Using namedtuple to contain the flavor transformations in chain

python/snewpy/flavor_transformation/TransformationChain.py

@@ -10,9 +10,13 @@
 from .in_sn import SNTransformation
 from .in_vacuum import VacuumTransformation, NoVacuumTransformation
 from .in_earth import EarthTransformation, NoEarthMatter
+from collections import namedtuple
 
+#a tuple to hold the transformations list
+TransformationsTuple = namedtuple('TransformationsTuple',['in_sn','in_vacuum','in_earth'])
 
 class TransformationChain(FlavorTransformation):
+
     r"""This class calculates the probability matrix :math:`P_{\beta\alpha}` of the :math:`\nu_\alpha\to\nu_\beta` flavor transition as multiplication of :math:`P^{SN}` transformation in SN, :math:`P^{Vac}` transformation in vacuum and :math:`P^{Earth}` transformation in Earth:
 
     .. math::
@@ -42,10 +46,7 @@ def __init__(self,
             Neutrino mixing parameters (to be applied to all individual transformations in chain)
             By default use standard `MixingParameters` with normal neutrino ordering
         """
-        self.in_sn = in_sn
-        self.in_vacuum = in_vacuum
-        self.in_earth = in_earth
-        self.transforms = [in_sn, in_vacuum, in_earth]
+        self.transforms = TransformationsTuple(in_sn, in_vacuum, in_earth)
         self.set_mixing_params(mixing_params)
 
     def set_mixing_params(self, mixing_params):
@@ -61,8 +62,9 @@ def __call__(self, mixing_params):
         return self
 
     def P_ff(self, t, E)->FlavorMatrix:
-        in_sn, in_vacuum, in_earth = self.transforms
-        return in_earth.P_fm(t,E) @ in_vacuum.P_mm(t,E) @ in_sn.P_mf(t,E)
+        return self.transforms.in_earth.P_fm(t,E) @ \
+               self.transforms.in_vacuum.P_mm(t,E) @ \
+               self.transforms.in_sn.P_mf(t,E)
 
     def __str__(self):
         s = '+'.join([t.__class__.__name__ for t in self.transforms])+'_'+self.mixing_params.mass_order.name

python/snewpy/test/test_04_xforms.py

@@ -170,7 +170,7 @@ def test_AdiabaticMSWes_NMO(self):
         xform = xforms.TransformationChain(xforms.in_sn.AdiabaticMSWes(), mixing_params=mixpars)
         #test the probability matrix shape
         U2 = xform.mixing_params.Pmf_HighDensityLimit().T
-        P_SN = xform.in_sn.P_mf(t,E)
+        P_SN = xform.transforms.in_sn.P_mf(t,E)
         #check with eq (A48) from snewpy_v2.0 paper
         assert np.allclose(P_SN['NU','NU'], 
                            [[0, U2['mu','1'], U2['tau','1'],0],
@@ -219,7 +219,7 @@ def test_AdiabaticMSWes_IMO(self):
         xform = xforms.TransformationChain(xforms.in_sn.AdiabaticMSWes(), mixing_params=mixpars)
         #test the probability matrix shape
         U2 = xform.mixing_params.Pmf_HighDensityLimit().T
-        P_SN = xform.in_sn.P_mf(t,E)
+        P_SN = xform.transforms.in_sn.P_mf(t,E)
         #check with eq (A50) from snewpy_v2.0 paper
         assert np.allclose(P_SN['NU','NU'], 
                            [[0, U2['mu','1'], U2['tau','1'],0],
@@ -267,7 +267,7 @@ def test_NonAdiabaticMSWes_NMO(self):
         xform = xforms.TransformationChain(xforms.in_sn.NonAdiabaticMSWes(), mixing_params=mixpars)
         #test the probability matrix shape
         U2 = xform.mixing_params.Pmf_HighDensityLimit().T
-        P_SN = xform.in_sn.P_mf(t,E)
+        P_SN = xform.transforms.in_sn.P_mf(t,E)
         #check with eq (A52) from snewpy_v2.0 paper
         assert np.allclose(P_SN['NU','NU'], 
                            [[0, U2['mu','1'], U2['tau','1'],0],
@@ -309,7 +309,7 @@ def test_NonAdiabaticMSWes_IMO(self):
         xform = xforms.TransformationChain(xforms.in_sn.NonAdiabaticMSWes(), mixing_params=mixpars)
         #test the probability matrix shape
         U2 = xform.mixing_params.Pmf_HighDensityLimit().T
-        P_SN = xform.in_sn.P_mf(t,E)
+        P_SN = xform.transforms.in_sn.P_mf(t,E)
         #check with eq (A54) from snewpy_v2.0 paper
         assert np.allclose(P_SN['NU','NU'], 
                            [[0, U2['mu','1'], U2['tau','1'],0],
@@ -354,7 +354,7 @@ def test_TwoFlavorDecoherence_NMO(self):
 
         #test the probability matrix shape
         U2 = xform.mixing_params.Pmf_HighDensityLimit().T
-        P_SN = xform.in_sn.P_mf(t,E)
+        P_SN = xform.transforms.in_sn.P_mf(t,E)
         #check with eq (A29) from snewpy_v2.0 paper
         assert np.allclose(P_SN['NU','NU'], 
                            [
@@ -392,7 +392,7 @@ def test_TwoFlavorDecoherence_IMO(self):
         xform = xforms.TransformationChain(xforms.in_sn.TwoFlavorDecoherence(), mixing_params=mixpars)
         #test the probability matrix shape
         U2 = xform.mixing_params.Pmf_HighDensityLimit().T
-        P_SN = xform.in_sn.P_mf(t,E)
+        P_SN = xform.transforms.in_sn.P_mf(t,E)
         #check with eq (A30) from snewpy_v2.0 paper
         assert np.allclose(P_SN['NU_BAR','NU_BAR'], 
                            [
@@ -454,7 +454,7 @@ def test_NeutrinoDecay_NMO(self, t, E):
                                            mixing_params=mixpars)
 
         #test the probability matrix shape
-        P_V = xform.in_vacuum.P_mm(t,E[0])
+        P_V = xform.transforms.in_vacuum.P_mm(t,E[0])
         #check with eq (8) from snewpy_v2.0 paper
         assert np.allclose(P_V['NU','NU'], 
                            [
@@ -503,7 +503,7 @@ def test_NeutrinoDecay_IMO(self, t, E):
                                            in_vacuum=xforms.in_vacuum.NeutrinoDecay(mass=mass, tau=lifetime, dist=distance),
                                            mixing_params=mixpars)
         #test the probability matrix shape
-        P_V = xform.in_vacuum.P_mm(t,E[0])
+        P_V = xform.transforms.in_vacuum.P_mm(t,E[0])
         #check with eq (9) from snewpy_v2.0 paper
         assert np.allclose(P_V['NU_BAR','NU_BAR'], 
                            [
@@ -557,7 +557,7 @@ def test_QuantumDecoherence_NMO(self, t, E):
                                                             E0=energy_ref),
             mixing_params=mixpars)
         #check the flavor transformation matrix
-        Pmm = xform.in_vacuum.P_mm(t, E)
+        Pmm = xform.transforms.in_vacuum.P_mm(t, E)
         x = (E/energy_ref)**n
         # Test computation survival and transition probabilities of mass states.
         #check with the formulas (10-13) in snewpy v2.0 paper
@@ -628,7 +628,7 @@ def test_QuantumDecoherence_IMO(self, t, E):
                                                             E0=energy_ref),
             mixing_params=mixpars)
         #check the flavor transformation matrix
-        Pmm = xform.in_vacuum.P_mm(t, E)
+        Pmm = xform.transforms.in_vacuum.P_mm(t, E)
         x = (E/energy_ref)**n
         # Test computation survival and transition probabilities of mass states.
         #check with the formulas (10-13) in snewpy v2.0 paper