diff --git a/notebooks/plotting/collapse_maps/plot_collapse_maps.ipynb b/notebooks/plotting/collapse_maps/plot_collapse_maps.ipynb
index 721627e..c45c802 100644
--- a/notebooks/plotting/collapse_maps/plot_collapse_maps.ipynb
+++ b/notebooks/plotting/collapse_maps/plot_collapse_maps.ipynb
@@ -2,7 +2,10 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "# Plot Collapse Maps\n",
     "\n",
@@ -19,85 +22,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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AgN3sznGNGTNGrVu3VpcuXWSM0erVq/Uf//EfPrdnah0AEDCVMrX+ty5+60+S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\ngMX+P+zQNP3l4WKnAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x1100f8290>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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YjMVuVZZt20p96U3ZpaVq9z/9ZHmq9z8WAOBnUJFXXZ/NXaT/HT1VklSUW6DO\n44cFeEQAgCqH28+qruM5+aft5wVwJACAqouKvMrqPO5/VJSTr7KSEl33uxGBHg4AoCqq5lPrlm3b\ndrlvWpYqeBsAgPPidq5YliX76CrX+pMkK/RWo7KvSlfkAACcUzWvyAlyAIDhWOwGAIC5qMgBADAY\nz1oHAMBgVOQAAJiMIAcAwFw82Q0AAJNRkQMAYC4qcgAATEZFDgCAuVi1DgCAwZhaBwDAZFTkAACY\ni6l1AABMxtQ6AADm4lnrAAAYrJpPrVu2bdvlvlnN/zgAAPdVEDvnzVc55eYYfa3CIAcAAFVb9V4h\nAACA4QhyAAAMRpADAGAwghwAAIMR5AAAGOz/AQ25NstQAF7KAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x11088c8d0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x1116ac810>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x110990290>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x1116245d0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
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-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x1114b78d0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
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sBJThZly5H7gcN/RhLDCJ2iEKC4CDvPvv4YYvvIUbErEVG/YafQHYDuiNW8h3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\nYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x1111cf750>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "%matplotlib inline\n",
-    "import plot_collapse_maps as plt\n",
+    "from rmtk.plotting.collapse_maps import plot_collapse_maps as plt\n",
     "\n",
     "collapse_map = '../input_models/collapse_map.xml'\n",
     "exposure_model = '../input_models/exposure_model.xml'\n",
@@ -115,7 +49,9 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": []
@@ -137,7 +73,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython2",
-   "version": "2.7.9"
+   "version": "2.7.13"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/plotting/damage_dist/plot_damage_dist.ipynb b/notebooks/plotting/damage_dist/plot_damage_dist.ipynb
index 3a04c18..017dca9 100644
--- a/notebooks/plotting/damage_dist/plot_damage_dist.ipynb
+++ b/notebooks/plotting/damage_dist/plot_damage_dist.ipynb
@@ -2,7 +2,10 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "# Plot Damage Distribution\n",
     "\n",
@@ -21,12 +24,14 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
     "%matplotlib inline\n",
-    "import plot_damage_dist as plotdd\n",
+    "from rmtk.plotting.damage_dist import plot_damage_dist as plotdd\n",
     "tax_dmg_dist_file = '../sample_outputs/scenario_damage/dmg_dist_per_taxonomy.xml'\n",
     "\n",
     "taxonomy_list=[]\n",
@@ -38,7 +43,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "## Total damage distribution\n",
     "\n",
@@ -49,7 +57,9 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -76,7 +86,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython2",
-   "version": "2.7.9"
+   "version": "2.7.13"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/plotting/hazard_outputs/plot_hazard_curves.ipynb b/notebooks/plotting/hazard_outputs/plot_hazard_curves.ipynb
index 7cca8d8..13298ee 100644
--- a/notebooks/plotting/hazard_outputs/plot_hazard_curves.ipynb
+++ b/notebooks/plotting/hazard_outputs/plot_hazard_curves.ipynb
@@ -2,7 +2,10 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "# Hazard Curves and Uniform Hazard Spectra \n",
     "\n",
@@ -15,13 +18,15 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
     "%matplotlib inline\n",
     "import matplotlib.pyplot as plt\n",
-    "from plot_hazard_outputs import HazardCurve, UniformHazardSpectra\n",
+    "from rmtk.plotting.hazard_outputs.plot_hazard_outputs import HazardCurve, UniformHazardSpectra\n",
     "\n",
     "hazard_curve_file = \"../sample_outputs/hazard/hazard_curve.xml\"\n",
     "hazard_curves = HazardCurve(hazard_curve_file)"
@@ -29,7 +34,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "## Hazard Curve"
    ]
@@ -38,7 +46,9 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -49,7 +59,9 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -58,7 +70,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "## Uniform Hazard Spectra"
    ]
@@ -67,7 +82,9 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -93,7 +110,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython2",
-   "version": "2.7.9"
+   "version": "2.7.13"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/plotting/hazard_outputs/plot_hazard_maps.ipynb b/notebooks/plotting/hazard_outputs/plot_hazard_maps.ipynb
index a2fa02c..b61fddf 100644
--- a/notebooks/plotting/hazard_outputs/plot_hazard_maps.ipynb
+++ b/notebooks/plotting/hazard_outputs/plot_hazard_maps.ipynb
@@ -2,7 +2,10 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "# Hazard Maps\n",
     "\n",
@@ -19,12 +22,14 @@
    "execution_count": null,
    "metadata": {
     "collapsed": false,
+    "deletable": true,
+    "editable": true,
     "scrolled": true
    },
    "outputs": [],
    "source": [
     "%matplotlib inline\n",
-    "from plot_hazard_outputs import HazardMap\n",
+    "from rmtk.plotting.hazard_outputs.plot_hazard_outputs import HazardMap\n",
     "\n",
     "hazard_map_file = \"../sample_outputs/hazard/hazard_map-poe_0.02.xml\"\n",
     "\n",
@@ -55,7 +60,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython2",
-   "version": "2.7.9"
+   "version": "2.7.13"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/plotting/loss_curves/plot_loss_curves.ipynb b/notebooks/plotting/loss_curves/plot_loss_curves.ipynb
index 3fd55a9..e1ad25f 100644
--- a/notebooks/plotting/loss_curves/plot_loss_curves.ipynb
+++ b/notebooks/plotting/loss_curves/plot_loss_curves.ipynb
@@ -2,7 +2,10 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "# Plot Loss Curves\n",
     "\n",
@@ -15,12 +18,14 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
     "%matplotlib inline\n",
-    "import plot_loss_curves as plotlc\n",
+    "from rmtk.plotting.loss_curves import plot_loss_curves as plotlc\n",
     "\n",
     "loss_curves_file = '../sample_outputs/classical_risk/loss-curves-structural.xml'\n",
     "assets_list = ['a1', 'a10', 'a100']\n",
@@ -48,7 +53,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython2",
-   "version": "2.7.9"
+   "version": "2.7.13"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/plotting/risk_maps/plot_risk_maps.ipynb b/notebooks/plotting/risk_maps/plot_risk_maps.ipynb
index 179371e..f4b1830 100644
--- a/notebooks/plotting/risk_maps/plot_risk_maps.ipynb
+++ b/notebooks/plotting/risk_maps/plot_risk_maps.ipynb
@@ -2,7 +2,10 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "# Plot Risk Maps\n",
     "\n",
@@ -21,12 +24,14 @@
    "execution_count": null,
    "metadata": {
     "collapsed": false,
+    "deletable": true,
+    "editable": true,
     "scrolled": false
    },
    "outputs": [],
    "source": [
     "%matplotlib inline\n",
-    "import plot_risk_maps as plotrm\n",
+    "import rmtk.plotting.risk_maps.plot_risk_maps as plotrm\n",
     "\n",
     "loss_map_file = '../sample_outputs/scenario_risk/loss-maps-structural.xml'\n",
     "exposure_model = '../input_models/exposure_model_nepal.xml'\n",
@@ -44,7 +49,9 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": []
@@ -66,7 +73,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython2",
-   "version": "2.7.9"
+   "version": "2.7.13"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/risk/closest_curves/closest_curves.ipynb b/notebooks/risk/closest_curves/closest_curves.ipynb
index 9e1074c..4f8b302 100755
--- a/notebooks/risk/closest_curves/closest_curves.ipynb
+++ b/notebooks/risk/closest_curves/closest_curves.ipynb
@@ -2,41 +2,54 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "# Logic tree branch selector"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "This feature of the Risk Modeller's Toolkit allows users to analyse sets of hazard curves derived using the Classical PSHA-based approach, and identify the branch of the logic tree that generated the hazard curve closest to the mean hazard curve, or a specific hazard curve fractile. This IPython notebook allows the user to sort a list of hazard curves in descending order of the root mean square relative error between the branch hazard curves and mean hazard curve. An average of the RMSE distance across all locations is used for the sorting."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
-    "import closest_curves as cc"
+    "import rmtk.risk.closest_curves.closest_curves as cc"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "Please specify below the path of the directory containing the hazard curve NRML files:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -45,118 +58,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "gsimlt_path=CauzziFaccioli2008_Toro2002SHARE_*_*_*_*_*,smlt_path=b2 0.0847919470655\n",
-      "gsimlt_path=ZhaoEtAl2006Asc_Toro2002SHARE_*_*_*_*_*,smlt_path=b2 0.0848318418032\n",
-      "gsimlt_path=AkkarBommer2010_Toro2002SHARE_*_*_*_*_*,smlt_path=b2 0.0848420322196\n",
-      "gsimlt_path=ChiouYoungs2008_Toro2002SHARE_*_*_*_*_*,smlt_path=b2 0.0849064677496\n",
-      "gsimlt_path=ChiouYoungs2008_Toro2002SHARE_Toro2002SHARE_*_*_FaccioliEtAl2010_*,smlt_path=b1 0.0850259901133\n",
-      "gsimlt_path=ChiouYoungs2008_Toro2002SHARE_Campbell2003SHARE_*_*_FaccioliEtAl2010_*,smlt_path=b1 0.0850260944879\n",
-      "gsimlt_path=AkkarBommer2010_Toro2002SHARE_Toro2002SHARE_*_*_FaccioliEtAl2010_*,smlt_path=b1 0.0850653783651\n",
-      "gsimlt_path=AkkarBommer2010_Toro2002SHARE_Campbell2003SHARE_*_*_FaccioliEtAl2010_*,smlt_path=b1 0.0850654826913\n",
-      "gsimlt_path=ZhaoEtAl2006Asc_Toro2002SHARE_Toro2002SHARE_*_*_FaccioliEtAl2010_*,smlt_path=b1 0.0850668072792\n",
-      "gsimlt_path=ZhaoEtAl2006Asc_Toro2002SHARE_Campbell2003SHARE_*_*_FaccioliEtAl2010_*,smlt_path=b1 0.0850669116037\n",
-      "gsimlt_path=CauzziFaccioli2008_Toro2002SHARE_Toro2002SHARE_*_*_FaccioliEtAl2010_*,smlt_path=b1 0.0854499431266\n",
-      "gsimlt_path=CauzziFaccioli2008_Toro2002SHARE_Campbell2003SHARE_*_*_FaccioliEtAl2010_*,smlt_path=b1 0.0854500469833\n",
-      "gsimlt_path=ChiouYoungs2008_CauzziFaccioli2008_Campbell2003SHARE_*_*_FaccioliEtAl2010_*,smlt_path=b3 0.130789343431\n",
-      "gsimlt_path=ChiouYoungs2008_CauzziFaccioli2008_Toro2002SHARE_*_*_FaccioliEtAl2010_*,smlt_path=b3 0.13078963624\n",
-      "gsimlt_path=AkkarBommer2010_CauzziFaccioli2008_Campbell2003SHARE_*_*_FaccioliEtAl2010_*,smlt_path=b3 0.130873927961\n",
-      "gsimlt_path=AkkarBommer2010_CauzziFaccioli2008_Toro2002SHARE_*_*_FaccioliEtAl2010_*,smlt_path=b3 0.13087422058\n",
-      "gsimlt_path=CauzziFaccioli2008_CauzziFaccioli2008_Campbell2003SHARE_*_*_FaccioliEtAl2010_*,smlt_path=b3 0.130929956138\n",
-      "gsimlt_path=CauzziFaccioli2008_CauzziFaccioli2008_Toro2002SHARE_*_*_FaccioliEtAl2010_*,smlt_path=b3 0.130930248633\n",
-      "gsimlt_path=ZhaoEtAl2006Asc_CauzziFaccioli2008_Campbell2003SHARE_*_*_FaccioliEtAl2010_*,smlt_path=b3 0.133892636771\n",
-      "gsimlt_path=ZhaoEtAl2006Asc_CauzziFaccioli2008_Toro2002SHARE_*_*_FaccioliEtAl2010_*,smlt_path=b3 0.133892922793\n",
-      "gsimlt_path=CauzziFaccioli2008_Campbell2003SHARE_*_*_*_*_*,smlt_path=b2 0.135733885976\n",
-      "gsimlt_path=CauzziFaccioli2008_Campbell2003SHARE_Toro2002SHARE_*_*_FaccioliEtAl2010_*,smlt_path=b1 0.135764812219\n",
-      "gsimlt_path=CauzziFaccioli2008_Campbell2003SHARE_Campbell2003SHARE_*_*_FaccioliEtAl2010_*,smlt_path=b1 0.135764993002\n",
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-      "gsimlt_path=AkkarBommer2010_Campbell2003SHARE_*_*_*_*_*,smlt_path=b2 0.135779376957\n",
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-      "gsimlt_path=ChiouYoungs2008_Campbell2003SHARE_Toro2002SHARE_*_*_FaccioliEtAl2010_*,smlt_path=b1 0.135921928347\n",
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-      "gsimlt_path=ZhaoEtAl2006Asc_Campbell2003SHARE_Toro2002SHARE_*_*_FaccioliEtAl2010_*,smlt_path=b1 0.135983794726\n",
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-      "gsimlt_path=CauzziFaccioli2008_AkkarBommer2010_Toro2002SHARE_*_*_FaccioliEtAl2010_*,smlt_path=b1 0.136894940324\n",
-      "gsimlt_path=CauzziFaccioli2008_AkkarBommer2010_Campbell2003SHARE_*_*_FaccioliEtAl2010_*,smlt_path=b1 0.136894956804\n",
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-      "gsimlt_path=CauzziFaccioli2008_ChiouYoungs2008_Campbell2003SHARE_*_*_FaccioliEtAl2010_*,smlt_path=b3 0.141646548848\n",
-      "gsimlt_path=CauzziFaccioli2008_ChiouYoungs2008_Toro2002SHARE_*_*_FaccioliEtAl2010_*,smlt_path=b3 0.141646787638\n",
-      "gsimlt_path=ZhaoEtAl2006Asc_ChiouYoungs2008_Campbell2003SHARE_*_*_FaccioliEtAl2010_*,smlt_path=b3 0.141738048883\n",
-      "gsimlt_path=ZhaoEtAl2006Asc_ChiouYoungs2008_Toro2002SHARE_*_*_FaccioliEtAl2010_*,smlt_path=b3 0.14173828752\n",
-      "gsimlt_path=ChiouYoungs2008_Campbell2003SHARE_Campbell2003SHARE_*_*_FaccioliEtAl2010_*,smlt_path=b3 0.145216604883\n",
-      "gsimlt_path=ChiouYoungs2008_Campbell2003SHARE_Toro2002SHARE_*_*_FaccioliEtAl2010_*,smlt_path=b3 0.145216654481\n",
-      "gsimlt_path=CauzziFaccioli2008_Campbell2003SHARE_Campbell2003SHARE_*_*_FaccioliEtAl2010_*,smlt_path=b3 0.145362515121\n",
-      "gsimlt_path=CauzziFaccioli2008_Campbell2003SHARE_Toro2002SHARE_*_*_FaccioliEtAl2010_*,smlt_path=b3 0.145362564668\n",
-      "gsimlt_path=AkkarBommer2010_Campbell2003SHARE_Campbell2003SHARE_*_*_FaccioliEtAl2010_*,smlt_path=b3 0.145407138941\n",
-      "gsimlt_path=AkkarBommer2010_Campbell2003SHARE_Toro2002SHARE_*_*_FaccioliEtAl2010_*,smlt_path=b3 0.145407188474\n",
-      "gsimlt_path=ZhaoEtAl2006Asc_Campbell2003SHARE_Campbell2003SHARE_*_*_FaccioliEtAl2010_*,smlt_path=b3 0.145522047807\n",
-      "gsimlt_path=ZhaoEtAl2006Asc_Campbell2003SHARE_Toro2002SHARE_*_*_FaccioliEtAl2010_*,smlt_path=b3 0.145522097301\n",
-      "gsimlt_path=AkkarBommer2010_CauzziFaccioli2008_Campbell2003SHARE_*_*_FaccioliEtAl2010_*,smlt_path=b1 0.272436870971\n",
-      "gsimlt_path=AkkarBommer2010_CauzziFaccioli2008_Toro2002SHARE_*_*_FaccioliEtAl2010_*,smlt_path=b1 0.272436878179\n",
-      "gsimlt_path=ZhaoEtAl2006Asc_CauzziFaccioli2008_Campbell2003SHARE_*_*_FaccioliEtAl2010_*,smlt_path=b1 0.27243847263\n",
-      "gsimlt_path=ZhaoEtAl2006Asc_CauzziFaccioli2008_Toro2002SHARE_*_*_FaccioliEtAl2010_*,smlt_path=b1 0.272438479838\n",
-      "gsimlt_path=ChiouYoungs2008_CauzziFaccioli2008_Campbell2003SHARE_*_*_FaccioliEtAl2010_*,smlt_path=b1 0.272445204902\n",
-      "gsimlt_path=ChiouYoungs2008_CauzziFaccioli2008_Toro2002SHARE_*_*_FaccioliEtAl2010_*,smlt_path=b1 0.27244521211\n",
-      "gsimlt_path=CauzziFaccioli2008_CauzziFaccioli2008_Campbell2003SHARE_*_*_FaccioliEtAl2010_*,smlt_path=b1 0.273085939673\n",
-      "gsimlt_path=CauzziFaccioli2008_CauzziFaccioli2008_Toro2002SHARE_*_*_FaccioliEtAl2010_*,smlt_path=b1 0.273085946864\n",
-      "gsimlt_path=ZhaoEtAl2006Asc_CauzziFaccioli2008_*_*_*_*_*,smlt_path=b2 0.273209868842\n",
-      "gsimlt_path=CauzziFaccioli2008_CauzziFaccioli2008_*_*_*_*_*,smlt_path=b2 0.273212080723\n",
-      "gsimlt_path=AkkarBommer2010_CauzziFaccioli2008_*_*_*_*_*,smlt_path=b2 0.273213499085\n",
-      "gsimlt_path=ChiouYoungs2008_CauzziFaccioli2008_*_*_*_*_*,smlt_path=b2 0.273243965883\n",
-      "gsimlt_path=ChiouYoungs2008_Toro2002SHARE_Campbell2003SHARE_*_*_FaccioliEtAl2010_*,smlt_path=b3 0.999535378154\n",
-      "gsimlt_path=ChiouYoungs2008_Toro2002SHARE_Toro2002SHARE_*_*_FaccioliEtAl2010_*,smlt_path=b3 0.999535398355\n",
-      "gsimlt_path=AkkarBommer2010_Toro2002SHARE_Campbell2003SHARE_*_*_FaccioliEtAl2010_*,smlt_path=b3 0.999558146551\n",
-      "gsimlt_path=AkkarBommer2010_Toro2002SHARE_Toro2002SHARE_*_*_FaccioliEtAl2010_*,smlt_path=b3 0.999558166751\n",
-      "gsimlt_path=CauzziFaccioli2008_Toro2002SHARE_Campbell2003SHARE_*_*_FaccioliEtAl2010_*,smlt_path=b3 0.999567009306\n",
-      "gsimlt_path=CauzziFaccioli2008_Toro2002SHARE_Toro2002SHARE_*_*_FaccioliEtAl2010_*,smlt_path=b3 0.999567029506\n",
-      "gsimlt_path=ZhaoEtAl2006Asc_Toro2002SHARE_Campbell2003SHARE_*_*_FaccioliEtAl2010_*,smlt_path=b3 0.999999979808\n",
-      "gsimlt_path=ZhaoEtAl2006Asc_Toro2002SHARE_Toro2002SHARE_*_*_FaccioliEtAl2010_*,smlt_path=b3 1.0\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "cc.list_closest_curves(input_dir)"
    ]
@@ -178,7 +86,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython2",
-   "version": "2.7.9"
+   "version": "2.7.13"
   }
  },
  "nbformat": 4,
diff --git a/rmtk/risk/event_loss_tables/Nepal/agg_losses-rlzs-structural-b1,b1.csv b/notebooks/risk/event_loss_tables/Nepal/agg_losses-rlzs-structural-b1,b1.csv
similarity index 100%
rename from rmtk/risk/event_loss_tables/Nepal/agg_losses-rlzs-structural-b1,b1.csv
rename to notebooks/risk/event_loss_tables/Nepal/agg_losses-rlzs-structural-b1,b1.csv
diff --git a/rmtk/risk/event_loss_tables/Nepal/output-182-ses-0-smltp_b1.xml b/notebooks/risk/event_loss_tables/Nepal/output-182-ses-0-smltp_b1.xml
similarity index 100%
rename from rmtk/risk/event_loss_tables/Nepal/output-182-ses-0-smltp_b1.xml
rename to notebooks/risk/event_loss_tables/Nepal/output-182-ses-0-smltp_b1.xml
diff --git a/notebooks/risk/event_loss_tables/loss_modelling.ipynb b/notebooks/risk/event_loss_tables/loss_modelling.ipynb
index f29b31b..ec6e596 100644
--- a/notebooks/risk/event_loss_tables/loss_modelling.ipynb
+++ b/notebooks/risk/event_loss_tables/loss_modelling.ipynb
@@ -2,7 +2,10 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "# Probable Maximum Losses (PML)\n",
     "\n",
@@ -14,57 +17,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {
     "collapsed": false,
+    "deletable": true,
+    "editable": true,
     "scrolled": false
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "10000\n",
-      "21222\n",
-      "Maximum loss:\n",
-      "8366565400.0\n",
-      "Average annual loss:\n",
-      "336644923.844\n",
-      "Average annual loss ratio (%):\n",
-      "0.542975683619\n",
-      "Loss for a return period of 100 years.\n",
-      "2155725100.0\n",
-      "Loss for a return period of 475 years.\n",
-      "3415264500.0\n",
-      "Loss for a return period of 950 years.\n",
-      "4500805600.0\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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C+Pv7w8XFBfXq1cOff/5puNahQwccOHDAagESERFZTvlg3gAA+WU/u8sUC9WWpCQmLCwM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\nlJaWYuLEiWjVqlWNnq9WqzkWhoiInJ7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-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x112c86b10>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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REannFOYiItXg2glNu59JKAl4mB8/fpzzzz+fvn37kpiYSEZGRqBLEBGpkczM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\nmIuIiNjc/wcVxzSSVFftKgAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x11329af10>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "from loss_modelling import lossModelling\n",
+    "from rmtk.risk.event_loss_tables.loss_modelling import lossModelling\n",
     "%matplotlib inline\n",
     "\n",
+    "import os\n",
+    "print os.getcwd()\n",
+    "\n",
     "event_loss_table_folder = 'Nepal'\n",
     "total_cost = 62e9\n",
     "investigationTime = 10000\n",
@@ -78,93 +45,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {
     "collapsed": false,
+    "deletable": true,
+    "editable": true,
     "scrolled": false
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "10000.0\n",
-      "21222\n",
-      "Ruptures causing losses for a return period of 500 years\n",
-      " \n",
-      "col=00~ses=0058~src=229-52~rup=005-01\n",
-      "Mag: 7.25 Lon:85.67 Lat:28.04 Depth:15.00\n",
-      "col=00~ses=0058~src=230-46~rup=003-01\n",
-      "Mag: 6.25 Lon:85.05 Lat:27.53 Depth:15.00\n",
-      "col=00~ses=0193~src=230-60~rup=006-01\n",
-      "Mag: 7.75 Lon:86.47 Lat:27.53 Depth:15.00\n",
-      "col=00~ses=0122~src=230-49~rup=003-01\n",
-      "Mag: 6.25 Lon:85.35 Lat:27.53 Depth:15.00\n",
-      "col=00~ses=0072~src=229-90~rup=004-01\n",
-      "Mag: 6.75 Lon:85.26 Lat:27.86 Depth:15.00\n",
-      "col=00~ses=0054~src=230-69~rup=004-01\n",
-      "Mag: 6.75 Lon:85.35 Lat:27.44 Depth:15.00\n",
-      "col=00~ses=0197~src=229-64~rup=005-01\n",
-      "Mag: 7.25 Lon:84.75 Lat:27.95 Depth:15.00\n",
-      "col=00~ses=0092~src=230-31~rup=002-01\n",
-      "Mag: 5.75 Lon:85.15 Lat:27.62 Depth:15.00\n",
-      "col=00~ses=0013~src=231-238~rup=006-01\n",
-      "Mag: 7.75 Lon:83.47 Lat:27.41 Depth:15.00\n",
-      "col=00~ses=0141~src=230-225~rup=007-01\n",
-      "Mag: 8.25 Lon:86.77 Lat:26.99 Depth:15.00\n",
-      " \n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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TFL+7kJAQnD9/nn//woULCA0N5ZcVFBQobd+FCxcQEhLSbZ6WlpZ47LHHsG3bNmzbtg0L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\nqqGhwR8zUgqUunDB93kFOK+99ppqbGzUuxu65pVXXlEWi8XtNHl5eX5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tyEg2DB/Ot62t\n90j18uXLZGZmMmfOnID30VBrQ6QqUgURCohUQeoABp5UTSYTs+9Y599On85vr1yh+uJFHXvVNYYa\nVEWqIlUQoYBIFaQOYOBJFWB9bCzhZ844/39txgx+VF5O8x03MdcTX4ZaGyJVkSqIUECkClIHMPCk\nCjB7yhSeqKoCx37U69cpWbSIl29fMP/QoUPMnj1bt/4ZalAVqYpUQYQCIlWQOoCBKVWAN+fPZ9GO\nHdDZCSNGgNnM2ZAQWlpasFqtRERE6NY3Q60NkapIFUQoIFIFqQMYmFIFGDV8OJ8tWcKT27fDd9+B\nUlwADhw44PVdZfwdQw2qIlWRKohQQKQKUgcwcKUKMDgsjI9XrCC1vh4sFuqDghg3bhznzp3TtV+G\nWhsiVZEqiFBApApSBzBwpepIUFAQS6Kjee7AAb6LiOC39fVs+ugjKqqqdKsNQ23qilRFqiBCAZEq\nSB3AwJaqIyNHjmTDsmWMPXSI/zdrFreystj82WdMvHSJ6M5OooHJt27x6pIlAblUoaE+lSJVkSqI\nUECkClIHIFKF/7n27//OyuJ7xcW8aLVyfeZMLgwbxoXYWA4DwXV1PFFVxaypUzXvj6E2cUSqIlUQ\noYBIFaQOQKQKXa/9++zDD/NYbS3MmAGnTjlPu7FFRfFFfX1A+mOoT6VIVaQKIhQQqYLUAYhU4d67\n1PxZdDQTgZupqZivXyds5EgAJk6aFJD+mFRPd3cNYAJ1ax6JRCKRSHyJq6GzT0m1D43vEolEIpF4\nnYH9Y7xEIpFIJH6MDKoSiUQikfgpfWZQ3bRpE4mJicyYMYOnn36atrY2XnjhBeLj40lKSmLVqlVc\nv37dOf2aNWtITk5m165dANTW1jJ48GBSUlKc/7Zt26bX2/E63b3/n//85yQlJZGSksLixYu5dOmS\nc/r+9v6h+2XgyK9+9SvMZjPfffed82/9cRlA98vhlVdeITIy0vm+du/e7Zy+Py4HV7Xw1ltvER8f\nT2JiIhs2bHBO39+WQXfv/6mnnnK+l9jYWFJSUpzT97f3D90vg+PHj5OWlkZKSgoPPfQQpaWlzun7\nzDJQfSD19fUqNjZW3bp1Syml1OrVq9WWLVvU3r17VUdHh1JKqQ0bNqgNGzYopZQ6ceKEevnll5XN\nZlOrV69WSil19uxZlZiYqM8b8DGu3v+NGzec02zevFn95Cc/UUr1v/evlOtloJRS58+fV4sXL1Yx\nMTHq2rVrSqn+uQyUcr0cXnnlFfWrX/3qnun743JwtQwKCgpUdna2slqtSimlvv32W6VU/1sG7j4L\njjz//PPq7/7u75RS/e/9K+V6GSxatEjl5uYqpZT64osv1IIFC5RSfWsZ9Bmp2mw2bt686WwnTpxI\ndna28xys1NRU6m+fZxQcHIzFYukiGaOnu/c/bNgw5+MtLS3OZdEf3z90vwwAfvazn/EP//APXabt\nr8sAXC8H1c2BfP11Ody9DCZMmMA777zDSy+95DxPe/To0UD/XAauagDsdfDJJ5/wwx/+EOif7x+6\nrwGTyeT8xbKpqcm5XPrSMugTg+rEiRN5/vnniY6OZsKECYwcOZLs7Owu07z//vssW7YMgGnTpmGz\n2cjIyGDdunXOaWpqarpQv/j2/fX6ety9/40bNxIdHc1HH33EL37xC6D/vX9wvQx27NhBZGQkM2fO\n7DJ9f1wG4L4W3nrrLZKSkli7di1NTU1A/1wO3S2DnJwcqqqqOHDgAGlpaSxYsIAvv/wS6H/LoKfv\nw6KiIsaOHcvkyZOB/vf+wXUN/OM//iMvvPAC0dHRvPDCC7z22mtAH1sGAbdxN/nuu+9UZmamunr1\nqmpvb1crVqxQ27Ztcz7+6quvqlWrVrmdh5F/7ujp/Sul1C9/+Uv18ssvu5yHkd+/Ut0vgw8++ECl\npqaq69evK6WUiomJUVevXnU5D6MvA6Vc10JDQ4Pq7OxUnZ2dauPGjWrNmjUu52H05eBqGSQmJqq/\n/uu/VkopdfToURUbG+tyHkZeBj19H/zkJz9Rb775ptt5GPn9K+V6Gfz0pz9Vv//975VSSn3yyScq\nOzvb5TwG9M+/eXl5xMbGEhERQXBwMKtWreLQoUMAbNmyhS+++IL/+I//0LmX2sXd+3fk6aef5tNP\nP9Wph9qnu2WwZcsWamtrSUpKIjY2lvr6embPns23336rd3c1i6taGDNmDCaTCZPJxDPPPMPRo0f1\n7qpmcbUMIiMjWbVqFQAPPfQQZrOZa9eu6dxb/8fd94HNZmP79u089dRTOvdS23S3DIqLi9m6dSsr\nV64E4Mknn+yTn4M+MahOmjSJI0eO0NrailKKvLw8EhISyM3N5fXXX2fHjh2EhYXp3U3N4ur9V1dX\nO6fZsWMH8fHxOvZS23S3DJ544gkuX77M2bNnOXv2LJGRkXz11VeMGTNG7+5qFle1cPnyZec027dv\nZ8aMGTr2Utu4WgYrVqygoKAAgKqqKqxWKxERETr31v9x9f7BPtjEx8czYcIEnXupbe5eBvn5+SQk\nJDBx4kT2798PQEFBAXFxcTr39N70iSsqzZkzhyeffJJZs2YRHBzMrFmz+NGPfsT06dOxWq3k5OQA\nMHfuXP7lX/7F5Xwcv587snbtWtavX695/32Nq/f/9NNPc/r0acxmMzExMbzzzjtu52PU9w/dL4Nn\nn322yzSeXMbSyMsAXNfCM888w/HjxzGZTMTGxvLuu++6nY+Rl4O7WlizZg0zZswgJCSErVu3up2P\nUZeBu/f/29/+1nmAUk8x6vuH7pfBj3/8Y1JSUvibv/kbbDYbgwcP5l//9V/dzkePZdCnrv0rkUgk\nEomR0yd+/pVIJBKJpD9EBlWJRCKRSPwUGVQlEolEIvFTZFCVSCQSicRPkUFVIpFIJBI/RQZViUQi\nkUj8FBlUJRKJRCLxU2RQlUgkEonET/n/NGHzC/39tXoAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x110960050>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Ruptures causing losses for a return period of 5000 years\n",
-      " \n",
-      "col=00~ses=0116~src=229-118~rup=006-01\n",
-      "Mag: 7.75 Lon:86.07 Lat:27.77 Depth:15.00\n",
-      "col=00~ses=0049~src=229-113~rup=006-01\n",
-      "Mag: 7.75 Lon:85.56 Lat:27.77 Depth:15.00\n",
-      "col=00~ses=0156~src=230-32~rup=006-01\n",
-      "Mag: 7.75 Lon:85.25 Lat:27.62 Depth:15.00\n",
-      "col=00~ses=0025~src=229-134~rup=006-01\n",
-      "Mag: 7.75 Lon:86.07 Lat:27.68 Depth:15.00\n",
-      "col=00~ses=0129~src=230-93~rup=006-01\n",
-      "Mag: 7.75 Lon:85.35 Lat:27.35 Depth:15.00\n",
-      "col=00~ses=0133~src=231-156~rup=006-01\n",
-      "Mag: 7.75 Lon:83.48 Lat:27.77 Depth:15.00\n",
-      "col=00~ses=0132~src=229-46~rup=006-01\n",
-      "Mag: 7.75 Lon:85.06 Lat:28.04 Depth:15.00\n",
-      "col=00~ses=0025~src=229-113~rup=005-01\n",
-      "Mag: 7.25 Lon:85.56 Lat:27.77 Depth:15.00\n",
-      "col=00~ses=0128~src=230-16~rup=006-01\n",
-      "Mag: 7.75 Lon:84.95 Lat:27.71 Depth:15.00\n",
-      "col=00~ses=0014~src=231-58~rup=006-01\n",
-      "Mag: 7.75 Lon:83.28 Lat:28.13 Depth:15.00\n",
-      " \n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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REymFSH9CFY1chL6eHvaHhmJ0bGy7fRUcDpo6u/WuAJiR08HGkasxzMjpYEZO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\nYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x11354ae10>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "from loss_modelling import selectRuptures \n",
     "return_periods = [500,5000]\n",
@@ -176,7 +64,9 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": []
@@ -198,7 +88,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython2",
-   "version": "2.7.9"
+   "version": "2.7.13"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/vulnerability/Convolution_module/Convolution.ipynb b/notebooks/vulnerability/Convolution_module/Convolution.ipynb
index fcc0e95..310a3af 100644
--- a/notebooks/vulnerability/Convolution_module/Convolution.ipynb
+++ b/notebooks/vulnerability/Convolution_module/Convolution.ipynb
@@ -2,9 +2,12 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
-    "#Risk-Hazard Convolution Module\n",
+    "# Risk-Hazard Convolution Module\n",
     "\n",
     "This script takes a fragility function as input and does the convolution with a hazard curve in order to estimate the annual probability of collapse. \n"
    ]
@@ -12,10 +15,12 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "source": [
-    "##Load hazard curve\n",
+    "## Load hazard curve\n",
     "\n",
     "In order to use this methodology, it is necessary to provide a hazard curve, defined according to the format established on the [RMTK manual](../../../../../rmtk-docs.pdf). Please provide the location of the file containing this input using the parameter capacity_curves_file."
    ]
@@ -25,6 +30,8 @@
    "execution_count": 30,
    "metadata": {
     "collapsed": false,
+    "deletable": true,
+    "editable": true,
     "scrolled": false
    },
    "outputs": [
@@ -51,10 +58,12 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "source": [
-    "##Load fragility function\n",
+    "## Load fragility function\n",
     "\n",
     "The fragility function representing the risk of the building class being analysed should be provided here. This function should follow the format established in the [RMTK manual](../../../../../rmtk-docs.pdf). Please provide the location of the file containing this input using the parameter fragility_function_file."
    ]
@@ -64,7 +73,9 @@
    "execution_count": 44,
    "metadata": {
     "collapsed": false,
-    "scrolled": false
+    "deletable": true,
+    "editable": true,
+    "scrolled": true
    },
    "outputs": [
     {
@@ -87,9 +98,12 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
-    "##Calculate annual probability of damage\n",
+    "## Calculate annual probability of damage\n",
     "\n",
     "\n",
     "In order to obtain the fragility model, it is necessary to input the return period for which the hazard curve was derived, as well as the damage state of interest (slight, collapse, etc.), as described in the [RMTK manual](../../../../../rmtk-docs.pdf). \n",
@@ -102,6 +116,8 @@
    "execution_count": 45,
    "metadata": {
     "collapsed": false,
+    "deletable": true,
+    "editable": true,
     "scrolled": false
    },
    "outputs": [
@@ -323,7 +339,7 @@
     }
    ],
    "source": [
-    "import Convolution\n",
+    "from rmtk.vulnerability.Convolution_module import Convolution\n",
     "\n",
     "return_period = 50\n",
     "damage_state = 'Collapse'\n",
@@ -336,7 +352,9 @@
    "cell_type": "code",
    "execution_count": 208,
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -403,6 +421,8 @@
    "execution_count": 209,
    "metadata": {
     "collapsed": false,
+    "deletable": true,
+    "editable": true,
     "scrolled": true
    },
    "outputs": [
@@ -436,7 +456,9 @@
    "cell_type": "code",
    "execution_count": 210,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -510,7 +532,9 @@
    "cell_type": "code",
    "execution_count": 211,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [
     {
@@ -532,7 +556,9 @@
    "cell_type": "code",
    "execution_count": 227,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [
     {
@@ -553,7 +579,9 @@
    "cell_type": "code",
    "execution_count": 345,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -628,7 +656,9 @@
    "cell_type": "code",
    "execution_count": 346,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [
     {
@@ -664,7 +694,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython2",
-   "version": "2.7.10"
+   "version": "2.7.13"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/vulnerability/derivation_fragility/NLTHA_on_SDOF/2MSA_on_SDOF.ipynb b/notebooks/vulnerability/derivation_fragility/NLTHA_on_SDOF/2MSA_on_SDOF.ipynb
index e97fb39..ecf8103 100644
--- a/notebooks/vulnerability/derivation_fragility/NLTHA_on_SDOF/2MSA_on_SDOF.ipynb
+++ b/notebooks/vulnerability/derivation_fragility/NLTHA_on_SDOF/2MSA_on_SDOF.ipynb
@@ -2,14 +2,20 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "# Double Multiple Stripe Analysis (2MSA) for Single Degree of Freedom (SDOF) Oscillators"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "<img src=\"../../../../figures/intact-damaged.jpg\" width=\"500\" align=\"middle\">"
    ]
@@ -18,21 +24,26 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
-    "from rmtk.vulnerability.common import utils\n",
-    "import double_MSA_on_SDOF\n",
     "import numpy\n",
+    "from rmtk.vulnerability.common import utils\n",
+    "from rmtk.vulnerability.derivation_fragility.NLTHA_on_SDOF import MSA_utils\n",
     "from rmtk.vulnerability.derivation_fragility.NLTHA_on_SDOF.read_pinching_parameters import read_parameters\n",
-    "import MSA_utils\n",
+    "from rmtk.vulnerability.derivation_fragility.NLTHA_on_SDOF import double_MSA_on_SDOF\n",
     "%matplotlib inline"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "---\n",
     "### Load capacity curves\n",
@@ -48,7 +59,9 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -63,7 +76,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "### Load ground motion records\n",
     "For what concerns the ground motions to be used in the Double Multiple Stripe Analysis the following inputs are required:\n",
@@ -85,7 +101,9 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -109,7 +127,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "### Load damage state thresholds\n",
     "\n",
@@ -123,7 +144,9 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -134,7 +157,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "### Calculate fragility function\n",
     "\n",
@@ -145,7 +171,9 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -160,7 +188,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "### Fit lognormal CDF fragility curves\n",
     "\n",
@@ -174,7 +205,9 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -189,7 +222,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "### Plot fragility functions\n",
     "\n",
@@ -201,7 +237,9 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -212,7 +250,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "### Save fragility functions\n",
     " \n",
@@ -226,7 +267,9 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -242,7 +285,9 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": []
@@ -264,7 +309,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython2",
-   "version": "2.7.10"
+   "version": "2.7.13"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/vulnerability/derivation_fragility/NLTHA_on_SDOF/NLTHA_on_SDOF.ipynb b/notebooks/vulnerability/derivation_fragility/NLTHA_on_SDOF/NLTHA_on_SDOF.ipynb
index c2778fe..6b3eaf7 100644
--- a/notebooks/vulnerability/derivation_fragility/NLTHA_on_SDOF/NLTHA_on_SDOF.ipynb
+++ b/notebooks/vulnerability/derivation_fragility/NLTHA_on_SDOF/NLTHA_on_SDOF.ipynb
@@ -23,7 +23,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -55,7 +55,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 2,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -70,7 +70,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 3,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -80,9 +80,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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s75unIAA+kjivRXytOd7tg+MMPiO9FqBFrqUPNoy4khgw8nwjVNVyLO1hvVr3pHIcJ4Pr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\nFks6lBDLfgIh9+zcmIQcMzvezC6MCTr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7fiA7NZsRuSNs+/xn8X/8f/R39LuDv3z9FwAykjP8bVpmtqRXTi8mLJ3AziM7ad+gPXfN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\nGwvHcRwnJW4sHMdxnJT8P9DXmV8jRg9hAAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11572a1d0>"
+       "<matplotlib.figure.Figure at 0x7f9a8b1a5d90>"
       ]
      },
      "metadata": {},
@@ -114,7 +114,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 4,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -145,7 +145,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 5,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -156,7 +156,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "['Slight', 'fraction Sdy', 'normal', '0.7', '0']\n",
+      "['Slight', 'Sdy', 'normal', '0.5', '0']\n",
       "['Moderate', '3 Sdy 1 Sdu', 'normal', '', '0']\n",
       "['Extensive', '1 Sdy 1 Sdu', 'normal', '', '0']\n",
       "['Collapse', 'Sdu', 'normal', '', '0']\n"
@@ -184,7 +184,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 6,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -196,56 +196,20 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2%\n",
-      "4%\n",
-      "6%\n",
-      "8%\n",
-      "10%\n",
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-      "96%\n",
-      "98%\n",
-      "100%\n"
+      "2%\n"
+     ]
+    },
+    {
+     "ename": "OSError",
+     "evalue": "OpenSees not found",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-6-c7f96b51ca5c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mdamping_ratio\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.05\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mdegradation\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mPDM\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mNLTHA_on_SDOF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcalculate_fragility\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcapacity_curves\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhysteresis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgmrs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdamage_model\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdamping_ratio\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdegradation\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mPDM\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'../../../../../rmtk_data/PDM.csv'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/home/daniele/GIT/gem/rmtk/rmtk/vulnerability/derivation_fragility/NLTHA_on_SDOF/NLTHA_on_SDOF.pyc\u001b[0m in \u001b[0;36mcalculate_fragility\u001b[0;34m(capacity_curves, hysteresis, gmrs, damage_model, damping, degradation)\u001b[0m\n\u001b[1;32m     18\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0migmr\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mno_gmrs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m             \u001b[0mlimit_states\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdefine_limit_states\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcapacity_curves\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0micc\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdamage_model\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 20\u001b[0;31m             \u001b[0mtime\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdisps\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrun_time_history_analysis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcapacity_curves\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mhysteresis\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0micc\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mgmrs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0migmr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdamping\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdegradation\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     21\u001b[0m             \u001b[0mSdi\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mabs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdisps\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     22\u001b[0m             \u001b[0;34m[\u001b[0m\u001b[0mPDM\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mds\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mallocate_damage\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0migmr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mPDM\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mSdi\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlimit_states\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/home/daniele/GIT/gem/rmtk/rmtk/vulnerability/derivation_fragility/NLTHA_on_SDOF/NLTHA_on_SDOF.pyc\u001b[0m in \u001b[0;36mrun_time_history_analysis\u001b[0;34m(capacity_curves, hysteresis, icc, gmrs, igmr, damping, degradation)\u001b[0m\n\u001b[1;32m     37\u001b[0m             \u001b[0msubprocess\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"./OpenSees\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtcl\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     38\u001b[0m         \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 39\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mOSError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'OpenSees not found'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     40\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     41\u001b[0m     \u001b[0mdisps\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mOSError\u001b[0m: OpenSees not found"
      ]
     }
    ],
@@ -270,52 +234,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "metadata": {
     "collapsed": false,
     "deletable": true,
     "editable": true,
     "scrolled": false
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "5%\n",
-      "10%\n",
-      "15%\n",
-      "20%\n",
-      "25%\n",
-      "30%\n",
-      "35%\n",
-      "40%\n",
-      "45%\n",
-      "50%\n",
-      "55%\n",
-      "60%\n",
-      "65%\n",
-      "70%\n",
-      "75%\n",
-      "80%\n",
-      "85%\n",
-      "90%\n",
-      "95%\n",
-      "100%\n",
-      "The best damage-intensity correlation was achieved for Sa at T=0.7s\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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hjMp6LqE25biddCzey3q+QKILNRnj55rxcfPG0RDgBoChQ+kfpuhPbr2VnpMmsYfEXZK6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\n4TiO4xTk/wM4xcLlH4t//wAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x114d021d0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "minT, maxT,stepT  = 0.0, 2.0, 0.1\n",
     "regression_method = 'least squares'\n",
@@ -339,7 +265,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -369,34 +295,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": null,
    "metadata": {
     "collapsed": false,
     "deletable": true,
     "editable": true
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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PAjdIugooL4k3M/tx5c2KDIMYoM6H7+Likl1m3Jy3MfVEqJg+Fb8RPS1J7KGcTYoMkawO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\nHvgQWNysX8n13k79WGA28DE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F/z/0TzOrSae4rMQppvzZDhgN3GYd9tZgG9cbErsAh+Pz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\n/gJPSXsMplNKsgAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x111801610>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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YZPG5LG2pN9w57DQRjkniNlPKWRB2NrA6Xu9weqn2ZVmwVsnzx7FG4LGZ/sAZUWS39PaY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\nQHg8Ho8nI/8Psxf3DW76ZhwAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x114e5d090>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "minIML, maxIML = 0.0, 1.2\n",
     "utils.plot_fragility_model(fragility_model, minIML, maxIML)\n",
@@ -420,7 +325,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -455,7 +360,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {
     "collapsed": true,
     "deletable": true,
@@ -485,25 +390,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "metadata": {
     "collapsed": false,
     "deletable": true,
     "editable": true,
     "scrolled": true
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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I2Cl9vROwKiLcbDIKiSnA7HTxG1nGYmatp15JYi+SDtVhj6dl+eYBB0t6Argb30GtXO8n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\nYWZmJf1/GJN/fBqxJSEAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x10a81edd0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "utils.plot_vulnerability_model(vulnerability_model)"
    ]
@@ -524,7 +418,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {
     "collapsed": true,
     "deletable": true,
diff --git a/notebooks/vulnerability/derivation_fragility/batch_facility.ipynb b/notebooks/vulnerability/derivation_fragility/batch_facility.ipynb
index c34c0ee..6c16578 100644
--- a/notebooks/vulnerability/derivation_fragility/batch_facility.ipynb
+++ b/notebooks/vulnerability/derivation_fragility/batch_facility.ipynb
@@ -2,7 +2,10 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "# RMTK batch facility\n",
     "\n",
@@ -14,7 +17,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "##Load capacity curves\n",
     "\n",
@@ -23,24 +29,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Models loaded:\n",
-      "Masonry\n",
-      "RC\n",
-      "Steel\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from rmtk.vulnerability.common import utils\n",
+    "from rmtk.vulnerability.derivation_fragility.hybrid_methods.N2 import N2Method\n",
+    "\n",
     "%matplotlib inline \n",
     "files_folder = '../../../../rmtk_data/portfolio_Italy'\n",
     "models = utils.read_set_models(files_folder)"
@@ -48,7 +47,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "## Load ground motion records\n",
     "\n",
@@ -57,22 +59,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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uYkJq+C6E6AP6WdhVeuxeBl4HFrbcFqy+ixBCCCEaZANgMWCqZz61y7Y0ynvS8nVgE8tt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R2Sf89Y3UvdlbVWOPQrFrNQnYVOQ/x2lVXDa5bHKcpiMie4vIa1Kr5mAuzJFTU/M7wvL1wNmp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\njtPm/D8PirUWkwU6gQAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x10ac64c50>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "gmrs_folder = '../../../../rmtk_data/accelerograms'\n",
     "gmrs = utils.read_gmrs(gmrs_folder)\n",
@@ -83,69 +76,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Masonry.csv\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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hI4FvAr/Gs9r3sMxObvAcwkuIr5d2HWaZ/bgJ4qJc6+JvcGPxiKudLbPLG5i3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\nJjsJSVKTnYQkqclOQpLUZCchSWr6BapPLOAkfNvzAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x10ae7da10>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "5%\n",
-      "10%\n",
-      "15%\n",
-      "20%\n",
-      "25%\n",
-      "30%\n",
-      "35%\n",
-      "40%\n",
-      "45%\n",
-      "50%\n",
-      "55%\n",
-      "60%\n",
-      "65%\n",
-      "70%\n",
-      "75%\n",
-      "80%\n",
-      "85%\n",
-      "90%\n",
-      "95%\n",
-      "100%\n"
-     ]
-    },
-    {
-     "ename": "TypeError",
-     "evalue": "plot_fragility_model() takes exactly 4 arguments (3 given)",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-3-6c23ee788a2a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     10\u001b[0m     \u001b[0mPDM\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mN2Method\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcalculate_fragility\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcapacity_curves\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mgmrs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdamage_model\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdamping\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m     \u001b[0mfragility_model\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcalculate_mean_fragility\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgmrs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mPDM\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdamping\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'Sa'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdamage_model\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m     \u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot_fragility_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfragility_model\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0.01\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m: plot_fragility_model() takes exactly 4 arguments (3 given)"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "import N2Method\n",
-    "damage_model = utils.read_damage_model('../../../../rmtk_data/damage_model.csv')\n",
+    "damage_model = utils.read_damage_model('../../../../../rmtk_data/damage_model.csv')\n",
     "damping = 0.05\n",
     "T = 2.0\n",
     "\n",
@@ -162,7 +101,9 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": []
@@ -184,7 +125,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython2",
-   "version": "2.7.9"
+   "version": "2.7.13"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/vulnerability/derivation_fragility/hybrid_methods/N2/N2.ipynb b/notebooks/vulnerability/derivation_fragility/hybrid_methods/N2/N2.ipynb
index 1dee6d0..708a1cf 100644
--- a/notebooks/vulnerability/derivation_fragility/hybrid_methods/N2/N2.ipynb
+++ b/notebooks/vulnerability/derivation_fragility/hybrid_methods/N2/N2.ipynb
@@ -126,7 +126,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "collapsed": true,
+    "collapsed": false,
     "deletable": true,
     "editable": true
    },
diff --git a/notebooks/vulnerability/model_generator/DBELA_approach/DBELA.ipynb b/notebooks/vulnerability/model_generator/DBELA_approach/DBELA.ipynb
index 43ce207..fe9933a 100644
--- a/notebooks/vulnerability/model_generator/DBELA_approach/DBELA.ipynb
+++ b/notebooks/vulnerability/model_generator/DBELA_approach/DBELA.ipynb
@@ -2,7 +2,10 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "# Generation of capacity curves using DBELA\n",
     "This notebook enables the user to generate capacity curves (in terms of spectral acceleration vs. spectral displacement) using the Displacement-based Earthquake Loss Assessment (DBELA) approach. The DBELA methodology permits the calculation of the displacement capacity of a collection of structures at a number of limit states (which could be structural or non-structural). These displacements are derived based on the capacity of an equivalent SDOF structure, following the principles of structural mechanics ([Crowley et al., 2004](http://link.springer.com/article/10.1007%2Fs10518-004-2290-8); [Bal et al., 2010](http://www.iusspress.it/pc/viewPrd.asp?idcategory=25&idproduct=132); [Silva et al., 2013](http://www.sciencedirect.com/science/article/pii/S0141029613002071)). The figure below illustrates various capacity curves generated using this approach.\n",
@@ -19,11 +22,13 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "collapsed": true
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
-    "import DBELA\n",
+    "from rmtk.vulnerability.model_generator.DBELA_approach import DBELA\n",
     "from rmtk.vulnerability.common import utils\n",
     "%matplotlib inline"
    ]
@@ -31,7 +36,9 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "source": [
     "---\n",
@@ -44,7 +51,9 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -54,7 +63,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "### Number of samples\n",
     "\n",
@@ -65,7 +77,9 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -74,7 +88,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "### Generate the capacity curves"
    ]
@@ -83,7 +100,9 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -95,7 +114,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "### Plot the capacity curves"
    ]
@@ -105,6 +127,8 @@
    "execution_count": null,
    "metadata": {
     "collapsed": false,
+    "deletable": true,
+    "editable": true,
     "scrolled": false
    },
    "outputs": [],
@@ -114,7 +138,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "### Include additional information\n",
     "Additional information can be added to the capacity curves generated using the above method. For instance, by setting appropriate values for the parameters `gamma` and `yielding_point_index` in the cell below, the `add_information` function can be used to include this data in the previously generated capacity curves."
@@ -124,7 +151,9 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -136,7 +165,9 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -146,7 +177,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "### Save capacity curves\n",
     "Please specify below the path for the output file to save the capacity curves:"
@@ -156,7 +190,9 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -167,7 +203,9 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -191,7 +229,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython2",
-   "version": "2.7.10"
+   "version": "2.7.13"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/vulnerability/model_generator/SPBELA_approach/SPBELA.ipynb b/notebooks/vulnerability/model_generator/SPBELA_approach/SPBELA.ipynb
index 73af69b..9644aa8 100644
--- a/notebooks/vulnerability/model_generator/SPBELA_approach/SPBELA.ipynb
+++ b/notebooks/vulnerability/model_generator/SPBELA_approach/SPBELA.ipynb
@@ -2,7 +2,10 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "# Generation of capacity curves using SP-BELA\n",
     "The Simplified Pushover-based Earthquake Loss Assessment (SP-BELA) methodology allows the calculation of the displacement capacity (i.e. spectral displacement) and collapse multiplier (i.e. spectral acceleration) using a simplified mechanics-based procedure, similar to what has been proposed by [Cosenza et al. 2005](http://www.tandfonline.com/doi/abs/10.1080/13632460509350531). The methodology currently implemented in the Risk Modeller's Toolkit only supports reinforced concrete frames.\n",
@@ -17,13 +20,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
-    "import SPBELA\n",
+    "from rmtk.vulnerability.model_generator.SPBELA_approach import SPBELA\n",
     "from rmtk.vulnerability.common import utils\n",
     "%matplotlib inline "
    ]
@@ -31,7 +36,9 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "source": [
     "---\n",
@@ -42,9 +49,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -54,7 +63,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "### Number of samples\n",
     "\n",
@@ -63,9 +75,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -74,16 +88,21 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "### Generate the capacity curves"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {
     "collapsed": false,
+    "deletable": true,
+    "editable": true,
     "scrolled": false
    },
    "outputs": [],
@@ -96,365 +115,33 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "### Plot the capacity curves"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": [
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-       "tdyhx+cVUB1QuO5vkxvDWCvHpNz0qHCZiki1rPTy8GVnZruk46n/91imwcx2SGm7sY8vVpV9uHtj\n",
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-       "FCRERCRLQUJERLJ+ADvP8WsTmEsZAAAAAElFTkSuQmCC\n"
-      ],
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x10b873450>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "utils.plot_capacity_curves(capacity_curves)"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "### Adding additional information\n",
     "Additional information can be added to the capacity curves generated using the above method. For instance, by setting appropriate values for the parameters `gamma` and `yielding_point_index` in the cell below, the `add_information` function can be used to include this data in the previously generated capacity curves."
@@ -462,9 +149,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -474,9 +163,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -486,7 +177,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "### Save capacity curves\n",
     "Please specify below the path for the output file to save the capacity curves:"
@@ -494,9 +188,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -505,9 +201,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -531,7 +229,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython2",
-   "version": "2.7.9"
+   "version": "2.7.13"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/vulnerability/model_generator/model_generator.ipynb b/notebooks/vulnerability/model_generator/model_generator.ipynb
index 6c83719..f7d2a1a 100644
--- a/notebooks/vulnerability/model_generator/model_generator.ipynb
+++ b/notebooks/vulnerability/model_generator/model_generator.ipynb
@@ -2,7 +2,10 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "# Model Generator\n",
     "This feature of the Risk Modeller's Toolkit enables the generation of various models (capacity curves), as a way to propogate the building-to-building variability to the final fragility or vulnerability models. The figure below illustrates various capacity curves (in terms of spectral acceleration versus spectral displacement) generated using the Displacement-based Earthquake Loss Assessment (DBELA) approach.\n",
@@ -13,7 +16,9 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "source": [
     "---\n",
@@ -26,7 +31,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "## Generation of capacity curves using SP-BELA (Borzi et al. 2008)\n",
     "\n",
@@ -37,7 +45,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "## Generation of capacity curves using point dispersion\n",
     "This module can be used to generate many synthethic pushover curves (base shear vs. roof displacement or spectral acceleration vs. spectral displacement), starting from a median pushover curve and dispersion levels for a number of specific points on the curve. <br/>\n",
@@ -62,7 +73,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython2",
-   "version": "2.7.9"
+   "version": "2.7.13"
   }
  },
  "nbformat": 4,