On 04/19/2012 08:41 AM, Stefan Behnel wrote:
Dag Sverre Seljebotn, 18.04.2012 23:35:
from numpy import sqrt, sin
cdef double f(double x):
return sqrt(x * x) # or sin(x * x)
Of course, here one could get the pointer in the module at import time.
That optimisation would actually be very worthwhile all by itself. I mean,
we know what signatures we need for globally imported functions throughout
the module, so we can reduce the call to a single jump through a function
pointer (although likely with a preceding NULL check, which the branch
prediction would be happy to give us for free). At least as long as sqrt is
not being reassigned, but that should hit the 99% case.
However, here:
from numpy import sqrt
Correction: "import numpy as np"
cdef double f(double x):
return np.sqrt(x * x) # or np.sin(x * x)
the __getattr__ on np sure is larger than any effect we discuss.
Yes, that would have to stay a .pxd case, I guess.
How about this mini-CEP:
Modules are allowed to specify __nomonkey__ (or __const__, or
__notreassigned__), a list of strings naming module-level variables
where "we don't hold you responsible if you assume no monkey-patching of
these".
When doing "import numpy as np", then (assuming "np" is never reassigned
in the module), at import time we check all names looked up from it in
__nomonkey__, and if so treat them as "from numpy import sqrt as
'np.sqrt'", i.e. the "np." is just a namespace mechanism.
Needs a bit more work, it ignores the possibility that others could
monkey-patch "np" in the Cython module.
Problem with .pxd is that currently you need to pick one overload
(np.sqrt works for n-dimensional arrays too, or takes a list and returns
an array). And even after adding 3-4 language features to Cython to make
this work, you're stuck with having to reimplement parts of NumPy in the
pxd files just so that you can early bind from Cython.
Dag
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