lp_speed_test.py

#! /usr/bin/env python

import pyart
import numpy as np

from pyart.testing.sample_objects import make_empty_ppi_radar, \
                                         _EXAMPLE_RAYS_FILE


def pproc(LP_solver, proc=1):
    """ Phase process using LP_solver and number of processors. """
    # make a example radar to phase process
    radar = make_empty_ppi_radar(983, 80, 1)
    radar.range['data'] = 117.8784 + np.arange(983) * 119.91698
    f = np.load(_EXAMPLE_RAYS_FILE)
    for field_name in f:
        fdata = f[field_name]
        fdata = np.tile(fdata, (80, 1))
        radar.fields[field_name] = {'data': fdata}
    f.close()

    # phase processing
    phidp, kdp = pyart.correct.phase_proc_lp(radar, 0.0, LP_solver=LP_solver,
                                             proc=proc)

if __name__ == '__main__':
    import pstats, cProfile
    cProfile.runctx("pproc('cvxopt')", globals(), locals(), "Profile.prof")
    s = pstats.Stats("Profile.prof")
    s.strip_dirs().sort_stats("time").print_stats(10)

Please test also on plain command line (from shell) with no ipython or idle involved, because this will improve dramatically. At least "idle" tried to handle the separate processes obviously through another pipe or something, which added much overhead. I'am very interested in the outcome, because the speedup here was significant.