Interfacing a dynamic shared library gives me different results in 2.7 versus 3.5

[Siyi Deng]

I have a dynamic library doing some numerical computations. 

I used ctypes to interact it by passing numpy arrays back and forth. 

Python 3.5 gives me the correct results. 

Python 2.7 gives me different, erroneous results, but it never crashes. 

How is this possible? There is no string operations involved whatsoever.
-- 
https://mail.python.org/mailman/listinfo/python-list

Re: Interfacing a dynamic shared library gives me different results in 2.7 versus 3.5

[Siyi Deng]

Thanks for all the replies.  

It turned out that the Apple OS X stock python 2.7 gives the wrong results, but 
other distributions like 2.7 from miniconda gives the correct results. Facepalm.
-- 
https://mail.python.org/mailman/listinfo/python-list

Re: Interfacing a dynamic shared library gives me different results in 2.7 versus 3.5

[Siyi Deng]

Hello ChrisA,
I don't quite understand, the binary shared library contains no python 
interfaces, it should be independent of python. As a matter of fact, I have 
successfully used it in Conda python 2.7, 3.5,  Julialang as well as c++ 
executables. I think the fact that only stock python 2.7 failed to run 
correctly indicates some bug in that python distribution. 


> When you use a binary shared library, it has to be compiled against
> the correct Python. You're messing around with ctypes, so basically
> you've voided your warranty; *everything* you're doing is
> platform-specific. Have fun. :)
> 
> ChrisA
-- 
https://mail.python.org/mailman/listinfo/python-list

Re: Interfacing a dynamic shared library gives me different results in 2.7 versus 3.5

[Siyi Deng]

The c function has a signature as follows:

int cfun(int len_data, float* data, int* a, int num_a,
int flag1, int flag2, int flag3, float* param,
float* out1, float* out2, float* out3)


and in python:

import numpy as np
import ctypes as ct

data = np.atleast_2d(np.float32(data))
a = np.atleast_2d(np.int32(a)) if a else np.zeros((2, 1), dtype=np.int32)
param = np.atleast_2d(np.float32(param))

num_a = activity.shape[1]
len_data = data.shape[1]
num_inc = len_data//256
cf = ct.POINTER(ct.c_float)

hr = np.zeros((2, num_inc), dtype=np.float32)
of = np.zeros((num_inc, 207), dtype=np.float32)
gait = np.zeros((num_inc, 14), dtype=np.float32)
pt_of = of.ctypes.data_as(cf) if do_of else None
pt_gait = gait.ctypes.data_as(cf) if do_gait else None
pt_param = param.ctypes.data_as(cf) if param else None
dl.run_plt_hrm(len_data, data.ctypes.data_as(cf),
activity.ctypes.data_as(ct.POINTER(ct.c_int)),
num_act, do_long_fft+0, do_cls_mitigate+0, do_weighted_average+0,
pt_param, hr.ctypes.data_as(cf), pt_of, pt_gait)

-- 
https://mail.python.org/mailman/listinfo/python-list

Re: Interfacing a dynamic shared library gives me different results in 2.7 versus 3.5

[Siyi Deng]

Here is a summary of what I did with numpy and the dll

I have verified that the values entering the last dll call (dl.cfunction) are 
identical across platforms.


 

The c function has a signature as follows: 

int cfunction(int len_data, float* data, int* ac, int num_ac, 
int flag1, int flag2, int flag3, float* param, 
float* out) 


and in python: 

import numpy as np 
import ctypes as ct 

dl = ct.cdll.LoadLibrary('xxx.dylib')

# data, ac, param are loaded from somewhere else.
data = np.atleast_2d(np.float32(data)) 
ac = np.atleast_2d(np.int32(a)) if a else np.zeros((2, 1), dtype=np.int32) 
param = np.atleast_2d(np.float32(param)) 
flag1 = True
flag2 = True
flag3 = True
num_ac = ac.shape[1] 
len_data = data.shape[1] 
num_inc = len_data//200
out = np.zeros((2, num_inc), dtype=np.float32) 
pt_param = param.ctypes.data_as(cf) if param else None

cf = ct.POINTER(ct.c_float) 
dl.cfunction(len_data, data.ctypes.data_as(cf), 
ac.ctypes.data_as(ct.POINTER(ct.c_int)), 
num_ac, flag1+0, flag2+0, flag3+0, 
pt_param, out.ctypes.data_as(cf)) 


-- 
https://mail.python.org/mailman/listinfo/python-list