[NumPy-Tickets] [NumPy] #1767: f2py assumed shape merge - test failure

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#1767: f2py assumed shape merge - test failure
-------------------------+--------------------------------------------------
  Reporter:  rgommers    |       Owner:  pearu       
      Type:  defect      |      Status:  needs_review
  Priority:  low         |   Milestone:  1.6.0       
 Component:  numpy.f2py  |     Version:  devel       
Resolution:              |    Keywords:              
-------------------------+--------------------------------------------------
Changes (by derek):

  * status:  needs_info => needs_review


Comment:

 Thanks, this test passes now! It was the float128 indeed...
 If you still need the info:

 print(numpy.finfo(numpy.float128))
 Warning: overflow encountered in add
 Warning: invalid value encountered in subtract
 Warning: invalid value encountered in subtract
 Warning: overflow encountered in add
 Warning: invalid value encountered in subtract
 Warning: invalid value encountered in subtract
 Machine parameters for float128
 ---------------------------------------------------------------------
 precision= 75   resolution= 1e-75
 machep=    -4   eps=        1.3817869701e-76
 negep =    -4   epsneg=     1.3817869701e-76
 minexp=    -1   tiny=       -1.08420217274e-19
 maxexp=     1   max=        -9.22337203471e+18
 nexp  =     1   min=        -max
 ---------------------------------------------------------------------

 compared to i386:
 print(np.finfo(np.float128))
 Machine parameters for float128
 ---------------------------------------------------------------------
 precision= 18   resolution= 1e-18
 machep=   -63   eps=        1.08420217249e-19
 negep =   -64   epsneg=     5.42101086243e-20
 minexp=-16382   tiny=       3.36210314311e-4932
 maxexp= 16384   max=        1.18973149536e+4932
 nexp  =    15   min=        -max
 ---------------------------------------------------------------------

 The tiny and max certainly seem wrong, but maybe it's just print() can't
 handle them?
 Anyway the other failures (notably fpe 'overflow' and 'underflow' for
 numpy.float128) are still present on ppc.

 Cheers

-- 
Ticket URL: <http://projects.scipy.org/numpy/ticket/1767#comment:7>
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