DAMASK_EICMD/lib/damask/asciitable.py

613 lines
28 KiB
Python

# -*- coding: UTF-8 no BOM -*-
import os,sys
import numpy as np
# ------------------------------------------------------------------
# python 3 has no unicode object, this ensures that the code works on Python 2&3
try:
test=isinstance('test', unicode)
except(NameError):
unicode=str
# ------------------------------------------------------------------
class ASCIItable():
"""Read and write to ASCII tables"""
__slots__ = ['__IO__',
'info',
'labeled',
'data',
]
tmpext = '_tmp' # filename extension for in-place access
# ------------------------------------------------------------------
def __init__(self,
name = None,
outname = None,
buffered = False, # flush writes
labeled = True, # assume table has labels
readonly = False, # no reading from file
):
self.__IO__ = {'output': [],
'buffered': buffered,
'labeled': labeled, # header contains labels
'tags': [], # labels according to file info
'readBuffer': [], # buffer to hold non-advancing reads
'dataStart': 0,
}
self.__IO__['inPlace'] = not outname and name and not readonly
if self.__IO__['inPlace']: outname = name + self.tmpext # transparently create tmp file
try:
self.__IO__['in'] = (open( name,'r') if os.access( name, os.R_OK) else None) if name else sys.stdin
except TypeError:
self.__IO__['in'] = name
try:
self.__IO__['out'] = (open(outname,'w') if (not os.path.isfile(outname) or
os.access( outname, os.W_OK)
) and
(not self.__IO__['inPlace'] or
not os.path.isfile(name) or
os.access( name, os.W_OK)
) else None) if outname else sys.stdout
except TypeError:
self.__IO__['out'] = outname
self.info = []
self.tags = []
self.data = []
self.line = ''
if self.__IO__['in'] is None \
or self.__IO__['out'] is None: raise IOError # complain if any required file access not possible
# ------------------------------------------------------------------
def _transliterateToFloat(self,
x):
try:
return float(x)
except:
return 0.0
# ------------------------------------------------------------------
def _removeCRLF(self,
string):
try:
return string.replace('\n','').replace('\r','')
except:
return string
# ------------------------------------------------------------------
def _quote(self,
what):
"""quote empty or white space-containing output"""
import re
return '{quote}{content}{quote}'.format(
quote = ('"' if str(what)=='' or re.search(r"\s",str(what)) else ''),
content = what)
# ------------------------------------------------------------------
def close(self,
dismiss = False):
self.input_close()
self.output_flush()
self.output_close(dismiss)
# ------------------------------------------------------------------
def input_close(self):
try:
if self.__IO__['in'] != sys.stdin: self.__IO__['in'].close()
except:
pass
# ------------------------------------------------------------------
def output_write(self,
what):
"""aggregate a single row (string) or list of (possibly containing further lists of) rows into output"""
if not isinstance(what, (str, unicode)):
try:
for item in what: self.output_write(item)
except:
self.__IO__['output'] += [str(what)]
else:
self.__IO__['output'] += [what]
return self.__IO__['buffered'] or self.output_flush()
# ------------------------------------------------------------------
def output_flush(self,
clear = True):
try:
self.__IO__['output'] == [] or self.__IO__['out'].write('\n'.join(self.__IO__['output']) + '\n')
except IOError:
return False
if clear: self.output_clear()
return True
# ------------------------------------------------------------------
def output_clear(self):
self.__IO__['output'] = []
# ------------------------------------------------------------------
def output_close(self,
dismiss = False):
try:
if self.__IO__['out'] != sys.stdout: self.__IO__['out'].close()
except:
pass
if dismiss and os.path.isfile(self.__IO__['out'].name):
os.remove(self.__IO__['out'].name)
elif self.__IO__['inPlace']:
os.rename(self.__IO__['out'].name, self.__IO__['out'].name[:-len(self.tmpext)])
# ------------------------------------------------------------------
def head_read(self):
"""
get column labels
by either reading the first row or,
if keyword "head[*]" is present, the last line of the header
"""
import re,shlex
try:
self.__IO__['in'].seek(0)
except:
pass
firstline = self.__IO__['in'].readline().strip()
m = re.search('(\d+)\s+head', firstline.lower()) # search for "head" keyword
if m: # proper ASCIItable format
if self.__IO__['labeled']: # table features labels
self.info = [self.__IO__['in'].readline().strip() for i in range(1,int(m.group(1)))]
self.tags = shlex.split(self.__IO__['in'].readline()) # store tags found in last line
else:
self.info = [self.__IO__['in'].readline().strip() for i in range(0,int(m.group(1)))] # all header is info ...
else: # other table format
try:
self.__IO__['in'].seek(0) # try to rewind
except:
self.__IO__['readBuffer'] = [firstline] # or at least save data in buffer
while self.data_read(advance = False, respectLabels = False):
if self.line[0] in ['#','!','%','/','|','*','$']: # "typical" comment indicators
self.info_append(self.line) # store comment as info
self.data_read() # wind forward one line
else: break # last line of comments
if self.__IO__['labeled']: # table features labels
self.tags = self.data # get tags from last line in "header"...
self.data_read() # ...and remove from buffer
if self.__IO__['labeled']: # table features tags
self.__IO__['tags'] = list(self.tags) # backup tags (make COPY, not link)
try:
self.__IO__['dataStart'] = self.__IO__['in'].tell() # current file position is at start of data
except IOError:
pass
# ------------------------------------------------------------------
def head_write(self,
header = True):
"""write current header information (info + labels)"""
head = ['{}\theader'.format(len(self.info)+self.__IO__['labeled'])] if header else []
head.append(self.info)
if self.__IO__['labeled']: head.append('\t'.join(map(self._quote,self.tags)))
return self.output_write(head)
# ------------------------------------------------------------------
def head_getGeom(self):
"""interpret geom header"""
identifiers = {
'grid': ['a','b','c'],
'size': ['x','y','z'],
'origin': ['x','y','z'],
}
mappings = {
'grid': lambda x: int(x),
'size': lambda x: float(x),
'origin': lambda x: float(x),
'homogenization': lambda x: int(x),
'microstructures': lambda x: int(x),
}
info = {
'grid': np.zeros(3,'i'),
'size': np.zeros(3,'d'),
'origin': np.zeros(3,'d'),
'homogenization': 0,
'microstructures': 0,
}
extra_header = []
for header in self.info:
headitems = list(map(str.lower,header.split()))
if len(headitems) == 0: continue # skip blank lines
if headitems[0] in list(mappings.keys()):
if headitems[0] in list(identifiers.keys()):
for i in range(len(identifiers[headitems[0]])):
info[headitems[0]][i] = \
mappings[headitems[0]](headitems[headitems.index(identifiers[headitems[0]][i])+1])
else:
info[headitems[0]] = mappings[headitems[0]](headitems[1])
else:
extra_header.append(header)
return info,extra_header
# ------------------------------------------------------------------
def head_putGeom(self,info):
"""translate geometry description to header"""
self.info_append([
"grid\ta {}\tb {}\tc {}".format(*info['grid']),
"size\tx {}\ty {}\tz {}".format(*info['size']),
"origin\tx {}\ty {}\tz {}".format(*info['origin']),
"homogenization\t{}".format(info['homogenization']),
"microstructures\t{}".format(info['microstructures']),
])
# ------------------------------------------------------------------
def labels_append(self,
what,
reset = False):
"""add item or list to existing set of labels (and switch on labeling)"""
if not isinstance(what, (str, unicode)):
try:
for item in what: self.labels_append(item)
except:
self.tags += [self._removeCRLF(str(what))]
else:
self.tags += [self._removeCRLF(what)]
self.__IO__['labeled'] = True # switch on processing (in particular writing) of tags
if reset: self.__IO__['tags'] = list(self.tags) # subsequent data_read uses current tags as data size
# ------------------------------------------------------------------
def labels_clear(self):
"""delete existing labels and switch to no labeling"""
self.tags = []
self.__IO__['labeled'] = False
# ------------------------------------------------------------------
def labels(self,
tags = None,
raw = False):
"""
tell abstract labels.
"x" for "1_x","2_x",... unless raw output is requested.
operates on object tags or given list.
"""
from collections import Iterable
if tags is None: tags = self.tags
if isinstance(tags, Iterable) and not raw: # check whether list of tags is requested
id = 0
dim = 1
labelList = []
while id < len(tags):
if not tags[id].startswith('1_'):
labelList.append(tags[id])
else:
label = tags[id][2:] # get label
while id < len(tags) and tags[id] == '{}_{}'.format(dim,label): # check successors
id += 1 # next label...
dim += 1 # ...should be one higher dimension
labelList.append(label) # reached end --> store
id -= 1 # rewind one to consider again
id += 1
dim = 1
else:
labelList = self.tags
return labelList
# ------------------------------------------------------------------
def label_index(self,
labels):
"""
tell index of column label(s).
return numpy array if asked for list of labels.
transparently deals with label positions implicitly given as numbers or their headings given as strings.
"""
from collections import Iterable
if isinstance(labels, Iterable) and not isinstance(labels, str): # check whether list of labels is requested
idx = []
for label in labels:
if label is not None:
try:
idx.append(int(label)-1) # column given as integer number?
except ValueError:
label = label[1:-1] if label[0] == label[-1] and label[0] in ('"',"'") else label # remove outermost quotations
try:
idx.append(self.tags.index(label)) # locate string in label list
except ValueError:
try:
idx.append(self.tags.index('1_'+label)) # locate '1_'+string in label list
except ValueError:
idx.append(-1) # not found...
else:
try:
idx = int(labels)-1 # offset for python array indexing
except ValueError:
try:
labels = labels[1:-1] if labels[0] == labels[-1] and labels[0] in ('"',"'") else labels # remove outermost quotations
idx = self.tags.index(labels)
except ValueError:
try:
idx = self.tags.index('1_'+labels) # locate '1_'+string in label list
except ValueError:
idx = None if labels is None else -1
return np.array(idx) if isinstance(idx,Iterable) else idx
# ------------------------------------------------------------------
def label_dimension(self,
labels):
"""
tell dimension (length) of column label(s).
return numpy array if asked for list of labels.
transparently deals with label positions implicitly given as numbers or their headings given as strings.
"""
from collections import Iterable
if isinstance(labels, Iterable) and not isinstance(labels, str): # check whether list of labels is requested
dim = []
for label in labels:
if label is not None:
myDim = -1
try: # column given as number?
idx = int(label)-1
myDim = 1 # if found has at least dimension 1
if self.tags[idx].startswith('1_'): # column has multidim indicator?
while idx+myDim < len(self.tags) and self.tags[idx+myDim].startswith("%i_"%(myDim+1)):
myDim += 1 # add while found
except ValueError: # column has string label
label = label[1:-1] if label[0] == label[-1] and label[0] in ('"',"'") else label # remove outermost quotations
if label in self.tags: # can be directly found?
myDim = 1 # scalar by definition
elif '1_'+label in self.tags: # look for first entry of possible multidim object
idx = self.tags.index('1_'+label) # get starting column
myDim = 1 # (at least) one-dimensional
while idx+myDim < len(self.tags) and self.tags[idx+myDim].startswith("%i_"%(myDim+1)):
myDim += 1 # keep adding while going through object
dim.append(myDim)
else:
dim = -1 # assume invalid label
idx = -1
try: # column given as number?
idx = int(labels)-1
dim = 1 # if found has at least dimension 1
if self.tags[idx].startswith('1_'): # column has multidim indicator?
while idx+dim < len(self.tags) and self.tags[idx+dim].startswith("%i_"%(dim+1)):
dim += 1 # add as long as found
except ValueError: # column has string label
labels = labels[1:-1] if labels[0] == labels[-1] and labels[0] in ('"',"'") else labels # remove outermost quotations
if labels in self.tags: # can be directly found?
dim = 1 # scalar by definition
elif '1_'+labels in self.tags: # look for first entry of possible multidim object
idx = self.tags.index('1_'+labels) # get starting column
dim = 1 # is (at least) one-dimensional
while idx+dim < len(self.tags) and self.tags[idx+dim].startswith("%i_"%(dim+1)):
dim += 1 # keep adding while going through object
return np.array(dim) if isinstance(dim,Iterable) else dim
# ------------------------------------------------------------------
def label_indexrange(self,
labels):
"""
tell index range for given label(s).
return numpy array if asked for list of labels.
transparently deals with label positions implicitly given as numbers or their headings given as strings.
"""
from collections import Iterable
start = self.label_index(labels)
dim = self.label_dimension(labels)
return np.hstack([range(c[0],c[0]+c[1]) for c in zip(start,dim)]) \
if isinstance(labels, Iterable) and not isinstance(labels, str) \
else range(start,start+dim)
# ------------------------------------------------------------------
def info_append(self,
what):
"""add item or list to existing set of infos"""
if not isinstance(what, (str, unicode)):
try:
for item in what: self.info_append(item)
except:
self.info += [self._removeCRLF(str(what))]
else:
self.info += [self._removeCRLF(what)]
# ------------------------------------------------------------------
def info_clear(self):
"""delete any info block"""
self.info = []
# ------------------------------------------------------------------
def data_rewind(self):
self.__IO__['in'].seek(self.__IO__['dataStart']) # position file to start of data section
self.__IO__['readBuffer'] = [] # delete any non-advancing data reads
self.tags = list(self.__IO__['tags']) # restore label info found in header (as COPY, not link)
self.__IO__['labeled'] = len(self.tags) > 0
# ------------------------------------------------------------------
def data_skipLines(self,
count):
"""wind forward by count number of lines"""
for i in range(count):
alive = self.data_read()
return alive
# ------------------------------------------------------------------
def data_read(self,
advance = True,
respectLabels = True):
"""read next line (possibly buffered) and parse it into data array"""
import shlex
self.line = self.__IO__['readBuffer'].pop(0) if len(self.__IO__['readBuffer']) > 0 \
else self.__IO__['in'].readline().strip() # take buffered content or get next data row from file
if not advance:
self.__IO__['readBuffer'].append(self.line) # keep line just read in buffer
self.line = self.line.rstrip('\n')
if self.__IO__['labeled'] and respectLabels: # if table has labels
items = shlex.split(self.line)[:len(self.__IO__['tags'])] # use up to label count (from original file info)
self.data = items if len(items) == len(self.__IO__['tags']) else [] # take entries if label count matches
else:
self.data = shlex.split(self.line) # otherwise take all
return self.data != []
# ------------------------------------------------------------------
def data_readArray(self,
labels = []):
"""read whole data of all (given) labels as numpy array"""
from collections import Iterable
try:
self.data_rewind() # try to wind back to start of data
except:
pass # assume/hope we are at data start already...
if labels is None or labels == []:
use = None # use all columns (and keep labels intact)
labels_missing = []
else:
if isinstance(labels, str) or not isinstance(labels, Iterable): # check whether labels are a list or single item
labels = [labels]
indices = self.label_index(labels) # check requested labels ...
dimensions = self.label_dimension(labels) # ... and remember their dimension
present = np.where(indices >= 0)[0] # positions in request list of labels that are present ...
missing = np.where(indices < 0)[0] # ... and missing in table
labels_missing = np.array(labels)[missing] # labels of missing data
columns = []
for i,(c,d) in enumerate(zip(indices[present],dimensions[present])): # for all valid labels ...
# ... transparently add all components unless column referenced by number or with explicit dimension
columns += list(range(c,c + \
(d if str(c) != str(labels[present[i]]) else \
1)))
use = np.array(columns) if len(columns) > 0 else None
self.tags = list(np.array(self.tags)[use]) # update labels with valid subset
self.data = np.loadtxt(self.__IO__['in'],usecols=use,ndmin=2)
return labels_missing
# ------------------------------------------------------------------
def data_write(self,
delimiter = '\t'):
"""write current data array and report alive output back"""
if len(self.data) == 0: return True
if isinstance(self.data[0],list):
return self.output_write([delimiter.join(map(self._quote,items)) for items in self.data])
else:
return self.output_write( delimiter.join(map(self._quote,self.data)))
# ------------------------------------------------------------------
def data_writeArray(self,
fmt = None,
delimiter = '\t'):
"""write whole numpy array data"""
for row in self.data:
try:
output = [fmt % value for value in row] if fmt else list(map(repr,row))
except:
output = [fmt % row] if fmt else [repr(row)]
self.__IO__['out'].write(delimiter.join(output) + '\n')
# ------------------------------------------------------------------
def data_append(self,
what):
if not isinstance(what, (str, unicode)):
try:
for item in what: self.data_append(item)
except:
self.data += [str(what)]
else:
self.data += [what]
# ------------------------------------------------------------------
def data_set(self,
what, where):
"""update data entry in column "where". grows data array if needed."""
idx = -1
try:
idx = self.label_index(where)
if len(self.data) <= idx:
self.data_append(['n/a' for i in range(idx+1-len(self.data))]) # grow data if too short
self.data[idx] = str(what)
except(ValueError):
pass
return idx
# ------------------------------------------------------------------
def data_clear(self):
self.data = []
# ------------------------------------------------------------------
def data_asFloat(self):
return list(map(self._transliterateToFloat,self.data))
# ------------------------------------------------------------------
def microstructure_read(self,
grid,
type = 'i',
strict = False):
"""read microstructure data (from .geom format)"""
def datatype(item):
return int(item) if type.lower() == 'i' else float(item)
N = grid.prod() # expected number of microstructure indices in data
microstructure = np.zeros(N,type) # initialize as flat array
i = 0
while i < N and self.data_read():
items = self.data
if len(items) > 2:
if items[1].lower() == 'of': items = np.ones(datatype(items[0]))*datatype(items[2])
elif items[1].lower() == 'to': items = np.arange(datatype(items[0]),1+datatype(items[2]))
else: items = list(map(datatype,items))
else: items = list(map(datatype,items))
s = min(len(items), N-i) # prevent overflow of microstructure array
microstructure[i:i+s] = items[:s]
i += len(items)
return (microstructure, i == N and not self.data_read()) if strict else microstructure # check for proper point count and end of file