diff --git a/python/snewpy/flavor_transformation.py b/python/snewpy/flavor_transformation.py
index bab1190..d30959c 100644
--- a/python/snewpy/flavor_transformation.py
+++ b/python/snewpy/flavor_transformation.py
@@ -1144,23 +1144,14 @@ class NeutrinoDecay(FlavorTransformation):
         prob : float or ndarray
             Transition probability.
         """
-        pe_array = []
-
         # NMO case.
         if self.mass_order == MassHierarchy.NORMAL:
-            for energy in E:
-                pe_array.append(
-                    self.De1*(1-np.exp(-self.gamma(energy)*self.d)) +
-                    self.De3*np.exp(-self.gamma(energy)*self.d))
-            pe_array = np.array(pe_array)
+            pe_array = self.De1*(1-np.exp(-self.gamma(E)*self.d)) + \
+                       self.De3*np.exp(-self.gamma(E)*self.d)
         # IMO case.
         else:
-            for energy in E:
-                pe_array.append(
-                    self.De2*np.exp(-self.gamma(energy)*self.d) +
-                    self.De3*(1-np.exp(-self.gamma(energy)*self.d)))
-            pe_array = np.array(pe_array)
-
+            pe_array = self.De2*np.exp(-self.gamma(E)*self.d) + \
+                       self.De3*(1-np.exp(-self.gamma(E)*self.d))
         return pe_array
 
     def prob_ex(self, t, E):
@@ -1251,23 +1242,14 @@ class NeutrinoDecay(FlavorTransformation):
         prob : float or ndarray
             Transition probability.
         """
-        pxbar_array = []
-
         # NMO case.
         if self.mass_order == MassHierarchy.NORMAL:
-            for energy in E:
-                pxbar_array.append(
-                    self.De1*(1-np.exp(-self.gamma(energy)*self.d)) +
-                    self.De2 + self.De3*np.exp(-self.gamma(energy)*self.d))
-            pxbar_array = np.array(pxbar_array)
+            pxbar_array = self.De1*(1-np.exp(-self.gamma(E)*self.d)) + \
+                          self.De2 + self.De3*np.exp(-self.gamma(E)*self.d)
         # IMO case.
         else:
-            for energy in E:
-                pxbar_array.append(
-                    self.De1 + self.De2*np.exp(-self.gamma(energy)*self.d) +
-                    self.De3*(1-np.exp(-self.gamma(energy)*self.d)))
-            pxbar_array = np.array(pxbar_array)
-
+            pxbar_array = self.De1 + self.De2*np.exp(-self.gamma(E)*self.d) + \
+                          self.De3*(1-np.exp(-self.gamma(E)*self.d))
         return pxbar_array
 
     def prob_xxbar(self, t, E):
diff --git a/python/snewpy/models.py b/python/snewpy/models.py
index e807386..4778d0a 100644
--- a/python/snewpy/models.py
+++ b/python/snewpy/models.py
@@ -1018,7 +1018,7 @@ class Warren_2020(SupernovaModel):
         # Set model metadata.
         self.filename = os.path.basename(filename)
         self.EOS = eos
-        self.progenitor_mass = float(filename.split('_')[-1].strip('m%.h5')) * u.Msun
+        self.progenitor_mass = float(filename.split('_')[-1][1:-3]) * u.Msun
         self.turbmixing_param = float(filename.split('_')[-2].strip('a%'))
 
         # Get grid of model times.
@@ -1251,7 +1251,12 @@ class Fornax_2019(SupernovaModel):
         # Set up model metadata.
         self.filename = filename
 
-        self.progenitor_mass = float(filename.split('_')[-1][:-4]) * u.Msun
+        mass_str = filename.split('_')[-1]
+        if 'M' in mass_str:
+            self.progenitor_mass = float(mass_str[:-4]) * u.Msun
+        else:
+            mass_str = filename.split('_')[-2]
+            self.progenitor_mass = float(mass_str[:-1]) * u.Msun
 
         self.fluxunit = 1e50 * u.erg/(u.s*u.MeV)
         self.time = None
@@ -1637,7 +1642,7 @@ class Fornax_2021(SupernovaModel):
             Absolute or relative path to FITS file with model data.
         """
         # Set up model metadata.
-        self.progenitor_mass = float(filename.split('_')[-3][:-1]) * u.Msun
+        self.progenitor_mass = float(filename.split('/')[-1].split('_')[2][:-1]) * u.Msun
 
         # Conversion of flavor to key name in the model HDF5 file.
         self._flavorkeys = { Flavor.NU_E : 'nu0',

diff --git a/python/snewpy/test/test_models.py b/python/snewpy/test/test_models.py
index 4dc7990..bca7655 100644
--- a/python/snewpy/test/test_models.py
+++ b/python/snewpy/test/test_models.py
@@ -5,7 +5,10 @@ import unittest
 
 from snewpy.neutrino import Flavor
 from snewpy.flavor_transformation import NoTransformation
-from snewpy.models import Nakazato_2013
+from snewpy.models import Nakazato_2013, Tamborra_2014, OConnor_2015, \
+                          Sukhbold_2015, Bollig_2016, Walk_2018, \
+                          Walk_2019, Fornax_2019, Warren_2020, \
+                          Kuroda_2020, Fornax_2021, Zha_2021
 
 from astropy import units as u
 
@@ -13,23 +16,286 @@ import numpy as np
 
 class TestModels(unittest.TestCase):
 
-    def test_Nakazato_vanilla(self):
+    def test_Nakazato_2013(self):
         """
-        Instantiate a 'Nakazato 2013' model
+        Instantiate a set of 'Nakazato 2013' models
         """
-        xform = NoTransformation()
-        mfile = 'models/Nakazato_2013/nakazato-shen-z0.004-t_rev100ms-s13.0.fits'
-        model = Nakazato_2013(mfile)
+        for z in [0.004, 0.02]:
+            for trev in [100, 200, 300]:
+                for mass in [13., 20., 50.]:
+                    mfile = 'models/Nakazato_2013/nakazato-shen-z{}-t_rev{}ms-s{:.1f}.fits'.format(z, trev, mass)
+                    model = Nakazato_2013(mfile)
 
-        self.assertEqual(model.EOS, 'SHEN')
-        self.assertEqual(model.progenitor_mass, 13.*u.Msun)
-        self.assertEqual(model.revival_time, 100.*u.ms)
+                    self.assertEqual(model.EOS, 'SHEN')
+                    self.assertEqual(model.progenitor_mass, mass*u.Msun)
+                    self.assertEqual(model.revival_time, trev*u.ms)
+                    self.assertEqual(model.metallicity, z)
 
-        self.assertEqual(model.time[0], -50*u.ms)
+                    # Check that times are in proper units.
+                    t = model.get_time()
+                    self.assertTrue(t.unit, u.s)
+                    self.assertEqual(model.time[0], -50*u.ms)
 
-#        tunit = model.time.unit
-#        Eunit = model.meanE[Flavor.NU_E].unit
-#
-#        t = -50*u.ms
-#        self.assertTrue(np.interp(t.to(tunit), model.time, model.meanE[Flavor.NU_E])*Eunit == 6.79147181061522*u.MeV)
+                    # Check that we can compute flux dictionaries.
+                    f = model.get_initial_spectra(0*u.s, 10*u.MeV)
+                    self.assertEqual(type(f), dict)
+                    self.assertEqual(len(f), len(Flavor))
+                    self.assertEqual(f[Flavor.NU_E].unit, 1/(u.erg * u.s))
+
+    def test_Tamborra_2014(self):
+        """
+        Instantiate a set of 'Tamborra 2014' models
+        """
+        for mass in [20., 27.]:
+            mfile = 'models/Tamborra_2014/s{:.1f}c_3D_dir1'.format(mass)
+            model = Tamborra_2014(mfile, eos='LS220')
+
+            self.assertEqual(model.EOS, 'LS220')
+            self.assertEqual(model.progenitor_mass, mass*u.Msun)
+
+            # Check that times are in proper units.
+            t = model.get_time()
+            self.assertTrue(t.unit, u.s)
+
+            # Check that we can compute flux dictionaries.
+            f = model.get_initial_spectra(0*u.s, 10*u.MeV)
+            self.assertEqual(type(f), dict)
+            self.assertEqual(len(f), len(Flavor))
+            self.assertEqual(f[Flavor.NU_E].unit, 1/(u.erg * u.s))
+
+    def test_OConnor_2015(self):
+        """
+        Instantiate a set of "O'Connor 2015" models
+        """
+        mfile = 'models/OConnor_2015/M1_neutrinos.dat'
+        model = OConnor_2015(mfile, eos='LS220')
+
+        self.assertEqual(model.EOS, 'LS220')
+        self.assertEqual(model.progenitor_mass, 40*u.Msun)
+
+        # Check that times are in proper units.
+        t = model.get_time()
+        self.assertTrue(t.unit, u.s)
+
+        # Check that we can compute flux dictionaries.
+        f = model.get_initial_spectra(0*u.s, 10*u.MeV)
+        self.assertEqual(type(f), dict)
+        self.assertEqual(len(f), len(Flavor))
+        self.assertEqual(f[Flavor.NU_E].unit, 1/(u.erg * u.s))
+
+    def test_Sukhbold_2015(self):
+        """
+        Instantiate a set of 'Sukhbold 2015' models
+        """
+        for mass in ['z9.6', 's27.0']:
+            for eos in ['LS220', 'SFHo']:
+                mfile = 'models/Sukhbold_2015/sukhbold-{}-{}.fits'.format(eos, mass)
+                massval = float(mass[1:]) * u.Msun
+                model = Sukhbold_2015(mfile)
+
+                self.assertEqual(model.EOS, eos)
+                self.assertEqual(model.progenitor_mass, massval)
+
+                # Check that times are in proper units.
+                t = model.get_time()
+                self.assertTrue(t.unit, u.s)
+
+                # Check that we can compute flux dictionaries.
+                f = model.get_initial_spectra(0*u.s, 10*u.MeV)
+                self.assertEqual(type(f), dict)
+                self.assertEqual(len(f), len(Flavor))
+                self.assertEqual(f[Flavor.NU_E].unit, 1/(u.erg * u.s))
+
+    def test_Bollig_2016(self):
+        """
+        Instantiate a set of 'Bollig 2016' models
+        """
+        for mass in [11.2, 27.]:
+            mfile = 'models/Bollig_2016/s{:.1f}c'.format(mass)
+            model = Bollig_2016(mfile, eos='LS220')
+
+            self.assertEqual(model.EOS, 'LS220')
+            self.assertEqual(model.progenitor_mass, mass*u.Msun)
+
+            # Check that times are in proper units.
+            t = model.get_time()
+            self.assertTrue(t.unit, u.s)
+
+            # Check that we can compute flux dictionaries.
+            f = model.get_initial_spectra(0*u.s, 10*u.MeV)
+            self.assertEqual(type(f), dict)
+            self.assertEqual(len(f), len(Flavor))
+            self.assertEqual(f[Flavor.NU_E].unit, 1/(u.erg * u.s))
+
+    def test_Walk_2018(self):
+        """
+        Instantiate a set of 'Walk 2018' models
+        """
+        mass = 15.
+        mfile = 'models/Walk_2018/s{:.1f}c_3D_nonrot_dir1'.format(mass)
+        model = Walk_2018(mfile, eos='LS220')
+
+        self.assertEqual(model.EOS, 'LS220')
+        self.assertEqual(model.progenitor_mass, mass*u.Msun)
+
+        # Check that times are in proper units.
+        t = model.get_time()
+        self.assertTrue(t.unit, u.s)
+
+        # Check that we can compute flux dictionaries.
+        f = model.get_initial_spectra(0*u.s, 10*u.MeV)
+        self.assertEqual(type(f), dict)
+        self.assertEqual(len(f), len(Flavor))
+        self.assertEqual(f[Flavor.NU_E].unit, 1/(u.erg * u.s))
+
+    def test_Walk_2019(self):
+        """
+        Instantiate a set of 'Walk 2019' models
+        """
+        mass = 40.
+        mfile = 'models/Walk_2019/s{:.1f}c_3DBH_dir1'.format(mass)
+        model = Walk_2019(mfile, eos='LS220')
+
+        self.assertEqual(model.EOS, 'LS220')
+        self.assertEqual(model.progenitor_mass, mass*u.Msun)
+
+        # Check that times are in proper units.
+        t = model.get_time()
+        self.assertTrue(t.unit, u.s)
+
+        # Check that we can compute flux dictionaries.
+        f = model.get_initial_spectra(0*u.s, 10*u.MeV)
+        self.assertEqual(type(f), dict)
+        self.assertEqual(len(f), len(Flavor))
+        self.assertEqual(f[Flavor.NU_E].unit, 1/(u.erg * u.s))
+
+    def test_Fornax_2019(self):
+        """
+        Instantiate a set of 'Fornax 2019' models
+        """
+        for mass in [9, 10, 12, 13, 14, 15, 19, 25, 60]:
+            mfile = 'models/Fornax_2019/lum_spec_{}M.h5'.format(mass)
+            model = Fornax_2019(mfile)
+
+            self.assertEqual(model.progenitor_mass, mass*u.Msun)
+
+            # Check that times are in proper units.
+            t = model.get_time()
+            self.assertTrue(t.unit, u.s)
+
+            # Check that we can compute flux dictionaries.
+            f = model.get_initial_spectra(0*u.s, 10*u.MeV, theta=23*u.degree, phi=22*u.degree)
+            self.assertEqual(type(f), dict)
+            self.assertEqual(len(f), len(Flavor))
+            self.assertEqual(f[Flavor.NU_E].unit, u.erg/(u.MeV * u.s))
+
+    def test_Warren_2020(self):
+        """
+        Instantiate a set of 'Warren 2020' models
+        """
+        masses = [
+        '10.0', '10.25', '10.5', '10.75', '100', '11.0', '11.25', '11.5', '11.75',
+        '12.0', '12.25', '12.5', '12.75', '120', '13.0', '13.1', '13.2', '13.3',
+        '13.4', '13.5', '13.6', '13.7', '13.8', '13.9', '14.0', '14.1', '14.2',
+        '14.3', '14.4', '14.5', '14.6', '14.7', '14.8', '14.9', '15.0', '15.1',
+        '15.2', '15.3', '15.4', '15.5', '15.6', '15.7', '15.8', '15.9', '16.0',
+        '16.1', '16.2', '16.3', '16.4', '16.5', '16.6', '16.7', '16.8', '16.9',
+        '17.0', '17.1', '17.2', '17.3', '17.4', '17.5', '17.6', '17.7', '17.8',
+        '17.9', '18.0', '18.1', '18.2', '18.3', '18.4', '18.5', '18.6', '18.7',
+        '18.8', '18.9', '19.0', '19.1', '19.2', '19.3', '19.4', '19.5', '19.6',
+        '19.7', '19.8', '19.9', '20.0', '20.1', '20.2', '20.3', '20.4', '20.5',
+        '20.6', '20.7', '20.8', '20.9', '21.0', '21.1', '21.2', '21.3', '21.4',
+        '21.5', '21.6', '21.7', '21.8', '21.9', '22.0', '22.1', '22.2', '22.3',
+        '22.4', '22.5', '22.6', '22.7', '22.8', '22.9', '23.0', '23.1', '23.2',
+        '23.3', '23.4', '23.5', '23.6', '23.7', '23.8', '23.9', '24.0', '24.1',
+        '24.2', '24.3', '24.4', '24.5', '24.6', '24.7', '24.8', '24.9', '25.0',
+        '25.1', '25.2', '25.3', '25.4', '25.5', '25.6', '25.7', '25.8', '25.9',
+        '26.0', '26.1', '26.2', '26.3', '26.4', '26.5', '26.6', '26.7', '26.8',
+        '26.9', '27.0', '27.1', '27.2', '27.3', '27.4', '27.5', '27.6', '27.7',
+        '27.8', '27.9', '28.0', '28.1', '28.2', '28.3', '28.4', '28.5', '28.6',
+        '28.7', '28.8', '28.9', '29.0', '29.1', '29.2', '29.3', '29.4', '29.5',
+        '29.6', '29.7', '29.8', '29.9', '30.0', '31', '32', '33', '35', '40', '45',
+        '50', '55', '60', '70', '80', '9.0', '9.25', '9.5', '9.75']
+
+        for mixing in [1.23, 1.25, 1.27]:
+            for mass in masses:
+                mfile = 'models/Warren_2020/stir_a{}/stir_multimessenger_a{}_m{}.h5'.format(mixing, mixing, mass)
+    
+                model = Warren_2020(mfile)
+
+                self.assertEqual(model.progenitor_mass, float(mass)*u.Msun)
+                self.assertEqual(model.turbmixing_param, mixing)
+                self.assertEqual(model.EOS, 'LS220')
+
+                # Check that times are in proper units.
+                t = model.get_time()
+                self.assertTrue(t.unit, u.s)
+
+                # Check that we can compute flux dictionaries.
+                f = model.get_initial_spectra(0*u.s, 10*u.MeV)
+                self.assertEqual(type(f), dict)
+                self.assertEqual(len(f), len(Flavor))
+                self.assertEqual(f[Flavor.NU_E].unit, 1./(u.erg * u.s))
+
+    def test_Kuroda_2020(self):
+        """
+        Instantiate a set of 'Kuroda 2020' models
+        """
+        for field in ['R00B00', 'R10B12', 'R10B13']:
+            mfile = 'models/Kuroda_2020/Lnu{}.dat'.format(field)
+            model = Kuroda_2020(mfile)
+
+            self.assertEqual(model.EOS, 'LS220')
+            self.assertEqual(model.progenitor_mass, 20*u.Msun)
+
+            # Check that times are in proper units.
+            t = model.get_time()
+            self.assertTrue(t.unit, u.s)
+
+            # Check that we can compute flux dictionaries.
+            f = model.get_initial_spectra(0*u.s, 10*u.MeV)
+            self.assertEqual(type(f), dict)
+            self.assertEqual(len(f), len(Flavor))
+            self.assertEqual(f[Flavor.NU_E].unit, 1/(u.erg * u.s))
+
+    def test_Fornax_2021(self):
+        """
+        Instantiate a set of 'Fornax 2021' models
+        """
+        for mass in ['12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '25', '26', '26.99']:
+            mfile = 'models/Fornax_2021/lum_spec_{}M_r10000_dat.h5'.format(mass)
+            model = Fornax_2021(mfile)
+
+            self.assertEqual(model.progenitor_mass, float(mass)*u.Msun)
+
+            # Check that times are in proper units.
+            t = model.get_time()
+            self.assertTrue(t.unit, u.s)
+
+            # Check that we can compute flux dictionaries.
+            f = model.get_initial_spectra(0*u.s, 10*u.MeV)
+            self.assertEqual(type(f), dict)
+            self.assertEqual(len(f), len(Flavor))
+            self.assertEqual(f[Flavor.NU_E].unit, u.erg/(u.MeV * u.s))
+
+    def test_Zha_2021(self):
+        """
+        Instantiate a set of 'Zha 2021' models
+        """
+        for mass in ['16', '17', '18', '19', '19.89', '20', '21', '22.39', '23', '24', '25', '26', '30', '33']:
+            mfile = 'models/Zha_2021/s{}.dat'.format(mass)
+            model = Zha_2021(mfile)
+
+            self.assertEqual(model.progenitor_mass, float(mass)*u.Msun)
+            self.assertEqual(model.EOS, 'STOS_B145')
+
+            # Check that times are in proper units.
+            t = model.get_time()
+            self.assertTrue(t.unit, u.s)
+
+            # Check that we can compute flux dictionaries.
+            f = model.get_initial_spectra(0*u.s, 10*u.MeV)
+            self.assertEqual(type(f), dict)
+            self.assertEqual(len(f), len(Flavor))
+            self.assertEqual(f[Flavor.NU_E].unit, 1./(u.erg * u.s))
 

Vectorize NeutrinoDecay calculation in flavor transformation module
The NeutrinoDecay transformation is extremely slow; the loop over energy spectra can be vectorized.
python/snewpy/test/test_models.py::TestModels::test_Warren_2020
python/snewpy/test/test_models.py::TestModels::test_Bollig_2016 python/snewpy/test/test_models.py::TestModels::test_Fornax_2019 python/snewpy/test/test_models.py::TestModels::test_Fornax_2021 python/snewpy/test/test_models.py::TestModels::test_Kuroda_2020 python/snewpy/test/test_models.py::TestModels::test_Nakazato_2013 python/snewpy/test/test_models.py::TestModels::test_OConnor_2015 python/snewpy/test/test_models.py::TestModels::test_Sukhbold_2015 python/snewpy/test/test_models.py::TestModels::test_Tamborra_2014 python/snewpy/test/test_models.py::TestModels::test_Walk_2018 python/snewpy/test/test_models.py::TestModels::test_Walk_2019 python/snewpy/test/test_models.py::TestModels::test_Zha_2021