1 files changed, 0 insertions, 64 deletions
diff --git a/tests/test_sample_cdf.py b/tests/test_sample_cdf.py
deleted file mode 100644
index 2baa2ac..0000000
--- a/tests/test_sample_cdf.py
+++ /dev/null
@@ -1,64 +0,0 @@
-import pycuda.autoinit
-import pycuda.driver as cuda
-from pycuda.compiler import SourceModule
-from pycuda import gpuarray
-import numpy as np
-import ROOT
-import os
-
-current_directory = os.path.split(os.path.realpath(__file__))[0]
-source_directory = current_directory + '/../src'
-
-source = open(current_directory + '/test_sample_cdf.cu').read()
-
-mod = SourceModule(source, options=['-I' + source_directory], no_extern_c=True, cache_dir=False)
-
-test_sample_cdf = mod.get_function('test_sample_cdf')
-
-def compare_sampling(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):
-        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():
-    '''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 = compare_sampling(gaussian, reps=50)
-
-    assert prob > 0.01
-