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 if i != self.data.shape[1]: raise IndexError('Mismatch between array shape and headings') 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): """ Create table from ASCII file. The first line needs to indicate the number of subsequent header lines as 'n header'. Vector data labels are indicated by '1_v, 2_v, ..., n_v'. Tensor data labels are indicated by '3x3:1_T, 3x3:2_T, ..., 3x3:9_T'. Parameters ---------- fname : file, str, or pathlib.Path Filename or file for reading. """ 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 = f.readline().split() headings = {} for label in labels: tensor_column = re.search(r'[0-9,x]*?:[0-9]*?_',label) if tensor_column: my_shape = tensor_column.group().split(':',1)[0].split('x') headings[label.split('_',1)[1]] = tuple([int(d) for d in my_shape]) else: vector_column = re.match(r'[0-9]*?_',label) if vector_column: headings[label.split('_',1)[1]] = (int(label.split('_',1)[0]),) else: headings[label]=(1,) return Table(np.loadtxt(f),headings,comments) def get_array(self,label): """ Return data as array. Parameters ---------- label : str Label of the array. """ if re.match(r'[0-9]*?_',label): idx,key = label.split('_',1) return self.data[key].to_numpy()[:,int(idx)-1] else: return self.data[label].to_numpy().reshape((-1,)+self.headings[label]) def set_array(self,label,array,info): """ Modify data in the spreadsheet. Parameters ---------- label : str Label for the new data. array : np.ndarray New data. info : str Human-readable information about the new data. """ 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)) if re.match(r'[0-9]*?_',label): idx,key = label.split('_',1) iloc = self.data.columns.get_loc(key).tolist().index(True) + int(idx) -1 self.data.iloc[:,iloc] = array else: self.data[label] = array.reshape(self.data[label].shape) def get_labels(self): """Return the labels of all columns.""" return [label for label in self.headings] def add_array(self,label,array,info): """ Add data to the spreadsheet. Parameters ---------- label : str Label for the new data. array : np.ndarray New data. info : str Human-readable information about the new data. """ 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): """ Store as plain text file. Parameters ---------- fname : file, str, or pathlib.Path Filename or file for reading. """ 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('x'.join([str(d) for d in self.headings[l]]),i+1,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)