DAMASK_EICMD/python/damask/table.py

import re

import pandas as pd
import numpy as np

from . import version

class Table():
    """Store spreadsheet-like data."""
  
    def __init__(self,data,shapes,comments=None):
        """
        New spreadsheet.
        
        Parameters
        ----------
        data : numpy.ndarray
            Data.
        shapes : dict with str:tuple pairs
            Shapes of the columns. Example 'F':(3,3) for a deformation gradient. 
        comments : iterable of str, optional
            Additional, human-readable information.
        
        """
        self.comments = ['table.py v {}'.format(version)] if not comments else [c for c in comments]
        self.data = pd.DataFrame(data=data)
        self.shapes = shapes
        self.__label_condensed()


    def __label_flat(self):
        """Label data individually, e.g. v v v ==> 1_v 2_v 3_v."""
        labels = []
        for label,shape in self.shapes.items():
            size = np.prod(shape)
            labels += ['{}{}'.format('' if size == 1 else '{}_'.format(i+1),label) for i in range(size)]
        self.data.columns = labels
        
        
    def __label_condensed(self):
        """Label data condensed, e.g. 1_v 2_v 3_v ==> v v v."""
        labels = []
        for label,shape in self.shapes.items():
            labels += [label] * np.prod(shape)
        self.data.columns = labels


    def __add_comment(self,label,shape,info):
        if info is not None:
            self.comments.append('{}{}: {}'.format(label,
                                                   ' '+str(shape) if np.prod(shape,dtype=int) > 1 else '',
                                                   info))

    
    @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 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)

        header,keyword = f.readline().split()
        if keyword == 'header':
            header = int(header)
        else:
            raise Exception
        
        comments = ['table.py:from_ASCII v {}'.format(version)]
        comments+= [f.readline()[:-1] for i in range(1,header)]
        labels   = f.readline().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+').to_numpy()

        return Table(data,shapes,comments)

    @staticmethod
    def from_ang(fname):
        """
        Create table from TSL 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.

        """
        shapes = {'eu':(3,), 'pos':(2,),
                  'IQ':(1,), 'CI':(1,), 'ID':(1,), 'intensity':(1,), 'fit':(1,)}
        try:
            f = open(fname)
        except TypeError:
            f = fname
            f.seek(0)
        
        content = f.readlines()

        comments = ['table.py:from_ang v {}'.format(version)]
        for line in content:
            if line.startswith('#'):
                comments.append(line.strip())
            else:
                break
       
        data = np.loadtxt(content)

        return Table(data,shapes,comments)

    @property
    def labels(self):
        return list(self.shapes.keys())


    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.

        """
        self.__add_comment(label,data.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] = data
        else: 
            self.data[label]       = data.reshape(self.data[label].shape)


    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.

        """
        self.__add_comment(label,data.shape[1:],info)

        self.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 = self.data.index
        self.data = pd.concat([self.data,new],axis=1)


    def delete(self,label):
        """
        Delete column data.

        Parameters
        ----------
        label : str
            Column label.

        """
        self.data.drop(columns=label,inplace=True)

        del self.shapes[label]


    def rename(self,label_old,label_new,info=None):
        """
        Rename column data.

        Parameters
        ----------
        label_old : str
            Old column label.
        label_new : str
            New column label.

        """
        self.data.rename(columns={label_old:label_new},inplace=True)

        self.comments.append('{} => {}{}'.format(label_old,
                                                 label_new,
                                                 '' if info is None else ': {}'.format(info),
                                                 ))

        self.shapes = {(label if label is not label_old else label_new):self.shapes[label] for label in self.shapes}


    def sort_by(self,labels,ascending=True):
        """
        Get column data.

        Parameters
        ----------
        label : str or list
            Column labels.
        ascending : bool or list, optional
            Set sort order.

        """
        self.__label_flat()
        self.data.sort_values(labels,axis=0,inplace=True,ascending=ascending)
        self.__label_condensed()
        self.comments.append('sorted by [{}]'.format(', '.join(labels)))


    def to_ASCII(self,fname):
        """
        Store as plain text file.

        Parameters
        ----------
        fname : file, str, or pathlib.Path
            Filename or file for reading.

        """
        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('{}'.format(l))
            elif(len(self.shapes[l]) == 1):
                labels += ['{}_{}'.format(i+1,l) \
                          for i in range(self.shapes[l][0])]
            else:
                labels += ['{}:{}_{}'.format('x'.join([str(d) for d in self.shapes[l]]),i+1,l) \
                          for i in range(np.prod(self.shapes[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)