From ce86f1ddf190f61bb2a94fcbeb01cac64477b78e Mon Sep 17 00:00:00 2001 From: adamchengtkc Date: Sun, 5 Nov 2023 18:36:40 +0000 Subject: [PATCH] deploy: 6dc397d3f03b11dea7fb497307f33de76b1d6884 --- notebooks/Build_within_Python.html | 12 +- notebooks/Build_within_Python.ipynb | 408 ++++++++++++++-------------- searchindex.js | 2 +- 3 files changed, 211 insertions(+), 211 deletions(-) diff --git a/notebooks/Build_within_Python.html b/notebooks/Build_within_Python.html index c5382f9..62914a9 100644 --- a/notebooks/Build_within_Python.html +++ b/notebooks/Build_within_Python.html @@ -131,7 +131,7 @@

Build 1D model 
-/tmp/ipykernel_2149/1143012394.py:13: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
+/tmp/ipykernel_2072/1143012394.py:13: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
   node_template = pd.concat([node_template, new], ignore_index=True)
@@ -336,8 +336,8 @@

Build 1D model 
-CPU times: user 617 ms, sys: 267 ms, total: 883 ms
-Wall time: 552 ms
+CPU times: user 672 ms, sys: 46.9 ms, total: 719 ms
+Wall time: 613 ms
@@ -411,7 +411,7 @@

Build 1D model

-
+ 
[12]:
@@ -450,7 +450,7 @@ 
Build 1D model
-
+
[13]:
@@ -470,7 +470,7 @@ 
Build 1D model
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node\u001b[38;5;241m.\u001b[39mresult\u001b[38;5;241m.\u001b[39meffective_conductivity(age)\n\u001b[0;32m----> 3\u001b[0m     \u001b[43mplt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mt\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mvalues\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m(\u001b[49m\u001b[43mt\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdepth\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43mt\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdepth\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m/\u001b[39;49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m      4\u001b[0m     plt\u001b[38;5;241m.\u001b[39mgca()\u001b[38;5;241m.\u001b[39minvert_yaxis()\n\u001b[1;32m      5\u001b[0m     plt\u001b[38;5;241m.\u001b[39mxlabel(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mConductivity\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", "File \u001b[0;32m~/.cache/pypoetry/virtualenvs/warmth-atxSUjif-py3.10/lib/python3.10/site-packages/matplotlib/pyplot.py:3578\u001b[0m, in \u001b[0;36mplot\u001b[0;34m(scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   3570\u001b[0m \u001b[38;5;129m@_copy_docstring_and_deprecators\u001b[39m(Axes\u001b[38;5;241m.\u001b[39mplot)\n\u001b[1;32m   3571\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mplot\u001b[39m(\n\u001b[1;32m   3572\u001b[0m     \u001b[38;5;241m*\u001b[39margs: \u001b[38;5;28mfloat\u001b[39m \u001b[38;5;241m|\u001b[39m ArrayLike \u001b[38;5;241m|\u001b[39m \u001b[38;5;28mstr\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   3576\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m   3577\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mlist\u001b[39m[Line2D]:\n\u001b[0;32m-> 3578\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mgca\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   3579\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3580\u001b[0m \u001b[43m        \u001b[49m\u001b[43mscalex\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mscalex\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3581\u001b[0m \u001b[43m        \u001b[49m\u001b[43mscaley\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mscaley\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3582\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdata\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m}\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43m{\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3583\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3584\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n", "File \u001b[0;32m~/.cache/pypoetry/virtualenvs/warmth-atxSUjif-py3.10/lib/python3.10/site-packages/matplotlib/axes/_axes.py:1721\u001b[0m, in \u001b[0;36mAxes.plot\u001b[0;34m(self, scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1478\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   1479\u001b[0m \u001b[38;5;124;03mPlot y versus x as lines and/or markers.\u001b[39;00m\n\u001b[1;32m   1480\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1718\u001b[0m \u001b[38;5;124;03m(``'green'``) or hex strings (``'#008000'``).\u001b[39;00m\n\u001b[1;32m   1719\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   1720\u001b[0m kwargs \u001b[38;5;241m=\u001b[39m cbook\u001b[38;5;241m.\u001b[39mnormalize_kwargs(kwargs, mlines\u001b[38;5;241m.\u001b[39mLine2D)\n\u001b[0;32m-> 1721\u001b[0m lines \u001b[38;5;241m=\u001b[39m [\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_lines(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, data\u001b[38;5;241m=\u001b[39mdata, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)]\n\u001b[1;32m   1722\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m line \u001b[38;5;129;01min\u001b[39;00m lines:\n\u001b[1;32m   1723\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39madd_line(line)\n", "File \u001b[0;32m~/.cache/pypoetry/virtualenvs/warmth-atxSUjif-py3.10/lib/python3.10/site-packages/matplotlib/axes/_base.py:303\u001b[0m, in \u001b[0;36m_process_plot_var_args.__call__\u001b[0;34m(self, axes, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m    301\u001b[0m     this \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m args[\u001b[38;5;241m0\u001b[39m],\n\u001b[1;32m    302\u001b[0m     args \u001b[38;5;241m=\u001b[39m args[\u001b[38;5;241m1\u001b[39m:]\n\u001b[0;32m--> 303\u001b[0m \u001b[38;5;28;01myield from\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_plot_args\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    304\u001b[0m \u001b[43m    \u001b[49m\u001b[43maxes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mthis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mambiguous_fmt_datakey\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mambiguous_fmt_datakey\u001b[49m\u001b[43m)\u001b[49m\n", "File \u001b[0;32m~/.cache/pypoetry/virtualenvs/warmth-atxSUjif-py3.10/lib/python3.10/site-packages/matplotlib/axes/_base.py:499\u001b[0m, in \u001b[0;36m_process_plot_var_args._plot_args\u001b[0;34m(self, axes, tup, kwargs, return_kwargs, ambiguous_fmt_datakey)\u001b[0m\n\u001b[1;32m    496\u001b[0m     axes\u001b[38;5;241m.\u001b[39myaxis\u001b[38;5;241m.\u001b[39mupdate_units(y)\n\u001b[1;32m    498\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m x\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m!=\u001b[39m y\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m]:\n\u001b[0;32m--> 499\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mx and y must have same first dimension, but \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    500\u001b[0m                      \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhave shapes \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mx\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m and \u001b[39m\u001b[38;5;132;01m{\u001b[39;00my\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    501\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m x\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m2\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m y\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m2\u001b[39m:\n\u001b[1;32m    502\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mx and y can be no greater than 2D, but have \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    503\u001b[0m                      \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mshapes \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mx\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m and \u001b[39m\u001b[38;5;132;01m{\u001b[39;00my\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n", "\u001b[0;31mValueError\u001b[0m: x and y must have same first dimension, but have shapes (342,) and (341,)"]}], "tabbable": null, "tooltip": null}}}, "version_major": 2, 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FFJVXyVwNERHRrRiayCpEt/BFK38PlFYasCr1vNzlEBER3YKhiayCQqGQzjb9e/c5Po+OiIisDkMTWY3hXZrBQ+2MzEvXsCvzitzlEBERmWBoIqvhoXbG8C41z6P7PiVL3mKIiIhuwtBEVmVMz5pLdElH83CxsEzmaoiIiP7A0ERWpVWAJ6Jb+MIogGV7+Tw6IiKyHgxNZHVG9wwBACzbl8Pn0RERkdVgaCKrM6h9IPw8VMgvrkDysXy5yyEiIgLA0ERWSOWsxF+61TyP7ideoiMiIivB0ERWaWT3mkt0205eQvaVUpmrISIiYmgiKxXi645+rRsDAJbu49kmIiKSH0MTWa1RPWrONq3cn4PKag4IJyIieTE0kdUa2M4fARo1LpdU4n9HdXKXQ0REDo6hiayWi5MST9cOCN/DS3RERCQvhiayak/3CIFSAezKvIIzl0rkLoeIiBwYQxNZtaZebhjQxh8AsJTTDxARkYwYmsjq1c4QvjL1PMqrDDJXQ0REjoqhiaxe/9b+aOrlhsLSKmzI4IBwIiKSB0MTWT0npQLPdOeAcCIikhdDE9mEp7oHQ6kA9mYVIJMDwomISAYMTWQTAjSu+FPbmgHhK/blyFwNERE5IoYmshlPX38e3X8OnOcM4URE1OAYmshmDGjTGP6eNTOEbzqeJ3c5RETkYBiayGY4Oynx567NAADLeImOiIgaGEMT2ZSnrj9WZevJS7hQWCZzNURE5EgYmsimhPk1QnQLXwgBrNzPs01ERNRwGJrI5jzTo+Zs08r952EwCpmrISIiR8HQRDYntkMgtG4uuFBYhh2nL8tdDhEROQiGJrI5ri5OeLJzUwDA8n2cIZyIiBoGQxPZpKevP1Yl6WgeLpdUyFwNERE5AoYmskntmmgQGeyFKoPAqgPn5S6HiIgcAEMT2azah/gu25cDITggnIiILIuhiWzW0MgguLk44cylaziQfVXucoiIyM4xNJHN8lA7Y0hEIICa6QeIiIgsiaGJbNpfutZcoltzKBelldUyV0NERPaMoYlsWlRzHwT7uKGkohobMnRyl0NERHaMoYlsmlKpwJ+7/DFDOBERkaUwNJHNG9G1KRQKIOXMFeQUlMpdDhER2SmGJrJ5zbzd0aulLwDg51SebSIiIstgaCK7UDsg/OfU8zDyIb5ERGQBDE1kFwZ3DISnqzMuFJZh95krcpdDRER2iKGJ7IKrixOGRgYBAFbyEh0REVkAQxPZjb90bQYAWJ+Ri6LyKpmrISIie8PQRHbjoWAvhPt7oLzKiLWHcuUuh4iI7AxDE9kNhUIhnW1auT9H5mqIiMjeMDSRXXmyS1M4KRU4kF2IM5dK5C6HiIjsCEMT2RV/T1f0a+UHAPjl4AWZqyEiInvC0ER2Z3iXmkt0qw5c4JxNRERkNgxNZHceaR8AT3XNnE17swrkLoeIiOyErKFp27ZtGDp0KIKCgqBQKLB69WqT7UIIzJo1C02aNIGbmxtiYmJw6tQpkzYFBQUYPXo0NBoNvLy8MGHCBJSUmI5lOXToEPr27QtXV1cEBwfjo48+uqWWlStXom3btnB1dUVERATWrVtn9v5Sw3B1cUJcpyYAgFUHOGcTERGZh6yh6dq1a4iMjMT8+fNvu/2jjz7C559/ji+//BJ79uxBo0aNEBsbi/LycqnN6NGjceTIESQlJWHNmjXYtm0bXnjhBWl7UVERBg0ahNDQUKSmpuLjjz/GnDlz8PXXX0ttdu3ahZEjR2LChAk4ePAghg0bhmHDhiEjI8NynSeLqr1Et+6wDmWVBpmrISIiuyCsBADxyy+/SK+NRqMIDAwUH3/8sbSusLBQqNVqsXTpUiGEEEePHhUAxL59+6Q269evFwqFQly4cEEIIcSCBQuEt7e3qKiokNokJCSINm3aSK+feuopERcXZ1JPVFSU+Nvf/lbn+vV6vQAg9Hp9nd9DlmMwGEWfvyeL0IQ1YvXB83KXQ0REVqo+399WO6bp7Nmz0Ol0iImJkdZptVpERUUhJSUFAJCSkgIvLy9069ZNahMTEwOlUok9e/ZIbfr16weVSiW1iY2NxYkTJ3D16lWpzY2fU9um9nNup6KiAkVFRSYLWQ+lUoEnO/8xIJyIiOhBWW1o0ul0AICAgACT9QEBAdI2nU4Hf39/k+3Ozs7w8fExaXO7fdz4GXdqU7v9dhITE6HVaqUlODi4vl0kCxveuSkAYPupS8grKr9HayIioruz2tBk7WbOnAm9Xi8tOTmcgdrahPk1QtdQbxgF8N80nm0iIqIHY7WhKTAwEACQl5dnsj4vL0/aFhgYiPz8fJPt1dXVKCgoMGlzu33c+Bl3alO7/XbUajU0Go3JQtZneJeas03/Sb0AIThnExER3T+rDU3NmzdHYGAgkpOTpXVFRUXYs2cPoqOjAQDR0dEoLCxEamqq1GbTpk0wGo2IioqS2mzbtg1VVX889T4pKQlt2rSBt7e31ObGz6ltU/s5ZLseiwiCykmJE3nFOJrLcWdERHT/ZA1NJSUlSEtLQ1paGoCawd9paWnIzs6GQqHA1KlT8X//93/49ddfcfjwYTz33HMICgrCsGHDAADt2rXD4MGD8fzzz2Pv3r3YuXMnpkyZgmeeeQZBQUEAgFGjRkGlUmHChAk4cuQIli9fjnnz5mH69OlSHa+88go2bNiATz75BMePH8ecOXOwf/9+TJkypaEPCZmZ1t0FMe1rxr1xQDgRET0Qy9/Md2ebN28WAG5Zxo4dK4SomXbgnXfeEQEBAUKtVouBAweKEydOmOzjypUrYuTIkcLDw0NoNBoxfvx4UVxcbNImPT1d9OnTR6jVatG0aVMxd+7cW2pZsWKFaN26tVCpVKJDhw5i7dq19eoLpxywXklHdCI0YY3o+v7/RFW1Qe5yiIjIitTn+1shBAd6mENRURG0Wi30ej3HN1mZKoMRUR8mo+BaJRaP746H2/jf+01EROQQ6vP9bbVjmojMxcVJibiImseq/DftoszVEBGRrWJoIocw7PqcTRuP6FBaWS1zNUREZIsYmsghdAnxQoiPO0orDUg6mnfvNxAREd2EoYkcgkKhwLCHau6oXH2Qd9EREVH9MTSRw3ji+iW6bacu40pJhczVEBGRrWFoIofRsrEHIppqYTAKrDmUK3c5RERkYxiayKHUDghfzWfRERFRPTE0kUMZGtkESgVwMLsQ565ck7scIiKyIQxN5FD8PV3RO9wPALD6IOdsIiKiumNoIocz7KGaS3T/TbsATohPRER1xdBEDie2YyBcXZQ4c/kaDl/Qy10OERHZiHqFpmPHjmH27Nn405/+hJYtW6JJkybo1KkTxo4di59++gkVFbyNm6yfh9oZj7QPBAD8wjmbiIiojuoUmg4cOICYmBh07twZO3bsQFRUFKZOnYr3338fzz77LIQQeOuttxAUFIS///3vDE9k9WonulxzKBcGIy/RERHRvTnXpdGIESMwY8YM/Pzzz/Dy8rpju5SUFMybNw+ffPIJ3nzzTXPVSGR2fVs1htbNBZeKK7DnzBX0uj44nIiI6E7qFJpOnjwJFxeXe7aLjo5GdHQ0qqqqHrgwIktSOSvxaEQglu7Nwa/pFxmaiIjonup0ea4ugelB2hPJYWinmkt06zN0qKw2ylwNERFZuzqdabrZvn37sHnzZuTn58NoNP2y+fTTT81SGJGlRbXwhb+nGvnFFdh+6hIGtguQuyQiIrJi9Q5NH374Id5++220adMGAQEBUCgU0rYbfyaydk5KBeI6NcGinVn4Nf0iQxMREd1VvUPTvHnz8N1332HcuHEWKIeoYT0eGYRFO7OQdDQPZZUGuKmc5C6JiIisVL0nt1Qqlejdu7claiFqcA8FeyHYxw2llQb8fixP7nKIiMiK1Ts0TZs2DfPnz7dELUQNTqFQSAPCf0vns+iIiOjO6n157rXXXkNcXBxatmyJ9u3b33Kn3KpVq8xWHFFDePyhICzYkoktJy5BX1YFrRvv/iQiolvV+0zTyy+/jM2bN6N169bw9fWFVqs1WYhsTdtADVoHeKDSYMTGIzq5yyEiIitV7zNNS5YswX/+8x/ExcVZoh4iWTweGYR//O8kfku/iKe6BctdDhERWaF6n2ny8fFBy5YtLVELkWweuz6uaefpy7hcwmcnEhHRreodmubMmYPZs2ejtLTUEvUQySLMrxEim2lhFMD6w7lyl0NERFao3pfnPv/8c2RmZiIgIABhYWG3DAQ/cOCA2YojakhxnZog/bweaw/nYkx0mNzlEBGRlal3aBo2bJgFyiCS36MRTfDhuuPYe7YA+cXl8Pd0lbskIiKyIvUOTbNnz7ZEHUSya+btjshgL6TnFGJjho5nm4iIyESdxjQJISxdB5FViIsIBACs5bgmIiK6SZ1CU4cOHbBs2TJUVlbetd2pU6cwefJkzJ071yzFETW0RyOaAIB0iY6IiKhWnS7PffHFF0hISMCLL76IRx55BN26dUNQUBBcXV1x9epVHD16FDt27MCRI0cwZcoUTJ482dJ1E1kEL9EREdGd1Ck0DRw4EPv378eOHTuwfPly/Pjjjzh37hzKysrg5+eHzp0747nnnsPo0aPh7e1t6ZqJLCouIhDpOYW8i46IiEzUayB4nz590KdPH0vVQmQVeBcdERHdTr0ntySyd7WX6IwC2JjBZ9EREVENhiai2+BddEREdDOGJqLb4F10RER0M4YmotvgJToiIroZQxPRHfASHRER3ajej1EBAKPRiNOnTyM/Px9Go9FkW79+/cxSGJHchnT84y66S8UVaOyplrskIiKSUb1D0+7duzFq1CicO3fulserKBQKGAwGsxVHJKdgnz8mutxwRIcxPUPlLomIiGRU78tzkyZNQrdu3ZCRkYGCggJcvXpVWgoKCixRI5FspEt0hy7KXAkREcmt3meaTp06hZ9//hnh4eGWqIfIqvASHRER1ar3maaoqCicPn3aErUQWZ3aS3RGAWw4wrvoiIgcWZ3ONB06dEj6+aWXXsKrr74KnU6HiIgIuLi4mLTt1KmTeSskkpn0LLpDFzmuiYjIgSnEzaO5b0OpVEKhUNwy8FvayfVtjjwQvKioCFqtFnq9HhqNRu5yyIxyCkrR96PNUCqAPW/G8BIdEZEdqc/3d53ONJ09e9YshRHZIt5FR0REQB3HNIWGhkrLuXPn0LRpU5N1oaGhaNq0Kc6dO2fpeolkwbvoiIio3gPBBwwYcNupBfR6PQYMGGCWooiszZCOfzyL7lJxhczVEBGRHOodmmrHLt3sypUraNSokVmKIrI2vIuOiIjqPE/T8OHDAdQM+h43bhzU6j8GwxoMBhw6dAi9evUyf4VEVoJ30RERObY6hyatVgug5kyTp6cn3NzcpG0qlQo9e/bE888/b/4KiawEJ7okInJsdQ5NixYtAgCEhYXhtdde46U4cjjBPu6IbKZF+nk9/ndUh9FRPNtERORI6j2mafbs2WjUqBHy8/Oxfft2bN++Hfn5+ZaojcjqxHasuYtu45E8mSshIqKGVu/QVFxcjDFjxqBp06bo378/+vfvj6ZNm+LZZ5+FXq+3RI1EViO2Q01oSsm8jKLyKpmrISKihlTv0DRx4kTs2bMHa9asQWFhIQoLC7FmzRrs378ff/vb3yxRI5HVaNnYA+H+HqgyCGw+zjOsRESOpN6hac2aNfjuu+8QGxsLjUYDjUaD2NhYfPPNN/jtt9/qta9t27Zh6NChCAoKgkKhwOrVq022jxs3DgqFwmQZPHiwSZuCggKMHj0aGo0GXl5emDBhAkpKSkzaHDp0CH379oWrqyuCg4Px0Ucf3VLLypUr0bZtW7i6uiIiIgLr1q2rV1/IccR2CAAAbOTUA0REDqXeocnX11e6k+5GWq0W3t7e9drXtWvXEBkZifnz59+xzeDBg5GbmystS5cuNdk+evRoHDlyBElJSVizZg22bduGF154QdpeVFSEQYMGITQ0FKmpqfj4448xZ84cfP3111KbXbt2YeTIkZgwYQIOHjyIYcOGYdiwYcjIyKhXf8gx1F6i23LiEsqrHPNZi0REDknU01dffSViYmJEbm6utC43N1cMGjRIfPnll/XdnQSA+OWXX0zWjR07VjzxxBN3fM/Ro0cFALFv3z5p3fr164VCoRAXLlwQQgixYMEC4e3tLSoqKqQ2CQkJok2bNtLrp556SsTFxZnsOyoqSvztb3+rc/16vV4AEHq9vs7vIdtkNBpF9Ie/i9CENSLpiE7ucoiI6AHU5/u73meaFi5ciN27dyMkJATh4eEIDw9HSEgIdu3aha+++gpdunSRFnPYsmUL/P390aZNG0yePBlXrlyRtqWkpMDLywvdunWT1sXExECpVGLPnj1Sm379+kGlUkltYmNjceLECVy9elVqExMTY/K5sbGxSElJuWNdFRUVKCoqMlnIMSgUCgy6fraJs4MTETmOOs/TVGvYsGEWKOP2Bg8ejOHDh6N58+bIzMzEm2++iSFDhiAlJQVOTk7Q6XTw9/c3eY+zszN8fHyg09V8mel0OjRv3tykTUBAgLTN29sbOp1OWndjm9p93E5iYiLeffddc3STbNCgDgFYvCsLycfyUG0wwtmp3v/+ICIiG1Pv0DR79mxL1HFbzzzzjPRzREQEOnXqhJYtW2LLli0YOHBgg9VxOzNnzsT06dOl10VFRQgODpaxImpIPcJ84O3ugqulVdibVYBeLf3kLomIiCzsvv55XFhYiG+//RYzZ85EQUEBAODAgQO4cOGCWYu7WYsWLeDn54fTp08DAAIDA2+ZWLO6uhoFBQUIDAyU2uTlmU5EWPv6Xm1qt9+OWq2W7h6sXchxODspMbBdzdnJ/3GiSyIih1Dv0HTo0CG0bt0af//73/GPf/wDhYWFAIBVq1Zh5syZ5q7PxPnz53HlyhU0adIEABAdHY3CwkKkpqZKbTZt2gSj0YioqCipzbZt21BV9cdEhElJSWjTpo10t190dDSSk5NNPispKQnR0dEW7Q/ZtsHXxzX974gOQgiZqyEiIkurd2iaPn06xo0bh1OnTsHV1VVa/+ijj2Lbtm312ldJSQnS0tKQlpYGADh79izS0tKQnZ2NkpISzJgxA7t370ZWVhaSk5PxxBNPIDw8HLGxsQCAdu3aYfDgwXj++eexd+9e7Ny5E1OmTMEzzzyDoKAgAMCoUaOgUqkwYcIEHDlyBMuXL8e8efNMLq298sor2LBhAz755BMcP34cc+bMwf79+zFlypT6Hh5yIH1a+cFd5YSL+nIcvsDZ8ImI7F59b83TaDTi9OnTQgghPDw8RGZmphBCiKysLKFWq+u1r82bNwsAtyxjx44VpaWlYtCgQaJx48bCxcVFhIaGiueff17odKa3eF+5ckWMHDlSeHh4CI1GI8aPHy+Ki4tN2qSnp4s+ffoItVotmjZtKubOnXtLLStWrBCtW7cWKpVKdOjQQaxdu7ZefeGUA45p8g/7RWjCGvHRhmNyl0JERPehPt/fCiHqd13B398fGzduROfOneHp6Yn09HS0aNECSUlJ+Otf/4qcnByzBztbUFRUBK1WC71ez/FNDuS/aRfwyrI0hPt74Pfp/eUuh4iI6qk+39/1vjz3+OOP47333pPGCCkUCmRnZyMhIQEjRoy4v4qJbNSAtv5wcVLgdH4JMi+V3PsNRERks+odmj755BOUlJTA398fZWVl6N+/P8LDw+Hp6YkPPvjAEjUSWS2Nqwuir083wGfRERHZt3rP06TVapGUlIQdO3bg0KFDKCkpQZcuXW6ZUZvIUcR2CMC2k5ewMUOHFx8Ol7scIiKykHqHplp9+vRBnz59zFkLkU16pH0A3l6dgfTzeuTqy9BE6yZ3SUREZAH1ujxnNBrx3Xff4bHHHkPHjh0RERGBxx9/HN9//z3nqSGH5e/pii4hNXN+caJLIiL7VefQJITA448/jokTJ+LChQuIiIhAhw4dcO7cOYwbNw5PPvmkJesksmqxHWpmB+e4JiIi+1Xny3OLFy/Gtm3bkJycjAEDBphs27RpE4YNG4bvv/8ezz33nNmLJLJ2sR0C8eG649hztgBXr1XCu5FK7pKIiMjM6nymaenSpXjzzTdvCUwA8Kc//QlvvPEGfvzxR7MWR2QrQn0boW2gJwxGgeTj+fd+AxER2Zw6h6ZDhw5h8ODBd9w+ZMgQpKenm6UoIlsUe/1ZdLxER0Rkn+ocmgoKChAQEHDH7QEBAbh69apZiiKyRbWhadvJSyitrJa5GiIiMrc6hyaDwQBn5zsPgXJyckJ1Nb8oyHG1a+KJYB83VFQbse3kJbnLISIiM6vzQHAhBMaNGwe1Wn3b7RUVFWYrisgWKRQKxLYPxLc7zmLjkTwM7thE7pKIiMiM6hyaxo4de882vHOOHN2gDjWhadPxfFQbjHB2qveTioiIyErVOTQtWrTIknUQ2YUuIV7wdnfB1dIq7D93FT1b+MpdEhERmQn/GUxkRs5OSgxo4w8ASD7G2cGJiOwJQxORmcW0r7nL9PdjnK+JiMieMDQRmVnfVn5wcVLg7OVryLxUInc5RERkJgxNRGbm6eoijWX6/Sgv0RER2QuGJiILiGlXc4kumZfoiIjsBkMTkQUMbFczGHz/uZoH+BIRke1jaCKygGbe7mgb6AmjADaf4NkmIiJ7wNBEZCG1l+h+59QDRER2gaGJyEJqpx7YdvIyKquNMldDREQPiqGJyEI6NdWisacaJRXV2HP2itzlEBHRA2JoIrIQpVKBgW1rBoRz6gEiItvH0ERkQQPb/TE7uBBC5mqIiOhBMDQRWVCfcD+onZW4UFiG47piucshIqIHwNBEZEFuKif0CfcDwAf4EhHZOoYmIgurvUSXxNnBiYhsGkMTkYXVzg6enlOI/OJymashIqL7xdBEZGEBGld0aqYFAGzi2SYiIpvF0ETUAGJuuIuOiIhsE0MTUQOovUS34/QllFcZZK6GiIjuB0MTUQNo30SDIK0ryquM2Hn6stzlEBHRfWBoImoACoXihokuOfUAEZEtYmgiaiC1D/BNPpYPo5GzgxMR2RqGJqIG0rOFDxqpnJBfXIGMi3q5yyEionpiaCJqIGpnJ/Rt1RgAH+BLRGSLGJqIGlDtJTpOPUBEZHsYmoga0IA2jaFQAEdzi6DTc3ZwIiJbwtBE1IB8PdTo1MwLALDlBM82ERHZEoYmogb2pzY1E11uZmgiIrIpDE1EDWxA25rB4DtOXUZltVHmaoiIqK4YmogaWMcgLfw8VLhWacD+rAK5yyEiojpiaCJqYEqlAv1b8xIdEZGtYWgiksGf2taEpk3HGZqIiGwFQxORDPq08oOTUoHMS9eQfaVU7nKIiKgOGJqIZKB1c0HXUG8AwJaTPNtERGQLGJqIZDKgduoBXqIjIrIJDE1EMqkd17Qr8wrKqwwyV0NERPfC0EQkk9YBHgjSuqKi2oiUzCtyl0NERPfA0EQkE4VCgYfbcuoBIiJbwdBEJKPaR6psOp4PIYTM1RAR0d0wNBHJqFe4L1ROSpy/WobMS9fkLoeIiO6CoYlIRu4qZ0S18AEAbOElOiIiq8bQRCSzAW04OzgRkS2QNTQlJiaie/fu8PT0hL+/P4YNG4YTJ06YtCkvL0d8fDx8fX3h4eGBESNGIC8vz6RNdnY24uLi4O7uDn9/f8yYMQPV1dUmbbZs2YIuXbpArVYjPDwcixcvvqWe+fPnIywsDK6uroiKisLevXvN3meim9VOPbAvqwDF5VUyV0NERHcia2jaunUr4uPjsXv3biQlJaGqqgqDBg3CtWt/jO2YNm0afvvtN6xcuRJbt27FxYsXMXz4cGm7wWBAXFwcKisrsWvXLixZsgSLFy/GrFmzpDZnz55FXFwcBgwYgLS0NEydOhUTJ07Exo0bpTbLly/H9OnTMXv2bBw4cACRkZGIjY1Ffj7/9U+WFebXCM39GqHKILDzNKceICKyWsKK5OfnCwBi69atQgghCgsLhYuLi1i5cqXU5tixYwKASElJEUIIsW7dOqFUKoVOp5PaLFy4UGg0GlFRUSGEEOL1118XHTp0MPmsp59+WsTGxkqve/ToIeLj46XXBoNBBAUFicTExDrVrtfrBQCh1+vr2WsiIeb8miFCE9aIhJ/T5S6FiMih1Of726rGNOn1egCAj0/NwNjU1FRUVVUhJiZGatO2bVuEhIQgJSUFAJCSkoKIiAgEBARIbWJjY1FUVIQjR45IbW7cR22b2n1UVlYiNTXVpI1SqURMTIzU5mYVFRUoKioyWYju159umK9JcOoBIiKrZDWhyWg0YurUqejduzc6duwIANDpdFCpVPDy8jJpGxAQAJ1OJ7W5MTDVbq/ddrc2RUVFKCsrw+XLl2EwGG7bpnYfN0tMTIRWq5WW4ODg++s4EYAezX3g5uKEvKIKHM1lACciskZWE5ri4+ORkZGBZcuWyV1KncycORN6vV5acnJy5C6JbJja2Qm9w/0AAFtOXJK5GiIiuh2rCE1TpkzBmjVrsHnzZjRr1kxaHxgYiMrKShQWFpq0z8vLQ2BgoNTm5rvpal/fq41Go4Gbmxv8/Pzg5OR02za1+7iZWq2GRqMxWYgexIC2jQEAmzn1ABGRVZI1NAkhMGXKFPzyyy/YtGkTmjdvbrK9a9eucHFxQXJysrTuxIkTyM7ORnR0NAAgOjoahw8fNrnLLSkpCRqNBu3bt5fa3LiP2ja1+1CpVOjatatJG6PRiOTkZKkNkaXVztd0IPsqCksrZa6GiIhuJmtoio+Pxw8//ICffvoJnp6e0Ol00Ol0KCsrAwBotVpMmDAB06dPx+bNm5Gamorx48cjOjoaPXv2BAAMGjQI7du3x5gxY5Ceno6NGzfi7bffRnx8PNRqNQBg0qRJOHPmDF5//XUcP34cCxYswIoVKzBt2jSplunTp+Obb77BkiVLcOzYMUyePBnXrl3D+PHjG/7AkEMK8nJD20BPGAWw9SQv0RERWR2L38t3FwBuuyxatEhqU1ZWJl588UXh7e0t3N3dxZNPPilyc3NN9pOVlSWGDBki3NzchJ+fn3j11VdFVVWVSZvNmzeLhx56SKhUKtGiRQuTz6j1xRdfiJCQEKFSqUSPHj3E7t2769wXTjlA5pC47pgITVgjpi47KHcpREQOoT7f3woheH+zORQVFUGr1UKv13N8E923lMwrGPnNbvh5qLD3zRgolQq5SyIismv1+f62ioHgRFSja6g3GqmccLmkklMPEBFZGYYmIiuiclYiumXN1AMc10REZF0YmoisTP82NVMPbGNoIiKyKgxNRFamf6ua0JR67iqKy6tkroaIiGoxNBFZmRBfdzT3a4Rqo0BK5hW5yyEiousYmoisUL9WHNdERGRtGJqIrJA0runUJXBWECIi68DQRGSFerbwhcpJiZyCMmRdKZW7HCIiAkMTkVVyVzmjW5g3AGDrCT7Al4jIGjA0EVmp/q1rL9FdlrkSIiICGJqIrFa/66EpJfMKKqoNMldDREQMTURWqm2gJ/w91SirMmB/1lW5yyEicngMTURWSqFQSGebOPUAEZH8GJqIrJg0romhiYhIdgxNRFasT7gfFArguK4YOn253OUQETk0hiYiK+bdSIXIZl4Aaia6JCIi+TA0EVk5jmsiIrIODE1EVq52XNOOU5dhMPKRKkREcmFoIrJykc200Lg6Q19WhfTzhXKXQ0TksBiaiKycs5MSfVvxLjoiIrkxNBHZgH6t/QBwXBMRkZwYmohsQO1g8PScQhSWVspcDRGRY2JoIrIBTbRuaB3gAaMAdpzmA3yJiOTA0ERkI/pxXBMRkawYmohsRP82f8zXJASnHiAiamgMTUQ2onuYD1xdlMgrqsCp/BK5yyEicjgMTUQ2wtXFCd3DfAAA209xXBMRUUNjaCKyIX1b1Uw9sIPPoSMianAMTUQ2pE94zbimPWcLUFltlLkaIiLHwtBEZEPaBnrCz0OF0koDDmRflbscIiKHwtBEZEOUSgV6h9deouO4JiKihsTQRGRj+lwPTds5ySURUYNiaCKyMbUP7z18vhD60iqZqyEichwMTUQ2JlDrinD/mkeq7Mrk2SYioobC0ERkg3iJjoio4TE0EdmgP+ZrYmgiImooDE1ENiiqhS+clQpkF5Qi+0qp3OUQETkEhiYiG+ShdkaXEG8AwPbTnB2ciKghMDQR2ag+vERHRNSgGJqIbFRtaNqVeQUGo5C5GiIi+8fQRGSjOjXVwtPVGfqyKhy+oJe7HCIiu8fQRGSjnJ2U6NXSFwCw4xTHNRERWRpDE5EN63N9dvDtHNdERGRxDE1ENqzv9UkuD2RfxbWKapmrISKybwxNRDYs1NcdzbzdUGUQ2Hu2QO5yiIjsGkMTkQ1TKBTSA3x5iY6IyLIYmohsnPRIFU5ySURkUQxNRDauV0tfKBTAybwS5BWVy10OEZHdYmgisnFe7ip0aqoFwNnBiYgsiaGJyA7Uzg6+nfM1ERFZDEMTkR3oE14zGHzH6SsQgo9UISKyBIYmIjvQJdQLbi5OuFxSgeO6YrnLISKySwxNRHZA7eyEHs19AAA7T3NcExGRJTA0EdmJ3uE1z6HblXlF5kqIiOwTQxORnejVsmYw+J4zV1BlMMpcDRGR/ZE1NCUmJqJ79+7w9PSEv78/hg0bhhMnTpi0efjhh6FQKEyWSZMmmbTJzs5GXFwc3N3d4e/vjxkzZqC62vQ5XFu2bEGXLl2gVqsRHh6OxYsX31LP/PnzERYWBldXV0RFRWHv3r1m7zORpbRvooG3uwuuVRpw6Hyh3OUQEdkdWUPT1q1bER8fj927dyMpKQlVVVUYNGgQrl27ZtLu+eefR25urrR89NFH0jaDwYC4uDhUVlZi165dWLJkCRYvXoxZs2ZJbc6ePYu4uDgMGDAAaWlpmDp1KiZOnIiNGzdKbZYvX47p06dj9uzZOHDgACIjIxEbG4v8/HzLHwgiM1AqFYhuef0S3WleoiMiMjeFsKL7ky9dugR/f39s3boV/fr1A1Bzpumhhx7CZ599dtv3rF+/Ho899hguXryIgIAAAMCXX36JhIQEXLp0CSqVCgkJCVi7di0yMjKk9z3zzDMoLCzEhg0bAABRUVHo3r07/vWvfwEAjEYjgoOD8dJLL+GNN964Z+1FRUXQarXQ6/XQaDQPchiI7tsPu8/h7dUZ6NnCB8teiJa7HCIiq1ef72+rGtOk1+sBAD4+Pibrf/zxR/j5+aFjx46YOXMmSktLpW0pKSmIiIiQAhMAxMbGoqioCEeOHJHaxMTEmOwzNjYWKSkpAIDKykqkpqaatFEqlYiJiZHaENmCXtfPNB04V4iySoPM1RAR2RdnuQuoZTQaMXXqVPTu3RsdO3aU1o8aNQqhoaEICgrCoUOHkJCQgBMnTmDVqlUAAJ1OZxKYAEivdTrdXdsUFRWhrKwMV69ehcFguG2b48eP37beiooKVFRUSK+Liorus+dE5tPcrxGaaF2Rqy/H/nMF6NuqsdwlERHZDasJTfHx8cjIyMCOHTtM1r/wwgvSzxEREWjSpAkGDhyIzMxMtGzZsqHLlCQmJuLdd9+V7fOJbkehUKBXSz/858B57Dx9haGJiMiMrOLy3JQpU7BmzRps3rwZzZo1u2vbqKgoAMDp06cBAIGBgcjLyzNpU/s6MDDwrm00Gg3c3Nzg5+cHJyen27ap3cfNZs6cCb1eLy05OTl17C2RZf0xXxMnuSQiMidZQ5MQAlOmTMEvv/yCTZs2oXnz5vd8T1paGgCgSZMmAIDo6GgcPnzY5C63pKQkaDQatG/fXmqTnJxssp+kpCRER9cMlFWpVOjatatJG6PRiOTkZKnNzdRqNTQajclCZA16h9fM13T4gh760iqZqyEish+yhqb4+Hj88MMP+Omnn+Dp6QmdTgedToeysjIAQGZmJt5//32kpqYiKysLv/76K5577jn069cPnTp1AgAMGjQI7du3x5gxY5Ceno6NGzfi7bffRnx8PNRqNQBg0qRJOHPmDF5//XUcP34cCxYswIoVKzBt2jSplunTp+Obb77BkiVLcOzYMUyePBnXrl3D+PHjG/7AED2AAI0rWjZuBCGAlDOceoCIyGyEjADcdlm0aJEQQojs7GzRr18/4ePjI9RqtQgPDxczZswQer3eZD9ZWVliyJAhws3NTfj5+YlXX31VVFVVmbTZvHmzeOihh4RKpRItWrSQPuNGX3zxhQgJCREqlUr06NFD7N69u8590ev1AsAttRHJ4Z3Vh0Vowhoxa/VhuUshIrJq9fn+tqp5mmwZ52kia7IhIxeTfjiAcH8P/D69v9zlEBFZLZudp4mIzKNnC18oFMDp/BLkFZXLXQ4RkV1gaCKyQ17uKnQM0gLgXXRERObC0ERkp3pdn3pgJ59DR0RkFgxNRHaqd8uaqQd2nb4MDl0kInpwDE1Edqp7mA9UTkpc1Jcj60rpvd9ARER3xdBEZKfcVE7oHOIFANh5muOaiIgeFEMTkR3rdf0SXUomxzURET0ohiYiO3bjc+iMRo5rIiJ6EAxNRHYsMtgLjVROuFpahWO6IrnLISKyaQxNRHbMxUmJHs19AAC7OPUAEdEDYWgisnO9w2vGNe3kJJdERA+EoYnIztUOBt97tgCV1UaZqyEisl0MTUR2rm2gJ3waqVBaaUD6+UK5yyEislkMTUR2TqlUILpF7SNVeImOiOh+MTQROYBe0tQDHAxORHS/GJqIHEDtuKa07EJUGTiuiYjofjA0ETmAUB93eKidUWkw4syla3KXQ0RkkxiaiByAUqlA20BPAMCxXE5ySUR0PxiaiBxEuyYaAAxNRET3i6GJyEHUhqajDE1ERPeFoYnIQbRrUnt5rljmSoiIbBNDE5GDaBPoCYUCuFxSgfzicrnLISKyOQxNRA7CXeWM5r6NAPBsExHR/WBoInIgHAxORHT/GJqIHMgf45oYmoiI6ouhiciB8EwTEdH9Y2giciC1oSnz0jWUVxlkroaIyLYwNBE5kCZaV2jdXGAwCpz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\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    241\u001b[0m show_inline_matplotlib_plots()\n\u001b[1;32m    242\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mauto_display \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mresult \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", "Cell \u001b[0;32mIn[13], line 3\u001b[0m, in \u001b[0;36mplot_cell_properties\u001b[0;34m(age)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mplot_cell_properties\u001b[39m(age):\n\u001b[1;32m      2\u001b[0m     t \u001b[38;5;241m=\u001b[39m node\u001b[38;5;241m.\u001b[39mresult\u001b[38;5;241m.\u001b[39meffective_conductivity(age)\n\u001b[0;32m----> 3\u001b[0m     \u001b[43mplt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mt\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mvalues\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m(\u001b[49m\u001b[43mt\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdepth\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43mt\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdepth\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m/\u001b[39;49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m      4\u001b[0m     plt\u001b[38;5;241m.\u001b[39mgca()\u001b[38;5;241m.\u001b[39minvert_yaxis()\n\u001b[1;32m      5\u001b[0m     plt\u001b[38;5;241m.\u001b[39mxlabel(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mConductivity\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", "File \u001b[0;32m~/.cache/pypoetry/virtualenvs/warmth-atxSUjif-py3.10/lib/python3.10/site-packages/matplotlib/pyplot.py:3578\u001b[0m, in \u001b[0;36mplot\u001b[0;34m(scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   3570\u001b[0m \u001b[38;5;129m@_copy_docstring_and_deprecators\u001b[39m(Axes\u001b[38;5;241m.\u001b[39mplot)\n\u001b[1;32m   3571\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mplot\u001b[39m(\n\u001b[1;32m   3572\u001b[0m     \u001b[38;5;241m*\u001b[39margs: \u001b[38;5;28mfloat\u001b[39m \u001b[38;5;241m|\u001b[39m ArrayLike \u001b[38;5;241m|\u001b[39m \u001b[38;5;28mstr\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   3576\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m   3577\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mlist\u001b[39m[Line2D]:\n\u001b[0;32m-> 3578\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mgca\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   3579\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3580\u001b[0m \u001b[43m        \u001b[49m\u001b[43mscalex\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mscalex\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3581\u001b[0m \u001b[43m        \u001b[49m\u001b[43mscaley\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mscaley\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3582\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdata\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m}\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43m{\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3583\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3584\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n", "File \u001b[0;32m~/.cache/pypoetry/virtualenvs/warmth-atxSUjif-py3.10/lib/python3.10/site-packages/matplotlib/axes/_axes.py:1721\u001b[0m, in \u001b[0;36mAxes.plot\u001b[0;34m(self, scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1478\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   1479\u001b[0m \u001b[38;5;124;03mPlot y versus x as lines and/or markers.\u001b[39;00m\n\u001b[1;32m   1480\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1718\u001b[0m \u001b[38;5;124;03m(``'green'``) or hex strings (``'#008000'``).\u001b[39;00m\n\u001b[1;32m   1719\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   1720\u001b[0m kwargs \u001b[38;5;241m=\u001b[39m cbook\u001b[38;5;241m.\u001b[39mnormalize_kwargs(kwargs, mlines\u001b[38;5;241m.\u001b[39mLine2D)\n\u001b[0;32m-> 1721\u001b[0m lines \u001b[38;5;241m=\u001b[39m [\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_lines(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, data\u001b[38;5;241m=\u001b[39mdata, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)]\n\u001b[1;32m   1722\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m line \u001b[38;5;129;01min\u001b[39;00m lines:\n\u001b[1;32m   1723\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39madd_line(line)\n", "File \u001b[0;32m~/.cache/pypoetry/virtualenvs/warmth-atxSUjif-py3.10/lib/python3.10/site-packages/matplotlib/axes/_base.py:303\u001b[0m, in \u001b[0;36m_process_plot_var_args.__call__\u001b[0;34m(self, axes, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m    301\u001b[0m     this \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m args[\u001b[38;5;241m0\u001b[39m],\n\u001b[1;32m    302\u001b[0m     args \u001b[38;5;241m=\u001b[39m args[\u001b[38;5;241m1\u001b[39m:]\n\u001b[0;32m--> 303\u001b[0m \u001b[38;5;28;01myield from\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_plot_args\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    304\u001b[0m \u001b[43m    \u001b[49m\u001b[43maxes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mthis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mambiguous_fmt_datakey\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mambiguous_fmt_datakey\u001b[49m\u001b[43m)\u001b[49m\n", "File \u001b[0;32m~/.cache/pypoetry/virtualenvs/warmth-atxSUjif-py3.10/lib/python3.10/site-packages/matplotlib/axes/_base.py:499\u001b[0m, in \u001b[0;36m_process_plot_var_args._plot_args\u001b[0;34m(self, axes, tup, kwargs, return_kwargs, ambiguous_fmt_datakey)\u001b[0m\n\u001b[1;32m    496\u001b[0m     axes\u001b[38;5;241m.\u001b[39myaxis\u001b[38;5;241m.\u001b[39mupdate_units(y)\n\u001b[1;32m    498\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m x\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m!=\u001b[39m y\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m]:\n\u001b[0;32m--> 499\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mx and y must have same first dimension, but \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    500\u001b[0m                      \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhave shapes \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mx\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m and \u001b[39m\u001b[38;5;132;01m{\u001b[39;00my\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    501\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m x\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m2\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m y\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m2\u001b[39m:\n\u001b[1;32m    502\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mx and y can be no greater than 2D, but have \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    503\u001b[0m                      \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mshapes \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mx\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m and \u001b[39m\u001b[38;5;132;01m{\u001b[39;00my\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n", "\u001b[0;31mValueError\u001b[0m: x and y must have same first dimension, but have shapes (342,) and (341,)"]}], "tabbable": null, "tooltip": null}}}, "version_major": 2, "version_minor": 0}
 
 
 
diff --git a/notebooks/Build_within_Python.ipynb b/notebooks/Build_within_Python.ipynb
index 92906ae..d80dd08 100644
--- a/notebooks/Build_within_Python.ipynb
+++ b/notebooks/Build_within_Python.ipynb
@@ -12,10 +12,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-04T15:32:24.572655Z",
-     "iopub.status.busy": "2023-11-04T15:32:24.572466Z",
-     "iopub.status.idle": "2023-11-04T15:32:25.231371Z",
-     "shell.execute_reply": "2023-11-04T15:32:25.230801Z"
+     "iopub.execute_input": "2023-11-05T18:36:33.026597Z",
+     "iopub.status.busy": "2023-11-05T18:36:33.026171Z",
+     "iopub.status.idle": "2023-11-05T18:36:33.920549Z",
+     "shell.execute_reply": "2023-11-05T18:36:33.919846Z"
     },
     "scrolled": true
    },
@@ -33,10 +33,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-04T15:32:25.234173Z",
-     "iopub.status.busy": "2023-11-04T15:32:25.233663Z",
-     "iopub.status.idle": "2023-11-04T15:32:25.236544Z",
-     "shell.execute_reply": "2023-11-04T15:32:25.236041Z"
+     "iopub.execute_input": "2023-11-05T18:36:33.924533Z",
+     "iopub.status.busy": "2023-11-05T18:36:33.923707Z",
+     "iopub.status.idle": "2023-11-05T18:36:33.928549Z",
+     "shell.execute_reply": "2023-11-05T18:36:33.927979Z"
     }
    },
    "outputs": [],
@@ -49,10 +49,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-04T15:32:25.238727Z",
-     "iopub.status.busy": "2023-11-04T15:32:25.238390Z",
-     "iopub.status.idle": "2023-11-04T15:32:25.249107Z",
-     "shell.execute_reply": "2023-11-04T15:32:25.248553Z"
+     "iopub.execute_input": "2023-11-05T18:36:33.931561Z",
+     "iopub.status.busy": "2023-11-05T18:36:33.931009Z",
+     "iopub.status.idle": "2023-11-05T18:36:33.944437Z",
+     "shell.execute_reply": "2023-11-05T18:36:33.943912Z"
     }
    },
    "outputs": [
@@ -60,7 +60,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/tmp/ipykernel_2149/1143012394.py:13: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
+      "/tmp/ipykernel_2072/1143012394.py:13: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
       "  node_template = pd.concat([node_template, new], ignore_index=True)\n"
      ]
     }
@@ -94,10 +94,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-04T15:32:25.272349Z",
-     "iopub.status.busy": "2023-11-04T15:32:25.272160Z",
-     "iopub.status.idle": "2023-11-04T15:32:25.294349Z",
-     "shell.execute_reply": "2023-11-04T15:32:25.293790Z"
+     "iopub.execute_input": "2023-11-05T18:36:33.978965Z",
+     "iopub.status.busy": "2023-11-05T18:36:33.978537Z",
+     "iopub.status.idle": "2023-11-05T18:36:34.010522Z",
+     "shell.execute_reply": "2023-11-05T18:36:34.009935Z"
     }
    },
    "outputs": [
@@ -272,10 +272,10 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-04T15:32:25.296514Z",
-     "iopub.status.busy": "2023-11-04T15:32:25.296175Z",
-     "iopub.status.idle": "2023-11-04T15:32:25.299083Z",
-     "shell.execute_reply": "2023-11-04T15:32:25.298603Z"
+     "iopub.execute_input": "2023-11-05T18:36:34.014338Z",
+     "iopub.status.busy": "2023-11-05T18:36:34.013718Z",
+     "iopub.status.idle": "2023-11-05T18:36:34.018336Z",
+     "shell.execute_reply": "2023-11-05T18:36:34.017743Z"
     }
    },
    "outputs": [],
@@ -289,10 +289,10 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-04T15:32:25.301070Z",
-     "iopub.status.busy": "2023-11-04T15:32:25.300767Z",
-     "iopub.status.idle": "2023-11-04T15:32:25.303611Z",
-     "shell.execute_reply": "2023-11-04T15:32:25.303048Z"
+     "iopub.execute_input": "2023-11-05T18:36:34.021105Z",
+     "iopub.status.busy": "2023-11-05T18:36:34.020873Z",
+     "iopub.status.idle": "2023-11-05T18:36:34.025015Z",
+     "shell.execute_reply": "2023-11-05T18:36:34.024443Z"
     }
    },
    "outputs": [],
@@ -308,10 +308,10 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-04T15:32:25.305768Z",
-     "iopub.status.busy": "2023-11-04T15:32:25.305366Z",
-     "iopub.status.idle": "2023-11-04T15:32:25.307987Z",
-     "shell.execute_reply": "2023-11-04T15:32:25.307521Z"
+     "iopub.execute_input": "2023-11-05T18:36:34.027691Z",
+     "iopub.status.busy": "2023-11-05T18:36:34.027464Z",
+     "iopub.status.idle": "2023-11-05T18:36:34.031352Z",
+     "shell.execute_reply": "2023-11-05T18:36:34.030793Z"
     }
    },
    "outputs": [],
@@ -324,10 +324,10 @@
    "execution_count": 8,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-04T15:32:25.310053Z",
-     "iopub.status.busy": "2023-11-04T15:32:25.309753Z",
-     "iopub.status.idle": "2023-11-04T15:32:25.312554Z",
-     "shell.execute_reply": "2023-11-04T15:32:25.312062Z"
+     "iopub.execute_input": "2023-11-05T18:36:34.034185Z",
+     "iopub.status.busy": "2023-11-05T18:36:34.033684Z",
+     "iopub.status.idle": "2023-11-05T18:36:34.038232Z",
+     "shell.execute_reply": "2023-11-05T18:36:34.037668Z"
     }
    },
    "outputs": [],
@@ -341,10 +341,10 @@
    "execution_count": 9,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-04T15:32:25.314842Z",
-     "iopub.status.busy": "2023-11-04T15:32:25.314448Z",
-     "iopub.status.idle": "2023-11-04T15:32:25.869853Z",
-     "shell.execute_reply": "2023-11-04T15:32:25.869256Z"
+     "iopub.execute_input": "2023-11-05T18:36:34.040927Z",
+     "iopub.status.busy": "2023-11-05T18:36:34.040512Z",
+     "iopub.status.idle": "2023-11-05T18:36:34.658645Z",
+     "shell.execute_reply": "2023-11-05T18:36:34.657860Z"
     }
    },
    "outputs": [
@@ -352,8 +352,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 617 ms, sys: 267 ms, total: 883 ms\n",
-      "Wall time: 552 ms\n"
+      "CPU times: user 672 ms, sys: 46.9 ms, total: 719 ms\n",
+      "Wall time: 613 ms\n"
      ]
     }
    ],
@@ -367,10 +367,10 @@
    "execution_count": 10,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-04T15:32:25.871994Z",
-     "iopub.status.busy": "2023-11-04T15:32:25.871809Z",
-     "iopub.status.idle": "2023-11-04T15:32:25.876066Z",
-     "shell.execute_reply": "2023-11-04T15:32:25.875590Z"
+     "iopub.execute_input": "2023-11-05T18:36:34.662265Z",
+     "iopub.status.busy": "2023-11-05T18:36:34.661656Z",
+     "iopub.status.idle": "2023-11-05T18:36:34.666513Z",
+     "shell.execute_reply": "2023-11-05T18:36:34.665984Z"
     }
    },
    "outputs": [
@@ -394,10 +394,10 @@
    "execution_count": 11,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-04T15:32:25.878124Z",
-     "iopub.status.busy": "2023-11-04T15:32:25.877807Z",
-     "iopub.status.idle": "2023-11-04T15:32:26.166663Z",
-     "shell.execute_reply": "2023-11-04T15:32:26.166143Z"
+     "iopub.execute_input": "2023-11-05T18:36:34.669464Z",
+     "iopub.status.busy": "2023-11-05T18:36:34.668999Z",
+     "iopub.status.idle": "2023-11-05T18:36:34.985975Z",
+     "shell.execute_reply": "2023-11-05T18:36:34.985258Z"
     }
    },
    "outputs": [
@@ -435,17 +435,17 @@
    "execution_count": 12,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-04T15:32:26.169025Z",
-     "iopub.status.busy": "2023-11-04T15:32:26.168674Z",
-     "iopub.status.idle": "2023-11-04T15:32:26.366652Z",
-     "shell.execute_reply": "2023-11-04T15:32:26.366153Z"
+     "iopub.execute_input": "2023-11-05T18:36:34.989368Z",
+     "iopub.status.busy": "2023-11-05T18:36:34.988823Z",
+     "iopub.status.idle": "2023-11-05T18:36:35.213584Z",
+     "shell.execute_reply": "2023-11-05T18:36:35.212839Z"
     }
    },
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "8960dcf3e9754ff1a71dc965d0eccc3e",
+       "model_id": "ffefdbf9be0f4d8b83298f013df6c373",
        "version_major": 2,
        "version_minor": 0
       },
@@ -488,10 +488,10 @@
    "execution_count": 13,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-04T15:32:26.370365Z",
-     "iopub.status.busy": "2023-11-04T15:32:26.370194Z",
-     "iopub.status.idle": "2023-11-04T15:32:27.179400Z",
-     "shell.execute_reply": "2023-11-04T15:32:27.178855Z"
+     "iopub.execute_input": "2023-11-05T18:36:35.218285Z",
+     "iopub.status.busy": "2023-11-05T18:36:35.218053Z",
+     "iopub.status.idle": "2023-11-05T18:36:36.377848Z",
+     "shell.execute_reply": "2023-11-05T18:36:36.377253Z"
     }
    },
    "outputs": [
@@ -508,7 +508,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "7392955a28c74a8ca7cfd8f34c02abe5",
+       "model_id": "8e07e7f07fee4edc918f353f11e3c687",
        "version_major": 2,
        "version_minor": 0
       },
@@ -581,38 +581,23 @@
   "widgets": {
    "application/vnd.jupyter.widget-state+json": {
     "state": {
-     "0b10e79a69bd4684a3f472f5b76f53fb": {
+     "0d8396c8806d40459800c369c8ea2d2e": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "2.0.0",
-      "model_name": "IntSliderModel",
+      "model_name": "SliderStyleModel",
       "state": {
-       "_dom_classes": [],
        "_model_module": "@jupyter-widgets/controls",
        "_model_module_version": "2.0.0",
-       "_model_name": "IntSliderModel",
+       "_model_name": "SliderStyleModel",
        "_view_count": null,
-       "_view_module": "@jupyter-widgets/controls",
+       "_view_module": "@jupyter-widgets/base",
        "_view_module_version": "2.0.0",
-       "_view_name": "IntSliderView",
-       "behavior": "drag-tap",
-       "continuous_update": true,
-       "description": "age",
-       "description_allow_html": false,
-       "disabled": false,
-       "layout": "IPY_MODEL_1a580141ce0c46f5a63b88833d66f575",
-       "max": 160,
-       "min": 0,
-       "orientation": "horizontal",
-       "readout": true,
-       "readout_format": "d",
-       "step": 1,
-       "style": "IPY_MODEL_3500212c2f0d4cdcb753c7beac71c310",
-       "tabbable": null,
-       "tooltip": null,
-       "value": 0
+       "_view_name": "StyleView",
+       "description_width": "",
+       "handle_color": null
       }
      },
-     "1a580141ce0c46f5a63b88833d66f575": {
+     "13f9c9bc45b54ddfa056a030f8c83ba3": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "2.0.0",
       "model_name": "LayoutModel",
@@ -665,7 +650,36 @@
        "width": null
       }
      },
-     "1d57fbb65e8b47bd91eb1ae18226190b": {
+     "311aa4270e43497ab600c21ea16dcb9a": {
+      "model_module": "@jupyter-widgets/output",
+      "model_module_version": "1.0.0",
+      "model_name": "OutputModel",
+      "state": {
+       "_dom_classes": [],
+       "_model_module": "@jupyter-widgets/output",
+       "_model_module_version": "1.0.0",
+       "_model_name": "OutputModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/output",
+       "_view_module_version": "1.0.0",
+       "_view_name": "OutputView",
+       "layout": "IPY_MODEL_c767341aa9e54abaad2709fc9886bb5b",
+       "msg_id": "",
+       "outputs": [
+        {
+         "data": {
+          "image/png": 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",
+          "text/plain": "
"
+         },
+         "metadata": {},
+         "output_type": "display_data"
+        }
+       ],
+       "tabbable": null,
+       "tooltip": null
+      }
+     },
+     "37918d0ccda04d0890cac407616a0b62": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "2.0.0",
       "model_name": "LayoutModel",
@@ -718,7 +732,7 @@
        "width": null
       }
      },
-     "1e24ecc36d2e470eb1232bccf2f9e3f7": {
+     "47b0c753a23e4d1fa97560818db0c282": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "2.0.0",
       "model_name": "LayoutModel",
@@ -771,7 +785,23 @@
        "width": null
       }
      },
-     "2dfca8e21a504a91bfaf9da68d0c474f": {
+     "63f2f8ea638842c4b7abbd512efb04e0": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "SliderStyleModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "SliderStyleModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "2.0.0",
+       "_view_name": "StyleView",
+       "description_width": "",
+       "handle_color": null
+      }
+     },
+     "6dce777fe72640f0bea41d7cc5b940b8": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "2.0.0",
       "model_name": "LayoutModel",
@@ -824,23 +854,7 @@
        "width": null
       }
      },
-     "3500212c2f0d4cdcb753c7beac71c310": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "2.0.0",
-      "model_name": "SliderStyleModel",
-      "state": {
-       "_model_module": "@jupyter-widgets/controls",
-       "_model_module_version": "2.0.0",
-       "_model_name": "SliderStyleModel",
-       "_view_count": null,
-       "_view_module": "@jupyter-widgets/base",
-       "_view_module_version": "2.0.0",
-       "_view_name": "StyleView",
-       "description_width": "",
-       "handle_color": null
-      }
-     },
-     "3a8805535ad54666b8e456f775700b29": {
+     "76aa13605586491980aa2bbb77b0732f": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "2.0.0",
       "model_name": "IntSliderModel",
@@ -858,90 +872,51 @@
        "description": "age",
        "description_allow_html": false,
        "disabled": false,
-       "layout": "IPY_MODEL_1d57fbb65e8b47bd91eb1ae18226190b",
+       "layout": "IPY_MODEL_6dce777fe72640f0bea41d7cc5b940b8",
        "max": 160,
        "min": 0,
        "orientation": "horizontal",
        "readout": true,
        "readout_format": "d",
        "step": 1,
-       "style": "IPY_MODEL_852c3e1a1f2b46b0ab5facdf34d26345",
+       "style": "IPY_MODEL_63f2f8ea638842c4b7abbd512efb04e0",
        "tabbable": null,
        "tooltip": null,
        "value": 0
       }
      },
-     "5d05b854a3e6467ab5088ec21d4cae1b": {
-      "model_module": "@jupyter-widgets/output",
-      "model_module_version": "1.0.0",
-      "model_name": "OutputModel",
-      "state": {
-       "_dom_classes": [],
-       "_model_module": "@jupyter-widgets/output",
-       "_model_module_version": "1.0.0",
-       "_model_name": "OutputModel",
-       "_view_count": null,
-       "_view_module": "@jupyter-widgets/output",
-       "_view_module_version": "1.0.0",
-       "_view_name": "OutputView",
-       "layout": "IPY_MODEL_f6561ad6e33c4089bd3e687e35c284bd",
-       "msg_id": "",
-       "outputs": [
-        {
-         "data": {
-          "image/png": 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",
-          "text/plain": "
"
-         },
-         "metadata": {},
-         "output_type": "display_data"
-        }
-       ],
-       "tabbable": null,
-       "tooltip": null
-      }
-     },
-     "7392955a28c74a8ca7cfd8f34c02abe5": {
+     "81ff5a1ed27945719704edc4513aecab": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "2.0.0",
-      "model_name": "VBoxModel",
+      "model_name": "IntSliderModel",
       "state": {
-       "_dom_classes": [
-        "widget-interact"
-       ],
+       "_dom_classes": [],
        "_model_module": "@jupyter-widgets/controls",
        "_model_module_version": "2.0.0",
-       "_model_name": "VBoxModel",
+       "_model_name": "IntSliderModel",
        "_view_count": null,
        "_view_module": "@jupyter-widgets/controls",
        "_view_module_version": "2.0.0",
-       "_view_name": "VBoxView",
-       "box_style": "",
-       "children": [
-        "IPY_MODEL_0b10e79a69bd4684a3f472f5b76f53fb",
-        "IPY_MODEL_8f9636d17fd5406199643a35677acfd3"
-       ],
-       "layout": "IPY_MODEL_9072f71b1a824904a7b652988f60c3c3",
+       "_view_name": "IntSliderView",
+       "behavior": "drag-tap",
+       "continuous_update": true,
+       "description": "age",
+       "description_allow_html": false,
+       "disabled": false,
+       "layout": "IPY_MODEL_9f73ec8614e24faaba5fce5a0c2324fa",
+       "max": 160,
+       "min": 0,
+       "orientation": "horizontal",
+       "readout": true,
+       "readout_format": "d",
+       "step": 1,
+       "style": "IPY_MODEL_0d8396c8806d40459800c369c8ea2d2e",
        "tabbable": null,
-       "tooltip": null
-      }
-     },
-     "852c3e1a1f2b46b0ab5facdf34d26345": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "2.0.0",
-      "model_name": "SliderStyleModel",
-      "state": {
-       "_model_module": "@jupyter-widgets/controls",
-       "_model_module_version": "2.0.0",
-       "_model_name": "SliderStyleModel",
-       "_view_count": null,
-       "_view_module": "@jupyter-widgets/base",
-       "_view_module_version": "2.0.0",
-       "_view_name": "StyleView",
-       "description_width": "",
-       "handle_color": null
+       "tooltip": null,
+       "value": 0
       }
      },
-     "8960dcf3e9754ff1a71dc965d0eccc3e": {
+     "8e07e7f07fee4edc918f353f11e3c687": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "2.0.0",
       "model_name": "VBoxModel",
@@ -958,52 +933,15 @@
        "_view_name": "VBoxView",
        "box_style": "",
        "children": [
-        "IPY_MODEL_3a8805535ad54666b8e456f775700b29",
-        "IPY_MODEL_5d05b854a3e6467ab5088ec21d4cae1b"
+        "IPY_MODEL_81ff5a1ed27945719704edc4513aecab",
+        "IPY_MODEL_b32d2dc629174a5fa99e8e8082db13ed"
        ],
-       "layout": "IPY_MODEL_1e24ecc36d2e470eb1232bccf2f9e3f7",
+       "layout": "IPY_MODEL_47b0c753a23e4d1fa97560818db0c282",
        "tabbable": null,
        "tooltip": null
       }
      },
-     "8f9636d17fd5406199643a35677acfd3": {
-      "model_module": "@jupyter-widgets/output",
-      "model_module_version": "1.0.0",
-      "model_name": "OutputModel",
-      "state": {
-       "_dom_classes": [],
-       "_model_module": "@jupyter-widgets/output",
-       "_model_module_version": "1.0.0",
-       "_model_name": "OutputModel",
-       "_view_count": null,
-       "_view_module": "@jupyter-widgets/output",
-       "_view_module_version": "1.0.0",
-       "_view_name": "OutputView",
-       "layout": "IPY_MODEL_2dfca8e21a504a91bfaf9da68d0c474f",
-       "msg_id": "",
-       "outputs": [
-        {
-         "ename": "ValueError",
-         "evalue": "x and y must have same first dimension, but have shapes (342,) and (341,)",
-         "output_type": "error",
-         "traceback": [
-          "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-          "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-          "File \u001b[0;32m~/.cache/pypoetry/virtualenvs/warmth-atxSUjif-py3.10/lib/python3.10/site-packages/ipywidgets/widgets/interaction.py:240\u001b[0m, in \u001b[0;36minteractive.update\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m    238\u001b[0m     value \u001b[38;5;241m=\u001b[39m widget\u001b[38;5;241m.\u001b[39mget_interact_value()\n\u001b[1;32m    239\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkwargs[widget\u001b[38;5;241m.\u001b[39m_kwarg] \u001b[38;5;241m=\u001b[39m value\n\u001b[0;32m--> 240\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mresult \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    241\u001b[0m show_inline_matplotlib_plots()\n\u001b[1;32m    242\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mauto_display \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mresult \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
-          "Cell \u001b[0;32mIn[13], line 3\u001b[0m, in \u001b[0;36mplot_cell_properties\u001b[0;34m(age)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mplot_cell_properties\u001b[39m(age):\n\u001b[1;32m      2\u001b[0m     t \u001b[38;5;241m=\u001b[39m node\u001b[38;5;241m.\u001b[39mresult\u001b[38;5;241m.\u001b[39meffective_conductivity(age)\n\u001b[0;32m----> 3\u001b[0m     \u001b[43mplt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mt\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mvalues\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m(\u001b[49m\u001b[43mt\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdepth\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43mt\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdepth\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m/\u001b[39;49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m      4\u001b[0m     plt\u001b[38;5;241m.\u001b[39mgca()\u001b[38;5;241m.\u001b[39minvert_yaxis()\n\u001b[1;32m      5\u001b[0m     plt\u001b[38;5;241m.\u001b[39mxlabel(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mConductivity\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
-          "File \u001b[0;32m~/.cache/pypoetry/virtualenvs/warmth-atxSUjif-py3.10/lib/python3.10/site-packages/matplotlib/pyplot.py:3578\u001b[0m, in \u001b[0;36mplot\u001b[0;34m(scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   3570\u001b[0m \u001b[38;5;129m@_copy_docstring_and_deprecators\u001b[39m(Axes\u001b[38;5;241m.\u001b[39mplot)\n\u001b[1;32m   3571\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mplot\u001b[39m(\n\u001b[1;32m   3572\u001b[0m     \u001b[38;5;241m*\u001b[39margs: \u001b[38;5;28mfloat\u001b[39m \u001b[38;5;241m|\u001b[39m ArrayLike \u001b[38;5;241m|\u001b[39m \u001b[38;5;28mstr\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   3576\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m   3577\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mlist\u001b[39m[Line2D]:\n\u001b[0;32m-> 3578\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mgca\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   3579\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3580\u001b[0m \u001b[43m        \u001b[49m\u001b[43mscalex\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mscalex\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3581\u001b[0m \u001b[43m        \u001b[49m\u001b[43mscaley\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mscaley\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3582\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdata\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m}\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43m{\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3583\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3584\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
-          "File \u001b[0;32m~/.cache/pypoetry/virtualenvs/warmth-atxSUjif-py3.10/lib/python3.10/site-packages/matplotlib/axes/_axes.py:1721\u001b[0m, in \u001b[0;36mAxes.plot\u001b[0;34m(self, scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1478\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   1479\u001b[0m \u001b[38;5;124;03mPlot y versus x as lines and/or markers.\u001b[39;00m\n\u001b[1;32m   1480\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1718\u001b[0m \u001b[38;5;124;03m(``'green'``) or hex strings (``'#008000'``).\u001b[39;00m\n\u001b[1;32m   1719\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   1720\u001b[0m kwargs \u001b[38;5;241m=\u001b[39m cbook\u001b[38;5;241m.\u001b[39mnormalize_kwargs(kwargs, mlines\u001b[38;5;241m.\u001b[39mLine2D)\n\u001b[0;32m-> 1721\u001b[0m lines \u001b[38;5;241m=\u001b[39m [\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_lines(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, data\u001b[38;5;241m=\u001b[39mdata, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)]\n\u001b[1;32m   1722\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m line \u001b[38;5;129;01min\u001b[39;00m lines:\n\u001b[1;32m   1723\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39madd_line(line)\n",
-          "File \u001b[0;32m~/.cache/pypoetry/virtualenvs/warmth-atxSUjif-py3.10/lib/python3.10/site-packages/matplotlib/axes/_base.py:303\u001b[0m, in \u001b[0;36m_process_plot_var_args.__call__\u001b[0;34m(self, axes, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m    301\u001b[0m     this \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m args[\u001b[38;5;241m0\u001b[39m],\n\u001b[1;32m    302\u001b[0m     args \u001b[38;5;241m=\u001b[39m args[\u001b[38;5;241m1\u001b[39m:]\n\u001b[0;32m--> 303\u001b[0m \u001b[38;5;28;01myield from\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_plot_args\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    304\u001b[0m \u001b[43m    \u001b[49m\u001b[43maxes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mthis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mambiguous_fmt_datakey\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mambiguous_fmt_datakey\u001b[49m\u001b[43m)\u001b[49m\n",
-          "File \u001b[0;32m~/.cache/pypoetry/virtualenvs/warmth-atxSUjif-py3.10/lib/python3.10/site-packages/matplotlib/axes/_base.py:499\u001b[0m, in \u001b[0;36m_process_plot_var_args._plot_args\u001b[0;34m(self, axes, tup, kwargs, return_kwargs, ambiguous_fmt_datakey)\u001b[0m\n\u001b[1;32m    496\u001b[0m     axes\u001b[38;5;241m.\u001b[39myaxis\u001b[38;5;241m.\u001b[39mupdate_units(y)\n\u001b[1;32m    498\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m x\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m!=\u001b[39m y\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m]:\n\u001b[0;32m--> 499\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mx and y must have same first dimension, but \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    500\u001b[0m                      \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhave shapes \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mx\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m and \u001b[39m\u001b[38;5;132;01m{\u001b[39;00my\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    501\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m x\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m2\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m y\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m2\u001b[39m:\n\u001b[1;32m    502\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mx and y can be no greater than 2D, but have \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    503\u001b[0m                      \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mshapes \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mx\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m and \u001b[39m\u001b[38;5;132;01m{\u001b[39;00my\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n",
-          "\u001b[0;31mValueError\u001b[0m: x and y must have same first dimension, but have shapes (342,) and (341,)"
-         ]
-        }
-       ],
-       "tabbable": null,
-       "tooltip": null
-      }
-     },
-     "9072f71b1a824904a7b652988f60c3c3": {
+     "9f73ec8614e24faaba5fce5a0c2324fa": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "2.0.0",
       "model_name": "LayoutModel",
@@ -1056,7 +994,44 @@
        "width": null
       }
      },
-     "f6561ad6e33c4089bd3e687e35c284bd": {
+     "b32d2dc629174a5fa99e8e8082db13ed": {
+      "model_module": "@jupyter-widgets/output",
+      "model_module_version": "1.0.0",
+      "model_name": "OutputModel",
+      "state": {
+       "_dom_classes": [],
+       "_model_module": "@jupyter-widgets/output",
+       "_model_module_version": "1.0.0",
+       "_model_name": "OutputModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/output",
+       "_view_module_version": "1.0.0",
+       "_view_name": "OutputView",
+       "layout": "IPY_MODEL_37918d0ccda04d0890cac407616a0b62",
+       "msg_id": "",
+       "outputs": [
+        {
+         "ename": "ValueError",
+         "evalue": "x and y must have same first dimension, but have shapes (342,) and (341,)",
+         "output_type": "error",
+         "traceback": [
+          "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+          "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+          "File \u001b[0;32m~/.cache/pypoetry/virtualenvs/warmth-atxSUjif-py3.10/lib/python3.10/site-packages/ipywidgets/widgets/interaction.py:240\u001b[0m, in \u001b[0;36minteractive.update\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m    238\u001b[0m     value \u001b[38;5;241m=\u001b[39m widget\u001b[38;5;241m.\u001b[39mget_interact_value()\n\u001b[1;32m    239\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkwargs[widget\u001b[38;5;241m.\u001b[39m_kwarg] \u001b[38;5;241m=\u001b[39m value\n\u001b[0;32m--> 240\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mresult \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    241\u001b[0m show_inline_matplotlib_plots()\n\u001b[1;32m    242\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mauto_display \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mresult \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
+          "Cell \u001b[0;32mIn[13], line 3\u001b[0m, in \u001b[0;36mplot_cell_properties\u001b[0;34m(age)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mplot_cell_properties\u001b[39m(age):\n\u001b[1;32m      2\u001b[0m     t \u001b[38;5;241m=\u001b[39m node\u001b[38;5;241m.\u001b[39mresult\u001b[38;5;241m.\u001b[39meffective_conductivity(age)\n\u001b[0;32m----> 3\u001b[0m     \u001b[43mplt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mt\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mvalues\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m(\u001b[49m\u001b[43mt\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdepth\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43mt\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdepth\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m/\u001b[39;49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m      4\u001b[0m     plt\u001b[38;5;241m.\u001b[39mgca()\u001b[38;5;241m.\u001b[39minvert_yaxis()\n\u001b[1;32m      5\u001b[0m     plt\u001b[38;5;241m.\u001b[39mxlabel(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mConductivity\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
+          "File \u001b[0;32m~/.cache/pypoetry/virtualenvs/warmth-atxSUjif-py3.10/lib/python3.10/site-packages/matplotlib/pyplot.py:3578\u001b[0m, in \u001b[0;36mplot\u001b[0;34m(scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   3570\u001b[0m \u001b[38;5;129m@_copy_docstring_and_deprecators\u001b[39m(Axes\u001b[38;5;241m.\u001b[39mplot)\n\u001b[1;32m   3571\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mplot\u001b[39m(\n\u001b[1;32m   3572\u001b[0m     \u001b[38;5;241m*\u001b[39margs: \u001b[38;5;28mfloat\u001b[39m \u001b[38;5;241m|\u001b[39m ArrayLike \u001b[38;5;241m|\u001b[39m \u001b[38;5;28mstr\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   3576\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m   3577\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mlist\u001b[39m[Line2D]:\n\u001b[0;32m-> 3578\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mgca\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   3579\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3580\u001b[0m \u001b[43m        \u001b[49m\u001b[43mscalex\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mscalex\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3581\u001b[0m \u001b[43m        \u001b[49m\u001b[43mscaley\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mscaley\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3582\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdata\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m}\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43m{\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3583\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3584\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
+          "File \u001b[0;32m~/.cache/pypoetry/virtualenvs/warmth-atxSUjif-py3.10/lib/python3.10/site-packages/matplotlib/axes/_axes.py:1721\u001b[0m, in \u001b[0;36mAxes.plot\u001b[0;34m(self, scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1478\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   1479\u001b[0m \u001b[38;5;124;03mPlot y versus x as lines and/or markers.\u001b[39;00m\n\u001b[1;32m   1480\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1718\u001b[0m \u001b[38;5;124;03m(``'green'``) or hex strings (``'#008000'``).\u001b[39;00m\n\u001b[1;32m   1719\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   1720\u001b[0m kwargs \u001b[38;5;241m=\u001b[39m cbook\u001b[38;5;241m.\u001b[39mnormalize_kwargs(kwargs, mlines\u001b[38;5;241m.\u001b[39mLine2D)\n\u001b[0;32m-> 1721\u001b[0m lines \u001b[38;5;241m=\u001b[39m [\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_lines(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, data\u001b[38;5;241m=\u001b[39mdata, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)]\n\u001b[1;32m   1722\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m line \u001b[38;5;129;01min\u001b[39;00m lines:\n\u001b[1;32m   1723\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39madd_line(line)\n",
+          "File \u001b[0;32m~/.cache/pypoetry/virtualenvs/warmth-atxSUjif-py3.10/lib/python3.10/site-packages/matplotlib/axes/_base.py:303\u001b[0m, in \u001b[0;36m_process_plot_var_args.__call__\u001b[0;34m(self, axes, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m    301\u001b[0m     this \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m args[\u001b[38;5;241m0\u001b[39m],\n\u001b[1;32m    302\u001b[0m     args \u001b[38;5;241m=\u001b[39m args[\u001b[38;5;241m1\u001b[39m:]\n\u001b[0;32m--> 303\u001b[0m \u001b[38;5;28;01myield from\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_plot_args\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    304\u001b[0m \u001b[43m    \u001b[49m\u001b[43maxes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mthis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mambiguous_fmt_datakey\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mambiguous_fmt_datakey\u001b[49m\u001b[43m)\u001b[49m\n",
+          "File \u001b[0;32m~/.cache/pypoetry/virtualenvs/warmth-atxSUjif-py3.10/lib/python3.10/site-packages/matplotlib/axes/_base.py:499\u001b[0m, in \u001b[0;36m_process_plot_var_args._plot_args\u001b[0;34m(self, axes, tup, kwargs, return_kwargs, ambiguous_fmt_datakey)\u001b[0m\n\u001b[1;32m    496\u001b[0m     axes\u001b[38;5;241m.\u001b[39myaxis\u001b[38;5;241m.\u001b[39mupdate_units(y)\n\u001b[1;32m    498\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m x\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m!=\u001b[39m y\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m]:\n\u001b[0;32m--> 499\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mx and y must have same first dimension, but \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    500\u001b[0m                      \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhave shapes \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mx\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m and \u001b[39m\u001b[38;5;132;01m{\u001b[39;00my\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    501\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m x\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m2\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m y\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m2\u001b[39m:\n\u001b[1;32m    502\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mx and y can be no greater than 2D, but have \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    503\u001b[0m                      \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mshapes \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mx\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m and \u001b[39m\u001b[38;5;132;01m{\u001b[39;00my\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n",
+          "\u001b[0;31mValueError\u001b[0m: x and y must have same first dimension, but have shapes (342,) and (341,)"
+         ]
+        }
+       ],
+       "tabbable": null,
+       "tooltip": null
+      }
+     },
+     "c767341aa9e54abaad2709fc9886bb5b": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "2.0.0",
       "model_name": "LayoutModel",
@@ -1108,6 +1083,31 @@
        "visibility": null,
        "width": null
       }
+     },
+     "ffefdbf9be0f4d8b83298f013df6c373": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "VBoxModel",
+      "state": {
+       "_dom_classes": [
+        "widget-interact"
+       ],
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "VBoxModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/controls",
+       "_view_module_version": "2.0.0",
+       "_view_name": "VBoxView",
+       "box_style": "",
+       "children": [
+        "IPY_MODEL_76aa13605586491980aa2bbb77b0732f",
+        "IPY_MODEL_311aa4270e43497ab600c21ea16dcb9a"
+       ],
+       "layout": "IPY_MODEL_13f9c9bc45b54ddfa056a030f8c83ba3",
+       "tabbable": null,
+       "tooltip": null
+      }
      }
     },
     "version_major": 2,
diff --git a/searchindex.js b/searchindex.js
index 4dbd62c..959f77d 100644
--- a/searchindex.js
+++ b/searchindex.js
@@ -1 +1 @@
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\ No newline at end of file