From binaries to HDF5 using Python

I have used this script to convert the Millennium II data from the unformatted fortran binary formato to the DF5 one.
The core of the script is a module (modified_read_snapshots) built on the basis of a script kindly provided by Mike Boylan-Kolchin from the group that perform the Millennium II simulation.

#!/usr/bin/env python    

import time
import kd3hdf5
import tables as tb
import modified_read_snapshots as rs

t = time.time()

The usual imports and time initialization!:P

def bin2hdf5(bin_file, h5_file, tree = False):
    snap = rs.read_snapshot(bin_file)
    h5f = kd3hdf5.KDTree(h5_file, 'w')
    if tree == True:

This function accept as arguments the name of the binary file, the name of the HDF5 file to be created and give the user the possibility to create a KDTree with the data. By default it won’t create this tree. If no KDTree must be created the function only uses the part of the kd3hdf5 module that store the data into the HDF5 file. We will have a brief view of this at the end of the post.

def main():
    print "start"
    for i in [0, 10, 100, 200, 511]:
        t2 = time.time()
        print "Loop ", i
        t3 = time.time()
        bin2hdf5('../binary/snap_newMillen_subidorder_067.'+str(i), '../hdf5/data_'+str(i))
        print "Loop ", i, " finished in ", time.time()-t2

    print "That's all folks, in ", time.time()-t, "!!!"

if __name__ == "__main__":

Nothing more than calling the previous function on the data files looping on their names!:)
Respect to the other posts here we make use of the main function but is nothing extraordinary!:P

The code from the kd3hdf5 module is

class KDTree(object):
    #Docs [...]

    def __init__(self, filename, mode):
        if mode == 'read' or mode == 'r':
            self.h5file = tb.openFile(filename, mode = "r")
        elif mode == 'append' or mode == 'a':
            self.h5file = tb.openFile(filename, mode = "a")
        elif mode == 'build' or mode == 'w' or mode == 'write':
            self.h5file = tb.openFile(filename, mode = "w")

This provide the creation of the object, linked to an HDF5 file and

def data_store(self, data):
        t = time.time()
        self.h5file.createArray(self.h5file.root, 'data', np.asarray(data), title='data') = data.shape[0] = data.shape[1] = np.amax(data,axis=0)   # maxes and mins for each coord = np.amin(data,axis=0)
        print, " Stored in ", time.time()-t, "seconds."
        t = time.time()
        print time.time()-t, " seconds to commit changes."

fill the file with the data and some metadata (table dimension and maxes and mins of the data).

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