diff --git a/pfam_matrix.py b/pfam_matrix.py
new file mode 100644
index 0000000..9b0db92
--- /dev/null
+++ b/pfam_matrix.py
@@ -0,0 +1,137 @@
+import pandas as pd 
+import numpy as np 
+import progressbar 
+import os
+import json
+import sys
+from prepare_synteny_matrix import read_data_homology,load_neighbor_genes
+from process_data import create_map_list
+from process_negative import read_database_txt
+
+
+def get_score_overlap(x,y,pfam_db,pfam_map):
+    df_1=pfam_db.loc[pfam_map[x]]
+    df_2=pfam_db.loc[pfam_map[y]]
+    l=list(df_2.domain)
+    c=0
+    c_1=0
+    for _,row in df_1.iterrows():
+        if row.domain in l:#check if the domain exists in the list
+            c_1+=1
+            st=int(df_2[df_2["domain"]==row.domain].hmm_from)#get the start
+            end=int(df_2[df_2["domain"]==row.domain].hmm_to)#get the end
+            if (int(row.hmm_from)>st and int(row.hmm_from)<end) or (int(row.hmm_to)>st and int(row.hmm_from)<end):#check if the domain is a ovelapping domain
+                c+=1
+    return c/max(len(df_1),len(df_2))  
+
+def pfam_matrix(g1,g2,n,pfam_db,gmap,pfam_map):
+    pm=np.zeros((n,n))
+    for i in range(n):
+        if g1[i]=="NULL_GENE":
+            continue
+        try:
+            _=gmap[g1[i]]
+        except:
+            continue
+        for j in range(n):
+            if g2[j]=="NULL_GENE":
+                continue
+            try:
+                _=gmap[g2[j]]
+            except:
+                continue
+            pm[i][j]=get_score_overlap(g1[i],g2[j],pfam_db,pfam_map)
+    return pm
+
+def create_pfam_matrix(df,lsy,pfam_db,pfam_map):
+    n=3
+    glist=list(pfam_db.gene_stable_id)
+    gmap=create_map_list(glist)
+    pg=[]
+    indexes=[]
+    for index,row in progressbar.progressbar(df.iterrows()):
+        g1=str(row["gene_stable_id"])
+        g2=str(row["homology_gene_stable_id"])
+        x=[]
+        y=[]
+        try:
+            _=lsy[g1]
+            _=lsy[g2]
+        except:
+            continue
+        for i in range(len(lsy[g1]['b'])-1,-1,-1):
+            x.append(lsy[g1]['b'][i])
+        x.append(g1)
+        for k in lsy[g1]['f']:
+            x.append(k)
+
+        for i in range(len(lsy[g2]['b'])-1,-1,-1):
+            y.append(lsy[g2]['b'][i])
+        y.append(g2)
+        for k in lsy[g2]['f']:
+            y.append(k)
+        
+        assert(len(x)==len(y))
+        assert(len(x)==(2*n+1))
+        pmtemp=pfam_matrix(x,y,2*n+1,pfam_db,gmap,pfam_map)
+        pg.append(pmtemp)
+        indexes.append(index)
+    return np.array(pg),np.array(indexes)
+
+def create_pfam_map(pfam_db):
+    pfam_map={}
+    for index,row in progressbar.progressbar(pfam_db.iterrows()):
+        try:
+            _=pfam_map[row.gene_stable_id]
+        except:
+            pfam_map[row.gene_stable_id]=[]
+        pfam_map[row.gene_stable_id].append(index)
+    return pfam_map
+
+def main_positive():
+    if not os.path.isdir("processed/pfam_matrices"):
+        os.mkdir("processed/pfam_matrices")
+    a_h,d_h=read_data_homology("data_homology")
+    lsy=load_neighbor_genes()
+    pfam_db=pd.read_hdf("pfam_db_positive.h5")
+    pfam_map=create_pfam_map(pfam_db)
+    ndir="processed/pfam_matrices/"
+    nf1="pfam_matrices"
+    nf3="pfam_indexes"
+    for i in range(len(a_h)):
+        df=a_h[i]
+        print(len(df))
+        pfam_matrices,indexes=create_pfam_matrix(df,lsy,pfam_db,pfam_map)
+        np.save(ndir+str(d_h[i])+"_"+nf1,pfam_matrices)
+        np.save(ndir+str(d_h[i])+"_"+nf3,indexes)
+        print(len(indexes))
+
+def read_data_negative(arg):
+    df=read_database_txt(arg[-1])
+    name=arg[-1].split(".")[0]
+    ind=np.load("processed/synteny_matrices/"+name+"_indexes.npy")
+    df=df.loc[ind]
+    pfam_db=pd.read_hdf("pfam_db_negative.h5")
+    pfam_map=create_pfam_map(pfam_db)
+    with open("processed/neighbor_genes_"+name+".json","r") as file:
+        lsy=dict(json.load(file))
+    return df,pfam_db,pfam_map,lsy,name
+
+def main_negative(arg):
+    df,pfam_db,pfam_map,lsy,name=read_data_negative(arg)
+    ndir="processed/pfam_matrices/"
+    nf1="pfam_matrices"
+    nf3="pfam_indexes"
+    print(len(df))
+    pfam_matrices,indexes=create_pfam_matrix(df,lsy,pfam_db,pfam_map)
+    np.save(ndir+name+"_"+nf1,pfam_matrices)
+    np.save(ndir+name+"_"+nf3,indexes)
+    print(len(indexes))
+
+def main():
+    arg=sys.argv
+    #main_positive()
+    main_negative(arg)
+
+if __name__=="__main__":
+    main()