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    <p>NumPy provides <a href="http://docs.scipy.org/doc/numpy/reference/generated/numpy.fromfile.html" rel="noreferrer"><code>fromfile()</code></a> to read binary data.</p> <pre><code>a = numpy.fromfile("filename", dtype=numpy.float32) </code></pre> <p>will create a one-dimensional array containing your data. To access it as a two-dimensional Fortran-ordered <code>n x m</code> matrix, you can reshape it:</p> <pre><code>a = a.reshape((n, m), order="FORTRAN") </code></pre> <p>[EDIT: The <code>reshape()</code> actually copies the data in this case (see the comments). To do it without cpoying, use</p> <pre><code>a = a.reshape((m, n)).T </code></pre> <p>Thanks to Joe Kingtion for pointing this out.]</p> <p>But to be honest, if your matrix has several gigabytes, I would go for a HDF5 tool like <a href="http://h5py.alfven.org/" rel="noreferrer">h5py</a> or <a href="http://www.pytables.org/moin" rel="noreferrer">PyTables</a>. Both of the tools have FAQ entries comparing the tool to the other one. I generally prefer h5py, though PyTables seems to be more commonly used (and the scopes of both projects are slightly different).</p> <p><a href="http://en.wikipedia.org/wiki/Hierarchical_Data_Format#Interfaces" rel="noreferrer">HDF5</a> files can be written from most programming language used in data analysis. The list of interfaces in the linked Wikipedia article is not complete, for example there is also an <a href="http://cran.r-project.org/web/packages/hdf5/index.html" rel="noreferrer">R interface</a>. But I actually don't know which language you want to use to write the data...</p>
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    1. CO@Sven Thanks. What would you say are the advantages of using HDF5 over a simple `n*m` array for storing one large dense matrix of floats?
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    2. CO@aix: to name a few: the ability of transparently having only part of the matrix in memory of a given time, the ability of transparent compression and meta-data taking care of the matrix dimensions, eliminating an important source of erros. And since it is really easy to use, I don't see real disadvantages.
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    3. CO@Sven Could you also clarify something in your example. If I do `a = numpy.fromfile("filename", dtype=numpy.float32)` followed by `a = a.reshape((n, m), order="FORTRAN")` on a 4GB file, is this potentially going to create a 4GB "C" matrix in memory only to immediately make another 4GB in-memory copy to flip it into the Fortran format?
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