diff --git a/src/grid.py b/src/grid.py
index d132ef9..19a3867 100644
--- a/src/grid.py
+++ b/src/grid.py
@@ -717,7 +717,6 @@ def reinitialization(self,initialization,microbes_pp,output,mode,pulse,*args):
         # last: assign microbes to each grid box randomly based on prior densities
         choose_taxa = np.zeros((self.n_taxa,self.gridsize), dtype='int8')
         for i in range(self.n_taxa):
-            choose_taxa[i,:] = np.random.binomial(1, frequencies[i], self.gridsize)
             freq=np.float64(frequencies.iloc[1]) #working independent of indexing (based on location not index labels)
             p_vec = np.array([freq, 1.0 - freq], dtype=np.float64) #solve numoy issues
             p_vec = p_vec / p_vec.sum()
diff --git a/src/utility.py b/src/utility.py
index eeb6405..3e97c41 100644
--- a/src/utility.py
+++ b/src/utility.py
@@ -10,7 +10,7 @@
 import numpy as np
 import pandas as pd
 import pickle
-from scipy.stats import distributions
+from scipy.stats import uniform, norm # instead of calling the whole distributions package (heavy) we call only the needed objects
 
 
 def LHS(n,loc,upc,dist):
@@ -34,9 +34,9 @@ def LHS(n,loc,upc,dist):
     
     scale = upc - loc
     if dist == 'uniform':
-        rv = distributions.uniform(loc=loc, scale=scale)  
+        rv = uniform(loc=loc, scale=scale)  
     elif dist == 'normal':
-        rv = distributions.norm(loc=loc, scale=scale)
+        rv = norm(loc=loc, scale=scale)
     
     lhs = rv.ppf(points)
     
@@ -87,4 +87,4 @@ def export(output,site,name):
     """
     
     with open(site + "_" + str(name) + ".pickle", "wb") as f:
-        pickle.dump(output, f, pickle.HIGHEST_PROTOCOL)
\ No newline at end of file
+        pickle.dump(output, f, pickle.HIGHEST_PROTOCOL)