391 lines
12 KiB
Python
391 lines
12 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.int,int)) else v for k,v in shapes.items() }
|
|
self._label_uniform()
|
|
|
|
def __repr__(self):
|
|
"""Brief overview."""
|
|
return '\n'.join(['# '+c for c in self.comments])+'\n'+self.data.__repr__()
|
|
|
|
def __getitem__(self,item):
|
|
"""Return slice according to item."""
|
|
return self.__class__(data=self.data[item],shapes=self.shapes,comments=self.comments)
|
|
|
|
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 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 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.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.
|
|
|
|
"""
|
|
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)
|