diff --git a/examples/test_coordinate.ipynb b/examples/test_coordinate.ipynb
index 3167fdb57..4cdf1f869 100644
--- a/examples/test_coordinate.ipynb
+++ b/examples/test_coordinate.ipynb
@@ -702,34 +702,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 51,
    "id": "276c6b03-5612-45e1-b202-1464b8331550",
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "ModuleNotFoundError",
-     "evalue": "No module named 'colossus'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[36], line 6\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpyplot\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mplt\u001b[39;00m\n\u001b[1;32m      5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mscipy\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m spatial\n\u001b[0;32m----> 6\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mcolossus\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcosmology\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m cosmology\n",
-      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'colossus'"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import numpy as np\n",
     "from astropy.io import fits\n",
     "import matplotlib as mpl\n",
     "import matplotlib.pyplot as plt\n",
     "from scipy import spatial\n",
-    "from colossus.cosmology import cosmology"
+    "#from colossus.cosmology import cosmology\n",
+    "from astropy.cosmology import WMAP5"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 42,
    "id": "878ea70c-99f9-45b7-aef7-b1e14ee4ad86",
    "metadata": {},
    "outputs": [],
@@ -740,17 +729,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 52,
    "id": "51dfa7d4-d8d0-4dab-ba6a-463c9da9d5c9",
    "metadata": {},
    "outputs": [],
    "source": [
-    "cosmo = cosmology.setCosmology('planck18')"
+    "#cosmo = cosmology.setCosmology('planck18')\n",
+    "cosmo = WMAP5"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 53,
    "id": "da723c9a-f702-463b-b1e3-5bcf404d0fe4",
    "metadata": {},
    "outputs": [],
@@ -769,10 +759,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 60,
    "id": "d1262e48-b5f0-4f31-8507-84af6bdeed80",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1\n"
+     ]
+    }
+   ],
    "source": [
     "#TS: This is a simplified way to measure lensing signals (without worrying about resposivity, multiplicative and additive biases)\n",
     "d2r = np.pi/180.\n",
@@ -815,7 +813,8 @@
     "    sel_z = source[sel]['photoz']>l['z'][i] #Try to change the source galaxy selection\n",
     "    source_bg = source[sel][sel_z]\n",
     "    theta = calcDistanceAngle(l['RA'][i]*d2r, l['Dec'][i]*d2r, source_bg['RA']*d2r, source_bg['Dec']*d2r)  #Compute an angle between the lens and the source\n",
-    "    l_chi =  cosmo.comovingDistance(z_min=0.0,z_max=l['z'][i])#Compute the comoving distance of the lens\n",
+    "    #l_chi =  cosmo.comovingDistance(z_min=0.0,z_max=l['z'][i])#Compute the comoving distance of the lens\n",
+    "    l_chi =  cosmo.comoving_distance((l['z'][i])).value   #Compute the comoving distance of the lens\n",
     "    r = theta*l_chi                                                                                                 \n",
     "    assign_r = np.digitize(r, bin_edges)\n",
     "\n",
@@ -831,8 +830,11 @@
     "        sin2p = 2.*sinp*cosp\n",
     "        et = -(source_bg_inbin[\"e1\"]*cos2p+source_bg_inbin[\"e2\"]*sin2p)\n",
     "        ex = -(-source_bg_inbin[\"e1\"]*sin2p+source_bg_inbin[\"e2\"]*cos2p)\n",
+    "        \n",
     "        # critical surface mass density [M_sun/ comoving Mpc^2]. (1+zl)**-2 is for comoving coordinates.  \n",
-    "        Sigma_cr = Sigma_cr_fact/(1. - l_chi/cosmo.comovingDistance(z_min=0.0,z_max=source_bg_inbin[\"photoz\"]))/l_chi/(1.+l[\"z\"][i])/10**12  \n",
+    "        # comoving_distance = cosmo.comovingDistance(z_min=0.0,z_max=source_bg_inbin[\"photoz\"])\n",
+    "        comoving_distance = cosmo.comoving_distance((source_bg_inbin[\"photoz\"])).value\n",
+    "        Sigma_cr = Sigma_cr_fact/(1. - l_chi/comoving_distance)/l_chi/(1.+l[\"z\"][i])/10**12  \n",
     "                              \n",
     "        sum[\"n\"][i_r] += sel.sum()\n",
     "                              \n",
@@ -853,7 +855,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 61,
    "id": "7caca756-3e93-4e8b-8502-fe1a001ba5c0",
    "metadata": {},
    "outputs": [],
@@ -867,12 +869,42 @@
     "sigma_cr = 1./(sum[\"wsigmainv\"]/sum[\"w\"])"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "e68eef0e-e363-484c-92bc-0d429c16bbeb",
+   "metadata": {},
+   "source": [
+    "Below, we compare the explicitly calculated lensing signal against the CLMM calculated signal."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 62,
    "id": "366b3a5e-52e4-453d-ba93-e3c0a8cf22f5",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "'GalaxyCluster' object has no attribute 'profile'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[62], line 5\u001b[0m\n\u001b[1;32m      2\u001b[0m plt\u001b[38;5;241m.\u001b[39mfigure(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m6\u001b[39m,\u001b[38;5;241m6\u001b[39m))\n\u001b[1;32m      3\u001b[0m plt\u001b[38;5;241m.\u001b[39merrorbar(radial_bin,gt,yerr\u001b[38;5;241m=\u001b[39mgt_err,label\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124moriginal\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m      4\u001b[0m plt\u001b[38;5;241m.\u001b[39merrorbar(\n\u001b[0;32m----> 5\u001b[0m     \u001b[43mcluster\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mprofile\u001b[49m[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mradius\u001b[39m\u001b[38;5;124m\"\u001b[39m],\n\u001b[1;32m      6\u001b[0m     cluster\u001b[38;5;241m.\u001b[39mprofile[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDS_t\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m/\u001b[39m \u001b[38;5;241m1e13\u001b[39m,\n\u001b[1;32m      7\u001b[0m     cluster\u001b[38;5;241m.\u001b[39mprofile[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDS_t_err\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m/\u001b[39m\u001b[38;5;241m1e13\u001b[39m,\n\u001b[1;32m      8\u001b[0m     label\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCLMM\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m      9\u001b[0m )\n\u001b[1;32m     10\u001b[0m plt\u001b[38;5;241m.\u001b[39mloglog()\n\u001b[1;32m     11\u001b[0m plt\u001b[38;5;241m.\u001b[39mlegend(fontsize\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m20\u001b[39m)\n",
+      "\u001b[0;31mAttributeError\u001b[0m: 'GalaxyCluster' object has no attribute 'profile'"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "#Figure for the lensing signal\n",
     "plt.figure(figsize=(6,6))\n",
@@ -889,6 +921,14 @@
     "plt.ylabel(r'$\\Delta\\Sigma(R)$',fontsize=20)\n",
     "plt.savefig('weaklens_hsc_clust.png')"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "219ecd61-b5cc-44f5-b7bf-ee8649f9c5a1",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {