Automatic copies of NumPy arrays

[Jan Kybic]

Hello,
	I have recently switched from Matlab to Numeric
Python. Overall the experience has been largely positive: Python is so
much better suited for bigger projects. However, one of the things
that is bothering me, is that in NumPy, I have to decide, whether I
want to pass arrays directly or as copies. Whether I should write
call to myfunc() as:

myfunc(x) or myfunc(array(x))

The first variant is clearly faster but I need to know that either
myfunc does not change x, or that x is no longer needed afterwards. In
Matlab this was handled for me automatically. I just wandered, if
anybody has also found it to be a problem, and if they perhaps had a
solution. I thought along the lines of subclassing arrays to be
unmutable such as:

...
myfunc(unmutable(x))
...


with corresponding definition:

def myfunc(x):
	x=makemutable(x)
	...

def makemutable(x):
	if not mutable(x):
		return array(x)
	return x

Any comments


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