optimize redshifting for speed

pcigale/sed_modules/redshifting.py

@@ -76,10 +76,10 @@ def igm_transmission(wavelength, redshift):
     # n = 3 - 9 -> 1
     for n in range(3, n_transitions_max):
         if n <= 5:
-            w = np.where(z_n[n, :] < 3)
+            w = z_n[n, :] < 3
             tau_n[n, w] = (tau_a * fact[n] *
                            np.power(0.25 * (1. + z_n[n, w]), (1. / 3.)))
-            w = np.where(z_n[n, :] >= 3)
+            w = z_n[n, :] >= 3
             tau_n[n, w] = (tau_a * fact[n] *
                            np.power(0.25 * (1. + z_n[n, w]), (1. / 6.)))
         elif 5 < n <= 9:
@@ -91,11 +91,11 @@ def igm_transmission(wavelength, redshift):
 
     for n in range(2, n_transitions_max):
         # If z_n>=redshift or z_n<0, the photon cannot be absorbed by Lyman n->1
-        w = np.where( (z_n[n, :] >= redshift) | (z_n[n, :] < 0) )
+        w = (z_n[n, :] >= redshift) | (z_n[n, :] < 0)
         tau_n[n, w] = 0.
 
     z_l = wavelength / lambda_limit - 1.
-    w = np.where( z_l < redshift )
+    w = z_l < redshift
 
     tau_l_igm = np.zeros_like(wavelength)
     tau_l_igm[w] = (0.805 * np.power(1. + z_l[w], 3) *
@@ -120,7 +120,7 @@ def igm_transmission(wavelength, redshift):
     tau_l_lls[w] = n0 * ((term1 - term2) * term3 - term4)
 
     # Reset for short wavelength (z_l<0)
-    w = np.where( z_l<0 )
+    w = z_l<0
     # Get the normalization factor at z_l=0
     tau_norm_l_igm = np.interp(0, z_l, tau_l_igm)
     tau_norm_l_lls = np.interp(0, z_l, tau_l_lls)