- improved reporting and slicing of table.
- implemented numpy-like `allclose` and `isclose`
This commit is contained in:
Philip Eisenlohr
Martin Diehl
parent
03d54be58a
commit
07c9cf5f1a
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@ -27,20 +27,69 @@ class Table:
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self.comments = [] if comments_ is None else [c for c in comments_]
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self.data = pd.DataFrame(data=data)
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self.shapes = { k:(v,) if isinstance(v,(np.int64,np.int32,int)) else v for k,v in shapes.items() }
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self._label_uniform()
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self._relabel('uniform')
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def __repr__(self):
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"""Brief overview."""
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return '\n'.join(['# '+c for c in self.comments])+'\n'+self.data.__repr__()
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self._relabel('shapes')
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data_repr = self.data.__repr__()
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self._relabel('uniform')
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return '\n'.join(['# '+c for c in self.comments])+'\n'+data_repr
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def __getitem__(self,item):
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"""Return slice according to item."""
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return self.__class__(data=self.data[item],shapes=self.shapes,comments=self.comments)
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"""
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Slice the Table according to item.
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Parameters
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----------
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item : row and/or column indexer
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Slice to select from Table.
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Returns
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-------
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slice : Table
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Sliced part of the Table.
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Examples
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--------
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>>> import damask
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>>> import numpy as np
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>>> tbl = damask.Table(data=np.arange(12).reshape((4,3)),
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... shapes=dict(colA=(1,),colB=(1,),colC=(1,)))
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>>> tbl['colA','colB']
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colA colB
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0 0 1
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1 3 4
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2 6 7
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3 9 10
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>>> tbl[::2,['colB','colA']]
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colB colA
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0 1 0
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2 7 6
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>>> tbl[1:2,'colB']
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colB
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1 4
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2 7
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"""
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item = (item,slice(None,None,None)) if isinstance(item,slice) else \
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item if isinstance(item[0],slice) else \
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(slice(None,None,None),item)
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sliced = self.data.loc[item]
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cols = np.array(sliced.columns if isinstance(sliced,pd.core.frame.DataFrame) else [item[1]])
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_,idx = np.unique(cols,return_index=True)
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return self.__class__(data=sliced,
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shapes = {k:self.shapes[k] for k in cols[np.sort(idx)]},
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comments=self.comments)
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def __len__(self):
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"""Number of rows."""
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return len(self.data)
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def __copy__(self):
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"""Create deep copy."""
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return copy.deepcopy(self)
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@ -48,21 +97,51 @@ class Table:
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copy = __copy__
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def _label_discrete(self):
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"""Label data individually, e.g. v v v ==> 1_v 2_v 3_v."""
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def _label(self,what,how):
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"""
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Expand labels according to data shape.
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Parameters
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----------
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what : str or list
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Labels to expand.
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how : str
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Mode of labeling.
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'uniform' ==> v v v
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'shapes' ==> 3:v v v
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'linear' ==> 1_v 2_v 3_v
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"""
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what = [what] if isinstance(what,str) else what
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labels = []
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for label,shape in self.shapes.items():
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size = int(np.prod(shape))
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labels += [('' if size == 1 else f'{i+1}_')+label for i in range(size)]
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self.data.columns = labels
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for label in what:
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shape = self.shapes[label]
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size = np.prod(shape,dtype=int)
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if how == 'uniform':
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labels += [label] * size
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elif how == 'shapes':
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labels += [('' if size == 1 or i>0 else f'{util.srepr(shape,"x")}:')+label for i in range(size)]
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elif how == 'linear':
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labels += [('' if size == 1 else f'{i+1}_')+label for i in range(size)]
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else:
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raise KeyError
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return labels
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def _label_uniform(self):
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"""Label data uniformly, e.g. 1_v 2_v 3_v ==> v v v."""
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labels = []
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for label,shape in self.shapes.items():
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labels += [label] * int(np.prod(shape))
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self.data.columns = labels
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def _relabel(self,how):
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"""
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Modify labeling of data in-place.
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Parameters
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----------
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how : str
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Mode of labeling.
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'uniform' ==> v v v
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'shapes' ==> 3:v v v
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'linear' ==> 1_v 2_v 3_v
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"""
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self.data.columns = self._label(self.shapes,how)
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def _add_comment(self,label,shape,info):
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@ -72,6 +151,62 @@ class Table:
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self.comments.append(f'{specific} / {general}')
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def isclose(self,other,rtol=1e-5,atol=1e-8,equal_nan=True):
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"""
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Report where values are approximately equal to corresponding ones of other Table.
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Parameters
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----------
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other : Table
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Table to compare against.
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rtol : float, optional
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Relative tolerance of equality.
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atol : float, optional
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Absolute tolerance of equality.
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equal_nan : bool, optional
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Consider matching NaN values as equal. Defaults to True.
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Returns
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-------
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mask : numpy.ndarray bool
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Mask indicating where corresponding table values are close.
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"""
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return np.isclose( self.data.to_numpy(),
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other.data.to_numpy(),
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rtol=rtol,
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atol=atol,
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equal_nan=equal_nan)
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def allclose(self,other,rtol=1e-5,atol=1e-8,equal_nan=True):
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"""
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Test whether all values are approximately equal to corresponding ones of other Table.
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Parameters
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----------
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other : Table
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Table to compare against.
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rtol : float, optional
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Relative tolerance of equality.
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atol : float, optional
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Absolute tolerance of equality.
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equal_nan : bool, optional
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Consider matching NaN values as equal. Defaults to True.
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Returns
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-------
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answer : bool
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Whether corresponding values are close between both tables.
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"""
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return np.allclose( self.data.to_numpy(),
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other.data.to_numpy(),
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rtol=rtol,
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atol=atol,
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equal_nan=equal_nan)
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@staticmethod
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def load(fname):
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"""
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@ -130,12 +265,13 @@ class Table:
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return Table(data,shapes,comments)
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@staticmethod
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def load_ang(fname):
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"""
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Load from ang file.
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A valid TSL ang file needs to contains the following columns:
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A valid TSL ang file has to have the following columns:
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* Euler angles (Bunge notation) in radians, 3 floats, label 'eu'.
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* Spatial position in meters, 2 floats, label 'pos'.
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* Image quality, 1 float, label 'IQ'.
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@ -225,10 +361,12 @@ class Table:
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"""
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dup = self.copy()
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dup._add_comment(label,data.shape[1:],info)
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if re.match(r'[0-9]*?_',label):
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idx,key = label.split('_',1)
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iloc = dup.data.columns.get_loc(key).tolist().index(True) + int(idx) -1
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m = re.match(r'(.*)\[((\d+,)*(\d+))\]',label)
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if m:
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key = m.group(1)
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idx = np.ravel_multi_index(tuple(map(int,m.group(2).split(","))),
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self.shapes[key])
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iloc = dup.data.columns.get_loc(key).tolist().index(True) + idx
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dup.data.iloc[:,iloc] = data
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else:
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dup.data[label] = data.reshape(dup.data[label].shape)
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@ -331,10 +469,18 @@ class Table:
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Updated table.
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"""
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labels_ = [labels] if isinstance(labels,str) else labels.copy()
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for i,l in enumerate(labels_):
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m = re.match(r'(.*)\[((\d+,)*(\d+))\]',l)
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if m:
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idx = np.ravel_multi_index(tuple(map(int,m.group(2).split(','))),
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self.shapes[m.group(1)])
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labels_[i] = f'{1+idx}_{m.group(1)}'
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dup = self.copy()
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dup._label_discrete()
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dup.data.sort_values(labels,axis=0,inplace=True,ascending=ascending)
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dup._label_uniform()
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dup._relabel('linear')
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dup.data.sort_values(labels_,axis=0,inplace=True,ascending=ascending)
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dup._relabel('uniform')
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dup.comments.append(f'sorted {"ascending" if ascending else "descending"} by {labels}')
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return dup
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@ -399,7 +545,7 @@ class Table:
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----------
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fname : file, str, or pathlib.Path
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Filename or file for writing.
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legacy : Boolean, optional
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legacy : bool, optional
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Write table in legacy style, indicating header lines by "N header"
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in contrast to using comment sign ('#') at beginning of lines.
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@ -399,7 +399,7 @@ class Test:
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tables = [damask.Table.load(filename) for filename in files]
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for table in tables:
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table._label_discrete()
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table._relabel('linear')
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columns += [columns[0]]*(len(files)-len(columns)) # extend to same length as files
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columns = columns[:len(files)] # truncate to same length as files
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@ -419,7 +419,7 @@ class Test:
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data = []
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for table,labels in zip(tables,columns):
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table._label_uniform()
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table._relabel('uniform')
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data.append(np.hstack(list(table.get(label) for label in labels)))
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@ -86,9 +86,12 @@ class TestConfigMaterial:
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def test_from_table(self):
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N = np.random.randint(3,10)
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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
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t = Table(a,{'varying':1,'constant':4})
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c = ConfigMaterial.from_table(t,**{'phase':'varying','O':'constant','homogenization':'4_constant'})
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a = np.vstack((np.hstack((np.arange(N),np.arange(N)[::-1])),
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np.ones(N*2),np.zeros(N*2),np.ones(N*2),np.ones(N*2),
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np.ones(N*2),
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)).T
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t = Table(a,{'varying':1,'constant':4,'ones':1})
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c = ConfigMaterial.from_table(t,**{'phase':'varying','O':'constant','homogenization':'ones'})
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assert len(c['material']) == N
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for i,m in enumerate(c['material']):
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assert m['homogenization'] == 1 and (m['constituents'][0]['O'] == [1,0,1,1]).all()
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@ -407,7 +407,8 @@ class TestGrid:
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z=np.ones(cells.prod())
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z[cells[:2].prod()*int(cells[2]/2):]=0
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t = Table(np.column_stack((coords,z)),{'coords':3,'z':1})
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g = Grid.from_table(t,'coords',['1_coords','z'])
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t = t.add('indicator',t.get('coords')[:,0])
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g = Grid.from_table(t,'coords',['indicator','z'])
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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:
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d = default.get('F')
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assert np.allclose(d,1.0) and d.shape[1:] == (3,3)
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def test_get_component(self,default):
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d = default.get('5_F')
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assert np.allclose(d,1.0) and d.shape[1:] == (1,)
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def test_set(self,default):
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d = default.set('F',np.zeros((5,3,3)),'set to zero').get('F')
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assert np.allclose(d,0.0) and d.shape[1:] == (3,3)
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@pytest.mark.parametrize('N',[10,40])
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def test_getitem(self,N):
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assert len(Table(np.random.rand(N,1),{'X':1})[:N//2]) == N//2
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def test_set_component(self,default):
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d = default.set('F[0,0]',np.zeros((5)),'set to zero').get('F')
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assert np.allclose(d[...,0,0],0.0) and d.shape[1:] == (3,3)
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def test_labels(self,default):
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assert default.labels == ['F','v','s']
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def test_add(self,default):
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d = np.random.random((5,9))
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assert np.allclose(d,default.add('nine',d,'random data').get('nine'))
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def test_isclose(self,default):
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assert default.isclose(default).all()
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def test_allclose(self,default):
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assert default.allclose(default)
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@pytest.mark.parametrize('N',[1,3,4])
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def test_slice(self,default,N):
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assert len(default[:N]) == 1+N
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assert len(default[:N,['F','s']]) == 1+N
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assert default[N:].get('F').shape == (len(default)-N,3,3)
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assert (default[:N,['v','s']].data == default['v','s'][:N].data).all().all()
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@pytest.mark.parametrize('mode',['str','path'])
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def test_write_read(self,default,tmp_path,mode):
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@ -91,21 +111,6 @@ class TestTable:
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with open(ref_path/fname) as f:
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Table.load(f)
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def test_set(self,default):
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d = default.set('F',np.zeros((5,3,3)),'set to zero').get('F')
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assert np.allclose(d,0.0) and d.shape[1:] == (3,3)
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def test_set_component(self,default):
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d = default.set('1_F',np.zeros((5)),'set to zero').get('F')
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assert np.allclose(d[...,0,0],0.0) and d.shape[1:] == (3,3)
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def test_labels(self,default):
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assert default.labels == ['F','v','s']
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def test_add(self,default):
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d = np.random.random((5,9))
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assert np.allclose(d,default.add('nine',d,'random data').get('nine'))
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def test_rename_equivalent(self):
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x = np.random.random((5,13))
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t = Table(x,{'F':(3,3),'v':(3,),'s':(1,)},['random test data'])
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@ -176,15 +181,15 @@ class TestTable:
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def test_sort_component(self):
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x = np.random.random((5,12))
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t = Table(x,{'F':(3,3),'v':(3,)},['random test data'])
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unsort = t.get('4_F')
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sort = t.sort_by('4_F').get('4_F')
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unsort = t.get('F')[:,1,0]
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sort = t.sort_by('F[1,0]').get('F')[:,1,0]
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assert np.all(np.sort(unsort,0)==sort)
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def test_sort_revert(self):
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x = np.random.random((5,12))
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t = Table(x,{'F':(3,3),'v':(3,)},['random test data'])
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sort = t.sort_by('4_F',ascending=False).get('4_F')
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assert np.all(np.sort(sort,0)==sort[::-1,:])
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sort = t.sort_by('F[1,0]',ascending=False).get('F')[:,1,0]
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assert np.all(np.sort(sort,0)==sort[::-1])
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def test_sort(self):
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t = Table(np.array([[0,1,],[2,1,]]),
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@ -192,4 +197,4 @@ class TestTable:
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['test data'])\
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.add('s',np.array(['b','a']))\
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.sort_by('s')
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assert np.all(t.get('1_v') == np.array([2,0]).reshape(2,1))
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assert np.all(t.get('v')[:,0] == np.array([2,0]))
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