On Fri, Apr 8, 2011 at 9:23 PM, Robert Love <rblove_li...@comcast.net>wrote:
> > Using np.loadtxt I can easily read my file that has columns of time, mode, > 3 float64 for position and 3 for velocity like this. > > dt = dtype([('time', '|S12'), > ('mode','|S3'),('rx','f8'),('ry','f8'),('rz','f8'),('vx','f8'),('vy','f8'),('vz','f8')]) > > data = np.loadtxt('file', dtype=dt) > > > I can then put the two pairs of 3 components into np.arrays and start > performing the vector operations I need. > > How can I read them directly into np.arrays? > > > dt = dtype([('time', '|S12'), ('mode','|S3'),np.array('r'), np.array('v')]) > > I've seen examples for nested data that create a tuple but not an array. > Any tips appreciated. > If you do this: >>> dt = dtype([('time', '|S12'), ('mode','|S3'), ('r','f8', 3), ('v','f8',3)]) >>> data = np.loadtxt('file', dtype=dt) then data['r'] and data['v'] are the arrays of positions and velocities. You can then give them more convenient names: >>> r = data['r'] >>> v = data['v'] Warren > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion >
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