Hi -- We are subclassing from np.rec.recarray and are confused about how
some methods of np.rec.recarray relate to (differ from) analogous methods of
its parent, np.ndarray. Below are specific questions about the __eq__,
__getitem__ and view methods, we'd appreciate answers to our specific
questions and/or more general points that we may be not understanding about
subclassing from np.ndarray (and np.rec.recarray).
---
1) Suppose I have a recarray object, x. How come np.ndarray.__getitem__(x,
'column_name') returns a recarray object rather than a ndarray? e.g.,
In [230]: x = np.rec.fromrecords([(1,'dd'), (2,'cc')], names=['a','b'])
In [231]: np.ndarray.__getitem__(x, 'a')
Out[231]: rec.array([1, 2])
In [232]: np.ndarray.__getitem__(x, 'a').dtype
Out[232]: dtype('int32')
The returned object is a recarray but it does not have a structured dtype.
This generally seems to be the case when passing the instance of a subclass
of np.ndarray (such as a np.rec.recarray object) to np.ndarray.__getitem__
---
2)a) When I use the __getitem__ method of recarray to get an individual
column, the returned object is an ndarray when the column is a numeric type
but it is a recarray when the column is a string type. Why doesn't
__getitem__ always return an ndarray for an individual column? e.g.,
In [175]: x = np.rec.fromrecords([(1,'dd'), (2,'cc')], names=['a','b'])
In [176]: x['a']
Out[176]: array([1, 2])
In [177]: x['b']
Out[177]: rec.array(['dd', 'cc'], dtype='|S2')
2)b) Suppose I have a subclass of recarray, NewRecarray, that attaches some
new attribute, e.g. 'info'.
x = NewRecarray(data, names = ['a','b'], formats = '<i4, |S2')
Now say I want to use recarray's __getitem__ method to get an individual
column. Then
x['a'] is an ndarray
x['b'] is a NewRecarray and x['b'].info == x.info
Is this the expected / proper behavior? Is there something wrong with the
way I've subclassed recarray?
---
3)a) If I have two recarrays with the same len and column headers, the
__eq__ method returns the rich comparison. Why is the result a recarray
rather than an ndarray?
In [162]: x = np.rec.fromrecords([(1,'dd'), (2,'cc')], names=['a','b'])
In [163]: y = np.rec.fromrecords([(1,'dd'), (2,'cc')], names=['a','b'])
In [164]: x == y
Out[164]: rec.array([ True, True], dtype=bool)
3)b) Suppose I have a subclass of recarray, NewRecarray, that attaches some
new attribute, e.g. 'info'.
x = NewRecarray(data)
y = NewRecarray(data)
z = x == y
Then z is a NewRecarray object and z.info = x.info.
Is this the expected / proper behavior? Is there something wrong with the
way I've subclassed recarray? [Dan Yamins asked this a couple days ago]
---
4) Suppose I have a subclass of np.ndarray, NewArray, that attaches some
new attribute, e.g. 'info'. When I view a NewArray object as a ndarray, the
result has no 'info' attribute. Is the memory corresponding to the 'info'
attribute garbage collected? What happens to it?
x = NewArray(data)
x.view(np.ndarray) has no 'info' attribute
---
Thanks for any help! (And thanks for reading if you read any or all of
this!)
Elaine
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