[SciPy-User] writing data to binary for fortran

[Francesc Alted]

A Tuesday 11 May 2010 21:09:32 Gideon escrigué:
> I've previously used the FortranFile.py to read in binary data
> generated by fortran computations, but now I'd like to write data from
> NumPy/SciPy to binary which can be read in by a fortran program.  Does
> anyone have an example of using fortranfile.py to create and write
> data to binary?  Alternatively, can anyone suggest a way to write
> numpy arrays to binary in away that permits me to specify the correct
> offset (4 bytes on my machine) for fortran to then properly read the
> data in?

Just for completeness to other solutions offered, I'm attaching a BinaryFile 
class that allows you to read/write fortran files (in general, binary files).  
From its docstrings:

"""
BinaryFile: A class for accessing data to/from large binary files
=================================================================

The data is meant to be read/write sequentially from/to a binary file.
One can request to read a piece of data with a specific type and shape
from it.  Also, it supports the notion of Fortran and C ordered data,
so that the returned data is always well-behaved (C-contiguous and
aligned).

This class is seeking capable.
"""

It differs from the solutions that other presented here in that it does not 
use the struct module at all, so it is much more faster.  For example, when 
using Neil's fortranfile module, one have:

In [1]: import fortranfile

In [2]: import numpy as np

In [3]: f = fortranfile.FortranFile('/tmp/test.unf',mode='w')

In [5]: time f.writeReals(np.arange(1e7))
CPU times: user 6.06 s, sys: 0.14 s, total: 6.21 s
Wall time: 6.41 s

In [7]: f.close()

In [8]: f = fortranfile.FortranFile('/tmp/test.unf',mode='r')

In [9]: time f.readReals()
CPU times: user 0.64 s, sys: 0.35 s, total: 0.99 s
Wall time: 1.00 s
Out[10]:
array([  0.00000000e+00,   1.00000000e+00,   2.00000000e+00, ...,
         9.99999700e+06,   9.99999800e+06,   9.99999900e+06], dtype=float32)

while using my binaryfile module gives:

In [1]: import numpy as np

In [2]: from binaryfile import BinaryFile

In [3]: f = BinaryFile('/tmp/test.bin', mode="w+", order='fortran')

In [4]: time f.write(np.arange(1e7))
CPU times: user 0.04 s, sys: 0.19 s, total: 0.24 s
Wall time: 0.24 s        # 26x times faster than fortranfile           

In [6]: f.seek(0)

In [7]: time f.read('f8', (int(1e7),))
CPU times: user 0.03 s, sys: 0.12 s, total: 0.15 s
Wall time: 0.15 s       # 6.6 times faster than fortranfile                          
Out[8]:                                           
array([  0.00000000e+00,   1.00000000e+00,   2.00000000e+00, ...,
         9.99999700e+06,   9.99999800e+06,   9.99999900e+06])

Also, binaryfile supports all the types in NumPy, even strings and records.

HTH,

-- 
Francesc Alted
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