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 | import exceptions |
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52 | class TitleValueError(exceptions.Exception): pass |
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53 | class DataMissingValuesError(exceptions.Exception): pass |
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54 | class DataFileNotOpenError(exceptions.Exception): pass |
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55 | class DataTimeError(exceptions.Exception): pass |
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56 | class DataDomainError(exceptions.Exception): pass |
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57 | class NewQuantity(exceptions.Exception): pass |
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58 | |
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59 | |
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60 | |
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61 | import csv |
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62 | import os, sys |
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63 | import shutil |
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64 | from struct import unpack |
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65 | import array as p_array |
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66 | #import time, os |
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67 | from os import sep, path, remove, mkdir, access, F_OK, W_OK, getcwd |
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68 | |
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69 | |
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70 | from Numeric import concatenate, array, Float, Int, Int32, resize, sometrue, \ |
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71 | searchsorted, zeros, allclose, around, reshape, transpose, sort, \ |
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72 | NewAxis, ArrayType, compress, take, arange, argmax, alltrue,shape,Float |
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73 | |
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74 | import string |
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75 | |
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76 | from Scientific.IO.NetCDF import NetCDFFile |
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77 | #from shutil import copy |
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78 | from os.path import exists, basename, join |
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79 | from os import getcwd |
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80 | |
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81 | |
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82 | from anuga.coordinate_transforms.redfearn import redfearn, \ |
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83 | convert_from_latlon_to_utm |
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84 | from anuga.coordinate_transforms.geo_reference import Geo_reference, \ |
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85 | write_NetCDF_georeference, ensure_geo_reference |
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86 | from anuga.geospatial_data.geospatial_data import Geospatial_data,\ |
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87 | ensure_absolute |
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88 | from anuga.config import minimum_storable_height as default_minimum_storable_height |
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89 | from anuga.utilities.numerical_tools import ensure_numeric, mean |
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90 | from anuga.caching.caching import myhash |
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91 | from anuga.utilities.anuga_exceptions import ANUGAError |
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92 | from anuga.shallow_water import Domain |
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93 | from anuga.abstract_2d_finite_volumes.pmesh2domain import \ |
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94 | pmesh_to_domain_instance |
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95 | from anuga.abstract_2d_finite_volumes.util import get_revision_number, \ |
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96 | remove_lone_verts, sww2timeseries, get_centroid_values |
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97 | from anuga.load_mesh.loadASCII import export_mesh_file |
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98 | # formula mappings |
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99 | |
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100 | quantity_formula = {'momentum':'(xmomentum**2 + ymomentum**2)**0.5', |
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101 | 'depth':'stage-elevation', |
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102 | 'speed': \ |
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103 | '(xmomentum**2 + ymomentum**2)**0.5/(stage-elevation+1.e-6/(stage-elevation))'} |
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104 | |
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105 | |
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106 | |
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107 | def make_filename(s): |
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108 | """Transform argument string into a Sexsuitable filename |
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109 | """ |
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110 | |
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111 | s = s.strip() |
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112 | s = s.replace(' ', '_') |
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113 | s = s.replace('(', '') |
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114 | s = s.replace(')', '') |
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115 | s = s.replace('__', '_') |
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116 | |
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117 | return s |
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118 | |
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119 | |
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120 | def check_dir(path, verbose=None): |
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121 | """Check that specified path exists. |
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122 | If path does not exist it will be created if possible |
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123 | |
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124 | USAGE: |
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125 | checkdir(path, verbose): |
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126 | |
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127 | ARGUMENTS: |
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128 | path -- Directory |
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129 | verbose -- Flag verbose output (default: None) |
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130 | |
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131 | RETURN VALUE: |
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132 | Verified path including trailing separator |
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133 | |
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134 | """ |
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135 | |
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136 | import os.path |
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137 | |
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138 | if sys.platform in ['nt', 'dos', 'win32', 'what else?']: |
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139 | unix = 0 |
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140 | else: |
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141 | unix = 1 |
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142 | |
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143 | |
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144 | if path[-1] != os.sep: |
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145 | path = path + os.sep # Add separator for directories |
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146 | |
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147 | path = os.path.expanduser(path) # Expand ~ or ~user in pathname |
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148 | if not (os.access(path,os.R_OK and os.W_OK) or path == ''): |
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149 | try: |
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150 | exitcode=os.mkdir(path) |
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151 | |
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152 | # Change access rights if possible |
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153 | # |
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154 | if unix: |
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155 | exitcode=os.system('chmod 775 '+path) |
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156 | else: |
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157 | pass # FIXME: What about acces rights under Windows? |
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158 | |
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159 | if verbose: print 'MESSAGE: Directory', path, 'created.' |
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160 | |
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161 | except: |
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162 | print 'WARNING: Directory', path, 'could not be created.' |
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163 | if unix: |
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164 | path = '/tmp/' |
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165 | else: |
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166 | path = 'C:' |
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167 | |
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168 | print 'Using directory %s instead' %path |
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169 | |
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170 | return(path) |
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171 | |
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172 | |
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173 | |
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174 | def del_dir(path): |
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175 | """Recursively delete directory path and all its contents |
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176 | """ |
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177 | |
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178 | import os |
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179 | |
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180 | if os.path.isdir(path): |
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181 | for file in os.listdir(path): |
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182 | X = os.path.join(path, file) |
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183 | |
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184 | |
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185 | if os.path.isdir(X) and not os.path.islink(X): |
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186 | del_dir(X) |
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187 | else: |
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188 | try: |
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189 | os.remove(X) |
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190 | except: |
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191 | print "Could not remove file %s" %X |
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192 | |
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193 | os.rmdir(path) |
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194 | |
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195 | |
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196 | # ANOTHER OPTION, IF NEED IN THE FUTURE, Nick B 7/2007 |
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197 | def rmgeneric(path, __func__,verbose=False): |
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198 | ERROR_STR= """Error removing %(path)s, %(error)s """ |
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199 | |
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200 | try: |
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201 | __func__(path) |
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202 | if verbose: print 'Removed ', path |
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203 | except OSError, (errno, strerror): |
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204 | print ERROR_STR % {'path' : path, 'error': strerror } |
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205 | |
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206 | def removeall(path,verbose=False): |
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207 | |
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208 | if not os.path.isdir(path): |
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209 | return |
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210 | |
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211 | files=os.listdir(path) |
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212 | |
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213 | for x in files: |
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214 | fullpath=os.path.join(path, x) |
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215 | if os.path.isfile(fullpath): |
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216 | f=os.remove |
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217 | rmgeneric(fullpath, f) |
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218 | elif os.path.isdir(fullpath): |
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219 | removeall(fullpath) |
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220 | f=os.rmdir |
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221 | rmgeneric(fullpath, f,verbose) |
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222 | |
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223 | |
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224 | |
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225 | def create_filename(datadir, filename, format, size=None, time=None): |
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226 | |
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227 | import os |
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228 | #from anuga.config import data_dir |
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229 | |
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230 | FN = check_dir(datadir) + filename |
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231 | |
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232 | if size is not None: |
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233 | FN += '_size%d' %size |
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234 | |
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235 | if time is not None: |
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236 | FN += '_time%.2f' %time |
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237 | |
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238 | FN += '.' + format |
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239 | return FN |
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240 | |
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241 | |
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242 | def get_files(datadir, filename, format, size): |
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243 | """Get all file (names) with given name, size and format |
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244 | """ |
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245 | |
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246 | import glob |
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247 | |
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248 | import os |
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249 | #from anuga.config import data_dir |
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250 | |
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251 | dir = check_dir(datadir) |
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252 | |
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253 | pattern = dir + os.sep + filename + '_size=%d*.%s' %(size, format) |
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254 | return glob.glob(pattern) |
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255 | |
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256 | |
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257 | |
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258 | #Generic class for storing output to e.g. visualisation or checkpointing |
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259 | class Data_format: |
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260 | """Generic interface to data formats |
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261 | """ |
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262 | |
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263 | |
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264 | def __init__(self, domain, extension, mode = 'w'): |
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265 | assert mode in ['r', 'w', 'a'], '''Mode %s must be either:''' %mode +\ |
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266 | ''' 'w' (write)'''+\ |
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267 | ''' 'r' (read)''' +\ |
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268 | ''' 'a' (append)''' |
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269 | |
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270 | #Create filename |
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271 | self.filename = create_filename(domain.get_datadir(), |
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272 | domain.get_name(), extension) |
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273 | |
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274 | #print 'F', self.filename |
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275 | self.timestep = 0 |
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276 | self.domain = domain |
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277 | |
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278 | |
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279 | |
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280 | # Exclude ghosts in case this is a parallel domain |
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281 | self.number_of_nodes = domain.number_of_full_nodes |
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282 | self.number_of_volumes = domain.number_of_full_triangles |
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283 | #self.number_of_volumes = len(domain) |
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284 | |
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285 | |
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286 | |
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287 | |
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288 | #FIXME: Should we have a general set_precision function? |
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289 | |
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290 | |
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291 | |
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292 | #Class for storing output to e.g. visualisation |
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293 | class Data_format_sww(Data_format): |
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294 | """Interface to native NetCDF format (.sww) for storing model output |
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295 | |
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296 | There are two kinds of data |
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297 | |
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298 | 1: Constant data: Vertex coordinates and field values. Stored once |
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299 | 2: Variable data: Conserved quantities. Stored once per timestep. |
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300 | |
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301 | All data is assumed to reside at vertex locations. |
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302 | """ |
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303 | |
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304 | |
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305 | def __init__(self, domain, mode = 'w',\ |
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306 | max_size = 2000000000, |
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307 | recursion = False): |
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308 | from Scientific.IO.NetCDF import NetCDFFile |
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309 | from Numeric import Int, Float, Float32 |
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310 | |
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311 | self.precision = Float32 #Use single precision for quantities |
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312 | if hasattr(domain, 'max_size'): |
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313 | self.max_size = domain.max_size #file size max is 2Gig |
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314 | else: |
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315 | self.max_size = max_size |
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316 | self.recursion = recursion |
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317 | self.mode = mode |
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318 | |
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319 | Data_format.__init__(self, domain, 'sww', mode) |
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320 | |
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321 | if hasattr(domain, 'minimum_storable_height'): |
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322 | self.minimum_storable_height = domain.minimum_storable_height |
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323 | else: |
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324 | self.minimum_storable_height = default_minimum_storable_height |
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325 | |
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326 | # NetCDF file definition |
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327 | fid = NetCDFFile(self.filename, mode) |
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328 | |
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329 | if mode == 'w': |
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330 | description = 'Output from anuga.abstract_2d_finite_volumes suitable for plotting' |
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331 | self.writer = Write_sww() |
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332 | self.writer.header(fid, domain.starttime, |
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333 | self.number_of_volumes, |
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334 | self.domain.number_of_full_nodes, |
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335 | description=description, |
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336 | smoothing=domain.smooth, |
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337 | order=domain.default_order) |
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338 | if hasattr(domain, 'texture'): |
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339 | fid.texture = domain.texture |
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340 | # if domain.geo_reference is not None: |
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341 | # domain.geo_reference.write_NetCDF(fid) |
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342 | |
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343 | # fid.institution = 'Geoscience Australia' |
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344 | # fid.description = 'Output from anuga.abstract_2d_finite_volumes suitable for plotting' |
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345 | |
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346 | # if domain.smooth: |
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347 | # fid.smoothing = 'Yes' |
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348 | # else: |
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349 | # fid.smoothing = 'No' |
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350 | |
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351 | # fid.order = domain.default_order |
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352 | |
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353 | # #Reference point |
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354 | # #Start time in seconds since the epoch (midnight 1/1/1970) |
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355 | # #FIXME: Use Georef |
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356 | # fid.starttime = domain.starttime |
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357 | |
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358 | # # dimension definitions |
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359 | # fid.createDimension('number_of_volumes', self.number_of_volumes) |
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360 | # fid.createDimension('number_of_vertices', 3) |
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361 | |
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362 | # if domain.smooth is True: |
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363 | # #fid.createDimension('number_of_points', len(domain.vertexlist)) |
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364 | # fid.createDimension('number_of_points', self.domain.number_of_full_nodes) |
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365 | |
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366 | # # FIXME(Ole): This will cause sww files for paralle domains to |
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367 | # # have ghost nodes stored (but not used by triangles). |
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368 | # # To clean this up, we have to change get_vertex_values and friends in |
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369 | # # quantity.py (but I can't be bothered right now) |
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370 | # else: |
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371 | # fid.createDimension('number_of_points', 3*self.number_of_volumes) |
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372 | |
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373 | # fid.createDimension('number_of_timesteps', None) #extensible |
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374 | |
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375 | # # variable definitions |
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376 | # fid.createVariable('x', self.precision, ('number_of_points',)) |
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377 | # fid.createVariable('y', self.precision, ('number_of_points',)) |
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378 | # fid.createVariable('elevation', self.precision, ('number_of_points',)) |
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379 | # if domain.geo_reference is not None: |
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380 | # domain.geo_reference.write_NetCDF(fid) |
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381 | |
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382 | # #FIXME: Backwards compatibility |
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383 | # fid.createVariable('z', self.precision, ('number_of_points',)) |
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384 | # ################################# |
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385 | |
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386 | # fid.createVariable('volumes', Int, ('number_of_volumes', |
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387 | # 'number_of_vertices')) |
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388 | |
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389 | # fid.createVariable('time', Float, # Always use full precision lest two timesteps |
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390 | # # close to each other may appear as the same step |
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391 | # ('number_of_timesteps',)) |
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392 | |
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393 | # fid.createVariable('stage', self.precision, |
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394 | # ('number_of_timesteps', |
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395 | # 'number_of_points')) |
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396 | |
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397 | # fid.createVariable('xmomentum', self.precision, |
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398 | # ('number_of_timesteps', |
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399 | # 'number_of_points')) |
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400 | |
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401 | # fid.createVariable('ymomentum', self.precision, |
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402 | # ('number_of_timesteps', |
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403 | # 'number_of_points')) |
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404 | |
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405 | #Close |
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406 | fid.close() |
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407 | |
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408 | |
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409 | def store_connectivity(self): |
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410 | """Specialisation of store_connectivity for net CDF format |
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411 | |
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412 | Writes x,y,z coordinates of triangles constituting |
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413 | the bed elevation. |
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414 | """ |
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415 | |
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416 | from Scientific.IO.NetCDF import NetCDFFile |
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417 | |
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418 | from Numeric import concatenate, Int |
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419 | |
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420 | domain = self.domain |
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421 | |
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422 | #Get NetCDF |
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423 | fid = NetCDFFile(self.filename, 'a') #Open existing file for append |
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424 | |
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425 | # Get the variables |
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426 | x = fid.variables['x'] |
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427 | y = fid.variables['y'] |
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428 | z = fid.variables['elevation'] |
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429 | |
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430 | volumes = fid.variables['volumes'] |
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431 | |
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432 | # Get X, Y and bed elevation Z |
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433 | Q = domain.quantities['elevation'] |
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434 | X,Y,Z,V = Q.get_vertex_values(xy=True, |
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435 | precision=self.precision) |
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436 | |
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437 | # |
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438 | points = concatenate( (X[:,NewAxis],Y[:,NewAxis]), axis=1 ) |
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439 | self.writer.triangulation(fid, points, |
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440 | V.astype(volumes.typecode()), |
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441 | Z, |
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442 | points_georeference= \ |
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443 | domain.geo_reference) |
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444 | # |
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445 | |
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446 | # volumes[:] = V.astype(volumes.typecode()) |
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447 | # x[:] = X |
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448 | # y[:] = Y |
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449 | # z[:] = Z |
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450 | |
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451 | # #FIXME: Backwards compatibility |
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452 | # z = fid.variables['z'] |
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453 | # z[:] = Z |
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454 | ################################ |
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455 | |
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456 | |
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457 | |
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458 | #Close |
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459 | fid.close() |
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460 | |
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461 | def store_timestep(self, names): |
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462 | """Store time and named quantities to file |
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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 | from Numeric import choose |
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470 | |
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471 | #Get NetCDF |
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472 | retries = 0 |
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473 | file_open = False |
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474 | while not file_open and retries < 10: |
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475 | try: |
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476 | fid = NetCDFFile(self.filename, 'a') #Open existing file |
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477 | except IOError: |
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478 | #This could happen if someone was reading the file. |
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479 | #In that case, wait a while and try again |
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480 | msg = 'Warning (store_timestep): File %s could not be opened'\ |
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481 | %self.filename |
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482 | msg += ' - trying step %s again' %self.domain.time |
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483 | print msg |
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484 | retries += 1 |
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485 | sleep(1) |
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486 | else: |
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487 | file_open = True |
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488 | |
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489 | if not file_open: |
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490 | msg = 'File %s could not be opened for append' %self.filename |
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491 | raise DataFileNotOpenError, msg |
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492 | |
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493 | |
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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 domian.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 | #remember the old filename, then give domain a |
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511 | #name with the extension |
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512 | old_domain_filename = self.domain.get_name() |
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513 | if not self.recursion: |
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514 | self.domain.set_name(old_domain_filename+filename_ext) |
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515 | |
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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 | |
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521 | #build a new data_structure. |
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522 | next_data_structure=\ |
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523 | Data_format_sww(self.domain, mode=self.mode,\ |
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524 | max_size = self.max_size,\ |
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525 | recursion = self.recursion+1) |
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526 | if not self.recursion: |
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527 | print ' file_size = %s'%file_size |
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528 | print ' saving file to %s'%next_data_structure.filename |
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529 | #set up the new data_structure |
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530 | self.domain.writer = next_data_structure |
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531 | |
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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 and 'xmomentum' in names and \ |
---|
555 | 'ymomentum' in names: |
---|
556 | |
---|
557 | # Get stage |
---|
558 | Q = domain.quantities['stage'] |
---|
559 | A,_ = Q.get_vertex_values(xy = False, |
---|
560 | precision = self.precision) |
---|
561 | z = fid.variables['elevation'] |
---|
562 | stage = choose(A-z[:] >= self.minimum_storable_height, |
---|
563 | (z[:], A)) |
---|
564 | |
---|
565 | # Get xmomentum |
---|
566 | Q = domain.quantities['xmomentum'] |
---|
567 | xmomentum, _ = Q.get_vertex_values(xy = False, |
---|
568 | precision = self.precision) |
---|
569 | |
---|
570 | # Get ymomentum |
---|
571 | Q = domain.quantities['ymomentum'] |
---|
572 | ymomentum, _ = Q.get_vertex_values(xy = False, |
---|
573 | precision = self.precision) |
---|
574 | self.writer.quantities(fid, |
---|
575 | time=self.domain.time, |
---|
576 | precision=self.precision, |
---|
577 | stage=stage, |
---|
578 | xmomentum=xmomentum, |
---|
579 | ymomentum=ymomentum) |
---|
580 | else: |
---|
581 | # This is producing a sww that is not standard. |
---|
582 | #Store time |
---|
583 | time[i] = self.domain.time |
---|
584 | |
---|
585 | for name in names: |
---|
586 | # Get quantity |
---|
587 | Q = domain.quantities[name] |
---|
588 | A,V = Q.get_vertex_values(xy = False, |
---|
589 | precision = self.precision) |
---|
590 | |
---|
591 | #FIXME: Make this general (see below) |
---|
592 | if name == 'stage': |
---|
593 | z = fid.variables['elevation'] |
---|
594 | A = choose(A-z[:] >= self.minimum_storable_height, |
---|
595 | (z[:], A)) |
---|
596 | stage[i,:] = A.astype(self.precision) |
---|
597 | elif name == 'xmomentum': |
---|
598 | xmomentum[i,:] = A.astype(self.precision) |
---|
599 | elif name == 'ymomentum': |
---|
600 | ymomentum[i,:] = A.astype(self.precision) |
---|
601 | |
---|
602 | #As in.... |
---|
603 | #eval( name + '[i,:] = A.astype(self.precision)' ) |
---|
604 | #FIXME: But we need a UNIT test for that before |
---|
605 | # refactoring |
---|
606 | |
---|
607 | |
---|
608 | |
---|
609 | #Flush and close |
---|
610 | fid.sync() |
---|
611 | fid.close() |
---|
612 | |
---|
613 | |
---|
614 | |
---|
615 | #Class for handling checkpoints data |
---|
616 | class Data_format_cpt(Data_format): |
---|
617 | """Interface to native NetCDF format (.cpt) |
---|
618 | """ |
---|
619 | |
---|
620 | |
---|
621 | def __init__(self, domain, mode = 'w'): |
---|
622 | from Scientific.IO.NetCDF import NetCDFFile |
---|
623 | from Numeric import Int, Float, Float |
---|
624 | |
---|
625 | self.precision = Float #Use full precision |
---|
626 | |
---|
627 | Data_format.__init__(self, domain, 'sww', mode) |
---|
628 | |
---|
629 | |
---|
630 | # NetCDF file definition |
---|
631 | fid = NetCDFFile(self.filename, mode) |
---|
632 | |
---|
633 | if mode == 'w': |
---|
634 | #Create new file |
---|
635 | fid.institution = 'Geoscience Australia' |
---|
636 | fid.description = 'Checkpoint data' |
---|
637 | #fid.smooth = domain.smooth |
---|
638 | fid.order = domain.default_order |
---|
639 | |
---|
640 | # dimension definitions |
---|
641 | fid.createDimension('number_of_volumes', self.number_of_volumes) |
---|
642 | fid.createDimension('number_of_vertices', 3) |
---|
643 | |
---|
644 | #Store info at all vertices (no smoothing) |
---|
645 | fid.createDimension('number_of_points', 3*self.number_of_volumes) |
---|
646 | fid.createDimension('number_of_timesteps', None) #extensible |
---|
647 | |
---|
648 | # variable definitions |
---|
649 | |
---|
650 | #Mesh |
---|
651 | fid.createVariable('x', self.precision, ('number_of_points',)) |
---|
652 | fid.createVariable('y', self.precision, ('number_of_points',)) |
---|
653 | |
---|
654 | |
---|
655 | fid.createVariable('volumes', Int, ('number_of_volumes', |
---|
656 | 'number_of_vertices')) |
---|
657 | |
---|
658 | fid.createVariable('time', self.precision, |
---|
659 | ('number_of_timesteps',)) |
---|
660 | |
---|
661 | #Allocate space for all quantities |
---|
662 | for name in domain.quantities.keys(): |
---|
663 | fid.createVariable(name, self.precision, |
---|
664 | ('number_of_timesteps', |
---|
665 | 'number_of_points')) |
---|
666 | |
---|
667 | #Close |
---|
668 | fid.close() |
---|
669 | |
---|
670 | |
---|
671 | def store_checkpoint(self): |
---|
672 | """ |
---|
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 | from Numeric import concatenate |
---|
682 | |
---|
683 | domain = self.domain |
---|
684 | |
---|
685 | #Get NetCDF |
---|
686 | fid = NetCDFFile(self.filename, 'a') #Open existing file for append |
---|
687 | |
---|
688 | # Get the variables |
---|
689 | x = fid.variables['x'] |
---|
690 | y = fid.variables['y'] |
---|
691 | |
---|
692 | volumes = fid.variables['volumes'] |
---|
693 | |
---|
694 | # Get X, Y and bed elevation Z |
---|
695 | Q = domain.quantities['elevation'] |
---|
696 | X,Y,Z,V = Q.get_vertex_values(xy=True, |
---|
697 | precision = self.precision) |
---|
698 | |
---|
699 | |
---|
700 | |
---|
701 | x[:] = X.astype(self.precision) |
---|
702 | y[:] = Y.astype(self.precision) |
---|
703 | z[:] = Z.astype(self.precision) |
---|
704 | |
---|
705 | volumes[:] = V |
---|
706 | |
---|
707 | #Close |
---|
708 | fid.close() |
---|
709 | |
---|
710 | |
---|
711 | def store_timestep(self, name): |
---|
712 | """Store time and named quantity to file |
---|
713 | """ |
---|
714 | from Scientific.IO.NetCDF import NetCDFFile |
---|
715 | from time import sleep |
---|
716 | |
---|
717 | #Get NetCDF |
---|
718 | retries = 0 |
---|
719 | file_open = False |
---|
720 | while not file_open and retries < 10: |
---|
721 | try: |
---|
722 | fid = NetCDFFile(self.filename, 'a') #Open existing file |
---|
723 | except IOError: |
---|
724 | #This could happen if someone was reading the file. |
---|
725 | #In that case, wait a while and try again |
---|
726 | msg = 'Warning (store_timestep): File %s could not be opened'\ |
---|
727 | %self.filename |
---|
728 | msg += ' - trying again' |
---|
729 | print msg |
---|
730 | retries += 1 |
---|
731 | sleep(1) |
---|
732 | else: |
---|
733 | file_open = True |
---|
734 | |
---|
735 | if not file_open: |
---|
736 | msg = 'File %s could not be opened for append' %self.filename |
---|
737 | raise DataFileNotOPenError, msg |
---|
738 | |
---|
739 | |
---|
740 | domain = self.domain |
---|
741 | |
---|
742 | # Get the variables |
---|
743 | time = fid.variables['time'] |
---|
744 | stage = fid.variables['stage'] |
---|
745 | i = len(time) |
---|
746 | |
---|
747 | #Store stage |
---|
748 | time[i] = self.domain.time |
---|
749 | |
---|
750 | # Get quantity |
---|
751 | Q = domain.quantities[name] |
---|
752 | A,V = Q.get_vertex_values(xy=False, |
---|
753 | precision = self.precision) |
---|
754 | |
---|
755 | stage[i,:] = A.astype(self.precision) |
---|
756 | |
---|
757 | #Flush and close |
---|
758 | fid.sync() |
---|
759 | fid.close() |
---|
760 | |
---|
761 | |
---|
762 | #### NED is national exposure database (name changed to NEXIS) |
---|
763 | |
---|
764 | LAT_TITLE = 'LATITUDE' |
---|
765 | LONG_TITLE = 'LONGITUDE' |
---|
766 | X_TITLE = 'x' |
---|
767 | Y_TITLE = 'y' |
---|
768 | class Exposure_csv: |
---|
769 | def __init__(self,file_name, latitude_title=LAT_TITLE, |
---|
770 | longitude_title=LONG_TITLE, is_x_y_locations=None, |
---|
771 | x_title=X_TITLE, y_title=Y_TITLE, |
---|
772 | refine_polygon=None, title_check_list=None): |
---|
773 | """ |
---|
774 | This class is for handling the exposure csv file. |
---|
775 | It reads the file in and converts the lats and longs to a geospatial |
---|
776 | data object. |
---|
777 | Use the methods to read and write columns. |
---|
778 | |
---|
779 | The format of the csv files it reads is; |
---|
780 | The first row is a title row. |
---|
781 | comma's are the delimiters |
---|
782 | each column is a 'set' of data |
---|
783 | |
---|
784 | Feel free to use/expand it to read other csv files. |
---|
785 | |
---|
786 | |
---|
787 | It is not for adding and deleting rows |
---|
788 | |
---|
789 | Can geospatial handle string attributes? It's not made for them. |
---|
790 | Currently it can't load and save string att's. |
---|
791 | |
---|
792 | So just use geospatial to hold the x, y and georef? Bad, since |
---|
793 | different att's are in diferent structures. Not so bad, the info |
---|
794 | to write if the .csv file is saved is in attribute_dic |
---|
795 | |
---|
796 | The location info is in the geospatial attribute. |
---|
797 | |
---|
798 | |
---|
799 | """ |
---|
800 | self._file_name = file_name |
---|
801 | self._geospatial = None # |
---|
802 | |
---|
803 | # self._attribute_dic is a dictionary. |
---|
804 | #The keys are the column titles. |
---|
805 | #The values are lists of column data |
---|
806 | |
---|
807 | # self._title_index_dic is a dictionary. |
---|
808 | #The keys are the column titles. |
---|
809 | #The values are the index positions of file columns. |
---|
810 | self._attribute_dic, self._title_index_dic = \ |
---|
811 | csv2dict(self._file_name, title_check_list=title_check_list) |
---|
812 | try: |
---|
813 | #Have code here that handles caps or lower |
---|
814 | lats = self._attribute_dic[latitude_title] |
---|
815 | longs = self._attribute_dic[longitude_title] |
---|
816 | |
---|
817 | except KeyError: |
---|
818 | # maybe a warning.. |
---|
819 | #Let's see if this works.. |
---|
820 | if False != is_x_y_locations: |
---|
821 | is_x_y_locations = True |
---|
822 | pass |
---|
823 | else: |
---|
824 | self._geospatial = Geospatial_data(latitudes = lats, |
---|
825 | longitudes = longs) |
---|
826 | |
---|
827 | if is_x_y_locations is True: |
---|
828 | if self._geospatial is not None: |
---|
829 | pass #fixme throw an error |
---|
830 | try: |
---|
831 | xs = self._attribute_dic[x_title] |
---|
832 | ys = self._attribute_dic[y_title] |
---|
833 | points = [[float(i),float(j)] for i,j in map(None,xs,ys)] |
---|
834 | except KeyError: |
---|
835 | # maybe a warning.. |
---|
836 | msg = "Could not find location information." |
---|
837 | raise TitleValueError, msg |
---|
838 | else: |
---|
839 | self._geospatial = Geospatial_data(data_points=points) |
---|
840 | |
---|
841 | # create a list of points that are in the refining_polygon |
---|
842 | # described by a list of indexes representing the points |
---|
843 | |
---|
844 | def __cmp__(self, other): |
---|
845 | #print "self._attribute_dic",self._attribute_dic |
---|
846 | #print "other._attribute_dic",other._attribute_dic |
---|
847 | #print "self._title_index_dic", self._title_index_dic |
---|
848 | #print "other._title_index_dic", other._title_index_dic |
---|
849 | |
---|
850 | #check that a is an instance of this class |
---|
851 | if isinstance(self, type(other)): |
---|
852 | result = cmp(self._attribute_dic, other._attribute_dic) |
---|
853 | if result <>0: |
---|
854 | return result |
---|
855 | # The order of the columns is important. Therefore.. |
---|
856 | result = cmp(self._title_index_dic, other._title_index_dic) |
---|
857 | if result <>0: |
---|
858 | return result |
---|
859 | for self_ls, other_ls in map(None,self._attribute_dic, \ |
---|
860 | other._attribute_dic): |
---|
861 | result = cmp(self._attribute_dic[self_ls], |
---|
862 | other._attribute_dic[other_ls]) |
---|
863 | if result <>0: |
---|
864 | return result |
---|
865 | return 0 |
---|
866 | else: |
---|
867 | return 1 |
---|
868 | |
---|
869 | |
---|
870 | def get_column(self, column_name, use_refind_polygon=False): |
---|
871 | """ |
---|
872 | Given a column name return a list of the column values |
---|
873 | |
---|
874 | Note, the type of the values will be String! |
---|
875 | do this to change a list of strings to a list of floats |
---|
876 | time = [float(x) for x in time] |
---|
877 | |
---|
878 | Not implemented: |
---|
879 | if use_refind_polygon is True, only return values in the |
---|
880 | refined polygon |
---|
881 | """ |
---|
882 | if not self._attribute_dic.has_key(column_name): |
---|
883 | msg = 'Therer is no column called %s!' %column_name |
---|
884 | raise TitleValueError, msg |
---|
885 | return self._attribute_dic[column_name] |
---|
886 | |
---|
887 | |
---|
888 | def get_value(self, value_column_name, |
---|
889 | known_column_name, |
---|
890 | known_values, |
---|
891 | use_refind_polygon=False): |
---|
892 | """ |
---|
893 | Do linear interpolation on the known_colum, using the known_value, |
---|
894 | to return a value of the column_value_name. |
---|
895 | """ |
---|
896 | pass |
---|
897 | |
---|
898 | |
---|
899 | def get_location(self, use_refind_polygon=False): |
---|
900 | """ |
---|
901 | Return a geospatial object which describes the |
---|
902 | locations of the location file. |
---|
903 | |
---|
904 | Note, if there is not location info, this returns None. |
---|
905 | |
---|
906 | Not implemented: |
---|
907 | if use_refind_polygon is True, only return values in the |
---|
908 | refined polygon |
---|
909 | """ |
---|
910 | return self._geospatial |
---|
911 | |
---|
912 | def set_column(self, column_name, column_values, overwrite=False): |
---|
913 | """ |
---|
914 | Add a column to the 'end' (with the right most column being the end) |
---|
915 | of the csv file. |
---|
916 | |
---|
917 | Set overwrite to True if you want to overwrite a column. |
---|
918 | |
---|
919 | Note, in column_name white space is removed and case is not checked. |
---|
920 | Precondition |
---|
921 | The column_name and column_values cannot have comma's in it. |
---|
922 | """ |
---|
923 | |
---|
924 | value_row_count = \ |
---|
925 | len(self._attribute_dic[self._title_index_dic.keys()[0]]) |
---|
926 | if len(column_values) <> value_row_count: |
---|
927 | msg = 'The number of column values must equal the number of rows.' |
---|
928 | raise DataMissingValuesError, msg |
---|
929 | |
---|
930 | if self._attribute_dic.has_key(column_name): |
---|
931 | if not overwrite: |
---|
932 | msg = 'Column name %s already in use!' %column_name |
---|
933 | raise TitleValueError, msg |
---|
934 | else: |
---|
935 | # New title. Add it to the title index. |
---|
936 | self._title_index_dic[column_name] = len(self._title_index_dic) |
---|
937 | self._attribute_dic[column_name] = column_values |
---|
938 | #print "self._title_index_dic[column_name]",self._title_index_dic |
---|
939 | |
---|
940 | def save(self, file_name=None): |
---|
941 | """ |
---|
942 | Save the exposure csv file |
---|
943 | """ |
---|
944 | if file_name is None: |
---|
945 | file_name = self._file_name |
---|
946 | |
---|
947 | fd = open(file_name,'wb') |
---|
948 | writer = csv.writer(fd) |
---|
949 | |
---|
950 | #Write the title to a cvs file |
---|
951 | line = [None]* len(self._title_index_dic) |
---|
952 | for title in self._title_index_dic.iterkeys(): |
---|
953 | line[self._title_index_dic[title]]= title |
---|
954 | writer.writerow(line) |
---|
955 | |
---|
956 | # Write the values to a cvs file |
---|
957 | value_row_count = \ |
---|
958 | len(self._attribute_dic[self._title_index_dic.keys()[0]]) |
---|
959 | for row_i in range(value_row_count): |
---|
960 | line = [None]* len(self._title_index_dic) |
---|
961 | for title in self._title_index_dic.iterkeys(): |
---|
962 | line[self._title_index_dic[title]]= \ |
---|
963 | self._attribute_dic[title][row_i] |
---|
964 | writer.writerow(line) |
---|
965 | |
---|
966 | |
---|
967 | def csv2dict(file_name, title_check_list=None): |
---|
968 | """ |
---|
969 | Load in the csv as a dic, title as key and column info as value, . |
---|
970 | Also, create a dic, title as key and column index as value, |
---|
971 | to keep track of the column order. |
---|
972 | |
---|
973 | WARNING: Vaules are returned as strings. |
---|
974 | do this to change a list of strings to a list of floats |
---|
975 | time = [float(x) for x in time] |
---|
976 | """ |
---|
977 | |
---|
978 | # |
---|
979 | attribute_dic = {} |
---|
980 | title_index_dic = {} |
---|
981 | titles_stripped = [] # list of titles |
---|
982 | reader = csv.reader(file(file_name)) |
---|
983 | |
---|
984 | # Read in and manipulate the title info |
---|
985 | titles = reader.next() |
---|
986 | for i,title in enumerate(titles): |
---|
987 | titles_stripped.append(title.strip()) |
---|
988 | title_index_dic[title.strip()] = i |
---|
989 | title_count = len(titles_stripped) |
---|
990 | #print "title_index_dic",title_index_dic |
---|
991 | if title_check_list is not None: |
---|
992 | for title_check in title_check_list: |
---|
993 | #msg = "Reading error. This row is not present ", title_check |
---|
994 | #assert title_index_dic.has_key(title_check), msg |
---|
995 | if not title_index_dic.has_key(title_check): |
---|
996 | #reader.close() |
---|
997 | msg = "Reading error. This row is not present ", \ |
---|
998 | title_check |
---|
999 | raise IOError, msg |
---|
1000 | |
---|
1001 | |
---|
1002 | |
---|
1003 | #create a dic of colum values, indexed by column title |
---|
1004 | for line in reader: |
---|
1005 | if len(line) <> title_count: |
---|
1006 | raise IOError #FIXME make this nicer |
---|
1007 | for i, value in enumerate(line): |
---|
1008 | attribute_dic.setdefault(titles_stripped[i],[]).append(value) |
---|
1009 | |
---|
1010 | return attribute_dic, title_index_dic |
---|
1011 | |
---|
1012 | |
---|
1013 | #Auxiliary |
---|
1014 | def write_obj(filename,x,y,z): |
---|
1015 | """Store x,y,z vectors into filename (obj format) |
---|
1016 | Vectors are assumed to have dimension (M,3) where |
---|
1017 | M corresponds to the number elements. |
---|
1018 | triangles are assumed to be disconnected |
---|
1019 | |
---|
1020 | The three numbers in each vector correspond to three vertices, |
---|
1021 | |
---|
1022 | e.g. the x coordinate of vertex 1 of element i is in x[i,1] |
---|
1023 | |
---|
1024 | """ |
---|
1025 | #print 'Writing obj to %s' % filename |
---|
1026 | |
---|
1027 | import os.path |
---|
1028 | |
---|
1029 | root, ext = os.path.splitext(filename) |
---|
1030 | if ext == '.obj': |
---|
1031 | FN = filename |
---|
1032 | else: |
---|
1033 | FN = filename + '.obj' |
---|
1034 | |
---|
1035 | |
---|
1036 | outfile = open(FN, 'wb') |
---|
1037 | outfile.write("# Triangulation as an obj file\n") |
---|
1038 | |
---|
1039 | M, N = x.shape |
---|
1040 | assert N==3 #Assuming three vertices per element |
---|
1041 | |
---|
1042 | for i in range(M): |
---|
1043 | for j in range(N): |
---|
1044 | outfile.write("v %f %f %f\n" % (x[i,j],y[i,j],z[i,j])) |
---|
1045 | |
---|
1046 | for i in range(M): |
---|
1047 | base = i*N |
---|
1048 | outfile.write("f %d %d %d\n" % (base+1,base+2,base+3)) |
---|
1049 | |
---|
1050 | outfile.close() |
---|
1051 | |
---|
1052 | |
---|
1053 | ######################################################### |
---|
1054 | #Conversion routines |
---|
1055 | ######################################################## |
---|
1056 | |
---|
1057 | def sww2obj(basefilename, size): |
---|
1058 | """Convert netcdf based data output to obj |
---|
1059 | """ |
---|
1060 | from Scientific.IO.NetCDF import NetCDFFile |
---|
1061 | |
---|
1062 | from Numeric import Float, zeros |
---|
1063 | |
---|
1064 | #Get NetCDF |
---|
1065 | FN = create_filename('.', basefilename, 'sww', size) |
---|
1066 | print 'Reading from ', FN |
---|
1067 | fid = NetCDFFile(FN, 'r') #Open existing file for read |
---|
1068 | |
---|
1069 | |
---|
1070 | # Get the variables |
---|
1071 | x = fid.variables['x'] |
---|
1072 | y = fid.variables['y'] |
---|
1073 | z = fid.variables['elevation'] |
---|
1074 | time = fid.variables['time'] |
---|
1075 | stage = fid.variables['stage'] |
---|
1076 | |
---|
1077 | M = size #Number of lines |
---|
1078 | xx = zeros((M,3), Float) |
---|
1079 | yy = zeros((M,3), Float) |
---|
1080 | zz = zeros((M,3), Float) |
---|
1081 | |
---|
1082 | for i in range(M): |
---|
1083 | for j in range(3): |
---|
1084 | xx[i,j] = x[i+j*M] |
---|
1085 | yy[i,j] = y[i+j*M] |
---|
1086 | zz[i,j] = z[i+j*M] |
---|
1087 | |
---|
1088 | #Write obj for bathymetry |
---|
1089 | FN = create_filename('.', basefilename, 'obj', size) |
---|
1090 | write_obj(FN,xx,yy,zz) |
---|
1091 | |
---|
1092 | |
---|
1093 | #Now read all the data with variable information, combine with |
---|
1094 | #x,y info and store as obj |
---|
1095 | |
---|
1096 | for k in range(len(time)): |
---|
1097 | t = time[k] |
---|
1098 | print 'Processing timestep %f' %t |
---|
1099 | |
---|
1100 | for i in range(M): |
---|
1101 | for j in range(3): |
---|
1102 | zz[i,j] = stage[k,i+j*M] |
---|
1103 | |
---|
1104 | |
---|
1105 | #Write obj for variable data |
---|
1106 | #FN = create_filename(basefilename, 'obj', size, time=t) |
---|
1107 | FN = create_filename('.', basefilename[:5], 'obj', size, time=t) |
---|
1108 | write_obj(FN,xx,yy,zz) |
---|
1109 | |
---|
1110 | |
---|
1111 | def dat2obj(basefilename): |
---|
1112 | """Convert line based data output to obj |
---|
1113 | FIXME: Obsolete? |
---|
1114 | """ |
---|
1115 | |
---|
1116 | import glob, os |
---|
1117 | from anuga.config import data_dir |
---|
1118 | |
---|
1119 | |
---|
1120 | #Get bathymetry and x,y's |
---|
1121 | lines = open(data_dir+os.sep+basefilename+'_geometry.dat', 'r').readlines() |
---|
1122 | |
---|
1123 | from Numeric import zeros, Float |
---|
1124 | |
---|
1125 | M = len(lines) #Number of lines |
---|
1126 | x = zeros((M,3), Float) |
---|
1127 | y = zeros((M,3), Float) |
---|
1128 | z = zeros((M,3), Float) |
---|
1129 | |
---|
1130 | ##i = 0 |
---|
1131 | for i, line in enumerate(lines): |
---|
1132 | tokens = line.split() |
---|
1133 | values = map(float,tokens) |
---|
1134 | |
---|
1135 | for j in range(3): |
---|
1136 | x[i,j] = values[j*3] |
---|
1137 | y[i,j] = values[j*3+1] |
---|
1138 | z[i,j] = values[j*3+2] |
---|
1139 | |
---|
1140 | ##i += 1 |
---|
1141 | |
---|
1142 | |
---|
1143 | #Write obj for bathymetry |
---|
1144 | write_obj(data_dir+os.sep+basefilename+'_geometry',x,y,z) |
---|
1145 | |
---|
1146 | |
---|
1147 | #Now read all the data files with variable information, combine with |
---|
1148 | #x,y info |
---|
1149 | #and store as obj |
---|
1150 | |
---|
1151 | files = glob.glob(data_dir+os.sep+basefilename+'*.dat') |
---|
1152 | |
---|
1153 | for filename in files: |
---|
1154 | print 'Processing %s' % filename |
---|
1155 | |
---|
1156 | lines = open(data_dir+os.sep+filename,'r').readlines() |
---|
1157 | assert len(lines) == M |
---|
1158 | root, ext = os.path.splitext(filename) |
---|
1159 | |
---|
1160 | #Get time from filename |
---|
1161 | i0 = filename.find('_time=') |
---|
1162 | if i0 == -1: |
---|
1163 | #Skip bathymetry file |
---|
1164 | continue |
---|
1165 | |
---|
1166 | i0 += 6 #Position where time starts |
---|
1167 | i1 = filename.find('.dat') |
---|
1168 | |
---|
1169 | if i1 > i0: |
---|
1170 | t = float(filename[i0:i1]) |
---|
1171 | else: |
---|
1172 | raise DataTimeError, 'Hmmmm' |
---|
1173 | |
---|
1174 | |
---|
1175 | |
---|
1176 | ##i = 0 |
---|
1177 | for i, line in enumerate(lines): |
---|
1178 | tokens = line.split() |
---|
1179 | values = map(float,tokens) |
---|
1180 | |
---|
1181 | for j in range(3): |
---|
1182 | z[i,j] = values[j] |
---|
1183 | |
---|
1184 | ##i += 1 |
---|
1185 | |
---|
1186 | #Write obj for variable data |
---|
1187 | write_obj(data_dir+os.sep+basefilename+'_time=%.4f' %t,x,y,z) |
---|
1188 | |
---|
1189 | |
---|
1190 | def filter_netcdf(filename1, filename2, first=0, last=None, step = 1): |
---|
1191 | """Read netcdf filename1, pick timesteps first:step:last and save to |
---|
1192 | nettcdf file filename2 |
---|
1193 | """ |
---|
1194 | from Scientific.IO.NetCDF import NetCDFFile |
---|
1195 | |
---|
1196 | #Get NetCDF |
---|
1197 | infile = NetCDFFile(filename1, 'r') #Open existing file for read |
---|
1198 | outfile = NetCDFFile(filename2, 'w') #Open new file |
---|
1199 | |
---|
1200 | |
---|
1201 | #Copy dimensions |
---|
1202 | for d in infile.dimensions: |
---|
1203 | outfile.createDimension(d, infile.dimensions[d]) |
---|
1204 | |
---|
1205 | for name in infile.variables: |
---|
1206 | var = infile.variables[name] |
---|
1207 | outfile.createVariable(name, var.typecode(), var.dimensions) |
---|
1208 | |
---|
1209 | |
---|
1210 | #Copy the static variables |
---|
1211 | for name in infile.variables: |
---|
1212 | if name == 'time' or name == 'stage': |
---|
1213 | pass |
---|
1214 | else: |
---|
1215 | #Copy |
---|
1216 | outfile.variables[name][:] = infile.variables[name][:] |
---|
1217 | |
---|
1218 | #Copy selected timesteps |
---|
1219 | time = infile.variables['time'] |
---|
1220 | stage = infile.variables['stage'] |
---|
1221 | |
---|
1222 | newtime = outfile.variables['time'] |
---|
1223 | newstage = outfile.variables['stage'] |
---|
1224 | |
---|
1225 | if last is None: |
---|
1226 | last = len(time) |
---|
1227 | |
---|
1228 | selection = range(first, last, step) |
---|
1229 | for i, j in enumerate(selection): |
---|
1230 | print 'Copying timestep %d of %d (%f)' %(j, last-first, time[j]) |
---|
1231 | newtime[i] = time[j] |
---|
1232 | newstage[i,:] = stage[j,:] |
---|
1233 | |
---|
1234 | #Close |
---|
1235 | infile.close() |
---|
1236 | outfile.close() |
---|
1237 | |
---|
1238 | |
---|
1239 | #Get data objects |
---|
1240 | def get_dataobject(domain, mode='w'): |
---|
1241 | """Return instance of class of given format using filename |
---|
1242 | """ |
---|
1243 | |
---|
1244 | cls = eval('Data_format_%s' %domain.format) |
---|
1245 | return cls(domain, mode) |
---|
1246 | |
---|
1247 | |
---|
1248 | |
---|
1249 | |
---|
1250 | def dem2pts(basename_in, basename_out=None, |
---|
1251 | easting_min=None, easting_max=None, |
---|
1252 | northing_min=None, northing_max=None, |
---|
1253 | use_cache=False, verbose=False,): |
---|
1254 | """Read Digitial Elevation model from the following NetCDF format (.dem) |
---|
1255 | |
---|
1256 | Example: |
---|
1257 | |
---|
1258 | ncols 3121 |
---|
1259 | nrows 1800 |
---|
1260 | xllcorner 722000 |
---|
1261 | yllcorner 5893000 |
---|
1262 | cellsize 25 |
---|
1263 | NODATA_value -9999 |
---|
1264 | 138.3698 137.4194 136.5062 135.5558 .......... |
---|
1265 | |
---|
1266 | Convert to NetCDF pts format which is |
---|
1267 | |
---|
1268 | points: (Nx2) Float array |
---|
1269 | elevation: N Float array |
---|
1270 | """ |
---|
1271 | |
---|
1272 | |
---|
1273 | |
---|
1274 | kwargs = {'basename_out': basename_out, |
---|
1275 | 'easting_min': easting_min, |
---|
1276 | 'easting_max': easting_max, |
---|
1277 | 'northing_min': northing_min, |
---|
1278 | 'northing_max': northing_max, |
---|
1279 | 'verbose': verbose} |
---|
1280 | |
---|
1281 | if use_cache is True: |
---|
1282 | from caching import cache |
---|
1283 | result = cache(_dem2pts, basename_in, kwargs, |
---|
1284 | dependencies = [basename_in + '.dem'], |
---|
1285 | verbose = verbose) |
---|
1286 | |
---|
1287 | else: |
---|
1288 | result = apply(_dem2pts, [basename_in], kwargs) |
---|
1289 | |
---|
1290 | return result |
---|
1291 | |
---|
1292 | |
---|
1293 | def _dem2pts(basename_in, basename_out=None, verbose=False, |
---|
1294 | easting_min=None, easting_max=None, |
---|
1295 | northing_min=None, northing_max=None): |
---|
1296 | """Read Digitial Elevation model from the following NetCDF format (.dem) |
---|
1297 | |
---|
1298 | Internal function. See public function dem2pts for details. |
---|
1299 | """ |
---|
1300 | |
---|
1301 | #FIXME: Can this be written feasibly using write_pts? |
---|
1302 | |
---|
1303 | import os |
---|
1304 | from Scientific.IO.NetCDF import NetCDFFile |
---|
1305 | from Numeric import Float, zeros, reshape, sum |
---|
1306 | |
---|
1307 | root = basename_in |
---|
1308 | |
---|
1309 | #Get NetCDF |
---|
1310 | infile = NetCDFFile(root + '.dem', 'r') #Open existing netcdf file for read |
---|
1311 | |
---|
1312 | if verbose: print 'Reading DEM from %s' %(root + '.dem') |
---|
1313 | |
---|
1314 | ncols = infile.ncols[0] |
---|
1315 | nrows = infile.nrows[0] |
---|
1316 | xllcorner = infile.xllcorner[0] #Easting of lower left corner |
---|
1317 | yllcorner = infile.yllcorner[0] #Northing of lower left corner |
---|
1318 | cellsize = infile.cellsize[0] |
---|
1319 | NODATA_value = infile.NODATA_value[0] |
---|
1320 | dem_elevation = infile.variables['elevation'] |
---|
1321 | |
---|
1322 | zone = infile.zone[0] |
---|
1323 | false_easting = infile.false_easting[0] |
---|
1324 | false_northing = infile.false_northing[0] |
---|
1325 | |
---|
1326 | #Text strings |
---|
1327 | projection = infile.projection |
---|
1328 | datum = infile.datum |
---|
1329 | units = infile.units |
---|
1330 | |
---|
1331 | |
---|
1332 | #Get output file |
---|
1333 | if basename_out == None: |
---|
1334 | ptsname = root + '.pts' |
---|
1335 | else: |
---|
1336 | ptsname = basename_out + '.pts' |
---|
1337 | |
---|
1338 | if verbose: print 'Store to NetCDF file %s' %ptsname |
---|
1339 | # NetCDF file definition |
---|
1340 | outfile = NetCDFFile(ptsname, 'w') |
---|
1341 | |
---|
1342 | #Create new file |
---|
1343 | outfile.institution = 'Geoscience Australia' |
---|
1344 | outfile.description = 'NetCDF pts format for compact and portable storage ' +\ |
---|
1345 | 'of spatial point data' |
---|
1346 | #assign default values |
---|
1347 | if easting_min is None: easting_min = xllcorner |
---|
1348 | if easting_max is None: easting_max = xllcorner + ncols*cellsize |
---|
1349 | if northing_min is None: northing_min = yllcorner |
---|
1350 | if northing_max is None: northing_max = yllcorner + nrows*cellsize |
---|
1351 | |
---|
1352 | #compute offsets to update georeferencing |
---|
1353 | easting_offset = xllcorner - easting_min |
---|
1354 | northing_offset = yllcorner - northing_min |
---|
1355 | |
---|
1356 | #Georeferencing |
---|
1357 | outfile.zone = zone |
---|
1358 | outfile.xllcorner = easting_min #Easting of lower left corner |
---|
1359 | outfile.yllcorner = northing_min #Northing of lower left corner |
---|
1360 | outfile.false_easting = false_easting |
---|
1361 | outfile.false_northing = false_northing |
---|
1362 | |
---|
1363 | outfile.projection = projection |
---|
1364 | outfile.datum = datum |
---|
1365 | outfile.units = units |
---|
1366 | |
---|
1367 | |
---|
1368 | #Grid info (FIXME: probably not going to be used, but heck) |
---|
1369 | outfile.ncols = ncols |
---|
1370 | outfile.nrows = nrows |
---|
1371 | |
---|
1372 | dem_elevation_r = reshape(dem_elevation, (nrows, ncols)) |
---|
1373 | totalnopoints = nrows*ncols |
---|
1374 | |
---|
1375 | # calculating number of NODATA_values for each row in clipped region |
---|
1376 | #FIXME: use array operations to do faster |
---|
1377 | nn = 0 |
---|
1378 | k = 0 |
---|
1379 | i1_0 = 0 |
---|
1380 | j1_0 = 0 |
---|
1381 | thisj = 0 |
---|
1382 | thisi = 0 |
---|
1383 | for i in range(nrows): |
---|
1384 | y = (nrows-i-1)*cellsize + yllcorner |
---|
1385 | for j in range(ncols): |
---|
1386 | x = j*cellsize + xllcorner |
---|
1387 | if easting_min <= x <= easting_max and \ |
---|
1388 | northing_min <= y <= northing_max: |
---|
1389 | thisj = j |
---|
1390 | thisi = i |
---|
1391 | if dem_elevation_r[i,j] == NODATA_value: nn += 1 |
---|
1392 | |
---|
1393 | if k == 0: |
---|
1394 | i1_0 = i |
---|
1395 | j1_0 = j |
---|
1396 | k += 1 |
---|
1397 | |
---|
1398 | index1 = j1_0 |
---|
1399 | index2 = thisj |
---|
1400 | |
---|
1401 | # dimension definitions |
---|
1402 | nrows_in_bounding_box = int(round((northing_max-northing_min)/cellsize)) |
---|
1403 | ncols_in_bounding_box = int(round((easting_max-easting_min)/cellsize)) |
---|
1404 | |
---|
1405 | clippednopoints = (thisi+1-i1_0)*(thisj+1-j1_0) |
---|
1406 | nopoints = clippednopoints-nn |
---|
1407 | |
---|
1408 | clipped_dem_elev = dem_elevation_r[i1_0:thisi+1,j1_0:thisj+1] |
---|
1409 | |
---|
1410 | if verbose: |
---|
1411 | print 'There are %d values in the elevation' %totalnopoints |
---|
1412 | print 'There are %d values in the clipped elevation' %clippednopoints |
---|
1413 | print 'There are %d NODATA_values in the clipped elevation' %nn |
---|
1414 | |
---|
1415 | outfile.createDimension('number_of_points', nopoints) |
---|
1416 | outfile.createDimension('number_of_dimensions', 2) #This is 2d data |
---|
1417 | |
---|
1418 | # variable definitions |
---|
1419 | outfile.createVariable('points', Float, ('number_of_points', |
---|
1420 | 'number_of_dimensions')) |
---|
1421 | outfile.createVariable('elevation', Float, ('number_of_points',)) |
---|
1422 | |
---|
1423 | # Get handles to the variables |
---|
1424 | points = outfile.variables['points'] |
---|
1425 | elevation = outfile.variables['elevation'] |
---|
1426 | |
---|
1427 | lenv = index2-index1+1 |
---|
1428 | #Store data |
---|
1429 | global_index = 0 |
---|
1430 | #for i in range(nrows): |
---|
1431 | for i in range(i1_0,thisi+1,1): |
---|
1432 | if verbose and i%((nrows+10)/10)==0: |
---|
1433 | print 'Processing row %d of %d' %(i, nrows) |
---|
1434 | |
---|
1435 | lower_index = global_index |
---|
1436 | |
---|
1437 | v = dem_elevation_r[i,index1:index2+1] |
---|
1438 | no_NODATA = sum(v == NODATA_value) |
---|
1439 | if no_NODATA > 0: |
---|
1440 | newcols = lenv - no_NODATA #ncols_in_bounding_box - no_NODATA |
---|
1441 | else: |
---|
1442 | newcols = lenv #ncols_in_bounding_box |
---|
1443 | |
---|
1444 | telev = zeros(newcols, Float) |
---|
1445 | tpoints = zeros((newcols, 2), Float) |
---|
1446 | |
---|
1447 | local_index = 0 |
---|
1448 | |
---|
1449 | y = (nrows-i-1)*cellsize + yllcorner |
---|
1450 | #for j in range(ncols): |
---|
1451 | for j in range(j1_0,index2+1,1): |
---|
1452 | |
---|
1453 | x = j*cellsize + xllcorner |
---|
1454 | if easting_min <= x <= easting_max and \ |
---|
1455 | northing_min <= y <= northing_max and \ |
---|
1456 | dem_elevation_r[i,j] <> NODATA_value: |
---|
1457 | tpoints[local_index, :] = [x-easting_min,y-northing_min] |
---|
1458 | telev[local_index] = dem_elevation_r[i, j] |
---|
1459 | global_index += 1 |
---|
1460 | local_index += 1 |
---|
1461 | |
---|
1462 | upper_index = global_index |
---|
1463 | |
---|
1464 | if upper_index == lower_index + newcols: |
---|
1465 | points[lower_index:upper_index, :] = tpoints |
---|
1466 | elevation[lower_index:upper_index] = telev |
---|
1467 | |
---|
1468 | assert global_index == nopoints, 'index not equal to number of points' |
---|
1469 | |
---|
1470 | infile.close() |
---|
1471 | outfile.close() |
---|
1472 | |
---|
1473 | |
---|
1474 | |
---|
1475 | def _read_hecras_cross_sections(lines): |
---|
1476 | """Return block of surface lines for each cross section |
---|
1477 | Starts with SURFACE LINE, |
---|
1478 | Ends with END CROSS-SECTION |
---|
1479 | """ |
---|
1480 | |
---|
1481 | points = [] |
---|
1482 | |
---|
1483 | reading_surface = False |
---|
1484 | for i, line in enumerate(lines): |
---|
1485 | |
---|
1486 | if len(line.strip()) == 0: #Ignore blanks |
---|
1487 | continue |
---|
1488 | |
---|
1489 | if lines[i].strip().startswith('SURFACE LINE'): |
---|
1490 | reading_surface = True |
---|
1491 | continue |
---|
1492 | |
---|
1493 | if lines[i].strip().startswith('END') and reading_surface: |
---|
1494 | yield points |
---|
1495 | reading_surface = False |
---|
1496 | points = [] |
---|
1497 | |
---|
1498 | if reading_surface: |
---|
1499 | fields = line.strip().split(',') |
---|
1500 | easting = float(fields[0]) |
---|
1501 | northing = float(fields[1]) |
---|
1502 | elevation = float(fields[2]) |
---|
1503 | points.append([easting, northing, elevation]) |
---|
1504 | |
---|
1505 | |
---|
1506 | |
---|
1507 | |
---|
1508 | def hecras_cross_sections2pts(basename_in, |
---|
1509 | basename_out=None, |
---|
1510 | verbose=False): |
---|
1511 | """Read HEC-RAS Elevation datal from the following ASCII format (.sdf) |
---|
1512 | |
---|
1513 | Example: |
---|
1514 | |
---|
1515 | |
---|
1516 | # RAS export file created on Mon 15Aug2005 11:42 |
---|
1517 | # by HEC-RAS Version 3.1.1 |
---|
1518 | |
---|
1519 | BEGIN HEADER: |
---|
1520 | UNITS: METRIC |
---|
1521 | DTM TYPE: TIN |
---|
1522 | DTM: v:\1\cit\perth_topo\river_tin |
---|
1523 | STREAM LAYER: c:\local\hecras\21_02_03\up_canning_cent3d.shp |
---|
1524 | CROSS-SECTION LAYER: c:\local\hecras\21_02_03\up_can_xs3d.shp |
---|
1525 | MAP PROJECTION: UTM |
---|
1526 | PROJECTION ZONE: 50 |
---|
1527 | DATUM: AGD66 |
---|
1528 | VERTICAL DATUM: |
---|
1529 | NUMBER OF REACHES: 19 |
---|
1530 | NUMBER OF CROSS-SECTIONS: 14206 |
---|
1531 | END HEADER: |
---|
1532 | |
---|
1533 | |
---|
1534 | Only the SURFACE LINE data of the following form will be utilised |
---|
1535 | |
---|
1536 | CROSS-SECTION: |
---|
1537 | STREAM ID:Southern-Wungong |
---|
1538 | REACH ID:Southern-Wungong |
---|
1539 | STATION:19040.* |
---|
1540 | CUT LINE: |
---|
1541 | 405548.671603161 , 6438142.7594925 |
---|
1542 | 405734.536092045 , 6438326.10404912 |
---|
1543 | 405745.130459356 , 6438331.48627354 |
---|
1544 | 405813.89633823 , 6438368.6272789 |
---|
1545 | SURFACE LINE: |
---|
1546 | 405548.67, 6438142.76, 35.37 |
---|
1547 | 405552.24, 6438146.28, 35.41 |
---|
1548 | 405554.78, 6438148.78, 35.44 |
---|
1549 | 405555.80, 6438149.79, 35.44 |
---|
1550 | 405559.37, 6438153.31, 35.45 |
---|
1551 | 405560.88, 6438154.81, 35.44 |
---|
1552 | 405562.93, 6438156.83, 35.42 |
---|
1553 | 405566.50, 6438160.35, 35.38 |
---|
1554 | 405566.99, 6438160.83, 35.37 |
---|
1555 | ... |
---|
1556 | END CROSS-SECTION |
---|
1557 | |
---|
1558 | Convert to NetCDF pts format which is |
---|
1559 | |
---|
1560 | points: (Nx2) Float array |
---|
1561 | elevation: N Float array |
---|
1562 | """ |
---|
1563 | |
---|
1564 | import os |
---|
1565 | from Scientific.IO.NetCDF import NetCDFFile |
---|
1566 | from Numeric import Float, zeros, reshape |
---|
1567 | |
---|
1568 | root = basename_in |
---|
1569 | |
---|
1570 | #Get ASCII file |
---|
1571 | infile = open(root + '.sdf', 'r') #Open SDF file for read |
---|
1572 | |
---|
1573 | if verbose: print 'Reading DEM from %s' %(root + '.sdf') |
---|
1574 | |
---|
1575 | lines = infile.readlines() |
---|
1576 | infile.close() |
---|
1577 | |
---|
1578 | if verbose: print 'Converting to pts format' |
---|
1579 | |
---|
1580 | i = 0 |
---|
1581 | while lines[i].strip() == '' or lines[i].strip().startswith('#'): |
---|
1582 | i += 1 |
---|
1583 | |
---|
1584 | assert lines[i].strip().upper() == 'BEGIN HEADER:' |
---|
1585 | i += 1 |
---|
1586 | |
---|
1587 | assert lines[i].strip().upper().startswith('UNITS:') |
---|
1588 | units = lines[i].strip().split()[1] |
---|
1589 | i += 1 |
---|
1590 | |
---|
1591 | assert lines[i].strip().upper().startswith('DTM TYPE:') |
---|
1592 | i += 1 |
---|
1593 | |
---|
1594 | assert lines[i].strip().upper().startswith('DTM:') |
---|
1595 | i += 1 |
---|
1596 | |
---|
1597 | assert lines[i].strip().upper().startswith('STREAM') |
---|
1598 | i += 1 |
---|
1599 | |
---|
1600 | assert lines[i].strip().upper().startswith('CROSS') |
---|
1601 | i += 1 |
---|
1602 | |
---|
1603 | assert lines[i].strip().upper().startswith('MAP PROJECTION:') |
---|
1604 | projection = lines[i].strip().split(':')[1] |
---|
1605 | i += 1 |
---|
1606 | |
---|
1607 | assert lines[i].strip().upper().startswith('PROJECTION ZONE:') |
---|
1608 | zone = int(lines[i].strip().split(':')[1]) |
---|
1609 | i += 1 |
---|
1610 | |
---|
1611 | assert lines[i].strip().upper().startswith('DATUM:') |
---|
1612 | datum = lines[i].strip().split(':')[1] |
---|
1613 | i += 1 |
---|
1614 | |
---|
1615 | assert lines[i].strip().upper().startswith('VERTICAL DATUM:') |
---|
1616 | i += 1 |
---|
1617 | |
---|
1618 | assert lines[i].strip().upper().startswith('NUMBER OF REACHES:') |
---|
1619 | i += 1 |
---|
1620 | |
---|
1621 | assert lines[i].strip().upper().startswith('NUMBER OF CROSS-SECTIONS:') |
---|
1622 | number_of_cross_sections = int(lines[i].strip().split(':')[1]) |
---|
1623 | i += 1 |
---|
1624 | |
---|
1625 | |
---|
1626 | #Now read all points |
---|
1627 | points = [] |
---|
1628 | elevation = [] |
---|
1629 | for j, entries in enumerate(_read_hecras_cross_sections(lines[i:])): |
---|
1630 | for k, entry in enumerate(entries): |
---|
1631 | points.append(entry[:2]) |
---|
1632 | elevation.append(entry[2]) |
---|
1633 | |
---|
1634 | |
---|
1635 | msg = 'Actual #number_of_cross_sections == %d, Reported as %d'\ |
---|
1636 | %(j+1, number_of_cross_sections) |
---|
1637 | assert j+1 == number_of_cross_sections, msg |
---|
1638 | |
---|
1639 | #Get output file |
---|
1640 | if basename_out == None: |
---|
1641 | ptsname = root + '.pts' |
---|
1642 | else: |
---|
1643 | ptsname = basename_out + '.pts' |
---|
1644 | |
---|
1645 | geo_ref = Geo_reference(zone, 0, 0, datum, projection, units) |
---|
1646 | geo = Geospatial_data(points, {"elevation":elevation}, |
---|
1647 | verbose=verbose, geo_reference=geo_ref) |
---|
1648 | geo.export_points_file(ptsname) |
---|
1649 | |
---|
1650 | def export_grid(basename_in, extra_name_out = None, |
---|
1651 | quantities = None, # defaults to elevation |
---|
1652 | timestep = None, |
---|
1653 | reduction = None, |
---|
1654 | cellsize = 10, |
---|
1655 | NODATA_value = -9999, |
---|
1656 | easting_min = None, |
---|
1657 | easting_max = None, |
---|
1658 | northing_min = None, |
---|
1659 | northing_max = None, |
---|
1660 | verbose = False, |
---|
1661 | origin = None, |
---|
1662 | datum = 'WGS84', |
---|
1663 | format = 'ers'): |
---|
1664 | """ |
---|
1665 | |
---|
1666 | Wrapper for sww2dem. - see sww2dem to find out what most of the |
---|
1667 | parameters do. |
---|
1668 | |
---|
1669 | Quantities is a list of quantities. Each quantity will be |
---|
1670 | calculated for each sww file. |
---|
1671 | |
---|
1672 | This returns the basenames of the files returned, which is made up |
---|
1673 | of the dir and all of the file name, except the extension. |
---|
1674 | |
---|
1675 | This function returns the names of the files produced. |
---|
1676 | |
---|
1677 | It will also produce as many output files as there are input sww files. |
---|
1678 | """ |
---|
1679 | |
---|
1680 | if quantities is None: |
---|
1681 | quantities = ['elevation'] |
---|
1682 | |
---|
1683 | if type(quantities) is str: |
---|
1684 | quantities = [quantities] |
---|
1685 | |
---|
1686 | # How many sww files are there? |
---|
1687 | dir, base = os.path.split(basename_in) |
---|
1688 | #print "basename_in",basename_in |
---|
1689 | #print "base",base |
---|
1690 | |
---|
1691 | iterate_over = get_all_swwfiles(dir,base,verbose) |
---|
1692 | |
---|
1693 | if dir == "": |
---|
1694 | dir = "." # Unix compatibility |
---|
1695 | |
---|
1696 | files_out = [] |
---|
1697 | #print 'sww_file',iterate_over |
---|
1698 | for sww_file in iterate_over: |
---|
1699 | for quantity in quantities: |
---|
1700 | if extra_name_out is None: |
---|
1701 | basename_out = sww_file + '_' + quantity |
---|
1702 | else: |
---|
1703 | basename_out = sww_file + '_' + quantity + '_' \ |
---|
1704 | + extra_name_out |
---|
1705 | # print "basename_out", basename_out |
---|
1706 | |
---|
1707 | file_out = sww2dem(dir+sep+sww_file, dir+sep+basename_out, |
---|
1708 | quantity, |
---|
1709 | timestep, |
---|
1710 | reduction, |
---|
1711 | cellsize, |
---|
1712 | NODATA_value, |
---|
1713 | easting_min, |
---|
1714 | easting_max, |
---|
1715 | northing_min, |
---|
1716 | northing_max, |
---|
1717 | verbose, |
---|
1718 | origin, |
---|
1719 | datum, |
---|
1720 | format) |
---|
1721 | files_out.append(file_out) |
---|
1722 | #print "basenames_out after",basenames_out |
---|
1723 | return files_out |
---|
1724 | |
---|
1725 | |
---|
1726 | def get_timeseries(production_dirs, output_dir, scenario_name, gauges_dir_name, |
---|
1727 | plot_quantity, generate_fig = False, |
---|
1728 | reportname = None, surface = False, time_min = None, |
---|
1729 | time_max = None, title_on = False, verbose = True, |
---|
1730 | nodes=None): |
---|
1731 | """ |
---|
1732 | nodes - number of processes used. |
---|
1733 | |
---|
1734 | warning - this function has no tests |
---|
1735 | """ |
---|
1736 | if reportname == None: |
---|
1737 | report = False |
---|
1738 | else: |
---|
1739 | report = True |
---|
1740 | |
---|
1741 | if nodes is None: |
---|
1742 | is_parallel = False |
---|
1743 | else: |
---|
1744 | is_parallel = True |
---|
1745 | |
---|
1746 | # Generate figures |
---|
1747 | swwfiles = {} |
---|
1748 | |
---|
1749 | if is_parallel is True: |
---|
1750 | for i in range(nodes): |
---|
1751 | print 'Sending node %d of %d' %(i,nodes) |
---|
1752 | swwfiles = {} |
---|
1753 | if not reportname == None: |
---|
1754 | reportname = report_name + '_%s' %(i) |
---|
1755 | for label_id in production_dirs.keys(): |
---|
1756 | if label_id == 'boundaries': |
---|
1757 | swwfile = best_boundary_sww |
---|
1758 | else: |
---|
1759 | file_loc = output_dir + label_id + sep |
---|
1760 | sww_extra = '_P%s_%s' %(i,nodes) |
---|
1761 | swwfile = file_loc + scenario_name + sww_extra + '.sww' |
---|
1762 | print 'swwfile',swwfile |
---|
1763 | swwfiles[swwfile] = label_id |
---|
1764 | |
---|
1765 | texname, elev_output = sww2timeseries(swwfiles, |
---|
1766 | gauges_dir_name, |
---|
1767 | production_dirs, |
---|
1768 | report = report, |
---|
1769 | reportname = reportname, |
---|
1770 | plot_quantity = plot_quantity, |
---|
1771 | generate_fig = generate_fig, |
---|
1772 | surface = surface, |
---|
1773 | time_min = time_min, |
---|
1774 | time_max = time_max, |
---|
1775 | title_on = title_on, |
---|
1776 | verbose = verbose) |
---|
1777 | else: |
---|
1778 | for label_id in production_dirs.keys(): |
---|
1779 | if label_id == 'boundaries': |
---|
1780 | print 'boundaries' |
---|
1781 | file_loc = project.boundaries_in_dir |
---|
1782 | swwfile = project.boundaries_dir_name3 + '.sww' |
---|
1783 | # swwfile = boundary_dir_filename |
---|
1784 | else: |
---|
1785 | file_loc = output_dir + label_id + sep |
---|
1786 | swwfile = file_loc + scenario_name + '.sww' |
---|
1787 | swwfiles[swwfile] = label_id |
---|
1788 | |
---|
1789 | texname, elev_output = sww2timeseries(swwfiles, |
---|
1790 | gauges_dir_name, |
---|
1791 | production_dirs, |
---|
1792 | report = report, |
---|
1793 | reportname = reportname, |
---|
1794 | plot_quantity = plot_quantity, |
---|
1795 | generate_fig = generate_fig, |
---|
1796 | surface = surface, |
---|
1797 | time_min = time_min, |
---|
1798 | time_max = time_max, |
---|
1799 | title_on = title_on, |
---|
1800 | verbose = verbose) |
---|
1801 | |
---|
1802 | |
---|
1803 | |
---|
1804 | def sww2dem(basename_in, basename_out = None, |
---|
1805 | quantity = None, # defaults to elevation |
---|
1806 | timestep = None, |
---|
1807 | reduction = None, |
---|
1808 | cellsize = 10, |
---|
1809 | NODATA_value = -9999, |
---|
1810 | easting_min = None, |
---|
1811 | easting_max = None, |
---|
1812 | northing_min = None, |
---|
1813 | northing_max = None, |
---|
1814 | verbose = False, |
---|
1815 | origin = None, |
---|
1816 | datum = 'WGS84', |
---|
1817 | format = 'ers'): |
---|
1818 | |
---|
1819 | """Read SWW file and convert to Digitial Elevation model format |
---|
1820 | (.asc or .ers) |
---|
1821 | |
---|
1822 | Example (ASC): |
---|
1823 | |
---|
1824 | ncols 3121 |
---|
1825 | nrows 1800 |
---|
1826 | xllcorner 722000 |
---|
1827 | yllcorner 5893000 |
---|
1828 | cellsize 25 |
---|
1829 | NODATA_value -9999 |
---|
1830 | 138.3698 137.4194 136.5062 135.5558 .......... |
---|
1831 | |
---|
1832 | Also write accompanying file with same basename_in but extension .prj |
---|
1833 | used to fix the UTM zone, datum, false northings and eastings. |
---|
1834 | |
---|
1835 | The prj format is assumed to be as |
---|
1836 | |
---|
1837 | Projection UTM |
---|
1838 | Zone 56 |
---|
1839 | Datum WGS84 |
---|
1840 | Zunits NO |
---|
1841 | Units METERS |
---|
1842 | Spheroid WGS84 |
---|
1843 | Xshift 0.0000000000 |
---|
1844 | Yshift 10000000.0000000000 |
---|
1845 | Parameters |
---|
1846 | |
---|
1847 | |
---|
1848 | The parameter quantity must be the name of an existing quantity or |
---|
1849 | an expression involving existing quantities. The default is |
---|
1850 | 'elevation'. Quantity is not a list of quantities. |
---|
1851 | |
---|
1852 | if timestep (an index) is given, output quantity at that timestep |
---|
1853 | |
---|
1854 | if reduction is given use that to reduce quantity over all timesteps. |
---|
1855 | |
---|
1856 | datum |
---|
1857 | |
---|
1858 | format can be either 'asc' or 'ers' |
---|
1859 | """ |
---|
1860 | |
---|
1861 | import sys |
---|
1862 | from Numeric import array, Float, concatenate, NewAxis, zeros, reshape, \ |
---|
1863 | sometrue |
---|
1864 | from Numeric import array2string |
---|
1865 | |
---|
1866 | from anuga.utilities.polygon import inside_polygon, outside_polygon, \ |
---|
1867 | separate_points_by_polygon |
---|
1868 | from anuga.abstract_2d_finite_volumes.util import \ |
---|
1869 | apply_expression_to_dictionary |
---|
1870 | |
---|
1871 | msg = 'Format must be either asc or ers' |
---|
1872 | assert format.lower() in ['asc', 'ers'], msg |
---|
1873 | |
---|
1874 | |
---|
1875 | false_easting = 500000 |
---|
1876 | false_northing = 10000000 |
---|
1877 | |
---|
1878 | if quantity is None: |
---|
1879 | quantity = 'elevation' |
---|
1880 | |
---|
1881 | if reduction is None: |
---|
1882 | reduction = max |
---|
1883 | |
---|
1884 | if basename_out is None: |
---|
1885 | basename_out = basename_in + '_%s' %quantity |
---|
1886 | |
---|
1887 | if quantity_formula.has_key(quantity): |
---|
1888 | quantity = quantity_formula[quantity] |
---|
1889 | |
---|
1890 | swwfile = basename_in + '.sww' |
---|
1891 | demfile = basename_out + '.' + format |
---|
1892 | # Note the use of a .ers extension is optional (write_ermapper_grid will |
---|
1893 | # deal with either option |
---|
1894 | |
---|
1895 | #if verbose: bye= nsuadsfd[0] # uncomment to check catching verbose errors |
---|
1896 | |
---|
1897 | # Read sww file |
---|
1898 | if verbose: |
---|
1899 | print 'Reading from %s' %swwfile |
---|
1900 | print 'Output directory is %s' %basename_out |
---|
1901 | |
---|
1902 | from Scientific.IO.NetCDF import NetCDFFile |
---|
1903 | fid = NetCDFFile(swwfile) |
---|
1904 | |
---|
1905 | #Get extent and reference |
---|
1906 | x = fid.variables['x'][:] |
---|
1907 | y = fid.variables['y'][:] |
---|
1908 | volumes = fid.variables['volumes'][:] |
---|
1909 | if timestep is not None: |
---|
1910 | times = fid.variables['time'][timestep] |
---|
1911 | else: |
---|
1912 | times = fid.variables['time'][:] |
---|
1913 | |
---|
1914 | number_of_timesteps = fid.dimensions['number_of_timesteps'] |
---|
1915 | number_of_points = fid.dimensions['number_of_points'] |
---|
1916 | |
---|
1917 | if origin is None: |
---|
1918 | |
---|
1919 | # Get geo_reference |
---|
1920 | # sww files don't have to have a geo_ref |
---|
1921 | try: |
---|
1922 | geo_reference = Geo_reference(NetCDFObject=fid) |
---|
1923 | except AttributeError, e: |
---|
1924 | geo_reference = Geo_reference() # Default georef object |
---|
1925 | |
---|
1926 | xllcorner = geo_reference.get_xllcorner() |
---|
1927 | yllcorner = geo_reference.get_yllcorner() |
---|
1928 | zone = geo_reference.get_zone() |
---|
1929 | else: |
---|
1930 | zone = origin[0] |
---|
1931 | xllcorner = origin[1] |
---|
1932 | yllcorner = origin[2] |
---|
1933 | |
---|
1934 | |
---|
1935 | |
---|
1936 | # FIXME: Refactor using code from Interpolation_function.statistics |
---|
1937 | # (in interpolate.py) |
---|
1938 | # Something like print swwstats(swwname) |
---|
1939 | if verbose: |
---|
1940 | print '------------------------------------------------' |
---|
1941 | print 'Statistics of SWW file:' |
---|
1942 | print ' Name: %s' %swwfile |
---|
1943 | print ' Reference:' |
---|
1944 | print ' Lower left corner: [%f, %f]'\ |
---|
1945 | %(xllcorner, yllcorner) |
---|
1946 | if timestep is not None: |
---|
1947 | print ' Time: %f' %(times) |
---|
1948 | else: |
---|
1949 | print ' Start time: %f' %fid.starttime[0] |
---|
1950 | print ' Extent:' |
---|
1951 | print ' x [m] in [%f, %f], len(x) == %d'\ |
---|
1952 | %(min(x.flat), max(x.flat), len(x.flat)) |
---|
1953 | print ' y [m] in [%f, %f], len(y) == %d'\ |
---|
1954 | %(min(y.flat), max(y.flat), len(y.flat)) |
---|
1955 | if timestep is not None: |
---|
1956 | print ' t [s] = %f, len(t) == %d' %(times, 1) |
---|
1957 | else: |
---|
1958 | print ' t [s] in [%f, %f], len(t) == %d'\ |
---|
1959 | %(min(times), max(times), len(times)) |
---|
1960 | print ' Quantities [SI units]:' |
---|
1961 | for name in ['stage', 'xmomentum', 'ymomentum']: |
---|
1962 | q = fid.variables[name][:].flat |
---|
1963 | if timestep is not None: |
---|
1964 | q = q[timestep*len(x):(timestep+1)*len(x)] |
---|
1965 | if verbose: print ' %s in [%f, %f]' %(name, min(q), max(q)) |
---|
1966 | for name in ['elevation']: |
---|
1967 | q = fid.variables[name][:].flat |
---|
1968 | if verbose: print ' %s in [%f, %f]' %(name, min(q), max(q)) |
---|
1969 | |
---|
1970 | # Get quantity and reduce if applicable |
---|
1971 | if verbose: print 'Processing quantity %s' %quantity |
---|
1972 | |
---|
1973 | # Turn NetCDF objects into Numeric arrays |
---|
1974 | quantity_dict = {} |
---|
1975 | for name in fid.variables.keys(): |
---|
1976 | quantity_dict[name] = fid.variables[name][:] |
---|
1977 | |
---|
1978 | |
---|
1979 | # Convert quantity expression to quantities found in sww file |
---|
1980 | q = apply_expression_to_dictionary(quantity, quantity_dict) |
---|
1981 | |
---|
1982 | if len(q.shape) == 2: |
---|
1983 | #q has a time component and needs to be reduced along |
---|
1984 | #the temporal dimension |
---|
1985 | if verbose: print 'Reducing quantity %s' %quantity |
---|
1986 | q_reduced = zeros( number_of_points, Float ) |
---|
1987 | |
---|
1988 | if timestep is not None: |
---|
1989 | for k in range(number_of_points): |
---|
1990 | q_reduced[k] = q[timestep,k] |
---|
1991 | else: |
---|
1992 | for k in range(number_of_points): |
---|
1993 | q_reduced[k] = reduction( q[:,k] ) |
---|
1994 | |
---|
1995 | q = q_reduced |
---|
1996 | |
---|
1997 | #Post condition: Now q has dimension: number_of_points |
---|
1998 | assert len(q.shape) == 1 |
---|
1999 | assert q.shape[0] == number_of_points |
---|
2000 | |
---|
2001 | |
---|
2002 | if verbose: |
---|
2003 | print 'Processed values for %s are in [%f, %f]' %(quantity, min(q), max(q)) |
---|
2004 | |
---|
2005 | |
---|
2006 | #Create grid and update xll/yll corner and x,y |
---|
2007 | |
---|
2008 | #Relative extent |
---|
2009 | if easting_min is None: |
---|
2010 | xmin = min(x) |
---|
2011 | else: |
---|
2012 | xmin = easting_min - xllcorner |
---|
2013 | |
---|
2014 | if easting_max is None: |
---|
2015 | xmax = max(x) |
---|
2016 | else: |
---|
2017 | xmax = easting_max - xllcorner |
---|
2018 | |
---|
2019 | if northing_min is None: |
---|
2020 | ymin = min(y) |
---|
2021 | else: |
---|
2022 | ymin = northing_min - yllcorner |
---|
2023 | |
---|
2024 | if northing_max is None: |
---|
2025 | ymax = max(y) |
---|
2026 | else: |
---|
2027 | ymax = northing_max - yllcorner |
---|
2028 | |
---|
2029 | |
---|
2030 | |
---|
2031 | if verbose: print 'Creating grid' |
---|
2032 | ncols = int((xmax-xmin)/cellsize)+1 |
---|
2033 | nrows = int((ymax-ymin)/cellsize)+1 |
---|
2034 | |
---|
2035 | |
---|
2036 | #New absolute reference and coordinates |
---|
2037 | newxllcorner = xmin+xllcorner |
---|
2038 | newyllcorner = ymin+yllcorner |
---|
2039 | |
---|
2040 | x = x+xllcorner-newxllcorner |
---|
2041 | y = y+yllcorner-newyllcorner |
---|
2042 | |
---|
2043 | vertex_points = concatenate ((x[:, NewAxis] ,y[:, NewAxis]), axis = 1) |
---|
2044 | assert len(vertex_points.shape) == 2 |
---|
2045 | |
---|
2046 | grid_points = zeros ( (ncols*nrows, 2), Float ) |
---|
2047 | |
---|
2048 | |
---|
2049 | for i in xrange(nrows): |
---|
2050 | if format.lower() == 'asc': |
---|
2051 | yg = i*cellsize |
---|
2052 | else: |
---|
2053 | #this will flip the order of the y values for ers |
---|
2054 | yg = (nrows-i)*cellsize |
---|
2055 | |
---|
2056 | for j in xrange(ncols): |
---|
2057 | xg = j*cellsize |
---|
2058 | k = i*ncols + j |
---|
2059 | |
---|
2060 | grid_points[k,0] = xg |
---|
2061 | grid_points[k,1] = yg |
---|
2062 | |
---|
2063 | #Interpolate |
---|
2064 | from anuga.fit_interpolate.interpolate import Interpolate |
---|
2065 | |
---|
2066 | # Remove loners from vertex_points, volumes here |
---|
2067 | vertex_points, volumes = remove_lone_verts(vertex_points, volumes) |
---|
2068 | #export_mesh_file('monkey.tsh',{'vertices':vertex_points, 'triangles':volumes}) |
---|
2069 | #import sys; sys.exit() |
---|
2070 | interp = Interpolate(vertex_points, volumes, verbose = verbose) |
---|
2071 | |
---|
2072 | #Interpolate using quantity values |
---|
2073 | if verbose: print 'Interpolating' |
---|
2074 | grid_values = interp.interpolate(q, grid_points).flat |
---|
2075 | |
---|
2076 | |
---|
2077 | if verbose: |
---|
2078 | print 'Interpolated values are in [%f, %f]' %(min(grid_values), |
---|
2079 | max(grid_values)) |
---|
2080 | |
---|
2081 | #Assign NODATA_value to all points outside bounding polygon (from interpolation mesh) |
---|
2082 | P = interp.mesh.get_boundary_polygon() |
---|
2083 | outside_indices = outside_polygon(grid_points, P, closed=True) |
---|
2084 | |
---|
2085 | for i in outside_indices: |
---|
2086 | grid_values[i] = NODATA_value |
---|
2087 | |
---|
2088 | |
---|
2089 | |
---|
2090 | |
---|
2091 | if format.lower() == 'ers': |
---|
2092 | # setup ERS header information |
---|
2093 | grid_values = reshape(grid_values,(nrows, ncols)) |
---|
2094 | header = {} |
---|
2095 | header['datum'] = '"' + datum + '"' |
---|
2096 | # FIXME The use of hardwired UTM and zone number needs to be made optional |
---|
2097 | # FIXME Also need an automatic test for coordinate type (i.e. EN or LL) |
---|
2098 | header['projection'] = '"UTM-' + str(zone) + '"' |
---|
2099 | header['coordinatetype'] = 'EN' |
---|
2100 | if header['coordinatetype'] == 'LL': |
---|
2101 | header['longitude'] = str(newxllcorner) |
---|
2102 | header['latitude'] = str(newyllcorner) |
---|
2103 | elif header['coordinatetype'] == 'EN': |
---|
2104 | header['eastings'] = str(newxllcorner) |
---|
2105 | header['northings'] = str(newyllcorner) |
---|
2106 | header['nullcellvalue'] = str(NODATA_value) |
---|
2107 | header['xdimension'] = str(cellsize) |
---|
2108 | header['ydimension'] = str(cellsize) |
---|
2109 | header['value'] = '"' + quantity + '"' |
---|
2110 | #header['celltype'] = 'IEEE8ByteReal' #FIXME: Breaks unit test |
---|
2111 | |
---|
2112 | |
---|
2113 | #Write |
---|
2114 | if verbose: print 'Writing %s' %demfile |
---|
2115 | import ermapper_grids |
---|
2116 | ermapper_grids.write_ermapper_grid(demfile, grid_values, header) |
---|
2117 | |
---|
2118 | fid.close() |
---|
2119 | else: |
---|
2120 | #Write to Ascii format |
---|
2121 | |
---|
2122 | #Write prj file |
---|
2123 | prjfile = basename_out + '.prj' |
---|
2124 | |
---|
2125 | if verbose: print 'Writing %s' %prjfile |
---|
2126 | prjid = open(prjfile, 'w') |
---|
2127 | prjid.write('Projection %s\n' %'UTM') |
---|
2128 | prjid.write('Zone %d\n' %zone) |
---|
2129 | prjid.write('Datum %s\n' %datum) |
---|
2130 | prjid.write('Zunits NO\n') |
---|
2131 | prjid.write('Units METERS\n') |
---|
2132 | prjid.write('Spheroid %s\n' %datum) |
---|
2133 | prjid.write('Xshift %d\n' %false_easting) |
---|
2134 | prjid.write('Yshift %d\n' %false_northing) |
---|
2135 | prjid.write('Parameters\n') |
---|
2136 | prjid.close() |
---|
2137 | |
---|
2138 | |
---|
2139 | |
---|
2140 | if verbose: print 'Writing %s' %demfile |
---|
2141 | |
---|
2142 | ascid = open(demfile, 'w') |
---|
2143 | |
---|
2144 | ascid.write('ncols %d\n' %ncols) |
---|
2145 | ascid.write('nrows %d\n' %nrows) |
---|
2146 | ascid.write('xllcorner %d\n' %newxllcorner) |
---|
2147 | ascid.write('yllcorner %d\n' %newyllcorner) |
---|
2148 | ascid.write('cellsize %f\n' %cellsize) |
---|
2149 | ascid.write('NODATA_value %d\n' %NODATA_value) |
---|
2150 | |
---|
2151 | |
---|
2152 | #Get bounding polygon from mesh |
---|
2153 | #P = interp.mesh.get_boundary_polygon() |
---|
2154 | #inside_indices = inside_polygon(grid_points, P) |
---|
2155 | |
---|
2156 | for i in range(nrows): |
---|
2157 | if verbose and i%((nrows+10)/10)==0: |
---|
2158 | print 'Doing row %d of %d' %(i, nrows) |
---|
2159 | |
---|
2160 | base_index = (nrows-i-1)*ncols |
---|
2161 | |
---|
2162 | slice = grid_values[base_index:base_index+ncols] |
---|
2163 | s = array2string(slice, max_line_width=sys.maxint) |
---|
2164 | ascid.write(s[1:-1] + '\n') |
---|
2165 | |
---|
2166 | |
---|
2167 | #print |
---|
2168 | #for j in range(ncols): |
---|
2169 | # index = base_index+j# |
---|
2170 | # print grid_values[index], |
---|
2171 | # ascid.write('%f ' %grid_values[index]) |
---|
2172 | #ascid.write('\n') |
---|
2173 | |
---|
2174 | #Close |
---|
2175 | ascid.close() |
---|
2176 | fid.close() |
---|
2177 | return basename_out |
---|
2178 | |
---|
2179 | #Backwards compatibility |
---|
2180 | def sww2asc(basename_in, basename_out = None, |
---|
2181 | quantity = None, |
---|
2182 | timestep = None, |
---|
2183 | reduction = None, |
---|
2184 | cellsize = 10, |
---|
2185 | verbose = False, |
---|
2186 | origin = None): |
---|
2187 | print 'sww2asc will soon be obsoleted - please use sww2dem' |
---|
2188 | sww2dem(basename_in, |
---|
2189 | basename_out = basename_out, |
---|
2190 | quantity = quantity, |
---|
2191 | timestep = timestep, |
---|
2192 | reduction = reduction, |
---|
2193 | cellsize = cellsize, |
---|
2194 | verbose = verbose, |
---|
2195 | origin = origin, |
---|
2196 | datum = 'WGS84', |
---|
2197 | format = 'asc') |
---|
2198 | |
---|
2199 | def sww2ers(basename_in, basename_out = None, |
---|
2200 | quantity = None, |
---|
2201 | timestep = None, |
---|
2202 | reduction = None, |
---|
2203 | cellsize = 10, |
---|
2204 | verbose = False, |
---|
2205 | origin = None, |
---|
2206 | datum = 'WGS84'): |
---|
2207 | print 'sww2ers will soon be obsoleted - please use sww2dem' |
---|
2208 | sww2dem(basename_in, |
---|
2209 | basename_out = basename_out, |
---|
2210 | quantity = quantity, |
---|
2211 | timestep = timestep, |
---|
2212 | reduction = reduction, |
---|
2213 | cellsize = cellsize, |
---|
2214 | verbose = verbose, |
---|
2215 | origin = origin, |
---|
2216 | datum = datum, |
---|
2217 | format = 'ers') |
---|
2218 | ################################# END COMPATIBILITY ############## |
---|
2219 | |
---|
2220 | |
---|
2221 | |
---|
2222 | def sww2pts(basename_in, basename_out=None, |
---|
2223 | data_points=None, |
---|
2224 | quantity=None, |
---|
2225 | timestep=None, |
---|
2226 | reduction=None, |
---|
2227 | NODATA_value=-9999, |
---|
2228 | verbose=False, |
---|
2229 | origin=None): |
---|
2230 | #datum = 'WGS84') |
---|
2231 | |
---|
2232 | |
---|
2233 | """Read SWW file and convert to interpolated values at selected points |
---|
2234 | |
---|
2235 | The parameter quantity' must be the name of an existing quantity or |
---|
2236 | an expression involving existing quantities. The default is |
---|
2237 | 'elevation'. |
---|
2238 | |
---|
2239 | if timestep (an index) is given, output quantity at that timestep |
---|
2240 | |
---|
2241 | if reduction is given use that to reduce quantity over all timesteps. |
---|
2242 | |
---|
2243 | data_points (Nx2 array) give locations of points where quantity is to be computed. |
---|
2244 | |
---|
2245 | """ |
---|
2246 | |
---|
2247 | import sys |
---|
2248 | from Numeric import array, Float, concatenate, NewAxis, zeros, reshape, sometrue |
---|
2249 | from Numeric import array2string |
---|
2250 | |
---|
2251 | from anuga.utilities.polygon import inside_polygon, outside_polygon, separate_points_by_polygon |
---|
2252 | from anuga.abstract_2d_finite_volumes.util import apply_expression_to_dictionary |
---|
2253 | |
---|
2254 | from anuga.geospatial_data.geospatial_data import Geospatial_data |
---|
2255 | |
---|
2256 | if quantity is None: |
---|
2257 | quantity = 'elevation' |
---|
2258 | |
---|
2259 | if reduction is None: |
---|
2260 | reduction = max |
---|
2261 | |
---|
2262 | if basename_out is None: |
---|
2263 | basename_out = basename_in + '_%s' %quantity |
---|
2264 | |
---|
2265 | swwfile = basename_in + '.sww' |
---|
2266 | ptsfile = basename_out + '.pts' |
---|
2267 | |
---|
2268 | # Read sww file |
---|
2269 | if verbose: print 'Reading from %s' %swwfile |
---|
2270 | from Scientific.IO.NetCDF import NetCDFFile |
---|
2271 | fid = NetCDFFile(swwfile) |
---|
2272 | |
---|
2273 | # Get extent and reference |
---|
2274 | x = fid.variables['x'][:] |
---|
2275 | y = fid.variables['y'][:] |
---|
2276 | volumes = fid.variables['volumes'][:] |
---|
2277 | |
---|
2278 | number_of_timesteps = fid.dimensions['number_of_timesteps'] |
---|
2279 | number_of_points = fid.dimensions['number_of_points'] |
---|
2280 | if origin is None: |
---|
2281 | |
---|
2282 | # Get geo_reference |
---|
2283 | # sww files don't have to have a geo_ref |
---|
2284 | try: |
---|
2285 | geo_reference = Geo_reference(NetCDFObject=fid) |
---|
2286 | except AttributeError, e: |
---|
2287 | geo_reference = Geo_reference() #Default georef object |
---|
2288 | |
---|
2289 | xllcorner = geo_reference.get_xllcorner() |
---|
2290 | yllcorner = geo_reference.get_yllcorner() |
---|
2291 | zone = geo_reference.get_zone() |
---|
2292 | else: |
---|
2293 | zone = origin[0] |
---|
2294 | xllcorner = origin[1] |
---|
2295 | yllcorner = origin[2] |
---|
2296 | |
---|
2297 | |
---|
2298 | |
---|
2299 | # FIXME: Refactor using code from file_function.statistics |
---|
2300 | # Something like print swwstats(swwname) |
---|
2301 | if verbose: |
---|
2302 | x = fid.variables['x'][:] |
---|
2303 | y = fid.variables['y'][:] |
---|
2304 | times = fid.variables['time'][:] |
---|
2305 | print '------------------------------------------------' |
---|
2306 | print 'Statistics of SWW file:' |
---|
2307 | print ' Name: %s' %swwfile |
---|
2308 | print ' Reference:' |
---|
2309 | print ' Lower left corner: [%f, %f]'\ |
---|
2310 | %(xllcorner, yllcorner) |
---|
2311 | print ' Start time: %f' %fid.starttime[0] |
---|
2312 | print ' Extent:' |
---|
2313 | print ' x [m] in [%f, %f], len(x) == %d'\ |
---|
2314 | %(min(x.flat), max(x.flat), len(x.flat)) |
---|
2315 | print ' y [m] in [%f, %f], len(y) == %d'\ |
---|
2316 | %(min(y.flat), max(y.flat), len(y.flat)) |
---|
2317 | print ' t [s] in [%f, %f], len(t) == %d'\ |
---|
2318 | %(min(times), max(times), len(times)) |
---|
2319 | print ' Quantities [SI units]:' |
---|
2320 | for name in ['stage', 'xmomentum', 'ymomentum', 'elevation']: |
---|
2321 | q = fid.variables[name][:].flat |
---|
2322 | print ' %s in [%f, %f]' %(name, min(q), max(q)) |
---|
2323 | |
---|
2324 | |
---|
2325 | |
---|
2326 | # Get quantity and reduce if applicable |
---|
2327 | if verbose: print 'Processing quantity %s' %quantity |
---|
2328 | |
---|
2329 | # Turn NetCDF objects into Numeric arrays |
---|
2330 | quantity_dict = {} |
---|
2331 | for name in fid.variables.keys(): |
---|
2332 | quantity_dict[name] = fid.variables[name][:] |
---|
2333 | |
---|
2334 | |
---|
2335 | |
---|
2336 | # Convert quantity expression to quantities found in sww file |
---|
2337 | q = apply_expression_to_dictionary(quantity, quantity_dict) |
---|
2338 | |
---|
2339 | |
---|
2340 | |
---|
2341 | if len(q.shape) == 2: |
---|
2342 | # q has a time component and needs to be reduced along |
---|
2343 | # the temporal dimension |
---|
2344 | if verbose: print 'Reducing quantity %s' %quantity |
---|
2345 | q_reduced = zeros( number_of_points, Float ) |
---|
2346 | |
---|
2347 | for k in range(number_of_points): |
---|
2348 | q_reduced[k] = reduction( q[:,k] ) |
---|
2349 | |
---|
2350 | q = q_reduced |
---|
2351 | |
---|
2352 | # Post condition: Now q has dimension: number_of_points |
---|
2353 | assert len(q.shape) == 1 |
---|
2354 | assert q.shape[0] == number_of_points |
---|
2355 | |
---|
2356 | |
---|
2357 | if verbose: |
---|
2358 | print 'Processed values for %s are in [%f, %f]' %(quantity, min(q), max(q)) |
---|
2359 | |
---|
2360 | |
---|
2361 | # Create grid and update xll/yll corner and x,y |
---|
2362 | vertex_points = concatenate ((x[:, NewAxis] ,y[:, NewAxis]), axis = 1) |
---|
2363 | assert len(vertex_points.shape) == 2 |
---|
2364 | |
---|
2365 | # Interpolate |
---|
2366 | from anuga.fit_interpolate.interpolate import Interpolate |
---|
2367 | interp = Interpolate(vertex_points, volumes, verbose = verbose) |
---|
2368 | |
---|
2369 | # Interpolate using quantity values |
---|
2370 | if verbose: print 'Interpolating' |
---|
2371 | interpolated_values = interp.interpolate(q, data_points).flat |
---|
2372 | |
---|
2373 | |
---|
2374 | if verbose: |
---|
2375 | print 'Interpolated values are in [%f, %f]' %(min(interpolated_values), |
---|
2376 | max(interpolated_values)) |
---|
2377 | |
---|
2378 | # Assign NODATA_value to all points outside bounding polygon (from interpolation mesh) |
---|
2379 | P = interp.mesh.get_boundary_polygon() |
---|
2380 | outside_indices = outside_polygon(data_points, P, closed=True) |
---|
2381 | |
---|
2382 | for i in outside_indices: |
---|
2383 | interpolated_values[i] = NODATA_value |
---|
2384 | |
---|
2385 | |
---|
2386 | # Store results |
---|
2387 | G = Geospatial_data(data_points=data_points, |
---|
2388 | attributes=interpolated_values) |
---|
2389 | |
---|
2390 | G.export_points_file(ptsfile, absolute = True) |
---|
2391 | |
---|
2392 | fid.close() |
---|
2393 | |
---|
2394 | |
---|
2395 | def convert_dem_from_ascii2netcdf(basename_in, basename_out = None, |
---|
2396 | use_cache = False, |
---|
2397 | verbose = False): |
---|
2398 | """Read Digitial Elevation model from the following ASCII format (.asc) |
---|
2399 | |
---|
2400 | Example: |
---|
2401 | |
---|
2402 | ncols 3121 |
---|
2403 | nrows 1800 |
---|
2404 | xllcorner 722000 |
---|
2405 | yllcorner 5893000 |
---|
2406 | cellsize 25 |
---|
2407 | NODATA_value -9999 |
---|
2408 | 138.3698 137.4194 136.5062 135.5558 .......... |
---|
2409 | |
---|
2410 | Convert basename_in + '.asc' to NetCDF format (.dem) |
---|
2411 | mimicking the ASCII format closely. |
---|
2412 | |
---|
2413 | |
---|
2414 | An accompanying file with same basename_in but extension .prj must exist |
---|
2415 | and is used to fix the UTM zone, datum, false northings and eastings. |
---|
2416 | |
---|
2417 | The prj format is assumed to be as |
---|
2418 | |
---|
2419 | Projection UTM |
---|
2420 | Zone 56 |
---|
2421 | Datum WGS84 |
---|
2422 | Zunits NO |
---|
2423 | Units METERS |
---|
2424 | Spheroid WGS84 |
---|
2425 | Xshift 0.0000000000 |
---|
2426 | Yshift 10000000.0000000000 |
---|
2427 | Parameters |
---|
2428 | """ |
---|
2429 | |
---|
2430 | |
---|
2431 | |
---|
2432 | kwargs = {'basename_out': basename_out, 'verbose': verbose} |
---|
2433 | |
---|
2434 | if use_cache is True: |
---|
2435 | from caching import cache |
---|
2436 | result = cache(_convert_dem_from_ascii2netcdf, basename_in, kwargs, |
---|
2437 | dependencies = [basename_in + '.asc'], |
---|
2438 | verbose = verbose) |
---|
2439 | |
---|
2440 | else: |
---|
2441 | result = apply(_convert_dem_from_ascii2netcdf, [basename_in], kwargs) |
---|
2442 | |
---|
2443 | return result |
---|
2444 | |
---|
2445 | |
---|
2446 | |
---|
2447 | |
---|
2448 | |
---|
2449 | |
---|
2450 | def _convert_dem_from_ascii2netcdf(basename_in, basename_out = None, |
---|
2451 | verbose = False): |
---|
2452 | """Read Digitial Elevation model from the following ASCII format (.asc) |
---|
2453 | |
---|
2454 | Internal function. See public function convert_dem_from_ascii2netcdf for details. |
---|
2455 | """ |
---|
2456 | |
---|
2457 | import os |
---|
2458 | from Scientific.IO.NetCDF import NetCDFFile |
---|
2459 | from Numeric import Float, array |
---|
2460 | |
---|
2461 | #root, ext = os.path.splitext(basename_in) |
---|
2462 | root = basename_in |
---|
2463 | |
---|
2464 | ########################################### |
---|
2465 | # Read Meta data |
---|
2466 | if verbose: print 'Reading METADATA from %s' %root + '.prj' |
---|
2467 | metadatafile = open(root + '.prj') |
---|
2468 | metalines = metadatafile.readlines() |
---|
2469 | metadatafile.close() |
---|
2470 | |
---|
2471 | L = metalines[0].strip().split() |
---|
2472 | assert L[0].strip().lower() == 'projection' |
---|
2473 | projection = L[1].strip() #TEXT |
---|
2474 | |
---|
2475 | L = metalines[1].strip().split() |
---|
2476 | assert L[0].strip().lower() == 'zone' |
---|
2477 | zone = int(L[1].strip()) |
---|
2478 | |
---|
2479 | L = metalines[2].strip().split() |
---|
2480 | assert L[0].strip().lower() == 'datum' |
---|
2481 | datum = L[1].strip() #TEXT |
---|
2482 | |
---|
2483 | L = metalines[3].strip().split() |
---|
2484 | assert L[0].strip().lower() == 'zunits' #IGNORE |
---|
2485 | zunits = L[1].strip() #TEXT |
---|
2486 | |
---|
2487 | L = metalines[4].strip().split() |
---|
2488 | assert L[0].strip().lower() == 'units' |
---|
2489 | units = L[1].strip() #TEXT |
---|
2490 | |
---|
2491 | L = metalines[5].strip().split() |
---|
2492 | assert L[0].strip().lower() == 'spheroid' #IGNORE |
---|
2493 | spheroid = L[1].strip() #TEXT |
---|
2494 | |
---|
2495 | L = metalines[6].strip().split() |
---|
2496 | assert L[0].strip().lower() == 'xshift' |
---|
2497 | false_easting = float(L[1].strip()) |
---|
2498 | |
---|
2499 | L = metalines[7].strip().split() |
---|
2500 | assert L[0].strip().lower() == 'yshift' |
---|
2501 | false_northing = float(L[1].strip()) |
---|
2502 | |
---|
2503 | #print false_easting, false_northing, zone, datum |
---|
2504 | |
---|
2505 | |
---|
2506 | ########################################### |
---|
2507 | #Read DEM data |
---|
2508 | |
---|
2509 | datafile = open(basename_in + '.asc') |
---|
2510 | |
---|
2511 | if verbose: print 'Reading DEM from %s' %(basename_in + '.asc') |
---|
2512 | lines = datafile.readlines() |
---|
2513 | datafile.close() |
---|
2514 | |
---|
2515 | if verbose: print 'Got', len(lines), ' lines' |
---|
2516 | |
---|
2517 | ncols = int(lines[0].split()[1].strip()) |
---|
2518 | nrows = int(lines[1].split()[1].strip()) |
---|
2519 | xllcorner = float(lines[2].split()[1].strip()) |
---|
2520 | yllcorner = float(lines[3].split()[1].strip()) |
---|
2521 | cellsize = float(lines[4].split()[1].strip()) |
---|
2522 | NODATA_value = int(lines[5].split()[1].strip()) |
---|
2523 | |
---|
2524 | assert len(lines) == nrows + 6 |
---|
2525 | |
---|
2526 | |
---|
2527 | ########################################## |
---|
2528 | |
---|
2529 | |
---|
2530 | if basename_out == None: |
---|
2531 | netcdfname = root + '.dem' |
---|
2532 | else: |
---|
2533 | netcdfname = basename_out + '.dem' |
---|
2534 | |
---|
2535 | if verbose: print 'Store to NetCDF file %s' %netcdfname |
---|
2536 | # NetCDF file definition |
---|
2537 | fid = NetCDFFile(netcdfname, 'w') |
---|
2538 | |
---|
2539 | #Create new file |
---|
2540 | fid.institution = 'Geoscience Australia' |
---|
2541 | fid.description = 'NetCDF DEM format for compact and portable storage ' +\ |
---|
2542 | 'of spatial point data' |
---|
2543 | |
---|
2544 | fid.ncols = ncols |
---|
2545 | fid.nrows = nrows |
---|
2546 | fid.xllcorner = xllcorner |
---|
2547 | fid.yllcorner = yllcorner |
---|
2548 | fid.cellsize = cellsize |
---|
2549 | fid.NODATA_value = NODATA_value |
---|
2550 | |
---|
2551 | fid.zone = zone |
---|
2552 | fid.false_easting = false_easting |
---|
2553 | fid.false_northing = false_northing |
---|
2554 | fid.projection = projection |
---|
2555 | fid.datum = datum |
---|
2556 | fid.units = units |
---|
2557 | |
---|
2558 | |
---|
2559 | # dimension definitions |
---|
2560 | fid.createDimension('number_of_rows', nrows) |
---|
2561 | fid.createDimension('number_of_columns', ncols) |
---|
2562 | |
---|
2563 | # variable definitions |
---|
2564 | fid.createVariable('elevation', Float, ('number_of_rows', |
---|
2565 | 'number_of_columns')) |
---|
2566 | |
---|
2567 | # Get handles to the variables |
---|
2568 | elevation = fid.variables['elevation'] |
---|
2569 | |
---|
2570 | #Store data |
---|
2571 | n = len(lines[6:]) |
---|
2572 | for i, line in enumerate(lines[6:]): |
---|
2573 | fields = line.split() |
---|
2574 | if verbose and i%((n+10)/10)==0: |
---|
2575 | print 'Processing row %d of %d' %(i, nrows) |
---|
2576 | |
---|
2577 | elevation[i, :] = array([float(x) for x in fields]) |
---|
2578 | |
---|
2579 | fid.close() |
---|
2580 | |
---|
2581 | |
---|
2582 | |
---|
2583 | |
---|
2584 | |
---|
2585 | def ferret2sww(basename_in, basename_out = None, |
---|
2586 | verbose = False, |
---|
2587 | minlat = None, maxlat = None, |
---|
2588 | minlon = None, maxlon = None, |
---|
2589 | mint = None, maxt = None, mean_stage = 0, |
---|
2590 | origin = None, zscale = 1, |
---|
2591 | fail_on_NaN = True, |
---|
2592 | NaN_filler = 0, |
---|
2593 | elevation = None, |
---|
2594 | inverted_bathymetry = True |
---|
2595 | ): #FIXME: Bathymetry should be obtained |
---|
2596 | #from MOST somehow. |
---|
2597 | #Alternatively from elsewhere |
---|
2598 | #or, as a last resort, |
---|
2599 | #specified here. |
---|
2600 | #The value of -100 will work |
---|
2601 | #for the Wollongong tsunami |
---|
2602 | #scenario but is very hacky |
---|
2603 | """Convert MOST and 'Ferret' NetCDF format for wave propagation to |
---|
2604 | sww format native to abstract_2d_finite_volumes. |
---|
2605 | |
---|
2606 | Specify only basename_in and read files of the form |
---|
2607 | basefilename_ha.nc, basefilename_ua.nc, basefilename_va.nc containing |
---|
2608 | relative height, x-velocity and y-velocity, respectively. |
---|
2609 | |
---|
2610 | Also convert latitude and longitude to UTM. All coordinates are |
---|
2611 | assumed to be given in the GDA94 datum. |
---|
2612 | |
---|
2613 | min's and max's: If omitted - full extend is used. |
---|
2614 | To include a value min may equal it, while max must exceed it. |
---|
2615 | Lat and lon are assuemd to be in decimal degrees |
---|
2616 | |
---|
2617 | origin is a 3-tuple with geo referenced |
---|
2618 | UTM coordinates (zone, easting, northing) |
---|
2619 | |
---|
2620 | nc format has values organised as HA[TIME, LATITUDE, LONGITUDE] |
---|
2621 | which means that longitude is the fastest |
---|
2622 | varying dimension (row major order, so to speak) |
---|
2623 | |
---|
2624 | ferret2sww uses grid points as vertices in a triangular grid |
---|
2625 | counting vertices from lower left corner upwards, then right |
---|
2626 | """ |
---|
2627 | |
---|
2628 | import os |
---|
2629 | from Scientific.IO.NetCDF import NetCDFFile |
---|
2630 | from Numeric import Float, Int, Int32, searchsorted, zeros, array |
---|
2631 | from Numeric import allclose, around |
---|
2632 | |
---|
2633 | precision = Float |
---|
2634 | |
---|
2635 | msg = 'Must use latitudes and longitudes for minlat, maxlon etc' |
---|
2636 | |
---|
2637 | if minlat != None: |
---|
2638 | assert -90 < minlat < 90 , msg |
---|
2639 | if maxlat != None: |
---|
2640 | assert -90 < maxlat < 90 , msg |
---|
2641 | if minlat != None: |
---|
2642 | assert maxlat > minlat |
---|
2643 | if minlon != None: |
---|
2644 | assert -180 < minlon < 180 , msg |
---|
2645 | if maxlon != None: |
---|
2646 | assert -180 < maxlon < 180 , msg |
---|
2647 | if minlon != None: |
---|
2648 | assert maxlon > minlon |
---|
2649 | |
---|
2650 | |
---|
2651 | |
---|
2652 | #Get NetCDF data |
---|
2653 | if verbose: print 'Reading files %s_*.nc' %basename_in |
---|
2654 | #print "basename_in + '_ha.nc'",basename_in + '_ha.nc' |
---|
2655 | file_h = NetCDFFile(basename_in + '_ha.nc', 'r') #Wave amplitude (cm) |
---|
2656 | file_u = NetCDFFile(basename_in + '_ua.nc', 'r') #Velocity (x) (cm/s) |
---|
2657 | file_v = NetCDFFile(basename_in + '_va.nc', 'r') #Velocity (y) (cm/s) |
---|
2658 | file_e = NetCDFFile(basename_in + '_e.nc', 'r') #Elevation (z) (m) |
---|
2659 | |
---|
2660 | if basename_out is None: |
---|
2661 | swwname = basename_in + '.sww' |
---|
2662 | else: |
---|
2663 | swwname = basename_out + '.sww' |
---|
2664 | |
---|
2665 | # Get dimensions of file_h |
---|
2666 | for dimension in file_h.dimensions.keys(): |
---|
2667 | if dimension[:3] == 'LON': |
---|
2668 | dim_h_longitude = dimension |
---|
2669 | if dimension[:3] == 'LAT': |
---|
2670 | dim_h_latitude = dimension |
---|
2671 | if dimension[:4] == 'TIME': |
---|
2672 | dim_h_time = dimension |
---|
2673 | |
---|
2674 | # print 'long:', dim_h_longitude |
---|
2675 | # print 'lats:', dim_h_latitude |
---|
2676 | # print 'times:', dim_h_time |
---|
2677 | |
---|
2678 | times = file_h.variables[dim_h_time] |
---|
2679 | latitudes = file_h.variables[dim_h_latitude] |
---|
2680 | longitudes = file_h.variables[dim_h_longitude] |
---|
2681 | |
---|
2682 | # get dimensions for file_e |
---|
2683 | for dimension in file_e.dimensions.keys(): |
---|
2684 | if dimension[:3] == 'LON': |
---|
2685 | dim_e_longitude = dimension |
---|
2686 | if dimension[:3] == 'LAT': |
---|
2687 | dim_e_latitude = dimension |
---|
2688 | |
---|
2689 | # get dimensions for file_u |
---|
2690 | for dimension in file_u.dimensions.keys(): |
---|
2691 | if dimension[:3] == 'LON': |
---|
2692 | dim_u_longitude = dimension |
---|
2693 | if dimension[:3] == 'LAT': |
---|
2694 | dim_u_latitude = dimension |
---|
2695 | if dimension[:4] == 'TIME': |
---|
2696 | dim_u_time = dimension |
---|
2697 | |
---|
2698 | # get dimensions for file_v |
---|
2699 | for dimension in file_v.dimensions.keys(): |
---|
2700 | if dimension[:3] == 'LON': |
---|
2701 | dim_v_longitude = dimension |
---|
2702 | if dimension[:3] == 'LAT': |
---|
2703 | dim_v_latitude = dimension |
---|
2704 | if dimension[:4] == 'TIME': |
---|
2705 | dim_v_time = dimension |
---|
2706 | |
---|
2707 | |
---|
2708 | # Precision used by most for lat/lon is 4 or 5 decimals |
---|
2709 | e_lat = around(file_e.variables[dim_e_latitude][:], 5) |
---|
2710 | e_lon = around(file_e.variables[dim_e_longitude][:], 5) |
---|
2711 | |
---|
2712 | # Check that files are compatible |
---|
2713 | assert allclose(latitudes, file_u.variables[dim_u_latitude]) |
---|
2714 | assert allclose(latitudes, file_v.variables[dim_v_latitude]) |
---|
2715 | assert allclose(latitudes, e_lat) |
---|
2716 | |
---|
2717 | assert allclose(longitudes, file_u.variables[dim_u_longitude]) |
---|
2718 | assert allclose(longitudes, file_v.variables[dim_v_longitude]) |
---|
2719 | assert allclose(longitudes, e_lon) |
---|
2720 | |
---|
2721 | if mint is None: |
---|
2722 | jmin = 0 |
---|
2723 | mint = times[0] |
---|
2724 | else: |
---|
2725 | jmin = searchsorted(times, mint) |
---|
2726 | |
---|
2727 | if maxt is None: |
---|
2728 | jmax = len(times) |
---|
2729 | maxt = times[-1] |
---|
2730 | else: |
---|
2731 | jmax = searchsorted(times, maxt) |
---|
2732 | |
---|
2733 | #print "latitudes[:]",latitudes[:] |
---|
2734 | #print "longitudes[:]",longitudes [:] |
---|
2735 | kmin, kmax, lmin, lmax = _get_min_max_indexes(latitudes[:], |
---|
2736 | longitudes[:], |
---|
2737 | minlat, maxlat, |
---|
2738 | minlon, maxlon) |
---|
2739 | |
---|
2740 | |
---|
2741 | times = times[jmin:jmax] |
---|
2742 | latitudes = latitudes[kmin:kmax] |
---|
2743 | longitudes = longitudes[lmin:lmax] |
---|
2744 | |
---|
2745 | #print "latitudes[:]",latitudes[:] |
---|
2746 | #print "longitudes[:]",longitudes [:] |
---|
2747 | |
---|
2748 | if verbose: print 'cropping' |
---|
2749 | zname = 'ELEVATION' |
---|
2750 | |
---|
2751 | amplitudes = file_h.variables['HA'][jmin:jmax, kmin:kmax, lmin:lmax] |
---|
2752 | uspeed = file_u.variables['UA'][jmin:jmax, kmin:kmax, lmin:lmax] #Lon |
---|
2753 | vspeed = file_v.variables['VA'][jmin:jmax, kmin:kmax, lmin:lmax] #Lat |
---|
2754 | elevations = file_e.variables[zname][kmin:kmax, lmin:lmax] |
---|
2755 | |
---|
2756 | # if latitudes2[0]==latitudes[0] and latitudes2[-1]==latitudes[-1]: |
---|
2757 | # elevations = file_e.variables['ELEVATION'][kmin:kmax, lmin:lmax] |
---|
2758 | # elif latitudes2[0]==latitudes[-1] and latitudes2[-1]==latitudes[0]: |
---|
2759 | # from Numeric import asarray |
---|
2760 | # elevations=elevations.tolist() |
---|
2761 | # elevations.reverse() |
---|
2762 | # elevations=asarray(elevations) |
---|
2763 | # else: |
---|
2764 | # from Numeric import asarray |
---|
2765 | # elevations=elevations.tolist() |
---|
2766 | # elevations.reverse() |
---|
2767 | # elevations=asarray(elevations) |
---|
2768 | # 'print hmmm' |
---|
2769 | |
---|
2770 | |
---|
2771 | |
---|
2772 | #Get missing values |
---|
2773 | nan_ha = file_h.variables['HA'].missing_value[0] |
---|
2774 | nan_ua = file_u.variables['UA'].missing_value[0] |
---|
2775 | nan_va = file_v.variables['VA'].missing_value[0] |
---|
2776 | if hasattr(file_e.variables[zname],'missing_value'): |
---|
2777 | nan_e = file_e.variables[zname].missing_value[0] |
---|
2778 | else: |
---|
2779 | nan_e = None |
---|
2780 | |
---|
2781 | #Cleanup |
---|
2782 | from Numeric import sometrue |
---|
2783 | |
---|
2784 | missing = (amplitudes == nan_ha) |
---|
2785 | if sometrue (missing): |
---|
2786 | if fail_on_NaN: |
---|
2787 | msg = 'NetCDFFile %s contains missing values'\ |
---|
2788 | %(basename_in+'_ha.nc') |
---|
2789 | raise DataMissingValuesError, msg |
---|
2790 | else: |
---|
2791 | amplitudes = amplitudes*(missing==0) + missing*NaN_filler |
---|
2792 | |
---|
2793 | missing = (uspeed == nan_ua) |
---|
2794 | if sometrue (missing): |
---|
2795 | if fail_on_NaN: |
---|
2796 | msg = 'NetCDFFile %s contains missing values'\ |
---|
2797 | %(basename_in+'_ua.nc') |
---|
2798 | raise DataMissingValuesError, msg |
---|
2799 | else: |
---|
2800 | uspeed = uspeed*(missing==0) + missing*NaN_filler |
---|
2801 | |
---|
2802 | missing = (vspeed == nan_va) |
---|
2803 | if sometrue (missing): |
---|
2804 | if fail_on_NaN: |
---|
2805 | msg = 'NetCDFFile %s contains missing values'\ |
---|
2806 | %(basename_in+'_va.nc') |
---|
2807 | raise DataMissingValuesError, msg |
---|
2808 | else: |
---|
2809 | vspeed = vspeed*(missing==0) + missing*NaN_filler |
---|
2810 | |
---|
2811 | |
---|
2812 | missing = (elevations == nan_e) |
---|
2813 | if sometrue (missing): |
---|
2814 | if fail_on_NaN: |
---|
2815 | msg = 'NetCDFFile %s contains missing values'\ |
---|
2816 | %(basename_in+'_e.nc') |
---|
2817 | raise DataMissingValuesError, msg |
---|
2818 | else: |
---|
2819 | elevations = elevations*(missing==0) + missing*NaN_filler |
---|
2820 | |
---|
2821 | ####### |
---|
2822 | |
---|
2823 | |
---|
2824 | |
---|
2825 | number_of_times = times.shape[0] |
---|
2826 | number_of_latitudes = latitudes.shape[0] |
---|
2827 | number_of_longitudes = longitudes.shape[0] |
---|
2828 | |
---|
2829 | assert amplitudes.shape[0] == number_of_times |
---|
2830 | assert amplitudes.shape[1] == number_of_latitudes |
---|
2831 | assert amplitudes.shape[2] == number_of_longitudes |
---|
2832 | |
---|
2833 | if verbose: |
---|
2834 | print '------------------------------------------------' |
---|
2835 | print 'Statistics:' |
---|
2836 | print ' Extent (lat/lon):' |
---|
2837 | print ' lat in [%f, %f], len(lat) == %d'\ |
---|
2838 | %(min(latitudes.flat), max(latitudes.flat), |
---|
2839 | len(latitudes.flat)) |
---|
2840 | print ' lon in [%f, %f], len(lon) == %d'\ |
---|
2841 | %(min(longitudes.flat), max(longitudes.flat), |
---|
2842 | len(longitudes.flat)) |
---|
2843 | print ' t in [%f, %f], len(t) == %d'\ |
---|
2844 | %(min(times.flat), max(times.flat), len(times.flat)) |
---|
2845 | |
---|
2846 | q = amplitudes.flat |
---|
2847 | name = 'Amplitudes (ha) [cm]' |
---|
2848 | print ' %s in [%f, %f]' %(name, min(q), max(q)) |
---|
2849 | |
---|
2850 | q = uspeed.flat |
---|
2851 | name = 'Speeds (ua) [cm/s]' |
---|
2852 | print ' %s in [%f, %f]' %(name, min(q), max(q)) |
---|
2853 | |
---|
2854 | q = vspeed.flat |
---|
2855 | name = 'Speeds (va) [cm/s]' |
---|
2856 | print ' %s in [%f, %f]' %(name, min(q), max(q)) |
---|
2857 | |
---|
2858 | q = elevations.flat |
---|
2859 | name = 'Elevations (e) [m]' |
---|
2860 | print ' %s in [%f, %f]' %(name, min(q), max(q)) |
---|
2861 | |
---|
2862 | |
---|
2863 | #print number_of_latitudes, number_of_longitudes |
---|
2864 | number_of_points = number_of_latitudes*number_of_longitudes |
---|
2865 | number_of_volumes = (number_of_latitudes-1)*(number_of_longitudes-1)*2 |
---|
2866 | |
---|
2867 | |
---|
2868 | file_h.close() |
---|
2869 | file_u.close() |
---|
2870 | file_v.close() |
---|
2871 | file_e.close() |
---|
2872 | |
---|
2873 | |
---|
2874 | # NetCDF file definition |
---|
2875 | outfile = NetCDFFile(swwname, 'w') |
---|
2876 | |
---|
2877 | description = 'Converted from Ferret files: %s, %s, %s, %s'\ |
---|
2878 | %(basename_in + '_ha.nc', |
---|
2879 | basename_in + '_ua.nc', |
---|
2880 | basename_in + '_va.nc', |
---|
2881 | basename_in + '_e.nc') |
---|
2882 | |
---|
2883 | #Create new file |
---|
2884 | starttime = times[0] |
---|
2885 | sww = Write_sww() |
---|
2886 | sww.header(outfile, times, number_of_volumes, |
---|
2887 | number_of_points, description=description, |
---|
2888 | verbose=verbose) |
---|
2889 | |
---|
2890 | #Store |
---|
2891 | from anuga.coordinate_transforms.redfearn import redfearn |
---|
2892 | x = zeros(number_of_points, Float) #Easting |
---|
2893 | y = zeros(number_of_points, Float) #Northing |
---|
2894 | |
---|
2895 | |
---|
2896 | if verbose: print 'Making triangular grid' |
---|
2897 | #Check zone boundaries |
---|
2898 | refzone, _, _ = redfearn(latitudes[0],longitudes[0]) |
---|
2899 | |
---|
2900 | vertices = {} |
---|
2901 | i = 0 |
---|
2902 | for k, lat in enumerate(latitudes): #Y direction |
---|
2903 | for l, lon in enumerate(longitudes): #X direction |
---|
2904 | |
---|
2905 | vertices[l,k] = i |
---|
2906 | |
---|
2907 | zone, easting, northing = redfearn(lat,lon) |
---|
2908 | |
---|
2909 | msg = 'Zone boundary crossed at longitude =', lon |
---|
2910 | #assert zone == refzone, msg |
---|
2911 | #print '%7.2f %7.2f %8.2f %8.2f' %(lon, lat, easting, northing) |
---|
2912 | x[i] = easting |
---|
2913 | y[i] = northing |
---|
2914 | i += 1 |
---|
2915 | |
---|
2916 | #Construct 2 triangles per 'rectangular' element |
---|
2917 | volumes = [] |
---|
2918 | for l in range(number_of_longitudes-1): #X direction |
---|
2919 | for k in range(number_of_latitudes-1): #Y direction |
---|
2920 | v1 = vertices[l,k+1] |
---|
2921 | v2 = vertices[l,k] |
---|
2922 | v3 = vertices[l+1,k+1] |
---|
2923 | v4 = vertices[l+1,k] |
---|
2924 | |
---|
2925 | volumes.append([v1,v2,v3]) #Upper element |
---|
2926 | volumes.append([v4,v3,v2]) #Lower element |
---|
2927 | |
---|
2928 | volumes = array(volumes) |
---|
2929 | |
---|
2930 | if origin is None: |
---|
2931 | origin = Geo_reference(refzone,min(x),min(y)) |
---|
2932 | geo_ref = write_NetCDF_georeference(origin, outfile) |
---|
2933 | |
---|
2934 | if elevation is not None: |
---|
2935 | z = elevation |
---|
2936 | else: |
---|
2937 | if inverted_bathymetry: |
---|
2938 | z = -1*elevations |
---|
2939 | else: |
---|
2940 | z = elevations |
---|
2941 | #FIXME: z should be obtained from MOST and passed in here |
---|
2942 | |
---|
2943 | from Numeric import resize |
---|
2944 | z = resize(z,outfile.variables['z'][:].shape) |
---|
2945 | outfile.variables['x'][:] = x - geo_ref.get_xllcorner() |
---|
2946 | outfile.variables['y'][:] = y - geo_ref.get_yllcorner() |
---|
2947 | outfile.variables['z'][:] = z #FIXME HACK for bacwards compat. |
---|
2948 | outfile.variables['elevation'][:] = z |
---|
2949 | outfile.variables['volumes'][:] = volumes.astype(Int32) #For Opteron 64 |
---|
2950 | |
---|
2951 | |
---|
2952 | |
---|
2953 | #Time stepping |
---|
2954 | stage = outfile.variables['stage'] |
---|
2955 | xmomentum = outfile.variables['xmomentum'] |
---|
2956 | ymomentum = outfile.variables['ymomentum'] |
---|
2957 | |
---|
2958 | if verbose: print 'Converting quantities' |
---|
2959 | n = len(times) |
---|
2960 | for j in range(n): |
---|
2961 | if verbose and j%((n+10)/10)==0: print ' Doing %d of %d' %(j, n) |
---|
2962 | i = 0 |
---|
2963 | for k in range(number_of_latitudes): #Y direction |
---|
2964 | for l in range(number_of_longitudes): #X direction |
---|
2965 | w = zscale*amplitudes[j,k,l]/100 + mean_stage |
---|
2966 | stage[j,i] = w |
---|
2967 | h = w - z[i] |
---|
2968 | xmomentum[j,i] = uspeed[j,k,l]/100*h |
---|
2969 | ymomentum[j,i] = vspeed[j,k,l]/100*h |
---|
2970 | i += 1 |
---|
2971 | |
---|
2972 | #outfile.close() |
---|
2973 | |
---|
2974 | #FIXME: Refactor using code from file_function.statistics |
---|
2975 | #Something like print swwstats(swwname) |
---|
2976 | if verbose: |
---|
2977 | x = outfile.variables['x'][:] |
---|
2978 | y = outfile.variables['y'][:] |
---|
2979 | print '------------------------------------------------' |
---|
2980 | print 'Statistics of output file:' |
---|
2981 | print ' Name: %s' %swwname |
---|
2982 | print ' Reference:' |
---|
2983 | print ' Lower left corner: [%f, %f]'\ |
---|
2984 | %(geo_ref.get_xllcorner(), geo_ref.get_yllcorner()) |
---|
2985 | print ' Start time: %f' %starttime |
---|
2986 | print ' Min time: %f' %mint |
---|
2987 | print ' Max time: %f' %maxt |
---|
2988 | print ' Extent:' |
---|
2989 | print ' x [m] in [%f, %f], len(x) == %d'\ |
---|
2990 | %(min(x.flat), max(x.flat), len(x.flat)) |
---|
2991 | print ' y [m] in [%f, %f], len(y) == %d'\ |
---|
2992 | %(min(y.flat), max(y.flat), len(y.flat)) |
---|
2993 | print ' t [s] in [%f, %f], len(t) == %d'\ |
---|
2994 | %(min(times), max(times), len(times)) |
---|
2995 | print ' Quantities [SI units]:' |
---|
2996 | for name in ['stage', 'xmomentum', 'ymomentum', 'elevation']: |
---|
2997 | q = outfile.variables[name][:].flat |
---|
2998 | print ' %s in [%f, %f]' %(name, min(q), max(q)) |
---|
2999 | |
---|
3000 | |
---|
3001 | |
---|
3002 | outfile.close() |
---|
3003 | |
---|
3004 | |
---|
3005 | |
---|
3006 | |
---|
3007 | |
---|
3008 | def timefile2netcdf(filename, quantity_names=None, time_as_seconds=False): |
---|
3009 | """Template for converting typical text files with time series to |
---|
3010 | NetCDF tms file. |
---|
3011 | |
---|
3012 | |
---|
3013 | The file format is assumed to be either two fields separated by a comma: |
---|
3014 | |
---|
3015 | time [DD/MM/YY hh:mm:ss], value0 value1 value2 ... |
---|
3016 | |
---|
3017 | E.g |
---|
3018 | |
---|
3019 | 31/08/04 00:00:00, 1.328223 0 0 |
---|
3020 | 31/08/04 00:15:00, 1.292912 0 0 |
---|
3021 | |
---|
3022 | or time (seconds), value0 value1 value2 ... |
---|
3023 | |
---|
3024 | 0.0, 1.328223 0 0 |
---|
3025 | 0.1, 1.292912 0 0 |
---|
3026 | |
---|
3027 | will provide a time dependent function f(t) with three attributes |
---|
3028 | |
---|
3029 | filename is assumed to be the rootname with extenisons .txt and .sww |
---|
3030 | """ |
---|
3031 | |
---|
3032 | import time, calendar |
---|
3033 | from Numeric import array |
---|
3034 | from anuga.config import time_format |
---|
3035 | from anuga.utilities.numerical_tools import ensure_numeric |
---|
3036 | |
---|
3037 | |
---|
3038 | |
---|
3039 | fid = open(filename + '.txt') |
---|
3040 | line = fid.readline() |
---|
3041 | fid.close() |
---|
3042 | |
---|
3043 | fields = line.split(',') |
---|
3044 | msg = 'File %s must have the format date, value0 value1 value2 ...' |
---|
3045 | assert len(fields) == 2, msg |
---|
3046 | |
---|
3047 | if not time_as_seconds: |
---|
3048 | try: |
---|
3049 | starttime = calendar.timegm(time.strptime(fields[0], time_format)) |
---|
3050 | except ValueError: |
---|
3051 | msg = 'First field in file %s must be' %filename |
---|
3052 | msg += ' date-time with format %s.\n' %time_format |
---|
3053 | msg += 'I got %s instead.' %fields[0] |
---|
3054 | raise DataTimeError, msg |
---|
3055 | else: |
---|
3056 | try: |
---|
3057 | starttime = float(fields[0]) |
---|
3058 | except Error: |
---|
3059 | msg = "Bad time format" |
---|
3060 | raise DataTimeError, msg |
---|
3061 | |
---|
3062 | |
---|
3063 | #Split values |
---|
3064 | values = [] |
---|
3065 | for value in fields[1].split(): |
---|
3066 | values.append(float(value)) |
---|
3067 | |
---|
3068 | q = ensure_numeric(values) |
---|
3069 | |
---|
3070 | msg = 'ERROR: File must contain at least one independent value' |
---|
3071 | assert len(q.shape) == 1, msg |
---|
3072 | |
---|
3073 | |
---|
3074 | |
---|
3075 | #Read times proper |
---|
3076 | from Numeric import zeros, Float, alltrue |
---|
3077 | from anuga.config import time_format |
---|
3078 | import time, calendar |
---|
3079 | |
---|
3080 | fid = open(filename + '.txt') |
---|
3081 | lines = fid.readlines() |
---|
3082 | fid.close() |
---|
3083 | |
---|
3084 | N = len(lines) |
---|
3085 | d = len(q) |
---|
3086 | |
---|
3087 | T = zeros(N, Float) #Time |
---|
3088 | Q = zeros((N, d), Float) #Values |
---|
3089 | |
---|
3090 | for i, line in enumerate(lines): |
---|
3091 | fields = line.split(',') |
---|
3092 | if not time_as_seconds: |
---|
3093 | realtime = calendar.timegm(time.strptime(fields[0], time_format)) |
---|
3094 | else: |
---|
3095 | realtime = float(fields[0]) |
---|
3096 | T[i] = realtime - starttime |
---|
3097 | |
---|
3098 | for j, value in enumerate(fields[1].split()): |
---|
3099 | Q[i, j] = float(value) |
---|
3100 | |
---|
3101 | msg = 'File %s must list time as a monotonuosly ' %filename |
---|
3102 | msg += 'increasing sequence' |
---|
3103 | assert alltrue( T[1:] - T[:-1] > 0 ), msg |
---|
3104 | |
---|
3105 | #Create NetCDF file |
---|
3106 | from Scientific.IO.NetCDF import NetCDFFile |
---|
3107 | |
---|
3108 | fid = NetCDFFile(filename + '.tms', 'w') |
---|
3109 | |
---|
3110 | |
---|
3111 | fid.institution = 'Geoscience Australia' |
---|
3112 | fid.description = 'Time series' |
---|
3113 | |
---|
3114 | |
---|
3115 | #Reference point |
---|
3116 | #Start time in seconds since the epoch (midnight 1/1/1970) |
---|
3117 | #FIXME: Use Georef |
---|
3118 | fid.starttime = starttime |
---|
3119 | |
---|
3120 | # dimension definitions |
---|
3121 | #fid.createDimension('number_of_volumes', self.number_of_volumes) |
---|
3122 | #fid.createDimension('number_of_vertices', 3) |
---|
3123 | |
---|
3124 | |
---|
3125 | fid.createDimension('number_of_timesteps', len(T)) |
---|
3126 | |
---|
3127 | fid.createVariable('time', Float, ('number_of_timesteps',)) |
---|
3128 | |
---|
3129 | fid.variables['time'][:] = T |
---|
3130 | |
---|
3131 | for i in range(Q.shape[1]): |
---|
3132 | try: |
---|
3133 | name = quantity_names[i] |
---|
3134 | except: |
---|
3135 | name = 'Attribute%d'%i |
---|
3136 | |
---|
3137 | fid.createVariable(name, Float, ('number_of_timesteps',)) |
---|
3138 | fid.variables[name][:] = Q[:,i] |
---|
3139 | |
---|
3140 | fid.close() |
---|
3141 | |
---|
3142 | |
---|
3143 | def extent_sww(file_name): |
---|
3144 | """ |
---|
3145 | Read in an sww file. |
---|
3146 | |
---|
3147 | Input; |
---|
3148 | file_name - the sww file |
---|
3149 | |
---|
3150 | Output; |
---|
3151 | z - Vector of bed elevation |
---|
3152 | volumes - Array. Each row has 3 values, representing |
---|
3153 | the vertices that define the volume |
---|
3154 | time - Vector of the times where there is stage information |
---|
3155 | stage - array with respect to time and vertices (x,y) |
---|
3156 | """ |
---|
3157 | |
---|
3158 | |
---|
3159 | from Scientific.IO.NetCDF import NetCDFFile |
---|
3160 | |
---|
3161 | #Check contents |
---|
3162 | #Get NetCDF |
---|
3163 | fid = NetCDFFile(file_name, 'r') |
---|
3164 | |
---|
3165 | # Get the variables |
---|
3166 | x = fid.variables['x'][:] |
---|
3167 | y = fid.variables['y'][:] |
---|
3168 | stage = fid.variables['stage'][:] |
---|
3169 | #print "stage",stage |
---|
3170 | #print "stage.shap",stage.shape |
---|
3171 | #print "min(stage.flat), mpythonax(stage.flat)",min(stage.flat), max(stage.flat) |
---|
3172 | #print "min(stage)",min(stage) |
---|
3173 | |
---|
3174 | fid.close() |
---|
3175 | |
---|
3176 | return [min(x),max(x),min(y),max(y),min(stage.flat),max(stage.flat)] |
---|
3177 | |
---|
3178 | |
---|
3179 | def sww2domain(filename,boundary=None,t=None,\ |
---|
3180 | fail_if_NaN=True,NaN_filler=0\ |
---|
3181 | ,verbose = False,very_verbose = False): |
---|
3182 | """ |
---|
3183 | Usage: domain = sww2domain('file.sww',t=time (default = last time in file)) |
---|
3184 | |
---|
3185 | Boundary is not recommended if domain.smooth is not selected, as it |
---|
3186 | uses unique coordinates, but not unique boundaries. This means that |
---|
3187 | the boundary file will not be compatable with the coordinates, and will |
---|
3188 | give a different final boundary, or crash. |
---|
3189 | """ |
---|
3190 | NaN=9.969209968386869e+036 |
---|
3191 | #initialise NaN. |
---|
3192 | |
---|
3193 | from Scientific.IO.NetCDF import NetCDFFile |
---|
3194 | from shallow_water import Domain |
---|
3195 | from Numeric import asarray, transpose, resize |
---|
3196 | |
---|
3197 | if verbose: print 'Reading from ', filename |
---|
3198 | fid = NetCDFFile(filename, 'r') #Open existing file for read |
---|
3199 | time = fid.variables['time'] #Timesteps |
---|
3200 | if t is None: |
---|
3201 | t = time[-1] |
---|
3202 | time_interp = get_time_interp(time,t) |
---|
3203 | |
---|
3204 | # Get the variables as Numeric arrays |
---|
3205 | x = fid.variables['x'][:] #x-coordinates of vertices |
---|
3206 | y = fid.variables['y'][:] #y-coordinates of vertices |
---|
3207 | elevation = fid.variables['elevation'] #Elevation |
---|
3208 | stage = fid.variables['stage'] #Water level |
---|
3209 | xmomentum = fid.variables['xmomentum'] #Momentum in the x-direction |
---|
3210 | ymomentum = fid.variables['ymomentum'] #Momentum in the y-direction |
---|
3211 | |
---|
3212 | starttime = fid.starttime[0] |
---|
3213 | volumes = fid.variables['volumes'][:] #Connectivity |
---|
3214 | coordinates=transpose(asarray([x.tolist(),y.tolist()])) |
---|
3215 | |
---|
3216 | conserved_quantities = [] |
---|
3217 | interpolated_quantities = {} |
---|
3218 | other_quantities = [] |
---|
3219 | |
---|
3220 | # get geo_reference |
---|
3221 | #sww files don't have to have a geo_ref |
---|
3222 | try: |
---|
3223 | geo_reference = Geo_reference(NetCDFObject=fid) |
---|
3224 | except: #AttributeError, e: |
---|
3225 | geo_reference = None |
---|
3226 | |
---|
3227 | if verbose: print ' getting quantities' |
---|
3228 | for quantity in fid.variables.keys(): |
---|
3229 | dimensions = fid.variables[quantity].dimensions |
---|
3230 | if 'number_of_timesteps' in dimensions: |
---|
3231 | conserved_quantities.append(quantity) |
---|
3232 | interpolated_quantities[quantity]=\ |
---|
3233 | interpolated_quantity(fid.variables[quantity][:],time_interp) |
---|
3234 | else: other_quantities.append(quantity) |
---|
3235 | |
---|
3236 | other_quantities.remove('x') |
---|
3237 | other_quantities.remove('y') |
---|
3238 | other_quantities.remove('z') |
---|
3239 | other_quantities.remove('volumes') |
---|
3240 | try: |
---|
3241 | other_quantities.remove('stage_range') |
---|
3242 | other_quantities.remove('xmomentum_range') |
---|
3243 | other_quantities.remove('ymomentum_range') |
---|
3244 | other_quantities.remove('elevation_range') |
---|
3245 | except: |
---|
3246 | pass |
---|
3247 | |
---|
3248 | |
---|
3249 | conserved_quantities.remove('time') |
---|
3250 | |
---|
3251 | if verbose: print ' building domain' |
---|
3252 | # From domain.Domain: |
---|
3253 | # domain = Domain(coordinates, volumes,\ |
---|
3254 | # conserved_quantities = conserved_quantities,\ |
---|
3255 | # other_quantities = other_quantities,zone=zone,\ |
---|
3256 | # xllcorner=xllcorner, yllcorner=yllcorner) |
---|
3257 | |
---|
3258 | # From shallow_water.Domain: |
---|
3259 | coordinates=coordinates.tolist() |
---|
3260 | volumes=volumes.tolist() |
---|
3261 | #FIXME:should this be in mesh?(peter row) |
---|
3262 | if fid.smoothing == 'Yes': unique = False |
---|
3263 | else: unique = True |
---|
3264 | if unique: |
---|
3265 | coordinates,volumes,boundary=weed(coordinates,volumes,boundary) |
---|
3266 | |
---|
3267 | |
---|
3268 | try: |
---|
3269 | domain = Domain(coordinates, volumes, boundary) |
---|
3270 | except AssertionError, e: |
---|
3271 | fid.close() |
---|
3272 | msg = 'Domain could not be created: %s. Perhaps use "fail_if_NaN=False and NaN_filler = ..."' %e |
---|
3273 | raise DataDomainError, msg |
---|
3274 | |
---|
3275 | if not boundary is None: |
---|
3276 | domain.boundary = boundary |
---|
3277 | |
---|
3278 | domain.geo_reference = geo_reference |
---|
3279 | |
---|
3280 | domain.starttime=float(starttime)+float(t) |
---|
3281 | domain.time=0.0 |
---|
3282 | |
---|
3283 | for quantity in other_quantities: |
---|
3284 | try: |
---|
3285 | NaN = fid.variables[quantity].missing_value |
---|
3286 | except: |
---|
3287 | pass #quantity has no missing_value number |
---|
3288 | X = fid.variables[quantity][:] |
---|
3289 | if very_verbose: |
---|
3290 | print ' ',quantity |
---|
3291 | print ' NaN =',NaN |
---|
3292 | print ' max(X)' |
---|
3293 | print ' ',max(X) |
---|
3294 | print ' max(X)==NaN' |
---|
3295 | print ' ',max(X)==NaN |
---|
3296 | print '' |
---|
3297 | if (max(X)==NaN) or (min(X)==NaN): |
---|
3298 | if fail_if_NaN: |
---|
3299 | msg = 'quantity "%s" contains no_data entry'%quantity |
---|
3300 | raise DataMissingValuesError, msg |
---|
3301 | else: |
---|
3302 | data = (X<>NaN) |
---|
3303 | X = (X*data)+(data==0)*NaN_filler |
---|
3304 | if unique: |
---|
3305 | X = resize(X,(len(X)/3,3)) |
---|
3306 | domain.set_quantity(quantity,X) |
---|
3307 | # |
---|
3308 | for quantity in conserved_quantities: |
---|
3309 | try: |
---|
3310 | NaN = fid.variables[quantity].missing_value |
---|
3311 | except: |
---|
3312 | pass #quantity has no missing_value number |
---|
3313 | X = interpolated_quantities[quantity] |
---|
3314 | if very_verbose: |
---|
3315 | print ' ',quantity |
---|
3316 | print ' NaN =',NaN |
---|
3317 | print ' max(X)' |
---|
3318 | print ' ',max(X) |
---|
3319 | print ' max(X)==NaN' |
---|
3320 | print ' ',max(X)==NaN |
---|
3321 | print '' |
---|
3322 | if (max(X)==NaN) or (min(X)==NaN): |
---|
3323 | if fail_if_NaN: |
---|
3324 | msg = 'quantity "%s" contains no_data entry'%quantity |
---|
3325 | raise DataMissingValuesError, msg |
---|
3326 | else: |
---|
3327 | data = (X<>NaN) |
---|
3328 | X = (X*data)+(data==0)*NaN_filler |
---|
3329 | if unique: |
---|
3330 | X = resize(X,(X.shape[0]/3,3)) |
---|
3331 | domain.set_quantity(quantity,X) |
---|
3332 | |
---|
3333 | fid.close() |
---|
3334 | return domain |
---|
3335 | |
---|
3336 | def interpolated_quantity(saved_quantity,time_interp): |
---|
3337 | |
---|
3338 | #given an index and ratio, interpolate quantity with respect to time. |
---|
3339 | index,ratio = time_interp |
---|
3340 | Q = saved_quantity |
---|
3341 | if ratio > 0: |
---|
3342 | q = (1-ratio)*Q[index]+ ratio*Q[index+1] |
---|
3343 | else: |
---|
3344 | q = Q[index] |
---|
3345 | #Return vector of interpolated values |
---|
3346 | return q |
---|
3347 | |
---|
3348 | def get_time_interp(time,t=None): |
---|
3349 | #Finds the ratio and index for time interpolation. |
---|
3350 | #It is borrowed from previous abstract_2d_finite_volumes code. |
---|
3351 | if t is None: |
---|
3352 | t=time[-1] |
---|
3353 | index = -1 |
---|
3354 | ratio = 0. |
---|
3355 | else: |
---|
3356 | T = time |
---|
3357 | tau = t |
---|
3358 | index=0 |
---|
3359 | msg = 'Time interval derived from file %s [%s:%s]'\ |
---|
3360 | %('FIXMEfilename', T[0], T[-1]) |
---|
3361 | msg += ' does not match model time: %s' %tau |
---|
3362 | if tau < time[0]: raise DataTimeError, msg |
---|
3363 | if tau > time[-1]: raise DataTimeError, msg |
---|
3364 | while tau > time[index]: index += 1 |
---|
3365 | while tau < time[index]: index -= 1 |
---|
3366 | if tau == time[index]: |
---|
3367 | #Protect against case where tau == time[-1] (last time) |
---|
3368 | # - also works in general when tau == time[i] |
---|
3369 | ratio = 0 |
---|
3370 | else: |
---|
3371 | #t is now between index and index+1 |
---|
3372 | ratio = (tau - time[index])/(time[index+1] - time[index]) |
---|
3373 | return (index,ratio) |
---|
3374 | |
---|
3375 | |
---|
3376 | def weed(coordinates,volumes,boundary = None): |
---|
3377 | if type(coordinates)==ArrayType: |
---|
3378 | coordinates = coordinates.tolist() |
---|
3379 | if type(volumes)==ArrayType: |
---|
3380 | volumes = volumes.tolist() |
---|
3381 | |
---|
3382 | unique = False |
---|
3383 | point_dict = {} |
---|
3384 | same_point = {} |
---|
3385 | for i in range(len(coordinates)): |
---|
3386 | point = tuple(coordinates[i]) |
---|
3387 | if point_dict.has_key(point): |
---|
3388 | unique = True |
---|
3389 | same_point[i]=point |
---|
3390 | #to change all point i references to point j |
---|
3391 | else: |
---|
3392 | point_dict[point]=i |
---|
3393 | same_point[i]=point |
---|
3394 | |
---|
3395 | coordinates = [] |
---|
3396 | i = 0 |
---|
3397 | for point in point_dict.keys(): |
---|
3398 | point = tuple(point) |
---|
3399 | coordinates.append(list(point)) |
---|
3400 | point_dict[point]=i |
---|
3401 | i+=1 |
---|
3402 | |
---|
3403 | |
---|
3404 | for volume in volumes: |
---|
3405 | for i in range(len(volume)): |
---|
3406 | index = volume[i] |
---|
3407 | if index>-1: |
---|
3408 | volume[i]=point_dict[same_point[index]] |
---|
3409 | |
---|
3410 | new_boundary = {} |
---|
3411 | if not boundary is None: |
---|
3412 | for segment in boundary.keys(): |
---|
3413 | point0 = point_dict[same_point[segment[0]]] |
---|
3414 | point1 = point_dict[same_point[segment[1]]] |
---|
3415 | label = boundary[segment] |
---|
3416 | #FIXME should the bounday attributes be concaterated |
---|
3417 | #('exterior, pond') or replaced ('pond')(peter row) |
---|
3418 | |
---|
3419 | if new_boundary.has_key((point0,point1)): |
---|
3420 | new_boundary[(point0,point1)]=new_boundary[(point0,point1)]#\ |
---|
3421 | #+','+label |
---|
3422 | |
---|
3423 | elif new_boundary.has_key((point1,point0)): |
---|
3424 | new_boundary[(point1,point0)]=new_boundary[(point1,point0)]#\ |
---|
3425 | #+','+label |
---|
3426 | else: new_boundary[(point0,point1)]=label |
---|
3427 | |
---|
3428 | boundary = new_boundary |
---|
3429 | |
---|
3430 | return coordinates,volumes,boundary |
---|
3431 | |
---|
3432 | |
---|
3433 | def decimate_dem(basename_in, stencil, cellsize_new, basename_out=None, |
---|
3434 | verbose=False): |
---|
3435 | """Read Digitial Elevation model from the following NetCDF format (.dem) |
---|
3436 | |
---|
3437 | Example: |
---|
3438 | |
---|
3439 | ncols 3121 |
---|
3440 | nrows 1800 |
---|
3441 | xllcorner 722000 |
---|
3442 | yllcorner 5893000 |
---|
3443 | cellsize 25 |
---|
3444 | NODATA_value -9999 |
---|
3445 | 138.3698 137.4194 136.5062 135.5558 .......... |
---|
3446 | |
---|
3447 | Decimate data to cellsize_new using stencil and write to NetCDF dem format. |
---|
3448 | """ |
---|
3449 | |
---|
3450 | import os |
---|
3451 | from Scientific.IO.NetCDF import NetCDFFile |
---|
3452 | from Numeric import Float, zeros, sum, reshape, equal |
---|
3453 | |
---|
3454 | root = basename_in |
---|
3455 | inname = root + '.dem' |
---|
3456 | |
---|
3457 | #Open existing netcdf file to read |
---|
3458 | infile = NetCDFFile(inname, 'r') |
---|
3459 | if verbose: print 'Reading DEM from %s' %inname |
---|
3460 | |
---|
3461 | #Read metadata |
---|
3462 | ncols = infile.ncols[0] |
---|
3463 | nrows = infile.nrows[0] |
---|
3464 | xllcorner = infile.xllcorner[0] |
---|
3465 | yllcorner = infile.yllcorner[0] |
---|
3466 | cellsize = infile.cellsize[0] |
---|
3467 | NODATA_value = infile.NODATA_value[0] |
---|
3468 | zone = infile.zone[0] |
---|
3469 | false_easting = infile.false_easting[0] |
---|
3470 | false_northing = infile.false_northing[0] |
---|
3471 | projection = infile.projection |
---|
3472 | datum = infile.datum |
---|
3473 | units = infile.units |
---|
3474 | |
---|
3475 | dem_elevation = infile.variables['elevation'] |
---|
3476 | |
---|
3477 | #Get output file name |
---|
3478 | if basename_out == None: |
---|
3479 | outname = root + '_' + repr(cellsize_new) + '.dem' |
---|
3480 | else: |
---|
3481 | outname = basename_out + '.dem' |
---|
3482 | |
---|
3483 | if verbose: print 'Write decimated NetCDF file to %s' %outname |
---|
3484 | |
---|
3485 | #Determine some dimensions for decimated grid |
---|
3486 | (nrows_stencil, ncols_stencil) = stencil.shape |
---|
3487 | x_offset = ncols_stencil / 2 |
---|
3488 | y_offset = nrows_stencil / 2 |
---|
3489 | cellsize_ratio = int(cellsize_new / cellsize) |
---|
3490 | ncols_new = 1 + (ncols - ncols_stencil) / cellsize_ratio |
---|
3491 | nrows_new = 1 + (nrows - nrows_stencil) / cellsize_ratio |
---|
3492 | |
---|
3493 | #Open netcdf file for output |
---|
3494 | outfile = NetCDFFile(outname, 'w') |
---|
3495 | |
---|
3496 | #Create new file |
---|
3497 | outfile.institution = 'Geoscience Australia' |
---|
3498 | outfile.description = 'NetCDF DEM format for compact and portable storage ' +\ |
---|
3499 | 'of spatial point data' |
---|
3500 | #Georeferencing |
---|
3501 | outfile.zone = zone |
---|
3502 | outfile.projection = projection |
---|
3503 | outfile.datum = datum |
---|
3504 | outfile.units = units |
---|
3505 | |
---|
3506 | outfile.cellsize = cellsize_new |
---|
3507 | outfile.NODATA_value = NODATA_value |
---|
3508 | outfile.false_easting = false_easting |
---|
3509 | outfile.false_northing = false_northing |
---|
3510 | |
---|
3511 | outfile.xllcorner = xllcorner + (x_offset * cellsize) |
---|
3512 | outfile.yllcorner = yllcorner + (y_offset * cellsize) |
---|
3513 | outfile.ncols = ncols_new |
---|
3514 | outfile.nrows = nrows_new |
---|
3515 | |
---|
3516 | # dimension definition |
---|
3517 | outfile.createDimension('number_of_points', nrows_new*ncols_new) |
---|
3518 | |
---|
3519 | # variable definition |
---|
3520 | outfile.createVariable('elevation', Float, ('number_of_points',)) |
---|
3521 | |
---|
3522 | # Get handle to the variable |
---|
3523 | elevation = outfile.variables['elevation'] |
---|
3524 | |
---|
3525 | dem_elevation_r = reshape(dem_elevation, (nrows, ncols)) |
---|
3526 | |
---|
3527 | #Store data |
---|
3528 | global_index = 0 |
---|
3529 | for i in range(nrows_new): |
---|
3530 | if verbose: print 'Processing row %d of %d' %(i, nrows_new) |
---|
3531 | lower_index = global_index |
---|
3532 | telev = zeros(ncols_new, Float) |
---|
3533 | local_index = 0 |
---|
3534 | trow = i * cellsize_ratio |
---|
3535 | |
---|
3536 | for j in range(ncols_new): |
---|
3537 | tcol = j * cellsize_ratio |
---|
3538 | tmp = dem_elevation_r[trow:trow+nrows_stencil, tcol:tcol+ncols_stencil] |
---|
3539 | |
---|
3540 | #if dem contains 1 or more NODATA_values set value in |
---|
3541 | #decimated dem to NODATA_value, else compute decimated |
---|
3542 | #value using stencil |
---|
3543 | if sum(sum(equal(tmp, NODATA_value))) > 0: |
---|
3544 | telev[local_index] = NODATA_value |
---|
3545 | else: |
---|
3546 | telev[local_index] = sum(sum(tmp * stencil)) |
---|
3547 | |
---|
3548 | global_index += 1 |
---|
3549 | local_index += 1 |
---|
3550 | |
---|
3551 | upper_index = global_index |
---|
3552 | |
---|
3553 | elevation[lower_index:upper_index] = telev |
---|
3554 | |
---|
3555 | assert global_index == nrows_new*ncols_new, 'index not equal to number of points' |
---|
3556 | |
---|
3557 | infile.close() |
---|
3558 | outfile.close() |
---|
3559 | |
---|
3560 | |
---|
3561 | |
---|
3562 | |
---|
3563 | def tsh2sww(filename, verbose=False): #test_tsh2sww |
---|
3564 | """ |
---|
3565 | to check if a tsh/msh file 'looks' good. |
---|
3566 | """ |
---|
3567 | |
---|
3568 | |
---|
3569 | if verbose == True:print 'Creating domain from', filename |
---|
3570 | domain = pmesh_to_domain_instance(filename, Domain) |
---|
3571 | if verbose == True:print "Number of triangles = ", len(domain) |
---|
3572 | |
---|
3573 | domain.smooth = True |
---|
3574 | domain.format = 'sww' #Native netcdf visualisation format |
---|
3575 | file_path, filename = path.split(filename) |
---|
3576 | filename, ext = path.splitext(filename) |
---|
3577 | domain.set_name(filename) |
---|
3578 | domain.reduction = mean |
---|
3579 | if verbose == True:print "file_path",file_path |
---|
3580 | if file_path == "":file_path = "." |
---|
3581 | domain.set_datadir(file_path) |
---|
3582 | |
---|
3583 | if verbose == True: |
---|
3584 | print "Output written to " + domain.get_datadir() + sep + \ |
---|
3585 | domain.get_name() + "." + domain.format |
---|
3586 | sww = get_dataobject(domain) |
---|
3587 | sww.store_connectivity() |
---|
3588 | sww.store_timestep('stage') |
---|
3589 | |
---|
3590 | |
---|
3591 | def asc_csiro2sww(bath_dir, |
---|
3592 | elevation_dir, |
---|
3593 | ucur_dir, |
---|
3594 | vcur_dir, |
---|
3595 | sww_file, |
---|
3596 | minlat = None, maxlat = None, |
---|
3597 | minlon = None, maxlon = None, |
---|
3598 | zscale=1, |
---|
3599 | mean_stage = 0, |
---|
3600 | fail_on_NaN = True, |
---|
3601 | elevation_NaN_filler = 0, |
---|
3602 | bath_prefix='ba', |
---|
3603 | elevation_prefix='el', |
---|
3604 | verbose=False): |
---|
3605 | """ |
---|
3606 | Produce an sww boundary file, from esri ascii data from CSIRO. |
---|
3607 | |
---|
3608 | Also convert latitude and longitude to UTM. All coordinates are |
---|
3609 | assumed to be given in the GDA94 datum. |
---|
3610 | |
---|
3611 | assume: |
---|
3612 | All files are in esri ascii format |
---|
3613 | |
---|
3614 | 4 types of information |
---|
3615 | bathymetry |
---|
3616 | elevation |
---|
3617 | u velocity |
---|
3618 | v velocity |
---|
3619 | |
---|
3620 | Assumptions |
---|
3621 | The metadata of all the files is the same |
---|
3622 | Each type is in a seperate directory |
---|
3623 | One bath file with extention .000 |
---|
3624 | The time period is less than 24hrs and uniform. |
---|
3625 | """ |
---|
3626 | from Scientific.IO.NetCDF import NetCDFFile |
---|
3627 | |
---|
3628 | from anuga.coordinate_transforms.redfearn import redfearn |
---|
3629 | |
---|
3630 | precision = Float # So if we want to change the precision its done here |
---|
3631 | |
---|
3632 | # go in to the bath dir and load the only file, |
---|
3633 | bath_files = os.listdir(bath_dir) |
---|
3634 | |
---|
3635 | bath_file = bath_files[0] |
---|
3636 | bath_dir_file = bath_dir + os.sep + bath_file |
---|
3637 | bath_metadata,bath_grid = _read_asc(bath_dir_file) |
---|
3638 | |
---|
3639 | #Use the date.time of the bath file as a basis for |
---|
3640 | #the start time for other files |
---|
3641 | base_start = bath_file[-12:] |
---|
3642 | |
---|
3643 | #go into the elevation dir and load the 000 file |
---|
3644 | elevation_dir_file = elevation_dir + os.sep + elevation_prefix \ |
---|
3645 | + base_start |
---|
3646 | |
---|
3647 | elevation_files = os.listdir(elevation_dir) |
---|
3648 | ucur_files = os.listdir(ucur_dir) |
---|
3649 | vcur_files = os.listdir(vcur_dir) |
---|
3650 | elevation_files.sort() |
---|
3651 | # the first elevation file should be the |
---|
3652 | # file with the same base name as the bath data |
---|
3653 | assert elevation_files[0] == 'el' + base_start |
---|
3654 | |
---|
3655 | number_of_latitudes = bath_grid.shape[0] |
---|
3656 | number_of_longitudes = bath_grid.shape[1] |
---|
3657 | number_of_volumes = (number_of_latitudes-1)*(number_of_longitudes-1)*2 |
---|
3658 | |
---|
3659 | longitudes = [bath_metadata['xllcorner']+x*bath_metadata['cellsize'] \ |
---|
3660 | for x in range(number_of_longitudes)] |
---|
3661 | latitudes = [bath_metadata['yllcorner']+y*bath_metadata['cellsize'] \ |
---|
3662 | for y in range(number_of_latitudes)] |
---|
3663 | |
---|
3664 | # reverse order of lat, so the fist lat represents the first grid row |
---|
3665 | latitudes.reverse() |
---|
3666 | |
---|
3667 | kmin, kmax, lmin, lmax = _get_min_max_indexes(latitudes[:],longitudes[:], |
---|
3668 | minlat=minlat, maxlat=maxlat, |
---|
3669 | minlon=minlon, maxlon=maxlon) |
---|
3670 | |
---|
3671 | |
---|
3672 | bath_grid = bath_grid[kmin:kmax,lmin:lmax] |
---|
3673 | latitudes = latitudes[kmin:kmax] |
---|
3674 | longitudes = longitudes[lmin:lmax] |
---|
3675 | number_of_latitudes = len(latitudes) |
---|
3676 | number_of_longitudes = len(longitudes) |
---|
3677 | number_of_times = len(os.listdir(elevation_dir)) |
---|
3678 | number_of_points = number_of_latitudes*number_of_longitudes |
---|
3679 | number_of_volumes = (number_of_latitudes-1)*(number_of_longitudes-1)*2 |
---|
3680 | |
---|
3681 | #Work out the times |
---|
3682 | if len(elevation_files) > 1: |
---|
3683 | # Assume: The time period is less than 24hrs. |
---|
3684 | time_period = (int(elevation_files[1][-3:]) - \ |
---|
3685 | int(elevation_files[0][-3:]))*60*60 |
---|
3686 | times = [x*time_period for x in range(len(elevation_files))] |
---|
3687 | else: |
---|
3688 | times = [0.0] |
---|
3689 | |
---|
3690 | |
---|
3691 | if verbose: |
---|
3692 | print '------------------------------------------------' |
---|
3693 | print 'Statistics:' |
---|
3694 | print ' Extent (lat/lon):' |
---|
3695 | print ' lat in [%f, %f], len(lat) == %d'\ |
---|
3696 | %(min(latitudes), max(latitudes), |
---|
3697 | len(latitudes)) |
---|
3698 | print ' lon in [%f, %f], len(lon) == %d'\ |
---|
3699 | %(min(longitudes), max(longitudes), |
---|
3700 | len(longitudes)) |
---|
3701 | print ' t in [%f, %f], len(t) == %d'\ |
---|
3702 | %(min(times), max(times), len(times)) |
---|
3703 | |
---|
3704 | ######### WRITE THE SWW FILE ############# |
---|
3705 | # NetCDF file definition |
---|
3706 | outfile = NetCDFFile(sww_file, 'w') |
---|
3707 | |
---|
3708 | #Create new file |
---|
3709 | outfile.institution = 'Geoscience Australia' |
---|
3710 | outfile.description = 'Converted from XXX' |
---|
3711 | |
---|
3712 | |
---|
3713 | #For sww compatibility |
---|
3714 | outfile.smoothing = 'Yes' |
---|
3715 | outfile.order = 1 |
---|
3716 | |
---|
3717 | #Start time in seconds since the epoch (midnight 1/1/1970) |
---|
3718 | outfile.starttime = starttime = times[0] |
---|
3719 | |
---|
3720 | |
---|
3721 | # dimension definitions |
---|
3722 | outfile.createDimension('number_of_volumes', number_of_volumes) |
---|
3723 | |
---|
3724 | outfile.createDimension('number_of_vertices', 3) |
---|
3725 | outfile.createDimension('number_of_points', number_of_points) |
---|
3726 | outfile.createDimension('number_of_timesteps', number_of_times) |
---|
3727 | |
---|
3728 | # variable definitions |
---|
3729 | outfile.createVariable('x', precision, ('number_of_points',)) |
---|
3730 | outfile.createVariable('y', precision, ('number_of_points',)) |
---|
3731 | outfile.createVariable('elevation', precision, ('number_of_points',)) |
---|
3732 | |
---|
3733 | #FIXME: Backwards compatibility |
---|
3734 | outfile.createVariable('z', precision, ('number_of_points',)) |
---|
3735 | ################################# |
---|
3736 | |
---|
3737 | outfile.createVariable('volumes', Int, ('number_of_volumes', |
---|
3738 | 'number_of_vertices')) |
---|
3739 | |
---|
3740 | outfile.createVariable('time', precision, |
---|
3741 | ('number_of_timesteps',)) |
---|
3742 | |
---|
3743 | outfile.createVariable('stage', precision, |
---|
3744 | ('number_of_timesteps', |
---|
3745 | 'number_of_points')) |
---|
3746 | |
---|
3747 | outfile.createVariable('xmomentum', precision, |
---|
3748 | ('number_of_timesteps', |
---|
3749 | 'number_of_points')) |
---|
3750 | |
---|
3751 | outfile.createVariable('ymomentum', precision, |
---|
3752 | ('number_of_timesteps', |
---|
3753 | 'number_of_points')) |
---|
3754 | |
---|
3755 | #Store |
---|
3756 | from anuga.coordinate_transforms.redfearn import redfearn |
---|
3757 | x = zeros(number_of_points, Float) #Easting |
---|
3758 | y = zeros(number_of_points, Float) #Northing |
---|
3759 | |
---|
3760 | if verbose: print 'Making triangular grid' |
---|
3761 | #Get zone of 1st point. |
---|
3762 | refzone, _, _ = redfearn(latitudes[0],longitudes[0]) |
---|
3763 | |
---|
3764 | vertices = {} |
---|
3765 | i = 0 |
---|
3766 | for k, lat in enumerate(latitudes): |
---|
3767 | for l, lon in enumerate(longitudes): |
---|
3768 | |
---|
3769 | vertices[l,k] = i |
---|
3770 | |
---|
3771 | zone, easting, northing = redfearn(lat,lon) |
---|
3772 | |
---|
3773 | msg = 'Zone boundary crossed at longitude =', lon |
---|
3774 | #assert zone == refzone, msg |
---|
3775 | #print '%7.2f %7.2f %8.2f %8.2f' %(lon, lat, easting, northing) |
---|
3776 | x[i] = easting |
---|
3777 | y[i] = northing |
---|
3778 | i += 1 |
---|
3779 | |
---|
3780 | |
---|
3781 | #Construct 2 triangles per 'rectangular' element |
---|
3782 | volumes = [] |
---|
3783 | for l in range(number_of_longitudes-1): #X direction |
---|
3784 | for k in range(number_of_latitudes-1): #Y direction |
---|
3785 | v1 = vertices[l,k+1] |
---|
3786 | v2 = vertices[l,k] |
---|
3787 | v3 = vertices[l+1,k+1] |
---|
3788 | v4 = vertices[l+1,k] |
---|
3789 | |
---|
3790 | #Note, this is different to the ferrit2sww code |
---|
3791 | #since the order of the lats is reversed. |
---|
3792 | volumes.append([v1,v3,v2]) #Upper element |
---|
3793 | volumes.append([v4,v2,v3]) #Lower element |
---|
3794 | |
---|
3795 | volumes = array(volumes) |
---|
3796 | |
---|
3797 | geo_ref = Geo_reference(refzone,min(x),min(y)) |
---|
3798 | geo_ref.write_NetCDF(outfile) |
---|
3799 | |
---|
3800 | # This will put the geo ref in the middle |
---|
3801 | #geo_ref = Geo_reference(refzone,(max(x)+min(x))/2.0,(max(x)+min(y))/2.) |
---|
3802 | |
---|
3803 | |
---|
3804 | if verbose: |
---|
3805 | print '------------------------------------------------' |
---|
3806 | print 'More Statistics:' |
---|
3807 | print ' Extent (/lon):' |
---|
3808 | print ' x in [%f, %f], len(lat) == %d'\ |
---|
3809 | %(min(x), max(x), |
---|
3810 | len(x)) |
---|
3811 | print ' y in [%f, %f], len(lon) == %d'\ |
---|
3812 | %(min(y), max(y), |
---|
3813 | len(y)) |
---|
3814 | print 'geo_ref: ',geo_ref |
---|
3815 | |
---|
3816 | z = resize(bath_grid,outfile.variables['z'][:].shape) |
---|
3817 | outfile.variables['x'][:] = x - geo_ref.get_xllcorner() |
---|
3818 | outfile.variables['y'][:] = y - geo_ref.get_yllcorner() |
---|
3819 | outfile.variables['z'][:] = z |
---|
3820 | outfile.variables['elevation'][:] = z #FIXME HACK |
---|
3821 | outfile.variables['volumes'][:] = volumes.astype(Int32) #On Opteron 64 |
---|
3822 | |
---|
3823 | stage = outfile.variables['stage'] |
---|
3824 | xmomentum = outfile.variables['xmomentum'] |
---|
3825 | ymomentum = outfile.variables['ymomentum'] |
---|
3826 | |
---|
3827 | outfile.variables['time'][:] = times #Store time relative |
---|
3828 | |
---|
3829 | if verbose: print 'Converting quantities' |
---|
3830 | n = number_of_times |
---|
3831 | for j in range(number_of_times): |
---|
3832 | # load in files |
---|
3833 | elevation_meta, elevation_grid = \ |
---|
3834 | _read_asc(elevation_dir + os.sep + elevation_files[j]) |
---|
3835 | |
---|
3836 | _, u_momentum_grid = _read_asc(ucur_dir + os.sep + ucur_files[j]) |
---|
3837 | _, v_momentum_grid = _read_asc(vcur_dir + os.sep + vcur_files[j]) |
---|
3838 | |
---|
3839 | #cut matrix to desired size |
---|
3840 | elevation_grid = elevation_grid[kmin:kmax,lmin:lmax] |
---|
3841 | u_momentum_grid = u_momentum_grid[kmin:kmax,lmin:lmax] |
---|
3842 | v_momentum_grid = v_momentum_grid[kmin:kmax,lmin:lmax] |
---|
3843 | |
---|
3844 | # handle missing values |
---|
3845 | missing = (elevation_grid == elevation_meta['NODATA_value']) |
---|
3846 | if sometrue (missing): |
---|
3847 | if fail_on_NaN: |
---|
3848 | msg = 'File %s contains missing values'\ |
---|
3849 | %(elevation_files[j]) |
---|
3850 | raise DataMissingValuesError, msg |
---|
3851 | else: |
---|
3852 | elevation_grid = elevation_grid*(missing==0) + \ |
---|
3853 | missing*elevation_NaN_filler |
---|
3854 | |
---|
3855 | |
---|
3856 | if verbose and j%((n+10)/10)==0: print ' Doing %d of %d' %(j, n) |
---|
3857 | i = 0 |
---|
3858 | for k in range(number_of_latitudes): #Y direction |
---|
3859 | for l in range(number_of_longitudes): #X direction |
---|
3860 | w = zscale*elevation_grid[k,l] + mean_stage |
---|
3861 | stage[j,i] = w |
---|
3862 | h = w - z[i] |
---|
3863 | xmomentum[j,i] = u_momentum_grid[k,l]*h |
---|
3864 | ymomentum[j,i] = v_momentum_grid[k,l]*h |
---|
3865 | i += 1 |
---|
3866 | outfile.close() |
---|
3867 | |
---|
3868 | def _get_min_max_indexes(latitudes_ref,longitudes_ref, |
---|
3869 | minlat=None, maxlat=None, |
---|
3870 | minlon=None, maxlon=None): |
---|
3871 | """ |
---|
3872 | return max, min indexes (for slicing) of the lat and long arrays to cover the area |
---|
3873 | specified with min/max lat/long |
---|
3874 | |
---|
3875 | Think of the latitudes and longitudes describing a 2d surface. |
---|
3876 | The area returned is, if possible, just big enough to cover the |
---|
3877 | inputed max/min area. (This will not be possible if the max/min area |
---|
3878 | has a section outside of the latitudes/longitudes area.) |
---|
3879 | |
---|
3880 | asset longitudes are sorted, |
---|
3881 | long - from low to high (west to east, eg 148 - 151) |
---|
3882 | assert latitudes are sorted, ascending or decending |
---|
3883 | """ |
---|
3884 | latitudes = latitudes_ref[:] |
---|
3885 | longitudes = longitudes_ref[:] |
---|
3886 | |
---|
3887 | latitudes = ensure_numeric(latitudes) |
---|
3888 | longitudes = ensure_numeric(longitudes) |
---|
3889 | |
---|
3890 | assert allclose(sort(longitudes), longitudes) |
---|
3891 | |
---|
3892 | lat_ascending = True |
---|
3893 | if not allclose(sort(latitudes), latitudes): |
---|
3894 | lat_ascending = False |
---|
3895 | # reverse order of lat, so it's in ascending order |
---|
3896 | latitudes = latitudes[::-1] |
---|
3897 | assert allclose(sort(latitudes), latitudes) |
---|
3898 | #print "latitudes in funct", latitudes |
---|
3899 | |
---|
3900 | largest_lat_index = len(latitudes)-1 |
---|
3901 | #Cut out a smaller extent. |
---|
3902 | if minlat == None: |
---|
3903 | lat_min_index = 0 |
---|
3904 | else: |
---|
3905 | lat_min_index = searchsorted(latitudes, minlat)-1 |
---|
3906 | if lat_min_index <0: |
---|
3907 | lat_min_index = 0 |
---|
3908 | |
---|
3909 | |
---|
3910 | if maxlat == None: |
---|
3911 | lat_max_index = largest_lat_index #len(latitudes) |
---|
3912 | else: |
---|
3913 | lat_max_index = searchsorted(latitudes, maxlat) |
---|
3914 | if lat_max_index > largest_lat_index: |
---|
3915 | lat_max_index = largest_lat_index |
---|
3916 | |
---|
3917 | if minlon == None: |
---|
3918 | lon_min_index = 0 |
---|
3919 | else: |
---|
3920 | lon_min_index = searchsorted(longitudes, minlon)-1 |
---|
3921 | if lon_min_index <0: |
---|
3922 | lon_min_index = 0 |
---|
3923 | |
---|
3924 | if maxlon == None: |
---|
3925 | lon_max_index = len(longitudes) |
---|
3926 | else: |
---|
3927 | lon_max_index = searchsorted(longitudes, maxlon) |
---|
3928 | |
---|
3929 | # Reversing the indexes, if the lat array is decending |
---|
3930 | if lat_ascending is False: |
---|
3931 | lat_min_index, lat_max_index = largest_lat_index - lat_max_index , \ |
---|
3932 | largest_lat_index - lat_min_index |
---|
3933 | lat_max_index = lat_max_index + 1 # taking into account how slicing works |
---|
3934 | lon_max_index = lon_max_index + 1 # taking into account how slicing works |
---|
3935 | |
---|
3936 | return lat_min_index, lat_max_index, lon_min_index, lon_max_index |
---|
3937 | |
---|
3938 | |
---|
3939 | def _read_asc(filename, verbose=False): |
---|
3940 | """Read esri file from the following ASCII format (.asc) |
---|
3941 | |
---|
3942 | Example: |
---|
3943 | |
---|
3944 | ncols 3121 |
---|
3945 | nrows 1800 |
---|
3946 | xllcorner 722000 |
---|
3947 | yllcorner 5893000 |
---|
3948 | cellsize 25 |
---|
3949 | NODATA_value -9999 |
---|
3950 | 138.3698 137.4194 136.5062 135.5558 .......... |
---|
3951 | |
---|
3952 | """ |
---|
3953 | |
---|
3954 | datafile = open(filename) |
---|
3955 | |
---|
3956 | if verbose: print 'Reading DEM from %s' %(filename) |
---|
3957 | lines = datafile.readlines() |
---|
3958 | datafile.close() |
---|
3959 | |
---|
3960 | if verbose: print 'Got', len(lines), ' lines' |
---|
3961 | |
---|
3962 | ncols = int(lines.pop(0).split()[1].strip()) |
---|
3963 | nrows = int(lines.pop(0).split()[1].strip()) |
---|
3964 | xllcorner = float(lines.pop(0).split()[1].strip()) |
---|
3965 | yllcorner = float(lines.pop(0).split()[1].strip()) |
---|
3966 | cellsize = float(lines.pop(0).split()[1].strip()) |
---|
3967 | NODATA_value = float(lines.pop(0).split()[1].strip()) |
---|
3968 | |
---|
3969 | assert len(lines) == nrows |
---|
3970 | |
---|
3971 | #Store data |
---|
3972 | grid = [] |
---|
3973 | |
---|
3974 | n = len(lines) |
---|
3975 | for i, line in enumerate(lines): |
---|
3976 | cells = line.split() |
---|
3977 | assert len(cells) == ncols |
---|
3978 | grid.append(array([float(x) for x in cells])) |
---|
3979 | grid = array(grid) |
---|
3980 | |
---|
3981 | return {'xllcorner':xllcorner, |
---|
3982 | 'yllcorner':yllcorner, |
---|
3983 | 'cellsize':cellsize, |
---|
3984 | 'NODATA_value':NODATA_value}, grid |
---|
3985 | |
---|
3986 | |
---|
3987 | |
---|
3988 | #### URS 2 SWW ### |
---|
3989 | |
---|
3990 | lon_name = 'LON' |
---|
3991 | lat_name = 'LAT' |
---|
3992 | time_name = 'TIME' |
---|
3993 | precision = Float # So if we want to change the precision its done here |
---|
3994 | class Write_nc: |
---|
3995 | """ |
---|
3996 | Write an nc file. |
---|
3997 | |
---|
3998 | Note, this should be checked to meet cdc netcdf conventions for gridded |
---|
3999 | data. http://www.cdc.noaa.gov/cdc/conventions/cdc_netcdf_standard.shtml |
---|
4000 | |
---|
4001 | """ |
---|
4002 | def __init__(self, |
---|
4003 | quantity_name, |
---|
4004 | file_name, |
---|
4005 | time_step_count, |
---|
4006 | time_step, |
---|
4007 | lon, |
---|
4008 | lat): |
---|
4009 | """ |
---|
4010 | time_step_count is the number of time steps. |
---|
4011 | time_step is the time step size |
---|
4012 | |
---|
4013 | pre-condition: quantity_name must be 'HA' 'UA'or 'VA'. |
---|
4014 | """ |
---|
4015 | self.quantity_name = quantity_name |
---|
4016 | quantity_units = {'HA':'CENTIMETERS', |
---|
4017 | 'UA':'CENTIMETERS/SECOND', |
---|
4018 | 'VA':'CENTIMETERS/SECOND'} |
---|
4019 | |
---|
4020 | multiplier_dic = {'HA':100.0, # To convert from m to cm |
---|
4021 | 'UA':100.0, # m/s to cm/sec |
---|
4022 | 'VA':-100.0} # MUX files have positve x in the |
---|
4023 | # Southern direction. This corrects for it, when writing nc files. |
---|
4024 | |
---|
4025 | self.quantity_multiplier = multiplier_dic[self.quantity_name] |
---|
4026 | |
---|
4027 | #self.file_name = file_name |
---|
4028 | self.time_step_count = time_step_count |
---|
4029 | self.time_step = time_step |
---|
4030 | |
---|
4031 | # NetCDF file definition |
---|
4032 | self.outfile = NetCDFFile(file_name, 'w') |
---|
4033 | outfile = self.outfile |
---|
4034 | |
---|
4035 | #Create new file |
---|
4036 | nc_lon_lat_header(outfile, lon, lat) |
---|
4037 | |
---|
4038 | # TIME |
---|
4039 | outfile.createDimension(time_name, None) |
---|
4040 | outfile.createVariable(time_name, precision, (time_name,)) |
---|
4041 | |
---|
4042 | #QUANTITY |
---|
4043 | outfile.createVariable(self.quantity_name, precision, |
---|
4044 | (time_name, lat_name, lon_name)) |
---|
4045 | outfile.variables[self.quantity_name].missing_value=-1.e+034 |
---|
4046 | outfile.variables[self.quantity_name].units= \ |
---|
4047 | quantity_units[self.quantity_name] |
---|
4048 | outfile.variables[lon_name][:]= ensure_numeric(lon) |
---|
4049 | outfile.variables[lat_name][:]= ensure_numeric(lat) |
---|
4050 | |
---|
4051 | #Assume no one will be wanting to read this, while we are writing |
---|
4052 | #outfile.close() |
---|
4053 | |
---|
4054 | def store_timestep(self, quantity_slice): |
---|
4055 | """ |
---|
4056 | Write a time slice of quantity info |
---|
4057 | quantity_slice is the data to be stored at this time step |
---|
4058 | """ |
---|
4059 | |
---|
4060 | outfile = self.outfile |
---|
4061 | |
---|
4062 | # Get the variables |
---|
4063 | time = outfile.variables[time_name] |
---|
4064 | quantity = outfile.variables[self.quantity_name] |
---|
4065 | |
---|
4066 | i = len(time) |
---|
4067 | |
---|
4068 | #Store time |
---|
4069 | time[i] = i*self.time_step #self.domain.time |
---|
4070 | quantity[i,:] = quantity_slice* self.quantity_multiplier |
---|
4071 | |
---|
4072 | def close(self): |
---|
4073 | self.outfile.close() |
---|
4074 | |
---|
4075 | def urs2sww(basename_in='o', basename_out=None, verbose=False, |
---|
4076 | remove_nc_files=True, |
---|
4077 | minlat=None, maxlat=None, |
---|
4078 | minlon= None, maxlon=None, |
---|
4079 | mint=None, maxt=None, |
---|
4080 | mean_stage=0, |
---|
4081 | origin = None, |
---|
4082 | zscale=1, |
---|
4083 | fail_on_NaN=True, |
---|
4084 | NaN_filler=0, |
---|
4085 | elevation=None): |
---|
4086 | """ |
---|
4087 | Convert URS C binary format for wave propagation to |
---|
4088 | sww format native to abstract_2d_finite_volumes. |
---|
4089 | |
---|
4090 | Specify only basename_in and read files of the form |
---|
4091 | basefilename_velocity-z-mux, basefilename_velocity-e-mux and |
---|
4092 | basefilename_waveheight-n-mux containing relative height, |
---|
4093 | x-velocity and y-velocity, respectively. |
---|
4094 | |
---|
4095 | Also convert latitude and longitude to UTM. All coordinates are |
---|
4096 | assumed to be given in the GDA94 datum. The latitude and longitude |
---|
4097 | information is for a grid. |
---|
4098 | |
---|
4099 | min's and max's: If omitted - full extend is used. |
---|
4100 | To include a value min may equal it, while max must exceed it. |
---|
4101 | Lat and lon are assumed to be in decimal degrees. |
---|
4102 | NOTE: minlon is the most east boundary. |
---|
4103 | |
---|
4104 | origin is a 3-tuple with geo referenced |
---|
4105 | UTM coordinates (zone, easting, northing) |
---|
4106 | It will be the origin of the sww file. This shouldn't be used, |
---|
4107 | since all of anuga should be able to handle an arbitary origin. |
---|
4108 | |
---|
4109 | |
---|
4110 | URS C binary format has data orgainised as TIME, LONGITUDE, LATITUDE |
---|
4111 | which means that latitude is the fastest |
---|
4112 | varying dimension (row major order, so to speak) |
---|
4113 | |
---|
4114 | In URS C binary the latitudes and longitudes are in assending order. |
---|
4115 | """ |
---|
4116 | if basename_out == None: |
---|
4117 | basename_out = basename_in |
---|
4118 | files_out = urs2nc(basename_in, basename_out) |
---|
4119 | ferret2sww(basename_out, |
---|
4120 | minlat=minlat, |
---|
4121 | maxlat=maxlat, |
---|
4122 | minlon=minlon, |
---|
4123 | maxlon=maxlon, |
---|
4124 | mint=mint, |
---|
4125 | maxt=maxt, |
---|
4126 | mean_stage=mean_stage, |
---|
4127 | origin=origin, |
---|
4128 | zscale=zscale, |
---|
4129 | fail_on_NaN=fail_on_NaN, |
---|
4130 | NaN_filler=NaN_filler, |
---|
4131 | inverted_bathymetry=True, |
---|
4132 | verbose=verbose) |
---|
4133 | #print "files_out",files_out |
---|
4134 | if remove_nc_files: |
---|
4135 | for file_out in files_out: |
---|
4136 | os.remove(file_out) |
---|
4137 | |
---|
4138 | def urs2nc(basename_in = 'o', basename_out = 'urs'): |
---|
4139 | """ |
---|
4140 | Convert the 3 urs files to 4 nc files. |
---|
4141 | |
---|
4142 | The name of the urs file names must be; |
---|
4143 | [basename_in]_velocity-z-mux |
---|
4144 | [basename_in]_velocity-e-mux |
---|
4145 | [basename_in]_waveheight-n-mux |
---|
4146 | |
---|
4147 | """ |
---|
4148 | |
---|
4149 | files_in = [basename_in + WAVEHEIGHT_MUX_LABEL, |
---|
4150 | basename_in + EAST_VELOCITY_LABEL, |
---|
4151 | basename_in + NORTH_VELOCITY_LABEL] |
---|
4152 | files_out = [basename_out+'_ha.nc', |
---|
4153 | basename_out+'_ua.nc', |
---|
4154 | basename_out+'_va.nc'] |
---|
4155 | quantities = ['HA','UA','VA'] |
---|
4156 | |
---|
4157 | for file_name in files_in: |
---|
4158 | if os.access(file_name, os.F_OK) == 0 : |
---|
4159 | msg = 'File %s does not exist or is not accessible' %file_name |
---|
4160 | raise IOError, msg |
---|
4161 | |
---|
4162 | hashed_elevation = None |
---|
4163 | for file_in, file_out, quantity in map(None, files_in, |
---|
4164 | files_out, |
---|
4165 | quantities): |
---|
4166 | lonlatdep, lon, lat, depth = _binary_c2nc(file_in, |
---|
4167 | file_out, |
---|
4168 | quantity) |
---|
4169 | #print "lonlatdep", lonlatdep |
---|
4170 | if hashed_elevation == None: |
---|
4171 | elevation_file = basename_out+'_e.nc' |
---|
4172 | write_elevation_nc(elevation_file, |
---|
4173 | lon, |
---|
4174 | lat, |
---|
4175 | depth) |
---|
4176 | hashed_elevation = myhash(lonlatdep) |
---|
4177 | else: |
---|
4178 | msg = "The elevation information in the mux files is inconsistent" |
---|
4179 | assert hashed_elevation == myhash(lonlatdep), msg |
---|
4180 | files_out.append(elevation_file) |
---|
4181 | return files_out |
---|
4182 | |
---|
4183 | def _binary_c2nc(file_in, file_out, quantity): |
---|
4184 | """ |
---|
4185 | Reads in a quantity urs file and writes a quantity nc file. |
---|
4186 | additionally, returns the depth and lat, long info, |
---|
4187 | so it can be written to a file. |
---|
4188 | """ |
---|
4189 | columns = 3 # long, lat , depth |
---|
4190 | mux_file = open(file_in, 'rb') |
---|
4191 | |
---|
4192 | # Number of points/stations |
---|
4193 | (points_num,)= unpack('i',mux_file.read(4)) |
---|
4194 | |
---|
4195 | # nt, int - Number of time steps |
---|
4196 | (time_step_count,)= unpack('i',mux_file.read(4)) |
---|
4197 | |
---|
4198 | #dt, float - time step, seconds |
---|
4199 | (time_step,) = unpack('f', mux_file.read(4)) |
---|
4200 | |
---|
4201 | msg = "Bad data in the mux file." |
---|
4202 | if points_num < 0: |
---|
4203 | mux_file.close() |
---|
4204 | raise ANUGAError, msg |
---|
4205 | if time_step_count < 0: |
---|
4206 | mux_file.close() |
---|
4207 | raise ANUGAError, msg |
---|
4208 | if time_step < 0: |
---|
4209 | mux_file.close() |
---|
4210 | raise ANUGAError, msg |
---|
4211 | |
---|
4212 | lonlatdep = p_array.array('f') |
---|
4213 | lonlatdep.read(mux_file, columns * points_num) |
---|
4214 | lonlatdep = array(lonlatdep, typecode=Float) |
---|
4215 | lonlatdep = reshape(lonlatdep, (points_num, columns)) |
---|
4216 | |
---|
4217 | lon, lat, depth = lon_lat2grid(lonlatdep) |
---|
4218 | lon_sorted = list(lon) |
---|
4219 | lon_sorted.sort() |
---|
4220 | |
---|
4221 | if not lon == lon_sorted: |
---|
4222 | msg = "Longitudes in mux file are not in ascending order" |
---|
4223 | raise IOError, msg |
---|
4224 | lat_sorted = list(lat) |
---|
4225 | lat_sorted.sort() |
---|
4226 | |
---|
4227 | if not lat == lat_sorted: |
---|
4228 | msg = "Latitudes in mux file are not in ascending order" |
---|
4229 | |
---|
4230 | nc_file = Write_nc(quantity, |
---|
4231 | file_out, |
---|
4232 | time_step_count, |
---|
4233 | time_step, |
---|
4234 | lon, |
---|
4235 | lat) |
---|
4236 | |
---|
4237 | for i in range(time_step_count): |
---|
4238 | #Read in a time slice from mux file |
---|
4239 | hz_p_array = p_array.array('f') |
---|
4240 | hz_p_array.read(mux_file, points_num) |
---|
4241 | hz_p = array(hz_p_array, typecode=Float) |
---|
4242 | hz_p = reshape(hz_p, (len(lon), len(lat))) |
---|
4243 | hz_p = transpose(hz_p) #mux has lat varying fastest, nc has long v.f. |
---|
4244 | |
---|
4245 | #write time slice to nc file |
---|
4246 | nc_file.store_timestep(hz_p) |
---|
4247 | mux_file.close() |
---|
4248 | nc_file.close() |
---|
4249 | |
---|
4250 | return lonlatdep, lon, lat, depth |
---|
4251 | |
---|
4252 | |
---|
4253 | def write_elevation_nc(file_out, lon, lat, depth_vector): |
---|
4254 | """ |
---|
4255 | Write an nc elevation file. |
---|
4256 | """ |
---|
4257 | |
---|
4258 | # NetCDF file definition |
---|
4259 | outfile = NetCDFFile(file_out, 'w') |
---|
4260 | |
---|
4261 | #Create new file |
---|
4262 | nc_lon_lat_header(outfile, lon, lat) |
---|
4263 | |
---|
4264 | # ELEVATION |
---|
4265 | zname = 'ELEVATION' |
---|
4266 | outfile.createVariable(zname, precision, (lat_name, lon_name)) |
---|
4267 | outfile.variables[zname].units='CENTIMETERS' |
---|
4268 | outfile.variables[zname].missing_value=-1.e+034 |
---|
4269 | |
---|
4270 | outfile.variables[lon_name][:]= ensure_numeric(lon) |
---|
4271 | outfile.variables[lat_name][:]= ensure_numeric(lat) |
---|
4272 | |
---|
4273 | depth = reshape(depth_vector, ( len(lat), len(lon))) |
---|
4274 | outfile.variables[zname][:]= depth |
---|
4275 | |
---|
4276 | outfile.close() |
---|
4277 | |
---|
4278 | def nc_lon_lat_header(outfile, lon, lat): |
---|
4279 | """ |
---|
4280 | outfile is the netcdf file handle. |
---|
4281 | lon - a list/array of the longitudes |
---|
4282 | lat - a list/array of the latitudes |
---|
4283 | """ |
---|
4284 | |
---|
4285 | outfile.institution = 'Geoscience Australia' |
---|
4286 | outfile.description = 'Converted from URS binary C' |
---|
4287 | |
---|
4288 | # Longitude |
---|
4289 | outfile.createDimension(lon_name, len(lon)) |
---|
4290 | outfile.createVariable(lon_name, precision, (lon_name,)) |
---|
4291 | outfile.variables[lon_name].point_spacing='uneven' |
---|
4292 | outfile.variables[lon_name].units='degrees_east' |
---|
4293 | outfile.variables[lon_name].assignValue(lon) |
---|
4294 | |
---|
4295 | |
---|
4296 | # Latitude |
---|
4297 | outfile.createDimension(lat_name, len(lat)) |
---|
4298 | outfile.createVariable(lat_name, precision, (lat_name,)) |
---|
4299 | outfile.variables[lat_name].point_spacing='uneven' |
---|
4300 | outfile.variables[lat_name].units='degrees_north' |
---|
4301 | outfile.variables[lat_name].assignValue(lat) |
---|
4302 | |
---|
4303 | |
---|
4304 | |
---|
4305 | def lon_lat2grid(long_lat_dep): |
---|
4306 | """ |
---|
4307 | given a list of points that are assumed to be an a grid, |
---|
4308 | return the long's and lat's of the grid. |
---|
4309 | long_lat_dep is an array where each row is a position. |
---|
4310 | The first column is longitudes. |
---|
4311 | The second column is latitudes. |
---|
4312 | |
---|
4313 | The latitude is the fastest varying dimension - in mux files |
---|
4314 | """ |
---|
4315 | LONG = 0 |
---|
4316 | LAT = 1 |
---|
4317 | QUANTITY = 2 |
---|
4318 | |
---|
4319 | long_lat_dep = ensure_numeric(long_lat_dep, Float) |
---|
4320 | |
---|
4321 | num_points = long_lat_dep.shape[0] |
---|
4322 | this_rows_long = long_lat_dep[0,LONG] |
---|
4323 | |
---|
4324 | # Count the length of unique latitudes |
---|
4325 | i = 0 |
---|
4326 | while long_lat_dep[i,LONG] == this_rows_long and i < num_points: |
---|
4327 | i += 1 |
---|
4328 | # determine the lats and longsfrom the grid |
---|
4329 | lat = long_lat_dep[:i, LAT] |
---|
4330 | long = long_lat_dep[::i, LONG] |
---|
4331 | |
---|
4332 | lenlong = len(long) |
---|
4333 | lenlat = len(lat) |
---|
4334 | #print 'len lat', lat, len(lat) |
---|
4335 | #print 'len long', long, len(long) |
---|
4336 | |
---|
4337 | msg = 'Input data is not gridded' |
---|
4338 | assert num_points % lenlat == 0, msg |
---|
4339 | assert num_points % lenlong == 0, msg |
---|
4340 | |
---|
4341 | # Test that data is gridded |
---|
4342 | for i in range(lenlong): |
---|
4343 | msg = 'Data is not gridded. It must be for this operation' |
---|
4344 | first = i*lenlat |
---|
4345 | last = first + lenlat |
---|
4346 | |
---|
4347 | assert allclose(long_lat_dep[first:last,LAT], lat), msg |
---|
4348 | assert allclose(long_lat_dep[first:last,LONG], long[i]), msg |
---|
4349 | |
---|
4350 | |
---|
4351 | # print 'range long', min(long), max(long) |
---|
4352 | # print 'range lat', min(lat), max(lat) |
---|
4353 | # print 'ref long', min(long_lat_dep[:,0]), max(long_lat_dep[:,0]) |
---|
4354 | # print 'ref lat', min(long_lat_dep[:,1]), max(long_lat_dep[:,1]) |
---|
4355 | |
---|
4356 | |
---|
4357 | |
---|
4358 | msg = 'Out of range latitudes/longitudes' |
---|
4359 | for l in lat:assert -90 < l < 90 , msg |
---|
4360 | for l in long:assert -180 < l < 180 , msg |
---|
4361 | |
---|
4362 | # Changing quantity from lat being the fastest varying dimension to |
---|
4363 | # long being the fastest varying dimension |
---|
4364 | # FIXME - make this faster/do this a better way |
---|
4365 | # use numeric transpose, after reshaping the quantity vector |
---|
4366 | # quantity = zeros(len(long_lat_dep), Float) |
---|
4367 | quantity = zeros(num_points, Float) |
---|
4368 | |
---|
4369 | # print 'num',num_points |
---|
4370 | for lat_i, _ in enumerate(lat): |
---|
4371 | for long_i, _ in enumerate(long): |
---|
4372 | q_index = lat_i*lenlong+long_i |
---|
4373 | lld_index = long_i*lenlat+lat_i |
---|
4374 | # print 'lat_i', lat_i, 'long_i',long_i, 'q_index', q_index, 'lld_index', lld_index |
---|
4375 | temp = long_lat_dep[lld_index, QUANTITY] |
---|
4376 | quantity[q_index] = temp |
---|
4377 | |
---|
4378 | return long, lat, quantity |
---|
4379 | |
---|
4380 | #### END URS 2 SWW ### |
---|
4381 | |
---|
4382 | #### URS UNGRIDDED 2 SWW ### |
---|
4383 | |
---|
4384 | ### PRODUCING THE POINTS NEEDED FILE ### |
---|
4385 | LL_LAT = -50.0 |
---|
4386 | LL_LONG = 80.0 |
---|
4387 | GRID_SPACING = 1.0/60.0 |
---|
4388 | LAT_AMOUNT = 4800 |
---|
4389 | LONG_AMOUNT = 3600 |
---|
4390 | def URS_points_needed_to_file(file_name, boundary_polygon, zone, |
---|
4391 | ll_lat=LL_LAT, ll_long=LL_LONG, |
---|
4392 | grid_spacing=GRID_SPACING, |
---|
4393 | lat_amount=LAT_AMOUNT, long_amount=LONG_AMOUNT, |
---|
4394 | export_csv=False, use_cache=False, |
---|
4395 | verbose=False): |
---|
4396 | """ |
---|
4397 | file_name - name of the urs file produced for David. |
---|
4398 | boundary_polygon - a list of points that describes a polygon. |
---|
4399 | The last point is assumed ot join the first point. |
---|
4400 | This is in UTM (lat long would be better though) |
---|
4401 | |
---|
4402 | ll_lat - lower left latitude, in decimal degrees |
---|
4403 | ll-long - lower left longitude, in decimal degrees |
---|
4404 | grid_spacing - in deciamal degrees |
---|
4405 | |
---|
4406 | |
---|
4407 | Don't add the file extension. It will be added. |
---|
4408 | """ |
---|
4409 | geo = URS_points_needed(boundary_polygon, zone, ll_lat, ll_long, |
---|
4410 | grid_spacing, |
---|
4411 | lat_amount, long_amount,use_cache, verbose) |
---|
4412 | if not file_name[-4:] == ".urs": |
---|
4413 | file_name += ".urs" |
---|
4414 | geo.export_points_file(file_name) |
---|
4415 | if export_csv: |
---|
4416 | if file_name[-4:] == ".urs": |
---|
4417 | file_name = file_name[:-4] + ".csv" |
---|
4418 | geo.export_points_file(file_name) |
---|
4419 | |
---|
4420 | def URS_points_needed(boundary_polygon, zone, ll_lat=LL_LAT, |
---|
4421 | ll_long=LL_LONG, grid_spacing=GRID_SPACING, |
---|
4422 | lat_amount=LAT_AMOUNT, long_amount=LONG_AMOUNT, |
---|
4423 | use_cache=False, verbose=False): |
---|
4424 | args = (boundary_polygon, |
---|
4425 | zone) |
---|
4426 | kwargs = {'ll_lat': ll_lat, |
---|
4427 | 'll_long': ll_long, |
---|
4428 | 'grid_spacing': grid_spacing, |
---|
4429 | 'lat_amount': lat_amount, |
---|
4430 | 'long_amount': long_amount} |
---|
4431 | if use_cache is True: |
---|
4432 | try: |
---|
4433 | from anuga.caching import cache |
---|
4434 | except: |
---|
4435 | msg = 'Caching was requested, but caching module'+\ |
---|
4436 | 'could not be imported' |
---|
4437 | raise msg |
---|
4438 | |
---|
4439 | |
---|
4440 | geo = cache(_URS_points_needed, |
---|
4441 | args, kwargs, |
---|
4442 | verbose=verbose, |
---|
4443 | compression=False) |
---|
4444 | else: |
---|
4445 | #I was getting 'got multiple values for keyword argument' errors |
---|
4446 | #geo = apply(_URS_points_needed, args, kwargs) |
---|
4447 | geo = _URS_points_needed(boundary_polygon, |
---|
4448 | zone, ll_lat, |
---|
4449 | ll_long, grid_spacing, |
---|
4450 | lat_amount, long_amount) |
---|
4451 | |
---|
4452 | return geo |
---|
4453 | def _URS_points_needed(boundary_polygon, |
---|
4454 | zone, ll_lat=LL_LAT, |
---|
4455 | ll_long=LL_LONG, grid_spacing=GRID_SPACING, |
---|
4456 | lat_amount=LAT_AMOUNT, long_amount=LONG_AMOUNT): |
---|
4457 | """ |
---|
4458 | |
---|
4459 | boundary_polygon - a list of points that describes a polygon. |
---|
4460 | The last point is assumed ot join the first point. |
---|
4461 | This is in UTM (lat long would be better though) |
---|
4462 | |
---|
4463 | ll_lat - lower left latitude, in decimal degrees |
---|
4464 | ll-long - lower left longitude, in decimal degrees |
---|
4465 | grid_spacing - in deciamal degrees |
---|
4466 | |
---|
4467 | """ |
---|
4468 | |
---|
4469 | from sets import ImmutableSet |
---|
4470 | |
---|
4471 | msg = "grid_spacing can not be zero" |
---|
4472 | assert not grid_spacing ==0, msg |
---|
4473 | a = boundary_polygon |
---|
4474 | # List of segments. Each segment is two points. |
---|
4475 | segs = [i and [a[i-1], a[i]] or [a[len(a)-1], a[0]] for i in range(len(a))] |
---|
4476 | |
---|
4477 | # convert the segs to Lat's and longs. |
---|
4478 | |
---|
4479 | # Don't assume the zone of the segments is the same as the lower left |
---|
4480 | # corner of the lat long data!! They can easily be in different zones |
---|
4481 | |
---|
4482 | lat_long_set = ImmutableSet() |
---|
4483 | for seg in segs: |
---|
4484 | points_lat_long = points_needed(seg, ll_lat, ll_long, grid_spacing, |
---|
4485 | lat_amount, long_amount, zone) |
---|
4486 | lat_long_set |= ImmutableSet(points_lat_long) |
---|
4487 | #print "lat_long_set",lat_long_set |
---|
4488 | geo = Geospatial_data(data_points=list(lat_long_set), |
---|
4489 | points_are_lats_longs=True) |
---|
4490 | return geo |
---|
4491 | |
---|
4492 | def points_needed(seg, ll_lat, ll_long, grid_spacing, |
---|
4493 | lat_amount, long_amount, zone): |
---|
4494 | """ |
---|
4495 | return a list of the points, in lats and longs that are needed to |
---|
4496 | interpolate any point on the segment. |
---|
4497 | """ |
---|
4498 | from math import sqrt |
---|
4499 | #print "zone",zone |
---|
4500 | geo_reference = Geo_reference(zone=zone) |
---|
4501 | #print "seg",seg |
---|
4502 | geo = Geospatial_data(seg,geo_reference=geo_reference) |
---|
4503 | seg_lat_long = geo.get_data_points(as_lat_long=True) |
---|
4504 | #print "seg_lat_long", seg_lat_long |
---|
4505 | # 1.415 = 2^0.5, rounded up.... |
---|
4506 | sqrt_2_rounded_up = 1.415 |
---|
4507 | buffer = sqrt_2_rounded_up * grid_spacing |
---|
4508 | |
---|
4509 | max_lat = max(seg_lat_long[0][0], seg_lat_long[1][0]) + buffer |
---|
4510 | max_long = max(seg_lat_long[0][1], seg_lat_long[1][1]) + buffer |
---|
4511 | min_lat = min(seg_lat_long[0][0], seg_lat_long[1][0]) - buffer |
---|
4512 | min_long = min(seg_lat_long[0][1], seg_lat_long[1][1]) - buffer |
---|
4513 | |
---|
4514 | first_row = (min_long - ll_long)/grid_spacing |
---|
4515 | # To round up |
---|
4516 | first_row_long = int(round(first_row + 0.5)) |
---|
4517 | #print "first_row", first_row_long |
---|
4518 | |
---|
4519 | last_row = (max_long - ll_long)/grid_spacing # round down |
---|
4520 | last_row_long = int(round(last_row)) |
---|
4521 | #print "last_row",last_row _long |
---|
4522 | |
---|
4523 | first_row = (min_lat - ll_lat)/grid_spacing |
---|
4524 | # To round up |
---|
4525 | first_row_lat = int(round(first_row + 0.5)) |
---|
4526 | #print "first_row", first_row_lat |
---|
4527 | |
---|
4528 | last_row = (max_lat - ll_lat)/grid_spacing # round down |
---|
4529 | last_row_lat = int(round(last_row)) |
---|
4530 | #print "last_row",last_row_lat |
---|
4531 | |
---|
4532 | # to work out the max distance - |
---|
4533 | # 111120 - horizontal distance between 1 deg latitude. |
---|
4534 | #max_distance = sqrt_2_rounded_up * 111120 * grid_spacing |
---|
4535 | max_distance = 157147.4112 * grid_spacing |
---|
4536 | #print "max_distance", max_distance #2619.12 m for 1 minute |
---|
4537 | points_lat_long = [] |
---|
4538 | # Create a list of the lat long points to include. |
---|
4539 | for index_lat in range(first_row_lat, last_row_lat + 1): |
---|
4540 | for index_long in range(first_row_long, last_row_long + 1): |
---|
4541 | lat = ll_lat + index_lat*grid_spacing |
---|
4542 | long = ll_long + index_long*grid_spacing |
---|
4543 | |
---|
4544 | #filter here to keep good points |
---|
4545 | if keep_point(lat, long, seg, max_distance): |
---|
4546 | points_lat_long.append((lat, long)) #must be hashable |
---|
4547 | #print "points_lat_long", points_lat_long |
---|
4548 | |
---|
4549 | # Now that we have these points, lets throw ones out that are too far away |
---|
4550 | return points_lat_long |
---|
4551 | |
---|
4552 | def keep_point(lat, long, seg, max_distance): |
---|
4553 | """ |
---|
4554 | seg is two points, UTM |
---|
4555 | """ |
---|
4556 | from math import sqrt |
---|
4557 | _ , x0, y0 = redfearn(lat, long) |
---|
4558 | x1 = seg[0][0] |
---|
4559 | y1 = seg[0][1] |
---|
4560 | x2 = seg[1][0] |
---|
4561 | y2 = seg[1][1] |
---|
4562 | |
---|
4563 | x2_1 = x2-x1 |
---|
4564 | y2_1 = y2-y1 |
---|
4565 | d = abs((x2_1)*(y1-y0)-(x1-x0)*(y2_1))/sqrt((x2_1)*(x2_1)+(y2_1)*(y2_1)) |
---|
4566 | if d <= max_distance: |
---|
4567 | return True |
---|
4568 | else: |
---|
4569 | return False |
---|
4570 | |
---|
4571 | #### CONVERTING UNGRIDDED URS DATA TO AN SWW FILE #### |
---|
4572 | |
---|
4573 | WAVEHEIGHT_MUX_LABEL = '_waveheight-z-mux' |
---|
4574 | EAST_VELOCITY_LABEL = '_velocity-e-mux' |
---|
4575 | NORTH_VELOCITY_LABEL = '_velocity-n-mux' |
---|
4576 | def urs_ungridded2sww(basename_in='o', basename_out=None, verbose=False, |
---|
4577 | mint=None, maxt=None, |
---|
4578 | mean_stage=0, |
---|
4579 | origin=None, |
---|
4580 | hole_points_UTM=None, |
---|
4581 | zscale=1): |
---|
4582 | """ |
---|
4583 | Convert URS C binary format for wave propagation to |
---|
4584 | sww format native to abstract_2d_finite_volumes. |
---|
4585 | |
---|
4586 | |
---|
4587 | Specify only basename_in and read files of the form |
---|
4588 | basefilename_velocity-z-mux, basefilename_velocity-e-mux and |
---|
4589 | basefilename_waveheight-n-mux containing relative height, |
---|
4590 | x-velocity and y-velocity, respectively. |
---|
4591 | |
---|
4592 | Also convert latitude and longitude to UTM. All coordinates are |
---|
4593 | assumed to be given in the GDA94 datum. The latitude and longitude |
---|
4594 | information is assumed ungridded grid. |
---|
4595 | |
---|
4596 | min's and max's: If omitted - full extend is used. |
---|
4597 | To include a value min ans max may equal it. |
---|
4598 | Lat and lon are assumed to be in decimal degrees. |
---|
4599 | |
---|
4600 | origin is a 3-tuple with geo referenced |
---|
4601 | UTM coordinates (zone, easting, northing) |
---|
4602 | It will be the origin of the sww file. This shouldn't be used, |
---|
4603 | since all of anuga should be able to handle an arbitary origin. |
---|
4604 | The mux point info is NOT relative to this origin. |
---|
4605 | |
---|
4606 | |
---|
4607 | URS C binary format has data orgainised as TIME, LONGITUDE, LATITUDE |
---|
4608 | which means that latitude is the fastest |
---|
4609 | varying dimension (row major order, so to speak) |
---|
4610 | |
---|
4611 | In URS C binary the latitudes and longitudes are in assending order. |
---|
4612 | |
---|
4613 | Note, interpolations of the resulting sww file will be different |
---|
4614 | from results of urs2sww. This is due to the interpolation |
---|
4615 | function used, and the different grid structure between urs2sww |
---|
4616 | and this function. |
---|
4617 | |
---|
4618 | Interpolating data that has an underlying gridded source can |
---|
4619 | easily end up with different values, depending on the underlying |
---|
4620 | mesh. |
---|
4621 | |
---|
4622 | consider these 4 points |
---|
4623 | 50 -50 |
---|
4624 | |
---|
4625 | 0 0 |
---|
4626 | |
---|
4627 | The grid can be |
---|
4628 | - |
---|
4629 | |\| A |
---|
4630 | - |
---|
4631 | or; |
---|
4632 | - |
---|
4633 | |/| B |
---|
4634 | - |
---|
4635 | If a point is just below the center of the midpoint, it will have a |
---|
4636 | +ve value in grid A and a -ve value in grid B. |
---|
4637 | """ |
---|
4638 | from anuga.pmesh.mesh import Mesh, NoTrianglesError |
---|
4639 | |
---|
4640 | files_in = [basename_in + WAVEHEIGHT_MUX_LABEL, |
---|
4641 | basename_in + EAST_VELOCITY_LABEL, |
---|
4642 | basename_in + NORTH_VELOCITY_LABEL] |
---|
4643 | quantities = ['HA','UA','VA'] |
---|
4644 | |
---|
4645 | # instanciate urs_points of the three mux files. |
---|
4646 | mux = {} |
---|
4647 | for quantity, file in map(None, quantities, files_in): |
---|
4648 | mux[quantity] = Urs_points(file) |
---|
4649 | |
---|
4650 | # Could check that the depth is the same. (hashing) |
---|
4651 | |
---|
4652 | # handle to a mux file to do depth stuff |
---|
4653 | a_mux = mux[quantities[0]] |
---|
4654 | |
---|
4655 | # Convert to utm |
---|
4656 | lat = a_mux.lonlatdep[:,1] |
---|
4657 | long = a_mux.lonlatdep[:,0] |
---|
4658 | points_utm, zone = convert_from_latlon_to_utm( \ |
---|
4659 | latitudes=lat, longitudes=long) |
---|
4660 | #print "points_utm", points_utm |
---|
4661 | #print "zone", zone |
---|
4662 | |
---|
4663 | elevation = a_mux.lonlatdep[:,2] * -1 # |
---|
4664 | |
---|
4665 | # grid ( create a mesh from the selected points) |
---|
4666 | # This mesh has a problem. Triangles are streched over ungridded areas. |
---|
4667 | # If these areas could be described as holes in pmesh, that would be great |
---|
4668 | |
---|
4669 | # I can't just get the user to selection a point in the middle. |
---|
4670 | # A boundary is needed around these points. |
---|
4671 | # But if the zone of points is obvious enough auto-segment should do |
---|
4672 | # a good boundary. |
---|
4673 | mesh = Mesh() |
---|
4674 | mesh.add_vertices(points_utm) |
---|
4675 | mesh.auto_segment(smooth_indents=True, expand_pinch=True) |
---|
4676 | # To try and avoid alpha shape 'hugging' too much |
---|
4677 | mesh.auto_segment( mesh.shape.get_alpha()*1.1 ) |
---|
4678 | if hole_points_UTM is not None: |
---|
4679 | point = ensure_absolute(hole_points_UTM) |
---|
4680 | mesh.add_hole(point[0], point[1]) |
---|
4681 | try: |
---|
4682 | mesh.generate_mesh(minimum_triangle_angle=0.0, verbose=False) |
---|
4683 | except NoTrianglesError: |
---|
4684 | # This is a bit of a hack, going in and changing the |
---|
4685 | # data structure. |
---|
4686 | mesh.holes = [] |
---|
4687 | mesh.generate_mesh(minimum_triangle_angle=0.0, verbose=False) |
---|
4688 | mesh_dic = mesh.Mesh2MeshList() |
---|
4689 | |
---|
4690 | #mesh.export_mesh_file(basename_in + '_168.tsh') |
---|
4691 | #import sys; sys.exit() |
---|
4692 | # These are the times of the mux file |
---|
4693 | mux_times = [] |
---|
4694 | for i in range(a_mux.time_step_count): |
---|
4695 | mux_times.append(a_mux.time_step * i) |
---|
4696 | mux_times_start_i, mux_times_fin_i = mux2sww_time(mux_times, mint, maxt) |
---|
4697 | times = mux_times[mux_times_start_i:mux_times_fin_i] |
---|
4698 | |
---|
4699 | if mux_times_start_i == mux_times_fin_i: |
---|
4700 | # Close the mux files |
---|
4701 | for quantity, file in map(None, quantities, files_in): |
---|
4702 | mux[quantity].close() |
---|
4703 | msg="Due to mint and maxt there's no time info in the boundary SWW." |
---|
4704 | raise Exception, msg |
---|
4705 | |
---|
4706 | # If this raise is removed there is currently no downstream errors |
---|
4707 | |
---|
4708 | points_utm=ensure_numeric(points_utm) |
---|
4709 | #print "mesh_dic['generatedpointlist']", mesh_dic['generatedpointlist'] |
---|
4710 | #print "points_utm", points_utm |
---|
4711 | assert ensure_numeric(mesh_dic['generatedpointlist']) == \ |
---|
4712 | ensure_numeric(points_utm) |
---|
4713 | |
---|
4714 | volumes = mesh_dic['generatedtrianglelist'] |
---|
4715 | |
---|
4716 | # write sww intro and grid stuff. |
---|
4717 | if basename_out is None: |
---|
4718 | swwname = basename_in + '.sww' |
---|
4719 | else: |
---|
4720 | swwname = basename_out + '.sww' |
---|
4721 | |
---|
4722 | if verbose: print 'Output to ', swwname |
---|
4723 | outfile = NetCDFFile(swwname, 'w') |
---|
4724 | # For a different way of doing this, check out tsh2sww |
---|
4725 | # work out sww_times and the index range this covers |
---|
4726 | sww = Write_sww() |
---|
4727 | sww.header(outfile, times, len(volumes), len(points_utm), |
---|
4728 | verbose=verbose) |
---|
4729 | outfile.mean_stage = mean_stage |
---|
4730 | outfile.zscale = zscale |
---|
4731 | |
---|
4732 | sww.triangulation(outfile, points_utm, volumes, |
---|
4733 | elevation, zone, new_origin=origin, |
---|
4734 | verbose=verbose) |
---|
4735 | |
---|
4736 | if verbose: print 'Converting quantities' |
---|
4737 | j = 0 |
---|
4738 | # Read in a time slice from each mux file and write it to the sww file |
---|
4739 | for ha, ua, va in map(None, mux['HA'], mux['UA'], mux['VA']): |
---|
4740 | if j >= mux_times_start_i and j < mux_times_fin_i: |
---|
4741 | stage = zscale*ha + mean_stage |
---|
4742 | h = stage - elevation |
---|
4743 | xmomentum = ua*h |
---|
4744 | ymomentum = -1*va*h # -1 since in mux files south is positive. |
---|
4745 | sww.quantities(outfile, |
---|
4746 | slice_index=j - mux_times_start_i, |
---|
4747 | verbose=verbose, |
---|
4748 | stage=stage, |
---|
4749 | xmomentum=xmomentum, |
---|
4750 | ymomentum=ymomentum) |
---|
4751 | j += 1 |
---|
4752 | if verbose: sww.verbose_quantities(outfile) |
---|
4753 | outfile.close() |
---|
4754 | # |
---|
4755 | # Do some conversions while writing the sww file |
---|
4756 | |
---|
4757 | |
---|
4758 | def mux2sww_time(mux_times, mint, maxt): |
---|
4759 | """ |
---|
4760 | """ |
---|
4761 | |
---|
4762 | if mint == None: |
---|
4763 | mux_times_start_i = 0 |
---|
4764 | else: |
---|
4765 | mux_times_start_i = searchsorted(mux_times, mint) |
---|
4766 | |
---|
4767 | if maxt == None: |
---|
4768 | mux_times_fin_i = len(mux_times) |
---|
4769 | else: |
---|
4770 | maxt += 0.5 # so if you specify a time where there is |
---|
4771 | # data that time will be included |
---|
4772 | mux_times_fin_i = searchsorted(mux_times, maxt) |
---|
4773 | |
---|
4774 | return mux_times_start_i, mux_times_fin_i |
---|
4775 | |
---|
4776 | |
---|
4777 | class Write_sww: |
---|
4778 | from anuga.shallow_water.shallow_water_domain import Domain |
---|
4779 | sww_quantities = Domain.conserved_quantities |
---|
4780 | RANGE = '_range' |
---|
4781 | def __init__(self): |
---|
4782 | pass |
---|
4783 | |
---|
4784 | def header(self,outfile, times, number_of_volumes, |
---|
4785 | number_of_points, |
---|
4786 | description='Converted from XXX', |
---|
4787 | smoothing=True, |
---|
4788 | order=1, verbose=False): |
---|
4789 | """ |
---|
4790 | outfile - the name of the file that will be written |
---|
4791 | times - A list of the time slice times OR a start time |
---|
4792 | Note, if a list is given the info will be made relative. |
---|
4793 | number_of_volumes - the number of triangles |
---|
4794 | """ |
---|
4795 | |
---|
4796 | outfile.institution = 'Geoscience Australia' |
---|
4797 | outfile.description = description |
---|
4798 | |
---|
4799 | #For sww compatibility |
---|
4800 | if smoothing is True: |
---|
4801 | # Smoothing to be depreciated |
---|
4802 | outfile.smoothing = 'Yes' |
---|
4803 | outfile.vertices_are_stored_uniquely = 'False' |
---|
4804 | else: |
---|
4805 | # Smoothing to be depreciated |
---|
4806 | outfile.smoothing = 'No' |
---|
4807 | outfile.vertices_are_stored_uniquely = 'True' |
---|
4808 | outfile.order = order |
---|
4809 | |
---|
4810 | try: |
---|
4811 | revision_number = get_revision_number() |
---|
4812 | except: |
---|
4813 | revision_number = None |
---|
4814 | # writing a string so None can be written |
---|
4815 | outfile.revision_number = str(revision_number) |
---|
4816 | #times - A list or array of the time slice times OR a start time |
---|
4817 | #times = ensure_numeric(times) |
---|
4818 | #Start time in seconds since the epoch (midnight 1/1/1970) |
---|
4819 | |
---|
4820 | # this is being used to seperate one number from a list. |
---|
4821 | # what it is actually doing is sorting lists from numeric arrays. |
---|
4822 | if type(times) is list or type(times) is ArrayType: |
---|
4823 | number_of_times = len(times) |
---|
4824 | times = ensure_numeric(times) |
---|
4825 | if number_of_times == 0: |
---|
4826 | starttime = 0 |
---|
4827 | else: |
---|
4828 | starttime = times[0] |
---|
4829 | times = times - starttime #Store relative times |
---|
4830 | else: |
---|
4831 | number_of_times = 0 |
---|
4832 | starttime = times |
---|
4833 | #times = ensure_numeric([]) |
---|
4834 | outfile.starttime = starttime |
---|
4835 | # dimension definitions |
---|
4836 | outfile.createDimension('number_of_volumes', number_of_volumes) |
---|
4837 | outfile.createDimension('number_of_vertices', 3) |
---|
4838 | outfile.createDimension('numbers_in_range', 2) |
---|
4839 | |
---|
4840 | if smoothing is True: |
---|
4841 | outfile.createDimension('number_of_points', number_of_points) |
---|
4842 | |
---|
4843 | # FIXME(Ole): This will cause sww files for paralle domains to |
---|
4844 | # have ghost nodes stored (but not used by triangles). |
---|
4845 | # To clean this up, we have to change get_vertex_values and |
---|
4846 | # friends in quantity.py (but I can't be bothered right now) |
---|
4847 | else: |
---|
4848 | outfile.createDimension('number_of_points', 3*number_of_volumes) |
---|
4849 | outfile.createDimension('number_of_timesteps', number_of_times) |
---|
4850 | |
---|
4851 | # variable definitions |
---|
4852 | outfile.createVariable('x', precision, ('number_of_points',)) |
---|
4853 | outfile.createVariable('y', precision, ('number_of_points',)) |
---|
4854 | outfile.createVariable('elevation', precision, ('number_of_points',)) |
---|
4855 | q = 'elevation' |
---|
4856 | outfile.createVariable(q+Write_sww.RANGE, precision, |
---|
4857 | ('numbers_in_range',)) |
---|
4858 | # The values are initally filled with large (10e+36) numbers. |
---|
4859 | # I'm relying on this feature. Maybe I shouldn't? |
---|
4860 | outfile.variables[q+Write_sww.RANGE][1] = \ |
---|
4861 | -1*outfile.variables[q+Write_sww.RANGE][1] |
---|
4862 | |
---|
4863 | #FIXME: Backwards compatibility |
---|
4864 | outfile.createVariable('z', precision, ('number_of_points',)) |
---|
4865 | ################################# |
---|
4866 | |
---|
4867 | outfile.createVariable('volumes', Int, ('number_of_volumes', |
---|
4868 | 'number_of_vertices')) |
---|
4869 | |
---|
4870 | outfile.createVariable('time', precision, |
---|
4871 | ('number_of_timesteps',)) |
---|
4872 | |
---|
4873 | for q in Write_sww.sww_quantities: |
---|
4874 | outfile.createVariable(q, precision, |
---|
4875 | ('number_of_timesteps', |
---|
4876 | 'number_of_points')) |
---|
4877 | outfile.createVariable(q+Write_sww.RANGE, precision, |
---|
4878 | ('numbers_in_range',)) |
---|
4879 | # Initialising the max value to a very small number. |
---|
4880 | # It assumes that netcdf initialises it to a very large number |
---|
4881 | outfile.variables[q+Write_sww.RANGE][1] = \ |
---|
4882 | -outfile.variables[q+Write_sww.RANGE][1] |
---|
4883 | |
---|
4884 | if type(times) is list or type(times) is ArrayType: |
---|
4885 | outfile.variables['time'][:] = times #Store time relative |
---|
4886 | |
---|
4887 | if verbose: |
---|
4888 | print '------------------------------------------------' |
---|
4889 | print 'Statistics:' |
---|
4890 | print ' t in [%f, %f], len(t) == %d'\ |
---|
4891 | %(min(times.flat), max(times.flat), len(times.flat)) |
---|
4892 | |
---|
4893 | |
---|
4894 | def triangulation(self,outfile, points_utm, volumes, |
---|
4895 | elevation, zone=None, new_origin=None, |
---|
4896 | points_georeference=None, verbose=False): |
---|
4897 | """ |
---|
4898 | |
---|
4899 | new_origin - qa georeference that the points can be set to. (Maybe |
---|
4900 | do this before calling this function.) |
---|
4901 | |
---|
4902 | points_utm - currently a list or array of the points in UTM. |
---|
4903 | points_georeference - the georeference of the points_utm |
---|
4904 | |
---|
4905 | How about passing new_origin and current_origin. |
---|
4906 | If you get both, do a convertion from the old to the new. |
---|
4907 | |
---|
4908 | If you only get new_origin, the points are absolute, |
---|
4909 | convert to relative |
---|
4910 | |
---|
4911 | if you only get the current_origin the points are relative, store |
---|
4912 | as relative. |
---|
4913 | |
---|
4914 | if you get no georefs create a new georef based on the minimums of |
---|
4915 | points_utm. (Another option would be to default to absolute) |
---|
4916 | |
---|
4917 | Yes, and this is done in another part of the code. |
---|
4918 | Probably geospatial. |
---|
4919 | |
---|
4920 | If you don't supply either geo_refs, then supply a zone. If not |
---|
4921 | the default zone will be used. |
---|
4922 | |
---|
4923 | |
---|
4924 | precon |
---|
4925 | |
---|
4926 | header has been called. |
---|
4927 | """ |
---|
4928 | |
---|
4929 | number_of_points = len(points_utm) |
---|
4930 | volumes = array(volumes) |
---|
4931 | points_utm = array(points_utm) |
---|
4932 | |
---|
4933 | # given the two geo_refs and the points, do the stuff |
---|
4934 | # described in the method header |
---|
4935 | # if this is needed else where, pull out as a function |
---|
4936 | points_georeference = ensure_geo_reference(points_georeference) |
---|
4937 | new_origin = ensure_geo_reference(new_origin) |
---|
4938 | if new_origin is None and points_georeference is not None: |
---|
4939 | points = points_utm |
---|
4940 | geo_ref = points_georeference |
---|
4941 | else: |
---|
4942 | if new_origin is None: |
---|
4943 | new_origin = Geo_reference(zone,min(points_utm[:,0]), |
---|
4944 | min(points_utm[:,1])) |
---|
4945 | points = new_origin.change_points_geo_ref(points_utm, |
---|
4946 | points_georeference) |
---|
4947 | geo_ref = new_origin |
---|
4948 | |
---|
4949 | # At this stage I need a georef and points |
---|
4950 | # the points are relative to the georef |
---|
4951 | geo_ref.write_NetCDF(outfile) |
---|
4952 | |
---|
4953 | # This will put the geo ref in the middle |
---|
4954 | #geo_ref=Geo_reference(refzone,(max(x)+min(x))/2.0,(max(x)+min(y))/2.) |
---|
4955 | |
---|
4956 | x = points[:,0] |
---|
4957 | y = points[:,1] |
---|
4958 | z = outfile.variables['z'][:] |
---|
4959 | |
---|
4960 | if verbose: |
---|
4961 | print '------------------------------------------------' |
---|
4962 | print 'More Statistics:' |
---|
4963 | print ' Extent (/lon):' |
---|
4964 | print ' x in [%f, %f], len(lat) == %d'\ |
---|
4965 | %(min(x), max(x), |
---|
4966 | len(x)) |
---|
4967 | print ' y in [%f, %f], len(lon) == %d'\ |
---|
4968 | %(min(y), max(y), |
---|
4969 | len(y)) |
---|
4970 | print ' z in [%f, %f], len(z) == %d'\ |
---|
4971 | %(min(elevation), max(elevation), |
---|
4972 | len(elevation)) |
---|
4973 | print 'geo_ref: ',geo_ref |
---|
4974 | print '------------------------------------------------' |
---|
4975 | |
---|
4976 | #z = resize(bath_grid,outfile.variables['z'][:].shape) |
---|
4977 | outfile.variables['x'][:] = points[:,0] #- geo_ref.get_xllcorner() |
---|
4978 | outfile.variables['y'][:] = points[:,1] #- geo_ref.get_yllcorner() |
---|
4979 | outfile.variables['z'][:] = elevation |
---|
4980 | outfile.variables['elevation'][:] = elevation #FIXME HACK |
---|
4981 | outfile.variables['volumes'][:] = volumes.astype(Int32) #On Opteron 64 |
---|
4982 | |
---|
4983 | q = 'elevation' |
---|
4984 | # This updates the _range values |
---|
4985 | outfile.variables[q+Write_sww.RANGE][0] = min(elevation) |
---|
4986 | outfile.variables[q+Write_sww.RANGE][1] = max(elevation) |
---|
4987 | |
---|
4988 | def quantities(self, outfile, precision=Float, |
---|
4989 | slice_index=None, time=None, |
---|
4990 | verbose=False, **quant): |
---|
4991 | """ |
---|
4992 | Write the quantity info. |
---|
4993 | |
---|
4994 | **quant is extra keyword arguments passed in. These must be |
---|
4995 | the sww quantities, currently; stage, xmomentum, ymomentum. |
---|
4996 | |
---|
4997 | if the time array is already been built, use the slice_index |
---|
4998 | to specify the index. |
---|
4999 | |
---|
5000 | Otherwise, use time to increase the time dimension |
---|
5001 | |
---|
5002 | Maybe make this general, but the viewer assumes these quantities, |
---|
5003 | so maybe we don't want it general - unless the viewer is general |
---|
5004 | |
---|
5005 | precon |
---|
5006 | triangulation and |
---|
5007 | header have been called. |
---|
5008 | """ |
---|
5009 | |
---|
5010 | if time is not None: |
---|
5011 | file_time = outfile.variables['time'] |
---|
5012 | slice_index = len(file_time) |
---|
5013 | file_time[slice_index] = time |
---|
5014 | |
---|
5015 | # write the conserved quantities from Doamin. |
---|
5016 | # Typically stage, xmomentum, ymomentum |
---|
5017 | # other quantities will be ignored, silently. |
---|
5018 | for q in Write_sww.sww_quantities: |
---|
5019 | if not quant.has_key(q): |
---|
5020 | msg = 'SWW file can not write quantity %s' %q |
---|
5021 | raise NewQuantity, msg |
---|
5022 | else: |
---|
5023 | q_values = quant[q] |
---|
5024 | outfile.variables[q][slice_index] = q_values.astype(precision) |
---|
5025 | |
---|
5026 | # This updates the _range values |
---|
5027 | q_range = outfile.variables[q+Write_sww.RANGE][:] |
---|
5028 | q_values_min = min(q_values) |
---|
5029 | if q_values_min < q_range[0]: |
---|
5030 | outfile.variables[q+Write_sww.RANGE][0] = q_values_min |
---|
5031 | q_values_max = max(q_values) |
---|
5032 | if q_values_max > q_range[1]: |
---|
5033 | outfile.variables[q+Write_sww.RANGE][1] = q_values_max |
---|
5034 | |
---|
5035 | def verbose_quantities(self, outfile): |
---|
5036 | print '------------------------------------------------' |
---|
5037 | print 'More Statistics:' |
---|
5038 | for q in Write_sww.sww_quantities: |
---|
5039 | print ' %s in [%f, %f]' %(q |
---|
5040 | ,outfile.variables[q+Write_sww.RANGE][0] |
---|
5041 | ,outfile.variables[q+Write_sww.RANGE][1] |
---|
5042 | ) |
---|
5043 | print '------------------------------------------------' |
---|
5044 | |
---|
5045 | def obsolete_write_sww_time_slices(outfile, has, uas, vas, elevation, |
---|
5046 | mean_stage=0, zscale=1, |
---|
5047 | verbose=False): |
---|
5048 | #Time stepping |
---|
5049 | stage = outfile.variables['stage'] |
---|
5050 | xmomentum = outfile.variables['xmomentum'] |
---|
5051 | ymomentum = outfile.variables['ymomentum'] |
---|
5052 | |
---|
5053 | n = len(has) |
---|
5054 | j=0 |
---|
5055 | for ha, ua, va in map(None, has, uas, vas): |
---|
5056 | if verbose and j%((n+10)/10)==0: print ' Doing %d of %d' %(j, n) |
---|
5057 | w = zscale*ha + mean_stage |
---|
5058 | stage[j] = w |
---|
5059 | h = w - elevation |
---|
5060 | xmomentum[j] = ua*h |
---|
5061 | ymomentum[j] = -1*va*h # -1 since in mux files south is positive. |
---|
5062 | j += 1 |
---|
5063 | |
---|
5064 | def urs2txt(basename_in, location_index=None): |
---|
5065 | """ |
---|
5066 | Not finished or tested |
---|
5067 | """ |
---|
5068 | |
---|
5069 | files_in = [basename_in + WAVEHEIGHT_MUX_LABEL, |
---|
5070 | basename_in + EAST_VELOCITY_LABEL, |
---|
5071 | basename_in + NORTH_VELOCITY_LABEL] |
---|
5072 | quantities = ['HA','UA','VA'] |
---|
5073 | |
---|
5074 | d = "," |
---|
5075 | |
---|
5076 | # instanciate urs_points of the three mux files. |
---|
5077 | mux = {} |
---|
5078 | for quantity, file in map(None, quantities, files_in): |
---|
5079 | mux[quantity] = Urs_points(file) |
---|
5080 | |
---|
5081 | # Could check that the depth is the same. (hashing) |
---|
5082 | |
---|
5083 | # handle to a mux file to do depth stuff |
---|
5084 | a_mux = mux[quantities[0]] |
---|
5085 | |
---|
5086 | # Convert to utm |
---|
5087 | latitudes = a_mux.lonlatdep[:,1] |
---|
5088 | longitudes = a_mux.lonlatdep[:,0] |
---|
5089 | points_utm, zone = convert_from_latlon_to_utm( \ |
---|
5090 | latitudes=latitudes, longitudes=longitudes) |
---|
5091 | #print "points_utm", points_utm |
---|
5092 | #print "zone", zone |
---|
5093 | depths = a_mux.lonlatdep[:,2] # |
---|
5094 | |
---|
5095 | fid = open(basename_in + '.txt', 'w') |
---|
5096 | |
---|
5097 | fid.write("zone: " + str(zone) + "\n") |
---|
5098 | |
---|
5099 | if location_index is not None: |
---|
5100 | #Title |
---|
5101 | li = location_index |
---|
5102 | fid.write('location_index'+d+'lat'+d+ 'long' +d+ 'Easting' +d+ \ |
---|
5103 | 'Northing' + "\n") |
---|
5104 | fid.write(str(li) +d+ str(latitudes[li])+d+ \ |
---|
5105 | str(longitudes[li]) +d+ str(points_utm[li][0]) +d+ \ |
---|
5106 | str(points_utm[li][01]) + "\n") |
---|
5107 | |
---|
5108 | # the non-time dependent stuff |
---|
5109 | #Title |
---|
5110 | fid.write('location_index'+d+'lat'+d+ 'long' +d+ 'Easting' +d+ \ |
---|
5111 | 'Northing' +d+ 'depth m' + "\n") |
---|
5112 | i = 0 |
---|
5113 | for depth, point_utm, lat, long in map(None, depths, |
---|
5114 | points_utm, latitudes, |
---|
5115 | longitudes): |
---|
5116 | |
---|
5117 | fid.write(str(i) +d+ str(lat)+d+ str(long) +d+ str(point_utm[0]) +d+ \ |
---|
5118 | str(point_utm[01]) +d+ str(depth) + "\n") |
---|
5119 | i +=1 |
---|
5120 | #Time dependent |
---|
5121 | if location_index is not None: |
---|
5122 | time_step = a_mux.time_step |
---|
5123 | i = 0 |
---|
5124 | #Title |
---|
5125 | fid.write('time' +d+ 'HA depth m'+d+ \ |
---|
5126 | 'UA momentum East x m/sec' +d+ 'VA momentum North y m/sec' \ |
---|
5127 | + "\n") |
---|
5128 | for HA, UA, VA in map(None, mux['HA'], mux['UA'], mux['VA']): |
---|
5129 | fid.write(str(i*time_step) +d+ str(HA[location_index])+d+ \ |
---|
5130 | str(UA[location_index]) +d+ str(VA[location_index]) \ |
---|
5131 | + "\n") |
---|
5132 | |
---|
5133 | i +=1 |
---|
5134 | |
---|
5135 | class Urs_points: |
---|
5136 | """ |
---|
5137 | Read the info in URS mux files. |
---|
5138 | |
---|
5139 | for the quantities heres a correlation between the file names and |
---|
5140 | what they mean; |
---|
5141 | z-mux is height above sea level, m |
---|
5142 | e-mux is velocity is Eastern direction, m/s |
---|
5143 | n-mux is velocity is Northern direction, m/s |
---|
5144 | """ |
---|
5145 | def __init__(self,urs_file): |
---|
5146 | self.iterated = False |
---|
5147 | columns = 3 # long, lat , depth |
---|
5148 | mux_file = open(urs_file, 'rb') |
---|
5149 | |
---|
5150 | # Number of points/stations |
---|
5151 | (self.points_num,)= unpack('i',mux_file.read(4)) |
---|
5152 | |
---|
5153 | # nt, int - Number of time steps |
---|
5154 | (self.time_step_count,)= unpack('i',mux_file.read(4)) |
---|
5155 | #print "self.time_step_count", self.time_step_count |
---|
5156 | #dt, float - time step, seconds |
---|
5157 | (self.time_step,) = unpack('f', mux_file.read(4)) |
---|
5158 | #print "self.time_step", self.time_step |
---|
5159 | msg = "Bad data in the urs file." |
---|
5160 | if self.points_num < 0: |
---|
5161 | mux_file.close() |
---|
5162 | raise ANUGAError, msg |
---|
5163 | if self.time_step_count < 0: |
---|
5164 | mux_file.close() |
---|
5165 | raise ANUGAError, msg |
---|
5166 | if self.time_step < 0: |
---|
5167 | mux_file.close() |
---|
5168 | raise ANUGAError, msg |
---|
5169 | |
---|
5170 | # the depth is in meters, and it is the distance from the ocean |
---|
5171 | # to the sea bottom. |
---|
5172 | lonlatdep = p_array.array('f') |
---|
5173 | lonlatdep.read(mux_file, columns * self.points_num) |
---|
5174 | lonlatdep = array(lonlatdep, typecode=Float) |
---|
5175 | lonlatdep = reshape(lonlatdep, (self.points_num, columns)) |
---|
5176 | #print 'lonlatdep',lonlatdep |
---|
5177 | self.lonlatdep = lonlatdep |
---|
5178 | |
---|
5179 | self.mux_file = mux_file |
---|
5180 | # check this array |
---|
5181 | |
---|
5182 | def __iter__(self): |
---|
5183 | """ |
---|
5184 | iterate over quantity data which is with respect to time. |
---|
5185 | |
---|
5186 | Note: You can only interate once over an object |
---|
5187 | |
---|
5188 | returns quantity infomation for each time slice |
---|
5189 | """ |
---|
5190 | msg = "You can only interate once over a urs file." |
---|
5191 | assert not self.iterated, msg |
---|
5192 | self.iter_time_step = 0 |
---|
5193 | self.iterated = True |
---|
5194 | return self |
---|
5195 | |
---|
5196 | def next(self): |
---|
5197 | if self.time_step_count == self.iter_time_step: |
---|
5198 | self.close() |
---|
5199 | raise StopIteration |
---|
5200 | #Read in a time slice from mux file |
---|
5201 | hz_p_array = p_array.array('f') |
---|
5202 | hz_p_array.read(self.mux_file, self.points_num) |
---|
5203 | hz_p = array(hz_p_array, typecode=Float) |
---|
5204 | self.iter_time_step += 1 |
---|
5205 | |
---|
5206 | return hz_p |
---|
5207 | |
---|
5208 | def close(self): |
---|
5209 | self.mux_file.close() |
---|
5210 | |
---|
5211 | #### END URS UNGRIDDED 2 SWW ### |
---|
5212 | |
---|
5213 | |
---|
5214 | def start_screen_catcher(dir_name, myid='', numprocs='', extra_info='', |
---|
5215 | print_to_screen=False, verbose=False): |
---|
5216 | """ |
---|
5217 | Used to store screen output and errors to file, if run on multiple |
---|
5218 | processes eachprocessor will have its own output and error file. |
---|
5219 | |
---|
5220 | extra_info - is used as a string that can identify outputs with another |
---|
5221 | string eg. '_other' |
---|
5222 | """ |
---|
5223 | import sys |
---|
5224 | dir_name = dir_name |
---|
5225 | if access(dir_name,W_OK) == 0: |
---|
5226 | if verbose: print 'Make directory %s' %dir_name |
---|
5227 | if verbose: print "myid", myid |
---|
5228 | mkdir (dir_name,0777) |
---|
5229 | if myid <>'': |
---|
5230 | myid = '_'+str(myid) |
---|
5231 | if numprocs <>'': |
---|
5232 | numprocs = '_'+str(numprocs) |
---|
5233 | if extra_info <>'': |
---|
5234 | extra_info = '_'+str(extra_info) |
---|
5235 | screen_output_name = dir_name + "screen_output%s%s%s.txt" %(myid, |
---|
5236 | numprocs, |
---|
5237 | extra_info) |
---|
5238 | screen_error_name = dir_name + "screen_error%s%s%s.txt" %(myid, |
---|
5239 | numprocs, |
---|
5240 | extra_info) |
---|
5241 | print screen_output_name |
---|
5242 | #used to catch screen output to file |
---|
5243 | sys.stdout = Screen_Catcher(screen_output_name) |
---|
5244 | sys.stderr = Screen_Catcher(screen_error_name) |
---|
5245 | |
---|
5246 | class Screen_Catcher: |
---|
5247 | """this simply catches the screen output and stores it to file defined by |
---|
5248 | start_screen_catcher (above) |
---|
5249 | """ |
---|
5250 | |
---|
5251 | def __init__(self, filename): |
---|
5252 | self.filename = filename |
---|
5253 | |
---|
5254 | if exists(self.filename)is True: |
---|
5255 | print'Old existing file "%s" has been deleted' %(self.filename) |
---|
5256 | remove(self.filename) |
---|
5257 | |
---|
5258 | def write(self, stuff): |
---|
5259 | fid = open(self.filename, 'a') |
---|
5260 | fid.write(stuff) |
---|
5261 | # if print_to_screen: print stuff |
---|
5262 | |
---|
5263 | def copy_code_files(dir_name, filename1, filename2): |
---|
5264 | """Copies "filename1" and "filename2" to "dir_name". Very useful for |
---|
5265 | information management |
---|
5266 | filename1 and filename2 are both absolute pathnames |
---|
5267 | """ |
---|
5268 | |
---|
5269 | if access(dir_name,F_OK) == 0: |
---|
5270 | print 'Make directory %s' %dir_name |
---|
5271 | mkdir (dir_name,0777) |
---|
5272 | shutil.copy(filename1, dir_name + sep + basename(filename1)) |
---|
5273 | shutil.copy(filename2, dir_name + sep + basename(filename2)) |
---|
5274 | # copy (__file__, project.output_run_time_dir + basename(__file__)) |
---|
5275 | print 'Files %s and %s copied' %(filename1, filename2) |
---|
5276 | |
---|
5277 | def get_data_from_file(filename,separator_value = ','): |
---|
5278 | """ |
---|
5279 | Read in data information from file and |
---|
5280 | |
---|
5281 | Returns: |
---|
5282 | header_fields, a string? of the first line separated |
---|
5283 | by the 'separator_value' |
---|
5284 | |
---|
5285 | data, a array (N data columns X M lines) in the file |
---|
5286 | excluding the header |
---|
5287 | |
---|
5288 | NOTE: wont deal with columns with different lenghts and there must be |
---|
5289 | no blank lines at the end. |
---|
5290 | """ |
---|
5291 | |
---|
5292 | fid = open(filename) |
---|
5293 | lines = fid.readlines() |
---|
5294 | |
---|
5295 | fid.close() |
---|
5296 | |
---|
5297 | header_line = lines[0] |
---|
5298 | header_fields = header_line.split(separator_value) |
---|
5299 | |
---|
5300 | #array to store data, number in there is to allow float... |
---|
5301 | #i'm sure there is a better way! |
---|
5302 | data=array([],typecode=Float) |
---|
5303 | data=resize(data,((len(lines)-1),len(header_fields))) |
---|
5304 | # print 'number of fields',range(len(header_fields)) |
---|
5305 | # print 'number of lines',len(lines), shape(data) |
---|
5306 | # print'data',data[1,1],header_line |
---|
5307 | |
---|
5308 | array_number = 0 |
---|
5309 | line_number = 1 |
---|
5310 | while line_number < (len(lines)): |
---|
5311 | for i in range(len(header_fields)): |
---|
5312 | #this get line below the header, explaining the +1 |
---|
5313 | #and also the line_number can be used as the array index |
---|
5314 | fields = lines[line_number].split(separator_value) |
---|
5315 | #assign to array |
---|
5316 | data[array_number,i] = float(fields[i]) |
---|
5317 | |
---|
5318 | line_number = line_number +1 |
---|
5319 | array_number = array_number +1 |
---|
5320 | |
---|
5321 | return header_fields, data |
---|
5322 | |
---|
5323 | def store_parameters(verbose=False,**kwargs): |
---|
5324 | """ |
---|
5325 | Store "kwargs" into a temp csv file, if "completed" is a kwargs csv file is |
---|
5326 | kwargs[file_name] else it is kwargs[output_dir] + details_temp.csv |
---|
5327 | |
---|
5328 | Must have a file_name keyword arg, this is what is writing to. |
---|
5329 | might be a better way to do this using CSV module Writer and writeDict |
---|
5330 | |
---|
5331 | writes file to "output_dir" unless "completed" is in kwargs, then |
---|
5332 | it writes to "file_name" kwargs |
---|
5333 | |
---|
5334 | """ |
---|
5335 | import types |
---|
5336 | # import os |
---|
5337 | |
---|
5338 | # Check that kwargs is a dictionary |
---|
5339 | if type(kwargs) != types.DictType: |
---|
5340 | raise TypeError |
---|
5341 | |
---|
5342 | #is completed is kwargs? |
---|
5343 | try: |
---|
5344 | kwargs['completed'] |
---|
5345 | completed=True |
---|
5346 | except: |
---|
5347 | completed=False |
---|
5348 | |
---|
5349 | #get file name and removes from dict and assert that a file_name exists |
---|
5350 | if completed: |
---|
5351 | try: |
---|
5352 | file = str(kwargs['file_name']) |
---|
5353 | except: |
---|
5354 | raise 'kwargs must have file_name' |
---|
5355 | else: |
---|
5356 | #write temp file in output directory |
---|
5357 | try: |
---|
5358 | file = str(kwargs['output_dir'])+'detail_temp.csv' |
---|
5359 | except: |
---|
5360 | raise 'kwargs must have output_dir' |
---|
5361 | |
---|
5362 | #extracts the header info and the new line info |
---|
5363 | line='' |
---|
5364 | header='' |
---|
5365 | count=0 |
---|
5366 | keys = kwargs.keys() |
---|
5367 | keys.sort() |
---|
5368 | |
---|
5369 | #used the sorted keys to create the header and line data |
---|
5370 | for k in keys: |
---|
5371 | # print "%s = %s" %(k, kwargs[k]) |
---|
5372 | header = header+str(k) |
---|
5373 | line = line+str(kwargs[k]) |
---|
5374 | count+=1 |
---|
5375 | if count <len(kwargs): |
---|
5376 | header = header+',' |
---|
5377 | line = line+',' |
---|
5378 | header+='\n' |
---|
5379 | line+='\n' |
---|
5380 | |
---|
5381 | # checks the header info, if the same, then write, if not create a new file |
---|
5382 | #try to open! |
---|
5383 | try: |
---|
5384 | fid = open(file,"r") |
---|
5385 | file_header=fid.readline() |
---|
5386 | fid.close() |
---|
5387 | if verbose: print 'read file header %s' %file_header |
---|
5388 | |
---|
5389 | except: |
---|
5390 | msg = 'try to create new file',file |
---|
5391 | if verbose: print msg |
---|
5392 | #tries to open file, maybe directory is bad |
---|
5393 | try: |
---|
5394 | fid = open(file,"w") |
---|
5395 | fid.write(header) |
---|
5396 | fid.close() |
---|
5397 | file_header=header |
---|
5398 | except: |
---|
5399 | msg = 'cannot create new file',file |
---|
5400 | raise msg |
---|
5401 | |
---|
5402 | #if header is same or this is a new file |
---|
5403 | if file_header==str(header): |
---|
5404 | fid=open(file,"a") |
---|
5405 | #write new line |
---|
5406 | fid.write(line) |
---|
5407 | fid.close() |
---|
5408 | else: |
---|
5409 | #backup plan, |
---|
5410 | # if header is different and has completed will append info to |
---|
5411 | #end of details_temp.cvs file in output directory |
---|
5412 | file = str(kwargs['output_dir'])+'detail_temp.csv' |
---|
5413 | fid=open(file,"a") |
---|
5414 | fid.write(header) |
---|
5415 | fid.write(line) |
---|
5416 | fid.close() |
---|
5417 | if verbose: print 'file',file_header.strip('\n') |
---|
5418 | if verbose: print 'head',header.strip('\n') |
---|
5419 | if file_header.strip('\n')==str(header): print 'they equal' |
---|
5420 | msg = 'WARNING: File header does not match input info, the input variables have changed, suggest to change file name' |
---|
5421 | print msg |
---|
5422 | |
---|
5423 | |
---|
5424 | |
---|
5425 | |
---|
5426 | |
---|
5427 | def get_maximum_inundation_elevation(filename, |
---|
5428 | polygon=None, |
---|
5429 | time_interval=None, |
---|
5430 | verbose=False): |
---|
5431 | |
---|
5432 | """Return highest elevation where depth > 0 |
---|
5433 | |
---|
5434 | Usage: |
---|
5435 | max_runup = get_maximum_inundation_elevation(filename, |
---|
5436 | polygon=None, |
---|
5437 | time_interval=None, |
---|
5438 | verbose=False) |
---|
5439 | |
---|
5440 | filename is a NetCDF sww file containing ANUGA model output. |
---|
5441 | Optional arguments polygon and time_interval restricts the maximum |
---|
5442 | runup calculation |
---|
5443 | to a points that lie within the specified polygon and time interval. |
---|
5444 | |
---|
5445 | If no inundation is found within polygon and time_interval the return value |
---|
5446 | is None signifying "No Runup" or "Everything is dry". |
---|
5447 | |
---|
5448 | See general function get_maximum_inundation_data for details. |
---|
5449 | |
---|
5450 | """ |
---|
5451 | |
---|
5452 | runup, _ = get_maximum_inundation_data(filename, |
---|
5453 | polygon=polygon, |
---|
5454 | time_interval=time_interval, |
---|
5455 | verbose=verbose) |
---|
5456 | return runup |
---|
5457 | |
---|
5458 | |
---|
5459 | |
---|
5460 | |
---|
5461 | def get_maximum_inundation_location(filename, |
---|
5462 | polygon=None, |
---|
5463 | time_interval=None, |
---|
5464 | verbose=False): |
---|
5465 | """Return location of highest elevation where h > 0 |
---|
5466 | |
---|
5467 | |
---|
5468 | Usage: |
---|
5469 | max_runup_location = get_maximum_inundation_location(filename, |
---|
5470 | polygon=None, |
---|
5471 | time_interval=None, |
---|
5472 | verbose=False) |
---|
5473 | |
---|
5474 | filename is a NetCDF sww file containing ANUGA model output. |
---|
5475 | Optional arguments polygon and time_interval restricts the maximum |
---|
5476 | runup calculation |
---|
5477 | to a points that lie within the specified polygon and time interval. |
---|
5478 | |
---|
5479 | If no inundation is found within polygon and time_interval the return value |
---|
5480 | is None signifying "No Runup" or "Everything is dry". |
---|
5481 | |
---|
5482 | See general function get_maximum_inundation_data for details. |
---|
5483 | """ |
---|
5484 | |
---|
5485 | _, max_loc = get_maximum_inundation_data(filename, |
---|
5486 | polygon=polygon, |
---|
5487 | time_interval=time_interval, |
---|
5488 | verbose=verbose) |
---|
5489 | return max_loc |
---|
5490 | |
---|
5491 | |
---|
5492 | |
---|
5493 | def get_maximum_inundation_data(filename, polygon=None, time_interval=None, |
---|
5494 | use_centroid_values=False, |
---|
5495 | verbose=False): |
---|
5496 | """Compute maximum run up height from sww file. |
---|
5497 | |
---|
5498 | |
---|
5499 | Usage: |
---|
5500 | runup, location = get_maximum_inundation_data(filename, |
---|
5501 | polygon=None, |
---|
5502 | time_interval=None, |
---|
5503 | verbose=False) |
---|
5504 | |
---|
5505 | |
---|
5506 | Algorithm is as in get_maximum_inundation_elevation from |
---|
5507 | shallow_water_domain |
---|
5508 | except that this function works with the sww file and computes the maximal |
---|
5509 | runup height over multiple timesteps. |
---|
5510 | |
---|
5511 | Optional arguments polygon and time_interval restricts the |
---|
5512 | maximum runup calculation |
---|
5513 | to a points that lie within the specified polygon and time interval. |
---|
5514 | Polygon is |
---|
5515 | assumed to be in (absolute) UTM coordinates in the same zone as domain. |
---|
5516 | |
---|
5517 | If no inundation is found within polygon and time_interval the return value |
---|
5518 | is None signifying "No Runup" or "Everything is dry". |
---|
5519 | """ |
---|
5520 | |
---|
5521 | # We are using nodal values here as that is what is stored in sww files. |
---|
5522 | |
---|
5523 | # Water depth below which it is considered to be 0 in the model |
---|
5524 | # FIXME (Ole): Allow this to be specified as a keyword argument as well |
---|
5525 | |
---|
5526 | from anuga.utilities.polygon import inside_polygon |
---|
5527 | from anuga.config import minimum_allowed_height |
---|
5528 | from Scientific.IO.NetCDF import NetCDFFile |
---|
5529 | |
---|
5530 | dir, base = os.path.split(filename) |
---|
5531 | |
---|
5532 | iterate_over = get_all_swwfiles(dir,base) |
---|
5533 | |
---|
5534 | # Read sww file |
---|
5535 | if verbose: |
---|
5536 | print 'Reading from %s' %filename |
---|
5537 | # FIXME: Use general swwstats (when done) |
---|
5538 | |
---|
5539 | maximal_runup = None |
---|
5540 | maximal_runup_location = None |
---|
5541 | |
---|
5542 | for file, swwfile in enumerate (iterate_over): |
---|
5543 | |
---|
5544 | # Read sww file |
---|
5545 | filename = join(dir,swwfile+'.sww') |
---|
5546 | |
---|
5547 | if verbose: |
---|
5548 | print 'Reading from %s' %filename |
---|
5549 | # FIXME: Use general swwstats (when done) |
---|
5550 | |
---|
5551 | fid = NetCDFFile(filename) |
---|
5552 | |
---|
5553 | # Get geo_reference |
---|
5554 | # sww files don't have to have a geo_ref |
---|
5555 | try: |
---|
5556 | geo_reference = Geo_reference(NetCDFObject=fid) |
---|
5557 | except AttributeError, e: |
---|
5558 | geo_reference = Geo_reference() # Default georef object |
---|
5559 | |
---|
5560 | xllcorner = geo_reference.get_xllcorner() |
---|
5561 | yllcorner = geo_reference.get_yllcorner() |
---|
5562 | zone = geo_reference.get_zone() |
---|
5563 | |
---|
5564 | # Get extent |
---|
5565 | volumes = fid.variables['volumes'][:] |
---|
5566 | x = fid.variables['x'][:] + xllcorner |
---|
5567 | y = fid.variables['y'][:] + yllcorner |
---|
5568 | |
---|
5569 | |
---|
5570 | # Get the relevant quantities |
---|
5571 | elevation = fid.variables['elevation'][:] |
---|
5572 | stage = fid.variables['stage'][:] |
---|
5573 | |
---|
5574 | |
---|
5575 | # Here's where one could convert nodal information to centroid |
---|
5576 | # information |
---|
5577 | # but is probably something we need to write in C. |
---|
5578 | # Here's a Python thought which is NOT finished!!! |
---|
5579 | if use_centroid_values is True: |
---|
5580 | x = get_centroid_values(x, volumes) |
---|
5581 | y = get_centroid_values(y, volumes) |
---|
5582 | elevation = get_centroid_values(elevation, volumes) |
---|
5583 | |
---|
5584 | |
---|
5585 | # Spatial restriction |
---|
5586 | if polygon is not None: |
---|
5587 | msg = 'polygon must be a sequence of points.' |
---|
5588 | assert len(polygon[0]) == 2, msg |
---|
5589 | # FIXME (Ole): Make a generic polygon input check in polygon.py |
---|
5590 | # and call it here |
---|
5591 | |
---|
5592 | points = concatenate((x[:,NewAxis], y[:,NewAxis]), axis=1) |
---|
5593 | |
---|
5594 | point_indices = inside_polygon(points, polygon) |
---|
5595 | |
---|
5596 | # Restrict quantities to polygon |
---|
5597 | elevation = take(elevation, point_indices) |
---|
5598 | stage = take(stage, point_indices, axis=1) |
---|
5599 | |
---|
5600 | # Get info for location of maximal runup |
---|
5601 | points_in_polygon = take(points, point_indices) |
---|
5602 | x = points_in_polygon[:,0] |
---|
5603 | y = points_in_polygon[:,1] |
---|
5604 | else: |
---|
5605 | # Take all points |
---|
5606 | point_indices = arange(len(x)) |
---|
5607 | |
---|
5608 | |
---|
5609 | # Temporal restriction |
---|
5610 | time = fid.variables['time'][:] |
---|
5611 | all_timeindices = arange(len(time)) |
---|
5612 | if time_interval is not None: |
---|
5613 | |
---|
5614 | msg = 'time_interval must be a sequence of length 2.' |
---|
5615 | assert len(time_interval) == 2, msg |
---|
5616 | msg = 'time_interval %s must not be decreasing.' %(time_interval) |
---|
5617 | assert time_interval[1] >= time_interval[0], msg |
---|
5618 | |
---|
5619 | msg = 'Specified time interval [%.8f:%.8f]' %tuple(time_interval) |
---|
5620 | msg += ' must does not match model time interval: [%.8f, %.8f]\n'\ |
---|
5621 | %(time[0], time[-1]) |
---|
5622 | if time_interval[1] < time[0]: raise ValueError(msg) |
---|
5623 | if time_interval[0] > time[-1]: raise ValueError(msg) |
---|
5624 | |
---|
5625 | # Take time indices corresponding to interval (& is bitwise AND) |
---|
5626 | timesteps = compress((time_interval[0] <= time) & (time <= time_interval[1]), |
---|
5627 | all_timeindices) |
---|
5628 | |
---|
5629 | |
---|
5630 | msg = 'time_interval %s did not include any model timesteps.' %(time_interval) |
---|
5631 | assert not alltrue(timesteps == 0), msg |
---|
5632 | |
---|
5633 | |
---|
5634 | else: |
---|
5635 | # Take them all |
---|
5636 | timesteps = all_timeindices |
---|
5637 | |
---|
5638 | |
---|
5639 | fid.close() |
---|
5640 | |
---|
5641 | # Compute maximal runup for each timestep |
---|
5642 | #maximal_runup = None |
---|
5643 | #maximal_runup_location = None |
---|
5644 | #maximal_runups = [None] |
---|
5645 | #maximal_runup_locations = [None] |
---|
5646 | |
---|
5647 | for i in timesteps: |
---|
5648 | if use_centroid_values is True: |
---|
5649 | stage_i = get_centroid_values(stage[i,:], volumes) |
---|
5650 | else: |
---|
5651 | stage_i = stage[i,:] |
---|
5652 | |
---|
5653 | depth = stage_i - elevation |
---|
5654 | |
---|
5655 | # Get wet nodes i.e. nodes with depth>0 within given region and timesteps |
---|
5656 | wet_nodes = compress(depth > minimum_allowed_height, arange(len(depth))) |
---|
5657 | |
---|
5658 | if alltrue(wet_nodes == 0): |
---|
5659 | runup = None |
---|
5660 | else: |
---|
5661 | # Find maximum elevation among wet nodes |
---|
5662 | wet_elevation = take(elevation, wet_nodes) |
---|
5663 | |
---|
5664 | runup_index = argmax(wet_elevation) |
---|
5665 | runup = max(wet_elevation) |
---|
5666 | assert wet_elevation[runup_index] == runup # Must always be True |
---|
5667 | if runup > maximal_runup: |
---|
5668 | maximal_runup = runup # This works even if maximal_runups is None |
---|
5669 | #print "NEW RUNUP",runup |
---|
5670 | |
---|
5671 | # Record location |
---|
5672 | wet_x = take(x, wet_nodes) |
---|
5673 | wet_y = take(y, wet_nodes) |
---|
5674 | maximal_runup_location = [wet_x[runup_index], wet_y[runup_index]] |
---|
5675 | |
---|
5676 | #print 'maximal_runup, maximal_runup_location',maximal_runup, maximal_runup_location |
---|
5677 | return maximal_runup, maximal_runup_location |
---|
5678 | |
---|
5679 | def get_all_swwfiles(look_in_dir='',base_name='',verbose=False): |
---|
5680 | ''' |
---|
5681 | Finds all the sww files in a "look_in_dir" which contains a "base_name". |
---|
5682 | will accept base_name with or without the extension ".sww" |
---|
5683 | |
---|
5684 | Returns: a list of strings |
---|
5685 | |
---|
5686 | Usage: iterate_over = get_all_swwfiles(dir, name) |
---|
5687 | then |
---|
5688 | for swwfile in iterate_over: |
---|
5689 | do stuff |
---|
5690 | |
---|
5691 | Check "export_grids" and "get_maximum_inundation_data" for examples |
---|
5692 | ''' |
---|
5693 | |
---|
5694 | #plus tests the extension |
---|
5695 | name, extension = os.path.splitext(base_name) |
---|
5696 | |
---|
5697 | if extension <>'' and extension <> '.sww': |
---|
5698 | msg = msg='file %s %s must be an NetCDF sww file!'%(base_name,extension) |
---|
5699 | raise IOError, msg |
---|
5700 | |
---|
5701 | if look_in_dir == "": |
---|
5702 | look_in_dir = "." # Unix compatibility |
---|
5703 | |
---|
5704 | dir_ls = os.listdir(look_in_dir) |
---|
5705 | #print 'dir_ls',dir_ls, base |
---|
5706 | iterate_over = [x[:-4] for x in dir_ls if name in x and x[-4:] == '.sww'] |
---|
5707 | if len(iterate_over) == 0: |
---|
5708 | msg = 'No files of the base name %s'\ |
---|
5709 | %(name) |
---|
5710 | raise IOError, msg |
---|
5711 | if verbose: print 'iterate over %s' %(iterate_over) |
---|
5712 | |
---|
5713 | return iterate_over |
---|
5714 | |
---|
5715 | def get_all_files_with_extension(look_in_dir='',base_name='',extension='.sww',verbose=False): |
---|
5716 | ''' |
---|
5717 | Finds all the sww files in a "look_in_dir" which contains a "base_name". |
---|
5718 | |
---|
5719 | |
---|
5720 | Returns: a list of strings |
---|
5721 | |
---|
5722 | Usage: iterate_over = get_all_swwfiles(dir, name) |
---|
5723 | then |
---|
5724 | for swwfile in iterate_over: |
---|
5725 | do stuff |
---|
5726 | |
---|
5727 | Check "export_grids" and "get_maximum_inundation_data" for examples |
---|
5728 | ''' |
---|
5729 | |
---|
5730 | #plus tests the extension |
---|
5731 | name, ext = os.path.splitext(base_name) |
---|
5732 | # print 'look_in_dir',look_in_dir |
---|
5733 | |
---|
5734 | if ext <>'' and ext <> extension: |
---|
5735 | msg = msg='base_name %s must be an file with %s extension!'%(base_name,extension) |
---|
5736 | raise IOError, msg |
---|
5737 | |
---|
5738 | if look_in_dir == "": |
---|
5739 | look_in_dir = "." # Unix compatibility |
---|
5740 | # print 'look_in_dir',look_in_dir, getcwd() |
---|
5741 | dir_ls = os.listdir(look_in_dir) |
---|
5742 | #print 'dir_ls',dir_ls, base_name |
---|
5743 | iterate_over = [x[:-4] for x in dir_ls if name in x and x[-4:] == extension] |
---|
5744 | if len(iterate_over) == 0: |
---|
5745 | msg = 'No files of the base name %s in %s'\ |
---|
5746 | %(name, look_in_dir) |
---|
5747 | raise IOError, msg |
---|
5748 | if verbose: print 'iterate over %s' %(iterate_over) |
---|
5749 | |
---|
5750 | return iterate_over |
---|
5751 | |
---|
5752 | def get_all_directories_with_name(look_in_dir='',base_name='',verbose=False): |
---|
5753 | ''' |
---|
5754 | Finds all the sww files in a "look_in_dir" which contains a "base_name". |
---|
5755 | |
---|
5756 | |
---|
5757 | Returns: a list of strings |
---|
5758 | |
---|
5759 | Usage: iterate_over = get_all_swwfiles(dir, name) |
---|
5760 | then |
---|
5761 | for swwfile in iterate_over: |
---|
5762 | do stuff |
---|
5763 | |
---|
5764 | Check "export_grids" and "get_maximum_inundation_data" for examples |
---|
5765 | ''' |
---|
5766 | |
---|
5767 | #plus tests the extension |
---|
5768 | |
---|
5769 | if look_in_dir == "": |
---|
5770 | look_in_dir = "." # Unix compatibility |
---|
5771 | # print 'look_in_dir',look_in_dir |
---|
5772 | dir_ls = os.listdir(look_in_dir) |
---|
5773 | # print 'dir_ls',dir_ls |
---|
5774 | iterate_over = [x for x in dir_ls if base_name in x] |
---|
5775 | if len(iterate_over) == 0: |
---|
5776 | msg = 'No files of the base name %s'\ |
---|
5777 | %(name) |
---|
5778 | raise IOError, msg |
---|
5779 | if verbose: print 'iterate over %s' %(iterate_over) |
---|
5780 | |
---|
5781 | return iterate_over |
---|
5782 | |
---|
5783 | |
---|
5784 | #------------------------------------------------------------- |
---|
5785 | if __name__ == "__main__": |
---|
5786 | #setting umask from config to force permissions for all files and directories |
---|
5787 | # created to the same. (it was noticed the "mpirun" doesn't honour the umask |
---|
5788 | # set in your .bashrc etc file) |
---|
5789 | from config import umask |
---|
5790 | import os |
---|
5791 | os.umask(umask) |
---|
5792 | |
---|