|
6327 | ('numbers_in_range',)) |
---|
6328 | |
---|
6329 | # Initialise ranges with small and large sentinels. |
---|
6330 | # If this was in pure Python we could have used None sensibly |
---|
6331 | outfile.variables[q + Write_sts.RANGE][0] = max_float # Min |
---|
6332 | outfile.variables[q + Write_sts.RANGE][1] = -max_float # Max |
---|
6333 | |
---|
6334 | # Doing sts_precision instead of Float gives cast errors. |
---|
6335 | outfile.createVariable('time', netcdf_float, ('number_of_timesteps',)) |
---|
6336 | |
---|
6337 | for q in Write_sts.sts_quantities: |
---|
6338 | outfile.createVariable(q, sts_precision, ('number_of_timesteps', |
---|
6339 | 'number_of_points')) |
---|
6340 | outfile.createVariable(q + Write_sts.RANGE, sts_precision, |
---|
6341 | ('numbers_in_range',)) |
---|
6342 | # Initialise ranges with small and large sentinels. |
---|
6343 | # If this was in pure Python we could have used None sensibly |
---|
6344 | outfile.variables[q + Write_sts.RANGE][0] = max_float # Min |
---|
6345 | outfile.variables[q + Write_sts.RANGE][1] = -max_float # Max |
---|
6346 | |
---|
6347 | if isinstance(times, (list, num.ndarray)): |
---|
6348 | outfile.variables['time'][:] = times #Store time relative |
---|
6349 | |
---|
6350 | if verbose: |
---|
6351 | print '------------------------------------------------' |
---|
6352 | print 'Statistics:' |
---|
6353 | print ' t in [%f, %f], len(t) == %d' \ |
---|
6354 | % (num.min(times), num.max(times), len(times.flat)) |
---|
6355 | |
---|
6356 | ## |
---|
6357 | # @brief |
---|
6358 | # @param outfile |
---|
6359 | # @param points_utm |
---|
6360 | # @param elevation |
---|
6361 | # @param zone |
---|
6362 | # @param new_origin |
---|
6363 | # @param points_georeference |
---|
6364 | # @param verbose True if this function is to be verbose. |
---|
6365 | def store_points(self, |
---|
6366 | outfile, |
---|
6367 | points_utm, |
---|
6368 | elevation, zone=None, new_origin=None, |
---|
6369 | points_georeference=None, verbose=False): |
---|
6370 | |
---|
6371 | """ |
---|
6372 | points_utm - currently a list or array of the points in UTM. |
---|
6373 | points_georeference - the georeference of the points_utm |
---|
6374 | |
---|
6375 | How about passing new_origin and current_origin. |
---|
6376 | If you get both, do a convertion from the old to the new. |
---|
6377 | |
---|
6378 | If you only get new_origin, the points are absolute, |
---|
6379 | convert to relative |
---|
6380 | |
---|
6381 | if you only get the current_origin the points are relative, store |
---|