windhorst et al 2023 F200W mag Priors

astropath/chance.py

@@ -3,6 +3,9 @@
 
 from scipy import interpolate
 
+from importlib.resources import files
+import os
+
 from IPython import embed
 
 
@@ -12,7 +15,8 @@
 driver_spl = interpolate.UnivariateSpline._from_tck(driver_tck)
 
 
-def driver_sigma(rmag):
+
+def driver_sigma(mag):
     """
     Estimated incidence of galaxies per sq arcsec with r > rmag
     using Driver et al. 2016 number counts.
@@ -26,7 +30,32 @@ def driver_sigma(rmag):
         float or np.ndarray:  Galaxy number density
 
     """
-    return 10**driver_spl(rmag)
+    return 10**driver_spl(mag)
+
+def windhorst_sigma(mag):
+    """
+    Estimated incidence of galaxies per sq arcsec with F200W > mag
+    using Windhorst et al. 2024 number counts.
+
+    Spline parameters (globals) are for F200W vs Num counts
+
+    Args:
+        mag (float or np.ndarray): F200W band magnitude of galaxy
+
+    Returns:
+        float or np.ndarray:  Galaxy number density
+
+    """
+    data_path = os.path.join(files('astropath'),'data','galaxy_num_counts','windhorst2023_F200W.npz')
+    data = np.load(data_path)
+    mag_f200w = data['mag']
+    Num = data['Num(N/arcsec/0.5mag))']
+
+    winhorst_spline = interpolate.interp1d(mag_f200w, Num, kind='cubic', fill_value='extrapolate')
+
+    num_counts = winhorst_spline(mag)
+
+    return num_counts
 
 
 def bloom_sigma(rmag):

astropath/path.py

@@ -11,6 +11,8 @@
 from astropath import localization
 from astropath import priors
 
+from IPython import embed
+
 
 class PATH(object):
     """Convenience class to run PATH analysis
@@ -122,7 +124,7 @@ def init_localization(self, ltype:str, **kwargs):
         assert localization.vet_localization(self.localiz), 'Bad candidate prior input'
         logging.info("Localization is ready!")
 
-    def calc_priors(self):
+    def calc_priors(self, ifilter:str='r'):
         """Calculate and normalize the P(O) values for the candidates
 
         Raises:
@@ -138,7 +140,8 @@ def calc_priors(self):
         logging.info("Calculating priors")
         self.raw_prior_Oi = priors.raw_prior_Oi(
             self.cand_prior['P_O_method'], self.candidates['ang_size'], 
-            mag=self.candidates['mag'] if 'mag' in self.candidates.keys() else None)
+            mag=self.candidates['mag'] if 'mag' in self.candidates.keys() else None,
+            filter=ifilter)
 
         # Normalize
         logging.info("Normalizing priors")

astropath/priors.py

@@ -27,6 +27,10 @@
                 help='Label for this prior.'),
 }
 
+splines = {
+    'r': 'driver',
+    'F200W':'windhorst'
+}
 
 
 def raw_prior_Oi(method, ang_size, mag=None, filter='r'):
@@ -52,11 +56,18 @@ def raw_prior_Oi(method, ang_size, mag=None, filter='r'):
         float or np.ndarray:
 
     """
+    # Setting spline_fit
     # Convenience
     if method not in ['identical']:
-        if filter != 'r':
-            raise IOError("Not ready for this.  Best to go with what you have that is closest to r-band")
-        Sigma_m = chance.driver_sigma(mag)
+        if filter not in splines.keys():
+                raise IOError("Not ready for this.  Best to go with what you have that is closest to r-band or F200W")
+        else:
+            spline_fit = splines[filter]
+            if spline_fit == 'driver':
+                Sigma_m = chance.driver_sigma(mag)
+
+            elif spline_fit == 'windhorst':
+                Sigma_m = chance.windhorst_sigma(mag)
 
     # Do it
     if method == 'inverse':