def test_init_abscissa_float(self):
"""brightness.Distribution.__init__: Python, float abscissa"""
d1 = brightness.Distribution(self.masses, 5000, engine=self.engine)
d2 = brightness.Distribution(self.masses,
np.arange(100, 5001),
engine=self.engine)
np.testing.assert_allclose(d1.graph[:, 100:], d2.graph)
def test_init_df(self):
"""brightness.Distribution.__init__: Python, DataFrame"""
# This assumes that the numba array version works
absc = 5000
bd1 = brightness.Distribution(self.peak_data, absc, engine=self.engine)
bd2 = brightness.Distribution(self.masses, absc, engine=self.engine)
np.testing.assert_allclose(bd1.graph, bd2.graph)
def test_init_list(self):
"""brightness.Distribution.__init__: Python, list of DataFrames"""
# This assumes that the array version works
l = [self.peak_data.loc[[0, 1]], self.peak_data.loc[[2]]]
absc = 5000
np.testing.assert_allclose(
brightness.Distribution(l, absc, engine=self.engine).graph,
brightness.Distribution(self.masses, absc,
engine=self.engine).graph)
def test_init_cam_eff(self):
"""brightness.Distribution.__init__: Python, cam_eff"""
eff = 20
absc = 5000
d1 = brightness.Distribution(self.masses,
absc,
cam_eff=eff,
engine=self.engine)
d2 = brightness.Distribution(self.masses / eff,
absc,
cam_eff=1,
engine=self.engine)
np.testing.assert_allclose(d1.graph, d2.graph)
def test_init_abscissa_none(self):
"""brightness.Distribution.__init__: Python, `None` abscissa"""
smth = 1
d1 = brightness.Distribution(self.masses,
None,
bw=smth,
engine=self.engine)
a = np.max(self.masses) + 2 * smth * np.sqrt(np.max(self.masses)) - 1
d2 = brightness.Distribution(self.masses,
a,
bw=smth,
engine=self.engine)
np.testing.assert_allclose(d1.graph, d2.graph)
def test_init_kern_width(self):
"""brightness.Distribution.__init__: Python, truncated kernel"""
smth = 1
x, y = self._calc_graph(smth)
d = brightness.Distribution(self.masses,
x,
bw=smth,
cam_eff=1,
kern_width=5,
engine=self.engine)
np.testing.assert_allclose([x, y], d.graph, atol=1e-6)
def test_init_array(self):
"""brightness.Distribution.__init__: Python, full kernel, ndarray"""
smth = 1
x, y = self._calc_graph(smth)
d = brightness.Distribution(self.masses,
x,
bw=smth,
cam_eff=1,
kern_width=np.inf,
engine=self.engine)
np.testing.assert_allclose([x, y], d.graph)
def test_most_probable(self):
"""brightness.Distribution.most_probable: Python"""
absc = 5000
d = brightness.Distribution(self.masses, absc, engine=self.engine)
np.testing.assert_allclose(d.most_probable(), self.most_probable)
def test_std(self):
"""brightness.Distribution.std: Python"""
absc = 5000
d = brightness.Distribution(self.masses, absc, engine=self.engine)
var = np.sum((d.graph[0] - d.mean())**2 * d.graph[1])
np.testing.assert_allclose(d.std(), np.sqrt(var))
def test_mean(self):
"""brightness.Distribution.mean: Python"""
absc = 5000
d = brightness.Distribution(self.masses, absc, engine=self.engine)
mean = np.sum(d.graph[0] * d.graph[1])
np.testing.assert_allclose(d.mean(), mean)