DAMASK_EICMD/python/damask/_table.py

575 lines
17 KiB
Python

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.int64,np.int32,int)) else v for k,v in shapes.items() }
self._relabel('uniform')
def __repr__(self):
"""Brief overview."""
self._relabel('shapes')
data_repr = self.data.__repr__()
self._relabel('uniform')
return '\n'.join(['# '+c for c in self.comments])+'\n'+data_repr
def __getitem__(self,item):
"""
Slice the Table according to item.
Parameters
----------
item : row and/or column indexer
Slice to select from Table.
Returns
-------
slice : Table
Sliced part of the Table.
Examples
--------
>>> import damask
>>> import numpy as np
>>> tbl = damask.Table(data=np.arange(12).reshape((4,3)),
... shapes=dict(colA=(1,),colB=(1,),colC=(1,)))
>>> tbl['colA','colB']
colA colB
0 0 1
1 3 4
2 6 7
3 9 10
>>> tbl[::2,['colB','colA']]
colB colA
0 1 0
2 7 6
>>> tbl[1:2,'colB']
colB
1 4
2 7
"""
item = (item,slice(None,None,None)) if isinstance(item,slice) else \
item if isinstance(item[0],slice) else \
(slice(None,None,None),item)
sliced = self.data.loc[item]
cols = np.array(sliced.columns if isinstance(sliced,pd.core.frame.DataFrame) else [item[1]])
_,idx = np.unique(cols,return_index=True)
return self.__class__(data=sliced,
shapes = {k:self.shapes[k] for k in cols[np.sort(idx)]},
comments=self.comments)
def __len__(self):
"""Number of rows."""
return len(self.data)
def __copy__(self):
"""Create deep copy."""
return copy.deepcopy(self)
copy = __copy__
def _label(self,what,how):
"""
Expand labels according to data shape.
Parameters
----------
what : str or list
Labels to expand.
how : str
Mode of labeling.
'uniform' ==> v v v
'shapes' ==> 3:v v v
'linear' ==> 1_v 2_v 3_v
"""
what = [what] if isinstance(what,str) else what
labels = []
for label in what:
shape = self.shapes[label]
size = np.prod(shape,dtype=int)
if how == 'uniform':
labels += [label] * size
elif how == 'shapes':
labels += [('' if size == 1 or i>0 else f'{util.srepr(shape,"x")}:')+label for i in range(size)]
elif how == 'linear':
labels += [('' if size == 1 else f'{i+1}_')+label for i in range(size)]
else:
raise KeyError
return labels
def _relabel(self,how):
"""
Modify labeling of data in-place.
Parameters
----------
how : str
Mode of labeling.
'uniform' ==> v v v
'shapes' ==> 3:v v v
'linear' ==> 1_v 2_v 3_v
"""
self.data.columns = self._label(self.shapes,how)
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}')
def isclose(self,other,rtol=1e-5,atol=1e-8,equal_nan=True):
"""
Report where values are approximately equal to corresponding ones of other Table.
Parameters
----------
other : Table
Table to compare against.
rtol : float, optional
Relative tolerance of equality.
atol : float, optional
Absolute tolerance of equality.
equal_nan : bool, optional
Consider matching NaN values as equal. Defaults to True.
Returns
-------
mask : numpy.ndarray bool
Mask indicating where corresponding table values are close.
"""
return np.isclose( self.data.to_numpy(),
other.data.to_numpy(),
rtol=rtol,
atol=atol,
equal_nan=equal_nan)
def allclose(self,other,rtol=1e-5,atol=1e-8,equal_nan=True):
"""
Test whether all values are approximately equal to corresponding ones of other Table.
Parameters
----------
other : Table
Table to compare against.
rtol : float, optional
Relative tolerance of equality.
atol : float, optional
Absolute tolerance of equality.
equal_nan : bool, optional
Consider matching NaN values as equal. Defaults to True.
Returns
-------
answer : bool
Whether corresponding values are close between both tables.
"""
return np.allclose( self.data.to_numpy(),
other.data.to_numpy(),
rtol=rtol,
atol=atol,
equal_nan=equal_nan)
@staticmethod
def load(fname):
"""
Load from 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 from ang file.
A valid TSL ang file has to have 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.split('#',1)[1].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.
Returns
-------
data : numpy.ndarray
Array of column data.
"""
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.
Returns
-------
table : Table
Updated table.
"""
dup = self.copy()
dup._add_comment(label,data.shape[1:],info)
m = re.match(r'(.*)\[((\d+,)*(\d+))\]',label)
if m:
key = m.group(1)
idx = np.ravel_multi_index(tuple(map(int,m.group(2).split(","))),
self.shapes[key])
iloc = dup.data.columns.get_loc(key).tolist().index(True) + idx
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.
Returns
-------
table : Table
Updated table.
"""
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.
Returns
-------
table : Table
Updated table.
"""
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).
Returns
-------
table : Table
Updated table.
"""
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.
Returns
-------
table : Table
Updated table.
"""
labels_ = [labels] if isinstance(labels,str) else labels.copy()
for i,l in enumerate(labels_):
m = re.match(r'(.*)\[((\d+,)*(\d+))\]',l)
if m:
idx = np.ravel_multi_index(tuple(map(int,m.group(2).split(','))),
self.shapes[m.group(1)])
labels_[i] = f'{1+idx}_{m.group(1)}'
dup = self.copy()
dup._relabel('linear')
dup.data.sort_values(labels_,axis=0,inplace=True,ascending=ascending)
dup._relabel('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.
Returns
-------
table : Table
Concatenated table.
"""
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.
Returns
-------
table : Table
Joined table.
"""
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 : bool, 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',newline='\n')
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)