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)