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
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