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import pycuda.driver as cuda
from pycuda.compiler import SourceModule
from pycuda import gpuarray
import numpy as np
import ROOT
import os
import unittest
import chroma
class TestSampling(unittest.TestCase):
def setUp(self):
self.context = chroma.gpu.create_cuda_context()
current_directory = os.path.split(os.path.realpath(__file__))[0]
from chroma.cuda import srcdir as source_directory
source = open(current_directory + '/test_sample_cdf.cu').read()
self.mod = SourceModule(source, options=['-I' + source_directory], no_extern_c=True, cache_dir=False)
self.test_sample_cdf = self.mod.get_function('test_sample_cdf')
def compare_sampling(self, hist, reps=10):
nbins = hist.GetNbinsX();
xaxis = hist.GetXaxis()
intg = hist.GetIntegral()
cdf_y = np.empty(nbins+1, dtype=float)
cdf_x = np.empty_like(cdf_y)
cdf_x[0] = xaxis.GetBinLowEdge(1)
cdf_y[0] = 0.0
for i in xrange(1,len(cdf_x)):
cdf_y[i] = intg[i]
cdf_x[i] = xaxis.GetBinUpEdge(i)
cdf_x_gpu = gpuarray.to_gpu(cdf_x.astype(np.float32))
cdf_y_gpu = gpuarray.to_gpu(cdf_y.astype(np.float32))
block =(128,1,1)
grid = (128, 1)
out_gpu = gpuarray.empty(shape=int(block[0]*grid[0]), dtype=np.float32)
out_h = ROOT.TH1D('out_h', '', hist.GetNbinsX(),
xaxis.GetXmin(),
xaxis.GetXmax())
out_h.SetLineColor(ROOT.kGreen)
for i in xrange(reps):
self.test_sample_cdf(np.int32(i),
np.int32(len(cdf_x_gpu)),
cdf_x_gpu, cdf_y_gpu, out_gpu, block=block, grid=grid)
out = out_gpu.get()
for v in out:
out_h.Fill(v)
prob = out_h.KolmogorovTest(hist)
return prob, out_h
def test_sampling(self):
'''Verify that the CDF-based sampler on the GPU reproduces a binned
Gaussian distribution'''
f = ROOT.TF1('f_gaussian', 'gaus(0)', -5, 5)
f.SetParameters(1.0/np.sqrt(np.pi * 2), 0.0, 1.0)
gaussian = ROOT.TH1D('gaussian', '', 100, -5, 5)
gaussian.Add(f)
prob, out_h = self.compare_sampling(gaussian, reps=50)
assert prob > 0.01
def tearDown(self):
self.context.pop()
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