From 8626cb05258f1b25624133f164eff0e58290434a Mon Sep 17 00:00:00 2001
From: Florian <33749653+flo-schu@users.noreply.github.com>
Date: Fri, 21 Feb 2025 09:48:41 +0100
Subject: [PATCH 01/16] Update LICENSE

Add full name
---
 LICENSE | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/LICENSE b/LICENSE
index f39dc41df..4a26ffa19 100644
--- a/LICENSE
+++ b/LICENSE
@@ -1,6 +1,6 @@
 MIT License
 
-Copyright (c) 2023 Florian
+Copyright (c) 2023 Florian Schunck
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal

From fa06e31e841ba593ac41b1684afb7134a0cea813 Mon Sep 17 00:00:00 2001
From: mariegrho <mgrholthaus@gmail.com>
Date: Wed, 7 May 2025 18:14:32 +0200
Subject: [PATCH 02/16] first draft of the superquickstart

---
 docs/source/user_guide/superquickstart.ipynb | 1442 ++++++++++++++++++
 1 file changed, 1442 insertions(+)
 create mode 100644 docs/source/user_guide/superquickstart.ipynb

diff --git a/docs/source/user_guide/superquickstart.ipynb b/docs/source/user_guide/superquickstart.ipynb
new file mode 100644
index 000000000..b37480b68
--- /dev/null
+++ b/docs/source/user_guide/superquickstart.ipynb
@@ -0,0 +1,1442 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Pymob quickstart"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This super-quick quickstart gives an introduction to the basic Pymob workflow and key functionalities.  \n",
+    "For this, we will investigate a simple linear regression model, which we want to fit to a noisy dataset.  \n",
+    "Pymob supports our modeling process by providing several tools for *structuring our data*, for the *parameter estimation* and *visualization of the results*.  \n",
+    "  \n",
+    "Before starting the modeling process, we let's have a look at the main steps and modules of pymob:\n",
+    "\n",
+    "1. __Simulation:__   \n",
+    "First, we need to initialize a Simulation object by calling the {class}`pymob.simulation.SimulationBase` class from the simulation module.   \n",
+    "Optionally, we can configure the simulation with `sim.config.case_study.name = \"linear-regression\"`, `sim.config.case_study.scenario = \"test\"` and many more options. \n",
+    "\n",
+    "2. __Model:__   \n",
+    "Our model will be defined as a python function.  \n",
+    "We will then assign it to our Simulation object by `.model` \n",
+    "\n",
+    "3. __Observations:__   \n",
+    "Our observation data needs to be structured as a [xarray.Dataset](https://docs.xarray.dev/en/stable/generated/xarray.Dataset.html).  \n",
+    "We assign it to our Simulation object by  `.observations.`  \n",
+    "`sim.config.data_structure` will give us some information about the layout of our data.\n",
+    "\n",
+    "4. __Solver:__  \n",
+    "Solvers are needed to solve the model. \n",
+    "In our simple case, we will use the solver \"solve_analytic_1d\" from the \"pymob.solver.analytic module.\n",
+    "We  assign it to our Simulation object by `.solver` \n",
+    "For more complex models, the JaxSolver from the diffrax module is a more powerful option.  \n",
+    "User can also implement their own solver as a subclass of `pymob.solver.SolverBase`. \n",
+    "  \n",
+    "5. __Inferer:__  \n",
+    "The inferer serves as the parameter estimator.  \n",
+    "Pymob provided [various backends](https://pymob.readthedocs.io/en/stable/user_guide/framework_overview.html). In our example, we will work with *numpyro*.  \n",
+    "We assign the inferer to our Simulation object by `.inferer` and configurate the kernel we want to use (here *nuts*).  \n",
+    "But before, we need to parameterize our model using the *Param* class. The parameters can be marked as free or fixed, depending on whether they should be variable during an optimization procedure.  \n",
+    "We assign the parameters to our Simulation object by `sim.model_parameters`. This is a dictionary that holds the model input data. The keys it takes by default are `parameters`, `y0` and `x_in`. \n",
+    "\n",
+    "7. __Evaluator:__  \n",
+    "The Evaluator is an instance to evaluate a model. \n",
+    "\n",
+    "6. __Config:__  \n",
+    "Our settings will be saved in a configuration file `.cfg`.  \n",
+    "The config file contains information about our simulation in different sections. -> Learn more [here](https://pymob.readthedocs.io/en/stable/user_guide/case_studies.html#configuration).\n",
+    "We can further use it to create new Simulations by loading the settings from a config file. \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![framework-overview](.\\figures\\pymob_overview.png)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# First, import the necessary python packages\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import xarray as xr\n",
+    "\n",
+    "# Import the pymob modules\n",
+    "from pymob.simulation import SimulationBase\n",
+    "from pymob.sim.solvetools import solve_analytic_1d\n",
+    "from pymob.sim.config import Param"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Since no measured data is provided, we will generate an artificial dataset.  \n",
+    "$y_{obs}$ represents the observation data over the time $t$ [0, 10].  \n",
+    "In order to use the data later, we need to convert it into a xarray-Dataset.  \n",
+    "In your application later, you would use your measuered experimental data.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
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+       "  * t        (t) float64 0.0 0.101 0.202 0.303 0.404 ... 9.697 9.798 9.899 10.0\n",
+       "Data variables:\n",
+       "    y        (t) float64 2.22 -0.3948 1.384 0.7673 ... 32.87 35.55 32.99 33.12</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-17c72c86-d721-47b5-b133-7508a065bda8' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-17c72c86-d721-47b5-b133-7508a065bda8' 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'>t</span>: 100</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-6554160b-17ea-4774-b555-345e2ca22509' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6554160b-17ea-4774-b555-345e2ca22509' class='xr-section-summary' >Coordinates: <span>(1)</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'>t</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.101 0.202 ... 9.899 10.0</div><input id='attrs-27ff8595-7a94-401c-a97e-a8c9b1c00224' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-27ff8595-7a94-401c-a97e-a8c9b1c00224' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4bb6fc91-035e-4502-bcba-ea3f547731f3' class='xr-var-data-in' type='checkbox'><label for='data-4bb6fc91-035e-4502-bcba-ea3f547731f3' 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.      ,  0.10101 ,  0.20202 ,  0.30303 ,  0.40404 ,  0.505051,\n",
+       "        0.606061,  0.707071,  0.808081,  0.909091,  1.010101,  1.111111,\n",
+       "        1.212121,  1.313131,  1.414141,  1.515152,  1.616162,  1.717172,\n",
+       "        1.818182,  1.919192,  2.020202,  2.121212,  2.222222,  2.323232,\n",
+       "        2.424242,  2.525253,  2.626263,  2.727273,  2.828283,  2.929293,\n",
+       "        3.030303,  3.131313,  3.232323,  3.333333,  3.434343,  3.535354,\n",
+       "        3.636364,  3.737374,  3.838384,  3.939394,  4.040404,  4.141414,\n",
+       "        4.242424,  4.343434,  4.444444,  4.545455,  4.646465,  4.747475,\n",
+       "        4.848485,  4.949495,  5.050505,  5.151515,  5.252525,  5.353535,\n",
+       "        5.454545,  5.555556,  5.656566,  5.757576,  5.858586,  5.959596,\n",
+       "        6.060606,  6.161616,  6.262626,  6.363636,  6.464646,  6.565657,\n",
+       "        6.666667,  6.767677,  6.868687,  6.969697,  7.070707,  7.171717,\n",
+       "        7.272727,  7.373737,  7.474747,  7.575758,  7.676768,  7.777778,\n",
+       "        7.878788,  7.979798,  8.080808,  8.181818,  8.282828,  8.383838,\n",
+       "        8.484848,  8.585859,  8.686869,  8.787879,  8.888889,  8.989899,\n",
+       "        9.090909,  9.191919,  9.292929,  9.393939,  9.494949,  9.59596 ,\n",
+       "        9.69697 ,  9.79798 ,  9.89899 , 10.      ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-32aa05c4-933c-4efb-aa55-afaa9525bc4d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-32aa05c4-933c-4efb-aa55-afaa9525bc4d' class='xr-section-summary' >Data variables: <span>(1)</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>y</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.22 -0.3948 1.384 ... 32.99 33.12</div><input id='attrs-eb9df45f-e589-49e0-85a2-e5018bc93c83' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-eb9df45f-e589-49e0-85a2-e5018bc93c83' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e718f40f-0803-49fa-bd52-df90ac338187' class='xr-var-data-in' type='checkbox'><label for='data-e718f40f-0803-49fa-bd52-df90ac338187' 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([ 2.21982221, -0.39481712,  1.38373391,  0.76734977,  3.53128984,\n",
+       "        2.83480537,  1.56855402,  3.98072621,  5.95755583,  5.52551877,\n",
+       "        2.23964013,  3.10663967,  5.68419342,  1.14802872,  7.64059029,\n",
+       "        7.81415541,  7.73834928,  7.40872897,  8.33693323,  9.10099101,\n",
+       "        9.19634601, 10.3405425 , 11.24527459,  9.40017356, 10.56387531,\n",
+       "        8.80908111,  9.16921146,  8.58392666,  9.97546341,  9.74083896,\n",
+       "       11.95826555, 13.93410308, 12.19266982,  9.62461685, 14.72994292,\n",
+       "       10.54623444, 12.63065813, 13.25493083, 14.07108398, 12.25928216,\n",
+       "       15.78096936, 12.627227  , 14.11730366, 14.95651657, 14.51064769,\n",
+       "       17.48640518, 17.0615146 , 14.11524981, 14.86710255, 18.71381567,\n",
+       "       17.51924538, 23.58892978, 19.90362788, 19.45425992, 17.09233205,\n",
+       "       20.59558364, 21.58057381, 18.49053291, 20.01779783, 21.07720316,\n",
+       "       20.8838165 , 21.1425492 , 21.21008105, 24.45880636, 20.44022179,\n",
+       "       25.68197026, 23.10267878, 25.24313608, 22.02298164, 22.24198888,\n",
+       "       22.89092846, 21.94086175, 23.82941087, 26.45258291, 24.83685328,\n",
+       "       29.37746348, 27.24916928, 25.83365091, 26.60719058, 28.44142883,\n",
+       "       28.06419799, 30.02134903, 28.37587939, 27.00740759, 29.28454614,\n",
+       "       27.74011134, 28.74545372, 29.4329712 , 28.3208646 , 33.99737959,\n",
+       "       27.74735084, 30.78183787, 31.86772239, 33.16865905, 33.14755493,\n",
+       "       31.62916884, 32.86912176, 35.55412696, 32.9939107 , 33.12241191])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d26f26d2-36bc-40d6-b65f-7458dd620d0d' class='xr-section-summary-in' type='checkbox'  ><label for='section-d26f26d2-36bc-40d6-b65f-7458dd620d0d' class='xr-section-summary' >Indexes: <span>(1)</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>t</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f7b4bde7-2a92-4667-bc0a-e3532af01bd2' class='xr-index-data-in' type='checkbox'/><label for='index-f7b4bde7-2a92-4667-bc0a-e3532af01bd2' 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([                0.0, 0.10101010101010101, 0.20202020202020202,\n",
+       "       0.30303030303030304, 0.40404040404040403,  0.5050505050505051,\n",
+       "        0.6060606060606061,  0.7070707070707071,  0.8080808080808081,\n",
+       "        0.9090909090909091,  1.0101010101010102,  1.1111111111111112,\n",
+       "        1.2121212121212122,  1.3131313131313131,  1.4141414141414141,\n",
+       "        1.5151515151515151,  1.6161616161616161,  1.7171717171717171,\n",
+       "        1.8181818181818181,  1.9191919191919191,  2.0202020202020203,\n",
+       "         2.121212121212121,  2.2222222222222223,   2.323232323232323,\n",
+       "        2.4242424242424243,   2.525252525252525,  2.6262626262626263,\n",
+       "         2.727272727272727,  2.8282828282828283,   2.929292929292929,\n",
+       "        3.0303030303030303,   3.131313131313131,  3.2323232323232323,\n",
+       "        3.3333333333333335,  3.4343434343434343,  3.5353535353535355,\n",
+       "        3.6363636363636362,  3.7373737373737375,  3.8383838383838382,\n",
+       "        3.9393939393939394,   4.040404040404041,   4.141414141414141,\n",
+       "         4.242424242424242,   4.343434343434343,   4.444444444444445,\n",
+       "         4.545454545454545,   4.646464646464646,   4.747474747474747,\n",
+       "         4.848484848484849,    4.94949494949495,    5.05050505050505,\n",
+       "         5.151515151515151,   5.252525252525253,   5.353535353535354,\n",
+       "         5.454545454545454,   5.555555555555555,   5.656565656565657,\n",
+       "         5.757575757575758,   5.858585858585858,   5.959595959595959,\n",
+       "        6.0606060606060606,   6.161616161616162,   6.262626262626262,\n",
+       "         6.363636363636363,  6.4646464646464645,   6.565656565656566,\n",
+       "         6.666666666666667,   6.767676767676767,  6.8686868686868685,\n",
+       "          6.96969696969697,   7.070707070707071,   7.171717171717171,\n",
+       "        7.2727272727272725,   7.373737373737374,   7.474747474747475,\n",
+       "         7.575757575757575,  7.6767676767676765,   7.777777777777778,\n",
+       "         7.878787878787879,   7.979797979797979,   8.080808080808081,\n",
+       "         8.181818181818182,   8.282828282828282,   8.383838383838384,\n",
+       "         8.484848484848484,   8.585858585858587,   8.686868686868687,\n",
+       "         8.787878787878787,    8.88888888888889,    8.98989898989899,\n",
+       "          9.09090909090909,   9.191919191919192,   9.292929292929292,\n",
+       "         9.393939393939394,   9.494949494949495,   9.595959595959595,\n",
+       "         9.696969696969697,   9.797979797979798,     9.8989898989899,\n",
+       "                      10.0],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;t&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-74439760-c4be-4922-923b-30f332d32610' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-74439760-c4be-4922-923b-30f332d32610' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset>\n",
+       "Dimensions:  (t: 100)\n",
+       "Coordinates:\n",
+       "  * t        (t) float64 0.0 0.101 0.202 0.303 0.404 ... 9.697 9.798 9.899 10.0\n",
+       "Data variables:\n",
+       "    y        (t) float64 2.22 -0.3948 1.384 0.7673 ... 32.87 35.55 32.99 33.12"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 500x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Parameter for the artificial data generation\n",
+    "slope = np.random.uniform(2.0, 4.0) \n",
+    "intercept = 1.0\n",
+    "num_points = 100\n",
+    "noise_level = 1.7\n",
+    "\n",
+    "# generating time values\n",
+    "t = np.linspace(0, 10, num_points)\n",
+    "\n",
+    "# generating y-values with noise\n",
+    "noise = np.random.normal(0, noise_level, num_points)\n",
+    "y_obs = slope * t + intercept + noise\n",
+    "\n",
+    "# visualizing our data\n",
+    "fig, ax = plt.subplots(figsize=(5, 4))\n",
+    "ax.scatter(t, y_obs, label='Datapoints')\n",
+    "ax.set(xlabel='t [-]', ylabel='y_obs [-]', title ='Artificial Data')\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "# convert the data to an xr-Dataset\n",
+    "data_obs = xr.DataArray(y_obs, coords={\"t\": t}).to_dataset(name=\"y\")\n",
+    "data_obs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "\n",
+    "## Initialize a simulation\n",
+    "\n",
+    "In pymob a Simulation object is initialized by calling the {class}`pymob.simulation.SimulationBase` class from the simulation module.  \n",
+    "We will chose a linear regression model, since it seems to be a good approximation to the data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```{admonition} x-dimension\n",
+    ":class: note\n",
+    "The x_dimension of our simulation can have any name, for expample t as often used for time series data.\n",
+    "You can specified it via `sim.config.simulation.x_dimension`.\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MinMaxScaler(variable=y, min=-0.39481712290701676, max=35.554126963859574)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\mgrho\\pymob\\pymob\\simulation.py:303: UserWarning: `sim.config.data_structure.y = Datavariable(dimensions=['t'] min=-0.39481712290701676 max=35.554126963859574 observed=True dimensions_evaluator=None)` has been assumed from `sim.observations`. If the order of the dimensions should be different, specify `sim.config.data_structure.y = DataVariable(dimensions=[...], ...)` manually.\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Initialize the Simulation object\n",
+    "sim = SimulationBase()\n",
+    "\n",
+    "# Define the linear regression model\n",
+    "def linreg(x, a, b):\n",
+    "    return a + x * b\n",
+    "\n",
+    "# Add the model to the simulation\n",
+    "sim.model = linreg\n",
+    "\n",
+    "# Adding our dataset to the simulation\n",
+    "sim.observations = data_obs\n",
+    "\n",
+    "# Defining a solver\n",
+    "sim.solver = solve_analytic_1d"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```{admonition} Scalers\n",
+    ":class: note\n",
+    "We notice a mysterious Scaler message. This tells us that our data variable has been identified and a scaler was constructed, which transforms the variable between [0, 1].   \n",
+    "This has no effect at the moment, but it can be used later. Scaling can be powerful to help parameter estimation in more complex models.\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "## Running the model 🏃\n",
+    "\n",
+    "Next, we define the model parameters *a* and *b*.  \n",
+    "The parameter *a* is set as fixed (`free = False`), meaning its value is known and will not be estimated during optimization.  \n",
+    "The parameter *b* is marked as free (`free = True`), allowing it to be optimized to fit our data. As an initial guess, we assume b = 3.   \n",
+    "\n",
+    "Our model is now prepared with a parameter set.  \n",
+    "In order to intialize the *Evaluator* class, we need to execute `sim.dispatch_constructor()`.   \n",
+    "This step is very important and needs to be done everytime when we made changes in our model.  \n",
+    "\n",
+    "The returned dataset (`evaluator.results`) has the exact same shape as our observation data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\mgrho\\pymob\\pymob\\simulation.py:552: UserWarning: The number of ODE states was not specified in the config file [simulation] > 'n_ode_states = <n>'. Extracted the return arguments ['a+x*b'] from the source code. Setting 'n_ode_states=1.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
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+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
+       "Dimensions:  (t: 100)\n",
+       "Coordinates:\n",
+       "  * t        (t) float64 0.0 0.101 0.202 0.303 0.404 ... 9.697 9.798 9.899 10.0\n",
+       "Data variables:\n",
+       "    y        (t) float64 1.0 1.303 1.606 1.909 2.212 ... 30.09 30.39 30.7 31.0</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-482e4360-65c5-455a-844a-801c2a7d65ff' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-482e4360-65c5-455a-844a-801c2a7d65ff' 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'>t</span>: 100</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-23abf1fe-49d0-4b6f-b23d-312f68845896' class='xr-section-summary-in' type='checkbox'  checked><label for='section-23abf1fe-49d0-4b6f-b23d-312f68845896' class='xr-section-summary' >Coordinates: <span>(1)</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'>t</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.101 0.202 ... 9.899 10.0</div><input id='attrs-5bf72154-86cc-4516-9327-466025d82bad' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5bf72154-86cc-4516-9327-466025d82bad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bed3c049-c054-40c3-adb7-6d201f215605' class='xr-var-data-in' type='checkbox'><label for='data-bed3c049-c054-40c3-adb7-6d201f215605' 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.      ,  0.10101 ,  0.20202 ,  0.30303 ,  0.40404 ,  0.505051,\n",
+       "        0.606061,  0.707071,  0.808081,  0.909091,  1.010101,  1.111111,\n",
+       "        1.212121,  1.313131,  1.414141,  1.515152,  1.616162,  1.717172,\n",
+       "        1.818182,  1.919192,  2.020202,  2.121212,  2.222222,  2.323232,\n",
+       "        2.424242,  2.525253,  2.626263,  2.727273,  2.828283,  2.929293,\n",
+       "        3.030303,  3.131313,  3.232323,  3.333333,  3.434343,  3.535354,\n",
+       "        3.636364,  3.737374,  3.838384,  3.939394,  4.040404,  4.141414,\n",
+       "        4.242424,  4.343434,  4.444444,  4.545455,  4.646465,  4.747475,\n",
+       "        4.848485,  4.949495,  5.050505,  5.151515,  5.252525,  5.353535,\n",
+       "        5.454545,  5.555556,  5.656566,  5.757576,  5.858586,  5.959596,\n",
+       "        6.060606,  6.161616,  6.262626,  6.363636,  6.464646,  6.565657,\n",
+       "        6.666667,  6.767677,  6.868687,  6.969697,  7.070707,  7.171717,\n",
+       "        7.272727,  7.373737,  7.474747,  7.575758,  7.676768,  7.777778,\n",
+       "        7.878788,  7.979798,  8.080808,  8.181818,  8.282828,  8.383838,\n",
+       "        8.484848,  8.585859,  8.686869,  8.787879,  8.888889,  8.989899,\n",
+       "        9.090909,  9.191919,  9.292929,  9.393939,  9.494949,  9.59596 ,\n",
+       "        9.69697 ,  9.79798 ,  9.89899 , 10.      ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b2eef1e6-5191-4a06-a692-78267f88e56b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b2eef1e6-5191-4a06-a692-78267f88e56b' class='xr-section-summary' >Data variables: <span>(1)</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>y</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.0 1.303 1.606 ... 30.39 30.7 31.0</div><input id='attrs-9ca67db2-09ec-4230-97b8-f3758d48a767' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9ca67db2-09ec-4230-97b8-f3758d48a767' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c272703a-e29f-4edb-83cb-ff5d5451a14f' class='xr-var-data-in' type='checkbox'><label for='data-c272703a-e29f-4edb-83cb-ff5d5451a14f' 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([ 1.        ,  1.3030303 ,  1.60606061,  1.90909091,  2.21212121,\n",
+       "        2.51515152,  2.81818182,  3.12121212,  3.42424242,  3.72727273,\n",
+       "        4.03030303,  4.33333333,  4.63636364,  4.93939394,  5.24242424,\n",
+       "        5.54545455,  5.84848485,  6.15151515,  6.45454545,  6.75757576,\n",
+       "        7.06060606,  7.36363636,  7.66666667,  7.96969697,  8.27272727,\n",
+       "        8.57575758,  8.87878788,  9.18181818,  9.48484848,  9.78787879,\n",
+       "       10.09090909, 10.39393939, 10.6969697 , 11.        , 11.3030303 ,\n",
+       "       11.60606061, 11.90909091, 12.21212121, 12.51515152, 12.81818182,\n",
+       "       13.12121212, 13.42424242, 13.72727273, 14.03030303, 14.33333333,\n",
+       "       14.63636364, 14.93939394, 15.24242424, 15.54545455, 15.84848485,\n",
+       "       16.15151515, 16.45454545, 16.75757576, 17.06060606, 17.36363636,\n",
+       "       17.66666667, 17.96969697, 18.27272727, 18.57575758, 18.87878788,\n",
+       "       19.18181818, 19.48484848, 19.78787879, 20.09090909, 20.39393939,\n",
+       "       20.6969697 , 21.        , 21.3030303 , 21.60606061, 21.90909091,\n",
+       "       22.21212121, 22.51515152, 22.81818182, 23.12121212, 23.42424242,\n",
+       "       23.72727273, 24.03030303, 24.33333333, 24.63636364, 24.93939394,\n",
+       "       25.24242424, 25.54545455, 25.84848485, 26.15151515, 26.45454545,\n",
+       "       26.75757576, 27.06060606, 27.36363636, 27.66666667, 27.96969697,\n",
+       "       28.27272727, 28.57575758, 28.87878788, 29.18181818, 29.48484848,\n",
+       "       29.78787879, 30.09090909, 30.39393939, 30.6969697 , 31.        ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-97883329-2daa-484d-a4a1-1c83b93ea06f' class='xr-section-summary-in' type='checkbox'  ><label for='section-97883329-2daa-484d-a4a1-1c83b93ea06f' class='xr-section-summary' >Indexes: <span>(1)</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>t</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e038dafd-9629-4d24-bef2-24e2ae414a80' class='xr-index-data-in' type='checkbox'/><label for='index-e038dafd-9629-4d24-bef2-24e2ae414a80' 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([                0.0, 0.10101010101010101, 0.20202020202020202,\n",
+       "       0.30303030303030304, 0.40404040404040403,  0.5050505050505051,\n",
+       "        0.6060606060606061,  0.7070707070707071,  0.8080808080808081,\n",
+       "        0.9090909090909091,  1.0101010101010102,  1.1111111111111112,\n",
+       "        1.2121212121212122,  1.3131313131313131,  1.4141414141414141,\n",
+       "        1.5151515151515151,  1.6161616161616161,  1.7171717171717171,\n",
+       "        1.8181818181818181,  1.9191919191919191,  2.0202020202020203,\n",
+       "         2.121212121212121,  2.2222222222222223,   2.323232323232323,\n",
+       "        2.4242424242424243,   2.525252525252525,  2.6262626262626263,\n",
+       "         2.727272727272727,  2.8282828282828283,   2.929292929292929,\n",
+       "        3.0303030303030303,   3.131313131313131,  3.2323232323232323,\n",
+       "        3.3333333333333335,  3.4343434343434343,  3.5353535353535355,\n",
+       "        3.6363636363636362,  3.7373737373737375,  3.8383838383838382,\n",
+       "        3.9393939393939394,   4.040404040404041,   4.141414141414141,\n",
+       "         4.242424242424242,   4.343434343434343,   4.444444444444445,\n",
+       "         4.545454545454545,   4.646464646464646,   4.747474747474747,\n",
+       "         4.848484848484849,    4.94949494949495,    5.05050505050505,\n",
+       "         5.151515151515151,   5.252525252525253,   5.353535353535354,\n",
+       "         5.454545454545454,   5.555555555555555,   5.656565656565657,\n",
+       "         5.757575757575758,   5.858585858585858,   5.959595959595959,\n",
+       "        6.0606060606060606,   6.161616161616162,   6.262626262626262,\n",
+       "         6.363636363636363,  6.4646464646464645,   6.565656565656566,\n",
+       "         6.666666666666667,   6.767676767676767,  6.8686868686868685,\n",
+       "          6.96969696969697,   7.070707070707071,   7.171717171717171,\n",
+       "        7.2727272727272725,   7.373737373737374,   7.474747474747475,\n",
+       "         7.575757575757575,  7.6767676767676765,   7.777777777777778,\n",
+       "         7.878787878787879,   7.979797979797979,   8.080808080808081,\n",
+       "         8.181818181818182,   8.282828282828282,   8.383838383838384,\n",
+       "         8.484848484848484,   8.585858585858587,   8.686868686868687,\n",
+       "         8.787878787878787,    8.88888888888889,    8.98989898989899,\n",
+       "          9.09090909090909,   9.191919191919192,   9.292929292929292,\n",
+       "         9.393939393939394,   9.494949494949495,   9.595959595959595,\n",
+       "         9.696969696969697,   9.797979797979798,     9.8989898989899,\n",
+       "                      10.0],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;t&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-724365f3-1619-4348-a8dc-f9919d5316bf' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-724365f3-1619-4348-a8dc-f9919d5316bf' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset>\n",
+       "Dimensions:  (t: 100)\n",
+       "Coordinates:\n",
+       "  * t        (t) float64 0.0 0.101 0.202 0.303 0.404 ... 9.697 9.798 9.899 10.0\n",
+       "Data variables:\n",
+       "    y        (t) float64 1.0 1.303 1.606 1.909 2.212 ... 30.09 30.39 30.7 31.0"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Parameterizing the model\n",
+    "sim.config.model_parameters.a = Param(value=1, free=False)\n",
+    "sim.config.model_parameters.b = Param(value=3, free=True)\n",
+    "# this makes sure the model parameters are available to the model.\n",
+    "sim.model_parameters[\"parameters\"] = sim.config.model_parameters.value_dict\n",
+    "\n",
+    "# put everything in place for running the simulation\n",
+    "sim.dispatch_constructor()\n",
+    "\n",
+    "# run\n",
+    "evaluator = sim.dispatch(theta={\"b\":3})\n",
+    "evaluator()\n",
+    "evaluator.results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's have a look at the results.  \n",
+    "You can vary the parameter *b* in the previous step to investigate it's influence on the model fit.   \n",
+    "\n",
+    "In the [beginner guide](), you can try out the *manual parameter estimation*, which is provided by Pymob."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x23e228b6610>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(5, 4))\n",
+    "data_res = evaluator.results\n",
+    "ax.plot(data_obs.t, data_obs.y, ls=\"\", marker=\"o\", color=\"tab:blue\", alpha=.5, label =\"observation data\")\n",
+    "ax.plot(data_res.t, data_res.y, color=\"black\", label =\"result\")\n",
+    "ax.legend()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Estimating parameters \n",
+    "\n",
+    "We are almost set infer the parameters of the model. We add another parameter to also estimate the error of the parameters, We use a lognormal distribution for it. We also specify an error model for the distribution. This will be \n",
+    "\n",
+    "$$y_{obs} \\sim Normal (y, \\sigma_y)$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Jax 64 bit mode: False\n",
+      "Absolute tolerance: 1e-07\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\mgrho\\pymob\\pymob\\inference\\numpyro_backend.py:552: UserWarning: Model is not rendered, because the graphviz executable is not found. Try search for 'graphviz executables not found' and the used OS. This should be an easy fix :-)\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Trace Shapes:      \n",
+      " Param Sites:      \n",
+      "Sample Sites:      \n",
+      "       b dist     |\n",
+      "        value     |\n",
+      " sigma_y dist     |\n",
+      "        value     |\n",
+      "   y_obs dist 100 |\n",
+      "        value 100 |\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "sample: 100%|██████████| 3000/3000 [00:04<00:00, 639.77it/s, 3 steps of size 8.64e-01. acc. prob=0.92] \n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "                mean       std    median      5.0%     95.0%     n_eff     r_hat\n",
+      "         b      3.30      0.03      3.30      3.26      3.35   1785.61      1.00\n",
+      "   sigma_y      1.69      0.12      1.68      1.50      1.90   1189.45      1.00\n",
+      "\n",
+      "Number of divergences: 0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 400x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sim.config.model_parameters.sigma_y = Param(free=True , prior=\"lognorm(scale=1,s=1)\", min=0, max=1)\n",
+    "sim.config.model_parameters.b.prior = \"lognorm(scale=1,s=1)\"\n",
+    "sim.config.model_parameters.b.min = -5\n",
+    "sim.config.model_parameters.b.max = 5\n",
+    "\n",
+    "sim.config.error_model.y = \"normal(loc=y,scale=sigma_y)\"\n",
+    "\n",
+    "\n",
+    "sim.set_inferer(\"numpyro\")\n",
+    "sim.inferer.config.inference_numpyro.kernel = \"nuts\"\n",
+    "sim.inferer.run()\n",
+    "\n",
+    "sim.inferer.idata.posterior\n",
+    "\n",
+    "# Plot the results\n",
+    "sim.config.simulation.x_dimension = \"t\"\n",
+    "sim.posterior_predictive_checks(pred_hdi_style={\"alpha\": 0.1})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Estimating parameters and uncertainty with MCMC\n",
+    "\n",
+    "Of course this example is very simple, we can in fact optimize the parameters perfectly by hand. But just for the fun of it, let's use *Markov Chain Monte Carlo* (MCMC) to estimate the parameters, their uncertainty and the uncertainty in the data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```{admonition} numpyro distributions\n",
+    ":class: warning\n",
+    "Currently only few distributions are implemented in the numpyro backend. This API will soon change, so that basically any distribution can be used to specifcy parameters. \n",
+    "```\n",
+    "\n",
+    "Finally, we let our inferer run the paramter estimation procedure with the numpyro backend and a NUTS kernel. This does the job in a few seconds"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can inspect our estimates and see that the parameters are well esimtated by the model. Note that we only get an estimate for $b$. This is because earlier we set the parameter `a` with the flag `free=False` this effectively excludes it from estimation and uses the default value, which was set to the true value `a=0`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "```{admonition} Customize the posterior predictive checks\n",
+    ":class: hint\n",
+    "You can explore the API of {class}`pymob.sim.plot.SimulationPlot` to find out how you can work on the default predictions. Of course you can always make your own plot, by accessing {attr}`pymob.simulation.inferer.idata` and {attr}`pymob.simulation.observations`\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Report the results\n",
+    "\n",
+    "```{admonition} numpyro distributions\n",
+    ":class: warning\n",
+    "Automated reporting is already implemented in a different branch. This will be soon explained here.\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TODO: Call report when done"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "## Exporting the simulation and running it via the case study API\n",
+    "\n",
+    "After constructing the simulation, all settings of the simulation can be exported to a comprehensive configuration file, along with all the default settings. This is as simple as "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Scenario directory exists at 'c:\\Users\\mgrho\\pymob\\docs\\source\\user_guide\\case_studies\\quickstart\\scenarios\\test'.\n",
+      "Results directory exists at 'c:\\Users\\mgrho\\pymob\\docs\\source\\user_guide\\case_studies\\quickstart\\results\\test'.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "sim.config.case_study.name = \"quickstart\"\n",
+    "sim.config.case_study.scenario = \"test\"\n",
+    "sim.config.create_directory(\"scenario\", force=True)\n",
+    "sim.config.create_directory(\"results\", force=True)\n",
+    "\n",
+    "# usually we expect to have a data directory in the case\n",
+    "os.makedirs(sim.data_path, exist_ok=True)\n",
+    "sim.save_observations(force=True)\n",
+    "sim.config.save(force=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The simulation will be saved to the default path (`CASE_STUDY/scenarios/SCENARIO/settings.cfg`) or to a custom path spcified with the `fp` keyword. `force=True` will overwrite any existing config file, which is the reasonable choice in most cases.\n",
+    "\n",
+    "From there on, the simulation is (almost) ready to be executable from the commandline.\n",
+    "\n",
+    "### Commandline API\n",
+    "\n",
+    "The commandline API runs a series of commands that load the case study, execute the {meth}`pymob.simulation.SimulationBase.initialize` method and perform some more initialization tasks, before running the required job.\n",
+    "\n",
+    "+ `pymob-infer`: Runs an inference job e.g. `pymob-infer --case_study=quickstart --scenario=test --inference_backend=numpyro`. While there are more commandline options, these are the two required "
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "pymob",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From 3249cd0ba1daff041ae15eb86d0b2d38f00ee929 Mon Sep 17 00:00:00 2001
From: mariegrho <mgrholthaus@gmail.com>
Date: Wed, 7 May 2025 18:19:03 +0200
Subject: [PATCH 03/16] Markdown version of the first superquickstart version

---
 docs/source/user_guide/superquickstart.md | 1236 +++++++++++++++++++++
 1 file changed, 1236 insertions(+)
 create mode 100644 docs/source/user_guide/superquickstart.md

diff --git a/docs/source/user_guide/superquickstart.md b/docs/source/user_guide/superquickstart.md
new file mode 100644
index 000000000..72e47c180
--- /dev/null
+++ b/docs/source/user_guide/superquickstart.md
@@ -0,0 +1,1236 @@
+# Pymob quickstart
+
+This super-quick quickstart gives an introduction to the basic Pymob workflow and key functionalities.  
+For this, we will investigate a simple linear regression model, which we want to fit to a noisy dataset.  
+Pymob supports our modeling process by providing several tools for *structuring our data*, for the *parameter estimation* and *visualization of the results*.  
+  
+Before starting the modeling process, we let's have a look at the main steps and modules of pymob:
+
+1. __Simulation:__   
+First, we need to initialize a Simulation object by calling the {class}`pymob.simulation.SimulationBase` class from the simulation module.   
+Optionally, we can configure the simulation with `sim.config.case_study.name = "linear-regression"`, `sim.config.case_study.scenario = "test"` and many more options. 
+
+2. __Model:__   
+Our model will be defined as a python function.  
+We will then assign it to our Simulation object by `.model` 
+
+3. __Observations:__   
+Our observation data needs to be structured as a [xarray.Dataset](https://docs.xarray.dev/en/stable/generated/xarray.Dataset.html).  
+We assign it to our Simulation object by  `.observations.`  
+`sim.config.data_structure` will give us some information about the layout of our data.
+
+4. __Solver:__  
+Solvers are needed to solve the model. 
+In our simple case, we will use the solver "solve_analytic_1d" from the "pymob.solver.analytic module.
+We  assign it to our Simulation object by `.solver` 
+For more complex models, the JaxSolver from the diffrax module is a more powerful option.  
+User can also implement their own solver as a subclass of `pymob.solver.SolverBase`. 
+  
+5. __Inferer:__  
+The inferer serves as the parameter estimator.  
+Pymob provided [various backends](https://pymob.readthedocs.io/en/stable/user_guide/framework_overview.html). In our example, we will work with *numpyro*.  
+We assign the inferer to our Simulation object by `.inferer` and configurate the kernel we want to use (here *nuts*).  
+But before, we need to parameterize our model using the *Param* class. The parameters can be marked as free or fixed, depending on whether they should be variable during an optimization procedure.  
+We assign the parameters to our Simulation object by `sim.model_parameters`. This is a dictionary that holds the model input data. The keys it takes by default are `parameters`, `y0` and `x_in`. 
+
+7. __Evaluator:__  
+The Evaluator is an instance to evaluate a model. 
+
+6. __Config:__  
+Our settings will be saved in a configuration file `.cfg`.  
+The config file contains information about our simulation in different sections. -> Learn more [here](https://pymob.readthedocs.io/en/stable/user_guide/case_studies.html#configuration).
+We can further use it to create new Simulations by loading the settings from a config file. 
+
+
+![framework-overview](.\figures\pymob_overview.png)
+
+
+```python
+# First, import the necessary python packages
+import numpy as np
+import matplotlib.pyplot as plt
+import xarray as xr
+
+# Import the pymob modules
+from pymob.simulation import SimulationBase
+from pymob.sim.solvetools import solve_analytic_1d
+from pymob.sim.config import Param
+```
+
+Since no measured data is provided, we will generate an artificial dataset.  
+$y_{obs}$ represents the observation data over the time $t$ [0, 10].  
+In order to use the data later, we need to convert it into a xarray-Dataset.  
+In your application later, you would use your measuered experimental data.  
+
+
+```python
+# Parameter for the artificial data generation
+slope = np.random.uniform(2.0, 4.0) 
+intercept = 1.0
+num_points = 100
+noise_level = 1.7
+
+# generating time values
+t = np.linspace(0, 10, num_points)
+
+# generating y-values with noise
+noise = np.random.normal(0, noise_level, num_points)
+y_obs = slope * t + intercept + noise
+
+# visualizing our data
+fig, ax = plt.subplots(figsize=(5, 4))
+ax.scatter(t, y_obs, label='Datapoints')
+ax.set(xlabel='t [-]', ylabel='y_obs [-]', title ='Artificial Data')
+plt.tight_layout()
+
+# convert the data to an xr-Dataset
+data_obs = xr.DataArray(y_obs, coords={"t": t}).to_dataset(name="y")
+data_obs
+```
+
+
+
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+  * t        (t) float64 0.0 0.101 0.202 0.303 0.404 ... 9.697 9.798 9.899 10.0
+Data variables:
+    y        (t) float64 -0.6628 2.548 0.904 1.836 ... 41.31 40.07 40.09 38.61</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-91073f8c-b769-4179-8bf5-9115e9f0d656' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-91073f8c-b769-4179-8bf5-9115e9f0d656' 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'>t</span>: 100</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-c5a336db-4db1-485e-913f-870d2575167c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c5a336db-4db1-485e-913f-870d2575167c' class='xr-section-summary' >Coordinates: <span>(1)</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'>t</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.101 0.202 ... 9.899 10.0</div><input id='attrs-419eb748-f363-4360-b3a8-0ae23df5e05b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-419eb748-f363-4360-b3a8-0ae23df5e05b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-305abda1-4dce-4873-a66f-b80bbeac3f22' class='xr-var-data-in' type='checkbox'><label for='data-305abda1-4dce-4873-a66f-b80bbeac3f22' 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.      ,  0.10101 ,  0.20202 ,  0.30303 ,  0.40404 ,  0.505051,
+        0.606061,  0.707071,  0.808081,  0.909091,  1.010101,  1.111111,
+        1.212121,  1.313131,  1.414141,  1.515152,  1.616162,  1.717172,
+        1.818182,  1.919192,  2.020202,  2.121212,  2.222222,  2.323232,
+        2.424242,  2.525253,  2.626263,  2.727273,  2.828283,  2.929293,
+        3.030303,  3.131313,  3.232323,  3.333333,  3.434343,  3.535354,
+        3.636364,  3.737374,  3.838384,  3.939394,  4.040404,  4.141414,
+        4.242424,  4.343434,  4.444444,  4.545455,  4.646465,  4.747475,
+        4.848485,  4.949495,  5.050505,  5.151515,  5.252525,  5.353535,
+        5.454545,  5.555556,  5.656566,  5.757576,  5.858586,  5.959596,
+        6.060606,  6.161616,  6.262626,  6.363636,  6.464646,  6.565657,
+        6.666667,  6.767677,  6.868687,  6.969697,  7.070707,  7.171717,
+        7.272727,  7.373737,  7.474747,  7.575758,  7.676768,  7.777778,
+        7.878788,  7.979798,  8.080808,  8.181818,  8.282828,  8.383838,
+        8.484848,  8.585859,  8.686869,  8.787879,  8.888889,  8.989899,
+        9.090909,  9.191919,  9.292929,  9.393939,  9.494949,  9.59596 ,
+        9.69697 ,  9.79798 ,  9.89899 , 10.      ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8d3d2f0e-e56d-49f6-b82d-32ce26702057' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8d3d2f0e-e56d-49f6-b82d-32ce26702057' class='xr-section-summary' >Data variables: <span>(1)</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>y</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.6628 2.548 0.904 ... 40.09 38.61</div><input id='attrs-34399cae-1b61-4664-82d6-2552d2e39701' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-34399cae-1b61-4664-82d6-2552d2e39701' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-89c5bce0-09f6-4bd5-84b2-6c6d6d88a30f' class='xr-var-data-in' type='checkbox'><label for='data-89c5bce0-09f6-4bd5-84b2-6c6d6d88a30f' 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([-0.66279759,  2.54838195,  0.90402378,  1.8359012 ,  0.5293172 ,
+        1.80744333,  2.59868535,  3.67090295,  2.83376176,  4.07329674,
+        6.76920911,  4.36085823,  5.20194196,  7.38577124,  5.63337025,
+        7.39286231,  9.73184766,  8.00042407,  6.45994362,  7.1827322 ,
+        4.73620223, 10.11247429, 11.1347963 ,  8.0156748 ,  8.72705155,
+        7.82912066, 11.33148295,  8.07588462, 11.14310521, 12.84006373,
+       11.85141345, 12.2609556 , 14.14259846, 12.67848459, 13.24807901,
+       14.88929832, 15.33969195, 18.34985947, 17.05053606, 15.37027947,
+       19.89569387, 17.61449201, 18.2711486 , 16.02393223, 17.35700651,
+       18.03726618, 21.98245202, 16.86950815, 16.88034406, 18.44249038,
+       20.972587  , 18.57095052, 22.12011127, 19.2342568 , 20.85517476,
+       22.02376379, 22.91952443, 22.57665199, 24.86933789, 24.34129814,
+       26.17710891, 23.43682452, 23.60375076, 23.8623379 , 26.26546831,
+       25.44175443, 27.9531178 , 25.575388  , 27.58545487, 28.96124999,
+       27.15385814, 29.14941119, 28.49437023, 28.3901884 , 30.4857327 ,
+       29.18909622, 28.99954318, 31.46041456, 32.08189935, 31.83879134,
+       33.49777158, 33.39883355, 33.33298454, 35.64371523, 33.17544264,
+       33.86539446, 35.12255748, 39.45756359, 33.48485126, 35.75819551,
+       36.24946202, 37.99676006, 39.51851734, 34.66114129, 36.76887495,
+       37.95513145, 41.31069764, 40.07119133, 40.09468708, 38.60892577])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9c5dc49e-feab-4514-9c5c-35be9fdef510' class='xr-section-summary-in' type='checkbox'  ><label for='section-9c5dc49e-feab-4514-9c5c-35be9fdef510' class='xr-section-summary' >Indexes: <span>(1)</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>t</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-74dccaea-7782-4646-b497-9e1af0111db3' class='xr-index-data-in' type='checkbox'/><label for='index-74dccaea-7782-4646-b497-9e1af0111db3' 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([                0.0, 0.10101010101010101, 0.20202020202020202,
+       0.30303030303030304, 0.40404040404040403,  0.5050505050505051,
+        0.6060606060606061,  0.7070707070707071,  0.8080808080808081,
+        0.9090909090909091,  1.0101010101010102,  1.1111111111111112,
+        1.2121212121212122,  1.3131313131313131,  1.4141414141414141,
+        1.5151515151515151,  1.6161616161616161,  1.7171717171717171,
+        1.8181818181818181,  1.9191919191919191,  2.0202020202020203,
+         2.121212121212121,  2.2222222222222223,   2.323232323232323,
+        2.4242424242424243,   2.525252525252525,  2.6262626262626263,
+         2.727272727272727,  2.8282828282828283,   2.929292929292929,
+        3.0303030303030303,   3.131313131313131,  3.2323232323232323,
+        3.3333333333333335,  3.4343434343434343,  3.5353535353535355,
+        3.6363636363636362,  3.7373737373737375,  3.8383838383838382,
+        3.9393939393939394,   4.040404040404041,   4.141414141414141,
+         4.242424242424242,   4.343434343434343,   4.444444444444445,
+         4.545454545454545,   4.646464646464646,   4.747474747474747,
+         4.848484848484849,    4.94949494949495,    5.05050505050505,
+         5.151515151515151,   5.252525252525253,   5.353535353535354,
+         5.454545454545454,   5.555555555555555,   5.656565656565657,
+         5.757575757575758,   5.858585858585858,   5.959595959595959,
+        6.0606060606060606,   6.161616161616162,   6.262626262626262,
+         6.363636363636363,  6.4646464646464645,   6.565656565656566,
+         6.666666666666667,   6.767676767676767,  6.8686868686868685,
+          6.96969696969697,   7.070707070707071,   7.171717171717171,
+        7.2727272727272725,   7.373737373737374,   7.474747474747475,
+         7.575757575757575,  7.6767676767676765,   7.777777777777778,
+         7.878787878787879,   7.979797979797979,   8.080808080808081,
+         8.181818181818182,   8.282828282828282,   8.383838383838384,
+         8.484848484848484,   8.585858585858587,   8.686868686868687,
+         8.787878787878787,    8.88888888888889,    8.98989898989899,
+          9.09090909090909,   9.191919191919192,   9.292929292929292,
+         9.393939393939394,   9.494949494949495,   9.595959595959595,
+         9.696969696969697,   9.797979797979798,     9.8989898989899,
+                      10.0],
+      dtype=&#x27;float64&#x27;, name=&#x27;t&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4871e23a-307c-4643-964c-9b76c43f19f7' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-4871e23a-307c-4643-964c-9b76c43f19f7' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>
+
+
+
+
+    
+![png](superquickstart_files/superquickstart_5_1.png)
+    
+
+
+
+
+## Initialize a simulation
+
+In pymob a Simulation object is initialized by calling the {class}`pymob.simulation.SimulationBase` class from the simulation module.  
+We will chose a linear regression model, since it seems to be a good approximation to the data.
+
+```{admonition} x-dimension
+:class: note
+The x_dimension of our simulation can have any name, for expample t as often used for time series data.
+You can specified it via `sim.config.simulation.x_dimension`.
+```
+
+
+```python
+# Initialize the Simulation object
+sim = SimulationBase()
+
+# Define the linear regression model
+def linreg(x, a, b):
+    return a + x * b
+
+# Add the model to the simulation
+sim.model = linreg
+
+# Adding our dataset to the simulation
+sim.observations = data_obs
+
+# Defining a solver
+sim.solver = solve_analytic_1d
+```
+
+    MinMaxScaler(variable=y, min=-0.6627975885756643, max=41.31069763798674)
+    
+
+    C:\Users\mgrho\pymob\pymob\simulation.py:303: UserWarning: `sim.config.data_structure.y = Datavariable(dimensions=['t'] min=-0.6627975885756643 max=41.31069763798674 observed=True dimensions_evaluator=None)` has been assumed from `sim.observations`. If the order of the dimensions should be different, specify `sim.config.data_structure.y = DataVariable(dimensions=[...], ...)` manually.
+      warnings.warn(
+    
+
+```{admonition} Scalers
+:class: note
+We notice a mysterious Scaler message. This tells us that our data variable has been identified and a scaler was constructed, which transforms the variable between [0, 1].   
+This has no effect at the moment, but it can be used later. Scaling can be powerful to help parameter estimation in more complex models.
+```
+
+
+## Running the model 🏃
+
+Next, we define the model parameters *a* and *b*.  
+The parameter *a* is set as fixed (`free = False`), meaning its value is known and will not be estimated during optimization.  
+The parameter *b* is marked as free (`free = True`), allowing it to be optimized to fit our data. As an initial guess, we assume b = 3.   
+
+Our model is now prepared with a parameter set.  
+In order to intialize the *Evaluator* class, we need to execute `sim.dispatch_constructor()`.   
+This step is very important and needs to be done everytime when we made changes in our model.  
+
+The returned dataset (`evaluator.results`) has the exact same shape as our observation data.
+
+
+```python
+# Parameterizing the model
+sim.config.model_parameters.a = Param(value=1, free=False)
+sim.config.model_parameters.b = Param(value=3, free=True)
+# this makes sure the model parameters are available to the model.
+sim.model_parameters["parameters"] = sim.config.model_parameters.value_dict
+
+# put everything in place for running the simulation
+sim.dispatch_constructor()
+
+# run
+evaluator = sim.dispatch(theta={"b":3})
+evaluator()
+evaluator.results
+```
+
+    C:\Users\mgrho\pymob\pymob\simulation.py:552: UserWarning: The number of ODE states was not specified in the config file [simulation] > 'n_ode_states = <n>'. Extracted the return arguments ['a+x*b'] from the source code. Setting 'n_ode_states=1.
+      warnings.warn(
+    
+
+
+
+
+<div><svg style="position: absolute; width: 0; height: 0; overflow: hidden">
+<defs>
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+</symbol>
+</defs>
+</svg>
+<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.
+ *
+ */
+
+:root {
+  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));
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+  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);
+  --xr-background-color: var(--jp-layout-color0, white);
+  --xr-background-color-row-even: var(--jp-layout-color1, white);
+  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);
+}
+
+html[theme=dark],
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+body.vscode-dark {
+  --xr-font-color0: rgba(255, 255, 255, 1);
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+  --xr-disabled-color: #515151;
+  --xr-background-color: #111111;
+  --xr-background-color-row-even: #111111;
+  --xr-background-color-row-odd: #313131;
+}
+
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+  display: block !important;
+  min-width: 300px;
+  max-width: 700px;
+}
+
+.xr-text-repr-fallback {
+  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */
+  display: none;
+}
+
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+  padding-top: 6px;
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+  margin-bottom: 4px;
+  border-bottom: solid 1px var(--xr-border-color);
+}
+
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+  display: inline;
+  margin-top: 0;
+  margin-bottom: 0;
+}
+
+.xr-obj-type,
+.xr-array-name {
+  margin-left: 2px;
+  margin-right: 10px;
+}
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+.xr-obj-type {
+  color: var(--xr-font-color2);
+}
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+  display: grid;
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
+
+.xr-section-summary,
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+  padding-top: 4px;
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+}
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+}
+
+.xr-section-details {
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+  grid-column: 1 / -1;
+  margin-bottom: 5px;
+}
+
+.xr-section-summary-in:checked ~ .xr-section-details {
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+}
+
+.xr-array-wrap {
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+}
+
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+}
+
+.xr-preview {
+  color: var(--xr-font-color3);
+}
+
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+}
+
+.xr-array-data,
+.xr-array-in:checked ~ .xr-array-preview {
+  display: none;
+}
+
+.xr-array-in:checked ~ .xr-array-data,
+.xr-array-preview {
+  display: inline-block;
+}
+
+.xr-dim-list {
+  display: inline-block !important;
+  list-style: none;
+  padding: 0 !important;
+  margin: 0;
+}
+
+.xr-dim-list li {
+  display: inline-block;
+  padding: 0;
+  margin: 0;
+}
+
+.xr-dim-list:before {
+  content: '(';
+}
+
+.xr-dim-list:after {
+  content: ')';
+}
+
+.xr-dim-list li:not(:last-child):after {
+  content: ',';
+  padding-right: 5px;
+}
+
+.xr-has-index {
+  font-weight: bold;
+}
+
+.xr-var-list,
+.xr-var-item {
+  display: contents;
+}
+
+.xr-var-item > div,
+.xr-var-item label,
+.xr-var-item > .xr-var-name span {
+  background-color: var(--xr-background-color-row-even);
+  margin-bottom: 0;
+}
+
+.xr-var-item > .xr-var-name:hover span {
+  padding-right: 5px;
+}
+
+.xr-var-list > li:nth-child(odd) > div,
+.xr-var-list > li:nth-child(odd) > label,
+.xr-var-list > li:nth-child(odd) > .xr-var-name span {
+  background-color: var(--xr-background-color-row-odd);
+}
+
+.xr-var-name {
+  grid-column: 1;
+}
+
+.xr-var-dims {
+  grid-column: 2;
+}
+
+.xr-var-dtype {
+  grid-column: 3;
+  text-align: right;
+  color: var(--xr-font-color2);
+}
+
+.xr-var-preview {
+  grid-column: 4;
+}
+
+.xr-index-preview {
+  grid-column: 2 / 5;
+  color: var(--xr-font-color2);
+}
+
+.xr-var-name,
+.xr-var-dims,
+.xr-var-dtype,
+.xr-preview,
+.xr-attrs dt {
+  white-space: nowrap;
+  overflow: hidden;
+  text-overflow: ellipsis;
+  padding-right: 10px;
+}
+
+.xr-var-name:hover,
+.xr-var-dims:hover,
+.xr-var-dtype:hover,
+.xr-attrs dt:hover {
+  overflow: visible;
+  width: auto;
+  z-index: 1;
+}
+
+.xr-var-attrs,
+.xr-var-data,
+.xr-index-data {
+  display: none;
+  background-color: var(--xr-background-color) !important;
+  padding-bottom: 5px !important;
+}
+
+.xr-var-attrs-in:checked ~ .xr-var-attrs,
+.xr-var-data-in:checked ~ .xr-var-data,
+.xr-index-data-in:checked ~ .xr-index-data {
+  display: block;
+}
+
+.xr-var-data > table {
+  float: right;
+}
+
+.xr-var-name span,
+.xr-var-data,
+.xr-index-name div,
+.xr-index-data,
+.xr-attrs {
+  padding-left: 25px !important;
+}
+
+.xr-attrs,
+.xr-var-attrs,
+.xr-var-data,
+.xr-index-data {
+  grid-column: 1 / -1;
+}
+
+dl.xr-attrs {
+  padding: 0;
+  margin: 0;
+  display: grid;
+  grid-template-columns: 125px auto;
+}
+
+.xr-attrs dt,
+.xr-attrs dd {
+  padding: 0;
+  margin: 0;
+  float: left;
+  padding-right: 10px;
+  width: auto;
+}
+
+.xr-attrs dt {
+  font-weight: normal;
+  grid-column: 1;
+}
+
+.xr-attrs dt:hover span {
+  display: inline-block;
+  background: var(--xr-background-color);
+  padding-right: 10px;
+}
+
+.xr-attrs dd {
+  grid-column: 2;
+  white-space: pre-wrap;
+  word-break: break-all;
+}
+
+.xr-icon-database,
+.xr-icon-file-text2,
+.xr-no-icon {
+  display: inline-block;
+  vertical-align: middle;
+  width: 1em;
+  height: 1.5em !important;
+  stroke-width: 0;
+  stroke: currentColor;
+  fill: currentColor;
+}
+</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;
+Dimensions:  (t: 100)
+Coordinates:
+  * t        (t) float64 0.0 0.101 0.202 0.303 0.404 ... 9.697 9.798 9.899 10.0
+Data variables:
+    y        (t) float64 1.0 1.303 1.606 1.909 2.212 ... 30.09 30.39 30.7 31.0</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-1c661578-14ae-4cc6-8945-9d26ea758117' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1c661578-14ae-4cc6-8945-9d26ea758117' 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'>t</span>: 100</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-c196b71b-0b4f-4ea1-b124-8c8aa025dc43' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c196b71b-0b4f-4ea1-b124-8c8aa025dc43' class='xr-section-summary' >Coordinates: <span>(1)</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'>t</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.101 0.202 ... 9.899 10.0</div><input id='attrs-ebb610be-409f-48a0-9490-5a4f37adc244' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ebb610be-409f-48a0-9490-5a4f37adc244' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-572e0bca-7d9f-4975-a9bd-ccc87d99f66a' class='xr-var-data-in' type='checkbox'><label for='data-572e0bca-7d9f-4975-a9bd-ccc87d99f66a' 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.      ,  0.10101 ,  0.20202 ,  0.30303 ,  0.40404 ,  0.505051,
+        0.606061,  0.707071,  0.808081,  0.909091,  1.010101,  1.111111,
+        1.212121,  1.313131,  1.414141,  1.515152,  1.616162,  1.717172,
+        1.818182,  1.919192,  2.020202,  2.121212,  2.222222,  2.323232,
+        2.424242,  2.525253,  2.626263,  2.727273,  2.828283,  2.929293,
+        3.030303,  3.131313,  3.232323,  3.333333,  3.434343,  3.535354,
+        3.636364,  3.737374,  3.838384,  3.939394,  4.040404,  4.141414,
+        4.242424,  4.343434,  4.444444,  4.545455,  4.646465,  4.747475,
+        4.848485,  4.949495,  5.050505,  5.151515,  5.252525,  5.353535,
+        5.454545,  5.555556,  5.656566,  5.757576,  5.858586,  5.959596,
+        6.060606,  6.161616,  6.262626,  6.363636,  6.464646,  6.565657,
+        6.666667,  6.767677,  6.868687,  6.969697,  7.070707,  7.171717,
+        7.272727,  7.373737,  7.474747,  7.575758,  7.676768,  7.777778,
+        7.878788,  7.979798,  8.080808,  8.181818,  8.282828,  8.383838,
+        8.484848,  8.585859,  8.686869,  8.787879,  8.888889,  8.989899,
+        9.090909,  9.191919,  9.292929,  9.393939,  9.494949,  9.59596 ,
+        9.69697 ,  9.79798 ,  9.89899 , 10.      ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b0d02921-dccf-4ebc-b53b-cfae69747f72' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b0d02921-dccf-4ebc-b53b-cfae69747f72' class='xr-section-summary' >Data variables: <span>(1)</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>y</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.0 1.303 1.606 ... 30.39 30.7 31.0</div><input id='attrs-7c8f9a91-83a2-4c24-b8db-74a5709fd2a2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7c8f9a91-83a2-4c24-b8db-74a5709fd2a2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-436b20a7-b19b-4a32-ae07-11fd3cb6fcd7' class='xr-var-data-in' type='checkbox'><label for='data-436b20a7-b19b-4a32-ae07-11fd3cb6fcd7' 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([ 1.        ,  1.3030303 ,  1.60606061,  1.90909091,  2.21212121,
+        2.51515152,  2.81818182,  3.12121212,  3.42424242,  3.72727273,
+        4.03030303,  4.33333333,  4.63636364,  4.93939394,  5.24242424,
+        5.54545455,  5.84848485,  6.15151515,  6.45454545,  6.75757576,
+        7.06060606,  7.36363636,  7.66666667,  7.96969697,  8.27272727,
+        8.57575758,  8.87878788,  9.18181818,  9.48484848,  9.78787879,
+       10.09090909, 10.39393939, 10.6969697 , 11.        , 11.3030303 ,
+       11.60606061, 11.90909091, 12.21212121, 12.51515152, 12.81818182,
+       13.12121212, 13.42424242, 13.72727273, 14.03030303, 14.33333333,
+       14.63636364, 14.93939394, 15.24242424, 15.54545455, 15.84848485,
+       16.15151515, 16.45454545, 16.75757576, 17.06060606, 17.36363636,
+       17.66666667, 17.96969697, 18.27272727, 18.57575758, 18.87878788,
+       19.18181818, 19.48484848, 19.78787879, 20.09090909, 20.39393939,
+       20.6969697 , 21.        , 21.3030303 , 21.60606061, 21.90909091,
+       22.21212121, 22.51515152, 22.81818182, 23.12121212, 23.42424242,
+       23.72727273, 24.03030303, 24.33333333, 24.63636364, 24.93939394,
+       25.24242424, 25.54545455, 25.84848485, 26.15151515, 26.45454545,
+       26.75757576, 27.06060606, 27.36363636, 27.66666667, 27.96969697,
+       28.27272727, 28.57575758, 28.87878788, 29.18181818, 29.48484848,
+       29.78787879, 30.09090909, 30.39393939, 30.6969697 , 31.        ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-54e0b947-846c-4e4c-b0fc-0bac4ea098f6' class='xr-section-summary-in' type='checkbox'  ><label for='section-54e0b947-846c-4e4c-b0fc-0bac4ea098f6' class='xr-section-summary' >Indexes: <span>(1)</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>t</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d890c701-c395-4f66-89a6-ea93b577272e' class='xr-index-data-in' type='checkbox'/><label for='index-d890c701-c395-4f66-89a6-ea93b577272e' 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([                0.0, 0.10101010101010101, 0.20202020202020202,
+       0.30303030303030304, 0.40404040404040403,  0.5050505050505051,
+        0.6060606060606061,  0.7070707070707071,  0.8080808080808081,
+        0.9090909090909091,  1.0101010101010102,  1.1111111111111112,
+        1.2121212121212122,  1.3131313131313131,  1.4141414141414141,
+        1.5151515151515151,  1.6161616161616161,  1.7171717171717171,
+        1.8181818181818181,  1.9191919191919191,  2.0202020202020203,
+         2.121212121212121,  2.2222222222222223,   2.323232323232323,
+        2.4242424242424243,   2.525252525252525,  2.6262626262626263,
+         2.727272727272727,  2.8282828282828283,   2.929292929292929,
+        3.0303030303030303,   3.131313131313131,  3.2323232323232323,
+        3.3333333333333335,  3.4343434343434343,  3.5353535353535355,
+        3.6363636363636362,  3.7373737373737375,  3.8383838383838382,
+        3.9393939393939394,   4.040404040404041,   4.141414141414141,
+         4.242424242424242,   4.343434343434343,   4.444444444444445,
+         4.545454545454545,   4.646464646464646,   4.747474747474747,
+         4.848484848484849,    4.94949494949495,    5.05050505050505,
+         5.151515151515151,   5.252525252525253,   5.353535353535354,
+         5.454545454545454,   5.555555555555555,   5.656565656565657,
+         5.757575757575758,   5.858585858585858,   5.959595959595959,
+        6.0606060606060606,   6.161616161616162,   6.262626262626262,
+         6.363636363636363,  6.4646464646464645,   6.565656565656566,
+         6.666666666666667,   6.767676767676767,  6.8686868686868685,
+          6.96969696969697,   7.070707070707071,   7.171717171717171,
+        7.2727272727272725,   7.373737373737374,   7.474747474747475,
+         7.575757575757575,  7.6767676767676765,   7.777777777777778,
+         7.878787878787879,   7.979797979797979,   8.080808080808081,
+         8.181818181818182,   8.282828282828282,   8.383838383838384,
+         8.484848484848484,   8.585858585858587,   8.686868686868687,
+         8.787878787878787,    8.88888888888889,    8.98989898989899,
+          9.09090909090909,   9.191919191919192,   9.292929292929292,
+         9.393939393939394,   9.494949494949495,   9.595959595959595,
+         9.696969696969697,   9.797979797979798,     9.8989898989899,
+                      10.0],
+      dtype=&#x27;float64&#x27;, name=&#x27;t&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-63487aed-70e1-4b19-aba9-3e2b4a406fd5' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-63487aed-70e1-4b19-aba9-3e2b4a406fd5' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>
+
+
+
+Let's have a look at the results.  
+You can vary the parameter *b* in the previous step to investigate it's influence on the model fit.   
+
+In the [beginner guide](), you can try out the *manual parameter estimation*, which is provided by Pymob.
+
+
+```python
+fig, ax = plt.subplots(figsize=(5, 4))
+data_res = evaluator.results
+ax.plot(data_obs.t, data_obs.y, ls="", marker="o", color="tab:blue", alpha=.5, label ="observation data")
+ax.plot(data_res.t, data_res.y, color="black", label ="result")
+ax.legend()
+```
+
+
+
+
+    <matplotlib.legend.Legend at 0x2a94a6a0d10>
+
+
+
+
+    
+![png](superquickstart_files/superquickstart_13_1.png)
+    
+
+
+
+
+## Estimating parameters 
+
+We are almost set infer the parameters of the model. We add another parameter to also estimate the error of the parameters, We use a lognormal distribution for it. We also specify an error model for the distribution. This will be 
+
+$$y_{obs} \sim Normal (y, \sigma_y)$$
+
+
+```python
+sim.config.model_parameters.sigma_y = Param(free=True , prior="lognorm(scale=1,s=1)", min=0, max=1)
+sim.config.model_parameters.b.prior = "lognorm(scale=1,s=1)"
+sim.config.model_parameters.b.min = -5
+sim.config.model_parameters.b.max = 5
+
+sim.config.error_model.y = "normal(loc=y,scale=sigma_y)"
+
+
+sim.set_inferer("numpyro")
+sim.inferer.config.inference_numpyro.kernel = "nuts"
+sim.inferer.run()
+
+sim.inferer.idata.posterior
+
+# Plot the results
+sim.config.simulation.x_dimension = "t"
+sim.posterior_predictive_checks(pred_hdi_style={"alpha": 0.1})
+```
+
+    Jax 64 bit mode: False
+    Absolute tolerance: 1e-07
+    
+
+    C:\Users\mgrho\pymob\pymob\inference\numpyro_backend.py:552: UserWarning: Model is not rendered, because the graphviz executable is not found. Try search for 'graphviz executables not found' and the used OS. This should be an easy fix :-)
+      warnings.warn(
+    
+
+    Trace Shapes:      
+     Param Sites:      
+    Sample Sites:      
+           b dist     |
+            value     |
+     sigma_y dist     |
+            value     |
+       y_obs dist 100 |
+            value 100 |
+    
+
+    
  0%|                                                                                                                                                                                                                                                                   | 0/3000 [00:00<?, ?it/s]
+
+    
warmup:   0%|                                                                                                                                                                                                       | 1/3000 [00:01<1:35:41,  1.91s/it, 1 steps of size 1.87e+00. acc. prob=0.00]
+
+    
warmup:  14%|██████████████████████████▉                                                                                                                                                                           | 408/3000 [00:02<00:09, 283.03it/s, 1 steps of size 2.65e-01. acc. prob=0.78]
+
+    
warmup:  29%|████████████████████████████████████████████████████████▋                                                                                                                                             | 858/3000 [00:02<00:03, 663.36it/s, 3 steps of size 1.05e+00. acc. prob=0.79]
+
+    
sample:  47%|███████████████████████████████████████████████████████████████████████████████████████████▊                                                                                                        | 1406/3000 [00:02<00:01, 1195.50it/s, 3 steps of size 7.21e-01. acc. prob=0.95]
+
+    
sample:  61%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                            | 1831/3000 [00:02<00:00, 1613.43it/s, 7 steps of size 7.21e-01. acc. prob=0.95]
+
+    
sample:  78%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                           | 2330/3000 [00:02<00:00, 2168.25it/s, 7 steps of size 7.21e-01. acc. prob=0.94]
+
+    
sample:  93%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊             | 2799/3000 [00:02<00:00, 2647.78it/s, 7 steps of size 7.21e-01. acc. prob=0.95]
+
+    
sample: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3000/3000 [00:02<00:00, 1169.97it/s, 7 steps of size 7.21e-01. acc. prob=0.95]
+
+    
+    
+
+    
+                    mean       std    median      5.0%     95.0%     n_eff     r_hat
+             b      3.86      0.03      3.86      3.82      3.91   1439.12      1.00
+       sigma_y      1.56      0.11      1.55      1.38      1.76   1283.13      1.00
+    
+    Number of divergences: 0
+    
+
+
+    
+![png](superquickstart_files/superquickstart_16_14.png)
+    
+
+
+### Estimating parameters and uncertainty with MCMC
+
+Of course this example is very simple, we can in fact optimize the parameters perfectly by hand. But just for the fun of it, let's use *Markov Chain Monte Carlo* (MCMC) to estimate the parameters, their uncertainty and the uncertainty in the data.
+
+```{admonition} numpyro distributions
+:class: warning
+Currently only few distributions are implemented in the numpyro backend. This API will soon change, so that basically any distribution can be used to specifcy parameters. 
+```
+
+Finally, we let our inferer run the paramter estimation procedure with the numpyro backend and a NUTS kernel. This does the job in a few seconds
+
+We can inspect our estimates and see that the parameters are well esimtated by the model. Note that we only get an estimate for $b$. This is because earlier we set the parameter `a` with the flag `free=False` this effectively excludes it from estimation and uses the default value, which was set to the true value `a=0`.
+
+
+```{admonition} Customize the posterior predictive checks
+:class: hint
+You can explore the API of {class}`pymob.sim.plot.SimulationPlot` to find out how you can work on the default predictions. Of course you can always make your own plot, by accessing {attr}`pymob.simulation.inferer.idata` and {attr}`pymob.simulation.observations`
+```
+
+### Report the results
+
+```{admonition} numpyro distributions
+:class: warning
+Automated reporting is already implemented in a different branch. This will be soon explained here.
+```
+
+
+```python
+# TODO: Call report when done
+```
+
+
+## Exporting the simulation and running it via the case study API
+
+After constructing the simulation, all settings of the simulation can be exported to a comprehensive configuration file, along with all the default settings. This is as simple as 
+
+
+```python
+import os
+sim.config.case_study.name = "quickstart"
+sim.config.case_study.scenario = "test"
+sim.config.create_directory("scenario", force=True)
+sim.config.create_directory("results", force=True)
+
+# usually we expect to have a data directory in the case
+os.makedirs(sim.data_path, exist_ok=True)
+sim.save_observations(force=True)
+sim.config.save(force=True)
+```
+
+    Scenario directory exists at 'C:\Users\mgrho\pymob\docs\source\user_guide\case_studies\quickstart\scenarios\test'.
+    Results directory exists at 'C:\Users\mgrho\pymob\docs\source\user_guide\case_studies\quickstart\results\test'.
+    
+
+The simulation will be saved to the default path (`CASE_STUDY/scenarios/SCENARIO/settings.cfg`) or to a custom path spcified with the `fp` keyword. `force=True` will overwrite any existing config file, which is the reasonable choice in most cases.
+
+From there on, the simulation is (almost) ready to be executable from the commandline.
+
+### Commandline API
+
+The commandline API runs a series of commands that load the case study, execute the {meth}`pymob.simulation.SimulationBase.initialize` method and perform some more initialization tasks, before running the required job.
+
++ `pymob-infer`: Runs an inference job e.g. `pymob-infer --case_study=quickstart --scenario=test --inference_backend=numpyro`. While there are more commandline options, these are the two required 

From 9bb56a95821744384890204f9c360779c225e3ce Mon Sep 17 00:00:00 2001
From: mariegrho <mgrholthaus@gmail.com>
Date: Thu, 12 Jun 2025 07:45:33 +0200
Subject: [PATCH 04/16] updated superquickstart, improved wording

---
 docs/source/user_guide/superquickstart.md | 2465 ++++++++++-----------
 1 file changed, 1229 insertions(+), 1236 deletions(-)

diff --git a/docs/source/user_guide/superquickstart.md b/docs/source/user_guide/superquickstart.md
index 72e47c180..d2daebbe8 100644
--- a/docs/source/user_guide/superquickstart.md
+++ b/docs/source/user_guide/superquickstart.md
@@ -1,1236 +1,1229 @@
-# Pymob quickstart
-
-This super-quick quickstart gives an introduction to the basic Pymob workflow and key functionalities.  
-For this, we will investigate a simple linear regression model, which we want to fit to a noisy dataset.  
-Pymob supports our modeling process by providing several tools for *structuring our data*, for the *parameter estimation* and *visualization of the results*.  
-  
-Before starting the modeling process, we let's have a look at the main steps and modules of pymob:
-
-1. __Simulation:__   
-First, we need to initialize a Simulation object by calling the {class}`pymob.simulation.SimulationBase` class from the simulation module.   
-Optionally, we can configure the simulation with `sim.config.case_study.name = "linear-regression"`, `sim.config.case_study.scenario = "test"` and many more options. 
-
-2. __Model:__   
-Our model will be defined as a python function.  
-We will then assign it to our Simulation object by `.model` 
-
-3. __Observations:__   
-Our observation data needs to be structured as a [xarray.Dataset](https://docs.xarray.dev/en/stable/generated/xarray.Dataset.html).  
-We assign it to our Simulation object by  `.observations.`  
-`sim.config.data_structure` will give us some information about the layout of our data.
-
-4. __Solver:__  
-Solvers are needed to solve the model. 
-In our simple case, we will use the solver "solve_analytic_1d" from the "pymob.solver.analytic module.
-We  assign it to our Simulation object by `.solver` 
-For more complex models, the JaxSolver from the diffrax module is a more powerful option.  
-User can also implement their own solver as a subclass of `pymob.solver.SolverBase`. 
-  
-5. __Inferer:__  
-The inferer serves as the parameter estimator.  
-Pymob provided [various backends](https://pymob.readthedocs.io/en/stable/user_guide/framework_overview.html). In our example, we will work with *numpyro*.  
-We assign the inferer to our Simulation object by `.inferer` and configurate the kernel we want to use (here *nuts*).  
-But before, we need to parameterize our model using the *Param* class. The parameters can be marked as free or fixed, depending on whether they should be variable during an optimization procedure.  
-We assign the parameters to our Simulation object by `sim.model_parameters`. This is a dictionary that holds the model input data. The keys it takes by default are `parameters`, `y0` and `x_in`. 
-
-7. __Evaluator:__  
-The Evaluator is an instance to evaluate a model. 
-
-6. __Config:__  
-Our settings will be saved in a configuration file `.cfg`.  
-The config file contains information about our simulation in different sections. -> Learn more [here](https://pymob.readthedocs.io/en/stable/user_guide/case_studies.html#configuration).
-We can further use it to create new Simulations by loading the settings from a config file. 
-
-
-![framework-overview](.\figures\pymob_overview.png)
-
-
-```python
-# First, import the necessary python packages
-import numpy as np
-import matplotlib.pyplot as plt
-import xarray as xr
-
-# Import the pymob modules
-from pymob.simulation import SimulationBase
-from pymob.sim.solvetools import solve_analytic_1d
-from pymob.sim.config import Param
-```
-
-Since no measured data is provided, we will generate an artificial dataset.  
-$y_{obs}$ represents the observation data over the time $t$ [0, 10].  
-In order to use the data later, we need to convert it into a xarray-Dataset.  
-In your application later, you would use your measuered experimental data.  
-
-
-```python
-# Parameter for the artificial data generation
-slope = np.random.uniform(2.0, 4.0) 
-intercept = 1.0
-num_points = 100
-noise_level = 1.7
-
-# generating time values
-t = np.linspace(0, 10, num_points)
-
-# generating y-values with noise
-noise = np.random.normal(0, noise_level, num_points)
-y_obs = slope * t + intercept + noise
-
-# visualizing our data
-fig, ax = plt.subplots(figsize=(5, 4))
-ax.scatter(t, y_obs, label='Datapoints')
-ax.set(xlabel='t [-]', ylabel='y_obs [-]', title ='Artificial Data')
-plt.tight_layout()
-
-# convert the data to an xr-Dataset
-data_obs = xr.DataArray(y_obs, coords={"t": t}).to_dataset(name="y")
-data_obs
-```
-
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-</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;
-Dimensions:  (t: 100)
-Coordinates:
-  * t        (t) float64 0.0 0.101 0.202 0.303 0.404 ... 9.697 9.798 9.899 10.0
-Data variables:
-    y        (t) float64 -0.6628 2.548 0.904 1.836 ... 41.31 40.07 40.09 38.61</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-91073f8c-b769-4179-8bf5-9115e9f0d656' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-91073f8c-b769-4179-8bf5-9115e9f0d656' 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'>t</span>: 100</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-c5a336db-4db1-485e-913f-870d2575167c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c5a336db-4db1-485e-913f-870d2575167c' class='xr-section-summary' >Coordinates: <span>(1)</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'>t</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.101 0.202 ... 9.899 10.0</div><input id='attrs-419eb748-f363-4360-b3a8-0ae23df5e05b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-419eb748-f363-4360-b3a8-0ae23df5e05b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-305abda1-4dce-4873-a66f-b80bbeac3f22' class='xr-var-data-in' type='checkbox'><label for='data-305abda1-4dce-4873-a66f-b80bbeac3f22' 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.      ,  0.10101 ,  0.20202 ,  0.30303 ,  0.40404 ,  0.505051,
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-        4.242424,  4.343434,  4.444444,  4.545455,  4.646465,  4.747475,
-        4.848485,  4.949495,  5.050505,  5.151515,  5.252525,  5.353535,
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-        6.060606,  6.161616,  6.262626,  6.363636,  6.464646,  6.565657,
-        6.666667,  6.767677,  6.868687,  6.969697,  7.070707,  7.171717,
-        7.272727,  7.373737,  7.474747,  7.575758,  7.676768,  7.777778,
-        7.878788,  7.979798,  8.080808,  8.181818,  8.282828,  8.383838,
-        8.484848,  8.585859,  8.686869,  8.787879,  8.888889,  8.989899,
-        9.090909,  9.191919,  9.292929,  9.393939,  9.494949,  9.59596 ,
-        9.69697 ,  9.79798 ,  9.89899 , 10.      ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8d3d2f0e-e56d-49f6-b82d-32ce26702057' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8d3d2f0e-e56d-49f6-b82d-32ce26702057' class='xr-section-summary' >Data variables: <span>(1)</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>y</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.6628 2.548 0.904 ... 40.09 38.61</div><input id='attrs-34399cae-1b61-4664-82d6-2552d2e39701' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-34399cae-1b61-4664-82d6-2552d2e39701' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-89c5bce0-09f6-4bd5-84b2-6c6d6d88a30f' class='xr-var-data-in' type='checkbox'><label for='data-89c5bce0-09f6-4bd5-84b2-6c6d6d88a30f' 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([-0.66279759,  2.54838195,  0.90402378,  1.8359012 ,  0.5293172 ,
-        1.80744333,  2.59868535,  3.67090295,  2.83376176,  4.07329674,
-        6.76920911,  4.36085823,  5.20194196,  7.38577124,  5.63337025,
-        7.39286231,  9.73184766,  8.00042407,  6.45994362,  7.1827322 ,
-        4.73620223, 10.11247429, 11.1347963 ,  8.0156748 ,  8.72705155,
-        7.82912066, 11.33148295,  8.07588462, 11.14310521, 12.84006373,
-       11.85141345, 12.2609556 , 14.14259846, 12.67848459, 13.24807901,
-       14.88929832, 15.33969195, 18.34985947, 17.05053606, 15.37027947,
-       19.89569387, 17.61449201, 18.2711486 , 16.02393223, 17.35700651,
-       18.03726618, 21.98245202, 16.86950815, 16.88034406, 18.44249038,
-       20.972587  , 18.57095052, 22.12011127, 19.2342568 , 20.85517476,
-       22.02376379, 22.91952443, 22.57665199, 24.86933789, 24.34129814,
-       26.17710891, 23.43682452, 23.60375076, 23.8623379 , 26.26546831,
-       25.44175443, 27.9531178 , 25.575388  , 27.58545487, 28.96124999,
-       27.15385814, 29.14941119, 28.49437023, 28.3901884 , 30.4857327 ,
-       29.18909622, 28.99954318, 31.46041456, 32.08189935, 31.83879134,
-       33.49777158, 33.39883355, 33.33298454, 35.64371523, 33.17544264,
-       33.86539446, 35.12255748, 39.45756359, 33.48485126, 35.75819551,
-       36.24946202, 37.99676006, 39.51851734, 34.66114129, 36.76887495,
-       37.95513145, 41.31069764, 40.07119133, 40.09468708, 38.60892577])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9c5dc49e-feab-4514-9c5c-35be9fdef510' class='xr-section-summary-in' type='checkbox'  ><label for='section-9c5dc49e-feab-4514-9c5c-35be9fdef510' class='xr-section-summary' >Indexes: <span>(1)</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>t</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-74dccaea-7782-4646-b497-9e1af0111db3' class='xr-index-data-in' type='checkbox'/><label for='index-74dccaea-7782-4646-b497-9e1af0111db3' 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([                0.0, 0.10101010101010101, 0.20202020202020202,
-       0.30303030303030304, 0.40404040404040403,  0.5050505050505051,
-        0.6060606060606061,  0.7070707070707071,  0.8080808080808081,
-        0.9090909090909091,  1.0101010101010102,  1.1111111111111112,
-        1.2121212121212122,  1.3131313131313131,  1.4141414141414141,
-        1.5151515151515151,  1.6161616161616161,  1.7171717171717171,
-        1.8181818181818181,  1.9191919191919191,  2.0202020202020203,
-         2.121212121212121,  2.2222222222222223,   2.323232323232323,
-        2.4242424242424243,   2.525252525252525,  2.6262626262626263,
-         2.727272727272727,  2.8282828282828283,   2.929292929292929,
-        3.0303030303030303,   3.131313131313131,  3.2323232323232323,
-        3.3333333333333335,  3.4343434343434343,  3.5353535353535355,
-        3.6363636363636362,  3.7373737373737375,  3.8383838383838382,
-        3.9393939393939394,   4.040404040404041,   4.141414141414141,
-         4.242424242424242,   4.343434343434343,   4.444444444444445,
-         4.545454545454545,   4.646464646464646,   4.747474747474747,
-         4.848484848484849,    4.94949494949495,    5.05050505050505,
-         5.151515151515151,   5.252525252525253,   5.353535353535354,
-         5.454545454545454,   5.555555555555555,   5.656565656565657,
-         5.757575757575758,   5.858585858585858,   5.959595959595959,
-        6.0606060606060606,   6.161616161616162,   6.262626262626262,
-         6.363636363636363,  6.4646464646464645,   6.565656565656566,
-         6.666666666666667,   6.767676767676767,  6.8686868686868685,
-          6.96969696969697,   7.070707070707071,   7.171717171717171,
-        7.2727272727272725,   7.373737373737374,   7.474747474747475,
-         7.575757575757575,  7.6767676767676765,   7.777777777777778,
-         7.878787878787879,   7.979797979797979,   8.080808080808081,
-         8.181818181818182,   8.282828282828282,   8.383838383838384,
-         8.484848484848484,   8.585858585858587,   8.686868686868687,
-         8.787878787878787,    8.88888888888889,    8.98989898989899,
-          9.09090909090909,   9.191919191919192,   9.292929292929292,
-         9.393939393939394,   9.494949494949495,   9.595959595959595,
-         9.696969696969697,   9.797979797979798,     9.8989898989899,
-                      10.0],
-      dtype=&#x27;float64&#x27;, name=&#x27;t&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4871e23a-307c-4643-964c-9b76c43f19f7' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-4871e23a-307c-4643-964c-9b76c43f19f7' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>
-
-
-
-
-    
-![png](superquickstart_files/superquickstart_5_1.png)
-    
-
-
-
-
-## Initialize a simulation
-
-In pymob a Simulation object is initialized by calling the {class}`pymob.simulation.SimulationBase` class from the simulation module.  
-We will chose a linear regression model, since it seems to be a good approximation to the data.
-
-```{admonition} x-dimension
-:class: note
-The x_dimension of our simulation can have any name, for expample t as often used for time series data.
-You can specified it via `sim.config.simulation.x_dimension`.
-```
-
-
-```python
-# Initialize the Simulation object
-sim = SimulationBase()
-
-# Define the linear regression model
-def linreg(x, a, b):
-    return a + x * b
-
-# Add the model to the simulation
-sim.model = linreg
-
-# Adding our dataset to the simulation
-sim.observations = data_obs
-
-# Defining a solver
-sim.solver = solve_analytic_1d
-```
-
-    MinMaxScaler(variable=y, min=-0.6627975885756643, max=41.31069763798674)
-    
-
-    C:\Users\mgrho\pymob\pymob\simulation.py:303: UserWarning: `sim.config.data_structure.y = Datavariable(dimensions=['t'] min=-0.6627975885756643 max=41.31069763798674 observed=True dimensions_evaluator=None)` has been assumed from `sim.observations`. If the order of the dimensions should be different, specify `sim.config.data_structure.y = DataVariable(dimensions=[...], ...)` manually.
-      warnings.warn(
-    
-
-```{admonition} Scalers
-:class: note
-We notice a mysterious Scaler message. This tells us that our data variable has been identified and a scaler was constructed, which transforms the variable between [0, 1].   
-This has no effect at the moment, but it can be used later. Scaling can be powerful to help parameter estimation in more complex models.
-```
-
-
-## Running the model 🏃
-
-Next, we define the model parameters *a* and *b*.  
-The parameter *a* is set as fixed (`free = False`), meaning its value is known and will not be estimated during optimization.  
-The parameter *b* is marked as free (`free = True`), allowing it to be optimized to fit our data. As an initial guess, we assume b = 3.   
-
-Our model is now prepared with a parameter set.  
-In order to intialize the *Evaluator* class, we need to execute `sim.dispatch_constructor()`.   
-This step is very important and needs to be done everytime when we made changes in our model.  
-
-The returned dataset (`evaluator.results`) has the exact same shape as our observation data.
-
-
-```python
-# Parameterizing the model
-sim.config.model_parameters.a = Param(value=1, free=False)
-sim.config.model_parameters.b = Param(value=3, free=True)
-# this makes sure the model parameters are available to the model.
-sim.model_parameters["parameters"] = sim.config.model_parameters.value_dict
-
-# put everything in place for running the simulation
-sim.dispatch_constructor()
-
-# run
-evaluator = sim.dispatch(theta={"b":3})
-evaluator()
-evaluator.results
-```
-
-    C:\Users\mgrho\pymob\pymob\simulation.py:552: UserWarning: The number of ODE states was not specified in the config file [simulation] > 'n_ode_states = <n>'. Extracted the return arguments ['a+x*b'] from the source code. Setting 'n_ode_states=1.
-      warnings.warn(
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-dl.xr-attrs {
-  padding: 0;
-  margin: 0;
-  display: grid;
-  grid-template-columns: 125px auto;
-}
-
-.xr-attrs dt,
-.xr-attrs dd {
-  padding: 0;
-  margin: 0;
-  float: left;
-  padding-right: 10px;
-  width: auto;
-}
-
-.xr-attrs dt {
-  font-weight: normal;
-  grid-column: 1;
-}
-
-.xr-attrs dt:hover span {
-  display: inline-block;
-  background: var(--xr-background-color);
-  padding-right: 10px;
-}
-
-.xr-attrs dd {
-  grid-column: 2;
-  white-space: pre-wrap;
-  word-break: break-all;
-}
-
-.xr-icon-database,
-.xr-icon-file-text2,
-.xr-no-icon {
-  display: inline-block;
-  vertical-align: middle;
-  width: 1em;
-  height: 1.5em !important;
-  stroke-width: 0;
-  stroke: currentColor;
-  fill: currentColor;
-}
-</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;
-Dimensions:  (t: 100)
-Coordinates:
-  * t        (t) float64 0.0 0.101 0.202 0.303 0.404 ... 9.697 9.798 9.899 10.0
-Data variables:
-    y        (t) float64 1.0 1.303 1.606 1.909 2.212 ... 30.09 30.39 30.7 31.0</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-1c661578-14ae-4cc6-8945-9d26ea758117' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1c661578-14ae-4cc6-8945-9d26ea758117' 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'>t</span>: 100</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-c196b71b-0b4f-4ea1-b124-8c8aa025dc43' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c196b71b-0b4f-4ea1-b124-8c8aa025dc43' class='xr-section-summary' >Coordinates: <span>(1)</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'>t</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.101 0.202 ... 9.899 10.0</div><input id='attrs-ebb610be-409f-48a0-9490-5a4f37adc244' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ebb610be-409f-48a0-9490-5a4f37adc244' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-572e0bca-7d9f-4975-a9bd-ccc87d99f66a' class='xr-var-data-in' type='checkbox'><label for='data-572e0bca-7d9f-4975-a9bd-ccc87d99f66a' 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.      ,  0.10101 ,  0.20202 ,  0.30303 ,  0.40404 ,  0.505051,
-        0.606061,  0.707071,  0.808081,  0.909091,  1.010101,  1.111111,
-        1.212121,  1.313131,  1.414141,  1.515152,  1.616162,  1.717172,
-        1.818182,  1.919192,  2.020202,  2.121212,  2.222222,  2.323232,
-        2.424242,  2.525253,  2.626263,  2.727273,  2.828283,  2.929293,
-        3.030303,  3.131313,  3.232323,  3.333333,  3.434343,  3.535354,
-        3.636364,  3.737374,  3.838384,  3.939394,  4.040404,  4.141414,
-        4.242424,  4.343434,  4.444444,  4.545455,  4.646465,  4.747475,
-        4.848485,  4.949495,  5.050505,  5.151515,  5.252525,  5.353535,
-        5.454545,  5.555556,  5.656566,  5.757576,  5.858586,  5.959596,
-        6.060606,  6.161616,  6.262626,  6.363636,  6.464646,  6.565657,
-        6.666667,  6.767677,  6.868687,  6.969697,  7.070707,  7.171717,
-        7.272727,  7.373737,  7.474747,  7.575758,  7.676768,  7.777778,
-        7.878788,  7.979798,  8.080808,  8.181818,  8.282828,  8.383838,
-        8.484848,  8.585859,  8.686869,  8.787879,  8.888889,  8.989899,
-        9.090909,  9.191919,  9.292929,  9.393939,  9.494949,  9.59596 ,
-        9.69697 ,  9.79798 ,  9.89899 , 10.      ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b0d02921-dccf-4ebc-b53b-cfae69747f72' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b0d02921-dccf-4ebc-b53b-cfae69747f72' class='xr-section-summary' >Data variables: <span>(1)</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>y</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.0 1.303 1.606 ... 30.39 30.7 31.0</div><input id='attrs-7c8f9a91-83a2-4c24-b8db-74a5709fd2a2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7c8f9a91-83a2-4c24-b8db-74a5709fd2a2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-436b20a7-b19b-4a32-ae07-11fd3cb6fcd7' class='xr-var-data-in' type='checkbox'><label for='data-436b20a7-b19b-4a32-ae07-11fd3cb6fcd7' 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([ 1.        ,  1.3030303 ,  1.60606061,  1.90909091,  2.21212121,
-        2.51515152,  2.81818182,  3.12121212,  3.42424242,  3.72727273,
-        4.03030303,  4.33333333,  4.63636364,  4.93939394,  5.24242424,
-        5.54545455,  5.84848485,  6.15151515,  6.45454545,  6.75757576,
-        7.06060606,  7.36363636,  7.66666667,  7.96969697,  8.27272727,
-        8.57575758,  8.87878788,  9.18181818,  9.48484848,  9.78787879,
-       10.09090909, 10.39393939, 10.6969697 , 11.        , 11.3030303 ,
-       11.60606061, 11.90909091, 12.21212121, 12.51515152, 12.81818182,
-       13.12121212, 13.42424242, 13.72727273, 14.03030303, 14.33333333,
-       14.63636364, 14.93939394, 15.24242424, 15.54545455, 15.84848485,
-       16.15151515, 16.45454545, 16.75757576, 17.06060606, 17.36363636,
-       17.66666667, 17.96969697, 18.27272727, 18.57575758, 18.87878788,
-       19.18181818, 19.48484848, 19.78787879, 20.09090909, 20.39393939,
-       20.6969697 , 21.        , 21.3030303 , 21.60606061, 21.90909091,
-       22.21212121, 22.51515152, 22.81818182, 23.12121212, 23.42424242,
-       23.72727273, 24.03030303, 24.33333333, 24.63636364, 24.93939394,
-       25.24242424, 25.54545455, 25.84848485, 26.15151515, 26.45454545,
-       26.75757576, 27.06060606, 27.36363636, 27.66666667, 27.96969697,
-       28.27272727, 28.57575758, 28.87878788, 29.18181818, 29.48484848,
-       29.78787879, 30.09090909, 30.39393939, 30.6969697 , 31.        ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-54e0b947-846c-4e4c-b0fc-0bac4ea098f6' class='xr-section-summary-in' type='checkbox'  ><label for='section-54e0b947-846c-4e4c-b0fc-0bac4ea098f6' class='xr-section-summary' >Indexes: <span>(1)</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>t</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d890c701-c395-4f66-89a6-ea93b577272e' class='xr-index-data-in' type='checkbox'/><label for='index-d890c701-c395-4f66-89a6-ea93b577272e' 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([                0.0, 0.10101010101010101, 0.20202020202020202,
-       0.30303030303030304, 0.40404040404040403,  0.5050505050505051,
-        0.6060606060606061,  0.7070707070707071,  0.8080808080808081,
-        0.9090909090909091,  1.0101010101010102,  1.1111111111111112,
-        1.2121212121212122,  1.3131313131313131,  1.4141414141414141,
-        1.5151515151515151,  1.6161616161616161,  1.7171717171717171,
-        1.8181818181818181,  1.9191919191919191,  2.0202020202020203,
-         2.121212121212121,  2.2222222222222223,   2.323232323232323,
-        2.4242424242424243,   2.525252525252525,  2.6262626262626263,
-         2.727272727272727,  2.8282828282828283,   2.929292929292929,
-        3.0303030303030303,   3.131313131313131,  3.2323232323232323,
-        3.3333333333333335,  3.4343434343434343,  3.5353535353535355,
-        3.6363636363636362,  3.7373737373737375,  3.8383838383838382,
-        3.9393939393939394,   4.040404040404041,   4.141414141414141,
-         4.242424242424242,   4.343434343434343,   4.444444444444445,
-         4.545454545454545,   4.646464646464646,   4.747474747474747,
-         4.848484848484849,    4.94949494949495,    5.05050505050505,
-         5.151515151515151,   5.252525252525253,   5.353535353535354,
-         5.454545454545454,   5.555555555555555,   5.656565656565657,
-         5.757575757575758,   5.858585858585858,   5.959595959595959,
-        6.0606060606060606,   6.161616161616162,   6.262626262626262,
-         6.363636363636363,  6.4646464646464645,   6.565656565656566,
-         6.666666666666667,   6.767676767676767,  6.8686868686868685,
-          6.96969696969697,   7.070707070707071,   7.171717171717171,
-        7.2727272727272725,   7.373737373737374,   7.474747474747475,
-         7.575757575757575,  7.6767676767676765,   7.777777777777778,
-         7.878787878787879,   7.979797979797979,   8.080808080808081,
-         8.181818181818182,   8.282828282828282,   8.383838383838384,
-         8.484848484848484,   8.585858585858587,   8.686868686868687,
-         8.787878787878787,    8.88888888888889,    8.98989898989899,
-          9.09090909090909,   9.191919191919192,   9.292929292929292,
-         9.393939393939394,   9.494949494949495,   9.595959595959595,
-         9.696969696969697,   9.797979797979798,     9.8989898989899,
-                      10.0],
-      dtype=&#x27;float64&#x27;, name=&#x27;t&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-63487aed-70e1-4b19-aba9-3e2b4a406fd5' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-63487aed-70e1-4b19-aba9-3e2b4a406fd5' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>
-
-
-
-Let's have a look at the results.  
-You can vary the parameter *b* in the previous step to investigate it's influence on the model fit.   
-
-In the [beginner guide](), you can try out the *manual parameter estimation*, which is provided by Pymob.
-
-
-```python
-fig, ax = plt.subplots(figsize=(5, 4))
-data_res = evaluator.results
-ax.plot(data_obs.t, data_obs.y, ls="", marker="o", color="tab:blue", alpha=.5, label ="observation data")
-ax.plot(data_res.t, data_res.y, color="black", label ="result")
-ax.legend()
-```
-
-
-
-
-    <matplotlib.legend.Legend at 0x2a94a6a0d10>
-
-
-
-
-    
-![png](superquickstart_files/superquickstart_13_1.png)
-    
-
-
-
-
-## Estimating parameters 
-
-We are almost set infer the parameters of the model. We add another parameter to also estimate the error of the parameters, We use a lognormal distribution for it. We also specify an error model for the distribution. This will be 
-
-$$y_{obs} \sim Normal (y, \sigma_y)$$
-
-
-```python
-sim.config.model_parameters.sigma_y = Param(free=True , prior="lognorm(scale=1,s=1)", min=0, max=1)
-sim.config.model_parameters.b.prior = "lognorm(scale=1,s=1)"
-sim.config.model_parameters.b.min = -5
-sim.config.model_parameters.b.max = 5
-
-sim.config.error_model.y = "normal(loc=y,scale=sigma_y)"
-
-
-sim.set_inferer("numpyro")
-sim.inferer.config.inference_numpyro.kernel = "nuts"
-sim.inferer.run()
-
-sim.inferer.idata.posterior
-
-# Plot the results
-sim.config.simulation.x_dimension = "t"
-sim.posterior_predictive_checks(pred_hdi_style={"alpha": 0.1})
-```
-
-    Jax 64 bit mode: False
-    Absolute tolerance: 1e-07
-    
-
-    C:\Users\mgrho\pymob\pymob\inference\numpyro_backend.py:552: UserWarning: Model is not rendered, because the graphviz executable is not found. Try search for 'graphviz executables not found' and the used OS. This should be an easy fix :-)
-      warnings.warn(
-    
-
-    Trace Shapes:      
-     Param Sites:      
-    Sample Sites:      
-           b dist     |
-            value     |
-     sigma_y dist     |
-            value     |
-       y_obs dist 100 |
-            value 100 |
-    
-
-    
  0%|                                                                                                                                                                                                                                                                   | 0/3000 [00:00<?, ?it/s]
-
-    
warmup:   0%|                                                                                                                                                                                                       | 1/3000 [00:01<1:35:41,  1.91s/it, 1 steps of size 1.87e+00. acc. prob=0.00]
-
-    
warmup:  14%|██████████████████████████▉                                                                                                                                                                           | 408/3000 [00:02<00:09, 283.03it/s, 1 steps of size 2.65e-01. acc. prob=0.78]
-
-    
warmup:  29%|████████████████████████████████████████████████████████▋                                                                                                                                             | 858/3000 [00:02<00:03, 663.36it/s, 3 steps of size 1.05e+00. acc. prob=0.79]
-
-    
sample:  47%|███████████████████████████████████████████████████████████████████████████████████████████▊                                                                                                        | 1406/3000 [00:02<00:01, 1195.50it/s, 3 steps of size 7.21e-01. acc. prob=0.95]
-
-    
sample:  61%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                            | 1831/3000 [00:02<00:00, 1613.43it/s, 7 steps of size 7.21e-01. acc. prob=0.95]
-
-    
sample:  78%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                           | 2330/3000 [00:02<00:00, 2168.25it/s, 7 steps of size 7.21e-01. acc. prob=0.94]
-
-    
sample:  93%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊             | 2799/3000 [00:02<00:00, 2647.78it/s, 7 steps of size 7.21e-01. acc. prob=0.95]
-
-    
sample: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3000/3000 [00:02<00:00, 1169.97it/s, 7 steps of size 7.21e-01. acc. prob=0.95]
-
-    
-    
-
-    
-                    mean       std    median      5.0%     95.0%     n_eff     r_hat
-             b      3.86      0.03      3.86      3.82      3.91   1439.12      1.00
-       sigma_y      1.56      0.11      1.55      1.38      1.76   1283.13      1.00
-    
-    Number of divergences: 0
-    
-
-
-    
-![png](superquickstart_files/superquickstart_16_14.png)
-    
-
-
-### Estimating parameters and uncertainty with MCMC
-
-Of course this example is very simple, we can in fact optimize the parameters perfectly by hand. But just for the fun of it, let's use *Markov Chain Monte Carlo* (MCMC) to estimate the parameters, their uncertainty and the uncertainty in the data.
-
-```{admonition} numpyro distributions
-:class: warning
-Currently only few distributions are implemented in the numpyro backend. This API will soon change, so that basically any distribution can be used to specifcy parameters. 
-```
-
-Finally, we let our inferer run the paramter estimation procedure with the numpyro backend and a NUTS kernel. This does the job in a few seconds
-
-We can inspect our estimates and see that the parameters are well esimtated by the model. Note that we only get an estimate for $b$. This is because earlier we set the parameter `a` with the flag `free=False` this effectively excludes it from estimation and uses the default value, which was set to the true value `a=0`.
-
-
-```{admonition} Customize the posterior predictive checks
-:class: hint
-You can explore the API of {class}`pymob.sim.plot.SimulationPlot` to find out how you can work on the default predictions. Of course you can always make your own plot, by accessing {attr}`pymob.simulation.inferer.idata` and {attr}`pymob.simulation.observations`
-```
-
-### Report the results
-
-```{admonition} numpyro distributions
-:class: warning
-Automated reporting is already implemented in a different branch. This will be soon explained here.
-```
-
-
-```python
-# TODO: Call report when done
-```
-
-
-## Exporting the simulation and running it via the case study API
-
-After constructing the simulation, all settings of the simulation can be exported to a comprehensive configuration file, along with all the default settings. This is as simple as 
-
-
-```python
-import os
-sim.config.case_study.name = "quickstart"
-sim.config.case_study.scenario = "test"
-sim.config.create_directory("scenario", force=True)
-sim.config.create_directory("results", force=True)
-
-# usually we expect to have a data directory in the case
-os.makedirs(sim.data_path, exist_ok=True)
-sim.save_observations(force=True)
-sim.config.save(force=True)
-```
-
-    Scenario directory exists at 'C:\Users\mgrho\pymob\docs\source\user_guide\case_studies\quickstart\scenarios\test'.
-    Results directory exists at 'C:\Users\mgrho\pymob\docs\source\user_guide\case_studies\quickstart\results\test'.
-    
-
-The simulation will be saved to the default path (`CASE_STUDY/scenarios/SCENARIO/settings.cfg`) or to a custom path spcified with the `fp` keyword. `force=True` will overwrite any existing config file, which is the reasonable choice in most cases.
-
-From there on, the simulation is (almost) ready to be executable from the commandline.
-
-### Commandline API
-
-The commandline API runs a series of commands that load the case study, execute the {meth}`pymob.simulation.SimulationBase.initialize` method and perform some more initialization tasks, before running the required job.
-
-+ `pymob-infer`: Runs an inference job e.g. `pymob-infer --case_study=quickstart --scenario=test --inference_backend=numpyro`. While there are more commandline options, these are the two required 
+# Pymob quickstart
+
+This super-quick quickstart provides an introduction to the basic Pymob workflow and its key functionalities.  
+We will explore a simple linear regression model that we want to fit to a noisy dataset.  
+Pymob supports the modeling process by providing several tools for *data structuring*, *parameter estimation* and *visualization of results*.  
+  
+If you are looking for a more detailed introduction, [click here]().  
+If you want to learn how to work with ODE models, check out [this tutorial]().
+
+## Pymob components
+
+Before starting the modeling process, let's take a look at the main steps and modules of pymob:
+
+1. __Simulation:__   
+First, we need to initialize a Simulation object by creating an instance of the {class}`pymob.simulation.SimulationBase` class from the simulation module.   
+Optionally, we can configure the simulation with `sim.config.case_study.name = "linear-regression"`, `sim.config.case_study.scenario = "test"` and many other options. 
+
+2. __Model:__   
+Our model will be defined as a standard python function.  
+We will then assign it to the Simulation object by accessing the `.model` attribute. 
+
+3. __Observations:__   
+Our observation data must be structured as a [xarray.Dataset](https://docs.xarray.dev/en/stable/generated/xarray.Dataset.html).  
+We assign it to the `.observations` attribute of our Simulation object.   
+Calling `sim.config.data_structure` will give us further information about the layout of our data.  
+
+4. __Solver:__  
+A [solver](https://pymob.readthedocs.io/en/stable/api/pymob.solvers.html) is required to solve the model.   
+In our simple case, we will use the `solve_analytic_1d` solver from the `pymob.solver.analytic` module.  
+We assign it to our Simulation object using the `.solver` attribute.   
+Since our model already provides an analytical solution, this solver basically does nothing. It is still needed to fulfill Pymob's requirement for a solver component.   
+For more complex models (e.g. ODEs), the `JaxSolver` from the `pymob.solvers.diffrax` module is a more powerful option.   
+Users can also implement custom solvers as a subclass of `pymob.solver.SolverBase`.   
+  
+5. __Inferer:__  
+The inferer handels the parameter estimation.  
+Pymob supports [various backends](https://pymob.readthedocs.io/en/stable/user_guide/framework_overview.html). In this example, we will work with *NumPyro*.  
+We assign the inferer to our Simulation object via the `.inferer` attribute and configure the desired kernel (e.g. *nuts*).  
+But before inference, we need to parameterize our model using the *Param* class.   
+Each parameter can be marked either as free or fixed, depending on whether it should be variable during the optimization procedure.   
+The parameters are stored in the `sim.model_parameters` dictionary, which holds model input values.
+By default, it takes the keys: `parameters`, `y0` and `x_in`. 
+
+6. __Evaluator:__  
+The Evaluator is an instance to manage model evaluations.
+It sets up tasks, coordinates parallel runs of the simulation and keeps track of the results from each simulation or parameter inference process.  
+
+7. __Config:__  
+The simulation settings will be saved in a `.cfg` configuration file.  
+The config file contains information about our simulation in various sections. -> [Learn more here](https://pymob.readthedocs.io/en/stable/user_guide/case_studies.html#configuration).  
+We can further use it to create new simulations by loading settings from a config file. 
+
+
+![framework-overview](.\figures\pymob_overview.png)
+
+
+```python
+# First, import the necessary python packages
+import numpy as np
+import matplotlib.pyplot as plt
+import xarray as xr
+
+# Import the pymob modules
+from pymob.simulation import SimulationBase
+from pymob.sim.solvetools import solve_analytic_1d
+from pymob.sim.config import Param
+```
+
+Since no measured data is provided, we will generate an artificial dataset.  
+$y_{obs}$ represents the observed data over the time $t$ [0, 10].  
+To use this data later in the simulation, we need to convert it into a xarray-Dataset.  
+In your own application, you would replace this with your measured experimental data.  
+
+
+```python
+# Parameter for the artificial data generation
+slope = np.random.uniform(2.0, 4.0) 
+intercept = 1.0
+num_points = 100
+noise_level = 1.7
+
+# generating time values
+t = np.linspace(0, 10, num_points)
+
+# generating y-values with noise
+noise = np.random.normal(0, noise_level, num_points)
+y_obs = slope * t + intercept + noise
+
+# visualizing our data
+fig, ax = plt.subplots(figsize=(5, 4))
+ax.scatter(t, y_obs, label='Datapoints')
+ax.set(xlabel='t [-]', ylabel='y_obs [-]', title ='Artificial Data')
+plt.tight_layout()
+
+# convert the data to an xr-Dataset
+data_obs = xr.DataArray(y_obs, coords={"t": t}).to_dataset(name="y")
+data_obs
+```
+
+
+
+
+<div><svg style="position: absolute; width: 0; height: 0; overflow: hidden">
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+  --xr-background-color: var(--jp-layout-color0, white);
+  --xr-background-color-row-even: var(--jp-layout-color1, white);
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+.xr-var-data,
+.xr-index-data {
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+}
+
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+  padding: 0;
+  margin: 0;
+  display: grid;
+  grid-template-columns: 125px auto;
+}
+
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+.xr-attrs dd {
+  padding: 0;
+  margin: 0;
+  float: left;
+  padding-right: 10px;
+  width: auto;
+}
+
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+  font-weight: normal;
+  grid-column: 1;
+}
+
+.xr-attrs dt:hover span {
+  display: inline-block;
+  background: var(--xr-background-color);
+  padding-right: 10px;
+}
+
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+  grid-column: 2;
+  white-space: pre-wrap;
+  word-break: break-all;
+}
+
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+.xr-icon-file-text2,
+.xr-no-icon {
+  display: inline-block;
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+  width: 1em;
+  height: 1.5em !important;
+  stroke-width: 0;
+  stroke: currentColor;
+  fill: currentColor;
+}
+</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;
+Dimensions:  (t: 100)
+Coordinates:
+  * t        (t) float64 0.0 0.101 0.202 0.303 0.404 ... 9.697 9.798 9.899 10.0
+Data variables:
+    y        (t) float64 2.537 0.762 3.105 3.813 ... 33.87 34.32 37.92 39.47</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-3cba92b8-0944-42c5-b82a-35783b953cd4' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-3cba92b8-0944-42c5-b82a-35783b953cd4' 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'>t</span>: 100</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-a797f301-1248-4646-9731-91e28642f5e8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a797f301-1248-4646-9731-91e28642f5e8' class='xr-section-summary' >Coordinates: <span>(1)</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'>t</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.101 0.202 ... 9.899 10.0</div><input id='attrs-c80e20c8-6a9d-4f7e-9478-466ed8adac53' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c80e20c8-6a9d-4f7e-9478-466ed8adac53' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-60347dee-7fd7-40d1-b503-b9cd31e0334a' class='xr-var-data-in' type='checkbox'><label for='data-60347dee-7fd7-40d1-b503-b9cd31e0334a' 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.      ,  0.10101 ,  0.20202 ,  0.30303 ,  0.40404 ,  0.505051,
+        0.606061,  0.707071,  0.808081,  0.909091,  1.010101,  1.111111,
+        1.212121,  1.313131,  1.414141,  1.515152,  1.616162,  1.717172,
+        1.818182,  1.919192,  2.020202,  2.121212,  2.222222,  2.323232,
+        2.424242,  2.525253,  2.626263,  2.727273,  2.828283,  2.929293,
+        3.030303,  3.131313,  3.232323,  3.333333,  3.434343,  3.535354,
+        3.636364,  3.737374,  3.838384,  3.939394,  4.040404,  4.141414,
+        4.242424,  4.343434,  4.444444,  4.545455,  4.646465,  4.747475,
+        4.848485,  4.949495,  5.050505,  5.151515,  5.252525,  5.353535,
+        5.454545,  5.555556,  5.656566,  5.757576,  5.858586,  5.959596,
+        6.060606,  6.161616,  6.262626,  6.363636,  6.464646,  6.565657,
+        6.666667,  6.767677,  6.868687,  6.969697,  7.070707,  7.171717,
+        7.272727,  7.373737,  7.474747,  7.575758,  7.676768,  7.777778,
+        7.878788,  7.979798,  8.080808,  8.181818,  8.282828,  8.383838,
+        8.484848,  8.585859,  8.686869,  8.787879,  8.888889,  8.989899,
+        9.090909,  9.191919,  9.292929,  9.393939,  9.494949,  9.59596 ,
+        9.69697 ,  9.79798 ,  9.89899 , 10.      ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-73cae99e-8b2f-4feb-824c-55817b00ee92' class='xr-section-summary-in' type='checkbox'  checked><label for='section-73cae99e-8b2f-4feb-824c-55817b00ee92' class='xr-section-summary' >Data variables: <span>(1)</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>y</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.537 0.762 3.105 ... 37.92 39.47</div><input id='attrs-44ebf413-4dc2-4ed5-b79b-4a22c6c5d3c2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-44ebf413-4dc2-4ed5-b79b-4a22c6c5d3c2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c8abb7e6-b7f3-4dfa-914b-0afef1078d02' class='xr-var-data-in' type='checkbox'><label for='data-c8abb7e6-b7f3-4dfa-914b-0afef1078d02' 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([ 2.53735306,  0.76202974,  3.10502331,  3.81269502,  1.65493898,
+        4.7382624 ,  1.91659115,  5.09948271,  4.09436031,  2.93203812,
+        2.41660779,  3.52721064,  4.52038892,  5.25244692,  6.49443682,
+        7.15817191,  7.37549405,  6.45793422, 10.42760157,  7.0160899 ,
+        8.44228028,  8.14150763,  8.33164821,  9.06734271, 10.1364285 ,
+        9.89235673, 11.86570089,  8.92148715,  8.00979706, 12.612303  ,
+       12.45222116, 14.50730789, 11.04216221, 13.04196904, 10.78455757,
+       14.51374821, 15.78655397, 11.85873628, 15.17499665, 12.55360977,
+       17.20997672, 14.13169503, 13.91247417, 17.84122887, 19.53174081,
+       18.97160079, 20.69099192, 19.09570155, 19.80465631, 21.57411683,
+       18.67932246, 15.86782202, 21.89297698, 21.14301831, 20.60363498,
+       23.19466707, 21.78505986, 24.02588231, 19.93938713, 24.11449125,
+       23.38553814, 20.22692378, 22.04672104, 24.69358621, 26.35163201,
+       26.87845261, 25.66642845, 24.78028588, 28.13555625, 28.77362647,
+       29.8394705 , 28.96746404, 26.8770062 , 28.38000956, 27.32974723,
+       28.48364593, 28.19300058, 31.5562261 , 27.70039454, 30.37674089,
+       33.23517464, 31.15096457, 31.92976207, 30.66092298, 37.31956867,
+       33.85527604, 30.32904845, 31.79863742, 32.96114687, 35.05260485,
+       36.40587357, 32.88890392, 33.20810035, 35.01590153, 35.25401727,
+       39.29738961, 33.86973557, 34.3242026 , 37.91957645, 39.46912001])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1bb34202-8817-4d17-b85c-9a071e56812b' class='xr-section-summary-in' type='checkbox'  ><label for='section-1bb34202-8817-4d17-b85c-9a071e56812b' class='xr-section-summary' >Indexes: <span>(1)</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>t</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f2ac7879-0c60-4f2e-8504-7516258bff90' class='xr-index-data-in' type='checkbox'/><label for='index-f2ac7879-0c60-4f2e-8504-7516258bff90' 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([                0.0, 0.10101010101010101, 0.20202020202020202,
+       0.30303030303030304, 0.40404040404040403,  0.5050505050505051,
+        0.6060606060606061,  0.7070707070707071,  0.8080808080808081,
+        0.9090909090909091,  1.0101010101010102,  1.1111111111111112,
+        1.2121212121212122,  1.3131313131313131,  1.4141414141414141,
+        1.5151515151515151,  1.6161616161616161,  1.7171717171717171,
+        1.8181818181818181,  1.9191919191919191,  2.0202020202020203,
+         2.121212121212121,  2.2222222222222223,   2.323232323232323,
+        2.4242424242424243,   2.525252525252525,  2.6262626262626263,
+         2.727272727272727,  2.8282828282828283,   2.929292929292929,
+        3.0303030303030303,   3.131313131313131,  3.2323232323232323,
+        3.3333333333333335,  3.4343434343434343,  3.5353535353535355,
+        3.6363636363636362,  3.7373737373737375,  3.8383838383838382,
+        3.9393939393939394,   4.040404040404041,   4.141414141414141,
+         4.242424242424242,   4.343434343434343,   4.444444444444445,
+         4.545454545454545,   4.646464646464646,   4.747474747474747,
+         4.848484848484849,    4.94949494949495,    5.05050505050505,
+         5.151515151515151,   5.252525252525253,   5.353535353535354,
+         5.454545454545454,   5.555555555555555,   5.656565656565657,
+         5.757575757575758,   5.858585858585858,   5.959595959595959,
+        6.0606060606060606,   6.161616161616162,   6.262626262626262,
+         6.363636363636363,  6.4646464646464645,   6.565656565656566,
+         6.666666666666667,   6.767676767676767,  6.8686868686868685,
+          6.96969696969697,   7.070707070707071,   7.171717171717171,
+        7.2727272727272725,   7.373737373737374,   7.474747474747475,
+         7.575757575757575,  7.6767676767676765,   7.777777777777778,
+         7.878787878787879,   7.979797979797979,   8.080808080808081,
+         8.181818181818182,   8.282828282828282,   8.383838383838384,
+         8.484848484848484,   8.585858585858587,   8.686868686868687,
+         8.787878787878787,    8.88888888888889,    8.98989898989899,
+          9.09090909090909,   9.191919191919192,   9.292929292929292,
+         9.393939393939394,   9.494949494949495,   9.595959595959595,
+         9.696969696969697,   9.797979797979798,     9.8989898989899,
+                      10.0],
+      dtype=&#x27;float64&#x27;, name=&#x27;t&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-89be94e4-c853-4f55-9cc5-db089c3863c0' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-89be94e4-c853-4f55-9cc5-db089c3863c0' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>
+
+
+
+
+    
+![png](superquickstart_files/superquickstart_6_1.png)
+    
+
+
+## Initialize a simulation
+
+In pymob, a Simulation object is initialized by creating an instance of the {class}`pymob.simulation.SimulationBase` class from the simulation module.  
+We will choose a linear regression model, as it provides a good approximation of the data: $ y = a + b*x $
+
+```{admonition} x-dimension
+:class: note
+The x_dimension of our simulation can have any name, for example t as often used for time series data.
+You can specify it via `sim.config.simulation.x_dimension`.
+```
+
+
+```python
+# Initialize the Simulation object
+sim = SimulationBase()
+
+# Define the linear regression model
+def linreg(x, a, b):
+    return a + b * x
+
+# Add the model to the simulation
+sim.model = linreg
+
+# Adding our dataset to the simulation
+sim.observations = data_obs
+
+# Defining a solver
+sim.solver = solve_analytic_1d
+```
+
+    MinMaxScaler(variable=y, min=0.7620297399871993, max=39.46912001079589)
+    
+
+    C:\Users\mgrho\pymob\pymob\simulation.py:303: UserWarning: `sim.config.data_structure.y = Datavariable(dimensions=['t'] min=0.7620297399871993 max=39.46912001079589 observed=True dimensions_evaluator=None)` has been assumed from `sim.observations`. If the order of the dimensions should be different, specify `sim.config.data_structure.y = DataVariable(dimensions=[...], ...)` manually.
+      warnings.warn(
+    
+
+```{admonition} Scalers
+:class: note
+We notice a mysterious Scaler message. This tells us that our data variable has been identified and a scaler was constructed, which transforms the variable between [0, 1].   
+This has no effect at the moment, but it can be used later. Scaling can be powerful to help parameter estimation in more complex models.
+```
+
+
+## Running the model 🏃
+
+Next, we define the model parameters *a* and *b*.  
+Parameter *a* is set as fixed (`free = False`), meaning its value is known and will not be estimated during optimization.  
+Parameter *b* is marked as free (`free = True`), allowing it to be optimized to fit the data. As an initial guess, we assume b = 3.   
+
+Our model is now prepared with a defined parameter set.  
+To initialize the *Evaluator* class, we call `sim.dispatch_constructor()`.   
+This step is essential and must be executed every time changes are made to the model. 
+
+The returned dataset (`evaluator.results`) has the exact same shape as the observation data.
+
+
+```python
+# Parameterizing the model
+sim.config.model_parameters.a = Param(value=1, free=False)
+sim.config.model_parameters.b = Param(value=3, free=True)
+# this makes sure the model parameters are available to the model.
+sim.model_parameters["parameters"] = sim.config.model_parameters.value_dict
+
+# put everything in place for running the simulation
+sim.dispatch_constructor()
+
+# run
+evaluator = sim.dispatch(theta={"b":3})
+evaluator()
+evaluator.results
+```
+
+    C:\Users\mgrho\pymob\pymob\simulation.py:552: UserWarning: The number of ODE states was not specified in the config file [simulation] > 'n_ode_states = <n>'. Extracted the return arguments ['a+b*x'] from the source code. Setting 'n_ode_states=1.
+      warnings.warn(
+    
+
+
+
+
+<div><svg style="position: absolute; width: 0; height: 0; overflow: hidden">
+<defs>
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+.xr-no-icon {
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+  stroke-width: 0;
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+</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;
+Dimensions:  (t: 100)
+Coordinates:
+  * t        (t) float64 0.0 0.101 0.202 0.303 0.404 ... 9.697 9.798 9.899 10.0
+Data variables:
+    y        (t) float64 1.0 1.303 1.606 1.909 2.212 ... 30.09 30.39 30.7 31.0</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-1c65b503-5085-41c4-8541-11fd2419ad56' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1c65b503-5085-41c4-8541-11fd2419ad56' 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'>t</span>: 100</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-5019ed9d-7d01-441b-bef4-dfd10f6916b8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5019ed9d-7d01-441b-bef4-dfd10f6916b8' class='xr-section-summary' >Coordinates: <span>(1)</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'>t</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.101 0.202 ... 9.899 10.0</div><input id='attrs-3bd2447e-bca7-41d2-b649-383901267a04' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3bd2447e-bca7-41d2-b649-383901267a04' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cbd54d71-5413-4299-8c7b-d851d64cc0a2' class='xr-var-data-in' type='checkbox'><label for='data-cbd54d71-5413-4299-8c7b-d851d64cc0a2' 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.      ,  0.10101 ,  0.20202 ,  0.30303 ,  0.40404 ,  0.505051,
+        0.606061,  0.707071,  0.808081,  0.909091,  1.010101,  1.111111,
+        1.212121,  1.313131,  1.414141,  1.515152,  1.616162,  1.717172,
+        1.818182,  1.919192,  2.020202,  2.121212,  2.222222,  2.323232,
+        2.424242,  2.525253,  2.626263,  2.727273,  2.828283,  2.929293,
+        3.030303,  3.131313,  3.232323,  3.333333,  3.434343,  3.535354,
+        3.636364,  3.737374,  3.838384,  3.939394,  4.040404,  4.141414,
+        4.242424,  4.343434,  4.444444,  4.545455,  4.646465,  4.747475,
+        4.848485,  4.949495,  5.050505,  5.151515,  5.252525,  5.353535,
+        5.454545,  5.555556,  5.656566,  5.757576,  5.858586,  5.959596,
+        6.060606,  6.161616,  6.262626,  6.363636,  6.464646,  6.565657,
+        6.666667,  6.767677,  6.868687,  6.969697,  7.070707,  7.171717,
+        7.272727,  7.373737,  7.474747,  7.575758,  7.676768,  7.777778,
+        7.878788,  7.979798,  8.080808,  8.181818,  8.282828,  8.383838,
+        8.484848,  8.585859,  8.686869,  8.787879,  8.888889,  8.989899,
+        9.090909,  9.191919,  9.292929,  9.393939,  9.494949,  9.59596 ,
+        9.69697 ,  9.79798 ,  9.89899 , 10.      ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d063be66-88cf-4f10-a8f4-1bcb5972929f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d063be66-88cf-4f10-a8f4-1bcb5972929f' class='xr-section-summary' >Data variables: <span>(1)</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>y</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.0 1.303 1.606 ... 30.39 30.7 31.0</div><input id='attrs-af1fff76-0563-4521-8e7c-7c457f5042e1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-af1fff76-0563-4521-8e7c-7c457f5042e1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f046512e-5cb8-499e-93c7-5827a840e920' class='xr-var-data-in' type='checkbox'><label for='data-f046512e-5cb8-499e-93c7-5827a840e920' 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([ 1.        ,  1.3030303 ,  1.60606061,  1.90909091,  2.21212121,
+        2.51515152,  2.81818182,  3.12121212,  3.42424242,  3.72727273,
+        4.03030303,  4.33333333,  4.63636364,  4.93939394,  5.24242424,
+        5.54545455,  5.84848485,  6.15151515,  6.45454545,  6.75757576,
+        7.06060606,  7.36363636,  7.66666667,  7.96969697,  8.27272727,
+        8.57575758,  8.87878788,  9.18181818,  9.48484848,  9.78787879,
+       10.09090909, 10.39393939, 10.6969697 , 11.        , 11.3030303 ,
+       11.60606061, 11.90909091, 12.21212121, 12.51515152, 12.81818182,
+       13.12121212, 13.42424242, 13.72727273, 14.03030303, 14.33333333,
+       14.63636364, 14.93939394, 15.24242424, 15.54545455, 15.84848485,
+       16.15151515, 16.45454545, 16.75757576, 17.06060606, 17.36363636,
+       17.66666667, 17.96969697, 18.27272727, 18.57575758, 18.87878788,
+       19.18181818, 19.48484848, 19.78787879, 20.09090909, 20.39393939,
+       20.6969697 , 21.        , 21.3030303 , 21.60606061, 21.90909091,
+       22.21212121, 22.51515152, 22.81818182, 23.12121212, 23.42424242,
+       23.72727273, 24.03030303, 24.33333333, 24.63636364, 24.93939394,
+       25.24242424, 25.54545455, 25.84848485, 26.15151515, 26.45454545,
+       26.75757576, 27.06060606, 27.36363636, 27.66666667, 27.96969697,
+       28.27272727, 28.57575758, 28.87878788, 29.18181818, 29.48484848,
+       29.78787879, 30.09090909, 30.39393939, 30.6969697 , 31.        ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-205c757b-6e11-466d-8fab-67d9291fac41' class='xr-section-summary-in' type='checkbox'  ><label for='section-205c757b-6e11-466d-8fab-67d9291fac41' class='xr-section-summary' >Indexes: <span>(1)</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>t</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-389aa368-2271-4700-81bd-a9e5b3a74441' class='xr-index-data-in' type='checkbox'/><label for='index-389aa368-2271-4700-81bd-a9e5b3a74441' 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([                0.0, 0.10101010101010101, 0.20202020202020202,
+       0.30303030303030304, 0.40404040404040403,  0.5050505050505051,
+        0.6060606060606061,  0.7070707070707071,  0.8080808080808081,
+        0.9090909090909091,  1.0101010101010102,  1.1111111111111112,
+        1.2121212121212122,  1.3131313131313131,  1.4141414141414141,
+        1.5151515151515151,  1.6161616161616161,  1.7171717171717171,
+        1.8181818181818181,  1.9191919191919191,  2.0202020202020203,
+         2.121212121212121,  2.2222222222222223,   2.323232323232323,
+        2.4242424242424243,   2.525252525252525,  2.6262626262626263,
+         2.727272727272727,  2.8282828282828283,   2.929292929292929,
+        3.0303030303030303,   3.131313131313131,  3.2323232323232323,
+        3.3333333333333335,  3.4343434343434343,  3.5353535353535355,
+        3.6363636363636362,  3.7373737373737375,  3.8383838383838382,
+        3.9393939393939394,   4.040404040404041,   4.141414141414141,
+         4.242424242424242,   4.343434343434343,   4.444444444444445,
+         4.545454545454545,   4.646464646464646,   4.747474747474747,
+         4.848484848484849,    4.94949494949495,    5.05050505050505,
+         5.151515151515151,   5.252525252525253,   5.353535353535354,
+         5.454545454545454,   5.555555555555555,   5.656565656565657,
+         5.757575757575758,   5.858585858585858,   5.959595959595959,
+        6.0606060606060606,   6.161616161616162,   6.262626262626262,
+         6.363636363636363,  6.4646464646464645,   6.565656565656566,
+         6.666666666666667,   6.767676767676767,  6.8686868686868685,
+          6.96969696969697,   7.070707070707071,   7.171717171717171,
+        7.2727272727272725,   7.373737373737374,   7.474747474747475,
+         7.575757575757575,  7.6767676767676765,   7.777777777777778,
+         7.878787878787879,   7.979797979797979,   8.080808080808081,
+         8.181818181818182,   8.282828282828282,   8.383838383838384,
+         8.484848484848484,   8.585858585858587,   8.686868686868687,
+         8.787878787878787,    8.88888888888889,    8.98989898989899,
+          9.09090909090909,   9.191919191919192,   9.292929292929292,
+         9.393939393939394,   9.494949494949495,   9.595959595959595,
+         9.696969696969697,   9.797979797979798,     9.8989898989899,
+                      10.0],
+      dtype=&#x27;float64&#x27;, name=&#x27;t&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-cd67cdd0-d69e-4aab-b3c9-91c81ac9fe9b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-cd67cdd0-d69e-4aab-b3c9-91c81ac9fe9b' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>
+
+
+
+Let's take a look at the results.  
+
+You can vary the parameter *b* in the previous step to investigate its influence on the model fit.  
+In the [Beginner Guide](), you can try out the *manual parameter estimation*, which is a feature provided by Pymob.  
+
+
+```python
+fig, ax = plt.subplots(figsize=(5, 4))
+data_res = evaluator.results
+ax.plot(data_obs.t, data_obs.y, ls="", marker="o", color="tab:blue", alpha=.5, label ="observation data")
+ax.plot(data_res.t, data_res.y, color="black", label ="result")
+ax.legend()
+```
+
+
+
+
+    <matplotlib.legend.Legend at 0x25f8941ebd0>
+
+
+
+
+    
+![png](superquickstart_files/superquickstart_14_1.png)
+    
+
+
+## Estimating parameters and uncertainty with MCMC
+Of course this example is very simple - we could, in fact, optimize the parameters perfectly by hand. But just for fun, let's use *Markov Chain Monte Carlo (MCMC)* to estimate the parameters, their uncertainty and the uncertainty in the data. We’ll run the parameter estimation with our inferer, using the NumPyro backend with a NUTS kernel. This completes the job in a few seconds.
+
+We are almost ready to infer the model parameters. To also estimate the uncertainty of the parameters, we add another parameter representing the error and assume that it follows a lognormal distribution. Additionally, we specify an error model for the data distribution. This will be: $$y_{obs} \sim Normal (y, \sigma_y)$$  
+
+
+```python
+sim.config.model_parameters.sigma_y = Param(free=True , prior="lognorm(scale=1,s=1)", min=0, max=1)
+sim.config.model_parameters.b.prior = "lognorm(scale=1,s=1)"
+
+sim.config.error_model.y = "normal(loc=y,scale=sigma_y)"
+
+
+sim.set_inferer("numpyro")
+sim.inferer.config.inference_numpyro.kernel = "nuts"
+sim.inferer.run()
+
+sim.inferer.idata.posterior
+
+# Plot the results
+sim.config.simulation.x_dimension = "t"
+sim.posterior_predictive_checks(pred_hdi_style={"alpha": 0.1})
+```
+
+    C:\Users\mgrho\pymob\pymob\inference\numpyro_backend.py:552: UserWarning: Model is not rendered, because the graphviz executable is not found. Try search for 'graphviz executables not found' and the used OS. This should be an easy fix :-)
+      warnings.warn(
+    
+
+    Jax 64 bit mode: False
+    Absolute tolerance: 1e-07
+    Trace Shapes:      
+     Param Sites:      
+    Sample Sites:      
+           b dist     |
+            value     |
+     sigma_y dist     |
+            value     |
+       y_obs dist 100 |
+            value 100 |
+    
+
+    sample: 100%|██████████| 3000/3000 [00:02<00:00, 1240.00it/s, 3 steps of size 7.01e-01. acc. prob=0.94]
+    
+
+    
+                    mean       std    median      5.0%     95.0%     n_eff     r_hat
+             b      3.68      0.03      3.68      3.63      3.73   1376.15      1.00
+       sigma_y      1.75      0.13      1.74      1.54      1.97   1188.08      1.00
+    
+    Number of divergences: 0
+    
+
+
+    
+![png](superquickstart_files/superquickstart_16_4.png)
+    
+
+
+```{admonition} numpyro distributions
+:class: warning
+Currently only few distributions are implemented in the numpyro backend. This API will soon change, so that basically any distribution can be used to specifcy parameters. 
+```
+
+We can inspect our estimates and ssee that the model provides a good fit for the parameters.  
+Note that we only get an estimate for $b$. Previously, we set the parameter $a$ with the flag `free = False`.   
+This effectively excludes it from the estimation and uses its default value, which was set to the true value `a = 0`.
+
+
+```{admonition} Customize the posterior predictive checks
+:class: hint
+You can explore the API of {class}`pymob.sim.plot.SimulationPlot` to find out how you can work on the default predictions. Of course you can always make your own plot, by accessing {attr}`pymob.simulation.inferer.idata` and {attr}`pymob.simulation.observations`
+```
+
+## Report the results
+
+Pymob provides an option to generate an automated report of the parameter distribution.  
+The report can be configured by modifying the options in `sim.config.report`.
+
+
+```python
+# report the results
+sim.report()
+```
+
+
+    
+![png](superquickstart_files/superquickstart_21_0.png)
+    
+
+
+
+    
+![png](superquickstart_files/superquickstart_21_1.png)
+    
+
+
+
+## Exporting the simulation and running it via the case study API
+
+After constructing the simulation, all settings - custom and default - can be exported to a comprehensive configuration file.   
+The simulation will be saved to the default path (`CASE_STUDY/scenarios/SCENARIO/settings.cfg`) or to a custom path, specified with the file path `fp` keyword.   
+Setting `force=True` will overwrite any existing config file, which is a reasonable choice in most cases.
+From this point on, the simulation is (almost) ready to be executed from the command-line. 
+
+
+```python
+import os
+sim.config.case_study.name = "quickstart"
+sim.config.case_study.scenario = "test"
+sim.config.create_directory("scenario", force=True)
+sim.config.create_directory("results", force=True)
+
+# usually we expect to have a data directory in the case
+os.makedirs(sim.data_path, exist_ok=True)
+sim.save_observations(force=True)
+sim.config.save(force=True)
+```
+
+    Scenario directory exists at 'c:\Users\mgrho\pymob\docs\source\user_guide\case_studies\quickstart\scenarios\test'.
+    Results directory exists at 'c:\Users\mgrho\pymob\docs\source\user_guide\case_studies\quickstart\results\test'.
+    
+
+### Commandline API
+
+The command-line API runs a series of commands that load the case study, execute the {meth}`pymob.simulation.SimulationBase.initialize` method and perform some more initialization tasks before running the required job.
+
++ `pymob-infer` runs an inference job, for example:  
+
+  `pymob-infer --case_study=quickstart --scenario=test --inference_backend=numpyro`.   
+  While there are more command-line options, these two (--case_study and --scenario) are required.
+
+

From 9beed0713c9c1d612f094794000197496449c721 Mon Sep 17 00:00:00 2001
From: amelieleo <aleonardi@uni-osnabrueck.de>
Date: Mon, 16 Jun 2025 11:05:44 +0200
Subject: [PATCH 05/16] new long introduction - 1. version

---
 docs/source/user_guide/Introduction.ipynb | 2184 +++++++++++++++++++++
 1 file changed, 2184 insertions(+)
 create mode 100644 docs/source/user_guide/Introduction.ipynb

diff --git a/docs/source/user_guide/Introduction.ipynb b/docs/source/user_guide/Introduction.ipynb
new file mode 100644
index 000000000..a72381cf8
--- /dev/null
+++ b/docs/source/user_guide/Introduction.ipynb
@@ -0,0 +1,2184 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Pymob  Introduction\n",
+    "## Overview\n",
+    "**Pymob** is a Python-based platform for parameter estimation across a wide range of models. It abstracts repetitive tasks in the modeling process so that you can focus on building models, asking questions to the real world and learn from observations. <br>\n",
+    "The idea of pymob originated from the frustration with fitting complex models to complicated datasets (missing observations, non-uniform data structure, non-linear models, ODE models). In such scenarios a lot of time is spent matching observations with model results. <br>\n",
+    "One of Pymob’s key strengths is its streamlined model definition workflow. This not only simplifies the process of building models but also lets you apply a host of advanced optimization and inference algorithms, giving you the flexibility to iterate and discover solutions more effectively. <br>\n",
+    "\n",
+    "### What's the focus of this introduction?\n",
+    "This introduction will give you an overview of the pymob package and an easy example on how to use it. After, you can explore more advanced tutorials and deepen your pymob kowledge. <br> \n",
+    "First the general structure of the pymob package will be explained. You will get to know the function of the components. Subsequentenly you will get instructions to use pymob for your first parameter estimation with a simple example. \n",
+    "\n",
+    "### How pymob is structured:\n",
+    "Here  you can see the structure of the structure of pymob package: <br>\n",
+    "![Structure of the pymob package](..\\user_guide\\figures\\pymob_overview.png) <br>\n",
+    "The Pymob package consists of several elements: \n",
+    "\n",
+    "\n",
+    "1) __simulation__ <br>\n",
+    "First, we need to initialize a Simulation object by calling the {class}`pymob.simulation.SimulationBase` class from the simulation module.   \n",
+    "Optionally, we can configure the simulation object with `sim.config.case_study.name` = \"linear-regression\", `sim.config.case_study.scenario` = \"test\" and many more options. \n",
+    "\n",
+    "2) __model__ <br>\n",
+    "The model is a python function you define. With the model you try to describe the data you observed. A classical model is, for example, the Lotka-Volterra model to describe the interactions of predators and prey. In the tutorial today, the model will be a simple linear function. <br>\n",
+    "The model will be added to the simualtion by using `.model`\n",
+    "\n",
+    "3) __observations__ <br>\n",
+    "The obseravtions are the data points, to which we want to fit our model. The observation data needs to be an `xarray.Dataset` ([learn more here](https://docs.xarray.dev/en/stable/getting-started-guide/quick-overview.html)).  \n",
+    "We assign it to our Simulation object by  `.observations`.  \n",
+    "`sim.config.data_structure` will give us some information about the layout of our data.\n",
+    "\n",
+    "4) __solver__ <br>\n",
+    "A solver is required for many models e.g. models that contain differential equations. Solvers in pymob are callables that need to return a dictionary of results mapped to the data variables. <br>\n",
+    "The solver is assigned to the Simulation object by `.solver`. <br>\n",
+    "These solvers are currently implemented in pymob: \n",
+    "    - analytic module\n",
+    "        - solve_analytic_1d\n",
+    "    - base module \n",
+    "        - SolverBase\n",
+    "        - curve_jumps\n",
+    "        - jump_interpolation\n",
+    "        - mappar\n",
+    "        - radius_interpolation\n",
+    "        - rect_interpolation\n",
+    "        - smoothed_interpolation\n",
+    "    - diffrax module\n",
+    "        - JaxSolver\n",
+    "    - scipy module\n",
+    "        - solve_ivp_1d\n",
+    "\n",
+    "The documentation can be found [here](https://pymob.readthedocs.io/en/stable/api/pymob.solvers.html) \n",
+    "\n",
+    "5) __inferer__ <br>\n",
+    "    The inferer serves as the parameter estimator. Pymob provides various backends. You can find detailed information [here](https://pymob.readthedocs.io/en/stable/user_guide/framework_overview.html). <br>\n",
+    "    Currently, supported inference backends are:\n",
+    "    * interactive (interactive backend in jupyter notebookswith parameter sliders)\n",
+    "    * numpyro (bayesian inference and stochastic variational inference)\n",
+    "    * pyabc (approximate bayesian inference)\n",
+    "    * pymoo (experimental multi-objective optimization)\n",
+    "\n",
+    "6)  __config__ <br>\n",
+    "Pymob uses `pydantic` models to validate configuration files, with the configuration organized into separate sections. You can modify these configurations either by editing the files before initializing a simulation from a config file, or directly within the script. During parameter estimation setup, all configuration settings are stored in a config object, which can later be exported as a `.cfg` file.\n",
+    "7) __evaluator__ <br>\n",
+    "    - for running the model\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "### Let's get started 🎉\n",
+    "You will need several packages during this introduction:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# imports from pymob\n",
+    "from pymob.simulation import SimulationBase\n",
+    "from pymob.sim.solvetools import solve_analytic_1d\n",
+    "from pymob.sim.config import Param\n",
+    "\n",
+    "# other imports\n",
+    "import numpy as np\n",
+    "import xarray as xr\n",
+    "from matplotlib import pyplot as plt\n",
+    "import os\n",
+    "from numpy import random"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the following tutorial, you’ll notice some import statements included as comments. These are provided to indicate which package is required for each step."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Generate artificial data\n",
+    "\n",
+    "In the real world, you will have measured a dataset. For demonstration, we generate some artifical data. Later we will fit the model to our artifical data. <br>\n",
+    "$y_{obs}$ represents the observation data over the time $t$ [0, 10].  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Parameter for the artificial data generation\n",
+    "rng = np.random.default_rng(seed=1)  # for reproducibility\n",
+    "slope = rng.uniform(1,4)\n",
+    "intercept = 1.0\n",
+    "num_points = 100\n",
+    "noise_level = 1.7\n",
+    "\n",
+    "# generating x-values\n",
+    "x = np.linspace(0, 10, num_points)\n",
+    "\n",
+    "# generating y-values with noise\n",
+    "noise = np.random.normal(0, noise_level, num_points)\n",
+    "y_obs = slope * x + intercept + noise\n",
+    "\n",
+    "data = np.array(y_obs)\n",
+    "\n",
+    "# visualising our data\n",
+    "plt.scatter(x, y_obs, label='Datapoints')\n",
+    "plt.xlabel('t [-]')\n",
+    "plt.ylabel('values [-]')\n",
+    "plt.title('Artificial Data')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Above you can see you're generated artificial data. At the moment it's stored in a normal array as you can see below: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[-1.69991666  0.7770054  -2.36015111  2.91014224  0.63066817  2.40394824\n",
+      "  4.31395361  4.42931318  5.4571753   1.03117589  1.45487093  1.86606004\n",
+      "  3.72839181  1.47528361  5.42852574  3.78836883  4.70564204  4.21259739\n",
+      "  6.27489982  4.98923399  9.03610435  5.43110992  6.79454758  9.00144808\n",
+      "  5.86470108  7.05892069  5.95518505  8.36331162  7.22203679  6.32809727\n",
+      " 10.75460038  7.23512537  7.93339034  9.19854853 11.37894337  7.77096616\n",
+      "  8.40322914 12.14786636 10.14789604  8.75451075 10.49370251 14.96185282\n",
+      "  9.60044473 13.18702914  9.57685206 13.31587966  9.58160647 13.68228838\n",
+      " 14.33005712 12.4450883  14.97002787 14.73098301 14.38551919 13.01083281\n",
+      " 13.25281733 13.77742185 12.17359029 17.49924141 14.48031642 18.25112152\n",
+      " 19.52123994 15.21968595 14.3528846  14.44941175 16.05506821 19.02447177\n",
+      " 17.4895854  14.25242034 17.14824633 20.33126544 19.31609374 20.56085204\n",
+      " 19.10507125 19.33591233 20.23652105 19.25595876 21.50762196 23.11688583\n",
+      " 21.84582015 21.16707988 20.47574538 22.19884108 21.29531012 22.66865517\n",
+      " 19.43942123 22.52109512 21.1049189  21.81951277 25.1887952  24.33157026\n",
+      " 22.99033129 25.66906984 24.94970416 24.74672747 26.66680386 24.52966849\n",
+      " 27.12576605 28.26974095 24.62870675 22.66858819]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# our artificial data is now in the variable data\n",
+    "print(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The pymob package operates with `xarray.Dataset`. We avoid most of the mess by using `xarray` as a common input/output format. So we have to transform our data into a `xarray.Dataset`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "obs_data = xr.DataArray(data, dims = (\"t\"), coords={\"t\": x}).to_dataset(name=\"data\") "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note: If you want to rename your data-dimension you have to change every `.data` to the new name!\n",
+    "\n",
+    "It can be helpful to look at the data befor going forward, especially if you never worked with *xarray Datasets*. At the section 'Data variables' you'll find the data you just generated. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "<defs>\n",
+       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
+       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "</symbol>\n",
+       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
+       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
+       " *\n",
+       " */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
+       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
+       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
+       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
+       "\n",
+       "html[theme=dark],\n",
+       "body[data-theme=dark],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
+       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
+       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
+       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-disabled-color: #515151;\n",
+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
+       "Dimensions:  (t: 100)\n",
+       "Coordinates:\n",
+       "  * t        (t) float64 0.0 0.101 0.202 0.303 0.404 ... 9.697 9.798 9.899 10.0\n",
+       "Data variables:\n",
+       "    data     (t) float64 -1.7 0.777 -2.36 2.91 ... 27.13 28.27 24.63 22.67</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-6ecf6756-3925-41d8-92ae-1aff9f4a60cd' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-6ecf6756-3925-41d8-92ae-1aff9f4a60cd' 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'>t</span>: 100</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-ac86f16a-5fb1-4739-9258-2034bb0ff1f2' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ac86f16a-5fb1-4739-9258-2034bb0ff1f2' class='xr-section-summary' >Coordinates: <span>(1)</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'>t</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.101 0.202 ... 9.899 10.0</div><input id='attrs-3e101e79-bd89-432e-999e-373ccb8f405f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3e101e79-bd89-432e-999e-373ccb8f405f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-49e2deca-a228-4c30-af25-cb53b243a54f' class='xr-var-data-in' type='checkbox'><label for='data-49e2deca-a228-4c30-af25-cb53b243a54f' 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.      ,  0.10101 ,  0.20202 ,  0.30303 ,  0.40404 ,  0.505051,\n",
+       "        0.606061,  0.707071,  0.808081,  0.909091,  1.010101,  1.111111,\n",
+       "        1.212121,  1.313131,  1.414141,  1.515152,  1.616162,  1.717172,\n",
+       "        1.818182,  1.919192,  2.020202,  2.121212,  2.222222,  2.323232,\n",
+       "        2.424242,  2.525253,  2.626263,  2.727273,  2.828283,  2.929293,\n",
+       "        3.030303,  3.131313,  3.232323,  3.333333,  3.434343,  3.535354,\n",
+       "        3.636364,  3.737374,  3.838384,  3.939394,  4.040404,  4.141414,\n",
+       "        4.242424,  4.343434,  4.444444,  4.545455,  4.646465,  4.747475,\n",
+       "        4.848485,  4.949495,  5.050505,  5.151515,  5.252525,  5.353535,\n",
+       "        5.454545,  5.555556,  5.656566,  5.757576,  5.858586,  5.959596,\n",
+       "        6.060606,  6.161616,  6.262626,  6.363636,  6.464646,  6.565657,\n",
+       "        6.666667,  6.767677,  6.868687,  6.969697,  7.070707,  7.171717,\n",
+       "        7.272727,  7.373737,  7.474747,  7.575758,  7.676768,  7.777778,\n",
+       "        7.878788,  7.979798,  8.080808,  8.181818,  8.282828,  8.383838,\n",
+       "        8.484848,  8.585859,  8.686869,  8.787879,  8.888889,  8.989899,\n",
+       "        9.090909,  9.191919,  9.292929,  9.393939,  9.494949,  9.59596 ,\n",
+       "        9.69697 ,  9.79798 ,  9.89899 , 10.      ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f0c7e538-4bb6-41ea-8451-f1ea823e6f27' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f0c7e538-4bb6-41ea-8451-f1ea823e6f27' class='xr-section-summary' >Data variables: <span>(1)</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>data</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.7 0.777 -2.36 ... 24.63 22.67</div><input id='attrs-b4325c81-ee34-4fc8-b74e-f502cab4ab55' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b4325c81-ee34-4fc8-b74e-f502cab4ab55' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-692299a7-ebe2-4d2b-a439-1686de231dbe' class='xr-var-data-in' type='checkbox'><label for='data-692299a7-ebe2-4d2b-a439-1686de231dbe' 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([-1.69991666,  0.7770054 , -2.36015111,  2.91014224,  0.63066817,\n",
+       "        2.40394824,  4.31395361,  4.42931318,  5.4571753 ,  1.03117589,\n",
+       "        1.45487093,  1.86606004,  3.72839181,  1.47528361,  5.42852574,\n",
+       "        3.78836883,  4.70564204,  4.21259739,  6.27489982,  4.98923399,\n",
+       "        9.03610435,  5.43110992,  6.79454758,  9.00144808,  5.86470108,\n",
+       "        7.05892069,  5.95518505,  8.36331162,  7.22203679,  6.32809727,\n",
+       "       10.75460038,  7.23512537,  7.93339034,  9.19854853, 11.37894337,\n",
+       "        7.77096616,  8.40322914, 12.14786636, 10.14789604,  8.75451075,\n",
+       "       10.49370251, 14.96185282,  9.60044473, 13.18702914,  9.57685206,\n",
+       "       13.31587966,  9.58160647, 13.68228838, 14.33005712, 12.4450883 ,\n",
+       "       14.97002787, 14.73098301, 14.38551919, 13.01083281, 13.25281733,\n",
+       "       13.77742185, 12.17359029, 17.49924141, 14.48031642, 18.25112152,\n",
+       "       19.52123994, 15.21968595, 14.3528846 , 14.44941175, 16.05506821,\n",
+       "       19.02447177, 17.4895854 , 14.25242034, 17.14824633, 20.33126544,\n",
+       "       19.31609374, 20.56085204, 19.10507125, 19.33591233, 20.23652105,\n",
+       "       19.25595876, 21.50762196, 23.11688583, 21.84582015, 21.16707988,\n",
+       "       20.47574538, 22.19884108, 21.29531012, 22.66865517, 19.43942123,\n",
+       "       22.52109512, 21.1049189 , 21.81951277, 25.1887952 , 24.33157026,\n",
+       "       22.99033129, 25.66906984, 24.94970416, 24.74672747, 26.66680386,\n",
+       "       24.52966849, 27.12576605, 28.26974095, 24.62870675, 22.66858819])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ba8c4568-c459-4b85-92a7-7a860bfe8177' class='xr-section-summary-in' type='checkbox'  ><label for='section-ba8c4568-c459-4b85-92a7-7a860bfe8177' class='xr-section-summary' >Indexes: <span>(1)</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>t</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c7b2a37f-f161-49aa-b176-b1fde2595b0b' class='xr-index-data-in' type='checkbox'/><label for='index-c7b2a37f-f161-49aa-b176-b1fde2595b0b' 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([                0.0, 0.10101010101010101, 0.20202020202020202,\n",
+       "       0.30303030303030304, 0.40404040404040403,  0.5050505050505051,\n",
+       "        0.6060606060606061,  0.7070707070707071,  0.8080808080808081,\n",
+       "        0.9090909090909091,  1.0101010101010102,  1.1111111111111112,\n",
+       "        1.2121212121212122,  1.3131313131313131,  1.4141414141414141,\n",
+       "        1.5151515151515151,  1.6161616161616161,  1.7171717171717171,\n",
+       "        1.8181818181818181,  1.9191919191919191,  2.0202020202020203,\n",
+       "         2.121212121212121,  2.2222222222222223,   2.323232323232323,\n",
+       "        2.4242424242424243,   2.525252525252525,  2.6262626262626263,\n",
+       "         2.727272727272727,  2.8282828282828283,   2.929292929292929,\n",
+       "        3.0303030303030303,   3.131313131313131,  3.2323232323232323,\n",
+       "        3.3333333333333335,  3.4343434343434343,  3.5353535353535355,\n",
+       "        3.6363636363636362,  3.7373737373737375,  3.8383838383838382,\n",
+       "        3.9393939393939394,   4.040404040404041,   4.141414141414141,\n",
+       "         4.242424242424242,   4.343434343434343,   4.444444444444445,\n",
+       "         4.545454545454545,   4.646464646464646,   4.747474747474747,\n",
+       "         4.848484848484849,    4.94949494949495,    5.05050505050505,\n",
+       "         5.151515151515151,   5.252525252525253,   5.353535353535354,\n",
+       "         5.454545454545454,   5.555555555555555,   5.656565656565657,\n",
+       "         5.757575757575758,   5.858585858585858,   5.959595959595959,\n",
+       "        6.0606060606060606,   6.161616161616162,   6.262626262626262,\n",
+       "         6.363636363636363,  6.4646464646464645,   6.565656565656566,\n",
+       "         6.666666666666667,   6.767676767676767,  6.8686868686868685,\n",
+       "          6.96969696969697,   7.070707070707071,   7.171717171717171,\n",
+       "        7.2727272727272725,   7.373737373737374,   7.474747474747475,\n",
+       "         7.575757575757575,  7.6767676767676765,   7.777777777777778,\n",
+       "         7.878787878787879,   7.979797979797979,   8.080808080808081,\n",
+       "         8.181818181818182,   8.282828282828282,   8.383838383838384,\n",
+       "         8.484848484848484,   8.585858585858587,   8.686868686868687,\n",
+       "         8.787878787878787,    8.88888888888889,    8.98989898989899,\n",
+       "          9.09090909090909,   9.191919191919192,   9.292929292929292,\n",
+       "         9.393939393939394,   9.494949494949495,   9.595959595959595,\n",
+       "         9.696969696969697,   9.797979797979798,     9.8989898989899,\n",
+       "                      10.0],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;t&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5e5160bf-362c-4e9a-9b72-55cb8c7e23ae' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-5e5160bf-362c-4e9a-9b72-55cb8c7e23ae' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset>\n",
+       "Dimensions:  (t: 100)\n",
+       "Coordinates:\n",
+       "  * t        (t) float64 0.0 0.101 0.202 0.303 0.404 ... 9.697 9.798 9.899 10.0\n",
+       "Data variables:\n",
+       "    data     (t) float64 -1.7 0.777 -2.36 2.91 ... 27.13 28.27 24.63 22.67"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "obs_data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Initialize a simulation\n",
+    "First, we initialize an object of the class simulation. This is the center of the whole package and will manage all processes from now on. <br>\n",
+    "In pymob a Simulation object is initialized by calling the {class}`pymob.simulation.SimulationBase` class from the simulation module."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#from pymob.simulation import SimulationBase\n",
+    "\n",
+    "sim = SimulationBase()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```{admonition} Configuring the simulation\n",
+    ":class: note\n",
+    "Optionally, we can configure the simulation at this stage with \n",
+    "`sim.config.case_study.name = \"linear-regression\"`, `sim.config.case_study.scenario = \"test\"`, and many more options. \n",
+    "```\n",
+    "Case studies are a principled approach to the modelling process. In essence, they are a simple template that contains building blocks for model and names and stores them in an intuitive and reproducible way. [Here](https://pymob.readthedocs.io/en/stable/user_guide/case_studies.html#configuration) you'll find some additional information oon case studies. <br>\n",
+    "\n",
+    "At the moment, it is sufficient to only create a simulation object without making any further configurations.\n",
+    "\n",
+    "## Define a model \n",
+    "\n",
+    "Now the model needs to be defined. In Pymob, every model is represented as a Python function. Here, you’ll specify the model whose parameters will be estimated.\n",
+    "\n",
+    "In this tutorial, we’ll use linear regression as our example, since it’s the simplest form of modeling."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# definition of the model: \n",
+    "def linreg(t, a, b):\n",
+    "    return a + t * b"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "So we assume that this model describes our data well. So we add it to the simulation by"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sim.model = linreg"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "## Defining a solver\n",
+    "\n",
+    "As described above: A solver is required for many models. So we define a solver by `.solver`. <br>\n",
+    "In our case the model gives the exact solution of the model. Therefore, we choose `solve_analytic_1d`. An Overwiev of the solvers currently implemented in pymob can be found at the beginning of this tutorial [here](#How-pymob-is-structured:)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# from pymob.sim.solvetools import solve_analytic_1d\n",
+    "sim.solver = solve_analytic_1d"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## The pymob magic\n",
+    "\n",
+    "So far we have not done anythin special. Pymob exists, because wrangling dimensions of input and output data, nested data-structures, missing data is painful. <br>\n",
+    "\n",
+    "Now we add our data, which is already transformed into a *xarray Dataset*, by using `.observations`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MinMaxScaler(variable=data, min=-2.360151110471945, max=28.269740948520962)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\ameli\\OneDrive\\Dokumente\\01_Uni\\04_Jobs\\01_TKTD\\pymob\\pymob\\simulation.py:303: UserWarning: `sim.config.data_structure.data = Datavariable(dimensions=['t'] min=-2.360151110471945 max=28.269740948520962 observed=True dimensions_evaluator=None)` has been assumed from `sim.observations`. If the order of the dimensions should be different, specify `sim.config.data_structure.data = DataVariable(dimensions=[...], ...)` manually.\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "# import xarray as xr\n",
+    "\n",
+    "sim.observations = obs_data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This worked 🎉 `sim.config.data_structure` will now give us some information about the layout of our data, which will handle the data transformations in the background."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Datastructure(data=DataVariable(dimensions=['t'], min=-2.360151110471945, max=28.269740948520962, observed=True, dimensions_evaluator=None))"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sim.config.data_structure"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```{admonition} What happens when we assign a Dataset to the observations attribute?\n",
+    ":class: hint\n",
+    "\n",
+    "Debug into the function and discover what happens!\n",
+    "```\n",
+    "\n",
+    "We can give `pymob` additional information about the data structure of our observations and intermediate (unobserved) variables that are simulated. This can be done with `sim.config.data_structure.y = DataVariable(dimensions=[\"x\"])`.\n",
+    "These information can be used to switch the dimensional order of the observations or provide data variables that have differing dimensions from the observations, if needed. But if the dataset is ordinary, simply setting `sim.observations` property with a `xr.Dataset` will be sufficient.\n",
+    "\n",
+    "```{admonition} Scalers\n",
+    ":class: note\n",
+    "We also notice a mysterious Scaler message. This tells us that our data variable has been identified and a scaler was constructed, which transforms the variable between [0, 1]. This has no effect at the moment, but it can be used later. Scaling can be powerful to help parameter estimation in more complex models.\n",
+    "```\n",
+    "\n",
+    "## Parameterizing a model\n",
+    "\n",
+    "Parameters are specified via the `FloatParam` or `ArrayParam` class. Parameters can be marked free or fixed depending on whether they should be variable during an optimization procedure. <br>\n",
+    "\n",
+    "In this tutorial we want to fit the parameter $b$ and assume that we know parameter $a$: <br>\n",
+    "* The parameter $a$ is set as fixed (`free = False`), meaning its value is known and will not be estimated during optimization.\n",
+    "* The parameter $b$ is marked as free (`free = True`), allowing it to be optimized to fit our data. As an initial guess, we assume $b = 3$.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pymob.sim.config import Param\n",
+    "sim.config.model_parameters.a = Param(value=0, free=False)\n",
+    "sim.config.model_parameters.b = Param(value=3, free=True)\n",
+    "\n",
+    "# this makes sure the model parameters are available to the model.\n",
+    "sim.model_parameters[\"parameters\"] = sim.config.model_parameters.value_dict"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To make the parameters available to the simulation one has to use `sim.model_parameters[\"parameters\"] = sim.config.model_parameters.value_dict`. This step is particularly important for all fixed parameters.\n",
+    "\n",
+    "`sim.model_parameters` is a dictionary that stores the input data for the model. By default, it includes the keys `parameters`, `y0`, and `x_in`. For our analytic model, we only need the `parameters` key. In situations where initial values for variables are required, you can provide them using `sim.model_parameters[\"y0\"] = ...`.\n",
+    "\n",
+    "For example, when working with a Lotka-Volterra model, you would specify the initial conditions for the predator and prey populations with `y0`. For more details on such use cases, please refer to the advanced tutorial.\n",
+    "\n",
+    "```{admonition} generating input for solvers\n",
+    ":class: note\n",
+    "A helpful function to generate `y0` or `x_in` from observations is `SimulationBase.parse_input`, combined with settings of `config.simulation.y0`\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'a': 0.0, 'b': 3.0}"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sim.model_parameters['parameters']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "## Running the model 🏃\n",
+    "\n",
+    "The model is prepared with a parameter set and ready to be executed. With `sim.dispatch_constructor()`, everything is prepared for the run of the model. It initiaizes an `evaluator`, makes preliminary calculations and checks. \n",
+    "\n",
+    "For the parameter estimation it is not necessary to run the model, but it can be helpfull. By using `.dispatch()` all the parameters with the setting `free=True` get fixed. Therefore, we have to fix parameter $b$. \n",
+    "\n",
+    "*Try changing the value of $b$ and see what effect it has on the next steps?* <br>\n",
+    "\n",
+    "**`sim.dispatch_constructor()` should be executed every time you change something in your simulation settings, even if you don't run the model.** <br>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\ameli\\OneDrive\\Dokumente\\01_Uni\\04_Jobs\\01_TKTD\\pymob\\pymob\\simulation.py:552: UserWarning: The number of ODE states was not specified in the config file [simulation] > 'n_ode_states = <n>'. Extracted the return arguments ['a+t*b'] from the source code. Setting 'n_ode_states=1.\n",
+      "  warnings.warn(\n"
+     ]
+    },
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+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
+       "Dimensions:  (t: 100)\n",
+       "Coordinates:\n",
+       "  * t        (t) float64 0.0 0.101 0.202 0.303 0.404 ... 9.697 9.798 9.899 10.0\n",
+       "Data variables:\n",
+       "    data     (t) float64 0.0 0.303 0.6061 0.9091 1.212 ... 29.09 29.39 29.7 30.0</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-7e0f253f-5ca0-4a14-84dd-52cb4bf84b3a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-7e0f253f-5ca0-4a14-84dd-52cb4bf84b3a' 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'>t</span>: 100</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-2cda1e5f-cbd7-4694-b984-e923c99479f0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2cda1e5f-cbd7-4694-b984-e923c99479f0' class='xr-section-summary' >Coordinates: <span>(1)</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'>t</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.101 0.202 ... 9.899 10.0</div><input id='attrs-7c6d93e4-3689-4659-9d9b-dbe1b41dc045' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7c6d93e4-3689-4659-9d9b-dbe1b41dc045' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-99cfafed-fd60-4291-9b2e-17806cbd0ba3' class='xr-var-data-in' type='checkbox'><label for='data-99cfafed-fd60-4291-9b2e-17806cbd0ba3' 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.      ,  0.10101 ,  0.20202 ,  0.30303 ,  0.40404 ,  0.505051,\n",
+       "        0.606061,  0.707071,  0.808081,  0.909091,  1.010101,  1.111111,\n",
+       "        1.212121,  1.313131,  1.414141,  1.515152,  1.616162,  1.717172,\n",
+       "        1.818182,  1.919192,  2.020202,  2.121212,  2.222222,  2.323232,\n",
+       "        2.424242,  2.525253,  2.626263,  2.727273,  2.828283,  2.929293,\n",
+       "        3.030303,  3.131313,  3.232323,  3.333333,  3.434343,  3.535354,\n",
+       "        3.636364,  3.737374,  3.838384,  3.939394,  4.040404,  4.141414,\n",
+       "        4.242424,  4.343434,  4.444444,  4.545455,  4.646465,  4.747475,\n",
+       "        4.848485,  4.949495,  5.050505,  5.151515,  5.252525,  5.353535,\n",
+       "        5.454545,  5.555556,  5.656566,  5.757576,  5.858586,  5.959596,\n",
+       "        6.060606,  6.161616,  6.262626,  6.363636,  6.464646,  6.565657,\n",
+       "        6.666667,  6.767677,  6.868687,  6.969697,  7.070707,  7.171717,\n",
+       "        7.272727,  7.373737,  7.474747,  7.575758,  7.676768,  7.777778,\n",
+       "        7.878788,  7.979798,  8.080808,  8.181818,  8.282828,  8.383838,\n",
+       "        8.484848,  8.585859,  8.686869,  8.787879,  8.888889,  8.989899,\n",
+       "        9.090909,  9.191919,  9.292929,  9.393939,  9.494949,  9.59596 ,\n",
+       "        9.69697 ,  9.79798 ,  9.89899 , 10.      ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-90eeda1d-3d05-46fc-b54f-445978b391ce' class='xr-section-summary-in' type='checkbox'  checked><label for='section-90eeda1d-3d05-46fc-b54f-445978b391ce' class='xr-section-summary' >Data variables: <span>(1)</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>data</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.303 0.6061 ... 29.7 30.0</div><input id='attrs-9e09554e-765f-43d4-b3f5-c22bcd33fc90' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9e09554e-765f-43d4-b3f5-c22bcd33fc90' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bcafc74a-b977-4629-94fa-f8b729cc9773' class='xr-var-data-in' type='checkbox'><label for='data-bcafc74a-b977-4629-94fa-f8b729cc9773' 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.        ,  0.3030303 ,  0.60606061,  0.90909091,  1.21212121,\n",
+       "        1.51515152,  1.81818182,  2.12121212,  2.42424242,  2.72727273,\n",
+       "        3.03030303,  3.33333333,  3.63636364,  3.93939394,  4.24242424,\n",
+       "        4.54545455,  4.84848485,  5.15151515,  5.45454545,  5.75757576,\n",
+       "        6.06060606,  6.36363636,  6.66666667,  6.96969697,  7.27272727,\n",
+       "        7.57575758,  7.87878788,  8.18181818,  8.48484848,  8.78787879,\n",
+       "        9.09090909,  9.39393939,  9.6969697 , 10.        , 10.3030303 ,\n",
+       "       10.60606061, 10.90909091, 11.21212121, 11.51515152, 11.81818182,\n",
+       "       12.12121212, 12.42424242, 12.72727273, 13.03030303, 13.33333333,\n",
+       "       13.63636364, 13.93939394, 14.24242424, 14.54545455, 14.84848485,\n",
+       "       15.15151515, 15.45454545, 15.75757576, 16.06060606, 16.36363636,\n",
+       "       16.66666667, 16.96969697, 17.27272727, 17.57575758, 17.87878788,\n",
+       "       18.18181818, 18.48484848, 18.78787879, 19.09090909, 19.39393939,\n",
+       "       19.6969697 , 20.        , 20.3030303 , 20.60606061, 20.90909091,\n",
+       "       21.21212121, 21.51515152, 21.81818182, 22.12121212, 22.42424242,\n",
+       "       22.72727273, 23.03030303, 23.33333333, 23.63636364, 23.93939394,\n",
+       "       24.24242424, 24.54545455, 24.84848485, 25.15151515, 25.45454545,\n",
+       "       25.75757576, 26.06060606, 26.36363636, 26.66666667, 26.96969697,\n",
+       "       27.27272727, 27.57575758, 27.87878788, 28.18181818, 28.48484848,\n",
+       "       28.78787879, 29.09090909, 29.39393939, 29.6969697 , 30.        ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-fcb00663-791e-42e2-ab5f-ecfa3be659ed' class='xr-section-summary-in' type='checkbox'  ><label for='section-fcb00663-791e-42e2-ab5f-ecfa3be659ed' class='xr-section-summary' >Indexes: <span>(1)</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>t</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-76e76f52-204f-4383-a41a-ae6586cf54f3' class='xr-index-data-in' type='checkbox'/><label for='index-76e76f52-204f-4383-a41a-ae6586cf54f3' 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([                0.0, 0.10101010101010101, 0.20202020202020202,\n",
+       "       0.30303030303030304, 0.40404040404040403,  0.5050505050505051,\n",
+       "        0.6060606060606061,  0.7070707070707071,  0.8080808080808081,\n",
+       "        0.9090909090909091,  1.0101010101010102,  1.1111111111111112,\n",
+       "        1.2121212121212122,  1.3131313131313131,  1.4141414141414141,\n",
+       "        1.5151515151515151,  1.6161616161616161,  1.7171717171717171,\n",
+       "        1.8181818181818181,  1.9191919191919191,  2.0202020202020203,\n",
+       "         2.121212121212121,  2.2222222222222223,   2.323232323232323,\n",
+       "        2.4242424242424243,   2.525252525252525,  2.6262626262626263,\n",
+       "         2.727272727272727,  2.8282828282828283,   2.929292929292929,\n",
+       "        3.0303030303030303,   3.131313131313131,  3.2323232323232323,\n",
+       "        3.3333333333333335,  3.4343434343434343,  3.5353535353535355,\n",
+       "        3.6363636363636362,  3.7373737373737375,  3.8383838383838382,\n",
+       "        3.9393939393939394,   4.040404040404041,   4.141414141414141,\n",
+       "         4.242424242424242,   4.343434343434343,   4.444444444444445,\n",
+       "         4.545454545454545,   4.646464646464646,   4.747474747474747,\n",
+       "         4.848484848484849,    4.94949494949495,    5.05050505050505,\n",
+       "         5.151515151515151,   5.252525252525253,   5.353535353535354,\n",
+       "         5.454545454545454,   5.555555555555555,   5.656565656565657,\n",
+       "         5.757575757575758,   5.858585858585858,   5.959595959595959,\n",
+       "        6.0606060606060606,   6.161616161616162,   6.262626262626262,\n",
+       "         6.363636363636363,  6.4646464646464645,   6.565656565656566,\n",
+       "         6.666666666666667,   6.767676767676767,  6.8686868686868685,\n",
+       "          6.96969696969697,   7.070707070707071,   7.171717171717171,\n",
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+       "                      10.0],\n",
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+      ],
+      "text/plain": [
+       "<xarray.Dataset>\n",
+       "Dimensions:  (t: 100)\n",
+       "Coordinates:\n",
+       "  * t        (t) float64 0.0 0.101 0.202 0.303 0.404 ... 9.697 9.798 9.899 10.0\n",
+       "Data variables:\n",
+       "    data     (t) float64 0.0 0.303 0.6061 0.9091 1.212 ... 29.09 29.39 29.7 30.0"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# put everything in place for running the simulation\n",
+    "sim.dispatch_constructor()\n",
+    "\n",
+    "# run\n",
+    "evaluator = sim.dispatch(theta={\"b\":3}) # makes sure that the parameter b is set to 3\n",
+    "evaluator()\n",
+    "evaluator.results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This returns a dataset which is of the exact same shape as the observation dataset, plus intermediate variables that were created during the simulation, if they are tracked by the solver.\n",
+    "\n",
+    "Although this API seems to be a bit clunky, it is necessary, to make sure that simulations that are executed in parallel are isolated from each other.\n",
+    "\n",
+    "\n",
+    "## Estimating parameters \n",
+    "\n",
+    "We are almost set to infer the parameters of the model. We add another parameter to also estimate the error of the parameters, We use a lognormal distribution for it. We also specify an error model for the distribution. This will be \n",
+    "\n",
+    "$$y_{obs} \\sim Normal (y, \\sigma_y)$$\n",
+    "\n",
+    "Further we also have to make some assumptions for the parameter $b$ which we want to fit. First, we have to define the prior function from which we draw the parameter values during the parameter estimation. Additionally, we set the `min` and `max` values for our parameters. This can also be done in one step,  as can be seen for the error-model parameter `sigma_y`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sim.config.model_parameters.b.prior = \"lognorm(scale=1,s=1)\"\n",
+    "sim.config.model_parameters.b.min = -5\n",
+    "sim.config.model_parameters.b.max = 5\n",
+    "\n",
+    "#construction the error model\n",
+    "sim.config.model_parameters.sigma_y = Param(free=True , prior=\"lognorm(scale=1,s=1)\", min=0, max=1)\n",
+    "\n",
+    "sim.config.error_model.data = \"normal(loc=data,scale=sigma_y)\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As `sima_y` is not a fixed parameter, the new parameter does not have to be passed to the simulation class."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'a': 0.0, 'b': 3.0, 'sigma_y': 0.0}"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sim.model_parameters[\"parameters\"] = sim.config.model_parameters.value_dict\n",
+    "sim.model_parameters['parameters']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Manual estimation\n",
+    "\n",
+    "First, we try estimating the parameters by hand. For this we have a simple interactive backend. <br>\n",
+    "Note that changing sigma_y has no effect on the model fit because sigma_y is only used for the error model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from matplotlib import pyplot as plt\n",
+    "def plot(results: xr.Dataset):\n",
+    "    obs = sim.observations\n",
+    "\n",
+    "    fig, ax = plt.subplots(1,1)\n",
+    "    ax.plot(results.t, results.data, lw=2, color=\"black\")\n",
+    "    ax.plot(obs.t, obs.data, ls=\"\", marker=\"o\", color=\"tab:blue\", alpha=.5)\n",
+    "    ax.set_xlim(-1,12)\n",
+    "    ax.set_ylim(-1,30)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2174c0da8e5e497392655ec9bf58e9e1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(VBox(children=(FloatSlider(value=3.0, description='b', max=5.0, min=-5.0, step=None), FloatSlid…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sim.plot = plot\n",
+    "sim.interactive()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Estimating parameters and uncertainty with MCMC\n",
+    "\n",
+    "Of course this example is very simple, we can in fact optimize the parameters perfectly by hand. But just for the fun of it, let's use *Markov Chain Monte Carlo* (MCMC) to estimate the parameters, their uncertainty and the uncertainty in the data. <br>\n",
+    "\n",
+    "The inferer serves as the parameter estimator. Different inferer are implemented in numpy and can be found at the beginning of the tuorial and in the API. The method for the parameter estimation is defined by using `set_inferer()`. This automatically translates the pymob data in the format of the selected inferer. Numpyro additionally needs a kernel. To start the estimation you use `.run()`.\n",
+    "\n",
+    "\n",
+    "*Note that other methods often don't need a kernel.*\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```{admonition} numpyro distributions\n",
+    ":class: warning\n",
+    "Currently only few distributions are implemented in the numpyro backend. This API will soon change, so that basically any distribution can be used to specifcy parameters. \n",
+    "```\n",
+    "\n",
+    "Finally, we let our inferer run the paramter estimation procedure with the numpyro backend and a NUTS kernel. This does the job in a few seconds. <br>\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Jax 64 bit mode: False\n",
+      "Absolute tolerance: 1e-07\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\ameli\\OneDrive\\Dokumente\\01_Uni\\04_Jobs\\01_TKTD\\pymob\\pymob\\inference\\numpyro_backend.py:552: UserWarning: Model is not rendered, because the graphviz executable is not found. Try search for 'graphviz executables not found' and the used OS. This should be an easy fix :-)\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Trace Shapes:      \n",
+      " Param Sites:      \n",
+      "Sample Sites:      \n",
+      "       b dist     |\n",
+      "        value     |\n",
+      " sigma_y dist     |\n",
+      "        value     |\n",
+      "data_obs dist 100 |\n",
+      "        value 100 |\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "sample: 100%|██████████| 3000/3000 [00:03<00:00, 877.81it/s, 7 steps of size 7.01e-01. acc. prob=0.95] \n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "                mean       std    median      5.0%     95.0%     n_eff     r_hat\n",
+      "         b      2.65      0.03      2.65      2.60      2.70   1421.06      1.00\n",
+      "   sigma_y      1.71      0.13      1.70      1.50      1.92   1315.22      1.00\n",
+      "\n",
+      "Number of divergences: 0\n"
+     ]
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+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
+       "Dimensions:  (chain: 1, draw: 2000)\n",
+       "Coordinates:\n",
+       "  * chain    (chain) int32 0\n",
+       "  * draw     (draw) int32 0 1 2 3 4 5 6 7 ... 1993 1994 1995 1996 1997 1998 1999\n",
+       "    cluster  (chain) int32 0\n",
+       "Data variables:\n",
+       "    b        (chain, draw) float32 2.634 2.687 2.613 2.639 ... 2.618 2.653 2.625\n",
+       "    sigma_y  (chain, draw) float32 1.727 1.798 1.775 1.828 ... 1.549 1.938 1.531\n",
+       "Attributes:\n",
+       "    created_at:     2025-06-10T08:58:04.047307+00:00\n",
+       "    arviz_version:  0.20.0</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-9d295794-74cb-40a3-96e7-0196f9dd9480' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-9d295794-74cb-40a3-96e7-0196f9dd9480' 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>: 2000</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-5339adac-aa1d-4b05-b13f-2f73c52841b6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5339adac-aa1d-4b05-b13f-2f73c52841b6' class='xr-section-summary' >Coordinates: <span>(3)</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'>int32</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-2b6cae9f-7d26-487b-b214-39c143a0f0c6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2b6cae9f-7d26-487b-b214-39c143a0f0c6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d644c351-8e2b-4cf9-adeb-a5cae61f9382' class='xr-var-data-in' type='checkbox'><label for='data-d644c351-8e2b-4cf9-adeb-a5cae61f9382' 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'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 ... 1996 1997 1998 1999</div><input id='attrs-08c7eab3-0d65-40b1-8d11-7f95845162c1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-08c7eab3-0d65-40b1-8d11-7f95845162c1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4f8612de-a9ee-4575-ad3e-079f65c23515' class='xr-var-data-in' type='checkbox'><label for='data-4f8612de-a9ee-4575-ad3e-079f65c23515' 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, ..., 1997, 1998, 1999])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cluster</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-260a1002-2706-4f24-834d-fe2646d5c5b1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-260a1002-2706-4f24-834d-fe2646d5c5b1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a2963598-b89b-4315-9696-a69a46d95569' class='xr-var-data-in' type='checkbox'><label for='data-a2963598-b89b-4315-9696-a69a46d95569' 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></ul></div></li><li class='xr-section-item'><input id='section-daf4baa5-027e-4615-a0a5-03e4e9ff1df3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-daf4baa5-027e-4615-a0a5-03e4e9ff1df3' class='xr-section-summary' >Data variables: <span>(2)</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>b</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>2.634 2.687 2.613 ... 2.653 2.625</div><input id='attrs-b729e2a0-89d8-43cc-bd89-c0bac41c29c4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b729e2a0-89d8-43cc-bd89-c0bac41c29c4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f160f82c-8a2f-4fca-8a89-ca32b3c409aa' class='xr-var-data-in' type='checkbox'><label for='data-f160f82c-8a2f-4fca-8a89-ca32b3c409aa' 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([[2.634382 , 2.6873431, 2.6128893, ..., 2.6182663, 2.652883 ,\n",
+       "        2.6254828]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sigma_y</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.727 1.798 1.775 ... 1.938 1.531</div><input id='attrs-51c7e6a3-79ae-4f29-9d92-53e5cf6ae5a3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-51c7e6a3-79ae-4f29-9d92-53e5cf6ae5a3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-733f5e5d-70a0-4a31-9ba0-a8fd5e70a5e2' class='xr-var-data-in' type='checkbox'><label for='data-733f5e5d-70a0-4a31-9ba0-a8fd5e70a5e2' 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([[1.7270529, 1.797513 , 1.7745078, ..., 1.5492175, 1.9377252,\n",
+       "        1.5306886]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-bbe7a462-35dc-4a1d-a4b4-79fa218ed8cc' class='xr-section-summary-in' type='checkbox'  ><label for='section-bbe7a462-35dc-4a1d-a4b4-79fa218ed8cc' class='xr-section-summary' >Indexes: <span>(2)</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-71d885f2-f9f9-48fc-ab40-20d76efbba25' class='xr-index-data-in' type='checkbox'/><label for='index-71d885f2-f9f9-48fc-ab40-20d76efbba25' 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;int32&#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-101b469f-5670-4458-b4b0-c357d5f8b0dc' class='xr-index-data-in' type='checkbox'/><label for='index-101b469f-5670-4458-b4b0-c357d5f8b0dc' 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",
+       "       1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999],\n",
+       "      dtype=&#x27;int32&#x27;, name=&#x27;draw&#x27;, length=2000))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-66ca2e6c-2f67-469d-8b30-33545f93f2cd' class='xr-section-summary-in' type='checkbox'  checked><label for='section-66ca2e6c-2f67-469d-8b30-33545f93f2cd' class='xr-section-summary' >Attributes: <span>(2)</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>2025-06-10T08:58:04.047307+00:00</dd><dt><span>arviz_version :</span></dt><dd>0.20.0</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset>\n",
+       "Dimensions:  (chain: 1, draw: 2000)\n",
+       "Coordinates:\n",
+       "  * chain    (chain) int32 0\n",
+       "  * draw     (draw) int32 0 1 2 3 4 5 6 7 ... 1993 1994 1995 1996 1997 1998 1999\n",
+       "    cluster  (chain) int32 0\n",
+       "Data variables:\n",
+       "    b        (chain, draw) float32 2.634 2.687 2.613 2.639 ... 2.618 2.653 2.625\n",
+       "    sigma_y  (chain, draw) float32 1.727 1.798 1.775 1.828 ... 1.549 1.938 1.531\n",
+       "Attributes:\n",
+       "    created_at:     2025-06-10T08:58:04.047307+00:00\n",
+       "    arviz_version:  0.20.0"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sim.dispatch_constructor() # important to call this before running the inferer\n",
+    "\n",
+    "sim.set_inferer(\"numpyro\")\n",
+    "sim.inferer.config.inference_numpyro.kernel = \"nuts\"\n",
+    "sim.inferer.run()\n",
+    "\n",
+    "sim.inferer.idata.posterior"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can inspect our estimates and see that the parameters are well esimtated by the model. Note that we only get an estimate for `b`. This is because earlier we set the parameter `a` with the flag `free=False` this effectively excludes it from estimation and uses the default value, which was set to the true value `a=0`. <br>\n",
+    "\n",
+    "The `mean`of `b` is the value of the estimated parameter. It shloud be the same or close to estimation you did manually. The `sigma_y` is the mean error of this estimation."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Plot the results\n",
+    "\n",
+    "Pymob provides a very basic utility for plotting posterior predictions. We see that the mean is a perfect fit and also that the uncertainty in the data is correctly displayed. Fantstic 🎉"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 400x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sim.config.simulation.x_dimension = \"t\"\n",
+    "sim.posterior_predictive_checks(pred_hdi_style={\"alpha\": 0.1})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "```{admonition} Customize the posterior predictive checks\n",
+    ":class: hint\n",
+    "You can explore the API of {class}`pymob.sim.plot.SimulationPlot` to find out how you can work on the default predictions. Of course you can always make your own plot, by accessing {attr}`pymob.simulation.inferer.idata` and {attr}`pymob.simulation.observations`\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Report the results\n",
+    "The command `.report()` can be used to generate an automated report. The report can be configured with options in `.config.report()`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x400 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sim.report()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "## Exporting the simulation and running it via the case study API\n",
+    "\n",
+    "After constructing the simulation, all settings of the simulation can be exported to a comprehensive configuration file, along with all the default settings. This is as simple as "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Scenario directory exists at 'c:\\Users\\ameli\\OneDrive\\Dokumente\\01_Uni\\04_Jobs\\01_TKTD\\pymob_new\\pymob\\docs\\source\\user_guide\\case_studies\\quickstart\\scenarios\\test'.\n",
+      "Results directory exists at 'c:\\Users\\ameli\\OneDrive\\Dokumente\\01_Uni\\04_Jobs\\01_TKTD\\pymob_new\\pymob\\docs\\source\\user_guide\\case_studies\\quickstart\\results\\test'.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "sim.config.case_study.name = \"quickstart\"\n",
+    "sim.config.case_study.scenario = \"test\"\n",
+    "sim.config.create_directory(\"scenario\", force=True)\n",
+    "sim.config.create_directory(\"results\", force=True)\n",
+    "\n",
+    "# usually we expect to have a data directory in the case\n",
+    "os.makedirs(sim.data_path, exist_ok=True)\n",
+    "sim.save_observations(force=True)\n",
+    "sim.config.save(force=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The simulation will be saved to the default path (`CASE_STUDY/scenarios/SCENARIO/settings.cfg`) or to a custom file path specified with the `fp` keyword. `force=True` will overwrite any existing config file, which is the reasonable choice in most cases.\n",
+    "\n",
+    "From there on, the simulation is (almost) ready to be executable from the commandline.\n",
+    "\n",
+    "### Commandline API\n",
+    "\n",
+    "The commandline API runs a series of commands that load the case study, execute the {meth}`pymob.simulation.SimulationBase.initialize` method and perform some more initialization tasks, before running the required job.\n",
+    "\n",
+    "+ `pymob-infer`: Runs an inference job e.g. `pymob-infer --case_study=quickstart --scenario=test --inference_backend=numpyro`. While there are more commandline options, these are the two required "
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "pymob",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From 54cb20d90291e4da1eee1e6a30d5f04d39a7f5d5 Mon Sep 17 00:00:00 2001
From: amelieleo <aleonardi@uni-osnabrueck.de>
Date: Mon, 23 Jun 2025 11:45:34 +0200
Subject: [PATCH 06/16] some fine-tuning of the introduction tutorial

---
 docs/source/user_guide/Introduction.ipynb | 226 ++++++++++++----------
 1 file changed, 120 insertions(+), 106 deletions(-)

diff --git a/docs/source/user_guide/Introduction.ipynb b/docs/source/user_guide/Introduction.ipynb
index a72381cf8..dddffa3ab 100644
--- a/docs/source/user_guide/Introduction.ipynb
+++ b/docs/source/user_guide/Introduction.ipynb
@@ -20,27 +20,26 @@
     "The Pymob package consists of several elements: \n",
     "\n",
     "\n",
-    "1) __simulation__ <br>\n",
+    "1) __Simulation__ <br>\n",
     "First, we need to initialize a Simulation object by calling the {class}`pymob.simulation.SimulationBase` class from the simulation module.   \n",
-    "Optionally, we can configure the simulation object with `sim.config.case_study.name` = \"linear-regression\", `sim.config.case_study.scenario` = \"test\" and many more options. \n",
+    "Optionally, we can configure the simulation object with {attr}`pymob.simulation.SimulationBase.config.case_study.name` = \"linear-regression\", {attr}`pymob.simulation.SimulationBase.config.case_study.scenario` = \"test\" and many more options. \n",
     "\n",
-    "2) __model__ <br>\n",
+    "2) __Model__ <br>\n",
     "The model is a python function you define. With the model you try to describe the data you observed. A classical model is, for example, the Lotka-Volterra model to describe the interactions of predators and prey. In the tutorial today, the model will be a simple linear function. <br>\n",
-    "The model will be added to the simualtion by using `.model`\n",
+    "The model will be added to the simualtion by using {class}`pymob.simulation.SimulationBase.model`\n",
     "\n",
-    "3) __observations__ <br>\n",
+    "3) __Observations__ <br>\n",
     "The obseravtions are the data points, to which we want to fit our model. The observation data needs to be an `xarray.Dataset` ([learn more here](https://docs.xarray.dev/en/stable/getting-started-guide/quick-overview.html)).  \n",
-    "We assign it to our Simulation object by  `.observations`.  \n",
-    "`sim.config.data_structure` will give us some information about the layout of our data.\n",
+    "We assign it to our Simulation object by  {attr}`pymob.simulation.SimulationBase.observations`.  \n",
+    "{attr}`pymob.simulation.SimulationBase.config.data_structure` will give us some information about the layout of our data.\n",
     "\n",
-    "4) __solver__ <br>\n",
+    "4) __Solver__ <br>\n",
     "A solver is required for many models e.g. models that contain differential equations. Solvers in pymob are callables that need to return a dictionary of results mapped to the data variables. <br>\n",
-    "The solver is assigned to the Simulation object by `.solver`. <br>\n",
+    "The solver is assigned to the Simulation object by {class}`pymob.simulation.SimulationBase.solver`. <br>\n",
     "These solvers are currently implemented in pymob: \n",
     "    - analytic module\n",
     "        - solve_analytic_1d\n",
     "    - base module \n",
-    "        - SolverBase\n",
     "        - curve_jumps\n",
     "        - jump_interpolation\n",
     "        - mappar\n",
@@ -54,7 +53,7 @@
     "\n",
     "The documentation can be found [here](https://pymob.readthedocs.io/en/stable/api/pymob.solvers.html) \n",
     "\n",
-    "5) __inferer__ <br>\n",
+    "5) __Inferer__ <br>\n",
     "    The inferer serves as the parameter estimator. Pymob provides various backends. You can find detailed information [here](https://pymob.readthedocs.io/en/stable/user_guide/framework_overview.html). <br>\n",
     "    Currently, supported inference backends are:\n",
     "    * interactive (interactive backend in jupyter notebookswith parameter sliders)\n",
@@ -62,10 +61,13 @@
     "    * pyabc (approximate bayesian inference)\n",
     "    * pymoo (experimental multi-objective optimization)\n",
     "\n",
-    "6)  __config__ <br>\n",
+    "6) __Evaluator__ <br>\n",
+    "The Evaluator is an instance to manage model evaluations. It sets up tasks, coordinates parallel runs of the simulation and keeps track of the results from each simulation or parameter inference process.\n",
+    "\n",
+    "7) __Config__ <br>\n",
     "Pymob uses `pydantic` models to validate configuration files, with the configuration organized into separate sections. You can modify these configurations either by editing the files before initializing a simulation from a config file, or directly within the script. During parameter estimation setup, all configuration settings are stored in a config object, which can later be exported as a `.cfg` file.\n",
-    "7) __evaluator__ <br>\n",
-    "    - for running the model\n",
+    "\n",
+    "\n",
     "\n",
     "\n",
     "\n",
@@ -78,7 +80,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -114,12 +116,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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9Zh5nBWAA4DYvnQLal+YJXO0j89QlR0tBfvnvN72ut7vRZ4aamTRTbRpRR/qfQc4ff7FOptp2795tpn7rF/vmzZtl1qxZMmHCBJON+etf/2pOFmkxrmYqdMtIA4lJkyaVew2to9E6mLlz55rtGj1BpFtLGgzo87S+Rl9Hi4EjGT9+vDk9pH1ubrvtNqlfv76cc8455r4RI0aYOht97oUXXiiLFi2Sxx9/3NTrpEprYjRwefvtt01xsJ5e0uJmXf9pp51mamk+/vhj+fnnn8uCKgDwU2lC10MO9Hyzu9M7NDI1oV6p/SEz4+NUmwYvjRo1MgGJFse+//77plhWj2dr5kODEy3CnThxonTo0MFsGWmwE05PNGnAosGGnka67777zE89cfTqq6+abRvN0DzwwAMR16D3aW+bTp06mcDqrbfeKsue6HHxV155RaZNm2bef8yYMSb4iVX8G4/W/mj25eabbzYB1JAhQ8x22IIFC0xwo1mf22+/3WSj+vTpk/L7AEDQShNm2bzNpd9vGnidfdTB5qebRcw5oYCfXy0uLjYndrSXin7phdu1a5epq9D6Eu2TEoSWzpmix5+154tuLbnZQTgVdvy9A0AydDtHi33jmXplF0cyM/tKQ6bQOFp2yNpNWDiqh2dOVsX6/q6IbaY0eS3VBgDwHrdLE5Z4ZJvLKQQzNrBSbQAAxCpN0O0cDVxCGS5N2OKBbS4nUTODlJx88smmn43ftpgAwC3JngKyc+5fA4+ewLULmRkAADxWmmB3PeZxHjiB6yQyM390zEX24O8bgJvinQJyorleZQ+cwHVSVgczVqfZeK39ESzW33fFTsMAEOTmeqd7sNmdXbJ6m0l7sWjNh9XdVhvGWTONEMyMjAYy+vetf++pdiEGAKc4fero9ICewM3qYEZZ06JjDTJEsGggU3FKOAB4QSZOHVUO4AncrA9mNBOjXXS1Db62/kew6dYSGRkAXhX0U0dOyfpgxqJfcHzJAQDcFPRTR07J6gJgAAC8JOinjpxCMAMAgId49dTRPhub+NmNbSYAADzGa6eOZnl8qHJWT80GAACxWU38KgYLVljlVLYome9vtpkAAEDELaXpy36UW6evcKSJn53YZgIAwKc0iFhi81ZUpC0lp5r42YVgBgAAH3KijmVWlC0lJ5v42YFtJgAAfMaJYZT7YsyF8noTP4IZAAB8xKlhlEvizIWKJOePbJDbTfwIZgAACOgwSie3irzUxI+aGQAAfMSpYZQNktwqKvBQnxlXMzMTJkyQY489VmrXrm0GPZ5zzjmyatWqco85+eSTzTDI8Ms111zj2poBAAjiMMrj/pgLFSvHUq9mFXnowqNk6pVdZOGoHp4IZFwPZubPny+DBw+WxYsXy+zZs83U6tNOO01KSkrKPe7KK6+UTZs2lV3uu+8+19YMAICb4gUdOSnWscSbC6WXe849Qs7908HmGLbbW0ue2WaaNWtWuetTpkwxGZqlS5fKiSeeWHZ7jRo1pKCgwIUVAgDgrV4wVtChp5b0VUI21rFYc6EqHvn20paS58cZrFmzRlq3bi1ffvmldOjQoWyb6auvvhJdpgY0/fr1k9GjR5sAJ5Ldu3ebS3g75KZNmzLOAAAQqJlGTr72Pgea8Tk5zsAzwUxpaamcddZZsn37dlm4cGHZ7U8//bQ0b95cGjduLF988YWMGjVKjjvuOHnjjTcivs7YsWNl3Lhx+91OMAMACNpMIy8EHU7xZTBz7bXXynvvvWcCmSZNmkR93Lx586Rnz54mi3PIIYfsdz+ZGQCA2zTI6D5xXtQj1Dl/bN1oEa2dwce+FIMbLwZFyQQznjiaPWTIEHn77bdlwYIFMQMZ1blzZ/MzWjCTm5trLgAA+KEXjF0zjWaluO3k5HZVVpxm0qSQBjLTp083GZeWLVvGfc7y5cvNz0aN/PEBAwCyT7K9YKwp1TOW/2R+RureG+sxs1Icb+DEWAQ3uJqZ0WPZL730ksyYMcP0miksLDS3a1qpevXqsnbtWnP/GWecIQceeKCpmRk2bJg56XTkkUe6uXQAAGzpBZNIZiTWY05tVxBzvIFuFun9+rjwraN4YxGiPc+LXM3MPPXUU2YvTE8saabFurz88svm/qpVq8qcOXNM75nDDz9cRowYIeedd5689dZbbi4bAABbesH8WrInbmYkXvbk8XmrUxpv4NRYhKzLzMSrPdbCXW2sBwCAnyTSC2Z037Zy5zuxMyNjZ35l/hTrMZM/Wp/S1pdTYxHcwKBJAAAcYDWg01NL4fS63l63Zm7czEhh8W4pLI79mO0796a09eXUWAQ3eOI0EwAAQQ1otOYk0rFnLeS1S53qVaRo596IGRzrGHjF8QbWVphuVyXzPC8iMwMACIxETgVl+rU0cNHj12cfVX6mkZ0Zj0HdWkadqRRtvEG8WUzRnudFZGYAAIFgZ7+UVF8rmeZziWRGGuZp37Qc2VwcO3sypMeh0qagVtIzlfw6i8mzHYC90EEQAOBPdo4OSPW1UgmArPeSKEXC+l4q3mNO/+P1s7UDMMEMAMDX7BwdkOprpRNMpdtn5nSfZE8CP84AAAAvjA5I5bXSbT4Xq0g4mcdkM4IZAICv2dkvJZXXsiOYsoqEY0nkMdmKYAYA4Gt29ktJ5bW83nxunwfrYexGMAMA8DU7+6Wk8lpebj43K0tqbegzAwDwNTv7paTyWonOYcp087lZAZmInQiCGQBA4EcHVMxCxGqIl+xrebH53L44RclK70+nqaCXcDQbABAYidSHJLr1kmytiZe2dBat3SYDnlkc93FTr+zi2aJijmYDALJSvBM/0frBWFsv4ZmXZE8Peen49BaPFyXbjWAGAJAV0u0HkwivHJ9u4OGiZCdQMwMAyArJ9IPxu+M8WpTsFIIZAEBWyKatl8oeLEp2EsEMACArZNvWy+lJnsryM2pmAABZId3men7spHu6h4qSnUQwAwDICtbWi55a0q/yUBJbL146du3XomQnsc0EAMgaqWy9ZFMnXb8iMwMAyCrJbL1k4jg30kcwAwDIOoluvSRznDvoWzleRjADAAi0dAp3s+k4t58RzAAAAivdwt1Ej2mv3rzDzEMK4kkhP2DQJADAk9I9Ch1tDpP1Con0WtE1dJ84L+px7or8csIpaN/fBDMAgMBlVKwgJFq9i9VTZuGoHnEDJCsoUvG+MJMJlGDf9zdHswEAnmLHUWg75zBFO84d7XWVBmIaUCEzCGYAAJ4R7yh0ooGC3YW7GtBoFmfqlV1kyCmHxHxskAZW+gUFwAAAz9THfLTmZ1uOQjsxh8k6zs0JJ+8hmAEAeK4+Jt1AId05TLFk28BKP2CbCQDgufqYdAMFaw6TqljeG28OUzxWoBTtmXp7oxQDJaSGYAYA4Ln6GLEhUEhlDlMinAyUkBq2mQAArvSHiXfiSGwIFJKZw5QMK1CquD2mgRJ9ZjKPYAYA4Ep/mGQLZFMNFBKdw5QspwIlJI9gBgBgi2gdd63+MBW3dhItkB1yyqHS7dD6ngwUnAqUkBxqZgAArvSHSbSQdtiph5mAwWuBDLyDYAYAkLZUOu5SSAu7EMwAANKWbCM5zdDolOnd/ymVob0Ok4Z59p44QnahZgYAkLZkGslFKhIuyMuVYb1aS4v6NSmkRdLIzAAA0pZo/cuvJXsiNsnbXLxbHp6zWnIPqER9DJJGMAMASFsi9S+j+7aVO99JfYiktTU1Y/lP5qdej3Qbsg/bTAAAW8RrJJdfvWrKQyQjbU3VqVHF/Nz++964PW0QbAQzAICMNJLT7EkqxcTR+teEBzHxetog2AhmAAAZaSSXyrTpZOc3hf7Y1tLnaFBF7U12oGYGAJARqUybTnZ+U7SeNgg2ghkAQEak0iQv2flN4dJ5LvyFYAYAkLJkTxNZRcJaFJxIk7xEt6YiSee58BdXa2YmTJggb7zxhqxcuVKqV68uxx9/vEycOFHatGlT9phdu3bJiBEjZNq0abJ7927p3bu3PPnkk9KwYUM3lw4AWS+ZCdmpTpu2tqa0sDfRupmcP4Kj8O0qBJurmZn58+fL4MGDZfHixTJ79mzZu3evnHbaaVJSUlL2mGHDhslbb70lr776qnn8xo0bpX///m4uGwCynnXCqGI9i3WaSO9PpEj47KMOjtkkL9bWVCSpznSiX42/5YRCIc/8jf3888/SoEEDE7SceOKJUlRUJAcddJC89NJL8uc//9k8RrM4bdu2lUWLFkmXLl3ivmZxcbHk5+eb18rLy8vAbwEAwaZf9N0nzotamGtlRhaO6mHbaSIn+8ykmmGCs5L5/vbU0WxdsKpX77+pwaVLl5psTa9evcoec/jhh0uzZs2iBjO6FaWX8A8DAODOhOxIR7RTEW1rylpPvO2qaKL1sKFfjb94JpgpLS2VoUOHSrdu3aRDhw7mtsLCQqlatarUqVOn3GO1Xkbvi1aHM27cuIysGQCyUbITsp3uX5NqwBSrhw39avzFM6eZtHZmxYoVptA3HbfccovJ8FiXDRs22LZGAEBqze/8nmGCt3kiMzNkyBB5++23ZcGCBdKkSZOy2wsKCmTPnj2yffv2ctmZzZs3m/siyc3NNRcAgDPinTDyy2kitzJMCFhmRmuPNZCZPn26zJs3T1q2bFnu/k6dOkmVKlVk7ty5ZbetWrVKfvjhB+natasLKwYApNL8zouCkmGCy8GMbi298MIL5rRS7dq1TR2MXnbu3Gnu1yrmK664QoYPHy7vv/++KQgeNGiQCWQSOckEAHBGss3vgjJeAd7k6tHsnJzI/xOaPHmyXHbZZeWa5k2dOrVc07xo20wVcTQbAJyjRbTpnCZym3WaSYV/GVq/gV8CsyBK5vvbU31mnEAwAwCIhT4z3uTbPjMAAGRaMuMV4E0EMwCArNhSSqWHDfyBYAYAslAigUn4Y9Zv/V2mLvlBCovZioH3EMwAQJZJpEYk0mMqouU/vIICYADIItFmEYWf3lGRHpOpoZKAogAYAJDSLKKxM78yf0r0X7lODJUEkkUwAwBZIpFZRIXFu1N6bVr+w00EMwCQJcW9TgYctPyHmwhmACBLinudCDj8MlQSwebqbCYAgP3FvRW3kqxTR7+W7I47i6ggL1cK8qI/puLj/TJUEsFGMAMAWVDcq+585xsZ3Tf2tOuxZ7WXsWdFfkxFfhoqiWBjmwkAsqS4V++vW7OqCUAqbkUVVOgzE/Exebky4Lhm0qJ+zcB1AIa/EcwAQAAkWtyrjzv7qIPjziJiXhH8hGAGAAIg0eJe63GJzCJiXhH8gpoZAAgAzZrEK+7V+zl1hCAimAGAANAsita8xCrujXXqSAuIF63dJjOW/2R+6nXAL9hmAoCA0DqXRIp7Uxk8CXgZgyYBIEs6AKc6eJKABm5g0CQAZLFEC3cTGTyp9+upJk4xwcuomQGAgItWD5Nobxp9HOBlZGYAIMBi1cPs/k9pQq/BRGx4HZkZAMjSWU3rt5Yk9DpMxIbXEcwAQJbOapq65IeYQyXpTQO/IJgBgABKpB6msHi3mbWUam8awCsIZgDABU43qUu0zqVF/Rrm+LX2ognHRGz4CQXAAJBhmWhSl8ysJj3GzVBJ+BnBDABkULQmdVZRrl3ZEGtWk75upJxPzh/ZF6sehqGS8DO2mQDA4S2kPf8pNT+nL/tRbp2+ImpRrl5ufv1L+WjN1rS3ndKd1QT4CeMMAMDhLSSNF5KNTRLddoo3uoC5S8iG72+CGQBweAspFYnMRko0UElmVhPgFQQzYQhmgGCL9EWtMv3lrevoPnFezOPQybLqWhaO6rHf+hkQiaArZtAkgGwQKTNRp0YV83P773szuq0Sr69LKsJnI4UX5zIgEiiPAmAAgWrVr0FMeCATflJIn+MUJ+cXVXxtBkQC5RHMAPCdWJmJSKzH6XPsbk6XiflFFV870cCJAZHIFgQzAHwnlS0dp7MVVl+XZDZ16tY4QPKr/3dbLJnZSMk0xAOyAcEMAN9JJ+PgVLYiVl+XinL+uEzof6RMPO+IsusVHxOtF0y8wIkBkcg2BDMAfCedjIOT2QotMI4056hiDW743KNoz4k1G4mGeEB5HM0G4DvWMehorfqTPebsxPrCj4Z3al5Xln7/a8yj4qn0gqEhHoKsmD4z/4dgBgj2aSYV7z9iQe69QkM8BBXBTBiCGSC4vNRnBoC9aJoHILAqZiLm33jKfls4imwFkD0SCmaOPvropF40JydHZs6cKQcffHCq6wKApGpEzj6q/H9vwjvmAgi2hIKZ5cuXy4gRI6RWrVpxH6u7Vvfee6/s3r3bjvUBQMxZRFZ33yDWwwQFdT1wWkI1M5UqVZLCwkJp0KBBQi9au3Zt+fzzz6VVq1biNmpmAP+LN8QxkyeVkBxOXCET398J9ZlZt26dHHTQQQkv4Ouvv5bmzZsn/HgAiCVbZxFpELdo7TaZsfwn89OpUQyZnp+ViVlZyC4JbTMlG5g0bdo01fUAwH6ycRaR3zMaTPaGbzoAH3HEEbJhwwb7VgMAEWTbLKIgZDSyNZsGHwYz69evl717/6+XAwA4IZtmEcXLaDg9/dsu2ZhNQ5bOZlqwYIH069dPGjdubI5zv/nmm+Xuv+yyy8zt4ZfTTz/dtfUCcEemZhElUqPidB1LUDIa2ZZNg7vSapp3wgknSPXq1VN+fklJiXTs2FEuv/xy6d+/f8THaPAyefLksuu5ubkpvx8A/7IGMlasIymwqY4kkRqVTNSxBCWjYWXTos3Psk6gBSGbBp8HM++++25ab96nTx9ziUWDl4KCgrTeB0AwaMCgBaN29yxJpIeNykSfm6BkNKxsmn42+rcT/rkx2RuubDNpN99kamM0yNm5c6fY4YMPPjD9bdq0aSPXXnutbNu2LebjtVmfnk0PvwAIDv3y0+6+2vFXf9qxtRSvRmXszK9k7Mzoj9HLza9/KR+t2Zr2tlOQ6oOsbJpmYMLpdZocIuNN8ypXrmya5iXaa0ab22jX4GSa5mk9zPTp0+Wcc84pu23atGlSo0YNadmypaxdu1ZuvfVW04V40aJFZk2RjB07VsaNG7ff7TTNAxCJ1r0MeGaxba9nx7ZTtIngfp3+TQdgeGJqtnYA1u2gROtV3n77bVm5cmXawUxF3333nRxyyCEyZ84c6dmzZ9TMTPgoBf0wtO8NwQyASF+w763YJP9a9L1tr2tXwOH3PjOA56ZmDxw4MKkFXHzxxY4EDhoc1a9fX9asWRM1mNGAiyJhAMkGCnaxqyGcU/VBQBAlFMyEnyZy048//mhqZho14l8lAOwt9o1GQ4eGefoPpBzZXBz5ZE6s49PpTO+26oMAOHiaKV07duwwWZbwGVBaa1OvXj1z0dqX8847z5xm0pqZm266SQ499FDp3bu3m8sG4FOxin0jsXIgY89qb35GOpnj5+PTQFC42jTv008/lT/96U/mooYPH27+PGbMGFPg+8UXX8hZZ50lhx12mFxxxRXSqVMn+fDDD9lGAuBIQ7qKwk/dRDuZ4+fj00BQuJqZOfnkkyVW/fH//u//ZnQ9AIJ96iXRTMlfuzaXPh0a7ff+Vh3L4rXbZPBLy2T7zsgtK2gIB2RRMAMAmTy9k2imRAOZaLUqGtx0a11f7j3viJjHp2kIB/hsm2n79u12vAyALJWpKdF2NqSjIRzg48zMxIkTpUWLFnLhhRea6xdccIG8/vrrpkhXO//qrCUAsKsDrx3HnJ1qsc/xacCnmZlJkyaZJnRq9uzZ5vLee++Zpno33nijE2sEEGCZmBIdPuk6v3pVeeIv9mVU7B6vACADmRkda2AFM9rpVzMzp512msnWdO7cOYUlAMhmTk+JjlaLM7pvW6lbM5eMCpCNmZm6devKhg0bzJ9nzZolvXr1Mn/WU0n79u2zf4UAAs3JKdGxanEGv/SZFO3c47uMSniWSX+mO9gSyMrMTP/+/eUvf/mLtG7d2nTj1e0l9dlnn5mGdgCQSlGuBhghG485Z7IWJ1OY1wTYlJl56KGHZMiQIdKuXTtTL6NTrNWmTZvkuuuuS/blAGQ5qyhXVQwp0jnmnIlanCCe+AKyIjNTpUoVGTly5H63Dxs2zK41AfB48zm7WcecK2YdCtLIOjhdi5NJQcwyAa43zVu1apU89thj8s0335jrbdu2lb/97W/Spk0bWxcHIHu2Iuw+5uxkLU6mJZNlYjAlslHS20zaU6ZDhw6ydOlS01NGL8uWLTO36X0A3OP3rQg7jznb2SDPbUHKMgGeyMzo5OpbbrlFxo8fX+72O+64w9ynU64BZB5bEc42yHNTkLJMgCcyM1ro+9e//nW/2y+55BJzHwB3BK3g1Q5BGTkQpCwT4InMjE66/vDDD/c7hr1w4UI54YQT7FwbgCSwFRHckQNByjIBrgUzM2fOLPvzWWedJaNGjTI1M126dDG3LV68WF599VUZN26cI4sEEB9bEfFrcfzMiRNfQFDkhLR1bxyVKiW2G5WTk+O5LsDFxcWSn58vRUVFkpeX5/ZyAEdrZrpPnBe3+dzCUT1c+Re8346Le3XNXlwT4Pb3d0KZmdLSUrvWBiALtyL8eFzcq2sOQpYJcL0AGIB3ebHg1Y/Hxf24ZiCbJbTNVNH8+fPlgQceKGuap6MNbrzxRk8WALPNhGzkla0Ia+sr2ikrt7e+grJmIIiS+f5OOjPzwgsvmEnZNWrUkOuvv95cqlevLj179pSXXnopnXUD8GDzOTePi7sxIZoj7kAWHM2+++675b777is3i0kDmgcffFDuvPNOM1EbANI9Lu5WzQpH3AH/SToz891330m/fv32u12PbK9bt86udQHwgHQzI6keF3ezZoUj7kAWZGaaNm0qc+fO3a9p3pw5c8x9AILBjsyI1bk23nHx8M61bo9lSGXNAHyWmRkxYoTZVrr22mvl+eefN5drrrlGhg4dKiNHjnRmlQAyyq7MiHVcXFUMO6IdF3e7ZiWVNQPwWTCjQcy0adPkyy+/NAGMXlasWCEvv/yyXH311c6sEkDGxMuMKL0/0S2nZI+Le6FmxYtH3AHYuM2kzj33XHOJZerUqaaOpmbNmqm8BQCXJJMZSbR5WzLzkbxSsxKEmU5AtkgpmEmEZmk6d+4srVq1cuotAM/ySp+XVCSbGUn0d020c62XalbotgtkeTCTQi8+IBC82gY/UclkRpz4Xb08lgGANzHOALBRENrgW5mRaKGC3q73/1qyx7HflZoVAJ7IzADZxu0jxXZJJDMyum9bufMdZ39XalYAJIrMDGATt48U2yleZqRuzdyM/K5eGcsAwNvIzAA28cKRYjvFyoxoR+Ag/a4A/M2xYKZ58+ZSpUoVp14e8ByvHCnOxGmeIP6uALJom2ngwIGyYMGCuI/TRnqMN0A2SbRw1u9t8LU2qLQ0JHWqR//HSlB+VwABDWaKioqkV69e0rp1a7nnnnvkp58SSzcDQZcNbfD1hFL3ifPk4v/5WLbv3BvxMUH5XQEEOJh58803TQCjYw10hEGLFi2kT58+8tprr8nevZH/4wZki3SPFKc7pdqNY+cVcXwaQKblhNLsbrds2TKZPHmy/POf/5RatWrJJZdcItddd53J3HhBcXGx5Ofnm4xSXl6e28tBlkilA3A6Deic7jisr68ZmViBjG47PXHx0dKlVWKnjvzcJRmAt76/0yoA3rRpk8yePdtcKleuLGeccYYZQNmuXTu57777ZNiwYem8POBbybbBt7IeFf9lYTWgi5XpyETH4XjHzpVuO1XKyUkoIPF7l2QAPt9m0q2k119/Xc4880xzYunVV181k7M3btwozz33nMyZM0deeeUVGT9+vDMrBgImnSnVmeo4bOex8yB0SQbgLUlnZho1aiSlpaUyYMAAWbJkiRx11FH7PeaUU06ROnXq2LVGINBSnVKdyY7Ddh3FDkqXZAA+z8w89NBDJgvzxBNPRAxklAYy69ats2N9QOClmvXIZMdhu46dB6lLMgAfBzOXXnqpVKtGIyzALqlmPTLZcdiuY+dB65IMwBuYzQS4LNWsR6a78NoxyZrOwQCcwGwmwAdTqiNlPawgSAtnI9Wg5PwRaNjZhTfdSdZurBlA8JGZATwglayHWx2H05lknQ1dkgH4sGme19E0D36S6WZ7bvHjmgF49/ubYAYIAD920/XjmgEEsAMwAH92HPYCP64ZgDe5WjOzYMEC6devnzRu3FhycnLMEMtwmjQaM2aMadRXvXp1M6179erVrq0XAAB4j6vBTElJiXTs2NE04ItE5zs9+uijMmnSJPn444+lZs2a0rt3b9m1ix4UAADAA9tMffr0MZdINCvz8MMPy+233y5nn322ue1f//qXNGzY0GRwLrroogyvFgAAeJFnj2brOITCwkKztWTRQqDOnTvLokWLoj5v9+7dpmgo/AIAAILLs8GMBjJKMzHh9Lp1XyQTJkwwQY91adq0qeNrBbD/SaVFa7fJjOU/mZ+RJn4DgF0Cd5rplltukeHDh5dd18wMAQ2QOfSQAZBpns3MFBQUmJ+bN28ud7tet+6LJDc315xHD78AyFwgo2MZKk7G1vEFerveDwBZE8y0bNnSBC1z584tl2XRU01du3Z1dW1ARWyr/Pcz0IxMpN/cuk3vz8bPBkCAt5l27Ngha9asKVf0u3z5cqlXr540a9ZMhg4dKnfddZe0bt3aBDejR482PWnOOeccN5cNlMO2yn9pN9+KGZlwGsLo/fo4muUBCEww8+mnn8opp5xSdt2qdRk4cKBMmTJFbrrpJtOL5qqrrpLt27dL9+7dZdasWVKtWvlhfIDb2yoVcw3Wtkq0IZFBpGMJ7HwcACSK2UwIpEzM/dH36D5xXtRsRM4fU68XjuqRFTOHdHttwDOL4z5u6pVdyMwAiIvZTMjq4GX91t9l6pIfpLDY2W0ftlXK04BRP2fNSoViBHf6OACwE8EMAlmzIhnY9nFrW8Wr06Z1DRow6uesqwkPaKzV6f1eWCuAYCGYQSBrVirS+/UrVIOeU9sV2PKFqoGEnY/zQrFxuoGSrkEDxoprLMjCgmgAmUMwg0AeBc7Etk+mt1WcLja2K1DSx2rA6MXsEYBg8myfGSDdmhWnt32sbRVV8Wva7m0VO3q4xOqFY3ezO/2dNWA8+6iDzU8CGQBOIjMD30o1KLFz2ydT2yrpFhvHyrpoFiVWoGT39hwA2I1gBr6VbFDi1GmaTGyrpFNsHG97amiv1pzKAuBrBDPwrXg1K5LB0zTWtordrILc1Zt/SynAi7c9pZ/E5I/WJ/TaNLsD4FUEM/CtWEeBK/LjaZpEjpzHyzolsj21fefejG/PAYCdCGbga1FrVvJyZcBxzaRF/Zq+PE2T6JHzeFmnRLMpdapXkaKde2l2B8CXCGbge0E7CpzskfNYWadEsymDurWUh+d8S7M7AL5EMINAcKpmxctHzoeccqh0O7R+zMAt0V44Q3ocKm0KatHsDoAvEcwAHpPo1lDrhrXiBnDJjBgIWoYLQPYgmAE8xu4xCcn0wglShgtA9iCYATw26NGJMQlkXQAEGcEMPCWTE6FTeS+nBz06OX2arAuAoMoJhUKJHprwpeLiYsnPz5eioiLJy8tzezmwIVCwI+BJJSiJdlzaeud0Bz3asUYAyMbvb4IZeEKigYIdX/CpBCUaQHWfOC/qKSNr62fhqB62ZpIymakCAL9+fzM1G65LdCL0u1+kP9k51enTyQx6tBPTpwEgPoIZuC7RQOH2GSuSDkLC6X1TPlqXUlCSzqDHWOtZtHabzFj+k/kZa+0AgOgoAIbrEg0AfinZk/Jk52TmHKn3/sjyWNs6dh+Xph4GAOxDMAPX2TnAMFJglMycI8u/Fn1vLlaAocea7TouHW091naZ3YXE0VCPAyAoCGbgukT6qtStWUV+KdmbdGCU7JyjinRN17ywTIb1ai19OhTIsx+tT+u4dLyaHX223q/Bk5OBBZkhAEFCzQxcZ/VVURW/vq3rd53dwXzZRvt619sbRciMJDrnKBor6HhozmoTyJj3qrAIzcgkmk1xq5A4UmYonUJqAPASghl4gtVyXwODcHr9ib/8SerWzDWZESt7IQlmRpIpyE2UVad7RbcWMvXKLuY4dqLZDCcKiZOR6mkuAPAytpl8wO3ahky9f6SW+7+W7JE73ym/HaKZkfDuSLEmO9tZjxNOf/t3VxTKrX2T68RrdyFxspLJDNEtGIBfEMx4nNu1DZl+//CW+/reg1/av1A2PDPSq11BzODKznocO770nZi7lAy3M0MA4AS2mTzM7doGN98/XuGulRmJlyWyox7Hzi/9RNaTytwlv2SGAMAJBDMe5XZtg9vvb2ehbKx6HL39jCMbRw0wnPjSj7ceJzNuVmYo2UJqAPAytpk8yu3aBrff3+7tkEj1OOFZHSvASLSxXrrbQfHW4xSnJnIDgJsIZjzK7doGt9/fie2Q8HqcRAKM9Vt/l4fnfGvuc+JLP956nBItcItVSA0AXkYw41Fu1za4/f5uFcpWDDDaFNQK5Je+W5khAHACwYxHuX3qxe3398p2SJC/9N3KDAGA3SgA9ii3T73Een/5I7g4o8N/v+SdKgJ2s1A20pf+2UcdbH4GIZABgCDJCYXC248FT3FxseTn50tRUZHk5eWJ33ixz4x+l4fHL06vx+2mgQAAb39/E8z4gNtf5tb7z/66sGw+UThrJZnMlgAAgq04ie9vamZ8wO3aBn1/DaCGv7I84v2ZnPYMAEBF1MwgIV6Y9gwAQCRkZuCLvjNB4PZ2IQAEFcEMfNF3xu/cLuQGgCBjmwkJYaaPfweGAkDQEczAF31v/MrtgZ0AkA0IZuC7JnZ+QuE0ADiPmpksYkcBajrt/bOxAJbCaQBwHsFMlrCzADWVvjfZWgBL4TQAOI9tpizgdgGq2+/vJgqnAcB5BDMB53YBqtvv7zYKpwHAeQQzAed2Aarb7+8FFE4DQJbXzIwdO1bGjRtX7rY2bdrIypUrXVuTn7hdgOr2+3tFOoXTAACfBzOqffv2MmfOnLLrBxzgi2V7gtsFqG6/v5e4PTAUAILKF1GBBi8FBQVuL8PXBahabBupKiXnj+0OpwpQ3X5/AEDw+aJmZvXq1dK4cWNp1aqVXHzxxfLDDz9Efezu3buluLi43CWbuV2A6vb7AwCCz/PBTOfOnWXKlCkya9Yseeqpp2TdunVywgknyG+//Rbx8RMmTJD8/PyyS9OmTSXbuV2A6vb7AwCCLScUCvnqTOz27dulefPm8uCDD8oVV1wRMTOjF4tmZjSgKSoqkry8PMlmbnfgdfv9AQD+od/fmpRI5PvbFzUz4erUqSOHHXaYrFmzJuL9ubm55gJ3C1CjBS4UwAIA7Oa7YGbHjh2ydu1aufTSS91eCqLI1tEFAAB3eL5mZuTIkTJ//nxZv369/Pvf/5Zzzz1XKleuLAMGDBCv0+zEorXbZMbyn8zPoHa5DZfNowsAAO7wfGbmxx9/NIHLtm3b5KCDDpLu3bvL4sWLzZ+9LBuzE/FGF2h1jN6vzeOolQEAZE0wM23aNPFrdqLil7qVnQjqCZ5kRhdQOwMAyJptJr/J5sGKjC4AALiBYMZm2TxYkdEFAAA3EMzYLJuzE9bogmjVMHq73s/oAgCAnQhmbOb37ESqJ7CsvjJ9OhSUFfuGY3QBACBrC4D9xs+DFVM9gRXpeTk5IuG9pQsCfpILAOAeMjM28+tgxVT7w0R7npXQuaJbC5l6ZRdZOKoHgQwAwBEEMw7w22DFVE9gxXqe0nDt3RWFzGACADiKbSaHaMCizeH8MFgx1f4w9JUBAHgBwYyD/DJYMdUTWNl8cgsA4B1sMyHlE1h+P7kFAAgGghmk3B/Gjr4y2TiMEwBgL7aZUHYCS08laQASSvAEVqrPy+ZhnAAA+5GZQVonsFJ9XqpHwQEAqCgnFApvbRY8xcXFkp+fL0VFRZKXlydeZ3XSdesEVKrvn8zz9LHdJ86LehLKaiyovWm8ePoLAOCt72+2mTzEC9suqZ7ASuZ5HOkGANiJbSaPSGbbxe9FsxzpBgDYicyMDzrw6kaL3q9N+GZ/Xeh69iZdHOkGANiJzIwHJLrt8vi8NYEomrXjSDcAABaCGQ9IdDtl8kfrkpqf5NXtKL8O4wQAeBPbTB6Q6HbK9p17Ey6aTbSY2K3TU9aR7oprLPDZlhkAwH0czfYA66iybhdF+svQ0CK/epWYwYzlkYuOktwDKpltp4qvZYUoVv8XL5yecvsoOgDA/9/fbDP5ZNtlULcWCb1W/Zq5MYuJld7/7hfeaFpnHek++6iDzU8CGQBAsghmPCJeJ90hPVonVDSrf0ikmPj2GSuSqr8BAMCrqJnxWECjx6+jbbskMgdp647dCb3XLyV7ot5H0zoAgJ+QmfGYWNsuicxBsrM3C03rAAB+QGbGZckWwEbL3ig9fl1YtFPq1awqv5bsiVpMXLdmFfmlJH4xMU3rAAB+QDDjolRPE1WcgxTpdSKxQqS7zu4gd77zTczTU5rtoWkdAMAP2GbywSymVF4nEms76owjG9O0DgAQGGRmPD6LKVZAEet1LPVqVpHRZ7aXgrzyW1g0rQMABAXBjIdnMcU7TRTvdZTWxmggE+l14p2eAgDADwhmXJDoKaF4j7PjdSrW3wAA4DfUzLgg0VNC8R5n1+sAAOBnBDMpSmcitW7lJNLNN95pIrteBwAAP2ObKQXpDmi0ZjHF6+Ybr3bFrtcBAMDPyMy4dKQ6kW6+mXwdAAD8KicUCgV6mmAyI8Tj0a2k7hPnRT1BZDWbWziqR8LZkGQ7ADv9OgAA+O37m20mF45UO3GaiFNJAIBsxTaTC0eqAQCAfQhmksBRaAAAvIdgJgkchQYAwHsIZpJgHYVWDGgEAMAbCGaSxFFoAAC8haPZKap4FLpT87qy9PtfORoNAIANOJqdAeFHobVR3kn3v59yR2AAAJA6tpk80hEYAACkhmAmza0mndEUaZ/Ouk3vT2YIJQAASA7BTIY6AgMAAGcQzKSBjsAAALjPF8HME088IS1atJBq1apJ586dZcmSJeIFdAQGAMB9ng9mXn75ZRk+fLjccccdsmzZMunYsaP07t1btmzZ4vbS6AgMAIAHeD6YefDBB+XKK6+UQYMGSbt27WTSpElSo0YNefbZZ91eGh2BAQDwAE8HM3v27JGlS5dKr169ym6rVKmSub5o0aKIz9m9e7dptBN+cRIdgQEAcJenm+Zt3bpV9u3bJw0bNix3u15fuXJlxOdMmDBBxo0bJ5mkAcup7QrKdQSmAzAAAJnh6WAmFbfccoupsbFoZqZp06YZ7QgMAAAyx9PBTP369aVy5cqyefPmcrfr9YKCgojPyc3NNRcAAJAdPF0zU7VqVenUqZPMnTu37LbS0lJzvWvXruI32gl40dptMmP5T+YnnYEBAAh4ZkbpltHAgQPlmGOOkeOOO04efvhhKSkpMaeb/ERnNOloA4ZRAgCQZcHMhRdeKD///LOMGTNGCgsL5aijjpJZs2btVxTsh2GUFfMw1jBKTj0BAJC6nFAoFOi9Di0Azs/Pl6KiIsnLy8v4++tWUveJ86LOcMr54xj3wlE9OP0EAEAK39+erpkJAoZRAgDgLIIZhzGMEgAAZxHMOIxhlAAAOItgxmEMowQAwFkEMw5jGCUAAM4imMkAhlECAJDFfWaCgmGUAAA4g2AmgxhGCQCA/dhmAgAAvkYwAwAAfI1gBgAA+BrBDAAA8DWCGQAA4GsEMwAAwNcIZgAAgK8RzAAAAF8jmAEAAL4W+A7AoVDI/CwuLnZ7KQAAIEHW97b1PZ7Vwcxvv/1mfjZt2tTtpQAAgBS+x/Pz82M+JieUSMjjY6WlpbJx40apXbu25OTk2B41apC0YcMGycvLs/W18X/4nDODzzkz+Jwzg8/Z/5+zhicayDRu3FgqVaqU3ZkZ/QCaNGni6HvoXyD/z+I8PufM4HPODD7nzOBz9vfnHC8jY6EAGAAA+BrBDAAA8DWCmTTk5ubKHXfcYX7COXzOmcHnnBl8zpnB55xdn3PgC4ABAECwkZkBAAC+RjADAAB8jWAGAAD4GsEMAADwNYKZFD3xxBPSokULqVatmnTu3FmWLFni9pICZcKECXLssceazs0NGjSQc845R1atWuX2sgLv3nvvNZ2yhw4d6vZSAumnn36SSy65RA488ECpXr26HHHEEfLpp5+6vaxA2bdvn4wePVpatmxpPuNDDjlE7rzzzoTm+yC6BQsWSL9+/Uw3Xv1vxJtvvlnufv18x4wZI40aNTKfe69evWT16tWSKQQzKXj55Zdl+PDh5jjasmXLpGPHjtK7d2/ZsmWL20sLjPnz58vgwYNl8eLFMnv2bNm7d6+cdtppUlJS4vbSAuuTTz6Rf/zjH3LkkUe6vZRA+vXXX6Vbt25SpUoVee+99+Trr7+Wv//971K3bl23lxYoEydOlKeeekoef/xx+eabb8z1++67Tx577DG3l+ZrJSUl5rtO/yEfiX7Gjz76qEyaNEk+/vhjqVmzpvle3LVrV2YWqEezkZzjjjsuNHjw4LLr+/btCzVu3Dg0YcIEV9cVZFu2bNF/VoXmz5/v9lIC6bfffgu1bt06NHv27NBJJ50UuuGGG9xeUuCMGjUq1L17d7eXEXh9+/YNXX755eVu69+/f+jiiy92bU1BIyKh6dOnl10vLS0NFRQUhO6///6y27Zv3x7Kzc0NTZ06NSNrIjOTpD179sjSpUtNCi18/pNeX7RokatrC7KioiLzs169em4vJZA0C9a3b99y/7uGvWbOnCnHHHOMnH/++Wbr9E9/+pM888wzbi8rcI4//niZO3eufPvtt+b6559/LgsXLpQ+ffq4vbTAWrdunRQWFpb774fOVNISjEx9LwZ+0KTdtm7davZkGzZsWO52vb5y5UrX1hX0yedaw6Ep+g4dOri9nMCZNm2a2S7VbSY457vvvjPbH7pFfeutt5rP+/rrr5eqVavKwIED3V5eYNx8881mkvPhhx8ulStXNv+9vvvuu+Xiiy92e2mBVVhYaH5G+l607nMawQx8kTVYsWKF+dcV7LVhwwa54YYbTF2SFrPD2aBcMzP33HOPua6ZGf3ftdYYEMzY55VXXpEXX3xRXnrpJWnfvr0sX77c/GNIC1f5nIOLbaYk1a9f30T7mzdvLne7Xi8oKHBtXUE1ZMgQefvtt+X999+XJk2auL2cwNEtUy1cP/roo+WAAw4wFy2+1kI+/bP+qxb20FMe7dq1K3db27Zt5YcffnBtTUF04403muzMRRddZE6LXXrppTJs2DBzQhLOsL773PxeJJhJkqaEO3XqZPZkw//Fpde7du3q6tqCRGvMNJCZPn26zJs3zxyzhP169uwpX375pfnXq3XR7IGm5PXPGrjDHrpNWrG9gNZ1NG/e3LU1BdHvv/9u6hjD6f+O9b/TcIb+91mDlvDvRd3q01NNmfpeZJspBbrnrelK/Y/+cccdJw8//LA5tjZo0CC3lxaorSVNE8+YMcP0mrH2XbWoTHsYwB762VasQ9IjldoHhfoke2l2QItTdZvpggsuML2pnn76aXOBfbQXitbINGvWzGwzffbZZ/Lggw/K5Zdf7vbSfG3Hjh2yZs2ackW/+g8ePZShn7Vu5d11113SunVrE9xorx/d2tMeYRmRkTNTAfTYY4+FmjVrFqpatao5qr148WK3lxQo+j/NSJfJkye7vbTA42i2c956661Qhw4dzJHVww8/PPT000+7vaTAKS4uNv/71f8+V6tWLdSqVavQbbfdFtq9e7fbS/O1999/P+J/kwcOHFh2PHv06NGhhg0bmv999+zZM7Rq1aqMrS9H/09mwiYAAAD7UTMDAAB8jWAGAAD4GsEMAADwNYIZAADgawQzAADA1whmAACArxHMAAAAXyOYAeBLLVq0kJycHHPZvn171MdNmTKl7HHapRRA8BDMAPCUk08+OeGgY/z48bJp0yYz5iKaCy+80DyG2WlAcDGbCYCvZ0vFm8qrs7z0okNiAQQTmRkAnnHZZZfJ/Pnz5ZFHHinbGlq/fr3bywLgcWRmAHiGBjHffvutmditW0jqoIMOcntZADyOYAaAZ2jti24H1ahRI+72EQBY2GYCEBi1atUqu1xzzTVuLwdAhpCZARAYy5cvL/tzXl6eq2sBkDkEMwA8RbeZ9u3bl9JzDz30UNvXA8D72GYC4LlmeB9//LE5xbR161YpLS11e0kAPI5gBoCnjBw5UipXrizt2rUzJ5l++OEHt5cEwOPYZgLgKYcddpgsWrTI7WUA8BEyMwB8a9SoUebkUlFRUdTHvPjii+YxH374YUbXBiBzckKhUCiD7wcAtvj+++9l79695s+tWrWSSpUi/9vst99+k82bN5s/16lTR+rXr5/RdQJwHsEMAADwNbaZAACArxHMAAAAXyOYAQAAvkYwAwAAfI1gBgAA+BrBDAAA8DWCGQAA4GsEMwAAwNcIZgAAgPjZ/weJwq9I26ogQAAAAABJRU5ErkJggg==",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -148,7 +150,7 @@
     "# visualising our data\n",
     "plt.scatter(x, y_obs, label='Datapoints')\n",
     "plt.xlabel('t [-]')\n",
-    "plt.ylabel('values [-]')\n",
+    "plt.ylabel('y_obs [-]')\n",
     "plt.title('Artificial Data')\n",
     "plt.legend()\n",
     "plt.show()"
@@ -170,23 +172,23 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[-1.69991666  0.7770054  -2.36015111  2.91014224  0.63066817  2.40394824\n",
-      "  4.31395361  4.42931318  5.4571753   1.03117589  1.45487093  1.86606004\n",
-      "  3.72839181  1.47528361  5.42852574  3.78836883  4.70564204  4.21259739\n",
-      "  6.27489982  4.98923399  9.03610435  5.43110992  6.79454758  9.00144808\n",
-      "  5.86470108  7.05892069  5.95518505  8.36331162  7.22203679  6.32809727\n",
-      " 10.75460038  7.23512537  7.93339034  9.19854853 11.37894337  7.77096616\n",
-      "  8.40322914 12.14786636 10.14789604  8.75451075 10.49370251 14.96185282\n",
-      "  9.60044473 13.18702914  9.57685206 13.31587966  9.58160647 13.68228838\n",
-      " 14.33005712 12.4450883  14.97002787 14.73098301 14.38551919 13.01083281\n",
-      " 13.25281733 13.77742185 12.17359029 17.49924141 14.48031642 18.25112152\n",
-      " 19.52123994 15.21968595 14.3528846  14.44941175 16.05506821 19.02447177\n",
-      " 17.4895854  14.25242034 17.14824633 20.33126544 19.31609374 20.56085204\n",
-      " 19.10507125 19.33591233 20.23652105 19.25595876 21.50762196 23.11688583\n",
-      " 21.84582015 21.16707988 20.47574538 22.19884108 21.29531012 22.66865517\n",
-      " 19.43942123 22.52109512 21.1049189  21.81951277 25.1887952  24.33157026\n",
-      " 22.99033129 25.66906984 24.94970416 24.74672747 26.66680386 24.52966849\n",
-      " 27.12576605 28.26974095 24.62870675 22.66858819]\n"
+      "[ 0.44668493 -1.05339278  2.88210883  0.54770906  4.90974856  3.1063565\n",
+      "  4.1153076   3.60259822  1.69447086  6.11825235  2.56857373  6.38476746\n",
+      "  2.93053129  3.59011671  3.42634276  6.02443788  5.72637654  3.22811334\n",
+      "  4.84727615  3.9733141   5.65347452  5.50991143  8.54505759  5.6833806\n",
+      "  7.65710427  5.64452999  7.10133308  7.00760147  6.75841725  9.37537888\n",
+      "  8.14045588  6.85651275 10.12309432 11.08196899 11.52097808  7.51548696\n",
+      "  8.0297615  10.85079118 12.93975746 10.2212721  16.0213019  14.17261046\n",
+      " 11.14047691 11.05711712 12.680791   10.39508488 13.02588009 14.54587264\n",
+      " 11.06522809 15.05341466 15.88021161 13.5149888  12.35195892 13.75650635\n",
+      " 14.42424165 11.76829229 14.74964692 16.40062315 15.11131069 15.20300216\n",
+      " 14.99451106 18.36247128 17.63770869 18.36809463 15.54230347 15.94216816\n",
+      " 19.04781969 17.34864417 18.07014272 18.20120197 19.87433198 18.7962511\n",
+      " 18.7543702  18.2084891  23.12944126 20.59857353 18.77284008 23.88329856\n",
+      " 23.3321688  23.02580195 23.21747082 23.25404914 26.31811671 21.88010027\n",
+      " 20.52659898 19.98693753 21.82025114 23.45593097 27.15569488 25.87688644\n",
+      " 23.81774822 23.07077554 24.3808879  24.50083914 27.6189827  27.27833748\n",
+      " 28.74494774 25.67215921 23.97065903 30.70085225]\n"
      ]
     }
    ],
@@ -215,7 +217,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Note: If you want to rename your data-dimension you have to change every `.data` to the new name!\n",
+    "Note: If you want to rename your data-dimension you have to change every {class}`sim.config.data_structure.data` to the new name!\n",
     "\n",
     "It can be helpful to look at the data befor going forward, especially if you never worked with *xarray Datasets*. At the section 'Data variables' you'll find the data you just generated. "
    ]
@@ -596,7 +598,7 @@
        "Coordinates:\n",
        "  * t        (t) float64 0.0 0.101 0.202 0.303 0.404 ... 9.697 9.798 9.899 10.0\n",
        "Data variables:\n",
-       "    data     (t) float64 -1.7 0.777 -2.36 2.91 ... 27.13 28.27 24.63 22.67</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-6ecf6756-3925-41d8-92ae-1aff9f4a60cd' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-6ecf6756-3925-41d8-92ae-1aff9f4a60cd' 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'>t</span>: 100</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-ac86f16a-5fb1-4739-9258-2034bb0ff1f2' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ac86f16a-5fb1-4739-9258-2034bb0ff1f2' class='xr-section-summary' >Coordinates: <span>(1)</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'>t</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.101 0.202 ... 9.899 10.0</div><input id='attrs-3e101e79-bd89-432e-999e-373ccb8f405f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3e101e79-bd89-432e-999e-373ccb8f405f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-49e2deca-a228-4c30-af25-cb53b243a54f' class='xr-var-data-in' type='checkbox'><label for='data-49e2deca-a228-4c30-af25-cb53b243a54f' 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.      ,  0.10101 ,  0.20202 ,  0.30303 ,  0.40404 ,  0.505051,\n",
+       "    data     (t) float64 0.4467 -1.053 2.882 0.5477 ... 28.74 25.67 23.97 30.7</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-eb64e66e-f78a-4179-b4f0-29402da0b279' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-eb64e66e-f78a-4179-b4f0-29402da0b279' 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'>t</span>: 100</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-2e684245-490d-4808-92f5-ba21fa3cac66' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2e684245-490d-4808-92f5-ba21fa3cac66' class='xr-section-summary' >Coordinates: <span>(1)</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'>t</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.101 0.202 ... 9.899 10.0</div><input id='attrs-893bf04c-da48-4bf9-a149-18342b421e93' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-893bf04c-da48-4bf9-a149-18342b421e93' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0fab4542-7a5c-40d7-a920-88ce71516cc3' class='xr-var-data-in' type='checkbox'><label for='data-0fab4542-7a5c-40d7-a920-88ce71516cc3' 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.      ,  0.10101 ,  0.20202 ,  0.30303 ,  0.40404 ,  0.505051,\n",
        "        0.606061,  0.707071,  0.808081,  0.909091,  1.010101,  1.111111,\n",
        "        1.212121,  1.313131,  1.414141,  1.515152,  1.616162,  1.717172,\n",
        "        1.818182,  1.919192,  2.020202,  2.121212,  2.222222,  2.323232,\n",
@@ -612,26 +614,26 @@
        "        7.878788,  7.979798,  8.080808,  8.181818,  8.282828,  8.383838,\n",
        "        8.484848,  8.585859,  8.686869,  8.787879,  8.888889,  8.989899,\n",
        "        9.090909,  9.191919,  9.292929,  9.393939,  9.494949,  9.59596 ,\n",
-       "        9.69697 ,  9.79798 ,  9.89899 , 10.      ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f0c7e538-4bb6-41ea-8451-f1ea823e6f27' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f0c7e538-4bb6-41ea-8451-f1ea823e6f27' class='xr-section-summary' >Data variables: <span>(1)</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>data</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.7 0.777 -2.36 ... 24.63 22.67</div><input id='attrs-b4325c81-ee34-4fc8-b74e-f502cab4ab55' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b4325c81-ee34-4fc8-b74e-f502cab4ab55' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-692299a7-ebe2-4d2b-a439-1686de231dbe' class='xr-var-data-in' type='checkbox'><label for='data-692299a7-ebe2-4d2b-a439-1686de231dbe' 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([-1.69991666,  0.7770054 , -2.36015111,  2.91014224,  0.63066817,\n",
-       "        2.40394824,  4.31395361,  4.42931318,  5.4571753 ,  1.03117589,\n",
-       "        1.45487093,  1.86606004,  3.72839181,  1.47528361,  5.42852574,\n",
-       "        3.78836883,  4.70564204,  4.21259739,  6.27489982,  4.98923399,\n",
-       "        9.03610435,  5.43110992,  6.79454758,  9.00144808,  5.86470108,\n",
-       "        7.05892069,  5.95518505,  8.36331162,  7.22203679,  6.32809727,\n",
-       "       10.75460038,  7.23512537,  7.93339034,  9.19854853, 11.37894337,\n",
-       "        7.77096616,  8.40322914, 12.14786636, 10.14789604,  8.75451075,\n",
-       "       10.49370251, 14.96185282,  9.60044473, 13.18702914,  9.57685206,\n",
-       "       13.31587966,  9.58160647, 13.68228838, 14.33005712, 12.4450883 ,\n",
-       "       14.97002787, 14.73098301, 14.38551919, 13.01083281, 13.25281733,\n",
-       "       13.77742185, 12.17359029, 17.49924141, 14.48031642, 18.25112152,\n",
-       "       19.52123994, 15.21968595, 14.3528846 , 14.44941175, 16.05506821,\n",
-       "       19.02447177, 17.4895854 , 14.25242034, 17.14824633, 20.33126544,\n",
-       "       19.31609374, 20.56085204, 19.10507125, 19.33591233, 20.23652105,\n",
-       "       19.25595876, 21.50762196, 23.11688583, 21.84582015, 21.16707988,\n",
-       "       20.47574538, 22.19884108, 21.29531012, 22.66865517, 19.43942123,\n",
-       "       22.52109512, 21.1049189 , 21.81951277, 25.1887952 , 24.33157026,\n",
-       "       22.99033129, 25.66906984, 24.94970416, 24.74672747, 26.66680386,\n",
-       "       24.52966849, 27.12576605, 28.26974095, 24.62870675, 22.66858819])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ba8c4568-c459-4b85-92a7-7a860bfe8177' class='xr-section-summary-in' type='checkbox'  ><label for='section-ba8c4568-c459-4b85-92a7-7a860bfe8177' class='xr-section-summary' >Indexes: <span>(1)</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>t</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c7b2a37f-f161-49aa-b176-b1fde2595b0b' class='xr-index-data-in' type='checkbox'/><label for='index-c7b2a37f-f161-49aa-b176-b1fde2595b0b' 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([                0.0, 0.10101010101010101, 0.20202020202020202,\n",
+       "        9.69697 ,  9.79798 ,  9.89899 , 10.      ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e9f18c3d-e626-4ae4-b046-709b1083cd35' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e9f18c3d-e626-4ae4-b046-709b1083cd35' class='xr-section-summary' >Data variables: <span>(1)</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>data</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.4467 -1.053 2.882 ... 23.97 30.7</div><input id='attrs-9aa8e80e-3f34-4cfd-8d46-a32fdc5a29cf' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9aa8e80e-3f34-4cfd-8d46-a32fdc5a29cf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6e8e20dc-5dcd-4f03-85bc-aacb8d56d976' class='xr-var-data-in' type='checkbox'><label for='data-6e8e20dc-5dcd-4f03-85bc-aacb8d56d976' 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([ 0.44668493, -1.05339278,  2.88210883,  0.54770906,  4.90974856,\n",
+       "        3.1063565 ,  4.1153076 ,  3.60259822,  1.69447086,  6.11825235,\n",
+       "        2.56857373,  6.38476746,  2.93053129,  3.59011671,  3.42634276,\n",
+       "        6.02443788,  5.72637654,  3.22811334,  4.84727615,  3.9733141 ,\n",
+       "        5.65347452,  5.50991143,  8.54505759,  5.6833806 ,  7.65710427,\n",
+       "        5.64452999,  7.10133308,  7.00760147,  6.75841725,  9.37537888,\n",
+       "        8.14045588,  6.85651275, 10.12309432, 11.08196899, 11.52097808,\n",
+       "        7.51548696,  8.0297615 , 10.85079118, 12.93975746, 10.2212721 ,\n",
+       "       16.0213019 , 14.17261046, 11.14047691, 11.05711712, 12.680791  ,\n",
+       "       10.39508488, 13.02588009, 14.54587264, 11.06522809, 15.05341466,\n",
+       "       15.88021161, 13.5149888 , 12.35195892, 13.75650635, 14.42424165,\n",
+       "       11.76829229, 14.74964692, 16.40062315, 15.11131069, 15.20300216,\n",
+       "       14.99451106, 18.36247128, 17.63770869, 18.36809463, 15.54230347,\n",
+       "       15.94216816, 19.04781969, 17.34864417, 18.07014272, 18.20120197,\n",
+       "       19.87433198, 18.7962511 , 18.7543702 , 18.2084891 , 23.12944126,\n",
+       "       20.59857353, 18.77284008, 23.88329856, 23.3321688 , 23.02580195,\n",
+       "       23.21747082, 23.25404914, 26.31811671, 21.88010027, 20.52659898,\n",
+       "       19.98693753, 21.82025114, 23.45593097, 27.15569488, 25.87688644,\n",
+       "       23.81774822, 23.07077554, 24.3808879 , 24.50083914, 27.6189827 ,\n",
+       "       27.27833748, 28.74494774, 25.67215921, 23.97065903, 30.70085225])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e94c8525-16d4-4862-be9a-099f112cf923' class='xr-section-summary-in' type='checkbox'  ><label for='section-e94c8525-16d4-4862-be9a-099f112cf923' class='xr-section-summary' >Indexes: <span>(1)</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>t</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a97352a5-8862-4ab7-b694-b6980a867697' class='xr-index-data-in' type='checkbox'/><label for='index-a97352a5-8862-4ab7-b694-b6980a867697' 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([                0.0, 0.10101010101010101, 0.20202020202020202,\n",
        "       0.30303030303030304, 0.40404040404040403,  0.5050505050505051,\n",
        "        0.6060606060606061,  0.7070707070707071,  0.8080808080808081,\n",
        "        0.9090909090909091,  1.0101010101010102,  1.1111111111111112,\n",
@@ -665,7 +667,7 @@
        "         9.393939393939394,   9.494949494949495,   9.595959595959595,\n",
        "         9.696969696969697,   9.797979797979798,     9.8989898989899,\n",
        "                      10.0],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;t&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5e5160bf-362c-4e9a-9b72-55cb8c7e23ae' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-5e5160bf-362c-4e9a-9b72-55cb8c7e23ae' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+       "      dtype=&#x27;float64&#x27;, name=&#x27;t&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-21b36329-3667-4288-8b2d-de495a908644' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-21b36329-3667-4288-8b2d-de495a908644' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
        "<xarray.Dataset>\n",
@@ -673,7 +675,7 @@
        "Coordinates:\n",
        "  * t        (t) float64 0.0 0.101 0.202 0.303 0.404 ... 9.697 9.798 9.899 10.0\n",
        "Data variables:\n",
-       "    data     (t) float64 -1.7 0.777 -2.36 2.91 ... 27.13 28.27 24.63 22.67"
+       "    data     (t) float64 0.4467 -1.053 2.882 0.5477 ... 28.74 25.67 23.97 30.7"
       ]
      },
      "execution_count": 5,
@@ -714,7 +716,7 @@
     "Optionally, we can configure the simulation at this stage with \n",
     "`sim.config.case_study.name = \"linear-regression\"`, `sim.config.case_study.scenario = \"test\"`, and many more options. \n",
     "```\n",
-    "Case studies are a principled approach to the modelling process. In essence, they are a simple template that contains building blocks for model and names and stores them in an intuitive and reproducible way. [Here](https://pymob.readthedocs.io/en/stable/user_guide/case_studies.html#configuration) you'll find some additional information oon case studies. <br>\n",
+    "Case studies are a principled approach to the modelling process. In essence, they are a simple template that contains building blocks for model and names and stores them in an intuitive and reproducible way. [Here](https://pymob.readthedocs.io/en/stable/user_guide/case_studies.html#configuration) you'll find some additional information on case studies. <br>\n",
     "\n",
     "At the moment, it is sufficient to only create a simulation object without making any further configurations.\n",
     "\n",
@@ -759,8 +761,8 @@
     "\n",
     "## Defining a solver\n",
     "\n",
-    "As described above: A solver is required for many models. So we define a solver by `.solver`. <br>\n",
-    "In our case the model gives the exact solution of the model. Therefore, we choose `solve_analytic_1d`. An Overwiev of the solvers currently implemented in pymob can be found at the beginning of this tutorial [here](#How-pymob-is-structured:)."
+    "As described above: A solver is required for many models. So we define a solver by {class}`pymob.simulation.SimulationBase.solver`. <br>\n",
+    "In our case the model gives the exact solution of the model. Therefore, we choose `solve_analytic_1d`. An overwiev of the solvers currently implemented in pymob can be found at the beginning of this tutorial [here](#How-pymob-is-structured:)."
    ]
   },
   {
@@ -779,9 +781,9 @@
    "source": [
     "## The pymob magic\n",
     "\n",
-    "So far we have not done anythin special. Pymob exists, because wrangling dimensions of input and output data, nested data-structures, missing data is painful. <br>\n",
+    "So far we have not done anything special. Pymob exists, because wrangling dimensions of input and output data, nested data-structures, missing data is painful. <br>\n",
     "\n",
-    "Now we add our data, which is already transformed into a *xarray Dataset*, by using `.observations`."
+    "Now we add our data, which is already transformed into a *xarray Dataset*, by using {attr}`pymob.simulation.SimulationBase.observations`."
    ]
   },
   {
@@ -793,14 +795,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "MinMaxScaler(variable=data, min=-2.360151110471945, max=28.269740948520962)\n"
+      "MinMaxScaler(variable=data, min=-1.0533927803793315, max=30.700852250682072)\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "C:\\Users\\ameli\\OneDrive\\Dokumente\\01_Uni\\04_Jobs\\01_TKTD\\pymob\\pymob\\simulation.py:303: UserWarning: `sim.config.data_structure.data = Datavariable(dimensions=['t'] min=-2.360151110471945 max=28.269740948520962 observed=True dimensions_evaluator=None)` has been assumed from `sim.observations`. If the order of the dimensions should be different, specify `sim.config.data_structure.data = DataVariable(dimensions=[...], ...)` manually.\n",
+      "C:\\Users\\ameli\\OneDrive\\Dokumente\\01_Uni\\04_Jobs\\01_TKTD\\pymob\\pymob\\simulation.py:303: UserWarning: `sim.config.data_structure.data = Datavariable(dimensions=['t'] min=-1.0533927803793315 max=30.700852250682072 observed=True dimensions_evaluator=None)` has been assumed from `sim.observations`. If the order of the dimensions should be different, specify `sim.config.data_structure.data = DataVariable(dimensions=[...], ...)` manually.\n",
       "  warnings.warn(\n"
      ]
     }
@@ -815,7 +817,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This worked 🎉 `sim.config.data_structure` will now give us some information about the layout of our data, which will handle the data transformations in the background."
+    "This worked 🎉 {attr}`pymob.simulation.SimulationBase.config.data_structure` will now give us some information about the layout of our data, which will handle the data transformations in the background."
    ]
   },
   {
@@ -826,7 +828,7 @@
     {
      "data": {
       "text/plain": [
-       "Datastructure(data=DataVariable(dimensions=['t'], min=-2.360151110471945, max=28.269740948520962, observed=True, dimensions_evaluator=None))"
+       "Datastructure(data=DataVariable(dimensions=['t'], min=-1.0533927803793315, max=30.700852250682072, observed=True, dimensions_evaluator=None))"
       ]
      },
      "execution_count": 11,
@@ -848,8 +850,8 @@
     "Debug into the function and discover what happens!\n",
     "```\n",
     "\n",
-    "We can give `pymob` additional information about the data structure of our observations and intermediate (unobserved) variables that are simulated. This can be done with `sim.config.data_structure.y = DataVariable(dimensions=[\"x\"])`.\n",
-    "These information can be used to switch the dimensional order of the observations or provide data variables that have differing dimensions from the observations, if needed. But if the dataset is ordinary, simply setting `sim.observations` property with a `xr.Dataset` will be sufficient.\n",
+    "We can give `pymob` additional information about the data structure of our observations and intermediate (unobserved) variables that are simulated. This can be done with {attr}`sim.config.data_structure.y` = `DataVariable(dimensions=[\"x\"])`.\n",
+    "These information can be used to switch the dimensional order of the observations or provide data variables that have differing dimensions from the observations, if needed. But if the dataset is ordinary, simply setting {attr}`pymob.simulation.SimulationBase.observations` property with a `xr.Dataset` will be sufficient.\n",
     "\n",
     "```{admonition} Scalers\n",
     ":class: note\n",
@@ -867,11 +869,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "from pymob.sim.config import Param\n",
+    "#from pymob.sim.config import Param\n",
     "sim.config.model_parameters.a = Param(value=0, free=False)\n",
     "sim.config.model_parameters.b = Param(value=3, free=True)\n",
     "\n",
@@ -883,9 +885,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "To make the parameters available to the simulation one has to use `sim.model_parameters[\"parameters\"] = sim.config.model_parameters.value_dict`. This step is particularly important for all fixed parameters.\n",
+    "To make the parameters available to the simulation one has to use {attr}`sim.model_parameters[\"parameters\"]` = {attr}`sim.config.model_parameters.value_dict`. This step is particularly important for all fixed parameters.\n",
     "\n",
-    "`sim.model_parameters` is a dictionary that stores the input data for the model. By default, it includes the keys `parameters`, `y0`, and `x_in`. For our analytic model, we only need the `parameters` key. In situations where initial values for variables are required, you can provide them using `sim.model_parameters[\"y0\"] = ...`.\n",
+    "{attr}`pymob.simulation.SimulationBase.model_parameters` is a dictionary that stores the input data for the model. By default, it includes the keys `parameters`, `y0`, and `x_in`. For our analytic model, we only need the `parameters` key. In situations where initial values for variables are required, you can provide them using {attr}`pymob.simulation.SimulationBase.model_parameters[\"y0\"]` = ... .\n",
     "\n",
     "For example, when working with a Lotka-Volterra model, you would specify the initial conditions for the predator and prey populations with `y0`. For more details on such use cases, please refer to the advanced tutorial.\n",
     "\n",
@@ -919,16 +921,25 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n",
     "## Running the model 🏃\n",
     "\n",
-    "The model is prepared with a parameter set and ready to be executed. With `sim.dispatch_constructor()`, everything is prepared for the run of the model. It initiaizes an `evaluator`, makes preliminary calculations and checks. \n",
+    "The model is prepared with a parameter set and ready to be executed. With {class}`pymob.simulation.SimulationBase.dispatch_constructor()`, everything is prepared for the run of the model. It initiaizes an `evaluator`, makes preliminary calculations and checks. \n",
     "\n",
-    "For the parameter estimation it is not necessary to run the model, but it can be helpfull. By using `.dispatch()` all the parameters with the setting `free=True` get fixed. Therefore, we have to fix parameter $b$. \n",
+    "ℹ️ What does the dispatch constructor do?: <br>\n",
+    "Behind the scenes, the dispatch constructor assembles a lightweight {class}`pymob.simulation.SimulationBase.evaluator` object from the Simulation object, that takes the least necessary amount of information, runs it through some dimension checks, and also connects it to the specified solver and initializes it. The purpose of the dispatch constructor is manyfold: <br>\n",
+    "By executing the entire overhead of a model evaluation and packing it into a new {class}`pymob.simulation.SimulationBase.evaluator` instance {meth}`pymob.simulation.SimulationBase.dispatch_constructor()` to make single model evaluations as fast as possible and allow parallel evaluations, because each evaluator created by {meth}`pymob.simulation.SimulationBase.dispatch()` is it's a fully independent model instance with a separate set of parameters that can be solved.\n",
+    "Evaluators store the raw output from a simulation and can generate an xarray object from it that corresponds to the data-structure of the observations with the {attr}`pymob.simulation.SimulationBase.evaluator.results` property. This automatically aligns simulations results with observations, for simple computation of loss functions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For the parameter estimation it is not necessary to run the model, but it can be helpfull. By using {meth}`pymob.simulation.SimulationBase.dispatch()` all the parameters with the setting `free=True` get fixed. Therefore, we have to fix parameter $b$. \n",
     "\n",
     "*Try changing the value of $b$ and see what effect it has on the next steps?* <br>\n",
     "\n",
-    "**`sim.dispatch_constructor()` should be executed every time you change something in your simulation settings, even if you don't run the model.** <br>"
+    "**{meth}`pymob.simulation.SimulationBase.dispatch_constructor()` should be executed every time you change something in your simulation settings, even if you don't run the model.** <br>"
    ]
   },
   {
@@ -1315,7 +1326,7 @@
        "Coordinates:\n",
        "  * t        (t) float64 0.0 0.101 0.202 0.303 0.404 ... 9.697 9.798 9.899 10.0\n",
        "Data variables:\n",
-       "    data     (t) float64 0.0 0.303 0.6061 0.9091 1.212 ... 29.09 29.39 29.7 30.0</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-7e0f253f-5ca0-4a14-84dd-52cb4bf84b3a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-7e0f253f-5ca0-4a14-84dd-52cb4bf84b3a' 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'>t</span>: 100</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-2cda1e5f-cbd7-4694-b984-e923c99479f0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2cda1e5f-cbd7-4694-b984-e923c99479f0' class='xr-section-summary' >Coordinates: <span>(1)</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'>t</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.101 0.202 ... 9.899 10.0</div><input id='attrs-7c6d93e4-3689-4659-9d9b-dbe1b41dc045' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7c6d93e4-3689-4659-9d9b-dbe1b41dc045' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-99cfafed-fd60-4291-9b2e-17806cbd0ba3' class='xr-var-data-in' type='checkbox'><label for='data-99cfafed-fd60-4291-9b2e-17806cbd0ba3' 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.      ,  0.10101 ,  0.20202 ,  0.30303 ,  0.40404 ,  0.505051,\n",
+       "    data     (t) float64 0.0 0.303 0.6061 0.9091 1.212 ... 29.09 29.39 29.7 30.0</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-fc2821af-170e-473c-a33c-edf1a6f44f4d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-fc2821af-170e-473c-a33c-edf1a6f44f4d' 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'>t</span>: 100</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-98019c1c-8e31-4c6b-a82f-2d2cf3019f5f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-98019c1c-8e31-4c6b-a82f-2d2cf3019f5f' class='xr-section-summary' >Coordinates: <span>(1)</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'>t</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.101 0.202 ... 9.899 10.0</div><input id='attrs-b8702041-a250-4762-bc1e-6a741d6c9fb2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b8702041-a250-4762-bc1e-6a741d6c9fb2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d5a71a87-853e-4246-a3e2-dc555f78b7c6' class='xr-var-data-in' type='checkbox'><label for='data-d5a71a87-853e-4246-a3e2-dc555f78b7c6' 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.      ,  0.10101 ,  0.20202 ,  0.30303 ,  0.40404 ,  0.505051,\n",
        "        0.606061,  0.707071,  0.808081,  0.909091,  1.010101,  1.111111,\n",
        "        1.212121,  1.313131,  1.414141,  1.515152,  1.616162,  1.717172,\n",
        "        1.818182,  1.919192,  2.020202,  2.121212,  2.222222,  2.323232,\n",
@@ -1331,7 +1342,7 @@
        "        7.878788,  7.979798,  8.080808,  8.181818,  8.282828,  8.383838,\n",
        "        8.484848,  8.585859,  8.686869,  8.787879,  8.888889,  8.989899,\n",
        "        9.090909,  9.191919,  9.292929,  9.393939,  9.494949,  9.59596 ,\n",
-       "        9.69697 ,  9.79798 ,  9.89899 , 10.      ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-90eeda1d-3d05-46fc-b54f-445978b391ce' class='xr-section-summary-in' type='checkbox'  checked><label for='section-90eeda1d-3d05-46fc-b54f-445978b391ce' class='xr-section-summary' >Data variables: <span>(1)</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>data</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.303 0.6061 ... 29.7 30.0</div><input id='attrs-9e09554e-765f-43d4-b3f5-c22bcd33fc90' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9e09554e-765f-43d4-b3f5-c22bcd33fc90' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bcafc74a-b977-4629-94fa-f8b729cc9773' class='xr-var-data-in' type='checkbox'><label for='data-bcafc74a-b977-4629-94fa-f8b729cc9773' 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.        ,  0.3030303 ,  0.60606061,  0.90909091,  1.21212121,\n",
+       "        9.69697 ,  9.79798 ,  9.89899 , 10.      ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6a2c89d8-533a-4272-80cc-1f25ff824740' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6a2c89d8-533a-4272-80cc-1f25ff824740' class='xr-section-summary' >Data variables: <span>(1)</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>data</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.303 0.6061 ... 29.7 30.0</div><input id='attrs-93345447-c959-4e2f-b2ea-42df6a894c9d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-93345447-c959-4e2f-b2ea-42df6a894c9d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3147747c-8ec9-4d6b-95cf-3a7eee442d61' class='xr-var-data-in' type='checkbox'><label for='data-3147747c-8ec9-4d6b-95cf-3a7eee442d61' 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.        ,  0.3030303 ,  0.60606061,  0.90909091,  1.21212121,\n",
        "        1.51515152,  1.81818182,  2.12121212,  2.42424242,  2.72727273,\n",
        "        3.03030303,  3.33333333,  3.63636364,  3.93939394,  4.24242424,\n",
        "        4.54545455,  4.84848485,  5.15151515,  5.45454545,  5.75757576,\n",
@@ -1350,7 +1361,7 @@
        "       24.24242424, 24.54545455, 24.84848485, 25.15151515, 25.45454545,\n",
        "       25.75757576, 26.06060606, 26.36363636, 26.66666667, 26.96969697,\n",
        "       27.27272727, 27.57575758, 27.87878788, 28.18181818, 28.48484848,\n",
-       "       28.78787879, 29.09090909, 29.39393939, 29.6969697 , 30.        ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-fcb00663-791e-42e2-ab5f-ecfa3be659ed' class='xr-section-summary-in' type='checkbox'  ><label for='section-fcb00663-791e-42e2-ab5f-ecfa3be659ed' class='xr-section-summary' >Indexes: <span>(1)</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>t</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-76e76f52-204f-4383-a41a-ae6586cf54f3' class='xr-index-data-in' type='checkbox'/><label for='index-76e76f52-204f-4383-a41a-ae6586cf54f3' 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([                0.0, 0.10101010101010101, 0.20202020202020202,\n",
+       "       28.78787879, 29.09090909, 29.39393939, 29.6969697 , 30.        ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1a417faa-0933-4ccd-a117-1683379b65ca' class='xr-section-summary-in' type='checkbox'  ><label for='section-1a417faa-0933-4ccd-a117-1683379b65ca' class='xr-section-summary' >Indexes: <span>(1)</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>t</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-647bb2ee-a6a2-404f-b433-d29cad010a96' class='xr-index-data-in' type='checkbox'/><label for='index-647bb2ee-a6a2-404f-b433-d29cad010a96' 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([                0.0, 0.10101010101010101, 0.20202020202020202,\n",
        "       0.30303030303030304, 0.40404040404040403,  0.5050505050505051,\n",
        "        0.6060606060606061,  0.7070707070707071,  0.8080808080808081,\n",
        "        0.9090909090909091,  1.0101010101010102,  1.1111111111111112,\n",
@@ -1384,7 +1395,7 @@
        "         9.393939393939394,   9.494949494949495,   9.595959595959595,\n",
        "         9.696969696969697,   9.797979797979798,     9.8989898989899,\n",
        "                      10.0],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;t&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-30354674-ed5c-47f4-ada1-42b9ef1c6aac' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-30354674-ed5c-47f4-ada1-42b9ef1c6aac' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+       "      dtype=&#x27;float64&#x27;, name=&#x27;t&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-85b552d0-fa73-45c2-bbdc-6737f34f1487' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-85b552d0-fa73-45c2-bbdc-6737f34f1487' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
        "<xarray.Dataset>\n",
@@ -1448,7 +1459,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "As `sima_y` is not a fixed parameter, the new parameter does not have to be passed to the simulation class."
+    "As `sigma_y` is not a fixed parameter, the new parameter does not have to be passed to the simulation class."
    ]
   },
   {
@@ -1484,7 +1495,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1492,22 +1503,25 @@
     "def plot(results: xr.Dataset):\n",
     "    obs = sim.observations\n",
     "\n",
+    "    SSE = ((results.data - obs.data) ** 2).sum(dim=\"t\") #calculating the sum of squared errors\n",
+    "\n",
     "    fig, ax = plt.subplots(1,1)\n",
     "    ax.plot(results.t, results.data, lw=2, color=\"black\")\n",
     "    ax.plot(obs.t, obs.data, ls=\"\", marker=\"o\", color=\"tab:blue\", alpha=.5)\n",
     "    ax.set_xlim(-1,12)\n",
-    "    ax.set_ylim(-1,30)"
+    "    ax.set_ylim(-1,30)\n",
+    "    ax.text(0.05, 0.95, f\"SSE={np.round(SSE.values, 2)}\", transform=ax.transAxes, ha=\"left\", va=\"top\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "2174c0da8e5e497392655ec9bf58e9e1",
+       "model_id": "776bc2d6e3fb4ab4a3d4ad2534849bfe",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1532,7 +1546,7 @@
     "\n",
     "Of course this example is very simple, we can in fact optimize the parameters perfectly by hand. But just for the fun of it, let's use *Markov Chain Monte Carlo* (MCMC) to estimate the parameters, their uncertainty and the uncertainty in the data. <br>\n",
     "\n",
-    "The inferer serves as the parameter estimator. Different inferer are implemented in numpy and can be found at the beginning of the tuorial and in the API. The method for the parameter estimation is defined by using `set_inferer()`. This automatically translates the pymob data in the format of the selected inferer. Numpyro additionally needs a kernel. To start the estimation you use `.run()`.\n",
+    "The inferer serves as the parameter estimator. Different inferer are implemented in numpy and can be found at the beginning of the tuorial and in the API. The method for the parameter estimation is defined by using {meth}`pymob.simulation.SimulationBase.set_inferer()`. This automatically translates the pymob data in the format of the selected inferer. Numpyro additionally needs a kernel. To start the estimation you use {meth}`pymob.simulation.SimulationBase.inferer.run()`.\n",
     "\n",
     "\n",
     "*Note that other methods often don't need a kernel.*\n"
@@ -1590,7 +1604,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "sample: 100%|██████████| 3000/3000 [00:03<00:00, 877.81it/s, 7 steps of size 7.01e-01. acc. prob=0.95] \n"
+      "sample: 100%|██████████| 3000/3000 [00:07<00:00, 420.73it/s, 3 steps of size 7.38e-01. acc. prob=0.94] \n"
      ]
     },
     {
@@ -1599,8 +1613,8 @@
      "text": [
       "\n",
       "                mean       std    median      5.0%     95.0%     n_eff     r_hat\n",
-      "         b      2.65      0.03      2.65      2.60      2.70   1421.06      1.00\n",
-      "   sigma_y      1.71      0.13      1.70      1.50      1.92   1315.22      1.00\n",
+      "         b      2.73      0.03      2.73      2.68      2.78   1645.00      1.00\n",
+      "   sigma_y      1.80      0.13      1.79      1.60      2.02   1113.95      1.00\n",
       "\n",
       "Number of divergences: 0\n"
      ]
@@ -1978,16 +1992,16 @@
        "  * draw     (draw) int32 0 1 2 3 4 5 6 7 ... 1993 1994 1995 1996 1997 1998 1999\n",
        "    cluster  (chain) int32 0\n",
        "Data variables:\n",
-       "    b        (chain, draw) float32 2.634 2.687 2.613 2.639 ... 2.618 2.653 2.625\n",
-       "    sigma_y  (chain, draw) float32 1.727 1.798 1.775 1.828 ... 1.549 1.938 1.531\n",
+       "    b        (chain, draw) float32 2.783 2.69 2.673 2.697 ... 2.706 2.696 2.709\n",
+       "    sigma_y  (chain, draw) float32 1.704 2.01 1.895 1.962 ... 1.627 2.016 1.74\n",
        "Attributes:\n",
-       "    created_at:     2025-06-10T08:58:04.047307+00:00\n",
-       "    arviz_version:  0.20.0</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-9d295794-74cb-40a3-96e7-0196f9dd9480' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-9d295794-74cb-40a3-96e7-0196f9dd9480' 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>: 2000</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-5339adac-aa1d-4b05-b13f-2f73c52841b6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5339adac-aa1d-4b05-b13f-2f73c52841b6' class='xr-section-summary' >Coordinates: <span>(3)</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'>int32</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-2b6cae9f-7d26-487b-b214-39c143a0f0c6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2b6cae9f-7d26-487b-b214-39c143a0f0c6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d644c351-8e2b-4cf9-adeb-a5cae61f9382' class='xr-var-data-in' type='checkbox'><label for='data-d644c351-8e2b-4cf9-adeb-a5cae61f9382' 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'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 ... 1996 1997 1998 1999</div><input id='attrs-08c7eab3-0d65-40b1-8d11-7f95845162c1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-08c7eab3-0d65-40b1-8d11-7f95845162c1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4f8612de-a9ee-4575-ad3e-079f65c23515' class='xr-var-data-in' type='checkbox'><label for='data-4f8612de-a9ee-4575-ad3e-079f65c23515' 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, ..., 1997, 1998, 1999])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cluster</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-260a1002-2706-4f24-834d-fe2646d5c5b1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-260a1002-2706-4f24-834d-fe2646d5c5b1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a2963598-b89b-4315-9696-a69a46d95569' class='xr-var-data-in' type='checkbox'><label for='data-a2963598-b89b-4315-9696-a69a46d95569' 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></ul></div></li><li class='xr-section-item'><input id='section-daf4baa5-027e-4615-a0a5-03e4e9ff1df3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-daf4baa5-027e-4615-a0a5-03e4e9ff1df3' class='xr-section-summary' >Data variables: <span>(2)</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>b</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>2.634 2.687 2.613 ... 2.653 2.625</div><input id='attrs-b729e2a0-89d8-43cc-bd89-c0bac41c29c4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b729e2a0-89d8-43cc-bd89-c0bac41c29c4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f160f82c-8a2f-4fca-8a89-ca32b3c409aa' class='xr-var-data-in' type='checkbox'><label for='data-f160f82c-8a2f-4fca-8a89-ca32b3c409aa' 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([[2.634382 , 2.6873431, 2.6128893, ..., 2.6182663, 2.652883 ,\n",
-       "        2.6254828]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sigma_y</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.727 1.798 1.775 ... 1.938 1.531</div><input id='attrs-51c7e6a3-79ae-4f29-9d92-53e5cf6ae5a3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-51c7e6a3-79ae-4f29-9d92-53e5cf6ae5a3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-733f5e5d-70a0-4a31-9ba0-a8fd5e70a5e2' class='xr-var-data-in' type='checkbox'><label for='data-733f5e5d-70a0-4a31-9ba0-a8fd5e70a5e2' 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([[1.7270529, 1.797513 , 1.7745078, ..., 1.5492175, 1.9377252,\n",
-       "        1.5306886]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-bbe7a462-35dc-4a1d-a4b4-79fa218ed8cc' class='xr-section-summary-in' type='checkbox'  ><label for='section-bbe7a462-35dc-4a1d-a4b4-79fa218ed8cc' class='xr-section-summary' >Indexes: <span>(2)</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-71d885f2-f9f9-48fc-ab40-20d76efbba25' class='xr-index-data-in' type='checkbox'/><label for='index-71d885f2-f9f9-48fc-ab40-20d76efbba25' 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;int32&#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-101b469f-5670-4458-b4b0-c357d5f8b0dc' class='xr-index-data-in' type='checkbox'/><label for='index-101b469f-5670-4458-b4b0-c357d5f8b0dc' 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",
+       "    created_at:     2025-06-23T08:31:52.794154+00:00\n",
+       "    arviz_version:  0.20.0</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-6714f8b0-49ad-43ff-9c51-2cb069c8d04f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-6714f8b0-49ad-43ff-9c51-2cb069c8d04f' 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>: 2000</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-2d34e7ef-e9d2-4aa4-871e-629b4671d608' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2d34e7ef-e9d2-4aa4-871e-629b4671d608' class='xr-section-summary' >Coordinates: <span>(3)</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'>int32</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-c1a26b3d-fafc-4c96-9160-6614ad843803' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c1a26b3d-fafc-4c96-9160-6614ad843803' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9f5960b3-a949-44d9-9a38-cc85bc4645c1' class='xr-var-data-in' type='checkbox'><label for='data-9f5960b3-a949-44d9-9a38-cc85bc4645c1' 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'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 ... 1996 1997 1998 1999</div><input id='attrs-fae29af7-7e2b-4f67-a600-3cb873b8f584' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fae29af7-7e2b-4f67-a600-3cb873b8f584' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dc1e28bd-a7e4-432d-b118-3d6c02896276' class='xr-var-data-in' type='checkbox'><label for='data-dc1e28bd-a7e4-432d-b118-3d6c02896276' 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, ..., 1997, 1998, 1999])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cluster</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-b948e590-6470-4df8-a62f-b7cbcdc5b539' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b948e590-6470-4df8-a62f-b7cbcdc5b539' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f24bee39-60e2-4eae-9929-8db2471316ed' class='xr-var-data-in' type='checkbox'><label for='data-f24bee39-60e2-4eae-9929-8db2471316ed' 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></ul></div></li><li class='xr-section-item'><input id='section-9398c299-6349-4390-ace2-48b207d3ade2' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9398c299-6349-4390-ace2-48b207d3ade2' class='xr-section-summary' >Data variables: <span>(2)</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>b</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>2.783 2.69 2.673 ... 2.696 2.709</div><input id='attrs-f767a4da-4899-4ebd-8965-1d3fc4d2b4dd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f767a4da-4899-4ebd-8965-1d3fc4d2b4dd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-69c13ebf-a428-42ee-9a65-d14a9f2d3a5c' class='xr-var-data-in' type='checkbox'><label for='data-69c13ebf-a428-42ee-9a65-d14a9f2d3a5c' 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([[2.7828448, 2.6895475, 2.6731958, ..., 2.7059813, 2.695733 ,\n",
+       "        2.708754 ]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sigma_y</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.704 2.01 1.895 ... 2.016 1.74</div><input id='attrs-da4cdb94-8804-47bd-b483-fbc1e92b231e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-da4cdb94-8804-47bd-b483-fbc1e92b231e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fb665ea2-fe1a-4458-8c55-f2bb722160c1' class='xr-var-data-in' type='checkbox'><label for='data-fb665ea2-fe1a-4458-8c55-f2bb722160c1' 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([[1.7042773, 2.0098093, 1.8947011, ..., 1.6274247, 2.0155187,\n",
+       "        1.7396609]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d1083e2d-18ef-4d7d-865e-c3fd0b0f82cb' class='xr-section-summary-in' type='checkbox'  ><label for='section-d1083e2d-18ef-4d7d-865e-c3fd0b0f82cb' class='xr-section-summary' >Indexes: <span>(2)</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-2c273231-0019-47c5-9620-8afa43de35a2' class='xr-index-data-in' type='checkbox'/><label for='index-2c273231-0019-47c5-9620-8afa43de35a2' 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;int32&#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-0dabac1c-a98d-4c8f-bfc2-a588d07dfd8f' class='xr-index-data-in' type='checkbox'/><label for='index-0dabac1c-a98d-4c8f-bfc2-a588d07dfd8f' 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",
        "       1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999],\n",
-       "      dtype=&#x27;int32&#x27;, name=&#x27;draw&#x27;, length=2000))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-66ca2e6c-2f67-469d-8b30-33545f93f2cd' class='xr-section-summary-in' type='checkbox'  checked><label for='section-66ca2e6c-2f67-469d-8b30-33545f93f2cd' class='xr-section-summary' >Attributes: <span>(2)</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>2025-06-10T08:58:04.047307+00:00</dd><dt><span>arviz_version :</span></dt><dd>0.20.0</dd></dl></div></li></ul></div></div>"
+       "      dtype=&#x27;int32&#x27;, name=&#x27;draw&#x27;, length=2000))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-780efcf7-e613-411c-bb8d-919f128ba5f0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-780efcf7-e613-411c-bb8d-919f128ba5f0' class='xr-section-summary' >Attributes: <span>(2)</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>2025-06-23T08:31:52.794154+00:00</dd><dt><span>arviz_version :</span></dt><dd>0.20.0</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
        "<xarray.Dataset>\n",
@@ -1997,10 +2011,10 @@
        "  * draw     (draw) int32 0 1 2 3 4 5 6 7 ... 1993 1994 1995 1996 1997 1998 1999\n",
        "    cluster  (chain) int32 0\n",
        "Data variables:\n",
-       "    b        (chain, draw) float32 2.634 2.687 2.613 2.639 ... 2.618 2.653 2.625\n",
-       "    sigma_y  (chain, draw) float32 1.727 1.798 1.775 1.828 ... 1.549 1.938 1.531\n",
+       "    b        (chain, draw) float32 2.783 2.69 2.673 2.697 ... 2.706 2.696 2.709\n",
+       "    sigma_y  (chain, draw) float32 1.704 2.01 1.895 1.962 ... 1.627 2.016 1.74\n",
        "Attributes:\n",
-       "    created_at:     2025-06-10T08:58:04.047307+00:00\n",
+       "    created_at:     2025-06-23T08:31:52.794154+00:00\n",
        "    arviz_version:  0.20.0"
       ]
      },
@@ -2025,7 +2039,7 @@
    "source": [
     "We can inspect our estimates and see that the parameters are well esimtated by the model. Note that we only get an estimate for `b`. This is because earlier we set the parameter `a` with the flag `free=False` this effectively excludes it from estimation and uses the default value, which was set to the true value `a=0`. <br>\n",
     "\n",
-    "The `mean`of `b` is the value of the estimated parameter. It shloud be the same or close to estimation you did manually. The `sigma_y` is the mean error of this estimation."
+    "The `mean`of `b` is the value of the estimated parameter. It should be the same or close to estimation you did manually. The `sigma_y` is the mean error of this estimation."
    ]
   },
   {
@@ -2044,7 +2058,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 400x300 with 1 Axes>"
       ]
@@ -2074,7 +2088,7 @@
    "metadata": {},
    "source": [
     "### Report the results\n",
-    "The command `.report()` can be used to generate an automated report. The report can be configured with options in `.config.report()`."
+    "The command {meth}`pymob.simulation.SimulationBase.report()` can be used to generate an automated report. The report can be configured with options in {meth}`pymob.simulation.SimulationBase.config.report()`."
    ]
   },
   {
@@ -2084,7 +2098,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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ekLr5uaU0vl8JPf3tGVRSkJvoJoE4kJOdRX+7dArVN7fSj19dKSoSpVr8OwAgtUP2nn/+eXrjjTeoqKgonPOJw+c6d+4s/n311VdT//79RXgdc+edd9IJJ5xAf/nLX+iss84S4X5LliyhRx99NCzMcG6q3/3udzRy5EghUv385z+nfv360fnnn0/JiPQSaG4N0sZ9NeIdXFrUKS6GhNPEysloQHDagaE9C5WX9+sIYslFE/DJKKxtbBFGU68i6zw8iU6wbUcwbh56AaXvvNItgjphRlvhL17UNPiX+F/fF80qTWYF7Puh9KjhSVXgL36e4X2hEE3uD0WdUtvenLuhXPyOl+0kheRYgKcUSFrmrCunW577UghS/74OglSmwcnOH7xyGh01tAf930vLRUw/AADEg4ceekiEyp144onUt2/f8M+LL74YXmbHjh1UVtaR1Proo48WQhaLUJMnT6ZXXnmFXn/99Yjk6D/84Q/p9ttvpxtuuIGmT59OtbW19N5771GnTupCTzyRM/8cNsV5UxZsrnC0fix6gtYI5LwuWw8cpnQp2GKWe0YeckVto/jR0tLaRodcViKUYW/ahNhOMbqWbMRbei9ZfPfx2n2m3imq24g33BYvjC89vE3O28X93AzVS+eViKc977zveCatN26Pf9uWQhQLehyGpL3G8jvppRT2YtKcD5kDy29hMpZTsLeqQTx7APADL/o+PKVAUsJJHG/891Ia27eInrluRsor1sAd7CH12LeOpKseX0S3PvclPXHNdDp2ZM9ENwsAkOaoGMJz586N+uziiy8WP2awgfOb3/xG/KQCnMukqbWN9lWbJ/l15G0TDFINz0KHKsxZhfVJI5CXkSE8TryOkpWVu6tMZ7A5bIQ9hzhpsn4Zzqm0t7qBzpzYN5xjRhW+hozT9ez4bOMBOlTnTihTZdehxCV517N6T7XIsXbGhL5i4iySQMweiTsO1lG/ru2emORo6x3Pq8JQlTgvURUzWfxcvquSJg0oofycbNP7uqWtzfT7RMAOTq2hyobvrdobce/JYzfKJSXxOqzO7PUTi0C7aKuzCYVkI57adCK8AgE8pUASMm/jAfrOM0todB8WpGZSMQSpjIbLYz/57Rk0orSL6BfxyC8AAACAw1Haf8eS7FzLxvJa4QX91ooyenP5HstlpZ2njYhhbxKzhNvpAAtWZoZnhUsvKaa5pX2buaHcNCx+8bl063klsROkgklotMaSLF0mw2Zvmo79enCUMW6Cm+D1WFnbJO41KrrL9orDwttry35zr0YWXKXw46w9/vWmjrxRQVNvUZXwvUQ49XEhApkDyS2cM5BzByaTV2IiSFRyf6+oT2AOQzd5I/VAlAJJxfKdlXTDv5fQiF5d2kP2OkOQAiT6AfeH/t0607VPLqaVu9wl0QMAAJC4GePKcAUr++G+XEbfBjMhJVYDIlkMEG1ib6O8UCzKsaB0QBfeZ4U0qKXdIMUVrpaU6PMTT0OYq/j9b1WZa2EzJyTqtbBbjQ/hK+yRaCYwqHgrsWHoh3hjt2vum9pwU6vlK332rHODFL6NuqI8FnkPGS0jw/f8xmgvX+44RHPX749puyt2VYmJB650yOIUh9YmK/GorppUFVwVKa9poA/W7LUMAfaKw6EcbFq8OGUQpUDSwA/Ebz+1WLiuP33tDOpakJfoJoEkokeXfHr2upnUrTCPvvn4IvHiBAAAEAkb3DxATQakAWc3yOccS1y1x2hd9X0li6zkzENBlrxXDQWSgpI+pJLXM0to7sWZcXN+VVbx255nkUeeF3mu3XqIZYfUC61wGO7jLtvH4qJ2e4cOG4tSVvdQUKEB8zcdEOXa3Vw7IXZZXEyu0skeUNJjKPUwr24nk5eHRanQ3aQ91PA/fUgyH488YjmhY+RryLaYTPwu4RxbsRRL8IJU7Vnx4lDouaG/dn4I+x+t3Rc1KWLmKcXPjQ/XqHlGQpQCSQEPrq5+/AsxE/Tva2faVmMBmUmfkk70/HdmUnHnHLriXwtFZSMAAAAdzF1f7jghuV9JnPVeG2aG7acb9wuDWfu906pmqWgPcwEPLnsfVPCUsjtOvu5czjzV4PxCbtEaymYeQrPXldPnoRxdMg+UzLFlx7KdlfTeqo5zKnNyadscS7/jcBtum3YsY3YcKvdBwKA9LFJ/teMQ7a9tjBJ+DRPXG/Q/sV2L9WRoqZ/3oJ/bVqmwZ7X/eD17/NoP52+14v3Ve8V9lO50eOfKv6O/S1aaQ880KTC6EZtUolCk6KUX9s32yu8z1ZBpiFIg4fAL81tPfCF+P33tdBrUoyDRTQJJzIBuBfTCDbOEJ92Vjy0Sg0YAAACxJ3Fm4yOWnDt6OrxIVNM0uzcC4mEy+GWYaI3hVoPQsIg2hI5Uf06tZsjt2m3lBSFnwJ0eOosbHFok0eZhimwbuYKPV1WEk+F6eSFRSbWPc46kRs2yUpSSOboiroeLmJ/mkLil3YcZVvcQn0Ozb1ms5ATqKrDw8NaKUK63oC500FKUifQe8jNRNHszcdVGL3PLdeSUshAqwkpF9DJ6L6p4ob2v+X5w4glXXt0QvidVQkP9qDqZbMizWZfA3ExuibWYBRfS2HLAPnek3L7cH+c040INXoQ8QpQCCYUfctc/vVgkRXz06iNofL+SRDcJpAD9u3amF288iroX5tFVjy0Ss4AAAADcI8PBEh2mIVEx77itUowJV8dyaReq5bkiX+gc8lToUZhPczeUe96GoMW6UtxhAcaJh8+iLRW2SdCrNcLB7LXGx2XnGWaGEwNcVWDjPmDVDzrltptNdc0t4XDAdXudt8MVVuF7waA4NqEduTidbFiyiGglbMoKgVbEQ5ThcEwOYfVyQlIWdHAiOvt5pPp2dFx6vedpx78Xbq4QnnBm4q+eBVsqaNGW+BUO2lReI7xC3bBlfy01hkUx/xM+cQVJPUnuKEUyz53XFVb16IV9nnjg4iVeCNEQpUDC4Bfg915cRku2H6L7LptCRw/vmegmgRSib0lnevGGWdSzKJ+uevwLWrodVfkAAMAthjOdMY4zVUPw9J4WqnDokz4pbyzhYJzjyaoqnZPmqXgfSKQuI3MWeZGny2gdI2ob2oUINvLLqtSS5K4pq6K9upxWevSeQ9qQOW3uHqscWlYiAQspbjE7d2+vKDNMGr0wJMDJSmwsyrFRxkZ2LB4kRodndsh+meJ8Htmw3KoTJbUC0+GmFqXE+E7uX6fJweXS8h4xSrbsFtlXtU2S7VOpyGYl+rrBbDNWbXEj7spzaHQ/yOPnfH+xwGImC97siavPn6cqQnJVUv7xG8sQzRi3zeeTc+F66Yms+qxkEderAlHy/tOHusJTCqQ0f/pgPf1v1V76xdnj6IyJfRPdHJCiOaZeuOEoKi1uF6bmbWzPGwEAAMAZcqYzqJh/QqX6WyzhbiqrakN4pBHdbBP+ppJEfOehupiPR+YvUlnHSJiR3lNRbXBlHgWVKyCbbsHhbq30Na1w2GLhmefEzvaiKhobWkYeQfqk8rxZfV4qv/03rMID+Sg7osucHTN7tKmKqKoGtYqBGmtybifrcz/QX0MtAYNtRm/f3MsuUfmGtPv1umIcHz9v385zk2HvLLO+wV40sXi1BRNZGc/Dy8qCLieRX7fXOq+bH3C4q0poXiyg+h5IWV5aspMemruZrp41mL59zNBENwekML2LO9FLN86iIT0K6dqnFtP/UjDRKwAAJAsqBhbnn5DJoz3dd8S/3VkEViKH6r5jWcbNIN3onOebiVJuwveC7pdT9Z5yIqJondmsRMRYhYugj+KB9JqSaP9kbzutwMceKaoCmSvJkXNKBdzlcpLnX4qo2m3qsXPkc+QpZbCsauiZU9bvqxHebmZCulFYp5n3k9VzyW9pSr99qy7FIhGntnD7PORKika5zlhAZ2FF25/fW7VXFG3wA680KL732WPL7TMglmfHnsr68MSDU4e2tWXV4nmiisp7k707VcJxjbdvjJucenogSoGEKLY/eXUlnTCql/CSAiBWenbJpxduPIqmDOxKtz7/Jb20eGeimwQAAKmFizElGyacj4gH3Zbhe27a4zAUSNoMqpXVLHdtmnMoOvRJHz5oNUg3M2ykocIGRX5OtichkUbrxKLJaI0dle1YtY8NXolVDjO9B5njZOsee7A42dq2UDgciwNcQj3W8COVy+02p5QR9QZhiXbeVE6EZP214TAxFjdMtx1OJk6uw9TMvHncepn5herzx6q96/fWiAT3diGXjNFl5X0ZiVKc34m3veXA4ZjDplmEtEtYr3+OupU+th44LDy2tlWYh/36IThW1DbS4m0Hwx5STsWtDYq58+RWtUUYjGCRkguasAdbLOgPA+F7IOXgh9nNz35Jw3t1oX9eMZVyfE7IBjKH4k659PS1M+jE0aX0w/+uoEc/3ZzoJgEAQMpgVFwqYJDLT2+4sqhgV2pe4mQ47kTQsg678Q690cK5eKySQ2ux8pRxFIoULltufGZ4Vt1tHhgvBD1SuG7a47XKhWOVkFwF/Xk18pzyyztHL6zJ0FC/+mWsiYa155SFAjak9WTZuEp1VN9zjlvPDRXkkW0qtwlhMgrN03zI11LmYDPqg36F8anmmDK6l1Xy1Bkh86bpkbervLeMQo9ZiFE5F5+s3y+qQ8YDKbA58RzzQqSU+5W//ZY9v9p5iDZaCFmLt7kvDMXPSrOCGGa9zMktAUUAxA1OHMrhVbnZAXr8miOpqFNuopsE0ozOedn0yFVH0HlT+tE9766je/+31nFOCQAAyETsKpMZGTjlIUPbbILJqY2mNWSchgLJ5WsaWpS8A9zgRCQJi3zB9hCMt1bsMRVgwuKJ+BXyCDHZmfycE42bVZ7VJyH3IgG142tp9Z3my1YLDwutR5UbTJOGhy7OmrJq4Z1jV3FSVpzSbDnKYDXtG6HlVD0JzK675frh8D3nGG2XBQkjbMP3TD7nSo36xPSyz/O531tlnTTfK2T+LFVPIe1vXoarsnGfkdh5+XiNE5FE9mm31dg+3bhfKXRX7yHFgtS8TQdowz5rAZA9TI288fwS+jquo1UZS/v1GZ6IUG2XFO2kl2Gsh1NR22hb5MGsCAW3uareebJ5bdi+VlznSRkJwvdAysDq6o3PLqWyqgb619VH0oBuBYluEkhT+AX8t0um0DVHD6FHPtlCd7zwVUzVcQAAIBVxPZg3yKti9smKUOnsXDNrVWPQOdy1kgEmB/p6jxirmWK/4NAQWaVNf8zbQyEjZpXmInLZ2By2/L6yrkmE5zB23lryXBp5ZEWJK6bb8BDdxtizy0gM03s1yLa6zZGl/3vz/sOW16VPcSfxuzA/J2p9lapsVtu2XCmOaMVTSZaJdWgbvmdyYdhI1hqwjDwtX+2opEVbK5QECn07zWAPH22lN6N1WAjT9zm7TVfrRCi9l08sl5PHqVJIinr2KYbzaT8zqx5ohJmXXUWtvYCh335DyCOottFasFP2MPXoHnEqDpvBnkIsqPHEh4p3n3zm5rj0WNPDgp/+XlJ9h/DnsYhi2nBO9sTTimNm59WJkApRCvgOv6R+8uoq+mLrQfrrJVNo6qBuiW4SSHPYxfyX54yjn589jt5ZWUbffGyRq1K0AACQqjgdfMaSV0XvKcXPW/aOjmyPdQUsN+3Xzj5H5DxS3ov9/thQVKk6xsaK9vi0g3T5b7N9aIWLoENxiI1Ps7xW4XW0jlg22HloqaK6eF1Tq6jwtWpPdM4lvWeZc2+tqIA93fba/zbKnROZ60thXzb5wvysGsa7CHiYU0qfxF1idwwqu9YLyYeb2sUJ1RDg8L4sdvb55gP02cb9mmUjF+ZnEwthq/dE7jMy0XlI1An9za3uZFJ8wAs4z4+2zSrHbBX2Kz1XYglnToocW7omuPXIUQkttfTuDH0rxTT2GuK8THbRGPLrjtBXd+c06OA6ejmxwO81Tr0TtQH9ddHvy0W/gygFfOehTzbTf7/cRXedNprOmtQ30c0BGQK/uK47dig9dOU0kWD0Gw/Np226xIwAAJCuqBojPNvJeYhkaEvEGFtxXMlhfQs2V9DKXe3CAhtXHP7RYdQEaI/DEB2zXWttEjnO5zAvN7aX3ovWyGOAjY//rSqjuqaWiLAmbciX1b6DBp5lZuJL0IGnlETF+0mlnR3r2n8eVDivZpXO9O2TglClQR6sFpPKfFb7r2lopg9W7xVt0PZl9roKC0S662yX18YwfxCpIQVH1ZxPpv3eYn2vUxmZ5SHSt4E9ErX5y7ShbnrvH9M2m7Q9bAi7QO/RpN+FbBPf09rjMrpvHIkBMV4HDj023K7Z/WyxLXkNrN4D7O1i5e0T2/GY91ez56CfwphS+J7JOvp/O3nPyvtfekq5dmAOOlnWm4kF6QHMNpQoJmJxLfSn1c1xQpQCvsKx+n98bz1dOLU/3XLi8EQ3B2Qgp0/oSy/ccJQYpFzw4OeGyTsBACDdUA17kKFlHetpvXbUPFU4V2R5TQNtOVBrurxKfj+5PBu6zQreSXIg3NoajKwAGFT3TrBDej9xyXMW2iRcddCybQZGmVmuD6tzbrVs+/L2dKyifg3cwAYYGzGz15XTKotqc9quID3qjELDnFT0ku3mKlscCsbhWVojjL2mHXvj6MIFrcIHw15Vui+l8OFZbXszxPYDjo14lVxyxhUYgyK/0mebtN49waj7a97GA2Ybs8S0WqEL49ws1FIvUPhYI8EQFu02G4hvVuFXkX8H7T3SbG4hqypsSn0iQrjhyQH7lfgejdyG+Tr7qtULBPA7YOl26zF+QDMZ47YghBa7o5XPa7siAXYs3FpheJ7YK1nFk5fRr83v5OgKp8ZHpJ/42R816aC7l8LbI2UgSgHfWL2nir734jI6YnA3uvcbEz1JggaAGzhk9LVbjqHuhXl0xb8W0gtf7Eh0kwAAwFe04gXnH1LFjWGWY5KExo2Nt+tQXUQ4l9XQQY4ruNy2ak6auOFgyCMNg4icH8qGqcIyoY2xV9Kmcut8W2I+3MgzSOFico4rDvewSxyu3b68bkY2W5TBZLC+aUinF/lodGJUxFdRbTAWQNjTTn6tX8d4uwmqvmdxt44o7RL12Qdr9kWJiR1tD/heVU/FpOgIv7QW1y0TnYeXsd+hnSDI4oE2lxLn02Lx9tMNamF7Kvnf9N5qsYTvWa1r1iYJ79/o2jt9tvCzPWK7pu0JiknnXYfqDSdA9DmlOC8Tv2es2qHfypz15VEVHO1Ob5SnFLljf00jvbl8j27fQeGVzKGoZuHr3Gaztn60dp8Q67XCsVuPMP3z202/gygFfIFnTL/z9BIhAnA1tPwc/+KwAVBhUI8Ceu3WY+jYET3px6+upF+8scp24AwAAKmKdkz4yYb95km2owydIP1vZRmt2VOtbCCbJUaWqCc6D4bDubTt7azL5cIz3Py9NIj1glSsIR9BD5bvSCBtv7VITym7fTkXNrR/rymzEaVMG2B/HG5n7M2Mfssk4Xb7aE80FhNms/1uNssGut6o9JpYckpZnepiWS07GB36qg31Cypcu3B+tXDOJmcNtvJWY8OdCyuFl7VJphYVcmTrCUQxwc9hbf43ed9rqwLqx6Wz1+0zTQoe1Cyqz4smL4uKOGCepNqeCG/YIImCVlZeWEukSOtwP3Y0a0J9jaqcakN33Y799WGhsj8aeRyFvw99rFJ9b3dlvWXYsxlRoc9B4zbrA/D4vcn3SMXhjn2aNY897qza7oXXIUQp4Dn8orrhmaVUWd9Mj33rSOrZJT/RTQIgPLB67FvT6eYTh9MzC7bTVY8jAToAIDWxqyr6wZq9rgx8HkxyrpGN5TVRA0seNDupZuomh4XRKtoBLwsfPMO9bOeh8OyzG09s/XF4ndRX2yaj1hldDzYObM+ZgqeUExZvizYSDXcb2g/nZowIlXQhShnBl5K9Rt5b1REWadZlDcWD0KcdibRVZve1Xj7m4p7+t1EbzPSPks4hQccAlVtSViC0rGQfdJdInUPH2LNNn+enQjcusto2h+5q2+FFX3ADX7/5mw/Qwi0d4Vtmp9c855r5BTlQ06j8jOBnC59XDt0U+foONwnhIhzKGcKoIpu+4p02z5SZB6S27do8fqp9TKUqqBlBXX+SzwazLmP07IjJm6stKPqa9lwYHY88P9yX5SQGV+uOWMbi+vI70QjeLIcrv71iT8xV/5ZsO0ifbzog8uJx2Dj3ZytMwzxjyCnI3q4bDKrXivA9i/X0hwdPKZBw+Kb/0X9X0PJdlXTfZVNpbN/iRDcJgAh4Vu9Hp4+h+y6bInJfnPvPecIjAAAAUon1urAGO1QNKrvBJFfS9RM7UYYHx8yhw82meTpUxsNOxDU/4PeP3fVi40SPipFpFa4UFUIW9bf1+dOHr7jJAWW2fTZqtR4f+rapCKsdlQ6Dpn3ZyFDUelpEL6/7O2jlVRX5ZVGnnOhlg0GR04ZDVa32VlZVL8Jr7MJvw9X3HHqdmOX9WqfzptOLvlIo03+nNfybQ33BTDD2In8TCxLloTxtsmvUakQc7i8criqfGXpky6z1go4qgeyJpWf9Xs25Cu1m4ZYKcX+zgc9wLjoWLvTkaAQ9NxgJJR2eOfJv+xNtfl/ZrxsRiqspBMFhdKo46Qv67sTnmgtRaJF9L2IfmvVleJ9ZQn8jrHIiygqSZBVOKZPpB4PCU8suj9YXWw8a9jfDfZjs0w0sTnPuPb03GfcRqzabets5aAtEKeApD87dTG8s20M/PG0MfX1c70Q3BwBTzpvSn1656WjxouHKfByuAgAAqYJT7yD15Lkd/zYy6o2EB+1iPNsb/ly21UEbVcewQRNPA317zDCyMb7aqeY1FL2/6I3ZFQBXCdP4zCBJtF5wCucscoFhkmWbSl9moZrcH9WvnXl4jdVnMveJ1fXV5pSy8m7QLsvoDVttO1Xy4Jh5Shm1lYU9zmmjT/ish5O1W1VlY9hLxEnOODfo+7JReJQ+kb9Dhz9ltNtdvaeaFmypEMevFcQkG/fVimX0HvGqz0KrZa2qBOpD6cyEi2y7uGcLuO9wxVO794OZIKfFtkKiBWaLOPGUicVLVSbc1u6OC1+YikOBgCaUz3iZjr+D9l6bik0Pi4ShIhn6MMbmiGugdkYihPCIiYfY2kqhNmo9+4SQZrG8PqcdPKVAQmGX6z+9v54unNafbjphWKKbA4AtE/qX0Ju3H0sT+hfTzc99SX/9YL1ShSgAAEg0Tov5qA4S7bxR7Aw3DvGzWlbvrcQeI07aqdXEjCq2+RlaaEWeLhTErml2++evjYxF/eUxErfkprngzNz15Y6MPrNmycp1sSbUbt9J9EdGXgv6vmjliaVP8CzCUG1CyOzuIb3Q1BHGZ2T0Wot52qrU0ZWrOmARRe+9xWFGZk1dubsynJdICIMe9Gt9f5HnlMUx9kxSCgVWbEYszZW5llh8NNpMU2urWvVOC9FxndYTygDtmvp+YnavqNz7dp+zF51hFUVN+KAqbvykeP/tYqDa8pb793jYbSyahkRLbUU8B+8Ps2umf1/x+0xWFDW6PrKaqz6MsVnzrFc9H6ptiixiQcpwxVkJhxVaEZWfzcU1hSgFPIFdgL/34vL2SnsXotIeSB0459lz1x9Fl88YRP+YvYlu+PcSqjJIZggAAMmEU3FAVW/XGpxe2QpmYwIuGc8eI9aDWfMZWBn+0qoTK+zazUab/jkfi2EUdHF97PZnmtzYQUPZI4eP08mxWS16mNvkjyZlGOZmF36nh5NcNza3hdc18ljRYjdWjfBEMPsyalnd58HoNtqFjsp7UBqqLPSaebw0tUhvLvIdFjnZM0lF4DbzMtOGMbnBVEBx4OWk/XhHRV3Yk0q/PIsujjx+HD5jYxIQLbx6vtR44Sh5O7nwrOHnCidu9+Il4Sh8z1To0/7bXGRk5DVVSXTfsY7x5/qP+X3G4ZpG+1V971aKZ7bKPebw8xivldX6+gkUeY6dTIhAlAIxw7Mm33kGlfZA6pKXkyXE1N9fMEG8ZM/757yoMrQAAJBMyAH1xn01SvmR9MYVl5LmhKZBi1lm5Rlbk4En5/twipERGDD5Xs50t+jCNewG9DwDvNIkn47XcFP01QEZt5N3QZ/X4XNntjyXEDfzPpJ5ktT24f4zq77OXkhbDtSG1xUimsX+7TwlwsmjTQQoo23qxcRYhAdp6HHuKbOiLFztmuFD0eeUMgrPdENUaE6bdUUyzutk501VXtNoen3cEBYaNJ/Zn/lAZMihNnE4V1VzLehYG+Sb9tdaFtkxD6G1nzDg/qYVMNW6n7rHjX5s7EWBCKP98PnhJPGqaNvBYvRby/dEhFaGRWPdvcrhzypejm6S4ztZrk1zT5kVklBFRYx1uWXTb2T+tFj2BVEKxAS/UK59erF4KaHSHkh1rpw5mF66cRY1NLfR+Q987uiFCAAA8YSNUJ7JX1NWbZs0W5bn1ooGPOiXIVla7IxJ48pG6oaiFSI1hs1oViX3kPU+zBNga5dxgtHi4TAyk+G5a4dyF4ay/njMci0pbt41MqzF6JxU1kcb6UbXicPUVC4Pr2vmbaYcvhf1O1K0iFyWPbNahdgr4TGEG89r9nBTWVd7XcPhW8GOCAazROZ26M+vvq/a5SnaVnHYNuyNBXEWOb3ALKrT3ANIK2zHtm9tSKmqQMPJ0bmfGD23vCpI4WR7sYgY2nWdhAya7YffUSzGWhVUsGsHeyvx+dFOPGhTc2jPHQu+h+qa6asdh6xzs7k8R2GPQYPv+B0swzBbFItERGzbdOfmAlosIqKz92zkbxUgSgHXcPWNW5//ktaW1dCD3zwClfZAWjB1UDd6+45jaerAbnTnC8voV2+uNk0CCQAAiYK9PKRAxHkqVGZXN5ZbG4rRolP0iNLI66dOU3UpFjg/kn4Qq0+wqp+RFa3UrWQ1DjYzlu1yZtgRNEsO7bHK44VnQkTFMP32bTYfiw3PYS1suBrtw6jSlLNkydGYJeRWLtNuYlgZNisY7bFndr/YoeqpvWJXh9Etqu8F2o1s9pAy85KyE+q8KvdeYeEJpMXp/WEkGLNBr8//pdJO4V0WUT0w0pvSrceK3M42m0T2xtUX1cLpVMPJYooQjCGEzAnyOrEIy4LS8p2VjsfdtrkIw/dytAcc27NG75W2GBKdz1lfLhKFW4kzX2ytEFX2+J3b6uJCmXnO+eUp5eSauPEQhSgFXMGd7Wevr6K56/fTvRdMpBNG9Up0kwDwDPb4+/d1M+jG44fRU/O30eWPLgyXHQYAZAaffvopnXPOOdSvXz9huLz++uuWy19zzTViOf3P+PHjw8v86le/ivp+zJgxrtrHtpR22Ld420Fb706VyVjt4Fx1XKlPuusWNkj0ogsbBtqQrY6QKhGIEVpGfR9GxodbYz0WVEIu3aI30ByZB8JbzXyNWHOGcrLsYAyGpl3ycu26doVLVI/FSAhUCekz3Z5HQuWuQx33XfvzpP3fVh5SH6/dZxtmGdU83WlSKQhj1of8SDkrytWHWq01nI36D1cVkxUrA7oE+/ocZE7DH6Pzh3k7oRlUOL9csdFMjFXZruM2edCZ5SbkhAhHK5gJIGb9x+yQpbehNhS3WXddzNbVVjc0nQzQfKwNR+UIIhY22WvQbH3ZP9qfVeQZwRg8kb0iLAI6WAeiFHDFA3M20QuLd9IdJ4+kS6YPTHRzAPCcnOwsuvvMsfTgldNEiMtZ988TMxoAgMzg8OHDNHnyZHrggQeUlr/vvvuorKws/LNz507q3r07XXzxxRHLsUilXW7evHmu2pctKm05W4fLknOlXK1XkN6o4BAkr2FDQtXrxTg8z3pd/Syz1eJOzhnPoKtgaLBoqsB5iYrx4uR8OPXE8kJTCMbQFzhU1Sr8UGuMmieLV8wpFU7WHbmeURAMf6cquPhhGApPKcWrwyGQzrYduV03Xh1eEdQ8E6QIwP3EouBaBHqxQxu+t35fjadtdItRRU3VffB4VZvnS+VSxRINEPTpeWAWump2z5o9U7j6qPg+9LfwxNoVGe6u8m5S0fkWbbXIoWixPu+/1Y2nlMkqZsfDYnS87lw3YmV0qQsAbHj1y1305w820DemDaDvnTIy0c0BwFfOnNiXRvXuQjf+eyld8a+F9JMzx9K3jxmCCpMApDlnnHGG+FGlpKRE/EjYs+rQoUP07W9/O2K5nJwc6tOnT8zta/cTcj7w49nZRgWjR+7DC3h8qpL3ymnIVsf2HfmpKC/5zsoycov06HFzPHbCoh3RniwOwuB4Nt3i+ywPprNVTwkfB3uy6POYcX606G1GLmOWSLpzbrayAWgUEma6rGJibD/6RPi6+CSK6Yc7Dh1xIrx47LZtxex1kYn25b0gcuUY5h+zzsnF4zityBGr1w+v/dGafTGJPJwknvMEGm4/IoG5Yh9W6L1GobOq1DV6M4nB50wlr5LWs81Jn7S655REKZtQYEZW/jRezhzefZvbmyqE9r4wOxz2/IuX7RJ0cU/BUwo4gmdXf/jKCjp2RE9RrQyGOcgERpQW0Ru3HUtfH9ebfvP2GpFrimPFAQDAjMcff5xOOeUUGjx4cMTnGzduFCGBw4YNoyuvvJJ27NjhavtiIO3jtGdx59y4ufprUa3gxmN4q0SvnLtIhulEfBN0b0CbwfvRCydOBA2vMWuLCnaLZseoSjlpCx8GewR6CW+T+xiHuBkZTJEihfwdHatnJIJxaJxaG7zvFezNpHqmnCb6j8op5dKANvJEMhMZzEI/jULiuJLfbAOhUkXk8LJ7rSurVhKNLbdhkyBeotqF/H6Gf7Uz+hnrFG4jh1XKcDmra2KWb001p5Thd23294BZSGR1fYtaAQkb7902Dy+UtQAWnzfS9oq6iKIPKkCUAsqs2VNNN/17KY0o7UIPfXMa5eWg+4DMoUt+jgjlu/uMMfT2ij10wQPzaatNEksAQGayZ88e+t///kfXX399xOczZ86kp556it577z166KGHaOvWrXTcccdRTY25IdLY2EjV1dURPx0D2djbajZG5RAQNxWBDPfhyVbUB9fNrUHhmaVNJMxhiRyCrRL6ZRuKYeBpoDcqBvcosG1n3EQpB+vatTcnxnJlIvTNgUeR15OfMjzVLF9QhCil+TyW66gXXrzKNRRxf+qSdlsR63PDac6ijv1Gr5eXHbstwYUe3OD1tLoXeemsxIlgKHn6oi0V6qIUJT9O22iUgN5elApaVoKMalNQLZRbVZSz8lhz8kw0T3zv3IvOT/jdy0K9k5ZAVQBK8Mv76icWiZnTp749g4o65Sa6SQDEHR7w3XjCcHr2upm0v7aRzr1/nkhkCAAAWp5++mnq2rUrnX/++RGfczgg55iaNGkSnXbaafTuu+9SZWUlvfTSS6bbuvfee8OhgfwzcGB7Hkcume1EODGjusF5yfpkQHhKmX4X/c17q/Z6lpDdjpLOuWFxIxHmQXMMnlINLR2VzIyI1d5pd/BTzSnVHr6nQr2LCpBGVSNZ6JGCVWS4VORyTk6D195ejFGIWMCBwbhJoRJneLu69utFT1X4fOrbyGOpRJIE9ruj4gicD2lvDIV3CvOSL3OPUxGFi3rosZs/sdqHkXdb0GCiIxasDtGLyaW2JO3H+qTyVkCUAkovr28+tki87p69fib1KemU6CYBkFCOHtGT3r79WBpW2oW+88wS+vP7610P0gAA6QUPfp944gm66qqrKC8vz3JZFq5GjRpFmzZtMl3m7rvvpqqqqvAPJ1AH7eKB25LtdlSaJNl1g1H+I7+JCj1xkONmybaDlgKPF2EmchNWnj0cYscz7aqOWQu2OBdozXL/cGgfnzPtkUZ4IjjcT1aM3mWqONG+Vu+pVt+u7m+34x0vK4x5QaplIHGT+0n/LCjIy3a9/6Xb/Sn247eHGU/gOO17+u1tdCDiGmF1x2zZX+v4mSLyqGnW8iMk2AtUPZMZiFLA1iX2yscWidmkZ6+fQUN7Fia6SQAkBf26dqaXbjyKrpg5iP45ZxNd8+QXdMgksSoAIHP45JNPhMh03XXX2S5bW1tLmzdvpr59+5ouk5+fT8XFxRE/qUYyhBM4QVu9Kt4Gb35ONvXskh/TvvWnW+85ZcdOTdijnliNH15bbsFMq+H+wlWy2pdJjHLASe61hyrarf3bwWmINeTRCKMcSupZpZyhvwRuK3TGmm8JOIdv/fWaPFWxhMPuOuTPJABXDIwVq+fSxn01jp9bXr+yrN6BnAokGGP1vWR9xza3INE58ABO8nnZowuporaJnr52Bo3pk3oDYQD8hI2Hey6YSH+8aBIt2nqQzr5/Hq3cVZXoZgEAPIAFo2XLlokfhvM/8b9lYnL2YLr66qsNE5xz7qgJEyZEffeDH/xAiFbbtm2j+fPn0wUXXEDZ2dl0+eWXx+GIUofCOIaYjOpd5Pk2YzEPjh/VU+QwNHvnqO0/sgWtDt0E3CbsVeFwYwuVhTzczAQnbc6iWDWp0qJYvPuNw/ecngM/PKU4lEuPm2qcKujFrnTJp8nHlaS2vGewN6A2OXhyeocFHIdH67F6xHG1TafOfV7fS3ZbCzrdnm6FZA3WaHbw7oEoBQzhvAuXPbqADtQ00jPXzaApA7smukkAJC2XHDmQXr35aPHvbzw8n15ajPAaAFKdJUuW0NSpU8UP83//93/i37/4xS/E32VlZVGV8zi87r///a+pl9SuXbuEADV69Gi65JJLqEePHrRw4ULq1asXZTJ6saUg332IiR67GeTkNNJia2uUp5TDfChWFRBjNdYO1DaG8wiZHU6Lpr2xeHaw6DUolHDeDXweO3KDBV2fBz9yShnB4Y6+kEL3iBPKa9znZko0ql5n+lDLRHkeWuG0SUbPBCtPqPZKrQlWbbz2vKLITSZr+J72WW5H8mU7AwmHczRc/q+F4uX27+tnQpACQIEJ/UtEnqk7XviKfvjfFfTVzkr61bnjlGe2AQDJxYknnmg5kOUqeno4EXldnblB/8ILL1AmYjcsnTSgJCJ5rZdGPBsk2Rab8y3kyeV2rdZTNSj13dbIq8YtXuYFUhGcYrk6nCQ9Ficl9hjLzc6ittb26lhSiGIDcNmOSkft8JvuBXmehJ0a4aWOwV4ryQLnMgoGU1eYclMpMQk1qah73K6JRu9ly6qFwlMqkNjwPZu3YNBV9T1tTilKSg7VqQvl8JQCUbNjHLLHuXG4whgEKQDU6VaYJ6pT3n7SCPrPFzvokocX0G6fEvECAIBfxMOItjKUvAx3qrcRZBJppE0b1M1Re1Sb6ueseTxm5LUGXCzXh7tRrJ4hRl2xqr7Ztl9FbsNZG7oWWBdIMGJi/xKKJ8Uuq3AnW1EYJ9cxFdGH7iahJhUlTrsJdbPrVsnqSdRB0NnSwSDVNHR4yyXcE8wiN7UqEKVAmDV7qunCh+aL8tDPXX8UTYYgBYArY+77p46mf119JG3Zf5jOuX8efb7pQKKbBQAASRduZIaXISYfr91nOWD35UgV7YNOudmOVs9SHLU7MU8KHebv8tKIN7vMWgMzFk+2xpa2mEVHaTBru5BTA9epyFvookKanxX+DLecjOqGC5LTlI9mdB93ue/0obuxhMP6RcDpc9LgotmJMrHmbIoVu+0Fg84rxGq9i1WKMxV1Su4AOYhSQLBgcwVd+sgCysvOolduOpomDojvjAsA6cbXx/WmN28/lnp1yaerHl9Ef/1gvWn5aQAASCa8NnDlgFvV+1rFiC9wIKa8uXyP6XdWRprfHmP5uc6G4aoCDYfryep1doztl3xFbLQGZqw2dOyeUoGwEOXWUM1OcWsrGYUMr0hWDxM9nU0EbDv0obsFLgRPv9F3L/0lGdS9IMqTkj1wtGNqGabIXoZGz22n13lPlbdRDl53s4MO88fx/v0KVfeKFH9MAi94Z0UZfeuJL6h/t8706i1H04jSLoluEgBpwdCehfTarUfTN6YNoH/M3kQXPPi5KE0LAACZ6CllNijWf66iBXnVQqtDzVF1TXK4XavzzB+ZGVBOLos+WblRxSqxTUocZoZapKdUYkUpKSjFYlNmG/Sj2KoCxpdAHPvNmRP7+rTl1MYr71H2lom3yGj3HNU/B/T5l/Qi0+HGVlq4pYKW76yMCgudMqAr9S2J/d5atdu7Stp8vp2EJBZpPJrYjvCuIZTUQJTKYHjQ8+DcTXTbf76kqYO60os3zqLexanzkgQgFeDZ/D9dPJkeveoIKqtsoLPun0cPzNlETS3wmgIAxJc+iu/4bKvM4ElggHUvzItLLqi8HHc7Ua3M5kXVKVXYuOlRmC884mNpQ1zQilI+5IRyF77nXpbSn/PTJ/ShIT3dVwWMN0aXwC8HI04snwq4yfvFFDoMl/XDa9NNeGgs2N3CQZu+pV9dhs9qQ4nDXlMB4/dFIgmohBcGO/4t04BxAaVJA4y9i9eWVaeER6DZZIgRqXHnA8+pb2ql2//zFf3xvfV0/pT+9PS1Mxx1HACAM04d34fe/97xIqzvT++vpzP/8ZkImwUAgHiFfHBeiYR6SgXUPrczwGYM7e6ZmGIlgMXiKeXWc8y6+p77ffE57VqQG2UAJjakI+hrovNZw3t4IGq1r79k+yHaVnHY1TZKCiLH13ZVeZ022VNvCgOM+gjnn03GBOzJDJ+L3JyshHpK8f0QbyHabnf5unMS9YwK2Iew7Qh5hrYvG7CsQJgI7D2lglEVKrvke5sDKhFPeif9FqJUBsIu3d94aD69u7KMfnbWWPrrJZNNk20CALyjZ5d8euCKaUIE5lmdy/+1kP7vpWVU7qA6BQAAqDKsV2Q4vtX48Lwp/Wl4aHnvcyk5MwpybPbPYpFXYkogQV4bhXkcRkNxNQ74R5+kO5GeUirhe7EY4zy2jbUrS4G2ur7ZtXjJ2yjWTfymSCqjmPvI4B6FGZe7yur56dZjJR4VUSebeOXEitPL6nWlvFZdsvdE4CTReTD0h5fXPJig+8vJLiFKZRifbNhP5z3wuShTz4bx9ccNS5uXAACpwgmjetH73z2e7jh5JL29vIxO+NNc+vP766lGceYRAABUDAj9693OwJffezErb2SMmnpK6f7OsRGDxGA9icP3VJg5rLvh5yKnlMk6scz48znjbes3EXDgweA1QQXDPRa7jPtxPML/7MI1uQlfG11q+v0Rg7u5aVrH/oPOPcjihZPrl0rWiBtxiddwnyzffxHey6qnVnvlsDRLEUr/Z9BZqKz+MOLlKWWWJ669PdZtaNW0UUYi2k3OWCGTw/cqytckOo9kQLcCOnZEz/Df3QryaFhP67zSHALuBCeTRxClMgT2yvjD/9aJhOalRfn0xq3H0HEjeyW6WQBkLDyD+39fH0Uff/8EOm18b/rnnE1CnPrn7I1UVQdxCgAQO3ojw87mkN97YQCpVtozwmowLo/JbQv52LRhEX4kOhcGgO25DhiGWQZ8mvHnc9rSyhXkzLfxtTGlEcc8tm8xnTGhb2ITnccSshgIxByKqipqFXcyT4FhtIWgzjiMJ/EM2YxFFJw1rEM861uiFnqcDJiJO9zfVfPN+RlSbVpwwqduYTc5EvTQU4rH1mY5qPxmYPfOouK2EU6a0KrzlDpKcx+o0vGe7Dgb2tPOOdH4Ha314CzMz6bOBvnGBnTrHH5Hje5T5Lgtym32bcsgadh1qI4ufWQBPfzJZrrqqMH0+q3H0BCf488BAGoM7F5Af79sKr19+7HiBfHnDzbQ0X/4mH7/zhra7jJ/BQAg/ZEDzMK8HDp3cj/jZVzOhPsVtWZkoIoBd8B52Fwsxi7nVlIx0P0MmdEaSkF9pSYTA8btjD8XseFkv1VGIWiBSGHlrEl9w7PrfG7yfPSWMjPQtZ/HEkLJXSRWQ1u1D7CAJ4lKTuy4Df6KRk67dbwCKvT70d7jPbokNlm1F/Tr2imcxNopZvr4tEHdHPVXq2vpmyil+1vfRL1QHilK21eu03vx6N8N4SToLrHzHpKI/RqcQ36OBp28D4Lt/5YTBPz85vB6fY5IK+S7XvYHfqZq33XFnXIM+4pZESaZC639mareURC+B8K8t2ovnXnfZ7SxvJYeunIa/fb8CcgfBUASwu7MT1wznd777nEiKfoTn28TnlNX/Gshvbl8DzVoqowAAIB2YGg2SIw28tS27XUYh5nIwvsxqvqbY1H9T4YtqrSQE6LbYXWofopSwTaNZ5rPVj+XSOc+0mxgETtNtq56Xp30iygRJ9gxmy/znLkJL2wP3+v4+9RxfcgvZFcpzMuJ8vQzup+k4dlfsfiAlzgVdN16Vjl9jug9E7Wr222Lc3bGGmblFQGTMR5XY3brtWP2LNLbdLE8S/wL37PuTzUNLaahbE5aVNTJ28TgksEmlTL118SsrSIk2+aybz1w2Jf3j90m9F8bCXjc9uaQWMWTFFbbPHFUacSku5MjgCiVpnBVjLteXk43PbuUhvbqQu/ecRydMdE/F2wAgDeM6VNMf7t0Cs3/8Ul012mjRf63O/7zFc2852ORFP29VWVU1xT5AgcAZB76wV4fA3FH1f4Z368kYnknRqsTQ0a/ZNDESLHyjhnUo91AsNsth1F07WzsXaE9L1bb8br6EecOGllaJEImunTKEVXY2MPmKE2OHz/MQnmNhll4yRtVYDY7N0UWoWpOkJdB7wEgbdIxfYosDTO7SVYWh7T90yg0xQ6tgaxKloKxKvugPD7VyphGjCjt4qx9HnUyffJ2PU41jmNHduS3YbTXzq7N04eoCaWcN8dtTq3TxquJmkbHLT8KuhRQVJ+zdveElcDo9HrZ9VnZt/XvE7v9NLZETsI61fHiFZxqFGZudn7dhG3m6LbvZAtyf+FzbxJSrr02uw7Vm26vKSRW8USAVV/UVhoVXnwOLoY/kiJIeDLzH/93BZXXNNIdJ42g204a6av7NQDAe9h74NavjaCbTxhOC7dW0Ktf7qaP1+4Tv/l+5uSERw/vIQZXY/sURw2CAQDpjX5cOHNYD1q2szIi7Deq0prBCPHksb3D4kt4IOugHaeO7031Ta1i7OG0zWb5jbSDcfaU2by/NnpbNq3kcAMjEYJ3ydtkwb+9TYFwzprymoawCMHeQJw018wY2xNa3zynVHT7RO6gbkTjqCPUa1TvItOkuEbV8twg3w+cfJ4T4LL3rZazJvZ1JAB49bYxqzIljzmgMInD15HTVMhzuWFfjfj3OZP6ieNmjzQrWBCrt/BEVjEmRXMD5t4qViKv/O7Iwd1od0knWrr9EDnNu1ToUDz1KqfUxP4l9PmmA57tRy8Ca8+j3THyonb3ZazeQNpVbVLb2/Z5Fo/0HkJWmImz+sOZPrS7GCu6aZ7TczO4e4Hl+f76uN4iXFHfR5zsRmXZ9pDnoKvt8/3Pj3wOrTTyVvIqZ6Obx3iWy3E9v9M3l7e/M7WbMNqa/jOz9w1PWhw83CTeiXKb3IftojicPAMgSqURtY0tIg/Nf77YSaN6d6FHrzqSJg6IrHAAAEgt+KV09HAWoHpSS2ubGLB+uGYfzV5fTrPXlYdfFmxAcU6qSQNKxECRwx4AAOmL0WBPP4ZVcfLQeqm4GTgbtcNJ7gsjtJ5SQ3sWGotSATUjjkMIdh5sFy0k3QrzhCcVe5WbbY8NZE4MbkTXzrm2xq8XcMjPil2VjtbhfFD7axpNjXsjgcSu2iHnLFu3tyYs+HgNGzqcTFfO1IeNdZtrzNFenFyYRSnuc1oxUxp0dnYdCxnsrfb2ikihLkzQ+X2gz/9j5SkVXibiGpEyMv8Xw5NUCzZX2LfV4Ty1Wx0n5miwQOQzgfPqvLFst+miRwzqpiBKke/0KekUlRNUXt+wh5zDkyMrSdpV/bPz7lTNKcXn+/TxfWhrxWFatbvKlacce4Ia78eJdy3nYwqaer0dqmsKnxOjpTg8Vk5AGMHHyQUeGDNRKhDKV1VxuDFq4kMraHNbzbzkVF+tozTCuh7V9zP3gaC+3ynej9oxA9sUPNHFzBzaQ0Rp8PY4D+S2ijrx3N5Ybv1OcNLNIUqlCXPWl9PPXltFZVX1dMuJw+nOU0aaPgwAAKkJGw7sDcE/Pzt7HO2rbqCFWypo4ZaD9MXWCvpo7b7wS4tnw1mUnixEqq40oX+xZyEXAIDEozbADNquY5e3gnO1HKiNHIzbccLoXkphembtytXklDL1EFCcxZ46sKvwjJq7vl3El8gQnj1VDYZ5Mfh5WVnXpNReFVQrrGkNNjcGNIdx6kUprXChQlAn7HCbWNyzE6U439dyhyIaw96/nFdKilLr9lY7Nlg5D1nQpQHsRe4W7W70oZCWQoCLfbFIuLashoaXFkYcH+dKsvP8Mtsni79OPUXsCGg8Nzgnzacb7b0ptTi5LHwelDxLYrjUql4fk/qXRItSGsFqx8E60z5nJCrL9Z2IG2ZYHUFEpbbQ/W+Vt821WOlkWYuFh/QoFKJU9PbbV2Kxua7Jm3ysx4zoIbyBtcUi9OfGJM+5QC8m8vtAenhqtze2bzHtrWqImDCJhYBL7+Ujh3SPaDM/o/Ny8joq9xXk0dqyak+vNUSpFKe8uoF+/fYaemdFGY3uXUT/vflomqqrxAAASE9kRQ7+YWoammn1nmoxs75iVxWt3F0lng2SYb0KxcwHG2n8nODSrrFUNgIAJA5jV3zjMCgzeOBpuO2AuihlZDSYz5BbNkeznLpxaWbESZuPt2WcLykkuERuTsD5nqzbYd8+/RI9HVQPC+f20m1F2xz2ojLyYNCHlLDhZiW6GB2i3L9Z6IfZaWHPBSvMPD1YUOJ3Eecg+XLHIUsRgMMseQLW6LidiBhsADZqKk2dPqGP8ESWfcnIaLQiYNH/jfpRR84X9TZrtzeuX0cIaOR2zeFzzImMjcK0WMy08hQx3FcwcjzCE2WR7dR47zjTRWPOV+fFNqP2obiqlTjGwi0LD6v3VJmGY+rDa+W+jcJ53RyN2ToR97vm+WkEV+rU3p+WXmy6TTgK37P4Tu+RKJ8tcvvc34NB6xBJrRcW5/wzC5/l88DPt0hRSnef2+Rj0mIs9gXCudWMc9mpS5LyXMj+zn9q+74sDKC/vnK37HVqUoivo7Uqr2kHFxuiVIrS1hak57/YQf/vvXWifOMPTx9N3zluGAxMADIYntlnt1pRYj0Ez/SzOMUi1fKdlTRv4wGRl0q+FDnUb9rgbsJrYMaQ7o5zUwAAEoRRmIA+fK/NepUehZEigrR3+FnCCbl50D+kZ4Gt54qqB4GKxKOv7GZmRMp9mu1bvx4bEJxAVz+s7whzkNvi0tkdn7kh9ixQ9vsvzDN+VltVLlRFGmqODXibxdno1UZEclg6e43IsauR8ar9jAXI8f2Kw6KU+C4YCLfVSYRASec8kUNMbp/X1R6vDAU0u5YcFsTCEIfTt4fG2At/LAJLY1T2W+0+PUgfZgm/7zlc0etqvuaeeJHnhKvPOSnUoj2l/7+9MwGvojr//5sASQgJIZAFAmHfdxBBxSoqLqi41brUqrV119bWpWqtora/aqt/F/q41Fr3Xauoxa2KiAqCsiiirAJhXwLImoQk83++JzmXuXNn7szcJbnL9+MzXpLcO3fmnTNnzvmed3Frip4FoxhVp0PfudMkUJhtHO74IFjlZKKdOnmNhv4eXoTKEyz6W1t9rWOfGvT7YNHeytjeRRH3j77ExowGAWWJ7PLt4QhhxnxLQfRGgnmn/IcQopGPWYd762dGYAEjw0VYUv2Vt3Oz66czTGKaXQS8m91wPKN7NIz/dZ9q/h79ceQWhOerHVoMM3t9+e2WILrqEGyG76U4GBz+8fWFMq9ih/ykT5H85bTBqoERQogVuNn+pE+x2vRDesOPVSpOfH7FdvX61MxV8tiMH9QgHJ5Uh/UuUn0LHuDxLIlOCIkcL0JQaPieyyDelOhce2LYlYg2g9woGOgjdAiTfC/HES7/CTxhzHjpgsYPKJUd+/bLsk27glayzWAVfOYK56TMdtMvp0lAJN2iUyXAWCe6bWlxH4ho4hiowmi/H69hmHb2NMuCEDPMgoabiBqu8hN+7Zbjxky73FaqvToJQTiuVZV7pHtRG1tPwQMipv2xBr3XJGRpkEMLITrwWHZ6fyQgz4uT6IRjxjPd1oRhvtTLRBvnBk8ptL/axgtp/dj4ASUBLyAnT7/gQ/IjXjS898i+xWGLLvi5pdCvIC2C3WcHlxVIeWGubNpVJUs2HhBMehbn2YZDOp0LBKfK3dVh8x4VNlY0s7sOfjxRDnzG/vfm8zvgadrwinZj9t5BOBfynHoBtsDiqMbfuDJDiVIIWbV6kHkRt8z3d4e8rMBntNcmckU5vd+NkPA90zXW+a6cwHuPG9hRPRt1G8tw+T6MzSsq9zrmcepTmi/tGxeaIAzlZrVQx4Gwazzb9fFZcz62yWope2pqZWCntgFPUTzXa13T+dsfsbl4h58rTVEqiUB1m8nTlsm/ZvygHqYPnjNc3aSRdEiEkPQE/QVWSrGdOKST+h0GsPNWb5eZKyrl8xVb5aGPl8vkj5aph9sx/UtUBRWIWpGU0yaEJE5OKSvhJviBf3sUTuBt6RS+ETgM084QLmVN9mv2dEGfk+FRGIKHJzZzHhfrxMfJXuZwsjE928uqrXsO5FKy/0hg4G8mXPjKyUPLfE3EbMwVgt35IGFv9f76qD1DAjmlgvJbRT/WdNtDnU9XoQa/Nv1v+zBNtwmlk+iKCR2uqbU8feC7VeLp0N9rzwR4kuiqY3bjdLQxCDN+wEIRihqFA57SEIfscnvtb3RTi1X1PQ32B88LCFOfLNsiO/fZ29RsB+R5cxOlgr8jFLvQYizEaTHJrnKn25wJIhFSIGAyb/UcD8r3lpmhCibo64HwR3P6FOwnSJRy+Fr0gfCgW+fQd5i/17oPCO1+Ucm4PXhp6XtJ5yKFrUPDM721Iwh1ZlHKXJDADdyHTt/lthdr+nOzYJ/VIkPG9i628Tj13geVFuTId6a8SmbPTlSmczsvPaZGDtgKSzEOp0UciNhmUQrj+HcWbgixB543sLvOi2juWq2mHFDWVhZU7FD3S4e8bNm7eru6/lt85pKMFopSSQAGTB99v1nu+O8iWbNtn5w7ulxuPKE/q2sRQmICHp5YgcF2vfRTq7efL9uqwhI++G6TvDp3reS0ylSTxOMHdZTjB5UyaTohzYyXYb11RTTDNcmpfp/Z5T/6CaxdonOzAGX3HXbCj5eJ2oG/W372GCaic22EE2LgJeA2bjN/NFKPU79CUJusllJXtz8u4Upe0kO42zj8O6x5VJTwY9orVv+dFkf0rr3mgspuvDeQAkMTNHGzCa8zY/29zhuj77n2pjG61yugBS279gXBEXl83IB94N2F47EmIt4TRtAKJ1Q5/cVaFQ1Cjfm90YYjmk1s1/4wcYY3irXdQEzSuTatopTbraj/7PW20fc2vjNcv+CUV8nL9+jvMLe5EeWFqq1v21MT1muupG2O8sIyi++OApnJOEjroIUQCB8QpEJEKYkMp2qfGF++v2hj4OeeRXnSt2OD55ktDgeAZPK47kVtsmW7yT4ooKHN0L5Ntu04toVJuAp9hgT/Itz9GO66QrC2e85keGgM1vZrXtix66vMx1zV2NdZPbwgJmsvTjx3xw8sDfqs033sLaeUeIaiVIIDlf3OtxfJx0u2qETmr1x2aEi+BUIIiSV40E4Y0kltcM+es2qbEqj0dssbmWol8tThZTKuX4nrBI0QEgdsRnvmwSMGnr1L8lRI05yV25TXlPUj1kGs4TBRwbgD+/ALJjTwJHArYx5RAlyX91onHAdKshsOQpz374iF11A4UKnMLu+I+SenIwi5xpnhE47b7cfOJuYJq7MQmBHWcyyrZYaEc/RxS+KMnESh3xnqPRLOg0Qnxs9r3JddEmJvOc0OAFvqUFSEKlmPy2tzQegqQughNizfHCymeBGkrCE02MzXAFEWTscTSZPWx6TP2SvWyTe8jOzCGM04iaJH9SsJGyalj1NXM3MPYXa/x81eMJ0KcmRQWYGrDazf68fcuhqpbSLyMJ/D2Az3Aopi1TR6yWW3ynS81maxwmzvhoThoe+PtBuEp5RVgLLzLiprlxM2T5zuD/CMQ34vfYgQfLQoae4ztHcjBN42Tjn5Go2M3GDLGu9Br6eZYXpvOPHTLEg1vDe8Z27YduSxn4Ft9jXmc7PrR+3Qfb4Oxw05Fk978d5IKEolKEgEiBCaf81YqTqQSRMHyvmHdHNUlwkhJB6gz0EyWmy3nTxQJUyfsmCdvP31Bpm6cIMa5J48tJOcPaqrDO7cluHEhDQReoXUHPKkPReQDLpHUZ66H1EVS73VblIh4asXmSfLWHXXE3i7sBg7UEUIQrY5eaod0fQadp/F+Zvz93j5Dj99Vzxy7SEXztbdDZNrbWfrRNxN2sNRaU8iLQhGFr4XeWU4hHHuqbYPeWvdqmUgAa43Tyn792lRTeWoCiOu2YGCHvv21ynREu0Yoez2X9Lw4nSpzaE6Vu8cK36uQbxyxPbv2FaJXZHgdPgQEaznDJuaKyhaGdc3dCEL+7GL/jC3f6d7TofuOnF0/xL1WfRDwO3WDSfWAggaORYvT4j/bmRE0d+0alSWze1I52ILtxttMywqID8angnWqnlB3xNmjqkfMwhr7FzY2vYcrHmnwh2XJ89LFxuhj0NeJoRwOrW5/pZwNzeBVy8E1LqcB0Luwh9vho/PBb/6ISik1FZtDg3f9bqQrNuPgybl8fi8v5eiVIKBzvCdhRvlL1O/U8mIf3ZQF7lxQv8QVZUQQpoaPPyGlbdT2y0nDlA5qKbMXyf/mbtOnvuiQoVWnHNwuZw2vLMUNK7IEkLigx48mycBeuKAAb/dJM46GYkkkTbCeNu1buVJlMIqN8IBdPJUfBu8LP0KJRMGd5J3v23Im+FFGLKb4DrnlHL+XqfjtObC0ujVf1WNzafUhmPWx60nD9Ywi6BjdfR2aBAKkK9n95bdtufQpyRPJb91wi6MU4dLYVLqBN6NhPfi4PRSnJ+lFl2d8iKFiFL4LyNMuXOULa8N4+pmEsqyW7QILLTkN06Iw+V00p5PThPjcLl5wn2uudDeNk7E6miRWwoTeuRlssNubOAlnCxSID6a84K59T2BFujwNr8ea9H0P079NMZa2qvI7vPDurRTOcV0wmnkCcLmdiy6z7ETL0vaZqvvHN61naMNIADi3p61ojLs+YQTpMxenF4uP0R4fT52ttC52+wqJYY7Nrt8jPp7kC/MzjNOeUq5iNnwhAw5RpdQdCewWGwmrKeUYQTOyet95eop5WE3fk6JolQCgaoxk95apCZ6cBt86LyRKqkhIYQkGhjYH9G3WG13nLpfeU69/NUa1Yf93zvfy4mDO8rZB3eVQ3q2T7jBOSGpgB48u1XHs9KmsdKOHahEhISrbhOvaG5pO68GPSh3mqBhZffw3kVBK7zae8Br/+IkIJgrDoZ8xmHXkYh5fsBqOiotwzstGMNxco1Jkvm49ATE7lAxqUUuGruqckHfYvkswrVBuOpubm32mAGljgnhQyaCLvtDqI0O3AonECmhzAO6LYzp0cF14gZxzSuR3C99SvJVyJk1j08ssA/f83aQ1qpytvsP/Mtj2G6Ya4d7wZygOij0NA634YFQquh3rsPKzPvVZDjk5QqLi+ChQU4xbE6EE+ZOGNxQAMcKBCmI7uFAWJhbaJjbvQhh66NANbrYXGB473mle4c2qrAYPrO6cq9D7kX7a2Z+PtnZGAsydvdZpOJrsalyacNxhu5HP0PQFnVlWqdFFafjciuY4iZSL67w9l6KUgkAMuP/Y9pyeXrmKlVW+a4zhshZo8pZip0QkhRgJfLnY7qqDYlVX/5yjQrxm7JgvXJrR+jx6SM7R7zCSAjx5vlwYGUz1ONETRAzRCUxdRIFunbIDayIO5FhquKJXDB+cJr46jlnuMGveaXfLt+S+5e7HZvvj8QN5NfpW9oQfumEtYIVFjPNaFNGFL4X4STEbRKp/w7xc7/N6jsm0hBFMWlyy/Wk23s0oYaOx2nZF3IGLVr/Y4hXl9fvjGRyPbCsrboOb329XpqCcFMOczu0VpWz35e/87W+HeFmVY1VJM0V7dR7HQonhMMs7oY7NuRf0u+NxRQM+a5QgRHib2j4XvDPEEGs1c7g4Wcv8kR3cM25Tjiqe3hnC3go4pyV6OvxOPEcKs7LVqF60dIigqqY4Ig+xY0LLs5eT07PLP3crosiTM6pzcKTDAs68MJd01jhz+v118+Y2kbPXSvFeXj+7wyEituBSC8klff0fd4Oi8QDuJM+O2u1EqR2Ve1XE7rrj+vHqnqEkKQFK/C3nzJIbprQX4XboI+D99Tf3lusQkp+cUhXNcAnhESHniiaw6m095TTINILbqWs9YD24O7eiq4EVfJzeI/2hPCzIqsnlxg/eTqOGIbvWdGCeyTJrd2u78ShZUpkRH9qPlbrwqVVwPKSsNmJhuTC+x0XR6M9N+Qac7LjyUPLZMbSLVKD5NUuzUGdm3v0nme0B4G1TfQsahMqSqFwgMf9RmovXFMIeD2LY5tnyk7MiZVXitqXx12ZE5Bbc9iF3bfh344Qepds3BWS+B/ef9OXbFb/Rtia9kyLuYe3ZXfWymZ2QgjsY9cfRyuYNafLgzl0D2GGudktHMP9PIsnyH3a275PiQXWe8Ou+RVaqsXa9btOfbFeYKqPslyl0/6tCzpenV60KNUhL8sxFBfj+gVrdjiKUn7aG7NmNwNY+fjvN+vl2PtmyF+mfq+U+fd+d4T85bQhFKQIISkBBlKnj+gir185Vqb+9nD14EL+qZMmfyZnPPy5vDF/rWMICCGJwIwZM2TixIlSVlamJihTpkwJ+/7p06c3VvcJ3jZuDK4w9NBDD0n37t0lJydHxowZI3PmzIn4GBEKhdX0WOaAiAZ4OIRLO+AcDtfw6iVJrmZ3dcNkNtxgOOi7HX4fzusC4XCjurdXyY2RPN1Mm6yWAa8K5M0K2md0c4uQY9BzCPNuzSFNdngNcbKbfGNciuuo8yp5xf27vO0n4DngIvxg0hS4fjFo3LqYkPX+sds1hEKv3+k1XMZJwHNMxh5LPBxiaxfBOrCrDO+hPX7eHyJyZ0Q/cUeeNDNaGIh1X+kkGITrKqw55ezyIkXS1cT7OYBk9l4WLeAZGS6cr7njhTI8LsQGfSacx6HD72NVxCzDa//q8Y04LowxhpeH927LMIU+2tG2tbfnCEWpJubLVdvkjEdmytUvzFcP++cvHiNP/PLgQDI6QghJNeAZhbDk2bccI7dPHCg7q2rl9y9/LYfdPU3ufndxwKWYkERiz549MmzYMCUi+WHJkiWyYcOGwFZS0pCHB7z88sty7bXXyqRJk2TevHlq/8cff7xs3tywUu8XjCPMK8+oOAehxFrhp6myAcDDQU807XCqaBTJpB3VxPzgJiA4/Rk2hcdC75LgcRrCIBFi1RTY5dxyW+3WpnY6L53/xG43+Fu469hU5+smUkKwCyRlj0Eb1x4LXsRRiH6ePQASLB2HfU6p8J+ByIAiB9727/V8dT437/aJ9DprndG9+l7j90hscQrfCxcq62TH6too47ziLPfAg8aPkApvQD/nj0qlWABpauwOB/N3cwXKjEg8pRobpRHlYobX+8hPf4QxhtuzRp8WqnHbMdBjdATD95oIlKq8/39L5dNlW6VTQY78v58Nk9NHdE64BxUhhMQLTOx+ObaHXHhYd/nih23y3Ber5fFPf5B/zlgh4/oWyy8O6aZc6ZlPjyQCEyZMUJtfIEK1axdaYQfcd999cskll8hFF12kfn700Udl6tSp8sQTT8hNN90U9TEjf8SEIaGJatU9VRebqUg0YT5Oldsiued1wnTP3hsxzp8UaahiRPsNeEoZjjmlrJTkZ8va7XsdvZ0QOoN8H9F66CMXjq7EGCtPqT6leSoPD7xY7CambUzJ+n0linZBt0Nd/VBjdwzR5n9pTuwug9ul8SMyeJakIhAUI72vDuQe87aHRC7Q4sej1I5EG2KN7d3BVpDJCFepVJoPL48MP8LvgeefkdTXNdpHKUWpOIM4S4hRnyzdoh7+fzppgJw3ppsaOBJCSDqCwR5CjrBt3lklL325Rl6cUyG/fvor5ZVwzsHlcvbB5VLiM4kyIYnA8OHDpbq6WgYPHiy33367jB07Vv2+pqZG5s6dKzfffHPgvZmZmTJ+/HiZNWtWXI+pVctM2be/LobTd/+TQbj2O+coimw0fcLgjkGrz+WFubJlV7WtEBNJ+IzdxNwuh1W856+wD/LddOuQqxInA7dFTXg6dSls7WhbeEPZlTX3G6aNXDhalHLD63XGeHnisDLHCThC2lAhK9aeLQglWr55t+OKv3lSPKBTvut1V9UKm+OmcxANm0qEObCr8Ps8EHrpZ98H3uwnd57XdoKk2bgvkIPKWjktGqz2PSA0e2d4eTs1r/RbcdXtWJqbhlB38dRn+y2uEQv0YXhZkEGhBrC3sX8K3k9G2AWW6trkvK6xykdHUSoOYNVt1g+V8s9PflBiVIc2WXLLiQOUFwDFKEIIOQCEp98e00euHNdLpi3eLM/PrpD7PlwqD360TI4dWKpE/MN6daBXKUl4OnXqpDyfRo0apUSpxx9/XMaNGyezZ8+WkSNHytatW6Wurk5KS4MnN/h58eLFjvvFvrBpdu7c6fvYWmVGnwA9gM9bcdueGvVa6mEy4Tcpd0NC7mAhxinsLKdVpqrqZA1t1Hj5aq/J3eOhRCDPkxWEjThVUoznJMVpRdzt+sXqaCCGWRNAx+JUUWL9xCGdgkJincKHvGDNV5Qo2LULv+YL5zml24Gr51wE4bvmt9pVcHT7nNudibEGKgyahUpURtfCQaywJjr3gg65dQqD9gqqIq+uDF9BMRGwNgtzmFyigucPUlLU+hAOdV9W7jFkGs8Cuz7KzesXC0Or4nDdIxFY7aAoFUPQAN/5dqM8NmOFfLtup3q43Tyhv5x/aDeHkp6EEEJ0QsXjBnVUW0XlXnnxywp55cs18u63G6V7h1xVnfTMg8pDEgoTkij069dPbZrDDjtMVqxYIffff788++yzEe/3rrvukjvuuCOqYytv31oq91QrYaap0Z4ubvfumB4dJC+nZVwn4hC6nT0o/KsaYecAaaiju4UUulV2jMSMRowN7iZIpQIZUQrCpzR6sTnu3+Ou4JEE75CexXmev9u8az8iuz6/ep+CjrmQRHzwfjzFedlqbmlOrh1J+DE8SeFlCtt/vHizp8IJTvQpyXf1LEwVDrRrZ5vDWxeiFIo0+OGkIZ08h7HDa9YOt+8cVt5ObfEi2lB4KiUxYPueGvnPvLXy1MxVsnb7PqVA//2nQ+XUEWUhq3iEJAoh5aNjWS6IkCjo2iFXbjyhv/x+fF95f9FGeX72avnrO4vl3veXyolDOsp5h3STUd0KE84FnRAro0ePls8++0z9u6ioSFq0aCGbNm0Keg9+7tixo+M+EO6H5OhmT6ny8nIpKCjw3G9369BGhcZGU+Un2ttNJ5F2omNBc4Xrxq7alp5UtE1QD5l4Y+2TUYVr+tLNgRxW8VhU8FplkITHbD94CtXV18ui9fZemW7PXu355NY9oT9C+Ke/4zSF7/nwlAoUC7D5G0Lz/HppRsuBROehf8N909Kmv4S9DutVFJPvx/xUfzeq/J0wODQXoRe8FntAYQp4nCUTkYwx9TPA75SqZQzE8ObSHNCvr9gS/XMvuVpHAoGH4OyV21QeFKzk19TWy5ge7eXOUwepzoShJoQQEh1wVUdeEWzLN++SF2avkdfmrpEpC9ZLr+I2csbILqpgRJOUyyYkAhYsWKDC+kBWVpYcdNBB8tFHH8lpp52mfldfX69+vvrqqx33kZ2drbZoiXbQ6zUkx4lEFZFjuR6DCQkqlGHytX1vQ9hiKqIvpbVkvV0VLthiV1Wt8vCIB/GqluYHp+phiYr5VoRgbS4WgDasc40hysPN+y2eiZvtMB+OP0+phtf6xgZzVP8SVakUxDo0zwvhukPcN01JU/TN1hxdyUy4Z4ZuZ9Emo/fD0f1LVHGG5goXxhj8uIEdo05RRFHKJ5W7q+X1eeuUGPXD1j2qAZw3pqucO7qrKgtJCCEk9qAc+20TB8oNx/eTqQs3KHHqnveXyL0fLFE5p84Y0UW5ozfH4JKkJrt375bly5cHfl65cqUSmdq3by9du3ZVHkzr1q2TZ555Rv39gQcekB49esigQYOkqqpK5ZSaNm2afPDBB4F9wOPpwgsvVHmn4EWFz+zZsydQjS+RSfWqmLGal2lvoFglf01EsCI/orxQStq6izFaOIh3+2ku0RMTwkjCEhMFJGzH9ayuDU4cDyJd8Innwjw8q774odK3p5RuH/ojqAbcnARySknz4yenV1OAyqGrKmsT5pnjp0pkoJ2ZbiSIvig0Ei/ym7ktg1jkzObo3QOotPLBok3y1tfr5bPlW5X6ObpHe5WcF5OgZH4YEUJIMoEH35kHdVEb4vanzF8nr89fJ9e9+rXc+ua3qk9Gvgu4uOukoIREwldffSVHHXVU4GcdQgdR6amnnpINGzZIRUVF4O+ornfdddcpoSo3N1eGDh0qH374YdA+zj77bNmyZYvcdtttsnHjRlWp77333gtJfp6IJMoEIdYEKoBFISKlsgAVLszanFfGadKF8fKKLXtcvaqccJsINnXmAeQ+S7QJYWwSncdOJNHeVfG4NiiYcPygjiq0H94hkXpKNTdl7XJk8cad0qnZwpabzzPLjSGdC6RPaV6z53eLpFe3C99DFc6aKCsmpgMUpRyo2l8n05dsljcXrFcVoZAMDhVbrjiyl5w2orPKG0UIIaT5QKWS3xzTR64+urfMX7ND/jN3rbz99XrlzYqQkWMHlMrxgzuqSklcPCB+QeW8cDmbIEyZ+cMf/qA2NxCqFy5cL1HRg22/iYKRywrhW4lKNHNUI0H6wU0/VjXrMYTLK9MuN0sO6ha/AhWGzgkmTUPz5T6LD1qMOpDjKPpWHS5/UyyAwNmpoLUK4/cjZi1c96N0KWzacH/0f1t3V0uuxZMEYmYsqskZUXgLYlw0tneRtEuwHHjwtEvkAmHh2rVd+B4WSLlI6k7iXvFmYMuualWF4MPvN8mny7aqVR/EwKPqE1beh5e3S9icCIQQkq6gXx7ZtVBtkyYOks9XbJX3Fm6UD77bqLyoMBjEwOuIvsVyZJ/ioBV+QtIReBr4nXvqEA+/5chHdW8viUxA1EjS4R36vXQGYgNEih4+BAoS6tUUy0TfAQE7Tl5JeObDA88PCO2PhQjkl+5FbZRTQ1PkGo7kG4rykisfWnPixSNWe3dBvF6xZXcTHFXqkNaiFFb7vt+4U6Yv2aKEqAVrdqhBGlTts0Z1UYO2MT07pKzLOiGEpBpYjTqqX4na/q9usMxZtU3e/3ajTF+6Rf73XUPVs+4dcpVAhTCMkd3aqRVXQtKJSDwHsbK/ZXd1RImPk4FYn1WyilyJhl4Mdpo8oy1HWjmMHMj/FMvmqvsIcwL1dCaegpS5P6Y3TnzRIcg6Qb6TKIU0EngPRSl/pJUoBZfU1ZV71Sr6zOWVMuuHStm2p0YNHOAFdf1x/eSYASXSrzSfHlGEEJLk6PLJuoTy6so9MmPpFvlk6VYV6vfMrNXq98jpAG+DEV3byaCyAunfMV8K41C6nJBkZlBZWynKz1LhWKlELJw5OGSML6hqnZtNgSOexLINYw4FD8n2KdZXJCLojzGHRX6tRMhPlcp065CrHFXcQkBROID4J6VFqX01dSp+eH7FdplfsUPmr9kum3ZWq7/BG2r8gBIV0oEJS7xK1RJCCEkMUPr6/EOxdZfaunpZvHGXzKvYLvNWb5d5FTtUVT9z9ReUMMYihXa/x4ZqRFyNJOm62p+KXoV+KitZaZvTMIy2E7HjpVMhaW6KOqslTSLmVCTWi/GYZ5GmG9uQprlHkMOPxIeWqeIBhXxQSzftliWbdsmyTbtk0fqd8v2GnYHcBz2K2sjYXkUysluhHN67SKmd9IYihJD09aJCaWlsFxzaXf2ucne1EqqWNG6LN+2SF+ZUyN6aA1WlMBnExLy0bbaU5OeokugQsEra5jS85udIhzx4k7TiahkhSQDGgxXb9krndv4nGx3yslWqB7twSPQx8aAgwZISk8QGCykIIyri4jshvggk/0+IkhapT8tkyPuEqi3b99aobevuGlm3fa+s27FP1u+okrU79qmQjB179wc+g7jzAZ3y5cpxvWRE10Ll1shQDEIIIW4TzLG9sTWE+wUWPXZXy5pte9XEtaJyn3rdvKtKVm7dI7NXVsp20/PHTJusFurZU5ibpV7b57ZSrvbt1e9aqRw9qBKI17xsvDZs+He8JrSEkNAEyMgBEuv8XDr/CCHNCZ43zZHgm5Bkp29pvvKm5wJjmolSby5YJw9+tExqausbtrqG16r9dWJX6AXJ8zoXtlbuoUOGdlIhFn1K81UDQgdMCCGERAs8apVHVH6OHNTNvtpPdW2d8tbdjG1ntezYWyPbsJCyB4sp+9UrfoawhTyGP+6zF7Gsz7iASJXTSoUJaeEqL7uV5OW0VO9p3SpTWme1UBNjbOp3+LklXjMDv1cliVtkqokyvYQJiT9alOL9RhKRYweWBpWt1zAUjJAGEKrHcL00FKUgJA0uK2gYOJsGzxhMIwwCK814hRcUXFGxyswHPSGEkOYGq2hdCnPV5gXks4IwBS/g3dW1srNqv+yuqlU/78K/qxv/rV8b/w7BK/D+6tqIEzS3apHR8IyFWNUiM+i5i1c8e82v5vfgXM2/w75aZGZKi4yGMuDIO9Qi48Cr/h0qBKHkOMIfEeoID2ZCUhmMUREeXNSGYVMk8cjNCp0CnjKsjHMrQkh6i1I/6VOsNkIIISSVQWgeQgWxRQrCCuFRXFVTL/v21ymvYryqf9fUSVVtnewz/c3sgQzPLrNXcrX+t/pbwyvyaCEs3vq5wHvr6iMWxU4Y1FEePf+giM+dkGShV3Fecx8CIZ6hIEUIkXQXpQghhBDiffIAryVsBdL0iY8hiqGQyP66ehUCUl8vUmcYDf9ufDX/u77x/RCyEIZICCGEEEII4MiQEEIIIb5FMYTutWJCdkJImkBBnRDilZFdC1nwwgfsXQkhhBBCCCHEgZOHlgmD2wghXmGSdH9QlCKEEEIIIYQQB1C0gRBCSHygTxkhhBBCCCGEEEIIaXIoShFCCCGEEEIIIYSQJoeiFCGEEEIIIYQQQghpcihKEUIIIYQQQgghhJAmh6IUIYQQQgghhBBCCGlyKEoRQgghhBBCCCGEkCaHohQhhBBCCCGEEEIIaXIoShFCCCGEEEIIIYSQJqellzcZhqFed+7cGe/jIYQ0E7y/CSGp1JfpsUuqYT4v9tuEEEIISfYxmSdRateuXeq1vLw8FsdGCElACgoKmvsQCCEkZmDskor9WmVlZeDfqXh+hBBCCEmvMVmG4WEpsb6+XtavXy/5+fmSkZEhzam0QRhbs2aNtG3bVtIR2oA2ALQBbQBoA9oA0AbBNsBYBYOfsrIyycxMvSwFO3bskMLCQqmoqKAo1QjvAXtoF3toF3tol1BoE3toF3tol1AgNXkZk3nylMIOunTpIokCLnK6X2jagDYAtAFtAGgD2gDQBgdskMpijR7U4RzT/Xpb4T1gD+1iD+1iD+0SCm1iD+1iD+0SjJcxWeotIRJCCCGEEEIIIYSQhIeiFCGEEEIIIYQQQghpcpJKlMrOzpZJkyap13SFNqANAG1AGwDagDYAtEF62SCdztUrtIk9tIs9tIs9tEsotIk9tIs9tEvkeEp0TgghhBBCCCGEEEJI2npKEUIIIYQQQgghhJDUgKIUIYQQQgghhBBCCGlyKEoRQgghhBBCCCGEkCaHohQhhBBCCCGEEEIISW9RasaMGTJx4kQpKyuTjIwMmTJliufPfv7559KyZUsZPny4JDN+bTB9+nT1Puu2ceNGSVYiaQfV1dVyyy23SLdu3VTFg+7du8sTTzwh6WKDX/7yl7btYNCgQZIubeD555+XYcOGSW5urnTq1El+9atfSWVlpSQrkdjgoYcekgEDBkjr1q2lX79+8swzz0gyc9ddd8nBBx8s+fn5UlJSIqeddposWbLE9XOvvvqq9O/fX3JycmTIkCHyzjvvSDrZYNGiRfLTn/5U9YNoOw888IAkM5HY4F//+pf85Cc/kcLCQrWNHz9e5syZI8kO7nFcV7TtMWPGpMQ5RXPdx40bF/Lcu/zyy4PeU1FRISeddJJ6NmA/N9xwg9TW1kqycvvtt4ecM/o7TVVVlVx11VXSoUMHycvLU33Bpk2bUtomQPd31g22SKe24jZ2QH2r2267TY2TMFZA37hs2bKg92zbtk3OO+88adu2rbRr105+/etfy+7du4Pe880336g+Fn1ReXm5/P3vf5dktMn+/fvlxhtvVGOFNm3aqPdccMEFsn79etf2dffddyetTby0Fbu5xQknnJDSbcWLXez6GWz33HNPSreXtBKl9uzZoyaVGHT5YceOHaoDOeaYYyTZidQGGKht2LAhsOFhmk42OOuss+Sjjz6Sf//738oWL774opqUp4sNHnzwwaDrv2bNGmnfvr387Gc/k3Q4f4jS6APwMMSEHKIEJmuXXHKJJCt+bfDII4/IzTffrCYssMEdd9yhBuNvv/22JCuffPKJOocvvvhC/ve//6nB43HHHads48TMmTPl3HPPVW1h/vz5aiKL7dtvv5V0scHevXulZ8+eagDUsWNHSXYisQEWbNAOPv74Y5k1a5Ya8OEz69atk2Tl5ZdflmuvvVaVm543b57qH44//njZvHmzpCJerzv6efPzzzywr6urUyJDTU2N6huefvppeeqpp9SkPJnBgpP5nD/77LPA337/+9+rfh/PQdgQk+szzjgj5W3y5ZdfBtkEbQaYx0Hp0Fbcxg4458mTJ8ujjz4qs2fPVkIM+hGImRqIDBhHwIb//e9/1ST90ksvDfx9586d6l7EQvDcuXPVZBxjj8cee0ySzSZ4XqI/vfXWW9Xr66+/ruYRp5xySsh777zzzqD285vf/CZpbeJ1nAkRynzOmF+ZSbW24sUuZntggxMERCcsAKRye4k7RoKCQ3vjjTc8vffss882/vSnPxmTJk0yhg0bZqQKXmzw8ccfq/dt377dSEW82ODdd981CgoKjMrKSiMV8XMvaPD+jIwMY9WqVUY6nP8999xj9OzZM+h3kydPNjp37mykAl5scOihhxrXX3990O+uvfZaY+zYsUaqsHnzZmWLTz75xPE9Z511lnHSSScF/W7MmDHGZZddZqSLDcx069bNuP/++41Uwq8NQG1trZGfn288/fTTRrIyevRo46qrrgr8XFdXZ5SVlRl33XWXkQ7YXfcjjzzSuOaaaxw/88477xiZmZnGxo0bA7975JFHjLZt2xrV1dVGMhJurLtjxw6jVatWxquvvhr43ffff6/sNmvWrJS1iR1oF7169TLq6+vTtq1Yxw6wRceOHdWYydxmsrOzjRdffFH9/N1336nPffnll0HjbIwp161bp35++OGHjcLCwiC73HjjjUa/fv2MVBhPzZkzR71v9erVnp+lyWwTJ7tceOGFxqmnnur4mVRvK17bC2x09NFHB/0u1dtLPEgoT6lIePLJJ+WHH35QK4fpDMIW4Yp77LHHKq+RdOKtt96SUaNGqdWfzp07S9++feX666+Xffv2SboCjzG4ZEOBTwcOPfRQ5R2GMC08QxCq8Nprr8mJJ54o6QJCWOECbAau+fAYg4dBKvDjjz+qV3gBOgGvGLR9M1gFxu/TxQapTiQ2wGo47oNktRu8N7Caam7bmZmZ6udUaduRXneEbhcVFcngwYOVtyiutQa2QVhOaWlpUH+AVWqs7icrCLdCaAk8IuGpgLAzgDaCdm5uJwjt69q1a6CdpKpNrPfLc889p8L44cGQzm3FzMqVK1V6D3P7KCgoUKHA5vaBMCyMqzV4P/obeFbp9xxxxBGSlZUVZCt4GG3fvl1Soa9Bu4EdzMD7GGGxI0aMUJ4t5tDOVLUJvI4RfYPokyuuuCIoLQbbiqj5xtSpU5V3vpV0bC/R0FKSGDyUb7rpJvn0009VPql0BEIUXHDRIWBS+vjjj6u4eXQGI0eOlHQAoiRc1zEhf+ONN2Tr1q1y5ZVXqo4TomW6AVf9d999V1544QVJF8aOHasGm2effbZyQUfHj3hwv2GwyQweZrj/EaqGex+TE/yMCQruCfQVyUx9fb387ne/U9caEwonMOA2TyoAfk7mPHt+bZDKRGoD5AzBJN4qWCYLuIcRXmTXthcvXizpet1//vOfq8UXXFvk58B1xqAeITjh+gP9t2QEAgLCyjBJREgIQrWRlwQhyjgnTHKsk2lzH5iKNrGCHDBI7YGcOOncVqzo8wj3jMSrNQUI5lgQg83v6dGjR8g+9N+Qxy9ZwRgSbQPh38iTpPntb3+rxlawA8I7IWri/rvvvvtS1iYI3UPoL85rxYoV8sc//lEmTJigBJUWLVqkfVsBCPNF3kNziHS6tpdoSVolB4MzPGDwMIZnTLqCQYk5d9Jhhx2mOo77779fnn32WUkHMFjFigZECaz4ANz0Z555pjz88MPKWySdQAeJASnEiXThu+++k2uuuUblfoA4g44fCUqRxBReY+kA8iHgQXbIIYcobzE83C688ELlQYhVq2QHuWUw6TLnTkk3aIPIbIDVypdeekmt+Fq9CUlyX3dz7hJ4uUB8R35RjIN69eolqQgmhZqhQ4cqkQpiyyuvvJJ24x0n8NyHnSBApXNbIf7AIh5y1GIMhTydZpDPz3zfQfy97LLLVEEGFFhKRc4555ygewbnjXsFz9JUyOMcC5BPCt6q1rFFOraXaEnamcquXbvkq6++kquvvlqpstiQUOzrr79W/542bZqkK6NHj5bly5dLuoCBBcL2tCAFUIEMD5W1a9dKOoFzRgd5/vnnB7mEpjro5LGCDiEKnT+EKQiSsAUEqnQAkxGcL8IRVq1apcI5UP0DKzjFxcWSzKCfRwJNJK3u0qVL2Pcisbe10hR+TvaE335skKpEYoN7771XiVIffPCB6huSFYQcYWU6Fdt2LK87BBqgx0BO/YH+WyqARSgszuKccU4IXYOXkFM7SXWbrF69Wj788EO5+OKLw74vHduKPo9w/QhercUT4H2OKmup3Ia0IIX2g6TdZi8pp/YDu2C8lao2sYJwYTyLzPdMOrYVDSK14G3p1teka3tJG1EKncXChQtlwYIFgQ1eEfAawr/1wyYdwfkne6iOHyBGIGTNXIJ06dKlyjsk3SZvqLSDh4VdbHMqAyHG6g2ECRxoyFOYPrRq1Uq1e5w/vENOPvnkpPWUwrXDhBRhuVhosLo6O+UXQyVOMxhg4vfpYoNUI1IbwEvwz3/+s7z33ntBOS+SESwyHHTQQUFtG17C+DlZ23Y8rjvGP0CPgWAbjBXNEyc94Rw4cKCkAhj7wNsH54w2gmeAuZ1g0oRFCt1OUt0mSNuAkCJU0gtHOrYV3EOY8JrbB3JmIeWHuX1A1EQKAA3uP/Q3em6F96DKmjlfJWyFOVgyhh1pQQppYSBoIg+QG2g/GFvp8LVUs4kdWOhHahTzPZNubcXqkYk+bISuZQAABIFJREFUF5X63EjH9uIbI4HYtWuXMX/+fLXh0O677z71b1394KabbjLOP/98x8+nQvU9vzZAZv8pU6YYy5YtMxYuXKgqi6B6yIcffmikiw3w/i5duhhnnnmmsWjRIlWZp0+fPsbFF19sJCuR3gu/+MUvVKWxZMfv+T/55JNGy5YtVTWLFStWGJ999pkxatQoVa0qXWywZMkS49lnnzWWLl1qzJ49W1Ulbd++vbFy5UojWbniiitUZc3p06cbGzZsCGx79+4NvAc2gC00n3/+uWoL9957r6o6hecCqlGhf0wXG6Cai247nTp1UlUZ8W88J9LFBnfffbeRlZVlvPbaa0GfwX2VrLz00kuqStZTTz2lqh5deumlRrt27YKqhaUSbtd9+fLlxp133ml89dVXqp978803VRXWI444Iqjq4uDBg43jjjvOWLBggfHee+8ZxcXFxs0332wkK9ddd52yCc4Z/d348eONoqIiVZ0QXH755UbXrl2NadOmKdugMiu2VLaJuSIlzh1VrMykU1txGzugb0S/ARt88803qnJYjx49jH379gX2ccIJJxgjRoxQYwmMpzCmPvfcc4Mq9pWWlqp+99tvv1V9U25urvHPf/7TSDab1NTUGKeccoqaR+C6m/saXRlt5syZar6Fv2OM+dxzz6m2ccEFFyStTdzsgr9h7ICqnbhnMK8cOXKkagtVVVUp21a83EPgxx9/VOeBCp1WUrW9xJuEEqU+/vhjdfGtG0pSAryipGsqi1J+bfC3v/1NlbzNyclRE9Bx48apgUgyE0k7wOQTA7PWrVurB8u1114bNGFJBxugg8P5P/bYY0ayE8n5T5482Rg4cKCyASbi5513nrF27VojXWyASerw4cPV+aOENQaaixcvNpIZu/PHBhFSAxtom2heeeUVo2/fvkqUGDRokDF16lQjnWyAAaTdZ8I9P1PNBijHbPcZjBOSmX/84x9q0o22DdH9iy++MFIVt+teUVGhRAWMfSDW9e7d27jhhhvUZMHMqlWrjAkTJqi+EeINRJ39+/cbyQoWHPCMQxvo3Lmz+hmiiwbiwpVXXqnKjWOSc/rpp6sJdirbRPP++++rNoJFGjPp1Fbcxg719fXGrbfeqibEsMUxxxwTYq/KykolLOTl5anxxEUXXRQi6H/99dfG4YcfrvaBdgixKxlt4vS8xIbPgblz56oFX4jkmG8NGDDA+Otf/xokziSbTdzsgjkUBFqIKVjYwzP1kksuCVkESbW24uUeAhCP0E9g7mUlVdtLvMnA//z7VxFCCCGEEEIIIYQQEjnJmWiEEEIIIYQQQgghhCQ1FKUIIYQQQgghhBBCSJNDUYoQQgghhBBCCCGENDkUpQghhBBCCCGEEEJIk0NRihBCCCGEEEIIIYQ0ORSlCCGEEEIIIYQQQkiTQ1GKEEIIIYQQQgghhDQ5FKUIIYQQQgghhBBCSJNDUYoQQgghhBBCCCGENDkUpQghhBBCCCGEEEJIk0NRihBCCCGEEEIIIYQ0ORSlCCGEEEIIIYQQQog0Nf8fWmGsW/sRPaAAAAAASUVORK5CYII=",
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",
       "text/plain": [
        "<Figure size 1200x400 with 4 Axes>"
       ]
@@ -2094,7 +2108,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]

From 5f7304dd80eab8a24f1acdcc274e6c029b3385c9 Mon Sep 17 00:00:00 2001
From: mariegrho <mgrholthaus@gmail.com>
Date: Mon, 23 Jun 2025 13:23:09 +0200
Subject: [PATCH 07/16] updated API references and info box on the evaluator

---
 docs/source/user_guide/superquickstart.ipynb | 696 +++++++++++++------
 1 file changed, 466 insertions(+), 230 deletions(-)

diff --git a/docs/source/user_guide/superquickstart.ipynb b/docs/source/user_guide/superquickstart.ipynb
index b37480b68..d4df2f353 100644
--- a/docs/source/user_guide/superquickstart.ipynb
+++ b/docs/source/user_guide/superquickstart.ipynb
@@ -11,46 +11,60 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This super-quick quickstart gives an introduction to the basic Pymob workflow and key functionalities.  \n",
-    "For this, we will investigate a simple linear regression model, which we want to fit to a noisy dataset.  \n",
-    "Pymob supports our modeling process by providing several tools for *structuring our data*, for the *parameter estimation* and *visualization of the results*.  \n",
+    "This quickstart provides an introduction to the basic Pymob workflow and its key functionalities.  \n",
+    "We will explore a simple linear regression model that we want to fit to a noisy dataset.  \n",
+    "Pymob supports the modeling process by providing several tools for *data structuring*, *parameter estimation* and *visualization of results*.  \n",
     "  \n",
-    "Before starting the modeling process, we let's have a look at the main steps and modules of pymob:\n",
+    "If you are looking for a more detailed introduction, [click here]().  \n",
+    "If you want to learn how to work with ODE models, check out [this tutorial]()."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Pymob components 🧩\n",
+    "\n",
+    "Before starting the modeling process, let's take a look at the main steps and modules of pymob:\n",
     "\n",
     "1. __Simulation:__   \n",
-    "First, we need to initialize a Simulation object by calling the {class}`pymob.simulation.SimulationBase` class from the simulation module.   \n",
-    "Optionally, we can configure the simulation with `sim.config.case_study.name = \"linear-regression\"`, `sim.config.case_study.scenario = \"test\"` and many more options. \n",
+    "First, we need to initialize a Simulation object by creating an instance of the {class}`pymob.simulation.SimulationBase` class from the simulation module.   \n",
+    "Optionally, we can configure the simulation with `sim.config.case_study.name = \"linear-regression\"`, `sim.config.case_study.scenario = \"test\"` and many other options. \n",
     "\n",
     "2. __Model:__   \n",
-    "Our model will be defined as a python function.  \n",
-    "We will then assign it to our Simulation object by `.model` \n",
+    "Our model will be defined as a standard python function.  \n",
+    "We will then assign it to the Simulation object by accessing the `.model` attribute. \n",
     "\n",
     "3. __Observations:__   \n",
-    "Our observation data needs to be structured as a [xarray.Dataset](https://docs.xarray.dev/en/stable/generated/xarray.Dataset.html).  \n",
-    "We assign it to our Simulation object by  `.observations.`  \n",
-    "`sim.config.data_structure` will give us some information about the layout of our data.\n",
+    "Our observation data must be structured as an [xarray.Dataset](https://docs.xarray.dev/en/stable/generated/xarray.Dataset.html).  \n",
+    "We assign it to the `.observations` attribute of our Simulation object.   \n",
+    "Calling `sim.config.data_structure` will give us further information about the layout of our data.  \n",
     "\n",
     "4. __Solver:__  \n",
-    "Solvers are needed to solve the model. \n",
-    "In our simple case, we will use the solver \"solve_analytic_1d\" from the \"pymob.solver.analytic module.\n",
-    "We  assign it to our Simulation object by `.solver` \n",
-    "For more complex models, the JaxSolver from the diffrax module is a more powerful option.  \n",
-    "User can also implement their own solver as a subclass of `pymob.solver.SolverBase`. \n",
+    "A [solver](https://pymob.readthedocs.io/en/stable/api/pymob.solvers.html) is required to solve the model.   \n",
+    "In our simple case, we will use the `solve_analytic_1d` solver from the `pymob.solver.analytic` module.  \n",
+    "We assign it to our Simulation object using the {attr}`pymob.simulation.solver` attribute.   \n",
+    "Since our model already provides an analytical solution, this solver basically does nothing. It is still needed to fulfill Pymob's requirement for a solver component.   \n",
+    "For more complex models (e.g. ODEs), the `JaxSolver` from the `pymob.solver.diffrax` module is a more powerful option.   \n",
+    "Users can also implement custom solvers as a subclass of {class}`pymob.solver.SolverBase`.   \n",
     "  \n",
     "5. __Inferer:__  \n",
-    "The inferer serves as the parameter estimator.  \n",
-    "Pymob provided [various backends](https://pymob.readthedocs.io/en/stable/user_guide/framework_overview.html). In our example, we will work with *numpyro*.  \n",
-    "We assign the inferer to our Simulation object by `.inferer` and configurate the kernel we want to use (here *nuts*).  \n",
-    "But before, we need to parameterize our model using the *Param* class. The parameters can be marked as free or fixed, depending on whether they should be variable during an optimization procedure.  \n",
-    "We assign the parameters to our Simulation object by `sim.model_parameters`. This is a dictionary that holds the model input data. The keys it takes by default are `parameters`, `y0` and `x_in`. \n",
+    "The inferer handels the parameter estimation.  \n",
+    "Pymob supports [various backends](https://pymob.readthedocs.io/en/stable/user_guide/framework_overview.html). In this example, we will work with *NumPyro*.  \n",
+    "We assign the inferer to our Simulation object via the {attr}`pymob.simulation.inferer` attribute and configure the desired kernel (e.g. *nuts*).  \n",
+    "But before inference, we need to parameterize our model using the *Param* class.   \n",
+    "Each parameter can be marked either as free or fixed, depending on whether it should be variable during the optimization procedure.   \n",
+    "The parameters are stored in the {attr}`pymob.simulation.SimulationBase.model_parameters` dictionary, which holds model input values.\n",
+    "By default, it takes the keys: `parameters`, `y0` and `x_in`. \n",
     "\n",
-    "7. __Evaluator:__  \n",
-    "The Evaluator is an instance to evaluate a model. \n",
+    "6. __Evaluator:__  \n",
+    "The Evaluator is an instance to manage model evaluations.\n",
+    "It sets up tasks, coordinates parallel runs of the simulation and keeps track of the results from each simulation or parameter inference process.  \n",
     "\n",
-    "6. __Config:__  \n",
-    "Our settings will be saved in a configuration file `.cfg`.  \n",
-    "The config file contains information about our simulation in different sections. -> Learn more [here](https://pymob.readthedocs.io/en/stable/user_guide/case_studies.html#configuration).\n",
-    "We can further use it to create new Simulations by loading the settings from a config file. \n"
+    "7. __Config:__  \n",
+    "The simulation settings will be saved in a `.cfg` configuration file.  \n",
+    "The config file contains information about our simulation in various sections. -> [Learn more here](https://pymob.readthedocs.io/en/stable/user_guide/case_studies.html#configuration).  \n",
+    "We can further use it to create new simulations by loading settings from a config file. \n"
    ]
   },
   {
@@ -60,9 +74,16 @@
     "![framework-overview](.\\figures\\pymob_overview.png)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Getting started 🛫"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 84,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -82,14 +103,14 @@
    "metadata": {},
    "source": [
     "Since no measured data is provided, we will generate an artificial dataset.  \n",
-    "$y_{obs}$ represents the observation data over the time $t$ [0, 10].  \n",
-    "In order to use the data later, we need to convert it into a xarray-Dataset.  \n",
-    "In your application later, you would use your measuered experimental data.  "
+    "$y_{obs}$ represents the **observed data** over the time $t$ [0, 10].  \n",
+    "To use this data later in the simulation, we need to convert it into an **xarray-Dataset**.  \n",
+    "In your own application, you would replace this with your measured experimental data.  "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 85,
    "metadata": {},
    "outputs": [
     {
@@ -115,27 +136,76 @@
        " */\n",
        "\n",
        ":root {\n",
-       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
-       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
-       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
-       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
-       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
-       "  --xr-background-color: var(--jp-layout-color0, white);\n",
-       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
-       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
-       "}\n",
-       "\n",
-       "html[theme=dark],\n",
-       "body[data-theme=dark],\n",
+       "  --xr-font-color0: var(\n",
+       "    --jp-content-font-color0,\n",
+       "    var(--pst-color-text-base rgba(0, 0, 0, 1))\n",
+       "  );\n",
+       "  --xr-font-color2: var(\n",
+       "    --jp-content-font-color2,\n",
+       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.54))\n",
+       "  );\n",
+       "  --xr-font-color3: var(\n",
+       "    --jp-content-font-color3,\n",
+       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.38))\n",
+       "  );\n",
+       "  --xr-border-color: var(\n",
+       "    --jp-border-color2,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 10))\n",
+       "  );\n",
+       "  --xr-disabled-color: var(\n",
+       "    --jp-layout-color3,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 40))\n",
+       "  );\n",
+       "  --xr-background-color: var(\n",
+       "    --jp-layout-color0,\n",
+       "    var(--pst-color-on-background, white)\n",
+       "  );\n",
+       "  --xr-background-color-row-even: var(\n",
+       "    --jp-layout-color1,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 5))\n",
+       "  );\n",
+       "  --xr-background-color-row-odd: var(\n",
+       "    --jp-layout-color2,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 15))\n",
+       "  );\n",
+       "}\n",
+       "\n",
+       "html[theme=\"dark\"],\n",
+       "html[data-theme=\"dark\"],\n",
+       "body[data-theme=\"dark\"],\n",
        "body.vscode-dark {\n",
-       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
-       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
-       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
-       "  --xr-border-color: #1F1F1F;\n",
-       "  --xr-disabled-color: #515151;\n",
-       "  --xr-background-color: #111111;\n",
-       "  --xr-background-color-row-even: #111111;\n",
-       "  --xr-background-color-row-odd: #313131;\n",
+       "  --xr-font-color0: var(\n",
+       "    --jp-content-font-color0,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 1))\n",
+       "  );\n",
+       "  --xr-font-color2: var(\n",
+       "    --jp-content-font-color2,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.54))\n",
+       "  );\n",
+       "  --xr-font-color3: var(\n",
+       "    --jp-content-font-color3,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.38))\n",
+       "  );\n",
+       "  --xr-border-color: var(\n",
+       "    --jp-border-color2,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 10))\n",
+       "  );\n",
+       "  --xr-disabled-color: var(\n",
+       "    --jp-layout-color3,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 40))\n",
+       "  );\n",
+       "  --xr-background-color: var(\n",
+       "    --jp-layout-color0,\n",
+       "    var(--pst-color-on-background, #111111)\n",
+       "  );\n",
+       "  --xr-background-color-row-even: var(\n",
+       "    --jp-layout-color1,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 5))\n",
+       "  );\n",
+       "  --xr-background-color-row-odd: var(\n",
+       "    --jp-layout-color2,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 15))\n",
+       "  );\n",
        "}\n",
        "\n",
        ".xr-wrap {\n",
@@ -176,7 +246,7 @@
        ".xr-sections {\n",
        "  padding-left: 0 !important;\n",
        "  display: grid;\n",
-       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
        "}\n",
        "\n",
        ".xr-section-item {\n",
@@ -184,11 +254,14 @@
        "}\n",
        "\n",
        ".xr-section-item input {\n",
-       "  display: none;\n",
+       "  display: inline-block;\n",
+       "  opacity: 0;\n",
+       "  height: 0;\n",
        "}\n",
        "\n",
        ".xr-section-item input + label {\n",
        "  color: var(--xr-disabled-color);\n",
+       "  border: 2px solid transparent !important;\n",
        "}\n",
        "\n",
        ".xr-section-item input:enabled + label {\n",
@@ -196,6 +269,10 @@
        "  color: var(--xr-font-color2);\n",
        "}\n",
        "\n",
+       ".xr-section-item input:focus + label {\n",
+       "  border: 2px solid var(--xr-font-color0) !important;\n",
+       "}\n",
+       "\n",
        ".xr-section-item input:enabled + label:hover {\n",
        "  color: var(--xr-font-color0);\n",
        "}\n",
@@ -217,7 +294,7 @@
        "\n",
        ".xr-section-summary-in + label:before {\n",
        "  display: inline-block;\n",
-       "  content: '►';\n",
+       "  content: \"►\";\n",
        "  font-size: 11px;\n",
        "  width: 15px;\n",
        "  text-align: center;\n",
@@ -228,7 +305,7 @@
        "}\n",
        "\n",
        ".xr-section-summary-in:checked + label:before {\n",
-       "  content: '▼';\n",
+       "  content: \"▼\";\n",
        "}\n",
        "\n",
        ".xr-section-summary-in:checked + label > span {\n",
@@ -300,15 +377,15 @@
        "}\n",
        "\n",
        ".xr-dim-list:before {\n",
-       "  content: '(';\n",
+       "  content: \"(\";\n",
        "}\n",
        "\n",
        ".xr-dim-list:after {\n",
-       "  content: ')';\n",
+       "  content: \")\";\n",
        "}\n",
        "\n",
        ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: ',';\n",
+       "  content: \",\";\n",
        "  padding-right: 5px;\n",
        "}\n",
        "\n",
@@ -325,7 +402,9 @@
        ".xr-var-item label,\n",
        ".xr-var-item > .xr-var-name span {\n",
        "  background-color: var(--xr-background-color-row-even);\n",
+       "  border-color: var(--xr-background-color-row-odd);\n",
        "  margin-bottom: 0;\n",
+       "  padding-top: 2px;\n",
        "}\n",
        "\n",
        ".xr-var-item > .xr-var-name:hover span {\n",
@@ -336,6 +415,7 @@
        ".xr-var-list > li:nth-child(odd) > label,\n",
        ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
        "  background-color: var(--xr-background-color-row-odd);\n",
+       "  border-color: var(--xr-background-color-row-even);\n",
        "}\n",
        "\n",
        ".xr-var-name {\n",
@@ -385,8 +465,15 @@
        ".xr-var-data,\n",
        ".xr-index-data {\n",
        "  display: none;\n",
-       "  background-color: var(--xr-background-color) !important;\n",
-       "  padding-bottom: 5px !important;\n",
+       "  border-top: 2px dotted var(--xr-background-color);\n",
+       "  padding-bottom: 20px !important;\n",
+       "  padding-top: 10px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in + label,\n",
+       ".xr-var-data-in + label,\n",
+       ".xr-index-data-in + label {\n",
+       "  padding: 0 1px;\n",
        "}\n",
        "\n",
        ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
@@ -399,6 +486,12 @@
        "  float: right;\n",
        "}\n",
        "\n",
+       ".xr-var-data > pre,\n",
+       ".xr-index-data > pre,\n",
+       ".xr-var-data > table > tbody > tr {\n",
+       "  background-color: transparent !important;\n",
+       "}\n",
+       "\n",
        ".xr-var-name span,\n",
        ".xr-var-data,\n",
        ".xr-index-name div,\n",
@@ -458,12 +551,20 @@
        "  stroke: currentColor;\n",
        "  fill: currentColor;\n",
        "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
+       "\n",
+       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
+       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
+       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
+       "  color: var(--xr-font-color0);\n",
+       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
+       "  stroke-width: 0.8px;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 2kB\n",
        "Dimensions:  (t: 100)\n",
        "Coordinates:\n",
-       "  * t        (t) float64 0.0 0.101 0.202 0.303 0.404 ... 9.697 9.798 9.899 10.0\n",
+       "  * t        (t) float64 800B 0.0 0.101 0.202 0.303 ... 9.697 9.798 9.899 10.0\n",
        "Data variables:\n",
-       "    y        (t) float64 2.22 -0.3948 1.384 0.7673 ... 32.87 35.55 32.99 33.12</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-17c72c86-d721-47b5-b133-7508a065bda8' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-17c72c86-d721-47b5-b133-7508a065bda8' 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'>t</span>: 100</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-6554160b-17ea-4774-b555-345e2ca22509' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6554160b-17ea-4774-b555-345e2ca22509' class='xr-section-summary' >Coordinates: <span>(1)</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'>t</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.101 0.202 ... 9.899 10.0</div><input id='attrs-27ff8595-7a94-401c-a97e-a8c9b1c00224' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-27ff8595-7a94-401c-a97e-a8c9b1c00224' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4bb6fc91-035e-4502-bcba-ea3f547731f3' class='xr-var-data-in' type='checkbox'><label for='data-4bb6fc91-035e-4502-bcba-ea3f547731f3' 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.      ,  0.10101 ,  0.20202 ,  0.30303 ,  0.40404 ,  0.505051,\n",
+       "    y        (t) float64 800B -0.5149 -0.7114 -1.253 3.426 ... 29.12 31.78 32.77</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-cb22c682-387e-497e-a297-695495b750ff' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-cb22c682-387e-497e-a297-695495b750ff' 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'>t</span>: 100</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-0514a1e4-a436-4f2c-a31f-fbcdb00dc73f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0514a1e4-a436-4f2c-a31f-fbcdb00dc73f' class='xr-section-summary' >Coordinates: <span>(1)</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'>t</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.101 0.202 ... 9.899 10.0</div><input id='attrs-0281450d-db12-43b7-b078-b02f2e95a9c1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0281450d-db12-43b7-b078-b02f2e95a9c1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-91e61dfe-0c60-4ed0-8c88-dbfd85f4f586' class='xr-var-data-in' type='checkbox'><label for='data-91e61dfe-0c60-4ed0-8c88-dbfd85f4f586' 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.      ,  0.10101 ,  0.20202 ,  0.30303 ,  0.40404 ,  0.505051,\n",
        "        0.606061,  0.707071,  0.808081,  0.909091,  1.010101,  1.111111,\n",
        "        1.212121,  1.313131,  1.414141,  1.515152,  1.616162,  1.717172,\n",
        "        1.818182,  1.919192,  2.020202,  2.121212,  2.222222,  2.323232,\n",
@@ -479,26 +580,26 @@
        "        7.878788,  7.979798,  8.080808,  8.181818,  8.282828,  8.383838,\n",
        "        8.484848,  8.585859,  8.686869,  8.787879,  8.888889,  8.989899,\n",
        "        9.090909,  9.191919,  9.292929,  9.393939,  9.494949,  9.59596 ,\n",
-       "        9.69697 ,  9.79798 ,  9.89899 , 10.      ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-32aa05c4-933c-4efb-aa55-afaa9525bc4d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-32aa05c4-933c-4efb-aa55-afaa9525bc4d' class='xr-section-summary' >Data variables: <span>(1)</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>y</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.22 -0.3948 1.384 ... 32.99 33.12</div><input id='attrs-eb9df45f-e589-49e0-85a2-e5018bc93c83' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-eb9df45f-e589-49e0-85a2-e5018bc93c83' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e718f40f-0803-49fa-bd52-df90ac338187' class='xr-var-data-in' type='checkbox'><label for='data-e718f40f-0803-49fa-bd52-df90ac338187' 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([ 2.21982221, -0.39481712,  1.38373391,  0.76734977,  3.53128984,\n",
-       "        2.83480537,  1.56855402,  3.98072621,  5.95755583,  5.52551877,\n",
-       "        2.23964013,  3.10663967,  5.68419342,  1.14802872,  7.64059029,\n",
-       "        7.81415541,  7.73834928,  7.40872897,  8.33693323,  9.10099101,\n",
-       "        9.19634601, 10.3405425 , 11.24527459,  9.40017356, 10.56387531,\n",
-       "        8.80908111,  9.16921146,  8.58392666,  9.97546341,  9.74083896,\n",
-       "       11.95826555, 13.93410308, 12.19266982,  9.62461685, 14.72994292,\n",
-       "       10.54623444, 12.63065813, 13.25493083, 14.07108398, 12.25928216,\n",
-       "       15.78096936, 12.627227  , 14.11730366, 14.95651657, 14.51064769,\n",
-       "       17.48640518, 17.0615146 , 14.11524981, 14.86710255, 18.71381567,\n",
-       "       17.51924538, 23.58892978, 19.90362788, 19.45425992, 17.09233205,\n",
-       "       20.59558364, 21.58057381, 18.49053291, 20.01779783, 21.07720316,\n",
-       "       20.8838165 , 21.1425492 , 21.21008105, 24.45880636, 20.44022179,\n",
-       "       25.68197026, 23.10267878, 25.24313608, 22.02298164, 22.24198888,\n",
-       "       22.89092846, 21.94086175, 23.82941087, 26.45258291, 24.83685328,\n",
-       "       29.37746348, 27.24916928, 25.83365091, 26.60719058, 28.44142883,\n",
-       "       28.06419799, 30.02134903, 28.37587939, 27.00740759, 29.28454614,\n",
-       "       27.74011134, 28.74545372, 29.4329712 , 28.3208646 , 33.99737959,\n",
-       "       27.74735084, 30.78183787, 31.86772239, 33.16865905, 33.14755493,\n",
-       "       31.62916884, 32.86912176, 35.55412696, 32.9939107 , 33.12241191])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d26f26d2-36bc-40d6-b65f-7458dd620d0d' class='xr-section-summary-in' type='checkbox'  ><label for='section-d26f26d2-36bc-40d6-b65f-7458dd620d0d' class='xr-section-summary' >Indexes: <span>(1)</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>t</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f7b4bde7-2a92-4667-bc0a-e3532af01bd2' class='xr-index-data-in' type='checkbox'/><label for='index-f7b4bde7-2a92-4667-bc0a-e3532af01bd2' 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([                0.0, 0.10101010101010101, 0.20202020202020202,\n",
+       "        9.69697 ,  9.79798 ,  9.89899 , 10.      ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7b6d7b8a-3a33-4ac2-92c6-72dd22cffdeb' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7b6d7b8a-3a33-4ac2-92c6-72dd22cffdeb' class='xr-section-summary' >Data variables: <span>(1)</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>y</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.5149 -0.7114 ... 31.78 32.77</div><input id='attrs-f45ad545-a6de-4cb9-aa08-db81961682eb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f45ad545-a6de-4cb9-aa08-db81961682eb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-66163812-f915-4a3e-b4fa-2a4be5796afb' class='xr-var-data-in' type='checkbox'><label for='data-66163812-f915-4a3e-b4fa-2a4be5796afb' 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([-0.51485544, -0.71136009, -1.25293135,  3.42554816,  3.02802318,\n",
+       "        2.28996175,  1.30369905,  1.12342635,  4.696483  ,  3.55592901,\n",
+       "        6.07850176,  5.65225376,  6.30163965,  6.21062672,  7.48114549,\n",
+       "       10.4209593 ,  4.30685901,  3.34331655, 11.06410455,  8.0075721 ,\n",
+       "        7.62191623,  5.771721  ,  8.9993412 ,  8.25634047, 10.1705519 ,\n",
+       "        5.7614147 ,  7.63852438, 11.69554624,  9.2803849 ,  8.74384082,\n",
+       "        4.70197652,  9.55192526,  8.01544118, 11.25992123, 10.25462628,\n",
+       "       13.39701217, 13.07742985, 14.18054669, 14.94229116, 12.36204004,\n",
+       "       11.47591127, 14.14875177, 12.76613791, 13.68796203, 15.8061059 ,\n",
+       "       12.87511648, 13.97928359, 15.32178202, 16.82801938, 14.50979942,\n",
+       "       14.93651816, 16.60607159, 18.55546995, 13.24463006, 18.3729172 ,\n",
+       "       14.16656354, 17.32953375, 21.29800181, 16.82317851, 19.04953345,\n",
+       "       19.77974641, 20.66126185, 19.86304768, 18.24686502, 17.73073581,\n",
+       "       21.52896061, 22.41719182, 20.40032284, 24.38217873, 23.72367109,\n",
+       "       22.01080873, 24.19549703, 21.77467738, 21.14727555, 24.36605289,\n",
+       "       24.81749318, 21.43139972, 25.37209188, 23.56605562, 25.6332203 ,\n",
+       "       26.54650393, 25.38526454, 25.54376272, 25.05455707, 24.78854931,\n",
+       "       25.10641378, 29.38972575, 26.33082303, 27.44406156, 27.87010378,\n",
+       "       26.88792478, 27.54458101, 30.97867305, 26.06115315, 26.74526954,\n",
+       "       28.48091395, 31.67196315, 29.12184748, 31.77992649, 32.77431831])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4ded40ce-d19b-49e4-b2f5-6f6a3f4610af' class='xr-section-summary-in' type='checkbox'  ><label for='section-4ded40ce-d19b-49e4-b2f5-6f6a3f4610af' class='xr-section-summary' >Indexes: <span>(1)</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>t</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-513a7ad3-ffd7-4709-afae-634504bfc914' class='xr-index-data-in' type='checkbox'/><label for='index-513a7ad3-ffd7-4709-afae-634504bfc914' 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([                0.0, 0.10101010101010101, 0.20202020202020202,\n",
        "       0.30303030303030304, 0.40404040404040403,  0.5050505050505051,\n",
        "        0.6060606060606061,  0.7070707070707071,  0.8080808080808081,\n",
        "        0.9090909090909091,  1.0101010101010102,  1.1111111111111112,\n",
@@ -532,24 +633,24 @@
        "         9.393939393939394,   9.494949494949495,   9.595959595959595,\n",
        "         9.696969696969697,   9.797979797979798,     9.8989898989899,\n",
        "                      10.0],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;t&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-74439760-c4be-4922-923b-30f332d32610' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-74439760-c4be-4922-923b-30f332d32610' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+       "      dtype=&#x27;float64&#x27;, name=&#x27;t&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4ad51e0f-9925-4afc-b00e-de34757f6870' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-4ad51e0f-9925-4afc-b00e-de34757f6870' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
-       "<xarray.Dataset>\n",
+       "<xarray.Dataset> Size: 2kB\n",
        "Dimensions:  (t: 100)\n",
        "Coordinates:\n",
-       "  * t        (t) float64 0.0 0.101 0.202 0.303 0.404 ... 9.697 9.798 9.899 10.0\n",
+       "  * t        (t) float64 800B 0.0 0.101 0.202 0.303 ... 9.697 9.798 9.899 10.0\n",
        "Data variables:\n",
-       "    y        (t) float64 2.22 -0.3948 1.384 0.7673 ... 32.87 35.55 32.99 33.12"
+       "    y        (t) float64 800B -0.5149 -0.7114 -1.253 3.426 ... 29.12 31.78 32.77"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 85,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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GwbXQ/hajkRqclEcL0HgRqAEASVk3XfzLC6O6N5febXND1kL7W4yWqaMOCupeRqAGAHjW8QglVkrD8j/WF8id/cPPjDUYe6nTmF0EagCAZ31so8QqsGVoMA3Kkd73MpLJAACetTtFSqySNlDPnDlTOnToYPqc6uPMM8+UhQsXlrx/5MgRGT16tNStW1eqVasmgwcPll27drk5ZABIaf7DK+bn7zBf3e7aVT9FSqySdum7cePG8sADD0heXp7o2SAvvviiDBw4UD777DNp166djBs3Tt599115/fXXTfPyMWPGyKBBg2TlypVuDhsA0u5EKreSrs74pcSqYP+RkPvUGb8khnm9xCqlTs+qU6eOPPTQQ/Lb3/5W6tWrJ3PnzjV/Vl999ZW0adNGVq1aJd26dbN0P07PAgDnT6RyY2wSpsTKzbGl1elZx48fl1deeUUKCwvNEviaNWvk2LFj0rt375JrWrduLU2bNjWBGgDgDK8fXvHrX0qsdOYcSJ8nY5BOuqzvdevWmcCs+9G6Dz1v3jxp27at5OfnS8WKFaVWrVqlrm/QoIEUFBSEvV9RUZF5BP7WAgBIfGa11c5isZRI/TrJS6ySOlCfcsopJijr9P+NN96QESNGyPLly2O+39SpU2Xy5MmOjhEAvCSegOd0ZrXVsTix/10hiUuskjpQ66y5VatW5s+dO3eWTz75RB577DG59NJL5ejRo7Jv375Ss2rN+s7NzQ17v4kTJ8r48eNLzaibNGmS4L8FACRvwlesmdVWxxJu/1sTxPT1dFi+jodn9qj9iouLzdK1Bu2srCxZsmRJyXsbN26Ubdu2maXycLKzs0vKvfwPAEgF/oAXvEztD3j6fixiObzC6li8vv+dDFwN1Dr7/fDDD2Xr1q1mr1qff/DBBzJs2DCTDTdq1CgzO162bJlJLrvyyitNkLaa8Q0AqSKRAc9/eIVYOJHK7ljs7H/Dg0vfu3fvliuuuEJ27txpArM2P3nvvffkggsuMO8/+uijkpmZaRqd6Cy7b9++8tRTT7k5ZABIyYQvO4dX2BkLncWSPFA///zzEd+vVKmSzJgxwzwAIJ2VR8Czmllt9WcU7P9Jfig8KuneWSzpk8kAAN5ppWkls9rqz7j33S+jBup06CwWLwI1ACQBp1pp2i3tCnV9tLH4WQnSwfvfKItADQBJwJ/wpRnVGWFaaUYLeHZLuyJdH24sdjSokS1Dz2gqRT8XmwNA0qWBSdL3+nYavb4BpJJY66jt9vK2cr0KHkudqlnyQ+GxqH+P33Y6UVZs2isFB7xzAIhXYxOBGgCSTCzL12dPWxoxU7tW5SyZMayTdGv53/3pSNf7l9lXTOhlngeORQPvuFfzY/p7JfMhG4mMTSx9A0CSsdtKM1o5ldr30zEZ9txHZlY7pEsTW6VggWPRJexY+X4J1jpL1+xzlsE92pkMAOAsOyVbmiD26Ptfx3RfnbkXF/vM7DxWgb8E6P1Wbd4r8/N3mK/p2r2MGTUApDg7JVu+GO8bau88mJ3Es8UbCmT8a/mO9jRPVsyoASDFRevlbVdw7+9wfb+D6b72uN55ln7GCyu3Ot7TPFkRqAEgxUXq5R1NtN7fkfp+++lS+JzfdzXJZ2N65UX9pSHc1rQvTQ/xIFADQBKzuo/r7+Wts1qrxvU+ucz1+jwwK9tqolpmRoYJ7FYOAIkUg31peIgHe9QAkCY11f5e3qs375XRc9eaABqp/GpMr1bmEakULJYe5JEOALmwfa48v3KrrfulOgI1ACShcA1J/Pu44WqRNch2z8uRBwafaq4TC13OAsuv/DN4f+DOqZYdU0JbuANA9LmVQF0/jQ7xIFADQJKJdh60lVpkO8daRprB59bIllpVsmT/4WO2e5CHqgd3qqd5KiFQA0Cank1t9VjLSDP4XQeKSl6LtQe50z3NUw2BGgCSjJ194WjtRq10ObMyg69ZJUsqnVChVO/uSLPzSGKZ7acyAjUAeEy04Gp1f3brnsNlenbH0jTEygx+3+FjMmdUJ8nMzLDcgzwSO7P9VEegBoAky+S2so+rM9zp7//bdrJZPDP4PYVFMrDjieJWT/NURR01AHhEuA5fwR25otUi+4NzuKVqu01DrM7g0ykTuzwRqAHAA6LtAwcH13ANTPxtOnUp2qmmIdFakAa3FIWzWPoGgCTN5A63j/vOF985uqRNJra7CNQA4AGxdPgKtY+rM+49B4scX6omE9s9BGoAiCHz2mlO7ANbOWoynqYhZGK7g0ANAHH20HZCvB25wjUkCXWfeJaqycQufySTAUAMmddOs3KqVLjgauWoyXCnX8H7CNQAEGPmtdMiZXJHCq5WjppUk/q3MWdCE6STC0vfAGAz81qPiXSqA5cT+8BWE9Fyqmezn5yECNQAYDPgBZ/l7PT+td19YBqSpDZXl76nTp0qXbp0kerVq0v9+vXloosuko0bN5a6pmfPnpKRkVHqcd1117k2ZgCpy2ogCwzS5bF/HQ0NSVKbq4F6+fLlMnr0aFm9erUsXrxYjh07Jn369JHCwsJS11199dWyc+fOkseDDz7o2pgBpK5oAS+cRO9f6z1Xbd4r8/N3mK/BPyOeRDR4n6tL34sWLSr1fPbs2WZmvWbNGunRo0fJ61WqVJHc3FwXRgggnUTqwBWN1TOgE1UqRkOS1OWpPer9+/ebr3XqlF6emTNnjrz00ksmWA8YMEAmTZpkgncoRUVF5uF34MCBBI8aQCoJF/BqVc4qs+Qdzz63FeFqo8OdgEVDktTkmUBdXFwsY8eOle7du0v79u1LXr/sssukWbNm0qhRI/niiy9kwoQJZh/7rbfeCrvvPXny5HIcOYBU6zwWKuAV+3wy7LmPyi1hK1qpmI5U39dxBgZiGpKkngyfz5eYgkCbrr/+elm4cKGsWLFCGjduHPa6pUuXyvnnny+bNm2Sk046ydKMukmTJma2XqNGjYSNH0Bqdx7TwHn2tKVRO4dpnbITM1jdix767Oqo1718dTcCcxLS2FSzZk1LsckTDU/GjBkj77zzjixbtixikFZdu3Y1XzVQh5KdnW3+0oEPAIi381h5J2zFekgHUo+rgVon8xqk582bZ2bKLVq0iPo9+fn55mvDhiRGACjfzmOxdg6LBbXR8MQetZZmzZ07V+bPn29qqQsKCszruhxQuXJl2bx5s3n/wgsvlLp165o96nHjxpmM8A4dOrg5dABpcuZzsEQkbIXaL4/3kA6kDlcD9cyZM0uamgSaNWuWjBw5UipWrCjvv/++TJ8+3dRW617z4MGD5a677nJpxACSmVPLyU4mbEXaLw9XKkZtdHpxNVBHy2PTwKxNUQB4U3mf2Zxqy8lWyq+ojYZnyrMAJBc3zmyOl5eWk62WX2kWObXR6c0TWd8AkotbZzbHy0utNu3sl/uX2gd2PNF8JUinFwI1gKQ6szle5Zm5HQnlV7CKpW8A5Z457bZYMred3o/32n45vItADSAtZ4J2MrcTsR/vpf1yeBtL3wBsSbeZoBP78aGOqfTSfjm8jRk1AFvSaSYY68EYdmbjlF8hGgI1AMfObE61mWC8+/FWj6mk/AqRsPQNIGkzp728H28nO57yK0TCjBpATNJhJhjPfnwqZMfDGwjUAGLmZM/rVNqP11nyyk17UiI7Hu4jUANAnPvxSrO5Nehu3XNYXv54mxQcOJJW2fFIHAI1AEQQLTNbnT1tacRl7nBqVc6SYp+vZJ8aCCXDF+0IqyR34MABc771/v37pUaNGm4PB0CSCtWZbPGGgpBZ3XZ5/TATuBubCNQAEGPgjnUmHcw/l06ljHk4F5sozwLgGaE6eHlVtKxuO5LhMBO4hz1qAJ6QbOdbO52tTbkWwmFGDcB1yXi+daKytSnXQjACNYCUPN86eBn96M/Fji6r+2usreRq28nnplwLwVj6BuCqWDt4RTofOtQyur4VGJvjXVaPVGMdTEu5JvVvI/e++2VaHGYCZxGoASRdP+1QgTi3RrYMPaOp7P/pmLywcmuZ7w+eQAcfjOFojfUvY2meU7XULxGZmRlpcZgJnEWgBpBU/bTDnkh1oEgeff9ryz/X6jGVTvY851hLxIJADcA1unxdXOwzHbr2/XQs5DWBS8KR9rPdzLS20/M8HQ4zgQuBulOnTrZumpGRIQsWLJATTzwx1nEBSHGhlq+DBS8JaxKYU7XLbmZap/phJnAhUOfn58stt9wi1apVi3qtNjp74IEHpKioyInxAUhB4ZavgwUvCScqoOqsNlJyGpAUS9+33Xab1K9f39K1Dz/8cDxjApDCrCxf61L4jGGdpFvLuqWCpdOlS/5l9R8Lj5ZpB+rlZitIL5bqqLds2SL16tWzfNMNGzZIs2bN4hkXgBRlpfWm7ldnZmSUmdHaqV2Oxn+P35zWUEbPTa5mK0gvlgK1Bl3dd7aqSZMmUqFChajXTZ06Vbp06SLVq1c3s/WLLrpINm7cWOqaI0eOyOjRo6Vu3bpm6X3w4MGya9cuy2MBkPzlWIGGdGkaUzJZ8Cq2zqRnXHa6LPh8p+PNVgDPZH2feuqp8o9//MME5lgsX77cBGEN1j///LPceeed0qdPHzMjr1q1qrlm3Lhx8u6778rrr79uThoZM2aMDBo0SFauXBnP0AEkgJV9XrvlWHaSz0IZ1b259G6bK52b1ZY13/5YamyxNlsBkiZQb926VY4dC11SYcWiRYtKPZ89e7aZWa9Zs0Z69Ohhjv96/vnnZe7cudKrVy9zzaxZs6RNmzayevVq6datWzzDB+DCoRr+5Ws7HbqiJZ/9T4eG8unWH6XgQOSfHRxs453dA2lXR62BWdWp89//QDVg6y8CvXv3LrmmdevW0rRpU1m1ahWBGvCIsE1IQnT/itR6M1SHrmjJZ3qVzpQ/vP28MjPmaFnbsc7ugaQJ1Oecc45UrlzZkYEUFxfL2LFjpXv37tK+fXvzWkFBgVSsWFFq1apV6toGDRqY90LRsrDA0jA9nBuAu4dq3PHmOqleKaski9tOhy6ry9MapO0uT8cyuweSKlDr/rRTdK96/fr1smLFirjuowlqkydPdmxcAJzJ4h723EellqOtduhK5PK03dk94AZLWd/aZczOXrQG8J9++sny9Zog9s4778iyZcukcePGJa/n5ubK0aNHZd++faWu16xvfS+UiRMnmiV0/2P79u2WxwHAPjsBMrjkyd+ha2DHE83XUAEx0cvT/tm9zpwD6fN4DuwAynVGffHFF5ulZqu11EOGDDHdzFq2bBm1i9mNN94o8+bNkw8++EBatGhR6v3OnTtLVlaWLFmyxJRlKS3f2rZtm5x55pkh75mdnW0eAMqHnQAZy0EY5bE8Tf9tJH2g1oA6cuRIywFQa5+tLndrRvf8+fNNLbV/31nLsHTvW7+OGjVKxo8fbxLMatSoYQK7BmkSyQBviBZI4y15Kq/lafpvI6kD9YgRI2zddNiwYSaoRjNz5kzztWfPnqVe1xIs/cVAPfroo5KZmWlm1Jok1rdvX3nqqadsjQdA4kQKpE4tmXM8JNJZhk+nyylMs751Zq771VZ+eQBQmtXDKuw2JHn56m62Z7AcnIF0jE2eqqMGkJxNTAL3eVdv3mt6Z1s5X9oulqeRjixlfQNI3yYmdg6r0EDaPS9HHhh8qgnIwXNdu3vKOoPWM6jn5+8wX+m5jXTEjBqA7SYm0TK3ndhTtjObB1IZgRpAGU4cVhFPyZOdlqRAqnMkUGtDkuA2nwCSl1PdwGLZU453Ng9Iuu9RT5s2TV599dWS55dccok5K/rEE0+Uzz//3OnxAXCBm4dV2JnNA+nAdqB++umnS86fXrx4sXksXLhQ+vXrJ7fddlsixgjApSYm4ear+nrDBB1WwdGTQJxL39o9zB+otT+3zqj79OkjzZs3l65du9q9HQAPcvOwCo6eBOKcUdeuXbvkoItFixaVnBWtfVOOHz9u93YAEizWEqdwh1XUrpolV3VvLjUrV0xIuZSbs3kgJWbUgwYNkssuu0zy8vJk7969ZslbffbZZ9KqVatEjBFAjOItcQrM3F68oUDezv9Ofig8Ks+v3GoeiSiX4uhJIM4Ztfbe1mMp27Zta/anq1WrZl7fuXOn3HDDDXZvB8BDDUtC0YC4/6ejMmvlVhOk47mXVRw9Cfx/9PoGUpAuSZ89bWnY7Gl/G88VE3pFnZk6fS87ddX09kaqSnivbz0T+oknnpAvv/zSPG/Tpo05fvKUU06JbcQAXClxmr1yi4zs3iJi8HOi+Umsy/D09gZiWPp+8803pX379rJmzRo57bTTzGPt2rXmNX0PgPusli7d++6XZrYcaenaiXIpp5bhgXRke0Z9++23y8SJE2XKlCmlXr/nnnvMe3puNAB32SlditaWM95yKTqNAeU8o9aksSuuuKLM68OHDzfvAXBftBKnQP4AqsEyVLlVvOVSdBoDyjlQ9+zZU/71r3+VeX3FihVyzjnnxDkcAE6WOCmrwTpcsIx0LyvlUnQaA8ph6XvBggUlf/7Nb34jEyZMMHvU3bp1M6+tXr1aXn/9dZk8eXKcwwHglHBHTcYSLOM5tpJOY0A5lGdlZlqbeGdkZHiuOxnlWUh3upyt2d2aOBbNy1d3i5hlHUu5lL+8S/fCfXGWdwGpwk5sshSBi4uLLT28FqQB/HfpWkuwnGjL6S+XGtjxRPPVSmCNd+kcSHe296gBJB+3gyWdxoBy7ky2fPly+fOf/1zS8ETbieoRl15MJmPpG8koUR254u39HS86jQH2Y5PtQP3SSy/JlVdeaQ7n6N69u3lt5cqVMm/ePJk9e7Y5sMNLCNRINokOpgRLIMUDtbYLveaaa2TcuHGlXn/kkUfk2WefLZllewWBGsnE38Er+D9KfxhlmRhIDY4nkwX65ptvZMCAAWVe17KtLVu22L0dAIsdvCI1JQGQumwH6iZNmsiSJUvKvP7++++b9wDEhg5eABzp9X3LLbfITTfdJPn5+XLWWWeV7FHr/vRjjz1m93YAfkEHLwCOBOrrr79ecnNz5eGHH5bXXnutZN/61VdflYEDB9q9HYBf0MELgGN11BdffLHp7b13717z0D8HB+mXX35ZCgsLI97nww8/NPvdjRo1Ml3N3n777VLvjxw50rwe+Pj1r38dy5ABz4v18Avds161ea/Mz99hvsa7h+30/QCU84zaqmuvvVa6du0qLVu2DHuNBnI9z/qqq64y5V6haGCeNWtWyfPs7OyEjBdwu8TJ35REs771Tj4LTUmcLuVyu84aQDkGaitVX/369TOPSDQw61I74BWJDGZ2Dr8IV8oV7XzpSH8vJ+8HwOOB2ikffPCB1K9fX2rXri29evWS++67T+rWDX9oQFFRkXkE1qoBTimPYKbff0Hb3IgzdiulXHe8uU6qV8qSbi2j9+SOdj/9bn1fx0VzFKB8ebrXty57//WvfzXlYNOmTTOtS3UGHunwj6lTp5oicv+DkjEkY51ztMMvopVyqX0/HZNhz31kTq7SXzAioTQM8C5PB+ohQ4aYRiqnnnqqXHTRRfLOO+/IJ598YmbZ4UycONF0evE/tm/fXq5jRuryUjCzU6Lln+1HCtaUhgHe5elAHUwT03JycmTTpk0R97S1HVvgA3CCV4KZztj3HPz/2zvRRJvt27kfpWFACu1RN2vWTLKyshy953/+8x9TDtawIQktSO4651izxkMlslkRONvXpXS798v4JaEt2nnVADwQqEeMGCGjRo2SHj16RLxu/fr1Ue916NChUrNj7RWuHc/q1KljHpMnT5bBgwebrO/NmzfL7bffLq1atZK+ffvaHTYQd/D01znrUrIvjmAWa9Z4uEQ2OwJn+1bvVx7nVQNwMFDrvm/v3r3NjFmPu9TAfeKJJ0osPv30UznvvPNKno8fP9581XvOnDlTvvjiC3nxxRdl3759pilKnz595N5776WWGo6zEjxjqXN2Kms8UiKbHf7Zvp37hSoNA1B+bB9zqb7//nv529/+ZoLohg0bTODWWbZ2J3N6uTteHHMJp4+WjHVGrMFRM7DDLTP7Z+QrJvQqE+y1Q9jQZ1fH9PcLdW+r95vUv42M7N6CmTTgYmyKaY+6Xr16Zvarj7Vr15rOYZdffrlUq1ZNhg8fLjfccIPk5eXFOn6g3MRSP2ylzjnerPHAfWS7CWpWZvtW75dTPZsgDSRz1vfOnTtl8eLF5lGhQgW58MILZd26ddK2bVt59NFHnRsl4LGSq2h1zk5njVtNZBvX+2Qzcw6kz4NXBTgABEgetmfUx44dkwULFphZ9D//+U/p0KGDjB07Vi677LKS6fu8efNM/+5x48YlYsxA0pVcxVsCZTWRbUyvVuYRbbbvVGIcAA8Gai2NKi4ulqFDh8rHH38sHTt2LHONJojVqlXLqTECCVMeM0snSqDsJrIFL53Hez8ASbT0rUva3333ncyYMSNkkFYapLXUCkjVoyXtJqpZCdLRgqP/wA4rS9tWOH0/AB7K+k4mZH3DajCVMDPLWINWtCzvQHZO33L6iM1EHNkJwOWsbyCV2Dla0g4rB2fEUgLlT2RzitP3A+AsAjUQR8lVpFnqyk17HCuBYtYLpC8CNeDwzNJuP+5oiWqxNlgBkBqS6vQswOusJo8FJ6rpjFm7hc3P32G++k+5Cnc/K0dXAkgNzKgBh5ab7fTPDszyXryhIOSMWfeu7333S1td0wCkHgI14NBys9XkscBENRXukI4b5n4W8R6RWo4CSB0EaqStcDPmWE+4stq9bMx5J8m4C04xf9byrXAzZqvi7ZoGwNsI1EhL4WbM8Sw3W+1e1r1VvZITrKzOwCOhHzeQ2kgmQ9qJlKCly82xHNIRS5ezeGfC8XZNA5AcCNRIK9GOtbQqVJD1989WGRZahNqZCVu5H4DURKBGWrGT8BVJuCBrp3+21Rn4U5fRjxtIZ+xRw1Xl3XHLieXmaMc/Wu1yZvUEK71f3/bOdU0DkFwI1HCNGx237C43x3r8o9UuZ1b7jNOPG0hfnJ4FV4QrgYr3xCqrJ1pp4pgvwox5Uv+2cu+75fdLBL28gfRywEZsIlCj3EU7/tEfLFdM6JWQYGX1WEuCJwAvxCaSyeC5hK5IJVBOsJrw5V9uHtjxRPOVIA3ADexRo9xZTehKZMctp4+1BIBEIVCj3FlN6Mqpmm26dyUqkDqZoMUyOYBEIVCjXGlAKy72Sa3KWbLvp2Mhr9HwVrNKltzy+udScMD7ZzBzXjSARCKZDK4GtGglUcHvKS81+nArex1AciOZDEnTXztYgxrZUqtKVsj3/MFQg73OzJOhHalXxgogebkaqD/88EMZMGCANGrUSDIyMuTtt98u9b5O9u+++25p2LChVK5cWXr37i1ff/21a+OF8wHNT5fC5/y+qzx8SUfZdzj0krjdjHD9ubrHPT9/h/nqdMB0O3sdQHpwdY+6sLBQTjvtNLnqqqtk0KBBZd5/8MEH5fHHH5cXX3xRWrRoIZMmTZK+ffvKhg0bpFIljvZLpf7aul+dmZHhWEZ4eewbeyF7HUDqczVQ9+vXzzxC0dn09OnT5a677pKBAwea1/76179KgwYNzMx7yJAh5TxalEdAs5oRHum6cPvGGrSve2mtjOreXHq3zY07M9uJsQJANJ7do96yZYsUFBSY5W4/3Xjv2rWrrFq1ytWxIXEBze6ZzrEssz+/cqsMfXa16Y6mQd2KUMvo8Y4VAJK6PEuDtNIZdCB97n8vlKKiIvMIzKyDu/wBLVp/bX9AG9KlqTz6/r9DXhftUAw7x1jqeHTmHS0zO9IyupXTr6inBpCSM+pYTZ061cy8/Y8mTZq4PaS05z/OUQWHrMCAtnhDgZnlhgrSVs9gtrMfbCUzO1y2uj/IK6vnTwNASs2oc3Nzzdddu3aZrG8/fd6xY8ew3zdx4kQZP358qRk1wdo9/o5dRT8Xy9jeJ8vLH28r1cTEf5yjCrWv7Deud56M6ZVXanYaqhuY3f3gwMzs4C5l0cqvdCT6vh4eQjtSAGkXqDXLW4P1kiVLSgKzBt2PPvpIrr/++rDfl52dbR5wR2Dw3LrncNnAXCPbBN3mOVVLAprSmXSkRievfLLdBOpoy9GT+reJuMxuZyZup/xKgzznRQNIuUB96NAh2bRpU6kEsvz8fKlTp440bdpUxo4dK/fdd5/k5eWVlGdpzfVFF13k5rARR+exXQeKZPr7X5tlYX9g0+QsOwExXFa3BufRcz+Ta3q0kL98uCVil7NgoWbilF8BkHTfo/7000/l9NNPNw+lS9b6Z21yom6//Xa58cYb5ZprrpEuXbqYwL5o0SJqqJO481iofWGrga5g/0+y8us9cseb6yJ2A1vw+U6ZcVnZfWO7mdmUXwGQdJ9R9+zZ09RLh6PdyqZMmWIe8C4rJVGRZshWA929734pPxQetXTv2lUrmr1j/RmapPbCyq22M7PtZqsDQCKkXNY3yp+dkqhA/pl0tHpkv2hBOtDKTd+br/qLwN0D2snTMWRmW81WJ2kMQFomkyF5xLpH659J+wNiqHrkWD25bLO8uXZHSctQfcSSma3fp8E8eO/dn61O+RWAROOYS8S97D175RazLG2Vf8lYl6YDA2WoZLQ6VbPkh8Lwh3RE+znKiXrmUKVgzKQBlEdsYkaNhGZ5i40l41CzXi3tGvdqfkzjC6x11vvGE1j1eym/AuAGAjViEq5EKppoS8bBAVFLt+IRqaEJACQDAjUSmuWtDU6GntG0VIMTOzPbaJnXVlHrDCBZEagRcT82p2q2WT/ec6ioJNBazfLWLmEju7eIe8k50sEX8TQ0AYBkQKBG1LafgXR2e2H7//ZhjyanerYjCVeRMq/1lwFNZKPWGUCqIlCnObsJYRoQ9Tzn8p7FRiqvyszM4KhJACmLQJ3GYkkI81lYdk7ULDZc5jW1zgBSGYE6Tdlt+xksUpB2YxYba0MTAPA6AnWairXtZzRuzmKpdQaQigjUacrpcqValbNkxrBO0q1lXWaxAOAgDuVIU06XK+376ZhkZmQQpAHAYQTqNGX1xCo7aCoCAM4jUKepSEc4xoqmIgDgPPao01jYsqaAtp/amazY55MbX/7MLG+HQlMRAEgcAnWas1rW9MDgU03NtaKpCACUH86jRlxdzHSfm6YiAGAP51EjIWgqAgDlj0CdZIdmuB0caSoCAOWLQO1xLDcDQHqjPCsJDs0IbvWpJ1jp6/q+1Rn5qs17ZX7+DvNVnwMAkgMz6iQ8NMP/2h1vrpPqlbIitu1kRg4AyY0ZdRIfmqF1zcOe+0jOnrY05OzaqRk5AMA9BGqPstOOM1TgtTIj1/dZBgcAbyNQe5SddpyhAm+0Gblepe/rdQAA7yJQp8ihGcGB1+qMnIM0AMDbPB+o//jHP0pGRkapR+vWrSXVxXpohj/wWp2Rc5AGAHib5wO1ateunezcubPksWLFCkmnQzP0wAur/IE32oxcX9f3OUgDALwtKQL1CSecILm5uSWPnJwcSRcarFdM6CVzRnWVWpWzwl4XHHgjzcg5SAMAkkdSBOqvv/5aGjVqJC1btpRhw4bJtm3bwl5bVFRkmp0HPpKdBtPueTnmBKsMG4E33Ixcn+vr1FEDgPd5/vSshQsXyqFDh+SUU04xy96TJ0+WHTt2yPr166V69eoh97T1mmCpcnpWLA1MvNQrHAAgtk7P8nygDrZv3z5p1qyZPPLIIzJq1KiQM2p9BH4YTZo0SZlArQi8AJDcUvqYy1q1asnJJ58smzZtCvl+dna2eaQyTrACgPSRFHvUgXQZfPPmzdKwYerur3KIBgAgaWbUt956qwwYMMAsd3/33Xdyzz33SIUKFWTo0KGSijhEAwCQVDPq//znPyYoazLZJZdcInXr1pXVq1dLvXr1JNVwiAYAIOlm1K+88oqkg2iHaGiqmL5/QdtcEscAII14fkadLvvOHKIBAEjKGXW67DtziAYAIBRm1B7Zd+YQDQBAKARqF/edA8+QdvIQDcq7ACB1sPSdQHb2nbWBiS6F6yxbg7IvxkM0KO8CgNTCjDqBrO4nr9z0vZn1xnuIBuVdAJB6mFEnkNX95CeXbZY31+4omfVqCZbdXt6UdwFAamJGnUDR9p3DzXr9vbwHdjzRfLUSWCnvAoDURKBOIA2wOktW0UJtcHKZXZR3AUBqIlAnWLh9Z6dnvZR3AUBqIlBbFE/JkwbrFRN6yZjzWiVk1qtjKS72Sa3KWWGvsVPeBQDwDpLJLHCi5EmXwbu3ypEnl4U+RzvWWW+osQWzU94FAPAWZtRROFny5GRTk0hjC2a1vAsA4D0Eaoc6i8WbXGZ31htpbH66FD7n913NsjtBGgCSE4G6nEue4m1qYnVsat9PxyQzI4PlbgBIYuxRu1DyFKqpSedmtWXNtz+aZDUrTU4oxwKA9ECgdqjkSZei7XQT8zc18e81n/vQMlvJapRjAUB6IFBbSP7SxLFQe8EZvyxZ/1h4VM6etjSmrHB/Qljw/f3JauGWw62OjXIsAEhu7FHHmfz1m9Mayui5sWWFx5OsFk9iGsdgAkDyIFDHkfw147LTZcHnO2POCo83WS2WxDT9xUFn/0OfXS03v5JvvupzTtYCAG9i6duCcCda2T1vOhEJYXZO24p1mR0A4B4CtUWByV+xnDcdKng6lRAWamzBOAYTAJITS9/ldN50qOVlpzuVRcIxmACQnAjULpw3nYhOZdFQdw0AyYlA7fJ50051KouGumsASE7sUcfJH2ijnWAVKbnMTkJYrKi7BoDkxIzaAU6cN+1PCBvY8UTz1emErvJcZgcApFmgnjFjhjRv3lwqVaokXbt2lY8//li8xn/etJeXl8trmR0AkEZL36+++qqMHz9enn76aROkp0+fLn379pWNGzdK/fr1xUuSYXm5PJbZAQDOyfD5fJ7uH6nBuUuXLvLkk0+a58XFxdKkSRO58cYb5Y477oj6/QcOHJCaNWvK/v37pUaNGgkfr7+piAr8YP1hkJkrAOCAjdjk6aXvo0ePypo1a6R3794lr2VmZprnq1atcnVs4fpls7wMAEibpe89e/bI8ePHpUGDBqVe1+dfffVVyO8pKioyj8DfWhIxaw7O8g48LYvlZQCAUzw9o47F1KlTzXKC/6HL5IlY2o52Wlais7gBAOnB04E6JydHKlSoILt27Sr1uj7Pzc0N+T0TJ040a/7+x/bt2x0bTzzHUgIAkHKBumLFitK5c2dZsmRJyWuaTKbPzzzzzJDfk52dbTbmAx9OoV82AKC8eXqPWmlp1ogRI+RXv/qVnHHGGaY8q7CwUK688spyHwv9sgEA5c3zgfrSSy+V77//Xu6++24pKCiQjh07yqJFi8okmJUH+mUDAMqb5+uo4+VkHbXuPetxldEammg7UZLHAAApX0ftNfTLBgCUNwK1TTQ0AQCUJ8/vUXsRDU0AAOWFQB0jf0MTAAASiaVvAAA8jEANAICHEagBAPAwAjUAAB5GoAYAwMMI1AAAeFjKl2f5O6RquzYAALzAH5OsdPFO+UB98OBB87VJkyZuDwUAgDIxSnt+p/WhHHp+9XfffSfVq1eXjIyMuH8D0oC/fft2R8+5TmV8ZvbxmdnHZ2Yfn5m7n5mGXg3SjRo1kszMzPSeUesH0LhxY0fvqf+A+BfbHj4z+/jM7OMzs4/PzL3PLNpM2o9kMgAAPIxADQCAhxGobcjOzpZ77rnHfIU1fGb28ZnZx2dmH59Z8nxmKZ9MBgBAMmNGDQCAhxGoAQDwMAI1AAAeRqC2YcaMGdK8eXOpVKmSdO3aVT7++GO3h+RZU6dOlS5duphGM/Xr15eLLrpINm7c6PawksYDDzxgGvSMHTvW7aF43o4dO2T48OFSt25dqVy5spx66qny6aefuj0sTzp+/LhMmjRJWrRoYT6rk046Se69915LbSzTyYcffigDBgwwzUj0v8O333671Pv6ed19993SsGFD8zn27t1bvv7664SNh0Bt0auvvirjx483GX9r166V0047Tfr27Su7d+92e2ietHz5chk9erSsXr1aFi9eLMeOHZM+ffpIYWGh20PzvE8++USeeeYZ6dChg9tD8bwff/xRunfvLllZWbJw4ULZsGGDPPzww1K7dm23h+ZJ06ZNk5kzZ8qTTz4pX375pXn+4IMPyhNPPOH20DylsLDQ/D9eJ2eh6Gf2+OOPy9NPPy0fffSRVK1a1cSDI0eOJGZAmvWN6M444wzf6NGjS54fP37c16hRI9/UqVNdHVey2L17t/7K7lu+fLnbQ/G0gwcP+vLy8nyLFy/2nXvuub6bb77Z7SF52oQJE3xnn32228NIGv379/ddddVVpV4bNGiQb9iwYa6NyetExDdv3ryS58XFxb7c3FzfQw89VPLavn37fNnZ2b6XX345IWNgRm3B0aNHZc2aNWZ5I7A1qT5ftWqVq2NLFvv37zdf69Sp4/ZQPE1XIfr371/q3zWEt2DBAvnVr34lv/vd78wWy+mnny7PPvus28PyrLPOOkuWLFki//73v83zzz//XFasWCH9+vVze2hJY8uWLVJQUFDqv1FtBarboYmKBynf69sJe/bsMXs7DRo0KPW6Pv/qq69cG1cyHYyie626RNm+fXu3h+NZr7zyitlW0aVvWPPNN9+YpVzdlrrzzjvNZ3fTTTdJxYoVZcSIEW4Pz3PuuOMOc7BE69atpUKFCub/a/fff78MGzbM7aEljYKCAvM1VDzwv+c0AjXKZZa4fv1685s7QtPTeG6++Wazn6/JirD+S6DOqP/0pz+Z5zqj1n/XdO+QQF3Wa6+9JnPmzJG5c+dKu3btJD8/3/wSrUlTfF7exdK3BTk5Oea3z127dpV6XZ/n5ua6Nq5kMGbMGHnnnXdk2bJljp9ilkp0a0UTEzt16iQnnHCCeWhCnias6J915oOyNOu2bdu2pV5r06aNbNu2zbUxedltt91mZtVDhgwx2fGXX365jBs3zlRpwBr///PLMx4QqC3QZbTOnTubvZ3A3+T1+Zlnnunq2LxKczA0SM+bN0+WLl1qykEQ3vnnny/r1q0zMxz/Q2eKuiSpf9ZfFFGWbqcEl/3p/muzZs1cG5OXHT58uMzZx/rvlv7/DNbo/8s0IAfGA91O0OzvRMUDlr4t0j0wXRrS/3meccYZMn36dJPCf+WVV7o9NM8ud+vy2vz5800ttX/vRpMutO4QpelnFLx/ryUfWhvMvn54OhvUBCld+r7kkktMb4O//OUv5oGytDZY96SbNm1qlr4/++wzeeSRR+Sqq65ye2iecujQIdm0aVOpBDL9hVmTYfWz0+2C++67T/Ly8kzg1tp03T7QfhEJkZBc8hT1xBNP+Jo2beqrWLGiKddavXq120PyLP1XK9Rj1qxZbg8taVCeZc3f//53X/v27U15TOvWrX1/+ctf3B6SZx04cMD8O6X/H6tUqZKvZcuWvj/84Q++oqIit4fmKcuWLQv5/68RI0aUlGhNmjTJ16BBA/Pv3fnnn+/buHFjwsbD6VkAAHgYe9QAAHgYgRoAAA8jUAMA4GEEagAAPIxADQCAhxGoAQDwMAI1AAAeRqAGAMDDCNQALGnevLlkZGSYx759+8JeN3v27JLrtNUigPgQqIE017NnT8sBdcqUKbJz507Tsz2cSy+91FzDgTWAMziUA4Ctw0OiHeWnh67oQ0+dAxA/ZtRAGhs5cqQ59/qxxx4rWa7eunWr28MCEIAZNZDGNEDr+c16lKYua6t69eq5PSwAAQjUQBrTvWZdoq5SpUrUJW0A7mDpG0DMqlWrVvK47rrr3B4OkJKYUQOIWX5+fsmfa9So4epYgFRFoAbSnC59Hz9+PKbvbdWqlePjAVAaS99AmtNGJh999JHJ9t6zZ48UFxe7PSQAAQjUQJq79dZbpUKFCtK2bVuT8b1t2za3hwQgAEvfQJo7+eSTZdWqVW4PA0AYzKgBWDZhwgST4b1///6w18yZM8dc869//atcxwakqgyfz+dzexAAvO/bb7+VY8eOmT+3bNlSMjND/55/8OBB2bVrl/lzrVq1JCcnp1zHCaQaAjUAAB7G0jcAAB5GoAYAwMMI1AAAeBiBGgAADyNQAwDgYQRqAAA8jEANAICHEagBAPAwAjUAAOJd/w/unFILfC8KhAAAAABJRU5ErkJggg==",
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",
       "text/plain": [
        "<Figure size 500x400 with 1 Axes>"
       ]
@@ -560,7 +661,8 @@
    ],
    "source": [
     "# Parameter for the artificial data generation\n",
-    "slope = np.random.uniform(2.0, 4.0) \n",
+    "rng = np.random.default_rng(seed=1)  # for reproducibility\n",
+    "slope = rng.uniform(2,4)\n",
     "intercept = 1.0\n",
     "num_points = 100\n",
     "noise_level = 1.7\n",
@@ -587,12 +689,10 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "## Initialize a simulation ✨\n",
     "\n",
-    "\n",
-    "## Initialize a simulation\n",
-    "\n",
-    "In pymob a Simulation object is initialized by calling the {class}`pymob.simulation.SimulationBase` class from the simulation module.  \n",
-    "We will chose a linear regression model, since it seems to be a good approximation to the data."
+    "In pymob, a **simulation object** is initialized by creating an instance of the {class}`pymob.simulation.SimulationBase` class from the simulation module.  \n",
+    "We will choose a linear regression model, as it provides a good approximation of the data: $ y = a + b*x $"
    ]
   },
   {
@@ -601,39 +701,53 @@
    "source": [
     "```{admonition} x-dimension\n",
     ":class: note\n",
-    "The x_dimension of our simulation can have any name, for expample t as often used for time series data.\n",
-    "You can specified it via `sim.config.simulation.x_dimension`.\n",
+    "The x_dimension of our simulation can have any name, for example t as often used for time series data.\n",
+    "You can specify it via `sim.config.simulation.x_dimension`.\n",
     "```"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 86,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "MinMaxScaler(variable=y, min=-0.39481712290701676, max=35.554126963859574)\n"
+      "MinMaxScaler(variable=y, min=-1.2529313454358775, max=32.77431830696904)\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "C:\\Users\\mgrho\\pymob\\pymob\\simulation.py:303: UserWarning: `sim.config.data_structure.y = Datavariable(dimensions=['t'] min=-0.39481712290701676 max=35.554126963859574 observed=True dimensions_evaluator=None)` has been assumed from `sim.observations`. If the order of the dimensions should be different, specify `sim.config.data_structure.y = DataVariable(dimensions=[...], ...)` manually.\n",
+      "C:\\Pymob\\pymob\\pymob\\simulation.py:307: UserWarning: `sim.config.data_structure.y = Datavariable(dimensions=['t'] min=-1.2529313454358775 max=32.77431830696904 observed=True dimensions_evaluator=None)` has been assumed from `sim.observations`. If the order of the dimensions should be different, specify `sim.config.data_structure.y = DataVariable(dimensions=[...], ...)` manually.\n",
       "  warnings.warn(\n"
      ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Datastructure(y=DataVariable(dimensions=['t'], min=-1.2529313454358775, max=32.77431830696904, observed=True, dimensions_evaluator=None))"
+      ]
+     },
+     "execution_count": 86,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
     "# Initialize the Simulation object\n",
     "sim = SimulationBase()\n",
     "\n",
+    "# configurate the case study\n",
+    "sim.config.case_study.name = \"superquickstart\"\n",
+    "sim.config.case_study.scenario = \"linreg\"\n",
+    "\n",
     "# Define the linear regression model\n",
     "def linreg(x, a, b):\n",
-    "    return a + x * b\n",
+    "    return a + b * x\n",
     "\n",
     "# Add the model to the simulation\n",
     "sim.model = linreg\n",
@@ -642,7 +756,10 @@
     "sim.observations = data_obs\n",
     "\n",
     "# Defining a solver\n",
-    "sim.solver = solve_analytic_1d"
+    "sim.solver = solve_analytic_1d\n",
+    "\n",
+    "# Take a look at the layut of the data\n",
+    "sim.config.data_structure"
    ]
   },
   {
@@ -661,29 +778,60 @@
    "metadata": {},
    "source": [
     "\n",
-    "## Running the model 🏃\n",
+    "## Parameterizing and running the model 🏃\n",
     "\n",
-    "Next, we define the model parameters *a* and *b*.  \n",
-    "The parameter *a* is set as fixed (`free = False`), meaning its value is known and will not be estimated during optimization.  \n",
-    "The parameter *b* is marked as free (`free = True`), allowing it to be optimized to fit our data. As an initial guess, we assume b = 3.   \n",
+    "Next, we define the **model parameters** $a$ and $b$.  \n",
+    "Parameter $a$ is set as fixed (`free = False`), meaning its value is known and will not be estimated during optimization.  \n",
+    "Parameter $b$ is marked as free (`free = True`), allowing it to be optimized to fit the data. As an initial guess, we assume $b = 3$.   "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'a': 1.0, 'b': 3.0}"
+      ]
+     },
+     "execution_count": 87,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Parameterizing the model\n",
+    "sim.config.model_parameters.a = Param(value=1.0, free=False)\n",
+    "sim.config.model_parameters.b = Param(value=3.0, free=True)\n",
+    "# this makes sure the model parameters are available to the model.\n",
+    "sim.model_parameters[\"parameters\"] = sim.config.model_parameters.value_dict\n",
     "\n",
-    "Our model is now prepared with a parameter set.  \n",
-    "In order to intialize the *Evaluator* class, we need to execute `sim.dispatch_constructor()`.   \n",
-    "This step is very important and needs to be done everytime when we made changes in our model.  \n",
+    "sim.model_parameters[\"parameters\"] "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Our model is now prepared with a defined parameter set.  \n",
+    "To initialize the **Evaluator**, we call {meth}`pymob.simulation.SimulationBase.dispatch_constructor()`.   \n",
+    "This step is essential and must be executed every time changes are made to the model. \n",
     "\n",
-    "The returned dataset (`evaluator.results`) has the exact same shape as our observation data."
+    "The returned dataset (`evaluator.results`) has the exact same shape as the observation data."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 88,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "C:\\Users\\mgrho\\pymob\\pymob\\simulation.py:552: UserWarning: The number of ODE states was not specified in the config file [simulation] > 'n_ode_states = <n>'. Extracted the return arguments ['a+x*b'] from the source code. Setting 'n_ode_states=1.\n",
+      "C:\\Pymob\\pymob\\pymob\\simulation.py:567: UserWarning: The number of ODE states was not specified in the config file [simulation] > 'n_ode_states = <n>'. Extracted the return arguments ['a+b*x'] from the source code. Setting 'n_ode_states=1.\n",
       "  warnings.warn(\n"
      ]
     },
@@ -710,27 +858,76 @@
        " */\n",
        "\n",
        ":root {\n",
-       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
-       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
-       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
-       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
-       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
-       "  --xr-background-color: var(--jp-layout-color0, white);\n",
-       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
-       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
-       "}\n",
-       "\n",
-       "html[theme=dark],\n",
-       "body[data-theme=dark],\n",
+       "  --xr-font-color0: var(\n",
+       "    --jp-content-font-color0,\n",
+       "    var(--pst-color-text-base rgba(0, 0, 0, 1))\n",
+       "  );\n",
+       "  --xr-font-color2: var(\n",
+       "    --jp-content-font-color2,\n",
+       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.54))\n",
+       "  );\n",
+       "  --xr-font-color3: var(\n",
+       "    --jp-content-font-color3,\n",
+       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.38))\n",
+       "  );\n",
+       "  --xr-border-color: var(\n",
+       "    --jp-border-color2,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 10))\n",
+       "  );\n",
+       "  --xr-disabled-color: var(\n",
+       "    --jp-layout-color3,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 40))\n",
+       "  );\n",
+       "  --xr-background-color: var(\n",
+       "    --jp-layout-color0,\n",
+       "    var(--pst-color-on-background, white)\n",
+       "  );\n",
+       "  --xr-background-color-row-even: var(\n",
+       "    --jp-layout-color1,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 5))\n",
+       "  );\n",
+       "  --xr-background-color-row-odd: var(\n",
+       "    --jp-layout-color2,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 15))\n",
+       "  );\n",
+       "}\n",
+       "\n",
+       "html[theme=\"dark\"],\n",
+       "html[data-theme=\"dark\"],\n",
+       "body[data-theme=\"dark\"],\n",
        "body.vscode-dark {\n",
-       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
-       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
-       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
-       "  --xr-border-color: #1F1F1F;\n",
-       "  --xr-disabled-color: #515151;\n",
-       "  --xr-background-color: #111111;\n",
-       "  --xr-background-color-row-even: #111111;\n",
-       "  --xr-background-color-row-odd: #313131;\n",
+       "  --xr-font-color0: var(\n",
+       "    --jp-content-font-color0,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 1))\n",
+       "  );\n",
+       "  --xr-font-color2: var(\n",
+       "    --jp-content-font-color2,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.54))\n",
+       "  );\n",
+       "  --xr-font-color3: var(\n",
+       "    --jp-content-font-color3,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.38))\n",
+       "  );\n",
+       "  --xr-border-color: var(\n",
+       "    --jp-border-color2,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 10))\n",
+       "  );\n",
+       "  --xr-disabled-color: var(\n",
+       "    --jp-layout-color3,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 40))\n",
+       "  );\n",
+       "  --xr-background-color: var(\n",
+       "    --jp-layout-color0,\n",
+       "    var(--pst-color-on-background, #111111)\n",
+       "  );\n",
+       "  --xr-background-color-row-even: var(\n",
+       "    --jp-layout-color1,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 5))\n",
+       "  );\n",
+       "  --xr-background-color-row-odd: var(\n",
+       "    --jp-layout-color2,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 15))\n",
+       "  );\n",
        "}\n",
        "\n",
        ".xr-wrap {\n",
@@ -771,7 +968,7 @@
        ".xr-sections {\n",
        "  padding-left: 0 !important;\n",
        "  display: grid;\n",
-       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
        "}\n",
        "\n",
        ".xr-section-item {\n",
@@ -779,11 +976,14 @@
        "}\n",
        "\n",
        ".xr-section-item input {\n",
-       "  display: none;\n",
+       "  display: inline-block;\n",
+       "  opacity: 0;\n",
+       "  height: 0;\n",
        "}\n",
        "\n",
        ".xr-section-item input + label {\n",
        "  color: var(--xr-disabled-color);\n",
+       "  border: 2px solid transparent !important;\n",
        "}\n",
        "\n",
        ".xr-section-item input:enabled + label {\n",
@@ -791,6 +991,10 @@
        "  color: var(--xr-font-color2);\n",
        "}\n",
        "\n",
+       ".xr-section-item input:focus + label {\n",
+       "  border: 2px solid var(--xr-font-color0) !important;\n",
+       "}\n",
+       "\n",
        ".xr-section-item input:enabled + label:hover {\n",
        "  color: var(--xr-font-color0);\n",
        "}\n",
@@ -812,7 +1016,7 @@
        "\n",
        ".xr-section-summary-in + label:before {\n",
        "  display: inline-block;\n",
-       "  content: '►';\n",
+       "  content: \"►\";\n",
        "  font-size: 11px;\n",
        "  width: 15px;\n",
        "  text-align: center;\n",
@@ -823,7 +1027,7 @@
        "}\n",
        "\n",
        ".xr-section-summary-in:checked + label:before {\n",
-       "  content: '▼';\n",
+       "  content: \"▼\";\n",
        "}\n",
        "\n",
        ".xr-section-summary-in:checked + label > span {\n",
@@ -895,15 +1099,15 @@
        "}\n",
        "\n",
        ".xr-dim-list:before {\n",
-       "  content: '(';\n",
+       "  content: \"(\";\n",
        "}\n",
        "\n",
        ".xr-dim-list:after {\n",
-       "  content: ')';\n",
+       "  content: \")\";\n",
        "}\n",
        "\n",
        ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: ',';\n",
+       "  content: \",\";\n",
        "  padding-right: 5px;\n",
        "}\n",
        "\n",
@@ -920,7 +1124,9 @@
        ".xr-var-item label,\n",
        ".xr-var-item > .xr-var-name span {\n",
        "  background-color: var(--xr-background-color-row-even);\n",
+       "  border-color: var(--xr-background-color-row-odd);\n",
        "  margin-bottom: 0;\n",
+       "  padding-top: 2px;\n",
        "}\n",
        "\n",
        ".xr-var-item > .xr-var-name:hover span {\n",
@@ -931,6 +1137,7 @@
        ".xr-var-list > li:nth-child(odd) > label,\n",
        ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
        "  background-color: var(--xr-background-color-row-odd);\n",
+       "  border-color: var(--xr-background-color-row-even);\n",
        "}\n",
        "\n",
        ".xr-var-name {\n",
@@ -980,8 +1187,15 @@
        ".xr-var-data,\n",
        ".xr-index-data {\n",
        "  display: none;\n",
-       "  background-color: var(--xr-background-color) !important;\n",
-       "  padding-bottom: 5px !important;\n",
+       "  border-top: 2px dotted var(--xr-background-color);\n",
+       "  padding-bottom: 20px !important;\n",
+       "  padding-top: 10px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in + label,\n",
+       ".xr-var-data-in + label,\n",
+       ".xr-index-data-in + label {\n",
+       "  padding: 0 1px;\n",
        "}\n",
        "\n",
        ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
@@ -994,6 +1208,12 @@
        "  float: right;\n",
        "}\n",
        "\n",
+       ".xr-var-data > pre,\n",
+       ".xr-index-data > pre,\n",
+       ".xr-var-data > table > tbody > tr {\n",
+       "  background-color: transparent !important;\n",
+       "}\n",
+       "\n",
        ".xr-var-name span,\n",
        ".xr-var-data,\n",
        ".xr-index-name div,\n",
@@ -1053,12 +1273,20 @@
        "  stroke: currentColor;\n",
        "  fill: currentColor;\n",
        "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
+       "\n",
+       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
+       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
+       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
+       "  color: var(--xr-font-color0);\n",
+       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
+       "  stroke-width: 0.8px;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 2kB\n",
        "Dimensions:  (t: 100)\n",
        "Coordinates:\n",
-       "  * t        (t) float64 0.0 0.101 0.202 0.303 0.404 ... 9.697 9.798 9.899 10.0\n",
+       "  * t        (t) float64 800B 0.0 0.101 0.202 0.303 ... 9.697 9.798 9.899 10.0\n",
        "Data variables:\n",
-       "    y        (t) float64 1.0 1.303 1.606 1.909 2.212 ... 30.09 30.39 30.7 31.0</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-482e4360-65c5-455a-844a-801c2a7d65ff' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-482e4360-65c5-455a-844a-801c2a7d65ff' 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'>t</span>: 100</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-23abf1fe-49d0-4b6f-b23d-312f68845896' class='xr-section-summary-in' type='checkbox'  checked><label for='section-23abf1fe-49d0-4b6f-b23d-312f68845896' class='xr-section-summary' >Coordinates: <span>(1)</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'>t</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.101 0.202 ... 9.899 10.0</div><input id='attrs-5bf72154-86cc-4516-9327-466025d82bad' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5bf72154-86cc-4516-9327-466025d82bad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bed3c049-c054-40c3-adb7-6d201f215605' class='xr-var-data-in' type='checkbox'><label for='data-bed3c049-c054-40c3-adb7-6d201f215605' 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.      ,  0.10101 ,  0.20202 ,  0.30303 ,  0.40404 ,  0.505051,\n",
+       "    y        (t) float64 800B 1.0 1.303 1.606 1.909 ... 30.09 30.39 30.7 31.0</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-bf80eaa8-9884-4187-be3c-beae3e76c5cd' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-bf80eaa8-9884-4187-be3c-beae3e76c5cd' 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'>t</span>: 100</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-8a9f7f00-6941-475e-86d3-da832a6b23b7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8a9f7f00-6941-475e-86d3-da832a6b23b7' class='xr-section-summary' >Coordinates: <span>(1)</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'>t</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.101 0.202 ... 9.899 10.0</div><input id='attrs-01b2ced7-51bd-42a0-989b-23685df30390' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-01b2ced7-51bd-42a0-989b-23685df30390' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-202ca2de-722e-427c-9aab-8db7f9a71528' class='xr-var-data-in' type='checkbox'><label for='data-202ca2de-722e-427c-9aab-8db7f9a71528' 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.      ,  0.10101 ,  0.20202 ,  0.30303 ,  0.40404 ,  0.505051,\n",
        "        0.606061,  0.707071,  0.808081,  0.909091,  1.010101,  1.111111,\n",
        "        1.212121,  1.313131,  1.414141,  1.515152,  1.616162,  1.717172,\n",
        "        1.818182,  1.919192,  2.020202,  2.121212,  2.222222,  2.323232,\n",
@@ -1074,7 +1302,7 @@
        "        7.878788,  7.979798,  8.080808,  8.181818,  8.282828,  8.383838,\n",
        "        8.484848,  8.585859,  8.686869,  8.787879,  8.888889,  8.989899,\n",
        "        9.090909,  9.191919,  9.292929,  9.393939,  9.494949,  9.59596 ,\n",
-       "        9.69697 ,  9.79798 ,  9.89899 , 10.      ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b2eef1e6-5191-4a06-a692-78267f88e56b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b2eef1e6-5191-4a06-a692-78267f88e56b' class='xr-section-summary' >Data variables: <span>(1)</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>y</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.0 1.303 1.606 ... 30.39 30.7 31.0</div><input id='attrs-9ca67db2-09ec-4230-97b8-f3758d48a767' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9ca67db2-09ec-4230-97b8-f3758d48a767' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c272703a-e29f-4edb-83cb-ff5d5451a14f' class='xr-var-data-in' type='checkbox'><label for='data-c272703a-e29f-4edb-83cb-ff5d5451a14f' 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([ 1.        ,  1.3030303 ,  1.60606061,  1.90909091,  2.21212121,\n",
+       "        9.69697 ,  9.79798 ,  9.89899 , 10.      ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-bf16fc3b-2d10-481c-8d0b-20669c196b3b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-bf16fc3b-2d10-481c-8d0b-20669c196b3b' class='xr-section-summary' >Data variables: <span>(1)</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>y</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.0 1.303 1.606 ... 30.39 30.7 31.0</div><input id='attrs-6334fbe2-584c-43f9-bb4a-d9330aaf2fbd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6334fbe2-584c-43f9-bb4a-d9330aaf2fbd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0dc278ee-7131-4f84-97ca-bd6b2242f94c' class='xr-var-data-in' type='checkbox'><label for='data-0dc278ee-7131-4f84-97ca-bd6b2242f94c' 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([ 1.        ,  1.3030303 ,  1.60606061,  1.90909091,  2.21212121,\n",
        "        2.51515152,  2.81818182,  3.12121212,  3.42424242,  3.72727273,\n",
        "        4.03030303,  4.33333333,  4.63636364,  4.93939394,  5.24242424,\n",
        "        5.54545455,  5.84848485,  6.15151515,  6.45454545,  6.75757576,\n",
@@ -1093,7 +1321,7 @@
        "       25.24242424, 25.54545455, 25.84848485, 26.15151515, 26.45454545,\n",
        "       26.75757576, 27.06060606, 27.36363636, 27.66666667, 27.96969697,\n",
        "       28.27272727, 28.57575758, 28.87878788, 29.18181818, 29.48484848,\n",
-       "       29.78787879, 30.09090909, 30.39393939, 30.6969697 , 31.        ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-97883329-2daa-484d-a4a1-1c83b93ea06f' class='xr-section-summary-in' type='checkbox'  ><label for='section-97883329-2daa-484d-a4a1-1c83b93ea06f' class='xr-section-summary' >Indexes: <span>(1)</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>t</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e038dafd-9629-4d24-bef2-24e2ae414a80' class='xr-index-data-in' type='checkbox'/><label for='index-e038dafd-9629-4d24-bef2-24e2ae414a80' 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([                0.0, 0.10101010101010101, 0.20202020202020202,\n",
+       "       29.78787879, 30.09090909, 30.39393939, 30.6969697 , 31.        ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e789d90c-b446-4d5c-8ab1-417cdaa87542' class='xr-section-summary-in' type='checkbox'  ><label for='section-e789d90c-b446-4d5c-8ab1-417cdaa87542' class='xr-section-summary' >Indexes: <span>(1)</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>t</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-b435329b-68e7-4db5-b4f8-37c8c4966a05' class='xr-index-data-in' type='checkbox'/><label for='index-b435329b-68e7-4db5-b4f8-37c8c4966a05' 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([                0.0, 0.10101010101010101, 0.20202020202020202,\n",
        "       0.30303030303030304, 0.40404040404040403,  0.5050505050505051,\n",
        "        0.6060606060606061,  0.7070707070707071,  0.8080808080808081,\n",
        "        0.9090909090909091,  1.0101010101010102,  1.1111111111111112,\n",
@@ -1127,29 +1355,23 @@
        "         9.393939393939394,   9.494949494949495,   9.595959595959595,\n",
        "         9.696969696969697,   9.797979797979798,     9.8989898989899,\n",
        "                      10.0],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;t&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-724365f3-1619-4348-a8dc-f9919d5316bf' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-724365f3-1619-4348-a8dc-f9919d5316bf' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+       "      dtype=&#x27;float64&#x27;, name=&#x27;t&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7c36564d-d456-4bb2-9623-ea54a6fa2551' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-7c36564d-d456-4bb2-9623-ea54a6fa2551' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
-       "<xarray.Dataset>\n",
+       "<xarray.Dataset> Size: 2kB\n",
        "Dimensions:  (t: 100)\n",
        "Coordinates:\n",
-       "  * t        (t) float64 0.0 0.101 0.202 0.303 0.404 ... 9.697 9.798 9.899 10.0\n",
+       "  * t        (t) float64 800B 0.0 0.101 0.202 0.303 ... 9.697 9.798 9.899 10.0\n",
        "Data variables:\n",
-       "    y        (t) float64 1.0 1.303 1.606 1.909 2.212 ... 30.09 30.39 30.7 31.0"
+       "    y        (t) float64 800B 1.0 1.303 1.606 1.909 ... 30.09 30.39 30.7 31.0"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 88,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "# Parameterizing the model\n",
-    "sim.config.model_parameters.a = Param(value=1, free=False)\n",
-    "sim.config.model_parameters.b = Param(value=3, free=True)\n",
-    "# this makes sure the model parameters are available to the model.\n",
-    "sim.model_parameters[\"parameters\"] = sim.config.model_parameters.value_dict\n",
-    "\n",
     "# put everything in place for running the simulation\n",
     "sim.dispatch_constructor()\n",
     "\n",
@@ -1163,30 +1385,42 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's have a look at the results.  \n",
-    "You can vary the parameter *b* in the previous step to investigate it's influence on the model fit.   \n",
+    "```{admonition} What does the dispatch constructor do?\n",
+    ":class: hint\n",
+    "Behind the scenes, the dispatch constructor assembles a lightweight Evaluator object from the Simulation object, that takes the least necessary amount of information, runs it through some dimension checks, and also connects it to the specified solver and initializes it. The purpose of the dispatch constructor is manyfold:\n",
+    "By executing the entire overhead of a model evaluation and packing it into a new Evaluator instance sim.dispatch_constructor() to make single model evaluations as fast as possible and allow parallel evaluations, because each evaluator created by sim.dispatch() is it's a fully independent model instance with a separate set of parameters that can be solved.\n",
+    "Evaluators store the raw output from a simulation and can generate an xarray object from it that corresponds to the data-structure of the observations with the evaluator.results property. This automatically aligns simulations results with observations, for simple computation of loss functions.\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's take a look at the **results**.  \n",
     "\n",
-    "In the [beginner guide](), you can try out the *manual parameter estimation*, which is provided by Pymob."
+    "You can vary the parameter $b$ in the previous step to investigate its influence on the model fit.  \n",
+    "In the [Introduction](https://pymob.readthedocs.io/en/stable/user_guide/introduction.html), you can try out the *manual parameter estimation*, which is a feature provided by Pymob.  "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 89,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x23e228b6610>"
+       "<matplotlib.legend.Legend at 0x24c375963d0>"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 89,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 500x400 with 1 Axes>"
       ]
@@ -1203,25 +1437,24 @@
     "ax.legend()"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": []
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Estimating parameters \n",
+    "## Estimating parameters and uncertainty with MCMC 🤔\n",
+    "Of course this example is very simple. In fact, we could optimize the parameters perfectly by hand.   \n",
+    "But just for fun, let's use *Markov Chain Monte Carlo (MCMC)* to estimate the parameters, their uncertainty and the uncertainty in the data.   \n",
+    "We’ll run the parameter estimation with our **inferer**, using the NumPyro backend with a NUTS kernel. This completes the job in a few seconds.\n",
     "\n",
-    "We are almost set infer the parameters of the model. We add another parameter to also estimate the error of the parameters, We use a lognormal distribution for it. We also specify an error model for the distribution. This will be \n",
+    "We are almost ready to infer the model parameters. To also estimate the uncertainty of the parameters, we add another parameter representing the error and assume that it follows a lognormal distribution.   \n",
+    "Additionally, we specify an error model for the data distribution. This will be: $$y_{obs} \\sim Normal (y, \\sigma_y)$$  \n",
     "\n",
-    "$$y_{obs} \\sim Normal (y, \\sigma_y)$$"
+    "Since $\\sigma_y$ is not a fixed parameter, it doesn't need to be passed to the simulation class."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 90,
    "metadata": {},
    "outputs": [
     {
@@ -1229,21 +1462,7 @@
      "output_type": "stream",
      "text": [
       "Jax 64 bit mode: False\n",
-      "Absolute tolerance: 1e-07\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\mgrho\\pymob\\pymob\\inference\\numpyro_backend.py:552: UserWarning: Model is not rendered, because the graphviz executable is not found. Try search for 'graphviz executables not found' and the used OS. This should be an easy fix :-)\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "Absolute tolerance: 1e-07\n",
       "Trace Shapes:      \n",
       " Param Sites:      \n",
       "Sample Sites:      \n",
@@ -1259,7 +1478,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "sample: 100%|██████████| 3000/3000 [00:04<00:00, 639.77it/s, 3 steps of size 8.64e-01. acc. prob=0.92] \n"
+      "sample: 100%|██████████| 3000/3000 [00:01<00:00, 1963.23it/s, 7 steps of size 8.27e-01. acc. prob=0.92]\n"
      ]
     },
     {
@@ -1268,15 +1487,15 @@
      "text": [
       "\n",
       "                mean       std    median      5.0%     95.0%     n_eff     r_hat\n",
-      "         b      3.30      0.03      3.30      3.26      3.35   1785.61      1.00\n",
-      "   sigma_y      1.69      0.12      1.68      1.50      1.90   1189.45      1.00\n",
+      "         b      2.98      0.03      2.98      2.92      3.03   1611.92      1.00\n",
+      "   sigma_y      1.83      0.13      1.82      1.61      2.04   1703.02      1.00\n",
       "\n",
       "Number of divergences: 0\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 400x300 with 1 Axes>"
       ]
@@ -1288,8 +1507,6 @@
    "source": [
     "sim.config.model_parameters.sigma_y = Param(free=True , prior=\"lognorm(scale=1,s=1)\", min=0, max=1)\n",
     "sim.config.model_parameters.b.prior = \"lognorm(scale=1,s=1)\"\n",
-    "sim.config.model_parameters.b.min = -5\n",
-    "sim.config.model_parameters.b.max = 5\n",
     "\n",
     "sim.config.error_model.y = \"normal(loc=y,scale=sigma_y)\"\n",
     "\n",
@@ -1305,15 +1522,6 @@
     "sim.posterior_predictive_checks(pred_hdi_style={\"alpha\": 0.1})"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Estimating parameters and uncertainty with MCMC\n",
-    "\n",
-    "Of course this example is very simple, we can in fact optimize the parameters perfectly by hand. But just for the fun of it, let's use *Markov Chain Monte Carlo* (MCMC) to estimate the parameters, their uncertainty and the uncertainty in the data."
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -1321,16 +1529,16 @@
     "```{admonition} numpyro distributions\n",
     ":class: warning\n",
     "Currently only few distributions are implemented in the numpyro backend. This API will soon change, so that basically any distribution can be used to specifcy parameters. \n",
-    "```\n",
-    "\n",
-    "Finally, we let our inferer run the paramter estimation procedure with the numpyro backend and a NUTS kernel. This does the job in a few seconds"
+    "```"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We can inspect our estimates and see that the parameters are well esimtated by the model. Note that we only get an estimate for $b$. This is because earlier we set the parameter `a` with the flag `free=False` this effectively excludes it from estimation and uses the default value, which was set to the true value `a=0`."
+    "We can **inspect our estimates** and see that the model provides a good fit for the parameters.  \n",
+    "Note that we only get an estimate for $b$. Previously, we set the parameter $a$ with the flag `free = False`.   \n",
+    "This effectively excludes it from the estimation and uses its default value, which was set to the true value `a = 0`."
    ]
   },
   {
@@ -1348,21 +1556,44 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Report the results\n",
+    "## Report the results 🗒️\n",
     "\n",
-    "```{admonition} numpyro distributions\n",
-    ":class: warning\n",
-    "Automated reporting is already implemented in a different branch. This will be soon explained here.\n",
-    "```"
+    "Pymob provides the option to generate an automated report of the parameter distribution for a simulation.  \n",
+    "The report can be configured by modifying the options in {meth}`pymob.simulation.SimulationBase.config.report`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 91,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# TODO: Call report when done"
+    "# report the results\n",
+    "sim.report()"
+   ]
+  },
+  {
+   "attachments": {
+    "posterior_trace.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![posterior_trace.png](attachment:posterior_trace.png)"
+   ]
+  },
+  {
+   "attachments": {
+    "posterior_pairs.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![posterior_pairs.png](attachment:posterior_pairs.png)"
    ]
   },
   {
@@ -1370,29 +1601,30 @@
    "metadata": {},
    "source": [
     "\n",
-    "## Exporting the simulation and running it via the case study API\n",
+    "## Exporting the simulation and running it via the case study API 📤\n",
     "\n",
-    "After constructing the simulation, all settings of the simulation can be exported to a comprehensive configuration file, along with all the default settings. This is as simple as "
+    "After constructing the simulation, all settings - custom and default - can be exported to a comprehensive configuration file.   \n",
+    "The simulation will be saved to the default path (`CASE_STUDY/scenarios/SCENARIO/settings.cfg`) or to a custom path, specified with the file path keyword `fp`.   \n",
+    "Setting `force=True` will overwrite any existing config file, which is a reasonable choice in most cases.\n",
+    "From this point on, the simulation is (almost) ready to be executed from the command-line. "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 92,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Scenario directory exists at 'c:\\Users\\mgrho\\pymob\\docs\\source\\user_guide\\case_studies\\quickstart\\scenarios\\test'.\n",
-      "Results directory exists at 'c:\\Users\\mgrho\\pymob\\docs\\source\\user_guide\\case_studies\\quickstart\\results\\test'.\n"
+      "Scenario directory exists at 'c:\\Users\\mgrho\\pymob\\docs\\source\\user_guide\\case_studies\\superquickstart\\scenarios\\linreg'.\n",
+      "Results directory exists at 'c:\\Users\\mgrho\\pymob\\docs\\source\\user_guide\\case_studies\\superquickstart\\results\\linreg'.\n"
      ]
     }
    ],
    "source": [
     "import os\n",
-    "sim.config.case_study.name = \"quickstart\"\n",
-    "sim.config.case_study.scenario = \"test\"\n",
     "sim.config.create_directory(\"scenario\", force=True)\n",
     "sim.config.create_directory(\"results\", force=True)\n",
     "\n",
@@ -1406,21 +1638,25 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The simulation will be saved to the default path (`CASE_STUDY/scenarios/SCENARIO/settings.cfg`) or to a custom path spcified with the `fp` keyword. `force=True` will overwrite any existing config file, which is the reasonable choice in most cases.\n",
-    "\n",
-    "From there on, the simulation is (almost) ready to be executable from the commandline.\n",
-    "\n",
     "### Commandline API\n",
     "\n",
-    "The commandline API runs a series of commands that load the case study, execute the {meth}`pymob.simulation.SimulationBase.initialize` method and perform some more initialization tasks, before running the required job.\n",
+    "The command-line API runs a series of commands that load the case study, execute the {meth}`pymob.simulation.SimulationBase.initialize` method and perform some more initialization tasks before running the required job.\n",
     "\n",
-    "+ `pymob-infer`: Runs an inference job e.g. `pymob-infer --case_study=quickstart --scenario=test --inference_backend=numpyro`. While there are more commandline options, these are the two required "
+    "+ `pymob-infer` runs an inference job, for example:  \n",
+    "\n",
+    "  `pymob-infer --case_study=quickstart --scenario=test --inference_backend=numpyro`.   \n",
+    "  While there are more command-line options, these two (--case_study and --scenario) are required."
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "pymob",
+   "display_name": "pymobnew",
    "language": "python",
    "name": "python3"
   },
@@ -1434,7 +1670,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.11"
+   "version": "3.11.13"
   }
  },
  "nbformat": 4,

From d0e19ebfc1d967c37a626fc7f20d261f4a65e7fd Mon Sep 17 00:00:00 2001
From: mariegrho <mgrholthaus@gmail.com>
Date: Mon, 23 Jun 2025 13:31:51 +0200
Subject: [PATCH 08/16] Updated md version

---
 docs/source/user_guide/superquickstart.md | 491 +++++++++++++++-------
 1 file changed, 339 insertions(+), 152 deletions(-)

diff --git a/docs/source/user_guide/superquickstart.md b/docs/source/user_guide/superquickstart.md
index d2daebbe8..b1439d7b8 100644
--- a/docs/source/user_guide/superquickstart.md
+++ b/docs/source/user_guide/superquickstart.md
@@ -1,13 +1,13 @@
 # Pymob quickstart
 
-This super-quick quickstart provides an introduction to the basic Pymob workflow and its key functionalities.  
+This quickstart provides an introduction to the basic Pymob workflow and its key functionalities.  
 We will explore a simple linear regression model that we want to fit to a noisy dataset.  
 Pymob supports the modeling process by providing several tools for *data structuring*, *parameter estimation* and *visualization of results*.  
   
 If you are looking for a more detailed introduction, [click here]().  
 If you want to learn how to work with ODE models, check out [this tutorial]().
 
-## Pymob components
+## Pymob components 🧩
 
 Before starting the modeling process, let's take a look at the main steps and modules of pymob:
 
@@ -20,25 +20,25 @@ Our model will be defined as a standard python function.
 We will then assign it to the Simulation object by accessing the `.model` attribute. 
 
 3. __Observations:__   
-Our observation data must be structured as a [xarray.Dataset](https://docs.xarray.dev/en/stable/generated/xarray.Dataset.html).  
+Our observation data must be structured as an [xarray.Dataset](https://docs.xarray.dev/en/stable/generated/xarray.Dataset.html).  
 We assign it to the `.observations` attribute of our Simulation object.   
 Calling `sim.config.data_structure` will give us further information about the layout of our data.  
 
 4. __Solver:__  
 A [solver](https://pymob.readthedocs.io/en/stable/api/pymob.solvers.html) is required to solve the model.   
 In our simple case, we will use the `solve_analytic_1d` solver from the `pymob.solver.analytic` module.  
-We assign it to our Simulation object using the `.solver` attribute.   
+We assign it to our Simulation object using the {attr}`pymob.simulation.solver` attribute.   
 Since our model already provides an analytical solution, this solver basically does nothing. It is still needed to fulfill Pymob's requirement for a solver component.   
-For more complex models (e.g. ODEs), the `JaxSolver` from the `pymob.solvers.diffrax` module is a more powerful option.   
-Users can also implement custom solvers as a subclass of `pymob.solver.SolverBase`.   
+For more complex models (e.g. ODEs), the `JaxSolver` from the `pymob.solver.diffrax` module is a more powerful option.   
+Users can also implement custom solvers as a subclass of {class}`pymob.solver.SolverBase`.   
   
 5. __Inferer:__  
 The inferer handels the parameter estimation.  
 Pymob supports [various backends](https://pymob.readthedocs.io/en/stable/user_guide/framework_overview.html). In this example, we will work with *NumPyro*.  
-We assign the inferer to our Simulation object via the `.inferer` attribute and configure the desired kernel (e.g. *nuts*).  
+We assign the inferer to our Simulation object via the {attr}`pymob.simulation.inferer` attribute and configure the desired kernel (e.g. *nuts*).  
 But before inference, we need to parameterize our model using the *Param* class.   
 Each parameter can be marked either as free or fixed, depending on whether it should be variable during the optimization procedure.   
-The parameters are stored in the `sim.model_parameters` dictionary, which holds model input values.
+The parameters are stored in the {attr}`pymob.simulation.SimulationBase.model_parameters` dictionary, which holds model input values.
 By default, it takes the keys: `parameters`, `y0` and `x_in`. 
 
 6. __Evaluator:__  
@@ -53,6 +53,8 @@ We can further use it to create new simulations by loading settings from a confi
 
 ![framework-overview](.\figures\pymob_overview.png)
 
+## Getting started 🛫
+
 
 ```python
 # First, import the necessary python packages
@@ -67,14 +69,15 @@ from pymob.sim.config import Param
 ```
 
 Since no measured data is provided, we will generate an artificial dataset.  
-$y_{obs}$ represents the observed data over the time $t$ [0, 10].  
-To use this data later in the simulation, we need to convert it into a xarray-Dataset.  
+$y_{obs}$ represents the **observed data** over the time $t$ [0, 10].  
+To use this data later in the simulation, we need to convert it into an **xarray-Dataset**.  
 In your own application, you would replace this with your measured experimental data.  
 
 
 ```python
 # Parameter for the artificial data generation
-slope = np.random.uniform(2.0, 4.0) 
+rng = np.random.default_rng(seed=1)  # for reproducibility
+slope = rng.uniform(2,4)
 intercept = 1.0
 num_points = 100
 noise_level = 1.7
@@ -120,27 +123,76 @@ data_obs
  */
 
 :root {
-  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));
-  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));
-  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));
-  --xr-border-color: var(--jp-border-color2, #e0e0e0);
-  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);
-  --xr-background-color: var(--jp-layout-color0, white);
-  --xr-background-color-row-even: var(--jp-layout-color1, white);
-  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);
-}
-
-html[theme=dark],
-body[data-theme=dark],
+  --xr-font-color0: var(
+    --jp-content-font-color0,
+    var(--pst-color-text-base rgba(0, 0, 0, 1))
+  );
+  --xr-font-color2: var(
+    --jp-content-font-color2,
+    var(--pst-color-text-base, rgba(0, 0, 0, 0.54))
+  );
+  --xr-font-color3: var(
+    --jp-content-font-color3,
+    var(--pst-color-text-base, rgba(0, 0, 0, 0.38))
+  );
+  --xr-border-color: var(
+    --jp-border-color2,
+    hsl(from var(--pst-color-on-background, white) h s calc(l - 10))
+  );
+  --xr-disabled-color: var(
+    --jp-layout-color3,
+    hsl(from var(--pst-color-on-background, white) h s calc(l - 40))
+  );
+  --xr-background-color: var(
+    --jp-layout-color0,
+    var(--pst-color-on-background, white)
+  );
+  --xr-background-color-row-even: var(
+    --jp-layout-color1,
+    hsl(from var(--pst-color-on-background, white) h s calc(l - 5))
+  );
+  --xr-background-color-row-odd: var(
+    --jp-layout-color2,
+    hsl(from var(--pst-color-on-background, white) h s calc(l - 15))
+  );
+}
+
+html[theme="dark"],
+html[data-theme="dark"],
+body[data-theme="dark"],
 body.vscode-dark {
-  --xr-font-color0: rgba(255, 255, 255, 1);
-  --xr-font-color2: rgba(255, 255, 255, 0.54);
-  --xr-font-color3: rgba(255, 255, 255, 0.38);
-  --xr-border-color: #1F1F1F;
-  --xr-disabled-color: #515151;
-  --xr-background-color: #111111;
-  --xr-background-color-row-even: #111111;
-  --xr-background-color-row-odd: #313131;
+  --xr-font-color0: var(
+    --jp-content-font-color0,
+    var(--pst-color-text-base, rgba(255, 255, 255, 1))
+  );
+  --xr-font-color2: var(
+    --jp-content-font-color2,
+    var(--pst-color-text-base, rgba(255, 255, 255, 0.54))
+  );
+  --xr-font-color3: var(
+    --jp-content-font-color3,
+    var(--pst-color-text-base, rgba(255, 255, 255, 0.38))
+  );
+  --xr-border-color: var(
+    --jp-border-color2,
+    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 10))
+  );
+  --xr-disabled-color: var(
+    --jp-layout-color3,
+    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 40))
+  );
+  --xr-background-color: var(
+    --jp-layout-color0,
+    var(--pst-color-on-background, #111111)
+  );
+  --xr-background-color-row-even: var(
+    --jp-layout-color1,
+    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 5))
+  );
+  --xr-background-color-row-odd: var(
+    --jp-layout-color2,
+    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 15))
+  );
 }
 
 .xr-wrap {
@@ -181,7 +233,7 @@ body.vscode-dark {
 .xr-sections {
   padding-left: 0 !important;
   display: grid;
-  grid-template-columns: 150px auto auto 1fr 20px 20px;
+  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;
 }
 
 .xr-section-item {
@@ -189,11 +241,14 @@ body.vscode-dark {
 }
 
 .xr-section-item input {
-  display: none;
+  display: inline-block;
+  opacity: 0;
+  height: 0;
 }
 
 .xr-section-item input + label {
   color: var(--xr-disabled-color);
+  border: 2px solid transparent !important;
 }
 
 .xr-section-item input:enabled + label {
@@ -201,6 +256,10 @@ body.vscode-dark {
   color: var(--xr-font-color2);
 }
 
+.xr-section-item input:focus + label {
+  border: 2px solid var(--xr-font-color0) !important;
+}
+
 .xr-section-item input:enabled + label:hover {
   color: var(--xr-font-color0);
 }
@@ -222,7 +281,7 @@ body.vscode-dark {
 
 .xr-section-summary-in + label:before {
   display: inline-block;
-  content: '►';
+  content: "►";
   font-size: 11px;
   width: 15px;
   text-align: center;
@@ -233,7 +292,7 @@ body.vscode-dark {
 }
 
 .xr-section-summary-in:checked + label:before {
-  content: '▼';
+  content: "▼";
 }
 
 .xr-section-summary-in:checked + label > span {
@@ -305,15 +364,15 @@ body.vscode-dark {
 }
 
 .xr-dim-list:before {
-  content: '(';
+  content: "(";
 }
 
 .xr-dim-list:after {
-  content: ')';
+  content: ")";
 }
 
 .xr-dim-list li:not(:last-child):after {
-  content: ',';
+  content: ",";
   padding-right: 5px;
 }
 
@@ -330,7 +389,9 @@ body.vscode-dark {
 .xr-var-item label,
 .xr-var-item > .xr-var-name span {
   background-color: var(--xr-background-color-row-even);
+  border-color: var(--xr-background-color-row-odd);
   margin-bottom: 0;
+  padding-top: 2px;
 }
 
 .xr-var-item > .xr-var-name:hover span {
@@ -341,6 +402,7 @@ body.vscode-dark {
 .xr-var-list > li:nth-child(odd) > label,
 .xr-var-list > li:nth-child(odd) > .xr-var-name span {
   background-color: var(--xr-background-color-row-odd);
+  border-color: var(--xr-background-color-row-even);
 }
 
 .xr-var-name {
@@ -390,8 +452,15 @@ body.vscode-dark {
 .xr-var-data,
 .xr-index-data {
   display: none;
-  background-color: var(--xr-background-color) !important;
-  padding-bottom: 5px !important;
+  border-top: 2px dotted var(--xr-background-color);
+  padding-bottom: 20px !important;
+  padding-top: 10px !important;
+}
+
+.xr-var-attrs-in + label,
+.xr-var-data-in + label,
+.xr-index-data-in + label {
+  padding: 0 1px;
 }
 
 .xr-var-attrs-in:checked ~ .xr-var-attrs,
@@ -404,6 +473,12 @@ body.vscode-dark {
   float: right;
 }
 
+.xr-var-data > pre,
+.xr-index-data > pre,
+.xr-var-data > table > tbody > tr {
+  background-color: transparent !important;
+}
+
 .xr-var-name span,
 .xr-var-data,
 .xr-index-name div,
@@ -463,12 +538,20 @@ dl.xr-attrs {
   stroke: currentColor;
   fill: currentColor;
 }
-</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;
+
+.xr-var-attrs-in:checked + label > .xr-icon-file-text2,
+.xr-var-data-in:checked + label > .xr-icon-database,
+.xr-index-data-in:checked + label > .xr-icon-database {
+  color: var(--xr-font-color0);
+  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));
+  stroke-width: 0.8px;
+}
+</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 2kB
 Dimensions:  (t: 100)
 Coordinates:
-  * t        (t) float64 0.0 0.101 0.202 0.303 0.404 ... 9.697 9.798 9.899 10.0
+  * t        (t) float64 800B 0.0 0.101 0.202 0.303 ... 9.697 9.798 9.899 10.0
 Data variables:
-    y        (t) float64 2.537 0.762 3.105 3.813 ... 33.87 34.32 37.92 39.47</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-3cba92b8-0944-42c5-b82a-35783b953cd4' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-3cba92b8-0944-42c5-b82a-35783b953cd4' 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'>t</span>: 100</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-a797f301-1248-4646-9731-91e28642f5e8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a797f301-1248-4646-9731-91e28642f5e8' class='xr-section-summary' >Coordinates: <span>(1)</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'>t</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.101 0.202 ... 9.899 10.0</div><input id='attrs-c80e20c8-6a9d-4f7e-9478-466ed8adac53' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c80e20c8-6a9d-4f7e-9478-466ed8adac53' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-60347dee-7fd7-40d1-b503-b9cd31e0334a' class='xr-var-data-in' type='checkbox'><label for='data-60347dee-7fd7-40d1-b503-b9cd31e0334a' 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.      ,  0.10101 ,  0.20202 ,  0.30303 ,  0.40404 ,  0.505051,
+    y        (t) float64 800B -0.5149 -0.7114 -1.253 3.426 ... 29.12 31.78 32.77</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-cb22c682-387e-497e-a297-695495b750ff' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-cb22c682-387e-497e-a297-695495b750ff' 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'>t</span>: 100</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-0514a1e4-a436-4f2c-a31f-fbcdb00dc73f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0514a1e4-a436-4f2c-a31f-fbcdb00dc73f' class='xr-section-summary' >Coordinates: <span>(1)</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'>t</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.101 0.202 ... 9.899 10.0</div><input id='attrs-0281450d-db12-43b7-b078-b02f2e95a9c1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0281450d-db12-43b7-b078-b02f2e95a9c1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-91e61dfe-0c60-4ed0-8c88-dbfd85f4f586' class='xr-var-data-in' type='checkbox'><label for='data-91e61dfe-0c60-4ed0-8c88-dbfd85f4f586' 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.      ,  0.10101 ,  0.20202 ,  0.30303 ,  0.40404 ,  0.505051,
         0.606061,  0.707071,  0.808081,  0.909091,  1.010101,  1.111111,
         1.212121,  1.313131,  1.414141,  1.515152,  1.616162,  1.717172,
         1.818182,  1.919192,  2.020202,  2.121212,  2.222222,  2.323232,
@@ -484,26 +567,26 @@ Data variables:
         7.878788,  7.979798,  8.080808,  8.181818,  8.282828,  8.383838,
         8.484848,  8.585859,  8.686869,  8.787879,  8.888889,  8.989899,
         9.090909,  9.191919,  9.292929,  9.393939,  9.494949,  9.59596 ,
-        9.69697 ,  9.79798 ,  9.89899 , 10.      ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-73cae99e-8b2f-4feb-824c-55817b00ee92' class='xr-section-summary-in' type='checkbox'  checked><label for='section-73cae99e-8b2f-4feb-824c-55817b00ee92' class='xr-section-summary' >Data variables: <span>(1)</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>y</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.537 0.762 3.105 ... 37.92 39.47</div><input id='attrs-44ebf413-4dc2-4ed5-b79b-4a22c6c5d3c2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-44ebf413-4dc2-4ed5-b79b-4a22c6c5d3c2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c8abb7e6-b7f3-4dfa-914b-0afef1078d02' class='xr-var-data-in' type='checkbox'><label for='data-c8abb7e6-b7f3-4dfa-914b-0afef1078d02' 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([ 2.53735306,  0.76202974,  3.10502331,  3.81269502,  1.65493898,
-        4.7382624 ,  1.91659115,  5.09948271,  4.09436031,  2.93203812,
-        2.41660779,  3.52721064,  4.52038892,  5.25244692,  6.49443682,
-        7.15817191,  7.37549405,  6.45793422, 10.42760157,  7.0160899 ,
-        8.44228028,  8.14150763,  8.33164821,  9.06734271, 10.1364285 ,
-        9.89235673, 11.86570089,  8.92148715,  8.00979706, 12.612303  ,
-       12.45222116, 14.50730789, 11.04216221, 13.04196904, 10.78455757,
-       14.51374821, 15.78655397, 11.85873628, 15.17499665, 12.55360977,
-       17.20997672, 14.13169503, 13.91247417, 17.84122887, 19.53174081,
-       18.97160079, 20.69099192, 19.09570155, 19.80465631, 21.57411683,
-       18.67932246, 15.86782202, 21.89297698, 21.14301831, 20.60363498,
-       23.19466707, 21.78505986, 24.02588231, 19.93938713, 24.11449125,
-       23.38553814, 20.22692378, 22.04672104, 24.69358621, 26.35163201,
-       26.87845261, 25.66642845, 24.78028588, 28.13555625, 28.77362647,
-       29.8394705 , 28.96746404, 26.8770062 , 28.38000956, 27.32974723,
-       28.48364593, 28.19300058, 31.5562261 , 27.70039454, 30.37674089,
-       33.23517464, 31.15096457, 31.92976207, 30.66092298, 37.31956867,
-       33.85527604, 30.32904845, 31.79863742, 32.96114687, 35.05260485,
-       36.40587357, 32.88890392, 33.20810035, 35.01590153, 35.25401727,
-       39.29738961, 33.86973557, 34.3242026 , 37.91957645, 39.46912001])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1bb34202-8817-4d17-b85c-9a071e56812b' class='xr-section-summary-in' type='checkbox'  ><label for='section-1bb34202-8817-4d17-b85c-9a071e56812b' class='xr-section-summary' >Indexes: <span>(1)</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>t</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f2ac7879-0c60-4f2e-8504-7516258bff90' class='xr-index-data-in' type='checkbox'/><label for='index-f2ac7879-0c60-4f2e-8504-7516258bff90' 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([                0.0, 0.10101010101010101, 0.20202020202020202,
+        9.69697 ,  9.79798 ,  9.89899 , 10.      ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7b6d7b8a-3a33-4ac2-92c6-72dd22cffdeb' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7b6d7b8a-3a33-4ac2-92c6-72dd22cffdeb' class='xr-section-summary' >Data variables: <span>(1)</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>y</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.5149 -0.7114 ... 31.78 32.77</div><input id='attrs-f45ad545-a6de-4cb9-aa08-db81961682eb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f45ad545-a6de-4cb9-aa08-db81961682eb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-66163812-f915-4a3e-b4fa-2a4be5796afb' class='xr-var-data-in' type='checkbox'><label for='data-66163812-f915-4a3e-b4fa-2a4be5796afb' 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([-0.51485544, -0.71136009, -1.25293135,  3.42554816,  3.02802318,
+        2.28996175,  1.30369905,  1.12342635,  4.696483  ,  3.55592901,
+        6.07850176,  5.65225376,  6.30163965,  6.21062672,  7.48114549,
+       10.4209593 ,  4.30685901,  3.34331655, 11.06410455,  8.0075721 ,
+        7.62191623,  5.771721  ,  8.9993412 ,  8.25634047, 10.1705519 ,
+        5.7614147 ,  7.63852438, 11.69554624,  9.2803849 ,  8.74384082,
+        4.70197652,  9.55192526,  8.01544118, 11.25992123, 10.25462628,
+       13.39701217, 13.07742985, 14.18054669, 14.94229116, 12.36204004,
+       11.47591127, 14.14875177, 12.76613791, 13.68796203, 15.8061059 ,
+       12.87511648, 13.97928359, 15.32178202, 16.82801938, 14.50979942,
+       14.93651816, 16.60607159, 18.55546995, 13.24463006, 18.3729172 ,
+       14.16656354, 17.32953375, 21.29800181, 16.82317851, 19.04953345,
+       19.77974641, 20.66126185, 19.86304768, 18.24686502, 17.73073581,
+       21.52896061, 22.41719182, 20.40032284, 24.38217873, 23.72367109,
+       22.01080873, 24.19549703, 21.77467738, 21.14727555, 24.36605289,
+       24.81749318, 21.43139972, 25.37209188, 23.56605562, 25.6332203 ,
+       26.54650393, 25.38526454, 25.54376272, 25.05455707, 24.78854931,
+       25.10641378, 29.38972575, 26.33082303, 27.44406156, 27.87010378,
+       26.88792478, 27.54458101, 30.97867305, 26.06115315, 26.74526954,
+       28.48091395, 31.67196315, 29.12184748, 31.77992649, 32.77431831])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4ded40ce-d19b-49e4-b2f5-6f6a3f4610af' class='xr-section-summary-in' type='checkbox'  ><label for='section-4ded40ce-d19b-49e4-b2f5-6f6a3f4610af' class='xr-section-summary' >Indexes: <span>(1)</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>t</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-513a7ad3-ffd7-4709-afae-634504bfc914' class='xr-index-data-in' type='checkbox'/><label for='index-513a7ad3-ffd7-4709-afae-634504bfc914' 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([                0.0, 0.10101010101010101, 0.20202020202020202,
        0.30303030303030304, 0.40404040404040403,  0.5050505050505051,
         0.6060606060606061,  0.7070707070707071,  0.8080808080808081,
         0.9090909090909091,  1.0101010101010102,  1.1111111111111112,
@@ -537,19 +620,19 @@ Data variables:
          9.393939393939394,   9.494949494949495,   9.595959595959595,
          9.696969696969697,   9.797979797979798,     9.8989898989899,
                       10.0],
-      dtype=&#x27;float64&#x27;, name=&#x27;t&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-89be94e4-c853-4f55-9cc5-db089c3863c0' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-89be94e4-c853-4f55-9cc5-db089c3863c0' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>
+      dtype=&#x27;float64&#x27;, name=&#x27;t&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4ad51e0f-9925-4afc-b00e-de34757f6870' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-4ad51e0f-9925-4afc-b00e-de34757f6870' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>
 
 
 
 
     
-![png](superquickstart_files/superquickstart_6_1.png)
+![png](superquickstart_files/superquickstart_7_1.png)
     
 
 
-## Initialize a simulation
+## Initialize a simulation ✨
 
-In pymob, a Simulation object is initialized by creating an instance of the {class}`pymob.simulation.SimulationBase` class from the simulation module.  
+In pymob, a **simulation object** is initialized by creating an instance of the {class}`pymob.simulation.SimulationBase` class from the simulation module.  
 We will choose a linear regression model, as it provides a good approximation of the data: $ y = a + b*x $
 
 ```{admonition} x-dimension
@@ -563,6 +646,10 @@ You can specify it via `sim.config.simulation.x_dimension`.
 # Initialize the Simulation object
 sim = SimulationBase()
 
+# configurate the case study
+sim.config.case_study.name = "superquickstart"
+sim.config.case_study.scenario = "linreg"
+
 # Define the linear regression model
 def linreg(x, a, b):
     return a + b * x
@@ -575,15 +662,25 @@ sim.observations = data_obs
 
 # Defining a solver
 sim.solver = solve_analytic_1d
+
+# Take a look at the layut of the data
+sim.config.data_structure
 ```
 
-    MinMaxScaler(variable=y, min=0.7620297399871993, max=39.46912001079589)
+    MinMaxScaler(variable=y, min=-1.2529313454358775, max=32.77431830696904)
     
 
-    C:\Users\mgrho\pymob\pymob\simulation.py:303: UserWarning: `sim.config.data_structure.y = Datavariable(dimensions=['t'] min=0.7620297399871993 max=39.46912001079589 observed=True dimensions_evaluator=None)` has been assumed from `sim.observations`. If the order of the dimensions should be different, specify `sim.config.data_structure.y = DataVariable(dimensions=[...], ...)` manually.
+    C:\Pymob\pymob\pymob\simulation.py:307: UserWarning: `sim.config.data_structure.y = Datavariable(dimensions=['t'] min=-1.2529313454358775 max=32.77431830696904 observed=True dimensions_evaluator=None)` has been assumed from `sim.observations`. If the order of the dimensions should be different, specify `sim.config.data_structure.y = DataVariable(dimensions=[...], ...)` manually.
       warnings.warn(
     
 
+
+
+
+    Datastructure(y=DataVariable(dimensions=['t'], min=-1.2529313454358775, max=32.77431830696904, observed=True, dimensions_evaluator=None))
+
+
+
 ```{admonition} Scalers
 :class: note
 We notice a mysterious Scaler message. This tells us that our data variable has been identified and a scaler was constructed, which transforms the variable between [0, 1].   
@@ -591,26 +688,38 @@ This has no effect at the moment, but it can be used later. Scaling can be power
 ```
 
 
-## Running the model 🏃
+## Parameterizing and running the model 🏃
+
+Next, we define the **model parameters** $a$ and $b$.  
+Parameter $a$ is set as fixed (`free = False`), meaning its value is known and will not be estimated during optimization.  
+Parameter $b$ is marked as free (`free = True`), allowing it to be optimized to fit the data. As an initial guess, we assume $b = 3$.   
+
+
+```python
+# Parameterizing the model
+sim.config.model_parameters.a = Param(value=1.0, free=False)
+sim.config.model_parameters.b = Param(value=3.0, free=True)
+# this makes sure the model parameters are available to the model.
+sim.model_parameters["parameters"] = sim.config.model_parameters.value_dict
+
+sim.model_parameters["parameters"] 
+```
+
+
+
+
+    {'a': 1.0, 'b': 3.0}
+
 
-Next, we define the model parameters *a* and *b*.  
-Parameter *a* is set as fixed (`free = False`), meaning its value is known and will not be estimated during optimization.  
-Parameter *b* is marked as free (`free = True`), allowing it to be optimized to fit the data. As an initial guess, we assume b = 3.   
 
 Our model is now prepared with a defined parameter set.  
-To initialize the *Evaluator* class, we call `sim.dispatch_constructor()`.   
+To initialize the **Evaluator**, we call {meth}`pymob.simulation.SimulationBase.dispatch_constructor()`.   
 This step is essential and must be executed every time changes are made to the model. 
 
 The returned dataset (`evaluator.results`) has the exact same shape as the observation data.
 
 
 ```python
-# Parameterizing the model
-sim.config.model_parameters.a = Param(value=1, free=False)
-sim.config.model_parameters.b = Param(value=3, free=True)
-# this makes sure the model parameters are available to the model.
-sim.model_parameters["parameters"] = sim.config.model_parameters.value_dict
-
 # put everything in place for running the simulation
 sim.dispatch_constructor()
 
@@ -620,7 +729,7 @@ evaluator()
 evaluator.results
 ```
 
-    C:\Users\mgrho\pymob\pymob\simulation.py:552: UserWarning: The number of ODE states was not specified in the config file [simulation] > 'n_ode_states = <n>'. Extracted the return arguments ['a+b*x'] from the source code. Setting 'n_ode_states=1.
+    C:\Pymob\pymob\pymob\simulation.py:567: UserWarning: The number of ODE states was not specified in the config file [simulation] > 'n_ode_states = <n>'. Extracted the return arguments ['a+b*x'] from the source code. Setting 'n_ode_states=1.
       warnings.warn(
     
 
@@ -647,27 +756,76 @@ evaluator.results
  */
 
 :root {
-  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));
-  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));
-  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));
-  --xr-border-color: var(--jp-border-color2, #e0e0e0);
-  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);
-  --xr-background-color: var(--jp-layout-color0, white);
-  --xr-background-color-row-even: var(--jp-layout-color1, white);
-  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);
-}
-
-html[theme=dark],
-body[data-theme=dark],
+  --xr-font-color0: var(
+    --jp-content-font-color0,
+    var(--pst-color-text-base rgba(0, 0, 0, 1))
+  );
+  --xr-font-color2: var(
+    --jp-content-font-color2,
+    var(--pst-color-text-base, rgba(0, 0, 0, 0.54))
+  );
+  --xr-font-color3: var(
+    --jp-content-font-color3,
+    var(--pst-color-text-base, rgba(0, 0, 0, 0.38))
+  );
+  --xr-border-color: var(
+    --jp-border-color2,
+    hsl(from var(--pst-color-on-background, white) h s calc(l - 10))
+  );
+  --xr-disabled-color: var(
+    --jp-layout-color3,
+    hsl(from var(--pst-color-on-background, white) h s calc(l - 40))
+  );
+  --xr-background-color: var(
+    --jp-layout-color0,
+    var(--pst-color-on-background, white)
+  );
+  --xr-background-color-row-even: var(
+    --jp-layout-color1,
+    hsl(from var(--pst-color-on-background, white) h s calc(l - 5))
+  );
+  --xr-background-color-row-odd: var(
+    --jp-layout-color2,
+    hsl(from var(--pst-color-on-background, white) h s calc(l - 15))
+  );
+}
+
+html[theme="dark"],
+html[data-theme="dark"],
+body[data-theme="dark"],
 body.vscode-dark {
-  --xr-font-color0: rgba(255, 255, 255, 1);
-  --xr-font-color2: rgba(255, 255, 255, 0.54);
-  --xr-font-color3: rgba(255, 255, 255, 0.38);
-  --xr-border-color: #1F1F1F;
-  --xr-disabled-color: #515151;
-  --xr-background-color: #111111;
-  --xr-background-color-row-even: #111111;
-  --xr-background-color-row-odd: #313131;
+  --xr-font-color0: var(
+    --jp-content-font-color0,
+    var(--pst-color-text-base, rgba(255, 255, 255, 1))
+  );
+  --xr-font-color2: var(
+    --jp-content-font-color2,
+    var(--pst-color-text-base, rgba(255, 255, 255, 0.54))
+  );
+  --xr-font-color3: var(
+    --jp-content-font-color3,
+    var(--pst-color-text-base, rgba(255, 255, 255, 0.38))
+  );
+  --xr-border-color: var(
+    --jp-border-color2,
+    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 10))
+  );
+  --xr-disabled-color: var(
+    --jp-layout-color3,
+    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 40))
+  );
+  --xr-background-color: var(
+    --jp-layout-color0,
+    var(--pst-color-on-background, #111111)
+  );
+  --xr-background-color-row-even: var(
+    --jp-layout-color1,
+    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 5))
+  );
+  --xr-background-color-row-odd: var(
+    --jp-layout-color2,
+    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 15))
+  );
 }
 
 .xr-wrap {
@@ -708,7 +866,7 @@ body.vscode-dark {
 .xr-sections {
   padding-left: 0 !important;
   display: grid;
-  grid-template-columns: 150px auto auto 1fr 20px 20px;
+  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;
 }
 
 .xr-section-item {
@@ -716,11 +874,14 @@ body.vscode-dark {
 }
 
 .xr-section-item input {
-  display: none;
+  display: inline-block;
+  opacity: 0;
+  height: 0;
 }
 
 .xr-section-item input + label {
   color: var(--xr-disabled-color);
+  border: 2px solid transparent !important;
 }
 
 .xr-section-item input:enabled + label {
@@ -728,6 +889,10 @@ body.vscode-dark {
   color: var(--xr-font-color2);
 }
 
+.xr-section-item input:focus + label {
+  border: 2px solid var(--xr-font-color0) !important;
+}
+
 .xr-section-item input:enabled + label:hover {
   color: var(--xr-font-color0);
 }
@@ -749,7 +914,7 @@ body.vscode-dark {
 
 .xr-section-summary-in + label:before {
   display: inline-block;
-  content: '►';
+  content: "►";
   font-size: 11px;
   width: 15px;
   text-align: center;
@@ -760,7 +925,7 @@ body.vscode-dark {
 }
 
 .xr-section-summary-in:checked + label:before {
-  content: '▼';
+  content: "▼";
 }
 
 .xr-section-summary-in:checked + label > span {
@@ -832,15 +997,15 @@ body.vscode-dark {
 }
 
 .xr-dim-list:before {
-  content: '(';
+  content: "(";
 }
 
 .xr-dim-list:after {
-  content: ')';
+  content: ")";
 }
 
 .xr-dim-list li:not(:last-child):after {
-  content: ',';
+  content: ",";
   padding-right: 5px;
 }
 
@@ -857,7 +1022,9 @@ body.vscode-dark {
 .xr-var-item label,
 .xr-var-item > .xr-var-name span {
   background-color: var(--xr-background-color-row-even);
+  border-color: var(--xr-background-color-row-odd);
   margin-bottom: 0;
+  padding-top: 2px;
 }
 
 .xr-var-item > .xr-var-name:hover span {
@@ -868,6 +1035,7 @@ body.vscode-dark {
 .xr-var-list > li:nth-child(odd) > label,
 .xr-var-list > li:nth-child(odd) > .xr-var-name span {
   background-color: var(--xr-background-color-row-odd);
+  border-color: var(--xr-background-color-row-even);
 }
 
 .xr-var-name {
@@ -917,8 +1085,15 @@ body.vscode-dark {
 .xr-var-data,
 .xr-index-data {
   display: none;
-  background-color: var(--xr-background-color) !important;
-  padding-bottom: 5px !important;
+  border-top: 2px dotted var(--xr-background-color);
+  padding-bottom: 20px !important;
+  padding-top: 10px !important;
+}
+
+.xr-var-attrs-in + label,
+.xr-var-data-in + label,
+.xr-index-data-in + label {
+  padding: 0 1px;
 }
 
 .xr-var-attrs-in:checked ~ .xr-var-attrs,
@@ -931,6 +1106,12 @@ body.vscode-dark {
   float: right;
 }
 
+.xr-var-data > pre,
+.xr-index-data > pre,
+.xr-var-data > table > tbody > tr {
+  background-color: transparent !important;
+}
+
 .xr-var-name span,
 .xr-var-data,
 .xr-index-name div,
@@ -990,12 +1171,20 @@ dl.xr-attrs {
   stroke: currentColor;
   fill: currentColor;
 }
-</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;
+
+.xr-var-attrs-in:checked + label > .xr-icon-file-text2,
+.xr-var-data-in:checked + label > .xr-icon-database,
+.xr-index-data-in:checked + label > .xr-icon-database {
+  color: var(--xr-font-color0);
+  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));
+  stroke-width: 0.8px;
+}
+</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 2kB
 Dimensions:  (t: 100)
 Coordinates:
-  * t        (t) float64 0.0 0.101 0.202 0.303 0.404 ... 9.697 9.798 9.899 10.0
+  * t        (t) float64 800B 0.0 0.101 0.202 0.303 ... 9.697 9.798 9.899 10.0
 Data variables:
-    y        (t) float64 1.0 1.303 1.606 1.909 2.212 ... 30.09 30.39 30.7 31.0</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-1c65b503-5085-41c4-8541-11fd2419ad56' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1c65b503-5085-41c4-8541-11fd2419ad56' 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'>t</span>: 100</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-5019ed9d-7d01-441b-bef4-dfd10f6916b8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5019ed9d-7d01-441b-bef4-dfd10f6916b8' class='xr-section-summary' >Coordinates: <span>(1)</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'>t</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.101 0.202 ... 9.899 10.0</div><input id='attrs-3bd2447e-bca7-41d2-b649-383901267a04' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3bd2447e-bca7-41d2-b649-383901267a04' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cbd54d71-5413-4299-8c7b-d851d64cc0a2' class='xr-var-data-in' type='checkbox'><label for='data-cbd54d71-5413-4299-8c7b-d851d64cc0a2' 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.      ,  0.10101 ,  0.20202 ,  0.30303 ,  0.40404 ,  0.505051,
+    y        (t) float64 800B 1.0 1.303 1.606 1.909 ... 30.09 30.39 30.7 31.0</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-bf80eaa8-9884-4187-be3c-beae3e76c5cd' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-bf80eaa8-9884-4187-be3c-beae3e76c5cd' 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'>t</span>: 100</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-8a9f7f00-6941-475e-86d3-da832a6b23b7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8a9f7f00-6941-475e-86d3-da832a6b23b7' class='xr-section-summary' >Coordinates: <span>(1)</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'>t</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.101 0.202 ... 9.899 10.0</div><input id='attrs-01b2ced7-51bd-42a0-989b-23685df30390' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-01b2ced7-51bd-42a0-989b-23685df30390' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-202ca2de-722e-427c-9aab-8db7f9a71528' class='xr-var-data-in' type='checkbox'><label for='data-202ca2de-722e-427c-9aab-8db7f9a71528' 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.      ,  0.10101 ,  0.20202 ,  0.30303 ,  0.40404 ,  0.505051,
         0.606061,  0.707071,  0.808081,  0.909091,  1.010101,  1.111111,
         1.212121,  1.313131,  1.414141,  1.515152,  1.616162,  1.717172,
         1.818182,  1.919192,  2.020202,  2.121212,  2.222222,  2.323232,
@@ -1011,7 +1200,7 @@ Data variables:
         7.878788,  7.979798,  8.080808,  8.181818,  8.282828,  8.383838,
         8.484848,  8.585859,  8.686869,  8.787879,  8.888889,  8.989899,
         9.090909,  9.191919,  9.292929,  9.393939,  9.494949,  9.59596 ,
-        9.69697 ,  9.79798 ,  9.89899 , 10.      ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d063be66-88cf-4f10-a8f4-1bcb5972929f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d063be66-88cf-4f10-a8f4-1bcb5972929f' class='xr-section-summary' >Data variables: <span>(1)</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>y</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.0 1.303 1.606 ... 30.39 30.7 31.0</div><input id='attrs-af1fff76-0563-4521-8e7c-7c457f5042e1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-af1fff76-0563-4521-8e7c-7c457f5042e1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f046512e-5cb8-499e-93c7-5827a840e920' class='xr-var-data-in' type='checkbox'><label for='data-f046512e-5cb8-499e-93c7-5827a840e920' 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([ 1.        ,  1.3030303 ,  1.60606061,  1.90909091,  2.21212121,
+        9.69697 ,  9.79798 ,  9.89899 , 10.      ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-bf16fc3b-2d10-481c-8d0b-20669c196b3b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-bf16fc3b-2d10-481c-8d0b-20669c196b3b' class='xr-section-summary' >Data variables: <span>(1)</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>y</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.0 1.303 1.606 ... 30.39 30.7 31.0</div><input id='attrs-6334fbe2-584c-43f9-bb4a-d9330aaf2fbd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6334fbe2-584c-43f9-bb4a-d9330aaf2fbd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0dc278ee-7131-4f84-97ca-bd6b2242f94c' class='xr-var-data-in' type='checkbox'><label for='data-0dc278ee-7131-4f84-97ca-bd6b2242f94c' 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([ 1.        ,  1.3030303 ,  1.60606061,  1.90909091,  2.21212121,
         2.51515152,  2.81818182,  3.12121212,  3.42424242,  3.72727273,
         4.03030303,  4.33333333,  4.63636364,  4.93939394,  5.24242424,
         5.54545455,  5.84848485,  6.15151515,  6.45454545,  6.75757576,
@@ -1030,7 +1219,7 @@ Data variables:
        25.24242424, 25.54545455, 25.84848485, 26.15151515, 26.45454545,
        26.75757576, 27.06060606, 27.36363636, 27.66666667, 27.96969697,
        28.27272727, 28.57575758, 28.87878788, 29.18181818, 29.48484848,
-       29.78787879, 30.09090909, 30.39393939, 30.6969697 , 31.        ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-205c757b-6e11-466d-8fab-67d9291fac41' class='xr-section-summary-in' type='checkbox'  ><label for='section-205c757b-6e11-466d-8fab-67d9291fac41' class='xr-section-summary' >Indexes: <span>(1)</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>t</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-389aa368-2271-4700-81bd-a9e5b3a74441' class='xr-index-data-in' type='checkbox'/><label for='index-389aa368-2271-4700-81bd-a9e5b3a74441' 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([                0.0, 0.10101010101010101, 0.20202020202020202,
+       29.78787879, 30.09090909, 30.39393939, 30.6969697 , 31.        ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e789d90c-b446-4d5c-8ab1-417cdaa87542' class='xr-section-summary-in' type='checkbox'  ><label for='section-e789d90c-b446-4d5c-8ab1-417cdaa87542' class='xr-section-summary' >Indexes: <span>(1)</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>t</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-b435329b-68e7-4db5-b4f8-37c8c4966a05' class='xr-index-data-in' type='checkbox'/><label for='index-b435329b-68e7-4db5-b4f8-37c8c4966a05' 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([                0.0, 0.10101010101010101, 0.20202020202020202,
        0.30303030303030304, 0.40404040404040403,  0.5050505050505051,
         0.6060606060606061,  0.7070707070707071,  0.8080808080808081,
         0.9090909090909091,  1.0101010101010102,  1.1111111111111112,
@@ -1064,14 +1253,21 @@ Data variables:
          9.393939393939394,   9.494949494949495,   9.595959595959595,
          9.696969696969697,   9.797979797979798,     9.8989898989899,
                       10.0],
-      dtype=&#x27;float64&#x27;, name=&#x27;t&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-cd67cdd0-d69e-4aab-b3c9-91c81ac9fe9b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-cd67cdd0-d69e-4aab-b3c9-91c81ac9fe9b' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>
+      dtype=&#x27;float64&#x27;, name=&#x27;t&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7c36564d-d456-4bb2-9623-ea54a6fa2551' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-7c36564d-d456-4bb2-9623-ea54a6fa2551' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>
+
 
 
+```{admonition} What does the dispatch constructor do?
+:class: hint
+Behind the scenes, the dispatch constructor assembles a lightweight Evaluator object from the Simulation object, that takes the least necessary amount of information, runs it through some dimension checks, and also connects it to the specified solver and initializes it. The purpose of the dispatch constructor is manyfold:
+By executing the entire overhead of a model evaluation and packing it into a new Evaluator instance sim.dispatch_constructor() to make single model evaluations as fast as possible and allow parallel evaluations, because each evaluator created by sim.dispatch() is it's a fully independent model instance with a separate set of parameters that can be solved.
+Evaluators store the raw output from a simulation and can generate an xarray object from it that corresponds to the data-structure of the observations with the evaluator.results property. This automatically aligns simulations results with observations, for simple computation of loss functions.
+```
 
-Let's take a look at the results.  
+Let's take a look at the **results**.  
 
-You can vary the parameter *b* in the previous step to investigate its influence on the model fit.  
-In the [Beginner Guide](), you can try out the *manual parameter estimation*, which is a feature provided by Pymob.  
+You can vary the parameter $b$ in the previous step to investigate its influence on the model fit.  
+In the [Introduction](https://pymob.readthedocs.io/en/stable/user_guide/introduction.html), you can try out the *manual parameter estimation*, which is a feature provided by Pymob.  
 
 
 ```python
@@ -1085,20 +1281,25 @@ ax.legend()
 
 
 
-    <matplotlib.legend.Legend at 0x25f8941ebd0>
+    <matplotlib.legend.Legend at 0x24c375963d0>
 
 
 
 
     
-![png](superquickstart_files/superquickstart_14_1.png)
+![png](superquickstart_files/superquickstart_18_1.png)
     
 
 
-## Estimating parameters and uncertainty with MCMC
-Of course this example is very simple - we could, in fact, optimize the parameters perfectly by hand. But just for fun, let's use *Markov Chain Monte Carlo (MCMC)* to estimate the parameters, their uncertainty and the uncertainty in the data. We’ll run the parameter estimation with our inferer, using the NumPyro backend with a NUTS kernel. This completes the job in a few seconds.
+## Estimating parameters and uncertainty with MCMC 🤔
+Of course this example is very simple. In fact, we could optimize the parameters perfectly by hand.   
+But just for fun, let's use *Markov Chain Monte Carlo (MCMC)* to estimate the parameters, their uncertainty and the uncertainty in the data.   
+We’ll run the parameter estimation with our **inferer**, using the NumPyro backend with a NUTS kernel. This completes the job in a few seconds.
 
-We are almost ready to infer the model parameters. To also estimate the uncertainty of the parameters, we add another parameter representing the error and assume that it follows a lognormal distribution. Additionally, we specify an error model for the data distribution. This will be: $$y_{obs} \sim Normal (y, \sigma_y)$$  
+We are almost ready to infer the model parameters. To also estimate the uncertainty of the parameters, we add another parameter representing the error and assume that it follows a lognormal distribution.   
+Additionally, we specify an error model for the data distribution. This will be: $$y_{obs} \sim Normal (y, \sigma_y)$$  
+
+Since $\sigma_y$ is not a fixed parameter, it doesn't need to be passed to the simulation class.
 
 
 ```python
@@ -1119,10 +1320,6 @@ sim.config.simulation.x_dimension = "t"
 sim.posterior_predictive_checks(pred_hdi_style={"alpha": 0.1})
 ```
 
-    C:\Users\mgrho\pymob\pymob\inference\numpyro_backend.py:552: UserWarning: Model is not rendered, because the graphviz executable is not found. Try search for 'graphviz executables not found' and the used OS. This should be an easy fix :-)
-      warnings.warn(
-    
-
     Jax 64 bit mode: False
     Absolute tolerance: 1e-07
     Trace Shapes:      
@@ -1136,20 +1333,20 @@ sim.posterior_predictive_checks(pred_hdi_style={"alpha": 0.1})
             value 100 |
     
 
-    sample: 100%|██████████| 3000/3000 [00:02<00:00, 1240.00it/s, 3 steps of size 7.01e-01. acc. prob=0.94]
+    sample: 100%|██████████| 3000/3000 [00:01<00:00, 1963.23it/s, 7 steps of size 8.27e-01. acc. prob=0.92]
     
 
     
                     mean       std    median      5.0%     95.0%     n_eff     r_hat
-             b      3.68      0.03      3.68      3.63      3.73   1376.15      1.00
-       sigma_y      1.75      0.13      1.74      1.54      1.97   1188.08      1.00
+             b      2.98      0.03      2.98      2.92      3.03   1611.92      1.00
+       sigma_y      1.83      0.13      1.82      1.61      2.04   1703.02      1.00
     
     Number of divergences: 0
     
 
 
     
-![png](superquickstart_files/superquickstart_16_4.png)
+![png](superquickstart_files/superquickstart_20_3.png)
     
 
 
@@ -1158,7 +1355,7 @@ sim.posterior_predictive_checks(pred_hdi_style={"alpha": 0.1})
 Currently only few distributions are implemented in the numpyro backend. This API will soon change, so that basically any distribution can be used to specifcy parameters. 
 ```
 
-We can inspect our estimates and ssee that the model provides a good fit for the parameters.  
+We can **inspect our estimates** and see that the model provides a good fit for the parameters.  
 Note that we only get an estimate for $b$. Previously, we set the parameter $a$ with the flag `free = False`.   
 This effectively excludes it from the estimation and uses its default value, which was set to the true value `a = 0`.
 
@@ -1168,10 +1365,10 @@ This effectively excludes it from the estimation and uses its default value, whi
 You can explore the API of {class}`pymob.sim.plot.SimulationPlot` to find out how you can work on the default predictions. Of course you can always make your own plot, by accessing {attr}`pymob.simulation.inferer.idata` and {attr}`pymob.simulation.observations`
 ```
 
-## Report the results
+## Report the results 🗒️
 
-Pymob provides an option to generate an automated report of the parameter distribution.  
-The report can be configured by modifying the options in `sim.config.report`.
+Pymob provides the option to generate an automated report of the parameter distribution for a simulation.  
+The report can be configured by modifying the options in {meth}`pymob.simulation.SimulationBase.config.report`.
 
 
 ```python
@@ -1179,31 +1376,21 @@ The report can be configured by modifying the options in `sim.config.report`.
 sim.report()
 ```
 
+![posterior_trace.png](superquickstart_files/posterior_trace.png)
 
-    
-![png](superquickstart_files/superquickstart_21_0.png)
-    
-
-
-
-    
-![png](superquickstart_files/superquickstart_21_1.png)
-    
-
+![posterior_pairs.png](superquickstart_files/posterior_pairs.png)
 
 
-## Exporting the simulation and running it via the case study API
+## Exporting the simulation and running it via the case study API 📤
 
 After constructing the simulation, all settings - custom and default - can be exported to a comprehensive configuration file.   
-The simulation will be saved to the default path (`CASE_STUDY/scenarios/SCENARIO/settings.cfg`) or to a custom path, specified with the file path `fp` keyword.   
+The simulation will be saved to the default path (`CASE_STUDY/scenarios/SCENARIO/settings.cfg`) or to a custom path, specified with the file path keyword `fp`.   
 Setting `force=True` will overwrite any existing config file, which is a reasonable choice in most cases.
 From this point on, the simulation is (almost) ready to be executed from the command-line. 
 
 
 ```python
 import os
-sim.config.case_study.name = "quickstart"
-sim.config.case_study.scenario = "test"
 sim.config.create_directory("scenario", force=True)
 sim.config.create_directory("results", force=True)
 
@@ -1213,8 +1400,8 @@ sim.save_observations(force=True)
 sim.config.save(force=True)
 ```
 
-    Scenario directory exists at 'c:\Users\mgrho\pymob\docs\source\user_guide\case_studies\quickstart\scenarios\test'.
-    Results directory exists at 'c:\Users\mgrho\pymob\docs\source\user_guide\case_studies\quickstart\results\test'.
+    Scenario directory exists at 'c:\Users\mgrho\pymob\docs\source\user_guide\case_studies\superquickstart\scenarios\linreg'.
+    Results directory exists at 'c:\Users\mgrho\pymob\docs\source\user_guide\case_studies\superquickstart\results\linreg'.
     
 
 ### Commandline API

From 2cbb1ab0a6392e58c035777abe74ce2e8c858f32 Mon Sep 17 00:00:00 2001
From: mariegrho <mgrholthaus@gmail.com>
Date: Fri, 27 Jun 2025 14:21:39 +0200
Subject: [PATCH 09/16] changes titel and updated the api links with a tilde

---
 docs/source/user_guide/superquickstart.ipynb |  138 +-
 docs/source/user_guide/superquickstart.md    | 2888 +++++++++---------
 2 files changed, 1540 insertions(+), 1486 deletions(-)

diff --git a/docs/source/user_guide/superquickstart.ipynb b/docs/source/user_guide/superquickstart.ipynb
index d4df2f353..25553a4d4 100644
--- a/docs/source/user_guide/superquickstart.ipynb
+++ b/docs/source/user_guide/superquickstart.ipynb
@@ -4,19 +4,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Pymob quickstart"
+    "# Pymob in minutes - the basics"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This quickstart provides an introduction to the basic Pymob workflow and its key functionalities.  \n",
+    "This guide provides a streamlined introduction to the basic Pymob workflow and its key functionalities.  \n",
     "We will explore a simple linear regression model that we want to fit to a noisy dataset.  \n",
     "Pymob supports the modeling process by providing several tools for *data structuring*, *parameter estimation* and *visualization of results*.  \n",
     "  \n",
-    "If you are looking for a more detailed introduction, [click here]().  \n",
-    "If you want to learn how to work with ODE models, check out [this tutorial]()."
+    "If you are looking for a more detailed introduction, [click here](https://pymob.readthedocs.io/en/stable/user_guide/introduction.html).  \n",
+    "If you want to learn how to work with ODE models, check out [this tutorial](). "
    ]
   },
   {
@@ -37,29 +37,29 @@
     "\n",
     "3. __Observations:__   \n",
     "Our observation data must be structured as an [xarray.Dataset](https://docs.xarray.dev/en/stable/generated/xarray.Dataset.html).  \n",
-    "We assign it to the `.observations` attribute of our Simulation object.   \n",
+    "We assign it to the `~pymob.sim.config.Casestudy.observations ` attribute of our Simulation object.   \n",
     "Calling `sim.config.data_structure` will give us further information about the layout of our data.  \n",
     "\n",
     "4. __Solver:__  \n",
     "A [solver](https://pymob.readthedocs.io/en/stable/api/pymob.solvers.html) is required to solve the model.   \n",
-    "In our simple case, we will use the `solve_analytic_1d` solver from the `pymob.solver.analytic` module.  \n",
+    "In our simple case, we will use the `solve_analytic_1d` solver from the `~pymob.solver.analytic` module.  \n",
     "We assign it to our Simulation object using the {attr}`pymob.simulation.solver` attribute.   \n",
     "Since our model already provides an analytical solution, this solver basically does nothing. It is still needed to fulfill Pymob's requirement for a solver component.   \n",
-    "For more complex models (e.g. ODEs), the `JaxSolver` from the `pymob.solver.diffrax` module is a more powerful option.   \n",
+    "For more complex models (e.g. ODEs), the `JaxSolver` from the `~pymob.solver.diffrax` module is a more powerful option.   \n",
     "Users can also implement custom solvers as a subclass of {class}`pymob.solver.SolverBase`.   \n",
     "  \n",
     "5. __Inferer:__  \n",
     "The inferer handels the parameter estimation.  \n",
     "Pymob supports [various backends](https://pymob.readthedocs.io/en/stable/user_guide/framework_overview.html). In this example, we will work with *NumPyro*.  \n",
-    "We assign the inferer to our Simulation object via the {attr}`pymob.simulation.inferer` attribute and configure the desired kernel (e.g. *nuts*).  \n",
+    "We assign the inferer to our Simulation object via the {attr}`~pymob.simulation.inferer` attribute and configure the desired kernel (e.g. *nuts*).  \n",
     "But before inference, we need to parameterize our model using the *Param* class.   \n",
     "Each parameter can be marked either as free or fixed, depending on whether it should be variable during the optimization procedure.   \n",
-    "The parameters are stored in the {attr}`pymob.simulation.SimulationBase.model_parameters` dictionary, which holds model input values.\n",
+    "The parameters are stored in the {attr}`~pymob.simulation.SimulationBase.model_parameters` dictionary, which holds model input values.\n",
     "By default, it takes the keys: `parameters`, `y0` and `x_in`. \n",
     "\n",
     "6. __Evaluator:__  \n",
-    "The Evaluator is an instance to manage model evaluations.\n",
-    "It sets up tasks, coordinates parallel runs of the simulation and keeps track of the results from each simulation or parameter inference process.  \n",
+    "The Evaluator is an instance to manage model evaluations. It sets up tasks, coordinates parallel runs of the simulation and keeps track of the results from each simulation or parameter inference process.   \n",
+    "Evaluators store the raw output from a simulation and can generate an xarray object from it that corresponds to the data-structure of the observations with the ~pymob.sim.evaluator.Evaluator.results` property. This automatically aligns the simulations results with the observations, for simple computation of loss functions.  \n",
     "\n",
     "7. __Config:__  \n",
     "The simulation settings will be saved in a `.cfg` configuration file.  \n",
@@ -83,7 +83,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -110,7 +110,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -564,7 +564,7 @@
        "Coordinates:\n",
        "  * t        (t) float64 800B 0.0 0.101 0.202 0.303 ... 9.697 9.798 9.899 10.0\n",
        "Data variables:\n",
-       "    y        (t) float64 800B -0.5149 -0.7114 -1.253 3.426 ... 29.12 31.78 32.77</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-cb22c682-387e-497e-a297-695495b750ff' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-cb22c682-387e-497e-a297-695495b750ff' 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'>t</span>: 100</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-0514a1e4-a436-4f2c-a31f-fbcdb00dc73f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0514a1e4-a436-4f2c-a31f-fbcdb00dc73f' class='xr-section-summary' >Coordinates: <span>(1)</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'>t</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.101 0.202 ... 9.899 10.0</div><input id='attrs-0281450d-db12-43b7-b078-b02f2e95a9c1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0281450d-db12-43b7-b078-b02f2e95a9c1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-91e61dfe-0c60-4ed0-8c88-dbfd85f4f586' class='xr-var-data-in' type='checkbox'><label for='data-91e61dfe-0c60-4ed0-8c88-dbfd85f4f586' 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.      ,  0.10101 ,  0.20202 ,  0.30303 ,  0.40404 ,  0.505051,\n",
+       "    y        (t) float64 800B -1.859 4.002 2.278 1.5 ... 29.9 27.81 31.68 32.25</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-0bbdad02-502b-4742-8aab-8a0d91dbd7c5' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-0bbdad02-502b-4742-8aab-8a0d91dbd7c5' 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'>t</span>: 100</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-a707166c-1a0a-44c2-a769-da7ff0dcd866' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a707166c-1a0a-44c2-a769-da7ff0dcd866' class='xr-section-summary' >Coordinates: <span>(1)</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'>t</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.101 0.202 ... 9.899 10.0</div><input id='attrs-2c528975-1b9f-4be7-97d1-0df153dd1a8a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2c528975-1b9f-4be7-97d1-0df153dd1a8a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-14221b05-11a5-430d-bda3-0478e24c5ce5' class='xr-var-data-in' type='checkbox'><label for='data-14221b05-11a5-430d-bda3-0478e24c5ce5' 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.      ,  0.10101 ,  0.20202 ,  0.30303 ,  0.40404 ,  0.505051,\n",
        "        0.606061,  0.707071,  0.808081,  0.909091,  1.010101,  1.111111,\n",
        "        1.212121,  1.313131,  1.414141,  1.515152,  1.616162,  1.717172,\n",
        "        1.818182,  1.919192,  2.020202,  2.121212,  2.222222,  2.323232,\n",
@@ -580,26 +580,26 @@
        "        7.878788,  7.979798,  8.080808,  8.181818,  8.282828,  8.383838,\n",
        "        8.484848,  8.585859,  8.686869,  8.787879,  8.888889,  8.989899,\n",
        "        9.090909,  9.191919,  9.292929,  9.393939,  9.494949,  9.59596 ,\n",
-       "        9.69697 ,  9.79798 ,  9.89899 , 10.      ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7b6d7b8a-3a33-4ac2-92c6-72dd22cffdeb' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7b6d7b8a-3a33-4ac2-92c6-72dd22cffdeb' class='xr-section-summary' >Data variables: <span>(1)</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>y</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.5149 -0.7114 ... 31.78 32.77</div><input id='attrs-f45ad545-a6de-4cb9-aa08-db81961682eb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f45ad545-a6de-4cb9-aa08-db81961682eb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-66163812-f915-4a3e-b4fa-2a4be5796afb' class='xr-var-data-in' type='checkbox'><label for='data-66163812-f915-4a3e-b4fa-2a4be5796afb' 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([-0.51485544, -0.71136009, -1.25293135,  3.42554816,  3.02802318,\n",
-       "        2.28996175,  1.30369905,  1.12342635,  4.696483  ,  3.55592901,\n",
-       "        6.07850176,  5.65225376,  6.30163965,  6.21062672,  7.48114549,\n",
-       "       10.4209593 ,  4.30685901,  3.34331655, 11.06410455,  8.0075721 ,\n",
-       "        7.62191623,  5.771721  ,  8.9993412 ,  8.25634047, 10.1705519 ,\n",
-       "        5.7614147 ,  7.63852438, 11.69554624,  9.2803849 ,  8.74384082,\n",
-       "        4.70197652,  9.55192526,  8.01544118, 11.25992123, 10.25462628,\n",
-       "       13.39701217, 13.07742985, 14.18054669, 14.94229116, 12.36204004,\n",
-       "       11.47591127, 14.14875177, 12.76613791, 13.68796203, 15.8061059 ,\n",
-       "       12.87511648, 13.97928359, 15.32178202, 16.82801938, 14.50979942,\n",
-       "       14.93651816, 16.60607159, 18.55546995, 13.24463006, 18.3729172 ,\n",
-       "       14.16656354, 17.32953375, 21.29800181, 16.82317851, 19.04953345,\n",
-       "       19.77974641, 20.66126185, 19.86304768, 18.24686502, 17.73073581,\n",
-       "       21.52896061, 22.41719182, 20.40032284, 24.38217873, 23.72367109,\n",
-       "       22.01080873, 24.19549703, 21.77467738, 21.14727555, 24.36605289,\n",
-       "       24.81749318, 21.43139972, 25.37209188, 23.56605562, 25.6332203 ,\n",
-       "       26.54650393, 25.38526454, 25.54376272, 25.05455707, 24.78854931,\n",
-       "       25.10641378, 29.38972575, 26.33082303, 27.44406156, 27.87010378,\n",
-       "       26.88792478, 27.54458101, 30.97867305, 26.06115315, 26.74526954,\n",
-       "       28.48091395, 31.67196315, 29.12184748, 31.77992649, 32.77431831])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4ded40ce-d19b-49e4-b2f5-6f6a3f4610af' class='xr-section-summary-in' type='checkbox'  ><label for='section-4ded40ce-d19b-49e4-b2f5-6f6a3f4610af' class='xr-section-summary' >Indexes: <span>(1)</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>t</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-513a7ad3-ffd7-4709-afae-634504bfc914' class='xr-index-data-in' type='checkbox'/><label for='index-513a7ad3-ffd7-4709-afae-634504bfc914' 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([                0.0, 0.10101010101010101, 0.20202020202020202,\n",
+       "        9.69697 ,  9.79798 ,  9.89899 , 10.      ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d652bc6e-69a3-4c00-b5d6-cf34c2ee5605' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d652bc6e-69a3-4c00-b5d6-cf34c2ee5605' class='xr-section-summary' >Data variables: <span>(1)</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>y</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.859 4.002 2.278 ... 31.68 32.25</div><input id='attrs-511ccb26-d960-4c31-8045-880929874026' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-511ccb26-d960-4c31-8045-880929874026' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ed959cf6-ad35-4b37-995a-ff2488e65974' class='xr-var-data-in' type='checkbox'><label for='data-ed959cf6-ad35-4b37-995a-ff2488e65974' 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([-1.85944047,  4.0017863 ,  2.27774178,  1.49993008,  2.25572637,\n",
+       "        5.97506098,  4.28861329,  1.48989749,  2.54840738,  6.00176557,\n",
+       "        5.19237785,  4.84129396,  5.93507375, -0.90023433,  5.32909845,\n",
+       "        5.43301534,  5.06823295,  6.38765919,  7.73241466,  8.36162623,\n",
+       "        3.59142011,  8.51912826, 10.83197262,  9.69217013,  5.58780283,\n",
+       "        8.63797095,  8.17536178, 10.41306493,  9.05779623, 10.35476686,\n",
+       "       11.79329806,  9.09134391, 14.38982907, 13.00039266, 11.8792888 ,\n",
+       "       10.61317107, 11.03240126, 10.55073022, 11.92232672, 15.54988696,\n",
+       "       13.25952982, 12.90309411, 12.73696535, 14.77726639, 17.50000192,\n",
+       "       12.80559198, 16.29095349, 16.24516375, 15.49882565, 17.40908366,\n",
+       "       15.60839363, 17.0775438 , 18.29367702, 15.9263197 , 19.16664567,\n",
+       "       19.25783323, 16.06462271, 16.80720809, 20.51733645, 18.32847375,\n",
+       "       15.85198324, 21.47444749, 19.88608661, 21.23243998, 18.88006365,\n",
+       "       18.41194379, 19.00600483, 21.81444858, 21.27952537, 22.84800896,\n",
+       "       23.85697258, 23.31593258, 22.68282408, 22.95792202, 24.52231748,\n",
+       "       24.6493255 , 23.69740026, 26.59968312, 24.7479687 , 21.96248218,\n",
+       "       25.59637031, 25.68290203, 29.80989984, 23.29474923, 23.65893307,\n",
+       "       29.42755947, 31.19393529, 27.86332172, 28.35144343, 28.50637287,\n",
+       "       29.1473367 , 28.92850415, 33.32833186, 29.00200625, 32.46986583,\n",
+       "       28.1559924 , 29.89935856, 27.80621022, 31.68101752, 32.25217908])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-173fb448-2b7e-4404-85b5-81ea335a9292' class='xr-section-summary-in' type='checkbox'  ><label for='section-173fb448-2b7e-4404-85b5-81ea335a9292' class='xr-section-summary' >Indexes: <span>(1)</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>t</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-ea5efa77-864b-4b0d-b035-0719798c206d' class='xr-index-data-in' type='checkbox'/><label for='index-ea5efa77-864b-4b0d-b035-0719798c206d' 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([                0.0, 0.10101010101010101, 0.20202020202020202,\n",
        "       0.30303030303030304, 0.40404040404040403,  0.5050505050505051,\n",
        "        0.6060606060606061,  0.7070707070707071,  0.8080808080808081,\n",
        "        0.9090909090909091,  1.0101010101010102,  1.1111111111111112,\n",
@@ -633,7 +633,7 @@
        "         9.393939393939394,   9.494949494949495,   9.595959595959595,\n",
        "         9.696969696969697,   9.797979797979798,     9.8989898989899,\n",
        "                      10.0],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;t&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4ad51e0f-9925-4afc-b00e-de34757f6870' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-4ad51e0f-9925-4afc-b00e-de34757f6870' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+       "      dtype=&#x27;float64&#x27;, name=&#x27;t&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-26db7585-42ad-476e-a0b3-73fca3430337' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-26db7585-42ad-476e-a0b3-73fca3430337' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
        "<xarray.Dataset> Size: 2kB\n",
@@ -641,16 +641,16 @@
        "Coordinates:\n",
        "  * t        (t) float64 800B 0.0 0.101 0.202 0.303 ... 9.697 9.798 9.899 10.0\n",
        "Data variables:\n",
-       "    y        (t) float64 800B -0.5149 -0.7114 -1.253 3.426 ... 29.12 31.78 32.77"
+       "    y        (t) float64 800B -1.859 4.002 2.278 1.5 ... 29.9 27.81 31.68 32.25"
       ]
      },
-     "execution_count": 85,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 500x400 with 1 Axes>"
       ]
@@ -691,7 +691,7 @@
    "source": [
     "## Initialize a simulation ✨\n",
     "\n",
-    "In pymob, a **simulation object** is initialized by creating an instance of the {class}`pymob.simulation.SimulationBase` class from the simulation module.  \n",
+    "In pymob, a **simulation object** is initialized by creating an instance of the {class}`~pymob.simulation.SimulationBase` class from the simulation module.  \n",
     "We will choose a linear regression model, as it provides a good approximation of the data: $ y = a + b*x $"
    ]
   },
@@ -708,31 +708,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "MinMaxScaler(variable=y, min=-1.2529313454358775, max=32.77431830696904)\n"
+      "MinMaxScaler(variable=y, min=-1.8594404709936558, max=33.32833185958829)\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "C:\\Pymob\\pymob\\pymob\\simulation.py:307: UserWarning: `sim.config.data_structure.y = Datavariable(dimensions=['t'] min=-1.2529313454358775 max=32.77431830696904 observed=True dimensions_evaluator=None)` has been assumed from `sim.observations`. If the order of the dimensions should be different, specify `sim.config.data_structure.y = DataVariable(dimensions=[...], ...)` manually.\n",
+      "C:\\Pymob\\pymob\\pymob\\simulation.py:307: UserWarning: `sim.config.data_structure.y = Datavariable(dimensions=['t'] min=-1.8594404709936558 max=33.32833185958829 observed=True dimensions_evaluator=None)` has been assumed from `sim.observations`. If the order of the dimensions should be different, specify `sim.config.data_structure.y = DataVariable(dimensions=[...], ...)` manually.\n",
       "  warnings.warn(\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "Datastructure(y=DataVariable(dimensions=['t'], min=-1.2529313454358775, max=32.77431830696904, observed=True, dimensions_evaluator=None))"
+       "Datastructure(y=DataVariable(dimensions=['t'], min=-1.8594404709936558, max=33.32833185958829, observed=True, dimensions_evaluator=None))"
       ]
      },
-     "execution_count": 86,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -787,7 +787,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -796,7 +796,7 @@
        "{'a': 1.0, 'b': 3.0}"
       ]
      },
-     "execution_count": 87,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -816,7 +816,7 @@
    "metadata": {},
    "source": [
     "Our model is now prepared with a defined parameter set.  \n",
-    "To initialize the **Evaluator**, we call {meth}`pymob.simulation.SimulationBase.dispatch_constructor()`.   \n",
+    "To initialize the **Evaluator**, we call {meth}`~pymob.simulation.SimulationBase.dispatch_constructor()`.   \n",
     "This step is essential and must be executed every time changes are made to the model. \n",
     "\n",
     "The returned dataset (`evaluator.results`) has the exact same shape as the observation data."
@@ -824,7 +824,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -1286,7 +1286,7 @@
        "Coordinates:\n",
        "  * t        (t) float64 800B 0.0 0.101 0.202 0.303 ... 9.697 9.798 9.899 10.0\n",
        "Data variables:\n",
-       "    y        (t) float64 800B 1.0 1.303 1.606 1.909 ... 30.09 30.39 30.7 31.0</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-bf80eaa8-9884-4187-be3c-beae3e76c5cd' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-bf80eaa8-9884-4187-be3c-beae3e76c5cd' 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'>t</span>: 100</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-8a9f7f00-6941-475e-86d3-da832a6b23b7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8a9f7f00-6941-475e-86d3-da832a6b23b7' class='xr-section-summary' >Coordinates: <span>(1)</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'>t</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.101 0.202 ... 9.899 10.0</div><input id='attrs-01b2ced7-51bd-42a0-989b-23685df30390' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-01b2ced7-51bd-42a0-989b-23685df30390' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-202ca2de-722e-427c-9aab-8db7f9a71528' class='xr-var-data-in' type='checkbox'><label for='data-202ca2de-722e-427c-9aab-8db7f9a71528' 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.      ,  0.10101 ,  0.20202 ,  0.30303 ,  0.40404 ,  0.505051,\n",
+       "    y        (t) float64 800B 1.0 1.303 1.606 1.909 ... 30.09 30.39 30.7 31.0</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-0874a8f8-0240-4ecd-95d9-f50b3fa444f0' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-0874a8f8-0240-4ecd-95d9-f50b3fa444f0' 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'>t</span>: 100</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-b20ed496-9f95-476b-976b-1e68e2470341' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b20ed496-9f95-476b-976b-1e68e2470341' class='xr-section-summary' >Coordinates: <span>(1)</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'>t</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.101 0.202 ... 9.899 10.0</div><input id='attrs-0178aa80-aa69-42d8-b260-76e173c8f254' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0178aa80-aa69-42d8-b260-76e173c8f254' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b7c34ec6-58bc-42c6-adef-eaa25e213363' class='xr-var-data-in' type='checkbox'><label for='data-b7c34ec6-58bc-42c6-adef-eaa25e213363' 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.      ,  0.10101 ,  0.20202 ,  0.30303 ,  0.40404 ,  0.505051,\n",
        "        0.606061,  0.707071,  0.808081,  0.909091,  1.010101,  1.111111,\n",
        "        1.212121,  1.313131,  1.414141,  1.515152,  1.616162,  1.717172,\n",
        "        1.818182,  1.919192,  2.020202,  2.121212,  2.222222,  2.323232,\n",
@@ -1302,7 +1302,7 @@
        "        7.878788,  7.979798,  8.080808,  8.181818,  8.282828,  8.383838,\n",
        "        8.484848,  8.585859,  8.686869,  8.787879,  8.888889,  8.989899,\n",
        "        9.090909,  9.191919,  9.292929,  9.393939,  9.494949,  9.59596 ,\n",
-       "        9.69697 ,  9.79798 ,  9.89899 , 10.      ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-bf16fc3b-2d10-481c-8d0b-20669c196b3b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-bf16fc3b-2d10-481c-8d0b-20669c196b3b' class='xr-section-summary' >Data variables: <span>(1)</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>y</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.0 1.303 1.606 ... 30.39 30.7 31.0</div><input id='attrs-6334fbe2-584c-43f9-bb4a-d9330aaf2fbd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6334fbe2-584c-43f9-bb4a-d9330aaf2fbd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0dc278ee-7131-4f84-97ca-bd6b2242f94c' class='xr-var-data-in' type='checkbox'><label for='data-0dc278ee-7131-4f84-97ca-bd6b2242f94c' 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([ 1.        ,  1.3030303 ,  1.60606061,  1.90909091,  2.21212121,\n",
+       "        9.69697 ,  9.79798 ,  9.89899 , 10.      ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ae018595-cc78-4256-b30b-a20df289dfd7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ae018595-cc78-4256-b30b-a20df289dfd7' class='xr-section-summary' >Data variables: <span>(1)</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>y</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.0 1.303 1.606 ... 30.39 30.7 31.0</div><input id='attrs-bda4057c-d482-40f3-b3e1-50b53d47e9ac' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bda4057c-d482-40f3-b3e1-50b53d47e9ac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-76f7debe-f2f7-48d2-ba4d-cd1cb5d76418' class='xr-var-data-in' type='checkbox'><label for='data-76f7debe-f2f7-48d2-ba4d-cd1cb5d76418' 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([ 1.        ,  1.3030303 ,  1.60606061,  1.90909091,  2.21212121,\n",
        "        2.51515152,  2.81818182,  3.12121212,  3.42424242,  3.72727273,\n",
        "        4.03030303,  4.33333333,  4.63636364,  4.93939394,  5.24242424,\n",
        "        5.54545455,  5.84848485,  6.15151515,  6.45454545,  6.75757576,\n",
@@ -1321,7 +1321,7 @@
        "       25.24242424, 25.54545455, 25.84848485, 26.15151515, 26.45454545,\n",
        "       26.75757576, 27.06060606, 27.36363636, 27.66666667, 27.96969697,\n",
        "       28.27272727, 28.57575758, 28.87878788, 29.18181818, 29.48484848,\n",
-       "       29.78787879, 30.09090909, 30.39393939, 30.6969697 , 31.        ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e789d90c-b446-4d5c-8ab1-417cdaa87542' class='xr-section-summary-in' type='checkbox'  ><label for='section-e789d90c-b446-4d5c-8ab1-417cdaa87542' class='xr-section-summary' >Indexes: <span>(1)</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>t</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-b435329b-68e7-4db5-b4f8-37c8c4966a05' class='xr-index-data-in' type='checkbox'/><label for='index-b435329b-68e7-4db5-b4f8-37c8c4966a05' 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([                0.0, 0.10101010101010101, 0.20202020202020202,\n",
+       "       29.78787879, 30.09090909, 30.39393939, 30.6969697 , 31.        ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a5800b86-fe93-4c37-ba41-83d099825c2c' class='xr-section-summary-in' type='checkbox'  ><label for='section-a5800b86-fe93-4c37-ba41-83d099825c2c' class='xr-section-summary' >Indexes: <span>(1)</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>t</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-176e951f-3364-488e-be5f-da3da8c3bb1c' class='xr-index-data-in' type='checkbox'/><label for='index-176e951f-3364-488e-be5f-da3da8c3bb1c' 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([                0.0, 0.10101010101010101, 0.20202020202020202,\n",
        "       0.30303030303030304, 0.40404040404040403,  0.5050505050505051,\n",
        "        0.6060606060606061,  0.7070707070707071,  0.8080808080808081,\n",
        "        0.9090909090909091,  1.0101010101010102,  1.1111111111111112,\n",
@@ -1355,7 +1355,7 @@
        "         9.393939393939394,   9.494949494949495,   9.595959595959595,\n",
        "         9.696969696969697,   9.797979797979798,     9.8989898989899,\n",
        "                      10.0],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;t&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7c36564d-d456-4bb2-9623-ea54a6fa2551' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-7c36564d-d456-4bb2-9623-ea54a6fa2551' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+       "      dtype=&#x27;float64&#x27;, name=&#x27;t&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-bf3c48cd-fe25-4883-bd84-e96d5b5cf5c3' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-bf3c48cd-fe25-4883-bd84-e96d5b5cf5c3' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
        "<xarray.Dataset> Size: 2kB\n",
@@ -1366,7 +1366,7 @@
        "    y        (t) float64 800B 1.0 1.303 1.606 1.909 ... 30.09 30.39 30.7 31.0"
       ]
      },
-     "execution_count": 88,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1387,9 +1387,7 @@
    "source": [
     "```{admonition} What does the dispatch constructor do?\n",
     ":class: hint\n",
-    "Behind the scenes, the dispatch constructor assembles a lightweight Evaluator object from the Simulation object, that takes the least necessary amount of information, runs it through some dimension checks, and also connects it to the specified solver and initializes it. The purpose of the dispatch constructor is manyfold:\n",
-    "By executing the entire overhead of a model evaluation and packing it into a new Evaluator instance sim.dispatch_constructor() to make single model evaluations as fast as possible and allow parallel evaluations, because each evaluator created by sim.dispatch() is it's a fully independent model instance with a separate set of parameters that can be solved.\n",
-    "Evaluators store the raw output from a simulation and can generate an xarray object from it that corresponds to the data-structure of the observations with the evaluator.results property. This automatically aligns simulations results with observations, for simple computation of loss functions.\n",
+    "Behind the scenes, the dispatch constructor assembles a lightweight Evaluator object from the Simulation object, that takes the least necessary amount of information, runs it through some dimension checks, and also connects it to the specified solver and initializes it.\n",
     "```"
    ]
   },
@@ -1405,22 +1403,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x24c375963d0>"
+       "<matplotlib.legend.Legend at 0x2c1fe179110>"
       ]
      },
-     "execution_count": 89,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 500x400 with 1 Axes>"
       ]
@@ -1444,7 +1442,7 @@
     "## Estimating parameters and uncertainty with MCMC 🤔\n",
     "Of course this example is very simple. In fact, we could optimize the parameters perfectly by hand.   \n",
     "But just for fun, let's use *Markov Chain Monte Carlo (MCMC)* to estimate the parameters, their uncertainty and the uncertainty in the data.   \n",
-    "We’ll run the parameter estimation with our **inferer**, using the NumPyro backend with a NUTS kernel. This completes the job in a few seconds.\n",
+    "We’ll run the parameter estimation with our **{attr}`~pymob.simulation.inferer`**, using the NumPyro backend with a NUTS kernel. This completes the job in a few seconds.\n",
     "\n",
     "We are almost ready to infer the model parameters. To also estimate the uncertainty of the parameters, we add another parameter representing the error and assume that it follows a lognormal distribution.   \n",
     "Additionally, we specify an error model for the data distribution. This will be: $$y_{obs} \\sim Normal (y, \\sigma_y)$$  \n",
@@ -1454,7 +1452,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -1478,7 +1476,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "sample: 100%|██████████| 3000/3000 [00:01<00:00, 1963.23it/s, 7 steps of size 8.27e-01. acc. prob=0.92]\n"
+      "sample: 100%|██████████| 3000/3000 [00:02<00:00, 1450.53it/s, 3 steps of size 7.98e-01. acc. prob=0.93] \n"
      ]
     },
     {
@@ -1487,15 +1485,15 @@
      "text": [
       "\n",
       "                mean       std    median      5.0%     95.0%     n_eff     r_hat\n",
-      "         b      2.98      0.03      2.98      2.92      3.03   1611.92      1.00\n",
-      "   sigma_y      1.83      0.13      1.82      1.61      2.04   1703.02      1.00\n",
+      "         b      3.05      0.03      3.05      3.00      3.10   1484.06      1.00\n",
+      "   sigma_y      1.84      0.13      1.83      1.61      2.03   1336.43      1.00\n",
       "\n",
       "Number of divergences: 0\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 400x300 with 1 Axes>"
       ]
@@ -1559,12 +1557,12 @@
     "## Report the results 🗒️\n",
     "\n",
     "Pymob provides the option to generate an automated report of the parameter distribution for a simulation.  \n",
-    "The report can be configured by modifying the options in {meth}`pymob.simulation.SimulationBase.config.report`."
+    "The report can be configured by modifying the options in {meth}`~pymob.simulation.SimulationBase.config.report`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 91,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1611,7 +1609,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 92,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -1640,7 +1638,7 @@
    "source": [
     "### Commandline API\n",
     "\n",
-    "The command-line API runs a series of commands that load the case study, execute the {meth}`pymob.simulation.SimulationBase.initialize` method and perform some more initialization tasks before running the required job.\n",
+    "The command-line API runs a series of commands that load the case study, execute the {meth}`~pymob.simulation.SimulationBase.initialize` method and perform some more initialization tasks before running the required job.\n",
     "\n",
     "+ `pymob-infer` runs an inference job, for example:  \n",
     "\n",
diff --git a/docs/source/user_guide/superquickstart.md b/docs/source/user_guide/superquickstart.md
index b1439d7b8..e92f85e55 100644
--- a/docs/source/user_guide/superquickstart.md
+++ b/docs/source/user_guide/superquickstart.md
@@ -1,1416 +1,1472 @@
-# Pymob quickstart
-
-This quickstart provides an introduction to the basic Pymob workflow and its key functionalities.  
-We will explore a simple linear regression model that we want to fit to a noisy dataset.  
-Pymob supports the modeling process by providing several tools for *data structuring*, *parameter estimation* and *visualization of results*.  
-  
-If you are looking for a more detailed introduction, [click here]().  
-If you want to learn how to work with ODE models, check out [this tutorial]().
-
-## Pymob components 🧩
-
-Before starting the modeling process, let's take a look at the main steps and modules of pymob:
-
-1. __Simulation:__   
-First, we need to initialize a Simulation object by creating an instance of the {class}`pymob.simulation.SimulationBase` class from the simulation module.   
-Optionally, we can configure the simulation with `sim.config.case_study.name = "linear-regression"`, `sim.config.case_study.scenario = "test"` and many other options. 
-
-2. __Model:__   
-Our model will be defined as a standard python function.  
-We will then assign it to the Simulation object by accessing the `.model` attribute. 
-
-3. __Observations:__   
-Our observation data must be structured as an [xarray.Dataset](https://docs.xarray.dev/en/stable/generated/xarray.Dataset.html).  
-We assign it to the `.observations` attribute of our Simulation object.   
-Calling `sim.config.data_structure` will give us further information about the layout of our data.  
-
-4. __Solver:__  
-A [solver](https://pymob.readthedocs.io/en/stable/api/pymob.solvers.html) is required to solve the model.   
-In our simple case, we will use the `solve_analytic_1d` solver from the `pymob.solver.analytic` module.  
-We assign it to our Simulation object using the {attr}`pymob.simulation.solver` attribute.   
-Since our model already provides an analytical solution, this solver basically does nothing. It is still needed to fulfill Pymob's requirement for a solver component.   
-For more complex models (e.g. ODEs), the `JaxSolver` from the `pymob.solver.diffrax` module is a more powerful option.   
-Users can also implement custom solvers as a subclass of {class}`pymob.solver.SolverBase`.   
-  
-5. __Inferer:__  
-The inferer handels the parameter estimation.  
-Pymob supports [various backends](https://pymob.readthedocs.io/en/stable/user_guide/framework_overview.html). In this example, we will work with *NumPyro*.  
-We assign the inferer to our Simulation object via the {attr}`pymob.simulation.inferer` attribute and configure the desired kernel (e.g. *nuts*).  
-But before inference, we need to parameterize our model using the *Param* class.   
-Each parameter can be marked either as free or fixed, depending on whether it should be variable during the optimization procedure.   
-The parameters are stored in the {attr}`pymob.simulation.SimulationBase.model_parameters` dictionary, which holds model input values.
-By default, it takes the keys: `parameters`, `y0` and `x_in`. 
-
-6. __Evaluator:__  
-The Evaluator is an instance to manage model evaluations.
-It sets up tasks, coordinates parallel runs of the simulation and keeps track of the results from each simulation or parameter inference process.  
-
-7. __Config:__  
-The simulation settings will be saved in a `.cfg` configuration file.  
-The config file contains information about our simulation in various sections. -> [Learn more here](https://pymob.readthedocs.io/en/stable/user_guide/case_studies.html#configuration).  
-We can further use it to create new simulations by loading settings from a config file. 
-
-
-![framework-overview](.\figures\pymob_overview.png)
-
-## Getting started 🛫
-
-
-```python
-# First, import the necessary python packages
-import numpy as np
-import matplotlib.pyplot as plt
-import xarray as xr
-
-# Import the pymob modules
-from pymob.simulation import SimulationBase
-from pymob.sim.solvetools import solve_analytic_1d
-from pymob.sim.config import Param
-```
-
-Since no measured data is provided, we will generate an artificial dataset.  
-$y_{obs}$ represents the **observed data** over the time $t$ [0, 10].  
-To use this data later in the simulation, we need to convert it into an **xarray-Dataset**.  
-In your own application, you would replace this with your measured experimental data.  
-
-
-```python
-# Parameter for the artificial data generation
-rng = np.random.default_rng(seed=1)  # for reproducibility
-slope = rng.uniform(2,4)
-intercept = 1.0
-num_points = 100
-noise_level = 1.7
-
-# generating time values
-t = np.linspace(0, 10, num_points)
-
-# generating y-values with noise
-noise = np.random.normal(0, noise_level, num_points)
-y_obs = slope * t + intercept + noise
-
-# visualizing our data
-fig, ax = plt.subplots(figsize=(5, 4))
-ax.scatter(t, y_obs, label='Datapoints')
-ax.set(xlabel='t [-]', ylabel='y_obs [-]', title ='Artificial Data')
-plt.tight_layout()
-
-# convert the data to an xr-Dataset
-data_obs = xr.DataArray(y_obs, coords={"t": t}).to_dataset(name="y")
-data_obs
-```
-
-
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-</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 2kB
-Dimensions:  (t: 100)
-Coordinates:
-  * t        (t) float64 800B 0.0 0.101 0.202 0.303 ... 9.697 9.798 9.899 10.0
-Data variables:
-    y        (t) float64 800B -0.5149 -0.7114 -1.253 3.426 ... 29.12 31.78 32.77</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-cb22c682-387e-497e-a297-695495b750ff' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-cb22c682-387e-497e-a297-695495b750ff' 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'>t</span>: 100</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-0514a1e4-a436-4f2c-a31f-fbcdb00dc73f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0514a1e4-a436-4f2c-a31f-fbcdb00dc73f' class='xr-section-summary' >Coordinates: <span>(1)</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'>t</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.101 0.202 ... 9.899 10.0</div><input id='attrs-0281450d-db12-43b7-b078-b02f2e95a9c1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0281450d-db12-43b7-b078-b02f2e95a9c1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-91e61dfe-0c60-4ed0-8c88-dbfd85f4f586' class='xr-var-data-in' type='checkbox'><label for='data-91e61dfe-0c60-4ed0-8c88-dbfd85f4f586' 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.      ,  0.10101 ,  0.20202 ,  0.30303 ,  0.40404 ,  0.505051,
-        0.606061,  0.707071,  0.808081,  0.909091,  1.010101,  1.111111,
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-        1.818182,  1.919192,  2.020202,  2.121212,  2.222222,  2.323232,
-        2.424242,  2.525253,  2.626263,  2.727273,  2.828283,  2.929293,
-        3.030303,  3.131313,  3.232323,  3.333333,  3.434343,  3.535354,
-        3.636364,  3.737374,  3.838384,  3.939394,  4.040404,  4.141414,
-        4.242424,  4.343434,  4.444444,  4.545455,  4.646465,  4.747475,
-        4.848485,  4.949495,  5.050505,  5.151515,  5.252525,  5.353535,
-        5.454545,  5.555556,  5.656566,  5.757576,  5.858586,  5.959596,
-        6.060606,  6.161616,  6.262626,  6.363636,  6.464646,  6.565657,
-        6.666667,  6.767677,  6.868687,  6.969697,  7.070707,  7.171717,
-        7.272727,  7.373737,  7.474747,  7.575758,  7.676768,  7.777778,
-        7.878788,  7.979798,  8.080808,  8.181818,  8.282828,  8.383838,
-        8.484848,  8.585859,  8.686869,  8.787879,  8.888889,  8.989899,
-        9.090909,  9.191919,  9.292929,  9.393939,  9.494949,  9.59596 ,
-        9.69697 ,  9.79798 ,  9.89899 , 10.      ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7b6d7b8a-3a33-4ac2-92c6-72dd22cffdeb' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7b6d7b8a-3a33-4ac2-92c6-72dd22cffdeb' class='xr-section-summary' >Data variables: <span>(1)</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>y</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.5149 -0.7114 ... 31.78 32.77</div><input id='attrs-f45ad545-a6de-4cb9-aa08-db81961682eb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f45ad545-a6de-4cb9-aa08-db81961682eb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-66163812-f915-4a3e-b4fa-2a4be5796afb' class='xr-var-data-in' type='checkbox'><label for='data-66163812-f915-4a3e-b4fa-2a4be5796afb' 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([-0.51485544, -0.71136009, -1.25293135,  3.42554816,  3.02802318,
-        2.28996175,  1.30369905,  1.12342635,  4.696483  ,  3.55592901,
-        6.07850176,  5.65225376,  6.30163965,  6.21062672,  7.48114549,
-       10.4209593 ,  4.30685901,  3.34331655, 11.06410455,  8.0075721 ,
-        7.62191623,  5.771721  ,  8.9993412 ,  8.25634047, 10.1705519 ,
-        5.7614147 ,  7.63852438, 11.69554624,  9.2803849 ,  8.74384082,
-        4.70197652,  9.55192526,  8.01544118, 11.25992123, 10.25462628,
-       13.39701217, 13.07742985, 14.18054669, 14.94229116, 12.36204004,
-       11.47591127, 14.14875177, 12.76613791, 13.68796203, 15.8061059 ,
-       12.87511648, 13.97928359, 15.32178202, 16.82801938, 14.50979942,
-       14.93651816, 16.60607159, 18.55546995, 13.24463006, 18.3729172 ,
-       14.16656354, 17.32953375, 21.29800181, 16.82317851, 19.04953345,
-       19.77974641, 20.66126185, 19.86304768, 18.24686502, 17.73073581,
-       21.52896061, 22.41719182, 20.40032284, 24.38217873, 23.72367109,
-       22.01080873, 24.19549703, 21.77467738, 21.14727555, 24.36605289,
-       24.81749318, 21.43139972, 25.37209188, 23.56605562, 25.6332203 ,
-       26.54650393, 25.38526454, 25.54376272, 25.05455707, 24.78854931,
-       25.10641378, 29.38972575, 26.33082303, 27.44406156, 27.87010378,
-       26.88792478, 27.54458101, 30.97867305, 26.06115315, 26.74526954,
-       28.48091395, 31.67196315, 29.12184748, 31.77992649, 32.77431831])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4ded40ce-d19b-49e4-b2f5-6f6a3f4610af' class='xr-section-summary-in' type='checkbox'  ><label for='section-4ded40ce-d19b-49e4-b2f5-6f6a3f4610af' class='xr-section-summary' >Indexes: <span>(1)</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>t</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-513a7ad3-ffd7-4709-afae-634504bfc914' class='xr-index-data-in' type='checkbox'/><label for='index-513a7ad3-ffd7-4709-afae-634504bfc914' 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([                0.0, 0.10101010101010101, 0.20202020202020202,
-       0.30303030303030304, 0.40404040404040403,  0.5050505050505051,
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-        1.5151515151515151,  1.6161616161616161,  1.7171717171717171,
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-        3.0303030303030303,   3.131313131313131,  3.2323232323232323,
-        3.3333333333333335,  3.4343434343434343,  3.5353535353535355,
-        3.6363636363636362,  3.7373737373737375,  3.8383838383838382,
-        3.9393939393939394,   4.040404040404041,   4.141414141414141,
-         4.242424242424242,   4.343434343434343,   4.444444444444445,
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-         7.575757575757575,  7.6767676767676765,   7.777777777777778,
-         7.878787878787879,   7.979797979797979,   8.080808080808081,
-         8.181818181818182,   8.282828282828282,   8.383838383838384,
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-         9.696969696969697,   9.797979797979798,     9.8989898989899,
-                      10.0],
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-
-
-
-
-    
-![png](superquickstart_files/superquickstart_7_1.png)
-    
-
-
-## Initialize a simulation ✨
-
-In pymob, a **simulation object** is initialized by creating an instance of the {class}`pymob.simulation.SimulationBase` class from the simulation module.  
-We will choose a linear regression model, as it provides a good approximation of the data: $ y = a + b*x $
-
-```{admonition} x-dimension
-:class: note
-The x_dimension of our simulation can have any name, for example t as often used for time series data.
-You can specify it via `sim.config.simulation.x_dimension`.
-```
-
-
-```python
-# Initialize the Simulation object
-sim = SimulationBase()
-
-# configurate the case study
-sim.config.case_study.name = "superquickstart"
-sim.config.case_study.scenario = "linreg"
-
-# Define the linear regression model
-def linreg(x, a, b):
-    return a + b * x
-
-# Add the model to the simulation
-sim.model = linreg
-
-# Adding our dataset to the simulation
-sim.observations = data_obs
-
-# Defining a solver
-sim.solver = solve_analytic_1d
-
-# Take a look at the layut of the data
-sim.config.data_structure
-```
-
-    MinMaxScaler(variable=y, min=-1.2529313454358775, max=32.77431830696904)
-    
-
-    C:\Pymob\pymob\pymob\simulation.py:307: UserWarning: `sim.config.data_structure.y = Datavariable(dimensions=['t'] min=-1.2529313454358775 max=32.77431830696904 observed=True dimensions_evaluator=None)` has been assumed from `sim.observations`. If the order of the dimensions should be different, specify `sim.config.data_structure.y = DataVariable(dimensions=[...], ...)` manually.
-      warnings.warn(
-    
-
-
-
-
-    Datastructure(y=DataVariable(dimensions=['t'], min=-1.2529313454358775, max=32.77431830696904, observed=True, dimensions_evaluator=None))
-
-
-
-```{admonition} Scalers
-:class: note
-We notice a mysterious Scaler message. This tells us that our data variable has been identified and a scaler was constructed, which transforms the variable between [0, 1].   
-This has no effect at the moment, but it can be used later. Scaling can be powerful to help parameter estimation in more complex models.
-```
-
-
-## Parameterizing and running the model 🏃
-
-Next, we define the **model parameters** $a$ and $b$.  
-Parameter $a$ is set as fixed (`free = False`), meaning its value is known and will not be estimated during optimization.  
-Parameter $b$ is marked as free (`free = True`), allowing it to be optimized to fit the data. As an initial guess, we assume $b = 3$.   
-
-
-```python
-# Parameterizing the model
-sim.config.model_parameters.a = Param(value=1.0, free=False)
-sim.config.model_parameters.b = Param(value=3.0, free=True)
-# this makes sure the model parameters are available to the model.
-sim.model_parameters["parameters"] = sim.config.model_parameters.value_dict
-
-sim.model_parameters["parameters"] 
-```
-
-
-
-
-    {'a': 1.0, 'b': 3.0}
-
-
-
-Our model is now prepared with a defined parameter set.  
-To initialize the **Evaluator**, we call {meth}`pymob.simulation.SimulationBase.dispatch_constructor()`.   
-This step is essential and must be executed every time changes are made to the model. 
-
-The returned dataset (`evaluator.results`) has the exact same shape as the observation data.
-
-
-```python
-# put everything in place for running the simulation
-sim.dispatch_constructor()
-
-# run
-evaluator = sim.dispatch(theta={"b":3})
-evaluator()
-evaluator.results
-```
-
-    C:\Pymob\pymob\pymob\simulation.py:567: UserWarning: The number of ODE states was not specified in the config file [simulation] > 'n_ode_states = <n>'. Extracted the return arguments ['a+b*x'] from the source code. Setting 'n_ode_states=1.
-      warnings.warn(
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-.xr-icon-file-text2,
-.xr-no-icon {
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-}
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-.xr-var-attrs-in:checked + label > .xr-icon-file-text2,
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-.xr-index-data-in:checked + label > .xr-icon-database {
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-  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));
-  stroke-width: 0.8px;
-}
-</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 2kB
-Dimensions:  (t: 100)
-Coordinates:
-  * t        (t) float64 800B 0.0 0.101 0.202 0.303 ... 9.697 9.798 9.899 10.0
-Data variables:
-    y        (t) float64 800B 1.0 1.303 1.606 1.909 ... 30.09 30.39 30.7 31.0</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-bf80eaa8-9884-4187-be3c-beae3e76c5cd' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-bf80eaa8-9884-4187-be3c-beae3e76c5cd' 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'>t</span>: 100</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-8a9f7f00-6941-475e-86d3-da832a6b23b7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8a9f7f00-6941-475e-86d3-da832a6b23b7' class='xr-section-summary' >Coordinates: <span>(1)</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'>t</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.101 0.202 ... 9.899 10.0</div><input id='attrs-01b2ced7-51bd-42a0-989b-23685df30390' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-01b2ced7-51bd-42a0-989b-23685df30390' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-202ca2de-722e-427c-9aab-8db7f9a71528' class='xr-var-data-in' type='checkbox'><label for='data-202ca2de-722e-427c-9aab-8db7f9a71528' 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.      ,  0.10101 ,  0.20202 ,  0.30303 ,  0.40404 ,  0.505051,
-        0.606061,  0.707071,  0.808081,  0.909091,  1.010101,  1.111111,
-        1.212121,  1.313131,  1.414141,  1.515152,  1.616162,  1.717172,
-        1.818182,  1.919192,  2.020202,  2.121212,  2.222222,  2.323232,
-        2.424242,  2.525253,  2.626263,  2.727273,  2.828283,  2.929293,
-        3.030303,  3.131313,  3.232323,  3.333333,  3.434343,  3.535354,
-        3.636364,  3.737374,  3.838384,  3.939394,  4.040404,  4.141414,
-        4.242424,  4.343434,  4.444444,  4.545455,  4.646465,  4.747475,
-        4.848485,  4.949495,  5.050505,  5.151515,  5.252525,  5.353535,
-        5.454545,  5.555556,  5.656566,  5.757576,  5.858586,  5.959596,
-        6.060606,  6.161616,  6.262626,  6.363636,  6.464646,  6.565657,
-        6.666667,  6.767677,  6.868687,  6.969697,  7.070707,  7.171717,
-        7.272727,  7.373737,  7.474747,  7.575758,  7.676768,  7.777778,
-        7.878788,  7.979798,  8.080808,  8.181818,  8.282828,  8.383838,
-        8.484848,  8.585859,  8.686869,  8.787879,  8.888889,  8.989899,
-        9.090909,  9.191919,  9.292929,  9.393939,  9.494949,  9.59596 ,
-        9.69697 ,  9.79798 ,  9.89899 , 10.      ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-bf16fc3b-2d10-481c-8d0b-20669c196b3b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-bf16fc3b-2d10-481c-8d0b-20669c196b3b' class='xr-section-summary' >Data variables: <span>(1)</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>y</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.0 1.303 1.606 ... 30.39 30.7 31.0</div><input id='attrs-6334fbe2-584c-43f9-bb4a-d9330aaf2fbd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6334fbe2-584c-43f9-bb4a-d9330aaf2fbd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0dc278ee-7131-4f84-97ca-bd6b2242f94c' class='xr-var-data-in' type='checkbox'><label for='data-0dc278ee-7131-4f84-97ca-bd6b2242f94c' 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([ 1.        ,  1.3030303 ,  1.60606061,  1.90909091,  2.21212121,
-        2.51515152,  2.81818182,  3.12121212,  3.42424242,  3.72727273,
-        4.03030303,  4.33333333,  4.63636364,  4.93939394,  5.24242424,
-        5.54545455,  5.84848485,  6.15151515,  6.45454545,  6.75757576,
-        7.06060606,  7.36363636,  7.66666667,  7.96969697,  8.27272727,
-        8.57575758,  8.87878788,  9.18181818,  9.48484848,  9.78787879,
-       10.09090909, 10.39393939, 10.6969697 , 11.        , 11.3030303 ,
-       11.60606061, 11.90909091, 12.21212121, 12.51515152, 12.81818182,
-       13.12121212, 13.42424242, 13.72727273, 14.03030303, 14.33333333,
-       14.63636364, 14.93939394, 15.24242424, 15.54545455, 15.84848485,
-       16.15151515, 16.45454545, 16.75757576, 17.06060606, 17.36363636,
-       17.66666667, 17.96969697, 18.27272727, 18.57575758, 18.87878788,
-       19.18181818, 19.48484848, 19.78787879, 20.09090909, 20.39393939,
-       20.6969697 , 21.        , 21.3030303 , 21.60606061, 21.90909091,
-       22.21212121, 22.51515152, 22.81818182, 23.12121212, 23.42424242,
-       23.72727273, 24.03030303, 24.33333333, 24.63636364, 24.93939394,
-       25.24242424, 25.54545455, 25.84848485, 26.15151515, 26.45454545,
-       26.75757576, 27.06060606, 27.36363636, 27.66666667, 27.96969697,
-       28.27272727, 28.57575758, 28.87878788, 29.18181818, 29.48484848,
-       29.78787879, 30.09090909, 30.39393939, 30.6969697 , 31.        ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e789d90c-b446-4d5c-8ab1-417cdaa87542' class='xr-section-summary-in' type='checkbox'  ><label for='section-e789d90c-b446-4d5c-8ab1-417cdaa87542' class='xr-section-summary' >Indexes: <span>(1)</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>t</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-b435329b-68e7-4db5-b4f8-37c8c4966a05' class='xr-index-data-in' type='checkbox'/><label for='index-b435329b-68e7-4db5-b4f8-37c8c4966a05' 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([                0.0, 0.10101010101010101, 0.20202020202020202,
-       0.30303030303030304, 0.40404040404040403,  0.5050505050505051,
-        0.6060606060606061,  0.7070707070707071,  0.8080808080808081,
-        0.9090909090909091,  1.0101010101010102,  1.1111111111111112,
-        1.2121212121212122,  1.3131313131313131,  1.4141414141414141,
-        1.5151515151515151,  1.6161616161616161,  1.7171717171717171,
-        1.8181818181818181,  1.9191919191919191,  2.0202020202020203,
-         2.121212121212121,  2.2222222222222223,   2.323232323232323,
-        2.4242424242424243,   2.525252525252525,  2.6262626262626263,
-         2.727272727272727,  2.8282828282828283,   2.929292929292929,
-        3.0303030303030303,   3.131313131313131,  3.2323232323232323,
-        3.3333333333333335,  3.4343434343434343,  3.5353535353535355,
-        3.6363636363636362,  3.7373737373737375,  3.8383838383838382,
-        3.9393939393939394,   4.040404040404041,   4.141414141414141,
-         4.242424242424242,   4.343434343434343,   4.444444444444445,
-         4.545454545454545,   4.646464646464646,   4.747474747474747,
-         4.848484848484849,    4.94949494949495,    5.05050505050505,
-         5.151515151515151,   5.252525252525253,   5.353535353535354,
-         5.454545454545454,   5.555555555555555,   5.656565656565657,
-         5.757575757575758,   5.858585858585858,   5.959595959595959,
-        6.0606060606060606,   6.161616161616162,   6.262626262626262,
-         6.363636363636363,  6.4646464646464645,   6.565656565656566,
-         6.666666666666667,   6.767676767676767,  6.8686868686868685,
-          6.96969696969697,   7.070707070707071,   7.171717171717171,
-        7.2727272727272725,   7.373737373737374,   7.474747474747475,
-         7.575757575757575,  7.6767676767676765,   7.777777777777778,
-         7.878787878787879,   7.979797979797979,   8.080808080808081,
-         8.181818181818182,   8.282828282828282,   8.383838383838384,
-         8.484848484848484,   8.585858585858587,   8.686868686868687,
-         8.787878787878787,    8.88888888888889,    8.98989898989899,
-          9.09090909090909,   9.191919191919192,   9.292929292929292,
-         9.393939393939394,   9.494949494949495,   9.595959595959595,
-         9.696969696969697,   9.797979797979798,     9.8989898989899,
-                      10.0],
-      dtype=&#x27;float64&#x27;, name=&#x27;t&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7c36564d-d456-4bb2-9623-ea54a6fa2551' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-7c36564d-d456-4bb2-9623-ea54a6fa2551' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>
-
-
-
-```{admonition} What does the dispatch constructor do?
-:class: hint
-Behind the scenes, the dispatch constructor assembles a lightweight Evaluator object from the Simulation object, that takes the least necessary amount of information, runs it through some dimension checks, and also connects it to the specified solver and initializes it. The purpose of the dispatch constructor is manyfold:
-By executing the entire overhead of a model evaluation and packing it into a new Evaluator instance sim.dispatch_constructor() to make single model evaluations as fast as possible and allow parallel evaluations, because each evaluator created by sim.dispatch() is it's a fully independent model instance with a separate set of parameters that can be solved.
-Evaluators store the raw output from a simulation and can generate an xarray object from it that corresponds to the data-structure of the observations with the evaluator.results property. This automatically aligns simulations results with observations, for simple computation of loss functions.
-```
-
-Let's take a look at the **results**.  
-
-You can vary the parameter $b$ in the previous step to investigate its influence on the model fit.  
-In the [Introduction](https://pymob.readthedocs.io/en/stable/user_guide/introduction.html), you can try out the *manual parameter estimation*, which is a feature provided by Pymob.  
-
-
-```python
-fig, ax = plt.subplots(figsize=(5, 4))
-data_res = evaluator.results
-ax.plot(data_obs.t, data_obs.y, ls="", marker="o", color="tab:blue", alpha=.5, label ="observation data")
-ax.plot(data_res.t, data_res.y, color="black", label ="result")
-ax.legend()
-```
-
-
-
-
-    <matplotlib.legend.Legend at 0x24c375963d0>
-
-
-
-
-    
-![png](superquickstart_files/superquickstart_18_1.png)
-    
-
-
-## Estimating parameters and uncertainty with MCMC 🤔
-Of course this example is very simple. In fact, we could optimize the parameters perfectly by hand.   
-But just for fun, let's use *Markov Chain Monte Carlo (MCMC)* to estimate the parameters, their uncertainty and the uncertainty in the data.   
-We’ll run the parameter estimation with our **inferer**, using the NumPyro backend with a NUTS kernel. This completes the job in a few seconds.
-
-We are almost ready to infer the model parameters. To also estimate the uncertainty of the parameters, we add another parameter representing the error and assume that it follows a lognormal distribution.   
-Additionally, we specify an error model for the data distribution. This will be: $$y_{obs} \sim Normal (y, \sigma_y)$$  
-
-Since $\sigma_y$ is not a fixed parameter, it doesn't need to be passed to the simulation class.
-
-
-```python
-sim.config.model_parameters.sigma_y = Param(free=True , prior="lognorm(scale=1,s=1)", min=0, max=1)
-sim.config.model_parameters.b.prior = "lognorm(scale=1,s=1)"
-
-sim.config.error_model.y = "normal(loc=y,scale=sigma_y)"
-
-
-sim.set_inferer("numpyro")
-sim.inferer.config.inference_numpyro.kernel = "nuts"
-sim.inferer.run()
-
-sim.inferer.idata.posterior
-
-# Plot the results
-sim.config.simulation.x_dimension = "t"
-sim.posterior_predictive_checks(pred_hdi_style={"alpha": 0.1})
-```
-
-    Jax 64 bit mode: False
-    Absolute tolerance: 1e-07
-    Trace Shapes:      
-     Param Sites:      
-    Sample Sites:      
-           b dist     |
-            value     |
-     sigma_y dist     |
-            value     |
-       y_obs dist 100 |
-            value 100 |
-    
-
-    sample: 100%|██████████| 3000/3000 [00:01<00:00, 1963.23it/s, 7 steps of size 8.27e-01. acc. prob=0.92]
-    
-
-    
-                    mean       std    median      5.0%     95.0%     n_eff     r_hat
-             b      2.98      0.03      2.98      2.92      3.03   1611.92      1.00
-       sigma_y      1.83      0.13      1.82      1.61      2.04   1703.02      1.00
-    
-    Number of divergences: 0
-    
-
-
-    
-![png](superquickstart_files/superquickstart_20_3.png)
-    
-
-
-```{admonition} numpyro distributions
-:class: warning
-Currently only few distributions are implemented in the numpyro backend. This API will soon change, so that basically any distribution can be used to specifcy parameters. 
-```
-
-We can **inspect our estimates** and see that the model provides a good fit for the parameters.  
-Note that we only get an estimate for $b$. Previously, we set the parameter $a$ with the flag `free = False`.   
-This effectively excludes it from the estimation and uses its default value, which was set to the true value `a = 0`.
-
-
-```{admonition} Customize the posterior predictive checks
-:class: hint
-You can explore the API of {class}`pymob.sim.plot.SimulationPlot` to find out how you can work on the default predictions. Of course you can always make your own plot, by accessing {attr}`pymob.simulation.inferer.idata` and {attr}`pymob.simulation.observations`
-```
-
-## Report the results 🗒️
-
-Pymob provides the option to generate an automated report of the parameter distribution for a simulation.  
-The report can be configured by modifying the options in {meth}`pymob.simulation.SimulationBase.config.report`.
-
-
-```python
-# report the results
-sim.report()
-```
-
-![posterior_trace.png](superquickstart_files/posterior_trace.png)
-
-![posterior_pairs.png](superquickstart_files/posterior_pairs.png)
-
-
-## Exporting the simulation and running it via the case study API 📤
-
-After constructing the simulation, all settings - custom and default - can be exported to a comprehensive configuration file.   
-The simulation will be saved to the default path (`CASE_STUDY/scenarios/SCENARIO/settings.cfg`) or to a custom path, specified with the file path keyword `fp`.   
-Setting `force=True` will overwrite any existing config file, which is a reasonable choice in most cases.
-From this point on, the simulation is (almost) ready to be executed from the command-line. 
-
-
-```python
-import os
-sim.config.create_directory("scenario", force=True)
-sim.config.create_directory("results", force=True)
-
-# usually we expect to have a data directory in the case
-os.makedirs(sim.data_path, exist_ok=True)
-sim.save_observations(force=True)
-sim.config.save(force=True)
-```
-
-    Scenario directory exists at 'c:\Users\mgrho\pymob\docs\source\user_guide\case_studies\superquickstart\scenarios\linreg'.
-    Results directory exists at 'c:\Users\mgrho\pymob\docs\source\user_guide\case_studies\superquickstart\results\linreg'.
-    
-
-### Commandline API
-
-The command-line API runs a series of commands that load the case study, execute the {meth}`pymob.simulation.SimulationBase.initialize` method and perform some more initialization tasks before running the required job.
-
-+ `pymob-infer` runs an inference job, for example:  
-
-  `pymob-infer --case_study=quickstart --scenario=test --inference_backend=numpyro`.   
-  While there are more command-line options, these two (--case_study and --scenario) are required.
-
-
+# Pymob in minutes - the basics
+
+This guide provides a streamlined introduction to the basic Pymob workflow and its key functionalities.  
+We will explore a simple linear regression model that we want to fit to a noisy dataset.  
+Pymob supports the modeling process by providing several tools for *data structuring*, *parameter estimation* and *visualization of results*.  
+  
+If you are looking for a more detailed introduction, [click here](https://pymob.readthedocs.io/en/stable/user_guide/introduction.html).  
+If you want to learn how to work with ODE models, check out [this tutorial]().
+
+## Pymob components 🧩
+
+Before starting the modeling process, let's take a look at the main steps and modules of pymob:
+
+1. __Simulation:__   
+First, we need to initialize a Simulation object by creating an instance of the {class}`pymob.simulation.SimulationBase` class from the simulation module.   
+Optionally, we can configure the simulation with `sim.config.case_study.name = "linear-regression"`, `sim.config.case_study.scenario = "test"` and many other options. 
+
+2. __Model:__   
+Our model will be defined as a standard python function.  
+We will then assign it to the Simulation object by accessing the `.model` attribute. 
+
+3. __Observations:__   
+Our observation data must be structured as an [xarray.Dataset](https://docs.xarray.dev/en/stable/generated/xarray.Dataset.html).  
+We assign it to the `~pymob.sim.config.Casestudy.observations ` attribute of our Simulation object.   
+Calling `sim.config.data_structure` will give us further information about the layout of our data.  
+
+4. __Solver:__  
+A [solver](https://pymob.readthedocs.io/en/stable/api/pymob.solvers.html) is required to solve the model.   
+In our simple case, we will use the `solve_analytic_1d` solver from the `~pymob.solver.analytic` module.  
+We assign it to our Simulation object using the {attr}`pymob.simulation.solver` attribute.   
+Since our model already provides an analytical solution, this solver basically does nothing. It is still needed to fulfill Pymob's requirement for a solver component.   
+For more complex models (e.g. ODEs), the `JaxSolver` from the `~pymob.solver.diffrax` module is a more powerful option.   
+Users can also implement custom solvers as a subclass of {class}`pymob.solver.SolverBase`.   
+  
+5. __Inferer:__  
+The inferer handels the parameter estimation.  
+Pymob supports [various backends](https://pymob.readthedocs.io/en/stable/user_guide/framework_overview.html). In this example, we will work with *NumPyro*.  
+We assign the inferer to our Simulation object via the {attr}`~pymob.simulation.inferer` attribute and configure the desired kernel (e.g. *nuts*).  
+But before inference, we need to parameterize our model using the *Param* class.   
+Each parameter can be marked either as free or fixed, depending on whether it should be variable during the optimization procedure.   
+The parameters are stored in the {attr}`~pymob.simulation.SimulationBase.model_parameters` dictionary, which holds model input values.
+By default, it takes the keys: `parameters`, `y0` and `x_in`. 
+
+6. __Evaluator:__  
+The Evaluator is an instance to manage model evaluations. It sets up tasks, coordinates parallel runs of the simulation and keeps track of the results from each simulation or parameter inference process.   
+Evaluators store the raw output from a simulation and can generate an xarray object from it that corresponds to the data-structure of the observations with the ~pymob.sim.evaluator.Evaluator.results` property. This automatically aligns the simulations results with the observations, for simple computation of loss functions.  
+
+7. __Config:__  
+The simulation settings will be saved in a `.cfg` configuration file.  
+The config file contains information about our simulation in various sections. -> [Learn more here](https://pymob.readthedocs.io/en/stable/user_guide/case_studies.html#configuration).  
+We can further use it to create new simulations by loading settings from a config file. 
+
+
+![framework-overview](.\figures\pymob_overview.png)
+
+## Getting started 🛫
+
+
+```python
+# First, import the necessary python packages
+import numpy as np
+import matplotlib.pyplot as plt
+import xarray as xr
+
+# Import the pymob modules
+from pymob.simulation import SimulationBase
+from pymob.sim.solvetools import solve_analytic_1d
+from pymob.sim.config import Param
+```
+
+Since no measured data is provided, we will generate an artificial dataset.  
+$y_{obs}$ represents the **observed data** over the time $t$ [0, 10].  
+To use this data later in the simulation, we need to convert it into an **xarray-Dataset**.  
+In your own application, you would replace this with your measured experimental data.  
+
+
+```python
+# Parameter for the artificial data generation
+rng = np.random.default_rng(seed=1)  # for reproducibility
+slope = rng.uniform(2,4)
+intercept = 1.0
+num_points = 100
+noise_level = 1.7
+
+# generating time values
+t = np.linspace(0, 10, num_points)
+
+# generating y-values with noise
+noise = np.random.normal(0, noise_level, num_points)
+y_obs = slope * t + intercept + noise
+
+# visualizing our data
+fig, ax = plt.subplots(figsize=(5, 4))
+ax.scatter(t, y_obs, label='Datapoints')
+ax.set(xlabel='t [-]', ylabel='y_obs [-]', title ='Artificial Data')
+plt.tight_layout()
+
+# convert the data to an xr-Dataset
+data_obs = xr.DataArray(y_obs, coords={"t": t}).to_dataset(name="y")
+data_obs
+```
+
+
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+  * t        (t) float64 800B 0.0 0.101 0.202 0.303 ... 9.697 9.798 9.899 10.0
+Data variables:
+    y        (t) float64 800B 2.313 3.534 1.349 2.437 ... 31.34 32.63 32.2 29.24</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-33e5c0c5-7b93-4c38-8ed4-024270a3232d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-33e5c0c5-7b93-4c38-8ed4-024270a3232d' 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'>t</span>: 100</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-bb5d2338-b720-4f3c-b96f-2736a3ffe7be' class='xr-section-summary-in' type='checkbox'  checked><label for='section-bb5d2338-b720-4f3c-b96f-2736a3ffe7be' class='xr-section-summary' >Coordinates: <span>(1)</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'>t</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.101 0.202 ... 9.899 10.0</div><input id='attrs-dc9c427f-013f-46c5-b504-23723fcbf3c6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dc9c427f-013f-46c5-b504-23723fcbf3c6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1f2ba592-2ada-4ccc-830a-d9beb5322ec8' class='xr-var-data-in' type='checkbox'><label for='data-1f2ba592-2ada-4ccc-830a-d9beb5322ec8' 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.      ,  0.10101 ,  0.20202 ,  0.30303 ,  0.40404 ,  0.505051,
+        0.606061,  0.707071,  0.808081,  0.909091,  1.010101,  1.111111,
+        1.212121,  1.313131,  1.414141,  1.515152,  1.616162,  1.717172,
+        1.818182,  1.919192,  2.020202,  2.121212,  2.222222,  2.323232,
+        2.424242,  2.525253,  2.626263,  2.727273,  2.828283,  2.929293,
+        3.030303,  3.131313,  3.232323,  3.333333,  3.434343,  3.535354,
+        3.636364,  3.737374,  3.838384,  3.939394,  4.040404,  4.141414,
+        4.242424,  4.343434,  4.444444,  4.545455,  4.646465,  4.747475,
+        4.848485,  4.949495,  5.050505,  5.151515,  5.252525,  5.353535,
+        5.454545,  5.555556,  5.656566,  5.757576,  5.858586,  5.959596,
+        6.060606,  6.161616,  6.262626,  6.363636,  6.464646,  6.565657,
+        6.666667,  6.767677,  6.868687,  6.969697,  7.070707,  7.171717,
+        7.272727,  7.373737,  7.474747,  7.575758,  7.676768,  7.777778,
+        7.878788,  7.979798,  8.080808,  8.181818,  8.282828,  8.383838,
+        8.484848,  8.585859,  8.686869,  8.787879,  8.888889,  8.989899,
+        9.090909,  9.191919,  9.292929,  9.393939,  9.494949,  9.59596 ,
+        9.69697 ,  9.79798 ,  9.89899 , 10.      ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9a0dc0ba-cfd4-4b53-9ea4-9851b667c217' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9a0dc0ba-cfd4-4b53-9ea4-9851b667c217' class='xr-section-summary' >Data variables: <span>(1)</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>y</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.313 3.534 1.349 ... 32.2 29.24</div><input id='attrs-23a2e428-887a-451e-9211-3a1e8e051fdd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-23a2e428-887a-451e-9211-3a1e8e051fdd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-116d2e00-93d8-4039-b1f1-64e232315d88' class='xr-var-data-in' type='checkbox'><label for='data-116d2e00-93d8-4039-b1f1-64e232315d88' 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([ 2.31344255,  3.53433593,  1.34908632,  2.43689987,  0.52129732,
+        3.37612748,  1.90897385,  6.29056489,  3.25194086,  3.37744078,
+        9.0193145 ,  5.8191261 ,  5.38044197,  7.66364914,  6.60103552,
+        6.19300329,  5.27990676,  7.52361384,  7.10757993,  6.0356579 ,
+        7.2188985 ,  5.85003633,  6.17868995,  9.50163557,  7.92126308,
+        7.98138724,  8.48669928,  7.88744995, 12.14526444,  9.34691599,
+        8.85736082,  6.8697178 ,  8.28332729, 10.89691933, 14.76845224,
+       10.19777631, 13.00627447, 12.2166908 , 13.28187352, 11.63794867,
+       13.10039385, 14.74542854, 15.34570065, 13.59974961, 15.05159625,
+       12.64807788, 15.76381504, 15.86640267, 15.54728581, 17.85995723,
+       15.50995487, 14.79101145, 18.16856046, 20.17045554, 16.49446633,
+       18.41673529, 17.24657162, 15.49351063, 22.49363636, 20.10535031,
+       19.28376332, 19.72132889, 19.68426233, 19.92852017, 21.63053763,
+       19.43820789, 22.77571803, 21.52550027, 21.36025545, 23.63246365,
+       22.4934742 , 22.81210646, 20.97514826, 21.98038786, 25.49873142,
+       24.81276344, 22.36927849, 24.81073349, 22.51059971, 27.99373936,
+       24.28979222, 26.35288122, 27.66829318, 24.70447271, 27.59622693,
+       29.05306128, 27.99765931, 27.0760332 , 28.29855623, 27.65515164,
+       29.21482719, 26.26966137, 28.90332653, 30.18122177, 31.47070239,
+       30.51950515, 31.33660832, 32.63472848, 32.19772335, 29.23970369])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b06c176f-f31a-43ae-9008-25ae32f4df4d' class='xr-section-summary-in' type='checkbox'  ><label for='section-b06c176f-f31a-43ae-9008-25ae32f4df4d' class='xr-section-summary' >Indexes: <span>(1)</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>t</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-94e3529b-6d2d-468e-aca1-30ca04af86c8' class='xr-index-data-in' type='checkbox'/><label for='index-94e3529b-6d2d-468e-aca1-30ca04af86c8' 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([                0.0, 0.10101010101010101, 0.20202020202020202,
+       0.30303030303030304, 0.40404040404040403,  0.5050505050505051,
+        0.6060606060606061,  0.7070707070707071,  0.8080808080808081,
+        0.9090909090909091,  1.0101010101010102,  1.1111111111111112,
+        1.2121212121212122,  1.3131313131313131,  1.4141414141414141,
+        1.5151515151515151,  1.6161616161616161,  1.7171717171717171,
+        1.8181818181818181,  1.9191919191919191,  2.0202020202020203,
+         2.121212121212121,  2.2222222222222223,   2.323232323232323,
+        2.4242424242424243,   2.525252525252525,  2.6262626262626263,
+         2.727272727272727,  2.8282828282828283,   2.929292929292929,
+        3.0303030303030303,   3.131313131313131,  3.2323232323232323,
+        3.3333333333333335,  3.4343434343434343,  3.5353535353535355,
+        3.6363636363636362,  3.7373737373737375,  3.8383838383838382,
+        3.9393939393939394,   4.040404040404041,   4.141414141414141,
+         4.242424242424242,   4.343434343434343,   4.444444444444445,
+         4.545454545454545,   4.646464646464646,   4.747474747474747,
+         4.848484848484849,    4.94949494949495,    5.05050505050505,
+         5.151515151515151,   5.252525252525253,   5.353535353535354,
+         5.454545454545454,   5.555555555555555,   5.656565656565657,
+         5.757575757575758,   5.858585858585858,   5.959595959595959,
+        6.0606060606060606,   6.161616161616162,   6.262626262626262,
+         6.363636363636363,  6.4646464646464645,   6.565656565656566,
+         6.666666666666667,   6.767676767676767,  6.8686868686868685,
+          6.96969696969697,   7.070707070707071,   7.171717171717171,
+        7.2727272727272725,   7.373737373737374,   7.474747474747475,
+         7.575757575757575,  7.6767676767676765,   7.777777777777778,
+         7.878787878787879,   7.979797979797979,   8.080808080808081,
+         8.181818181818182,   8.282828282828282,   8.383838383838384,
+         8.484848484848484,   8.585858585858587,   8.686868686868687,
+         8.787878787878787,    8.88888888888889,    8.98989898989899,
+          9.09090909090909,   9.191919191919192,   9.292929292929292,
+         9.393939393939394,   9.494949494949495,   9.595959595959595,
+         9.696969696969697,   9.797979797979798,     9.8989898989899,
+                      10.0],
+      dtype=&#x27;float64&#x27;, name=&#x27;t&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-adcd8708-1985-4edd-b64e-9ea75926ce6b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-adcd8708-1985-4edd-b64e-9ea75926ce6b' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>
+
+
+
+
+    
+![png](superquickstart_files/superquickstart_7_1.png)
+    
+
+
+## Initialize a simulation ✨
+
+In pymob, a **simulation object** is initialized by creating an instance of the {class}`~pymob.simulation.SimulationBase` class from the simulation module.  
+We will choose a linear regression model, as it provides a good approximation of the data: $ y = a + b*x $
+
+```{admonition} x-dimension
+:class: note
+The x_dimension of our simulation can have any name, for example t as often used for time series data.
+You can specify it via `sim.config.simulation.x_dimension`.
+```
+
+
+```python
+# Initialize the Simulation object
+sim = SimulationBase()
+
+# configurate the case study
+sim.config.case_study.name = "superquickstart"
+sim.config.case_study.scenario = "linreg"
+
+# Define the linear regression model
+def linreg(x, a, b):
+    return a + b * x
+
+# Add the model to the simulation
+sim.model = linreg
+
+# Adding our dataset to the simulation
+sim.observations = data_obs
+
+# Defining a solver
+sim.solver = solve_analytic_1d
+
+# Take a look at the layut of the data
+sim.config.data_structure
+```
+
+    MinMaxScaler(variable=y, min=0.5212973246575279, max=32.634728477251194)
+    
+
+    C:\Pymob\pymob\pymob\simulation.py:307: UserWarning: `sim.config.data_structure.y = Datavariable(dimensions=['t'] min=0.5212973246575279 max=32.634728477251194 observed=True dimensions_evaluator=None)` has been assumed from `sim.observations`. If the order of the dimensions should be different, specify `sim.config.data_structure.y = DataVariable(dimensions=[...], ...)` manually.
+      warnings.warn(
+    
+
+
+
+
+    Datastructure(y=DataVariable(dimensions=['t'], min=0.5212973246575279, max=32.634728477251194, observed=True, dimensions_evaluator=None))
+
+
+
+```{admonition} Scalers
+:class: note
+We notice a mysterious Scaler message. This tells us that our data variable has been identified and a scaler was constructed, which transforms the variable between [0, 1].   
+This has no effect at the moment, but it can be used later. Scaling can be powerful to help parameter estimation in more complex models.
+```
+
+
+## Parameterizing and running the model 🏃
+
+Next, we define the **model parameters** $a$ and $b$.  
+Parameter $a$ is set as fixed (`free = False`), meaning its value is known and will not be estimated during optimization.  
+Parameter $b$ is marked as free (`free = True`), allowing it to be optimized to fit the data. As an initial guess, we assume $b = 3$.   
+
+
+```python
+# Parameterizing the model
+sim.config.model_parameters.a = Param(value=1.0, free=False)
+sim.config.model_parameters.b = Param(value=3.0, free=True)
+# this makes sure the model parameters are available to the model.
+sim.model_parameters["parameters"] = sim.config.model_parameters.value_dict
+
+sim.model_parameters["parameters"] 
+```
+
+
+
+
+    {'a': 1.0, 'b': 3.0}
+
+
+
+Our model is now prepared with a defined parameter set.  
+To initialize the **Evaluator**, we call {meth}`~pymob.simulation.SimulationBase.dispatch_constructor()`.   
+This step is essential and must be executed every time changes are made to the model. 
+
+The returned dataset (`evaluator.results`) has the exact same shape as the observation data.
+
+
+```python
+# put everything in place for running the simulation
+sim.dispatch_constructor()
+
+# run
+evaluator = sim.dispatch(theta={"b":3})
+evaluator()
+evaluator.results
+```
+
+    C:\Pymob\pymob\pymob\simulation.py:567: UserWarning: The number of ODE states was not specified in the config file [simulation] > 'n_ode_states = <n>'. Extracted the return arguments ['a+b*x'] from the source code. Setting 'n_ode_states=1.
+      warnings.warn(
+    
+
+
+
+
+<div><svg style="position: absolute; width: 0; height: 0; overflow: hidden">
+<defs>
+<symbol id="icon-database" viewBox="0 0 32 32">
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+    hsl(from var(--pst-color-on-background, white) h s calc(l - 40))
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+    var(--pst-color-on-background, white)
+  );
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+}
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+  color: var(--xr-disabled-color);
+  border: 2px solid transparent !important;
+}
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+  color: var(--xr-font-color2);
+}
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+}
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+}
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+}
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+  padding: 0 5px !important;
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+}
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+}
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+.xr-array-preview {
+  display: inline-block;
+}
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+  display: inline-block !important;
+  list-style: none;
+  padding: 0 !important;
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+}
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+}
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+}
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+  background-color: var(--xr-background-color-row-even);
+  border-color: var(--xr-background-color-row-odd);
+  margin-bottom: 0;
+  padding-top: 2px;
+}
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+  padding-right: 5px;
+}
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+  background-color: var(--xr-background-color-row-odd);
+  border-color: var(--xr-background-color-row-even);
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+}
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+}
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+  overflow: visible;
+  width: auto;
+  z-index: 1;
+}
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+  display: none;
+  border-top: 2px dotted var(--xr-background-color);
+  padding-bottom: 20px !important;
+  padding-top: 10px !important;
+}
+
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+.xr-index-data-in + label {
+  padding: 0 1px;
+}
+
+.xr-var-attrs-in:checked ~ .xr-var-attrs,
+.xr-var-data-in:checked ~ .xr-var-data,
+.xr-index-data-in:checked ~ .xr-index-data {
+  display: block;
+}
+
+.xr-var-data > table {
+  float: right;
+}
+
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+.xr-index-data > pre,
+.xr-var-data > table > tbody > tr {
+  background-color: transparent !important;
+}
+
+.xr-var-name span,
+.xr-var-data,
+.xr-index-name div,
+.xr-index-data,
+.xr-attrs {
+  padding-left: 25px !important;
+}
+
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+  grid-column: 1 / -1;
+}
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+  margin: 0;
+  display: grid;
+  grid-template-columns: 125px auto;
+}
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+  padding: 0;
+  margin: 0;
+  float: left;
+  padding-right: 10px;
+  width: auto;
+}
+
+.xr-attrs dt {
+  font-weight: normal;
+  grid-column: 1;
+}
+
+.xr-attrs dt:hover span {
+  display: inline-block;
+  background: var(--xr-background-color);
+  padding-right: 10px;
+}
+
+.xr-attrs dd {
+  grid-column: 2;
+  white-space: pre-wrap;
+  word-break: break-all;
+}
+
+.xr-icon-database,
+.xr-icon-file-text2,
+.xr-no-icon {
+  display: inline-block;
+  vertical-align: middle;
+  width: 1em;
+  height: 1.5em !important;
+  stroke-width: 0;
+  stroke: currentColor;
+  fill: currentColor;
+}
+
+.xr-var-attrs-in:checked + label > .xr-icon-file-text2,
+.xr-var-data-in:checked + label > .xr-icon-database,
+.xr-index-data-in:checked + label > .xr-icon-database {
+  color: var(--xr-font-color0);
+  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));
+  stroke-width: 0.8px;
+}
+</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 2kB
+Dimensions:  (t: 100)
+Coordinates:
+  * t        (t) float64 800B 0.0 0.101 0.202 0.303 ... 9.697 9.798 9.899 10.0
+Data variables:
+    y        (t) float64 800B 1.0 1.303 1.606 1.909 ... 30.09 30.39 30.7 31.0</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-9d27d71e-f5db-467f-a3fd-a65cfe042cc0' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-9d27d71e-f5db-467f-a3fd-a65cfe042cc0' 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'>t</span>: 100</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-1bef969a-2d93-4669-ab41-b5a64a363492' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1bef969a-2d93-4669-ab41-b5a64a363492' class='xr-section-summary' >Coordinates: <span>(1)</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'>t</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.101 0.202 ... 9.899 10.0</div><input id='attrs-8e1facdb-7ef4-433a-a5eb-2241f2284cae' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8e1facdb-7ef4-433a-a5eb-2241f2284cae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bc0ad2fe-efcb-4148-a881-c47399660fcd' class='xr-var-data-in' type='checkbox'><label for='data-bc0ad2fe-efcb-4148-a881-c47399660fcd' 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.      ,  0.10101 ,  0.20202 ,  0.30303 ,  0.40404 ,  0.505051,
+        0.606061,  0.707071,  0.808081,  0.909091,  1.010101,  1.111111,
+        1.212121,  1.313131,  1.414141,  1.515152,  1.616162,  1.717172,
+        1.818182,  1.919192,  2.020202,  2.121212,  2.222222,  2.323232,
+        2.424242,  2.525253,  2.626263,  2.727273,  2.828283,  2.929293,
+        3.030303,  3.131313,  3.232323,  3.333333,  3.434343,  3.535354,
+        3.636364,  3.737374,  3.838384,  3.939394,  4.040404,  4.141414,
+        4.242424,  4.343434,  4.444444,  4.545455,  4.646465,  4.747475,
+        4.848485,  4.949495,  5.050505,  5.151515,  5.252525,  5.353535,
+        5.454545,  5.555556,  5.656566,  5.757576,  5.858586,  5.959596,
+        6.060606,  6.161616,  6.262626,  6.363636,  6.464646,  6.565657,
+        6.666667,  6.767677,  6.868687,  6.969697,  7.070707,  7.171717,
+        7.272727,  7.373737,  7.474747,  7.575758,  7.676768,  7.777778,
+        7.878788,  7.979798,  8.080808,  8.181818,  8.282828,  8.383838,
+        8.484848,  8.585859,  8.686869,  8.787879,  8.888889,  8.989899,
+        9.090909,  9.191919,  9.292929,  9.393939,  9.494949,  9.59596 ,
+        9.69697 ,  9.79798 ,  9.89899 , 10.      ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-39530410-ec77-4f64-ad06-7988693f0afc' class='xr-section-summary-in' type='checkbox'  checked><label for='section-39530410-ec77-4f64-ad06-7988693f0afc' class='xr-section-summary' >Data variables: <span>(1)</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>y</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.0 1.303 1.606 ... 30.39 30.7 31.0</div><input id='attrs-d5bc885b-3334-49a2-ad8c-0080d0bc0e1e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d5bc885b-3334-49a2-ad8c-0080d0bc0e1e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fad708e8-94f6-4b4e-bc29-2bc97653d0fb' class='xr-var-data-in' type='checkbox'><label for='data-fad708e8-94f6-4b4e-bc29-2bc97653d0fb' 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([ 1.        ,  1.3030303 ,  1.60606061,  1.90909091,  2.21212121,
+        2.51515152,  2.81818182,  3.12121212,  3.42424242,  3.72727273,
+        4.03030303,  4.33333333,  4.63636364,  4.93939394,  5.24242424,
+        5.54545455,  5.84848485,  6.15151515,  6.45454545,  6.75757576,
+        7.06060606,  7.36363636,  7.66666667,  7.96969697,  8.27272727,
+        8.57575758,  8.87878788,  9.18181818,  9.48484848,  9.78787879,
+       10.09090909, 10.39393939, 10.6969697 , 11.        , 11.3030303 ,
+       11.60606061, 11.90909091, 12.21212121, 12.51515152, 12.81818182,
+       13.12121212, 13.42424242, 13.72727273, 14.03030303, 14.33333333,
+       14.63636364, 14.93939394, 15.24242424, 15.54545455, 15.84848485,
+       16.15151515, 16.45454545, 16.75757576, 17.06060606, 17.36363636,
+       17.66666667, 17.96969697, 18.27272727, 18.57575758, 18.87878788,
+       19.18181818, 19.48484848, 19.78787879, 20.09090909, 20.39393939,
+       20.6969697 , 21.        , 21.3030303 , 21.60606061, 21.90909091,
+       22.21212121, 22.51515152, 22.81818182, 23.12121212, 23.42424242,
+       23.72727273, 24.03030303, 24.33333333, 24.63636364, 24.93939394,
+       25.24242424, 25.54545455, 25.84848485, 26.15151515, 26.45454545,
+       26.75757576, 27.06060606, 27.36363636, 27.66666667, 27.96969697,
+       28.27272727, 28.57575758, 28.87878788, 29.18181818, 29.48484848,
+       29.78787879, 30.09090909, 30.39393939, 30.6969697 , 31.        ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f35b6182-0eec-4c39-9437-982bdb0049ed' class='xr-section-summary-in' type='checkbox'  ><label for='section-f35b6182-0eec-4c39-9437-982bdb0049ed' class='xr-section-summary' >Indexes: <span>(1)</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>t</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-b65f8685-9167-400b-954e-47b3561e3ffb' class='xr-index-data-in' type='checkbox'/><label for='index-b65f8685-9167-400b-954e-47b3561e3ffb' 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([                0.0, 0.10101010101010101, 0.20202020202020202,
+       0.30303030303030304, 0.40404040404040403,  0.5050505050505051,
+        0.6060606060606061,  0.7070707070707071,  0.8080808080808081,
+        0.9090909090909091,  1.0101010101010102,  1.1111111111111112,
+        1.2121212121212122,  1.3131313131313131,  1.4141414141414141,
+        1.5151515151515151,  1.6161616161616161,  1.7171717171717171,
+        1.8181818181818181,  1.9191919191919191,  2.0202020202020203,
+         2.121212121212121,  2.2222222222222223,   2.323232323232323,
+        2.4242424242424243,   2.525252525252525,  2.6262626262626263,
+         2.727272727272727,  2.8282828282828283,   2.929292929292929,
+        3.0303030303030303,   3.131313131313131,  3.2323232323232323,
+        3.3333333333333335,  3.4343434343434343,  3.5353535353535355,
+        3.6363636363636362,  3.7373737373737375,  3.8383838383838382,
+        3.9393939393939394,   4.040404040404041,   4.141414141414141,
+         4.242424242424242,   4.343434343434343,   4.444444444444445,
+         4.545454545454545,   4.646464646464646,   4.747474747474747,
+         4.848484848484849,    4.94949494949495,    5.05050505050505,
+         5.151515151515151,   5.252525252525253,   5.353535353535354,
+         5.454545454545454,   5.555555555555555,   5.656565656565657,
+         5.757575757575758,   5.858585858585858,   5.959595959595959,
+        6.0606060606060606,   6.161616161616162,   6.262626262626262,
+         6.363636363636363,  6.4646464646464645,   6.565656565656566,
+         6.666666666666667,   6.767676767676767,  6.8686868686868685,
+          6.96969696969697,   7.070707070707071,   7.171717171717171,
+        7.2727272727272725,   7.373737373737374,   7.474747474747475,
+         7.575757575757575,  7.6767676767676765,   7.777777777777778,
+         7.878787878787879,   7.979797979797979,   8.080808080808081,
+         8.181818181818182,   8.282828282828282,   8.383838383838384,
+         8.484848484848484,   8.585858585858587,   8.686868686868687,
+         8.787878787878787,    8.88888888888889,    8.98989898989899,
+          9.09090909090909,   9.191919191919192,   9.292929292929292,
+         9.393939393939394,   9.494949494949495,   9.595959595959595,
+         9.696969696969697,   9.797979797979798,     9.8989898989899,
+                      10.0],
+      dtype=&#x27;float64&#x27;, name=&#x27;t&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-97fbef1d-6c52-4d6d-b1c9-38f90ceab01b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-97fbef1d-6c52-4d6d-b1c9-38f90ceab01b' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>
+
+
+
+```{admonition} What does the dispatch constructor do?
+:class: hint
+Behind the scenes, the dispatch constructor assembles a lightweight Evaluator object from the Simulation object, that takes the least necessary amount of information, runs it through some dimension checks, and also connects it to the specified solver and initializes it.
+```
+
+Let's take a look at the **results**.  
+
+You can vary the parameter $b$ in the previous step to investigate its influence on the model fit.  
+In the [Introduction](https://pymob.readthedocs.io/en/stable/user_guide/introduction.html), you can try out the *manual parameter estimation*, which is a feature provided by Pymob.  
+
+
+```python
+fig, ax = plt.subplots(figsize=(5, 4))
+data_res = evaluator.results
+ax.plot(data_obs.t, data_obs.y, ls="", marker="o", color="tab:blue", alpha=.5, label ="observation data")
+ax.plot(data_res.t, data_res.y, color="black", label ="result")
+ax.legend()
+```
+
+
+
+
+    <matplotlib.legend.Legend at 0x2d2757dd250>
+
+
+
+
+    
+![png](superquickstart_files/superquickstart_18_1.png)
+    
+
+
+## Estimating parameters and uncertainty with MCMC 🤔
+Of course this example is very simple. In fact, we could optimize the parameters perfectly by hand.   
+But just for fun, let's use *Markov Chain Monte Carlo (MCMC)* to estimate the parameters, their uncertainty and the uncertainty in the data.   
+We’ll run the parameter estimation with our **{attr}`~pymob.simulation.inferer`**, using the NumPyro backend with a NUTS kernel. This completes the job in a few seconds.
+
+We are almost ready to infer the model parameters. To also estimate the uncertainty of the parameters, we add another parameter representing the error and assume that it follows a lognormal distribution.   
+Additionally, we specify an error model for the data distribution. This will be: $$y_{obs} \sim Normal (y, \sigma_y)$$  
+
+Since $\sigma_y$ is not a fixed parameter, it doesn't need to be passed to the simulation class.
+
+
+```python
+sim.config.model_parameters.sigma_y = Param(free=True , prior="lognorm(scale=1,s=1)", min=0, max=1)
+sim.config.model_parameters.b.prior = "lognorm(scale=1,s=1)"
+
+sim.config.error_model.y = "normal(loc=y,scale=sigma_y)"
+
+
+sim.set_inferer("numpyro")
+sim.inferer.config.inference_numpyro.kernel = "nuts"
+sim.inferer.run()
+
+sim.inferer.idata.posterior
+
+# Plot the results
+sim.config.simulation.x_dimension = "t"
+sim.posterior_predictive_checks(pred_hdi_style={"alpha": 0.1})
+```
+
+    Jax 64 bit mode: False
+    Absolute tolerance: 1e-07
+    
+
+    Trace Shapes:      
+     Param Sites:      
+    Sample Sites:      
+           b dist     |
+            value     |
+     sigma_y dist     |
+            value     |
+       y_obs dist 100 |
+            value 100 |
+    
+
+    
  0%|                                                                                                    | 0/3000 [00:00<?, ?it/s]
+
+    
warmup:   0%|                                        | 1/3000 [00:02<2:24:08,  2.88s/it, 1 steps of size 1.87e+00. acc. prob=0.00]
+
+    
warmup:   2%|█                                        | 75/3000 [00:02<01:23, 35.20it/s, 5 steps of size 1.36e-02. acc. prob=0.76]
+
+    
warmup:   5%|██                                      | 158/3000 [00:03<00:33, 84.73it/s, 5 steps of size 1.28e+00. acc. prob=0.78]
+
+    
warmup:   9%|███▌                                   | 270/3000 [00:03<00:16, 168.27it/s, 3 steps of size 1.37e+00. acc. prob=0.78]
+
+    
warmup:  13%|████▉                                  | 379/3000 [00:03<00:10, 261.46it/s, 1 steps of size 7.30e-01. acc. prob=0.79]
+
+    
warmup:  16%|██████▍                                | 492/3000 [00:03<00:06, 372.25it/s, 3 steps of size 9.54e-01. acc. prob=0.79]
+
+    
warmup:  21%|████████▏                              | 626/3000 [00:03<00:04, 521.55it/s, 3 steps of size 1.30e+00. acc. prob=0.79]
+
+    
warmup:  26%|█████████▉                             | 765/3000 [00:03<00:03, 678.16it/s, 3 steps of size 1.06e+00. acc. prob=0.79]
+
+    
warmup:  30%|███████████▌                           | 891/3000 [00:03<00:02, 773.18it/s, 3 steps of size 1.35e+00. acc. prob=0.79]
+
+    
sample:  34%|████████████▉                         | 1021/3000 [00:03<00:02, 886.43it/s, 7 steps of size 7.70e-01. acc. prob=0.94]
+
+    
sample:  39%|██████████████▋                       | 1156/3000 [00:03<00:01, 993.67it/s, 3 steps of size 7.70e-01. acc. prob=0.93]
+
+    
sample:  43%|███████████████▉                     | 1292/3000 [00:04<00:01, 1074.35it/s, 3 steps of size 7.70e-01. acc. prob=0.93]
+
+    
sample:  48%|█████████████████▌                   | 1425/3000 [00:04<00:01, 1140.72it/s, 3 steps of size 7.70e-01. acc. prob=0.93]
+
+    
sample:  52%|███████████████████▏                 | 1553/3000 [00:04<00:01, 1175.55it/s, 7 steps of size 7.70e-01. acc. prob=0.93]
+
+    
sample:  56%|████████████████████▊                | 1686/3000 [00:04<00:01, 1218.48it/s, 7 steps of size 7.70e-01. acc. prob=0.93]
+
+    
sample:  60%|██████████████████████▍              | 1815/3000 [00:04<00:00, 1227.49it/s, 3 steps of size 7.70e-01. acc. prob=0.93]
+
+    
sample:  65%|███████████████████████▉             | 1943/3000 [00:04<00:00, 1177.93it/s, 3 steps of size 7.70e-01. acc. prob=0.92]
+
+    
sample:  69%|█████████████████████████▍           | 2065/3000 [00:04<00:00, 1156.28it/s, 3 steps of size 7.70e-01. acc. prob=0.92]
+
+    
sample:  73%|██████████████████████████▉          | 2184/3000 [00:04<00:00, 1158.87it/s, 3 steps of size 7.70e-01. acc. prob=0.92]
+
+    
sample:  77%|████████████████████████████▍        | 2302/3000 [00:04<00:00, 1150.36it/s, 3 steps of size 7.70e-01. acc. prob=0.92]
+
+    
sample:  81%|█████████████████████████████▊       | 2419/3000 [00:05<00:00, 1073.61it/s, 5 steps of size 7.70e-01. acc. prob=0.93]
+
+    
sample:  85%|███████████████████████████████▎     | 2541/3000 [00:05<00:00, 1081.20it/s, 7 steps of size 7.70e-01. acc. prob=0.93]
+
+    
sample:  89%|████████████████████████████████▊    | 2663/3000 [00:05<00:00, 1087.50it/s, 3 steps of size 7.70e-01. acc. prob=0.92]
+
+    
sample:  93%|██████████████████████████████████▎  | 2784/3000 [00:05<00:00, 1088.87it/s, 3 steps of size 7.70e-01. acc. prob=0.92]
+
+    
sample:  97%|███████████████████████████████████▊ | 2905/3000 [00:05<00:00, 1089.82it/s, 7 steps of size 7.70e-01. acc. prob=0.92]
+
+    
sample: 100%|██████████████████████████████████████| 3000/3000 [00:05<00:00, 542.00it/s, 3 steps of size 7.70e-01. acc. prob=0.92]
+
+    
+    
+
+    
+                    mean       std    median      5.0%     95.0%     n_eff     r_hat
+             b      3.05      0.03      3.05      3.01      3.09   1831.90      1.00
+       sigma_y      1.56      0.11      1.55      1.38      1.74   1341.54      1.00
+    
+    
+
+    Number of divergences: 0
+    
+
+
+    
+![png](superquickstart_files/superquickstart_20_32.png)
+    
+
+
+```{admonition} numpyro distributions
+:class: warning
+Currently only few distributions are implemented in the numpyro backend. This API will soon change, so that basically any distribution can be used to specifcy parameters. 
+```
+
+We can **inspect our estimates** and see that the model provides a good fit for the parameters.  
+Note that we only get an estimate for $b$. Previously, we set the parameter $a$ with the flag `free = False`.   
+This effectively excludes it from the estimation and uses its default value, which was set to the true value `a = 0`.
+
+
+```{admonition} Customize the posterior predictive checks
+:class: hint
+You can explore the API of {class}`pymob.sim.plot.SimulationPlot` to find out how you can work on the default predictions. Of course you can always make your own plot, by accessing {attr}`pymob.simulation.inferer.idata` and {attr}`pymob.simulation.observations`
+```
+
+## Report the results 🗒️
+
+Pymob provides the option to generate an automated report of the parameter distribution for a simulation.  
+The report can be configured by modifying the options in {meth}`~pymob.simulation.SimulationBase.config.report`.
+
+
+```python
+# report the results
+sim.report()
+```
+
+![posterior_trace.png](superquickstart_files/posterior_trace.png)
+
+![posterior_pairs.png](superquickstart_files/posterior_pairs.png)
+
+
+## Exporting the simulation and running it via the case study API 📤
+
+After constructing the simulation, all settings - custom and default - can be exported to a comprehensive configuration file.   
+The simulation will be saved to the default path (`CASE_STUDY/scenarios/SCENARIO/settings.cfg`) or to a custom path, specified with the file path keyword `fp`.   
+Setting `force=True` will overwrite any existing config file, which is a reasonable choice in most cases.
+From this point on, the simulation is (almost) ready to be executed from the command-line. 
+
+
+```python
+import os
+sim.config.create_directory("scenario", force=True)
+sim.config.create_directory("results", force=True)
+
+# usually we expect to have a data directory in the case
+os.makedirs(sim.data_path, exist_ok=True)
+sim.save_observations(force=True)
+sim.config.save(force=True)
+```
+
+    Scenario directory exists at 'C:\Users\mgrho\pymob\docs\source\user_guide\case_studies\superquickstart\scenarios\linreg'.
+    Results directory exists at 'C:\Users\mgrho\pymob\docs\source\user_guide\case_studies\superquickstart\results\linreg'.
+    
+
+### Commandline API
+
+The command-line API runs a series of commands that load the case study, execute the {meth}`~pymob.simulation.SimulationBase.initialize` method and perform some more initialization tasks before running the required job.
+
++ `pymob-infer` runs an inference job, for example:  
+
+  `pymob-infer --case_study=quickstart --scenario=test --inference_backend=numpyro`.   
+  While there are more command-line options, these two (--case_study and --scenario) are required.
+
+

From a3182e0688d50c7048c4489d51874a598519cbcb Mon Sep 17 00:00:00 2001
From: mariegrho <mgrholthaus@gmail.com>
Date: Mon, 30 Jun 2025 13:33:39 +0200
Subject: [PATCH 10/16] minor fixes in the cross reference

---
 docs/source/user_guide/superquickstart.ipynb | 116 ++++++++--------
 docs/source/user_guide/superquickstart.md    | 137 +++++++------------
 2 files changed, 110 insertions(+), 143 deletions(-)

diff --git a/docs/source/user_guide/superquickstart.ipynb b/docs/source/user_guide/superquickstart.ipynb
index 25553a4d4..be4c1b1b7 100644
--- a/docs/source/user_guide/superquickstart.ipynb
+++ b/docs/source/user_guide/superquickstart.ipynb
@@ -37,15 +37,15 @@
     "\n",
     "3. __Observations:__   \n",
     "Our observation data must be structured as an [xarray.Dataset](https://docs.xarray.dev/en/stable/generated/xarray.Dataset.html).  \n",
-    "We assign it to the `~pymob.sim.config.Casestudy.observations ` attribute of our Simulation object.   \n",
+    "We assign it to the {attr}`~pymob.sim.config.Casestudy.observations` attribute of our Simulation object.   \n",
     "Calling `sim.config.data_structure` will give us further information about the layout of our data.  \n",
     "\n",
     "4. __Solver:__  \n",
     "A [solver](https://pymob.readthedocs.io/en/stable/api/pymob.solvers.html) is required to solve the model.   \n",
-    "In our simple case, we will use the `solve_analytic_1d` solver from the `~pymob.solver.analytic` module.  \n",
-    "We assign it to our Simulation object using the {attr}`pymob.simulation.solver` attribute.   \n",
+    "In our simple case, we will use the `solve_analytic_1d` solver from the {mod}`~pymob.solver.analytic` module.  \n",
+    "We assign it to our Simulation object using the {attr}`~pymob.simulation.solver` attribute.   \n",
     "Since our model already provides an analytical solution, this solver basically does nothing. It is still needed to fulfill Pymob's requirement for a solver component.   \n",
-    "For more complex models (e.g. ODEs), the `JaxSolver` from the `~pymob.solver.diffrax` module is a more powerful option.   \n",
+    "For more complex models (e.g. ODEs), the `JaxSolver` from the {mod}`~pymob.solver.diffrax` module is a more powerful option.   \n",
     "Users can also implement custom solvers as a subclass of {class}`pymob.solver.SolverBase`.   \n",
     "  \n",
     "5. __Inferer:__  \n",
@@ -59,12 +59,12 @@
     "\n",
     "6. __Evaluator:__  \n",
     "The Evaluator is an instance to manage model evaluations. It sets up tasks, coordinates parallel runs of the simulation and keeps track of the results from each simulation or parameter inference process.   \n",
-    "Evaluators store the raw output from a simulation and can generate an xarray object from it that corresponds to the data-structure of the observations with the ~pymob.sim.evaluator.Evaluator.results` property. This automatically aligns the simulations results with the observations, for simple computation of loss functions.  \n",
+    "Evaluators store the raw output from a simulation and can generate an xarray object from it that corresponds to the data-structure of the observations with the {attr}`~pymob.sim.evaluator.Evaluator.results` property. This automatically aligns the simulations results with the observations, for simple computation of loss functions.  \n",
     "\n",
     "7. __Config:__  \n",
     "The simulation settings will be saved in a `.cfg` configuration file.  \n",
-    "The config file contains information about our simulation in various sections. -> [Learn more here](https://pymob.readthedocs.io/en/stable/user_guide/case_studies.html#configuration).  \n",
-    "We can further use it to create new simulations by loading settings from a config file. \n"
+    "The config file contains information about our simulation in various sections. [Learn more here](https://pymob.readthedocs.io/en/stable/user_guide/case_studies.html#configuration).  \n",
+    "We can further use it to create new simulations by loading settings from a config file. "
    ]
   },
   {
@@ -83,7 +83,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -110,7 +110,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [
     {
@@ -564,7 +564,7 @@
        "Coordinates:\n",
        "  * t        (t) float64 800B 0.0 0.101 0.202 0.303 ... 9.697 9.798 9.899 10.0\n",
        "Data variables:\n",
-       "    y        (t) float64 800B -1.859 4.002 2.278 1.5 ... 29.9 27.81 31.68 32.25</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-0bbdad02-502b-4742-8aab-8a0d91dbd7c5' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-0bbdad02-502b-4742-8aab-8a0d91dbd7c5' 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'>t</span>: 100</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-a707166c-1a0a-44c2-a769-da7ff0dcd866' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a707166c-1a0a-44c2-a769-da7ff0dcd866' class='xr-section-summary' >Coordinates: <span>(1)</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'>t</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.101 0.202 ... 9.899 10.0</div><input id='attrs-2c528975-1b9f-4be7-97d1-0df153dd1a8a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2c528975-1b9f-4be7-97d1-0df153dd1a8a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-14221b05-11a5-430d-bda3-0478e24c5ce5' class='xr-var-data-in' type='checkbox'><label for='data-14221b05-11a5-430d-bda3-0478e24c5ce5' 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.      ,  0.10101 ,  0.20202 ,  0.30303 ,  0.40404 ,  0.505051,\n",
+       "    y        (t) float64 800B 0.281 4.775 -0.2706 2.471 ... 30.34 30.21 34.78</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-43460ff4-23ca-493d-b7c5-513a26efa32e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-43460ff4-23ca-493d-b7c5-513a26efa32e' 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'>t</span>: 100</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-4ffcf9c4-87ea-41ad-91ad-474e7c5bfa0a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4ffcf9c4-87ea-41ad-91ad-474e7c5bfa0a' class='xr-section-summary' >Coordinates: <span>(1)</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'>t</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.101 0.202 ... 9.899 10.0</div><input id='attrs-19d47ec4-9eba-4f7a-8300-36db6065b4ba' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-19d47ec4-9eba-4f7a-8300-36db6065b4ba' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-57eee83b-5cfa-4f55-b6e4-5fdb17513410' class='xr-var-data-in' type='checkbox'><label for='data-57eee83b-5cfa-4f55-b6e4-5fdb17513410' 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.      ,  0.10101 ,  0.20202 ,  0.30303 ,  0.40404 ,  0.505051,\n",
        "        0.606061,  0.707071,  0.808081,  0.909091,  1.010101,  1.111111,\n",
        "        1.212121,  1.313131,  1.414141,  1.515152,  1.616162,  1.717172,\n",
        "        1.818182,  1.919192,  2.020202,  2.121212,  2.222222,  2.323232,\n",
@@ -580,26 +580,26 @@
        "        7.878788,  7.979798,  8.080808,  8.181818,  8.282828,  8.383838,\n",
        "        8.484848,  8.585859,  8.686869,  8.787879,  8.888889,  8.989899,\n",
        "        9.090909,  9.191919,  9.292929,  9.393939,  9.494949,  9.59596 ,\n",
-       "        9.69697 ,  9.79798 ,  9.89899 , 10.      ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d652bc6e-69a3-4c00-b5d6-cf34c2ee5605' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d652bc6e-69a3-4c00-b5d6-cf34c2ee5605' class='xr-section-summary' >Data variables: <span>(1)</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>y</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.859 4.002 2.278 ... 31.68 32.25</div><input id='attrs-511ccb26-d960-4c31-8045-880929874026' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-511ccb26-d960-4c31-8045-880929874026' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ed959cf6-ad35-4b37-995a-ff2488e65974' class='xr-var-data-in' type='checkbox'><label for='data-ed959cf6-ad35-4b37-995a-ff2488e65974' 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([-1.85944047,  4.0017863 ,  2.27774178,  1.49993008,  2.25572637,\n",
-       "        5.97506098,  4.28861329,  1.48989749,  2.54840738,  6.00176557,\n",
-       "        5.19237785,  4.84129396,  5.93507375, -0.90023433,  5.32909845,\n",
-       "        5.43301534,  5.06823295,  6.38765919,  7.73241466,  8.36162623,\n",
-       "        3.59142011,  8.51912826, 10.83197262,  9.69217013,  5.58780283,\n",
-       "        8.63797095,  8.17536178, 10.41306493,  9.05779623, 10.35476686,\n",
-       "       11.79329806,  9.09134391, 14.38982907, 13.00039266, 11.8792888 ,\n",
-       "       10.61317107, 11.03240126, 10.55073022, 11.92232672, 15.54988696,\n",
-       "       13.25952982, 12.90309411, 12.73696535, 14.77726639, 17.50000192,\n",
-       "       12.80559198, 16.29095349, 16.24516375, 15.49882565, 17.40908366,\n",
-       "       15.60839363, 17.0775438 , 18.29367702, 15.9263197 , 19.16664567,\n",
-       "       19.25783323, 16.06462271, 16.80720809, 20.51733645, 18.32847375,\n",
-       "       15.85198324, 21.47444749, 19.88608661, 21.23243998, 18.88006365,\n",
-       "       18.41194379, 19.00600483, 21.81444858, 21.27952537, 22.84800896,\n",
-       "       23.85697258, 23.31593258, 22.68282408, 22.95792202, 24.52231748,\n",
-       "       24.6493255 , 23.69740026, 26.59968312, 24.7479687 , 21.96248218,\n",
-       "       25.59637031, 25.68290203, 29.80989984, 23.29474923, 23.65893307,\n",
-       "       29.42755947, 31.19393529, 27.86332172, 28.35144343, 28.50637287,\n",
-       "       29.1473367 , 28.92850415, 33.32833186, 29.00200625, 32.46986583,\n",
-       "       28.1559924 , 29.89935856, 27.80621022, 31.68101752, 32.25217908])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-173fb448-2b7e-4404-85b5-81ea335a9292' class='xr-section-summary-in' type='checkbox'  ><label for='section-173fb448-2b7e-4404-85b5-81ea335a9292' class='xr-section-summary' >Indexes: <span>(1)</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>t</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-ea5efa77-864b-4b0d-b035-0719798c206d' class='xr-index-data-in' type='checkbox'/><label for='index-ea5efa77-864b-4b0d-b035-0719798c206d' 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([                0.0, 0.10101010101010101, 0.20202020202020202,\n",
+       "        9.69697 ,  9.79798 ,  9.89899 , 10.      ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-84777266-abbb-4342-a9de-43233de46e53' class='xr-section-summary-in' type='checkbox'  checked><label for='section-84777266-abbb-4342-a9de-43233de46e53' class='xr-section-summary' >Data variables: <span>(1)</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>y</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.281 4.775 -0.2706 ... 30.21 34.78</div><input id='attrs-45ed58d4-d042-4454-966b-1979c907c8f5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-45ed58d4-d042-4454-966b-1979c907c8f5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8cbcf34d-db2e-467f-b4ee-a679829f0fc3' class='xr-var-data-in' type='checkbox'><label for='data-8cbcf34d-db2e-467f-b4ee-a679829f0fc3' 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([ 0.28098171,  4.77538938, -0.27060725,  2.47128633,  1.01466105,\n",
+       "        3.2679597 ,  4.70051422,  1.93217318,  5.082623  ,  3.6843063 ,\n",
+       "        4.51998017,  3.26803103,  3.07512908,  3.57395258,  3.6241049 ,\n",
+       "        4.58810694,  3.82884281,  8.14571552,  8.24818809,  9.72236549,\n",
+       "        4.70146737,  9.05930222,  5.94992415,  8.52722744,  6.55250397,\n",
+       "       10.79632652,  7.68199537,  9.05824464, 10.93596586,  9.73823156,\n",
+       "        9.45764319,  6.9213614 ,  9.33596133, 12.53372142, 10.43895052,\n",
+       "       14.44158404,  8.96241515, 12.48336367, 11.61954073, 14.5480077 ,\n",
+       "       13.31325139, 15.65173068, 16.82876826, 14.44113823, 13.46262926,\n",
+       "       13.6351774 , 17.85634528, 14.49969756, 12.93861167, 14.75127382,\n",
+       "       15.25800612, 14.3458396 , 16.81402628, 19.10140957, 17.11511876,\n",
+       "       16.47810444, 19.27617769, 17.65928777, 21.4870153 , 18.99787141,\n",
+       "       17.60658913, 20.1158257 , 20.65111889, 21.90430844, 19.94000956,\n",
+       "       24.40937823, 21.66264338, 17.40165908, 19.30289728, 19.00432468,\n",
+       "       24.90563978, 22.97673914, 21.2001953 , 24.72272489, 24.97765503,\n",
+       "       22.77239235, 26.77775649, 22.33896691, 24.70444908, 25.76145285,\n",
+       "       25.97630266, 25.53291933, 25.533891  , 25.67715573, 23.99234918,\n",
+       "       30.93568447, 24.89612835, 28.67876116, 24.64567766, 29.56081186,\n",
+       "       28.6222925 , 26.68473011, 29.20094564, 30.03540899, 28.74281582,\n",
+       "       28.96913372, 30.78992037, 30.34161476, 30.21371687, 34.78050506])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f852956d-972e-4a11-abc5-73444c340197' class='xr-section-summary-in' type='checkbox'  ><label for='section-f852956d-972e-4a11-abc5-73444c340197' class='xr-section-summary' >Indexes: <span>(1)</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>t</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-a27f7b89-858d-47a1-8885-a1db0ed03448' class='xr-index-data-in' type='checkbox'/><label for='index-a27f7b89-858d-47a1-8885-a1db0ed03448' 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([                0.0, 0.10101010101010101, 0.20202020202020202,\n",
        "       0.30303030303030304, 0.40404040404040403,  0.5050505050505051,\n",
        "        0.6060606060606061,  0.7070707070707071,  0.8080808080808081,\n",
        "        0.9090909090909091,  1.0101010101010102,  1.1111111111111112,\n",
@@ -633,7 +633,7 @@
        "         9.393939393939394,   9.494949494949495,   9.595959595959595,\n",
        "         9.696969696969697,   9.797979797979798,     9.8989898989899,\n",
        "                      10.0],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;t&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-26db7585-42ad-476e-a0b3-73fca3430337' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-26db7585-42ad-476e-a0b3-73fca3430337' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+       "      dtype=&#x27;float64&#x27;, name=&#x27;t&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b490ab1a-0611-40b0-ba86-4185b5ce5737' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b490ab1a-0611-40b0-ba86-4185b5ce5737' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
        "<xarray.Dataset> Size: 2kB\n",
@@ -641,16 +641,16 @@
        "Coordinates:\n",
        "  * t        (t) float64 800B 0.0 0.101 0.202 0.303 ... 9.697 9.798 9.899 10.0\n",
        "Data variables:\n",
-       "    y        (t) float64 800B -1.859 4.002 2.278 1.5 ... 29.9 27.81 31.68 32.25"
+       "    y        (t) float64 800B 0.281 4.775 -0.2706 2.471 ... 30.34 30.21 34.78"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 47,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 500x400 with 1 Axes>"
       ]
@@ -708,31 +708,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "MinMaxScaler(variable=y, min=-1.8594404709936558, max=33.32833185958829)\n"
+      "MinMaxScaler(variable=y, min=-0.2706072545541467, max=34.780505060222275)\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "C:\\Pymob\\pymob\\pymob\\simulation.py:307: UserWarning: `sim.config.data_structure.y = Datavariable(dimensions=['t'] min=-1.8594404709936558 max=33.32833185958829 observed=True dimensions_evaluator=None)` has been assumed from `sim.observations`. If the order of the dimensions should be different, specify `sim.config.data_structure.y = DataVariable(dimensions=[...], ...)` manually.\n",
+      "C:\\Pymob\\pymob\\pymob\\simulation.py:307: UserWarning: `sim.config.data_structure.y = Datavariable(dimensions=['t'] min=-0.2706072545541467 max=34.780505060222275 observed=True dimensions_evaluator=None)` has been assumed from `sim.observations`. If the order of the dimensions should be different, specify `sim.config.data_structure.y = DataVariable(dimensions=[...], ...)` manually.\n",
       "  warnings.warn(\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "Datastructure(y=DataVariable(dimensions=['t'], min=-1.8594404709936558, max=33.32833185958829, observed=True, dimensions_evaluator=None))"
+       "Datastructure(y=DataVariable(dimensions=['t'], min=-0.2706072545541467, max=34.780505060222275, observed=True, dimensions_evaluator=None))"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 48,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -787,7 +787,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [
     {
@@ -796,7 +796,7 @@
        "{'a': 1.0, 'b': 3.0}"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 49,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -824,7 +824,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 50,
    "metadata": {},
    "outputs": [
     {
@@ -1286,7 +1286,7 @@
        "Coordinates:\n",
        "  * t        (t) float64 800B 0.0 0.101 0.202 0.303 ... 9.697 9.798 9.899 10.0\n",
        "Data variables:\n",
-       "    y        (t) float64 800B 1.0 1.303 1.606 1.909 ... 30.09 30.39 30.7 31.0</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-0874a8f8-0240-4ecd-95d9-f50b3fa444f0' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-0874a8f8-0240-4ecd-95d9-f50b3fa444f0' 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'>t</span>: 100</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-b20ed496-9f95-476b-976b-1e68e2470341' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b20ed496-9f95-476b-976b-1e68e2470341' class='xr-section-summary' >Coordinates: <span>(1)</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'>t</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.101 0.202 ... 9.899 10.0</div><input id='attrs-0178aa80-aa69-42d8-b260-76e173c8f254' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0178aa80-aa69-42d8-b260-76e173c8f254' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b7c34ec6-58bc-42c6-adef-eaa25e213363' class='xr-var-data-in' type='checkbox'><label for='data-b7c34ec6-58bc-42c6-adef-eaa25e213363' 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.      ,  0.10101 ,  0.20202 ,  0.30303 ,  0.40404 ,  0.505051,\n",
+       "    y        (t) float64 800B 1.0 1.303 1.606 1.909 ... 30.09 30.39 30.7 31.0</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-39a93839-9bc3-4564-a7d9-b11aa8ad8906' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-39a93839-9bc3-4564-a7d9-b11aa8ad8906' 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'>t</span>: 100</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-b6c0b2f5-bd64-4be5-b162-65a94f81c4cb' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b6c0b2f5-bd64-4be5-b162-65a94f81c4cb' class='xr-section-summary' >Coordinates: <span>(1)</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'>t</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.101 0.202 ... 9.899 10.0</div><input id='attrs-7acde639-b15b-487d-8eee-27320ac37010' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7acde639-b15b-487d-8eee-27320ac37010' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-54ba89b1-643c-474b-8c8a-2117c729b910' class='xr-var-data-in' type='checkbox'><label for='data-54ba89b1-643c-474b-8c8a-2117c729b910' 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.      ,  0.10101 ,  0.20202 ,  0.30303 ,  0.40404 ,  0.505051,\n",
        "        0.606061,  0.707071,  0.808081,  0.909091,  1.010101,  1.111111,\n",
        "        1.212121,  1.313131,  1.414141,  1.515152,  1.616162,  1.717172,\n",
        "        1.818182,  1.919192,  2.020202,  2.121212,  2.222222,  2.323232,\n",
@@ -1302,7 +1302,7 @@
        "        7.878788,  7.979798,  8.080808,  8.181818,  8.282828,  8.383838,\n",
        "        8.484848,  8.585859,  8.686869,  8.787879,  8.888889,  8.989899,\n",
        "        9.090909,  9.191919,  9.292929,  9.393939,  9.494949,  9.59596 ,\n",
-       "        9.69697 ,  9.79798 ,  9.89899 , 10.      ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ae018595-cc78-4256-b30b-a20df289dfd7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ae018595-cc78-4256-b30b-a20df289dfd7' class='xr-section-summary' >Data variables: <span>(1)</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>y</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.0 1.303 1.606 ... 30.39 30.7 31.0</div><input id='attrs-bda4057c-d482-40f3-b3e1-50b53d47e9ac' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bda4057c-d482-40f3-b3e1-50b53d47e9ac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-76f7debe-f2f7-48d2-ba4d-cd1cb5d76418' class='xr-var-data-in' type='checkbox'><label for='data-76f7debe-f2f7-48d2-ba4d-cd1cb5d76418' 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([ 1.        ,  1.3030303 ,  1.60606061,  1.90909091,  2.21212121,\n",
+       "        9.69697 ,  9.79798 ,  9.89899 , 10.      ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e410dd31-a596-4be8-a21d-15a92c09754d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e410dd31-a596-4be8-a21d-15a92c09754d' class='xr-section-summary' >Data variables: <span>(1)</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>y</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.0 1.303 1.606 ... 30.39 30.7 31.0</div><input id='attrs-090abd7e-faf8-4829-b0f1-3fcba85c565e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-090abd7e-faf8-4829-b0f1-3fcba85c565e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-484e2a86-0b9d-4598-96dc-5db9d961dd9d' class='xr-var-data-in' type='checkbox'><label for='data-484e2a86-0b9d-4598-96dc-5db9d961dd9d' 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([ 1.        ,  1.3030303 ,  1.60606061,  1.90909091,  2.21212121,\n",
        "        2.51515152,  2.81818182,  3.12121212,  3.42424242,  3.72727273,\n",
        "        4.03030303,  4.33333333,  4.63636364,  4.93939394,  5.24242424,\n",
        "        5.54545455,  5.84848485,  6.15151515,  6.45454545,  6.75757576,\n",
@@ -1321,7 +1321,7 @@
        "       25.24242424, 25.54545455, 25.84848485, 26.15151515, 26.45454545,\n",
        "       26.75757576, 27.06060606, 27.36363636, 27.66666667, 27.96969697,\n",
        "       28.27272727, 28.57575758, 28.87878788, 29.18181818, 29.48484848,\n",
-       "       29.78787879, 30.09090909, 30.39393939, 30.6969697 , 31.        ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a5800b86-fe93-4c37-ba41-83d099825c2c' class='xr-section-summary-in' type='checkbox'  ><label for='section-a5800b86-fe93-4c37-ba41-83d099825c2c' class='xr-section-summary' >Indexes: <span>(1)</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>t</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-176e951f-3364-488e-be5f-da3da8c3bb1c' class='xr-index-data-in' type='checkbox'/><label for='index-176e951f-3364-488e-be5f-da3da8c3bb1c' 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([                0.0, 0.10101010101010101, 0.20202020202020202,\n",
+       "       29.78787879, 30.09090909, 30.39393939, 30.6969697 , 31.        ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-037e92c7-7dc1-4481-8456-1583f2540c8b' class='xr-section-summary-in' type='checkbox'  ><label for='section-037e92c7-7dc1-4481-8456-1583f2540c8b' class='xr-section-summary' >Indexes: <span>(1)</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>t</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-f275c998-3e11-45e0-aadf-c811a094c8d2' class='xr-index-data-in' type='checkbox'/><label for='index-f275c998-3e11-45e0-aadf-c811a094c8d2' 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([                0.0, 0.10101010101010101, 0.20202020202020202,\n",
        "       0.30303030303030304, 0.40404040404040403,  0.5050505050505051,\n",
        "        0.6060606060606061,  0.7070707070707071,  0.8080808080808081,\n",
        "        0.9090909090909091,  1.0101010101010102,  1.1111111111111112,\n",
@@ -1355,7 +1355,7 @@
        "         9.393939393939394,   9.494949494949495,   9.595959595959595,\n",
        "         9.696969696969697,   9.797979797979798,     9.8989898989899,\n",
        "                      10.0],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;t&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-bf3c48cd-fe25-4883-bd84-e96d5b5cf5c3' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-bf3c48cd-fe25-4883-bd84-e96d5b5cf5c3' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+       "      dtype=&#x27;float64&#x27;, name=&#x27;t&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6b875a85-df51-43da-8e93-99db196ee38d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-6b875a85-df51-43da-8e93-99db196ee38d' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
        "<xarray.Dataset> Size: 2kB\n",
@@ -1366,7 +1366,7 @@
        "    y        (t) float64 800B 1.0 1.303 1.606 1.909 ... 30.09 30.39 30.7 31.0"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 50,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1403,22 +1403,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 51,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x2c1fe179110>"
+       "<matplotlib.legend.Legend at 0x181a0cae3d0>"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 51,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 500x400 with 1 Axes>"
       ]
@@ -1452,7 +1452,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 52,
    "metadata": {},
    "outputs": [
     {
@@ -1476,7 +1476,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "sample: 100%|██████████| 3000/3000 [00:02<00:00, 1450.53it/s, 3 steps of size 7.98e-01. acc. prob=0.93] \n"
+      "sample: 100%|██████████| 3000/3000 [00:02<00:00, 1429.92it/s, 7 steps of size 8.95e-01. acc. prob=0.89]\n"
      ]
     },
     {
@@ -1485,15 +1485,15 @@
      "text": [
       "\n",
       "                mean       std    median      5.0%     95.0%     n_eff     r_hat\n",
-      "         b      3.05      0.03      3.05      3.00      3.10   1484.06      1.00\n",
-      "   sigma_y      1.84      0.13      1.83      1.61      2.03   1336.43      1.00\n",
+      "         b      3.01      0.03      3.01      2.96      3.06   1985.04      1.00\n",
+      "   sigma_y      1.79      0.13      1.78      1.56      1.97   2102.94      1.00\n",
       "\n",
       "Number of divergences: 0\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 400x300 with 1 Axes>"
       ]
@@ -1562,7 +1562,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 53,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1609,7 +1609,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 54,
    "metadata": {},
    "outputs": [
     {
diff --git a/docs/source/user_guide/superquickstart.md b/docs/source/user_guide/superquickstart.md
index e92f85e55..5ddafa5b4 100644
--- a/docs/source/user_guide/superquickstart.md
+++ b/docs/source/user_guide/superquickstart.md
@@ -5,7 +5,7 @@ We will explore a simple linear regression model that we want to fit to a noisy
 Pymob supports the modeling process by providing several tools for *data structuring*, *parameter estimation* and *visualization of results*.  
   
 If you are looking for a more detailed introduction, [click here](https://pymob.readthedocs.io/en/stable/user_guide/introduction.html).  
-If you want to learn how to work with ODE models, check out [this tutorial]().
+If you want to learn how to work with ODE models, check out [this tutorial](). 
 
 ## Pymob components 🧩
 
@@ -21,15 +21,15 @@ We will then assign it to the Simulation object by accessing the `.model` attrib
 
 3. __Observations:__   
 Our observation data must be structured as an [xarray.Dataset](https://docs.xarray.dev/en/stable/generated/xarray.Dataset.html).  
-We assign it to the `~pymob.sim.config.Casestudy.observations ` attribute of our Simulation object.   
+We assign it to the {attr}`~pymob.sim.config.Casestudy.observations` attribute of our Simulation object.   
 Calling `sim.config.data_structure` will give us further information about the layout of our data.  
 
 4. __Solver:__  
 A [solver](https://pymob.readthedocs.io/en/stable/api/pymob.solvers.html) is required to solve the model.   
-In our simple case, we will use the `solve_analytic_1d` solver from the `~pymob.solver.analytic` module.  
-We assign it to our Simulation object using the {attr}`pymob.simulation.solver` attribute.   
+In our simple case, we will use the `solve_analytic_1d` solver from the {mod}`~pymob.solver.analytic` module.  
+We assign it to our Simulation object using the {attr}`~pymob.simulation.solver` attribute.   
 Since our model already provides an analytical solution, this solver basically does nothing. It is still needed to fulfill Pymob's requirement for a solver component.   
-For more complex models (e.g. ODEs), the `JaxSolver` from the `~pymob.solver.diffrax` module is a more powerful option.   
+For more complex models (e.g. ODEs), the `JaxSolver` from the {mod}`~pymob.solver.diffrax` module is a more powerful option.   
 Users can also implement custom solvers as a subclass of {class}`pymob.solver.SolverBase`.   
   
 5. __Inferer:__  
@@ -43,14 +43,13 @@ By default, it takes the keys: `parameters`, `y0` and `x_in`.
 
 6. __Evaluator:__  
 The Evaluator is an instance to manage model evaluations. It sets up tasks, coordinates parallel runs of the simulation and keeps track of the results from each simulation or parameter inference process.   
-Evaluators store the raw output from a simulation and can generate an xarray object from it that corresponds to the data-structure of the observations with the ~pymob.sim.evaluator.Evaluator.results` property. This automatically aligns the simulations results with the observations, for simple computation of loss functions.  
+Evaluators store the raw output from a simulation and can generate an xarray object from it that corresponds to the data-structure of the observations with the {attr}`~pymob.sim.evaluator.Evaluator.results` property. This automatically aligns the simulations results with the observations, for simple computation of loss functions.  
 
 7. __Config:__  
 The simulation settings will be saved in a `.cfg` configuration file.  
-The config file contains information about our simulation in various sections. -> [Learn more here](https://pymob.readthedocs.io/en/stable/user_guide/case_studies.html#configuration).  
+The config file contains information about our simulation in various sections. [Learn more here](https://pymob.readthedocs.io/en/stable/user_guide/case_studies.html#configuration).  
 We can further use it to create new simulations by loading settings from a config file. 
 
-
 ![framework-overview](.\figures\pymob_overview.png)
 
 ## Getting started 🛫
@@ -551,7 +550,7 @@ Dimensions:  (t: 100)
 Coordinates:
   * t        (t) float64 800B 0.0 0.101 0.202 0.303 ... 9.697 9.798 9.899 10.0
 Data variables:
-    y        (t) float64 800B 2.313 3.534 1.349 2.437 ... 31.34 32.63 32.2 29.24</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-33e5c0c5-7b93-4c38-8ed4-024270a3232d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-33e5c0c5-7b93-4c38-8ed4-024270a3232d' 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'>t</span>: 100</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-bb5d2338-b720-4f3c-b96f-2736a3ffe7be' class='xr-section-summary-in' type='checkbox'  checked><label for='section-bb5d2338-b720-4f3c-b96f-2736a3ffe7be' class='xr-section-summary' >Coordinates: <span>(1)</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'>t</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.101 0.202 ... 9.899 10.0</div><input id='attrs-dc9c427f-013f-46c5-b504-23723fcbf3c6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dc9c427f-013f-46c5-b504-23723fcbf3c6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1f2ba592-2ada-4ccc-830a-d9beb5322ec8' class='xr-var-data-in' type='checkbox'><label for='data-1f2ba592-2ada-4ccc-830a-d9beb5322ec8' 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.      ,  0.10101 ,  0.20202 ,  0.30303 ,  0.40404 ,  0.505051,
+    y        (t) float64 800B 1.59 2.136 1.343 2.532 ... 31.06 27.02 30.87 32.09</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-b6e66973-2041-42d5-8705-ecf533c55a89' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b6e66973-2041-42d5-8705-ecf533c55a89' 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'>t</span>: 100</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-77264d36-2121-47a0-9814-7048a5037bab' class='xr-section-summary-in' type='checkbox'  checked><label for='section-77264d36-2121-47a0-9814-7048a5037bab' class='xr-section-summary' >Coordinates: <span>(1)</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'>t</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.101 0.202 ... 9.899 10.0</div><input id='attrs-914d5174-447d-4c2c-b6c2-2cd02c911b23' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-914d5174-447d-4c2c-b6c2-2cd02c911b23' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c0677693-e243-4c3d-bd9f-6ceef5966d2f' class='xr-var-data-in' type='checkbox'><label for='data-c0677693-e243-4c3d-bd9f-6ceef5966d2f' 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.      ,  0.10101 ,  0.20202 ,  0.30303 ,  0.40404 ,  0.505051,
         0.606061,  0.707071,  0.808081,  0.909091,  1.010101,  1.111111,
         1.212121,  1.313131,  1.414141,  1.515152,  1.616162,  1.717172,
         1.818182,  1.919192,  2.020202,  2.121212,  2.222222,  2.323232,
@@ -567,26 +566,26 @@ Data variables:
         7.878788,  7.979798,  8.080808,  8.181818,  8.282828,  8.383838,
         8.484848,  8.585859,  8.686869,  8.787879,  8.888889,  8.989899,
         9.090909,  9.191919,  9.292929,  9.393939,  9.494949,  9.59596 ,
-        9.69697 ,  9.79798 ,  9.89899 , 10.      ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9a0dc0ba-cfd4-4b53-9ea4-9851b667c217' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9a0dc0ba-cfd4-4b53-9ea4-9851b667c217' class='xr-section-summary' >Data variables: <span>(1)</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>y</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.313 3.534 1.349 ... 32.2 29.24</div><input id='attrs-23a2e428-887a-451e-9211-3a1e8e051fdd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-23a2e428-887a-451e-9211-3a1e8e051fdd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-116d2e00-93d8-4039-b1f1-64e232315d88' class='xr-var-data-in' type='checkbox'><label for='data-116d2e00-93d8-4039-b1f1-64e232315d88' 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([ 2.31344255,  3.53433593,  1.34908632,  2.43689987,  0.52129732,
-        3.37612748,  1.90897385,  6.29056489,  3.25194086,  3.37744078,
-        9.0193145 ,  5.8191261 ,  5.38044197,  7.66364914,  6.60103552,
-        6.19300329,  5.27990676,  7.52361384,  7.10757993,  6.0356579 ,
-        7.2188985 ,  5.85003633,  6.17868995,  9.50163557,  7.92126308,
-        7.98138724,  8.48669928,  7.88744995, 12.14526444,  9.34691599,
-        8.85736082,  6.8697178 ,  8.28332729, 10.89691933, 14.76845224,
-       10.19777631, 13.00627447, 12.2166908 , 13.28187352, 11.63794867,
-       13.10039385, 14.74542854, 15.34570065, 13.59974961, 15.05159625,
-       12.64807788, 15.76381504, 15.86640267, 15.54728581, 17.85995723,
-       15.50995487, 14.79101145, 18.16856046, 20.17045554, 16.49446633,
-       18.41673529, 17.24657162, 15.49351063, 22.49363636, 20.10535031,
-       19.28376332, 19.72132889, 19.68426233, 19.92852017, 21.63053763,
-       19.43820789, 22.77571803, 21.52550027, 21.36025545, 23.63246365,
-       22.4934742 , 22.81210646, 20.97514826, 21.98038786, 25.49873142,
-       24.81276344, 22.36927849, 24.81073349, 22.51059971, 27.99373936,
-       24.28979222, 26.35288122, 27.66829318, 24.70447271, 27.59622693,
-       29.05306128, 27.99765931, 27.0760332 , 28.29855623, 27.65515164,
-       29.21482719, 26.26966137, 28.90332653, 30.18122177, 31.47070239,
-       30.51950515, 31.33660832, 32.63472848, 32.19772335, 29.23970369])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b06c176f-f31a-43ae-9008-25ae32f4df4d' class='xr-section-summary-in' type='checkbox'  ><label for='section-b06c176f-f31a-43ae-9008-25ae32f4df4d' class='xr-section-summary' >Indexes: <span>(1)</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>t</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-94e3529b-6d2d-468e-aca1-30ca04af86c8' class='xr-index-data-in' type='checkbox'/><label for='index-94e3529b-6d2d-468e-aca1-30ca04af86c8' 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([                0.0, 0.10101010101010101, 0.20202020202020202,
+        9.69697 ,  9.79798 ,  9.89899 , 10.      ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-80914dc8-dc91-498c-b781-0761b0f62517' class='xr-section-summary-in' type='checkbox'  checked><label for='section-80914dc8-dc91-498c-b781-0761b0f62517' class='xr-section-summary' >Data variables: <span>(1)</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>y</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.59 2.136 1.343 ... 30.87 32.09</div><input id='attrs-0c997349-0f4e-4053-836f-48a9cad4415e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0c997349-0f4e-4053-836f-48a9cad4415e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-86a643cf-ad83-418a-b5e3-5f52ca258650' class='xr-var-data-in' type='checkbox'><label for='data-86a643cf-ad83-418a-b5e3-5f52ca258650' 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([ 1.58976396,  2.13626768,  1.34289925,  2.53150209,  2.56102578,
+       -0.21899969,  1.26689629,  2.064798  ,  4.04579132,  2.33368407,
+        5.56713047,  4.64800357,  4.12075281,  2.8115269 ,  6.05279673,
+        4.72113173,  9.74281027,  6.23928579,  6.02924706,  8.057377  ,
+        8.67482637,  6.44336997,  9.71115216,  6.84397881,  9.06017053,
+        6.49025613, 10.12391111,  9.5022547 ,  8.05685756,  9.53097276,
+        8.43057554, 14.57156966,  8.77743968,  8.75197054, 11.73207016,
+       10.08294682, 13.55943288, 14.76145888, 14.13237262, 15.826024  ,
+       11.69458685, 12.51989808, 14.40128584, 14.39427702, 12.66837215,
+       15.92469856, 17.83174566, 17.44936835, 17.64962036, 14.75949057,
+       15.63025287, 17.29111458, 19.47794167, 16.11896414, 19.22733081,
+       15.55895925, 17.50982302, 16.59275063, 19.37052338, 18.53681002,
+       21.55941223, 19.05813279, 18.82898749, 18.51376136, 19.01012364,
+       20.79403644, 22.02425154, 21.93984824, 21.0715503 , 20.06227644,
+       22.79902669, 20.19672578, 24.33260566, 25.66898506, 22.59631756,
+       24.35184169, 24.93279036, 27.10831817, 26.88697449, 25.80286533,
+       27.3116128 , 25.4060967 , 27.55552521, 28.30159717, 25.17681247,
+       28.26655258, 27.82207145, 28.22772349, 30.45361189, 30.23373524,
+       28.89402766, 30.98637061, 31.13245499, 29.01262802, 29.44403878,
+       29.12494048, 31.06098902, 27.02074993, 30.87370365, 32.092818  ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a0200bb1-5afd-402c-8284-a6c1f4971599' class='xr-section-summary-in' type='checkbox'  ><label for='section-a0200bb1-5afd-402c-8284-a6c1f4971599' class='xr-section-summary' >Indexes: <span>(1)</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>t</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-0f6569cc-0300-40f9-81f5-07b531e578bc' class='xr-index-data-in' type='checkbox'/><label for='index-0f6569cc-0300-40f9-81f5-07b531e578bc' 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([                0.0, 0.10101010101010101, 0.20202020202020202,
        0.30303030303030304, 0.40404040404040403,  0.5050505050505051,
         0.6060606060606061,  0.7070707070707071,  0.8080808080808081,
         0.9090909090909091,  1.0101010101010102,  1.1111111111111112,
@@ -620,7 +619,7 @@ Data variables:
          9.393939393939394,   9.494949494949495,   9.595959595959595,
          9.696969696969697,   9.797979797979798,     9.8989898989899,
                       10.0],
-      dtype=&#x27;float64&#x27;, name=&#x27;t&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-adcd8708-1985-4edd-b64e-9ea75926ce6b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-adcd8708-1985-4edd-b64e-9ea75926ce6b' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>
+      dtype=&#x27;float64&#x27;, name=&#x27;t&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-65faa5e2-c491-48df-8cb4-0b387f00d0f0' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-65faa5e2-c491-48df-8cb4-0b387f00d0f0' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>
 
 
 
@@ -667,17 +666,17 @@ sim.solver = solve_analytic_1d
 sim.config.data_structure
 ```
 
-    MinMaxScaler(variable=y, min=0.5212973246575279, max=32.634728477251194)
+    MinMaxScaler(variable=y, min=-0.21899969389420804, max=32.09281799761304)
     
 
-    C:\Pymob\pymob\pymob\simulation.py:307: UserWarning: `sim.config.data_structure.y = Datavariable(dimensions=['t'] min=0.5212973246575279 max=32.634728477251194 observed=True dimensions_evaluator=None)` has been assumed from `sim.observations`. If the order of the dimensions should be different, specify `sim.config.data_structure.y = DataVariable(dimensions=[...], ...)` manually.
+    C:\Pymob\pymob\pymob\simulation.py:307: UserWarning: `sim.config.data_structure.y = Datavariable(dimensions=['t'] min=-0.21899969389420804 max=32.09281799761304 observed=True dimensions_evaluator=None)` has been assumed from `sim.observations`. If the order of the dimensions should be different, specify `sim.config.data_structure.y = DataVariable(dimensions=[...], ...)` manually.
       warnings.warn(
     
 
 
 
 
-    Datastructure(y=DataVariable(dimensions=['t'], min=0.5212973246575279, max=32.634728477251194, observed=True, dimensions_evaluator=None))
+    Datastructure(y=DataVariable(dimensions=['t'], min=-0.21899969389420804, max=32.09281799761304, observed=True, dimensions_evaluator=None))
 
 
 
@@ -1184,7 +1183,7 @@ Dimensions:  (t: 100)
 Coordinates:
   * t        (t) float64 800B 0.0 0.101 0.202 0.303 ... 9.697 9.798 9.899 10.0
 Data variables:
-    y        (t) float64 800B 1.0 1.303 1.606 1.909 ... 30.09 30.39 30.7 31.0</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-9d27d71e-f5db-467f-a3fd-a65cfe042cc0' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-9d27d71e-f5db-467f-a3fd-a65cfe042cc0' 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'>t</span>: 100</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-1bef969a-2d93-4669-ab41-b5a64a363492' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1bef969a-2d93-4669-ab41-b5a64a363492' class='xr-section-summary' >Coordinates: <span>(1)</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'>t</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.101 0.202 ... 9.899 10.0</div><input id='attrs-8e1facdb-7ef4-433a-a5eb-2241f2284cae' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8e1facdb-7ef4-433a-a5eb-2241f2284cae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bc0ad2fe-efcb-4148-a881-c47399660fcd' class='xr-var-data-in' type='checkbox'><label for='data-bc0ad2fe-efcb-4148-a881-c47399660fcd' 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.      ,  0.10101 ,  0.20202 ,  0.30303 ,  0.40404 ,  0.505051,
+    y        (t) float64 800B 1.0 1.303 1.606 1.909 ... 30.09 30.39 30.7 31.0</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-843c93d5-f2e9-4819-9f89-2ea8de587d95' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-843c93d5-f2e9-4819-9f89-2ea8de587d95' 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'>t</span>: 100</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-8dae8c55-0d33-4a1c-ac84-9e679dd2d172' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8dae8c55-0d33-4a1c-ac84-9e679dd2d172' class='xr-section-summary' >Coordinates: <span>(1)</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'>t</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.101 0.202 ... 9.899 10.0</div><input id='attrs-e0a4798e-87be-49d8-99e3-be36163fb43f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e0a4798e-87be-49d8-99e3-be36163fb43f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-846ef747-2ea0-4f0b-8375-fbd69975f778' class='xr-var-data-in' type='checkbox'><label for='data-846ef747-2ea0-4f0b-8375-fbd69975f778' 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.      ,  0.10101 ,  0.20202 ,  0.30303 ,  0.40404 ,  0.505051,
         0.606061,  0.707071,  0.808081,  0.909091,  1.010101,  1.111111,
         1.212121,  1.313131,  1.414141,  1.515152,  1.616162,  1.717172,
         1.818182,  1.919192,  2.020202,  2.121212,  2.222222,  2.323232,
@@ -1200,7 +1199,7 @@ Data variables:
         7.878788,  7.979798,  8.080808,  8.181818,  8.282828,  8.383838,
         8.484848,  8.585859,  8.686869,  8.787879,  8.888889,  8.989899,
         9.090909,  9.191919,  9.292929,  9.393939,  9.494949,  9.59596 ,
-        9.69697 ,  9.79798 ,  9.89899 , 10.      ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-39530410-ec77-4f64-ad06-7988693f0afc' class='xr-section-summary-in' type='checkbox'  checked><label for='section-39530410-ec77-4f64-ad06-7988693f0afc' class='xr-section-summary' >Data variables: <span>(1)</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>y</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.0 1.303 1.606 ... 30.39 30.7 31.0</div><input id='attrs-d5bc885b-3334-49a2-ad8c-0080d0bc0e1e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d5bc885b-3334-49a2-ad8c-0080d0bc0e1e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fad708e8-94f6-4b4e-bc29-2bc97653d0fb' class='xr-var-data-in' type='checkbox'><label for='data-fad708e8-94f6-4b4e-bc29-2bc97653d0fb' 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([ 1.        ,  1.3030303 ,  1.60606061,  1.90909091,  2.21212121,
+        9.69697 ,  9.79798 ,  9.89899 , 10.      ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e40a3c57-c1c9-4619-b14d-8a6db46c8dfe' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e40a3c57-c1c9-4619-b14d-8a6db46c8dfe' class='xr-section-summary' >Data variables: <span>(1)</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>y</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.0 1.303 1.606 ... 30.39 30.7 31.0</div><input id='attrs-454c1729-5e7a-457c-9212-b2f6447d7024' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-454c1729-5e7a-457c-9212-b2f6447d7024' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-54742c46-e2f7-4d6c-b8ca-0ce1fdb9f1c4' class='xr-var-data-in' type='checkbox'><label for='data-54742c46-e2f7-4d6c-b8ca-0ce1fdb9f1c4' 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([ 1.        ,  1.3030303 ,  1.60606061,  1.90909091,  2.21212121,
         2.51515152,  2.81818182,  3.12121212,  3.42424242,  3.72727273,
         4.03030303,  4.33333333,  4.63636364,  4.93939394,  5.24242424,
         5.54545455,  5.84848485,  6.15151515,  6.45454545,  6.75757576,
@@ -1219,7 +1218,7 @@ Data variables:
        25.24242424, 25.54545455, 25.84848485, 26.15151515, 26.45454545,
        26.75757576, 27.06060606, 27.36363636, 27.66666667, 27.96969697,
        28.27272727, 28.57575758, 28.87878788, 29.18181818, 29.48484848,
-       29.78787879, 30.09090909, 30.39393939, 30.6969697 , 31.        ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f35b6182-0eec-4c39-9437-982bdb0049ed' class='xr-section-summary-in' type='checkbox'  ><label for='section-f35b6182-0eec-4c39-9437-982bdb0049ed' class='xr-section-summary' >Indexes: <span>(1)</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>t</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-b65f8685-9167-400b-954e-47b3561e3ffb' class='xr-index-data-in' type='checkbox'/><label for='index-b65f8685-9167-400b-954e-47b3561e3ffb' 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([                0.0, 0.10101010101010101, 0.20202020202020202,
+       29.78787879, 30.09090909, 30.39393939, 30.6969697 , 31.        ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-963b279c-24d3-4340-a7db-d217683908c5' class='xr-section-summary-in' type='checkbox'  ><label for='section-963b279c-24d3-4340-a7db-d217683908c5' class='xr-section-summary' >Indexes: <span>(1)</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>t</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-4a0e9593-b218-4999-a36a-0b6a7108a6ca' class='xr-index-data-in' type='checkbox'/><label for='index-4a0e9593-b218-4999-a36a-0b6a7108a6ca' 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([                0.0, 0.10101010101010101, 0.20202020202020202,
        0.30303030303030304, 0.40404040404040403,  0.5050505050505051,
         0.6060606060606061,  0.7070707070707071,  0.8080808080808081,
         0.9090909090909091,  1.0101010101010102,  1.1111111111111112,
@@ -1253,7 +1252,7 @@ Data variables:
          9.393939393939394,   9.494949494949495,   9.595959595959595,
          9.696969696969697,   9.797979797979798,     9.8989898989899,
                       10.0],
-      dtype=&#x27;float64&#x27;, name=&#x27;t&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-97fbef1d-6c52-4d6d-b1c9-38f90ceab01b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-97fbef1d-6c52-4d6d-b1c9-38f90ceab01b' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>
+      dtype=&#x27;float64&#x27;, name=&#x27;t&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4a6abe8f-ee48-42ef-a2ab-7f3e31f0ecdb' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-4a6abe8f-ee48-42ef-a2ab-7f3e31f0ecdb' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>
 
 
 
@@ -1279,7 +1278,7 @@ ax.legend()
 
 
 
-    <matplotlib.legend.Legend at 0x2d2757dd250>
+    <matplotlib.legend.Legend at 0x1df6d279990>
 
 
 
@@ -1333,76 +1332,44 @@ sim.posterior_predictive_checks(pred_hdi_style={"alpha": 0.1})
             value 100 |
     
 
-    
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-
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-
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-
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warmup:   5%|██                                      | 158/3000 [00:03<00:33, 84.73it/s, 5 steps of size 1.28e+00. acc. prob=0.78]
-
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warmup:   9%|███▌                                   | 270/3000 [00:03<00:16, 168.27it/s, 3 steps of size 1.37e+00. acc. prob=0.78]
-
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warmup:  13%|████▉                                  | 379/3000 [00:03<00:10, 261.46it/s, 1 steps of size 7.30e-01. acc. prob=0.79]
-
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warmup:  16%|██████▍                                | 492/3000 [00:03<00:06, 372.25it/s, 3 steps of size 9.54e-01. acc. prob=0.79]
-
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warmup:  21%|████████▏                              | 626/3000 [00:03<00:04, 521.55it/s, 3 steps of size 1.30e+00. acc. prob=0.79]
-
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warmup:  26%|█████████▉                             | 765/3000 [00:03<00:03, 678.16it/s, 3 steps of size 1.06e+00. acc. prob=0.79]
+    
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-    
warmup:  30%|███████████▌                           | 891/3000 [00:03<00:02, 773.18it/s, 3 steps of size 1.35e+00. acc. prob=0.79]
+    
warmup:   0%|                                                                                                                                                     | 1/3000 [00:01<51:56,  1.04s/it, 1 steps of size 1.87e+00. acc. prob=0.00]
 
-    
sample:  34%|████████████▉                         | 1021/3000 [00:03<00:02, 886.43it/s, 7 steps of size 7.70e-01. acc. prob=0.94]
+    
warmup:  10%|███████████████▎                                                                                                                                  | 315/3000 [00:01<00:07, 375.41it/s, 3 steps of size 5.98e-01. acc. prob=0.78]
 
-    
sample:  39%|██████████████▋                       | 1156/3000 [00:03<00:01, 993.67it/s, 3 steps of size 7.70e-01. acc. prob=0.93]
+    
warmup:  20%|█████████████████████████████▉                                                                                                                    | 614/3000 [00:01<00:03, 755.62it/s, 7 steps of size 8.67e-01. acc. prob=0.79]
 
-    
sample:  43%|███████████████▉                     | 1292/3000 [00:04<00:01, 1074.35it/s, 3 steps of size 7.70e-01. acc. prob=0.93]
+    
warmup:  31%|████████████████████████████████████████████▌                                                                                                    | 922/3000 [00:01<00:01, 1153.67it/s, 1 steps of size 8.99e-01. acc. prob=0.79]
 
-    
sample:  48%|█████████████████▌                   | 1425/3000 [00:04<00:01, 1140.72it/s, 3 steps of size 7.70e-01. acc. prob=0.93]
+    
sample:  41%|███████████████████████████████████████████████████████████▋                                                                                    | 1244/3000 [00:01<00:01, 1538.23it/s, 3 steps of size 8.50e-01. acc. prob=0.92]
 
-    
sample:  52%|███████████████████▏                 | 1553/3000 [00:04<00:01, 1175.55it/s, 7 steps of size 7.70e-01. acc. prob=0.93]
+    
sample:  52%|██████████████████████████████████████████████████████████████████████████▍                                                                     | 1552/3000 [00:01<00:00, 1867.98it/s, 3 steps of size 8.50e-01. acc. prob=0.92]
 
-    
sample:  56%|████████████████████▊                | 1686/3000 [00:04<00:01, 1218.48it/s, 7 steps of size 7.70e-01. acc. prob=0.93]
+    
sample:  61%|████████████████████████████████████████████████████████████████████████████████████████▏                                                       | 1836/3000 [00:01<00:00, 2094.01it/s, 7 steps of size 8.50e-01. acc. prob=0.91]
 
-    
sample:  60%|██████████████████████▍              | 1815/3000 [00:04<00:00, 1227.49it/s, 3 steps of size 7.70e-01. acc. prob=0.93]
+    
sample:  74%|█████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                     | 2210/3000 [00:01<00:00, 2416.20it/s, 11 steps of size 8.50e-01. acc. prob=0.91]
 
-    
sample:  65%|███████████████████████▉             | 1943/3000 [00:04<00:00, 1177.93it/s, 3 steps of size 7.70e-01. acc. prob=0.92]
+    
sample:  86%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                   | 2595/3000 [00:01<00:00, 2743.24it/s, 3 steps of size 8.50e-01. acc. prob=0.91]
 
-    
sample:  69%|█████████████████████████▍           | 2065/3000 [00:04<00:00, 1156.28it/s, 3 steps of size 7.70e-01. acc. prob=0.92]
+    
sample: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉| 2999/3000 [00:01<00:00, 3005.45it/s, 3 steps of size 8.50e-01. acc. prob=0.91]
 
-    
sample:  73%|██████████████████████████▉          | 2184/3000 [00:04<00:00, 1158.87it/s, 3 steps of size 7.70e-01. acc. prob=0.92]
-
-    
sample:  77%|████████████████████████████▍        | 2302/3000 [00:04<00:00, 1150.36it/s, 3 steps of size 7.70e-01. acc. prob=0.92]
-
-    
sample:  81%|█████████████████████████████▊       | 2419/3000 [00:05<00:00, 1073.61it/s, 5 steps of size 7.70e-01. acc. prob=0.93]
-
-    
sample:  85%|███████████████████████████████▎     | 2541/3000 [00:05<00:00, 1081.20it/s, 7 steps of size 7.70e-01. acc. prob=0.93]
-
-    
sample:  89%|████████████████████████████████▊    | 2663/3000 [00:05<00:00, 1087.50it/s, 3 steps of size 7.70e-01. acc. prob=0.92]
-
-    
sample:  93%|██████████████████████████████████▎  | 2784/3000 [00:05<00:00, 1088.87it/s, 3 steps of size 7.70e-01. acc. prob=0.92]
-
-    
sample:  97%|███████████████████████████████████▊ | 2905/3000 [00:05<00:00, 1089.82it/s, 7 steps of size 7.70e-01. acc. prob=0.92]
-
-    
sample: 100%|██████████████████████████████████████| 3000/3000 [00:05<00:00, 542.00it/s, 3 steps of size 7.70e-01. acc. prob=0.92]
+    
sample: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3000/3000 [00:01<00:00, 1505.68it/s, 1 steps of size 8.50e-01. acc. prob=0.91]
 
     
     
 
     
                     mean       std    median      5.0%     95.0%     n_eff     r_hat
-             b      3.05      0.03      3.05      3.01      3.09   1831.90      1.00
-       sigma_y      1.56      0.11      1.55      1.38      1.74   1341.54      1.00
+             b      3.07      0.03      3.07      3.02      3.11   2085.24      1.00
+       sigma_y      1.57      0.12      1.57      1.38      1.76   1661.94      1.00
     
-    
-
     Number of divergences: 0
     
 
 
     
-![png](superquickstart_files/superquickstart_20_32.png)
+![png](superquickstart_files/superquickstart_20_16.png)
     
 
 

From 9c4ecf9f0024953ddc55555b165ec7d12dbc9fda Mon Sep 17 00:00:00 2001
From: flo-schu <fluncki@protonmail.com>
Date: Sun, 29 Jun 2025 15:14:57 +0200
Subject: [PATCH 11/16] Add superquickstart and introduction to user guide
 index

---
 docs/source/user_guide/index.md | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/docs/source/user_guide/index.md b/docs/source/user_guide/index.md
index da9101c2c..0bac1e9e1 100644
--- a/docs/source/user_guide/index.md
+++ b/docs/source/user_guide/index.md
@@ -8,6 +8,7 @@ This guide is an overview and explains the important features.
 :maxdepth: 1
 
 installation
+superquickstart
 quickstart
 ```
 
@@ -15,6 +16,7 @@ quickstart
 :caption: Usage
 :maxdepth: 2
 
+Introduction
 framework_overview
 case_studies
 simulation

From 3dda3eb0b90add9905a14ce45ca0f331122c1f2d Mon Sep 17 00:00:00 2001
From: flo-schu <fluncki@protonmail.com>
Date: Tue, 1 Jul 2025 08:45:00 +0200
Subject: [PATCH 12/16] Fix one reference and paths to pymob_overview for unix
 compatibility

---
 docs/source/user_guide/Introduction.ipynb    | 4 ++--
 docs/source/user_guide/superquickstart.ipynb | 6 +++---
 2 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/docs/source/user_guide/Introduction.ipynb b/docs/source/user_guide/Introduction.ipynb
index dddffa3ab..d1ef27069 100644
--- a/docs/source/user_guide/Introduction.ipynb
+++ b/docs/source/user_guide/Introduction.ipynb
@@ -16,7 +16,7 @@
     "\n",
     "### How pymob is structured:\n",
     "Here  you can see the structure of the structure of pymob package: <br>\n",
-    "![Structure of the pymob package](..\\user_guide\\figures\\pymob_overview.png) <br>\n",
+    "![Structure of the pymob package](./figures/pymob_overview.png) <br>\n",
     "The Pymob package consists of several elements: \n",
     "\n",
     "\n",
@@ -762,7 +762,7 @@
     "## Defining a solver\n",
     "\n",
     "As described above: A solver is required for many models. So we define a solver by {class}`pymob.simulation.SimulationBase.solver`. <br>\n",
-    "In our case the model gives the exact solution of the model. Therefore, we choose `solve_analytic_1d`. An overwiev of the solvers currently implemented in pymob can be found at the beginning of this tutorial [here](#How-pymob-is-structured:)."
+    "In our case the model gives the exact solution of the model. Therefore, we choose `solve_analytic_1d`. An overwiev of the solvers currently implemented in pymob can be found at the beginning of this tutorial [here](#how-pymob-is-structured)."
    ]
   },
   {
diff --git a/docs/source/user_guide/superquickstart.ipynb b/docs/source/user_guide/superquickstart.ipynb
index be4c1b1b7..c77d1d4ee 100644
--- a/docs/source/user_guide/superquickstart.ipynb
+++ b/docs/source/user_guide/superquickstart.ipynb
@@ -71,7 +71,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "![framework-overview](.\\figures\\pymob_overview.png)"
+    "![framework-overview](./figures/pymob_overview.png)"
    ]
   },
   {
@@ -1654,7 +1654,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "pymobnew",
+   "display_name": "pymob",
    "language": "python",
    "name": "python3"
   },
@@ -1668,7 +1668,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.13"
+   "version": "3.11.11"
   }
  },
  "nbformat": 4,

From a2f7dde541c98808e838d3d08d5aaf5bab649204 Mon Sep 17 00:00:00 2001
From: merkuns <mholdreich@uni-osnabrueck.de>
Date: Thu, 17 Jul 2025 11:30:39 +0200
Subject: [PATCH 13/16] Made some very small changes (reject those if you want,
 I don't care) and added a question as a comment in the code

---
 docs/source/user_guide/superquickstart.ipynb | 624 ++++++-------------
 1 file changed, 206 insertions(+), 418 deletions(-)

diff --git a/docs/source/user_guide/superquickstart.ipynb b/docs/source/user_guide/superquickstart.ipynb
index c77d1d4ee..6dccfa8cc 100644
--- a/docs/source/user_guide/superquickstart.ipynb
+++ b/docs/source/user_guide/superquickstart.ipynb
@@ -52,7 +52,7 @@
     "The inferer handels the parameter estimation.  \n",
     "Pymob supports [various backends](https://pymob.readthedocs.io/en/stable/user_guide/framework_overview.html). In this example, we will work with *NumPyro*.  \n",
     "We assign the inferer to our Simulation object via the {attr}`~pymob.simulation.inferer` attribute and configure the desired kernel (e.g. *nuts*).  \n",
-    "But before inference, we need to parameterize our model using the *Param* class.   \n",
+    "But before inference, we need to parameterize our model using the {class}`sim.parameters.Param` class.   \n",
     "Each parameter can be marked either as free or fixed, depending on whether it should be variable during the optimization procedure.   \n",
     "The parameters are stored in the {attr}`~pymob.simulation.SimulationBase.model_parameters` dictionary, which holds model input values.\n",
     "By default, it takes the keys: `parameters`, `y0` and `x_in`. \n",
@@ -83,7 +83,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -104,13 +104,13 @@
    "source": [
     "Since no measured data is provided, we will generate an artificial dataset.  \n",
     "$y_{obs}$ represents the **observed data** over the time $t$ [0, 10].  \n",
-    "To use this data later in the simulation, we need to convert it into an **xarray-Dataset**.  \n",
+    "To use this data later in the simulation, we need to convert it into an **xarray dataset**.  \n",
     "In your own application, you would replace this with your measured experimental data.  "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -136,76 +136,27 @@
        " */\n",
        "\n",
        ":root {\n",
-       "  --xr-font-color0: var(\n",
-       "    --jp-content-font-color0,\n",
-       "    var(--pst-color-text-base rgba(0, 0, 0, 1))\n",
-       "  );\n",
-       "  --xr-font-color2: var(\n",
-       "    --jp-content-font-color2,\n",
-       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.54))\n",
-       "  );\n",
-       "  --xr-font-color3: var(\n",
-       "    --jp-content-font-color3,\n",
-       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.38))\n",
-       "  );\n",
-       "  --xr-border-color: var(\n",
-       "    --jp-border-color2,\n",
-       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 10))\n",
-       "  );\n",
-       "  --xr-disabled-color: var(\n",
-       "    --jp-layout-color3,\n",
-       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 40))\n",
-       "  );\n",
-       "  --xr-background-color: var(\n",
-       "    --jp-layout-color0,\n",
-       "    var(--pst-color-on-background, white)\n",
-       "  );\n",
-       "  --xr-background-color-row-even: var(\n",
-       "    --jp-layout-color1,\n",
-       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 5))\n",
-       "  );\n",
-       "  --xr-background-color-row-odd: var(\n",
-       "    --jp-layout-color2,\n",
-       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 15))\n",
-       "  );\n",
-       "}\n",
-       "\n",
-       "html[theme=\"dark\"],\n",
-       "html[data-theme=\"dark\"],\n",
-       "body[data-theme=\"dark\"],\n",
+       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
+       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
+       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
+       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
+       "\n",
+       "html[theme=dark],\n",
+       "body[data-theme=dark],\n",
        "body.vscode-dark {\n",
-       "  --xr-font-color0: var(\n",
-       "    --jp-content-font-color0,\n",
-       "    var(--pst-color-text-base, rgba(255, 255, 255, 1))\n",
-       "  );\n",
-       "  --xr-font-color2: var(\n",
-       "    --jp-content-font-color2,\n",
-       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.54))\n",
-       "  );\n",
-       "  --xr-font-color3: var(\n",
-       "    --jp-content-font-color3,\n",
-       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.38))\n",
-       "  );\n",
-       "  --xr-border-color: var(\n",
-       "    --jp-border-color2,\n",
-       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 10))\n",
-       "  );\n",
-       "  --xr-disabled-color: var(\n",
-       "    --jp-layout-color3,\n",
-       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 40))\n",
-       "  );\n",
-       "  --xr-background-color: var(\n",
-       "    --jp-layout-color0,\n",
-       "    var(--pst-color-on-background, #111111)\n",
-       "  );\n",
-       "  --xr-background-color-row-even: var(\n",
-       "    --jp-layout-color1,\n",
-       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 5))\n",
-       "  );\n",
-       "  --xr-background-color-row-odd: var(\n",
-       "    --jp-layout-color2,\n",
-       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 15))\n",
-       "  );\n",
+       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
+       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
+       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
+       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-disabled-color: #515151;\n",
+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
        "}\n",
        "\n",
        ".xr-wrap {\n",
@@ -246,7 +197,7 @@
        ".xr-sections {\n",
        "  padding-left: 0 !important;\n",
        "  display: grid;\n",
-       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
        "}\n",
        "\n",
        ".xr-section-item {\n",
@@ -254,14 +205,11 @@
        "}\n",
        "\n",
        ".xr-section-item input {\n",
-       "  display: inline-block;\n",
-       "  opacity: 0;\n",
-       "  height: 0;\n",
+       "  display: none;\n",
        "}\n",
        "\n",
        ".xr-section-item input + label {\n",
        "  color: var(--xr-disabled-color);\n",
-       "  border: 2px solid transparent !important;\n",
        "}\n",
        "\n",
        ".xr-section-item input:enabled + label {\n",
@@ -269,10 +217,6 @@
        "  color: var(--xr-font-color2);\n",
        "}\n",
        "\n",
-       ".xr-section-item input:focus + label {\n",
-       "  border: 2px solid var(--xr-font-color0) !important;\n",
-       "}\n",
-       "\n",
        ".xr-section-item input:enabled + label:hover {\n",
        "  color: var(--xr-font-color0);\n",
        "}\n",
@@ -294,7 +238,7 @@
        "\n",
        ".xr-section-summary-in + label:before {\n",
        "  display: inline-block;\n",
-       "  content: \"►\";\n",
+       "  content: '►';\n",
        "  font-size: 11px;\n",
        "  width: 15px;\n",
        "  text-align: center;\n",
@@ -305,7 +249,7 @@
        "}\n",
        "\n",
        ".xr-section-summary-in:checked + label:before {\n",
-       "  content: \"▼\";\n",
+       "  content: '▼';\n",
        "}\n",
        "\n",
        ".xr-section-summary-in:checked + label > span {\n",
@@ -377,15 +321,15 @@
        "}\n",
        "\n",
        ".xr-dim-list:before {\n",
-       "  content: \"(\";\n",
+       "  content: '(';\n",
        "}\n",
        "\n",
        ".xr-dim-list:after {\n",
-       "  content: \")\";\n",
+       "  content: ')';\n",
        "}\n",
        "\n",
        ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: \",\";\n",
+       "  content: ',';\n",
        "  padding-right: 5px;\n",
        "}\n",
        "\n",
@@ -402,9 +346,7 @@
        ".xr-var-item label,\n",
        ".xr-var-item > .xr-var-name span {\n",
        "  background-color: var(--xr-background-color-row-even);\n",
-       "  border-color: var(--xr-background-color-row-odd);\n",
        "  margin-bottom: 0;\n",
-       "  padding-top: 2px;\n",
        "}\n",
        "\n",
        ".xr-var-item > .xr-var-name:hover span {\n",
@@ -415,7 +357,6 @@
        ".xr-var-list > li:nth-child(odd) > label,\n",
        ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
        "  background-color: var(--xr-background-color-row-odd);\n",
-       "  border-color: var(--xr-background-color-row-even);\n",
        "}\n",
        "\n",
        ".xr-var-name {\n",
@@ -465,15 +406,8 @@
        ".xr-var-data,\n",
        ".xr-index-data {\n",
        "  display: none;\n",
-       "  border-top: 2px dotted var(--xr-background-color);\n",
-       "  padding-bottom: 20px !important;\n",
-       "  padding-top: 10px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in + label,\n",
-       ".xr-var-data-in + label,\n",
-       ".xr-index-data-in + label {\n",
-       "  padding: 0 1px;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
        "}\n",
        "\n",
        ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
@@ -486,12 +420,6 @@
        "  float: right;\n",
        "}\n",
        "\n",
-       ".xr-var-data > pre,\n",
-       ".xr-index-data > pre,\n",
-       ".xr-var-data > table > tbody > tr {\n",
-       "  background-color: transparent !important;\n",
-       "}\n",
-       "\n",
        ".xr-var-name span,\n",
        ".xr-var-data,\n",
        ".xr-index-name div,\n",
@@ -551,106 +479,66 @@
        "  stroke: currentColor;\n",
        "  fill: currentColor;\n",
        "}\n",
-       "\n",
-       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
-       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
-       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
-       "  color: var(--xr-font-color0);\n",
-       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
-       "  stroke-width: 0.8px;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 2kB\n",
-       "Dimensions:  (t: 100)\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
+       "Dimensions:  (t: 101)\n",
        "Coordinates:\n",
-       "  * t        (t) float64 800B 0.0 0.101 0.202 0.303 ... 9.697 9.798 9.899 10.0\n",
+       "  * t        (t) float64 0.0 0.1 0.2 0.3 0.4 0.5 ... 9.5 9.6 9.7 9.8 9.9 10.0\n",
        "Data variables:\n",
-       "    y        (t) float64 800B 0.281 4.775 -0.2706 2.471 ... 30.34 30.21 34.78</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-43460ff4-23ca-493d-b7c5-513a26efa32e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-43460ff4-23ca-493d-b7c5-513a26efa32e' 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'>t</span>: 100</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-4ffcf9c4-87ea-41ad-91ad-474e7c5bfa0a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4ffcf9c4-87ea-41ad-91ad-474e7c5bfa0a' class='xr-section-summary' >Coordinates: <span>(1)</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'>t</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.101 0.202 ... 9.899 10.0</div><input id='attrs-19d47ec4-9eba-4f7a-8300-36db6065b4ba' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-19d47ec4-9eba-4f7a-8300-36db6065b4ba' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-57eee83b-5cfa-4f55-b6e4-5fdb17513410' class='xr-var-data-in' type='checkbox'><label for='data-57eee83b-5cfa-4f55-b6e4-5fdb17513410' 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.      ,  0.10101 ,  0.20202 ,  0.30303 ,  0.40404 ,  0.505051,\n",
-       "        0.606061,  0.707071,  0.808081,  0.909091,  1.010101,  1.111111,\n",
-       "        1.212121,  1.313131,  1.414141,  1.515152,  1.616162,  1.717172,\n",
-       "        1.818182,  1.919192,  2.020202,  2.121212,  2.222222,  2.323232,\n",
-       "        2.424242,  2.525253,  2.626263,  2.727273,  2.828283,  2.929293,\n",
-       "        3.030303,  3.131313,  3.232323,  3.333333,  3.434343,  3.535354,\n",
-       "        3.636364,  3.737374,  3.838384,  3.939394,  4.040404,  4.141414,\n",
-       "        4.242424,  4.343434,  4.444444,  4.545455,  4.646465,  4.747475,\n",
-       "        4.848485,  4.949495,  5.050505,  5.151515,  5.252525,  5.353535,\n",
-       "        5.454545,  5.555556,  5.656566,  5.757576,  5.858586,  5.959596,\n",
-       "        6.060606,  6.161616,  6.262626,  6.363636,  6.464646,  6.565657,\n",
-       "        6.666667,  6.767677,  6.868687,  6.969697,  7.070707,  7.171717,\n",
-       "        7.272727,  7.373737,  7.474747,  7.575758,  7.676768,  7.777778,\n",
-       "        7.878788,  7.979798,  8.080808,  8.181818,  8.282828,  8.383838,\n",
-       "        8.484848,  8.585859,  8.686869,  8.787879,  8.888889,  8.989899,\n",
-       "        9.090909,  9.191919,  9.292929,  9.393939,  9.494949,  9.59596 ,\n",
-       "        9.69697 ,  9.79798 ,  9.89899 , 10.      ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-84777266-abbb-4342-a9de-43233de46e53' class='xr-section-summary-in' type='checkbox'  checked><label for='section-84777266-abbb-4342-a9de-43233de46e53' class='xr-section-summary' >Data variables: <span>(1)</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>y</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.281 4.775 -0.2706 ... 30.21 34.78</div><input id='attrs-45ed58d4-d042-4454-966b-1979c907c8f5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-45ed58d4-d042-4454-966b-1979c907c8f5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8cbcf34d-db2e-467f-b4ee-a679829f0fc3' class='xr-var-data-in' type='checkbox'><label for='data-8cbcf34d-db2e-467f-b4ee-a679829f0fc3' 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([ 0.28098171,  4.77538938, -0.27060725,  2.47128633,  1.01466105,\n",
-       "        3.2679597 ,  4.70051422,  1.93217318,  5.082623  ,  3.6843063 ,\n",
-       "        4.51998017,  3.26803103,  3.07512908,  3.57395258,  3.6241049 ,\n",
-       "        4.58810694,  3.82884281,  8.14571552,  8.24818809,  9.72236549,\n",
-       "        4.70146737,  9.05930222,  5.94992415,  8.52722744,  6.55250397,\n",
-       "       10.79632652,  7.68199537,  9.05824464, 10.93596586,  9.73823156,\n",
-       "        9.45764319,  6.9213614 ,  9.33596133, 12.53372142, 10.43895052,\n",
-       "       14.44158404,  8.96241515, 12.48336367, 11.61954073, 14.5480077 ,\n",
-       "       13.31325139, 15.65173068, 16.82876826, 14.44113823, 13.46262926,\n",
-       "       13.6351774 , 17.85634528, 14.49969756, 12.93861167, 14.75127382,\n",
-       "       15.25800612, 14.3458396 , 16.81402628, 19.10140957, 17.11511876,\n",
-       "       16.47810444, 19.27617769, 17.65928777, 21.4870153 , 18.99787141,\n",
-       "       17.60658913, 20.1158257 , 20.65111889, 21.90430844, 19.94000956,\n",
-       "       24.40937823, 21.66264338, 17.40165908, 19.30289728, 19.00432468,\n",
-       "       24.90563978, 22.97673914, 21.2001953 , 24.72272489, 24.97765503,\n",
-       "       22.77239235, 26.77775649, 22.33896691, 24.70444908, 25.76145285,\n",
-       "       25.97630266, 25.53291933, 25.533891  , 25.67715573, 23.99234918,\n",
-       "       30.93568447, 24.89612835, 28.67876116, 24.64567766, 29.56081186,\n",
-       "       28.6222925 , 26.68473011, 29.20094564, 30.03540899, 28.74281582,\n",
-       "       28.96913372, 30.78992037, 30.34161476, 30.21371687, 34.78050506])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f852956d-972e-4a11-abc5-73444c340197' class='xr-section-summary-in' type='checkbox'  ><label for='section-f852956d-972e-4a11-abc5-73444c340197' class='xr-section-summary' >Indexes: <span>(1)</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>t</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-a27f7b89-858d-47a1-8885-a1db0ed03448' class='xr-index-data-in' type='checkbox'/><label for='index-a27f7b89-858d-47a1-8885-a1db0ed03448' 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([                0.0, 0.10101010101010101, 0.20202020202020202,\n",
-       "       0.30303030303030304, 0.40404040404040403,  0.5050505050505051,\n",
-       "        0.6060606060606061,  0.7070707070707071,  0.8080808080808081,\n",
-       "        0.9090909090909091,  1.0101010101010102,  1.1111111111111112,\n",
-       "        1.2121212121212122,  1.3131313131313131,  1.4141414141414141,\n",
-       "        1.5151515151515151,  1.6161616161616161,  1.7171717171717171,\n",
-       "        1.8181818181818181,  1.9191919191919191,  2.0202020202020203,\n",
-       "         2.121212121212121,  2.2222222222222223,   2.323232323232323,\n",
-       "        2.4242424242424243,   2.525252525252525,  2.6262626262626263,\n",
-       "         2.727272727272727,  2.8282828282828283,   2.929292929292929,\n",
-       "        3.0303030303030303,   3.131313131313131,  3.2323232323232323,\n",
-       "        3.3333333333333335,  3.4343434343434343,  3.5353535353535355,\n",
-       "        3.6363636363636362,  3.7373737373737375,  3.8383838383838382,\n",
-       "        3.9393939393939394,   4.040404040404041,   4.141414141414141,\n",
-       "         4.242424242424242,   4.343434343434343,   4.444444444444445,\n",
-       "         4.545454545454545,   4.646464646464646,   4.747474747474747,\n",
-       "         4.848484848484849,    4.94949494949495,    5.05050505050505,\n",
-       "         5.151515151515151,   5.252525252525253,   5.353535353535354,\n",
-       "         5.454545454545454,   5.555555555555555,   5.656565656565657,\n",
-       "         5.757575757575758,   5.858585858585858,   5.959595959595959,\n",
-       "        6.0606060606060606,   6.161616161616162,   6.262626262626262,\n",
-       "         6.363636363636363,  6.4646464646464645,   6.565656565656566,\n",
-       "         6.666666666666667,   6.767676767676767,  6.8686868686868685,\n",
-       "          6.96969696969697,   7.070707070707071,   7.171717171717171,\n",
-       "        7.2727272727272725,   7.373737373737374,   7.474747474747475,\n",
-       "         7.575757575757575,  7.6767676767676765,   7.777777777777778,\n",
-       "         7.878787878787879,   7.979797979797979,   8.080808080808081,\n",
-       "         8.181818181818182,   8.282828282828282,   8.383838383838384,\n",
-       "         8.484848484848484,   8.585858585858587,   8.686868686868687,\n",
-       "         8.787878787878787,    8.88888888888889,    8.98989898989899,\n",
-       "          9.09090909090909,   9.191919191919192,   9.292929292929292,\n",
-       "         9.393939393939394,   9.494949494949495,   9.595959595959595,\n",
-       "         9.696969696969697,   9.797979797979798,     9.8989898989899,\n",
+       "    y        (t) float64 4.028 1.37 1.506 -0.1024 ... 31.42 33.42 29.35 32.74</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-5ee9938c-20c6-411e-8835-794938fbacc5' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-5ee9938c-20c6-411e-8835-794938fbacc5' 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'>t</span>: 101</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-d24e7663-61b2-4f17-8ac7-529abd0dcb9e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d24e7663-61b2-4f17-8ac7-529abd0dcb9e' class='xr-section-summary' >Coordinates: <span>(1)</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'>t</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.1 0.2 0.3 ... 9.8 9.9 10.0</div><input id='attrs-9ad731f7-e217-4470-b156-1eb634f22edf' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9ad731f7-e217-4470-b156-1eb634f22edf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cabf65af-d128-47e8-b2fc-4d72fd4be4d3' class='xr-var-data-in' type='checkbox'><label for='data-cabf65af-d128-47e8-b2fc-4d72fd4be4d3' 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. ,  0.1,  0.2,  0.3,  0.4,  0.5,  0.6,  0.7,  0.8,  0.9,  1. ,  1.1,\n",
+       "        1.2,  1.3,  1.4,  1.5,  1.6,  1.7,  1.8,  1.9,  2. ,  2.1,  2.2,  2.3,\n",
+       "        2.4,  2.5,  2.6,  2.7,  2.8,  2.9,  3. ,  3.1,  3.2,  3.3,  3.4,  3.5,\n",
+       "        3.6,  3.7,  3.8,  3.9,  4. ,  4.1,  4.2,  4.3,  4.4,  4.5,  4.6,  4.7,\n",
+       "        4.8,  4.9,  5. ,  5.1,  5.2,  5.3,  5.4,  5.5,  5.6,  5.7,  5.8,  5.9,\n",
+       "        6. ,  6.1,  6.2,  6.3,  6.4,  6.5,  6.6,  6.7,  6.8,  6.9,  7. ,  7.1,\n",
+       "        7.2,  7.3,  7.4,  7.5,  7.6,  7.7,  7.8,  7.9,  8. ,  8.1,  8.2,  8.3,\n",
+       "        8.4,  8.5,  8.6,  8.7,  8.8,  8.9,  9. ,  9.1,  9.2,  9.3,  9.4,  9.5,\n",
+       "        9.6,  9.7,  9.8,  9.9, 10. ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-32113b8c-0987-4de4-b314-994ae4f4671d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-32113b8c-0987-4de4-b314-994ae4f4671d' class='xr-section-summary' >Data variables: <span>(1)</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>y</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.028 1.37 1.506 ... 29.35 32.74</div><input id='attrs-eac7af59-b12c-4595-b92f-ef631db4ffdd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-eac7af59-b12c-4595-b92f-ef631db4ffdd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c64767ae-1ee3-4c86-8901-f80bb3f4bf03' class='xr-var-data-in' type='checkbox'><label for='data-c64767ae-1ee3-4c86-8901-f80bb3f4bf03' 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([ 4.02838695,  1.37032163,  1.5058789 , -0.1023566 ,  1.12812715,\n",
+       "       -0.50440519,  4.92087469,  2.92540714,  1.5095558 ,  5.80320885,\n",
+       "        4.59705594, -0.72073345,  4.66018611,  4.11794134,  5.96940087,\n",
+       "        3.31291739,  6.79659354,  3.75619909,  7.39629986,  6.27685582,\n",
+       "        7.20791104,  6.60574684, 10.89076531,  8.99487528,  8.45276379,\n",
+       "        7.94093974,  9.64866376, 12.35314246,  8.92075151,  9.06058763,\n",
+       "       10.74204086, 11.38791295, 12.64666093, 12.73118546, 15.84105012,\n",
+       "       12.48788591, 10.76807751, 17.11361543, 14.69969533, 14.25328245,\n",
+       "       10.88640772, 13.97251212, 11.9017861 , 13.85105715, 11.55327017,\n",
+       "       12.46803935, 16.16629758, 16.0732593 , 14.35144056, 15.46688564,\n",
+       "       14.78195572, 17.13462543, 15.60952458, 18.1101187 , 15.79153842,\n",
+       "       16.59987436, 13.13150084, 18.7204421 , 20.30191879, 19.40942286,\n",
+       "       20.68303218, 18.96360434, 19.90339138, 21.66396511, 18.87725489,\n",
+       "       19.44583516, 21.02652977, 23.41136604, 19.46488225, 20.60072757,\n",
+       "       24.64160105, 22.00015593, 19.95634443, 22.11655647, 25.79612665,\n",
+       "       25.5652377 , 20.03882792, 25.42004912, 22.84889615, 26.30146051,\n",
+       "       27.16532825, 25.72762564, 24.72205709, 25.33922566, 25.57633255,\n",
+       "       28.31664231, 26.7084131 , 26.42207821, 26.8125442 , 29.79686026,\n",
+       "       27.8095757 , 26.29596553, 30.54519835, 28.95846456, 29.53248537,\n",
+       "       27.94281117, 31.39903464, 31.42011852, 33.41947384, 29.35454881,\n",
+       "       32.73808715])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-48323806-5424-420f-a66e-80bd7feaf00b' class='xr-section-summary-in' type='checkbox'  ><label for='section-48323806-5424-420f-a66e-80bd7feaf00b' class='xr-section-summary' >Indexes: <span>(1)</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>t</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-0271a992-5c90-4a91-9a6a-a9691714efb8' class='xr-index-data-in' type='checkbox'/><label for='index-0271a992-5c90-4a91-9a6a-a9691714efb8' 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([                0.0,                 0.1,                 0.2,\n",
+       "       0.30000000000000004,                 0.4,                 0.5,\n",
+       "        0.6000000000000001,  0.7000000000000001,                 0.8,\n",
+       "                       0.9,\n",
+       "       ...\n",
+       "                       9.1,   9.200000000000001,                 9.3,\n",
+       "                       9.4,                 9.5,   9.600000000000001,\n",
+       "         9.700000000000001,                 9.8,                 9.9,\n",
        "                      10.0],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;t&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b490ab1a-0611-40b0-ba86-4185b5ce5737' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b490ab1a-0611-40b0-ba86-4185b5ce5737' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+       "      dtype=&#x27;float64&#x27;, name=&#x27;t&#x27;, length=101))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-18ef19bb-1657-4f09-a452-ee63d9ccb7aa' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-18ef19bb-1657-4f09-a452-ee63d9ccb7aa' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
-       "<xarray.Dataset> Size: 2kB\n",
-       "Dimensions:  (t: 100)\n",
+       "<xarray.Dataset>\n",
+       "Dimensions:  (t: 101)\n",
        "Coordinates:\n",
-       "  * t        (t) float64 800B 0.0 0.101 0.202 0.303 ... 9.697 9.798 9.899 10.0\n",
+       "  * t        (t) float64 0.0 0.1 0.2 0.3 0.4 0.5 ... 9.5 9.6 9.7 9.8 9.9 10.0\n",
        "Data variables:\n",
-       "    y        (t) float64 800B 0.281 4.775 -0.2706 2.471 ... 30.34 30.21 34.78"
+       "    y        (t) float64 4.028 1.37 1.506 -0.1024 ... 31.42 33.42 29.35 32.74"
       ]
      },
-     "execution_count": 47,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 500x400 with 1 Axes>"
       ]
@@ -664,7 +552,7 @@
     "rng = np.random.default_rng(seed=1)  # for reproducibility\n",
     "slope = rng.uniform(2,4)\n",
     "intercept = 1.0\n",
-    "num_points = 100\n",
+    "num_points = 101\n",
     "noise_level = 1.7\n",
     "\n",
     "# generating time values\n",
@@ -708,31 +596,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "MinMaxScaler(variable=y, min=-0.2706072545541467, max=34.780505060222275)\n"
+      "MinMaxScaler(variable=y, min=-0.7207334529232607, max=33.419473836088876)\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "C:\\Pymob\\pymob\\pymob\\simulation.py:307: UserWarning: `sim.config.data_structure.y = Datavariable(dimensions=['t'] min=-0.2706072545541467 max=34.780505060222275 observed=True dimensions_evaluator=None)` has been assumed from `sim.observations`. If the order of the dimensions should be different, specify `sim.config.data_structure.y = DataVariable(dimensions=[...], ...)` manually.\n",
+      "C:\\Users\\Markus\\pymob\\pymob\\pymob\\simulation.py:303: UserWarning: `sim.config.data_structure.y = Datavariable(dimensions=['t'] min=-0.7207334529232607 max=33.419473836088876 observed=True dimensions_evaluator=None)` has been assumed from `sim.observations`. If the order of the dimensions should be different, specify `sim.config.data_structure.y = DataVariable(dimensions=[...], ...)` manually.\n",
       "  warnings.warn(\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "Datastructure(y=DataVariable(dimensions=['t'], min=-0.2706072545541467, max=34.780505060222275, observed=True, dimensions_evaluator=None))"
+       "Datastructure(y=DataVariable(dimensions=['t'], min=-0.7207334529232607, max=33.419473836088876, observed=True, dimensions_evaluator=None))"
       ]
      },
-     "execution_count": 48,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -787,7 +675,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -796,7 +684,7 @@
        "{'a': 1.0, 'b': 3.0}"
       ]
      },
-     "execution_count": 49,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -824,17 +712,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Pymob\\pymob\\pymob\\simulation.py:567: UserWarning: The number of ODE states was not specified in the config file [simulation] > 'n_ode_states = <n>'. Extracted the return arguments ['a+b*x'] from the source code. Setting 'n_ode_states=1.\n",
-      "  warnings.warn(\n"
-     ]
-    },
     {
      "data": {
       "text/html": [
@@ -858,76 +738,27 @@
        " */\n",
        "\n",
        ":root {\n",
-       "  --xr-font-color0: var(\n",
-       "    --jp-content-font-color0,\n",
-       "    var(--pst-color-text-base rgba(0, 0, 0, 1))\n",
-       "  );\n",
-       "  --xr-font-color2: var(\n",
-       "    --jp-content-font-color2,\n",
-       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.54))\n",
-       "  );\n",
-       "  --xr-font-color3: var(\n",
-       "    --jp-content-font-color3,\n",
-       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.38))\n",
-       "  );\n",
-       "  --xr-border-color: var(\n",
-       "    --jp-border-color2,\n",
-       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 10))\n",
-       "  );\n",
-       "  --xr-disabled-color: var(\n",
-       "    --jp-layout-color3,\n",
-       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 40))\n",
-       "  );\n",
-       "  --xr-background-color: var(\n",
-       "    --jp-layout-color0,\n",
-       "    var(--pst-color-on-background, white)\n",
-       "  );\n",
-       "  --xr-background-color-row-even: var(\n",
-       "    --jp-layout-color1,\n",
-       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 5))\n",
-       "  );\n",
-       "  --xr-background-color-row-odd: var(\n",
-       "    --jp-layout-color2,\n",
-       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 15))\n",
-       "  );\n",
-       "}\n",
-       "\n",
-       "html[theme=\"dark\"],\n",
-       "html[data-theme=\"dark\"],\n",
-       "body[data-theme=\"dark\"],\n",
+       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
+       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
+       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
+       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
+       "\n",
+       "html[theme=dark],\n",
+       "body[data-theme=dark],\n",
        "body.vscode-dark {\n",
-       "  --xr-font-color0: var(\n",
-       "    --jp-content-font-color0,\n",
-       "    var(--pst-color-text-base, rgba(255, 255, 255, 1))\n",
-       "  );\n",
-       "  --xr-font-color2: var(\n",
-       "    --jp-content-font-color2,\n",
-       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.54))\n",
-       "  );\n",
-       "  --xr-font-color3: var(\n",
-       "    --jp-content-font-color3,\n",
-       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.38))\n",
-       "  );\n",
-       "  --xr-border-color: var(\n",
-       "    --jp-border-color2,\n",
-       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 10))\n",
-       "  );\n",
-       "  --xr-disabled-color: var(\n",
-       "    --jp-layout-color3,\n",
-       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 40))\n",
-       "  );\n",
-       "  --xr-background-color: var(\n",
-       "    --jp-layout-color0,\n",
-       "    var(--pst-color-on-background, #111111)\n",
-       "  );\n",
-       "  --xr-background-color-row-even: var(\n",
-       "    --jp-layout-color1,\n",
-       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 5))\n",
-       "  );\n",
-       "  --xr-background-color-row-odd: var(\n",
-       "    --jp-layout-color2,\n",
-       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 15))\n",
-       "  );\n",
+       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
+       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
+       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
+       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-disabled-color: #515151;\n",
+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
        "}\n",
        "\n",
        ".xr-wrap {\n",
@@ -968,7 +799,7 @@
        ".xr-sections {\n",
        "  padding-left: 0 !important;\n",
        "  display: grid;\n",
-       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
        "}\n",
        "\n",
        ".xr-section-item {\n",
@@ -976,14 +807,11 @@
        "}\n",
        "\n",
        ".xr-section-item input {\n",
-       "  display: inline-block;\n",
-       "  opacity: 0;\n",
-       "  height: 0;\n",
+       "  display: none;\n",
        "}\n",
        "\n",
        ".xr-section-item input + label {\n",
        "  color: var(--xr-disabled-color);\n",
-       "  border: 2px solid transparent !important;\n",
        "}\n",
        "\n",
        ".xr-section-item input:enabled + label {\n",
@@ -991,10 +819,6 @@
        "  color: var(--xr-font-color2);\n",
        "}\n",
        "\n",
-       ".xr-section-item input:focus + label {\n",
-       "  border: 2px solid var(--xr-font-color0) !important;\n",
-       "}\n",
-       "\n",
        ".xr-section-item input:enabled + label:hover {\n",
        "  color: var(--xr-font-color0);\n",
        "}\n",
@@ -1016,7 +840,7 @@
        "\n",
        ".xr-section-summary-in + label:before {\n",
        "  display: inline-block;\n",
-       "  content: \"►\";\n",
+       "  content: '►';\n",
        "  font-size: 11px;\n",
        "  width: 15px;\n",
        "  text-align: center;\n",
@@ -1027,7 +851,7 @@
        "}\n",
        "\n",
        ".xr-section-summary-in:checked + label:before {\n",
-       "  content: \"▼\";\n",
+       "  content: '▼';\n",
        "}\n",
        "\n",
        ".xr-section-summary-in:checked + label > span {\n",
@@ -1099,15 +923,15 @@
        "}\n",
        "\n",
        ".xr-dim-list:before {\n",
-       "  content: \"(\";\n",
+       "  content: '(';\n",
        "}\n",
        "\n",
        ".xr-dim-list:after {\n",
-       "  content: \")\";\n",
+       "  content: ')';\n",
        "}\n",
        "\n",
        ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: \",\";\n",
+       "  content: ',';\n",
        "  padding-right: 5px;\n",
        "}\n",
        "\n",
@@ -1124,9 +948,7 @@
        ".xr-var-item label,\n",
        ".xr-var-item > .xr-var-name span {\n",
        "  background-color: var(--xr-background-color-row-even);\n",
-       "  border-color: var(--xr-background-color-row-odd);\n",
        "  margin-bottom: 0;\n",
-       "  padding-top: 2px;\n",
        "}\n",
        "\n",
        ".xr-var-item > .xr-var-name:hover span {\n",
@@ -1137,7 +959,6 @@
        ".xr-var-list > li:nth-child(odd) > label,\n",
        ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
        "  background-color: var(--xr-background-color-row-odd);\n",
-       "  border-color: var(--xr-background-color-row-even);\n",
        "}\n",
        "\n",
        ".xr-var-name {\n",
@@ -1187,15 +1008,8 @@
        ".xr-var-data,\n",
        ".xr-index-data {\n",
        "  display: none;\n",
-       "  border-top: 2px dotted var(--xr-background-color);\n",
-       "  padding-bottom: 20px !important;\n",
-       "  padding-top: 10px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in + label,\n",
-       ".xr-var-data-in + label,\n",
-       ".xr-index-data-in + label {\n",
-       "  padding: 0 1px;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
        "}\n",
        "\n",
        ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
@@ -1208,12 +1022,6 @@
        "  float: right;\n",
        "}\n",
        "\n",
-       ".xr-var-data > pre,\n",
-       ".xr-index-data > pre,\n",
-       ".xr-var-data > table > tbody > tr {\n",
-       "  background-color: transparent !important;\n",
-       "}\n",
-       "\n",
        ".xr-var-name span,\n",
        ".xr-var-data,\n",
        ".xr-index-name div,\n",
@@ -1273,100 +1081,49 @@
        "  stroke: currentColor;\n",
        "  fill: currentColor;\n",
        "}\n",
-       "\n",
-       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
-       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
-       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
-       "  color: var(--xr-font-color0);\n",
-       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
-       "  stroke-width: 0.8px;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 2kB\n",
-       "Dimensions:  (t: 100)\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
+       "Dimensions:  (t: 101)\n",
        "Coordinates:\n",
-       "  * t        (t) float64 800B 0.0 0.101 0.202 0.303 ... 9.697 9.798 9.899 10.0\n",
+       "  * t        (t) float64 0.0 0.1 0.2 0.3 0.4 0.5 ... 9.5 9.6 9.7 9.8 9.9 10.0\n",
        "Data variables:\n",
-       "    y        (t) float64 800B 1.0 1.303 1.606 1.909 ... 30.09 30.39 30.7 31.0</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-39a93839-9bc3-4564-a7d9-b11aa8ad8906' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-39a93839-9bc3-4564-a7d9-b11aa8ad8906' 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'>t</span>: 100</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-b6c0b2f5-bd64-4be5-b162-65a94f81c4cb' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b6c0b2f5-bd64-4be5-b162-65a94f81c4cb' class='xr-section-summary' >Coordinates: <span>(1)</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'>t</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.101 0.202 ... 9.899 10.0</div><input id='attrs-7acde639-b15b-487d-8eee-27320ac37010' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7acde639-b15b-487d-8eee-27320ac37010' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-54ba89b1-643c-474b-8c8a-2117c729b910' class='xr-var-data-in' type='checkbox'><label for='data-54ba89b1-643c-474b-8c8a-2117c729b910' 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.      ,  0.10101 ,  0.20202 ,  0.30303 ,  0.40404 ,  0.505051,\n",
-       "        0.606061,  0.707071,  0.808081,  0.909091,  1.010101,  1.111111,\n",
-       "        1.212121,  1.313131,  1.414141,  1.515152,  1.616162,  1.717172,\n",
-       "        1.818182,  1.919192,  2.020202,  2.121212,  2.222222,  2.323232,\n",
-       "        2.424242,  2.525253,  2.626263,  2.727273,  2.828283,  2.929293,\n",
-       "        3.030303,  3.131313,  3.232323,  3.333333,  3.434343,  3.535354,\n",
-       "        3.636364,  3.737374,  3.838384,  3.939394,  4.040404,  4.141414,\n",
-       "        4.242424,  4.343434,  4.444444,  4.545455,  4.646465,  4.747475,\n",
-       "        4.848485,  4.949495,  5.050505,  5.151515,  5.252525,  5.353535,\n",
-       "        5.454545,  5.555556,  5.656566,  5.757576,  5.858586,  5.959596,\n",
-       "        6.060606,  6.161616,  6.262626,  6.363636,  6.464646,  6.565657,\n",
-       "        6.666667,  6.767677,  6.868687,  6.969697,  7.070707,  7.171717,\n",
-       "        7.272727,  7.373737,  7.474747,  7.575758,  7.676768,  7.777778,\n",
-       "        7.878788,  7.979798,  8.080808,  8.181818,  8.282828,  8.383838,\n",
-       "        8.484848,  8.585859,  8.686869,  8.787879,  8.888889,  8.989899,\n",
-       "        9.090909,  9.191919,  9.292929,  9.393939,  9.494949,  9.59596 ,\n",
-       "        9.69697 ,  9.79798 ,  9.89899 , 10.      ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e410dd31-a596-4be8-a21d-15a92c09754d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e410dd31-a596-4be8-a21d-15a92c09754d' class='xr-section-summary' >Data variables: <span>(1)</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>y</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.0 1.303 1.606 ... 30.39 30.7 31.0</div><input id='attrs-090abd7e-faf8-4829-b0f1-3fcba85c565e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-090abd7e-faf8-4829-b0f1-3fcba85c565e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-484e2a86-0b9d-4598-96dc-5db9d961dd9d' class='xr-var-data-in' type='checkbox'><label for='data-484e2a86-0b9d-4598-96dc-5db9d961dd9d' 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([ 1.        ,  1.3030303 ,  1.60606061,  1.90909091,  2.21212121,\n",
-       "        2.51515152,  2.81818182,  3.12121212,  3.42424242,  3.72727273,\n",
-       "        4.03030303,  4.33333333,  4.63636364,  4.93939394,  5.24242424,\n",
-       "        5.54545455,  5.84848485,  6.15151515,  6.45454545,  6.75757576,\n",
-       "        7.06060606,  7.36363636,  7.66666667,  7.96969697,  8.27272727,\n",
-       "        8.57575758,  8.87878788,  9.18181818,  9.48484848,  9.78787879,\n",
-       "       10.09090909, 10.39393939, 10.6969697 , 11.        , 11.3030303 ,\n",
-       "       11.60606061, 11.90909091, 12.21212121, 12.51515152, 12.81818182,\n",
-       "       13.12121212, 13.42424242, 13.72727273, 14.03030303, 14.33333333,\n",
-       "       14.63636364, 14.93939394, 15.24242424, 15.54545455, 15.84848485,\n",
-       "       16.15151515, 16.45454545, 16.75757576, 17.06060606, 17.36363636,\n",
-       "       17.66666667, 17.96969697, 18.27272727, 18.57575758, 18.87878788,\n",
-       "       19.18181818, 19.48484848, 19.78787879, 20.09090909, 20.39393939,\n",
-       "       20.6969697 , 21.        , 21.3030303 , 21.60606061, 21.90909091,\n",
-       "       22.21212121, 22.51515152, 22.81818182, 23.12121212, 23.42424242,\n",
-       "       23.72727273, 24.03030303, 24.33333333, 24.63636364, 24.93939394,\n",
-       "       25.24242424, 25.54545455, 25.84848485, 26.15151515, 26.45454545,\n",
-       "       26.75757576, 27.06060606, 27.36363636, 27.66666667, 27.96969697,\n",
-       "       28.27272727, 28.57575758, 28.87878788, 29.18181818, 29.48484848,\n",
-       "       29.78787879, 30.09090909, 30.39393939, 30.6969697 , 31.        ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-037e92c7-7dc1-4481-8456-1583f2540c8b' class='xr-section-summary-in' type='checkbox'  ><label for='section-037e92c7-7dc1-4481-8456-1583f2540c8b' class='xr-section-summary' >Indexes: <span>(1)</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>t</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-f275c998-3e11-45e0-aadf-c811a094c8d2' class='xr-index-data-in' type='checkbox'/><label for='index-f275c998-3e11-45e0-aadf-c811a094c8d2' 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([                0.0, 0.10101010101010101, 0.20202020202020202,\n",
-       "       0.30303030303030304, 0.40404040404040403,  0.5050505050505051,\n",
-       "        0.6060606060606061,  0.7070707070707071,  0.8080808080808081,\n",
-       "        0.9090909090909091,  1.0101010101010102,  1.1111111111111112,\n",
-       "        1.2121212121212122,  1.3131313131313131,  1.4141414141414141,\n",
-       "        1.5151515151515151,  1.6161616161616161,  1.7171717171717171,\n",
-       "        1.8181818181818181,  1.9191919191919191,  2.0202020202020203,\n",
-       "         2.121212121212121,  2.2222222222222223,   2.323232323232323,\n",
-       "        2.4242424242424243,   2.525252525252525,  2.6262626262626263,\n",
-       "         2.727272727272727,  2.8282828282828283,   2.929292929292929,\n",
-       "        3.0303030303030303,   3.131313131313131,  3.2323232323232323,\n",
-       "        3.3333333333333335,  3.4343434343434343,  3.5353535353535355,\n",
-       "        3.6363636363636362,  3.7373737373737375,  3.8383838383838382,\n",
-       "        3.9393939393939394,   4.040404040404041,   4.141414141414141,\n",
-       "         4.242424242424242,   4.343434343434343,   4.444444444444445,\n",
-       "         4.545454545454545,   4.646464646464646,   4.747474747474747,\n",
-       "         4.848484848484849,    4.94949494949495,    5.05050505050505,\n",
-       "         5.151515151515151,   5.252525252525253,   5.353535353535354,\n",
-       "         5.454545454545454,   5.555555555555555,   5.656565656565657,\n",
-       "         5.757575757575758,   5.858585858585858,   5.959595959595959,\n",
-       "        6.0606060606060606,   6.161616161616162,   6.262626262626262,\n",
-       "         6.363636363636363,  6.4646464646464645,   6.565656565656566,\n",
-       "         6.666666666666667,   6.767676767676767,  6.8686868686868685,\n",
-       "          6.96969696969697,   7.070707070707071,   7.171717171717171,\n",
-       "        7.2727272727272725,   7.373737373737374,   7.474747474747475,\n",
-       "         7.575757575757575,  7.6767676767676765,   7.777777777777778,\n",
-       "         7.878787878787879,   7.979797979797979,   8.080808080808081,\n",
-       "         8.181818181818182,   8.282828282828282,   8.383838383838384,\n",
-       "         8.484848484848484,   8.585858585858587,   8.686868686868687,\n",
-       "         8.787878787878787,    8.88888888888889,    8.98989898989899,\n",
-       "          9.09090909090909,   9.191919191919192,   9.292929292929292,\n",
-       "         9.393939393939394,   9.494949494949495,   9.595959595959595,\n",
-       "         9.696969696969697,   9.797979797979798,     9.8989898989899,\n",
+       "    y        (t) float64 1.0 1.3 1.6 1.9 2.2 2.5 ... 29.8 30.1 30.4 30.7 31.0</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-e20ef49a-12e6-4100-a7e8-2b78c441958e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e20ef49a-12e6-4100-a7e8-2b78c441958e' 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'>t</span>: 101</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-74d8b306-460e-419e-bc40-45053a64973b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-74d8b306-460e-419e-bc40-45053a64973b' class='xr-section-summary' >Coordinates: <span>(1)</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'>t</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.1 0.2 0.3 ... 9.8 9.9 10.0</div><input id='attrs-b94370ef-43d5-416e-8603-a21e21fac238' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b94370ef-43d5-416e-8603-a21e21fac238' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-27721230-cb1b-4bda-b55e-6df747632e3c' class='xr-var-data-in' type='checkbox'><label for='data-27721230-cb1b-4bda-b55e-6df747632e3c' 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. ,  0.1,  0.2,  0.3,  0.4,  0.5,  0.6,  0.7,  0.8,  0.9,  1. ,  1.1,\n",
+       "        1.2,  1.3,  1.4,  1.5,  1.6,  1.7,  1.8,  1.9,  2. ,  2.1,  2.2,  2.3,\n",
+       "        2.4,  2.5,  2.6,  2.7,  2.8,  2.9,  3. ,  3.1,  3.2,  3.3,  3.4,  3.5,\n",
+       "        3.6,  3.7,  3.8,  3.9,  4. ,  4.1,  4.2,  4.3,  4.4,  4.5,  4.6,  4.7,\n",
+       "        4.8,  4.9,  5. ,  5.1,  5.2,  5.3,  5.4,  5.5,  5.6,  5.7,  5.8,  5.9,\n",
+       "        6. ,  6.1,  6.2,  6.3,  6.4,  6.5,  6.6,  6.7,  6.8,  6.9,  7. ,  7.1,\n",
+       "        7.2,  7.3,  7.4,  7.5,  7.6,  7.7,  7.8,  7.9,  8. ,  8.1,  8.2,  8.3,\n",
+       "        8.4,  8.5,  8.6,  8.7,  8.8,  8.9,  9. ,  9.1,  9.2,  9.3,  9.4,  9.5,\n",
+       "        9.6,  9.7,  9.8,  9.9, 10. ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d0c2e7f5-e48e-4f47-85df-b18e6060ede0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d0c2e7f5-e48e-4f47-85df-b18e6060ede0' class='xr-section-summary' >Data variables: <span>(1)</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>y</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.0 1.3 1.6 1.9 ... 30.4 30.7 31.0</div><input id='attrs-4a387cb5-8d14-4b1d-a803-29a0eae53181' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4a387cb5-8d14-4b1d-a803-29a0eae53181' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-54be3784-0c9e-4e36-8597-d707eb7999a2' class='xr-var-data-in' type='checkbox'><label for='data-54be3784-0c9e-4e36-8597-d707eb7999a2' 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([ 1. ,  1.3,  1.6,  1.9,  2.2,  2.5,  2.8,  3.1,  3.4,  3.7,  4. ,\n",
+       "        4.3,  4.6,  4.9,  5.2,  5.5,  5.8,  6.1,  6.4,  6.7,  7. ,  7.3,\n",
+       "        7.6,  7.9,  8.2,  8.5,  8.8,  9.1,  9.4,  9.7, 10. , 10.3, 10.6,\n",
+       "       10.9, 11.2, 11.5, 11.8, 12.1, 12.4, 12.7, 13. , 13.3, 13.6, 13.9,\n",
+       "       14.2, 14.5, 14.8, 15.1, 15.4, 15.7, 16. , 16.3, 16.6, 16.9, 17.2,\n",
+       "       17.5, 17.8, 18.1, 18.4, 18.7, 19. , 19.3, 19.6, 19.9, 20.2, 20.5,\n",
+       "       20.8, 21.1, 21.4, 21.7, 22. , 22.3, 22.6, 22.9, 23.2, 23.5, 23.8,\n",
+       "       24.1, 24.4, 24.7, 25. , 25.3, 25.6, 25.9, 26.2, 26.5, 26.8, 27.1,\n",
+       "       27.4, 27.7, 28. , 28.3, 28.6, 28.9, 29.2, 29.5, 29.8, 30.1, 30.4,\n",
+       "       30.7, 31. ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c5c291ca-2947-4ed3-ae03-733301f7366f' class='xr-section-summary-in' type='checkbox'  ><label for='section-c5c291ca-2947-4ed3-ae03-733301f7366f' class='xr-section-summary' >Indexes: <span>(1)</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>t</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-6bb21e26-3315-4cf1-aae4-63caab232252' class='xr-index-data-in' type='checkbox'/><label for='index-6bb21e26-3315-4cf1-aae4-63caab232252' 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([                0.0,                 0.1,                 0.2,\n",
+       "       0.30000000000000004,                 0.4,                 0.5,\n",
+       "        0.6000000000000001,  0.7000000000000001,                 0.8,\n",
+       "                       0.9,\n",
+       "       ...\n",
+       "                       9.1,   9.200000000000001,                 9.3,\n",
+       "                       9.4,                 9.5,   9.600000000000001,\n",
+       "         9.700000000000001,                 9.8,                 9.9,\n",
        "                      10.0],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;t&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6b875a85-df51-43da-8e93-99db196ee38d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-6b875a85-df51-43da-8e93-99db196ee38d' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+       "      dtype=&#x27;float64&#x27;, name=&#x27;t&#x27;, length=101))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ed34689d-58b9-4f09-84c9-35a5773794f8' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ed34689d-58b9-4f09-84c9-35a5773794f8' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
-       "<xarray.Dataset> Size: 2kB\n",
-       "Dimensions:  (t: 100)\n",
+       "<xarray.Dataset>\n",
+       "Dimensions:  (t: 101)\n",
        "Coordinates:\n",
-       "  * t        (t) float64 800B 0.0 0.101 0.202 0.303 ... 9.697 9.798 9.899 10.0\n",
+       "  * t        (t) float64 0.0 0.1 0.2 0.3 0.4 0.5 ... 9.5 9.6 9.7 9.8 9.9 10.0\n",
        "Data variables:\n",
-       "    y        (t) float64 800B 1.0 1.303 1.606 1.909 ... 30.09 30.39 30.7 31.0"
+       "    y        (t) float64 1.0 1.3 1.6 1.9 2.2 2.5 ... 29.8 30.1 30.4 30.7 31.0"
       ]
      },
-     "execution_count": 50,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1403,22 +1160,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x181a0cae3d0>"
+       "<matplotlib.legend.Legend at 0x1cdc4883450>"
       ]
      },
-     "execution_count": 51,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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7d2YRo6kRNF2eRj7VpIjxuVZm50WLFvFYeepCTDcaKf/tt9+W6PxNY0booktvhEZ20Hyq6oQKjnRxd8WtsoMDVq5cWTxckkaa3HzzzTx6hGZlmWd53X///bxvUlISzxYbOnQo/vrrL/z00088isTRYQXm+VvLly/nY1vP4xIEwXtt9NZU1TZPjkRKI9J8Qcou3XnnnSxmNPrIFVQqtVi3bl2+ANPAQbqg0oWZZk/RCPhWrVrx7Kavv/4an3zyCbfepwiELr6WU4GdDQ1mrGn1N5OTk1Ph9GVL6HObO3cu/zxz5kwWsVdeeaX4+Q8++IAnNdPwRzq2wWDgz4+mFBMUnTkKfVsiaD5ZbGysw8cRBMH1VDSGSmtho6d0orMmNtN1f+3atRyw0GgVuv69+eabGD58eKnIryap1Lvo379/ie1Zs2ZxlEaj5Unk3n//fX6TFGmYv/3TEEh6/rrrroOvQ8Myzezfv58dPrZEmKYR03BM+sZD4kVpSNq+9957qzSEUhAE33Ai1vnHRk/GDqqJWYqM2TZPZo3KTGxOT0/nKe/r16/nbRpSu3r1ajRp0gSuxmGzB00ipsiLUmyUYqQJwjQenuo4Zlq0aIF69eph586dZQoZuV3oZjlIrTIEBQVx9OIK6LUrg2X0RudMXwyspxkTcXFxUCqV2Lx5M08l/uGHH7Bw4UKefvzbb7+xnZWsrdZpRvr8BUHwficimTiozhWkDuSoiwSLLPNmF6EtGz2lEykSs7TNE7S2rKLRLFu2bOH6+4ULF/jaNG3aNDz33HM8Hd4dqPRZUB6UhIvqYRRNbNiwAS1btsS+ffu4/kOpK0vIxJCcnFzm8WbPno0XX3zRsbP/p3BZmfSeu3Dttdfis88+Y9NFWX8M9N569OjBN/rDoRQjfd4TJkzgVCHVuiy/WJB55KabbirzNf39/Xk/QRC834nY5B8bvTl6u5RVwOlEisTMIrbop5PlRnb5+fmYMmUK3nrrreLyCEVhXbp0gTtRaSFr3rw5ixblR8lVR4vfaNGbo9CHRBdmy4iM6kTeDuWYly1bxoaOiRMnIjIyEidOnMC6devw3nvvsQFk69atnFKMjo7mSCw1NZVTtQSlb+lzo5okrdkguyutqC8PEk06JgmjRqORNKUgeKkTMeGfwZZlTWw+dTmnwsgu5+JJttUfPnyYj0VpxXnz5tkMHCqq2bmdkFHUZc6JUs2H3G+k1oMGDWL3Cl1MLaMycuKVZy6gCyrdfI34+Hg2wUyaNInFitKrFHHddtttnDYkV+iOHTswf/58Fnd67vXXX2e7PkFuRqqzPfrooxzRkdGmvGiMoN8n8SMBrVOnDs6cOVND71YQBOc5EW3XtQLVSo66rJ2I1hObK4rsjiVn4pkXXsI3K97icgVl1ciIdvvttzule0h1UOUEJ/XfooswiRqlrugbP9nuiWPHjrFNk1KRvg7Z562hMP3zzz+3uT9FXmTJLwv6rN99912+lYW1UFFNztqwIwiCZ6B1khPxQjmRXfqlC/h+7iT8fXh38bpTWhdGa1+rUrNzKyGjNCBFBGTgyM7OZociXaC///57ttsPGzaMv/FTmowiirFjx7KIiWNREAShajjLiZhdoMeVvEKoVWQYo0jsqgzs3rwRn7/7MnR5ufAPCMKM2fMw5alRZdrqq9o9xGVClpKSwqksMhmQcNHiaBKxW2+9lZ+n9QSUFqOIjKI0so2XFzEIgiAI9mGvE1FRjmhQBLVx70WcTMnBmcs5CPBXQWvMx5FPX8PRnVt4n3rXdEDvkS/hkYdvLndtmCM1O7cQMlonVh4BAQF45513+CYIgiA4l4qciE3KSeOZ04BpOTpEh2i48W/m8d+xbc2rKMxKg0KpQt9HxiDhpgfQLiGywsjO0ZpddeAeiwAEQRAEuyjLiagoJxKzTANSF/twtQnr1r2Bv//vao0+KLo+ug6dhnot2yEquOLIjqiu7iGOIEImCILgYVg7ESvCMg14PvEQ1rz6LFLOXx16We/6gajXdzj0Kg3qRQXi/k4Jdhk0qqN7iE8JGTklBcEdkb9NoTLU1Pqr3EID8nQ6HPlmBTaveQfGIgNCI2tj0NOvoE7rbsjXGzgNOKBDHbtdhs6o2fmkkNEaNjKTXLx4kTtb0LYrG1UKguU3UFpHSYvW6W9UZs0JFVGT669SL5zFlzNHICnxAG+3u74v7n3qRWhDrzZFoMtoRJAGIZrSKcLqqtn5rJDRBYL6DJJrksRMENwN6r9Jy1Pob1UQyqKm1l+ZTCZezEyzC6m/qzpQi3vGTEOnW+4uDgKqmgZ0pGbn00JG0DddulDQiBPpGyi4E9RMlbqsSJZAKI+aWn+VkpKCESNG8LBLonO3HugyeCqKtLWQozNUqYlwVWt28HUhI+gfnjpb0E0QBMGTqI71V0arWtu+X37E8OHDWMzoOknzD59++mmcTsurUhNhd8UjhUwQBMFTcfb6qxMWtbbsnBz8tm4+Dm75jJ+jgcdr1qxBu3btqtxE2J3FTIRMEAShBtE6cf2VZa2t6FIiNr45GZcv/M3Pden/CD54+3W0qnd1OryjTYRrstWUo4iQCYIg1CDOWn9lFqDUzDz8vXUVtqxdDKOxCOG1YjHomdnwq9MG209m4pq6tcoVIHdqNeUoImSCIAg1iLPWX5Gw7D5wGJsXTcWF4wf5sQ433cmuxKCQMG4ObI8AuVOrKUcRIRMEQahhqrr+im317y3F6mlTYNAVIEAbgnufnIFrb7qz0gKkdaNWU47ivmcmCILgxV05HF1/RcOKaWQWTYcnGrbtiocnzkFEdJxDAuROraYcRYRMEATBRV05Krv+itaEDR8+HJcvX+Y1tXcMfRrR3f6D8NqhJfarjAC5U6spRxEhEwRBqASumIpMg4ypOwd16SCuadUa69auRVBsAz6XqgqQu7SachQRMkEQBDupbqu6rXTlb7/twqAHHsS5v89wU8T2tz+M6/87Bv93ORB9Y+E0AXKHVlOOIkImCIJgJ9VpVbdOV/rDiOPfrcAPbKs3IjgqFvc9PRstr+1WKgIc1auxUwTI1a2mHEWETBAEwU6qy6puna7MuZSENXOewcUTR/j5htfdhmETX0ZQcKjNCHDkjcEeKUDOQoRMEATBTrTVYFW3TFc2qa3Fr199hE3L5kKvK4BGG4o6dz6Jlt37ICAoxCMXK9cEImSCIAh2Co7RZEJooIrTf23rhJUY1+OoVd2crgwuysZ7L4zH0d3/x4/HXNMFDQc+jUy/EJxJy8UvJy+jVXwoIrWaCiNAYw0N7HQXRMgEQRAqUb+6nKPjUSdJGQVoXScUceGBVbKqk9gc2bkZ/7f8FeRlZUCl1qB5/5GI6XoX1GoVdJkF0BuNSM3WYd+5DLRPCC8WM1sR4IkaHNjpLoiQCYIgVKJ+FR8eiFrBahy6mIW9ZzNwOacQtYI1DjkFs7KyMHX8E/h+3RrertOkJdo+9AIKQ+JYFIl0fwX0BUZEBPkjr7AIJ1NzERF09TnrCPCEC5YGuAMiZIIgCJW02ydEalk8DlzIZKs9CUTdiKBKRWL/93//h0cffRRnzpyBn0KB9ncMxh2PjsUf57MR7K/k16J0pVqhgEmjQi5HXwqOCEnAaDimZQRo9IIu9o4iQiYIgtfjaM2oPLs91ccoZZeRp4fpn33tOX5hYSGmT5+OOXPmsFA1bNgQr7y1GAcNcTidlosCfRG0GiV0hiLkFBgQFuSPhrW0SM0uRFquDln5elzJ1aFj/cgSEeAFL+hi7ygiZIIgeDVVqRnZY7cncVj+y2lk5RsqPP7hw4fx8MMPY9++fbw9ZMgQzJ8/H6GhoeiUko2P/ziPv9PykJajQ4C/CtGhAWhcW8s1sQZRZCbJx5VcPR6/sRE61Y8sIZa5XtDF3lFEyARB8Fps1YxydXr8fiYNh5My8WCXeujeuOx5XdoK7PZJGfls/KAAiMSrrJoULWheuHAhJk2aBJ1Oh4jIKMyYOx//+c9ABAdfFR7ab2Lf5uR/xJGkLDSpHYzQQP8S0VWOrggd60eUEjFC6wVd7B3F+96RIAhCGfUt+pkiqPRcHdLz9fj7ci76tYnDba1j/xGckinIOI6IbHeGJ3Eiw4e/UlHCim9dk1IXZGDYsKHYsmULP9+yy43oMeR5HA2IxJubj5eI3lQqBe7vnMDieylbx2Jlb//EOl7Qxd5RRMgEQfBKrGtGJGJkX88vNCA4wB8x/grk6orwx5krfJG/uUU0jiZll0pBtogLsdkZnvbTG4zoUC+8xHoyy5rU1xs/w5SVryIrMwOagAB0e2AcmvW6B3UiAhGkVtmM3hxt4Kvwgi72jiJCJgiCV2JZM6KIhCIxEjG6oJPQGE1+fJGnCOVsWh4WbE1EXFgg4sNL29YtRc4sLOT+M5mA+PDSxon8nCxsXPgS9m7bxNsxjVqi8f2ToQuNZ7NGdKgGSoVfmY5CRxv4NvHwLvaOIkImCIJXorWoGZHgpOcVciRmTrnpi4xQKhRQKxW8Dy047pAQXlxfshSZY8nZePyGRkj6xyxBxyZxnL8lsVRNKnHfLnw0bzIyUpMAPwXa3vkYej8wEoeT81i8UnN0yCk0FC9sLstR6GgD3yYe3MXeUUrGwxUwe/ZsdO7cGSEhIYiOjsaAAQNw7NixEvv06tWL/2EsbyNHjnT2eQuCIJSLuWZEaTWyshuKjPBXXr2YkwiRtd286DhbZ4Ba5Qe9kYz0/2IpMiRiJCwtYkP5ntaNmY9PxzMUFuLLpXOweNJjLGKBUfHoPGYhHh39LAIDAlBkNEGrUfFrXk1N5vLvEZQCpHN0lqNQ8Y8Ims/Vm0Ws0hHZ9u3bMXr0aBYzg8GA5557Dn369MGRI0eg1WqL9/vf//6Hl156qXg7KMi71iwIguD+WNaMzqfn8VqvQoORHYYkYiQeZG0n8aLHKQVH0Zm9tnXL4+/asw87lkzFpb8T+bmmN9yNmFtHoHPTOK6f0XFVSgX0RSZe1BwcoOKaXXaBgZ2J3uworAkq9al99913JbZXrFjBkdmePXtwww03lBCu2NhY552lIAiCAwubzTWj7w4mIzU7mQUpLNC/xPqszLxCFrKoYDVCAkpfEssTmUa1tDAe2IQNs2bAoNcjMDQCt46Yhg49b2ELvVql5JpYsEbJbaVSswug1qrZ6ZirM6CwyOj1jsKaoEryn5mZyfeRkZElHl+zZg1Wr17NYta/f39MnTq1zKiM1lTQzbL3mCAIgrMWNtNzT9wUjHb1wvHR72dZQEiAgjQqZBfokZxVgNohGnYRWlOeyJw9exaPPfYYtm3bxtu9+/bDjHkLYAoIw47jqfx7KdkFvLA5MkiN2iFqbitFkZhapeC0JQko1eC82VHo1kJGayjGjRuHHj16oHXr1sWPP/jgg6hfvz7i4+Nx4MABXgBIdbTPP/+8zLrbiy++6OhpCILgA1S1GS4JxPVNa/PvmsUwJVvHkVbbuuG4r1MIfjyaYrdtfe3atXjiiSf4yzyVVd58800MHz6cj0vnSZ05okM03L5Ko/JjQcvW6f9pNaXj7h0U/VHdztsdhTWBn8lcbawko0aNwrfffouff/4ZdevWLXO/H3/8Eb1798aJEyfQuHFjuyKyhIQE/gOhti2CIPg2lE5c9NNJFi3LZrgEXb5IfEgMRt7Y2K6Ipqz0pGXER8YLErkm0cElRCY9PZ0FbN26dbzdtWtXzj41adKk1Hmm5+mL162RySOnQI9wrRq1tBoEqJW4o00crokL9XpHYVUgPQgLC6tQDxyKyMaMGYOvvvoKO3bsKFfEzP/QRFlCptFo+CYIgmALZzfDLcvWXpFtfevWrRg8eDAuXLgApVKJadOmseFNpVLZPE+K5Mhiz51E8gpRZAJSsqjZbwTu75QgEZgTqZSQ0befsWPHYsOGDfjpp5+4a3NFmJtjxsXFOX6WgiD4LDXZDNeWyBUUFGDKlCnc3Jdo2rQpR2FdunSp8DxJzDo3iGB3Yr7ewOc5oEMdETFXChlZ7yk3/MUXX/BasuTkZH6cQr/AwECcPHmSn7/99tsRFRXFNbLx48ezo7Ft27bOPndBEHwArQub4dIXcepWT13rzSWVefPmlVhuVNF5UnR2tfkvEBGkQYim9HsQanBB9KJFizhXSYueKcIy39avX8/Pq9VqboxJa8tatGiBp59+Gvfccw82bbrapkUQBKEqC5utS/pmVyHVspxpXS8qKuJ5YRR1kYjFxMTg66+/xrvvvmtTxFx1noKDqcXyIJMGLZoWBEFwFjXdDJcmNtPkZprgTFAHo6VLl6J27dpudZ6CE1yLrnapCILgW9jjKqwKdCn88MMP2QeQnZ2N4OBgvPXWWzz80tpk4srz9CWy7NQDETJBEDymW0dlO3vYS1paGveE/fTTT3m7e/fuWLVqFRo1auTQ8arrPH2NrOq03wuCILiiW4ejHeHL4/vvv+eoKykpia301KCBGjmQxd5RquM8hbIRIRMEwSO7dVSVvLw8Fqy3336bt8mgRrb6jh07VttrCm7gWhQEQXAGlHqjSIxEjLpgkF3dPGiStulxGjRJ+1UH1OicBMssYtTkwfyY4HmIkAmC4NbdOpwJjZ+aNWsWrrvuOhw9epSXD9FUj4ULF8q4KQ9GUouCIHh1tw4zp06dwiOPPIJff/2Vt++9914sXryYmzeUhZg2PAMRMkEQahxtDXbrIGP28uXL8dRTTyEnJ4e7ElFKkUStPFu9o2NjhJpHhEwQhBrH3AWDjB3BGlWpjvbOGjSZmpqKESNGYOPGjbxN7fJorRiNmnJnI4pQOaRGJghCjWPugkHdLqgLBg24NBiNfO+sQZPffPMN2rRpwyLm7++PuXPn8lipikTM1UYUofKIkAmC4BIooqHIpnV8GA+gPHM5l+8pEqtKxJObm8vNfe+44w5cunQJrVq1wh9//IFnn33WrrVhrjKiCI4jqUVBEFxGRTPAKsvvv//O3eoTExN5m6ZvvPLKKwgICHBrI4pQNUTIBEFwKc7ogkG2ehKsl156iTvX16lTB/MWLkGHbtcjNc+IOmqT3eKodeHYGMEx5F9CEASPxGyNP3z0GJ578nHs3fMHP37H3QNx45Dn8EeBEv+3NbHSbsOaMqIIzkOETBAEj4Nchd8dTMbGdR9i+6rXYNAVIFAbgnHT58CvSU+cyia3ob9DbkMZx+J5iJAJguBxIrbw6z34YsE0/L3v6sywhm26oNuQqTjsH47QK3nokBBeHElRepAiKxIlchs2qhVcoQiZjSjmdWRUE6N0IkViMo7F/RAhEwTBKdREFwx6jdeWrsXKuVNQkJ0Opb8/7hgyATcMfAzZBQb8cOQSVMrSr2ntNrSnJudsI4pQfYiQCYJQZWqiCwZ15Rgx+kl89OFy3o5r2AwPTX4N8Q2b87beaIJapUBOgYFFLTTQv8puQxnH4hmIkAmCUCVqogvGzp07uaXUyZMnKbzCjQOH4I4h46FSq4v3USsVLGQ0lbmwyFjqGOI29F5kQbQgCA5T3V0w9Ho9pk6dip49e7KIxdepi3tfWIqbB08oIWJESIAKIRoVCg0m+Ful/8xuwybRweI29EJEyARBcJjq7IJx7NgxdO/eHTNnzoTRaOSFzgcPHECvXr1YlEicrAlSq1A7RIPkLF21tL0S3BOJsQVBcBhHumBUZAohgXr33Xe5pVR+fj4iIiKwaNEiDBo0iJ/v21pVpjW+XlQQbm4RjaNJ2eI29CFEyARBcBhtJbtgVGQKSUpKwtChQ3nYJXHLLbdgxYoV3KmjMtb4m5pHi9vQhxAhEwTBYSrTBaMiU0h8xgFMf/YppKWlcW/EV199FWPHjoVCoai0NV7chr6FCJkgCGVSURrQ3i4YhKUpxHKxsspQgA/ffB7H/m8TP9ahQwesXr0aLVu25Nc/dyVPxEooFxEyQRCqtDbMnlQfiZEtU8ipg7uxdu5EXLl0AX5+Cjzx1AS8MWcW1Gq1TGgW7EaETBCEKq8NqyjVZ20KMegL8d2HC7Ht42WcgoyIqYNeI17EmCfuKxYxmdAs2IsImSD4ONbpw7jQgDLTgOX1LCwv1WdpCslNPoM1c57FhZN/8XOd+wzELUMnogAa3s96bVpVeiYKvoEImSD4MLbSd7WCNTh1OQf1IoMqXBtmb42KorOGUUFYt3Ipfl+/kCMybWg47n3qJbTt2YfFqU2dq4uVK7M2TWpkAiFCJgg+SlnpuyNJmTiblofoEI1NS70jPQuTki5i/cyR+HX7Nt5ucm1P/PfpWfAPiSq1WLmmJzTXRLNjwY06e8yePRudO3dGSEgIoqOjMWDAAF59b0lBQQFGjx6NqKgoBAcH45577sGlS5ecfd6CIFRTa6kmtYNhMJpw7FK2ze4Z5rVhgf5KNnEcTc7i+7LaUH388cdo06YNftm+DQGBgbj/yRm489kFuGLSIiNPz6YQy5qX1iINaYuyeiaaHY4VnY+1mC/66STe3HwcC7Ym8j1t0+OCl0Zk27dvZ5EiMaPR4s899xz69OmDI0eOQKvV8j7jx4/H119/jU8++QRhYWEYM2YMBg4ciF9++aW63oMgCJWkvPQddY2nx5MyCpCVr0dYkLrU2rD4sABs2n8Rp1Jzy3QUZmZm8v9/stITnTp1wqpVq9CsWfNyIyBHJjQ74nAUQ4n34Gey9ZXLTlJTUzkyI4G74YYb+A+3du3aWLt2Le69917e5+jRo7jmmmu4e/V1111X4TGzsrJYAOlYoaGhjp6aIAjlQFELRSBkmKBIzJqU7AL8cuIy18maxYSUWBtm3r/IaPpHAFQsAOZ1YyQAF/76E48++ijOnj3LC5qff/55bv7r7186VWmPyFivTbMUmdKCVPp8rAWJojWKvEi0LA0lBF0Sr9bswjDyxsaSZnQh9upBlWpkdHAiMjKS7/fs2cPdqqmtjJkWLVqgXr16ZQqZTqfjm+WJC4JQvWgraC1FaUMSsEa1tLicU1i8Nqx1fCjScgtZJGw5Co9euIJRT07A1o/fZ0Fo1KgRR2HU/Lcy2Duh2VGHoxhKvAuHhYy6UY8bNw49evRA69at+bHk5GReAxIeHl5i35iYGH6urLrbiy++6OhpCILgAOWl7+j/Nl3k6eL/YNd6UPj5IU9fxOJH4jR/S6JNAUg+k4gvZz+NS2eO8/bw4cPx5ptvcq28MoYK875Up7uzXRxoL/PrW/+eo4JU04YSwU2FjGplhw4dws8//1ylE5gyZQomTJhQIiJLSEio0jEFQSifslpLJWXk49DFLOgNRlDRYcHWE8W1JhICSklaCwAJ347PV+Lr5a+jSK9HYGgEXlvwLp4Y/N9K16/K29dWZOSoIGkr2exYcG8c+leiAu5XX32FHTt2oG7dusWPx8bGorCwEBkZGSWiMnIt0nO20Gg0fBMEoWaxTt9R5EJuP3+lAh3qhSM+PKiU+UFrJQDpKUlY99pkJO7bxcds1vlG9Bo2Ff37d6u0ocIR84X1+dgrSI4YSgQvETL6B6Zu1Bs2bMBPP/2Ehg0blni+Y8eOXMzdunUr2+4JsudTwbdbt6t/2IIguA/m1lLn0vOw/JfToOt52zphxR3nrWtNI65vVCwAx3/5Fp+9/RIKcrOh1gTirpGTEdXxdrStG84CUJn6FeFIrctRQbK32bEYPbxQyCidSI7EL774gteSmete5CoJDAzk+2HDhnGqkAwg5DIh4SMRs8exKAhCzUMXa6qDZeUbWBSsx6aUqDVl5qNhqAnzn38OJ3b9wM/Xa9EWA8bNRqE2poQAlNUouKzp0Y7UuqoiSPYaSgQvEzKa0krQqHFLli9fjscee4x/puIu/UegiIzciH379uVpr4IguC/21JpISKa++xE2zH8eOVdS4KdQonGfR9H1P8OgDNOiTXRwsQBQNEbikJpTwJESRUfWAmVdv3LUfFEVQaqo2bHgpanFiqCBeO+88w7fBEFwT6xdhEH+ynJrTedS0rFtxes4//NnvF0rvj4GjHsFhVGNodWoMPDaOujeuBYLgNmwceB8Bk6m5uJiRgFiQgLQOFqLSK2mzPpVVcwXVREkmWvm+YglRxB8DFvOQFovFh7kz+k461rTucTD+ODlCdy1nuh2xyDcNWIyNIFBxYuHD5zPZCGzNGzEhwcgM1/Px7yUlY9snR7tE8JZzGzVr6pqvhBB8l1EyATBhyjLGXg4KYs7dtDNXGvSKIGtH3+ArasXwFhkQFBYJB58ZjZadu1ls35FhhFrw0bTmBDkFhbxa1C7Kzr2NbF+SM7SlapfiflCcBQRMkHwEexxEVIPxQitGnsPJ+K7d6fi4tE/eZ+67W/AsMmvIiwyqsz61enLuaUMGyRAFIWR0FHbKzKAhAX6o13d8FL1KzFfCI4iQiYIPoI9XTBI5LTnfsUnzz+D7OwsaLXBmP7KXFyMvg4Ki+bBtupXZRk2SMw6N4hAel4hzqTl4oEu9XBD09plOgnFfCFUFhEyQfBgKtP6qSJnorEgC5venIqTv2/lbeqPSH0SGzRoWNxgt7z6FdXZyjJs0O/QQuvawQH/WPzLFiapdQmVRYRMEDyUyo4u0ZbTBePY7p+x9rXJyL6SCpVKxf1PJ02aBKVSaXf9qm5EkHTLEFyCCJkgeCCOtHOy1QWjsCAfX73/Gn7+4urMsJh6jbDp0/Xo3LlTid+1t34lhg3BFYiQCYKH4ejoEusuGKbLp7DhjclIOXeKn+/Y7wEsf/dNtGkQY/N17alfiWFDcAUiZILgYVRllhYJyaPXJeDZF17G1yuv2uq1EbXw2OS5GPfYvRUKjT31KzFsCDWNCJkgeBhVmaV16tQpDH3kEfz666+83eeOuzBv/tto3aiuU4VGDBtCTVKyO6ggCG6P1sK0YQtb7ZzIbPHBBx+gXbt2LGLU9HvlypX4btNGtG2SINGS4NFIRCYIHkZlR5ekpqZixIgR2LhxI29ff/31+PDDD9GgQQOXvQdBcCYiZILXUpk1Vp72Pux1B37zzTcYOnQoD7elWYEvv/wynnnmmWJbvSB4AyJkgldS2TVWnvg+ynMHxmkVGDVqFBYvXszHadWqFVavXo327du7+i0JgtMRIRO8DkfWWHnq+xjVq3GpaG337j9w+8MPIzExkY8zbtw4zJ49m0csCYI3ImYPwavXWNHaKuroTve0TY/TGivazxveB0HuwBaxoYgLVWPmzJe5tRSJWJ06dbB582YedisiJngzImSCz66x8qb3QcLVs2dPTJ8+HUVFRRg0aBD27duP5td2x9HkLO467+7iLQiOIqlFwauoyhorT3wfOTo9li5divHjxyMvLw9hYWE8nb3LLf2x/kAKTqae8+gaoSDYgwiZ4FVoy2mMW9YaK3dEa8f7MORkYOyQB7D1+2/5sV69evHasMKACK+oEQqCvUhqUfDKNVZkQ6c1VZaY11g1iQ52+w7sFb2Pndu+x9rJ97OIqdVqvPbaa9i6dSvq1k3wihqhIFQG9/5aKgiVxLoxris7sFdlHVtZ7yMjMxufL3oFR3+6uri5TZs2bKtv27Ytb1MtzNE+jILgqYiQCV6HO3Rgt7X+iwZPtq8XjlohGruEzfp97N/zO7Yuno7MS+dYlJ5++mnMnDkTGo3G62qEglAZRMgEj8OeSMeVHdhtrf+6mJGHLw9cxGd/nudIqFawxi7zBT2X0EODSS9Mx8Y35sJoNCIhIYFbTFFNzBqtl9QIBaEyyF+z4LUdO1zRgd3WrDD6OTElF0VGE9e3CouMCAv0t8t8cezYMTz88MPYvXs3b9PPCxcuRHh4uM3XNppMCA1U8efTtk4YFIp/y+AypVnwVkTIBI/BEzp2WK//IvGgmlR+oQFRWjWLWEaenvcloStrCCb93qJFi7gvYn5+PiIiIrjd1L333sevkZycVSLKtBT4yzk6rpUlZRSgdZ1QxIUHypRmwasRIRO8eipyTWNdo8ouMCA9rxDBAf58zv5KBXJ1Bha0sswXSUlJGDZsGL799qqt/pZbbsGKFSuQ7x+KRT+dLBWNtogLwY9HU4oFPj48ELWC1Th0MQt7z2bgck4hpzJlSrPgrYiQCV4/Fbkm0VrVqEiwDEVG+Adc/a+mLzJCqVBArVSUMF9k6/QcRW3Y8DlmTByH9CtpbOKYM2cOxo4di1OXc21GowcvZOCHI8kIDfRHh4Tw4s8mIVLL0dqBC5ks7hSt1o0IkkhM8EpEyASPwFPceNazwkiwVEoF9EUmqJVAToEB0aEBCPlH2CjlpzMYse7nY/jsnVk4sv1Lfrxuk2uw9P0V6HdDl3Kj0dhQEw6cz4RKWVqgqD5G50KpTPodETHBWxEhEzwCrYe48azXf8WGahAWqEJypg4qBRCkUaFxbW1x/SzxUg5OH96Dj1fPQvbli/x4j4FDcc2dw/FzWiCapmTz+yorGtUbTVCrFCyQlMakyMwdBV4QqhMRMsErpyK7Euv1XyREFAxRSpEiKhKb7AI9zl3Owm8fv4sTW9bCZDIiMqYOHpw4F43adLoqcv/U/Xo1r11mNEoRHwmZzlDEaUx3FXhBcKsWVTt27ED//v0RHx/PFxPz+HQzjz32GD9uebvtttucec6CD2KOdMh1Rxd4EgKD0cj3tO1ubjwSM5oVNv7WZnjujmvw0oDWuKt9PFX0cOZyLk4mHsM3s4YjcfNqFrHOfQbimcVfsohZ1/1ydIbiaNQaSlGGaFQoNJjgb/XePakllyBUhUp/TcvNzUW7du14fPrAgQNt7kPCtXz58uJty84DguDJHTsqg+U6thaxQI/GtXDuSi6WLVmE92dOg66gAJrgMNw/7iV0uOG2EgJEacJ8PTkedQjSKMuMRokgtQq1QzRIztLxa7qqJZcgeIyQ9evXj2/lQcIVGxtblfMSBJtUpWNHVXofOoOkpIsYMXQofvjhB97u1ftWNL33WdStQ5HaVcjQQVEYWfYL9EW8iPrLvRfRtVFUmf0j60UF4eYW0TialO0RAi8IzqZaEuc//fQToqOjeRHnzTffzP3goqKibO6r0+n4ZiYrK6s6TknwIhzp2FGZjiDVwSeffILHH38c6enpCAwMxLx58zBy5Cgs3n6qONJKz9Nj37kMXjxN23pDEaJCNTh7hcQ3pUKxuql5tEuFWhC8RsgorUgpx4YNG+LkyZN47rnnOILbuXMnlEplqf1nz56NF1980dmnIQhu0REkMzMTY8aM4Q71RMeOHfnnFi1a8LbZ4Xj8UjZSs3XI0xkQHKDkuhg5HFvGhSIi6Gpd8FhyNh6/oRGS/nEhaq3EyhUtuQTBHfAzWQ87qswv+/lhw4YNGDBgQJn7nDp1Co0bN8aWLVvQu3dvuyIyaopKF4DQ0FBHT00QitOJ1A2DRMtyDRb92Wfl63EiNQet4sPwbJ/mUJE/3ols374djz76KM6ePctruqZMmYLp06fD39+/RJqTBGzH8VR8dygZtE46wF/FtS2y6Udqr9aXydRC68HIPCJiJfgKWVlZPPW8Ij2odk9uo0aNUKtWLZw4ccKmkFE9TcwgQk12BLGuQ/2dlsfCdn/nBKdEZvTFbOrUqTzsko5L/wdWrVqF7t27l5nmJOchnWOj2sEI9FeyG9HS1CHrwQTBhROiz58/j7S0NMTFxVX3SwlCOR1BVMUiRnWo1OwCBPgrERWs5ijoSFIWpx9JZKrCwYMH0aVLF66BkYgNHz4c+/btKyFi9DoUIYYH+XP7KLo/cyWXI7NCQxGvM7N2Jsp6MEFwopDl5OTwf0y6EadPn+afKX1Czz377LPYtWsXzpw5w6PX7777bjRp0gR9+/at7EsJQpXRWnQEsexET6k7jUrBrkBK5TWpHcwiRwuQDQYj9z08mpzF95QGrAiaE/bGG2+gU6dOOHDgAGchaI3lsmXLEBJyNcqzbjVFHUqUCj++p5Er/ioFDl3I4mNZIuvBBKF8Kv31juYi3XTTTcXbEyZM4PvBgwfz2An6T7xy5UpkZGTwouk+ffrg5ZdflvSh4PKOIDEhmhKd6EkgzL0PKQois8SfZ9Mx9/uj3DHelrvRloX/woXz3Ajgxx9/5Ne844478N5775VaglJe42OqobWOD+Vu9dTol15T1oMJQjUJGU2lLc8f8v3331f2kIJQI70PydhBNTGtRsktnUjESCzMvQ/z9UXsHqR9msWElHI3WtrfzSKXfnAbPn/7JWRlZiAoKIijshEjRpQSKnsaH9PcMBLQhrW0bOyQ9WCCYB+ScBd8piPIx3+cZ2NHWo6O04kUiZmdgfTl7HhyNgxFJk4zmhsTm+edUaS0YGsi4sICER8eAOgK8cmCqTj0f1dnhrXt0BGfrFuLZs2alXkeWjsaH9PcsCE9GkLh5yfrwQTBTkTIBJ8Rs4l9m1PFiY0dJFaWpgqy4lMKLy78aprRGhIfMmPQzK/kv3bjo3mTkXE5GQqFEtfePQyPjhqHJk2aOqXxcYLMDROESiFCJvgMtE6MLPbkGryUXbIvIaUdVQo/NI8JKZUWpL6H2ToDVCY9vn1/Ln7ftIofr12nAXerj2zYEqev6Coc6mk94sW61ZTUwQTBMUTIBJ+irMbDtCia7Ph0s4bGo1w5m4ij62YhN/kMP9btjkG4a8RkaAKDuAu/vWu8PK3xsSB4AiJkgs9hq/FwXGgAluz4t++hOSozFhXh943L8eeahTAVGRAcHoVBE2ah1XU3ObzGqyqNjx3F1Q2TBaE6ESETfBJbfQmt034F6clYO3cy/j68m5+v2/4G/G/KqwiJiKryUM+a7Ivo6obJglDdiJAJglXa77uDyfjq8/XYtvxVFObnQh0QhLHPz4S6ZW8k5emBAr3H1LZc2TBZEGoKETLB7anJtFikSo/ti5/H959+wtsdu1yHj1avQtOmTUpENp5Q27LuJGJOl5qXFFDkSZ1MqE2WO4qwINiLCJng1uL1V3IWdp++wtZ3XZGxWtNimzdv5g4dFy9ehEqlwowZMzBp0iT+2VW1rapQXicR2qbHqWVXRW5LQXB3RMgEt8Mc+ew9l86dNmiRMl10m8eGsKvQMi1G0URVhSU/P58Fa+HChbzdvHlznhlGfRMrU9tyN0NFRZ1EpKO+4C2IkAlOpaoXc3NNh7pvpOcWwl+hQESQEpn5ehy8kIn2CeGcJqO02Ee/nUWEVo1TqbkOmxj+/PNPPPTQQzh69Chvjx49GnPnzuV2U55uqNDa0UlEOuoL3oD8BQtOo6oXc8uaTmxoAM6k5SEk0J+71NMFlx4/mZqLTvXVCPRX4MejKagXFcSvUVkTQ1FREebMmcODLg0GA48Z+uCDD3jCubcYKuztJCId9QVPR4RMcArOuJhb1nR0BiMMRUb4B1z9E6WLcHCAio9P7aRoX2rySxdh676IFZkYaGo5TW7+5ZdfePuee+7BkiVLEBX1r63eGwwV0klE8BWqfbCm4P2UN2eLts1zviqa62U5BFOtVEClVEBf9O/v+CtpfpgR6fl67hKv1ag4UivPxGAJRSEUdbVr145FjOaErVixAp988olDIlZZQ4UrlxS0jg/jjvpnLufyPUViYr0XvAWJyAS3ccdpS9R0VIgIUvMkZ7VWzcfRFxmhVCgAowk5OgOPO6H97DExpKam4vHHH8eGDRt4u2fPnli1ahUaNGhQpbqfJxgqPM1tKQiVRYRMqDLOuphb1nQokqOJyCRYFNFR9JVToEe4Vs0RWZC/EvE2hNOWieGbb77B0KFDcenSJfj7+/Og12eeeQZKZem+ipWt+3mKoaImO4kIQk0jqUWhymgtLua2sPdibq7pUO2Gajr+Sj+0rhOKsEB/XkemN5o4SuvaMBI3tYhGvt5Yasir2cRAIhjub8QTTzzBE5tJxFq2bInffvuNrfb2ihjV/UhYw4P8uc5F97RNj9PzZvGl1yzvXMRQIQjVh0Rkglu546y7w9Mk53qRQejUIBId60fgmrhQPs6pyzksJmWZGGJ059Cp0x04fvw4H/epp57C7NmzERgY6FQTx4jrtWibEIbDSZnYfz4DjWppEaRRiaFCEGoQETLBLdxx1nWox29ohKR/0pG2ajpljUNpGavFic2rMOj1OWyxj4+PZ0PHrbfe6vS6359n0zH3+6NsPMkpMPA9RY405Zlu7ty+ShC8CREywSlUZc5WeXWoFrGhdpsYUs//jYljh2DXrl38/KBBg/Duu+8iMjLS6XU/sv5T15ECfRGaxYQgPjwQuTo9Tl3O5XrewGvroHvjWhKJCUINIEImuNQdV9X1Z3TsuhGBeO+99zB+/Hjk5uYiLCyMBeyBBx6waQaxh/JMHJQuPZ58tXVWk9pXlxsQoYFqtKvrz1HpgfOZLGSCIFQ/ImSCy9xxzlhMnJKSguHDh2PTpk28fdNNN3EqsV69etVW96MF2ZQyjQsPQGhgSZGTZryCUPOIa1EoFpVzV/JwNDmL7ytavOwMqrqYmMSrdevWfK9Wq/Haa69hy5YtDomY9fsnLB2U2QV6GIxGvj+RmgOVwg/NY0JsRnxUHySTijTjFYSaQSIywWUNb0kUruQVQq1SgJzrtLjZUhjKWn+Wk5ODCRMmYNmyZbzdpk0b7lbftm3bYkGqzMLf8t6/rbpfq/gw7sJPN1u4y9oxQfAV5H+aj+Oqhrf0uhv3XsTJlBycuZyDAH8VIoPUaBytRaRWU6YgkJHjkUcewYkTJ1j0SNBmzpyJgIAAhwTZnvc/qlfjEnW/uNAALNlxSprxCoKbIKlFH8ZZPRIri1k8zl7JRXSIBko/6m7vh5TsAuw7l4ErubpSi4n1ej2mTZuGHj16sIglJCTgxx9/5HSiWcQqWrzs6PsnqNZFDkq6V6kUZaYdaVvWjglCzSJC5sO4ouGtpXiQbb1lfBgvIM7VFbFtPU9nwJGkLBy/9K8gJCYeZwGj1lJGo5Hnhx04cAC9evWqkiBX5f1LM15BcB8ktejDuKLhrbV4kFjRsEwSjPS8QlCz+5QsHXfxuK9jXWz+bDWefvppnuIcHh6OxYsX8/qw8o5pr4uwqu9fmvEKgnsgQubDaF3Q8NaWeJCYdW4QgewCA/L1BhaPHnVUGDf0AW74S/Tu3Ztt9XXr1rXrmPYIkjPevzTjFQTXI6lFH8YVDW+1ZTQYpsiJ1mTRLLK0Q7/gPzd3ZxHTaDSYP38+fvjhB5siVt4xKxIkafgrCD4qZDt27ED//v25hx1dfDZu3FjqAkBFeRodTw1ab7nlFiQmJjrznAUnYd1tviZMC+WJR35uNta+NgWb3nga6VfS0L59e+zZs4cb/ipoDpkDxyxPkFzx/gVBcAMhoxZANGH3nXfesfn83LlzsWDBAq5l0MgMrVaLvn37oqCgwBnnKziZmjYtlCUeh/78Da+OuBvHdmziL0g0aoX+flq1auXwMe0RJDFtCILn42ey/gpbmV/28+OJuwMGDOBtOhRFalScp8GFRGZmJmJiYri+8d///rfCY2ZlZXGvPPq90NCyG8YKzqW8KcjV8bvmNV/Hk9Lx8/pF2PvVSphMRtRJqIe1q1fhhhtuqPR7sFxHRp01KJ1IkZg9Heir8v4FQage7NUDp5o9Tp8+jeTkZE4nmqGT6Nq1K3bu3GlTyHQ6Hd8sT1yoeRw1LTjaFYSe08Wcw+IJI3Bo/z5+7NFHB2PBgrf4b8YRquIiFNOGIHguTjV7kIgRFIFZQtvm56yhYYd04TLfaKGr4Bk4sgiZoLVgCxcuRKdOHVnEaMzKJ598gpUrVzgsYtaCZF68LFGVIHg/LnctTpkyhcNG8+3cuXOuPiXBDhxehHzhAvr164cnn3yS66ZUPz148CDuvfdel70XQRA8G6cKWWxsLN9funS1rY8Z2jY/Zw3Zqyn3aXkT3B9HumJQ1EUNfslKT22l3n77bXz77bdcVxUEQXALIWvYsCEL1tatW0vUvMh91q1bN2e+lOBi/l2EbLvMajnKhCJtavR7//33Iz09HS3btMM3237FqFFPODz4UhAEwWGzB43QoKatlgaPffuu1jloDtS4ceO4G3nTpk1Z2KZOncrfuM3ORsEzqMjFZ29XjP2//4pbRo/AhfPn4KdQoPNdQ9B+wHBs+tsPR/JOVvuoGEEQvJ9KC9nu3bt5Cq8ZGqNBDB48mC32EydO5LVmI0aMQEZGBnr27InvvvuOU0mCZ2CPE7G8Ccq0DOP85Swc2bQM8zeu4O2AyDg0uW8y6rbtiKiQIJ7lVd2jYgRB8A2qtI6sOpB1ZK6l9HwuFUdd1B2DFhZbio71vpROpEjs8KFD2Lp4GtLOHuf96ne/E/X7jUJ4eAh3uaf9qFFwRNDVBcy0+HjkjY3FYSgIguvXkQne5UQ0R1mUOqSoi0SHnIhksyfRMXfFMEdvyRl5OLx5HX5dtxAGvR4BIeG4e/QMZMR04AhMo6K5Y0o+/snUXHSqry6zM70gCIK9iJAJdjkRiWCNEnv+Tsfuv6+gU/3IYjGjRch/HE7EuCf+h10/7+B9G3boiXvGzYQ6JBK/nUqDf8DVPzU6bnCAisWMut0HaZw/KkYQBN9ChEyocBwKiQ5FTWm5OmTl67Fk+0n8US+9uGa2fv06PPHEE1wTDQoKwsTps5Ac3xMxtSmVaIBKqYC+yMRToAl/pYIfLywywq8QTh8VIwiCbyFXD6EYrQ0nIonYvnMZyC80QK1SICzQH1FaDRs1Tl64hMQN8/HVhk953y5dumDVqlUIrFUXb24+/s9xVFwLS80ugFqr5ohMX2SEUqGAv8KPa29UI5NRKYIgeGxnD8F9sB6HQjeKxEjEIoL8UWgwIipYg9iwAODiISwddw+LmFKpxIwZM/DLL7+gWbNmJY5DUOPeQPXVdGKBvgjZ+XpoNUokZ+lkVIogCFVGIjKh1DgUssSTsYNqYpROpEgsPU/PbsP6YSp8ueRVbP98Bf9OWEw9rPhwJQb06VXmcajm1rpOKI4nZ7O4qZR+HKW1rRtmV2d6QRCE8hAhE0pg6UT882w618QonRgdGoDg3At4/9nJSD17dUF8/HX9kdBvBPYV1ELrlOwSgmTtaKQuH/Uig9CpQSQ61o/ANXGhMipFEASnIOvIhDKt+OROJGNHRKAKx7asw7cr5qPIoId/cASufXgKoltdx+vC6kYEom5EkM2FzTLnSxAER5F1ZEKVILEhi/33mkNYNGMCko7+yY/XatUD3QdPgSYkgmteMaEBaFsnDCdSc0usMbM8jqwPEwShOhEhE2xCgfratWsw94nRyMnOgkoTiCZ3jUbT6++Cn8KPRYxqZo1ra6FQKGRhsyAILkOEzMtwRirvypUrGDlyJI9dITp06oKew2ZgT7qaLfUqpZJrZiRikVoN70OiJgubBUFwBSJkPtTs1x6Ro1lhQ4YMwcWLF6FSqTB9+nRMnjwZFzN1mPXNXwjyVyI8SM3rwyy7f5i73dNxBUEQahK56nhts99Ajp7MHeZvbhGNo0nZZYpcfn4+Jk2ahIULF/LxmjdvjtWrV6NTp068XTdCibZ1wvl41iJGaUhZ2CwIgqsQIfOBZr97z2ZgwdZExIUFIj68tMh1CcnA5LEjcPToUf49ajc1b948bjdV3towc7d7c2d8WdgsCIIrECHzgWa/JFqp2Tp0SAgvbj1F90EqP3yy/B28+vlSGIsMPN17+fLluO2222y+jvXaMKqJUTqRIjFZ2CwIgqsQIfNArGtd2QV6m81+Ceown62jPol+0Bv/XTKYlnQOa+dOxOnDV231/e68Gx8ufw+1atUq97XN3e5lbZggCO6CCJkXGDpqBWu4D6Jls18z1GGenqPISa1UcD3rjx8+x4Z3Z0KXnwd1oBYd/zsez04cg8jIKLvOQdaGCYLgToiQeYGh4+yVXE4d6gxGTh9aphepw/zVZr9q+OmysWLedBz8+Qd+rlaTdmh63yT4hURj3R/ncORidrH5QxAEwVMQIfMCQ0ezGBW3iqK+iMcv5bChw2zESM4qQO0QDdKP/Y7XXpiN7CupUKhUaNZvGOKuvw9Gkx9iwwMRHxZYbP6w1WpKEATBXREh8wJDB203jQnG2St5qBcZiMs5hcVGjOZRapz+cgG2fbqK962d0BjtH3kBurD6oMOQlZ6EMTTQn38mR6KtVlOCIAjuigiZh09vNkMRmEalwIBr6yBE48/77/hlF6Y+PhIp50/zPg1uvA8NbhuGjCIFQpR+3CfRsjsHCaK0mhIEwdMQIfMQZyJ11LCe3myJubMGiVhcqBrPvPAKFr42h231IZHR+O8zs1GnVRccuJCJ/PR8tI4PRf0obanoTlpNCYLgaYiQeYgzsVEtLcKD/HnxMS1yLquzRkHaRdxw56PYuXMnP9f+xttxz9jp0IaG83ab+DAkZRTgfEY+C5k10mpKEARPQ65Wbta4tyxn4uGkLCgVfnyz1VkjIsgf6X9+iw79pyA3NxfqwGD0H/UCuvcdUEL0qBZGv0tixkMzg9TFz0mrKUEQPBERshpu3FuVVlMkYPFhAYjQqnEqNbfY0NEgyIDNS6dh6/ff8P5dul+PZvdPQrsWTWwaQ5rFhiA1R4cTqTloFhMiraYEQfBoRMhqsHFvRbb2ipyJ9Hh6nh6Pdm8AhZ8fR3y//vgDpjw5GikpKVCr1Zg1axbue2wk3tp6osx6WqC/kgWM0pWWDkdpNSUIgiciQuYk7ImmKrK12+NMJNHJ1xehbrACM6c8i6VLl/JzrVu35m717dq143OhKJAEtKx62rX1IjDi+kZI+sfYoZVWU4IgeCgiZE7CnmiqIlu7Vq2yy5l4bP+f6D/mfzhx4gQ/PmHCBI7EAgICKtWpXqVSiMVeEASPR4TMSdgbTZVna6eIqLxI6nxaDs5vW40FaxahqKgIdevWxcqVK3HzzTeXOpZ0qhcEwVcQIXMSWjujKdqvLMqLpP46egzbl05H0olDvO9DDz2Et99+G+HhV231tpBO9YIg+AIKZx9wxowZHElY3lq0aAFPh+pO567k4WhyFt/Ttq1oilJ3FD1ZYq5LNYkOrtDWbo6kWseHISNPj9OpOdj+xVp8Pu1hFjESro8++ojrYeWJmHWn+haxoXwvIiYIgrdRLRFZq1atsGXLln9fRKXyeku9MycomyOpvcdOY8LYUdix9Wq3+t69e2PFihWcUhQEQRCuUi0KQ8JF04Z9zVJf2bpUeQunv/zyC/zvf//D5cuXodFoMGfOHIwdOxYKhdODaEEQBI+mWoQsMTER8fHx7KLr1q0bZs+ejXr16tncV6fT8c1MVlYWPNlSb12XojVb9EyevohTkmaxKivK69EgCAtmvoDly5fz8chOT2lEstcLgiAINSBkXbt25fRX8+bNkZSUhBdffBHXX389Dh06hJCQ0k45Ejnax5ss9ea6FInVV/uTSolVi7gQ/Hg0pVSUt/mn7Xhh6XRkXLrAx584cSJ/NhSRCYIgCLbxM1k7E5xMRkYG6tevjzfeeAPDhg2zKyJLSEhAZmYmQkND4UrI2LFgayJHXNTj0BqD0Ygzl3MxtndTNlOUn5JUsVhdzMjnmhn1PDRPczboC/HD6newdf1SmIxGRMbUwWfr1qBXrxtr8N0KgiC4F6QHYWFhFepBtbswyFnXrFmz4sW71lC04a4Rh9ZBS315KcnYUBMOnM+ESnn1sUtnT2LNq8/i/InDvN3+5rvR/eGn0bhtxxp4h4IgCJ5PtTsHcnJycPLkScTFxcFdrPL24qilvryUpN5oglqlQHZeIbZ8uhKvP/EfFrGgkHAMfuEt3Dn2ZWQZ/fn3HT1vQRAEX8LpEdkzzzyD/v37czrx4sWLmD59OpRKJR544AG4e/d5axy11JfX5UOtVMCYewW7P3oV6cd382PNO/XEbaNexCVDEH49mYaCwiJ89PtZHL6Q5dB5C4Ig+BJOF7Lz58+zaKWlpaF27dro2bMndu3axT+7e/d5WzjS6klbTkry1B9b8Nub01CYmwWVWoO7RkzCNTffi/3nM5FXWICiIiMSooIQHxZYpfMWBEHwFZwuZOvWrYOndp93VqsnWz0T83OzseGdl7F7yxe8T1hCM/Qd/QpaXtMcR5KyeMilSgGEBPrz+ZMZJCTAvvOuyiBPQRAET8ezW244uft8eZgt9Y6kJHXnDmHj/OeQnnIRfn4KdLtnGKa+MBUn03Q4cD4D59PzEeCvRExoABrX1iJSq7H7vJ2ZShUEQfBEvEbInNF93pmQiDzYKQ7jJz6PzeuXkTsEYdF1MfyF1zFyUD9+niKpHYmpeP/nU2gYFYzwIP9SIlzeeVdHKlUQBMHT8Boh0zqh+7wzOXz4MHeo379/P2/f8+AjePmVeWieEF2c9qN7ip5qBwewHd9axCzPmzqEkAPTnD6MCw2otlSqIAiCJ+E1QlbRLC9yGZJBo6Lu81XFaDRiwYIFmDx5Mi/0rlWrFpYtW4YBAwY4fN7xYQHYtP8iTqXmFqcPawVrcOpyDupFBlVLKlUQBMFT8Bohc2b3+ao4Nh977DFs3bqVt2+//Xa8//775TZQrui8qaPIpWwdLmYWlEgfHknKxNm0PESHaGxGoDWdShUEQXAVXtVK3XqWF7WPonuKxJxdL7JedL127Udo06YNi1hQUBAWLVqEr776yq4pAGWdd+v4UBaqIqOJ04ckWCRsdN+kdjAMRhOOXcoutVjbFalUQRAEV+F1V7mamIps6RTMzMzArx/OxdFfvuXnOnfuzN3qqS1XVc+bBGr+lkSbTkyy59PjSRkFbN0PC1K7JJUqCILgarxOyCprla8slk7B/DP7sfGt55F5ORl+CiV63DscS994Bc3qRDrlvCnaK8uJScLWLDYEqTk6nEjNQbOYkBpPpQqCILgDXilk1b3oOiU9G4nfvIcdn6/g6KdWfH08OGkuCiMbY9vxK2geF+EUAdFW4MQkJyMJWKNaWlzOKbSr64ggCIK3IUJWCSjtt2v3Hvzw7lSk/J3Ij113+yDc/fgkaAK1yC7QO9UpaI+j8dp6ERhxfSMk/WPs0EpnD0EQfAwRMjspKirCgvmvY+3sl2E06BEcHoVB42eiVbebq80paK8TU6VSiMVeEASfRYTMDv7++28MHjwY27dv5+3mXXrhwadfQUhEVLU7BR1pWiwIguBLiJCVA6XvyIE4ZswYnlSq1Wpx1+NTENq+L4LDSwpIdToFa8KJKQiC4KmIkJXBlStXMGrUKHz88ce8fd1112HVqlVAaAy7Fmt60XV1OjEFQRA8GREyG2zevJk7dNBgUBoKSsNBp0yZApXq6sclqT5BEAT3QYTMgvz8fO6RSL0SiebNm3MURoucLZFUnyAIgvsgQvYPe/fuxcMPP4wjR47w9hNPPIF58+ZxuylbSKpPEATBPfA5IbOephwbosbrr7+GadOmQa/Xc2/EDz74AP369XP4mBKdCYIg1BxeL2SWIpOarcP+cxnF41B0V5KxfekMnDy0m/cdOHAglixZwqNX7EUmNAuCILgWrxYyS5G5nKPjLvX+SgVaxYcg7c8f8MWiWdDl50IdqMVLr76GiWMftzncsrzjy4RmQRAE1+K1QmYpMrGhGlzMyIfRBORnp2Pdq9OQvH8H79ewVUd0GzoNIW1aoqjIhKQs+1KE5r6LMqFZEATBtXilkFmLTHaBARn5ehSe2YN9a15FQVYaFEoVbhv8JG6+bzhy9Ub8eTYdc78/ys137UkRUrqSIj1bI1ZkQrMgCELN4ZVCZi0yuXm5OPTJG7jw6xf8fHBsfbR56AV0uaEbFEol8vP0OH4pGwX6Iu4mb0+KkKK2skasEDKhWRAEoWbwSiGzFJmzxw5g1exnkXbxDD/X9Ob70frukcg2+KGwyMitpY4nZ8NQZOKpy+ZxKRWlCLUVjFiRCc2CIAg1g1deZbVqFdR+Jnzz4dvYvn4xjEUGBIbXQvP7J6Fph+4sYEqjEWqlgqcrU3upuPAAnrpsb4rQnhErMqFZEASh+vFKIStMT8aXs0bg9JG9vN3+xn7oPex5HMswIS23EIYiI+JYYEw8XVml8EPzmBCbjsWyUoT2jlgRo4cgCEL14pVCRnpEgy81QcHo8ehE3NDvPwjSqGBU5+PQxSwYjX4cjWXmG9AqPgwB/kq+2aK8FKGMWBEEQXA9XilkjRo1wscfr4c2pj4OZWpYZFKydSwyd7eLR7uEcNQK0bA4xYUGYMmOUw6nCKXvoiAIgmvxSiEjbr/9dr6/3o72UVVNEUrfRUEQBNfhtUJWGZGRFKEgCILnUm1C9s4773D3+OTkZLRr1w4LFy5Ely5d4K69GA1GE+5sFweKu/L0RZIiFARB8GUhW79+PSZMmIDFixeja9eumD9/Pvr27Ytjx44hOjoa7kJ5DX8lVSgIguAZ+JnI0eBkSLxoGOXbb7/N20ajEQkJCRg7diwPriyPrKwshIWFITMzE6GhoaguSjf8VfHiZnNdTBr+CoIguBZ79UDh7BcuLCzEnj17cMstt/z7IgoFb+/cubPU/jqdjk/W8lbTvRipM4dS4cf3tE2PUzcP2k8QBEFwb5wuZJcvX0ZRURFiYmJKPE7bVC+zZvbs2ay45htFbtWNvQ1/z6Xn8eiXo8lZfC/CJgiC4H643LU4ZcoUrqeZoYisusXMnoa/JGTLfzmNrHyDUwdmyjRpQRAENxcymq6sVCpx6dKlEo/TdmxsbKn9NRoN32oSbQUNf5My8jkCo2CNxMtZAzNlmrQgCIIHpBbVajU6duyIrVu3Fj9GZg/a7tatG9wBc8NfMnZYe13oXKmNFU2SblsnzGn1M7O5hMQwPMifu+nTPW3T4/S8IAiC4AZCRlCqcNmyZVi5ciX++usvjBo1Crm5uRgyZAjcAXPDX3InUjeP7AI9DEYj3x+4kAm9wYjWdULZpFJeN3x7EXOJIAiCh9XIBg0ahNTUVEybNo0NHu3bt8d3331XygDiSsrq5kGREgVp8eFBThuYKdOkBUEQPNDsMWbMGL65M7Ya/lKqcf6WRKcOzJRp0oIgCF7sWnS3XoyU3nP2wEytTJMWBEHwrBqZJ1Ne/Yy2HRmYWZ65xCyOTaKDZZq0IAiCA4iQlVM/ax0fhow8Pc5czuV7isQcsd5XhzgKgiAI1dhrsSrUVK9FVyxetlxHpjNcTSdSJCajYgRBEBzXAynKlIOzB2bKNGlBEATnI0JWw8g0aUEQBOciNTJBEATBoxEhEwRBEDwaETJBEATBoxEhEwRBEDwaETJBEATBoxEhEwRBEDwat7Pfm9dn00I4QRAEwXfJ+kcHKurb4XZClp19dcBkQkKCq09FEARBcBNdoA4fHtOiiiY0X7x4ESEhIaVmd1VWyUkMz5075/JWV+6EfC5lI5+NbeRzKRv5bKr3cyF5IhGLj48vNejYrSMyOtm6des67Xj0IcofWGnkcykb+WxsI59L2chnU32fS3mRmBkxewiCIAgejQiZIAiC4NF4rZBpNBpMnz6d74V/kc+lbOSzsY18LmUjn417fC5uZ/YQBEEQhMrgtRGZIAiC4BuIkAmCIAgejQiZIAiC4NGIkAmCIAgejVcK2TvvvIMGDRogICAAXbt2xe+//w5fZ/bs2ejcuTN3TImOjsaAAQNw7NgxV5+W2/Hqq69yR5lx48a5+lTcggsXLuDhhx9GVFQUAgMD0aZNG+zevRu+TFFREaZOnYqGDRvyZ9K4cWO8/PLLFfYD9EZ27NiB/v37c+cN+n+zcePGEs/TZzJt2jTExcXxZ3XLLbcgMTHR6efhdUK2fv16TJgwga2ff/75J9q1a4e+ffsiJSUFvsz27dsxevRo7Nq1C5s3b4Zer0efPn2Qm5vr6lNzG/744w8sWbIEbdu2dfWpuAXp6eno0aMH/P398e233+LIkSN4/fXXERERAV9mzpw5WLRoEd5++2389ddfvD137lwsXLgQvkZubi5fYyl4sAV9LgsWLMDixYvx22+/QavV8vW4oKDAuSdi8jK6dOliGj16dPF2UVGRKT4+3jR79myXnpe7kZKSQl8fTdu3b3f1qbgF2dnZpqZNm5o2b95suvHGG01PPfWUydeZNGmSqWfPnq4+DbfjjjvuMA0dOrTEYwMHDjQ99NBDJl8GgGnDhg3F20aj0RQbG2uaN29e8WMZGRkmjUZj+uijj5z62l4VkRUWFmLPnj0cvlr2bqTtnTt3uvTc3I3MzEy+j4yMdPWpuAUUrd5xxx0l/nZ8nS+//BKdOnXCfffdx+noDh06YNmyZfB1unfvjq1bt+L48eO8vX//fvz888/o16+fq0/NrTh9+jSSk5NL/J+ivolU7nH29djtmgZXhcuXL3P+OiYmpsTjtH306FGXnZe7QRMGqAZEaaPWrVvD11m3bh2noSm1KPzLqVOnOIVGqfrnnnuOP58nn3wSarUagwcPhq8yefJk7u7eokULKJVKvubMmjULDz30kKtPza0gESNsXY/NzzkLrxIywf7o49ChQ/wt0tehMRNPPfUU1w3JHCSU/MJDEdkrr7zC2xSR0d8N1Tt8Wcg+/vhjrFmzBmvXrkWrVq2wb98+/mJIhgdf/lxciVelFmvVqsXfkC5dulTicdqOjY112Xm5E2PGjMFXX32Fbdu2OXVcjqdCqWgyAl177bVQqVR8I2MMFajpZ/q27auQ06xly5YlHrvmmmtw9uxZ+DLPPvssR2X//e9/2cX5yCOPYPz48ewMFv7FfM2tieuxVwkZpTw6duzI+WvLb5W03a1bN/gyVIslEduwYQN+/PFHtg4LQO/evXHw4EH+Vm2+URRCaSL6mb4Y+SqUerZeokF1ofr168OXycvLKzXkkf5O6Foj/AtdY0iwLK/HlJIl96Kzr8del1qkfD6F93Qx6tKlC+bPn88W0SFDhsDX04mUCvniiy94LZk5R03FV1rf4avQZ2FdJySLMK2b8vX6IUUZZGyg1OL999/P6zGXLl3KN1+G1k1RTaxevXqcWty7dy/eeOMNDB06FL5GTk4OTpw4UcLgQV8AyURGnw+lXGfOnImmTZuysNH6O0rB0jpWp2LyQhYuXGiqV6+eSa1Wsx1/165dJl+H/qlt3ZYvX+7qU3M7xH7/L5s2bTK1bt2aLdMtWrQwLV261OTrZGVl8d8HXWMCAgJMjRo1Mj3//PMmnU5n8jW2bdtm87oyePDgYgv+1KlTTTExMfw31Lt3b9OxY8ecfh4yxkUQBEHwaLyqRiYIgiD4HiJkgiAIgkcjQiYIgiB4NCJkgiAIgkcjQiYIgiB4NCJkgiAIgkcjQiYIgiB4NCJkgiAIgkcjQiYIgiB4NCJkgiAIgkcjQiYIgiB4NCJkgiAIAjyZ/wcbEqIZtldS+QAAAABJRU5ErkJggg==",
       "text/plain": [
        "<Figure size 500x400 with 1 Axes>"
       ]
@@ -1452,7 +1209,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -1460,7 +1217,21 @@
      "output_type": "stream",
      "text": [
       "Jax 64 bit mode: False\n",
-      "Absolute tolerance: 1e-07\n",
+      "Absolute tolerance: 1e-07\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Markus\\pymob\\pymob\\pymob\\inference\\numpyro_backend.py:552: UserWarning: Model is not rendered, because the graphviz executable is not found. Try search for 'graphviz executables not found' and the used OS. This should be an easy fix :-)\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "Trace Shapes:      \n",
       " Param Sites:      \n",
       "Sample Sites:      \n",
@@ -1468,15 +1239,15 @@
       "        value     |\n",
       " sigma_y dist     |\n",
       "        value     |\n",
-      "   y_obs dist 100 |\n",
-      "        value 100 |\n"
+      "   y_obs dist 101 |\n",
+      "        value 101 |\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "sample: 100%|██████████| 3000/3000 [00:02<00:00, 1429.92it/s, 7 steps of size 8.95e-01. acc. prob=0.89]\n"
+      "sample: 100%|██████████| 3000/3000 [00:07<00:00, 415.08it/s, 3 steps of size 7.20e-01. acc. prob=0.95] \n"
      ]
     },
     {
@@ -1485,15 +1256,15 @@
      "text": [
       "\n",
       "                mean       std    median      5.0%     95.0%     n_eff     r_hat\n",
-      "         b      3.01      0.03      3.01      2.96      3.06   1985.04      1.00\n",
-      "   sigma_y      1.79      0.13      1.78      1.56      1.97   2102.94      1.00\n",
+      "         b      3.03      0.03      3.03      2.98      3.08   1525.63      1.00\n",
+      "   sigma_y      1.82      0.13      1.81      1.62      2.05   1082.19      1.00\n",
       "\n",
       "Number of divergences: 0\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 400x300 with 1 Axes>"
       ]
@@ -1513,6 +1284,7 @@
     "sim.inferer.config.inference_numpyro.kernel = \"nuts\"\n",
     "sim.inferer.run()\n",
     "\n",
+    "# Was genaus macht diese Zeile? Ich kann keinen Unterschied erkennen, wenn ich sie auskommentiere.\n",
     "sim.inferer.idata.posterior\n",
     "\n",
     "# Plot the results\n",
@@ -1562,9 +1334,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 16,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x400 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# report the results\n",
     "sim.report()"
@@ -1609,15 +1402,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Scenario directory exists at 'c:\\Users\\mgrho\\pymob\\docs\\source\\user_guide\\case_studies\\superquickstart\\scenarios\\linreg'.\n",
-      "Results directory exists at 'c:\\Users\\mgrho\\pymob\\docs\\source\\user_guide\\case_studies\\superquickstart\\results\\linreg'.\n"
+      "Scenario directory created at 'c:\\Users\\Markus\\pymob\\pymob\\docs\\source\\user_guide\\case_studies\\superquickstart\\scenarios\\linreg'.\n",
+      "Results directory exists at 'c:\\Users\\Markus\\pymob\\pymob\\docs\\source\\user_guide\\case_studies\\superquickstart\\results\\linreg'.\n"
      ]
     }
    ],
@@ -1645,16 +1438,11 @@
     "  `pymob-infer --case_study=quickstart --scenario=test --inference_backend=numpyro`.   \n",
     "  While there are more command-line options, these two (--case_study and --scenario) are required."
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "pymob",
+   "display_name": "pymob2",
    "language": "python",
    "name": "python3"
   },
@@ -1668,7 +1456,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.11"
+   "version": "3.11.12"
   }
  },
  "nbformat": 4,

From 609bf6278572e6044e15a2e3e35f6d4e5b2e207b Mon Sep 17 00:00:00 2001
From: mariegrho <mgrholthaus@gmail.com>
Date: Mon, 21 Jul 2025 11:19:54 +0200
Subject: [PATCH 14/16] updated commtent for posterior distribution to avoid
 confusion

---
 docs/source/user_guide/superquickstart.ipynb | 453 ++++++++++++-------
 docs/source/user_guide/superquickstart.md    | 260 ++++-------
 2 files changed, 385 insertions(+), 328 deletions(-)

diff --git a/docs/source/user_guide/superquickstart.ipynb b/docs/source/user_guide/superquickstart.ipynb
index 6dccfa8cc..d0258518d 100644
--- a/docs/source/user_guide/superquickstart.ipynb
+++ b/docs/source/user_guide/superquickstart.ipynb
@@ -110,7 +110,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -136,27 +136,76 @@
        " */\n",
        "\n",
        ":root {\n",
-       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
-       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
-       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
-       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
-       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
-       "  --xr-background-color: var(--jp-layout-color0, white);\n",
-       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
-       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
-       "}\n",
-       "\n",
-       "html[theme=dark],\n",
-       "body[data-theme=dark],\n",
+       "  --xr-font-color0: var(\n",
+       "    --jp-content-font-color0,\n",
+       "    var(--pst-color-text-base rgba(0, 0, 0, 1))\n",
+       "  );\n",
+       "  --xr-font-color2: var(\n",
+       "    --jp-content-font-color2,\n",
+       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.54))\n",
+       "  );\n",
+       "  --xr-font-color3: var(\n",
+       "    --jp-content-font-color3,\n",
+       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.38))\n",
+       "  );\n",
+       "  --xr-border-color: var(\n",
+       "    --jp-border-color2,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 10))\n",
+       "  );\n",
+       "  --xr-disabled-color: var(\n",
+       "    --jp-layout-color3,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 40))\n",
+       "  );\n",
+       "  --xr-background-color: var(\n",
+       "    --jp-layout-color0,\n",
+       "    var(--pst-color-on-background, white)\n",
+       "  );\n",
+       "  --xr-background-color-row-even: var(\n",
+       "    --jp-layout-color1,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 5))\n",
+       "  );\n",
+       "  --xr-background-color-row-odd: var(\n",
+       "    --jp-layout-color2,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 15))\n",
+       "  );\n",
+       "}\n",
+       "\n",
+       "html[theme=\"dark\"],\n",
+       "html[data-theme=\"dark\"],\n",
+       "body[data-theme=\"dark\"],\n",
        "body.vscode-dark {\n",
-       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
-       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
-       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
-       "  --xr-border-color: #1F1F1F;\n",
-       "  --xr-disabled-color: #515151;\n",
-       "  --xr-background-color: #111111;\n",
-       "  --xr-background-color-row-even: #111111;\n",
-       "  --xr-background-color-row-odd: #313131;\n",
+       "  --xr-font-color0: var(\n",
+       "    --jp-content-font-color0,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 1))\n",
+       "  );\n",
+       "  --xr-font-color2: var(\n",
+       "    --jp-content-font-color2,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.54))\n",
+       "  );\n",
+       "  --xr-font-color3: var(\n",
+       "    --jp-content-font-color3,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.38))\n",
+       "  );\n",
+       "  --xr-border-color: var(\n",
+       "    --jp-border-color2,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 10))\n",
+       "  );\n",
+       "  --xr-disabled-color: var(\n",
+       "    --jp-layout-color3,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 40))\n",
+       "  );\n",
+       "  --xr-background-color: var(\n",
+       "    --jp-layout-color0,\n",
+       "    var(--pst-color-on-background, #111111)\n",
+       "  );\n",
+       "  --xr-background-color-row-even: var(\n",
+       "    --jp-layout-color1,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 5))\n",
+       "  );\n",
+       "  --xr-background-color-row-odd: var(\n",
+       "    --jp-layout-color2,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 15))\n",
+       "  );\n",
        "}\n",
        "\n",
        ".xr-wrap {\n",
@@ -197,7 +246,7 @@
        ".xr-sections {\n",
        "  padding-left: 0 !important;\n",
        "  display: grid;\n",
-       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
        "}\n",
        "\n",
        ".xr-section-item {\n",
@@ -205,11 +254,14 @@
        "}\n",
        "\n",
        ".xr-section-item input {\n",
-       "  display: none;\n",
+       "  display: inline-block;\n",
+       "  opacity: 0;\n",
+       "  height: 0;\n",
        "}\n",
        "\n",
        ".xr-section-item input + label {\n",
        "  color: var(--xr-disabled-color);\n",
+       "  border: 2px solid transparent !important;\n",
        "}\n",
        "\n",
        ".xr-section-item input:enabled + label {\n",
@@ -217,6 +269,10 @@
        "  color: var(--xr-font-color2);\n",
        "}\n",
        "\n",
+       ".xr-section-item input:focus + label {\n",
+       "  border: 2px solid var(--xr-font-color0) !important;\n",
+       "}\n",
+       "\n",
        ".xr-section-item input:enabled + label:hover {\n",
        "  color: var(--xr-font-color0);\n",
        "}\n",
@@ -238,7 +294,7 @@
        "\n",
        ".xr-section-summary-in + label:before {\n",
        "  display: inline-block;\n",
-       "  content: '►';\n",
+       "  content: \"►\";\n",
        "  font-size: 11px;\n",
        "  width: 15px;\n",
        "  text-align: center;\n",
@@ -249,7 +305,7 @@
        "}\n",
        "\n",
        ".xr-section-summary-in:checked + label:before {\n",
-       "  content: '▼';\n",
+       "  content: \"▼\";\n",
        "}\n",
        "\n",
        ".xr-section-summary-in:checked + label > span {\n",
@@ -321,15 +377,15 @@
        "}\n",
        "\n",
        ".xr-dim-list:before {\n",
-       "  content: '(';\n",
+       "  content: \"(\";\n",
        "}\n",
        "\n",
        ".xr-dim-list:after {\n",
-       "  content: ')';\n",
+       "  content: \")\";\n",
        "}\n",
        "\n",
        ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: ',';\n",
+       "  content: \",\";\n",
        "  padding-right: 5px;\n",
        "}\n",
        "\n",
@@ -346,7 +402,9 @@
        ".xr-var-item label,\n",
        ".xr-var-item > .xr-var-name span {\n",
        "  background-color: var(--xr-background-color-row-even);\n",
+       "  border-color: var(--xr-background-color-row-odd);\n",
        "  margin-bottom: 0;\n",
+       "  padding-top: 2px;\n",
        "}\n",
        "\n",
        ".xr-var-item > .xr-var-name:hover span {\n",
@@ -357,6 +415,7 @@
        ".xr-var-list > li:nth-child(odd) > label,\n",
        ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
        "  background-color: var(--xr-background-color-row-odd);\n",
+       "  border-color: var(--xr-background-color-row-even);\n",
        "}\n",
        "\n",
        ".xr-var-name {\n",
@@ -406,8 +465,15 @@
        ".xr-var-data,\n",
        ".xr-index-data {\n",
        "  display: none;\n",
-       "  background-color: var(--xr-background-color) !important;\n",
-       "  padding-bottom: 5px !important;\n",
+       "  border-top: 2px dotted var(--xr-background-color);\n",
+       "  padding-bottom: 20px !important;\n",
+       "  padding-top: 10px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in + label,\n",
+       ".xr-var-data-in + label,\n",
+       ".xr-index-data-in + label {\n",
+       "  padding: 0 1px;\n",
        "}\n",
        "\n",
        ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
@@ -420,6 +486,12 @@
        "  float: right;\n",
        "}\n",
        "\n",
+       ".xr-var-data > pre,\n",
+       ".xr-index-data > pre,\n",
+       ".xr-var-data > table > tbody > tr {\n",
+       "  background-color: transparent !important;\n",
+       "}\n",
+       "\n",
        ".xr-var-name span,\n",
        ".xr-var-data,\n",
        ".xr-index-name div,\n",
@@ -479,12 +551,20 @@
        "  stroke: currentColor;\n",
        "  fill: currentColor;\n",
        "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
+       "\n",
+       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
+       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
+       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
+       "  color: var(--xr-font-color0);\n",
+       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
+       "  stroke-width: 0.8px;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 2kB\n",
        "Dimensions:  (t: 101)\n",
        "Coordinates:\n",
-       "  * t        (t) float64 0.0 0.1 0.2 0.3 0.4 0.5 ... 9.5 9.6 9.7 9.8 9.9 10.0\n",
+       "  * t        (t) float64 808B 0.0 0.1 0.2 0.3 0.4 0.5 ... 9.6 9.7 9.8 9.9 10.0\n",
        "Data variables:\n",
-       "    y        (t) float64 4.028 1.37 1.506 -0.1024 ... 31.42 33.42 29.35 32.74</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-5ee9938c-20c6-411e-8835-794938fbacc5' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-5ee9938c-20c6-411e-8835-794938fbacc5' 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'>t</span>: 101</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-d24e7663-61b2-4f17-8ac7-529abd0dcb9e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d24e7663-61b2-4f17-8ac7-529abd0dcb9e' class='xr-section-summary' >Coordinates: <span>(1)</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'>t</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.1 0.2 0.3 ... 9.8 9.9 10.0</div><input id='attrs-9ad731f7-e217-4470-b156-1eb634f22edf' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9ad731f7-e217-4470-b156-1eb634f22edf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cabf65af-d128-47e8-b2fc-4d72fd4be4d3' class='xr-var-data-in' type='checkbox'><label for='data-cabf65af-d128-47e8-b2fc-4d72fd4be4d3' 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. ,  0.1,  0.2,  0.3,  0.4,  0.5,  0.6,  0.7,  0.8,  0.9,  1. ,  1.1,\n",
+       "    y        (t) float64 808B 1.23 -1.047 3.266 4.534 ... 30.26 30.72 31.78</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-9bc63df5-2af1-4743-b6f2-4f52b720b145' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-9bc63df5-2af1-4743-b6f2-4f52b720b145' 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'>t</span>: 101</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-597f96fc-65e6-4b7a-8e10-a73e0a38162b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-597f96fc-65e6-4b7a-8e10-a73e0a38162b' class='xr-section-summary' >Coordinates: <span>(1)</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'>t</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.1 0.2 0.3 ... 9.8 9.9 10.0</div><input id='attrs-0c706c46-1aac-4c05-af44-97d7afa9ddc7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0c706c46-1aac-4c05-af44-97d7afa9ddc7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-73059a11-efbd-4fec-b676-b2193e673b80' class='xr-var-data-in' type='checkbox'><label for='data-73059a11-efbd-4fec-b676-b2193e673b80' 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. ,  0.1,  0.2,  0.3,  0.4,  0.5,  0.6,  0.7,  0.8,  0.9,  1. ,  1.1,\n",
        "        1.2,  1.3,  1.4,  1.5,  1.6,  1.7,  1.8,  1.9,  2. ,  2.1,  2.2,  2.3,\n",
        "        2.4,  2.5,  2.6,  2.7,  2.8,  2.9,  3. ,  3.1,  3.2,  3.3,  3.4,  3.5,\n",
        "        3.6,  3.7,  3.8,  3.9,  4. ,  4.1,  4.2,  4.3,  4.4,  4.5,  4.6,  4.7,\n",
@@ -492,27 +572,27 @@
        "        6. ,  6.1,  6.2,  6.3,  6.4,  6.5,  6.6,  6.7,  6.8,  6.9,  7. ,  7.1,\n",
        "        7.2,  7.3,  7.4,  7.5,  7.6,  7.7,  7.8,  7.9,  8. ,  8.1,  8.2,  8.3,\n",
        "        8.4,  8.5,  8.6,  8.7,  8.8,  8.9,  9. ,  9.1,  9.2,  9.3,  9.4,  9.5,\n",
-       "        9.6,  9.7,  9.8,  9.9, 10. ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-32113b8c-0987-4de4-b314-994ae4f4671d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-32113b8c-0987-4de4-b314-994ae4f4671d' class='xr-section-summary' >Data variables: <span>(1)</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>y</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.028 1.37 1.506 ... 29.35 32.74</div><input id='attrs-eac7af59-b12c-4595-b92f-ef631db4ffdd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-eac7af59-b12c-4595-b92f-ef631db4ffdd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c64767ae-1ee3-4c86-8901-f80bb3f4bf03' class='xr-var-data-in' type='checkbox'><label for='data-c64767ae-1ee3-4c86-8901-f80bb3f4bf03' 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([ 4.02838695,  1.37032163,  1.5058789 , -0.1023566 ,  1.12812715,\n",
-       "       -0.50440519,  4.92087469,  2.92540714,  1.5095558 ,  5.80320885,\n",
-       "        4.59705594, -0.72073345,  4.66018611,  4.11794134,  5.96940087,\n",
-       "        3.31291739,  6.79659354,  3.75619909,  7.39629986,  6.27685582,\n",
-       "        7.20791104,  6.60574684, 10.89076531,  8.99487528,  8.45276379,\n",
-       "        7.94093974,  9.64866376, 12.35314246,  8.92075151,  9.06058763,\n",
-       "       10.74204086, 11.38791295, 12.64666093, 12.73118546, 15.84105012,\n",
-       "       12.48788591, 10.76807751, 17.11361543, 14.69969533, 14.25328245,\n",
-       "       10.88640772, 13.97251212, 11.9017861 , 13.85105715, 11.55327017,\n",
-       "       12.46803935, 16.16629758, 16.0732593 , 14.35144056, 15.46688564,\n",
-       "       14.78195572, 17.13462543, 15.60952458, 18.1101187 , 15.79153842,\n",
-       "       16.59987436, 13.13150084, 18.7204421 , 20.30191879, 19.40942286,\n",
-       "       20.68303218, 18.96360434, 19.90339138, 21.66396511, 18.87725489,\n",
-       "       19.44583516, 21.02652977, 23.41136604, 19.46488225, 20.60072757,\n",
-       "       24.64160105, 22.00015593, 19.95634443, 22.11655647, 25.79612665,\n",
-       "       25.5652377 , 20.03882792, 25.42004912, 22.84889615, 26.30146051,\n",
-       "       27.16532825, 25.72762564, 24.72205709, 25.33922566, 25.57633255,\n",
-       "       28.31664231, 26.7084131 , 26.42207821, 26.8125442 , 29.79686026,\n",
-       "       27.8095757 , 26.29596553, 30.54519835, 28.95846456, 29.53248537,\n",
-       "       27.94281117, 31.39903464, 31.42011852, 33.41947384, 29.35454881,\n",
-       "       32.73808715])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-48323806-5424-420f-a66e-80bd7feaf00b' class='xr-section-summary-in' type='checkbox'  ><label for='section-48323806-5424-420f-a66e-80bd7feaf00b' class='xr-section-summary' >Indexes: <span>(1)</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>t</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-0271a992-5c90-4a91-9a6a-a9691714efb8' class='xr-index-data-in' type='checkbox'/><label for='index-0271a992-5c90-4a91-9a6a-a9691714efb8' 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([                0.0,                 0.1,                 0.2,\n",
+       "        9.6,  9.7,  9.8,  9.9, 10. ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-487f62f3-db64-4ef1-9e63-396f24cc8e40' class='xr-section-summary-in' type='checkbox'  checked><label for='section-487f62f3-db64-4ef1-9e63-396f24cc8e40' class='xr-section-summary' >Data variables: <span>(1)</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>y</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.23 -1.047 3.266 ... 30.72 31.78</div><input id='attrs-04e46b7b-066f-4e83-a533-8b43400c6e7d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-04e46b7b-066f-4e83-a533-8b43400c6e7d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d37026cf-9545-462b-9d3a-36313dc93ea6' class='xr-var-data-in' type='checkbox'><label for='data-d37026cf-9545-462b-9d3a-36313dc93ea6' 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([ 1.22976341, -1.04655608,  3.2663395 ,  4.53431072,  4.23776931,\n",
+       "        4.45073522,  6.52567047,  0.29665366,  6.33615348,  3.28200789,\n",
+       "        3.07767615,  3.59402733,  7.39484909,  4.30321806,  5.83122278,\n",
+       "        2.97635964,  4.55086797,  6.45862688,  9.18578756,  5.33482202,\n",
+       "        6.71494669,  8.21052043,  8.74591202,  9.83845053,  6.75816717,\n",
+       "        6.23673361,  8.38101656,  7.46444103,  8.68615071, 13.32678057,\n",
+       "        8.88212373,  8.88252235, 10.77848501, 10.90335846, 10.43258584,\n",
+       "       11.30942105, 13.64763251, 13.23968283, 12.76870946, 12.60118114,\n",
+       "       14.89643901, 13.57957546, 12.90127828, 12.9065928 , 16.22289857,\n",
+       "       15.4752407 , 13.20064713, 14.49709013, 15.7142081 , 15.48008663,\n",
+       "       15.15420418, 15.87595124, 18.69492503, 19.52187194, 18.21216017,\n",
+       "       18.08072175, 12.8157894 , 17.71323914, 19.4071676 , 14.92766165,\n",
+       "       19.44135291, 19.25401464, 18.04114805, 17.57667418, 21.32571394,\n",
+       "       20.70517294, 22.33936779, 21.61152343, 17.36170699, 20.8799282 ,\n",
+       "       22.76487523, 22.38001707, 21.97427494, 24.63834728, 22.66893927,\n",
+       "       23.40982269, 23.91073962, 26.370772  , 24.27157531, 21.7355613 ,\n",
+       "       26.63193139, 25.58322001, 24.95530861, 28.22779359, 28.57040507,\n",
+       "       24.80400595, 27.75891697, 27.67127171, 27.1611122 , 28.57306242,\n",
+       "       26.88583892, 29.47300221, 26.18066409, 27.46141492, 28.88998537,\n",
+       "       29.56806835, 27.3572519 , 32.843706  , 30.26274097, 30.72126862,\n",
+       "       31.77564013])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e52eb455-8d0c-406e-83f5-182be75e53a3' class='xr-section-summary-in' type='checkbox'  ><label for='section-e52eb455-8d0c-406e-83f5-182be75e53a3' class='xr-section-summary' >Indexes: <span>(1)</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>t</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-61bc477f-e5a2-414c-9695-1220d268ce49' class='xr-index-data-in' type='checkbox'/><label for='index-61bc477f-e5a2-414c-9695-1220d268ce49' 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([                0.0,                 0.1,                 0.2,\n",
        "       0.30000000000000004,                 0.4,                 0.5,\n",
        "        0.6000000000000001,  0.7000000000000001,                 0.8,\n",
        "                       0.9,\n",
@@ -521,24 +601,24 @@
        "                       9.4,                 9.5,   9.600000000000001,\n",
        "         9.700000000000001,                 9.8,                 9.9,\n",
        "                      10.0],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;t&#x27;, length=101))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-18ef19bb-1657-4f09-a452-ee63d9ccb7aa' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-18ef19bb-1657-4f09-a452-ee63d9ccb7aa' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+       "      dtype=&#x27;float64&#x27;, name=&#x27;t&#x27;, length=101))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2e32461d-63cf-41f3-9f9c-63303bc6d05c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-2e32461d-63cf-41f3-9f9c-63303bc6d05c' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
-       "<xarray.Dataset>\n",
+       "<xarray.Dataset> Size: 2kB\n",
        "Dimensions:  (t: 101)\n",
        "Coordinates:\n",
-       "  * t        (t) float64 0.0 0.1 0.2 0.3 0.4 0.5 ... 9.5 9.6 9.7 9.8 9.9 10.0\n",
+       "  * t        (t) float64 808B 0.0 0.1 0.2 0.3 0.4 0.5 ... 9.6 9.7 9.8 9.9 10.0\n",
        "Data variables:\n",
-       "    y        (t) float64 4.028 1.37 1.506 -0.1024 ... 31.42 33.42 29.35 32.74"
+       "    y        (t) float64 808B 1.23 -1.047 3.266 4.534 ... 30.26 30.72 31.78"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 500x400 with 1 Axes>"
       ]
@@ -596,31 +676,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "MinMaxScaler(variable=y, min=-0.7207334529232607, max=33.419473836088876)\n"
+      "MinMaxScaler(variable=y, min=-1.0465560756676948, max=32.84370600090758)\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "C:\\Users\\Markus\\pymob\\pymob\\pymob\\simulation.py:303: UserWarning: `sim.config.data_structure.y = Datavariable(dimensions=['t'] min=-0.7207334529232607 max=33.419473836088876 observed=True dimensions_evaluator=None)` has been assumed from `sim.observations`. If the order of the dimensions should be different, specify `sim.config.data_structure.y = DataVariable(dimensions=[...], ...)` manually.\n",
+      "C:\\Pymob\\pymob\\pymob\\simulation.py:307: UserWarning: `sim.config.data_structure.y = Datavariable(dimensions=['t'] min=-1.0465560756676948 max=32.84370600090758 observed=True dimensions_evaluator=None)` has been assumed from `sim.observations`. If the order of the dimensions should be different, specify `sim.config.data_structure.y = DataVariable(dimensions=[...], ...)` manually.\n",
       "  warnings.warn(\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "Datastructure(y=DataVariable(dimensions=['t'], min=-0.7207334529232607, max=33.419473836088876, observed=True, dimensions_evaluator=None))"
+       "Datastructure(y=DataVariable(dimensions=['t'], min=-1.0465560756676948, max=32.84370600090758, observed=True, dimensions_evaluator=None))"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -675,7 +755,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -684,7 +764,7 @@
        "{'a': 1.0, 'b': 3.0}"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -712,9 +792,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Pymob\\pymob\\pymob\\simulation.py:567: UserWarning: The number of ODE states was not specified in the config file [simulation] > 'n_ode_states = <n>'. Extracted the return arguments ['a+b*x'] from the source code. Setting 'n_ode_states=1.\n",
+      "  warnings.warn(\n"
+     ]
+    },
     {
      "data": {
       "text/html": [
@@ -738,27 +826,76 @@
        " */\n",
        "\n",
        ":root {\n",
-       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
-       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
-       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
-       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
-       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
-       "  --xr-background-color: var(--jp-layout-color0, white);\n",
-       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
-       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
-       "}\n",
-       "\n",
-       "html[theme=dark],\n",
-       "body[data-theme=dark],\n",
+       "  --xr-font-color0: var(\n",
+       "    --jp-content-font-color0,\n",
+       "    var(--pst-color-text-base rgba(0, 0, 0, 1))\n",
+       "  );\n",
+       "  --xr-font-color2: var(\n",
+       "    --jp-content-font-color2,\n",
+       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.54))\n",
+       "  );\n",
+       "  --xr-font-color3: var(\n",
+       "    --jp-content-font-color3,\n",
+       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.38))\n",
+       "  );\n",
+       "  --xr-border-color: var(\n",
+       "    --jp-border-color2,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 10))\n",
+       "  );\n",
+       "  --xr-disabled-color: var(\n",
+       "    --jp-layout-color3,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 40))\n",
+       "  );\n",
+       "  --xr-background-color: var(\n",
+       "    --jp-layout-color0,\n",
+       "    var(--pst-color-on-background, white)\n",
+       "  );\n",
+       "  --xr-background-color-row-even: var(\n",
+       "    --jp-layout-color1,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 5))\n",
+       "  );\n",
+       "  --xr-background-color-row-odd: var(\n",
+       "    --jp-layout-color2,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 15))\n",
+       "  );\n",
+       "}\n",
+       "\n",
+       "html[theme=\"dark\"],\n",
+       "html[data-theme=\"dark\"],\n",
+       "body[data-theme=\"dark\"],\n",
        "body.vscode-dark {\n",
-       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
-       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
-       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
-       "  --xr-border-color: #1F1F1F;\n",
-       "  --xr-disabled-color: #515151;\n",
-       "  --xr-background-color: #111111;\n",
-       "  --xr-background-color-row-even: #111111;\n",
-       "  --xr-background-color-row-odd: #313131;\n",
+       "  --xr-font-color0: var(\n",
+       "    --jp-content-font-color0,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 1))\n",
+       "  );\n",
+       "  --xr-font-color2: var(\n",
+       "    --jp-content-font-color2,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.54))\n",
+       "  );\n",
+       "  --xr-font-color3: var(\n",
+       "    --jp-content-font-color3,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.38))\n",
+       "  );\n",
+       "  --xr-border-color: var(\n",
+       "    --jp-border-color2,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 10))\n",
+       "  );\n",
+       "  --xr-disabled-color: var(\n",
+       "    --jp-layout-color3,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 40))\n",
+       "  );\n",
+       "  --xr-background-color: var(\n",
+       "    --jp-layout-color0,\n",
+       "    var(--pst-color-on-background, #111111)\n",
+       "  );\n",
+       "  --xr-background-color-row-even: var(\n",
+       "    --jp-layout-color1,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 5))\n",
+       "  );\n",
+       "  --xr-background-color-row-odd: var(\n",
+       "    --jp-layout-color2,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 15))\n",
+       "  );\n",
        "}\n",
        "\n",
        ".xr-wrap {\n",
@@ -799,7 +936,7 @@
        ".xr-sections {\n",
        "  padding-left: 0 !important;\n",
        "  display: grid;\n",
-       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
        "}\n",
        "\n",
        ".xr-section-item {\n",
@@ -807,11 +944,14 @@
        "}\n",
        "\n",
        ".xr-section-item input {\n",
-       "  display: none;\n",
+       "  display: inline-block;\n",
+       "  opacity: 0;\n",
+       "  height: 0;\n",
        "}\n",
        "\n",
        ".xr-section-item input + label {\n",
        "  color: var(--xr-disabled-color);\n",
+       "  border: 2px solid transparent !important;\n",
        "}\n",
        "\n",
        ".xr-section-item input:enabled + label {\n",
@@ -819,6 +959,10 @@
        "  color: var(--xr-font-color2);\n",
        "}\n",
        "\n",
+       ".xr-section-item input:focus + label {\n",
+       "  border: 2px solid var(--xr-font-color0) !important;\n",
+       "}\n",
+       "\n",
        ".xr-section-item input:enabled + label:hover {\n",
        "  color: var(--xr-font-color0);\n",
        "}\n",
@@ -840,7 +984,7 @@
        "\n",
        ".xr-section-summary-in + label:before {\n",
        "  display: inline-block;\n",
-       "  content: '►';\n",
+       "  content: \"►\";\n",
        "  font-size: 11px;\n",
        "  width: 15px;\n",
        "  text-align: center;\n",
@@ -851,7 +995,7 @@
        "}\n",
        "\n",
        ".xr-section-summary-in:checked + label:before {\n",
-       "  content: '▼';\n",
+       "  content: \"▼\";\n",
        "}\n",
        "\n",
        ".xr-section-summary-in:checked + label > span {\n",
@@ -923,15 +1067,15 @@
        "}\n",
        "\n",
        ".xr-dim-list:before {\n",
-       "  content: '(';\n",
+       "  content: \"(\";\n",
        "}\n",
        "\n",
        ".xr-dim-list:after {\n",
-       "  content: ')';\n",
+       "  content: \")\";\n",
        "}\n",
        "\n",
        ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: ',';\n",
+       "  content: \",\";\n",
        "  padding-right: 5px;\n",
        "}\n",
        "\n",
@@ -948,7 +1092,9 @@
        ".xr-var-item label,\n",
        ".xr-var-item > .xr-var-name span {\n",
        "  background-color: var(--xr-background-color-row-even);\n",
+       "  border-color: var(--xr-background-color-row-odd);\n",
        "  margin-bottom: 0;\n",
+       "  padding-top: 2px;\n",
        "}\n",
        "\n",
        ".xr-var-item > .xr-var-name:hover span {\n",
@@ -959,6 +1105,7 @@
        ".xr-var-list > li:nth-child(odd) > label,\n",
        ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
        "  background-color: var(--xr-background-color-row-odd);\n",
+       "  border-color: var(--xr-background-color-row-even);\n",
        "}\n",
        "\n",
        ".xr-var-name {\n",
@@ -1008,8 +1155,15 @@
        ".xr-var-data,\n",
        ".xr-index-data {\n",
        "  display: none;\n",
-       "  background-color: var(--xr-background-color) !important;\n",
-       "  padding-bottom: 5px !important;\n",
+       "  border-top: 2px dotted var(--xr-background-color);\n",
+       "  padding-bottom: 20px !important;\n",
+       "  padding-top: 10px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in + label,\n",
+       ".xr-var-data-in + label,\n",
+       ".xr-index-data-in + label {\n",
+       "  padding: 0 1px;\n",
        "}\n",
        "\n",
        ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
@@ -1022,6 +1176,12 @@
        "  float: right;\n",
        "}\n",
        "\n",
+       ".xr-var-data > pre,\n",
+       ".xr-index-data > pre,\n",
+       ".xr-var-data > table > tbody > tr {\n",
+       "  background-color: transparent !important;\n",
+       "}\n",
+       "\n",
        ".xr-var-name span,\n",
        ".xr-var-data,\n",
        ".xr-index-name div,\n",
@@ -1081,12 +1241,20 @@
        "  stroke: currentColor;\n",
        "  fill: currentColor;\n",
        "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
+       "\n",
+       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
+       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
+       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
+       "  color: var(--xr-font-color0);\n",
+       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
+       "  stroke-width: 0.8px;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 2kB\n",
        "Dimensions:  (t: 101)\n",
        "Coordinates:\n",
-       "  * t        (t) float64 0.0 0.1 0.2 0.3 0.4 0.5 ... 9.5 9.6 9.7 9.8 9.9 10.0\n",
+       "  * t        (t) float64 808B 0.0 0.1 0.2 0.3 0.4 0.5 ... 9.6 9.7 9.8 9.9 10.0\n",
        "Data variables:\n",
-       "    y        (t) float64 1.0 1.3 1.6 1.9 2.2 2.5 ... 29.8 30.1 30.4 30.7 31.0</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-e20ef49a-12e6-4100-a7e8-2b78c441958e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e20ef49a-12e6-4100-a7e8-2b78c441958e' 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'>t</span>: 101</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-74d8b306-460e-419e-bc40-45053a64973b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-74d8b306-460e-419e-bc40-45053a64973b' class='xr-section-summary' >Coordinates: <span>(1)</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'>t</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.1 0.2 0.3 ... 9.8 9.9 10.0</div><input id='attrs-b94370ef-43d5-416e-8603-a21e21fac238' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b94370ef-43d5-416e-8603-a21e21fac238' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-27721230-cb1b-4bda-b55e-6df747632e3c' class='xr-var-data-in' type='checkbox'><label for='data-27721230-cb1b-4bda-b55e-6df747632e3c' 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. ,  0.1,  0.2,  0.3,  0.4,  0.5,  0.6,  0.7,  0.8,  0.9,  1. ,  1.1,\n",
+       "    y        (t) float64 808B 1.0 1.3 1.6 1.9 2.2 ... 29.8 30.1 30.4 30.7 31.0</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-cc8d2e81-b255-4073-9b78-d1fffaeae3ba' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-cc8d2e81-b255-4073-9b78-d1fffaeae3ba' 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'>t</span>: 101</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-a80a99c6-ff77-4097-9d50-717c4fc3050b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a80a99c6-ff77-4097-9d50-717c4fc3050b' class='xr-section-summary' >Coordinates: <span>(1)</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'>t</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.1 0.2 0.3 ... 9.8 9.9 10.0</div><input id='attrs-9d4acdb7-cbda-477c-8166-4fbc4fe6f57e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9d4acdb7-cbda-477c-8166-4fbc4fe6f57e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0e02f0a5-b54e-4639-a3a9-96bedc8f9dae' class='xr-var-data-in' type='checkbox'><label for='data-0e02f0a5-b54e-4639-a3a9-96bedc8f9dae' 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. ,  0.1,  0.2,  0.3,  0.4,  0.5,  0.6,  0.7,  0.8,  0.9,  1. ,  1.1,\n",
        "        1.2,  1.3,  1.4,  1.5,  1.6,  1.7,  1.8,  1.9,  2. ,  2.1,  2.2,  2.3,\n",
        "        2.4,  2.5,  2.6,  2.7,  2.8,  2.9,  3. ,  3.1,  3.2,  3.3,  3.4,  3.5,\n",
        "        3.6,  3.7,  3.8,  3.9,  4. ,  4.1,  4.2,  4.3,  4.4,  4.5,  4.6,  4.7,\n",
@@ -1094,7 +1262,7 @@
        "        6. ,  6.1,  6.2,  6.3,  6.4,  6.5,  6.6,  6.7,  6.8,  6.9,  7. ,  7.1,\n",
        "        7.2,  7.3,  7.4,  7.5,  7.6,  7.7,  7.8,  7.9,  8. ,  8.1,  8.2,  8.3,\n",
        "        8.4,  8.5,  8.6,  8.7,  8.8,  8.9,  9. ,  9.1,  9.2,  9.3,  9.4,  9.5,\n",
-       "        9.6,  9.7,  9.8,  9.9, 10. ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d0c2e7f5-e48e-4f47-85df-b18e6060ede0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d0c2e7f5-e48e-4f47-85df-b18e6060ede0' class='xr-section-summary' >Data variables: <span>(1)</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>y</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.0 1.3 1.6 1.9 ... 30.4 30.7 31.0</div><input id='attrs-4a387cb5-8d14-4b1d-a803-29a0eae53181' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4a387cb5-8d14-4b1d-a803-29a0eae53181' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-54be3784-0c9e-4e36-8597-d707eb7999a2' class='xr-var-data-in' type='checkbox'><label for='data-54be3784-0c9e-4e36-8597-d707eb7999a2' 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([ 1. ,  1.3,  1.6,  1.9,  2.2,  2.5,  2.8,  3.1,  3.4,  3.7,  4. ,\n",
+       "        9.6,  9.7,  9.8,  9.9, 10. ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-004c1f6b-283b-4ed9-b4bd-44f3dca51ebb' class='xr-section-summary-in' type='checkbox'  checked><label for='section-004c1f6b-283b-4ed9-b4bd-44f3dca51ebb' class='xr-section-summary' >Data variables: <span>(1)</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>y</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.0 1.3 1.6 1.9 ... 30.4 30.7 31.0</div><input id='attrs-1a0438c5-f531-4262-ad38-204d74a4a5f8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1a0438c5-f531-4262-ad38-204d74a4a5f8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d8f009ae-96a4-4f51-af68-87e0f8862aa6' class='xr-var-data-in' type='checkbox'><label for='data-d8f009ae-96a4-4f51-af68-87e0f8862aa6' 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([ 1. ,  1.3,  1.6,  1.9,  2.2,  2.5,  2.8,  3.1,  3.4,  3.7,  4. ,\n",
        "        4.3,  4.6,  4.9,  5.2,  5.5,  5.8,  6.1,  6.4,  6.7,  7. ,  7.3,\n",
        "        7.6,  7.9,  8.2,  8.5,  8.8,  9.1,  9.4,  9.7, 10. , 10.3, 10.6,\n",
        "       10.9, 11.2, 11.5, 11.8, 12.1, 12.4, 12.7, 13. , 13.3, 13.6, 13.9,\n",
@@ -1103,7 +1271,7 @@
        "       20.8, 21.1, 21.4, 21.7, 22. , 22.3, 22.6, 22.9, 23.2, 23.5, 23.8,\n",
        "       24.1, 24.4, 24.7, 25. , 25.3, 25.6, 25.9, 26.2, 26.5, 26.8, 27.1,\n",
        "       27.4, 27.7, 28. , 28.3, 28.6, 28.9, 29.2, 29.5, 29.8, 30.1, 30.4,\n",
-       "       30.7, 31. ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c5c291ca-2947-4ed3-ae03-733301f7366f' class='xr-section-summary-in' type='checkbox'  ><label for='section-c5c291ca-2947-4ed3-ae03-733301f7366f' class='xr-section-summary' >Indexes: <span>(1)</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>t</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-6bb21e26-3315-4cf1-aae4-63caab232252' class='xr-index-data-in' type='checkbox'/><label for='index-6bb21e26-3315-4cf1-aae4-63caab232252' 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([                0.0,                 0.1,                 0.2,\n",
+       "       30.7, 31. ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f25bfcaa-c60c-48d1-b80c-29d7fb48eb11' class='xr-section-summary-in' type='checkbox'  ><label for='section-f25bfcaa-c60c-48d1-b80c-29d7fb48eb11' class='xr-section-summary' >Indexes: <span>(1)</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>t</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-354469b2-1f21-4050-a788-5216712dfed4' class='xr-index-data-in' type='checkbox'/><label for='index-354469b2-1f21-4050-a788-5216712dfed4' 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([                0.0,                 0.1,                 0.2,\n",
        "       0.30000000000000004,                 0.4,                 0.5,\n",
        "        0.6000000000000001,  0.7000000000000001,                 0.8,\n",
        "                       0.9,\n",
@@ -1112,18 +1280,18 @@
        "                       9.4,                 9.5,   9.600000000000001,\n",
        "         9.700000000000001,                 9.8,                 9.9,\n",
        "                      10.0],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;t&#x27;, length=101))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ed34689d-58b9-4f09-84c9-35a5773794f8' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ed34689d-58b9-4f09-84c9-35a5773794f8' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+       "      dtype=&#x27;float64&#x27;, name=&#x27;t&#x27;, length=101))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0084ba6a-99e9-4475-91f2-f934a658769e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-0084ba6a-99e9-4475-91f2-f934a658769e' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
-       "<xarray.Dataset>\n",
+       "<xarray.Dataset> Size: 2kB\n",
        "Dimensions:  (t: 101)\n",
        "Coordinates:\n",
-       "  * t        (t) float64 0.0 0.1 0.2 0.3 0.4 0.5 ... 9.5 9.6 9.7 9.8 9.9 10.0\n",
+       "  * t        (t) float64 808B 0.0 0.1 0.2 0.3 0.4 0.5 ... 9.6 9.7 9.8 9.9 10.0\n",
        "Data variables:\n",
-       "    y        (t) float64 1.0 1.3 1.6 1.9 2.2 2.5 ... 29.8 30.1 30.4 30.7 31.0"
+       "    y        (t) float64 808B 1.0 1.3 1.6 1.9 2.2 ... 29.8 30.1 30.4 30.7 31.0"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1160,22 +1328,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x1cdc4883450>"
+       "<matplotlib.legend.Legend at 0x178111263d0>"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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7d2YRo6kRNF2eRj7VpIjxuVZm50WLFvFYeepCTDcaKf/tt9+W6PxNY0booktvhEZ20Hyq6oQKjnRxd8WtsoMDVq5cWTxckkaa3HzzzTx6hGZlmWd53X///bxvUlISzxYbOnQo/vrrL/z00088isTRYQXm+VvLly/nY1vP4xIEwXtt9NZU1TZPjkRKI9J8Qcou3XnnnSxmNPrIFVQqtVi3bl2+ANPAQbqg0oWZZk/RCPhWrVrx7Kavv/4an3zyCbfepwiELr6WU4GdDQ1mrGn1N5OTk1Ph9GVL6HObO3cu/zxz5kwWsVdeeaX4+Q8++IAnNdPwRzq2wWDgz4+mFBMUnTkKfVsiaD5ZbGysw8cRBMH1VDSGSmtho6d0orMmNtN1f+3atRyw0GgVuv69+eabGD58eKnIryap1Lvo379/ie1Zs2ZxlEaj5Unk3n//fX6TFGmYv/3TEEh6/rrrroOvQ8Myzezfv58dPrZEmKYR03BM+sZD4kVpSNq+9957qzSEUhAE33Ai1vnHRk/GDqqJWYqM2TZPZo3KTGxOT0/nKe/r16/nbRpSu3r1ajRp0gSuxmGzB00ipsiLUmyUYqQJwjQenuo4Zlq0aIF69eph586dZQoZuV3oZjlIrTIEBQVx9OIK6LUrg2X0RudMXwyspxkTcXFxUCqV2Lx5M08l/uGHH7Bw4UKefvzbb7+xnZWsrdZpRvr8BUHwficimTiozhWkDuSoiwSLLPNmF6EtGz2lEykSs7TNE7S2rKLRLFu2bOH6+4ULF/jaNG3aNDz33HM8Hd4dqPRZUB6UhIvqYRRNbNiwAS1btsS+ffu4/kOpK0vIxJCcnFzm8WbPno0XX3zRsbP/p3BZmfSeu3Dttdfis88+Y9NFWX8M9N569OjBN/rDoRQjfd4TJkzgVCHVuiy/WJB55KabbirzNf39/Xk/QRC834nY5B8bvTl6u5RVwOlEisTMIrbop5PlRnb5+fmYMmUK3nrrreLyCEVhXbp0gTtRaSFr3rw5ixblR8lVR4vfaNGbo9CHRBdmy4iM6kTeDuWYly1bxoaOiRMnIjIyEidOnMC6devw3nvvsQFk69atnFKMjo7mSCw1NZVTtQSlb+lzo5okrdkguyutqC8PEk06JgmjRqORNKUgeKkTMeGfwZZlTWw+dTmnwsgu5+JJttUfPnyYj0VpxXnz5tkMHCqq2bmdkFHUZc6JUs2H3G+k1oMGDWL3Cl1MLaMycuKVZy6gCyrdfI34+Hg2wUyaNInFitKrFHHddtttnDYkV+iOHTswf/58Fnd67vXXX2e7PkFuRqqzPfrooxzRkdGmvGiMoN8n8SMBrVOnDs6cOVND71YQBOc5EW3XtQLVSo66rJ2I1hObK4rsjiVn4pkXXsI3K97icgVl1ciIdvvttzule0h1UOUEJ/XfooswiRqlrugbP9nuiWPHjrFNk1KRvg7Z562hMP3zzz+3uT9FXmTJLwv6rN99912+lYW1UFFNztqwIwiCZ6B1khPxQjmRXfqlC/h+7iT8fXh38bpTWhdGa1+rUrNzKyGjNCBFBGTgyM7OZociXaC///57ttsPGzaMv/FTmowiirFjx7KIiWNREAShajjLiZhdoMeVvEKoVWQYo0jsqgzs3rwRn7/7MnR5ufAPCMKM2fMw5alRZdrqq9o9xGVClpKSwqksMhmQcNHiaBKxW2+9lZ+n9QSUFqOIjKI0so2XFzEIgiAI9mGvE1FRjmhQBLVx70WcTMnBmcs5CPBXQWvMx5FPX8PRnVt4n3rXdEDvkS/hkYdvLndtmCM1O7cQMlonVh4BAQF45513+CYIgiA4l4qciE3KSeOZ04BpOTpEh2i48W/m8d+xbc2rKMxKg0KpQt9HxiDhpgfQLiGywsjO0ZpddeAeiwAEQRAEuyjLiagoJxKzTANSF/twtQnr1r2Bv//vao0+KLo+ug6dhnot2yEquOLIjqiu7iGOIEImCILgYVg7ESvCMg14PvEQ1rz6LFLOXx16We/6gajXdzj0Kg3qRQXi/k4Jdhk0qqN7iE8JGTklBcEdkb9NoTLU1Pqr3EID8nQ6HPlmBTaveQfGIgNCI2tj0NOvoE7rbsjXGzgNOKBDHbtdhs6o2fmkkNEaNjKTXLx4kTtb0LYrG1UKguU3UFpHSYvW6W9UZs0JFVGT669SL5zFlzNHICnxAG+3u74v7n3qRWhDrzZFoMtoRJAGIZrSKcLqqtn5rJDRBYL6DJJrksRMENwN6r9Jy1Pob1UQyqKm1l+ZTCZezEyzC6m/qzpQi3vGTEOnW+4uDgKqmgZ0pGbn00JG0DddulDQiBPpGyi4E9RMlbqsSJZAKI+aWn+VkpKCESNG8LBLonO3HugyeCqKtLWQozNUqYlwVWt28HUhI+gfnjpb0E0QBMGTqI71V0arWtu+X37E8OHDWMzoOknzD59++mmcTsurUhNhd8UjhUwQBMFTcfb6qxMWtbbsnBz8tm4+Dm75jJ+jgcdr1qxBu3btqtxE2J3FTIRMEAShBtE6cf2VZa2t6FIiNr45GZcv/M3Pden/CD54+3W0qnd1OryjTYRrstWUo4iQCYIg1CDOWn9lFqDUzDz8vXUVtqxdDKOxCOG1YjHomdnwq9MG209m4pq6tcoVIHdqNeUoImSCIAg1iLPWX5Gw7D5wGJsXTcWF4wf5sQ433cmuxKCQMG4ObI8AuVOrKUcRIRMEQahhqrr+im317y3F6mlTYNAVIEAbgnufnIFrb7qz0gKkdaNWU47ivmcmCILgxV05HF1/RcOKaWQWTYcnGrbtiocnzkFEdJxDAuROraYcRYRMEATBRV05Krv+itaEDR8+HJcvX+Y1tXcMfRrR3f6D8NqhJfarjAC5U6spRxEhEwRBqASumIpMg4ypOwd16SCuadUa69auRVBsAz6XqgqQu7SachQRMkEQBDupbqu6rXTlb7/twqAHHsS5v89wU8T2tz+M6/87Bv93ORB9Y+E0AXKHVlOOIkImCIJgJ9VpVbdOV/rDiOPfrcAPbKs3IjgqFvc9PRstr+1WKgIc1auxUwTI1a2mHEWETBAEwU6qy6puna7MuZSENXOewcUTR/j5htfdhmETX0ZQcKjNCHDkjcEeKUDOQoRMEATBTrTVYFW3TFc2qa3Fr199hE3L5kKvK4BGG4o6dz6Jlt37ICAoxCMXK9cEImSCIAh2Co7RZEJooIrTf23rhJUY1+OoVd2crgwuysZ7L4zH0d3/x4/HXNMFDQc+jUy/EJxJy8UvJy+jVXwoIrWaCiNAYw0N7HQXRMgEQRAqUb+6nKPjUSdJGQVoXScUceGBVbKqk9gc2bkZ/7f8FeRlZUCl1qB5/5GI6XoX1GoVdJkF0BuNSM3WYd+5DLRPCC8WM1sR4IkaHNjpLoiQCYIgVKJ+FR8eiFrBahy6mIW9ZzNwOacQtYI1DjkFs7KyMHX8E/h+3RrertOkJdo+9AIKQ+JYFIl0fwX0BUZEBPkjr7AIJ1NzERF09TnrCPCEC5YGuAMiZIIgCJW02ydEalk8DlzIZKs9CUTdiKBKRWL/93//h0cffRRnzpyBn0KB9ncMxh2PjsUf57MR7K/k16J0pVqhgEmjQi5HXwqOCEnAaDimZQRo9IIu9o4iQiYIgtfjaM2oPLs91ccoZZeRp4fpn33tOX5hYSGmT5+OOXPmsFA1bNgQr7y1GAcNcTidlosCfRG0GiV0hiLkFBgQFuSPhrW0SM0uRFquDln5elzJ1aFj/cgSEeAFL+hi7ygiZIIgeDVVqRnZY7cncVj+y2lk5RsqPP7hw4fx8MMPY9++fbw9ZMgQzJ8/H6GhoeiUko2P/ziPv9PykJajQ4C/CtGhAWhcW8s1sQZRZCbJx5VcPR6/sRE61Y8sIZa5XtDF3lFEyARB8Fps1YxydXr8fiYNh5My8WCXeujeuOx5XdoK7PZJGfls/KAAiMSrrJoULWheuHAhJk2aBJ1Oh4jIKMyYOx//+c9ABAdfFR7ab2Lf5uR/xJGkLDSpHYzQQP8S0VWOrggd60eUEjFC6wVd7B3F+96RIAhCGfUt+pkiqPRcHdLz9fj7ci76tYnDba1j/xGckinIOI6IbHeGJ3Eiw4e/UlHCim9dk1IXZGDYsKHYsmULP9+yy43oMeR5HA2IxJubj5eI3lQqBe7vnMDieylbx2Jlb//EOl7Qxd5RRMgEQfBKrGtGJGJkX88vNCA4wB8x/grk6orwx5krfJG/uUU0jiZll0pBtogLsdkZnvbTG4zoUC+8xHoyy5rU1xs/w5SVryIrMwOagAB0e2AcmvW6B3UiAhGkVtmM3hxt4Kvwgi72jiJCJgiCV2JZM6KIhCIxEjG6oJPQGE1+fJGnCOVsWh4WbE1EXFgg4sNL29YtRc4sLOT+M5mA+PDSxon8nCxsXPgS9m7bxNsxjVqi8f2ToQuNZ7NGdKgGSoVfmY5CRxv4NvHwLvaOIkImCIJXorWoGZHgpOcVciRmTrnpi4xQKhRQKxW8Dy047pAQXlxfshSZY8nZePyGRkj6xyxBxyZxnL8lsVRNKnHfLnw0bzIyUpMAPwXa3vkYej8wEoeT81i8UnN0yCk0FC9sLstR6GgD3yYe3MXeUUrGwxUwe/ZsdO7cGSEhIYiOjsaAAQNw7NixEvv06tWL/2EsbyNHjnT2eQuCIJSLuWZEaTWyshuKjPBXXr2YkwiRtd286DhbZ4Ba5Qe9kYz0/2IpMiRiJCwtYkP5ntaNmY9PxzMUFuLLpXOweNJjLGKBUfHoPGYhHh39LAIDAlBkNEGrUfFrXk1N5vLvEZQCpHN0lqNQ8Y8Ims/Vm0Ws0hHZ9u3bMXr0aBYzg8GA5557Dn369MGRI0eg1WqL9/vf//6Hl156qXg7KMi71iwIguD+WNaMzqfn8VqvQoORHYYkYiQeZG0n8aLHKQVH0Zm9tnXL4+/asw87lkzFpb8T+bmmN9yNmFtHoHPTOK6f0XFVSgX0RSZe1BwcoOKaXXaBgZ2J3uworAkq9al99913JbZXrFjBkdmePXtwww03lBCu2NhY552lIAiCAwubzTWj7w4mIzU7mQUpLNC/xPqszLxCFrKoYDVCAkpfEssTmUa1tDAe2IQNs2bAoNcjMDQCt46Yhg49b2ELvVql5JpYsEbJbaVSswug1qrZ6ZirM6CwyOj1jsKaoEryn5mZyfeRkZElHl+zZg1Wr17NYta/f39MnTq1zKiM1lTQzbL3mCAIgrMWNtNzT9wUjHb1wvHR72dZQEiAgjQqZBfokZxVgNohGnYRWlOeyJw9exaPPfYYtm3bxtu9+/bDjHkLYAoIw47jqfx7KdkFvLA5MkiN2iFqbitFkZhapeC0JQko1eC82VHo1kJGayjGjRuHHj16oHXr1sWPP/jgg6hfvz7i4+Nx4MABXgBIdbTPP/+8zLrbiy++6OhpCILgA1S1GS4JxPVNa/PvmsUwJVvHkVbbuuG4r1MIfjyaYrdtfe3atXjiiSf4yzyVVd58800MHz6cj0vnSZ05okM03L5Ko/JjQcvW6f9pNaXj7h0U/VHdztsdhTWBn8lcbawko0aNwrfffouff/4ZdevWLXO/H3/8Eb1798aJEyfQuHFjuyKyhIQE/gOhti2CIPg2lE5c9NNJFi3LZrgEXb5IfEgMRt7Y2K6Ipqz0pGXER8YLErkm0cElRCY9PZ0FbN26dbzdtWtXzj41adKk1Hmm5+mL162RySOnQI9wrRq1tBoEqJW4o00crokL9XpHYVUgPQgLC6tQDxyKyMaMGYOvvvoKO3bsKFfEzP/QRFlCptFo+CYIgmALZzfDLcvWXpFtfevWrRg8eDAuXLgApVKJadOmseFNpVLZPE+K5Mhiz51E8gpRZAJSsqjZbwTu75QgEZgTqZSQ0befsWPHYsOGDfjpp5+4a3NFmJtjxsXFOX6WgiD4LDXZDNeWyBUUFGDKlCnc3Jdo2rQpR2FdunSp8DxJzDo3iGB3Yr7ewOc5oEMdETFXChlZ7yk3/MUXX/BasuTkZH6cQr/AwECcPHmSn7/99tsRFRXFNbLx48ezo7Ft27bOPndBEHwArQub4dIXcepWT13rzSWVefPmlVhuVNF5UnR2tfkvEBGkQYim9HsQanBB9KJFizhXSYueKcIy39avX8/Pq9VqboxJa8tatGiBp59+Gvfccw82bbrapkUQBKEqC5utS/pmVyHVspxpXS8qKuJ5YRR1kYjFxMTg66+/xrvvvmtTxFx1noKDqcXyIJMGLZoWBEFwFjXdDJcmNtPkZprgTFAHo6VLl6J27dpudZ6CE1yLrnapCILgW9jjKqwKdCn88MMP2QeQnZ2N4OBgvPXWWzz80tpk4srz9CWy7NQDETJBEDymW0dlO3vYS1paGveE/fTTT3m7e/fuWLVqFRo1auTQ8arrPH2NrOq03wuCILiiW4ejHeHL4/vvv+eoKykpia301KCBGjmQxd5RquM8hbIRIRMEwSO7dVSVvLw8Fqy3336bt8mgRrb6jh07VttrCm7gWhQEQXAGlHqjSIxEjLpgkF3dPGiStulxGjRJ+1UH1OicBMssYtTkwfyY4HmIkAmC4NbdOpwJjZ+aNWsWrrvuOhw9epSXD9FUj4ULF8q4KQ9GUouCIHh1tw4zp06dwiOPPIJff/2Vt++9914sXryYmzeUhZg2PAMRMkEQahxtDXbrIGP28uXL8dRTTyEnJ4e7ElFKkUStPFu9o2NjhJpHhEwQhBrH3AWDjB3BGlWpjvbOGjSZmpqKESNGYOPGjbxN7fJorRiNmnJnI4pQOaRGJghCjWPugkHdLqgLBg24NBiNfO+sQZPffPMN2rRpwyLm7++PuXPn8lipikTM1UYUofKIkAmC4BIooqHIpnV8GA+gPHM5l+8pEqtKxJObm8vNfe+44w5cunQJrVq1wh9//IFnn33WrrVhrjKiCI4jqUVBEFxGRTPAKsvvv//O3eoTExN5m6ZvvPLKKwgICHBrI4pQNUTIBEFwKc7ogkG2ehKsl156iTvX16lTB/MWLkGHbtcjNc+IOmqT3eKodeHYGMEx5F9CEASPxGyNP3z0GJ578nHs3fMHP37H3QNx45Dn8EeBEv+3NbHSbsOaMqIIzkOETBAEj4Nchd8dTMbGdR9i+6rXYNAVIFAbgnHT58CvSU+cyia3ob9DbkMZx+J5iJAJguBxIrbw6z34YsE0/L3v6sywhm26oNuQqTjsH47QK3nokBBeHElRepAiKxIlchs2qhVcoQiZjSjmdWRUE6N0IkViMo7F/RAhEwTBKdREFwx6jdeWrsXKuVNQkJ0Opb8/7hgyATcMfAzZBQb8cOQSVMrSr2ntNrSnJudsI4pQfYiQCYJQZWqiCwZ15Rgx+kl89OFy3o5r2AwPTX4N8Q2b87beaIJapUBOgYFFLTTQv8puQxnH4hmIkAmCUCVqogvGzp07uaXUyZMnKbzCjQOH4I4h46FSq4v3USsVLGQ0lbmwyFjqGOI29F5kQbQgCA5T3V0w9Ho9pk6dip49e7KIxdepi3tfWIqbB08oIWJESIAKIRoVCg0m+Ful/8xuwybRweI29EJEyARBcJjq7IJx7NgxdO/eHTNnzoTRaOSFzgcPHECvXr1YlEicrAlSq1A7RIPkLF21tL0S3BOJsQVBcBhHumBUZAohgXr33Xe5pVR+fj4iIiKwaNEiDBo0iJ/v21pVpjW+XlQQbm4RjaNJ2eI29CFEyARBcBhtJbtgVGQKSUpKwtChQ3nYJXHLLbdgxYoV3KmjMtb4m5pHi9vQhxAhEwTBYSrTBaMiU0h8xgFMf/YppKWlcW/EV199FWPHjoVCoai0NV7chr6FCJkgCGVSURrQ3i4YhKUpxHKxsspQgA/ffB7H/m8TP9ahQwesXr0aLVu25Nc/dyVPxEooFxEyQRCqtDbMnlQfiZEtU8ipg7uxdu5EXLl0AX5+Cjzx1AS8MWcW1Gq1TGgW7EaETBCEKq8NqyjVZ20KMegL8d2HC7Ht42WcgoyIqYNeI17EmCfuKxYxmdAs2IsImSD4ONbpw7jQgDLTgOX1LCwv1WdpCslNPoM1c57FhZN/8XOd+wzELUMnogAa3s96bVpVeiYKvoEImSD4MLbSd7WCNTh1OQf1IoMqXBtmb42KorOGUUFYt3Ipfl+/kCMybWg47n3qJbTt2YfFqU2dq4uVK7M2TWpkAiFCJgg+SlnpuyNJmTiblofoEI1NS70jPQuTki5i/cyR+HX7Nt5ucm1P/PfpWfAPiSq1WLmmJzTXRLNjwY06e8yePRudO3dGSEgIoqOjMWDAAF59b0lBQQFGjx6NqKgoBAcH45577sGlS5ecfd6CIFRTa6kmtYNhMJpw7FK2ze4Z5rVhgf5KNnEcTc7i+7LaUH388cdo06YNftm+DQGBgbj/yRm489kFuGLSIiNPz6YQy5qX1iINaYuyeiaaHY4VnY+1mC/66STe3HwcC7Ym8j1t0+OCl0Zk27dvZ5EiMaPR4s899xz69OmDI0eOQKvV8j7jx4/H119/jU8++QRhYWEYM2YMBg4ciF9++aW63oMgCJWkvPQddY2nx5MyCpCVr0dYkLrU2rD4sABs2n8Rp1Jzy3QUZmZm8v9/stITnTp1wqpVq9CsWfNyIyBHJjQ74nAUQ4n34Gey9ZXLTlJTUzkyI4G74YYb+A+3du3aWLt2Le69917e5+jRo7jmmmu4e/V1111X4TGzsrJYAOlYoaGhjp6aIAjlQFELRSBkmKBIzJqU7AL8cuIy18maxYSUWBtm3r/IaPpHAFQsAOZ1YyQAF/76E48++ijOnj3LC5qff/55bv7r7186VWmPyFivTbMUmdKCVPp8rAWJojWKvEi0LA0lBF0Sr9bswjDyxsaSZnQh9upBlWpkdHAiMjKS7/fs2cPdqqmtjJkWLVqgXr16ZQqZTqfjm+WJC4JQvWgraC1FaUMSsEa1tLicU1i8Nqx1fCjScgtZJGw5Co9euIJRT07A1o/fZ0Fo1KgRR2HU/Lcy2Duh2VGHoxhKvAuHhYy6UY8bNw49evRA69at+bHk5GReAxIeHl5i35iYGH6urLrbiy++6OhpCILgAOWl7+j/Nl3k6eL/YNd6UPj5IU9fxOJH4jR/S6JNAUg+k4gvZz+NS2eO8/bw4cPx5ptvcq28MoYK875Up7uzXRxoL/PrW/+eo4JU04YSwU2FjGplhw4dws8//1ylE5gyZQomTJhQIiJLSEio0jEFQSifslpLJWXk49DFLOgNRlDRYcHWE8W1JhICSklaCwAJ347PV+Lr5a+jSK9HYGgEXlvwLp4Y/N9K16/K29dWZOSoIGkr2exYcG8c+leiAu5XX32FHTt2oG7dusWPx8bGorCwEBkZGSWiMnIt0nO20Gg0fBMEoWaxTt9R5EJuP3+lAh3qhSM+PKiU+UFrJQDpKUlY99pkJO7bxcds1vlG9Bo2Ff37d6u0ocIR84X1+dgrSI4YSgQvETL6B6Zu1Bs2bMBPP/2Ehg0blni+Y8eOXMzdunUr2+4JsudTwbdbt6t/2IIguA/m1lLn0vOw/JfToOt52zphxR3nrWtNI65vVCwAx3/5Fp+9/RIKcrOh1gTirpGTEdXxdrStG84CUJn6FeFIrctRQbK32bEYPbxQyCidSI7EL774gteSmete5CoJDAzk+2HDhnGqkAwg5DIh4SMRs8exKAhCzUMXa6qDZeUbWBSsx6aUqDVl5qNhqAnzn38OJ3b9wM/Xa9EWA8bNRqE2poQAlNUouKzp0Y7UuqoiSPYaSgQvEzKa0krQqHFLli9fjscee4x/puIu/UegiIzciH379uVpr4IguC/21JpISKa++xE2zH8eOVdS4KdQonGfR9H1P8OgDNOiTXRwsQBQNEbikJpTwJESRUfWAmVdv3LUfFEVQaqo2bHgpanFiqCBeO+88w7fBEFwT6xdhEH+ynJrTedS0rFtxes4//NnvF0rvj4GjHsFhVGNodWoMPDaOujeuBYLgNmwceB8Bk6m5uJiRgFiQgLQOFqLSK2mzPpVVcwXVREkmWvm+YglRxB8DFvOQFovFh7kz+k461rTucTD+ODlCdy1nuh2xyDcNWIyNIFBxYuHD5zPZCGzNGzEhwcgM1/Px7yUlY9snR7tE8JZzGzVr6pqvhBB8l1EyATBhyjLGXg4KYs7dtDNXGvSKIGtH3+ArasXwFhkQFBYJB58ZjZadu1ls35FhhFrw0bTmBDkFhbxa1C7Kzr2NbF+SM7SlapfiflCcBQRMkHwEexxEVIPxQitGnsPJ+K7d6fi4tE/eZ+67W/AsMmvIiwyqsz61enLuaUMGyRAFIWR0FHbKzKAhAX6o13d8FL1KzFfCI4iQiYIPoI9XTBI5LTnfsUnzz+D7OwsaLXBmP7KXFyMvg4Ki+bBtupXZRk2SMw6N4hAel4hzqTl4oEu9XBD09plOgnFfCFUFhEyQfBgKtP6qSJnorEgC5venIqTv2/lbeqPSH0SGzRoWNxgt7z6FdXZyjJs0O/QQuvawQH/WPzLFiapdQmVRYRMEDyUyo4u0ZbTBePY7p+x9rXJyL6SCpVKxf1PJ02aBKVSaXf9qm5EkHTLEFyCCJkgeCCOtHOy1QWjsCAfX73/Gn7+4urMsJh6jbDp0/Xo3LlTid+1t34lhg3BFYiQCYKH4ejoEusuGKbLp7DhjclIOXeKn+/Y7wEsf/dNtGkQY/N17alfiWFDcAUiZILgYVRllhYJyaPXJeDZF17G1yuv2uq1EbXw2OS5GPfYvRUKjT31KzFsCDWNCJkgeBhVmaV16tQpDH3kEfz666+83eeOuzBv/tto3aiuU4VGDBtCTVKyO6ggCG6P1sK0YQtb7ZzIbPHBBx+gXbt2LGLU9HvlypX4btNGtG2SINGS4NFIRCYIHkZlR5ekpqZixIgR2LhxI29ff/31+PDDD9GgQQOXvQdBcCYiZILXUpk1Vp72Pux1B37zzTcYOnQoD7elWYEvv/wynnnmmWJbvSB4AyJkgldS2TVWnvg+ynMHxmkVGDVqFBYvXszHadWqFVavXo327du7+i0JgtMRIRO8DkfWWHnq+xjVq3GpaG337j9w+8MPIzExkY8zbtw4zJ49m0csCYI3ImYPwavXWNHaKuroTve0TY/TGivazxveB0HuwBaxoYgLVWPmzJe5tRSJWJ06dbB582YedisiJngzImSCz66x8qb3QcLVs2dPTJ8+HUVFRRg0aBD27duP5td2x9HkLO467+7iLQiOIqlFwauoyhorT3wfOTo9li5divHjxyMvLw9hYWE8nb3LLf2x/kAKTqae8+gaoSDYgwiZ4FVoy2mMW9YaK3dEa8f7MORkYOyQB7D1+2/5sV69evHasMKACK+oEQqCvUhqUfDKNVZkQ6c1VZaY11g1iQ52+w7sFb2Pndu+x9rJ97OIqdVqvPbaa9i6dSvq1k3wihqhIFQG9/5aKgiVxLoxris7sFdlHVtZ7yMjMxufL3oFR3+6uri5TZs2bKtv27Ytb1MtzNE+jILgqYiQCV6HO3Rgt7X+iwZPtq8XjlohGruEzfp97N/zO7Yuno7MS+dYlJ5++mnMnDkTGo3G62qEglAZRMgEj8OeSMeVHdhtrf+6mJGHLw9cxGd/nudIqFawxi7zBT2X0EODSS9Mx8Y35sJoNCIhIYFbTFFNzBqtl9QIBaEyyF+z4LUdO1zRgd3WrDD6OTElF0VGE9e3CouMCAv0t8t8cezYMTz88MPYvXs3b9PPCxcuRHh4uM3XNppMCA1U8efTtk4YFIp/y+AypVnwVkTIBI/BEzp2WK//IvGgmlR+oQFRWjWLWEaenvcloStrCCb93qJFi7gvYn5+PiIiIrjd1L333sevkZycVSLKtBT4yzk6rpUlZRSgdZ1QxIUHypRmwasRIRO8eipyTWNdo8ouMCA9rxDBAf58zv5KBXJ1Bha0sswXSUlJGDZsGL799qqt/pZbbsGKFSuQ7x+KRT+dLBWNtogLwY9HU4oFPj48ELWC1Th0MQt7z2bgck4hpzJlSrPgrYiQCV4/Fbkm0VrVqEiwDEVG+Adc/a+mLzJCqVBArVSUMF9k6/QcRW3Y8DlmTByH9CtpbOKYM2cOxo4di1OXc21GowcvZOCHI8kIDfRHh4Tw4s8mIVLL0dqBC5ks7hSt1o0IkkhM8EpEyASPwFPceNazwkiwVEoF9EUmqJVAToEB0aEBCPlH2CjlpzMYse7nY/jsnVk4sv1Lfrxuk2uw9P0V6HdDl3Kj0dhQEw6cz4RKWVqgqD5G50KpTPodETHBWxEhEzwCrYe48azXf8WGahAWqEJypg4qBRCkUaFxbW1x/SzxUg5OH96Dj1fPQvbli/x4j4FDcc2dw/FzWiCapmTz+yorGtUbTVCrFCyQlMakyMwdBV4QqhMRMsErpyK7Euv1XyREFAxRSpEiKhKb7AI9zl3Owm8fv4sTW9bCZDIiMqYOHpw4F43adLoqcv/U/Xo1r11mNEoRHwmZzlDEaUx3FXhBcKsWVTt27ED//v0RHx/PFxPz+HQzjz32GD9uebvtttucec6CD2KOdMh1Rxd4EgKD0cj3tO1ubjwSM5oVNv7WZnjujmvw0oDWuKt9PFX0cOZyLk4mHsM3s4YjcfNqFrHOfQbimcVfsohZ1/1ydIbiaNQaSlGGaFQoNJjgb/XePakllyBUhUp/TcvNzUW7du14fPrAgQNt7kPCtXz58uJty84DguDJHTsqg+U6thaxQI/GtXDuSi6WLVmE92dOg66gAJrgMNw/7iV0uOG2EgJEacJ8PTkedQjSKMuMRokgtQq1QzRIztLxa7qqJZcgeIyQ9evXj2/lQcIVGxtblfMSBJtUpWNHVXofOoOkpIsYMXQofvjhB97u1ftWNL33WdStQ5HaVcjQQVEYWfYL9EW8iPrLvRfRtVFUmf0j60UF4eYW0TialO0RAi8IzqZaEuc//fQToqOjeRHnzTffzP3goqKibO6r0+n4ZiYrK6s6TknwIhzp2FGZjiDVwSeffILHH38c6enpCAwMxLx58zBy5Cgs3n6qONJKz9Nj37kMXjxN23pDEaJCNTh7hcQ3pUKxuql5tEuFWhC8RsgorUgpx4YNG+LkyZN47rnnOILbuXMnlEplqf1nz56NF1980dmnIQhu0REkMzMTY8aM4Q71RMeOHfnnFi1a8LbZ4Xj8UjZSs3XI0xkQHKDkuhg5HFvGhSIi6Gpd8FhyNh6/oRGS/nEhaq3EyhUtuQTBHfAzWQ87qswv+/lhw4YNGDBgQJn7nDp1Co0bN8aWLVvQu3dvuyIyaopKF4DQ0FBHT00QitOJ1A2DRMtyDRb92Wfl63EiNQet4sPwbJ/mUJE/3ols374djz76KM6ePctruqZMmYLp06fD39+/RJqTBGzH8VR8dygZtE46wF/FtS2y6Udqr9aXydRC68HIPCJiJfgKWVlZPPW8Ij2odk9uo0aNUKtWLZw4ccKmkFE9TcwgQk12BLGuQ/2dlsfCdn/nBKdEZvTFbOrUqTzsko5L/wdWrVqF7t27l5nmJOchnWOj2sEI9FeyG9HS1CHrwQTBhROiz58/j7S0NMTFxVX3SwlCOR1BVMUiRnWo1OwCBPgrERWs5ijoSFIWpx9JZKrCwYMH0aVLF66BkYgNHz4c+/btKyFi9DoUIYYH+XP7KLo/cyWXI7NCQxGvM7N2Jsp6MEFwopDl5OTwf0y6EadPn+afKX1Czz377LPYtWsXzpw5w6PX7777bjRp0gR9+/at7EsJQpXRWnQEsexET6k7jUrBrkBK5TWpHcwiRwuQDQYj9z08mpzF95QGrAiaE/bGG2+gU6dOOHDgAGchaI3lsmXLEBJyNcqzbjVFHUqUCj++p5Er/ioFDl3I4mNZIuvBBKF8Kv31juYi3XTTTcXbEyZM4PvBgwfz2An6T7xy5UpkZGTwouk+ffrg5ZdflvSh4PKOIDEhmhKd6EkgzL0PKQois8SfZ9Mx9/uj3DHelrvRloX/woXz3Ajgxx9/5Ne844478N5775VaglJe42OqobWOD+Vu9dTol15T1oMJQjUJGU2lLc8f8v3331f2kIJQI70PydhBNTGtRsktnUjESCzMvQ/z9UXsHqR9msWElHI3WtrfzSKXfnAbPn/7JWRlZiAoKIijshEjRpQSKnsaH9PcMBLQhrW0bOyQ9WCCYB+ScBd8piPIx3+cZ2NHWo6O04kUiZmdgfTl7HhyNgxFJk4zmhsTm+edUaS0YGsi4sICER8eAOgK8cmCqTj0f1dnhrXt0BGfrFuLZs2alXkeWjsaH9PcsCE9GkLh5yfrwQTBTkTIBJ8Rs4l9m1PFiY0dJFaWpgqy4lMKLy78aprRGhIfMmPQzK/kv3bjo3mTkXE5GQqFEtfePQyPjhqHJk2aOqXxcYLMDROESiFCJvgMtE6MLPbkGryUXbIvIaUdVQo/NI8JKZUWpL6H2ToDVCY9vn1/Ln7ftIofr12nAXerj2zYEqev6Coc6mk94sW61ZTUwQTBMUTIBJ+irMbDtCia7Ph0s4bGo1w5m4ij62YhN/kMP9btjkG4a8RkaAKDuAu/vWu8PK3xsSB4AiJkgs9hq/FwXGgAluz4t++hOSozFhXh943L8eeahTAVGRAcHoVBE2ah1XU3ObzGqyqNjx3F1Q2TBaE6ESETfBJbfQmt034F6clYO3cy/j68m5+v2/4G/G/KqwiJiKryUM+a7Ivo6obJglDdiJAJglXa77uDyfjq8/XYtvxVFObnQh0QhLHPz4S6ZW8k5emBAr3H1LZc2TBZEGoKETLB7anJtFikSo/ti5/H959+wtsdu1yHj1avQtOmTUpENp5Q27LuJGJOl5qXFFDkSZ1MqE2WO4qwINiLCJng1uL1V3IWdp++wtZ3XZGxWtNimzdv5g4dFy9ehEqlwowZMzBp0iT+2VW1rapQXicR2qbHqWVXRW5LQXB3RMgEt8Mc+ew9l86dNmiRMl10m8eGsKvQMi1G0URVhSU/P58Fa+HChbzdvHlznhlGfRMrU9tyN0NFRZ1EpKO+4C2IkAlOpaoXc3NNh7pvpOcWwl+hQESQEpn5ehy8kIn2CeGcJqO02Ee/nUWEVo1TqbkOmxj+/PNPPPTQQzh69Chvjx49GnPnzuV2U55uqNDa0UlEOuoL3oD8BQtOo6oXc8uaTmxoAM6k5SEk0J+71NMFlx4/mZqLTvXVCPRX4MejKagXFcSvUVkTQ1FREebMmcODLg0GA48Z+uCDD3jCubcYKuztJCId9QVPR4RMcArOuJhb1nR0BiMMRUb4B1z9E6WLcHCAio9P7aRoX2rySxdh676IFZkYaGo5TW7+5ZdfePuee+7BkiVLEBX1r63eGwwV0klE8BWqfbCm4P2UN2eLts1zviqa62U5BFOtVEClVEBf9O/v+CtpfpgR6fl67hKv1ag4UivPxGAJRSEUdbVr145FjOaErVixAp988olDIlZZQ4UrlxS0jg/jjvpnLufyPUViYr0XvAWJyAS3ccdpS9R0VIgIUvMkZ7VWzcfRFxmhVCgAowk5OgOPO6H97DExpKam4vHHH8eGDRt4u2fPnli1ahUaNGhQpbqfJxgqPM1tKQiVRYRMqDLOuphb1nQokqOJyCRYFNFR9JVToEe4Vs0RWZC/EvE2hNOWieGbb77B0KFDcenSJfj7+/Og12eeeQZKZem+ipWt+3mKoaImO4kIQk0jqUWhymgtLua2sPdibq7pUO2Gajr+Sj+0rhOKsEB/XkemN5o4SuvaMBI3tYhGvt5Yasir2cRAIhjub8QTTzzBE5tJxFq2bInffvuNrfb2ihjV/UhYw4P8uc5F97RNj9PzZvGl1yzvXMRQIQjVh0Rkglu546y7w9Mk53qRQejUIBId60fgmrhQPs6pyzksJmWZGGJ059Cp0x04fvw4H/epp57C7NmzERgY6FQTx4jrtWibEIbDSZnYfz4DjWppEaRRiaFCEGoQETLBLdxx1nWox29ohKR/0pG2ajpljUNpGavFic2rMOj1OWyxj4+PZ0PHrbfe6vS6359n0zH3+6NsPMkpMPA9RY405Zlu7ty+ShC8CREywSlUZc5WeXWoFrGhdpsYUs//jYljh2DXrl38/KBBg/Duu+8iMjLS6XU/sv5T15ECfRGaxYQgPjwQuTo9Tl3O5XrewGvroHvjWhKJCUINIEImuNQdV9X1Z3TsuhGBeO+99zB+/Hjk5uYiLCyMBeyBBx6waQaxh/JMHJQuPZ58tXVWk9pXlxsQoYFqtKvrz1HpgfOZLGSCIFQ/ImSCy9xxzlhMnJKSguHDh2PTpk28fdNNN3EqsV69etVW96MF2ZQyjQsPQGhgSZGTZryCUPOIa1EoFpVzV/JwNDmL7ytavOwMqrqYmMSrdevWfK9Wq/Haa69hy5YtDomY9fsnLB2U2QV6GIxGvj+RmgOVwg/NY0JsRnxUHySTijTjFYSaQSIywWUNb0kUruQVQq1SgJzrtLjZUhjKWn+Wk5ODCRMmYNmyZbzdpk0b7lbftm3bYkGqzMLf8t6/rbpfq/gw7sJPN1u4y9oxQfAV5H+aj+Oqhrf0uhv3XsTJlBycuZyDAH8VIoPUaBytRaRWU6YgkJHjkUcewYkTJ1j0SNBmzpyJgIAAhwTZnvc/qlfjEnW/uNAALNlxSprxCoKbIKlFH8ZZPRIri1k8zl7JRXSIBko/6m7vh5TsAuw7l4ErubpSi4n1ej2mTZuGHj16sIglJCTgxx9/5HSiWcQqWrzs6PsnqNZFDkq6V6kUZaYdaVvWjglCzSJC5sO4ouGtpXiQbb1lfBgvIM7VFbFtPU9nwJGkLBy/9K8gJCYeZwGj1lJGo5Hnhx04cAC9evWqkiBX5f1LM15BcB8ktejDuKLhrbV4kFjRsEwSjPS8QlCz+5QsHXfxuK9jXWz+bDWefvppnuIcHh6OxYsX8/qw8o5pr4uwqu9fmvEKgnsgQubDaF3Q8NaWeJCYdW4QgewCA/L1BhaPHnVUGDf0AW74S/Tu3Ztt9XXr1rXrmPYIkjPevzTjFQTXI6lFH8YVDW+1ZTQYpsiJ1mTRLLK0Q7/gPzd3ZxHTaDSYP38+fvjhB5siVt4xKxIkafgrCD4qZDt27ED//v25hx1dfDZu3FjqAkBFeRodTw1ab7nlFiQmJjrznAUnYd1tviZMC+WJR35uNta+NgWb3nga6VfS0L59e+zZs4cb/ipoDpkDxyxPkFzx/gVBcAMhoxZANGH3nXfesfn83LlzsWDBAq5l0MgMrVaLvn37oqCgwBnnKziZmjYtlCUeh/78Da+OuBvHdmziL0g0aoX+flq1auXwMe0RJDFtCILn42ey/gpbmV/28+OJuwMGDOBtOhRFalScp8GFRGZmJmJiYri+8d///rfCY2ZlZXGvPPq90NCyG8YKzqW8KcjV8bvmNV/Hk9Lx8/pF2PvVSphMRtRJqIe1q1fhhhtuqPR7sFxHRp01KJ1IkZg9Heir8v4FQage7NUDp5o9Tp8+jeTkZE4nmqGT6Nq1K3bu3GlTyHQ6Hd8sT1yoeRw1LTjaFYSe08Wcw+IJI3Bo/z5+7NFHB2PBgrf4b8YRquIiFNOGIHguTjV7kIgRFIFZQtvm56yhYYd04TLfaKGr4Bk4sgiZoLVgCxcuRKdOHVnEaMzKJ598gpUrVzgsYtaCZF68LFGVIHg/LnctTpkyhcNG8+3cuXOuPiXBDhxehHzhAvr164cnn3yS66ZUPz148CDuvfdel70XQRA8G6cKWWxsLN9funS1rY8Z2jY/Zw3Zqyn3aXkT3B9HumJQ1EUNfslKT22l3n77bXz77bdcVxUEQXALIWvYsCEL1tatW0vUvMh91q1bN2e+lOBi/l2EbLvMajnKhCJtavR7//33Iz09HS3btMM3237FqFFPODz4UhAEwWGzB43QoKatlgaPffuu1jloDtS4ceO4G3nTpk1Z2KZOncrfuM3ORsEzqMjFZ29XjP2//4pbRo/AhfPn4KdQoPNdQ9B+wHBs+tsPR/JOVvuoGEEQvJ9KC9nu3bt5Cq8ZGqNBDB48mC32EydO5LVmI0aMQEZGBnr27InvvvuOU0mCZ2CPE7G8Ccq0DOP85Swc2bQM8zeu4O2AyDg0uW8y6rbtiKiQIJ7lVd2jYgRB8A2qtI6sOpB1ZK6l9HwuFUdd1B2DFhZbio71vpROpEjs8KFD2Lp4GtLOHuf96ne/E/X7jUJ4eAh3uaf9qFFwRNDVBcy0+HjkjY3FYSgIguvXkQne5UQ0R1mUOqSoi0SHnIhksyfRMXfFMEdvyRl5OLx5HX5dtxAGvR4BIeG4e/QMZMR04AhMo6K5Y0o+/snUXHSqry6zM70gCIK9iJAJdjkRiWCNEnv+Tsfuv6+gU/3IYjGjRch/HE7EuCf+h10/7+B9G3boiXvGzYQ6JBK/nUqDf8DVPzU6bnCAisWMut0HaZw/KkYQBN9ChEyocBwKiQ5FTWm5OmTl67Fk+0n8US+9uGa2fv06PPHEE1wTDQoKwsTps5Ac3xMxtSmVaIBKqYC+yMRToAl/pYIfLywywq8QTh8VIwiCbyFXD6EYrQ0nIonYvnMZyC80QK1SICzQH1FaDRs1Tl64hMQN8/HVhk953y5dumDVqlUIrFUXb24+/s9xVFwLS80ugFqr5ohMX2SEUqGAv8KPa29UI5NRKYIgeGxnD8F9sB6HQjeKxEjEIoL8UWgwIipYg9iwAODiISwddw+LmFKpxIwZM/DLL7+gWbNmJY5DUOPeQPXVdGKBvgjZ+XpoNUokZ+lkVIogCFVGIjKh1DgUssSTsYNqYpROpEgsPU/PbsP6YSp8ueRVbP98Bf9OWEw9rPhwJQb06VXmcajm1rpOKI4nZ7O4qZR+HKW1rRtmV2d6QRCE8hAhE0pg6UT882w618QonRgdGoDg3At4/9nJSD17dUF8/HX9kdBvBPYV1ELrlOwSgmTtaKQuH/Uig9CpQSQ61o/ANXGhMipFEASnIOvIhDKt+OROJGNHRKAKx7asw7cr5qPIoId/cASufXgKoltdx+vC6kYEom5EkM2FzTLnSxAER5F1ZEKVILEhi/33mkNYNGMCko7+yY/XatUD3QdPgSYkgmteMaEBaFsnDCdSc0usMbM8jqwPEwShOhEhE2xCgfratWsw94nRyMnOgkoTiCZ3jUbT6++Cn8KPRYxqZo1ra6FQKGRhsyAILkOEzMtwRirvypUrGDlyJI9dITp06oKew2ZgT7qaLfUqpZJrZiRikVoN70OiJgubBUFwBSJkPtTs1x6Ro1lhQ4YMwcWLF6FSqTB9+nRMnjwZFzN1mPXNXwjyVyI8SM3rwyy7f5i73dNxBUEQahK56nhts99Ajp7MHeZvbhGNo0nZZYpcfn4+Jk2ahIULF/LxmjdvjtWrV6NTp068XTdCibZ1wvl41iJGaUhZ2CwIgqsQIfOBZr97z2ZgwdZExIUFIj68tMh1CcnA5LEjcPToUf49ajc1b948bjdV3towc7d7c2d8WdgsCIIrECHzgWa/JFqp2Tp0SAgvbj1F90EqP3yy/B28+vlSGIsMPN17+fLluO2222y+jvXaMKqJUTqRIjFZ2CwIgqsQIfNArGtd2QV6m81+Ceown62jPol+0Bv/XTKYlnQOa+dOxOnDV231/e68Gx8ufw+1atUq97XN3e5lbZggCO6CCJkXGDpqBWu4D6Jls18z1GGenqPISa1UcD3rjx8+x4Z3Z0KXnwd1oBYd/zsez04cg8jIKLvOQdaGCYLgToiQeYGh4+yVXE4d6gxGTh9aphepw/zVZr9q+OmysWLedBz8+Qd+rlaTdmh63yT4hURj3R/ncORidrH5QxAEwVMQIfMCQ0ezGBW3iqK+iMcv5bChw2zESM4qQO0QDdKP/Y7XXpiN7CupUKhUaNZvGOKuvw9Gkx9iwwMRHxZYbP6w1WpKEATBXREh8wJDB203jQnG2St5qBcZiMs5hcVGjOZRapz+cgG2fbqK962d0BjtH3kBurD6oMOQlZ6EMTTQn38mR6KtVlOCIAjuigiZh09vNkMRmEalwIBr6yBE48/77/hlF6Y+PhIp50/zPg1uvA8NbhuGjCIFQpR+3CfRsjsHCaK0mhIEwdMQIfMQZyJ11LCe3myJubMGiVhcqBrPvPAKFr42h231IZHR+O8zs1GnVRccuJCJ/PR8tI4PRf0obanoTlpNCYLgaYiQeYgzsVEtLcKD/HnxMS1yLquzRkHaRdxw56PYuXMnP9f+xttxz9jp0IaG83ab+DAkZRTgfEY+C5k10mpKEARPQ65Wbta4tyxn4uGkLCgVfnyz1VkjIsgf6X9+iw79pyA3NxfqwGD0H/UCuvcdUEL0qBZGv0tixkMzg9TFz0mrKUEQPBERshpu3FuVVlMkYPFhAYjQqnEqNbfY0NEgyIDNS6dh6/ff8P5dul+PZvdPQrsWTWwaQ5rFhiA1R4cTqTloFhMiraYEQfBoRMhqsHFvRbb2ipyJ9Hh6nh6Pdm8AhZ8fR3y//vgDpjw5GikpKVCr1Zg1axbue2wk3tp6osx6WqC/kgWM0pWWDkdpNSUIgiciQuYk7ImmKrK12+NMJNHJ1xehbrACM6c8i6VLl/JzrVu35m717dq143OhKJAEtKx62rX1IjDi+kZI+sfYoZVWU4IgeCgiZE7CnmiqIlu7Vq2yy5l4bP+f6D/mfzhx4gQ/PmHCBI7EAgICKtWpXqVSiMVeEASPR4TMSdgbTZVna6eIqLxI6nxaDs5vW40FaxahqKgIdevWxcqVK3HzzTeXOpZ0qhcEwVcQIXMSWjujKdqvLMqLpP46egzbl05H0olDvO9DDz2Et99+G+HhV231tpBO9YIg+AIKZx9wxowZHElY3lq0aAFPh+pO567k4WhyFt/Ttq1oilJ3FD1ZYq5LNYkOrtDWbo6kWseHISNPj9OpOdj+xVp8Pu1hFjESro8++ojrYeWJmHWn+haxoXwvIiYIgrdRLRFZq1atsGXLln9fRKXyeku9MycomyOpvcdOY8LYUdix9Wq3+t69e2PFihWcUhQEQRCuUi0KQ8JF04Z9zVJf2bpUeQunv/zyC/zvf//D5cuXodFoMGfOHIwdOxYKhdODaEEQBI+mWoQsMTER8fHx7KLr1q0bZs+ejXr16tncV6fT8c1MVlYWPNlSb12XojVb9EyevohTkmaxKivK69EgCAtmvoDly5fz8chOT2lEstcLgiAINSBkXbt25fRX8+bNkZSUhBdffBHXX389Dh06hJCQ0k45Ejnax5ss9ea6FInVV/uTSolVi7gQ/Hg0pVSUt/mn7Xhh6XRkXLrAx584cSJ/NhSRCYIgCLbxM1k7E5xMRkYG6tevjzfeeAPDhg2zKyJLSEhAZmYmQkND4UrI2LFgayJHXNTj0BqD0Ygzl3MxtndTNlOUn5JUsVhdzMjnmhn1PDRPczboC/HD6newdf1SmIxGRMbUwWfr1qBXrxtr8N0KgiC4F6QHYWFhFepBtbswyFnXrFmz4sW71lC04a4Rh9ZBS315KcnYUBMOnM+ESnn1sUtnT2LNq8/i/InDvN3+5rvR/eGn0bhtxxp4h4IgCJ5PtTsHcnJycPLkScTFxcFdrPL24qilvryUpN5oglqlQHZeIbZ8uhKvP/EfFrGgkHAMfuEt3Dn2ZWQZ/fn3HT1vQRAEX8LpEdkzzzyD/v37czrx4sWLmD59OpRKJR544AG4e/d5axy11JfX5UOtVMCYewW7P3oV6cd382PNO/XEbaNexCVDEH49mYaCwiJ89PtZHL6Q5dB5C4Ig+BJOF7Lz58+zaKWlpaF27dro2bMndu3axT+7e/d5WzjS6klbTkry1B9b8Nub01CYmwWVWoO7RkzCNTffi/3nM5FXWICiIiMSooIQHxZYpfMWBEHwFZwuZOvWrYOndp93VqsnWz0T83OzseGdl7F7yxe8T1hCM/Qd/QpaXtMcR5KyeMilSgGEBPrz+ZMZJCTAvvOuyiBPQRAET8ezW244uft8eZgt9Y6kJHXnDmHj/OeQnnIRfn4KdLtnGKa+MBUn03Q4cD4D59PzEeCvRExoABrX1iJSq7H7vJ2ZShUEQfBEvEbInNF93pmQiDzYKQ7jJz6PzeuXkTsEYdF1MfyF1zFyUD9+niKpHYmpeP/nU2gYFYzwIP9SIlzeeVdHKlUQBMHT8Boh0zqh+7wzOXz4MHeo379/P2/f8+AjePmVeWieEF2c9qN7ip5qBwewHd9axCzPmzqEkAPTnD6MCw2otlSqIAiCJ+E1QlbRLC9yGZJBo6Lu81XFaDRiwYIFmDx5Mi/0rlWrFpYtW4YBAwY4fN7xYQHYtP8iTqXmFqcPawVrcOpyDupFBlVLKlUQBMFT8Bohc2b3+ao4Nh977DFs3bqVt2+//Xa8//775TZQrui8qaPIpWwdLmYWlEgfHknKxNm0PESHaGxGoDWdShUEQXAVXtVK3XqWF7WPonuKxJxdL7JedL127Udo06YNi1hQUBAWLVqEr776yq4pAGWdd+v4UBaqIqOJ04ckWCRsdN+kdjAMRhOOXcoutVjbFalUQRAEV+F1V7mamIps6RTMzMzArx/OxdFfvuXnOnfuzN3qqS1XVc+bBGr+lkSbTkyy59PjSRkFbN0PC1K7JJUqCILgarxOyCprla8slk7B/DP7sfGt55F5ORl+CiV63DscS994Bc3qRDrlvCnaK8uJScLWLDYEqTk6nEjNQbOYkBpPpQqCILgDXilk1b3oOiU9G4nfvIcdn6/g6KdWfH08OGkuCiMbY9vxK2geF+EUAdFW4MQkJyMJWKNaWlzOKbSr64ggCIK3IUJWCSjtt2v3Hvzw7lSk/J3Ij113+yDc/fgkaAK1yC7QO9UpaI+j8dp6ERhxfSMk/WPs0EpnD0EQfAwRMjspKirCgvmvY+3sl2E06BEcHoVB42eiVbebq80paK8TU6VSiMVeEASfRYTMDv7++28MHjwY27dv5+3mXXrhwadfQUhEVLU7BR1pWiwIguBLiJCVA6XvyIE4ZswYnlSq1Wpx1+NTENq+L4LDSwpIdToFa8KJKQiC4KmIkJXBlStXMGrUKHz88ce8fd1112HVqlVAaAy7Fmt60XV1OjEFQRA8GREyG2zevJk7dNBgUBoKSsNBp0yZApXq6sclqT5BEAT3QYTMgvz8fO6RSL0SiebNm3MURoucLZFUnyAIgvsgQvYPe/fuxcMPP4wjR47w9hNPPIF58+ZxuylbSKpPEATBPfA5IbOephwbosbrr7+GadOmQa/Xc2/EDz74AP369XP4mBKdCYIg1BxeL2SWIpOarcP+cxnF41B0V5KxfekMnDy0m/cdOHAglixZwqNX7EUmNAuCILgWrxYyS5G5nKPjLvX+SgVaxYcg7c8f8MWiWdDl50IdqMVLr76GiWMftzncsrzjy4RmQRAE1+K1QmYpMrGhGlzMyIfRBORnp2Pdq9OQvH8H79ewVUd0GzoNIW1aoqjIhKQs+1KE5r6LMqFZEATBtXilkFmLTHaBARn5ehSe2YN9a15FQVYaFEoVbhv8JG6+bzhy9Ub8eTYdc78/ys137UkRUrqSIj1bI1ZkQrMgCELN4ZVCZi0yuXm5OPTJG7jw6xf8fHBsfbR56AV0uaEbFEol8vP0OH4pGwX6Iu4mb0+KkKK2skasEDKhWRAEoWbwSiGzFJmzxw5g1exnkXbxDD/X9Ob70frukcg2+KGwyMitpY4nZ8NQZOKpy+ZxKRWlCLUVjFiRCc2CIAg1g1deZbVqFdR+Jnzz4dvYvn4xjEUGBIbXQvP7J6Fph+4sYEqjEWqlgqcrU3upuPAAnrpsb4rQnhErMqFZEASh+vFKIStMT8aXs0bg9JG9vN3+xn7oPex5HMswIS23EIYiI+JYYEw8XVml8EPzmBCbjsWyUoT2jlgRo4cgCEL14pVCRnpEgy81QcHo8ehE3NDvPwjSqGBU5+PQxSwYjX4cjWXmG9AqPgwB/kq+2aK8FKGMWBEEQXA9XilkjRo1wscfr4c2pj4OZWpYZFKydSwyd7eLR7uEcNQK0bA4xYUGYMmOUw6nCKXvoiAIgmvxSiEjbr/9dr6/3o72UVVNEUrfRUEQBNfhtUJWGZGRFKEgCILnUm1C9s4773D3+OTkZLRr1w4LFy5Ely5d4K69GA1GE+5sFweKu/L0RZIiFARB8GUhW79+PSZMmIDFixeja9eumD9/Pvr27Ytjx44hOjoa7kJ5DX8lVSgIguAZ+JnI0eBkSLxoGOXbb7/N20ajEQkJCRg7diwPriyPrKwshIWFITMzE6GhoaguSjf8VfHiZnNdTBr+CoIguBZ79UDh7BcuLCzEnj17cMstt/z7IgoFb+/cubPU/jqdjk/W8lbTvRipM4dS4cf3tE2PUzcP2k8QBEFwb5wuZJcvX0ZRURFiYmJKPE7bVC+zZvbs2ay45htFbtWNvQ1/z6Xn8eiXo8lZfC/CJgiC4H643LU4ZcoUrqeZoYisusXMnoa/JGTLfzmNrHyDUwdmyjRpQRAENxcymq6sVCpx6dKlEo/TdmxsbKn9NRoN32oSbQUNf5My8jkCo2CNxMtZAzNlmrQgCIIHpBbVajU6duyIrVu3Fj9GZg/a7tatG9wBc8NfMnZYe13oXKmNFU2SblsnzGn1M7O5hMQwPMifu+nTPW3T4/S8IAiC4AZCRlCqcNmyZVi5ciX++usvjBo1Crm5uRgyZAjcAXPDX3InUjeP7AI9DEYj3x+4kAm9wYjWdULZpFJeN3x7EXOJIAiCh9XIBg0ahNTUVEybNo0NHu3bt8d3331XygDiSsrq5kGREgVp8eFBThuYKdOkBUEQPNDsMWbMGL65M7Ya/lKqcf6WRKcOzJRp0oIgCF7sWnS3XoyU3nP2wEytTJMWBEHwrBqZJ1Ne/Yy2HRmYWZ65xCyOTaKDZZq0IAiCA4iQlVM/ax0fhow8Pc5czuV7isQcsd5XhzgKgiAI1dhrsSrUVK9FVyxetlxHpjNcTSdSJCajYgRBEBzXAynKlIOzB2bKNGlBEATnI0JWw8g0aUEQBOciNTJBEATBoxEhEwRBEDwaETJBEATBoxEhEwRBEDwaETJBEATBoxEhEwRBEDwat7Pfm9dn00I4QRAEwXfJ+kcHKurb4XZClp19dcBkQkKCq09FEARBcBNdoA4fHtOiiiY0X7x4ESEhIaVmd1VWyUkMz5075/JWV+6EfC5lI5+NbeRzKRv5bKr3cyF5IhGLj48vNejYrSMyOtm6des67Xj0IcofWGnkcykb+WxsI59L2chnU32fS3mRmBkxewiCIAgejQiZIAiC4NF4rZBpNBpMnz6d74V/kc+lbOSzsY18LmUjn417fC5uZ/YQBEEQhMrgtRGZIAiC4BuIkAmCIAgejQiZIAiC4NGIkAmCIAgejVcK2TvvvIMGDRogICAAXbt2xe+//w5fZ/bs2ejcuTN3TImOjsaAAQNw7NgxV5+W2/Hqq69yR5lx48a5+lTcggsXLuDhhx9GVFQUAgMD0aZNG+zevRu+TFFREaZOnYqGDRvyZ9K4cWO8/PLLFfYD9EZ27NiB/v37c+cN+n+zcePGEs/TZzJt2jTExcXxZ3XLLbcgMTHR6efhdUK2fv16TJgwga2ff/75J9q1a4e+ffsiJSUFvsz27dsxevRo7Nq1C5s3b4Zer0efPn2Qm5vr6lNzG/744w8sWbIEbdu2dfWpuAXp6eno0aMH/P398e233+LIkSN4/fXXERERAV9mzpw5WLRoEd5++2389ddfvD137lwsXLgQvkZubi5fYyl4sAV9LgsWLMDixYvx22+/QavV8vW4oKDAuSdi8jK6dOliGj16dPF2UVGRKT4+3jR79myXnpe7kZKSQl8fTdu3b3f1qbgF2dnZpqZNm5o2b95suvHGG01PPfWUydeZNGmSqWfPnq4+DbfjjjvuMA0dOrTEYwMHDjQ99NBDJl8GgGnDhg3F20aj0RQbG2uaN29e8WMZGRkmjUZj+uijj5z62l4VkRUWFmLPnj0cvlr2bqTtnTt3uvTc3I3MzEy+j4yMdPWpuAUUrd5xxx0l/nZ8nS+//BKdOnXCfffdx+noDh06YNmyZfB1unfvjq1bt+L48eO8vX//fvz888/o16+fq0/NrTh9+jSSk5NL/J+ivolU7nH29djtmgZXhcuXL3P+OiYmpsTjtH306FGXnZe7QRMGqAZEaaPWrVvD11m3bh2noSm1KPzLqVOnOIVGqfrnnnuOP58nn3wSarUagwcPhq8yefJk7u7eokULKJVKvubMmjULDz30kKtPza0gESNsXY/NzzkLrxIywf7o49ChQ/wt0tehMRNPPfUU1w3JHCSU/MJDEdkrr7zC2xSR0d8N1Tt8Wcg+/vhjrFmzBmvXrkWrVq2wb98+/mJIhgdf/lxciVelFmvVqsXfkC5dulTicdqOjY112Xm5E2PGjMFXX32Fbdu2OXVcjqdCqWgyAl177bVQqVR8I2MMFajpZ/q27auQ06xly5YlHrvmmmtw9uxZ+DLPPvssR2X//e9/2cX5yCOPYPz48ewMFv7FfM2tieuxVwkZpTw6duzI+WvLb5W03a1bN/gyVIslEduwYQN+/PFHtg4LQO/evXHw4EH+Vm2+URRCaSL6mb4Y+SqUerZeokF1ofr168OXycvLKzXkkf5O6Foj/AtdY0iwLK/HlJIl96Kzr8del1qkfD6F93Qx6tKlC+bPn88W0SFDhsDX04mUCvniiy94LZk5R03FV1rf4avQZ2FdJySLMK2b8vX6IUUZZGyg1OL999/P6zGXLl3KN1+G1k1RTaxevXqcWty7dy/eeOMNDB06FL5GTk4OTpw4UcLgQV8AyURGnw+lXGfOnImmTZuysNH6O0rB0jpWp2LyQhYuXGiqV6+eSa1Wsx1/165dJl+H/qlt3ZYvX+7qU3M7xH7/L5s2bTK1bt2aLdMtWrQwLV261OTrZGVl8d8HXWMCAgJMjRo1Mj3//PMmnU5n8jW2bdtm87oyePDgYgv+1KlTTTExMfw31Lt3b9OxY8ecfh4yxkUQBEHwaLyqRiYIgiD4HiJkgiAIgkcjQiYIgiB4NCJkgiAIgkcjQiYIgiB4NCJkgiAIgkcjQiYIgiB4NCJkgiAIgkcjQiYIgiB4NCJkgiAIgkcjQiYIgiB4NCJkgiAIAjyZ/wcbEqIZtldS+QAAAABJRU5ErkJggg==",
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",
       "text/plain": [
        "<Figure size 500x400 with 1 Axes>"
       ]
@@ -1209,7 +1377,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -1217,21 +1385,7 @@
      "output_type": "stream",
      "text": [
       "Jax 64 bit mode: False\n",
-      "Absolute tolerance: 1e-07\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\Markus\\pymob\\pymob\\pymob\\inference\\numpyro_backend.py:552: UserWarning: Model is not rendered, because the graphviz executable is not found. Try search for 'graphviz executables not found' and the used OS. This should be an easy fix :-)\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "Absolute tolerance: 1e-07\n",
       "Trace Shapes:      \n",
       " Param Sites:      \n",
       "Sample Sites:      \n",
@@ -1247,7 +1401,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "sample: 100%|██████████| 3000/3000 [00:07<00:00, 415.08it/s, 3 steps of size 7.20e-01. acc. prob=0.95] \n"
+      "sample: 100%|██████████| 3000/3000 [00:04<00:00, 695.09it/s, 3 steps of size 7.54e-01. acc. prob=0.93] \n"
      ]
     },
     {
@@ -1256,15 +1410,15 @@
      "text": [
       "\n",
       "                mean       std    median      5.0%     95.0%     n_eff     r_hat\n",
-      "         b      3.03      0.03      3.03      2.98      3.08   1525.63      1.00\n",
-      "   sigma_y      1.82      0.13      1.81      1.62      2.05   1082.19      1.00\n",
+      "         b      3.00      0.03      3.00      2.95      3.04   1290.04      1.00\n",
+      "   sigma_y      1.67      0.12      1.67      1.46      1.85   1417.10      1.00\n",
       "\n",
       "Number of divergences: 0\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 400x300 with 1 Axes>"
       ]
@@ -1284,7 +1438,7 @@
     "sim.inferer.config.inference_numpyro.kernel = \"nuts\"\n",
     "sim.inferer.run()\n",
     "\n",
-    "# Was genaus macht diese Zeile? Ich kann keinen Unterschied erkennen, wenn ich sie auskommentiere.\n",
+    "# you can access the posterior distrubution by:\n",
     "sim.inferer.idata.posterior\n",
     "\n",
     "# Plot the results\n",
@@ -1334,30 +1488,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 9,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x400 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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AK0HxYQByAq6W1pNmmatWnacyuP9A8eA6AFUGiRKgbUEgcDUC4nGeAAAgDLhfEwL3a72Aq6Wa56kV561dro126ScAZQQxdQUDUQdAsbQixq5VxyhaTCGuFIB6aQq4X0GhVKnQZ5XaWmdaURS12ccooh6a7fpFgVkA6gVEHSiMKhX6rFJb6wyN+4x33mt4r1/vXonEdog4b3YcX6vFlOv6Rbwi0ODhtdog+xUURpWyT6vU1jJhczGmdTtqV+HeH13PjF1vra4F70MWgg9dOL7ZhVfTZAG7CBlP1/WLArMgzfcDlBeIOlAYeU5sWQiZFMvS1qpPEETaSUMLk9HDBphlK1d128a3vyTivJmlLfISU6GTsO/6bfcSHmWIbSwLeHitPhB1oDDKYCUoi+WmjpNZiIsxyaThEiZJxHaZxHkeYip0Eq7j9ZsHsEyV9/sB0gFRBwqlaCtBWSw3dZzM8hBhIcIkiVipm7jRY0zxhTZgjbLTTpapkGugbt+PdgQlTRKCkib1ol1LOqRZrqvomLo60Iy+f/++l7viCm3XcLte4yG0y9i0Sz+rCJYJAyBH2vXJtFVuFpt1s04WzyRCrVnW0bi4wnayRiWlXb7/uAbaB4g60PbUTWS0YjLL2+KUdX9FWf+SCLVmTaxxLljESflph+8/roH2AaIOtBXt7PrLazLL2+KUdX9pPp/XdZBEqDVzYt1q1BDz3KwF0d/kiqXX3I4yWqOa+T3Ed7w7ZbwGQHNA8WHQNpSlgHDVi3vmXTg36/6Sfj7P66Do4r3cFxZ0rjGgSZxiJsswmTfze1iW73gZKdM1AEoo6pYsWZJvSwBosoAqw5JIRU86eQhKl5BJu++swijp5/O+DqgIMv3EWQibcf259lG0e813LTTze1iG73i7PeSBmoi6kSNHmi996UvmmWf+lVEDQJnQAsoWa9Rqipx08hKU7MqhjFkWMln2zftjcZRHe5ptXaP+nXD9M1Ff73/hzegnbpJuhlVP7yNEXDabuGuhmdbNMnzHq/SQB+pH6pi6Hj16mKuvvtr87Gc/M1tuuaU54YQTzGGHHYYyH6A0aMH0yCtvmZMnfCTKFiwqrqTIgGU9Hrc8Myv1GOh4vDySAFgY0e+kwkS3xxdXlUeSiCwPocfAFt/H7cn7+itjrFTctdCsNtMYy9IuNNZlGA8fyEoFpbHUzZ0711x77bVmxx13NM8995w56aSTIuvdsccea/70pz/l20oAUqAF0/OzO6KbfpGTX1KrUjPHg8RTFsuAtEjpfc94571E+85iwdSWsRDrR5b4Ip/L0yac2aJH7WnG9Ve2WKkQS1xIm5O6JfXY61IvZaTomExQP1KLuv79+5ujjjrKPP744+bFF180p556qhkwYICZMmWK2Xnnnc348ePNj370IzN//vx8WwxAQgG1+QaDShVnU9QkvKfFvZl2LLRwItiFyoIxiTsp7eRmE3DNdnH7XJ424Sxds81oT9nI48EljVuyigKpyIc8UE9yyX4dN26c+cEPfmBmz55tbrrpJrPHHntEQu9rX/ua2WCDDcwRRxxhHnvssTwOBUAi6Cb51Yljc4mhqkMw89j11spl4nO5jUYPG+DdLu/JzdaOZk/uuq1Xi/bK/7niA8ssNuKu85DvQR4lRdII86oKpLJZWkG1acoyYQsWLDDnn3++ufzyy/91oB49IuvdRRddZPbff39TVbBMWDXJMtHUZYkd3Q+KOfr6PuNSjZFrTFo9Vr52FB1npttGIu9z24wq7bUTd+5Czm1e59+3nzKcWwDaYpkwssZR4sStt95qli5davr162cOOuigyHJ38803mwceeMB85jOfMf/5n/9pTjzxxDwPDUDTqsbXJZjZF3OUtICvK9i91YH7vnYUfY7KmMQg0eIo7joP+R5QDKF+nabfrrFr1lJrANSFzKJu3rx55rrrrjPXXHONeeWVVwwZ/j7ykY9E2bDHHHOMGTbsA1cDJVD8+c9/Nnvvvbe57LLLIOpAZajLEju+fqQRri7h1CxB5bLQZDmebZ95WoLKIC5t2MRR3HXe6u+Bbezq8oAFQOlEHVndyCp35513mhUrVphevXqZyZMnR7Xr9txzT+tntttuO7PffvuZW265JUubAUhE1km67BaXPPqhJ2zOXm1lX33nKcRCk/Q82/ZJlNkS1MzlzSiuy3edh3wPyL0sE0PodZ7U5QELgNLF1PXs+UGOxahRo8zxxx9vjjvuOLP++uvHfu7b3/525IqdNu1fX8wqgZi6alHmeLiyxQZRe8hdJiflVo1X3Hmi4Hw5mVMwPImQ0M/b0Pvc+5+JHr7jlEXYZj03tvjKvOrnNfu6Ltv3BpSTBypyneStKVJnv5LF7be//W0kzs4666wgQUdccskllRV0oHqUddmgMlaSz5K92uzzFJfRmuY828qPFLkiQdw1kfValpmrMlOUBB3Vz8vrWmx2NieyRUEV76+tIrWou+uuu6IsVrbYJeEXv/hFFGMHQLMpunaVqwREWcVmlvHKUvYl7rhx5SrStNtWt++VNxcVVhYjq7BNOsmxONJFestyLeZNXcoSgXieKun9tbIlTeKgBIrrr7/erFpV/orfGrhfq0dRZvi4sgx1cAvztmThkks0pelP1vOUR7vTtj0tss1ESMmQNGPkc1+X+VrMi3boI/gXVTrfpS5pAkAZKSoD0ZepV+bki9Dxcq2BmiQrUYuULOOQtt1bjRpinpu1IHHbs2JL1Ii7JtKOkS/BoMzXYl4ga7a92LMNrmkXEHUA5IQWKHGZemUtdxGKz6UR4hrMq+ZYUuuVbve6a/Wztj1kv1msi64M1CzXhK/si6vuG79nSwjJWrS7LJNqSNZsmdrbjuQ9/ntW/P6aFrhfEwL3K6jaygatypRNmkEZ5xIMGbc0bhbbZwh5vFasnpC3iyjp/rKuINGqTN28qFp724l2Hv8OuF8ByJ+s4svl3qnj02LI8lch4+myniSx4KUtmuxahSLJfvU2JHJlfJyt/zKWjwRwnqVEko5FlhUk4s5RGd2dvu9iGdvbTmD8S5D9CkC7pL+HZM0VnWXbSvRSUITNwhNSToAEIf1IUZAkcy3tuMeVxQjZr60kCvfZ1n85LlxChH7nZcVNOhZx2/teJ83UJRFb5szTZnx/kW0bTjvdP5sNLHWg7clikWjHwNy3Fi3L/NStrX1y5YEkqwbkMe42q2LIfuU2tAKHdEXb+m8Tw/L/WUk6FnHb+/4fEi/Kn5UZxq1cpSOJ9T3v7y/WqE1GO90/mw1EHWh78loTtVmu1u/f97J55JW3zG5j1zVf32ecKRKarGSmqG0pKF3AV7/OOzM4y7j7Jt+Q/fI2tB+XqKM++P4fuiRbiEhJOhZx22vraVLRSz9krcpbxMaNRRpRlef3F+7E5NQxVKUIIOpA25PGItGqBAgSdGzleH52R/Q7Ttg1s216siLXqT6GLmarXxNlyQzOa/LV1xDvi8+BFjZURoWybkno8Y9PeGiRYotjbAZZRW8z1msNEWxFiyqsUQvaStQVkHALgBfXBGUTfEmtAFlEFlno9GufqGu220dPVrYF20MmtDhLT6tEc9rJ1+WylW31PRxQgkQS4aG3DRGCeZCHOMrbtRbSptDz2qzrDO5E0Fai7owzzojKmgBQBfRknWSiyyqyyOXKFjp+7aPZFoqksWau7E9fseFWxiPp2C/tZrSR1rVnGxOf8JBjpUVKsy1QMks3zpUeQp6W1zweGlpxncGdCCot6hYsWGAWLVrktMJtuOGGXX+PGzcu+gGgiiSx7mQVWWyVC42pS2p5SmOpSBJrlmYSTTNmWSwuvH0zy6jwcXg7bq+rpInN3Urb0tq0MjavGW49ncSy3+YjzO+enxv9TaEA5DouUqyEWsHirtOiXbQAlE7UvfHGG+ass84yv/3tb80777jLDvTo0cOsXLkyy6EAKA1JXCt5xNaQkAtNkLDFdlE8V1zB1VZk6IVOommEadZ4syQTfNZzGlJo1edupb41062njz3z3SWlEz9asKUR9UXHywJQKlE3d+5cs+2225o5c+aYDTbYwKyzzjrmrbfeMjvuuKN5/fXXzZtvvhmJOXrdp0+ffFsNQMGEulaKiK2RGZl5F4h1TXhZig1nHbM84s1aWUYlTUwYQ2VRRg8b0LRric4jZeP6QgBCRGwrhVHah5M84mUBqI2ou/DCCyNBd/7550fWOl7664knnoj+/+ijj5ovf/nLkbC7995782wzAJUibWxNs1a5yMsiJpdCy7uWn2/M4tbYdfXXR1KhliVeKklMmF6Kjf+mz9tct1nK3/hWCiGXa+jYlNUCnHe8bFJgAQSlFnW///3vzZgxYyJBZ2PXXXc1999/fxQ7d8EFF5iLLroo0f5nz55tfvOb35h77rnHvPTSS5Grd+jQoWannXYy3/zmN832228fvK/Vq1ebH//4x+bqq682r732mhk4cKDZc889ozZ9+MMfTtQuAELJuhh61okxSYHYNBYxfn3Fg690ez9tnFPacbEJoKRu0VYGtpNgInxuYmlxtRU41gV9qX6gq/yN7VrU7+nzSxZB3jZkbGQ78xBGod+fPMuHNKsUCSyAoPSijkTXfvvt1/W6V68PsqKWLVtm+vXrF/1NbtkJEyaYW265JbGou/LKK82ll15qNt54Y7P33ntH7t1XX33V3HHHHdHPr371K3PIIYcE7evEE08011xzjdlss83MV77ylcjCSG0i0fnkk0+aTTbZJFHbAGj2TTwP12hopmpaFyVlQkrLjtwuDSGTuGtctADKag3Ra7TmZV2xraQR12bZN9+qFU++/o61/I3tWiT0e1kEjaynmMf1kOT7k2eIQ7PCJZCUAUov6gYNGtTwesiQIV1iT1q/1lhjjei9pGy33XZm6tSpZrfddmt4/7HHHjMTJ06MXLuTJk3qEpAuHn744UjQkeXwD3/4g+nbt2/0/mGHHWb23Xdfc/LJJ5v77rsvcfsAaOZNPC/XaJ7WJz3h6WWvNt9gkPnqxLGprTIhk3geRYuTrEbA5GVd0deFtC4mES9y6S2C+kLv2crfuCys+j1aCzeNoKHx0oKOLJFZYv+Sfn/yvs7zFlwoRgxKL+qoRMnMmTO7Xo8fPz76Te5SEkrEe++9F8XYjRgxIvH+DzzwQOv7u+yyS2T9Iyvb888/b7bZ5oOnThc/+9nPot/kAmZBR3zqU58yu+++e7Qf6ocsuQJA0TfxPFyjzbAE+KxGLOjSWMtC25/VkpJmNYK4NsUdLyT+L/QYUnDoODd+X8fUua5F23tpBI1tvOKyj+W48D7k+WxFaZ5W0iwLIAC5ibo99tjDXHHFFebtt9+OXKOf/vSnzZprrmm+8Y1vmP/7v/+LXK833nhjlAVLVrU84Wza3r3jm0/WPmoXxeJp9tlnn+j/jzzyiDniiCNybSNob/K4idsmWNfk1WpLgGu5sLRu51a1P0vmaZbSJTr+TwqakDjAkBUsXOVvZMxhM0SGHi9K4IgTdHJcGG1hDm1fVeLVWhmzCdqX1KLuC1/4gpk1a5Z54YUXIhcpJTH89Kc/jbJgv/e970VZr1SImOLYksbT+SCr2gMPPBBZ/zbffHPvtkuWLIlKr5AVkWP+JBxLR7F6LihGkH6Yjo5/uTcAaOVNPG4dzlZaAlzLhWUpzBvS/qwTeNLVCOYsWBrVaSPLV1K3blz8HxO3okXaPmtrGItHWe4ljeWRxSFb43znziZGXZZQOUZJHmrKFq9WdqshqDepRd2WW25pfv3rXze8d+ihh0YWMXLBzp8/34wdOzay4OVVp27FihWRRY1EFiVR2ISaZOHChdHvwYMHe+MCeTsbF198sTnvvPMytRuAPIibvFppCXBN5FksbiHtzzqBhwoQQmabUqyabyUFm/BKUpePcAk3HbsYmjSjCzIn3YdvnzZxaBNgtj75XNC+6yV0f0XGq4UIcIg+UKm1Xyk27Utf+lLeu43Kkhx99NFR/bvjjz++Ze5SWqf2tNNOa7DUjRrVfRFzAJpNmSYvwjaRN9timMcYhAgQn6C09c0mNpMkHrjEqi12MaTPPmtY6D40WlzKdoa0QVoqXQkfSY5Pr5O6apPQjNjQqriKQXXJXdQ1AxJ0xx57bFTG5PDDDzc/+clPgj7HFjqXJY5dqS5LHkHZtXEZtgC0gqoEWzfTYpjU1RdKnAjSJVz0hKzFJln5qD1xYyFLp0hkAoGEkyMI3q+t37o9Y9dbq+vvpEuo8TFs5VR84tAnwHlcaAm7tBbEtxYtKyTUIctDR9lcxaB+ZBZ106dPj6xnFLsmY88kFF939tlnpxZ0vFoFuXenTJlievbsGfRZSpCg2Ltp06aZVatWdXPXciwd6tSBqpAkeUL/jyhSEIaKrrhVEZK4+kLxuQR5ZQXfhCyTEXiZsrilyrQ7U68QYVuyiwoM04+r3hztg8bMZw3j+MckdCtMPLS/OXv/zWLHOK7IchLLK+1DCksaBxbOedOs2NCyWdtB/Ugt6pYuXRq5Qcl6RlBShIu0ok4KOio0fMMNN8TG0WkoieOmm26KSqtQrToJ16fT7wNQFXyL2cuCsK4swzTHC0lmsK1eECK6ZJv1qgi+Y+Qda8f7kH0gESHR1rWk7dDbkqAjl61ryS5Cihr6vBZ9PHYsDml/WaxhLjESJ+hsRZazWp/pfzQOegyaIeqaFRtaFWs7aENR961vfcv88pe/NOuuu26UCUsFh2n5rbxdriToDj744Kg8ik/QzZs3L/oZPnx49MOccMIJkagjUSmLD9N6tFTOhFarGD16dG7tBqCVaGHAFiKy2PhilbIGyvuCwG3b2Iru2iY2stDZVkWIO0aWSVgKRBZVhO4bCSXfaz5uaDt823azjP2zkK8UNCQqbS5RvXxYXnGIzaqbmMR1qq113Je8kw+aKb5amdAE2o/Uou7mm2+OxNNzzz1n1l9//XxbZYw5//zzzXXXXRcJRcqivfDCC7ttQytKbLXVVtHfV111VZSles4555hzzz23axsqVHzcccdFq0p87GMfi5Y2I1cxtZ/KsNByZCB/2jHDq4g+u9yGWhzZPpeUkCxM12Su2+laRWHDoWs2rIpArzVZkxLSum2TlkNxlSjh49L/tMvVdywtNELiALOMja1vIfGBdIxmuRltYiur6931vYX4Am0l6hYvXmw++clPNkXQcaweH8dV526jjTbqEnU+qH4e1bS7+uqro4LJJBQnT54c7ZfWlgX50o4ZXkX1WcdyMRSPJsURiQe9AkESQrMwfQkDPBnrhemlOBw5ZI2G/enXtmPkuRpCXu482Ud9PWjXpO1acVmKdB/lOJBrkhIi9PJh8nN0bHLHSkEUUt4lTf1Am/jK46FHj4HNCtwMUQ9ArUUdFfRtZiFeSoign1DIOictdBJKrPjKV74S/YDmU/cMr7IVQNXH4Zg6m4iLa5Nr4nWtIGFriy9hgGP9tAuNj0vFfiVx1rCksX2aJBalkHNsWzdWb+vbj25zXIyZbRxc4l0LGOmil4ImTSymy3qaNKbSh8wUltZNmxU4NIGi7vcq0H6kFnWnn356FEv37LPPmq233jrfVoFKU+cMrzIVQJWTnC27Manlyjfx6v75Mih9CQN68XcSFoRNCPmWmwpxBYaICJcwCikTYjvHLpeo3NZlzZTjECp8bOPgGhvdtrv+NsfqXk8Ti9nsUh42sSzHKG0CRZ3vVaA9SS3qKHmB1njda6+9zMknnxz9pvVeXeVGqCgxaA/qnOHliitrdZ9dFiFuY5rjh5bsCME1WdoyPl37tCUihJI2UJ+XwXK5TpOW6JDZyHHWzDxWfeA+2K7DbmLy3cbMWVvShUa60yVx17+2wOrM4ThcYpnHyJVA0c73KtCeZKpTt8UWW0TJBhdccEH04ytpsnLlyiyHAhWjzEHGaWN74uLKWtFnbrsuZSGhNpILzVYKJKvVwrZ+aJLJ0nYMV1/iJmbfeUxigXFZPLWgyKtEBx8vjjRWI5eFko9JrllZmmX00AHdxJ0PV/29uFqJv3t+birB7irObIsZTCvOynyvAqBlou7uu+82Bx54YCTWKAuWyoLkWdIEgGaQJbYnNK6sCOucnqxJnPjWKrURNzHarF/8O3QidR1DimWbdSvpeUwSd+caU8aWaZrFiuY6HvWZixyntRq5zpHrmAdsObKb655+yIpIKzbo2nxyv6HxcjYBGyJYXcWZdUwdb1sGa1tZ2gHal9SijkqHUMHha6+91hx55JGRNQ6AspNlck4SV5blRp8kWYHrl9km7jTCw2e10P33LZvlm+j1MXwCLHQsZMxeaKKBbT8SLS5DLH9JBY48h3HJLGkTP7rVvBs6wIxbf62GhBrpAichp0W2zqqVlrO475Ruky9WUu/HVZw564NaM8QXMmlBpUXdiy++GK3EcNRRR+XbIgCaSJbA6KRxZWlu9EmTFeS+dMHhvIO+tfjyTea2MhO+8hY2AZZkLDiDNumk6qrzl3aN1KQCh9+LE7K2vvH+9Zjaat/pWDr6kQ8l0q2uIdHJrny+vuj3K28uivbh+k5xfCKx3+YjzMx3lziXfsvyXU36oNYs8YVMWlBpUadXbgDtS5ldDjbxkDUwOjSuLM2NPs4C5SpWS9BkmaUWXQhafLkmXVuZibhSGmkSN2Rf0yyHFVo/L7QWWpwQcSVJyNi30BU5XGNKaMsovb7iwVcaahdy/+Li+7gPOg5Ott1XEFhCxw8NCwj9riZ9UGuW+EImLSgD9lTVAD772c+aRx99NFoDFrQvfPOmmxn95tIMZW4b3cBlDa08YpZc6Bt73I3etr3sB03ccQH4rRLXPOkev8uYBlHC4or/p7M69WoXIWVAbK/1eUw61no/2p2uRaqExMwJluvdNia245H1yxWfaHs/ri9xY0rH/OrEsdb++fYtXaU+S5k+F77vRUiSiGx33Hc1ZMwlaa+TkLbSeG2+waBgFzMApRF1tGwXrejw6U9/2vzjH//It1WgMiQVOVVvW9IJIemEY9s+pB9FiWs56eo2EDax1EuVPZJ12vIeu6R9cX2e/tbilISdbaxDhIjrOnK9r9umx5Tcmr79+5D75rqBDFnV9HZ6HGzH8h2/GRasLA9qecE1GMkaSb/L9IAL2ofU7tf999/f9OrVyzz44INm0003jQSeq04dJVHQdqB+lNnl0Iy2pXHfhgTs+wL8Q/pRhngeVxu0y5EzKjlj11UmI3Tssmyf5PO6FprPFRtnNXVdR77rS7dNb2dzvct2xLmzXS5s/m3riyvuUMefUqKFK2wgybjlQbO+K2X4DgKQWtRNnTq16+9Vq1ZF1jqXxQ6ZsfUljxi1qrUtq3BoRnmOvAVsmoxdXW9OBszbrIurVq/OpWhy2jYnRYtT17JU+nzqLFrZTls2Z9rrS39Ot0Na3XwWNl+Gs47di1tZJEk/WpU92qwH0TI/4IL2oUcn1SVJwYwZMxJtT3Xs6gCtdzt48GCzcOFCM2jQoKKbA2oAWUfkZEBuMNtk3ypRY6sPRkkYvsXf5fZSxPjqsmmBQNiyOtO0OU22ZRIonk4KO4qjopg1tnbZMmq5b7KdNuESch5d50jiakdcHUBt3ZP7oH7KZAva19X/7FcSbH20fQ+4DXkL9WY9AGQpbF7GB2NQPU2R2lJXF5EGyk/db3h5POGHjlHIdtqqRsJr2rwlXasCaCuKzQrnCvzXddmoLIYUR74lunzoMjPcVhIg1PaRQ9ZIdf24xku7Yuk4JLKova4yKa5xkha80P7r/tqKTbvaQeMfl3jA/9fFhzccumaDqNNWyjzXT/bVQSybtT3Lfouub1f3+2u7kTpRAoB2z67Ni6wB/nFjREuGHXDlY+akX/61YTt6n6wjenubqNTLPEmBYssM5WPo5Z1IDMmA9rgCznQczqh1nXvb8m267SHXjz6ObVx1di9ZrnR7XQkFtP6pz0VNx9D9SJLcw9vqNmq3a5J1V3UZkxWrVkfFi9O20bY9v9bfA33spMeJu27KQpHJZu1wf01KVa6b3C11M2fODNqub9++Zu211zb9+vVLeyjQxlQ5+DhuPcy44rt5jBEJN3ZxSgsL4aoVx6UZbOug2oScr9YbTcy+gs06XpCQn4+z1tA4Uv01yZjha0bWOddY2cbZZi2Jqw1H25DLVbpBZbYq/cjxl8J49ND+5oAtN/BaO7n/tuvFlrRB20q3MLdx243WbrC4kXWUJq2Q69JVb1CS1LLss0zr70FaC3bR1q+iY/FCrW9Vvr82gwcqdN3kLuoo2zU0AYK2GzdunDniiCPMaaedFgk9UDxFmt1Dj13V4GPfwuppXYxpxkjXL3Ohb+YUnyVdrhyn5nJj8mSsrWZaqNFvHf/ly+r0TTqueL0z9/23SMRQ38ldKPvgq7WmX7vcl3Ibsjr6klhcC9fPePd976oM8vOua0lnl9pEOAleXe5EFy3mc+E7jk2wyzjCpA8zXESbrJfURjpfOiYwS6JTlcRK3gldSYRJVe+vzeKpCl03uYs6WiJsxYoV5k9/+lP0eujQoWbDDTeM/p41a5Z5550PBmeHHXYw8+bNM6+++qo588wzzZ133mkefvhhs8Yaa+TVB1CxJ5Ikxy5zdm0al4pNhGS9cfjGiCZ0aaFjYUZWHd+SYnSOKNFAQp+LS+CwtUWXybDFf+l9ELQP7SqU7dRjLEUG/XByB7kMWfjQZ0hA6PIatsktzoqorXI24oShLOdCQueuv82JlvCSn7dZDLlNnKSgx5ihc08/LKK0MJPC0leSxibYeayliCNC1gPma1HGPhI2YZfmu1E1sZJnjF8SYVLV+2uz2L5i102uou53v/ud2WOPPcxWW21lLrvsMjNx4sSG/z/00EPmm9/8ZlTu5NlnnzXz5883Rx99dCToLr/8cvPtb387j/aDCj6RJD12s4KaW31zcLnY8qqfZxsjniTJaqUzQfVC7ozLAmYTfiy8SDDwb12qwyZs2Dpjq10mXZaEa2k0vV9tNdL90K5DKTpck5vPihh3TVI/aNxJvJCw9MX96TIpOkPVteQa7fvHX/hYt7GguDcpDkm4kQC0xR+ysNSFh/X5to2RrXyL67sdFztGY5VXpnJWsZLVi1GkFySpMKni/bVZ7FkDkZu6pMnpp59upkyZYl577bUoZs7Gu+++azbZZBNz5JFHmh/+8IfmzTffjF7Tz1/+8hdTRepS0kRPeK201Nkm7WaUnSgafWP3lf4o0zWgS0tIC5gUcr6YOy3E9DnX2NY+tR3f1gfXDdhV0kOStnxMHLq/XGjZJbroWpBiS7eL+6ktbdr6ymNO6HHkMXadizQlRPQY637KEjX6mtlm9NrmmRnzG/pBArVogZT13ljkvVW2ocrCpJ3oKEtJk//+7/+OLHUuQccu2QkTJphbb701EnXrrbee+fjHP26eecZetwq0xxOJjjNyxR3lTRFro+rjsCWjKDEXssKAdnmShc8luFzoJAwS7TTh60Xl9bFt1kxZLsQVy2frp840tZFnULoUVTqWcf6SZQ2vz97/A8EW59plXC5QQsYMSnHsGmM+F3SOpACTSTtprUK0PynoCZfLlQSdFHb0/mRVHkUKUI4B1FbhvL9HWb0YZYjLgvWtfUkt6t56660opi4Ocr++/fbbXa9HjBgRvQeKp6gvfhFxC0VnNWlBFFfKo9lt4AlSwufBJbrTllqQsVk06dtEHR/bF4MWMjnqRBQXlCG7yboDE/XDdTwtdNktKvtJma62ZbzSuHYH9e9tOt5faf2fTxxrsS4FHY1HWjGrrXPs6rXF+uk4TVfZEpc1WL9uxndZj1OSEjB1icsC1SVT8WGKm5s7d24k1GzMmTMnWvNVFiomFyxZ8ED7UoSVsJlPz2kK+sYd3+a6zTpeug1cbkTv1zUp6fe11UT+tiVh+FywJAri+hU3ObosiWQdfWvRsgbhQZm99MNJCpwtK+MO05xXhtyhND56n1muuRBLqU8cywQV3W4aC9q3zoaVlkRfKIGExpTONe1Ht0Mn7uhixpQNG2oNTvNdDjmnWT0JdYjLAm0o6o455hhzxhlnRFmw559/vjnooIO6SpUsX77c3Hbbbeb//b//Z5YsWWKOPfbY6P2VK1ea5557zmy33Xb59QBUklZYCeUNvFlPz6EWwCTHt1nUXDXlkqDbQO5JndTgC4iXpShck5VrO3rfF1PHgkOLDXqP6qyFTI4ugUVZr3MXvu89Ngs+Fhh0XF8WJ4+Ny4rD7Y2LFbXFKLrOsS3bl0SSbVz5HGp3Nwsgl0WUzwMh+8+4avjpGEEpIPW1JC2Wej/akieRrltJ6He5Gd9VF3B/gsqJOkqUeOqpp8wdd9xhDj/88KgG3fDhw6P/UQkTyr+gn8mTJ0fbEi+++GIk6EgQAtBMbDfwZjw9J7HAhcbT2bICQ4/hgydYdk/Kchq2WDU+FgkeKRxck6EvQNxVfFj3S4skEoehffXVetMWIsn8JcsbXt/1t9lWt2BItieJm3HrrxXUXp+1S59jW4wg9YlKoEhonKVrV7u7ZRkWV4HpODe7rYbf+A0GN4g62QctcPRrnyVPPxxMtsQvhl4fod9VWNpAW4q63r17R9a4G2+80fzkJz+Jkh8ozo7o06dPJN6+9KUvmS984Qtdn9l8883Nvffem0/LAUh4A5fLU+VFyFN90ni6OJdVUsuBdjmFTG5JBId8P2Q7F9ptq5e3SipaGTkxsztUWou0aKXCwK76eHGCh4QN/bjEssS3L3mObVnTrmLDEm0dZYEsrwdqoy1hQq/5KqGx0cLH1peQ6zTOkheaGBNCEgscLG2g7UQdQ1Y6+iHXKhccHjZsWCT6ACiKtC6UpLFrIU/1aeryJZnofH0gtGWJBIHENjahgiN0zG3LWkmoXdo6RsLClfUaNxlzjTjO3CU465P7xm5n+v/jr81rEDK2eEObtYz6xYV7dbmRuPPsilHU59hm5aRkBBvygcEWQ2mzYN9x0k5dCSaEFrmcUMPv8W8aT5fFzSYgXWMRZ8nLC1jgQDuQuk5du1KXOnXtQFKB1qz6Uq2sW2Wz6tjEVEhsnNwPbc9Cwuc+lmJKx5NJ4aAtTbR/GccmoT6MHjbA2lbbOXaNt07U8K12YFtjNq7GoGv/Ictm6Rpzcnu5niuhs03lGMoxt/VJWzFpXyzqXJZZdjHr68g2RvJhQh/LFmeZ1pWaBNRsA2WmNHXqACg7SZ/4m5Uh22wLgZwcQ9d6pUlUJ0joyU8vkcWCxeValO4+chfrpcBs50NafzjuUbsDXevkugLfbedRHku+z+KCBAi1d921+llFa7fEgGEDugkauX8K6qfP3P7s7K7gfm4j709eC76khDgrJ6MtnbZz+OzMfxX7JWic+bwnRX8/+Py6BKKuN9eK8iRFlzICoNUEizoqNNyjRw9z3XXXmQ996EPR61Doc1TaBIAyP3m73Id5HK9ZLqW4MhfsHrTFmbn2o5fPIsha5ArITyuKbckIJEyloNNWKblP7Za84O6/J1qijd63jZ8t5tHnWqZ9UJapxJalyW3WItWXREL/o5pvLM6o5Idr3za3eJzQciU+2MZDC0tX5m+IQLzZ0ee8HqTkChx57R8WP1ArUTd16tRInL333ntdr0OhzwFQ9idvm0Wt7E/6rgmUy11wX3jdT9ek5BNk9LmX31iUqmyM75h6+6enz+/mViTLmRYSVNDWJmAowYHOFWWg8tJZvJ2e3DnmSxfH1X1nOFtUx+klWWXDRkihZGmJ1YJunbX6ma1HDUmcUS3RllnXEnC6bImrfps+r7Y6gW8vWub8bFZ858S1/zjBVvb7AACJRd20aR98STfYYIOG1wC0klBrUNqnam1RS+uSbdVTvcvCIuuX8QoHLHJs7fFZKW0TJIkIWz097e6T/9dt0Nmqtjgxir2Tokf3yZeBygH+sv0kTA7YcmRX7Jlt/Gxi1OZaJrSFzgfHDYa4UmX/faLx4smbO+Pa5Pu2fuq4QD4nWuh+7ZbnzKKlK4MFUtzDkTw+ubHzjqnT31lbTKYvmcgm2LQ1NS+LIgCFiTq5KoTtNQCtIGQJnzyfqtNk0bbyqd4VMK+zI3Vcl80SZYv7cxW8tbm2OC6OY/W0OLC1wRYDJ9F12Gz7c0FWNW1NIrEnC+NKYUnWJLIMyuxPud5oUgubhAQoC0ktfF37WWdg3ygr14Wu4ee77tjSKC1wLuuevua1oKNr4KsTxyZ+WLLF0SVJYgp9SNLt1/2MqzVoqxGoz1FexcsByJueaT9Iy309+uij0W/JP/7xD/P5z3/ejB8/3uy7777mySefzKOdAAQv4aOfqmkiIYFhW+Q8Dp706cavb/4uXIH6SaC2hrTZlhhBgs4nOFztob7qWn568mILIE2GtmPIffsmvtDtdEFbF3RudF07aqtr3/L41F+a+EnMUZ9s642Sa1jy5OvJzuljr74dxSXS+ZTjzNfX8buM6db+txcv7ybaSRzStvQZnV1ssyYxdFxbEWsb3KZBa9if+eMEHYsmukboN1/D1F7+LhE01vL/SfenvyP8muAxDVmZQ6OvGT2uOgFItjPtfQaAvEid/XrJJZeYH/3oR9EqEeutt15Xau7OO+8cFSGmSikvvPCCeeSRR6KlwTbZZJPcGg3ai6zLfdHESD9ZrGYsYEKKymZdZiiJpc+3UoKvfaEWEW3B0xMcLQRP64ba9u2yzsjt5LJiPjHKFkK2QOqSKGRlo33oNVxp/7aYLhmbF2ctJLS46rC4IyXrrdXPvCnixmh71woePM46GUVa7EjgsXXStmQZ9UePHSVVUB9dMXI62UNbwnQfScyErPDhC1lwZSf7XMiuh6S4pfR0drfPkse1Bmms5PHob7p2fHGeId9ZJFmA0os6SpT46Ec/asaOHdv13pQpUyLL3WGHHWbOOecc87vf/c6cdtpp5gc/+EG06gQArVjuy1cCIk0sTB7Fg5t1PLbWkJty7TX7dsWRyf5zweG4uCVfBix/RouDTdYdaM7c99+cfZUFf3XslI6z8q0godc4pXMsJ3JZqJj2Q+Jz0o+faBBj7EbV66zaCjJvM3pt88yMRuucDbJmHbnjRt3KmVDsoCub1HU+dfwgs8HaA7pEnfx8XJJGnHvad971Ncj17Fyfl+c/7qFG/18nv+i2hGQzJ1lKz/f9tJWW0diyo+MSjZBkAUov6mbPnm123HHHhvdIxNFKEpdffnm0Duypp54alUAhax0AaUiz3JeOk9JLICUljeUtSwmTpMcj4aRdcaGiUk7ILouIjP/SFisZr8Tb2+L1bG3Qx9P7lsVqfa7F0Fg3rs2nY/3YgmYTfXGQNYstWPwZElMsrm3YSuUQ8pgkKnnNWl2UWRYozgILD9t517GqO3/kg3W9GWkh1AKZrom4649dsNLiaotvo0QUcvfa9pdlKT3bNekbT18B7LjvbLPqX4Lm8kBFraupRd2iRYvMgAEDul6vWrXK/OlPfzIf//jHI0HHbLrppubuu+/O3lLQlmRxZWprVVzZh2ZZ3oo4XoiotGWvSmjC9gWU0+ukZV98rnS9bxaqvkB1n0VFw59xHZdFn8sN6nKt2sQAuYmpD8/OWtBQvoPjsfSYaSsltUXGrtG5YWsso4UXWQhXrFrtXLFDu7c5y1fHC7JFVbfHJuQ00sVsc38mLQFDQo0tsHJ/WZfSsxFXqy+tdT5rOAZoPQ9U2LqaWtSNHDnSvPTSS12vH3/8cbN48WKz++67N2xHa8L27fuvGxFoX9I8+aQVOHErACQli+Utbb+bfRPRY6TXO42zBLEbKqTcA6/aoAvvumqj+dYwZTFJyOK9riK5VMbk7P391l0pknQMlTwuH0MKE/qczlJ1WQs5HivOSinFjMzGpcQRet+WkUsuXyl8pMghaHtp/dT9YPj/2kWaRIy5rFFx1xSJURpjstBJy5urdqAtNjHt91Dfa/i4od9b1/Fb/VAIslNl62pqUUeu11//+teRq3XixInmrLPOiooMH3DAAQ3bUSIF17YD7UuWJ580AqcsT8dFPvHFTWJ6jKhECb0nhYEroFzGxdlWGpBWHVcChHaluywttsB237WhkzO0oHMJCxIztH6rrUyMfjCQ7v1QV61su88qFOJOpmPaLKsy+YPbqvfB16Ct8DK3TYcw3PW32c422taidX3fbOdSWhW5LA5ZKW0u57QPTKHfQ5tQzINWPKSB/CjL/NFSUXfGGWeY2267zZx++unRa8p2nTBhgvnEJz7Rtc306dOjDNgvfvGL+bQWlI7Qm2qrn3zK8nRcxBOfyypms3SwW48sQDo707WKQpxAChU5+kYpJz7fOrRx4yeTM2zbuwRVXCydbm9onTq9kH1e6FqEOvuTsFnX+BrU42ALUQjpI8XbSVHH694StmtOn0tX3GzcOfcJtZAMWogsUPb5o6WibrPNNotcrldccYWZN29eFEv3jW98o2Gb++67z2y55ZZm0qRJebQVlIwkVqginnzK8HTc6n67YpZcLlGbiJHZlXoVhVCB5COuNIatOCyJDR2j5XugiDv3XOaEXaJsgdS4VuIISVIYPbS/OXv/zYLckHJ1BVm2RQu3UMh9OWJwf+v/+BqU1jhCCzpbH6m0DO1Xij3ptteJE66HiZCYs7hz6BJqtvtS0u9hVYPkq972MrFnCeaPloo64mMf+1iU3erixBNPjH5APUny9FvlJ58sN89W99slNmyTWNy2IedXCoNQy9Wq1R8E87vQx7XVd0vr1tailz9nSxggWNBpy1OImKW1aF24XMqybbZEH1mHz7fkGIlwW/1Cm5h21WC09ZHcorpt0m2vXbpxFjFZp1DG+kk3sguXUEtr+StbkHwacVaWtoOKijrQ3iR9+q3qk0/am6e8KbsKoeaNzaVGFh850bmyT6X7jdqulwFznV8+r7K/LD42HLpmlJEpRYBMArBdD7al37jdMk5Mwmuw6v1RjJwsRuwq2+JKAtAZwLLNNF4vv9HRIN70ovfUTtvEbAvK960jS1Y8QlpOSQjZMmx9cHIFnyt9jnV7XZY8KeRlSZjQa8YlsIlQUeJ6YEpr+WPK4KpNK87K0HZQLBB1IDV1tL6FEnfzLOqJWZ4TduVJdxjjK+SsJ9vQcjBS3EnxQcegz19w998bBFASl7Cu76YtVDahSIJOtoNWvpi9oNF65sry5fVNk4rAnj17NLz2xTXK8YrLLNUrHRBJEjR0GRPX8WztdSUU+NqjrxltdaLXWsTazoNLFMtEnLzvS2UIkk8rzsrQdlAsEHUgE3WwvqVxc8TdPIt8YubjxAkFnX0q37dlfWZJiqHPaHdkEpcwxbbxMX3xbHKc9SoDeoUF6YrUbkaZGBK3moGEl0yT65vGWRP1/oYP7GvmiRUkXGVGbJClcPwGg7v1VcYxahepLXuV2+USZTwWrvbITGFbPUTfsmVynzaRaROlWjRnuS+V4WE1rTgrQ9tBsfQs+PgAFIprsfDQm6dr0XB9E6Z4LV7QnY9Lr+V7eRISyO8rO6Ff28bJtYC5tp7YrEwkPpK0iUQKH8c3wZH7j7cj12+IK5LPpSwATKKD90MCjX5IjFA/aE3VOEjU6OWk2Jqoz7nuz1pr9Om2P+kOpWvOtWIFlW+hOEDNthut3SDG4tYyldul+Y7IY+hzr8U2WUWlVZD7qItd835c13bINR8KtSNu5ZpmEnd/KXPbQbHAUgfamiwWNZ81QCcQsCWEY4+kpcK2yHsaQldrYNdiklglbd3xlUyxrUaghR4X0ZXWFz6eK/GCz41uH40t1VEjS6CM77KJG5fwYHeitFjpNtiSEug4vNasXq2B26kL6dpKx8g+s7WPkVnH8pqTa+rKLFnbudd91fF8vtVX9HeELY4uEcXt4mPp9pDYluOhr0XpltZj6ou5TOpqLHuWaB28IKD1QNSVkLLfbOqEzaqUFluiQK+e3Y3h2lKRREy6ro2TfvnXLpdb3GoNPIkmKQmix0WvuiDbbxtTV2kOEjJyxQR2z5Gli1yu0o2ohYmO35PQe/R5G64VJtKUZ6F+kYih9toyOKnvVALElokqx8wWo8bQ+FA/bTFlfCweAxaLLhen6/z6XHZ6XNji6LIWUnvpR4p917WYtKadL+YyCTLeElmioE5A1JUMpKS3FptVKY/z5kMvPh5qZXBdGzRB6RgqHS+ni/Emvc70uGiXHbs9aR96W9eKEtJqJdFrlvoK9/pcbjQmtjVPQ5YMIyhrOK5MC4lbXwanD5ub23Xt6Pg235q8JJTnLmyMX3TF84U8RLK4uuDuFxoye2lcpVij80TXgRwzaV21WXz1g0jcw0VoPKUPmzB0fRYP2KBqQNSVDKSktxZfQHKSG7pvsqEitOPWHxT9zXFWXPyWC9+GnGPXtWGz/GlrWdzkKOOVQqw1vLyTbTWKNBYvFyQUfOVg4o5Fn7/6yG2s5zJu1YGbnp7ZUHyY0G5WLW5D47rYyqWX9aLrwiYkZawgiSsJlVWR2D7vKiNjE/fcDzlW9Jv6LkUdvy+3px+byzSpcHXh+3yold12juRDSSsfsCEaQd5A1JUMpKS3Fle2WNIbum+yOWDLDaIaaXq/RJKJwnVt2Cx/cRZHvS9bPTZCWmG0e9Hl9rzjpJ28MVtJiJuoaexc2ZvcT97ON2FT3/SYUAYqW/ukuNGuT5uIiRO1Nz8zy+r+I7Es9zdm+JpRjJ3NoslwVrEt47Z/n57m/RWrncJJCxxfrKRuG722fU98rlyfKzbkXkefd7mW6T296oksfcLXru27yuMr4wnzeMD2iTZ4ZUAzgKgrGUhJL0dAsm2y850Lfd5uf3Z2198s6PJIzLBdG7R/mvhd8Wch+9LFfF0rRMQtBi/jv2S/XBOxT5Dpido1QbqyN2mCdsWh2WqscSzazx9/vUEIkSVUnsOQeDR+j7Kebf3TxYJ1EgiNP1lyfWOjobg+bSmT/bBZpLTAefWtxc7rPiSBxlUmR2JL9khyr/MtneZyUzNaeGp3sc/inOQBO2T9ZXhlQDOAqCshyHpqPnFuD31Dp5uzds+4zpuMcaPfkz2TqCwbETK5ua6NH3/hY9FxfIVa9fv8d5IlvniST+pG05ZDzsANiT9jwemyavhi3/ic2SxztuPY9kPJLr5zbzsnMpEjNL5OWpXSWDZ9Gbe80oW0SNG40XnZZvTa5pkZHyyRpjNv4/qa1bOQ9F5nKzwdWkrFJjxd+6NrQSe/hHyntJjT+5SfgVcGNAOIOlAZ8oo/CXF72OKbQp6k44KwbdaOvNwwtgnSJmakC9UmONgF5Zs82Q1GViwSISwKXJOTnsB4sXntjiMrjF72iqxHPsupFozrrNUv+ry0uujPszvZl0nJrkuylvmWNZNijK1I7MLjcaLYQ15tQgonslTt/JHhQcKaXLEU4yeP4Yp/0+d23PprNcTD+dy5El1rL4lnIcn3NW1ha75W9feJz4cNqjMoYxltJXTk3/q8uzJn4wS8zWoHrwxoK1F34403mscee8z85S9/Mc8//7xZvny5ufbaa83RRx+daD8LFiww//Ef/2HuuOMOM23aNNOvXz8zZswYc9RRR5njjjvOrLGGv54VKJ48409C3R7aAhQSiG2zDmiRE5K0kNR64ZoYbG5GV9Ykw4LEVS+OA/aTZKnq/cl6clzHziWwSAQ9O7PRDSktp/q8DOjT+JrHRltFOB7PFR+nXZe2JatcEzmLST1OssAxE1qig8aB3cR8fBIYT77+gYDRLuq0cY1x5zLNw4Tv+5pkW1vCjmtf3Bd68JBWS1vGrRw7VwZv3EObyzJoi3XUWcEAtIWoO+uss8yMGTPM8OHDzYgRI6K/k0KC7uMf/7h5/fXXzc4772xOPPFEs2zZMnPvvfeaU045xdx+++3mD3/4g+lpqScGykOe8Sehbg9tAeIsU9+Ep/ctg+y1i4YnpSwZuHETYpJMVF10Vk52WoxpQchjImuO6bbbJj6XoBm0Rm/TsXRl12tpCWR4X3ofOkOTx5PbbOsjw8Lz5TcWdduPzdLiy3Y97TfPmc7OxvfmL1nmfa0TJGzwta/XtiWxwp/hNsrMYZdIl9D1KuMHfddtEoua7/saty31k757lBBEbdNi1Vfeha5LcvO7LGjaim5zyco4RN9Dm/6uye+Tq5ByXUAWb3kotai75pprzCabbGJGjx5tLrnkEnPGGWck3sfVV18dCbpTTz3V/PCHP+x6n6x+O+20k3nooYfM448/bnbdddecWw/yJM/4k1C3h63oKj/x2wQU39h8lg5tSWCLTtoM3LgJUfaV3E4ymYIskdp1JfvhE2ManT0rXZuy7aEic5dN1ulWe09jS3gIiduT1h3bZGQTPTqhg8fZZ73teP9folRmQksROn6DId3WxI2LbeP4Oyq54vsMt1H2UZZ34YxQmRkaYk3TlkidXZo0Xsy3rRauBAk7bqdO1NBFsaWLNUnGLZcconPuS57QmdGu+0qdXa3I4i0XpRZ1e+65Z+Z9kKAj9t1334b3+/bta/bee2/zzDPPmLfffjvzcUBzyfumGOL2cAWdh2TauW5sNhFC1o+rUwg2V2kSGS8k+6onQJqE9Ti4btA2txf9sOVGZyTq2nnS3eSKZdPxfi/M7XCKGzmZukQir5xhy9B0xTLazg/FsU3eeoOG88tt1Nbcvr16mOWrlHnOGDN8YF/z+W03bCgNowWGLRN4UP/eDeKQkhpCE1tY/NnOZ9IYN9u4XHTPi93OD1vL6NoI/b76vtv6OtKZyPq6lOOnBRf/zcu62fpK+Kx6ZPmMK9ni6mtdXa3I4i0XpRZ1eTB+/Pjo9z333GP22muvBksduV379+9vdtxxxwJbCEIp4qZoCzoPybRz3diSuENDrB0uK0SIK9ZmZXIlJNgmXl8moq6dJ9tuq6FHQsBVy48zNW2xabpd3Ae9VJdEbqf7bjs/ZDEkdygd8/W3F5t+fXpF4sEmdm2Cjo8p228TkLbSLNraZ3NDa6TbzydoXWNmK8VhGxeX4JaWLV/h6JDvtr6O6LX+nMtl7qvVyP2T168vzpTg8ZFuWv0A1Y6uSGTxlovai7ovfvGL5pe//KW5/PLLo4SL7bffPoqpI5G3ePFic/PNN5uRI0cW3cy2oKo3Oy2cbK6q0BubzVLF7kCb2zPE2uGyxNkmcHlsrgHH29L/faVc+PgsRmyZiFwrjbZ11SBzCVtX3BLtU0/Qtr5Jq4wtYN62LquE92GzJDa8Xroyek1ij6x41G9a1cHmRiVoHGhVCt1+codLeJWOLGubkvikJcK4TqLuI8WH6exNxleihsaG+qkzk33kYbFhoS9j6mzYHi5c38G4UAKJr6SJzQLajq7IOruWq0jtRR1Z4ihu7stf/rK57rrromxaolevXlGixCc+8Qnv50kA0g/T0dHdDQfiqfrNLs5KmOTGRhNT6FqsSayTNmEZsk6otMrQJMY1zeLclbrWW+hyZzbhFFL0VVsZT7j+GWdxVz1xkyiwxdfZJm1bhqovGzUOHifZfh3fSOKQzzUdn/YrXYlc+kQeb52Bfc3ANfo0WMz4b2ndkuvfhpYyccVLJiGrxUYKdpeYs51rjqUMjZWVcFhB3PfYZZlvV1dkXV3LVaT2oo7i5T7zmc9Ev8k6R8kR7733nrnzzjvN6aefbu6+++7Igjdo0Adrc2ouvvhic95557W83XWjHW52SW5sets8xscmLF0rP7iwiRSXu5KEgi2jNK4+Hm3nco354pZcwe7ys7ytLcmF2qNFLfWBLIF6P2kgMUz14Fz149hNSPzv7MbYuZnv/kuYsTiQom7bjdbuehhgIf724uXRD0Hvz1+yvFvGLkFjrfsYAu2TXMKPvzav2/vUHttqGdpaG2Kdt2XVZilz4hN0cS57ae314XrwgCsSFE3tRd3XvvY186c//cn87W9/M1tssUX0Hgk4Km2ydOnSKCv2yiuvNGeeeab185Rxe9pppzVY6kaN8hfkBN3Bzc6PK+4rKVos6v1qSwThstxoi4fNNSv3y9CETMklEh3r57KWcKKHLd7P5Wa2jVlckovugwzwd7WNxMzcBe+bNx3uRxJULKp4P7LEizyeRseKub4vtB+9pBtB4oqsfTZRR58NXW4spG4gCT0S3rYafeQ+tsVGuoSZLauWa8uFPuT4rOS+1VT0a9/2oceEKxIUTe1FHdWjGzp0aJegk0yYMCH6/eyzzzo/T4WK6QdkAzc7N7qgKWftZY1B1PtlFx8h9+eygLGg43boGDCJzUXr21Za43glBlo5QgsJtvL5Mh45liwkycW3pJh0AftiKE/65V8bXKe8goXej+yDtNLJNg/s18saKyYTACS+5BSK29MZtLz+bZz1cfTQ/lG5FW6Hz8LLAp7bKF3F9DedS35wiBNmNoFqE5N6zVqNFmkha69mDRFxWebhigRFUvuKu5TlStY1+q3hUiZVF22chcWV/ssK3ejiFvtuR/TkRyKCJxiaXOh3mnOr90tChILkfXFLNJEdv8uYboHf1A5f3TiZOSmhuC/XthKafF3ZlDTJy3Zz3JmEPmsbJxYe3CdfRqQWH9ROtlrRe7Rv+tHjEJI4QOU/tBijNpOgIxF0guMcs0DkvvnEGSfC2ASYHnM9fpTkQeM86cdPRMfx1W/TFi5yxeo2U3vzsD6T2JT7DPke8DWrxztO2NpEKABVozaWunnz5kU/tPoE/TAUQ3ffffeZCy64IPphyPV64YUXNljsqkjVExCA3dWWR4ydzaLFVhASElxc1VfDLHRi4xIfmgF96RbT/YEqdKF72W4WIiQcXe5E2zjJPunPkfVyxarV3ar929oXV/LCh0+wMmwldCV72Equ6KxeGhseJ1kihZNTOIuUtrGNv1zn1rYChW0tWJe72hdzKfcn90/nQ4rmtdfs15BVHPI9cF2zcaIyrxAIAIqk9CtK0GoPBK39yu9NnTo1+puW/aK1W4mrrroqSmg455xzzLnnntuQ6ED7IAFHdeko2/X999+P3LK07BjVqDvyyCNNVWmHBIS6l2ZxuaZ9MYghbQgqz5GwuDEX0u3Ro0eDhYqLwmr35vgNBneL8woRimv27WmWLO++9mpc5iYLMld8lY7V+t/ZC6PkBp3IkTTBRENFgkNqyvnc2ORytMXGUULG2mv2bSium2QNWooxpM/6lg1jF3lIGAC9R+JN7ydODLqu/clixQt9rfqEllwpw7f8XZIQiCpQ1VJRoA1FHYkxKkMieeKJJ6IfhkWdi6233tr89a9/Nd/97nfNww8/HIm/3r17R8uPkeWOMmBpdYmqggSEelhGbYHbvuDv0DZwjFRI6Q1XcWMtDC85cItIXMj3Nhy6ZiSEtBChOC/9eVuJEtqGsizZkqYFXUiWJglKVxyVS/RwcoO0ktkElW0lDYoBtFngWCCmEXU0NrKOnBQlcsypzdI66hPK9D/dH9oP9ZdWMtHHI6Qw0scIsbi5llULvfZtwlq7fiW6D3FL9envlS0EogrAUwMqJeqmTJkS/YRA1jlpoZOMHTs2eD9VAwkI9bWMugKuk7ZBlsKQLleG/ueyTNCkIZe2koWRCbJ8kaBzxdvZChFzm21lTmxuVbmGqxQOesF0m2hz1Q+zwdvYVrOQ7dTJChT3JV2EHIcXAteQk9YsLbCoRAgdV2cUyyWzfLXXKMHFZo2j64HPiS3hgc6ZrbyIzfJlKxNiW1YtCbpPLmuftrLxObCtZmErtUNjWNWHY3hqQKVEHQijyGyrdjP9l+HmrwVDqICQEztlXrKVyRWfZluuS/aXJkP6sVlUZK0yeY361sh1CRNZVkVnpYbGUflEj9zWtpqFzkzV24xbf1CDqLPtRxcRdlmRbG5f7kPcklm2tWN1DTyJtmhSwoP8PItI3ReZJewrExL3sBni1g15YLWNdehqElK8pnk4Lvr+V4b7ESgXtc9+Bc0jjwzNqqGzKYu4kWvXUGhGp2T16s7YidE2mdvOs55IyKKis5w5Q1tnx8pj8NiSKCTRSbFj2oLIWak0GcvrzjaZ8WTNn6MgfB33Rtm5VJKEj6P3Y9uvrb/6mnAJbXahyoxa375l/0lc0muyXMoyL/w9dIlzsqRq1lPrzFKb9LG5YLOrL7ZyJKHZ7qH3Dr0PW6Z/XGZv3PmUbuUk2flluP+V4X4EygVEHUhNu5YAKLo0S4jwiPuMTl4IES8MixGeXKUYk9mhvA3FO/Hkp92A+hi0LxJJZEWkNupyJnx8/Zpj/yQkcuREq9dfpZi3aFWGRcui43BfpHDyWZBIPJAoZNcfXxO2GDWNFqWuSVpbCen1Xafs0vB+3PeOXOPUFyl2dAFlGiv6iays/ywjIh8aqC0ksvMqr5Tm3iGvIz1u1G7uH2fw2tpgu05CrVu6b2W5/xV9PwLlAu5XkBqY/ovB5SryuYLkZyhoXsem+cSLzpCUa4Gy647gbXhVAJew+WBJq2VRuQpb5uJdf5vjdJH5rjsSOrzGacjSYRqelGWWqDyuTQQRJAxpPFg46X6z0HVl7sqMVz5/tngwFyGuZRJm2r2qcZ0veT1J17mOcUsStG8LIfBdv7a4OVsMpP6/7fi2tZfjsPUN9z9QRiDqQGqQpFFcbI2OXwqZUGVcmxQYlAjgg+LKZFJDiIVClw6RsLCgODRqsysWTB9DulJlzTXZT52FSUkCu33v4a4SIL6lw0hY6GQEm+Ci17b+keiwWbNkMoetVIeuixcniHRbbBnKuqQKHZPGWYoQXRMuJC7S9323udZdfdAhAyTG9VJy8rOuuLm4Ysxx4xaK7Zo/a//NGt7b61JjOjsbwxoAaDUQdaCySRqtxlf/rOiyAkmy4LQIkEs7ufpFyH7ZLBTyPR3Uz6JQWwmJkHVJ5QQtrTZ8DJlBysKDBB2LFnLlsjWNEi+0dYdi+GyWKrYg6fOr++dya/fv09Pc/uzsru8Jj5+2FOkECdf5c11r0vpEbdaik8Y+SU24uLputnbFudb1/+KspvL4tqLLtmvR1z7XdzTkgUwfnwRd757GrBSVd+g11W+EsANFAlEHKkORmWa+ScFluWLLRdIJMg1JXUHaUmLLaNT90iUw9LmQa5WSwNBru8atvWqDLF8HbDmyYQLW1jTbqgzkvjzgyse67Y/rs2nLlm/FB72KhCzb4WJQ/96m4/2V5v0Vqz8Qlr/8q/nxFz7mfBiKW82A4slIqPXq2dNrwXS5I3l/+rg+oRlSDsRlvfXVlOPjyutFXxe2WEvbNecLK5BjY7uWdTa274FMHoctdJ/cuJc5c9d+Zvy6vcz/vrXKXPToMnP3q6sqKeyKzuIF+QFRBypB0dYwnyVMCyoZc0bYyj80g7glmRhfcV3ZL7J0+UpgcNyXdCvKuDqOO9JjwbXZbDXzeNku/h8nS3AQfMjSYtwHyvq0WdN4tQQdf+fi5Tc+EKTe0ieqVp2e031uQh0vRmMgz58v8YLGnKx8PBlrFyi16+z9Nwu69uKs7rZyIFLgSVw15TQ6DtNV6sXXPldYgRSG+jtK29nW0o2zcO+12fqRRY4E3Z2HDjA9e/SI/rfDh3qbOw/tZT7z6/fM7/9RLWFX9L0V5AtEHagERRfZ9FnCtBXBVR4kj/banqi1m1RPqGzlsa35qePZ2GJkW7je1h/fGq4uIUNiiuK1eGWG+1WGKgkuEip6RYJQ+NzobFeGBC2122Y1pKxXGicpotilKovuajFKwongAs9zF75vFi1d2fX/If37RLF9PXv2MJusO7BBeOu+0biPue/lLoFz199mN/x/0Bq9zQ4fHha13yesCEpGyet74nOZ0mogSbGt4pAkQUTji/mzLWXG2yVNdiCXK1noWNAx9Po7u/Qzd79qf2AqK0XfW0G+QNSBSpj4i840iwsS11YEPfnl0V7XE7Wr9hv9fnbmgq6Aec7mlFBGJBXDlfF17IbzIVeGiNtGj4W0+JEQsVlXXOc7Ln5Kuv30PljAsqvZBhc6pm0vuPsF67q1FCPnErx6v3179TDD1uxrXnt7SYOrV1pvbWJJijVNx9KV1vbTZ/R4clkXHSuZpiiw/A488OJbDS7rl9/ssLo3W/2d1kJZJqj06dXouqYHmLTJXuRyTfJ+mV2hRd9bQb5A1IGW3FCymvjLkGkbmhSi44Xyiqlzxe7pCZ7cpi7r2fwlyxte2yyL1G5qsw5Mt8Vc2QLYOX7ONha6vVwDzXZebe5k3he5RLXg4m19FlRXUgZb6ORY6P37XLXUJrLOaZav6jRzOxprwmmLiC17NS00ntoixTGIITFkvrg5mexB1k4p6t5Xa/WStVK6hV33mTiXa1Js7XeNK8eVpkn2ohg6crna3q+aK7QM99Yy80DF4g0h6kBLbih5mPirlGnbjLbanqhtljLXslAEJR1ocaaFDgkCmUXK27mKueo1P3kSZWsUweUx+H3ZBz1W2qVLbkb5f5u4oj6tq1ZJkLF+/NsFJSBI65hOjogjSfKHLR5OZ6+GCDwal1ffWtwgrkhsabcyrwyhxTVxwd1/j367XMGuMiNxCS9cxy9OOBJJ7zO+SVa331daJ6lFiq9/iqmjpAiKoZMu2NWdnea7jy3rlhVbBVdole6treSBCsYbQtSBltxQYOJv3hO1dtG5ym0QXExXxuPZJj5OJrDF7embm9yfLs0hY+b4c3FWAZuwYLexznzVMXBSSNqslWxJjCuropMjfMh1dF3v6deySLEcT5mJquMNXX2WsGuZ+klFnKW18Yzbn49Wz5BwrUCfK9gWR+kSncMH9jVrrdGnoa/6PpLlPhM3yer2u0rrJLW68HFHf+tuM+PS/aMsV0qKoBg6zn4lQUfvE2mTJNLcJ6tmSaoST1Uw3hCiDrREeMHE35wnaperl60+T0+f3y3hgD/vy6rU5z705qatYeSGk1A7r4552nXFmNlcpxQDZ2ubK7uXAvopjtBXN4+wuXZdDO7fp9t7lAxx5r7/1mDBlCJTWxWli1QXfdYidMTg/rGJCdS3ceuv1dAPLehcrmDbKiKhcZTzFi+PfiS2ci1p7zNx16HtPpN09Yi447KwoyxXmRRBFjoiS9Zr0vtkFS1JVWL7ChojIOpAy4RXO5r4mxmjKPfrStzQri6+KdmsLVQTjoSALQbQdnPTbdD71PF1ofBELEWcjgUkaxUJOkILOFlaxbe8F5dckdvSGEghxPXmfNjEJruwZTandlPbXKQ2EUefk5nCIa5eiqvUgtqHzub21Z2LKxxsE5j6WnHdZ+K+LyGTrO3BJ+t3Tx/3st+/ZL7xyU0btiGXKwm6rN/5JO2toiWpSuxZQWNEj86qFNMpCR0dHWbw4MFm4cKFZtCgQUU3B5SYLLFD9FlXokXIfuXEQuibErlJ9cQc1z69T90G+p/cJy1Kr+vU6WPIcitykXrdRx3wbnOxcskR3Y511upntVJR+0h0+fqlIdE3foPB3nIvev8u6LgU0ybr2/n2YztnTMhSa3KcKCZOrsKhz/tJv/xrQx+pdp4snuyz8mr0cmQuF2jo90XGSuadZBGaFWy71vlaSvOdTysEs8YngvppCljqAMiIS5i4nqJDJg9f8eK4p3ObS0aLC1vWapLJwNaGpK4KKQzIUkVxWCwcbAkYGh30T5YpW+IHCTGbqGO3oLaMaIHESRh0HsiKF+qatbVbCxKqIxcn6lxlXrhdnPHrE6O2tVxlm2Smqq1GIb2mJcX4s3pFEnkcXVQ6NPki1OrE78W5HZMIpSRrJzO64HiSPiQ9dp0sSaC5QNQBkAEtTAgWdi6XZdwN3BazJOt+xYmnkIkla+wOWV+0u0/ukzNOtWtUxo7pBA0tHOQkOunHT8SOEddk04JDLmxvS8DQfdfxbvq1D0qI0MWFGV+hZhvUNim2tVjl13wsOfbaikaWOZsQs12Prpg5+VBii1mkttK1r/upY+xs+wy5rqX12rcf3k4n6Piu8axuTL6W0sRgZT12O4a1ADeN1RgBAInQwkS+5omWXDM8qbhqzUlsEwEva8TQBEIWKJuFTX/eNbHQ52SGqw/dTl02hQQZF7ml49EkR5Objv/i2DHalpbxijsOC2ctaOgYJFRsn88SeE/o/YYueSULC1N7yfolz5lLLNFSXr74P9qPz93pS1xgocnng8fe9Vnf+MmHEj6vdB3SDx2Dsm2pnXzdU9yjhF7rVS90LJ/+vjDyuLa4QhkrKtsXOkah3xnf/liMcR+or/SeLgXEllF+Xx+LS93YSggBEAcsdQBkiGvRJRPote8pOjTQmyYGvaIBTyTSCmKzNjXDJRNXKkK2T5cdsUHb2pbxso2HFs4kZm0CmT9vS7RwYbMG6WQPHr8kteiki5GsmiSCbSKWOGDLDZyCLSRuTVuANbrcirQE6cxUssDR/nQBY06U0CVr2C0t20vHo3OrrxG5Wofr2nRZnWwWOptL2SXe4kRa0u+MLVmEj+FzD7ss9TYrK7JZQRog6gDIENfCrlZbTJ0N2icJBd7edQw9MfiKDbvcq3lOBq5JT4oOmpBC3Ys8AYbE9WlxQIkKvA/X5+NcpmRZopIjca5vTibQ/SfBSJYpWsvVJpwkHJ9GfSCBR4JHB/lzXbpnZy3wlh5heD/8eS22fPgyoGWdOynWyFrpcru6+utKirBdm2keqEKytJMQ8p2R7fStGuNyqbre5x99HpHNCpICUQdAxrgWEnJxYo6REymvxeqb4EKKDfusEGkmS9/an/K1XAXBJTjl8mJSyBD0XkgxWHbvsXDi2DvbdgxNsj7L2sB+vaz91aKA3d5y4rWJoVDL4J+nv2uePnNP6//iLIEyMSKNmOHsYJsg1+jl21yZxnHtpv34MoDjHqhc67aSqNXuWfmdIWuxfBDIQxzZ2kk1F224LPJxlvoq1kUD5QKiDoAWVnLPmrnKFiOakKl2Gy375RKFaVw5oRZLW8kVm6ihSV2KQR3ETvja5XIrhlgsOROThBDFyUkRI+vC6THSrke9X9uxtWWQxape+J6scCwSZR9tLmsWxDxeMjFCw9eFFjO+rNf5S9wWQSpaTZ/ha09bkGifvD95PepyL1kSBXwuZem6t12z5Oq11WdMA3+ftJXSJxRdlu04Ny+yWUFWIOoASOAWDV343IWOYdKvQy2GPMHrmDrfRMj7cpVgsR1fZt3a+k6wu85m+ZGTqU+gucbMFSNF40b9tglxnVTAopG216Lnrr/N7tZfbeWLs6YQJBq5Rp68BnSdPtlfm8DVrl9bxir1zWVJJeTxbFY9VyzfNqPX7soUpuvLt3yYTnCQxyArahJR4tq3L7lBHt/2nSExmoc48n2fQuL0bMeNc/MimxVkAaIOgEC3qCS09pZGT9L6dYjF0Cf8XCUfWAhRaRAWhLoEi+340v3oOr4tmUGu+MA10NIEsbvciq46aTbXqIxbIuQkrWvE8UoQSQUBHZOtqrrumxa89H5I8d64VTFoXHTZkxBLj7QwywcZGlNd/kWOXeiY5CFKbPF7rgcnV+mgPKxd+pr1WT5bCdZ7BS4g6gBIEVPni2HyfTZOtIVMnq590I1eCwGeCG21ywia0KWoo+PFuR9tfafMTp3pSGhrpkQLEhs8HtqiZROJIaJRjq9r3Vcap1WrV3dlMkuB5juOzk5mgS9j/QgSZKGrMciyJ7Yixdqixn0McWdz+/j80/9s8ZpSQNji45KsxGD7v806rDNvfRZH/Z0hQkMI4oSRvtZ97WgVWO8V+ICoAyBFTJ2cSLRg8n1Wf44nNJsbzfY/vQ+f2KDJkCdsV3akLsFCxLkfpdAidLwaCzV9TBKX0moVuqyVTFDwJY2EZtTK/dlEnbZk8j45UcFWHy+J0NRWzRB8ZU/4OCETu+/hha2KN9N57ew0h2y7oVWYu5ZzswkMW9FqKu9C151MoPEh4/dcyO9ASOxbqDAqIsYtqQhGhiyQQNQBkPLmLS0ivtpbts8Rvoy/pEsWuawKrv+R5YeEgi1r1zUWerLh923izRU/qN2mSSYk3WdXO0MXi3clF7hgsccxhBTzxokY0oIjx5kEhhaB2qrpypz9yDprmgmbrttQ9oTaT6t36GW8pLU2jZWXPyuFI8drSnQMZ5zA0P+X5V1crlXC91CRR+xbEmHUyhi3kO8+MmSBD4g6AHK4eSf9rG9SCZ1wXCIlRFyevf9mieKifJONa5KxxQ/GJYr4sAkW27jb3vMltuhMyRAef21egxCTAlqufyrrvtHxtCDjDFfb8V97e4n55Phe3foyeesNoh+dfRz6MOC6RmxWRZvYlNejPvf6fIbWjdOlT5I+YCWNfWuWMMoa6xby3S/CegiqA0QdAAXgm1RCJhzXBO4SNLpkRlKXTZzbzjbJ2Pqh9+NKeNDIxII0cUS+xBYSAFLkkZC5/k/TTcfSlc79abGj4+k0ZNUj8aX/zyVfXNnDHPPoqpHGIsLWR9c5dj2AhAowHaMo264tefLasFkYXWIw6UNS0ti3ZgijPGLdQsVmK62HoFpA1AFQAFkmlSQizeWWCrVMyJp3vs/bLHv0OVvGYpp6gFrwyFIrIYH6vpUQpDWNP3/Vw42CjmLBVqxa3fAZCR3bl3lM25P1TsP950LOF93zYkNdO455dMXq+RJReA3RJOEEMlaSrY1yebaz9/9grWA55nEudXltjPlnOZ1ePXs27DtU3DcrfCIP8oh1gxUOZAWiDoCCcE0qvskhqUjT++JSI75YM/m+PJZrRQP9ef05nZmZdNLyiaUkgfoEWeV0YgdD7/GyYLZit+wedCVYaNZZq1/Dkl9UnFdiW7NX1hGk2LuogPELb1rdnFrYy1UgkhaelueQV0nQY0eFrmkbmb1rE5M+oc6rr+h95+H+LNp6lZdLt+h+gGoDUQdAyfBNDiEizbcvm6BziSJ9LLaskCjylY+wlajQsX+hk5ZLQHEMW9JAfYqzYquYrfCvS6xJS6Wr7AuNi3xvgyH9G0QdiSKdJaz7yoJsxOD+Xa5KHldXtjUjx/cER8FjGz5XftwxbUuKJbEK1skiVcc+geoBUQdAhSaHOJFms5z5JhqfKHLFWEkrjc6O5OPoAsZ5xcKRC1BajLRbVVtHXAJZWsVCasbpWDFd9sUmelgEyzprOkvatqSbDRKgHPgfJ+ypTzb3cNp4SfpxlcThfaexLrk+U+XCurCygaKBqAOgQpNDnEjzWV30SgdxVkEZY+Vb3UBCIov24Srwqy1GSUtwaGuXFnu2TEHf8m8+q53GZ+1yuW11nTV5Xn1lODRSGGshLQWdLf5w7xihEeI21NuELouXFBTWBSAbEHUAVAyfNcBldQlxsdkmaCkIQyxKnHQgl8zSFi0WlkTSEhy2shvEjHff62ZNC13+TfbRJ+58bnAfofGONkiQEbJNLKRtK3LY9inLraR1G7bKtZi2nA8A4AMg6kBbUvVJwdV+l9UlxMXm26/chsREXMFe3r+09lE2pS4l4mqTbgsLRF8Wq20fOsmCju863y6XrE082Upo0I+MNYy7vlzWLy7SLF20vpUvfPukrF0+977rPMRt2ArXoi0hRFuXYc0DwA1EHWg7qj4p+NrvsqhkqX2n4fek65DEg6xBpvcfki3qs+LpOm6cxUpuT9/qA7qMCAkh6qfP+vP09MZF7bfdaO1uYpPEoi0bWAthm8vbVULEZmXkz7rc4LbyIWkzYMvwABXS/jxKh4DiqPoDddmBqANtRxkmhSw3trj22ywqIe6zpEsn6f1NdvTJ5WbU2aI+K55eL5WzWOPGkkSXrZ++WESNXkpLb+dyb4aKZB4Ddlu73OVUakRbEW3i3JXcUNR1nvQBKq79SUqHQECUi6o/UFcBiDrQNoQW0i37jS1uaaa07rO4ydK2LFmcmPStO5uk4CwlOUh3r07ocPVLZ6na+uUTniwu2WJm244tbXxtses0RCS7tnEVG6Z9hyYpNGsprFY9QPkyl0Pi+yAgykcZHqjrDkQdaAu0haVZ2Xu24+rJJ+TG5rMwxC3NlLZtvskypMivL55Mroc64933ozbrOm0+Kx71T7oqQ9EuTtfyUa7yLdQO2W+deUpQv7T1LrQor0u42IS7q6BzKDT2vqxpOUZEiIVLf05vm0VY+q7HkPg+CIjyUYYHjboDUQfaAn2D1wuINwOXEAqxiOl4MnY1yvZnmbCSrB3L+3cdT++LscXC6T7ISZv3a7NysXCwuSrjCE0CkLFcLmubLTnB5uK19S9JVql+P41A0Z9xxdfpBx4ppn0WLtvnaFs+tqtOYtIYuzQPK5yYI4GAKJ5QKytID0QdaAvyfkIMmZhcE3Hcjc02GbMlLi8XclKR4HP5+kpz6Fg4vU+bC5exFTC2rf+aV9yUtKTKfdkseGR55GLArvbRGIW0yyVc9PtJXe4u6yP30WU59m2r39fIZA7bCiV6ibG8XaK2mEdZ/BkUTxqhDsLpmWBbACoN3dzpJ+tEwhMHTUr0m17bsK1uwNDxyVLocgVqaEKUxw1ZBcGHPgaVCnH1g9tLGa4MHZ+39wlKXpBes9YavbsyUSleTR+bhW8knob2t+6DxAMlDoScCxv62Pq80r5lOyQkEqQFTbtlxwxfMxqjNO2ywS532/j7PkNtP36XMQ3nTovCuAcC1/9DHiTirtsk9f5CsO1PFn8GoO7AUgdqj356jyvGmpeVK62rQcfMEb7Cu742+I4hy2TwjyvWkMZQliyRx9T95P/x37TaAxUHlixaurKhfy6rTVwpFG0JlMWW4+LHtEXJZyHV+yR8sXarV3da2+VDWmFt5yCNy92VSSr3ZYs75P37rlvX53SJGd91m7dL1GadhNsVtBMQdaD25B0wncSVm9bVwMtXhbgC49rga1to3JVtYvZloLIICl0Gi48h92Fb45Qsf1IM2rJifTGJrjbxOOvx1W0ii6atALKOq9PLmdmK6HJ7dE02Rp+DLCEEcZ+1Xadp4xW1wNfj1cwkpdDEGADqCkQdqD15x9O1KtjXJpRcFrG0bQiNu7KtfpA0UJ9E2fvLV5nX3l7ibIuvbbzGqRS75MIl9+zaa/brck/Sahcui5vLasRjaLOQEnECVa8oIdvpKqIbInrlOQi57nwrgiS5ZrPEKUrLpuyfLbatGXXkQh6kUL8O1JUenZ2djX4C4KWjo8MMHjzYLFy40AwaNKjo5oBAcBN347IWucqWuNyDtrIYvlUnPrLOmmbgGn3M/CXLI8uWzvCNO2+6EK/ev4Rj4risSqjQIOhvsjZpVzCJNlptIu6aIgudFKcU40bxlPp9G0niP7WIShs7mtd+XP3O+zhJKeq4ALRCU8BSB9oCZFyFjY3N5Su3I2ylUFzlLaR1SFvQpMXOVWvPd950PN1DL7nj77Qg82VEuixNGhKHNoulFqKhtei0KA2xhkpC6x/GuSbzCleIs5AXVUcO9etAnYGoA4VSRQtaFducl/gNXQGB/yczfEkEyfg3DYmNJOO64dA1G/b3/orV1tgtm5VNZkS6zqcudEyic/6SZVHxZN3/uPp/vlp0fBzKmGWLYppYsCT1D321/vIKV4hz+RZViBYFcEGdgagDhVHFZXyq2OY8CbU6yTIpPD7sXiULG7lwn5kxv2F7mY1qK7jMsAhbsapRxEl0coRrqbAk55OSIWi/UhjJPtLvC+7+e2IrkCyonCVDO05EuYS3zTqaV8yo7yEh63HSPly1Kia2XR8MQbFA1IHCqKIbpIptzpM4qxO5UWcveN+8vWhZQ5kUFlic1SuFEb0mwSRFjcsdG5pRG1eyw5XVK8+nXjeWP+cqBWOrHWjLxo3LKtbtyEtEJSn30apwhbTHSfNwZVsWrwja/cEQNBcUHwaF4SvOW1aq2OYQXEWAbfgKJ5OLlQSdRBfK1UKGkg1slimXZUlC7kpbcV1byY6rj9wm+pHtjisQzcV75cRLv8l9G7dyBrXN5pqWr7MU9k0K9ydLEW7bdZLk2skKH0u7xuOKGIcWDG8FvusBgKzAUgcKowxukHZoc6ssB77JSSZJPD290e1KrlhbORG56gFbWeYsWNrw2VffWtxlQZusslZtdeGSnk+ZNCH3p61eul4ewULVF8OlLX/NdvH5LFRxLkHbdUIkuXayuB19Vto4EVwmC3vamD64bEEIKGmSEJQ0AXUjrvSED9cqC0mQx9NlSrRwcCGzWfMsWaFXn5D705OsL7M0bkLOcg5c7U4yBiHb6zZS3cERg/s3jI2v3VnPiz6+XH83xPVapjImSQVa2doPyqspSu1+vfHGG82JJ55ottlmG9OvXz/To0cPM2XKlFT7WrRokTnnnHPM+PHjzYABA8yQIUPMxz72MXPeeefl3m4AqkRal7J2aRG2dVKTHN+2FJZ2tdkgYcFutaSuubj+aQsa70+7oV0uXtu2Iecgi1szqYsvZHvdRrJM2hJQXO3O6nbUxyfh7BtTicuVXhRx14MGLltQC/frWWedZWbMmGGGDx9uRowYEf2dhpkzZ5o99tjDvP7662bPPfc0++23n1m2bJl57bXXzK233hqJPQDalbQuZdtEwxOVb/krX604m2vKtiyXC5sbM218mm/1iWafAyKLSzypiy9ke24judKlq1lazHztzlpKJGvoQ5HJEVlBGRZQC1F3zTXXmE022cSMHj3aXHLJJeaMM85IvI+VK1eagw46yMyZM8c8+OCDZsKECd3+D0C7oye8EPdQXKyYraAx4duva+KWQo1i7155c1G0OgQhV4hIQlwfdf98BYvzQI4ZWbqyxIAlFUA2UWmLSeS/pStQjomv3XnEo1ZZmGWhjrG8oM1j6ljUXXvtteboo48O/txNN91kDj30UHP22Web888/P3M7EFMHqkiSGJ64+B3bMlpJYoPSTEy+z8XF9dmWp3LFyeXV3qwUGUMVcmzXuCD2C4BkYJmwhNx8883R74MPPtjMmjXL/O53vzMLFiwwG2+8sfnUpz5lBg4cWHQTQQuoQ+ZYFkGUxJXnyxS07StJUkVal6K00Njqjcn96OW2dBatLenCZQlrpWVI94stM9R+GcfX7GOHZIq6xiUvi1Idvq8AFEHtRd1f/vKX6Pejjz5qTj/99CiWjllnnXXMLbfcYnbffXfn52l7+RlS1aBa1KHYZ5Y+JC3n4HOrZikNkUdZiZBxGDlkjYbXMvki7zi5vMSHr19Zr12fVU3HPfJqHpI0sW/NGgsAQIWzX/Pgrbc+KAr61a9+1Zx66qmRte7tt982P/rRjyJz56RJk8zcuf96qtdcfPHFkWmUf0aNSr58DygWPZFToHeRxUfTkCX7LWl2qy9TMEvx5TwKN6fJ0pSvpdWOSFuEN++Ctq5+Zc16dLVRvq8TWUgEk7CjkiX0u9WCquhMz1YWUwYgb2ov6lav/mB9yP333z+Ky/vQhz4UZdOecsopkcgjYffzn//c+XmK46Nt+IdEIagWtlIMRVeVT0oWQZSmnIOr5EKW0hB5lJUIGQfXceh8SwFDgsVWfqQI8eHqV1YhHCoWJWy5o++JXg2k7qu2lGnlCQDSUHv3K1nX5s2bZz796U93+x+9973vfc8884y7sCnVx6MfUF1cpRiqtG5rmco58H7SxHllbUfoONiOo4WMromX1KWapsyEa9+ufrnez9pG/T4JXBqP0Ji6umZ6Ju07Yv9A2ai9qBs3blwk6qjYsIbfe//99wtoGWgltlIMZa71lHSJp1a3rciYp7TjECLCkvQtqfiI27cv+cCVYZq2jXFtL7omWlHXehKhnuQ8QPyBVlF7UUdFh5944gnzwgsvmAMPPLDhf/QesdFGGxXUOtBKqlLrqWjRFEezLDnNnvh855+PrQsdx/UtifhIM262MUm6n1CxWLXvSTNI0vfQ81D27zOoF7WJqSNr3EsvvRT9lhxzzDGR+/TKK680s2fPblg27Lvf/W709+c+97mWtxcUgytWrEwUHSheRMxTq2KZbOdfHjuv1ShsQfdJx801JlmWdQtNACjj96RVCQyhfQ89D2X/PoeCBJJqUPoVJR5//PHo7+eff77rvalTp0Z/77zzzua4446L/r7qqquidVxpya9zzz23ax9jxowxl112mfnKV75ittxySzN58uRI5FG9uunTp0dry06cOLGQ/oHWUwU3iHYBkfWI2l2W9jbDklNEHJfLOpdkofi4/WsLTdy4hdSLSzP+VbcWlbH9oeehDkt8lXH8QQVFHQm66667ruE9cqXSD8OizgdlupKLlcQdrTBBS4Ntttlm5swzzwz6PKgHVbkx8WTBqx7wT5nam3fMU6snPlcRYiKvpcB8a+PGtSmuXlzS8XcJxLwfcvLcX9KCyEUQch7q4M4u6/iDiom6KVOmRD8hkHVOWug0BxxwQPQD2pcq3ZioXVVqb1ZaPfHpsc3LOpdFqNqyc/MaE1tb8n7IyXN/SQRuFShLklNa6mBtbBdKLeoAKDKzregn63a7kbZy4tNjm5d1Ls82yWXQmiGaKT4qjxU+mmFNSyNwy/CdrSt1sDa2Cz06Ozs7i25EOy++C1pLyI2/TIuSxy3xVOQNNstatEW3PUs7Qj9HokmKNCqGHLdObivHJut1rj9P1jRZ3DkvS13Ivsr0nQWgSE0BSx1oK0IsH2Vye9raW1RsoBQcRJo2lCmu0TW2cdag0PansbS20lqZ1frSTHcxu8RDrahl+s4CUCQQdQBUzO1ZVKaoFDM84SZtQ5FZrnFCI0SwJWl/qGgq0nKZRUQ2y12srW4k6tK0BYB2BKIOgIrFjxQxgcXV1gptQ5FZrj7LGm1Hy8jFCbak7Y8TOWWyXJble5JG+Jf9OwtAq4CoA6BgN1hSS00RE5gtsYB+krah6CxXm0BwlTexCba82191t2EzvidphX/VM0wByAMkSiQEiRIgT6oU4F2WBIe8x1cnNGy+wSDz1YljWxanmDVZoWrnpJ37BUCzNQVEXUIg6kCepMmQBMlIkvyQp7BOEsuXNgs3tN0QSQCUE2S/AlAjEODdfOLcctKl2q93ry6XaBaLWZJYubRuQywoDwDQ9Oz2DgCgZbCgIAsdJtvioHEnQUZ11kj4kAgKXbicRZP8XCsWcW+3BeXjwILzAMBSB0DhVDHAu47uvLRJC/pzlEW729h1G95rhgU2NGmjHazBsEYC8AEQdQCARNR1Ak0rfvTnnp/dEf3QCgtUkLeZwjfkgaAdyn1UPYsYgLyA+xUAkIi6uvPSusL5c5Q1KyFBR0kvRS+Fxmu8Ft2WZhLqigag7sBSBwBIRJ3deWld4fwZmY1a9LjU1aLartZIAEKAqAMAJKLqE2iz4gHLNi7t5pKsYmwqAHmDOnUJQZ06AKqbSFGlYs9Zaae+AlBVOlCnDgBQZsrs9msn61XZLIcAgOaDRAkAQNskUrRbQD0JuTonSAAAGoGlDgDQNokUsF4BAOoMYuoSgpg6AKobU9fufQEA1FtTQNQlBKIOgPYRY0g2AABUSVMgpg4AUDts67HWLT7QBtY/BaC9gagDANSOvMRYlRIr8hKyAIDqAlEHAKgdeYmxtEuHFWF1q5pVEQCQP4ipSwhi6gCoT0xdWZMg0sTyIf4PgOqB4sMAAJDDslF1K5KMci0AAIg6AEBbooXTFQ++Ev0ugxhKW+sP658C0N4gpg4A0JZoofT87I7SJBgUGcsHAKguEHUAgLaEhdPmGwwqZYKBbYkvlCwBAPiAqAMAtC0kmL46cWxpypb4RBtKlgAA4kBMHQCgrSlLgkFc4kaa5AkAQHsBSx0AoO2xuTpbTVyduSoVQgYAFAMsdQAAUALiMl7LYlEEAJQXFB9OCIoPAwCaRVmLIQMAmgOKDwMAQE1BnTkAQBYQUwcAAAAAUANgqQOgQsA9BwAAwAUsdQBUBNQpAwAA4AOiDoCKEFfyAgAAQHsDUQdARUCdMgAAAD4QUwdARUCdMgAAAD4g6gCoECh5AQAAwAXcrwAAAAAANQCiDgAAAACgBkDUAQAAAADUAIg6AAAAAIAaAFEHAAAAAFADIOoAAAAAAGoARB0AAAAAQA2AqAMAAAAAqAEQdQAAAAAANQCiDgAAAACgBkDUAQAAAADUAIg6AAAAAIAaAFEHAAAAAFADehfdgKrR2dkZ/e7o6Ci6KQAAAACoMB3/1BKsLbICUZeQRYsWRb9HjRpVdFMAAAAAUBNtMXjw4Mz76dGZlzxsE1avXm3mzJlj1lprLdOjR4+im1PJpxISxLNmzTKDBg0qujkgEJy3aoLzVk1w3trnvHV2dkaCbuTIkaZnz+wRcbDUJYQG/UMf+lDRzag8dMHjZlU9cN6qCc5bNcF5a4/zNjgHCx2DRAkAAAAAgBoAUQcAAAAAUAMg6kBL6devnznnnHOi36A64LxVE5y3aoLzVk36leC8IVECAAAAAKAGwFIHAAAAAFADIOoAAAAAAGoARB0AAAAAQA2AqAOxzJ4921x++eVm7733NhtuuKHp27evWX/99c1BBx1knnrqqUT7+r//+z9z4okndu2HCi4ec8wxUbFGGxtttFFU5Nn2s/vuu+fUw3qydOlSc9ppp5ldd901Guc11lgjOm877bSTufbaa82KFSsSFd2+8sorzeabb2769+9v1llnHXPooYea119/3fmZ++67z+y2225RoW6q2TRhwgTz4IMP5tS7+lLkeXN91+jn6KOPzrGX9SOv8/bWW2+Ziy++2Hz2s581Y8aM6Rr/OJ5++mmz7777miFDhpg111zT7LDDDuaWW27JoWf1ZmmB560Z8xsSJUAs3/72t82ll15qNt544+hCo4nh1VdfNXfccUdUDftXv/qVOeSQQ2L3849//MN84hOfiC5+EohbbLFFtJ/f/va30T7/+Mc/RsfQF/2CBQvMqaee2m1/9D9MNG7mzZsXVTffbrvtzNixY6Mxnj9/vrn33nvNjBkzonNAf4dUMT/++OPNNddcYzbbbDOz3377Rauq0IQxcOBA8+STT5pNNtmkYfsbb7zRHHHEEdEx+dq4+eabozbR5+jGB8p33mgyGT16tPV7tdVWW5lJkybl2tc6kdd5mzp1avQAROeCzg89CL/33nvetUEffvhhs88++0SC5POf/3z0IHXrrbdGx/3+979vTj/99Cb0uB7MK/C8NWV+I1EHgI9bb721c+rUqd3ef/TRRzv79OnTufbaa3cuXbo0dj/77bcfXd2dV1xxRcP7t9xyS/T+Pvvs0+0zo0ePjn5AclatWtW5bNmybu+vWLGic/fdd4/G/O67747dz0MPPRRtu+uuuzbs75577one33vvvRu2f/fddzuHDBnSOXz48M5Zs2Z1vU9/03v009HRkbl/daWo80bQ+7vttlsOvWg/8jpvb7zxRucjjzzS9R0ZN25c9FkXtP+NN964s1+/fp3PPvts1/sLFizoHDt2bGffvn07p0+fnrpfdWdVQeetWfMb3K8glgMPPDByo2l22WWX6MmEnmqef/75WBM3uePWW289c8oppzT87+CDD46sAPR/nzsPJIOeLMnFrendu7eZPHly9Pdrr70Wu5+f/exn0e8LLrigYX+f+tSnIsvt/fffb2bOnNn1/m9+85vo6ZPOs1xSj/4++eSToyfj22+/PXP/6kpR5w2U47zRPZJcgWRtC+Ghhx6KvCCHHXZYdB+VS0995zvfMcuXLzfXXXddor60Ez0LOm/NAqIOZKJPnz5dXwAf77zzjlm5cmXk2rHFGVAMArsRNMuWLTNTpkwx3/3ud81VV12VOI4PdI+z+v3vfx/9PX78+Njtya1AMToUY6Ihlw/xyCOPNGxPkNsiZHtQjvPGkCC/+uqro+/bT37yk9gHNpDveUsKvm/VPG/Nmt/8MzEAHugp/4EHHjAjRoyIArF9rL322qZXr15RjAJ5ebSwmzZtWvT7lVde6fbZN954I0qmkGy77bbm17/+dbcYPNAdelKnGwaNO4lrSlZ46aWXojGdOHGi97NLliwxc+fOjW5qdP40HJNFsZEM/63jtVzbg3KcN+Zvf/tblMwk+eQnPxlZe9Zdd93M/ao7Wc5bGnzfNwr4p/hJfN/Kd96aNr/l6swFbcPy5cujWB26hK6//vqgz0yYMCHa/qqrruoWs0fv088JJ5zQ8L9zzz2388EHH+x88803O5csWRLFjBxxxBHRthSLgNiseBYtWtQ1vvTTo0ePzq9//etRzEgcs2fPjj6z0047Wf9///33R///yle+0vXeJptsEr1n2z9dN/S/LbbYImOv6k+rzxtx+umnd/7xj3/snDdvXvTdor8/9alPRdtuu+22nStXrsytf3Uly3mzERebtddee0X/f/XVV63/HzlyZOegQYNSHbudWNTi89as+Q2iDqQKLD3ssMOiC+/4448P/txzzz3XOXDgwK6kiG984xudkydP7uzZs2c0ydP7X/rSl4L2xRf+D37wgww9ab/zRskK//mf/xklMtCEv3DhQu9nIOra57z5jk/JE7Q9PYCB5p03GxB19TxvzZrfEFMHEscZHHvssVEZk8MPPzyKuQllyy23jGopfe5znzN//etfzRVXXGFefvll89Of/jQqf0GEunfYPfTEE0+k7El7BgRTssKXv/zlKGaKxu6iiy7yfoaCrYmFCxda/9/R0dGwXdxnbNuDcpw33/GpNAqB71tzz1saQs41vm/lO2/Nmt8g6kAiQUe+f4qtoQKmFNwZUitLsummm0b1yqhWHQWI/v3vfzfHHXec+d///d/o/9tss03QfoYPH94VOwSSw0HVHGTtggLtKWaSYh5XrVoVFM/ji9fyxf+AYs+bD3zfWnPe0uD7vlG81uLFi/F9K+F5a9b3DaIOJBJ0119/fVRM9oYbbrAGYKdh0aJF5q677jLDhg0ze+21V9BnOEOICjSC5FARWpm97IPK2dDNxfbUSGVoCErll9sTVDLDtb2tRA4o9rz5wPetdectKfi+VfO8Ne37lthhC9oyxuCoo46KfPwHH3xwbODo22+/3fniiy9GvyXvvfdet89S0WLap60oMe2Dgkc19P76668ffYaKPQI7f//7363jR+998pOfjMbvoosuij1vaYoPDx48GMWHK3be/ud//ieKedQ88cQTnQMGDIgKjb/22ms59bJ+5HXeNCHFhz/84Q97iw9PmzYtdb/qzt8LOm/Nmt8g6kAs55xzTnSBUZLDmWeeGb3WP/JmwtvTb8ljjz3Wuc4660RJFt/61rc6v/zlL3duuOGGXQkXq1ev7nbctdZaK1qJ4t///d+jxIrPfOYz0eRCnznjjDNaNgZVhMePshdprGnMDz/88M5hw4ZF47fLLrtEQjvuvBHHHXdc9L/NNtus85vf/GYUyEuTxdChQztffvnlbtvfcMMN0fZ0vk8++eToh/6mjDJaQQSU77zRgxsJ7kmTJnWecsopnaeddlqU0ETnjJKZ/uu//qsl/a8qeZ43Ohf8Q0kOtJ18jyZ+LeDpvkjHp3spnTvKnqTPff/7329J/6vKOQWdt2bNbxB1IBa20vl+rr322tiLfsaMGZFVbtSoUdHEQplFe+yxR+d///d/W49LS5N97nOfi7Ip6QvSu3fv6AmGLvz77ruv6f2uOk8//XR0g6cJncaaxo9uVFRa5qc//Wk3q6nvZkXWWrKk0r7IIkD7OeSQQ7yWm3vvvTe6Ia655prRAwFlUP7hD39oSl/rRFHn7bbbbou+W2PGjInOGU0u9F099NBDO5966qmm9rkO5Hne4u63Dz/8cLfP0DkiyxLdK/v379+53Xbbdd50001N7XMdeLqg89as+a3HPxsCAAAAAAAqDBIlAAAAAABqAEQdAAAAAEANgKgDAAAAAKgBEHUAAAAAADUAog4AAAAAoAZA1AEAAAAA1ACIOgAAAACAGgBRBwAAAABQAyDqAAAAAABqAEQdAADkwPTp002PHj3M7rvvXnRTAABtCkQdAAAAAEANgKgDAAAAAKgBEHUAAAAAADUAog4AAHKmo6PDfPWrXzWjRo0ya6yxhvm3f/s388Mf/tCsXr266KYBAGpMj87Ozs6iGwEAAHVIlBgzZozZYYcdzIoVK8w//vEPs8cee5jly5ebBx980Lz//vvmqKOOMlOmTCm6qQCAmgJRBwAAOYo6YosttoiE3PDhw6PXJPB23XVXM2fOHHP77bebSZMmFdxaAEAdgfsVAABy5vvf/36XoCM23nhjc/bZZ0d/X3XVVQW2DABQZ2CpAwCAHC11Q4cONe+88063/y9cuNAMGTLE9O/f3yxevNj07IlnagBAvuCuAgAAOTJ69Gjr+4MHD45EHcXWzZ8/v+XtAgDUH4g6AAAAAIAaAFEHAAA5MnPmTGeZkwULFkTuV7LYAQBA3kDUAQBAjlA8HWW+am666abo94477mh69epVQMsAAHUHog4AAHLm61//ekOyxLRp08z5558f/X3SSScV2DIAQJ3pXXQDAACgTlDxYSo4/JGPfCQqPkyFiMly995775nDDz/cHHjggUU3EQBQU2CpAwCAHOnXr5956KGHzGGHHWaefPJJc99990XLhVHtOqwmAQBoJqhTBwAAAABQA2CpAwAAAACoARB1AAAAAAA1AKIOAAAAAKAGQNQBAAAAANQAiDoAAAAAgBoAUQcAAAAAUAMg6gAAAAAAagBEHQAAAABADYCoAwAAAACoARB1AAAAAAA1AKIOAAAAAKAGQNQBAAAAANQAiDoAAAAAAFN9/j8qE60AcoWrHgAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# report the results\n",
     "sim.report()"
@@ -1402,15 +1535,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Scenario directory created at 'c:\\Users\\Markus\\pymob\\pymob\\docs\\source\\user_guide\\case_studies\\superquickstart\\scenarios\\linreg'.\n",
-      "Results directory exists at 'c:\\Users\\Markus\\pymob\\pymob\\docs\\source\\user_guide\\case_studies\\superquickstart\\results\\linreg'.\n"
+      "Scenario directory exists at 'c:\\Users\\mgrho\\pymob\\docs\\source\\user_guide\\case_studies\\superquickstart\\scenarios\\linreg'.\n",
+      "Results directory exists at 'c:\\Users\\mgrho\\pymob\\docs\\source\\user_guide\\case_studies\\superquickstart\\results\\linreg'.\n"
      ]
     }
    ],
@@ -1442,7 +1575,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "pymob2",
+   "display_name": "pymobnew",
    "language": "python",
    "name": "python3"
   },
@@ -1456,7 +1589,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.12"
+   "version": "3.11.13"
   }
  },
  "nbformat": 4,
diff --git a/docs/source/user_guide/superquickstart.md b/docs/source/user_guide/superquickstart.md
index 5ddafa5b4..af75c22e4 100644
--- a/docs/source/user_guide/superquickstart.md
+++ b/docs/source/user_guide/superquickstart.md
@@ -36,7 +36,7 @@ Users can also implement custom solvers as a subclass of {class}`pymob.solver.So
 The inferer handels the parameter estimation.  
 Pymob supports [various backends](https://pymob.readthedocs.io/en/stable/user_guide/framework_overview.html). In this example, we will work with *NumPyro*.  
 We assign the inferer to our Simulation object via the {attr}`~pymob.simulation.inferer` attribute and configure the desired kernel (e.g. *nuts*).  
-But before inference, we need to parameterize our model using the *Param* class.   
+But before inference, we need to parameterize our model using the {class}`sim.parameters.Param` class.   
 Each parameter can be marked either as free or fixed, depending on whether it should be variable during the optimization procedure.   
 The parameters are stored in the {attr}`~pymob.simulation.SimulationBase.model_parameters` dictionary, which holds model input values.
 By default, it takes the keys: `parameters`, `y0` and `x_in`. 
@@ -50,7 +50,7 @@ The simulation settings will be saved in a `.cfg` configuration file.
 The config file contains information about our simulation in various sections. [Learn more here](https://pymob.readthedocs.io/en/stable/user_guide/case_studies.html#configuration).  
 We can further use it to create new simulations by loading settings from a config file. 
 
-![framework-overview](.\figures\pymob_overview.png)
+![framework-overview](./figures/pymob_overview.png)
 
 ## Getting started 🛫
 
@@ -69,7 +69,7 @@ from pymob.sim.config import Param
 
 Since no measured data is provided, we will generate an artificial dataset.  
 $y_{obs}$ represents the **observed data** over the time $t$ [0, 10].  
-To use this data later in the simulation, we need to convert it into an **xarray-Dataset**.  
+To use this data later in the simulation, we need to convert it into an **xarray dataset**.  
 In your own application, you would replace this with your measured experimental data.  
 
 
@@ -78,7 +78,7 @@ In your own application, you would replace this with your measured experimental
 rng = np.random.default_rng(seed=1)  # for reproducibility
 slope = rng.uniform(2,4)
 intercept = 1.0
-num_points = 100
+num_points = 101
 noise_level = 1.7
 
 # generating time values
@@ -546,80 +546,48 @@ dl.xr-attrs {
   stroke-width: 0.8px;
 }
 </style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 2kB
-Dimensions:  (t: 100)
+Dimensions:  (t: 101)
 Coordinates:
-  * t        (t) float64 800B 0.0 0.101 0.202 0.303 ... 9.697 9.798 9.899 10.0
+  * t        (t) float64 808B 0.0 0.1 0.2 0.3 0.4 0.5 ... 9.6 9.7 9.8 9.9 10.0
 Data variables:
-    y        (t) float64 800B 1.59 2.136 1.343 2.532 ... 31.06 27.02 30.87 32.09</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-b6e66973-2041-42d5-8705-ecf533c55a89' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b6e66973-2041-42d5-8705-ecf533c55a89' 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'>t</span>: 100</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-77264d36-2121-47a0-9814-7048a5037bab' class='xr-section-summary-in' type='checkbox'  checked><label for='section-77264d36-2121-47a0-9814-7048a5037bab' class='xr-section-summary' >Coordinates: <span>(1)</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'>t</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.101 0.202 ... 9.899 10.0</div><input id='attrs-914d5174-447d-4c2c-b6c2-2cd02c911b23' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-914d5174-447d-4c2c-b6c2-2cd02c911b23' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c0677693-e243-4c3d-bd9f-6ceef5966d2f' class='xr-var-data-in' type='checkbox'><label for='data-c0677693-e243-4c3d-bd9f-6ceef5966d2f' 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.      ,  0.10101 ,  0.20202 ,  0.30303 ,  0.40404 ,  0.505051,
-        0.606061,  0.707071,  0.808081,  0.909091,  1.010101,  1.111111,
-        1.212121,  1.313131,  1.414141,  1.515152,  1.616162,  1.717172,
-        1.818182,  1.919192,  2.020202,  2.121212,  2.222222,  2.323232,
-        2.424242,  2.525253,  2.626263,  2.727273,  2.828283,  2.929293,
-        3.030303,  3.131313,  3.232323,  3.333333,  3.434343,  3.535354,
-        3.636364,  3.737374,  3.838384,  3.939394,  4.040404,  4.141414,
-        4.242424,  4.343434,  4.444444,  4.545455,  4.646465,  4.747475,
-        4.848485,  4.949495,  5.050505,  5.151515,  5.252525,  5.353535,
-        5.454545,  5.555556,  5.656566,  5.757576,  5.858586,  5.959596,
-        6.060606,  6.161616,  6.262626,  6.363636,  6.464646,  6.565657,
-        6.666667,  6.767677,  6.868687,  6.969697,  7.070707,  7.171717,
-        7.272727,  7.373737,  7.474747,  7.575758,  7.676768,  7.777778,
-        7.878788,  7.979798,  8.080808,  8.181818,  8.282828,  8.383838,
-        8.484848,  8.585859,  8.686869,  8.787879,  8.888889,  8.989899,
-        9.090909,  9.191919,  9.292929,  9.393939,  9.494949,  9.59596 ,
-        9.69697 ,  9.79798 ,  9.89899 , 10.      ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-80914dc8-dc91-498c-b781-0761b0f62517' class='xr-section-summary-in' type='checkbox'  checked><label for='section-80914dc8-dc91-498c-b781-0761b0f62517' class='xr-section-summary' >Data variables: <span>(1)</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>y</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.59 2.136 1.343 ... 30.87 32.09</div><input id='attrs-0c997349-0f4e-4053-836f-48a9cad4415e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0c997349-0f4e-4053-836f-48a9cad4415e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-86a643cf-ad83-418a-b5e3-5f52ca258650' class='xr-var-data-in' type='checkbox'><label for='data-86a643cf-ad83-418a-b5e3-5f52ca258650' 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([ 1.58976396,  2.13626768,  1.34289925,  2.53150209,  2.56102578,
-       -0.21899969,  1.26689629,  2.064798  ,  4.04579132,  2.33368407,
-        5.56713047,  4.64800357,  4.12075281,  2.8115269 ,  6.05279673,
-        4.72113173,  9.74281027,  6.23928579,  6.02924706,  8.057377  ,
-        8.67482637,  6.44336997,  9.71115216,  6.84397881,  9.06017053,
-        6.49025613, 10.12391111,  9.5022547 ,  8.05685756,  9.53097276,
-        8.43057554, 14.57156966,  8.77743968,  8.75197054, 11.73207016,
-       10.08294682, 13.55943288, 14.76145888, 14.13237262, 15.826024  ,
-       11.69458685, 12.51989808, 14.40128584, 14.39427702, 12.66837215,
-       15.92469856, 17.83174566, 17.44936835, 17.64962036, 14.75949057,
-       15.63025287, 17.29111458, 19.47794167, 16.11896414, 19.22733081,
-       15.55895925, 17.50982302, 16.59275063, 19.37052338, 18.53681002,
-       21.55941223, 19.05813279, 18.82898749, 18.51376136, 19.01012364,
-       20.79403644, 22.02425154, 21.93984824, 21.0715503 , 20.06227644,
-       22.79902669, 20.19672578, 24.33260566, 25.66898506, 22.59631756,
-       24.35184169, 24.93279036, 27.10831817, 26.88697449, 25.80286533,
-       27.3116128 , 25.4060967 , 27.55552521, 28.30159717, 25.17681247,
-       28.26655258, 27.82207145, 28.22772349, 30.45361189, 30.23373524,
-       28.89402766, 30.98637061, 31.13245499, 29.01262802, 29.44403878,
-       29.12494048, 31.06098902, 27.02074993, 30.87370365, 32.092818  ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a0200bb1-5afd-402c-8284-a6c1f4971599' class='xr-section-summary-in' type='checkbox'  ><label for='section-a0200bb1-5afd-402c-8284-a6c1f4971599' class='xr-section-summary' >Indexes: <span>(1)</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>t</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-0f6569cc-0300-40f9-81f5-07b531e578bc' class='xr-index-data-in' type='checkbox'/><label for='index-0f6569cc-0300-40f9-81f5-07b531e578bc' 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([                0.0, 0.10101010101010101, 0.20202020202020202,
-       0.30303030303030304, 0.40404040404040403,  0.5050505050505051,
-        0.6060606060606061,  0.7070707070707071,  0.8080808080808081,
-        0.9090909090909091,  1.0101010101010102,  1.1111111111111112,
-        1.2121212121212122,  1.3131313131313131,  1.4141414141414141,
-        1.5151515151515151,  1.6161616161616161,  1.7171717171717171,
-        1.8181818181818181,  1.9191919191919191,  2.0202020202020203,
-         2.121212121212121,  2.2222222222222223,   2.323232323232323,
-        2.4242424242424243,   2.525252525252525,  2.6262626262626263,
-         2.727272727272727,  2.8282828282828283,   2.929292929292929,
-        3.0303030303030303,   3.131313131313131,  3.2323232323232323,
-        3.3333333333333335,  3.4343434343434343,  3.5353535353535355,
-        3.6363636363636362,  3.7373737373737375,  3.8383838383838382,
-        3.9393939393939394,   4.040404040404041,   4.141414141414141,
-         4.242424242424242,   4.343434343434343,   4.444444444444445,
-         4.545454545454545,   4.646464646464646,   4.747474747474747,
-         4.848484848484849,    4.94949494949495,    5.05050505050505,
-         5.151515151515151,   5.252525252525253,   5.353535353535354,
-         5.454545454545454,   5.555555555555555,   5.656565656565657,
-         5.757575757575758,   5.858585858585858,   5.959595959595959,
-        6.0606060606060606,   6.161616161616162,   6.262626262626262,
-         6.363636363636363,  6.4646464646464645,   6.565656565656566,
-         6.666666666666667,   6.767676767676767,  6.8686868686868685,
-          6.96969696969697,   7.070707070707071,   7.171717171717171,
-        7.2727272727272725,   7.373737373737374,   7.474747474747475,
-         7.575757575757575,  7.6767676767676765,   7.777777777777778,
-         7.878787878787879,   7.979797979797979,   8.080808080808081,
-         8.181818181818182,   8.282828282828282,   8.383838383838384,
-         8.484848484848484,   8.585858585858587,   8.686868686868687,
-         8.787878787878787,    8.88888888888889,    8.98989898989899,
-          9.09090909090909,   9.191919191919192,   9.292929292929292,
-         9.393939393939394,   9.494949494949495,   9.595959595959595,
-         9.696969696969697,   9.797979797979798,     9.8989898989899,
+    y        (t) float64 808B 0.3908 -0.3918 0.8276 3.156 ... 31.15 30.01 34.0</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-f694765b-7190-4a7f-badf-dfcbf2290055' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f694765b-7190-4a7f-badf-dfcbf2290055' 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'>t</span>: 101</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-8622ff98-cc98-46a5-9d0d-ef39a77640e2' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8622ff98-cc98-46a5-9d0d-ef39a77640e2' class='xr-section-summary' >Coordinates: <span>(1)</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'>t</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.1 0.2 0.3 ... 9.8 9.9 10.0</div><input id='attrs-5c494f52-c3bd-4970-872f-03965d5c0e51' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5c494f52-c3bd-4970-872f-03965d5c0e51' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-95144df5-551f-4f62-b70d-7edf2ba7e67c' class='xr-var-data-in' type='checkbox'><label for='data-95144df5-551f-4f62-b70d-7edf2ba7e67c' 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. ,  0.1,  0.2,  0.3,  0.4,  0.5,  0.6,  0.7,  0.8,  0.9,  1. ,  1.1,
+        1.2,  1.3,  1.4,  1.5,  1.6,  1.7,  1.8,  1.9,  2. ,  2.1,  2.2,  2.3,
+        2.4,  2.5,  2.6,  2.7,  2.8,  2.9,  3. ,  3.1,  3.2,  3.3,  3.4,  3.5,
+        3.6,  3.7,  3.8,  3.9,  4. ,  4.1,  4.2,  4.3,  4.4,  4.5,  4.6,  4.7,
+        4.8,  4.9,  5. ,  5.1,  5.2,  5.3,  5.4,  5.5,  5.6,  5.7,  5.8,  5.9,
+        6. ,  6.1,  6.2,  6.3,  6.4,  6.5,  6.6,  6.7,  6.8,  6.9,  7. ,  7.1,
+        7.2,  7.3,  7.4,  7.5,  7.6,  7.7,  7.8,  7.9,  8. ,  8.1,  8.2,  8.3,
+        8.4,  8.5,  8.6,  8.7,  8.8,  8.9,  9. ,  9.1,  9.2,  9.3,  9.4,  9.5,
+        9.6,  9.7,  9.8,  9.9, 10. ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f7340656-3ffa-49f5-9219-223c024b65e3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f7340656-3ffa-49f5-9219-223c024b65e3' class='xr-section-summary' >Data variables: <span>(1)</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>y</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.3908 -0.3918 ... 30.01 34.0</div><input id='attrs-b47fad1c-78ef-47a3-a131-28bf176cf1d6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b47fad1c-78ef-47a3-a131-28bf176cf1d6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e0d22dde-c08a-442a-bbf8-be83fa8d064b' class='xr-var-data-in' type='checkbox'><label for='data-e0d22dde-c08a-442a-bbf8-be83fa8d064b' 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([ 0.39079492, -0.39175535,  0.82763596,  3.15618136,  2.01285176,
+        1.95283272,  3.85547661,  3.09599209,  3.33960694,  6.36552902,
+        7.06203247,  8.13500043,  6.67894376,  4.27773142,  4.49357852,
+        7.06572   ,  3.08708572,  5.02765617,  9.61498023,  6.15033602,
+        7.06840353,  9.85153287,  7.89139009, 10.54173076,  9.94828007,
+        5.96000158,  6.24899319,  7.94907589,  9.79232732,  8.66821005,
+        8.93151735, 12.26781126, 16.09515715, 12.11394117, 11.2904242 ,
+        9.83146948, 15.16436768, 14.12965637, 15.98539961, 13.52253083,
+       13.58992133, 13.60836609, 13.24495716, 14.48019284, 15.88365617,
+       15.81425659, 13.46733329, 13.15867419, 13.45737717, 17.61523855,
+       14.06675657, 15.84539765, 16.52929276, 17.39914921, 13.02141939,
+       18.42999402, 16.09000421, 14.77746634, 18.02244084, 19.48583459,
+       18.16219559, 18.1813225 , 20.7326549 , 21.39514027, 19.99235063,
+       20.99838942, 21.21453906, 20.94423511, 22.5408263 , 17.69934953,
+       24.49226654, 23.43777925, 20.56362939, 21.57852696, 22.4708503 ,
+       22.07018762, 25.95316873, 23.84921314, 26.88319091, 24.64641577,
+       26.58470172, 27.73115116, 25.24151267, 28.08410175, 26.01094206,
+       24.04774622, 26.54920391, 27.94994462, 29.22530081, 25.98647605,
+       28.58272953, 29.75064965, 30.1260468 , 30.65475777, 29.32251387,
+       27.36895217, 29.43841263, 32.83137326, 31.15325649, 30.01147967,
+       33.99673899])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-42d8ccba-ea8d-43d5-ab46-93aab147b65c' class='xr-section-summary-in' type='checkbox'  ><label for='section-42d8ccba-ea8d-43d5-ab46-93aab147b65c' class='xr-section-summary' >Indexes: <span>(1)</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>t</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-e01b19cf-800b-44bd-a844-aa7dd6378eab' class='xr-index-data-in' type='checkbox'/><label for='index-e01b19cf-800b-44bd-a844-aa7dd6378eab' 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([                0.0,                 0.1,                 0.2,
+       0.30000000000000004,                 0.4,                 0.5,
+        0.6000000000000001,  0.7000000000000001,                 0.8,
+                       0.9,
+       ...
+                       9.1,   9.200000000000001,                 9.3,
+                       9.4,                 9.5,   9.600000000000001,
+         9.700000000000001,                 9.8,                 9.9,
                       10.0],
-      dtype=&#x27;float64&#x27;, name=&#x27;t&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-65faa5e2-c491-48df-8cb4-0b387f00d0f0' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-65faa5e2-c491-48df-8cb4-0b387f00d0f0' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>
+      dtype=&#x27;float64&#x27;, name=&#x27;t&#x27;, length=101))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a714ca33-f3a4-4494-8ddc-6c83255dad56' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a714ca33-f3a4-4494-8ddc-6c83255dad56' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>
 
 
 
@@ -666,17 +634,17 @@ sim.solver = solve_analytic_1d
 sim.config.data_structure
 ```
 
-    MinMaxScaler(variable=y, min=-0.21899969389420804, max=32.09281799761304)
+    MinMaxScaler(variable=y, min=-0.39175534608056317, max=33.9967389893923)
     
 
-    C:\Pymob\pymob\pymob\simulation.py:307: UserWarning: `sim.config.data_structure.y = Datavariable(dimensions=['t'] min=-0.21899969389420804 max=32.09281799761304 observed=True dimensions_evaluator=None)` has been assumed from `sim.observations`. If the order of the dimensions should be different, specify `sim.config.data_structure.y = DataVariable(dimensions=[...], ...)` manually.
+    C:\Pymob\pymob\pymob\simulation.py:307: UserWarning: `sim.config.data_structure.y = Datavariable(dimensions=['t'] min=-0.39175534608056317 max=33.9967389893923 observed=True dimensions_evaluator=None)` has been assumed from `sim.observations`. If the order of the dimensions should be different, specify `sim.config.data_structure.y = DataVariable(dimensions=[...], ...)` manually.
       warnings.warn(
     
 
 
 
 
-    Datastructure(y=DataVariable(dimensions=['t'], min=-0.21899969389420804, max=32.09281799761304, observed=True, dimensions_evaluator=None))
+    Datastructure(y=DataVariable(dimensions=['t'], min=-0.39175534608056317, max=33.9967389893923, observed=True, dimensions_evaluator=None))
 
 
 
@@ -1179,80 +1147,37 @@ dl.xr-attrs {
   stroke-width: 0.8px;
 }
 </style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 2kB
-Dimensions:  (t: 100)
+Dimensions:  (t: 101)
 Coordinates:
-  * t        (t) float64 800B 0.0 0.101 0.202 0.303 ... 9.697 9.798 9.899 10.0
+  * t        (t) float64 808B 0.0 0.1 0.2 0.3 0.4 0.5 ... 9.6 9.7 9.8 9.9 10.0
 Data variables:
-    y        (t) float64 800B 1.0 1.303 1.606 1.909 ... 30.09 30.39 30.7 31.0</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-843c93d5-f2e9-4819-9f89-2ea8de587d95' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-843c93d5-f2e9-4819-9f89-2ea8de587d95' 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'>t</span>: 100</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-8dae8c55-0d33-4a1c-ac84-9e679dd2d172' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8dae8c55-0d33-4a1c-ac84-9e679dd2d172' class='xr-section-summary' >Coordinates: <span>(1)</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'>t</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.101 0.202 ... 9.899 10.0</div><input id='attrs-e0a4798e-87be-49d8-99e3-be36163fb43f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e0a4798e-87be-49d8-99e3-be36163fb43f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-846ef747-2ea0-4f0b-8375-fbd69975f778' class='xr-var-data-in' type='checkbox'><label for='data-846ef747-2ea0-4f0b-8375-fbd69975f778' 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.      ,  0.10101 ,  0.20202 ,  0.30303 ,  0.40404 ,  0.505051,
-        0.606061,  0.707071,  0.808081,  0.909091,  1.010101,  1.111111,
-        1.212121,  1.313131,  1.414141,  1.515152,  1.616162,  1.717172,
-        1.818182,  1.919192,  2.020202,  2.121212,  2.222222,  2.323232,
-        2.424242,  2.525253,  2.626263,  2.727273,  2.828283,  2.929293,
-        3.030303,  3.131313,  3.232323,  3.333333,  3.434343,  3.535354,
-        3.636364,  3.737374,  3.838384,  3.939394,  4.040404,  4.141414,
-        4.242424,  4.343434,  4.444444,  4.545455,  4.646465,  4.747475,
-        4.848485,  4.949495,  5.050505,  5.151515,  5.252525,  5.353535,
-        5.454545,  5.555556,  5.656566,  5.757576,  5.858586,  5.959596,
-        6.060606,  6.161616,  6.262626,  6.363636,  6.464646,  6.565657,
-        6.666667,  6.767677,  6.868687,  6.969697,  7.070707,  7.171717,
-        7.272727,  7.373737,  7.474747,  7.575758,  7.676768,  7.777778,
-        7.878788,  7.979798,  8.080808,  8.181818,  8.282828,  8.383838,
-        8.484848,  8.585859,  8.686869,  8.787879,  8.888889,  8.989899,
-        9.090909,  9.191919,  9.292929,  9.393939,  9.494949,  9.59596 ,
-        9.69697 ,  9.79798 ,  9.89899 , 10.      ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e40a3c57-c1c9-4619-b14d-8a6db46c8dfe' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e40a3c57-c1c9-4619-b14d-8a6db46c8dfe' class='xr-section-summary' >Data variables: <span>(1)</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>y</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.0 1.303 1.606 ... 30.39 30.7 31.0</div><input id='attrs-454c1729-5e7a-457c-9212-b2f6447d7024' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-454c1729-5e7a-457c-9212-b2f6447d7024' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-54742c46-e2f7-4d6c-b8ca-0ce1fdb9f1c4' class='xr-var-data-in' type='checkbox'><label for='data-54742c46-e2f7-4d6c-b8ca-0ce1fdb9f1c4' 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([ 1.        ,  1.3030303 ,  1.60606061,  1.90909091,  2.21212121,
-        2.51515152,  2.81818182,  3.12121212,  3.42424242,  3.72727273,
-        4.03030303,  4.33333333,  4.63636364,  4.93939394,  5.24242424,
-        5.54545455,  5.84848485,  6.15151515,  6.45454545,  6.75757576,
-        7.06060606,  7.36363636,  7.66666667,  7.96969697,  8.27272727,
-        8.57575758,  8.87878788,  9.18181818,  9.48484848,  9.78787879,
-       10.09090909, 10.39393939, 10.6969697 , 11.        , 11.3030303 ,
-       11.60606061, 11.90909091, 12.21212121, 12.51515152, 12.81818182,
-       13.12121212, 13.42424242, 13.72727273, 14.03030303, 14.33333333,
-       14.63636364, 14.93939394, 15.24242424, 15.54545455, 15.84848485,
-       16.15151515, 16.45454545, 16.75757576, 17.06060606, 17.36363636,
-       17.66666667, 17.96969697, 18.27272727, 18.57575758, 18.87878788,
-       19.18181818, 19.48484848, 19.78787879, 20.09090909, 20.39393939,
-       20.6969697 , 21.        , 21.3030303 , 21.60606061, 21.90909091,
-       22.21212121, 22.51515152, 22.81818182, 23.12121212, 23.42424242,
-       23.72727273, 24.03030303, 24.33333333, 24.63636364, 24.93939394,
-       25.24242424, 25.54545455, 25.84848485, 26.15151515, 26.45454545,
-       26.75757576, 27.06060606, 27.36363636, 27.66666667, 27.96969697,
-       28.27272727, 28.57575758, 28.87878788, 29.18181818, 29.48484848,
-       29.78787879, 30.09090909, 30.39393939, 30.6969697 , 31.        ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-963b279c-24d3-4340-a7db-d217683908c5' class='xr-section-summary-in' type='checkbox'  ><label for='section-963b279c-24d3-4340-a7db-d217683908c5' class='xr-section-summary' >Indexes: <span>(1)</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>t</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-4a0e9593-b218-4999-a36a-0b6a7108a6ca' class='xr-index-data-in' type='checkbox'/><label for='index-4a0e9593-b218-4999-a36a-0b6a7108a6ca' 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([                0.0, 0.10101010101010101, 0.20202020202020202,
-       0.30303030303030304, 0.40404040404040403,  0.5050505050505051,
-        0.6060606060606061,  0.7070707070707071,  0.8080808080808081,
-        0.9090909090909091,  1.0101010101010102,  1.1111111111111112,
-        1.2121212121212122,  1.3131313131313131,  1.4141414141414141,
-        1.5151515151515151,  1.6161616161616161,  1.7171717171717171,
-        1.8181818181818181,  1.9191919191919191,  2.0202020202020203,
-         2.121212121212121,  2.2222222222222223,   2.323232323232323,
-        2.4242424242424243,   2.525252525252525,  2.6262626262626263,
-         2.727272727272727,  2.8282828282828283,   2.929292929292929,
-        3.0303030303030303,   3.131313131313131,  3.2323232323232323,
-        3.3333333333333335,  3.4343434343434343,  3.5353535353535355,
-        3.6363636363636362,  3.7373737373737375,  3.8383838383838382,
-        3.9393939393939394,   4.040404040404041,   4.141414141414141,
-         4.242424242424242,   4.343434343434343,   4.444444444444445,
-         4.545454545454545,   4.646464646464646,   4.747474747474747,
-         4.848484848484849,    4.94949494949495,    5.05050505050505,
-         5.151515151515151,   5.252525252525253,   5.353535353535354,
-         5.454545454545454,   5.555555555555555,   5.656565656565657,
-         5.757575757575758,   5.858585858585858,   5.959595959595959,
-        6.0606060606060606,   6.161616161616162,   6.262626262626262,
-         6.363636363636363,  6.4646464646464645,   6.565656565656566,
-         6.666666666666667,   6.767676767676767,  6.8686868686868685,
-          6.96969696969697,   7.070707070707071,   7.171717171717171,
-        7.2727272727272725,   7.373737373737374,   7.474747474747475,
-         7.575757575757575,  7.6767676767676765,   7.777777777777778,
-         7.878787878787879,   7.979797979797979,   8.080808080808081,
-         8.181818181818182,   8.282828282828282,   8.383838383838384,
-         8.484848484848484,   8.585858585858587,   8.686868686868687,
-         8.787878787878787,    8.88888888888889,    8.98989898989899,
-          9.09090909090909,   9.191919191919192,   9.292929292929292,
-         9.393939393939394,   9.494949494949495,   9.595959595959595,
-         9.696969696969697,   9.797979797979798,     9.8989898989899,
+    y        (t) float64 808B 1.0 1.3 1.6 1.9 2.2 ... 29.8 30.1 30.4 30.7 31.0</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-7b6c6f3e-398c-4687-baad-c20a6690ae2b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-7b6c6f3e-398c-4687-baad-c20a6690ae2b' 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'>t</span>: 101</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-33ba9ccc-210d-4255-b54a-b66ae6415b5d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-33ba9ccc-210d-4255-b54a-b66ae6415b5d' class='xr-section-summary' >Coordinates: <span>(1)</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'>t</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.1 0.2 0.3 ... 9.8 9.9 10.0</div><input id='attrs-0c4d6f4e-669b-43e0-95d7-7dfe1d3c8890' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0c4d6f4e-669b-43e0-95d7-7dfe1d3c8890' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b75aee23-5ecd-42dd-8c27-1666d0255b07' class='xr-var-data-in' type='checkbox'><label for='data-b75aee23-5ecd-42dd-8c27-1666d0255b07' 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. ,  0.1,  0.2,  0.3,  0.4,  0.5,  0.6,  0.7,  0.8,  0.9,  1. ,  1.1,
+        1.2,  1.3,  1.4,  1.5,  1.6,  1.7,  1.8,  1.9,  2. ,  2.1,  2.2,  2.3,
+        2.4,  2.5,  2.6,  2.7,  2.8,  2.9,  3. ,  3.1,  3.2,  3.3,  3.4,  3.5,
+        3.6,  3.7,  3.8,  3.9,  4. ,  4.1,  4.2,  4.3,  4.4,  4.5,  4.6,  4.7,
+        4.8,  4.9,  5. ,  5.1,  5.2,  5.3,  5.4,  5.5,  5.6,  5.7,  5.8,  5.9,
+        6. ,  6.1,  6.2,  6.3,  6.4,  6.5,  6.6,  6.7,  6.8,  6.9,  7. ,  7.1,
+        7.2,  7.3,  7.4,  7.5,  7.6,  7.7,  7.8,  7.9,  8. ,  8.1,  8.2,  8.3,
+        8.4,  8.5,  8.6,  8.7,  8.8,  8.9,  9. ,  9.1,  9.2,  9.3,  9.4,  9.5,
+        9.6,  9.7,  9.8,  9.9, 10. ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-63b956f7-97ac-440b-82d3-5da433d918e7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-63b956f7-97ac-440b-82d3-5da433d918e7' class='xr-section-summary' >Data variables: <span>(1)</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>y</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.0 1.3 1.6 1.9 ... 30.4 30.7 31.0</div><input id='attrs-3b324bef-212f-4afa-90a8-b46b04184dde' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3b324bef-212f-4afa-90a8-b46b04184dde' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e76ba371-f486-41ee-af6e-45373eade611' class='xr-var-data-in' type='checkbox'><label for='data-e76ba371-f486-41ee-af6e-45373eade611' 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([ 1. ,  1.3,  1.6,  1.9,  2.2,  2.5,  2.8,  3.1,  3.4,  3.7,  4. ,
+        4.3,  4.6,  4.9,  5.2,  5.5,  5.8,  6.1,  6.4,  6.7,  7. ,  7.3,
+        7.6,  7.9,  8.2,  8.5,  8.8,  9.1,  9.4,  9.7, 10. , 10.3, 10.6,
+       10.9, 11.2, 11.5, 11.8, 12.1, 12.4, 12.7, 13. , 13.3, 13.6, 13.9,
+       14.2, 14.5, 14.8, 15.1, 15.4, 15.7, 16. , 16.3, 16.6, 16.9, 17.2,
+       17.5, 17.8, 18.1, 18.4, 18.7, 19. , 19.3, 19.6, 19.9, 20.2, 20.5,
+       20.8, 21.1, 21.4, 21.7, 22. , 22.3, 22.6, 22.9, 23.2, 23.5, 23.8,
+       24.1, 24.4, 24.7, 25. , 25.3, 25.6, 25.9, 26.2, 26.5, 26.8, 27.1,
+       27.4, 27.7, 28. , 28.3, 28.6, 28.9, 29.2, 29.5, 29.8, 30.1, 30.4,
+       30.7, 31. ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c52e374a-a464-43ff-b6b2-3755fd25e350' class='xr-section-summary-in' type='checkbox'  ><label for='section-c52e374a-a464-43ff-b6b2-3755fd25e350' class='xr-section-summary' >Indexes: <span>(1)</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>t</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-bf9fd621-98be-4718-8802-94b75e56b0a5' class='xr-index-data-in' type='checkbox'/><label for='index-bf9fd621-98be-4718-8802-94b75e56b0a5' 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([                0.0,                 0.1,                 0.2,
+       0.30000000000000004,                 0.4,                 0.5,
+        0.6000000000000001,  0.7000000000000001,                 0.8,
+                       0.9,
+       ...
+                       9.1,   9.200000000000001,                 9.3,
+                       9.4,                 9.5,   9.600000000000001,
+         9.700000000000001,                 9.8,                 9.9,
                       10.0],
-      dtype=&#x27;float64&#x27;, name=&#x27;t&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4a6abe8f-ee48-42ef-a2ab-7f3e31f0ecdb' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-4a6abe8f-ee48-42ef-a2ab-7f3e31f0ecdb' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>
+      dtype=&#x27;float64&#x27;, name=&#x27;t&#x27;, length=101))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f9e9e217-fb1b-4b3a-9138-70db47fcc03c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f9e9e217-fb1b-4b3a-9138-70db47fcc03c' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>
 
 
 
@@ -1278,7 +1203,7 @@ ax.legend()
 
 
 
-    <matplotlib.legend.Legend at 0x1df6d279990>
+    <matplotlib.legend.Legend at 0x2886e8b3390>
 
 
 
@@ -1310,6 +1235,7 @@ sim.set_inferer("numpyro")
 sim.inferer.config.inference_numpyro.kernel = "nuts"
 sim.inferer.run()
 
+# you can access the posterior distrubution by:
 sim.inferer.idata.posterior
 
 # Plot the results
@@ -1328,41 +1254,41 @@ sim.posterior_predictive_checks(pred_hdi_style={"alpha": 0.1})
             value     |
      sigma_y dist     |
             value     |
-       y_obs dist 100 |
-            value 100 |
+       y_obs dist 101 |
+            value 101 |
     
 
-    
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+    
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-    
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+    
warmup:   0%|                      | 1/3000 [00:01<51:17,  1.03s/it, 1 steps of size 1.87e+00. acc. prob=0.00]
 
-    
warmup:  10%|███████████████▎                                                                                                                                  | 315/3000 [00:01<00:07, 375.41it/s, 3 steps of size 5.98e-01. acc. prob=0.78]
+    
warmup:   8%|█▌                 | 250/3000 [00:01<00:09, 304.93it/s, 1 steps of size 1.64e+00. acc. prob=0.79]
 
-    
warmup:  20%|█████████████████████████████▉                                                                                                                    | 614/3000 [00:01<00:03, 755.62it/s, 7 steps of size 8.67e-01. acc. prob=0.79]
+    
warmup:  17%|███▎               | 514/3000 [00:01<00:03, 651.34it/s, 3 steps of size 1.80e+00. acc. prob=0.79]
 
-    
warmup:  31%|████████████████████████████████████████████▌                                                                                                    | 922/3000 [00:01<00:01, 1153.67it/s, 1 steps of size 8.99e-01. acc. prob=0.79]
+    
warmup:  27%|████▊             | 801/3000 [00:01<00:02, 1038.40it/s, 3 steps of size 1.31e+00. acc. prob=0.79]
 
-    
sample:  41%|███████████████████████████████████████████████████████████▋                                                                                    | 1244/3000 [00:01<00:01, 1538.23it/s, 3 steps of size 8.50e-01. acc. prob=0.92]
+    
sample:  37%|██████▎          | 1120/3000 [00:01<00:01, 1466.63it/s, 7 steps of size 7.84e-01. acc. prob=0.94]
 
-    
sample:  52%|██████████████████████████████████████████████████████████████████████████▍                                                                     | 1552/3000 [00:01<00:00, 1867.98it/s, 3 steps of size 8.50e-01. acc. prob=0.92]
+    
sample:  46%|███████▉         | 1393/3000 [00:01<00:00, 1745.39it/s, 3 steps of size 7.84e-01. acc. prob=0.93]
 
-    
sample:  61%|████████████████████████████████████████████████████████████████████████████████████████▏                                                       | 1836/3000 [00:01<00:00, 2094.01it/s, 7 steps of size 8.50e-01. acc. prob=0.91]
+    
sample:  56%|█████████▌       | 1692/3000 [00:01<00:00, 2038.12it/s, 3 steps of size 7.84e-01. acc. prob=0.93]
 
-    
sample:  74%|█████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                     | 2210/3000 [00:01<00:00, 2416.20it/s, 11 steps of size 8.50e-01. acc. prob=0.91]
+    
sample:  70%|███████████▊     | 2087/3000 [00:01<00:00, 2500.03it/s, 3 steps of size 7.84e-01. acc. prob=0.92]
 
-    
sample:  86%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                   | 2595/3000 [00:01<00:00, 2743.24it/s, 3 steps of size 8.50e-01. acc. prob=0.91]
+    
sample:  83%|██████████████   | 2483/3000 [00:01<00:00, 2867.60it/s, 1 steps of size 7.84e-01. acc. prob=0.92]
 
-    
sample: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉| 2999/3000 [00:01<00:00, 3005.45it/s, 3 steps of size 8.50e-01. acc. prob=0.91]
+    
sample:  97%|████████████████▍| 2905/3000 [00:01<00:00, 3148.32it/s, 7 steps of size 7.84e-01. acc. prob=0.92]
 
-    
sample: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3000/3000 [00:01<00:00, 1505.68it/s, 1 steps of size 8.50e-01. acc. prob=0.91]
+    
sample: 100%|█████████████████| 3000/3000 [00:01<00:00, 1522.15it/s, 7 steps of size 7.84e-01. acc. prob=0.92]
 
     
     
 
     
                     mean       std    median      5.0%     95.0%     n_eff     r_hat
-             b      3.07      0.03      3.07      3.02      3.11   2085.24      1.00
-       sigma_y      1.57      0.12      1.57      1.38      1.76   1661.94      1.00
+             b      3.04      0.03      3.04      2.99      3.09   1489.99      1.00
+       sigma_y      1.80      0.13      1.80      1.57      1.99   1742.50      1.00
     
     Number of divergences: 0
     
@@ -1435,5 +1361,3 @@ The command-line API runs a series of commands that load the case study, execute
 
   `pymob-infer --case_study=quickstart --scenario=test --inference_backend=numpyro`.   
   While there are more command-line options, these two (--case_study and --scenario) are required.
-
-

From d44a09c9fddb8821d31e29eb5be2b5cbf12f258b Mon Sep 17 00:00:00 2001
From: merkuns <mholdreich@uni-osnabrueck.de>
Date: Wed, 23 Jul 2025 11:12:27 +0200
Subject: [PATCH 15/16] ODE system tutorial v1.0

---
 .../advanced_tutorial_ODE_system.ipynb        | 1564 +++++++++++++++++
 1 file changed, 1564 insertions(+)
 create mode 100644 docs/source/user_guide/advanced_tutorial_ODE_system.ipynb

diff --git a/docs/source/user_guide/advanced_tutorial_ODE_system.ipynb b/docs/source/user_guide/advanced_tutorial_ODE_system.ipynb
new file mode 100644
index 000000000..883008e34
--- /dev/null
+++ b/docs/source/user_guide/advanced_tutorial_ODE_system.ipynb
@@ -0,0 +1,1564 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "a4675559",
+   "metadata": {},
+   "source": [
+    "# Implementing an ODE model in Pymob\n",
+    "\n",
+    "In this tutorial, we will implement a simple ODE model, create simulation results and infer an unknown parameter from artificially generated data. It is recommended to work through this notebook after the introductiory tutorial where something very similar is done for a linear regression model.\n",
+    "\n",
+    "After setting up the simulation manually (Chapter 1), we will save our settings and create a new simulation from those settings (Chapter 2).\n",
+    "\n",
+    "# Chapter 1: Setting up the model 👩‍💻\n",
+    "\n",
+    "👉 Let's begin with setting up a Pymob simulation for an ODE model. This will follow roughly the same procedure as the introductory tutorial. We do, however, need to make some tweaks to allow for the needs of an ODE model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "04efc9a5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# First, import the necessary python packages\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import xarray as xr\n",
+    "from scipy.integrate import solve_ivp\n",
+    "\n",
+    "# Import the pymob modules\n",
+    "from pymob.simulation import SimulationBase\n",
+    "from pymob.solvers.diffrax import JaxSolver\n",
+    "from pymob.sim.config import Param, DataVariable"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ef4f2e47",
+   "metadata": {},
+   "source": [
+    "## 1.1 Creating the `sim` object 🧩\n",
+    "\n",
+    "👉 As an example for a relatively simple ODE model, we will use the well-known **Lotka-Volterra model** describing a predator-prey relationship.\n",
+    "\n",
+    "👉 The equations for this model look like this ($X$ and $Y$ denote prey and predator, respectively):\n",
+    "\n",
+    "$\\frac{dX}{dt} = \\alpha X - \\beta X Y$\n",
+    "\n",
+    "$\\frac{dY}{dt} = \\gamma X Y - \\delta Y$\n",
+    "\n",
+    "$\\newline \\alpha, \\beta, \\gamma, \\delta > 0$\n",
+    "\n",
+    "👉 In the following cell, we will define our model. To work with our solver (we will later use {class}`pymob.solvers.diffrax.JaxSolver` which calls `diffrax.diffeqsolve`), our Python function needs to have a signature of the form `fun(t, y, *args)` where `t` represents the current time within the system, `y` represents the current system state and `*args` is a placeholder for all model parameters.\n",
+    "\n",
+    "👉 Note that the argument `t` is not used inside the function as the derivatives generated by the Lotka Volterra model are independent from time. It still needs to be included in the signature to satisfy the needs of the solver."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "e9c2bc1f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def lotkavolterra(t, y, alpha, beta, gamma, delta):\n",
+    "    X, Y = y\n",
+    "    dXdt = alpha * X - beta * X * Y\n",
+    "    dYdt = gamma * X * Y - delta * Y\n",
+    "    return dXdt, dYdt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3f98649f",
+   "metadata": {},
+   "source": [
+    "👉 We can then create our simulation object and assign the model and the solver to it:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "db7bbc83",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Initialize the simulation object\n",
+    "sim = SimulationBase()\n",
+    "\n",
+    "# Configure the case study\n",
+    "sim.config.case_study.name = \"ODEtutorial\"\n",
+    "sim.config.case_study.scenario = \"lotkavolterra\"\n",
+    "\n",
+    "# Add the model to the simulation\n",
+    "sim.model = lotkavolterra\n",
+    "\n",
+    "# Define a solver\n",
+    "sim.solver = JaxSolver"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c7bc6365",
+   "metadata": {},
+   "source": [
+    "## 1.2 Generating artificial data 📈\n",
+    "\n",
+    "👉 Now we generate some artificial data that we will later use as our **observations**. To do this, we generate a time series of the Lotka-Volterra model with parameters $\\alpha = 0.7, \\beta = 0.1, \\gamma = 0.1, \\delta = 0.9$ from the initial condition $X = 10, Y = 5$ using `solve_ivp` (we could also use `diffrax.diffeqsolve` here, that would make no difference). This is done for 101 steps with $\\Delta t = 0.5$.\n",
+    "\n",
+    "👉 We then add some noise to the data and make sure that predator and prey abundances in our data are always positive as negative abundances would never be measured in reality.\n",
+    "\n",
+    "👉 After running the code, you can take a look at our artificial data and recognize the characteristic periodic oscillations produced by the Lotka-Volterra model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "55902090",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Generate Lotka Volterra time series\n",
+    "sol = solve_ivp(lotkavolterra, (0, 50), np.array([10,5]), \"LSODA\", np.linspace(0,50,101), args=[0.7,0.1,0.1,0.9])\n",
+    "\n",
+    "# Add \"random\" noise (example is made reproducible by setting a fixed seed)\n",
+    "rng = np.random.default_rng(seed=1)\n",
+    "noise = rng.normal(0, 0.5, (2,101))\n",
+    "y_obs = sol.y + noise\n",
+    "y_obs = np.greater(y_obs, np.zeros(y_obs.shape)) * y_obs\n",
+    "\n",
+    "# Save the evaluated time points\n",
+    "t = sol.t\n",
+    "\n",
+    "# Plot the generated data\n",
+    "fig, ax = plt.subplots(figsize=(5, 4))\n",
+    "ax.plot(t, y_obs.transpose(), label='Datapoints')\n",
+    "ax.set(xlabel='t [-]', ylabel='y_obs [-]', title ='Artificial Data')\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0a1a2716",
+   "metadata": {},
+   "source": [
+    "## 1.3 Adding data to the `sim` object 🤝\n",
+    "\n",
+    "👉 Let's prepare our observations. As seen in the introductory tutorial, Pymob uses `xArray` datasets. Because our model has two state variables, the dataset containing our artificial data also needs to have two data variables. It also needs to include the time points we generated the data for as a coordinate axis. This can be achieved like this (or probably in an easier way):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "1075ba4a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
+       " *\n",
+       " */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
+       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
+       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
+       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
+       "\n",
+       "html[theme=dark],\n",
+       "body[data-theme=dark],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
+       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
+       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
+       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-disabled-color: #515151;\n",
+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
+       "Dimensions:   (time: 101)\n",
+       "Coordinates:\n",
+       "  * time      (time) float64 0.0 0.5 1.0 1.5 2.0 ... 48.0 48.5 49.0 49.5 50.0\n",
+       "Data variables:\n",
+       "    prey      (time) float64 10.17 11.36 11.85 11.33 ... 11.08 11.16 12.37 11.56\n",
+       "    predator  (time) float64 5.431 5.33 6.397 7.604 ... 5.544 5.436 7.871 9.127</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-19b0fb0c-409a-4321-80db-2480bba0723d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-19b0fb0c-409a-4321-80db-2480bba0723d' 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'>time</span>: 101</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-7d4a5124-8cc4-4ffd-906a-1d45cbd4c9dd' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7d4a5124-8cc4-4ffd-906a-1d45cbd4c9dd' class='xr-section-summary' >Coordinates: <span>(1)</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'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.5 1.0 1.5 ... 49.0 49.5 50.0</div><input id='attrs-647e6c37-34c5-487f-b209-f49c8beb0c99' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-647e6c37-34c5-487f-b209-f49c8beb0c99' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5c0b55b3-ce1c-4ad9-be12-ea1bb0e4e279' class='xr-var-data-in' type='checkbox'><label for='data-5c0b55b3-ce1c-4ad9-be12-ea1bb0e4e279' 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. ,  0.5,  1. ,  1.5,  2. ,  2.5,  3. ,  3.5,  4. ,  4.5,  5. ,  5.5,\n",
+       "        6. ,  6.5,  7. ,  7.5,  8. ,  8.5,  9. ,  9.5, 10. , 10.5, 11. , 11.5,\n",
+       "       12. , 12.5, 13. , 13.5, 14. , 14.5, 15. , 15.5, 16. , 16.5, 17. , 17.5,\n",
+       "       18. , 18.5, 19. , 19.5, 20. , 20.5, 21. , 21.5, 22. , 22.5, 23. , 23.5,\n",
+       "       24. , 24.5, 25. , 25.5, 26. , 26.5, 27. , 27.5, 28. , 28.5, 29. , 29.5,\n",
+       "       30. , 30.5, 31. , 31.5, 32. , 32.5, 33. , 33.5, 34. , 34.5, 35. , 35.5,\n",
+       "       36. , 36.5, 37. , 37.5, 38. , 38.5, 39. , 39.5, 40. , 40.5, 41. , 41.5,\n",
+       "       42. , 42.5, 43. , 43.5, 44. , 44.5, 45. , 45.5, 46. , 46.5, 47. , 47.5,\n",
+       "       48. , 48.5, 49. , 49.5, 50. ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3ad470b1-db7f-4d89-b569-4dff691bdd72' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3ad470b1-db7f-4d89-b569-4dff691bdd72' class='xr-section-summary' >Data variables: <span>(2)</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>prey</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>10.17 11.36 11.85 ... 12.37 11.56</div><input id='attrs-66ae39f1-e39c-4b6d-8bbb-7a904fc64f87' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-66ae39f1-e39c-4b6d-8bbb-7a904fc64f87' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5b16a95a-34a7-4c6c-87a5-66cca8b9b554' class='xr-var-data-in' type='checkbox'><label for='data-5b16a95a-34a7-4c6c-87a5-66cca8b9b554' 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([10.1727921 , 11.36356787, 11.85133418, 11.32758206, 12.13024324,\n",
+       "       11.03241642,  9.34444201,  8.70483115,  7.64174857,  6.98965608,\n",
+       "        6.57516732,  6.85312033,  6.49556603,  7.30309667,  7.87383721,\n",
+       "        9.31440432, 10.03074436, 10.83049243, 11.32881408, 11.88840763,\n",
+       "       11.71207183, 10.68510596, 10.25517752,  8.89699376,  6.06892994,\n",
+       "        5.85565751,  6.43105706,  6.33033608,  6.93909897,  7.47152959,\n",
+       "        9.16525117,  8.46085511,  9.83597796, 12.02511963, 12.08239879,\n",
+       "       12.40010983, 11.5018881 , 10.03518014,  9.70578761,  8.44397176,\n",
+       "        6.79703598,  6.44069241,  6.4624991 ,  6.04794303,  6.76127886,\n",
+       "        7.38864432,  8.10315132,  8.75177457, 10.32322071, 11.46761437,\n",
+       "       11.95473981, 11.6996615 , 12.1583948 , 10.62373256, 10.056329  ,\n",
+       "        7.83245256,  7.83863179,  6.74086035,  5.84417289,  6.3370874 ,\n",
+       "        6.81762112,  7.46631758,  7.59446965,  8.46235261, 10.14320294,\n",
+       "       10.81344819, 11.94354311, 12.5259487 , 11.00313129, 11.02428433,\n",
+       "       10.23476387,  8.21252765,  6.96298935,  7.11201886,  6.57890043,\n",
+       "        6.92512831,  6.60095158,  6.57253108,  8.01592551,  8.7834307 ,\n",
+       "       10.43360022, 11.15591357, 11.03255373, 11.57796771, 12.299899  ,\n",
+       "       11.26081546,  9.31381041,  8.35946656,  7.57995973,  6.95289035,\n",
+       "        6.87040252,  6.58466325,  6.70585993,  7.16553957,  8.58367122,\n",
+       "        7.87094733,  9.97407174, 11.08383673, 11.15598075, 12.37157364,\n",
+       "       11.56283879])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>predator</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.431 5.33 6.397 ... 7.871 9.127</div><input id='attrs-8f8a731d-b7d4-42d8-b245-031601831183' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8f8a731d-b7d4-42d8-b245-031601831183' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e64dc7ee-e8eb-4540-b096-f753a88b13dc' class='xr-var-data-in' type='checkbox'><label for='data-e64dc7ee-e8eb-4540-b096-f753a88b13dc' 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([ 5.4312224 ,  5.33024327,  6.39672032,  7.60376055,  8.25824253,\n",
+       "        8.59705194,  8.84949092, 10.49034665,  9.05239184,  7.94732558,\n",
+       "        7.45133951,  6.42920702,  6.24083527,  5.30774194,  4.97430678,\n",
+       "        4.50230252,  5.22067198,  4.85811968,  6.38407349,  7.75677694,\n",
+       "        7.32121594,  7.83192608,  9.95254717, 10.92525808,  8.63594762,\n",
+       "        7.67745524,  7.66731934,  6.08481283,  5.53333341,  5.08116931,\n",
+       "        5.19473698,  4.61815895,  5.08871216,  5.25544464,  5.60412135,\n",
+       "        6.81784786,  7.60981582,  9.09197531,  9.1191402 ,  9.14099243,\n",
+       "        9.89174691,  8.29277154,  7.35407016,  6.76122864,  5.57187966,\n",
+       "        5.13419767,  5.13146441,  4.94832922,  4.35267683,  5.74192103,\n",
+       "        5.7182678 ,  6.45352596,  7.6566888 ,  8.9296395 , 10.53578011,\n",
+       "        9.1344126 ,  9.26436743,  7.2566352 ,  7.37426989,  6.93945498,\n",
+       "        5.63987591,  4.90414734,  5.00326636,  5.1269287 ,  5.23927496,\n",
+       "        6.29258134,  6.10537371,  6.67899595,  8.04966224,  9.10969044,\n",
+       "        9.80562505,  9.73391147,  9.28981634,  7.5005858 ,  7.79300184,\n",
+       "        6.20167932,  5.16791414,  5.53098832,  5.53606625,  5.01004803,\n",
+       "        4.97135418,  4.82327212,  7.42236034,  7.40758985,  7.54550247,\n",
+       "        8.77120917,  9.81951815,  8.99908534,  9.31052548,  8.12160111,\n",
+       "        7.99771137,  6.96905479,  4.39192311,  5.22164132,  4.0539337 ,\n",
+       "        5.30106873,  4.95572986,  5.54363339,  5.43624167,  7.87109895,\n",
+       "        9.12710988])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-09e47e5f-8ea4-4eed-bf8e-21abb3964b43' class='xr-section-summary-in' type='checkbox'  ><label for='section-09e47e5f-8ea4-4eed-bf8e-21abb3964b43' class='xr-section-summary' >Indexes: <span>(1)</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>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-6c080840-f6bb-4655-a364-8ba7c2058e51' class='xr-index-data-in' type='checkbox'/><label for='index-6c080840-f6bb-4655-a364-8ba7c2058e51' 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([ 0.0,  0.5,  1.0,  1.5,  2.0,  2.5,  3.0,  3.5,  4.0,  4.5,\n",
+       "       ...\n",
+       "       45.5, 46.0, 46.5, 47.0, 47.5, 48.0, 48.5, 49.0, 49.5, 50.0],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;time&#x27;, length=101))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c24fa593-54a5-444f-8c6a-cd3e7b5c3fce' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c24fa593-54a5-444f-8c6a-cd3e7b5c3fce' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset>\n",
+       "Dimensions:   (time: 101)\n",
+       "Coordinates:\n",
+       "  * time      (time) float64 0.0 0.5 1.0 1.5 2.0 ... 48.0 48.5 49.0 49.5 50.0\n",
+       "Data variables:\n",
+       "    prey      (time) float64 10.17 11.36 11.85 11.33 ... 11.08 11.16 12.37 11.56\n",
+       "    predator  (time) float64 5.431 5.33 6.397 7.604 ... 5.544 5.436 7.871 9.127"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Create an xArray dataset containing the artificial data\n",
+    "data_obs_1 = xr.DataArray(y_obs[0], coords={\"time\": t}).to_dataset(name=\"prey\")\n",
+    "data_obs_2 = xr.DataArray(y_obs[1], coords={\"time\": t}).to_dataset(name=\"predator\")\n",
+    "data_obs = xr.merge([data_obs_1, data_obs_2])\n",
+    "\n",
+    "# Look at the structure of the generated datatset\n",
+    "data_obs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "44cdcecd",
+   "metadata": {},
+   "source": [
+    "👉 As our next step, we add our artificial data to the model. As you can see in the cell output, Pymob automatically detects the two data variables and the time axis and creates two {class}`pymob.sim.config.DataVariable` objects within the simulation's {class}`pymob.sim.config.DataStructure` instance. That's why it's so important to prepare the data in the way we did above!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "6a9bf1d1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MinMaxScaler(variable=prey, min=5.844172888098338, max=12.52594869826619)\n",
+      "MinMaxScaler(variable=predator, min=4.053933700151361, max=10.925258075625722)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Markus\\pymob\\pymob\\pymob\\simulation.py:303: UserWarning: `sim.config.data_structure.prey = Datavariable(dimensions=['time'] min=5.844172888098338 max=12.52594869826619 observed=True dimensions_evaluator=None)` has been assumed from `sim.observations`. If the order of the dimensions should be different, specify `sim.config.data_structure.prey = DataVariable(dimensions=[...], ...)` manually.\n",
+      "  warnings.warn(\n",
+      "C:\\Users\\Markus\\pymob\\pymob\\pymob\\simulation.py:303: UserWarning: `sim.config.data_structure.predator = Datavariable(dimensions=['time'] min=4.053933700151361 max=10.925258075625722 observed=True dimensions_evaluator=None)` has been assumed from `sim.observations`. If the order of the dimensions should be different, specify `sim.config.data_structure.predator = DataVariable(dimensions=[...], ...)` manually.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Datastructure(prey=DataVariable(dimensions=['time'], min=5.844172888098338, max=12.52594869826619, observed=True, dimensions_evaluator=None), predator=DataVariable(dimensions=['time'], min=4.053933700151361, max=10.925258075625722, observed=True, dimensions_evaluator=None))"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Add our dataset to the simulation\n",
+    "sim.observations = data_obs\n",
+    "\n",
+    "# Take a look at the layout of the data\n",
+    "sim.config.data_structure"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "42f82d26",
+   "metadata": {},
+   "source": [
+    "👉 Because the results of ODE models strongly depend on their **initial conditions**, our simulation object need to know those. The correct place to put this information is {attr}`~pymob.sim.model_parameters[\"y0\"]`.\n",
+    "\n",
+    "👉 The initial conditions also have to be an xArray dataset with two data variables (but without the time coordinate). We can do this manually like before by creating a {class}`xArray.Dataset` object from our initial conditions..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "c74e4f81",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create an xArray dataset\n",
+    "y0_obs_1 = xr.DataArray(10).to_dataset(name=\"prey\")\n",
+    "y0_obs_2 = xr.DataArray(5).to_dataset(name=\"predator\")\n",
+    "y0_obs = xr.merge([y0_obs_1, y0_obs_2])\n",
+    "\n",
+    "# Add the initial condition to the simulation\n",
+    "sim.model_parameters[\"y0\"] = y0_obs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6e4e7050",
+   "metadata": {},
+   "source": [
+    "👉 ... or we can use {method}`pymob.sim.parse_input()` which extracts all the necessary information from the configuration (which we first have to define in this case)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "e8f61deb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Pass the initial condition to the simulation\n",
+    "#\n",
+    "# Note: The input needs to be a list containing a separate string for every state variable.\n",
+    "#       Those strings must have the format \"variableName=initialValue\" (without any spaces!).\n",
+    "sim.config.simulation.y0 = [\"prey=10\", \"predator=5\"]\n",
+    "\n",
+    "# Let parse_input() create an xArray dataset\n",
+    "#\n",
+    "# Note: The input variable drop_dims makes sure that the dataset only contains a single value\n",
+    "#       instead of a full time series filled with the same value over and over again.\n",
+    "y0_obs = sim.parse_input(\"y0\", drop_dims=['time'])\n",
+    "\n",
+    "# Add the initial condition to the simulation\n",
+    "sim.model_parameters[\"y0\"] = y0_obs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "be620f2e",
+   "metadata": {},
+   "source": [
+    "## 1.4 Setting parameters and running the model 👟\n",
+    "\n",
+    "👉 The next step is defining the **parameters** of the system, similarly as in the introductiory tutorial. In this case, we want to have three fixed parameters ($\\alpha = 0.7, \\beta = 0.1, \\gamma = 0.1$) and a single free parameter ($\\delta$). You will soon see why we made that choice."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "e6a7ecbd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'alpha': 0.7, 'beta': 0.1, 'gamma': 0.1, 'delta': 0.9}"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Parameterize the model\n",
+    "sim.config.model_parameters.alpha = Param(value=0.7, free=False)\n",
+    "sim.config.model_parameters.beta = Param(value=0.1, free=False)\n",
+    "sim.config.model_parameters.gamma = Param(value=0.1, free=False)\n",
+    "sim.config.model_parameters.delta = Param(value=0.9, free=True)\n",
+    "\n",
+    "# Make sure the model parameters are available to the model\n",
+    "sim.model_parameters[\"parameters\"] = sim.config.model_parameters.value_dict\n",
+    "\n",
+    "# Look at the parameter values passed to the model\n",
+    "sim.model_parameters[\"parameters\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d7d969e9",
+   "metadata": {},
+   "source": [
+    "👉 We do not need to define {attr}`~pymob.sim.model_parameters[\"x_in\"]` as we don't wave any input data in this case. If we wanted to make the growth rates in our model depend on weather conditions and use a corresponding dataset, {attr}`~pymob.sim.model_parameters[\"x_in\"]` would be the place to include our external data.\n",
+    "\n",
+    "👉 Instead, we follow the same routine as in the introductory tutorial, let Pymob initialize the simulation and look at the resulting time series (with $\\delta = 0.9$):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "452b9e06",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Markus\\pymob\\pymob\\pymob\\simulation.py:552: UserWarning: The number of ODE states was not specified in the config file [simulation] > 'n_ode_states = <n>'. Extracted the return arguments ['dXdt', 'dYdt'] from the source code. Setting 'n_ode_states=2.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x2050f079b10>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Put everything in place for running the simulation\n",
+    "sim.dispatch_constructor()\n",
+    "\n",
+    "# Create an evaluator, run the simulation and obtain the results\n",
+    "evaluator = sim.dispatch(theta={\"delta\":0.9})\n",
+    "evaluator()\n",
+    "data_res = evaluator.results\n",
+    "\n",
+    "# Plot the results\n",
+    "fig, ax = plt.subplots(figsize=(5, 4))\n",
+    "ax.plot(data_obs.time, data_obs.prey, ls=\"-\", color=\"tab:blue\", alpha=.5, label =\"observation data\")\n",
+    "ax.plot(data_obs.time, data_obs.predator, ls=\"-\", color=\"tab:blue\", alpha=.5, label =\"observation data\")\n",
+    "ax.plot(data_res.time, data_res.prey, color=\"black\", label =\"result\")\n",
+    "ax.plot(data_res.time, data_res.predator, color=\"black\", label =\"result\")\n",
+    "ax.legend()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c6919c67",
+   "metadata": {},
+   "source": [
+    "## 1.5 Finding out the value of $\\delta$ 🔎\n",
+    "\n",
+    "👉 Now let's see which value for $\\delta$ best fits our data. To do that, we use the **inferer** in the same way as in the introductory tutorial. We do, however, need to apply our error model to both of our state variables. Also, we changed the prior for $\\delta$ to a uniform distribution from 0.5 to 1.5 because that's a better guess.\n",
+    "\n",
+    "👉 Note: **The following code will throw an error.** This is not your fault, just look at the error message and continue with the next markdown cell."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "231463eb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Jax 64 bit mode: False\n",
+      "Absolute tolerance: 1e-07\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Markus\\pymob\\pymob\\pymob\\inference\\numpyro_backend.py:552: UserWarning: Model is not rendered, because the graphviz executable is not found. Try search for 'graphviz executables not found' and the used OS. This should be an easy fix :-)\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "      Trace Shapes:      \n",
+      "       Param Sites:      \n",
+      "      Sample Sites:      \n",
+      "         delta dist     |\n",
+      "              value     |\n",
+      "    sigma_prey dist     |\n",
+      "              value     |\n",
+      "sigma_predator dist     |\n",
+      "              value     |\n",
+      "      prey_obs dist 101 |\n",
+      "              value 101 |\n",
+      "  predator_obs dist 101 |\n",
+      "              value 101 |\n"
+     ]
+    },
+    {
+     "ename": "XlaRuntimeError",
+     "evalue": "INTERNAL: Generated function failed: CpuCallback error: _EquinoxRuntimeError: The maximum number of solver steps was reached. Try increasing `max_steps`.\n\n\n--------------------\nAn error occurred during the runtime of your JAX program! Unfortunately you do not appear to be using `equinox.filter_jit` (perhaps you are using `jax.jit` instead?) and so further information about the error cannot be displayed. (Probably you are seeing a very large but uninformative error message right now.) Please wrap your program with `equinox.filter_jit`.\n--------------------\n\n\nAt:\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\equinox\\_errors.py(89): raises\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\callback.py(258): _flat_callback\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\callback.py(52): pure_callback_impl\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\callback.py(188): _callback\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\interpreters\\mlir.py(2327): _wrapped_callback\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\interpreters\\pxla.py(1145): __call__\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\profiler.py(334): wrapper\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\pjit.py(1178): _pjit_call_impl_python\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\pjit.py(1222): call_impl_cache_miss\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\pjit.py(1238): _pjit_call_impl\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\core.py(893): process_primitive\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\core.py(405): bind_with_trace\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\core.py(2682): bind\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\pjit.py(166): _python_pjit_helper\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\pjit.py(255): cache_miss\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\traceback_util.py(177): reraise_with_filtered_traceback\n  C:\\Users\\Markus\\pymob\\pymob\\pymob\\solvers\\base.py(82): __call__\n  C:\\Users\\Markus\\pymob\\pymob\\pymob\\sim\\evaluator.py(351): __call__\n  C:\\Users\\Markus\\pymob\\pymob\\pymob\\inference\\numpyro_backend.py(261): evaluator\n  C:\\Users\\Markus\\pymob\\pymob\\pymob\\inference\\numpyro_backend.py(485): model\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\primitives.py(105): __call__\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\primitives.py(105): __call__\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\primitives.py(105): __call__\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\handlers.py(171): get_trace\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\infer\\util.py(450): _get_model_transforms\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\infer\\util.py(656): initialize_model\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\infer\\hmc.py(657): _init_state\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\infer\\hmc.py(713): init\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\infer\\mcmc.py(416): _single_chain_mcmc\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\infer\\mcmc.py(634): run\n  C:\\Users\\Markus\\pymob\\pymob\\pymob\\inference\\numpyro_backend.py(652): run_mcmc\n  C:\\Users\\Markus\\pymob\\pymob\\pymob\\inference\\numpyro_backend.py(566): run\n  C:\\Users\\Markus\\AppData\\Local\\Temp\\ipykernel_10328\\906244579.py(15): <module>\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\IPython\\core\\interactiveshell.py(3548): run_code\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\IPython\\core\\interactiveshell.py(3488): run_ast_nodes\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\IPython\\core\\interactiveshell.py(3306): run_cell_async\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\IPython\\core\\async_helpers.py(129): _pseudo_sync_runner\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\IPython\\core\\interactiveshell.py(3101): _run_cell\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\IPython\\core\\interactiveshell.py(3046): run_cell\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel\\zmqshell.py(549): run_cell\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel\\ipkernel.py(449): do_execute\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel\\kernelbase.py(778): execute_request\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel\\ipkernel.py(362): execute_request\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel\\kernelbase.py(437): dispatch_shell\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel\\kernelbase.py(534): process_one\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel\\kernelbase.py(545): dispatch_queue\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\asyncio\\events.py(84): _run\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\asyncio\\base_events.py(1936): _run_once\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\asyncio\\base_events.py(608): run_forever\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\tornado\\platform\\asyncio.py(211): start\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel\\kernelapp.py(739): start\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\traitlets\\config\\application.py(1075): launch_instance\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel_launcher.py(18): <module>\n  <frozen runpy>(88): _run_code\n  <frozen runpy>(198): _run_module_as_main\n",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mXlaRuntimeError\u001b[0m                           Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[10], line 15\u001b[0m\n\u001b[0;32m     13\u001b[0m sim\u001b[38;5;241m.\u001b[39mset_inferer(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnumpyro\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m     14\u001b[0m sim\u001b[38;5;241m.\u001b[39minferer\u001b[38;5;241m.\u001b[39mconfig\u001b[38;5;241m.\u001b[39minference_numpyro\u001b[38;5;241m.\u001b[39mkernel \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnuts\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m---> 15\u001b[0m \u001b[43msim\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minferer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     17\u001b[0m \u001b[38;5;66;03m# Plot the results\u001b[39;00m\n\u001b[0;32m     18\u001b[0m sim\u001b[38;5;241m.\u001b[39mconfig\u001b[38;5;241m.\u001b[39msimulation\u001b[38;5;241m.\u001b[39mx_dimension \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtime\u001b[39m\u001b[38;5;124m\"\u001b[39m\n",
+      "File \u001b[1;32m~\\pymob\\pymob\\pymob\\inference\\numpyro_backend.py:566\u001b[0m, in \u001b[0;36mNumpyroBackend.run\u001b[1;34m(self, print_debug, render_model)\u001b[0m\n\u001b[0;32m    564\u001b[0m \u001b[38;5;66;03m# run inference\u001b[39;00m\n\u001b[0;32m    565\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkernel\u001b[38;5;241m.\u001b[39mlower() \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msa\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkernel\u001b[38;5;241m.\u001b[39mlower() \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnuts\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m--> 566\u001b[0m     sampler, mcmc \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_mcmc\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    567\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    568\u001b[0m \u001b[43m        \u001b[49m\u001b[43mkeys\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkeys\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    569\u001b[0m \u001b[43m        \u001b[49m\u001b[43mkernel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkernel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlower\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    570\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    572\u001b[0m     \u001b[38;5;66;03m# create arviz idata\u001b[39;00m\n\u001b[0;32m    573\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39midata \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnuts_posterior(\n\u001b[0;32m    574\u001b[0m         mcmc\u001b[38;5;241m=\u001b[39mmcmc, model\u001b[38;5;241m=\u001b[39mmodel, key\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mnext\u001b[39m(keys), obs\u001b[38;5;241m=\u001b[39mobs\n\u001b[0;32m    575\u001b[0m     )\n",
+      "File \u001b[1;32m~\\pymob\\pymob\\pymob\\inference\\numpyro_backend.py:652\u001b[0m, in \u001b[0;36mNumpyroBackend.run_mcmc\u001b[1;34m(self, model, keys, kernel)\u001b[0m\n\u001b[0;32m    642\u001b[0m mcmc \u001b[38;5;241m=\u001b[39m infer\u001b[38;5;241m.\u001b[39mMCMC(\n\u001b[0;32m    643\u001b[0m     sampler\u001b[38;5;241m=\u001b[39msampler,\n\u001b[0;32m    644\u001b[0m     num_warmup\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mwarmup,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    648\u001b[0m     progress_bar\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[0;32m    649\u001b[0m )\n\u001b[0;32m    651\u001b[0m \u001b[38;5;66;03m# run inference\u001b[39;00m\n\u001b[1;32m--> 652\u001b[0m \u001b[43mmcmc\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mnext\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mkeys\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    653\u001b[0m mcmc\u001b[38;5;241m.\u001b[39mprint_summary()\n\u001b[0;32m    655\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m sampler, mcmc\n",
+      "File \u001b[1;32mc:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\infer\\mcmc.py:634\u001b[0m, in \u001b[0;36mMCMC.run\u001b[1;34m(self, rng_key, extra_fields, init_params, *args, **kwargs)\u001b[0m\n\u001b[0;32m    632\u001b[0m map_args \u001b[38;5;241m=\u001b[39m (rng_key, init_state, init_params)\n\u001b[0;32m    633\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnum_chains \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m--> 634\u001b[0m     states_flat, last_state \u001b[38;5;241m=\u001b[39m \u001b[43mpartial_map_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmap_args\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    635\u001b[0m     states \u001b[38;5;241m=\u001b[39m tree_map(\u001b[38;5;28;01mlambda\u001b[39;00m x: x[jnp\u001b[38;5;241m.\u001b[39mnewaxis, \u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m], states_flat)\n\u001b[0;32m    636\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
+      "File \u001b[1;32mc:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\infer\\mcmc.py:416\u001b[0m, in \u001b[0;36mMCMC._single_chain_mcmc\u001b[1;34m(self, init, args, kwargs, collect_fields)\u001b[0m\n\u001b[0;32m    414\u001b[0m \u001b[38;5;66;03m# Check if _sample_fn is None, then we need to initialize the sampler.\u001b[39;00m\n\u001b[0;32m    415\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m init_state \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mor\u001b[39;00m (\u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msampler, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_sample_fn\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m--> 416\u001b[0m     new_init_state \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msampler\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minit\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    417\u001b[0m \u001b[43m        \u001b[49m\u001b[43mrng_key\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    418\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnum_warmup\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    419\u001b[0m \u001b[43m        \u001b[49m\u001b[43minit_params\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    420\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmodel_args\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    421\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmodel_kwargs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    422\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    423\u001b[0m     init_state \u001b[38;5;241m=\u001b[39m new_init_state \u001b[38;5;28;01mif\u001b[39;00m init_state \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m init_state\n\u001b[0;32m    424\u001b[0m sample_fn, postprocess_fn \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_cached_fns()\n",
+      "File \u001b[1;32mc:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\infer\\hmc.py:713\u001b[0m, in \u001b[0;36mHMC.init\u001b[1;34m(self, rng_key, num_warmup, init_params, model_args, model_kwargs)\u001b[0m\n\u001b[0;32m    708\u001b[0m \u001b[38;5;66;03m# vectorized\u001b[39;00m\n\u001b[0;32m    709\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    710\u001b[0m     rng_key, rng_key_init_model \u001b[38;5;241m=\u001b[39m jnp\u001b[38;5;241m.\u001b[39mswapaxes(\n\u001b[0;32m    711\u001b[0m         vmap(random\u001b[38;5;241m.\u001b[39msplit)(rng_key), \u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m    712\u001b[0m     )\n\u001b[1;32m--> 713\u001b[0m init_params \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_init_state\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    714\u001b[0m \u001b[43m    \u001b[49m\u001b[43mrng_key_init_model\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel_kwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minit_params\u001b[49m\n\u001b[0;32m    715\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    716\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_potential_fn \u001b[38;5;129;01mand\u001b[39;00m init_params \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    717\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m    718\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mValid value of `init_params` must be provided with\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m `potential_fn`.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    719\u001b[0m     )\n",
+      "File \u001b[1;32mc:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\infer\\hmc.py:657\u001b[0m, in \u001b[0;36mHMC._init_state\u001b[1;34m(self, rng_key, model_args, model_kwargs, init_params)\u001b[0m\n\u001b[0;32m    650\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21m_init_state\u001b[39m(\u001b[38;5;28mself\u001b[39m, rng_key, model_args, model_kwargs, init_params):\n\u001b[0;32m    651\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_model \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    652\u001b[0m         (\n\u001b[0;32m    653\u001b[0m             new_init_params,\n\u001b[0;32m    654\u001b[0m             potential_fn,\n\u001b[0;32m    655\u001b[0m             postprocess_fn,\n\u001b[0;32m    656\u001b[0m             model_trace,\n\u001b[1;32m--> 657\u001b[0m         ) \u001b[38;5;241m=\u001b[39m \u001b[43minitialize_model\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    658\u001b[0m \u001b[43m            \u001b[49m\u001b[43mrng_key\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    659\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_model\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    660\u001b[0m \u001b[43m            \u001b[49m\u001b[43mdynamic_args\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m    661\u001b[0m \u001b[43m            \u001b[49m\u001b[43minit_strategy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_init_strategy\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    662\u001b[0m \u001b[43m            \u001b[49m\u001b[43mmodel_args\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmodel_args\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    663\u001b[0m \u001b[43m            \u001b[49m\u001b[43mmodel_kwargs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmodel_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    664\u001b[0m \u001b[43m            \u001b[49m\u001b[43mforward_mode_differentiation\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_forward_mode_differentiation\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    665\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    666\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m init_params \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    667\u001b[0m             init_params \u001b[38;5;241m=\u001b[39m new_init_params\n",
+      "File \u001b[1;32mc:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\infer\\util.py:656\u001b[0m, in \u001b[0;36minitialize_model\u001b[1;34m(rng_key, model, init_strategy, dynamic_args, model_args, model_kwargs, forward_mode_differentiation, validate_grad)\u001b[0m\n\u001b[0;32m    646\u001b[0m model_kwargs \u001b[38;5;241m=\u001b[39m {} \u001b[38;5;28;01mif\u001b[39;00m model_kwargs \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m model_kwargs\n\u001b[0;32m    647\u001b[0m substituted_model \u001b[38;5;241m=\u001b[39m substitute(\n\u001b[0;32m    648\u001b[0m     seed(model, rng_key \u001b[38;5;28;01mif\u001b[39;00m is_prng_key(rng_key) \u001b[38;5;28;01melse\u001b[39;00m rng_key[\u001b[38;5;241m0\u001b[39m]),\n\u001b[0;32m    649\u001b[0m     substitute_fn\u001b[38;5;241m=\u001b[39minit_strategy,\n\u001b[0;32m    650\u001b[0m )\n\u001b[0;32m    651\u001b[0m (\n\u001b[0;32m    652\u001b[0m     inv_transforms,\n\u001b[0;32m    653\u001b[0m     replay_model,\n\u001b[0;32m    654\u001b[0m     has_enumerate_support,\n\u001b[0;32m    655\u001b[0m     model_trace,\n\u001b[1;32m--> 656\u001b[0m ) \u001b[38;5;241m=\u001b[39m \u001b[43m_get_model_transforms\u001b[49m\u001b[43m(\u001b[49m\u001b[43msubstituted_model\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    657\u001b[0m \u001b[38;5;66;03m# substitute param sites from model_trace to model so\u001b[39;00m\n\u001b[0;32m    658\u001b[0m \u001b[38;5;66;03m# we don't need to generate again parameters of `numpyro.module`\u001b[39;00m\n\u001b[0;32m    659\u001b[0m model \u001b[38;5;241m=\u001b[39m substitute(\n\u001b[0;32m    660\u001b[0m     model,\n\u001b[0;32m    661\u001b[0m     data\u001b[38;5;241m=\u001b[39m{\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    665\u001b[0m     },\n\u001b[0;32m    666\u001b[0m )\n",
+      "File \u001b[1;32mc:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\infer\\util.py:450\u001b[0m, in \u001b[0;36m_get_model_transforms\u001b[1;34m(model, model_args, model_kwargs)\u001b[0m\n\u001b[0;32m    448\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21m_get_model_transforms\u001b[39m(model, model_args\u001b[38;5;241m=\u001b[39m(), model_kwargs\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[0;32m    449\u001b[0m     model_kwargs \u001b[38;5;241m=\u001b[39m {} \u001b[38;5;28;01mif\u001b[39;00m model_kwargs \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m model_kwargs\n\u001b[1;32m--> 450\u001b[0m     model_trace \u001b[38;5;241m=\u001b[39m \u001b[43mtrace\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_trace\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mmodel_args\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[43mmodel_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    451\u001b[0m     inv_transforms \u001b[38;5;241m=\u001b[39m {}\n\u001b[0;32m    452\u001b[0m     \u001b[38;5;66;03m# model code may need to be replayed in the presence of deterministic sites\u001b[39;00m\n",
+      "File \u001b[1;32mc:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\handlers.py:171\u001b[0m, in \u001b[0;36mtrace.get_trace\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    163\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mget_trace\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m    164\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    165\u001b[0m \u001b[38;5;124;03m    Run the wrapped callable and return the recorded trace.\u001b[39;00m\n\u001b[0;32m    166\u001b[0m \n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    169\u001b[0m \u001b[38;5;124;03m    :return: `OrderedDict` containing the execution trace.\u001b[39;00m\n\u001b[0;32m    170\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 171\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\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[0;32m    172\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrace\n",
+      "File \u001b[1;32mc:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\primitives.py:105\u001b[0m, in \u001b[0;36mMessenger.__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    103\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\n\u001b[0;32m    104\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m:\n\u001b[1;32m--> 105\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\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",
+      "File \u001b[1;32mc:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\primitives.py:105\u001b[0m, in \u001b[0;36mMessenger.__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    103\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\n\u001b[0;32m    104\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m:\n\u001b[1;32m--> 105\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\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",
+      "File \u001b[1;32mc:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\primitives.py:105\u001b[0m, in \u001b[0;36mMessenger.__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    103\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\n\u001b[0;32m    104\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m:\n\u001b[1;32m--> 105\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\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",
+      "File \u001b[1;32m~\\pymob\\pymob\\pymob\\inference\\numpyro_backend.py:485\u001b[0m, in \u001b[0;36mNumpyroBackend.parse_probabilistic_model.<locals>.model\u001b[1;34m(solver, obs, masks, only_prior, user_error_model, make_predictions)\u001b[0m\n\u001b[0;32m    483\u001b[0m y0 \u001b[38;5;241m=\u001b[39m {k\u001b[38;5;241m.\u001b[39mreplace(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_y0\u001b[39m\u001b[38;5;124m\"\u001b[39m,\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m): v \u001b[38;5;28;01mfor\u001b[39;00m k, v \u001b[38;5;129;01min\u001b[39;00m theta_\u001b[38;5;241m.\u001b[39mitems() \u001b[38;5;28;01mif\u001b[39;00m k \u001b[38;5;129;01min\u001b[39;00m data_variables_y0}\n\u001b[0;32m    484\u001b[0m theta \u001b[38;5;241m=\u001b[39m {k: v \u001b[38;5;28;01mfor\u001b[39;00m k, v \u001b[38;5;129;01min\u001b[39;00m theta_\u001b[38;5;241m.\u001b[39mitems() \u001b[38;5;28;01mif\u001b[39;00m k \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m data_variables_y0}\n\u001b[1;32m--> 485\u001b[0m sim_results \u001b[38;5;241m=\u001b[39m \u001b[43msolver\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtheta\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtheta\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my0\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43my0\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    487\u001b[0m \u001b[38;5;66;03m# store data_variables as deterministic model output\u001b[39;00m\n\u001b[0;32m    488\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m deterministic_name, deterministic_value \u001b[38;5;129;01min\u001b[39;00m sim_results\u001b[38;5;241m.\u001b[39mitems():\n",
+      "File \u001b[1;32m~\\pymob\\pymob\\pymob\\inference\\numpyro_backend.py:261\u001b[0m, in \u001b[0;36mNumpyroBackend.parse_deterministic_model.<locals>.evaluator\u001b[1;34m(theta, y0, x_in, seed)\u001b[0m\n\u001b[0;32m    259\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mevaluator\u001b[39m(theta, y0\u001b[38;5;241m=\u001b[39m{}, x_in\u001b[38;5;241m=\u001b[39m{}, seed\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[0;32m    260\u001b[0m     evaluator \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msimulation\u001b[38;5;241m.\u001b[39mdispatch(theta\u001b[38;5;241m=\u001b[39mtheta, y0\u001b[38;5;241m=\u001b[39my0, x_in\u001b[38;5;241m=\u001b[39mx_in)\n\u001b[1;32m--> 261\u001b[0m     \u001b[43mevaluator\u001b[49m\u001b[43m(\u001b[49m\u001b[43mseed\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    262\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m evaluator\u001b[38;5;241m.\u001b[39mY\n",
+      "File \u001b[1;32m~\\pymob\\pymob\\pymob\\sim\\evaluator.py:351\u001b[0m, in \u001b[0;36mEvaluator.__call__\u001b[1;34m(self, seed)\u001b[0m\n\u001b[0;32m    348\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_signature\u001b[38;5;241m.\u001b[39mupdate({\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mseed\u001b[39m\u001b[38;5;124m\"\u001b[39m: seed})\n\u001b[0;32m    350\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_solver, SolverBase):\n\u001b[1;32m--> 351\u001b[0m     Y_ \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_solver\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparameters\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    353\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    354\u001b[0m     Y_ \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_solver(parameters\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mparameters, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_signature)\n",
+      "File \u001b[1;32m~\\pymob\\pymob\\pymob\\solvers\\base.py:82\u001b[0m, in \u001b[0;36mSolverBase.__call__\u001b[1;34m(self, **kwargs)\u001b[0m\n\u001b[0;32m     81\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m---> 82\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msolve\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;31m[... skipping hidden 10 frame]\u001b[0m\n",
+      "File \u001b[1;32mc:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\interpreters\\pxla.py:1145\u001b[0m, in \u001b[0;36mExecuteReplicated.__call__\u001b[1;34m(self, *args)\u001b[0m\n\u001b[0;32m   1142\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mordered_effects \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhas_unordered_effects\n\u001b[0;32m   1143\u001b[0m     \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhas_host_callbacks):\n\u001b[0;32m   1144\u001b[0m   input_bufs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_add_tokens_to_inputs(input_bufs)\n\u001b[1;32m-> 1145\u001b[0m   results \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mxla_executable\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexecute_sharded\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   1146\u001b[0m \u001b[43m      \u001b[49m\u001b[43minput_bufs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwith_tokens\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\n\u001b[0;32m   1147\u001b[0m \u001b[43m  \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1148\u001b[0m   result_token_bufs \u001b[38;5;241m=\u001b[39m results\u001b[38;5;241m.\u001b[39mdisassemble_prefix_into_single_device_arrays(\n\u001b[0;32m   1149\u001b[0m       \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mordered_effects))\n\u001b[0;32m   1150\u001b[0m   sharded_runtime_token \u001b[38;5;241m=\u001b[39m results\u001b[38;5;241m.\u001b[39mconsume_token()\n",
+      "\u001b[1;31mXlaRuntimeError\u001b[0m: INTERNAL: Generated function failed: CpuCallback error: _EquinoxRuntimeError: The maximum number of solver steps was reached. Try increasing `max_steps`.\n\n\n--------------------\nAn error occurred during the runtime of your JAX program! Unfortunately you do not appear to be using `equinox.filter_jit` (perhaps you are using `jax.jit` instead?) and so further information about the error cannot be displayed. (Probably you are seeing a very large but uninformative error message right now.) Please wrap your program with `equinox.filter_jit`.\n--------------------\n\n\nAt:\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\equinox\\_errors.py(89): raises\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\callback.py(258): _flat_callback\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\callback.py(52): pure_callback_impl\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\callback.py(188): _callback\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\interpreters\\mlir.py(2327): _wrapped_callback\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\interpreters\\pxla.py(1145): __call__\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\profiler.py(334): wrapper\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\pjit.py(1178): _pjit_call_impl_python\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\pjit.py(1222): call_impl_cache_miss\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\pjit.py(1238): _pjit_call_impl\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\core.py(893): process_primitive\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\core.py(405): bind_with_trace\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\core.py(2682): bind\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\pjit.py(166): _python_pjit_helper\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\pjit.py(255): cache_miss\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\traceback_util.py(177): reraise_with_filtered_traceback\n  C:\\Users\\Markus\\pymob\\pymob\\pymob\\solvers\\base.py(82): __call__\n  C:\\Users\\Markus\\pymob\\pymob\\pymob\\sim\\evaluator.py(351): __call__\n  C:\\Users\\Markus\\pymob\\pymob\\pymob\\inference\\numpyro_backend.py(261): evaluator\n  C:\\Users\\Markus\\pymob\\pymob\\pymob\\inference\\numpyro_backend.py(485): model\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\primitives.py(105): __call__\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\primitives.py(105): __call__\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\primitives.py(105): __call__\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\handlers.py(171): get_trace\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\infer\\util.py(450): _get_model_transforms\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\infer\\util.py(656): initialize_model\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\infer\\hmc.py(657): _init_state\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\infer\\hmc.py(713): init\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\infer\\mcmc.py(416): _single_chain_mcmc\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\infer\\mcmc.py(634): run\n  C:\\Users\\Markus\\pymob\\pymob\\pymob\\inference\\numpyro_backend.py(652): run_mcmc\n  C:\\Users\\Markus\\pymob\\pymob\\pymob\\inference\\numpyro_backend.py(566): run\n  C:\\Users\\Markus\\AppData\\Local\\Temp\\ipykernel_10328\\906244579.py(15): <module>\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\IPython\\core\\interactiveshell.py(3548): run_code\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\IPython\\core\\interactiveshell.py(3488): run_ast_nodes\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\IPython\\core\\interactiveshell.py(3306): run_cell_async\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\IPython\\core\\async_helpers.py(129): _pseudo_sync_runner\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\IPython\\core\\interactiveshell.py(3101): _run_cell\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\IPython\\core\\interactiveshell.py(3046): run_cell\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel\\zmqshell.py(549): run_cell\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel\\ipkernel.py(449): do_execute\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel\\kernelbase.py(778): execute_request\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel\\ipkernel.py(362): execute_request\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel\\kernelbase.py(437): dispatch_shell\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel\\kernelbase.py(534): process_one\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel\\kernelbase.py(545): dispatch_queue\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\asyncio\\events.py(84): _run\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\asyncio\\base_events.py(1936): _run_once\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\asyncio\\base_events.py(608): run_forever\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\tornado\\platform\\asyncio.py(211): start\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel\\kernelapp.py(739): start\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\traitlets\\config\\application.py(1075): launch_instance\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel_launcher.py(18): <module>\n  <frozen runpy>(88): _run_code\n  <frozen runpy>(198): _run_module_as_main\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Add parameters to use in our error model\n",
+    "sim.config.model_parameters.sigma_prey = Param(free=True , prior=\"lognorm(scale=1,s=1)\", min=0, max=1)\n",
+    "sim.config.model_parameters.sigma_predator = Param(free=True , prior=\"lognorm(scale=1,s=1)\", min=0, max=1)\n",
+    "\n",
+    "# Define the error model for both state variables\n",
+    "sim.config.error_model.prey = \"normal(loc=prey,scale=sigma_prey)\"\n",
+    "sim.config.error_model.predator = \"normal(loc=predator,scale=sigma_predator)\"\n",
+    "\n",
+    "# Choose a prior distribution for delta\n",
+    "sim.config.model_parameters.delta.prior = \"uniform(loc=0.5,scale=1)\"\n",
+    "\n",
+    "# Create the inferer (NumPyro backend, NUTS kernel) and let it do its work\n",
+    "sim.set_inferer(\"numpyro\")\n",
+    "sim.inferer.config.inference_numpyro.kernel = \"nuts\"\n",
+    "sim.inferer.run()\n",
+    "\n",
+    "# Plot the results\n",
+    "sim.config.simulation.x_dimension = \"time\"\n",
+    "sim.posterior_predictive_checks(pred_hdi_style={\"alpha\": 0.1})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "12d28ca8",
+   "metadata": {},
+   "source": [
+    "👉 What you see is an error that originated during runtime. The error message should tell you:\n",
+    "\n",
+    "`_EquinoxRuntimeError: The maximum number of solver steps was reached. Try increasing 'max_steps'.`\n",
+    "\n",
+    "👉 This means that our solver has to deal with a very difficult problem. To accomodate that, it needs to be very precise and work with extremely small time steps which causes it to exceed the maximum number of steps it is allowed to take.\n",
+    "\n",
+    "👉 We can solve this in two ways:\n",
+    "\n",
+    "1. Increase {attr}`~pymob.sim.config.max_steps`: The simplest work to deal with this problem. It might not always work, though, because with very extreme model dynamics, even a high number of steps can be exceeded.\n",
+    "\n",
+    "2. Set {attr}`~pymob.sim.config.throw_exception` to `False`: With this setting, exceeding the maximum number of steps will not result in an error but return `inf` values as the result. In that case, the loss would also be infinite and the corresponding value of $\\delta$ would simply be rejected. That means that difficult problems are being thrown out and we make our decision about $\\delta$ based on the remaining runs. In many cases, extreme model behavior resulting in {attr}`~pymob.sim.config.max_steps` being exceeded will not fit the data anyway and rejecting the corresponding parameter value is justified. But to make such an assumption, you should know your system very well and check whether the assumption is valid.\n",
+    "\n",
+    "👉 We will first try option 1:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "d31c1ce7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "      Trace Shapes:      \n",
+      "       Param Sites:      \n",
+      "      Sample Sites:      \n",
+      "         delta dist     |\n",
+      "              value     |\n",
+      "    sigma_prey dist     |\n",
+      "              value     |\n",
+      "sigma_predator dist     |\n",
+      "              value     |\n",
+      "      prey_obs dist 101 |\n",
+      "              value 101 |\n",
+      "  predator_obs dist 101 |\n",
+      "              value 101 |\n"
+     ]
+    },
+    {
+     "ename": "XlaRuntimeError",
+     "evalue": "INTERNAL: Generated function failed: CpuCallback error: _EquinoxRuntimeError: The maximum number of solver steps was reached. Try increasing `max_steps`.\n\n\n--------------------\nAn error occurred during the runtime of your JAX program! Unfortunately you do not appear to be using `equinox.filter_jit` (perhaps you are using `jax.jit` instead?) and so further information about the error cannot be displayed. (Probably you are seeing a very large but uninformative error message right now.) Please wrap your program with `equinox.filter_jit`.\n--------------------\n\n\nAt:\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\equinox\\_errors.py(89): raises\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\callback.py(258): _flat_callback\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\callback.py(52): pure_callback_impl\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\callback.py(188): _callback\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\interpreters\\mlir.py(2327): _wrapped_callback\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\interpreters\\pxla.py(1145): __call__\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\profiler.py(334): wrapper\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\pjit.py(1178): _pjit_call_impl_python\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\pjit.py(1222): call_impl_cache_miss\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\pjit.py(1238): _pjit_call_impl\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\core.py(893): process_primitive\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\core.py(405): bind_with_trace\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\core.py(2682): bind\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\pjit.py(166): _python_pjit_helper\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\pjit.py(255): cache_miss\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\traceback_util.py(177): reraise_with_filtered_traceback\n  C:\\Users\\Markus\\pymob\\pymob\\pymob\\solvers\\base.py(82): __call__\n  C:\\Users\\Markus\\pymob\\pymob\\pymob\\sim\\evaluator.py(351): __call__\n  C:\\Users\\Markus\\pymob\\pymob\\pymob\\inference\\numpyro_backend.py(261): evaluator\n  C:\\Users\\Markus\\pymob\\pymob\\pymob\\inference\\numpyro_backend.py(485): model\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\primitives.py(105): __call__\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\primitives.py(105): __call__\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\primitives.py(105): __call__\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\handlers.py(171): get_trace\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\infer\\util.py(450): _get_model_transforms\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\infer\\util.py(656): initialize_model\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\infer\\hmc.py(657): _init_state\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\infer\\hmc.py(713): init\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\infer\\mcmc.py(416): _single_chain_mcmc\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\infer\\mcmc.py(634): run\n  C:\\Users\\Markus\\pymob\\pymob\\pymob\\inference\\numpyro_backend.py(652): run_mcmc\n  C:\\Users\\Markus\\pymob\\pymob\\pymob\\inference\\numpyro_backend.py(566): run\n  C:\\Users\\Markus\\AppData\\Local\\Temp\\ipykernel_10328\\3769994282.py(8): <module>\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\IPython\\core\\interactiveshell.py(3548): run_code\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\IPython\\core\\interactiveshell.py(3488): run_ast_nodes\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\IPython\\core\\interactiveshell.py(3306): run_cell_async\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\IPython\\core\\async_helpers.py(129): _pseudo_sync_runner\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\IPython\\core\\interactiveshell.py(3101): _run_cell\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\IPython\\core\\interactiveshell.py(3046): run_cell\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel\\zmqshell.py(549): run_cell\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel\\ipkernel.py(449): do_execute\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel\\kernelbase.py(778): execute_request\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel\\ipkernel.py(362): execute_request\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel\\kernelbase.py(437): dispatch_shell\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel\\kernelbase.py(534): process_one\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel\\kernelbase.py(545): dispatch_queue\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\asyncio\\events.py(84): _run\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\asyncio\\base_events.py(1936): _run_once\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\asyncio\\base_events.py(608): run_forever\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\tornado\\platform\\asyncio.py(211): start\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel\\kernelapp.py(739): start\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\traitlets\\config\\application.py(1075): launch_instance\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel_launcher.py(18): <module>\n  <frozen runpy>(88): _run_code\n  <frozen runpy>(198): _run_module_as_main\n",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mXlaRuntimeError\u001b[0m                           Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[11], line 8\u001b[0m\n\u001b[0;32m      5\u001b[0m sim\u001b[38;5;241m.\u001b[39mdispatch_constructor()\n\u001b[0;32m      7\u001b[0m \u001b[38;5;66;03m# Try running the inferer again\u001b[39;00m\n\u001b[1;32m----> 8\u001b[0m \u001b[43msim\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minferer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     10\u001b[0m \u001b[38;5;66;03m# Plot the results\u001b[39;00m\n\u001b[0;32m     11\u001b[0m sim\u001b[38;5;241m.\u001b[39mconfig\u001b[38;5;241m.\u001b[39msimulation\u001b[38;5;241m.\u001b[39mx_dimension \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtime\u001b[39m\u001b[38;5;124m\"\u001b[39m\n",
+      "File \u001b[1;32m~\\pymob\\pymob\\pymob\\inference\\numpyro_backend.py:566\u001b[0m, in \u001b[0;36mNumpyroBackend.run\u001b[1;34m(self, print_debug, render_model)\u001b[0m\n\u001b[0;32m    564\u001b[0m \u001b[38;5;66;03m# run inference\u001b[39;00m\n\u001b[0;32m    565\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkernel\u001b[38;5;241m.\u001b[39mlower() \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msa\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkernel\u001b[38;5;241m.\u001b[39mlower() \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnuts\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m--> 566\u001b[0m     sampler, mcmc \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_mcmc\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    567\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    568\u001b[0m \u001b[43m        \u001b[49m\u001b[43mkeys\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkeys\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    569\u001b[0m \u001b[43m        \u001b[49m\u001b[43mkernel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkernel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlower\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    570\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    572\u001b[0m     \u001b[38;5;66;03m# create arviz idata\u001b[39;00m\n\u001b[0;32m    573\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39midata \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnuts_posterior(\n\u001b[0;32m    574\u001b[0m         mcmc\u001b[38;5;241m=\u001b[39mmcmc, model\u001b[38;5;241m=\u001b[39mmodel, key\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mnext\u001b[39m(keys), obs\u001b[38;5;241m=\u001b[39mobs\n\u001b[0;32m    575\u001b[0m     )\n",
+      "File \u001b[1;32m~\\pymob\\pymob\\pymob\\inference\\numpyro_backend.py:652\u001b[0m, in \u001b[0;36mNumpyroBackend.run_mcmc\u001b[1;34m(self, model, keys, kernel)\u001b[0m\n\u001b[0;32m    642\u001b[0m mcmc \u001b[38;5;241m=\u001b[39m infer\u001b[38;5;241m.\u001b[39mMCMC(\n\u001b[0;32m    643\u001b[0m     sampler\u001b[38;5;241m=\u001b[39msampler,\n\u001b[0;32m    644\u001b[0m     num_warmup\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mwarmup,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    648\u001b[0m     progress_bar\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[0;32m    649\u001b[0m )\n\u001b[0;32m    651\u001b[0m \u001b[38;5;66;03m# run inference\u001b[39;00m\n\u001b[1;32m--> 652\u001b[0m \u001b[43mmcmc\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mnext\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mkeys\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    653\u001b[0m mcmc\u001b[38;5;241m.\u001b[39mprint_summary()\n\u001b[0;32m    655\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m sampler, mcmc\n",
+      "File \u001b[1;32mc:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\infer\\mcmc.py:634\u001b[0m, in \u001b[0;36mMCMC.run\u001b[1;34m(self, rng_key, extra_fields, init_params, *args, **kwargs)\u001b[0m\n\u001b[0;32m    632\u001b[0m map_args \u001b[38;5;241m=\u001b[39m (rng_key, init_state, init_params)\n\u001b[0;32m    633\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnum_chains \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m--> 634\u001b[0m     states_flat, last_state \u001b[38;5;241m=\u001b[39m \u001b[43mpartial_map_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmap_args\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    635\u001b[0m     states \u001b[38;5;241m=\u001b[39m tree_map(\u001b[38;5;28;01mlambda\u001b[39;00m x: x[jnp\u001b[38;5;241m.\u001b[39mnewaxis, \u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m], states_flat)\n\u001b[0;32m    636\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
+      "File \u001b[1;32mc:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\infer\\mcmc.py:416\u001b[0m, in \u001b[0;36mMCMC._single_chain_mcmc\u001b[1;34m(self, init, args, kwargs, collect_fields)\u001b[0m\n\u001b[0;32m    414\u001b[0m \u001b[38;5;66;03m# Check if _sample_fn is None, then we need to initialize the sampler.\u001b[39;00m\n\u001b[0;32m    415\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m init_state \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mor\u001b[39;00m (\u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msampler, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_sample_fn\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m--> 416\u001b[0m     new_init_state \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msampler\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minit\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    417\u001b[0m \u001b[43m        \u001b[49m\u001b[43mrng_key\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    418\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnum_warmup\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    419\u001b[0m \u001b[43m        \u001b[49m\u001b[43minit_params\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    420\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmodel_args\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    421\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmodel_kwargs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    422\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    423\u001b[0m     init_state \u001b[38;5;241m=\u001b[39m new_init_state \u001b[38;5;28;01mif\u001b[39;00m init_state \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m init_state\n\u001b[0;32m    424\u001b[0m sample_fn, postprocess_fn \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_cached_fns()\n",
+      "File \u001b[1;32mc:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\infer\\hmc.py:713\u001b[0m, in \u001b[0;36mHMC.init\u001b[1;34m(self, rng_key, num_warmup, init_params, model_args, model_kwargs)\u001b[0m\n\u001b[0;32m    708\u001b[0m \u001b[38;5;66;03m# vectorized\u001b[39;00m\n\u001b[0;32m    709\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    710\u001b[0m     rng_key, rng_key_init_model \u001b[38;5;241m=\u001b[39m jnp\u001b[38;5;241m.\u001b[39mswapaxes(\n\u001b[0;32m    711\u001b[0m         vmap(random\u001b[38;5;241m.\u001b[39msplit)(rng_key), \u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m    712\u001b[0m     )\n\u001b[1;32m--> 713\u001b[0m init_params \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_init_state\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    714\u001b[0m \u001b[43m    \u001b[49m\u001b[43mrng_key_init_model\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel_kwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minit_params\u001b[49m\n\u001b[0;32m    715\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    716\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_potential_fn \u001b[38;5;129;01mand\u001b[39;00m init_params \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    717\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m    718\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mValid value of `init_params` must be provided with\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m `potential_fn`.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    719\u001b[0m     )\n",
+      "File \u001b[1;32mc:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\infer\\hmc.py:657\u001b[0m, in \u001b[0;36mHMC._init_state\u001b[1;34m(self, rng_key, model_args, model_kwargs, init_params)\u001b[0m\n\u001b[0;32m    650\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21m_init_state\u001b[39m(\u001b[38;5;28mself\u001b[39m, rng_key, model_args, model_kwargs, init_params):\n\u001b[0;32m    651\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_model \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    652\u001b[0m         (\n\u001b[0;32m    653\u001b[0m             new_init_params,\n\u001b[0;32m    654\u001b[0m             potential_fn,\n\u001b[0;32m    655\u001b[0m             postprocess_fn,\n\u001b[0;32m    656\u001b[0m             model_trace,\n\u001b[1;32m--> 657\u001b[0m         ) \u001b[38;5;241m=\u001b[39m \u001b[43minitialize_model\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    658\u001b[0m \u001b[43m            \u001b[49m\u001b[43mrng_key\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    659\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_model\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    660\u001b[0m \u001b[43m            \u001b[49m\u001b[43mdynamic_args\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m    661\u001b[0m \u001b[43m            \u001b[49m\u001b[43minit_strategy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_init_strategy\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    662\u001b[0m \u001b[43m            \u001b[49m\u001b[43mmodel_args\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmodel_args\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    663\u001b[0m \u001b[43m            \u001b[49m\u001b[43mmodel_kwargs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmodel_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    664\u001b[0m \u001b[43m            \u001b[49m\u001b[43mforward_mode_differentiation\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_forward_mode_differentiation\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    665\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    666\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m init_params \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    667\u001b[0m             init_params \u001b[38;5;241m=\u001b[39m new_init_params\n",
+      "File \u001b[1;32mc:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\infer\\util.py:656\u001b[0m, in \u001b[0;36minitialize_model\u001b[1;34m(rng_key, model, init_strategy, dynamic_args, model_args, model_kwargs, forward_mode_differentiation, validate_grad)\u001b[0m\n\u001b[0;32m    646\u001b[0m model_kwargs \u001b[38;5;241m=\u001b[39m {} \u001b[38;5;28;01mif\u001b[39;00m model_kwargs \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m model_kwargs\n\u001b[0;32m    647\u001b[0m substituted_model \u001b[38;5;241m=\u001b[39m substitute(\n\u001b[0;32m    648\u001b[0m     seed(model, rng_key \u001b[38;5;28;01mif\u001b[39;00m is_prng_key(rng_key) \u001b[38;5;28;01melse\u001b[39;00m rng_key[\u001b[38;5;241m0\u001b[39m]),\n\u001b[0;32m    649\u001b[0m     substitute_fn\u001b[38;5;241m=\u001b[39minit_strategy,\n\u001b[0;32m    650\u001b[0m )\n\u001b[0;32m    651\u001b[0m (\n\u001b[0;32m    652\u001b[0m     inv_transforms,\n\u001b[0;32m    653\u001b[0m     replay_model,\n\u001b[0;32m    654\u001b[0m     has_enumerate_support,\n\u001b[0;32m    655\u001b[0m     model_trace,\n\u001b[1;32m--> 656\u001b[0m ) \u001b[38;5;241m=\u001b[39m \u001b[43m_get_model_transforms\u001b[49m\u001b[43m(\u001b[49m\u001b[43msubstituted_model\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    657\u001b[0m \u001b[38;5;66;03m# substitute param sites from model_trace to model so\u001b[39;00m\n\u001b[0;32m    658\u001b[0m \u001b[38;5;66;03m# we don't need to generate again parameters of `numpyro.module`\u001b[39;00m\n\u001b[0;32m    659\u001b[0m model \u001b[38;5;241m=\u001b[39m substitute(\n\u001b[0;32m    660\u001b[0m     model,\n\u001b[0;32m    661\u001b[0m     data\u001b[38;5;241m=\u001b[39m{\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    665\u001b[0m     },\n\u001b[0;32m    666\u001b[0m )\n",
+      "File \u001b[1;32mc:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\infer\\util.py:450\u001b[0m, in \u001b[0;36m_get_model_transforms\u001b[1;34m(model, model_args, model_kwargs)\u001b[0m\n\u001b[0;32m    448\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21m_get_model_transforms\u001b[39m(model, model_args\u001b[38;5;241m=\u001b[39m(), model_kwargs\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[0;32m    449\u001b[0m     model_kwargs \u001b[38;5;241m=\u001b[39m {} \u001b[38;5;28;01mif\u001b[39;00m model_kwargs \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m model_kwargs\n\u001b[1;32m--> 450\u001b[0m     model_trace \u001b[38;5;241m=\u001b[39m \u001b[43mtrace\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_trace\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mmodel_args\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[43mmodel_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    451\u001b[0m     inv_transforms \u001b[38;5;241m=\u001b[39m {}\n\u001b[0;32m    452\u001b[0m     \u001b[38;5;66;03m# model code may need to be replayed in the presence of deterministic sites\u001b[39;00m\n",
+      "File \u001b[1;32mc:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\handlers.py:171\u001b[0m, in \u001b[0;36mtrace.get_trace\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    163\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mget_trace\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m    164\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    165\u001b[0m \u001b[38;5;124;03m    Run the wrapped callable and return the recorded trace.\u001b[39;00m\n\u001b[0;32m    166\u001b[0m \n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    169\u001b[0m \u001b[38;5;124;03m    :return: `OrderedDict` containing the execution trace.\u001b[39;00m\n\u001b[0;32m    170\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 171\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\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[0;32m    172\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrace\n",
+      "File \u001b[1;32mc:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\primitives.py:105\u001b[0m, in \u001b[0;36mMessenger.__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    103\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\n\u001b[0;32m    104\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m:\n\u001b[1;32m--> 105\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\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",
+      "File \u001b[1;32mc:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\primitives.py:105\u001b[0m, in \u001b[0;36mMessenger.__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    103\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\n\u001b[0;32m    104\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m:\n\u001b[1;32m--> 105\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\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",
+      "File \u001b[1;32mc:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\primitives.py:105\u001b[0m, in \u001b[0;36mMessenger.__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    103\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\n\u001b[0;32m    104\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m:\n\u001b[1;32m--> 105\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\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",
+      "File \u001b[1;32m~\\pymob\\pymob\\pymob\\inference\\numpyro_backend.py:485\u001b[0m, in \u001b[0;36mNumpyroBackend.parse_probabilistic_model.<locals>.model\u001b[1;34m(solver, obs, masks, only_prior, user_error_model, make_predictions)\u001b[0m\n\u001b[0;32m    483\u001b[0m y0 \u001b[38;5;241m=\u001b[39m {k\u001b[38;5;241m.\u001b[39mreplace(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_y0\u001b[39m\u001b[38;5;124m\"\u001b[39m,\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m): v \u001b[38;5;28;01mfor\u001b[39;00m k, v \u001b[38;5;129;01min\u001b[39;00m theta_\u001b[38;5;241m.\u001b[39mitems() \u001b[38;5;28;01mif\u001b[39;00m k \u001b[38;5;129;01min\u001b[39;00m data_variables_y0}\n\u001b[0;32m    484\u001b[0m theta \u001b[38;5;241m=\u001b[39m {k: v \u001b[38;5;28;01mfor\u001b[39;00m k, v \u001b[38;5;129;01min\u001b[39;00m theta_\u001b[38;5;241m.\u001b[39mitems() \u001b[38;5;28;01mif\u001b[39;00m k \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m data_variables_y0}\n\u001b[1;32m--> 485\u001b[0m sim_results \u001b[38;5;241m=\u001b[39m \u001b[43msolver\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtheta\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtheta\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my0\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43my0\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    487\u001b[0m \u001b[38;5;66;03m# store data_variables as deterministic model output\u001b[39;00m\n\u001b[0;32m    488\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m deterministic_name, deterministic_value \u001b[38;5;129;01min\u001b[39;00m sim_results\u001b[38;5;241m.\u001b[39mitems():\n",
+      "File \u001b[1;32m~\\pymob\\pymob\\pymob\\inference\\numpyro_backend.py:261\u001b[0m, in \u001b[0;36mNumpyroBackend.parse_deterministic_model.<locals>.evaluator\u001b[1;34m(theta, y0, x_in, seed)\u001b[0m\n\u001b[0;32m    259\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mevaluator\u001b[39m(theta, y0\u001b[38;5;241m=\u001b[39m{}, x_in\u001b[38;5;241m=\u001b[39m{}, seed\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[0;32m    260\u001b[0m     evaluator \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msimulation\u001b[38;5;241m.\u001b[39mdispatch(theta\u001b[38;5;241m=\u001b[39mtheta, y0\u001b[38;5;241m=\u001b[39my0, x_in\u001b[38;5;241m=\u001b[39mx_in)\n\u001b[1;32m--> 261\u001b[0m     \u001b[43mevaluator\u001b[49m\u001b[43m(\u001b[49m\u001b[43mseed\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    262\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m evaluator\u001b[38;5;241m.\u001b[39mY\n",
+      "File \u001b[1;32m~\\pymob\\pymob\\pymob\\sim\\evaluator.py:351\u001b[0m, in \u001b[0;36mEvaluator.__call__\u001b[1;34m(self, seed)\u001b[0m\n\u001b[0;32m    348\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_signature\u001b[38;5;241m.\u001b[39mupdate({\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mseed\u001b[39m\u001b[38;5;124m\"\u001b[39m: seed})\n\u001b[0;32m    350\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_solver, SolverBase):\n\u001b[1;32m--> 351\u001b[0m     Y_ \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_solver\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparameters\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    353\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    354\u001b[0m     Y_ \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_solver(parameters\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mparameters, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_signature)\n",
+      "File \u001b[1;32m~\\pymob\\pymob\\pymob\\solvers\\base.py:82\u001b[0m, in \u001b[0;36mSolverBase.__call__\u001b[1;34m(self, **kwargs)\u001b[0m\n\u001b[0;32m     81\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m---> 82\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msolve\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;31m[... skipping hidden 10 frame]\u001b[0m\n",
+      "File \u001b[1;32mc:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\interpreters\\pxla.py:1145\u001b[0m, in \u001b[0;36mExecuteReplicated.__call__\u001b[1;34m(self, *args)\u001b[0m\n\u001b[0;32m   1142\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mordered_effects \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhas_unordered_effects\n\u001b[0;32m   1143\u001b[0m     \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhas_host_callbacks):\n\u001b[0;32m   1144\u001b[0m   input_bufs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_add_tokens_to_inputs(input_bufs)\n\u001b[1;32m-> 1145\u001b[0m   results \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mxla_executable\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexecute_sharded\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   1146\u001b[0m \u001b[43m      \u001b[49m\u001b[43minput_bufs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwith_tokens\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\n\u001b[0;32m   1147\u001b[0m \u001b[43m  \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1148\u001b[0m   result_token_bufs \u001b[38;5;241m=\u001b[39m results\u001b[38;5;241m.\u001b[39mdisassemble_prefix_into_single_device_arrays(\n\u001b[0;32m   1149\u001b[0m       \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mordered_effects))\n\u001b[0;32m   1150\u001b[0m   sharded_runtime_token \u001b[38;5;241m=\u001b[39m results\u001b[38;5;241m.\u001b[39mconsume_token()\n",
+      "\u001b[1;31mXlaRuntimeError\u001b[0m: INTERNAL: Generated function failed: CpuCallback error: _EquinoxRuntimeError: The maximum number of solver steps was reached. Try increasing `max_steps`.\n\n\n--------------------\nAn error occurred during the runtime of your JAX program! Unfortunately you do not appear to be using `equinox.filter_jit` (perhaps you are using `jax.jit` instead?) and so further information about the error cannot be displayed. (Probably you are seeing a very large but uninformative error message right now.) Please wrap your program with `equinox.filter_jit`.\n--------------------\n\n\nAt:\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\equinox\\_errors.py(89): raises\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\callback.py(258): _flat_callback\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\callback.py(52): pure_callback_impl\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\callback.py(188): _callback\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\interpreters\\mlir.py(2327): _wrapped_callback\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\interpreters\\pxla.py(1145): __call__\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\profiler.py(334): wrapper\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\pjit.py(1178): _pjit_call_impl_python\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\pjit.py(1222): call_impl_cache_miss\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\pjit.py(1238): _pjit_call_impl\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\core.py(893): process_primitive\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\core.py(405): bind_with_trace\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\core.py(2682): bind\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\pjit.py(166): _python_pjit_helper\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\pjit.py(255): cache_miss\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\traceback_util.py(177): reraise_with_filtered_traceback\n  C:\\Users\\Markus\\pymob\\pymob\\pymob\\solvers\\base.py(82): __call__\n  C:\\Users\\Markus\\pymob\\pymob\\pymob\\sim\\evaluator.py(351): __call__\n  C:\\Users\\Markus\\pymob\\pymob\\pymob\\inference\\numpyro_backend.py(261): evaluator\n  C:\\Users\\Markus\\pymob\\pymob\\pymob\\inference\\numpyro_backend.py(485): model\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\primitives.py(105): __call__\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\primitives.py(105): __call__\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\primitives.py(105): __call__\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\handlers.py(171): get_trace\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\infer\\util.py(450): _get_model_transforms\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\infer\\util.py(656): initialize_model\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\infer\\hmc.py(657): _init_state\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\infer\\hmc.py(713): init\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\infer\\mcmc.py(416): _single_chain_mcmc\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\infer\\mcmc.py(634): run\n  C:\\Users\\Markus\\pymob\\pymob\\pymob\\inference\\numpyro_backend.py(652): run_mcmc\n  C:\\Users\\Markus\\pymob\\pymob\\pymob\\inference\\numpyro_backend.py(566): run\n  C:\\Users\\Markus\\AppData\\Local\\Temp\\ipykernel_10328\\3769994282.py(8): <module>\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\IPython\\core\\interactiveshell.py(3548): run_code\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\IPython\\core\\interactiveshell.py(3488): run_ast_nodes\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\IPython\\core\\interactiveshell.py(3306): run_cell_async\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\IPython\\core\\async_helpers.py(129): _pseudo_sync_runner\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\IPython\\core\\interactiveshell.py(3101): _run_cell\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\IPython\\core\\interactiveshell.py(3046): run_cell\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel\\zmqshell.py(549): run_cell\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel\\ipkernel.py(449): do_execute\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel\\kernelbase.py(778): execute_request\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel\\ipkernel.py(362): execute_request\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel\\kernelbase.py(437): dispatch_shell\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel\\kernelbase.py(534): process_one\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel\\kernelbase.py(545): dispatch_queue\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\asyncio\\events.py(84): _run\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\asyncio\\base_events.py(1936): _run_once\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\asyncio\\base_events.py(608): run_forever\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\tornado\\platform\\asyncio.py(211): start\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel\\kernelapp.py(739): start\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\traitlets\\config\\application.py(1075): launch_instance\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel_launcher.py(18): <module>\n  <frozen runpy>(88): _run_code\n  <frozen runpy>(198): _run_module_as_main\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Increase max_steps\n",
+    "sim.config.jaxsolver.max_steps = 100000000\n",
+    "\n",
+    "# Put everything in place (needs to be run again because we changed an important setting)\n",
+    "sim.dispatch_constructor()\n",
+    "\n",
+    "# Try running the inferer again\n",
+    "sim.inferer.run()\n",
+    "\n",
+    "# Plot the results\n",
+    "sim.config.simulation.x_dimension = \"time\"\n",
+    "sim.posterior_predictive_checks(pred_hdi_style={\"alpha\": 0.1})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8614a6c4",
+   "metadata": {},
+   "source": [
+    "👉 Even with {attr}`~pymob.sim.config.max_steps` set to 100.000.000 (the default value is 4096), we still get a runtime error, it just needs a little longer to appear. That means that we probably have an extremely sensitive numerical problem for some of our prior values, exceeding even an unreasonable amount of solver steps. So let's try option 2:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "badbb5e0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "      Trace Shapes:      \n",
+      "       Param Sites:      \n",
+      "      Sample Sites:      \n",
+      "         delta dist     |\n",
+      "              value     |\n",
+      "    sigma_prey dist     |\n",
+      "              value     |\n",
+      "sigma_predator dist     |\n",
+      "              value     |\n",
+      "      prey_obs dist 101 |\n",
+      "              value 101 |\n",
+      "  predator_obs dist 101 |\n",
+      "              value 101 |\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "sample: 100%|██████████| 3000/3000 [00:18<00:00, 163.14it/s, 15 steps of size 4.32e-01. acc. prob=0.93]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "                      mean       std    median      5.0%     95.0%     n_eff     r_hat\n",
+      "           delta      0.90      0.00      0.90      0.89      0.90   2707.28      1.00\n",
+      "  sigma_predator      0.52      0.04      0.52      0.46      0.58   1255.02      1.00\n",
+      "      sigma_prey      0.44      0.03      0.43      0.39      0.49   1217.63      1.00\n",
+      "\n",
+      "Number of divergences: 0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 400x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Decrease max_steps to a reasonable value and set throw_exception to False\n",
+    "sim.config.jaxsolver.max_steps = 10000\n",
+    "sim.config.jaxsolver.throw_exception = False\n",
+    "\n",
+    "# Put everything in place (needs to be run again because we changed an important setting)\n",
+    "sim.dispatch_constructor()\n",
+    "\n",
+    "# Try running the inferer again\n",
+    "sim.inferer.run()\n",
+    "\n",
+    "# Plot the results\n",
+    "sim.config.simulation.x_dimension = \"time\"\n",
+    "sim.posterior_predictive_checks(pred_hdi_style={\"alpha\": 0.1})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f2aeb666",
+   "metadata": {},
+   "source": [
+    "👉 This worked, so now we can have a look at the results:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "4af0a3f3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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WIpyx0TmeY+EmIQakweRwNwAAEPkcKa+mJ2aspw+X7KbebTPpo5vH09E9WoW7WTHLmJ6t6LPfHkPXTl9El722kJ69ZASdORSpkQCEm3vuuYeuueYaOvroo2nMmDH07LPPiigoTsljrr76aiE+sZ8Ts3DhQtq7dy+NGDFC/H700UeF6PSHP/zB9jrZD+qGG24Qy7Vq1UpEPN1xxx1CkBo3bpxn+8aeFCv3HKYerTOpVWaqZ+sF4UvfAyCWyStyF6Xkxf0WL/dsYoPgpGtZFCfHIJJRYugcQJQCAJjCJqkPfr6GCkqr6LYTe9MdJ/Wl9JSkcDcr5unWOoM+vXUC3fT2Urrt/WX05zMH0o3H1af8AADCwyWXXEIHDhyghx9+WJiMs9g0c+ZMn1E5Ry+p/aIqKirowQcfpG3btom0vTPPPJPeeecdP9Nyq3Uy//rXv8R6L7zwQlFVjw3UX3zxRU/37VBpFe0+VCbEqVaZmHQIJQpGdyFP+6pTFEpOQsJIPONNdEn0R6iYkdBwi/D9YrhM6JoT8+wqKKM9h8toQu82jj4XC8FqEKUAALocLKmkR79cS1+v2k+DO2XTm9eNpsGdcsLdrLiiRUYqvX3DGLrv41X0+Iz1Ilz87lP7IVQagDDCZuX8o8esWbP8/j7++ONp3bp1Aa1Tpv9NnTpV/AS7kpKZsSvEE2/Bozw8/Lr1oBBhJ4/oHO6mxBX8bNlZUEZ92jWnaCdenoUyUkqv0p48BniOecfy3YWOlpfnJRZOAUQpAIAfPEv+waJd9M/vNlJZVS394fT+NOW4XpSCGcWwwFFp/75kBGWlJ9NzP22hipo6uv+MARCmAACe0mhaq/dskQOTkDYp5pHHGsc1tLAgBYJTsYyv6B5tMnXfX7P3CO06VEbZzZKpXVY6xQKx3hWTu1enM1chn1tF5TW0ZMchGtW9JfqmURw9unjHIRrUKZuy0sNTuAqiFADABz+QHvliLa3bX0TH9GlNj00eQr3bRv+MViwYoP/tvCGUlpxIr87eJown/3LuYPE6AAB4gUzPwGMl9CgNg4JfNh+goZ1zqE3ztHA3CQDHrNpTXyG0WWoSpScnNanKXFXToGzEgAgbL0Ky3E299D35Sk1dnTBCH9gxmzLTIC2EEkX+I8DvbbZoyS2qoFpFcZw66BW4cgAAoqTtk/9bT1+s2EedWzSjl688iiYN7oAZjwiCz8XDZw+iZilJ9OKsraJz9+QFQyFMAQA8IV4GWeEqq74ht5iGdc4Rz2wur87Rx+qv2JKqGpGizdEkJ/RvF87mAodU1tRSWjK8NiULthWI39r0SJ9ZNrotUYee0bleSh8ILUoMnQKIUgDEMWVVNTRt7nYhcnDa3t2n9KObj+8FI/MIFqbum9SfUpMT6dkfNlNKcgL9dfIQiIcAgIDxjRdNnieR3AHef6ScVu05QqcObB9xYj1HH7OJfJvmqdSlZQat3ntEvC4jkb0a3G3MLaYNuUXwSgoCLCTyuePvXzV8XpftKhRCYk6z8KS9RAvSg0j6FHm2XkUJWz8osp403iOfTfqeUiDU7DtcTh1z0ptc796Y9ocXmMQAEKezetN/3U4T/zGL/vndJjqhf1v68d7j6c5TUFkv0uEvojtP7ku/PaE3vbtgF/1txnrMVgEAAsZnmKrTt40G3Xv1niMiIqnKxKjdTNDKt1lefk9hmUi1i0RYkIp1+Fz9uD4vpN97R8qqhZC4fFehblEYhqPcgg1HSM9ck0uHy6LUE6vhlAXjccL3PvdtOY3sixV7qbSyxvNtFJRUUo2L50vsRkpF1vfE5rxi2pJfHJR18/VVVNF4j/M9yNcZT+4HA0Xn+cYCONussHebbzmPpMFwnzsGkVIAxBH8Zfrp8r307x82iy/uY/u0od9POppGdG0sTw6iJ2KqorqOXp+7Xfg33Hta/3A3CwAQZRRXVFNBSZUwJpZ94IQ4rDi1aPsh8dtOhNHSnYVU1K5GGMLaJcFiEBebRzU4rNx9RIgP1bUKpSaHZiTFPisMe65okRELodDI2KCd931zfgmN7tEqKFFDXJK+VfNUah4EbyApbAQjqunbtbniN1tQMEfKqz31N+LjPnfLQeqY04zG9Gw89vESqa7vKaVEXEQq06ddlmfrlLstry/5HcGG/cyB4krq3jrw66yuTqH1qkkF3q720uJrkOG+v5ZYuAwhSgEQJ2LUV6v20fM/bqFtB0uFCPXUb4bRhD7hMbMDgcMdoYfOHii+pJ7/aYuIcLvtxD7hbhYAIIr4eeMBMYhVi1JmICiznvLq+sGBXYwGrnrV97wY5IYzncmpKPrThnw6vl9bapGRGvLt80Aw0FRP+XG9QXs4WL+/2JFgqleSPikxgc4e1omCl75HQUNe916fD7a4kNcsY7R6vvf2H6mgTg3imFnUFacxtsxMteX7unB7AZ0+pEPIvcvMjM4jTJMKKYFcXtW1dULU6tKyme987j1cTlvySxrXb7JNv0d7DJ0DiFIAxLgY9eXKfUK04HDPwZ2y6bWrj6ZTBraLig4rMIfPIXtK8QDpqW83UuvMVLp0TLdwNwsAECWoUwTkgFE3fc+3TOQSyW0zipRqRLE1yOEUEk4ftIoE4PFzUpC/4jlthWfsW9kYVBuRV1TpG5CFSpRiMYBUA+3EhrNTWFplSyDQIv2RZAXFcPuZ5RdX0CByL0qpBZigRUp5nMCnvndYUNO+Fky0e7JmbxFtO1giMhFam1TR5KgrbXQm+wVxetapg9pTRmrjEJ3XxxRX1FBa88ix2IjQLGZPCUY02N7CclHQgu+zfu2zdEW/OkWhJM3V1ej72HSdsTCigygFQIyKUVxJ7/mfNtOOgjIa0jmbXr/6aDoZYlTMwR3gv184THSoH/hstegEcYcGAACc0NgnjpzvCB7kc5TvgA7Z1L+DdykZgaO4jkLp1jrD97fTr+Pluw4L0aFt83TKyUixEBuDex6/X5cnfgdiqu5LX9Q5nNK3y2uRR+05JgfV0qycU+KsIlyM9qGkokZcq0d1a0ldWzWe41ATyTpBKHzAgh25JldrJFZIjzE3Zu4szjJF5TV+olSknVQWUzhKP5LS98LlrepGYK2pq7MUfxUTDy/1NiPhDPBkCROoJzGMzgGIMTHq46V76JRnfqF7P1pJWekpQoz66vZj6ZRB7SFIxShcWnzqFUfR8K4t6Pb3l4mZNgAAcILZLGy4Ov7VDZ13tbFrJOD0MBgdUzNRRg85iLEacEfCQMUOZj2S79fnCZHHa9SHTh7H0gaz4hIX5thSfJAmyJy6pceSHYdo7b76qotOYd+arQdKIkoEcEMoHh/yfIQqikd7b8tzFMjmjc5zQoScM+5jsijt9nyyb1mwDMK9xjBNs+F3RYPPkxPktWkmXCo614BZNLOX/Lwx39F3LvttSc+tQIAoBUAMwDOKXAWCxajfN4hRb1xzNH15+zEQo+IEnlWbds1oMUN7w/TFcVGFCQAQhOp7ZssEcbtm8Kw8D4QCEcX4sz+syxPRCKEW16xm0522xmr5/YcrhNFzpGPWN5Gz70Z4IdDIy6BRyLC3TvYVYp8fPWHR6NrSesZYec6oKzzO23pQpPtEO42Cjbf3n3ptCR5ESm07UNJENNHew8aeUuQ5kSZF5plUKtVe//y3+jW+rjli9NctBRTNcAoes77BXN0J8to0CwJVTCOlzJcLCKW+kuiqPYfFM/inDXlUXuVceHMDRCkAopwF2wrovBd/pTv/s0JUGpl2bb0YdfJAiFHxBvthvH39GHEdXDNtkfAeAQAAZ5FSTb83gvVdwsIJ+x7KAX6TNqk63Oy3crjMvdDCUUYcEcMRK7xNLunt5X6YDU6MDp+fGGJjcNEogJgvzIO+WRvzKRC0g8lgYrYZTq0zuj5cbUt3cNhwHuqM2uffQDZnZ+Pp+vfqX/PyFvlm9X73Ec9K8D25NuUVu/qs0fH1ksZz6e5AsAC+eu8RWrgteBHnbu8r7XOYRQNpvB40TJpqZy/mbS0Qz1stspKcVwTrUaVYpOC5QX5UfT4Vxf7+6D1rgrH7/OxlH7Odh0ITqQxRCoAoZXNesYiIufTVBaKk978vHSHS9E4aADEqnmE/jLeuHyMMaK+etkiUkAYAAD9xRidNSQ6UeAb4iIH4Y7fjz7OsHL1rhRSG2CfJDrM3HyC3aJvO6VBerWveloNioO7UIFpPDDH69uZzVtYwYx0Kqeh/a3LpuwbfqGDRaKBvvEfs9SQFIDvwdTx380FxTfE5+WXTAdsRC6GooGc3RTDXJBolnD08Nuh2Eh3y4/o88TwI1XVrJ33PrB3yEqixeS8bRVDpCU/cH+Nn3obcYkfn1eiy5JQpFkjdwM8qmXLqJdq2So8t3/sUXjgVtqomBOqoCfI5YzZUKyj1P25Gvo/y2cnfD2zhogdP6LgRae2kl3tZFAGiFABRBndo/vr1Ojr933PETNoDZw6gH+89XpiNhrvqC4gMuJoHR8zxF9F10xfrDkABAPHJku2H6If1eSaDsTqatcl/oOP7ZrHZ/7TrRxGsalx2CKTaW5NZbZfrkR5DdsQQPmcypSgUAUycQsaRGMGqxKYe9PD1YpYSpIfRMaiuVcSAbtnOw0I80UbE6YkFRul7LCJsyTePCmoUIRp+W7Tb6X7GQp9VPg/ksQ/m9RuowOgTDCyWc7P2OZsPCJE0v7jSfN0GK/fyKbli92H6eUN+QAKNm4ivxjTx0D/z+ZnGqbAcLevlPvJz0slxtHPYlu4sFGmkdsUsPp8LdKL7WBTkseJ6V5Ye5ueIo92+9tD3D6IUAFECPxj/t3o/nfL0LzTt1+10+ZhuNPsPJ9JNE3sHXPEAxB6jureiFy47SvhQ3Pb+MvFlDAAAB3VmYMM1g+0boIRgfKIdCCSbTOKw949RtJgbrPYvmMJPoNj1QXKDemDKVgReICuY2UGxiK5hEWHtviLTe0MOFO36JHl1qXN/UDtoddKOcBCslqmFA6fFAwxNqBP9hbU1Nk3qFRttVPulcWobp0Sar9Pec5LTrbSRSUZwxWb1s4f7ivuPNN473CaOpDK7nvRSzqwOu3yfJz+c9EvrLCK77BiOy/YWVXg7UcsRa3oTPUbYFUxLK/X3ychTisV4vgb0CCT13ai5lR5HnEGUAiAK4C+K66cvplvfW0ZtslLp898eQ389b0hAM70g9mGT+yfOH0KzNh6gP3+2OmwlcwEAkYfWp8fO48GrAW9+UYV4HvkipSIsyJcjmLTRYnYwesZaRQWoByl2jkUohYdAvFNCDUdFyVQxo+Ood4qkAKE+D2qjeLPBl/yIvJat7iOv7BWqauuE91EwYSHArTeTGcG9egM7vtLUWX2eFm0vEJHntqrv+URKf9SHUS1K8fJS+FWbtLNgs8MXYWav7Zzq+uuWg7aW1RrCczrzou2H/NI0OZLKa9T7sm6f/eidFXvqI7vUXlQcdVgfzVgiqgFa0Xiu7F+B2iWNMg8cCWwNB6Gyus6RiC7x86KipteAWphMaXi4WRWO0LtrfAJviKpBJnu8PgCAh/CX1cdL99BjX68TsxmPnDOIrh7fg5KQpgdscsnobpR7pJL+9cMm6pDTjO45tV+4mwQAiAC0hRDcpLvw9xILOAM7ZlNKkr15Tvb6mb+tQHxGCmNmpbHtotd8jnDgiCiOJtZ2rAPR6LXik1XrrXbP8bGPkfmFQE47n1ttlDh7KTqhaaSHovtvs+pTdgRCFmElXvTe7HgiuVqv5sPfr88Tg+2zh3VqsiyLdmv3HqGxvVpHVJ9URtOYR/gYv8fpXbbS9wzWYSdSyk7qliQr3d5Q3Up00CKfuRzhc+bQjobLmTVbv0Kc/gc4DZajDicN7uB7TXrk2eFQSZVPyBrRtYUQZmR0ZbI6rM0GRvvEAuy6/UXUt31zw8/y8WK7lECQAuXmhtTgtkM62nqk61Xf012/jpDsJho3wSqdVPMAZ4GtXVaa7b6AFkRKARChcCfmxreW0H0fr6LBnbLp27sm0nXH9IyoL38QHfzu5D502Ziu9NyPm+n9hbvC3RwAQIRRX2nNznL+f+8sKBV+MU6qcVVU1fkGJIUNfj88QGKRzE3FUKMKVOzxwSbLnFqh13Y9eBbcTlUopWGWXp3uIl/XwhEWVseHJ9nzDMzedxWUNYkoCGW2nxPvFz522mOijWTia8bsOpS+WVboRYTUqiPObKyDRVE715ypKKVjbK0dmLMI62uXB104MzHNS8w8xTjd60BJpV9EWTDhthilkqpbaJS+5BQvhHI1isvoOb6mFRsilHzOiW0pCq3cfdj0ulXfIPI57JQmQr9mP9URO1vyS5tEFHG0n/1t1bPrUJkwKzeL5jS6Jq1uE34Gc8QYi2fynATj1tLer3YnJRRNGievx6qKrPxMRU2dreeEEkBUJ3t1sbeVWyBKARCB/LQhjyY9O5t+3XqQHj1nEL1/4zjq2ioj3M0CUQp/sfx18hA6eUA7evDz1fRDkCsqAQCiD72OMQsIPPhsDOPXx0nHXVvxjOH1c3SAOkLA7no5pUOvAtX/1uw3/ZxiMAv+03p7KSs8S69OdzHCjmDHA3+jimbLdxf6ZtQj3Tdo/tb6Y2IkZLDJs3rQoh3z8GCQ03DclouvrXV2XDbkFolrzuo6KzeJQlGa/FaETxCLAkwg37fsa6Z3LLWDS3WVXbN9WbitQPQvw40bEY3Tkjgq064Ipt2EevCueCBKKYbpe/rbN484UmyJwmoBVC1CaWGxcEdBKa1sSGXVwmJRkeo4mrVNrwqcnc9phePGdMHG992a0avFZz1mbcwXop1RyqVicK8VlTcWknAr9OpFrPGkCacVByoeK75IqfqDaWSYryfe8rY36nwXsTDv1tNQ7w5xEv2mBaIUABEEzyA88c16un76EurUohl987vj6NpjeqKqHgiY5KREev7ykTSsSwu6/YNlooMHAAASvf4yCwg8c9y4jLPOq3p5Tov5YsVelSjV+L0WSAUou7PtdpuuXt/qPUccraumVqHNecXOj5NDkSlSfdGlYGB3/7Uz8TyYDsRAVx0xYTTLb960BEe+WjwA9UV+adbLogBTqon8chJ5xr5m6upWWv8qtf+PHXKLKqjYxOTZzZiZB/5ufHGcwveWQHGXrsZpWU7200gw4fPJ6VHBCU4zumb9faqs7n95jRm1URtlaBbdYnq96LTTqnKgnym94Zp1tqX6nJ3JAN5HrjqnTmWTz1m9NvK9xiK1aFeC+5RiFgu1ohC3lyOKAxFs9IRQo2tUPke15BVVNhFqF24vEN50PlFdsZ++p0cgwhtEKQAiBA45vujl+fTq7G107YQe9OlvJ1CvtsZ5zQA4JSM1maZdO5o65jSjG6Yv1q3cAwCID9Qd7/qqScFVOmTKxc6CsiYdarXwJd5riByq1hEDjFJSZHSNXp9YLYY5YdtBo3QhRfdY8qCGB797Cp0N0v0HAtYjolgtWiGFSqXOnRG32xl/q8Np9P6SHY2TO7ar7yW4P7fqaCwjtCJYsOF7l9N2Qg1H1bnFToqofF5w6qxWmDF6LtRj5DVlEg1lUwUJZpqmG5y0x6jCpVusDPhZIDKCRWbz6DTVOdFZTBvRK+F0cUabXuhbr8lpVuyk1jVJ+zNelp+FM9fkCiFauw7+4cgwjlxlylTHSh0ZJyPtQhWZC1EKgAhg7uaDdM4Lc4VI8PKVo+jRcwdTWrK/gScAXtAqM5Xeum4MJSUm0jVvLhKmwwCA+IbTk+wMFhSHgym9frY0IjYTENg3hNPeOOpIy3frck19i4yo0WwvkAGe0aBDbsOpAOa0KV4VxGMhz0nVKLvY3R3t1ZPUcD1ZpedIZq7NpRmrG9M01adYu26+nrivpTfAshp0GZ1PtWjqW0QJnojgW5+VN45mIBoMtLsm72tbn/X4+OgbbtffpyxIa9NB7fjeyHVqU2cZ9S2jfgbyvSSj/LwqrMCr14tIsoMXYoJ6DXaeFYaV2nSie5x4FmnXKiMRnXxOfdjMRE2rdjn1PtSm3bk5K3Ucnac181fMxWm+7jfm+l+/87YWiPTiQ2ovKr7GTBpVWlnr/jwdLKUZqmhPMyBKARBG+IHy+pxtdPW0hdQhO52+vuM4On1IY2UKAIJBt9YZNP260aKayfXTF5vOKAEAYhN1NM+czQdMhZRGY9XGwYl/WXMKWCRSI0UJozHQ4TLn5srBijKoH/QGJuyY+baYwc/uQPaLZ9KdeB5xRAzvrxWyqqJVqp/W80VGsNgV9fg6VC9r9jmOvDM6zlabsyXYGggjevcSe8zwwJYHjXw81ebcdqqdWTXHznc6R0poxZpA7hD2dnMSjcPnjgfH2lQnLvLjRZ+Eo5lkJT3eV6dYVxBrerRWGaT7BoJaHtHRPfU/EyTHj29UArBeO8zaZZRyxhMMZs+LmobvGu19se2APVHK6JnAEX5GEWIJQRL2jM6L0T1fUcP7rYh2frVqn+85wfeM3jGxbIei+Mznnfj28eS1NppZrE93I00nmOwCUQqAMMEPlHv/u5Ien7GezhjSUaTrsVgAQCgY0jmHXrpyFG3YX0y3vrs0KLPlAIDogEUVrbhhNpBjM2ozo12JYscjxgWmA3eDrWojs7yO1lC3Sy24qWfdnVYzMtwOKWLgzukim/ICS8N2UgHLzDuoUGW2zZ6F2qpQWjGLzYi165OG4k4jzZbvKhTr90uDdHCorbambo9e1T/1Mn6RGTrL8TXAxvycAlTWEIEgU1qt2qI10Q7kemIPnXlbCky2pXgqpLK/j3qV0kdu16FS8ayRAgFXKpRpUBLOIpAV4pxEZEkUB8+bzi2aid9tslJtLc/VMdnEmnFr0C8xPJ2a824bDx5tdkRSO8vLFEh1lGdCg98S+xrpwdcIR0Lqfdd40Wc1evbxvSwF8orqOsPIWCOamt+bnwij76x9DX5tXBVWC7fJzBvW6lrRRlBZiW8HbR4DXo8U0pwCUQqAMMAzAxe/Mp8+W7GX7pvUn164fKTw+wEglEzs15b+fuEwmrP5IP3xk1Ux61MCALBGGw1SWdu0Uyk7rnJAEIjE4tb/h3HjD2WUEmYl6rhpJ1cs5LQF7cBJlmoPFE4/WtWwHrdRVnaxo3uwz9fszfX+JJJqExHAyqi4strZgFPOxtu5LvQWsZrNV383qsvc+y2js36979QEG4bEVtewnf3kAT1HZOm1Rf7bTODxujugFoVY2JMDbRYpWCD70aTqpdqknAfi9Wm99hvIBQukqOUV8vhwFAmbWNe/qF5Af3mnsPDoNBVQXcmTxRWONnKCupqjFp+xf31LHLWrafqe+bJmoovdx7Kb465+HrBgJiPuAkVGdzmpzLhy9xHdQgZeFiuy42Ood7z10hh5Xzbnlwghka8jJ/cdRsEAhBjurHLKFM8ITrtmNJ04oF24mwTimAtHdRFGiE99u5FaZaTSn88a6NlsPgAgemiSMqPXUVaMJ1q6tcqgnIwU29tjP5605ETd1LdABsRc1jtfU2VIz4dJbqOkstpSlEqyWQFXPfBnk1g9wciuF4oVPkEjDHMJnGKlRs+Q3mzm3coTzO2Ay22klIzqcppe479M04X0I6WaigfqY2U2OSQjcnzpQAbL8etztxwQ0SmTR3RuMqg0Egz9hCvyFu2x3d+QsiVFCqMKh3qwCNS6eartKA8n5u964mKk4Kse10QMUnRFJaUhqrVN8zQ6pk8b29vhdG71Vo38uNwcIyuRn0U+FlS7tMzQeKNpI13tbdz/3vJ/r6KqlmprFV+EppewYMNRd/WiYqM/WYec9CbPP35fMViPk/siICwOJ6cS8nlJTU4Uz3/2PdZGW4nVKPz9UP/dpE3NtQKiFAAhhEOS7/hgufgyfX/KWOrTLivcTQKAfntCb5F68frc7ZTTLIXuOLlvuJsEAAgxReXVhn1Uo7LgcqDJnXqOdpADYN86xChAf+jMgwG3Az+zzxmlguhFStnx5XATlSVx4zXldE6AxakZq/bTWcM6mooZw7q0MF0Pfwfw8z/RhgDHKVZqdA+Rx4N69qDaW1huej7U57jEwJzXKKXOTAyyE5UhlzEbBNvBzkfsRDVr71m1GCD/bSa2eh05bbQto2PLqXAcwcGDYLvrcgpfS05E50B8hux8JsHgNa2JvtNTYxTdFyhurhCjSqMshE3o3Ybmb61/tnC0jdl1YvcYsM9XzzaZwrdK64ukfY55BYtp/MMijraKul7lvlBlKRzRfMc7+c7h/sH/1uynE/q3szxu2ohquyB9D4AQMf3X7TTl7SXUr30WffbbYyBIgYiBO+QcIXXJ0V3p6e83iWsVABDf6PWT1Wk0ttbR4H2xUKcTayQu1A+YGyICDJaRnV47pd1929OManigxikGZj5JsmPN6Wl2aNJal2MN9hJx4p1jVeLcl17UcHy35Bf7Lc9Rcpx+5/T8SvS2zYebU3203lJuYQ8qjjQwG+jU+lXDs3/wpSZhNDYzFMIUHU+pEIwvpR+TEXoisnofZGRbcmKC4T2k3Q0e0KvTAZ3ec4makW9jRJL+AeMqYTwIFlXHgnRMObWoiXm3TeHHqkna9wOpvifZXVime7y2qe5vr3HkKaVY76daOFbvG0d3laiuW+0kibYwht3JAn6WcrqonlF3qESgQK+lUGC3DVZFAxSXfoUMIqUACDLcAXx8xjp689cddMaQDvTMxSOoWWpSuJsFQBNh6okLhlJxZTU9+tU6ykpPEal9AID4RG9m32im1Qz2f9HzzjGKkGBD5Daq1Bzdtin1fjh6aXp2U8Ls7svWA6UaHxXzdnkBHwNGG3nmtpqhGhZ2OO1CXV1MmtJqB4J24EGyXkoOXz8y1cfJfgSCHTNrvUE9Cya1VL8fXJHwqO4tKCWx8fhYiaP1y8jX9N+3YlNesaiuZafynFW6kfZa4ParzaI5SlFG8nF617F92lDr5ml+rdVug60nrCvS+fO9yrBcK0pJUcFo4CqvRbvXtVu04oYdMZNThJ0SyF7IJnHF1HZZ6dQ2K63JuTH7XCixuubdRp5Go+cpe1ON7NbSOs03GLum2F/ULxrPAqvzx/vndn8gSgEQRHgG6ncfrKAf1ufRTRN70Z9OH2ArNB6AcMAh7P+6ZASVVC6lP3yyipqnJ9OkwR3C3SwAQAShjhrabGESzp1To0Gn0SBDpj5YdYytStB7hbqqnBu8ihSywqgqnJbqmvrjrh7ry4EGi4d8fvdpPE/YA6VVprFQqJcaGY4xJPsjWqGOGpOw2MIRWCwIcdrY+v3FNKxzjqM0zMZIKfP0PaPjojantotx2o3/RrQeUtrzxUbK43u3ppYZjedYfX/JfXKajqOuwKXt+tq9PjhaSosTo2in0Xh2xGorTzS9NpmJKhwlxGbkdvw82YsrGB5IbmjibSX+Nt8HtYCd4GGqdGRjHSqlRMmuW12ijXHOzguhIH0PgCDBZn2XvLKAft6YT4+fN4QeOHMgBCkQ8bB54ctXHkUju7agO95fbnugAwCILYw6yd+ty3VkIGzU12axyiq83+izoey/O4l24e99NW6CPNz6v9iN/JL749cdUbWTK02p18VCBFfL4wgi3fUp+ubFkTrG0otEcts10xWdVP8usCloej0g1a5PO9jXG/wXlhpfP1p/Hzc07f/qCZn2DkRLE4HUDXz98rbV170b3yg1fN98sWKvLQGLhdBlu/SrcvIhUbeE12dXwAx0H+q3Zyz0OhFdJWZtD3ZUXKTixXkKBL0Kj0ZYpiKq3tdGR1oBUQqAIMAh2Be9PJ+2HSih1685mq4c1z3cTQLANhmpyfTGtaOpT7vmwgdtuYelZwEA0YHTWWvDDmgQ+ttaf6hg4jA4pMlgM9KQh05tMuxvEF4j+jDa5Y3gt3VFKdU62cMqnFgJfdrUu4DmD1WHYklDGqbB27YrkgXQBNuRRXz7Gt3yBSWBR/s1kaR0trV2nz2xJTPNWwsM9mTj1LhA4BRfdZqyFOxlBTIlAFHZbeqa9mMFJZWWzyQW0ux+DwQspiYE9z6IFKyOkxeib6iq9nFVWXP0PcPsAFEKAI9hr4YLX5on8uHfnzKOTuzfLtxNAsAxXIXp7RvGUIfsdLpm2iJDzwIAQGyiVyXIDNkBVZsw8+xrMFIyjEraB4NABkoV1SEq5+0AOcBVDxjMTpGdcYVVxIRdsSFYWEX82on4CyZOTO3JxfnQpuvpXdNmQpwXYoHWfF2xEWlohIge8vARwON3reccpx7a8fcyigDac6jcvyqjRXvtGs47NXJX7w+nac7Z5F30u3r9elFdVnARDDuVU6MZPu7qiGDFwIz9UIBp4lr4XDtpI0fDegFHu0nDevXEhx0gSgHgcSf+4lfmC2+ej24ZT8O7mpdgBiCSadM8jd69cSzlZKTQFa8vpLX7IEwBAJx5wATDKyOU3iOBRDsFa7baDflFFfTj+jzdFJlAjicPqKLFD8USVRSZ4jp9z9oI2CtYLGIvIqttaEUlPZGpqsFrLFjXMQtvVn5bdozqjT4bCHqeaBw5xfeLW6TQ6eR8GxVU8GJ35X3vpQDrNtU4npBFKyKZQg99D8UzqeF5gUgpAMLE7E0H6MrXF1LrzFT6+NYJ1KddVribBEDAdGrRjD6YMo6y0pPF9e3GjBUAEPsYRcsEMqAyGs/FappHMOFKeBz5wSKS9tgGojlwhImecBeNpsXqNuuJPVr0BvjaiCAtXh+WGaubGoE33ab/RvM1ESqyKqORx5tX95tfxIjOgXBr4O209Hww9s/IDN+3my43oY0mcksoHG2FyXUU3vfBxG4EVEIMWg47vRQgSgHgAV+v2kc3vLWYerfLFBFSnVs0C3eTAPCMLi0zhDDFXlMcMcUpqgAAYMuUPAiDFC9nduMFbWWv5bsLTSNFJIdsHOt1OpMV0Tg2lW3mQzV/W/SmEima+08bHWckGK7are8X4ySNzS6B6EDaaLRAK3EGU+SWbXVrZr0htygq7yXgjMQYFKUKSp0JqhClAAiQ9xfuojs+WE5HdWspBu6tm6eFu0kAeE7XVvXCVFpyIl3+2gLanAdhCgBgLj5xdE6klC+PRrYe8M4A12zMYxYVxP6YbojGSKmyEFyr0vg6mOQVNXoz8WloUn3PQIRRG3UHG7v+UXqYVQqMNFEqgjJ4gw5fZ2pPQWCfPQEa7ccCEKUACIDX52yjBz5bTScPaE9vXT+GstJTwt0kAIJGt9b1wlRyUgJd9trCkHSuAQDRK0ypB8ehHrjGAl4WmDBLDwlGKfZAU6rCagIfRabmdqIK52/1j/qK9uxXjsDY25CG6gX8nOFI8GDgy94L4Jh74ekVilO+bGehI4PteIRNxYE+EKUAcMmrs7fS4zPW0znDO9HLVx5F6SnelqgFIBLp0SZTCFMcaswRU17O5AMAopuvVln724DIw67BtBOiOs07hlJp9EQC+P74wxNsRtXvAoXXy9FDqwMQmL2I5ArFOUdULAgEiFIAuODFWVvoiW820OQRnehfFw+n5CTcSiB+6NW2Ob0/ZZyYbb30VaTyAQDqwWA3cjE7NZFUJRAEH9yloYULxITbBw/nHEQ6GEkD4JDnf9xM/5i5kS4Y2ZmeuXgEBCkQl/Rp15z+c1N9xNQlry6gdftQlQ8AACIVs1LwqGaoIcYPRzT6fUUzwUiPdYoSoO6cDXsSEGQwmgbAAc/+sIme/n4TXTSqCz110XBKisVyCQA4EKb+e/N4apaSRJe9toBWGlTuAQAAELlEo/9TrIsIwSTGdy/iiAQR0G31v2D1HQHQAlEKAJtM/XkLPfvDZrp0dFf6+4XDIEgBQETdW2fSf28ZTy0yUujK1xfSkh2Hwt0kAAAADoD5rj/hTrWKB5EknoiESMRAm+ClqIXxE9ADohQANnhj7nZ66tuNdMFRnemJ84dSIh6oAPjo3KKZiJhql51GV09b1KTSDwAAaDl5YPtwNwE0UFwRHJNnEJkUlYe2AmC8Ewkjhkjy+0s0KwUK4haIUgBY8N7CnfTXr9fRWcM60j8uHAZBCgAd2men04c3j6durTLo2jcX0S+bDoS7SQCACCYpzAOTSBqkRQu92yLtBoBoJOBIKQ8flxhFAT0gSgFgwsdL99CfP1tDpw5qT89eAlNzAMxo0zyNPpgyjvq2b05T3lpC367NDXeTAAARSrgnyytr4KPkFEQ4AOCcSJC/N+YWR44oFcePkexm/obxKRhX+sCRAMCAr1buoz98vJIm9mtLL1w+Eg8OAGzQMjOV3rtxHA3pnE23vruUPlqyO9xNAsBTpk6dSj169KD09HQaO3YsLVq0yHT5Z599lvr370/NmjWjrl270t13300VFRW+94uLi+muu+6i7t27i2UmTJhAixcv9lvHtddeSwkJCX4/p59+etD2EQA94nUwyfdbqIDfTuxRFwGeUgWllRSP91Ok0QKilCE4EgDo8N3aXLr7wxU0pmcreuXKUZSWnBTuJgEQNeQ0S6F3bxxLx/ZtS/d9vIpen7Mt3E0CwBM+/PBDuueee+iRRx6hZcuW0fDhw2nSpEmUn5+vu/z7779Pf/rTn8Ty69evpzfeeEOs44EHHvAtc+ONN9L3339P77zzDq1evZpOO+00OuWUU2jv3r1+62IRav/+/b6fDz74gKKZOB6XRC3xGimVEEdprfFGZmpy0AUULzSpDtnpFE68lNXi+QrXis4pSaE9Gs1SInc8C1EKAA3shXP7+8tpWJcceuOa0dQsNXJvYAAilYzUZHr96qPp7GEd6fEZ6+mf326EhwuIep555hmaMmUKXXfddTRo0CB6+eWXKSMjg6ZNm6a7/Lx58+iYY46hyy+/XERXseB02WWX+aKrysvL6ZNPPqF//OMfNHHiROrTpw89+uij4vdLL73kt660tDTq0KGD76dly5YUqSkJdkiI66FJdBLJekkwoy9Cud+dWjSjSKVt8zSKNdpmme+TF4FrtS76PpkasaxFRirFCm7uVQ4SsEOkHyetsJ8a4kipo3vYO45u6JgT2LMLohQAKrhq2E1vL6H+HbJo+vVjKDPNfAYFAGBManIi/fvSkXTF2G70ws9b6MHP10REaWQA3FBVVUVLly4VUUySxMRE8ff8+fN1P8OpePwZKUJt27aNvvnmGzrzzDPF3zU1NVRbWytSAdVwGt/cuXP9Xps1axa1a9dOpALeeuutVFAQOVUu05KddyeRpRR9uD1neqJluyxvIz9i4XLq1aY5dW2V4fl6R3Rt4epzWenJTdLzo4kerTNpQIds02VKK2uCHh3oJn2vZab/PZOclCAEy3AVG/ByUjHBpkepl4JHMDIC3KC9nkKdypgUxC/eHm0Ce3ZBlAKggaU7C+mGtxZTzzaZ9Pb1Yyg73d0DBwDg/wX4+HlD6PYT+9B7C3fRnf9ZTlUwGAZRyMGDB4WA1L59e7/X+e/cXH1Tf46Qeuyxx+jYY4+llJQU6t27N51wwgm+9L2srCwaP348/fWvf6V9+/aJ9b/77rtC5OIUPXXq3ttvv00//vgj/f3vf6dffvmFzjjjDLG8EZWVlVRUVOT3EzRcjFfi2VckknCSFqR3zuwMzvTO9NAuORSJ8KRkONMWW2Wm0rherT1b38CO2dS9daYn64r0W5avxRMHtPMJGHb8egpKq0zf92IMX+eBoMNpnaN7tKJ22WnRn75n45i6Peyhish3+/3F4mKsTs4kBfiAgCgFABGt2nOYrp22iDrkpNM7N4yNutkgACIZ/vL+/aT+9OBZA+nrVftpyttLqKzKfHYSgFiAo5ueeOIJevHFF4UH1aeffkozZswQIpSEvaS4I925c2eRovfcc8+JFD+OwpJceumldO6559LQoUPpvPPOo6+//lqYofP6jXjyyScpJyfH98Mm60x7jyNU3KfvgUjASQSC1TlrbyBwhduLyipFS004s8zTUhI9j2Zwcuyba7IDtMci3OfRjl8OTyh3bdVMN9pID6tjnax6DrvFXYR4gm47vUp75gn48EVKBVGVChF2b1G2slD7OGmvt1BPziTY2Fw3l9Ga2n1z2i+AKAXinvX7i+iqNxYJIer9G8c56rwAAOxz43G96KnfDKO5Ww6Ke+5IWXW4mwSAbdq0aUNJSUmUl5fn9zr/zR5Pejz00EN01VVXCTNzFpTOP/98IVKxYFRXVx8xyNFTHPlUUlJCu3fvFql+1dXV1KtXL8O28Hvcni1bthguc//999ORI0d8P7xuplNLd2kQJ/RrZ/iem5QSs84xpzHFKpkWxsrBTnPT4mRMZCVKGA1m9D4WyqFYuwD7dcFu6/jerWlk15bUp+E+ynR4jYSKSI/qkNoPC60nqSKm3F7T/dpneZJO6UWklIywsbpf7QoBQzs7i1SsrQt1pFSEX2wOvLBGdGtMn03W3ERe3FNnDe1oe1k7m+ts4GtnVfRLK7A53TWIUiCu2ZJfTFe+vpAyU5Po/SljRaQUACB4XHR0V3rpiqNo9d4jdMmr8ym/qCLcTQLAFqmpqTRq1CiRQidhYYn/5hQ8PcrKyvwinhgWtvRmnjMzM6ljx45UWFhI3377LU2ePNmwLXv27BGeUry8ERx1lZ2d7ffDuO0Da/1l1LiZ7OUOrJEXlZcFRjh9KZJQD1DC7QHi9Nzpiks2VhDuAaadNpoZDgc7mCE9JYm6tc6gxIZzzde/Vx46Zm13em+EIqojEONnRZVklmViwaEW0c1uL17Oi9uv0gPLArvPAbvV1Zyey5qGSZSQiVKqZax8wcKBk2ea2bJe3FLJNu+Z4/q2DWg7VmNk7b44vcYgSoG4ZcfBUrr8tYXiQf/+lHHUpaX35pIAgKacNrgDTb9uNO0+VEa/eXk+bT9YGu4mAWCLe+65h1577TV66623aP369cJwvLS0VFTjY66++moRoSQ555xzRBW9//znP7R9+3b6/vvvRfQUvy7FKRagZs6c6Xv/xBNPpAEDBvjWyRFU9913Hy1YsIB27NghRDAWrLhC36RJk0K278EYj54+RF9U81KH4VUNcRgVEEwyHApu6sGoE0P504d0EKkjVjgZOLgVl8Kd9WVn85MGd6Czh3WyXEPrzDRPjP71165/3k8Z6O9jZwb7KNmtQNZV0++1Ok9WxzHcmQZ2A5LU58tM7AnldcteYmbbln49Vk1yEpTF0VKcchuMqorH9zMWQOwJ2Y0eb3o+b+FGuwtG134ToabJGUzw5BlihwS7z3uDRay+lxMC/B6HKAXikj2FZXTF6wtFnjdHSPVwmFsNAAiMCb3b0Ac3jRPeUhe+NI+W7yoMd5MAsOSSSy6hf/7zn/Twww/TiBEjaMWKFUJQkubnu3bt8jMof/DBB+nee+8VvwcNGkQ33HCDEJJeeeUV3zKcWnfbbbcJIYpFLTZFZ6GKjdEZFq9WrVolPKX69esn1sERW3PmzBHRUKHCTZREpss0JK+9a1I88IXxCt43rfG0mSGzetBsFv2hl2ph5ygmBGDSG1iElatVudy++cbG9mwtopT4WOsN6tUf1xt88vnT+jE5QbERSeUkXchMEAjkHFjdl04EEW11NS+wu327++3k8Jw7vBOND8Cg3iqdTnpbeRmt1qttc2GqH4yKj2bRrsEM/gyVJVyChcBrhjpCUUZMp4ZClEoILBXZ6v5vUlnQ4dYi51sagBCRe6RCREiVVNYIU/M+7SJPgQcgHhjWpQV9cusEUTHnstcW0Pfr/L16AIhEbr/9dtq5c6eobrdw4UIaO3as7z02Hp8+fbrv7+TkZHrkkUeE91N5ebkQraZOnUotWjSmcF188cW0detWsT4WtF544QVhTC5p1qyZEKny8/OpqqpKREu9+uqrTaoA2iWUYgAP4CeP6OzYSNX2oNHmginJwdtpI/+NQOABroxyUlc0CoYJt5PrQYpSfF65yll9m6wbpTc48Tqlz2w/zLbEUSKWaSmav7XV8QIVUa0OIYtlXHnNzX2QYDNljn1prM6J1W46MfRm8UyPqgDMi/S8m9QpfU6PvaMoQpGObC4emkZlaY59k6iThhF7QgT50ZkR6P0dCdVZZfSSnfvb9PmT0NSrTMLPec7UOdrk/g4WR3Vr6Wh5y0hKzft92jnzhoQoBeKKA8WVdPnrC6iwtIreuWEMDeoUeXnKAMQTHDHAwhTPHN38zhJ6Z8HOcDcJgLjFqHqaHlphidOfMjWRUXqdWB6YjbToDNsd0IzrZd2R5zFnusVgMRCcRnzY81NJ0B3IWg2w3WzLiaAiozVYfJCfstMiuQlOw4mEwWYgcPP5PlHvB//TrsG0blScYu2xFIzDJtfJUWJ2fGmsfI1qHaimwbgKvDAUD8ioOTF4G/NV39Msp41yUj8jvBTMuzgokMH3QopJVGUwPea8XPNJA9rTaYM66N97Nv2TmiTrNfkc0ajuLUXVSIk9Ecws7dReSqphhJzBbWTVLvV55UkLpz7NEKVAXEVIXfrqfPH7zetGiygNAED4adVQ+ZJ9Mx76fA39feYGT8sPAwDsoY0AMaO3ahaU+6qcYpSiSUHQG3xwqpQVdgfgWWn2hACurjuia4uATJS9RN9VRH+AHXSjc3IeKVXDopQ8STYe1T4By2DZMwy8xbzadzeCzuBOOY7SVkZ2bSEGl1YCZkubfk9y34d1te6rqlunPcZmA1S5rG8Ji+NkdRzqHERKcbu44qAWq2gjM5QAP8NG0OrnE+8uR3LbhdNrjfzr+HoKpFsjjz2LF2oPKLNol66tmlFPD+xJOC1xVPdWIoLTzrk6sX+7gMVntx+XwrkdzNKmZUodpyHqXffOjM5N3ksILF1XD22FP3WbAxEErURf9b6oRTa7RMa3MwAh8JDiSl95RZX09vVjwhImCQAwhr/4X7pyFF01rju9NGsr3f3hCqqorg13swCIOQLplMqULSbTlol2/W91FIkdM2T76XvkKCrTbmqQk8pnTgdPdo+/7P/7RUoFJX0vwXE6Cw845L/teHLKbajbr94sD6L02qHVODgdKdAKUlq0h1RGmqibY3WIeNDKkUachmNW/cxoNXoRcIM6ZlPfdlnUqSHawDzNjGwz0cHx027TMlLKgSjFcMVBtajC59Yorc+OOOZmMos/c9KAdmK7PEGmju7ga7Kdg+hRs4iiFhkpAVUuk0IFR7VN6NNGdx2Zqcl+3lS8DjdVHDnqVX3O5THQtsltxfIAvLYD8r3Ttv+MIY376bQtTQ3MG+HoKr5/uWJtpgtfxQm9bUzcmLzn1rxf3ld6dxGL6XpCk5fecBClQMyzs6CULnllAR0uq6b3bhwLQQqACIW/SB+bPJj+dMYA+mLlPuEzxSm3AIDw0qtNfVQUd0p50M2mzup+rxRajAaFPHMeDKHEa0N0yVHdWnhWet2t6bc8lv7pe97j5BByZASbaHPUGUcZsF+YOhLDqH1yF5ymV2mvJ45caGkxuA92upDemtT3AgstTtHTcnhf2WLCJ+i53L+OGuGAowaN1+PufhvcKVtcF040Kb01sShkZficmZpsKCa4EW2VhggnN+KNE/T2V31vG0W3OHnOje7ZyrIYAotWVtGqdiN1EoJQgdHOs/8YA1HO6jiyUKSOMjPbljqKyiqiqn5d/pOsfdtnifQ/FhFNP6dzFDMDKJpgegzY6NykObJYg/Y+4uf8xH5tdcUu9SuBfh1DlAIxzea8Yrr4lflUXl1LH0wZR8NthEEDAMIHdxJuOb43vXLlKNqYW0yTX5hLa/cdCXezAIgZ3HQc1Z85bXAHOnlg+6B6AxmtmaNzpEDGqPvIdiIs7GJnEGhn4HDqoEYzenm87A4w5QDf3+hcoU4B+sRoDbPl2qWxuhHStLZFRqru4ERb0t5vGx5eKnrXndkg1Qqt7456QDa8SwuR0mrVfrUQYCqA6bzFg2Q7KWJuU9rNBAY52LZbfc0oAqNZarK4LrSio50y906v56Skpv5XvB2+rwaoqprZqQAcjOhDo/Ovd92q04m1aWd+KZl2t60nbms+zREvTiKc7Aq6fP7H925NY2xM/Jut0c69YBadYyYgZaYl2UpR53Ri9bOb90umKrr53gvGd2WiTd8oJ5+TJ8aRd6HO5JRbIEqBmGXBtgJRap758KZxMDUHIIrgge/Ht0wQX+a/eWk+zVyTG+4mAQAs0HZl3fbFjT7HA5+BHbN0O9hOvGis0pDUs9tGPkF2dk2/6py94yIH+OqxKr+SYVJqvcm2EqwjGdQDJk47UafuaI+9EWcO7UjHNAzy9UhNqm+zURSMXjszU5OFKGSHFqqBrN6g1uh4s0eOWfohvydMzVXnUW9dHCnkBhYCORXLjneWV+l7ani7Zw/rRAM6ZNkaPKsFUj0vKfV5YPp3yDYUxeSqtPue4CpSLYHOGd7JgbF3AmU3kyJsaPwrrU4xR7eohXU3rZKb4HREjlzTbUeQPOp4tZxeqxYMDQtn2Enfc9lMM882vev3hH7tqIOmnfycUotbPAHRp515mrJdQUbP6NwNaTYEX71tG93D4v2G306EWvV3sFmUmq11BfRpACKUr1buo6vfWCRmAz777TEijBIAEF2wkPz5bceI37e8u5Sm/rwFBugABEgwhiRG/Vy3M6dmn1MPnNUDrARNCoJXsE+QrlmwnFW2+UiSzy79Y5Vg2+h8YAdvJ9jk6rl9nHbixmSXB3B6g132VGH6tm8uvIP6tPU3x1ejfbafMqi9Lb8qLRwJYTeFyLRKlb0/BOp9txosOq2gaObvJAfggdzTvB67kRwJieZt4/QxFiZ8yzek7h7rIJLNuuy8N08wn39OiLoUVs9CvoY4hZArVGpx2u/hyD2fiKz5qJVw4ETs4EPIqbxG+8f3opHxuxGN++rd+enRuv45kpOR0uT64tfMfKi8EG6C8Z2bkWbmXZcgjPX1olfNREk395aMsuTniN66nRSrgCgFYgp+mL02exvd8cFyOqp7C/rolgkBh7oDAMIHDy7enzKWLjiqMz317UZxb5dW1oS7WQAAGzjp46pTiGxkGBhiJz3O6UBHr1qvnHE280nS949y1vH369QrgUc6JGjEJJm2Fwx/Lh4MnjOskxC6+Pwatd3L9JZAVyX7jG1UwpaTVXZ34CnlRGzQilkc4SQFRKN9dlq9UC7NKbKcomuXlpkpvutJa+jNUSd6qa5uz7lXV4pPXPBofb71JnjfcLUPkvm2rTdidU1wFWQnyOhHo03LS9yp2M0TAWwa31slZLuFrVvOGtrRMJrWjmeUpLHoqOJNVLCDZUeponbNqi5yNC2nW2vTHK2/O+t/Kzpp3kb+ZO0sJgA4+tbus8Tb6SQAwghX6nrw8zX08dI9IiT7qYuGBVRaFgAQGfB9/PRFw0WEwP/N3EDr9xeJSn39EAEJQEiwGjv7OrsBjPBGdGlBR8qrqai82nEHnwc88jNa4UOv7V4MROV21ObO3Ennv9U+eJwOx1XJflif53tNHVVwQv92wm7AcDtqTymLlg/okE0dW6T7BDOr6AxO8+FBJQ921BUSvSRY6UJ+29BcMHUNRRZZcCurcjaJwdEFbOwbSCQPD/ydVqKzQvc6Nrkx+bpTHxf2ybFLm6xU3YhD6YHEg92DJY1FSKzMtZ2gvmY5wmruloP+73t0OQXTE09/e/qvc8XBkgrza9SuX57eJhQbopQ6Ukjr12W+vaaRqkZ0bZkhnu/5xRWmzyWukrpqz2Fx/Y3q7r/fHP11uKzKdDtGz0i5X3rnnQ3QWchZu6/I1rOmlreh2YzZ/vtX8nR/3bVUpSYaiXxsYt+6eaqryRdZvCNFVXHRDDur5+8Xu4IkIqVATLCnsIwuenk+fbJsD917aj969pIREKQAiCH4i3zKxF6iYEFxRQ1NfuFX+mz5nnA3C4DowyB6x2w2vtZlDoWT/jeLFzJ1xDx9r+nMPqcIySaaaSBsWKsdzNv1oZFG303TfxrX1att8ybVzrhDzpEiaoN2s/3R4m90br4se6NwhUQzE3b1oEgOFjhFUa/ct5oUBykuduBzbOZTZXeQxpFY/mmcCb7Ulv4NExfa6B3HbTU5SU7EHh7Fqc2tndxV5ss2bR+fW7WHF//bqrKd3E2j+4/Xyce7V9tMmwPwpq9ZoV62tc4zyatKiizOsLhm5BmnxqpanR0STERQdcXG7q0yhZ8aRwnJFFi7QpHecU7XjIX0PIW0nkpW6/R7X+6Z4XKNactGgpG6yAJHSLEorHetHtenjfCvE//uq++ZxXBKod3oMoaPr0zxsyLBSGx3Gyll83NHdWvZxE+QJ2a7aYoUsG2Nk8gvdaozp47yduwIUhIvvxUgSoGohw2Qz3l+Lu0sKKVp14ymO07uG5KZOQBA6OEZwxm/O050Ou7+cCXd/+lqESUJAHAPD97ZcNkIq6gPo0E7z467Qb06juZRRwpot8UCDA9gfMbgCQmGM7N6JthH92hlK82JPTrkZBfPKHMlJz3BTm9QzgztkiOiuPWwO8suUZuR+x0PkxVxNSlRNZGcw6kcaq+gQFBvf3yv1pZ+Q9qqZFr0+nvDOueI64YH+yyicCSP01Q2v22YHGI9jx4z8ZBTQXU9yizQi4qSnlt2Kpa5QTE43l77MMkIDTt4GeDEVRsNzbg1A30z4UadSmWYvWez4ezrxn5qbrzd9AQ79kzKVIk+eveLWdvsXltG6b9614p2UbMUaL9tJCb4BBezap8cbWX2faaHbV811WJaQSiQZ4wVetfpwI7ZNNIkjc/OPnIVSvk6t99uJc5gAFEKRC1FFdV0z39XCAPkbq0z6cvbj6UTB7QLd7MAAEGGZ3bevXEs3X5iH/pg0S5RZXPHwdJwNwuAqMBupIF6UGR30DCyWws/HwuziB01vlltRd/3yFZ1sobfvKSsJmZ33+0OsmWfngezUoyQqWJ6gzPt9vQGBQkNA1EtetHean8WOZA/2azfk+AfjWDXBJ6jR9RiUTC8OflQsJioFw2j5oT+bZtsn8Uss/Vy5INMKZODcG0EmxO0g0/t9rQomkGf+nXeZ46oc4piMFDlqJL0FG+Gc0b3B6eFqlOH7D4P7MJ9dym0qiPJvKg65lVTzUQLo4G8OprHrVzhRWQYC1OMUbSm29RBm3p4k2W1IkuwzOb5OSGN2ANFtlFeB9o283kyql5qluZoN6snweIAs2gaqDjt5lrT3hcT+7Y1jWIzA6IUiErmbTlIZzw7h75YsY/uPqUffXLLeFdVWgAA0QkPUn8/qT+9ee1o2nu4nM58bg59uHgXqvMBYIHde0SWebcVKdXwm9OxWLDRg307ZPqcGk7H4CpNom267XU3aJCo9Sz1vzldLZBOfKJLo3O913ngp00jYjFGeyzVW5L76TRyxM7ynM5nJRZ5jVHECgubWkEp1PYMHHmhPe4ycsYqwsKrtEf1ZTa4U3ZQI4m062Dz9on92joXcRueDFaDXRaepQhulOkgI+bYtJqxk3YXDDiyksUdo0gmeT3w80EdzcN/B5pGaoXVuWevMLtwZCg/I+2mghndB/KrQ6TVNkRspSQmime+TMVz22+Tz2COipTpx+pVcRqa+t71ArmXeqmITvyTJHYjkxITzKtkju3VWvgTmiE/zSI/C81OsSMIt8xMNY1iMwNG5yCqyCuqoCe+WS/EqN5tM+mz307QrYoDAIgPeIZ15p0T6fcfraQ/frKaflyfT/934TDXX4oAxDo1Ng2Yq2rrdAUqt/Q1KEygN+jx9z1KpOJGT2VD5CBB229XCxjq9bqJrG4UvoxLyjuJHlC3kYWgpTsLfa/x4IZ/1Ma+6sGb3rqdefeE1+YgQSdFsKqmjr5bl9tkWTmYzWlW/1v6zUhDX7P1Bouju7ekyhr9cvf+56mpJ5hvYOvgtpJaDQtSfdr530tOKyfy4FIvLdITQStIZ4DTT1kAls+L1pmq6ogOGx7IfrInE6fiDumcTV+u3Odom+xN9b81+ykaYA89rY+ek+eudkKDTxsXgmifneaL3OJoU57I4PTxhduNiz0YwcL94bJqERXJzwLOoHGL3UvCd6+ZPPe16E1ccMqnmcdbVnqySH9ft7/efN2Lu0q2wk5lPT14ooInlvKLKmnrgRK/dnkxHwxRCkQF5VW19Oa87TT1py2iQ33XKX3pluN7u8q5BgDEFuz38Pb1Y2j6vB2iOt+kZ2fTU78ZZjlrBEA84qYqWLpOelkw0A7axb9tjiAbjc55WNz4mTRVapNRFqCiSrWyW61NDijUlauapGp4LPz4R0q5W0e4xSiz6Fe9NEaGJxmO79fWF9kmfHcGtm9i/BsK1EbHRu3V/YD6JRengI2Y+d7VS8Fy2hc+3aY5u1EECxcHWLarUUB16sPDXjhOsTJpjwac7IOb6KFw3NpWBSbUXn98j3fMaeb3LOL72i0sRnlZ+dEOak3K73ls8hm1KCU/bxUhddKA9uK3T5Ty4NzWqCaa9LCzDfYWPGBnlsgFEKVARFNZU0v/WbSbXvh5i7gJThvUnh46e1BYjdgAAJEHDxCuP7anSHe58z/L6do3F9NlY7rS/WcOtKwqBUA8UeNwsMMRCWwcHYrBEFecKiitNBzsmzVdHcWkRu1xZSXItM5M9atGpEX9cU4pG96lheiPbMor1l0mIRSDI8V4e2bbl5XpvMTJQNroXHAEgZ73lbZSn5FfWaSKbn5RJaokICffcUbRhtJ0P1Bkq6yOoN1iQnqngs9PNE4o272sjI3OnW0vMzVZ/Azu7FzAs9smLzCP3VRHSgX3vgw0Qs/u+TGKkDXDC1OJhACfaywGB6MggpePW4hSICIprqgWBsbT5u6g3KIKmtC7Nb185aiw5ZADAKIDLmn7xe3H0L9/2EyvzN5GP284QH87f4ioOgUAMJ7RNqJddprtkuSBwt6Qbv0h5Wy0evfYT8TJYMj+wLPe30Ovrf5RXna2ab99/gJUw+CoYR1OBCE2x45UgjXpyB45fNT2FJYFtB4vPLy81s04ckmmfnppq8j+TWv2HrE0H7dCvbte+T5GsPboiWjCwh9X4QvmNrR9p8LSasfnQJ5Pw/Q9JTSiVMjwRUrZv44jwep0cKcc7yN3Pd4viFIgomDD4rfm7aAPFu6i4soaOq5vG3rm4uGOS3sCAOIX9mf5w+kDhInmfR+vohveWkKTR3SiB88aZBoFAUA8wD4VlQm1tKew3HQ5NkPdd7jcVqWtUEWnmG1GzmDbNeZV42WBBL/qe54fl6ZhUdz2BJU4FS0khHi9HJ3DE5uBilKBtCdYg1M+75xWk19cEeCKmprPGxnQczRbSaW9VFc1HtjThQVZ7VKm61rda9q3Q6HJBHL7uzG+Nkq5ViP9CCNdlLL77HRzPxt5zIWSBA83q01R5+q5biqKaoEoBSIiRe+Hdfn0n8W7aO6Wg6JDd86wjjRlYq+GmS0AAHDOkM459OXtx9DLs7bS8z9toZ825NM9p/ajq8Z1D1nkBwCRBnciOYLISpRqmZFSL0qZ21BEDGygyxX+OHpp/2F3g3MvRB170VHu1q03CKoXozzKEQGWOB1Uqq8pJQIjKNwapp/Yvx19taqp0bd6FVLQ8r+vrHe6ZUYqFTYY/LMpMxc5MsPrYT4bkms1FH62cGUxdTqwblsMGhMKwdhoC5z6ypP+UljzChZ6jaqeNknfC5FgHuxbSu4n77fdXVILscFIoQsWXAzEzjPBq8ARiFIgbGzMLaYPF++mz5bvocKyauElcfcp/eiio7v4GeEBAIBbOGrijpP70tnDO9FfvlpLf/lqnXjuPDZ5iCjDDkA8YmcwLDvfdiKlgo2sFGbmRcPtNfLcCSWhilbq3iqDVu89QunJSQ1CSX3ElJdwtSx1lTMv8fowcSUvJyb+sqKfUzid1QksbnBqFPd5JZEa0CYHoHYOo5WvFEfe6Bl723mcsGVHda3ii9jkn1AXT9HbX06TdEtCGJ89XK2cr1sv/TWP7tGKOuWk065D9ZGHRpfDgI7ZopJrJFVE5qrtrTQedXaR+6k4iIrlSnrSL8+qOEKXlv7py6cP6UChQrsLofZjhSgFQu4V9fWq/WJQuGL3YZGnPmlIB7rk6K7iS8iueSIAADihZ5tMevPa0fT9ujx67Ot1dPEr8+mCkZ3pT2cOEGkPAMQTWj8Mvf600zLzwYQHNJx6ZXfCyqzpvds2p3aaNF4eLGzJry9x7Wb9HJXpVrtLcCmIcLqETJkI1qnyIiUjVHCRC46osepHntCvHSUkNqZjOcWpSTffR3zdsijl899R+YFFEjKAOBAhWh5+v4pjqvftrJkjmZOjzwu9URxvl+UTsDjCiiuohfNxym3yWmCQ+9eYvqe/gxwZdFxf9xX2gtUfdIq8bn33rqLYTsnkogznDOtk+WyarOP1x3YUXhA53+bGQJQCQYdv3MU7Cum/S3bTjFX7qby6lgZ0yKJHzhlE543oLMJhAQAgFB2z0wZ3oIn92tKLs7bSy79spW/X5tItx/emG47r6XrmHICow2RkyCXnD5ZW+jqxkRAppTeD7BYWkPTMtQMx2GahywluDimLKCO7tnQ04IiMM+dPsFIN7ZaHz8kITwl5q9ciARnt4STiTGctptf4kDiw5RjUqdGfiaM3QxXBGY7LyldgIszXdLC3X1FdK36npyT6PLKcTNyEO+hCocgHPXAQNHKPVNAny/bQR0t2046CMspKS6bzj+osoqKGdfG+CgAAANid6WZvqQuP6kx/n7mBnv5+E72zYKd47TejusBvCsQ8Zh3UoV3qB43SENrMoPakAe1i9rucUy6CFUXpSpRKTTIc2MhTEA2nYmDHbFHRLVavGy08cJUD2VCnoTlF+v4EokPrRkqpTrVV+lIo8OrK4ygWnmSPFEJxS7HfljrKMBrEDif0aJ1pGiHJkV/sz8Up5ZEUTewFzk3YvT37EKWAp1TV1NGP6/NEVNQvmw6IvHROy7vrlH40aXCHiPgyAgAAhs2eX7xiFC3bVUhPfrOe/vTpapr263b60xkDhIlrvAyaQPxhpzPN6RkV1XWmqQ5GkSknD2wfkmpTwayqd9IAZ+XYneCknLg9GiqCRUiSBnv5VdfW6Zr6clSZ08iyaL/X+HhwNVj2vZKvMREShOhDtqs2oPS9yNy3YBBKvx8tacmJvmPMz+jtB0vD4rcl2xApAk0gz/xzh3cy7Pf1aUj7Lquqj5hKSUqgBqvDiEVp+J5hX0A3VWmNaNEQbep1SihEKeAJuw+V0fuLdtF/F++mgtIqYX53+0l96aJRXQIKiQcAgGDDfjL/vXm88Jv6v5kb6PrpS2hsz1ZCnBrZTT9dBoBopm1WGg3v0oJKq2oMvZS4c96nnTvxgMvFBwrPRPdqm2lowG2WqiVNdb1K+fMao3ETDxykKa6EhR0W0M3OheF4MEzCAA9cB3fK9swPJdrgAbqMFJLirJeDQqNrPr+4wheV5QY5yA4kZVdei94Lr0ANT/Srn4dcrTwcE2k+nzQPNp0ZZgsFs+PHUapcxbC6ttInzKizXCNDktOnYwtvI3454vOUge2FV5aXQJQCruGc89mbDoi0l5835osv4dMGtafLxnQThpNmIf8AABCJflOcjvThkt30r+830/kvzqMT+7cVkZ7Du7YIdxMB8JQebTL9KoJFGmcN6+jagJs7y3qmsZGIehzE0TRN30+gERbPn0jsbcWaIHVsnza61eSsRCm9ga58xUvhpl/75mLwacdXy7L6XgCeUjJaz2/AHmSxxOnqm2uE32hEfUz530lheghwijMbuXfIDkz4OGNIx6gYN/KEDo9xW2em0tYDoYlOCzbS5s/JfeS1IMVE/10JQs6h0iqRnvfewp20+1A5tc9OoztP7kuXju6mW0YVAACiBfaTumJsdzp/ZGd6e/5OenX2Npo89Vc6eUA7IU5Jvx0AYgFOQaj/HeF5CDFGsMboEZJBE5O0bu5fsdEMvp1q6kJ7nryosMYREJvyiqldAAKDL1IqQvL3tM04cUC7kJe6N8JuNdFIhgsHeDEBYFfwNaJN8zTaeqBERDMFG94WE+kaWp92zam0spa6WWQsRcadClEK2IS/XJbtOkzvLtgpKuhV1dYJr6gHzhhIpwxqjw4tACCm4Ep8XJXvqnHd6a35O+i12dvonBfmipDlu07pq1vBC4Bog71IOE2ua6voHxxFE177r0SKj0+4tx8pWEUGyfcjzVuLU0UDFRikGX9ABfyCSKQIUuxHlYqxk2dwUARHmoZyPMpiz+q9RyhSSUtOojE9W9lePtwaG0QpYEppZQ19sWKfEKM4PJO9Dq4Y101EErj1mgAAgGiBQ5R/e0Ifunp8D3pr3g56bc42Ovv5uSKtb8pxvWh879YwRAdRC1+73VpHpu9SJOPlLZ/uQZqbkY9PuHx94v2JmJGa5Cshb0S0pJc6JTHCIqUilVhLb40EQh0gwZH17OXGGUToBgYORCmgy4bcInpvwS76bPleKqmsoUEds+n/LhhK547oJCIIAAAgnmDj5ttOZHGqu0jrmz5vB13++kLxbLxpYi/hf4OIUQCAHeQAhs2JZWSJF+uDDhAZjO7Rir5dm0vxiPSUwqUIQLR5SiWEtR1QF4APntX535r9QoxasrNQlBs9Z3gnunJcdxreJTxVHUD0oL0+MEsGYhE2kWVx6sbjeooo0jfmbKe7PlxBf5+5ga6d0IMuG9stYtIDANAjJycHz+eISd/z9jzgrEYG6SlJNLZnazpSXk3xhtRYAzFLtwOGJADEFhCl4hzumK7dV0SfLNtDny/fS4Vl1dS7bSY9fPYguvCoLsLADgAAQNPQ+4uP7koXjepCszcfpNfnbKMn/7eBnvtxM00e2ZkuH9MNvlMAgJBENvVs05xW7TksJhPVhFp7bJedJtpgVR0xXjxu4rL4jxSllOB/B3dtlUG7D5UFd0MAgJAAUSpOySuqECIUi1Gb8kpE1YNJgzuIgdS4Xq0QFQUAADbgZ+Xx/dqKn3X7imj6vO302bK99P7CXTS0cw5dNqabSHvm9D8AQHwwoXcbURDGCDaXZ7xI3ZOG9fwTblgoOH1Ix3A3A4SR1plpojrZ4M7ZTd7LbubtRHfP1plClLIjvrbMSI0IkfDkge2pxuTZAKKLWAo6Tgjz9tFLjiMKSirph/V59PWq/fTrloNiFmN0j5b05AVDRcUCrroBAADAHYM6ZdM/fjOcHjp7kEjtY2Hqgc9W0+Mz1tE5wzqJdGgW/dkcEwAQu7TNqi8ZbkQvISApYlANQCyRlJhAx/Rp0+T1E/q3o2YpScGpOmlj2U4tmkVEgSZMUMW2lxpwD+6MGGff4XL6bm0uzVybS4u2HxJCVPfWGXT7SX3pwqM6U3d0iAAAwHPfKfbiu2JsN1Eu+INFu+irlfvpwyW7qXVmqigFzcbo7DnCHXgAQHzBEVJ92mWFbBafo0QACCdBmfj2pQoay1IpSfUL4bsWBINmqUlUWMbP9HC3JPqBKBVjlFfV0oLtBTRn00Gas/kAbc4vEa8P6JBFd5zUVwyG+N9IzwMAgODCz9lhXVqIn0fOGUyzNx0Qkapc1fS9hbuEQDWxX1s6oX9bmti3LbXMxMARABCcanAAxBpSZzJLoerdtrn4Lu7ROiNk7QLxw4iuLahjTrqYjIx2EsIsDUCUioGKeSt3HxbV8uZtPUiLtxcKH4P0lEQxC3/J6K4ifzkSvAYAACCeqzGdNriD+OHn9s8b8un79XlCqGKRijvX3Lk5tk8bOrpHKxrZrUVMdHIAAACAYOBL3zNRpeqjEsOftgdik5SkROrSEoKnF0CUiiL4oZtbVFEvQu0oFELU2n1HqLq2/mE8sGM2XXdsDzHjPqp7SzEIAgAAEFnws/mMoR3FD5fN5gqoP2/Mp1kb8+nFWVuppm6LEKn6d8imEV1zaFDHbOFXNaBDNmXCjwIAYJOMtCQqraoJ+ww4AMHAiacUAECf5unJVFReTeEGvdsIpbZOoe0HS8RghSs6id/7i+hQaZV4nyOhhndpQTdN7EVHd29FR3VrSTkZmFUHAIBogmdxh3bJET+/O7kvlVXV0Irdh2npjkJavLOQvl2bRx8s2i2W5f53j9aZ1LttpvAD7MEVt1rzvzOEiSs8MwAAarh/yP1GTFKCWCTBhqcUAMCcCb1b05Hy6rBb+0CUCjNcFnR3YTltzS+hrQdKaEt+ifC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+      "text/plain": [
+       "<Figure size 1200x600 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1472x1104 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Report the results\n",
+    "sim.report()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a82590f5",
+   "metadata": {},
+   "source": [
+    "# Chapter 2: Saving and retrieving a simulation 💾\n",
+    "\n",
+    "👉 In this chapter, we will save our Pymob simulation and create a new simulation from it. You will see that this makes the process much shorter than above.\n",
+    "\n",
+    "👉 Let's start by **saving** our configuration and observations.\n",
+    "\n",
+    "(Note: The observations have to be saved before the configuration. Otherwise the configuration doesn't save the location the observations were saved in which causes problems down the line.)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "497891c1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Scenario directory exists at 'c:\\Users\\Markus\\pymob\\pymob\\docs\\source\\user_guide\\case_studies\\ODEtutorial\\scenarios\\lotkavolterra'.\n",
+      "Results directory exists at 'c:\\Users\\Markus\\pymob\\pymob\\docs\\source\\user_guide\\case_studies\\ODEtutorial\\results\\lotkavolterra'.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Set the data paths we want to save to and create the necessary folders if they don't exist yet\n",
+    "import os\n",
+    "sim.config.create_directory(\"scenario\", force=True)\n",
+    "sim.config.create_directory(\"results\", force=True)\n",
+    "os.makedirs(sim.data_path, exist_ok=True)\n",
+    "\n",
+    "# Save our configuration and observations\n",
+    "sim.save_observations(force=True)\n",
+    "sim.config.save(force=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "08d4078f",
+   "metadata": {},
+   "source": [
+    "## 2.1 Creating a new `sim` file from a saved configuration 🆕\n",
+    "\n",
+    "👉 In the next part we try to generate a new simulation object from the configuration file we just created. To do this, we first have to make an additional import:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "bef01c1f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pymob import Config"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e9560316",
+   "metadata": {},
+   "source": [
+    "👉 After we've done that, we can now create a {class}`pymob.config.Config` object from our file. This can then be passed to the constructor of {class}`pymob.SimulationBase`. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "c6fafa7e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load configuration to a Config instance\n",
+    "config = Config(\"case_studies\\\\ODEtutorial\\\\scenarios\\\\lotkavolterra\\\\settings.cfg\")\n",
+    "\n",
+    "# Create a new simulation from the configuration\n",
+    "sim2 = SimulationBase(config)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b5d8e849",
+   "metadata": {},
+   "source": [
+    "👉 Essentially, passing the {class}`pymob.config.Config` file to the {class}`pymob.SimulationBase` constructor just copies it to {attr}`~pymob.sim2.config`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "6ba0762d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "config == sim2.config"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d9a70478",
+   "metadata": {},
+   "source": [
+    "👉 Now that our simulation knows about its configuration, we can call the {meth}`pymob.sim.SimulationBase.initialize()` function which prepares all of our data for us. It fetches the observation data from the specified location and handles the initial condition as well as external inputs (which we don't have here). That means that a well-prepared config file can save a lot of work!\n",
+    "\n",
+    "👉 We do, however, still need to specify some additional features of the {class}`pymob.sim.SimulationBase` object. That includes the model, its parameters and the solver.\n",
+    "\n",
+    "(Note: By subclassing {class}`pymob.solvers.diffrax.JaxSolver` and writing a customized `initialize()` function that also includes these tasks, this can be avoided. But for now, we will keep it simple and do it manually.)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "c3621119",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MinMaxScaler(variable=prey, min=5.844172888098338, max=12.52594869826619)\n",
+      "MinMaxScaler(variable=predator, min=4.053933700151361, max=10.925258075625722)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Markus\\pymob\\pymob\\pymob\\simulation.py:1385: UserWarning: Using default initialize method, (load observations, define 'y0', define 'x_in'). This may be insufficient for more complex simulations.\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Add data and initial conditions to the simulation\n",
+    "sim2.initialize(config)\n",
+    "\n",
+    "# Add model, model parameters, and solver to the simulation\n",
+    "sim2.model = lotkavolterra\n",
+    "sim2.model_parameters[\"parameters\"] = sim2.config.model_parameters.value_dict\n",
+    "sim2.solver = JaxSolver"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "21bca37e",
+   "metadata": {},
+   "source": [
+    "## 2.2 Running the model and parameter inference 👟🔍\n",
+    "\n",
+    "👉 As before, we want to create an evaluator for running the system. This is essentially the same code as above, let's see how it goes:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "69c0aaad",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "vmap in_axes must be an int, None, or a tuple of entries corresponding to the positional arguments passed to the function, but got len(in_axes)=6, len(args)=4",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[19], line 6\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[38;5;66;03m# Create an evaluator, run the simulation and obtain the results\u001b[39;00m\n\u001b[0;32m      5\u001b[0m evaluator2 \u001b[38;5;241m=\u001b[39m sim2\u001b[38;5;241m.\u001b[39mdispatch(theta\u001b[38;5;241m=\u001b[39m{\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdelta\u001b[39m\u001b[38;5;124m\"\u001b[39m:\u001b[38;5;241m0.9\u001b[39m})\n\u001b[1;32m----> 6\u001b[0m \u001b[43mevaluator2\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m      7\u001b[0m data_res2 \u001b[38;5;241m=\u001b[39m evaluator2\u001b[38;5;241m.\u001b[39mresults\n\u001b[0;32m      9\u001b[0m \u001b[38;5;66;03m# Plot the results\u001b[39;00m\n",
+      "File \u001b[1;32m~\\pymob\\pymob\\pymob\\sim\\evaluator.py:351\u001b[0m, in \u001b[0;36mEvaluator.__call__\u001b[1;34m(self, seed)\u001b[0m\n\u001b[0;32m    348\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_signature\u001b[38;5;241m.\u001b[39mupdate({\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mseed\u001b[39m\u001b[38;5;124m\"\u001b[39m: seed})\n\u001b[0;32m    350\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_solver, SolverBase):\n\u001b[1;32m--> 351\u001b[0m     Y_ \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_solver\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparameters\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    353\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    354\u001b[0m     Y_ \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_solver(parameters\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mparameters, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_signature)\n",
+      "File \u001b[1;32m~\\pymob\\pymob\\pymob\\solvers\\base.py:82\u001b[0m, in \u001b[0;36mSolverBase.__call__\u001b[1;34m(self, **kwargs)\u001b[0m\n\u001b[0;32m     81\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m---> 82\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msolve\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;31m[... skipping hidden 12 frame]\u001b[0m\n",
+      "File \u001b[1;32m~\\pymob\\pymob\\pymob\\solvers\\diffrax.py:129\u001b[0m, in \u001b[0;36mJaxSolver.solve\u001b[1;34m(self, parameters, y0, x_in)\u001b[0m\n\u001b[0;32m    112\u001b[0m initialized_eval_func \u001b[38;5;241m=\u001b[39m partial(\n\u001b[0;32m    113\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39modesolve_splitargs,\n\u001b[0;32m    114\u001b[0m     odestates \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtuple\u001b[39m(y0\u001b[38;5;241m.\u001b[39mkeys()),\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    117\u001b[0m     n_xin\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mlen\u001b[39m(x_in_flat)\n\u001b[0;32m    118\u001b[0m )\n\u001b[0;32m    120\u001b[0m loop_eval \u001b[38;5;241m=\u001b[39m jax\u001b[38;5;241m.\u001b[39mvmap(\n\u001b[0;32m    121\u001b[0m     initialized_eval_func, \n\u001b[0;32m    122\u001b[0m     in_axes\u001b[38;5;241m=\u001b[39m(\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    127\u001b[0m     )\n\u001b[0;32m    128\u001b[0m )\n\u001b[1;32m--> 129\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mloop_eval\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mY_0\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mode_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mpp_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mx_in_flat\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    131\u001b[0m \u001b[38;5;66;03m# if self.batch_dimension not in self.coordinates:    \u001b[39;00m\n\u001b[0;32m    132\u001b[0m \u001b[38;5;66;03m# this is not yet stable, because it may remove extra dimensions\u001b[39;00m\n\u001b[0;32m    133\u001b[0m \u001b[38;5;66;03m# if there is a batch dimension of explicitly one specified\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    136\u001b[0m \u001b[38;5;66;03m# this is added at the 0-axis\u001b[39;00m\n\u001b[0;32m    137\u001b[0m \u001b[38;5;66;03m# if parameters are scalars, the returned shape is \u001b[39;00m\n\u001b[0;32m    138\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m v, val \u001b[38;5;129;01min\u001b[39;00m result\u001b[38;5;241m.\u001b[39mitems():\n",
+      "    \u001b[1;31m[... skipping hidden 1 frame]\u001b[0m\n",
+      "File \u001b[1;32mc:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\api.py:1249\u001b[0m, in \u001b[0;36mvmap.<locals>.vmap_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m   1245\u001b[0m \u001b[38;5;129m@wraps\u001b[39m(fun, docstr\u001b[38;5;241m=\u001b[39mdocstr)\n\u001b[0;32m   1246\u001b[0m \u001b[38;5;129m@api_boundary\u001b[39m\n\u001b[0;32m   1247\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mvmap_f\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m   1248\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(in_axes, \u001b[38;5;28mtuple\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(in_axes) \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28mlen\u001b[39m(args):\n\u001b[1;32m-> 1249\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvmap in_axes must be an int, None, or a tuple of entries corresponding \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   1250\u001b[0m                      \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mto the positional arguments passed to the function, \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   1251\u001b[0m                      \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbut got \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(in_axes)\u001b[38;5;132;01m=}\u001b[39;00m\u001b[38;5;124m, \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(args)\u001b[38;5;132;01m=}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m   1252\u001b[0m   args_flat, in_tree  \u001b[38;5;241m=\u001b[39m tree_flatten((args, kwargs), is_leaf\u001b[38;5;241m=\u001b[39mbatching\u001b[38;5;241m.\u001b[39mis_vmappable)\n\u001b[0;32m   1253\u001b[0m   f \u001b[38;5;241m=\u001b[39m lu\u001b[38;5;241m.\u001b[39mwrap_init(fun)\n",
+      "\u001b[1;31mValueError\u001b[0m: vmap in_axes must be an int, None, or a tuple of entries corresponding to the positional arguments passed to the function, but got len(in_axes)=6, len(args)=4"
+     ]
+    }
+   ],
+   "source": [
+    "# Put everything in place for running the simulation\n",
+    "sim2.dispatch_constructor()\n",
+    "\n",
+    "# Create an evaluator, run the simulation and obtain the results\n",
+    "evaluator2 = sim2.dispatch(theta={\"delta\":0.9})\n",
+    "evaluator2()\n",
+    "data_res2 = evaluator2.results\n",
+    "\n",
+    "# Plot the results\n",
+    "fig, ax = plt.subplots(figsize=(5, 4))\n",
+    "ax.plot(data_obs.time, data_obs.prey, ls=\"-\", color=\"tab:blue\", alpha=.5, label =\"observation data\")\n",
+    "ax.plot(data_obs.time, data_obs.predator, ls=\"-\", color=\"tab:blue\", alpha=.5, label =\"observation data\")\n",
+    "ax.plot(data_res2.time, data_res2.prey, color=\"black\", label =\"result\")\n",
+    "ax.plot(data_res2.time, data_res2.predator, color=\"black\", label =\"result\")\n",
+    "ax.legend()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "821b1cec",
+   "metadata": {},
+   "source": [
+    "👉 If you chose to ignore the bit about {method}`pymob.sim.parse_input()` in the beginning of this notebook and added the initial conditions manually, you should see the following error message now:\n",
+    "\n",
+    "```\n",
+    "ValueError: vmap in_axes must be an int, None, or a tuple of entries corresponding to the positional arguments passed to the function, but got len(in_axes)=6, len(args)=4\n",
+    "```\n",
+    "\n",
+    "👉 The reason for this is that our model takes four parameters ($\\alpha, \\beta, \\gamma, \\delta$) along with two initial conditions (for prey and predator, respectively) but we only gave it the model parameters. If we had chosen the {method}`pymob.sim.parse_input()` formulation before, we would have run the following line of code:\n",
+    "\n",
+    "```\n",
+    "sim.config.simulation.y0 = [\"prey=10\", \"predator=5\"]\n",
+    "```\n",
+    "\n",
+    "👉 In this case, the function {meth}`pymob.SimulationBase.initialize()` above would have run {method}`pymob.sim.parse_input()` and added the initial condition $X = 10, Y = 5$ to `sim2`. But in our case, because the initial condition has never been defined in the configuration, this doesn't happen and we get this error. If you ran into this problem, run the following cell which sets the initial conditions manually. Otherwise, just scroll past it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "27b20bcd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x2052fea2690>"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "y0_obs_1 = xr.DataArray(10).to_dataset(name=\"prey\")\n",
+    "y0_obs_2 = xr.DataArray(5).to_dataset(name=\"predator\")\n",
+    "y0_obs = xr.merge([y0_obs_1, y0_obs_2])\n",
+    "\n",
+    "sim2.model_parameters[\"y0\"] = y0_obs\n",
+    "\n",
+    "# put everything in place for running the simulation\n",
+    "sim2.dispatch_constructor()\n",
+    "\n",
+    "# run\n",
+    "evaluator2 = sim2.dispatch(theta={\"delta\":0.9})\n",
+    "evaluator2()\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(5, 4))\n",
+    "data_res = evaluator2.results\n",
+    "ax.plot(data_obs.time, data_obs.prey, ls=\"-\", color=\"tab:blue\", alpha=.5, label =\"observation data\")\n",
+    "ax.plot(data_obs.time, data_obs.predator, ls=\"-\", color=\"tab:blue\", alpha=.5, label =\"observation data\")\n",
+    "ax.plot(data_res.time, data_res.prey, color=\"black\", label =\"result\")\n",
+    "ax.plot(data_res.time, data_res.predator, color=\"black\", label =\"result\")\n",
+    "ax.legend()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a551b9b5",
+   "metadata": {},
+   "source": [
+    "👉 Now let's start the parameter inference again. The result should be the same as before."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "3ced1952",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Jax 64 bit mode: False\n",
+      "Absolute tolerance: 1e-07\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Markus\\pymob\\pymob\\pymob\\inference\\numpyro_backend.py:552: UserWarning: Model is not rendered, because the graphviz executable is not found. Try search for 'graphviz executables not found' and the used OS. This should be an easy fix :-)\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "      Trace Shapes:      \n",
+      "       Param Sites:      \n",
+      "      Sample Sites:      \n",
+      "         delta dist     |\n",
+      "              value     |\n",
+      "    sigma_prey dist     |\n",
+      "              value     |\n",
+      "sigma_predator dist     |\n",
+      "              value     |\n",
+      "      prey_obs dist 101 |\n",
+      "              value 101 |\n",
+      "  predator_obs dist 101 |\n",
+      "              value 101 |\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "sample: 100%|██████████| 3000/3000 [00:17<00:00, 176.28it/s, 15 steps of size 4.32e-01. acc. prob=0.93]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "                      mean       std    median      5.0%     95.0%     n_eff     r_hat\n",
+      "           delta      0.90      0.00      0.90      0.89      0.90   2707.28      1.00\n",
+      "  sigma_predator      0.52      0.04      0.52      0.46      0.58   1255.02      1.00\n",
+      "      sigma_prey      0.44      0.03      0.43      0.39      0.49   1217.63      1.00\n",
+      "\n",
+      "Number of divergences: 0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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j0yzfd7ZFmFUYgHXHgcba4j6A+bMgluuOn8kOsWFad9CHHvqmtV7XOxpDb1iy2Vjr1hkrwMyV3UkyZTVs6w6aYI3BU4o54UMTEScJtzrnmGBYXJBjgnamj9cbAqJndIJe2jxKW/q9FQ7hSCMIObBpcJOwwXgDySwG3Dj4LuVgkDC135abjemN15qhuSyibYUhq4ob0LGlI6egSbcGC27WcIPOTGJlgq2Ylqne+vpaN5Xlxsx8DguD4vVkeuR6Yu/zz3hvhaXLKk9RlPv9kXVjdNOTe+t2vnhkLZ08q0jRzkH/MLjwJqb4jFTi5H7HmM/rHupWCsa/v/C8p9Pngj/5WQDp52ZTXx4AMH8WFLwpcYPDoP3JzcaHnA+AqTXDV05sVfGUMLjCpKXG9OiMZ3A8V3VdRUEW1zKEgXZed57qV1+aN21u8oVH16v3pTAIi2DgBnRO6877ndNUw5KJp+93aNg3Pbk1pm7nu8/1qff5TIeJ9glBkxTIXH8k3b5eTp+zFkMSkAU8bC7rB4U1Q86JDks9ANPOTFPX/lDxvLgul97pHqS5jZU0p6laFdSEgUFxoaCkXa57pIJ7Q2SmQU4fXf7uTjq+NZI+GTRAA2b29u7KpdKJMSUA9LnJpTm7VDCUmYBkCEGCO9VybMe033lfc71OWLrb8n6HNanmJg/vdizUa6/d2yYmLErcxJRg0Pd7vOlzXrTfsYIhCfBNktqT7iKSB0XWAwTd4oA3Gws00KbT3lxZRKWEwsCI9ROWegBeQwgydss5ze/F5eH1grPaqD0EguFXL2ygK3+/Tml4WO0vHFFF5zVjbnJkdjKwY8dEzP4KSyNAvYuwac/I/c4IA3Nl6573e1tloWOhnqQ3DMrE7qm4DtMv97t5+lyOZ806rSspCSiTdGQ3vbB+UJmgJj8q30BsNj5EYdtsQCLaw3bIpUtO0u7U7lxpiAHTDvP+qnvfjJr9eEZGkt6hVGeu0lUZJHTXisnq1WkPS+0OM1WmH0qPyV0KC05eY1gUoUlBu9zvJrfvtcuWeNas01oMSeA3f9lE1/7fxmj++SXHN9JHDq1WBx0MCtK8tmR6/ULQDErfbBBq2wd30YL8XTGzDEy1F2E55IDpoDgFztuqigJnrk59huBC0keSAlJDxHPQgf9klAmddq6ADhqgAesohe0Zi6tixmPWl+VTd3e3WmfOZguDYNg9Nb5WjwUyuCPv6xt76PCFs2l+W71ntFjBkIT2t+L/1sXkn9/8ZDftmsyn25/aHM10QDvlI+sjmhNbDWHabPe/sZ1ufaZvqoXy5mhHVbkBJVMKA+3M4ONpULJL7L+e1E5NFYWBCwanPkNgShJYZxYG7KvnQsmgBYPcvybB4ER7WGpHdNr1OeEyLhKWIrddCRItAFxDQXOJsoS8hHUlJcCabWbt77+ejAgF2U55+9hkDDMOy2br2zkZFQp6R1VA+jLDdlBk4NmkQWF+77dPaaA7Tm+k9+1fGQp3DMz7r5zYFmP2IyPJNLNXj02FIZuNU1Cd1p3BjIv3TBiUCaY9HnOV19Q1sJP+1rVD1QUEXYexS0sRDjJxxVoMCdBucFnoPZMA/HzryCTVFEXM2DAwKN5sTsVV0rUh2yeHxWLQD7mpNkFpUK1l6nfZUpOpokEADAbDeA5pmUtvbelXgc5FsxqMvysLrJDNBrqDdskky1x5fXmtsd+DrN3hGgaTxaAD1/eHNwfojhe3RC3Ob+8opo8dNYuCgDx7zDeCrMWxFkMCNJYX0EXH1Mdof184rtU4zrO9KsJkpTsmSC2Es3pQsObUUdUp/ZCZaxiyqfQMDR26tRO01s1rF+kMW6S6qSbSutk3HgbapVIQb92lxcDNI4OmHY/u4V2qtqV3R6SewQRY0UgIkFb//7vbu7qAVFPiVYVznJGqXsNaDAmAm/UP+5XTkZ0VtLprgGbXldG8tnqqLM6fNgWtuaoompeOisWgq0F5s0G46R1VQa8pAM2aa9C+bs6M4e+Pt4aSuXLFa5DrzrTz98fT/PTAv6wdCPu668qE3kjSb+C773t9O93y9LZIbQthzG5RNJYm0TUyYbSivaoLSGVmyv+9M9VXi7pjJiz6CSsYEoAPLFxE0P7KyyOtC+TMXmjeYLJcxh4WBsWCAcyGO6r2786P0htmX7c0rSV9JvDvyGKroGnnjJdEtIct7VNW+Uv6TAjbntm0fYRufmpbTJowlDecU32/d1YXG9voe1UXkAhsHb/ezUIhNh5ougYvYV1JGaRM8jhP6acHYMr+dfMIbR2MTHILCrzZmAb005f0QpDJAUO6KyloBiVzuuNp3bo7JgzMFTC1T0kkGIJO+2TanXz0UHb065ICOUjakShisgJe3jIy7XdbqkuUFS1dxFeeNj8QawHAuqEX0pd+t7fthR4P9BPWYkgA2ZzNdFD6+/vVexj8EWsGRkzZfxsrofNP2D90h5zb+nL7annIGUELNT0f3QmyQI819aBpl88m5gqaIexkqrAefAwD7XLdsbZoV63cRnmlqoanaDzye+vXDVB13m7qDNBiMA3TAq58cC2Nju+JccfgumBFH9xcRDvzy6kqdxcdON+cIOAHNveP0q3P9Bp/Zkp19hpWMCQAd0nlAKg85LrQ6B2diDED8Xz1/W/Rew7sCEQTYbeAqXIYNEMgcCaJnpcOBOkWwLrLlF+duaKfExgrWhGDbqmxBu2n52EqbI1J2pm5gsaamppocNeUlRJk8zx2i0ra8R7W++6/d9Ntz66JBm4jzLhbPV95WjF96sQFgdCO5o9IFMEwLSkcuF3KkZ3l1FSxl8Hi2uA5qq0todHRYJWJt7funYsiYYoH+gHrSooDLoBxapyHAw5LAQwKh9lpvgECWkHOMjD1d2LBwA3/+HdMWmMQAE0sqEwCGQxUDliRGTL4/SCZq5zFoWf1gGYwV5kOqvvqg0oV5vkLTs3zcF2jVKSGOcn0bakIffOB1YFl9mBfIE340qOrp/0M9L6+oVe1xzfFpoKu3WkszZmW6YiX3//I/srSkTErP2AFQ4oN6BjMgKD1IQMJhwZtGkxpoUEFtHgYickVA8aJcYGgnQ+EHsQNikGxQHbq7IkRmFIg6zn1QdaQ6LTrsREw3urqaiUcWCuXgiHIGEm8hougG4rE9vH8aQWfYVCEuEU7sLC+0HgOZ9WVGC3ooC21PXv2UN2UtSNjHpef0kkHNJepa4PLGlayX7CupDjQA7CSQYE5MWPFMzSRxtryaXOh0aYhCDeSDATqPZJYQ+LhMBjuztcHARd0iwN9opYUajzvora2Vv0OGBaYr7QYpMAIes9I2nnOMMejsGd4AhrTjp9zsZ7faZ+87qZMMAixhoYGKi+qmFbwGQZFiONLeKBBHpjsrc/sPYeYZT63uTZi9UxZ+jJVmNt0B1GgNzG1Z963fxUdUJtLPTtz6ICO+qj7CCnwZWVlak/7NYbUCoY4YM1PtxhY66iqqorOkeVhJacvqlU3t29Xrqp5wM0NYrPxIf/T2yP03ee2x/RIOmVOiaKZW4Kzj9t0UMJQZKULZLbSWCCb6jCCqn7W2z7rPvq6ujpFHwf9OZYCML1BpX0y7aBNKRMD47SgYBfVFEfcYRUVFWrdr/7AQrrq3jeitQJAZH8RXXZCS6DxNF730xfV0MkLm1Q2T0tFPpVMjqn9gt/bsGFDtHmebmUGkV4+IZQJCLXO+lKqnBIKfF319fVqr3R1dflCkxUMccAbjQ8pH2BoHDgkkOIADjgYLR9y3NzW6kIqKyuKMii/tT98L/rAsFCQOdELz26nxXPbopPnuKBNZ1BBMlcO+AO8dji4oBkCGZACWffT43eDoF23GPj72fWIfQPAqgGj6u3tje4j2V0zKFcScP8b/dEALpSJi45poI8c1ha1MM87Zg7NLx+nTYO7qLO2VFmcW0f20P4t1apzaVDrzmsMYA80lkQa50HjLiwsVe47/B6EG4Q0u/l43dnKzvdZMLACKmlngHbsd1g4oBvuJPAfr2FjDHHAmr6edogbiUPNNxLPeM1N3ySDCvKQbxzcZQyGo3cSH3IcCDArnq1sOihhEciwFnBIeA4uBDIOO6d/8t/yPQuKdlOaLRgRH24GXktrMuh5GKo1+8jumKwe7BdUEo/sKYjSifsxp7mGFtXlU2t1iVKEFjcUUEN5QeBCTY+pceyBM8CwxxHjkXtGV4TCkiLMPavgNmXaYTn40UPJCoYEkOap1IL0m4MDL38eBgbVUm7ukTS7viwqGJh2Nq3hQvj71p20bSQ21TXIvvQssEAjtGspkCHUOG1YuuuCcsdwbEM/5Lgm0CppxLpDsElrjV1oQdAOOrpG9hiVCVgHEqCdtVy574NadyfmCgGAvc5WGYD7AKVCrjvv9SCF2oRGOywDCAWpTIB2dmF7CSsYEkBqINyjHv/XzU1sNK4L4JvG7pAgNhvoQBsPvboTboHO+soY+kE3BB3y0z93bzdd9WgfnfP/valcCkHQzkFv3ZUkA7UMHBrdncTMKah1d0pV1ZUJMCUOKqJlOxq/wf0X1J7BurdXmzN65tSXx7zHewa0S8EdlCLErkN9z0AwgJHKfQPXKaw1VihMll4QaeWmDEKpwPGeaW5uVvR7CRtjSADeMFLzwybTGRQf8r6+PvUzDmQBQWmu3CPpyI5yGs4pUdWTBbtHYrQnPiiDu/PoPx/fHONCQL76B4/cQfNK/c0ykdlQidZdj+8o2kX7bb9hop3diibfNZgr2j+j6jXi0++jC4+up483TwSSZltXkjet4eIlxzVRe135tD3DWrdUhPizgqp70RkraNKZKwDaZQYW0xyUpcYAPezG1et3GH4ksljBkAAcOJYmJw6FKZjM/fT1zRYEg+KKbQABuP2rK9TrgYEco4+ye3TSOJBobc8wzWv1r7OjrulL5moSDDgkcMf09PRMm13t9yHXCyJ5H7BWbTrkcN2xUFCfMUkqzfLdi5qVP9kvsCuFlYkjOspoJKeU6osm1f7R9zuvO4Ln8h4FYe1wHEEXDHx24805l78bRGHk5NQMiWQVIb9gXUkJYAo8S5+fBN9UKUSC2GyyNYSki90tJsGwX2OF0YXQNjVjIuiiQhYMTgKZ23rw7wYxWtWphiEe7ZsGzAkCa3unN37zk/bG8kiDSKSqQrs2aanYRzLjjpmr3wLZad1ZIJv2O59nvfrZ77M6ocXxJD1WMIQYekCILQYT9M0WVBWu00FhLcR0UFprSunCo2u1eES9ci2EobiNaytMwO/IvPQgD7mTQHZSJuY2RgrGJPAaw5X8bIGgC2S96M4E/I5JIPu97noNg9wHslGhBN6T7we9Z5z2e1DT8KwrKQH4psnMi3jjAvmw8N9yQDpI5iozXhC0Mm020I3Ky2PmVKtxlJ01Jao1sd/Vz6xBmQ55PAbFTQABZlZhoR3vOSkTHXXldOnxzfSdJ7qiPn0UidWV5vla/6K7geIFzeOtexC9nkAzWtxvGhyn+uJJqqrKS2q/czo50x7EWNgJh+4K8QSyH7CCIQFkpkAi8443Gx8MZlBBbTaT9hfPDYZHXX4OFTWiDXduYIfcKR89nkDmQTH8GbzufladO6UdAk60g7azDmykAxvyaV3fKLVWFtL8tjrf22LEW3cnGvgs6Ovut0D+9Yub6Or7N0Zb3X/5xDxadlCkL1I8K1MKhqAKIye0defvxbrbmc8hha79JQoIxWNQfgbk4h3yeNqf3h46iKIfE3M1dVhNRDvfhyDWXdLO6xjvkIN5wULAhMDa4r0txP1cdzBE2TiRaXcK3vLv8P3Rs8H8coOhk+s1f3grpsPrjY9vic5Kdlp3rvqXtAd5VidS2O9+wAqGOJA3izd8ooCQ1FBYCwmCQfEBZ1OfD7nTQcHvSTeYDCT6STu0Tb0dRrzgrYl2eQ1+075teFzVIyDbKBllAuDeSYwg6gE4qycVRYgFnm6V+bnua3pGjNl067fviO4LJyCNVafd73Ufn3Izp+KZ8APWlRQHer983MREhSWcssoIovrZlNMN2mUXUiehxlPdcGBQ/bxm2zAVVo3SrMZIf6Iw1TDotPN0Mam5+ql1r3ypi254ZFPUpXH5KcWqYaEe6NTB18VMQcaogsql5/XD/+PRDuaKViWyUt1Pa2dOfdm0bq8cvC8oiG+psRtMt4z3+LhnTDUM3FrHWgxZEniO57NkYCPKNgFBFLnJQTEy8JxIMHBXUvwNRpSe+9NV9LUHN9PJ33mCfvn8et9yunWhxpZXPAaF+6LXkPipuWI0478/GvFzy0H0WwYiLRnixTlknUwQe0YPmst+X4kyY/SU1W0j4/SXTcO0abs/6baYyIbgvcymQwdhpNnGszLl8CqZJOB3Hca4oYYhkYXsB6zFEAc41K/3TlBB1SRhJlQyfj+ZOik1J782m1OBGDYgdyV1Artjtu+cjBlRCm3s6ytfoXft3+BpS+V4GUnclTTRuuvVz34x17e7h4wujY39O2nxnOmVtxLMBHRXmF97Rk+zlcqEqWrYtGfwt1Am7nhxSyS76k9dtGLZUjrniE5PaQeNmNp2UGOBqv1Aq/t5bXWqCykaW8YDu4ilpeDnnpmYcvnqBZHcpDNIWIvBAdCQ0S/ohhfG6BO/Xkv3vLItYRAR0CW93+apfshZkOH7E1k7fFA2D++eVnQ1MTnp+WSueAViiWhn7U8KBj+1v85qWAWx70F7bSlP7BLQBQNbbX6lOfO6w3WI+Ejfzr0xmmRoB629O/YoZYKFIysTXo/5ZOaKzq4I3jdVFqW0Z/SkBT8LIyccUlXjpTf7BSsYDMBmXr7y5ZhNfsMjG1Wjs2QsBv2Q47VfKXz6ZsOhTTbLgRlUa3n+tCrovJwczydzOQkG/f/J1pBIv7fXAGO68Ki6GJfGV0/uUO0kEq07D+3R94yfguGBVYN0/m/WqwaKUIR+/2pv0hYyfgcDcYJQJljjZsWL6U2Wdj2L0O91n3AYHRxkfCFwwfD444/TGWecQa2trepG3n333TE/x4JdeeWV1NLSovLvTz31VHrrrbcCy3ToGt475SxZ7Y/93n5pITz0Q2Y5xKt41mlXPd/L8qd1Zb122RLPJ3M5pe4xbalaan7m1INmuDR+eGYLXXNSLf3ivP3ptAWRrp7JHHKOkQBItfx7107a1OePn37z9tFp/ZoQH4EVkA3KhHxmZSBeBh6DPQBsXQdhqe3R2mHwvg1aMAT67chmOOigg+jTn/40LVu2bNrPb7jhBrrlllvoxz/+Mc2ZM4euuOIKeu9730uvvfZaQt+nF5kOcxti++mbgJ9zCp8sy/eTQWFzdQ+P05bh3bS0dIKqCiKWTqKDwkFQ0M5dWbt3TNL8lmo6ZEGnL7TL52TTPfl34QJZ3bVDVb+2Fu5R1+unWwBAq/PqxiJqqCqOtnVO5pAzg4KfnuM72HPf3lFMHztqlueKkKlf09aRxIoQM1dYTHpXVj+UCU5WkHsmXtNCHbKWgWNUu32a/ZzJfvcagX77aaedph4m4MbcdNNN9I1vfIPOPPNM9d5PfvITampqUpbFueee6xld2MzfOvMA+sbvXosZJt7ZkFxASAot3mDs7/Y60wDf8eDqIfruszznuY8uPraRPnxoxCpLhnYIbKC2JJfa60opPz/HV2sH4GyRZA85YkLLV66KziH+4pE1dPZBTerv/TjkpvnguBfoQJpMFS2ur88Q9P9/d79CJy1s9JTBqoFOOdire9/Dnu+ojoxOTWbP9Pf3K2Xi0NYS6t+dr0Z+Hrmk07fUbLnueA8WWDJnTXfhydTVPB/OKsBK3IFle6gyP35RIe3rMYY1a9aowddwHzGQVXPUUUfR008/7fn3Lzu4he78UCd9/egyWvnJxcpNkOxG0S0GZhJ+BKA39g5HhQIzl5uf6qbB3cndalmH4Xc9gEyz1RuhxWOuekwIT//1/HbqHY0dEeo37clk9TDACLbtiNA+vfW5t3766iKaFh+5+LhGaqvdOy0vHqQ7BpXbh7SVU11JxC3iZ7ttrr+AMpDsustUYb+rn3ft2kV/XD0UHY71oTtfo3tf6wu0eV7o01UhFABYCBJ4zT9zOqDykA4ODqb1/SrToSyf8hsjA8W3bx9JWorLnkmsefBm87r/ydvbhsyjGQd20aIk/p6ZsOzvxNfhdf8Y6dtNJXXPKSaEpmpVhfm+WGpO8wBS2TPwx+MTJqeNYi31vHYEis+S+jzq2ZlDB3TUU+HEaNJN3DgjTILjXF7uGaesHlxTslk9en8zPwv0NvaN0K3P9E1T4k5d0kptFCxCazGkixUrVijLgh8dHR1pfQ42C48v5A2eLHORvnoZhPZ6s+HzW8qc5jwnNwqQU1blXGU/NCinDA28l+iQc0zINEvCjyI3nXZmhqn0u8H1NlcW02cO3nsteL7q/fN9qR0B6kvz6JC2MqUIpcpcuWo3iD3DkA3oUjmrejuVPT7sGXwP6i5MStyWIX8bV2aVYMBcU2Dr1q0x7+M1/8yE5cuXq+IWfmzYsCGt70cWFAZxw0fMA26S3Wwc6NULlfw4KIgLwL+uz1Voq0lO69Sze/igeC3UZH8n9BlCPj2yc5JhrmCcKKaS1/z5w6qUxecX7brmynGNZPcMN3V79+xiuuP0RrrxA530o7Pb6cyljYEI5HSYK6+zX8wV+0Vq+rLIMVXr3u+6o4mJCWqtyM9IidsnXUnIQoIAeOihh+jggw+OuoWeffZZuuCCCxz/Dlq+233Mk2nJkKjrJH+OH4f8PfPK6cDGAhqaLFJBwKqCPRlpUH4UijHtf3p7hG57NmJe5+ZsUkH/z89JTDsqbI+dW0vPvvo2VeWNx/i4/aJdupJSVSYAZmbQ3PerKlWD7L3OqjLVjjBzTUURkl1Bec/4wVz1jKRU113ud656nvRhv4PO2uJc+tJRtfTd5/qiSS5Q4tprY2ey73OCYXh4mFavXh0TcH7ppZeUpt7Z2UmXXHIJfetb36L58+dH01VR83DWWWf5SidXDSfrL9WzCvzUQnhjg7nsX1s59Tp5lwa7Bfw+KPh8WAgsFAAcltue7aUPH7ebOpMweHCgxtsrVIaM3+suGaHMpU9FMMjUSb+KrfS+TJL2ZPeMXg/gJ3Pl72M6uM9QKlk9Mu7HwmaPT1YmlLilDfk0TMXUXl1M1YXJW2ozVjC88MILdPLJJ0dfX3bZZer5/PPPpzvvvJO++tWvqtTJz372s+qwH3/88fTAAw94WsNgQjJ+7nibza9iK86/5gOJwwGtk5l9MuA6jCAOyuah6a04IBw29O9MKlWY3THsjnprYJiay3KpyQetWz5zpXuqjdCkls5JC14LBmaumWjdgGy/rWvyXgFrLAvSeN0TZbGF4axOiD0DJW5BXeW0bs77rGA46aST4g70wOa65ppr1CNIpCsYdO3PD7eArANgDQpxklQgr5VTVv1gUM3lucasHMxFTmXd//TOCN3xwmCk/XUO0fJT8uizp9aTV+CpX6YahlSgd1jFtXhdh8H3VdKejlCTgsGvan8uIJTtMNA2PlFrfB26IlRYWOjbWZUdCnA9qXgmvERaFFx11VW0bt062peQqhTXS+396JdkmmWQjlAzuTT8OOT1pdNbcaCFcipZOX0790SFAoDLuO6hdZ42c9PXnRuxpbru0n3Dfn6vXTKwKN2gXVqlbO14ud/xHSw09RkSqcYYZUaVXxbDuFC0WIlLZ91DJRh+97vf0X777UennHIK/exnP4upG5hpSLZXjw5piuvVz37OYYg3KznZg+KHUAPt+E5Uz/7PWW1027J5KivnI4elltG9cWCXQ5HYiOcujUwbofG6c0aP14WRzFzdUCaYMfvFXJ0aLqaSTSX/VrrwcqcEspfuUwhkU91LVgsGBIiff/55Wrx4MV188cUqewiZQnhvpiHVDA3TQfHrkMve7m4eFK+rn/WUSeTRH9peoZr5pUr7nPpyYwpgR02Jp7TLACgjHWWCrTW9MNKPbKpMBtHLPcOtJbxkrk60pzMrWWYR+jHSdnKqqFB+PyPoVhiMtJ1ZhxxyiGpwt3nzZvrv//5v2rhxIx133HF04IEH0s0336xqCGYC0sku0dPg/Ci1N6VMpppLH8+09rIeQNYw6Icj1YPSWl1CFx5dO629Q1NFoae0u7XuuF4Wkl6nCpvqAEz/TwbczVTuES/3u1NvqnSVOFmHkeOxIgTa3bJ2vEKuW9KPh7jX1NTQbbfdpiqOf/nLX9JMQLqCgQ+KDMp5vdkkg+LUvUyEGsNLDcok1FJNmWTgb96/sIZuP60+2v76PfuVe067/P50snr4b+Xf6KmkboMtTMlc2VJJ1w0m94zXipAUmnK/Z+L29aNAb8KwZ9KlPXSC4cUXX6QvfelLalbCpZdeqiyI119/nR577DE1M+Hb3/42XXTRRTQTkGr6G8Cai19FbqYK1kysHUk7f77XDEq6YzJlrnWleWqil9fVzzpzlbn0qdLOVob01Xu9Z+T3uLHuMnvKa+Yq06h53dOxGPD78nzvmVLmvNwz+hyGdNc9VIJh6dKldPTRR6uCNLiR0Hbiuuuuo3nz5kV/52Mf+xht27aNZgLSCQjpm83rthimKtBUezyZXEl+aVDyWWqumdLudbGVTju7JNI95DIjzOtUYW78J2lPV5ngdWfBoCtEXiQrsPID8P+xfqmm9uoCedJj2t3c714hLbvlox/9qBqu09bmnDFSX1/vS4dCryGLplL9O1gaaP3cvXOCFuSNUUW+d5sNzGPr0Bht6B9Tc4abRA1DqgeFhZqMM3hZ5MZph9La4Xz0VGnXBaHXtOvVt+nWMJg6lXpd/czMVdLOswxStZA56CuL3LwUyJzFJoUa9ky67XDkGZ/wWInj+6kXRKZzVkMlGNCaAsDFwGpA6mpYfGNuIxMp/oc3B+i6h7jvT5/qg3JunTcpfL/96xa67qEtkaIuIrr8lGJ6V3t+2ulvrLlKjdKrgyLTGjPJR2dIrdFr2sGg9Fz6TPLRpWDgtt1eCAaeZWCiPVGb82TXnT/PbbD1KjsAs5BIt609/q5nZDdt3TFJc+oi42293O+Sdo5LhSVVNW1XEiTzP//zPysJh5TV9evXq/cvvPBC5VKaSUh3/ioKqjA3V/b9ueXpHtrc7/7QFf278IzX6D2U7kGRwWcvDzm+g7U//l5+P11lQ3fHeDXHV6ddKhDprrt0aUj3oNsWD/u53cyl19fdq8JITrTQU7PTSVVl/P7VXrrgD71qYM75v91Af1w97Fkdhq4IpZsiHDrBcPnll9Pf/vY3evTRR2P6FmHa2kzJRMrUYnAaHuNFodXb3UOOg2rStXak9udl3x69NUAmhXkMPYDrFe1Mtz6HgWlIB6Z+SV4Ez/U023RmGSQqcvOqMNJEuy4kUlWsvvXg2zGK1S1Pb6PN/Ts8T80OYw1D2oIBM5eRkoqmdtInBuvh7bffppmEdLJL4g2Paa0ocN1E7awuNg+qqSxI+5DL4LlXWjcO5JOrt9GWgdi2DJkG4iTtnKnihWAw1QGwFu7WuntV5GbSutOtv4hX5CaLLt2CrPNwI93TSYlbh0E6HtCup6pmItRCJRiQbdTYOH2ACDqhhiV44hbSyeoB0N/n305fEFNo9ZUTW9UgHbe1v4byArrw6PqY7/rXk9pVBbEb2p8Xh/yXz6+n4657mM6/8y/0iV+vU7Nv9TqATCwGqXV7VaCnH/JMsnpMRW687l4JBj0jKd0aBr/X3dQRNpN1d1TiKmML9rxSJsJWwwCkRcnhhx9O9913n4opAMxAfvjDH9IxxxxDMwnp1DAwPnp4O3UWDNPa3lG1yRbNqlczKDIJkpmAjYa+7gc1FdDG/jFa2FanhAJ3n0wHzCC4/5Ic15jpBoalsHzly1EtDU+Yw7CkvpGqq1OfB+BEu+xhxAFLNztX8gE31TBkQjvceKOjo56m27JgcKOGQRdq6AMkLQe+n27Szp+daTEnK3Hf/OABdMU9r0UH5lx2QosapOM27XrtSKZCzSuktXuvvfZaOu200+i1115TNwktMPD/p556ShW4zRSkm6oqD0pLdQmV5qCNccTv7UU9AJvWNUU5VN1YRPVVxcp1kqk7hg+59Bu7oUE5me5dw7tpwVTKJGJX6VqfenGhVwFcXnfZRx/rnsr8i2TqMPi73AQzb44BSEstU1eSvG9eWDvcFpyVFm7Yl26KMHDOER00p2QHrdq8nVorC2lea7X6fC8sBtNQp3SFmldIa/citoDgMy4SxW5//OMflWvp6aefpsMOO4xmCpBHj2ly6UI/ZF7lduuaK/u63WRQbh5yJ9O9uTyiaeM7MhnGpK87r7nb6w4hwO4S/t5M0w71dfciI4xTVfUaBqY9XYHM666PhXU7M4lpl26wTNcdn9dUUUSLGwrV4ByvijpZYTMV5oUJ+elc2Oc+9zlVy/CDH/yAZjIyleBOh9ztzaa32+bvyPSg6NWgbgk1mO4rli2lr698hSbQvTaH6POHVVFjeWSGBQ5kJq42pypcL9dd5tJnsu6ccullVhULSVMdQCbz0nnNvWzpIbN6pAsvkyw2WZCq75ndLgs17BluwT+jBAMW77e//W20yM3CGXpetVfZPbJgRvp2Mz3kptYSbh2Uc47opBPm19PTL6+mhmKi8ryIG8ONLpP6eFJmUF5o3V6kHeq0u532yRlJTKcMGGca+9LTnN1u6aHHLKTykqkip9fuuC3UTHuGhUSYAs9AWn6Gs846S6WsWqRXD+DmITelv7nVwldqUF2DY/Ry9xht2u5eHUZDWQEd2Fyiqkz1nvpu0O5lgR7HLExMyQ3a5fdwYNgtf7duMcgCMbfXXcYCvGpZnWmarVNbjFyXlTinosIwtdtmpCWm5s+fr+YwP/nkkyqmUFZWFvPzmdJV1Q3oYzLxmlsRuJHay4fcVAXqxkHB5z60Zgfd/uIWNSYzN6eLViybUBq/G7SbUvfSTRHWaZffA4blBYMy1QFkqv3p1c/sSnIrq4rXXAbNmWG5Ye3o+53bhri13/WiQrca0Ok1JIVTtLsF3u/8HewCc+Osuo20dgE6qlZXV6vW23hI4CKtYIhfD+BW2qdJg+J89HRbeUjg79EE8PYXBmJaeyA28K79G1KaxWyCninEgWc3MjQko2CNzAuBbJoh4RbtHNfB/5FF5Faas95VNdN0T512dskw7SzU3Nrv/NwzOkH9Q7to9sQOqil2Z91NsZ1Jl/aMyWIIY6oqkNadQuM8hkwJtEjuoEALcfOguDXLwET71tHJaTOUETBe2zOasWBgM92NnvqJ5ifzZ/OBd3PdwaDWjO6kjqpJqi3JfN31hAVmIG75uyFkpItHMqhM9yQrQpyowF1PsV5u7HfQjM+5/43tdOsz3KCS6NLjm+lL89wRyBMijuGmUOP7x8LNjYJIr5C2XQqrYcmSJSqtEA/8HwVuFs5ayNahXfTSllHaOhgRDGHrqW+iva2qwDhDeXZ9+jnjOu3yoHAANFOXCbsFmEHxgXcrzsCpqg+sGqTP3dtNX753PZ37s7fUazfWnV0y0m3iRowBn6Gne0rB4Ma6s7UjW567te4QatvHJqNCga3Y7zzRRV2De1urpAM54nNyata7mwKZ97tMEU53ZorXSGsXXHnllXTxxRfTGWecQb/+9a/VA//HJDf8zGIvcNNxyNGt8TP3dNFFd71Nn1y5kX794kZXPh8Vsnr6m1vMFX/fWF5Inz+8MqbdxtUfWJCxtQBAk5R5726m7jFzlXN8dbdbprT37Zyk/3q+P4ZB3fjnLaqyOxPoM4jdTPvklgyy/sKN2hGTUHObdhbsm4d2T7NiIw0qR12z7hluKhNsqcnYDrsHw+ZxScs+uv3221UNA6a0MT74wQ/SgQceqNpkIDBtQdGb37+L6Ht/GYrp3nj1/W/Rew7syIjBYoPJVFU2d/F+RUWFK7TjM0+dU0qHNBfTaG4p1eTvpkMWTu+TlSpwIGTGB1tWbgkGPcbiJoNirXvT4C5HBpXJfTUVRrqVVcWCgVOZ3ap7cao65/V2I7sHnwHaO6qKlBU76bIVq6/7Hg/2DIMDzpmmlXuF3HRvEPol6UCGkhf917MZuPlbhic80XBwH2Q/JD2jIlPIg4Jq0INaStUsZTcOCh9yfZaBW6l70pXkdl46095eVeiJm00v0GM3jxtpzhwbka5Hhht+dF53fdiQG9k9bPGhaeQFh1fFWLFXnDYvYyvWVJA66VJRJ2hHF2G4khGTcrN2xAuktRM+/vGPK6vhxhtvjHn/+9//Pp133nlu0TYjgI3mlYYDBiX7F7l9yHUG5WYVrp5+6WYdgNRc5ZwBt4qtWOuGmw0M6o4XB6LN1648bb4rDEqvOndTMEg3khszJBKNVmXBkGl2j2yed+rcUjqqs4KGqJgqaCcddoDzmOFMqs5zXaplQDfhK3+/Njph8cKj6+jc+sjPwhZfAPIzCT6jR9LRRx+tXj/77LNqktsnPvEJuuyyy6K/pwuPfRFttWX0uUMr6Pt/HYrp3pgpA4HPUs699Yq5MlhzdUv7k9qYF4E4PafeLeYqZ/aCQR07t4b6d+crBnX44swZlKSd3Tx4zVlVmcSOuHmeKTPGrepb3YXHtGea3aMnWjRVFtG8inIaGHCvA6pOe/5Ul95MgJjTVfe+GeNKvu3ZXnr3omYqmEmC4ZVXXqFDDz1U/Z8H89TX16sHfsYIW0AlKOBgvHt2MR3aWqIYyOy6MjWXQZb2pxt4xmfjsHuVF60zKLcK9PRAXN+OPfRqzwA1lhDNc5F2qf25VeSmM6iWqmKaVVhIIyPutTbQhRr80PjeTIrc8Hm87gie87pv7h6g5vJc19ZdLy4sKSlxJUWbaWfB7EW6p16QWjS17pnsd6duwuu376T5VZnXG3mBtCh65JFH3KdkBoMZFPz07cVFVF5erA5mJoIBhwMPfDYGJLmdjy5pZ0hXUiaHHIcM188uhv97Z5TueHFL1Jr69tnl9LEjZ2VMO9MHn27P9mGa11jpikAG7VgXZq5stbktkHUXHmvd6fqk2UfPGry+7tfuKqdzj8y8ol3uCz1VON1AK9aAO5PKdttur7te5JbrQv0LdxOWwgGvWypQUOhOXY3bcG9qiUXSpfZuFCzJwLOedui2O0ZnUPoUqlTBf68K6IZ2qcpqPjR4/sZdr2ac8gng8x9au0PVGXz9T1tVncF9r2/PaN05m0rWAXix7qY9k2kb6ETr/v/uesW1dZfJBPL7M6Ud+1Gve3F73fOnBJs8q5nQ3lxZrArwYqc5tlFdSYTuMAqG8NkwMxB6AJfrDjLZbGzeyiAiuzcyGXLjxKCYKbnBoDirB9rjluHdnlVWbxvZTXe8MBhTZ3DzU910xhHDNLe5NiMGhTXW+/Xgetxcd12ByDRDRtZxeLnuevUzkGm7eV53vWYH7qVMBvQ40d4zOkFdw7toaVEFFWWoxIHu986voCM7yunNzX1q7sjiOQ1Rq9nNqYJuwQoGH+DFIYc5rQcRAbcKlRisjXH2kxt96rlnDGhtKc+flrGVl5PjSmX1hu07HdKER9IWDCzUdNeDW7Uj8nOhHXOFtRszJWSfIS/X3bRn8F4m8R3TOE836y9irMw1O+jWZ3oj7TYe66UvHVVLn+nIXKg1VpRQQWNRDO1uCjU3ET5RNYOrnzkvmgNZ6TJX/D3iCrKyV7od3MyL9qLoR7YGQNwFqXvSzL522RJXKqvnNpSTrsDj89sq02ckrLXqmWBu1Y44VeGyqyoTgczKBD7Ly3XXaXejloEzg0zr7uZ+7x7aRbc+GxEK6vMnI/PIN29Pv+aIG/HJVhiA20LNTViLwQfowWAO3KabBiddMTw0nj/f7d7usvcNM8RM0z5h/rOgBE5fVKNS917b0ENHLJpN89umErwzRGtNKV1yXCPd/GR3NMB64dH1qkjP7RRht9fdVEHs9rq/f2E1nbyoid7c1EdHLZ5Lc5pqXKW9Z2Q3dY3uoUWFO5UgYldWOuvEzfO8Ss1mIFNo0pBBhMyiBZ3uWTtAGAf0MMJJ1QwDF4q5dcg5IwlzMPTN5nZvd1N7BvbtpgNoTay5yjTbhpICOri1lNpq3DOt8bkfWFRLCyv30NbRPbR/Sw3VFOdkpLkyc+WUSS/XnUduulFcyAFUue/AlGoLcumQtnJqqylzlfYH3xqkG//cM9X9tE8FW0+ZUxKlIRXg3LBgYNq9EgxzG8uNGUTNZe7WjjDCGHgGrCvJB3AAV5qQMg0uVcge8aYOmW5qIRyMM2VVpUs7Z5eYaHdb68YDE+KWNBYpSyGTAj2Z1eNlYR5Duhm4YjnTdZdaN38e3nNzz2wdGqPvPLE1xh3zH49toq6BnWm5wvxcdwjIy05oiXGxXXp8E1Wn2c5I1o5It68+WCtssBaDT9DHNXKxFTZ8qn5G1kD0njecRunmZtPnJzO96dLOh1x3C3gh1PR0W84m4iSAVNcJf481xmeA+W0c2EWLCvdQU743tOuFYvheLhRLNZPFxFzZ7+9GJ16Jtb2jxoKuTYPjdFAaFg/TjkCtniLsRbrnWQc20oKqPbRlaFwVo7bXlae9Z2TtiZ5mG9ZU1dBbDFjQK664gubMmaOqJ/fbbz/65je/GZMbnS2QDIq1ND3DJRUNRGrcrNF7tdl0BsW0p+PWYFeAzHCS2p/bqXu6QM4kL50L++59rY/+5XdddNWjfXTuT1fR71/t9WTdJcOWNSTprjsH/GUQ1IvunlzQJYHX7dVFaVlr3BPMyUJ2e93z8/NVTARWJgoied3TSbjgMy7rjbykfZ8QDNdff71q1nfbbbfR66+/rl7fcMMNdOutt1K2QWeunI+d6kFhxmAKxHGWg9utSPT+Mfz56cRIECyX/lY97dBt2vUCPW7DkQ5zxb3CqNMbHtkwbQZD747MehglKnJjVxL2TDq0I4tNdyNxZpzbggGZTV9/z5wYd8zX3t1JrdUlKnc/VcUOf8NuL9QXvLptXAW2WVi6ve6FYh+yIsQt7lMFCxTpRnJzUuE+6Up66qmn6Mwzz6TTTz9dvZ49ezb9/Oc/p+eee46yDaaiH2w4MMqamuSzQVhTh1nNLRl4c6Xj2kkGXIQj6zDSoR2HQ7d2vKadtTJd4wOTLy8vT/pzcN+Gh4epe0dsYBLA6+7RzKerOdEu6wGwh1JVJnDd7DLyKwD6oYNbaG7JTtrYPzZV0FUb3bscY0oGuG6sO4TXPa/00H8+3j0V0O6hi49tpI8c5k7TwnhjYdPdM/w3piw2twtR9ymL4dhjj6WHHnqIVq1apV7/7W9/oyeeeIJOO+00yjZwwRLAhxyMkIe8pxN41g853veit7ve4oBzx7nfU7KAxgX6cd1+0S7dUyyUZX+pZMEa46y6UqObZE5Dagwj3ZRV0M4pyskCdOOBdTdlsXmRMonPb6oooqVNxcotk677lPcMhl3BMtOr2DHm0wva80TPpHT3DIB7Fc+6DytCLRguv/xyOvfcc2nhwoXqxhxyyCF0ySWXxJ35AAk9ODgY8wgDmDlJ1wB3Kk3FRJUT20zan1eHXKZjpku79LcGRbse+E8W+H3Qj3Tai46pj3GTfOmoOmqvLfeFQaXLXDmGo7fb9sqdwW4vWZHPWngqrjDQrsZ5Du42WmpbhtwfDJarzQhhJg4lLhXaWZmQ6c1SOQzj5LascCX96le/op/+9Kf0s5/9jBYvXkwvvfSSEgytra10/vnnG/9mxYoVdPXVV1PYwIecNQfZ5RKbB8H1VFpt88aDz3Xdzp00uy6XoH94dcglM+eDwjUJydLOZjXTDsgKWa9o16ehwYTHOmLdkxVGsvXyKXNK6YDaRhqaLKL9Gispf3zYE9pNNSRYa1hqqdDOWWyALhjw7BXtciCQTJRIRZngueAd1U7Drso9p33Pnj1qz8BiYJdcKo0uKysrlTuMPxvYNjxOWzYM0eLcYleqzfcpwfCv//qvUasBWLp0Ka1bt04xfyfBsHz58phBQbAYOjo6KGjoU7mkaYnNX1VVlXRnT053++PqYdUhM+JzJbromAa6YJ53h1wGcQHu619dXZ2SWa3HWbxM3ePPlcN1ZOA/2V413IKEmSvcIwvrq9W6DA25n6rKtCPYvWrbODWV5dIojdH23gmqzBmntiQ1V9k+RTZu5GIxfTa2l0WdgGxXnmx8AUpIVUkhfeGIqmhH2Mh+r6f22jLPaM8Rljn/H0I22TiDk6WGWMn1D2+iSdqkrmPFsqV0zhGZtzvfZwQDGImecSDTvkyAeRZGE03PnuCDgk2Pg5vMZC4O3kF72dy/IyoUAByWW57eRh86bpw6XL5+k+YKsL87GdqlWc2HhA8gV0J7aTHotKcSPAe9MnjLlgMzV69oxzjI5StXR10orDGDR3391Hz6zClVSfvouX5DH0TvVQBUBs4BXSAlUxPAtHOFPyy1g5qKaKyggprK8qimyBv3Y+6UEieVCT6rEFR1dXVJrZkMPPOewWAkPavt6ytfoXft3xAqyyHUMYYzzjiDvv3tb9N9991Ha9eupbvuukuNCj377LMp22ByaQDsf0zGdynnJK/fvsPYOXRj/07PrR0+5KnEGfj3TJkxXlsMetU5055s4F8Gb03dbL0obsNchOUrX47xq0dHQ04Srfi/tUnNTpADnUy0e6VE6QKZBQTv92RiJBwHkm7M5soiOqyj0tNZBnmGdiRMOwurRIAgYLevnDi3oX9sWqyE252HCaEWDKhX+PCHP0xf+MIXaNGiRfSVr3yFPve5z6kit2yDPmhcaq6skaYSeG4uy1MaJE3zubpvWjOdDL1QJxnBwEVKulkNeFXD4DQmMx2hFo92WSvhFkzjICXws7e3Jk6swL5ySlZwuyOsBAtjPaaUSpGejI04pTd7sWdytL5X6ewZvkY9NRuxEj2rza125/uMYEB/+5tuuknFFeCXxHzpb33rW6FO84oHSTcmaL24YUi1+U22Vz0P9gBgRl9weFVMdszyU2d7Zo7qBXoM0J6Mz9gpAMqf5+U91avOeT4AGGMyh1yuu9fzL+JVD0vgZ62VBSknK3jZiTeeMiHbTgOJBIOML/i57k5FbpNiKFMyDSQ58Kxbao0Vhar+gu8thIJb7c73mRjDTANvaszajcQHtkabdJ1ROkL19fWOGhBnAEkGdercUjppQSN175ikCtpJB85v9YW58iHHIeEYSbxh6TIAyrSbtG6vwMxFFhhy0R4OebzAP64zHnP1inYwCgQl2Z3EK8uJBhgeU1eSl3RshGnVaxi8FAxYdymE2Q2KtUvEXGV8ga9Ft3a8bFmdP5Ukoe8ZrGUycQZWOEyzO/5hvzI6dcnBNLinWFkKYRMKgBUMPgIbC1kmetAYnSgPaSmhlpZdjj5fWfEss3owc6C9Lpe2b0+9nXG6ud3cawgHHw8wTjAgJw1Ozo/QM5IYXtPO2jFXQOO7OZjY0NDgGDyXDErP6vGyQAxApsqRnRX07KvvUGMpChonaJiKaW5DBRVORNY8HvBzTpcEmHYUhW3cOkC1hZM0z2PmymvN9QhgrJyZFE+ZwM9li24/1126H/EdWwZ20tr1g2rwU01xJDaF64kXn+G5HYCphqG9powWJ5nNFwSsYPAR2CjoMGkKGq/v20FLd+503GxgvtyVlDeanhLo5UHhA8qCQY+R4CA7CQY+SGCuekaSH5qrUzCRBUO8ucGSQcl1lxXJXtKONtCYlxAJxBLNrSyh0lJYaeMJK6CHhoai9HI21UNrYK1uiaZ8fmu8nP7x6NmerrspzTkecwWdSDNnS082/uP0Zq/7DOVOnas/vT2yd8znVM+n41py1No7nVUOPHOlub5PvBZqMz7GMNOAjdFWCZdM7PvYcJ21JY5V2thc27dvj25EP7N6EtUyADgAoN3UHA3v9ff3R11RfmX1xKvClYFQps9EO64R684Cz0S727MMUkkVjhcI5Q4AXHzIxZD/9XykDkBd3yTRFb97NanspnTAgliuF9MOIctFXybapaKhN1xkQe31uveOTkwb83n9w+tpcHeu2jNOafNs3bPSBHDiCTehDGvzPIYVDD4Cm0EFn45pmNZ5EoU67JLRAf88fsZarSlDww/BYCrQA3CAcZBNtINuMACmHWb5y91jtH3qV/1oPxyPdlgxAwMDxgA6aDcxKClgvKbdVGwlM2TAoEzAmkuNvGtwJz21YXqK88QkeZYqqbeWkCmqEFig3ZS2ylaaLlBkhpIfysSmIfQli30fwqFnZyThwinpAu+bMpL8UibcQLipm2HgQ37agio6oDaHenbm0AEd9UpYAOzWkCaqrnEHZTHIdsGShkTuJLaCQBvmFlz30ProuMevvXtSBdD9cAuYcuqlLxlrDGYlfd5wxUgXlMlX7FVLCQa+nwP8Ou2owO3r61PP0hWGe8HXA6h1f3j9NCbndapkPCsT+wTWGK5LBv9xLyCo5RnQ151bg3jZmTQ3N5c6qouNYz47a4ppcveIOq8cHNetTD2bSiYveH1W3YC1GHwEbwhs/sggkMKoUODNo7uToLWCQcmD79ccBp12J+YK4CDjQEuXDCwI0I5DjLRcmOG6WQ5N1g/aTQV6DBxurLvUAJl2ue5dg2PK2sHsBf4cP1KnnVKFuV9Vb29vzP0As2UhHV13g1AAk/MyVVIX+JJ2Fni6SwaKka5gmCw1r7sb5KnusIUqY1C37nFmsaexZ/S0W6w9HrxvpBsM4M7Ebs+QcBvWYvARrLnyptCrbrm5G5gSb3wwW90naTooXjMoHvHJ7iIT7WzxsKbK7gxotBu6h4zdMdGvf36bt4dcD9LL2QYAt1RmTRVrzU32uC/OPa9so+sf7hLWTicd3xpJ1/UasjCSA7G8h1DrA7qReQRa2VpgC9NUaQt85rAa+uix+9P8tnrP6JYWg0z75L2MfQLhi7XmdeYWKyaBwu9xy3ovkTsl1E5bUE0Lqiapa3g3LZ3TrFqJ836HtQZ62eJhy9NU2CfPKu5Z2BFusTXDwFoSQ6ZuyqAcmKsKFvb0qI0mzVX8/raR3fRK9y7Vd0VqIV5DanF6wRL3xeGDjWfpzjBVfOI1Kri9pp3XnXPqnawGaN5ovdLd3a1oZ+EMrfuGRzZOs3bQIdOPdWdrx+Srx3ugc+vWrbRmzRpFv7R0nNb9+FllqoW4l5DuR2aMUsPm64HmzfEc/F93perpzV4X5gHS3dNYXhAZ81kcyy5x7yGU+ZrYuufzqmdT8Xth7OWmwwoGn8H9dpih6KYobzZUe3d1dSlmLJnP717eRp+7t5uufLSXlt35qvIf+5XlIHO7TbSza4AZFAQcC5NGB7O8rtQffyuvu56ZJK+NG+pBIMPaYaFm0rrVLIBhb2tHGPwdTrSDETEjA824jihTqyiky05oiVn3r5zYRs1VxZ4HQLEvuXmfE+0cJ+E9A8tNMk49vZmFux/B28KpdFMn2iF8IcjWr1+v4go4twCvPZ8P6cb0Q6i5AetK8hmSuZqaieFgY4PhQOGAS987NFd9ihU014Vnt9McHw4Kx0fk3GR5iNk1gAMF14buR0V3zAVVjTQwUUDzm6sV0wJT8Esw8JQ4LlrT50hgrfGe/r7TLIC2qiJfGJReh2FqJxFvJgYqbfevbKTt43m0oLWWqgpjXTp+CGTsE87WkcD61dbWqv/LucgM2YAOYOHuJ+0FDnsGNOCMQgHatGmTek+6ifTAM5ANqaqAtRh8Bm9w3ix6Hjp+jnJ7aIF6QNZJc+0aifXJes2g2B2mH3IcaPhbcXhMwTX8PoLuh3dWKaHgZ+peIq07HkDrF46ojtG6v/yuVmqpKvZt3eMFzxPBtO5+ZcboVmY82vXsMYBjWrKGx+tUVQafUScLWSoTEBAQClIIgHbUjrzWs1spdX6k2bqF8FM4wyAriFM95O3VhUbNtaMmEjD1m0GlMuZQVkuzYPGTQemCIVXaT5lTQgc1FapZAEhjrMif3tbDj2w2fe53IuD3TOmeyQ4ocitwLr8bmWibBsaVJSaz8kxwGonpR1ZP3tSay1RTp3WX18j4w5v9qqCQq6Yve1cLvW9+pbUYLJwPuWzPkMxMAKC6cHpH1UuPb6bmSn80VzbhOdMEtCcr2Ngy0judel3DwGDLRGbIpEo7LATMAmCt2+s0W4Zc83juJBNYiEi/vNedSU20c80HGkh++Mev04V3rY7GyOLRzpXCkjn7RXu+2O8c30h2z2zaPhoVCmzZ3/j4FlVNnQ2CwVoMPoMZFB8U9tUns1nweygIO3pWJQ1OFlF7VRGV0JjvDIqzoNjvmozWrGt+gJcTxJwEMndDzZR2vMe+cT/ARW6yIV0yKZsm2r3uTGra71j3/jGa1kASMbKjOiuMloOkXSoTfrXdz5vaMxzXkI0AE2Fd76ixJxo6Ifux3zOFtRh8BmsfMsUUvWp4NkMymis6qh7aHjlM2Kh+uQVYc3NKP0yVQYH2eEFTL4RavCwTJ5iCiNLF4Ac4Q8YpvpNoz/DfybGkfkAK5K2jk+apgwPmLrF6fIERBO0FDlmETmgs2dsqnQELf7+mSKfbsMMKBp/BwVvWPGBa/9Mv1yY0raWvWK+FCIJB6fMJ4sGJdi8niJkABpPqITf56P1mrqkGcSV02ln79Yt2rBPWHT2yhnZNGpklLF8TTLERP2nPzc2dpgh1DexMSomrLNgzze37paPqqL02UsgXdlhXUgCA+wT5zwPjuUmb1k6+Ymg0fgsGae0kEwjd1DdCr3ePUXtVITWL6lW/mSu37WAfdbJztvV1Z+bq57qzqzGVdQedJh+9303c/rBqgL794NroPucECtliAgCzReYdgtI1xXs7kcq+Q37TXlRUpFx42DN7B2ztpf2MxXWO64404X9Y2kabBnapaXvFe3b6umcygRUMAYCnQ3WNTDia1ibBYPJz49D4udn4UPIENA5AO9EACwjCTk0hyyG6/N2F6jDJiVh+0s5CTQag4zEakxspCAYls9mSWfd4PnowOb969cBNeu0f10zb51ed2kaHdERqWfR9AqZ7yXFN01qOBNGZtHBKEUKXAV2JQ0PI0oJcWtpSFnNe5brXVhSpNhqRmFZBVqSqAtaVFAA4Da6zujiuaQ0Nis1W3VcMcADSzywH+V2J4gzcwI1rL5Btidd4nwO/fjNX2R8pHu2mbCoGF2z52QhNz+7JhHa/snqANT0j02pv8LKiMCfGUpD7BM83PblV1QDo+93vdhJ5U2cVlowu3PD6igfWTnMBO617ttQwAFYwBAA+5A3lBXTxsfXG7o3YaNhwHHu4/41I3339oPgVvNVpl4FQp2ExTgV5sIj47/1krjxa9YUNg9F5EIl89U5Bcz+Za7qBUGjr6AaLbKCg4jpz6suMvZoaS3ISF24Ox2Ze+Zlmy+AUZ9QQ6ddBmguY4w7xEi2yISMJyA7xNcPA+dHY6B84oI4OqM2l3rEcWtQemc1g0qBuf6GfDm4uomYtM8bPQw6w1pNMAJobuOn97GERjY+P+JruCax8qYuuuGdD1F3x+cOq6P0LndcPTHTr0C7VE2lp6SQ1FwW77lIwQJve1jtMi3NLjKmekW6wW2K6wcKF53dcBy29v3XmYvrG716NWffqosT7pLUy1k0qg+9+r3tdSZ6qGfrOE13GbrWs8NSX7Z3apitxfgu1TGAFQwDQ86PRrqCpIo8apg64kwYF4cHg4jC/GRTHFvD9sk2BqfcOGNZFx9TTLU/3RJkCW0R9fcO+xhegPV9xz2sxwvaOFwfo0NYScpJPd/2ti278c3cMc/3AAbWBzOzlQkCsNeYQ3/AI09U9LQjq1A328PZSKgvAnfGxo2bR3JKdtGlwF81pKCcajUxu4/bh2A+4BhljgPBord5rDfPv+k17vqjDOHNpAy2o2kOrenfRfz6DcbDTFR70g1JCewdRbtnuqNDOhjnPEtlD6QwCa93SZ4qNB5cMGL2TBjW3cW8ONI8O9DvLQc5lYMEEutFyWO8zj8N8YkchHVDbSCM5pcqtgIMSREaSydeN15sHd9F+Yv5FbMPCrdOYKwRJVUEwQUSs+8a+Efr3RzfGzWRzUizW9+2gpU3ed1XVoVwxdeVUW4K5C8XUt3vvnuE5DBBsuIYN/TupePcw1ZXkTptKh+sPwmLIFYoQhvdAkZvML6LvPLF1msLzs2c203ef2x4jtN+/sDqa+potsDGGgMD1ANyEC+DxjaxBydjDF4+sVXOhgww8S9rZXObDC01JTm8D0HUS7zVXFtFRc2qijCuIdE8nX3dzeX503SXe3jZkzBhb3zfqe4qwXHcwTqe4DaOpLNeY1NBcHlFIghBqcKPwnuH9DsEg9wz2x6K6fCUUQKMeXwgqeFs0Vf8C8KwFKDwrP7mYbls2Tz1DsG3oGYoKBSm0t/TvCESoZQIrGAKCZK682aCFc+AKGw0b7tunNNAdpzcqM1YCv+dXxbNTCh8feB7SA0EggYMP6HQGke4JX/eKZUtjhO2/ntRODWWRVgd6AL2uYMLIXJvK8nxr46ED69Vanm8UcLJIrCJv97TiKigaNUWRYrMgwKnCLBh4z4CZyoIxHq86MlkQ8z5b10Gse1FRUYxQg/aP1yhi4w4EoPOPb/SalYntOwITyOkieyidYZCbBP8HswFjXbu1nwYni5U7CX3zoUFJq4IRRABU0isB2jDYBoea6eS+Mibaubun33Nvzzmikw5pLqYXV62PBvpR8Aa6QT8Hw/nQg7kiDiHdBTVFsZPs/ASYKYKbuj8eFbVsjbGARk+tkxY2UffoHiU08HMMkwlSMEimjvt/19+20u0vcIAcA4Ra6ejGSXpoDQrJtkSv74JjW6m9bA8dvF8ltQVEO4P3M6xMKD7YC7IGQ4fKwCrN9T17MFNYwRAQdOYKX+u9r/XFVFbiwJ/UGRk8jjGSXBUK5hCkz1J3X7Fg4MZ0oIutBZN2jd8Jirmiz9R4cwnVTDFSWGsQDCbaT19UQ+9Z2q7cNMxcMVgo6HVnf/y6vlEqmRhRPm+2wth9Bxqba8uoXQusB0W7LIjE89BE3rSCsf94bDOteHcd/RfeF0kC331ys/p/bk6Xsvog4P2mXQJCDYIBewZdVOMJBQjx2uLgLLV0YQVDQOBiKz4oqrLyxdiDctuzvbSkvpFWd++k/3js7agGxX3dg2RQ8pDjNVs8mOAm+ws5ubuCsnb0dWdrDUHdV3u6VNuOqoLJKO1FRYVRbZxbYwS57pyyCpoigmqPYlA8OY+FmsnCDDIzRmbigU7MYzC5XV7v2RWT7aP//OsrX6F37d+gXINB7Zk8sd/f3NxnFAoXndBK755XE7XUssmNBGQXtTMIsh4Am01VVhqCimuGcug/Ht8Uk2aJvu5HdVbSvJAccmZEYK5buoeopTySgov3dCbKwcYgGRSve8/IbrXur3XtoDuejqR/wraBCwmZJLqWF0S1tlOGDFsPsHggGBCf4m6kJvddEHEdCf5u3jNOmXdLW0op9+9DRmYLTExO0tqeUV8FQ75IWWX3J9YdggGFeqbhWSwUuOI5mzKSACsYAi5y4zQ8p4NSWlJizELpiY3z+gp5UBh/XD1M1z/cHbVqENg9c2nVtL8NKu1QX/d7XukxFivhJeIKcCHpCKJXj1NhJAPCC0wKQou1WVOQNuiWDKAHFlh/f6SC31S7oGIM8xvpa+NFju6ZvJwcml1fGphQK5hi8Hiurq6miooJ+vKJeUpZ01NXw7Df00V2UTuDgEMMrQ5dVvFsOih4jQZdJoGxn6hpCOKQgxnB3w7aTZXayLU/ZnZkxrBE0BqU8m/vzqMbn+iK67IwNTKEZo7ZvkG1NWBLgPcMQ9aP4F6s2opYVIT5ynVHHCvIlgwyZVXGSmQMR3//9a2jdPtTm9U9gVC4dtkSX60FKdTgEpLrznGyZQeV0fFza6ddh6x4zoapbRJWMAQIbDYw10QHxZSF0iZqGoIANjtrrk4FVQ+v3h41qSVzhaYVJIPq3hFp6OcEpxkBuN6gs0vw/XLPSOgdSmVFtJ8DnZwgu9ry/edYiQ5+H+mgRzbl0q6ialoyu9F3oSDXvaenh5zgdB1hWPd0YAVDgOCRnIkOihQYyKOvzN/bpygocAM8BORMbjDglj9vptue2BzDoPwcRO+E/ZoqpvmFGborgMGBx6CC5gyn+26y2rgiGs0agTDQrrtkEgHrDvrnzGkMVCgXiv2eSpo1fj/bMpIAW+AWILDZuKVEIrD2hNS3MBTL4GCDdvi29Uptp86T7KMP+qB01FXQJcc3xlaWH9caU8WqI4jZF4mYa7KdbJn2MAmGZIGAOvZL0HumQOz3ZBHEzBG3YC2GAMFl/7z5kwH7iv0uDtPBDB41AHhmqwbuI1gKJgZVMXW9YWBQZxxQR0d2lFPvWO40v7AJYAjQWMMgkPHgDKnkOtmGQ6g5xUjigV2PYdvvydLOAiXbYC2GgIEgbioaVBAzGJwAl5DUoMBcEVNwatkAARgGocbB8+pCirY0SARcJ7cuCRJYO9migWHqr8UuMTCoMNAOYO+mst/D4Hp02u+JwIHnoPd7OrAWQ8DAIdebzyVC0JpfPNqdsqu41XZYhJoMnieDIFuQ6MAaopWHDqfkBdAetCsmnb3LtRdhob0oxbPKVmY2IvSCYdOmTfS1r32N/vCHP6iqznnz5tGPfvQjOvzww2kmAMyG/a6J3BRB9aQ3zTZAG+u2iohbQ6fdxKC43URYmKspQ4Yhh9KHkXbQ4cSg9OQFvjdhoV3GGRLtY25DHxbBUDjljuP9kOzfZCNCLRiQN3zcccfRySefrARDQ0MDvfXWWyqXfKZAbrZEB4V/J0iL4ZfPr6flK1/e257jhBY6bcH0YLjOoHDIccDDclBk4F8yHlPK5ylzSkLho2eADu5OmshNEdTcjlRjJCbg3tTX14dmHGaBoD3RenLmVVjWfUYJhuuvv546OjqUhcCYM2cOzSQ4Fc+YEHQVJSwFFgrR9hx/3kIHNhbQvAS0B10cpoMFLA45CwanlM9Fyzpo4azmUNOeaN3D4ufmGIlpBoYOCL4wuWJyRGFnIoRBicsE4dgtDrjnnnuUy+gjH/kINTY20iGHHEI/+MEPaKYBmz8Zf3fQgWenKWgb5bR5A9jtEbaZt9xKInHKZ6xVETSY4SQTxA1DUV46AWjWysO07qnEpnheSlgEcqoINdXvvPMO3X777TR//nx68MEH6YILLqCLLrqIfvzjHzv+DTJfkA4nH2GHLJ6JBzlnOQg4TUFDR9J4tHO6ZNgOOeiRdHPK57Trqw6PCyyVDBl2NYVNa40XI5HnGEw4jLTnJnFWOSMpWxFqwYDFP/TQQ+naa69V1sJnP/tZ+sxnPkN33HGH49+sWLGCqqqqog+4osIOZprcHTPRvOWgwFPQ0LMGwPNX37tAtZjY1DeSdYdc+uqdUj4vPb6Z2mpKQycYJHOFC0xOO9ODt2Gjndc9nuYN2nF+w+K+S+WsckJD2NZ9xsQYWlpa6IADDoh5b9GiRfTb3/7W8W+WL19Ol112WfQ1LIawCwccEuT39/b2Opr9QbdNZmBICvrho/Xx3zf10/V/eEO5W3B+LxetLySg2VZWBtf0zwlcUYuiJc7z1zOqCiciA+vD1gSNs6rQJfaGRzYY+yOhLTTiC0HvGR1gmDzPwFRfwQItLPULEtgHEFhdXV1xz2o2B55DbzEgI+nNN9+MeW/VqlU0a9Ysx7/BQQcTko9sAA4INA0nExsaSliCWbAc0PqYhQIwKVpfmHzFYTSr4RLAOE+srT6UHoVvmJSH9yEYwgYwzsHx3KhQ0NuP8FhVMLEwrjsElr7uDBbUYdW4QVs8iwfXFaYsthknGC699FJ65plnlCtp9erV9LOf/Yy+//3v0xe/+EWaaQDjdDJRwVzxQOpeWIJZjoHogVj6oRVGJqGFK74gDzlrryYGBdrDFrwFoCQMUbHjPUDWD5SiMNIOQNiCNn3dISjg2guzQldcXKz2BfaHDtAftgy8dBAOLuOAI444gu666y76+c9/TkuWLKFvfvObdNNNN9F5551HMw046GBSumDAIcHYxrq6ulAdFudA9PSpZ3JeQNjAA1dMhxz3Igx9epxwQGeD8R60VOSrfRN0e/NE+9207hAUUCLCKtAAtsRMzS9BP2gPSwuSdBHOHS/wgQ98gF5++WW14K+//roKPs9UQIvCgZbmNWIk2IRhKvQxBaLBkC48ul65X3Q3UpgPOQDBxc0Ms4n2tpoyuvoDC6b1RyrLicR0wuij19cdQkBaDbgHoD1scREdWFt9zwAQdBB4Yac/EbKb+hkGmKiyGhdCAc+o4Qhb8FMPRHfUFNPY9i51UJiZshsprL5iBtYYwheDWNjlhQMOxhXG2IjEx4+bR4e0lNDf39lCnbUl1DTVNC/M1gID+wLr3t3drWhlpSgbtO3CwkK1PzCqlPcMxxbCGJNKFVYwhAjQUMFIIRDAVLHJkJkVVv88Ww48VWvbRKU65CwY2I0UdgYFQEuFyw5VrVh3BBbD7AKTWNjZRI3lBUoggLmCsWYDc+V1Hx4eVuvNLpowW2kSEADYL9yKG73cYNmH+bwmCysYQgYwI2w2CIjW1tasOSQAV3qivQc0v7C7YiRAZ3t7uzrkEMxhTZc0AW4LWJXZCFhknZ2dSijgEdZ4jgkQvk1NTarTLfYMrPowxQEzgRUMIWRQCDQjjTLsbgwdLMw47TYMozBTAdYbD7hhePqWhffI1nXOzc1VFgIykKBQZHu1s0R23pEZDGjZYK7ZiLDmzadzHdnKrCyCK1CdScgeu83CwsLCwhdYwWBhYWFhEQMrGCwsLCwsYmAFg4WFhYVFDKxgsLCwsLCIgRUMFhYWFhYxmPE5edx3KBsmuVlYWFh4BeaBiabn7ROCAW0OgLAP67GwsLDwiycmqjfKmUxGfGQx0Dtm8+bNafXs4elvGzZsmDGl7vvydQL2Wmcm9pVrHczgOsHqIRRQQJuo9ciMtxiwAOiBkwmyaRJcJthXrhOw1zozsa9ca2Wa15lsZwIbfLawsLCwiIEVDBYWFhYWMbCCIQ7QV/2qq66aEf3V42FfuU7AXuvMxL5yrUU+XeeMDz5bWFhYWKQGazFYWFhYWMTACgYLCwsLixhYwWBhYWFhEQMrGCwsMsCjjz6qCif7+/uDJsXCwjXY4LOFRQo46aST6OCDD6abbrpJvd61axf19fWpofCpVtZbWIQVM77y2cLCSxQWFlJzc3PQZFhYuArrSrKwSBKf/OQn6bHHHqObb75ZWQd43HnnnTGuJLyurq6me++9lxYsWEClpaX04Q9/mEZHR+nHP/4xzZ49m2pqauiiiy6iiYmJ6GePjY3RV77yFWpra6OysjI66qijlJvKwiIIWIvBwiJJQCCsWrWKlixZQtdcc41679VXX532exACt9xyC/3iF79QTcuWLVtGZ599thIY999/P73zzjv0oQ99iI477jg655xz1N986Utfotdee039DZqc3XXXXfS+972PXn75ZZo/f77v12qxb8MKBguLFBqQwXUEK4DdR2+88ca03xsfH6fbb7+d9ttvP/UaFsP//u//0tatW6m8vJwOOOAAOvnkk+mRRx5RgmH9+vX0ox/9SD1DKACwHh544AH1/rXXXuvzlVrs67CCwcLCZUBwsFAAEJiGCwlCQb7X3d2t/g+rAG6l/fffP+Zz4F6qq6vzkXILiwisYLCwcBkFBQUxrxGDML2HWSHA8PAw5eXl0YsvvqieJaQwsbDwC1YwWFikALiSZNDYDRxyyCHqM2FBnHDCCa5+toVFOrBZSRYWKQAuoWeffZbWrl1LPT09Ua0/E8CFdN5559EnPvEJWrlyJa1Zs4aee+45WrFiBd13332u0G1hkQqsYLCwSAEICsPdgwByQ0ODChi7AQSZIRi+/OUvqzTXs846i55//nnq7Ox05fMtLFKBrXy2sLCwsIiBtRgsLCwsLGJgBYOFhYWFRQysYLCwsLCwiIEVDBYWFhYWMbCCwcLCwsIiBlYwWFhYWFjEwAoGCwsLC4sYWMFgYWFhYREDKxgsLCwsLGJgBYOFhYWFRQysYLCwsLCwiIEVDBYWFhYWMbCCwcLCwsIiBlYwWFhYWFjEwAoGCwsLC4sYWMFgYWFhYbFvzXzG6MXNmzdTRUWFGsBuYWFhsS9icnKShoaGqLW1lXJzc/dtwQCh0NHRETQZFhYWFqHAhg0bqL29fd8WDLAUeDEqKyuDJsfCwsIiEAwODiolmXniPi0Y2H0EoWAFg4WFxb6OnCRc6jb4bGFhYWERAysYLCwsLCxiYAWDhYWFhUUMrGCwSApbBnbQU2/3qGcLC4uZjRkffLbIHL98fj0tX/ky7Zkkys0hWrFsKZ1zRGfQZFlYWHgEazFYxAUsBBYKAJ6/vvIVazlYWMxgWMFgERdrekaiQoExMTlJa3tGgyLJwsLCY1jBYBEXc+rLlPtIIi8nh2bXlwZFkoWFhcewgsEiLlqqSlRMAcIAwPO1y5ao9y0sLGYmbPDZIiEQaH7X/g3KfQRLwQoFC4uZDSsYLJIChIEVCBYW+wasK8kH7Nq1S7X/trDYl4A9v337dtq9e3fQpFikCCsYfOiBjtbf3d3dVjhYpFVMODExQdm473t7e9W+37lzJ2WjMrd161YaHx+nfRHWleQxsLHGxsZoeHhYdTVsaGhIOCQjTMDhyM/Pp7KyMiouLqZsY05Y+x07dtDo6Kha+8LCQsqmYsKvntxB75lXroarZNP69/f3R5WhbLMYxsbGqKurSw21KS0tpYKCAtrXYAWDD4IBBwM90F9fu4Ve2DBIh8xro9bq0qygfWBgQGlPOBzl5eXU1NSkBEU2AIcb9DNjQtv1MAsGUzHhDY9soIVVbVRfX581ggF9/7H2JSUlislmk8UAWru6umhkZEQpcqA/mfkFMw3ZccKzGGCu0FwfWDVI1z+8aUoTXJUVbSVwKEB/TU2NYq7QAsFcs+GggG5YaRAEoBm+bgi4bCsmxOstw7tDTzsDFkJPTw/l5eUpQYbXYLY4A2EfrQsaYSGPjo5SdXW1Eg74/76I7PFpZClwKLaPEV3/8PqsaysBwQDgQLM5ze+FHWCkeBQVFanXsHLgUsq2YkK87qwtyRoGBYGMdWfrBuvOVnPYAbpxXsvLy6N7Hu9lA+1uwwoGjzUQHOito3uysq0Ea9wM/B/v4brCDhxoqaXikEOohTmQy8WELBzw/LV3dyq3I64nzLQzmJGyuzHbBIOkvWBKMOyLAWjrSvIQfCBm1ZaqQy6FA16Hua0EB82lYJAHJcy+egDWgYyFgHa4BkA73BxhBdyL+5WN05ptQzS/uZoaKwqj7piw0w5gz0iXERItQD9oR8wh22ifnJxUez7stLsNazF4CBwGPFqrS5TmJzXBqz+wINQFYxxfkBkZ+D+7CsIMMCIIBkk7GCo07rBrf1Akqgr20OGdVUooSOYa9nUHIHx1pQHMNhtohzWsZyDlTAWg9zVYi8FDMBPC5jpjcR0d1VlBGwfGqIJ20kH711OYwZkkUoPi/+OgwA8bVrBVgxRbHWEXDBwbQZpktjFXVhr0rDUw27DHd5h2XagVFhYqd3A2BM/dhLUYPAQYqKxZgAZ4aHsFNVUWhf6gmDQ/6ZIJM9gfr7tdsiEAzcxfZ0LZQruJuYL2sMdIWJnQLYb8KdqzIUbiJqxg8AjQMHR3BgPvQQsJayU0DgKEmhPt+FmYD4ruK5aHHJZQWNcdwJ4xxRGygblyara+9uyCDPOe0ZMVJO2gO+zWmtuwgsEj8GYyFYNBo2Lmm03aUzbQjsMNi8ZJqHHcJ4yAwILCkI20mwL+DAg6nIcw026KLwAQFNkS35kxguHxxx+nM844Q5X74wbcfffd0w75lVdeSS0tLSor4NRTT6W33nqLsgF8iE2bjQOhYd1spvgCg98LK+0skE3rDqYVZgYVTyCHnbnGE2q8b8JKO9bVyUKWlua+hEAFAzS7gw46iL773e8af37DDTfQLbfcQnfccQc9++yzKpj43ve+NytukpNZLQ96WK8j3gHngxLWgqt4zJURVgbF7hanliNhZq6J0pjDvN+dYiPZ4vqdcVlJp512mnqYAKZ600030Te+8Q0688wz1Xs/+clPVK8eWBbnnnsuZVPgWQcXi4WtqR7nnMfLl+csE1OAN6zBWz2IizYf2RIb0QVybW0thVWoOe0H3jNhzO7h+ILTOSyYoh3XyJX0Mx3h4Uga1qxZo5pZwX3EqKqqoqOOOoqefvppCjucAs9h9xmD2TPD7x7aRS9uGFLPulALI+0AGGe8Jn98yMOo/TllgjFwXRAeYaQ9kVBjV1gYA9BOAX/dBRlG2ve5OgYIBQAWggRe88+cNqgMjKLTo9/gVsOJNhsYMH4vTFoIC4YHVg3QDY9sjLZ/RoEeajEAaFZMexjbbMcTDOhb9Xb3IOVXDNGsxioKC3jPxLMe2aURRs01kVCTtIepjXW87EEdYdvv+6TFkC5WrFihLAt+dHR0+E4DNlAybhZsyrBtNtANC4GFAoBnNAGUlgO0wzDSDgbrxFx//2ovffR/36CvPbCZTv7OE2r2QRgttWSUiTAhGaHG2T1ho53XPRl37ngILeR9TjA0NzerZ7TBlcBr/pkJy5cvVz34+bFhwwbyG3x4EwmGMDJX0LNpaNzY/hlV22GmPR5zhVALc4dbpj2ZWRdhXfdEtId5z4B2J9epdOPtKwitK2nOnDlKADz00EN08MEHR91CyE664IILHP8OJnbQZjY2WrxgVpg3G2hvqygwNv1rr9q7rmC+YaTdSSBv6B9z7HAbhp5ViaydMLfG0ONSWOuO6qJor6cwp32ydX//G/1RxSFXc53KsxrG4PmMEwzIylm9enVMwPmll15SWRednZ10ySWX0Le+9S2aP3++EhRXXHGFqnk466yzKMzAZkumNXUYU/jAdHCgcTD0gyIPehgPCgtkEz1gVLqwy8vJCU2H22Q16bAyVwi1+17fHrNnLji2lRY2lkaFBNMetj2zbXg8OkRLuk6P6qyI7nmcVbiSkrXqsh2BXuELL7xAJ598cvT1ZZddpp7PP/98uvPOO+mrX/2qCmp99rOfVdPDjj/+eHrggQdCP+Iwkb9VL3QLU9onV2ufsbgq2vQPloKu/eH6WNsKy0GJ1y7CJOyuXbYkFNYCkGyrC+wT3KNkrIugmet3n9ys/s+KxXvnV0T7DoUlAA3aNw/vdnSdNk7te05zjldnMpMQ6BWedNJJcTVraBXXXHONemQToEknw+hZ604mHuEHcC+gFTHDwaHQBYJOe5gEQyKBzB1u39k2RC0V+XTkkjYKC8AwU90zYZmJ4cRcGayBH9GxiIr37A6VYMC6Q/FJ5DrNnWp9HrYYiVcIh8oxg5BMyqRuMYRls7FLIBkGFTbak2WuEHSYdVBbHLF4slEwhK0hHWjvqC6eNpZUAkx382CkG0CYaMdZba4smjYv5Wua65QRJtq9RDhUvRkEMNZkmWvYNhsz+lRcdWGh3cRcnXzZXGwVlk6luqUWD7ieMDNXmfklEdXAQ9Tvidcd+0HOS2k3uE7DGvj3ClYwuAyukGQzHwF27iuEjYVNiHYMkmGFhUFxZgwzV/hUESwEw8ID2qo+4jAstINumY+OVGWuaAXdyFTj4TdhY65sqbF7BXSz1Qn6sZekwAtT2qesYQBzXVKfS+90D9Pb23fRT/42OC15YXBwZ2iC5/q6VxVOUlHFHtqze4T6+obVNaEWis9qGAP/XsEKBpehB5MRPJdxFO6qyim1YcpM0pn80NDQtPYLOBx8kMKUssoWANYV681ryveDu2cy7WFirrqlpq87aK2vr4/uqTAxKF5fVoRKaRctbihQj2Pbi6lreDctbKuj9rrIxL8wZSbp6450eP0MlJSUxJxVWBhhCvx7hZl9dQGmqnInTP5/XV1ddINJU5oHsIQB8lCw9QDaMcaTYyY67WERDFIg8z3A4YV1xsJA0s7ZPWGz1OS6gynJgfQMyVzDVCAm9z72e1tNKS1pLKLqounV22GwNPV1Z5oqKyujgk7f7/tKzyQrGFwGNhdrQrypWFM1CYYw+bvBfFgTYkYEuiEYWKuSDEqmrIaptQEfXHYh8bpL2sNqqTGNoB1uDHbd6XsmLMyV3TFYd6f9rgu1sMyVkPVGTA/oKy0tNe53KxgsXGGuvNlY+3DSusOy2WSarTzk8lmnPSyZSSbmqtMu6QyTQJY0JLvuYdkzXFSYLO1hasAolTimJx7tOSGLTXkJKxhchkxVNR0UbibGzCAsaZ8yQ0PXoKRwY/M7jMzVZKnJZ9ZuwybUpDKhMyjp0mDaOac+DFq3ad15z/B+570lETZFyKRM5BhoD1NsyktYweAi+LDqxTAy4MmHRvdvB73ZpL9VHgZmTCbadW0rDKmqUqPjdeeMKv69sAk1ZlBy3SXturBmhG3d9T0jr2Nz/2i0QV1YYlNSMOh7xon2MCVceAmbleRRhob0FcsMBh7Qox/qoBkU0w7fqmSeMk0StLN/WNY6BE27ZFDMnJxo53UPi1tAWmqcrCAFGdOuqov7R6lPFZMVUVFubiiC5/q6S0EG4CygQd3tL26hyanU1UuPb6IzFgfb1oatdlYQOOAv170wpLT7ASsYPMrQQJoqoJf+O2XIBB0IZaYJWpBHb6IdB4VHHDLACILWoGSBmO7O0Ce3SWYaBrcAW2osuJzW/d7X+uj2F7YQvPlgUJcc10Qf1GpKgrSQnWjvHyO6/YUBRbf6m0mi7zyxlQ5rK6X2APsOyfRmaaXJFNp+B9oPby+jjhD1N/MC1pXkcYaG3s/GKZgYtPZnCt7Go50DjmGhnd1gqax7GFJWZZqt7ueWU+d0BnXTk1vVLIkgrbVkaO8a2ROlmwT9G7ZH+j2FoUW7kzLRNWqmfX3fzsAVCq9hBYOL4ENq8hUz2LUkfycM/u54GRom2vl3WAgGOYdYMijTIYdv+O9dO6l3x54Y2sNiqem0y3UH7Y+vGXJgrsEyKL2GwbRnOmuKSS9jg8XTXBbJTgrDzBRH2qvDRzuUgafe7vF8wJR1JbkIPtiywMpkKrOWzV0mOaAVZJdV6RIw+Vt12nl2bxi6fTJz3TYyTi9vGaWW8nzKKSPa2D1Eb3SP0u1PbVaMFIf8gsOr6ENV49F2E0G3PWcGJTPVmEFhFGm83kNNpcEzV9DdPbyLXt48ota9sTGWuaINxsXH1tMtT/fEtMeoD1gwSIHqpMQ1Ktob6Jant02jPQiBjFG0y1e+HKVlxbKldM4RnZ58lxUMHgbinPyn2ICb+3fQqv4BWtiWRw3lkeBikAdFp133tzI4sI4HCoE4nhI0g3rwrSG67dm1USaaQ93TtGy8vuPFATp6dhXNKymJ9tgPWjAAerKCPopUghlUXWmwMRJ894NvDUbXHbvl8lOKYyafAWcsrqcDanOpZ2cOHdBRrxju9u3bQyEYZHcC03k9Y3EdHVCbQz07iQ7oaFC09/X1+U47LAQWCnI07bv2b/BkpogVDB4wVycfPeNPb4/Qfz4eYVy5OVvUIT++Ndg20DrtTv3ydV99GLJ7Nm8fpVuf6Y0RBE7NItQAlv6dNK91rwsvSGuHM5J0d4ZpFCnw6UMq6YMHd4SCuSJL6tZn+qJrjWd98hlfU31pHjWU5SglCIDwCzK+wwOpnNxI3VMjSlsr8xXtGPTHtEsFyi+s6RnxdTStFQwedPfkw2rSQLDhbvxzJLtEDjFZcFYbtQZ0yOW8YaeDwtAL3TgVN0gG9c62YUdBYNK264tjW3KHUSCbRpHi9dFtxVRXmheKjDB0UTXFPuTkM2kF8RnB66BrGeIpQr8XLjw1ovTwajplTkmM69dvoTanvszX0bQ2+OzBHAbZq0eHSRPE662jwVWyStrjCTXZOhyQNQFBan/N5blxh8Qw+JBDA5TCIAwuvK1DY/Ry9xj17ZyMGUUqh8d84ciaGNqDzghrKpu+7vrkMwbvJz1pIYhGgCygZLoy06e78PZMIiOsn3pG91bJs2Dwk3ZYBYgp5In94OVoWmsxeFDcxhvG5Ld20gTbq4sC06BkAzrZ7sIJehO3oBlUdWGk8Ag55uzrBjjnH0PpFzWVKoaVPz4cE/gPUutmq+uBVYP0n493TdHbpwQCfNv68JiC3SPRcap6RpjfbaCxx2uKsO7N9J0nuqbNXdChW5bsjglizCcrQlw4KAWDk+LWNbybZot1D2LeOQLNx8ypoRfeWEtL57TQ/LZ6z77LCgaXwIyShQI2DzQS9lVCIPAMZX0oPV43V0YqjoPoU+9EuxNkVhXAWVVB0I4Dju993/5VtKBqUh3gA+e0qJ+ZpnENDEQEXhiEGmiApWByLbKfXs7dHhoai2aAMe0sKPwWDCzU3r+wmhZU7VHrvnROMzVVTLcWmFZA7hkO/PstGKQSJ3tnxVPcmssjvbWCnnfeVFFIB7WUUmOleZ3dghUMLoE3jdS4dV+l1ASXNuTR6q4Bml1XRvPa6qIpoEFsNk5PlYc2HkyHPCjtjw+5GmZTmqcOTsMUIzVprk60B6F14ztR6GXSUHU/vRPtHDwPat2xZnvX3ZlZ8Z6SFoNM0Q3CYjApQibF7dLjm6m+NMfY+JJbi/sFv7IXrWBwCSwQ+Llvxx66/uGNMb5KqQlCsyreU0SFhbkxxVbYbH4LBl2oJWKQ+iGXaZ9BMShes0RCjX8uBXhQdRigu6U8z+xaNPjp9XXXiw3DvO66UGMERTsLB8QOtu2YICrZFRXEZ2guvJriHOrt7Y2hFesehFCzgiHLwDeLn9EKIJ4maNL+gqplYBqkRhQPTLtkULKbrJ+QbcBTEQzSHRNUHQa+s74sny4+rpFufrI7oZ9eCgZ22wXFoHjNpdadzJ5hTZ019KBox/ff93of3fhnThvvjlr0gHTh8TWyMGELI0hrx2tYweBydglvls7a4riaoDwoQad9csV1oowkBn5XMiVdk/UTzCRTdYPJdQ9SqIH29+xXTouqiQYmCmh+c7VRKJjWndNAg8hm0xWhROsOuuVaB5X2yTT3jOxWyQpOsR1TJp50nwW57n4IBpuu6hLkHAYAriI93VBqgqa0T/3/fgu1ZF1JCKi/2jMek8IHZAuDMqXbBrHu+E6ue4Gf/tD26UxJh8nSDCKrKtV1DxPt+P7Nw7sdLXoT9D0TFO1+nTFrMbjcUx8bBwxz3c4dSvtY+cnFxuwYk/soCA1KtqxORuuWAXXIvMtOyKEPHdIaWMESM1eZH58I+rpDMAQh1PSq52Rp5yQFmVXld0aYaQ5DIphiJLIjsV8AzR1TDfKkbHCK7TgFz4NYd7/2qbUYXAC7JbBB/rh6mD53bzddes9aWnbnq/Ts+iFHTdC02fxmrtLnm6iGQS/+wRPMcTnZyu+CJd2Fl4rmqqcf+g2d9mSSDsISm+JxpIlol3tCp53HqwZBO7KovnBEtaNFn8y6B5FV5ZfiaC0GF8Cbu3d0YlrffNXuojISZORuq2g+JzuvSo3Rbw1K+k2lL9gEp+IfWESLGwqnZar4pUGx3z1ZwWByC/it/UmmEi+Ai0y10dFRqqioiPrlASkI/c5mk1ZmPGUCmWp44HdKSkqMGWG8Z/zKZpPBerS5OKipkMYKKpQFIYXC5OSkWldMKpTuR0k7fu7nfvcrvgBYi8EF8ObeMDBm7B3TvWOS2tvbqampKWYGgCnt028NSi9u0w+4nNjGxT9kML+5IZmftDNzlYfVibFLS8bEXDkv3e91d6Id7w8MDKi1Lysri+m+GrTFwHTHK4gE3djnlZWV01yUUhEKYt0l7Y3lBXRYR2VMBtLIyIhqUMjXYKJd1pDMtIwkwAoGF8CMtb2y0DjYY1ZtKVVVVVFdXZ3S/PRDHiSDkoeEaZA/w8HAQRkaGlLdJfWA+ucPq1KHKoiDIovbdNrZjQHmikOOBx9yJ5dGEAzKifb+/n5lWXZ0dCjBoLdy15lEkMxVpx3vY7/U19cr2vV+RJJ2v9M+9XXTacc+yc/PV+uOM6ufVf3vg1Di/LBqrSvJBfBGqS3JVYNg0POfc9IvOqaBOhsqozcTlZK6+S1TJ4PQ/uSzPCg4FKC3oaFB9aDHoUELBC7+KRoforqSiPsLB8fvQ64fFOmKwXpCoEEYg8EODg4qlwxcA3yNPCBHdv4MYhi9TjunczY3N6v1lw3bZOqkLMoLgnZ2/+guMAhjKEBYe1icYaI9XmEer3tra2u0ohmFbU60B73fQyUYcJM3bNhAjY2N6pBZxDLXU+eW0vHzaqlvV160aRv8qwxsKA5c4UCFYbM51QHgPdAOzQma36ZNm5TW3VhRpqyEnp5IxbA8aEFYDKZDDmaK/QmhxhYBhIPTIfe7glgXyNJPDesAr5nx6v53Uzabn8Fz3jOm+AK/B2uBr8G038NIu2ndc6eUBhPtfs+U8EsopOVKwqLOmzdPCQeL2AAobxjUMCATCa4XvC8PNpgQT0EDTMVhfqZOcuDZVMPA/m0AB6a8vDzmIASdVcUHxYm5Quvj90zMVdIuU179ol0GQnUGxQkKAI8h1V0yejM9P2mXzzrt2N+scTOj1V1hQe0Z3ucm2rGeUCZyp9Yd1yFpD3q/+zlXPWXBgEWbP39+1MSy2JuPrm82nosse/DgZ9h88Q6Kn1pIonRPyVDZQnRKPwz6kDsJNYA1VylEUG/ywobBaLotxyD8ANZMCjVJO96T1jhox8NJMMg20H6Av8dJmcB+Zxp5KI9TAJrviV9ML57FwBYyA3RDwOlnVW/A6FeKtp+KS1rB5+uuu47+9V//lV555RX3KcpCYHP07dxDL20ZVcxGbn55SBjQBp0Oit9toPFdJq2b4wZSqOGQSGsn6AwZnbnK2AHel7Qzc+VD/qe3I/UmX3tgs6o3Qf2Jn8NXnJQJ/n5JOxivFAxBZ7PpNQw6c8X+lsDreFq3n0kLbCGbXKdYe92yLDWcVUm7n+1UeM/4gbSCz5/4xCdUIO+ggw5SG1hKWQCByn0F2BioBuZhJXIgOg4Q0vV0vyAfemxEk0uDD7nXm0BvwCZrGHR/K4DX0GRx7yEkTAwKwUbuveSXUNOzY5wsNdCMgPTgeO60Pjm4f4e1lVCnT3npTvUXHADVGRTO2PDwcELm6keH2HjFbTgPeitqvA5LkRsrNfq68wRDfd2LEtDuZ1dhXZnwEmmdgJtuusl9SrIUG/tGokJBH4iebzgkuvYab7N5zVzjpR2CNgSd9YwTxBmQdSJ/n81zyaD86FMvLSuZS8/uDBNzBe0bhmObGwJ4vWH7TjrUh0IxLhAz1QGYBDKgD5XRmxj6FTzn72F6pTLBiQA67ZJeU0aYn4F/+T36upsEcuFUsohUdiTtfqU5y6JCP5DWCTj//PPdpyRL8c62ISOTWb99J82rjLgAdLA2K32avNn81P7ipXtyIE4HGD4Hq03dPv065JwyqWtzLDCqq6uNlhp+32lKV1OpPwFo3aeuB81ramqm0S6ZK2dVcVyB74Nf6y5z+XVlwsRcOb4ju6oGkbKqC2S57kyLKcZWIJS47WOTtHFgFy0q3EHttWW+ZRHynvGryjo/E0Lvvvtuev3119XrxYsX0wc/+EHffGBhQWeNuRlXcxmEQp6RuWMzwXfZ09OjnoNKneQDzvdMakRg/ibaIRg4t55rAmTKql8N6XTmKoUa6Hey1EBjVWmecbxqfZk/zDUe7XjfJJCZdl5n0wxlP2JT8ZQJDvjrPEAGoKVgYNr92jNsHZtakOD7oUzoyM3NVWcUlub/vTNK1z0UO5v7hLY832iXZ9VrpGWXrF69mhYtWqRiDStXrlSPf/qnf1LC4e2333aNOGycK664gubMmaM06/3224+++c1v+t6oLR4aygrowqPrpjXjwtQnU+CZgcOva13yoPjFoEwZGqwdmQQDfgcHxSndFn/nR3aPbjFIH70T7VL7Qwzof85qo2tOqqWfnbufeu1Xj30n2vm1ycpk5hoviOvHujND3zaym17uHqO+nZNxA88A1hX7PV4Q149sNj2WoddfOFnoJSUl1DWwUykSeh80XL+ftIc6+HzRRRcpJv3MM89QbW2teg/pqxAO+Nl9993nCnHXX3893X777fTjH/9YCZ0XXniBPvWpTynfN74nDMDNes+8cjqstYTW9Y3S/i01qtIZVcJwCTiBzVZ5s4PQ/ky59E7ZVAwcfk5XNgkG7qjpZTGO0yE3BZ7l7+B9xHCA5qpiqizYQ+Ule+sF/GKuUnNNhnYwV1hBCPwzrU4ZYV4yD3z+A6sG6dZnewnkRzTnyDhMwCkIC+aKNh9MK38WX4sfTQz1mBqvIX+vE+2FhYWO8xueXDdCBfn51NrqD+1YJ5wvr91XaQmGxx57LEYoACh/Rxrrcccd5xpxTz31FJ155pl0+umnq9ezZ8+mn//85/Tcc89RWMCBOLTDqGgsoprKouhmixcjYAbAqa6ru8dozp4Cmlde7qv2Z8qld8qmYnBGkqnimYUauw28pN2US8/xBacgHRgU+vgwrSba/WBQJpdGIoEMrZsD//w3MiDtR9LC5u2jdOszvdM058PaSql8SvCagL2gC0K9uFDuJy+g9zmSGXim2AgD78+uK50WlwJuf3Ybfe+5bfSt0SL6x6Nnk1fgvlrIqsMe8NpKScuVBMbAh0sC6XRuBkyPPfZYeuihh2jVqlXq9d/+9jd64okn6LTTTnP8GywYWh/Ih9/56MxY4m1y/B4e977WRx//1Vq66tE+Ov8361Xqq1/1AHKCGNMUz0dvqt42MSg/8tJ1wSCZjZ4+LeHUq4pp9yPLRBfITAfWE9aYk1DSU4f5s/gz/Fj3NT0jxg7C63pH4jJXrt6WfnK/6zB0C1nPpnI6r/n5+dRcWUyXHt88rbswX/83fvcqbRnY4Snt6rsSzEwJVDB84AMfoM9+9rP07LPPRjUfWBCf//znVQDaLVx++eV07rnn0sKFC9WGO+SQQ+iSSy6h8847z/FvVqxYoVxN/ECXRC8BQaT3peeNlujmqXz6J7dOS3WF39KPQ26qA4jn59YLrmSWhKzC9aPoB59vcoPFcwkAMvhpshj8WHeuYTAJqHgCma9Lr39hRcSPpIWWCiQYxL4HZtlYmqsEspOlxsFzWaTlt1Dj9TbF1EC7k0DOyclR9wUu49+cv4g+eVDF9M+eJFrbE3HzuQ0InKff7lXzXkItGG655RYVYzjmmGOUeYsHXEjooeRmjcOvfvUr+ulPf0o/+9nP6C9/+YuKNfzHf/yHenbC8uXLlanFDy97OukdObGBWBPEjUtkFm8ZxqHWPnOSaMvQuOctDsBEWDDInO4tAzvplW271LD0eMBBMeV2+xU8l7RzLj2ve7xDY6ohYb+zX0INyoSpDiCRlck1Amzp8Wf4mbRQXThJFx/TMC3ZorY4J66lxrTL9Fqddj8sZLlHpcWQqCFo0dR+h+VwXGepsb3+7PrpgfdM8cvn19Nx1z1MX/jNm3T+yg2qQt8PwZCWQw8+3N/97ncqO4nTVZGlBMHgJtB2g60GYOnSpbRu3TplFTjVUnA6pR/QmbfUhLgbYzzMaSg3prpimtTk+LCnh9wUvJXznHMf3Ewrli2lc47oNP49XEnsEtHTbf3IMtGFGl9TIoHMP2etXQoDMC6vmSvn0kt6kqVdrwfg65AWqpdJC0z7+/avpANqc2jryB5aOrtJddpFskWimBKYrwxA4zq29O+graOTVJmzm1p8sNQYiVphxIuRNFcWTWuv//X3zKGWKmfBmK6lsHzly3s9CpOkvnPeic7WTaCC4ZprrqGvfOUrShBIYYDg17//+7/TlVde6QpxyMDQTVP2U4YBepGOZFDxtCcGCmSQ6nrbs73RDXbZu1qmDpq3DGpT3wj9ZeMwtVcXEijt27GHrn94Y3QT4vnrK1+hd+3fYNzwkoHpMRGvs3ucMpKSFchgUJyZxBkx3NbA61bKnF3iJNTi0Y6/wb5C3Ey6bfxKWZW015fmUUtVMdVVRCqy5ajaRFo3gN9HXcAdL26JtJLJIVp+Sh599tS9CS1eWmoyNpaMdZ8/VaPDf4v2+ifMr6PesVyqzt9Nc5un10C4Ec8xZUJt8y6UkZkr6eqrr472bdEZOX7mFs444wz69re/rdJf165dS3fddRfdeOONdPbZZ1MYwH5uUwA0GasFv3/6ohr61ccX0op/aKI7Tm+k982vjP7MK60b5unJ33mCvvbgZhX4xgGFW0vfhBOTk45+Uy6ykv5uuQ5eusJYIPPhlgyStf54wL0xBSD5ury0dnRfuvz+ZIQaBIKkVdLuddKC/tkymyqRUGN6+d5sHyM1H11qw9c9tM6zAK4btOdpmXg1RTmqvT4sCAhkt+ur5tSXGUfptlQUhFMwOKXzIWtIprBmiltvvZU+/OEP0xe+8AXlqoKV8rnPfU4VuYUBTnUAyZau82bDFDRsMGhhXjMo3TzdM2WelhblTduEeTk5jn5T1rI29+9QhU7oKov4xIsbhlT6rZfBxHguvGQEsrw3JuYKl4NXVqle9Sz3TDJCTbfU9D5bXgpk/mx9HGky1o4++KYL8TXt59iLa7ZNVzi9UCZSpT1fVG+bgudeTACEpQ53rj5Kt6HM+7YYKX0D93DBY//994/ZxFgUWBHITHILGA+IYHZYm/Y5pXuy3z0RmLnKzWbqse9moMnJPB3fkxPTJgJC4dplSxz9pqD7wbeGYhoIcrwEG/hLR9XRZ9sjw3K8Zq7SLZBM7QRbOzJ1UmYmQfvzqleVTDuEMH1neIzmUREVT04k9X0yq0pPFZbZPV7R7qQIcQ+teJDV2+hXZYqvYeqhF9AVlVRpzzOcVV2J86IOAzG+w9rK6PnX11B7dTGV5URqhLxGSlcBBo2N8elPf1q5jJAOysBGRAEaMpX2FWzoHaJV3WPUUDxJ1YURBpVMDYME1g2MiBmoXrDkdgtrNk/1BnIIeB9dW0YLq/bQZHk9LWirjRtM2zo0FiMUAFn0hLjJ6YeO0ILycnIbrLVGmevIDprXWEAQCcmsldT+4tVheMFc+VA/sGqAbn2mLypILzy6ns4/oTHh35uyqmRmj/wOt8Hfo7vh8H1Q4hIB94azqlqqS6YFcLEGmHroJe1y/jTTjmLOZFBUFHEZ6Y3/ZPKFF4oQLIQDmyNnEU4EPzqspiQYOBMIvYtQfOZHD/KwAn765Stfi85gwCY/98j6pH2WeqaGU069V+Ypu5PYPEV7CHwXnmfPrk+4wU2WxzS3QM8wLehsIrfBGUV/eHOAvvvc9hgr5XOzk3fh4Xr5kPO6e10PgMA26lRYKPBa3fpMD51+6BxK5Ill5qorExwABrxyJXENAmf3SKGajBDlegDQjn2PAO7BzUU0VlChFJOC3SOeusHY2oEysW7HTppTn0cFSdIuY1MyRdtUD+M2eL1NE/O8Qlp2z4knnhj9P26ynsWRrATOVkxLI6OIn/4flk5QVcFkUsVtDC4q87NP/UcP76BZhaOqfXBl7piKbXCwO5kMDSfLQwI/a3A3ey8K0In2xywUpJWy7Bh0+EzMoMCY4PrkwiZ5yL1MWcVZcfKv437sn0Q9Jqqj0XlAptuyawkPr4LnPAaWM5/kHk/WQsa6Y5AX12Fg79XXl6q/7+vzLkWbhdof3uyn/3p+QCgTtfQvSRbB5k+1lZcp2lIweCXUeN11gewl0voGZB996UtfosbGRtVmF7EH+ZjpcPLTbxrYFfVbJysY+EDJuITUXr04KPjMutI8Ori1VB1Mjhtx0DwZ2mF5XPX++dHAGJ444oT30D4A7jW3g7hcmLd5cLcDc02OKcrUSZO15gVzxVrgcLdW5hsLpOY0JJBoU+DW7IBp0JMXtMuCSNNgp1T2+7bhcZWk0D+lT+pxNS8AYQZLgYXCXmWij3pGk/vOfMPAIT9Shbn1jF9Vz2lbDCg8e+SRR1Tn049//OP03e9+lzZt2kTf+973VCO9mQ4nPz0CZ7t37zS2Hk7GrYHnrUO76J2RQZrXWEnFHjEovf5CMkYI+lQsjznFO6l/opCqcsdobNcuGtxTRPOaKlXbcdDutq+etTRmrnrwEkWDyUC6QU1xBq/WHY/G8sJp/vWLj2uktprk1l5m9zgFoN1OWuCAvz51LtmsHsbdf++mK+/dpNJT2QW77GBvUrRh2UOJm1VbogTy5sFxszLRP0Zzmynp2NSEwUrwalY7W4OmSvnQCYbf//739JOf/IROOukk1Qb7hBNOUIVus2bNUi0s4vUymgmAtnzlafPpmj+8FT3YMEkjhWkjKTFCme3wf++M0M1P9UTN3EuPb6IzFscv1XczqyfZdM+Y5mJVxTSrqIhQLzY6OkGzyoqooiKi0aITpFeCwcRcv3Jia9LMVbZc5kPtdUYY0w7GDv/6kR3lNJxTQg3FRPVlybsfOQDNsyf4s/m6YNG7nbSQKN0zGVcSGPVV974RbQPDLthj59bQ3NJSV1u2R2KAe+Nonz68TgVxTcrE3MbkXN/5ImnB1FWYkxbczEziPaOnCHuNtK4APsK5c+dG4wl4DRx//PF0wQUX0L6AM5c20OyiUdq2k6gqb5w66vZqqqkE5XGjcdi6BnfSLU9HhMLeAfVb6fD2MupwmUHxZtZNU2aSqVo7poPCrim3XQOyPTOYK2ZrD1GxugfzWutSol1qf3AzvNk/TIvaiqiutCAq1NxedxYMABSJ/asrFCNPReuW2T1Me0/fMB3QUaw+04sMGd2fnmphXjwX7IbtO2huc41r7bdNtTo/fD4yP0RPq77shBbVgSAZ5E41j+TGmSYr0yvBwJ8Z6pnPEApr1qyhzs5O1fkUze6OPPJIZUmYxuPNRMA0baosoqZKtALZmw8v/Y/JgAOh69fvrQKNmR3dt4MOER0pvUg7REHaW4ODVJUzQXNSpJ0H38iGdPLnbgsGWQcA4B7Mr6xQvXpStXZY+0M9xn8+3j3FLLpUPcfxrbmeMFf5LLXuZFqo6K0xoJA9tHYH/edjTPvWKO1uT6LT112nPRkN38kF21we+SzOtpKpuOkgUcYc8M33zaY5lURzmlLjV0VFRUppYHexPijJC0VIt+79mGCZlviB+whVzgCa3CHGAOZ26aWXqvjDvgDO4NGrH1PR/Bhgrs2lucbydwyod/uQc5YDaEc7DLTFuOiut+lTd22ku/62NaXP4iCubjF4FcTlz9eZq/7/RGD3Byy1G/+8ZdrgGXSX9UKomeYwpOrCA/D7WwfH6MbHp9OO9sxu+7vjCYZkaXeq5EVrCfV6qlg003U3tZKQwHrVlOarjrCprnthYaQ3lCyQk4zaK0XItN+9RFpiGQKAceqpp9Ibb7xBL774ooozHHjggTTTwRkaLAwA2fY5VW0HmhJ8zF89uZ1ueCTSyI7bGTeUuy8YWKjB5Ea/GslYrvz963TK4pakO0XqB0Wm23oRxOUMDb3IKlVLDVCWWl+kgeG01ufDu2mxR+su3W3puPCA3h0T9Of1o0bau3dM0qypJoFu146Yekyl4jpFJe+hraX0/OtraVFHHU2ObI+pw3DDymQBhCaQ6PelgxNFJnePpLzu+VMpq5warHfm9cpSk4IhdJXPTkDQGY99BRy85f7yAOcZp2Mx8O9/cEkD7Vc2ruYxHNDZSK3VkTGU3AnUDfBGxqbe2L/LmKWBxnnJCgZO4XNqv+12QA6CgdsqADI7Jx1LraU8z1wJXlPsevohCwYWlulaOzKwqoOr2Lkdt1saJitCeopvsu1fJODTH28tpcqyAurdOX3PuLHuEEDoDIy9/OdX19EdT28xdDBOTzDkinYqnDXEbdDd3jOsCMnECD+Qn8pwnmRx0UUX0UwGMzuYoTKnGwcemzvVm8ebTVUeVxappnrVxRFtkjebTE10I5gFOpvKcqdlacRrnBcveM4HRXaxdDsgx3UAen8qDs6lyqDwN7DUZI8ottQwkMWtDBmmnRm1dMeYagJSCaxKMO2tNaXKDy6npXnhOtVrKZKFHDhk2jNQhNxYdyg3uI9NOQP0rlml9FbXgNrzCzvro+3D09kz+SJpgQUwgNdg5G6dVakIsWswdK6k73znOzGvt23bprIpONiMtg4IyKDobaYLBlO6J5vZ6QQr4202HCAcFLznRiBUdshEcdsXjqiKtj8GY4nXOM+JdtYkTUU/bgbkpFDTBXK6Ljzcu9MXVdGiGlhKw7RfUyXNaapR681CzY3WLzIjSdKeah2AU2D1UwdV0CkL6lR2D3+fm+vOjFTWMaRKu0kRMtUDyOFDrqU3VxRS4UTkPvJ3x5vz7AS+XqdUYTdrd3gwkiku5TWSXhVkITEwavO//uu/6L//+79pwYIF6r0333yTPvOZz6i22DMdTgFQ2XsnFbDW7XRQpIXi1kHhA/7e+ZX0D0va6fWNPXTYglm0f3tDSp9n6lOvMyS3GJTeO4oFMldsp2Op8frCUivPHaeS4shn4H1OWXVDMLCA5M/S6wCSPfBOmT3HdJRQjdgebmaEmVImmVmlY6mZstlk5TYUzkwzkxh6+isXiLEykSrtuVOxM3at6bSjzYpbgoEFshz96pcrKa1vueKKK9SsBBYKAP4Pq+Ib3/gGzXToGRqZBEDlQXFirvo4yExp14OI0KbQvbGtJvWZtfgsOcvXlLLqJu1SqMkAqNsCmbVjN2nH5+muo1TqAGRgFS4/Ern4sP50K9atwL909Uja043tmGaGy/3oZv2LU4EYxwjTYbRFWjM9fc+4TTvDL6EApCWSt2zZYrx4XMTWramlO2YjTP5WRrqmHjYbtA29pS9/pltBLVOnRj7g6W48zu1mi0buDTcDcnzAdYGcrobGQs3UStkLaweQw+j5+5AdlYo/nQOrr6ztpoKd22luSy319PRMW3e3kha4M6kukLmFSjqxAHapmRQhN5UJ2VXVDWUCYCVOCgaOibhpqekFkX65kYC0OMEpp5yiXEZ/+ctfou8hXRVVz0hfncnABmATWNdy0tWeANx8OflNasYcZ3CjsAVMUNLOAi4T2jllVRa5yQArB+QyBTMLXSCnk+7JAFOWlppcdzfrMOK5H9NxEcJyOHpubUwrDdCNugw0qNu+M6K5usGkeE3col2mfeppzvz5XisTmQiG/Km9AjpZKO6dXjjpKu1yXUJvMfzP//yPms1w+OGHR6UZNuB73/te+uEPf0gzGWzesaaZaXEbg7M82H/LB1FOvcrU3w0aweikFu8m7Xo7YlnL4IbfFcKRBSh/L5BOdgmDP0/mpct1d0swmARyKlPnTJCMCf9HBfcdL+5Ny7zomHr6fHt7xr563DtTABT3PF3aZUxIT3Pms+VGZhLiFaZ1ZxrSQZ7mIXhg1WDMul92wi76YktLxrQ7FRUCbmTKxUNaIqihoYHuv/9+Vdj261//Wj1ef/119R6ykmYyZF6+vGHpZmiYWvrq5jVP7MrUvGbaJXN1w5WkMyhJuwyeZwKsL6fu6SmTmQgGU8qoqTFaJsDns0A2aX+Z0M7rAQuBs8sAPKP31sa+EcoUukDONKbGtHudzSYVITfdvnlTtDutOyrp3Vh3Tjd2c78ni4xsE4zyRND5/e9/v5oBvS+AB8XrufRuMFepvXsR1JJFTzLriS2gdLUQvXW4V7TjAToxVvTl7jHV+iFTgWwSaqaMMLcyY+RnSxoyXfeukT3GYsU124Yyoh2frTNXfWBNurTzZ5rSPtlCdmvPmBQhV9Z91Lzu73Rntu5YX1g7sohWJkp47VZKe1DPP//zP6u6hcWLF9P69evV+xdeeOGMn8cgfcV6PjrM4HSZazwNipFp/xsOxAG6tZOJmydebrf87kzAjOL+N/rpn+/eQlc92kfn/uwtuufVnowtNSeh5pbmysJF1/4yZVDS/dVZW2wc/oNeW24wV1NcKpN1ZzejXHdZ3etGRhjvGV2ouaFM5E4x6c4a87q3VWXmNgXdJgs503hgskhr1yxfvlw10Xv00UdV8I6BwPMvf/lLmsnglrumQFwm/n9pMZgyNfDZEMiZ0i6zJmQ+ulu0m+YOuxHExSGHhYAKZdnbCU3k8L4bLg1e966pQGL30C7XrB0ZROR89EwZlExzRoUv5lPIBnWY51FZENvkLR3a5b10i/Z4aZ8MN9bdFNjOVOvOESnapnXHbBakD7slkCXtmSoTySKt6Mvdd9+tBMDRRx8doyHDenj77bdpJsOLIKI85GD+ptRJfDYPk0k3aMa+Yp32TH2WknanlNVMBQM+G2a7qWnc1tFIVa4bLjx0m+XGgjjkFx5dT+c1Zm6pASZlIlPminUfHBxU6475FIe2ltDO/HLVJK66KGJlZpK0oCtCMvPMDdqdanfcSHNmV4yuCLnBXIuLi1WKNp6x7oe0FNNYQYVa98KJ0Yyte/576QJj6zATz0SySEtkoh2GKciMhfKa4CDBbgVdimcaiGPghuvuGJmyis2SrnmNzzJpIIxMadcLlmQ7YvYZp0s7BxFn1ZQYW5PPrk9unGcirRsdS/Vus7c+00Pre4Y8qXtxg0Hpac5oJX1wa5kqWuSkhUyYlEzNxkAgxHZgSWUal0qUssp1GOmmOePv4ilxmWbIFWjrjv5mB7WUqnV3Q6ixQNabFmbq9vVUMCBN9b777ou+5s2BVNVjjjmGZiqkz9KNLpNORT+s2QB6rUS6h1yappJ2DiJmGsyStMsCKDeEGtPeVlumZiPHmu11SU/gSiSQNw/udug2G2mNkWlGksliyPSQy/2nu/Eyrd6WmWD3vb6dPndvN339T1tp2Z2v0v1vbHeFdqdstkyFmkzvNs2QcIP2Sa2JIK+7tO7TAY/FNWWCZeqZSBZp+SSuvfZaOu200+i1115TF3/zzTer/z/11FP02GOP0UwFZ5d4lUImXUQyM0MW1ECLqqqqSjubSqfdrWCWflCkC4MPP97jyVepAH/H6/6e/cppUTXR4J5CmtdURfnjw64J5I7qIuNM4IbiSNFSeXl5RpYaMznJSNxkrvq683vYM+lMVmSBPDJZQDc9GZkSJ1Nh33fQLGrKgHY98C9bQLACg+uRccxUaOezw7E5t9y+enq5LpBxT9FUlBWCdGmHFc6WB39OJsWcqSAtNRGznRF8BvFLly6lP/7xj8q19PTTT9Nhhx1GMxWmrB63AnFObikZZ8CGgyaRTjDRiXY3fMVMO2eT6PNw+efptmiQGi/WA4G9Q9rKldnupkDG5114dF2MRRIZlrS3mDGTuhe9LYN0RWRCu55VJdedkxbScckwg3KypDYNZuZHN6WsSi3bzT3Dnyc/OxPkxUkWYUGdCe1Ongm/KqDz0yEa7TDQSO8HP/gB7UvgoRmArtm4xVz1FD49AI1DDjpSbUXA/laddp5v4AZzZQYVL6sqnWpW2YVTT/d0QzBIgXz6ohpaUp9H/bvzaf+WGiUsQDcCvLW1tSkfSicrU//uTGjXBYNp3dNp2459hvV1sqRm12fmwpNpnybaoQihfxgKalNdd+x3XVDKz8iUueZNnVWn9HKmva6uLuX9zta9qZUHf7fXSHl1sNF++9vf0r4IbDbO6pE1DJkWtyXLXFlopOp35f5O7LPU09/YLM4EieowODskVdplEFH263EjH92kueKzYJEsbihUQgEAQ4VbIJ3MKm4nwYFVBHD/1rVDBXDdEmpOHWIz2TOynQT6MekpmWi30VaTmWCQvapM1g6YK+hOZ79zRhIg94ybykSusLjl9wDsBkpn3U2KENPuR6qq+r50/uiss85SKaty9vNMB08PM2l+6XTITHTIsRHARLb2jNDSgojbBMB3gEFVVFSk7M7g7Bs3axhMGpSTUGPBkIrmyn5uxCZkUBX0c098L7RuvQ4Dr3HQS0qSH2Kkz6jemwrbvbeX0X6Z0c459dgTOiPU6UgFLExYEZKpsE1leVRVsJchuhH453XVXUmckZZKnIFdMfgbXRFyS5nImcpmg2vXRDvvd+yZVPZ7PCWOla5QupKA+fPn0zXXXENPPvmkiimg9a7ETJzgxpttaCKf1vYOUmXOBLVWF7qeQsbtt//vnRG6/uFIwC83p0f5us9YXKc2DDZjfX19yrQjiPjOtiGqoL20u5G6x8DnyEMsh7yny6Ckv5U1dplL71bgnJm3U/ttrhdAEDcVBYC1vy39O6alwiKAu+zYcepMI7iq0yYZFGiX687uJLg1UhXIONvsr8cwo9raiqkGd+7065FBctk8kj8b1wDaU0m4MPnopSLkhmDgs4o9IV2RknYOfKdKOx5sSem0h9piwOQ2HBC02sZDAhcwEwUDNhhS9m57tlcdarCGi4+tp4/W1rrKXLHZ3t7cSzc8sjGGiaDi96jOCqou2pv6maymD9ofWDVAtz27Jko7tNVzamtdzXJg7Y83L9d98Nqkw6CkINGDt3idqgafSCBLBiWtH+kaSFYD5OvHWmzo32EM4G7s30md9clbf/Hanjutu+xwm+y9ln21OPCuB83d0Fz1eAtolLOquXAylTnKuEccyzIV5rnh9pXZbPge2VdL0s5TAFNZd10RkoIn1IJBjvlkX/tMLmwDNvWN0K3P9EYPN56h8Z20oInAnt26WdgQGwd3GSt8Nw6MUUNbudpsbOYng3XbBunWZ/uIk5nwhMKtkxe6S7ukR08/5GvDZpdaVSKYhhdJd49bOd1g9gMDA9HrYOHLBxrvDQ0NpeQaYEYB4YUh9KYA7pyG9IvznJirvu5s7aQyLnND75CKhRyQX0bFk9PXPZ20YxNYwLCw4WC9pB17APsmWSUAv6+71XRlwg1+lad15uX7zfsDz6mmrbIiJAPPXqx7IqQtNmE1LFmyRJk8eOD/M3kWw6ot/WaNbyBWqmcKfE5bRYGxwhfl9qzNJuuSweZ6c/NeoaDTLtNL3aCdwQdTpg2mGkzE78nparKVt9vFPqaKcN2dhN8B00knVRUVyXoAF+5BDNzxet05jTjZdf/Fc+vpA9/7a0wxGyBjR25ZyIliUyzokg384xrhvmPm7EVRoSlFW86lYbDwSSXVmQP+8rO8UIQ8sRiuvPJKuvHGG1U3Va50Rg0DgtHotIr4w0wCNltt4W7jEHZ0UcxJQQNOBGwK5M1/9eQOuuGRDdHhHxcc20ob+iOHo3jKd1lTU5Pw83CgGktyjLS3VhZQ3qQ7QURZbOXUCFBWbyej/bHrhgvLvEzdk5lZPDFPrxgGs+HUz2QOqF47ggDuifs30LadRBW0kxbPbfFE6+bvluCkhUTYMrCDvn7XyzHzBW57to+W1DdSTc3eOQxuuR+Z9njN9NhXn0yRHvYM7g/vGbd7miXbmZcBIQRLE6nOiawU3DPsOycL2a+qZyCtu3v77berGoaPfexj0fc++MEP0oEHHqiExUwTDLhZlfl7lIYHXz8z66+c2EZ1JSjCcS9TgANjpy0oU1kgb2zsoXf6d9PtT20WE6Ja6H37x2pZ8Q4K+rg40T425k4gTqZ9xmsdnsqQeqy7XFfdXwy4LdRkcaHOXNlnLDOAEq293h6kpbqEOvLzafv29JshJpMqbKId1g6SFuLt1TU9I0Y3ZtfwblrkwbrLrKqB8Vx6tXuMWiv2UG1tLO0QDMnsdzBhSZsXfcFM+91kqTHtycamsAb4PQ5We0l7IqS1M3Hx6JekAxlKmbbKDRvAKOB7xo0/Y3EFHdFRRq+s3UrN5fm0eE5DNB3R7c0GJthaXUL9/flq9kBMq+k/b6EldbnU1LQjYdoqB1RNtHNzNzdpZw1KmvIycMgaFALQ8b4XfwPa+XO8Sjs0uTSkf1oW5PGwdzCpRO0xVAbb0FA0ZVKfEudG00XTujtlhOF97ONEtM+pLzNal9gzXk0Qw55Y+dIWFbPj5IivnlxAZy5tiPHVJ2pLAr4kO/wCpspht2jPnZopIZm+npmUSmwK55HrLJxo92vuc1rf8vGPf1xZDTq+//3v03nnnUczCbih2Gzs+qgtzqUljUXUNNWOwe2iEznABP/f6jCZq2tkIuF8BhwUuSFhIYD2xvJI/yIuznFrs+Ezucd+z8huerVnXNViSGUB6whLAIclHkA3DoruK/Yi7VAyV5lVZdIAwWBBe6IWE5J+nXa3maucDyA/V3fj4cEBdic0VRQpi5RjIXg6Y34Z5eXtDd7yWrmF7WOTdPNTEaEA4OnfH92oigD5+pLx1bNmzq4YXSB7IdSKi4ujRYxObjzZrynZRAsT7fz/rAg+/8u//It6oGcS3EvYfJdddln0kSk2bdpE//RP/6Q0TDAVfM8LL7xAfgE3VFYiO/n93MzK4hbWALcjID2bpb5cMah4FhozJ1MFKL92uyEXDsq9r/WpoOUVD/eojpy/f7V3mvDo7e2NSztrT7orRtLupmDA98gCQCeXDFdBJ2JS3ILeKWXSTdoBmffuxKCQ0QLmE492MFbEQn79iYX0j4dGWuv/btUI/cvvutR9dHvdgU0D43ETO2TNQDyBjLPKax5PmXBT6y6cShWOt+4ybTXZRAuddi/2TDykxRVeeeUVOvTQQ9X/eTAPfJd44GeMTJnl9u3b6bjjjqOTTz6Z/vCHP6ieKW+99VZSQVc3gJsBE1aagHoA1M0sBwa+j7+nuSoyIeqOFyMDxzmbpb2uPKF5Dc3c6aDwtbhNe9+OPdPSer/zRBedMK8uWr0NBoV7C8HmdC/xM0mbU/thNw85WwPxWidzFgqEg1PqoHQjOaVMun3IZYtmWbeg/w4EAx5OlcT8d3kFxfSLv+7tqIpnxKgOatxP3Uc3aZ/TUGZM5UUWnl5n4pS2qq+5kyLk9rrnT7Vqkd9jUiYSnVXO1uNiYT9od10wPPLII+QHrr/+euro6KAf/ehH0ffmzJlDfgEaCDZiZWVl9D3TDXObuerph9Dgjuwop+GcEnVYZHsMaCKmzQYm4XRQvMxyQA2Gk/Yn6QZdfX19inadBl17crIY9Ip7twqW5PeYtDz8XrwqaAhk7BvOojGlTLpdqJQMg5KxBtBmshax50HXmv4xYxB6Q/9OpZS4aSGj59LFxzbQLU9viyo/GI/J+yWZtiRsHcvMJZMi5LYrKV9khDlZmdIV5iQYOFHBSYnjPRPqGINfuOeee1SQ+yMf+Yhq633IIYf42tEVN1ivuPQymMWQvdf5s2uKc+jQ9oqYwwJNxMmdxBkO8ZirF7Tv11BBOs/QtT8ZawCDdUo5lLR7mY/OkOsSj7kmapCmuzSc6i/cZK7MoGQqqZNgwLqbfN5YUx4tCxemqZYGfZK8WHd0tf3FefvTTR+cTXec3kjvnj2d+XPiglOCiGSsfD1eu33zRbKIqZZBP6umlvl4T8YXTLQzL7CCgYjeeecdFeRGb6YHH3yQLrjgAtVu48c//rHj34AhgtnIRybQNxEHI/UqXzdh6lPP2T36ITf5u7GJcN16hbEfggHa5GXHN8UUcn3+sCrVoVNfVwgHuJRkgJdp1w+ATrsXA0tM3W2x5nrKLU8XM/nqTS4Nk7XjhZXJwfN4tHOgGtaa/jPuIAsmBgUELku9IA+pz25bmczw8NlHzKpW3W1lFhoDawahpgtkWM1w1egWpB/rni+SFmTKs7628Tr04nrwfiIL2Y+RngzvRwFlAGwMWAyYGAfAYkAM44477qDzzz/f+DcrVqygq6++2hN6ZPqiV1kOeuokGAwzK73ilLVSaBtgskwHDklPT09MKqs8aF628MXnnbG4ng5rK6W+XXlUMjFMNUU5xmpZXBsEA+jnWANnLMlDrtPO1+6VQOa2BiwAZO8e+btMpxRQXOfALg0T7V4xKKm5mnr3MODOwLojAYBnt7PrUVbCv39hNc0p2UlbRyZo6exmJSzwd26vOwsrCFrZd0ifIcG1GNLNCLpxHXxmJFjh8HLdc6aSKdhKZCGh7xl+H7Tr8R22kOV5NQkGv4rbQm8xtLS00AEHHBDz3qJFi1R1tROWL1+uzEp+bNiwwTV65EbzImWSwZ+ZKEMGAGOCENi8eXPUNdPV1aUEhdxIOu1uDRcyARsfwgCuL6Q/OtEuYw18rbhnujWg0y4bl3nFXAGnIj1edwjgLVu2KEEAmsA0sfbSXSFdj9L6dHvdZZpzItpBB4QD9g0LA/x/27ZtMcwJ6w7t/eDWsmn+frch03rj0Y6fYY+ztQb6ISx0371U4uQ+8cIVUySyCPXsRQnsC+6dxIBAwbrrbiRdmXBj2t+MsRiQkfTmm2/GvLdq1SqaNWtW3JuU6qSqZKHHF7xmrrz54w0Xx8+gneKAcBYSNpCulZgOnVe0y0OeaDA6u5O4mA2HXs/2MQlIL2mX687anA58N9YdgowtDAg4XI8Mjprcd/Ja3KadYwdcTWyiXfat6u7uVvsGzxAKkk4/94ye9mnKqgIgALhQD20m8H/8rc7wdWWC4QVzLUgyaQG0QzBs3LhRKb2gC4oFflcmuMg946UykbUWA3ovPfPMM8qVtHr1avrZz36miui++MUvBkIP3zDWxL2cqCRz6uNtNgA0oIye6TBl6+ixETdbJ6cbxJUDTyAccMj1oLP8W73+wotDLoVaMusO4QAmBvpxuKVQQIHWixuGVJGfnnboFe2JGgHqjIqFgik7zOSK8Yp2WcQVb93xe3A74ndgnYF+U9qwKeDvhfsxlf2O7wftWFfUZ8HKh3CWQkH+rfxcr2jPSovhiCOOoLvuuku5h9B/CamqN910U2DV1SYG5dY8gHTTDyXi0WLS/ryyrJJp6iaBgw1Niie16XDyt3oh1CRzlFkmTvMAcGD1gw2gGIx7UwEfWTpO5x1eRFWFk74wV9m7J96cbTAqmWopYUrN9koRkp+ZzJ7hrs5O16YrQl5UbJsaAco940Qb9gs3YzQN8THR7tWeyUrBAHzgAx9Qj6DBg1v8yhSQ/nT+Pw5wKkM/Em02L2mXfYcS0c5Vx9C84R7QYVr3RL2K3Fh3OfhGD4TGAywFKRSAX7+8nX7zynaVsXX6olrPGJTcM307J2nT4BgtKhil9lpzzUc8TdS07m6MsE0meM57JlEnWydanCrlvWCu+VrSAitFJuuXEW+ugp8uvKx0JYUJvNGkxiT7sLsN3sTc/8ape2M62VRuN3GLl/aZLO1g9CahwFk9cMdgcAyYrtfWDh9yIJ1132AoDgOgzH/nia00OB6bb+/FnoHF8pl7ulQDxnN/uiqmLUkykBqvZK5erbuecJHJfpdpurpg8MLKzHORdqy5SYnza3IbwwqGJOFnEJG/h1Pc5CZJdWZyvECcV7RzthHTzlpTOrTjM/7vnVHVc+miu95WPZgefGvvnF2v171/F9HL3WNqZnOyMBWHxTZAjN+AL1PaQSssFn00LDelS3e/e9FChcFMm5krC6BM9rsUBF4KtZyp/c60Z7rfASkIpBXlF6xgSBKmAivAKwbF2h8HEzO1GPgzvQ6am1L4MF9IMdeB5JkrA39z+wsDMUwOg2O2jXjT3l12iIWW/U+/XKO07k+u3Ji01s3FYaZjDIEx14VxnvH2jMli0ZvSpbpnvLYyOQlB17pV5tRUEB/P8v+JaNeFmleCAZCKUCZnFUIdZ2VqJpeCbKfuF0IfYwgLnAJCXjNXTp2U4y1TGYzudyBOT+GTQdhIn/28aJ/9ZLC+b6ex99KmgV00r5U8AQ7hO1v66PqHIxP0ZBO5ozpj25I44YzFdXRAbQ795u899PtVIzE9gNoc/P1u7RnMlzbNVNDbksSDqcLf68wYbpTH34vv++PqYbrjxU3R/QNMikpsrHMytHsp1HQhlExczYSYs5JDdPm7USxa5+vkNoa1GJKE122fTdB7+fN3p2qiOgWzvMxywGf3jk7EBGH1PvvJoKk0x9x23COtG8AhRDPATLXumiKi8w+qpJ+dux/dtmwe/erjC+n9CyOpll7uGZ7YJ9tZXHp8pHI5zHuGZ0oAYKwDu3KUtSj3TzLuMSe3r5e054nYXToxQT1hYVJcn9dCzQRrMcTB1sExFfBcmFcCW3RaXrTXgkGmrALYbFxun4ppaaq/8KqGgaHyzEf3ODLXZJkUmgfqbcfB5JyybNyiva2iIGOtW47z7JwqOJuY8Kb+Qk9ZhaYJ6+atrn6qyhun9tqS0CtC+md374xtxa3DtJdk9qCfwdt8rb8ZF3YmO9/cyf338pYROrTB3xoGwAoGB/zy+fW0fGVkKHpuzlbVCO59+1fGtMXFDfcyIKS3gOAmYqn4LmV2idT+4qXLuUV7Z01xRsyVs0vQdvwflrYp91Ft4QS115Z7LtSaKovoX09qVxaOUyvoRLSbeiR57cKTQge0VhVWTWtUmG5Gkh/MVZ6n2bWl0+Y0SJj2ktN+92Pd80QTw1QtBk5Y0IXDlQ+upYuPaaAL5vnLqq0rySHgyUIBwDNM2qc2RNIlvc5yMKUfmoqWkoHJrDY1tPOC9paqErrkuOmdVhvKC1JuQYKeS+i9hB5MXtPO2t9751fQyk8uppvPnKNaQZ88K1JQlSrtsm+S13tGz6qSE8HiVUEn2jNc9+KlIiSbRwKIxcBalGNG+ds5xqALat1aYNqlK9ZL2ie0zCSOCSYCruOyd7VMc5tiu2FORfdw6oHsTGAtBgPW9ESChRJ4ed2feyj3iR61IY9vdb/9sA7ukglBwMwKGxybZO3afjW4PZEGG4S/lQF31z/sV0bvmr9YmfzcaVUOT081M8bLlEn9kGPdGyuK1Rp3d++KBhOTue9OzNVrwQDauCssa+D8Olm3hmnmiF+0s2Bg2tHh9eDmIhqaLKL9GiMV5thLcmBVvDYeXhfmmWZdACyIcC1Yd931CwUT7iNYCnwd75tfSXvGdtCNz/RPd5n176SOOu/iajqsYDAADNdk1smg18Kz26nDY78fNhcOI1o5Mx5euzM6OjNeZgaDOznqzNQPnyX37mmsKlSbv78/MoEr2SpiHCgUtm0fHKcFBbuiB8gPocaDhBj4TtCTqBKXwQkCklYvZkiY9gyYEGf3ALJ9eDzBwMyqgnZQZUHsnvGj7TNo58aFkvb60l3UUVJAVVP3P54yZNrvfgg1PVlExgT11h4y+0ieYdC+sL7Q6H6dXe9dTM0E60oyAC6QFcuWxi1S2jw0vUe/1/nROLi3PdubVGYG3nt+3QBtGYgcMj4YXrWsNkH/Dr3wJ15OOui897U+Vdj2tQc2q8I2riPwY91lt08T7fEA2vn3glh3MH/JjJIpjsTaYo0vvGs1fequTaqoUGemfqw7Yl+p0s7A33G1vT6n3Y8hN/mG/Q7F5oX1g9E9rmcf8RnuGoxMBESb86+c2Bbjfl1+6mzFk/yEtRgccM4RnXRwUxHd/9wbdMsLw9MGlbdVFfnGoNivHa9wSWpReu3AhUfX0rnN3g2iTyV4DuAA3PPKNrrhkb2BXd3y2dQ3Qv/1fP80IXjAh2fRvACEmmRQ8ZrS8e9wrYn08XvVEVaHrtmDSfIMDpPFMy1Vkkhlgb1n6QQ1VuT52t0TtGHt2Hppr47ENbB++qhaJ2tBrxL2K90zXzSPxDNqMG54pHvKut+q9nhrZaHxDK/ZNkxzyiIW5VkH1tOxc6rVua6gnXTgfI8KduLAWgxxgMyUQxsoJgCG5y+/q5WaK/0RDNxFFJvNaQ6vzMwwHXJUCrPG4uUsg0TBRM7WgBbFQsHJ8kGcx1TYhmlifq87M9ftY5MqfXlDz1Bca4cZlNRa2W/uF+2ydw+7JAHpHgNA/0NvbY9bs+Fnd0/QDobK1suH7nyNHl0fWWPTWEwJ/rn05/ttIedPBaCxrsho0xWbkoJI8aEEXtcXTcbQDkUPyRZ1pf42z2NYiyEOoF2BMfzDfpXRdEkw4Yr86UFRr4OJYCzcakH3T0prIZFV4Uc7DFOGDGupw3vyVXZXPBpxqLYN7piWqojr7agu9o12FmrQUiNWWNcU3X1R2kzWjolBsY/eLyuT4wr8fTz4CQ9uE663BndSOPxUJnpGdit3qVQabn2mhxbXNVJe3s6YCXMS2Ne6+47f99NCzpva7xv6dxv3+M7de6ad4a+e3E6VBXtUBpIepPZ7DgPDCoY4kIGsmoqi6JhKBINNU6O81rrxnWBAh7eX0qvruqm5PJ8WdlYlzIeWh9zLGRI62JXCWmoyjEj/HcmALz6uUaUw+tFMjJkJ1h19a3S6dU2QW2WAObGfWx/u7sTU3AZbCDzNDZDtoCEc0OE13r2QCoefzNVJsYGlWF/q7E7is6pbZX5aajli/nO8cwhLAPuFs6ugaGJIle5qDGIOQ5RW378xi2ByCfiZ5cCbDYxcFsogEHVYR6UKVOmuAbYqpOtLHnI/aZfN9EwzChhMI2D6nW++b7aqJ/iHuWW+BBH1DrFObbRNbhe5Z6QA87qJmw59z/D1ABt7h43uI+BTB1XQTz4yO8YC8qOGgTG3sdzoaplVWzrNnSTdeU5n1U8LGeA9YzqHXzqqLnoO2VWEZ87CMo3j9XsOA8NaDA5wMk29HnKTaGSjPPg4DBt6h2m0f5I6qyP59sAHDqiluaVjtGVonBa218e0j/B7qDhn92wYMjPXi05opXfPq1G045Cb6kdqSvPVz7dvH/G1mRgOKqqGO6qd05d1i8yJQfnd70YmLcg9c88rPTHdavXrOKajZFq7ET8KIqXS8/X3zKFr/7gmxl3aXlempvyBicLy0lM+P35gJc2tyadF7RVUodGOUbd+tawuEPOfuS3Jur5RKpkYUYocM3sGzsbm/h0qy3FxcRXJSoUg5jAwrGBwgJNp6lc+utNmY4DxPLRmRzRzR8+HRiO1xvJSatAOuV+BOAZ/l5NpfcLsvd1KE7nBAD8FA9Oux3b0Lp8XHNuqrIpx+OJ3747Rzv0OgJoC0Lx/t++cjCsUUJXeVFE4TQj43d3zo4e107zSXTScUxItZOM1xPVs7BuZlvL5478NTl1HX0zMx28LOU/jFaAdj76+iKIJC19OH7zrb11045+3Tu2l6bR73XbHCVYwOEBvKcDgkY9+H3JZEQpsGx6n21+Yns6J+EPe2JCjpYON6zdzxXdWleZNC7qBEVXk7c1ZR6uMLx5ZS999rm9acJ0DoH7Tzum2rP3BZVSdv1v5kbftmKRNO/Lp9qc2RwUGMthOX1QTc5iDcAnICmh8L2cfmYTCRce30sH1k1RdOH3PMPxWJnDP51eVR9eR/fewGN7pnm5ZkkPMx2+hli+mF8r7DQYPwYD4JK4DNMFSYKFgot1voRZzHYF8axYA5mpTU9O0qkVuNeDnZpOZSXxAnYJ0b2zspUV1Efo42Mk54Y0lRHWl/uTSm1L4JHNtKCYq2D2iGCwOEMx9BOBOnlVEBzU102huKXXWlEStCc6/93vdWSDj/6z94Vr6+rAvdtE3HtoSPdic/3/yomaqEZ/jZw0DA2sKpgINNVHQ//DmfCrPjQgQfZY2++iDUIT0mclgrhAMaKQYr7kex3ygaMgW2H4gX2Sz6YIBtMOah0sMY2zf3NRnTMnm7Dw/qs2dYAVDHOBA6JlHQRxyU6sAJ7dLQ0kkva26ulodCt0X+5UTW+kL8/zTXPV5uMxcgaGhSIbX0NCQOjAc09mvpdZo7aAq1o9MsHjptnxNNTU19Pete4WCnkHTVhO8rxjMKDJwaG/NiAR3jIVQAKqqqqatL197UMqEBPYEaMzJGYxpxa4DQmP76G7atH2UqgsLfI0H5mszwyVAe19fn/pZT08PNZTszboLg+tUwmYlpQjcVD8acukAU5RZJqasB9W5tCxfCQV2H+i+2P94bHNaIzbTBbsATAcFFg1rqCwU8J7JfE51BoXXtOPwH9BRbxwipLeCDmI0IwCGuGlo3Mg8Lzi6QXWMPakzQivug4mBBpEyyTEaU8tqCDto22iBD/q/eXI9XXBMc0wHVuCKB9bSuT9dRX98e9j3dM8ihz0DocsKG9yTCEZ/+cQWYwah3r7bb1iLIU3B4DdMh1a6Zloq4A6ItHXmzeTkblrbM+pr7xXQBPPZBDAkHBhYDVhXuJRMwEHyU/NjgCa4uEzAGqI4Sc5scGoFHQTt+E4IKZNl+d5FjVRCY9GaHN2FJGnHz/xWhDgjzATs77q6Oiot3UGLiiIdCN67sE4NtcH8As7TwDXf+PgW+tCxi3zf7xMOLc7B7CEcBgcHlbK3rLmMjp9bO61jbBCu0xg6A/nWLAYYVBA3S+87xJCuGR1O7qbZ9d4O6dGRaL1wQOJlX7CfO4h1T/SdmF99zOyquK2gk/kcL4DvbKsppctOaKEb/7zFILwKFQOOp1H7MUMi2Uw8CewVOWwK11PdPxYVCkEqQvna5EUdWM+Ghoa4Z5gFsp+uUwkrGFIAV4AGUYkIrQ6PZGcZAKYWGtec4a/2BPB6xWs+F08jDVJ7Sob2eMKZ/zaIXHQOQGMmxvH7LTYKr2TWNAhrh+MaenZPPIRFEcrXmumlA+x5r6csxoMVDCkgiIwkBr6Xy+1T0eDY3fTOtiHlbjpyySzyGzJ10onJmAaXMDg7I1sEcliUCQDMBa6wxpq99SLJgrXeoNad5yYn28LFpAh968wDfFeEioqK1AMJFZm0nwnKjQRYwZAC/GyEZgJMS/gmUwUOTFlusTpsQdCONcN3OwkGp8ElDPydX32GTLRz/nw6gsHPBnTJVkAniyAy8BjQtLHfe3t7U2KurAit3jpAnbWldOSSOeQ38qbSflOlXc8EC1Iw2KykLMhIYnC/mnQOOmj3q3meDtCMg2IatuI0uES2sw6y0Adwoj0ZBNnvRq+BSRWcGROUtYP9msy8ZJMidEB9Ac1ujG0w6belNjk5mdZZDdIzwbCCIQUEzaCkeW1CvBkBfvd30oF1Mx2SeG3CpW8/yEPCnUnTOeRsZQYVRJQtuNPVXIOinTPs0hFquFdBntXi4oiFno5CwSNYg1p3wLqSsiAjKRm3RiJ3jN+9epwYlKzeTqY/UtD53JJBpRNnCKqGQc/eQepnqhZjUKnZpvhOKnsXVobe9txvFBQUqHVHGnY6eyYo655hLYYs8LdKIM9f1/4SuWOC6JGUrOaaqE140O4MAN8NBpmO9hdkWwMGaE/HJePXrGQngLkjtpTqugddA7BlYAc99XYPjexJz9oBgt4z1mJIEmHQXE3D5ZOZ2sY+yyCZK2iFUENLAF0bkoV6ejpl0PncqQb+9eyqoCZwSTBzTzSr2oSgFSFTy/lE4ILCIGj/5fPrafnKl6OW+8XHNtLZlbFWcjJ8JkiBDFjBkOJmC8Mh11M/k5naFgbaobk6VYQ61QKwvzVoJMNcdXfeV0/uoBPagktVTTYrzIQgp4fpgoHdScnSjt9FL6sgLIXlU0IBwPPNT3XTER1lNLuxOmsCz4B1JSWJsDAoeciTdceEhXYWTk7CQQe3OA8yiKjnpscL/OvuvBse2UDbx/yZDZ7qnkmEMLjwANCNdU+Wds4ECmLPrOkZMVru63r3jlhNtrAtqMxHhrUYUthsYWCunPrZ3d2dtDsGpniQQURTMDGZtcTvccA9aCQqMHRy53UNR2JTQQNuvOHhYcdeVDqCLCrUgf3e1dWVFO1sFQWxZ+bUT5/0h9dtlZG5EMm4Q4NMK5ewFkMSYDM2DMw1XuqnnCOra35hYK7QnPVZxPHAAiQMzIkZlFMwkd15EnjdWVsSuMWQTqFbUD2STOBzlwz92DOcKuo3WqpKaMWypZQ3pe3j+dtnL6G22rKYWdVOYOERdHwBCMeJCzmQHoqOiEH7/RgcWOPsi3jgFMswbDYAZjIC0MkA15eshusHsI6w2Ezan6kdAwKP+vzkoABmyanOySg4QaeqOrWYSEQT9oxsUOc3zjmik961f4Nq3IceTRAWsO4xfyGRJcAKaBgEshUMCQAtBYzAqS1x0H7XZARDY2Nj4D5LBuiGBp2Idma+YTgkOnN16oGju/Pyx4dDQz/2DJQbMKlkmCsUjzC4NADQAgUBrdvj0c71C0GveUtVSUx/JlkFnahZJPhMGCxM60pKMhspLNqTTP1MZJ6yUAsT7aAFk6wwByAZSyfoQy4BQQXa5SQ9J3deTXGkoDDIDpk6UBMAmhLVBeD6sL/CtPagJ1HaKs5D2PaMrlCEuaNq1gqG6667TjHFSy65xNfvhRQPixtJbrZEPtcgA3FOwP3jKVbxYg0cXwiD9iTBWnQiJoUgNRhx2IRyZWWloi0ecF/we2GxMgHsYXafxtszYKxh2zP5UxbPxr4Rx5Y1YbF2ss6V9Pzzz9P3vvc9OvDAA339XgiEMPm5E7WYMAXiwibUwFyhecM1ACFhAq4rjOuO9cQjXktlHHI8IvOJw8NcAV53p33DWndY3Ehyv4MmWDNO+5nngocRD741RFffv17Ndza1rAlbLDArLAak2Z133nn0gx/8wPfCFTCBsB0SPfUzkc8ybEhkNXBxVVgOiQTogiUQzy0AjRxCLYxMCnsZ1oCTKw+M12n+c5BI1B6DBV2YLDRZ+HbNH95SQsGpgzDPNA9LBl5WCIYvfvGLdPrpp9Opp56a8HdxYNG6QD4y9eWHTeOWtDkdFHZ1hMU01QGmCeFgYlBhys5ItaUy3gP9uLag23g47RtYDaBTLzTk98KoTABgnE4FkmHTuJMpfOMOwmHMwAvfztXwi1/8gv7yl7/QihUrkvp9/B42Pj86OjrS/m5oKEGmvmXSHI0PShg1KAYzT124sdYaRsbKWjfW1RSE5nTQsDJXAAwI9OlCGbTj2sJo6ejtMSQgKEB72OIieuGbhGxZw9lKYVKEwnnyprBhwwa6+OKL6ac//WnSDG758uVqlCE/8BnpAowpbIGsZPrVQ1jAnRHGQJwEmBBcg5JBsQ8ZQj2s4AldumDAmsNixTWFxSVgApgQaARDZcUCzGnHjh1KGQrrnsF5BPOX6w664RXA9TjFq4JGy1Thm1PLmjBaO+HdvUT04osvqrzrQw89NPoeNvPjjz9Ot912mzqE+iYOY7qaH4N7mBHh0IBB4ZDU1e0NboUVONA42GBKEP6gvaWlJdSWDgDBgF77KNbDPeBGhe3t7YGNIU3XagC9uAd4XVtbS2GnG3sD647/Y9/gGbU6YRVosvDt1XXdVLCzn+a27BViOLOcShwWhIcSA0455RR6+eWXY9771Kc+RQsXLqSvfe1rod4IfvdNYt821gSMFQw3rK4YCTBTMKMtW7ZEg+Vh1fwkwIxmzZqlBBksU9wLuB3DLtAY2BvYI+vXr1fXgH3T1NQUKuZkAqxguIex5hAO2D+gO4xxQJPl0Ly0kzZvzo9m5EEgY83DtudDvQsgRZcsWTLtQEIT1t/fV8HFMzgYOOg4OGH1ETsBbiMcdGhO9fX1WSPw2eUF9wYQRv92PEAI44GsP1g6Ycy+MwHCAEIY/CEszS2TBfYI+BeEcW9vr7qG5ubm0F1DqAWDRWJwERWYVLYxJgY0Jhx0dgtkG7J13WE1gElxXUm2IVusMxPdUIDgxoMLLEyxBUbOZDoTzrMIMNVYI2XNzsLCwiJoTKYxUc8vXhh+J7SFhYXFDEROiC1NKxgsLCwsLGJgBYOFhYWFRQysYLCwsLCwiIEVDBYWFhYWMbCCwcLCwsJi36pj4GzcTLqsWlhYWGQ7mAcmU6Ew4wUD+tkAmXRZtbCwsJhJPDFRQeOML3BD98jNmzerCuFU84YhYSFQ0KF1JhfH7SvXCdhrnZnYV651MIPrBKuHUGhtbU3YR23GWwxYAPSByQS4ATN5s+1r1wnYa52Z2FeutTLN60y29YkNPltYWFhYxMAKBgsLCwuLGFjBEAdoZ33VVVfN+ME/+8p1AvZaZyb2lWst8uk6Z3zw2cLCwsIiNViLwcLCwsIiBlYwWFhYWFjEwAoGCwsLC4sYWMHggO9+97s0e/ZsNYbvqKOOoueee46yHY8//jidccYZqsAFxX533313zM8RbrryyiuppaVFjXs89dRT6a233qJsw4oVK+iII45QRY0YnXjWWWfRm2++GfM7mC/9xS9+UY22xNzjD33oQ7R161bKNtx+++104IEHRvPajznmGPrDH/4w467ThOuuu07t40suuWTGXe+//du/qWuTj4ULF/p2nVYwGPDLX/6SLrvsMhX9/8tf/kIHHXQQvfe976Xu7m7KZmDGLK4FQs+EG264gW655Ra644476Nlnn1Xzl3Hd2ITZhMcee0wdmmeeeYb+9Kc/0fj4OL3nPe9R18+49NJL6fe//z39+te/Vr+P6vhly5ZRtgHFm2CQL774Ir3wwgv07ne/m84880x69dVXZ9R16nj++efpe9/7nhKKEjPpehcvXkxbtmyJPp544gn/rhNZSRaxOPLIIye/+MUvRl9PTExMtra2Tq5YsWJypgC3/q677oq+3rNnz2Rzc/Pkv//7v0ff6+/vnywqKpr8+c9/PpnN6O7uVtf72GOPRa+roKBg8te//nX0d15//XX1O08//fRktqOmpmbyhz/84Yy9zqGhocn58+dP/ulPf5o88cQTJy+++GL1/ky63quuumryoIMOMv7Mj+u0FoOGXbt2Ke0LbhTZVgOvn376aZqpWLNmDXV1dcVcN8rn4UbL9uvG8HOgtrZWPeP+woqQ1wozvbOzM6uvdWJign7xi18oywgupZl6nbAGTz/99JjrAmba9b711lvK7Tt37lw677zzaP369b5d54zvlZQqenp61AFramqKeR+v33jjDZqpgFAATNfNP8vWJorwQR933HG0ZMkS9R6up7CwkKqrq2fEtb788stKEMDlB3/zXXfdRQcccAC99NJLM+o6AQg+uHfhStIxk+7rUUcdRXfeeSctWLBAuZGuvvpqOuGEE+iVV17x5TqtYLCY0YB2icMk/bMzDWAeEAKwjH7zm9/Q+eefr/zOMw3oKHrxxReruBGSQmYyTjvttOj/EUeBoJg1axb96le/UokhXsO6kjTU19dTXl7etAg/Xjc3N9NMBV/bTLruL33pS3TvvffSI488EtNhF9cDl2F/f/+MuFZoj/PmzaPDDjtMZWQhweDmm2+ecdcJFwoSQA499FDKz89XDwhAJEzg/9CYZ9L1SsA62H///Wn16tW+3FcrGAyHDAfsoYceinFH4DXM9ZmKOXPmqE0lrxu935GdlG3Xjdg6hAJcKg8//LC6Ngnc34KCgphrRTorfLjZdq0mYL+OjY3NuOs85ZRTlNsM1hE/Dj/8cOV/5//PpOuVGB4eprffflulkvtyX10JYc8w/OIXv1DZOHfeeefka6+9NvnZz352srq6erKrq2sym4Fsjr/+9a/qgVt/4403qv+vW7dO/fy6665T1/m73/1u8u9///vkmWeeOTlnzpzJHTt2TGYTLrjggsmqqqrJRx99dHLLli3Rx+joaPR3Pv/5z092dnZOPvzww5MvvPDC5DHHHKMe2YbLL79cZVutWbNG3TO8zsnJmfzjH/84o67TCTIraSZd75e//GW1f3Ffn3zyyclTTz11sr6+XmXY+XGdVjA44NZbb1ULX1hYqNJXn3nmmclsxyOPPKIEgv44//zzoymrV1xxxWRTU5MSjKeccsrkm2++OZltMF0jHj/60Y+ivwNh94UvfEGldpaWlk6effbZSnhkGz796U9Pzpo1S+3ThoYGdc9YKMyk60xWMMyU6z3nnHMmW1pa1H1ta2tTr1evXu3bddruqhYWFhYWMbAxBgsLCwuLGFjBYGFhYWERAysYLCwsLCxiYAWDhYWFhUUMrGCwsLCwsIiBFQwWFhYWFjGwgsHCwsLCIgZWMFhYWFhYxMAKBguLDPDoo4+qsYt6QzMLi2yGrXy2sEgBJ510Eh188MF00003qdfoctnX16c6e0JAWFjMBNh5DBYWGXbjzfaWzhYWOqwrycIiSXzyk59U/f8x6wDWAR6YsiVdSXiN3vmYA4EBOqWlpfThD3+YRkdH6cc//jHNnj2bampq6KKLLlKTAhlok/2Vr3yF2traqKysTA1mgZvKwiIIWIvBwiJJQCCsWrVKjQi95ppr1HuvvvrqtN+DEMDwGIyhHBoaomXLltHZZ5+tBMb9999P77zzDn3oQx9S40bPOecc9TeYH/Haa6+pv8GcX8ySeN/73qfmD8yfP9/3a7XYt2EFg4VFkqiqqlKuI1gB7D4yzQHHoPbbb7+d9ttvP/UaFsP//u//qglbmMmMecwnn3yymiwHwYABKz/60Y/UM4QCAOvhgQceUO9fe+21Pl+pxb4OKxgsLFwGBAcLBQCBabiQIBTkexhTCcAqgFsJoxsl4F6qq6vzkXILiwisYLCwcBkYuyiBGITpPYzg5LGNmDOOmcZ4lpDCxMLCL1jBYGGRAuBKkkFjN3DIIYeoz4QFccIJJ7j62RYW6cBmJVlYpAC4hJ599llau3Yt9fT0RLX+TAAXEgbaf+ITn6CVK1fSmjVr6LnnnqMVK1bQfffd5wrdFhapwAoGC4sUgKAw3D0IIDc0NKiAsRtAkBmC4ctf/rJKcz3rrLPo+eefp87OTlc+38IiFdjKZwsLCwuLGFiLwcLCwsIiBlYwWFhYWFjEwAoGCwsLC4sYWMFgYWFhYREDKxgsLCwsLGJgBYOFhYWFRQysYLCwsLCwiIEVDBYWFhYWMbCCwcLCwsIiBlYwWFhYWFjEwAoGCwsLC4sYWMFgYWFhYUES/z+v/xTmjD1FAwAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 400x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create the inferer (NumPyro backend, NUTS kernel) and let it do its work\n",
+    "sim2.set_inferer(\"numpyro\")\n",
+    "sim2.inferer.config.inference_numpyro.kernel = \"nuts\"\n",
+    "sim2.inferer.run()\n",
+    "\n",
+    "# Plot the results\n",
+    "sim2.config.simulation.x_dimension = \"time\"\n",
+    "sim2.posterior_predictive_checks(pred_hdi_style={\"alpha\": 0.1})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7212637c",
+   "metadata": {},
+   "source": [
+    "## 2.3 Summary\n",
+    "\n",
+    "👉 Creating the simulation from a pre-saved configuration saved us the following steps:\n",
+    "\n",
+    "- Adding data to the simulation\n",
+    "- If done right: Adding initial conditions to the simulation\n",
+    "- Creating the Lotka-Volterra parameters\n",
+    "- Specifying the error model along with the corresponding parameters\n",
+    "- Telling the evaluator not to throw exceptions if max_steps is exceeded\n",
+    "- Chossing a prior for parameter inference\n",
+    "\n",
+    "👉 We still had to:\n",
+    "\n",
+    "- Define a model\n",
+    "- Pass parameter values to the simulation\n",
+    "- Specify the solver\n",
+    "\n",
+    "👉 By subclassing {class}`pymob.SimulationBase`, even those last steps can be avoided. This will, however, not be explained in this tutorial as it only makes sense in the context of __case studies__."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "pymob2",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From d04fa5887a1935e1b1b069a6ba6e4e37ae6ef64e Mon Sep 17 00:00:00 2001
From: merkuns <mholdreich@uni-osnabrueck.de>
Date: Mon, 28 Jul 2025 18:05:49 +0200
Subject: [PATCH 16/16] Added subclassing and made some small tweaks

---
 .../advanced_tutorial_ODE_system.ipynb        | 590 +++++++++++++-----
 1 file changed, 433 insertions(+), 157 deletions(-)

diff --git a/docs/source/user_guide/advanced_tutorial_ODE_system.ipynb b/docs/source/user_guide/advanced_tutorial_ODE_system.ipynb
index 883008e34..e3e30e98d 100644
--- a/docs/source/user_guide/advanced_tutorial_ODE_system.ipynb
+++ b/docs/source/user_guide/advanced_tutorial_ODE_system.ipynb
@@ -539,7 +539,7 @@
        "  * time      (time) float64 0.0 0.5 1.0 1.5 2.0 ... 48.0 48.5 49.0 49.5 50.0\n",
        "Data variables:\n",
        "    prey      (time) float64 10.17 11.36 11.85 11.33 ... 11.08 11.16 12.37 11.56\n",
-       "    predator  (time) float64 5.431 5.33 6.397 7.604 ... 5.544 5.436 7.871 9.127</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-19b0fb0c-409a-4321-80db-2480bba0723d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-19b0fb0c-409a-4321-80db-2480bba0723d' 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'>time</span>: 101</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-7d4a5124-8cc4-4ffd-906a-1d45cbd4c9dd' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7d4a5124-8cc4-4ffd-906a-1d45cbd4c9dd' class='xr-section-summary' >Coordinates: <span>(1)</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'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.5 1.0 1.5 ... 49.0 49.5 50.0</div><input id='attrs-647e6c37-34c5-487f-b209-f49c8beb0c99' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-647e6c37-34c5-487f-b209-f49c8beb0c99' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5c0b55b3-ce1c-4ad9-be12-ea1bb0e4e279' class='xr-var-data-in' type='checkbox'><label for='data-5c0b55b3-ce1c-4ad9-be12-ea1bb0e4e279' 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. ,  0.5,  1. ,  1.5,  2. ,  2.5,  3. ,  3.5,  4. ,  4.5,  5. ,  5.5,\n",
+       "    predator  (time) float64 5.431 5.33 6.397 7.604 ... 5.544 5.436 7.871 9.127</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-3b0dad06-e130-4af7-be75-48c55a522edc' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-3b0dad06-e130-4af7-be75-48c55a522edc' 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'>time</span>: 101</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-64635400-7500-42ac-9831-d3dae645cec7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-64635400-7500-42ac-9831-d3dae645cec7' class='xr-section-summary' >Coordinates: <span>(1)</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'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.5 1.0 1.5 ... 49.0 49.5 50.0</div><input id='attrs-d4ebfd0a-841b-4e03-8551-0da2c0456df6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d4ebfd0a-841b-4e03-8551-0da2c0456df6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-acb5d6e4-252a-455e-b525-ef5ec42d46e0' class='xr-var-data-in' type='checkbox'><label for='data-acb5d6e4-252a-455e-b525-ef5ec42d46e0' 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. ,  0.5,  1. ,  1.5,  2. ,  2.5,  3. ,  3.5,  4. ,  4.5,  5. ,  5.5,\n",
        "        6. ,  6.5,  7. ,  7.5,  8. ,  8.5,  9. ,  9.5, 10. , 10.5, 11. , 11.5,\n",
        "       12. , 12.5, 13. , 13.5, 14. , 14.5, 15. , 15.5, 16. , 16.5, 17. , 17.5,\n",
        "       18. , 18.5, 19. , 19.5, 20. , 20.5, 21. , 21.5, 22. , 22.5, 23. , 23.5,\n",
@@ -547,7 +547,7 @@
        "       30. , 30.5, 31. , 31.5, 32. , 32.5, 33. , 33.5, 34. , 34.5, 35. , 35.5,\n",
        "       36. , 36.5, 37. , 37.5, 38. , 38.5, 39. , 39.5, 40. , 40.5, 41. , 41.5,\n",
        "       42. , 42.5, 43. , 43.5, 44. , 44.5, 45. , 45.5, 46. , 46.5, 47. , 47.5,\n",
-       "       48. , 48.5, 49. , 49.5, 50. ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3ad470b1-db7f-4d89-b569-4dff691bdd72' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3ad470b1-db7f-4d89-b569-4dff691bdd72' class='xr-section-summary' >Data variables: <span>(2)</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>prey</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>10.17 11.36 11.85 ... 12.37 11.56</div><input id='attrs-66ae39f1-e39c-4b6d-8bbb-7a904fc64f87' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-66ae39f1-e39c-4b6d-8bbb-7a904fc64f87' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5b16a95a-34a7-4c6c-87a5-66cca8b9b554' class='xr-var-data-in' type='checkbox'><label for='data-5b16a95a-34a7-4c6c-87a5-66cca8b9b554' 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([10.1727921 , 11.36356787, 11.85133418, 11.32758206, 12.13024324,\n",
+       "       48. , 48.5, 49. , 49.5, 50. ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5d21fab7-150f-43d3-a4d3-bfbe3464d931' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5d21fab7-150f-43d3-a4d3-bfbe3464d931' class='xr-section-summary' >Data variables: <span>(2)</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>prey</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>10.17 11.36 11.85 ... 12.37 11.56</div><input id='attrs-bbe2c41f-fbe9-4d58-b2ee-5ec11b181cc8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bbe2c41f-fbe9-4d58-b2ee-5ec11b181cc8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6993d986-4817-42da-a7a1-982f9cb5b61c' class='xr-var-data-in' type='checkbox'><label for='data-6993d986-4817-42da-a7a1-982f9cb5b61c' 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([10.1727921 , 11.36356787, 11.85133418, 11.32758206, 12.13024324,\n",
        "       11.03241642,  9.34444201,  8.70483115,  7.64174857,  6.98965608,\n",
        "        6.57516732,  6.85312033,  6.49556603,  7.30309667,  7.87383721,\n",
        "        9.31440432, 10.03074436, 10.83049243, 11.32881408, 11.88840763,\n",
@@ -567,7 +567,7 @@
        "       11.26081546,  9.31381041,  8.35946656,  7.57995973,  6.95289035,\n",
        "        6.87040252,  6.58466325,  6.70585993,  7.16553957,  8.58367122,\n",
        "        7.87094733,  9.97407174, 11.08383673, 11.15598075, 12.37157364,\n",
-       "       11.56283879])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>predator</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.431 5.33 6.397 ... 7.871 9.127</div><input id='attrs-8f8a731d-b7d4-42d8-b245-031601831183' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8f8a731d-b7d4-42d8-b245-031601831183' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e64dc7ee-e8eb-4540-b096-f753a88b13dc' class='xr-var-data-in' type='checkbox'><label for='data-e64dc7ee-e8eb-4540-b096-f753a88b13dc' 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([ 5.4312224 ,  5.33024327,  6.39672032,  7.60376055,  8.25824253,\n",
+       "       11.56283879])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>predator</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.431 5.33 6.397 ... 7.871 9.127</div><input id='attrs-293a3d9f-b59f-41b5-990a-464ce32e2d02' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-293a3d9f-b59f-41b5-990a-464ce32e2d02' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e9d8cfbd-34c2-4b2c-a8b6-174cb9fc2df2' class='xr-var-data-in' type='checkbox'><label for='data-e9d8cfbd-34c2-4b2c-a8b6-174cb9fc2df2' 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([ 5.4312224 ,  5.33024327,  6.39672032,  7.60376055,  8.25824253,\n",
        "        8.59705194,  8.84949092, 10.49034665,  9.05239184,  7.94732558,\n",
        "        7.45133951,  6.42920702,  6.24083527,  5.30774194,  4.97430678,\n",
        "        4.50230252,  5.22067198,  4.85811968,  6.38407349,  7.75677694,\n",
@@ -587,10 +587,10 @@
        "        8.77120917,  9.81951815,  8.99908534,  9.31052548,  8.12160111,\n",
        "        7.99771137,  6.96905479,  4.39192311,  5.22164132,  4.0539337 ,\n",
        "        5.30106873,  4.95572986,  5.54363339,  5.43624167,  7.87109895,\n",
-       "        9.12710988])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-09e47e5f-8ea4-4eed-bf8e-21abb3964b43' class='xr-section-summary-in' type='checkbox'  ><label for='section-09e47e5f-8ea4-4eed-bf8e-21abb3964b43' class='xr-section-summary' >Indexes: <span>(1)</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>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-6c080840-f6bb-4655-a364-8ba7c2058e51' class='xr-index-data-in' type='checkbox'/><label for='index-6c080840-f6bb-4655-a364-8ba7c2058e51' 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([ 0.0,  0.5,  1.0,  1.5,  2.0,  2.5,  3.0,  3.5,  4.0,  4.5,\n",
+       "        9.12710988])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-19b093dd-1b79-4c37-83fd-eab1d138faeb' class='xr-section-summary-in' type='checkbox'  ><label for='section-19b093dd-1b79-4c37-83fd-eab1d138faeb' class='xr-section-summary' >Indexes: <span>(1)</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>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a6f19019-6ae4-4422-8230-918fbd227169' class='xr-index-data-in' type='checkbox'/><label for='index-a6f19019-6ae4-4422-8230-918fbd227169' 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([ 0.0,  0.5,  1.0,  1.5,  2.0,  2.5,  3.0,  3.5,  4.0,  4.5,\n",
        "       ...\n",
        "       45.5, 46.0, 46.5, 47.0, 47.5, 48.0, 48.5, 49.0, 49.5, 50.0],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;time&#x27;, length=101))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c24fa593-54a5-444f-8c6a-cd3e7b5c3fce' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c24fa593-54a5-444f-8c6a-cd3e7b5c3fce' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+       "      dtype=&#x27;float64&#x27;, name=&#x27;time&#x27;, length=101))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a38700d6-a9c0-411c-9c7d-dab6ae6cfd4b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a38700d6-a9c0-411c-9c7d-dab6ae6cfd4b' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
        "<xarray.Dataset>\n",
@@ -675,7 +675,7 @@
    "source": [
     "👉 Because the results of ODE models strongly depend on their **initial conditions**, our simulation object need to know those. The correct place to put this information is {attr}`~pymob.sim.model_parameters[\"y0\"]`.\n",
     "\n",
-    "👉 The initial conditions also have to be an xArray dataset with two data variables (but without the time coordinate). We can do this manually like before by creating a {class}`xArray.Dataset` object from our initial conditions..."
+    "👉 The initial conditions also have to be an xArray dataset with two data variables (but without the time coordinate). We can do this manually like before by creating a {class}`xArray.Dataset` object from our initial conditions:"
    ]
   },
   {
@@ -699,7 +699,10 @@
    "id": "6e4e7050",
    "metadata": {},
    "source": [
-    "👉 ... or we can use {method}`pymob.sim.parse_input()` which extracts all the necessary information from the configuration (which we first have to define in this case)."
+    "```{admonition} Using parse_input()\n",
+    ":class: note\n",
+    "Otherwise we can use {method}`pymob.sim.parse_input()` which extracts all the necessary information from the configuration. This is, however, only possible after we give add this information to the configuration. This might seem unnecessary at the moment but you will later see why it makes sense in certain situations.\n",
+    "```"
    ]
   },
   {
@@ -737,7 +740,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "id": "e6a7ecbd",
    "metadata": {},
    "outputs": [
@@ -747,7 +750,7 @@
        "{'alpha': 0.7, 'beta': 0.1, 'gamma': 0.1, 'delta': 0.9}"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -778,7 +781,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "id": "452b9e06",
    "metadata": {},
    "outputs": [
@@ -793,10 +796,10 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x2050f079b10>"
+       "<matplotlib.legend.Legend at 0x247d7273d90>"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -838,12 +841,25 @@
     "\n",
     "👉 Now let's see which value for $\\delta$ best fits our data. To do that, we use the **inferer** in the same way as in the introductory tutorial. We do, however, need to apply our error model to both of our state variables. Also, we changed the prior for $\\delta$ to a uniform distribution from 0.5 to 1.5 because that's a better guess.\n",
     "\n",
-    "👉 Note: **The following code will throw an error.** This is not your fault, just look at the error message and continue with the next markdown cell."
+    "```{admonition} Caution\n",
+    ":class: caution\n",
+    "The following code will throw an error. This is not your fault, just look at the error message and continue with the next markdown cell.\n",
+    "```"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
+   "id": "7c386f22",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from jaxlib.xla_extension import XlaRuntimeError"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
    "id": "231463eb",
    "metadata": {},
    "outputs": [
@@ -879,36 +895,72 @@
       "      prey_obs dist 101 |\n",
       "              value 101 |\n",
       "  predator_obs dist 101 |\n",
-      "              value 101 |\n"
-     ]
-    },
-    {
-     "ename": "XlaRuntimeError",
-     "evalue": "INTERNAL: Generated function failed: CpuCallback error: _EquinoxRuntimeError: The maximum number of solver steps was reached. Try increasing `max_steps`.\n\n\n--------------------\nAn error occurred during the runtime of your JAX program! Unfortunately you do not appear to be using `equinox.filter_jit` (perhaps you are using `jax.jit` instead?) and so further information about the error cannot be displayed. (Probably you are seeing a very large but uninformative error message right now.) Please wrap your program with `equinox.filter_jit`.\n--------------------\n\n\nAt:\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\equinox\\_errors.py(89): raises\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\callback.py(258): _flat_callback\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\callback.py(52): pure_callback_impl\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\callback.py(188): _callback\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\interpreters\\mlir.py(2327): _wrapped_callback\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\interpreters\\pxla.py(1145): __call__\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\profiler.py(334): wrapper\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\pjit.py(1178): _pjit_call_impl_python\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\pjit.py(1222): call_impl_cache_miss\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\pjit.py(1238): _pjit_call_impl\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\core.py(893): process_primitive\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\core.py(405): bind_with_trace\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\core.py(2682): bind\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\pjit.py(166): _python_pjit_helper\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\pjit.py(255): cache_miss\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\traceback_util.py(177): reraise_with_filtered_traceback\n  C:\\Users\\Markus\\pymob\\pymob\\pymob\\solvers\\base.py(82): __call__\n  C:\\Users\\Markus\\pymob\\pymob\\pymob\\sim\\evaluator.py(351): __call__\n  C:\\Users\\Markus\\pymob\\pymob\\pymob\\inference\\numpyro_backend.py(261): evaluator\n  C:\\Users\\Markus\\pymob\\pymob\\pymob\\inference\\numpyro_backend.py(485): model\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\primitives.py(105): __call__\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\primitives.py(105): __call__\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\primitives.py(105): __call__\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\handlers.py(171): get_trace\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\infer\\util.py(450): _get_model_transforms\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\infer\\util.py(656): initialize_model\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\infer\\hmc.py(657): _init_state\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\infer\\hmc.py(713): init\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\infer\\mcmc.py(416): _single_chain_mcmc\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\infer\\mcmc.py(634): run\n  C:\\Users\\Markus\\pymob\\pymob\\pymob\\inference\\numpyro_backend.py(652): run_mcmc\n  C:\\Users\\Markus\\pymob\\pymob\\pymob\\inference\\numpyro_backend.py(566): run\n  C:\\Users\\Markus\\AppData\\Local\\Temp\\ipykernel_10328\\906244579.py(15): <module>\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\IPython\\core\\interactiveshell.py(3548): run_code\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\IPython\\core\\interactiveshell.py(3488): run_ast_nodes\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\IPython\\core\\interactiveshell.py(3306): run_cell_async\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\IPython\\core\\async_helpers.py(129): _pseudo_sync_runner\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\IPython\\core\\interactiveshell.py(3101): _run_cell\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\IPython\\core\\interactiveshell.py(3046): run_cell\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel\\zmqshell.py(549): run_cell\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel\\ipkernel.py(449): do_execute\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel\\kernelbase.py(778): execute_request\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel\\ipkernel.py(362): execute_request\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel\\kernelbase.py(437): dispatch_shell\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel\\kernelbase.py(534): process_one\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel\\kernelbase.py(545): dispatch_queue\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\asyncio\\events.py(84): _run\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\asyncio\\base_events.py(1936): _run_once\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\asyncio\\base_events.py(608): run_forever\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\tornado\\platform\\asyncio.py(211): start\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel\\kernelapp.py(739): start\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\traitlets\\config\\application.py(1075): launch_instance\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel_launcher.py(18): <module>\n  <frozen runpy>(88): _run_code\n  <frozen runpy>(198): _run_module_as_main\n",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mXlaRuntimeError\u001b[0m                           Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn[10], line 15\u001b[0m\n\u001b[0;32m     13\u001b[0m sim\u001b[38;5;241m.\u001b[39mset_inferer(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnumpyro\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m     14\u001b[0m sim\u001b[38;5;241m.\u001b[39minferer\u001b[38;5;241m.\u001b[39mconfig\u001b[38;5;241m.\u001b[39minference_numpyro\u001b[38;5;241m.\u001b[39mkernel \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnuts\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m---> 15\u001b[0m \u001b[43msim\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minferer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     17\u001b[0m \u001b[38;5;66;03m# Plot the results\u001b[39;00m\n\u001b[0;32m     18\u001b[0m sim\u001b[38;5;241m.\u001b[39mconfig\u001b[38;5;241m.\u001b[39msimulation\u001b[38;5;241m.\u001b[39mx_dimension \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtime\u001b[39m\u001b[38;5;124m\"\u001b[39m\n",
-      "File \u001b[1;32m~\\pymob\\pymob\\pymob\\inference\\numpyro_backend.py:566\u001b[0m, in \u001b[0;36mNumpyroBackend.run\u001b[1;34m(self, print_debug, render_model)\u001b[0m\n\u001b[0;32m    564\u001b[0m \u001b[38;5;66;03m# run inference\u001b[39;00m\n\u001b[0;32m    565\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkernel\u001b[38;5;241m.\u001b[39mlower() \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msa\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkernel\u001b[38;5;241m.\u001b[39mlower() \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnuts\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m--> 566\u001b[0m     sampler, mcmc \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_mcmc\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    567\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    568\u001b[0m \u001b[43m        \u001b[49m\u001b[43mkeys\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkeys\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    569\u001b[0m \u001b[43m        \u001b[49m\u001b[43mkernel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkernel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlower\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    570\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    572\u001b[0m     \u001b[38;5;66;03m# create arviz idata\u001b[39;00m\n\u001b[0;32m    573\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39midata \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnuts_posterior(\n\u001b[0;32m    574\u001b[0m         mcmc\u001b[38;5;241m=\u001b[39mmcmc, model\u001b[38;5;241m=\u001b[39mmodel, key\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mnext\u001b[39m(keys), obs\u001b[38;5;241m=\u001b[39mobs\n\u001b[0;32m    575\u001b[0m     )\n",
-      "File \u001b[1;32m~\\pymob\\pymob\\pymob\\inference\\numpyro_backend.py:652\u001b[0m, in \u001b[0;36mNumpyroBackend.run_mcmc\u001b[1;34m(self, model, keys, kernel)\u001b[0m\n\u001b[0;32m    642\u001b[0m mcmc \u001b[38;5;241m=\u001b[39m infer\u001b[38;5;241m.\u001b[39mMCMC(\n\u001b[0;32m    643\u001b[0m     sampler\u001b[38;5;241m=\u001b[39msampler,\n\u001b[0;32m    644\u001b[0m     num_warmup\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mwarmup,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    648\u001b[0m     progress_bar\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[0;32m    649\u001b[0m )\n\u001b[0;32m    651\u001b[0m \u001b[38;5;66;03m# run inference\u001b[39;00m\n\u001b[1;32m--> 652\u001b[0m \u001b[43mmcmc\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mnext\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mkeys\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    653\u001b[0m mcmc\u001b[38;5;241m.\u001b[39mprint_summary()\n\u001b[0;32m    655\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m sampler, mcmc\n",
-      "File \u001b[1;32mc:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\infer\\mcmc.py:634\u001b[0m, in \u001b[0;36mMCMC.run\u001b[1;34m(self, rng_key, extra_fields, init_params, *args, **kwargs)\u001b[0m\n\u001b[0;32m    632\u001b[0m map_args \u001b[38;5;241m=\u001b[39m (rng_key, init_state, init_params)\n\u001b[0;32m    633\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnum_chains \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m--> 634\u001b[0m     states_flat, last_state \u001b[38;5;241m=\u001b[39m \u001b[43mpartial_map_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmap_args\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    635\u001b[0m     states \u001b[38;5;241m=\u001b[39m tree_map(\u001b[38;5;28;01mlambda\u001b[39;00m x: x[jnp\u001b[38;5;241m.\u001b[39mnewaxis, \u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m], states_flat)\n\u001b[0;32m    636\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
-      "File \u001b[1;32mc:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\infer\\mcmc.py:416\u001b[0m, in \u001b[0;36mMCMC._single_chain_mcmc\u001b[1;34m(self, init, args, kwargs, collect_fields)\u001b[0m\n\u001b[0;32m    414\u001b[0m \u001b[38;5;66;03m# Check if _sample_fn is None, then we need to initialize the sampler.\u001b[39;00m\n\u001b[0;32m    415\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m init_state \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mor\u001b[39;00m (\u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msampler, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_sample_fn\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m--> 416\u001b[0m     new_init_state \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msampler\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minit\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    417\u001b[0m \u001b[43m        \u001b[49m\u001b[43mrng_key\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    418\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnum_warmup\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    419\u001b[0m \u001b[43m        \u001b[49m\u001b[43minit_params\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    420\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmodel_args\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    421\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmodel_kwargs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    422\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    423\u001b[0m     init_state \u001b[38;5;241m=\u001b[39m new_init_state \u001b[38;5;28;01mif\u001b[39;00m init_state \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m init_state\n\u001b[0;32m    424\u001b[0m sample_fn, postprocess_fn \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_cached_fns()\n",
-      "File \u001b[1;32mc:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\infer\\hmc.py:713\u001b[0m, in \u001b[0;36mHMC.init\u001b[1;34m(self, rng_key, num_warmup, init_params, model_args, model_kwargs)\u001b[0m\n\u001b[0;32m    708\u001b[0m \u001b[38;5;66;03m# vectorized\u001b[39;00m\n\u001b[0;32m    709\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    710\u001b[0m     rng_key, rng_key_init_model \u001b[38;5;241m=\u001b[39m jnp\u001b[38;5;241m.\u001b[39mswapaxes(\n\u001b[0;32m    711\u001b[0m         vmap(random\u001b[38;5;241m.\u001b[39msplit)(rng_key), \u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m    712\u001b[0m     )\n\u001b[1;32m--> 713\u001b[0m init_params \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_init_state\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    714\u001b[0m \u001b[43m    \u001b[49m\u001b[43mrng_key_init_model\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel_kwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minit_params\u001b[49m\n\u001b[0;32m    715\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    716\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_potential_fn \u001b[38;5;129;01mand\u001b[39;00m init_params \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    717\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m    718\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mValid value of `init_params` must be provided with\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m `potential_fn`.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    719\u001b[0m     )\n",
-      "File \u001b[1;32mc:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\infer\\hmc.py:657\u001b[0m, in \u001b[0;36mHMC._init_state\u001b[1;34m(self, rng_key, model_args, model_kwargs, init_params)\u001b[0m\n\u001b[0;32m    650\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21m_init_state\u001b[39m(\u001b[38;5;28mself\u001b[39m, rng_key, model_args, model_kwargs, init_params):\n\u001b[0;32m    651\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_model \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    652\u001b[0m         (\n\u001b[0;32m    653\u001b[0m             new_init_params,\n\u001b[0;32m    654\u001b[0m             potential_fn,\n\u001b[0;32m    655\u001b[0m             postprocess_fn,\n\u001b[0;32m    656\u001b[0m             model_trace,\n\u001b[1;32m--> 657\u001b[0m         ) \u001b[38;5;241m=\u001b[39m \u001b[43minitialize_model\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    658\u001b[0m \u001b[43m            \u001b[49m\u001b[43mrng_key\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    659\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_model\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    660\u001b[0m \u001b[43m            \u001b[49m\u001b[43mdynamic_args\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m    661\u001b[0m \u001b[43m            \u001b[49m\u001b[43minit_strategy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_init_strategy\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    662\u001b[0m \u001b[43m            \u001b[49m\u001b[43mmodel_args\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmodel_args\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    663\u001b[0m \u001b[43m            \u001b[49m\u001b[43mmodel_kwargs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmodel_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    664\u001b[0m \u001b[43m            \u001b[49m\u001b[43mforward_mode_differentiation\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_forward_mode_differentiation\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    665\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    666\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m init_params \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    667\u001b[0m             init_params \u001b[38;5;241m=\u001b[39m new_init_params\n",
-      "File \u001b[1;32mc:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\infer\\util.py:656\u001b[0m, in \u001b[0;36minitialize_model\u001b[1;34m(rng_key, model, init_strategy, dynamic_args, model_args, model_kwargs, forward_mode_differentiation, validate_grad)\u001b[0m\n\u001b[0;32m    646\u001b[0m model_kwargs \u001b[38;5;241m=\u001b[39m {} \u001b[38;5;28;01mif\u001b[39;00m model_kwargs \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m model_kwargs\n\u001b[0;32m    647\u001b[0m substituted_model \u001b[38;5;241m=\u001b[39m substitute(\n\u001b[0;32m    648\u001b[0m     seed(model, rng_key \u001b[38;5;28;01mif\u001b[39;00m is_prng_key(rng_key) \u001b[38;5;28;01melse\u001b[39;00m rng_key[\u001b[38;5;241m0\u001b[39m]),\n\u001b[0;32m    649\u001b[0m     substitute_fn\u001b[38;5;241m=\u001b[39minit_strategy,\n\u001b[0;32m    650\u001b[0m )\n\u001b[0;32m    651\u001b[0m (\n\u001b[0;32m    652\u001b[0m     inv_transforms,\n\u001b[0;32m    653\u001b[0m     replay_model,\n\u001b[0;32m    654\u001b[0m     has_enumerate_support,\n\u001b[0;32m    655\u001b[0m     model_trace,\n\u001b[1;32m--> 656\u001b[0m ) \u001b[38;5;241m=\u001b[39m \u001b[43m_get_model_transforms\u001b[49m\u001b[43m(\u001b[49m\u001b[43msubstituted_model\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    657\u001b[0m \u001b[38;5;66;03m# substitute param sites from model_trace to model so\u001b[39;00m\n\u001b[0;32m    658\u001b[0m \u001b[38;5;66;03m# we don't need to generate again parameters of `numpyro.module`\u001b[39;00m\n\u001b[0;32m    659\u001b[0m model \u001b[38;5;241m=\u001b[39m substitute(\n\u001b[0;32m    660\u001b[0m     model,\n\u001b[0;32m    661\u001b[0m     data\u001b[38;5;241m=\u001b[39m{\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    665\u001b[0m     },\n\u001b[0;32m    666\u001b[0m )\n",
-      "File \u001b[1;32mc:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\infer\\util.py:450\u001b[0m, in \u001b[0;36m_get_model_transforms\u001b[1;34m(model, model_args, model_kwargs)\u001b[0m\n\u001b[0;32m    448\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21m_get_model_transforms\u001b[39m(model, model_args\u001b[38;5;241m=\u001b[39m(), model_kwargs\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[0;32m    449\u001b[0m     model_kwargs \u001b[38;5;241m=\u001b[39m {} \u001b[38;5;28;01mif\u001b[39;00m model_kwargs \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m model_kwargs\n\u001b[1;32m--> 450\u001b[0m     model_trace \u001b[38;5;241m=\u001b[39m \u001b[43mtrace\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_trace\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mmodel_args\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[43mmodel_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    451\u001b[0m     inv_transforms \u001b[38;5;241m=\u001b[39m {}\n\u001b[0;32m    452\u001b[0m     \u001b[38;5;66;03m# model code may need to be replayed in the presence of deterministic sites\u001b[39;00m\n",
-      "File \u001b[1;32mc:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\handlers.py:171\u001b[0m, in \u001b[0;36mtrace.get_trace\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    163\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mget_trace\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m    164\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    165\u001b[0m \u001b[38;5;124;03m    Run the wrapped callable and return the recorded trace.\u001b[39;00m\n\u001b[0;32m    166\u001b[0m \n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    169\u001b[0m \u001b[38;5;124;03m    :return: `OrderedDict` containing the execution trace.\u001b[39;00m\n\u001b[0;32m    170\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 171\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\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[0;32m    172\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrace\n",
-      "File \u001b[1;32mc:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\primitives.py:105\u001b[0m, in \u001b[0;36mMessenger.__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    103\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\n\u001b[0;32m    104\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m:\n\u001b[1;32m--> 105\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\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",
-      "File \u001b[1;32mc:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\primitives.py:105\u001b[0m, in \u001b[0;36mMessenger.__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    103\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\n\u001b[0;32m    104\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m:\n\u001b[1;32m--> 105\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\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",
-      "File \u001b[1;32mc:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\primitives.py:105\u001b[0m, in \u001b[0;36mMessenger.__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    103\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\n\u001b[0;32m    104\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m:\n\u001b[1;32m--> 105\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\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",
-      "File \u001b[1;32m~\\pymob\\pymob\\pymob\\inference\\numpyro_backend.py:485\u001b[0m, in \u001b[0;36mNumpyroBackend.parse_probabilistic_model.<locals>.model\u001b[1;34m(solver, obs, masks, only_prior, user_error_model, make_predictions)\u001b[0m\n\u001b[0;32m    483\u001b[0m y0 \u001b[38;5;241m=\u001b[39m {k\u001b[38;5;241m.\u001b[39mreplace(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_y0\u001b[39m\u001b[38;5;124m\"\u001b[39m,\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m): v \u001b[38;5;28;01mfor\u001b[39;00m k, v \u001b[38;5;129;01min\u001b[39;00m theta_\u001b[38;5;241m.\u001b[39mitems() \u001b[38;5;28;01mif\u001b[39;00m k \u001b[38;5;129;01min\u001b[39;00m data_variables_y0}\n\u001b[0;32m    484\u001b[0m theta \u001b[38;5;241m=\u001b[39m {k: v \u001b[38;5;28;01mfor\u001b[39;00m k, v \u001b[38;5;129;01min\u001b[39;00m theta_\u001b[38;5;241m.\u001b[39mitems() \u001b[38;5;28;01mif\u001b[39;00m k \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m data_variables_y0}\n\u001b[1;32m--> 485\u001b[0m sim_results \u001b[38;5;241m=\u001b[39m \u001b[43msolver\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtheta\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtheta\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my0\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43my0\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    487\u001b[0m \u001b[38;5;66;03m# store data_variables as deterministic model output\u001b[39;00m\n\u001b[0;32m    488\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m deterministic_name, deterministic_value \u001b[38;5;129;01min\u001b[39;00m sim_results\u001b[38;5;241m.\u001b[39mitems():\n",
-      "File \u001b[1;32m~\\pymob\\pymob\\pymob\\inference\\numpyro_backend.py:261\u001b[0m, in \u001b[0;36mNumpyroBackend.parse_deterministic_model.<locals>.evaluator\u001b[1;34m(theta, y0, x_in, seed)\u001b[0m\n\u001b[0;32m    259\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mevaluator\u001b[39m(theta, y0\u001b[38;5;241m=\u001b[39m{}, x_in\u001b[38;5;241m=\u001b[39m{}, seed\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[0;32m    260\u001b[0m     evaluator \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msimulation\u001b[38;5;241m.\u001b[39mdispatch(theta\u001b[38;5;241m=\u001b[39mtheta, y0\u001b[38;5;241m=\u001b[39my0, x_in\u001b[38;5;241m=\u001b[39mx_in)\n\u001b[1;32m--> 261\u001b[0m     \u001b[43mevaluator\u001b[49m\u001b[43m(\u001b[49m\u001b[43mseed\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    262\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m evaluator\u001b[38;5;241m.\u001b[39mY\n",
-      "File \u001b[1;32m~\\pymob\\pymob\\pymob\\sim\\evaluator.py:351\u001b[0m, in \u001b[0;36mEvaluator.__call__\u001b[1;34m(self, seed)\u001b[0m\n\u001b[0;32m    348\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_signature\u001b[38;5;241m.\u001b[39mupdate({\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mseed\u001b[39m\u001b[38;5;124m\"\u001b[39m: seed})\n\u001b[0;32m    350\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_solver, SolverBase):\n\u001b[1;32m--> 351\u001b[0m     Y_ \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_solver\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparameters\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    353\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    354\u001b[0m     Y_ \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_solver(parameters\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mparameters, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_signature)\n",
-      "File \u001b[1;32m~\\pymob\\pymob\\pymob\\solvers\\base.py:82\u001b[0m, in \u001b[0;36mSolverBase.__call__\u001b[1;34m(self, **kwargs)\u001b[0m\n\u001b[0;32m     81\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m---> 82\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msolve\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;31m[... skipping hidden 10 frame]\u001b[0m\n",
-      "File \u001b[1;32mc:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\interpreters\\pxla.py:1145\u001b[0m, in \u001b[0;36mExecuteReplicated.__call__\u001b[1;34m(self, *args)\u001b[0m\n\u001b[0;32m   1142\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mordered_effects \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhas_unordered_effects\n\u001b[0;32m   1143\u001b[0m     \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhas_host_callbacks):\n\u001b[0;32m   1144\u001b[0m   input_bufs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_add_tokens_to_inputs(input_bufs)\n\u001b[1;32m-> 1145\u001b[0m   results \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mxla_executable\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexecute_sharded\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   1146\u001b[0m \u001b[43m      \u001b[49m\u001b[43minput_bufs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwith_tokens\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\n\u001b[0;32m   1147\u001b[0m \u001b[43m  \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1148\u001b[0m   result_token_bufs \u001b[38;5;241m=\u001b[39m results\u001b[38;5;241m.\u001b[39mdisassemble_prefix_into_single_device_arrays(\n\u001b[0;32m   1149\u001b[0m       \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mordered_effects))\n\u001b[0;32m   1150\u001b[0m   sharded_runtime_token \u001b[38;5;241m=\u001b[39m results\u001b[38;5;241m.\u001b[39mconsume_token()\n",
-      "\u001b[1;31mXlaRuntimeError\u001b[0m: INTERNAL: Generated function failed: CpuCallback error: _EquinoxRuntimeError: The maximum number of solver steps was reached. Try increasing `max_steps`.\n\n\n--------------------\nAn error occurred during the runtime of your JAX program! Unfortunately you do not appear to be using `equinox.filter_jit` (perhaps you are using `jax.jit` instead?) and so further information about the error cannot be displayed. (Probably you are seeing a very large but uninformative error message right now.) Please wrap your program with `equinox.filter_jit`.\n--------------------\n\n\nAt:\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\equinox\\_errors.py(89): raises\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\callback.py(258): _flat_callback\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\callback.py(52): pure_callback_impl\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\callback.py(188): _callback\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\interpreters\\mlir.py(2327): _wrapped_callback\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\interpreters\\pxla.py(1145): __call__\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\profiler.py(334): wrapper\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\pjit.py(1178): _pjit_call_impl_python\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\pjit.py(1222): call_impl_cache_miss\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\pjit.py(1238): _pjit_call_impl\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\core.py(893): process_primitive\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\core.py(405): bind_with_trace\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\core.py(2682): bind\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\pjit.py(166): _python_pjit_helper\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\pjit.py(255): cache_miss\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\traceback_util.py(177): reraise_with_filtered_traceback\n  C:\\Users\\Markus\\pymob\\pymob\\pymob\\solvers\\base.py(82): __call__\n  C:\\Users\\Markus\\pymob\\pymob\\pymob\\sim\\evaluator.py(351): __call__\n  C:\\Users\\Markus\\pymob\\pymob\\pymob\\inference\\numpyro_backend.py(261): evaluator\n  C:\\Users\\Markus\\pymob\\pymob\\pymob\\inference\\numpyro_backend.py(485): model\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\primitives.py(105): __call__\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\primitives.py(105): __call__\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\primitives.py(105): __call__\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\handlers.py(171): get_trace\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\infer\\util.py(450): _get_model_transforms\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\infer\\util.py(656): initialize_model\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\infer\\hmc.py(657): _init_state\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\infer\\hmc.py(713): init\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\infer\\mcmc.py(416): _single_chain_mcmc\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\infer\\mcmc.py(634): run\n  C:\\Users\\Markus\\pymob\\pymob\\pymob\\inference\\numpyro_backend.py(652): run_mcmc\n  C:\\Users\\Markus\\pymob\\pymob\\pymob\\inference\\numpyro_backend.py(566): run\n  C:\\Users\\Markus\\AppData\\Local\\Temp\\ipykernel_10328\\906244579.py(15): <module>\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\IPython\\core\\interactiveshell.py(3548): run_code\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\IPython\\core\\interactiveshell.py(3488): run_ast_nodes\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\IPython\\core\\interactiveshell.py(3306): run_cell_async\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\IPython\\core\\async_helpers.py(129): _pseudo_sync_runner\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\IPython\\core\\interactiveshell.py(3101): _run_cell\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\IPython\\core\\interactiveshell.py(3046): run_cell\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel\\zmqshell.py(549): run_cell\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel\\ipkernel.py(449): do_execute\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel\\kernelbase.py(778): execute_request\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel\\ipkernel.py(362): execute_request\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel\\kernelbase.py(437): dispatch_shell\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel\\kernelbase.py(534): process_one\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel\\kernelbase.py(545): dispatch_queue\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\asyncio\\events.py(84): _run\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\asyncio\\base_events.py(1936): _run_once\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\asyncio\\base_events.py(608): run_forever\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\tornado\\platform\\asyncio.py(211): start\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel\\kernelapp.py(739): start\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\traitlets\\config\\application.py(1075): launch_instance\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel_launcher.py(18): <module>\n  <frozen runpy>(88): _run_code\n  <frozen runpy>(198): _run_module_as_main\n"
+      "              value 101 |\n",
+      "An error occurred: XlaRuntimeError : INTERNAL: Generated function failed: CpuCallback error: _EquinoxRuntimeError: The maximum number of solver steps was reached. Try increasing `max_steps`.\n",
+      "\n",
+      "\n",
+      "--------------------\n",
+      "An error occurred during the runtime of your JAX program! Unfortunately you do not appear to be using `equinox.filter_jit` (perhaps you are using `jax.jit` instead?) and so further information about the error cannot be displayed. (Probably you are seeing a very large but uninformative error message right now.) Please wrap your program with `equinox.filter_jit`.\n",
+      "--------------------\n",
+      "\n",
+      "\n",
+      "At:\n",
+      "  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\equinox\\_errors.py(89): raises\n",
+      "  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\callback.py(258): _flat_callback\n",
+      "  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\callback.py(52): pure_callback_impl\n",
+      "  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\callback.py(188): _callback\n",
+      "  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\interpreters\\mlir.py(2327): _wrapped_callback\n",
+      "  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\interpreters\\pxla.py(1145): __call__\n",
+      "  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\profiler.py(334): wrapper\n",
+      "  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\pjit.py(1178): _pjit_call_impl_python\n",
+      "  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\pjit.py(1222): call_impl_cache_miss\n",
+      "  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\pjit.py(1238): _pjit_call_impl\n",
+      "  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\core.py(893): process_primitive\n",
+      "  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\core.py(405): bind_with_trace\n",
+      "  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\core.py(2682): bind\n",
+      "  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\pjit.py(166): _python_pjit_helper\n",
+      "  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\pjit.py(255): cache_miss\n",
+      "  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\traceback_util.py(177): reraise_with_filtered_traceback\n",
+      "  C:\\Users\\Markus\\pymob\\pymob\\pymob\\solvers\\base.py(82): __call__\n",
+      "  C:\\Users\\Markus\\pymob\\pymob\\pymob\\sim\\evaluator.py(351): __call__\n",
+      "  C:\\Users\\Markus\\pymob\\pymob\\pymob\\inference\\numpyro_backend.py(261): evaluator\n",
+      "  C:\\Users\\Markus\\pymob\\pymob\\pymob\\inference\\numpyro_backend.py(485): model\n",
+      "  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\primitives.py(105): __call__\n",
+      "  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\primitives.py(105): __call__\n",
+      "  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\primitives.py(105): __call__\n",
+      "  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\handlers.py(171): get_trace\n",
+      "  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\infer\\util.py(450): _get_model_transforms\n",
+      "  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\infer\\util.py(656): initialize_model\n",
+      "  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\infer\\hmc.py(657): _init_state\n",
+      "  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\infer\\hmc.py(713): init\n",
+      "  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\infer\\mcmc.py(416): _single_chain_mcmc\n",
+      "  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\infer\\mcmc.py(634): run\n",
+      "  C:\\Users\\Markus\\pymob\\pymob\\pymob\\inference\\numpyro_backend.py(652): run_mcmc\n",
+      "  C:\\Users\\Markus\\pymob\\pymob\\pymob\\inference\\numpyro_backend.py(566): run\n",
+      "  C:\\Users\\Markus\\AppData\\Local\\Temp\\ipykernel_3884\\119426844.py(17): <module>\n",
+      "  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\IPython\\core\\interactiveshell.py(3548): run_code\n",
+      "  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\IPython\\core\\interactiveshell.py(3488): run_ast_nodes\n",
+      "  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\IPython\\core\\interactiveshell.py(3306): run_cell_async\n",
+      "  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\IPython\\core\\async_helpers.py(129): _pseudo_sync_runner\n",
+      "  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\IPython\\core\\interactiveshell.py(3101): _run_cell\n",
+      "  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\IPython\\core\\interactiveshell.py(3046): run_cell\n",
+      "  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel\\zmqshell.py(549): run_cell\n",
+      "  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel\\ipkernel.py(449): do_execute\n",
+      "  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel\\kernelbase.py(778): execute_request\n",
+      "  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel\\ipkernel.py(362): execute_request\n",
+      "  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel\\kernelbase.py(437): dispatch_shell\n",
+      "  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel\\kernelbase.py(534): process_one\n",
+      "  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel\\kernelbase.py(545): dispatch_queue\n",
+      "  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\asyncio\\events.py(84): _run\n",
+      "  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\asyncio\\base_events.py(1936): _run_once\n",
+      "  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\asyncio\\base_events.py(608): run_forever\n",
+      "  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\tornado\\platform\\asyncio.py(211): start\n",
+      "  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel\\kernelapp.py(739): start\n",
+      "  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\traitlets\\config\\application.py(1075): launch_instance\n",
+      "  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel_launcher.py(18): <module>\n",
+      "  <frozen runpy>(88): _run_code\n",
+      "  <frozen runpy>(198): _run_module_as_main\n",
+      "\n"
      ]
     }
    ],
@@ -924,14 +976,21 @@
     "# Choose a prior distribution for delta\n",
     "sim.config.model_parameters.delta.prior = \"uniform(loc=0.5,scale=1)\"\n",
     "\n",
-    "# Create the inferer (NumPyro backend, NUTS kernel) and let it do its work\n",
-    "sim.set_inferer(\"numpyro\")\n",
-    "sim.inferer.config.inference_numpyro.kernel = \"nuts\"\n",
-    "sim.inferer.run()\n",
+    "try:\n",
     "\n",
-    "# Plot the results\n",
-    "sim.config.simulation.x_dimension = \"time\"\n",
-    "sim.posterior_predictive_checks(pred_hdi_style={\"alpha\": 0.1})"
+    "    # Create the inferer (NumPyro backend, NUTS kernel) and let it do its work\n",
+    "    sim.set_inferer(\"numpyro\")\n",
+    "    sim.inferer.config.inference_numpyro.kernel = \"nuts\"\n",
+    "    sim.inferer.run()\n",
+    "\n",
+    "    # Plot the results\n",
+    "    sim.config.simulation.x_dimension = \"time\"\n",
+    "    sim.posterior_predictive_checks(pred_hdi_style={\"alpha\": 0.1})\n",
+    "\n",
+    "except XlaRuntimeError as e:\n",
+    "\n",
+    "    # Print the error message\n",
+    "    print(\"An error occurred:\", type(e).__name__, \":\", e)"
    ]
   },
   {
@@ -956,7 +1015,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 13,
    "id": "d31c1ce7",
    "metadata": {},
    "outputs": [
@@ -976,36 +1035,72 @@
       "      prey_obs dist 101 |\n",
       "              value 101 |\n",
       "  predator_obs dist 101 |\n",
-      "              value 101 |\n"
-     ]
-    },
-    {
-     "ename": "XlaRuntimeError",
-     "evalue": "INTERNAL: Generated function failed: CpuCallback error: _EquinoxRuntimeError: The maximum number of solver steps was reached. Try increasing `max_steps`.\n\n\n--------------------\nAn error occurred during the runtime of your JAX program! Unfortunately you do not appear to be using `equinox.filter_jit` (perhaps you are using `jax.jit` instead?) and so further information about the error cannot be displayed. (Probably you are seeing a very large but uninformative error message right now.) Please wrap your program with `equinox.filter_jit`.\n--------------------\n\n\nAt:\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\equinox\\_errors.py(89): raises\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\callback.py(258): _flat_callback\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\callback.py(52): pure_callback_impl\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\callback.py(188): _callback\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\interpreters\\mlir.py(2327): _wrapped_callback\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\interpreters\\pxla.py(1145): __call__\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\profiler.py(334): wrapper\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\pjit.py(1178): _pjit_call_impl_python\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\pjit.py(1222): call_impl_cache_miss\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\pjit.py(1238): _pjit_call_impl\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\core.py(893): process_primitive\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\core.py(405): bind_with_trace\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\core.py(2682): bind\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\pjit.py(166): _python_pjit_helper\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\pjit.py(255): cache_miss\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\traceback_util.py(177): reraise_with_filtered_traceback\n  C:\\Users\\Markus\\pymob\\pymob\\pymob\\solvers\\base.py(82): __call__\n  C:\\Users\\Markus\\pymob\\pymob\\pymob\\sim\\evaluator.py(351): __call__\n  C:\\Users\\Markus\\pymob\\pymob\\pymob\\inference\\numpyro_backend.py(261): evaluator\n  C:\\Users\\Markus\\pymob\\pymob\\pymob\\inference\\numpyro_backend.py(485): model\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\primitives.py(105): __call__\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\primitives.py(105): __call__\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\primitives.py(105): __call__\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\handlers.py(171): get_trace\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\infer\\util.py(450): _get_model_transforms\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\infer\\util.py(656): initialize_model\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\infer\\hmc.py(657): _init_state\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\infer\\hmc.py(713): init\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\infer\\mcmc.py(416): _single_chain_mcmc\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\infer\\mcmc.py(634): run\n  C:\\Users\\Markus\\pymob\\pymob\\pymob\\inference\\numpyro_backend.py(652): run_mcmc\n  C:\\Users\\Markus\\pymob\\pymob\\pymob\\inference\\numpyro_backend.py(566): run\n  C:\\Users\\Markus\\AppData\\Local\\Temp\\ipykernel_10328\\3769994282.py(8): <module>\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\IPython\\core\\interactiveshell.py(3548): run_code\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\IPython\\core\\interactiveshell.py(3488): run_ast_nodes\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\IPython\\core\\interactiveshell.py(3306): run_cell_async\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\IPython\\core\\async_helpers.py(129): _pseudo_sync_runner\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\IPython\\core\\interactiveshell.py(3101): _run_cell\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\IPython\\core\\interactiveshell.py(3046): run_cell\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel\\zmqshell.py(549): run_cell\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel\\ipkernel.py(449): do_execute\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel\\kernelbase.py(778): execute_request\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel\\ipkernel.py(362): execute_request\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel\\kernelbase.py(437): dispatch_shell\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel\\kernelbase.py(534): process_one\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel\\kernelbase.py(545): dispatch_queue\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\asyncio\\events.py(84): _run\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\asyncio\\base_events.py(1936): _run_once\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\asyncio\\base_events.py(608): run_forever\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\tornado\\platform\\asyncio.py(211): start\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel\\kernelapp.py(739): start\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\traitlets\\config\\application.py(1075): launch_instance\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel_launcher.py(18): <module>\n  <frozen runpy>(88): _run_code\n  <frozen runpy>(198): _run_module_as_main\n",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mXlaRuntimeError\u001b[0m                           Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn[11], line 8\u001b[0m\n\u001b[0;32m      5\u001b[0m sim\u001b[38;5;241m.\u001b[39mdispatch_constructor()\n\u001b[0;32m      7\u001b[0m \u001b[38;5;66;03m# Try running the inferer again\u001b[39;00m\n\u001b[1;32m----> 8\u001b[0m \u001b[43msim\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minferer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     10\u001b[0m \u001b[38;5;66;03m# Plot the results\u001b[39;00m\n\u001b[0;32m     11\u001b[0m sim\u001b[38;5;241m.\u001b[39mconfig\u001b[38;5;241m.\u001b[39msimulation\u001b[38;5;241m.\u001b[39mx_dimension \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtime\u001b[39m\u001b[38;5;124m\"\u001b[39m\n",
-      "File \u001b[1;32m~\\pymob\\pymob\\pymob\\inference\\numpyro_backend.py:566\u001b[0m, in \u001b[0;36mNumpyroBackend.run\u001b[1;34m(self, print_debug, render_model)\u001b[0m\n\u001b[0;32m    564\u001b[0m \u001b[38;5;66;03m# run inference\u001b[39;00m\n\u001b[0;32m    565\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkernel\u001b[38;5;241m.\u001b[39mlower() \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msa\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkernel\u001b[38;5;241m.\u001b[39mlower() \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnuts\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m--> 566\u001b[0m     sampler, mcmc \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_mcmc\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    567\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    568\u001b[0m \u001b[43m        \u001b[49m\u001b[43mkeys\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkeys\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    569\u001b[0m \u001b[43m        \u001b[49m\u001b[43mkernel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkernel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlower\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    570\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    572\u001b[0m     \u001b[38;5;66;03m# create arviz idata\u001b[39;00m\n\u001b[0;32m    573\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39midata \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnuts_posterior(\n\u001b[0;32m    574\u001b[0m         mcmc\u001b[38;5;241m=\u001b[39mmcmc, model\u001b[38;5;241m=\u001b[39mmodel, key\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mnext\u001b[39m(keys), obs\u001b[38;5;241m=\u001b[39mobs\n\u001b[0;32m    575\u001b[0m     )\n",
-      "File \u001b[1;32m~\\pymob\\pymob\\pymob\\inference\\numpyro_backend.py:652\u001b[0m, in \u001b[0;36mNumpyroBackend.run_mcmc\u001b[1;34m(self, model, keys, kernel)\u001b[0m\n\u001b[0;32m    642\u001b[0m mcmc \u001b[38;5;241m=\u001b[39m infer\u001b[38;5;241m.\u001b[39mMCMC(\n\u001b[0;32m    643\u001b[0m     sampler\u001b[38;5;241m=\u001b[39msampler,\n\u001b[0;32m    644\u001b[0m     num_warmup\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mwarmup,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    648\u001b[0m     progress_bar\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[0;32m    649\u001b[0m )\n\u001b[0;32m    651\u001b[0m \u001b[38;5;66;03m# run inference\u001b[39;00m\n\u001b[1;32m--> 652\u001b[0m \u001b[43mmcmc\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mnext\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mkeys\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    653\u001b[0m mcmc\u001b[38;5;241m.\u001b[39mprint_summary()\n\u001b[0;32m    655\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m sampler, mcmc\n",
-      "File \u001b[1;32mc:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\infer\\mcmc.py:634\u001b[0m, in \u001b[0;36mMCMC.run\u001b[1;34m(self, rng_key, extra_fields, init_params, *args, **kwargs)\u001b[0m\n\u001b[0;32m    632\u001b[0m map_args \u001b[38;5;241m=\u001b[39m (rng_key, init_state, init_params)\n\u001b[0;32m    633\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnum_chains \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m--> 634\u001b[0m     states_flat, last_state \u001b[38;5;241m=\u001b[39m \u001b[43mpartial_map_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmap_args\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    635\u001b[0m     states \u001b[38;5;241m=\u001b[39m tree_map(\u001b[38;5;28;01mlambda\u001b[39;00m x: x[jnp\u001b[38;5;241m.\u001b[39mnewaxis, \u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m], states_flat)\n\u001b[0;32m    636\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
-      "File \u001b[1;32mc:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\infer\\mcmc.py:416\u001b[0m, in \u001b[0;36mMCMC._single_chain_mcmc\u001b[1;34m(self, init, args, kwargs, collect_fields)\u001b[0m\n\u001b[0;32m    414\u001b[0m \u001b[38;5;66;03m# Check if _sample_fn is None, then we need to initialize the sampler.\u001b[39;00m\n\u001b[0;32m    415\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m init_state \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mor\u001b[39;00m (\u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msampler, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_sample_fn\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m--> 416\u001b[0m     new_init_state \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msampler\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minit\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    417\u001b[0m \u001b[43m        \u001b[49m\u001b[43mrng_key\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    418\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnum_warmup\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    419\u001b[0m \u001b[43m        \u001b[49m\u001b[43minit_params\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    420\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmodel_args\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    421\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmodel_kwargs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    422\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    423\u001b[0m     init_state \u001b[38;5;241m=\u001b[39m new_init_state \u001b[38;5;28;01mif\u001b[39;00m init_state \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m init_state\n\u001b[0;32m    424\u001b[0m sample_fn, postprocess_fn \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_cached_fns()\n",
-      "File \u001b[1;32mc:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\infer\\hmc.py:713\u001b[0m, in \u001b[0;36mHMC.init\u001b[1;34m(self, rng_key, num_warmup, init_params, model_args, model_kwargs)\u001b[0m\n\u001b[0;32m    708\u001b[0m \u001b[38;5;66;03m# vectorized\u001b[39;00m\n\u001b[0;32m    709\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    710\u001b[0m     rng_key, rng_key_init_model \u001b[38;5;241m=\u001b[39m jnp\u001b[38;5;241m.\u001b[39mswapaxes(\n\u001b[0;32m    711\u001b[0m         vmap(random\u001b[38;5;241m.\u001b[39msplit)(rng_key), \u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m    712\u001b[0m     )\n\u001b[1;32m--> 713\u001b[0m init_params \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_init_state\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    714\u001b[0m \u001b[43m    \u001b[49m\u001b[43mrng_key_init_model\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel_kwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minit_params\u001b[49m\n\u001b[0;32m    715\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    716\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_potential_fn \u001b[38;5;129;01mand\u001b[39;00m init_params \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    717\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m    718\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mValid value of `init_params` must be provided with\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m `potential_fn`.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    719\u001b[0m     )\n",
-      "File \u001b[1;32mc:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\infer\\hmc.py:657\u001b[0m, in \u001b[0;36mHMC._init_state\u001b[1;34m(self, rng_key, model_args, model_kwargs, init_params)\u001b[0m\n\u001b[0;32m    650\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21m_init_state\u001b[39m(\u001b[38;5;28mself\u001b[39m, rng_key, model_args, model_kwargs, init_params):\n\u001b[0;32m    651\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_model \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    652\u001b[0m         (\n\u001b[0;32m    653\u001b[0m             new_init_params,\n\u001b[0;32m    654\u001b[0m             potential_fn,\n\u001b[0;32m    655\u001b[0m             postprocess_fn,\n\u001b[0;32m    656\u001b[0m             model_trace,\n\u001b[1;32m--> 657\u001b[0m         ) \u001b[38;5;241m=\u001b[39m \u001b[43minitialize_model\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    658\u001b[0m \u001b[43m            \u001b[49m\u001b[43mrng_key\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    659\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_model\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    660\u001b[0m \u001b[43m            \u001b[49m\u001b[43mdynamic_args\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m    661\u001b[0m \u001b[43m            \u001b[49m\u001b[43minit_strategy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_init_strategy\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    662\u001b[0m \u001b[43m            \u001b[49m\u001b[43mmodel_args\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmodel_args\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    663\u001b[0m \u001b[43m            \u001b[49m\u001b[43mmodel_kwargs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmodel_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    664\u001b[0m \u001b[43m            \u001b[49m\u001b[43mforward_mode_differentiation\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_forward_mode_differentiation\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    665\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    666\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m init_params \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    667\u001b[0m             init_params \u001b[38;5;241m=\u001b[39m new_init_params\n",
-      "File \u001b[1;32mc:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\infer\\util.py:656\u001b[0m, in \u001b[0;36minitialize_model\u001b[1;34m(rng_key, model, init_strategy, dynamic_args, model_args, model_kwargs, forward_mode_differentiation, validate_grad)\u001b[0m\n\u001b[0;32m    646\u001b[0m model_kwargs \u001b[38;5;241m=\u001b[39m {} \u001b[38;5;28;01mif\u001b[39;00m model_kwargs \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m model_kwargs\n\u001b[0;32m    647\u001b[0m substituted_model \u001b[38;5;241m=\u001b[39m substitute(\n\u001b[0;32m    648\u001b[0m     seed(model, rng_key \u001b[38;5;28;01mif\u001b[39;00m is_prng_key(rng_key) \u001b[38;5;28;01melse\u001b[39;00m rng_key[\u001b[38;5;241m0\u001b[39m]),\n\u001b[0;32m    649\u001b[0m     substitute_fn\u001b[38;5;241m=\u001b[39minit_strategy,\n\u001b[0;32m    650\u001b[0m )\n\u001b[0;32m    651\u001b[0m (\n\u001b[0;32m    652\u001b[0m     inv_transforms,\n\u001b[0;32m    653\u001b[0m     replay_model,\n\u001b[0;32m    654\u001b[0m     has_enumerate_support,\n\u001b[0;32m    655\u001b[0m     model_trace,\n\u001b[1;32m--> 656\u001b[0m ) \u001b[38;5;241m=\u001b[39m \u001b[43m_get_model_transforms\u001b[49m\u001b[43m(\u001b[49m\u001b[43msubstituted_model\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    657\u001b[0m \u001b[38;5;66;03m# substitute param sites from model_trace to model so\u001b[39;00m\n\u001b[0;32m    658\u001b[0m \u001b[38;5;66;03m# we don't need to generate again parameters of `numpyro.module`\u001b[39;00m\n\u001b[0;32m    659\u001b[0m model \u001b[38;5;241m=\u001b[39m substitute(\n\u001b[0;32m    660\u001b[0m     model,\n\u001b[0;32m    661\u001b[0m     data\u001b[38;5;241m=\u001b[39m{\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    665\u001b[0m     },\n\u001b[0;32m    666\u001b[0m )\n",
-      "File \u001b[1;32mc:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\infer\\util.py:450\u001b[0m, in \u001b[0;36m_get_model_transforms\u001b[1;34m(model, model_args, model_kwargs)\u001b[0m\n\u001b[0;32m    448\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21m_get_model_transforms\u001b[39m(model, model_args\u001b[38;5;241m=\u001b[39m(), model_kwargs\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[0;32m    449\u001b[0m     model_kwargs \u001b[38;5;241m=\u001b[39m {} \u001b[38;5;28;01mif\u001b[39;00m model_kwargs \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m model_kwargs\n\u001b[1;32m--> 450\u001b[0m     model_trace \u001b[38;5;241m=\u001b[39m \u001b[43mtrace\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_trace\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mmodel_args\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[43mmodel_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    451\u001b[0m     inv_transforms \u001b[38;5;241m=\u001b[39m {}\n\u001b[0;32m    452\u001b[0m     \u001b[38;5;66;03m# model code may need to be replayed in the presence of deterministic sites\u001b[39;00m\n",
-      "File \u001b[1;32mc:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\handlers.py:171\u001b[0m, in \u001b[0;36mtrace.get_trace\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    163\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mget_trace\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m    164\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    165\u001b[0m \u001b[38;5;124;03m    Run the wrapped callable and return the recorded trace.\u001b[39;00m\n\u001b[0;32m    166\u001b[0m \n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    169\u001b[0m \u001b[38;5;124;03m    :return: `OrderedDict` containing the execution trace.\u001b[39;00m\n\u001b[0;32m    170\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 171\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\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[0;32m    172\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrace\n",
-      "File \u001b[1;32mc:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\primitives.py:105\u001b[0m, in \u001b[0;36mMessenger.__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    103\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\n\u001b[0;32m    104\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m:\n\u001b[1;32m--> 105\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\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",
-      "File \u001b[1;32mc:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\primitives.py:105\u001b[0m, in \u001b[0;36mMessenger.__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    103\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\n\u001b[0;32m    104\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m:\n\u001b[1;32m--> 105\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\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",
-      "File \u001b[1;32mc:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\primitives.py:105\u001b[0m, in \u001b[0;36mMessenger.__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    103\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\n\u001b[0;32m    104\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m:\n\u001b[1;32m--> 105\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\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",
-      "File \u001b[1;32m~\\pymob\\pymob\\pymob\\inference\\numpyro_backend.py:485\u001b[0m, in \u001b[0;36mNumpyroBackend.parse_probabilistic_model.<locals>.model\u001b[1;34m(solver, obs, masks, only_prior, user_error_model, make_predictions)\u001b[0m\n\u001b[0;32m    483\u001b[0m y0 \u001b[38;5;241m=\u001b[39m {k\u001b[38;5;241m.\u001b[39mreplace(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_y0\u001b[39m\u001b[38;5;124m\"\u001b[39m,\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m): v \u001b[38;5;28;01mfor\u001b[39;00m k, v \u001b[38;5;129;01min\u001b[39;00m theta_\u001b[38;5;241m.\u001b[39mitems() \u001b[38;5;28;01mif\u001b[39;00m k \u001b[38;5;129;01min\u001b[39;00m data_variables_y0}\n\u001b[0;32m    484\u001b[0m theta \u001b[38;5;241m=\u001b[39m {k: v \u001b[38;5;28;01mfor\u001b[39;00m k, v \u001b[38;5;129;01min\u001b[39;00m theta_\u001b[38;5;241m.\u001b[39mitems() \u001b[38;5;28;01mif\u001b[39;00m k \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m data_variables_y0}\n\u001b[1;32m--> 485\u001b[0m sim_results \u001b[38;5;241m=\u001b[39m \u001b[43msolver\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtheta\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtheta\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my0\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43my0\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    487\u001b[0m \u001b[38;5;66;03m# store data_variables as deterministic model output\u001b[39;00m\n\u001b[0;32m    488\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m deterministic_name, deterministic_value \u001b[38;5;129;01min\u001b[39;00m sim_results\u001b[38;5;241m.\u001b[39mitems():\n",
-      "File \u001b[1;32m~\\pymob\\pymob\\pymob\\inference\\numpyro_backend.py:261\u001b[0m, in \u001b[0;36mNumpyroBackend.parse_deterministic_model.<locals>.evaluator\u001b[1;34m(theta, y0, x_in, seed)\u001b[0m\n\u001b[0;32m    259\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mevaluator\u001b[39m(theta, y0\u001b[38;5;241m=\u001b[39m{}, x_in\u001b[38;5;241m=\u001b[39m{}, seed\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[0;32m    260\u001b[0m     evaluator \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msimulation\u001b[38;5;241m.\u001b[39mdispatch(theta\u001b[38;5;241m=\u001b[39mtheta, y0\u001b[38;5;241m=\u001b[39my0, x_in\u001b[38;5;241m=\u001b[39mx_in)\n\u001b[1;32m--> 261\u001b[0m     \u001b[43mevaluator\u001b[49m\u001b[43m(\u001b[49m\u001b[43mseed\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    262\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m evaluator\u001b[38;5;241m.\u001b[39mY\n",
-      "File \u001b[1;32m~\\pymob\\pymob\\pymob\\sim\\evaluator.py:351\u001b[0m, in \u001b[0;36mEvaluator.__call__\u001b[1;34m(self, seed)\u001b[0m\n\u001b[0;32m    348\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_signature\u001b[38;5;241m.\u001b[39mupdate({\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mseed\u001b[39m\u001b[38;5;124m\"\u001b[39m: seed})\n\u001b[0;32m    350\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_solver, SolverBase):\n\u001b[1;32m--> 351\u001b[0m     Y_ \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_solver\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparameters\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    353\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    354\u001b[0m     Y_ \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_solver(parameters\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mparameters, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_signature)\n",
-      "File \u001b[1;32m~\\pymob\\pymob\\pymob\\solvers\\base.py:82\u001b[0m, in \u001b[0;36mSolverBase.__call__\u001b[1;34m(self, **kwargs)\u001b[0m\n\u001b[0;32m     81\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m---> 82\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msolve\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;31m[... skipping hidden 10 frame]\u001b[0m\n",
-      "File \u001b[1;32mc:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\interpreters\\pxla.py:1145\u001b[0m, in \u001b[0;36mExecuteReplicated.__call__\u001b[1;34m(self, *args)\u001b[0m\n\u001b[0;32m   1142\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mordered_effects \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhas_unordered_effects\n\u001b[0;32m   1143\u001b[0m     \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhas_host_callbacks):\n\u001b[0;32m   1144\u001b[0m   input_bufs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_add_tokens_to_inputs(input_bufs)\n\u001b[1;32m-> 1145\u001b[0m   results \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mxla_executable\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexecute_sharded\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   1146\u001b[0m \u001b[43m      \u001b[49m\u001b[43minput_bufs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwith_tokens\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\n\u001b[0;32m   1147\u001b[0m \u001b[43m  \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1148\u001b[0m   result_token_bufs \u001b[38;5;241m=\u001b[39m results\u001b[38;5;241m.\u001b[39mdisassemble_prefix_into_single_device_arrays(\n\u001b[0;32m   1149\u001b[0m       \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mordered_effects))\n\u001b[0;32m   1150\u001b[0m   sharded_runtime_token \u001b[38;5;241m=\u001b[39m results\u001b[38;5;241m.\u001b[39mconsume_token()\n",
-      "\u001b[1;31mXlaRuntimeError\u001b[0m: INTERNAL: Generated function failed: CpuCallback error: _EquinoxRuntimeError: The maximum number of solver steps was reached. Try increasing `max_steps`.\n\n\n--------------------\nAn error occurred during the runtime of your JAX program! Unfortunately you do not appear to be using `equinox.filter_jit` (perhaps you are using `jax.jit` instead?) and so further information about the error cannot be displayed. (Probably you are seeing a very large but uninformative error message right now.) Please wrap your program with `equinox.filter_jit`.\n--------------------\n\n\nAt:\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\equinox\\_errors.py(89): raises\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\callback.py(258): _flat_callback\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\callback.py(52): pure_callback_impl\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\callback.py(188): _callback\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\interpreters\\mlir.py(2327): _wrapped_callback\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\interpreters\\pxla.py(1145): __call__\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\profiler.py(334): wrapper\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\pjit.py(1178): _pjit_call_impl_python\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\pjit.py(1222): call_impl_cache_miss\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\pjit.py(1238): _pjit_call_impl\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\core.py(893): process_primitive\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\core.py(405): bind_with_trace\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\core.py(2682): bind\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\pjit.py(166): _python_pjit_helper\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\pjit.py(255): cache_miss\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\traceback_util.py(177): reraise_with_filtered_traceback\n  C:\\Users\\Markus\\pymob\\pymob\\pymob\\solvers\\base.py(82): __call__\n  C:\\Users\\Markus\\pymob\\pymob\\pymob\\sim\\evaluator.py(351): __call__\n  C:\\Users\\Markus\\pymob\\pymob\\pymob\\inference\\numpyro_backend.py(261): evaluator\n  C:\\Users\\Markus\\pymob\\pymob\\pymob\\inference\\numpyro_backend.py(485): model\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\primitives.py(105): __call__\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\primitives.py(105): __call__\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\primitives.py(105): __call__\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\handlers.py(171): get_trace\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\infer\\util.py(450): _get_model_transforms\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\infer\\util.py(656): initialize_model\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\infer\\hmc.py(657): _init_state\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\infer\\hmc.py(713): init\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\infer\\mcmc.py(416): _single_chain_mcmc\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\infer\\mcmc.py(634): run\n  C:\\Users\\Markus\\pymob\\pymob\\pymob\\inference\\numpyro_backend.py(652): run_mcmc\n  C:\\Users\\Markus\\pymob\\pymob\\pymob\\inference\\numpyro_backend.py(566): run\n  C:\\Users\\Markus\\AppData\\Local\\Temp\\ipykernel_10328\\3769994282.py(8): <module>\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\IPython\\core\\interactiveshell.py(3548): run_code\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\IPython\\core\\interactiveshell.py(3488): run_ast_nodes\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\IPython\\core\\interactiveshell.py(3306): run_cell_async\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\IPython\\core\\async_helpers.py(129): _pseudo_sync_runner\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\IPython\\core\\interactiveshell.py(3101): _run_cell\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\IPython\\core\\interactiveshell.py(3046): run_cell\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel\\zmqshell.py(549): run_cell\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel\\ipkernel.py(449): do_execute\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel\\kernelbase.py(778): execute_request\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel\\ipkernel.py(362): execute_request\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel\\kernelbase.py(437): dispatch_shell\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel\\kernelbase.py(534): process_one\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel\\kernelbase.py(545): dispatch_queue\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\asyncio\\events.py(84): _run\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\asyncio\\base_events.py(1936): _run_once\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\asyncio\\base_events.py(608): run_forever\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\tornado\\platform\\asyncio.py(211): start\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel\\kernelapp.py(739): start\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\traitlets\\config\\application.py(1075): launch_instance\n  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel_launcher.py(18): <module>\n  <frozen runpy>(88): _run_code\n  <frozen runpy>(198): _run_module_as_main\n"
+      "              value 101 |\n",
+      "An error occurred: XlaRuntimeError : INTERNAL: Generated function failed: CpuCallback error: _EquinoxRuntimeError: The maximum number of solver steps was reached. Try increasing `max_steps`.\n",
+      "\n",
+      "\n",
+      "--------------------\n",
+      "An error occurred during the runtime of your JAX program! Unfortunately you do not appear to be using `equinox.filter_jit` (perhaps you are using `jax.jit` instead?) and so further information about the error cannot be displayed. (Probably you are seeing a very large but uninformative error message right now.) Please wrap your program with `equinox.filter_jit`.\n",
+      "--------------------\n",
+      "\n",
+      "\n",
+      "At:\n",
+      "  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\equinox\\_errors.py(89): raises\n",
+      "  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\callback.py(258): _flat_callback\n",
+      "  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\callback.py(52): pure_callback_impl\n",
+      "  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\callback.py(188): _callback\n",
+      "  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\interpreters\\mlir.py(2327): _wrapped_callback\n",
+      "  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\interpreters\\pxla.py(1145): __call__\n",
+      "  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\profiler.py(334): wrapper\n",
+      "  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\pjit.py(1178): _pjit_call_impl_python\n",
+      "  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\pjit.py(1222): call_impl_cache_miss\n",
+      "  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\pjit.py(1238): _pjit_call_impl\n",
+      "  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\core.py(893): process_primitive\n",
+      "  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\core.py(405): bind_with_trace\n",
+      "  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\core.py(2682): bind\n",
+      "  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\pjit.py(166): _python_pjit_helper\n",
+      "  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\pjit.py(255): cache_miss\n",
+      "  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\traceback_util.py(177): reraise_with_filtered_traceback\n",
+      "  C:\\Users\\Markus\\pymob\\pymob\\pymob\\solvers\\base.py(82): __call__\n",
+      "  C:\\Users\\Markus\\pymob\\pymob\\pymob\\sim\\evaluator.py(351): __call__\n",
+      "  C:\\Users\\Markus\\pymob\\pymob\\pymob\\inference\\numpyro_backend.py(261): evaluator\n",
+      "  C:\\Users\\Markus\\pymob\\pymob\\pymob\\inference\\numpyro_backend.py(485): model\n",
+      "  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\primitives.py(105): __call__\n",
+      "  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\primitives.py(105): __call__\n",
+      "  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\primitives.py(105): __call__\n",
+      "  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\handlers.py(171): get_trace\n",
+      "  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\infer\\util.py(450): _get_model_transforms\n",
+      "  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\infer\\util.py(656): initialize_model\n",
+      "  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\infer\\hmc.py(657): _init_state\n",
+      "  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\infer\\hmc.py(713): init\n",
+      "  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\infer\\mcmc.py(416): _single_chain_mcmc\n",
+      "  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\numpyro\\infer\\mcmc.py(634): run\n",
+      "  C:\\Users\\Markus\\pymob\\pymob\\pymob\\inference\\numpyro_backend.py(652): run_mcmc\n",
+      "  C:\\Users\\Markus\\pymob\\pymob\\pymob\\inference\\numpyro_backend.py(566): run\n",
+      "  C:\\Users\\Markus\\AppData\\Local\\Temp\\ipykernel_3884\\2085724305.py(10): <module>\n",
+      "  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\IPython\\core\\interactiveshell.py(3548): run_code\n",
+      "  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\IPython\\core\\interactiveshell.py(3488): run_ast_nodes\n",
+      "  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\IPython\\core\\interactiveshell.py(3306): run_cell_async\n",
+      "  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\IPython\\core\\async_helpers.py(129): _pseudo_sync_runner\n",
+      "  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\IPython\\core\\interactiveshell.py(3101): _run_cell\n",
+      "  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\IPython\\core\\interactiveshell.py(3046): run_cell\n",
+      "  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel\\zmqshell.py(549): run_cell\n",
+      "  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel\\ipkernel.py(449): do_execute\n",
+      "  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel\\kernelbase.py(778): execute_request\n",
+      "  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel\\ipkernel.py(362): execute_request\n",
+      "  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel\\kernelbase.py(437): dispatch_shell\n",
+      "  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel\\kernelbase.py(534): process_one\n",
+      "  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel\\kernelbase.py(545): dispatch_queue\n",
+      "  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\asyncio\\events.py(84): _run\n",
+      "  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\asyncio\\base_events.py(1936): _run_once\n",
+      "  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\asyncio\\base_events.py(608): run_forever\n",
+      "  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\tornado\\platform\\asyncio.py(211): start\n",
+      "  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel\\kernelapp.py(739): start\n",
+      "  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\traitlets\\config\\application.py(1075): launch_instance\n",
+      "  c:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\ipykernel_launcher.py(18): <module>\n",
+      "  <frozen runpy>(88): _run_code\n",
+      "  <frozen runpy>(198): _run_module_as_main\n",
+      "\n"
      ]
     }
    ],
@@ -1016,12 +1111,19 @@
     "# Put everything in place (needs to be run again because we changed an important setting)\n",
     "sim.dispatch_constructor()\n",
     "\n",
-    "# Try running the inferer again\n",
-    "sim.inferer.run()\n",
+    "try:\n",
     "\n",
-    "# Plot the results\n",
-    "sim.config.simulation.x_dimension = \"time\"\n",
-    "sim.posterior_predictive_checks(pred_hdi_style={\"alpha\": 0.1})"
+    "    # Try running the inferer again\n",
+    "    sim.inferer.run()\n",
+    "\n",
+    "    # Plot the results\n",
+    "    sim.config.simulation.x_dimension = \"time\"\n",
+    "    sim.posterior_predictive_checks(pred_hdi_style={\"alpha\": 0.1})\n",
+    "\n",
+    "except XlaRuntimeError as e:\n",
+    "\n",
+    "    # Print the error message\n",
+    "    print(\"An error occurred:\", type(e).__name__, \":\", e)"
    ]
   },
   {
@@ -1034,7 +1136,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 14,
    "id": "badbb5e0",
    "metadata": {},
    "outputs": [
@@ -1061,7 +1163,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "sample: 100%|██████████| 3000/3000 [00:18<00:00, 163.14it/s, 15 steps of size 4.32e-01. acc. prob=0.93]\n"
+      "sample: 100%|██████████| 3000/3000 [00:15<00:00, 188.91it/s, 15 steps of size 4.32e-01. acc. prob=0.93]\n"
      ]
     },
     {
@@ -1096,12 +1198,19 @@
     "# Put everything in place (needs to be run again because we changed an important setting)\n",
     "sim.dispatch_constructor()\n",
     "\n",
-    "# Try running the inferer again\n",
-    "sim.inferer.run()\n",
+    "try:\n",
     "\n",
-    "# Plot the results\n",
-    "sim.config.simulation.x_dimension = \"time\"\n",
-    "sim.posterior_predictive_checks(pred_hdi_style={\"alpha\": 0.1})"
+    "    # Try running the inferer again\n",
+    "    sim.inferer.run()\n",
+    "\n",
+    "    # Plot the results\n",
+    "    sim.config.simulation.x_dimension = \"time\"\n",
+    "    sim.posterior_predictive_checks(pred_hdi_style={\"alpha\": 0.1})\n",
+    "\n",
+    "except XlaRuntimeError as e:\n",
+    "\n",
+    "    # Print the error message\n",
+    "    print(\"An error occurred:\", type(e).__name__, \":\", e)"
    ]
   },
   {
@@ -1114,7 +1223,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 15,
    "id": "4af0a3f3",
    "metadata": {},
    "outputs": [
@@ -1155,12 +1264,15 @@
     "\n",
     "👉 Let's start by **saving** our configuration and observations.\n",
     "\n",
-    "(Note: The observations have to be saved before the configuration. Otherwise the configuration doesn't save the location the observations were saved in which causes problems down the line.)"
+    "```{admonition} Caution\n",
+    ":class: caution\n",
+    "The observations have to be saved before the configuration. Otherwise the configuration doesn't save the location the observations were saved in which causes problems down the line.\n",
+    "```"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 16,
    "id": "497891c1",
    "metadata": {},
    "outputs": [
@@ -1197,7 +1309,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 17,
    "id": "bef01c1f",
    "metadata": {},
    "outputs": [],
@@ -1215,13 +1327,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 18,
    "id": "c6fafa7e",
    "metadata": {},
    "outputs": [],
    "source": [
     "# Load configuration to a Config instance\n",
-    "config = Config(\"case_studies\\\\ODEtutorial\\\\scenarios\\\\lotkavolterra\\\\settings.cfg\")\n",
+    "config = Config(\"case_studies/ODEtutorial/scenarios/lotkavolterra/settings.cfg\")\n",
     "\n",
     "# Create a new simulation from the configuration\n",
     "sim2 = SimulationBase(config)"
@@ -1237,7 +1349,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 19,
    "id": "6ba0762d",
    "metadata": {},
    "outputs": [
@@ -1247,7 +1359,7 @@
        "True"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1265,12 +1377,15 @@
     "\n",
     "👉 We do, however, still need to specify some additional features of the {class}`pymob.sim.SimulationBase` object. That includes the model, its parameters and the solver.\n",
     "\n",
-    "(Note: By subclassing {class}`pymob.solvers.diffrax.JaxSolver` and writing a customized `initialize()` function that also includes these tasks, this can be avoided. But for now, we will keep it simple and do it manually.)"
+    "```{admonition} Subclassing SimulationBase\n",
+    ":class: note\n",
+    "By subclassing {class}`pymob.SimulationBase` and writing a customized `initialize()` function that also includes these tasks, this can be avoided (see the last three cells of this notebook). But for now, we will keep it simple and do it manually.\n",
+    "```"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 20,
    "id": "c3621119",
    "metadata": {},
    "outputs": [
@@ -1313,44 +1428,44 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 21,
    "id": "69c0aaad",
    "metadata": {},
    "outputs": [
     {
-     "ename": "ValueError",
-     "evalue": "vmap in_axes must be an int, None, or a tuple of entries corresponding to the positional arguments passed to the function, but got len(in_axes)=6, len(args)=4",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn[19], line 6\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[38;5;66;03m# Create an evaluator, run the simulation and obtain the results\u001b[39;00m\n\u001b[0;32m      5\u001b[0m evaluator2 \u001b[38;5;241m=\u001b[39m sim2\u001b[38;5;241m.\u001b[39mdispatch(theta\u001b[38;5;241m=\u001b[39m{\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdelta\u001b[39m\u001b[38;5;124m\"\u001b[39m:\u001b[38;5;241m0.9\u001b[39m})\n\u001b[1;32m----> 6\u001b[0m \u001b[43mevaluator2\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m      7\u001b[0m data_res2 \u001b[38;5;241m=\u001b[39m evaluator2\u001b[38;5;241m.\u001b[39mresults\n\u001b[0;32m      9\u001b[0m \u001b[38;5;66;03m# Plot the results\u001b[39;00m\n",
-      "File \u001b[1;32m~\\pymob\\pymob\\pymob\\sim\\evaluator.py:351\u001b[0m, in \u001b[0;36mEvaluator.__call__\u001b[1;34m(self, seed)\u001b[0m\n\u001b[0;32m    348\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_signature\u001b[38;5;241m.\u001b[39mupdate({\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mseed\u001b[39m\u001b[38;5;124m\"\u001b[39m: seed})\n\u001b[0;32m    350\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_solver, SolverBase):\n\u001b[1;32m--> 351\u001b[0m     Y_ \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_solver\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparameters\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    353\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    354\u001b[0m     Y_ \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_solver(parameters\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mparameters, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_signature)\n",
-      "File \u001b[1;32m~\\pymob\\pymob\\pymob\\solvers\\base.py:82\u001b[0m, in \u001b[0;36mSolverBase.__call__\u001b[1;34m(self, **kwargs)\u001b[0m\n\u001b[0;32m     81\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m---> 82\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msolve\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;31m[... skipping hidden 12 frame]\u001b[0m\n",
-      "File \u001b[1;32m~\\pymob\\pymob\\pymob\\solvers\\diffrax.py:129\u001b[0m, in \u001b[0;36mJaxSolver.solve\u001b[1;34m(self, parameters, y0, x_in)\u001b[0m\n\u001b[0;32m    112\u001b[0m initialized_eval_func \u001b[38;5;241m=\u001b[39m partial(\n\u001b[0;32m    113\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39modesolve_splitargs,\n\u001b[0;32m    114\u001b[0m     odestates \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtuple\u001b[39m(y0\u001b[38;5;241m.\u001b[39mkeys()),\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    117\u001b[0m     n_xin\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mlen\u001b[39m(x_in_flat)\n\u001b[0;32m    118\u001b[0m )\n\u001b[0;32m    120\u001b[0m loop_eval \u001b[38;5;241m=\u001b[39m jax\u001b[38;5;241m.\u001b[39mvmap(\n\u001b[0;32m    121\u001b[0m     initialized_eval_func, \n\u001b[0;32m    122\u001b[0m     in_axes\u001b[38;5;241m=\u001b[39m(\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    127\u001b[0m     )\n\u001b[0;32m    128\u001b[0m )\n\u001b[1;32m--> 129\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mloop_eval\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mY_0\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mode_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mpp_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mx_in_flat\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    131\u001b[0m \u001b[38;5;66;03m# if self.batch_dimension not in self.coordinates:    \u001b[39;00m\n\u001b[0;32m    132\u001b[0m \u001b[38;5;66;03m# this is not yet stable, because it may remove extra dimensions\u001b[39;00m\n\u001b[0;32m    133\u001b[0m \u001b[38;5;66;03m# if there is a batch dimension of explicitly one specified\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    136\u001b[0m \u001b[38;5;66;03m# this is added at the 0-axis\u001b[39;00m\n\u001b[0;32m    137\u001b[0m \u001b[38;5;66;03m# if parameters are scalars, the returned shape is \u001b[39;00m\n\u001b[0;32m    138\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m v, val \u001b[38;5;129;01min\u001b[39;00m result\u001b[38;5;241m.\u001b[39mitems():\n",
-      "    \u001b[1;31m[... skipping hidden 1 frame]\u001b[0m\n",
-      "File \u001b[1;32mc:\\Users\\Markus\\anaconda3\\envs\\pymob2\\Lib\\site-packages\\jax\\_src\\api.py:1249\u001b[0m, in \u001b[0;36mvmap.<locals>.vmap_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m   1245\u001b[0m \u001b[38;5;129m@wraps\u001b[39m(fun, docstr\u001b[38;5;241m=\u001b[39mdocstr)\n\u001b[0;32m   1246\u001b[0m \u001b[38;5;129m@api_boundary\u001b[39m\n\u001b[0;32m   1247\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mvmap_f\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m   1248\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(in_axes, \u001b[38;5;28mtuple\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(in_axes) \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28mlen\u001b[39m(args):\n\u001b[1;32m-> 1249\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvmap in_axes must be an int, None, or a tuple of entries corresponding \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   1250\u001b[0m                      \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mto the positional arguments passed to the function, \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   1251\u001b[0m                      \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbut got \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(in_axes)\u001b[38;5;132;01m=}\u001b[39;00m\u001b[38;5;124m, \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(args)\u001b[38;5;132;01m=}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m   1252\u001b[0m   args_flat, in_tree  \u001b[38;5;241m=\u001b[39m tree_flatten((args, kwargs), is_leaf\u001b[38;5;241m=\u001b[39mbatching\u001b[38;5;241m.\u001b[39mis_vmappable)\n\u001b[0;32m   1253\u001b[0m   f \u001b[38;5;241m=\u001b[39m lu\u001b[38;5;241m.\u001b[39mwrap_init(fun)\n",
-      "\u001b[1;31mValueError\u001b[0m: vmap in_axes must be an int, None, or a tuple of entries corresponding to the positional arguments passed to the function, but got len(in_axes)=6, len(args)=4"
-     ]
+     "data": {
+      "image/png": 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zUx52BwTCKsYfQB2Be47YAQLAgmcWHVXu20UjQwMhXydr6xs1PAfv/Pldhh8f7tFjb2ynIkWrhnIg46xrdHjI2IgAuH4LXQN5qCTmilkVWF/MgXpkeFDct+Hm3jlTYu06WDNQqKmpEecS5sCeor2lmaan1UFgaOvIUOgHMg5iWEQxNA6eY1ZDc++wUH9hdhHABpx7nnt0UYk9MIIByC6WIzvg+ebO083qGDPZTnE2ArzwwgvCIgitHjAqnZwYp6N7Xgl55SLuT2B0eJCe/tc94vO1a9eJ1yOvvWgMDx0JcbEH0NnWKl7hOwiMDMmFfrGgXXkQwjIrPDycppGtjI1QXW0tZSXF0IF9e52oVAwIxfyxUMAHPvABMaCzv1caIseoazg6NGj9QIbRBzzuAIC1CD6HKhEX7JprrhECCjhCYzcC+g4f/rbx90TG/c89DfTz+58S84jgWA16Cqjc/xoN9nYaGVkoP1C4FhBBAJe9/1bKLioX3/vvfx1ZZ6jVx7CLNSuhOBt773vfawwyBL2IfsBQRkufVCbueepfNDzQR/nFZfSFb3xXfO/o7pcpI0FacI2GckY2RTQ2Mkz9/X3GwEt4FE5NTtDwiKPFwGrwZB1DJs3ZGHpq4+ITjA2KGWblInQCswnjrIjhYXmt4pNSKT0j08iyWQBi2UCGIAWjSXwAcHXG5xgih4It6jCYNgrDSAx44w+YR1oBrb2jwpNv55P/Fl+juRtmnBs2nyR2S9WvyRHnuBDDIbxQPPLII8JVOzU9g9ZsO5fWnX6h+P6f/nY/LYb6GO41pkphCoudIXB45/PUMyTrEaEIPPzNvSM0NTlJO/4tN1inX3kDZa3YTFHRsdTb2UaVRw6FNLWIY8Tz1dcl6ypgbZCR8UI/qKhG1JUwGDMrK0t48J155pkzHNgBGP/yoMhTTz1VuOAzUN7AMw6TXWTumIYMt3cGfhbtRDDSzc7OFua8qGkxzj33XGGACzNhuOpfcsklwj0eZrpmYJOIf//zn/8svoZbPcagYK4ZBnoie2lr7zCyTvgKAheedSrFx0SKv+OOWpzvHDC9+swzzwjz3ri4OPF30b87FyBww7qN34n/h7lhZiAJgcM+3ieuDcyBkS0yvvGNbwhGCyzP1q1bac3KCnrz9dfFv/3oju/T6VukKQY2KOgrxvmxVCDDCWdvLfMH0mHY+rv7N3zwhbICbTM5OUF7nn1YfA3nEeCcS2XT9u7nHhWml6FeJ2ORx5lvuYYiIqPo/EvlQv/is09RV59jGGUoABsP1+nCGJGBh+2ss84STjJ4wBMSE2mgt4te37ObQhVQzg6PTdKhHc9QY30tJSSn0kkXXkWH24apYtOp4meee+ZJ47x44/aP5xGbg2B8iPVALNAjItsEcnJyxCuun1kQ8fnPf16obrFgwmABM7EQSDB6xQyMOfnxj38sFvjMzEwRuHjhxCYHwQDtNXB7h8s7T4RGbe38888XCzo26Ag+2PyhNGIG/j7PBcPYFrjewz0e7BQDojFI7K+++mrxNdqI8HsQ2LC5R7b50Zs+Iuqcw0ODxvyv+//9KB2tqhOqVHdY6Dn48pe/LM4BjgPO+h/84Adnvf5439j4oaQCh34EJQzRNAPvs6CgQCieQd8jUYF69h//+If4d/w83gcmS7+yazftO1ZN5553nmC4EEx/+NOf0xt794vgh/lvP/3pT8lSDdGhDtA2x17fTv1d7ZSUkmo0d2897zKi279O+3fvoPGBLqKoZCHBz0wMPSduZMSPPfaY+HzThW8Xrx+++gL60edyqLu9hf73rw/R1z7+Xgo1apHrY6B3EMh4oQLwAJ19/kX06L//Ra888wR97r0ycIcakI0Br/xb1m+v+8CHKComVog/Vm09iw7tfI6eeepJuvKUd4jvoSkaXoWeAAHwF8+doGDglvMqRDPwoKqrIFPi+lhiYhK1UCMNQ7DT3y/Uztgg8zOKBRHisd///vcieJlbaC666CLxORZ8LMDI1hFIUPIA7YzNDoAMwWzWgCCG1hRzXRktQJjBhZEvPJvrjjvucBp4iWwDfwMZHHDvvfcKB6NEFYwxFgYjVyBmwygXBA0E2ZraOiorLTEGd6ampVFmVjalqpl6ri5KCz0H3/3ud+mcc84Rn3/xi1+kyy+/XLQ/uZt2gPeKQIXfgX9Hloo1A7PPGHie0JLEQGa2Y8cOEchwXhGsEDAR4EvKV1B4RCTFREeJTQIyueT0LMrPz6OVy8tF0IPyFkHZX1hSpsFM26DfCDj5/CuMuUUxqTlUtGqj+Jn9Lz0Z0hJ83Pi4UU8/4yxKzS0WmUxGQowx8+jRh/9tCApCSezBikXQGai7YifPO2DgLW+5XLzueekZCuXxNONjo3Rs3x7x9edvvcUY87Hh1LPF68svv0zTY8MhSy+CVhwfGzFqQozY2BgKj4iQz+D+/SKrMs/5wuIKl6DDhw87/T5kBYy0tDRBg/HPgJb7zne+I34PAh4GQTJQI8egTSy+/LFq1Srxb+ZpyJhzZgYWcCzm0AFwwPn3v//tNFwTWQyyJAQUHCPYKqCzq9v4P/PJ7/EeFnoONmzYYHyOUg7Q1tbm9vfi/zIV6+4cmmvQOHYEYJwbbB6xMQC4nQAIC48UmxNQnAjkTz75JF379quotKhA/D/0P/L/8xeWVEYG2qZ/cJgOvCJttDacd4WgcUAl4t82nX0Z1R3ZS68+8witvuCdIUstQs0HXPw2KYDISIimsLBldP27rqG7f/d/dHDHs1TZ2k+5yXInHCoZGdfIMJ4eQP2BNyLAlVdcTp+8OYwaKg9TZXUtlZcWU6gBG62OxlqxmGMBLCspprXjHbSntps2rV0lqCXs9Kv37aTSk871SrmIjQ0yo2AAf3t0YpomlGjCvJhCjh4dl0BDfT1OC70v+PCHPywoMNSMscDCgAEU3Cc/+UlBsYGGdB0qaQ4GAGdPZiBoIQNCsECGhKwSgzsZyNRQ18JATQQNOHps3LCeloXJTYmZltRhUxUZ6Zj/xYYB2Mx6C2RQyKRwrhDkEKBwLDt37nQSeUCJiT/HrQRMQ95000fpmvdcT4U5meJ34ff4E0sqI8Mi0dlcL3a80bHxVLhiPbX1jwipKEyCN559ifi5A3teFY2noZqRgRYBsoqXi1emR/HgJSaniDrSazt3UMiJPRS1yMeHnbcZBbnZVLpGipAe/I+sgYYSELAxoLC9sUZ8DWoLi9K2sjTaVppGZy3PNCimQ7te9Dojw+8ETRuMD/zt8YkJ0SrhGsjwbzFxCQblyHUp4/yMj4s6GLfLMLjeBHR3d4v7AxOhGaAKP/axj4k61Gc/+1lBzwEQeWFThGwJGwTzh7vgZQYEFfi99913n8jM3vnOdxrBBNOUq6qqRJ0KQg+8F3wPiFLHyzU81Hmlye7MYAYKc6HnwBPg/SAzBfXo7hwC+Js4RkyFBv2Kc2LOUvF/cbz8tjmQoUaHTcAHP3gjrVq1WtCyENz4G0sqkIG24UWioLhUPDitfaPCvw4XJCs3X9BV2A13tTSEpE0VdkrsjhKTkS9esxLlw4Mb74xzLhCf79lpDRWpRzUyJfbgQMY1DDNOOkse32OPhF6bQZuastvbXOsUqKMjwun0igxKi48ydv37Xn1B3KehSC2OjEhaCjQiaDoG6KmYOEcA+ehHPyrqQBBhYKf/kY98RAgqUIMx41vf+pZQ7UGBCNUf1IOs/IPaEEIMtAlBLAEqkYMc6qsQTcBCD8EBCzV+9sYbbzSMEuYC1IsQfyAjM9OKCMKQ0KOGhhFWoMI/9zkppoiKjBTPIf4dWdxzTz9F7W2t1N09s6kbwRR1q4WcA0+A9421D78LvxMqzh/96EdOP4MAhKCE84HnDcpDs1oSgQwtBceOHaUTx48J6y0EWTyToP2R/R4+dECIPQJhxLCkAhnTNkBZuaRWWvtGDI9FjB1HYy3Q1doUkoML8TBigcODMhouFwWzYGX5CpmlNTUEzgrMV4xNKFcPl4zMbSDbJusJeIhCDS2qbtnb4hzIzID6FwKC9uYG6miqC0m/RSgWARyHGVhcIyIiKTIq2hAtQKgBmg7ZEyhVLKwIFGZ8//vfF36bqOdgEYWikClnBCQELAQvbAJwz/zyl78U/4aFGJkHfgbiDAhCEPjw7EBlOB8QvBAI8vPznepY+H2oyx05ckRI02+77Tb69ve+L/4NfZCg6RDAIab4y92/o40ry+jtV7t388CxLeQceALUrXCOUIdEtoVaniu9ik3E29/+dtFmAFN1ZJTIzsyBDJuF8orldMm5Z1B5Ub44lxC83HTTTUIc87a3XErbt+8QQdDfWDI1MlfaZtWqFUYgY8ufpNhIMWoGPHB3W2NIUou8yCNQD49PiZQ/XTXQiu+XyqJzW3MjhQrMNTKo2XhQK3aNrihVdbG2lmbRkGre8YeKYtFMLboCajF8H4tQZ3MdjY5LKjWUMKoysujomYq6ZapOBvofAeHnP/+5+JirFQjgPkJX3HXXXXO+F9xDs0nfAbPFmysQHN01/UIIgcUfQ4dZhNEzNCY2KpibhxogMkHU5z708VvF/Y21B3B19QD1utBzwEAP73zNyOi3c7WjMv8fbDJgqMCmCgzUGAUTMDIigukDDz1MU2ERlBAdQfHR8llDEENWuywsjNZv2EhREeFig+BPLJmMjGmbrmapntmwZpUoUsKXjxcQc0bW09YsqMVQc/eANRhQWFIuXtPiI50aiStUIOtobQrKSHJfG6L5+KCkcrcrLcrPo7DwiBked6FCfQP11ZWzZmQAT4joaW8JySnRY5yRuZGGg15E36P4OYu4AXkKFkKY63/cC4nZa1x/A0XIrZGhNJdsbGxMCEmw6QhXdUHz6DFstpDRwmBicGgoIO9pyQQypm06myRts2bVSlFzACrbB4xAxsM/u9qaxM1n9vkLKaFHoQxYrn1wy8vk93vammhgdDzkMrLZhB6MlPhoSsnIFp8HcpKCr4A/JDZVQ33d1NPdNWvG6RzImmk0BKlFZFtArJtAJuhFtTiGaiBjEQUHMmwYWcyBsTVMewpVoTLbDaXRNSPq+JC1uYo9+BqCvoSikSYDw2otmUCGrAs7wY4WBy3FIgje8Zszst52uZsPNQk+ZyxJ2TIgZ6pjZHCghh9aY6vDisfK4M1EdIQjkLmj3QBQHClZeeJzf/eu+CMbG+tsMK4TdrbzBbJQy8jAcBiBLNZ9Robm2sUUyNh9hR3vscAz5T2pJhuECjtiPj6IVfhto73HDDRQg+L0pZbnCZZMIMNC0dkkFzb050DZlJ3knK2YA1l3qwx4oaZc5IU+LrNAvGa5ZGS4+ZJS5USCE1WyFhM6FlXzBzI0D6dk5oRcIOsYkIv2YHv9nMdnDmTdbc0hJ/YYGcWImmls2ynGRewBiIVeBTKo4HzphbJMIFOrfaRpsWepPrchhFAcIzN1ypmkSxwTx2cegOtvLIlAxrRNh6IVkY3hJGcnOe8IWewh/k9Pp3DnDiXBB/zd4BUHxKbLQObOYgttBkBVjf/7O/TWyJbNG8hiI8MpNQQzMh4b1FpXNSd1CvA9KjKyEJPfDw/LRT4yMsqtMlA014omW7kI+ttsVjcQeNn1ggMZU+NmKzFDVcmBbCoUqcUY43tmajEYWBKBjGmboY4Gp9oDFnm+AKCksOOH9Jb90rBQhBK1aAgh4JQdnyACM9sbmZGbL4NcTYgEMmMhCJs/kOF4U7OkK0NtbSgFMnmfNdbOH8jMYg8404QSeBFkib0r8DyGcp2MgxjoQ866JtyYXnMgm+DG6ACPPdFxDaNi5DXEEhrI7GvJBjI0PQN9rfVOgQyBK01J00ErArggvOMFvRhKAzZ5kcf8KmA2w+OCQnl8jQ11IRXIkCVjMCiuEVwP3AF1iIwcmZHVhmBGVl8zt2IRgCkuMD46Qt3dnSGzAALDvAi6oRXNFBX6yUIxkJlpN9ynyLTYSxGKRddANj7uOL5QuIzj4+PGxPIotRkJdjYm3gMtAbA6r7W+ZoYaLFst9tzHARh1MpGRTYRcRpZVIGcdZSa4Xyw4UDc1yAw1VMQetZXSsR2WQu5cvRnZuXKhh6N3qACZP2aQ1VVXzVsjg1oMPn5AV2uzcMAPOem9mx4ygHf25jpZaNfHlNBj2TInQQRnazg+jgOzmQdbMhuLiiJaJsOHHcgCTNs0KNrGHMjW5ieLhuFVOZJOBByCj8aQohY5I0vOkYHKVczCKFXHFwpN0ULlppw9qqtOzLvIA/mFMpB1d3VqM5/19zEiI+tqbRQZCBZB3mzMBiflYgjVyZh6c9dDBvBaH6rKRddAZvSPmWhFc0aG40OQCxUJ/ojp+GYTegQDSyKQDY5NCrk53B5cA1l+SizdcFoJlWQ4PN4MarGtKaTEHpyRxSvFYk6y+8WiQvWSdbRYP5BB8cUPTHXl8QUFsrSUVIpWnn2h0EuGjBMLXltDtXF/zmeR5BB8tIRMRoYmda4JzZZRGxlZkGpkyPbvvPNO7dJ7symBayDjjCYUBB8jZum9er92RhYgDI9BsVhnjFXHzKK5YGRkwt0jNHa72NVzRpaZXyJqfnFR7u2ZVpRL6rG3s40GlIrM6vUxoPLEwjIyIfjIzAuZQMaent1NNfPWx0I5I+NFEKrEqEj39ybv7jHjygoZGQIrrKYWArZucsrI1GIPoZIZTC1C5ThtNEVTiEnvScAOZAEAdg2gFjuUf91sbglmOMQejWKR8GacfKDR0dEhRrcD6XlFs2ZjQH5utrABwoN3otragohxk2HwfIpFRmxUWEj1knHW36UC2XzH5xTIQqiXzKxYnI2OctTIImYEsmAHtfnAfW/CLzI6WjAJ3OjsOsXb3BQ9pST406FKLYYF+U0thUAG5wOc73bler+QQGb4LXa0igI8qMmQkd7n5FNUdMycgQy0VXp2Xkg0RbPQI5ymhPP3gjKyiPCQcvfgGm5bg+cZWXcIZmQRkY62F1fw91EjgwP79773PeFsDwMDDMjEqBbMZIMFUnZ2tnCFxyaO8cADDwgXe1BfYF8wD4zrpDDYdTWvhYM7Rr+4A091xhRyBCf+ejYMDclsBUEMzxhvgEWTt5vIbcjzOZCR9anhMdNAVEeNLPgZWehYg3sJpga7mxceyDAYDrslyEz7utppaExSdVYGZyvp+fJhy3Fp9nZFZm4+tTbUiEnKoRDI+jpaxEOERWI+IUS0qZcsFKhFVsa21FV7QS22eBzIhLgkQGauZsDxHX87AhnZHAoBLIzT4XJpwmRnBDSMCAHjcP7554upzz/96U8FzfWFL3yBrr32WjHzCybRmC0G93UEH0xKeOmll7zOdDB/C+pQOMBjBIzwDpwDPQMyYIYrtsPhSOP+WFEnwzFMjo9TRJT1qcVRJdTB2jgy6RCysFglmFj0gYwbRs2uHvMBNyx6dWpqauQ4l9HNIZORpeUWiYVgth4yRm5+IR14Db1W1g5k40rIYKaG5xNCwN0jJTM3pDKy0eFB6mxrXjC1yMG8t6OVBj00f0YQQ0YTDLz44oui/2iupQ8xbgoOH2FhImBjbhdMCjDjC/OzkKUx/vCHP4ifwUZuYGBAbD4xR4tZFWRn3gITFgCYJGDg7kLH04RFRAkWx6AVZ7lfzU3RESFALQ6r+hgCNbMIeNZcFZnBwKKnFrn+wLTNQgJZKAo+DKFHQYkIYq4qKVcUFMgdfb3FF3oWe8w1o8sVMZFhRkYWCoEMGVm7uj9nG0/jjjXAhmtqckIMkwwloBl6LicI/je8rlq1yqCz9u7dKyY8IwjzB/6dB8pu3LiRLrjgAhG83vnOd9Jvf/tb6u7uDsgxTZnMkFEDxDUdVZnybAu90Us2IY/P4nGM4JPJgQyb5ZTYSNF/G2xXjyWRkWHnMDI4QL1dHR4FMrO7Ryg0RZsViznJc2djQFGxaopubAgJapE9CBcWyBx+i6AWsdO1wsM21z3K0vuF0IoAglhmdi61NDVQQz2C9aYF/z246iN7CSQQjFDfiomJpahox6BXd2DWcdmyMFHrGh0bE9cQ7xnDKF2nGZsD+1NPPUXbt2+nJ598UgzVxPRjDMqFG7uYkeUSLXQ1XCP7mlABKT5O0vr8l+aiFgFuSZiyeJVsZGTMeN/p8fDKtM4ztQQC2YSxmwffnZSU5GFGhl4ya2dkUEqxEAIZmasZsjuUqkDW2mTxQKaoRa4fLTSQJadnGwIDiAGYJrIqa9DhgRiJkZefLwJZc6Nn/YAI6jzcMVBAAEFQQn9Y+DzUsLHpUK99A8MUNTgmaEVMc4boYrbJ3/i/Z5xxhvj42te+Jp7jBx98kD7zmc+Ie8A8bBXiBQTX8847b9b3gqwJP7eQQIZaF5AcH0sjU2Givw9r/WxiCIdNFasWydIYV3Za0VHWCmJLglqU0nvPFwlzIBu1+MynpqYmUfcICwuntOx8yk2Onff/lBtN0U2W5ua5oNzSIK9hRUXFvP9H8PZRUZSYlhkS9CL6yCDaMN93C0F+vqSHW0PAoYXpQXgozqdyMxIY9XNwiEeg+NjNHxeCEQg6IMQAnfjEE0/QjTfeKIINMi/Uz3bv3i2uOYJee3s7rV69WvweCEUgHsHHkSNH6OabbzZaVmYDguYzzzwj6Nu5aMrRsXHjOUKAAuUWFxVOiTGzU29GIFPnxsKPoVPA5fdtJYQtBbEHZ2SeBDKHu4f1p/BWVUnaLTU7j+Jioyk1bn6F5QoVyCAyCFQdwdsaGTLOjla5k55PscgDOIHUTOsrF6U9FQJZs5MacSEoKpI/26aGxYZCIIOsfr7dfGxUuAgCnLnxqJPsnFyhXkTQuvjii0UtDHJ6iDFAG4JtgZjkLW95i8jcv/KVr9CPf/xjIdcHPvjBD9L73/9+uuGGG+icc86hsrKyObMxAP8fdCWuCzLC2TA6yscXId4LgjWCmLvpE+6aotHmY+UNpTkjs2IgW/TU4qDysANw43qekcmmaCuDzXFTsnKF7H4h9aD05ERKSEmjgZ4uOlZZTae6uJ3goXr6cBtBM3L+KknTBatGNtjbLR4iHFdenqx9zQUslNGRaIrOpbqj+yydkaGZGUIB9Cx6HMgUPdzR3Gj5OqBTIJvnbSKAJcaE0dNPPUmHDh0y+qyQlWEzikzLHZB5Pf7443MGjl/+8pfiYzZAqWwGanL4mA+jSugRFbnwRR41PXwgMCNYw/HEqtdxclKaWgPR89Q4g4ElkZExbbOQ3TyDF5TRIWtnLOaMAxnIfP1jDDRopmfLAZvH3TRFoy54oLGX9tb3OtlEBaNGxtkKJNC8i50PcsCm9ZWLrKrtVcfII1oWAq5zdrU3G1OILR/IIuenFhm888cCiqxlwsIZy9iYd7SbIfgw3D3I0tdvWVgYRc5SnwwmFnUgY9qmt6PF40UCyq6MDFljQUE9FDKy5Iwcyp7D0WPWSdFumqIHRhxKzWAGMvxt3oh4kq2A0kGGanVqEfWxkaEBGh7s9/gYOZB50xQdzBrZQhtoka1wzyAyFqua6uJ9TbAQwsNsxQjWasbXtEWVi2Me1DiDgUUdyKAagk0M7+g9CWRm6qa1scHS/DUPkIS/YK4HgSxHTYp2N4CSZ7iZ/Q6DFsg6vAlkkloMhYyMAzVqPZ40KjPDMNDdQX2DIyEhFJA1soX9H1BsvNAjkFk168T74jqetxkZ/3+LHiIZNUCRUZPlsKgDGfq/sNvFCBevAplaKDrbmox+Jiuitk5mHLl5+bM63rtDvmqKrnPj7tFvysiCeewIot4IIeC3GArUItpDvDk+AHJyqDOxyapR94AVAVrQKZB5sKM3FvpxqVy04oZycmrK60DGVDn/fwsengB6+Tgjs2INb1EHMqEGU7Y/cEvwtHemxDRg08oznxoVtehJDdD8881umqIHTE3gQa2RmahFTzYiMVGOpmj0Dll10jBcY7yhTgEsKBnK/Lmm1rpWY3zu8X4jIyM8WggdGcuEZYdPTmrMyKwYqM3UYqQFFYtLI5B5QUsx+P9gbpdVJfgw8uzsaBeflxZ7dow8KdpdU7RVamRmsYenGVl8chpFKgPXRg+bhkMhIwMyc9jlvz4kFIuz+Q7OK1GfdCgXrUgtwuNRSyAjsnYgi7QDWVAWCZiqekMrAhgTwTUIqzZF8wINR/HCXM9k8uUlMpB1dbTNcC/ot0hGhr/d65XYI0wIBTKMhb7O8hmZN/coC3YaG0IjkLkbZ+LJQm/FQIZmaOYEF6qqnc2myqoZ2biFe8gWfSCT0nvvd7scyPp7uixLLZql9ylxnt1kBXnZguaZnpqizs7OWTOysSCKPUbHJ0RG7OlCj6ZaIE3VyVjZac2MzHvWIFcJdqyacbpK7z11Srd6IEPgGWOz4Mgoj+tHrk3RFjs8ATGSxnD1sOY4q0UdyNAL5ctu1ykjsyi16JDeZ3s8My0tMYbik6XTelWdY6EXBq0WyMgga+7uaJfNomFhwhjWE2oRSEhNF6+trTIzt+Y96v1mK08FMiu3iJil2xFeZmS8kFotkKFmN+HDIs9N0QDucysmZJNwHZmSa4CdkYXgbhcmw8BgXzcNqllDVkNNLUvvcz0OZNGYpJyWIT4/VOWgpobHHbOUghnIIPToVtcPjh6zGcW6A1sDJSTLQNbWJrM6KwHneGjUx4xMzcnqbJd1UutTi54tOa5N0ZN+XunhrXjnnXcGROjhLuu0Yh/ZmLp+cB6JmGe4aLCwyAOZd83QDIxXF9Tb9DS1m8apWwnVKpClZ+eKupCnyMqSWeeJmia3tGIw5feiPubl9YOzh3hNTrNsRoaN1vBAH42NeNceAuSrumh3pzXvT6eMzIseJHNTNJqGA52R4fl/6KGH5u4h89FM1+HuMWHJjGzMyKjlHDIrYtH3kflC2+AhSlbUVEuL9RZCczMzRnp409+RpxbC6oZGt0IPswN9oIG/6+31g9ciEGfhjMxsFoxNE8aceIrlxTL49Xa106RFex3H1EKP8R/e3KOciQ8PD4kakpUk+HIOmW+BzKiTwb3EQsc2M6OGITJZEhZ9W74DWVRXV4/RDJ2fL9VdniJdzbGy4o4eaKhv8Ho3DxQXSFVfW1u7URdzzcjGgyR08daeih3wsXuEMbJVr5/YaPmgqgUqiuV9jenE9W1d9OKxdvrT9hrLWFZJQ1x5P0VHzz/w1Yxzzz2XPvGJT9CPfvQjuvDCC+l973oHHT50kN5y2WXCAQU17Pe9731i3hzjgQceEK742BSkp6eL/zc4OGj8Prjlm3HVVVfRBz7wgVlpRuDqq68WAZi/1k0tcqAWtSiybjN0OGbJWTQjs577oyaADutobfK6GZqRkZlFlUcPU4dLDQLWV0da+qkkI54SooN3GlvULCr23fMUeTlK0NLTQY3dw7QyJ9EIaLhnsUEMWo1M9JB53yyMrCwxJcPaGVmb94wBgAU9OjZObNiO1tRT/USycGVp6xulovS42T1Ih+QGz9/AYNPh4WFhNotgOz0d7VFW9qc//Yne/e530+9+9zsKj46la664TIxjQR0Lv/cLX/gCXXvttfTss8+KxnfMKrvjjjtE8Onv76eXXnrJa0k7Zp6hTv7HP/6RLr30UkOUocueamavHKhF64WyMWVPFeGFKjNQWLSBTAwr9KEZmpGhMrKOdueF8GhrPz11qJXW5iXRxWtlwT3QwCLR0yVl8xWlM3eLHrUYdHdSQ/eQCGT9I/LBTImNpO6h8aDVyMZ88MnkOpk5I7PaiAxv55C5Ii09g5ob6qiyrommMhMMwc6sf3doyCNPR50YGBjwaFOJsS1f/vKXRdb1t7/fR+s3bKSvf+s7FK82j3/4wx/EuTt27Jj43agzvf3tbzfGMCE78xY8VRwemJi84ArcT9jQcsbpc0Zm8RpZpIc9coHEoqUWh8Z9k94zspUYorOjza0XodmTMNDg3qHI6BgqzJUPnbfKzIEeBLJhp2NKjY8Kco3M+4wMgPglISXdcEDBDt2qhsG+BLLMTHkNq+uajGs1VyALJZx00knGAnro0EF65aUXKDtdmivjY9WqVeLfMC1648aNdMEFF4jg9c53vpN++9vf+nUEk5lWxAbJE1XtbBmZxboLBMbGre3qsagzsmHh6uH7IpGd414Vxg3SwVww6kyu9542Q7vLyLoGx0TdhqnFVPE7B4NGLY6MTlCfaob2LpCFC9otNi6ehocGRVaGKcKWYg00ZGS5uTm07w2iuqYWytuElXDZnDUyjChC9hIINDU1U0tLM8UlpdDK8lLxtz0BsjcOEIMDg3TxpW+hb37ne5Tscr+jxxDUH6Y5b9++nZ588km66667RDa3c+dOKi0tFepHV+rOFw9OtAKYFYveZvuGcTCoRYtVyabNfXIWHKi56AOZtP7xnpZi5KqFvqfLJZCphSKYRfUTao5YSkaOGKvuSyAb7O0UNy3oRRZ7pBkZWXACWXNLE01NTQq1FL9PT8C9ZGkZmdRYNyjqZKCqFltGxhJ89JKhrhgVET7nBgsLrrc1Y08RHhEuhBfJycle05m80K9es5qeffY5yi8qppyU+FmP7YwzzhAfX/va1wTF+OCDD9JnPvMZQRWijsaAuOLAgQN03nnnzfm3Xe3bGIiJvioWAQ7Usldu2lIU+Pg4mrRlcI2yqcVg1R88Hx/virxcyY33dXU47eZGVEYWzEBWVSMzssycfI897FypxfGxMTHypqpjwJj7lBInb1wsjsFAQ72kTtMzs90W2hcayFLSrOnuMTgybtRxfdpsqfpNf3eHcAoBRtSrVeorOhb6d117raAKP/KB99GuXbsEnfjEE0/QjTfeKIINMq/vfe97tHv3bsFW/Otf/6L29nZavXq1+P/nn38+PfLII+LjyJEjdPPNN1NPT8+cfxtKxWeeeYZaWlpm0JSQyjO16Ev9yExJIphNL5Jm9kDCuu9Mo6u4L4tEvgpkA92dTn6LnJGhJoGCbzCboXO9bC0AsFtOTEw0FsKqdilVjosKN5qKg1Uja1LjZbKVMa6niImQt3dSmjWVi20dHTShfPq8bQ8xb0b6ezqFU4iVamRjY44eMm/BQSItLY0efvJZsdhfcsklohYGOT3EGKANQRu/+OKL9Ja3vIVWrFhBX/nKV+jHP/4xXXbZZeL/Q+34/ve/n2644QY655xzqKysbM5sDMD/B12JzfDmzZud/g37Wl8Vi671NUEvTlszkIVZJEtcYtSiHtqGM7L+3k6RffEu3xzUsGgkhocFbQ5Zvg+BmhdCCCEg+GjtyxftBAkx0RSpAkGwAjX7B2bnyl43T8HGwSzBt1JGhnPa1izbQ0Cbetpj5dYTtKfTkZFZxBt0QgkFvDm+559/3qmOBUVixcpV9Id77hNsASzWzEDm9fjjj88ZEH/5y1+Kj9lQU1Pj9PUVV1whPtxBZGQ+unqY3xuOD+4lkvmxRtAYV8cHV5YgLHELhoXfmm/o6OryyfrHdbeLG7a9s8v4vjmQBWvRaGqS1FuJhwM1Z1sIl430CRoR8nsEsyh154pemSDIqVrVQs8O757C8FtUykUrZWS4f3QIPZw8QXu7BBNhlYxsUvkjArE+CAXM1Nuy6UnLmAfrqpEBdkbmGxZtIGNpekpqmsdKKTNiYmIoNl5Sb41NMsNzrY0Fq07W3iIX+vKSIi0L4bKRXhHIIJRJjImgSNMWLBiCj1bV7M0O756CHfDjlMO/lTIy3DM6GAMnwU4PWIMpmpyasoSzx8iopE3DwsLFZGhvISdLS3pxeso6gcxcI9ORkRkSfAtVyUbVNbQDWZDQrAKZL7UHRrKqsTS1yIUHyiKzACIYiwZcDTAnDVhR7l0ztLuFEE3ICFoJ0XIIIotIghHIOFB7S52yiXJsUprlMjKIhXTUcM0bkdHBPrGwQuiE+zNYlDBjRE2MgLWRr4ugoexTA24tEMfEhgH1Oq0ZmcWaosfGHNSihePY4gxkWHQ7Wpm28W2RAFLT2W9RLoSuThfBoHFY6BEVHUtFOd41Q7sGMigzxQI4NW3YbnFWFgzBRydfQ28DmaqRRSdaz28RYiFf7akYEEGwqnNsoMdRJwvyMNgRjY4QDom6pE6tYOU0rhZ5CE28UdWGgk3VuKkZ2iotAUsmkJkbTYt8XCSA1HRnsYBrBhaMGtmxKlmUTs3KNUQNvu7oe7s7RSDDRgDUIhAZHpyMDIXvbh+aoc3UYrwFa2S4Z3RRi1hI2U5ppK/LoVx0keAHeoFkjz4djhBG07Cyg7LCWj+hcZF3Mg62wLEBqG+y2CPSj5OhddyXizKQJcVGUG7EkJZFwuy32NrWOkPoEayM7ESVbIbOzMnz+SHijKyro51GJ50zsiilXAx0L1lTU5OYSgtunpWjngJBGNQo+y2iZ4g5/2BjZEKPF+gMerG/26RclK9GtqYypEDv5nVMFeZANsEZmQXqSOM+TIaeu0ZmDUyoTQMUlP5shmYDa5968Tz9D+jT+OEPf0h79uwRXfLomscoBHN0/frXvy58zrBwoMP+V7/6VUAdFbCwcw+SlkDGfovKAX/UJQMLRo2spq7eJ2m6u0Wws6OdyjBfCTWyGFdqMbCPV329PL7k9CyK9lIogPsAvXDjCclix4sHEw2yvtakdGB4FBZqvo1wcbcZGenvFvZseA75vsSxQ/CEY8diwYMq/Q3eNGCfBYNrHbv2sdERihgbpally2gkPHjBDO9ndHTYmJzs6/GxewjsoEZHhiliOvguGoNq/A2OD/2OI2F61wCewgCmBL2AvtCzEd4cHMw50VwIl2lXYITCz3/+czF+Af5mX/3qV0Xz4qFDh4QCMFBoUD1WOhaJTBcHfOymgx3IeKEvKNS3CHYY1NsyilAij2DVyPj4UjJzjazQW8HHwGiYcIhva20R9LAVAllLW6sQZiDY5uXp24yMDXRTRHiYYAmYKcDfgBdhdXU11dbKTD4QaGlukuKFZWE02N/n0++CN2RnZ6d4je0fFsfUozZbwQAW4db2ThodHhSCCPYj9BbYZMm5astoWXiE0ToSTAwNDYn3hPEtYRERM/r2dGG26QKewOM7AV3y3Cnv7uJiThA66q+88krxvT//+c9iocS4cMwVCgTwPngh1JGRmak3c0bG87qCEci4h6ywQB8t1d/fR5PjoxQXFSfoUzxMwaqR1auBocmZOcZ78AZmv0UEMqvUyVhVm5aRpUUMwffo+EC3qA1i42GukYHeAysSSHrx6ndcQyNDg/Tn+/5F60tLffpdzz33nLCUWr1mLV352R9RVGQYXbdajmoJBgZGx+nTX/42HXtjh/B0vO6663wOGtx4/fO/PUYXb/btfOkA1m5Yfq3aehbd8cOfUGmObEPSCdz7vgplAK1bGuz44EmGqawMmIVu27aNduzY4TaQgX4w1y36+nzbuQG9vb1GWqxj9827BTYOHlUZGYx6+4bHZxTVAwF2hfC1h4x3RLihwPmPD/RQVHKCOCYZyFSNLMCBrE5l1CkZ2RTpAxWWnxorxtPEJFlLudjcJK9fjoZszFzHHe3rFlko+qxca7egFAPFisAp5ujhQ+LzwsIin/9uRkaGyCYnJibp4qlwGhmTbiHBUtL1jy+jwwf3U0NtrWBsfD0+/P+29g4xpaGpvZNiYqQ/ZDBx/Phxcc6Lt5xLCfGxAWXUPIVWshxBDHB1KsfX/G+uuP3220Ww4w8dGRToh7PPPlt4o/nSDM3IzVaqPhXIWKWIwZPi6wALIdDH1qmk2yvKfd+VYjEwzIMHuoWjBy+CBrUY4GNkajgtM4fCvDREBpZnyV1kZHyKeLVKRgbaDcjVUOPkjJNrZNGR4aJZN5g2VXz9YuISKD0t2effx/dne3ubYFzk5PLg1ciwme1VqlodvarmzQhqmVYylUjOyKa4KGu7GQZdtfilL31JZFD8wZSgL0AW9sILL9Drr7+u5T3m5+aKV9AkoAA4I2N3eFCLgZQ2t/X002CvdOJeUaqHXuHNx8Rgj6hJOQIZU4vTQXmIMnLkufcWGQlR4jqxBN8qGVkbN3t76VriihTVtD/S3yWuGTKyYLp7sBgJ1DC3QWipAY6N0ejwQFDHCwEDw6M00C03trpqrhlqQGqnyzT6YG9GUjJyKNqHOnUgoPXdMQXnuljg69mKeaAH4Fpt/rAa0lOTKSIq2tjRs/yeAxlimKsk3584WiWboSOjoik9XS7QugIZ5NuQrDNdyn6LgV40mjiQZeX6nG0iK5vPbxEbkZePd9DhZt+p7YWgs00yFPkFenbzKarXcaSvW1CxyMiC6bdYy2Kd9GyfapzupjSgVy6Y44WAhsZGcc9EREQK2lMHWFTW5TLE1woZWdRSCmRQKSJgYX6PueaFOUGnnXYahSrgEJGoFsLm5hZjpxsbGWFc4EDWyU6oOWTIVnTVCIwdb3+3sBPiY2QH/EDWyNCI2doiqdMsDdRbRVaCcf1mo7jb+kfptZouevGY/2kdLIDsWlJcpGc3n6Ac/of6ugglRVEjC+JMsjol1knL1n+PIlgHo25rRkODmpWXBeo7TOtm0nWIbzAwPT3tFMjMvqtWRJg39ac333xTfLDAA59jkB1uWMwH+s53vkP/+c9/aP/+/WL2D+TF5l6zUANkpwmpciFsbG4xsq/oyDBDFecqyQ+EPVV2jp76inMfUpfMyFxrZAFcNJA1QY68TDhWeD4Z2hXZSdGUpeqcjc3uqcXuIanmGw4ATQyatkf1kBVrqAkDCcoYGWNAJocHJLUYwHvSFY2mhV73PTpsgYyMj89X6tuMbHWPog0GEyiCic7OTkOEJwRXGrJqSwUyTF+FiIKHzGGEOD6HBBX4/Oc/T5/85Cfppptuoq1bt4rAhxlBVla8zAcs7EkqkDW3tBpDNcEbszFtIHe/vNv1dQ6Z+92uCmTqeIIhv2duPjE1g2JjfHeFwAZrTZkMGC2z1Mh6hsYDRhMLoYBy9Sgp0hPIpsOjKEZNacBCD2oRbSLBconn9pDM7Fzt9+ihyjpq6R0xatXBQLOazJCt8fhyVKBuaG6h+17zXSvgCzgbS0hOo9jYGEv7LHoVyM4991ylGnL+uPvuu8W/44C/9a1vCQoH3e5PP/20mNYa6khJk/x1E6hFtdAhG+MpyoFUiHH9qFhjIHPsdrspfJkjIzNqZBOBWxDNlIauxtDNK+WEgN6uThp3swByIAP8HcjaO3todNj3WXlmYHPF9CkCGe87giX44HtUh/OMayDr7uykms5BemRfM/Warltw2if01DiBPKUjAHXaNTgWVLFOg9pM4hm0utADsP47tAhY3tzS2mI0RMuMTC60gSqsg05pV/Wj8lLfe8hcF4mh3k6RkRk1siD0kZnVUr4aIjPWl8vMZ2pqkvZXyt9vRu+wo1GYM25/U8NxiUkUHx+v5Xdic8X0NyZFc9kmWIIPrnHm5PqH/gaae0forztr6UBjLwUa/AzqcGVh5OQ4jg+ZNHpUg4VGYzMJQwLrhwnrv0OLwOGA3yZoG66dcUbm78WP0TM8Rr2drVppKfMiMdQLsYCJWlS7sUBSi+aMjM+vr4iJiabEJNlL9sbRmTZN3QHMyOrZ1SNTX/0I9x8rMwd6ukRWDQRjVw+JPEvI8zT1WDkZI/d1U1lmPKXFR4mN3dOHW6k3wIt+e6tvs/LmM35GIAv0Mbl/BrMsr1gErP8OLQLu8WAHfGQtqB9B8AEEqrAOCqxXjf/Q6RnI0t+h/h5aNjVFwyrrDGaNTGcgAzKz5DEerXHOyLDYm2uc/l78uVdSZ/0IGRlTizIjC14gg5k4D9TMVudcZ8PwcH8XpcRG0Rnl6ZSTHCPqmg3dkqoNFLh9wttZeXNtJscG+8RmIJiBrIGfQdE+Yf0wYf13aBFkskO86roHrcju6sDwWGAW+o6+YerratceyNBQi+PB6JSRgR6xAMJBxNFHFoQaWXo2xUbpu0W5lxHuEGa4Lhj+zsgMIYRGxZs5I+vv7jAmMgfqvpxtEYzxcnKBO8QlS5uxoe4OscEam5qmwlTp3NPU45v7vCeAJqC7vVVr+wQPSIVSF2iqPkp9QVQuNqpnMCUzx87IFhOyVEbW0d4qbmQugBry+wDtfKvrm0SdB0abrlZgvmBqWRjFJkorocYTB4ws0yy/D5R7iVEjgyuExoyMs4Pdzz5KXT19boUegbiWzY1KKJCbrzcjUzWyN194jOoP7FLfD3xGZqaGdQoFohJki0FPaz3d84PPUf/AIOWlSDV0YwAzMjjC81DNIk0N7QD60WIT5DP45y/fSK/u2E7BQoNpMxJlcek9YAeyBYKDxmBfL3357Vvpex97B33oQx+iptrKgBbV2fonMztHi2s0w+yl+Puvfox+fPNVoh+wqUH+vUB527k2YuqkFnm68IHtT9HyijLh7I2G/R7VQxaojKy1Ra9hMGdkcUlyoR8e6KN/fOsm+sedX6P2Ttk8HDRrI0W960B4vDw+4PVnH6bP3nAVTQ10iikUqHEOqsnYgWp/QQacGBer9XdHRMnAPD46TN+65b1i/mMw0MjPoJ2RLS5EhDkW8ZHBfjp+4A36wx/+QD/6ztfl9wIUyIweMo2yX1YlYj4Wo7HyMN3+7W/Sadu20uTEWMDqZObJBZJa1BfI+PcCXZ2d9OUvf5nKy8vp8LFKp3qgv6+lbp9FDr4To8702quP3kfvv/xs2rVLZmfBycj0Xb+JCEfQwOTwE4f20ZmnbaOuKskgNPbIQZf+Ro1SnfrDg5BZj7DwCNGigZFZzz77LAUSg4ODYihyqLh6ANZ/hxZBzbHDxufv/8rP6EOflQ3gb+7ZHbBABkFCu1oEizRy88DQyBiNjciF4LJrb6R3fea7FB+fIDr8O+orAxbIeBGMS0yhqJhYLYazDBwLkF1UTj/91e+ppKRE0ESPPnSf+H5mYnRAegI7WpXPYr6ejAwKN6j3epSaFXWW93zjd5SeW0gdrU1022230WKgFusaHfZi133u+1RYsUr4uP7ksx8QWWigAhlvJlMys32azOAKTGZHJgZUnHI+rTr5LGFSfvnll2szQPfk+sXGxYvpBVwntzKs/w4tguOH9xufR8XG0duv/4DgtFtamoXdEGg33Ij+BGyUWHqvUy0FHD50SAg9gJi4WNp26TW0bvNJ4uvG4wcC1kvmUCxmifqYzoWC1YLY8154xTvoU5/6lPj62EF5bbOTJK3jT8cI2P70dcuAWqypfYLfb0ejbCvAdSxatZFu/OavxddYBGH5FcrNtMhU3tzzmvE1XEy+8usHxNinwYF+qj92gBq7AxvIMjSqToH27n4aGewzegw/+I1f0hlnnyOMJf76179SoANZelaOEIBxC46VYf13aBEcObDP+Hygu5OSExNp7dq14uuGY/sDUidDIOvxg/QeePNNx45vsEcutCvWbhSvDccPiteJANTIzI2YsRrrK3Ca4YxsfHhIZNAnnSQDde3RA06BzJ8ZWZNyhMD4+BxN0nRu0G+ucrAGowM9lJpXTNGxcWIhPHr0KAVDdYrZaDqAZwt0PgO9cmFRsWJor/iblYepY2A0IMxIo1Kd6g5k23fuksVoiK/GxigiKoouvfKd4uu9e/dSoDciqarP0c7IFhEO7XfcSP09HWKnCS9JoIlVfn6mpEQPmcrIdA3zY+xXJtBAn3LfLlu93mmhDwS16NRDprE+Bo9QxkBftxAGbNq0SXzd095Mk0M9lKQGpfozIzOOT6MiE8pE0MJN1ccc3+vrpGlaRgXlctLwG284goC/JxeYpdsxmnbzsGyqPey4R/t7OgVDwNewvfaoiAFw+wiU/VaWxvYJYPv2V4zPMSAV4OsHY/ZAqYYb1fGlKsNnW+yxSAABQnWVrBMB/d2doojNgaz+qMzI/L0bFNSi6l/RnZEd3OdY6HiMRMlKGcjqThyhibGxgAYyFNJ1Su/NgWx8dIQ6e/rE7LuSsnLxva7aowYN5s8NiXOPVbi2jKyx8hBNTU5SRESEYf4MB5pctRAGqsaCmuP4+LigpNIysihC026+s3+E6tRzxk3fqAtu3ChZg+aqI+I1EPRis5rurdNHEnht56vG54PK4T+9sEyok7u6uowAE8iMGrAzskUCTuvZXRwNp3C950BWc3S/2C35O5BhV8oZmc5AhvrJscOSPgS6O2TDdVJWHqWmpgo1Y3PNMRoLgHGwPwyDgddek/WVyEiZdaG2CSxfs8Gg5fjvISPz1+6X2ycwGkNXtoKMrPawpL550CroYew78gOckXGgTkjNoLhYKZ7RgT1v7qWxkSExTJafQVyidRtkRtZQfYLGRkeoscf//WRtKpDlagxkuN/27nGoSwd7ZCAbngqnVatWBZRebDCxIoBdI1sk4EUAKjCgrxvUYjitX7+eoqKiRG9ZZ3O9X2tkuNGbWtpFNqHbrPTw4cM0OjIsVIJAZ0eboIhGxqfp5JNPFt9DMT3g1KKmQIZzx4EsNS3d8MwEilasE691xw4aGZk/R7nU1yvaJjNHW7aCjKzu6D6nDQ4yFtioFSxfG1BqyrGbz9Iqvd+tWgiKSkoNVgRIz8oW9mqTk5PUUnOMWvtG/XqfYizVQH+f9vaJY8eOUV9PN4VHRhnlC1wvGAdz1hmoQNbII1zSpAmEnZEtEjAtk1+63EEtRoaJIMYcPehFf1JS/aMT1KmEHhitrnO+2549e8Qr796RoQ0P9IrAzIEMgo9Ayu911sigVmxvbxe0W4EaZNmm5pLlla8Rr8cO7hP9MhFKJemvQNagGsx1CgXABNQdkYscj0zCPYpjQasBslD0BdXU1FBAm6E17uT3vyE3IuvWywx6oKfDECDxM9hZe0y0ImBWmb/vz+i4eEpLlS4cOvDKK7I+lr9cbqwmx8dFS8HA6ARt2LDR2IzUdw1Rk5/bDBp4HqBNLS7OjGzVOvnADIBaVLtNXujrju33a0bWazIL1i304EBWuGI9JSYlGwuhWdlXf/yA3+X3w8PDhrJQjHDRlJFxNoYMOjdXBhC4s2PHm1osF/6Guhrq7u520It+upaNSrWoUyiAYaFdrXKB5d17f3e7CGRQRy5ftSZgdTKnHjJNqlMEp2P75Xs/84zTDWoRMAs+uuql2MWf/WT+CtTbt0s7qtK1WyjaGJDaKdiBitUyq37jzTfpX6830kNvNgofVH9gfHxcKHyB+FSVkdnUYugDi+uhQ4fE5yefKh+iob4ew+nDLPjwZ40Mvxv9av4QenAgAw2VrjwlsVCgmL5p8xbxdXP1MRoY8u9OkKXpUdExFJuQpK1GxkIPbDpylXFwb3eH2O1SVAKl5RQYCz0vvv7KyJqVdDtbozPLvjfkIl9cvlw0efNGBNQisGqtzGIeevpl+vOOGr9OMze3T+iiFmsaW6mtvlp8/pZLLjLk95OTE+Ie5Wn19ccPGbXkgLQWaKROOZCVrdsiJqMDo2ruWlGFZEoqT5yg4aFBQSX7a9Pc0tIiNnhgL9iEmh1vrAw7kM2DAwcOCP4ddN769eswApump6eor7vLKZA1nDhEgyP+e4Bw4/aqQKYzIwONCMoCKFyxzhhXw71kmbn5lJyaRlOTE3T8kEMQ4u/+FTFZIEpvRoZrlZebYwRqpqBKVq4zAhln2v7YlOA+aleuHjkqM9SBg3vlRmTD5pMNT1CwBhzIylfL49vz+hvUOTBGTb3DIdUM/cIrO8RrTmEprVy5UhgRYLEd7O0WGw7OyI4fkcpNf7ZPOIuRNLUWdHWJOjVQumYzpaarkUpK8BGdlCYmN+CYIboCBscm/Hp8ubl54jzjHtJVy/UnrP8OgwymY7Zs2UIp8bEUrdypeRQIFEVxcfFCUVV13H9Np6i/9Xbob4ZGoyyyTjTOZuaXGMP9eAovXNXXKGXY4f2OPh5/PkRJSi2lg1qEaMWckfEol/6uDmrtk4FshcpY/J2RtbW1iY0DLKRyNVKLR1TrxKaTTjKOr88UyOJyK4zNFuDPWqc/fBZ37JCBbM3Gk4QUnWfnIevEsaAuGBsbK7KVjuY6kaUFJFBH6j2+rIJSSkvPEG0LTA/zmKE162QrTFOlbDPwV1bdqK4fT/YOBZ9FIDTepQXqY6Av4qIjKCZRzkRqapZBBQ/W+k2S2jhs6sXSjRE/ZWRMKxYtX0NhGA2jFsLhXhXIxqZo3UZJLx49qFc1hYfxmcOt1KwyBF4kktL1BbLKykrRBwhxzLp164yMBQ217f2j4vN1Gzcb5yLajxkZLxKgjuI1SdOxSz9xUG4wTj75FOP4YGw9qUaNxGSXiQy3v6tdzLILxEKfojFjeWO3VCxu2XqKeDWuYbc8FvEMrueF/rBfpxf4w0eSacWStZspPiqcMrOynYwJ+oYnKK9MSvCbVL/c4OikX69fjlJFh0J9DAiNd2mRjCwybBnFJMlAVq1k1PLfpCDihOrl8Re16I8amUPosc5lkegw/u4GtdAfP6T3+E60DdC+hl7aWdU1Y5HAaA4dCwXTiqCfoN6LV7x/d2c7HWqWMuotqsZy/PhxmhiVDvn+WAz9IRQ4ceIEDfb3ClHHxo0bKCUlhSKjopzoYWTbecWy8bvxxCG/BTKMxOnv79eakSFQ8wbx9NNOE69GVt0t3T0ArpM1nDhsWHaFCnW6f79s9C5cvo7ioyMoO1tmZL0qkCEji88td2r8Hh73D7VYVyed/XPy5Brj7SyyQ019tLe+J2BTru1ANgdAA/FNhgdlbHLaWAhrG2RDLXDKKaox+vA+v/XqiIzMD83QhtCjQiqjcnOyneTNCGQbt8iMrL5S0pC6wAXrTlWcdywSOdoMg3ftkoEsq2wN/f7lanqxftxY5DHDClhbVijMZ4G6owf9npHpbPZm2jSvfDUlxcWKzCtdUVODvTKQwXrrjG0nG56E/gpkhmt6fCJFx8ZrUS2ibWJooE8c19YtG2ZkZDxHj+tkyMj8qa7lhT41K1/bNWQz69ScAkqIjqA8VT/t6ZTlCzAHyQWy9ae5+qigy/2VkdWr95KV61tG9kZ9Nz17pM2vwhsz7EA2B44cOSIMVxMTE8XcKixurChqaHIEstNPVcalVUeof8g/PSw9ff003N+rlVqE+ICp0xzVT5XHNRa1GwT9V1xYSAnJEHxM0r59+rIyLsr3j4yLyQFmRZgu6f3zO2Qgi8tbLppLk9Pk9ZscG6GKlHB6/+klVJQeZ7QZVB7Z7/eMTAZqPY9eVVWVeM0uLDMCR4aqIU0MdNOVm/LoPacU0taT5fE1HD/kt4WeF/mMHHl/6shYKqtl7xueu6zkBJdA1kmjk86BDM8gArU/5OnYxCGwAhk5eUbPobbgmJkrMrL8fA5k8hmE1VhmQYlwNRkZHhLmC0N+qpHVqfeSlVvgU42Ma3hxGv1S54IdyBZAK+IhgYIHixsvhM0tMjsClleUU1xismhifHOfNNjVjRYlTceMMHgE6kBtba2YdxQdHS0KzQCr+vghQvCOiginAkU9mj0LfQVTQEhie4bHHdRbpr76SnOj3GGetG4VXbU5n269bD3FxMaJ70WO9VFafJRBHQPH1UgXf2Rkjt18rj5peq2yvMp0yN0zlWCnq6OdyjITKC4qwqDe4Mnor4yMjy8lSy7EOo7xRHWtcc44O3AIdhz1PtTIzHVAf25EomLiBIWLv6djiCVUi3zeEMgK8ziQtRvK3fDwCFqh+gGRdQ75SbVY57IZ8SYjAyvFgVan8fdcsAPZPLYxAI9rQQaRogJZe5sjkOGGzi6Q/TvHTpzwq1Fpbl6elgfITCMUFBQKoQd+LQey7i5pkQP6D30k7PqBdgRdMC82bT2DRh9ZalaeFtoG77+rTYpyztq0kkoz4sXiyhlLi2kzwoHs0AEpnPCHS4s5kOkK1HX18nem5+QZKsXsbHkNuzokNQVwIMNuvqtbOqv7LZBlciDz/RirVCDLzHFYspkFOxzI4uPjhTTfn/Sp8/XTSyuCjsUHqMXSwnyDFUmIkucQz+DWLYo+rTril4xsbGzMaIZGxin/bphXzzWa2AFdzMp8sAPZAnZgRUVFxuKWlpFp7Hb5YgG5hcXi9cQJSfXoBBbktibn96Lz+HKVZxx2X7zbHR8bExY5oAhgGso+k9XVsjFVB8z9PseqasVxRkVFU3xympYHADTQxPiYCPwlpiGWWWqhb1MtFACr3uqqq/zWi+TIWPK0Tb5uUEMes9ws9BC0MNLS0ig3X56D44f9wxrw8SVlqECmIVhzoM5W4gPA3EJhbiVw1MmO+Pn66Qtk/DvTlGVZfHQ4lRbKazk1NUkTQ1I8g8x6y5bNpkCmPyNrbGwUzyAUvnFJqV5nZEwr4v8GSr5vB7IF7JZYCICHgwMZ+nR6hhyFzPwiGciqqvUHMuxwutpktlJaIv+OzuMzpLbhYeImTk5mm6oOkZHh+xzIuCajA+as53iVDJBZefmCxtVBSdTU1hlO7Enx0hAZYFVYR5sjkKHuCFUjLHogqtGdkUE45JjzlKvNvqmxkeXSjoWe3UuYHmaUL1d2XLU1IUMt1qtAbTbodWRk0n2GYYx0qT7qF2rRUcvK03b9+BlE3RRARhYTHUXxyTKQxE/1U15KDJ1SmmYcX2PlERoem9IuLKtTx4f1blxt0r3xWWQRV6CyMcAOZAvIWFgliJpOQmqmMWahvd8h7CgqktRivR8WCdRrultlICsuLtbfM5LrzIebqRv8beyq0pWNE+pqUE3pgHnXXFOjuPnsfG0PAQcyOIWYaS5e6DvbW43FAL1InO12tjRoH+UC2hTimvCISEpMzdSSkaG+0tsjacI8kwAoV9HDvV3tTsdQrOyrWhrlefEn9YZ7ialOX9Ckapxs9mzOyODsMTQqewGBigrZ+A3fSb8GMo01Tv6dyYqORY1M/A3l7jHc203v2lpEGQnRBmuAQbDDQwPaN1t16r3gOeANgjcZ2VCAhR6AHchmARYA10CGuU+JKWlG2l9VL/lkoLRUiiUa6iSnr72HrK1ZeyDj3SDXH5gGMAJZV4egCbAegfoICwsXPDrXsnyFebGpVxRSWrZ8Lzqom2oOZFl4745F1VBmdnc4vQe+hl0tDdpHuTjqRzkUExWhpbWAr590Ypc7eCA319ELaKa/2YexVQUHncDmxpCRZ+Vp67FqVd6UxcUOSh0z17DxALo6OmZcP96I+JUa1lXjNIKjzPL4GWTmp8H0rGE2IEQmADa2um2q6kyBjClbb6jB4QALPQA7kM0CjL2Aos8sd0dGJnbUKu2vrnfcZBUVsmGxpbFeW8bCwM6rW1GL/qiRZXLPiGsg6+6gialp8RETFUWpKsjooBexUTA3rrY3NzrRUjoeglo1xDJTvW9GnlKFQb5tLprzQtjTKs+LXwKZH4QCEFeYd78F6npKCydHICsrLROv7U36A1lra6ugZUELJ4lZZGFa6FhkzUCpaQOHv5GpPEE7THVOwzAZysUB2dhu9YzMcQ1zBK3IYHePpibHZtl8jAjWum2q6k2lFCMj8yKQcf0OatlAwQ5k8yzyMAuGjxt2tsz9suqtzrRbKispFhkLxAW6MhbG0OiEXwIZ37jpKnjwJFjDeFY5Q2DmE1RT7BKvQ/CBBRb9MRy0utvl8SUroYAOapFnf3FzJ8N8fGYXcUcga9Q+ysWpvqIpWzEvgubzxdQibKr6BhwTk8vLHRmLv+or2bl5QiquY6HHczQ9JTePxQXO3pRZqs7Z3SHH8bCgJS5e9prV1uhlRvA3nLInTdfQnOXFmxZ+vkdblMm0O9YgEBlZVITnzMGQem5satECcEjT5eJd2T4gdilQFeWrHX1zc6tB3STHRWvNWMxobm011He6XD3Q6M3NnelZOU67L65BGMMLp6YEV56eo0+5yNRP2LJllJUYbVCnCarorSOQNRlCCOcGcl4k0G80bFoMyspkxtLlx4ws1U8ZmTmDhVgnIjJSfN7Y7GjcX14uWYPezjbqHxz2q7WRFsUi148ysikpVvb7MQxzZKFclM8gno/cArnRq6vVG8gwJw/PjM4RLgiOBh2rmqEZ7O7RZmrzMWdkqAPqluDXmWtk6pxGKQrXE9jUooXgWh97s75HvK7LT6b8XMdDxMpF3IScsVRW6g1kvLtMy8wWU6l1gBV0UCnGJKY47b7cZ2RhxvHpCNQcJLDgpcRGGmKWhPQcbQ9BM4+kcAlkRqCehVpk6k1nU7Q/6ivmjMxM42BBT0qTrEGz6gsCcrIyhO8iUKmxjcLZEUKfq0eVEgCluCzyZsGO2W8RyFdtMPV1ekVXxvVLz6SIqCgt17Cjo0MER1wvBGsztVig3D261GbTXUY2pNmmqs6N2CPSm4zMFntYM5C19Y9QY/ewyB42FKQ4yX/ZJxAZBEvUj52o1Ppe6hvkDcZ9QLqPjxMPV7EHFnpgfGrKSbmoJyNTgSwijJaNDdDY6LCxaOkwDEadsq21eYbizXx8+Jsd3dL2y7xIdLe30vjYqN8yMt31FezmXReNFA5kakoD15b4Hj2hebPFx8fCIR3HWFUjg1F6du4M0YHDOLjD8FsEClQbTGO93ozMTCsCmISh7XdmZAnTZ7A9jEIVyLo7HdTpzIxMH7XY29trGD4L+T1Ti7b8PrRhXuj31svFriIrQeyazF5vGFQIQIXGtEalZmqxUS1YeRrNgvn43BV2XR3wjRqZxqZorj9hwRvqajX89OAnhwfAV/cSiA8mxsfF7K98l4wsISHBsKlqanZQN6iHwiEC6G5t9EtGhoXQvGDpohZd6cqU9AzjPJiRaWTV/snI0rP1ZWS1yn7L3AzNMN+j5ozMaDFQ9VH9zd4ygIJF0PU7WalrzqpLChzuHu5YA5GRaaQW6/j6padTXFycKSPzRrUoA6xNLVoA5h6roy1y3MfGwuQZD1G3uSla0Rq6F4lmVeuBlZQ/aoCG1NZF7AF5OnaDCGSiKVotgqAluV6gIyPrbW/Wrlg0ZpulZVKCS30FSFfy5mZTDQnB01nCPaVtt4sPpgHT46O11lckteh8ztKUqs9swwVkqay+usY/gSxVOVToGDpZq36nKzXsmpGZm6L91StnpoZRL9ZBm5mvH2D+nWwVN9jXTb1DozMyMrjutHdKj0bdtCLq/lz79zQjM/ss2qpFC4BvsvHYFFFMzkiMpvyU2BnU28CoI70vKin1S1N0K9tT+aEZeq6MDDZVUL4xtQi3gTiVsaAxWleNrL25wVD0ATqahc2OCe7EFZnqGDG12QzzjldXRsaLRHxSihhvwkbFvgCBcWBgwCH2cDlG7kNqV/J1Rk6+ZA1qFW1nZZ9FFuvku9nAmTeT5g1HWZm8fm2aWwzM1DDG4ujwO3WIWXJnBDJMwQabANVmbWOzE5uAKdJAvcae1To39TFvAhlahZgJtanFIMO82+2aThSvmwocbtfmGtmgKZDxIojajM65XW1qoTf30ujMyJia4S5+tBuwwz431SJbw/HnKPrUV8GHmVp0NNLKBzpG424Xrh7uAlm26tMxmz87KRc1ZmTm3TxuodS4SG3HF5eYQkmJCTMarHkmmdmGC8grLNK+2UK/JYQLQKKi3nSIIVq4Gdrkk8kwD9c0+y2Wq2dwsK9HDPrUH6jztNCK5t/J58ycwaDhOzlVzj6sM80+NGdlTQ112too6s09ZEYz9DKPG/e5bodnToezy0JhBzI3wAMA+x8gPDFDZA2rcmVAc1b1ddHAyLhxM+VmZQqXBaBG4463Syn6ykrlDaw7I3NX2DXXAfHvvFvMyi3SUiczU4sOWipP207OSZruLpCpAaLmhlp/Z2QQZWARjNBgpDoXrWhuqHXNyPIU/a3TgYbfC+b2hcckaBF7IDj2KrFRiRt/Ub4/h/p7aNA0AzA9NUVkvrqfQXNGlqJhI+J6j0JI5hr83bl7mNeBjiY9m62W3hE6dLxqpmLRq2bowCsWATuQzbHIw8EjKiaW1uQmOV1UfogmJ8apt6fHuJniYyK19loBvX39NNAr/fQqykr8k5G5Keyas07UyFganK4G7vkjkGG3q68Zeu4hlmxTBYd48xBGo0bW7J+MLC3B9/rYXD1kDGMmmYt8u7BY3kPwaNSVsTjTUtNaqEXz7K/cTJmZmAG7pvAIeU+2mLJq/F1uEzlRWal9vAnOd4qbmquvwTE2SjIeZmSxu4dJkOTa76hD8PHw3ibaf7TSTTO0D4pFO5AFH4ZQIF3eSMuzHdkYgEGU7HmGXiumF+OjwrX2WgEnVA8ZMr2sdIefnq5maHNGBirBXUaGhmgOZMlZ+XqoRdUQDVEA19uYWsRD7SvqeKHPck8tFqimdqjC4KE5I5C1+iEjy8qldA31sYVkZDk8k8w0ykX8fHKSkbHo2myZA5lxXX3MyMx0c0LMzAwIrQRsrNtqErSAVXAEsiqt403QP5aQkkbJGqhF2Hmx0EhuRmYKIwx3D1MvoJMEH+4eptKGN0DZAHX+bmVIoCsjC2R9DLAD2RwPUVJGtlgkcpNi5iw2D6rGRDRt6p7bVVnFvTRyvInuZmjY+vCNG23q4ncIWqRzQmKMfNASM/P1ZGTKZ3HZ5LjxoHKPjg7ni3rlswg6z112wDZOwqbKjbx5uL+Xuk09ZroCmQ6hx0Ko0yym3gb6neq10aaFXncgk5silZH5WCPjzQ2OL2EW9RsrT83uF6jpZCrWoFKTethsLyaalzVQixwco6Ll/L04N9eQ3T1c67gG/d3a6GSx5g3w/zF/r7ejdWapwYdZZDa1aAGYaSkMtHNX8DRnLOx5Fh/lCGS6MrJqlZHx6HHd9TGU93jxMXfxm48PuzbXjEwXtdjd3mIITHgGk6+7OYxL4Yna2bn5bhVm5o2ImZ6BKiw9Q6rCmhvksE+d1GJ6gv6MzB2Nk5oCm6qoGb1kWJz8FcjyTOpCbxpp3Y3gERnnLH13GYp6a3PplctmZaYmmyqz4TMEDIkam6Fxf2KD6m7hN9w9OpzH8ZgzMtTofQFYB7TZTE1OiCnxubm5xrPpXTN04HvIADuQzaN4QxO0OzgWwnYjvccDp9umqk4V5d01hepULLreuA5VWLvYoUGggIctVTW8YjpAd7es3XkDpqDaWxoNSiNRUUiudkSeAhkeghlMnPk6zaV6c93Vskt8h4Y6mXmgZlpWLqXGaQ5kWXD1mHm+QNkmKncPcyAzW43pDmR8jyIb83VMDY/gyczJn5XiynDjgA+wMUGtpl45c0YNWlHnCJ6MnNmdQgoN+rudBk2bLQ5ko8ND1NrmPDzVmwyqRxmSwzwcaklfMrJg9JABdiBzgyrlKABrnMJUx2RhM8wZC/eS4YHLVfJmNJzq3M27Gt/qci3hmxaqKbNc1qlGpjK2hJgI4dXHC4gvCyEHCJ6NhUB23qosMQkXJsJaqOH0LIqLcR84DJuqkSHqdKEQuRdJhwTfPFCzID9Py+h386w8UKfuMlj8ncSU9Bk1Fmn+7J9AhkAN6MxY5rrvmT7tdAlkht9ireaMOjNPS33M/DvTDVcPN9SiadzQwIijFoaSAGejVT4zI5NGfYx7AG3V4iIB0xqryktmlUq7q5EBhYVytzTQ3y8cs31Fk1qwzLSNzp4RphWxwJkpOPOUaIg9AKYXWcLt7UJonkXGvUIIZMh+z6jI8LnZ1Ey7zVZvA4UYHSs3Kc0ts9UgGnwe5WJ2cM9InFlr9cVsVvzedFnHdQWEO4mqD2k2alGHPN08UDNJNfaiYdhXcKDOy5+dieB7FMpTM/hZGRwc0PIMOmVkmqT35t8JuG0RMbE+A6POFGK+2jDX+dgPODw2JSZOA4np2SKImdcEz3+fLfawDFpVfWXzajn2wh3AJQN9nW1OyqHUpHiRCeja8TarjKVQ3bi6MzLH7mvZ7F526mdY8ME1CG/rgKAzeRYZj7L3x+Rr6UE4+y2eplRvc8188jUjM3ss6rCmMv/OpNQMoaRzV48ATZyYOpNaNKv6cH/6mrFA/To6Oio2H7EpmU73ibfAe2JbtsI55u8xPdzjEsgS4+MM938dwdp8DXU1QztYg5muHq7Hh+buzn5ng4Ui1Ubhaz/g8LgpI8vKo57hMRqbnHS7JiwEdkZmEdQ0d9DwoHSBPmXd8ll/DtkM0NPe4mRTBQm+LsEHKKm2Fh6o6R97KqYWXZV9/BBNjI1Sr+pj4xqWr9QUBwdQmQ3qgfbH5GtkZHN5/mVkymDd2uq+Kbqzud5nCb55EdStWEx249HnRC2muqcW07KkAAZN/+zI4bPQIy+PhtWpSnIjl/cEqL8OD0lDghI3rh6uo1xcAxlqdLrqgOaBmrifUjTVOPl3JmRkz1pTgqIYNSvx8yabKnOdjDe63gL3N9fIkB32DI0bvYCeij0gCuPnxa6RBRm7Dh4Xr/GJyZSRKk2C5w5kzcKWhXe2KNryQu9rIBOihYkJIVooVG7YusUeo7Pw4VARpqYpako5CzC1mJLtYyAbdwRPVpb5Y/K1oBbn6Gcy/BZbmmeVN+sLZLl+UCzKkTfujhEBKyF1pgM+FidkccmaWANzD1nf8IQWapGPD/1u6SnOPZxm8FzA3i7nYIxhkLoCGYKq2dNStz1VXKq8B91l1VAzpimrsboGZ3ePinIpSGpt9M2mSmRkilrEZqtrEBmZd2IPflZwT/7fL++ir33ta3TkyBEKBOxA5oI3D8vgk5sni7DzBbKRoQHhkMABIUEoF/X0kvEij503XEP82wztxv1C1RramhudAlmS6vfyNlCzYjEqfJnTQqgL87leMLj+wnU6Bt4LFpHx0RFqaHIOct62T4C20a5YVDVAdyo6XE9Qj0CT6Rh4cUrVtNA7BTIlBfeVWjS3K5iHTbqCJ7XDCd6pVw4ZmVLX+np8RlBNThU1VR31P6wXPA2Bx8LMRsUxxd9kmisHLFeBrLOl0Ul57EtGhvsJg4LHvRR7mJuh//jHP9K3v/1trTZhc8EOZCagFlStgkdp8dwLK8QCsMkBetqaHRJ89JJpXiSgTNPRJDxXM7S73Ve+Wug5kPECFad4fdykKPZ7Cg76Y4O9xoRcnsStf6Gf/Rbn4NmqTJkZmMLNAyJ9dYeoUfcTHNy9KZ7PF6hnWwBR30AgAOrqHSNN+D3ovkcxvJQpdl+pRfP1m6sVIzM9zeiVqzdRb2abKl8XUnNjNqh1HUa4Rp9qipyGgGsyW9Bg+hQDUs2Z14qKcseAzVHv3T26urtFDQ7AOesGtehlRmZuhmbXEtYS+Bt2IDMBs8W4Qded4/Zc9KLh7hEVYZjf8kPuLcxmuroCmbk+hgAyV0bGk5XbXTKy+LRswd3Dg848z8tTarGvo9mox8H2SwecrH8y5qYWOZDx8ZlRqGqSvs7talQDHsvcGN/6Y6AmA9c2W0nXm5uaxHkBIsKWiVYLXdQbB4rs3ALRXI/f72uh3zwSZq5AhoWWZ3lVqcyXv68rUFcqv8aM3EJttCJaMoAc7iGb43zl53EdsM2pFm88v6MjTmNePEWtOj/pmVkUE5cg1kBvp0MPqWboyLBpg/WxA1kQAH64V7mFc5CaC+4EH5j+y1ZLeCB94a95kcDv09Upb66PAbz7cmfjVFAof6ZDUQ/cFB0eHkF5+fleB2umFntaHf5uuoAghnOOvi3UiObaAHDW3ammC5hRYsyWq/OpvtKvjHmXl+kX68zms8hITc+g8MhIkTXz4onFDw4uaZoX+mzVhKxjVldDY5PRWgDx1GzA30lXjjdV1Y7MCz6P5ozMG9aAceLECfGanleszfWeN1rpqhdsrmvIzwaUhRBimFkD7ts7fsJ7c+TGumrDiBiXDZvMvuFxrzIyphZH+2TLQ0REhJi6HgjYgcwlkPV0yIxsIVSXU0bGNlXREaJnCA+ZuR7lDWrVIopAFqOJljJnZPM1PxapfrFOpZzkpmggR7k4eBfI5N/saPGf9B7XAHWuuahFHofR09FqZCyMkhK5gLSooabe4PhxKRyCw0ZBVhrpAII0ByUc41yLYHRkhNHkar5OQoKvaki+2DghQPBCn5FXoqU+Zh5bgoV+vpE3cP4AalwyMgR5DKZEa4Cr6a431zAzv1hbMzRfP57i7c4wmMHPRndbI/WqAMMwZgNWe0efYupDU538v8srKgxVsmFZF+4dtTjY02EwLbr8YeeDHchM6ERG5kEgM++WuEaGzCYmOtqwB/KFXqyqlvWZrLwCLTOs3GVkczU/Mr3KdKuZXszM9SUjU83Q9XI3uHz57G0OnsJY5JUqb66MrCg/V2RumMJbU+9ML5bzzCcU073sJTMWwbxibYpFNPhy0E1KzaTYyNkXQWFHZWIHGLhHdbAGyCwgsgDNnMjN0BpESS0qY5nNXsyMHFXHrXU5PlxXWMz5GqwdgVp/Rpai7lF3hsEzAllr04xAlpcvn88jx73LyPAcdjTJc7NixfIZA1+9zcgGuwJLK/olkKH36atf/aqQMEPCXV5eLtQruiaZ+hNdA6OCJvQqI1M1MmRikOCbFwpvz2OVom3yi6VCSedCn6+owdkaos0DDXFOxhT/zTtupnS8WSTYLaOpVn8g40UCDbGgSuaaixUlMpaZNRagQtlUdbc1OdUmPMHhI0fFa0Z+sTbFIh9fYnLKrM3QTpmJug/N1wnf18EacKDGsz7EPWQaspZWlUHNpxw2L+b19Y7j42vu6zOIGjDT+xn5RZSsaQ6Z+R4F5sqqHRlZk1AUmrF6hRR8HD1RRW/UdXulWOxolOdmxXIEMufj87Qhmj1LexdDIPvBD35Av/rVr+h///d/6fDhw+LrO+64g+666y6yMiYmp6itu09Iec0L/UJrZGZ3D/D6bD3j7W4QmRMeJNQ4WHShM5DxTTaX2KMwP0/0sMEZu5EXULXjTlE7cF8ysobaKr8FssS0LJGNzVevgZ8mUO2ibuNsG9LkvmHnBWShOHxULvT5xaXaxDoOIcv89RVQiHwfOlGLEWFC7ZepZpZ5u9BztlJRUWHUVZJifaMWcc93d3U6yevnAl+nZkWZmzOJZDe0qifAswv6NDI6hpLSsrRRi3wNE1Qgm2szwmuMaAVpdm7c37CqwmCEXjjWTpXtst/Nk8DDGRmuoWvG6anYY1iVV3o720I/kG3fvp2uvPJKuvzyy0X3+TXXXEMXX3wx7dq1i6wMyE57lUNAXFwcJSUlLTyQdbRQv2mcgo6MjHe7GblFFBetZycIuMpijVlkbjIXZCzYuZtrEEwtJvoYyMZGR4z+LX9lZAupK/J4HK5Hul5bOIw3tnrn13f8hLyGJWWzW535upufy9MOmxN3NTLetLBbvbebLb5Hcf36laktb3S8BTdvh4VHUK6acr2QQNbSVG+wPliAsX/xtQ7ooBXlZAZd7RN8DeNSpBBiLmUm1LxZasNhrgOas7XhLkjziR7b30xtfdKDcyHo6ukTzvocyMwZmav3qifUYlfHIghkp59+Oj3zzDN07Ngx8fXevXvp5Zdfpssuu8ztz6MYiwZB80cwANlpX5fjAizkIiJrw8/Bxqm1zTEzCE3RKT4GMj5/oKV0KRbx/rjwDUshs2rRXUaGY0vN4l17vVMgi0vN8km12NmkzHSTk7UqmzjjTErPXFAWhHlQQL2p1woALZ6SLt/XCS8HNNZUSWp45fIVpAvGbl75KM51b4AacrehinIJZL5mZCgfcCBL8lHsYVy/1AyKX8DvKlHK05HhYerq6jLuWygX3QVxrzaT+SU+Z5rurmFscsaCDHZLVMBCsDY7zXAQ72xtoqK0WFHvfnhfsxBxLATH1PVLSE4RPbFOgcwH5/tONQiU15iQDGRf/OIX6d3vfjetWrWKIiMjafPmzfTpT3+arr/+erc/f/vtt4vFjD8WInv3BzoHEMg843axW+KCNEQBTJmhKdpXatGhliqZU3nnrVCAvRTnmz1k7GrVYsA1sqgUGcgwk6y/X3pTLhSQ+LY31hq7eV/l2rNlLAvZALD6kj0fzchV/8ZNzZ4Ai2pfj6xbrF2tP+OMV64dc1GLkULU4bgPjYxFXWtW/Pm60IM6hQk0moXjffTYM65fuqSG5wNMuhPVuXCtA7qjVb3NyHRRw3hW4HEp3mOitICbr++Oa9Wugg9eKwcGBuj0wljRHwiKd0BRfPOBa/B5haXGs40+QG/qYyjNMLvTrrLqkM7I/vGPf9A999xD9957L73++uv0pz/9iX70ox+JV3f40pe+JOxa+INVdcGQ3vcpatGTC+CuTpaggVrkjEwGMr31lfT0dNGHspDZQxi2BzQ0OGdkETHxhrOJp8co1VI12mlF14UQu/L5kKvEAjwux4x8dW29uSd5EcT7yM2Q58kfYpa5Gr6xq2ZmAIsd+trMgSwjx/vNFoIiH2N2QYlxb/g6dNJpI7KA+x7S9dTZlJnaqMVibWNJOONMTEyk6YjoBRnsMoXY1dbo1EsG1iBL0a+tTQ2ihxUw1+vnQrWymCtUPZO4dlwni1rAs2PGkMoUsZnh6ewhHcg+97nPGVnZ+vXr6X3vex/ddtttIvOaLatBPcr8EQx0DY46UYsLhTvlYkZCtPFwQRFm9oHznNYo1h7IzMc3X0bGMntezLkp2uzF6EkgE7PIJiadMjJdgFCA50+JGtkCMlkW0rS4cfcoVu4ejS60o0fUcJ4+xaIT9aYW+rkCBzYnUdExlKzMn/k6cT00TZk/e7PZwvtg6X1yVr42xaJjI7KwjBrSdXfsB46RFakI4N6ULPyRkfHxZatNBLKo+e7TuST4TC/W1dUZm8yFBrJa1d5TrCaiA+zu77FiUdGKMeGOOmdIB7KhoaEZTXC42X3prvc3wClD7OFNRmao29qbDZl2RkIUJSQli2nK3iwUoP/YcSGzoFT7bpCPD8dt9JHNkpGx52CjKSvhpuhc7uHxYMeLmhwYrg4/BDKu/0VERFJ8UuqCFp8CFYz7e3tmUKRlpcWG16Sn7SOHjshAlqWxkdY5Y8mad6HnxciVQuTs2xfqjRd5CLqU6b3P9TFn1elCM7JwI/OqNjUGY2MGy6Wk5BSvsuqJiQnjGRQZmaY6NR8fT7eOjZpfVDGXBN8cyOJVIBswDfqdC/VKNVxqEiPxpsvbHrKxwR6x1uOYOFsMyUB2xRVX0He/+1165JFHRA/Ggw8+SD/5yU/o6quvJquiZ3hczNIZ6PaeWhRN0YqbRtaSnRTjteADDxD6yKJiYkVj71wO4D4pFk2u2bPduOzX16QGHQL8fljx58nxGa4ejfqpRYc0HbQbdrrzLz5pqUkUm5DkdrGrKCsxiumeDtg8fFT2kBWUlGkxmjUGTpqo0/kWer6mCekyM3nghTfpX6830DJa5iRP94Y1YMZASO9Z6KEhYDsyzoXVyIRBsPI2ZcNv+X35f3MUdewpvYh7GsEsKjpaKHfnonA9AV+/DCWimsvVY4bxgpuMjINcbW2tscEcUNdjPrCrB64hozBNTk3P8nCaOdvODfdIRgRBDBZVIRvI0C8Gyf3HP/5xWr16Nf3P//wPffSjHxVN0VYF6mPAgLJW8URtY6YWzY2z2ckxXtfJeJFIzysSC3JmYrRfAhnvorBzn22xzVaedZ0d7aJ51iz4cOcasRChx+jwoNFr4g9XD3ZMWMiOXqjbZslMytVcsu7WRkOVt1CcUP53ZeWORcJXgB7jgANqcb7aSmK0DCyJalQIlJm1nUPUOTgqvo6MTRS1Gm/uUc7IpPRez/gWoMnkejFXMzsDzwcLdlypRSDL5EDT0jtiLLgLfQaRUc9ndebNM5jKfYALuEc5WA3191Brp6xzzkUtDiyAWsSzzB6jK5Y77tHi9Hj62DnldGqZZ5Zq42oYpzflGUsGMjwYd955p7ip8NDBVPQ73/mOIS6wciDr86KRb7am6FwRyLyjbhwPUYlYHHzl5yHoAI3IDxEH6lbVczJXoExJS6Wo6Fgnn0buFUrOnOkaMR+wkHQo6T3GyOBDF8z1FWAhiw9+JlUdh+t14kUCataOXs+aTWurZSBbsaJCv2IxIVFk66Cl5gJ215dvyKVt66T8f6S71VCYcX3UvBB6nZFxM7QGeyo+Rgw9XaialSluFiSZA1mGosZfP3Sc/rarjraf6PQoUMNjEdDf0C7vURZozAVWdPMx8vWbQS1GLbxGBtYHGX50XDzl5zhbgYFG9VRJPK5KR/1K+R1I6T1gey0qocfE2Jghl/amRoYg2K92ukBOkiMjc3WNWHgPWYnP2RgCxx9eqab799TPyMg4kGUlzU4jRLnJWHjnl6B2+p4sgiNCeu9fxWKi6rFaGDXlEAu4Hgf620AtAdW1C6+xQHCCmhuwdtVK0n18aZmqvjKHzyKAxWhFdiKdunGV+LpXTQJmShn1UW8DmdnVo18TtQg6vb1NbiZzlBhiISjkfioTRYrhmgCPczl0rMrwU/XI9T63yC+BjL1YF0ItOikXW5zNg52oxWgVyBYgvzfEZKj/aShd8DDOHi90BjpgBzJ1c/ep+pgYj+BBloA+MnDBU1OT1KioLQAFfqY1qqprveshK/A9kEGuC0VRU88INTa5D2QIurMBfSXcWMo1JH5gYlVTNIZ1op6w4IxMCT1WrNDXKOyuWXghdQ2hbpslkImCda5cCCtdXBUWcv1QW8lLl2IDvWaz83v0zTV3bUzRQAho5oXQG+l9XnEpTUxNi1YAX2u5bW1tUigQFuaRUCArI52iYuKc7tGocHlueJPS1Ci/b24ongt8fClq2rsusQfT33yPLvQamgUf5kBmWHQ1N1PkMnlsC6HBjx7jQFakpf7HojHMTQPsQBZg4KHsRiBTKTEahT1Jq6HIzFWCiMbGBkPdht+xXBnPmp25PR0dkeVjIGNZrHD1MGVkELe09Y3OG8igcHPNWLgWEhaXShGRkWInfeC4pCrmA0QT/pDeOzcLZzrtyudCdOTcDhCY7OzOHmguHONFAmbB8foUi2YhhDeBrKOthSYnMAFYKcwmpgxq3JOMDOcZ6mTc+9wwjyDmq6jF2IikpFO8Bwa9cdGRxjBbDsh87VnQginuwEJFO4b0Pl8KfmJ021Mt0NVjRkbW2iTEaWbWIDZWUv/dnHHDAm6e4zx2/IRRA/RUaj8Xtdit5jnagSzA6BueELuJQS8Ui4witRh0tjY7PSirV8j+DHgKLrT9AEVYXlQw4ykzwTP10GyO1COD/TQyMmwcY+fAqNhJ44GfazxF+CwZGb4/RcsoWdGLv3/8NXq9zrkQPZvYwx+KRddmWvTnLEQsIEeaOB+fGQVqlE2DB71kBw4fNRqFdSlOnTPODI/orszMTNGviY1Gb0crjY5Pz2gI9ySQ8SKPxXVkMkx/D9kCpfcMBPRUl80IX/sUVf/EcU9OTiwoIxOTJ1SzMDIWYbKsYYwSaE+YPgAxyQtz9ZgvI8OGuUhtVFqbGg2l6nx1Mh6ImldUqsVZh6nFTjuQBQddqi9jfKDb6yJlsfJ7g3LRnNavqygRNMnE+JjRJDgfcINhwYmJT6S0jAyfPd44kHHGiYZzmCK3qmwsOzFmzhs5Qvj1OS8SeKjfsj6H1uUnG4V2tB+0989vWOqvZugZhsGRCzM9Ncu3EchcNxwlqinakwGbR1SNs7CkzC/2W3ELGP9hBlR3nHl1tTXRyPiEkT1xU7sn1KLZLLhvRL/Qw9NAhp91zch4QR+PShKzyUD9o47NwqeFTJ5AmSElI2dBGyKPPBaRQUVJKnQ+5elMCX4j9ZrcPWYKPsIXpFysUj6gsBfTAWyKATuQBVHoAYyp8dzeXACzBN/csFiYkSjGtQPHKqs9pxWT5g4yC8GIohZZkZmTKx/4Fq6PJc+d8Zkd1M0ZS0VWIl20Jpu2rFluUDcLoW26enppQPWa6AxkqNHxZkEGsoUthKK9ITtXbDjQiO664ShXvWSwAFpoU3SlYabrn0DNtNRCF0HXsTRDY1MGnZSjaHF3QXwhQg9eVHU2Q4vrF+VhRubSCmLYky0Do5BjZDPAfPcpH19hcQmFhYdrb4bGGgPRExDrY0Y2s5csct5Ahvu8oa52hquHL4ACFs9HZ7tdIwsKuJmz34f+B3NTtFkVhcWUnTH2H5EPhyceizr6x1wzstSMLKdAhsbtuSDEHqaMzHUxN49iXwhtw47waRmZWu3IIBTAe0P2gRqLJ5QexuTwhsOVYuM6J5pR+VzOBbwHlt6vXulf6tST3ia+TthwwOCXt0dpWTmi1uUuiM+32QJ1erhZWj9laLhXvacWHX6LrhkZwP/W3yF//3z3KQeyAjXQVnczdHZOjpHBeEotYkPa2TfolFUWOfWSze+3iHME+hRz1nQFHGS66HMbHxtzMiUPFJZ8IGMxRI8PM3TMGRn3pDHyC+RNdsTDjExI7xN8Xxx45zeomr1RX8FN1zUg32d2UvT8GZmqg5mNZ2dQHm3Nxt+aC3VqkS8p1Tejy7nRNFPsoj0ZS4864WwS/FJ2Hm9vNvql5pPeD/bLxX3NSv+oMmUztGe9Pnyd+juljRdfqSkKM4bILpRe5IW+bVmKWJDLsxJoeVYC+QpPne+dbKpmZGRhM/xC+zpaPMrIChTtpjsj4/YJBNvZzLpdARUn1zm72lrcKhfrnGyqJhbkIelJVj8XcB+wxR9MyfFeA4klH8gcw+C853YdfostM/pUSkrkv1UvUPXmyMh8VywCRhYxJGuAUYlp1D4wKnblyFrmG4SIWgqab5NS0twKIsyBbCGuCQ01VdodL5wCmXL18MTfcK7ZVQUFBSJgYEJvbaNcCBeyEUHwz83Ql3Fi9Acb3wp7Kg8XV75O3EtGakOPTY0nvWRm6X1CVqHIxC5d65nSd0E+kl7WyJgiBZPAdcDlih7mY58vI+NrmFOoFIuaXD1YdToSIe+LkvT4Bf9fMA2OZ63JcGdxpRbjFxDInHrINPXHgVr0dAyWTtiBTDUPdqhhcL5kZKj9tHf3O6X9qyokPdHghpabS7qdXVhKafFR2gLZWL+sS0UmplNtx6BhozUfuJaSlu1+oTdTi8hu5zvGpjqZmVaYbHH80WjqSSDDQjVbLxl2lkzHnjCZ0s6Gw0eOOqT3Gl3v+fhiYmKFGe5CHCHM4EUQ8m1gUl2nUQ8DGd4HgipqigWFRfS2jXnaJyd7Si3i72dk51BYmKRI8XsQWJGVg4LdrOq4oIcXkpHxZjKroNgv1GJ0UrpQeV6w2jNTXbMLPuYnuqUWo+anFvfv3y9es4vKRfuJDkD57Y0zki7YgWxsUshyO9q9302ggZp7OTpam53S/tXLZSCDr5l5lpA7gLpraZE3+/IVy7VIfkdcqFMs9HsbeuftH2NEqEkGZmWfuyA+OjQoKDVujHQHYZOlAtkqPzVD8/h4TzMypqbcSfDZOHkhWTVL73OKSrW5QTjNksuS1k3zuXrM2kvWIp38Wdcx7mFT9Pbde+X7yCmgq08u0ebs72yIDLGHZ/d+fIw09zUHZATZd20tpLUrJI3N3oJzZWQI0keV4XNemXRE8UR44nq/o1+TcaRKvq/UzCx664Zcj+8PYzPS1ujE/BQo1gCtO6MDPfM64L/xxhviNb98tZ2RLQbgRsNN3d/dKR4kFL3Rc+MpcBOxg3RbfZXTTWbUWNqanRoZ50r5E5LTqCTX8/fhChwTZ2RtrZIWg5s+P8jz1cdYfg9wnw4mGpgBKT+aMpnymIte7BgYpdYGGcg2rJWLhL8CmSd9TaKXbI6m6AJV56yrnT9jeXOvXOjLluuzpnJXX1moSMB1wzEyPERD/b00OT3lFbW4Y9dr4nX5mg2UlyI3b7qnl8O5ZLaxQrMB54Ozag7ImK0FVa7D2UQqT+fKyPbt2yd+BotxbFK61xkZnrHfvlRFdz17nH7/cjU9sKeB6hqks8rZG1fMK7KaPyNzUItRUVFG8OhsaTIyMnfsCM4xZ2T5FWu01f/QRxYsw2Ba6oEMU01xrVmxCLspBDNvgCGiQHP1MSfBBz9EUPS0d83dMHzw4EHxmllYqkWxCAsi3hG2qlldoG0YC3mYuBidXSgzy0OHDs35gM0l+Hj94FEa7u8VfT1r1qwhnXAaOBkV7tFuFz/LI3fQCOu6ALCXX2PD/Av9/r1yt7tx02bSCYc9laSjPKUWzdOEIUqaUJkz7hG+R9EiMh81/Kbaza/dsJH84sqSlEKJcbEe19zcSfAZfHzDQ4M0PNA3Z0aGqfbA5s2baURtyrxZ7LFpA9uD0wmRUH3XkJGxnLrOO6GTWYKP+YnmbK/IlHED+Dd3KtsjR47Q6OgoxcYnUFpOgZb6HxICIfYIkmEwsLQDmaqPjfrQQ8bYsGGDeG2uPmr0pgFwrY5PlMXdE1VzUzevvSZ3u4XL1/ns6AGMjMmgMjU2bAyNZHuj1LjIBS32KJoDOWUrjR2rK4widPvcgo9Xd+0WrxUr12hXNZmFAp7SXcjIsgpLhdqxu7tbeEeawVk1HFrmG+zZ0doiFuGTtvgnkBlmsx5SizwEE2hvqDYWQWRkSSobhZVaZfvcLv9HDsiM86QtW8g/x5flFZUnBB8uEnzj32JjDaZFqmvnD2RbtmxxTD32YrHnjQKG7F67tZDOW55Kg73emy44t1A0ietn7lktUs8g3PE5W3cn+ODjA60IAYkOapHtqWxqMUjgG3W41/dAZs7IXJWLbANUUzu3WGAXB7IV67T2kHGgBg2YnSFNbBdKbXCNLLt4ubHbxWI/m+vAXBnZG6/vEa8bN+tdBF2l2x4HssgwioyKprxiuVPeq+hBxoryUoOaQi1gNuzZI48vq7CMCrP0jadx9pHkZmjPF6CNG2UW1VR11HDAB3vwRqe8xsiWj9RLdsIdoJpksc7p204mfxwfqG9vFlfIyHnkijvWwNwQPhe1yPUjBDL+OW/eD98nEFPkp8RSerjs24yMjBTydN8ysmaampx0WmeK3SgXB93Uyfj4cstXi1cddVyui/MIFzuQBRh8oQe7O7RlZKiRtXUPOFE0UHcBtXMEMjhTvPnmm+LzVes3aeGuOZAN9zqOrzwr0RigtxBwjQxDGPlhYY7dHeUx224X9MOR/fL4Ttu2lXQCcmtkQ0wtep6RyXNdVLHabSBj+TaoU9c+QTNe2y0zzoLlayldQw+g20CmaoBxHlKLwKZNm8RrY+VhtdBOU0P3ME2ExxpTsg8cle0R7rBrtwzUyHyWF0kBjC54q1hk4HnJU4sznqPZGve75rhHYUt14MAB8fn6DRsN411vFnte3Fn1y9S3p6bkrsEY2SUs7zAKCfSl+d9cB2y6Uy5yICsoX7NgP9L5gPloON92RhYkDI/LCz3gg2EwA02lKSkpcpxLTaXhGGKmdCDBnw2HDx+mkeFhIa1epamRljPOIdUMjeM7ozyd3n1KIa3OlQFtoYHMnHW60oulapJyZ0vDrLtdPHT1x2UN8OzTt5FOQCjAY2QSU9M9DmRMHeUqlZprIOPrN9DbRXVtztmoGTsVdVqycp0WyyYzeCGEdBvwppHVHMiwzrO7BKYZlJXKY6yqrjHuG1ds3ykZg6IVa7WJBNxRp/MNDHUHZKg5xRUUEREpmvZd6cWFZGSoUUMMkZqaStmKRUHM8Waxn1B0GzMarrMAvQHq93wNG04cml2CHx3hdpwLNny8Wc6vWL1gP9L5gOwequUxkyl5oLGkA9mQizTdlyIlbggHvYg6meMmK1M2Ry2NdfPWx7CbT43Xs5vnjGygq8M4Pkj6c5MXXkyPVA8isHbd+jkX+u6WRhqdZbe7Z/8RUWiPiIyidevWkU44xn+kid/vbUaWU7rS7fFhYeM658Gjs1uNvfGGrD+s3bBZq1mw+xqZ54EE9yfelzDPHegWWQOyjas251O5CmRdrY3U2DPk9v/v3iOPb9U6vUKPmYbP3lCL4eLa55Uud8o8GOZm4tkyMnN9jIMd3os319I1I3Odzu4t8N6AxhOHnNaYYrfU4sSMqdCgh1GfRg+ZrvYQ1AN7O1udTMkDDTuQ4eZWF8HXncRsgo8V5VLx195U76Q0MmO3oqVQH9PVm8MPLA8N9eb4wkwOCWvWuc/IzBlLd690n3DF9p07xWvFqrVCLuyPbIUnQyd7YE8F8I47q2SF0QaBeVtmMD187IR7qzH4FLY2N4lFb4tmoQdUZl1dXY4aUlS4V7O/EhMTjTaRvoYTIjt/zymFlJEQbWTVXS0NVN8td9au2P8mKzJlVqAT5h4yb6lFoKBijdtAxvcoJizPlpGZFYuclXorhuAamc6MzBzIGo4fpO6hMUHrAUaLQXs7hU+NuZ0Uzedk+co1Qjm8WHrIgCUeyOSF7lTj1X29CE6CD1Pav3J5ubHbnc06xh+BTIePJMCL5qo1MpNCHQGmowxQqolJyeLz+lnoU5Ztb9asdnPdzeO9JnhIu3EgQyCERB0UDNdKXLPqquqZ8nyz0COzoJQKNAs9uP6HDUBcYopXQg8GU1NNVUeEswt6rQBzIGt0E8jQKFxbJfsct56sV+jhrn3CUzDVmqPoYddAZj4+1L7cbSidhR7eKxbNqsXICP8EMmRkqDvzGKqUlBTDhLunTaprXdcaPr4Va9f71Og9VyALhvQeWOKBbFIsWl0+LvSuGVmTC7XIDxHkt22dPW6LzExnFa5Yr2VIoZla7Pbx+JgeKSwto5iYGJGt8OBBRoGa21VfN1PQgkXjqJJtn3qKXqGHayBDbQpZpCcA3crHuHbdhlmUi3Iz0tpYT4NuakgcyJARpGuwFnPr6pHJrh4aAlnlYSdVG2csqHOinulKv+F8IIDjHK8slfUjXXBy9fDQZ5HB/4cFH7MFMrAGo8ODM9pEsDHj+pGU3nsv9DBL0iPVvciB2tc1Bv2X2NAMD/aLoMwb5mXLlhnHCOYHGHCpkfE5KV25VrzGabSn6lWGzHAZCQaWdCBDxoLgAqEAbgQ0RPsCrv2gBlHb1Grs3EUvmcpYjp2YqQqDChDBDLttNClqy8jUYtTZ1uLTQ8T0yDSFGcfoutBzIGty0zTc3j9M9ccO+EXo4Sq95wzDU3CdbPVaeXy8qLlmZKCmOvodtLFrRl2wfB2lJ/gpkKmGZq6BeAND8FF1xKmGwosgxBC4bRt7nLOyXa85jk+HB6gZqNtgerIvqkVk4mijgK0UnmX0Aj6y8wg9tr9ZZC54BlHrNOhFlzYR2FLhPcTHx4s5edwM7XUgU/Ql28xxb6KvCz2CGDM/oBfdbZhbVS1e2O+ZMk8OZKlFshbsjbvIbBlZjxqoaQeyAANBBheabVVgs4QeD1+AGgTfTHUnjjil9jl5chd7vLp6TloR6b6uIizvqtt9MEQGOFvBQ8G9SLPVyZrrZwayPfuO0MjQAEVGR9PatXI36K+MzNtNABZBYKWiXVwDtZmaMsueGbtVRla2ep1Hs9A8c/aXGy1fFIMcyNAm0t0nm+SdM5Zuca1c6cWdKpCVrFzrE7XpDg0Ncvo2NnKYtOAt5YUMA6rfkjKZPT/87HY60tJPTb3DM64hByrXRR7nB43CjmZo794LK0JZ9cvHqGOhN+pkJw453Yul6vgwNJPLAVwnAz2NDwT5mCz5rOalxGjPyHgkUKCxZAMZCr5YmHU7Ns9mVZVXKDOWmqq5A5mubIwD2fjYKPX2+OYowLtK7LyYPp1Ngt/aLB9YM3bs2iVel69a5/NmYV5q0cvzx3565Yp2wfGZJyYbLQatMwMZhno2qR33xo36FYu8CKZm5fhMCeE+T0vPoOmpKTpsahzGJowbdbHQo7/M3UK/Zv0mvx1fsvKR9JY65TpZ+SqZVTdUyuNr7Rud2SbikpGZFYvmTaCvYg94RiLTQ4uI7kCGOpm5Fl9WVmb4oRruHope5OtXsWIlhUXFigCtK7PGsfaqMVh2RhYkxeKQahbWVaQ018nMnfdFSh5br0aMu7WmWrFeq5s4eH7utgclwdSKp2CbKuwy+fhcM5ZyRb11NNXPEEOwo8cmPwg9nBbCjGyfM7K8knJxrmDpZTZI5owT7he1zfKczhR6lFBhjneuDXOBHflTlLu7L9QigtDa9fIaHjvk3Nhuzlja+keMOhIW4hNHDzstov64fpjhhux/ocMmXcGZakxOuVEHBNr7R+bNyMyKRTMt76vYAxkZ04qgLUFx+gp+j6AWYVM1pmhMPj7I7NEbCDB9zIGMgzyyMV0bEkEt2jWy4IAv8HCP7/ZUs2Zkpt2SoynaOZBhkWCFnM6MDBknhmf2mFJ+b29cpkfMGRkW+d5eOQ4GWFHBozIaDfsjAFnvsQMyezt92ymkGyjS80KB4ZieTIZ2p1ycWhZm0J/mYJ2QkEDpyuW/sqrGkD2bF0F/1I/MC31CuszIfG1G3rhR0ouVR2SDuus9OtTZLOpkzT0jRg0X5xlTGVaUyw2ZfzYiOT7R6pyFsOCjq+6Y24ysyyUjw8bLrFgE+N91yO/NtKKO4IFnEM3REK70drYJGb5rIHMdsOlqTaVzcsHIyJixYbYDWYDBO67+rlat3C4v9C01x6nJ1FhartL+FqUoYmCxxCKB0RW+ZBSuYGpkQPXI8RgPX8Qe2GVi9hrfrGaJOjfUImNp7eh2FnqckAvmWafpD2To38L5w1BFqVr0NiMLNxYwrgO6Zp1laqHoaG4wZM/mjKxw+VrRk+WvjCw+VYo9fK1RsaFx3fFDTgGZF8LhLknVMr1oGM0uX+PX40PG6Usg4wAPQ1ygrrpSKBSx0CO7NNPDZlUmFn5sypCJ81QGn8UeqkaGDFNnfQyATdXq1fIYG48fpHYlPuKNCI5lamTAbSBLLpAN47kLGKq7ULS2tojNQERkpFdjsHRgyQYyoxm6rdnnhd4MNJyic35sZIiOnag0HpiValI0FkEz9ca0YvFK6bqgW7HYryGQsdiDbXfc1clQY0lMkf1TJyorje+/tveQsK+Jio7RPrrFvAgiiCXGRXk9rZgzMixgswUyR1NtA3X0zwxk0mNRb0aGe8UQQxiBzDcxydaTNhvmwf0jjhl5vND3tUupODt8GIrMirV+zTgxT8yX1oK8ZJllnLmhXDAsOHc9DcdFdonF3pyRVXcM0K7qLvEzO1WzPtgUruH6LPZQGwTQpLoDmavg48Xj7VTVPiBNwZXyukvVqrExg21VZWWlWF/Si1YKIYguxSLQ0izvl0wxpTs4IWXpBjK1U+nSHMgiIiIMaqqp6hg1dMvFYLWaUouhhm2djoyFF4m85fL/6G6G5kDmy0NkzsiA2epkGbnyb1RVOWpLr+6UQo8Va9aLc+O33XxWrk/njuX3c2VkxkJoEnzg7/P8q+Wr1/scZFwBkQCcPXRmZCtXrhRu/9hsHTwi6Tfz8bU0ynPaNzwhBC+PP/GE+Lp87WbtikzXGpkvtGlJRjx97JxyOm9lllFH6q4/btCLbOOEjdVz+6rolRMdVNUxSA8++KD4/gUXXCBeEdx4ioOOGpk/A1lHzRFxz/77zSbafqLDIbpqqjM2n//85z/F5yefeoaY94aBut7WId2hTQ3zzMkNjmJxaQcytdC3q4ugK5CZ62RN1UeovkvSM6nJSZSQLMUWr+0/Kgq0WCSee+458b38inXCoDTRS2pstoysp8P3QM01MpYUzybBz86XNjnV1Y5euWcef0S8bt12Gvlf6OF9tsAZGWqLfHzsTTeXBP++++4Tr2XrTqb8bP8JPTIzsygiSmacvi5C2FAUVcx0wODjq6+tMaaLb9++nRobGig6Lp7OOOc87YpF12voq20SAiHeIwcys+ADlFxGlsxYOhrl39xf3Ub//e9/xefvete7nOrLvmRkXCeGV6k/A1lbzRHaVCRHM+2s7qLIFHl8LaqfE9L4Bx54QHx+ynmXiVd4reoErNnE7w2Sq8fSDmTjkzQ6PET9vT3aAxlnLA3HDlC9ysiALNVL9tenXqOnDrXS888/LxaqpKRkWr75NLHb9cZDzx2Y0uxu1RDIVEbGBWw+PogAzBL13AL5N9h5HHPL9rz0lPj8+uuvI3/AyMgyfcvIeMHCeUMdkM+XOVg73C8ajUB2zz33iNct51/hl/oRL4K5qoarq4eL1WvmrJMzloGBARrs6xZCnXv+9nfxvfWnX0Q5adICSSegDmXREAKZrh5KDmTVRw44CT64cZ9tnB5+5L9CcFVeXm78HxZ6+KKg9HdGxv2AuP/XpS2jy9bnCHVxdKoUrTWrQNba0ig2I8CKbRdoF3oAHWqNyc2zM7KAY3hsQox85/qODlks48ILLxSvR3a/TDX1zYZCkjOWjpYGMYn3j3+8W3x9yduuFlSPzh4yttjpaG3Sl5Gph3PFihXC1w0LHmeUQL7qlatTgeyv9/6dJsbHKbdkBZ11qn5/PidaKjPHR2rRkZEBnJW99NJLMzKW7tYG0Z+zZ+8+4QASHhFBG8++RHt9zHx8mTl5WgPZKuVgcnCfw8EE9mOs3u1vb6LJyQn65/33i683n/sWvx4fpgugmVnXeBgOSsePHhbzu1jwUVysBBHtjYIB2f3so+Lra6+91sg2HdJ7794LNgCc0fmrRoY1C88hZ9WrcpJoRU6icAYCmurlM/jKU/L4Tjv9dJqMTdXaCO0ayPKDpFhc0oEMPnM97S3aszHOWE455RSanBin3U8/ZGRlnLGAmhoaHKB//etf4uuLrpSUhtZANj5JE2Nj1N3ZoV3sAWrqfe97n/j8V7/61aw2VX/561/F65mXXU1RqgblP8VbjtE74w140YITAiyNrrrqKvH1b37zG8Mg2aixDA+JjOXuP8tsbO0pZ1N8Uqr2YZrm48vIlgEmVlMNbutpZ4jX17a/aLQvmIN1f0czVe57jdrb2wQlvmLL6ZSZoHcBBHiRT8tSx6cpI8NxoG8S1m+Nh14zBB9Zas4YAnVJUhgd2vW8Echc2QyvFYsmJej05IRQ1vpDms704naVca3JTaJ0FcgaVCDb8YwMZBdc9jbxmhoXqb2OyzqDgiC5eizpQIaFnjMy3YEM+PCHPyxedz52P9V3ykAWn55r+NntfekJGhoaFL5uhas2+iWQ8Ywg7LS9Ha/uTC061JYf/ehHxetDDz1kGKLybhe7QZgKv/bqdrHLveyqa8hfMFOLvmQrUOOh/gRaqbV/hK677jpBMaJf7tFHH52RsXQ2N9CD98v62MZz3ipedZsFOy/0MiOL15SxbNqwQdT1Jicm6Je//OWMQNbb1khvPC/rm+vPvFhIq7OT/UedpmbmaA1kuO9uuOEG8flzD/zBoBcT0uViO9LVTA17X6aJsVHKKiihdapJXEdGxoEME5hbW+QaAyWzL8+gO1x2max5/frXv6aRkREqSI2lohK2qaoTG/Vje6UqetPZl/iFVgS6lc9ioZ2RBRYQWuDDXxkZ8O53v5vi4uKpraGannn+BVE8X5aYZTScvvaUVErhYeNJrp7O0ZoLI2OT1K0Cta+NmEwtmg1IIWg544wzRLbyhz/8wSljGR4apN/97nfi84pNp9LKchngdANmz2xPlZyZ49NOE7XJkvR48XlV+6AQBnzoQx8SX991110zFvpjr79CjfW1FBcfT2tOPY/io/V5ZM7l6qGLekPD7Nlv/4CxEPL8NT6+jqZa2vfyk+LzzedeLoI0Kzv9EciSlI9kjBfToWfDbbfdJhqH9+18WbhgQPARniyfwYGOJnrqkYfE5xvOutTJJNlXe6rZ6mO6hTLvec97xByy1tZW8Qzi95+xcaXoqYQ13e6nHhLrzrZt22gyLt0vgQw18h5lT1VUpH8dXSiWZCBjaXqvBkXfXBz2u979bvH50w/eS8daByiOM7LWBqrcu0vceJdedS31Do/7JSPr0dRawAVvM2UC3HzzzU70W3JCvOjnMosgTrrgbZTpB8oNgAmqaIYOj6AUMVnYt9u5LFMFso5B8frxj39cXKOnnnqKjhw54rTQH971gni94JLLKTo2zmvX/fnAC2FihvJZ1EQLQVi07rTzKT23UAzt/Mtf/uLsDnF4v2huxyBPZG66lW6ugTqBDZE1bgawsWIlIrKy2s4hikqV57G3rYkef+wx8fmmcy6j462ygRjwVXrPI1yi/FQfY6Dn7XOf+5z4/I477qDx8XHaUJgm6sXAmy/JtokL3/I2w6UlX3Mga21tEyWUZWFhVGCrFgOLoXGZAfWrnYQ/Ahnw0Zs+Il5BIz6y+zhlqj4r1Fg4WxmOTjW677UHMk0ZJyspWX7PeMc73iHoEixGoN/w4HOxGb1VkdExtOHMi/1SO3KSbadnUXxspM87XmRk+BUY04LNBVSKV1xxhfi3X/ziF84O4yekIe25l79dvKb6IZCZm6ET0tQIF00ZWVJsBIWFh9OZV75XfH3nnXeK3TUrM5vqpLn16jMuFj+Xo9EJYq6MTGcgA3ih3/vCY8IeLjUrl5aFhYvFF/Wz8uUrKbd0pRBfoTbqz4zMHwBrkJ2dLZTC9957r2B1cpXoilsPopafJsQnK3MSKVUz/V2teigTUzMoLsY/m7mFYGkGMp6c7McaGQDBR/nKNYKHf/nxhyg+Lo4SUqVfH7D1oqvoYFOfKESjPqPrIcYDiYdR1/GhFwYw2xlxzejGG280RB+weUrLdhR8151+ISUkJFKKxgDtbjcP2baOTAW0HbtDVKus7JOf/KR4vfvuu0VPGS/0uKZwMlm+6TSjiK4baIZG7QOITs7QSi2CJoRR8rZLrqGExESRcT755JNGoIYYCSg86UK/1Vaca2TSJ5MnLeiUqV900UU0NTVJL/zrbgoPj6BExRoA1737WoqLjhBrAtOLHMjYtsxrn0U/Z2QAKPDPfOYz4vPbb79dMBRlxfy8T1NayWpKSM8TmdjFa3ybt+gOdfXczJ6trXXIGyzNQDY6KXa7nZpdPVyBDOF9H/ig+PylB/9M//3F12hkUM6Aio6R2QrTnBg/ootDh4QcwVHX1FaHafDM8fAs+nj88cfpu1/9Eh19XSqogJMveBulJUR5PLHZc+m9b0IPd/QiLIzY7WHVqlWi1eCWW24RmQtjy7mXUb9yePIHtcjHl5WVReMkA7VOxRma72PiE+ja698vvv7Wt75FX/ziF41/T8nKo5TiNSLg+SNQO49wyfFLH545K9v52AP0yB9+Qv1qBiHqZ9dffz2VZyaIr5890kY7KjsNH03vDYOV9D7M/xkZ8LGPfYxSUlLEcFAEteeflOIkoOKcdwim54qNedo3CUB9Xb0h1vFHs/xCsTQD2diECCgjQ4N+DWTAxz70foqIjBLF85cfuV/s5IFtp55OxdnSm9AftCLAgczX43Mn9jB7S2LHi43Br/73ZzTY2yW+n5mTSytOOoPS4/2zODkrFiH00CTbzpCBDI4sEATh4fzEJz4hvvfXv/7VMEpetiyMLrjuFuoalNfTHws9Hx/6c3hUh86hlkmqXeEd7/uw8MjbsWMH/eMf/zD+/cx3f4ImpqXBrD8WqcHBQdE0z+0TmYn+uVfQ17l8zXoaGx2mZ/7+f2IWG3D1u64Xdl1r8pIEpYz5ga9WdVKbap722p5K/X5/9ZC5IikpiT71qU+Jz3/+85+LJnMgOb+clp95Ob1lfa62TN4VDap1I121TwQLSzOQjTsUfZBYw2zTX8jOzKBPfvk7tH7b2fS5L3yRtl1wufh+UVkFlamdoHlR0QGmRnTVyJha5CK2K7797W8Lf8lrrrmGLnjXTeJ7UTHxgsbJTIwKSCDT1V8FGT42FQjadV1DhrIUDbZoQMWxYtGfnp6i0fFxQUlhEdS5EZnh6qEcE0DdcOO2DnDfXXJWPn3kIx8Rx4W2g61btxqmAaDJ/CX04ONDIzQyQ38FMgThb3/zG+K1uLSczrr0SvH9MVUrB+32wTNL6aI12bQiO1Es+tgweFsXDGSNjPGpT31KTLk393hOjQ7S6twk7X1jZnAPYrrqcwwWlmYg82MztDv85Oufo32vvkB3fP92Ovn0c8T3amqqjd2/PzKysZFhMbZeZ0bGD6grIO9FpnL//ffTGW+VjaVtasCmPzMyM7WoSwSBxa6U1YvtA4YCFaNMQN185StfMc4nbICQKWF0jD9oGz6+7FyHq4fOzIhH3sABH71ksGqC2pS9QjGpAddc58iP2TwWAX8FMuBd17xd9DueOHaETj/vYvG9+roap3OxLj+ZLt+QSx89u4xuOrvM6wDAPoth01NGe4i/A1l6erqwU4PoAwpGYKCrjaLDpp3mA+pGk/KszMgOnmJx6QYy2FP5uT42G0p5HHlVJWUkRInaGKBzPAbqbjxQE1NpwZ/7Am6IFtY7buhFM3LyCoQUF30sGLaX4cfFySH28M013RXlGTJTrukcFMEYqlIENUzjBVgQ0d5YL5Sc/hht4s7VQ/fOmg2q+0YmRDaGeVxOc63aG4XazR+KTNeNCHrwEv3grG9GTk6ONExW/Y5N9VJx5wpsFnzZMPCGr6+rXShB8TdR5/Q3cnNzxaR7qBghAgGFijFVTHX6A81NMiPLyrEzskXn6jEX2B8N7hfo+7hsXQ6dtTyDitLitFKLvaaM09ddPGdks9GLCG4YVwNVY0JsNKVkyt1Zb2u9tkxprmZoFJp1LvL5qbFCRQobs9+8WEW/fbFKjMm4f3eDOFYjkDXXi4zM26nUnrp66KyPmanFPtXHyDCUix3N4m/OvXXRk5FhUxcosYBx/VqaxDOoG6zuNU9nD+ScrmXLljkMrpvrZ2VSfAU2eS3K1ccOZIvMZ3E+lBUXUVRMHE1NTophd5A1n1ySpvUhNveQ6aA04KrNcPdQ7G/sFYv8A3sahEIxNU8+RAOttX5bnBDEsNtFM3RCSrrWRR61qDJF+3INDB/IzNoHRo3NSEdjtdjt+jtjSVQ9ZLpngXEgw/NgzrTLFGsw0NEo/iYra/1Z4/QnreiKvNxcioiKFvcPz5LTCZ4O3dWm7xn0FKWmkUP+ohZ7enpoZGTYif4OFpZcIJvU3GPlKYrS44S3G/DCLufBjVqpRY3Hh2DkMA6eGci4/6a5d4TerO+hxGzp8t/TLI1L/d1DhoZd3dnKuSuz6JK1OXTt1kL6+LkVJjXjkFC6Ad1NtSIj81czNB9jTJqsIaXG6838eGwQ6MOBMSl8APj4hrpaKYrGDBWs/zKywAYyCIPgaAKcOHFC++8fVwpTbu8JZiDrbJF1Tn/AmFyQnErxcf4RBC0UYUuxPgYEKyNDXWKVWiie3fnmjCZjHcDC0635+FjM4O79wlUcwKKIIBqeJlV2LfWOAZv+HN/iD0cI1Nwgy4aiDTRjoaJ+MckAfWVAb3OtUPWlaA4wAGyjuBk6LF6O3/BlcOhsGxTO8tjvE0hJTTOGwI52NBrPjP8mQ2cHNJBB+ZlVKLNOth7TCa5JdbYGL5AtX75cvLY31sywlvPHZG+dE6e9wZILZKBRsNvV1WPlDU7dIlVhtVUnaG+DHOypE3Bw151xMr3ompHhIcGsJ+AdJxUIuipJZWT1Vfp3u+7Gt6Bh1x+qQTMKU2Uga+oZoZLSMtFMOzE6JJpr/SFScEyGzqShyXC/9aoxvQjlIqNzYJQy1ULf21JjtHPoRj3XALPz/KpudQWEJdnq+A4fljZOOsEN0Tx9PhiBbJXabLXVVbplUXTXOO1AFmCgzjHY2y1UdVyIDTTWrJY3WXtDtRhPrnvHOzIxqT1QcyBz3d11DowJFxFQe8he0IvDu9262hoaHZXn2Z8ZWZwfXOddATECsjRQiV0jU4afXX9rnV/qgHx83Aztr141Q7k47LgHQRHzNWyrrzYs3XzF9hMdtKdWNszDbb+rs1N8XlJcGFB7o0BlZG1qhEswAtnq1avFa0dTHQ2PjPo9kJkFYcHAkgtkmNbM2QpkqpgTFGhwDaKjsUZkT3AT0AlZA9RbaHZQi9NuaUWmhtLio2nb2jKKjU8QxXQIWvwtvYdXnr+BYMVZGepkBSUVBr3oD7hOhkbA8UfWCfNg14wMgYwzlrb6Ki1iD5gwY9P24rEOcf64kRbCp+Ich/dhwDKyIv8FsnHYoeDcNQcvIysoKKCY2FhhjoyeVX9Ti3D6DyaWXiBDD1mQhB4MVr3193TRYF8P7W/oo94hPTJgQZv29dPwQJ/WY3SdEs1oHxhxCmTY7cK+Ka9YLhRoIg4Vn8X5UJgmC9pYiHPV8fU014hz7q9Ana6k9/7yOnQ0RTsyspbeYSNjaUUg00At8oQH4Plj7VSnPPpQH8tK8k/D9dwZmRRDYJYX22TpAlpUsIlrDSK1GBYWRkVlsk5WffyYX/4Gb0bgk+lvan8+LL1ANqpfCOEpEhISDEpzqqdJqMZa+mRA0MHPc5EZHmz40AFuinbl210zMh4umVNU7rcdr3mhT83S57M4HwpURtbSO0IZ+XIh7GmqFSbN/na98FevmmuNDNl899C4EcjaG2pocFjWQH1lQhgYk7PzwHFjEcwKoNCDs2s8F3xudW+2wFoM9HSKXkcEFDRiBwMl5XLDXHXimH9ZkXTUyGxqMWjUYrACmZleZGpqYFRPRjY6oW+g5nw2VchEOgbkIsfO5Wy0mllQ6reMDE2sxmRouHpE+p9a5KwIKj8E85hMeW67m2ucFmndi0S86iHz1+BOs7uHaHDtlRuqstIS4fQBk2t+LzoyMl7wdh08bmTU/nK9nwtQuWYXlvtF8IE6Mq8x7CYSDJRVyIysplJ/IEPGWVUlVcloZbCpxQADD1SwpPfuAllbQ7WxkOgAptuyo4DWQMbGwSaxB+oeECJACJKmFlrMuQLS80v8FsgQxLDoRkREimbo+OjAZGSiTqboRQ7UPW1N1Nkj3cb9YqibogJZrH8zMlxHZJaojwH5afFUrhbCumrf1acc7NfmJYsNQXuzw9rIX87scyE+OoKy/FQnAyvS3ig3qOXlMlgGA+UrVvlNPYz7E+0hMAZPzfbPiBhPELYkfRYtlJE11VXNqFFYTejhlJGZqEWmFTEBmmeOcUaWmltsLBK6a0gGrZiZLaibQFGLZnoxITmNYhKSxeeHjhzVvtvlY4xMkgM1/eUeAtk0n7++kXFqVRR3TnKsIeFurDnhc78jBzKIS85ekSkoS6C8XIpmAg1k1v5SLuJc8fHxcx4MVCyX1GJ99XHtz+Dx4zKjzswvEsFsUVKLKAK+973vFY7MMK+Em/bu3bvJCq4ekBJbKSOrV7vdAU2BTFCLfgjUfKOaMzLYNbE0ncEZWUZ+ichgYGPT3t5OOlFTIxcJ7AQBXSNcFgJujAYyVFZ25KjehRC2SdjtRkZGUkJGLoWhpuOnjMxVgs8ZWU5SjNEm0qZB8DEwKv8/PDHhktKlKPX1a6VMPCgZmR8CGay+sNlDI7JZ2BUMVFRUUFhYOA0PDhhUvC4cOybpymz1DCy6PjIogM444wzxED722GN06NAh+vGPf0ypqdIpINjZ2KTosWq1TCCrq64Svotm+bOv1CKMQgE2DtUBvlHNmaOr0ANAPxCooqjoGCooLPILvci/jwUXgegjY6CXi/u5coslbXRcPdS6jw+zs7DbRRbjzz4rphfhWoKMHlQxrin3IqGXzFcJPmdkyITQQ8aCpMvP2kLBQEK0Q4KPFpGxMd8FLWbGAj2iwQ5kcbExhhWX7jogB7L0/OLFGch+8IMfiADxxz/+kU455RTh+XXxxRcHlSt2Uiy2NdHU5IQoZGPkQbBQVFQketjQMIz3hExRh10VFiLsoAGmhnSgJF16DR5t6TeCrrtAJr5WxfvC0gq/BDLeQRuBLEA1MgbPKyuvkItU9QlJs+gCn6+i0nK/0oqugexEq5y/lpUULQKn4Q6hJSObMDIhpqXA2ORmBbaHjIH3kZSWJfodJycntXkugrEAjWeFjCwyPMxvdUDjGiqD8EVHLf7nP/+hk08+md75zneKGTyYrPvb3/521p/HQt7X1+f04S/gYUJfDN9gsBkKFvC32Q+ts7F6Rq+Nt+jo6hFzkHTz86DUClJjBT27u6ZbBEzOzlxVZ1gIgeyiUr88RLzQoxcI2UOgFVNnVmTQFRvz6DTDasw/gSxHLULJfpLeM5i25PsvW/V1Gf2O3R3U3Nbh9e+HkAQfAIQ5fHzBrB8hMwT1zedY1z0KVS/O1+jQoKjf8iSBoAWywjK/ZmQZeUWLMyODJPNXv/qVWKSfeOIJuvnmm8UY7j/96U9uf/7222+n5ORk48OfdB+oRXiPAUybBBOGBL+lVpvg48QxRbtlZYvzqROnlqUbY1uqOwaNRZB7xxhZiXIhTM3Vr1yEEIIfIjykoDEDNceKgYe2IiuBVqsaUmNNpXhfusDnK11lnP7OyJJURsbITY41JmOnZ+X6fA2ZVoT5MmqofP2Cma2wWXJmgd5AhmZoFnqA2g+Gc5BZoJXth35OtL9UV1c7qXcXXSDDA71lyxb63ve+J7Kxm266iT7ykY/Qr3/9a7c//6UvfYl6e3uNDx09KwvJyHTSbr4Gsk5FQ+gIZFWqZ6RU0V46Yc7KXjgmsz53ruX8vbisQu2BzBBCREVRWna+9qnJnmDl8nIKj4ik0ZFhw+VAB/h8JecW+VV67yr2YOQkO5w2isskPXzMh2to0IpKHWmFjAzUopNgR2NGZgVaEYgM809GBrEVmr0h5EtKzxJipEB6ZQYkkGHc9po1a5y+h+xntgF22LGwA4VOJ4rZamRWzMha6+XuRofgo6ZS0lzlSnqrG5yVcfHfrFhkoE8Iu++0vFIjS9dVTOcFp6ikTMwhC1QPmTskx8VqL6YPDg465jxlFAa0RgZAim/O0ErVfVTpg80RbOHMwcMKGRkyCExNyFJN0doyMiG9D77QA4iMcGRkTU1N2so2XB8rK4cqMkz8nWBDeyCDYtF1B44bt7hYqluCCVAcLISwUiBrrK3UlpFx8+OKFf7Z7XJWxnBnLwSqD4KP5PQsilPFdF3mwXxvsZBE9xwyTwCRCe94Dx7WsxDyIp+Wnk7RiSlip2sONP4AziEX65GNmana5SqQ1fjQVGtWLEIIYYWMjN+P2TxYR6+VDGTWyMgiwsIoNj6RktIytQZrvkdLVQ9gsF09AO3v4LbbbqNXX31VUItQAt177730m9/8hm655RYKNppaWoVJLx7UYN9kTi74rS00MjSgReyBeo2/AzVnZUBmgnvD18ykaHGeC0rKtdKL/DCyKXEwqUXs6lmCf+iQnoyMz1NpuRQCQerPzeb+Aq4T04tcH2OsVBQ89zt6y4RwRtbW1iYyA/zNYCuZEciQUUN41d/fL7IWX2GVHjKANye6++U4kBWXlTuNeFpUgWzr1q304IMP0t/+9jdat24dffvb36Y777yTrr/+ego2qpV5ZkFREcXFORpbg4WUlBSh7ARAR/hKLaII26ascdau8V8NEFkZgtm20rRZFXUswc/S7LnoUPTJhygY9kZmFCqJ/FFNvWR8fLwB8JdZsCuQZaPWUZLh/FysWSU3RK31NeL+8iUjQyDj44MQIiYmsK73rsD7iYiMovyiEm0L/cjYuJgBZoVAtmzZMhFkmF7URX8ztVisWJHIiEWYkQFvfetbaf/+/aIoj5MHsUewgY57lkmvWhl8oQdj7dq14rWx8gj1+5iRnaisEvOHoqJjqbzEv1TuaeXpdHqFtE9yB5bgp+TJ94HGeK09ZCpABrNGZs6cjiu1qK/ghT5bjRnxl1mwK85flUUfPafMUJwyykoKxcywyckJwyTWUzDTgAzICvUxV+VioRp3oiOQ1dXWiWcwMio6qIYLDAQZf2VkhSVlhqgk2Aj+OwgQUHBuU76Ga9cEvz7GQM8dUH9svxiyCYspb7H/oAwWmYWlFBMgR/jZgNH1qO9kl8hNw549e3z+naCk2GqHvRzjgnycZcslPdzS1EhdXXL6sTfAdd9d00V7D8hrmKYaTf01h8zd7t21jQLA0FKe3XVQ3V/eZ2TW6CFjsPgkR2PGUqXEVvlFpUIIEWxEaM7IhoeHDWV5PgeyxSj2sCrA07daSLFopmKBxuMHfRZ8HFR1mtyicr/XVeYDghgapQtXrjcyMtQhtGQr2dm0LDreEtRidkYaZSqnf1/8RA829dGLx9qpUrmETCdL15mU2MBkZLMhJiKc8kpl0Nm12/PNCAQUg0rhar2MLNypF+rAgQM+/87qSllLLFCUc7ARaWqKhmbBV/UwRFu4puhRhXE2/41gI/jvIJAZmYUUizMCWdVRmhgb88k8+Kgyr7XKQ4R+MiimsnLyxM3/+uuvawlk6AFky6RAOt+7AwJp4Yp14vNdu3Z5/XuwgentbKPR4SFh9AoRAor1GYnBDWTYEJWv3SQ+37lzp8f/f2zS4eoBYY6VMrKEaJnt5pSvMVgDKGx9Qa1Sd8In0wqIDA8TA0STklPEsR08KDfM3sK8EUE/qXnEUzAR/HcQIHT09AlPQ6sFMrQlwHMOvHpj1RGfMjJuWuUR58EGS/PL1mwUr6+99ppPv48XQQwMhFIaKvFgyu+B+KgIKlq1wedAhr68drXRKisrpRvOrKD3nlocVFUmY9WGzeL19T27PXYwYcUierbCaMpow7BCRsb11aScUjG1fWBgwOdabl11pZOiL9iICF8maOMNW04SX0NRrkPogeuHTQoQZVOLgcPhI0dFVpCUkkYZGbOLFAIN3GSclTUc2++1chHHVqlUmSVKgBBssOAjp3ytlkDGxepSNcIdQSzYFCoywqKVjkDmbS8SamQ8ZBXZCvwOAyX0mA8Vq9ZQRFQ09fX2eGyua+4hg60RO0LonJXnLbBJEC1zYWG0RdWqvck6zeA2BRYBBRuRSoK/fvPJWgIZZ2SwIMQAUcDOyAKIY2r4oT+sm3wFB7K6Ywe8Vi5i5ldfj+yRK7HIbhA1MiwUORXrtGZkLHkPNq0o3kN0BOWVrxbjVlpbWw1XDm8yMnMgsxLSEmKpoGKNVwu9w54qwmkRtIIQAnVcvoc2bvY9kEEI0aYmX8P1wgqIVPWrdZtO1p6R8bQOu0YWQJw4fsRpaqqVYCgXjyIjm/ApW0nNzqfkRCmECDZwg6fFRxk1JOzIvR2yCX6fF8KEbKlYtELGgllomL2WV7bSJ3oRNb/2emsGsuTYKCpetdGr47OqYtFVubh2k+/UG2ersQlJlJkZnPE0ruBsaeV6SQ/jGers7CQ9GZlNLQYcXIRdaaH6mGtG1lpfKcaw+BLIoFCC0swqQJ0MNjlFynzWW2UfvDox8gfenMPRckhrmZoLFkwkxESIRmJWZ/oSyDgjs0L9yIzU+EiDPvU6I7OYYtG1l2zFOjngE2KII3Wt9MzhVnpsfzP9+81G+tfrDXSiTc5qmwt8fFCxRlnkGYxU1GJcYoqxgfD2Hq1pahesA0+ftqnFIKBBNUOvVaM3rAQYLefl5dP01BQdObjPqzoLB7LswjKKDrIAwoxM1WBbunqDT/QiH195RQV1DE4IyrI0I94SWWdOcrRTncxTQP01MDhM3a2NlstY2LSYBS1vvvmmMDrwxp7KihkZB7KY5HQx7BbP3u8efJr2NfTSkZZ+qmofpNrOIdpe2bHwQFZQIkQWVkCEov0mpqbo1FNP9SnrfOG1feI1ISWdXm8ZNTIym1oMEMbHJ6hV7XY3rpPCA6vh5JMltVF9eL9X03idMrJI61zW/BTp3ZdV5ptEnRfBPNXcmZccawlFH5CfEmcEMmScnkq4MaS0o7FWLKKYAZaTk0NWAvwe03IKRN8QbKr27t3rldjDihkZU4t4n9u2bROfH977ushkzl6RSeeulBRh1+DYvGYF5ozMCm4X5owMQcfXQLb/kFxjMvOLaW99L9V1DTn9jWDCGmfbD6hqH6Dm3mHx+cFjx2lyXNrGrKyQzY9WwymnnGI4fHjTS+aYmoxAZqWMLFrc6LkVDuWiLxknz+iyAq3IyE+NFec9OjZeSLg9tQJCIGOjWWQrgR4UOh9wP2HB56zME3qRqcXpsWHDlNdKgYwzMrxPXujrjuwVfqInFafS5qJUMX0At2x7/+icv2vfPpmxwAnFCm4X5mwJNCAfH66fN4Ng39wtr/v6jZsFI8KPsZ2R+QlNPcP0333N9K/XG6m+a4j2H5SOFxjZEBFhjV38bHWy+mMHqM/DQAaqhye2Wi2QQRmWkxxL+eWrxbmH+7k3w1M5UMdmciBLIKsgNzmGwiPCqWDFWq+yTmTgzdXHLDPw1R1gXuwpfSpcPVQgO/ymdAWB/2BqqqxxWikjGzBlZLVH91FJusM8Ga0QQGvf7IEMQ4FBuwIla0+yRN3I7EyPgZ8wcYdZOt6rpybeyOgOvSED2bVXXEwXrs42/s0K6401zrYfZN+gtOAo8NAbjfTSLnmDFZZao7fDHU46SVKLHU211NjqmbIPTZxYNOISkykhJY1iLOBGbUZeSozIhkuWr/aqTgY6i0Ui2SUrhP8g1JBWAR5kZJ7e1smQkZ3Yt9OY52dFQCHqaUY2OjElxpoAr77yong999xzyUrgpmgE3NXrNlJYeAT1d7VT+FDnjMb+tr7Za4Mvv/yyyHJAu6VkZFuCbjNnS6iRYSPJCmlP6cXK+hZjs3XheefQuvxketumPGEenq36RYMJa614moDpxFduyhP0Ex6knTvlwlK2wpq7XQDuHnmFUla+Z49nyr5nn31WvBav3jSr+asV6mT5Xlo5IfCBsktKSaXc0pWWysbMx+htIOvuHaSaQ2+Iz88//3yyIlJiI6lwxXpDZr4QCTfTirgfX3jhefH5eeedR1ZCorKpGhqbpKaBScNX8tDe191kZLMHsuefl8dXvuEUJ5GFVQLZmLIJ87ZO9tRzL4jXgtLlwusUKM9MEOOcrECFW+Ns+wG4kd66IY+WZ8TS8Td3iO9tO/McsjLWbpQS4L0eehI+9dRT4nXFltPFa7TFMjI5dZgou8w7h4+nn35avFZsPFU00lqpPsYoSIXgY71RK0Fz7EKxa+cOUcPNyM4V/TlWRGp8FMUnpVCOcsJfyDVkWjFicsT4easFMgijQH8DB5t63WadHMi6h8ZF9uwOL7wgF/qy9bJEYJWMLEK9D86MvQ1kL70oM+rNp5xGVoS1VjzNwA2aOlRPwwN9gnY7/yxrXgTGZuWHdmif3J0vtD72orrJVm45Q2SjVtkNMqIjwsWcK14kQBN6UmzmQFa+6TRh0gvFohUzspTMXEpMzRA2TFwvWQiYdtty6pmW2N3OlpEBntCLnJHVHX5dKDkxTBMfVgLON9fJmnpGBKvheny455LU8bsTfGC8EI8p4ozMKqrFKKYWlVSe64Bw+nc3jQLqzD+8XE2Hmvqcvv/6zu3i9cyzziIrwhpn24946qknxetll1xEa/OtU2R2hwvOlTfJgd3bqadfSlvnwyuvvCKCWU5uLmUXV1guGzPXyTAXKSY2VjxAC5Vwg1Lk3ePyzaeJ3rFg+yu6AxY7WSdb7/GOd/eOl8TrqWdZlzHgSeD5y9cvOJBxD9lRJRKwWjbmOs4FWLFOOmAgMJknYnMdyB29iGcQG7OS0jJKzcoVG2ir3KMRKiMbU83LeXl5ol8O79edOUFl+wD1Do/TrupOp0BddVSOuLnofGveo9Zc9TTiySdlILv0kkvI6rjwnDMpJSOLRgb76Z5/PTy/yWzfiEErnn7WeZasj5kzFvgRrj3lbPH1/fffv6D/99JLL4kFJT2nQIw2KbcgrWiW4TO19O9//3tB/weLxLEDMns708KBDFk1hBElazYZ4ob5GqOxu+eNmRWFHgzOyIBTNq2hlJQUcWzmsUNzKReZVjzjLHlvW6UZGmD1JGdkwLZtkl58+RV5XcxArZBpVL5+z7zwkjBrwDO4foU1fFyXVCDr7u42do6XhEAgQ/3n4suvEp8/MM9C/8LRdrpnZx3997EnxNfbzpKLhFUDWZ4SfKw981Lxet9999H4xKSgMJiCcodnnnlGvFZsPk1kPKUZ1hN6mBujN51zmfgcdO9CDITxc1OTk5SRV0ylFqPd3CkX8yvWUm5egciq//vf/876s/saeuhwcx+NDA7Q0QN7LZ6ROQJZWVYiXXjhhcY9yshWDjXuMjIWemw7/UwnOs8KiHSpkQHl62UJ475/PTTj54fHJpyyM+CpZ+Xxrd6yzXJlC4Y135UmYBFECo35Y+hfCQXccP27xeurzz1BvQODc/bKDfR00aH9cpHYcprcDVrJ1cN114tepNWnnEuxcXFUVVVFP7vvcXriYIsIyrPh0SdkRr1i82l0wepsozBv1YwsNSuPStedJNohzAvhfIrTik2nBn3a9UKsqrDZuvCKd4iv77nnHrc/hwD27JE28Xl42xHxDJaXl1v2GeRAhvJkSXo8ve997xNf33vvvaLeaR5JBNrNLPgA9c0U3dbTznDq3bJWQ/SUYUSw5ZxLxfDWg2/sntFPxhkZm0oA219+WbyevE2KyawIa656mmnFiy++mEIFl55/NqVm5tDI0ADd+8//zurN1zs8Qcff2CFuzvXr11NCqpyxZiXDYHdZWXRsHJ1+nsyO//vgP8Vryyyy5sbmFjp8YL/4/KrLLzayOqsCCyJ63Lac91bx9e//9Fc60Ng7Z/8RB7LlCGQWzaYZ2IgAp19ypXh95JFHqKury+lnTrT105MHW4Xrw6bCFGo9usfS2Zh5igKUp2A0Lr30UjGzEAa5TN3j+3z8bSZ6EfUxFrLk5MlAbaWsJUJlZLgenJVFJqbTypNl9vinP/1p1kDW3DtCHT39dFC1Ipx1ttwsWxHWOeOagQX+iSeeCBlakREeHk4XXn7lnHWkvuFxmpqepqOvvyK+Pu3s84xdolWpRXM/WfFWSd3sffExsVvH8biTNf/2PlknLChfRZefYt0eQDOwGG48S+54D+9/k+558lX6+2v11K3qDWZ0dHQYopeKjdssm00zEKSB5Pxy2rhxo6hdPvDAA8a/N3QP0aP7W8S9uSYvSfgUcv3IyoEMdddL1ubQxWtlf1RUVBS9+92SGfnLX/4ys07W79iY8PGh/oemYytJ713Vk3D34Kxy68VXG8dn9gbFXDwA6mcEv4efeoEmxscoKT2LNq+17jNo7SfHB8DAE6M/cFOec451i+jucMN18iHa8dzjNDA4U73YPTQmAvWx17cb/WMj41PGSHmrB7KVJ59FMXEJ1NPeQm0npD9dx4BzER1fP/Gk3A1fctGFQmwQCthamkZnrC+lLafLe+7wy4+JDPrF4+2z1lZyS1dQUlq6pbNpnkvG9991110nPv/rX/8qvzc4Rg/vbRbHWpGVQBetzhZWSG+88YalhR4ARFIIvEkxMlADN9xwg3h98MEHhSBnNuUiX0OsMWMTKuOxUEYWFrbMoDrHpyS9iI3j2lPPFy1JqOMyK4B/Y8PyldmJ4vWJZ1Sj9/qtlKkCuRVhnTOuGZyNnXXWWcJfLJRw2flnUVpWLo0MDdI9//zPjH+Hoqi9oZp62pspPDKSkss2Gs7cVl4MQc2gHwd2VRdffoX43oGXHnXbn4PxGcfekI3s73jbWyhUAKf481dl06du+oBxfMvU8dR0DM5aH0OgtopkezYwtTY6PkVXX/MuEQCgKj1yvJIeerNRZNXwnbx0XY44FiFkmZoSJsGQfYcSYOUEA2eoF//5T0mBoxfSrFwcHBw0Gr3NGZmVqEXz+xmfmKLBsUlBMeIZ3HSOfK7uvvtuw1IMGxFgfUGyeH19p2R9Vm4+heItXMO11hnXiFCkFc304vmXvU18ft8//jHj33uGxuioysZK126h/vEw6hiQ1JWV6SksfNdsKaD3nFJEH7tRFtR3PP0oTU5OzAhkbxw8Ql0tDRQeESE2I6GGq666imJiYqjy+HGKH5AmyS8cazcWCizwXH+R9THrXjcGMg04wQPxaVlGlvXNO39DPUPjIojDf48zkoceesjytOJc9ypnZX/+85+dBB/IaOBaArELxCDoy0KNjAdNRlpsQxJpUi7ivTO2Xvx2I+tE9mymFeEv2VF1gE7slarvU884x7LN+oD1nx4vgEnCnPKHYiAD3vseSS9uf/ZJGnShF1u7+2nX47I2cbKisPgGtXKNjBtrYVkFiXNaWhp1d7ZT5b7XjEDMi/zvf/YD8fnWU06lhATrSu5nA+aKXXGFzDoPvvQIxUWFi76cN+vlBPAf//jHwrMwNjZOuEFYXbHoKoxA4Hr3e94jPn/ukX+JcffwN+UZcXBj+eMf/yg+f4/6uVDD9ddfL16xltTW1oqsmc2qf/rvHXTbZ/9HfH7rrbc69WpZLiMLc8wk6xtxBDI07y9fsUrYqaEeP6RoRdyrCNB//+lXBd249aKraeM6afhtVVjrjGusjyGrwYBCKPpCEW+98GxKy86j0eFBuvmTnzYsnXBj/e+3Pk+NlYcpNTWNrrv+vU7/z8o1MjMiIyPpmmuuEZ8/de8vqam9m6ZUtvLTu35Jrz39HyGY+P7t36NQBdeR7vvb36giXgbqV6s66aXtO+j//b//J77+3Ddup9j4RMtvQFytqiB4iKo4jSIio6i19gRlD9VQeoLMWHp6eujGG28Un3/84x8PuRo1o7i42Mg6Wd13RkW6yEr/9pOv0dBAPxWt2kgl51wjnksjI7OQ2AOIVG4/eH+9Q45Ahgzrbe98j0Evcg8ZAtmdd95JVUcPCX/NK276vOjhtDJCY9XzEAhecOeGosjK6fBcCA8Pow/f9mXx/v/yx9/SRz/6UaEu+vFP7qQdTzwoFvm/3vs32rbe2WQ2VBZE4JZbbqHY2Fiq3LuL7vrc++l4XZPoyfnS5z8r/v26T36Jzjk79GhFxmWXXSZ6p1paWuiGKy+kwaZjQjjwnvdcJ3a8COSXv/P6kLpuqfEykL1Z10PdE1G08SzZ2nLDO680hB+f+tSnhIigoqKC7rjjDgplML34jW98Q2w+ilKiKaZ2Ox3a+TxFRkbR9Z+7nWq7Ruj1um4hprCa2MOsXETG6Drr8KK3vUP0BqKN4FM33Sh6U/vam+nrX/+6+Pd33PwlMRmcFZuWxbTF0Nvbi22NeF3qeKOue/q6z/9gOiwsTJyTCy+80Pj8mlu+In5mampq+rcvVk7/5Mmj4mN0fHI6lLBjx47phKQUcUyl5cunS0pKxOfrTr9w+sWjbdOhjqqqqum1a9eKY4qJiZ0uW3ey+LyoqGi6q6tr+oWjbeK64TUUcLy137jXfv38iekj1Y3TF198sTgmfFxyySXiFffp9u3bp0Md4+Pj0x/84AeN4zvppJOm09LSxOff+c53pvfV94hz8bOnj03fu7NWfP5qZce0lfDPPfXifR1s7J2+f7f8/BfPHRevrxxvn7799tuNdSU+KWW6YvV68fk555wz3dIzNH28tc/y8cBaWwcbTshLjqGTL7yKPvCVn4qheKg7gGIEZ/32931Y/AwyNhjpAnC9sBqtMR8wVuKuvz0sHDGqK49TTU0NZeYV0Xv+53bKS7V2A/RCUFpaStu3bxfZ2cjIMFUd2E3LwsKEUACTklnuHCo1MtQ3UXMBvXbtyYW0siSPHn30UbGDx73IIqsvfvGLdNpp1p42sRDgufv9738vaki4XjATRhP4pk2b6POf/zyty08S7QYQ8bT0jliyRhZpGq7JtXSoSwGoGHGtYHJdtnINDfb10InD+0Xb0q9//WvKTo6liiwpxbcyrHXGbcyYdI3AtO7MS+kPf/mbaCPAPKBrbv0mpcY7OOsSFcjgDBGKVOqWDevok3f+nUpXrhMCkPd95WcUm5BkyXEt3iApKYkefvhh+tStnxbtEm/90P/QllOkcSs3glvd1cPsXvLBM0vp/aeXiBllAOrRoN4Q0HJzc0VNjKmpxQLQwPv37xcbErQSoKaEOi+et4vWZBtqTqt5LQK8ucVwzX5FLeYkyWdrSNXFtm7dSj+55zG6/IOfpczsXCFGWrXKug3QrnCcfRuWA3pxcpJjqb5riDadeRG1tbXRC5W9dLR1wHBZAErT4+nkklSjzyXUgEIyxsN/4f8epLPLUujxI11CHRYqWcpCgMX+Z3f+lNZd9TEaGF9GHf1jVJQeYUierdw2MZdbvBmwdmKjZNRdFhvy8/NFsIaww7xhRH0TziD/fL1BuGFYyf3e7IAP1SxcV5BRs3jDbEk1Nr2MLnj3TfTj736dVuZYPwszY/HdbYuQXgSae4cpPj7eKNbybpgD3lnLM0Pu5mOkx0cLw9ahsSlq6J90oj4WG3JS5TVqV04moWAt5gkQwBZjEDPDHetRmBZH563MEvdtcXqcJVWLncomDdkjRvKYp3gDBs0dgvfi4r7jFgFQk2ADT3b1MLssLAagAZNl3UdbpB2Q1Q2CfaGLzZZcw8paLBQXDxvO2FiYQu8+pcjopbMKIlUfGc8Xg7sOv0dkZOyKz9lZKDIhdiCzOHJVnQg3IfzseAefonzvFgsyFS3KvTiLNSPLTJTXrXNgTPTNGTWyEFw8bIQGIlTNDjUyAA4s6BUDIFKBNZX5XuR/CyXYgcziwALHbgKY88TUALKYxQRzwyWauvmYF2tG1jkwajgpWN0j00ZoI8KlZif8TsPDjDUEmRhoRSRmYE1DkR1YXKvhIgVnJ4dUIGOboMWEjATHMUGtGIrqy4UAu2GoyOB719I7bNTHrG4YbCN04aqixD0IsAkw6mRMK4bqvWgHshAA14tYOmtWLC7GjGyx0ooAAjRnZfVdHMjsx9BGADOyGLl+xCn1KbIxVs+GIq0I2E9QCMB1YV+MGRn6k/ghWqxCDwYHMgyiBEKRyrEROogIc5+RxZkzsvGJkL4XrSWvseEWqBehboQ5UIs1I0OmcvHaHDHOpWAROHrMhQyVfbLjvy30sBEoajEqIsxgAOJNykWG1RSXC4WdkYXIIm92uUhdhBkZAKutU0rTFm19zF09cDH1kNmwPrWYFCvdSMwZmRB72NSijUDSi2HLlomb0UboU4uMUKVzbIRgIItxZFyOXjKH2CNU2QE7kIUICtKkW0B6QpQwB7YRukAGZvbmszMyG4GiFpNMm+A4w91j0mmoZigiNAnRJYj8lFi6YmPuoqUVl2JWxipUOyOz4U9EmAIZCz2AeFNGxj8SqoHMzshCCBinwFN4bSweejE2yn4MbfgPESYGh6X3ZhoRtCKyMvm90Mxt7CfIho0gIENZVQE2tWjDn4icNSNz2FQxOxAXoveiHchs2Ah2Rhaii4eN0EC4GtuCuqy5dQeUI9p6AIx3CWWxR2jmkTZshDhQ68QiMjE5Pet8Lxs2dOHdWwtp2s306vioCBodHzMCXnSIerjaT5ANG0EAFo13bCkQjuQ2tWjD34iYZWq1yMAGKaQnzAN2ILNhI0jITlq8npI2QgPxJnFHqNKKQGjmkTZs2LBhw2fEqV4y8bkdyGzYsGHDRqghzkRr24HMhg0bNmyEHOJNQqNQ7SED7EBmw4YNG0sUcaYszM7IbNiwYcNGyCHOLPYIYfWs3wPZ97//fSHp/PSnP+3vP2XDhg0bNjyALfZYAF577TX6v//7P9qwYYM//4wNGzZs2PBZ7GHXyGZgYGCArr/+evrtb39Lqamp/vozNmzYsGHDh0Zp2FbBWNjswxhq8Fsgu+WWW+jyyy+nCy+8cM6fGx0dpb6+PqcPGzZs2LARGFxzciFdt60opBui/ZJL/v3vf6fXX39dUIvz4fbbb6dvfvOb/ngbNmzYsGFjHiRER4iPUIb2jKy+vp5uvfVWuueeeygmZn4Lni996UvU29trfOD/27Bhw4YNGwvFsulp5d+vCQ899BBdffXVFB7uSFMnJyeFcjEsLExQieZ/cwWoxeTkZBHUkpKSdL41GzZs2LARQlhoPNCeT15wwQW0f/9+p+/deOONtGrVKvrCF74wZxCzYcOGDRs2PIX2QJaYmEjr1q1z+l58fDylp6fP+L4NGzZs2LDhK2xnDxs2bNiwEdIIiFTl+eefD8SfsWHDhg0bSxB2RmbDhg0bNkIadiCzYcOGDRshDTuQ2bBhw4aNkIYdyGzYsGHDRkjDcr4k3J9tey7asGHDxtJGn4oD8/l2WC6Q9ff3i9fCwsJgvxUbNmzYsGGRuACHj4BZVPmKqakpampqEo3VsLXyJZIjGMK70ba6csA+L7PDPjfuYZ+X2WGfG/+eF4QnBLG8vDxhcRgyGRnebEFBgbbfh5No32AzYZ+X2WGfG/ewz8vssM+N/87LXJkYwxZ72LBhw4aNkIYdyGzYsGHDRkhj0Qay6Oho+vrXvy5ebThgn5fZYZ8b97DPy+ywz401zovlxB42bNiwYcOGJ1i0GZkNGzZs2FgasAOZDRs2bNgIadiBzIYNGzZshDTsQGbDhg0bNkIadiCzYcOGDRshjUUZyH7xi19QSUkJxcTE0LZt22jXrl201PDiiy/SFVdcIaxdYPX10EMPOf07xKpf+9rXKDc3l2JjY+nCCy+k48eP02LH7bffTlu3bhUWaFlZWXTVVVfR0aNHnX5mZGSEbrnlFkpPT6eEhAR6xzveQa2trbTY8atf/Yo2bNhguDGcdtpp9Nhjj9FSPy+u+P73vy+eqU9/+tO01M/NN77xDXEuzB+rVq0K+HlZdIHsvvvuo8985jOih+H111+njRs30iWXXEJtbW20lDA4OCiOHUHdHe644w76+c9/Tr/+9a9p586dFB8fL84TbrzFjBdeeEE8WK+++io99dRTND4+ThdffLE4X4zbbruNHn74Ybr//vvFz8P78+1vfzstdsAaDov0nj17aPfu3XT++efTlVdeSQcPHlzS58WM1157jf7v//5PBHwzlvK5Wbt2LTU3NxsfL7/8cuDPy/QiwymnnDJ9yy23GF9PTk5O5+XlTd9+++3TSxW4zA8++KDx9dTU1HROTs70D3/4Q+N7PT0909HR0dN/+9vfppcS2traxPl54YUXjPMQGRk5ff/99xs/c/jwYfEzO3bsmF5qSE1Nnf7d735nn5fp6en+/v7p5cuXTz/11FPT55xzzvStt94qvr+Uz83Xv/716Y0bN7r9t0Cel0WVkY2NjYndJGgyswkxvt6xY0dQ35uVUF1dTS0tLU7nCcacoGGX2nnq7e0Vr2lpaeIV9w+yNPO5AVVSVFS0pM7N5OQk/f3vfxeZKihG+7yQyOQvv/xyp3MALPVzc/z4cVHCKCsro+uvv57q6uoCfl4s537vCzo6OsQDmJ2d7fR9fH3kyJGgvS+rAUEMcHee+N+WAjAyCHWOM844g9atWye+h+OPioqilJSUJXlu9u/fLwIXKGbUNB588EFas2YNvfnmm0v6vCCoo1QBatEVS/me2bZtG9199920cuVKQSt+85vfpLPOOosOHDgQ0POyqAKZDRue7rDxwJk5/aUOLEgIWshUH3jgAXr/+98vahtLGZipdeutt4qaKgRkNhy47LLLjM9RN0RgKy4upn/84x9CRBYoLCpqMSMjg8LDw2eoYvB1Tk5O0N6X1cDnYimfp0984hP03//+l5577jmn+Xc4flDUPT09S/LcYAddUVFBJ510klB4QjD0s5/9bEmfF1BkEItt2bKFIiIixAeCO8RS+BwZxlI9N65A9rVixQo6ceJEQO+ZsMX2EOIBfOaZZ5zoI3wNusSGRGlpqbiRzOcJE12hXlzs5wnaFwQxUGbPPvusOBdm4P6JjIx0OjeQ54P3X+znxh3w/IyOji7p83LBBRcIyhWZKn+cfPLJoh7Eny/Vc+OKgYEBqqysFG09Ab1nphcZ/v73vwv13d133z196NCh6Ztuumk6JSVluqWlZXopAQqrN954Q3zgMv/kJz8Rn9fW1op///73vy/Oy7///e/pffv2TV955ZXTpaWl08PDw9OLGTfffPN0cnLy9PPPPz/d3NxsfAwNDRk/87GPfWy6qKho+tlnn53evXv39GmnnSY+Fju++MUvCvVmdXW1uCfw9bJly6affPLJJX1e3MGsWlzK5+azn/2seJZwz7zyyivTF1544XRGRoZQAwfyvCy6QAbcdddd4uRFRUUJOf6rr746vdTw3HPPiQDm+vH+97/fkOB/9atfnc7OzhaB/4ILLpg+evTo9GKHu3OCjz/+8Y/GzyCYf/zjHxfS87i4uOmrr75aBLvFjg9+8IPTxcXF4rnJzMwU9wQHsaV8XhYSyJbquXnXu941nZubK+6Z/Px88fWJEycCfl7seWQ2bNiwYSOksahqZDZs2LBhY+nBDmQ2bNiwYSOkYQcyGzZs2LAR0rADmQ0bNmzYCGnYgcyGDRs2bIQ07EBmw4YNGzZCGnYgs2HDhg0bIQ07kNmwYcOGjZCGHchs2LBhw0ZIww5kNmzYsGEjpGEHMhs2bNiwQaGM/w8Zzh5FAlNWggAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 500x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
     "# Put everything in place for running the simulation\n",
     "sim2.dispatch_constructor()\n",
     "\n",
-    "# Create an evaluator, run the simulation and obtain the results\n",
-    "evaluator2 = sim2.dispatch(theta={\"delta\":0.9})\n",
-    "evaluator2()\n",
-    "data_res2 = evaluator2.results\n",
+    "try:\n",
     "\n",
-    "# Plot the results\n",
-    "fig, ax = plt.subplots(figsize=(5, 4))\n",
-    "ax.plot(data_obs.time, data_obs.prey, ls=\"-\", color=\"tab:blue\", alpha=.5, label =\"observation data\")\n",
-    "ax.plot(data_obs.time, data_obs.predator, ls=\"-\", color=\"tab:blue\", alpha=.5, label =\"observation data\")\n",
-    "ax.plot(data_res2.time, data_res2.prey, color=\"black\", label =\"result\")\n",
-    "ax.plot(data_res2.time, data_res2.predator, color=\"black\", label =\"result\")\n",
-    "ax.legend()"
+    "    # Create an evaluator, run the simulation and obtain the results\n",
+    "    evaluator2 = sim2.dispatch(theta={\"delta\":0.9})\n",
+    "    evaluator2()\n",
+    "\n",
+    "    # Plot the results\n",
+    "    fig, ax = plt.subplots(figsize=(5, 4))\n",
+    "    data_res2 = evaluator2.results\n",
+    "    ax.plot(data_obs.time, data_obs.prey, ls=\"-\", color=\"tab:blue\", alpha=.5, label =\"observation data\")\n",
+    "    ax.plot(data_obs.time, data_obs.predator, ls=\"-\", color=\"tab:blue\", alpha=.5, label =\"observation data\")\n",
+    "    ax.plot(data_res2.time, data_res2.prey, color=\"black\", label =\"result\")\n",
+    "    ax.plot(data_res2.time, data_res2.predator, color=\"black\", label =\"result\")\n",
+    "    ax.legend()\n",
+    "\n",
+    "except ValueError as e:\n",
+    "\n",
+    "    # Print the error message\n",
+    "    print(\"An error occurred:\", type(e).__name__, \":\", e)"
    ]
   },
   {
@@ -1358,7 +1473,7 @@
    "id": "821b1cec",
    "metadata": {},
    "source": [
-    "👉 If you chose to ignore the bit about {method}`pymob.sim.parse_input()` in the beginning of this notebook and added the initial conditions manually, you should see the following error message now:\n",
+    "👉 If you chose to ignore the note about {method}`pymob.sim.parse_input()` in the beginning of this notebook and added the initial conditions manually, you should see the following error message now:\n",
     "\n",
     "```\n",
     "ValueError: vmap in_axes must be an int, None, or a tuple of entries corresponding to the positional arguments passed to the function, but got len(in_axes)=6, len(args)=4\n",
@@ -1375,20 +1490,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 22,
    "id": "27b20bcd",
    "metadata": {},
    "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.legend.Legend at 0x2052fea2690>"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
     {
      "data": {
       "image/png": 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",
@@ -1407,20 +1512,28 @@
     "\n",
     "sim2.model_parameters[\"y0\"] = y0_obs\n",
     "\n",
-    "# put everything in place for running the simulation\n",
+    "# Put everything in place for running the simulation\n",
     "sim2.dispatch_constructor()\n",
     "\n",
-    "# run\n",
-    "evaluator2 = sim2.dispatch(theta={\"delta\":0.9})\n",
-    "evaluator2()\n",
+    "try:\n",
     "\n",
-    "fig, ax = plt.subplots(figsize=(5, 4))\n",
-    "data_res = evaluator2.results\n",
-    "ax.plot(data_obs.time, data_obs.prey, ls=\"-\", color=\"tab:blue\", alpha=.5, label =\"observation data\")\n",
-    "ax.plot(data_obs.time, data_obs.predator, ls=\"-\", color=\"tab:blue\", alpha=.5, label =\"observation data\")\n",
-    "ax.plot(data_res.time, data_res.prey, color=\"black\", label =\"result\")\n",
-    "ax.plot(data_res.time, data_res.predator, color=\"black\", label =\"result\")\n",
-    "ax.legend()"
+    "    # Create an evaluator, run the simulation and obtain the results\n",
+    "    evaluator2 = sim2.dispatch(theta={\"delta\":0.9})\n",
+    "    evaluator2()\n",
+    "\n",
+    "    # Plot the results\n",
+    "    fig, ax = plt.subplots(figsize=(5, 4))\n",
+    "    data_res2 = evaluator2.results\n",
+    "    ax.plot(data_obs.time, data_obs.prey, ls=\"-\", color=\"tab:blue\", alpha=.5, label =\"observation data\")\n",
+    "    ax.plot(data_obs.time, data_obs.predator, ls=\"-\", color=\"tab:blue\", alpha=.5, label =\"observation data\")\n",
+    "    ax.plot(data_res2.time, data_res2.prey, color=\"black\", label =\"result\")\n",
+    "    ax.plot(data_res2.time, data_res2.predator, color=\"black\", label =\"result\")\n",
+    "    ax.legend()\n",
+    "\n",
+    "except ValueError as e:\n",
+    "\n",
+    "    # Print the error message\n",
+    "    print(\"An error occurred:\", type(e).__name__, \":\", e)"
    ]
   },
   {
@@ -1433,7 +1546,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 23,
    "id": "3ced1952",
    "metadata": {},
    "outputs": [
@@ -1476,7 +1589,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "sample: 100%|██████████| 3000/3000 [00:17<00:00, 176.28it/s, 15 steps of size 4.32e-01. acc. prob=0.93]\n"
+      "sample: 100%|██████████| 3000/3000 [00:21<00:00, 139.49it/s, 15 steps of size 4.32e-01. acc. prob=0.93]\n"
      ]
     },
     {
@@ -1536,7 +1649,170 @@
     "- Pass parameter values to the simulation\n",
     "- Specify the solver\n",
     "\n",
-    "👉 By subclassing {class}`pymob.SimulationBase`, even those last steps can be avoided. This will, however, not be explained in this tutorial as it only makes sense in the context of __case studies__."
+    "👉 By subclassing {class}`pymob.SimulationBase`, even those last steps can be avoided. This will be explained in detail in another tutorial as it mostly makes sense in the context of __case studies__. But in this case, it is pretty straightforward, so here's a little sneak peek:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "470e72e7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MinMaxScaler(variable=prey, min=5.844172888098338, max=12.52594869826619)\n",
+      "MinMaxScaler(variable=predator, min=4.053933700151361, max=10.925258075625722)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Markus\\pymob\\pymob\\pymob\\simulation.py:1385: UserWarning: Using default initialize method, (load observations, define 'y0', define 'x_in'). This may be insufficient for more complex simulations.\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Define the simulation class\n",
+    "class LotkaVolterraSim(SimulationBase):\n",
+    "    model = lotkavolterra\n",
+    "    solver = JaxSolver\n",
+    "    def initialize(self, input=None):\n",
+    "        super().initialize(input)\n",
+    "        self.model_parameters[\"parameters\"] = self.config.model_parameters.value_dict\n",
+    "        self.dispatch_constructor()\n",
+    "    \n",
+    "# Create and initialize simulation (no further steps necessary)\n",
+    "sim3 = LotkaVolterraSim(\"case_studies/ODEtutorial/scenarios/lotkavolterra/settings.cfg\")\n",
+    "sim3.initialize()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "fa12b690",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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zUx52BwTCKsYfQB2Be47YAQLAgmcWHVXu20UjQwMhXydr6xs1PAfv/Pldhh8f7tFjb2ynIkWrhnIg46xrdHjI2IgAuH4LXQN5qCTmilkVWF/MgXpkeFDct+Hm3jlTYu06WDNQqKmpEecS5sCeor2lmaan1UFgaOvIUOgHMg5iWEQxNA6eY1ZDc++wUH9hdhHABpx7nnt0UYk9MIIByC6WIzvg+ebO083qGDPZTnE2ArzwwgvCIgitHjAqnZwYp6N7Xgl55SLuT2B0eJCe/tc94vO1a9eJ1yOvvWgMDx0JcbEH0NnWKl7hOwiMDMmFfrGgXXkQwjIrPDycppGtjI1QXW0tZSXF0IF9e52oVAwIxfyxUMAHPvABMaCzv1caIseoazg6NGj9QIbRBzzuAIC1CD6HKhEX7JprrhECCjhCYzcC+g4f/rbx90TG/c89DfTz+58S84jgWA16Cqjc/xoN9nYaGVkoP1C4FhBBAJe9/1bKLioX3/vvfx1ZZ6jVx7CLNSuhOBt773vfawwyBL2IfsBQRkufVCbueepfNDzQR/nFZfSFb3xXfO/o7pcpI0FacI2GckY2RTQ2Mkz9/X3GwEt4FE5NTtDwiKPFwGrwZB1DJs3ZGHpq4+ITjA2KGWblInQCswnjrIjhYXmt4pNSKT0j08iyWQBi2UCGIAWjSXwAcHXG5xgih4It6jCYNgrDSAx44w+YR1oBrb2jwpNv55P/Fl+juRtmnBs2nyR2S9WvyRHnuBDDIbxQPPLII8JVOzU9g9ZsO5fWnX6h+P6f/nY/LYb6GO41pkphCoudIXB45/PUMyTrEaEIPPzNvSM0NTlJO/4tN1inX3kDZa3YTFHRsdTb2UaVRw6FNLWIY8Tz1dcl6ypgbZCR8UI/qKhG1JUwGDMrK0t48J155pkzHNgBGP/yoMhTTz1VuOAzUN7AMw6TXWTumIYMt3cGfhbtRDDSzc7OFua8qGkxzj33XGGACzNhuOpfcsklwj0eZrpmYJOIf//zn/8svoZbPcagYK4ZBnoie2lr7zCyTvgKAheedSrFx0SKv+OOWpzvHDC9+swzzwjz3ri4OPF30b87FyBww7qN34n/h7lhZiAJgcM+3ieuDcyBkS0yvvGNbwhGCyzP1q1bac3KCnrz9dfFv/3oju/T6VukKQY2KOgrxvmxVCDDCWdvLfMH0mHY+rv7N3zwhbICbTM5OUF7nn1YfA3nEeCcS2XT9u7nHhWml6FeJ2ORx5lvuYYiIqPo/EvlQv/is09RV59jGGUoABsP1+nCGJGBh+2ss84STjJ4wBMSE2mgt4te37ObQhVQzg6PTdKhHc9QY30tJSSn0kkXXkWH24apYtOp4meee+ZJ47x44/aP5xGbg2B8iPVALNAjItsEcnJyxCuun1kQ8fnPf16obrFgwmABM7EQSDB6xQyMOfnxj38sFvjMzEwRuHjhxCYHwQDtNXB7h8s7T4RGbe38888XCzo26Ag+2PyhNGIG/j7PBcPYFrjewz0e7BQDojFI7K+++mrxNdqI8HsQ2LC5R7b50Zs+Iuqcw0ODxvyv+//9KB2tqhOqVHdY6Dn48pe/LM4BjgPO+h/84Adnvf5439j4oaQCh34EJQzRNAPvs6CgQCieQd8jUYF69h//+If4d/w83gcmS7+yazftO1ZN5553nmC4EEx/+NOf0xt794vgh/lvP/3pT8lSDdGhDtA2x17fTv1d7ZSUkmo0d2897zKi279O+3fvoPGBLqKoZCHBz0wMPSduZMSPPfaY+HzThW8Xrx+++gL60edyqLu9hf73rw/R1z7+Xgo1apHrY6B3EMh4oQLwAJ19/kX06L//Ra888wR97r0ycIcakI0Br/xb1m+v+8CHKComVog/Vm09iw7tfI6eeepJuvKUd4jvoSkaXoWeAAHwF8+doGDglvMqRDPwoKqrIFPi+lhiYhK1UCMNQ7DT3y/Uztgg8zOKBRHisd///vcieJlbaC666CLxORZ8LMDI1hFIUPIA7YzNDoAMwWzWgCCG1hRzXRktQJjBhZEvPJvrjjvucBp4iWwDfwMZHHDvvfcKB6NEFYwxFgYjVyBmwygXBA0E2ZraOiorLTEGd6ampVFmVjalqpl6ri5KCz0H3/3ud+mcc84Rn3/xi1+kyy+/XLQ/uZt2gPeKQIXfgX9Hloo1A7PPGHie0JLEQGa2Y8cOEchwXhGsEDAR4EvKV1B4RCTFREeJTQIyueT0LMrPz6OVy8tF0IPyFkHZX1hSpsFM26DfCDj5/CuMuUUxqTlUtGqj+Jn9Lz0Z0hJ83Pi4UU8/4yxKzS0WmUxGQowx8+jRh/9tCApCSezBikXQGai7YifPO2DgLW+5XLzueekZCuXxNONjo3Rs3x7x9edvvcUY87Hh1LPF68svv0zTY8MhSy+CVhwfGzFqQozY2BgKj4iQz+D+/SKrMs/5wuIKl6DDhw87/T5kBYy0tDRBg/HPgJb7zne+I34PAh4GQTJQI8egTSy+/LFq1Srxb+ZpyJhzZgYWcCzm0AFwwPn3v//tNFwTWQyyJAQUHCPYKqCzq9v4P/PJ7/EeFnoONmzYYHyOUg7Q1tbm9vfi/zIV6+4cmmvQOHYEYJwbbB6xMQC4nQAIC48UmxNQnAjkTz75JF379quotKhA/D/0P/L/8xeWVEYG2qZ/cJgOvCJttDacd4WgcUAl4t82nX0Z1R3ZS68+8witvuCdIUstQs0HXPw2KYDISIimsLBldP27rqG7f/d/dHDHs1TZ2k+5yXInHCoZGdfIMJ4eQP2BNyLAlVdcTp+8OYwaKg9TZXUtlZcWU6gBG62OxlqxmGMBLCspprXjHbSntps2rV0lqCXs9Kv37aTSk871SrmIjQ0yo2AAf3t0YpomlGjCvJhCjh4dl0BDfT1OC70v+PCHPywoMNSMscDCgAEU3Cc/+UlBsYGGdB0qaQ4GAGdPZiBoIQNCsECGhKwSgzsZyNRQ18JATQQNOHps3LCeloXJTYmZltRhUxUZ6Zj/xYYB2Mx6C2RQyKRwrhDkEKBwLDt37nQSeUCJiT/HrQRMQ95000fpmvdcT4U5meJ34ff4E0sqI8Mi0dlcL3a80bHxVLhiPbX1jwipKEyCN559ifi5A3teFY2noZqRgRYBsoqXi1emR/HgJSaniDrSazt3UMiJPRS1yMeHnbcZBbnZVLpGipAe/I+sgYYSELAxoLC9sUZ8DWoLi9K2sjTaVppGZy3PNCimQ7te9Dojw+8ETRuMD/zt8YkJ0SrhGsjwbzFxCQblyHUp4/yMj4s6GLfLMLjeBHR3d4v7AxOhGaAKP/axj4k61Gc/+1lBzwEQeWFThGwJGwTzh7vgZQYEFfi99913n8jM3vnOdxrBBNOUq6qqRJ0KQg+8F3wPiFLHyzU81Hmlye7MYAYKc6HnwBPg/SAzBfXo7hwC+Js4RkyFBv2Kc2LOUvF/cbz8tjmQoUaHTcAHP3gjrVq1WtCyENz4G0sqkIG24UWioLhUPDitfaPCvw4XJCs3X9BV2A13tTSEpE0VdkrsjhKTkS9esxLlw4Mb74xzLhCf79lpDRWpRzUyJfbgQMY1DDNOOkse32OPhF6bQZuastvbXOsUqKMjwun0igxKi48ydv37Xn1B3KehSC2OjEhaCjQiaDoG6KmYOEcA+ehHPyrqQBBhYKf/kY98RAgqUIMx41vf+pZQ7UGBCNUf1IOs/IPaEEIMtAlBLAEqkYMc6qsQTcBCD8EBCzV+9sYbbzSMEuYC1IsQfyAjM9OKCMKQ0KOGhhFWoMI/9zkppoiKjBTPIf4dWdxzTz9F7W2t1N09s6kbwRR1q4WcA0+A9421D78LvxMqzh/96EdOP4MAhKCE84HnDcpDs1oSgQwtBceOHaUTx48J6y0EWTyToP2R/R4+dECIPQJhxLCkAhnTNkBZuaRWWvtGDI9FjB1HYy3Q1doUkoML8TBigcODMhouFwWzYGX5CpmlNTUEzgrMV4xNKFcPl4zMbSDbJusJeIhCDS2qbtnb4hzIzID6FwKC9uYG6miqC0m/RSgWARyHGVhcIyIiKTIq2hAtQKgBmg7ZEyhVLKwIFGZ8//vfF36bqOdgEYWikClnBCQELAQvbAJwz/zyl78U/4aFGJkHfgbiDAhCEPjw7EBlOB8QvBAI8vPznepY+H2oyx05ckRI02+77Tb69ve+L/4NfZCg6RDAIab4y92/o40ry+jtV7t388CxLeQceALUrXCOUIdEtoVaniu9ik3E29/+dtFmAFN1ZJTIzsyBDJuF8orldMm5Z1B5Ub44lxC83HTTTUIc87a3XErbt+8QQdDfWDI1MlfaZtWqFUYgY8ufpNhIMWoGPHB3W2NIUou8yCNQD49PiZQ/XTXQiu+XyqJzW3MjhQrMNTKo2XhQK3aNrihVdbG2lmbRkGre8YeKYtFMLboCajF8H4tQZ3MdjY5LKjWUMKoysujomYq6ZapOBvofAeHnP/+5+JirFQjgPkJX3HXXXXO+F9xDs0nfAbPFmysQHN01/UIIgcUfQ4dZhNEzNCY2KpibhxogMkHU5z708VvF/Y21B3B19QD1utBzwEAP73zNyOi3c7WjMv8fbDJgqMCmCgzUGAUTMDIigukDDz1MU2ERlBAdQfHR8llDEENWuywsjNZv2EhREeFig+BPLJmMjGmbrmapntmwZpUoUsKXjxcQc0bW09YsqMVQc/eANRhQWFIuXtPiI50aiStUIOtobQrKSHJfG6L5+KCkcrcrLcrPo7DwiBked6FCfQP11ZWzZmQAT4joaW8JySnRY5yRuZGGg15E36P4OYu4AXkKFkKY63/cC4nZa1x/A0XIrZGhNJdsbGxMCEmw6QhXdUHz6DFstpDRwmBicGgoIO9pyQQypm06myRts2bVSlFzACrbB4xAxsM/u9qaxM1n9vkLKaFHoQxYrn1wy8vk93vammhgdDzkMrLZhB6MlPhoSsnIFp8HcpKCr4A/JDZVQ33d1NPdNWvG6RzImmk0BKlFZFtArJtAJuhFtTiGaiBjEQUHMmwYWcyBsTVMewpVoTLbDaXRNSPq+JC1uYo9+BqCvoSikSYDw2otmUCGrAs7wY4WBy3FIgje8Zszst52uZsPNQk+ZyxJ2TIgZ6pjZHCghh9aY6vDisfK4M1EdIQjkLmj3QBQHClZeeJzf/eu+CMbG+tsMK4TdrbzBbJQy8jAcBiBLNZ9Robm2sUUyNh9hR3vscAz5T2pJhuECjtiPj6IVfhto73HDDRQg+L0pZbnCZZMIMNC0dkkFzb050DZlJ3knK2YA1l3qwx4oaZc5IU+LrNAvGa5ZGS4+ZJS5USCE1WyFhM6FlXzBzI0D6dk5oRcIOsYkIv2YHv9nMdnDmTdbc0hJ/YYGcWImmls2ynGRewBiIVeBTKo4HzphbJMIFOrfaRpsWepPrchhFAcIzN1ypmkSxwTx2cegOtvLIlAxrRNh6IVkY3hJGcnOe8IWewh/k9Pp3DnDiXBB/zd4BUHxKbLQObOYgttBkBVjf/7O/TWyJbNG8hiI8MpNQQzMh4b1FpXNSd1CvA9KjKyEJPfDw/LRT4yMsqtMlA014omW7kI+ttsVjcQeNn1ggMZU+NmKzFDVcmBbCoUqcUY43tmajEYWBKBjGmboY4Gp9oDFnm+AKCksOOH9Jb90rBQhBK1aAgh4JQdnyACM9sbmZGbL4NcTYgEMmMhCJs/kOF4U7OkK0NtbSgFMnmfNdbOH8jMYg8404QSeBFkib0r8DyGcp2MgxjoQ866JtyYXnMgm+DG6ACPPdFxDaNi5DXEEhrI7GvJBjI0PQN9rfVOgQyBK01J00ErArggvOMFvRhKAzZ5kcf8KmA2w+OCQnl8jQ11IRXIkCVjMCiuEVwP3AF1iIwcmZHVhmBGVl8zt2IRgCkuMD46Qt3dnSGzAALDvAi6oRXNFBX6yUIxkJlpN9ynyLTYSxGKRddANj7uOL5QuIzj4+PGxPIotRkJdjYm3gMtAbA6r7W+ZoYaLFst9tzHARh1MpGRTYRcRpZVIGcdZSa4Xyw4UDc1yAw1VMQetZXSsR2WQu5cvRnZuXKhh6N3qACZP2aQ1VVXzVsjg1oMPn5AV2uzcMAPOem9mx4ygHf25jpZaNfHlNBj2TInQQRnazg+jgOzmQdbMhuLiiJaJsOHHcgCTNs0KNrGHMjW5ieLhuFVOZJOBByCj8aQohY5I0vOkYHKVczCKFXHFwpN0ULlppw9qqtOzLvIA/mFMpB1d3VqM5/19zEiI+tqbRQZCBZB3mzMBiflYgjVyZh6c9dDBvBaH6rKRddAZvSPmWhFc0aG40OQCxUJ/ojp+GYTegQDSyKQDY5NCrk53B5cA1l+SizdcFoJlWQ4PN4MarGtKaTEHpyRxSvFYk6y+8WiQvWSdbRYP5BB8cUPTHXl8QUFsrSUVIpWnn2h0EuGjBMLXltDtXF/zmeR5BB8tIRMRoYmda4JzZZRGxlZkGpkyPbvvPNO7dJ7symBayDjjCYUBB8jZum9er92RhYgDI9BsVhnjFXHzKK5YGRkwt0jNHa72NVzRpaZXyJqfnFR7u2ZVpRL6rG3s40GlIrM6vUxoPLEwjIyIfjIzAuZQMaent1NNfPWx0I5I+NFEKrEqEj39ybv7jHjygoZGQIrrKYWArZucsrI1GIPoZIZTC1C5ThtNEVTiEnvScAOZAEAdg2gFjuUf91sbglmOMQejWKR8GacfKDR0dEhRrcD6XlFs2ZjQH5utrABwoN3otragohxk2HwfIpFRmxUWEj1knHW36UC2XzH5xTIQqiXzKxYnI2OctTIImYEsmAHtfnAfW/CLzI6WjAJ3OjsOsXb3BQ9pST406FKLYYF+U0thUAG5wOc73bler+QQGb4LXa0igI8qMmQkd7n5FNUdMycgQy0VXp2Xkg0RbPQI5ymhPP3gjKyiPCQcvfgGm5bg+cZWXcIZmQRkY62F1fw91EjgwP79773PeFsDwMDDMjEqBbMZIMFUnZ2tnCFxyaO8cADDwgXe1BfYF8wD4zrpDDYdTWvhYM7Rr+4A091xhRyBCf+ejYMDclsBUEMzxhvgEWTt5vIbcjzOZCR9anhMdNAVEeNLPgZWehYg3sJpga7mxceyDAYDrslyEz7utppaExSdVYGZyvp+fJhy3Fp9nZFZm4+tTbUiEnKoRDI+jpaxEOERWI+IUS0qZcsFKhFVsa21FV7QS22eBzIhLgkQGauZsDxHX87AhnZHAoBLIzT4XJpwmRnBDSMCAHjcP7554upzz/96U8FzfWFL3yBrr32WjHzCybRmC0G93UEH0xKeOmll7zOdDB/C+pQOMBjBIzwDpwDPQMyYIYrtsPhSOP+WFEnwzFMjo9TRJT1qcVRJdTB2jgy6RCysFglmFj0gYwbRs2uHvMBNyx6dWpqauQ4l9HNIZORpeUWiYVgth4yRm5+IR14Db1W1g5k40rIYKaG5xNCwN0jJTM3pDKy0eFB6mxrXjC1yMG8t6OVBj00f0YQQ0YTDLz44oui/2iupQ8xbgoOH2FhImBjbhdMCjDjC/OzkKUx/vCHP4ifwUZuYGBAbD4xR4tZFWRn3gITFgCYJGDg7kLH04RFRAkWx6AVZ7lfzU3RESFALQ6r+hgCNbMIeNZcFZnBwKKnFrn+wLTNQgJZKAo+DKFHQYkIYq4qKVcUFMgdfb3FF3oWe8w1o8sVMZFhRkYWCoEMGVm7uj9nG0/jjjXAhmtqckIMkwwloBl6LicI/je8rlq1yqCz9u7dKyY8IwjzB/6dB8pu3LiRLrjgAhG83vnOd9Jvf/tb6u7uDsgxTZnMkFEDxDUdVZnybAu90Us2IY/P4nGM4JPJgQyb5ZTYSNF/G2xXjyWRkWHnMDI4QL1dHR4FMrO7Ryg0RZsViznJc2djQFGxaopubAgJapE9CBcWyBx+i6AWsdO1wsM21z3K0vuF0IoAglhmdi61NDVQQz2C9aYF/z246iN7CSQQjFDfiomJpahox6BXd2DWcdmyMFHrGh0bE9cQ7xnDKF2nGZsD+1NPPUXbt2+nJ598UgzVxPRjDMqFG7uYkeUSLXQ1XCP7mlABKT5O0vr8l+aiFgFuSZiyeJVsZGTMeN/p8fDKtM4ztQQC2YSxmwffnZSU5GFGhl4ya2dkUEqxEAIZmasZsjuUqkDW2mTxQKaoRa4fLTSQJadnGwIDiAGYJrIqa9DhgRiJkZefLwJZc6Nn/YAI6jzcMVBAAEFQQn9Y+DzUsLHpUK99A8MUNTgmaEVMc4boYrbJ3/i/Z5xxhvj42te+Jp7jBx98kD7zmc+Ie8A8bBXiBQTX8847b9b3gqwJP7eQQIZaF5AcH0sjU2Givw9r/WxiCIdNFasWydIYV3Za0VHWCmJLglqU0nvPFwlzIBu1+MynpqYmUfcICwuntOx8yk2Onff/lBtN0U2W5ua5oNzSIK9hRUXFvP9H8PZRUZSYlhkS9CL6yCDaMN93C0F+vqSHW0PAoYXpQXgozqdyMxIY9XNwiEeg+NjNHxeCEQg6IMQAnfjEE0/QjTfeKIINMi/Uz3bv3i2uOYJee3s7rV69WvweCEUgHsHHkSNH6OabbzZaVmYDguYzzzwj6Nu5aMrRsXHjOUKAAuUWFxVOiTGzU29GIFPnxsKPoVPA5fdtJYQtBbEHZ2SeBDKHu4f1p/BWVUnaLTU7j+Jioyk1bn6F5QoVyCAyCFQdwdsaGTLOjla5k55PscgDOIHUTOsrF6U9FQJZs5MacSEoKpI/26aGxYZCIIOsfr7dfGxUuAgCnLnxqJPsnFyhXkTQuvjii0UtDHJ6iDFAG4JtgZjkLW95i8jcv/KVr9CPf/xjIdcHPvjBD9L73/9+uuGGG+icc86hsrKyObMxAP8fdCWuCzLC2TA6yscXId4LgjWCmLvpE+6aotHmY+UNpTkjs2IgW/TU4qDysANw43qekcmmaCuDzXFTsnKF7H4h9aD05ERKSEmjgZ4uOlZZTae6uJ3goXr6cBtBM3L+KknTBatGNtjbLR4iHFdenqx9zQUslNGRaIrOpbqj+yydkaGZGUIB9Cx6HMgUPdzR3Gj5OqBTIJvnbSKAJcaE0dNPPUmHDh0y+qyQlWEzikzLHZB5Pf7443MGjl/+8pfiYzZAqWwGanL4mA+jSugRFbnwRR41PXwgMCNYw/HEqtdxclKaWgPR89Q4g4ElkZExbbOQ3TyDF5TRIWtnLOaMAxnIfP1jDDRopmfLAZvH3TRFoy54oLGX9tb3OtlEBaNGxtkKJNC8i50PcsCm9ZWLrKrtVcfII1oWAq5zdrU3G1OILR/IIuenFhm888cCiqxlwsIZy9iYd7SbIfgw3D3I0tdvWVgYRc5SnwwmFnUgY9qmt6PF40UCyq6MDFljQUE9FDKy5Iwcyp7D0WPWSdFumqIHRhxKzWAGMvxt3oh4kq2A0kGGanVqEfWxkaEBGh7s9/gYOZB50xQdzBrZQhtoka1wzyAyFqua6uJ9TbAQwsNsxQjWasbXtEWVi2Me1DiDgUUdyKAagk0M7+g9CWRm6qa1scHS/DUPkIS/YK4HgSxHTYp2N4CSZ7iZ/Q6DFsg6vAlkkloMhYyMAzVqPZ40KjPDMNDdQX2DIyEhFJA1soX9H1BsvNAjkFk168T74jqetxkZ/3+LHiIZNUCRUZPlsKgDGfq/sNvFCBevAplaKDrbmox+Jiuitk5mHLl5+bM63rtDvmqKrnPj7tFvysiCeewIot4IIeC3GArUItpDvDk+AHJyqDOxyapR94AVAVrQKZB5sKM3FvpxqVy04oZycmrK60DGVDn/fwsengB6+Tgjs2INb1EHMqEGU7Y/cEvwtHemxDRg08oznxoVtehJDdD8881umqIHTE3gQa2RmahFTzYiMVGOpmj0Dll10jBcY7yhTgEsKBnK/Lmm1rpWY3zu8X4jIyM8WggdGcuEZYdPTmrMyKwYqM3UYqQFFYtLI5B5QUsx+P9gbpdVJfgw8uzsaBeflxZ7dow8KdpdU7RVamRmsYenGVl8chpFKgPXRg+bhkMhIwMyc9jlvz4kFIuz+Q7OK1GfdCgXrUgtwuNRSyAjsnYgi7QDWVAWCZiqekMrAhgTwTUIqzZF8wINR/HCXM9k8uUlMpB1dbTNcC/ot0hGhr/d65XYI0wIBTKMhb7O8hmZN/coC3YaG0IjkLkbZ+LJQm/FQIZmaOYEF6qqnc2myqoZ2biFe8gWfSCT0nvvd7scyPp7uixLLZql9ylxnt1kBXnZguaZnpqizs7OWTOysSCKPUbHJ0RG7OlCj6ZaIE3VyVjZac2MzHvWIFcJdqyacbpK7z11Srd6IEPgGWOz4Mgoj+tHrk3RFjs8ATGSxnD1sOY4q0UdyNAL5ctu1ykjsyi16JDeZ3s8My0tMYbik6XTelWdY6EXBq0WyMgga+7uaJfNomFhwhjWE2oRSEhNF6+trTIzt+Y96v1mK08FMiu3iJil2xFeZmS8kFotkKFmN+HDIs9N0QDucysmZJNwHZmSa4CdkYXgbhcmw8BgXzcNqllDVkNNLUvvcz0OZNGYpJyWIT4/VOWgpobHHbOUghnIIPToVtcPjh6zGcW6A1sDJSTLQNbWJrM6KwHneGjUx4xMzcnqbJd1UutTi54tOa5N0ZN+XunhrXjnnXcGROjhLuu0Yh/ZmLp+cB6JmGe4aLCwyAOZd83QDIxXF9Tb9DS1m8apWwnVKpClZ+eKupCnyMqSWeeJmia3tGIw5feiPubl9YOzh3hNTrNsRoaN1vBAH42NeNceAuSrumh3pzXvT6eMzIseJHNTNJqGA52R4fl/6KGH5u4h89FM1+HuMWHJjGzMyKjlHDIrYtH3kflC2+AhSlbUVEuL9RZCczMzRnp409+RpxbC6oZGt0IPswN9oIG/6+31g9ciEGfhjMxsFoxNE8aceIrlxTL49Xa106RFex3H1EKP8R/e3KOciQ8PD4kakpUk+HIOmW+BzKiTwb3EQsc2M6OGITJZEhZ9W74DWVRXV4/RDJ2fL9VdniJdzbGy4o4eaKhv8Ho3DxQXSFVfW1u7URdzzcjGgyR08daeih3wsXuEMbJVr5/YaPmgqgUqiuV9jenE9W1d9OKxdvrT9hrLWFZJQ1x5P0VHzz/w1Yxzzz2XPvGJT9CPfvQjuvDCC+l973oHHT50kN5y2WXCAQU17Pe9731i3hzjgQceEK742BSkp6eL/zc4OGj8Prjlm3HVVVfRBz7wgVlpRuDqq68WAZi/1k0tcqAWtSiybjN0OGbJWTQjs577oyaADutobfK6GZqRkZlFlUcPU4dLDQLWV0da+qkkI54SooN3GlvULCr23fMUeTlK0NLTQY3dw7QyJ9EIaLhnsUEMWo1M9JB53yyMrCwxJcPaGVmb94wBgAU9OjZObNiO1tRT/USycGVp6xulovS42T1Ih+QGz9/AYNPh4WFhNotgOz0d7VFW9qc//Yne/e530+9+9zsKj46la664TIxjQR0Lv/cLX/gCXXvttfTss8+KxnfMKrvjjjtE8Onv76eXXnrJa0k7Zp6hTv7HP/6RLr30UkOUocueamavHKhF64WyMWVPFeGFKjNQWLSBTAwr9KEZmpGhMrKOdueF8GhrPz11qJXW5iXRxWtlwT3QwCLR0yVl8xWlM3eLHrUYdHdSQ/eQCGT9I/LBTImNpO6h8aDVyMZ88MnkOpk5I7PaiAxv55C5Ii09g5ob6qiyrommMhMMwc6sf3doyCNPR50YGBjwaFOJsS1f/vKXRdb1t7/fR+s3bKSvf+s7FK82j3/4wx/EuTt27Jj43agzvf3tbzfGMCE78xY8VRwemJi84ArcT9jQcsbpc0Zm8RpZpIc9coHEoqUWh8Z9k94zspUYorOjza0XodmTMNDg3qHI6BgqzJUPnbfKzIEeBLJhp2NKjY8Kco3M+4wMgPglISXdcEDBDt2qhsG+BLLMTHkNq+uajGs1VyALJZx00knGAnro0EF65aUXKDtdmivjY9WqVeLfMC1648aNdMEFF4jg9c53vpN++9vf+nUEk5lWxAbJE1XtbBmZxboLBMbGre3qsagzsmHh6uH7IpGd414Vxg3SwVww6kyu9542Q7vLyLoGx0TdhqnFVPE7B4NGLY6MTlCfaob2LpCFC9otNi6ehocGRVaGKcKWYg00ZGS5uTm07w2iuqYWytuElXDZnDUyjChC9hIINDU1U0tLM8UlpdDK8lLxtz0BsjcOEIMDg3TxpW+hb37ne5Tscr+jxxDUH6Y5b9++nZ588km66667RDa3c+dOKi0tFepHV+rOFw9OtAKYFYveZvuGcTCoRYtVyabNfXIWHKi56AOZtP7xnpZi5KqFvqfLJZCphSKYRfUTao5YSkaOGKvuSyAb7O0UNy3oRRZ7pBkZWXACWXNLE01NTQq1FL9PT8C9ZGkZmdRYNyjqZKCqFltGxhJ89JKhrhgVET7nBgsLrrc1Y08RHhEuhBfJycle05m80K9es5qeffY5yi8qppyU+FmP7YwzzhAfX/va1wTF+OCDD9JnPvMZQRWijsaAuOLAgQN03nnnzfm3Xe3bGIiJvioWAQ7Usldu2lIU+Pg4mrRlcI2yqcVg1R88Hx/virxcyY33dXU47eZGVEYWzEBWVSMzssycfI897FypxfGxMTHypqpjwJj7lBInb1wsjsFAQ72kTtMzs90W2hcayFLSrOnuMTgybtRxfdpsqfpNf3eHcAoBRtSrVeorOhb6d117raAKP/KB99GuXbsEnfjEE0/QjTfeKIINMq/vfe97tHv3bsFW/Otf/6L29nZavXq1+P/nn38+PfLII+LjyJEjdPPNN1NPT8+cfxtKxWeeeYZaWlpm0JSQyjO16Ev9yExJIphNL5Jm9kDCuu9Mo6u4L4tEvgpkA92dTn6LnJGhJoGCbzCboXO9bC0AsFtOTEw0FsKqdilVjosKN5qKg1Uja1LjZbKVMa6niImQt3dSmjWVi20dHTShfPq8bQ8xb0b6ezqFU4iVamRjY44eMm/BQSItLY0efvJZsdhfcsklohYGOT3EGKANQRu/+OKL9Ja3vIVWrFhBX/nKV+jHP/4xXXbZZeL/Q+34/ve/n2644QY655xzqKysbM5sDMD/B12JzfDmzZud/g37Wl8Vi671NUEvTlszkIVZJEtcYtSiHtqGM7L+3k6RffEu3xzUsGgkhocFbQ5Zvg+BmhdCCCEg+GjtyxftBAkx0RSpAkGwAjX7B2bnyl43T8HGwSzBt1JGhnPa1izbQ0Cbetpj5dYTtKfTkZFZxBt0QgkFvDm+559/3qmOBUVixcpV9Id77hNsASzWzEDm9fjjj88ZEH/5y1+Kj9lQU1Pj9PUVV1whPtxBZGQ+unqY3xuOD+4lkvmxRtAYV8cHV5YgLHELhoXfmm/o6OryyfrHdbeLG7a9s8v4vjmQBWvRaGqS1FuJhwM1Z1sIl430CRoR8nsEsyh154pemSDIqVrVQs8O757C8FtUykUrZWS4f3QIPZw8QXu7BBNhlYxsUvkjArE+CAXM1Nuy6UnLmAfrqpEBdkbmGxZtIGNpekpqmsdKKTNiYmIoNl5Sb41NMsNzrY0Fq07W3iIX+vKSIi0L4bKRXhHIIJRJjImgSNMWLBiCj1bV7M0O756CHfDjlMO/lTIy3DM6GAMnwU4PWIMpmpyasoSzx8iopE3DwsLFZGhvISdLS3pxeso6gcxcI9ORkRkSfAtVyUbVNbQDWZDQrAKZL7UHRrKqsTS1yIUHyiKzACIYiwZcDTAnDVhR7l0ztLuFEE3ICFoJ0XIIIotIghHIOFB7S52yiXJsUprlMjKIhXTUcM0bkdHBPrGwQuiE+zNYlDBjRE2MgLWRr4ugoexTA24tEMfEhgH1Oq0ZmcWaosfGHNSihePY4gxkWHQ7Wpm28W2RAFLT2W9RLoSuThfBoHFY6BEVHUtFOd41Q7sGMigzxQI4NW3YbnFWFgzBRydfQ28DmaqRRSdaz28RYiFf7akYEEGwqnNsoMdRJwvyMNgRjY4QDom6pE6tYOU0rhZ5CE28UdWGgk3VuKkZ2iotAUsmkJkbTYt8XCSA1HRnsYBrBhaMGtmxKlmUTs3KNUQNvu7oe7s7RSDDRgDUIhAZHpyMDIXvbh+aoc3UYrwFa2S4Z3RRi1hI2U5ppK/LoVx0keAHeoFkjz4djhBG07Cyg7LCWj+hcZF3Mg62wLEBqG+y2CPSj5OhddyXizKQJcVGUG7EkJZFwuy32NrWOkPoEayM7ESVbIbOzMnz+SHijKyro51GJ50zsiilXAx0L1lTU5OYSgtunpWjngJBGNQo+y2iZ4g5/2BjZEKPF+gMerG/26RclK9GtqYypEDv5nVMFeZANsEZmQXqSOM+TIaeu0ZmDUyoTQMUlP5shmYDa5968Tz9D+jT+OEPf0h79uwRXfLomscoBHN0/frXvy58zrBwoMP+V7/6VUAdFbCwcw+SlkDGfovKAX/UJQMLRo2spq7eJ2m6u0Wws6OdyjBfCTWyGFdqMbCPV329PL7k9CyK9lIogPsAvXDjCclix4sHEw2yvtakdGB4FBZqvo1wcbcZGenvFvZseA75vsSxQ/CEY8diwYMq/Q3eNGCfBYNrHbv2sdERihgbpally2gkPHjBDO9ndHTYmJzs6/GxewjsoEZHhiliOvguGoNq/A2OD/2OI2F61wCewgCmBL2AvtCzEd4cHMw50VwIl2lXYITCz3/+czF+Af5mX/3qV0Xz4qFDh4QCMFBoUD1WOhaJTBcHfOymgx3IeKEvKNS3CHYY1NsyilAij2DVyPj4UjJzjazQW8HHwGiYcIhva20R9LAVAllLW6sQZiDY5uXp24yMDXRTRHiYYAmYKcDfgBdhdXU11dbKTD4QaGlukuKFZWE02N/n0++CN2RnZ6d4je0fFsfUozZbwQAW4db2ThodHhSCCPYj9BbYZMm5astoWXiE0ToSTAwNDYn3hPEtYRERM/r2dGG26QKewOM7AV3y3Cnv7uJiThA66q+88krxvT//+c9iocS4cMwVCgTwPngh1JGRmak3c0bG87qCEci4h6ywQB8t1d/fR5PjoxQXFSfoUzxMwaqR1auBocmZOcZ78AZmv0UEMqvUyVhVm5aRpUUMwffo+EC3qA1i42GukYHeAysSSHrx6ndcQyNDg/Tn+/5F60tLffpdzz33nLCUWr1mLV352R9RVGQYXbdajmoJBgZGx+nTX/42HXtjh/B0vO6663wOGtx4/fO/PUYXb/btfOkA1m5Yfq3aehbd8cOfUGmObEPSCdz7vgplAK1bGuz44EmGqawMmIVu27aNduzY4TaQgX4w1y36+nzbuQG9vb1GWqxj9827BTYOHlUZGYx6+4bHZxTVAwF2hfC1h4x3RLihwPmPD/RQVHKCOCYZyFSNLMCBrE5l1CkZ2RTpAxWWnxorxtPEJFlLudjcJK9fjoZszFzHHe3rFlko+qxca7egFAPFisAp5ujhQ+LzwsIin/9uRkaGyCYnJibp4qlwGhmTbiHBUtL1jy+jwwf3U0NtrWBsfD0+/P+29g4xpaGpvZNiYqQ/ZDBx/Phxcc6Lt5xLCfGxAWXUPIVWshxBDHB1KsfX/G+uuP3220Ww4w8dGRToh7PPPlt4o/nSDM3IzVaqPhXIWKWIwZPi6wALIdDH1qmk2yvKfd+VYjEwzIMHuoWjBy+CBrUY4GNkajgtM4fCvDREBpZnyV1kZHyKeLVKRgbaDcjVUOPkjJNrZNGR4aJZN5g2VXz9YuISKD0t2effx/dne3ubYFzk5PLg1ciwme1VqlodvarmzQhqmVYylUjOyKa4KGu7GQZdtfilL31JZFD8wZSgL0AW9sILL9Drr7+u5T3m5+aKV9AkoAA4I2N3eFCLgZQ2t/X002CvdOJeUaqHXuHNx8Rgj6hJOQIZU4vTQXmIMnLkufcWGQlR4jqxBN8qGVkbN3t76VriihTVtD/S3yWuGTKyYLp7sBgJ1DC3QWipAY6N0ejwQFDHCwEDw6M00C03trpqrhlqQGqnyzT6YG9GUjJyKNqHOnUgoPXdMQXnuljg69mKeaAH4Fpt/rAa0lOTKSIq2tjRs/yeAxlimKsk3584WiWboSOjoik9XS7QugIZ5NuQrDNdyn6LgV40mjiQZeX6nG0iK5vPbxEbkZePd9DhZt+p7YWgs00yFPkFenbzKarXcaSvW1CxyMiC6bdYy2Kd9GyfapzupjSgVy6Y44WAhsZGcc9EREQK2lMHWFTW5TLE1woZWdRSCmRQKSJgYX6PueaFOUGnnXYahSrgEJGoFsLm5hZjpxsbGWFc4EDWyU6oOWTIVnTVCIwdb3+3sBPiY2QH/EDWyNCI2doiqdMsDdRbRVaCcf1mo7jb+kfptZouevGY/2kdLIDsWlJcpGc3n6Ac/of6ugglRVEjC+JMsjol1knL1n+PIlgHo25rRkODmpWXBeo7TOtm0nWIbzAwPT3tFMjMvqtWRJg39ac333xTfLDAA59jkB1uWMwH+s53vkP/+c9/aP/+/WL2D+TF5l6zUANkpwmpciFsbG4xsq/oyDBDFecqyQ+EPVV2jp76inMfUpfMyFxrZAFcNJA1QY68TDhWeD4Z2hXZSdGUpeqcjc3uqcXuIanmGw4ATQyatkf1kBVrqAkDCcoYGWNAJocHJLUYwHvSFY2mhV73PTpsgYyMj89X6tuMbHWPog0GEyiCic7OTkOEJwRXGrJqSwUyTF+FiIKHzGGEOD6HBBX4/Oc/T5/85Cfppptuoq1bt4rAhxlBVla8zAcs7EkqkDW3tBpDNcEbszFtIHe/vNv1dQ6Z+92uCmTqeIIhv2duPjE1g2JjfHeFwAZrTZkMGC2z1Mh6hsYDRhMLoYBy9Sgp0hPIpsOjKEZNacBCD2oRbSLBconn9pDM7Fzt9+ihyjpq6R0xatXBQLOazJCt8fhyVKBuaG6h+17zXSvgCzgbS0hOo9jYGEv7LHoVyM4991ylGnL+uPvuu8W/44C/9a1vCQoH3e5PP/20mNYa6khJk/x1E6hFtdAhG+MpyoFUiHH9qFhjIHPsdrspfJkjIzNqZBOBWxDNlIauxtDNK+WEgN6uThp3swByIAP8HcjaO3todNj3WXlmYHPF9CkCGe87giX44HtUh/OMayDr7uykms5BemRfM/Warltw2if01DiBPKUjAHXaNTgWVLFOg9pM4hm0utADsP47tAhY3tzS2mI0RMuMTC60gSqsg05pV/Wj8lLfe8hcF4mh3k6RkRk1siD0kZnVUr4aIjPWl8vMZ2pqkvZXyt9vRu+wo1GYM25/U8NxiUkUHx+v5Xdic8X0NyZFc9kmWIIPrnHm5PqH/gaae0forztr6UBjLwUa/AzqcGVh5OQ4jg+ZNHpUg4VGYzMJQwLrhwnrv0OLwOGA3yZoG66dcUbm78WP0TM8Rr2drVppKfMiMdQLsYCJWlS7sUBSi+aMjM+vr4iJiabEJNlL9sbRmTZN3QHMyOrZ1SNTX/0I9x8rMwd6ukRWDQRjVw+JPEvI8zT1WDkZI/d1U1lmPKXFR4mN3dOHW6k3wIt+e6tvs/LmM35GIAv0Mbl/BrMsr1gErP8OLQLu8WAHfGQtqB9B8AEEqrAOCqxXjf/Q6RnI0t+h/h5aNjVFwyrrDGaNTGcgAzKz5DEerXHOyLDYm2uc/l78uVdSZ/0IGRlTizIjC14gg5k4D9TMVudcZ8PwcH8XpcRG0Rnl6ZSTHCPqmg3dkqoNFLh9wttZeXNtJscG+8RmIJiBrIGfQdE+Yf0wYf13aBFkskO86roHrcju6sDwWGAW+o6+YerratceyNBQi+PB6JSRgR6xAMJBxNFHFoQaWXo2xUbpu0W5lxHuEGa4Lhj+zsgMIYRGxZs5I+vv7jAmMgfqvpxtEYzxcnKBO8QlS5uxoe4OscEam5qmwlTp3NPU45v7vCeAJqC7vVVr+wQPSIVSF2iqPkp9QVQuNqpnMCUzx87IFhOyVEbW0d4qbmQugBry+wDtfKvrm0SdB0abrlZgvmBqWRjFJkorocYTB4ws0yy/D5R7iVEjgyuExoyMs4Pdzz5KXT19boUegbiWzY1KKJCbrzcjUzWyN194jOoP7FLfD3xGZqaGdQoFohJki0FPaz3d84PPUf/AIOWlSDV0YwAzMjjC81DNIk0N7QD60WIT5DP45y/fSK/u2E7BQoNpMxJlcek9YAeyBYKDxmBfL3357Vvpex97B33oQx+iptrKgBbV2fonMztHi2s0w+yl+Puvfox+fPNVoh+wqUH+vUB527k2YuqkFnm68IHtT9HyijLh7I2G/R7VQxaojKy1Ra9hMGdkcUlyoR8e6KN/fOsm+sedX6P2Ttk8HDRrI0W960B4vDw+4PVnH6bP3nAVTQ10iikUqHEOqsnYgWp/QQacGBer9XdHRMnAPD46TN+65b1i/mMw0MjPoJ2RLS5EhDkW8ZHBfjp+4A36wx/+QD/6ztfl9wIUyIweMo2yX1YlYj4Wo7HyMN3+7W/Sadu20uTEWMDqZObJBZJa1BfI+PcCXZ2d9OUvf5nKy8vp8LFKp3qgv6+lbp9FDr4To8702quP3kfvv/xs2rVLZmfBycj0Xb+JCEfQwOTwE4f20ZmnbaOuKskgNPbIQZf+Ro1SnfrDg5BZj7DwCNGigZFZzz77LAUSg4ODYihyqLh6ANZ/hxZBzbHDxufv/8rP6EOflQ3gb+7ZHbBABkFCu1oEizRy88DQyBiNjciF4LJrb6R3fea7FB+fIDr8O+orAxbIeBGMS0yhqJhYLYazDBwLkF1UTj/91e+ppKRE0ESPPnSf+H5mYnRAegI7WpXPYr6ejAwKN6j3epSaFXWW93zjd5SeW0gdrU1022230WKgFusaHfZi133u+1RYsUr4uP7ksx8QWWigAhlvJlMys32azOAKTGZHJgZUnHI+rTr5LGFSfvnll2szQPfk+sXGxYvpBVwntzKs/w4tguOH9xufR8XG0duv/4DgtFtamoXdEGg33Ij+BGyUWHqvUy0FHD50SAg9gJi4WNp26TW0bvNJ4uvG4wcC1kvmUCxmifqYzoWC1YLY8154xTvoU5/6lPj62EF5bbOTJK3jT8cI2P70dcuAWqypfYLfb0ejbCvAdSxatZFu/OavxddYBGH5FcrNtMhU3tzzmvE1XEy+8usHxNinwYF+qj92gBq7AxvIMjSqToH27n4aGewzegw/+I1f0hlnnyOMJf76179SoANZelaOEIBxC46VYf13aBEcObDP+Hygu5OSExNp7dq14uuGY/sDUidDIOvxg/QeePNNx45vsEcutCvWbhSvDccPiteJANTIzI2YsRrrK3Ca4YxsfHhIZNAnnSQDde3RA06BzJ8ZWZNyhMD4+BxN0nRu0G+ucrAGowM9lJpXTNGxcWIhPHr0KAVDdYrZaDqAZwt0PgO9cmFRsWJor/iblYepY2A0IMxIo1Kd6g5k23fuksVoiK/GxigiKoouvfKd4uu9e/dSoDciqarP0c7IFhEO7XfcSP09HWKnCS9JoIlVfn6mpEQPmcrIdA3zY+xXJtBAn3LfLlu93mmhDwS16NRDprE+Bo9QxkBftxAGbNq0SXzd095Mk0M9lKQGpfozIzOOT6MiE8pE0MJN1ccc3+vrpGlaRgXlctLwG284goC/JxeYpdsxmnbzsGyqPey4R/t7OgVDwNewvfaoiAFw+wiU/VaWxvYJYPv2V4zPMSAV4OsHY/ZAqYYb1fGlKsNnW+yxSAABQnWVrBMB/d2doojNgaz+qMzI/L0bFNSi6l/RnZEd3OdY6HiMRMlKGcjqThyhibGxgAYyFNJ1Su/NgWx8dIQ6e/rE7LuSsnLxva7aowYN5s8NiXOPVbi2jKyx8hBNTU5SRESEYf4MB5pctRAGqsaCmuP4+LigpNIysihC026+s3+E6tRzxk3fqAtu3ChZg+aqI+I1EPRis5rurdNHEnht56vG54PK4T+9sEyok7u6uowAE8iMGrAzskUCTuvZXRwNp3C950BWc3S/2C35O5BhV8oZmc5AhvrJscOSPgS6O2TDdVJWHqWmpgo1Y3PNMRoLgHGwPwyDgddek/WVyEiZdaG2CSxfs8Gg5fjvISPz1+6X2ycwGkNXtoKMrPawpL550CroYew78gOckXGgTkjNoLhYKZ7RgT1v7qWxkSExTJafQVyidRtkRtZQfYLGRkeoscf//WRtKpDlagxkuN/27nGoSwd7ZCAbngqnVatWBZRebDCxIoBdI1sk4EUAKjCgrxvUYjitX7+eoqKiRG9ZZ3O9X2tkuNGbWtpFNqHbrPTw4cM0OjIsVIJAZ0eboIhGxqfp5JNPFt9DMT3g1KKmQIZzx4EsNS3d8MwEilasE691xw4aGZk/R7nU1yvaJjNHW7aCjKzu6D6nDQ4yFtioFSxfG1BqyrGbz9Iqvd+tWgiKSkoNVgRIz8oW9mqTk5PUUnOMWvtG/XqfYizVQH+f9vaJY8eOUV9PN4VHRhnlC1wvGAdz1hmoQNbII1zSpAmEnZEtEjAtk1+63EEtRoaJIMYcPehFf1JS/aMT1KmEHhitrnO+2549e8Qr796RoQ0P9IrAzIEMgo9Ayu911sigVmxvbxe0W4EaZNmm5pLlla8Rr8cO7hP9MhFKJemvQNagGsx1CgXABNQdkYscj0zCPYpjQasBslD0BdXU1FBAm6E17uT3vyE3IuvWywx6oKfDECDxM9hZe0y0ImBWmb/vz+i4eEpLlS4cOvDKK7I+lr9cbqwmx8dFS8HA6ARt2LDR2IzUdw1Rk5/bDBp4HqBNLS7OjGzVOvnADIBaVLtNXujrju33a0bWazIL1i304EBWuGI9JSYlGwuhWdlXf/yA3+X3w8PDhrJQjHDRlJFxNoYMOjdXBhC4s2PHm1osF/6Guhrq7u520It+upaNSrWoUyiAYaFdrXKB5d17f3e7CGRQRy5ftSZgdTKnHjJNqlMEp2P75Xs/84zTDWoRMAs+uuql2MWf/WT+CtTbt0s7qtK1WyjaGJDaKdiBitUyq37jzTfpX6830kNvNgofVH9gfHxcKHyB+FSVkdnUYugDi+uhQ4fE5yefKh+iob4ew+nDLPjwZ40Mvxv9av4QenAgAw2VrjwlsVCgmL5p8xbxdXP1MRoY8u9OkKXpUdExFJuQpK1GxkIPbDpylXFwb3eH2O1SVAKl5RQYCz0vvv7KyJqVdDtbozPLvjfkIl9cvlw0efNGBNQisGqtzGIeevpl+vOOGr9OMze3T+iiFmsaW6mtvlp8/pZLLjLk95OTE+Ie5Wn19ccPGbXkgLQWaKROOZCVrdsiJqMDo2ruWlGFZEoqT5yg4aFBQSX7a9Pc0tIiNnhgL9iEmh1vrAw7kM2DAwcOCP4ddN769eswApump6eor7vLKZA1nDhEgyP+e4Bw4/aqQKYzIwONCMoCKFyxzhhXw71kmbn5lJyaRlOTE3T8kEMQ4u/+FTFZIEpvRoZrlZebYwRqpqBKVq4zAhln2v7YlOA+aleuHjkqM9SBg3vlRmTD5pMNT1CwBhzIylfL49vz+hvUOTBGTb3DIdUM/cIrO8RrTmEprVy5UhgRYLEd7O0WGw7OyI4fkcpNf7ZPOIuRNLUWdHWJOjVQumYzpaarkUpK8BGdlCYmN+CYIboCBscm/Hp8ubl54jzjHtJVy/UnrP8OgwymY7Zs2UIp8bEUrdypeRQIFEVxcfFCUVV13H9Np6i/9Xbob4ZGoyyyTjTOZuaXGMP9eAovXNXXKGXY4f2OPh5/PkRJSi2lg1qEaMWckfEol/6uDmrtk4FshcpY/J2RtbW1iY0DLKRyNVKLR1TrxKaTTjKOr88UyOJyK4zNFuDPWqc/fBZ37JCBbM3Gk4QUnWfnIevEsaAuGBsbK7KVjuY6kaUFJFBH6j2+rIJSSkvPEG0LTA/zmKE162QrTFOlbDPwV1bdqK4fT/YOBZ9FIDTepQXqY6Av4qIjKCZRzkRqapZBBQ/W+k2S2jhs6sXSjRE/ZWRMKxYtX0NhGA2jFsLhXhXIxqZo3UZJLx49qFc1hYfxmcOt1KwyBF4kktL1BbLKykrRBwhxzLp164yMBQ217f2j4vN1Gzcb5yLajxkZLxKgjuI1SdOxSz9xUG4wTj75FOP4YGw9qUaNxGSXiQy3v6tdzLILxEKfojFjeWO3VCxu2XqKeDWuYbc8FvEMrueF/rBfpxf4w0eSacWStZspPiqcMrOynYwJ+oYnKK9MSvCbVL/c4OikX69fjlJFh0J9DAiNd2mRjCwybBnFJMlAVq1k1PLfpCDihOrl8Re16I8amUPosc5lkegw/u4GtdAfP6T3+E60DdC+hl7aWdU1Y5HAaA4dCwXTiqCfoN6LV7x/d2c7HWqWMuotqsZy/PhxmhiVDvn+WAz9IRQ4ceIEDfb3ClHHxo0bKCUlhSKjopzoYWTbecWy8bvxxCG/BTKMxOnv79eakSFQ8wbx9NNOE69GVt0t3T0ArpM1nDhsWHaFCnW6f79s9C5cvo7ioyMoO1tmZL0qkCEji88td2r8Hh73D7VYVyed/XPy5Brj7SyyQ019tLe+J2BTru1ANgdAA/FNhgdlbHLaWAhrG2RDLXDKKaox+vA+v/XqiIzMD83QhtCjQiqjcnOyneTNCGQbt8iMrL5S0pC6wAXrTlWcdywSOdoMg3ftkoEsq2wN/f7lanqxftxY5DHDClhbVijMZ4G6owf9npHpbPZm2jSvfDUlxcWKzCtdUVODvTKQwXrrjG0nG56E/gpkhmt6fCJFx8ZrUS2ibWJooE8c19YtG2ZkZDxHj+tkyMj8qa7lhT41K1/bNWQz69ScAkqIjqA8VT/t6ZTlCzAHyQWy9ae5+qigy/2VkdWr95KV61tG9kZ9Nz17pM2vwhsz7EA2B44cOSIMVxMTE8XcKixurChqaHIEstNPVcalVUeof8g/PSw9ff003N+rlVqE+ICp0xzVT5XHNRa1GwT9V1xYSAnJEHxM0r59+rIyLsr3j4yLyQFmRZgu6f3zO2Qgi8tbLppLk9Pk9ZscG6GKlHB6/+klVJQeZ7QZVB7Z7/eMTAZqPY9eVVWVeM0uLDMCR4aqIU0MdNOVm/LoPacU0taT5fE1HD/kt4WeF/mMHHl/6shYKqtl7xueu6zkBJdA1kmjk86BDM8gArU/5OnYxCGwAhk5eUbPobbgmJkrMrL8fA5k8hmE1VhmQYlwNRkZHhLmC0N+qpHVqfeSlVvgU42Ma3hxGv1S54IdyBZAK+IhgYIHixsvhM0tMjsClleUU1xismhifHOfNNjVjRYlTceMMHgE6kBtba2YdxQdHS0KzQCr+vghQvCOiginAkU9mj0LfQVTQEhie4bHHdRbpr76SnOj3GGetG4VXbU5n269bD3FxMaJ70WO9VFafJRBHQPH1UgXf2Rkjt18rj5peq2yvMp0yN0zlWCnq6OdyjITKC4qwqDe4Mnor4yMjy8lSy7EOo7xRHWtcc44O3AIdhz1PtTIzHVAf25EomLiBIWLv6djiCVUi3zeEMgK8ziQtRvK3fDwCFqh+gGRdQ75SbVY57IZ8SYjAyvFgVan8fdcsAPZPLYxAI9rQQaRogJZe5sjkOGGzi6Q/TvHTpzwq1Fpbl6elgfITCMUFBQKoQd+LQey7i5pkQP6D30k7PqBdgRdMC82bT2DRh9ZalaeFtoG77+rTYpyztq0kkoz4sXiyhlLi2kzwoHs0AEpnPCHS4s5kOkK1HX18nem5+QZKsXsbHkNuzokNQVwIMNuvqtbOqv7LZBlciDz/RirVCDLzHFYspkFOxzI4uPjhTTfn/Sp8/XTSyuCjsUHqMXSwnyDFUmIkucQz+DWLYo+rTril4xsbGzMaIZGxin/bphXzzWa2AFdzMp8sAPZAnZgRUVFxuKWlpFp7Hb5YgG5hcXi9cQJSfXoBBbktibn96Lz+HKVZxx2X7zbHR8bExY5oAhgGso+k9XVsjFVB8z9PseqasVxRkVFU3xympYHADTQxPiYCPwlpiGWWWqhb1MtFACr3uqqq/zWi+TIWPK0Tb5uUEMes9ws9BC0MNLS0ig3X56D44f9wxrw8SVlqECmIVhzoM5W4gPA3EJhbiVw1MmO+Pn66Qtk/DvTlGVZfHQ4lRbKazk1NUkTQ1I8g8x6y5bNpkCmPyNrbGwUzyAUvnFJqV5nZEwr4v8GSr5vB7IF7JZYCICHgwMZ+nR6hhyFzPwiGciqqvUHMuxwutpktlJaIv+OzuMzpLbhYeImTk5mm6oOkZHh+xzIuCajA+as53iVDJBZefmCxtVBSdTU1hlO7Enx0hAZYFVYR5sjkKHuCFUjLHogqtGdkUE45JjzlKvNvqmxkeXSjoWe3UuYHmaUL1d2XLU1IUMt1qtAbTbodWRk0n2GYYx0qT7qF2rRUcvK03b9+BlE3RRARhYTHUXxyTKQxE/1U15KDJ1SmmYcX2PlERoem9IuLKtTx4f1blxt0r3xWWQRV6CyMcAOZAvIWFgliJpOQmqmMWahvd8h7CgqktRivR8WCdRrultlICsuLtbfM5LrzIebqRv8beyq0pWNE+pqUE3pgHnXXFOjuPnsfG0PAQcyOIWYaS5e6DvbW43FAL1InO12tjRoH+UC2hTimvCISEpMzdSSkaG+0tsjacI8kwAoV9HDvV3tTsdQrOyrWhrlefEn9YZ7ialOX9Ckapxs9mzOyODsMTQqewGBigrZ+A3fSb8GMo01Tv6dyYqORY1M/A3l7jHc203v2lpEGQnRBmuAQbDDQwPaN1t16r3gOeANgjcZ2VCAhR6AHchmARYA10CGuU+JKWlG2l9VL/lkoLRUiiUa6iSnr72HrK1ZeyDj3SDXH5gGMAJZV4egCbAegfoICwsXPDrXsnyFebGpVxRSWrZ8Lzqom2oOZFl4745F1VBmdnc4vQe+hl0tDdpHuTjqRzkUExWhpbWAr590Ypc7eCA319ELaKa/2YexVQUHncDmxpCRZ+Vp67FqVd6UxcUOSh0z17DxALo6OmZcP96I+JUa1lXjNIKjzPL4GWTmp8H0rGE2IEQmADa2um2q6kyBjClbb6jB4QALPQA7kM0CjL2Aos8sd0dGJnbUKu2vrnfcZBUVsmGxpbFeW8bCwM6rW1GL/qiRZXLPiGsg6+6gialp8RETFUWpKsjooBexUTA3rrY3NzrRUjoeglo1xDJTvW9GnlKFQb5tLprzQtjTKs+LXwKZH4QCEFeYd78F6npKCydHICsrLROv7U36A1lra6ugZUELJ4lZZGFa6FhkzUCpaQOHv5GpPEE7THVOwzAZysUB2dhu9YzMcQ1zBK3IYHePpibHZtl8jAjWum2q6k2lFCMj8yKQcf0OatlAwQ5k8yzyMAuGjxt2tsz9suqtzrRbKispFhkLxAW6MhbG0OiEXwIZ37jpKnjwJFjDeFY5Q2DmE1RT7BKvQ/CBBRb9MRy0utvl8SUroYAOapFnf3FzJ8N8fGYXcUcga9Q+ysWpvqIpWzEvgubzxdQibKr6BhwTk8vLHRmLv+or2bl5QiquY6HHczQ9JTePxQXO3pRZqs7Z3SHH8bCgJS5e9prV1uhlRvA3nLInTdfQnOXFmxZ+vkdblMm0O9YgEBlZVITnzMGQem5satECcEjT5eJd2T4gdilQFeWrHX1zc6tB3STHRWvNWMxobm011He6XD3Q6M3NnelZOU67L65BGMMLp6YEV56eo0+5yNRP2LJllJUYbVCnCarorSOQNRlCCOcGcl4k0G80bFoMyspkxtLlx4ws1U8ZmTmDhVgnIjJSfN7Y7GjcX14uWYPezjbqHxz2q7WRFsUi148ysikpVvb7MQxzZKFclM8gno/cArnRq6vVG8gwJw/PjM4RLgiOBh2rmqEZ7O7RZmrzMWdkqAPqluDXmWtk6pxGKQrXE9jUooXgWh97s75HvK7LT6b8XMdDxMpF3IScsVRW6g1kvLtMy8wWU6l1gBV0UCnGJKY47b7cZ2RhxvHpCNQcJLDgpcRGGmKWhPQcbQ9BM4+kcAlkRqCehVpk6k1nU7Q/6ivmjMxM42BBT0qTrEGz6gsCcrIyhO8iUKmxjcLZEUKfq0eVEgCluCzyZsGO2W8RyFdtMPV1ekVXxvVLz6SIqCgt17Cjo0MER1wvBGsztVig3D261GbTXUY2pNmmqs6N2CPSm4zMFntYM5C19Y9QY/ewyB42FKQ4yX/ZJxAZBEvUj52o1Ppe6hvkDcZ9QLqPjxMPV7EHFnpgfGrKSbmoJyNTgSwijJaNDdDY6LCxaOkwDEadsq21eYbizXx8+Jsd3dL2y7xIdLe30vjYqN8yMt31FezmXReNFA5kakoD15b4Hj2hebPFx8fCIR3HWFUjg1F6du4M0YHDOLjD8FsEClQbTGO93ozMTCsCmISh7XdmZAnTZ7A9jEIVyLo7HdTpzIxMH7XY29trGD4L+T1Ti7b8PrRhXuj31svFriIrQeyazF5vGFQIQIXGtEalZmqxUS1YeRrNgvn43BV2XR3wjRqZxqZorj9hwRvqajX89OAnhwfAV/cSiA8mxsfF7K98l4wsISHBsKlqanZQN6iHwiEC6G5t9EtGhoXQvGDpohZd6cqU9AzjPJiRaWTV/snI0rP1ZWS1yn7L3AzNMN+j5ozMaDFQ9VH9zd4ygIJF0PU7WalrzqpLChzuHu5YA5GRaaQW6/j6padTXFycKSPzRrUoA6xNLVoA5h6roy1y3MfGwuQZD1G3uSla0Rq6F4lmVeuBlZQ/aoCG1NZF7AF5OnaDCGSiKVotgqAluV6gIyPrbW/Wrlg0ZpulZVKCS30FSFfy5mZTDQnB01nCPaVtt4sPpgHT46O11lckteh8ztKUqs9swwVkqay+usY/gSxVOVToGDpZq36nKzXsmpGZm6L91StnpoZRL9ZBm5mvH2D+nWwVN9jXTb1DozMyMrjutHdKj0bdtCLq/lz79zQjM/ss2qpFC4BvsvHYFFFMzkiMpvyU2BnU28CoI70vKin1S1N0K9tT+aEZeq6MDDZVUL4xtQi3gTiVsaAxWleNrL25wVD0ATqahc2OCe7EFZnqGDG12QzzjldXRsaLRHxSihhvwkbFvgCBcWBgwCH2cDlG7kNqV/J1Rk6+ZA1qFW1nZZ9FFuvku9nAmTeT5g1HWZm8fm2aWwzM1DDG4ujwO3WIWXJnBDJMwQabANVmbWOzE5uAKdJAvcae1To39TFvAhlahZgJtanFIMO82+2aThSvmwocbtfmGtmgKZDxIojajM65XW1qoTf30ujMyJia4S5+tBuwwz431SJbw/HnKPrUV8GHmVp0NNLKBzpG424Xrh7uAlm26tMxmz87KRc1ZmTm3TxuodS4SG3HF5eYQkmJCTMarHkmmdmGC8grLNK+2UK/JYQLQKKi3nSIIVq4Gdrkk8kwD9c0+y2Wq2dwsK9HDPrUH6jztNCK5t/J58ycwaDhOzlVzj6sM80+NGdlTQ112too6s09ZEYz9DKPG/e5bodnToezy0JhBzI3wAMA+x8gPDFDZA2rcmVAc1b1ddHAyLhxM+VmZQqXBaBG4463Syn6ykrlDaw7I3NX2DXXAfHvvFvMyi3SUiczU4sOWipP207OSZruLpCpAaLmhlp/Z2QQZWARjNBgpDoXrWhuqHXNyPIU/a3TgYbfC+b2hcckaBF7IDj2KrFRiRt/Ub4/h/p7aNA0AzA9NUVkvrqfQXNGlqJhI+J6j0JI5hr83bl7mNeBjiY9m62W3hE6dLxqpmLRq2bowCsWATuQzbHIw8EjKiaW1uQmOV1UfogmJ8apt6fHuJniYyK19loBvX39NNAr/fQqykr8k5G5Keyas07UyFganK4G7vkjkGG3q68Zeu4hlmxTBYd48xBGo0bW7J+MLC3B9/rYXD1kDGMmmYt8u7BY3kPwaNSVsTjTUtNaqEXz7K/cTJmZmAG7pvAIeU+2mLJq/F1uEzlRWal9vAnOd4qbmquvwTE2SjIeZmSxu4dJkOTa76hD8PHw3ibaf7TSTTO0D4pFO5AFH4ZQIF3eSMuzHdkYgEGU7HmGXiumF+OjwrX2WgEnVA8ZMr2sdIefnq5maHNGBirBXUaGhmgOZMlZ+XqoRdUQDVEA19uYWsRD7SvqeKHPck8tFqimdqjC4KE5I5C1+iEjy8qldA31sYVkZDk8k8w0ykX8fHKSkbHo2myZA5lxXX3MyMx0c0LMzAwIrQRsrNtqErSAVXAEsiqt403QP5aQkkbJGqhF2Hmx0EhuRmYKIwx3D1MvoJMEH+4eptKGN0DZAHX+bmVIoCsjC2R9DLAD2RwPUVJGtlgkcpNi5iw2D6rGRDRt6p7bVVnFvTRyvInuZmjY+vCNG23q4ncIWqRzQmKMfNASM/P1ZGTKZ3HZ5LjxoHKPjg7ni3rlswg6z112wDZOwqbKjbx5uL+Xuk09ZroCmQ6hx0Ko0yym3gb6neq10aaFXncgk5silZH5WCPjzQ2OL2EW9RsrT83uF6jpZCrWoFKTethsLyaalzVQixwco6Ll/L04N9eQ3T1c67gG/d3a6GSx5g3w/zF/r7ejdWapwYdZZDa1aAGYaSkMtHNX8DRnLOx5Fh/lCGS6MrJqlZHx6HHd9TGU93jxMXfxm48PuzbXjEwXtdjd3mIITHgGk6+7OYxL4Yna2bn5bhVm5o2ImZ6BKiw9Q6rCmhvksE+d1GJ6gv6MzB2Nk5oCm6qoGb1kWJz8FcjyTOpCbxpp3Y3gERnnLH13GYp6a3PplctmZaYmmyqz4TMEDIkam6Fxf2KD6m7hN9w9OpzH8ZgzMtTofQFYB7TZTE1OiCnxubm5xrPpXTN04HvIADuQzaN4QxO0OzgWwnYjvccDp9umqk4V5d01hepULLreuA5VWLvYoUGggIctVTW8YjpAd7es3XkDpqDaWxoNSiNRUUiudkSeAhkeghlMnPk6zaV6c93Vskt8h4Y6mXmgZlpWLqXGaQ5kWXD1mHm+QNkmKncPcyAzW43pDmR8jyIb83VMDY/gyczJn5XiynDjgA+wMUGtpl45c0YNWlHnCJ6MnNmdQgoN+rudBk2bLQ5ko8ND1NrmPDzVmwyqRxmSwzwcaklfMrJg9JABdiBzgyrlKABrnMJUx2RhM8wZC/eS4YHLVfJmNJzq3M27Gt/qci3hmxaqKbNc1qlGpjK2hJgI4dXHC4gvCyEHCJ6NhUB23qosMQkXJsJaqOH0LIqLcR84DJuqkSHqdKEQuRdJhwTfPFCzID9Py+h386w8UKfuMlj8ncSU9Bk1Fmn+7J9AhkAN6MxY5rrvmT7tdAlkht9ireaMOjNPS33M/DvTDVcPN9SiadzQwIijFoaSAGejVT4zI5NGfYx7AG3V4iIB0xqryktmlUq7q5EBhYVytzTQ3y8cs31Fk1qwzLSNzp4RphWxwJkpOPOUaIg9AKYXWcLt7UJonkXGvUIIZMh+z6jI8LnZ1Ey7zVZvA4UYHSs3Kc0ts9UgGnwe5WJ2cM9InFlr9cVsVvzedFnHdQWEO4mqD2k2alGHPN08UDNJNfaiYdhXcKDOy5+dieB7FMpTM/hZGRwc0PIMOmVkmqT35t8JuG0RMbE+A6POFGK+2jDX+dgPODw2JSZOA4np2SKImdcEz3+fLfawDFpVfWXzajn2wh3AJQN9nW1OyqHUpHiRCeja8TarjKVQ3bi6MzLH7mvZ7F526mdY8ME1CG/rgKAzeRYZj7L3x+Rr6UE4+y2eplRvc8188jUjM3ss6rCmMv/OpNQMoaRzV48ATZyYOpNaNKv6cH/6mrFA/To6Oio2H7EpmU73ibfAe2JbtsI55u8xPdzjEsgS4+MM938dwdp8DXU1QztYg5muHq7Hh+buzn5ng4Ui1Ubhaz/g8LgpI8vKo57hMRqbnHS7JiwEdkZmEdQ0d9DwoHSBPmXd8ll/DtkM0NPe4mRTBQm+LsEHKKm2Fh6o6R97KqYWXZV9/BBNjI1Sr+pj4xqWr9QUBwdQmQ3qgfbH5GtkZHN5/mVkymDd2uq+Kbqzud5nCb55EdStWEx249HnRC2muqcW07KkAAZN/+zI4bPQIy+PhtWpSnIjl/cEqL8OD0lDghI3rh6uo1xcAxlqdLrqgOaBmrifUjTVOPl3JmRkz1pTgqIYNSvx8yabKnOdjDe63gL3N9fIkB32DI0bvYCeij0gCuPnxa6RBRm7Dh4Xr/GJyZSRKk2C5w5kzcKWhXe2KNryQu9rIBOihYkJIVooVG7YusUeo7Pw4VARpqYpako5CzC1mJLtYyAbdwRPVpb5Y/K1oBbn6Gcy/BZbmmeVN+sLZLl+UCzKkTfujhEBKyF1pgM+FidkccmaWANzD1nf8IQWapGPD/1u6SnOPZxm8FzA3i7nYIxhkLoCGYKq2dNStz1VXKq8B91l1VAzpimrsboGZ3ePinIpSGpt9M2mSmRkilrEZqtrEBmZd2IPflZwT/7fL++ir33ta3TkyBEKBOxA5oI3D8vgk5sni7DzBbKRoQHhkMABIUEoF/X0kvEij503XEP82wztxv1C1RramhudAlmS6vfyNlCzYjEqfJnTQqgL87leMLj+wnU6Bt4LFpHx0RFqaHIOct62T4C20a5YVDVAdyo6XE9Qj0CT6Rh4cUrVtNA7BTIlBfeVWjS3K5iHTbqCJ7XDCd6pVw4ZmVLX+np8RlBNThU1VR31P6wXPA2Bx8LMRsUxxd9kmisHLFeBrLOl0Ul57EtGhvsJg4LHvRR7mJuh//jHP9K3v/1trTZhc8EOZCagFlStgkdp8dwLK8QCsMkBetqaHRJ89JJpXiSgTNPRJDxXM7S73Ve+Wug5kPECFad4fdykKPZ7Cg76Y4O9xoRcnsStf6Gf/Rbn4NmqTJkZmMLNAyJ9dYeoUfcTHNy9KZ7PF6hnWwBR30AgAOrqHSNN+D3ovkcxvJQpdl+pRfP1m6sVIzM9zeiVqzdRb2abKl8XUnNjNqh1HUa4Rp9qipyGgGsyW9Bg+hQDUs2Z14qKcseAzVHv3T26urtFDQ7AOesGtehlRmZuhmbXEtYS+Bt2IDMBs8W4Qded4/Zc9KLh7hEVYZjf8kPuLcxmuroCmbk+hgAyV0bGk5XbXTKy+LRswd3Dg848z8tTarGvo9mox8H2SwecrH8y5qYWOZDx8ZlRqGqSvs7talQDHsvcGN/6Y6AmA9c2W0nXm5uaxHkBIsKWiVYLXdQbB4rs3ALRXI/f72uh3zwSZq5AhoWWZ3lVqcyXv68rUFcqv8aM3EJttCJaMoAc7iGb43zl53EdsM2pFm88v6MjTmNePEWtOj/pmVkUE5cg1kBvp0MPqWboyLBpg/WxA1kQAH64V7mFc5CaC+4EH5j+y1ZLeCB94a95kcDv09Upb66PAbz7cmfjVFAof6ZDUQ/cFB0eHkF5+fleB2umFntaHf5uuoAghnOOvi3UiObaAHDW3ammC5hRYsyWq/OpvtKvjHmXl+kX68zms8hITc+g8MhIkTXz4onFDw4uaZoX+mzVhKxjVldDY5PRWgDx1GzA30lXjjdV1Y7MCz6P5ozMG9aAceLECfGanleszfWeN1rpqhdsrmvIzwaUhRBimFkD7ts7fsJ7c+TGumrDiBiXDZvMvuFxrzIyphZH+2TLQ0REhJi6HgjYgcwlkPV0yIxsIVSXU0bGNlXREaJnCA+ZuR7lDWrVIopAFqOJljJnZPM1PxapfrFOpZzkpmggR7k4eBfI5N/saPGf9B7XAHWuuahFHofR09FqZCyMkhK5gLSooabe4PhxKRyCw0ZBVhrpAII0ByUc41yLYHRkhNHkar5OQoKvaki+2DghQPBCn5FXoqU+Zh5bgoV+vpE3cP4AalwyMgR5DKZEa4Cr6a431zAzv1hbMzRfP57i7c4wmMHPRndbI/WqAMMwZgNWe0efYupDU538v8srKgxVsmFZF+4dtTjY02EwLbr8YeeDHchM6ERG5kEgM++WuEaGzCYmOtqwB/KFXqyqlvWZrLwCLTOs3GVkczU/Mr3KdKuZXszM9SUjU83Q9XI3uHz57G0OnsJY5JUqb66MrCg/V2RumMJbU+9ML5bzzCcU073sJTMWwbxibYpFNPhy0E1KzaTYyNkXQWFHZWIHGLhHdbAGyCwgsgDNnMjN0BpESS0qY5nNXsyMHFXHrXU5PlxXWMz5GqwdgVp/Rpai7lF3hsEzAllr04xAlpcvn88jx73LyPAcdjTJc7NixfIZA1+9zcgGuwJLK/olkKH36atf/aqQMEPCXV5eLtQruiaZ+hNdA6OCJvQqI1M1MmRikOCbFwpvz2OVom3yi6VCSedCn6+owdkaos0DDXFOxhT/zTtupnS8WSTYLaOpVn8g40UCDbGgSuaaixUlMpaZNRagQtlUdbc1OdUmPMHhI0fFa0Z+sTbFIh9fYnLKrM3QTpmJug/N1wnf18EacKDGsz7EPWQaspZWlUHNpxw2L+b19Y7j42vu6zOIGjDT+xn5RZSsaQ6Z+R4F5sqqHRlZk1AUmrF6hRR8HD1RRW/UdXulWOxolOdmxXIEMufj87Qhmj1LexdDIPvBD35Av/rVr+h///d/6fDhw+LrO+64g+666y6yMiYmp6itu09Iec0L/UJrZGZ3D/D6bD3j7W4QmRMeJNQ4WHShM5DxTTaX2KMwP0/0sMEZu5EXULXjTlE7cF8ysobaKr8FssS0LJGNzVevgZ8mUO2ibuNsG9LkvmHnBWShOHxULvT5xaXaxDoOIcv89RVQiHwfOlGLEWFC7ZepZpZ5u9BztlJRUWHUVZJifaMWcc93d3U6yevnAl+nZkWZmzOJZDe0qifAswv6NDI6hpLSsrRRi3wNE1Qgm2szwmuMaAVpdm7c37CqwmCEXjjWTpXtst/Nk8DDGRmuoWvG6anYY1iVV3o720I/kG3fvp2uvPJKuvzyy0X3+TXXXEMXX3wx7dq1i6wMyE57lUNAXFwcJSUlLTyQdbRQv2mcgo6MjHe7GblFFBetZycIuMpijVlkbjIXZCzYuZtrEEwtJvoYyMZGR4z+LX9lZAupK/J4HK5Hul5bOIw3tnrn13f8hLyGJWWzW535upufy9MOmxN3NTLetLBbvbebLb5Hcf36laktb3S8BTdvh4VHUK6acr2QQNbSVG+wPliAsX/xtQ7ooBXlZAZd7RN8DeNSpBBiLmUm1LxZasNhrgOas7XhLkjziR7b30xtfdKDcyHo6ukTzvocyMwZmav3qifUYlfHIghkp59+Oj3zzDN07Ngx8fXevXvp5Zdfpssuu8ztz6MYiwZB80cwANlpX5fjAizkIiJrw8/Bxqm1zTEzCE3RKT4GMj5/oKV0KRbx/rjwDUshs2rRXUaGY0vN4l17vVMgi0vN8km12NmkzHSTk7UqmzjjTErPXFAWhHlQQL2p1woALZ6SLt/XCS8HNNZUSWp45fIVpAvGbl75KM51b4AacrehinIJZL5mZCgfcCBL8lHsYVy/1AyKX8DvKlHK05HhYerq6jLuWygX3QVxrzaT+SU+Z5rurmFscsaCDHZLVMBCsDY7zXAQ72xtoqK0WFHvfnhfsxBxLATH1PVLSE4RPbFOgcwH5/tONQiU15iQDGRf/OIX6d3vfjetWrWKIiMjafPmzfTpT3+arr/+erc/f/vtt4vFjD8WInv3BzoHEMg843axW+KCNEQBTJmhKdpXatGhliqZU3nnrVCAvRTnmz1k7GrVYsA1sqgUGcgwk6y/X3pTLhSQ+LY31hq7eV/l2rNlLAvZALD6kj0fzchV/8ZNzZ4Ai2pfj6xbrF2tP+OMV64dc1GLkULU4bgPjYxFXWtW/Pm60IM6hQk0moXjffTYM65fuqSG5wNMuhPVuXCtA7qjVb3NyHRRw3hW4HEp3mOitICbr++Oa9Wugg9eKwcGBuj0wljRHwiKd0BRfPOBa/B5haXGs40+QG/qYyjNMLvTrrLqkM7I/vGPf9A999xD9957L73++uv0pz/9iX70ox+JV3f40pe+JOxa+INVdcGQ3vcpatGTC+CuTpaggVrkjEwGMr31lfT0dNGHspDZQxi2BzQ0OGdkETHxhrOJp8co1VI12mlF14UQu/L5kKvEAjwux4x8dW29uSd5EcT7yM2Q58kfYpa5Gr6xq2ZmAIsd+trMgSwjx/vNFoIiH2N2QYlxb/g6dNJpI7KA+x7S9dTZlJnaqMVibWNJOONMTEyk6YjoBRnsMoXY1dbo1EsG1iBL0a+tTQ2ihxUw1+vnQrWymCtUPZO4dlwni1rAs2PGkMoUsZnh6ewhHcg+97nPGVnZ+vXr6X3vex/ddtttIvOaLatBPcr8EQx0DY46UYsLhTvlYkZCtPFwQRFm9oHznNYo1h7IzMc3X0bGMntezLkp2uzF6EkgE7PIJiadMjJdgFCA50+JGtkCMlkW0rS4cfcoVu4ejS60o0fUcJ4+xaIT9aYW+rkCBzYnUdExlKzMn/k6cT00TZk/e7PZwvtg6X1yVr42xaJjI7KwjBrSdXfsB46RFakI4N6ULPyRkfHxZatNBLKo+e7TuST4TC/W1dUZm8yFBrJa1d5TrCaiA+zu77FiUdGKMeGOOmdIB7KhoaEZTXC42X3prvc3wClD7OFNRmao29qbDZl2RkIUJSQli2nK3iwUoP/YcSGzoFT7bpCPD8dt9JHNkpGx52CjKSvhpuhc7uHxYMeLmhwYrg4/BDKu/0VERFJ8UuqCFp8CFYz7e3tmUKRlpcWG16Sn7SOHjshAlqWxkdY5Y8mad6HnxciVQuTs2xfqjRd5CLqU6b3P9TFn1elCM7JwI/OqNjUGY2MGy6Wk5BSvsuqJiQnjGRQZmaY6NR8fT7eOjZpfVDGXBN8cyOJVIBswDfqdC/VKNVxqEiPxpsvbHrKxwR6x1uOYOFsMyUB2xRVX0He/+1165JFHRA/Ggw8+SD/5yU/o6quvJquiZ3hczNIZ6PaeWhRN0YqbRtaSnRTjteADDxD6yKJiYkVj71wO4D4pFk2u2bPduOzX16QGHQL8fljx58nxGa4ejfqpRYc0HbQbdrrzLz5pqUkUm5DkdrGrKCsxiumeDtg8fFT2kBWUlGkxmjUGTpqo0/kWer6mCekyM3nghTfpX6830DJa5iRP94Y1YMZASO9Z6KEhYDsyzoXVyIRBsPI2ZcNv+X35f3MUdewpvYh7GsEsKjpaKHfnonA9AV+/DCWimsvVY4bxgpuMjINcbW2tscEcUNdjPrCrB64hozBNTk3P8nCaOdvODfdIRgRBDBZVIRvI0C8Gyf3HP/5xWr16Nf3P//wPffSjHxVN0VYF6mPAgLJW8URtY6YWzY2z2ckxXtfJeJFIzysSC3JmYrRfAhnvorBzn22xzVaedZ0d7aJ51iz4cOcasRChx+jwoNFr4g9XD3ZMWMiOXqjbZslMytVcsu7WRkOVt1CcUP53ZeWORcJXgB7jgANqcb7aSmK0DCyJalQIlJm1nUPUOTgqvo6MTRS1Gm/uUc7IpPRez/gWoMnkejFXMzsDzwcLdlypRSDL5EDT0jtiLLgLfQaRUc9ndebNM5jKfYALuEc5WA3191Brp6xzzkUtDiyAWsSzzB6jK5Y77tHi9Hj62DnldGqZZ5Zq42oYpzflGUsGMjwYd955p7ip8NDBVPQ73/mOIS6wciDr86KRb7am6FwRyLyjbhwPUYlYHHzl5yHoAI3IDxEH6lbVczJXoExJS6Wo6Fgnn0buFUrOnOkaMR+wkHQo6T3GyOBDF8z1FWAhiw9+JlUdh+t14kUCataOXs+aTWurZSBbsaJCv2IxIVFk66Cl5gJ215dvyKVt66T8f6S71VCYcX3UvBB6nZFxM7QGeyo+Rgw9XaialSluFiSZA1mGosZfP3Sc/rarjraf6PQoUMNjEdDf0C7vURZozAVWdPMx8vWbQS1GLbxGBtYHGX50XDzl5zhbgYFG9VRJPK5KR/1K+R1I6T1gey0qocfE2Jghl/amRoYg2K92ukBOkiMjc3WNWHgPWYnP2RgCxx9eqab799TPyMg4kGUlzU4jRLnJWHjnl6B2+p4sgiNCeu9fxWKi6rFaGDXlEAu4Hgf620AtAdW1C6+xQHCCmhuwdtVK0n18aZmqvjKHzyKAxWhFdiKdunGV+LpXTQJmShn1UW8DmdnVo18TtQg6vb1NbiZzlBhiISjkfioTRYrhmgCPczl0rMrwU/XI9T63yC+BjL1YF0ItOikXW5zNg52oxWgVyBYgvzfEZKj/aShd8DDOHi90BjpgBzJ1c/ep+pgYj+BBloA+MnDBU1OT1KioLQAFfqY1qqprveshK/A9kEGuC0VRU88INTa5D2QIurMBfSXcWMo1JH5gYlVTNIZ1op6w4IxMCT1WrNDXKOyuWXghdQ2hbpslkImCda5cCCtdXBUWcv1QW8lLl2IDvWaz83v0zTV3bUzRQAho5oXQG+l9XnEpTUxNi1YAX2u5bW1tUigQFuaRUCArI52iYuKc7tGocHlueJPS1Ci/b24ongt8fClq2rsusQfT33yPLvQamgUf5kBmWHQ1N1PkMnlsC6HBjx7jQFakpf7HojHMTQPsQBZg4KHsRiBTKTEahT1Jq6HIzFWCiMbGBkPdht+xXBnPmp25PR0dkeVjIGNZrHD1MGVkELe09Y3OG8igcHPNWLgWEhaXShGRkWInfeC4pCrmA0QT/pDeOzcLZzrtyudCdOTcDhCY7OzOHmguHONFAmbB8foUi2YhhDeBrKOthSYnMAFYKcwmpgxq3JOMDOcZ6mTc+9wwjyDmq6jF2IikpFO8Bwa9cdGRxjBbDsh87VnQginuwEJFO4b0Pl8KfmJ021Mt0NVjRkbW2iTEaWbWIDZWUv/dnHHDAm6e4zx2/IRRA/RUaj8Xtdit5jnagSzA6BueELuJQS8Ui4witRh0tjY7PSirV8j+DHgKLrT9AEVYXlQw4ykzwTP10GyO1COD/TQyMmwcY+fAqNhJ44GfazxF+CwZGb4/RcsoWdGLv3/8NXq9zrkQPZvYwx+KRddmWvTnLEQsIEeaOB+fGQVqlE2DB71kBw4fNRqFdSlOnTPODI/orszMTNGviY1Gb0crjY5Pz2gI9ySQ8SKPxXVkMkx/D9kCpfcMBPRUl80IX/sUVf/EcU9OTiwoIxOTJ1SzMDIWYbKsYYwSaE+YPgAxyQtz9ZgvI8OGuUhtVFqbGg2l6nx1Mh6ImldUqsVZh6nFTjuQBQddqi9jfKDb6yJlsfJ7g3LRnNavqygRNMnE+JjRJDgfcINhwYmJT6S0jAyfPd44kHHGiYZzmCK3qmwsOzFmzhs5Qvj1OS8SeKjfsj6H1uUnG4V2tB+0989vWOqvZugZhsGRCzM9Ncu3EchcNxwlqinakwGbR1SNs7CkzC/2W3ELGP9hBlR3nHl1tTXRyPiEkT1xU7sn1KLZLLhvRL/Qw9NAhp91zch4QR+PShKzyUD9o47NwqeFTJ5AmSElI2dBGyKPPBaRQUVJKnQ+5elMCX4j9ZrcPWYKPsIXpFysUj6gsBfTAWyKATuQBVHoAYyp8dzeXACzBN/csFiYkSjGtQPHKqs9pxWT5g4yC8GIohZZkZmTKx/4Fq6PJc+d8Zkd1M0ZS0VWIl20Jpu2rFluUDcLoW26enppQPWa6AxkqNHxZkEGsoUthKK9ITtXbDjQiO664ShXvWSwAFpoU3SlYabrn0DNtNRCF0HXsTRDY1MGnZSjaHF3QXwhQg9eVHU2Q4vrF+VhRubSCmLYky0Do5BjZDPAfPcpH19hcQmFhYdrb4bGGgPRExDrY0Y2s5csct5Ahvu8oa52hquHL4ACFs9HZ7tdIwsKuJmz34f+B3NTtFkVhcWUnTH2H5EPhyceizr6x1wzstSMLKdAhsbtuSDEHqaMzHUxN49iXwhtw47waRmZWu3IIBTAe0P2gRqLJ5QexuTwhsOVYuM6J5pR+VzOBbwHlt6vXulf6tST3ia+TthwwOCXt0dpWTmi1uUuiM+32QJ1erhZWj9laLhXvacWHX6LrhkZwP/W3yF//3z3KQeyAjXQVnczdHZOjpHBeEotYkPa2TfolFUWOfWSze+3iHME+hRz1nQFHGS66HMbHxtzMiUPFJZ8IGMxRI8PM3TMGRn3pDHyC+RNdsTDjExI7xN8Xxx45zeomr1RX8FN1zUg32d2UvT8GZmqg5mNZ2dQHm3Nxt+aC3VqkS8p1Tejy7nRNFPsoj0ZS4864WwS/FJ2Hm9vNvql5pPeD/bLxX3NSv+oMmUztGe9Pnyd+juljRdfqSkKM4bILpRe5IW+bVmKWJDLsxJoeVYC+QpPne+dbKpmZGRhM/xC+zpaPMrIChTtpjsj4/YJBNvZzLpdARUn1zm72lrcKhfrnGyqJhbkIelJVj8XcB+wxR9MyfFeA4klH8gcw+C853YdfostM/pUSkrkv1UvUPXmyMh8VywCRhYxJGuAUYlp1D4wKnblyFrmG4SIWgqab5NS0twKIsyBbCGuCQ01VdodL5wCmXL18MTfcK7ZVQUFBSJgYEJvbaNcCBeyEUHwz83Ql3Fi9Acb3wp7Kg8XV75O3EtGakOPTY0nvWRm6X1CVqHIxC5d65nSd0E+kl7WyJgiBZPAdcDlih7mY58vI+NrmFOoFIuaXD1YdToSIe+LkvT4Bf9fMA2OZ63JcGdxpRbjFxDInHrINPXHgVr0dAyWTtiBTDUPdqhhcL5kZKj9tHf3O6X9qyokPdHghpabS7qdXVhKafFR2gLZWL+sS0UmplNtx6BhozUfuJaSlu1+oTdTi8hu5zvGpjqZmVaYbHH80WjqSSDDQjVbLxl2lkzHnjCZ0s6Gw0eOOqT3Gl3v+fhiYmKFGe5CHCHM4EUQ8m1gUl2nUQ8DGd4HgipqigWFRfS2jXnaJyd7Si3i72dk51BYmKRI8XsQWJGVg4LdrOq4oIcXkpHxZjKroNgv1GJ0UrpQeV6w2jNTXbMLPuYnuqUWo+anFvfv3y9es4vKRfuJDkD57Y0zki7YgWxsUshyO9q9302ggZp7OTpam53S/tXLZSCDr5l5lpA7gLpraZE3+/IVy7VIfkdcqFMs9HsbeuftH2NEqEkGZmWfuyA+OjQoKDVujHQHYZOlAtkqPzVD8/h4TzMypqbcSfDZOHkhWTVL73OKSrW5QTjNksuS1k3zuXrM2kvWIp38Wdcx7mFT9Pbde+X7yCmgq08u0ebs72yIDLGHZ/d+fIw09zUHZATZd20tpLUrJI3N3oJzZWQI0keV4XNemXRE8UR44nq/o1+TcaRKvq/UzCx664Zcj+8PYzPS1ujE/BQo1gCtO6MDPfM64L/xxhviNb98tZ2RLQbgRsNN3d/dKR4kFL3Rc+MpcBOxg3RbfZXTTWbUWNqanRoZ50r5E5LTqCTX8/fhChwTZ2RtrZIWg5s+P8jz1cdYfg9wnw4mGpgBKT+aMpnymIte7BgYpdYGGcg2rJWLhL8CmSd9TaKXbI6m6AJV56yrnT9jeXOvXOjLluuzpnJXX1moSMB1wzEyPERD/b00OT3lFbW4Y9dr4nX5mg2UlyI3b7qnl8O5ZLaxQrMB54Ozag7ImK0FVa7D2UQqT+fKyPbt2yd+BotxbFK61xkZnrHfvlRFdz17nH7/cjU9sKeB6hqks8rZG1fMK7KaPyNzUItRUVFG8OhsaTIyMnfsCM4xZ2T5FWu01f/QRxYsw2Ba6oEMU01xrVmxCLspBDNvgCGiQHP1MSfBBz9EUPS0d83dMHzw4EHxmllYqkWxCAsi3hG2qlldoG0YC3mYuBidXSgzy0OHDs35gM0l+Hj94FEa7u8VfT1r1qwhnXAaOBkV7tFuFz/LI3fQCOu6ALCXX2PD/Av9/r1yt7tx02bSCYc9laSjPKUWzdOEIUqaUJkz7hG+R9EiMh81/Kbaza/dsJH84sqSlEKJcbEe19zcSfAZfHzDQ4M0PNA3Z0aGqfbA5s2baURtyrxZ7LFpA9uD0wmRUH3XkJGxnLrOO6GTWYKP+YnmbK/IlHED+Dd3KtsjR47Q6OgoxcYnUFpOgZb6HxICIfYIkmEwsLQDmaqPjfrQQ8bYsGGDeG2uPmr0pgFwrY5PlMXdE1VzUzevvSZ3u4XL1/ns6AGMjMmgMjU2bAyNZHuj1LjIBS32KJoDOWUrjR2rK4widPvcgo9Xd+0WrxUr12hXNZmFAp7SXcjIsgpLhdqxu7tbeEeawVk1HFrmG+zZ0doiFuGTtvgnkBlmsx5SizwEE2hvqDYWQWRkSSobhZVaZfvcLv9HDsiM86QtW8g/x5flFZUnBB8uEnzj32JjDaZFqmvnD2RbtmxxTD32YrHnjQKG7F67tZDOW55Kg73emy44t1A0ietn7lktUs8g3PE5W3cn+ODjA60IAYkOapHtqWxqMUjgG3W41/dAZs7IXJWLbANUUzu3WGAXB7IV67T2kHGgBg2YnSFNbBdKbXCNLLt4ubHbxWI/m+vAXBnZG6/vEa8bN+tdBF2l2x4HssgwioyKprxiuVPeq+hBxoryUoOaQi1gNuzZI48vq7CMCrP0jadx9pHkZmjPF6CNG2UW1VR11HDAB3vwRqe8xsiWj9RLdsIdoJpksc7p204mfxwfqG9vFlfIyHnkijvWwNwQPhe1yPUjBDL+OW/eD98nEFPkp8RSerjs24yMjBTydN8ysmaampx0WmeK3SgXB93Uyfj4cstXi1cddVyui/MIFzuQBRh8oQe7O7RlZKiRtXUPOFE0UHcBtXMEMjhTvPnmm+LzVes3aeGuOZAN9zqOrzwr0RigtxBwjQxDGPlhYY7dHeUx224X9MOR/fL4Ttu2lXQCcmtkQ0wtep6RyXNdVLHabSBj+TaoU9c+QTNe2y0zzoLlayldQw+g20CmaoBxHlKLwKZNm8RrY+VhtdBOU0P3ME2ExxpTsg8cle0R7rBrtwzUyHyWF0kBjC54q1hk4HnJU4sznqPZGve75rhHYUt14MAB8fn6DRsN411vFnte3Fn1y9S3p6bkrsEY2SUs7zAKCfSl+d9cB2y6Uy5yICsoX7NgP9L5gPloON92RhYkDI/LCz3gg2EwA02lKSkpcpxLTaXhGGKmdCDBnw2HDx+mkeFhIa1epamRljPOIdUMjeM7ozyd3n1KIa3OlQFtoYHMnHW60oulapJyZ0vDrLtdPHT1x2UN8OzTt5FOQCjAY2QSU9M9DmRMHeUqlZprIOPrN9DbRXVtztmoGTsVdVqycp0WyyYzeCGEdBvwppHVHMiwzrO7BKYZlJXKY6yqrjHuG1ds3ykZg6IVa7WJBNxRp/MNDHUHZKg5xRUUEREpmvZd6cWFZGSoUUMMkZqaStmKRUHM8Waxn1B0GzMarrMAvQHq93wNG04cml2CHx3hdpwLNny8Wc6vWL1gP9L5gOwequUxkyl5oLGkA9mQizTdlyIlbggHvYg6meMmK1M2Ry2NdfPWx7CbT43Xs5vnjGygq8M4Pkj6c5MXXkyPVA8isHbd+jkX+u6WRhqdZbe7Z/8RUWiPiIyidevWkU44xn+kid/vbUaWU7rS7fFhYeM658Gjs1uNvfGGrD+s3bBZq1mw+xqZ54EE9yfelzDPHegWWQOyjas251O5CmRdrY3U2DPk9v/v3iOPb9U6vUKPmYbP3lCL4eLa55Uud8o8GOZm4tkyMnN9jIMd3os319I1I3Odzu4t8N6AxhOHnNaYYrfU4sSMqdCgh1GfRg+ZrvYQ1AN7O1udTMkDDTuQ4eZWF8HXncRsgo8V5VLx195U76Q0MmO3oqVQH9PVm8MPLA8N9eb4wkwOCWvWuc/IzBlLd690n3DF9p07xWvFqrVCLuyPbIUnQyd7YE8F8I47q2SF0QaBeVtmMD187IR7qzH4FLY2N4lFb4tmoQdUZl1dXY4aUlS4V7O/EhMTjTaRvoYTIjt/zymFlJEQbWTVXS0NVN8td9au2P8mKzJlVqAT5h4yb6lFoKBijdtAxvcoJizPlpGZFYuclXorhuAamc6MzBzIGo4fpO6hMUHrAUaLQXs7hU+NuZ0Uzedk+co1Qjm8WHrIgCUeyOSF7lTj1X29CE6CD1Pav3J5ubHbnc06xh+BTIePJMCL5qo1MpNCHQGmowxQqolJyeLz+lnoU5Ztb9asdnPdzeO9JnhIu3EgQyCERB0UDNdKXLPqquqZ8nyz0COzoJQKNAs9uP6HDUBcYopXQg8GU1NNVUeEswt6rQBzIGt0E8jQKFxbJfsct56sV+jhrn3CUzDVmqPoYddAZj4+1L7cbSidhR7eKxbNqsXICP8EMmRkqDvzGKqUlBTDhLunTaprXdcaPr4Va9f71Og9VyALhvQeWOKBbFIsWl0+LvSuGVmTC7XIDxHkt22dPW6LzExnFa5Yr2VIoZla7Pbx+JgeKSwto5iYGJGt8OBBRoGa21VfN1PQgkXjqJJtn3qKXqGHayBDbQpZpCcA3crHuHbdhlmUi3Iz0tpYT4NuakgcyJARpGuwFnPr6pHJrh4aAlnlYSdVG2csqHOinulKv+F8IIDjHK8slfUjXXBy9fDQZ5HB/4cFH7MFMrAGo8ODM9pEsDHj+pGU3nsv9DBL0iPVvciB2tc1Bv2X2NAMD/aLoMwb5mXLlhnHCOYHGHCpkfE5KV25VrzGabSn6lWGzHAZCQaWdCBDxoLgAqEAbgQ0RPsCrv2gBlHb1Grs3EUvmcpYjp2YqQqDChDBDLttNClqy8jUYtTZ1uLTQ8T0yDSFGcfoutBzIGty0zTc3j9M9ccO+EXo4Sq95wzDU3CdbPVaeXy8qLlmZKCmOvodtLFrRl2wfB2lJ/gpkKmGZq6BeAND8FF1xKmGwosgxBC4bRt7nLOyXa85jk+HB6gZqNtgerIvqkVk4mijgK0UnmX0Aj6y8wg9tr9ZZC54BlHrNOhFlzYR2FLhPcTHx4s5edwM7XUgU/Ql28xxb6KvCz2CGDM/oBfdbZhbVS1e2O+ZMk8OZKlFshbsjbvIbBlZjxqoaQeyAANBBheabVVgs4QeD1+AGgTfTHUnjjil9jl5chd7vLp6TloR6b6uIizvqtt9MEQGOFvBQ8G9SLPVyZrrZwayPfuO0MjQAEVGR9PatXI36K+MzNtNABZBYKWiXVwDtZmaMsueGbtVRla2ep1Hs9A8c/aXGy1fFIMcyNAm0t0nm+SdM5Zuca1c6cWdKpCVrFzrE7XpDg0Ncvo2NnKYtOAt5YUMA6rfkjKZPT/87HY60tJPTb3DM64hByrXRR7nB43CjmZo794LK0JZ9cvHqGOhN+pkJw453Yul6vgwNJPLAVwnAz2NDwT5mCz5rOalxGjPyHgkUKCxZAMZCr5YmHU7Ns9mVZVXKDOWmqq5A5mubIwD2fjYKPX2+OYowLtK7LyYPp1Ngt/aLB9YM3bs2iVel69a5/NmYV5q0cvzx3565Yp2wfGZJyYbLQatMwMZhno2qR33xo36FYu8CKZm5fhMCeE+T0vPoOmpKTpsahzGJowbdbHQo7/M3UK/Zv0mvx1fsvKR9JY65TpZ+SqZVTdUyuNr7Rud2SbikpGZFYvmTaCvYg94RiLTQ4uI7kCGOpm5Fl9WVmb4oRruHope5OtXsWIlhUXFigCtK7PGsfaqMVh2RhYkxeKQahbWVaQ018nMnfdFSh5br0aMu7WmWrFeq5s4eH7utgclwdSKp2CbKuwy+fhcM5ZyRb11NNXPEEOwo8cmPwg9nBbCjGyfM7K8knJxrmDpZTZI5owT7he1zfKczhR6lFBhjneuDXOBHflTlLu7L9QigtDa9fIaHjvk3Nhuzlja+keMOhIW4hNHDzstov64fpjhhux/ocMmXcGZakxOuVEHBNr7R+bNyMyKRTMt76vYAxkZ04qgLUFx+gp+j6AWYVM1pmhMPj7I7NEbCDB9zIGMgzyyMV0bEkEt2jWy4IAv8HCP7/ZUs2Zkpt2SoynaOZBhkWCFnM6MDBknhmf2mFJ+b29cpkfMGRkW+d5eOQ4GWFHBozIaDfsjAFnvsQMyezt92ymkGyjS80KB4ZieTIZ2p1ycWhZm0J/mYJ2QkEDpyuW/sqrGkD2bF0F/1I/MC31CuszIfG1G3rhR0ouVR2SDuus9OtTZLOpkzT0jRg0X5xlTGVaUyw2ZfzYiOT7R6pyFsOCjq+6Y24ysyyUjw8bLrFgE+N91yO/NtKKO4IFnEM3REK70drYJGb5rIHMdsOlqTaVzcsHIyJixYbYDWYDBO67+rlat3C4v9C01x6nJ1FhartL+FqUoYmCxxCKB0RW+ZBSuYGpkQPXI8RgPX8Qe2GVi9hrfrGaJOjfUImNp7eh2FnqckAvmWafpD2To38L5w1BFqVr0NiMLNxYwrgO6Zp1laqHoaG4wZM/mjKxw+VrRk+WvjCw+VYo9fK1RsaFx3fFDTgGZF8LhLknVMr1oGM0uX+PX40PG6Usg4wAPQ1ygrrpSKBSx0CO7NNPDZlUmFn5sypCJ81QGn8UeqkaGDFNnfQyATdXq1fIYG48fpHYlPuKNCI5lamTAbSBLLpAN47kLGKq7ULS2tojNQERkpFdjsHRgyQYyoxm6rdnnhd4MNJyic35sZIiOnag0HpiValI0FkEz9ca0YvFK6bqgW7HYryGQsdiDbXfc1clQY0lMkf1TJyorje+/tveQsK+Jio7RPrrFvAgiiCXGRXk9rZgzMixgswUyR1NtA3X0zwxk0mNRb0aGe8UQQxiBzDcxydaTNhvmwf0jjhl5vND3tUupODt8GIrMirV+zTgxT8yX1oK8ZJllnLmhXDAsOHc9DcdFdonF3pyRVXcM0K7qLvEzO1WzPtgUruH6LPZQGwTQpLoDmavg48Xj7VTVPiBNwZXyukvVqrExg21VZWWlWF/Si1YKIYguxSLQ0izvl0wxpTs4IWXpBjK1U+nSHMgiIiIMaqqp6hg1dMvFYLWaUouhhm2djoyFF4m85fL/6G6G5kDmy0NkzsiA2epkGbnyb1RVOWpLr+6UQo8Va9aLc+O33XxWrk/njuX3c2VkxkJoEnzg7/P8q+Wr1/scZFwBkQCcPXRmZCtXrhRu/9hsHTwi6Tfz8bU0ynPaNzwhBC+PP/GE+Lp87WbtikzXGpkvtGlJRjx97JxyOm9lllFH6q4/btCLbOOEjdVz+6rolRMdVNUxSA8++KD4/gUXXCBeEdx4ioOOGpk/A1lHzRFxz/77zSbafqLDIbpqqjM2n//85z/F5yefeoaY94aBut7WId2hTQ3zzMkNjmJxaQcytdC3q4ugK5CZ62RN1UeovkvSM6nJSZSQLMUWr+0/Kgq0WCSee+458b38inXCoDTRS2pstoysp8P3QM01MpYUzybBz86XNjnV1Y5euWcef0S8bt12Gvlf6OF9tsAZGWqLfHzsTTeXBP++++4Tr2XrTqb8bP8JPTIzsygiSmacvi5C2FAUVcx0wODjq6+tMaaLb9++nRobGig6Lp7OOOc87YpF12voq20SAiHeIwcys+ADlFxGlsxYOhrl39xf3Ub//e9/xefvete7nOrLvmRkXCeGV6k/A1lbzRHaVCRHM+2s7qLIFHl8LaqfE9L4Bx54QHx+ynmXiVd4reoErNnE7w2Sq8fSDmTjkzQ6PET9vT3aAxlnLA3HDlC9ysiALNVL9tenXqOnDrXS888/LxaqpKRkWr75NLHb9cZDzx2Y0uxu1RDIVEbGBWw+PogAzBL13AL5N9h5HHPL9rz0lPj8+uuvI3/AyMgyfcvIeMHCeUMdkM+XOVg73C8ajUB2zz33iNct51/hl/oRL4K5qoarq4eL1WvmrJMzloGBARrs6xZCnXv+9nfxvfWnX0Q5adICSSegDmXREAKZrh5KDmTVRw44CT64cZ9tnB5+5L9CcFVeXm78HxZ6+KKg9HdGxv2AuP/XpS2jy9bnCHVxdKoUrTWrQNba0ig2I8CKbRdoF3oAHWqNyc2zM7KAY3hsQox85/qODlks48ILLxSvR3a/TDX1zYZCkjOWjpYGMYn3j3+8W3x9yduuFlSPzh4yttjpaG3Sl5Gph3PFihXC1w0LHmeUQL7qlatTgeyv9/6dJsbHKbdkBZ11qn5/PidaKjPHR2rRkZEBnJW99NJLMzKW7tYG0Z+zZ+8+4QASHhFBG8++RHt9zHx8mTl5WgPZKuVgcnCfw8EE9mOs3u1vb6LJyQn65/33i683n/sWvx4fpgugmVnXeBgOSsePHhbzu1jwUVysBBHtjYIB2f3so+Lra6+91sg2HdJ7794LNgCc0fmrRoY1C88hZ9WrcpJoRU6icAYCmurlM/jKU/L4Tjv9dJqMTdXaCO0ayPKDpFhc0oEMPnM97S3aszHOWE455RSanBin3U8/ZGRlnLGAmhoaHKB//etf4uuLrpSUhtZANj5JE2Nj1N3ZoV3sAWrqfe97n/j8V7/61aw2VX/561/F65mXXU1RqgblP8VbjtE74w140YITAiyNrrrqKvH1b37zG8Mg2aixDA+JjOXuP8tsbO0pZ1N8Uqr2YZrm48vIlgEmVlMNbutpZ4jX17a/aLQvmIN1f0czVe57jdrb2wQlvmLL6ZSZoHcBBHiRT8tSx6cpI8NxoG8S1m+Nh14zBB9Zas4YAnVJUhgd2vW8Echc2QyvFYsmJej05IRQ1vpDms704naVca3JTaJ0FcgaVCDb8YwMZBdc9jbxmhoXqb2OyzqDgiC5eizpQIaFnjMy3YEM+PCHPyxedz52P9V3ykAWn55r+NntfekJGhoaFL5uhas2+iWQ8Ywg7LS9Ha/uTC061JYf/ehHxetDDz1kGKLybhe7QZgKv/bqdrHLveyqa8hfMFOLvmQrUOOh/gRaqbV/hK677jpBMaJf7tFHH52RsXQ2N9CD98v62MZz3ipedZsFOy/0MiOL15SxbNqwQdT1Jicm6Je//OWMQNbb1khvPC/rm+vPvFhIq7OT/UedpmbmaA1kuO9uuOEG8flzD/zBoBcT0uViO9LVTA17X6aJsVHKKiihdapJXEdGxoEME5hbW+QaAyWzL8+gO1x2max5/frXv6aRkREqSI2lohK2qaoTG/Vje6UqetPZl/iFVgS6lc9ioZ2RBRYQWuDDXxkZ8O53v5vi4uKpraGannn+BVE8X5aYZTScvvaUVErhYeNJrp7O0ZoLI2OT1K0Cta+NmEwtmg1IIWg544wzRLbyhz/8wSljGR4apN/97nfi84pNp9LKchngdANmz2xPlZyZ49NOE7XJkvR48XlV+6AQBnzoQx8SX991110zFvpjr79CjfW1FBcfT2tOPY/io/V5ZM7l6qGLekPD7Nlv/4CxEPL8NT6+jqZa2vfyk+LzzedeLoI0Kzv9EciSlI9kjBfToWfDbbfdJhqH9+18WbhgQPARniyfwYGOJnrqkYfE5xvOutTJJNlXe6rZ6mO6hTLvec97xByy1tZW8Qzi95+xcaXoqYQ13e6nHhLrzrZt22gyLt0vgQw18h5lT1VUpH8dXSiWZCBjaXqvBkXfXBz2u979bvH50w/eS8daByiOM7LWBqrcu0vceJdedS31Do/7JSPr0dRawAVvM2UC3HzzzU70W3JCvOjnMosgTrrgbZTpB8oNgAmqaIYOj6AUMVnYt9u5LFMFso5B8frxj39cXKOnnnqKjhw54rTQH971gni94JLLKTo2zmvX/fnAC2FihvJZ1EQLQVi07rTzKT23UAzt/Mtf/uLsDnF4v2huxyBPZG66lW6ugTqBDZE1bgawsWIlIrKy2s4hikqV57G3rYkef+wx8fmmcy6j462ygRjwVXrPI1yi/FQfY6Dn7XOf+5z4/I477qDx8XHaUJgm6sXAmy/JtokL3/I2w6UlX3Mga21tEyWUZWFhVGCrFgOLoXGZAfWrnYQ/Ahnw0Zs+Il5BIz6y+zhlqj4r1Fg4WxmOTjW677UHMk0ZJyspWX7PeMc73iHoEixGoN/w4HOxGb1VkdExtOHMi/1SO3KSbadnUXxspM87XmRk+BUY04LNBVSKV1xxhfi3X/ziF84O4yekIe25l79dvKb6IZCZm6ET0tQIF00ZWVJsBIWFh9OZV75XfH3nnXeK3TUrM5vqpLn16jMuFj+Xo9EJYq6MTGcgA3ih3/vCY8IeLjUrl5aFhYvFF/Wz8uUrKbd0pRBfoTbqz4zMHwBrkJ2dLZTC9957r2B1cpXoilsPopafJsQnK3MSKVUz/V2teigTUzMoLsY/m7mFYGkGMp6c7McaGQDBR/nKNYKHf/nxhyg+Lo4SUqVfH7D1oqvoYFOfKESjPqPrIcYDiYdR1/GhFwYw2xlxzejGG280RB+weUrLdhR8151+ISUkJFKKxgDtbjcP2baOTAW0HbtDVKus7JOf/KR4vfvuu0VPGS/0uKZwMlm+6TSjiK4baIZG7QOITs7QSi2CJoRR8rZLrqGExESRcT755JNGoIYYCSg86UK/1Vaca2TSJ5MnLeiUqV900UU0NTVJL/zrbgoPj6BExRoA1737WoqLjhBrAtOLHMjYtsxrn0U/Z2QAKPDPfOYz4vPbb79dMBRlxfy8T1NayWpKSM8TmdjFa3ybt+gOdfXczJ6trXXIGyzNQDY6KXa7nZpdPVyBDOF9H/ig+PylB/9M//3F12hkUM6Aio6R2QrTnBg/ootDh4QcwVHX1FaHafDM8fAs+nj88cfpu1/9Eh19XSqogJMveBulJUR5PLHZc+m9b0IPd/QiLIzY7WHVqlWi1eCWW24RmQtjy7mXUb9yePIHtcjHl5WVReMkA7VOxRma72PiE+ja698vvv7Wt75FX/ziF41/T8nKo5TiNSLg+SNQO49wyfFLH545K9v52AP0yB9+Qv1qBiHqZ9dffz2VZyaIr5890kY7KjsNH03vDYOV9D7M/xkZ8LGPfYxSUlLEcFAEteeflOIkoOKcdwim54qNedo3CUB9Xb0h1vFHs/xCsTQD2diECCgjQ4N+DWTAxz70foqIjBLF85cfuV/s5IFtp55OxdnSm9AftCLAgczX43Mn9jB7S2LHi43Br/73ZzTY2yW+n5mTSytOOoPS4/2zODkrFiH00CTbzpCBDI4sEATh4fzEJz4hvvfXv/7VMEpetiyMLrjuFuoalNfTHws9Hx/6c3hUh86hlkmqXeEd7/uw8MjbsWMH/eMf/zD+/cx3f4ImpqXBrD8WqcHBQdE0z+0TmYn+uVfQ17l8zXoaGx2mZ/7+f2IWG3D1u64Xdl1r8pIEpYz5ga9WdVKbap722p5K/X5/9ZC5IikpiT71qU+Jz3/+85+LJnMgOb+clp95Ob1lfa62TN4VDap1I121TwQLSzOQjTsUfZBYw2zTX8jOzKBPfvk7tH7b2fS5L3yRtl1wufh+UVkFlamdoHlR0QGmRnTVyJha5CK2K7797W8Lf8lrrrmGLnjXTeJ7UTHxgsbJTIwKSCDT1V8FGT42FQjadV1DhrIUDbZoQMWxYtGfnp6i0fFxQUlhEdS5EZnh6qEcE0DdcOO2DnDfXXJWPn3kIx8Rx4W2g61btxqmAaDJ/CX04ONDIzQyQ38FMgThb3/zG+K1uLSczrr0SvH9MVUrB+32wTNL6aI12bQiO1Es+tgweFsXDGSNjPGpT31KTLk393hOjQ7S6twk7X1jZnAPYrrqcwwWlmYg82MztDv85Oufo32vvkB3fP92Ovn0c8T3amqqjd2/PzKysZFhMbZeZ0bGD6grIO9FpnL//ffTGW+VjaVtasCmPzMyM7WoSwSBxa6U1YvtA4YCFaNMQN185StfMc4nbICQKWF0jD9oGz6+7FyHq4fOzIhH3sABH71ksGqC2pS9QjGpAddc58iP2TwWAX8FMuBd17xd9DueOHaETj/vYvG9+roap3OxLj+ZLt+QSx89u4xuOrvM6wDAPoth01NGe4i/A1l6erqwU4PoAwpGYKCrjaLDpp3mA+pGk/KszMgOnmJx6QYy2FP5uT42G0p5HHlVJWUkRInaGKBzPAbqbjxQE1NpwZ/7Am6IFtY7buhFM3LyCoQUF30sGLaX4cfFySH28M013RXlGTJTrukcFMEYqlIENUzjBVgQ0d5YL5Sc/hht4s7VQ/fOmg2q+0YmRDaGeVxOc63aG4XazR+KTNeNCHrwEv3grG9GTk6ONExW/Y5N9VJx5wpsFnzZMPCGr6+rXShB8TdR5/Q3cnNzxaR7qBghAgGFijFVTHX6A81NMiPLyrEzskXn6jEX2B8N7hfo+7hsXQ6dtTyDitLitFKLvaaM09ddPGdks9GLCG4YVwNVY0JsNKVkyt1Zb2u9tkxprmZoFJp1LvL5qbFCRQobs9+8WEW/fbFKjMm4f3eDOFYjkDXXi4zM26nUnrp66KyPmanFPtXHyDCUix3N4m/OvXXRk5FhUxcosYBx/VqaxDOoG6zuNU9nD+ScrmXLljkMrpvrZ2VSfAU2eS3K1ccOZIvMZ3E+lBUXUVRMHE1NTophd5A1n1ySpvUhNveQ6aA04KrNcPdQ7G/sFYv8A3sahEIxNU8+RAOttX5bnBDEsNtFM3RCSrrWRR61qDJF+3INDB/IzNoHRo3NSEdjtdjt+jtjSVQ9ZLpngXEgw/NgzrTLFGsw0NEo/iYra/1Z4/QnreiKvNxcioiKFvcPz5LTCZ4O3dWm7xn0FKWmkUP+ohZ7enpoZGTYif4OFpZcIJvU3GPlKYrS44S3G/DCLufBjVqpRY3Hh2DkMA6eGci4/6a5d4TerO+hxGzp8t/TLI1L/d1DhoZd3dnKuSuz6JK1OXTt1kL6+LkVJjXjkFC6Ad1NtSIj81czNB9jTJqsIaXG6838eGwQ6MOBMSl8APj4hrpaKYrGDBWs/zKywAYyCIPgaAKcOHFC++8fVwpTbu8JZiDrbJF1Tn/AmFyQnErxcf4RBC0UYUuxPgYEKyNDXWKVWiie3fnmjCZjHcDC0635+FjM4O79wlUcwKKIIBqeJlV2LfWOAZv+HN/iD0cI1Nwgy4aiDTRjoaJ+MckAfWVAb3OtUPWlaA4wAGyjuBk6LF6O3/BlcOhsGxTO8tjvE0hJTTOGwI52NBrPjP8mQ2cHNJBB+ZlVKLNOth7TCa5JdbYGL5AtX75cvLY31sywlvPHZG+dE6e9wZILZKBRsNvV1WPlDU7dIlVhtVUnaG+DHOypE3Bw151xMr3ompHhIcGsJ+AdJxUIuipJZWT1Vfp3u+7Gt6Bh1x+qQTMKU2Uga+oZoZLSMtFMOzE6JJpr/SFScEyGzqShyXC/9aoxvQjlIqNzYJQy1ULf21JjtHPoRj3XALPz/KpudQWEJdnq+A4fljZOOsEN0Tx9PhiBbJXabLXVVbplUXTXOO1AFmCgzjHY2y1UdVyIDTTWrJY3WXtDtRhPrnvHOzIxqT1QcyBz3d11DowJFxFQe8he0IvDu9262hoaHZXn2Z8ZWZwfXOddATECsjRQiV0jU4afXX9rnV/qgHx83Aztr141Q7k47LgHQRHzNWyrrzYs3XzF9hMdtKdWNszDbb+rs1N8XlJcGFB7o0BlZG1qhEswAtnq1avFa0dTHQ2PjPo9kJkFYcHAkgtkmNbM2QpkqpgTFGhwDaKjsUZkT3AT0AlZA9RbaHZQi9NuaUWmhtLio2nb2jKKjU8QxXQIWvwtvYdXnr+BYMVZGepkBSUVBr3oD7hOhkbA8UfWCfNg14wMgYwzlrb6Ki1iD5gwY9P24rEOcf64kRbCp+Ich/dhwDKyIv8FsnHYoeDcNQcvIysoKKCY2FhhjoyeVX9Ti3D6DyaWXiBDD1mQhB4MVr3193TRYF8P7W/oo94hPTJgQZv29dPwQJ/WY3SdEs1oHxhxCmTY7cK+Ka9YLhRoIg4Vn8X5UJgmC9pYiHPV8fU014hz7q9Ana6k9/7yOnQ0RTsyspbeYSNjaUUg00At8oQH4Plj7VSnPPpQH8tK8k/D9dwZmRRDYJYX22TpAlpUsIlrDSK1GBYWRkVlsk5WffyYX/4Gb0bgk+lvan8+LL1ANqpfCOEpEhISDEpzqqdJqMZa+mRA0MHPc5EZHmz40AFuinbl210zMh4umVNU7rcdr3mhT83S57M4HwpURtbSO0IZ+XIh7GmqFSbN/na98FevmmuNDNl899C4EcjaG2pocFjWQH1lQhgYk7PzwHFjEcwKoNCDs2s8F3xudW+2wFoM9HSKXkcEFDRiBwMl5XLDXHXimH9ZkXTUyGxqMWjUYrACmZleZGpqYFRPRjY6oW+g5nw2VchEOgbkIsfO5Wy0mllQ6reMDE2sxmRouHpE+p9a5KwIKj8E85hMeW67m2ucFmndi0S86iHz1+BOs7uHaHDtlRuqstIS4fQBk2t+LzoyMl7wdh08bmTU/nK9nwtQuWYXlvtF8IE6Mq8x7CYSDJRVyIysplJ/IEPGWVUlVcloZbCpxQADD1SwpPfuAllbQ7WxkOgAptuyo4DWQMbGwSaxB+oeECJACJKmFlrMuQLS80v8FsgQxLDoRkREimbo+OjAZGSiTqboRQ7UPW1N1Nkj3cb9YqibogJZrH8zMlxHZJaojwH5afFUrhbCumrf1acc7NfmJYsNQXuzw9rIX87scyE+OoKy/FQnAyvS3ig3qOXlMlgGA+UrVvlNPYz7E+0hMAZPzfbPiBhPELYkfRYtlJE11VXNqFFYTejhlJGZqEWmFTEBmmeOcUaWmltsLBK6a0gGrZiZLaibQFGLZnoxITmNYhKSxeeHjhzVvtvlY4xMkgM1/eUeAtk0n7++kXFqVRR3TnKsIeFurDnhc78jBzKIS85ekSkoS6C8XIpmAg1k1v5SLuJc8fHxcx4MVCyX1GJ99XHtz+Dx4zKjzswvEsFsUVKLKAK+973vFY7MMK+Em/bu3bvJCq4ekBJbKSOrV7vdAU2BTFCLfgjUfKOaMzLYNbE0ncEZWUZ+ichgYGPT3t5OOlFTIxcJ7AQBXSNcFgJujAYyVFZ25KjehRC2SdjtRkZGUkJGLoWhpuOnjMxVgs8ZWU5SjNEm0qZB8DEwKv8/PDHhktKlKPX1a6VMPCgZmR8CGay+sNlDI7JZ2BUMVFRUUFhYOA0PDhhUvC4cOybpymz1DCy6PjIogM444wzxED722GN06NAh+vGPf0ypqdIpINjZ2KTosWq1TCCrq64Svotm+bOv1CKMQgE2DtUBvlHNmaOr0ANAPxCooqjoGCooLPILvci/jwUXgegjY6CXi/u5coslbXRcPdS6jw+zs7DbRRbjzz4rphfhWoKMHlQxrin3IqGXzFcJPmdkyITQQ8aCpMvP2kLBQEK0Q4KPFpGxMd8FLWbGAj2iwQ5kcbExhhWX7jogB7L0/OLFGch+8IMfiADxxz/+kU455RTh+XXxxRcHlSt2Uiy2NdHU5IQoZGPkQbBQVFQketjQMIz3hExRh10VFiLsoAGmhnSgJF16DR5t6TeCrrtAJr5WxfvC0gq/BDLeQRuBLEA1MgbPKyuvkItU9QlJs+gCn6+i0nK/0oqugexEq5y/lpUULQKn4Q6hJSObMDIhpqXA2ORmBbaHjIH3kZSWJfodJycntXkugrEAjWeFjCwyPMxvdUDjGiqD8EVHLf7nP/+hk08+md75zneKGTyYrPvb3/521p/HQt7X1+f04S/gYUJfDN9gsBkKFvC32Q+ts7F6Rq+Nt+jo6hFzkHTz86DUClJjBT27u6ZbBEzOzlxVZ1gIgeyiUr88RLzQoxcI2UOgFVNnVmTQFRvz6DTDasw/gSxHLULJfpLeM5i25PsvW/V1Gf2O3R3U3Nbh9e+HkAQfAIQ5fHzBrB8hMwT1zedY1z0KVS/O1+jQoKjf8iSBoAWywjK/ZmQZeUWLMyODJPNXv/qVWKSfeOIJuvnmm8UY7j/96U9uf/7222+n5ORk48OfdB+oRXiPAUybBBOGBL+lVpvg48QxRbtlZYvzqROnlqUbY1uqOwaNRZB7xxhZiXIhTM3Vr1yEEIIfIjykoDEDNceKgYe2IiuBVqsaUmNNpXhfusDnK11lnP7OyJJURsbITY41JmOnZ+X6fA2ZVoT5MmqofP2Cma2wWXJmgd5AhmZoFnqA2g+Gc5BZoJXth35OtL9UV1c7qXcXXSDDA71lyxb63ve+J7Kxm266iT7ykY/Qr3/9a7c//6UvfYl6e3uNDx09KwvJyHTSbr4Gsk5FQ+gIZFWqZ6RU0V46Yc7KXjgmsz53ruX8vbisQu2BzBBCREVRWna+9qnJnmDl8nIKj4ik0ZFhw+VAB/h8JecW+VV67yr2YOQkO5w2isskPXzMh2to0IpKHWmFjAzUopNgR2NGZgVaEYgM809GBrEVmr0h5EtKzxJipEB6ZQYkkGHc9po1a5y+h+xntgF22LGwA4VOJ4rZamRWzMha6+XuRofgo6ZS0lzlSnqrG5yVcfHfrFhkoE8Iu++0vFIjS9dVTOcFp6ikTMwhC1QPmTskx8VqL6YPDg465jxlFAa0RgZAim/O0ErVfVTpg80RbOHMwcMKGRkyCExNyFJN0doyMiG9D77QA4iMcGRkTU1N2so2XB8rK4cqMkz8nWBDeyCDYtF1B44bt7hYqluCCVAcLISwUiBrrK3UlpFx8+OKFf7Z7XJWxnBnLwSqD4KP5PQsilPFdF3mwXxvsZBE9xwyTwCRCe94Dx7WsxDyIp+Wnk7RiSlip2sONP4AziEX65GNmana5SqQ1fjQVGtWLEIIYYWMjN+P2TxYR6+VDGTWyMgiwsIoNj6RktIytQZrvkdLVQ9gsF09AO3v4LbbbqNXX31VUItQAt177730m9/8hm655RYKNppaWoVJLx7UYN9kTi74rS00MjSgReyBeo2/AzVnZUBmgnvD18ykaHGeC0rKtdKL/DCyKXEwqUXs6lmCf+iQnoyMz1NpuRQCQerPzeb+Aq4T04tcH2OsVBQ89zt6y4RwRtbW1iYyA/zNYCuZEciQUUN41d/fL7IWX2GVHjKANye6++U4kBWXlTuNeFpUgWzr1q304IMP0t/+9jdat24dffvb36Y777yTrr/+ego2qpV5ZkFREcXFORpbg4WUlBSh7ARAR/hKLaII26ascdau8V8NEFkZgtm20rRZFXUswc/S7LnoUPTJhygY9kZmFCqJ/FFNvWR8fLwB8JdZsCuQZaPWUZLh/FysWSU3RK31NeL+8iUjQyDj44MQIiYmsK73rsD7iYiMovyiEm0L/cjYuJgBZoVAtmzZMhFkmF7URX8ztVisWJHIiEWYkQFvfetbaf/+/aIoj5MHsUewgY57lkmvWhl8oQdj7dq14rWx8gj1+5iRnaisEvOHoqJjqbzEv1TuaeXpdHqFtE9yB5bgp+TJ94HGeK09ZCpABrNGZs6cjiu1qK/ghT5bjRnxl1mwK85flUUfPafMUJwyykoKxcywyckJwyTWUzDTgAzICvUxV+VioRp3oiOQ1dXWiWcwMio6qIYLDAQZf2VkhSVlhqgk2Aj+OwgQUHBuU76Ga9cEvz7GQM8dUH9svxiyCYspb7H/oAwWmYWlFBMgR/jZgNH1qO9kl8hNw549e3z+naCk2GqHvRzjgnycZcslPdzS1EhdXXL6sTfAdd9d00V7D8hrmKYaTf01h8zd7t21jQLA0FKe3XVQ3V/eZ2TW6CFjsPgkR2PGUqXEVvlFpUIIEWxEaM7IhoeHDWV5PgeyxSj2sCrA07daSLFopmKBxuMHfRZ8HFR1mtyicr/XVeYDghgapQtXrjcyMtQhtGQr2dm0LDreEtRidkYaZSqnf1/8RA829dGLx9qpUrmETCdL15mU2MBkZLMhJiKc8kpl0Nm12/PNCAQUg0rhar2MLNypF+rAgQM+/87qSllLLFCUc7ARaWqKhmbBV/UwRFu4puhRhXE2/41gI/jvIJAZmYUUizMCWdVRmhgb88k8+Kgyr7XKQ4R+MiimsnLyxM3/+uuvawlk6AFky6RAOt+7AwJp4Yp14vNdu3Z5/XuwgentbKPR4SFh9AoRAor1GYnBDWTYEJWv3SQ+37lzp8f/f2zS4eoBYY6VMrKEaJnt5pSvMVgDKGx9Qa1Sd8In0wqIDA8TA0STklPEsR08KDfM3sK8EUE/qXnEUzAR/HcQIHT09AlPQ6sFMrQlwHMOvHpj1RGfMjJuWuUR58EGS/PL1mwUr6+99ppPv48XQQwMhFIaKvFgyu+B+KgIKlq1wedAhr68drXRKisrpRvOrKD3nlocVFUmY9WGzeL19T27PXYwYcUierbCaMpow7BCRsb11aScUjG1fWBgwOdabl11pZOiL9iICF8maOMNW04SX0NRrkPogeuHTQoQZVOLgcPhI0dFVpCUkkYZGbOLFAIN3GSclTUc2++1chHHVqlUmSVKgBBssOAjp3ytlkDGxepSNcIdQSzYFCoywqKVjkDmbS8SamQ8ZBXZCvwOAyX0mA8Vq9ZQRFQ09fX2eGyua+4hg60RO0LonJXnLbBJEC1zYWG0RdWqvck6zeA2BRYBBRuRSoK/fvPJWgIZZ2SwIMQAUcDOyAKIY2r4oT+sm3wFB7K6Ywe8Vi5i5ldfj+yRK7HIbhA1MiwUORXrtGZkLHkPNq0o3kN0BOWVrxbjVlpbWw1XDm8yMnMgsxLSEmKpoGKNVwu9w54qwmkRtIIQAnVcvoc2bvY9kEEI0aYmX8P1wgqIVPWrdZtO1p6R8bQOu0YWQJw4fsRpaqqVYCgXjyIjm/ApW0nNzqfkRCmECDZwg6fFRxk1JOzIvR2yCX6fF8KEbKlYtELGgllomL2WV7bSJ3oRNb/2emsGsuTYKCpetdGr47OqYtFVubh2k+/UG2ersQlJlJkZnPE0ruBsaeV6SQ/jGers7CQ9GZlNLQYcXIRdaaH6mGtG1lpfKcaw+BLIoFCC0swqQJ0MNjlFynzWW2UfvDox8gfenMPRckhrmZoLFkwkxESIRmJWZ/oSyDgjs0L9yIzU+EiDPvU6I7OYYtG1l2zFOjngE2KII3Wt9MzhVnpsfzP9+81G+tfrDXSiTc5qmwt8fFCxRlnkGYxU1GJcYoqxgfD2Hq1pahesA0+ftqnFIKBBNUOvVaM3rAQYLefl5dP01BQdObjPqzoLB7LswjKKDrIAwoxM1WBbunqDT/QiH195RQV1DE4IyrI0I94SWWdOcrRTncxTQP01MDhM3a2NlstY2LSYBS1vvvmmMDrwxp7KihkZB7KY5HQx7BbP3u8efJr2NfTSkZZ+qmofpNrOIdpe2bHwQFZQIkQWVkCEov0mpqbo1FNP9SnrfOG1feI1ISWdXm8ZNTIym1oMEMbHJ6hV7XY3rpPCA6vh5JMltVF9eL9X03idMrJI61zW/BTp3ZdV5ptEnRfBPNXcmZccawlFH5CfEmcEMmScnkq4MaS0o7FWLKKYAZaTk0NWAvwe03IKRN8QbKr27t3rldjDihkZU4t4n9u2bROfH977ushkzl6RSeeulBRh1+DYvGYF5ozMCm4X5owMQcfXQLb/kFxjMvOLaW99L9V1DTn9jWDCGmfbD6hqH6Dm3mHx+cFjx2lyXNrGrKyQzY9WwymnnGI4fHjTS+aYmoxAZqWMLFrc6LkVDuWiLxknz+iyAq3IyE+NFec9OjZeSLg9tQJCIGOjWWQrgR4UOh9wP2HB56zME3qRqcXpsWHDlNdKgYwzMrxPXujrjuwVfqInFafS5qJUMX0At2x7/+icv2vfPpmxwAnFCm4X5mwJNCAfH66fN4Ng39wtr/v6jZsFI8KPsZ2R+QlNPcP0333N9K/XG6m+a4j2H5SOFxjZEBFhjV38bHWy+mMHqM/DQAaqhye2Wi2QQRmWkxxL+eWrxbmH+7k3w1M5UMdmciBLIKsgNzmGwiPCqWDFWq+yTmTgzdXHLDPw1R1gXuwpfSpcPVQgO/ymdAWB/2BqqqxxWikjGzBlZLVH91FJusM8Ga0QQGvf7IEMQ4FBuwIla0+yRN3I7EyPgZ8wcYdZOt6rpybeyOgOvSED2bVXXEwXrs42/s0K6401zrYfZN+gtOAo8NAbjfTSLnmDFZZao7fDHU46SVKLHU211NjqmbIPTZxYNOISkykhJY1iLOBGbUZeSozIhkuWr/aqTgY6i0Ui2SUrhP8g1JBWAR5kZJ7e1smQkZ3Yt9OY52dFQCHqaUY2OjElxpoAr77yong999xzyUrgpmgE3NXrNlJYeAT1d7VT+FDnjMb+tr7Za4Mvv/yyyHJAu6VkZFuCbjNnS6iRYSPJCmlP6cXK+hZjs3XheefQuvxketumPGEenq36RYMJa614moDpxFduyhP0Ex6knTvlwlK2wpq7XQDuHnmFUla+Z49nyr5nn31WvBav3jSr+asV6mT5Xlo5IfCBsktKSaXc0pWWysbMx+htIOvuHaSaQ2+Iz88//3yyIlJiI6lwxXpDZr4QCTfTirgfX3jhefH5eeedR1ZCorKpGhqbpKaBScNX8tDe191kZLMHsuefl8dXvuEUJ5GFVQLZmLIJ87ZO9tRzL4jXgtLlwusUKM9MEOOcrECFW+Ns+wG4kd66IY+WZ8TS8Td3iO9tO/McsjLWbpQS4L0eehI+9dRT4nXFltPFa7TFMjI5dZgou8w7h4+nn35avFZsPFU00lqpPsYoSIXgY71RK0Fz7EKxa+cOUcPNyM4V/TlWRGp8FMUnpVCOcsJfyDVkWjFicsT4easFMgijQH8DB5t63WadHMi6h8ZF9uwOL7wgF/qy9bJEYJWMLEK9D86MvQ1kL70oM+rNp5xGVoS1VjzNwA2aOlRPwwN9gnY7/yxrXgTGZuWHdmif3J0vtD72orrJVm45Q2SjVtkNMqIjwsWcK14kQBN6UmzmQFa+6TRh0gvFohUzspTMXEpMzRA2TFwvWQiYdtty6pmW2N3OlpEBntCLnJHVHX5dKDkxTBMfVgLON9fJmnpGBKvheny455LU8bsTfGC8EI8p4ozMKqrFKKYWlVSe64Bw+nc3jQLqzD+8XE2Hmvqcvv/6zu3i9cyzziIrwhpn24946qknxetll1xEa/OtU2R2hwvOlTfJgd3bqadfSlvnwyuvvCKCWU5uLmUXV1guGzPXyTAXKSY2VjxAC5Vwg1Lk3ePyzaeJ3rFg+yu6AxY7WSdb7/GOd/eOl8TrqWdZlzHgSeD5y9cvOJBxD9lRJRKwWjbmOs4FWLFOOmAgMJknYnMdyB29iGcQG7OS0jJKzcoVG2ir3KMRKiMbU83LeXl5ol8O79edOUFl+wD1Do/TrupOp0BddVSOuLnofGveo9Zc9TTiySdlILv0kkvI6rjwnDMpJSOLRgb76Z5/PTy/yWzfiEErnn7WeZasj5kzFvgRrj3lbPH1/fffv6D/99JLL4kFJT2nQIw2KbcgrWiW4TO19O9//3tB/weLxLEDMns708KBDFk1hBElazYZ4ob5GqOxu+eNmRWFHgzOyIBTNq2hlJQUcWzmsUNzKReZVjzjLHlvW6UZGmD1JGdkwLZtkl58+RV5XcxArZBpVL5+z7zwkjBrwDO4foU1fFyXVCDr7u42do6XhEAgQ/3n4suvEp8/MM9C/8LRdrpnZx3997EnxNfbzpKLhFUDWZ4SfKw981Lxet9999H4xKSgMJiCcodnnnlGvFZsPk1kPKUZ1hN6mBujN51zmfgcdO9CDITxc1OTk5SRV0ylFqPd3CkX8yvWUm5egciq//vf/876s/saeuhwcx+NDA7Q0QN7LZ6ROQJZWVYiXXjhhcY9yshWDjXuMjIWemw7/UwnOs8KiHSpkQHl62UJ475/PTTj54fHJpyyM+CpZ+Xxrd6yzXJlC4Y135UmYBFECo35Y+hfCQXccP27xeurzz1BvQODc/bKDfR00aH9cpHYcprcDVrJ1cN114tepNWnnEuxcXFUVVVFP7vvcXriYIsIyrPh0SdkRr1i82l0wepsozBv1YwsNSuPStedJNohzAvhfIrTik2nBn3a9UKsqrDZuvCKd4iv77nnHrc/hwD27JE28Xl42xHxDJaXl1v2GeRAhvJkSXo8ve997xNf33vvvaLeaR5JBNrNLPgA9c0U3dbTznDq3bJWQ/SUYUSw5ZxLxfDWg2/sntFPxhkZm0oA219+WbyevE2KyawIa656mmnFiy++mEIFl55/NqVm5tDI0ADd+8//zurN1zs8Qcff2CFuzvXr11NCqpyxZiXDYHdZWXRsHJ1+nsyO//vgP8Vryyyy5sbmFjp8YL/4/KrLLzayOqsCCyJ63Lac91bx9e//9Fc60Ng7Z/8RB7LlCGQWzaYZ2IgAp19ypXh95JFHqKury+lnTrT105MHW4Xrw6bCFGo9usfS2Zh5igKUp2A0Lr30UjGzEAa5TN3j+3z8bSZ6EfUxFrLk5MlAbaWsJUJlZLgenJVFJqbTypNl9vinP/1p1kDW3DtCHT39dFC1Ipx1ttwsWxHWOeOagQX+iSeeCBlakREeHk4XXn7lnHWkvuFxmpqepqOvvyK+Pu3s84xdolWpRXM/WfFWSd3sffExsVvH8biTNf/2PlknLChfRZefYt0eQDOwGG48S+54D+9/k+558lX6+2v11K3qDWZ0dHQYopeKjdssm00zEKSB5Pxy2rhxo6hdPvDAA8a/N3QP0aP7W8S9uSYvSfgUcv3IyoEMdddL1ubQxWtlf1RUVBS9+92SGfnLX/4ys07W79iY8PGh/oemYytJ713Vk3D34Kxy68VXG8dn9gbFXDwA6mcEv4efeoEmxscoKT2LNq+17jNo7SfHB8DAE6M/cFOec451i+jucMN18iHa8dzjNDA4U73YPTQmAvWx17cb/WMj41PGSHmrB7KVJ59FMXEJ1NPeQm0npD9dx4BzER1fP/Gk3A1fctGFQmwQCthamkZnrC+lLafLe+7wy4+JDPrF4+2z1lZyS1dQUlq6pbNpnkvG9991110nPv/rX/8qvzc4Rg/vbRbHWpGVQBetzhZWSG+88YalhR4ARFIIvEkxMlADN9xwg3h98MEHhSBnNuUiX0OsMWMTKuOxUEYWFrbMoDrHpyS9iI3j2lPPFy1JqOMyK4B/Y8PyldmJ4vWJZ1Sj9/qtlKkCuRVhnTOuGZyNnXXWWcJfLJRw2flnUVpWLo0MDdI9//zPjH+Hoqi9oZp62pspPDKSkss2Gs7cVl4MQc2gHwd2VRdffoX43oGXHnXbn4PxGcfekI3s73jbWyhUAKf481dl06du+oBxfMvU8dR0DM5aH0OgtopkezYwtTY6PkVXX/MuEQCgKj1yvJIeerNRZNXwnbx0XY44FiFkmZoSJsGQfYcSYOUEA2eoF//5T0mBoxfSrFwcHBw0Gr3NGZmVqEXz+xmfmKLBsUlBMeIZ3HSOfK7uvvtuw1IMGxFgfUGyeH19p2R9Vm4+heItXMO11hnXiFCkFc304vmXvU18ft8//jHj33uGxuioysZK126h/vEw6hiQ1JWV6SksfNdsKaD3nFJEH7tRFtR3PP0oTU5OzAhkbxw8Ql0tDRQeESE2I6GGq666imJiYqjy+HGKH5AmyS8cazcWCizwXH+R9THrXjcGMg04wQPxaVlGlvXNO39DPUPjIojDf48zkoceesjytOJc9ypnZX/+85+dBB/IaOBaArELxCDoy0KNjAdNRlpsQxJpUi7ivTO2Xvx2I+tE9mymFeEv2VF1gE7slarvU884x7LN+oD1nx4vgEnCnPKHYiAD3vseSS9uf/ZJGnShF1u7+2nX47I2cbKisPgGtXKNjBtrYVkFiXNaWhp1d7ZT5b7XjEDMi/zvf/YD8fnWU06lhATrSu5nA+aKXXGFzDoPvvQIxUWFi76cN+vlBPAf//jHwrMwNjZOuEFYXbHoKoxA4Hr3e94jPn/ukX+JcffwN+UZcXBj+eMf/yg+f4/6uVDD9ddfL16xltTW1oqsmc2qf/rvHXTbZ/9HfH7rrbc69WpZLiMLc8wk6xtxBDI07y9fsUrYqaEeP6RoRdyrCNB//+lXBd249aKraeM6afhtVVjrjGusjyGrwYBCKPpCEW+98GxKy86j0eFBuvmTnzYsnXBj/e+3Pk+NlYcpNTWNrrv+vU7/z8o1MjMiIyPpmmuuEZ8/de8vqam9m6ZUtvLTu35Jrz39HyGY+P7t36NQBdeR7vvb36giXgbqV6s66aXtO+j//b//J77+3Ddup9j4RMtvQFytqiB4iKo4jSIio6i19gRlD9VQeoLMWHp6eujGG28Un3/84x8PuRo1o7i42Mg6Wd13RkW6yEr/9pOv0dBAPxWt2kgl51wjnksjI7OQ2AOIVG4/eH+9Q45Ahgzrbe98j0Evcg8ZAtmdd95JVUcPCX/NK276vOjhtDJCY9XzEAhecOeGosjK6fBcCA8Pow/f9mXx/v/yx9/SRz/6UaEu+vFP7qQdTzwoFvm/3vs32rbe2WQ2VBZE4JZbbqHY2Fiq3LuL7vrc++l4XZPoyfnS5z8r/v26T36Jzjk79GhFxmWXXSZ6p1paWuiGKy+kwaZjQjjwnvdcJ3a8COSXv/P6kLpuqfEykL1Z10PdE1G08SzZ2nLDO680hB+f+tSnhIigoqKC7rjjDgplML34jW98Q2w+ilKiKaZ2Ox3a+TxFRkbR9Z+7nWq7Ruj1um4hprCa2MOsXETG6Drr8KK3vUP0BqKN4FM33Sh6U/vam+nrX/+6+Pd33PwlMRmcFZuWxbTF0Nvbi22NeF3qeKOue/q6z/9gOiwsTJyTCy+80Pj8mlu+In5mampq+rcvVk7/5Mmj4mN0fHI6lLBjx47phKQUcUyl5cunS0pKxOfrTr9w+sWjbdOhjqqqqum1a9eKY4qJiZ0uW3ey+LyoqGi6q6tr+oWjbeK64TUUcLy137jXfv38iekj1Y3TF198sTgmfFxyySXiFffp9u3bp0Md4+Pj0x/84AeN4zvppJOm09LSxOff+c53pvfV94hz8bOnj03fu7NWfP5qZce0lfDPPfXifR1s7J2+f7f8/BfPHRevrxxvn7799tuNdSU+KWW6YvV68fk555wz3dIzNH28tc/y8cBaWwcbTshLjqGTL7yKPvCVn4qheKg7gGIEZ/32931Y/AwyNhjpAnC9sBqtMR8wVuKuvz0sHDGqK49TTU0NZeYV0Xv+53bKS7V2A/RCUFpaStu3bxfZ2cjIMFUd2E3LwsKEUACTklnuHCo1MtQ3UXMBvXbtyYW0siSPHn30UbGDx73IIqsvfvGLdNpp1p42sRDgufv9738vaki4XjATRhP4pk2b6POf/zyty08S7QYQ8bT0jliyRhZpGq7JtXSoSwGoGHGtYHJdtnINDfb10InD+0Xb0q9//WvKTo6liiwpxbcyrHXGbcyYdI3AtO7MS+kPf/mbaCPAPKBrbv0mpcY7OOsSFcjgDBGKVOqWDevok3f+nUpXrhMCkPd95WcUm5BkyXEt3iApKYkefvhh+tStnxbtEm/90P/QllOkcSs3glvd1cPsXvLBM0vp/aeXiBllAOrRoN4Q0HJzc0VNjKmpxQLQwPv37xcbErQSoKaEOi+et4vWZBtqTqt5LQK8ucVwzX5FLeYkyWdrSNXFtm7dSj+55zG6/IOfpczsXCFGWrXKug3QrnCcfRuWA3pxcpJjqb5riDadeRG1tbXRC5W9dLR1wHBZAErT4+nkklSjzyXUgEIyxsN/4f8epLPLUujxI11CHRYqWcpCgMX+Z3f+lNZd9TEaGF9GHf1jVJQeYUierdw2MZdbvBmwdmKjZNRdFhvy8/NFsIaww7xhRH0TziD/fL1BuGFYyf3e7IAP1SxcV5BRs3jDbEk1Nr2MLnj3TfTj736dVuZYPwszY/HdbYuQXgSae4cpPj7eKNbybpgD3lnLM0Pu5mOkx0cLw9ahsSlq6J90oj4WG3JS5TVqV04moWAt5gkQwBZjEDPDHetRmBZH563MEvdtcXqcJVWLncomDdkjRvKYp3gDBs0dgvfi4r7jFgFQk2ADT3b1MLssLAagAZNl3UdbpB2Q1Q2CfaGLzZZcw8paLBQXDxvO2FiYQu8+pcjopbMKIlUfGc8Xg7sOv0dkZOyKz9lZKDIhdiCzOHJVnQg3IfzseAefonzvFgsyFS3KvTiLNSPLTJTXrXNgTPTNGTWyEFw8bIQGIlTNDjUyAA4s6BUDIFKBNZX5XuR/CyXYgcziwALHbgKY88TUALKYxQRzwyWauvmYF2tG1jkwajgpWN0j00ZoI8KlZif8TsPDjDUEmRhoRSRmYE1DkR1YXKvhIgVnJ4dUIGOboMWEjATHMUGtGIrqy4UAu2GoyOB719I7bNTHrG4YbCN04aqixD0IsAkw6mRMK4bqvWgHshAA14tYOmtWLC7GjGyx0ooAAjRnZfVdHMjsx9BGADOyGLl+xCn1KbIxVs+GIq0I2E9QCMB1YV+MGRn6k/ghWqxCDwYHMgyiBEKRyrEROogIc5+RxZkzsvGJkL4XrSWvseEWqBehboQ5UIs1I0OmcvHaHDHOpWAROHrMhQyVfbLjvy30sBEoajEqIsxgAOJNykWG1RSXC4WdkYXIIm92uUhdhBkZAKutU0rTFm19zF09cDH1kNmwPrWYFCvdSMwZmRB72NSijUDSi2HLlomb0UboU4uMUKVzbIRgIItxZFyOXjKH2CNU2QE7kIUICtKkW0B6QpQwB7YRukAGZvbmszMyG4GiFpNMm+A4w91j0mmoZigiNAnRJYj8lFi6YmPuoqUVl2JWxipUOyOz4U9EmAIZCz2AeFNGxj8SqoHMzshCCBinwFN4bSweejE2yn4MbfgPESYGh6X3ZhoRtCKyMvm90Mxt7CfIho0gIENZVQE2tWjDn4icNSNz2FQxOxAXoveiHchs2Ah2Rhaii4eN0EC4GtuCuqy5dQeUI9p6AIx3CWWxR2jmkTZshDhQ68QiMjE5Pet8Lxs2dOHdWwtp2s306vioCBodHzMCXnSIerjaT5ANG0EAFo13bCkQjuQ2tWjD34iYZWq1yMAGKaQnzAN2ILNhI0jITlq8npI2QgPxJnFHqNKKQGjmkTZs2LBhw2fEqV4y8bkdyGzYsGHDRqghzkRr24HMhg0bNmyEHOJNQqNQ7SED7EBmw4YNG0sUcaYszM7IbNiwYcNGyCHOLPYIYfWs3wPZ97//fSHp/PSnP+3vP2XDhg0bNjyALfZYAF577TX6v//7P9qwYYM//4wNGzZs2PBZ7GHXyGZgYGCArr/+evrtb39Lqamp/vozNmzYsGHDh0Zp2FbBWNjswxhq8Fsgu+WWW+jyyy+nCy+8cM6fGx0dpb6+PqcPGzZs2LARGFxzciFdt60opBui/ZJL/v3vf6fXX39dUIvz4fbbb6dvfvOb/ngbNmzYsGFjHiRER4iPUIb2jKy+vp5uvfVWuueeeygmZn4Lni996UvU29trfOD/27Bhw4YNGwvFsulp5d+vCQ899BBdffXVFB7uSFMnJyeFcjEsLExQieZ/cwWoxeTkZBHUkpKSdL41GzZs2LARQlhoPNCeT15wwQW0f/9+p+/deOONtGrVKvrCF74wZxCzYcOGDRs2PIX2QJaYmEjr1q1z+l58fDylp6fP+L4NGzZs2LDhK2xnDxs2bNiwEdIIiFTl+eefD8SfsWHDhg0bSxB2RmbDhg0bNkIadiCzYcOGDRshDTuQ2bBhw4aNkIYdyGzYsGHDRkjDcr4k3J9tey7asGHDxtJGn4oD8/l2WC6Q9ff3i9fCwsJgvxUbNmzYsGGRuACHj4BZVPmKqakpampqEo3VsLXyJZIjGMK70ba6csA+L7PDPjfuYZ+X2WGfG/+eF4QnBLG8vDxhcRgyGRnebEFBgbbfh5No32AzYZ+X2WGfG/ewz8vssM+N/87LXJkYwxZ72LBhw4aNkIYdyGzYsGHDRkhj0Qay6Oho+vrXvy5ebThgn5fZYZ8b97DPy+ywz401zovlxB42bNiwYcOGJ1i0GZkNGzZs2FgasAOZDRs2bNgIadiBzIYNGzZshDTsQGbDhg0bNkIadiCzYcOGDRshjUUZyH7xi19QSUkJxcTE0LZt22jXrl201PDiiy/SFVdcIaxdYPX10EMPOf07xKpf+9rXKDc3l2JjY+nCCy+k48eP02LH7bffTlu3bhUWaFlZWXTVVVfR0aNHnX5mZGSEbrnlFkpPT6eEhAR6xzveQa2trbTY8atf/Yo2bNhguDGcdtpp9Nhjj9FSPy+u+P73vy+eqU9/+tO01M/NN77xDXEuzB+rVq0K+HlZdIHsvvvuo8985jOih+H111+njRs30iWXXEJtbW20lDA4OCiOHUHdHe644w76+c9/Tr/+9a9p586dFB8fL84TbrzFjBdeeEE8WK+++io99dRTND4+ThdffLE4X4zbbruNHn74Ybr//vvFz8P78+1vfzstdsAaDov0nj17aPfu3XT++efTlVdeSQcPHlzS58WM1157jf7v//5PBHwzlvK5Wbt2LTU3NxsfL7/8cuDPy/QiwymnnDJ9yy23GF9PTk5O5+XlTd9+++3TSxW4zA8++KDx9dTU1HROTs70D3/4Q+N7PT0909HR0dN/+9vfppcS2traxPl54YUXjPMQGRk5ff/99xs/c/jwYfEzO3bsmF5qSE1Nnf7d735nn5fp6en+/v7p5cuXTz/11FPT55xzzvStt94qvr+Uz83Xv/716Y0bN7r9t0Cel0WVkY2NjYndJGgyswkxvt6xY0dQ35uVUF1dTS0tLU7nCcacoGGX2nnq7e0Vr2lpaeIV9w+yNPO5AVVSVFS0pM7N5OQk/f3vfxeZKihG+7yQyOQvv/xyp3MALPVzc/z4cVHCKCsro+uvv57q6uoCfl4s537vCzo6OsQDmJ2d7fR9fH3kyJGgvS+rAUEMcHee+N+WAjAyCHWOM844g9atWye+h+OPioqilJSUJXlu9u/fLwIXKGbUNB588EFas2YNvfnmm0v6vCCoo1QBatEVS/me2bZtG9199920cuVKQSt+85vfpLPOOosOHDgQ0POyqAKZDRue7rDxwJk5/aUOLEgIWshUH3jgAXr/+98vahtLGZipdeutt4qaKgRkNhy47LLLjM9RN0RgKy4upn/84x9CRBYoLCpqMSMjg8LDw2eoYvB1Tk5O0N6X1cDnYimfp0984hP03//+l5577jmn+Xc4flDUPT09S/LcYAddUVFBJ510klB4QjD0s5/9bEmfF1BkEItt2bKFIiIixAeCO8RS+BwZxlI9N65A9rVixQo6ceJEQO+ZsMX2EOIBfOaZZ5zoI3wNusSGRGlpqbiRzOcJE12hXlzs5wnaFwQxUGbPPvusOBdm4P6JjIx0OjeQ54P3X+znxh3w/IyOji7p83LBBRcIyhWZKn+cfPLJoh7Eny/Vc+OKgYEBqqysFG09Ab1nphcZ/v73vwv13d133z196NCh6Ztuumk6JSVluqWlZXopAQqrN954Q3zgMv/kJz8Rn9fW1op///73vy/Oy7///e/pffv2TV955ZXTpaWl08PDw9OLGTfffPN0cnLy9PPPPz/d3NxsfAwNDRk/87GPfWy6qKho+tlnn53evXv39GmnnSY+Fju++MUvCvVmdXW1uCfw9bJly6affPLJJX1e3MGsWlzK5+azn/2seJZwz7zyyivTF1544XRGRoZQAwfyvCy6QAbcdddd4uRFRUUJOf6rr746vdTw3HPPiQDm+vH+97/fkOB/9atfnc7OzhaB/4ILLpg+evTo9GKHu3OCjz/+8Y/GzyCYf/zjHxfS87i4uOmrr75aBLvFjg9+8IPTxcXF4rnJzMwU9wQHsaV8XhYSyJbquXnXu941nZubK+6Z/Px88fWJEycCfl7seWQ2bNiwYSOksahqZDZs2LBhY+nBDmQ2bNiwYSOkYQcyGzZs2LAR0rADmQ0bNmzYCGnYgcyGDRs2bIQ07EBmw4YNGzZCGnYgs2HDhg0bIQ07kNmwYcOGjZCGHchs2LBhw0ZIww5kNmzYsGEjpGEHMhs2bNiwQaGM/w8Zzh5FAlNWggAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 500x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Put everything in place for running the simulation\n",
+    "sim3.dispatch_constructor()\n",
+    "\n",
+    "try:\n",
+    "\n",
+    "    # Create an evaluator, run the simulation and obtain the results\n",
+    "    evaluator3 = sim3.dispatch(theta={\"delta\":0.9})\n",
+    "    evaluator3()\n",
+    "\n",
+    "    # Plot the results\n",
+    "    fig, ax = plt.subplots(figsize=(5, 4))\n",
+    "    data_res3 = evaluator3.results\n",
+    "    ax.plot(data_obs.time, data_obs.prey, ls=\"-\", color=\"tab:blue\", alpha=.5, label =\"observation data\")\n",
+    "    ax.plot(data_obs.time, data_obs.predator, ls=\"-\", color=\"tab:blue\", alpha=.5, label =\"observation data\")\n",
+    "    ax.plot(data_res3.time, data_res3.prey, color=\"black\", label =\"result\")\n",
+    "    ax.plot(data_res3.time, data_res3.predator, color=\"black\", label =\"result\")\n",
+    "    ax.legend()\n",
+    "\n",
+    "except ValueError as e:\n",
+    "\n",
+    "    # Print the error message\n",
+    "    print(\"An error occurred:\", type(e).__name__, \":\", e)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "9e3949d9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Jax 64 bit mode: False\n",
+      "Absolute tolerance: 1e-07\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Markus\\pymob\\pymob\\pymob\\inference\\numpyro_backend.py:552: UserWarning: Model is not rendered, because the graphviz executable is not found. Try search for 'graphviz executables not found' and the used OS. This should be an easy fix :-)\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "      Trace Shapes:      \n",
+      "       Param Sites:      \n",
+      "      Sample Sites:      \n",
+      "         delta dist     |\n",
+      "              value     |\n",
+      "    sigma_prey dist     |\n",
+      "              value     |\n",
+      "sigma_predator dist     |\n",
+      "              value     |\n",
+      "      prey_obs dist 101 |\n",
+      "              value 101 |\n",
+      "  predator_obs dist 101 |\n",
+      "              value 101 |\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "sample: 100%|██████████| 3000/3000 [00:20<00:00, 143.84it/s, 15 steps of size 4.32e-01. acc. prob=0.93]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "                      mean       std    median      5.0%     95.0%     n_eff     r_hat\n",
+      "           delta      0.90      0.00      0.90      0.89      0.90   2707.28      1.00\n",
+      "  sigma_predator      0.52      0.04      0.52      0.46      0.58   1255.02      1.00\n",
+      "      sigma_prey      0.44      0.03      0.43      0.39      0.49   1217.63      1.00\n",
+      "\n",
+      "Number of divergences: 0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 400x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create the inferer (NumPyro backend, NUTS kernel) and let it do its work\n",
+    "sim3.set_inferer(\"numpyro\")\n",
+    "sim3.inferer.config.inference_numpyro.kernel = \"nuts\"\n",
+    "sim3.inferer.run()\n",
+    "\n",
+    "# Plot the results\n",
+    "sim3.config.simulation.x_dimension = \"time\"\n",
+    "sim3.posterior_predictive_checks(pred_hdi_style={\"alpha\": 0.1})"
    ]
   }
  ],