112 lines
3.6 KiB
Python
112 lines
3.6 KiB
Python
import re
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import pandas as pd
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import numpy as np
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class Table():
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"""Store spreadsheet-like data."""
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def __init__(self,array,headings,comments=None):
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"""
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New spreadsheet data.
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Parameters
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----------
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array : numpy.ndarray
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Data.
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headings : dict
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Column headings. Labels as keys and shape as tuple. Example 'F':(3,3) for a deformation gradient.
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comments : iterable of str, optional
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Additional, human-readable information
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"""
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self.data = pd.DataFrame(data=array)
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d = {}
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i = 0
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for label in headings:
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for components in range(np.prod(headings[label])):
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d[i] = label
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i+=1
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self.data.rename(columns=d,inplace=True)
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if comments is None:
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self.comments = []
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else:
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self.comments = [c for c in comments]
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self.headings = headings
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@staticmethod
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def from_ASCII(fname):
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try:
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f = open(fname)
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except TypeError:
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f = fname
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header,keyword = f.readline().split()
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if keyword == 'header':
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header = int(header)
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else:
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raise Exception
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comments = [f.readline()[:-1] for i in range(header-1)]
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labels_raw = f.readline().split()
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labels = [l.split('_',1)[1] if '_' in l else l for l in labels_raw]
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headings = {}
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for l in labels_raw:
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tensor_column = re.search(':.*?_',l)
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if tensor_column:
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my_shape = tensor_column.group()[1:-1].split('x')
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headings[l.split('_',1)[1]] = tuple([int(d) for d in my_shape])
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else:
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vector_column = re.match('.*?_',l)
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if vector_column:
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headings[l.split('_',1)[1]] = (int(l.split('_',1)[0]),)
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else:
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headings[l]=(1,)
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return Table(np.loadtxt(f),headings,comments)
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def get_array(self,label):
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return self.data[label].to_numpy().reshape((-1,)+self.headings[label])
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def get_labels(self):
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return [label for label in self.headings]
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def add_array(self,label,array,info):
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if np.prod(array.shape[1:],dtype=int) == 1:
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self.comments.append('{}: {}'.format(label,info))
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else:
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self.comments.append('{} {}: {}'.format(label,array.shape[1:],info))
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self.headings[label] = array.shape[1:] if len(array.shape) > 1 else (1,)
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size = np.prod(array.shape[1:],dtype=int)
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new_data = pd.DataFrame(data=array.reshape(-1,size),
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columns=[label for l in range(size)])
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self.data = pd.concat([self.data,new_data],axis=1)
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def to_ASCII(self,fname):
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labels = []
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for l in self.headings:
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if(self.headings[l] == (1,)):
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labels.append('{}'.format(l))
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elif(len(self.headings[l]) == 1):
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labels+=['{}_{}'.format(i+1,l)\
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for i in range(self.headings[l][0])]
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else:
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labels+=['{}:{}_{}'.format(i+1,'x'.join([str(d) for d in self.headings[l]]),l)\
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for i in range(np.prod(self.headings[l],dtype=int))]
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header = ['{} header'.format(len(self.comments)+1)]\
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+ self.comments\
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+ [' '.join(labels)]
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try:
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f = open(fname,'w')
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except TypeError:
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f = fname
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for line in header: f.write(line+'\n')
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self.data.to_csv(f,sep=' ',index=False,header=False)
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