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HOPE: A Python Just-In-Time compiler for astrophysical computations

License: GNU General Public License v3.0

Makefile 0.18% Python 51.50% Jupyter Notebook 48.32%

hope's Introduction

HOPE - combine the ease of Python and the speed of C++

HOPE is a specialized method-at-a-time JIT compiler written in Python for translating Python source code into C++ and compiles this at runtime. In contrast to other existing JIT compliers, which are designed for general purpose, we have focused our development of the subset of the Python language that is most relevant for astrophysical calculations. By concentrating on this subset, HOPE is able to achieve the highest possible performance

By using HOPE, the user can benefit from being able to write common numerical code in Python and having the performance of compiled implementation. To enable the HOPE JIT compilation, the user needs to add a decorator to the function definition. The package does not require additional information, which ensures that HOPE is as non-intrusive as possible:

from hope import jit

@jit
def sum(x, y):
    return x + y

The HOPE package has been developed at ETH Zurich in the Software Lab of the Cosmology Research Group of the ETH Institute of Astronomy, and is now publicly available at GitHub.

Further information on the package can be found in our paper, on readthedocs.org and on our website.

Installation

The package has been uploaded to PyPI and can be installed at the command line via pip:

$ pip install hope

Or, if you have virtualenvwrapper installed:

$ mkvirtualenv hope
$ pip install hope

hope's People

Contributors

Stargazers

Watchers

hope's Issues

Travis CI can't find commands module for python3

For example:
https://travis-ci.org/cosmo-ethz/hope/jobs/38583739

The commands module was removed from the python3 standard library. We should replace commands with subprocess.
https://docs.python.org/2/library/commands.html
I'm working on this.

Proposing a PR to fix a few small typos

Issue Type

[x] Bug (Typo)

Steps to Replicate and Expected Behaviour

Examine hope/_transformer.py and observe unneccesary, however expect to see unnecessary.
Examine hope/jit.py and observe simpify, however expect to see simplify.
Examine hope/jit.py and observe retuns, however expect to see returns.
Examine hope/_generator.py and observe reasonamble, however expect to see reasonable.
Examine hope/config.py and observe purpos, however expect to see purpose.
Examine hope/options.py and observe propageted, however expect to see propagated.
Examine hope/_wrapper.py and observe proplem, however expect to see problem.
Examine hope/jit.py and observe multipications, however expect to see multiplications.

Notes

Semi-automated issue generated by
https://github.com/timgates42/meticulous/blob/master/docs/NOTE.md

To avoid wasting CI processing resources a branch with the fix has been
prepared but a pull request has not yet been created. A pull request fixing
the issue can be prepared from the link below, feel free to create it or
request @timgates42 create the PR. Alternatively if the fix is undesired please
close the issue with a small comment about the reasoning.

https://github.com/timgates42/hope/pull/new/bugfix_typos

Thanks.

Fail to reproduce timings

I installed hope from the git and run the following:

import numpy as np
import hope
@hope.jit
def pdf(density, dims, center, w2D, r50, b, a):
    for x in range(dims[0]):
        for y in range(dims[1]):
            dr = np.sqrt((x - center[0]) ** 2 + (y - center[1]) ** 2)
            density[x, y] = np.sum(w2D * 2 * (b - 1) / (2 * np.pi * (r50 * a)**2) * (1 + (dr / (r50 * a))**2)**(-b))
    return density

with:

 python -m timeit -s 'import numpy as np; b = 3.5; a = 1. / np.sqrt(2. ** (1. / (b - 1.)) - 1.) ; r50=20;center = np.array([10.141, 10.414]);dims = np.array([20, 20]) ; x1D = np.array([ 0.5 - 0.9491079123427585245262 / 2 , 0.5 - 0.7415311855993944398639 / 2 , 0.5 - 0.4058451513773971669066 / 2 , 0.5 , 0.5 + 0.4058451513773971669066 / 2 , 0.5 + 0.7415311855993944398639 / 2 , 0.5 + 0.9491079123427585245262 / 2 ], dtype=np.float32) ; w1D = np.array([ 0.1294849661688696932706 / 2 , 0.2797053914892766679015 / 2 , 0.38183005050511894495 / 2 , 0.4179591836734693877551 / 2 , 0.38183005050511894495 / 2 , 0.2797053914892766679015 / 2 , 0.1294849661688696932706 / 2 ], dtype=np.float32) ; w2D = np.outer(w1D, w1D) ; from pdf import pdf; density = np.zeros(dims, dtype=np.float32)' 'pdf(density, dims, center, w2D, r50, b, a)'

and the output is rather slow compared to the expected result. C++ module runs at the expected speed, so what did I do wrong?

JIT compile error OSX Yosemite running tests

On OS X Yosemite (10.10), I ran py.test test/test_functions.py.
I get failures which are variations on the following:

E           Exception: Error compiling function fkt (compiled to /var/folders/m1/78tf18750v38gm6y6y3dsm700000gn/T/hopemOTFNd)

hope/_wrapper.py:267: Exception
------------------------------------------------ Captured stdout call ------------------------------------------------
/var/folders/m1/78tf18750v38gm6y6y3dsm700000gn/T/hopemOTFNd/fkt_c90e045da53c2665412e2510a0e01b41d3f281dfe94b6581c8cb1895_0.cpp:67:52: warning: implicit conversion loses integer precision: 'std::size_t' (aka 'unsigned long') to 'int' [-Wshorten-64-to-32]
        char ** symbols = backtrace_symbols(stack, depth);
                          ~~~~~~~~~~~~~~~~~        ^~~~~
1 warning generated.
/var/folders/m1/78tf18750v38gm6y6y3dsm700000gn/T/hopemOTFNd/fkt_c90e045da53c2665412e2510a0e01b41d3f281dfe94b6581c8cb1895_0.cpp:38:24: error: non-constant-expression cannot be narrowed from type 'npy_int64' (aka 'long long') to 'npy_intp' (aka 'long') in initializer list [-Wc++11-narrowing]
        npy_intp d__ret0[] = {cshape};
                              ^~~~~~
/var/folders/m1/78tf18750v38gm6y6y3dsm700000gn/T/hopemOTFNd/fkt_c90e045da53c2665412e2510a0e01b41d3f281dfe94b6581c8cb1895_0.cpp:38:24: note: override this message by inserting an explicit cast
        npy_intp d__ret0[] = {cshape};
                              ^~~~~~
                              static_cast<npy_intp>( )
/var/folders/m1/78tf18750v38gm6y6y3dsm700000gn/T/hopemOTFNd/fkt_c90e045da53c2665412e2510a0e01b41d3f281dfe94b6581c8cb1895_0.cpp:38:24: warning: implicit conversion loses integer precision: 'npy_int64' (aka 'long long') to 'npy_intp' (aka 'long') [-Wshorten-64-to-32]
        npy_intp d__ret0[] = {cshape};
                             ~^~~~~~
1 warning and 1 error generated.
running build_ext
building 'fkt_c90e045da53c2665412e2510a0e01b41d3f281dfe94b6581c8cb1895_0' extension
C compiler: cc -fno-strict-aliasing -fno-common -dynamic -arch x86_64 -arch i386 -g -Os -pipe -fno-common -fno-strict-aliasing -fwrapv -DENABLE_DTRACE -DMACOSX -DNDEBUG -Wall -Wshorten-64-to-32 -DNDEBUG -g -fwrapv -Os -Wall -DENABLE_DTRACE -arch x86_64 -arch i386 -pipe

I'll look at fixing this tonight.

Hope PyPi name

Hello!

As I understand it, the project was abandoned about 6 years ago. Won't it be difficult for you to republish it under a different name in pypi?

I am just writing a Python framework and I really like the name, I would really appreciate it! The project will be ready for open source in a few days, now I can only tell you the details, if you're interested.

Thank you for your time.

Printing doubles

While testing the library, I found that doubles are printed with less precision.

For example, np.pi is printed in the CPP with only 15 decimal places (it is defined with 35 in the numpy math header).

I tried to add {:.35} to the format string, which works but prints the number in pythons internal representation (ie. it's not the same as the one defined in the headers because of floating point issues, I guess).

Another alternative (for np.pi) would be to include numpy/npy_math.h and use NPY_PI as constant. However, that includes to add a String type to the ast.

Best,

Wolf

Not working for sum function

I tried to see the performance, but it doesn't even compile. Maybe I'm doing something wrong?

Here's my code:

import time

from hope import jit


def normsum(n):
    return sum((i for i in xrange(n)))


@jit
def hopesum(n):
    return sum([i for i in range(n)])


st = time.time()
s = normsum(100000000)
print "Simple sum time: ", time.time()-st
print s

st = time.time()
hopesum(100000000)
print "Hope sum time: ", time.time()-st
print s

This is the output:

Simple sum time:  3.57045102119
4999999950000000
Traceback (most recent call last):
  File "hope_test.py", line 21, in <module>
    hopesum(100000000)
  File "/usr/local/lib/python2.7/dist-packages/hope-0.3.0-py2.7.egg/hope/_wrapper.py", line 66, in _hope_callback
    return self(*args) if self.cache is None else self.cache(*args)
  File "/usr/local/lib/python2.7/dist-packages/hope-0.3.0-py2.7.egg/hope/_wrapper.py", line 72, in __call__
    ASTTransformer(self.modtoken).module_visit(self.fkt, args)
  File "/usr/local/lib/python2.7/dist-packages/hope-0.3.0-py2.7.egg/hope/_transformer.py", line 633, in module_visit
    parsed = self.visit(fktast)
  File "/usr/lib/python2.7/ast.py", line 241, in visit
    return visitor(node)
  File "/usr/local/lib/python2.7/dist-packages/hope-0.3.0-py2.7.egg/hope/_transformer.py", line 570, in visit_FunctionDef
    self.token.body = self.body_visit(node.body) 
  File "/usr/local/lib/python2.7/dist-packages/hope-0.3.0-py2.7.egg/hope/_transformer.py", line 583, in body_visit
    self.exprs.append(self.visit(stmt))            
  File "/usr/lib/python2.7/ast.py", line 241, in visit
    return visitor(node)
  File "/usr/local/lib/python2.7/dist-packages/hope-0.3.0-py2.7.egg/hope/_transformer.py", line 537, in visit_Return
    value = self.visit(node.value)
  File "/usr/lib/python2.7/ast.py", line 241, in visit
    return visitor(node)
  File "/usr/local/lib/python2.7/dist-packages/hope-0.3.0-py2.7.egg/hope/_transformer.py", line 450, in visit_Call
    raise Exception("Function '{0}' not accessible form global scope of function '{1}': ({2})".format(node.func.id, self.fkt.__name__, ast.dump(node)))
Exception: Function 'sum' not accessible form global scope of function 'hopesum': (Call(func=Name(id='sum', ctx=Load()), args=[ListComp(elt=Name(id='i', ctx=Load()), generators=[comprehension(target=Name(id='i', ctx=Store()), iter=Call(func=Name(id='range', ctx=Load()), args=[Name(id='n', ctx=Load())], keywords=[], starargs=None, kwargs=None), ifs=[])])], keywords=[], starargs=None, kwargs=None))
in body return sum([i for i in range(n)])

Issue with numpy.interp

I found an issue when using the function numpy.interp within a Python function compiled using HOPE. The issue is that interpolation produces different results depending on whether HOPE is used or not. The following example illustrates this.

    import numpy as np
    import hope
    import pickle

    def interpolation(x, xp, fp):
        intp = np.interp(x, xp, fp)
        return intp

    @hope.jit
    def interpolation_hope(x, xp, fp):
        intp = np.interp(x, xp, fp)
        return intp

    with open('np.interp_github_issue.pkl', 'r') as f:
        x, xp, fp = pickle.load(f)
    x = x.astype(np.float32)
    xp = xp.astype(np.float32)
    fp = fp.astype(np.float32)

    intp = interpolation(x, xp, fp)
    intp_hope = interpolation_hope(x, xp, fp)
    np.allclose(intp, intp_hope)

np.interp_github_issue.pkl.zip

error when trying to compile function -- unsupported pickle protocol: 3

I'm using hope 0.3.1 (upgraded today), anaconda python2.7 on Mac 10.9.5 with clang 3.4.

When I try to follow the very simple example of the sum function using hope in ipython 2.2.0, I get this error.

In [1]: from hope import jit

In [2]: @jit
   ...: def jitsum(x,y):
   ...:     return x + y
   ...: 
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-2-cc998918d205> in <module>()
----> 1 @jit
      2 def jitsum(x,y):
      3     return x + y
      4 

/astro/grads/arh5361/code/anaconda/envs/py27/lib/python2.7/site-packages/hope/jit.pyc in jit(fkt)
     87 
     88     try:
---> 89         state = serialization.unserialize(filename)
     90         if not state is None:
     91             _check_state(fkt, state)

/astro/grads/arh5361/code/anaconda/envs/py27/lib/python2.7/site-packages/hope/serialization.pyc in unserialize(name)
     40 
     41     with open(os.path.join(config.prefix, "{0}.pck".format(name)), "rb") as fp:
---> 42         return pickle.load(fp)

/astro/grads/arh5361/code/anaconda/envs/py27/lib/python2.7/pickle.pyc in load(file)
   1376 
   1377 def load(file):
-> 1378     return Unpickler(file).load()
   1379 
   1380 def loads(str):

/astro/grads/arh5361/code/anaconda/envs/py27/lib/python2.7/pickle.pyc in load(self)
    856             while 1:
    857                 key = read(1)
--> 858                 dispatch[key](self)
    859         except _Stop, stopinst:
    860             return stopinst.value

/astro/grads/arh5361/code/anaconda/envs/py27/lib/python2.7/pickle.pyc in load_proto(self)
    884         proto = ord(self.read(1))
    885         if not 0 <= proto <= 2:
--> 886             raise ValueError, "unsupported pickle protocol: %d" % proto
    887     dispatch[PROTO] = load_proto
    888 

ValueError: unsupported pickle protocol: 3

Exception when iterating over a numpy array. What am I not getting?

Hi I'm trying to get a function that iterates over xrange and wants to extract a row from a numpy array (then do an expression).

But I can't seem to get it to access the row of the array?

i get this exception:

raise Exception("Extends of variable and subscript do not match")
Exception: Extends of variable and subscript do not match
in line c = t[i]
in body c = t[i]

These are what the types are:

ds = 1227, <type 'numpy.int32'>
dt 400 <type 'int'>
as 69 <type 'numpy.int32'>
mt 2147483647 <type 'int'>
ns 2680 <type 'int'>
248256 <type 'int'>

[[ 21 22 1 7 48650 0]
[ 26 27 1 3 84823 1]
[ 64 65 1 11 77351 2]
...,
[ 68 69 2948 2970 53287 248253]
[ 67 68 2952 2964 53498 248254]
[ 68 312 2980 3034 53498 248255]]

<type 'numpy.ndarray'> shape (248256, 6) dtype int32

And the code:

@jit
    def hope_loop(self, ds, dt, as, mt, ns, nr, t):
        earliest_arrival = np.zeros(ns, dtype=np.int32)
        earliest_arrival[:] = mt
        earliest_arrival[ds] = dt
        
        # Set up array of all stations to track back
        in_connection = np.zeros(ns, dtype=np.int32)
        in_connection[:] = -1
        for i in xrange(nr):
            c = t[i]
           # if c[2] >= earliest_arrival[c[0]] and c[3] < earliest_arrival[c[1]]:
           # continue
        return earliest_arrival

Examples not running on Python 3.6

The following code works in Python 2.7 on in macOS 10.13:

y=np.ones([n,n])
x=2*np.ones([n,n])
z=1
out=np.ones_like(x)


@hope.jit
def hope_exp(X,Y,Z,A):
        A[:] = hope.exp((X**2+Y**2)/Z)

hope_exp(x,y,z,out)

but in Python 3.6.1 it returns the following error:

AttributeError Traceback (most recent call last)
/Users/edouglas/anaconda/envs/astroconda/lib/python3.6/site-packages/hope/_transformer.py in visit_Assign(self, node)
286
--> 287 target, value = self.visit(node.targets[0]), self.visit(node.value)
288

/Users/edouglas/anaconda/envs/astroconda/lib/python3.6/ast.py in visit(self, node)
252 visitor = getattr(self, method, self.generic_visit)
--> 253 return visitor(node)
254

/Users/edouglas/anaconda/envs/astroconda/lib/python3.6/site-packages/hope/_transformer.py in visit_Call(self, node)
577 elif isinstance(node.func, ast.Attribute):
--> 578 if not node.starargs is None or not node.kwargs is None:
579 raise UnsupportedFeatureException("Only arguments without default values are supported in calls")

AttributeError: 'Call' object has no attribute 'starargs'

During handling of the above exception, another exception occurred:

AttributeError Traceback (most recent call last)
/Users/edouglas/anaconda/envs/astroconda/lib/python3.6/site-packages/hope/_transformer.py in body_visit(self, stmts)
641 try:
--> 642 self.exprs.append(self.visit(stmt))
643 except Exception as ex:

/Users/edouglas/anaconda/envs/astroconda/lib/python3.6/ast.py in visit(self, node)
252 visitor = getattr(self, method, self.generic_visit)
--> 253 return visitor(node)
254

/Users/edouglas/anaconda/envs/astroconda/lib/python3.6/site-packages/hope/_transformer.py in visit_Assign(self, node)
318 from hope._tosource import tosource
--> 319 ex.args = ((ex.args[0] if ex.args else "") + "\nin line " + tosource(node),) + ex.args[1:]
320 raise

/Users/edouglas/anaconda/envs/astroconda/lib/python3.6/site-packages/hope/_tosource.py in tosource(node, indent_with, add_line_information)
77 generator = SourceGenerator(indent_with, add_line_information)
---> 78 generator.visit(node)
79

/Users/edouglas/anaconda/envs/astroconda/lib/python3.6/ast.py in visit(self, node)
252 visitor = getattr(self, method, self.generic_visit)
--> 253 return visitor(node)
254

/Users/edouglas/anaconda/envs/astroconda/lib/python3.6/site-packages/hope/_tosource.py in visit_Assign(self, node)
173 self.write(' = ')
--> 174 self.visit(node.value)
175

/Users/edouglas/anaconda/envs/astroconda/lib/python3.6/ast.py in visit(self, node)
252 visitor = getattr(self, method, self.generic_visit)
--> 253 return visitor(node)
254

/Users/edouglas/anaconda/envs/astroconda/lib/python3.6/site-packages/hope/_tosource.py in visit_Call(self, node)
440 self.visit(keyword.value)
--> 441 if node.starargs is not None:
442 write_comma()

AttributeError: 'Call' object has no attribute 'starargs'

During handling of the above exception, another exception occurred:

AttributeError Traceback (most recent call last)
in ()
9 def hope_exp(X,Y,Z,A):
10 A[:] = hope.exp((X2+Y2)/Z)
---> 11 hope_exp(x,y,z,out)
12 np.exp((x2 + y2)/z)

/Users/edouglas/anaconda/envs/astroconda/lib/python3.6/site-packages/hope/_wrapper.py in _hope_callback(*args)
69
70 def _hope_callback(*args):
---> 71 return self(*args) if self.cache is None else self.cache(*args)
72 self.callback = _hope_callback
73 setattr(cache, str(id(self.callback)), fkt)

/Users/edouglas/anaconda/envs/astroconda/lib/python3.6/site-packages/hope/_wrapper.py in call(self, *args)
75
76 def call(self, *args):
---> 77 ASTTransformer(self.modtoken).module_visit(self.fkt, args)
78
79 if config.optimize:

/Users/edouglas/anaconda/envs/astroconda/lib/python3.6/site-packages/hope/_transformer.py in module_visit(self, fkt, args, argid)
690 if argid is None or not argid in self.modtoken.functions[fkt.name]:
691 #Create the HOPE AST from the builtin AST
--> 692 parsed = self.visit(fktast)
693 if not parsed.getId() in [arg.getId() for arg in self.modtoken.functions[fkt.name]]:
694 self.modtoken.functions[fkt.name].append(parsed)

/Users/edouglas/anaconda/envs/astroconda/lib/python3.6/ast.py in visit(self, node)
251 method = 'visit_' + node.class.name
252 visitor = getattr(self, method, self.generic_visit)
--> 253 return visitor(node)
254
255 def generic_visit(self, node):

/Users/edouglas/anaconda/envs/astroconda/lib/python3.6/site-packages/hope/_transformer.py in visit_FunctionDef(self, node)
627
628 # TODO: how do we parse recursive subfuncitons?
--> 629 self.token.body = self.body_visit(node.body)
630 AllocateVisitor(self.variables).visit(self.token.body)
631 merged = {}

/Users/edouglas/anaconda/envs/astroconda/lib/python3.6/site-packages/hope/_transformer.py in body_visit(self, stmts)
644 if "\nin body " not in (ex.args[0] if ex.args else ""):
645 from hope._tosource import tosource
--> 646 ex.args = ((ex.args[0] if ex.args else "") + "\nin body " + tosource(stmt),) + ex.args[1:]
647 raise
648 for expr in self.exprs:

/Users/edouglas/anaconda/envs/astroconda/lib/python3.6/site-packages/hope/_tosource.py in tosource(node, indent_with, add_line_information)
76
77 generator = SourceGenerator(indent_with, add_line_information)
---> 78 generator.visit(node)
79
80 return ''.join(generator.result)

/Users/edouglas/anaconda/envs/astroconda/lib/python3.6/ast.py in visit(self, node)
251 method = 'visit_' + node.class.name
252 visitor = getattr(self, method, self.generic_visit)
--> 253 return visitor(node)
254
255 def generic_visit(self, node):

/Users/edouglas/anaconda/envs/astroconda/lib/python3.6/site-packages/hope/_tosource.py in visit_Assign(self, node)
172 self.visit(target)
173 self.write(' = ')
--> 174 self.visit(node.value)
175
176 def visit_AugAssign(self, node):

/Users/edouglas/anaconda/envs/astroconda/lib/python3.6/ast.py in visit(self, node)
251 method = 'visit_' + node.class.name
252 visitor = getattr(self, method, self.generic_visit)
--> 253 return visitor(node)
254
255 def generic_visit(self, node):

/Users/edouglas/anaconda/envs/astroconda/lib/python3.6/site-packages/hope/_tosource.py in visit_Call(self, node)
439 self.write(keyword.arg + '=')
440 self.visit(keyword.value)
--> 441 if node.starargs is not None:
442 write_comma()
443 self.write('*')

AttributeError: 'Call' object has no attribute 'starargs'

Readme example:

Additionally, the standard sum example from the readme gives this error in 3.6.1 but works in 2.7:

TypeError Traceback (most recent call last)
in ()
4 def sums(x, y):
5 return x + y
----> 6 sums(x,y)

/Users/edouglas/anaconda/envs/astroconda/lib/python3.6/site-packages/hope/_wrapper.py in _hope_callback(*args)
69
70 def _hope_callback(*args):
---> 71 return self(*args) if self.cache is None else self.cache(*args)
72 self.callback = _hope_callback
73 setattr(cache, str(id(self.callback)), fkt)

/Users/edouglas/anaconda/envs/astroconda/lib/python3.6/site-packages/hope/_wrapper.py in call(self, *args)
102 fp.write(code)
103
--> 104 so_filename = _compile(tempfolder, localfilename, self.fkt.name)
105
106 self._store_state(tempfolder, localfilename)

/Users/edouglas/anaconda/envs/astroconda/lib/python3.6/site-packages/hope/_wrapper.py in _compile(target, localfilename, fkt_name)
271 try:
272 #trying to encode utf-8 to support AstroPy
--> 273 warnings.warn("A warning has been issued during compilation:\n{0}".format(out).encode('utf-8'))
274 except UnicodeError:
275 #encoding fails on Linux

TypeError: cannot use a string pattern on a bytes-like object

Missing file question

Hi Joel, did not find an email address of you so I write here, maybe we can follow up per email.

I'm working on the documentation and the benchmarks folder lacks a file util.py which is used within the notebooks. Any idea where to find it ?

Best
Uwe

Default OSX 10.9 clang is not supported

On OSX 10.9 I get the following error when trying to import hope:

hope.exceptions.UnsupportedCompilerException: Compiler 'gcc' with version '4.2.1
' is not supported. Minimum version is '4.7.0'

4.2.1 is indeed what the default gcc (a proxy for clang) reports. Are there any reasons it's not supported? And if there are, is there any way to point hope to a separately installed GCC, say, the one from homebrew?

error when trying to compile function -- ImportError: No module named 'winreg' from six library

I'm using python 3.3, hope 0.3.1, on Mac 10.9.5 with clang 3.4. When I try the basic sum example, I get this error from the six library. I have the most recent version of six (1.8.0) installed and I'm uncertain if this is an issue with hope or truly an issues with six.

In [1]: from hope import jit

In [2]: @jit
   ...: def jitsum(x,y):
   ...:     return x + y
   ...: 

In [3]: jitsum(1,2)
---------------------------------------------------------------------------
ImportError                               Traceback (most recent call last)
<ipython-input-3-64161ff6ad22> in <module>()
----> 1 jitsum(1,2)

/astro/grads/arh5361/code/anaconda/lib/python3.3/site-packages/hope/_wrapper.py in _hope_callback(*args)
     69 
     70         def _hope_callback(*args):
---> 71             return self(*args) if self.cache is None else self.cache(*args)
     72         self.callback = _hope_callback
     73         setattr(cache, str(id(self.callback)), fkt)

/astro/grads/arh5361/code/anaconda/lib/python3.3/site-packages/hope/_wrapper.py in __call__(self, *args)
    121 
    122             for name in list(sys.modules.keys()):
--> 123                 if hasattr(sys.modules[name], self.fkt.__name__) and getattr(sys.modules[name], self.fkt.__name__) is self.callback:
    124                     setattr(sys.modules[name], self.fkt.__name__, module.run)
    125             setattr(cache, str(id(module.run)), self.fkt)

/astro/grads/arh5361/code/anaconda/lib/python3.3/site-packages/astropy/extern/bundled/six.py in __getattr__(self, attr)
    114         if attr in ("__file__", "__name__") and self.mod not in sys.modules:
    115             raise AttributeError
--> 116         _module = self._resolve()
    117         value = getattr(_module, attr)
    118         setattr(self, attr, value)

/astro/grads/arh5361/code/anaconda/lib/python3.3/site-packages/astropy/extern/bundled/six.py in _resolve(self)
    103 
    104     def _resolve(self):
--> 105         return _import_module(self.mod)
    106 
    107     def __getattr__(self, attr):

/astro/grads/arh5361/code/anaconda/lib/python3.3/site-packages/astropy/extern/bundled/six.py in _import_module(name)
     74 def _import_module(name):
     75     """Import module, returning the module after the last dot."""
---> 76     __import__(name)
     77     return sys.modules[name]
     78 

ImportError: No module named 'winreg'

Please provides more examples

For some reason, this project is interesting to me. Could you/your team give more examples so people can quickly learn what this library can do and cannot do. Thank you!

Unknown compiler: x86_64-linux-gnu-gcc

I was trying to run the basic sum example, but seems the compiler does not get detected.

(test)sf@sf-MacBookPro:~/Projects/test/hope_test$ cat /etc/lsb-release
DISTRIB_ID=Ubuntu
DISTRIB_RELEASE=13.10
DISTRIB_CODENAME=saucy
DISTRIB_DESCRIPTION="Ubuntu 13.10"

#!/usr/bin/env python

from hope import jit

@jit
def sum(x, y):
    return x + y

(test)sf@sf-MacBookPro:~/Projects/test/hope_test$ python test.py 
Traceback (most recent call last):
  File "test.py", line 3, in <module>
    from hope import jit
  File "/home/sf/.virtualenvs/test/local/lib/python2.7/site-packages/hope/__init__.py", line 11, in <module>
    from hope import config
  File "/home/sf/.virtualenvs/test/local/lib/python2.7/site-packages/hope/config.py", line 9, in <module>
    cxxflags = get_cxxflags()
  File "/home/sf/.virtualenvs/test/local/lib/python2.7/site-packages/hope/options.py", line 72, in get_cxxflags
    raise UnsupportedCompilerException("Unknown compiler: {0}".format(compiler_name))
hope.exceptions.UnsupportedCompilerException: Unknown compiler: x86_64-linux-gnu-gcc

(test)sf@sf-MacBookPro:~/Projects/test/hope_test$ python -c "from distutils.ccompiler import new_compiler; print new_compiler().compiler[0].split('/')[-1]"
cc

Fix julialang.org fib example for Cython

Right now, the fibbonachi example (visible at http://nbviewer.ipython.org/url/refreweb.phys.ethz.ch/hope/notebooks/julialang.org.ipynb ... for some reason this doesn't match this repository?) reads:

cimport cython

@cython.boundscheck(False)
@cython.wraparound(False)
def cython_fib(int n):
    if n<2:
        return n
    return cython_fib(n-1)+cython_fib(n-2)

This is a mis-use of Cython, however. If you change it instead to

cimport cython

@cython.boundscheck(False)
@cython.wraparound(False)
cpdef int cython_fib(int n):
    if n<2:
        return n
    return cython_fib(n-1)+cython_fib(n-2)

That yields a drastic speedup, making the Cython example equivalent to native code (and hope).

The point is that using "def" in tight loops is a mis-use of Cython: it makes Cython functions act just like regular Python functions, rather than using it as a c function with an int return type, which is what makes the recursion here go fast.

compilation fails with python3

Hi,
on my box a basic example under python3 (3.4.2) does not work:

In [1]: from hope import jit
4.9.1

In [2]: @jit                
def sum(x, y):
        return x + y
   ...: 

In [3]: sum(4,8)            
cc1plus: warning: command line option ‘-Wstrict-prototypes’ is valid for C/ObjC but not for C++
running build_ext
building 'sum_900a4b7729fa81ded8e12217d97f52982df0978d23240cb0daa79505_0' extension
C compiler: gcc -pthread -Wno-unused-result -DDYNAMIC_ANNOTATIONS_ENABLED=1 -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -march=x86-64 -mtune=generic -O2 -pipe -fstack-protector-strong --param=ssp-buffer-size=4 -fPIC

compile options: '-I/usr/lib/python3.4/site-packages/numpy/core/include -I/usr/include/python3.4m -c'
extra options: '-Wall -Wno-unused-variable -std=c++11'
gcc: /tmp/hopexz2jayp3/sum_900a4b7729fa81ded8e12217d97f52982df0978d23240cb0daa79505_0.cpp
g++ -pthread -shared -Wl,-O1,--sort-common,--as-needed,-z,relro /tmp/hopexz2jayp3/sum_900a4b7729fa81ded8e12217d97f52982df0978d23240cb0daa79505_0.o -L/usr/lib -lpython3.4m -o /tmp/hopexz2jayp3/sum_900a4b7729fa81ded8e12217d97f52982df0978d23240cb0daa79505_0.cpython-34m.so

---------------------------------------------------------------------------
Exception                                 Traceback (most recent call last)
<ipython-input-3-2315bad740f1> in <module>()
----> 1 sum(4,8)

/home/xantares/projects/hope/hope/_wrapper.py in _hope_callback(*args)
     64 
     65         def _hope_callback(*args):
---> 66             return self(*args) if self.cache is None else self.cache(*args)
     67         self.callback = _hope_callback
     68         setattr(cache, str(id(self.callback)), fkt)

/home/xantares/projects/hope/hope/_wrapper.py in __call__(self, *args)
     97                 fp.write(code)
     98 
---> 99             _compile(tempfolder, localfilename, self.fkt.__name__)
    100 
    101             self._store_state(tempfolder, localfilename)

/home/xantares/projects/hope/hope/_wrapper.py in _compile(target, localfilename, fkt_name)
    248     if not os.path.isfile(os.path.join(target, "{0}.so".format(localfilename))) or out.find("error:") > -1:
    249         print(out)
--> 250         raise Exception("Error compiling function {0} (compiled to {1})".format(fkt_name, target))
    251 
    252     #TODO: add test case

Exception: Error compiling function sum (compiled to /tmp/hopexz2jayp3)

In [4]: 
Do you really want to exit ([y]/n)? 
xantares@archlinux:~/projects/hope$ g++ -pthread -shared -Wl,-O1,--sort-common,--as-needed,-z,relro /tmp/hopexz2jayp3/sum_900a4b7729fa81ded8e12217d97f52982df0978d23240cb0daa79505_0.o -L/usr/lib -lpython3.4m -o /tmp/hopexz2jayp3/sum_900a4b7729fa81ded8e12217d97f52982df0978d23240cb0daa79505_0.cpython-34m.so
g++: error: /tmp/hopexz2jayp3/sum_900a4b7729fa81ded8e12217d97f52982df0978d23240cb0daa79505_0.o: No such file or directory

Issue with self and np.array

When adding an np array to the test class and trying to access said array from a jitted function, it crashes while generating the CPP:

Traceback (most recent call last):
  File "examples/hope_class.py", line 56, in <module>
    example()
  File "examples/hope_class.py", line 47, in example
    r = dc.run(2)
  File "/home/wolfv/Programs/hope/hope/_wrapper.py", line 71, in _hope_callback
    return self(*args) if self.cache is None else self.cache(*args)
  File "/home/wolfv/Programs/hope/hope/_wrapper.py", line 77, in __call__
    ASTTransformer(self.modtoken).module_visit(self.fkt, args)
  File "/home/wolfv/Programs/hope/hope/_transformer.py", line 706, in module_visit
    parsed = self.visit(fktast)
  File "/usr/lib/python2.7/ast.py", line 241, in visit
    return visitor(node)
  File "/home/wolfv/Programs/hope/hope/_transformer.py", line 644, in visit_FunctionDef
    AllocateVisitor(self.variables).visit(self.token.body)
  File "/home/wolfv/Programs/hope/hope/_ast.py", line 387, in visit
    return getattr(self, 'visit_{0}'.format(type(node).__name__))(node)
  File "/home/wolfv/Programs/hope/hope/_transformer.py", line 152, in visit_Body
    start = check_blocks(start)
  File "/home/wolfv/Programs/hope/hope/_transformer.py", line 141, in check_blocks
    if self.variables[symbol].scope == "block":
KeyError: u'self.c'

Source:

# Copyright (C) 2014 ETH Zurich, Institute for Astronomy

'''
A simple example to demonstrate the supported functionality with class methods.

Note: this should be used with precaution as unboxing member variables causes some overhead

Created on Oct 23, 2014

author: jakeret
'''
from __future__ import print_function, division, absolute_import, unicode_literals

import os
import shutil
import hope

import numpy as np
prefix = "hope_class"

hope.config.keeptemp = True
hope.config.verbose = True
hope.config.prefix = prefix

if os.path.exists(prefix):
    shutil.rmtree(prefix)

@hope.jit
def external(a, b):
    return a + b

class DemoClass(object):
    def __init__(self):
        self.a = 1
        self.c = np.array([1, 2, 3])

    @hope.jit
    def run(self, b):
        return self.a + b + self.c

    def run_external(self, b):
        return external(self.a, b)


def example():
    dc = DemoClass()
    #jit compile a method of a class
    r = dc.run(2)
    assert r == 3

    #call jitted function from a class
    r = dc.run_external(2)
    assert r == 3

if __name__ == '__main__':
    example()

GPL issue

Is it possible to relax the license to a MIT or BSD like license - at least for the runtime code that is included into the users python programm and extensions?

is there any hope to let hope run with mingw on windows?

as the title says. Thanks

ln_python 10th polynomial benchmark variant

Hi,

I would like to contribute a variant on ln_python benchmark.

Instead of using numpy slice, we directly access to array elements inside a loop. This is of course terribly slow for interpreted Python but seems to be faster with compiled code like generated with HOPE and numba. In my tests, it happens to be fairly faster than the numpy slice version.

In the following code, I had to add a N parameter beacuse HOPE doesn't support np.ndarray.size attribute. I also use a Horner schema for the polynomial evaluation.

def loop_ln_python(X,Y, N):
    for i in xrange(N):
        xm1 = X[i] - 1.
        tmp = xm1 * (1./9.) - 1./8.
        tmp = tmp * xm1 + 1./7.
        tmp = tmp * xm1 - 1./6.
        tmp = tmp * xm1 + 1./5.
        tmp = tmp * xm1 - 1./4.
        tmp = tmp * xm1 + 1./3.
        tmp = tmp * xm1 - 1./2.
        tmp = tmp * xm1 + 1.
        Y[i] = tmp * xm1



loop_ln_hope = hope.jit(loop_ln_python)
loop_ln_numba = numba.autojit(loop_ln_python)

I test with the following:

X = np.random.random(10000).astype(np.float64)
Y = np.ones_like(X)

loop_ln_hope(X, Y, len(X))
loop_ln_numba(X, Y, len(X))

%timeit loop_ln_python(X, Y, len(X))
%timeit loop_ln_hope(X, Y, len(X))
%timeit loop_ln_numba(X, Y, len(X))

On my laptop, I get the following timings:

100 loops, best of 3: 15.1 ms per loop
10000 loops, best of 3: 46.1 µs per loop
100000 loops, best of 3: 12.7 µs per loop

To be compared to the ln_python_exp() timings:

1000 loops, best of 3: 257 µs per loop
10000 loops, best of 3: 76.8 µs per loop
10000 loops, best of 3: 110 µs per loop

I hope it will be of some interest for you.

For the sake of completeness, we can write a Horner schema version that doesn't add much to the compiled performances but which is more fair for numpy interpreted code:

def ln_python_horner(X, Y):
    Xm1 = X - 1
    Y[:] = Xm1 / 9. - 1./8.
    Y[:] = Y * Xm1 + 1./7.
    Y[:] = Y * Xm1 - 1./6.
    Y[:] = Y * Xm1 + 1./5.
    Y[:] = Y * Xm1 - 1./4.
    Y[:] = Y * Xm1 + 1./3.
    Y[:] = Y * Xm1 - 1./2.
    Y[:] = Y * Xm1 + 1.
    Y[:] = Y * Xm1

And with the timings:

10000 loops, best of 3: 169 µs per loop
10000 loops, best of 3: 104 µs per loop
10000 loops, best of 3: 119 µs per loop

Anyway, thanks for this nice package.

Cheers,

Pierre

segfault in pycosmo

The pycosmo test fails on my machine, with python 2.7.8 and python 3.4.2, I also have numpy 1.9.0, gcc 4.9.1.

It fails in test_pycosmo_1_opt, but only if I run test_pycosmo_1 first:

test_pycosmo_1()
test_pycosmo_1_opt()

If I set hope.config.optimize = False in test_pycosmo_1_opt it passes too.

Here is the output with hope.config.verbose=True :

$ python2 test/test_pycosmo.py 
[...]/hope-0.3.1-py2.7.egg/hope/jit.py:113: UserWarning: Recompiling... Reason: State is inconsistent with config. Inconsistent state key: [keeptemp].
  warnings.warn("Recompiling... Reason: {0}".format(le))
Compiling following functions:
pycosmo(float32^1 y, float64 lna, float64 k, float64 eta, float64 hubble_a, float64 tdot, float64 omega_gam, float64 omega_neu, float64 omega_dm_0, float64 omega_b_0, float64 ha, float64 rh, float64 r_bph_a, int64 xc_damp)
running build_ext
building 'pycosmo_595aba8b1f769fe4802a2e41fd8168d3a643953f6adc1192807b2249_0' extension
C compiler: gcc -pthread -fno-strict-aliasing -march=x86-64 -mtune=generic -O2 -pipe -fstack-protector-strong --param=ssp-buffer-size=4 -DNDEBUG -march=x86-64 -mtune=generic -O2 -pipe -fstack-protector-strong --param=ssp-buffer-size=4 -fPIC

compile options: '-I/usr/lib/python2.7/site-packages/numpy/core/include -I/usr/include/python2.7 -c'
extra options: '-Wall -Wno-unused-variable -std=c++11'
gcc: /tmp/hopeIYR0lp/pycosmo_595aba8b1f769fe4802a2e41fd8168d3a643953f6adc1192807b2249_0.cpp
g++ -pthread -shared -Wl,-O1,--sort-common,--as-needed,-z,relro /tmp/hopeIYR0lp/pycosmo_595aba8b1f769fe4802a2e41fd8168d3a643953f6adc1192807b2249_0.o -L/usr/lib -lpython2.7 -o /tmp/hopeIYR0lp/pycosmo_595aba8b1f769fe4802a2e41fd8168d3a643953f6adc1192807b2249_0.so

[...]/hope-0.3.1-py2.7.egg/hope/jit.py:113: UserWarning: Recompiling... Reason: State is inconsistent with config. Inconsistent state key: [optimize].
  warnings.warn("Recompiling... Reason: {0}".format(le))
pycosmo(float32^1 y, float64 lna, float64 k, float64 eta, float64 hubble_a, float64 tdot, float64 omega_gam, float64 omega_neu, float64 omega_dm_0, float64 omega_b_0, float64 ha, float64 rh, float64 r_bph_a, int64 xc_damp)
        new a.D
        a.D = numpy.exp(lna.D)
        new psi.d
        psi.d = (-y.f[0.J] - ((12.0.D / (((rh.D * k.D) * a.D) ** 2.J)) * ((omega_gam.D * y.f[9.J]) + (omega_neu.D * y.f[((6.J + (2.J * 10.J)) + 3.J)]))))
        new dphidlna.d
        dphidlna.d = ((psi.d - (((k.D ** 2.J) / ((3.0.D * (a.D ** 2.J)) * (ha.D ** 2.J))) * y.f[0.J])) + ((0.5.D / ((ha.D * rh.D) ** 2.J)) * (((((omega_dm_0.D * (a.D ** -3.J)) * y.f[1.J]) + ((omega_b_0.D * (a.D ** -3.J)) * y.f[3.J])) + (((4.0.D * omega_gam.D) * (a.D ** -4.J)) * y.f[5.J])) + (((4.0.D * omega_neu.D) * (a.D ** -4.J)) * y.f[((6.J + (2.J * 10.J)) + 1.J)]))))
        new ppi.f
        ppi.f = ((y.f[9.J] + y.f[6.J]) + y.f[10.J])
        new n_y.J
        n_y.J = (8.J + (3.J * 10.J))
        new dydlna.f[:n_y.J]
        (:n_y.J) {
                dydlna.f[:n_y.J] = numpy.zeros(n_y.J, dtype=numpy.float32)
        }
        dydlna.f[0.J] = dphidlna.d
        dydlna.f[1.J] = (((-k.D / (a.D * ha.D)) * y.f[2.J]) - (3.0.D * dphidlna.d))
        dydlna.f[2.J] = (-y.f[2.J] + ((k.D / (a.D * ha.D)) * psi.d))
        dydlna.f[3.J] = (((-k.D / (a.D * ha.D)) * y.f[4.J]) - (3.0.D * dphidlna.d))
        dydlna.f[4.J] = ((-y.f[4.J] + ((k.D / (a.D * ha.D)) * psi.d)) + (((tdot.D / r_bph_a.D) / (a.D * ha.D)) * (y.f[4.J] - (3.0.D * y.f[7.J]))))
        dydlna.f[5.J] = (((-k.D / (a.D * ha.D)) * y.f[7.J]) - dphidlna.d)
        dydlna.f[6.J] = (((k.D / (a.D * ha.D)) * -y.f[8.J]) + ((tdot.D / (a.D * ha.D)) * (y.f[6.J] - (ppi.f / 2.0.D))))
        dydlna.f[7.J] = (((k.D / ((3.0.D * a.D) * ha.D)) * ((y.f[5.J] - y.f[9.J]) + psi.d)) + ((tdot.D / (a.D * ha.D)) * (y.f[7.J] - (y.f[4.J] / 3.0.D))))
        dydlna.f[8.J] = ((((k.D / (a.D * ha.D)) / 3.0.D) * (y.f[6.J] - (2.0.D * y.f[10.J]))) + ((tdot.D / (a.D * ha.D)) * y.f[8.J]))
        dydlna.f[9.J] = (((k.D / ((5.0.D * a.D) * ha.D)) * ((2.0.D * y.f[7.J]) - (3.0.D * y.f[11.J]))) + ((tdot.D / (a.D * ha.D)) * (y.f[9.J] - (ppi.f / 10.0.D))))
        dydlna.f[10.J] = ((((k.D / (a.D * ha.D)) / 5.0.D) * ((2.0.D * y.f[8.J]) - (3.0.D * y.f[12.J]))) + ((tdot.D / (a.D * ha.D)) * (y.f[10.J] - (ppi.f / 5.0.D))))
        for i.l in (3.J:10.J) {
                dydlna.f[(5.J + (2.J * i.l))] = ((((k.D / (a.D * ha.D)) / ((2.0.D * i.l) + 1.0.D)) * ((i.l * y.f[(5.J + (2.J * (i.l - 1.J)))]) - ((i.l + 1.0.D) * y.f[(5.J + (2.J * (i.l + 1.J)))]))) + ((tdot.D / (a.D * ha.D)) * y.f[(5.J + (2.J * i.l))]))
                dydlna.f[(6.J + (2.J * i.l))] = ((((k.D / (a.D * ha.D)) / ((2.0.D * i.l) + 1.0.D)) * ((i.l * y.f[(6.J + (2.J * (i.l - 1.J)))]) - ((i.l + 1.0.D) * y.f[(6.J + (2.J * (i.l + 1.J)))]))) + ((tdot.D / (a.D * ha.D)) * y.f[(6.J + (2.J * i.l))]))
        }
        dydlna.f[(5.J + (2.J * 10.J))] = ((1.0.D / (a.D * ha.D)) * ((k.D * y.f[(5.J + (2.J * (10.J - 1.J)))]) - ((((10.J + 1.0.D) / eta.D) - tdot.D) * y.f[(5.J + (2.J * 10.J))])))
        dydlna.f[(6.J + (2.J * 10.J))] = ((1.0.D / (a.D * ha.D)) * ((k.D * y.f[(6.J + (2.J * (10.J - 1.J)))]) - ((((10.J + 1.0.D) / eta.D) - tdot.D) * y.f[(6.J + (2.J * 10.J))])))
        dydlna.f[((6.J + (2.J * 10.J)) + 1.J)] = (((-k.D / (a.D * ha.D)) * y.f[((6.J + (2.J * 10.J)) + 2.J)]) - dphidlna.d)
        dydlna.f[((6.J + (2.J * 10.J)) + 2.J)] = ((k.D / ((3.0.D * a.D) * ha.D)) * ((y.f[((6.J + (2.J * 10.J)) + 1.J)] - y.f[((6.J + (2.J * 10.J)) + 3.J)]) + psi.d))
        for j.l in (2.J:10.J) {
                dydlna.f[(((6.J + (2.J * 10.J)) + 1.J) + j.l)] = (((k.D / (a.D * ha.D)) / ((2.0.D * j.l) + 1.0.D)) * ((j.l * y.f[((((6.J + (2.J * 10.J)) + 1.J) + j.l) - 1.J)]) - ((j.l + 1.0.D) * y.f[((((6.J + (2.J * 10.J)) + 1.J) + j.l) + 1.J)])))
        }
        dydlna.f[(((6.J + (2.J * 10.J)) + 1.J) + 10.J)] = ((1.0.D / (a.D * ha.D)) * ((k.D * y.f[((((6.J + (2.J * 10.J)) + 1.J) + 10.J) - 1.J)]) - (((10.J + 1.0.D) / eta.D) * y.f[(((6.J + (2.J * 10.J)) + 1.J) + 10.J)])))
        if (xc_damp.J > 0.J) {
                (5.J:(n_y.J - 1.J)) {
                        dydlna.f[5.J:(n_y.J - 1.J)] = ((y.f[5.J:(n_y.J - 1.J)] * 0.5.D) * (1.0.D - numpy.tanh((((k.D * eta.D) - xc_damp.J) / 50.0.D))))
                }
        }
        return dydlna.f[:n_y.J]

Compiling following functions:
pycosmo(float32^1 y, float64 lna, float64 k, float64 eta, float64 hubble_a, float64 tdot, float64 omega_gam, float64 omega_neu, float64 omega_dm_0, float64 omega_b_0, float64 ha, float64 rh, float64 r_bph_a, int64 xc_damp)
running build_ext
building 'pycosmo_595aba8b1f769fe4802a2e41fd8168d3a643953f6adc1192807b2249_0' extension
C compiler: gcc -pthread -fno-strict-aliasing -march=x86-64 -mtune=generic -O2 -pipe -fstack-protector-strong --param=ssp-buffer-size=4 -DNDEBUG -march=x86-64 -mtune=generic -O2 -pipe -fstack-protector-strong --param=ssp-buffer-size=4 -fPIC

compile options: '-I/usr/lib/python2.7/site-packages/numpy/core/include -I/usr/include/python2.7 -c'
extra options: '-Wall -Wno-unused-variable -std=c++11'
gcc: /tmp/hopeJkZHki/pycosmo_595aba8b1f769fe4802a2e41fd8168d3a643953f6adc1192807b2249_0.cpp
g++ -pthread -shared -Wl,-O1,--sort-common,--as-needed,-z,relro /tmp/hopeJkZHki/pycosmo_595aba8b1f769fe4802a2e41fd8168d3a643953f6adc1192807b2249_0.o -L/usr/lib -lpython2.7 -o /tmp/hopeJkZHki/pycosmo_595aba8b1f769fe4802a2e41fd8168d3a643953f6adc1192807b2249_0.so

Segmentation fault (core dumped)

support for structured data

It is convenient to have data packed into structures. For example, if a
calculation requires a large number of pieces of information, it is preferable
to have the following ( I realize this is a bit of a contrived example)

def func(sarray):
    for i=0,range(sarray.size):
        x = sarray['a'][i] + sarray['b'][i] + ... sarray['z'][i]
        # do something with x

as opposed to

def func(a, b, c, d, ......, z):
    for i in xrange(a.size):
        x = a[i] + b[i]  ... + z[i];
        # do something with x

This could be solved by accepting structured arrays for input

sarray = zeros(n, dtype=[('a','f8'),('b','f8'),....('z','f8')])

res=func(sarray)

(edited for bugs)

hope.exceptions.UnsupportedCompilerException: Compiler 'gcc' with version '4.2.1' is not supported. Minimum version is '4.7.0'

When I just tried to import hope (without actually using any function), I got the following error. What is the fix for this? Thank you.

Traceback (most recent call last):
File "testHope.py", line 2, in
import hope
File "/Users/username/anaconda/lib/python2.7/site-packages/hope/init.py", line 11, in
from hope import config
File "/Users/username/anaconda/lib/python2.7/site-packages/hope/config.py", line 9, in
cxxflags = get_cxxflags()
File "/Users/username/anaconda/lib/python2.7/site-packages/hope/options.py", line 67, in get_cxxflags
_check_version(compiler_name, compiler.compiler[0])
File "/Users/username/anaconda/lib/python2.7/site-packages/hope/options.py", line 83, in _check_version
SUPPORTED_VERSIONS[compiler_name]))
hope.exceptions.UnsupportedCompilerException: Compiler 'gcc' with version '4.2.1' is not supported. Minimum version is '4.7.0'

problem with python 3.12

If this package is run with Python 3.12, there is an issue because of the numpy.distutils dependency, as this is discontinued. Is there any prospects of removing this dependency?

Specify additional dependencies

requirements.txt and setup.py do not specify some packages as a dependencies. They should because the makefile uses them. The following packages should be development dependencies:

tox
py.test
sphinx
flake8
coverage

Travis CI build not working

After fixing a typo in the .travis.yml the build are being kicked of on a commit.
Using @xantares pull request #11 will install gcc 4.8 which will allow to compile the tests. Pull request #7 will fix the version detection with py3.

Unfortunately it looks like that the build is taking to long and is being killed after 50 mins.

It's a large number of tests and all the tests are required to ensure the numerical accuracy up to type precision. Any clues on this?