On Thu, Sep 20, 2012 at 2:20 PM, Travis Oliphant <tra...@continuum.io>wrote:
> Here are a couple of scripts that might help (I used them to compare
> casting tables between various versions of NumPy):
>
> Casting Table Creation Script
> ========================
> import numpy as np
>
> operators = np.set_numeric_ops().values()
> types = '?bhilqpBHILQPfdgFDGO'
> to_check = ['add', 'divide', 'minimum', 'maximum', 'remainder',
> 'true_divide', 'logical_or', 'bitwise_or', 'right_shift', 'less', 'equal']
> operators = [op for op in operators if op.__name__ in to_check]
>
>
> def type_wrap(op):
> def func(obj1, obj2):
> try:
> result = op(obj1, obj2)
> char = result.dtype.char
> except:
> char = 'X'
> return char
>
> return func
>
> def coerce():
> result = {}
> for op in operators:
> d = {}
> name = op.__name__
> print name
> op = type_wrap(op)
> for type1 in types:
> s1 = np.dtype(type1).type(2)
> a1 = np.dtype(type1).type([1,2,3])
> for type2 in types:
> s2 = np.dtype(type2).type(1)
> a2 = np.dtype(type2).type([2,3,4])
> codes = []
> # scalar <op> scalar
> codes.append(op(s1, s2))
> # scalar <op> array
> codes.append(op(s1, a2))
> # array <op> scalar
> codes.append(op(a1, s2))
> # array <op> array
> codes.append(op(a1, a2))
> d[type1,type2] = codes
> result[name] = d
>
> #for check_key in to_check:
> # for key in result.keys():
> # if key == check_key:
> # continue
> # if result[key] == result[check_key]:
> # del result[key]
> #assert set(result.keys()) == set(to_check)
> return result
>
> import sys
> if sys.maxint > 2**33:
> bits = 64
> else:
> bits = 32
>
> def write():
> import cPickle
> file = open('coercion-%s-%sbit.pkl'%(np.__version__, bits),'w')
> cPickle.dump(coerce(),file,protocol=2)
> file.close()
>
> if __name__ == '__main__':
> write()
>
>
>
>
>
> Comparison Script
> ================
>
> import numpy as np
>
>
> def compare(result1, result2):
> for op in result1.keys():
> print "**** ", op, " ****"
> if op not in result2:
> print op, " not in the first"
> table1 = result1[op]
> table2 = result2[op]
> if table1 == table2:
> print "Tables are the same"
> else:
> if set(table1.keys()) != set(table2.keys()):
> print "Keys are not the same"
> continue
> for key in table1.keys():
> if table1[key] != table2[key]:
> print "Different at ", key, ": ", table1[key],
> table2[key]
>
> import cPickle
> import sys
>
> if __name__ == '__main__':
> name1 = 'coercion-1.5.1-64bit.pkl'
> name2 = 'coercion-1.6.1-64bit.pkl'
>
> if len(sys.argv) > 1:
> name1 = 'coercion-%s-64bit.pkl' % sys.argv[1]
> if len(sys.argv) > 2:
> name2 = 'coercion-%s-64bit.pkl' % sys.argv[2]
> result1 = cPickle.load(open(name1))
> result2 = cPickle.load(open(name2))
> compare(result1, result2)
>
>
>
> On Sep 20, 2012, at 3:09 PM, Nathaniel Smith wrote:
>
> > On Mon, Sep 17, 2012 at 10:22 AM, Matthew Brett <matthew.br...@gmail.com>
> wrote:
> >> Hi,
> >>
> >> On Sun, Sep 9, 2012 at 6:12 PM, Frédéric Bastien <no...@nouiz.org>
> wrote:
> >>> The third is releated to change to the casting rules in numpy. Before
> >>> a scalar complex128 * vector float32 gived a vector of dtype
> >>> complex128. Now it give a vector of complex64. The reason is that now
> >>> the scalar of different category only change the category, not the
> >>> precision. I would consider a must that we warn clearly about this
> >>> interface change. Most people won't see it, but people that optimize
> >>> there code heavily could depend on such thing.
> >>
> >> It seems to me that it would be a very good idea to put the casting
> >> table results into the tests to make sure we are keeping track of this
> >> kind of thing.
> >>
> >> I'm happy to try to do it if no-one else more qualified has time.
> >
> > I haven't seen any PRs show up from anyone else in the last few days,
> > and this would indeed be an excellent test to have, so that would be
> > awesome.
> >
>
IIRC, there are some scripts in the numpy repository. But I forget where I
saw them.
Chuck
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