Formatting fixes

fuyans · Aug 9, 2021 · c2c9079 · c2c9079
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commit c2c9079
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diff --git a/source/user_guide/distributions.ipynb b/source/user_guide/distributions.ipynb
@@ -2,109 +2,99 @@
  "cells": [
   {
    "cell_type": "markdown",
+   "source": [
+    "Generates minimalistic plots for a variety of distribution types.\n",
+    "\n",
+    "15th April 2021, Yan Fu"
+   ],
    "metadata": {
     "pycharm": {
      "name": "#%% md\n"
     }
-   },
+   }
+  },
+  {
+   "cell_type": "markdown",
    "source": [
-    "Generates minimalistic plots for a variety of distribution types.\n",
-    "\n",
-    "15th April 2021, Yan Fu"
-   ]
+    "# Sampling Distribution Parameters\r\n"
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "code",
    "execution_count": 1,
+   "source": [
+    "import matplotlib.pyplot as plt\r\n",
+    "import numpy as np\r\n",
+    "\r\n",
+    "from sfeprapy.mcs.mcs_gen_2 import InputParser\r\n",
+    "plt.style.use('seaborn-white')"
+   ],
+   "outputs": [],
    "metadata": {
     "pycharm": {
      "name": "#%%\n"
     }
-   },
-   "outputs": [],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "\n",
-    "from sfeprapy.mcs.mcs_gen_2 import InputParser\n",
-    "plt.style.use('seaborn-white')"
-   ]
+   }
   },
   {
    "cell_type": "code",
    "execution_count": 2,
+   "source": [
+    "# Sample defined distributions using `InputParser`, a thin layer built on top of scipy.stats module.\r\n",
+    "\r\n",
+    "dist_obj = InputParser()\r\n",
+    "df_dists = dist_obj.inputs2samples(\r\n",
+    "    dist_params={\r\n",
+    "        'Gumbel Type I': dict(\r\n",
+    "            dist = 'gumbel_r_',\r\n",
+    "            mean = 0,\r\n",
+    "            sd = 1,\r\n",
+    "            ubound=4,\r\n",
+    "            lbound=-4,\r\n",
+    "        ),\r\n",
+    "        'Normal': dict(\r\n",
+    "            dist = 'norm_',\r\n",
+    "            mean=0,\r\n",
+    "            sd=1,\r\n",
+    "            ubound=4,\r\n",
+    "            lbound=-4,\r\n",
+    "        ),\r\n",
+    "        'Uniform': dict(\r\n",
+    "            dist = 'uniform_',\r\n",
+    "            ubound=4,\r\n",
+    "            lbound=-4,\r\n",
+    "        ),\r\n",
+    "        'Lognorm': dict(\r\n",
+    "            dist = 'lognorm_',\r\n",
+    "            mean=1,\r\n",
+    "            sd=1,\r\n",
+    "            ubound=4,\r\n",
+    "            lbound=-4,\r\n",
+    "        ),\r\n",
+    "        'Complementary Lognorm': dict(\r\n",
+    "            dist='lognorm_mod_',\r\n",
+    "            ubound=1,\r\n",
+    "            lbound=0,\r\n",
+    "            mean=0.2,\r\n",
+    "            sd=0.2,\r\n",
+    "        )\r\n",
+    "    },\r\n",
+    "    num_samples = 10000\r\n",
+    ")\r\n",
+    "\r\n",
+    "df_dists.drop('index', axis=1, inplace=True)  # index column not used"
+   ],
+   "outputs": [],
    "metadata": {
     "pycharm": {
      "name": "#%%\n"
     }
-   },
-   "outputs": [],
-   "source": [
-    "# Sample defined distributions using `InputParser`, a thin layer built on top of scipy.stats module.\n",
-    "\n",
-    "dist_obj = InputParser()\n",
-    "df_dists = dist_obj.inputs2samples(\n",
-    "    dist_params={\n",
-    "        'Gumbel Type I': dict(\n",
-    "            dist = 'gumbel_r_',\n",
-    "            mean = 0,\n",
-    "            sd = 1,\n",
-    "            ubound=4,\n",
-    "            lbound=-4,\n",
-    "        ),\n",
-    "        'Normal': dict(\n",
-    "            dist = 'norm_',\n",
-    "            mean=0,\n",
-    "            sd=1,\n",
-    "            ubound=4,\n",
-    "            lbound=-4,\n",
-    "        ),\n",
-    "        'Uniform': dict(\n",
-    "            dist = 'uniform_',\n",
-    "            ubound=4,\n",
-    "            lbound=-4,\n",
-    "        ),\n",
-    "        'Lognorm': dict(\n",
-    "            dist = 'lognorm_',\n",
-    "            mean=1,\n",
-    "            sd=1,\n",
-    "            ubound=4,\n",
-    "            lbound=-4,\n",
-    "        ),\n",
-    "        'Complementary Lognorm': dict(\n",
-    "            dist='lognorm_mod_',\n",
-    "            ubound=1,\n",
-    "            lbound=0,\n",
-    "            mean=0.2,\n",
-    "            sd=0.2,\n",
-    "        )\n",
-    "    },\n",
-    "    num_samples = 10000\n",
-    ")\n",
-    "\n",
-    "df_dists.drop('index', axis=1, inplace=True)  # index column not used"
-   ]
+   }
   },
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 414x86.4 with 5 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
    "source": [
     "# Plots\n",
     "\n",
@@ -127,7 +117,24 @@
     "plt.tight_layout()\n",
     "plt.show()\n",
     "# fig.savefig('dists.png', dpi=300, bbox_inches='tight', pad_inches=0.015)"
-   ]
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "<Figure size 414x86.4 with 5 Axes>"
+      ],
+      "image/png": "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"
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
   }
  ],
  "metadata": {
@@ -151,4 +158,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}
diff --git a/source/user_guide/index.rst b/source/user_guide/index.rst
@@ -15,4 +15,5 @@ User Guide
 
 .. toctree::
     :hidden:
-    time_equivalence_vis
+
+    time_equivalence_vis
diff --git a/source/user_guide/mcs0.rst b/source/user_guide/mcs0.rst
@@ -265,13 +265,13 @@ Solver Settings (for Time Equivalence)
 Timber Properties
 -----------------
 
-|``timber_exposed_area``: float
+``timber_exposed_area``: float 
 
 	| [m²]
 	| Exposed timber surface within the compartment. Set
 	  ``timber_exposed_area`` to ‘0’ to omitt timber involvement.
 
-|``timber_charring_rate``: float
+``timber_charring_rate``: float
 
 	| [mm/min]
 	| Timber constant charring rate. This is currently independent of