diff --git a/binarycpython/utils/distribution_functions.py b/binarycpython/utils/distribution_functions.py
index ad14543f3b96eddad67c8634ea0c88c3d4cf94d0..f9b0fab6ca465aa474e4b600308e0c649f02bb90 100644
--- a/binarycpython/utils/distribution_functions.py
+++ b/binarycpython/utils/distribution_functions.py
@@ -298,7 +298,7 @@ def Kroupa2001(m, newopts=None):
     )
 
 
-def ktg93(m, newopts):
+def ktg93(m, newopts=None):
     """
     Wrapper for mass distribution of KTG93
     """
@@ -540,7 +540,7 @@ def Izzard2012_period_distribution(P, M1, log10Pmin=1):
     Mwas = M1
     M1 = max(1.15, min(16.3, M1))
 
-    print("Izzard2012 called for M={} (trunc'd to {}), P={}\n".format(Mwas, M1, P))
+    # print("Izzard2012 called for M={} (trunc'd to {}), P={}\n".format(Mwas, M1, P))
 
     # Calculate the normalisations
     # need to normalize the distribution for this mass
@@ -557,12 +557,12 @@ def Izzard2012_period_distribution(P, M1, log10Pmin=1):
             C += dlP * Izzard2012_period_distribution(10 ** lP, M1, log10Pmin)
 
         distribution_constants["Izzard2012"][M1][log10Pmin] = 1.0 / C
-    print(
-        "Normalization constant for Izzard2012 M={} (log10Pmin={}) is\
-        {}\n".format(
-            M1, log10Pmin, distribution_constants["Izzard2012"][M1][log10Pmin]
-        )
-    )
+    # print(
+    #     "Normalization constant for Izzard2012 M={} (log10Pmin={}) is\
+    #     {}\n".format(
+    #         M1, log10Pmin, distribution_constants["Izzard2012"][M1][log10Pmin]
+    #     )
+    # )
 
     lP = math.log10(P)
     # log period
@@ -575,11 +575,11 @@ def Izzard2012_period_distribution(P, M1, log10Pmin=1):
     g = 1.0 / (1.0 + 1e-30 ** (lP - nu))
 
     lPmu = lP - mu
-    print(
-        "M={} ({}) P={} : mu={} sigma={} K={} nu={} norm=%g\n".format(
-            Mwas, M1, P, mu, sigma, K, nu
-        )
-    )
+    # print(
+    #     "M={} ({}) P={} : mu={} sigma={} K={} nu={} norm=%g\n".format(
+    #         Mwas, M1, P, mu, sigma, K, nu
+    #     )
+    # )
 
     # print "FUNC $distdata{Izzard2012}{$M}{$log10Pmin} * (exp(- (x-$mu)**2/(2.0*$sigma*$sigma) ) + $K/MAX(0.1,$lP)) * $g;\n";