def test_true_start(self):
v = np.ones(100, dtype=bool)
self.assertEqual(vectors.true_start(v), 0)
v[:10] = False
self.assertEqual(vectors.true_start(v), 10)
v[-10:] = False
self.assertEqual(vectors.true_start(v), 10)
v[:] = False
with self.assertRaises(ValueError):
vectors.true_start(v)
def get_gauss_pdf_with_cutoff(t, rt_mean, sigma, cutoff) -> np.ndarray:
p: np.ndarray = peak_shapes.gaussian(t, rt_mean, sigma)
# cut at end
p = p[:true_end(p >= cutoff * p.max())]
# set to 0 at front
p[:true_start(p >= cutoff * p.max())] = 0
# return normalized profile (integral = 1)
return p / p.sum() / t[1]
def get_tanks_in_series_pdf_with_cutoff(t, rt_mean, n_tanks,
cutoff) -> np.ndarray:
p: np.ndarray = peak_shapes.tanks_in_series(t, rt_mean, n_tanks)
# cut at end
p = p[:true_end(p >= cutoff * p.max())]
# set to 0 at front
p[:true_start(p >= cutoff * p.max())] = 0
# return normalized profile (integral = 1)
return p / p.sum() / t[1]
def test_calculate(self):
# prepare
self.uo.v_void = 15.2
self.uo.v_init = 3.2
self.uo.losses_share = 0.2
self.uo.losses_species_list = [1]
self.uo._calculate()
self.assertTrue(hasattr(self.uo, '_v_void'))
self.assertTrue(hasattr(self.uo, '_v_init'))
self.assertTrue(hasattr(self.uo, '_c_init'))
self.assertTrue(hasattr(self.uo, '_p'))
self.assertAlmostEqual(0.56, self.uo._c.max())
self.assertAlmostEqual(0.2726, self.uo._c.mean(), 5)
self.assertEqual(51, vectors.true_start(self.uo._f > 0))
def test_sim_init_fill_up(self):
# prepare
self.uo._v_void = 15.5
self.uo._v_init = 5.5
self.uo._c_init = np.array([[0], [0.2]])
self.assert_defined_values("_v_void", self.uo._sim_init_fill_up)
self.assert_defined_values("_v_init", self.uo._sim_init_fill_up)
self.assert_defined_values("_c_init", self.uo._sim_init_fill_up)
# skip if `_v_init == _v_void`
self.uo._v_init = self.uo._v_void
c = self.uo._c.copy()
f = self.uo._f.copy()
c_init = self.uo._c_init
self.uo._sim_init_fill_up()
np.testing.assert_array_equal(c_init, self.uo._c_init)
np.testing.assert_array_equal(f, self.uo._f)
np.testing.assert_array_equal(c, self.uo._c)
# set v_init to < v_void
self.uo._v_init = 5.5
# target
vi = self.uo._v_init
vv = self.uo._v_void
c_init = (c_init * vi + c[:, 0:1] * (vv - vi)) / vv
# noinspection PyTypeChecker
i_v = vectors.true_start(np.cumsum(self.uo._f) * self.uo._dt >= vv - vi)
f[:i_v] = 0
c[:, :i_v] = 0
# run sim
self.uo._sim_init_fill_up()
# compare results
np.testing.assert_array_almost_equal(c_init, self.uo._c_init, 2)
np.testing.assert_array_equal(f, self.uo._f)
np.testing.assert_array_equal(c, self.uo._c)
def validate_point_on_slope(self, t: np.ndarray, p: np.ndarray, x: float,
y: float):
i = true_start(t >= x)
# y should be between p[i-1] and p[i]
self.assertTrue(p[i - 1] < y <= p[i] or p[i - 1] > y >= p[i])