Subclassing ndarray#
Introduction#
Subclassing ndarray is relatively simple, but it has some complications compared to other Python objects. On this page we explain the machinery that allows you to subclass ndarray, and the implications for implementing a subclass.
ndarrays and object creation#
Subclassing ndarray is complicated by the fact that new instances of ndarray classes can come about in three different ways. These are:
Explicit constructor call - as in
MySubClass(params)
. This is the usual route to Python instance creation.View casting - casting an existing ndarray as a given subclass
New from template - creating a new instance from a template instance. Examples include returning slices from a subclassed array, creating return types from ufuncs, and copying arrays. See Creating new from template for more details
The last two are characteristics of ndarrays - in order to support things like array slicing. The complications of subclassing ndarray are due to the mechanisms numpy has to support these latter two routes of instance creation.
When to use subclassing#
Besides the additional complexities of subclassing a NumPy array, subclasses can run into unexpected behaviour because some functions may convert the subclass to a baseclass and “forget” any additional information associated with the subclass. This can result in surprising behavior if you use NumPy methods or functions you have not explicitly tested.
On the other hand, compared to other interoperability approaches, subclassing can be a useful because many thing will “just work”.
This means that subclassing can be a convenient approach and for a long time it was also often the only available approach. However, NumPy now provides additional interoperability protocols described in “Interoperability with NumPy”. For many use-cases these interoperability protocols may now be a better fit or supplement the use of subclassing.
Subclassing can be a good fit if:
you are less worried about maintainability or users other than yourself: Subclass will be faster to implement and additional interoperability can be added “as-needed”. And with few users, possible surprises are not an issue.
you do not think it is problematic if the subclass information is ignored or lost silently. An example is
np.memmap
where “forgetting” about data being memory mapped cannot lead to a wrong result. An example of a subclass that sometimes confuses users are NumPy’s masked arrays. When they were introduced, subclassing was the only approach for implementation. However, today we would possibly try to avoid subclassing and rely only on interoperability protocols.
Note that also subclass authors may wish to study Interoperability with NumPy to support more complex use-cases or work around the surprising behavior.
astropy.units.Quantity
and xarray
are examples for array-like objects
that interoperate well with NumPy. Astropy’s Quantity
is an example
which uses a dual approach of both subclassing and interoperability protocols.
View casting#
View casting is the standard ndarray mechanism by which you take an ndarray of any subclass, and return a view of the array as another (specified) subclass:
>>> import numpy as np
>>> # create a completely useless ndarray subclass
>>> class C(np.ndarray): pass
>>> # create a standard ndarray
>>> arr = np.zeros((3,))
>>> # take a view of it, as our useless subclass
>>> c_arr = arr.view(C)
>>> type(c_arr)
<class '__main__.C'>
Creating new from template#
New instances of an ndarray subclass can also come about by a very similar mechanism to View casting, when numpy finds it needs to create a new instance from a template instance. The most obvious place this has to happen is when you are taking slices of subclassed arrays. For example:
>>> v = c_arr[1:]
>>> type(v) # the view is of type 'C'
<class '__main__.C'>
>>> v is c_arr # but it's a new instance
False
The slice is a view onto the original c_arr
data. So, when we
take a view from the ndarray, we return a new ndarray, of the same
class, that points to the data in the original.
There are other points in the use of ndarrays where we need such views,
such as copying arrays (c_arr.copy()
), creating ufunc output arrays
(see also __array_wrap__ for ufuncs and other functions), and reducing methods (like
c_arr.mean()
).
Relationship of view casting and new-from-template#
These paths both use the same machinery. We make the distinction here, because they result in different input to your methods. Specifically, View casting means you have created a new instance of your array type from any potential subclass of ndarray. Creating new from template means you have created a new instance of your class from a pre-existing instance, allowing you - for example - to copy across attributes that are particular to your subclass.
Implications for subclassing#
If we subclass ndarray, we need to deal not only with explicit construction of our array type, but also View casting or Creating new from template. NumPy has the machinery to do this, and it is this machinery that makes subclassing slightly non-standard.
There are two aspects to the machinery that ndarray uses to support views and new-from-template in subclasses.
The first is the use of the ndarray.__new__
method for the main work
of object initialization, rather then the more usual __init__
method. The second is the use of the __array_finalize__
method to
allow subclasses to clean up after the creation of views and new
instances from templates.
A brief Python primer on __new__
and __init__
#
__new__
is a standard Python method, and, if present, is called
before __init__
when we create a class instance. See the python
__new__ documentation for more detail.
For example, consider the following Python code:
>>> class C:
>>> def __new__(cls, *args):
>>> print('Cls in __new__:', cls)
>>> print('Args in __new__:', args)
>>> # The `object` type __new__ method takes a single argument.
>>> return object.__new__(cls)
>>> def __init__(self, *args):
>>> print('type(self) in __init__:', type(self))
>>> print('Args in __init__:', args)
meaning that we get:
>>> c = C('hello')
Cls in __new__: <class 'C'>
Args in __new__: ('hello',)
type(self) in __init__: <class 'C'>
Args in __init__: ('hello',)
When we call C('hello')
, the __new__
method gets its own class
as first argument, and the passed argument, which is the string
'hello'
. After python calls __new__
, it usually (see below)
calls our __init__
method, with the output of __new__
as the
first argument (now a class instance), and the passed arguments
following.
As you can see, the object can be initialized in the __new__
method or the __init__
method, or both, and in fact ndarray does
not have an __init__
method, because all the initialization is
done in the __new__
method.
Why use __new__
rather than just the usual __init__
? Because
in some cases, as for ndarray, we want to be able to return an object
of some other class. Consider the following:
class D(C):
def __new__(cls, *args):
print('D cls is:', cls)
print('D args in __new__:', args)
return C.__new__(C, *args)
def __init__(self, *args):
# we never get here
print('In D __init__')
meaning that:
>>> obj = D('hello')
D cls is: <class 'D'>
D args in __new__: ('hello',)
Cls in __new__: <class 'C'>
Args in __new__: ('hello',)
>>> type(obj)
<class 'C'>
The definition of C
is the same as before, but for D
, the
__new__
method returns an instance of class C
rather than
D
. Note that the __init__
method of D
does not get
called. In general, when the __new__
method returns an object of
class other than the class in which it is defined, the __init__
method of that class is not called.
This is how subclasses of the ndarray class are able to return views that preserve the class type. When taking a view, the standard ndarray machinery creates the new ndarray object with something like:
obj = ndarray.__new__(subtype, shape, ...
where subdtype
is the subclass. Thus the returned view is of the
same class as the subclass, rather than being of class ndarray
.
That solves the problem of returning views of the same type, but now
we have a new problem. The machinery of ndarray can set the class
this way, in its standard methods for taking views, but the ndarray
__new__
method knows nothing of what we have done in our own
__new__
method in order to set attributes, and so on. (Aside -
why not call obj = subdtype.__new__(...
then? Because we may not
have a __new__
method with the same call signature).
The role of __array_finalize__
#
__array_finalize__
is the mechanism that numpy provides to allow
subclasses to handle the various ways that new instances get created.
Remember that subclass instances can come about in these three ways:
explicit constructor call (
obj = MySubClass(params)
). This will call the usual sequence ofMySubClass.__new__
then (if it exists)MySubClass.__init__
.
Our MySubClass.__new__
method only gets called in the case of the
explicit constructor call, so we can’t rely on MySubClass.__new__
or
MySubClass.__init__
to deal with the view casting and
new-from-template. It turns out that MySubClass.__array_finalize__
does get called for all three methods of object creation, so this is
where our object creation housekeeping usually goes.
For the explicit constructor call, our subclass will need to create a new ndarray instance of its own class. In practice this means that we, the authors of the code, will need to make a call to
ndarray.__new__(MySubClass,...)
, a class-hierarchy prepared call tosuper().__new__(cls, ...)
, or do view casting of an existing array (see below)For view casting and new-from-template, the equivalent of
ndarray.__new__(MySubClass,...
is called, at the C level.
The arguments that __array_finalize__
receives differ for the three
methods of instance creation above.
The following code allows us to look at the call sequences and arguments:
import numpy as np
class C(np.ndarray):
def __new__(cls, *args, **kwargs):
print('In __new__ with class %s' % cls)
return super().__new__(cls, *args, **kwargs)
def __init__(self, *args, **kwargs):
# in practice you probably will not need or want an __init__
# method for your subclass
print('In __init__ with class %s' % self.__class__)
def __array_finalize__(self, obj):
print('In array_finalize:')
print(' self type is %s' % type(self))
print(' obj type is %s' % type(obj))
Now:
>>> # Explicit constructor
>>> c = C((10,))
In __new__ with class <class 'C'>
In array_finalize:
self type is <class 'C'>
obj type is <type 'NoneType'>
In __init__ with class <class 'C'>
>>> # View casting
>>> a = np.arange(10)
>>> cast_a = a.view(C)
In array_finalize:
self type is <class 'C'>
obj type is <type 'numpy.ndarray'>
>>> # Slicing (example of new-from-template)
>>> cv = c[:1]
In array_finalize:
self type is <class 'C'>
obj type is <class 'C'>
The signature of __array_finalize__
is:
def __array_finalize__(self, obj):
One sees that the super
call, which goes to
ndarray.__new__
, passes __array_finalize__
the new object, of our
own class (self
) as well as the object from which the view has been
taken (obj
). As you can see from the output above, the self
is
always a newly created instance of our subclass, and the type of obj
differs for the three instance creation methods:
When called from the explicit constructor,
obj
isNone
When called from view casting,
obj
can be an instance of any subclass of ndarray, including our own.When called in new-from-template,
obj
is another instance of our own subclass, that we might use to update the newself
instance.
Because __array_finalize__
is the only method that always sees new
instances being created, it is the sensible place to fill in instance
defaults for new object attributes, among other tasks.
This may be clearer with an example.
Simple example - adding an extra attribute to ndarray#
import numpy as np
class InfoArray(np.ndarray):
def __new__(subtype, shape, dtype=float, buffer=None, offset=0,
strides=None, order=None, info=None):
# Create the ndarray instance of our type, given the usual
# ndarray input arguments. This will call the standard
# ndarray constructor, but return an object of our type.
# It also triggers a call to InfoArray.__array_finalize__
obj = super().__new__(subtype, shape, dtype,
buffer, offset, strides, order)
# set the new 'info' attribute to the value passed
obj.info = info
# Finally, we must return the newly created object:
return obj
def __array_finalize__(self, obj):
# ``self`` is a new object resulting from
# ndarray.__new__(InfoArray, ...), therefore it only has
# attributes that the ndarray.__new__ constructor gave it -
# i.e. those of a standard ndarray.
#
# We could have got to the ndarray.__new__ call in 3 ways:
# From an explicit constructor - e.g. InfoArray():
# obj is None
# (we're in the middle of the InfoArray.__new__
# constructor, and self.info will be set when we return to
# InfoArray.__new__)
if obj is None: return
# From view casting - e.g arr.view(InfoArray):
# obj is arr
# (type(obj) can be InfoArray)
# From new-from-template - e.g infoarr[:3]
# type(obj) is InfoArray
#
# Note that it is here, rather than in the __new__ method,
# that we set the default value for 'info', because this
# method sees all creation of default objects - with the
# InfoArray.__new__ constructor, but also with
# arr.view(InfoArray).
self.info = getattr(obj, 'info', None)
# We do not need to return anything
Using the object looks like this:
>>> obj = InfoArray(shape=(3,)) # explicit constructor
>>> type(obj)
<class 'InfoArray'>
>>> obj.info is None
True
>>> obj = InfoArray(shape=(3,), info='information')
>>> obj.info
'information'
>>> v = obj[1:] # new-from-template - here - slicing
>>> type(v)
<class 'InfoArray'>
>>> v.info
'information'
>>> arr = np.arange(10)
>>> cast_arr = arr.view(InfoArray) # view casting
>>> type(cast_arr)
<class 'InfoArray'>
>>> cast_arr.info is None
True
This class isn’t very useful, because it has the same constructor as the
bare ndarray object, including passing in buffers and shapes and so on.
We would probably prefer the constructor to be able to take an already
formed ndarray from the usual numpy calls to np.array
and return an
object.
Slightly more realistic example - attribute added to existing array#
Here is a class that takes a standard ndarray that already exists, casts as our type, and adds an extra attribute.
import numpy as np
class RealisticInfoArray(np.ndarray):
def __new__(cls, input_array, info=None):
# Input array is an already formed ndarray instance
# We first cast to be our class type
obj = np.asarray(input_array).view(cls)
# add the new attribute to the created instance
obj.info = info
# Finally, we must return the newly created object:
return obj
def __array_finalize__(self, obj):
# see InfoArray.__array_finalize__ for comments
if obj is None: return
self.info = getattr(obj, 'info', None)
So:
>>> arr = np.arange(5)
>>> obj = RealisticInfoArray(arr, info='information')
>>> type(obj)
<class 'RealisticInfoArray'>
>>> obj.info
'information'
>>> v = obj[1:]
>>> type(v)
<class 'RealisticInfoArray'>
>>> v.info
'information'
__array_ufunc__
for ufuncs#
New in version 1.13.
A subclass can override what happens when executing numpy ufuncs on it by
overriding the default ndarray.__array_ufunc__
method. This method is
executed instead of the ufunc and should return either the result of the
operation, or NotImplemented
if the operation requested is not
implemented.
The signature of __array_ufunc__
is:
def __array_ufunc__(ufunc, method, *inputs, **kwargs):
- *ufunc* is the ufunc object that was called.
- *method* is a string indicating how the Ufunc was called, either
``"__call__"`` to indicate it was called directly, or one of its
:ref:`methods<ufuncs.methods>`: ``"reduce"``, ``"accumulate"``,
``"reduceat"``, ``"outer"``, or ``"at"``.
- *inputs* is a tuple of the input arguments to the ``ufunc``
- *kwargs* contains any optional or keyword arguments passed to the
function. This includes any ``out`` arguments, which are always
contained in a tuple.
A typical implementation would convert any inputs or outputs that are
instances of one’s own class, pass everything on to a superclass using
super()
, and finally return the results after possible
back-conversion. An example, taken from the test case
test_ufunc_override_with_super
in core/tests/test_umath.py
, is the
following.
input numpy as np
class A(np.ndarray):
def __array_ufunc__(self, ufunc, method, *inputs, out=None, **kwargs):
args = []
in_no = []
for i, input_ in enumerate(inputs):
if isinstance(input_, A):
in_no.append(i)
args.append(input_.view(np.ndarray))
else:
args.append(input_)
outputs = out
out_no = []
if outputs:
out_args = []
for j, output in enumerate(outputs):
if isinstance(output, A):
out_no.append(j)
out_args.append(output.view(np.ndarray))
else:
out_args.append(output)
kwargs['out'] = tuple(out_args)
else:
outputs = (None,) * ufunc.nout
info = {}
if in_no:
info['inputs'] = in_no
if out_no:
info['outputs'] = out_no
results = super().__array_ufunc__(ufunc, method, *args, **kwargs)
if results is NotImplemented:
return NotImplemented
if method == 'at':
if isinstance(inputs[0], A):
inputs[0].info = info
return
if ufunc.nout == 1:
results = (results,)
results = tuple((np.asarray(result).view(A)
if output is None else output)
for result, output in zip(results, outputs))
if results and isinstance(results[0], A):
results[0].info = info
return results[0] if len(results) == 1 else results
So, this class does not actually do anything interesting: it just
converts any instances of its own to regular ndarray (otherwise, we’d
get infinite recursion!), and adds an info
dictionary that tells
which inputs and outputs it converted. Hence, e.g.,
>>> a = np.arange(5.).view(A)
>>> b = np.sin(a)
>>> b.info
{'inputs': [0]}
>>> b = np.sin(np.arange(5.), out=(a,))
>>> b.info
{'outputs': [0]}
>>> a = np.arange(5.).view(A)
>>> b = np.ones(1).view(A)
>>> c = a + b
>>> c.info
{'inputs': [0, 1]}
>>> a += b
>>> a.info
{'inputs': [0, 1], 'outputs': [0]}
Note that another approach would be to use getattr(ufunc,
methods)(*inputs, **kwargs)
instead of the super
call. For this example,
the result would be identical, but there is a difference if another operand
also defines __array_ufunc__
. E.g., lets assume that we evalulate
np.add(a, b)
, where b
is an instance of another class B
that has
an override. If you use super
as in the example,
ndarray.__array_ufunc__
will notice that b
has an override, which
means it cannot evaluate the result itself. Thus, it will return
NotImplemented and so will our class A
. Then, control will be passed
over to b
, which either knows how to deal with us and produces a result,
or does not and returns NotImplemented, raising a TypeError
.
If instead, we replace our super
call with getattr(ufunc, method)
, we
effectively do np.add(a.view(np.ndarray), b)
. Again, B.__array_ufunc__
will be called, but now it sees an ndarray
as the other argument. Likely,
it will know how to handle this, and return a new instance of the B
class
to us. Our example class is not set up to handle this, but it might well be
the best approach if, e.g., one were to re-implement MaskedArray
using
__array_ufunc__
.
As a final note: if the super
route is suited to a given class, an
advantage of using it is that it helps in constructing class hierarchies.
E.g., suppose that our other class B
also used the super
in its
__array_ufunc__
implementation, and we created a class C
that depended
on both, i.e., class C(A, B)
(with, for simplicity, not another
__array_ufunc__
override). Then any ufunc on an instance of C
would
pass on to A.__array_ufunc__
, the super
call in A
would go to
B.__array_ufunc__
, and the super
call in B
would go to
ndarray.__array_ufunc__
, thus allowing A
and B
to collaborate.
__array_wrap__
for ufuncs and other functions#
Prior to numpy 1.13, the behaviour of ufuncs could only be tuned using
__array_wrap__
and __array_prepare__
. These two allowed one to
change the output type of a ufunc, but, in contrast to
__array_ufunc__
, did not allow one to make any changes to the inputs.
It is hoped to eventually deprecate these, but __array_wrap__
is also
used by other numpy functions and methods, such as squeeze
, so at the
present time is still needed for full functionality.
Conceptually, __array_wrap__
“wraps up the action” in the sense of
allowing a subclass to set the type of the return value and update
attributes and metadata. Let’s show how this works with an example. First
we return to the simpler example subclass, but with a different name and
some print statements:
import numpy as np
class MySubClass(np.ndarray):
def __new__(cls, input_array, info=None):
obj = np.asarray(input_array).view(cls)
obj.info = info
return obj
def __array_finalize__(self, obj):
print('In __array_finalize__:')
print(' self is %s' % repr(self))
print(' obj is %s' % repr(obj))
if obj is None: return
self.info = getattr(obj, 'info', None)
def __array_wrap__(self, out_arr, context=None):
print('In __array_wrap__:')
print(' self is %s' % repr(self))
print(' arr is %s' % repr(out_arr))
# then just call the parent
return super().__array_wrap__(self, out_arr, context)
We run a ufunc on an instance of our new array:
>>> obj = MySubClass(np.arange(5), info='spam')
In __array_finalize__:
self is MySubClass([0, 1, 2, 3, 4])
obj is array([0, 1, 2, 3, 4])
>>> arr2 = np.arange(5)+1
>>> ret = np.add(arr2, obj)
In __array_wrap__:
self is MySubClass([0, 1, 2, 3, 4])
arr is array([1, 3, 5, 7, 9])
In __array_finalize__:
self is MySubClass([1, 3, 5, 7, 9])
obj is MySubClass([0, 1, 2, 3, 4])
>>> ret
MySubClass([1, 3, 5, 7, 9])
>>> ret.info
'spam'
Note that the ufunc (np.add
) has called the __array_wrap__
method
with arguments self
as obj
, and out_arr
as the (ndarray) result
of the addition. In turn, the default __array_wrap__
(ndarray.__array_wrap__
) has cast the result to class MySubClass
,
and called __array_finalize__
- hence the copying of the info
attribute. This has all happened at the C level.
But, we could do anything we wanted:
class SillySubClass(np.ndarray):
def __array_wrap__(self, arr, context=None):
return 'I lost your data'
>>> arr1 = np.arange(5)
>>> obj = arr1.view(SillySubClass)
>>> arr2 = np.arange(5)
>>> ret = np.multiply(obj, arr2)
>>> ret
'I lost your data'
So, by defining a specific __array_wrap__
method for our subclass,
we can tweak the output from ufuncs. The __array_wrap__
method
requires self
, then an argument - which is the result of the ufunc -
and an optional parameter context. This parameter is returned by
ufuncs as a 3-element tuple: (name of the ufunc, arguments of the ufunc,
domain of the ufunc), but is not set by other numpy functions. Though,
as seen above, it is possible to do otherwise, __array_wrap__
should
return an instance of its containing class. See the masked array
subclass for an implementation.
In addition to __array_wrap__
, which is called on the way out of the
ufunc, there is also an __array_prepare__
method which is called on
the way into the ufunc, after the output arrays are created but before any
computation has been performed. The default implementation does nothing
but pass through the array. __array_prepare__
should not attempt to
access the array data or resize the array, it is intended for setting the
output array type, updating attributes and metadata, and performing any
checks based on the input that may be desired before computation begins.
Like __array_wrap__
, __array_prepare__
must return an ndarray or
subclass thereof or raise an error.
Extra gotchas - custom __del__
methods and ndarray.base#
One of the problems that ndarray solves is keeping track of memory
ownership of ndarrays and their views. Consider the case where we have
created an ndarray, arr
and have taken a slice with v = arr[1:]
.
The two objects are looking at the same memory. NumPy keeps track of
where the data came from for a particular array or view, with the
base
attribute:
>>> # A normal ndarray, that owns its own data
>>> arr = np.zeros((4,))
>>> # In this case, base is None
>>> arr.base is None
True
>>> # We take a view
>>> v1 = arr[1:]
>>> # base now points to the array that it derived from
>>> v1.base is arr
True
>>> # Take a view of a view
>>> v2 = v1[1:]
>>> # base points to the original array that it was derived from
>>> v2.base is arr
True
In general, if the array owns its own memory, as for arr
in this
case, then arr.base
will be None - there are some exceptions to this
- see the numpy book for more details.
The base
attribute is useful in being able to tell whether we have
a view or the original array. This in turn can be useful if we need
to know whether or not to do some specific cleanup when the subclassed
array is deleted. For example, we may only want to do the cleanup if
the original array is deleted, but not the views. For an example of
how this can work, have a look at the memmap
class in
numpy.core
.
Subclassing and Downstream Compatibility#
When sub-classing ndarray
or creating duck-types that mimic the ndarray
interface, it is your responsibility to decide how aligned your APIs will be
with those of numpy. For convenience, many numpy functions that have a corresponding
ndarray
method (e.g., sum
, mean
, take
, reshape
) work by checking
if the first argument to a function has a method of the same name. If it exists, the
method is called instead of coercing the arguments to a numpy array.
For example, if you want your sub-class or duck-type to be compatible with
numpy’s sum
function, the method signature for this object’s sum
method
should be the following:
def sum(self, axis=None, dtype=None, out=None, keepdims=False):
...
This is the exact same method signature for np.sum
, so now if a user calls
np.sum
on this object, numpy will call the object’s own sum
method and
pass in these arguments enumerated above in the signature, and no errors will
be raised because the signatures are completely compatible with each other.
If, however, you decide to deviate from this signature and do something like this:
def sum(self, axis=None, dtype=None):
...
This object is no longer compatible with np.sum
because if you call np.sum
,
it will pass in unexpected arguments out
and keepdims
, causing a TypeError
to be raised.
If you wish to maintain compatibility with numpy and its subsequent versions (which
might add new keyword arguments) but do not want to surface all of numpy’s arguments,
your function’s signature should accept **kwargs
. For example:
def sum(self, axis=None, dtype=None, **unused_kwargs):
...
This object is now compatible with np.sum
again because any extraneous arguments
(i.e. keywords that are not axis
or dtype
) will be hidden away in the
**unused_kwargs
parameter.