Merge branch 'new-ASCII' into grid-filters
This commit is contained in:
commit
b1ff178109
|
@ -1,4 +1,3 @@
|
|||
import random
|
||||
import re
|
||||
|
||||
import pandas as pd
|
||||
|
@ -21,27 +20,36 @@ class Table():
|
|||
Additional, human-readable information.
|
||||
|
||||
"""
|
||||
self.comments = [] if comments is None else [c for c in comments]
|
||||
self.data = pd.DataFrame(data=data)
|
||||
|
||||
labels = {}
|
||||
i = 0
|
||||
for label in shapes.keys():
|
||||
for components in range(np.prod(shapes[label])):
|
||||
labels[i] = label
|
||||
i+=1
|
||||
|
||||
if i != self.data.shape[1]:
|
||||
raise IndexError('Shape mismatch between shapes and data')
|
||||
|
||||
self.data.rename(columns=labels,inplace=True)
|
||||
|
||||
if comments is None:
|
||||
self.comments = []
|
||||
else:
|
||||
self.comments = [c for c in comments]
|
||||
|
||||
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):
|
||||
"""
|
||||
|
@ -67,7 +75,7 @@ class Table():
|
|||
header = int(header)
|
||||
else:
|
||||
raise Exception
|
||||
comments = [f.readline()[:-1] for i in range(header-1)]
|
||||
comments = [f.readline()[:-1] for i in range(1,header)]
|
||||
labels = f.readline().split()
|
||||
|
||||
shapes = {}
|
||||
|
@ -81,13 +89,13 @@ class Table():
|
|||
if vector_column:
|
||||
shapes[label.split('_',1)[1]] = (int(label.split('_',1)[0]),)
|
||||
else:
|
||||
shapes[label]=(1,)
|
||||
|
||||
data = pd.read_csv(f,names=[i for i in range(len(labels))],sep=r'\s+').to_numpy()
|
||||
shapes[label] = (1,)
|
||||
|
||||
data = pd.read_csv(f,names=list(range(len(labels))),sep=r'\s+').to_numpy()
|
||||
|
||||
return Table(data,shapes,comments)
|
||||
|
||||
|
||||
@property
|
||||
def labels(self):
|
||||
"""Return the labels of all columns."""
|
||||
return list(self.shapes.keys())
|
||||
|
@ -105,9 +113,12 @@ class Table():
|
|||
"""
|
||||
if re.match(r'[0-9]*?_',label):
|
||||
idx,key = label.split('_',1)
|
||||
return self.data[key].to_numpy()[:,int(idx)-1].reshape((-1,1))
|
||||
data = self.data[key].to_numpy()[:,int(idx)-1].reshape((-1,1))
|
||||
else:
|
||||
return self.data[label].to_numpy().reshape((-1,)+self.shapes[label])
|
||||
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):
|
||||
"""
|
||||
|
@ -123,11 +134,7 @@ class Table():
|
|||
Human-readable information about the new data.
|
||||
|
||||
"""
|
||||
if info is not None:
|
||||
if np.prod(data.shape[1:],dtype=int) == 1:
|
||||
self.comments.append('{}: {}'.format(label,info))
|
||||
else:
|
||||
self.comments.append('{} {}: {}'.format(label,data.shape[1:],info))
|
||||
self.__add_comment(label,data.shape[1:],info)
|
||||
|
||||
if re.match(r'[0-9]*?_',label):
|
||||
idx,key = label.split('_',1)
|
||||
|
@ -136,6 +143,7 @@ class Table():
|
|||
else:
|
||||
self.data[label] = data.reshape(self.data[label].shape)
|
||||
|
||||
|
||||
def add(self,label,data,info=None):
|
||||
"""
|
||||
Add column data.
|
||||
|
@ -150,17 +158,16 @@ class Table():
|
|||
Human-readable information about the modified data.
|
||||
|
||||
"""
|
||||
if info is not None:
|
||||
if np.prod(data.shape[1:],dtype=int) == 1:
|
||||
self.comments.append('{}: {}'.format(label,info))
|
||||
else:
|
||||
self.comments.append('{} {}: {}'.format(label,data.shape[1:],info))
|
||||
|
||||
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_data = pd.DataFrame(data=data.reshape(-1,size),
|
||||
columns=[label for l in range(size)])
|
||||
self.data = pd.concat([self.data,new_data],axis=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):
|
||||
"""
|
||||
|
@ -176,6 +183,7 @@ class Table():
|
|||
|
||||
del self.shapes[label]
|
||||
|
||||
|
||||
def rename(self,label_old,label_new,info=None):
|
||||
"""
|
||||
Rename column data.
|
||||
|
@ -190,9 +198,10 @@ class Table():
|
|||
"""
|
||||
self.data.rename(columns={label_old:label_new},inplace=True)
|
||||
|
||||
comments = '{} => {}'.format(label_old,label_new)
|
||||
comments += ': {}'.format(info) if info is not None else ''
|
||||
self.comments.append(comments)
|
||||
self.comments.append('{} => {}{}'.format(label_old,
|
||||
label_new,
|
||||
'' if info is None else ': {}'.format(info),
|
||||
))
|
||||
|
||||
self.shapes[label_new] = self.shapes.pop(label_old)
|
||||
|
||||
|
@ -203,26 +212,18 @@ class Table():
|
|||
|
||||
Parameters
|
||||
----------
|
||||
label : list of str or str
|
||||
label : str or list
|
||||
Column labels.
|
||||
ascending : bool, optional
|
||||
ascending : bool or list, optional
|
||||
Set sort order.
|
||||
|
||||
"""
|
||||
_temp = []
|
||||
_labels = []
|
||||
for label in labels if isinstance(labels,list) else [labels]:
|
||||
if re.match(r'[0-9]*?_',label):
|
||||
_temp.append(str(random.getrandbits(128)))
|
||||
self.add(_temp[-1],self.get(label))
|
||||
_labels.append(_temp[-1])
|
||||
else:
|
||||
_labels.append(label)
|
||||
|
||||
self.data.sort_values(_labels,axis=0,inplace=True,ascending=ascending)
|
||||
for t in _temp: self.delete(t)
|
||||
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.
|
||||
|
@ -238,14 +239,14 @@ class Table():
|
|||
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])]
|
||||
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))]
|
||||
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\
|
||||
header = ['{} header'.format(len(self.comments)+1)] \
|
||||
+ self.comments \
|
||||
+ [' '.join(labels)]
|
||||
|
||||
try:
|
||||
|
|
|
@ -9,7 +9,7 @@ from damask import Table
|
|||
@pytest.fixture
|
||||
def default():
|
||||
"""Simple Table."""
|
||||
x = np.ones((5,13))
|
||||
x = np.ones((5,13),dtype=float)
|
||||
return Table(x,{'F':(3,3),'v':(3,),'s':(1,)},['test data','contains only ones'])
|
||||
|
||||
@pytest.fixture
|
||||
|
@ -58,7 +58,7 @@ class TestTable:
|
|||
assert np.allclose(d,0.0) and d.shape[1:] == (3,3)
|
||||
|
||||
def test_labels(self,default):
|
||||
assert default.labels() == ['F','v','s']
|
||||
assert default.labels == ['F','v','s']
|
||||
|
||||
def test_add(self,default):
|
||||
d = np.random.random((5,9))
|
||||
|
@ -82,9 +82,9 @@ class TestTable:
|
|||
default.get('v')
|
||||
|
||||
|
||||
def test_invalid_initialization(self,default):
|
||||
x = default.get('v')
|
||||
with pytest.raises(IndexError):
|
||||
def test_invalid_initialization(self):
|
||||
x = np.random.random((5,10))
|
||||
with pytest.raises(ValueError):
|
||||
Table(x,{'F':(3,3)})
|
||||
|
||||
def test_invalid_set(self,default):
|
||||
|
@ -115,7 +115,14 @@ class TestTable:
|
|||
def test_sort_revert(self):
|
||||
x = np.random.random((5,12))
|
||||
t = Table(x,{'F':(3,3),'v':(3,)},['random test data'])
|
||||
t.sort_by('4_F',False)
|
||||
t.sort_by('4_F',ascending=False)
|
||||
sort = t.get('4_F')
|
||||
assert np.all(np.sort(sort,0)==sort[::-1,:])
|
||||
|
||||
def test_sort(self):
|
||||
t = Table(np.array([[0,1,],[2,1,]]),
|
||||
{'v':(2,)},
|
||||
['test data'])
|
||||
t.add('s',np.array(['b','a']))
|
||||
t.sort_by('s')
|
||||
assert np.all(t.get('1_v') == np.array([2,0]).reshape((2,1)))
|
||||
|
|
Loading…
Reference in New Issue