diff --git a/access_data_rest.py b/access_data_rest.py
new file mode 100644
index 0000000..368e336
--- /dev/null
+++ b/access_data_rest.py
@@ -0,0 +1,49 @@
+import json
+import requests
+import progressbar
+from create_synteny_matrix import update
+
+def update_rest(data):
+    gids={}
+    with open("processed/not_found.json","r") as file:
+        gids=dict(json.load(file))
+
+    gids=list(gids.keys())
+
+    geneseq={}
+
+    server = "https://rest.ensembl.org"
+    ext = "/sequence/id?type=cds"
+    headers={ "Content-Type" : "application/json", "Accept" : "application/json"}
+
+    for i in progressbar.progressbar(range(0,len(gids)-50,50)):
+        ids=dict(ids=list(gids[i:i+50]))
+        while(1):
+            try:
+                r = requests.post(server+ext, headers=headers, data=str(json.dumps(ids)))
+                if not r.ok:
+                    r.raise_for_status()
+                gs=r.json()
+                tgs={}
+                for g in gs:
+                    tgs[g["query"]]=g["seq"]
+                geneseq.update(tgs)
+                break
+            except Exception as e:
+                print("Error:",e)
+                continue
+
+    data.update(geneseq)
+    for genes in gids:
+        try:
+            _=data[genes]
+        except:
+            print(genes)
+            update(data,genes)
+
+    with open("processed/gene_sequences.json","w") as file:
+        json.dump(data,file)
+
+    print("Gene Sequences Updated Successfully")
+    return data
+            
diff --git a/create_synteny_matrix.py b/create_synteny_matrix.py
index ca7b2ee..5695dc7 100644
--- a/create_synteny_matrix.py
+++ b/create_synteny_matrix.py
@@ -12,17 +12,31 @@ def update(gene_seq,gene):
     while(1):
         try:
             server = "https://rest.ensembl.org"
-            ext = "/sequence/id/"+str(gene)+"?"
+            ext = "/sequence/id/"+str(gene)+"?type=cds;multiple_sequences=1"
 
-            r = requests.get(server+ext, headers={ "Content-Type" : "text/plain"})
+            r = requests.get(server+ext, headers={ "Content-Type" : "application/json"})
 
             if not r.ok:
                 r.raise_for_status()
                 sys.exit()
-
-            gene_seq[gene]=str(r.text)
-            return
-        except:
+            r=r.json()
+            if len(r)==1:
+                r=dict(r[0])
+                gene_seq[gene]=str(r["seq"])
+                return
+            else:
+                maxi=0
+                maxlen=0
+                for i in range(len(r)):
+                    m=r[i]
+                    m=dict(m)
+                    if len(m["seq"])>maxlen:
+                        maxi=i
+                r=dict(r[maxi])
+                gene_seq[gene]=str(r["seq"])
+                return
+        except Exception as e:
+            print("\nError:",e)
             continue
 
 def create_synteny_matrix_mul(gene_seq,g1,g2,n):
@@ -32,7 +46,7 @@ def create_synteny_matrix_mul(gene_seq,g1,g2,n):
         try:
             temp=gene_seq[gene]
         except:
-            #print("Updating gene sequences for gene:",gene)
+            print("Updating gene sequences for gene:",gene)
             update(gene_seq,gene)
     for gene in g2:
         if gene=="NULL_GENE":
@@ -40,7 +54,7 @@ def create_synteny_matrix_mul(gene_seq,g1,g2,n):
         try:
             temp=gene_seq[gene]
         except:
-            #print("Updating gene sequences for gene:",gene)
+            print("Updating gene sequences for gene:",gene)
             update(gene_seq,gene)
     #print(n)
     sm=np.zeros((n,n,2))
@@ -61,6 +75,7 @@ def create_synteny_matrix_mul(gene_seq,g1,g2,n):
                 sml[i][j][0]=result/(norm_len)
                 _,result,_=local_pairwise_align_ssw(DNA(gene_seq[g1[i]]),DNA(gene_seq[g2[j]][::-1]))
                 sml[i][j][1]=result/(norm_len)
+
             except:
                 return np.zeros((n,n,2)),np.zeros((n,n,2))
     return sm,sml
@@ -70,12 +85,12 @@ def synteny_matrix(gene_seq,hdf,lsy,n,enable_break):
     sl=[]
     t=0
     ind=[]
-    start=time.time()
     for index,row in progressbar.progressbar(hdf.iterrows()):
         g1=str(row["gene_stable_id"])
         g2=str(row["homology_gene_stable_id"])
         x=[]
         y=[]
+        t+=1
         try:
             temp=lsy[g1]
             temp=lsy[g2]
@@ -92,7 +107,7 @@ def synteny_matrix(gene_seq,hdf,lsy,n,enable_break):
         y.append(g2)
         for k in lsy[g2]['f']:
             y.append(k)
-
+        
         assert(len(x)==len(y))
         assert(len(x)==(2*n+1))
         smgtemp,smltemp=create_synteny_matrix_mul(gene_seq,x,y,2*n+1)
@@ -101,10 +116,9 @@ def synteny_matrix(gene_seq,hdf,lsy,n,enable_break):
         sg.append(smgtemp)
         sl.append(smltemp)
         ind.append(index)
-        t+=1
         if t==5 and enable_break==1:
             break
-    end=time.time()
     #print("Time Taken:",end-start)
     #print("Average Time:",(end-start)/len(sg))
+    print(t)
     return np.array(sg),np.array(sl),np.array(ind)
diff --git a/create_synteny_matrix_v2.py b/create_synteny_matrix_v2.py
new file mode 100644
index 0000000..2efe511
--- /dev/null
+++ b/create_synteny_matrix_v2.py
@@ -0,0 +1,119 @@
+import numpy as np
+import requests
+import edlib as ed
+import pandas as pd
+import time
+import sys
+import progressbar
+from skbio.alignment import local_pairwise_align_ssw
+from skbio import DNA,TabularMSA,RNA
+
+def update(gene_seq,gene):
+    while(1):
+        try:
+            server = "https://rest.ensembl.org"
+            ext = "/sequence/id/"+str(gene)+"?type=cds;multiple_sequences=1"
+
+            r = requests.get(server+ext, headers={ "Content-Type" : "application/json"})
+
+            if not r.ok:
+                r.raise_for_status()
+                sys.exit()
+            r=r.json()
+            if len(r)==1:
+                r=dict(r[0])
+                gene_seq[gene]=str(r["seq"])
+                return
+            else:
+                maxi=0
+                maxlen=0
+                for i in range(len(r)):
+                    m=r[i]
+                    m=dict(m)
+                    if len(m["seq"])>maxlen:
+                        maxi=i
+                r=dict(r[maxi])
+                gene_seq[gene]=str(r["seq"])
+                return
+        except Exception as e:
+            print("\nError:",e)
+            continue
+
+def create_synteny_matrix_mul(gene_seq,g1,g2,n):
+    for gene in g1:
+        if gene=="NULL_GENE":
+            continue
+        try:
+            temp=gene_seq[gene]
+        except:
+            print("Updating gene sequences for gene:",gene)
+            update(gene_seq,gene)
+    for gene in g2:
+        if gene=="NULL_GENE":
+            continue
+        try:
+            temp=gene_seq[gene]
+        except:
+            print("Updating gene sequences for gene:",gene)
+            update(gene_seq,gene)
+    #print(n)
+    sm=np.zeros((n,n,2))
+    sml=np.zeros((n,n,2))
+    for i in range(n):
+        if g1[i]=="NULL_GENE":
+            continue
+        for j in range(n):
+            if g2[j]=="NULL_GENE":
+                continue
+            norm_len=max(len(gene_seq[g1[i]]),len(gene_seq[g2[j]]))
+            try:
+                result = ed.align(gene_seq[g1[i]],gene_seq[g2[j]], mode="NW", task="distance")
+                sm[i][j][0]=result["editDistance"]/(norm_len)
+                result = ed.align(gene_seq[g1[i]],gene_seq[g2[j]][::-1], mode="NW", task="distance")
+                sm[i][j][1]=result["editDistance"]/(norm_len)
+            except:
+                return np.zeros((n,n,2)),np.zeros((n,n,2))
+    return sm,sml
+
+def synteny_matrix(gene_seq,hdf,lsy,n,enable_break):
+    sg=[]
+    sl=[]
+    t=0
+    ind=[]
+    for index,row in progressbar.progressbar(hdf.iterrows()):
+        g1=str(row["gene_stable_id"])
+        g2=str(row["homology_gene_stable_id"])
+        x=[]
+        y=[]
+        t+=1
+        try:
+            temp=lsy[g1]
+            temp=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))
+        smgtemp,smltemp=create_synteny_matrix_mul(gene_seq,x,y,2*n+1)
+        if np.all(smgtemp==0):
+            continue
+        sg.append(smgtemp)
+        sl.append(smltemp)
+        ind.append(index)
+        if t==5 and enable_break==1:
+            break
+    #print("Time Taken:",end-start)
+    #print("Average Time:",(end-start)/len(sg))
+    print(t)
+    return np.array(sg),np.array(sl),np.array(ind)
diff --git a/get_data.py b/get_data.py
index a2ee986..96634f4 100644
--- a/get_data.py
+++ b/get_data.py
@@ -2,7 +2,7 @@ import sys
 import os
 from req_data import get_data_file,download_data
 from read_data import read_data_genome,read_data_homology
-from process_data import list_dict_genomes
+from process_data import list_dict_genomes,create_chromosome_maps
 
 def get_data_genome(arg,dir):
     a=[]
@@ -22,15 +22,16 @@ def get_data_genome(arg,dir):
     if arg[4]=="-r":
         a,d=read_data_genome(dir,a,d)
         assert(len(a)==len(d))
-
+        print("Creating Maps:")
         ld,ldg=list_dict_genomes(a,d)
+        cmap,cimap=create_chromosome_maps(a,d)
 
         assert(len(ld)==len(ldg))
 
         for i in range(len(ld)):
             assert(len(ld[i])==len(ldg[i]))
 
-    return ld,ldg,a,d
+    return cmap,cimap,ld,ldg,a,d
 
 def get_data_homology(arg,dir):
     a_h=[]
diff --git a/main.py b/main.py
deleted file mode 100644
index 47d56e2..0000000
--- a/main.py
+++ /dev/null
@@ -1,53 +0,0 @@
-import sys
-import numpy as np
-import pandas as pd
-import json
-from get_data import get_data_homology,get_data_genome
-from process_data import create_data_homology_ls
-from read_get_gene_seq import read_gene_sequences
-from create_synteny_matrix import synteny_matrix
-from tree_data import create_tree_data
-from create_train_data import train_data
-from train import train
-
-arg=sys.argv
-arg=arg[1:]
-
-if len(arg)!=5:
-    print("No. of arguments more or less. Please check")
-    sys.exit(1)
-
-dir_g="data"
-ld,ldg,a,d=get_data_genome(arg,dir_g)
-
-dir_hom="data_homology"
-a_h,d_h=get_data_homology(arg,dir_hom)
-
-if arg[-1]=="-d":
-    sys.exit(1)
-
-print("Data Read")
-
-n=2 #no. of numbers neighbors
-save_after=50000 #to save data after n steps
-lsy=create_data_homology_ls(a_h,d_h,n,a,d,ld,ldg,save_after)
-print(len(lsy))
-
-print("Neighbor Genes Found")
-
-gene_sequences=read_gene_sequences(a_h[0],lsy,"geneseq","gene_sequences")
-
-synteny_matrices,indexes=synteny_matrix(gene_sequences,a_h[0],lsy,n)
-print("Synteny Matrices are created successfully\n",len(indexes),"\n",len(synteny_matrices))
-np.save("synteny_matrices",synteny_matrices)
-np.save("indexes",indexes)
-
-with open("gene_seq_updated.json","w") as file:
-    json.dump(gene_sequences,file)
-
-df=a_h[0].loc[indexes]
-branch_length_species,branch_length_homology_species,distance,dist_p_s,dist_p_hs=create_tree_data("species_tree.tree",df)
-
-train_synteny_matrices,train_branch_length_species,train_branch_length_homology_species,train_mean_gene_length,train_dist_p_s,train_dist_p_hs,train_distance,train_labels=train_data(indexes,synteny_matrices,df,branch_length_species,branch_length_homology_species,distance,dist_p_s,dist_p_hs,gene_sequences)
-
-train(train_synteny_matrices,train_branch_length_species,train_branch_length_homology_species,train_mean_gene_length,train_dist_p_s,train_dist_p_hs,train_distance,train_labels)
diff --git a/my.txt b/my.txt
deleted file mode 100644
index 6263a18..0000000
--- a/my.txt
+++ /dev/null
@@ -1,3 +0,0 @@
-ftp://ftp.ensembl.org/pub/release-96/gtf/lepisosteus_oculatus/Lepisosteus_oculatus.LepOcu1.96.gtf.gz
-ftp://ftp.ensembl.org/pub/release-96/gtf/mola_mola/Mola_mola.ASM169857v1.96.gtf.gz
-ftp://ftp.ensembl.org/pub/release-96/gtf/homo_sapiens/Homo_sapiens.GRCh38.96.gtf.gz
diff --git a/open_synteny_matrices.py b/open_synteny_matrices.py
index ee0dd85..5eff2ba 100644
--- a/open_synteny_matrices.py
+++ b/open_synteny_matrices.py
@@ -28,6 +28,7 @@ while(1):
         synteny_matrices_global=np.load(ndir+str(d_h[ch-1])+nf1+".npy")
         synteny_matrices_local=np.load(ndir+str(d_h[ch-1])+nf2+".npy")
         indexes=np.load(ndir+str(d_h[ch-1])+nf3+".npy")
+        print(len(indexes))
         break
     except:
         print("Choice invalid or incomplete files!!!!!. Try Another Index.")
diff --git a/open_synteny_matrices_negative.py b/open_synteny_matrices_negative.py
new file mode 100644
index 0000000..a37d8a1
--- /dev/null
+++ b/open_synteny_matrices_negative.py
@@ -0,0 +1,112 @@
+import numpy as np 
+import pandas as pd
+import os
+import json
+import gc
+from read_data import read_data_homology
+import matplotlib.pyplot as plt
+import seaborn as sns
+from matplotlib.backends.backend_pdf import PdfPages
+
+a_h=[]
+d_h=[]
+df=pd.read_hdf("negative_dataset.h5",key="ndf")
+
+ndir="processed/synteny_matrices/"
+nf1="_synteny_matrices_global"
+nf2="_synteny_matrices_local"
+nf3="_indexes"
+
+lsy={}
+with open("processed/neighbor_genes.json","r") as file:
+    lsy=dict(json.load(file))
+print("Neighbor Genes Loaded")
+
+synteny_matrices_global=np.load(ndir+"negative_dataset"+nf1+str(6)+".npy")
+synteny_matrices_local=np.load(ndir+"negative_dataset"+nf2+str(6)+".npy")
+indexes=np.load(ndir+"negative_dataset"+nf3+str(6)+".npy")
+df=df.loc[indexes]
+
+print(indexes[0:1000])
+ng=["Levenshtein Distance","Levenshtein Distance Reverse"]
+nl=["Local Alignment Score","Local Alignment Score Reverse"]
+
+inddict={}
+for i in range(len(indexes)):
+    inddict[indexes[i]]=i
+
+while(1):
+    try:
+        i=int(input("Enter the index:"))
+    except:
+        break
+    if i in inddict:
+        font = {'family': 'sans-serif',
+        'color':  'darkturquoise',
+        'weight': 'heavy',
+        'size': 20,
+        }
+        pdf=PdfPages(str(i)+".pdf")
+        print("Species",df.loc[i].species)
+        print("Homology Species",df.loc[i].homology_species)
+        print("Gene Stable Id:",df.loc[i].gene_stable_id)
+        print("Homology Gene Stable Id:",df.loc[i].homology_gene_stable_id)
+        text="Species:"+df.loc[i].species
+        text+="\n"+"Gene Stable Id:"+df.loc[i].gene_stable_id
+        text+="\n"+"Homology Species:"+df.loc[i].homology_species
+        text+="\n"+"Homology Gene Stable Id:"+df.loc[i].homology_gene_stable_id
+        text+="\n"+"Homology Type:"+"Non Homology"
+        fp=plt.figure(figsize=(10,10))
+        fp.text(0.5,0.5,text,ha="center",fontdict=font)
+        pdf.savefig()
+        plt.close()
+        print("Global aligned matrix:")
+
+        g1=df.loc[i].gene_stable_id
+        g2=df.loc[i].homology_gene_stable_id
+        x=[]
+        y=[]
+        for n in range(len(lsy[g1]['b'])-1,-1,-1):
+            x.append(lsy[g1]['b'][n])
+        x.append(g1)
+        for k in lsy[g1]['f']:
+            x.append(k)
+
+        for n in range(len(lsy[g2]['b'])-1,-1,-1):
+            y.append(lsy[g2]['b'][n])
+        y.append(g2)
+        for k in lsy[g2]['f']:
+            y.append(k)
+
+        loc=inddict[i]
+        sg=synteny_matrices_global[loc]
+        sl=synteny_matrices_local[loc]
+        for m in range(sg.shape[-1]):
+            matrix=sg[:,:,m]
+            print(matrix)            
+            fig,ax=plt.subplots(figsize=(8,8))
+            ax.set_xlabel(str(df.loc[i].homology_species),fontsize=10)
+            ax.set_ylabel(str(df.loc[i].species),fontsize=10)
+            hmap=sns.heatmap(matrix,xticklabels=y, yticklabels=x,annot=True,ax=ax,linewidths=.5,cmap="YlGnBu",annot_kws={"size": 10})
+            hmap.figure.subplots_adjust(left=0.33,bottom=0.33,right=0.79,top=0.79)
+            ax.set_title(ng[m])
+            #plt.text(1,0.5,text,size=10)
+            pdf.savefig()
+            plt.show()
+
+        print("Local Alignment Matrix")
+        for m in range(sg.shape[-1]):
+            matrix=sl[:,:,m]
+            print(matrix)
+            fig,ax=plt.subplots(figsize=(8,8))
+            ax.set_xlabel(df.loc[i].homology_species)
+            ax.set_ylabel(df.loc[i].species)
+            hmap=sns.heatmap(matrix,xticklabels=y, yticklabels=x,annot=True,ax=ax,linewidths=.5,cmap="YlGnBu",annot_kws={"size": 10})
+            hmap.figure.subplots_adjust(left=0.27,bottom=0.29,right=0.92)
+            ax.set_title(nl[m])
+            pdf.savefig()
+            plt.show()
+        pdf.close()
+    else:
+        print("Index not found")
+
diff --git a/prepare_data.py b/prepare_data.py
deleted file mode 100644
index b5ea833..0000000
--- a/prepare_data.py
+++ /dev/null
@@ -1,87 +0,0 @@
-import sys
-import numpy as np
-import pandas as pd
-import json
-import os
-from get_data import get_data_homology,get_data_genome
-from process_data import create_data_homology_ls
-from read_get_gene_seq import read_gene_sequences
-from create_synteny_matrix import synteny_matrix
-from tree_data import create_tree_data
-from prepare_train_data import train_data
-
-if not os.path.exists("processed"):
-    os.mkdir("processed")
-
-arg=sys.argv
-arg=arg[1:]
-
-enable_break=0
-
-if arg[-1]=="-test":
-    enable_break=1
-
-arg=arg[:-1]
-
-if len(arg)!=5:
-    print("No. of arguments more or less. Please check")
-    sys.exit(1)
-
-dir_g="data"
-ld,ldg,a,d=get_data_genome(arg,dir_g)
-
-dir_hom="data_homology"
-a_h,d_h=get_data_homology(arg,dir_hom)
-
-if arg[-1]=="-d":
-    sys.exit(1)
-
-print("Data Read")
-
-n=3 #no. of numbers neighbors
-save_after=0 #to save data after n steps
-
-if enable_break==1:
-    save_after=3
-else:
-    save_after=1000000
-
-lsy=create_data_homology_ls(a_h,d_h,n,a,d,ld,ldg,save_after,enable_break)
-print(len(lsy))
-
-print("Neighbor Genes Found")
-
-if enable_break==1:
-    gene_sequences=read_gene_sequences(a_h[0][0:10],lsy,"geneseq","gene_sequences")
-else:    
-    gene_sequences=read_gene_sequences(a_h[0],lsy,"geneseq","gene_sequences")
-
-if enable_break==1:
-    synteny_matrices_global,synteny_matrices_local,indexes=synteny_matrix(gene_sequences,a_h[0][0:10],lsy,n,enable_break)
-else:    
-    synteny_matrices_global,synteny_matrices_local,indexes=synteny_matrix(gene_sequences,a_h[0],lsy,n,enable_break)
-
-print("Synteny Matrices are created successfully\n",len(indexes),"\n",len(synteny_matrices_global))
-np.save("processed/synteny_matrices_global",synteny_matrices_global)
-np.save("processed/synteny_matrices_local",synteny_matrices_local)
-np.save("processed/indexes",indexes)
-
-with open("processed/gene_seq_updated.json","w") as file:
-    json.dump(gene_sequences,file)
-
-df=a_h[0].loc[indexes]
-branch_length_species,branch_length_homology_species,distance,dist_p_s,dist_p_hs=create_tree_data("species_tree.tree",df)
-
-train_synteny_matrices_global,train_synteny_matrices_local,train_branch_length_species,train_branch_length_homology_species,train_mean_gene_length,train_dist_p_s,train_dist_p_hs,train_distance,train_labels=train_data(indexes,synteny_matrices_global,synteny_matrices_local,df,branch_length_species,branch_length_homology_species,distance,dist_p_s,dist_p_hs,gene_sequences)
-
-np.save("processed/train_synteny_matrices_global",train_synteny_matrices_global)
-np.save("processed/train_synteny_matrices_local",train_synteny_matrices_local)
-np.save("processed/train_branch_length_species",train_branch_length_species)
-np.save("processed/train_branch_length_homology_species",train_branch_length_homology_species)
-np.save("processed/train_mean_gene_length",train_mean_gene_length)
-np.save("processed/train_dist_p_s",train_dist_p_s)
-np.save("processed/train_dist_p_hs",train_dist_p_hs)
-np.save("processed/train_distance",train_distance)
-np.save("processed/train_labels",train_labels)
-
-print("Data Saved Successfully to processed :)")
diff --git a/prepare_negative_dataset.py b/prepare_negative_dataset.py
new file mode 100644
index 0000000..151ebec
--- /dev/null
+++ b/prepare_negative_dataset.py
@@ -0,0 +1,82 @@
+import sys
+import numpy as np
+import pandas as pd
+import os
+import gc
+import progressbar
+import random
+from read_data import read_data_homology,read_data_genome
+
+arg=sys.argv
+arg=arg[1:]
+
+nos=int(arg[0])
+seed=int(arg[1])
+random.seed(seed)
+
+a_h,d_h=read_data_homology("data_homology")
+gmap={}
+indexes_hd=[]
+for df in progressbar.progressbar(a_h):
+    indexes_hd.append(list(df.index.values))
+    hgids=df.homology_gene_stable_id.unique()
+    for h in hgids:
+        gmap[h]=1
+
+a=[]
+d={}
+a,d=read_data_genome("data",a,d)
+assert(len(a)==len(d))
+indexes_gd=[list(df.index.values) for df in a]
+d=list(d.keys())
+ld=[]
+for i in progressbar.progressbar(range(len(a))):
+    df=a[i]
+    ldg=[]
+    for _,row in df.iterrows():
+        gid=row.gene_id
+        try:
+            temp=gmap[gid]
+        except:
+            ldg.append(gid)
+    ld.append(ldg)
+assert(len(ld)==len(a))
+
+negativesamp={}
+nohd=len(a_h)
+nogd=len(a)
+rows=[]
+for i in progressbar.progressbar(range(nos)):
+    while(1):
+        try:
+            slh=random.randrange(nohd)#sample a homology database
+            slg=random.randrange(nogd)#sample a gene annotation file
+            slhd=a_h[slh]#select the homology database
+            indexes=indexes_hd[slh]#select the respective indexes
+            ldg=ld[slg]#select the given gene_id annotations
+            ind1=random.randrange(len(ldg))#sample a gene
+            g1=ldg[ind1]#get the gene id
+            ind2=random.randrange(len(indexes))#sample a row
+            row=slhd.loc[indexes[ind2]]#get the row from the database
+            r={}
+            try:
+                _=negativesamp[row.gene_stable_id+g1]#check if they exist in the database
+                _=negativesamp[g1+row.gene_stable_id]#check if they exist in the database
+                continue
+            except:
+                r["gene_stable_id"]=row.gene_stable_id#add it to the row
+                r["species"]=row.species#add speccies to the row
+                r["homology_gene_stable_id"]=g1#add the gene_id to the row
+                r["homology_species"]=d[slg]#add the gene species to the row
+                rows.append(r)#add it to the rows dict
+                negativesamp[row.gene_stable_id+g1]=1#add to the map so duplicate samples are avoided
+                break 
+        except:
+            continue  
+
+a_h=[]
+gc.collect()
+
+ndf=pd.DataFrame(rows)
+print(ndf[0:10])
+ndf.to_hdf("negative_dataset.h5",key="ndf",mode="w")
diff --git a/prepare_other_factors.py b/prepare_other_factors.py
new file mode 100644
index 0000000..927faaa
--- /dev/null
+++ b/prepare_other_factors.py
@@ -0,0 +1,55 @@
+import pandas as pd
+import numpy as np 
+import json
+import gc
+import pickle
+from read_data import read_data_homology
+from tree_data import create_tree_data
+
+a_h,d_h=read_data_homology("data_homology")
+d_h=list(d_h.keys())
+
+smg_name="_synteny_matrices_global.npy"
+sml_name="_synteny_matrices_local.npy"
+smi_name="_indexes.npy"
+dir_name="processed/synteny_matrices/"
+label=dict(ortholog_one2one=0,other_paralog=1,ortholog_one2many=1,ortholog_many2many=1,within_species_paralog=0)
+
+rows=[]
+for i in range(len(a_h)):
+    df=a_h[i]
+    n=d_h[i]
+    try:
+        smg=np.load(dir_name+n+smg_name)
+        sml=np.load(dir_name+n+sml_name)
+        indexes=np.load(dir_name+n+smi_name)
+    except:
+        print("Incomplete data for:",n)
+        continue
+    df=df.loc[indexes]
+    branch_length_species,branch_length_homology_species,distance,dist_p_s,dist_p_hs=create_tree_data("species_tree.tree",df)
+    assert(len(branch_length_species)==len(df))
+    assert(len(sml)==len(distance))
+    for i in range(len(df)):
+        index=indexes[i]
+        row=df.loc[index]
+        r={}
+        r["species"]=row["species"]
+        r["homology_species"]=row["homology_species"]
+        r["gene_stable_id"]=row["gene_stable_id"]
+        r["homology_gene_stable_id"]=row["homology_gene_stable_id"]
+        r["label"]=label[row["homology_type"]]
+        r["global_alignment_matrix"]=smg[i]
+        r["local_alignment_matrix"]=sml[i]
+        r["index_homology_dataset"]=index
+        r["bls"]=branch_length_species[i]
+        r["blhs"]=branch_length_homology_species[i]
+        r["dis"]=distance[i]
+        r["dps"]=dist_p_s[i]
+        r["dphs"]=dist_p_hs[i]
+        rows.append(r)
+
+with open("dataset","wb") as file:
+    pickle.dump(rows,file)
+
+print("Data Saved Successfully:)")
\ No newline at end of file
diff --git a/prepare_synteny_matrix.py b/prepare_synteny_matrix.py
index 109bd23..6034189 100644
--- a/prepare_synteny_matrix.py
+++ b/prepare_synteny_matrix.py
@@ -7,6 +7,7 @@ import json
 from read_data import read_data_homology
 from read_get_gene_seq import read_gene_sequences
 from create_synteny_matrix import synteny_matrix
+from access_data_rest import update_rest
 
 if not os.path.isdir("processed/synteny_matrices"):
     os.mkdir("processed/synteny_matrices")
@@ -29,6 +30,8 @@ for i in range(len(a_h)):
     assert(len(df)==nos)
     a_h[i]=df
 
+print("Data Selected")
+
 with open("processed/neighbor_genes.json","r") as file:
     lsy=dict(json.load(file))
 print(len(lsy))
@@ -38,6 +41,8 @@ print("Neighbor Genes Loaded")
 gene_sequences=read_gene_sequences(a_h,lsy,"geneseq","gene_sequences")
 print("Gene Sequences Loaded")
 
+print("Going to update not found sequences:")
+gene_sequences=update_rest(gene_sequences)
 
 n=3
 ndir="processed/synteny_matrices/"
@@ -46,9 +51,10 @@ nf2="synteny_matrices_local"
 nf3="indexes"
 for i in range(len(a_h)):
     df=a_h[i]
+    print(len(df))
     synteny_matrices_global,synteny_matrices_local,indexes=synteny_matrix(gene_sequences,df,lsy,n,0)
     np.save(ndir+str(d_h[i])+"_"+nf1,synteny_matrices_global)
     np.save(ndir+str(d_h[i])+"_"+nf2,synteny_matrices_local)
     np.save(ndir+str(d_h[i])+"_"+nf3,indexes)
-        
+    print(len(indexes))    
 print("Synteny Matrices Created Successfully :)")
diff --git a/prepare_synteny_matrix_negative.py b/prepare_synteny_matrix_negative.py
new file mode 100644
index 0000000..909f58a
--- /dev/null
+++ b/prepare_synteny_matrix_negative.py
@@ -0,0 +1,42 @@
+import pandas as pd 
+import numpy as np 
+import os 
+import sys
+import progressbar
+import json
+from read_get_gene_seq import read_gene_sequences
+from create_synteny_matrix import synteny_matrix
+
+if not os.path.isdir("processed/synteny_matrices"):
+    os.mkdir("processed/synteny_matrices")
+
+df=pd.read_hdf("negative_dataset.h5",key="ndf")
+for _,row in progressbar.progressbar(df.iterrows()):
+    row["homology_species"]=row["homology_species"].lower()
+print(df[0:10])
+
+with open("processed/neighbor_genes.json","r") as file:
+    lsy=dict(json.load(file))
+print(len(lsy))
+print("Neighbor Genes Loaded")
+
+a_h=[]
+a_h.append(df)
+#gene_sequences=read_gene_sequences(a_h,lsy,"geneseq","gene_sequences")
+with open("processed/gene_sequences.json","r") as file:
+    gene_sequences=dict(json.load(file))
+print("Gene Sequences Loaded")
+
+
+
+n=3
+ndir="processed/synteny_matrices/"
+nf1="synteny_matrices_global"
+nf2="synteny_matrices_local"
+nf3="indexes"
+for i in range(10):
+    synteny_matrices_global,synteny_matrices_local,indexes=synteny_matrix(gene_sequences,df[i*100000:(i+1)*100000],lsy,n,0)
+    np.save(ndir+"negative_dataset"+"_"+nf1+str(i),synteny_matrices_global)
+    np.save(ndir+"negative_dataset"+"_"+nf2+str(i),synteny_matrices_local)
+    np.save(ndir+"negative_dataset"+"_"+nf3+str(i),indexes)
+print("Synteny Matrices Created Successfully :)")
diff --git a/process_data.py b/process_data.py
index 8a2cee1..b4da37a 100644
--- a/process_data.py
+++ b/process_data.py
@@ -16,6 +16,26 @@ def create_map_list(l): #this function maps the indexes to values
         t[l[i]]=i
 
     return t
+def create_chromosome_maps(a,n):
+    cmap=[]
+    cimap=[]
+    for df in progressbar.progressbar(a):
+        chmap={}
+        chindmap={}
+        for index,row in df.iterrows():
+            g=row.gene_id
+            try:
+                temp=chmap[g]
+            except:
+                chmap[g]=str(row.Chr)
+                if str(row.Chr) in chindmap:
+                    chindmap[str(row.Chr)].append(index)
+                else:
+                    chindmap[str(row.Chr)]=[]
+                    chindmap[str(row.Chr)].append(index)
+        cmap.append(chmap)
+        cimap.append(chindmap)
+    return cmap,cimap
 
 def list_dict_genomes(a,n):
     lst=[]
@@ -23,17 +43,20 @@ def list_dict_genomes(a,n):
     for x in a:
         ldgt={}
         uc=list(x["gene_id"])
-        for i in range(len(uc)):
-            ldgt[uc[i]]=i
+        for i,r in x.iterrows():
+            ldgt[r.gene_id]=i
         lst.append(uc)
         ldt.append(ldgt)
     return lst,ldt
 
-def get_nearest_neighbors(g,gs,n,a,d,ld,ldg):
+def get_nearest_neighbors(g,gs,n,a,d,ld,ldg,cmap,cimap):
+    #print("Finding Neighbor Genes")
     ne=[] #list to store the backward genes
     nr=[] #list to store the forward genes
     gi=d[gs.capitalize()] #get the address of the corresponding species to which the gene belongs whose neighbor has to be found
     sldf=a[gi]#select the dataframe
+    scmap=cmap[gi]#select the correct chromosome map
+    scimap=cimap[gi]#select the correct index maps
     try:
         sld=ld[gi]#see if the corresponding gene map exists
     except:
@@ -42,9 +65,12 @@ def get_nearest_neighbors(g,gs,n,a,d,ld,ldg):
     sldg=ldg[gi]#select the corresponding map
     if g not in sldg:#if the gene is not present in the dataframe return empty lists
         return ne,nr
-    i=sldg[g]#find the index of the genes
+    i=sldg[g]#find the index of the gene
+    chromosome_id=scmap[g]#get the chromosome no from the database.
+    scimap=scimap[chromosome_id]#select the correct chromosomes indexes
+    sldf=sldf.loc[scimap]#select only the same chromosome genes.
     #get the -n neighbors
-    start=int(sldf.iloc[i,[3]])#get the start location of the gene
+    start=int(sldf.loc[i]['start'])#get the start location of the gene
     flag=0
     for j in range(n):
         if flag==1:
@@ -52,9 +78,9 @@ def get_nearest_neighbors(g,gs,n,a,d,ld,ldg):
             continue
         itemp=0
         #select the column
-        end=sldf.iloc[:,4]
+        end=list(sldf.end)
         end=np.array(end)
-        assert(len(end)==len(sld))
+        assert(len(end)==len(sldf))
         end=end-start #subtract start from it so as to get relative position
         end_s=np.argsort(end)#sort them by the order of distance
         if end[end_s[0]]>=0:#if all the genes end ahead of the one in consideration
@@ -65,18 +91,19 @@ def get_nearest_neighbors(g,gs,n,a,d,ld,ldg):
             if end[k]<0 and end[k+1]>=0:#find the first value that is negative and the next one is positive to get the nearest gene
                 itemp=k
                 break
-        ne.append(sld[itemp])#push the gene in the array
-        start=int(sldf.iloc[itemp,[3]])#make "start" the start location of the current gene
+        itemp=scimap[itemp]
+        ne.append(sldf.loc[itemp].gene_id)
+        start=int(sldf.loc[itemp].start)#make "start" the start location of the current gene
         #print(start)
     #get the +n neighbors
     flag=0
-    end=int(sldf.iloc[i,[4]])
+    end=int(sldf.loc[i].end)
     for j in range(n):
         if flag==1:
             nr.append("NULL_GENE")
             continue
         itemp=0
-        start=sldf.iloc[:,3]
+        start=list(sldf.start)
         start=np.array(start)
         start=start-end
         start_s=np.argsort(start)
@@ -88,12 +115,12 @@ def get_nearest_neighbors(g,gs,n,a,d,ld,ldg):
             if start[k]>0:
                 itemp=k
                 break
-        nr.append(sld[itemp])
-        end=int(sldf.iloc[itemp,[4]])
-
+        itemp=scimap[itemp]
+        nr.append(sldf.loc[itemp].gene_id)
+        end=int(sldf.loc[itemp].end)
     return ne,nr
 
-def create_data_homology_ls(a_h,d_h,n,a,d,ld,ldg,save_after,enable_break,update):
+def create_data_homology_ls(a_h,d_h,n,a,d,ld,ldg,cmap,cimap,save_after,enable_break,update):
     lsy={} #dictionary which stores +/- n genes of the given gene by id. Each key is a gene id which corresponds to the one in center.
     t=0
     if os.path.exists("processed/neighbor_genes.json"):  
@@ -101,10 +128,11 @@ def create_data_homology_ls(a_h,d_h,n,a,d,ld,ldg,save_after,enable_break,update)
             lsy=dict(json.load(file))
         print("Existing neighbor genes read!!")
         print(len(lsy))
-    c=4
+    c=0
     lsytemp={}
     name="neighbor_genes"
     for df in a_h:
+        #df=df[0:2]
         for _,row in progressbar.progressbar(df.iterrows()):
             x=row["gene_stable_id"]
             y=row["homology_gene_stable_id"]
@@ -115,7 +143,7 @@ def create_data_homology_ls(a_h,d_h,n,a,d,ld,ldg,save_after,enable_break,update)
             except:
                 try:
                     t2=d[xs.capitalize()]#see if the species exist in genomic maps
-                    xl,xr=get_nearest_neighbors(x,xs,n,a,d,ld,ldg)
+                    xl,xr=get_nearest_neighbors(x,xs,n,a,d,ld,ldg,cmap,cimap)
                     if len(xl)!=0:#check if neighboring genes were successfully found
                         lsy[x]=dict(b=xl,f=xr)
                         lsytemp[x]=dict(b=xl,f=xr)
@@ -126,7 +154,7 @@ def create_data_homology_ls(a_h,d_h,n,a,d,ld,ldg,save_after,enable_break,update)
             except:
                 try:
                     t2=d[ys.capitalize()]
-                    yl,yr=get_nearest_neighbors(y,ys,n,a,d,ld,ldg)
+                    yl,yr=get_nearest_neighbors(y,ys,n,a,d,ld,ldg,cmap,cimap)
                     if len(yl)!=0:
                         lsy[y]=dict(b=yl,f=yr)
                         lsytemp[y]=dict(b=yl,f=yr)
diff --git a/read_data.py b/read_data.py
index ba43f96..f599dd7 100644
--- a/read_data.py
+++ b/read_data.py
@@ -2,6 +2,7 @@ import os
 import pandas as pd
 import gzip
 import sys
+import progressbar
 
 def clear_data(x):
     x=x.split()
@@ -18,23 +19,23 @@ def read_data_genome(dir_name,a,dict_ind_genome):
     if len(lf)==0:
         print("No files in the data directory!!!!!!")
         sys.exit(1)
+    colname=["Chr","source","feature","start","end","score","strand","frame","attribute"]
     print("Going to read data:")
-    for x in range(len(lf)):
-        data_gene=pd.read_csv(dir_name+"/"+lf[x],compression='gzip',sep='\t',comment='#',header=None)
+    for x in progressbar.progressbar(range(len(lf))):
+        data_gene=pd.read_csv(dir_name+"/"+lf[x],compression='gzip',sep='\t',comment='#',header=None,names=colname)
         #print(data_gene.head)
-        data_gene=data_gene[data_gene[2]=="gene"]
-        data_gene=data_gene.sort_values(3)
-        tmp=data_gene[8].str.split(";",expand=True)
+        data_gene=data_gene[data_gene["feature"]=="gene"]
+        tmp=data_gene["attribute"].str.split(";",expand=True)
         tmp=tmp.iloc[:,:5]
         data_gene[["gene_id","gene_version","gene_name","gene_source","gene_biotype"]]=tmp
-        data_gene=data_gene.drop(8,axis=1)
+        data_gene=data_gene.drop("attribute",axis=1)
         #print(data_gene[0:10])
         try:
             for y in ["gene_version","gene_name","gene_source","gene_biotype","gene_id"]:
                 data_gene[y]=data_gene[y].apply(clear_data)
         except:
             continue
-            #print(data_gene[0:10])
+        #print(data_gene[0:10])
         data_gene=data_gene[data_gene['gene_biotype']=='protein_coding']
         a.append(data_gene)
         n=lf[x].split(".")[0]
diff --git a/read_get_gene_seq.py b/read_get_gene_seq.py
index 490e0af..cf31437 100644
--- a/read_get_gene_seq.py
+++ b/read_get_gene_seq.py
@@ -108,8 +108,16 @@ def read_gene_sequences(hdf,lsy,data_dir,fname):
     s=[x.capitalize() for x in s]
     
     data=read_gene_seq(data_dir,s,gene_by_species_dict)
+    not_found={}
+    for species in gene_by_species_dict:
+        for gene in gene_by_species_dict[species]:
+            try:
+                _=data[gene]
+            except:
+                not_found[gene]=1
 
+    with open("processed/not_found.json","w") as file:
+        json.dump(not_found,file)
     with open("processed/"+fname+".json","w") as file:#save the data
         json.dump(data,file)
-
     return data
diff --git a/read_negative_dataset.py b/read_negative_dataset.py
new file mode 100644
index 0000000..df1068f
--- /dev/null
+++ b/read_negative_dataset.py
@@ -0,0 +1,7 @@
+import pandas as pd 
+
+df=pd.read_hdf("negative_dataset.h5",key="ndf")
+
+print(df.info())
+print(df.loc[33333:33433])
+print(df[df["species"]=="mola_mola"])
diff --git a/test_main.py b/test_main.py
deleted file mode 100644
index dddb487..0000000
--- a/test_main.py
+++ /dev/null
@@ -1,54 +0,0 @@
-import sys
-import numpy as np
-import pandas as pd
-import json
-from get_data import get_data_homology,get_data_genome
-from process_data import create_data_homology_ls
-from read_get_gene_seq import read_gene_sequences
-from create_synteny_matrix import synteny_matrix
-from tree_data import create_tree_data
-from create_train_data import train_data
-from train import train
-
-arg=sys.argv
-arg=arg[1:]
-
-if len(arg)!=5:
-    print("No. of arguments more or less. Please check")
-    sys.exit(1)
-
-dir_g="data"
-ld,ldg,a,d=get_data_genome(arg,dir_g)
-
-dir_hom="data_homology"
-a_h,d_h=get_data_homology(arg,dir_hom)
-
-if arg[-1]=="-d":
-    sys.exit(1)
-
-print("Data Read")
-
-n=2 #no. of numbers neighbors
-save_after=3 #to save data after n steps
-lsy=create_data_homology_ls(a_h,d_h,n,a,d,ld,ldg,save_after)
-print(len(lsy))
-
-print("Neighbor Genes Found")
-
-gene_sequences=read_gene_sequences(a_h[0],lsy,"geneseq","gene_sequences")
-
-synteny_matrices,indexes=synteny_matrix(gene_sequences,a_h[0][0:1000],lsy,n)
-print("Synteny Matrices are created successfully\n",len(indexes),"\n",len(synteny_matrices))
-np.save("synteny_matrices",synteny_matrices)
-np.save("indexes",indexes)
-
-with open("gene_seq_updated.json","w") as file:
-    json.dump(gene_sequences,file)
-
-df=a_h[0].loc[indexes]
-branch_length_species,branch_length_homology_species,distance,dist_p_s,dist_p_hs=create_tree_data("species_tree.tree",df)
-
-train_synteny_matrices,train_branch_length_species,train_branch_length_homology_species,train_mean_gene_length,train_dist_p_s,train_dist_p_hs,train_distance,train_labels=train_data(indexes,synteny_matrices,df,branch_length_species,branch_length_homology_species,distance,dist_p_s,dist_p_hs,gene_sequences)
-
-train(train_synteny_matrices,train_branch_length_species,train_branch_length_homology_species,train_mean_gene_length,
-            train_dist_p_s,train_dist_p_hs,train_distance,train_labels)
diff --git a/test_model.py b/test_model.py
new file mode 100644
index 0000000..62ab0e6
--- /dev/null
+++ b/test_model.py
@@ -0,0 +1,181 @@
+import tensorflow as tf 
+import numpy as np 
+import pandas as pd 
+import requests 
+import sys
+import pickle
+from get_data import get_data_genome
+from process_data import get_nearest_neighbors
+from create_synteny_matrix_v2 import create_synteny_matrix_mul
+from ete3 import Tree
+import traceback
+
+"""
+arg=sys.argv
+arg=arg[1:]
+
+enable_break=0
+
+if arg[-1]=="-test":
+    enable_break=1
+
+arg=arg[:-1]
+
+if len(arg)!=5:
+    print("No. of arguments more or less. Please check")
+    sys.exit(1)
+
+dir_g="data"
+cmap,cimap,ld,ldg,a,d=get_data_genome(arg,dir_g)"""
+
+data={}
+with open("genome_maps","rb") as file:
+    data=pickle.load(file)
+cmap=data["cmap"]
+cimap=data["cimap"]
+ld=data["ld"]
+ldg=data["ldg"]
+a=data["a"]
+d=data["d"]
+
+n=3
+
+server = "https://rest.ensembl.org"
+def get_gene_data(gid):
+    empty={}
+    ext = "/lookup/id/"+gid+"?" 
+    try:
+        r = requests.get(server+ext, headers={ "Content-Type" : "application/json"}) 
+        if not r.ok:
+            r.raise_for_status()
+    except:
+        return empty
+    decoded = r.json()
+    return dict(decoded)
+
+def create_branch_length_padding(bl):
+    maxlen=29
+    for x in bl:
+        for i in range(len(x),maxlen):
+            x.append(0)
+
+def create_tree_data(x,y):
+    t=Tree("species_tree.tree")
+    branch_lengths_s=[]
+    branch_lengths_hs=[]
+    dist=[]
+    ns=[]
+    nhs=[]
+    d=0
+    bl=[]
+    c=0
+    mca=t.get_common_ancestor(x,y)
+    node=t&x
+    while node.up!=mca:
+        d+=node.dist
+        bl.append(node.dist)
+        node=node.up
+        c+=1
+    ns.append(c)
+    c=0
+    branch_lengths_s.append(bl)
+    bl=[]
+    node=t&y
+    while node.up!=mca:
+        d+=node.dist
+        bl.append(node.dist)
+        node=node.up
+        c+=1
+    nhs.append(c)
+    branch_lengths_hs.append(bl)
+    dist.append(d)
+    create_branch_length_padding(branch_lengths_s)
+    create_branch_length_padding(branch_lengths_hs)
+    return np.array(branch_lengths_s),np.array(branch_lengths_hs),np.array(dist),np.array(ns),np.array(nhs)
+
+def filter_gene_details(gid,n,a,d,ld,ldg,cmap,cimap):
+    gd=get_gene_data(gid)
+    if len(gd)==0 or gd["biotype"]!="protein_coding":
+        print("Gene Id is Incorrect!!!. Try again.")
+        return list()
+    sp=gd["species"]
+    print(sp)
+    try:
+        t2=d[sp.capitalize()]
+    except:
+        print("Gene Species does not exist in gene maps. Try another species.")
+        return list(),sp
+    nl,nr=get_nearest_neighbors(gid,sp,n,a,d,ld,ldg,cmap,cimap)
+    if len(nl)==0 or len(nr)==0:
+        print("Gene is not present in gtf file. Please Try another gene.")
+        return list(),sp
+    print("Found nearest neighbors")
+    x=[]
+    for i in range(len(nl)-1,-1,-1):
+        x.append(nl[i])
+    x.append(gid)
+    for k in nr:
+        x.append(k)
+    assert(len(x)==2*n+1)
+    return x,sp
+
+def get_label(l):
+    if l==0:
+        return "Paralogous"
+    if l==1:
+        return "Orthologous"
+    return "Non Homologous"
+
+def softmax(arr):
+    arr=arr-np.amax(arr)
+    arr=np.exp(arr)
+    arr=arr/np.sum(arr)
+    return arr
+
+geneseq={}
+try:
+    model=tf.train.import_meta_graph('saved_models/model.ckpt.meta')
+except:
+    print("Something wrong with the model.")
+    sys.exit(1)
+with tf.Session() as sess:
+    try:
+        model.restore(sess,"saved_models/model.ckpt")
+        graph = tf.get_default_graph()
+        synmgt,synmlt,blst,blhst,dpst,dphst,dist,lrt,yt=graph.get_collection("input_nodes")
+        predictions=graph.get_tensor_by_name("Predictions/BiasAdd:0")
+        print("Model Loaded Successfully :)")
+    except:
+        print(":(")
+        sys.exit()
+    while(1):
+        try:
+            ch=input("Do you want to enter a gene id [y/n]:")
+            if ch=='n':
+                break
+            g1=input("Enter the first gene id:")
+            g1,sp1=filter_gene_details(g1,3,a,d,ld,ldg,cmap,cimap)
+            if len(g1)==0:
+                continue
+            g2=input("Enter the second gene id:")
+            g2,sp2=filter_gene_details(g2,3,a,d,ld,ldg,cmap,cimap)
+            if len(g2)==0:
+                continue
+            smg,sml=create_synteny_matrix_mul(geneseq,g1,g2,2*n+1)
+            bls,blhs,dis,dps,dphs=create_tree_data(sp1,sp2)
+            fd={synmgt:smg.reshape((1,7,7,2)),
+                synmlt:sml.reshape((1,7,7,2)),
+                blst:bls.reshape((1,29)),
+                blhst:blhs.reshape((1,29)),
+                dpst:dps.reshape((1,1)),
+                dist:dis.reshape((1,1)),
+                dphst:dphs.reshape((1,1))}
+            preds=sess.run([predictions],feed_dict=fd)
+            label=np.argmax(preds)
+            print(get_label(label))
+            print(softmax(preds))
+            
+        except Exception as e:
+            print("Some Error Was There Try Again:(",e)
+            traceback.print_exc()
+            continue
\ No newline at end of file
diff --git a/tree_data.py b/tree_data.py
index 8a19d55..035140f 100644
--- a/tree_data.py
+++ b/tree_data.py
@@ -1,5 +1,16 @@
 from ete3 import Tree
 import numpy as np
+import progressbar
+
+def create_branch_length_padding(bl):
+    maxlen=0
+    for x in bl:
+        if len(x)>maxlen:
+            maxlen=len(x)
+
+    for x in bl:
+        for i in range(len(x),maxlen):
+            x.append(0)
 
 def create_tree_data(treename,df):
     t=Tree(treename)
@@ -8,7 +19,7 @@ def create_tree_data(treename,df):
     dist=[]
     ns=[]
     nhs=[]
-    for index,row in df.iterrows():
+    for index,row in progressbar.progressbar(df.iterrows()):
         d=0
         x=row["species"]
         y=row["homology_species"]
@@ -34,4 +45,6 @@ def create_tree_data(treename,df):
         nhs.append(c)
         branch_lengths_hs.append(bl)
         dist.append(d)
+    create_branch_length_padding(branch_lengths_s)
+    create_branch_length_padding(branch_lengths_hs)
     return np.array(branch_lengths_s),np.array(branch_lengths_hs),np.array(dist),np.array(ns),np.array(nhs)
diff --git a/update_neighbor_genes.py b/update_neighbor_genes.py
index 8e6fdfb..42bbf92 100644
--- a/update_neighbor_genes.py
+++ b/update_neighbor_genes.py
@@ -25,7 +25,7 @@ if len(arg)!=5:
     sys.exit(1)
 
 dir_g="data"
-ld,ldg,a,d=get_data_genome(arg,dir_g)
+cmap,cimap,ld,ldg,a,d=get_data_genome(arg,dir_g)
 
 dir_hom="data_homology"
 a_h,d_h=get_data_homology(arg,dir_hom)
@@ -36,9 +36,9 @@ if arg[-1]=="-d":
 print("Data Read")
 
 n=3 #no. of numbers neighbors
-save_after=0 #to save data after n steps
+save_after=5 #to save data after n steps
 
-lsy=create_data_homology_ls(a_h,d_h,n,a,d,ld,ldg,save_after,enable_break,1)
+lsy=create_data_homology_ls(a_h,d_h,n,a,d,ld,ldg,cmap,cimap,save_after,enable_break,1)
 print(len(lsy))
 
 print("Neighbor Genes Updated Successfully")
diff --git a/update_neighbor_genes_ndf.py b/update_neighbor_genes_ndf.py
new file mode 100644
index 0000000..fd30397
--- /dev/null
+++ b/update_neighbor_genes_ndf.py
@@ -0,0 +1,42 @@
+import sys
+import numpy as np
+import pandas as pd
+import json
+import os
+import gc
+from get_data import get_data_homology,get_data_genome
+from process_data import create_data_homology_ls
+
+if not os.path.exists("processed"):
+    os.mkdir("processed")
+
+arg=sys.argv
+arg=arg[1:]
+
+enable_break=0
+
+if arg[-1]=="-test":
+    enable_break=1
+
+arg=arg[:-1]
+
+if len(arg)!=5:
+    print("No. of arguments more or less. Please check")
+    sys.exit(1)
+
+dir_g="data"
+cmap,cimap,ld,ldg,a,d=get_data_genome(arg,dir_g)
+
+df=pd.read_hdf("negative_dataset.h5",key="ndf")
+a_h=[]
+d_h=[]
+a_h.append(df)
+d_h.append("negative_dataset")
+print("Data Read")
+
+n=3 #no. of numbers neighbors
+save_after=5 #to save data after n steps
+
+lsy=create_data_homology_ls(a_h,d_h,n,a,d,ld,ldg,cmap,cimap,save_after,enable_break,1)
+print(len(lsy))
+print("Neighbor Genes Updated Successfully")
\ No newline at end of file