Merge remote-tracking branch 'origin/development' into spring-cleaning

This commit is contained in:
Martin Diehl 2021-04-01 00:09:51 +02:00
commit 8a3af87e3e
5 changed files with 210 additions and 55 deletions

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@ -1 +1 @@
v3.0.0-alpha2-670-ge6143f6ee
v3.0.0-alpha2-673-g0c08c9753

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@ -27,20 +27,69 @@ class Table:
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._label_uniform()
self._relabel('uniform')
def __repr__(self):
"""Brief overview."""
return '\n'.join(['# '+c for c in self.comments])+'\n'+self.data.__repr__()
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):
"""Return slice according to item."""
return self.__class__(data=self.data[item],shapes=self.shapes,comments=self.comments)
"""
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)
@ -48,21 +97,51 @@ class Table:
copy = __copy__
def _label_discrete(self):
"""Label data individually, e.g. v v v ==> 1_v 2_v 3_v."""
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,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
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 _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 _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):
@ -72,6 +151,62 @@ class 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):
"""
@ -120,12 +255,13 @@ class Table:
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:
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'.
@ -216,10 +352,12 @@ class Table:
"""
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
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)
@ -322,10 +460,18 @@ class 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._label_discrete()
dup.data.sort_values(labels,axis=0,inplace=True,ascending=ascending)
dup._label_uniform()
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

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@ -86,9 +86,12 @@ class TestConfigMaterial:
def test_from_table(self):
N = np.random.randint(3,10)
a = np.vstack((np.hstack((np.arange(N),np.arange(N)[::-1])),np.ones(N*2),np.zeros(N*2),np.ones(N*2),np.ones(N*2))).T
t = Table(a,{'varying':1,'constant':4})
c = ConfigMaterial.from_table(t,**{'phase':'varying','O':'constant','homogenization':'4_constant'})
a = np.vstack((np.hstack((np.arange(N),np.arange(N)[::-1])),
np.ones(N*2),np.zeros(N*2),np.ones(N*2),np.ones(N*2),
np.ones(N*2),
)).T
t = Table(a,{'varying':1,'constant':4,'ones':1})
c = ConfigMaterial.from_table(t,**{'phase':'varying','O':'constant','homogenization':'ones'})
assert len(c['material']) == N
for i,m in enumerate(c['material']):
assert m['homogenization'] == 1 and (m['constituents'][0]['O'] == [1,0,1,1]).all()

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@ -407,7 +407,8 @@ class TestGrid:
z=np.ones(cells.prod())
z[cells[:2].prod()*int(cells[2]/2):]=0
t = Table(np.column_stack((coords,z)),{'coords':3,'z':1})
g = Grid.from_table(t,'coords',['1_coords','z'])
t = t.add('indicator',t.get('coords')[:,0])
g = Grid.from_table(t,'coords',['indicator','z'])
assert g.N_materials == g.cells[0]*2 and (g.material[:,:,-1]-g.material[:,:,0] == cells[0]).all()

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@ -36,13 +36,33 @@ class TestTable:
d = default.get('F')
assert np.allclose(d,1.0) and d.shape[1:] == (3,3)
def test_get_component(self,default):
d = default.get('5_F')
assert np.allclose(d,1.0) and d.shape[1:] == (1,)
def test_set(self,default):
d = default.set('F',np.zeros((5,3,3)),'set to zero').get('F')
assert np.allclose(d,0.0) and d.shape[1:] == (3,3)
@pytest.mark.parametrize('N',[10,40])
def test_getitem(self,N):
assert len(Table(np.random.rand(N,1),{'X':1})[:N//2]) == N//2
def test_set_component(self,default):
d = default.set('F[0,0]',np.zeros((5)),'set to zero').get('F')
assert np.allclose(d[...,0,0],0.0) and d.shape[1:] == (3,3)
def test_labels(self,default):
assert default.labels == ['F','v','s']
def test_add(self,default):
d = np.random.random((5,9))
assert np.allclose(d,default.add('nine',d,'random data').get('nine'))
def test_isclose(self,default):
assert default.isclose(default).all()
def test_allclose(self,default):
assert default.allclose(default)
@pytest.mark.parametrize('N',[1,3,4])
def test_slice(self,default,N):
assert len(default[:N]) == 1+N
assert len(default[:N,['F','s']]) == 1+N
assert default[N:].get('F').shape == (len(default)-N,3,3)
assert (default[:N,['v','s']].data == default['v','s'][:N].data).all().all()
@pytest.mark.parametrize('mode',['str','path'])
def test_write_read(self,default,tmp_path,mode):
@ -84,21 +104,6 @@ class TestTable:
with open(ref_path/fname) as f:
Table.load(f)
def test_set(self,default):
d = default.set('F',np.zeros((5,3,3)),'set to zero').get('F')
assert np.allclose(d,0.0) and d.shape[1:] == (3,3)
def test_set_component(self,default):
d = default.set('1_F',np.zeros((5)),'set to zero').get('F')
assert np.allclose(d[...,0,0],0.0) and d.shape[1:] == (3,3)
def test_labels(self,default):
assert default.labels == ['F','v','s']
def test_add(self,default):
d = np.random.random((5,9))
assert np.allclose(d,default.add('nine',d,'random data').get('nine'))
def test_rename_equivalent(self):
x = np.random.random((5,13))
t = Table(x,{'F':(3,3),'v':(3,),'s':(1,)},['random test data'])
@ -169,15 +174,15 @@ class TestTable:
def test_sort_component(self):
x = np.random.random((5,12))
t = Table(x,{'F':(3,3),'v':(3,)},['random test data'])
unsort = t.get('4_F')
sort = t.sort_by('4_F').get('4_F')
unsort = t.get('F')[:,1,0]
sort = t.sort_by('F[1,0]').get('F')[:,1,0]
assert np.all(np.sort(unsort,0)==sort)
def test_sort_revert(self):
x = np.random.random((5,12))
t = Table(x,{'F':(3,3),'v':(3,)},['random test data'])
sort = t.sort_by('4_F',ascending=False).get('4_F')
assert np.all(np.sort(sort,0)==sort[::-1,:])
sort = t.sort_by('F[1,0]',ascending=False).get('F')[:,1,0]
assert np.all(np.sort(sort,0)==sort[::-1])
def test_sort(self):
t = Table(np.array([[0,1,],[2,1,]]),
@ -185,4 +190,4 @@ class TestTable:
['test data'])\
.add('s',np.array(['b','a']))\
.sort_by('s')
assert np.all(t.get('1_v') == np.array([2,0]).reshape(2,1))
assert np.all(t.get('v')[:,0] == np.array([2,0]))