def test_gen_toy(): np.random.seed(0) bound = (-1, 2) ntoy = 100000 toy = gen_toy(crystalball, ntoy, bound=bound, alpha=1., n=2., mean=1., sigma=0.3, quiet=False) assert_equal(len(toy), ntoy) htoy, bins = np.histogram(toy, bins=1000, range=bound) ncball = Normalized(crystalball, bound) f = lambda x: ncball(x, 1., 2., 1., 0.3) expected = vector_apply(f, mid(bins)) * ntoy * (bins[1] - bins[0]) # print htoy[:100] # print expected[:100] htoy = htoy * 1.0 err = np.sqrt(expected) chi2 = compute_chi2(htoy, expected, err) print chi2, len(bins), chi2 / len(bins) assert (0.9 < (chi2 / len(bins)) < 1.1)
def test_gen_toy(): np.random.seed(0) bound = (-1, 2) ntoy = 100000 toy = gen_toy(crystalball, ntoy, bound=bound, alpha=1., n=2., mean=1., sigma=0.3, quiet=False) assert_equal(len(toy), ntoy) htoy, bins = np.histogram(toy, bins=1000, range=bound) ncball = Normalized(crystalball, bound) f = lambda x: ncball(x, 1., 2., 1., 0.3) expected = vector_apply(f, mid(bins)) * ntoy * (bins[1] - bins[0]) # print htoy[:100] # print expected[:100] htoy = htoy * 1.0 err = np.sqrt(expected) chi2 = compute_chi2(htoy, expected, err) print chi2, len(bins), chi2 / len(bins) assert(0.9 < (chi2 / len(bins)) < 1.1)
def test_gen_toy2(): pdf = gaussian np.random.seed(0) toy = gen_toy(pdf, 10000, (-5, 5), mean=0, sigma=1) binlh = BinnedLH(pdf, toy, bound=(-5, 5), bins=100) lh = binlh(0.0, 1.0) for x in toy: assert x < 5 assert x >= -5 assert len(toy) == 10000 assert lh / 100.0 < 1.0
def test_gen_toy2(): pdf = gaussian np.random.seed(0) toy = gen_toy(pdf, 10000, (-5, 5), mean=0, sigma=1) binlh = BinnedLH(pdf, toy, bound=(-5, 5), bins=100) lh = binlh(0., 1.) for x in toy: assert (x < 5) assert (x >= -5) assert_equal(len(toy), 10000) assert (lh / 100. < 1.)
def test_gen_toy2(): pdf = gaussian np.random.seed(0) toy = gen_toy(pdf, 10000, (-5, 5), mean=0, sigma=1) binlh = BinnedLH(pdf, toy, bound=(-5, 5), bins=100) lh = binlh(0., 1.) for x in toy: assert(x < 5) assert(x >= -5) assert_equal(len(toy), 10000) assert(lh / 100. < 1.)