diff --git a/notebooks/py-graph_test.ipynb b/notebooks/py-graph_test.ipynb
index 04080e6..96e0d0d 100644
--- a/notebooks/py-graph_test.ipynb
+++ b/notebooks/py-graph_test.ipynb
@@ -2,1859 +2,64 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "autoscroll": false,
-    "collapsed": false,
-    "ein.tags": "worksheet-0",
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "import paths\n",
-    "\n",
-    "from ged.GED import ged\n",
-    "from utils.graphfiles import loadDataset\n",
-    "from ged.costfunctions import RiesenCostFunction, ConstantCostFunction\n",
-    "from ged.bipartiteGED import computeBipartiteCostMatrix, getOptimalMapping"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
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-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import networkx as nx\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot  as plt\n",
-    "\n",
-    "# We load a ds dataset\n",
-    "dataset, y = loadDataset(\"/home/bgauzere/work/Datasets/Acyclic/dataset_bps.ds\")"
-   ]
-  },
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-     "text": [
-      "\r",
-      " 21%|██▏       | 39/183 [01:16<04:42,  1.96s/it]"
-     ]
-    },
+   "execution_count": 1,
+   "metadata": {
+    "autoscroll": false,
+    "ein.tags": "worksheet-0",
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import paths\n",
+    "\n",
+    "import pygraph\n",
+    "\n",
+    "from pygraph.utils.graphfiles import loadDataset\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "autoscroll": false,
+    "ein.tags": "worksheet-0",
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import networkx as nx\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot  as plt\n",
+    "\n",
+    "# We load a ds dataset\n",
+    "# load it from https://brunl01.users.greyc.fr/CHEMISTRY/Acyclic.tar.gz\n",
+    "dataset, y = loadDataset(\"/home/bgauzere/work/Datasets/Acyclic/dataset_bps.ds\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "autoscroll": false,
+    "ein.tags": "worksheet-0",
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\r",
-      " 22%|██▏       | 40/183 [01:18<04:40,  1.96s/it]"
+      "100%|██████████| 183/183 [07:41<00:00,  2.52s/it]\n",
+      "100%|██████████| 183/183 [08:39<00:00,  2.84s/it]\n",
+      "100%|██████████| 183/183 [05:19<00:00,  1.75s/it]\n",
+      "100%|██████████| 183/183 [05:50<00:00,  1.91s/it]\n"
      ]
     }
    ],
@@ -1862,10 +67,12 @@
     "#Compute graph edit distances\n",
     "\n",
     "from tqdm import tqdm\n",
-    "from c_ext.lsape_binders import  lsap_solverHG\n",
+    "from pygraph.c_ext.lsape_binders import  lsap_solverHG\n",
+    "from pygraph.ged.costfunctions import ConstantCostFunction\n",
+    "from pygraph.ged.GED import ged\n",
     "import time\n",
     "\n",
-    "cf = ConstantCostFunction(3,1,3,1)\n",
+    "cf = ConstantCostFunction(1,3,1,3)\n",
     "N=len(dataset)\n",
     "\n",
     "methods=['Riesen + LSAP', 'Neigh + LSAP', 'Riesen + LSAPE', 'Neigh + LSAPE']\n",
@@ -1904,7 +111,6 @@
    "execution_count": 5,
    "metadata": {
     "autoscroll": false,
-    "collapsed": false,
     "ein.tags": "worksheet-0",
     "slideshow": {
      "slide_type": "-"
@@ -1915,27 +121,34 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      " method \t mean \t mean \t dev \t time\n",
-      " Riesen + LSAP \t  0.0 \t 0.0 \t -0.994535519125683 \t -14.283137746722804\n",
-      " Neigh + LSAP \t  0.0 \t 0.0 \t -0.994535519125683 \t -14.283137746722804\n",
-      " Riesen + LSAPE \t  19.770670966586042 \t 19.122338678372003 \t 0.4757055896177035 \t 4.839200931649199\n",
-      " Neigh + LSAPE \t  0.0 \t 0.0 \t -0.994535519125683 \t -14.283137746722804\n"
+      " method \t mean \t mean \t time\n",
+      " Riesen + LSAP \t 37.79903849025053 \t 35.31207262086058 \t 463.300405 \n",
+      " Neigh + LSAP \t 36.2281047508137 \t 33.85869987159963 \t 521.7821730000001 \n",
+      " Riesen + LSAPE \t 35.95508973095643 \t 34.10092866314312 \t 319.83455500000014 \n",
+      " Neigh + LSAPE \t 34.5005822807489 \t 32.5735614679447 \t 350.48029599999995 \n"
      ]
     }
    ],
    "source": [
     "print(\" method \\t mean \\t mean \\t time\")\n",
     "data = list()\n",
-    "\n",
     "for i in range(0,len(ged_distances)):\n",
     "    ged_ = np.minimum(ged_distances[i],ged_distances[i].transpose())\n",
-    "    print(\" {} \\t  {} \\t {} \\t {} \".format(methods[i], np.mean(ged_distances[i]),np.mean(ged_), times[i])\n"
+    "    print(\" {} \\t {} \\t {} \\t {} \".format(methods[i], np.mean(ged_distances[i]),np.mean(ged_), times[i]))\n"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
    "display_name": "Python 3",
+   "language": "python",
    "name": "python3"
   },
   "language_info": {
diff --git a/pygraph/ged/GED.py b/pygraph/ged/GED.py
index 1233329..06cb227 100644
--- a/pygraph/ged/GED.py
+++ b/pygraph/ged/GED.py
@@ -1,6 +1,6 @@
-from ged.costfunctions import ConstantCostFunction, RiesenCostFunction
-from ged.costfunctions import NeighboorhoodCostFunction
-from ged.bipartiteGED import computeBipartiteCostMatrix, getOptimalMapping
+from pygraph.ged.costfunctions import ConstantCostFunction, RiesenCostFunction
+from pygraph.ged.costfunctions import NeighboorhoodCostFunction
+from pygraph.ged.bipartiteGED import computeBipartiteCostMatrix, getOptimalMapping
 from scipy.optimize import linear_sum_assignment
 
 def ged(G1, G2, method='Riesen', rho=None, varrho=None,
@@ -29,18 +29,18 @@ def ged(G1, G2, method='Riesen', rho=None, varrho=None,
     n = G1.number_of_nodes()
     m = G2.number_of_nodes()
     ged = 0
-    for i in G1.nodes_iter():
+    for i in G1.nodes():
         phi_i = rho[i]
         if(phi_i >= m):
             ged += cf.cnd(i, G1)
         else:
             ged += cf.cns(i, phi_i, G1, G2)
-    for j in G2.nodes_iter():
+    for j in G2.nodes():
         phi_j = varrho[j]
         if(phi_j >= n):
             ged += cf.cni(j, G2)
 
-    for e in G1.edges_iter(data=True):
+    for e in G1.edges(data=True):
         i = e[0]
         j = e[1]
         phi_i = rho[i]
@@ -57,7 +57,7 @@ def ged(G1, G2, method='Riesen', rho=None, varrho=None,
                 ged += cf.ced(e, G1)
         else:
             ged += cf.ced(e, G1)
-    for e in G2.edges_iter(data=True):
+    for e in G2.edges(data=True):
         i = e[0]
         j = e[1]
         phi_i = varrho[i]
@@ -70,7 +70,3 @@ def ged(G1, G2, method='Riesen', rho=None, varrho=None,
         else:
             ged += cf.ced(e, G2)
     return ged, rho, varrho
-
-
-def computeDistanceMatrix(dataset):
-        pass
diff --git a/pygraph/ged/bipartiteGED.py b/pygraph/ged/bipartiteGED.py
index ab3aadf..b997f9e 100644
--- a/pygraph/ged/bipartiteGED.py
+++ b/pygraph/ged/bipartiteGED.py
@@ -1,6 +1,6 @@
 import numpy as np
 from scipy.optimize import linear_sum_assignment
-from ged.costfunctions import ConstantCostFunction
+from pygraph.ged.costfunctions import ConstantCostFunction
 
 
 def computeBipartiteCostMatrix(G1, G2, cf=ConstantCostFunction(1, 3, 1, 3)):
@@ -11,15 +11,15 @@ def computeBipartiteCostMatrix(G1, G2, cf=ConstantCostFunction(1, 3, 1, 3)):
     C = np.ones([nm, nm])*np.inf
     C[n:, m:] = 0
 
-    for u in G1.nodes_iter():
-        for v in G2.nodes_iter():
+    for u in G1.nodes():
+        for v in G2.nodes():
             cost = cf.cns(u, v, G1, G2)
             C[u, v] = cost
 
-    for v in G1.nodes_iter():
+    for v in G1.nodes():
         C[v, m + v] = cf.cnd(v, G1)
 
-    for v in G2.nodes_iter():
+    for v in G2.nodes():
         C[n + v, v] = cf.cni(v, G2)
     return C