Immutable numpy array?
Question:
Is there a simple way to create an immutable NumPy array?
If one has to derive a class from ndarray to do this, what’s the minimum set of methods that one has to override to achieve immutability?
Answers:
You can make a numpy array unwriteable:
a = np.arange(10)
a.flags.writeable = False
a[0] = 1
# Gives: ValueError: assignment destination is read-only
Also see the discussion in this thread:
http://mail.scipy.org/pipermail/numpy-discussion/2008-December/039274.html
and the documentation:
http://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.flags.html
I have a subclass of Array at this gist: https://gist.github.com/sfaleron/9791418d7023a9985bb803170c5d93d8
It makes a copy of its argument and marks that as read-only, so you should only be able to shoot yourself in the foot if you are very deliberate about it. My immediate need was for it to be hashable, so I could use them in sets, so that works too. It isn’t a lot of code, but about 70% of the lines are for testing, so I won’t post it directly.
Note that it’s not a drop-in replacement; it won’t accept any keyword args like a normal Array constructor. Instances will behave like Arrays, though.
Setting the flag directly didn’t work for me, but using ndarray.setflags did work:
a = np.arange(10)
a.setflags(write=False)
a[0] = 1 # ValueError
Is there a simple way to create an immutable NumPy array?
If one has to derive a class from ndarray to do this, what’s the minimum set of methods that one has to override to achieve immutability?
You can make a numpy array unwriteable:
a = np.arange(10)
a.flags.writeable = False
a[0] = 1
# Gives: ValueError: assignment destination is read-only
Also see the discussion in this thread:
http://mail.scipy.org/pipermail/numpy-discussion/2008-December/039274.html
and the documentation:
http://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.flags.html
I have a subclass of Array at this gist: https://gist.github.com/sfaleron/9791418d7023a9985bb803170c5d93d8
It makes a copy of its argument and marks that as read-only, so you should only be able to shoot yourself in the foot if you are very deliberate about it. My immediate need was for it to be hashable, so I could use them in sets, so that works too. It isn’t a lot of code, but about 70% of the lines are for testing, so I won’t post it directly.
Note that it’s not a drop-in replacement; it won’t accept any keyword args like a normal Array constructor. Instances will behave like Arrays, though.
Setting the flag directly didn’t work for me, but using ndarray.setflags did work:
a = np.arange(10)
a.setflags(write=False)
a[0] = 1 # ValueError