1 | ''' ops_GridSeriesFeature contains operations for GridSeriesFeatures''' |
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2 | from API import * |
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3 | from CSMLDocument import * |
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4 | from Scientific.IO.NetCDF import * #use this instead of cdms for now for it's simple write interface.. |
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5 | from Numeric import * |
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6 | |
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7 | |
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8 | def testmethod(self): |
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9 | print 'testmethod for gridseries feature' |
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10 | return 'testmethod - gridseries' |
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11 | |
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12 | def getAllowedSubsettings(self): |
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13 | return ['subsetToGridSeries'] |
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14 | |
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15 | def getDomainReference(self): |
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16 | #Inspects a time position list for the domain reference. |
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17 | #TODO, does not handle a file extract in place of a list. |
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18 | if isinstance(self.domain.domainReference,Parser.TimePositionList): |
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19 | time = {} |
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20 | time['t'] = self.domain.domainReference.timePositions |
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21 | domainref = time |
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22 | return domainref |
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23 | |
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24 | def getDomainComplement(self): |
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25 | #This will return a list containing one or more ordinates: |
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26 | #currently in form [Name, values] |
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27 | #assumes ordinate.axisValues is a file extract |
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28 | #TODO axisValues may be inline -see also domainReference for similar problem |
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29 | domaincomp ={} |
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30 | dc = self.domain.domainComplement |
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31 | #dc should be a grid! |
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32 | if isinstance(dc, Parser.Grid): |
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33 | for ordinate in dc.ordinates: |
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34 | domaincomp[ordinate.definesAxis]=ordinate.axisValues.getData() |
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35 | return domaincomp |
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36 | |
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37 | def getDomain(self): |
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38 | #returns both the domain reference axes and domain compliment axes in a single domain dictionary |
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39 | #axes are in no particular order |
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40 | domain = {} |
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41 | dr=getDomainReference(self) |
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42 | dc=getDomainComplement(self) |
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43 | for key in dc.keys(): |
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44 | domain[key]=dc[key] |
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45 | for key in dr.keys(): |
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46 | domain[key]=dr[key] |
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47 | return domain |
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48 | |
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49 | |
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50 | |
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51 | |
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52 | def subsetToGridSeries(self, timeSubset, csmlpath=None, ncpath=None,**kwargs): |
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53 | if csmlpath is not None: |
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54 | pathToSubsetCSML = csmlpath |
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55 | else: |
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56 | pathToSubsetCSML='temp.xml' |
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57 | if ncpath is not None: |
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58 | pathToSubsetNetCDF=ncpath |
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59 | else: |
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60 | pathToSubsetCSML='temp.nc' |
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61 | |
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62 | domainref = getDomainReference(self) |
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63 | self.times=timeSubset |
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64 | self.files=[] |
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65 | strTimes='' |
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66 | fulldata=[] |
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67 | if len(self.times) == 2: |
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68 | tone=ops_AbstractFeature.__getCDtime(self.times[0]) |
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69 | ttwo=ops_AbstractFeature.__getCDtime(self.times[1]) |
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70 | dr=getDomainReference(self) |
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71 | self.times=[] |
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72 | for time in dr['t'].split(): |
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73 | timeok=ops_AbstractFeature.__compareTimes(tone,time,ttwo) |
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74 | if timeok ==1: |
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75 | self.times.append(time) |
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76 | for time in self.times: |
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77 | listPosition=domainref['t'].split().index(time) |
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78 | strTimes= strTimes + ' ' + time |
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79 | for comp in self.rangeSet.aggregatedArray.component: |
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80 | data=comp.getData(fileposition=listPosition, **kwargs) |
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81 | self.files.append(comp.fileName.split()[listPosition]) |
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82 | if fulldata ==[]: |
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83 | fulldata = data.tolist() |
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84 | else: |
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85 | for item in data.tolist(): |
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86 | fulldata.append(item) |
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87 | #get the calendar type |
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88 | try: |
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89 | caltype, calunits = ops_AbstractFileExtract.__calendar(self.rangeSet.aggregatedArray.component[0].fileName.split()[0], 't') #TODO should accept any time dim!! |
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90 | csmltime.setcdtimeCalendar(caltype) |
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91 | except: |
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92 | csmltime.setcdtimeCalendar(csmltime.cdtime.DefaultCalendar) |
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93 | ### define domain and rangeSet to use for feature in csml document#### |
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94 | domain=Parser.GridSeriesDomain() |
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95 | domain.domainReference=Parser.TimePositionList(timePositions=strTimes) |
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96 | grid=Parser.Grid() |
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97 | dc = self.getDomainComplement() |
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98 | ordinates= [] |
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99 | i=0 |
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100 | valueStore=[] # use the values again later to generate netcdf |
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101 | arraySize=0 |
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102 | totalArraySize=1 |
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103 | for key in dc.keys(): |
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104 | arraySize=0 |
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105 | i=i+1 |
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106 | god=Parser.GridOrdinateDescription() |
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107 | god.gridAxesSpanned='dim%s'%i |
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108 | god.sequenceRule='+x+y+z' |
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109 | god.definesAxis=key |
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110 | straxisValues='' |
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111 | if key in kwargs: |
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112 | for val in dc[key]: |
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113 | if val >= kwargs[key][0]: |
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114 | if val <= kwargs[key] [1]: |
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115 | arraySize=arraySize+1 |
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116 | straxisValues=straxisValues+ str(val) + ', ' |
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117 | else: # this dimension has not been subsetted |
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118 | for val in dc[key]: |
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119 | arraySize=arraySize+1 |
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120 | straxisValues=straxisValues+ str(val) + ', ' |
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121 | totalArraySize=totalArraySize*arraySize |
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122 | god.axisValues=straxisValues[:-2] |
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123 | ordinates.append(god) |
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124 | totalArraySize=totalArraySize*len(self.times) |
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125 | grid.ordinates=ordinates |
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126 | domain.domainComplement=grid |
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127 | rangeSet=Parser.RangeSet() |
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128 | rangeSet.arrayDescriptor=Parser.NetCDFExtract(id=self.id,fileName='temp.nc',variableName=self.id,arraySize=[arraySize]) |
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129 | |
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130 | #### write csml document ##### -move this to the csmldocument module? |
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131 | subsetCSML=CSMLDocument() |
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132 | subsetCSML=subsetCSML.makeGridSeries(domain,rangeSet) |
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133 | output=open(pathToSubsetCSML,'w') |
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134 | output.write(subsetCSML) |
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135 | output.close() |
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136 | |
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137 | ### create and write netcdf - uses scientific python#### |
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138 | ncfile=NetCDFFile(pathToSubsetNetCDF,'w') |
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139 | # create the dimensions |
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140 | ncfile.createDimension ( 'time', len(self.times)) |
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141 | time_var = ncfile.createVariable ( 'time', Float, ('time',) ) |
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142 | time_var.longname = 'time' |
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143 | floatTimes=[] |
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144 | print len(fulldata[0]) |
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145 | for time in self.times: |
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146 | time=ops_AbstractFeature.__getCDtime(time).torel(calunits) |
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147 | floatTimes.append(time.value) |
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148 | time_var[:] =floatTimes[:] |
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149 | time_var.units=calunits |
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150 | time_var.calendar=caltype |
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151 | |
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152 | for ordinate in ordinates: |
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153 | ncfile.createDimension(ordinate.definesAxis, len(ordinate.axisValues.split())) |
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154 | item_var = ncfile.createVariable (ordinate.definesAxis, Float, (ordinate.definesAxis,) ) |
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155 | #convert to list |
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156 | vals=[] |
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157 | for val in ordinate.axisValues.split(','): |
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158 | vals.append(float(val)) |
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159 | ordinate.axisValues=vals |
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160 | item_var[:]=vals[:] |
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161 | print ordinate.definesAxis |
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162 | #this needs reconsidering - do the shapes always match up?? |
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163 | if len(ordinates)==3: |
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164 | feature_var = ncfile.createVariable (self.id, Float, ('time',ordinates[0].definesAxis,ordinates[1].definesAxis,ordinates[2].definesAxis)) |
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165 | elif len(grid.ordinates)==2: |
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166 | feature_var = ncfile.createVariable (self.id, Float, ('time',ordinates[0].definesAxis,ordinates[1].definesAxis)) |
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167 | print shape(feature_var) |
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168 | print shape(fulldata) |
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169 | feature_var[:]=fulldata[:] |
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170 | ncfile.close() |
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171 | |
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172 | return pathToSubsetCSML, pathToSubsetNetCDF, totalArraySize |
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