diff --git a/examples/ipython_notebooks/intro_to_transient_cosmology.ipynb b/examples/ipython_notebooks/intro_to_transient_cosmology.ipynb
index 991d5031..7ea99907 100644
--- a/examples/ipython_notebooks/intro_to_transient_cosmology.ipynb
+++ b/examples/ipython_notebooks/intro_to_transient_cosmology.ipynb
@@ -69,7 +69,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -100,7 +100,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 5,
    "metadata": {
     "scrolled": true
    },
@@ -120,7 +120,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -132,18 +132,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "INFO:flarestack.cosmo.icecube_diffuse_flux:Loaded contour 'northern_tracks_19' from https://arxiv.org/abs/1908.09551\n",
-      "INFO:flarestack.cosmo.icecube_diffuse_flux:Loaded contour 'northern_tracks_19' from https://arxiv.org/abs/1908.09551\n",
-      "INFO:flarestack.cosmo.neutrino_cosmology:Using the northern_tracks_19 best fit values of the diffuse flux.\n",
-      "INFO:flarestack.cosmo.neutrino_cosmology:Diffuse Flux at 1 GeV: 3.6171164613737875e-07 1 / (cm2 GeV s sr)\n",
-      "INFO:flarestack.cosmo.neutrino_cosmology:Diffuse Spectral Index is 2.28\n",
+      "INFO:flarestack.cosmo.icecube_diffuse_flux:Loaded contour 'joint_15' from https://arxiv.org/abs/1507.03991\n",
+      "INFO:flarestack.cosmo.icecube_diffuse_flux:Loaded contour 'joint_15' from https://arxiv.org/abs/1507.03991\n",
+      "INFO:flarestack.cosmo.neutrino_cosmology:Using the joint_15 best fit values of the diffuse flux.\n",
+      "INFO:flarestack.cosmo.neutrino_cosmology:Diffuse Flux at 1 GeV: 7.0624201077093805e-06 1 / (cm2 GeV s sr)\n",
+      "INFO:flarestack.cosmo.neutrino_cosmology:Diffuse Spectral Index is 2.5\n",
       "INFO:flarestack.core.energy_pdf:Minimum Energy is 1e+03 GeV.\n",
       "INFO:flarestack.core.energy_pdf:Maximum Energy is 1e+06 GeV.\n",
       "INFO:flarestack.cosmo.neutrino_cosmology:Neutrino Energy is 1.8e+43 erg\n",
@@ -152,14 +152,14 @@
       "INFO:flarestack.cosmo.neutrino_cosmology:Cumulative sources at z=8.0: 2.9e+11\n",
       "INFO:flarestack.cosmo.neutrino_cosmology:Cumulative flux at z=8.0 (1 GeV): 2.4e-10 1 / (cm2 GeV s sr)\n",
       "INFO:flarestack.cosmo.neutrino_cosmology:Cumulative annual flux at z=8.0 (1 GeV): 0.0075 1 / (cm2 GeV sr)\n",
-      "INFO:flarestack.cosmo.neutrino_cosmology:Fraction of diffuse flux at 1GeV: 0.00066\n",
+      "INFO:flarestack.cosmo.neutrino_cosmology:Fraction of diffuse flux at 1GeV: 3.4e-05\n",
       "INFO:flarestack.cosmo.neutrino_cosmology:Cumulative neutrino flux 2.4e-10 1 / (cm2 GeV s sr)\n",
       "INFO:flarestack.cosmo.neutrino_cosmology:Fraction of flux from nearby (z<0.1) sources: 0.057\n",
       "INFO:flarestack.cosmo.neutrino_cosmology:Fraction of flux from nearby (z<0.3) sources: 0.17\n",
-      "INFO:flarestack.cosmo.icecube_diffuse_flux:Loaded contour 'northern_tracks_19' from https://arxiv.org/abs/1908.09551\n",
-      "INFO:flarestack.cosmo.neutrino_cosmology:Using the northern_tracks_19 best fit values of the diffuse flux.\n",
-      "INFO:flarestack.cosmo.neutrino_cosmology:Diffuse Flux at 1 GeV: 3.6171164613737875e-07 1 / (cm2 GeV s sr)\n",
-      "INFO:flarestack.cosmo.neutrino_cosmology:Diffuse Spectral Index is 2.28\n",
+      "INFO:flarestack.cosmo.icecube_diffuse_flux:Loaded contour 'joint_15' from https://arxiv.org/abs/1507.03991\n",
+      "INFO:flarestack.cosmo.neutrino_cosmology:Using the joint_15 best fit values of the diffuse flux.\n",
+      "INFO:flarestack.cosmo.neutrino_cosmology:Diffuse Flux at 1 GeV: 7.0624201077093805e-06 1 / (cm2 GeV s sr)\n",
+      "INFO:flarestack.cosmo.neutrino_cosmology:Diffuse Spectral Index is 2.5\n",
       "INFO:flarestack.core.energy_pdf:Minimum Energy is 1e+03 GeV.\n",
       "INFO:flarestack.core.energy_pdf:Maximum Energy is 1e+06 GeV.\n",
       "INFO:flarestack.cosmo.neutrino_cosmology:Neutrino Energy is 1.8e+44 erg\n",
@@ -168,7 +168,7 @@
       "INFO:flarestack.cosmo.neutrino_cosmology:Cumulative sources at z=8.0: 2.9e+11\n",
       "INFO:flarestack.cosmo.neutrino_cosmology:Cumulative flux at z=8.0 (1 GeV): 2.4e-09 1 / (cm2 GeV s sr)\n",
       "INFO:flarestack.cosmo.neutrino_cosmology:Cumulative annual flux at z=8.0 (1 GeV): 0.075 1 / (cm2 GeV sr)\n",
-      "INFO:flarestack.cosmo.neutrino_cosmology:Fraction of diffuse flux at 1GeV: 0.0066\n",
+      "INFO:flarestack.cosmo.neutrino_cosmology:Fraction of diffuse flux at 1GeV: 0.00034\n",
       "INFO:flarestack.cosmo.neutrino_cosmology:Cumulative neutrino flux 2.4e-09 1 / (cm2 GeV s sr)\n",
       "INFO:flarestack.cosmo.neutrino_cosmology:Fraction of flux from nearby (z<0.1) sources: 0.057\n",
       "INFO:flarestack.cosmo.neutrino_cosmology:Fraction of flux from nearby (z<0.3) sources: 0.17\n"
@@ -177,16 +177,16 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x7fdf09d95590>"
+       "<matplotlib.legend.Legend at 0x7fc47194a510>"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -200,7 +200,7 @@
    "source": [
     "# Use joint diffuse flux fit (https://arxiv.org/abs/1507.03991)\n",
     "\n",
-    "fit = \"northern_tracks_19\"\n",
+    "fit = \"joint_15\"\n",
     "\n",
     "plt.figure()\n",
     "ax = plt.subplot(111)\n",