Custom data types in numpy arrays
Question:
I’m creating a numpy array which is to be filled with objects of a particular class I’ve made. I’d like to initialize the array such that it will only ever contain objects of that class. For example, here’s what I’d like to do, and what happens if I do it.
class Kernel:
pass
>>> L = np.empty(4,dtype=Kernel)
TypeError: data type not understood
I can do this:
>>> L = np.empty(4,dtype=object)
and then assign each element of L as a Kernel object (or any other type of object). It would be so neat were I able to have an array of Kernels, though, from both a programming point of view (type checking) and a mathematical one (operations on sets of functions).
Is there any way for me to specify the data type of a numpy array using an arbitrary class?
Answers:
It has to be a Numpy scalar type:
http://docs.scipy.org/doc/numpy/reference/arrays.scalars.html#arrays-scalars-built-in
or a subclass of ndarray:
http://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html#numpy.ndarray
As far as I know, enforcing a single type for elements in a numpy.ndarray has to be done manually (unless the array contains Numpy scalars): there is no built-in checking mechanism (your array has dtype=object). If you really want to enforce a single type, you have to subclass ndarray and implement the checks in the appropriate methods (__setitem__, etc.).
If you want to implement operations on a set of functions (Kernel objects), you might be able to do so by defining the proper operations directly in your Kernel class. This is what I did for my uncertainties.py module, which handles numpy.ndarrays of numbers with uncertainties.
If your Kernel class has a predictable amount of member data, then you could define a dtype for it instead of a class. e.g. if it’s parameterized by 9 floats and an int, you could do
kerneldt = np.dtype([('myintname', np.int32), ('myfloats', np.float64, 9)])
arr = np.empty(dims, dtype=kerneldt)
You’ll have to do some coercion to turn them into objects of class Kernel every time you want to manipulate methods of a single kernel but that’s one way to store the actual data in a NumPy array. If you want to only store a reference, then the object dtype is the best you can do without subclassing ndarray.
I’m creating a numpy array which is to be filled with objects of a particular class I’ve made. I’d like to initialize the array such that it will only ever contain objects of that class. For example, here’s what I’d like to do, and what happens if I do it.
class Kernel:
pass
>>> L = np.empty(4,dtype=Kernel)
TypeError: data type not understood
I can do this:
>>> L = np.empty(4,dtype=object)
and then assign each element of L as a Kernel object (or any other type of object). It would be so neat were I able to have an array of Kernels, though, from both a programming point of view (type checking) and a mathematical one (operations on sets of functions).
Is there any way for me to specify the data type of a numpy array using an arbitrary class?
It has to be a Numpy scalar type:
http://docs.scipy.org/doc/numpy/reference/arrays.scalars.html#arrays-scalars-built-in
or a subclass of ndarray:
http://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html#numpy.ndarray
As far as I know, enforcing a single type for elements in a numpy.ndarray has to be done manually (unless the array contains Numpy scalars): there is no built-in checking mechanism (your array has dtype=object). If you really want to enforce a single type, you have to subclass ndarray and implement the checks in the appropriate methods (__setitem__, etc.).
If you want to implement operations on a set of functions (Kernel objects), you might be able to do so by defining the proper operations directly in your Kernel class. This is what I did for my uncertainties.py module, which handles numpy.ndarrays of numbers with uncertainties.
If your Kernel class has a predictable amount of member data, then you could define a dtype for it instead of a class. e.g. if it’s parameterized by 9 floats and an int, you could do
kerneldt = np.dtype([('myintname', np.int32), ('myfloats', np.float64, 9)])
arr = np.empty(dims, dtype=kerneldt)
You’ll have to do some coercion to turn them into objects of class Kernel every time you want to manipulate methods of a single kernel but that’s one way to store the actual data in a NumPy array. If you want to only store a reference, then the object dtype is the best you can do without subclassing ndarray.