Hi ironpython,
Here's your Daily Digest of new issues for project "IronPython".
In today's digest:ISSUES
1. [New issue] Pickling Numpy arrays
2. [New comment] Pickling Numpy arrays
3. [New comment] Pickling Numpy arrays
4. [New comment] Pickling Numpy arrays
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ISSUES
1. [New issue] Pickling Numpy arrays
http://ironpython.codeplex.com/workitem/32007
User Hyang02116 has proposed the issue:
"When I pickle large Numpy arrays using IronPython [e.g. arrays that have 1
million elements], >99% of the numbers are recovered exactly when using
Unpickle, but <1% of the entries are quite different after loading the pickled
value, and the differences seem larger than can be accounted for by simple
floating point precision errors, e.g. -0.0241330987517 becomes -0.448629580028.
I wonder if anyone else has experienced this."-----------------
2. [New comment] Pickling Numpy arrays
http://ironpython.codeplex.com/workitem/32007
User slide_o_mix has commented on the issue:
"Can you please add a simple test case so this can be reproduced and
debugged."-----------------
3. [New comment] Pickling Numpy arrays
http://ironpython.codeplex.com/workitem/32007
User Hyang02116 has commented on the issue:
"import numpy, pickle
fname = "H:\\testfile"
fobj = open(fname, 'w')
old = numpy.zeros((1, 1))
old[0][0] = -0.0241330987517
pickle.dump(["h","a","y", old], fobj)
fobj.close()
fobj = open(fname)
new = pickle.Unpickler(fobj).load()
new = new[3]
fobj.close()
for i in range(1):
if old[i][0] != new[i][0]:
print str(i) + " " + str(old[i][0]) + " " + str(new[i][0])
"-----------------
4. [New comment] Pickling Numpy arrays
http://ironpython.codeplex.com/workitem/32007
User Hyang02116 has commented on the issue:
"Doing that, I get an old value of -0.02413309877517 and a new value of
-0.448629580027"
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