more general constructor for Table
reading from file is just one case (now handled by static method). General constructor needs data and header information as dictionary. Works only with python 3.7 where dict keeps the insertion order. Earlier python versions/other implementations might fail.
This commit is contained in:
Martin Diehl
parent
5661f60552
commit
2d96136a0d
|
|
@ -1,6 +1,8 @@
|
|||
#!/usr/bin/env python3
|
||||
|
||||
import os
|
||||
import sys
|
||||
from io import StringIO
|
||||
from optparse import OptionParser
|
||||
|
||||
import damask
|
||||
|
|
@ -34,9 +36,14 @@ parser.set_defaults(defgrad = 'f',
|
|||
|
||||
(options,filenames) = parser.parse_args()
|
||||
|
||||
if filenames == []: filenames = [None]
|
||||
|
||||
for name in filenames:
|
||||
table = damask.Table(name)
|
||||
table.add_array('Cauchy',damask.mechanics.Cauchy(table.get_array(options.defgrad).reshape(-1,3,3),
|
||||
table.get_array(options.stress).reshape(-1,3,3)).reshape(-1,9),
|
||||
damask.util.report(scriptName,name)
|
||||
|
||||
table = damask.Table.from_ASCII(StringIO(''.join(sys.stdin.read())) if name is None else name)
|
||||
table.add_array('Cauchy',
|
||||
damask.mechanics.Cauchy(table.get_array(options.defgrad).reshape(-1,3,3),
|
||||
table.get_array(options.stress).reshape(-1,3,3)).reshape(-1,9),
|
||||
scriptID)
|
||||
table.to_ASCII(name)
|
||||
table.to_ASCII(sys.stdout if name is None else name)
|
||||
|
|
|
|||
|
|
@ -4,9 +4,42 @@ import pandas as pd
|
|||
import numpy as np
|
||||
|
||||
class Table():
|
||||
"""Read and write to ASCII tables"""
|
||||
|
||||
def __init__(self,fname):
|
||||
"""Store spreadsheet-like data."""
|
||||
|
||||
def __init__(self,array,headings,comments=None):
|
||||
"""
|
||||
New spreadsheet data.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
array : numpy.ndarray
|
||||
Data.
|
||||
headings : dict
|
||||
Column headings. Labels as keys and shape as tuple. Example 'F':(3,3) for a deformation gradient.
|
||||
comments : iterable of str, optional
|
||||
Additional, human-readable information
|
||||
|
||||
"""
|
||||
self.data = pd.DataFrame(data=array)
|
||||
|
||||
d = {}
|
||||
i = 0
|
||||
for label in headings:
|
||||
for components in range(np.prod(headings[label])):
|
||||
d[i] = label
|
||||
i+=1
|
||||
|
||||
self.data.rename(columns=d,inplace=True)
|
||||
|
||||
if comments is None:
|
||||
self.comments = []
|
||||
else:
|
||||
self.comments = [c for c in comments]
|
||||
|
||||
self.headings = headings
|
||||
|
||||
@staticmethod
|
||||
def from_ASCII(fname):
|
||||
try:
|
||||
f = open(fname)
|
||||
except TypeError:
|
||||
|
|
@ -17,43 +50,38 @@ class Table():
|
|||
header = int(header)
|
||||
else:
|
||||
raise Exception
|
||||
self.comments = [f.readline()[:-1] for i in range(header-1)]
|
||||
labels_raw = f.readline().split()
|
||||
self.data = pd.read_csv(f,delim_whitespace=True,header=None)
|
||||
|
||||
labels_repeated = [l.split('_',1)[1] if '_' in l else l for l in labels_raw]
|
||||
self.data.rename(columns=dict(zip([l for l in self.data.columns],labels_repeated)),inplace=True)
|
||||
|
||||
self.shape = {}
|
||||
comments = [f.readline()[:-1] for i in range(header-1)]
|
||||
labels_raw = f.readline().split()
|
||||
labels = [l.split('_',1)[1] if '_' in l else l for l in labels_raw]
|
||||
|
||||
headings = {}
|
||||
for l in labels_raw:
|
||||
tensor_column = re.search(':.*?_',l)
|
||||
if tensor_column:
|
||||
my_shape = tensor_column.group()[1:-1].split('x')
|
||||
self.shape[l.split('_',1)[1]] = tuple([int(d) for d in my_shape])
|
||||
headings[l.split('_',1)[1]] = tuple([int(d) for d in my_shape])
|
||||
else:
|
||||
vector_column = re.match('.*?_',l)
|
||||
if vector_column:
|
||||
self.shape[l.split('_',1)[1]] = (int(l.split('_',1)[0]),)
|
||||
headings[l.split('_',1)[1]] = (int(l.split('_',1)[0]),)
|
||||
else:
|
||||
self.shape[l]=(1,)
|
||||
headings[l]=(1,)
|
||||
|
||||
self.labels = list(dict.fromkeys(labels_repeated))
|
||||
return Table(np.loadtxt(f),headings,comments)
|
||||
|
||||
|
||||
def get_array(self,label):
|
||||
return self.data[label].to_numpy().reshape((-1,)+self.shape[label])
|
||||
return self.data[label].to_numpy().reshape((-1,)+self.headings[label])
|
||||
|
||||
|
||||
def add_array(self,label,array,info):
|
||||
if np.product(array.shape[1:],dtype=int) == 1:
|
||||
if np.prod(array.shape[1:],dtype=int) == 1:
|
||||
self.comments.append('{}: {}'.format(label,info))
|
||||
|
||||
else:
|
||||
self.comments.append('{} {}: {}'.format(label,array.shape[1:],info))
|
||||
|
||||
self.shape[label] = array.shape[1:]
|
||||
self.labels.append(label)
|
||||
size = np.product(array.shape[1:])
|
||||
self.headings[label] = array.shape[1:] if len(array.shape) > 1 else (1,)
|
||||
size = np.prod(array.shape[1:],dtype=int)
|
||||
new_data = pd.DataFrame(data=array.reshape(-1,size),
|
||||
columns=[label for l in range(size)])
|
||||
self.data = pd.concat([self.data,new_data],axis=1)
|
||||
|
|
@ -61,15 +89,15 @@ class Table():
|
|||
|
||||
def to_ASCII(self,fname):
|
||||
labels = []
|
||||
for l in self.labels:
|
||||
if(self.shape[l] == (1,)):
|
||||
for l in self.headings:
|
||||
if(self.headings[l] == (1,)):
|
||||
labels.append('{}'.format(l))
|
||||
elif(len(self.shape[l]) == 1):
|
||||
elif(len(self.headings[l]) == 1):
|
||||
labels+=['{}_{}'.format(i+1,l)\
|
||||
for i in range(self.shape[l][0])]
|
||||
for i in range(self.headings[l][0])]
|
||||
else:
|
||||
labels+=['{}:{}_{}'.format(i+1,'x'.join([str(d) for d in self.shape[l]]),l)\
|
||||
for i in range(np.product(self.shape[l]))]
|
||||
labels+=['{}:{}_{}'.format(i+1,'x'.join([str(d) for d in self.headings[l]]),l)\
|
||||
for i in range(np.prod(self.headings[l],dtype=int))]
|
||||
|
||||
header = ['{} header'.format(len(self.comments)+1)]\
|
||||
+ self.comments\
|
||||
|
|
|
|||
Loading…
Reference in New Issue