def test_spline_c_idx():
p = get_particle(
"a",
model="spline_c_idx",
min_m=1.0,
max_m=3.0,
interp_N=20,
with_bound=True,
)
with variable_scope() as vm:
p.init_params()
for i in range(20):
vm.set(f"a_point_{i}r", 1.0)
vm.set(f"a_point_{i}i", 0.0)
amp = p(np.array([1.0, 3.0, 2.0]))
assert np.allclose(amp, 1.0)
p = get_particle(
"a",
model="spline_c_idx",
polar=False,
points=[1.0, 2.0, 3.0, 4.0, 5.0],
)
with variable_scope() as vm:
p.init_params()
for i in range(3):
vm.set(f"a_point_{i}r", i)
vm.set(f"a_point_{i}i", i)
amp = p(np.array([1.0, 3.0, 2.0, 4.0]))
assert np.allclose(amp, [0.0, 1 + 1j, 0.0, 2 + 2j])
def test_variable2():
set_config("polar", False)
with variable_scope() as vm:
b = Variable("R_total", cplx=True)
assert vm.complex_vars["R_total"] == False
set_config("polar", True)
with variable_scope() as vm:
b = Variable("R_total", cplx=True)
assert vm.complex_vars["R_total"] == True
def test_refresh_vars():
with variable_scope() as vm:
Variable("a", cplx=True)
Variable("b", value=1.0)
Variable("c", value=1.0, range_=[0, 3])
Variable("b", value=1.0)
Variable("d", value=1.0, fix=True)
vm.refresh_vars()
def test_hist_idx():
p = get_particle("a", model="hist_idx", min_m=1.0, max_m=3.0, interp_N=20)
with variable_scope() as vm:
p.init_params()
vm.set("a_point_0r", 1.0)
vm.set("a_point_0i", 0.0)
vm.set("a_point_1r", 1.0)
vm.set("a_point_1i", 0.0)
amp = p(np.array([1.0, 1.001, 2.0, 3.0, 4.0]))
assert np.allclose(amp[0:2], 1.0)
def test_minimize():
with variable_scope() as vm:
m = Variable("R_m", value=2.1)
vm.set_bound({"R_m": [-2, 3]})
def f():
return m() * m()
ret = vm.minimize(f)
print(ret)
assert np.allclose(m().numpy(), 0.0)
def test_rename():
with variable_scope() as vm:
Variable("a", value=1.0)
vm.rename_var("a", "b")
assert vm.get("b") == 1.0
assert "a" not in vm.variables
a = Variable("d", cplx=True) # BUG: cannot use "a"
a.set_value([2, 3])
vm.rename_var("d", "c", True)
assert vm.get("cr") == 2
assert vm.get("ci") == 3
def test_minimize():
with variable_scope() as vm:
m = Variable("R_m", value=2.1)
vm.set_bound({"R_m": (None, 3)})
def f():
return m() * m()
from scipy.optimize import minimize as mini
ret = vm.minimize(
f, method=lambda g, x: mini(g, x, jac=True, method="L-BFGS-B"))
print(ret)
assert abs(m().numpy()) < 1e-6
def test_variable():
with variable_scope() as vm:
m = Variable("R_m", value=2.1) # trainable is True by default
g_ls = Variable("A2BR_H", shape=[3], cplx=True)
fcr = Variable("R_total", cplx=True)
m1 = Variable("R1_m", value=2.3)
g_ls1 = Variable("A2BR1_H", shape=[3], cplx=True)
fcr1 = Variable("R1_total", cplx=True)
print(g_ls.value)
print(g_ls())
m.fixed(2.4)
g_ls.sameas(g_ls1)
fcr.r_shareto(fcr1)
def test_polar():
with variable_scope() as vm:
a = Variable("a", cplx=True)
b = a().numpy()
vm.rp2xy_all()
c = a().numpy()
vm.xy2rp_all()
d = a().numpy()
vm.std_polar_all()
e = vm.get("ai")
vm.trans_params(True)
f = a().numpy()
assert np.allclose(b, c)
assert np.allclose(c, d)
assert np.allclose(d, f)
assert -3.2 < e and e < 3.2
def test_minimize2():
data = np.linspace(-1.5, 1.5, 10000)
w = tf.cos(data) + 1
np.random.seed(2)
cut = np.random.random(data.shape) * np.max(w) * 1.1 < w
data = data[cut]
phsp = np.linspace(-1.5, 1.5, 10000)
with variable_scope() as vm:
a = Variable("R_m", cplx=True)
vm.set_bound({"R_mr": (-2, None)})
def f(x):
fx = tf.cos(x) + tf.abs(a())
return fx
ret = vm.minimize(nll_funciton(f, data, phsp))
print(ret)
assert abs(float(np.abs(a())) - 1.0) < 0.2