Pickle Containers

doc/scripts/TimeSeries.py

@@ -1,18 +1,27 @@
 #!/usr/bin/env python
 
-from snewpy import snowglobes
+from snewpy.models.ccsn import Nakazato_2013
+from snewpy.neutrino import MassHierarchy, MixingParameters
+from snewpy.flavor_transformation import AdiabaticMSW
+from snewpy.rate_calculator import RateCalculator
 
-SNOwGLoBES_path = None  # change to SNOwGLoBES directory if using a custom detector configuration
+import numpy as np
+import astropy.units as u
 
-# arguments for generate_time_series
-model_file = "/path/to/snewpy/models/Nakazato_2013/nakazato-LS220-BH-z0.004-s30.0.fits"
-modeltype = 'Nakazato_2013'
-transformation = 'AdiabaticMSW_NMO'
-d = 10  # Supernova distance in kpc
+model = Nakazato_2013(progenitor_mass=30*u.solMass, revival_time=0*u.ms, metallicity=0.004, eos='LS220')
 
-# Running the modules
-outfile = snowglobes.generate_time_series(model_file, modeltype, transformation, d)
-snowglobes.simulate(SNOwGLoBES_path, outfile, detector_input="icecube")
-snowglobes.collate(SNOwGLoBES_path, outfile)
+transformation = AdiabaticMSW(MixingParameters('NORMAL')) # Desired flavor transformation
+
+times    = model.get_time()
+energies = np.linspace(0,100,501)<<u.MeV
+distance = 10*u.kpc
 
-# An additional, optional argument in simulate() is the detector name, if one wants to only run 1 detector, rather than all of them.
+flux = model.get_flux(t=times, E=energies, distance=distance, flavor_xform=transformation)
+fluence = flux.integrate('time')
+
+detector = "wc100kt30prct"
+rc = RateCalculator()
+events = rc.run(fluence, detector, detector_effects=True)
+
+filename = f"{model}.{transformation}.{times[0]:.3f}-{times[-1]:.3f},{energies[0]:.3f}-{energies[-1]:.3f},{distance:.3f}.{detector}.npz"
+np.savez(filename,**events)

python/snewpy/flux.py

@@ -108,7 +108,7 @@ def __init__(self,
                  *,
                  integrable_axes: set[Axes] | None = None,
                  flavor_scheme: FlavorScheme | None = None
-    ):
+                ):
         """A container class storing the physical quantity (flux, fluence, rate...), which depends on flavor, time and energy.
 
         Parameters
@@ -484,8 +484,7 @@ def project_to(self, axis='energy', squeeze=False):
         if squeeze:
             return x, fP.array.squeeze().T
         else:
-            return x, fP
-
+            return x, fP        
 
     def plot(flux, projection='energy', styles=None, **kwargs):
         x, fP = flux.project_to(projection, squeeze=False)
@@ -511,6 +510,27 @@ def plot(flux, projection='energy', styles=None, **kwargs):
         plt.ylabel(f'{fP.__class__.__name__}, {x.unit._repr_latex_()}')
         return lines
 
+    @staticmethod
+    def _reconstruct(array, flavor, time, energy, integrable_axes, flavor_scheme):
+        return Container(array,
+                         flavor,
+                         time,
+                         energy,
+                         integrable_axes=integrable_axes,
+                         flavor_scheme=flavor_scheme,
+                         )
+
+    def __reduce__(self):
+        return ( Container._reconstruct,
+                      ( self.array,
+                        self.flavor,
+                        self.time,
+                        self.energy,
+                        self._integrable_axes,
+                        self.flavor_scheme,
+                       ),
+               )    
+
 #some standard container classes that can be used for 
 Flux = Container['1/(MeV*s*m**2)', "d2FdEdT"]
 Fluence = Container[Flux.unit*u.s, "dFdE"]

python/snewpy/models/base.py

@@ -133,9 +133,9 @@ def get_initial_spectra(self, t, E):
         """
         spectra_dict = self._get_initial_spectra_dict(t, E, flavors=ThreeFlavor)
         initial_spectra =  flux.Container['1/(MeV*s)'].from_dict(spectra_dict, 
-                                                                time=t,
-                                                                energy=E,
-                                                                flavor_scheme=ThreeFlavor)
+                                                                 time=t,
+                                                                 energy=E,
+                                                                 flavor_scheme=ThreeFlavor)
         return initial_spectra
 
     def get_transformed_spectra(self, t, E, flavor_xform):