diff --git a/index.ipynb b/index.ipynb
index 2d0110a70a1c82d5077da006878bb6b54adddb1c..81131e739bbbc951025e614a2564f10f9472bf22 100644
--- a/index.ipynb
+++ b/index.ipynb
@@ -295,18 +295,6 @@
     "[Cyclic pullout of textile fabrics and CFRP sheets](tour4_plastic_bond/4_3_PO_trc_cfrp_cyclic.ipynb#top) "
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "<div style=\"background-color:lightgreen;text-align:left\"> <img src=\"icons/rest.png\" alt=\"Step by step\" width=\"40\" height=\"40\">\n",
-    "    &nbsp; &nbsp; <b>Our current location</b> </div>"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -329,6 +317,18 @@
     "[Pull out simulation using damage model](tour5_damage_bond/5_2_PO_cfrp_damage.ipynb)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "<div style=\"background-color:lightgreen;text-align:left\"> <img src=\"icons/rest.png\" alt=\"Step by step\" width=\"40\" height=\"40\">\n",
+    "    &nbsp; &nbsp; <b>Our current location</b> </div>"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
diff --git a/tour2_constant_bond/2_1_2_PO_ELF_RLM_CAS.ipynb b/tour2_constant_bond/2_1_2_PO_ELF_RLM_CAS.ipynb
index 843be822579115ceda9c094a6baec2ddc65b0c4f..af7f9bda8e84738a99231135ae488a424260f9c2 100644
--- a/tour2_constant_bond/2_1_2_PO_ELF_RLM_CAS.ipynb
+++ b/tour2_constant_bond/2_1_2_PO_ELF_RLM_CAS.ipynb
@@ -2030,7 +2030,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -2044,7 +2044,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.10"
+   "version": "3.9.0"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/tour5_damage_bond/5_1_Introspect_Damage_Evolution_Damage_initiation.ipynb b/tour5_damage_bond/5_1_Introspect_Damage_Evolution_Damage_initiation.ipynb
index b543caa90982afce1c7afb30571b6f4373139812..2ffc7ff796450e43d5ced88737c99fa351df4f97 100644
--- a/tour5_damage_bond/5_1_Introspect_Damage_Evolution_Damage_initiation.ipynb
+++ b/tour5_damage_bond/5_1_Introspect_Damage_Evolution_Damage_initiation.ipynb
@@ -41,7 +41,7 @@
        "        "
       ],
       "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x7f315c750ac0>"
+       "<IPython.lib.display.YouTubeVideo at 0x7f7534115610>"
       ]
      },
      "execution_count": 1,
@@ -350,7 +350,7 @@
        "        "
       ],
       "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x7f31168cda90>"
+       "<IPython.lib.display.YouTubeVideo at 0x7f753692f730>"
       ]
      },
      "execution_count": 3,
@@ -416,21 +416,13 @@
    "execution_count": 4,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "trait <traits.ctrait.CTrait object at 0x7f3104265680>\n",
-      "<ibvpy.tmodel.mats1D5.vmats1D5_bondslip1D.MATS1D5BondSlipD object at 0x7f31043c1450> omega_fn <bmcs_utils.trait_types.either_type2.EitherType2 object at 0x7f3104603c10>\n"
-     ]
-    },
     {
      "data": {
       "text/latex": [
        "$\\displaystyle \\begin{cases} 0 & \\text{for}\\: \\kappa < \\kappa_{0} \\\\\\frac{\\kappa - \\kappa_{0}}{- \\kappa_{0} + \\kappa_{u}} & \\text{for}\\: \\kappa < \\kappa_{u} \\\\1 & \\text{otherwise} \\end{cases}$"
       ],
       "text/plain": [
-       "<ibvpy.tmodel.mats_damage_fn.LinearDamageFn at 0x7f310443b130>"
+       "<ibvpy.tmodel.mats_damage_fn.LinearDamageFn at 0x7f74dcc83cc0>"
       ]
      },
      "execution_count": 4,
@@ -459,7 +451,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a13df2bdb92444a8b9f3a59c59480dea",
+       "model_id": "6a0b4d601ab74495ad96ca94634bce3c",
        "version_major": 2,
        "version_minor": 0
       },
@@ -489,19 +481,10 @@
    "execution_count": 6,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "xtrait <bmcs_utils.trait_types.either_type2.EitherType2 object at 0x7f3104603c10>\n",
-      "name omega_fn_\n",
-      "name <ibvpy.tmodel.mats_damage_fn.LinearDamageFn object at 0x7f310443b130>\n"
-     ]
-    },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "34bb8a6b24524b20ab561d114ff5fe76",
+       "model_id": "ef4efc0f7dc54fea879b61ef02cbe583",
        "version_major": 2,
        "version_minor": 0
       },
@@ -609,21 +592,13 @@
    "execution_count": 7,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "trait <traits.ctrait.CTrait object at 0x7f30fc03a4a0>\n",
-      "<ibvpy.tmodel.mats1D5.vmats1D5_bondslip1D.MATS1D5BondSlipD object at 0x7f31043514f0> omega_fn <bmcs_utils.trait_types.either_type2.EitherType2 object at 0x7f3104603c10>\n"
-     ]
-    },
     {
      "data": {
       "text/latex": [
        "$\\displaystyle \\begin{cases} 0 & \\text{for}\\: \\kappa < 0 \\\\1 - e^{- \\left(\\frac{\\kappa}{\\lambda}\\right)^{m}} & \\text{otherwise} \\end{cases}$"
       ],
       "text/plain": [
-       "<ibvpy.tmodel.mats_damage_fn.WeibullDamageFn at 0x7f30fbfaf0e0>"
+       "<ibvpy.tmodel.mats_damage_fn.WeibullDamageFn at 0x7f74dcea84a0>"
       ]
      },
      "execution_count": 7,
@@ -653,7 +628,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "8e3d466d82984b2fab193425e03ce460",
+       "model_id": "63c43ffb7227496b9c2f0a1a12fe92ef",
        "version_major": 2,
        "version_minor": 0
       },
@@ -681,19 +656,10 @@
    "execution_count": 9,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "xtrait <bmcs_utils.trait_types.either_type2.EitherType2 object at 0x7f3104603c10>\n",
-      "name omega_fn_\n",
-      "name <ibvpy.tmodel.mats_damage_fn.WeibullDamageFn object at 0x7f30fbfaf0e0>\n"
-     ]
-    },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "be8dce314fdf4083a58bd393d7eb0886",
+       "model_id": "891cf6b930db41cfa92ec00f718c060c",
        "version_major": 2,
        "version_minor": 0
       },
@@ -774,7 +740,7 @@
        "        "
       ],
       "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x7f30fbe34b80>"
+       "<IPython.lib.display.YouTubeVideo at 0x7f74d4248760>"
       ]
      },
      "execution_count": 10,
@@ -853,7 +819,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "abeaa11934ab4bab909d0ee47c046b82",
+       "model_id": "af6f4d6ed90d4b7cb8eee9cf6bede1e6",
        "version_major": 2,
        "version_minor": 0
       },
@@ -922,21 +888,13 @@
    "execution_count": 13,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "trait <traits.ctrait.CTrait object at 0x7f30fbbac040>\n",
-      "<ibvpy.tmodel.mats1D5.vmats1D5_bondslip1D.MATS1D5BondSlipD object at 0x7f30fbe4f7c0> omega_fn <bmcs_utils.trait_types.either_type2.EitherType2 object at 0x7f3104603c10>\n"
-     ]
-    },
     {
      "data": {
       "text/latex": [
        "$\\displaystyle \\begin{cases} 0 & \\text{for}\\: \\kappa \\leq \\kappa_{0} \\\\1 - \\frac{\\kappa_{0} e^{\\frac{- \\kappa + \\kappa_{0}}{- \\kappa_{0} + \\kappa_\\mathrm{f}}}}{\\kappa} & \\text{otherwise} \\end{cases}$"
       ],
       "text/plain": [
-       "<ibvpy.tmodel.mats_damage_fn.ExpSlopeDamageFn at 0x7f30fbbb0c20>"
+       "<ibvpy.tmodel.mats_damage_fn.ExpSlopeDamageFn at 0x7f74d4b64db0>"
       ]
      },
      "execution_count": 13,
@@ -955,19 +913,10 @@
    "execution_count": 14,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "xtrait <bmcs_utils.trait_types.either_type2.EitherType2 object at 0x7f3104603c10>\n",
-      "name omega_fn_\n",
-      "name <ibvpy.tmodel.mats_damage_fn.ExpSlopeDamageFn object at 0x7f30fbbb0c20>\n"
-     ]
-    },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "43606dfae8e5486c89346f1d5119eaf9",
+       "model_id": "64879b0a706a4a3da92a6e9470ad316d",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1031,21 +980,13 @@
    "execution_count": 15,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "trait <traits.ctrait.CTrait object at 0x7f30fbc66400>\n",
-      "<ibvpy.tmodel.mats1D5.vmats1D5_bondslip1D.MATS1D5BondSlipD object at 0x7f30fbb78e50> omega_fn <bmcs_utils.trait_types.either_type2.EitherType2 object at 0x7f3104603c10>\n"
-     ]
-    },
     {
      "data": {
       "text/latex": [
-       "$\\displaystyle \\begin{cases} 0 & \\text{for}\\: \\kappa \\leq \\kappa_{0} \\\\1 - \\begin{cases} 1 & \\text{for}\\: \\kappa < \\kappa_{0} \\\\\\frac{\\kappa_{0} \\cdot \\left(1 - \\frac{1 - e^{- \\frac{\\alpha \\left(\\kappa - \\kappa_{0}\\right)}{- \\kappa_{0} + \\kappa_{u}}}}{1 - e^{- \\alpha}}\\right)}{\\kappa} & \\text{for}\\: \\kappa < \\kappa_{u} \\\\0 & \\text{otherwise} \\end{cases} & \\text{otherwise} \\end{cases}$"
+       "$\\displaystyle \\begin{cases} 0 & \\text{for}\\: \\kappa \\leq \\kappa_{0} \\\\1 - \\begin{cases} 1 & \\text{for}\\: \\kappa < \\kappa_{0} \\\\\\frac{\\kappa_{0} \\left(1 - \\frac{1 - e^{- \\frac{\\alpha \\left(\\kappa - \\kappa_{0}\\right)}{- \\kappa_{0} + \\kappa_{u}}}}{1 - e^{- \\alpha}}\\right)}{\\kappa} & \\text{for}\\: \\kappa < \\kappa_{u} \\\\0 & \\text{otherwise} \\end{cases} & \\text{otherwise} \\end{cases}$"
       ],
       "text/plain": [
-       "<ibvpy.tmodel.mats_damage_fn.AbaqusDamageFn at 0x7f30fbb060e0>"
+       "<ibvpy.tmodel.mats_damage_fn.AbaqusDamageFn at 0x7f74d4994bd0>"
       ]
      },
      "execution_count": 15,
@@ -1064,19 +1005,10 @@
    "execution_count": 16,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "xtrait <bmcs_utils.trait_types.either_type2.EitherType2 object at 0x7f3104603c10>\n",
-      "name omega_fn_\n",
-      "name <ibvpy.tmodel.mats_damage_fn.AbaqusDamageFn object at 0x7f30fbb060e0>\n"
-     ]
-    },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a1c6e954f7b541e3a1e7ea8b99980d7e",
+       "model_id": "5d3f8a5c2e0d4db5b80e237e83815c37",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1123,21 +1055,13 @@
    "execution_count": 17,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "trait <traits.ctrait.CTrait object at 0x7f30fbce2720>\n",
-      "<ibvpy.tmodel.mats1D5.vmats1D5_bondslip1D.MATS1D5BondSlipD object at 0x7f30fbae9b80> omega_fn <bmcs_utils.trait_types.either_type2.EitherType2 object at 0x7f3104603c10>\n"
-     ]
-    },
     {
      "data": {
       "text/latex": [
-       "$\\displaystyle 1 - \\begin{cases} 1 & \\text{for}\\: \\kappa < \\kappa_{0} \\\\e^{\\frac{\\left(\\kappa - \\kappa_{0}\\right) \\left(\\sqrt{E_{b}} \\sqrt{- E_{b} \\kappa_{0}^{2} + 4 G_{f}} + E_{b} \\kappa_{0}\\right)}{E_{b} \\kappa_{0}^{2} - 2 G_{f}}} & \\text{otherwise} \\end{cases}$"
+       "$\\displaystyle 1 - \\begin{cases} 1 & \\text{for}\\: \\kappa < \\kappa_{0} \\\\e^{- \\frac{\\left(\\kappa - \\kappa_{0}\\right) \\left(\\sqrt{E_{b}} \\sqrt{- E_{b} \\kappa_{0}^{2} + 4 G_{f}} - E_{b} \\kappa_{0}\\right)}{E_{b} \\kappa_{0}^{2} - 2 G_{f}}} & \\text{otherwise} \\end{cases}$"
       ],
       "text/plain": [
-       "<ibvpy.tmodel.mats_damage_fn.GfDamageFn at 0x7f30fb9f3b80>"
+       "<ibvpy.tmodel.mats_damage_fn.GfDamageFn at 0x7f74d41755e0>"
       ]
      },
      "execution_count": 17,
@@ -1159,7 +1083,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "dae3e2a5cf6b42ccb915765bc30ffb3a",
+       "model_id": "2aa78bd8960b4c8ca3b70629f0f6ae67",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1187,19 +1111,10 @@
    "execution_count": 19,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "xtrait <bmcs_utils.trait_types.either_type2.EitherType2 object at 0x7f3104603c10>\n",
-      "name omega_fn_\n",
-      "name <ibvpy.tmodel.mats_damage_fn.GfDamageFn object at 0x7f30fb9f3b80>\n"
-     ]
-    },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ea666c3cfe504ef3a322c0a8f46ad9de",
+       "model_id": "1351ab09b1ec43578c128d3753cb34a4",
        "version_major": 2,
        "version_minor": 0
       },
diff --git a/tour5_damage_bond/5_2_Bond_behavior_governed_by-damage.ipynb b/tour5_damage_bond/5_2_Bond_behavior_governed_by-damage.ipynb
index 37b6d4a1db8a7368453040b758253cc4ae07c428..5fd04c90a5e21471f3e7cd1206f420dfd91d38a6 100644
--- a/tour5_damage_bond/5_2_Bond_behavior_governed_by-damage.ipynb
+++ b/tour5_damage_bond/5_2_Bond_behavior_governed_by-damage.ipynb
@@ -18,10 +18,10 @@
      "evalue": "No module named 'bmcs'",
      "output_type": "error",
      "traceback": [
-      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
-      "\u001B[0;31mModuleNotFoundError\u001B[0m                       Traceback (most recent call last)",
-      "\u001B[0;32m<ipython-input-1-ca1eca9c0930>\u001B[0m in \u001B[0;36m<module>\u001B[0;34m\u001B[0m\n\u001B[1;32m      1\u001B[0m \u001B[0mget_ipython\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mrun_line_magic\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m'matplotlib'\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m'inline'\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m      2\u001B[0m \u001B[0;32mimport\u001B[0m \u001B[0mmatplotlib\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mpyplot\u001B[0m \u001B[0;32mas\u001B[0m \u001B[0mplt\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m----> 3\u001B[0;31m \u001B[0;32mfrom\u001B[0m \u001B[0mbmcs\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mbond_slip\u001B[0m \u001B[0;32mimport\u001B[0m \u001B[0mBondSlipModel\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m      4\u001B[0m \u001B[0;32mfrom\u001B[0m \u001B[0mIPython\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mdisplay\u001B[0m \u001B[0;32mimport\u001B[0m \u001B[0mLatex\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
-      "\u001B[0;31mModuleNotFoundError\u001B[0m: No module named 'bmcs'"
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-1-ca1eca9c0930>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'matplotlib'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'inline'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mbmcs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbond_slip\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mBondSlipModel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mIPython\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdisplay\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mLatex\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'bmcs'"
      ]
     }
    ],
@@ -510,7 +510,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -524,7 +524,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.8"
+   "version": "3.9.0"
   },
   "toc": {
    "base_numbering": 1,
@@ -542,4 +542,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
\ No newline at end of file
+}
diff --git a/tour5_damage_bond/5_2_PO_cfrp_damage.ipynb b/tour5_damage_bond/5_2_PO_cfrp_damage.ipynb
index bebac4fcf86b1d7361e18cca1654d856356404fc..30d20093c2b482236d4f8cef721ccc31fdf3dcc0 100644
--- a/tour5_damage_bond/5_2_PO_cfrp_damage.ipynb
+++ b/tour5_damage_bond/5_2_PO_cfrp_damage.ipynb
@@ -266,7 +266,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.4"
+   "version": "3.9.0"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/tour5_damage_bond/5_3_BS_DP_A.ipynb b/tour5_damage_bond/5_3_BS_DP_A.ipynb
index 94ac7885180af7cc0350456505749088cc608b9e..ffa0391ad1d0a91de8682e25d444e1ff15ead380 100644
--- a/tour5_damage_bond/5_3_BS_DP_A.ipynb
+++ b/tour5_damage_bond/5_3_BS_DP_A.ipynb
@@ -1009,10 +1009,10 @@
      "evalue": "name 'get_f_df' is not defined",
      "output_type": "error",
      "traceback": [
-      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
-      "\u001B[0;31mNameError\u001B[0m                                 Traceback (most recent call last)",
-      "\u001B[0;32m<ipython-input-22-f5e10656a81b>\u001B[0m in \u001B[0;36m<module>\u001B[0;34m\u001B[0m\n\u001B[1;32m      8\u001B[0m \u001B[0ms_n1_arr\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0ms_max\u001B[0m \u001B[0;34m*\u001B[0m \u001B[0mtheta\u001B[0m \u001B[0;31m# load history\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m      9\u001B[0m \u001B[0;32mfor\u001B[0m \u001B[0ms_n1\u001B[0m \u001B[0;32min\u001B[0m \u001B[0ms_n1_arr\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m---> 10\u001B[0;31m     \u001B[0mf_k\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mdf_k\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mtau_k\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mget_f_df\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0ms_n1\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mtau_k\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0momega_k\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mz_k\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m*\u001B[0m\u001B[0mmargs\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m     11\u001B[0m     \u001B[0;32mif\u001B[0m \u001B[0mf_k\u001B[0m \u001B[0;34m>\u001B[0m \u001B[0;36m0\u001B[0m\u001B[0;34m:\u001B[0m \u001B[0;31m# inelastic step - return mapping\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m     12\u001B[0m         \u001B[0mdelta_lambda_k\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mf_k\u001B[0m \u001B[0;34m/\u001B[0m \u001B[0mdf_k\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
-      "\u001B[0;31mNameError\u001B[0m: name 'get_f_df' is not defined"
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-22-f5e10656a81b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0ms_n1_arr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0ms_max\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mtheta\u001b[0m \u001b[0;31m# load history\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0ms_n1\u001b[0m \u001b[0;32min\u001b[0m \u001b[0ms_n1_arr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m     \u001b[0mf_k\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdf_k\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtau_k\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_f_df\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms_n1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtau_k\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0momega_k\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mz_k\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mmargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     11\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mf_k\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# inelastic step - return mapping\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m         \u001b[0mdelta_lambda_k\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf_k\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mdf_k\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'get_f_df' is not defined"
      ]
     }
    ],
@@ -1059,7 +1059,7 @@
  "metadata": {
   "celltoolbar": "Slideshow",
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -1073,7 +1073,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.8"
+   "version": "3.9.0"
   },
   "toc": {
    "base_numbering": 1,
@@ -1096,4 +1096,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
\ No newline at end of file
+}
diff --git a/tour6_energy/6_1_energy_dissipation.ipynb b/tour6_energy/6_1_energy_dissipation.ipynb
index 18d4a393e454f06a0269554e92e192cccf6d3228..b402416ebe7a393548e6d7e01d246e0a4b46af98 100644
--- a/tour6_energy/6_1_energy_dissipation.ipynb
+++ b/tour6_energy/6_1_energy_dissipation.ipynb
@@ -9,6 +9,14 @@
     "# **6.1 Energy flow - supply, storage, dissipation**"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "8cd02d2a-99e4-4a0a-8c82-cdeca9b9ced0",
+   "metadata": {},
+   "source": [
+    "[Slides to notebooks 6.1 and 6.2](slides/S0601_energy_games.pdf)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 3,
@@ -426,7 +434,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.4"
+   "version": "3.9.0"
   }
  },
  "nbformat": 4,
diff --git a/tour6_energy/6_2_Energy_released_in_pullout_constant_bond_and_rigid_matrix.ipynb b/tour6_energy/6_2_Energy_released_in_pullout_constant_bond_and_rigid_matrix.ipynb
index 79cec5aefbf61a72cc3df94ffd0114efa0b191fa..a0d6b1d934f1508abaaaee1fa56013311d4a802f 100644
--- a/tour6_energy/6_2_Energy_released_in_pullout_constant_bond_and_rigid_matrix.ipynb
+++ b/tour6_energy/6_2_Energy_released_in_pullout_constant_bond_and_rigid_matrix.ipynb
@@ -1125,7 +1125,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.4"
+   "version": "3.9.0"
   }
  },
  "nbformat": 4,
diff --git a/tour6_energy/6_3_localized_energy_dissipation.ipynb b/tour6_energy/6_3_localized_energy_dissipation.ipynb
index 6d19146e176f23db9625a7ce360d0df9367f13af..ee365e0c236faf05a1de716a24cf706801267a6f 100644
--- a/tour6_energy/6_3_localized_energy_dissipation.ipynb
+++ b/tour6_energy/6_3_localized_energy_dissipation.ipynb
@@ -11,13 +11,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 1,
    "id": "e3ba2c71-f9b2-4ba2-9dbb-7b97ef2a46b3",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/jpeg": 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\n",
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\n",
       "text/html": [
        "\n",
        "        <iframe\n",
@@ -31,10 +31,10 @@
        "        "
       ],
       "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x7fe90c740430>"
+       "<IPython.lib.display.YouTubeVideo at 0x7f68f81a95e0>"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 1,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -252,7 +252,7 @@
   },
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-   "execution_count": 1,
+   "execution_count": 2,
    "id": "applied-relief",
    "metadata": {},
    "outputs": [],
@@ -282,14 +282,14 @@
   },
   {
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-   "execution_count": 2,
+   "execution_count": 3,
    "id": "supported-watch",
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    "outputs": [
     {
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       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d7271c88b6f34673881e8ac5b72755a9",
+       "model_id": "2827342afc754fc28c5ab2580be74f2e",
        "version_major": 2,
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diff --git a/tour6_energy/slides/S0601_energy_games.pdf b/tour6_energy/slides/S0601_energy_games.pdf
new file mode 100755
index 0000000000000000000000000000000000000000..41f3f29585c252c014bef15af85c53c615a8c79b
Binary files /dev/null and b/tour6_energy/slides/S0601_energy_games.pdf differ
diff --git a/tour7_cracking/7_1_bending3pt_2d.ipynb b/tour7_cracking/7_1_bending3pt_2d.ipynb
index 34baa0b2e52dab3f73b28ebd7a381c6e81276e30..2630b4c5d167e902edcee10d2a568cb5425a9baa 100644
--- a/tour7_cracking/7_1_bending3pt_2d.ipynb
+++ b/tour7_cracking/7_1_bending3pt_2d.ipynb
@@ -1132,7 +1132,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.4"
+   "version": "3.9.0"
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