Mercurial > pylearn
annotate pmat.py @ 338:7d4792fc28ae
Automated merge with ssh://projects@lgcm.iro.umontreal.ca/hg/pylearn
author | Frederic Bastien <bastienf@iro.umontreal.ca> |
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date | Mon, 16 Jun 2008 17:17:50 -0400 |
parents | 9330d941fa1f |
children | c2f17f231960 |
rev | line source |
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1 ## Automatically adapted for numpy.numarray Jun 13, 2007 by python_numarray_to_numpy (-xsm) |
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2 |
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3 # PMat.py |
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4 # Copyright (C) 2005 Pascal Vincent |
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5 # |
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6 # Redistribution and use in source and binary forms, with or without |
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7 # modification, are permitted provided that the following conditions are met: |
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8 # |
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9 # 1. Redistributions of source code must retain the above copyright |
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10 # notice, this list of conditions and the following disclaimer. |
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11 # |
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12 # 2. Redistributions in binary form must reproduce the above copyright |
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13 # notice, this list of conditions and the following disclaimer in the |
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14 # documentation and/or other materials provided with the distribution. |
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15 # |
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16 # 3. The name of the authors may not be used to endorse or promote |
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17 # products derived from this software without specific prior written |
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18 # permission. |
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19 # |
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20 # THIS SOFTWARE IS PROVIDED BY THE AUTHORS ``AS IS'' AND ANY EXPRESS OR |
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21 # IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES |
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22 # OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN |
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23 # NO EVENT SHALL THE AUTHORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, |
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24 # SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED |
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25 # TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR |
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26 # PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF |
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27 # LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING |
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28 # NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS |
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29 # SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. |
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30 # |
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31 # This file is part of the PLearn library. For more information on the PLearn |
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32 # library, go to the PLearn Web site at www.plearn.org |
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33 |
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34 |
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35 # Author: Pascal Vincent |
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36 |
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37 #import numarray, sys, os, os.path |
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38 import numpy.numarray, sys, os, os.path |
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39 |
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40 def array_columns( a, cols ): |
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41 indices = None |
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42 if isinstance( cols, int ): |
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43 indices = [ cols ] |
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44 elif isinstance( cols, slice ): |
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45 #print cols |
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46 indices = range( *cols.indices(cols.stop) ) |
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47 else: |
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48 indices = list( cols ) |
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49 |
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50 return numpy.numarray.take(a, indices, axis=1) |
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51 |
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52 def load_pmat_as_array(fname): |
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53 s = file(fname,'rb').read() |
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54 formatstr = s[0:64] |
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55 datastr = s[64:] |
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56 structuretype, l, w, data_type, endianness = formatstr.split() |
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57 |
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58 if data_type=='DOUBLE': |
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59 elemtype = 'd' |
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60 elif data_type=='FLOAT': |
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61 elemtype = 'f' |
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62 else: |
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63 raise ValueError('Invalid data type in file header: '+data_type) |
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64 |
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65 if endianness=='LITTLE_ENDIAN': |
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66 byteorder = 'little' |
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67 elif endianness=='BIG_ENDIAN': |
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68 byteorder = 'big' |
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69 else: |
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70 raise ValueError('Invalid endianness in file header: '+endianness) |
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71 |
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72 l = int(l) |
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73 w = int(w) |
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74 X = numpy.numarray.fromstring(datastr,elemtype, shape=(l,w) ) |
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75 if byteorder!=sys.byteorder: |
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76 X.byteswap(True) |
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77 return X |
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78 |
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79 def load_pmat_as_array_dataset(fname): |
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80 import dataset,lookup_list |
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81 |
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82 #load the pmat as array |
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83 a=load_pmat_as_array(fname) |
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84 |
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85 #load the fieldnames |
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86 fieldnames = [] |
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87 fieldnamefile = os.path.join(fname+'.metadata','fieldnames') |
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88 if os.path.isfile(fieldnamefile): |
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89 f = open(fieldnamefile) |
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90 for row in f: |
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91 row = row.split() |
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92 if len(row)>0: |
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93 fieldnames.append(row[0]) |
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94 f.close() |
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95 else: |
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96 self.fieldnames = [ "field_"+str(i) for i in range(a.shape[1]) ] |
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97 |
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98 return dataset.ArrayDataSet(a,lookup_list.LookupList(fieldnames,[x for x in range(a.shape[1])])) |
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99 |
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100 def save_array_dataset_as_pmat(fname,ds): |
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101 ar=ds.data |
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102 save_array_as_pmat(fname,ar,ds.fieldNames()) |
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103 |
114
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104 def save_array_as_pmat( fname, ar, fieldnames=[] ): |
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105 s = file(fname,'wb') |
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106 |
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107 length, width = ar.shape |
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108 if fieldnames: |
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109 assert len(fieldnames) == width |
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110 metadatadir = fname+'.metadata' |
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111 if not os.path.isdir(metadatadir): |
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112 os.mkdir(metadatadir) |
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113 fieldnamefile = os.path.join(metadatadir,'fieldnames') |
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114 f = open(fieldnamefile,'wb') |
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115 for name in fieldnames: |
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116 f.write(name+'\t0\n') |
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117 f.close() |
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118 |
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119 header = 'MATRIX ' + str(length) + ' ' + str(width) + ' ' |
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120 if ar.dtype.char=='d': |
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121 header += 'DOUBLE ' |
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122 elemsize = 8 |
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123 |
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124 elif ar.dtype.char=='f': |
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125 header += 'FLOAT ' |
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126 elemsize = 4 |
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127 |
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128 else: |
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129 raise TypeError('Unsupported typecode: %s' % ar.dtype.char) |
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130 |
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131 rowsize = elemsize*width |
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132 |
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133 if sys.byteorder=='little': |
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134 header += 'LITTLE_ENDIAN ' |
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135 elif sys.byteorder=='big': |
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136 header += 'BIG_ENDIAN ' |
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137 else: |
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138 raise TypeError('Unsupported sys.byteorder: '+repr(sys.byteorder)) |
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139 |
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140 header += ' '*(63-len(header))+'\n' |
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141 s.write( header ) |
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142 s.write( ar.tostring() ) |
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143 s.close() |
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144 |
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145 |
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146 ####### Iterators ########################################################### |
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147 |
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148 class VMatIt: |
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149 def __init__(self, vmat): |
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150 self.vmat = vmat |
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151 self.cur_row = 0 |
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152 |
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153 def __iter__(self): |
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154 return self |
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155 |
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156 def next(self): |
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157 if self.cur_row==self.vmat.length: |
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158 raise StopIteration |
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159 row = self.vmat.getRow(self.cur_row) |
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160 self.cur_row += 1 |
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161 return row |
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162 |
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163 class ColumnIt: |
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164 def __init__(self, vmat, col): |
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165 self.vmat = vmat |
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166 self.col = col |
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167 self.cur_row = 0 |
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168 |
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169 def __iter__(self): |
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170 return self |
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171 |
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172 def next(self): |
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173 if self.cur_row==self.vmat.length: |
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174 raise StopIteration |
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175 val = self.vmat[self.cur_row, self.col] |
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176 self.cur_row += 1 |
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177 return val |
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178 |
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179 ####### VMat classes ######################################################## |
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180 |
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181 class VMat: |
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182 def __iter__(self): |
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183 return VMatIt(self) |
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184 |
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185 def __getitem__( self, key ): |
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186 if isinstance( key, slice ): |
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187 start, stop, step = key.start, key.stop, key.step |
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188 if step!=None: |
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189 raise IndexError('Extended slice with step not currently supported') |
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190 |
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191 if start is None: |
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192 start = 0 |
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193 |
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194 l = self.length |
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195 if stop is None or stop > l: |
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196 stop = l |
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197 |
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198 return self.getRows(start,stop-start) |
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199 |
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200 elif isinstance( key, tuple ): |
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201 # Basically returns a SubVMatrix |
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202 assert len(key) == 2 |
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203 rows = self.__getitem__( key[0] ) |
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204 |
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205 shape = rows.shape |
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206 if len(shape) == 1: |
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207 return rows[ key[1] ] |
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208 |
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209 cols = key[1] |
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210 if isinstance(cols, slice): |
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211 start, stop, step = cols.start, cols.stop, cols.step |
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212 if start is None: |
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213 start = 0 |
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214 |
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215 if stop is None: |
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216 stop = self.width |
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217 elif stop < 0: |
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218 stop = self.width+stop |
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219 |
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220 cols = slice(start, stop, step) |
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221 |
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222 return array_columns(rows, cols) |
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223 |
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224 elif isinstance( key, str ): |
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225 # The key is considered to be a fieldname and a column is |
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226 # returned. |
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227 try: |
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228 return array_columns( self.getRows(0,self.length), |
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229 self.fieldnames.index(key) ) |
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230 except ValueError: |
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231 print >>sys.stderr, "Key is '%s' while fieldnames are:" % key |
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232 print >>sys.stderr, self.fieldnames |
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233 raise |
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234 |
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235 else: |
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236 if key<0: key+=self.length |
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237 return self.getRow(key) |
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238 |
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239 def getFieldIndex(self, fieldname): |
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240 try: |
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241 return self.fieldnames.index(fieldname) |
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242 except ValueError: |
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243 raise ValueError( "VMat has no field named %s. Field names: %s" |
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244 %(fieldname, ','.join(self.fieldnames)) ) |
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245 |
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246 class PMat( VMat ): |
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247 |
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248 def __init__(self, fname, openmode='r', fieldnames=[], elemtype='d', |
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249 inputsize=-1, targetsize=-1, weightsize=-1, array = None): |
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250 self.fname = fname |
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251 self.inputsize = inputsize |
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252 self.targetsize = targetsize |
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253 self.weightsize = weightsize |
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254 if openmode=='r': |
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255 self.f = open(fname,'rb') |
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256 self.read_and_parse_header() |
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257 self.load_fieldnames() |
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258 |
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259 elif openmode=='w': |
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260 self.f = open(fname,'w+b') |
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261 self.fieldnames = fieldnames |
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262 self.save_fieldnames() |
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263 self.length = 0 |
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264 self.width = len(fieldnames) |
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265 self.elemtype = elemtype |
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266 self.swap_bytes = False |
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267 self.write_header() |
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268 |
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269 elif openmode=='a': |
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270 self.f = open(fname,'r+b') |
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271 self.read_and_parse_header() |
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272 self.load_fieldnames() |
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273 |
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274 else: |
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275 raise ValueError("Currently only supported openmodes are 'r', 'w' and 'a': "+repr(openmode)+" is not supported") |
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276 |
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277 if array is not None: |
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278 shape = array.shape |
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279 if len(shape) == 1: |
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280 row_format = lambda r: [ r ] |
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281 elif len(shape) == 2: |
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282 row_format = lambda r: r |
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283 |
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284 for row in array: |
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285 self.appendRow( row_format(row) ) |
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286 |
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287 def __del__(self): |
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288 self.close() |
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289 |
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290 def write_header(self): |
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291 header = 'MATRIX ' + str(self.length) + ' ' + str(self.width) + ' ' |
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292 |
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293 if self.elemtype=='d': |
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294 header += 'DOUBLE ' |
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295 self.elemsize = 8 |
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296 elif self.elemtype=='f': |
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297 header += 'FLOAT ' |
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298 self.elemsize = 4 |
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299 else: |
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300 raise TypeError('Unsupported elemtype: '+repr(elemtype)) |
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301 self.rowsize = self.elemsize*self.width |
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302 |
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303 if sys.byteorder=='little': |
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304 header += 'LITTLE_ENDIAN ' |
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305 elif sys.byteorder=='big': |
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306 header += 'BIG_ENDIAN ' |
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307 else: |
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308 raise TypeError('Unsupported sys.byteorder: '+repr(sys.byteorder)) |
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309 |
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310 header += ' '*(63-len(header))+'\n' |
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311 |
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312 self.f.seek(0) |
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313 self.f.write(header) |
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314 |
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315 def read_and_parse_header(self): |
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316 header = self.f.read(64) |
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317 mat_type, l, w, data_type, endianness = header.split() |
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318 if mat_type!='MATRIX': |
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319 raise ValueError('Invalid file header (should start with MATRIX)') |
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320 self.length = int(l) |
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321 self.width = int(w) |
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322 if endianness=='LITTLE_ENDIAN': |
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323 byteorder = 'little' |
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324 elif endianness=='BIG_ENDIAN': |
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325 byteorder = 'big' |
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326 else: |
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327 raise ValueError('Invalid endianness in file header: '+endianness) |
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328 self.swap_bytes = (byteorder!=sys.byteorder) |
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329 |
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330 if data_type=='DOUBLE': |
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331 self.elemtype = 'd' |
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332 self.elemsize = 8 |
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333 elif data_type=='FLOAT': |
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334 self.elemtype = 'f' |
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335 self.elemsize = 4 |
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336 else: |
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337 raise ValueError('Invalid data type in file header: '+data_type) |
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338 self.rowsize = self.elemsize*self.width |
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339 |
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340 def load_fieldnames(self): |
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341 self.fieldnames = [] |
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342 fieldnamefile = os.path.join(self.fname+'.metadata','fieldnames') |
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343 if os.path.isfile(fieldnamefile): |
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344 f = open(fieldnamefile) |
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345 for row in f: |
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346 row = row.split() |
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347 if len(row)>0: |
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348 self.fieldnames.append(row[0]) |
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349 f.close() |
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350 else: |
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351 self.fieldnames = [ "field_"+str(i) for i in range(self.width) ] |
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352 |
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353 def save_fieldnames(self): |
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354 metadatadir = self.fname+'.metadata' |
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355 if not os.path.isdir(metadatadir): |
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356 os.mkdir(metadatadir) |
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357 fieldnamefile = os.path.join(metadatadir,'fieldnames') |
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358 f = open(fieldnamefile,'wb') |
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359 for name in self.fieldnames: |
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360 f.write(name+'\t0\n') |
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361 f.close() |
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362 |
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363 def getRow(self,i): |
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364 if i<0 or i>=self.length: |
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365 raise IndexError('PMat index out of range') |
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366 self.f.seek(64+i*self.rowsize) |
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367 data = self.f.read(self.rowsize) |
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368 ar = numpy.numarray.fromstring(data, self.elemtype, (self.width,)) |
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369 if self.swap_bytes: |
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370 ar.byteswap(True) |
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371 return ar |
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372 |
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373 def getRows(self,i,l): |
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374 if i<0 or l<0 or i+l>self.length: |
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375 raise IndexError('PMat index out of range') |
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376 self.f.seek(64+i*self.rowsize) |
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377 data = self.f.read(l*self.rowsize) |
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378 ar = numpy.numarray.fromstring(data, self.elemtype, (l,self.width)) |
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379 if self.swap_bytes: |
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380 ar.byteswap(True) |
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381 return ar |
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382 |
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383 def checkzerorow(self,i): |
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384 if i<0 or i>self.length: |
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385 raise IndexError('PMat index out of range') |
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386 self.f.seek(64+i*self.rowsize) |
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387 data = self.f.read(self.rowsize) |
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388 ar = numpy.numarray.fromstring(data, self.elemtype, (len(data)/self.elemsize,)) |
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389 if self.swap_bytes: |
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390 ar.byteswap(True) |
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391 for elem in ar: |
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392 if elem!=0: |
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393 return False |
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394 return True |
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395 |
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396 def putRow(self,i,row): |
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397 if i<0 or i>=self.length: |
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398 raise IndexError('PMat index out of range') |
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399 if len(row)!=self.width: |
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400 raise TypeError('length of row ('+str(len(row))+ ') differs from matrix width ('+str(self.width)+')') |
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401 if i<0 or i>=self.length: |
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402 raise IndexError |
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403 if self.swap_bytes: # must make a copy and swap bytes |
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404 ar = numpy.numarray.numarray(row,type=self.elemtype) |
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405 ar.byteswap(True) |
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406 else: # asarray makes a copy if not already a numarray of the right type |
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407 ar = numpy.numarray.asarray(row,type=self.elemtype) |
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408 self.f.seek(64+i*self.rowsize) |
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409 self.f.write(ar.tostring()) |
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410 |
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411 def appendRow(self,row): |
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412 if len(row)!=self.width: |
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413 raise TypeError('length of row ('+str(len(row))+ ') differs from matrix width ('+str(self.width)+')') |
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414 if self.swap_bytes: # must make a copy and swap bytes |
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415 ar = numpy.numarray.numarray(row,type=self.elemtype) |
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416 ar.byteswap(True) |
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417 else: # asarray makes a copy if not already a numarray of the right type |
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418 ar = numpy.numarray.asarray(row,type=self.elemtype) |
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419 |
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420 self.f.seek(64+self.length*self.rowsize) |
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421 self.f.write(ar.tostring()) |
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422 self.length += 1 |
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423 self.write_header() # update length in header |
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424 |
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425 def flush(self): |
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426 self.f.flush() |
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427 |
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428 def close(self): |
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429 if hasattr(self, 'f'): |
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430 self.f.close() |
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431 |
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432 def append(self,row): |
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433 self.appendRow(row) |
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434 |
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435 def __setitem__(self, i, row): |
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436 l = self.length |
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437 if i<0: i+=l |
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438 self.putRow(i,row) |
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439 |
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440 def __len__(self): |
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441 return self.length |
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442 |
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443 |
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444 if __name__ == '__main__': |
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445 pmat = PMat( 'tmp.pmat', 'w', fieldnames=['F1', 'F2'] ) |
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446 pmat.append( [1, 2] ) |
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447 pmat.append( [3, 4] ) |
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448 pmat.close() |
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449 |
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450 pmat = PMat( 'tmp.pmat', 'r' ) |
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451 ar=load_pmat_as_array('tmp.pmat') |
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452 ds=load_pmat_as_array_dataset('tmp.pmat') |
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453 |
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454 print "PMat",pmat |
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455 print "PMat",pmat[:] |
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456 print "array",ar |
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457 print "ArrayDataSet",ds |
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458 for i in ds: |
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459 print i |
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460 save_array_dataset_as_pmat("tmp2.pmat",ds) |
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461 ds2=load_pmat_as_array_dataset('tmp2.pmat') |
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462 for i in ds2: |
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463 print i |
114
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464 # print "+++ tmp.pmat contains: " |
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465 # os.system( 'plearn vmat cat tmp.pmat' ) |
116
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466 import shutil |
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467 for fname in ["tmp.pmat", "tmp2.pmat"]: |
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468 os.remove( fname ) |
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469 if os.path.exists( fname+'.metadata' ): |
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470 shutil.rmtree( fname+'.metadata' ) |