def test_parallel_pdf(self):
"""check parallel PDFCalculator
"""
from diffpy.srreal.pdfcalculator import PDFCalculator
pdfc = PDFCalculator()
r0, g0 = pdfc(self.cdse)
ppdfc1 = createParallelCalculator(PDFCalculator(), 3, map)
r1, g1 = ppdfc1(self.cdse)
self.assertTrue(numpy.array_equal(r0, r1))
self.assertTrue(numpy.allclose(g0, g1))
ppdfc2 = createParallelCalculator(PDFCalculator(),
self.ncpu, self.pool.imap_unordered)
r2, g2 = ppdfc2(self.cdse)
self.assertTrue(numpy.array_equal(r0, r2))
self.assertTrue(numpy.allclose(g0, g2))
pdfc.rmax = ppdfc1.rmax = ppdfc2.rmax = 5
pdfc.qmax = ppdfc1.qmax = ppdfc2.qmax = 15
r0a, g0a = pdfc()
self.assertTrue(numpy.all(r0a <= 5))
self.assertFalse(numpy.allclose(g0a, numpy.interp(r0a, r0, g0)))
r1a, g1a = ppdfc1()
self.assertTrue(numpy.array_equal(r0a, r1a))
self.assertTrue(numpy.allclose(g0a, g1a))
r2a, g2a = ppdfc2()
self.assertTrue(numpy.array_equal(r0a, r2a))
self.assertTrue(numpy.allclose(g0a, g2a))
return
def test_parallel_bonds(self):
"""check parallel BondCalculator
"""
from diffpy.srreal.bondcalculator import BondCalculator
nickel = self.nickel
bc = BondCalculator()
d0 = bc(nickel)
pbc1 = createParallelCalculator(BondCalculator(), 3, map)
d1 = pbc1(nickel)
self.assertTrue(numpy.array_equal(d0, d1))
pbc2 = createParallelCalculator(BondCalculator(),
self.ncpu, self.pool.imap_unordered)
d2 = pbc2(nickel)
self.assertTrue(numpy.array_equal(d0, d2))
bc.rmax = pbc1.rmax = pbc2.rmax = 2.5
for bci in (bc, pbc1, pbc2):
bci.maskAllPairs(False)
bci.setPairMask(0, 'all', True)
bci.filterCone([1, 0, 0], 48)
d0a = bc(nickel)
self.assertEqual(8, len(d0a))
d1a = pbc1(nickel)
self.assertTrue(numpy.array_equal(d0a, d1a))
d2a = pbc2(nickel)
self.assertTrue(numpy.array_equal(d0a, d2a))
return
def test_parallel_bonds(self):
"""check parallel BondCalculator
"""
from diffpy.srreal.bondcalculator import BondCalculator
nickel = self.nickel
bc = BondCalculator()
d0 = bc(nickel)
pbc1 = createParallelCalculator(BondCalculator(), 3, map)
d1 = pbc1(nickel)
self.failUnless(numpy.array_equal(d0, d1))
pbc2 = createParallelCalculator(BondCalculator(), self.ncpu,
self.pool.imap_unordered)
d2 = pbc2(nickel)
self.failUnless(numpy.array_equal(d0, d2))
bc.rmax = pbc1.rmax = pbc2.rmax = 2.5
for bci in (bc, pbc1, pbc2):
bci.maskAllPairs(False)
bci.setPairMask(0, 'all', True)
bci.filterCone([1, 0, 0], 48)
d0a = bc(nickel)
self.assertEqual(8, len(d0a))
d1a = pbc1(nickel)
self.failUnless(numpy.array_equal(d0a, d1a))
d2a = pbc2(nickel)
self.failUnless(numpy.array_equal(d0a, d2a))
return
def test_parallel_pdf(self):
"""check parallel PDFCalculator
"""
from diffpy.srreal.pdfcalculator import PDFCalculator
pdfc = PDFCalculator()
r0, g0 = pdfc(self.cdse)
ppdfc1 = createParallelCalculator(PDFCalculator(), 3, map)
r1, g1 = ppdfc1(self.cdse)
self.failUnless(numpy.array_equal(r0, r1))
self.failUnless(numpy.allclose(g0, g1))
ppdfc2 = createParallelCalculator(PDFCalculator(), self.ncpu,
self.pool.imap_unordered)
r2, g2 = ppdfc2(self.cdse)
self.failUnless(numpy.array_equal(r0, r2))
self.failUnless(numpy.allclose(g0, g2))
pdfc.rmax = ppdfc1.rmax = ppdfc2.rmax = 5
pdfc.qmax = ppdfc1.qmax = ppdfc2.qmax = 15
r0a, g0a = pdfc()
self.failUnless(numpy.all(r0a <= 5))
self.assertFalse(numpy.allclose(g0a, numpy.interp(r0a, r0, g0)))
r1a, g1a = ppdfc1()
self.failUnless(numpy.array_equal(r0a, r1a))
self.failUnless(numpy.allclose(g0a, g1a))
r2a, g2a = ppdfc2()
self.failUnless(numpy.array_equal(r0a, r2a))
self.failUnless(numpy.allclose(g0a, g2a))
return
def parallel(self, ncpu, mapfunc = None):
"""Run calculation in parallel.
ncpu -- Number of parallel processes. Revert to serial mode when 1.
mapfunc -- A mapping function to use. If this is None (default),
multiprocessing.Pool.imap_unordered will be used.
No return value.
"""
from diffpy.srreal.parallel import createParallelCalculator
calc_serial = self._calc
if hasattr(calc_serial, 'pqobj'):
calc_serial = calc_serial.pqobj
# revert to serial calculator for ncpu <= 1
if ncpu <= 1:
self._calc = calc_serial
self._pool = None
return
# Why don't we let the user shoot his foot or test on single CPU?
# ncpu = min(ncpu, multiprocessing.cpu_count())
if mapfunc is None:
import multiprocessing
self._pool = multiprocessing.Pool(ncpu)
mapfunc = self._pool.imap_unordered
self._calc = createParallelCalculator(calc_serial, ncpu, mapfunc)
return
def parallel(self, ncpu, mapfunc=None):
"""Run calculation in parallel.
ncpu -- Number of parallel processes. Revert to serial mode when 1.
mapfunc -- A mapping function to use. If this is None (default),
multiprocessing.Pool.imap_unordered will be used.
No return value.
"""
from diffpy.srreal.parallel import createParallelCalculator
calc_serial = self._calc
if hasattr(calc_serial, 'pqobj'):
calc_serial = calc_serial.pqobj
# revert to serial calculator for ncpu <= 1
if ncpu <= 1:
self._calc = calc_serial
self._pool = None
return
# Why don't we let the user shoot his foot or test on single CPU?
# ncpu = min(ncpu, multiprocessing.cpu_count())
if mapfunc is None:
import multiprocessing
self._pool = multiprocessing.Pool(ncpu)
mapfunc = self._pool.imap_unordered
self._calc = createParallelCalculator(calc_serial, ncpu, mapfunc)
return
def test_parallel_evaluatortype(self):
"""check handling of the evaluatortype property
"""
from diffpy.srreal.pdfcalculator import PDFCalculator
pdfc = PDFCalculator()
self.assertEqual('OPTIMIZED', pdfc.evaluatortype)
ppdfc = createParallelCalculator(pdfc, 2, map)
self.assertEqual('BASIC', ppdfc.evaluatortype)
self.assertEqual('BASIC', pdfc.evaluatortype)
ppdfc.evaluatortype = 'BASIC'
self.assertRaises(ValueError,
setattr, ppdfc, 'evaluatortype', 'OPTIMIZED')
return
def test_parallel_evaluatortype(self):
"""check handling of the evaluatortype property
"""
from diffpy.srreal.pdfcalculator import PDFCalculator
pdfc = PDFCalculator()
self.assertEqual('OPTIMIZED', pdfc.evaluatortype)
ppdfc = createParallelCalculator(pdfc, 2, map)
self.assertEqual('BASIC', ppdfc.evaluatortype)
self.assertEqual('BASIC', pdfc.evaluatortype)
ppdfc.evaluatortype = 'BASIC'
self.assertRaises(ValueError, setattr, ppdfc, 'evaluatortype',
'OPTIMIZED')
return
def test_parallel(self):
"""check parallel run of OverlapCalculator
"""
import multiprocessing
from diffpy.srreal.parallel import createParallelCalculator
ncpu = 4
self.pool = multiprocessing.Pool(processes=ncpu)
olc = self.olc
polc = createParallelCalculator(OverlapCalculator(),
ncpu, self.pool.imap_unordered)
olc.atomradiitable.fromString('Ti:1.6, O:0.66')
polc.atomradiitable = olc.atomradiitable
self.assertTrue(numpy.array_equal(olc(self.rutile), polc(self.rutile)))
self.assertTrue(olc.totalsquareoverlap > 0.0)
self.assertEqual(olc.totalsquareoverlap, polc.totalsquareoverlap)
self.assertEqual(sorted(zip(olc.sites0, olc.sites1)),
sorted(zip(polc.sites0, polc.sites1)))
olc.atomradiitable.resetAll()
self.assertEqual(0.0, sum(olc(self.rutile)))
self.assertEqual(0.0, sum(polc(self.rutile)))
return
def test_parallel(self):
"""check parallel run of OverlapCalculator
"""
import multiprocessing
from diffpy.srreal.parallel import createParallelCalculator
ncpu = 4
self.pool = multiprocessing.Pool(processes=ncpu)
olc = self.olc
polc = createParallelCalculator(OverlapCalculator(), ncpu,
self.pool.imap_unordered)
olc.atomradiitable.fromString('Ti:1.6, O:0.66')
polc.atomradiitable = olc.atomradiitable
self.assertTrue(numpy.array_equal(olc(self.rutile), polc(self.rutile)))
self.assertTrue(olc.totalsquareoverlap > 0.0)
self.assertEqual(olc.totalsquareoverlap, polc.totalsquareoverlap)
self.assertEqual(sorted(zip(olc.sites0, olc.sites1)),
sorted(zip(polc.sites0, polc.sites1)))
olc.atomradiitable.resetAll()
self.assertEqual(0.0, sum(olc(self.rutile)))
self.assertEqual(0.0, sum(polc(self.rutile)))
return
# number of CPUs for parallel calculation ncpu = multiprocessing.cpu_count() # calculate PDF on a single core t0 = time.time() pc0 = PDFCalculator(**cfg) r0, g0 = pc0(menthol) t0 = time.time() - t0 print "Calculation time on 1 CPU: %g" % t0 # create a pool of workers pool = multiprocessing.Pool(processes=ncpu) t1 = time.time() # create a proxy parrallel calculator to PDFCalculator pc0, # that uses ncpu parallel jobs submitted via pool.imap_unordered pc1 = createParallelCalculator(pc0, ncpu, pool.imap_unordered) r1, g1 = pc1(menthol) t1 = time.time() - t1 print "Calculation time on %i CPUs: %g" % (ncpu, t1) print "Time ratio: %g" % (t0 / t1) # plot both results and the difference curve from pylab import plot, show, clf, draw clf() gd = g0 - g1 plot(r0, g0, r1, g1, r0, gd - 3) show()
} # number of CPUs for parallel calculation ncpu = multiprocessing.cpu_count() # calculate PDF on a single core t0 = time.time() pc0 = PDFCalculator(**cfg) r0, g0 = pc0(menthol) t0 = time.time() - t0 print "Calculation time on 1 CPU: %g" % t0 # create a pool of workers pool = multiprocessing.Pool(processes=ncpu) t1 = time.time() # create a proxy parrallel calculator to PDFCalculator pc0, # that uses ncpu parallel jobs submitted via pool.imap_unordered pc1 = createParallelCalculator(pc0, ncpu, pool.imap_unordered) r1, g1 = pc1(menthol) t1 = time.time() - t1 print "Calculation time on %i CPUs: %g" % (ncpu, t1) print "Time ratio: %g" % (t0 / t1) # plot both results and the difference curve from matplotlib.pyplot import plot, show, clf, draw clf() gd = g0 - g1 plot(r0, g0, r1, g1, r0, gd - 3) show()