Merge pull request #124 from Chamani8/g23

Add option to fit sed with G23 only

measure_extinction/modeldata.py

@@ -209,7 +209,7 @@ def stellar_sed(self, params, velocity=None):
 
         return sed
 
-    def dust_extinguished_sed(self, params, sed, velocity=0.0):
+    def dust_extinguished_sed(self, params, sed, fit_range="all", velocity=0.0):
         """
         Dust extinguished sed given the extinction parameters
 
@@ -221,6 +221,9 @@ def dust_extinguished_sed(self, params, sed, velocity=0.0):
         sed : dict
             fluxes for each spectral piece
 
+        fit_range : string, optional
+            keyword to toggle SED fitting to be done with G23 only or to also include curve_F99_method
+
         velocity : float, optional
             velocity of dust
 
@@ -231,42 +234,56 @@ def dust_extinguished_sed(self, params, sed, velocity=0.0):
         """
         Rv = params[1]
         g23mod = G23(Rv=Rv)
-        optnir_axav_x = np.flip(1.0 / (np.arange(0.35, 30.0, 0.1) * u.micron))
-        optnir_axav_y = g23mod(optnir_axav_x)
-
-        # updated F04 C1-C2 correlation
-        C1 = 2.18 - 2.91 * params[2]
 
         # create the extinguished sed
         ext_sed = {}
-        for cspec in self.fluxes.keys():
-            # get the dust extinguished SED (account for the
-            #  systemic velocity of the galaxy [opposite regular sense])
-            shifted_waves = (1.0 - velocity / 2.998e5) * self.waves[cspec]
-            axav = _curve_F99_method(
-                shifted_waves,
-                Rv,
-                C1,
-                params[2],
-                params[3],
-                params[4],
-                params[5],
-                params[6],
-                optnir_axav_x.value,
-                optnir_axav_y,
-                [0.033, 11.0],
-                "FM90_G23_measure_extinction",
-            )
-            ext_sed[cspec] = sed[cspec] * (10 ** (-0.4 * axav * params[0]))
+        if fit_range.lower() == "g23":
+            for cspec in self.fluxes.keys():
+                shifted_waves = (1.0 - velocity / 2.998e5) * self.waves[cspec]
+                axav = g23mod(shifted_waves)
+                ext_sed[cspec] = sed[cspec] * (10 ** (-0.4 * axav * params[0]))
+
+        elif fit_range.lower() == "all":
+            optnir_axav_x = np.flip(1.0 / (np.arange(0.35, 30.0, 0.1) * u.micron))
+            optnir_axav_y = g23mod(optnir_axav_x)
+
+            # updated F04 C1-C2 correlation
+            C1 = 2.18 - 2.91 * params[2]
+
+            for cspec in self.fluxes.keys():
+                # get the dust extinguished SED (account for the
+                #  systemic velocity of the galaxy [opposite regular sense])
+                shifted_waves = (1.0 - velocity / 2.998e5) * self.waves[cspec]
+                axav = _curve_F99_method(
+                    shifted_waves,
+                    Rv,
+                    C1,
+                    params[2],  # C2
+                    params[3],  # C3
+                    params[4],  # C4
+                    xo=params[5],  # xo
+                    gamma=params[6],  # gamma
+                    optnir_axav_x=optnir_axav_x.value,
+                    optnir_axav_y=optnir_axav_y,
+                    valid_x_range=[0.033, 11.0],
+                    model_name="FM90_G23_measure_extinction",
+                )
+
+                ext_sed[cspec] = sed[cspec] * (10 ** (-0.4 * axav * params[0]))
 
-        # # create the extinguished sed
-        # ext_sed = {}
-        # for cspec in self.fluxes.keys():
-        #     # get the dust extinguished SED (account for the
-        #     #  systemic velocity of the galaxy [opposite regular sense])
-        #     shifted_waves = (1.0 - velocity / 2.998e5) * self.waves[cspec]
-        #     axav = g23mod(shifted_waves)
-        #     ext_sed[cspec] = sed[cspec] * (10 ** (-0.4 * axav * params[0]))
+            # # create the extinguished sed
+            # ext_sed = {}
+            # for cspec in self.fluxes.keys():
+            #     # get the dust extinguished SED (account for the
+            #     #  systemic velocity of the galaxy [opposite regular sense])
+            #     shifted_waves = (1.0 - velocity / 2.998e5) * self.waves[cspec]
+            #     axav = g23mod(shifted_waves)
+            #     ext_sed[cspec] = sed[cspec] * (10 ** (-0.4 * axav * params[0]))
+
+        else:
+            raise ValueError(
+                "Incorrect input for fit_range argument in dust_extinguished_sed(). Available options are: g23, all"
+            )
 
         return ext_sed
 

measure_extinction/utils/fit_model.py

@@ -64,7 +64,7 @@ def __init__(
         self.weights = weights
         self.velocities = np.array([stellar_velocity, 0.0])
 
-    def lnlike(self, params, obsdata, modeldata):
+    def lnlike(self, params, obsdata, modeldata, fit_range="all"):
         """
         Compute the natural log of the likelihood that the data
         fits the model.
@@ -74,6 +74,7 @@ def lnlike(self, params, obsdata, modeldata):
         ----------
         params : floats
             parameters of the model
+            params = [logT, logg, logZ, Av, Rv, C2, C3, C4, x0, gamma, HI_gal, HI_mw]
 
         obsdata : StarData object
             observed data for a reddened star
@@ -86,7 +87,7 @@ def lnlike(self, params, obsdata, modeldata):
 
         # dust_extinguished sed
         ext_modsed = modeldata.dust_extinguished_sed(
-            params[3:10], modsed, velocity=self.velocities[0]
+            params[3:10], modsed, fit_range=fit_range, velocity=self.velocities[0]
         )
 
         # hi_abs sed
@@ -97,7 +98,12 @@ def lnlike(self, params, obsdata, modeldata):
 
         lnl = 0.0
         for cspec in hi_ext_modsed.keys():
-            gvals = (self.weights[cspec] > 0) & (np.isfinite(hi_ext_modsed[cspec]))
+            try:
+                gvals = (self.weights[cspec] > 0) & (np.isfinite(hi_ext_modsed[cspec]))
+            except ValueError:
+                raise ValueError(
+                    "Oops! The model data and reddened star data did not match.\n Hint: Make sure that the BAND name in the .dat files match."
+                )
             chiarr = np.square(
                 (
                     (
@@ -146,7 +152,7 @@ def lnprior(self, params):
         return lnp
 
     @staticmethod
-    def lnprob(params, obsdata, modeldata, fitinfo):
+    def lnprob(params, obsdata, modeldata, fitinfo, fit_range="all"):
         """
         Compute the natural log of the probability
 
@@ -170,7 +176,7 @@ def lnprob(params, obsdata, modeldata, fitinfo):
         if lnp == lnp_bignnum:
             return lnp
         else:
-            return lnp + fitinfo.lnlike(params, obsdata, modeldata)
+            return lnp + fitinfo.lnlike(params, obsdata, modeldata, fit_range=fit_range)
 
     def check_param_limits(self, params):
         """