Hi Matthieu,
I thought as much, regarding the computations, but was just presenting the
information.
Thanks for the set_printoptions but it doesn't seem to apply when accessing
a specific item:
>>> numpy.set_printoptions(precision=12)
>>> port_result.agg_matrix[0]
array([ 2.115495680000e+08, 4.037352320000e+08, 8.474664000000e+07,
3.996256000000e+07, 7.998535500000e+06, 6.682471000000e+06,
0.000000000000e+00, 1.000187900000e+07, 3.430652000000e+07,
1.752612400000e+07, 4.892464500000e+06, 2.065627875000e+06],
dtype=float32)
>>> port_result.agg_matrix[0][11]
2065627.9
No change in the vale output from a specific item in the matrix. Am I
missing something?
Hanni
2008/9/3 Matthieu Brucher <[EMAIL PROTECTED]>
> Hi,
>
> I can't help you with the first issues, but the display has nothing to
> do with the quality of the computation. Numpy only prints a part of a
> float value, but fir the computations, it obviously uses the correct
> value. All this can be parametrized by using set_printoptions().
>
> Matthieu
>
> 2008/9/3, Hanni Ali <[EMAIL PROTECTED]>:
> > Hi,
> >
> > I have encountered a worrying problem, during migration of software from
> > numarray to numpy, perhaps someone could help me determine how this could
> be
> > addressed.
> >
> > I have a large array or values 10000 long 12 items per line. The matrix
> > contains floats, dtype=float32 in numpy and type=Float32 in numarray.
> >
> > When I perform a mean of one of the columns we observe a discrepancies in
> > the output values.
> >
> > numarray:
> > >>> port_result.agg_matrix._array[::,2].mean()
> > 193955925.49500328
> >
> > numpy:
> >
> > >>> port_result.agg_matrix._array[::,2].mean()
> > 193954536.48896
> >
> > If we examine a specific line in the matrix the arrays appear identical:
> >
> > numarray:
> > >>> port_result.agg_matrix[0]
> > array([ 2.11549568e+08, 4.03735232e+08, 8.47466400e+07,
> > 3.99625600e+07, 7.99853550e+06, 6.68247100e+06,
> > 0.00000000e+00, 1.00018790e+07, 3.43065200e+07,
> > 1.75261240e+07, 4.89246450e+06, 2.06562788e+06],
> type=Float32)
> >
> > numpy:
> > >>> port_result.agg_matrix[0]
> > array([ 2.11549568e+08, 4.03735232e+08, 8.47466400e+07,
> > 3.99625600e+07, 7.99853550e+06, 6.68247100e+06,
> > 0.00000000e+00, 1.00018790e+07, 3.43065200e+07,
> > 1.75261240e+07, 4.89246450e+06, 2.06562788e+06],
> dtype=float32)
> >
> > However when examining a specific item numpy appears to report a value to
> 8
> > significant figures regardless of the true value, whereas numarray
> reported
> > the full value, however if I cast the output as a float the full value is
> > present, just not being output. Could this explain the difference in the
> > mean values? How can I get numpy to always provide the exact value in the
> > array, so behave in the same manner as numarray?
> >
> > numarray:
> > >>> port_result.agg_matrix[0][4]
> > 7998535.5
> > >>> port_result.agg_matrix[0][11]
> > 2065627.875
> >
> > numpy:
> > >>> port_result.agg_matrix[0][4]
> > 7998535.5
> > >>> port_result.agg_matrix[0][11]
> > 2065627.9
> > >>> float(port_result.agg_matrix[0][4])
> > 7998535.5
> > >>> float(port_result.agg_matrix[0][11])
> > 2065627.875
> >
> > I appreciate any help anyone can give, thank you.
> >
> > Hanni
> >
> >
> >
> > _______________________________________________
> > Numpy-discussion mailing list
> > Numpy-discussion@scipy.org
> > http://projects.scipy.org/mailman/listinfo/numpy-discussion
> >
> >
>
>
> --
> French PhD student
> Information System Engineer
> Website: http://matthieu-brucher.developpez.com/
> Blogs: http://matt.eifelle.com and http://blog.developpez.com/?blog=92
> LinkedIn: http://www.linkedin.com/in/matthieubrucher
> _______________________________________________
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