class TestSparseHistogram(object):
def setup(self):
data = [(0.0, 0.1), (0.2, 0.7), (0.3, 0.6), (0.6, 0.9)]
self.histo = SparseHistogram(bin_widths=(0.5, 0.3),
left_bin_edges=(0.0, -0.1))
self.histo.histogram(data)
def test_correct(self):
correct_results = collections.Counter({
(0, 0): 1,
(0, 2): 2,
(1, 3): 1
})
assert self.histo._histogram == correct_results
def test_call(self):
histo_fcn = self.histo()
# voxels we have filled
assert histo_fcn((0.25, 0.65)) == 2
assert histo_fcn((0.01, 0.09)) == 1
assert histo_fcn((0.61, 0.89)) == 1
# empty voxel gives 0
assert histo_fcn((2.00, 2.00)) == 0
def test_normalized(self):
raw_prob_normed = self.histo.normalized(raw_probability=True)
assert pytest.approx(raw_prob_normed((0.25, 0.65))) == 0.5
assert pytest.approx(raw_prob_normed((0.01, 0.09))) == 0.25
assert pytest.approx(raw_prob_normed((0.61, 0.89))) == 0.25
normed_fcn = self.histo.normalized()
assert pytest.approx(normed_fcn((0.25, 0.65))) == old_div(0.5, 0.15)
assert pytest.approx(normed_fcn((0.01, 0.09))) == old_div(0.25, 0.15)
assert pytest.approx(normed_fcn((0.61, 0.89))) == old_div(0.25, 0.15)
class testSparseHistogram(object):
def setup(self):
data = [(0.0, 0.1), (0.2, 0.7), (0.3, 0.6), (0.6, 0.9)]
self.histo = SparseHistogram(bin_widths=(0.5, 0.3),
left_bin_edges=(0.0, -0.1))
self.histo.histogram(data)
def test_correct(self):
correct_results = collections.Counter({
(0, 0): 1,
(0, 2): 2,
(1, 3): 1
})
assert_equal(self.histo._histogram, correct_results)
def test_call(self):
histo_fcn = self.histo()
# voxels we have filled
assert_equal(histo_fcn((0.25, 0.65)), 2)
assert_equal(histo_fcn((0.01, 0.09)), 1)
assert_equal(histo_fcn((0.61, 0.89)), 1)
# empty voxel gives 0
assert_equal(histo_fcn((2.00, 2.00)), 0)
def test_normalized(self):
raw_prob_normed = self.histo.normalized(raw_probability=True)
assert_almost_equal(raw_prob_normed((0.25, 0.65)), 0.5)
assert_almost_equal(raw_prob_normed((0.01, 0.09)), 0.25)
assert_almost_equal(raw_prob_normed((0.61, 0.89)), 0.25)
normed_fcn = self.histo.normalized()
assert_almost_equal(normed_fcn((0.25, 0.65)), 0.5 / 0.15)
assert_almost_equal(normed_fcn((0.01, 0.09)), 0.25 / 0.15)
assert_almost_equal(normed_fcn((0.61, 0.89)), 0.25 / 0.15)
class TestSparseHistogram(object):
def setup(self):
data = [(0.0, 0.1), (0.2, 0.7), (0.3, 0.6), (0.6, 0.9)]
self.histo = SparseHistogram(bin_widths=(0.5, 0.3),
left_bin_edges=(0.0, -0.1))
self.histo.histogram(data)
def test_correct(self):
correct_results = collections.Counter({
(0, 0) : 1,
(0, 2) : 2,
(1, 3) : 1
})
assert_equal(self.histo._histogram, correct_results)
def test_call(self):
histo_fcn = self.histo()
# voxels we have filled
assert_equal(histo_fcn((0.25, 0.65)), 2)
assert_equal(histo_fcn((0.01, 0.09)), 1)
assert_equal(histo_fcn((0.61, 0.89)), 1)
# empty voxel gives 0
assert_equal(histo_fcn((2.00, 2.00)), 0)
def test_normalized(self):
raw_prob_normed = self.histo.normalized(raw_probability=True)
assert_almost_equal(raw_prob_normed((0.25, 0.65)), 0.5)
assert_almost_equal(raw_prob_normed((0.01, 0.09)), 0.25)
assert_almost_equal(raw_prob_normed((0.61, 0.89)), 0.25)
normed_fcn = self.histo.normalized()
assert_almost_equal(normed_fcn((0.25, 0.65)), old_div(0.5,0.15))
assert_almost_equal(normed_fcn((0.01, 0.09)), old_div(0.25,0.15))
assert_almost_equal(normed_fcn((0.61, 0.89)), old_div(0.25,0.15))
class TestSparseHistogram(object):
def setup(self):
data = [(0.0, 0.1), (0.2, 0.7), (0.3, 0.6), (0.6, 0.9)]
self.histo = SparseHistogram(bin_widths=(0.5, 0.3),
left_bin_edges=(0.0, -0.1))
self.histo.histogram(data)
def test_correct(self):
correct_results = collections.Counter({
(0, 0): 1,
(0, 2): 2,
(1, 3): 1
})
assert self.histo._histogram == correct_results
def test_call(self):
histo_fcn = self.histo()
# voxels we have filled
assert histo_fcn((0.25, 0.65)) == 2
assert histo_fcn((0.01, 0.09)) == 1
assert histo_fcn((0.61, 0.89)) == 1
# empty voxel gives 0
assert histo_fcn((2.00, 2.00)) == 0
def test_normalized(self):
raw_prob_normed = self.histo.normalized(raw_probability=True)
assert pytest.approx(raw_prob_normed((0.25, 0.65))) == 0.5
assert pytest.approx(raw_prob_normed((0.01, 0.09))) == 0.25
assert pytest.approx(raw_prob_normed((0.61, 0.89))) == 0.25
normed_fcn = self.histo.normalized()
assert pytest.approx(normed_fcn((0.25, 0.65))) == old_div(0.5, 0.15)
assert pytest.approx(normed_fcn((0.01, 0.09))) == old_div(0.25, 0.15)
assert pytest.approx(normed_fcn((0.61, 0.89))) == old_div(0.25, 0.15)
def test_mangled_input(self):
# Sometimes singleton cvs are not unpacked properly
data = ([0.0], [0.1])
# This line might return mangled output
out = self.histo.map_to_bins(data)
# This raises on modern numpy if this is not 1D
_ = max(out)
def setup(self):
data = [(0.0, 0.1), (0.2, 0.7), (0.3, 0.6), (0.6, 0.9)]
histo = SparseHistogram(bin_widths=(0.5, 0.3),
left_bin_edges=(0.0, -0.1))
histo.histogram(data)
self.plotter = HistogramPlotter2D(histo)
def setup(self):
data = [(0.0, 0.1), (0.2, 0.7), (0.3, 0.6), (0.6, 0.9)]
self.histo = SparseHistogram(bin_widths=(0.5, 0.3),
left_bin_edges=(0.0, -0.1))
self.histo.histogram(data)
def setup(self):
data = [(0.0, 0.1), (0.2, 0.7), (0.3, 0.6), (0.6, 0.9)]
histo = SparseHistogram(bin_widths=(0.5, 0.3),
left_bin_edges=(0.0, -0.1))
histo.histogram(data)
self.plotter = HistogramPlotter2D(histo)
def setup(self):
data = [(0.0, 0.1), (0.2, 0.7), (0.3, 0.6), (0.6, 0.9)]
self.histo = SparseHistogram(bin_widths=(0.5, 0.3),
left_bin_edges=(0.0, -0.1))
self.histo.histogram(data)
