@david:
This is all in sympy master. The codegen stuff has had major work done to
it in the last development cycle.
On Fri, Sep 26, 2014 at 10:10 AM, David Shin <shin.da...@gmail.com> wrote:
> James,
>
> Your code doesn't seem to work as expected:
>
> $ python demo.py 10
> Traceback (most recent call last):
> File "demo.py", line 11, in <module>
> f = autowrap(summation, args=(x,))
> File
> "/opt/jump/x86_64/Python-2.7.3/lib/python2.7/site-packages/sympy/utilities/autowrap.py",
> line 387, in autowrap
> routine = Routine('autofunc', expr, args)
> File
> "/opt/jump/x86_64/Python-2.7.3/lib/python2.7/site-packages/sympy/utilities/codegen.py",
> line 229, in __init__
> new_args.append(InputArgument(symbol))
> File
> "/opt/jump/x86_64/Python-2.7.3/lib/python2.7/site-packages/sympy/utilities/codegen.py",
> line 350, in __init__
> Variable.__init__(self, name, datatype, dimensions, precision)
> File
> "/opt/jump/x86_64/Python-2.7.3/lib/python2.7/site-packages/sympy/utilities/codegen.py",
> line 300, in __init__
> raise TypeError("The first argument must be a sympy symbol.")
> TypeError: The first argument must be a sympy symbol.
>
> It appears to me that sum(x) has type int.
>
>
> On Thursday, September 25, 2014 7:52:12 PM UTC-5, James Crist wrote:
>>
>> I'd hazard that it's a limit in the internals of numpy for how they
>> handle broadcasting, but I can't be certain on that.
>>
>> However, we can handle this, you just need to frame your problem in a
>> better way. You're trying to do optimization, so generally you'd frame your
>> optimal condition as a vector. In SymPy you should do the same (using a
>> MatrixSymbol). You also don't need ufuncify, because you're not doing any
>> broadcasting. Instead, you can use `autowrap` to create a function. ufuncs
>> can't broadcast arrays, so ufuncify won't work for functions that require a
>> vector as an input for a single iteration. The resulting expression will be
>> just as fast as a ufunc for a single iteration though, so no harm done.
>>
>> Here's your toy example, done as described:
>>
>> import sympyfrom sympy.utilities.autowrap import autowrapimport sys
>>
>> N = int(sys.argv[1])
>>
>> x = sympy.MatrixSymbol('x', N, 1)
>> summation = sum(x)
>> values = list(range(N))
>>
>> f = autowrap(summation, args=(x,))print f(values)
>>
>>
>> This works regardless of how large N is. For your real problem, you can
>> formulate your expression using symbols, and use subs to form them into a
>> single vector input. Or you can work directly with the vector by using
>> elements of it as variables. It's up to you how to formulate it.
>>
>> -Jim
>>
>> On Thursday, September 25, 2014 6:55:31 PM UTC-5, Jason Moore wrote:
>>>
>>> I'm not saying that SymPy isn't well suited for this task. I'm just
>>> saying that we don't have an implementation for code generation for
>>> summations that would help with your problem. You could certainly add one.
>>>
>>> I use SymPy for optimization problems myself and generate the symbolic
>>> Jacobians and Hessians of the objective and constraint functions for
>>> non-linear programming problems and it works great. So SymPy can likely do
>>> what you want to do, but you may need to write a code printer for sympy.Sum
>>> or something similar.
>>>
>>>
>>> Jason
>>> moorepants.info
>>> +01 530-601-9791
>>>
>>> On Thu, Sep 25, 2014 at 7:03 PM, David Shin <shin....@gmail.com> wrote:
>>>
>>>> Thanks for the info, Jason.
>>>>
>>>> In my opinion, what I am trying to do is not particularly exotic.
>>>> Consider for example the task of implementing logistic regression
>>>> optimization via sympy and scipy. You have an n-by-k data matrix X of
>>>> independent variables, a length-n output vector Y of dependent variables,
>>>> and wish to approximate each Y[i] as a function of X[i], parameterized by a
>>>> set of k+1 parameters, theta. This boils down to writing an objective loss
>>>> function to minimize, which is a symbolic function of theta involving n
>>>> summands that are individually expressions in terms Y[i], X[i], and theta.
>>>> As the minimum does not have an analytic solution, you need to employ
>>>> approximation algorithms, hence scipy.optimize.
>>>>
>>>> Of course, there are numerous packages available for vanilla logistic
>>>> regression. However, if you want to customize it (for example by using an
>>>> alternative loss function or regularization penalty function), then you
>>>> kind of need to roll out your own implementation.
>>>>
>>>> From what you are saying, it sounds like sympy is not well-suited for
>>>> this type of task.
>>>>
>>>> On Thursday, September 25, 2014 5:31:27 PM UTC-5, Jason Moore wrote:
>>>>>
>>>>> I'm not sure this is supported. Ideally you'd create a sympy.Sum
>>>>> object representing your summation and then the code printers would print
>>>>> a
>>>>> loop that would look something like:
>>>>>
>>>>> >>> sum = sympy.Sum(a, (a, 1, 5))
>>>>> >>> sympy.ccode(sum, b)
>>>>> double b = 0;
>>>>> for (i = 0; i < 5; i++;){
>>>>> b = b + a[i]
>>>>> }
>>>>>
>>>>> I think Jim Crist is working on this functionality but it doesn't
>>>>> exist now.
>>>>>
>>>>>
>>>>> Jason
>>>>> moorepants.info
>>>>> +01 530-601-9791
>>>>>
>>>>> On Thu, Sep 25, 2014 at 5:46 PM, David Shin <shin....@gmail.com>
>>>>> wrote:
>>>>>
>>>>>> The function is a sum of thousands of summands, where each summand is
>>>>>> a small function of a few of the input parameters. These small summand
>>>>>> functions are composed of basic operations like multiplication, addition,
>>>>>> and exp().
>>>>>>
>>>>>> On Thursday, September 25, 2014 4:21:32 PM UTC-5, Jason Moore wrote:
>>>>>>>
>>>>>>> I see. What does your function look like? Does it have summations or
>>>>>>> things you want to iterate over? Or is it simply a scalar function?
>>>>>>>
>>>>>>>
>>>>>>> Jason
>>>>>>> moorepants.info
>>>>>>> +01 530-601-9791
>>>>>>>
>>>>>>> On Thu, Sep 25, 2014 at 5:09 PM, David Shin <shin....@gmail.com>
>>>>>>> wrote:
>>>>>>>
>>>>>>>> Also, I'd like to pass in the gradient and hessian of my function
>>>>>>>> into scipy.optimize.minimize() to help it along. I don't want to do the
>>>>>>>> calculus by hand, so I'm using sympy to do it for me.
>>>>>>>>
>>>>>>>>
>>>>>>>> On Thursday, September 25, 2014 4:08:02 PM UTC-5, David Shin wrote:
>>>>>>>>>
>>>>>>>>> I'm looking to minimize a complicated function of hundreds of
>>>>>>>>> variables. I want to ufuncify() my function so that I can pass it into
>>>>>>>>> scipy.optimize.minimize(). The summation func I gave is just a toy
>>>>>>>>> example,
>>>>>>>>> the actual function is much more complicated.
>>>>>>>>>
>>>>>>>>> Please let me know if I should be doing this some other way.
>>>>>>>>>
>>>>>>>>> On Thursday, September 25, 2014 4:00:04 PM UTC-5, Jason Moore
>>>>>>>>> wrote:
>>>>>>>>>>
>>>>>>>>>> First, why do you need sympy to do this? Would NumPy be
>>>>>>>>>> sufficient?
>>>>>>>>>>
>>>>>>>>>> import numpy as np
>>>>>>>>>> values = np.random.random(100)
>>>>>>>>>> np.sum(values)
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>> Jason
>>>>>>>>>> moorepants.info
>>>>>>>>>> +01 530-601-9791
>>>>>>>>>>
>>>>>>>>>> On Thu, Sep 25, 2014 at 4:52 PM, David Shin <shin....@gmail.com>
>>>>>>>>>> wrote:
>>>>>>>>>>
>>>>>>>>>>> Would you mind showing me how to convert my demo.py to have
>>>>>>>>>>> ufuncify generate code that has a single length-N array argument
>>>>>>>>>>> instead of
>>>>>>>>>>> N separate arguments?
>>>>>>>>>>>
>>>>>>>>>>> On Thursday, September 25, 2014 3:40:25 PM UTC-5, Jason Moore
>>>>>>>>>>> wrote:
>>>>>>>>>>>>
>>>>>>>>>>>> It works for the backend='f2py' in master but fails for the
>>>>>>>>>>>> default backend which is 'numpy'. I get a segmentation fault on
>>>>>>>>>>>> the 'numpy'
>>>>>>>>>>>> backend for values greater than 20 or so.
>>>>>>>>>>>>
>>>>>>>>>>>> This is an odd use of ufuncify, as summing would better be done
>>>>>>>>>>>> in a loop with an array as input to the function. You can probably
>>>>>>>>>>>> used the
>>>>>>>>>>>> IndexBased class to set up a loop based sum. If you use the
>>>>>>>>>>>> tempdir kwarg
>>>>>>>>>>>> to ufuncify you can see the code it generates, and you'll
>>>>>>>>>>>> basically get a
>>>>>>>>>>>> Fortran function that has as many input args as your integer value
>>>>>>>>>>>> which is
>>>>>>>>>>>> not very efficient.
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>> Jason
>>>>>>>>>>>> moorepants.info
>>>>>>>>>>>> +01 530-601-9791
>>>>>>>>>>>>
>>>>>>>>>>>> On Thu, Sep 25, 2014 at 4:29 PM, Jason Moore <
>>>>>>>>>>>> moore...@gmail.com> wrote:
>>>>>>>>>>>>
>>>>>>>>>>>>> David,
>>>>>>>>>>>>>
>>>>>>>>>>>>> This is because it wasn't wrapping lines correctly in the
>>>>>>>>>>>>> generated Fortran code. If you use the development version of
>>>>>>>>>>>>> SymPy it
>>>>>>>>>>>>> should work.
>>>>>>>>>>>>>
>>>>>>>>>>>>> Here is the PR that fixed it: https://github.com/sympy/sympy
>>>>>>>>>>>>> /pull/7968
>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>> Jason
>>>>>>>>>>>>> moorepants.info
>>>>>>>>>>>>> +01 530-601-9791
>>>>>>>>>>>>>
>>>>>>>>>>>>> On Thu, Sep 25, 2014 at 3:42 PM, David Shin <
>>>>>>>>>>>>> shin....@gmail.com> wrote:
>>>>>>>>>>>>>
>>>>>>>>>>>>>> Hi, I recently began trying out sympy and am running into
>>>>>>>>>>>>>> some difficulty.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> I wrote the following script, called demo.py:
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> import sympyfrom sympy.utilities.autowrap import ufuncifyimport
>>>>>>>>>>>>>> sys
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> N = int(sys.argv[1])
>>>>>>>>>>>>>> theta = []
>>>>>>>>>>>>>> values = []for n in range(N):
>>>>>>>>>>>>>> theta.append(sympy.symbols('x%s' % n))
>>>>>>>>>>>>>> values.append(n)
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> summation = sum(theta)
>>>>>>>>>>>>>> f = ufuncify(theta, summation)print f(*values)[0]
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Running it for small N, it works fine:
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> $ python demo.py 21
>>>>>>>>>>>>>> 210.0
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> But it fails for larger N. Can anyone advise? Thanks in
>>>>>>>>>>>>>> advance.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> $ python demo.py 22
>>>>>>>>>>>>>> Traceback (most recent call last):
>>>>>>>>>>>>>> File "demo.py", line 13, in
>>>>>>>>>>>>>> f = ufuncify(theta, summation)
>>>>>>>>>>>>>> File
>>>>>>>>>>>>>> "/opt/user/x86_64/Python-2.7.3/lib/python2.7/site-packages/sympy/utilities/autowrap.py",
>>>>>>>>>>>>>> line 485, in ufuncify
>>>>>>>>>>>>>> return autowrap(C.Equality(y[i], f(*args)), **kwargs)
>>>>>>>>>>>>>> File
>>>>>>>>>>>>>> "/opt/user/x86_64/Python-2.7.3/lib/python2.7/site-packages/sympy/utilities/autowrap.py",
>>>>>>>>>>>>>> line 403, in autowrap
>>>>>>>>>>>>>> return code_wrapper.wrap_code(routine, helpers=helps)
>>>>>>>>>>>>>> File
>>>>>>>>>>>>>> "/opt/user/x86_64/Python-2.7.3/lib/python2.7/site-packages/sympy/utilities/autowrap.py",
>>>>>>>>>>>>>> line 139, in wrap_code
>>>>>>>>>>>>>> self._process_files(routine)
>>>>>>>>>>>>>> File
>>>>>>>>>>>>>> "/opt/user/x86_64/Python-2.7.3/lib/python2.7/site-packages/sympy/utilities/autowrap.py",
>>>>>>>>>>>>>> line 158, in _process_files
>>>>>>>>>>>>>> " ".join(command), e.output))
>>>>>>>>>>>>>> sympy.utilities.autowrap.CodeWrapError: Error while executing
>>>>>>>>>>>>>> command: f2py -m wrapper_module_0 -c wrapped_code_0.f90. Command
>>>>>>>>>>>>>> output is:
>>>>>>>>>>>>>> running build
>>>>>>>>>>>>>> running config_cc
>>>>>>>>>>>>>> unifing config_cc, config, build_clib, build_ext, build commands
>>>>>>>>>>>>>> --compiler options
>>>>>>>>>>>>>> running config_fc
>>>>>>>>>>>>>> unifing config_fc, config, build_clib, build_ext, build commands
>>>>>>>>>>>>>> --fcompiler options
>>>>>>>>>>>>>> running build_src
>>>>>>>>>>>>>> build_src
>>>>>>>>>>>>>> building extension "wrapper_module_0" sources
>>>>>>>>>>>>>> f2py options: []
>>>>>>>>>>>>>> f2py:>
>>>>>>>>>>>>>> /tmp/tmpKbJQuO/src.linux-x86_64-2.7/wrapper_module_0module.c
>>>>>>>>>>>>>> creating /tmp/tmpKbJQuO
>>>>>>>>>>>>>> creating /tmp/tmpKbJQuO/src.linux-x86_64-2.7
>>>>>>>>>>>>>> Reading fortran codes...
>>>>>>>>>>>>>> Reading file 'wrapped_code_0.f90' (format:free)
>>>>>>>>>>>>>> Post-processing...
>>>>>>>>>>>>>> Block: wrapper_module_0
>>>>>>>>>>>>>> Block: autofunc
>>>>>>>>>>>>>> Post-processing (stage 2)...
>>>>>>>>>>>>>> Building modules...
>>>>>>>>>>>>>> Building module "wrapper_module_0"...
>>>>>>>>>>>>>> Constructing wrapper function "autofunc"...
>>>>>>>>>>>>>> y_15 =
>>>>>>>>>>>>>> autofunc(x_16,x1,x10,x11,x12,x13,x14,x15,x16,x17,x18,x19,x2,x20,x21,x3,x4,x5,x6,x7,x8,x9,[m_17])
>>>>>>>>>>>>>> Wrote C/API module "wrapper_module_0" to file
>>>>>>>>>>>>>> "/tmp/tmpKbJQuO/src.linux-x86_64-2.7/wrapper_module_0module.c"
>>>>>>>>>>>>>> adding '/tmp/tmpKbJQuO/src.linux-x86_64-2.7/fortranobject.c'
>>>>>>>>>>>>>> to sources.
>>>>>>>>>>>>>> adding '/tmp/tmpKbJQuO/src.linux-x86_64-2.7' to include_dirs.
>>>>>>>>>>>>>> copying
>>>>>>>>>>>>>> /opt/user/x86_64/Python-2.7.3/lib/python2.7/site-packages/numpy/f2py/src/fortranobject.c
>>>>>>>>>>>>>> -> /tmp/tmpKbJQuO/src.linux-x86_64-2.7
>>>>>>>>>>>>>> copying
>>>>>>>>>>>>>> /opt/user/x86_64/Python-2.7.3/lib/python2.7/site-packages/numpy/f2py/src/fortranobject.h
>>>>>>>>>>>>>> -> /tmp/tmpKbJQuO/src.linux-x86_64-2.7
>>>>>>>>>>>>>> build_src: building npy-pkg config files
>>>>>>>>>>>>>> running build_ext
>>>>>>>>>>>>>> customize UnixCCompiler
>>>>>>>>>>>>>> customize UnixCCompiler using build_ext
>>>>>>>>>>>>>> customize Gnu95FCompiler
>>>>>>>>>>>>>> Found executable /opt/user/x86_64/gcc-4.7.2/bin/gfortran
>>>>>>>>>>>>>> customize Gnu95FCompiler
>>>>>>>>>>>>>> customize Gnu95FCompiler using build_ext
>>>>>>>>>>>>>> building 'wrapper_module_0' extension
>>>>>>>>>>>>>> compiling C sources
>>>>>>>>>>>>>> C compiler: gcc -pthread -fno-strict-aliasing -g -O2 -DNDEBUG -g
>>>>>>>>>>>>>> -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> creating /tmp/tmpKbJQuO/tmp
>>>>>>>>>>>>>> creating /tmp/tmpKbJQuO/tmp/tmpKbJQuO
>>>>>>>>>>>>>> creating /tmp/tmpKbJQuO/tmp/tmpKbJQuO/src.linux-x86_64-2.7
>>>>>>>>>>>>>> compile options: '-I/tmp/tmpKbJQuO/src.linux-x86_64-2.7
>>>>>>>>>>>>>> -I/opt/user/x86_64/Python-2.7.3/lib/python2.7/site-packages/numpy/core/include
>>>>>>>>>>>>>> -I/opt/user/x86_64/Python-2.7.3/include/python2.7 -c'
>>>>>>>>>>>>>> gcc: /tmp/tmpKbJQuO/src.linux-x86_64-2.7/wrapper_module_0module.c
>>>>>>>>>>>>>> In file included from
>>>>>>>>>>>>>> /opt/user/x86_64/Python-2.7.3/lib/python2.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1728:0,
>>>>>>>>>>>>>> from
>>>>>>>>>>>>>> /opt/user/x86_64/Python-2.7.3/lib/python2.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:17,
>>>>>>>>>>>>>> from
>>>>>>>>>>>>>> /opt/user/x86_64/Python-2.7.3/lib/python2.7/site-packages/numpy/core/include/numpy/arrayobject.h:15,
>>>>>>>>>>>>>> from
>>>>>>>>>>>>>> /tmp/tmpKbJQuO/src.linux-x86_64-2.7/fortranobject.h:13,
>>>>>>>>>>>>>> from
>>>>>>>>>>>>>> /tmp/tmpKbJQuO/src.linux-x86_64-2.7/wrapper_module_0module.c:18:
>>>>>>>>>>>>>> /opt/user/x86_64/Python-2.7.3/lib/python2.7/site-packages/numpy/core/include/numpy/npy_deprecated_api.h:11:2:
>>>>>>>>>>>>>> warning: #warning "Using deprecated NumPy API, disable it by
>>>>>>>>>>>>>> #defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION" [-Wcpp]
>>>>>>>>>>>>>> /tmp/tmpKbJQuO/src.linux-x86_64-2.7/wrapper_module_0module.c:111:12:
>>>>>>>>>>>>>> warning: âpy_sizeâefined but not used [-Wunused-function]
>>>>>>>>>>>>>> gcc: /tmp/tmpKbJQuO/src.linux-x86_64-2.7/fortranobject.c
>>>>>>>>>>>>>> In file included from
>>>>>>>>>>>>>> /opt/user/x86_64/Python-2.7.3/lib/python2.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1728:0,
>>>>>>>>>>>>>> from
>>>>>>>>>>>>>> /opt/user/x86_64/Python-2.7.3/lib/python2.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:17,
>>>>>>>>>>>>>> from
>>>>>>>>>>>>>> /opt/user/x86_64/Python-2.7.3/lib/python2.7/site-packages/numpy/core/include/numpy/arrayobject.h:15,
>>>>>>>>>>>>>> from
>>>>>>>>>>>>>> /tmp/tmpKbJQuO/src.linux-x86_64-2.7/fortranobject.h:13,
>>>>>>>>>>>>>> from
>>>>>>>>>>>>>> /tmp/tmpKbJQuO/src.linux-x86_64-2.7/fortranobject.c:2:
>>>>>>>>>>>>>> /opt/user/x86_64/Python-2.7.3/lib/python2.7/site-packages/numpy/core/include/numpy/npy_deprecated_api.h:11:2:
>>>>>>>>>>>>>> warning: #warning "Using deprecated NumPy API, disable it by
>>>>>>>>>>>>>> #defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION" [-Wcpp]
>>>>>>>>>>>>>> compiling Fortran sources
>>>>>>>>>>>>>> Fortran f77 compiler: /opt/user/x86_64/gcc-4.7.2/bin/gfortran
>>>>>>>>>>>>>> -Wall -ffixed-form -fno-second-underscore -fPIC -O3
>>>>>>>>>>>>>> -funroll-loops
>>>>>>>>>>>>>> Fortran f90 compiler: /opt/user/x86_64/gcc-4.7.2/bin/gfortran
>>>>>>>>>>>>>> -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
>>>>>>>>>>>>>> Fortran fix compiler: /opt/user/x86_64/gcc-4.7.2/bin/gfortran
>>>>>>>>>>>>>> -Wall -ffixed-form -fno-second-underscore -Wall
>>>>>>>>>>>>>> -fno-second-underscore -fPIC -O3 -funroll-loops
>>>>>>>>>>>>>> compile options: '-I/tmp/tmpKbJQuO/src.linux-x86_64-2.7
>>>>>>>>>>>>>> -I/opt/user/x86_64/Python-2.7.3/lib/python2.7/site-packages/numpy/core/include
>>>>>>>>>>>>>> -I/opt/user/x86_64/Python-2.7.3/include/python2.7 -c'
>>>>>>>>>>>>>> gfortran:f90: wrapped_code_0.f90
>>>>>>>>>>>>>> wrapped_code_0.f90:1.133:
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> 4, x15, x16, x17, x18, x19, x2, x20, x21, x3, x4, x5, x6, x7,
>>>>>>>>>>>>>> x8, x9, y_15
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> 1
>>>>>>>>>>>>>> Warning: Line truncated at (1)
>>>>>>>>>>>>>> wrapped_code_0.f90:1.132:
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> 14, x15, x16, x17, x18, x19, x2, x20, x21, x3, x4, x5, x6, x7,
>>>>>>>>>>>>>> x8, x9, y_15
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> 1
>>>>>>>>>>>>>> Error: Unexpected junk in formal argument list at (1)
>>>>>>>>>>>>>> wrapped_code_0.f90:33.3:
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> end subroutine
>>>>>>>>>>>>>> 1
>>>>>>>>>>>>>> Error: Expecting END PROGRAM statement at (1)
>>>>>>>>>>>>>> Error: Unexpected end of file in 'wrapped_code_0.f90'
>>>>>>>>>>>>>> wrapped_code_0.f90:1.133:
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> 4, x15, x16, x17, x18, x19, x2, x20, x21, x3, x4, x5, x6, x7,
>>>>>>>>>>>>>> x8, x9, y_15
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> 1
>>>>>>>>>>>>>> Warning: Line truncated at (1)
>>>>>>>>>>>>>> wrapped_code_0.f90:1.132:
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> 14, x15, x16, x17, x18, x19, x2, x20, x21, x3, x4, x5, x6, x7,
>>>>>>>>>>>>>> x8, x9, y_15
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> 1
>>>>>>>>>>>>>> Error: Unexpected junk in formal argument list at (1)
>>>>>>>>>>>>>> wrapped_code_0.f90:33.3:
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> end subroutine
>>>>>>>>>>>>>> 1
>>>>>>>>>>>>>> Error: Expecting END PROGRAM statement at (1)
>>>>>>>>>>>>>> Error: Unexpected end of file in 'wrapped_code_0.f90'
>>>>>>>>>>>>>> error: Command "/opt/user/x86_64/gcc-4.7.2/bin/gfortran -Wall
>>>>>>>>>>>>>> -fno-second-underscore -fPIC -O3 -funroll-loops
>>>>>>>>>>>>>> -I/tmp/tmpKbJQuO/src.linux-x86_64-2.7
>>>>>>>>>>>>>> -I/opt/user/x86_64/Python-2.7.3/lib/python2.7/site-packages/numpy/core/include
>>>>>>>>>>>>>> -I/opt/user/x86_64/Python-2.7.3/include/python2.7 -c -c
>>>>>>>>>>>>>> wrapped_code_0.f90 -o /tmp/tmpKbJQuO/wrapped_code_0.o" failed
>>>>>>>>>>>>>> with exit status 1
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> --
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>>>>>>>>>>>>>> .
>>>>>>>>>>>>>> For more options, visit https://groups.google.com/d/optout.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>> --
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>>>>>>>>>>> .
>>>>>>>>>>>
>>>>>>>>>>> For more options, visit https://groups.google.com/d/optout.
>>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>> --
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>>>>>>>> e42-01b0f57dbe1a%40googlegroups.com
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>>>>>>>> .
>>>>>>>>
>>>>>>>> For more options, visit https://groups.google.com/d/optout.
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>>>>>>>
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>>>>>> .
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>>>>>>
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