changed "labels" property to "tags". added method to inquire about (abstract) labels, i.e. interpreting the tag list to distill underlying label "meaning".
This commit is contained in:
Aritra Chakraborty
parent
8540748220
commit
dc6260be58
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@ -1,6 +1,5 @@
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# -*- coding: UTF-8 no BOM -*-
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import os,sys
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import numpy as np
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@ -26,7 +25,7 @@ class ASCIItable():
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self.__IO__ = {'output': [],
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'buffered': buffered,
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'labeled': labeled, # header contains labels
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'labels': [], # labels according to file info
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'tags': [], # labels according to file info
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'readBuffer': [], # buffer to hold non-advancing reads
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'dataStart': 0,
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}
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@ -50,7 +49,7 @@ class ASCIItable():
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self.__IO__['out'] = outname
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self.info = []
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self.labels = []
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self.tags = []
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self.data = []
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self.line = ''
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@ -160,7 +159,7 @@ class ASCIItable():
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if self.__IO__['labeled']: # table features labels
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self.info = [self.__IO__['in'].readline().strip() for i in xrange(1,int(m.group(1)))]
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self.labels = shlex.split(self.__IO__['in'].readline()) # store labels found in last line
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self.tags = shlex.split(self.__IO__['in'].readline()) # store tags found in last line
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else:
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@ -179,11 +178,11 @@ class ASCIItable():
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else: break # last line of comments
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if self.__IO__['labeled']: # table features labels
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self.labels = self.data # get labels from last line in "header"...
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self.tags = self.data # get tags from last line in "header"...
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self.data_read() # ...and remove from buffer
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if self.__IO__['labeled']: # table features labels
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self.__IO__['labels'] = list(self.labels) # backup labels (make COPY, not link)
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if self.__IO__['labeled']: # table features tags
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self.__IO__['tags'] = list(self.tags) # backup tags (make COPY, not link)
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try:
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self.__IO__['dataStart'] = self.__IO__['in'].tell() # current file position is at start of data
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@ -196,7 +195,7 @@ class ASCIItable():
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"""write current header information (info + labels)"""
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head = ['{}\theader'.format(len(self.info)+self.__IO__['labeled'])] if header else []
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head.append(self.info)
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if self.__IO__['labeled']: head.append('\t'.join(map(self._quote,self.labels)))
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if self.__IO__['labeled']: head.append('\t'.join(map(self._quote,self.tags)))
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return self.output_write(head)
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@ -260,19 +259,58 @@ class ASCIItable():
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try:
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for item in what: self.labels_append(item)
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except:
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self.labels += [self._removeCRLF(str(what))]
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self.tags += [self._removeCRLF(str(what))]
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else:
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self.labels += [self._removeCRLF(what)]
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self.tags += [self._removeCRLF(what)]
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self.__IO__['labeled'] = True # switch on processing (in particular writing) of labels
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if reset: self.__IO__['labels'] = list(self.labels) # subsequent data_read uses current labels as data size
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self.__IO__['labeled'] = True # switch on processing (in particular writing) of tags
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if reset: self.__IO__['tags'] = list(self.tags) # subsequent data_read uses current tags as data size
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# ------------------------------------------------------------------
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def labels_clear(self):
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"""delete existing labels and switch to no labeling"""
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self.labels = []
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self.tags = []
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self.__IO__['labeled'] = False
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# ------------------------------------------------------------------
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def labels(self,
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tags = None,
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raw = False,
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):
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"""
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returns abstract labels (e.g. "x" for "1_x","2_x",...)
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unless raw output is requested.
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operates on object tags or given list.
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"""
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from collections import Iterable
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if tags is None: tags = self.tags
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if isinstance(tags, Iterable) and not raw: # check whether list of tags is requested
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id = 0
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dim = 1
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labelList = []
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while id < len(tags):
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if not tags[id].startswith('1_'):
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labelList.append(tags[id])
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else:
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label = tags[id][2:] # get label
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while id < len(tags) and tags[id] == '{}_{}'.format(dim,label): # check successors
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id += 1 # next label...
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dim += 1 # ...should be one higher dimension
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LabelList.append(label) # reached end --> store
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id -= 1 # rewind one to consider again
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id += 1
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dim = 1
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else:
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labelList = self.tags
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return labelList
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# ------------------------------------------------------------------
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def label_index(self,
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labels):
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@ -292,10 +330,10 @@ class ASCIItable():
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idx.append(int(label)-1) # column given as integer number?
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except ValueError:
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try:
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idx.append(self.labels.index(label)) # locate string in label list
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idx.append(self.tags.index(label)) # locate string in label list
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except ValueError:
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try:
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idx.append(self.labels.index('1_'+label)) # locate '1_'+string in label list
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idx.append(self.tags.index('1_'+label)) # locate '1_'+string in label list
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except ValueError:
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idx.append(-1) # not found...
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else:
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@ -303,10 +341,10 @@ class ASCIItable():
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idx = int(labels)-1 # offset for python array indexing
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except ValueError:
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try:
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idx = self.labels.index(labels)
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idx = self.tags.index(labels)
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except ValueError:
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try:
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idx = self.labels.index('1_'+labels) # locate '1_'+string in label list
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idx = self.tags.index('1_'+labels) # locate '1_'+string in label list
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except ValueError:
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idx = None if labels is None else -1
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@ -331,16 +369,16 @@ class ASCIItable():
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try: # column given as number?
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idx = int(label)-1
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myDim = 1 # if found has at least dimension 1
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if self.labels[idx].startswith('1_'): # column has multidim indicator?
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while idx+myDim < len(self.labels) and self.labels[idx+myDim].startswith("%i_"%(myDim+1)):
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if self.tags[idx].startswith('1_'): # column has multidim indicator?
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while idx+myDim < len(self.tags) and self.tags[idx+myDim].startswith("%i_"%(myDim+1)):
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myDim += 1 # add while found
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except ValueError: # column has string label
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if label in self.labels: # can be directly found?
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if label in self.tags: # can be directly found?
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myDim = 1 # scalar by definition
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elif '1_'+label in self.labels: # look for first entry of possible multidim object
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idx = self.labels.index('1_'+label) # get starting column
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elif '1_'+label in self.tags: # look for first entry of possible multidim object
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idx = self.tags.index('1_'+label) # get starting column
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myDim = 1 # (at least) one-dimensional
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while idx+myDim < len(self.labels) and self.labels[idx+myDim].startswith("%i_"%(myDim+1)):
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while idx+myDim < len(self.tags) and self.tags[idx+myDim].startswith("%i_"%(myDim+1)):
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myDim += 1 # keep adding while going through object
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dim.append(myDim)
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@ -350,16 +388,16 @@ class ASCIItable():
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try: # column given as number?
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idx = int(labels)-1
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dim = 1 # if found has at least dimension 1
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if self.labels[idx].startswith('1_'): # column has multidim indicator?
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while idx+dim < len(self.labels) and self.labels[idx+dim].startswith("%i_"%(dim+1)):
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if self.tags[idx].startswith('1_'): # column has multidim indicator?
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while idx+dim < len(self.tags) and self.tags[idx+dim].startswith("%i_"%(dim+1)):
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dim += 1 # add as long as found
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except ValueError: # column has string label
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if labels in self.labels: # can be directly found?
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if labels in self.tags: # can be directly found?
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dim = 1 # scalar by definition
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elif '1_'+labels in self.labels: # look for first entry of possible multidim object
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idx = self.labels.index('1_'+labels) # get starting column
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elif '1_'+labels in self.tags: # look for first entry of possible multidim object
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idx = self.tags.index('1_'+labels) # get starting column
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dim = 1 # is (at least) one-dimensional
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while idx+dim < len(self.labels) and self.labels[idx+dim].startswith("%i_"%(dim+1)):
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while idx+dim < len(self.tags) and self.tags[idx+dim].startswith("%i_"%(dim+1)):
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dim += 1 # keep adding while going through object
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return np.array(dim) if isinstance(dim,Iterable) else dim
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@ -403,8 +441,8 @@ class ASCIItable():
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def data_rewind(self):
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self.__IO__['in'].seek(self.__IO__['dataStart']) # position file to start of data section
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self.__IO__['readBuffer'] = [] # delete any non-advancing data reads
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self.labels = list(self.__IO__['labels']) # restore label info found in header (as COPY, not link)
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self.__IO__['labeled'] = len(self.labels) > 0
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self.tags = list(self.__IO__['tags']) # restore label info found in header (as COPY, not link)
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self.__IO__['labeled'] = len(self.tags) > 0
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# ------------------------------------------------------------------
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def data_skipLines(self,
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@ -431,8 +469,8 @@ class ASCIItable():
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self.line = self.line.rstrip('\n')
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if self.__IO__['labeled'] and respectLabels: # if table has labels
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items = shlex.split(self.line)[:len(self.__IO__['labels'])] # use up to label count (from original file info)
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self.data = items if len(items) == len(self.__IO__['labels']) else [] # take entries if label count matches
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items = shlex.split(self.line)[:len(self.__IO__['tags'])] # use up to label count (from original file info)
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self.data = items if len(items) == len(self.__IO__['tags']) else [] # take entries if label count matches
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else:
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self.data = shlex.split(self.line) # otherwise take all
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@ -469,7 +507,7 @@ class ASCIItable():
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1))
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use = np.array(columns)
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self.labels = list(np.array(self.labels)[use]) # update labels with valid subset
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self.tags = list(np.array(self.tags)[use]) # update labels with valid subset
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self.data = np.loadtxt(self.__IO__['in'],usecols=use,ndmin=2)
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@ -544,8 +582,8 @@ class ASCIItable():
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def datatype(item):
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return int(item) if type.lower() == 'i' else float(item)
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N = grid.prod() # expected number of microstructure indices in data
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microstructure = np.zeros(N,type) # initialize as flat array
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N = grid.prod() # expected number of microstructure indices in data
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microstructure = np.zeros(N,type) # initialize as flat array
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i = 0
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while i < N and self.data_read():
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@ -556,7 +594,7 @@ class ASCIItable():
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else: items = map(datatype,items)
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else: items = map(datatype,items)
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s = min(len(items), N-i) # prevent overflow of microstructure array
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s = min(len(items), N-i) # prevent overflow of microstructure array
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microstructure[i:i+s] = items[:s]
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i += len(items)
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