diff --git a/notebooks/wu2004.ipynb b/notebooks/wu2004.ipynb
index b618ad7..d3d97c3 100644
--- a/notebooks/wu2004.ipynb
+++ b/notebooks/wu2004.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -31,9 +31,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 2,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/k9/b8pxky2572sdtgy2vnxhcljw0000gn/T/ipykernel_86103/3008229323.py:4: DeprecationWarning: \n",
+      "Pyarrow will become a required dependency of pandas in the next major release of pandas (pandas 3.0),\n",
+      "(to allow more performant data types, such as the Arrow string type, and better interoperability with other libraries)\n",
+      "but was not found to be installed on your system.\n",
+      "If this would cause problems for you,\n",
+      "please provide us feedback at https://github.com/pandas-dev/pandas/issues/54466\n",
+      "        \n",
+      "  import pandas as pd\n",
+      "WARNING (pytensor.tensor.blas): Using NumPy C-API based implementation for BLAS functions.\n"
+     ]
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "np.random.seed(0)\n",
@@ -51,7 +67,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -72,9 +88,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Set parameter TokenServer to value \"leghorn.emsl.pnl.gov\"\n"
+     ]
+    }
+   ],
    "source": [
     "import cobra\n",
     "\n",
@@ -126,7 +150,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -139,7 +163,7 @@
        "       [ 0,  1,  0,  0, -1,  0]])"
       ]
      },
-     "execution_count": 41,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -152,7 +176,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -167,7 +191,7 @@
        "Name: fluxes, dtype: float64"
       ]
      },
-     "execution_count": 67,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -180,7 +204,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -218,6 +242,14 @@
     "Ex = emll.create_elasticity_matrix(model)\n",
     "display(Ex)\n",
     "\n",
+    "#### Original code to geneate Ey and ll ####\n",
+    "# Ey = np.zeros((3, 2))\n",
+    "# Ey[0, 0] = 1  # bpg (+) PGM\n",
+    "# Ey[2, 1] = 1  # adp (+) PK\n",
+    "\n",
+    "# ll = emll.LinLogSymbolic2x2(N, Ex, Ey, v_star)\n",
+    "\n",
+    "# Updated code to generate Ey and ll corresponding to updated cobra model\n",
     "Ey = np.zeros((6, 2))\n",
     "Ey[0, 0] = 1  # 2pg_e (+) EX_2pg_e\n",
     "Ey[5, 1] = 1  # pep_e (+) EX_pep_e\n",
@@ -235,7 +267,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -338,7 +370,7 @@
        "4  25.0  12.0  30.0   54.0   76.0  13.2  130.0  136.0"
       ]
      },
-     "execution_count": 84,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -358,7 +390,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -389,7 +421,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -398,7 +430,7 @@
        "array([87., 87., 87.])"
       ]
      },
-     "execution_count": 86,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -409,7 +441,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -418,7 +450,7 @@
        "19"
       ]
      },
-     "execution_count": 87,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -454,31 +486,6 @@
     "ll = emll.LinLogLeastNorm(N, Ex, Ey, v_star)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 88,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[1., 0.],\n",
-       "       [0., 0.],\n",
-       "       [0., 0.],\n",
-       "       [0., 0.],\n",
-       "       [0., 0.],\n",
-       "       [0., 1.]])"
-      ]
-     },
-     "execution_count": 88,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Ey"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -490,27 +497,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(5, 6)"
-      ]
-     },
-     "execution_count": 89,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "N.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 90,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -519,7 +506,7 @@
        "Text(0, 0.5, 'Probability density')"
       ]
      },
-     "execution_count": 90,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -559,7 +546,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 91,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -611,9 +598,16 @@
     "print(initialize_elasticity.__doc__)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Sample prior predictive trace"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 92,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -637,7 +631,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 93,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -651,8 +645,8 @@
        "              <ul class=\"xr-sections group-sections\">\n",
        "              \n",
        "            <li class = \"xr-section-item\">\n",
-       "                  <input id=\"idata_prior218e2860-dda1-4302-9f82-5a7d2e0a520a\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
-       "                  <label for=\"idata_prior218e2860-dda1-4302-9f82-5a7d2e0a520a\" class = \"xr-section-summary\">prior</label>\n",
+       "                  <input id=\"idata_prior962173c5-dfff-473b-8777-816e2e912edb\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
+       "                  <label for=\"idata_prior962173c5-dfff-473b-8777-816e2e912edb\" class = \"xr-section-summary\">prior</label>\n",
        "                  <div class=\"xr-section-inline-details\"></div>\n",
        "                  <div class=\"xr-section-details\">\n",
        "                      <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
@@ -1020,194 +1014,195 @@
        "  fill: currentColor;\n",
        "}\n",
        "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
-       "Dimensions:                    (chain: 1, draw: 500, Ey_dim_0: 6, Ey_dim_1: 2,\n",
-       "                                ey_kinetic_entries_dim_0: 2, Ex_dim_0: 6,\n",
-       "                                Ex_dim_1: 5, ex_kinetic_entries_dim_0: 10,\n",
-       "                                ey_capacity_entries_dim_0: 10,\n",
-       "                                ex_capacity_entries_dim_0: 20)\n",
+       "Dimensions:                    (chain: 1, draw: 500,\n",
+       "                                ey_kinetic_entries_dim_0: 2,\n",
+       "                                ex_kinetic_entries_dim_0: 10,\n",
+       "                                ex_capacity_entries_dim_0: 20, Ey_dim_0: 6,\n",
+       "                                Ey_dim_1: 2, Ex_dim_0: 6, Ex_dim_1: 5,\n",
+       "                                ey_capacity_entries_dim_0: 10)\n",
        "Coordinates:\n",
        "  * chain                      (chain) int64 0\n",
        "  * draw                       (draw) int64 0 1 2 3 4 5 ... 495 496 497 498 499\n",
+       "  * ey_kinetic_entries_dim_0   (ey_kinetic_entries_dim_0) int64 0 1\n",
+       "  * ex_kinetic_entries_dim_0   (ex_kinetic_entries_dim_0) int64 0 1 2 ... 7 8 9\n",
+       "  * ex_capacity_entries_dim_0  (ex_capacity_entries_dim_0) int64 0 1 2 ... 18 19\n",
        "  * Ey_dim_0                   (Ey_dim_0) int64 0 1 2 3 4 5\n",
        "  * Ey_dim_1                   (Ey_dim_1) int64 0 1\n",
-       "  * ey_kinetic_entries_dim_0   (ey_kinetic_entries_dim_0) int64 0 1\n",
        "  * Ex_dim_0                   (Ex_dim_0) int64 0 1 2 3 4 5\n",
        "  * Ex_dim_1                   (Ex_dim_1) int64 0 1 2 3 4\n",
-       "  * ex_kinetic_entries_dim_0   (ex_kinetic_entries_dim_0) int64 0 1 2 ... 7 8 9\n",
        "  * ey_capacity_entries_dim_0  (ey_capacity_entries_dim_0) int64 0 1 2 ... 7 8 9\n",
-       "  * ex_capacity_entries_dim_0  (ex_capacity_entries_dim_0) int64 0 1 2 ... 18 19\n",
        "Data variables:\n",
-       "    Ey                         (chain, draw, Ey_dim_0, Ey_dim_1) float64 0.98...\n",
        "    ey_kinetic_entries         (chain, draw, ey_kinetic_entries_dim_0) float64 ...\n",
-       "    Ex                         (chain, draw, Ex_dim_0, Ex_dim_1) float64 -0.6...\n",
        "    ex_kinetic_entries         (chain, draw, ex_kinetic_entries_dim_0) float64 ...\n",
-       "    ey_capacity_entries        (chain, draw, ey_capacity_entries_dim_0) float64 ...\n",
        "    ex_capacity_entries        (chain, draw, ex_capacity_entries_dim_0) float64 ...\n",
+       "    Ey                         (chain, draw, Ey_dim_0, Ey_dim_1) float64 0.09...\n",
+       "    Ex                         (chain, draw, Ex_dim_0, Ex_dim_1) float64 -0.3...\n",
+       "    ey_capacity_entries        (chain, draw, ey_capacity_entries_dim_0) float64 ...\n",
        "Attributes:\n",
-       "    created_at:                 2024-03-08T22:23:39.313824\n",
+       "    created_at:                 2024-03-09T23:44:54.466271\n",
        "    arviz_version:              0.17.0\n",
        "    inference_library:          pymc\n",
-       "    inference_library_version:  5.10.3</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-bbc87635-dd21-463c-9406-3f37e980683c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-bbc87635-dd21-463c-9406-3f37e980683c' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 1</li><li><span class='xr-has-index'>draw</span>: 500</li><li><span class='xr-has-index'>Ey_dim_0</span>: 6</li><li><span class='xr-has-index'>Ey_dim_1</span>: 2</li><li><span class='xr-has-index'>ey_kinetic_entries_dim_0</span>: 2</li><li><span class='xr-has-index'>Ex_dim_0</span>: 6</li><li><span class='xr-has-index'>Ex_dim_1</span>: 5</li><li><span class='xr-has-index'>ex_kinetic_entries_dim_0</span>: 10</li><li><span class='xr-has-index'>ey_capacity_entries_dim_0</span>: 10</li><li><span class='xr-has-index'>ex_capacity_entries_dim_0</span>: 20</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-7bf12b7d-b1c9-4526-9f77-f3bc1f7bc1cd' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7bf12b7d-b1c9-4526-9f77-f3bc1f7bc1cd' class='xr-section-summary' >Coordinates: <span>(10)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-838f63ee-e8f9-4be8-8456-42ddb45e2873' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-838f63ee-e8f9-4be8-8456-42ddb45e2873' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c355a935-7235-4a2d-a4f8-818a24302888' class='xr-var-data-in' type='checkbox'><label for='data-c355a935-7235-4a2d-a4f8-818a24302888' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 495 496 497 498 499</div><input id='attrs-c1a203c8-5616-4152-a4f5-07a8b773d10b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c1a203c8-5616-4152-a4f5-07a8b773d10b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-57e24a6e-8d43-472f-9156-9f48f245dbd8' class='xr-var-data-in' type='checkbox'><label for='data-57e24a6e-8d43-472f-9156-9f48f245dbd8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 497, 498, 499])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>Ey_dim_0</span></div><div class='xr-var-dims'>(Ey_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5</div><input id='attrs-9e5bfc82-3741-4500-9cd1-7c81aefdfd1f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9e5bfc82-3741-4500-9cd1-7c81aefdfd1f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-73511e19-3cdc-4760-af51-a9682e7d7935' class='xr-var-data-in' type='checkbox'><label for='data-73511e19-3cdc-4760-af51-a9682e7d7935' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3, 4, 5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>Ey_dim_1</span></div><div class='xr-var-dims'>(Ey_dim_1)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1</div><input id='attrs-27cbccdd-332e-449e-8265-737680d52787' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-27cbccdd-332e-449e-8265-737680d52787' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9cf508c2-266f-42ea-9bd7-1765c687b0a9' class='xr-var-data-in' type='checkbox'><label for='data-9cf508c2-266f-42ea-9bd7-1765c687b0a9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>ey_kinetic_entries_dim_0</span></div><div class='xr-var-dims'>(ey_kinetic_entries_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1</div><input id='attrs-be302f19-f872-46f7-b2f3-e44f9746fec2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-be302f19-f872-46f7-b2f3-e44f9746fec2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-29993eec-92a5-42ec-8e38-fa66fd24c528' class='xr-var-data-in' type='checkbox'><label for='data-29993eec-92a5-42ec-8e38-fa66fd24c528' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>Ex_dim_0</span></div><div class='xr-var-dims'>(Ex_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5</div><input id='attrs-4ee826c9-d04f-4fc1-a07b-f5f3b7930876' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4ee826c9-d04f-4fc1-a07b-f5f3b7930876' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6982483f-5592-441f-b1fd-ba18b8424708' class='xr-var-data-in' type='checkbox'><label for='data-6982483f-5592-441f-b1fd-ba18b8424708' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3, 4, 5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>Ex_dim_1</span></div><div class='xr-var-dims'>(Ex_dim_1)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4</div><input id='attrs-412b327d-d533-4779-afb9-879b10123517' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-412b327d-d533-4779-afb9-879b10123517' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4b03755b-08ce-4c6c-a278-673baf26e2a8' class='xr-var-data-in' type='checkbox'><label for='data-4b03755b-08ce-4c6c-a278-673baf26e2a8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3, 4])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>ex_kinetic_entries_dim_0</span></div><div class='xr-var-dims'>(ex_kinetic_entries_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 7 8 9</div><input id='attrs-9c93b8bc-3c7b-4346-b73b-d578cba14d36' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9c93b8bc-3c7b-4346-b73b-d578cba14d36' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-996a2218-b0ae-4296-976a-059e4e23eaa3' class='xr-var-data-in' type='checkbox'><label for='data-996a2218-b0ae-4296-976a-059e4e23eaa3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>ey_capacity_entries_dim_0</span></div><div class='xr-var-dims'>(ey_capacity_entries_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 7 8 9</div><input id='attrs-8e86dd2a-29df-49e7-8d0c-b34c21632c18' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8e86dd2a-29df-49e7-8d0c-b34c21632c18' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3c5288ff-beba-4386-b453-7e5e96201419' class='xr-var-data-in' type='checkbox'><label for='data-3c5288ff-beba-4386-b453-7e5e96201419' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>ex_capacity_entries_dim_0</span></div><div class='xr-var-dims'>(ex_capacity_entries_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 14 15 16 17 18 19</div><input id='attrs-0c30a457-f7ac-42fc-a031-a8d4eac52a84' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0c30a457-f7ac-42fc-a031-a8d4eac52a84' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1abd481f-56f2-4db8-904b-d4dbea3db3b7' class='xr-var-data-in' type='checkbox'><label for='data-1abd481f-56f2-4db8-904b-d4dbea3db3b7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
-       "       18, 19])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-77f5b668-1e48-42d4-ac8e-1f138c96924c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-77f5b668-1e48-42d4-ac8e-1f138c96924c' class='xr-section-summary' >Data variables: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>Ey</span></div><div class='xr-var-dims'>(chain, draw, Ey_dim_0, Ey_dim_1)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.9801 -0.007329 ... -0.1911</div><input id='attrs-c55e61da-835b-4342-b2d5-b62ec053f888' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c55e61da-835b-4342-b2d5-b62ec053f888' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-83c10681-bd08-4ab8-a475-3a2c13e33124' class='xr-var-data-in' type='checkbox'><label for='data-83c10681-bd08-4ab8-a475-3a2c13e33124' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[[ 9.80092127e-01, -7.32893250e-03],\n",
-       "         [ 6.15546056e-02, -7.92250458e-02],\n",
-       "         [ 6.87687300e-03,  5.04049500e-02],\n",
-       "         [ 1.55490558e-02,  2.35903168e-02],\n",
-       "         [ 9.47272041e-03,  2.39524597e-02],\n",
-       "         [-6.39700648e-02,  3.79186733e-01]],\n",
-       "\n",
-       "        [[ 1.43942867e-01,  7.97660052e-02],\n",
-       "         [ 9.86368660e-02, -9.56692850e-03],\n",
-       "         [-1.35160811e-01,  1.13512627e-02],\n",
-       "         [ 6.09708731e-02, -1.17556641e-01],\n",
-       "         [-3.65158103e-02,  7.21900411e-02],\n",
-       "         [ 8.46576201e-03,  1.66235917e+00]],\n",
-       "\n",
-       "        [[ 5.76263750e-01, -2.02013425e-02],\n",
-       "         [ 2.21754139e-02,  7.57086453e-02],\n",
-       "         [-1.56786922e-02,  5.19219502e-02],\n",
-       "         [ 1.32562825e-02, -3.56687029e-02],\n",
-       "         [ 3.72340686e-02,  6.20610072e-02],\n",
-       "         [-6.12300363e-02,  4.03728247e-01]],\n",
-       "...\n",
-       "        [[ 1.81008286e+00,  2.61554714e-04],\n",
-       "         [-5.57273083e-02,  1.30426061e-01],\n",
-       "         [ 2.90889677e-02, -5.58156970e-02],\n",
-       "         [ 3.59789872e-02, -7.81680404e-03],\n",
-       "         [ 1.91547496e-02,  2.04480540e-02],\n",
-       "         [-3.95517697e-02,  6.85689067e-01]],\n",
-       "\n",
-       "        [[ 1.30000305e+00,  2.11494823e-01],\n",
-       "         [-1.68202537e-02,  4.64714420e-03],\n",
-       "         [-1.33828817e-01,  5.76033729e-03],\n",
-       "         [ 3.04313056e-02, -4.99407927e-02],\n",
-       "         [-3.23985338e-02,  6.44310159e-03],\n",
-       "         [ 2.02270415e-02,  1.01651139e+00]],\n",
-       "\n",
-       "        [[ 1.18967451e+00, -7.95003210e-02],\n",
-       "         [ 4.02519784e-02,  3.54846317e-03],\n",
-       "         [-1.94175005e-02, -3.56566740e-03],\n",
-       "         [-1.87267936e-02, -1.06820926e-01],\n",
-       "         [ 2.28402949e-03,  2.33126440e-01],\n",
-       "         [-4.05092928e-02, -1.91137578e-01]]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ey_kinetic_entries</span></div><div class='xr-var-dims'>(chain, draw, ey_kinetic_entries_dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.9801 0.3792 ... 1.19 -0.1911</div><input id='attrs-151b14a3-e212-4ddc-b731-7d9481271bea' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-151b14a3-e212-4ddc-b731-7d9481271bea' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-82bac15f-8249-4766-8f86-5d0ed1e3a136' class='xr-var-data-in' type='checkbox'><label for='data-82bac15f-8249-4766-8f86-5d0ed1e3a136' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[ 9.80092127e-01,  3.79186733e-01],\n",
-       "        [ 1.43942867e-01,  1.66235917e+00],\n",
-       "        [ 5.76263750e-01,  4.03728247e-01],\n",
-       "        [ 5.09667728e-01,  7.16174114e-01],\n",
-       "        [ 5.46497684e-01,  1.25723420e+00],\n",
-       "        [ 1.46051803e+00,  3.79579015e-01],\n",
-       "        [ 7.66039009e-01,  1.11317423e+00],\n",
-       "        [ 2.09937823e+00,  3.08530259e-01],\n",
-       "        [ 1.64116812e-01, -1.43084781e-02],\n",
-       "        [ 3.37531512e-01,  1.03887209e+00],\n",
-       "        [ 1.10741264e+00,  1.48458794e+00],\n",
-       "        [ 5.55879435e-01,  7.14118820e-01],\n",
-       "        [-7.55589652e-02,  1.76160962e+00],\n",
-       "        [ 5.02669186e-01,  2.31435193e+00],\n",
-       "        [ 1.01634091e+00,  4.33578169e-01],\n",
-       "        [ 1.33941940e-01,  8.59237061e-01],\n",
-       "        [ 5.73272682e-01,  9.24772498e-01],\n",
-       "        [ 4.77316266e-01,  1.78710481e-01],\n",
-       "        [ 5.07940765e-01,  4.32428693e-01],\n",
-       "        [ 2.66781821e-01,  5.14032254e-01],\n",
-       "...\n",
-       "        [ 9.10991917e-02,  9.22490116e-01],\n",
-       "        [ 1.39578554e-01,  1.02109336e-01],\n",
-       "        [ 2.06812525e-01,  1.26624180e+00],\n",
-       "        [ 1.89548894e+00,  1.43352430e+00],\n",
-       "        [ 6.45660870e-01,  7.63315740e-01],\n",
-       "        [ 1.55844455e+00,  9.96748630e-01],\n",
-       "        [ 3.57450358e-01,  1.02648126e+00],\n",
-       "        [ 6.08438225e-01,  2.28331783e-01],\n",
-       "        [ 4.93188508e-01,  1.53093018e+00],\n",
-       "        [ 6.26259462e-01, -1.97396008e-01],\n",
-       "        [ 1.84224279e-03,  5.21310696e-01],\n",
-       "        [ 4.89096576e-01,  9.58394022e-01],\n",
-       "        [ 4.25201345e-01,  3.20932589e-01],\n",
-       "        [ 1.41807742e-01,  1.41736809e-01],\n",
-       "        [ 5.46244865e-01,  7.45094336e-01],\n",
-       "        [ 9.32080350e-01,  1.96965220e+00],\n",
-       "        [ 2.21893686e+00, -3.90885705e-01],\n",
-       "        [ 1.81008286e+00,  6.85689067e-01],\n",
-       "        [ 1.30000305e+00,  1.01651139e+00],\n",
-       "        [ 1.18967451e+00, -1.91137578e-01]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Ex</span></div><div class='xr-var-dims'>(chain, draw, Ex_dim_0, Ex_dim_1)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.6869 -0.02314 ... 0.06179</div><input id='attrs-279124f5-80aa-449b-abf0-300ab41a702e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-279124f5-80aa-449b-abf0-300ab41a702e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-082f5e3e-8745-4925-9b67-e1fcdb75600e' class='xr-var-data-in' type='checkbox'><label for='data-082f5e3e-8745-4925-9b67-e1fcdb75600e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[[-6.86943711e-01, -2.31365973e-02,  9.62986918e-02,\n",
-       "          -7.61972328e-02,  5.77900682e-02],\n",
-       "         [ 1.78336224e-02,  1.65158341e-02,  2.11468877e-02,\n",
-       "          -4.79313638e-02, -1.55601279e+00],\n",
-       "         [ 2.66914511e-01, -1.18045831e+00, -2.70722675e-02,\n",
-       "           2.72201485e-02,  1.73326976e-02],\n",
-       "         [ 9.92470752e-03, -2.22554089e-01,  3.74082221e-03,\n",
-       "           1.15921101e-01,  7.56024961e-03],\n",
-       "         [-8.46508282e-02, -4.40675732e-02, -3.44393997e+00,\n",
-       "           4.15032029e-01,  9.58664984e-01],\n",
-       "         [-2.37201328e-01, -1.14968052e-02,  5.33608577e-02,\n",
-       "          -1.38539765e-01,  6.04566247e-03]],\n",
-       "\n",
-       "        [[-1.66832405e+00, -1.68852092e-03,  2.94651653e-02,\n",
-       "           8.99758244e-04,  1.98248281e-02],\n",
-       "         [ 1.49869930e-02,  1.00833996e-01, -5.37950138e-02,\n",
-       "           2.05095182e-02, -1.67703803e+00],\n",
-       "         [ 3.82563689e-01, -2.93572562e-01, -8.94743679e-02,\n",
-       "          -6.83650857e-02, -5.61738225e-02],\n",
-       "         [ 1.38285385e-01,  3.19392474e-01,  3.58234926e-02,\n",
+       "    inference_library_version:  5.10.3</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-374fe097-9f9d-4376-a82e-5ab8c1bb5663' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-374fe097-9f9d-4376-a82e-5ab8c1bb5663' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 1</li><li><span class='xr-has-index'>draw</span>: 500</li><li><span class='xr-has-index'>ey_kinetic_entries_dim_0</span>: 2</li><li><span class='xr-has-index'>ex_kinetic_entries_dim_0</span>: 10</li><li><span class='xr-has-index'>ex_capacity_entries_dim_0</span>: 20</li><li><span class='xr-has-index'>Ey_dim_0</span>: 6</li><li><span class='xr-has-index'>Ey_dim_1</span>: 2</li><li><span class='xr-has-index'>Ex_dim_0</span>: 6</li><li><span class='xr-has-index'>Ex_dim_1</span>: 5</li><li><span class='xr-has-index'>ey_capacity_entries_dim_0</span>: 10</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-120f4db5-27ff-45c3-84be-eae380b26500' class='xr-section-summary-in' type='checkbox'  checked><label for='section-120f4db5-27ff-45c3-84be-eae380b26500' class='xr-section-summary' >Coordinates: <span>(10)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-e7f2047e-7e12-41fe-aaf6-4f4957a5d6ad' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e7f2047e-7e12-41fe-aaf6-4f4957a5d6ad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-54a07096-7969-4908-b942-523881837b0a' class='xr-var-data-in' type='checkbox'><label for='data-54a07096-7969-4908-b942-523881837b0a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 495 496 497 498 499</div><input id='attrs-be8497aa-8a0c-463c-93ab-e137cd03af58' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-be8497aa-8a0c-463c-93ab-e137cd03af58' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b68b8fe2-609b-41b2-8040-daaf9f8ed423' class='xr-var-data-in' type='checkbox'><label for='data-b68b8fe2-609b-41b2-8040-daaf9f8ed423' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 497, 498, 499])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>ey_kinetic_entries_dim_0</span></div><div class='xr-var-dims'>(ey_kinetic_entries_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1</div><input id='attrs-71936cb6-f198-488b-81c1-9f2c3a08bd37' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-71936cb6-f198-488b-81c1-9f2c3a08bd37' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d0795b9f-d068-49ec-b9de-cb69605973e2' class='xr-var-data-in' type='checkbox'><label for='data-d0795b9f-d068-49ec-b9de-cb69605973e2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>ex_kinetic_entries_dim_0</span></div><div class='xr-var-dims'>(ex_kinetic_entries_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 7 8 9</div><input id='attrs-51047040-8681-42c6-ad54-54e8df68469f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-51047040-8681-42c6-ad54-54e8df68469f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d12cedb0-2cee-4370-9c64-d2f5a163facb' class='xr-var-data-in' type='checkbox'><label for='data-d12cedb0-2cee-4370-9c64-d2f5a163facb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>ex_capacity_entries_dim_0</span></div><div class='xr-var-dims'>(ex_capacity_entries_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 14 15 16 17 18 19</div><input id='attrs-f392c0f4-1530-4be5-893a-ef6d41f480a5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f392c0f4-1530-4be5-893a-ef6d41f480a5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3e51ceaa-b345-44d3-9d1c-3f26eb1a94ba' class='xr-var-data-in' type='checkbox'><label for='data-3e51ceaa-b345-44d3-9d1c-3f26eb1a94ba' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+       "       18, 19])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>Ey_dim_0</span></div><div class='xr-var-dims'>(Ey_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5</div><input id='attrs-190c3eac-fb46-4797-a19a-8b3dad6af0f7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-190c3eac-fb46-4797-a19a-8b3dad6af0f7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8a78e362-34f4-47dc-b9f6-c97dc3440a65' class='xr-var-data-in' type='checkbox'><label for='data-8a78e362-34f4-47dc-b9f6-c97dc3440a65' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3, 4, 5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>Ey_dim_1</span></div><div class='xr-var-dims'>(Ey_dim_1)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1</div><input id='attrs-ff6a3794-9084-40e9-ac96-6275b79ca0de' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ff6a3794-9084-40e9-ac96-6275b79ca0de' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0528d9e0-cca6-49d9-a992-0da61050bd11' class='xr-var-data-in' type='checkbox'><label for='data-0528d9e0-cca6-49d9-a992-0da61050bd11' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>Ex_dim_0</span></div><div class='xr-var-dims'>(Ex_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5</div><input id='attrs-52ea1224-7e95-41d4-a2b0-dfdf801be70d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-52ea1224-7e95-41d4-a2b0-dfdf801be70d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-919311b6-62df-4d43-a6ba-d4f51ce00992' class='xr-var-data-in' type='checkbox'><label for='data-919311b6-62df-4d43-a6ba-d4f51ce00992' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3, 4, 5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>Ex_dim_1</span></div><div class='xr-var-dims'>(Ex_dim_1)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4</div><input id='attrs-2f611462-0e10-4cbb-b723-d7be1878788f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2f611462-0e10-4cbb-b723-d7be1878788f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0d6ad534-546c-4599-a77f-ed2095f2bb79' class='xr-var-data-in' type='checkbox'><label for='data-0d6ad534-546c-4599-a77f-ed2095f2bb79' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3, 4])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>ey_capacity_entries_dim_0</span></div><div class='xr-var-dims'>(ey_capacity_entries_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 7 8 9</div><input id='attrs-f7876caa-d87a-4c4b-a2a6-a69d17d45dac' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f7876caa-d87a-4c4b-a2a6-a69d17d45dac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6be34356-17f6-46ed-963b-d1a69f74e6b1' class='xr-var-data-in' type='checkbox'><label for='data-6be34356-17f6-46ed-963b-d1a69f74e6b1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2e03203a-bbf1-4178-83a9-7df87fa94501' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2e03203a-bbf1-4178-83a9-7df87fa94501' class='xr-section-summary' >Data variables: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>ey_kinetic_entries</span></div><div class='xr-var-dims'>(chain, draw, ey_kinetic_entries_dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.09345 0.7692 ... 0.3079 1.242</div><input id='attrs-0ad2fc27-2a7d-490c-ac2b-815f9f43adb7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0ad2fc27-2a7d-490c-ac2b-815f9f43adb7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cbe040ca-7701-43bb-9e3c-63a9b4cd924a' class='xr-var-data-in' type='checkbox'><label for='data-cbe040ca-7701-43bb-9e3c-63a9b4cd924a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[ 9.34537308e-02,  7.69182221e-01],\n",
+       "        [ 4.84518824e-01,  7.62468812e-01],\n",
+       "        [ 8.77640493e-01,  1.51019670e+00],\n",
+       "        [ 2.26553553e-01,  1.65187512e-01],\n",
+       "        [ 1.32897481e+00,  1.77030453e+00],\n",
+       "        [ 6.60692135e-01,  1.73370892e+00],\n",
+       "        [ 8.84862367e-01,  4.84465243e-02],\n",
+       "        [ 3.66752634e-01,  1.22475482e+00],\n",
+       "        [ 3.69142334e-01,  6.47755921e-02],\n",
+       "        [ 2.02010258e-01,  2.35315302e+00],\n",
+       "        [ 6.97924835e-01,  4.76262277e-01],\n",
+       "        [ 2.88866439e-01,  1.11096033e+00],\n",
+       "        [-1.47736450e-01,  2.08852976e+00],\n",
+       "        [ 2.20389264e-01,  9.72797490e-01],\n",
+       "        [ 3.14145491e-01,  6.37057628e-01],\n",
+       "        [ 5.18614425e-01,  2.65998071e-01],\n",
+       "        [ 6.82987344e-01,  6.80665795e-01],\n",
+       "        [ 2.06593082e+00,  8.92318881e-01],\n",
+       "        [ 2.45372390e+00,  1.36271148e-01],\n",
+       "        [ 1.61081549e-01,  2.49737652e-01],\n",
        "...\n",
-       "          -5.42337029e-02, -8.41256460e-03],\n",
-       "         [-5.34995414e-03,  3.42461864e-01,  8.33893455e-02,\n",
-       "          -7.58010914e-01, -6.30645002e-02],\n",
-       "         [-2.00764699e-02,  7.56111642e-02, -2.23723412e+00,\n",
-       "           1.06684976e+00,  5.11291270e-01],\n",
-       "         [ 6.41805298e-02,  1.04266851e-02,  1.60580294e+00,\n",
-       "          -4.18030196e-02,  3.22315157e-02]],\n",
-       "\n",
-       "        [[-1.07100396e+00,  2.88284570e-02, -4.75285091e-02,\n",
-       "          -1.01527679e-02, -4.43013349e-02],\n",
-       "         [ 1.40446661e-01,  1.52185055e-01, -9.50987070e-02,\n",
-       "           2.21869608e-02, -5.90826706e-01],\n",
-       "         [ 2.77633614e-01, -2.90770357e-01,  2.48642353e-02,\n",
-       "           1.93166606e-02,  1.04857745e-01],\n",
-       "         [-8.43260982e-03,  5.23016717e-01,  5.47939487e-03,\n",
-       "          -1.35995567e-01, -4.28291372e-02],\n",
-       "         [ 2.40016753e-02, -6.45524152e-02, -3.73509433e-01,\n",
-       "           2.24315989e-01,  1.27428166e+00],\n",
-       "         [-3.32538137e-02, -6.72813540e-02,  1.83228516e-01,\n",
-       "           4.92530605e-02,  6.17897729e-02]]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ex_kinetic_entries</span></div><div class='xr-var-dims'>(chain, draw, ex_kinetic_entries_dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.6869 1.556 ... 1.274 0.1832</div><input id='attrs-b6efb077-752e-4dd2-97a4-f3aac168456a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b6efb077-752e-4dd2-97a4-f3aac168456a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0a99fb9f-8289-4264-a8c8-0d92ebd64a05' class='xr-var-data-in' type='checkbox'><label for='data-0a99fb9f-8289-4264-a8c8-0d92ebd64a05' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[0.68694371, 1.55601279, 0.26691451, ..., 0.41503203,\n",
-       "         0.95866498, 0.05336086],\n",
-       "        [1.66832405, 1.67703803, 0.38256369, ..., 0.87594136,\n",
-       "         0.27166232, 1.09066142],\n",
-       "        [1.83599651, 1.35424365, 2.02806857, ..., 1.4702398 ,\n",
-       "         0.68986045, 0.62157243],\n",
+       "        [ 1.80055467e+00,  1.23323167e+00],\n",
+       "        [ 1.15474002e+00, -2.34714493e-02],\n",
+       "        [ 7.14204169e-01,  1.00823822e+00],\n",
+       "        [-2.31215664e-01,  6.05060657e-01],\n",
+       "        [ 4.36942135e-01,  1.25876216e+00],\n",
+       "        [ 1.52345979e+00,  2.86590475e-01],\n",
+       "        [ 1.24510066e+00,  1.01034669e+00],\n",
+       "        [ 9.42487335e-01,  1.39602560e+00],\n",
+       "        [ 1.95519911e+00,  2.20516889e+00],\n",
+       "        [ 6.06818174e-02,  6.48843530e-01],\n",
+       "        [ 1.39558625e+00,  9.78032058e-01],\n",
+       "        [ 4.29217844e-01,  5.38059698e-01],\n",
+       "        [ 7.08952767e-01,  1.10737255e-01],\n",
+       "        [ 7.42677275e-01,  7.62321216e-01],\n",
+       "        [ 1.85957202e+00,  9.19685509e-01],\n",
+       "        [ 4.07741165e-01,  6.11466904e-01],\n",
+       "        [ 4.01475995e-01,  1.07151855e+00],\n",
+       "        [-7.34881601e-02,  1.18373363e+00],\n",
+       "        [ 5.25727505e-01,  6.45598884e-01],\n",
+       "        [ 3.07915332e-01,  1.24208219e+00]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ex_kinetic_entries</span></div><div class='xr-var-dims'>(chain, draw, ex_kinetic_entries_dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.3878 0.8951 ... 1.239 -0.2781</div><input id='attrs-0c195cdf-66f2-4d45-bc2e-33aa5f8a57fc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0c195cdf-66f2-4d45-bc2e-33aa5f8a57fc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0953a9cb-ce8f-4fd1-91af-f2eb448a986d' class='xr-var-data-in' type='checkbox'><label for='data-0953a9cb-ce8f-4fd1-91af-f2eb448a986d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[ 0.38777316,  0.89514704,  0.91948396, ...,  0.62028104,\n",
+       "          1.47819303,  0.6457047 ],\n",
+       "        [-0.1937104 ,  0.70417817,  0.79235814, ...,  0.87393331,\n",
+       "          0.27373947,  0.82956865],\n",
+       "        [ 0.1583581 ,  0.58572878,  1.47547804, ...,  1.23923881,\n",
+       "          0.24287713,  3.35160718],\n",
        "        ...,\n",
-       "        [1.2916394 , 0.38084465, 0.56716142, ..., 1.07786404,\n",
-       "         0.96363819, 0.31915633],\n",
-       "        [1.13245741, 1.52188822, 0.00309283, ..., 1.06684976,\n",
-       "         0.51129127, 1.60580294],\n",
-       "        [1.07100396, 0.59082671, 0.27763361, ..., 0.22431599,\n",
-       "         1.27428166, 0.18322852]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ey_capacity_entries</span></div><div class='xr-var-dims'>(chain, draw, ey_capacity_entries_dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.007329 0.06155 ... -0.04051</div><input id='attrs-f90bb52c-d6cf-43a4-92ad-f5803eff0691' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f90bb52c-d6cf-43a4-92ad-f5803eff0691' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-54baf6c1-6f0c-4dac-8427-b147fba845eb' class='xr-var-data-in' type='checkbox'><label for='data-54baf6c1-6f0c-4dac-8427-b147fba845eb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[-0.00732893,  0.06155461, -0.07922505, ...,  0.00947272,\n",
-       "          0.02395246, -0.06397006],\n",
-       "        [ 0.07976601,  0.09863687, -0.00956693, ..., -0.03651581,\n",
-       "          0.07219004,  0.00846576],\n",
-       "        [-0.02020134,  0.02217541,  0.07570865, ...,  0.03723407,\n",
-       "          0.06206101, -0.06123004],\n",
+       "        [ 0.77063559,  0.75364294,  0.94370375, ...,  0.2055839 ,\n",
+       "          1.26107131,  1.44119865],\n",
+       "        [ 0.08230018,  0.60611572,  1.95867705, ...,  0.93768972,\n",
+       "          0.57810439,  1.99576825],\n",
+       "        [ 1.5100651 ,  0.53553071,  0.92326245, ...,  0.60210084,\n",
+       "          1.23927434, -0.2781215 ]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ex_capacity_entries</span></div><div class='xr-var-dims'>(chain, draw, ex_capacity_entries_dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.04521 -0.05129 ... -0.0311</div><input id='attrs-216589ed-cc88-4fc5-8522-7117896bd6db' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-216589ed-cc88-4fc5-8522-7117896bd6db' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1800b9fd-39a6-4a1f-a90d-024ad4d38088' class='xr-var-data-in' type='checkbox'><label for='data-1800b9fd-39a6-4a1f-a90d-024ad4d38088' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[ 0.04521431, -0.0512936 , -0.05987865, ...,  0.00039824,\n",
+       "         -0.08532333,  0.04079851],\n",
+       "        [-0.0131591 ,  0.03118034, -0.022018  , ...,  0.05727717,\n",
+       "         -0.00213896,  0.04820278],\n",
+       "        [-0.00024879, -0.01336701, -0.05531057, ..., -0.06749464,\n",
+       "         -0.09094858, -0.00230825],\n",
        "        ...,\n",
-       "        [ 0.00026155, -0.05572731,  0.13042606, ...,  0.01915475,\n",
-       "          0.02044805, -0.03955177],\n",
-       "        [ 0.21149482, -0.01682025,  0.00464714, ..., -0.03239853,\n",
-       "          0.0064431 ,  0.02022704],\n",
-       "        [-0.07950032,  0.04025198,  0.00354846, ...,  0.00228403,\n",
-       "          0.23312644, -0.04050929]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ex_capacity_entries</span></div><div class='xr-var-dims'>(chain, draw, ex_capacity_entries_dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.02314 0.0963 ... 0.04925 0.06179</div><input id='attrs-e99a7ee3-a2f7-4619-8a9f-6dc0b0f5fb1e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e99a7ee3-a2f7-4619-8a9f-6dc0b0f5fb1e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-50b9aa5e-c960-4502-b7de-561ef472a3c9' class='xr-var-data-in' type='checkbox'><label for='data-50b9aa5e-c960-4502-b7de-561ef472a3c9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[-0.0231366 ,  0.09629869, -0.07619723, ..., -0.01149681,\n",
-       "         -0.13853977,  0.00604566],\n",
-       "        [-0.00168852,  0.02946517,  0.00089976, ...,  0.06196752,\n",
-       "          0.02834678,  0.00397934],\n",
-       "        [-0.00731676,  0.01089503, -0.04146735, ...,  0.02909182,\n",
-       "          0.03340486,  0.01479266],\n",
+       "        [-0.00049025, -0.03857214, -0.01288019, ..., -0.04130573,\n",
+       "         -0.03551246,  0.2457068 ],\n",
+       "        [ 0.04309233,  0.19570556, -0.0214568 , ..., -0.07602045,\n",
+       "          0.04894327,  0.01736919],\n",
+       "        [-0.02179961,  0.14378324, -0.07634473, ...,  0.14057038,\n",
+       "         -0.00043905, -0.03109769]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Ey</span></div><div class='xr-var-dims'>(chain, draw, Ey_dim_0, Ey_dim_1)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.09345 -0.1555 ... -0.03429 1.242</div><input id='attrs-6422e173-8fc0-46b7-815d-40438640314e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6422e173-8fc0-46b7-815d-40438640314e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c7779750-193c-476b-8cef-3eca5204953b' class='xr-var-data-in' type='checkbox'><label for='data-c7779750-193c-476b-8cef-3eca5204953b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[[ 9.34537308e-02, -1.55487088e-01],\n",
+       "         [-1.16916149e-02, -1.07117538e-03],\n",
+       "         [ 4.56997009e-02, -6.07832236e-02],\n",
+       "         [ 5.58440056e-02, -2.66398510e-01],\n",
+       "         [-1.59538772e-02, -8.35210651e-02],\n",
+       "         [ 1.41021028e-01,  7.69182221e-01]],\n",
+       "\n",
+       "        [[ 4.84518824e-01,  7.85179738e-02],\n",
+       "         [-3.55748699e-02, -2.74938556e-02],\n",
+       "         [-6.36646114e-03,  1.05453459e-01],\n",
+       "         [-2.30019779e-02,  8.24803823e-02],\n",
+       "         [-1.41138114e-01, -2.37303451e-02],\n",
+       "         [-1.31712870e-01,  7.62468812e-01]],\n",
+       "\n",
+       "        [[ 8.77640493e-01, -5.82966524e-03],\n",
+       "         [-1.02250961e-01,  2.89415093e-01],\n",
+       "         [ 1.92583835e-03,  8.54108049e-02],\n",
+       "         [-8.39715752e-02, -1.24172641e-02],\n",
+       "         [-1.11805674e-01, -6.48725388e-02],\n",
+       "         [-3.18246554e-02,  1.51019670e+00]],\n",
+       "...\n",
+       "        [[-7.34881601e-02,  9.12292721e-03],\n",
+       "         [-7.13028927e-04, -1.82592672e-02],\n",
+       "         [-2.60781850e-01,  1.08611574e-01],\n",
+       "         [-1.31844365e-02,  2.69695163e-02],\n",
+       "         [ 2.90977170e-02, -4.67875308e-02],\n",
+       "         [-4.96216638e-02,  1.18373363e+00]],\n",
+       "\n",
+       "        [[ 5.25727505e-01,  3.42783829e-03],\n",
+       "         [ 1.27211707e-02, -9.85967134e-02],\n",
+       "         [-2.23731080e-02, -4.22573029e-02],\n",
+       "         [-5.79486634e-02,  3.41440767e-02],\n",
+       "         [ 7.67713201e-02,  1.85222582e-02],\n",
+       "         [ 6.88833167e-02,  6.45598884e-01]],\n",
+       "\n",
+       "        [[ 3.07915332e-01, -7.35585824e-02],\n",
+       "         [-4.28001800e-02,  5.45088794e-02],\n",
+       "         [-4.27795193e-03,  1.03169819e-01],\n",
+       "         [ 1.10511195e-01,  3.12700041e-03],\n",
+       "         [-4.66698607e-02,  1.07540675e-01],\n",
+       "         [-3.42939103e-02,  1.24208219e+00]]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Ex</span></div><div class='xr-var-dims'>(chain, draw, Ex_dim_0, Ex_dim_1)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.3878 0.04521 ... -0.0311</div><input id='attrs-730936a2-cea4-4d1c-a62b-06e9901e5f1c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-730936a2-cea4-4d1c-a62b-06e9901e5f1c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-839b0561-6b7b-476b-ae03-c417a49bc674' class='xr-var-data-in' type='checkbox'><label for='data-839b0561-6b7b-476b-ae03-c417a49bc674' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[[-3.87773157e-01,  4.52143145e-02, -5.12936043e-02,\n",
+       "          -5.98786461e-02,  3.57674737e-03],\n",
+       "         [-4.32439411e-03,  5.69244895e-02,  7.21801943e-03,\n",
+       "           5.67079513e-02, -8.95147042e-01],\n",
+       "         [ 9.19483957e-01, -1.53485982e+00, -2.48930596e-03,\n",
+       "           3.19260440e-02, -2.35195852e-02],\n",
+       "         [ 2.12384496e-02,  3.83850687e+00, -8.94455020e-03,\n",
+       "          -6.14804682e-01, -5.56743687e-02],\n",
+       "         [-4.53731795e-02, -2.70277174e-03, -1.99054662e+00,\n",
+       "           6.20281041e-01,  1.47819303e+00],\n",
+       "         [-5.25265109e-02,  3.98244230e-04,  6.45704702e-01,\n",
+       "          -8.53233325e-02,  4.07985053e-02]],\n",
+       "\n",
+       "        [[ 1.93710404e-01, -1.31590976e-02,  3.11803394e-02,\n",
+       "          -2.20179958e-02,  3.24965377e-02],\n",
+       "         [-3.13767563e-03,  8.79904562e-02, -1.35757116e-02,\n",
+       "          -1.27613829e-01, -7.04178173e-01],\n",
+       "         [ 7.92358142e-01, -2.22375657e+00,  1.48346186e-01,\n",
+       "           1.50046952e-01, -1.61589575e-02],\n",
+       "         [-2.96658294e-02,  9.16335185e-01,  1.59088637e-02,\n",
+       "...\n",
+       "           7.06355817e-02, -1.71415831e-02],\n",
+       "         [ 2.24421314e-02,  1.81725275e+00,  1.36123797e-01,\n",
+       "          -9.79338577e-01, -1.71622917e-02],\n",
+       "         [-1.80457188e-01, -2.93238505e-02, -1.44563374e+00,\n",
+       "           9.37689722e-01,  5.78104392e-01],\n",
+       "         [ 8.27428074e-02, -7.60204500e-02,  1.99576825e+00,\n",
+       "           4.89432707e-02,  1.73691921e-02]],\n",
+       "\n",
+       "        [[-1.51006510e+00, -2.17996083e-02,  1.43783240e-01,\n",
+       "          -7.63447298e-02, -1.27727150e-03],\n",
+       "         [ 1.47658281e-01,  9.90909355e-03,  6.39347454e-04,\n",
+       "           3.86193271e-04, -5.35530707e-01],\n",
+       "         [ 9.23262445e-01, -5.56582418e-01,  8.92427220e-02,\n",
+       "          -4.90698297e-03,  2.43478348e-02],\n",
+       "         [-7.82511119e-04,  5.61954674e-01,  4.62326009e-02,\n",
+       "          -3.55742477e-01,  1.06054730e-01],\n",
+       "         [-1.31666407e-02,  1.02315136e-02, -2.93148620e-01,\n",
+       "           6.02100838e-01,  1.23927434e+00],\n",
+       "         [-1.36148282e-02,  1.40570380e-01, -2.78121499e-01,\n",
+       "          -4.39045680e-04, -3.10976883e-02]]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ey_capacity_entries</span></div><div class='xr-var-dims'>(chain, draw, ey_capacity_entries_dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.1555 -0.01169 ... -0.03429</div><input id='attrs-a19037a9-9aac-425b-bde5-620a3e8970f3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a19037a9-9aac-425b-bde5-620a3e8970f3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-59afaa5f-24e9-4acd-8615-32f6b71ea1cc' class='xr-var-data-in' type='checkbox'><label for='data-59afaa5f-24e9-4acd-8615-32f6b71ea1cc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[-0.15548709, -0.01169161, -0.00107118, ..., -0.01595388,\n",
+       "         -0.08352107,  0.14102103],\n",
+       "        [ 0.07851797, -0.03557487, -0.02749386, ..., -0.14113811,\n",
+       "         -0.02373035, -0.13171287],\n",
+       "        [-0.00582967, -0.10225096,  0.28941509, ..., -0.11180567,\n",
+       "         -0.06487254, -0.03182466],\n",
        "        ...,\n",
-       "        [ 0.00746956, -0.00025921,  0.12260726, ..., -0.05187676,\n",
-       "          0.09149077, -0.01489737],\n",
-       "        [-0.02741032, -0.1356605 , -0.03127837, ...,  0.01042669,\n",
-       "         -0.04180302,  0.03223152],\n",
-       "        [ 0.02882846, -0.04752851, -0.01015277, ..., -0.06728135,\n",
-       "          0.04925306,  0.06178977]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ecf56216-61df-48d7-a3fa-eba4cc10fd11' class='xr-section-summary-in' type='checkbox'  ><label for='section-ecf56216-61df-48d7-a3fa-eba4cc10fd11' class='xr-section-summary' >Indexes: <span>(10)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-8493e1be-d77e-49de-8328-3b5f10728eb2' class='xr-index-data-in' type='checkbox'/><label for='index-8493e1be-d77e-49de-8328-3b5f10728eb2' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9fc4a9a9-5603-439b-865e-4f8650b1d20c' class='xr-index-data-in' type='checkbox'/><label for='index-9fc4a9a9-5603-439b-865e-4f8650b1d20c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
+       "        [ 0.00912293, -0.00071303, -0.01825927, ...,  0.02909772,\n",
+       "         -0.04678753, -0.04962166],\n",
+       "        [ 0.00342784,  0.01272117, -0.09859671, ...,  0.07677132,\n",
+       "          0.01852226,  0.06888332],\n",
+       "        [-0.07355858, -0.04280018,  0.05450888, ..., -0.04666986,\n",
+       "          0.10754068, -0.03429391]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-67be6841-32db-4bb6-bf5d-060266efcf1c' class='xr-section-summary-in' type='checkbox'  ><label for='section-67be6841-32db-4bb6-bf5d-060266efcf1c' class='xr-section-summary' >Indexes: <span>(10)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b44342e1-be7f-483c-9010-14e35da9487d' class='xr-index-data-in' type='checkbox'/><label for='index-b44342e1-be7f-483c-9010-14e35da9487d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-fe4058ed-fdbb-4f30-8c1c-56f28f471fbb' class='xr-index-data-in' type='checkbox'/><label for='index-fe4058ed-fdbb-4f30-8c1c-56f28f471fbb' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
        "       ...\n",
        "       490, 491, 492, 493, 494, 495, 496, 497, 498, 499],\n",
-       "      dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=500))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>Ey_dim_0</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-2a795c87-4c0c-4a4b-8a56-1513f6821aaf' class='xr-index-data-in' type='checkbox'/><label for='index-2a795c87-4c0c-4a4b-8a56-1513f6821aaf' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1, 2, 3, 4, 5], dtype=&#x27;int64&#x27;, name=&#x27;Ey_dim_0&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>Ey_dim_1</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ba24799f-6bb9-4783-b2e6-8ba05eb48b1a' class='xr-index-data-in' type='checkbox'/><label for='index-ba24799f-6bb9-4783-b2e6-8ba05eb48b1a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1], dtype=&#x27;int64&#x27;, name=&#x27;Ey_dim_1&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>ey_kinetic_entries_dim_0</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-32647758-7335-45b8-bad3-916832f2e21a' class='xr-index-data-in' type='checkbox'/><label for='index-32647758-7335-45b8-bad3-916832f2e21a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1], dtype=&#x27;int64&#x27;, name=&#x27;ey_kinetic_entries_dim_0&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>Ex_dim_0</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-289af3f1-f0db-4ac7-b664-16f2e1e52014' class='xr-index-data-in' type='checkbox'/><label for='index-289af3f1-f0db-4ac7-b664-16f2e1e52014' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1, 2, 3, 4, 5], dtype=&#x27;int64&#x27;, name=&#x27;Ex_dim_0&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>Ex_dim_1</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ba840883-5aa5-497d-bbc1-e9d13344fab3' class='xr-index-data-in' type='checkbox'/><label for='index-ba840883-5aa5-497d-bbc1-e9d13344fab3' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1, 2, 3, 4], dtype=&#x27;int64&#x27;, name=&#x27;Ex_dim_1&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>ex_kinetic_entries_dim_0</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-fe499dad-87f3-4674-af87-c13dff333624' class='xr-index-data-in' type='checkbox'/><label for='index-fe499dad-87f3-4674-af87-c13dff333624' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=&#x27;int64&#x27;, name=&#x27;ex_kinetic_entries_dim_0&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>ey_capacity_entries_dim_0</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-55014399-63f5-495b-8dfd-9ba66b39170f' class='xr-index-data-in' type='checkbox'/><label for='index-55014399-63f5-495b-8dfd-9ba66b39170f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=&#x27;int64&#x27;, name=&#x27;ey_capacity_entries_dim_0&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>ex_capacity_entries_dim_0</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a228589b-cae1-406e-89bc-b77fc28ab30e' class='xr-index-data-in' type='checkbox'/><label for='index-a228589b-cae1-406e-89bc-b77fc28ab30e' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19], dtype=&#x27;int64&#x27;, name=&#x27;ex_capacity_entries_dim_0&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-bbecaf96-3063-4514-b294-8f77589ef2de' class='xr-section-summary-in' type='checkbox'  checked><label for='section-bbecaf96-3063-4514-b294-8f77589ef2de' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2024-03-08T22:23:39.313824</dd><dt><span>arviz_version :</span></dt><dd>0.17.0</dd><dt><span>inference_library :</span></dt><dd>pymc</dd><dt><span>inference_library_version :</span></dt><dd>5.10.3</dd></dl></div></li></ul></div></div><br></div>\n",
+       "      dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=500))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>ey_kinetic_entries_dim_0</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-8b9730c0-65b4-4253-b5b7-211c33818203' class='xr-index-data-in' type='checkbox'/><label for='index-8b9730c0-65b4-4253-b5b7-211c33818203' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1], dtype=&#x27;int64&#x27;, name=&#x27;ey_kinetic_entries_dim_0&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>ex_kinetic_entries_dim_0</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f8f018e7-3a83-4126-8b8b-e683e0a78fa5' class='xr-index-data-in' type='checkbox'/><label for='index-f8f018e7-3a83-4126-8b8b-e683e0a78fa5' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=&#x27;int64&#x27;, name=&#x27;ex_kinetic_entries_dim_0&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>ex_capacity_entries_dim_0</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-1194644e-65c5-4873-8e77-4eab5649d3d3' class='xr-index-data-in' type='checkbox'/><label for='index-1194644e-65c5-4873-8e77-4eab5649d3d3' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19], dtype=&#x27;int64&#x27;, name=&#x27;ex_capacity_entries_dim_0&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>Ey_dim_0</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-90353d4f-e1d6-465d-bd84-9d62c5740e2d' class='xr-index-data-in' type='checkbox'/><label for='index-90353d4f-e1d6-465d-bd84-9d62c5740e2d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1, 2, 3, 4, 5], dtype=&#x27;int64&#x27;, name=&#x27;Ey_dim_0&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>Ey_dim_1</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-8f2077fd-96b9-4f0f-87c8-594de9c87604' class='xr-index-data-in' type='checkbox'/><label for='index-8f2077fd-96b9-4f0f-87c8-594de9c87604' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1], dtype=&#x27;int64&#x27;, name=&#x27;Ey_dim_1&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>Ex_dim_0</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9cfa45b0-de64-4754-a3d8-9a8ff2309d43' class='xr-index-data-in' type='checkbox'/><label for='index-9cfa45b0-de64-4754-a3d8-9a8ff2309d43' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1, 2, 3, 4, 5], dtype=&#x27;int64&#x27;, name=&#x27;Ex_dim_0&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>Ex_dim_1</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-4ef88ac2-90e7-4a2c-bf8c-68244ed87717' class='xr-index-data-in' type='checkbox'/><label for='index-4ef88ac2-90e7-4a2c-bf8c-68244ed87717' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1, 2, 3, 4], dtype=&#x27;int64&#x27;, name=&#x27;Ex_dim_1&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>ey_capacity_entries_dim_0</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-add96977-f74d-4c92-a700-413222c8198f' class='xr-index-data-in' type='checkbox'/><label for='index-add96977-f74d-4c92-a700-413222c8198f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=&#x27;int64&#x27;, name=&#x27;ey_capacity_entries_dim_0&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8b4a40d0-82f5-4c38-9ed7-6fa85b6eea43' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8b4a40d0-82f5-4c38-9ed7-6fa85b6eea43' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2024-03-09T23:44:54.466271</dd><dt><span>arviz_version :</span></dt><dd>0.17.0</dd><dt><span>inference_library :</span></dt><dd>pymc</dd><dt><span>inference_library_version :</span></dt><dd>5.10.3</dd></dl></div></li></ul></div></div><br></div>\n",
        "                      </ul>\n",
        "                  </div>\n",
        "            </li>\n",
@@ -1560,7 +1555,7 @@
        "\t> prior"
       ]
      },
-     "execution_count": 93,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1569,9 +1564,643 @@
     "trace_prior"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Sample FCCs from the prior predictive trace, and organize into dataframe\n"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 94,
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>FCC_ref</th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "      <th>4</th>\n",
+       "      <th>5</th>\n",
+       "      <th>6</th>\n",
+       "      <th>7</th>\n",
+       "      <th>8</th>\n",
+       "      <th>9</th>\n",
+       "      <th>...</th>\n",
+       "      <th>490</th>\n",
+       "      <th>491</th>\n",
+       "      <th>492</th>\n",
+       "      <th>493</th>\n",
+       "      <th>494</th>\n",
+       "      <th>495</th>\n",
+       "      <th>496</th>\n",
+       "      <th>497</th>\n",
+       "      <th>498</th>\n",
+       "      <th>499</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Reaction</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>ENO</th>\n",
+       "      <td>0.047295</td>\n",
+       "      <td>8.026206</td>\n",
+       "      <td>0.063011</td>\n",
+       "      <td>0.124888</td>\n",
+       "      <td>0.195212</td>\n",
+       "      <td>0.016624</td>\n",
+       "      <td>0.106775</td>\n",
+       "      <td>0.005381</td>\n",
+       "      <td>0.918388</td>\n",
+       "      <td>0.152977</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.441326</td>\n",
+       "      <td>0.345804</td>\n",
+       "      <td>0.039761</td>\n",
+       "      <td>0.018415</td>\n",
+       "      <td>0.060389</td>\n",
+       "      <td>0.075832</td>\n",
+       "      <td>1.643886</td>\n",
+       "      <td>0.068338</td>\n",
+       "      <td>0.017555</td>\n",
+       "      <td>0.176262</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>EX_2pg_e</th>\n",
+       "      <td>0.261289</td>\n",
+       "      <td>-12.788574</td>\n",
+       "      <td>0.682338</td>\n",
+       "      <td>0.174929</td>\n",
+       "      <td>0.849662</td>\n",
+       "      <td>0.370961</td>\n",
+       "      <td>-0.137570</td>\n",
+       "      <td>0.109006</td>\n",
+       "      <td>0.521470</td>\n",
+       "      <td>-0.052516</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.032697</td>\n",
+       "      <td>0.076065</td>\n",
+       "      <td>-0.073643</td>\n",
+       "      <td>0.006826</td>\n",
+       "      <td>0.797295</td>\n",
+       "      <td>0.074228</td>\n",
+       "      <td>-0.854880</td>\n",
+       "      <td>0.293006</td>\n",
+       "      <td>0.937428</td>\n",
+       "      <td>0.146382</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>EX_pep_e</th>\n",
+       "      <td>0.305514</td>\n",
+       "      <td>1.453545</td>\n",
+       "      <td>0.049101</td>\n",
+       "      <td>0.392880</td>\n",
+       "      <td>-0.019426</td>\n",
+       "      <td>0.377481</td>\n",
+       "      <td>0.421938</td>\n",
+       "      <td>0.072107</td>\n",
+       "      <td>-1.153854</td>\n",
+       "      <td>0.145936</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.010383</td>\n",
+       "      <td>0.356143</td>\n",
+       "      <td>0.018649</td>\n",
+       "      <td>0.975294</td>\n",
+       "      <td>-0.062271</td>\n",
+       "      <td>0.195938</td>\n",
+       "      <td>1.975325</td>\n",
+       "      <td>0.037652</td>\n",
+       "      <td>-0.068944</td>\n",
+       "      <td>0.035734</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>PGM</th>\n",
+       "      <td>0.131881</td>\n",
+       "      <td>3.358388</td>\n",
+       "      <td>0.074327</td>\n",
+       "      <td>0.084372</td>\n",
+       "      <td>0.211194</td>\n",
+       "      <td>0.088608</td>\n",
+       "      <td>-0.104577</td>\n",
+       "      <td>0.680955</td>\n",
+       "      <td>0.408771</td>\n",
+       "      <td>0.025443</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.214996</td>\n",
+       "      <td>0.076071</td>\n",
+       "      <td>0.026660</td>\n",
+       "      <td>-0.014842</td>\n",
+       "      <td>0.096365</td>\n",
+       "      <td>0.085245</td>\n",
+       "      <td>-3.718252</td>\n",
+       "      <td>0.222637</td>\n",
+       "      <td>0.045473</td>\n",
+       "      <td>0.189378</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>PK</th>\n",
+       "      <td>0.092579</td>\n",
+       "      <td>0.850861</td>\n",
+       "      <td>0.106388</td>\n",
+       "      <td>0.064738</td>\n",
+       "      <td>-0.069845</td>\n",
+       "      <td>0.104887</td>\n",
+       "      <td>0.493502</td>\n",
+       "      <td>0.045469</td>\n",
+       "      <td>0.015829</td>\n",
+       "      <td>0.156662</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.101326</td>\n",
+       "      <td>0.136658</td>\n",
+       "      <td>0.037847</td>\n",
+       "      <td>0.030976</td>\n",
+       "      <td>0.049741</td>\n",
+       "      <td>0.160982</td>\n",
+       "      <td>1.319145</td>\n",
+       "      <td>0.135841</td>\n",
+       "      <td>0.033574</td>\n",
+       "      <td>0.124061</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SK_adp_c</th>\n",
+       "      <td>0.161442</td>\n",
+       "      <td>0.099575</td>\n",
+       "      <td>0.024834</td>\n",
+       "      <td>0.158194</td>\n",
+       "      <td>-0.166797</td>\n",
+       "      <td>0.041439</td>\n",
+       "      <td>0.219932</td>\n",
+       "      <td>0.087082</td>\n",
+       "      <td>0.289396</td>\n",
+       "      <td>0.571498</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.199272</td>\n",
+       "      <td>0.009259</td>\n",
+       "      <td>0.950725</td>\n",
+       "      <td>-0.016668</td>\n",
+       "      <td>0.058480</td>\n",
+       "      <td>0.407775</td>\n",
+       "      <td>0.634775</td>\n",
+       "      <td>0.242527</td>\n",
+       "      <td>0.034913</td>\n",
+       "      <td>0.328183</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>6 rows × 500 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "FCC_ref        0          1         2         3         4         5    \\\n",
+       "Reaction                                                                \n",
+       "ENO       0.047295   8.026206  0.063011  0.124888  0.195212  0.016624   \n",
+       "EX_2pg_e  0.261289 -12.788574  0.682338  0.174929  0.849662  0.370961   \n",
+       "EX_pep_e  0.305514   1.453545  0.049101  0.392880 -0.019426  0.377481   \n",
+       "PGM       0.131881   3.358388  0.074327  0.084372  0.211194  0.088608   \n",
+       "PK        0.092579   0.850861  0.106388  0.064738 -0.069845  0.104887   \n",
+       "SK_adp_c  0.161442   0.099575  0.024834  0.158194 -0.166797  0.041439   \n",
+       "\n",
+       "FCC_ref        6         7         8         9    ...       490       491  \\\n",
+       "Reaction                                          ...                       \n",
+       "ENO       0.106775  0.005381  0.918388  0.152977  ...  0.441326  0.345804   \n",
+       "EX_2pg_e -0.137570  0.109006  0.521470 -0.052516  ...  0.032697  0.076065   \n",
+       "EX_pep_e  0.421938  0.072107 -1.153854  0.145936  ...  0.010383  0.356143   \n",
+       "PGM      -0.104577  0.680955  0.408771  0.025443  ...  0.214996  0.076071   \n",
+       "PK        0.493502  0.045469  0.015829  0.156662  ...  0.101326  0.136658   \n",
+       "SK_adp_c  0.219932  0.087082  0.289396  0.571498  ...  0.199272  0.009259   \n",
+       "\n",
+       "FCC_ref        492       493       494       495       496       497  \\\n",
+       "Reaction                                                               \n",
+       "ENO       0.039761  0.018415  0.060389  0.075832  1.643886  0.068338   \n",
+       "EX_2pg_e -0.073643  0.006826  0.797295  0.074228 -0.854880  0.293006   \n",
+       "EX_pep_e  0.018649  0.975294 -0.062271  0.195938  1.975325  0.037652   \n",
+       "PGM       0.026660 -0.014842  0.096365  0.085245 -3.718252  0.222637   \n",
+       "PK        0.037847  0.030976  0.049741  0.160982  1.319145  0.135841   \n",
+       "SK_adp_c  0.950725 -0.016668  0.058480  0.407775  0.634775  0.242527   \n",
+       "\n",
+       "FCC_ref        498       499  \n",
+       "Reaction                      \n",
+       "ENO       0.017555  0.176262  \n",
+       "EX_2pg_e  0.937428  0.146382  \n",
+       "EX_pep_e -0.068944  0.035734  \n",
+       "PGM       0.045473  0.189378  \n",
+       "PK        0.033574  0.124061  \n",
+       "SK_adp_c  0.034913  0.328183  \n",
+       "\n",
+       "[6 rows x 500 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Sample prior FCCs\n",
+    "fcc_prior = np.array([ll.flux_control_coefficient(Ex=ex) for ex in trace_prior.prior['Ex'][0].to_numpy()])\n",
+    "\n",
+    "# Organize prior FCCs as a dataframe\n",
+    "priors_df = pd.DataFrame(fcc_prior[:, 0], columns=[r.id for r in model.reactions]\n",
+    "                  ).stack().reset_index(level=1)\n",
+    "priors_df['type'] = 'Prior'\n",
+    "\n",
+    "# Copy prior_df now for plotting later\n",
+    "plot_df = priors_df.copy\n",
+    "\n",
+    "# Resphape dataframe to have rows for FCC samples for each reaction\n",
+    "priors_df['FCC_ref'] = [x for x in priors_df.index]\n",
+    "priors_df = priors_df.rename(columns={'level_1': 'Reaction', 0: 'FCC'}).reset_index()\n",
+    "priors_df = priors_df.pivot(index='Reaction', values='FCC', columns='FCC_ref')\n",
+    "\n",
+    "display(priors_df)\n",
+    "\n",
+    "# Save FCC prior predictives\n",
+    "priors_df.to_csv('../tests/test_data/expected_wu2004_FCCpriors_updated.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>index</th>\n",
+       "      <th>Reaction</th>\n",
+       "      <th>FCC</th>\n",
+       "      <th>Type</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0</td>\n",
+       "      <td>PGM</td>\n",
+       "      <td>0.131881</td>\n",
+       "      <td>New</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0</td>\n",
+       "      <td>ENO</td>\n",
+       "      <td>0.047295</td>\n",
+       "      <td>New</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>PK</td>\n",
+       "      <td>0.092579</td>\n",
+       "      <td>New</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>1</td>\n",
+       "      <td>PGM</td>\n",
+       "      <td>3.358388</td>\n",
+       "      <td>New</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>1</td>\n",
+       "      <td>ENO</td>\n",
+       "      <td>8.026206</td>\n",
+       "      <td>New</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2991</th>\n",
+       "      <td>498</td>\n",
+       "      <td>ENO</td>\n",
+       "      <td>0.017555</td>\n",
+       "      <td>New</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2992</th>\n",
+       "      <td>498</td>\n",
+       "      <td>PK</td>\n",
+       "      <td>0.033574</td>\n",
+       "      <td>New</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2996</th>\n",
+       "      <td>499</td>\n",
+       "      <td>PGM</td>\n",
+       "      <td>0.189378</td>\n",
+       "      <td>New</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2997</th>\n",
+       "      <td>499</td>\n",
+       "      <td>ENO</td>\n",
+       "      <td>0.176262</td>\n",
+       "      <td>New</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2998</th>\n",
+       "      <td>499</td>\n",
+       "      <td>PK</td>\n",
+       "      <td>0.124061</td>\n",
+       "      <td>New</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1500 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      index Reaction       FCC Type\n",
+       "2         0      PGM  0.131881  New\n",
+       "3         0      ENO  0.047295  New\n",
+       "4         0       PK  0.092579  New\n",
+       "8         1      PGM  3.358388  New\n",
+       "9         1      ENO  8.026206  New\n",
+       "...     ...      ...       ...  ...\n",
+       "2991    498      ENO  0.017555  New\n",
+       "2992    498       PK  0.033574  New\n",
+       "2996    499      PGM  0.189378  New\n",
+       "2997    499      ENO  0.176262  New\n",
+       "2998    499       PK  0.124061  New\n",
+       "\n",
+       "[1500 rows x 4 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>level_0</th>\n",
+       "      <th>index</th>\n",
+       "      <th>Reaction</th>\n",
+       "      <th>FCC</th>\n",
+       "      <th>Type</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>PGM</td>\n",
+       "      <td>0.131881</td>\n",
+       "      <td>New</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>ENO</td>\n",
+       "      <td>0.047295</td>\n",
+       "      <td>New</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>PK</td>\n",
+       "      <td>0.092579</td>\n",
+       "      <td>New</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>8</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>PGM</td>\n",
+       "      <td>3.358388</td>\n",
+       "      <td>New</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>9</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>ENO</td>\n",
+       "      <td>8.026206</td>\n",
+       "      <td>New</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2995</th>\n",
+       "      <td>498</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>PGM</td>\n",
+       "      <td>-0.216049</td>\n",
+       "      <td>Old</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2996</th>\n",
+       "      <td>498</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>PK</td>\n",
+       "      <td>0.876267</td>\n",
+       "      <td>Old</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2997</th>\n",
+       "      <td>499</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>ENO</td>\n",
+       "      <td>-0.160071</td>\n",
+       "      <td>Old</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2998</th>\n",
+       "      <td>499</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>PGM</td>\n",
+       "      <td>1.124280</td>\n",
+       "      <td>Old</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2999</th>\n",
+       "      <td>499</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>PK</td>\n",
+       "      <td>0.035792</td>\n",
+       "      <td>Old</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3000 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     level_0  index Reaction       FCC Type\n",
+       "0          2    0.0      PGM  0.131881  New\n",
+       "1          3    0.0      ENO  0.047295  New\n",
+       "2          4    0.0       PK  0.092579  New\n",
+       "3          8    1.0      PGM  3.358388  New\n",
+       "4          9    1.0      ENO  8.026206  New\n",
+       "...      ...    ...      ...       ...  ...\n",
+       "2995     498    NaN      PGM -0.216049  Old\n",
+       "2996     498    NaN       PK  0.876267  Old\n",
+       "2997     499    NaN      ENO -0.160071  Old\n",
+       "2998     499    NaN      PGM  1.124280  Old\n",
+       "2999     499    NaN       PK  0.035792  Old\n",
+       "\n",
+       "[3000 rows x 5 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Reaction', ylabel='FCC'>"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create violin plot for 'Prior' FCCs to compare results from original wu2004.ipynb vs. one with an updated cobra model & elasticity matrices\n",
+    "import seaborn as sns\n",
+    "data_prior = pd.DataFrame(fcc_prior[:, 0], columns=[r.id for r in model.reactions]\n",
+    "                  ).stack().reset_index(level=1)\n",
+    "data_prior['type'] = 'New'\n",
+    "data_prior.columns = ['Reaction', 'FCC', 'Type']\n",
+    "data_prior = data_prior.reset_index()\n",
+    "\n",
+    "# Filter df to only keep FCCs of reactions of interest\n",
+    "keep_rxns = ['PGM', 'ENO', 'PK']\n",
+    "plot_df = data_prior.iloc[[i for i in data_prior.index if data_prior.Reaction[i] in ['PGM', 'ENO', 'PK']]]\n",
+    "display(plot_df)\n",
+    "\n",
+    "# Load original cobra model prior FCC samples\n",
+    "orig_data_prior = pd.read_csv(\"../tests/test_data/expected_wu2004_FCCpriors.csv\", index_col=0)\n",
+    "orig_data_prior = orig_data_prior.T.stack().reset_index(level=1)\n",
+    "orig_data_prior['type'] = 'Old'\n",
+    "orig_data_prior.columns = ['Reaction', 'FCC', 'Type']\n",
+    "\n",
+    "plot_df = pd.concat([plot_df, orig_data_prior]).reset_index()\n",
+    "display(plot_df)\n",
+    "\n",
+    "this_pal = my_pal = {\"New\": \"r\", \"Old\": \"b\"}\n",
+    "\n",
+    "sns.violinplot(\n",
+    "    x='Reaction', y='FCC', hue='Type', data=plot_df,\n",
+    "    density_norm='width', width=0.5, palette=this_pal, saturation=1.\n",
+    ")\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, continue with posterior predictive"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -1587,7 +2216,7 @@
       "\u001b[0;31mValueError\u001b[0m: ",
       "\nDuring handling of the above exception, another exception occurred:\n",
       "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[94], line 8\u001b[0m\n\u001b[1;32m      5\u001b[0m x_err \u001b[38;5;241m=\u001b[39m pm\u001b[38;5;241m.\u001b[39mHalfNormal(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mx_error\u001b[39m\u001b[38;5;124m'\u001b[39m, sigma\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.05\u001b[39m, initval\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m.1\u001b[39m)\n\u001b[1;32m      7\u001b[0m \u001b[38;5;66;03m# Calculate steady-state concentrations and fluxes from elasticities\u001b[39;00m\n\u001b[0;32m----> 8\u001b[0m chi_ss, v_hat_ss \u001b[38;5;241m=\u001b[39m \u001b[43mll\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msteady_state_pytensor\u001b[49m\u001b[43m(\u001b[49m\u001b[43mEx_t\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mEy_t\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43men\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43myn\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     10\u001b[0m \u001b[38;5;66;03m# Error distributions for observed steady-state concentrations and fluxes\u001b[39;00m\n\u001b[1;32m     11\u001b[0m chi_obs \u001b[38;5;241m=\u001b[39m pm\u001b[38;5;241m.\u001b[39mNormal(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mchi_obs\u001b[39m\u001b[38;5;124m'\u001b[39m, mu\u001b[38;5;241m=\u001b[39mchi_ss, sigma\u001b[38;5;241m=\u001b[39mx_err, observed\u001b[38;5;241m=\u001b[39mxn)\n",
+      "Cell \u001b[0;32mIn[17], line 8\u001b[0m\n\u001b[1;32m      5\u001b[0m x_err \u001b[38;5;241m=\u001b[39m pm\u001b[38;5;241m.\u001b[39mHalfNormal(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mx_error\u001b[39m\u001b[38;5;124m'\u001b[39m, sigma\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.05\u001b[39m, initval\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m.1\u001b[39m)\n\u001b[1;32m      7\u001b[0m \u001b[38;5;66;03m# Calculate steady-state concentrations and fluxes from elasticities\u001b[39;00m\n\u001b[0;32m----> 8\u001b[0m chi_ss, v_hat_ss \u001b[38;5;241m=\u001b[39m \u001b[43mll\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msteady_state_pytensor\u001b[49m\u001b[43m(\u001b[49m\u001b[43mEx_t\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mEy_t\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43men\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43myn\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     10\u001b[0m \u001b[38;5;66;03m# Error distributions for observed steady-state concentrations and fluxes\u001b[39;00m\n\u001b[1;32m     11\u001b[0m chi_obs \u001b[38;5;241m=\u001b[39m pm\u001b[38;5;241m.\u001b[39mNormal(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mchi_obs\u001b[39m\u001b[38;5;124m'\u001b[39m, mu\u001b[38;5;241m=\u001b[39mchi_ss, sigma\u001b[38;5;241m=\u001b[39mx_err, observed\u001b[38;5;241m=\u001b[39mxn)\n",
       "File \u001b[0;32m~/Repos/emll/src/emll/linlog_model.py:146\u001b[0m, in \u001b[0;36mLinLogBase.steady_state_pytensor\u001b[0;34m(self, Ex, Ey, en, yn, method)\u001b[0m\n\u001b[1;32m    143\u001b[0m en \u001b[38;5;241m=\u001b[39m at\u001b[38;5;241m.\u001b[39mas_tensor_variable(en)\n\u001b[1;32m    144\u001b[0m yn \u001b[38;5;241m=\u001b[39m at\u001b[38;5;241m.\u001b[39mas_tensor_variable(yn)\n\u001b[0;32m--> 146\u001b[0m e_diag \u001b[38;5;241m=\u001b[39m \u001b[43men\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdimshuffle\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m0\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;124;43m\"\u001b[39;49m\u001b[38;5;124;43mx\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdiag\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mv_star\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    147\u001b[0m N_rep \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mNr\u001b[38;5;241m.\u001b[39mreshape((\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mNr\u001b[38;5;241m.\u001b[39mshape))\u001b[38;5;241m.\u001b[39mrepeat(n_exp, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m)\n\u001b[1;32m    148\u001b[0m N_hat \u001b[38;5;241m=\u001b[39m at\u001b[38;5;241m.\u001b[39mbatched_dot(N_rep, e_diag)\n",
       "File \u001b[0;32m~/.pyenv/versions/3.11.5/envs/bmca/lib/python3.11/site-packages/pytensor/tensor/variable.py:133\u001b[0m, in \u001b[0;36m_tensor_py_operators.__mul__\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m    129\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__mul__\u001b[39m(\u001b[38;5;28mself\u001b[39m, other):\n\u001b[1;32m    130\u001b[0m     \u001b[38;5;66;03m# See explanation in __add__ for the error caught\u001b[39;00m\n\u001b[1;32m    131\u001b[0m     \u001b[38;5;66;03m# and the return value in that case\u001b[39;00m\n\u001b[1;32m    132\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 133\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mpt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmath\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmul\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mother\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    134\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mNotImplementedError\u001b[39;00m, \u001b[38;5;167;01mTypeError\u001b[39;00m):\n\u001b[1;32m    135\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mNotImplemented\u001b[39m\n",
       "File \u001b[0;32m~/.pyenv/versions/3.11.5/envs/bmca/lib/python3.11/site-packages/pytensor/graph/op.py:295\u001b[0m, in \u001b[0;36mOp.__call__\u001b[0;34m(self, *inputs, **kwargs)\u001b[0m\n\u001b[1;32m    253\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"Construct an `Apply` node using :meth:`Op.make_node` and return its outputs.\u001b[39;00m\n\u001b[1;32m    254\u001b[0m \n\u001b[1;32m    255\u001b[0m \u001b[38;5;124;03mThis method is just a wrapper around :meth:`Op.make_node`.\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    292\u001b[0m \n\u001b[1;32m    293\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    294\u001b[0m return_list \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mpop(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mreturn_list\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[0;32m--> 295\u001b[0m node \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmake_node\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\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    297\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m config\u001b[38;5;241m.\u001b[39mcompute_test_value \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124moff\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m    298\u001b[0m     compute_test_value(node)\n",
@@ -3181,7 +3810,7 @@
    "source": [
     "fcc = np.array([ll.flux_control_coefficient(Ex=ex) for ex in trace.posterior['Ex'][0].to_numpy()])\n",
     "fcc_mb = np.array([ll.flux_control_coefficient(Ex=ex) for ex in trace_vi.posterior['Ex'][0].to_numpy()])\n",
-    "fcc_prior = np.array([ll.flux_control_coefficient(Ex=ex) for ex in trace_prior.prior['Ex'][0].to_numpy()])"
+    "# fcc_prior = np.array([ll.flux_control_coefficient(Ex=ex) for ex in trace_prior.prior['Ex'][0].to_numpy()])"
    ]
   },
   {
diff --git a/src/emll/pymc_utils.py b/src/emll/pymc_utils.py
index 8fa46a9..34e8888 100644
--- a/src/emll/pymc_utils.py
+++ b/src/emll/pymc_utils.py
@@ -52,9 +52,9 @@ def sample_prior_fccs(cobra_model):
     # Get stoichiometric matrix
     N = create_stoichiometric_matrix(cobra_model, dtype='int')
 
-    # Creaet linlog model
+    # Create linlog model
     ll = linlog_model.LinLogLeastNorm(N, Ex, Ey, v_star, driver='gelsy')
 
     fcc_prior = np.array([ll.flux_control_coefficient(Ex=ex) for ex in trace_prior.prior['Ex'][0].to_numpy()])
 
-    return fcc_prior
+    return fcc_prior
\ No newline at end of file
diff --git a/tests/test_data/expected_wu2004_FCCpriors_updated.csv b/tests/test_data/expected_wu2004_FCCpriors_updated.csv
new file mode 100644
index 0000000..6df4025
--- /dev/null
+++ b/tests/test_data/expected_wu2004_FCCpriors_updated.csv
@@ -0,0 +1,7 @@
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diff --git a/tests/test_priorpredictive.py b/tests/test_priorpredictive.py
index 6004227..47e7ba6 100644
--- a/tests/test_priorpredictive.py
+++ b/tests/test_priorpredictive.py
@@ -9,7 +9,7 @@
 @pytest.fixture(scope="module")
 def wu2004_bmca_obj():
     wu2004_paths = {
-        "model_path": "tests/test_models/wu2004_model.sbml",
+        "model_path": "tests/test_models/wu2004_model.json",
         "reference_state": None,
     }
     bmca_obj = emll.bmca.BMCA(
@@ -19,7 +19,10 @@ def wu2004_bmca_obj():
     return bmca_obj
 
 
-# def test_elasticity_matrix(wu2004_bmca_obj):
+# def test_build_unconditional_pymc_model(wu2004_bmca_obj):
+
+#def test_sample_prior_predictive(pymc_model)
+
 
 
 def test_priorpredictive(wu2004_bmca_obj):