import re import copy import pandas as pd import numpy as np from . import util class Table: """Store spreadsheet-like data.""" def __init__(self,data,shapes,comments=None): """ New spreadsheet. Parameters ---------- data : numpy.ndarray or pandas.DataFrame Data. Column labels from a pandas.DataFrame will be replaced. shapes : dict with str:tuple pairs Shapes of the columns. Example 'F':(3,3) for a deformation gradient. comments : str or iterable of str, optional Additional, human-readable information. """ comments_ = [comments] if isinstance(comments,str) else comments self.comments = [] if comments_ is None else [c for c in comments_] self.data = pd.DataFrame(data=data) self.shapes = { k:(v,) if isinstance(v,(np.int,int)) else v for k,v in shapes.items() } self._label_uniform() def __repr__(self): """Brief overview.""" return util.srepr(self.comments)+'\n'+self.data.__repr__() def __len__(self): """Number of rows.""" return len(self.data) def __copy__(self): """Copy Table.""" return copy.deepcopy(self) def copy(self): """Copy Table.""" return self.__copy__() def _label_discrete(self): """Label data individually, e.g. v v v ==> 1_v 2_v 3_v.""" labels = [] for label,shape in self.shapes.items(): size = int(np.prod(shape)) labels += [('' if size == 1 else f'{i+1}_')+label for i in range(size)] self.data.columns = labels def _label_uniform(self): """Label data uniformly, e.g. 1_v 2_v 3_v ==> v v v.""" labels = [] for label,shape in self.shapes.items(): labels += [label] * int(np.prod(shape)) self.data.columns = labels def _add_comment(self,label,shape,info): if info is not None: specific = f'{label}{" "+str(shape) if np.prod(shape,dtype=int) > 1 else ""}: {info}' general = util.execution_stamp('Table') self.comments.append(f'{specific} / {general}') @staticmethod def load(fname): """ Load ASCII table file. In legacy style, the first line indicates the number of subsequent header lines as "N header", with the last header line being interpreted as column labels. Alternatively, initial comments are marked by '#', with the first non-comment line containing the column labels. Vector data column labels are indicated by '1_v, 2_v, ..., n_v'. Tensor data column 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 f.seek(0) try: N_comment_lines,keyword = f.readline().strip().split(maxsplit=1) if keyword != 'header': raise ValueError else: comments = [f.readline().strip() for i in range(1,int(N_comment_lines))] labels = f.readline().split() except ValueError: f.seek(0) comments = [] line = f.readline().strip() while line.startswith('#'): comments.append(line.lstrip('#').strip()) line = f.readline().strip() labels = line.split() shapes = {} 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') shapes[label.split('_',1)[1]] = tuple([int(d) for d in my_shape]) else: vector_column = re.match(r'[0-9]*?_',label) if vector_column: shapes[label.split('_',1)[1]] = (int(label.split('_',1)[0]),) else: shapes[label] = (1,) data = pd.read_csv(f,names=list(range(len(labels))),sep=r'\s+') return Table(data,shapes,comments) @staticmethod def load_ang(fname): """ Load ang file. A valid TSL ang file needs to contains the following columns: * Euler angles (Bunge notation) in radians, 3 floats, label 'eu'. * Spatial position in meters, 2 floats, label 'pos'. * Image quality, 1 float, label 'IQ'. * Confidence index, 1 float, label 'CI'. * Phase ID, 1 int, label 'ID'. * SEM signal, 1 float, label 'intensity'. * Fit, 1 float, label 'fit'. Parameters ---------- fname : file, str, or pathlib.Path Filename or file for reading. """ try: f = open(fname) except TypeError: f = fname f.seek(0) content = f.readlines() comments = [util.execution_stamp('Table','from_ang')] for line in content: if line.startswith('#'): comments.append(line.strip()) else: break data = np.loadtxt(content) shapes = {'eu':3, 'pos':2, 'IQ':1, 'CI':1, 'ID':1, 'intensity':1, 'fit':1} remainder = data.shape[1]-sum(shapes.values()) if remainder > 0: # 3.8 can do: if (remainder := data.shape[1]-sum(shapes.values())) > 0 shapes['unknown'] = remainder return Table(data,shapes,comments) @property def labels(self): return list(self.shapes) def get(self,label): """ Get column data. Parameters ---------- label : str Column label. """ if re.match(r'[0-9]*?_',label): idx,key = label.split('_',1) data = self.data[key].to_numpy()[:,int(idx)-1].reshape(-1,1) else: data = self.data[label].to_numpy().reshape((-1,)+self.shapes[label]) return data.astype(type(data.flatten()[0])) def set(self,label,data,info=None): """ Set column data. Parameters ---------- label : str Column label. data : np.ndarray New data. info : str, optional Human-readable information about the new data. """ dup = self.copy() dup._add_comment(label,data.shape[1:],info) if re.match(r'[0-9]*?_',label): idx,key = label.split('_',1) iloc = dup.data.columns.get_loc(key).tolist().index(True) + int(idx) -1 dup.data.iloc[:,iloc] = data else: dup.data[label] = data.reshape(dup.data[label].shape) return dup def add(self,label,data,info=None): """ Add column data. Parameters ---------- label : str Column label. data : np.ndarray Modified data. info : str, optional Human-readable information about the modified data. """ dup = self.copy() dup._add_comment(label,data.shape[1:],info) dup.shapes[label] = data.shape[1:] if len(data.shape) > 1 else (1,) size = np.prod(data.shape[1:],dtype=int) new = pd.DataFrame(data=data.reshape(-1,size), columns=[label]*size, ) new.index = dup.data.index dup.data = pd.concat([dup.data,new],axis=1) return dup def delete(self,label): """ Delete column data. Parameters ---------- label : str Column label. """ dup = self.copy() dup.data.drop(columns=label,inplace=True) del dup.shapes[label] return dup def rename(self,old,new,info=None): """ Rename column data. Parameters ---------- label_old : str or iterable of str Old column label(s). label_new : str or iterable of str New column label(s). """ dup = self.copy() columns = dict(zip([old] if isinstance(old,str) else old, [new] if isinstance(new,str) else new)) dup.data.rename(columns=columns,inplace=True) dup.comments.append(f'{old} => {new}'+('' if info is None else f': {info}')) dup.shapes = {(label if label not in columns else columns[label]):dup.shapes[label] for label in dup.shapes} return dup def sort_by(self,labels,ascending=True): """ Sort table by values of given labels. Parameters ---------- label : str or list Column labels for sorting. ascending : bool or list, optional Set sort order. """ dup = self.copy() dup._label_discrete() dup.data.sort_values(labels,axis=0,inplace=True,ascending=ascending) dup._label_uniform() dup.comments.append(f'sorted {"ascending" if ascending else "descending"} by {labels}') return dup def append(self,other): """ Append other table vertically (similar to numpy.vstack). Requires matching labels/shapes and order. Parameters ---------- other : Table Table to append. """ if self.shapes != other.shapes or not self.data.columns.equals(other.data.columns): raise KeyError('Labels or shapes or order do not match') else: dup = self.copy() dup.data = dup.data.append(other.data,ignore_index=True) return dup def join(self,other): """ Append other table horizontally (similar to numpy.hstack). Requires matching number of rows and no common labels. Parameters ---------- other : Table Table to join. """ if set(self.shapes) & set(other.shapes) or self.data.shape[0] != other.data.shape[0]: raise KeyError('Dublicated keys or row count mismatch') else: dup = self.copy() dup.data = dup.data.join(other.data) for key in other.shapes: dup.shapes[key] = other.shapes[key] return dup def save(self,fname,legacy=False): """ Save as plain text file. Parameters ---------- fname : file, str, or pathlib.Path Filename or file for writing. legacy : Boolean, optional Write table in legacy style, indicating header lines by "N header" in contrast to using comment sign ('#') at beginning of lines. """ seen = set() labels = [] for l in [x for x in self.data.columns if not (x in seen or seen.add(x))]: if self.shapes[l] == (1,): labels.append(f'{l}') elif len(self.shapes[l]) == 1: labels += [f'{i+1}_{l}' \ for i in range(self.shapes[l][0])] else: labels += [f'{util.srepr(self.shapes[l],"x")}:{i+1}_{l}' \ for i in range(np.prod(self.shapes[l]))] header = ([f'{len(self.comments)+1} header'] + self.comments) if legacy else \ [f'# {comment}' for comment in self.comments] try: fhandle = open(fname,'w') except TypeError: fhandle = fname for line in header + [' '.join(labels)]: fhandle.write(line+'\n') self.data.to_csv(fhandle,sep=' ',na_rep='nan',index=False,header=False)