On Fri, Apr 8, 2011 at 9:23 PM, Robert Love <rblove_lists@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
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