diff --git a/README.md b/README.md
index 22c7ec82a..8cd863867 100644
--- a/README.md
+++ b/README.md
@@ -3,7 +3,7 @@
 FLORIS is a controls-focused wind farm simulation software incorporating
 steady-state engineering wake models into a performance-focused Python
 framework. It has been in active development at NREL since 2013 and the latest
-release is [FLORIS v.4.4.2](https://github.com/NREL/floris/releases/latest).
+release is [FLORIS v.4.5](https://github.com/NREL/floris/releases/latest).
 Online documentation is available at https://nrel.github.io/floris.
 
 The software is in active development and engagement with the development team
@@ -11,6 +11,16 @@ is highly encouraged. If you are interested in using FLORIS to conduct studies
 of a wind farm or extending FLORIS to include your own wake model, please join
 the conversation in [GitHub Discussions](https://github.com/NREL/floris/discussions/)!
 
+## WETO software
+
+FLORIS is primarily developed with the support from the U.S. Department of Energy and
+is part of the `WETO Software Stack <https://nrel.github.io/WETOStack>`_.
+For more information and other integrated modeling software, see:
+
+- [Portfolio Overview](https://nrel.github.io/WETOStack/portfolio_analysis/overview.html)
+- [Entry Guide](https://nrel.github.io/WETOStack/_static/entry_guide/index.html)
+- [Wind Farm Controls Workshop](https://www.youtube.com/watch?v=f-w6whxIBrA&list=PL6ksUtsZI1dwRXeWFCmJT6cEN1xijsHJz)
+
 ## Installation
 
 **If upgrading from a previous version, it is recommended to install FLORIS v4 into a new virtual environment**.
@@ -79,7 +89,7 @@ PACKAGE CONTENTS
     wind_data
 
 VERSION
-    4.4.2
+    4.5
 
 FILE
     ~/floris/floris/__init__.py
diff --git a/docs/_toc.yml b/docs/_toc.yml
index 2dd0f99e1..e7be3f2bd 100644
--- a/docs/_toc.yml
+++ b/docs/_toc.yml
@@ -14,14 +14,17 @@ parts:
     - file: intro_concepts
     - file: wind_data_user
     - file: floris_models
+    - file: input_reference_main
+    - file: turbine_models
+      sections:
+      - file: input_reference_turbine
+      - file: operation_models_user
+      - file: floating_wind_turbine
+      - file: multidimensional_wind_turbine
+      - file: turbine_library
     - file: advanced_concepts
     - file: heterogeneous_map
-    - file: floating_wind_turbine
-    - file: turbine_interaction
-    - file: operation_models_user
     - file: layout_optimization
-    - file: input_reference_main
-    - file: input_reference_turbine
     - file: examples
 
   - caption: Theory and Background
diff --git a/docs/api_docs.md b/docs/api_docs.md
index 2fb249f72..34c87bba6 100644
--- a/docs/api_docs.md
+++ b/docs/api_docs.md
@@ -1,11 +1,10 @@
 # API Documentation
 
-FLORIS is divided into two primary packages.
-{py:mod}`floris.simulation` is the core code that models the wind turbines
-and wind farms. It is low-level code that generally is not accessed
-by typical users. {py:mod}`floris.tools` is the set of analysis routines
-that define, drive, and post process a simulation. This is where
-more users will interface with the software.
+FLORIS is primarily divided into the {py:mod}`floris` package, which contains the user-level API,
+and {py:mod}`floris.core` is the core code that models the wind turbines and wind farms.
+Additionally, the {py:mod}`turbine_library` package contains turbine models that ship with FLORIS;
+and the {py:mod}`optimization` package contains high-level optimization routines that accept and
+work on instantiated `FlorisModel` objects.
 
 ```{eval-rst}
 .. autosummary::
diff --git a/docs/floating_wind_turbine.md b/docs/floating_wind_turbine.md
index c4dabe90e..37458d156 100644
--- a/docs/floating_wind_turbine.md
+++ b/docs/floating_wind_turbine.md
@@ -3,22 +3,14 @@
 
 The FLORIS wind turbine description includes a definition of the performance curves
 (`power` and `thrust_coefficient`) as a function of wind speed, and this lookup table is used
-directly in
-the calculation of power production for a steady-state atmospheric condition
+directly in the calculation of power production for a steady-state atmospheric condition
 (wind speed and wind direction). The power curve definition typically assumes a
-fixed-bottom wind turbine with no active or controllable tilt. However, floating
-wind turbines have additional rotational degrees of freedom including pitch which
+fixed-bottom wind turbine with a fixed shaft tilt. However, floating
+wind turbines have an additional rotational degrees of freedom in the platform pitch, which
 adds a tilt angle to the rotor. As the turbine tilts, its performance is affected
-similar to a yawed condition. The turbine is no longer operating on its defined
-performance curve, and corrections must be included to accurately predict the power
-production.
+because the turbine is no longer operating on its defined performance curve.
 
-Support for modeling this impact on a floating wind turbine were added in
-[PR#518](https://github.com/NREL/floris/pull/518/files) and allow for correcting the
-user-supplied performance curve for the average tilt. This is accomplished by including
-an additional input, `floating_tilt_table`, in the turbine definition which sets the
-steady tilt angle of the turbine based on wind speed. An interpolation is created and
-the tilt angle is computed for each turbine based on effective velocity. Taking into
-account the turbine rotor's built-in tilt, the absolute tilt change can then be used
-to correct the power and thrust coefficient.
-This tilt angle is then used directly in the selected wake models.
+FLORIS allows the user to correct for the tilt angle of the turbine as a function of wind speed.
+This is accomplished by including an additional input, `floating_tilt_table`, in the turbine definition that sets the steady tilt angle of the turbine based on wind speed. An interpolation is created and the tilt angle is computed for each turbine based on its rotor effective velocity. Taking into account the turbine rotor's built-in tilt, the absolute tilt is used to compute the power and thrust coefficient. To enable the use of the `floating_tilt_table`, the `correct_cp_ct_for_tilt` input on the turbine definition should be set to `True`.
+
+The tilt angle is then used directly in the selected wake models to compute wake effects of tilted turbines.
diff --git a/docs/heterogeneous_map.ipynb b/docs/heterogeneous_map.ipynb
index ab98409f7..81919a3e1 100644
--- a/docs/heterogeneous_map.ipynb
+++ b/docs/heterogeneous_map.ipynb
@@ -25,7 +25,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -56,7 +56,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -80,24 +80,49 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "HeterogeneousMap with 2 dimensions\n",
-      "Speeds-up defined for 5 points and\n",
-      "4 wind conditions\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Can print basic information about the map\n",
     "print(heterogeneous_map)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Interpolation\n",
+    "\n",
+    "By default the HeterogeneousMap uses linear interpolation to determine the speed up for a given wind speed and wind direction using `scipy.interpolate.LinearNDInterpolator`. This can be changed to nearest neighbor interpolation by setting the `interp_method` argument to `'nearest'`, which uses `scipy.interpolate.NearestNDInterpolator`. The default behavior is recovered by setting `interp_method` to `'linear'`.\n",
+    "\n",
+    "When `interp_method` is `'linear'`, any location outside of the convex hull of points defined by `x`, `y` and `z` are given a speed multiplier of 1.0 (that is, the freestream wind speed is applied) and an \"out of bounds\" warning is raised. When `interp_method` is `'nearest'`, points \"outside\" of the specified region still get assigned the speed multiplier of the nearest specified point, and \"out of bounds\" warning is raised."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "heterogeneous_map_nearest = HeterogeneousMap(\n",
+    "    x=np.array([0.0, 0.0, 250.0, 500.0, 500.0]),\n",
+    "    y=np.array([0.0, 500.0, 250.0, 0.0, 500.0]),\n",
+    "    speed_multipliers=np.array(\n",
+    "        [\n",
+    "            [1.0, 1.0, 1.0, 1.0, 1.0],\n",
+    "            [1.0, 1.0, 1.0, 1.0, 1.0],\n",
+    "            [1.5, 1.0, 1.25, 1.5, 1.0],\n",
+    "            [1.0, 1.5, 1.25, 1.0, 1.5],\n",
+    "        ]\n",
+    "    ),\n",
+    "    wind_directions=np.array([270.0, 270.0, 90.0, 90.0]),\n",
+    "    wind_speeds=np.array([5.0, 10.0, 5.0, 10.0]),\n",
+    "    interp_method=\"nearest\"\n",
+    ")\n",
+    "print(heterogeneous_map_nearest)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -114,30 +139,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0.5, 0.98, 'Heterogeneous speedup map for several directions and wind speeds')"
-      ]
-     },
-     "execution_count": 52,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1500x500 with 6 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Use the HeterogeneousMap object to plot the speedup map for 3 wd/ws combinations\n",
     "fig, axarr = plt.subplots(1, 3, sharex=True, sharey=True, figsize=(15, 5))\n",
@@ -180,32 +184,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\u001b[34mfloris.floris_model.FlorisModel\u001b[0m \u001b[1;30mWARNING\u001b[0m \u001b[33mDeleting stored wind_data information.\u001b[0m\n",
-      "\u001b[34mfloris.logging_manager.LoggingManager\u001b[0m \u001b[1;30mWARNING\u001b[0m \u001b[33mThe calculated flow field contains points outside of the the user-defined heterogeneous inflow bounds. For these points, the interpolated value has been filled with the freestream wind speed. If this is not the desired behavior, the user will need to expand the heterogeneous inflow bounds to fully cover the calculated flow field area.\u001b[0m\n",
-      "\u001b[34mfloris.floris_model.FlorisModel\u001b[0m \u001b[1;30mWARNING\u001b[0m \u001b[33mDeleting stored wind_data information.\u001b[0m\n",
-      "\u001b[34mfloris.logging_manager.LoggingManager\u001b[0m \u001b[1;30mWARNING\u001b[0m \u001b[33mThe calculated flow field contains points outside of the the user-defined heterogeneous inflow bounds. For these points, the interpolated value has been filled with the freestream wind speed. If this is not the desired behavior, the user will need to expand the heterogeneous inflow bounds to fully cover the calculated flow field area.\u001b[0m\n",
-      "\u001b[34mfloris.floris_model.FlorisModel\u001b[0m \u001b[1;30mWARNING\u001b[0m \u001b[33mDeleting stored wind_data information.\u001b[0m\n",
-      "\u001b[34mfloris.logging_manager.LoggingManager\u001b[0m \u001b[1;30mWARNING\u001b[0m \u001b[33mThe calculated flow field contains points outside of the the user-defined heterogeneous inflow bounds. For these points, the interpolated value has been filled with the freestream wind speed. If this is not the desired behavior, the user will need to expand the heterogeneous inflow bounds to fully cover the calculated flow field area.\u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x1000 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Initialize FlorisModel\n",
     "fmodel = FlorisModel(\"gch.yaml\")\n",
@@ -267,19 +248,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "HeterogeneousMap with 3 dimensions\n",
-      "Speeds-up defined for 12 points and\n",
-      "4 wind conditions\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Define a 3D heterogeneous map with two z-levels\n",
     "\n",
@@ -329,67 +300,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: xlabel='X (m)', ylabel='Y (m)'>"
-      ]
-     },
-     "execution_count": 55,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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fvx6CIEhXdHBmypQpEAQBr7/+eq3mzn2APMyRI0ewefNmiKIIQRCQlJSEuLg42fYaTeX2YKPRCFfnvaza1mQywWKxAAAUCgWUSqV0LRvbfTtr64xarZba17Vt9TlUn4fZbIbZLL8Bu+q4NbVVqVRQKBTNpq3FYoHJZJJtq1QqoVQqm01bURRhNBobpK1CoYBKpWrUtgBgMBgapK0gCFCr1XVq6+r92VhtgYb5jKhv24b4jKguKysLX3/9NQDrc3Lbbbehf//+suNS3ZSUlCAuLg4TJ07E6NGja2yv1+sRGhqK+fPnY/ny5S7bnj59Gk888QRuvPFG2TYbN27E999/X6fLWTEAeRCdTieFH8D6Ib9161Zs3brVaftWrVphzpw50u21a9fizJkzTtuq1Wo89dRT0u0NGzbg1KlTGDFiBAYOHCiV5+fn44svvsBDDz0klW3cuBHHjx+XnXdqaqr0Ybh582b89NNPsm2feOIJ+Pn5AQC2b9+Ow4cPS3V+fn5ITk5Gz549AdhfXHDnzp3IyMiQHXfq1KkICwsDAOzbtw979uyRbTt58mS0a9cOAPD999/jm2++kW07fvx4dOrUCQCQmZkpfeA6c99996Fbt24AgKNHj+KLL76QbXvnnXeiV69eAIATJ07g008/lW17++23SxcAPnXqFD766CPZtklJSdK/59mzZ/Hee+/Jtk1ISMD1118PwPpX2zvvvCPb9qabbsLQoUMBABcvXsSbb74p2zY+Ph7Dhw8HABQWFmLFihWyba+55hokJycDAEpLS/Hqq6/Kto2Li5P+SjQajVi8eLFs29jYWNx1113SbVdtr7rqKtx///3S7VdffVU2XHXs2BETJkyQbq9YsQKlpaVO20ZFRdm9j1atWoXCwkKnbUNDQ/Hoo49Kt99++21cvHjRadugoCC7v6rT0tJw/vx5p23r+xnx+++/O20LAAsXLpR+v1KfEXJEUcTmzZvRtWtXrzjD8ZWUlJSEpKQkt9t36tRJes+/++67su3MZjPGjh2LZ599Fvv27UNBQYFDm5ycHEyfPh3bt2+XPidqgwHIg/zzzz8u/zqrzmL5B/+cby/dNhqSAThPyaJYiqPnKtsW65Nx552PIzY2Vio7dOgQduzYAZPJhH2nuwIAikQfXCi5DkC07Dw++3MIFCrrasf5ojiXbVf/dhcUPtaVhrLLnQFESnUlJSXYsGED+vbti1tvvVX6K/dvfS5+Kt0HVy/n50/Mg/kv61/jfhdC4I82sm2f/PlllJ2xziH0TCtEIkC+7U//hu6sCWVGNdqdV6MbfGTbzj7yHi5mW/9abZevxNVQy7adeegT/PX7JwCA9gXA9ZC/kvS8Q1/iz1NfwWRUol2xBQmyLYEFP6Tj11+/BQBElJnh6mNr8fd7cOyoNVS2NZox0kXbFQcP4MGjmRBMAoLNZtzuou07hzLxaNYvAAA/iwWu/mZcd/gnzD78GwBAC9dtNx05jqcOZUNhBJQQMdpPvu2On37D8xmVf32OCZJvu+9oNl6dUtk2OdwIlczOA5kn/8LKSa9BWZGPbu5UBq3SedsTf+Qi/r5l0u2bOpfAV+YAnuyzf+Omka9Itwf2KoO/r/O2eXmFGJb4MhQG62utX38jAmS+8wsLSnHL9S9It/sMMCNI5q1RXmbAiLhnpNs9BivQOkJ+L4qknqmA3vpEXHWTD9p0kn+933HVbFgqFhi73OSP0O7y76O7O02Dqcz62GIS2iCyn4t/vCpEUcSlS5cYgNxQfZOhVquFVqu9onN47rnnEBYWhkmTJmHfvn0O9RaLBePGjcOcOXOkPxZri9cCq6WmvBaYTqdzsmRowUN3b0KAXxlKRMfldrXa+qlSZDHDZFJCFB2/SItF6ye0Sm3bxKFCh5C3EeR3i/UeRAN+y1+EXN3nUp/SKpsELGYFRIt13GKL46eyoDRDEIAiiw9Es+B8DuaKfkoLbAs7ollAkcn5p3yEbwzujXkGQZoQAMAlfT5W/7YIBYa/K8ar9mZVioAAFBl9AAsgWBznUGqxfkCLCsCWNwQLUGaU/+AuMavt2gpV3k16k30/iwIQBaDcpIJgARRO3nkGo9KuLWAd02KU/6IxmJQQK540QRShrDKuxWj/7WsWAFEQIBoFh7Y2QsUVoS0ALFXHBSAYnQcx0SRIbSGKdlFUUa2PbVyF0drWWT5QVGwdFAFYpPBnbauQ2aokGB3bSuNV23pnG1dREVSUcHwibH1Eseq4gFIQoTA6/9hUGAGLaN+2ss6+jwhrW6XBWq4QHMdUVuljqfKaVShEKeA4zMFgcWhbOQfHza4Ws1DRzwxFlde+jWAwVWlbpVwBKIwym0UNRinQ2NoKtpewXu84B9tzrddXtLWfhFilj6XKcyIoARgdXxAafxX6T+1iN44gCJgxY0aTfG5fqWuBJWx9BCq/ugcVU4ke39z6lkP5woULsWjRohr7C4KAjRs3utxfp6qhQ4eib9++Dvvu7N+/H/feey+ysrLQtm1bTJgwAQUFBXb7Fy1evBjffvsttm/fDkEQ0KlTJ8yYMaPGfYqq4gqQBwkMDERSUlKVzSwWJA3JgDboHxgAp+sJRVU+sVQq+w+/Ilvwgf2HWHjrFVL4MVtKcTw/BQXl+6BUW1d8qlMoLSiCNagoFI4fsEUWH9i+XwSlCKHKl01RRfARVPYf5kVmH6flgDXc/FH+F1aeegoPd16AUJ8ohGjDMPmqp/HKyedRYi4GVPZfJkXGKvNWAGKVL4VS23kzqn0Tl9oCjJNvaCkUVfmcFhXWh1ku06/cpLJrW/WZsgWf6n1MJqU05+pMtj5V5yAIMAlVgk+1fmKVMGJrayPYQpazjGNSWOdb/cvRNkDVckGACZXBp3rWVBirTEsQ7J4HW+hwtneUwmB95VSvs4UY+39x67hy49nNAYC5ygOwjed0DkaxYg72D8q24lP91WoWBSn4OPQxiHbByy44VYQii5N/DFvwqV4nV26xCFAYzC7GM1dpW1luCz7Oop6tzuHdqXe+aVC0AGKZY/CR6qsEHNECiJaKzfzlrvvI/fVeftmIU19fQNdbI6VN5bfddhtXf9x07tw5u+fqSq7+FBUVYdy4cXj77bfRtm1bp20yMzOxYsUKHDlyxG5XiNpiAPIwffv2lQLQ2Ls+Qetg50c3VA0+DnWi8zX5ArMPOgRNQ5i/dQOGRdTjl/yHUVj+vdPgA1QGGOdzkF/GlutnCz7OVF/VKTT+g9V/LMSULs8i1CcKYT7t8HDnx7Hi9yWwVHx92QWfakplThhWapJf8XG1GlQu069q8KnOYHT+byEFH2d1Mn0AxxUfG1Fm5QaoEnyc1jnvJ5jkx6u+4lNZLtvFYYXGrk5mxacu47ns47LO+Vetsi59DPKL7q7qXK34yPeR/xyQq6u64uN2nUzwsda5F3zsymsIPu7453c9rqr4cpw2bRratJHf7E32AgMDmyws/vHHHzh9+jRGjqzc6G7b0V2lUuHkyZPYt28f8vPz0aFDB6mN2WzG7Nmz8frrr+P06dNu3RcDkAfz93PcubKuwQcAWvsOQcfWMwAAomjBifzH8VdZFuBkv5amDD5VXdCXYcWppZjTfRGC1MG4KqAn7mh3L9JOfybbh8HHisHHnToGH5d1zTD4CE5WKwIC5Pfjo+alR48eOHr0qF3Z/PnzUVRUhBUrViA6Ohrjxo1DQoL93o6JiYkYN24cHnzwQbfviwGohahP8AEAtSIE3dpW7mR58vJynCnd7zhWMwk+VesuGf7GW38sx6xuz0ClUOHm8CQcvnQCx3T2R50w+Fgx+LhTx+Djss5Dgg81vuLiYpw6dUq6nZ2djaysLISEhKBDhw5ITU1FTk4O3n//falNVlaW1PfixYvIysqCRqNBbGwsfHx80Lt3b7v7CA4OBgCpvE2bNg4remq1GhEREejevbvbc2cA8nD1DT42XdosgEZpfUHlle7Gn4Vr7MdqhsGnqqOFOfjo7CcY1+k+AMBDXSbgyZ+eQblFz+BTgcHHnToGH5d1DD5UzeHDhzFs2DDp9qxZswBYTxGSlpaGCxcu4OzZs3Z9+vXrJ/2emZmJdevWoWPHjm5vumooDEAerMhihtrJ92Btgg8ABPtcj1C/2wAABnMBjl5cUDlWMw8+VffxSc/bhX6t49A7KBYhmhAkR43CB2c2OvRh8Klax+BjrWPwcVnnYcFH8NFCUNV951hy39ChQ12eniUtLc2hrLYHnzsbo7q6hCcGIA8jCAJCQ0NhMJ5E9Z3faxt8rH1aIa515cnNTlxaCoPlkkcFHxsRItZkf4DFVz8HjUKN5Mhh2Ja7Bxf1lwAw+NjXMfhY6xh8XNZ5YPAhchcDkIdRq9V49NFHcfqvyhME1i34WOsi/G5BoNa6zbRAfwy/Fn4DQCbENNPgY1Nq1uB0aSG+Or8TY9qPgFqhwp3tk7Ds5AbZPgw+FeUMPgAYfCQMPh6pzKiGysVnWk1MRvnXXkvEAOSh5EIP4F7wsekSNEn6Pevv1XB21g9PCD5VfZGTjhERQ+CnaoWbQgfh3extuGywP10Ag09FOYMPAAYfSQsKPqJSg58+uQwAUC+oeyiglosBqAWpTfABgGBtHIK01ktdXCo/jryyg/Z9PCz42FzUm/DV+e9wb4dboFaokBx5PT48Yz13EoNPRTmDDwAGH0kLCj5V+5Vdtj5H9TlZHrVc8p+czdySJUukU5vblJeXIyUlBW3atIG/vz/GjBmDvLw8u35nz55FcnIyWrVqhbCwMMyZM8flVa6bG6PRiP/85z9I//xO6Uu2wOwjG36KRB+n4afI7IsI/3uk278XflxZZ/GRDT9FZl+n4afI7CMbforNWtmAI1dXZPSRDT+lZo3T8FNqUkv7+Ww+vw9m0frhlxA+EOVGjWz4KTepnYafcpNKNvwYjEqn4cdkUsqGH5NRKRt+LEal0/AjGgXZ8CMYFbLhRzAKTsOPYBJkw4/CKDgNPwqjfLhQmFyEFYPz8FOX8Vz2cVnn/LIVSqN8+JHtYxBlA46rOoXB4jTkyJVb68yyAUeuTjCYZAOObJ3eKB9+9HrZ8CPq9U6DjFiulw0/cn0c5qrVyoYfwUcrG35c9SNyxiNXgA4dOoS33noLV199tV35zJkzsWXLFnzyyScICgrCtGnTMHr0aHz33XcArGeKTE5ORkREBA4cOIALFy7ggQcegFqtxksvvdQUD6XWRFGsuAp0axSafKEUnH/g1XTmZgVUaO83FABgtBTjXPFOj13xcbZz8z+GQhy5fBLXhsQi3CcEvYM64mjhabs2XPGx4opPDX244iNp7is+duW+WrTrY/3MMJvNUCrl34fknTwuABUXF2Ps2LF4++238cILlVcxLiwsxP/+9z+sW7cO//rXvwAAa9asQc+ePfH999/juuuuw44dO3D8+HF88803CA8PR9++ffH888/jySefxKJFi6DRyFyK2YO4e8mKUN8B0Citpzo/U3wABWbnX4KeFnxsyoxqpF/4CdeGWDfxxbftJQUgBh8rBp8a+jD4SDwp+KCiXFAA7ftaf2cAImc8bhNYSkoKkpOTHU6DnZmZCaPRaFfeo0cPdOjQARkZGQCAjIwM9OnTB+Hh4VKbxMRE6HQ6/PLLL1fmATQSV5u6nIWY8FYDpd/PFn/ndr/mtqmrujKjWtrUdfCfX2ERrV86g9p056auCtzUVUMfbuqSeNSmLq1WCj9E7vCoFaD169fjyJEjOHTokENdbm4uNBqNdMpsm/DwcOTm5kptqoYfW72tzhm9Xg99lTezTuf84qNNpa4XKW3jM0C6fb7sSI39PGHFp7pCYwlOFZ1Ht8D2iPGPgJ9KixJT5b8lV3xs5bJduOLjRh1XfFz3qa6xVnzIurqtcvE5WROTiYfBN0vnzp3D448/jvT0dPj4yH95NrTFixfj2WefvWL3VxtFohZK2H9YunMCQwFKhGi7AgAKDedQbi5oUcHHptykxk8F59AtsD0UggLdA9vhyKU/GXykctkuDD5u1DH4uO5T3RUNPlo1wC1eVAOPCUCZmZnIz89H//79pTKz2Yy9e/di5cqV2L59OwwGAwoKCuxWgfLy8hAREQEAiIiIwA8//GA3ru0oMVub6lJTU6VrmwDWFaDo6OiGelgNpjZnbg5UR0GlsH545JVny27qklPX4GMxW5CfdQFl/5TCt00rhPUNh0KpkIJPoMof/xd1M64K6ITlv63B+bIy2ftx93D2U0UXpN+jfSNwwHTWWRcGH1sdg0+NdQw+rvtUd8WDD5GbPCYA3XzzzTh69Khd2YMPPogePXrgySefRHR0NNRqNXbu3IkxY8YAAE6ePImzZ88iPj4eABAfH48XX3wR+fn5CAsLAwCkp6cjMDAQsbGxTu9Xq9VC24yWWAVBQFBQEMpM5wGhbtfqClBXnkW6wJBj36eRVnzOfnsah5d/j9L8EqnON9QPV8+4AT0Trsb/Rd2MWyOHQa1QQyEICNNG43zZbw7j1fY8PmdKLkq/R7dq41DP4FNRx+BTYx2Dj+s+1TH4UHPnMQEoICAAvXv3tivz8/NDmzZtpPJJkyZh1qxZCAkJQWBgIKZPn474+Hhcd911AIDhw4cjNjYW48aNw9KlS5Gbm4v58+cjJSWlWYUcV9RqNWbMmIGPT12LUjjfZFTT4exKZTvpts6YX1HeeJu6zn57GntTdzq08RN9MUoxFDP6zYRapYZSqAwC/Vv3QFttsHTbaJYPHXJ1RosSrTV+0u22PgHS7ww+FXUMPjXWMfi47lNdcwg+okYFFyfLJwLgQQHIHcuXL4dCocCYMWOg1+uRmJiI//znP1K9UqnE5s2bMXXqVMTHx8PPzw/jx4/Hc88914SzbjjunsdHq/CXfr9sLHN5VJccd/fxsZgtOLz8e7uytm3bYvbs2Xj88cehVquhUtm/DE0WM+6Kvln2vuuqX+sODD62OgafGusYfFz3qa65BB8bixn4+Vvr86d6pkV91VED8ehXxe7du+1u+/j4YNWqVVi1apVsn44dO2Lr1q2NPLMrq7YnMFQrKtsbLeUO9Q25c3N+Vp7dZq+pU6di2bJlToOPjUrROH+6hfsGOZQx+FSpY/Cxjsfg47JPdc0t+FRVYr0UGBQKjzvjC10BHh2AvJHRaERaWhould+AkJuPQFA5/1B2dVSX3lL5z171a6AxjuoqvGj/rTpkyBD4+vrCYmnawy0ZfKrUMfhYx2PwcdmnuuYcfGqqIwIYgDyOKIo4f/48gGCIAKp/FbpzOLulyqHzSkHdqIez+7RpZVc3btw4pKenY9GiRYiOjobFYrmif50x+FSpY/Cxjsfg47JPdZ4SfAQBiIyxlnnLmaD1ZhVMLk7zUROzWf513RIxALUQtTmPT7m5WPpdEBw3CwENdx6ftnGR0Ib6Q3/Rep8mkwnvvvsu3n//fTzwwAOVQUgUoahyxebfi3JQaHDcPAcAoigfEizV6tQKJfq36QAA2HXe8agygMGnPuMx+NRcx+DTNCs+ggLoVHFwr7cEIKodBiAPV5cTGP5jKJV+D1SH2NU1xgkMez42BFnP2O93VTUIzfv0NUxPHIcwn2CIoghBELDi169x+NIfdn3qcgLD7v7tsDHhIQBAfnmR/RwYfOo8HoNPzXUMPtzURc0bXzkerNjs67APkDuHs18y5EtlbTThdnXVNcSZmyOGdEXf52/FiTf2SitBAKANDUCXR4dhn/8ZHDjwEv4VNgiTu96MNpoA6IyVIa0+Z27u6F8Z8M6XFlrrGHzqPB6DT811DD5NH3wsGiWgFAF416UdqHYYgFqI2pzH52995dmRQ306yl6kVE5dLlkRFN8TgwZ2R+HRHBguFUMT4o+gPu0gKBXSCQy/yjmMreePIETjj4t6XYNcsqJrYKj0+2+XL7m8SKkzDD5u9GHwAcDg02yCD5GbGIA8XF1OYKi3lCG/PBdhPhFo59sBKkEFk2j9gG7Ma3UJSgWC+1ZeRqTcpAaqfS+YRQvOlZRC7qVZ2/P4xIVUnvTx+OU8h3oGH9fjMfjUXMfgw+BDnokByAO1atUKpaZCFJl9nB4G785RXX+W/I4wnwhoFBp08uuCHy+fke3TGBcpdV7esBcpFcxq9G3THgCQX1aMv0oKpToGH9fjMfjUXMfg03yDj0WjBBTcBEauMQB5GI1Ggzlz5uC5Y//nUFebw9lPFh3HdW1uBABc5T/AaQDy1OBj28wVHx6NALX1cWfknQbA4FPTeAw+Ndcx+DTz4OPFDEYllC72cayJuR59PREDUAtQl/P4/PDPSYzraIFCUOCa1v2x4dznUp2nBx+bEe17SL/vPPun0/DD4ONGHwYfAAw+nhZ8LBbgpyzrWHJnnSfvxleFB6vfCQx1+LXoN8QG9kCkbwS6+nfGz4V/Oe3jacEHADQKJZI79rTO0WTEzr/sD6ln8HGjD4MPAAYfTws+VesuVxxMykthkDMMQB7GaDRi7dq1MBX3AAafrjjUs1JtDmffe/E7xAZaV0luCr0ZPxe+Z1fvicHH5tZ2vRCitZ6Fese531FisqYGBh83+jD4AGDw8dzgw7BD7mEA8jCiKOLMmTNQws/uQl51OY/Pnos/Y2yHEgSo/XBD22uw/txXuKi/5NHBx2JUQgDwUOxAqey9X48w+LjTh8EHAINPSwk+giAiMtz6b8wzQZMzDEAern4nMDRiW+4e3BV9K1QKJW6PuhVv/P6x0z6eEHxsRnTojp4hYQCAH/PPI/P8ead9GHzcqWPwcVnH4OO0uDms+IhaBbp1tj5nDEDkDAOQBys2awGV/RdHbc/js+XCt0iO/BdaqXwwPOI6fJGzF2dKK0+U6EnBBwC0CiXmDbhJur38yHcOfRh83Klj8HFZx+DjtLg5BB9buQLyry0igAGoxajrCQxLTQZ8fC4dD8aMhFJQYPpVd2POT2+g1Cj/0miOwcfmkdjr0DGgNQAg48JZ7MnJluoYfNypY/BxWcfg47S4OQUfb2YyKSC6+EytidnkXc8hA5CHa4gzN2/K2YNbwgehfasw9ArqjFsjhuHTc/sc+jXn4CMaBfRuE47pfeOtY1gsWJDxDQAGH/fqGHxc1jH4OC1m8CFPxgDkwYqNPg6bwIDan8fHYDHilROfYsWARwEAj3RNxtGC0zhZdA5A8w8+ABCg1uDfw0ZCrbC2e/Pn7/F7/iUIcP7hyOBjq2PwcVnH4OO0uLkHH7NGgCj/ViYCAJlvB2rW1Go1LErHL4dSs8Zp+LFu6nL+QVZmVKPMqMZPBX/iozPfWsdXqPDC1RPgrwxxGn7KTSrZ8GMwKp2GH5NJKRt+TEaly4uUyp3A0BZ+lIKAFUNHonOQ9crvRy/m4o0fvnc6nmAUnIYfwSTIhh+FUXAafhRG+XChMLkIKwbn4acu47ns47JOdBpklEb58CPbxyDKBhxXdQqDxWnIkSu31pllA45cnWAwyQYc2Tq9UT786PWy4UfU650GGbFcLxt+5Po4zFWrlQ0xgo9WNvzI9tNq5cOPVu00/IgalWzAcVVn0Sidn6xQptxap3AacuTKAWvwMWuYfK6kvXv3YuTIkYiKioIgCNi0aZPL9hcuXMD999+Pbt26QaFQYMaMGQ5t3n77bdx4441o3bo1WrdujYSEBPzwww92bURRxIIFCxAZGQlfX18kJCTg999/r9XcGYA8jEajwVNPPYU/b8mBWLH6U5/gU9U7f3yNowXWfWZCfYKwfMCDCFT7SvXNLfjYvDB4OG7u0AUAcLm8DFN3fAWjxf4LlMHHVsfg47KOwYfBh2qlpKQEcXFxWLVqlVvt9Xo9QkNDMX/+fMTFxTlts3v3btx333349ttvkZGRgejoaAwfPhw5OTlSm6VLl+KNN97A6tWrcfDgQfj5+SExMRHl5eVuz52bwDyU3GYuoO4nMCwxKjH3x7X436BHEdUqBFcFRGL5gAcx9eAH0BnLnPa50pu6qhIAPD/4Ftzfw/omMpjNeHTHl/irqPKip9zUZavjpi6XddzU5bTYEzZ1yTGpBWSes+4jyUthNJ6kpCQkJSW53b5Tp05YsWIFAODdd9912mbt2rV2t9955x189tln2LlzJx544AGIoojXX38d8+fPx+233w4AeP/99xEeHo5Nmzbh3nvvdWsuXAFqQWq74mNTblJLm7ouG0rweOYa/KMvAgD0CorGu/GTEO4TaNenKVd8AOulLl4fchvG9exn7S+KmL1rKzLOW/db4oqPrY4rPi7ruOLT4lZ8bHUiBFwsUeFiiYqXwvBwpaWlMBqNCAmx7uaQnZ2N3NxcJCQkSG2CgoIwaNAgZGRkuD2ux7wqFi9ejGuvvRYBAQEICwvDqFGjcPLkSbs25eXlSElJQZs2beDv748xY8YgLy/Prs3Zs2eRnJyMVq1aISwsDHPmzIHJ5OLP92bGZDJh3bp16PRjMISK74CGCD5V/a4rwMPfp+HvcmsI6hoQjnU3TEW/1h2bPPgAQKQmEB8l3YtRV8UCAMwWC2bt2oqv/jjJ4CPVMfi4rGPwabHBh+pPp9PZ/ejdXLVsLE8++SSioqKkwJObmwsACA8Pt2sXHh4u1bnDYwLQnj17kJKSgu+//x7p6ekwGo0YPnw4SkpKpDYzZ87EV199hU8++QR79uzB+fPnMXr0aKnebDYjOTkZBoMBBw4cwHvvvYe0tDQsWLCgKR5SnVgsFvz+++8I/FuLsgYOPlX38fmjOB8TMt7GuZJLAIA2Wn+8Ez8RKT2GQiVUvmyuZPARjAqMaN8dW+58ANdEtKt4nEZM2fEFvjj+K4MPGHxqrGPw8ZrgI6qByBATIkNMMJvlN6W2JOaKz9y6/pgrPpOjo6MRFBQk/SxevLjJHtOSJUuwfv16bNy4ET4+8qd9qQuP2TC6bds2u9tpaWkICwtDZmYmhgwZgsLCQvzvf//DunXr8K9//QsAsGbNGvTs2RPff/89rrvuOuzYsQPHjx/HN998g/DwcPTt2xfPP/88nnzySSxatAgajfx+NZ6iIc/c/FfpZdy9+228du1diA/rDKWgQEqPobglsicW/fg1Dv99zmm/htrHx0YwKtDOPxBPDx2KW7t0q5xfkQ6Pbv0SR/PzHPtwHx8A3MdHwn18nBZ78j4+cnUWte3oUBFxYdYdYnkpjNo5d+4cAgMrd3vQunptNaJXX30VS5YswTfffIOrr75aKo+IiAAA5OXlITIyUirPy8tD37593R7fY1aAqisstO7oatsmmJmZCaPRaLdNsEePHujQoYO0TTAjIwN9+vSxWzZLTEyETqfDL7/8cgVn3/Dqu+JTnW1TV6GxDA9nfIgVx3fBVHFkVbegcKwbOgH/GXw3egZHSH0aY8UnTOWPp+OHYue9D9qFnx1/nsLI9R84hB+u+FhxxacCV3y8ZsXHohak8EP1ExgYaPfTFAFo6dKleP7557Ft2zZcc801dnUxMTGIiIjAzp07pTKdToeDBw8iPj7e7fvwmBWgqiwWC2bMmIHrr78evXv3BmDdJqjRaBAcHGzXtuo2wdzcXKfbDG11zuj1ervtnzqdrqEeRoO4EtfqMosWrDr+HXaf/wPP9U9Gn5AoAEBCVHckRHXHgdzTWP9HFnb8dRIGi/0XV11WfBRGBfpHROH+nnG4rUt3aKscwfF3aSle3L8bm06esOvDFR8rrvhU4IqP0+KWvOJDTaO4uBinTp2SbmdnZyMrKwshISHo0KEDUlNTkZOTg/fff19qk5WVJfW9ePEisrKyoNFoEBtr3a/z5ZdfxoIFC6z7u3bqJH0/+/v7w9/fH4IgYMaMGXjhhRdw1VVXISYmBs888wyioqIwatQot+fukQEoJSUFx44dw/79+xv9vhYvXoxnn3220e+nLsqMasDJ509jnbn5l4Jc3LXrXYzpFIfpsTchopV1iXRwRCcMjuiEIqMee87/gT0X/sD353NwrrjQYTy54BPi44sBIe1xY3Qn3NyxC9oH2B91Vm4y4oOff8K/D2WgyFCZQBh8rBh8KjD4OC32tuBjVsN6ngxqdIcPH8awYcOk27NmzQIAjB8/Hmlpabhw4QLOnj1r16dfv37S75mZmVi3bh06duyI06dPAwDefPNNGAwG3HnnnXb9Fi5ciEWLFgEA5s6di5KSEjz88MMoKCjADTfcgG3bttVqPyGPC0DTpk3D5s2bsXfvXrRv314qj4iIgMFgQEFBgd0qUF5enrS9MCIiwuFskrajxGxtqktNTZX+QQHrClB0dHRDPZwGdSUuWWGBiI9+P4rP/zyBMTF9MLnHIHQKsG6GDFBrcVvHWNzW0ZriC/Xl+K3wb/xVXIj84hIUG/XQm81QKRRopVKjjW8rtPcPQkxgiEPgsSkoL8OG48fw9o+H8XdpqVTO4GPF4FOBwcdpsVcGH7qihg4dClGU/9xIS0tzKHPVHoAUhFwRBAHPPfccnnvuuRrbyvGYACSKIqZPn46NGzdi9+7diImJsasfMGAA1Go1du7ciTFjxgAATp48ibNnz0rbBOPj4/Hiiy8iPz8fYWFhAID09HQEBgZKS2/VabXaJtsBzF1Nca0uvdmEdad+xEenfsSgsI4Y3fFqJER3RbC28szRQVofXBvWHteGtXc2nCyj2Yz9587gq99+xdd//I7yKqcpYPCxYvCpwODjtJjBh6hmHhOAUlJSsG7dOnzxxRcICAiQtgkGBQXB19cXQUFBmDRpEmbNmoWQkBAEBgZi+vTpiI+Px3XXXQcAGD58OGJjYzFu3DgsXboUubm5mD9/PlJSUpp9yLHRaDRYuHAhrtueaj3O08n3wpW8SKnZqMSBnL9wIOcvqAQF+odGYWB4NAa0bYfurUMR5e98ZaeqQn05Tl76Gz/n5+KHc38h469zKDbapw8GHysGnwoMPk6LGXzc69dSWUxKwMXntVv9vYjHBKA333wTgHW5rao1a9ZgwoQJAIDly5dDoVBgzJgx0Ov1SExMxH/+8x+prVKpxObNmzF16lTEx8fDz88P48ePr9cSWnPS1FdnN4kWHMzJwcEq12tppVIj0i8AbX394CdooVEqYbKYUW4y4VJ5GXJLinG5vIyXrJDqGHxc1jH4OC1m8HHsJ4giDl22rkjzUhjkjMe8KmraZggAPj4+WLVqlcuLsnXs2BFbt25tyKk1uaYOPoD8zs2lJiP+/LsAf6LAab1gFCA42VuRwceKwacCg4/TYgYf+X4iBJwvt3bgpTDIGY8JQGRlMpmwceNG9M1V44dOIiwy7+umDj6A9Tw+8nVc8bHWMfi4rGPwcVrM4FNzPwv3C6IaMAB5GIvFguPHjyMSSggdHb80GHwq6hh8aqxj8HHdpzoGH9flQPMKPgJERGqsry+LxcJVIHLAANRCMPhU1DH41FjH4OO6T3UMPq7LgeYVfGwUAAYFlgGwrpy3hEsdUcNiAPJwDD4VdQw+NdYx+LjuUx2Dj+tyoHkGHyJ3MQB5MINR6XAmaAafKnUMPtbxGHxc9qmOwcd1OdD8g49FzRNBU80YgFoIBp8qdQw+1vEYfFz2qY7Bx3U54BnBx5uJRgGiqu7Rz9XnfkvEAOThGHyq1DH4WMdj8HHZpzoGH9flAIMPtUwMQB7MZFZY9/SrXs7gU+fxGHxqrmPwYfBxVdccgo9FxU1gVDMGIA+jVquRmpqKuE1LYK72Dmfwqft4DD411zH4MPi4qmsuwYfIXXy5eBhBEKDRaGBWVH5oMPjUfTwGn5rrGHwYfFzVNdfgYwHwg94HgPUySETV1SoAnThxAuvXr8e+fftw5swZlJaWIjQ0FP369UNiYiLGjBnjMRcVbQkYfOo+HoNPzXUMPgw+ruqaa/CpnIOAbFjP/cMARM64FYCOHDmCuXPnYv/+/bj++usxaNAg3HHHHfD19cWlS5dw7NgxPP3005g+fTrmzp2LGTNmMAg1EpPJhM2bN2PQOREZYSIsCvsPEwYf1+Mx+NRcx+DD4OOqrrkHH+4QTe5yKwCNGTMGc+bMwaefforg4GDZdhkZGVixYgWWLVuGp556qqHmSFVYLBb89NNP6ArgYJh1mRdg8KlpPAafmusYfBh8XNV5WvARICJC8K5LYQhGBQRV3R+nq++ElsitAPTbb79Bra45VsfHxyM+Ph5Go4sPRWpQDD6ux2PwqbmOwYfBx1WdpwUfGwWAIWpeCoPkuRWA3Ak/9WlPdWMxKh0Og2fwcaMPgw8ABh8GH9d1nhp8AMCi4WHwVLM6HQV26NAhfPvtt8jPz4fFYv+h/NprrzXIxKh2GHzc6MPgA4DBh8HHdZ2nBx8id9U6AL300kuYP38+unfvjvDwcAhC5Yu+6u90ZTD4uNGHwQcAgw+Dj+s6Bh/yNrUOQCtWrMC7776LCRMmNMJ0qDZEo+CwCQxg8HGvjsHHZR2Dj9NiBp+a+zWH4GNRAYIIQP7tQlT7AKRQKHD99dc3xlyonhh83Klj8HFZx+DjtJjBp+Z+zSX4ELmr1i+XmTNnYtWqVXj99dcbYTpUE7VajSeeeAL9166ELbsw+LhTx+Djso7Bx2kxg0/N/Rh8mg/BKECox9Xg5b4vWqpav2yeeOIJJCcno0uXLoiNjXU44uvzzz9vsMmRI0EQ4Ofnh3KlwODjVh2Dj8s6Bh+nxQw+Nfdr7sHHpAYOKngpDJJX6wD02GOP4dtvv8WwYcPQpk0b7vjcROTCD4OPrY7Bx2Udg4/TYgafmvs19+BTOZ6A31S8FAbJq3UAeu+99/DZZ58hOTm5MeZzxaxatQqvvPIKcnNzERcXh3//+98YOHBgU0+rRiaTCdu3b8d1Oj1+CNDAUhFAGXxsdQw+LusYfJwWM/jU3M9zgg+Re2odgEJCQtClS5fGmMsV8/HHH2PWrFlYvXo1Bg0ahNdffx2JiYk4efIkwsLCmnp6LlksFhw+fBg9ARwO0MgeBs/gY8XgU4HBx2kxg0/N/Tw1+IgqC8LM1veQt1wKg2qn1q+IRYsWYeHChSgtLW2M+VwRr732Gh566CE8+OCDiI2NxerVq9GqVSu8++67TT21WnG26iOYBNnwozAKTsOPwigfLhQmF2HF4Dz81GU8l31c1olOg4zSKB9+ZPsYRNmA46pOYbA4DTly5dY6s2zAkasTDCbZgCNbpzfKhx+9Xjb8iHq90yAjlutlw49cH4e5arWyIUbw0cqGH9l+Wq18+NGqnYYfUaOSDTiu6iwapdOQI1durVM4DTly5YA13MgFHLk6i1qQDTFmtXz4ketnUbsIODJ1FpV8WHE5nsZ5+KnLeBa1CItahBLAiNIyjCgtg8nk4i848lq1XgF644038McffyA8PBydOnVy2An6yJEjDTa5xmAwGJCZmYnU1FSpTKFQICEhARkZGU04s/rhio8VV3wqcMXHaTFXfGru56krPha1/HuVyJlaB6BRo0Y1wjSunL///htmsxnh4eF25eHh4fj1118d2uv1euirfMDrdLpGn2NtMPhYMfhUYPBxWszgU3M/Bh/yNrUOQAsXLmyMeTRbixcvxrPPPtvU03BKMAlOr/jH4FNDHwYfCYOPcww+ruuae/ARVSJE0fuCkWCW3wXC3f7exK0AJIpiizncvW3btlAqlcjLy7Mrz8vLQ0REhEP71NRUzJo1S7qt0+kQHR3d6POsCwafGvow+EgYfJxj8HFd5wnBh8hdbu0E3atXL6xfvx4Gg8w3YoXff/8dU6dOxZIlSxpkco1Bo9FgwIAB2Llzp1RmsViwc+dOxMfHO7TXarUIDAy0+2luuHNzDX24c7OEOzdz52ZP37nZGVElMvxQrbkVgP7973/j1VdfRUREBO655x688sorWLt2LT777DO88847mDVrFgYOHIi+ffsiMDAQU6dObex518usWbPw9ttv47333sOJEycwdepUlJSU4MEHH2zqqdVIrVbj8ccfx+daf1gYfFz3YfCRMPgw+Hhb8BG5b9AVsXfvXowcORJRUVEQBAGbNm2qsc/u3bvRv39/aLVadO3aFWlpaXb1RUVFmDFjBjp27AhfX18MHjwYhw4dchjnxIkT+L//+z8EBQXBz88P1157Lc6ePev23N3aBHbzzTfj8OHD2L9/Pz7++GOsXbsWZ86cQVlZGdq2bYt+/frhgQcewNixY9G6dWu377yp3HPPPbh48SIWLFiA3Nxc9O3bF9u2bXPYMbo5EgQBwcHBKHFyTgtu6qrow01dEm7qco6bulzXefKmLlvwsYjAIX/r4DwTdOMpKSlBXFwcJk6ciNGjR9fYPjs7G8nJyZgyZQrWrl2LnTt3YvLkyYiMjERiYiIAYPLkyTh27Bg++OADREVF4cMPP0RCQgKOHz+Odu3aAQD++OMP3HDDDZg0aRKeffZZBAYG4pdffoGPj4/bcxdEb9xTrB50Oh2CgoJQWFjYpJvDui5dLv3O4FPRh8FHwuDjHIOP67qWEHycOT3lCfmJNLIr8Z1hu4+OL70IRS0CQHWW8nKceerpOs9VEARs3LjR5dHiTz75JLZs2YJjx45JZffeey8KCgqwbds2lJWVISAgAF988YXdFScGDBiApKQkvPDCC1IftVqNDz74oNbztOGpMT2M2WzGjh070N9YDpVB5KYucFNXVdzUxU1d3ripy1n4EdUW6YdqR6fT2f3o3fzjyB0ZGRlISEiwK0tMTJTOw2cymWA2mx1Wcnx9fbF//34A1v12t2zZgm7duiExMRFhYWEYNGiQW5vfqmIA8jBmsxkZGRnoZTI4OwKewceNOgYf130c5srg47IcYPCpz3iNFXwEUURbgwVtDRZYLN4RgmwHxNTnBwCio6MRFBQk/SxevLjB5pibm+v0PHw6nU5a/YmPj8fzzz+P8+fPw2w248MPP0RGRgYuXLgAAMjPz0dxcTGWLFmCESNGYMeOHbjjjjswevRo7Nmzx+251Po8QNQ8cVNXzXXc1OW6T3Xc1OW6HOCmrvqM19Cbuqqv9ChF4P/yra91k8kEjUZm8uTg3LlzdpvAtK7eu43ggw8+wMSJE9GuXTsolUr0798f9913HzIzMwFACrS33347Zs6cCQDo27cvDhw4gNWrV+Omm25y637cDkDnz59HVFRUbR8HNTIGn5rrGHxc96mOwcd1OcDgU5/xGjv4UP015ilfIiIinJ6HLzAwEL6+vgCALl26YM+ePSgpKYFOp0NkZCTuuecedO7cGYD1fH4qlQqxsbF24/Ts2VPaTOYOtwNQr169sGrVKtx///1uD06NS2EGnH0VM/hUjMfg47JPdQw+rssBBp/6jHclg4+gFiFYeHxPcxQfH4+tW7falaWnpzs9D5+fnx/8/Pxw+fJlbN++HUuXLgVgPZ/ftddei5MnT9q1/+2339CxY0e35+J2AHrxxRfxyCOPYOPGjXjrrbcQEhLi9p3QlcHgUzEeg4/LPtUx+LguBxh86jPelQ4+dGUVFxfj1KlT0u3s7GxkZWUhJCQEHTp0QGpqKnJycvD+++8DAKZMmYKVK1di7ty5mDhxInbt2oUNGzZgy5Yt0hjbt2+HKIro3r07Tp06hTlz5qBHjx525+qbM2cO7rnnHgwZMgTDhg3Dtm3b8NVXX2H37t1uz93tnaAfffRR/Pzzz/jnn38QGxuLr776yu07ocbFnZsrxuPOzS77OMyVOze7LAe4c3N9xmusnZudEdQiw08TOXz4MPr164d+/foBsJ5ouF+/fliwYAEA4MKFC3YnJ4yJicGWLVuQnp6OuLg4LFu2DO+88450DiAAKCwsREpKCnr06IEHHngAN9xwA7Zv3w61uvKFdscdd2D16tVYunQp+vTpg3feeQefffYZbrjhBrfnXqfzAK1cuRIzZ85Ez549oVLZvyuOHDlS2+E8SlOfB8hgMEh75H9qCIDZybFgXPFxXW6t44qP0zqu+ADgik99xmsOKz4KtRkqi4ixp6zvy9TU1CbbCfpKngco5tmX6n0eoOyFTzX5ee6ulFofBXbmzBl8/vnnaN26NW6//XaHAESNS61WY+rUqfi/N9532P+Hwcd1ubWOwcdpHYMPAAaf+ozXXIKPNxOMgKIeJ70WXXxstUS1Si9vv/02Zs+ejYSEBPzyyy8IDQ1trHmRDEEQEBYWBp1Y+Spn8HFdbq1j8HFax+ADgMGnPuM11+BjEYCsEOu/OS+FQc64HYBGjBiBH374AStXrsQDDzzQmHMiNzH4uC631jH4OK1j8AHA4FOf8Zpr8JHqNBb8Emn9nQGInHE7AJnNZvz8889o3759Y86HamA2m7Fv3z70RjlOQAux2j5ADD62OgYfp3UMPgAYfOozXnMPPiov3wxG7nM7AKWnpzfmPMhNZrMZe/bsQS8NcNKolfYDYvCx1TH4OK1j8AHA4FOf8Twu+IgigvS2X0UIgvy/I3kn7sHs4Rh8bHUMPk7rGHwAMPjUZzyPCz4VlCJw2x/W8YxGIy+FQQ4YgDyYwuR4JmgGn5rrGHwYfFzVMfjYyj0z+ACASmWG0gzA6SWjiawYgFoIBp+a6xh8GHxc1TH42Mo9O/gQuYsByMMx+NRcx+DD4OOqjsHHVs7g4+nkzvDvLrEefT0RA5AHUxhlLobK4AOAwYfBx3Udg4+tvOUFH43aDKUC4FccucJXRwvC4GPF4MPg46qOwcdW3jKDD5G7GIA8jEqlwuTJkzH21Y8qD4Fn8AHA4MPg47qOwcdWzuBDBDAAeRyFQoF27dqhwKRk8KnA4MPg46qOwcdW7l3BR6M24U+eCZpcYADyUHLhh8GnhjoGH6fFDD4192PwsZU37+Djo7K+90UAJztaf2cAImfkP3GakdOnT2PSpEmIiYmBr68vunTpgoULF8JgMNi1+/nnn3HjjTfCx8cH0dHRWLp0qcNYn3zyCXr06AEfHx/06dMHW7duvVIPo0GYzWZ899136Oqnh4DKDxuFUXQaZJRG+fAj28cgygYcV3UKg8VpyJErt9aZZQOOXJ1gMMkGHNk6vVE+/Oj1suFH1OudBhmxXC8bfuT6OMxVq5UNMYKPVjb8yPbTauXDj1btNPyIGpVswHFVZ9EonYYcuXJrncJpyJErB6zhRi7gyNVZ1IJsiDGr5cOPXD+L2lW4cF5nUcmHFZfjaZyHn7qMZ1GLsuFHVImy4UdUi07Dj6i2yIYfQS06DT8KtVk2/KjUZtnwo1KZnYYfjdosG358VCYp/BC5wyNWgH799VdYLBa89dZb6Nq1K44dO4aHHnoIJSUlePXVVwEAOp0Ow4cPR0JCAlavXo2jR49i4sSJCA4OxsMPPwwAOHDgAO677z4sXrwYt912G9atW4dRo0bhyJEj6N27d1M+RLeZzWZ888036BUIZJdqIMoFFa74WHHFx2kxV3xq7scVH1u5Z6z4OBCBYHPFapCXXApDYQQU9VjWEF18XLZEHhGARowYgREjRki3O3fujJMnT+LNN9+UAtDatWthMBjw7rvvQqPRoFevXsjKysJrr70mBaAVK1ZgxIgRmDNnDgDg+eefR3p6OlauXInVq1df+QdWTwqjCHO1M50y+FRg8HFazOBTcz8GH1u5hwYfAD4qIxRmIP5gAADAeAsvhUGOPCIAOVNYWIiQkBDpdkZGBoYMGWL3Ik9MTMTLL7+My5cvo3Xr1sjIyMCsWbPsxklMTMSmTZuu1LQbDYNPBQYfp8UMPjX3Y/CxlXt28CFyl0cGoFOnTuHf//63tPoDALm5uYiJibFrFx4eLtW1bt0aubm5UlnVNrm5ubL3pdfroa/yRafT6RriITQYBp8KDD5Oixl8au7H4GMrZ/Ah79KkO0HPmzcPgiC4/Pn111/t+uTk5GDEiBG466678NBDDzX6HBcvXoygoCDpJzo6utHv013cuRncuZk7N1v7cOdmAN63c7OPyug0/PiqjfBVMxSRa026AjR79mxMmDDBZZvOnTtLv58/fx7Dhg3D4MGD8d///teuXUREBPLy8uzKbLcjIiJctrHVO5Oammq32Uyn0zWrEFQVV3xsdVzxqW0dV3xc13HFx6q5r/gw9FBtNGkACg0NRWhoqFttc3JyMGzYMAwYMABr1qyBotqu7vHx8Xj66adhNBqhVls/QdLT09G9e3e0bt1aarNz507MmDFD6peeno74+HjZ+9VqtdC6+iJqBhh8bHUMPrWtY/BxXcfgY8XgQy2RR5wHKCcnB0OHDkWHDh3w6quv4uLFi8jNzbXbd+f++++HRqPBpEmT8Msvv+Djjz/GihUr7FZvHn/8cWzbtg3Lli3Dr7/+ikWLFuHw4cOYNm1aUzysOlGpVBg/fjy+z/GFaOCmLmsdN3U5w01dtnJu6rKWe9emLl/uF0Q18IidoNPT03Hq1CmcOnUK7du3t6sTReubNigoCDt27EBKSgoGDBiAtm3bYsGCBdIh8AAwePBgrFu3DvPnz8dTTz2Fq666Cps2bfKYcwAB1kthdOrUCQVFjl9SXPGpxBUf57ji47qOKz5Wnrzi08rWxwL83b4UABy2GLRUPA9Q7XhEAJowYUKN+woBwNVXX419+/a5bHPXXXfhrrvuaqCZNQ8MPpUYfJxj8HFdx+Bj1SKCTwVRAZzvWVQxP4/4qqMrjK8KD2M2m5GZmYkOwQacz1dBhPMvAQafinIGn1qVW+sYfGocj8HHWtcMg49UrjQ4LSeyYQDyMGazGV9//TViI4ALF1UQq32eMfhUlDP41KrcWsfgU+N4DD7WOk8IPiKgMFpfu95yKQyqHQagFoLBp6KcwadW5dY6Bp8ax2PwsdZ5QvCpIJgFdN7VDgBgHMRLYZAjBiAPx+BTUc7gU6tyax2DT43jMfhY6zwo+BC5iwHIgymNIixO9gFi8HHdpzoGH9flAINPfcZj8Klad2WCT4C6HOAmL6oBA1ALwuDjuk91DD6uywEGn/qMx+BTte4KBh8vpjABCvmPgBqJ8v+ELRIDUAvA4OO6T3UMPq7LAQaf+ozH4FO1jsGHmi8GIA+mMFgAi/ubwKx1DD5O6xh8ADD41Gc8Bp+qdU0ffPyV7n0ukPfyjtNjtiAqlQr33Xcffj6hhljt85GXrHCOl6xwXQ7wkhX1GY+XrKhaV/tLVrRSGZ2Gn1ZKg2z4CVCXy4Yff6We4ecK2rt3L0aOHImoqCgIgoBNmzbV2Gf37t3o378/tFotunbtirS0NLv6Tp06QRAEh5+UlBQAwKVLlzB9+nR0794dvr6+6NChAx577DEUFhbWau5cAfIwCoUC3bp1w6XLlV9+XPFxjis+rssBrvjUZzyu+FSta4YrPgJgjL4MwHsuhdEUSkpKEBcXh4kTJ2L06NE1ts/OzkZycjKmTJmCtWvXYufOnZg8eTIiIyORmJgIADh06BDM5srX4rFjx3DLLbdIV3E4f/48zp8/j1dffRWxsbE4c+YMpkyZgvPnz+PTTz91e+4MQB6Mwcc5Bh/X5QCDT33GY/CpWtcMg4+tXFMODPwTAC+F0ZiSkpKQlJTkdvvVq1cjJiYGy5YtAwD07NkT+/fvx/Lly6UAFBoaatdnyZIl6NKlC2666SYAQO/evfHZZ59J9V26dMGLL76I//f//h9MJpPb/958VXgYs9mMo0ePIjLEiPw8BUTR/suFwUemjsEHAINPfcZj8Kla14yDDzd/1ZtOp7O7rdVqoXX1eVgLGRkZSEhIsCtLTEzEjBkznLY3GAz48MMPMWvWLJdn8y4sLERgYGCtwi4DkIcxm8344osv0L0HcDFfIV0Kg8FHpo7BBwCDT33GY/CpWuc5wUcUAZi961IYClM9rwZf8U8fHR1tV75w4UIsWrSo7gNXkZubi/DwcLuy8PBw6HQ6lJWVwdfX165u06ZNKCgocHlB9L///hvPP/88Hn744VrNhQHIwzH4yNQx+ABg8KnPeAw+Ves8J/hIzArgi34AAGMsL4VRG+fOnUNgYKB0u6FWf+rif//7H5KSkhAVFeW0XqfTITk5GbGxsbUOaQxAHkxhNMucCZrBp1b9GHys4zH4VJQz+FTWeWDwARCgLIcoKlAk24JcCQwMtAtADSkiIgJ5eXl2ZXl5eQgMDHRY/Tlz5gy++eYbfP75507HKioqwogRIxAQEICNGzdCrXbxIeEEA1ALwuBTy34MPtbxGHwqyhl8Kus8N/hQ8xYfH4+tW7falaWnpyM+Pt6h7Zo1axAWFobk5GSHOp1Oh8TERGi1Wnz55Zfw8fGp9VwYgFoABp9a9mPwsY7H4FNRzuBTWcfgQ7VTXFyMU6dOSbezs7ORlZWFkJAQdOjQAampqcjJycH7778PAJgyZQpWrlyJuXPnYuLEidi1axc2bNiALVu22I1rsViwZs0ajB8/3mHHZp1Oh+HDh6O0tBQffvghdDqdtON2aGgolEr3rgfCAOTBFAYL4ORzjsGHwcdVHYOPrZzBp7KuZQWfAGUZLNwEdkUcPnwYw4YNk27PmjULADB+/HikpaXhwoULOHv2rFQfExODLVu2YObMmVixYgXat2+Pd955RzoE3uabb77B2bNnMXHiRIf7PHLkCA4ePAgA6Nq1q11ddnY2OnXq5NbcGYBaEAYfBh9XdQw+tnIGn8q6lhd86MoaOnQoRFH+fVP9LM+2Pj/++KPLcYcPHy47bk336S4GIA+jUqlw55134sXUT2Gp+Nxl8GHwcVXH4GMrZ/CprGPwIWIA8jAKhQK9evXCPxc+ZfBh8HFZx+BjK2fwqazznuAToCxHcYfzALznUhgKI6Cox+mORBdfDy0RA5CHcnWRUqcYfJwWM/jU3I/Bx1bO4AN4QPBRVPZpd+MRALwUBjnncbFYr9ejb9++EAQBWVlZdnU///wzbrzxRvj4+CA6OhpLly516P/JJ5+gR48e8PHxQZ8+fRwOx2vuLBYLfvnlF4S0E1D1FEC8Ojuvzs6rs9vKeXX2yrrme3V2V1dtD1CWOw0/Acoy2fAToCi3Cz9ENfG4WDx37lxERUXhp59+siu3HRaXkJCA1atX4+jRo5g4cSKCg4Ol02MfOHAA9913HxYvXozbbrsN69atw6hRo3DkyBH07t27KR5OrZlMJnz66afoPkiJg1+YIJZxxcd5HVd8AK742HDFx6qlr/jUph+RRwWgr7/+Gjt27MBnn32Gr7/+2q5u7dq1MBgMePfdd6HRaNCrVy9kZWXhtddekwLQihUrMGLECMyZMwcA8PzzzyM9PR0rV67E6tWrr/jjqS/BYILDxzCDj9NiBp+a+zH42MoZfADPDz5moxKH1t4FAEhINfBSGOTAYwJQXl4eHnroIWzatAmtWrVyqM/IyMCQIUPsXuSJiYl4+eWXcfnyZbRu3RoZGRnSOQqqttm0aVNjT7/xMfg4LWbwqbkfg4+tnMEH8PzgQ+QujwhAoihiwoQJmDJlCq655hqcPn3aoU1ubi5iYmLsymxXnM3NzUXr1q1lr0Kbm5sre996vR76Kl/4trNNNhsMPk6LGXxq7sfgYytn8AEYfMj7NGkAmjdvHl5++WWXbU6cOIEdO3agqKgIqampV2hmlRYvXoxnn332it+vWwwudm6WweDjHIOP6zoGHysGn0rNOfgECOUwCx7x932DUhhFKIS6nyBQNNb/5IKepElfIbNnz8aECRNctuncuTN27dqFjIwMaKt9IV5zzTUYO3Ys3nvvPdkrzALWq8/a/u+sja3emdTUVLvNZjqdDtHR0TU+tibB4FPrOgYf13UMPlYMPpWae/AhcleTBqDQ0FCEhobW2O6NN97ACy+8IN0+f/48EhMT8fHHH2PQoEEArFeYffrpp2E0GqFWWz9J09PT0b17d7Ru3Vpqs3PnTsyYMUMaS+4qtDZardYheDU7DD61rmPwcV3H4GPF4FOJwYdaGo9YI+zQoYPdbX9/fwBAly5d0L59ewDA/fffj2effRaTJk3Ck08+iWPHjmHFihVYvny51O/xxx/HTTfdhGXLliE5ORnr16/H4cOH8d///vfKPZh6UiqVuP322/HanPUQS/VwPAzMisHHOQYf13UMPlYMPpU8NfgE8QrxVAOPCEDuCAoKwo4dO5CSkoIBAwagbdu2WLBggXQIPAAMHjwY69atw/z58/HUU0/hqquuwqZNmzzmHECANQD17dsXF4//z2k9g49zDD6u6xh8rBh8Knlq8AmumINZVCK8vfUq5N5yKQyqHY8MQJ06dXJ6Jdirr74a+/btc9n3rrvuwl133dVYU2syDD7OMfi4rmPwsWLwqeTpwcdGqTLj+uHbAfBSGOQcXxUexmKx4NSpUwiOVqPgL6P8ZSkYfGpVbq1j8KlxPAYfax2Dj3W8Zhh87PvJ/3sQMQB5GJPJhI8++gg9koJw8M3zDrsAMfgw+LiqY/CxYvCpxOBD3ooBqIVg8GHwcVXH4GPF4FOppQcfo1GFDevGAwDmzPGOS2EojYBS/uOmZi7Oq9sSMQB5OAYfBh9XdQw+Vgw+lVp68KnKZHLxIievxwDkwWR3fGbwkalj8KlxPAYfax2Dj3U8Dw0+AQoljAr5zwIigAGoRWHwkevD4FPjeAw+1joGH+t4Hhx8iNzFANQCMPjI9WHwqXE8Bh9rHYOPdTwGH/IiDEAeTK02o7y45r3WGHxclwMMPvUZj8Gnah2Dj6t+VzL4+AmVL/KioiK0adNGti15JwYgD/Pzzz9Lv/ef2gV/bM/FxZ8L7dqIFkA0i1KAUTj5wpXCjSjCUuWzRaESIGidfzuKGi3Eqm2VAGxDaxy/HS3mynDj7HPKVieIgKXKd55CAVi01rCiqHagv0WthMUiVGlbWW8LOFX7WCxCZbkgVs63grniuVFAhEWsMq4gwlIRfJQOcxBgrlKkgAhBsI2Hij5V7kMEAAEWtQAFRCirXa3ZFjqUAKxPr1DxXxFwMp6tT/W2tnhncdLHAkCEAIvGOl+H8SoKlGJF24oHJIgihIrxFNWyh0Ut2s9BrBxXCj5V+pgrxhXVIgRRRPVsJKos0mTNgv0cVLbG1bKRQm22tq06h4qmSluIqdJHBGBRCFCpzBBEQFHte1UKPmbAIgCiorKjn8JU+UBQvZ/Rrq2i4j6l4FOlj1gxbiuVERCB6t/trRQVfUwCRIUIVBk3QKGX6qrzV+khPfgqbfwqgpRoqvIHgCBCUIpS8LGrA+BfEWAsJgUEAIKq8kn0s1jnZ7E4vqkDlKVQVGlrNimt8xb0FU9Dla8cQYRSZZaCj8mktD45VedR8eSYBBVUVYKnr0UDURRgdPJv4SdooFGbkPlbZ6ls5cqVGDlyJPr37+/YgbwWA5AH0el02Lp1q3RbUAjomhSJrkmRdu0u/1GKE5/nSrevfbQjlDKrH4V/6XF849/W8bRa9B8XArWv87bFf5txbEupdDtulB+0/s7blupE/PRN5adTn2FKtAp0vvJRXgoc2WX93aJRos8gMwKCnK8CGAwWfH+gMqD1vtqE4GDnqwJmM7Dnx1aVbbvo0TZYfnUh/Wc/az+NgD5R5YgIlF8F2P6nvxSCeoeWo32g/KrA13n+MFSEtt6B5Yjxk18N2H7JH6UWARY10MdHj25a+VWA7aV+0InWL6Geaj16aVy0hR8uVcSTbjCgH6qtBlR5qOmKVsgXVLCogW4mIwaVy/xlXg584+uLHLX1Y6Sz0YQb5NoC+DZIi9M+1rYd9WYMK5RfkdjbWo1TfioIahHtS824JUf+Ofs+TIGTwdbHFlYmYsRf8v9uWZEiToZZf29dBtzye/XXZOVH4olIC35tZ4GPygT/UgE3/uwjO+7Zdgr8EWN9PD56AfGH/WXb/t2+FOd7FgEAlEYBvfaEybbVRZUg/+pL1hUfk4Dwr7vKtjVFFUI/8Jx0229zL9m2qshLaHXjCel20RcDAXNloCmq0lYTdhmhN2dKKz6/f34LzHrnq8Ct2lxCr+Sd0u3jXwxHeYnz5yIg+DJuGf2pdPvbL+9AUUFrp239/Itw5z1rpRWfDV+MRP7fzi+k7etTjgdHf4X07wbZlW/evBldu3ZFYGCg034tgWASrX/o1ZHFVPe+nogXSPEg//zzj9NLgDQEV5vJ5DvV54QTjlxt1pKfQ4NOweWmMDliA7+LXG26kp1DA+8CUZc52K0+NAC5zV0up6CSD7h14WpzV0P2ccXV5q4rxdXmris3B/df5Jd1gaj+9SaKIi5dutTAsyJPJoiN9Y3aQul0OgQFBaGwsPCK/yWh0+nw+uuvO4SgadOmISAgQLqtUCjsrn1jMMivDAiCALVaXae2RqNRNpA1VlsAdic0q01bk8kEi0X+C7I2bdVqNYSKANhYbc1mM8xm+dWM2rRVqVTSBSGbQ1uLxQKTST4oKJVKKJXKZtNWFEUYjfKrULVpW/X92VhtAdfv5Zb+GVFeXu7wWSkIAmbMmNEkn9uN/Z1hu49rxrwAlVp+pbImJmM5Dn82v0m+35oCN4F5kMDAQNx2223YvHkzRFGEIAi47bbbaty5rzZnQK1N26ofSJ7QtjYXRGwObat+qba0tgqFwu3XWnNoKwiCR7UFGu997wmfERqNxulnpTd8qZP7GIA8TP/+/dG1a1dcunQJISEhfEMTETnBz0qqCQOQBwoMDOSbmYioBvysJFe4EzQRERF5Ha4AERERtQBKgwhlPY5rEo3edUwUV4CIiIjI6zAAERERkddhACIiIqI62bt3L0aOHImoqCgIgoBNmzbV2Gf37t3o378/tFotunbtirS0NNm2S5Yskc7hVFVubi7GjRuHiIgI+Pn5oX///vjss89qNXcGICIiIqqTkpISxMXFYdWqVW61z87ORnJyMoYNG4asrCzMmDEDkydPxvbt2x3aHjp0CG+99Rauvvpqh7oHHngAJ0+exJdffomjR49i9OjRuPvuu/Hjjz+6PXcGICIiIqqTpKQkvPDCC7jjjjvcar969WrExMRg2bJl6NmzJ6ZNm4Y777wTy5cvt2tXXFyMsWPH4u2330br1o7XiDtw4ACmT5+OgQMHonPnzpg/fz6Cg4ORmZnp9twZgIiIiOiKyMjIQEJCgl1ZYmIiMjIy7MpSUlKQnJzs0NZm8ODB+Pjjj3Hp0iVYLBasX78e5eXlGDp0qNtz8agAtGXLFgwaNAi+vr5o3bo1Ro0aZVd/9uxZJCcno1WrVggLC8OcOXMcrvVTm22PRERE3kan09n96PX6Bhs7NzcX4eHhdmXh4eHQ6XQoKysDAKxfvx5HjhzB4sWLZcfZsGEDjEYj2rRpA61Wi0ceeQQbN25E165d3Z6Lx5wH6LPPPsNDDz2El156Cf/6179gMplw7Ngxqd5sNiM5ORkRERE4cOAALly4gAceeABqtRovvfQSgMptj1OmTMHatWuxc+dOTJ48GZGRkUhMTGyqh0ZERFRvDXUeoOjoaLvyhQsXYtGiRfWZmtvOnTuHxx9/HOnp6fDxkb+w6zPPPIOCggJ88803aNu2LTZt2oS7774b+/btQ58+fdy6L48IQCaTCY8//jheeeUVTJo0SSqPjY2Vft+xYweOHz+Ob775BuHh4ejbty+ef/55PPnkk1i0aBE0Go3dtkcA6NmzJ/bv34/ly5czABEREcEaQqpeQkSr1TbY2BEREcjLy7Mry8vLQ2BgIHx9fZGZmYn8/Hz0799fqjebzdi7dy9WrlwJvV6P06dPY+XKlTh27Bh69eoFAIiLi8O+ffuwatUqrF692q25eEQAOnLkCHJycqBQKNCvXz/k5uaib9++eOWVV9C7d28A1u2Kffr0sVtaS0xMxNSpU/HLL7+gX79+stseqx9eV5Ver7db/issLARgXSIkIiJyxfZdIdZjZeZKa8xrqMXHx2Pr1q12Zenp6YiPjwcA3HzzzTh69Khd/YMPPogePXrgySefhFKpRGlpKQBAobDfi0epVMJisbg9F48IQH/++ScAYNGiRXjttdfQqVMnLFu2DEOHDsVvv/2GkJAQ2e2KgHWbo+3/rrY9+vr6Otz34sWL8eyzzzqUV18iJCIiklNUVISgoKCmnkaDKy4uxqlTp6Tb2dnZyMrKQkhICDp06IDU1FTk5OTg/fffBwBMmTIFK1euxNy5czFx4kTs2rULGzZswJYtWwAAAQEB0sKGjZ+fH9q0aSOV9+jRA127dsUjjzyCV199FW3atMGmTZuQnp6OzZs3uz33Jg1A8+bNw8svv+yyzYkTJ6RE9/TTT2PMmDEAgDVr1qB9+/b45JNP8MgjjzTaHFNTUzFr1izptsViwaVLl9CmTRsIgtBo9+uKTqdDdHS0wzIl1R6fy4bD57Jh8HlsOM3huRRFEUVFRYiKimqS+29shw8fxrBhw6Tbtu/L8ePHIy0tDRcuXMDZs2el+piYGGzZsgUzZ87EihUr0L59e7zzzju12g1FrVZj69atmDdvHkaOHIni4mJ07doV7733Hm699Va3x2nSADR79mxMmDDBZZvOnTvjwoULAOz3+dFqtejcubP0xEZEROCHH36w62vbzhgRESH939W2R2e0Wq3D9s/g4GDXD+wKacxlSm/D57Lh8LlsGHweG05TP5ctceXHZujQoS437zk70nro0KG1OmHh7t27HcquuuqqWp/5ubomDUChoaEIDQ2tsd2AAQOg1Wpx8uRJ3HDDDQAAo9GI06dPo2PHjgCs2xVffPFF5OfnIywsDIB1u2JgYKAUnGra9khERETewSP2AQoMDMSUKVOwcOFCREdHo2PHjnjllVcAAHfddRcAYPjw4YiNjcW4ceOwdOlS5ObmYv78+UhJSZFWcGra9khEROSpFEYLFKL7OwE79DfVva8n8ogABACvvPIKVCoVxo0bh7KyMgwaNAi7du2STpGtVCqxefNmTJ06FfHx8fDz88P48ePx3HPPSWM0xLbH5kCr1WLhwoUNemiit+Jz2XD4XDYMPo8Nh88luSKInnRsHhEREdnR6XQICgrCDf9aBJVK/uSBNTGZyrF/1yIUFhZ6xf5nHnUpDCIiIqKGwABEREREXocBiIiIiLwOAxARERF5HQagZuTFF1/E4MGD0apVK9mTLZ49exbJyclo1aoVwsLCMGfOHJhMJrs2u3fvRv/+/aHVatG1a1enJ6JatWoVOnXqBB8fHwwaNMjhJJItUadOnSAIgt3PkiVL7Nr8/PPPuPHGG+Hj44Po6GgsXbrUYZxPPvkEPXr0gI+PD/r06eNwbilv5I2vp9pYtGiRw2uvR48eUn15eTlSUlLQpk0b+Pv7Y8yYMQ4nbXXnvd8S7d27FyNHjkRUVBQEQcCmTZvs6kVRxIIFCxAZGQlfX18kJCTg999/t2tz6dIljB07FoGBgQgODsakSZNQXFxs18ad9z61LAxAzYjBYMBdd92FqVOnOq03m81ITk6GwWDAgQMH8N577yEtLQ0LFiyQ2mRnZyM5ORnDhg1DVlYWZsyYgcmTJ2P79u1Sm48//hizZs3CwoULceTIEcTFxSExMRH5+fmN/hib2nPPPYcLFy5IP9OnT5fqdDodhg8fjo4dOyIzMxOvvPIKFi1ahP/+979SmwMHDuC+++7DpEmT8OOPP2LUqFEYNWoUjh071hQPp1nw5tdTbfTq1cvutbd//36pbubMmfjqq6/wySefYM+ePTh//jxGjx4t1bvz3m+pSkpKEBcXh1WrVjmtX7p0Kd544w2sXr0aBw8ehJ+fHxITE1FeXi61GTt2LH755RfpWlF79+7Fww8/LNW78973BAqDpd4/XkWkZmfNmjViUFCQQ/nWrVtFhUIh5ubmSmVvvvmmGBgYKOr1elEURXHu3Llir1697Prdc889YmJionR74MCBYkpKinTbbDaLUVFR4uLFixv4kTQvHTt2FJcvXy5b/5///Eds3bq19FyKoig++eSTYvfu3aXbd999t5icnGzXb9CgQeIjjzzS4PP1FN76eqqNhQsXinFxcU7rCgoKRLVaLX7yySdS2YkTJ0QAYkZGhiiK7r33vQEAcePGjdJti8UiRkREiK+88opUVlBQIGq1WvGjjz4SRVEUjx8/LgIQDx06JLX5+uuvRUEQxJycHFEU3XvvN2eFhYUiAHHIDQvEfw19qc4/Q25YIAIQCwsLm/ohXRFcAfIgGRkZ6NOnj90V7RMTE6HT6fDLL79IbRISEuz6JSYmIiMjA4B1lSkzM9OujUKhQEJCgtSmJVuyZAnatGmDfv364ZVXXrHbhJCRkYEhQ4ZAo9FIZYmJiTh58iQuX74stXH1/Hobb3891cbvv/+OqKgodO7cGWPHjpWuY5iZmQmj0Wj3HPbo0QMdOnSQnkN33vveKDs7G7m5uXbPXVBQEAYNGmT33AUHB+Oaa66R2iQkJEChUODgwYNSm5re+9TyeMyZoAnIzc21+wAEIN3Ozc112Uan06GsrAyXL1+G2Wx22ubXX39txNk3vcceewz9+/dHSEgIDhw4gNTUVFy4cAGvvfYaAOtzFxMTY9en6vPbunVr2efX9vx7m7///ttrX0+1MWjQIKSlpaF79+64cOECnn32Wdx44404duwYcnNzodFoHPb7q/q6cue9741sj93VezI3N1e6PqSNSqVCSEiIXZua3vvU8jAANbJ58+bh5ZdfdtnmxIkTdjtEkvtq8/zOmjVLKrv66quh0WjwyCOPYPHixTxVPjWqpKQk6ferr74agwYNQseOHbFhwwb4+vo24cyIvBcDUCObPXs2JkyY4LJN586d3RorIiLC4ega25EiERER0v+rHz2Sl5eHwMBA+Pr6QqlUQqlUOm1jG8OT1Of5HTRoEEwmE06fPo3u3bvLPndAzc+vJz53DaFt27Yt6vV0pQQHB6Nbt244deoUbrnlFhgMBhQUFNitAlV9Dt1573sj22PPy8tDZGSkVJ6Xl4e+fftKbarvkG8ymXDp0qUa39dV74NaHu4D1MhCQ0PRo0cPlz9Vtzu7Eh8fj6NHj9q9mdPT0xEYGIjY2Fipzc6dO+36paenIz4+HgCg0WgwYMAAuzYWiwU7d+6U2niS+jy/WVlZUCgU0vJ4fHw89u7dC6PRKLVJT09H9+7dpSXwmp5fb9PSXk9XSnFxMf744w9ERkZiwIABUKvVds/hyZMncfbsWek5dOe9741iYmIQERFh99zpdDocPHjQ7rkrKChAZmam1GbXrl2wWCwYNGiQ1Kam9z61QE29FzZVOnPmjPjjjz+Kzz77rOjv7y/++OOP4o8//igWFRWJoiiKJpNJ7N27tzh8+HAxKytL3LZtmxgaGiqmpqZKY/z5559iq1atxDlz5ognTpwQV61aJSqVSnHbtm1Sm/Xr14tarVZMS0sTjx8/Lj788MNicHCw3REmLc2BAwfE5cuXi1lZWeIff/whfvjhh2JoaKj4wAMPSG0KCgrE8PBwcdy4ceKxY8fE9evXi61atRLfeustqc13330nqlQq8dVXXxVPnDghLly4UFSr1eLRo0eb4mE1C974eqqt2bNni7t37xazs7PF7777TkxISBDbtm0r5ufni6IoilOmTBE7dOgg7tq1Szx8+LAYHx8vxsfHS/3dee+3VEVFRdJnIQDxtddeE3/88UfxzJkzoiiK4pIlS8Tg4GDxiy++EH/++Wfx9ttvF2NiYsSysjJpjBEjRoj9+vUTDx48KO7fv1+86qqrxPvuu0+qd+e935zZjgIbOvBpMWHw83X+GTrwaa86CowBqBkZP368CMDh59tvv5XanD59WkxKShJ9fX3Ftm3birNnzxaNRqPdON9++63Yt29fUaPRiJ07dxbXrFnjcF///ve/xQ4dOogajUYcOHCg+P333zfyo2tamZmZ4qBBg8SgoCDRx8dH7Nmzp/jSSy+J5eXldu1++ukn8YYbbhC1Wq3Yrl07ccmSJQ5jbdiwQezWrZuo0WjEXr16iVu2bLlSD6PZ8rbXU23dc889YmRkpKjRaMR27dqJ99xzj3jq1CmpvqysTHz00UfF1q1bi61atRLvuOMO8cKFC3ZjuPPeb4m+/fZbp5+L48ePF0XReij8M888I4aHh4tarVa8+eabxZMnT9qN8c8//4j33Xef6O/vLwYGBooPPvig9IeljTvv/eaKAahuBFEUxSZYeCIiIqIGoNPpEBQUhKEDn4ZK5VPncUymcuz+4UUUFhYiMDCwAWfYPHEfICIiIvI6DEBERETkdRiAiIiIyOswABEREZHX4YkQiYiIWgCF0QyFxVz3/ua69/VEXAEiIiIir8MARERERF6HAYiIiIi8DgMQEdXLyZMnERERgaKionqNc9111+Gzzz5roFkREbnGAETk5cxmMwYPHozRo0fblRcWFiI6OhpPP/20y/6pqamYPn06AgIC6jWP+fPnY968ebBYLPUah4jIHQxARF5OqVQiLS0N27Ztw9q1a6Xy6dOnIyQkBAsXLpTte/bsWWzevBkTJkyo9zySkpJQVFSEr7/+ut5jERHVhAGIiNCtWzcsWbIE06dPx4ULF/DFF19g/fr1eP/996HRaGT7bdiwAXFxcWjXrp1UlpaWhuDgYGzevBndu3dHq1atcOedd6K0tBTvvfceOnXqhNatW+Oxxx6Ducpht0qlErfeeivWr1/fqI+ViAjgeYCIqML06dOxceNGjBs3DkePHsWCBQsQFxfnss++fftwzTXXOJSXlpbijTfewPr161FUVITRo0fjjjvuQHBwMLZu3Yo///wTY8aMwfXXX4977rlH6jdw4EAsWbKkwR8bkTcQDGYISlPd+3vZeYAYgIgIACAIAt5880307NkTffr0wbx582rsc+bMGacByGg04s0330SXLl0AAHfeeSc++OAD5OXlwd/fH7GxsRg2bBi+/fZbuwAUFRWFc+fOwWKxQKHgAjURNR5+whCR5N1330WrVq2QnZ2Nv/76q8b2ZWVl8PHxcShv1aqVFH4AIDw8HJ06dYK/v79dWX5+vl0/X19fWCwW6PX6ejwKIqKaMQAREQDgwIEDWL58OTZv3oyBAwdi0qRJEEXRZZ+2bdvi8uXLDuVqtdrutiAITsuqH/F16dIl+Pn5wdfXt46PgoiupL1792LkyJGIioqCIAjYtGlTjX12796N/v37Q6vVomvXrkhLS7OrX7x4Ma699loEBAQgLCwMo0aNwsmTJ52OJYoikpKS3L7vqhiAiAilpaWYMGECpk6dimHDhuF///sffvjhB6xevdplv379+uH48eMNNo9jx46hX79+DTYeETWukpISxMXFYdWqVW61z87ORnJyMoYNG4asrCzMmDEDkydPxvbt26U2e/bsQUpKCr7//nukp6fDaDRi+PDhKCkpcRjv9ddfhyAIdZo79wEiIqSmpkIURWkH5E6dOuHVV1/FE088gaSkJHTq1Mlpv8TEREyePBlmsxlKpbLe89i3bx+GDx9e73GI6MpISkpCUlKS2+1Xr16NmJgYLFu2DADQs2dP7N+/H8uXL0diYiIAYNu2bXZ90tLSEBYWhszMTAwZMkQqz8rKwrJly3D48GFERkbWeu5cASLycnv27MGqVauwZs0atGrVSip/5JFHMHjwYJebwpKSkqBSqfDNN9/Uex45OTk4cOAAHnzwwXqPRUR1p9Pp7H4acp+8jIwMJCQk2JUlJiYiIyNDtk9hYSEAICQkRCorLS3F/fffj1WrViEiIqJOc2EAIvJyN910E0wmE2644QaHuu3bt2Pnzp2yS8wqlQpPPfUUXnvtNalswoQJKCgosGu3aNEiZGVl2ZWlpaXZbbN/4403MGHCBLRv377Oj4XImwkGU71/ACA6OhpBQUHSz+LFixtsjrm5uQgPD7crCw8Ph06nQ1lZmUN7i8WCGTNm4Prrr0fv3r2l8pkzZ2Lw4MG4/fbb6zwXbgIjonp55JFHUFBQgKKionpdDiMsLAyzZs1qwJkRUV2cO3cOgYGB0m2tVttkc0lJScGxY8ewf/9+qezLL7/Erl278OOPP9ZrbAYgIqoXlUpV4/XC3DF79uwGmA0R1VdgYKBdAGpIERERyMvLsyvLy8tDYGCgw9Gf06ZNw+bNm7F37167leFdu3bhjz/+QHBwsF37MWPG4MYbb8Tu3bvdmgsDEBEREV0R8fHx2Lp1q11Zeno64uPjpduiKEpnpt+9ezdiYmLs2s+bNw+TJ0+2K+vTpw+WL1+OkSNHuj0XBiAiIiKqk+LiYpw6dUq6nZ2djaysLISEhKBDhw5ITU1FTk4O3n//fQDAlClTsHLlSsydOxcTJ07Erl27sGHDBmzZskUaIyUlBevWrcMXX3yBgIAA5ObmAgCCgoLg6+uLiIgIpzs+d+jQwSEsucKdoImIiKhODh8+jH79+knn75o1axb69euHBQsWAAAuXLiAs2fPSu1jYmKwZcsWpKenIy4uDsuWLcM777wjHQIPAG+++SYKCwsxdOhQREZGSj8ff/xxg85dEGs61SsRERE1WzqdDkFBQUi4aiZUyrrvsGwy6/HN78tRWFjYaPsANSdcASIiIiKvw32AiIiIWgKDEVDUY13DYmy4uXgArgARERGR12EAIiIiIq/DAERERERehwGIiIiIvA4DEBEREXkdBiAiIiLyOjwMnoiIqCXQG+q3rGExNNhUPAFXgIiIiMjrMAARERGR12EAIiIiIq/DAERERERehwGIiIiIvA4DEBEREXkdHgZPRETUAoh6PUSFWPf+PAyeiIiIqGVjACIiIiKvwwBEREREXocBiIiIiLwOAxARERF5HQYgIiIi8joMQEREROR1eB4gIiKiFkDU6yEK9TgPkMjzABERERG1aAxARERE5HUYgIiIiMjrMAARERGR12EAIiIiIq/DAEREREReh4fBExERtQCi3lDPw+CNDTib5o8rQEREROR1GICIiIjI6zAAERERUZ3s3bsXI0eORFRUFARBwKZNm2rss3v3bvTv3x9arRZdu3ZFWlqaQ5tVq1ahU6dO8PHxwaBBg/DDDz/Y1ZeXlyMlJQVt2rSBv78/xowZg7y8vFrNnQGIiIiI6qSkpARxcXFYtWqVW+2zs7ORnJyMYcOGISsrCzNmzMDkyZOxfft2qc3HH3+MWbNmYeHChThy5Aji4uKQmJiI/Px8qc3MmTPx1Vdf4ZNPPsGePXtw/vx5jB49ulZzF0RRrPseU0RERNSkdDodgoKCMEw5GipBXedxTKIR35o/R2FhIQIDA2vdXxAEbNy4EaNGjZJt8+STT2LLli04duyYVHbvvfeioKAA27ZtAwAMGjQI1157LVauXAkAsFgsiI6OxvTp0zFv3jwUFhYiNDQU69atw5133gkA+PXXX9GzZ09kZGTguuuuc2u+XAEiIiKiKyIjIwMJCQl2ZYmJicjIyAAAGAwGZGZm2rVRKBRISEiQ2mRmZsJoNNq16dGjBzp06CC1cQcPgyciImoBTDAC9dimY4L1MHidTmdXrtVqodVq6zM1SW5uLsLDw+3KwsPDodPpUFZWhsuXL8NsNjtt8+uvv0pjaDQaBAcHO7TJzc11ey4MQERERB5Mo9EgIiIC+3K/qvdY/v7+iI6OtitbuHAhFi1aVO+xmxsGICIiIg/m4+OD7OxsGAyGeo8liiIEQbAra6jVHwCIiIhwOForLy8PgYGB8PX1hVKphFKpdNomIiJCGsNgMKCgoMBuFahqG3cwABEREXk4Hx8f+Pj4NPU0ahQfH4+tW7falaWnpyM+Ph6AdTVrwIAB2Llzp7QztcViwc6dOzFt2jQAwIABA6BWq7Fz506MGTMGAHDy5EmcPXtWGscdDEBERERUJ8XFxTh16pR0Ozs7G1lZWQgJCUGHDh2QmpqKnJwcvP/++wCAKVOmYOXKlZg7dy4mTpyIXbt2YcOGDdiyZYs0xqxZszB+/Hhcc801GDhwIF5//XWUlJTgwQcfBAAEBQVh0qRJmDVrFkJCQhAYGIjp06cjPj7e7SPAAAYgIiIiqqPDhw9j2LBh0u1Zs2YBAMaPH4+0tDRcuHABZ8+elepjYmKwZcsWzJw5EytWrED79u3xzjvvIDExUWpzzz334OLFi1iwYAFyc3PRt29fbNu2zW7H6OXLl0OhUGDMmDHQ6/VITEzEf/7zn1rNnecBIiIiIq/D8wARERGR12EAIiIiIq/DAERERERehwGIiIiIvA4DEBEREXkdBiAiIiLyOgxARERE5HUYgIiIiMjrMAARERGR12EAIiIiIq/DAERERERehwGIiIiIvM7/B57wyKLet1sIAAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "heterogeneous_map.plot_single_speed_multiplier(wind_direction=90.0, wind_speed=5.0, z=100.0)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: xlabel='X (m)', ylabel='Y (m)'>"
-      ]
-     },
-     "execution_count": 56,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "heterogeneous_map.plot_single_speed_multiplier(wind_direction=90.0, wind_speed=5.0, z=200.0)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -410,40 +339,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\u001b[34mfloris.floris_model.FlorisModel\u001b[0m \u001b[1;30mWARNING\u001b[0m \u001b[33mDeleting stored wind_data information.\u001b[0m\n",
-      "\u001b[34mfloris.logging_manager.LoggingManager\u001b[0m \u001b[1;30mWARNING\u001b[0m \u001b[33mThe calculated flow field contains points outside of the the user-defined heterogeneous inflow bounds. For these points, the interpolated value has been filled with the freestream wind speed. If this is not the desired behavior, the user will need to expand the heterogeneous inflow bounds to fully cover the calculated flow field area.\u001b[0m\n",
-      "\u001b[34mfloris.floris_model.FlorisModel\u001b[0m \u001b[1;30mWARNING\u001b[0m \u001b[33mDeleting stored wind_data information.\u001b[0m\n",
-      "\u001b[34mfloris.logging_manager.LoggingManager\u001b[0m \u001b[1;30mWARNING\u001b[0m \u001b[33mThe calculated flow field contains points outside of the the user-defined heterogeneous inflow bounds. For these points, the interpolated value has been filled with the freestream wind speed. If this is not the desired behavior, the user will need to expand the heterogeneous inflow bounds to fully cover the calculated flow field area.\u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: title={'center': 'Wind Direction = 275.0\\nWind Speed = 7.0'}>"
-      ]
-     },
-     "execution_count": 58,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 500x700 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Show a horizontal and y plane slice (note that heterogeneity is only defined within the hull of points)\n",
     "\n",
@@ -501,7 +399,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.4"
+   "version": "3.13.2"
   }
  },
  "nbformat": 4,
diff --git a/docs/index.md b/docs/index.md
index 21663ee63..737eeaaef 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -13,6 +13,16 @@ Support for python version 3.8 was dropped in FLORIS v4.3, and support for pytho
 See {ref}`installation` for details. FLORIS v4.3 also made the move to requiring `numpy` version 2. See the [numpy documentation for details](https://numpy.org/doc/stable/numpy_2_0_migration_guide.html).
 ```
 
+## WETO software
+
+FLORIS is primarily developed with the support from the U.S. Department of Energy and
+is part of the `WETO Software Stack <https://nrel.github.io/WETOStack>`_.
+For more information and other integrated modeling software, see:
+
+- [Portfolio Overview](https://nrel.github.io/WETOStack/portfolio_analysis/overview.html)
+- [Entry Guide](https://nrel.github.io/WETOStack/_static/entry_guide/index.html)
+- [Wind Farm Controls Workshop](https://www.youtube.com/watch?v=f-w6whxIBrA&list=PL6ksUtsZI1dwRXeWFCmJT6cEN1xijsHJz)
+
 ## Quick Start
 
 FLORIS is a Python package run on the command line typically by providing
diff --git a/docs/input_reference_main.md b/docs/input_reference_main.md
index 9054ec8bf..19ab2d95d 100644
--- a/docs/input_reference_main.md
+++ b/docs/input_reference_main.md
@@ -1,6 +1,6 @@
 # Main Input File Reference
 
-In additional to reinitializing {py:class}`FlorisInterface`, users can configure FLORIS
+In addition to calling the `set()` method on {py:class}`FlorisModel`, users can configure FLORIS
 with an input file. The file must be YAML format with either "yaml" or "yml" extension.
 The below definitions guide a user to the top, mid, and lower level parameterizations. A few
 reference input files are available in the
diff --git a/docs/multidimensional_wind_turbine.ipynb b/docs/multidimensional_wind_turbine.ipynb
new file mode 100644
index 000000000..47a106d01
--- /dev/null
+++ b/docs/multidimensional_wind_turbine.ipynb
@@ -0,0 +1,129 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "ab10767e",
+   "metadata": {},
+   "source": [
+    "# Multidimensional Wind Turbine\n",
+    "\n",
+    "Many external factors can affect the power and thrust curves of wind turbines. FLORIS supports the\n",
+    "ability to define \"multidimensional\" wind turbines, where the power and thrust curves are defined\n",
+    "as a function of external parameters. To enable this functionality, rather than defining `power`\n",
+    "and `thrust_coefficient` as a function of `wind_speed` on the `power_thrust_table`, users should\n",
+    "instead provide a path to a data file (described below) as `power_thrust_data_file`. Additionally,\n",
+    "users must set the `multi_dimensional_cp_ct` option on the turbine definition to `True`.\n",
+    "\n",
+    "The power thrust data file should be a CSV file with the following columns:\n",
+    "(`<external_parameter_1>`, `<external_parameter_2>`, ..., `ws`, `power`,\n",
+    "`thrust_coefficient`). The external parameters can be any relevant factors that affect the turbine\n",
+    "performance, and the values to be used will be specified at run time or in the FLORIS input file.\n",
+    "For example, the external parameters could be air density, wave height, etc. The `ws` column should\n",
+    "contain the wind speed values for specification of the power and thrust coefficient (stored in the\n",
+    "`power` and `thrust_coefficient` columns, respectively). The wind speed, power, and thrust\n",
+    "coefficient values should be defined for each combination of the external parameters.\n",
+    "\n",
+    "The user can then specify the values of the external parameters either on the FLORIS input file\n",
+    "or using the `multidim_conditions` argument of `FlorisModel.set()`. The power and thrust coefficient\n",
+    "are determined based on the specified conditions using a nearest-neighbor approach.\n",
+    "\n",
+    "The following code snippet shows an example of a multidimensional wind turbine definition and its\n",
+    "corresponding power thrust data file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cc97a774",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from floris import FlorisModel, TimeSeries\n",
+    "\n",
+    "n_wind_speeds = 100\n",
+    "wind_speeds = np.linspace(0.1, 30, n_wind_speeds)\n",
+    "\n",
+    "fmodel = FlorisModel(\"defaults\") # Defaults to NREL 5MW turbine\n",
+    "fmodel.set(\n",
+    "    wind_data=TimeSeries(\n",
+    "        wind_directions=np.zeros(n_wind_speeds),\n",
+    "        wind_speeds=wind_speeds,\n",
+    "        turbulence_intensities=0.06\n",
+    "    ),\n",
+    "    layout_x=[0],\n",
+    "    layout_y=[0],\n",
+    "    wind_shear=0.0,\n",
+    "    turbine_type=[\"iea_15MW_floating_multi_dim_cp_ct\"],\n",
+    "    reference_wind_height=-1,\n",
+    ")\n",
+    "\n",
+    "# Now, we need to specify what external parameters to run the model for\n",
+    "fmodel.set(multidim_conditions={\"Tp\": 2, \"Hs\": 1})\n",
+    "fmodel.run()\n",
+    "\n",
+    "powers = fmodel.get_turbine_powers()\n",
+    "thrust_coefficients = fmodel.get_turbine_thrust_coefficients()\n",
+    "\n",
+    "fig, ax = plt.subplots(2, 1, sharex=True)\n",
+    "ax[0].plot(wind_speeds, powers, label=\"First condition\")\n",
+    "ax[0].grid()\n",
+    "ax[0].set_ylabel(\"Power [kW]\")\n",
+    "ax[1].plot(wind_speeds, thrust_coefficients)\n",
+    "ax[1].grid()\n",
+    "ax[1].set_ylabel(\"Thrust coefficient [-]\")\n",
+    "ax[1].set_xlabel(\"Wind speed [m/s]\")\n",
+    "ax[1].set_xlim([0, 30])\n",
+    "\n",
+    "# Set a second multidimensional condition and rerun\n",
+    "fmodel.set(multidim_conditions={\"Tp\": 4, \"Hs\": 5})\n",
+    "fmodel.run()\n",
+    "\n",
+    "powers = fmodel.get_turbine_powers()\n",
+    "thrust_coefficients = fmodel.get_turbine_thrust_coefficients()\n",
+    "ax[0].plot(wind_speeds, powers, label=\"Second condition\")\n",
+    "ax[0].legend(loc=\"upper left\")\n",
+    "ax[1].plot(wind_speeds, thrust_coefficients)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "98fd51f6",
+   "metadata": {},
+   "source": [
+    "Note that this example is not meant to be represntative of a real turbine, but rather to illustrate\n",
+    "the functionality. At this time, FLORIS only support a single external condition combination at a\n",
+    "time, but multiple combinations can be run sequentially as is shown above."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8c21f432",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "floris",
+   "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": "3.13.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/operation_models_user.ipynb b/docs/operation_models_user.ipynb
index b5def6f76..a1e8a02f2 100644
--- a/docs/operation_models_user.ipynb
+++ b/docs/operation_models_user.ipynb
@@ -141,10 +141,14 @@
     "The `\"cosine-loss\"` operation model describes the decrease in power and thrust produced by a \n",
     "wind turbine as it yaws (or tilts) away from the incoming wind. The thrust is reduced by a factor of \n",
     "$\\cos \\gamma$, where $\\gamma$ is the yaw misalignment angle, while the power is reduced by a factor \n",
-    "of $(\\cos\\gamma)^{p_P}$, where $p_P$ is the cosine loss exponent, specified by `cosine_loss_exponent_yaw`\n",
-    "(or `cosine_loss_exponent_tilt` for tilt angles). The power and thrust produced by the turbine\n",
+    "of $(\\cos\\gamma)^{p_P}$, where $p_P$ is the cosine loss exponent, specified by `cosine_loss_exponent_yaw`.\n",
+    "Similarly, the thrust and power are reduced by factors of $(\\cos \\theta / \\cos \\theta_{ref})$ and\n",
+    "$(\\cos \\theta / \\cos \\theta_{ref})^{p_T}$, respectively, where $\\theta$ is the tilt angle, \n",
+    "$\\theta_{ref}$ is the reference tilt angle under which the power and thrust curves are specified, and\n",
+    "$p_T$ is the cosine loss exponent for tilt, specified by `cosine_loss_exponent_tilt`.\n",
+    "The power and thrust produced by the turbine\n",
     "thus vary as a function of the turbine's yaw angle, set using the `yaw_angles` argument to \n",
-    "`FlorisModel.set()`."
+    "`FlorisModel.set()`, and tilt angle (if applicable, for example for floating wind turbines)."
    ]
   },
   {
diff --git a/docs/references.bib b/docs/references.bib
index f193de835..db685efa1 100644
--- a/docs/references.bib
+++ b/docs/references.bib
@@ -334,3 +334,30 @@ @article{HeckJohlasHowland2023_yawed_adm
 title = {Modelling the induction, thrust and power of a yaw-misaligned actuator disk},
 journal = {Journal of Fluid Mechanics},
 }
+
+@techreport{jonkman_NREL5MW_2009,
+	title = {Definition of a 5-{MW} Reference Wind Turbine for Offshore System Development},
+  institution = {National Renewable Energy Laboratory},
+	url = {http://www.osti.gov/servlets/purl/947422-nhrlni/},
+	number = {NREL/TP-500-38060},
+	author = {Jonkman, J. and Butterfield, S. and Musial, W. and Scott, G.},
+	year = {2009},
+	doi = {10.2172/947422},
+}
+
+@techreport{kainz_IEA10MW_2024,
+	title = {{IEA}-{Wind} 740-{10MW} Reference Offshore Wind Plants},
+  url = {https://research-hub.nrel.gov/en/publications/iea-wind-tcp-task-55-the-iea-wind-740-10-mw-reference-offshore-wi},
+	institution = {International Energy Agency},
+  author = {Kainz, Samuel and Quick, Julian and Souza de Alencar, Mauricio and Sanchez Perez-Moreno, Sebastian and Dykes, Katherine and Bay, Christopher and Zaaijer, Michiel B. and Bortolotti, Pietro},
+  year = {2024},
+}
+
+@techreport{gaertner_IEA15MW_2020,
+	title = {{IEA} {Wind} {TCP} {Task} 37: {Definition} of the {IEA} 15-{Megawatt} Offshore Reference Wind Turbine},
+	url = {https://research-hub.nrel.gov/en/publications/iea-wind-tcp-task-37-definition-of-the-iea-15-megawatt-offshore-r},
+	number = {NREL/TP-5000-75698},
+	institution = {International Energy Agency},
+	author = {Gaertner, Evan and Sethuraman, Latha and Anderson, Benjamin and Barter, Garrett and Abbas, Nikhar and Bortolotti, Pietro and Scott, George and Feil, Roland and Shields, Matthew and Rinker, Jennifer and Zahle, Frederik and Meng, Fanzhong and Skrzypinski, Witold and Bredmose, Henrik and Dykes, Katherine and Allen, Christopher and Viselli, Anthony},
+	year = {2020},
+}
diff --git a/docs/turbine_interaction.ipynb b/docs/turbine_interaction.ipynb
deleted file mode 100644
index bbc74fb0a..000000000
--- a/docs/turbine_interaction.ipynb
+++ /dev/null
@@ -1,437 +0,0 @@
-{
- "cells": [
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "ac224ce9-bd4f-4f5c-88b7-f0e9e49ee498",
-   "metadata": {},
-   "source": [
-    "# Turbine Library Interface\n",
-    "\n",
-    "FLORIS allows users to select from a set of pre-defined turbines as well as load an external\n",
-    "library of turbines. This reference demonstrates how to load, compare, and interact with the\n",
-    "basic turbine properties prior to wake modeling.\n",
-    "\n",
-    "## Setup"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "cf882c57-7d16-4f65-a6bb-cbc7e9603ac0",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "from floris.turbine_library import TurbineInterface, TurbineLibrary"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "71788b47-6641-4080-bb3f-eb799d969e0b",
-   "metadata": {},
-   "source": [
-    "## Interacting With A Single Turbine\n",
-    "\n",
-    "There are a few different ways that a ``TurbineInterface`` object can be created as demonstrated\n",
-    "below. For convenience, we'll only consider the object created from the internal library\n",
-    "(option 3).\n",
-    "\n",
-    "- Option 1: Load from a `Turbine` object:\n",
-    "  `ti = TurbineInterface(turbine_obj)`\n",
-    "- Option 2: Load from a turbine configuration dictionary:\n",
-    "  `ti = TurbineInterface.from_turbine_dict(turbine_dict)`\n",
-    "- Option 3a: Load a file from the internal turbine library:\n",
-    "  `ti = TurbineInterface.from_library(\"internal\", \"iea_15MW.yaml\")`\n",
-    "- Option 3b: Load a file from the internal turbine library:\n",
-    "  `ti = TurbineInterface.from_internal_library(\"iea_15MW.yaml\")`\n",
-    "- Option 4: Load a file from an external turbine library:\n",
-    "  `ti = TurbineInterface.from_library(\"path/to/user/library\", \"iea_15MW.yaml\")`\n",
-    "- Option 5: Load a file from anywhere:\n",
-    "  `ti = TurbineInterface.from_yaml(\"path/to/turbine.yaml\")`\n",
-    "\n",
-    "### Single Dimensional Turbine"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "2275840e-48a3-41d2-ace9-fad05da0dc02",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "ti = TurbineInterface.from_library(\"internal\", \"iea_15MW.yaml\")"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "f2576e8a-47ee-48b5-8707-aca0dc76929c",
-   "metadata": {},
-   "source": [
-    "#### Plot the core attributes\n",
-    "\n",
-    "For `TurbineInterface`, the core functionality is the power and thrust computation and plotting."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "b9a5f00a-0ead-4759-b911-3a1161e55791",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ti.plot_power_curve()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "722be425-9231-451a-bd84-7824db6a5098",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ti.plot_thrust_coefficient_curve()"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "b24cd983-a22b-4a77-87f1-1c43c6e44842",
-   "metadata": {},
-   "source": [
-    "### Interacting With A Multi-Dimensional Turbine"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "91eee045-9019-40e0-9dc4-c95d2737bb3c",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "ti_md = TurbineInterface.from_library(\"internal\", \"iea_15MW_multi_dim_cp_ct.yaml\")"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "62b901ba-67c9-40ea-b770-453e62ae50ed",
-   "metadata": {},
-   "source": [
-    "#### Plot the core attributes\n",
-    "\n",
-    "In this example, we'll demonstrate how the usage for a multi-dimensional turbine is exactly the same, and how to produce cleaner figures."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "f6e9f40c-4900-465a-882b-86ea86030f9b",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ti_md.plot_power_curve(\n",
-    "    fig_kwargs={\"figsize\": (6, 3)},  # The legend is a bit wider, so we'll need to change the dimensions\n",
-    "    legend_kwargs={\"fontsize\": 6},  # The labels are quite long, so let's shrink the font\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "b61286ae-ec6f-46e8-a863-0384b2e87855",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ti_md.plot_thrust_coefficient_curve(\n",
-    "    legend_kwargs={\"fontsize\": 6},  # The labels are quite long, so let's shrink the font\n",
-    ")"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "edf8e153-b756-4b0d-aba8-e82a39f18c89",
-   "metadata": {},
-   "source": [
-    "## Compare multiple turbines\n",
-    "\n",
-    "The other class provided by the turbine library is the `TurbineLibrary` object, which allows users\n",
-    "to simultaneously load internal and external turbine library configurations and compare them.\n",
-    "\n",
-    "Note that turbine names can overlap between the internal and external turbine libraries in this\n",
-    "interface, so a little more care is required. This is distinct from how turbine inputs are\n",
-    "treated through `FlorisInterface` via `floris.simulation.farm` where duplicate turbine names\n",
-    "will raise an error.\n",
-    "\n",
-    "### Loading the libraries\n",
-    "\n",
-    "Loading a turbine library is a 2 or more step process depending on how many turbine libraries\n",
-    "are going to be compared."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "52c74e32-c93c-449e-90f2-4ed5b2bf0f72",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "iea_15MW_multi_dim_cp_ct\n",
-      "nrel_5MW\n",
-      "iea_10MW\n",
-      "iea_15MW_floating\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Initialize the turbine library (no definitions required!)\n",
-    "tl = TurbineLibrary()\n",
-    "\n",
-    "# Load the internal library, except the IEA 15MW turbine\n",
-    "tl.load_internal_library(exclude=[\"iea_15MW.yaml\"])\n",
-    "for turbine in tl.turbine_map:\n",
-    "    print(turbine)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "533c277e-c181-42a1-b5dd-d76dc5d3bb5e",
-   "metadata": {},
-   "source": [
-    "In addition to the `load_internal_library` method, there is a `load_external_library` method with\n",
-    "the same parameterizations, but with an argument for a new library file path.\n",
-    "\n",
-    "We can also override previously ignored or loaded files by rerunning the load method again. Notice\n",
-    "how we use `which=[\"x_20MW.yaml\"]` to now include the file. This makes it so we only load the one\n",
-    "turbine configuration, however, the same could be achieved by specifying none of the keyword\n",
-    "arguments."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "273da4ef-0243-4296-9838-3f8e98615deb",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "iea_15MW_multi_dim_cp_ct\n",
-      "nrel_5MW\n",
-      "iea_10MW\n",
-      "iea_15MW_floating\n",
-      "iea_15MW\n"
-     ]
-    }
-   ],
-   "source": [
-    "tl.load_internal_library(which=[\"iea_15MW.yaml\"])\n",
-    "for turbine in tl.turbine_map:\n",
-    "    print(turbine)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "bac88742-33af-44f3-a35b-e178e60a49d3",
-   "metadata": {},
-   "source": [
-    "Notice that the \"iea_15MW\" turbine is now loaded.\n",
-    "\n",
-    "### Comparing turbines\n",
-    "\n",
-    "There are a number of methods that will plot the varying properties for each turbine against each\n",
-    "other, but here the primary output will be displayed.\n",
-    "\n",
-    "It should be noted that the 15MW turbines are all variations of each other, and so the\n",
-    "multi-dimensional example is removed in favor the of the floating 15MW turbine to highlight\n",
-    "a multi-dimensional turbine in relation to the standard 15 MW example."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "14008ddc-35be-4ac7-8371-f17cbf2f9ac3",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1600x1600 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "tl.plot_comparison(\n",
-    "    exclude=[\"iea_15MW_multi_dim_cp_ct\"],  # Remove a turbine just for demonstration\n",
-    "    wind_speeds=np.linspace(0, 30, 61),  # 0 -> 30 m/s, every 0.5 m/s\n",
-    "    fig_kwargs={\"figsize\": (8, 8)},  # Size the figure appropriately for the docs page\n",
-    "    plot_kwargs={\"linewidth\": 1},  # Ensure the line plots look nice\n",
-    "    legend_kwargs={\"fontsize\": 5},  # Ensure all the legend items fit\n",
-    ")"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "38f654ff-82f5-4019-8c43-e31c1b783cc1",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "Alternatively, these can all be ploted individually with:\n",
-    "\n",
-    "- `plot_power_curves()`\n",
-    "- `plot_Ct_curves()`\n",
-    "- `plot_rotor_diameters()`\n",
-    "- `plot_hub_heights()`\n",
-    "\n",
-    "For a text based approach, we can access the attributes like the following:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "5168be89-64be-482a-8a8a-c6889e64de88",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                  Turbine | Rotor Diameter (m) | Hub Height (m) |    TSR | Air Density (ρ) | Tilt (º)\n",
-      "-----------------------------------------------------------------------------------------------------\n",
-      " iea_15MW_multi_dim_cp_ct |             242.24 |          150.0 |    8.0 |           1.225 |    6.000\n",
-      "                 nrel_5MW |             125.88 |           90.0 |    8.0 |           1.225 |    5.000\n",
-      "                 iea_10MW |             198.00 |          119.0 |    8.0 |           1.225 |    6.000\n",
-      "        iea_15MW_floating |             242.24 |          150.0 |    8.0 |           1.225 |    6.000\n",
-      "                 iea_15MW |             242.24 |          150.0 |    8.0 |           1.225 |    6.000\n"
-     ]
-    }
-   ],
-   "source": [
-    "header = f\"\\\n",
-    "{'Turbine':>25} | \\\n",
-    "{'Rotor Diameter (m)':>18} | \\\n",
-    "{'Hub Height (m)':>14} | \\\n",
-    "{'TSR':>6} | \\\n",
-    "{'Air Density (ρ)':>15} | \\\n",
-    "{'Tilt (º)':>8}\\\n",
-    "\"\n",
-    "print(header)\n",
-    "print(\"-\" * len(header))\n",
-    "for name, t in tl.turbine_map.items():\n",
-    "    print(f\"{name:>25}\", end=\" | \")\n",
-    "    print(f\"{t.turbine.rotor_diameter:>18,.2f}\", end=\" | \")\n",
-    "    print(f\"{t.turbine.hub_height:>14,.1f}\", end=\" | \")\n",
-    "    print(f\"{t.turbine.TSR:>6,.1f}\", end=\" | \")\n",
-    "    if t.turbine.multi_dimensional_cp_ct:\n",
-    "        condition_keys = list(t.turbine.power_thrust_table.keys())\n",
-    "        print(f\"{t.turbine.power_thrust_table[condition_keys[0]]['ref_air_density']:>15,.3f}\", end=\" | \")\n",
-    "        print(f\"{t.turbine.power_thrust_table[condition_keys[0]]['ref_tilt']:>8,.3f}\")\n",
-    "    else:\n",
-    "        print(f\"{t.turbine.power_thrust_table['ref_air_density']:>15,.3f}\", end=\" | \")\n",
-    "        print(f\"{t.turbine.power_thrust_table['ref_tilt']:>8,.3f}\")"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "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": "3.10.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/docs/turbine_library.md b/docs/turbine_library.md
new file mode 100644
index 000000000..3db6b4ce4
--- /dev/null
+++ b/docs/turbine_library.md
@@ -0,0 +1,57 @@
+(turbine_library)=
+# Turbine Library
+
+FLORIS includes a library of predefined wind turbine models that can be used to quickly set up
+simulations without needing to define the turbine characteristics manually. These include standard
+reference wind turbines as well as fictional wind turbine models for the purpose of demonstrating
+various features of FLORIS. These turbines are stored in the `floris.turbine_library` module.
+
+## NREL 5MW reference wind turbine
+
+FLORIS representation of the NREL 5MW reference wind turbine {cite:t}`jonkman_NREL5MW_2009`. Data
+based on https://github.com/NREL/turbine-models/blob/master/Offshore/NREL_5MW_126_RWT_corrected.csv.
+Specified as `"nrel_5MW"` in the `turbine_type` field of the FLORIS input dictionary.
+
+The NREL 5MW turbine is the default turbine model used in most FLORIS examples and tutorials. It is
+also the model used if FLORIS is instantiated in the defaults configuration using
+`FlorisModel("defaults")`.
+
+
+## IEA 15MW reference wind turbine
+
+FLORIS representation of the IEA 15MW reference wind turbine {cite:t}`gaertner_IEA15MW_2020`. Data
+based on https://github.com/IEAWindTask37/IEA-15-240-RWT/blob/master/Documentation/IEA-15-240-RWT_tabular.xlsx.
+Specified as `"iea_15MW"` in the `turbine_type` field of the FLORIS input dictionary.
+
+The IEA 15MW turbine is used in the following examples:
+- examples/examples_control_types/004_helix_active_wake_mixing.py
+
+## IEA 10MW reference wind turbine
+
+FLORIS representation of the IEA 10MW reference wind turbine {cite:t}`kainz_IEA10MW_2024`. Data
+based on https://github.com/NREL/turbine-models/blob/master/Offshore/IEA_10MW_198_RWT.csv.
+Specified as `"iea_10MW"` in the `turbine_type` field of the FLORIS input dictionary.
+
+The IEA 10MW turbine is used in the following examples:
+- examples/examples_turbine/002_multiple_turbine_types.py
+
+## IEA 15MW multidimensional
+
+Fictional IEA 15MW turbine model used to demonstrate the use of multidimensional power and thrust
+coefficient data. Reads in fictional multidimensional data describing the power and thrust coefficient
+relationships on wave period `Tp` and wave height `Hs` from `iea_15MW_multi_dim_Tp_Hs.csv` in the
+`turbine_library` folder. Specified as `"iea_15MW_multi_dim"` in the `turbine_type` field of the FLORIS
+input dictionary. This data should be treated as fictional and for demonstrative purposes only.
+
+This fictional turbine model is not currently used in examples.
+
+## IEA 15MW floating, multidimensional
+
+The same as the multidimensional IEA 15MW turbine model above, but with an additional fictional
+floating platform tilt table. This model is used to demonstrate the floating wind turbine capabilities
+in FLORIS. Specified as `"iea_15MW_floating_multi_dim"` in the `turbine_type` field of the FLORIS input
+dictionary. This data should be treated as fictional and for demonstrative purposes only.
+
+This fictional turbine model is used in the following examples:
+- examples/examples_multidim/001_multi_dimensional_cp_ct.py
+- examples/examples_multidim/002_multi_dimensional_cp_ct_2Hs.py
diff --git a/docs/turbine_models.ipynb b/docs/turbine_models.ipynb
new file mode 100644
index 000000000..53c6e3b1e
--- /dev/null
+++ b/docs/turbine_models.ipynb
@@ -0,0 +1,170 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "ab10767e",
+   "metadata": {},
+   "source": [
+    "# Wind turbine models\n",
+    "\n",
+    "FLORIS generally represents wind turbines as actuator disks specified by a power curve and a\n",
+    "thrust coefficient curve (both specified as a function of wind speed). We can easily investigate the\n",
+    "power and thrust coefficients of a turbine by running a single-turbine `FlorisModel` using a range\n",
+    "of wind speeds."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cc97a774",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from floris import FlorisModel, TimeSeries\n",
+    "\n",
+    "n_wind_speeds = 100\n",
+    "wind_speeds = np.linspace(0.1, 30, n_wind_speeds)\n",
+    "\n",
+    "fmodel = FlorisModel(\"defaults\") # Defaults to NREL 5MW turbine\n",
+    "fmodel.set(\n",
+    "    wind_data=TimeSeries(\n",
+    "        wind_directions=np.zeros(n_wind_speeds),\n",
+    "        wind_speeds=wind_speeds,\n",
+    "        turbulence_intensities=0.06\n",
+    "    ),\n",
+    "    layout_x=[0],\n",
+    "    layout_y=[0],\n",
+    "    wind_shear=0.0\n",
+    ")\n",
+    "\n",
+    "fmodel.run()\n",
+    "\n",
+    "powers = fmodel.get_turbine_powers()\n",
+    "thrust_coefficients = fmodel.get_turbine_thrust_coefficients()\n",
+    "\n",
+    "fig, ax = plt.subplots(2, 1, sharex=True)\n",
+    "ax[0].plot(wind_speeds, powers)\n",
+    "ax[0].grid()\n",
+    "ax[0].set_ylabel(\"Power [kW]\")\n",
+    "ax[1].plot(wind_speeds, thrust_coefficients)\n",
+    "ax[1].grid()\n",
+    "ax[1].set_ylabel(\"Thrust coefficient [-]\")\n",
+    "ax[1].set_xlabel(\"Wind speed [m/s]\")\n",
+    "ax[1].set_xlim([0, 30])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "88d2ebce",
+   "metadata": {},
+   "source": [
+    "## Prepackaged turbine models\n",
+    "\n",
+    "FLORIS ships with three reference wind turbine models: the NREL 5MW turbine\n",
+    "{cite:t}`jonkman_NREL5MW_2009`,\n",
+    "the IEA 10 MW turbine {cite:t}`kainz_IEA10MW_2024`, and the IEA 15 MW turbine\n",
+    "{cite:t}`gaertner_IEA15MW_2020`. The\n",
+    "turbine models used for FLORIS simulations can be changed by specifying the `turbine_type` keyword\n",
+    "argument to `FlorisModel.set()`. See {ref}`turbine_library` for more details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7c1ec9ba",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, ax = plt.subplots(2, 1, sharex=True)\n",
+    "\n",
+    "turbine_models = [\"nrel_5MW\", \"iea_10MW\", \"iea_15MW\"]\n",
+    "\n",
+    "for turbine_model in turbine_models:\n",
+    "    fmodel.set(turbine_type=[turbine_model], reference_wind_height=-1)\n",
+    "\n",
+    "    fmodel.run()\n",
+    "\n",
+    "    powers = fmodel.get_turbine_powers()\n",
+    "    thrust_coefficients = fmodel.get_turbine_thrust_coefficients()\n",
+    "\n",
+    "    ax[0].plot(wind_speeds, powers, label=turbine_model)\n",
+    "    ax[1].plot(wind_speeds, thrust_coefficients)\n",
+    "\n",
+    "\n",
+    "ax[0].grid()\n",
+    "ax[0].set_ylabel(\"Power [kW]\")\n",
+    "ax[0].legend()\n",
+    "ax[1].grid()\n",
+    "ax[1].set_ylabel(\"Thrust coefficient [-]\")\n",
+    "ax[1].set_xlabel(\"Wind speed [m/s]\")\n",
+    "ax[1].set_xlim([0, 30])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd2c8544",
+   "metadata": {},
+   "source": [
+    "## User-defined wind turbine models\n",
+    "\n",
+    "Users may also provide their own wind turbine models, provided that they contain the appropriate\n",
+    "information. To include your own wind turbine model in your main FLORIS input YAML, use the\n",
+    "`!include` specifier, e.g.\n",
+    "```\n",
+    "  turbine_type:\n",
+    "  - !include path/to/your/turbine.yaml\n",
+    "```\n",
+    "\n",
+    "You can also `set` the `turbine_type` to your own turbine using the path, e.g.\n",
+    "```python\n",
+    "fmodel.set(turbine_type=[\"path/to/your/turbine.yaml\"])\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cdd9ad19",
+   "metadata": {},
+   "source": [
+    "The following pages describe in closer detail how wind turbines are implemented in FLORIS and\n",
+    "provide information on advanced wind turbine operation and modeling.\n",
+    "\n",
+    "```{note}\n",
+    "The `TurbineInterface` and `TurbineLibrary` classes are now deprecated and will be removed in a\n",
+    "future release. Users should simply use an instantiated `FlorisModel` to investigate wind turbine\n",
+    "models.\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "98fd51f6",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "floris",
+   "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": "3.13.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/examples_floating/001_floating_turbine_models.py b/examples/examples_floating/001_floating_turbine_models.py
index 75936b09a..900b588fe 100644
--- a/examples/examples_floating/001_floating_turbine_models.py
+++ b/examples/examples_floating/001_floating_turbine_models.py
@@ -11,9 +11,9 @@
 If `correct_cp_ct_for_tilt` is True, then the difference between the current tilt as
 interpolated from the floating tilt table is used to scale the turbine power and thrust.
 
-If `correct_cp_ct_for_tilt` is False, then it is assumed that the Cp/Ct tables provided
-already account for the variation in tilt with wind speed (for example they were computed from
-a turbine simulator with tilt degree-of-freedom enabled and the floating platform simulated),
+If `correct_cp_ct_for_tilt` is False, then it is assumed that the power/thrust coefficient tables
+provided already account for the variation in tilt with wind speed (for example they were computed
+from a turbine simulator with tilt degree-of-freedom enabled and the floating platform simulated),
 and no correction is made.
 
 In the example below, three single-turbine simulations are run to show the different behaviors.
@@ -21,7 +21,7 @@
 fmodel_fixed: Fixed bottom turbine (no tilt variation with wind speed)
 fmodel_floating: Floating turbine (tilt varies with wind speed)
 fmodel_floating_defined_floating: Floating turbine (tilt varies with wind speed, but
-    tilt does not scale cp/ct)
+    tilt does not scale power/thrust coefficient)
 """
 
 
@@ -89,7 +89,7 @@
     tilt_angles_floating_defined_floating,
     color="m",
     ls="--",
-    label="Floating (cp/ct not scaled by tilt)",
+    label="Floating (power/thrust coefficient not scaled by tilt)",
 )
 ax.grid(True)
 ax.legend()
@@ -104,7 +104,7 @@
     power_floating_defined_floating,
     color="m",
     ls="--",
-    label="Floating (cp/ct not scaled by tilt)",
+    label="Floating (power/thrust coefficient not scaled by tilt)",
 )
 ax.grid(True)
 ax.legend()
@@ -119,7 +119,7 @@
     power_floating_defined_floating - power_fixed,
     color="m",
     ls="--",
-    label="Floating (cp/ct not scaled by tilt)",
+    label="Floating (power/thrust coefficient not scaled by tilt)",
 )
 ax.grid(True)
 ax.legend()
@@ -134,7 +134,7 @@
     ct_floating_defined_floating,
     color="m",
     ls="--",
-    label="Floating (cp/ct not scaled by tilt)",
+    label="Floating (power/thrust coefficient not scaled by tilt)",
 )
 ax.grid(True)
 ax.legend()
diff --git a/examples/examples_get_flow/003_extract_turbulence_intensity_at_points.py b/examples/examples_get_flow/003_extract_turbulence_intensity_at_points.py
new file mode 100644
index 000000000..3cb79e38f
--- /dev/null
+++ b/examples/examples_get_flow/003_extract_turbulence_intensity_at_points.py
@@ -0,0 +1,80 @@
+"""Example: Extract turbulence intensity at points
+This example demonstrates the use of the sample_ti_at_points method of
+FlorisModel. sample_ti_at_points extracts the turbulence intensity (TI)
+information at user-specified locations in the flow.
+
+Specifically, this example returns the TI at a single x, y
+location and four different heights over a sweep of wind directions.
+This mimics the TI measurements of a met mast across all
+wind directions (at a fixed free stream wind speed).
+
+Try different values for met_mast_option to vary the location of the
+met mast within the two-turbine farm.
+"""
+
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+from floris import FlorisModel
+
+
+# User options
+# FLORIS model to use. Note that legacy "turbopark" is not available,
+# "turboparkgauss" is configured with none turbulence model by default,
+# and "emgauss" does not contain an explicit turbulence model.
+floris_model = "gch"  # Try "gch", "cc", "jensen"
+# Option to try different met mast locations
+met_mast_option = 0  # Try 0, 1, 2, 3
+
+# Instantiate FLORIS model
+fmodel = FlorisModel("../inputs/" + floris_model + ".yaml")
+
+# Set up a two-turbine farm
+D = 126
+fmodel.set(layout_x=[0, 3 * D], layout_y=[0, 3 * D])
+
+fig, ax = plt.subplots(1, 2)
+fig.set_size_inches(10, 4)
+ax[0].scatter(fmodel.layout_x, fmodel.layout_y, color="black", label="Turbine")
+
+# Set the wind direction to run 360 degrees
+wd_array = np.arange(0, 360, 1)
+ws_array = 8.0 * np.ones_like(wd_array)
+ti_array = 0.06 * np.ones_like(wd_array)
+fmodel.set(wind_directions=wd_array, wind_speeds=ws_array, turbulence_intensities=ti_array)
+
+# Simulate a met mast in between the turbines
+if met_mast_option == 0:
+    points_x = 4 * [3 * D]
+    points_y = 4 * [0]
+elif met_mast_option == 1:
+    points_x = 4 * [200.0]
+    points_y = 4 * [200.0]
+elif met_mast_option == 2:
+    points_x = 4 * [20.0]
+    points_y = 4 * [20.0]
+elif met_mast_option == 3:
+    points_x = 4 * [305.0]
+    points_y = 4 * [158.0]
+
+points_z = [30, 90, 150, 250]
+
+# Collect the points
+ti_at_points = fmodel.sample_ti_at_points(points_x, points_y, points_z)
+
+ax[0].scatter(points_x, points_y, color="red", marker="x", label="Met mast")
+ax[0].grid()
+ax[0].set_xlabel("x [m]")
+ax[0].set_ylabel("y [m]")
+ax[0].legend()
+
+# Plot the turbulence intensities
+for z_idx, z in enumerate(points_z):
+    ax[1].plot(wd_array, ti_at_points[:, z_idx].flatten(), label=f"TI at z={z} m")
+ax[1].grid()
+ax[1].legend()
+ax[1].set_xlabel("Wind Direction (deg)")
+ax[1].set_ylabel("Turbulence Intensity (-)")
+
+plt.show()
diff --git a/examples/examples_get_flow/003_plot_velocity_deficit_profiles.py b/examples/examples_get_flow/004_plot_velocity_deficit_profiles.py
similarity index 100%
rename from examples/examples_get_flow/003_plot_velocity_deficit_profiles.py
rename to examples/examples_get_flow/004_plot_velocity_deficit_profiles.py
diff --git a/examples/examples_multidim/001_multi_dimensional_cp_ct.py b/examples/examples_multidim/001_multi_dimensional_cp_ct.py
index b1bf0441b..c86b4e4fc 100644
--- a/examples/examples_multidim/001_multi_dimensional_cp_ct.py
+++ b/examples/examples_multidim/001_multi_dimensional_cp_ct.py
@@ -1,25 +1,27 @@
-"""Example: Multi-dimensional Cp/Ct data
+"""Example: Multi-dimensional power/thrust coefficient data
 This example creates a FLORIS instance and:
 1) Makes a two-turbine layout
 2) Demonstrates single ws/wd simulations
 3) Demonstrates multiple ws/wd simulations
 
-with the modification of using a turbine definition that has a multi-dimensional Cp/Ct table.
+with the modification of using a turbine definition that has a multi-dimensional
+power/thrust coefficient table.
 
 In the input file `gch_multi_dim_cp_ct.yaml`, the turbine_type points to a turbine definition,
 iea_15MW_floating_multi_dim_cp_ct.yaml located in the turbine_library,
-that supplies a multi-dimensional Cp/Ct data file in the form of a .csv file. This .csv file
-contains two additional conditions to define Cp/Ct values for: Tp for wave period, and Hs for wave
-height. For every combination of Tp and Hs defined, a Cp/Ct/Wind speed table of values is also
-defined. It is required for this .csv file to have the last 3 columns be ws, Cp, and Ct. In order
+that supplies a multi-dimensional power/thrust coefficient data file in the form of a .csv file.
+This .csv file contains two additional conditions to define power and thrust coefficient values for:
+Tp for wave period, and Hs for wave height. For every combination of Tp and Hs defined, a
+power/thrust coefficient/Wind speed table of values is also defined. It is required for this .csv
+file to have the last 3 columns be ws, power, and thrust coefficient. In order
 for this table to be used, the flag 'multi_dimensional_cp_ct' must be present and set to true in
 the turbine definition. With this flag enabled, the solver will down-select to use the
 interpolant defined at the closest conditions. The user must supply these conditions in the
 main input file under the 'flow_field' section, e.g.:
 
-NOTE: The multi-dimensional Cp/Ct data used in this example is fictional for the purposes of
-facilitating this example. The Cp/Ct values for the different wave conditions are scaled
-values of the original Cp/Ct data for the IEA 15MW turbine.
+NOTE: The multi-dimensional power/thrust coefficient data used in this example is fictional for the
+purposes of facilitating this example. The power/thrust coefficient values for the different wave
+conditions are scaled values of the original power/thrust coefficient data for the IEA 15MW turbine.
 
 flow_field:
   multidim_conditions:
@@ -30,7 +32,7 @@
 and used to select the interpolant at each turbine.
 
 Also note in the example below that there is a specific method for computing powers when
-using turbines with multi-dimensional Cp/Ct data under FlorisModel, called
+using turbines with multi-dimensional power/thrust coefficient data under FlorisModel, called
 'get_turbine_powers_multidim'. The normal 'get_turbine_powers' method will not work.
 """
 
diff --git a/examples/examples_multidim/002_multi_dimensional_cp_ct_2Hs.py b/examples/examples_multidim/002_multi_dimensional_cp_ct_2Hs.py
index 8cf206f07..10db44e46 100644
--- a/examples/examples_multidim/002_multi_dimensional_cp_ct_2Hs.py
+++ b/examples/examples_multidim/002_multi_dimensional_cp_ct_2Hs.py
@@ -1,9 +1,9 @@
-"""Example: Multi-dimensional Cp/Ct with 2 Hs values
+"""Example: Multi-dimensional power/thrust coefficient with 2 Hs values
 This example follows the previous example but shows the effect of changing the Hs setting.
 
-NOTE: The multi-dimensional Cp/Ct data used in this example is fictional for the purposes of
-facilitating this example. The Cp/Ct values for the different wave conditions are scaled
-values of the original Cp/Ct data for the IEA 15MW turbine.
+NOTE: The multi-dimensional power/thrust coefficient data used in this example is fictional for the
+purposes of facilitating this example. The power/thrust coefficient values for the different wave
+conditions are scaled values of the original power/thrust coefficient data for the IEA 15MW turbine.
 """
 
 
diff --git a/examples/examples_multidim/003_multi_dimensional_cp_ct_TI.py b/examples/examples_multidim/003_multi_dimensional_cp_ct_TI.py
new file mode 100644
index 000000000..97e48b488
--- /dev/null
+++ b/examples/examples_multidim/003_multi_dimensional_cp_ct_TI.py
@@ -0,0 +1,48 @@
+"""Example: Multi-dimensional power/thrust coefficients with turbulence intensity
+This example follows the previous example, but demonstrating how a multidimensional turbine can be
+used to model the effect of turbulence intensity on power and thrust coefficient.
+
+NOTE: The multi-dimensional power/thrust coefficient data used in this example is fictional for the
+purposes of facilitating this example and the power values shown should not be taken as
+representative of the actual effect of turbulence intensity on power/thrust coefficient.
+"""
+
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+from floris import FlorisModel, TimeSeries
+
+
+# Initialize FLORIS with the given input file.
+fmodel = FlorisModel("../inputs/gch_multi_dim_cp_ct_TI.yaml")
+
+# Set both cases to 3 turbine layout
+fmodel.set(layout_x=[0.0, 500.0, 1000.0], layout_y=[0.0, 0.0, 0.0])
+
+# Use a sweep of wind speeds
+wind_speeds = np.arange(5, 20, 0.1)
+time_series = TimeSeries(
+    wind_directions=270.0, wind_speeds=wind_speeds, turbulence_intensities=0.06
+)
+fmodel.set(wind_data=time_series)
+
+# Loop over different turbulence intensities using set()
+# When running with TI=0.10, the multidimensional data handler will find the nearest defined
+# value of 0.08 and use that data.
+fig, axarr = plt.subplots(1, 3, sharex=True, figsize=(12, 4))
+for ti, col in zip([0.06, 0.10], ["k", "r"]):
+    fmodel.set(multidim_conditions={"TI": ti})
+    fmodel.run()
+    turbine_powers = fmodel.get_turbine_powers() / 1000.0
+
+    for t_idx in range(3):
+        ax = axarr[t_idx]
+        ax.plot(wind_speeds, turbine_powers[:, t_idx], color=col, label="TI={0:.2f}".format(ti))
+for t_idx in range(3):
+    axarr[t_idx].grid(True)
+    axarr[t_idx].set_xlabel("Wind Speed (m/s)")
+    axarr[t_idx].set_title(f"Turbine {t_idx}")
+axarr[0].legend()
+
+plt.show()
diff --git a/examples/examples_turbine/001_check_turbine.py b/examples/examples_turbine/001_check_turbine.py
index 52a879dab..75698dda2 100644
--- a/examples/examples_turbine/001_check_turbine.py
+++ b/examples/examples_turbine/001_check_turbine.py
@@ -29,7 +29,7 @@
 )
 
 # Get a list of available turbine models provided through FLORIS, and remove
-# multi-dimensional Cp/Ct turbine definitions as they require different handling
+# multi-dimensional power/thrust coefficient turbine definitions as they require different handling
 turbines = [
     t.stem
     for t in fmodel.core.farm.internal_turbine_library.iterdir()
diff --git a/examples/inputs/gch.yaml b/examples/inputs/gch.yaml
index 35f285613..29e0e2de2 100644
--- a/examples/inputs/gch.yaml
+++ b/examples/inputs/gch.yaml
@@ -1,197 +1,202 @@
 
 ###
-# A name for this input file.
-# This is not currently only for the user's reference.
+# Name for the input file.
+# This is not used by FLORIS and is simply for the user's reference.
+# String type.
 name: GCH
 
 ###
-# A description of the contents of this input file.
-# This is not currently only for the user's reference.
+# Description of the contents of this input file.
+# This is not used by FLORIS and is simply for the user's reference.
+# String type.
 description: Three turbines using Gauss Curl Hybrid model
 
 ###
-# The earliest version of FLORIS this input file supports.
-# This is not currently only for the user's reference.
+# The FLORIS version that the file is defined for.
+# This is not used by FLORIS and is simply for the user's reference.
+# String type.
 floris_version: v4
 
 ###
-# Configure the logging level and where to show the logs.
+# Upper-level group of options for configuring logging.
 logging:
 
   ###
-  # Settings for logging to the console (i.e. terminal).
+  # Group of settings for logging to the console (i.e. terminal).
   console:
 
     ###
-    # Can be "true" or "false".
+    # Flag to enable console logging. Boolean type.
     enable: true
 
     ###
-    # Set the severity to show output. Messages at this level or higher will be shown.
-    # Can be one of "CRITICAL", "ERROR", "WARNING", "INFO", "DEBUG".
+    # Severity to show output in console. Messages at this level or higher will be shown.
+    # String type. Can be one of "CRITICAL", "ERROR", "WARNING", "INFO", "DEBUG".
     level: WARNING
 
   ###
-  # Settings for logging to a file.
+  # Group of settings for logging to a file.
   file:
 
     ###
-    # Can be "true" or "false".
+    # Flag to enable file logging. Boolean type.
     enable: false
 
     ###
-    # Set the severity to show output. Messages at this level or higher will be shown.
-    # Can be one of "CRITICAL", "ERROR", "WARNING", "INFO", "DEBUG".
+    # Severity to show output in file. Messages at this level or higher will be shown.
+    # String type. Can be one of "CRITICAL", "ERROR", "WARNING", "INFO", "DEBUG".
     level: WARNING
 
 ###
-# Configure the solver for the type of simulation.
+# Upper-level group of options for configuring solution grid.
 solver:
 
   ###
-  # Select the solver type.
-  # Can be one of: "turbine_grid", "flow_field_grid", "flow_field_planar_grid".
+  # Grid type for solving flow values at the turbines.
+  # String type. Can be one of: "turbine_grid", "turbine_cubature_grid".
   type: turbine_grid
 
   ###
-  # Options for the turbine type selected above. See the solver documentation for available parameters.
+  # Number of grid points per turbine for solve. For turbine_grid type solve, represents the number
+  # of points along each of the two axes. For turbine_cubature_grid type solve, represents the
+  # total number of points used in the cubature solve. Integer type.
   turbine_grid_points: 3
 
 ###
-# Configure the turbine types and their placement within the wind farm.
+# Group for setting the wind farm configuration.
 farm:
 
   ###
-  # Coordinates for the turbine locations in the x-direction which is typically considered
-  # to be the streamwise direction (left, right) when the wind is out of the west.
-  # The order of the coordinates here corresponds to the index of the turbine in the primary
-  # data structures.
+  # x-coordinates for the turbine locations, with the x axis corresponding to the
+  # "west to east" direction. The order of the coordinates corresponds to the index of the
+  # turbine in the primary data structures.
+  # List of float type.
   layout_x:
   - 0.0
   - 630.0
   - 1260.0
 
   ###
-  # Coordinates for the turbine locations in the y-direction which is typically considered
-  # to be the spanwise direction (up, down) when the wind is out of the west.
-  # The order of the coordinates here corresponds to the index of the turbine in the primary
-  # data structures.
+  # y-coordinates for the turbine locations, with the y axis corresponding to the
+  # "south to north" direction. The order of the coordinates corresponds to the index of the
+  # turbine in the primary data structures.
+  # List of float type.
   layout_y:
   - 0.0
   - 0.0
   - 0.0
 
   ###
-  # Listing of turbine types for placement at the x and y coordinates given above.
+  # Listing of turbine types for placement at the x and y coordinates.
   # The list length must be 1 or the same as ``layout_x`` and ``layout_y``. If it is a
   # single value, all turbines are of the same type. Otherwise, the turbine type
   # is mapped to the location at the same index in ``layout_x`` and ``layout_y``.
-  # The types can be either a name included in the turbine_library or
-  # a full definition of a wind turbine directly.
+  # The types can be either a string for a turbine included in the turbine_library,
+  # a string beginning with !include for a path to the user's turbine; or a full
+  # definition of a wind turbine (as a nested dictionary).
   turbine_type:
   - nrel_5MW
 
 ###
-# Configure the atmospheric conditions.
+# Group for defining the atmospheric conditions.
 flow_field:
 
   ###
-  # Air density.
+  # Air density. Float type.
   air_density: 1.225
 
   ###
-  # The height to consider the "center" of the vertical wind speed profile
+  # The height in meters to consider the "center" of the vertical wind speed profile
   # due to shear. With a shear exponent not 1, the wind speed at this height
   # will be the value given in ``wind_speeds``. Above and below this height,
   # the wind speed will change according to the shear profile; see
   # :py:meth:`.FlowField.initialize_velocity_field`.
   # For farms consisting of one wind turbine type, use ``reference_wind_height: -1``
   # to use the hub height of the wind turbine definition. For multiple wind turbine
-  # types, the reference wind height must be given explicitly.
+  # types, the reference wind height must be given explicitly. Float type (or -1).
   reference_wind_height: -1
 
   ###
-  # The turbulence intensities to include in the simulation, specified as a decimal.
+  # Turbulence intensities for the simulation, specified as a decimal value. Type list of floats.
   turbulence_intensities:
   - 0.06
 
   ###
-  # The wind directions to include in the simulation.
-  # 0 is north and 270 is west.
+  # Wind directions for the simulation, specified in degrees according to compass directions
+  # (0 is northerly, 90 is easterly, etc). Type list of floats.
   wind_directions:
   - 270.0
 
   ###
   # The exponent used to model the wind shear profile; see
-  # :py:meth:`.FlowField.initialize_velocity_field`.
+  # :py:meth:`.FlowField.initialize_velocity_field`. Float type.
   wind_shear: 0.12
 
   ###
-  # The wind speeds to include in the simulation.
+  # The wind speeds for the simulation, specified in m/s at the ``reference_wind_height``.
+  # Type list of floats.
   wind_speeds:
   - 8.0
 
   ###
-  # The wind veer as a constant value for all points in the grid.
+  # The wind veer (in degrees) as a constant value for all points in the grid. Only used in
+  # certain models. Float type.
   wind_veer: 0.0
 
   ###
-  # The conditions that are specified for use with the multi-dimensional Cp/Ct capability.
+  # Conditions that are specified for use with the multi-dimensional power/thrust capability.
   # These conditions are external to FLORIS and specified by the user. They are used internally
   # through a nearest-neighbor selection process to choose the correct Cp/Ct interpolants
-  # to use.
+  # to use. Type dictionary of string:float pairs.
   multidim_conditions:
     Tp: 2.5
     Hs: 3.01
 
 ###
-# Configure the wake model.
+# Group for defining the wake model parameters.
 wake:
 
   ###
-  # Select the models to use for the simulation.
-  # See :py:mod:`~.wake` for a list
-  # of available models and their descriptions.
+  # Group for selecting the model elements for the simulation.
+  # See :py:mod:`~.wake` for a list of available models and their descriptions.
   model_strings:
 
     ###
-    # Select the wake combination model.
+    # Wake combination model. String type..
     combination_model: sosfs
 
     ###
-    # Select the wake deflection model.
+    # Wake deflection model. String type.
     deflection_model: gauss
 
     ###
-    # Select the wake turbulence model.
+    # Wake turbulence model. String type..
     turbulence_model: crespo_hernandez
 
     ###
-    # Select the wake velocity deficit model.
+    # Wake velocity deficit model. String type.
     velocity_model: gauss
 
   ###
-  # Can be "true" or "false".
+  # Flag to include secondary steering effects. Only used in some models. Boolean type.
   enable_secondary_steering: true
 
   ###
-  # Can be "true" or "false".
+  # Flag to include yaw added recovery effects. Only used in some models. Boolean type.
   enable_yaw_added_recovery: true
 
   ###
-  # Can be "true" or "false".
+  # Flag to include active wake mixing effects. Only used in Empirical Guassian model. Boolean type.
   enable_active_wake_mixing: false
 
   ###
-  # Can be "true" or "false".
+  # Flag to compute transverse velocities across turbine rotors. Only used in some models.
+  # Boolean type.
   enable_transverse_velocities: true
 
   ###
-  # Configure the parameters for the wake deflection model
-  # selected above.
-  # Additional blocks can be provided for
-  # models that are not enabled, but the enabled model
-  # must have a corresponding parameter block.
+  # Parameters for the wake deflection model. See model descriptions and implementations for
+  # details of each parameter and its use.
   wake_deflection_parameters:
     gauss:
       ad: 0.0
@@ -207,11 +212,8 @@ wake:
       kd: 0.05
 
   ###
-  # Configure the parameters for the wake velocity deficit model
-  # selected above.
-  # Additional blocks can be provided for
-  # models that are not enabled, but the enabled model
-  # must have a corresponding parameter block.
+  # Parameters for the wake velocity deficit model. See model descriptions and implementations for
+  # details of each parameter and its use.
   wake_velocity_parameters:
     cc:
       a_s: 0.179367259
@@ -231,11 +233,8 @@ wake:
       we: 0.05
 
   ###
-  # Configure the parameters for the wake turbulence model
-  # selected above.
-  # Additional blocks can be provided for
-  # models that are not enabled, but the enabled model
-  # must have a corresponding parameter block.
+  # Parameters for the wake turbulence model. See model descriptions and implementations for
+  # details of each parameter and its use.
   wake_turbulence_parameters:
     crespo_hernandez:
       initial: 0.1
diff --git a/examples/inputs/gch_multi_dim_cp_ct.yaml b/examples/inputs/gch_multi_dim_cp_ct.yaml
index d1c788431..b3d1e27ed 100644
--- a/examples/inputs/gch_multi_dim_cp_ct.yaml
+++ b/examples/inputs/gch_multi_dim_cp_ct.yaml
@@ -1,5 +1,5 @@
 
-name: GCH multi dimensional Cp/Ct
+name: GCH multi dimensional power/thrust coefficient
 description: Three turbines using GCH model
 floris_version: v4
 
diff --git a/examples/inputs/gch_multi_dim_cp_ct_TI.yaml b/examples/inputs/gch_multi_dim_cp_ct_TI.yaml
new file mode 100644
index 000000000..23969b476
--- /dev/null
+++ b/examples/inputs/gch_multi_dim_cp_ct_TI.yaml
@@ -0,0 +1,114 @@
+
+name: GCH multi dimensional power/thrust coefficient
+description: Three turbines using GCH model
+floris_version: v4
+
+logging:
+  console:
+    enable: true
+    level: WARNING
+  file:
+    enable: false
+    level: WARNING
+
+solver:
+  type: turbine_grid
+  turbine_grid_points: 3
+
+farm:
+  layout_x:
+  - 0.0
+  - 630.0
+  - 1260.0
+  layout_y:
+  - 0.0
+  - 0.0
+  - 0.0
+  turbine_type:
+  - iea_15MW_multi_dim_TI.yaml
+  turbine_library_path: ../inputs/turbine_files/
+
+flow_field:
+  multidim_conditions:
+    TI: 0.06
+  air_density: 1.225
+  reference_wind_height: -1 # -1 is code for use the hub height
+  turbulence_intensities:
+  - 0.06
+  wind_directions:
+  - 270.0
+  wind_shear: 0.12
+  wind_speeds:
+  - 8.0
+  wind_veer: 0.0
+
+wake:
+  model_strings:
+    combination_model: sosfs
+    deflection_model: gauss
+    turbulence_model: crespo_hernandez
+    velocity_model: gauss
+
+  enable_secondary_steering: true
+  enable_yaw_added_recovery: true
+  enable_transverse_velocities: true
+  enable_active_wake_mixing: false
+
+  wake_deflection_parameters:
+    gauss:
+      ad: 0.0
+      alpha: 0.58
+      bd: 0.0
+      beta: 0.077
+      dm: 1.0
+      ka: 0.38
+      kb: 0.004
+    jimenez:
+      ad: 0.0
+      bd: 0.0
+      kd: 0.05
+    empirical_gauss:
+      horizontal_deflection_gain_D: 3.0
+      vertical_deflection_gain_D: -1
+      deflection_rate: 22
+      mixing_gain_deflection: 0.0
+      yaw_added_mixing_gain: 0.0
+
+  wake_velocity_parameters:
+    cc:
+      a_s: 0.179367259
+      b_s: 0.0118889215
+      c_s1: 0.0563691592
+      c_s2: 0.13290157
+      a_f: 3.11
+      b_f: -0.68
+      c_f: 2.41
+      alpha_mod: 1.0
+    gauss:
+      alpha: 0.58
+      beta: 0.077
+      ka: 0.38
+      kb: 0.004
+    jensen:
+      we: 0.05
+    turbopark:
+      A: 0.04
+      sigma_max_rel: 4.0
+    empirical_gauss:
+      wake_expansion_rates:
+      - 0.023
+      - 0.008
+      breakpoints_D:
+      - 10
+      sigma_0_D: 0.28
+      smoothing_length_D: 2.0
+      mixing_gain_velocity: 2.0
+
+  wake_turbulence_parameters:
+    crespo_hernandez:
+      initial: 0.01
+      constant: 0.9
+      ai: 0.83
+      downstream: -0.25
+    wake_induced_mixing:
+      atmospheric_ti_gain: 0.0
diff --git a/examples/inputs/turbine_files/iea_15MW_multi_dim_TI.csv b/examples/inputs/turbine_files/iea_15MW_multi_dim_TI.csv
new file mode 100644
index 000000000..656b8dabd
--- /dev/null
+++ b/examples/inputs/turbine_files/iea_15MW_multi_dim_TI.csv
@@ -0,0 +1,109 @@
+TI,ws,power,thrust_coefficient
+0.06,0.0,0.0,0.0
+0.06,2.9,0.0,0.0
+0.06,3.0,42.733312,0.80742173
+0.06,3.54953237,292.585981,0.784655297
+0.06,4.067900771,607.966543,0.781771245
+0.06,4.553906848,981.097693,0.785377072
+0.06,5.006427063,1401.98084,0.788045584
+0.06,5.424415288,1858.67086,0.789922119
+0.06,5.806905228,2337.575997,0.790464625
+0.06,6.153012649,2824.097302,0.789868339
+0.06,6.461937428,3303.06456,0.788727582
+0.06,6.732965398,3759.432328,0.787359348
+0.06,6.965470002,4178.637714,0.785895402
+0.06,7.158913742,4547.19121,0.778275899
+0.06,7.312849418,4855.342682,0.778275899
+0.06,7.426921164,5091.537139,0.778275899
+0.06,7.500865272,5248.453137,0.778275899
+0.06,7.534510799,5320.793207,0.778275899
+0.06,7.541241633,5335.345498,0.778275899
+0.06,7.58833327,5437.90563,0.778275899
+0.06,7.675676842,5631.253025,0.778275899
+0.06,7.803070431,5920.980626,0.778275899
+0.06,7.970219531,6315.115602,0.778275899
+0.06,8.176737731,6824.470067,0.778275899
+0.06,8.422147605,7462.846389,0.778275899
+0.06,8.70588182,8238.359448,0.778275899
+0.06,9.027284445,9167.96703,0.778275899
+0.06,9.385612468,10285.211,0.778275899
+0.06,9.780037514,11617.23699,0.778275899
+0.06,10.20964776,13194.41511,0.778275899
+0.06,10.67345004,15000.0,0.77176172
+0.06,10.86770694,15000.0,0.747149663
+0.06,11.17037214,15000.0,0.562338457
+0.06,11.6992653,15000.0,0.463477777
+0.06,12.25890683,15000.0,0.389083718
+0.06,12.84800295,15000.0,0.329822385
+0.06,13.46519181,15000.0,0.281465071
+0.06,14.10904661,15000.0,0.241494345
+0.06,14.77807889,15000.0,0.208180574
+0.06,15.470742,15000.0,0.180257568
+0.06,16.18543466,15000.0,0.156747535
+0.06,16.92050464,15000.0,0.136877529
+0.06,17.67425264,15000.0,0.120026379
+0.06,18.44493615,15000.0,0.105689427
+0.06,19.23077353,15000.0,0.093453742
+0.06,20.02994808,15000.0,0.082979637
+0.06,20.8406123,15000.0,0.073986457
+0.06,21.66089211,15000.0,0.066241166
+0.06,22.4888912,15000.0,0.059552107
+0.06,23.32269542,15000.0,0.053756866
+0.06,24.1603772,15000.0,0.048721662
+0.06,25.0,15000.0,0.044334197
+0.06,25.02,0.0,0.0
+0.06,50.0,0.0,0.0
+0.08,0.0,0.0,0.0
+0.08,2.9,0.0,0.0
+0.08,3.0,47.0066432,0.80742173
+0.08,3.54953237,321.84457910000003,0.784655297
+0.08,4.067900771,668.7631973,0.781771245
+0.08,4.553906848,1079.2074623,0.785377072
+0.08,5.006427063,1542.178924,0.788045584
+0.08,5.424415288,2044.5379460000001,0.789922119
+0.08,5.806905228,2571.3335967000003,0.790464625
+0.08,6.153012649,3106.5070322000006,0.789868339
+0.08,6.461937428,3633.371016,0.788727582
+0.08,6.732965398,4135.3755608,0.787359348
+0.08,6.965470002,4596.501485400001,0.785895402
+0.08,7.158913742,5001.910331,0.778275899
+0.08,7.312849418,5340.8769502000005,0.778275899
+0.08,7.426921164,5600.6908529,0.778275899
+0.08,7.500865272,5773.298450700001,0.778275899
+0.08,7.534510799,5852.8725277,0.778275899
+0.08,7.541241633,5868.8800478,0.778275899
+0.08,7.58833327,5981.696193000001,0.778275899
+0.08,7.675676842,6194.3783275000005,0.778275899
+0.08,7.803070431,6513.0786886,0.778275899
+0.08,7.970219531,6946.6271622,0.778275899
+0.08,8.176737731,7506.9170737,0.778275899
+0.08,8.422147605,8209.1310279,0.778275899
+0.08,8.70588182,9062.1953928,0.778275899
+0.08,9.027284445,10084.763733,0.778275899
+0.08,9.385612468,11313.732100000001,0.778275899
+0.08,9.780037514,12778.960689,0.778275899
+0.08,10.20964776,14513.856621,0.778275899
+0.08,10.67345004,15000.0,0.77176172
+0.08,10.86770694,15000.0,0.747149663
+0.08,11.17037214,15000.0,0.562338457
+0.08,11.6992653,15000.0,0.463477777
+0.08,12.25890683,15000.0,0.389083718
+0.08,12.84800295,15000.0,0.329822385
+0.08,13.46519181,15000.0,0.281465071
+0.08,14.10904661,15000.0,0.241494345
+0.08,14.77807889,15000.0,0.208180574
+0.08,15.470742,15000.0,0.180257568
+0.08,16.18543466,15000.0,0.156747535
+0.08,16.92050464,15000.0,0.136877529
+0.08,17.67425264,15000.0,0.120026379
+0.08,18.44493615,15000.0,0.105689427
+0.08,19.23077353,15000.0,0.093453742
+0.08,20.02994808,15000.0,0.082979637
+0.08,20.8406123,15000.0,0.073986457
+0.08,21.66089211,15000.0,0.066241166
+0.08,22.4888912,15000.0,0.059552107
+0.08,23.32269542,15000.0,0.053756866
+0.08,24.1603772,15000.0,0.048721662
+0.08,25.0,15000.0,0.044334197
+0.08,25.02,0.0,0.0
+0.08,50.0,0.0,0.0
diff --git a/examples/inputs/turbine_files/iea_15MW_multi_dim_TI.yaml b/examples/inputs/turbine_files/iea_15MW_multi_dim_TI.yaml
new file mode 100644
index 000000000..4737b6f6b
--- /dev/null
+++ b/examples/inputs/turbine_files/iea_15MW_multi_dim_TI.yaml
@@ -0,0 +1,13 @@
+turbine_type: 'iea_15MW_floating'
+hub_height: 150.0
+rotor_diameter: 242.24
+TSR: 8.0
+multi_dimensional_cp_ct: True
+power_thrust_table:
+  ref_air_density: 1.225
+  ref_tilt: 6.0
+  cosine_loss_exponent_yaw: 1.88
+  cosine_loss_exponent_tilt: 1.88
+  # Note that the multidimensional data provided here is fictional and does not represent a real
+  # wind turbine.
+  power_thrust_data_file: './iea_15MW_multi_dim_TI.csv'
diff --git a/floris/core/farm.py b/floris/core/farm.py
index 38235bc80..ada46a846 100644
--- a/floris/core/farm.py
+++ b/floris/core/farm.py
@@ -154,6 +154,9 @@ def __attrs_post_init__(self) -> None:
                             "Please specify a unique 'turbine_type' for each turbine definition."
                         )
                 self._turbine_definition_cache[t["turbine_type"]] = t
+                self._turbine_definition_cache[t["turbine_type"]]["turbine_library_path"] = (
+                    self.turbine_library_path
+                )
 
             # If a turbine type is a string, then it is expected in the internal or external
             # turbine library
@@ -186,6 +189,9 @@ def __attrs_post_init__(self) -> None:
                         " external turbine library."
                     )
                 self._turbine_definition_cache[t] = load_yaml(full_path)
+                self._turbine_definition_cache[t]["turbine_library_path"] = (
+                    self.turbine_library_path
+                )
 
         # Convert any dict entries in the turbine_type list to the type string. Since the
         # definition is saved above, we can make the whole list consistent now to use it
diff --git a/floris/core/flow_field.py b/floris/core/flow_field.py
index 037227d4f..500f9e6fb 100644
--- a/floris/core/flow_field.py
+++ b/floris/core/flow_field.py
@@ -7,13 +7,14 @@
 import matplotlib.path as mpltPath
 import numpy as np
 from attrs import define, field
-from scipy.interpolate import LinearNDInterpolator
+from scipy.interpolate import LinearNDInterpolator, NearestNDInterpolator
 from scipy.spatial import ConvexHull
 from shapely.geometry import Polygon
 
 from floris.core import (
     BaseClass,
     Grid,
+    PointsGrid,
 )
 from floris.type_dec import (
     floris_array_converter,
@@ -118,6 +119,10 @@ def heterogeneous_config_validator(self, instance: attrs.Attribute, value: dict
             # If only a 2D case, add "None" for the z locations
             value["z"] = None
 
+        if "interp_method" not in value:
+            # If no interpolation method is specified, default to linear
+            value["interp_method"] = "linear"
+
     @het_map.validator
     def het_map_validator(self, instance: attrs.Attribute, value: list | None) -> None:
         """Using this validator to make sure that the het_map has an interpolant defined for
@@ -149,14 +154,25 @@ def initialize_velocity_field(self, grid: Grid) -> None:
         # determined by this line. Since the right-most dimension on grid.z is storing the values
         # for height, using it here to apply the shear law makes that dimension store the vertical
         # wind profile.
-        wind_profile_plane = (grid.z_sorted / self.reference_wind_height) ** self.wind_shear
+
+        # Extract relevant x,y,z locations from the grid object
+        if isinstance(grid, PointsGrid):
+            x = np.tile(grid.points_x[None, :, None, None], (self.n_findex, 1, 1, 1))
+            y = np.tile(grid.points_y[None, :, None, None], (self.n_findex, 1, 1, 1))
+            z = grid.z_sorted
+        else:
+            x = grid.x_sorted_inertial_frame
+            y = grid.y_sorted_inertial_frame
+            z = grid.z_sorted
+
+        wind_profile_plane = (z / self.reference_wind_height) ** self.wind_shear
         dwind_profile_plane = (
             self.wind_shear
             * (1 / self.reference_wind_height) ** self.wind_shear
             * np.power(
-                grid.z_sorted,
+                z,
                 (self.wind_shear - 1),
-                where=grid.z_sorted != 0.0
+                where=z != 0.0
             )
         )
         # If no heterogeneous inflow defined, then set all speeds ups to 1.0
@@ -166,42 +182,33 @@ def initialize_velocity_field(self, grid: Grid) -> None:
         # If heterogeneous flow data is given, the speed ups at the defined
         # grid locations are determined in either 2 or 3 dimensions.
         else:
-            bounds = np.array(list(zip(
-                self.heterogeneous_inflow_config['x'],
-                self.heterogeneous_inflow_config['y']
-            )))
-            hull = ConvexHull(bounds)
-            polygon = Polygon(bounds[hull.vertices])
-            path = mpltPath.Path(polygon.boundary.coords)
-            points = np.column_stack(
-                (
-                    grid.x_sorted_inertial_frame.flatten(),
-                    grid.y_sorted_inertial_frame.flatten(),
-                )
-            )
-            inside = path.contains_points(points)
-            if not np.all(inside):
-                self.logger.warning(
-                    "The calculated flow field contains points outside of the the user-defined "
-                    "heterogeneous inflow bounds. For these points, the interpolated value has "
-                    "been filled with the freestream wind speed. If this is not the desired "
-                    "behavior, the user will need to expand the heterogeneous inflow bounds to "
-                    "fully cover the calculated flow field area."
-                )
+            if isinstance(self.het_map[0], NearestNDInterpolator):
+                # Do not check for being inside of bounds for nearest neighbor interpolation.
+                pass
+            else:
+                # Create a convex hull around the user-defined heterogeneous inflow bounds
+                # to check if the calculated flow field is within the bounds.
+                bounds = np.array(list(zip(
+                    self.heterogeneous_inflow_config['x'],
+                    self.heterogeneous_inflow_config['y']
+                )))
+                hull = ConvexHull(bounds)
+                polygon = Polygon(bounds[hull.vertices])
+                path = mpltPath.Path(polygon.boundary.coords)
+                inside = path.contains_points(np.column_stack((x.flatten(), y.flatten())))
+                if not np.all(inside):
+                    self.logger.warning(
+                        "The calculated flow field contains points outside of the the user-defined "
+                        "heterogeneous inflow bounds. For these points, the interpolated value has "
+                        "been filled with the freestream wind speed. If this is not the desired "
+                        "behavior, the user will need to expand the heterogeneous inflow bounds to "
+                        "fully cover the calculated flow field area."
+                    )
 
             if len(self.het_map[0].points[0]) == 2:
-                speed_ups = self.calculate_speed_ups(
-                    self.het_map,
-                    grid.x_sorted_inertial_frame,
-                    grid.y_sorted_inertial_frame
-                )
+                speed_ups = self.calculate_speed_ups(self.het_map, x, y)
             elif len(self.het_map[0].points[0]) == 3:
-                speed_ups = self.calculate_speed_ups(
-                    self.het_map,
-                    grid.x_sorted_inertial_frame,
-                    grid.y_sorted_inertial_frame,
-                    grid.z_sorted
-                )
+                speed_ups = self.calculate_speed_ups(self.het_map, x, y, z)
 
         # Create the sheer-law wind profile
         # This array is of shape (# wind directions, # wind speeds, grid.template_array)
@@ -284,13 +291,19 @@ def generate_heterogeneous_wind_map(self):
                 - **y**: A list of y locations at which the speed up factors are defined.
                 - **z** (optional): A list of z locations at which the speed up factors are defined.
         """
-        speed_multipliers = np.array(self.heterogeneous_inflow_config['speed_multipliers'])
-        x = self.heterogeneous_inflow_config['x']
-        y = self.heterogeneous_inflow_config['y']
-        z = self.heterogeneous_inflow_config['z']
+        speed_multipliers = np.array(self.heterogeneous_inflow_config["speed_multipliers"])
+        x = self.heterogeneous_inflow_config["x"]
+        y = self.heterogeneous_inflow_config["y"]
+        z = self.heterogeneous_inflow_config["z"]
+
+        if "interp_method" in self.heterogeneous_inflow_config.keys():
+            interp_method = self.heterogeneous_inflow_config["interp_method"]
+        else:
+            interp_method = "linear"
 
         # Declare an empty list to store interpolants by findex
         interps_f = np.empty(self.n_findex, dtype=object)
+
         if z is not None:
             # Compute the 3-dimensional interpolants for each wind direction
             # Linear interpolation is used for points within the user-defined area of values,
@@ -301,11 +314,17 @@ def generate_heterogeneous_wind_map(self):
 
             # Create triangulation using zeroth findex
             interp_3d = self.interpolate_multiplier_xyz(
-                x, y, z, speed_multipliers[0], fill_value=1.0
+                x,
+                y,
+                z,
+                speed_multipliers[0],
+                fill_value=1.0,
+                interp_method=interp_method,
             )
+            interp_shape = interp_3d.values.shape
             # Copy the interpolant for each findex and overwrite the values
             for findex in range(self.n_findex):
-                interp_3d.values = speed_multipliers[findex, :].reshape(-1, 1)
+                interp_3d.values = speed_multipliers[findex, :].reshape(interp_shape)
                 interps_f[findex] = copy.deepcopy(interp_3d)
 
         else:
@@ -317,19 +336,27 @@ def generate_heterogeneous_wind_map(self):
             # once and then overwrite the values for each findex.
 
             # Create triangulation using zeroth findex
-            interp_2d = self.interpolate_multiplier_xy(x, y, speed_multipliers[0], fill_value=1.0)
+            interp_2d = self.interpolate_multiplier_xy(
+                x, y, speed_multipliers[0], fill_value=1.0, interp_method=interp_method
+            )
             # Copy the interpolant for each findex and overwrite the values
             for findex in range(self.n_findex):
-                interp_2d.values = speed_multipliers[findex, :].reshape(-1, 1)
+                if interp_method == "linear":
+                    interp_2d.values = speed_multipliers[findex, :].reshape(-1, 1)
+                else:
+                    interp_2d.values = speed_multipliers[findex, :]
                 interps_f[findex] = copy.deepcopy(interp_2d)
 
         self.het_map = interps_f
 
     @staticmethod
-    def interpolate_multiplier_xy(x: NDArrayFloat,
-                                  y: NDArrayFloat,
-                                  multiplier: NDArrayFloat,
-                                  fill_value: float = 1.0):
+    def interpolate_multiplier_xy(
+        x: NDArrayFloat,
+        y: NDArrayFloat,
+        multiplier: NDArrayFloat,
+        fill_value: float = 1.0,
+        interp_method: str = "linear",
+    ):
         """Return an interpolant for a 2D multiplier field.
 
         Args:
@@ -337,20 +364,28 @@ def interpolate_multiplier_xy(x: NDArrayFloat,
             y (NDArrayFloat): y locations
             multiplier (NDArrayFloat): multipliers
             fill_value (float): fill value for points outside the region
+            interp_method (str): interpolation method, either "linear" or "nearest".
+                Default is "linear".
 
         Returns:
             LinearNDInterpolator: interpolant
         """
-
-        return LinearNDInterpolator(list(zip(x, y)), multiplier, fill_value=fill_value)
-
+        if interp_method == "linear":
+            return LinearNDInterpolator(list(zip(x, y)), multiplier, fill_value=fill_value)
+        elif interp_method == "nearest":
+            return NearestNDInterpolator(list(zip(x, y)), multiplier)
+        else:
+            raise UserWarning("Incompatible interpolation method specified.")
 
     @staticmethod
-    def interpolate_multiplier_xyz(x: NDArrayFloat,
-                                   y: NDArrayFloat,
-                                   z: NDArrayFloat,
-                                   multiplier: NDArrayFloat,
-                                   fill_value: float = 1.0):
+    def interpolate_multiplier_xyz(
+        x: NDArrayFloat,
+        y: NDArrayFloat,
+        z: NDArrayFloat,
+        multiplier: NDArrayFloat,
+        fill_value: float = 1.0,
+        interp_method: str = "linear",
+    ):
         """Return an interpolant for a 3D multiplier field.
 
         Args:
@@ -359,9 +394,16 @@ def interpolate_multiplier_xyz(x: NDArrayFloat,
             z (NDArrayFloat): z locations
             multiplier (NDArrayFloat): multipliers
             fill_value (float): fill value for points outside the region
+            interp_method (str): interpolation method, either "linear" or "nearest".
+                Default is "linear".
 
         Returns:
             LinearNDInterpolator: interpolant
         """
 
-        return LinearNDInterpolator(list(zip(x, y, z)), multiplier, fill_value=fill_value)
+        if interp_method == "linear":
+            return LinearNDInterpolator(list(zip(x, y, z)), multiplier, fill_value=fill_value)
+        elif interp_method == "nearest":
+            return NearestNDInterpolator(list(zip(x, y, z)), multiplier)
+        else:
+            raise UserWarning("Incompatible interpolation method specified.")
diff --git a/floris/core/rotor_velocity.py b/floris/core/rotor_velocity.py
index 43d4e3077..16007db30 100644
--- a/floris/core/rotor_velocity.py
+++ b/floris/core/rotor_velocity.py
@@ -47,11 +47,11 @@ def rotor_velocity_tilt_cosine_correction(
     tilt_angles = np.where(correct_cp_ct_for_tilt, tilt_angles, old_tilt_angle)
 
     # Compute the rotor effective velocity adjusting for tilt
-    relative_tilt = tilt_angles - ref_tilt
     rotor_effective_velocities = (
         rotor_effective_velocities
-        * cosd(relative_tilt) ** (cosine_loss_exponent_tilt / 3.0)
+        * (cosd(tilt_angles) / cosd(ref_tilt)) ** (cosine_loss_exponent_tilt / 3.0)
     )
+
     return rotor_effective_velocities
 
 def simple_mean(array, axis=0):
diff --git a/floris/core/solver.py b/floris/core/solver.py
index 679a97c4a..2f0766bb5 100644
--- a/floris/core/solver.py
+++ b/floris/core/solver.py
@@ -79,12 +79,9 @@ def sequential_solver(
     for i in range(grid.n_turbines):
 
         # Get the current turbine quantities
-        x_i = np.mean(grid.x_sorted[:, i:i+1], axis=(2, 3))
-        x_i = x_i[:, :, None, None]
-        y_i = np.mean(grid.y_sorted[:, i:i+1], axis=(2, 3))
-        y_i = y_i[:, :, None, None]
-        z_i = np.mean(grid.z_sorted[:, i:i+1], axis=(2, 3))
-        z_i = z_i[:, :, None, None]
+        x_i = np.mean(grid.x_sorted[:, i:i+1], axis=(2, 3), keepdims=True)
+        y_i = np.mean(grid.y_sorted[:, i:i+1], axis=(2, 3), keepdims=True)
+        z_i = np.mean(grid.z_sorted[:, i:i+1], axis=(2, 3), keepdims=True)
 
         u_i = flow_field.u_sorted[:, i:i+1]
         v_i = flow_field.v_sorted[:, i:i+1]
@@ -256,8 +253,9 @@ def sequential_solver(
     flow_field.turbulence_intensity_field_sorted = turbine_turbulence_intensity
     flow_field.turbulence_intensity_field_sorted_avg = np.mean(
         turbine_turbulence_intensity,
-        axis=(2,3)
-    )[:, :, None, None]
+        axis=(2,3),
+        keepdims=True
+    )
 
 
 def full_flow_sequential_solver(
@@ -313,16 +311,19 @@ def full_flow_sequential_solver(
     v_wake = np.zeros_like(flow_field.v_initial_sorted)
     w_wake = np.zeros_like(flow_field.w_initial_sorted)
 
+    # Initialize the turbulence intensity field over the entire flow field grid
+    n_points = flow_field_grid.x_sorted.shape[1]
+    ambient_turbulence_intensities = flow_field.turbulence_intensities[:, None, None, None]
+    ambient_turbulence_intensities = np.repeat(ambient_turbulence_intensities, n_points, axis=1)
+    turbulence_intensity_field = ambient_turbulence_intensities.copy()
+
     # Calculate the velocity deficit sequentially from upstream to downstream turbines
     for i in range(flow_field_grid.n_turbines):
 
         # Get the current turbine quantities
-        x_i = np.mean(turbine_grid.x_sorted[:, i:i+1], axis=(2, 3))
-        x_i = x_i[:, :, None, None]
-        y_i = np.mean(turbine_grid.y_sorted[:, i:i+1], axis=(2, 3))
-        y_i = y_i[:, :, None, None]
-        z_i = np.mean(turbine_grid.z_sorted[:, i:i+1], axis=(2, 3))
-        z_i = z_i[:, :, None, None]
+        x_i = np.mean(turbine_grid.x_sorted[:, i:i+1], axis=(2, 3), keepdims=True)
+        y_i = np.mean(turbine_grid.y_sorted[:, i:i+1], axis=(2, 3), keepdims=True)
+        z_i = np.mean(turbine_grid.z_sorted[:, i:i+1], axis=(2, 3), keepdims=True)
 
         u_i = turbine_grid_flow_field.u_sorted[:, i:i+1]
         v_i = turbine_grid_flow_field.v_sorted[:, i:i+1]
@@ -446,10 +447,37 @@ def full_flow_sequential_solver(
             velocity_deficit * flow_field.u_initial_sorted
         )
 
+        wake_added_ti = model_manager.turbulence_model.function(
+            ambient_turbulence_intensities,
+            flow_field_grid.x_sorted,
+            x_i,
+            rotor_diameter_i,
+            axial_induction_i,
+        )
+
+        # Calculate locations where wake-added turbulence (WAT) applies
+        area_overlap = np.where(velocity_deficit * flow_field.u_initial_sorted > 0.05, 1, 0)
+
+        # Modify wake added turbulence by wake area overlap
+        downstream_influence_length = 15 * rotor_diameter_i
+        ti_added = (
+            area_overlap
+            * np.nan_to_num(wake_added_ti, posinf=0.0)
+            * (flow_field_grid.x_sorted > x_i)
+            * (np.abs(y_i - flow_field_grid.y_sorted) < 2 * rotor_diameter_i)
+            * (flow_field_grid.x_sorted <= downstream_influence_length + x_i)
+        )
+        # Combine turbine TIs with WAT
+        turbulence_intensity_field = np.maximum(
+            np.sqrt(ti_added**2 + ambient_turbulence_intensities**2), turbulence_intensity_field
+        )
+
         flow_field.u_sorted = flow_field.u_initial_sorted - wake_field
         flow_field.v_sorted += v_wake
         flow_field.w_sorted += w_wake
 
+    flow_field.turbulence_intensity_field_sorted = turbulence_intensity_field
+
 
 def cc_solver(
     farm: Farm,
@@ -487,12 +515,9 @@ def cc_solver(
     for i in range(grid.n_turbines):
 
         # Get the current turbine quantities
-        x_i = np.mean(grid.x_sorted[:, i:i+1], axis=(2, 3))
-        x_i = x_i[:, :, None, None]
-        y_i = np.mean(grid.y_sorted[:, i:i+1], axis=(2, 3))
-        y_i = y_i[:, :, None, None]
-        z_i = np.mean(grid.z_sorted[:, i:i+1], axis=(2, 3))
-        z_i = z_i[:, :, None, None]
+        x_i = np.mean(grid.x_sorted[:, i:i+1], axis=(2, 3), keepdims=True)
+        y_i = np.mean(grid.y_sorted[:, i:i+1], axis=(2, 3), keepdims=True)
+        z_i = np.mean(grid.z_sorted[:, i:i+1], axis=(2, 3), keepdims=True)
 
         rotor_diameter_i = farm.rotor_diameters_sorted[:, i:i+1, None, None]
 
@@ -666,10 +691,9 @@ def cc_solver(
 
         # Calculate wake overlap for wake-added turbulence (WAT)
         area_overlap = 1 - (
-            np.sum(turb_u_wake <= 0.05, axis=(2, 3))
+            np.sum(turb_u_wake <= 0.05, axis=(2, 3), keepdims=True)
             / (grid.grid_resolution * grid.grid_resolution)
         )
-        area_overlap = area_overlap[:, :, None, None]
 
         # Modify wake added turbulence by wake area overlap
         downstream_influence_length = 15 * rotor_diameter_i
@@ -693,8 +717,9 @@ def cc_solver(
     flow_field.turbulence_intensity_field_sorted = turbine_turbulence_intensity
     flow_field.turbulence_intensity_field_sorted_avg = np.mean(
         turbine_turbulence_intensity,
-        axis=(2,3)
-    )[:, :, None, None]
+        axis=(2,3),
+        keepdims=True
+    )
 
 
 def full_flow_cc_solver(
@@ -749,6 +774,12 @@ def full_flow_cc_solver(
     w_wake = np.zeros_like(flow_field.w_initial_sorted)
     turb_u_wake = np.zeros_like(flow_field.u_initial_sorted)
 
+    # Initialize the turbulence intensity field over the entire flow field grid
+    n_points = flow_field_grid.x_sorted.shape[1]
+    ambient_turbulence_intensities = flow_field.turbulence_intensities[:, None, None, None]
+    ambient_turbulence_intensities = np.repeat(ambient_turbulence_intensities, n_points, axis=1)
+    turbulence_intensity_field = ambient_turbulence_intensities.copy()
+
     shape = (farm.n_turbines,) + np.shape(flow_field.u_initial_sorted)
     Ctmp = np.zeros((shape))
 
@@ -756,12 +787,9 @@ def full_flow_cc_solver(
     for i in range(flow_field_grid.n_turbines):
 
         # Get the current turbine quantities
-        x_i = np.mean(turbine_grid.x_sorted[:, i:i+1], axis=(2, 3))
-        x_i = x_i[:, :, None, None]
-        y_i = np.mean(turbine_grid.y_sorted[:, i:i+1], axis=(2, 3))
-        y_i = y_i[:, :, None, None]
-        z_i = np.mean(turbine_grid.z_sorted[:, i:i+1], axis=(2, 3))
-        z_i = z_i[:, :, None, None]
+        x_i = np.mean(turbine_grid.x_sorted[:, i:i+1], axis=(2, 3), keepdims=True)
+        y_i = np.mean(turbine_grid.y_sorted[:, i:i+1], axis=(2, 3), keepdims=True)
+        z_i = np.mean(turbine_grid.z_sorted[:, i:i+1], axis=(2, 3), keepdims=True)
 
         u_i = turbine_grid_flow_field.u_sorted[:, i:i+1]
         v_i = turbine_grid_flow_field.v_sorted[:, i:i+1]
@@ -882,9 +910,37 @@ def full_flow_cc_solver(
             **deficit_model_args,
         )
 
+        wake_added_turbulence_intensity = model_manager.turbulence_model.function(
+            ambient_turbulence_intensities,
+            flow_field_grid.x_sorted,
+            x_i,
+            rotor_diameter_i,
+            axial_induction_i
+        )
+
+        # Calculate wake overlap for wake-added turbulence (WAT)
+        area_overlap = np.where(turb_u_wake > 0.05, 1, 0)
+
+        # Modify wake added turbulence by wake area overlap
+        downstream_influence_length = 15 * rotor_diameter_i
+        ti_added = (
+            area_overlap
+            * np.nan_to_num(wake_added_turbulence_intensity, posinf=0.0)
+            * (flow_field_grid.x_sorted > x_i)
+            * (np.abs(y_i - flow_field_grid.y_sorted) < 2 * rotor_diameter_i)
+            * (flow_field_grid.x_sorted <= downstream_influence_length + x_i)
+        )
+
+        # Combine turbine TIs with WAT
+        turbulence_intensity_field = np.maximum(
+            np.sqrt(ti_added**2 + ambient_turbulence_intensities**2), turbulence_intensity_field
+        )
+
         flow_field.v_sorted += v_wake
         flow_field.w_sorted += w_wake
+
     flow_field.u_sorted = flow_field.u_initial_sorted - turb_u_wake
+    flow_field.turbulence_intensity_field_sorted = turbulence_intensity_field
 
 
 def turbopark_solver(
@@ -923,12 +979,9 @@ def turbopark_solver(
     # Calculate the velocity deficit sequentially from upstream to downstream turbines
     for i in range(grid.n_turbines):
         # Get the current turbine quantities
-        x_i = np.mean(grid.x_sorted[:, i:i+1], axis=(2, 3))
-        x_i = x_i[:, :, None, None]
-        y_i = np.mean(grid.y_sorted[:, i:i+1], axis=(2, 3))
-        y_i = y_i[:, :, None, None]
-        z_i = np.mean(grid.z_sorted[:, i:i+1], axis=(2, 3))
-        z_i = z_i[:, :, None, None]
+        x_i = np.mean(grid.x_sorted[:, i:i+1], axis=(2, 3), keepdims=True)
+        y_i = np.mean(grid.y_sorted[:, i:i+1], axis=(2, 3), keepdims=True)
+        z_i = np.mean(grid.z_sorted[:, i:i+1], axis=(2, 3), keepdims=True)
 
         Cts = thrust_coefficient(
             velocities=flow_field.u_sorted,
@@ -1013,10 +1066,8 @@ def turbopark_solver(
                 "and perform a thorough examination of the results."
             )
             for ii in range(i):
-                x_ii = np.mean(grid.x_sorted[:, ii:ii+1], axis=(2, 3))
-                x_ii = x_ii[:, :, None, None]
-                y_ii = np.mean(grid.y_sorted[:, ii:ii+1], axis=(2, 3))
-                y_ii = y_ii[:, :, None, None]
+                x_ii = np.mean(grid.x_sorted[:, ii:ii+1], axis=(2, 3), keepdims=True)
+                y_ii = np.mean(grid.y_sorted[:, ii:ii+1], axis=(2, 3), keepdims=True)
 
                 yaw_ii = farm.yaw_angles_sorted[:, ii:ii+1, None, None]
                 turbulence_intensity_ii = turbine_turbulence_intensity[:, ii:ii+1]
@@ -1094,10 +1145,13 @@ def turbopark_solver(
         # turbines; could use WAT_upstream
         # Calculate wake overlap for wake-added turbulence (WAT)
         area_overlap = (
-            np.sum(velocity_deficit * flow_field.u_initial_sorted > 0.05, axis=(2, 3))
+            np.sum(
+                velocity_deficit * flow_field.u_initial_sorted > 0.05,
+                axis=(2, 3),
+                keepdims=True
+            )
             / (grid.grid_resolution * grid.grid_resolution)
         )
-        area_overlap = area_overlap[:, :, None, None]
 
         # Modify wake added turbulence by wake area overlap
         downstream_influence_length = 15 * rotor_diameter_i
@@ -1121,8 +1175,9 @@ def turbopark_solver(
     flow_field.turbulence_intensity_field_sorted = turbine_turbulence_intensity
     flow_field.turbulence_intensity_field_sorted_avg = np.mean(
         turbine_turbulence_intensity,
-        axis=(2, 3)
-    )[:, :, None, None]
+        axis=(2, 3),
+        keepdims=True
+    )
 
 
 def full_flow_turbopark_solver(
@@ -1191,12 +1246,9 @@ def empirical_gauss_solver(
     for i in range(grid.n_turbines):
 
         # Get the current turbine quantities
-        x_i = np.mean(grid.x_sorted[:, i:i+1], axis=(2, 3))
-        x_i = x_i[:, :, None, None]
-        y_i = np.mean(grid.y_sorted[:, i:i+1], axis=(2, 3))
-        y_i = y_i[:, :, None, None]
-        z_i = np.mean(grid.z_sorted[:, i:i+1], axis=(2, 3))
-        z_i = z_i[:, :, None, None]
+        x_i = np.mean(grid.x_sorted[:, i:i+1], axis=(2, 3), keepdims=True)
+        y_i = np.mean(grid.y_sorted[:, i:i+1], axis=(2, 3), keepdims=True)
+        z_i = np.mean(grid.z_sorted[:, i:i+1], axis=(2, 3), keepdims=True)
 
         ct_i = thrust_coefficient(
             velocities=flow_field.u_sorted,
@@ -1398,6 +1450,12 @@ def full_flow_empirical_gauss_solver(
         model_manager
     )
 
+    # Create placeholder for TI, which is not currently used in the EmG model
+    n_points = flow_field_grid.x_sorted.shape[1]
+    ambient_turbulence_intensities = flow_field.turbulence_intensities[:, None, None, None]
+    ambient_turbulence_intensities = np.repeat(ambient_turbulence_intensities, n_points, axis=1)
+    turbulence_intensity_field = ambient_turbulence_intensities.copy()
+
     ### Referring to the quantities from above, calculate the wake in the full grid
 
     # Use full flow_field here to use the full grid in the wake models
@@ -1414,12 +1472,9 @@ def full_flow_empirical_gauss_solver(
     for i in range(flow_field_grid.n_turbines):
 
         # Get the current turbine quantities
-        x_i = np.mean(turbine_grid.x_sorted[:, i:i+1], axis=(2,3))
-        x_i = x_i[:, :, None, None]
-        y_i = np.mean(turbine_grid.y_sorted[:, i:i+1], axis=(2,3))
-        y_i = y_i[:, :, None, None]
-        z_i = np.mean(turbine_grid.z_sorted[:, i:i+1], axis=(2,3))
-        z_i = z_i[:, :, None, None]
+        x_i = np.mean(turbine_grid.x_sorted[:, i:i+1], axis=(2,3), keepdims=True)
+        y_i = np.mean(turbine_grid.y_sorted[:, i:i+1], axis=(2,3), keepdims=True)
+        z_i = np.mean(turbine_grid.z_sorted[:, i:i+1], axis=(2,3), keepdims=True)
 
         ct_i = thrust_coefficient(
             velocities=turbine_grid_flow_field.u_sorted,
@@ -1526,3 +1581,4 @@ def full_flow_empirical_gauss_solver(
         flow_field.u_sorted = flow_field.u_initial_sorted - wake_field
         flow_field.v_sorted += v_wake
         flow_field.w_sorted += w_wake
+        flow_field.turbulence_intensity_field_sorted = turbulence_intensity_field
diff --git a/floris/core/turbine/operation_models.py b/floris/core/turbine/operation_models.py
index 544086747..906ca0916 100644
--- a/floris/core/turbine/operation_models.py
+++ b/floris/core/turbine/operation_models.py
@@ -266,7 +266,8 @@ def thrust_coefficient(
         thrust_coefficient = (
             thrust_coefficient
             * cosd(yaw_angles)
-            * cosd(tilt_angles - power_thrust_table["ref_tilt"])
+            * cosd(tilt_angles)
+            / cosd(power_thrust_table["ref_tilt"])
         )
 
         return thrust_coefficient
@@ -294,7 +295,9 @@ def axial_induction(
             correct_cp_ct_for_tilt=correct_cp_ct_for_tilt
         )
 
-        misalignment_loss = cosd(yaw_angles) * cosd(tilt_angles - power_thrust_table["ref_tilt"])
+        misalignment_loss = (
+            cosd(yaw_angles) * cosd(tilt_angles) / cosd(power_thrust_table["ref_tilt"])
+        )
         return 0.5 / misalignment_loss * (1 - np.sqrt(1 - thrust_coefficient * misalignment_loss))
 
 @define
diff --git a/floris/core/turbine/turbine.py b/floris/core/turbine/turbine.py
index 039662ef1..0994b5c8f 100644
--- a/floris/core/turbine/turbine.py
+++ b/floris/core/turbine/turbine.py
@@ -51,23 +51,37 @@
 }
 
 
-def select_multidim_condition(
-    condition: dict | tuple,
-    specified_conditions: Iterable[tuple]
+def _select_multidim_condition(
+    condition: dict,
+    specified_conditions: Iterable[tuple],
+    condition_keys: list[str]
 ) -> tuple:
     """
     Convert condition to the type expected by power_thrust_table and select
     nearest specified condition
     """
-    if type(condition) is tuple:
-        pass
-    elif type(condition) is dict:
-        condition = tuple(condition.values())
+    if type(condition) is dict:
+        # Check valid keys
+        if set(condition.keys()) != set(condition_keys):
+            raise ValueError(
+                f"The provided condition keys {list(condition.keys())} do not match the "
+                f"expected keys {condition_keys}. A single value should be provided for "
+                "each dimension of the multidimensional power/thrust_coefficient table."
+            )
+        # Create a tuple of the condition values in the correct order
+        condition = tuple(condition[k] for k in condition_keys)
+    elif condition is None:
+        raise ValueError(
+            "multidim_condition must be provided if using multidimensional "
+            "power/thrust_coefficient."
+        )
     else:
-        raise TypeError("condition should be of type dict or tuple.")
+        raise TypeError("condition should be of type dict.")
 
     # Find the nearest key to the specified conditions.
     specified_conditions = np.array(specified_conditions)
+    if specified_conditions.ndim == 1: # Single specified condition
+        specified_conditions = specified_conditions.reshape(-1, 1)
 
     # Find the nearest key to the specified conditions.
     nearest_condition = np.zeros_like(condition)
@@ -76,7 +90,7 @@ def select_multidim_condition(
             specified_conditions[:, i][np.absolute(specified_conditions[:, i] - c).argmin()]
         )
 
-    return tuple(nearest_condition)
+    return tuple(nearest_condition)# if len(specified_conditions)
 
 
 def power(
@@ -96,7 +110,7 @@ def power(
     average_method: str = "cubic-mean",
     cubature_weights: NDArrayFloat | None = None,
     correct_cp_ct_for_tilt: bool = False,
-    multidim_condition: tuple | None = None, # Assuming only one condition at a time?
+    multidim_condition: dict | None = None,
 ) -> NDArrayFloat:
     """Power produced by a turbine adjusted for yaw and tilt. Value
     given in Watts.
@@ -130,7 +144,7 @@ def power(
             to determine a rotor-average wind speed. Defaults to "cubic-mean".
         cubature_weights (NDArrayFloat | None): Weights for cubature averaging methods. Defaults to
             None.
-        multidim_condition (tuple | None): The condition tuple used to select the appropriate
+        multidim_condition (dict | None): The condition dictionary used to select the appropriate
             thrust coefficient relationship for multidimensional power/thrust tables. Defaults to
             None.
 
@@ -163,9 +177,10 @@ def power(
         else: # assumed multidimensional, use multidim lookup
             # Currently, only works for single mutlidim condition. May need to
             # loop in the case where there are multiple conditions.
-            multidim_condition = select_multidim_condition(
+            multidim_condition = _select_multidim_condition(
                 multidim_condition,
-                list(turbine_power_thrust_tables[turb_type].keys())
+                [k for k in turbine_power_thrust_tables[turb_type].keys() if k != "condition_keys"],
+                turbine_power_thrust_tables[turb_type]["condition_keys"]
             )
             power_thrust_table = turbine_power_thrust_tables[turb_type][multidim_condition]
 
@@ -210,7 +225,7 @@ def thrust_coefficient(
     ix_filter: NDArrayFilter | Iterable[int] | None = None,
     average_method: str = "cubic-mean",
     cubature_weights: NDArrayFloat | None = None,
-    multidim_condition: tuple | None = None, # Assuming only one condition at a time?
+    multidim_condition: dict | None = None,
 ) -> NDArrayFloat:
 
     """Thrust coefficient of a turbine.
@@ -246,7 +261,7 @@ def thrust_coefficient(
             to determine a rotor-average wind speed. Defaults to "cubic-mean".
         cubature_weights (NDArrayFloat | None): Weights for cubature averaging methods. Defaults to
             None.
-        multidim_condition (tuple | None): The condition tuple used to select the appropriate
+        multidim_condition (dict | None): The condition dictionary used to select the appropriate
             thrust coefficient relationship for multidimensional power/thrust tables. Defaults to
             None.
 
@@ -279,9 +294,10 @@ def thrust_coefficient(
         else: # assumed multidimensional, use multidim lookup
             # Currently, only works for single mutlidim condition. May need to
             # loop in the case where there are multiple conditions.
-            multidim_condition = select_multidim_condition(
+            multidim_condition = _select_multidim_condition(
                 multidim_condition,
-                list(turbine_power_thrust_tables[turb_type].keys())
+                [k for k in turbine_power_thrust_tables[turb_type].keys() if k != "condition_keys"],
+                turbine_power_thrust_tables[turb_type]["condition_keys"]
             )
             power_thrust_table = turbine_power_thrust_tables[turb_type][multidim_condition]
 
@@ -329,7 +345,7 @@ def axial_induction(
     ix_filter: NDArrayFilter | Iterable[int] | None = None,
     average_method: str = "cubic-mean",
     cubature_weights: NDArrayFloat | None = None,
-    multidim_condition: tuple | None = None, # Assuming only one condition at a time?
+    multidim_condition: dict | None = None,
 ) -> NDArrayFloat:
     """Axial induction factor of the turbine incorporating
     the thrust coefficient and yaw angle.
@@ -361,7 +377,7 @@ def axial_induction(
             to determine a rotor-average wind speed. Defaults to "cubic-mean".
         cubature_weights (NDArrayFloat | None): Weights for cubature averaging methods. Defaults to
             None.
-        multidim_condition (tuple | None): The condition tuple used to select the appropriate
+        multidim_condition (dict | None): The condition dictionary used to select the appropriate
             thrust coefficient relationship for multidimensional power/thrust tables. Defaults to
             None.
 
@@ -393,10 +409,10 @@ def axial_induction(
             power_thrust_table = turbine_power_thrust_tables[turb_type]
         else: # assumed multidimensional, use multidim lookup
             # Currently, only works for single mutlidim condition. May need to
-            # loop in the case where there are multiple conditions.
-            multidim_condition = select_multidim_condition(
+            multidim_condition = _select_multidim_condition(
                 multidim_condition,
-                list(turbine_power_thrust_tables[turb_type].keys())
+                [k for k in turbine_power_thrust_tables[turb_type].keys() if k != "condition_keys"],
+                turbine_power_thrust_tables[turb_type]["condition_keys"]
             )
             power_thrust_table = turbine_power_thrust_tables[turb_type][multidim_condition]
 
@@ -592,6 +608,8 @@ def _initialize_multidim_power_thrust_table(self):
         for key in df2.index.unique():
             # Select the correct ws/Cp/Ct data
             data = df2.loc[key]
+            if type(key) is not tuple:
+                key = (key,)
 
             # Build the interpolants
             power_thrust_table_.update(
@@ -606,7 +624,8 @@ def _initialize_multidim_power_thrust_table(self):
             )
             # Add reference information at the lower level
 
-        # Set on-object version
+        # Save names of dimensions and set on-object version
+        power_thrust_table_.update({"condition_keys": self.condition_keys})
         self.power_thrust_table = power_thrust_table_
 
     @power_thrust_table.validator
@@ -617,14 +636,16 @@ def check_power_thrust_table(self, instance: attrs.Attribute, value: dict) -> No
         """
 
         if self.multi_dimensional_cp_ct:
-            if isinstance(list(value.keys())[0], tuple):
-                value = list(value.values())[0] # Check the first entry of multidim
-            elif "power_thrust_data_file" in value.keys():
+            if "power_thrust_data_file" in value.keys():
                 return None
             else:
-                raise ValueError(
-                    "power_thrust_data_file must be defined if multi_dimensional_cp_ct is True."
-                )
+                key_types = [type(k) for k in value.keys()]
+                if key_types[0] in (tuple, float, int):
+                    value = list(value.values())[0] # Check the first entry of multidim
+                else:
+                    raise ValueError(
+                        "power_thrust_data_file must be defined if multi_dimensional_cp_ct is True."
+                    )
 
         if not {"wind_speed", "power", "thrust_coefficient"} <= set(value.keys()):
             raise ValueError(
diff --git a/floris/core/wake_deflection/gauss.py b/floris/core/wake_deflection/gauss.py
index 8e1f7378f..060d52e67 100644
--- a/floris/core/wake_deflection/gauss.py
+++ b/floris/core/wake_deflection/gauss.py
@@ -289,7 +289,7 @@ def wake_added_yaw(
         scale,
     )
 
-    turbine_average_velocity = np.cbrt(np.mean(u_i ** 3, axis=(2, 3)))[:, :, None, None]
+    turbine_average_velocity = np.cbrt(np.mean(u_i ** 3, axis=(2, 3), keepdims=True))
     Gamma_wake_rotation = 0.25 * 2 * pi * D * (aI - aI ** 2) * turbine_average_velocity / TSR
 
     ### compute the spanwise and vertical velocities induced by yaw
@@ -384,7 +384,7 @@ def calculate_transverse_velocity(
         Ct,
         scale,
     )
-    turbine_average_velocity = np.cbrt(np.mean(u_i ** 3, axis=(2,3)))[:, :, None, None]
+    turbine_average_velocity = np.cbrt(np.mean(u_i ** 3, axis=(2,3), keepdims=True))
     Gamma_wake_rotation = 0.25 * 2 * pi * D * (aI - aI ** 2) * turbine_average_velocity / TSR
 
     ### compute the spanwise and vertical velocities induced by yaw
diff --git a/floris/core/wake_velocity/cumulative_gauss_curl.py b/floris/core/wake_velocity/cumulative_gauss_curl.py
index 86d8c982e..ba88c574d 100644
--- a/floris/core/wake_velocity/cumulative_gauss_curl.py
+++ b/floris/core/wake_velocity/cumulative_gauss_curl.py
@@ -84,8 +84,7 @@ def function(
         turbine_yaw = yaw_i
 
         # TODO Should this be cbrt? This is done to match v2
-        turb_avg_vels = np.cbrt(np.mean(u_i ** 3, axis=(2, 3)))
-        turb_avg_vels = turb_avg_vels[:, :, None, None]
+        turb_avg_vels = np.cbrt(np.mean(u_i ** 3, axis=(2, 3), keepdims=True))
 
         delta_x = x - x_i
 
@@ -100,22 +99,16 @@ def function(
             self.c_s2,
         )
 
-        x_i_loc = np.mean(x_i, axis=(2, 3))
-        x_i_loc = x_i_loc[:, :, None, None]
+        y_i_loc = np.mean(y_i, axis=(2, 3), keepdims=True)
+        z_i_loc = np.mean(z_i, axis=(2, 3), keepdims=True)
 
-        y_i_loc = np.mean(y_i, axis=(2, 3))
-        y_i_loc = y_i_loc[:, :, None, None]
-
-        z_i_loc = np.mean(z_i, axis=(2, 3))
-        z_i_loc = z_i_loc[:, :, None, None]
-
-        x_coord = np.mean(x, axis=(2, 3))[:, :, None, None]
+        x_coord = np.mean(x, axis=(2, 3), keepdims=True)
 
         y_loc = y
-        y_coord = np.mean(y, axis=(2, 3))[:, :, None, None]
+        y_coord = np.mean(y, axis=(2, 3), keepdims=True)
 
         z_loc = z  # np.mean(z, axis=(3,4))
-        z_coord = np.mean(z, axis=(2, 3))[:, :, None, None]
+        z_coord = np.mean(z, axis=(2, 3), keepdims=True)
 
         sum_lbda = np.zeros_like(u_initial)
 
diff --git a/floris/floris_model.py b/floris/floris_model.py
index 3547afe0f..aaa384029 100644
--- a/floris/floris_model.py
+++ b/floris/floris_model.py
@@ -142,6 +142,7 @@ def _reinitialize(
         solver_settings: dict | None = None,
         heterogeneous_inflow_config=None,
         wind_data: type[WindDataBase] | None = None,
+        multidim_conditions: dict | None = None,
     ):
         """
         Instantiate a new Floris object with updated conditions set by arguments. Any parameters
@@ -264,6 +265,8 @@ def _reinitialize(
 
             flow_field_dict["heterogeneous_inflow_config"] = heterogeneous_inflow_config
 
+        if multidim_conditions is not None:
+            flow_field_dict["multidim_conditions"] = multidim_conditions
 
 
         if solver_settings is not None:
@@ -401,6 +404,7 @@ def set(
         awc_amplitudes: NDArrayFloat | list[float] | list[float, None] | None = None,
         awc_frequencies: NDArrayFloat | list[float] | list[float, None] | None = None,
         disable_turbines: NDArrayBool | list[bool] | None = None,
+        multidim_conditions: dict | None = None,
     ):
         """
         Set the wind conditions and operation setpoints for the wind farm.
@@ -456,6 +460,7 @@ def set(
             solver_settings=solver_settings,
             heterogeneous_inflow_config=heterogeneous_inflow_config,
             wind_data=wind_data,
+            multidim_conditions=multidim_conditions,
         )
 
         # If the yaw angles or power setpoints are not the default, set them back to the
@@ -1058,6 +1063,7 @@ def set_for_viz(self, findex: int, solver_settings: dict) -> None:
                 'y': self.core.flow_field.heterogeneous_inflow_config['y'],
                 'speed_multipliers':
                     self.core.flow_field.heterogeneous_inflow_config['speed_multipliers'][findex:findex+1],
+                'interp_method': self.core.flow_field.heterogeneous_inflow_config['interp_method'],
             }
             if 'z' in self.core.flow_field.heterogeneous_inflow_config:
                 heterogeneous_inflow_config['z'] = (
@@ -1167,7 +1173,7 @@ def calculate_horizontal_plane(
                 Defaults to None.
             y_bounds (tuple, optional): Limits of output array (in m).
                 Defaults to None.
-            finder_for_viz (int, optional): Index of the condition to visualize.
+            findex_for_viz (int, optional): Index of the condition to visualize.
 
         Returns:
             :py:class:`~.tools.cut_plane.CutPlane`: containing values
@@ -1382,6 +1388,25 @@ def sample_flow_at_points(self, x: NDArrayFloat, y: NDArrayFloat, z: NDArrayFloa
 
         return self.core.solve_for_points(x, y, z)
 
+    def sample_ti_at_points(self, x: NDArrayFloat, y: NDArrayFloat, z: NDArrayFloat):
+        """
+        Extract the turbulence intensity at points in the flow.
+
+        Args:
+            x (1DArrayFloat | list): x-locations of points where TI is desired.
+            y (1DArrayFloat | list): y-locations of points where TI is desired.
+            z (1DArrayFloat | list): z-locations of points where TI is desired.
+
+        Returns:
+            3DArrayFloat containing turbulence intensity with dimensions
+            (# of findex, # of sample points)
+        """
+
+        self.sample_flow_at_points(x, y, z) # Solve, but ignore returned velocities
+
+        # Remove grid dimensions and return sorted TI field
+        return self.core.flow_field.turbulence_intensity_field_sorted[:, :, 0, 0]
+
     def sample_velocity_deficit_profiles(
         self,
         direction: str = "cross-stream",
diff --git a/floris/heterogeneous_map.py b/floris/heterogeneous_map.py
index 137998cd7..bcefbc334 100644
--- a/floris/heterogeneous_map.py
+++ b/floris/heterogeneous_map.py
@@ -29,7 +29,13 @@ class HeterogeneousMap(LoggingManager):
             (degrees). Optional.
         wind_speeds (NDArrayFloat, optional): A 1D NumPy array (size num_ws) of wind speeds (m/s).
             Optional.
-
+        interp_method (str, optional): Interpolation method used to calculate the heterogeneous
+            wind speeds at various locations in the wind farm. Options are 'linear' and 'nearest',
+            representing linear interpolation and nearest-neighbor interpolation respectively.
+            Linear interpolation is accurate, nearest-neighbor interpolation is very fast but
+            inaccurate. Note also that in the default 'linear' setting, speed-ups at locations
+            outside the convex hull of points defined by `x`, `y` and `z` are 1.0 while in
+            the nearest case will be the value of the nearest point. Defaults to 'linear'. Optional.
 
     Notes:
         * If wind_directions and wind_speeds are both defined, then they must be the same length
@@ -45,6 +51,7 @@ def __init__(
         z: NDArrayFloat = None,
         wind_directions: NDArrayFloat = None,
         wind_speeds: NDArrayFloat = None,
+        interp_method: str = "linear",
     ):
         # Check that x, y and speed_multipliers are lists or numpy arrays
         if not isinstance(x, (list, np.ndarray)):
@@ -62,6 +69,7 @@ def __init__(
         self.x = np.array(x)
         self.y = np.array(y)
         self.speed_multipliers = np.array(speed_multipliers)
+        self.interp_method = str(interp_method)
 
         # If z is provided, save it as an np array
         if z is not None:
@@ -163,12 +171,11 @@ def __str__(self) -> str:
         )
 
         return (
-            f"HeterogeneousMap with {num_dim} dimensions\n"
-            f"Speeds-up defined for {len(self.x)} points and\n"
-            f"{self.speed_multipliers.shape[0]} wind conditions"
-
+            f"HeterogeneousMap with {num_dim} dimensions "
+            f"using interpolation method \"{self.interp_method}\".\n"
+            f"Speed multipliers are defined for {len(self.x)} points and "
+            f"{self.speed_multipliers.shape[0]} wind conditions."
             f"\n\n{df}"
-
         )
 
     def get_heterogeneous_inflow_config(
@@ -251,6 +258,7 @@ def get_heterogeneous_inflow_config(
                 "x": self.x,
                 "y": self.y,
                 "speed_multipliers": speed_multipliers_by_findex,
+                "interp_method": self.interp_method,
             }
         else:
             return {
@@ -258,6 +266,7 @@ def get_heterogeneous_inflow_config(
                 "y": self.y,
                 "z": self.z,
                 "speed_multipliers": speed_multipliers_by_findex,
+                "interp_method": self.interp_method,
             }
 
     def get_heterogeneous_map_2d(self, z: float):
@@ -287,6 +296,7 @@ def get_heterogeneous_map_2d(self, z: float):
             speed_multipliers=speed_multipliers,
             wind_directions=self.wind_directions,
             wind_speeds=self.wind_speeds,
+            interp_method=self.interp_method,
         )
 
     @staticmethod
@@ -459,29 +469,22 @@ def plot_single_speed_multiplier(
             np.linspace(plot_min_y, plot_max_y, 100),
             indexing="ij",
         )
-        x_plot = x_plot.flatten()
-        y_plot = y_plot.flatten()
 
         try:
-            lin_interpolant = FlowField.interpolate_multiplier_xy(x, y, speed_multiplier_row)
+            interpolant = FlowField.interpolate_multiplier_xy(
+                x,
+                y,
+                speed_multiplier_row,
+                fill_value=1.0,
+                interp_method=self.interp_method
+            )
 
-            lin_values = lin_interpolant(x, y)
+            het_map_mesh = interpolant(x_plot.flatten(), y_plot.flatten()).reshape(x_plot.shape)
         except scipy.spatial._qhull.QhullError:
             self.logger.warning(
-                "QhullError occurred in computing visualize. Falling back to nearest neighbor. "
-                "Note this may not represent the exact speed multipliers used within FLORIS."
+                "QhullError occurred in computing visualize. Creating null visualization."
             )
-            lin_values = np.nan * np.ones_like(x)
-
-        nearest_interpolant = NearestNDInterpolator(
-            x=np.vstack([x, y]).T,
-            y=speed_multiplier_row,
-        )
-        nn_values = nearest_interpolant(x, y)
-        ids_isnan = np.isnan(lin_values)
-
-        het_map_mesh = np.array(lin_values, copy=True)
-        het_map_mesh[ids_isnan] = nn_values[ids_isnan]
+            het_map_mesh = np.nan * np.ones_like(x_plot)
 
         # If vmin is not provided, use a value rounded to the nearest 0.01 below the minimum
         if vmin is None:
@@ -492,9 +495,9 @@ def plot_single_speed_multiplier(
             vmax = np.ceil(het_map_mesh.max() * 100) / 100
 
         # Produce color plot of the speed multipliers
-        im = ax.tricontourf(
-            x,
-            y,
+        im = ax.contourf(
+            x_plot,
+            y_plot,
             het_map_mesh,
             cmap=cmap,
             vmin=vmin,
diff --git a/floris/optimization/yaw_optimization/yaw_optimizer_geometric.py b/floris/optimization/yaw_optimization/yaw_optimizer_geometric.py
index 68c687512..042683a1f 100644
--- a/floris/optimization/yaw_optimization/yaw_optimizer_geometric.py
+++ b/floris/optimization/yaw_optimization/yaw_optimizer_geometric.py
@@ -14,7 +14,7 @@ class YawOptimizationGeometric(YawOptimization):
     used to provide a rough estimate of optimal yaw angles based purely on the
     wind farm geometry. Main use case is for coupled layout and yaw optimization.
 
-    See Stanely et al. (2023) for details: https://wes.copernicus.org/articles/8/1341/2023/
+    See Stanley et al. (2023) for details: https://wes.copernicus.org/articles/8/1341/2023/
     """
 
     def __init__(
@@ -85,23 +85,30 @@ def geometric_yaw(
     bottom_right_yaw_lower=0.0,
 ):
     """
-    turbine_x: unrotated x turbine coords
-    turbine_y: unrotated y turbine coords
-    wind_direction: float, degrees
-    rotor_diameter: float
-    left_x: where we start the trapezoid. Should be left as 0.
-    top_left_y: trapezoid top left coord
-    right_x: where to stop the trapezoid downstream.
-        Max coord after which the upstream turbine won't yaw.
-    top_right_y: trapezoid top right coord
-    top_left_yaw_upper: yaw angle associated with top left point (upper trapezoid)
-    top_right_yaw_upper: yaw angle associated with top right point
-    bottom_left_yaw_upper: yaw angle associated with bottom left point
-    bottom_right_yaw_upper: yaw angle associated with bottom right point
-    top_left_yaw_lower: yaw angle associated with top left point (lower trapezoid)
-    top_right_yaw_lower: yaw angle associated with top right point
-    bottom_left_yaw_lower: yaw angle associated with bottom left point
-    bottom_right_yaw_lower: yaw angle associated with bottom right point
+    Main function to compute geometric yaw angles based on wind farm layout for a single wind
+    direction.
+
+    Arguments:
+        turbine_x: unrotated x turbine coords
+        turbine_y: unrotated y turbine coords
+        wind_direction: float, degrees
+        rotor_diameter: float
+        left_x: where we start the trapezoid. Should be left as 0.
+        top_left_y: trapezoid top left coord
+        right_x: where to stop the trapezoid downstream.
+            Max coord after which the upstream turbine won't yaw.
+        top_right_y: trapezoid top right coord
+        top_left_yaw_upper: yaw angle associated with top left point (upper trapezoid)
+        top_right_yaw_upper: yaw angle associated with top right point
+        bottom_left_yaw_upper: yaw angle associated with bottom left point
+        bottom_right_yaw_upper: yaw angle associated with bottom right point
+        top_left_yaw_lower: yaw angle associated with top left point (lower trapezoid)
+        top_right_yaw_lower: yaw angle associated with top right point
+        bottom_left_yaw_lower: yaw angle associated with bottom left point
+        bottom_right_yaw_lower: yaw angle associated with bottom right point
+
+    Returns:
+        yaw_array: yaw angles for all turbines for the present wind direction.
     """
 
     nturbs = len(turbine_x)
@@ -143,56 +150,37 @@ def _process_layout(
     spread=0.1
 ):
     """
-    returns the distance from each turbine to the nearest downstream waked turbine
-    normalized by the rotor diameter. Right now "waked" is determind by a Jensen-like
+    Returns the distance from each turbine to the nearest downstream waked turbine
+    normalized by the rotor diameter. Right now "waked" is determined by a Jensen-like
     wake spread, but this could/should be modified to be the same as the trapezoid rule
     used to determine the yaw angles.
 
-    turbine_x: turbine x coords (rotated)
-    turbine_y: turbine y coords (rotated)
-    rotor_diameter: turbine rotor diameter (float)
-    spread=0.1: Jensen alpha wake spread value
+    Arguments:
+        turbine_x: turbine x coords (rotated)
+        turbine_y: turbine y coords (rotated)
+        rotor_diameter: turbine rotor diameter (float)
+        spread=0.1: Jensen alpha wake spread value
+
+    Returns:
+        dx: distance to nearest downstream turbine in rotor diameters for all turbines
+        dy: lateral distance to nearest downstream turbine in rotor diameters for all turbines
     """
-    len(turbine_x)
-
-    # # Intialize storage
-    # dx = np.zeros(nturbs) + 1E10
-    # dy = np.zeros(nturbs)
-
-    # for waking_index in range(nturbs):
-    #     for waked_index in range(nturbs):
-    #         if turbine_x[waked_index] > turbine_x[waking_index]:
-    #             r = spread*(turbine_x[waked_index]-turbine_x[waking_index]) + rotor_diameter/2.0
-    #             if abs(turbine_y[waked_index]-turbine_y[waking_index]) < (r+rotor_diameter/2.0):
-    #                 if (turbine_x[waked_index] - turbine_x[waking_index]) < dx[waking_index]:
-    #                     dx[waking_index] = turbine_x[waked_index] - turbine_x[waking_index]
-    #                     dy[waking_index] = turbine_y[waked_index]- turbine_y[waking_index]
-    #     if dx[waking_index] == 1E10:
-    #         dx[waking_index] = 0.0
-
-    # dx_ = dx
-    # dy_ = dy
 
     # Compute distances
     x_dists = turbine_x.reshape(-1,1).T - turbine_x.reshape(-1,1)
     y_dists = turbine_y.reshape(-1,1).T - turbine_y.reshape(-1,1)
 
-    # Any turbines upstream or at the turbine location are ineligble
+    # Any turbines upstream or at the turbine location are ineligible
     x_dists[x_dists <= 0.] = np.inf
 
     # Check within Jensen model spread
-    in_Jensen_wake = (abs(y_dists) < spread * x_dists + rotor_diameter)
+    in_Jensen_wake = (abs(y_dists) < spread * x_dists + rotor_diameter/2.0)
     x_dists[~in_Jensen_wake] = np.inf
 
     # Get minimums (and arguments to select the correct y values also)
     dx = x_dists.min(axis=1)
     dy = y_dists[range(len(turbine_x)), x_dists.argmin(axis=1)]
 
-    # Handle last turbine downstream
-    furthest_ds_turb_idx = np.where(dx == np.inf)[0]
-    dx[furthest_ds_turb_idx] = 0.
-    dy[furthest_ds_turb_idx] = 0.
-
     return dx/rotor_diameter, dy/rotor_diameter
 
 
@@ -213,43 +201,43 @@ def _get_yaw_angles(
     bottom_right_yaw_lower
 ):
     """
-    _______2,5___________________________4,6
-    |.......................................
-    |......1,7...........................3,8
-    |.......................................
-    ________________________________________
+    For a given turbine, return the geometric yaw angle based on its position relative to the
+    nearest downstream turbine.
 
-    x and y: dx and dy to the nearest downstream turbine in rotor diameteters with
+    Arguments:
+        x: downstream distance to the nearest downstream turbine in rotor diameters with
         turbines rotated so wind is coming left to right
-    left_x: where we start the trapezoid. Should be left as 0.
-    top_left_y: trapezoid top left coord
-    right_x: where to stop the trapezoid downstream.
-        Max coord after which the upstream turbine won't yaw.
-    top_right_y: trapezoid top right coord
-    top_left_yaw_upper: yaw angle associated with top left point (upper trapezoid)
-    top_right_yaw_upper: yaw angle associated with top right point
-    bottom_left_yaw_upper: yaw angle associated with bottom left point
-    bottom_right_yaw_upper: yaw angle associated with bottom right point
-    top_left_yaw_lower: yaw angle associated with top left point (lower trapezoid)
-    top_right_yaw_lower: yaw angle associated with top right point
-    bottom_left_yaw_lower: yaw angle associated with bottom left point
-    bottom_right_yaw_lower: yaw angle associated with bottom right point
+        y: lateral distance to the nearest downstream turbine in rotor diameters with
+        turbines rotated so wind is coming left to right
+        left_x: where we start the trapezoid. Should be left as 0.
+        top_left_y: trapezoid top left coord
+        right_x: where to stop the trapezoid downstream.
+            Max coord after which the upstream turbine won't yaw.
+        top_right_y: trapezoid top right coord
+        top_left_yaw_upper: yaw angle associated with top left point (upper trapezoid)
+        top_right_yaw_upper: yaw angle associated with top right point
+        bottom_left_yaw_upper: yaw angle associated with bottom left point
+        bottom_right_yaw_upper: yaw angle associated with bottom right point
+        top_left_yaw_lower: yaw angle associated with top left point (lower trapezoid)
+        top_right_yaw_lower: yaw angle associated with top right point
+        bottom_left_yaw_lower: yaw angle associated with bottom left point
+        bottom_right_yaw_lower: yaw angle associated with bottom right point
+
+    Returns:
+        (yaw angle): float, geometric yaw angle for the given turbine
     """
 
-    if x <= 0:
+    dx = (x-left_x)/(right_x-left_x)
+    if dx >= 1.0:
+        return 0.0
+    edge_y = top_left_y + (top_right_y-top_left_y)*dx
+    if abs(y) > edge_y:
         return 0.0
-    else:
-        dx = (x-left_x)/(right_x-left_x)
-        if dx >= 1.0:
-            return 0.0
-        edge_y = top_left_y + (top_right_y-top_left_y)*dx
-        if abs(y) > edge_y:
-            return 0.0
-        elif y >= -0.01: # Tolerance to handle numerical issues
-            top_yaw = top_left_yaw_upper + (top_right_yaw_upper-top_left_yaw_upper)*dx
-            bottom_yaw = bottom_left_yaw_upper + (bottom_right_yaw_upper-bottom_left_yaw_upper)*dx
-            return bottom_yaw + (top_yaw-bottom_yaw)*abs(y)/edge_y
-        elif y < -0.01:
-            top_yaw = top_left_yaw_lower + (top_right_yaw_lower-top_left_yaw_lower)*dx
-            bottom_yaw = bottom_left_yaw_lower + (bottom_right_yaw_lower-bottom_left_yaw_lower)*dx
-            return bottom_yaw + (top_yaw-bottom_yaw)*abs(y)/edge_y
+    elif y >= -0.01: # Tolerance to handle numerical issues
+        top_yaw = top_left_yaw_upper + (top_right_yaw_upper-top_left_yaw_upper)*dx
+        bottom_yaw = bottom_left_yaw_upper + (bottom_right_yaw_upper-bottom_left_yaw_upper)*dx
+        return bottom_yaw + (top_yaw-bottom_yaw)*abs(y)/edge_y
+    elif y < -0.01:
+        top_yaw = top_left_yaw_lower + (top_right_yaw_lower-top_left_yaw_lower)*dx
+        bottom_yaw = bottom_left_yaw_lower + (bottom_right_yaw_lower-bottom_left_yaw_lower)*dx
+        return bottom_yaw + (top_yaw-bottom_yaw)*abs(y)/edge_y
diff --git a/floris/par_floris_model.py b/floris/par_floris_model.py
index 1efa4263f..21488efb0 100644
--- a/floris/par_floris_model.py
+++ b/floris/par_floris_model.py
@@ -177,7 +177,7 @@ def run(self) -> None:
 
     def sample_flow_at_points(self, x: NDArrayFloat, y: NDArrayFloat, z: NDArrayFloat):
         """
-        Sample the flow field at specified points.
+        Sample the flow field velocity at specified points.
 
         Args:
             x: The x-coordinates of the points.
@@ -187,15 +187,59 @@ def sample_flow_at_points(self, x: NDArrayFloat, y: NDArrayFloat, z: NDArrayFloa
         Returns:
             NDArrayFloat: The wind speeds at the specified points.
         """
+        return self._sample_value_at_points(x, y, z, value="velocity")
+
+    def sample_ti_at_points(self, x: NDArrayFloat, y: NDArrayFloat, z: NDArrayFloat):
+        """
+        Sample the turbulence intensity at specified points.
+
+        Args:
+            x: The x-coordinates of the points.
+            y: The y-coordinates of the points.
+            z: The z-coordinates of the points.
+
+        Returns:
+            NDArrayFloat: The turbulence intensity at the specified points.
+        """
+        return self._sample_value_at_points(x, y, z, value="turbulence_intensity")
+
+    def _sample_value_at_points(
+            self,
+            x: NDArrayFloat,
+            y: NDArrayFloat,
+            z: NDArrayFloat,
+            value: str
+        ):
+        """
+        Underlying method for sample values at (x,y,z) points for ParFlorisModel.
+
+        Args:
+            x: The x-coordinates of the points.
+            y: The y-coordinates of the points.
+            z: The z-coordinates of the points.
+            value: The type of value to sample ("velocity" or "turbulence_intensity").
+
+        Returns:
+            NDArrayFloat: The values at the specified points.
+        """
         if self.return_turbine_powers_only:
             raise NotImplementedError(
-                "Sampling flow at points is not supported when "
+                "Sampling at points is not supported when "
                 "return_turbine_powers_only is set to True on ParFlorisModel."
             )
 
+        valid_values = ["velocity", "turbulence_intensity"]
+        if value not in valid_values:
+            raise ValueError(
+                f"Invalid value '{value}' for sampling. Must be one of {valid_values}."
+            )
+
         if self.interface is None:
             t0 = timerpc()
-            sampled_wind_speeds = super().sample_flow_at_points(x, y, z)
+            if value == "velocity":
+                output = super().sample_flow_at_points(x, y, z)
+            elif value == "turbulence_intensity":
+                output = super().sample_ti_at_points(x, y, z)
             t1 = timerpc()
             self._print_timings(t0, t1, None, None)
         else:
@@ -207,30 +251,37 @@ def sample_flow_at_points(self, x: NDArrayFloat, y: NDArrayFloat, z: NDArrayFloa
                 for fmodel_dict, control_setpoints in parallel_run_inputs
             ]
             t1 = timerpc()
+            if value == "velocity":
+                _parallel_func = _parallel_sample_flow_at_points
+                _parallel_fmap = _parallel_sample_flow_at_points_map
+            elif value == "turbulence_intensity":
+                _parallel_func = _parallel_sample_ti_at_points
+                _parallel_fmap = _parallel_sample_ti_at_points_map
+
             if self.interface == "multiprocessing":
                 with self._PoolExecutor(self.max_workers) as p:
                     sampled_wind_speeds_p = p.starmap(
-                        _parallel_sample_flow_at_points,
+                        _parallel_func,
                         parallel_sample_flow_at_points_inputs
                     )
             elif self.interface == "pathos":
                 sampled_wind_speeds_p = self.pathos_pool.map(
-                    _parallel_sample_flow_at_points_map,
+                    _parallel_fmap,
                     parallel_sample_flow_at_points_inputs
                 )
             elif self.interface == "concurrent":
                 with self._PoolExecutor(self.max_workers) as p:
                     sampled_wind_speeds_p = p.map(
-                        _parallel_sample_flow_at_points_map,
+                        _parallel_fmap,
                         parallel_sample_flow_at_points_inputs
                     )
                     sampled_wind_speeds_p = list(sampled_wind_speeds_p)
             t2 = timerpc()
-            sampled_wind_speeds = np.concatenate(sampled_wind_speeds_p, axis=0)
+            output = np.concatenate(sampled_wind_speeds_p, axis=0)
             t3 = timerpc()
             self._print_timings(t0, t1, t2, t3)
 
-        return sampled_wind_speeds
+        return output
 
     def _preprocessing(self):
         """
@@ -438,3 +489,14 @@ def _parallel_sample_flow_at_points_map(x):
     Wrapper for unpacking inputs to _parallel_sample_flow_at_points() for use with map().
     """
     return _parallel_sample_flow_at_points(*x)
+
+def _parallel_sample_ti_at_points(fmodel_dict, set_kwargs, x, y, z):
+    fmodel = FlorisModel(fmodel_dict)
+    fmodel.set(**set_kwargs)
+    return fmodel.sample_ti_at_points(x, y, z)
+
+def _parallel_sample_ti_at_points_map(x):
+    """
+    Wrapper for unpacking inputs to _parallel_sample_ti_at_points() for use with map().
+    """
+    return _parallel_sample_ti_at_points(*x)
diff --git a/floris/parallel_floris_model.py b/floris/parallel_floris_model.py
index 9b0b0b355..e57bb34ab 100644
--- a/floris/parallel_floris_model.py
+++ b/floris/parallel_floris_model.py
@@ -9,7 +9,7 @@
 from floris.floris_model import FlorisModel
 from floris.logging_manager import LoggingManager
 from floris.optimization.yaw_optimization.yaw_optimizer_sr import YawOptimizationSR
-from floris.uncertain_floris_model import map_turbine_powers_uncertain, UncertainFlorisModel
+from floris.uncertain_floris_model import map_turbine_values_uncertain, UncertainFlorisModel
 
 
 def _get_turbine_powers_serial(fmodel_information, yaw_angles=None):
@@ -367,8 +367,8 @@ def get_turbine_powers(self, yaw_angles=None, no_wake=False):
         t2 = timerpc()
         turbine_powers = self._postprocessing(out)
         if self._is_uncertain:
-            turbine_powers = map_turbine_powers_uncertain(
-                unique_turbine_powers=turbine_powers,
+            turbine_powers = map_turbine_values_uncertain(
+                unique_turbine_values=turbine_powers,
                 map_to_expanded_inputs=self._map_to_expanded_inputs,
                 weights=self._weights,
                 n_unexpanded=self._n_unexpanded,
diff --git a/floris/turbine_library/iea_15MW_floating_multi_dim_cp_ct.yaml b/floris/turbine_library/iea_15MW_floating_multi_dim_cp_ct.yaml
index 646a4e86a..b8dc8bc62 100644
--- a/floris/turbine_library/iea_15MW_floating_multi_dim_cp_ct.yaml
+++ b/floris/turbine_library/iea_15MW_floating_multi_dim_cp_ct.yaml
@@ -8,7 +8,13 @@ power_thrust_table:
   ref_tilt: 6.0
   cosine_loss_exponent_yaw: 1.88
   cosine_loss_exponent_tilt: 1.88
+  # Note that the multidimensional data provided here is fictional and does not represent a real
+  # wind turbine.
   power_thrust_data_file: 'iea_15MW_multi_dim_Tp_Hs.csv'
+# The floating_tilt_table describes the steady state tilt of the floating platform as a function of
+# wind speed. This is used to adjust the power and thrust coefficients when correct_cp_ct_for_tilt
+# is True. The values specified here should be treated as a fictional example only, and may not
+# represent a real floating platform.
 floating_tilt_table:
   tilt:
     - 5.747296314800103
diff --git a/floris/turbine_library/iea_15MW_multi_dim_cp_ct.yaml b/floris/turbine_library/iea_15MW_multi_dim_cp_ct.yaml
index b08b348de..9e8f20461 100644
--- a/floris/turbine_library/iea_15MW_multi_dim_cp_ct.yaml
+++ b/floris/turbine_library/iea_15MW_multi_dim_cp_ct.yaml
@@ -8,4 +8,6 @@ power_thrust_table:
   ref_tilt: 6.0
   cosine_loss_exponent_yaw: 1.88
   cosine_loss_exponent_tilt: 1.88
+  # Note that the multidimensional data provided here is fictional and does not represent a real
+  # wind turbine.
   power_thrust_data_file: 'iea_15MW_multi_dim_Tp_Hs.csv'
diff --git a/floris/turbine_library/nrel_5MW.yaml b/floris/turbine_library/nrel_5MW.yaml
index 9a70ba270..ff760ee79 100644
--- a/floris/turbine_library/nrel_5MW.yaml
+++ b/floris/turbine_library/nrel_5MW.yaml
@@ -6,56 +6,72 @@
 
 ###
 # An ID for this type of turbine definition.
-# This is not currently used, but it will be enabled in the future. This should typically
-# match the root name of the file.
+# This is used to uniquely identify different turbines in the simulation, so should be different
+# for each different turbine definition being used in the same simulation.
+# String type.
 turbine_type: 'nrel_5MW'
 
 ###
-# Hub height.
+# Turbine hub height in meters. Float type.
 hub_height: 90.0
 
 ###
-# Rotor diameter.
+# Turbine rotor diameter in meters. Float type.
 rotor_diameter: 125.88
 
 ###
-# Tip speed ratio defined as linear blade tip speed normalized by the incoming wind speed.
+# Nominal wind turbine tip-speed ratio for below-rated operation. Only used in some wake models.
+# Float type.
 TSR: 8.0
 
 ###
-# Model for power and thrust curve interpretation.
+# Model for power and thrust curve interpretation. See floris.core.turbine.operation_models for
+# details. String type.
 operation_model: 'cosine-loss'
 
 ###
-# Parameters needed to evaluate the power and thrust produced by the turbine.
+# Group of parameters needed to evaluate the power and thrust produced by the turbine.
 power_thrust_table:
-  ### Power thrust table parameters
-  # The air density at which the power and thrust_coefficient curves are defined.
+  ###
+  # Air density at which the power and thrust_coefficient curves are defined (kg / m^3). Float type.
   ref_air_density: 1.225
   ###
-  # The tilt angle at which the Cp and Ct curves are defined. This is used to capture
-  # the effects of a floating platform on a turbine's power and wake.
+  # Tilt angle at which the power and thrust_coefficient curves are defined (degrees).
+  # Used to capture the effects of a floating platform on a turbine's power and wake.
+  # Float type.
   ref_tilt: 5.0
   ###
-  # Cosine exponent for power loss due to tilt.
+  # Cosine exponent for power loss due to tilt. Float type.
   cosine_loss_exponent_tilt: 1.88
   ###
-  # Cosine exponent for power loss due to yaw misalignment.
+  # Cosine exponent for power loss due to yaw misalignment. Float type.
   cosine_loss_exponent_yaw: 1.88
   ###
-  # Helix parameters
+  # Helix parameter a. See documentation for details. Float type.
   helix_a: 1.802
+  ###
+  # Helix parameter b for power calculation. See documentation for details. Float type.
   helix_power_b: 4.568e-03
+  ###
+  # Helix parameter c for power calculation. See documentation for details. Float type.
   helix_power_c: 1.629e-10
+  ###
+  # Helix parameter b for thrust calculation. See documentation for details. Float type.
   helix_thrust_b: 1.027e-03
+  ###
+  # Helix parameter c for thrust calculation. See documentation for details. Float type.
   helix_thrust_c: 1.378e-06
-  ### Peak shaving parameters
-  # Fraction of peak thrust by which to reduce
+  ###
+  # Fraction of peak thrust by which to reduce (specified as a decimal). Float type.
   peak_shaving_fraction: 0.2
-  # Threshold turbulence intensity above which to apply peak shaving
+  ###
+  # Threshold turbulence intensity above which to apply peak shaving (specified as a decimal).
+  # Float tpe.
   peak_shaving_TI_threshold: 0.1
 
-  ### Parameters for the 'controller-dependenter-dependent' operation model
+  ###
+  # Parameters for the 'controller-dependenter-dependent' operation model. See
+  # floris.core.turbine.controller_dependent_operation_model and documentation for details.
   controller_dependent_turbine_parameters:
     rated_rpm: 12.1
     rotor_solidity: 0.05132
@@ -67,8 +83,8 @@ power_thrust_table:
     cl_alfa: 4.275049
     cp_ct_data_file: "demo_cp_ct_surfaces/nrel_5MW_demo_cp_ct_surface.npz"
 
-  ### Power thrust table data
-  # wind speeds for look-up tables of power and thrust_coefficient
+  ###
+  # Wind speeds for look-up tables of power and thrust_coefficient. List of float type.
   wind_speed:
     - 0.0
     - 2.9
@@ -125,7 +141,7 @@ power_thrust_table:
     - 25.1
     - 50.0
   ###
-  # power values (specified in kW) for lookup by wind speed
+  # Power values (specified in kW) for lookup by wind speed. List of float type.
   power:
     - 0.0
     - 0.0
@@ -182,7 +198,7 @@ power_thrust_table:
     - 0.0
     - 0.0
   ###
-  # thrust coefficient values (unitless) for lookup by wind speed
+  # Thrust coefficient values (unitless) for lookup by wind speed. List of float type.
   thrust_coefficient:
     - 0.0
     - 0.0
@@ -240,14 +256,44 @@ power_thrust_table:
     - 0.0
 
 ###
-# A boolean flag used when the user wants FLORIS to use the user-supplied multi-dimensional
-# Cp/Ct information.
+# Boolean flag used when the user wants FLORIS to use the user-supplied multi-dimensional
+# power/thrust coefficient information information. Boolean type.
 multi_dimensional_cp_ct: False
 
 ###
-# The path to the .csv file that contains the multi-dimensional Cp/Ct data. The format of this
-# file is such that any external conditions, such as wave height or wave period, that the
-# Cp/Ct data is dependent on come first, in column format. The last three columns of the .csv
-# file must be ``ws``, ``Cp``, and ``Ct``, in that order. An example of fictional data is given
-# in ``floris/turbine_library/iea_15MW_multi_dim_Tp_Hs.csv``.
+# Path to the .csv file that contains the multi-dimensional power/thrust coefficient data.
+# The format of this file is such that any external conditions, such as wave height or wave period,
+# that the power/thrust data is dependent on come first, in column format. The last three columns
+# of the .csv file must be ``ws``, ``power``, and ``thrust_coefficient``, in that order. An example
+# of fictional data is given in ``floris/turbine_library/iea_15MW_multi_dim_Tp_Hs.csv``.
+# String type.
 power_thrust_data_file: '../floris/turbine_library/iea_15MW_multi_dim_Tp_Hs.csv'
+
+###
+# Group of parameters needed to evaluate the tilt angle of a floating turbine across wind speeds.
+floating_tilt_table:
+  ###
+  # Wind speeds at which steady tilt angles are defined (m/s). List of float type.
+  wind_speed:
+    - 4.0
+    - 6.0
+    - 8.0
+    - 10.0
+    - 12.0
+    - 14.0
+    - 16.0
+  ###
+  # Tilt angle for each wind speed (degrees, positive "tilted back"). List of float type.
+  tilt:
+    - 5.0
+    - 5.0
+    - 5.0
+    - 5.0
+    - 5.0
+    - 5.0
+    - 5.0
+
+###
+# Flag for whether to apply the floating tilt table to correct turbine power and thrust curves.
+# Boolean type.
+correct_cp_ct_for_tilt: false
diff --git a/floris/turbine_library/turbine_previewer.py b/floris/turbine_library/turbine_previewer.py
index d8b20064f..7623d4fbe 100644
--- a/floris/turbine_library/turbine_previewer.py
+++ b/floris/turbine_library/turbine_previewer.py
@@ -25,6 +25,11 @@
 INTERNAL_LIBRARY = Path(__file__).parent
 DEFAULT_WIND_SPEEDS = np.linspace(0, 40, 81)
 
+DEPRECATION_MESSAGE = (
+    "The TurbineInterface and TurbineLibrary classes are now deprecated as will be removed in a",
+    " future FLORIS release."
+)
+
 
 @define(auto_attribs=True)
 class TurbineInterface:
@@ -43,6 +48,7 @@ def from_library(cls, library_path: str | Path, file_name: str):
         Returns:
             (TurbineInterface): Creates a new ``TurbineInterface`` object.
         """
+        print(DEPRECATION_MESSAGE)
         # Use the pre-mapped internal turbine library or validate the user's library
         if library_path == "internal":
             library_path = INTERNAL_LIBRARY
@@ -64,6 +70,7 @@ def from_yaml(cls, file_path: str | Path):
         Returns:
             (TurbineInterface): Creates a new ``TurbineInterface`` object.
         """
+        print(DEPRECATION_MESSAGE)
         file_path = Path(file_path).resolve()
 
         # Add in the library specification if needed, and load from dict
@@ -81,6 +88,7 @@ def from_turbine_dict(cls, config_dict: dict):
         Returns:
             (`TurbineInterface`): Returns a ``TurbineInterface`` object.
         """
+        print(DEPRECATION_MESSAGE)
         return cls(turbine=Turbine.from_dict(config_dict))
 
     def power_curve(
@@ -351,6 +359,7 @@ def load_internal_library(self, which: list[str] = [], exclude: list[str] = [])
             exclude (list[str], optional): A list of file names to exclude from loading.
                 Defaults to [].
         """
+        print(DEPRECATION_MESSAGE)
         include = [el for el in INTERNAL_LIBRARY.iterdir() if el.suffix in (".yaml", ".yml")]
         which = [INTERNAL_LIBRARY / el for el in which] if which != [] else include
         exclude = [INTERNAL_LIBRARY / el for el in exclude]
@@ -379,6 +388,7 @@ def load_external_library(
             exclude (list[str], optional): A list of file names to exclude from loading.
                 Defaults to [].
         """
+        print(DEPRECATION_MESSAGE)
         library_path = Path(library_path).resolve()
         include = [el for el in library_path.iterdir() if el.suffix in (".yaml", ".yml")]
         which = [library_path / el for el in which] if which != [] else include
diff --git a/floris/uncertain_floris_model.py b/floris/uncertain_floris_model.py
index 5fcc03f0b..27d449da0 100644
--- a/floris/uncertain_floris_model.py
+++ b/floris/uncertain_floris_model.py
@@ -10,7 +10,7 @@
 import numpy as np
 
 from floris import FlorisModel
-from floris.core import State
+from floris.core import average_velocity, State
 from floris.logging_manager import LoggingManager
 from floris.par_floris_model import ParFlorisModel
 from floris.type_dec import (
@@ -85,6 +85,10 @@ def __init__(
         fix_yaw_to_nominal_direction=False,
         verbose=False,
     ):
+        # Check validity of inputs
+        if wd_std <= 0:
+            raise ValueError("wd_std must be strictly greater than 0.")
+
         # Save these inputs
         self.wd_resolution = wd_resolution
         self.ws_resolution = ws_resolution
@@ -272,8 +276,8 @@ def _get_turbine_powers(self):
         """
 
         # Pass to off-class function
-        result = map_turbine_powers_uncertain(
-            unique_turbine_powers=self.fmodel_expanded._get_turbine_powers(),
+        result = map_turbine_values_uncertain(
+            unique_turbine_values=self.fmodel_expanded._get_turbine_powers(),
             map_to_expanded_inputs=self.map_to_expanded_inputs,
             weights=self.weights,
             n_unexpanded=self.n_unexpanded,
@@ -1142,6 +1146,27 @@ def core(self):
         """
         return self.fmodel_unexpanded.core
 
+    @property
+    def turbine_average_velocities(self) -> NDArrayFloat:
+        # Get the expanded velocities
+        expanded_velocities = average_velocity(
+            velocities=self.fmodel_expanded.core.flow_field.u,
+            method=self.fmodel_expanded.core.grid.average_method,
+            cubature_weights=self.fmodel_expanded.core.grid.cubature_weights,
+        )
+
+        # Pass to off-class function
+        result = map_turbine_values_uncertain(
+            unique_turbine_values=expanded_velocities,
+            map_to_expanded_inputs=self.map_to_expanded_inputs,
+            weights=self.weights,
+            n_unexpanded=self.n_unexpanded,
+            n_sample_points=self.n_sample_points,
+            n_turbines=self.fmodel_unexpanded.core.farm.n_turbines,
+        )
+
+        return result
+
 
 def map_turbine_powers_uncertain(
     unique_turbine_powers,
@@ -1151,35 +1176,57 @@ def map_turbine_powers_uncertain(
     n_sample_points,
     n_turbines,
 ):
-    """Calculates the power at each turbine in the wind farm based on uncertainty weights.
+    """
+    Alias for map_turbine_values_uncertain.
+    """
+    # Deprecation warning
+    print("map_turbine_powers_uncertain is deprecated, use map_turbine_values_uncertain instead.")
+    return map_turbine_values_uncertain(
+        unique_turbine_values=unique_turbine_powers,
+        map_to_expanded_inputs=map_to_expanded_inputs,
+        weights=weights,
+        n_unexpanded=n_unexpanded,
+        n_sample_points=n_sample_points,
+        n_turbines=n_turbines,
+    )
+
+def map_turbine_values_uncertain(
+    unique_turbine_values,
+    map_to_expanded_inputs,
+    weights,
+    n_unexpanded,
+    n_sample_points,
+    n_turbines,
+):
+    """Calculates values at each turbine in the wind farm based on uncertainty weights.
 
-    This function calculates the power at each turbine in the wind farm, considering
-    the underlying turbine powers and applying a weighted sum to handle uncertainty.
+    This function calculates the values (e.g. power, velocity) at each turbine in the wind farm,
+    considering the underlying turbine values and applying a weighted sum to handle uncertainty.
 
     Args:
-        unique_turbine_powers (NDArrayFloat): An array of unique turbine powers from the
-            underlying FlorisModel
-        map_to_expanded_inputs (NDArrayFloat): An array of indices mapping the unique powers to
-            the expanded powers
+        unique_turbine_values (NDArrayFloat): An array of unique turbine powers, velocities, etc
+            from the underlying FlorisModel
+        map_to_expanded_inputs (NDArrayFloat): An array of indices mapping the unique values to
+            the expanded values
         weights (NDArrayFloat): An array of weights for each wind direction sample point
         n_unexpanded (int): The number of unexpanded conditions
         n_sample_points (int): The number of wind direction sample points
         n_turbines (int): The number of turbines in the wind farm
 
     Returns:
-        NDArrayFloat: An array containing the powers at each turbine for each findex.
+        NDArrayFloat: An array containing the values at each turbine for each findex.
 
     """
 
     # Expand back to the expanded value
-    expanded_turbine_powers = unique_turbine_powers[map_to_expanded_inputs]
+    expanded_turbine_values = unique_turbine_values[map_to_expanded_inputs]
 
     # Reshape the weights array to make it compatible with broadcasting
     weights_reshaped = weights[:, np.newaxis]
 
-    # Reshape expanded_turbine_powers into blocks
+    # Reshape expanded_turbine_values into blocks
     blocks = np.reshape(
-        expanded_turbine_powers,
+        expanded_turbine_values,
         (n_unexpanded, n_sample_points, n_turbines),
         order="F",
     )
@@ -1221,7 +1268,7 @@ def __init__(
             yaw_resolution,
             power_setpoint_resolution,
             awc_amplitude_resolution,
-            wd_std=1.0,
+            wd_std=1.0,  # Arbitrary nonzero value, not used since only one sample point
             wd_sample_points=[0],
             fix_yaw_to_nominal_direction=False,
             verbose=verbose,
diff --git a/floris/wind_data.py b/floris/wind_data.py
index 3d49dec4b..8ac770598 100644
--- a/floris/wind_data.py
+++ b/floris/wind_data.py
@@ -1587,16 +1587,12 @@ def upsample(self, wd_step=None, ws_step=None, ti_step=None, method="linear", in
                 )
             )
             freq_matrix = np.concatenate(
-                (freq_matrix[0, :, :][None, :, :], freq_matrix, freq_matrix[-1, :, :][None, :, :]),
+                (freq_matrix[0:1, :, :], freq_matrix, freq_matrix[-1:, :, :]),
                 axis=0
             )
             if self.value_table is not None:
                 value_matrix = np.concatenate(
-                    (
-                        value_matrix[0, :, :][None, :, :],
-                        value_matrix,
-                        value_matrix[-1, :, :][None, :, :]
-                    ),
+                    (value_matrix[0:1, :, :], value_matrix, value_matrix[-1:, :, :]),
                     axis=0
                 )
 
@@ -1615,15 +1611,11 @@ def upsample(self, wd_step=None, ws_step=None, ti_step=None, method="linear", in
 
             # Pad the remaining with the appropriate value
             freq_matrix = np.vstack(
-                (freq_matrix[-1, :, :][None, :, :], freq_matrix, freq_matrix[0, :, :][None, :, :])
+                (freq_matrix[-1:, :, :], freq_matrix, freq_matrix[0:1, :, :])
             )
             if self.value_table is not None:
                 value_matrix = np.vstack(
-                    (
-                        value_matrix[-1, :, :][None, :, :],
-                        value_matrix,
-                        value_matrix[0, :, :][None, :, :],
-                    )
+                    (value_matrix[-1:, :, :], value_matrix, value_matrix[0:1, :, :])
                 )
 
         # Pad out the wind speeds
@@ -1640,11 +1632,7 @@ def upsample(self, wd_step=None, ws_step=None, ti_step=None, method="linear", in
         )
         if self.value_table is not None:
             value_matrix = np.concatenate(
-                (
-                    value_matrix[:, 0, :][:, None, :],
-                    value_matrix,
-                    value_matrix[:, -1, :][:, None, :]
-                ),
+                (value_matrix[:, 0:1, :], value_matrix, value_matrix[:, -1:, :]),
                 axis=1
             )
 
@@ -1657,16 +1645,12 @@ def upsample(self, wd_step=None, ws_step=None, ti_step=None, method="linear", in
             )
         )
         freq_matrix = np.concatenate(
-            (freq_matrix[:, :, 0][:, :, None], freq_matrix, freq_matrix[:, :, -1][:, :, None]),
+            (freq_matrix[:, :, 0:1], freq_matrix, freq_matrix[:, :, -1:]),
             axis=2
         )
         if self.value_table is not None:
             value_matrix = np.concatenate(
-                (
-                    value_matrix[:, :, 0][:, :, None],
-                    value_matrix,
-                    value_matrix[:, :, -1][:, :, None]
-                ),
+                (value_matrix[:, :, 0:1], value_matrix, value_matrix[:, :, -1:]),
                 axis=2
             )
 
diff --git a/pyproject.toml b/pyproject.toml
index 3c62ccc2b..87bf6a9c4 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "floris"
-version = "4.4.2"
+version = "4.5"
 description = "A controls-oriented engineering wake model."
 readme = "README.md"
 requires-python = ">=3.9"
diff --git a/tests/data/iea_15MW_multi_dim_TI.csv b/tests/data/iea_15MW_multi_dim_TI.csv
new file mode 120000
index 000000000..761a14eae
--- /dev/null
+++ b/tests/data/iea_15MW_multi_dim_TI.csv
@@ -0,0 +1 @@
+../../examples/inputs/turbine_files/iea_15MW_multi_dim_TI.csv
\ No newline at end of file
diff --git a/tests/floris_model_integration_test.py b/tests/floris_model_integration_test.py
index 3ba94b955..f55e4b6a9 100644
--- a/tests/floris_model_integration_test.py
+++ b/tests/floris_model_integration_test.py
@@ -11,6 +11,7 @@
     WindRose,
 )
 from floris.core.turbine.operation_models import POWER_SETPOINT_DEFAULT, POWER_SETPOINT_DISABLED
+from tests.conftest import SampleInputs
 
 
 TEST_DATA = Path(__file__).resolve().parent / "data"
@@ -871,3 +872,122 @@ def test_merge_floris_models():
     fmodel_list = [fmodel1, "not a floris model"]
     with pytest.raises(TypeError):
         merged_fmodel = FlorisModel.merge_floris_models(fmodel_list)
+
+def test_sample_flow_at_points():
+
+    fmodel = FlorisModel(configuration=YAML_INPUT)
+    u_inf = 8.0
+    n_points = 10
+    fmodel.set(
+        layout_x=[0],
+        layout_y=[0],
+        wind_speeds=[u_inf, u_inf],
+        wind_directions=[270.0, 180.0],
+        turbulence_intensities=[0.06, 0.06],
+    )
+
+    # set up sample points
+    p = np.linspace(1, 1001, n_points)
+    x = np.concatenate((p, np.zeros_like(p)))
+    y = np.concatenate((np.zeros_like(p), p))
+    z = 100 * np.ones(n_points * 2)
+
+    # Use sample_flow_at_points to get the flow at the sample points
+    u_sampled_1 = fmodel.sample_flow_at_points(x, y, z)
+
+    # Check that the behind-turbine velocities match in the two directions
+    assert np.allclose(u_sampled_1[0,:n_points], u_sampled_1[1,n_points:])
+    # Check also that points outside the wake are all reasonable
+    assert np.allclose(u_sampled_1[0,n_points:], u_sampled_1[1,:n_points])
+    assert np.all(u_sampled_1[0,n_points:] > u_inf)
+    assert np.all(u_sampled_1[0,n_points:] == u_sampled_1[0,n_points])
+
+    # Run again at a higher speed and check all sampled velocities are higher
+    fmodel.set(wind_speeds=[u_inf + 2.0, u_inf + 2.0])
+    u_sampled_2 = fmodel.sample_flow_at_points(x, y, z)
+    assert np.all(u_sampled_2 > u_sampled_1)
+
+def test_sample_ti_at_points():
+
+    fmodel = FlorisModel(configuration=YAML_INPUT)
+    ti_inf = 0.8
+    n_points = 10
+    fmodel.set(
+        layout_x=[0],
+        layout_y=[0],
+        wind_speeds=[8.0, 8.0],
+        wind_directions=[270.0, 180.0],
+        turbulence_intensities=[ti_inf, ti_inf],
+    )
+
+    # set up sample points
+    p = np.linspace(1, 1001, n_points)
+    x = np.concatenate((p, np.zeros_like(p)))
+    y = np.concatenate((np.zeros_like(p), p))
+    z = 100 * np.ones(n_points * 2)
+
+    # Use sample_ti_at_points to get the TI at the sample points
+    ti_sampled_1 = fmodel.sample_ti_at_points(x, y, z)
+
+    # Check that the behind-turbine TIs match in the two directions
+    assert np.allclose(ti_sampled_1[0,:n_points], ti_sampled_1[1,n_points:])
+    # Check also that points outside the wake are all reasonable
+    assert np.allclose(ti_sampled_1[0,n_points:], ti_sampled_1[1,:n_points])
+    assert np.all(ti_sampled_1[0,n_points:] == ti_inf)
+
+    # Run again at a higher TI and check all sampled TIs are higher
+    fmodel.set(turbulence_intensities=[ti_inf + 0.02, ti_inf + 0.02])
+    ti_sampled_2 = fmodel.sample_ti_at_points(x, y, z)
+    assert np.all(ti_sampled_2 > ti_sampled_1)
+
+def test_set_multidim():
+    fmodel = FlorisModel(configuration=YAML_INPUT)
+    fmodel.set(turbine_type=[SampleInputs().turbine_multi_dim])
+
+    # No multidim condition has been set; should raise value error
+    with pytest.raises(ValueError):
+        fmodel.run()
+
+    # Set a multidim condition that is not a valid type
+    with pytest.raises(TypeError):
+        fmodel.set(multidim_conditions="invalid_type")
+        fmodel.run()
+
+    # Set an invalid multidim condition (not all dimensions specified)
+    with pytest.raises(ValueError):
+        fmodel.set(multidim_conditions={"Hs": 1.0})
+        fmodel.run()
+
+    # Set an invalid key (but the correct total number of keys)
+    with pytest.raises(ValueError):
+        fmodel.set(multidim_conditions={"invalid_key": 2.0, "Hs": 1.0})
+        fmodel.run()
+
+    # Set with an invalid key (as well as other keys being correct)
+    with pytest.raises(ValueError):
+        fmodel.set(multidim_conditions={"invalid_key": 2.0, "Hs": 1.0, "Tp": 8.0})
+        fmodel.run()
+
+    # Set a valid multidim condition, order of dictionary keys should not matter
+    fmodel.set(multidim_conditions={"Hs": 1.0, "Tp": 8.0})
+    fmodel.run()
+    powers_1 = fmodel.get_turbine_powers()
+    fmodel.set(multidim_conditions={"Tp": 8.0, "Hs": 1.0})
+    fmodel.run()
+    powers_2 = fmodel.get_turbine_powers()
+    assert np.array_equal(powers_1, powers_2)
+
+    # Create a single-dimensional table
+    turbine = SampleInputs().turbine_multi_dim
+    turbine["power_thrust_table"]["power_thrust_data_file"] = "iea_15MW_multi_dim_TI.csv"
+    fmodel.set(
+        turbine_type=[turbine],
+        turbine_library_path=Path(__file__).resolve().parent / "data/"
+    )
+
+    with pytest.raises(ValueError):
+        fmodel.set(multidim_conditions={"TI": 0.06, "Tp": 8.0})
+        fmodel.run()
+
+    fmodel.set(multidim_conditions={"TI": 0.06})
+    fmodel.run()
diff --git a/tests/geometric_yaw_unit_test.py b/tests/geometric_yaw_unit_test.py
index 2fb0452bb..61edafc45 100644
--- a/tests/geometric_yaw_unit_test.py
+++ b/tests/geometric_yaw_unit_test.py
@@ -3,7 +3,10 @@
 import pandas as pd
 
 from floris import FlorisModel
-from floris.optimization.yaw_optimization.yaw_optimizer_geometric import YawOptimizationGeometric
+from floris.optimization.yaw_optimization.yaw_optimizer_geometric import (
+    _process_layout,
+    YawOptimizationGeometric,
+)
 
 
 DEBUG = False
@@ -113,3 +116,40 @@ def test_disabled_turbines(sample_inputs_fixture):
     yaw_angles_opt_removed = df_opt.loc[3, "yaw_angles_opt"]
 
     assert np.allclose(yaw_angles_opt_disabled[[0, 2]], yaw_angles_opt_removed)
+
+def test_process_layout():
+
+    # Test inside Jensen-like wake
+    dx, dy = _process_layout(
+        turbine_x=np.array([0.0, 1000.0]),
+        turbine_y=np.array([0.0, 499.0]),
+        rotor_diameter=1.0,
+        spread=0.5
+    )
+    assert dx[0] == 1000.0 # Distance to downstream turbine, which is inside the wake
+
+    # Test outside Jensen-like wake
+    dx, dy, = _process_layout(
+        turbine_x=np.array([0.0, 1000.0]),
+        turbine_y=np.array([0.0, 501.0]),
+        rotor_diameter=1.0,
+        spread=0.5
+    )
+    assert dx[0] == np.inf # Distance to downstream turbine, which is outside the wake
+
+    # Test effect of rotor diameter
+    dx, dy, = _process_layout(
+        turbine_x=np.array([0.0, 1000.0]),
+        turbine_y=np.array([0.0, 549.0]),
+        rotor_diameter=100.0,
+        spread=0.5
+    )
+    assert dx[0] != np.inf # This turbine is now inside the wake
+
+    dx, dy = _process_layout(
+        turbine_x=np.array([0.0, 1000.0]),
+        turbine_y=np.array([0.0, 551.0]),
+        rotor_diameter=100.0,
+        spread=0.5
+    )
+    assert dx[0] == np.inf # This turbine is now outside the wake
diff --git a/tests/heterogeneous_map_integration_test.py b/tests/heterogeneous_map_integration_test.py
index c8ea43f83..a1a82ea07 100644
--- a/tests/heterogeneous_map_integration_test.py
+++ b/tests/heterogeneous_map_integration_test.py
@@ -1,9 +1,10 @@
+import logging
 from pathlib import Path
 
 import numpy as np
 import pytest
 
-from floris import FlorisModel
+from floris import FlorisModel, TimeSeries
 from floris.heterogeneous_map import HeterogeneousMap
 
 
@@ -396,7 +397,7 @@ def test_3d_het_and_shear():
         )
 
 
-def test_run_2d_het_map():
+def test_run_2d_het_map(caplog):
     # Define a 2D het map and confirm the results are as expected
     # when applied to FLORIS
 
@@ -417,7 +418,77 @@ def test_run_2d_het_map():
     # Get the FLORIS model
     fmodel = FlorisModel(configuration=YAML_INPUT)
 
-    from floris import TimeSeries
+    time_series = TimeSeries(
+        wind_directions=np.array([270.0, 90.0]),
+        wind_speeds=8.0,
+        turbulence_intensities=0.06,
+        heterogeneous_map=het_map,
+    )
+
+    # Set the model to a turbines perpendicular to east/west flow with 0 turbine closer to bottom
+    # and turbine 1 closer to top, while turbine 2 is outside of heterogeneous specification.
+    fmodel.set(
+        wind_data=time_series,
+        layout_x=[250.0, 250.0, 250.0],
+        layout_y=[100.0, 400.0, 700.0],
+        wind_shear=0.0,
+    )
+
+
+    # Run the model. Should raise warning due to turbine outside of bounds with linear interpolation
+    with caplog.at_level(logging.WARNING):
+        fmodel.run()
+    assert caplog.text != "" # Checking not empty
+
+    # Get the turbine powers
+    powers = fmodel.get_turbine_powers()
+
+    # Assert that in the first condition, turbine 1 has higher power
+    assert powers[0, 1] > powers[0, 0]
+
+    # Assert that in the second condition, turbine 0 has higher power
+    assert powers[1, 0] > powers[1, 1]
+
+    # Assert that the power of turbine 1 equals in the first condition
+    # equals the power of turbine 0 in the second condition
+    assert powers[0, 1] == powers[1, 0]
+
+    # Check that turbine 2 is simply seeing the freestream wind speed
+    velocities = fmodel.turbine_average_velocities
+    assert np.allclose(velocities[:, 2], 8.0)
+
+    # Check that sample_flow_at_points runs
+    # Middle of bottom, middle of left
+    v = fmodel.sample_flow_at_points(
+        np.array([250, 0]),
+        np.array([0, 250]),
+        np.array([90, 90])
+    )
+    assert np.allclose(v[:,0], np.array([8.0, 16.0])) # Not waked
+    assert np.allclose(v[0,1], 12.0) # Not waked
+    assert v[1,1] < 12.0 # Slightly waked
+
+def test_run_2d_het_map_nearest_neighbor(caplog):
+    # Define a 2D het map and confirm the results are as expected
+    # when applied to FLORIS
+
+    # The side of the flow which is accelerated reverses for east versus west
+    het_map = HeterogeneousMap(
+        x=np.array([0.0, 0.0, 500.0, 500.0]),
+        y=np.array([0.0, 500.0, 0.0, 500.0]),
+        speed_multipliers=np.array(
+            [
+                [1.0, 2.0, 1.0, 2.0],  # Top accelerated
+                [2.0, 1.0, 2.0, 1.0],  # Bottom accelerated
+            ]
+        ),
+        wind_directions=np.array([270.0, 90.0]),
+        wind_speeds=np.array([8.0, 8.0]),
+        interp_method='nearest',
+    )
+
+    # Get the FLORIS model
+    fmodel = FlorisModel(configuration=YAML_INPUT)
 
     time_series = TimeSeries(
         wind_directions=np.array([270.0, 90.0]),
@@ -426,17 +497,20 @@ def test_run_2d_het_map():
         heterogeneous_map=het_map,
     )
 
-    # Set the model to a turbines perpinducluar to
-    # east/west flow with 0 turbine closer to bottom and
-    # turbine 1 closer to top
+    # Set the model to a turbines perpendicular to east/west flow with 0 turbine closer to bottom
+    # and turbine 1 closer to top, while turbine 2 is outside of heterogeneous specification
+    # (but will still take on the nearest neighbor value).
     fmodel.set(
         wind_data=time_series,
-        layout_x=[250.0, 250.0],
-        layout_y=[100.0, 400.0],
+        layout_x=[250.0, 250.0, 250.0],
+        layout_y=[100.0, 400.0, 700.0],
+        wind_shear=0.0,
     )
 
-    # Run the model
-    fmodel.run()
+    # Run the model. No turbine outside of bounds warning raised for nearest neighbor interpolation
+    with caplog.at_level(logging.WARNING):
+        fmodel.run()
+    assert caplog.text == "" # Checking empty
 
     # Get the turbine powers
     powers = fmodel.get_turbine_powers()
@@ -451,6 +525,20 @@ def test_run_2d_het_map():
     # equals the power of turbine 0 in the second condition
     assert powers[0, 1] == powers[1, 0]
 
+    # Check that turbine 2 is takes the value of the top row.
+    velocities = fmodel.turbine_average_velocities
+    assert np.allclose(velocities[:, 2], 8.0*np.array([2.0, 1.0]))
+
+    # Check that sample_flow_at_points runs
+    # Middle of bottom, middle of left (slightly up)
+    v = fmodel.sample_flow_at_points(
+        np.array([250.0, 0]),
+        np.array([0, 251]),
+        np.array([90, 90])
+    )
+    assert np.allclose(v[:,0], np.array([8.0, 16.0])) # Not waked
+    assert np.allclose(v[0,1], 16.0) # Not waked
+    assert v[1,1] < 8.0 # Slightly waked
 
 def test_het_config():
 
diff --git a/tests/par_floris_model_unit_test.py b/tests/par_floris_model_unit_test.py
index 0c4d80b07..225695928 100644
--- a/tests/par_floris_model_unit_test.py
+++ b/tests/par_floris_model_unit_test.py
@@ -297,8 +297,8 @@ def test_sample_flow_at_points(sample_inputs_fixture):
     wind_speeds = np.array([8.0, 8.0, 9.0])
     wind_directions = np.array([270.0, 270.0, 270.0])
     fmodel.set(
-        wind_directions=wind_speeds.flatten(),
-        wind_speeds=wind_directions.flatten(),
+        wind_directions=wind_directions,
+        wind_speeds=wind_speeds,
         turbulence_intensities=0.06 * np.ones_like(wind_speeds.flatten()),
     )
 
@@ -313,6 +313,33 @@ def test_sample_flow_at_points(sample_inputs_fixture):
         ws_test = pfmodel.sample_flow_at_points(x_test, y_test, z_test)
         assert np.allclose(ws_base, ws_test)
 
+def test_sample_ti_at_points(sample_inputs_fixture):
+
+    sample_inputs_fixture.core["wake"]["model_strings"]["velocity_model"] = VELOCITY_MODEL
+    sample_inputs_fixture.core["wake"]["model_strings"]["deflection_model"] = DEFLECTION_MODEL
+    sample_inputs_fixture.core["wake"]["model_strings"]["turbulence_model"] = "crespo_hernandez"
+
+    fmodel = FlorisModel(sample_inputs_fixture.core)
+
+    wind_speeds = np.array([8.0, 8.0, 9.0])
+    wind_directions = np.array([270.0, 270.0, 270.0])
+    fmodel.set(
+        wind_directions=wind_directions,
+        wind_speeds=wind_speeds,
+        turbulence_intensities=0.06 * np.ones_like(wind_speeds.flatten()),
+    )
+
+    x_test = np.array([500.0, 750.0, 1000.0, 1250.0, 1500.0])
+    y_test = np.array([0.0, 0.0, 0.0, 0.0, 0.0])
+    z_test = np.array([90.0, 90.0, 90.0, 90.0, 90.0])
+
+    ti_base = fmodel.sample_ti_at_points(x_test, y_test, z_test)
+
+    for interface in ["multiprocessing", "pathos", "concurrent"]:
+        pfmodel = ParFlorisModel(fmodel, max_workers=2, interface=interface)
+        ti_test = pfmodel.sample_ti_at_points(x_test, y_test, z_test)
+        assert np.allclose(ti_base, ti_test)
+
 def test_copy(sample_inputs_fixture):
     """
     Check that the ParFlorisModel copies correctly as a ParFlorisModel.
diff --git a/tests/reg_tests/empirical_gauss_regression_test.py b/tests/reg_tests/empirical_gauss_regression_test.py
index a6bdaa991..e664881df 100644
--- a/tests/reg_tests/empirical_gauss_regression_test.py
+++ b/tests/reg_tests/empirical_gauss_regression_test.py
@@ -81,6 +81,36 @@
     ]
 )
 
+tilted_baseline = np.array(
+    [
+        # 8 m/s
+        [
+            [7.9736858, 0.7824685, 1734488.7059275, 0.2658985],
+            [5.8875889, 0.8705526, 704778.1875928, 0.3212047],
+            [5.9110415, 0.8692142, 712622.7895048, 0.3202651],
+        ],
+        # 9 m/s
+        [
+            [8.9703965, 0.7812020, 2471404.7824861, 0.2652278],
+            [6.6276158, 0.8354859, 1026885.3722728, 0.2980366],
+            [6.7091646, 0.8317630, 1063636.4762428, 0.2957326],
+        ],
+        # 10 m/s
+        [
+            [9.9671073, 0.7792154, 3383206.6144193, 0.2641794],
+            [7.3711242, 0.8050505, 1396968.1317078, 0.2799135],
+            [7.5109456, 0.8003819, 1477742.2826442, 0.2772650],
+        ],
+        # 11 m/s
+        [
+            [10.9638180, 0.7520150, 4470666.5322783, 0.2502624],
+            [8.2150645, 0.7898565, 1946589.2518193, 0.2714076],
+            [8.3045772, 0.7897407, 2013649.9637290, 0.2713440],
+        ]
+    ]
+)
+
+
 yaw_added_recovery_baseline = np.array(
     [
         # 8 m/s
@@ -453,6 +483,103 @@ def test_regression_yaw(sample_inputs_fixture):
 
     assert_results_arrays(test_results[0:4], yawed_baseline)
 
+
+def test_regression_tilt(sample_inputs_fixture):
+    """
+    Tandem turbines with the upstream turbine tilted
+    """
+    sample_inputs_fixture.core["wake"]["model_strings"]["velocity_model"] = VELOCITY_MODEL
+    sample_inputs_fixture.core["wake"]["model_strings"]["deflection_model"] = DEFLECTION_MODEL
+    sample_inputs_fixture.core["wake"]["model_strings"]["turbulence_model"] = TURBULENCE_MODEL
+
+    floris = Core.from_dict(sample_inputs_fixture.core)
+
+    tilt_angles = np.zeros((N_FINDEX, N_TURBINES))
+    tilt_angles[:,0] = 8.0
+    floris.farm.tilt_angles = tilt_angles
+
+    floris.initialize_domain()
+    floris.steady_state_atmospheric_condition()
+
+    n_turbines = floris.farm.n_turbines
+    n_findex = floris.flow_field.n_findex
+
+    velocities = floris.flow_field.u
+    turbulence_intensities = floris.flow_field.turbulence_intensity_field
+    air_density = floris.flow_field.air_density
+    yaw_angles = floris.farm.yaw_angles
+    tilt_angles = floris.farm.tilt_angles
+    power_setpoints = floris.farm.power_setpoints
+    awc_modes = floris.farm.awc_modes
+    awc_amplitudes = floris.farm.awc_amplitudes
+    test_results = np.zeros((n_findex, n_turbines, 4))
+
+    farm_avg_velocities = average_velocity(
+        velocities,
+    )
+    farm_cts = thrust_coefficient(
+        velocities,
+        turbulence_intensities,
+        air_density,
+        yaw_angles,
+        tilt_angles,
+        power_setpoints,
+        awc_modes,
+        awc_amplitudes,
+        floris.farm.turbine_thrust_coefficient_functions,
+        floris.farm.turbine_tilt_interps,
+        floris.farm.correct_cp_ct_for_tilt,
+        floris.farm.turbine_type_map,
+        floris.farm.turbine_power_thrust_tables,
+    )
+    farm_powers = power(
+        velocities,
+        turbulence_intensities,
+        air_density,
+        floris.farm.turbine_power_functions,
+        yaw_angles,
+        tilt_angles,
+        power_setpoints,
+        awc_modes,
+        awc_amplitudes,
+        floris.farm.turbine_tilt_interps,
+        floris.farm.turbine_type_map,
+        floris.farm.turbine_power_thrust_tables,
+    )
+    farm_axial_inductions = axial_induction(
+        velocities,
+        turbulence_intensities,
+        air_density,
+        yaw_angles,
+        tilt_angles,
+        power_setpoints,
+        awc_modes,
+        awc_amplitudes,
+        floris.farm.turbine_axial_induction_functions,
+        floris.farm.turbine_tilt_interps,
+        floris.farm.correct_cp_ct_for_tilt,
+        floris.farm.turbine_type_map,
+        floris.farm.turbine_power_thrust_tables,
+    )
+    for i in range(n_findex):
+        for j in range(n_turbines):
+            test_results[i, j, 0] = farm_avg_velocities[i, j]
+            test_results[i, j, 1] = farm_cts[i, j]
+            test_results[i, j, 2] = farm_powers[i, j]
+            test_results[i, j, 3] = farm_axial_inductions[i, j]
+
+    if DEBUG:
+        print_test_values(
+            farm_avg_velocities,
+            farm_cts,
+            farm_powers,
+            farm_axial_inductions,
+            max_findex_print=4,
+        )
+
+    assert_results_arrays(test_results[0:4], tilted_baseline)
+
+
 def test_regression_yaw_added_recovery(sample_inputs_fixture):
     """
     Tandem turbines with the upstream turbine yawed and yaw added recovery
diff --git a/tests/reg_tests/yaw_optimization_regression_test.py b/tests/reg_tests/yaw_optimization_regression_test.py
index 203856646..d87d40b39 100644
--- a/tests/reg_tests/yaw_optimization_regression_test.py
+++ b/tests/reg_tests/yaw_optimization_regression_test.py
@@ -183,4 +183,5 @@ def test_scipy_yaw_opt(sample_inputs_fixture):
         print(baseline_scipy.to_string())
         print(df_opt.to_string())
 
-    pd.testing.assert_frame_equal(df_opt, baseline_scipy)
+    # Only require matching up until 2 decimal places
+    pd.testing.assert_frame_equal(df_opt, baseline_scipy, check_exact=False, rtol=1e-5, atol=1e-2)
diff --git a/tests/rotor_velocity_unit_test.py b/tests/rotor_velocity_unit_test.py
index a83ed219e..ab6250e4a 100644
--- a/tests/rotor_velocity_unit_test.py
+++ b/tests/rotor_velocity_unit_test.py
@@ -145,6 +145,60 @@ def test_rotor_velocity_tilt_cosine_correction():
 
     np.testing.assert_allclose(tilt_corrected_velocities, wind_speed_N_TURBINES)
 
+    ## Test angles that are not the same as the reference tilt
+
+    # Test tilted "back" from reference tilt of 5 degrees
+    tilt_angle = 10.0 # Greater than the reference tilt
+    tilt_corrected_velocities = rotor_velocity_tilt_cosine_correction(
+        tilt_angles=tilt_angle*np.ones((1, N_TURBINES)),
+        ref_tilt=np.array([turbine_floating.power_thrust_table["ref_tilt"]] * N_TURBINES),
+        cosine_loss_exponent_tilt=np.array(
+            [turbine_floating.power_thrust_table["cosine_loss_exponent_tilt"]] * N_TURBINES
+        ),
+        tilt_interp=None, # Override wind-speed-based tilt interpolation
+        correct_cp_ct_for_tilt=np.array([[True] * N_TURBINES]),
+        rotor_effective_velocities=wind_speed_N_TURBINES,
+    )
+    assert (tilt_corrected_velocities < wind_speed_N_TURBINES).all()
+
+    # Test tilted "forward" from reference tilt of 5 degrees
+    tilt_angle = 0.0 # Less than the reference tilt
+    tilt_corrected_velocities = rotor_velocity_tilt_cosine_correction(
+        tilt_angles=tilt_angle*np.ones((1, N_TURBINES)),
+        ref_tilt=np.array([turbine_floating.power_thrust_table["ref_tilt"]] * N_TURBINES),
+        cosine_loss_exponent_tilt=np.array(
+            [turbine_floating.power_thrust_table["cosine_loss_exponent_tilt"]] * N_TURBINES
+        ),
+        tilt_interp=None, # Override wind-speed-based tilt interpolation
+        correct_cp_ct_for_tilt=np.array([[True] * N_TURBINES]),
+        rotor_effective_velocities=wind_speed_N_TURBINES,
+    )
+    assert (tilt_corrected_velocities > wind_speed_N_TURBINES).all()
+
+    # Test symmetry around zero tilt
+    tilt_angle = 3.0
+    tilt_negative = rotor_velocity_tilt_cosine_correction(
+        tilt_angles=-tilt_angle*np.ones((1, N_TURBINES)),
+        ref_tilt=np.array([turbine_floating.power_thrust_table["ref_tilt"]] * N_TURBINES),
+        cosine_loss_exponent_tilt=np.array(
+            [turbine_floating.power_thrust_table["cosine_loss_exponent_tilt"]] * N_TURBINES
+        ),
+        tilt_interp=None, # Override wind-speed-based tilt interpolation
+        correct_cp_ct_for_tilt=np.array([[True] * N_TURBINES]),
+        rotor_effective_velocities=wind_speed_N_TURBINES,
+    )
+    tilt_positive = rotor_velocity_tilt_cosine_correction(
+        tilt_angles=tilt_angle*np.ones((1, N_TURBINES)),
+        ref_tilt=np.array([turbine_floating.power_thrust_table["ref_tilt"]] * N_TURBINES),
+        cosine_loss_exponent_tilt=np.array(
+            [turbine_floating.power_thrust_table["cosine_loss_exponent_tilt"]] * N_TURBINES
+        ),
+        tilt_interp=None, # Override wind-speed-based tilt interpolation
+        correct_cp_ct_for_tilt=np.array([[True] * N_TURBINES]),
+        rotor_effective_velocities=wind_speed_N_TURBINES,
+    )
+    np.testing.assert_allclose(tilt_negative, tilt_positive)
+
 def test_compute_tilt_angles_for_floating_turbines():
     N_TURBINES = 4
 
diff --git a/tests/turbine_multi_dim_unit_test.py b/tests/turbine_multi_dim_unit_test.py
index 0c11c2564..a3a5c47dd 100644
--- a/tests/turbine_multi_dim_unit_test.py
+++ b/tests/turbine_multi_dim_unit_test.py
@@ -50,15 +50,15 @@
 def test_turbine_init():
     turbine_data = SampleInputs().turbine_multi_dim
     turbine = Turbine.from_dict(turbine_data)
-    condition = (2, 1)
+    condition_tuple = (2, 1)
     assert turbine.rotor_diameter == turbine_data["rotor_diameter"]
     assert turbine.hub_height == turbine_data["hub_height"]
     assert (
-        turbine.power_thrust_table[condition]["cosine_loss_exponent_yaw"]
+        turbine.power_thrust_table[condition_tuple]["cosine_loss_exponent_yaw"]
         == turbine_data["power_thrust_table"]["cosine_loss_exponent_yaw"]
     )
     assert (
-        turbine.power_thrust_table[condition]["cosine_loss_exponent_tilt"]
+        turbine.power_thrust_table[condition_tuple]["cosine_loss_exponent_tilt"]
         == turbine_data["power_thrust_table"]["cosine_loss_exponent_tilt"]
     )
 
@@ -75,7 +75,7 @@ def test_ct():
     turbine = Turbine.from_dict(turbine_data)
     turbine_type_map = np.array(N_TURBINES * [turbine.turbine_type])
     turbine_type_map = turbine_type_map[None, :]
-    condition = (2, 1)
+    condition = {"Tp":2, "Hs":1}
 
     # Single turbine
     # yaw angle / fCt are (n wind direction, n wind speed, n turbine)
@@ -97,7 +97,7 @@ def test_ct():
         multidim_condition=condition
     )
 
-    np.testing.assert_allclose(thrust, np.array([[0.77815736]]))
+    np.testing.assert_allclose(thrust, np.array([[0.77958497]]))
 
     # Multiple turbines with index filter
     # 4 turbines with 3 x 3 grid arrays
@@ -123,27 +123,26 @@ def test_ct():
     )
     assert len(thrusts[0]) == len(INDEX_FILTER)
 
-    thrusts_truth = np.array([
-        [0.77815736, 0.77815736],
-        [0.77815736, 0.77815736],
-        [0.77815736, 0.77815736],
-        [0.66626835,  0.66626835 ],
-
-        [0.77815736, 0.77815736],
-        [0.77815736, 0.77815736],
-        [0.77815736, 0.77815736],
-        [0.66626835,  0.66626835 ],
-
-        [0.77815736, 0.77815736],
-        [0.77815736, 0.77815736],
-        [0.77815736, 0.77815736],
-        [0.66626835,  0.66626835 ],
-
-        [0.77815736, 0.77815736],
-        [0.77815736, 0.77815736],
-        [0.77815736, 0.77815736],
-        [0.66626835,  0.66626835 ],
-    ])
+    thrusts_truth = np.array(
+        [
+            [0.77958497, 0.77958497],
+            [0.77958497, 0.77958497],
+            [0.77958497, 0.77958497],
+            [0.66749069, 0.66749069],
+            [0.77958497, 0.77958497],
+            [0.77958497, 0.77958497],
+            [0.77958497, 0.77958497],
+            [0.66749069, 0.66749069],
+            [0.77958497, 0.77958497],
+            [0.77958497, 0.77958497],
+            [0.77958497, 0.77958497],
+            [0.66749069, 0.66749069],
+            [0.77958497, 0.77958497],
+            [0.77958497, 0.77958497],
+            [0.77958497, 0.77958497],
+            [0.66749069, 0.66749069]
+        ]
+    )
     np.testing.assert_allclose(thrusts, thrusts_truth)
 
 def test_power():
@@ -154,7 +153,8 @@ def test_power():
     turbine = Turbine.from_dict(turbine_data)
     turbine_type_map = np.array(N_TURBINES * [turbine.turbine_type])
     turbine_type_map = turbine_type_map[None, :]
-    condition = (2, 1)
+    condition = {"Tp":2, "Hs":1}
+    condition_tuple = tuple(condition[k] for k in condition.keys())
 
     # Single turbine
     wind_speed = 10.0
@@ -164,14 +164,14 @@ def test_power():
         air_density=AIR_DENSITY,
         power_functions={turbine.turbine_type: turbine.power_function},
         yaw_angles=np.zeros((1, 1)), # 1 findex, 1 turbine
-        tilt_angles=turbine.power_thrust_table[condition]["ref_tilt"] * np.ones((1, 1)),
+        tilt_angles=turbine.power_thrust_table[condition_tuple]["ref_tilt"] * np.ones((1, 1)),
         power_setpoints=np.ones((1, 1)) * POWER_SETPOINT_DEFAULT,
         awc_modes=np.array([["baseline"]*N_TURBINES]*1),
         awc_amplitudes=np.zeros((1, 1)),
         tilt_interps={turbine.turbine_type: turbine.tilt_interp},
         turbine_type_map=turbine_type_map[:,0],
         turbine_power_thrust_tables={turbine.turbine_type: turbine.power_thrust_table},
-        multidim_condition=condition
+        multidim_condition=condition,
     )
 
     power_truth = 12424759.67683091
@@ -202,7 +202,7 @@ def test_power():
     assert len(p[0]) == len(INDEX_FILTER)
 
     power_truth = turbine.power_function(
-        power_thrust_table=turbine.power_thrust_table[condition],
+        power_thrust_table=turbine.power_thrust_table[condition_tuple],
         velocities=velocities,
         air_density=AIR_DENSITY,
         yaw_angles=np.zeros((1, N_TURBINES)),
@@ -220,9 +220,9 @@ def test_axial_induction():
     turbine = Turbine.from_dict(turbine_data)
     turbine_type_map = np.array(N_TURBINES * [turbine.turbine_type])
     turbine_type_map = turbine_type_map[None, :]
-    condition = (2, 1)
+    condition = {"Tp":2, "Hs":1}
 
-    baseline_ai = np.array([[0.26447651]])
+    baseline_ai = np.array([[0.26551081]])
 
     # Single turbine
     wind_speed = 10.0
diff --git a/tests/turbine_operation_models_unit_test.py b/tests/turbine_operation_models_unit_test.py
index 591c3e01c..9a2c7f670 100644
--- a/tests/turbine_operation_models_unit_test.py
+++ b/tests/turbine_operation_models_unit_test.py
@@ -185,8 +185,11 @@ def test_CosineLossTurbine():
         tilt_angles=tilt_angles_test,
         tilt_interp=None
     )
-    absolute_tilt = tilt_angles_test - turbine_data["power_thrust_table"]["ref_tilt"]
-    assert test_Ct == baseline_Ct * cosd(yaw_angles_test) * cosd(absolute_tilt)
+    assert test_Ct == (
+        baseline_Ct * cosd(yaw_angles_test)
+        * cosd(tilt_angles_test)
+        / cosd(turbine_data["power_thrust_table"]["ref_tilt"])
+    )
 
 
     # Check that thrust coefficient works as expected
@@ -200,7 +203,8 @@ def test_CosineLossTurbine():
     )
     baseline_misalignment_loss = (
         cosd(yaw_angles_nom)
-        * cosd(tilt_angles_nom - turbine_data["power_thrust_table"]["ref_tilt"])
+        * cosd(tilt_angles_nom)
+        / cosd(turbine_data["power_thrust_table"]["ref_tilt"])
     )
     baseline_ai = (
         1 - np.sqrt(1 - turbine_data["power_thrust_table"]["thrust_coefficient"][truth_index])
@@ -216,8 +220,13 @@ def test_CosineLossTurbine():
         tilt_angles=tilt_angles_test,
         tilt_interp=None
     )
-    absolute_tilt = tilt_angles_test - turbine_data["power_thrust_table"]["ref_tilt"]
-    assert test_Ct == baseline_Ct * cosd(yaw_angles_test) * cosd(absolute_tilt)
+
+    assert test_Ct == (
+        baseline_Ct
+        * cosd(yaw_angles_test)
+        * cosd(tilt_angles_test)
+        / cosd(turbine_data["power_thrust_table"]["ref_tilt"])
+    )
 
 
 def test_SimpleDeratingTurbine():
diff --git a/tests/uncertain_floris_model_integration_test.py b/tests/uncertain_floris_model_integration_test.py
index c471758fb..043b2f2fa 100644
--- a/tests/uncertain_floris_model_integration_test.py
+++ b/tests/uncertain_floris_model_integration_test.py
@@ -521,3 +521,104 @@ def test_copy():
     pufmodel_copy = pufmodel.copy()
     assert isinstance(pufmodel_copy, UncertainFlorisModel)
     assert isinstance(pufmodel_copy.fmodel_expanded, ParFlorisModel)
+
+def test_invalid_wd_std():
+    """
+    Test that the UncertainFlorisModel raises asn error with a wd_std of 0 or negative.
+    """
+    with pytest.raises(ValueError):
+        UncertainFlorisModel(configuration=YAML_INPUT, wd_std=0.0)
+
+    with pytest.raises(ValueError):
+        UncertainFlorisModel(configuration=YAML_INPUT, wd_std=-1.0)
+
+def test_turbine_average_velocities_shape_and_type():
+    """
+    Test that turbine_average_velocities returns the correct shape and type.
+    """
+    ufmodel = UncertainFlorisModel(configuration=YAML_INPUT)
+
+    # Set up a simple 2-turbine wind farm
+    ufmodel.set(
+        layout_x=[0, 500],
+        layout_y=[0, 0],
+        wind_speeds=[8.0, 10.0],
+        wind_directions=[270.0, 280.0],
+        turbulence_intensities=[0.06, 0.06],
+    )
+
+    ufmodel.run()
+
+    # Get turbine average velocities
+    velocities = ufmodel.turbine_average_velocities
+
+    # Check type
+    assert isinstance(velocities, np.ndarray)
+
+    # Check shape: should be (n_findex, n_turbines)
+    expected_shape = (ufmodel.n_findex, ufmodel.n_turbines)
+    assert velocities.shape == expected_shape
+
+    # Check that values are positive and reasonable
+    assert np.all(velocities > 0)
+    assert np.all(velocities < 20)  # Reasonable upper bound for wind speeds
+
+
+def test_turbine_average_velocities_free_stream():
+    """
+    Test that turbine_average_velocities returns the correct shape and type.
+    """
+    ufmodel = UncertainFlorisModel(configuration=YAML_INPUT)
+
+    # Set up a simple 2-turbine wind farm with no wake interactions
+    ufmodel.set(
+        layout_x=[0, 0],
+        layout_y=[0, 1000],
+        wind_speeds=[8.0, 10.0],
+        wind_directions=[270.0, 280.0],
+        turbulence_intensities=[0.06, 0.06],
+        wind_shear=0.0,  # Turn off shear to simplify the test
+    )
+
+    ufmodel.run()
+
+    # Get turbine average velocities
+    velocities = ufmodel.turbine_average_velocities
+
+    # Velocities should be the same as the wind speeds but n_turbines columns repeated
+    assert np.allclose(velocities, np.array([[8.0, 8.0], [10.0, 10.0]]))
+
+def test_turbine_average_velocities_uncertain_vs_certain():
+    """
+    Test that turbine_average_velocities returns the same values for uncertain and certain models.
+    """
+
+    # Set up (certain) FlorisModel
+    fmodel = FlorisModel(configuration=YAML_INPUT)
+    fmodel.set(
+        layout_x=[0, 500],
+        layout_y=[0, 0],
+        wind_speeds=[8.0, 10.0],
+        wind_directions=[270.0, 270.0],
+        turbulence_intensities=[0.06, 0.06],
+    )
+    fmodel.run()
+    velocities_certain = fmodel.turbine_average_velocities
+
+    # Create equivalent uncertain model
+    ufmodel = UncertainFlorisModel(configuration=fmodel)
+    ufmodel.run()
+    velocities_uncertain = ufmodel.turbine_average_velocities
+
+    # Check that the upstream turbine matches
+    assert np.allclose(velocities_uncertain[:, 0], velocities_certain[:, 0])
+    # Downstream turbine higher than certain when aligned
+    assert np.all(velocities_uncertain[:, 1] > velocities_certain[:, 1])
+
+    # Create a near 0-std uncertain model
+    ufmodel_zero_std = UncertainFlorisModel(configuration=fmodel, wd_std=1e-5)
+    ufmodel_zero_std.run()
+    velocities_uncertain_zero_std = ufmodel_zero_std.turbine_average_velocities
+
+    # Check that the uncertain model with 0 std matches the certain model
+    assert np.allclose(velocities_uncertain_zero_std, velocities_certain)