annotate dataset.py @ 5:8039918516fe

Added MinibatchIterator
author bengioy@bengiomac.local
date Sun, 23 Mar 2008 22:44:43 -0400
parents f7dcfb5f9d5b
children d5738b79089a
rev   line source
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3 class DataSet(object):
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4 """
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5 This is a virtual base class or interface for datasets.
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6 A dataset is basically an iterator over examples. It does not necessarily
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7 have a fixed length (this is useful for 'streams' which feed on-line learning).
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8 Datasets with fixed and known length are FiniteDataSet, a subclass of DataSet.
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9 Examples and datasets have named fields.
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10 One can obtain a sub-dataset by taking dataset.field or dataset(field1,field2,field3,...).
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11 Fields are not mutually exclusive, i.e. two fields can overlap in their actual content.
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12 The content of a field can be of any type, but often will be a numpy tensor.
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13 """
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14
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15 def __init__(self):
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16 pass
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17
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18 def __iter__(self):
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19 return self
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20
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21 def next(self):
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22 """Return the next example in the dataset."""
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23 raise NotImplementedError
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24
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25 def __getattr__(self,fieldname):
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26 """Return a sub-dataset containing only the given fieldname as field."""
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27 return self(fieldname)
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28
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29 def __call__(self,*fieldnames):
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30 """Return a sub-dataset containing only the given fieldnames as fields."""
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31 raise NotImplementedError
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32
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33 def fieldNames(self):
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34 """Return the list of field names that are supported by getattr and getFields."""
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35 raise NotImplementedError
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36
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37 class FiniteDataSet(DataSet):
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38 """
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39 Virtual interface, a subclass of DataSet for datasets which have a finite, known length.
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40 Examples are indexed by an integer between 0 and self.length()-1,
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41 and a subdataset can be obtained by slicing.
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42 """
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43
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44 def __init__(self):
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45 pass
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46
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47 def __len__(self):
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48 """len(dataset) returns the number of examples in the dataset."""
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49 raise NotImplementedError
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50
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51 def __getitem__(self,i):
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52 """dataset[i] returns the (i+1)-th example of the dataset."""
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53 raise NotImplementedError
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54
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55 def __getslice__(self,*slice_args):
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56 """dataset[i:j] returns the subdataset with examples i,i+1,...,j-1."""
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57 raise NotImplementedError
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58
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59 def minibatches(self,minibatch_size):
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60 """Return an iterator for the dataset that goes through minibatches of the given size."""
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61 return MinibatchIterator(self,minibatch_size)
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62
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63 class MinibatchIterator(object):
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64 """
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65 Iterator class for FiniteDataSet that can iterate by minibatches
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66 (sub-dataset of consecutive examples).
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67 """
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68 def __init__(self,dataset,minibatch_size):
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69 assert minibatch_size>0 and minibatch_size<len(dataset)
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70 self.dataset=dataset
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71 self.minibatch_size=minibatch_size
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72 self.current=-minibatch_size
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73 def __iter__(self):
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74 return self
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75 def next(self):
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76 self.current+=self.minibatch_size
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77 if self.current>=len(self.dataset):
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78 self.current=-self.minibatchsize
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79 raise StopIteration
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80 return self.dataset[self.current:self.current+self.minibatchsize]
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81
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82 # we may want ArrayDataSet defined in another python file
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83
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84 import numpy
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85
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86 class ArrayDataSet(FiniteDataSet):
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87 """
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88 A fixed-length and fixed-width dataset in which each element is a numpy.array
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89 or a number, hence the whole dataset corresponds to a numpy.array. Fields
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90 must correspond to a slice of columns. If the dataset has fields,
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91 each 'example' is just a one-row ArrayDataSet, otherwise it is a numpy.array.
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92 Any dataset can also be converted to a numpy.array (losing the notion of fields)
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93 by the asarray(dataset) call.
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94 """
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95
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96 def __init__(self,dataset=None,data=None,fields={}):
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97 """
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98 Construct an ArrayDataSet, either from a DataSet, or from
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99 a numpy.array plus an optional specification of fields (by
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100 a dictionary of column slices indexed by field names).
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101 """
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102 self.current_row=-1 # used for view of this dataset as an iterator
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103 if dataset!=None:
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104 assert data==None and fields=={}
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105 # convert dataset to an ArrayDataSet
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106 raise NotImplementedError
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107 if data!=None:
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108 assert dataset==None
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109 self.data=data
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110 self.fields=fields
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111 self.width = data.shape[1]
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112 for fieldname in fields:
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113 fieldslice=fields[fieldname]
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114 # make sure fieldslice.start and fieldslice.step are defined
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115 start=fieldslice.start
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116 step=fieldslice.step
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117 if not start:
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118 start=0
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119 if not step:
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120 step=1
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121 if not fieldslice.start or not fieldslice.step:
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122 fieldslice = slice(start,fieldslice.stop,step)
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123 # and coherent with the data array
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124 assert fieldslice.start>=0 and fieldslice.stop<=self.width
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125
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126 def next(self):
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127 """
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128 Return the next example in the dataset. If the dataset has fields,
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129 the 'example' is just a one-row ArrayDataSet, otherwise it is a numpy.array.
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130 """
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131 if self.fields:
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132 self.current_row+=1
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133 if self.current_row==len(self.data):
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134 self.current_row=-1
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135 raise StopIteration
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136 return self[self.current_row]
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137 else:
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138 return self.data[self.current_row]
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139
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140 def __getattr__(self,fieldname):
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141 """Return a sub-dataset containing only the given fieldname as field."""
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142 data=self.data[self.fields[fieldname]]
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143 if len(data)==1:
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144 return data
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145 else:
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146 return ArrayDataSet(data=data)
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147
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148 def __call__(self,*fieldnames):
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149 """Return a sub-dataset containing only the given fieldnames as fields."""
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150 min_col=self.data.shape[1]
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151 max_col=0
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152 for field_slice in self.fields.values():
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153 min_col=min(min_col,field_slice.start)
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154 max_col=max(max_col,field_slice.stop)
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155 new_fields={}
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156 for field in self.fields:
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157 new_fields[field[0]]=slice(field[1].start-min_col,field[1].stop-min_col,field[1].step)
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158 return ArrayDataSet(data=self.data[:,min_col:max_col],fields=new_fields)
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159
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160 def fieldNames(self):
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161 """Return the list of field names that are supported by getattr and getFields."""
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162 return self.fields.keys()
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163
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164 def __len__(self):
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165 """len(dataset) returns the number of examples in the dataset."""
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166 return len(self.data)
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167
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168 def __getitem__(self,i):
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169 """
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170 dataset[i] returns the (i+1)-th example of the dataset. If the dataset has fields
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171 then a one-example dataset is returned (to be able to handle example.field accesses).
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172 """
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173 if self.fields:
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174 if isinstance(i,slice):
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175 return ArrayDataSet(data=data[slice],fields=self.fields)
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176 return ArrayDataSet(data=self.data[i:i+1],fields=self.fields)
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177 else:
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178 return data[i]
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179
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180 def __getslice__(self,*slice_args):
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181 """dataset[i:j] returns the subdataset with examples i,i+1,...,j-1."""
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182 return ArrayDataSet(data=self.data[slice(slice_args)],fields=self.fields)
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183
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184 def asarray(self):
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185 if self.fields:
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186 columns_used = numpy.zeros((self.data.shape[1]),dtype=bool)
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187 for field_slice in self.fields.values():
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188 for c in xrange(field_slice.start,field_slice.stop,field_slice.step):
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189 columns_used[c]=True
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190 # try to figure out if we can map all the slices into one slice:
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191 mappable_to_one_slice = True
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192 start=0
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193 while start<len(columns_used) and not columns_used[start]:
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194 start+=1
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195 stop=len(columns_used)
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196 while stop>0 and not columns_used[stop-1]:
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197 stop-=1
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198 step=0
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199 i=start
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200 while i<stop:
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201 j=i+1
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202 while not columns_used[j] and j<stop:
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203 j+=1
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204 if step:
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205 if step!=j-i:
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206 mappable_to_one_slice = False
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207 break
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208 else:
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209 step = j-i
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210 if mappable_to_one_slice:
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211 return data[slice(start,stop,step)]
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212 # else make contiguous copy
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213 n_columns = sum(columns_used)
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214 result = zeros((len(self.data),n_columns)+self.data.shape[2:],self.data.dtype)
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215 c=0
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216 for field_slice in self.fields.values():
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217 slice_width=field_slice.stop-field_slice.start/field_slice.step
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218 # copy the field here
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219 result[:,slice(c,slice_width)]=self.data[field_slice]
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220 c+=slice_width
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221 return result
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222 return self.data
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223