diff --git a/binarycpython/utils/distribution_functions.py b/binarycpython/utils/distribution_functions.py
index e8e0176a5fefd1185b4dc200cc368ec5c8b3de92..9e6658e771d0ac16047bac4e87c741212667297c 100644
--- a/binarycpython/utils/distribution_functions.py
+++ b/binarycpython/utils/distribution_functions.py
@@ -24,6 +24,9 @@ import functools
 import math
 import json
 
+import traceback
+import sys
+
 from typing import Union
 
 import numpy as np
@@ -786,7 +789,7 @@ def interpolate_in_mass_izzard2012(
 
 
 def Izzard2012_period_distribution(
-    P: Union[int, float], M1: Union[int, float], log10Pmin: Union[int, float] = 1
+    P: Union[int, float], M1: Union[int, float], log10Pmin: Union[int, float] = -1.0
 ) -> Union[int, float]:
     """
     period distribution which interpolates between
@@ -809,7 +812,6 @@ def Izzard2012_period_distribution(
     """
 
     # Check if there is input and force it to be at least 1
-    log10Pmin //= -1.0
     log10Pmin = max(-1.0, log10Pmin)
 
     # save mass input and limit mass used (M1 from now on) to fitted range
@@ -828,16 +830,17 @@ def Izzard2012_period_distribution(
         N = 200.0  # Resolution for normalisation. I hope 1000 is enough
         dlP = (10.0 - log10Pmin) / N
         C = 0  # normalisation constant.
+        #print("LOOP",log10Pmin)
         for lP in np.arange(log10Pmin, 10, dlP):
             C += dlP * Izzard2012_period_distribution(10 ** lP, M1, log10Pmin)
 
         distribution_constants["Izzard2012"][M1][log10Pmin] = 1.0 / C
-    # print(
-    #     "Normalisation constant for Izzard2012 M={} (log10Pmin={}) is\
-    #     {}\n".format(
-    #         M1, log10Pmin, distribution_constants["Izzard2012"][M1][log10Pmin]
-    #     )
-    # )
+        #print(
+        # "Normalisation constant for Izzard2012 M={} (log10Pmin={}) is\
+        # {}\n".format(
+        #     M1, log10Pmin, distribution_constants["Izzard2012"][M1][log10Pmin]
+        # )
+        #)
 
     lP = math.log10(P)
     # log period