DAMASK_EICMD/python/damask/table.py

112 lines
3.6 KiB
Python

import re
import pandas as pd
import numpy as np
class Table():
"""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:
f = fname
header,keyword = f.readline().split()
if keyword == 'header':
header = int(header)
else:
raise Exception
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')
headings[l.split('_',1)[1]] = tuple([int(d) for d in my_shape])
else:
vector_column = re.match('.*?_',l)
if vector_column:
headings[l.split('_',1)[1]] = (int(l.split('_',1)[0]),)
else:
headings[l]=(1,)
return Table(np.loadtxt(f),headings,comments)
def get_array(self,label):
return self.data[label].to_numpy().reshape((-1,)+self.headings[label])
def get_labels(self):
return [label for label in self.headings]
def add_array(self,label,array,info):
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.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)
def to_ASCII(self,fname):
labels = []
for l in self.headings:
if(self.headings[l] == (1,)):
labels.append('{}'.format(l))
elif(len(self.headings[l]) == 1):
labels+=['{}_{}'.format(i+1,l)\
for i in range(self.headings[l][0])]
else:
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\
+ [' '.join(labels)]
try:
f = open(fname,'w')
except TypeError:
f = fname
for line in header: f.write(line+'\n')
self.data.to_csv(f,sep=' ',index=False,header=False)