339 lines
9.9 KiB
Python
339 lines
9.9 KiB
Python
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 != label_old else label_new):self.shapes[label] for label in self.shapes}
|
|
|
|
|
|
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.
|
|
|
|
"""
|
|
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 append(self,other):
|
|
"""
|
|
Append other table vertically (similar to numpy.vstack). Requires matching 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:
|
|
self.data = self.data.append(other.data,ignore_index=True)
|
|
|
|
|
|
def join(self,other):
|
|
"""
|
|
Append other table horizontally (similar to numpy.hstack). Requires matching number of rows
|
|
and no common lables
|
|
|
|
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:
|
|
self.data = self.data.join(other.data)
|
|
for key in other.shapes:
|
|
self.shapes[key] = other.shapes[key]
|
|
|
|
|
|
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)
|