[pythran] Re: ND arrays
- From: Pierre Augier <pierre.augier@xxxxxxxxxxxxxxxxxxxxxx>
- To: pythran@xxxxxxxxxxxxx
- Date: Tue, 27 Nov 2018 13:36:52 +0100
Le 26/11/2018 à 22:53, serge guelton a écrit :
On Mon, Nov 26, 2018 at 03:32:13PM -0500, Neal Becker wrote:
I have an algorithm that accepts ND arrays. I suppose pythran cannot
accelerate ND algorithms, only for known number of dimensions, correct?
Correct. The number of dimension is fixed, but you can instanciate your
function for different size:
.. code:: python
#pythran export foo(float64)
#pythran export foo(float64[:])
#pythran export foo(float64[:,:])
#pythran export foo(float64[:,:,:])
def foo(x):
return x *3.5 + 4.7
2 alternative solutions with
https://fluidpythran.readthedocs.io :-)
In the 2 cases, no need for compilation commands...
Ahead-Of-Time compilation (pythranized at first import and then just when
needed):
```
from fluidpythran import Array, NDim, pythran_def, set_pythranize_at_import
set_pythranize_at_import()
A = Array[float, NDim(1, 2, 3)]
@pythran_def
def foo(x: A):
return x *3.5 + 4.7
```
set_pythranize_at_import is of course not mandatory.
Cached Just-In-Time (compiled after the first call):
```
from fluidpythran import Array, NDim, cachedjit
A = Array[float, NDim(1, 2, 3)]
@cachedjit
def foo(x: A):
return x *3.5 + 4.7
```
To get more than one type for the ndarrays,
```
from fluidpythran import Array, NDim, Type
A = Array[Type(np.float32, complex, int), NDim(1, 2, 3)]
```
Pierre
--
Pierre Augier - CR CNRS
http://www.legi.grenoble-inp.fr
LEGI (UMR 5519) Laboratoire des Ecoulements Geophysiques et Industriels
BP53, 38041 Grenoble Cedex, France tel:+33.4.56.52.86.16
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