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):
"""Create deep copy."""
return copy.deepcopy(self)
copy = __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)