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Table.__eq__ for proper comparison; logical masks for slicing now work

This commit is contained in:
Philip Eisenlohr 2022-02-13 22:00:48 -05:00
parent e210a9873d
commit 0300912b30
2 changed files with 23 additions and 10 deletions
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27
python/damask/_table.py
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@ -44,6 +44,13 @@ class Table:
return '\n'.join(['# '+c for c in self.comments])+'\n'+data_repr return '\n'.join(['# '+c for c in self.comments])+'\n'+data_repr
def __eq__(self,
other: object) -> bool:
"""Compare to other Table."""
return NotImplemented if not isinstance(other,Table) else \
self.shapes == other.shapes and self.data.equals(other.data)
def __getitem__(self, def __getitem__(self,
item: Union[slice, Tuple[slice, ...]]) -> 'Table': item: Union[slice, Tuple[slice, ...]]) -> 'Table':
""" """
@ -75,20 +82,22 @@ class Table:
colB colA colB colA
0 1 0 0 1 0
2 7 6 2 7 6
>>> tbl[1:2,'colB'] >>> tbl[[True,False,False,True],'colB']
colB colB
1 4 0 1
2 7 3 10
""" """
item = (item,slice(None,None,None)) if isinstance(item,slice) else \ item_ = (item,slice(None,None,None)) if isinstance(item,(slice,np.ndarray)) else \
item if isinstance(item[0],slice) else \ (np.array(item),slice(None,None,None)) if isinstance(item,list) and np.array(item).dtype == np.bool_ else \
(slice(None,None,None),item) (np.array(item[0]),item[1]) if isinstance(item[0],list) else \
sliced = self.data.loc[item] item if isinstance(item[0],(slice,np.ndarray)) else \
cols = np.array(sliced.columns if isinstance(sliced,pd.core.frame.DataFrame) else [item[1]]) (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) _,idx = np.unique(cols,return_index=True)
return self.__class__(data=sliced, return self.__class__(data=sliced,
shapes = {k:self.shapes[k] for k in cols[np.sort(idx)]}, shapes={k:self.shapes[k] for k in cols[np.sort(idx)]},
comments=self.comments) comments=self.comments)

6
python/tests/test_Table.py
View File

@ -59,10 +59,14 @@ class TestTable:
@pytest.mark.parametrize('N',[1,3,4]) @pytest.mark.parametrize('N',[1,3,4])
def test_slice(self,default,N): def test_slice(self,default,N):
mask = np.random.choice([True,False],len(default))
assert len(default[:N]) == 1+N assert len(default[:N]) == 1+N
assert len(default[:N,['F','s']]) == 1+N assert len(default[:N,['F','s']]) == 1+N
assert len(default[mask,['F','s']]) == np.count_nonzero(mask)
assert default[mask,['F','s']] == default[mask][['F','s']] == default[['F','s']][mask]
assert default[np.logical_not(mask),['F','s']] != default[mask][['F','s']]
assert default[N:].get('F').shape == (len(default)-N,3,3) assert default[N:].get('F').shape == (len(default)-N,3,3)
assert (default[:N,['v','s']].data == default['v','s'][:N].data).all().all() assert default[:N,['v','s']].data.equals(default['v','s'][:N].data)
@pytest.mark.parametrize('mode',['str','path']) @pytest.mark.parametrize('mode',['str','path'])
def test_write_read(self,default,tmp_path,mode): def test_write_read(self,default,tmp_path,mode):