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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# **DPW7 - alpha sweep with different turbulence models and UDD example**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This notebook shows how to set up the simulations based on meshes provided during the [VII Drag prediction workshop](https://aiaa-dpw.larc.nasa.gov/Workshop7/workshop7.html). The meshes downloaded will correspond to the given angles of attack. Every AoA will be simulated using the Spallart-Allmaras and k-Omega SST turbulence models. Using the grid for AoA=2.5 degrees there will be an AoA-controlling user defined dynamic implemented that will \"search\" for an AoA corresponding to the coefficient of lift of 0.5.\n",
+    "\n",
+    "The tutorial will walk through all of the important steps of setting up CFD runs as well as through some post-processing steps including generating images using provided TecPlot macros and plotting different characteristics."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Firstly we will import all of the neessary libraries."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import tarfile\n",
+    "from urllib.request import urlretrieve\n",
+    "\n",
+    "import flow360 as fl\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import tecplot as tp\n",
+    "import vtk\n",
+    "from tecplot.constant import *\n",
+    "from tecplot.exception import *\n",
+    "from vtkmodules.util import numpy_support"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Turn off logging for clear cell outputs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from flow360.log import log, set_logging_level\n",
+    "\n",
+    "log.log_to_file = False\n",
+    "set_logging_level(\"INFO\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Then we will define some important variables that will be useful later on."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Reynolds number provided by Workshop\n",
+    "REYNOLDS = 20e06\n",
+    "\n",
+    "# MAC\n",
+    "CHORD = 1.0 * fl.u.m\n",
+    "\n",
+    "# Scale of the model\n",
+    "scale = 1 / 7.447\n",
+    "\n",
+    "# list of slices at which outputs will be defined, scaled to SI\n",
+    "Y_SLICES = (\n",
+    "    np.array(\n",
+    "        [\n",
+    "            121.459,\n",
+    "            133.026,\n",
+    "            144.594,\n",
+    "            151.074,\n",
+    "            232.444,\n",
+    "            327.074,\n",
+    "            396.765,\n",
+    "            427.998,\n",
+    "            459.37,\n",
+    "            581.148,\n",
+    "            697.333,\n",
+    "            840.704,\n",
+    "            978.148,\n",
+    "            1098.926,\n",
+    "            1122.048,\n",
+    "            1145.183,\n",
+    "        ]\n",
+    "    )\n",
+    "    * 0.0254\n",
+    "    * scale\n",
+    ")\n",
+    "\n",
+    "# AoA angles in the sweep\n",
+    "alphas = [2.50, 2.75, 3.00, 3.25, 3.50, 3.75, 4.00, 4.25]\n",
+    "\n",
+    "# studied turbulence models\n",
+    "tb_models = [\"SA\", \"SST\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Solution configuration"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The function defined below is used to generate SimulationParams objects used to run each case depending on the AoA of each case and the turbulence model. User defined dynamics is defined only for the case with AoA of 2.5 degrees. The UDD implements a simple PI controller that changes alpha a sthe controlled variable to obtained a given Cl as an output variable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Based on JAXA grids.\n",
+    "# Baseline DPW7 solver configuration\n",
+    "\n",
+    "\n",
+    "def generate_params(volume_mesh: fl.VolumeMesh, alpha, turbulence_model: str):\n",
+    "    if turbulence_model not in [\"SA\", \"SST\"]:\n",
+    "        raise ValueError(\n",
+    "            \"Choose correct turbulence model: 'SA' - SpalartAllmaras, 'SST' - kOmegaSST.\"\n",
+    "        )\n",
+    "\n",
+    "    vm = volume_mesh\n",
+    "    slip_walls = [\"20\", \"22\"]\n",
+    "    freestream = [\"21\"]\n",
+    "    walls = list(set(volume_mesh.boundary_names) - set(slip_walls) - set(freestream))\n",
+    "    walls = [vm[i] for i in walls]\n",
+    "\n",
+    "    with fl.SI_unit_system:\n",
+    "        params = fl.SimulationParams(\n",
+    "            reference_geometry=fl.ReferenceGeometry(\n",
+    "                area=3.90925521694,\n",
+    "                moment_center=(4.80746918057, 0.0, 0.64521392313),\n",
+    "                moment_length=1.0,\n",
+    "            ),\n",
+    "            operating_condition=fl.operating_condition_from_mach_reynolds(\n",
+    "                mach=0.85,\n",
+    "                reynolds=REYNOLDS,\n",
+    "                alpha=alpha * fl.u.deg,\n",
+    "                temperature=116.483 * fl.u.K,\n",
+    "                project_length_unit=1 * fl.u.m,\n",
+    "            ),\n",
+    "            time_stepping=fl.Steady(\n",
+    "                max_steps=4000, CFL=fl.RampCFL(initial=1, final=200, ramp_steps=2000)\n",
+    "            ),\n",
+    "            models=[\n",
+    "                fl.Wall(surfaces=walls, name=\"Wall\"),\n",
+    "                fl.Freestream(surfaces=[vm[i] for i in freestream], name=\"Freestream\"),\n",
+    "                fl.SlipWall(surfaces=[vm[i] for i in slip_walls], name=\"SlipWall\"),\n",
+    "                fl.Fluid(\n",
+    "                    navier_stokes_solver=fl.NavierStokesSolver(\n",
+    "                        absolute_tolerance=1e-10,\n",
+    "                        linear_solver=fl.LinearSolver(max_iterations=35),\n",
+    "                        update_jacobian_frequency=4,\n",
+    "                    ),\n",
+    "                    turbulence_model_solver=fl.SpalartAllmaras(\n",
+    "                        absolute_tolerance=1e-8,\n",
+    "                        linear_solver=fl.LinearSolver(max_iterations=25),\n",
+    "                        update_jacobian_frequency=4,\n",
+    "                        equation_evaluation_frequency=1,\n",
+    "                        rotation_correction=\"true\",\n",
+    "                        quadratic_constitutive_relation=\"true\",\n",
+    "                    )\n",
+    "                    if turbulence_model == \"SA\"\n",
+    "                    else fl.KOmegaSST(\n",
+    "                        absolute_tolerance=1e-8,\n",
+    "                        linear_solver=fl.LinearSolver(max_iterations=25),\n",
+    "                        update_jacobian_frequency=4,\n",
+    "                        equation_evaluation_frequency=1,\n",
+    "                        rotation_correction=\"true\",\n",
+    "                        quadratic_constitutive_relation=\"true\",\n",
+    "                    ),\n",
+    "                ),\n",
+    "            ],\n",
+    "            outputs=[\n",
+    "                fl.VolumeOutput(\n",
+    "                    output_format=\"tecplot\",\n",
+    "                    output_fields=[\"primitiveVars\", \"qcriterion\", \"Mach\", \"mutRatio\"],\n",
+    "                ),\n",
+    "                fl.SurfaceOutput(\n",
+    "                    output_format=\"tecplot\",\n",
+    "                    output_fields=[\n",
+    "                        \"primitiveVars\",\n",
+    "                        \"Cp\",\n",
+    "                        \"Cf\",\n",
+    "                        \"CfVec\",\n",
+    "                        \"yPlus\",\n",
+    "                        \"nodeForcesPerUnitArea\",\n",
+    "                    ],\n",
+    "                    surfaces=walls,\n",
+    "                ),\n",
+    "                fl.SurfaceSliceOutput(\n",
+    "                    name=\"wing_sections\",\n",
+    "                    entities=[\n",
+    "                        fl.Slice(name=f\"slice y={y:.2f}\", origin=(0, y, 0), normal=(0, 1, 0))\n",
+    "                        for y in Y_SLICES\n",
+    "                    ],\n",
+    "                    target_surfaces=walls,\n",
+    "                    output_fields=[\n",
+    "                        \"Cp\",\n",
+    "                        \"Cf\",\n",
+    "                        \"CfVec\",\n",
+    "                        \"yPlus\",\n",
+    "                    ],\n",
+    "                ),\n",
+    "            ],\n",
+    "            user_defined_dynamics=[\n",
+    "                fl.UserDefinedDynamic(\n",
+    "                    name=\"alphaController\",\n",
+    "                    input_vars=[\"CL\"],\n",
+    "                    constants={\"CLTarget\": 0.5, \"Kp\": 0.1, \"Ki\": 0.001},\n",
+    "                    output_vars={\"alphaAngle\": \"if (pseudoStep > 1500) state[0]; else alphaAngle;\"},\n",
+    "                    state_vars_initial_value=[\"alphaAngle\", \"2.5\"],\n",
+    "                    update_law=[\n",
+    "                        \"if (pseudoStep > 1500) state[0] + Kp * (CLTarget - CL) + Ki * state[1]; else state[0];\",\n",
+    "                        \"if (pseudoStep > 1500) state[1] + (CLTarget - CL); else state[1];\",\n",
+    "                    ],\n",
+    "                    input_boundary_patches=walls,\n",
+    "                )\n",
+    "            ]\n",
+    "            if alpha == 2.5\n",
+    "            else None,\n",
+    "        )\n",
+    "\n",
+    "    return params"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Uploading the meshes and running the cases"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For each mesh a different project will be created. The dataframe created below will store the information about the projects to then easily access them in the cloud by their id."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Creating a directory for the downloaded meshes\n",
+    "meshes_path = os.path.join(os.getcwd(), \"Meshes\")\n",
+    "os.makedirs(meshes_path, exist_ok=True)\n",
+    "print(f\"Meshes will be saved to: {meshes_path}\")\n",
+    "\n",
+    "# Creating a dataframe for the project info (or reading if is already existent)\n",
+    "project_file = \"project_list.csv\"\n",
+    "if os.path.exists(project_file):\n",
+    "    projects = pd.read_csv(project_file)\n",
+    "else:\n",
+    "    projects = pd.DataFrame(columns=[\"alpha\", \"project_name\", \"project_id\", \"volume_mesh_id\"])\n",
+    "\n",
+    "print(\"Project info:\")\n",
+    "projects"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Function for retrieving project IDs for certain alpha angles"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Two helper functions are then defined: to extract project with a given alpha angle and to extract a turbulence model from a ran case."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_project_id_for_alpha(alpha, projects):\n",
+    "    \"\"\"Returns project ID and the position in the dataframe\"\"\"\n",
+    "    matching_rows = projects[projects[\"alpha\"] == alpha]\n",
+    "    if not matching_rows.empty:\n",
+    "        return matching_rows[\"project_id\"].iloc[0], matching_rows.index[0]\n",
+    "    else:\n",
+    "        return None, len(projects.index)\n",
+    "\n",
+    "\n",
+    "def extract_turbulence_model_solver(case: fl.Case):\n",
+    "    models = case.params.models\n",
+    "    for model in models:\n",
+    "        if isinstance(model, fl.Fluid):\n",
+    "            return model.turbulence_model_solver\n",
+    "    return None"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Submitting and running the case for AoA = 2.5, SA model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The cells below provide a step by step instructions on how to submit a single case to cloud for solution."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Downloading the mesh"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "alpha = 2.5\n",
+    "tb_model = \"SA\"\n",
+    "\n",
+    "download_mesh_name = f\"CRM_wb_ih+0_UnstrMixed_JAXA_mediumRev00_LoQ{alpha:.2f}.b8.ugrid.gz\"\n",
+    "local_mesh_name = os.path.join(meshes_path, download_mesh_name)\n",
+    "\n",
+    "# if meshes not present in Meshes directory, try downloading them from DPW7 website\n",
+    "if not os.path.isfile(local_mesh_name):\n",
+    "    print(f\"DOWNLOADING MESH, {alpha=:.2f}\")\n",
+    "    url = f\"https://dpw.larc.nasa.gov/DPW7/JAXA_Grids.REV00/{download_mesh_name}\"\n",
+    "    urlretrieve(url, local_mesh_name)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Creating a project for the volume mesh"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "project_id, row_index = get_project_id_for_alpha(alpha, projects)\n",
+    "if project_id is None:\n",
+    "    # create project and upload volume mesh\n",
+    "    project = fl.Project.from_volume_mesh(\n",
+    "        local_mesh_name, name=f\"DPW7_JAXA_medium_LoQ_AoA{alpha:.2f}\"\n",
+    "    )\n",
+    "    projects.loc[row_index] = [alpha, project.metadata.name, project.id, project.volume_mesh.id]\n",
+    "    projects.to_csv(project_file, index=False)\n",
+    "else:\n",
+    "    project = fl.Project.from_cloud(project_id=project_id)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Running the case"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cases = project.get_case_ids()\n",
+    "\n",
+    "tb_models_calculated = [\n",
+    "    (\n",
+    "        \"SA\"\n",
+    "        if isinstance(extract_turbulence_model_solver(fl.Case(case_id)), fl.SpalartAllmaras)\n",
+    "        else (\n",
+    "            \"SST\"\n",
+    "            if isinstance(extract_turbulence_model_solver(fl.Case(case_id)), fl.KOmegaSST)\n",
+    "            else None\n",
+    "        )\n",
+    "    )\n",
+    "    for case_id in cases\n",
+    "]\n",
+    "\n",
+    "# run the case if it has not been ran previously\n",
+    "if tb_model not in tb_models_calculated:\n",
+    "    params = generate_params(project.volume_mesh, alpha, tb_model)\n",
+    "    project.run_case(params=params, name=f\"{project.metadata.name}_{tb_model}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Loop for all of the angles"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally the process is concentrated in a single block that loops through all AoA - turbulence model combinations and submits appropriate cases for solution."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for alpha in alphas:\n",
+    "    for tb_model in tb_models:\n",
+    "        download_mesh_name = f\"CRM_wb_ih+0_UnstrMixed_JAXA_mediumRev00_LoQ{alpha:.2f}.b8.ugrid.gz\"\n",
+    "        local_mesh_name = os.path.join(meshes_path, download_mesh_name)\n",
+    "\n",
+    "        # if meshes not present in Meshes directory, try downloading them from DPW7 website\n",
+    "        if not os.path.isfile(local_mesh_name):\n",
+    "            print(f\"DOWNLOADING MESH, {alpha=:.2f}\")\n",
+    "            url = f\"https://dpw.larc.nasa.gov/DPW7/JAXA_Grids.REV00/{download_mesh_name}\"\n",
+    "            urlretrieve(url, local_mesh_name)\n",
+    "\n",
+    "        project_id, row_index = get_project_id_for_alpha(alpha, projects)\n",
+    "        if project_id is None:\n",
+    "            # create project and upload volume mesh\n",
+    "            project = fl.Project.from_volume_mesh(\n",
+    "                local_mesh_name, name=f\"DPW7_JAXA_medium_LoQ_AoA{alpha:.2f}\"\n",
+    "            )\n",
+    "            projects.loc[row_index] = [\n",
+    "                alpha,\n",
+    "                project.metadata.name,\n",
+    "                project.id,\n",
+    "                project.volume_mesh.id,\n",
+    "                None,\n",
+    "            ]\n",
+    "            projects.to_csv(project_file, index=False)\n",
+    "        else:\n",
+    "            project = fl.Project.from_cloud(project_id=project_id)\n",
+    "\n",
+    "        cases = project.get_case_ids()\n",
+    "\n",
+    "        tb_models_calculated = [\n",
+    "            (\n",
+    "                \"SA\"\n",
+    "                if isinstance(extract_turbulence_model_solver(fl.Case(case_id)), fl.SpalartAllmaras)\n",
+    "                else (\n",
+    "                    \"SST\"\n",
+    "                    if isinstance(extract_turbulence_model_solver(fl.Case(case_id)), fl.KOmegaSST)\n",
+    "                    else None\n",
+    "                )\n",
+    "            )\n",
+    "            for case_id in cases\n",
+    "        ]\n",
+    "\n",
+    "        # run the case if it has not been ran previously\n",
+    "        if tb_model not in tb_models_calculated:\n",
+    "            params = generate_params(project.volume_mesh, alpha, tb_model)\n",
+    "            project.run_case(params=params, name=f\"{project.metadata.name}_{tb_model}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Downloading and postprocessing the results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "After the cases finished running (which can be checked in WebUI) we begin post-processing the results. This notebook shows a manual approach to working with the output data which consists of downloading the raw results and processing them inside the script. More automatic approaches are also available: different results objects have methods that make processing easier and the report plugin can help automate generating reports documenting the analyses. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Firstly the project list is read into the projects variable and a folder for results files is created."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# run if kernel was restarted\n",
+    "projects = pd.read_csv(\"project_list.csv\")\n",
+    "\n",
+    "results_path = os.path.join(os.getcwd(), \"Results\")\n",
+    "os.makedirs(results_path, exist_ok=True)\n",
+    "print(f\"Results will be saved to: {results_path}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Downloading"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Then we loop through all of the created projects and through cases ran inside them and download the numeric results files."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for i, row in projects.iterrows():\n",
+    "    for case_id in fl.Project.from_cloud(project_id=row[\"project_id\"]).get_case_ids():\n",
+    "        results = fl.Case(id=case_id).results\n",
+    "\n",
+    "        case_path = os.path.join(results_path, fl.Case(id=case_id).name)\n",
+    "        results.set_destination(folder_name=case_path)\n",
+    "        results.download(\n",
+    "            nonlinear_residuals=True,\n",
+    "            surface_forces=True,\n",
+    "            total_forces=True,\n",
+    "            user_defined_dynamics=True,\n",
+    "            overwrite=True,\n",
+    "        )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Extracting the loads"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Then a dataframe is prepared to store the processed quantities. The loads are averaged over the last 100 pseudo steps. The data is saved every 10 pseudo steps, this is why the result is the average of the last 10 datapoints. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>case_name</th>\n",
+       "      <th>alpha</th>\n",
+       "      <th>turbulence_model</th>\n",
+       "      <th>CL_total</th>\n",
+       "      <th>CD_total</th>\n",
+       "      <th>CDPressure_total</th>\n",
+       "      <th>CDSkinFriction_total</th>\n",
+       "      <th>CL2PA_total</th>\n",
+       "      <th>CMy_total</th>\n",
+       "      <th>CL_wing</th>\n",
+       "      <th>CD_wing</th>\n",
+       "      <th>CDPressure_wing</th>\n",
+       "      <th>CDSkinFriction_wing</th>\n",
+       "      <th>CMy_wing</th>\n",
+       "      <th>CL_fus</th>\n",
+       "      <th>CD_fus</th>\n",
+       "      <th>CDPressure_fus</th>\n",
+       "      <th>CDSkinFriction_fus</th>\n",
+       "      <th>CMy_fus</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Empty DataFrame\n",
+       "Columns: [case_name, alpha, turbulence_model, CL_total, CD_total, CDPressure_total, CDSkinFriction_total, CL2PA_total, CMy_total, CL_wing, CD_wing, CDPressure_wing, CDSkinFriction_wing, CMy_wing, CL_fus, CD_fus, CDPressure_fus, CDSkinFriction_fus, CMy_fus]\n",
+       "Index: []"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_dumping_rate = 10\n",
+    "n_steps_to_avg = 100\n",
+    "\n",
+    "entries_to_avg = n_steps_to_avg // data_dumping_rate\n",
+    "\n",
+    "aspect_ratio = 9.0\n",
+    "\n",
+    "results_path = os.path.join(os.getcwd(), \"Results\")\n",
+    "os.makedirs(results_path, exist_ok=True)\n",
+    "\n",
+    "alphas = [2.50, 2.75, 3.00, 3.25, 3.50, 3.75, 4.00, 4.25]\n",
+    "tb_models = [\"SA\", \"SST\"]\n",
+    "\n",
+    "wing_zone_ids = list(range(8, 20))\n",
+    "fuselage_zone_ids = list(range(1, 8))\n",
+    "\n",
+    "# preparing the data structure\n",
+    "loads = [\"CL\", \"CD\", \"CDPressure\", \"CDSkinFriction\", \"CL2PA\", \"CMy\"]\n",
+    "zones = [\"total\", \"wing\", \"fus\"]\n",
+    "\n",
+    "columns = [\"case_name\", \"alpha\", \"turbulence_model\"]\n",
+    "\n",
+    "for zone in zones:\n",
+    "    for load in loads:\n",
+    "        if not (load == \"CL2PA\" and zone != \"total\"):\n",
+    "            columns.append(f\"{load}_{zone}\")\n",
+    "\n",
+    "loads_df = pd.DataFrame(columns=columns)\n",
+    "loads_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The functions defined below are designed to compute the averages and create an entry to the loads_df dataframe."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def compute_loads_total(total_forces, loads):\n",
+    "    loads_total = dict()\n",
+    "    for load in loads:\n",
+    "        if load != \"CL2PA\":\n",
+    "            value = np.average(total_forces[load][-entries_to_avg:-1])\n",
+    "            loads_total[f\"{load}_total\"] = value\n",
+    "        else:\n",
+    "            # CL2PA corresponds to idealized profile drag\n",
+    "            value = (\n",
+    "                np.average(total_forces[\"CD\"][-entries_to_avg:-1])\n",
+    "                - pow(np.average(total_forces[\"CL\"][-entries_to_avg:-1]), 2) / np.pi / aspect_ratio\n",
+    "            )\n",
+    "            loads_total[f\"{load}_total\"] = value\n",
+    "    return loads_total\n",
+    "\n",
+    "\n",
+    "def compute_loads_partial(surface_forces, wing_zone_ids, fuselage_zone_ids, loads):\n",
+    "    loads_partial = dict()\n",
+    "\n",
+    "    for load in loads:\n",
+    "        if load != \"CL2PA\":\n",
+    "            value = 0\n",
+    "            for i in wing_zone_ids:\n",
+    "                value += np.average(surface_forces[str(i) + \"_\" + load][-entries_to_avg:-1])\n",
+    "            loads_partial[f\"{load}_wing\"] = value\n",
+    "\n",
+    "    for load in loads:\n",
+    "        if load != \"CL2PA\":\n",
+    "            value = 0\n",
+    "            for i in fuselage_zone_ids:\n",
+    "                value += np.average(surface_forces[str(i) + \"_\" + load][-entries_to_avg:-1])\n",
+    "            loads_partial[f\"{load}_fus\"] = value\n",
+    "\n",
+    "    return loads_partial"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The compute_alpha function extracts the effective averaged alpha from the case with the UDD defined.\n",
+    "\n",
+    "The get_case_object function is used to get a case object from the case name."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def compute_alpha(udd_data, entries_to_avg):\n",
+    "    return np.average(udd_data[\"alphaAngle\"][-entries_to_avg:-1])\n",
+    "\n",
+    "\n",
+    "def get_case_object(project, name):\n",
+    "    ids = project.get_case_ids()\n",
+    "    for id in ids:\n",
+    "        if fl.Case(id).name == name:\n",
+    "            return fl.Case(id)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The functions are then used in a loop that extracts the wanted loads from all of the performed simulations and stores the values in the file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for alpha in alphas:\n",
+    "    project = fl.Project.from_cloud(project_id=get_project_id_for_alpha(alpha, projects)[0])\n",
+    "    for tb_model in tb_models:\n",
+    "        case_name = f\"DPW7_JAXA_medium_LoQ_AoA{alpha:.2f}_{tb_model}\"\n",
+    "        csv_path_total = os.path.join(results_path, f\"{case_name}\", \"total_forces_v2.csv\")\n",
+    "        total_forces = pd.read_csv(csv_path_total, skipinitialspace=True)\n",
+    "        csv_path_surface = os.path.join(results_path, f\"{case_name}\", \"surface_forces_v2.csv\")\n",
+    "        surface_forces = pd.read_csv(csv_path_surface, skipinitialspace=True)\n",
+    "        loads_total = compute_loads_total(total_forces, loads)\n",
+    "        loads_partial = compute_loads_partial(\n",
+    "            surface_forces, wing_zone_ids, fuselage_zone_ids, loads\n",
+    "        )\n",
+    "\n",
+    "        if get_case_object(project, case_name).params.user_defined_dynamics is None:\n",
+    "            alpha_true = get_case_object(project, case_name).params.operating_condition.alpha\n",
+    "        else:\n",
+    "            csv_path = os.path.join(results_path, f\"{case_name}\", \"udd_alphaController_v2.csv\")\n",
+    "            udd_data = pd.read_csv(csv_path, skipinitialspace=True)\n",
+    "            alpha_true = compute_alpha(udd_data, entries_to_avg)\n",
+    "\n",
+    "        loads_row = {\"case_name\": case_name, \"alpha\": alpha_true, \"turbulence_model\": tb_model}\n",
+    "        loads_row.update(loads_total)\n",
+    "        loads_row.update(loads_partial)\n",
+    "\n",
+    "        new_row = pd.DataFrame(loads_row, index=[0])\n",
+    "\n",
+    "        loads_df = pd.concat([loads_df, new_row], ignore_index=True)\n",
+    "\n",
+    "loads_df.to_csv(os.path.join(results_path, \"loads.csv\"), index=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Extracting the solution history"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Another important part of each simulation is the convergence. The nonlinear residuals can be found in the **nonlinear_residual_v2.csv** file. Another quantity worth monitoring during the solution are the total loads. The code below extracts the from appropriate files and saves each history to the case-specific file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loads = [\"CL\", \"CD\", \"CMy\"]\n",
+    "residuals_sa = [\"0_cont\", \"1_momx\", \"2_momy\", \"3_momz\", \"4_energ\", \"5_nuHat\"]\n",
+    "residuals_sst = [\"0_cont\", \"1_momx\", \"2_momy\", \"3_momz\", \"4_energ\", \"5_k\", \"6_omega\"]\n",
+    "\n",
+    "for alpha in alphas:\n",
+    "    project = fl.Project.from_cloud(project_id=get_project_id_for_alpha(alpha, projects)[0])\n",
+    "    for tb_model in tb_models:\n",
+    "        case_name = f\"DPW7_JAXA_medium_LoQ_AoA{alpha:.2f}_{tb_model}\"\n",
+    "        csv_path_total_forces = os.path.join(results_path, f\"{case_name}\", \"total_forces_v2.csv\")\n",
+    "        total_forces = pd.read_csv(csv_path_total_forces, skipinitialspace=True)\n",
+    "        csv_path_residuals = os.path.join(results_path, f\"{case_name}\", \"nonlinear_residual_v2.csv\")\n",
+    "        residuals = pd.read_csv(csv_path_residuals, skipinitialspace=True)\n",
+    "\n",
+    "        if get_case_object(project, case_name).params.user_defined_dynamics is None:\n",
+    "            alpha_hist = pd.Series(\n",
+    "                [get_case_object(project, case_name).params.operating_condition.alpha]\n",
+    "                * len(residuals)\n",
+    "            )\n",
+    "        else:\n",
+    "            csv_path = os.path.join(results_path, f\"{case_name}\", \"udd_alphaController_v2.csv\")\n",
+    "            udd_data = pd.read_csv(csv_path, skipinitialspace=True)\n",
+    "            alpha_hist = udd_data[\"alphaAngle\"]\n",
+    "\n",
+    "        iterative_history = pd.DataFrame(total_forces[\"pseudo_step\"])\n",
+    "\n",
+    "        iterative_history = iterative_history.join(\n",
+    "            [\n",
+    "                alpha_hist,\n",
+    "                total_forces[loads],\n",
+    "                residuals[residuals_sa]\n",
+    "                if tb_model == \"SA\"\n",
+    "                else (residuals[residuals_sst] if tb_model == \"SST\" else None),\n",
+    "            ]\n",
+    "        )\n",
+    "\n",
+    "        case_path = os.path.join(results_path, case_name)\n",
+    "\n",
+    "        iterative_history.to_csv(os.path.join(case_path, \"iterative_history.csv\"), index=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Generating images"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Download surface results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for i, row in projects.iterrows():\n",
+    "    for case_id in fl.Project.from_cloud(project_id=row[\"project_id\"]).get_case_ids():\n",
+    "        results = fl.Case(id=case_id).results\n",
+    "\n",
+    "        case_path = os.path.join(results_path, fl.Case(id=case_id).name)\n",
+    "        results.set_destination(folder_name=case_path)\n",
+    "        results.download(surface=True, overwrite=True)\n",
+    "\n",
+    "        with tarfile.open(os.path.join(case_path, \"surfaces.tar.gz\")) as f:\n",
+    "            f.extractall(os.path.join(case_path, \"surfaces\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Using the vtk library we will extract the surface slice data from the .pvtu files and plot them using matplotlib."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Plotting a single profile.\n",
+    "First, necessary variables describing the slice and the locations of case output files are defined."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vtu_reader = vtk.vtkXMLPUnstructuredGridReader()\n",
+    "row = projects.iloc[0]\n",
+    "case_id = fl.Project.from_cloud(project_id=row[\"project_id\"]).case.id\n",
+    "slice = Y_SLICES[2]\n",
+    "case_path = os.path.join(results_path, fl.Case(id=case_id).name)\n",
+    "file_location = os.path.join(case_path, \"surfaces\")\n",
+    "filename = f\"surface_slice_slice y={slice:.2f}.pvtu\"\n",
+    "base_path = os.getcwd()\n",
+    "os.chdir(file_location)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next, using the vtu reader, the numeric point data is extracted from the vtu file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vtu_reader.SetFileName(filename)\n",
+    "vtu_reader.Update()\n",
+    "cps = numpy_support.vtk_to_numpy(vtu_reader.GetOutput().GetPointData().GetArray(\"Cp\"))\n",
+    "coords = numpy_support.vtk_to_numpy(vtu_reader.GetOutput().GetPoints().GetData())\n",
+    "os.chdir(base_path)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Then the plot is created using these points."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Piotr\\AppData\\Local\\Temp\\ipykernel_816\\2367370763.py:11: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
+      "  fig.show()\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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XXliQsSbKQ0WWlp122ildf/312fzJkyenPffcMz399NOpe/fWG+uSH0gS2SVaal+RnaehDCflKF6nvqCX1tpPY8Ez8b5VIr+dxRlAytGU59XRPPbYY2m//fYrCMCJIKOBAwemVVddNfXv3z/LmhNBG8WloDbYYIM2aWNrZMXJDzIDAAAAqoPgGgAAAGiin3/+OQuYyQ8uOOqoo3Llj84999z04IMPpi+//DKbfu6559IFF1yQDj744FZrU5REqs3+8uOPP2aBAC0RYBNlk/L98MMPFW+jeJ1oa3vtp7kiOOa7777Lvc6Vasrz6mgOP/zwgs/+Zpttli6++OLUr1+/dstUU6w48Gno0KGNtq/S8mYAAABA1ye4BgAAAJrouOOOS++++25ueokllsjm5Zc0Ou+889KOO+6Ym/f3v/89bbXVVllWj9bQt2/fXHBNBD6MHz8+yx7SXFH6KIJ0arN2NKXcTrQl38wzz1xnmeJ5TdlP/jo9evRoleCamWaaKRdcE2WCfvnll4qy37RGuaK2FJ/7V199NTe91FJLpdtuuy1NPfXUFZUua23FZcWij66yyipttn8AAACga2i9PNQAAADQhb344ovpnHPOyU1H4EmUgyoOLthhhx3S5ptvXpCxZJ999mm1di288MIF0y+99FKLbLdbt25p3nnnzU1HYMnYsWMr2sZrr71WMF1fgFHxvOJ1GvP+++8XlJuab775sra3tAUWWCD3/ylTpqR33nmnovVHjBiROrPIwpRvr732KiuwJrz55puprcw///wF0++9916b7RsAAADoOgTXAAAAQIUmT56c9thjjyyoolaUelpttdXqXT5K5cw444y56SgVdfXVV7dK29Zaa62C6fvuu6/Ftl38/B599NGK1i9evr7Xqy320RJWXnnlguknnnii7HV//fXX9PTTT6fO7PPPPy+YXnTRRctet9L3tLisWX4pqsass846zdo3AAAAQBBcAwAAABU65ZRT0htvvFGQxSTmlTLPPPOkM844o2DeYYcdlr744osWb9vGG29cMH3ttdemb775pkW2vfbaaxdMX3XVVWWvO3LkyIKAkmmmmSatuuqqdZaLYJj8DChPPfVURdlGrrzyygbb3FIGDRpUMH3NNdeUve69995bp0RWZ1Mc4BIBZ+WYNGlSnfeoMdNPP33B9I8//lj2uuuvv37q2fP/qqLfeOONnb4kFwAAAND2BNcAAABABaJM0emnn14w77LLLkvTTTddg+tFKaj8gIyvvvoqHXjggS3evhVWWCENHDiwoHzTAQcc0CLbjhJX+YEOw4YNS3fccUdZ6x5++OEF09ttt13q06dPneVmmmmm9Pvf/74giOOII44oax+33HJLevbZZ3PTvXv3ztrcGjbaaKMsaCq/TNitt97a6Hq//PJLOvroo1NnN+eccxZMRxBUOY477rg6WW8a07dv34LpMWPGlL3uHHPMkXbeeeeCsmwt1R8AAACA6iG4BgAAACoo5xPloCJAotbee++d1l133UbX7datWxaE06tXr9y8m2++Od15550t3s7Bgwdn+6t1ww03pEMOOaTscjqPPPJIvfMj8CWef76YHjFiRKPtiWwt+WV+Dj300JLLx9/ySwHFa9RQZqAwfPjwtNdeexXMi+n8clwtqUePHnWCo+KzkJ/RqFi8/tGmt99+O3V2a6yxRp3SZ41lGLrkkkvSkCFDKt7XkksuWSeIqhLHHntsQfDbf//737TvvvuWnW2nNhguPoN33313RfsGAAAAugbBNQAAAFCmM888M73yyiu56chcEvPKtdBCC6UTTzyxYN7++++fvv322xZtZ5TCibJT+c4777y04oorpttuuy3L3lEsAiNimZVWWilbv5R//OMfacCAAbnpr7/+Ogu0OOuss7L/54tAk2233TaddNJJBfOPPPLItPzyy5fcR7ShOPgmMp5ERpvi4JXYZwRsrLnmmgWv44ILLthoQE5zxWu8xBJL1HktLrjggixjUL4XXnghrbPOOrnyUfmvYWcUn+XVV189N/3999+n3/3ud1nAWAShFWd7+uMf/5j+/Oc/ZwFGiy++eEX7ioxPU001VW766quvTttss01WXur+++9PDz/8cO6RX3osv2zbFVdcUTDv0ksvTUsvvXQW8FZfJp1o5+jRo7OyarGvfv36ZZ9BJaUAAACgOv1f0WkAAACgpHfeeadOYExk66ivtFFjARk33XRTeumll7LpTz75JAs2iZv9LSlKV40bNy7bV61XX301C3aZeuqpU//+/bNyOxFo89FHH6VvvvmmrO3OMMMMWQDFhhtumAumiW1E6aYodzT//PNn2WI+/fTT9PHHH9dZf+ONN64TbFMqiCeCMiJgolaUXYrH3HPPnT0ioOP9998vyCQUZplllux555ewag3xOv7nP//Jgj9qX4to00EHHZS9p/FaRBviffjiiy9y6y2zzDJZoEkEVnVmEdQUz7329Y/3PMp9RTmuhRdeOMs+FJ+t/OCVeD2uv/76rHxZuWafffastFME09S6/fbbs0ex+FyPHTu2zvztt98+19d+++23bN67776blWuLx7zzzptmnXXW1LNnz6wvfPbZZ9l7CQAAABBkrgEAAIBGxM34KH80adKk3Lwdd9wxbb755k0qJxRZNPIzcVx++eXp8ccfTy0pggRuvPHGrCRO/r5ClMMZNWpUev7557NMMOUG1uRnlnnyySezjCD5IsgiAhYicKi+wJrddtst3XXXXVlQSmOmmWaarJTUn/70pzp/iyCJ2MfIkSPrBNZEUMdTTz1VUfBGc0SgzAMPPJAFgOSLz0oEZL388ssFgTWRtSWeV355sM4qsvRE5pfiz9fEiROzQK547vmBNTPPPHO65557GsxaVMq5556bNthgg2a1NwLb7rvvvjTXXHPV+VsEQEWbX3zxxaxv1BdYE5/J4vcZAAAAqA6CawAAAKAR559/fnr22Wdz07PNNltWQqk5ARlHHXVUQQmavffeO/3000+pJXXr1i2dfPLJ6e23384CW2aaaaYGl59jjjnSXnvtlZUwasxSSy2V3nrrrawsVnGQTXGQz3rrrZeGDRuWhg4dWicQoyERhBNleSLwaN111822VUqUgYrSVBEstNhii6W2tMoqq2SvcbyH0003XcmMP5E1JYI3osRQV7HrrrtmgVZREqqUaaedNgtOe/PNN7NMN00Rr9///ve/LJAptrXccstlmZcq+TyFjTbaKMt2FH06+mH0kYZEFp7NNtss/fvf/84y82y66aZNaj8AAADQuXWriSt4AAAAQJc3ZcqULHBm9OjR6csvv0w//vhjFjwwzzzzpCWXXDILSmks2KCUyNISmT8iS0tsN0ozxXYHDhxYcemsUiLDTmSlicw1EyZMyEoMRUBQBFosuuiiqSOIElmPPfZY+uCDD7JSUfHcI1tNvA4RZNKVRTmmp59+OgtCicw9EcwV70tkuCkVdNTeoh9EBqcoAxWfqchSFWXN5pxzzux9i0xIlQbwVCKC3q6++urc9JgxY9KAAQNSV1etzxsAAIDOS3ANAAAAALSDag0yqdbnDQAAQOelLBQAAAAAdADzzz9/lj0q/9HZnXDCCXWeU35gDQAAAHQGgmsAAAAAAAAAAKAEwTUAAAAAAAAAAFBCt5qamppSfwQAAAAAWsdbb72VPvnkkwaXWX/99VNn9v7772ePhgwcODBNO+20bdYmAAAAqJTgGgAAAAAAAAAAKEFZKAAAAAAAAAAAKEFwDQAAAAAAAAAAlCC4BgAAAAAAAAAAShBcAwAAAAAAAAAAJQiuAQAAAAAAAACAEgTXAAAAAAAAAABACYJrAAAAAAAAAACgBME1AAAAAAAAAABQguAaAAAAAAAAAAAoQXANAAAAAAAAAACUILgGAAAAAAAAAABKEFwDAAAAAAAAAAAlCK4BAAAAAAAAAIASBNcAAAAAAAAAAEAJgmsAAAAAAAAAAKAEwTUAAAAAAAAAAFCC4BoAAAAAAAAAAChBcA0AAAAAAAAAAJQguAYAAAAAAAAAAEoQXAMAAAAAAAAAACUIrgEAAAAAAAAAgBIE1wAAAAAAAAAAQAmCawAAAAAAAAAAoATBNQAAAAAAAAAAUILgGgAAAAAAAAAAKEFwDQAAAAAAAAAAlCC4BgAAAAAAAAAAShBcAwAAAAAAAAAAJQiuAQAAAAAAAACAEgTXAAAAAAAAAABACYJrAAAAAAAAAAAg1e//A3qR9LdHOBR/AAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 2400x1800 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(8, 6), dpi=300)\n",
+    "ax.scatter(coords[:, 0], cps, s=3)\n",
+    "ax.set_xlabel(\"X coordinate [m]\")\n",
+    "ax.set_ylabel(\"Cp\")\n",
+    "ax.grid(True)\n",
+    "ax_sec = ax.twinx()\n",
+    "ax_sec.scatter(coords[:, 0], coords[:, 2], c=\"r\", s=3)\n",
+    "ax_sec.set_aspect(\"equal\")\n",
+    "ax_sec.set_ylabel(\"Z coordinate [m]\")\n",
+    "ax.set_title(f\"Cp on surface slice y={slice:.2f} m\")\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Plotting in loop\n",
+    "Loop that creates the plots and saves them into images folders."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for i, row in projects.iterrows():\n",
+    "    for case_id in fl.Project.from_cloud(project_id=row[\"project_id\"]).get_case_ids():\n",
+    "        case_path = os.path.join(results_path, fl.Case(id=case_id).name)\n",
+    "        images_path = os.path.join(case_path, \"images\")\n",
+    "        os.makedirs(images_path, exist_ok=True)\n",
+    "        for slice in Y_SLICES:\n",
+    "            file_location = os.path.join(case_path, \"surfaces\")\n",
+    "            os.chdir(file_location)\n",
+    "            filename = f\"surface_slice_slice y={slice:.2f}.pvtu\"\n",
+    "            vtu_reader.SetFileName(filename)\n",
+    "            vtu_reader.Update()\n",
+    "            cps = numpy_support.vtk_to_numpy(vtu_reader.GetOutput().GetPointData().GetArray(\"Cp\"))\n",
+    "            coords = numpy_support.vtk_to_numpy(vtu_reader.GetOutput().GetPoints().GetData())\n",
+    "            os.chdir(base_path)\n",
+    "            fig, ax = plt.subplots(figsize=(8, 6), dpi=300)\n",
+    "            ax.scatter(coords[:, 0], cps, s=3)\n",
+    "            ax.set_xlabel(\"X coordinate [m]\")\n",
+    "            ax.set_ylabel(\"Cp\")\n",
+    "            ax.grid(True)\n",
+    "            ax_sec = ax.twinx()\n",
+    "            ax_sec.scatter(coords[:, 0], coords[:, 2], c=\"r\", s=3)\n",
+    "            ax_sec.set_aspect(\"equal\")\n",
+    "            ax_sec.set_ylabel(\"Z coordinate [m]\")\n",
+    "            ax.set_title(f\"Cp on surface slice y={slice:.2f} m\")\n",
+    "            fig_filepath = os.path.join(images_path, f\"surface_slice y={slice:.2f}.png\")\n",
+    "            fig.savefig(fig_filepath, format=\"png\")\n",
+    "            plt.clf()\n",
+    "            plt.close(fig)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next, we extract images showing different solution fields as well as streamlines using the macro provided by the DPW7. The pyTecPlot library is used."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The function below is used to load the solution data into TecPlot."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def loadData(datafiles):\n",
+    "    dataset = tp.data.load_tecplot_szl(\n",
+    "        datafiles, read_data_option=tp.constant.ReadDataOption.Replace\n",
+    "    )\n",
+    "    frame = tp.active_frame()\n",
+    "    frame.plot_type = tp.constant.PlotType.Cartesian3D\n",
+    "    frame.activate()\n",
+    "    return dataset"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Reading loads to include in headers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>case_name</th>\n",
+       "      <th>alpha</th>\n",
+       "      <th>turbulence_model</th>\n",
+       "      <th>CL_total</th>\n",
+       "      <th>CD_total</th>\n",
+       "      <th>CDPressure_total</th>\n",
+       "      <th>CDSkinFriction_total</th>\n",
+       "      <th>CL2PA_total</th>\n",
+       "      <th>CMy_total</th>\n",
+       "      <th>CL_wing</th>\n",
+       "      <th>CD_wing</th>\n",
+       "      <th>CDPressure_wing</th>\n",
+       "      <th>CDSkinFriction_wing</th>\n",
+       "      <th>CMy_wing</th>\n",
+       "      <th>CL_fus</th>\n",
+       "      <th>CD_fus</th>\n",
+       "      <th>CDPressure_fus</th>\n",
+       "      <th>CDSkinFriction_fus</th>\n",
+       "      <th>CMy_fus</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>DPW7_JAXA_medium_LoQ_AoA2.50_SA</td>\n",
+       "      <td>2.258009</td>\n",
+       "      <td>SA</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.022453</td>\n",
+       "      <td>0.013115</td>\n",
+       "      <td>0.009338</td>\n",
+       "      <td>0.013612</td>\n",
+       "      <td>-0.108635</td>\n",
+       "      <td>0.436259</td>\n",
+       "      <td>0.013515</td>\n",
+       "      <td>0.009301</td>\n",
+       "      <td>0.004214</td>\n",
+       "      <td>-0.143436</td>\n",
+       "      <td>0.063741</td>\n",
+       "      <td>0.008938</td>\n",
+       "      <td>0.003814</td>\n",
+       "      <td>0.005124</td>\n",
+       "      <td>0.034801</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>DPW7_JAXA_medium_LoQ_AoA2.50_SST</td>\n",
+       "      <td>2.335478</td>\n",
+       "      <td>SST</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.022653</td>\n",
+       "      <td>0.013397</td>\n",
+       "      <td>0.009257</td>\n",
+       "      <td>0.013811</td>\n",
+       "      <td>-0.101876</td>\n",
+       "      <td>0.435523</td>\n",
+       "      <td>0.013700</td>\n",
+       "      <td>0.009488</td>\n",
+       "      <td>0.004212</td>\n",
+       "      <td>-0.138081</td>\n",
+       "      <td>0.064477</td>\n",
+       "      <td>0.008953</td>\n",
+       "      <td>0.003908</td>\n",
+       "      <td>0.005045</td>\n",
+       "      <td>0.036205</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>DPW7_JAXA_medium_LoQ_AoA2.75_SA</td>\n",
+       "      <td>2.750000</td>\n",
+       "      <td>SA</td>\n",
+       "      <td>0.573302</td>\n",
+       "      <td>0.026896</td>\n",
+       "      <td>0.017653</td>\n",
+       "      <td>0.009243</td>\n",
+       "      <td>0.015271</td>\n",
+       "      <td>-0.119772</td>\n",
+       "      <td>0.500210</td>\n",
+       "      <td>0.017030</td>\n",
+       "      <td>0.012908</td>\n",
+       "      <td>0.004123</td>\n",
+       "      <td>-0.163835</td>\n",
+       "      <td>0.073092</td>\n",
+       "      <td>0.009865</td>\n",
+       "      <td>0.004745</td>\n",
+       "      <td>0.005120</td>\n",
+       "      <td>0.044063</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>DPW7_JAXA_medium_LoQ_AoA2.75_SST</td>\n",
+       "      <td>2.750000</td>\n",
+       "      <td>SST</td>\n",
+       "      <td>0.560985</td>\n",
+       "      <td>0.026405</td>\n",
+       "      <td>0.017221</td>\n",
+       "      <td>0.009184</td>\n",
+       "      <td>0.015274</td>\n",
+       "      <td>-0.110935</td>\n",
+       "      <td>0.488698</td>\n",
+       "      <td>0.016670</td>\n",
+       "      <td>0.012525</td>\n",
+       "      <td>0.004145</td>\n",
+       "      <td>-0.154953</td>\n",
+       "      <td>0.072287</td>\n",
+       "      <td>0.009735</td>\n",
+       "      <td>0.004696</td>\n",
+       "      <td>0.005039</td>\n",
+       "      <td>0.044018</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>DPW7_JAXA_medium_LoQ_AoA3.00_SA</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>SA</td>\n",
+       "      <td>0.605917</td>\n",
+       "      <td>0.029819</td>\n",
+       "      <td>0.020641</td>\n",
+       "      <td>0.009177</td>\n",
+       "      <td>0.016834</td>\n",
+       "      <td>-0.122869</td>\n",
+       "      <td>0.528264</td>\n",
+       "      <td>0.019428</td>\n",
+       "      <td>0.015368</td>\n",
+       "      <td>0.004060</td>\n",
+       "      <td>-0.171510</td>\n",
+       "      <td>0.077653</td>\n",
+       "      <td>0.010391</td>\n",
+       "      <td>0.005274</td>\n",
+       "      <td>0.005117</td>\n",
+       "      <td>0.048641</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>DPW7_JAXA_medium_LoQ_AoA3.00_SST</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>SST</td>\n",
+       "      <td>0.593051</td>\n",
+       "      <td>0.029253</td>\n",
+       "      <td>0.020125</td>\n",
+       "      <td>0.009128</td>\n",
+       "      <td>0.016814</td>\n",
+       "      <td>-0.113798</td>\n",
+       "      <td>0.516261</td>\n",
+       "      <td>0.019000</td>\n",
+       "      <td>0.014906</td>\n",
+       "      <td>0.004093</td>\n",
+       "      <td>-0.162423</td>\n",
+       "      <td>0.076790</td>\n",
+       "      <td>0.010254</td>\n",
+       "      <td>0.005219</td>\n",
+       "      <td>0.005035</td>\n",
+       "      <td>0.048625</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>DPW7_JAXA_medium_LoQ_AoA3.25_SA</td>\n",
+       "      <td>3.250000</td>\n",
+       "      <td>SA</td>\n",
+       "      <td>0.627955</td>\n",
+       "      <td>0.033043</td>\n",
+       "      <td>0.023936</td>\n",
+       "      <td>0.009106</td>\n",
+       "      <td>0.019096</td>\n",
+       "      <td>-0.118407</td>\n",
+       "      <td>0.546259</td>\n",
+       "      <td>0.022107</td>\n",
+       "      <td>0.018114</td>\n",
+       "      <td>0.003993</td>\n",
+       "      <td>-0.171518</td>\n",
+       "      <td>0.081696</td>\n",
+       "      <td>0.010936</td>\n",
+       "      <td>0.005823</td>\n",
+       "      <td>0.005113</td>\n",
+       "      <td>0.053111</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>DPW7_JAXA_medium_LoQ_AoA3.25_SST</td>\n",
+       "      <td>3.250000</td>\n",
+       "      <td>SST</td>\n",
+       "      <td>0.614736</td>\n",
+       "      <td>0.032374</td>\n",
+       "      <td>0.023311</td>\n",
+       "      <td>0.009063</td>\n",
+       "      <td>0.019008</td>\n",
+       "      <td>-0.109674</td>\n",
+       "      <td>0.533984</td>\n",
+       "      <td>0.021583</td>\n",
+       "      <td>0.017550</td>\n",
+       "      <td>0.004032</td>\n",
+       "      <td>-0.162804</td>\n",
+       "      <td>0.080752</td>\n",
+       "      <td>0.010791</td>\n",
+       "      <td>0.005761</td>\n",
+       "      <td>0.005030</td>\n",
+       "      <td>0.053130</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>DPW7_JAXA_medium_LoQ_AoA3.50_SA</td>\n",
+       "      <td>3.500000</td>\n",
+       "      <td>SA</td>\n",
+       "      <td>0.643325</td>\n",
+       "      <td>0.036727</td>\n",
+       "      <td>0.027688</td>\n",
+       "      <td>0.009039</td>\n",
+       "      <td>0.022089</td>\n",
+       "      <td>-0.108744</td>\n",
+       "      <td>0.557844</td>\n",
+       "      <td>0.025218</td>\n",
+       "      <td>0.021288</td>\n",
+       "      <td>0.003930</td>\n",
+       "      <td>-0.166256</td>\n",
+       "      <td>0.085481</td>\n",
+       "      <td>0.011508</td>\n",
+       "      <td>0.006400</td>\n",
+       "      <td>0.005108</td>\n",
+       "      <td>0.057512</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>DPW7_JAXA_medium_LoQ_AoA3.50_SST</td>\n",
+       "      <td>3.500000</td>\n",
+       "      <td>SST</td>\n",
+       "      <td>0.631086</td>\n",
+       "      <td>0.035907</td>\n",
+       "      <td>0.026912</td>\n",
+       "      <td>0.008995</td>\n",
+       "      <td>0.021821</td>\n",
+       "      <td>-0.101836</td>\n",
+       "      <td>0.546606</td>\n",
+       "      <td>0.024551</td>\n",
+       "      <td>0.020581</td>\n",
+       "      <td>0.003970</td>\n",
+       "      <td>-0.159411</td>\n",
+       "      <td>0.084480</td>\n",
+       "      <td>0.011356</td>\n",
+       "      <td>0.006331</td>\n",
+       "      <td>0.005025</td>\n",
+       "      <td>0.057575</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>DPW7_JAXA_medium_LoQ_AoA3.75_SA</td>\n",
+       "      <td>3.750000</td>\n",
+       "      <td>SA</td>\n",
+       "      <td>0.659799</td>\n",
+       "      <td>0.040832</td>\n",
+       "      <td>0.031858</td>\n",
+       "      <td>0.008974</td>\n",
+       "      <td>0.025435</td>\n",
+       "      <td>-0.099970</td>\n",
+       "      <td>0.570452</td>\n",
+       "      <td>0.028714</td>\n",
+       "      <td>0.024844</td>\n",
+       "      <td>0.003870</td>\n",
+       "      <td>-0.161849</td>\n",
+       "      <td>0.089347</td>\n",
+       "      <td>0.012118</td>\n",
+       "      <td>0.007014</td>\n",
+       "      <td>0.005103</td>\n",
+       "      <td>0.061879</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>DPW7_JAXA_medium_LoQ_AoA3.75_SST</td>\n",
+       "      <td>3.750000</td>\n",
+       "      <td>SST</td>\n",
+       "      <td>0.648348</td>\n",
+       "      <td>0.039822</td>\n",
+       "      <td>0.030890</td>\n",
+       "      <td>0.008931</td>\n",
+       "      <td>0.024955</td>\n",
+       "      <td>-0.094590</td>\n",
+       "      <td>0.560067</td>\n",
+       "      <td>0.027865</td>\n",
+       "      <td>0.023953</td>\n",
+       "      <td>0.003912</td>\n",
+       "      <td>-0.156589</td>\n",
+       "      <td>0.088281</td>\n",
+       "      <td>0.011957</td>\n",
+       "      <td>0.006937</td>\n",
+       "      <td>0.005019</td>\n",
+       "      <td>0.061999</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>DPW7_JAXA_medium_LoQ_AoA4.00_SA</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>SA</td>\n",
+       "      <td>0.676473</td>\n",
+       "      <td>0.045214</td>\n",
+       "      <td>0.036304</td>\n",
+       "      <td>0.008911</td>\n",
+       "      <td>0.029030</td>\n",
+       "      <td>-0.091622</td>\n",
+       "      <td>0.583243</td>\n",
+       "      <td>0.032454</td>\n",
+       "      <td>0.028641</td>\n",
+       "      <td>0.003813</td>\n",
+       "      <td>-0.157832</td>\n",
+       "      <td>0.093230</td>\n",
+       "      <td>0.012760</td>\n",
+       "      <td>0.007662</td>\n",
+       "      <td>0.005098</td>\n",
+       "      <td>0.066210</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>DPW7_JAXA_medium_LoQ_AoA4.00_SST</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>SST</td>\n",
+       "      <td>0.665006</td>\n",
+       "      <td>0.044008</td>\n",
+       "      <td>0.035139</td>\n",
+       "      <td>0.008869</td>\n",
+       "      <td>0.028367</td>\n",
+       "      <td>-0.086870</td>\n",
+       "      <td>0.572929</td>\n",
+       "      <td>0.031419</td>\n",
+       "      <td>0.027563</td>\n",
+       "      <td>0.003856</td>\n",
+       "      <td>-0.153262</td>\n",
+       "      <td>0.092077</td>\n",
+       "      <td>0.012590</td>\n",
+       "      <td>0.007576</td>\n",
+       "      <td>0.005014</td>\n",
+       "      <td>0.066392</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>DPW7_JAXA_medium_LoQ_AoA4.25_SA</td>\n",
+       "      <td>4.250000</td>\n",
+       "      <td>SA</td>\n",
+       "      <td>0.692715</td>\n",
+       "      <td>0.049805</td>\n",
+       "      <td>0.040955</td>\n",
+       "      <td>0.008851</td>\n",
+       "      <td>0.032834</td>\n",
+       "      <td>-0.083366</td>\n",
+       "      <td>0.595625</td>\n",
+       "      <td>0.036371</td>\n",
+       "      <td>0.032612</td>\n",
+       "      <td>0.003758</td>\n",
+       "      <td>-0.153870</td>\n",
+       "      <td>0.097090</td>\n",
+       "      <td>0.013435</td>\n",
+       "      <td>0.008343</td>\n",
+       "      <td>0.005092</td>\n",
+       "      <td>0.070504</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>DPW7_JAXA_medium_LoQ_AoA4.25_SST</td>\n",
+       "      <td>4.250000</td>\n",
+       "      <td>SST</td>\n",
+       "      <td>0.680845</td>\n",
+       "      <td>0.048412</td>\n",
+       "      <td>0.039603</td>\n",
+       "      <td>0.008809</td>\n",
+       "      <td>0.032017</td>\n",
+       "      <td>-0.078718</td>\n",
+       "      <td>0.585001</td>\n",
+       "      <td>0.035158</td>\n",
+       "      <td>0.031358</td>\n",
+       "      <td>0.003800</td>\n",
+       "      <td>-0.149470</td>\n",
+       "      <td>0.095845</td>\n",
+       "      <td>0.013254</td>\n",
+       "      <td>0.008245</td>\n",
+       "      <td>0.005008</td>\n",
+       "      <td>0.070752</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                           case_name     alpha turbulence_model  CL_total  \\\n",
+       "0    DPW7_JAXA_medium_LoQ_AoA2.50_SA  2.258009               SA  0.500000   \n",
+       "1   DPW7_JAXA_medium_LoQ_AoA2.50_SST  2.335478              SST  0.500000   \n",
+       "2    DPW7_JAXA_medium_LoQ_AoA2.75_SA  2.750000               SA  0.573302   \n",
+       "3   DPW7_JAXA_medium_LoQ_AoA2.75_SST  2.750000              SST  0.560985   \n",
+       "4    DPW7_JAXA_medium_LoQ_AoA3.00_SA  3.000000               SA  0.605917   \n",
+       "5   DPW7_JAXA_medium_LoQ_AoA3.00_SST  3.000000              SST  0.593051   \n",
+       "6    DPW7_JAXA_medium_LoQ_AoA3.25_SA  3.250000               SA  0.627955   \n",
+       "7   DPW7_JAXA_medium_LoQ_AoA3.25_SST  3.250000              SST  0.614736   \n",
+       "8    DPW7_JAXA_medium_LoQ_AoA3.50_SA  3.500000               SA  0.643325   \n",
+       "9   DPW7_JAXA_medium_LoQ_AoA3.50_SST  3.500000              SST  0.631086   \n",
+       "10   DPW7_JAXA_medium_LoQ_AoA3.75_SA  3.750000               SA  0.659799   \n",
+       "11  DPW7_JAXA_medium_LoQ_AoA3.75_SST  3.750000              SST  0.648348   \n",
+       "12   DPW7_JAXA_medium_LoQ_AoA4.00_SA  4.000000               SA  0.676473   \n",
+       "13  DPW7_JAXA_medium_LoQ_AoA4.00_SST  4.000000              SST  0.665006   \n",
+       "14   DPW7_JAXA_medium_LoQ_AoA4.25_SA  4.250000               SA  0.692715   \n",
+       "15  DPW7_JAXA_medium_LoQ_AoA4.25_SST  4.250000              SST  0.680845   \n",
+       "\n",
+       "    CD_total  CDPressure_total  CDSkinFriction_total  CL2PA_total  CMy_total  \\\n",
+       "0   0.022453          0.013115              0.009338     0.013612  -0.108635   \n",
+       "1   0.022653          0.013397              0.009257     0.013811  -0.101876   \n",
+       "2   0.026896          0.017653              0.009243     0.015271  -0.119772   \n",
+       "3   0.026405          0.017221              0.009184     0.015274  -0.110935   \n",
+       "4   0.029819          0.020641              0.009177     0.016834  -0.122869   \n",
+       "5   0.029253          0.020125              0.009128     0.016814  -0.113798   \n",
+       "6   0.033043          0.023936              0.009106     0.019096  -0.118407   \n",
+       "7   0.032374          0.023311              0.009063     0.019008  -0.109674   \n",
+       "8   0.036727          0.027688              0.009039     0.022089  -0.108744   \n",
+       "9   0.035907          0.026912              0.008995     0.021821  -0.101836   \n",
+       "10  0.040832          0.031858              0.008974     0.025435  -0.099970   \n",
+       "11  0.039822          0.030890              0.008931     0.024955  -0.094590   \n",
+       "12  0.045214          0.036304              0.008911     0.029030  -0.091622   \n",
+       "13  0.044008          0.035139              0.008869     0.028367  -0.086870   \n",
+       "14  0.049805          0.040955              0.008851     0.032834  -0.083366   \n",
+       "15  0.048412          0.039603              0.008809     0.032017  -0.078718   \n",
+       "\n",
+       "     CL_wing   CD_wing  CDPressure_wing  CDSkinFriction_wing  CMy_wing  \\\n",
+       "0   0.436259  0.013515         0.009301             0.004214 -0.143436   \n",
+       "1   0.435523  0.013700         0.009488             0.004212 -0.138081   \n",
+       "2   0.500210  0.017030         0.012908             0.004123 -0.163835   \n",
+       "3   0.488698  0.016670         0.012525             0.004145 -0.154953   \n",
+       "4   0.528264  0.019428         0.015368             0.004060 -0.171510   \n",
+       "5   0.516261  0.019000         0.014906             0.004093 -0.162423   \n",
+       "6   0.546259  0.022107         0.018114             0.003993 -0.171518   \n",
+       "7   0.533984  0.021583         0.017550             0.004032 -0.162804   \n",
+       "8   0.557844  0.025218         0.021288             0.003930 -0.166256   \n",
+       "9   0.546606  0.024551         0.020581             0.003970 -0.159411   \n",
+       "10  0.570452  0.028714         0.024844             0.003870 -0.161849   \n",
+       "11  0.560067  0.027865         0.023953             0.003912 -0.156589   \n",
+       "12  0.583243  0.032454         0.028641             0.003813 -0.157832   \n",
+       "13  0.572929  0.031419         0.027563             0.003856 -0.153262   \n",
+       "14  0.595625  0.036371         0.032612             0.003758 -0.153870   \n",
+       "15  0.585001  0.035158         0.031358             0.003800 -0.149470   \n",
+       "\n",
+       "      CL_fus    CD_fus  CDPressure_fus  CDSkinFriction_fus   CMy_fus  \n",
+       "0   0.063741  0.008938        0.003814            0.005124  0.034801  \n",
+       "1   0.064477  0.008953        0.003908            0.005045  0.036205  \n",
+       "2   0.073092  0.009865        0.004745            0.005120  0.044063  \n",
+       "3   0.072287  0.009735        0.004696            0.005039  0.044018  \n",
+       "4   0.077653  0.010391        0.005274            0.005117  0.048641  \n",
+       "5   0.076790  0.010254        0.005219            0.005035  0.048625  \n",
+       "6   0.081696  0.010936        0.005823            0.005113  0.053111  \n",
+       "7   0.080752  0.010791        0.005761            0.005030  0.053130  \n",
+       "8   0.085481  0.011508        0.006400            0.005108  0.057512  \n",
+       "9   0.084480  0.011356        0.006331            0.005025  0.057575  \n",
+       "10  0.089347  0.012118        0.007014            0.005103  0.061879  \n",
+       "11  0.088281  0.011957        0.006937            0.005019  0.061999  \n",
+       "12  0.093230  0.012760        0.007662            0.005098  0.066210  \n",
+       "13  0.092077  0.012590        0.007576            0.005014  0.066392  \n",
+       "14  0.097090  0.013435        0.008343            0.005092  0.070504  \n",
+       "15  0.095845  0.013254        0.008245            0.005008  0.070752  "
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "loads_df = pd.read_csv(os.path.join(results_path, \"loads.csv\"))\n",
+    "loads_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Then images are generated and saved. The image generation for all cases can take a long time (~ 1.5 hours)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "projects = pd.read_csv(\"project_list.csv\")\n",
+    "for idx, row in loads_df.iterrows():\n",
+    "    case_name = row[\"case_name\"]\n",
+    "    surfacesList = [\n",
+    "        os.path.join(results_path, case_name, \"surfaces\", \"surface_\" + str(x) + \".szplt\")\n",
+    "        for x in range(1, 20)\n",
+    "    ]\n",
+    "    dataset = loadData(surfacesList)\n",
+    "    ALPHA = row[\"alpha\"]\n",
+    "    CL = row[\"CL_total\"]\n",
+    "    CD = row[\"CD_total\"]\n",
+    "    CM = row[\"CMy_total\"]\n",
+    "    solution_label = \"DPW7_JAXA_medium_LoQ_AoA_{:.2f}_turb_mod_{}\".format(\n",
+    "        ALPHA, row[\"turbulence_model\"]\n",
+    "    )\n",
+    "\n",
+    "    filename = \"tp_macro.mcr\"\n",
+    "\n",
+    "    images_path = os.path.join(results_path, case_name, \"images\")\n",
+    "    os.makedirs(images_path, exist_ok=True)\n",
+    "\n",
+    "    macro_path = os.path.join(images_path, filename)\n",
+    "\n",
+    "    # update variables in new macro and write new macro file\n",
+    "    with open(macro_path, \"w\") as f:\n",
+    "        f.write(\"#!MC 1410\\n\")\n",
+    "        f.write(\"\\n\")\n",
+    "        f.write(\"$!VarSet |SolutionLabel|='\" + solution_label + \"'\\n\")\n",
+    "        f.write('$!VarSet |OutputDirectoryPath|=\"' + images_path + '\\\\\"\\n')\n",
+    "        f.write(\"$!VarSet |ALPHA|='\" + f\"{ALPHA:.2f}\" + \"'\\n\")\n",
+    "        f.write(\"$!VarSet |CL|='\" + f\"{CL:.3f}\" + \"'\\n\")\n",
+    "        f.write(\"$!VarSet |CD|='\" + f\"{CD:.3f}\" + \"'\\n\")\n",
+    "        f.write(\"$!VarSet |CMy|='\" + f\"{CM:.3f}\" + \"'\\n\")\n",
+    "        f.write(\"\\n\")\n",
+    "        dir = os.path.join(os.getcwd(), \"DPW-VII.Images_v7_mod.mcr\")\n",
+    "        with open(dir) as g:\n",
+    "            lines = g.readlines()\n",
+    "            for element in lines:\n",
+    "                f.write(element)\n",
+    "        f.write(\"\\n\")\n",
+    "\n",
+    "    tp.macro.execute_file(macro_path)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Processing generated files"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "After downloading and cleaning up the data it can be processed. Below the files will be loaded and example plots will be drawn using the built-in pandas Dataframe plotting interface."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Load data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>case_name</th>\n",
+       "      <th>alpha</th>\n",
+       "      <th>turbulence_model</th>\n",
+       "      <th>CL_total</th>\n",
+       "      <th>CD_total</th>\n",
+       "      <th>CDPressure_total</th>\n",
+       "      <th>CDSkinFriction_total</th>\n",
+       "      <th>CL2PA_total</th>\n",
+       "      <th>CMy_total</th>\n",
+       "      <th>CL_wing</th>\n",
+       "      <th>CD_wing</th>\n",
+       "      <th>CDPressure_wing</th>\n",
+       "      <th>CDSkinFriction_wing</th>\n",
+       "      <th>CMy_wing</th>\n",
+       "      <th>CL_fus</th>\n",
+       "      <th>CD_fus</th>\n",
+       "      <th>CDPressure_fus</th>\n",
+       "      <th>CDSkinFriction_fus</th>\n",
+       "      <th>CMy_fus</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>DPW7_JAXA_medium_LoQ_AoA2.50_SA</td>\n",
+       "      <td>2.258009</td>\n",
+       "      <td>SA</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.022453</td>\n",
+       "      <td>0.013115</td>\n",
+       "      <td>0.009338</td>\n",
+       "      <td>0.013612</td>\n",
+       "      <td>-0.108635</td>\n",
+       "      <td>0.436259</td>\n",
+       "      <td>0.013515</td>\n",
+       "      <td>0.009301</td>\n",
+       "      <td>0.004214</td>\n",
+       "      <td>-0.143436</td>\n",
+       "      <td>0.063741</td>\n",
+       "      <td>0.008938</td>\n",
+       "      <td>0.003814</td>\n",
+       "      <td>0.005124</td>\n",
+       "      <td>0.034801</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>DPW7_JAXA_medium_LoQ_AoA2.50_SST</td>\n",
+       "      <td>2.335478</td>\n",
+       "      <td>SST</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.022653</td>\n",
+       "      <td>0.013397</td>\n",
+       "      <td>0.009257</td>\n",
+       "      <td>0.013811</td>\n",
+       "      <td>-0.101876</td>\n",
+       "      <td>0.435523</td>\n",
+       "      <td>0.013700</td>\n",
+       "      <td>0.009488</td>\n",
+       "      <td>0.004212</td>\n",
+       "      <td>-0.138081</td>\n",
+       "      <td>0.064477</td>\n",
+       "      <td>0.008953</td>\n",
+       "      <td>0.003908</td>\n",
+       "      <td>0.005045</td>\n",
+       "      <td>0.036205</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>DPW7_JAXA_medium_LoQ_AoA2.75_SA</td>\n",
+       "      <td>2.750000</td>\n",
+       "      <td>SA</td>\n",
+       "      <td>0.573302</td>\n",
+       "      <td>0.026896</td>\n",
+       "      <td>0.017653</td>\n",
+       "      <td>0.009243</td>\n",
+       "      <td>0.015271</td>\n",
+       "      <td>-0.119772</td>\n",
+       "      <td>0.500210</td>\n",
+       "      <td>0.017030</td>\n",
+       "      <td>0.012908</td>\n",
+       "      <td>0.004123</td>\n",
+       "      <td>-0.163835</td>\n",
+       "      <td>0.073092</td>\n",
+       "      <td>0.009865</td>\n",
+       "      <td>0.004745</td>\n",
+       "      <td>0.005120</td>\n",
+       "      <td>0.044063</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>DPW7_JAXA_medium_LoQ_AoA2.75_SST</td>\n",
+       "      <td>2.750000</td>\n",
+       "      <td>SST</td>\n",
+       "      <td>0.560985</td>\n",
+       "      <td>0.026405</td>\n",
+       "      <td>0.017221</td>\n",
+       "      <td>0.009184</td>\n",
+       "      <td>0.015274</td>\n",
+       "      <td>-0.110935</td>\n",
+       "      <td>0.488698</td>\n",
+       "      <td>0.016670</td>\n",
+       "      <td>0.012525</td>\n",
+       "      <td>0.004145</td>\n",
+       "      <td>-0.154953</td>\n",
+       "      <td>0.072287</td>\n",
+       "      <td>0.009735</td>\n",
+       "      <td>0.004696</td>\n",
+       "      <td>0.005039</td>\n",
+       "      <td>0.044018</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>DPW7_JAXA_medium_LoQ_AoA3.00_SA</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>SA</td>\n",
+       "      <td>0.605917</td>\n",
+       "      <td>0.029819</td>\n",
+       "      <td>0.020641</td>\n",
+       "      <td>0.009177</td>\n",
+       "      <td>0.016834</td>\n",
+       "      <td>-0.122869</td>\n",
+       "      <td>0.528264</td>\n",
+       "      <td>0.019428</td>\n",
+       "      <td>0.015368</td>\n",
+       "      <td>0.004060</td>\n",
+       "      <td>-0.171510</td>\n",
+       "      <td>0.077653</td>\n",
+       "      <td>0.010391</td>\n",
+       "      <td>0.005274</td>\n",
+       "      <td>0.005117</td>\n",
+       "      <td>0.048641</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>DPW7_JAXA_medium_LoQ_AoA3.00_SST</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>SST</td>\n",
+       "      <td>0.593051</td>\n",
+       "      <td>0.029253</td>\n",
+       "      <td>0.020125</td>\n",
+       "      <td>0.009128</td>\n",
+       "      <td>0.016814</td>\n",
+       "      <td>-0.113798</td>\n",
+       "      <td>0.516261</td>\n",
+       "      <td>0.019000</td>\n",
+       "      <td>0.014906</td>\n",
+       "      <td>0.004093</td>\n",
+       "      <td>-0.162423</td>\n",
+       "      <td>0.076790</td>\n",
+       "      <td>0.010254</td>\n",
+       "      <td>0.005219</td>\n",
+       "      <td>0.005035</td>\n",
+       "      <td>0.048625</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>DPW7_JAXA_medium_LoQ_AoA3.25_SA</td>\n",
+       "      <td>3.250000</td>\n",
+       "      <td>SA</td>\n",
+       "      <td>0.627955</td>\n",
+       "      <td>0.033043</td>\n",
+       "      <td>0.023936</td>\n",
+       "      <td>0.009106</td>\n",
+       "      <td>0.019096</td>\n",
+       "      <td>-0.118407</td>\n",
+       "      <td>0.546259</td>\n",
+       "      <td>0.022107</td>\n",
+       "      <td>0.018114</td>\n",
+       "      <td>0.003993</td>\n",
+       "      <td>-0.171518</td>\n",
+       "      <td>0.081696</td>\n",
+       "      <td>0.010936</td>\n",
+       "      <td>0.005823</td>\n",
+       "      <td>0.005113</td>\n",
+       "      <td>0.053111</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>DPW7_JAXA_medium_LoQ_AoA3.25_SST</td>\n",
+       "      <td>3.250000</td>\n",
+       "      <td>SST</td>\n",
+       "      <td>0.614736</td>\n",
+       "      <td>0.032374</td>\n",
+       "      <td>0.023311</td>\n",
+       "      <td>0.009063</td>\n",
+       "      <td>0.019008</td>\n",
+       "      <td>-0.109674</td>\n",
+       "      <td>0.533984</td>\n",
+       "      <td>0.021583</td>\n",
+       "      <td>0.017550</td>\n",
+       "      <td>0.004032</td>\n",
+       "      <td>-0.162804</td>\n",
+       "      <td>0.080752</td>\n",
+       "      <td>0.010791</td>\n",
+       "      <td>0.005761</td>\n",
+       "      <td>0.005030</td>\n",
+       "      <td>0.053130</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>DPW7_JAXA_medium_LoQ_AoA3.50_SA</td>\n",
+       "      <td>3.500000</td>\n",
+       "      <td>SA</td>\n",
+       "      <td>0.643325</td>\n",
+       "      <td>0.036727</td>\n",
+       "      <td>0.027688</td>\n",
+       "      <td>0.009039</td>\n",
+       "      <td>0.022089</td>\n",
+       "      <td>-0.108744</td>\n",
+       "      <td>0.557844</td>\n",
+       "      <td>0.025218</td>\n",
+       "      <td>0.021288</td>\n",
+       "      <td>0.003930</td>\n",
+       "      <td>-0.166256</td>\n",
+       "      <td>0.085481</td>\n",
+       "      <td>0.011508</td>\n",
+       "      <td>0.006400</td>\n",
+       "      <td>0.005108</td>\n",
+       "      <td>0.057512</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>DPW7_JAXA_medium_LoQ_AoA3.50_SST</td>\n",
+       "      <td>3.500000</td>\n",
+       "      <td>SST</td>\n",
+       "      <td>0.631086</td>\n",
+       "      <td>0.035907</td>\n",
+       "      <td>0.026912</td>\n",
+       "      <td>0.008995</td>\n",
+       "      <td>0.021821</td>\n",
+       "      <td>-0.101836</td>\n",
+       "      <td>0.546606</td>\n",
+       "      <td>0.024551</td>\n",
+       "      <td>0.020581</td>\n",
+       "      <td>0.003970</td>\n",
+       "      <td>-0.159411</td>\n",
+       "      <td>0.084480</td>\n",
+       "      <td>0.011356</td>\n",
+       "      <td>0.006331</td>\n",
+       "      <td>0.005025</td>\n",
+       "      <td>0.057575</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>DPW7_JAXA_medium_LoQ_AoA3.75_SA</td>\n",
+       "      <td>3.750000</td>\n",
+       "      <td>SA</td>\n",
+       "      <td>0.659799</td>\n",
+       "      <td>0.040832</td>\n",
+       "      <td>0.031858</td>\n",
+       "      <td>0.008974</td>\n",
+       "      <td>0.025435</td>\n",
+       "      <td>-0.099970</td>\n",
+       "      <td>0.570452</td>\n",
+       "      <td>0.028714</td>\n",
+       "      <td>0.024844</td>\n",
+       "      <td>0.003870</td>\n",
+       "      <td>-0.161849</td>\n",
+       "      <td>0.089347</td>\n",
+       "      <td>0.012118</td>\n",
+       "      <td>0.007014</td>\n",
+       "      <td>0.005103</td>\n",
+       "      <td>0.061879</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>DPW7_JAXA_medium_LoQ_AoA3.75_SST</td>\n",
+       "      <td>3.750000</td>\n",
+       "      <td>SST</td>\n",
+       "      <td>0.648348</td>\n",
+       "      <td>0.039822</td>\n",
+       "      <td>0.030890</td>\n",
+       "      <td>0.008931</td>\n",
+       "      <td>0.024955</td>\n",
+       "      <td>-0.094590</td>\n",
+       "      <td>0.560067</td>\n",
+       "      <td>0.027865</td>\n",
+       "      <td>0.023953</td>\n",
+       "      <td>0.003912</td>\n",
+       "      <td>-0.156589</td>\n",
+       "      <td>0.088281</td>\n",
+       "      <td>0.011957</td>\n",
+       "      <td>0.006937</td>\n",
+       "      <td>0.005019</td>\n",
+       "      <td>0.061999</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>DPW7_JAXA_medium_LoQ_AoA4.00_SA</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>SA</td>\n",
+       "      <td>0.676473</td>\n",
+       "      <td>0.045214</td>\n",
+       "      <td>0.036304</td>\n",
+       "      <td>0.008911</td>\n",
+       "      <td>0.029030</td>\n",
+       "      <td>-0.091622</td>\n",
+       "      <td>0.583243</td>\n",
+       "      <td>0.032454</td>\n",
+       "      <td>0.028641</td>\n",
+       "      <td>0.003813</td>\n",
+       "      <td>-0.157832</td>\n",
+       "      <td>0.093230</td>\n",
+       "      <td>0.012760</td>\n",
+       "      <td>0.007662</td>\n",
+       "      <td>0.005098</td>\n",
+       "      <td>0.066210</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>DPW7_JAXA_medium_LoQ_AoA4.00_SST</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>SST</td>\n",
+       "      <td>0.665006</td>\n",
+       "      <td>0.044008</td>\n",
+       "      <td>0.035139</td>\n",
+       "      <td>0.008869</td>\n",
+       "      <td>0.028367</td>\n",
+       "      <td>-0.086870</td>\n",
+       "      <td>0.572929</td>\n",
+       "      <td>0.031419</td>\n",
+       "      <td>0.027563</td>\n",
+       "      <td>0.003856</td>\n",
+       "      <td>-0.153262</td>\n",
+       "      <td>0.092077</td>\n",
+       "      <td>0.012590</td>\n",
+       "      <td>0.007576</td>\n",
+       "      <td>0.005014</td>\n",
+       "      <td>0.066392</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>DPW7_JAXA_medium_LoQ_AoA4.25_SA</td>\n",
+       "      <td>4.250000</td>\n",
+       "      <td>SA</td>\n",
+       "      <td>0.692715</td>\n",
+       "      <td>0.049805</td>\n",
+       "      <td>0.040955</td>\n",
+       "      <td>0.008851</td>\n",
+       "      <td>0.032834</td>\n",
+       "      <td>-0.083366</td>\n",
+       "      <td>0.595625</td>\n",
+       "      <td>0.036371</td>\n",
+       "      <td>0.032612</td>\n",
+       "      <td>0.003758</td>\n",
+       "      <td>-0.153870</td>\n",
+       "      <td>0.097090</td>\n",
+       "      <td>0.013435</td>\n",
+       "      <td>0.008343</td>\n",
+       "      <td>0.005092</td>\n",
+       "      <td>0.070504</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>DPW7_JAXA_medium_LoQ_AoA4.25_SST</td>\n",
+       "      <td>4.250000</td>\n",
+       "      <td>SST</td>\n",
+       "      <td>0.680845</td>\n",
+       "      <td>0.048412</td>\n",
+       "      <td>0.039603</td>\n",
+       "      <td>0.008809</td>\n",
+       "      <td>0.032017</td>\n",
+       "      <td>-0.078718</td>\n",
+       "      <td>0.585001</td>\n",
+       "      <td>0.035158</td>\n",
+       "      <td>0.031358</td>\n",
+       "      <td>0.003800</td>\n",
+       "      <td>-0.149470</td>\n",
+       "      <td>0.095845</td>\n",
+       "      <td>0.013254</td>\n",
+       "      <td>0.008245</td>\n",
+       "      <td>0.005008</td>\n",
+       "      <td>0.070752</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                           case_name     alpha turbulence_model  CL_total  \\\n",
+       "0    DPW7_JAXA_medium_LoQ_AoA2.50_SA  2.258009               SA  0.500000   \n",
+       "1   DPW7_JAXA_medium_LoQ_AoA2.50_SST  2.335478              SST  0.500000   \n",
+       "2    DPW7_JAXA_medium_LoQ_AoA2.75_SA  2.750000               SA  0.573302   \n",
+       "3   DPW7_JAXA_medium_LoQ_AoA2.75_SST  2.750000              SST  0.560985   \n",
+       "4    DPW7_JAXA_medium_LoQ_AoA3.00_SA  3.000000               SA  0.605917   \n",
+       "5   DPW7_JAXA_medium_LoQ_AoA3.00_SST  3.000000              SST  0.593051   \n",
+       "6    DPW7_JAXA_medium_LoQ_AoA3.25_SA  3.250000               SA  0.627955   \n",
+       "7   DPW7_JAXA_medium_LoQ_AoA3.25_SST  3.250000              SST  0.614736   \n",
+       "8    DPW7_JAXA_medium_LoQ_AoA3.50_SA  3.500000               SA  0.643325   \n",
+       "9   DPW7_JAXA_medium_LoQ_AoA3.50_SST  3.500000              SST  0.631086   \n",
+       "10   DPW7_JAXA_medium_LoQ_AoA3.75_SA  3.750000               SA  0.659799   \n",
+       "11  DPW7_JAXA_medium_LoQ_AoA3.75_SST  3.750000              SST  0.648348   \n",
+       "12   DPW7_JAXA_medium_LoQ_AoA4.00_SA  4.000000               SA  0.676473   \n",
+       "13  DPW7_JAXA_medium_LoQ_AoA4.00_SST  4.000000              SST  0.665006   \n",
+       "14   DPW7_JAXA_medium_LoQ_AoA4.25_SA  4.250000               SA  0.692715   \n",
+       "15  DPW7_JAXA_medium_LoQ_AoA4.25_SST  4.250000              SST  0.680845   \n",
+       "\n",
+       "    CD_total  CDPressure_total  CDSkinFriction_total  CL2PA_total  CMy_total  \\\n",
+       "0   0.022453          0.013115              0.009338     0.013612  -0.108635   \n",
+       "1   0.022653          0.013397              0.009257     0.013811  -0.101876   \n",
+       "2   0.026896          0.017653              0.009243     0.015271  -0.119772   \n",
+       "3   0.026405          0.017221              0.009184     0.015274  -0.110935   \n",
+       "4   0.029819          0.020641              0.009177     0.016834  -0.122869   \n",
+       "5   0.029253          0.020125              0.009128     0.016814  -0.113798   \n",
+       "6   0.033043          0.023936              0.009106     0.019096  -0.118407   \n",
+       "7   0.032374          0.023311              0.009063     0.019008  -0.109674   \n",
+       "8   0.036727          0.027688              0.009039     0.022089  -0.108744   \n",
+       "9   0.035907          0.026912              0.008995     0.021821  -0.101836   \n",
+       "10  0.040832          0.031858              0.008974     0.025435  -0.099970   \n",
+       "11  0.039822          0.030890              0.008931     0.024955  -0.094590   \n",
+       "12  0.045214          0.036304              0.008911     0.029030  -0.091622   \n",
+       "13  0.044008          0.035139              0.008869     0.028367  -0.086870   \n",
+       "14  0.049805          0.040955              0.008851     0.032834  -0.083366   \n",
+       "15  0.048412          0.039603              0.008809     0.032017  -0.078718   \n",
+       "\n",
+       "     CL_wing   CD_wing  CDPressure_wing  CDSkinFriction_wing  CMy_wing  \\\n",
+       "0   0.436259  0.013515         0.009301             0.004214 -0.143436   \n",
+       "1   0.435523  0.013700         0.009488             0.004212 -0.138081   \n",
+       "2   0.500210  0.017030         0.012908             0.004123 -0.163835   \n",
+       "3   0.488698  0.016670         0.012525             0.004145 -0.154953   \n",
+       "4   0.528264  0.019428         0.015368             0.004060 -0.171510   \n",
+       "5   0.516261  0.019000         0.014906             0.004093 -0.162423   \n",
+       "6   0.546259  0.022107         0.018114             0.003993 -0.171518   \n",
+       "7   0.533984  0.021583         0.017550             0.004032 -0.162804   \n",
+       "8   0.557844  0.025218         0.021288             0.003930 -0.166256   \n",
+       "9   0.546606  0.024551         0.020581             0.003970 -0.159411   \n",
+       "10  0.570452  0.028714         0.024844             0.003870 -0.161849   \n",
+       "11  0.560067  0.027865         0.023953             0.003912 -0.156589   \n",
+       "12  0.583243  0.032454         0.028641             0.003813 -0.157832   \n",
+       "13  0.572929  0.031419         0.027563             0.003856 -0.153262   \n",
+       "14  0.595625  0.036371         0.032612             0.003758 -0.153870   \n",
+       "15  0.585001  0.035158         0.031358             0.003800 -0.149470   \n",
+       "\n",
+       "      CL_fus    CD_fus  CDPressure_fus  CDSkinFriction_fus   CMy_fus  \n",
+       "0   0.063741  0.008938        0.003814            0.005124  0.034801  \n",
+       "1   0.064477  0.008953        0.003908            0.005045  0.036205  \n",
+       "2   0.073092  0.009865        0.004745            0.005120  0.044063  \n",
+       "3   0.072287  0.009735        0.004696            0.005039  0.044018  \n",
+       "4   0.077653  0.010391        0.005274            0.005117  0.048641  \n",
+       "5   0.076790  0.010254        0.005219            0.005035  0.048625  \n",
+       "6   0.081696  0.010936        0.005823            0.005113  0.053111  \n",
+       "7   0.080752  0.010791        0.005761            0.005030  0.053130  \n",
+       "8   0.085481  0.011508        0.006400            0.005108  0.057512  \n",
+       "9   0.084480  0.011356        0.006331            0.005025  0.057575  \n",
+       "10  0.089347  0.012118        0.007014            0.005103  0.061879  \n",
+       "11  0.088281  0.011957        0.006937            0.005019  0.061999  \n",
+       "12  0.093230  0.012760        0.007662            0.005098  0.066210  \n",
+       "13  0.092077  0.012590        0.007576            0.005014  0.066392  \n",
+       "14  0.097090  0.013435        0.008343            0.005092  0.070504  \n",
+       "15  0.095845  0.013254        0.008245            0.005008  0.070752  "
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# importing the file\n",
+    "loads_df = pd.read_csv(os.path.join(results_path, \"loads.csv\"))\n",
+    "loads_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### CL vs AoA"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = None\n",
+    "for group_name, group_data in loads_df.groupby(\"turbulence_model\"):\n",
+    "    ax = group_data.plot(\n",
+    "        x=\"alpha\", y=\"CL_total\", ax=ax, grid=True, label=group_name, title=\"CL vs AoA\"\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### CL/CD vs Aoa"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>CL_total</th>\n",
+       "      <th>CD_total</th>\n",
+       "      <th>CL/CD_total</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.022453</td>\n",
+       "      <td>22.268258</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.022653</td>\n",
+       "      <td>22.071927</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.573302</td>\n",
+       "      <td>0.026896</td>\n",
+       "      <td>21.315787</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.560985</td>\n",
+       "      <td>0.026405</td>\n",
+       "      <td>21.245720</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.605917</td>\n",
+       "      <td>0.029819</td>\n",
+       "      <td>20.320023</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>0.593051</td>\n",
+       "      <td>0.029253</td>\n",
+       "      <td>20.273003</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>0.627955</td>\n",
+       "      <td>0.033043</td>\n",
+       "      <td>19.004203</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>0.614736</td>\n",
+       "      <td>0.032374</td>\n",
+       "      <td>18.988794</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>0.643325</td>\n",
+       "      <td>0.036727</td>\n",
+       "      <td>17.516626</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>0.631086</td>\n",
+       "      <td>0.035907</td>\n",
+       "      <td>17.575684</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>0.659799</td>\n",
+       "      <td>0.040832</td>\n",
+       "      <td>16.158997</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>0.648348</td>\n",
+       "      <td>0.039822</td>\n",
+       "      <td>16.281260</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>0.676473</td>\n",
+       "      <td>0.045214</td>\n",
+       "      <td>14.961460</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>0.665006</td>\n",
+       "      <td>0.044008</td>\n",
+       "      <td>15.110982</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>0.692715</td>\n",
+       "      <td>0.049805</td>\n",
+       "      <td>13.908404</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>0.680845</td>\n",
+       "      <td>0.048412</td>\n",
+       "      <td>14.063594</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    CL_total  CD_total  CL/CD_total\n",
+       "0   0.500000  0.022453    22.268258\n",
+       "1   0.500000  0.022653    22.071927\n",
+       "2   0.573302  0.026896    21.315787\n",
+       "3   0.560985  0.026405    21.245720\n",
+       "4   0.605917  0.029819    20.320023\n",
+       "5   0.593051  0.029253    20.273003\n",
+       "6   0.627955  0.033043    19.004203\n",
+       "7   0.614736  0.032374    18.988794\n",
+       "8   0.643325  0.036727    17.516626\n",
+       "9   0.631086  0.035907    17.575684\n",
+       "10  0.659799  0.040832    16.158997\n",
+       "11  0.648348  0.039822    16.281260\n",
+       "12  0.676473  0.045214    14.961460\n",
+       "13  0.665006  0.044008    15.110982\n",
+       "14  0.692715  0.049805    13.908404\n",
+       "15  0.680845  0.048412    14.063594"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "loads_df[\"CL/CD_total\"] = loads_df[\"CL_total\"] / loads_df[\"CD_total\"]\n",
+    "loads_df[[\"CL_total\", \"CD_total\", \"CL/CD_total\"]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = None\n",
+    "for group_name, group_data in loads_df.groupby(\"turbulence_model\"):\n",
+    "    ax = group_data.plot(\n",
+    "        x=\"alpha\", y=\"CL/CD_total\", ax=ax, grid=True, label=group_name, title=\"CL/CD vs AoA\"\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Drag decomposition"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### By type"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = None\n",
+    "for group_name, group_data in loads_df.groupby(\"turbulence_model\"):\n",
+    "    ax = group_data.plot(\n",
+    "        x=\"alpha\",\n",
+    "        y=\"CDPressure_total\",\n",
+    "        ax=ax,\n",
+    "        grid=True,\n",
+    "        label=f\"{group_name} - pressure\",\n",
+    "        title=\"Drag decomposition\",\n",
+    "    )\n",
+    "\n",
+    "for group_name, group_data in loads_df.groupby(\"turbulence_model\"):\n",
+    "    ax = group_data.plot(\n",
+    "        x=\"alpha\",\n",
+    "        y=\"CDSkinFriction_total\",\n",
+    "        ax=ax,\n",
+    "        grid=True,\n",
+    "        label=f\"{group_name} - skin friction\",\n",
+    "        title=\"Drag decomposition\",\n",
+    "        style=\"--\",\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### By part"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = None\n",
+    "for group_name, group_data in loads_df.groupby(\"turbulence_model\"):\n",
+    "    ax = group_data.plot(\n",
+    "        x=\"alpha\",\n",
+    "        y=\"CD_wing\",\n",
+    "        ax=ax,\n",
+    "        grid=True,\n",
+    "        label=f\"{group_name} - wing\",\n",
+    "        title=\"Drag decomposition\",\n",
+    "    )\n",
+    "\n",
+    "for group_name, group_data in loads_df.groupby(\"turbulence_model\"):\n",
+    "    ax = group_data.plot(\n",
+    "        x=\"alpha\",\n",
+    "        y=\"CD_fus\",\n",
+    "        ax=ax,\n",
+    "        grid=True,\n",
+    "        label=f\"{group_name} - fuselage\",\n",
+    "        title=\"Drag decomposition\",\n",
+    "        style=\"--\",\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Simulation history"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### On an example case - SA, 2.5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>pseudo_step</th>\n",
+       "      <th>alphaAngle</th>\n",
+       "      <th>CL</th>\n",
+       "      <th>CD</th>\n",
+       "      <th>CMy</th>\n",
+       "      <th>0_cont</th>\n",
+       "      <th>1_momx</th>\n",
+       "      <th>2_momy</th>\n",
+       "      <th>3_momz</th>\n",
+       "      <th>4_energ</th>\n",
+       "      <th>5_nuHat</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>2.500000</td>\n",
+       "      <td>0.090038</td>\n",
+       "      <td>1.251197</td>\n",
+       "      <td>0.201888</td>\n",
+       "      <td>2.812747e-03</td>\n",
+       "      <td>1.007261e-02</td>\n",
+       "      <td>4.770403e-14</td>\n",
+       "      <td>4.397797e-04</td>\n",
+       "      <td>8.047972e-03</td>\n",
+       "      <td>1.945773e-02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>10</td>\n",
+       "      <td>2.500000</td>\n",
+       "      <td>0.254437</td>\n",
+       "      <td>1.006006</td>\n",
+       "      <td>0.265718</td>\n",
+       "      <td>1.751079e-03</td>\n",
+       "      <td>1.277830e-03</td>\n",
+       "      <td>6.808128e-04</td>\n",
+       "      <td>1.062766e-03</td>\n",
+       "      <td>5.138124e-03</td>\n",
+       "      <td>1.843599e-03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>20</td>\n",
+       "      <td>2.500000</td>\n",
+       "      <td>0.240755</td>\n",
+       "      <td>0.810002</td>\n",
+       "      <td>0.235571</td>\n",
+       "      <td>1.693033e-03</td>\n",
+       "      <td>1.235297e-03</td>\n",
+       "      <td>8.075149e-04</td>\n",
+       "      <td>1.190353e-03</td>\n",
+       "      <td>4.914130e-03</td>\n",
+       "      <td>1.139797e-03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>30</td>\n",
+       "      <td>2.500000</td>\n",
+       "      <td>0.244433</td>\n",
+       "      <td>0.712796</td>\n",
+       "      <td>0.226364</td>\n",
+       "      <td>1.648514e-03</td>\n",
+       "      <td>1.193744e-03</td>\n",
+       "      <td>7.924807e-04</td>\n",
+       "      <td>1.165750e-03</td>\n",
+       "      <td>4.793525e-03</td>\n",
+       "      <td>8.670085e-04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>40</td>\n",
+       "      <td>2.500000</td>\n",
+       "      <td>0.245973</td>\n",
+       "      <td>0.648395</td>\n",
+       "      <td>0.219745</td>\n",
+       "      <td>1.634149e-03</td>\n",
+       "      <td>1.146978e-03</td>\n",
+       "      <td>7.923878e-04</td>\n",
+       "      <td>1.161709e-03</td>\n",
+       "      <td>4.749277e-03</td>\n",
+       "      <td>6.966269e-04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>396</th>\n",
+       "      <td>3960</td>\n",
+       "      <td>2.258009</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.022453</td>\n",
+       "      <td>-0.108635</td>\n",
+       "      <td>9.362475e-09</td>\n",
+       "      <td>2.048816e-08</td>\n",
+       "      <td>2.617556e-08</td>\n",
+       "      <td>2.672189e-08</td>\n",
+       "      <td>3.081626e-08</td>\n",
+       "      <td>3.449803e-08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>397</th>\n",
+       "      <td>3970</td>\n",
+       "      <td>2.258009</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.022453</td>\n",
+       "      <td>-0.108635</td>\n",
+       "      <td>9.361848e-09</td>\n",
+       "      <td>2.048735e-08</td>\n",
+       "      <td>2.617546e-08</td>\n",
+       "      <td>2.672337e-08</td>\n",
+       "      <td>3.081410e-08</td>\n",
+       "      <td>3.449955e-08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>398</th>\n",
+       "      <td>3980</td>\n",
+       "      <td>2.258009</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.022453</td>\n",
+       "      <td>-0.108635</td>\n",
+       "      <td>9.361923e-09</td>\n",
+       "      <td>2.048799e-08</td>\n",
+       "      <td>2.617594e-08</td>\n",
+       "      <td>2.672178e-08</td>\n",
+       "      <td>3.081494e-08</td>\n",
+       "      <td>3.449642e-08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>399</th>\n",
+       "      <td>3990</td>\n",
+       "      <td>2.258009</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.022453</td>\n",
+       "      <td>-0.108635</td>\n",
+       "      <td>9.361313e-09</td>\n",
+       "      <td>2.048775e-08</td>\n",
+       "      <td>2.617566e-08</td>\n",
+       "      <td>2.672333e-08</td>\n",
+       "      <td>3.081285e-08</td>\n",
+       "      <td>3.449798e-08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>400</th>\n",
+       "      <td>3999</td>\n",
+       "      <td>2.258009</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.022453</td>\n",
+       "      <td>-0.108635</td>\n",
+       "      <td>9.361240e-09</td>\n",
+       "      <td>2.048813e-08</td>\n",
+       "      <td>2.617541e-08</td>\n",
+       "      <td>2.672199e-08</td>\n",
+       "      <td>3.081303e-08</td>\n",
+       "      <td>3.449529e-08</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>401 rows × 11 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     pseudo_step  alphaAngle        CL        CD       CMy        0_cont  \\\n",
+       "0              0    2.500000  0.090038  1.251197  0.201888  2.812747e-03   \n",
+       "1             10    2.500000  0.254437  1.006006  0.265718  1.751079e-03   \n",
+       "2             20    2.500000  0.240755  0.810002  0.235571  1.693033e-03   \n",
+       "3             30    2.500000  0.244433  0.712796  0.226364  1.648514e-03   \n",
+       "4             40    2.500000  0.245973  0.648395  0.219745  1.634149e-03   \n",
+       "..           ...         ...       ...       ...       ...           ...   \n",
+       "396         3960    2.258009  0.500000  0.022453 -0.108635  9.362475e-09   \n",
+       "397         3970    2.258009  0.500000  0.022453 -0.108635  9.361848e-09   \n",
+       "398         3980    2.258009  0.500000  0.022453 -0.108635  9.361923e-09   \n",
+       "399         3990    2.258009  0.500000  0.022453 -0.108635  9.361313e-09   \n",
+       "400         3999    2.258009  0.500000  0.022453 -0.108635  9.361240e-09   \n",
+       "\n",
+       "           1_momx        2_momy        3_momz       4_energ       5_nuHat  \n",
+       "0    1.007261e-02  4.770403e-14  4.397797e-04  8.047972e-03  1.945773e-02  \n",
+       "1    1.277830e-03  6.808128e-04  1.062766e-03  5.138124e-03  1.843599e-03  \n",
+       "2    1.235297e-03  8.075149e-04  1.190353e-03  4.914130e-03  1.139797e-03  \n",
+       "3    1.193744e-03  7.924807e-04  1.165750e-03  4.793525e-03  8.670085e-04  \n",
+       "4    1.146978e-03  7.923878e-04  1.161709e-03  4.749277e-03  6.966269e-04  \n",
+       "..            ...           ...           ...           ...           ...  \n",
+       "396  2.048816e-08  2.617556e-08  2.672189e-08  3.081626e-08  3.449803e-08  \n",
+       "397  2.048735e-08  2.617546e-08  2.672337e-08  3.081410e-08  3.449955e-08  \n",
+       "398  2.048799e-08  2.617594e-08  2.672178e-08  3.081494e-08  3.449642e-08  \n",
+       "399  2.048775e-08  2.617566e-08  2.672333e-08  3.081285e-08  3.449798e-08  \n",
+       "400  2.048813e-08  2.617541e-08  2.672199e-08  3.081303e-08  3.449529e-08  \n",
+       "\n",
+       "[401 rows x 11 columns]"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "alpha_base = 2.5\n",
+    "tb_model = \"SA\"\n",
+    "case_name = f\"DPW7_JAXA_medium_LoQ_AoA{alpha_base:.2f}_{tb_model}\"\n",
+    "\n",
+    "history = pd.read_csv(os.path.join(results_path, case_name, \"iterative_history.csv\"))\n",
+    "history"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Convergence"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: title={'center': 'Residual history'}, xlabel='pseudo_step'>"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "residuals_sa = [\"0_cont\", \"1_momx\", \"2_momy\", \"3_momz\", \"4_energ\", \"5_nuHat\"]\n",
+    "history.plot(\n",
+    "    x=\"pseudo_step\",\n",
+    "    y=residuals_sa,\n",
+    "    title=\"Residual history\",\n",
+    "    grid=True,\n",
+    "    logy=True,\n",
+    "    ylim=[10e-10, 1],\n",
+    "    xlim=[10, 4000],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: title={'center': 'Drag history'}, xlabel='pseudo_step'>"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "history.plot(x=\"pseudo_step\", y=\"CD\", title=\"Drag history\", grid=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Alpha controller"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The plot below shows how the Alpha controller UDD changed the variables to obtain a CL of 0.5."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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DPFKG34xXtTbG98sLUrgAav78+TRx4sRy38c8fcZrMzEewcZr/NjzC3Dq1Clq2LChKr/gWmgj2uf+1N5GtbdPC21E+yqOR50rv+OhoaFWPYfXTly6dClt3ry53N98HkHGx2zcJda0aVMRhHFpj6+vr1XvaVMBDr8oR13r1q0zicr4PvfZlWXhwoWiDuiJJ56w5S0BAABAAyRJEoEQLyS8fv16qlevXrnP6d69u1hahmMRBQd2HCRZGwgxm6uRufuKh63xxF1c1MwrH3PWZ/jw4eLxp556yqTA2riLjOcMsGbFbQAAANCWkSNH0ty5c2nevHliriHO7vAlNzfXsI95jMExyM2bN2n06NEiCOLlZj7++GPxWrawuWZo8ODBYrgbD4/ng2zTpg2tXLmSoqKixONcNW4+4ovH/XO/3+rVq219OwAAANCAGTNmGCZrNjZ79mwxcWhpMQbXIa1atYr+85//UKtWrcT8QhwY8WgyW1SogJrTWHwpzcaNG0tsa9y4sUh/AQCAZZzq5zoHd6w54WHTeXl5pNPpSG3QPsvM63Uqw5o4obQYg8t0eKLGysBCrQAALoCDIF47zLj2wV3wjxgPouGRv7zcktqgfWWrVq2aeL47fzYIhgAAXODHiGfl57+w3XFyWQ7gsrKyKDg42O2O3Rpon+XvbU5OjmE5Li5adlcIhgAAnIxn2ecflZiYGAoMDCR37d7jYdRqDRbQvtIFBASIaw6IeL4fe3WZVTX1nVUAADej1GnYMhQYwFUE6gN4rj1yVwiGAABchDvXXIB2eajge4tgCAAAADQNwRAAAABoGoIhAACoFJ6Ad+zYsWJtK15XkkfEDRw40LB0E68rOXXqVGcfJoBFGE0GAAAVlpCQINaH4kU4P/30U2rdurUopOVZgXlJhBMnTjj7EAHKhWAIAAAq7KWXXhIFtGvXrhXzzChDs5s3b05PP/20sw8PwCoIhgAAXAxPZpdb6JxlHwJ8vKweHcQLZPLalB9++CEFBQWVOjMxgDtAMAQA4GI4EGr2/iqnvPexif0p0Ne6n4YzZ86IwI3XnwRwZyigBgCACsEC3KAWyAwBALgY7qriDI2z3ttajRo1El1qJ0+epLvuusuhxwXgSAiGAABcDAcY1nZVOVNERAT179+fpk+fTkOHDhUjyoylpaWhbgjcArrJAACgwqZNmybWVuvTpw/98ccfdPr0aTp+/Dj973//o65duxr2S0pKogMHDphcUlNTnXrsAAoEQwAAUGH169enPXv20O23306vv/46tWjRgvr27SsmXJwxY4Zhv88//5zatm1rclm2bJlTjx1A4fp5WAAAcGk8v9DkyZNFN5kyz5D5xIwArgyZIQAAANA0BEMAAACgaQiGAAAAQNMQDAEAAICmIRgCAAAATUMwBAAAAJqGYAgAAAA0DcEQAAAAaBqCIQAAANA0BEMAAOAQPPM0LzrL65BZa86cOW63uGvdunVp6tSplX6dH3/8kfr162f396vIeVB06dJFrDmndgiGAABAtSZNmkReXl5iuRBXlpeXR++99x6NGzeu3H13795Nzz33nF3ff968eRQREVFi+7vvvktvvvkmFRcXk5ohGAIAANWaNWsWjR07Vly7st9//12s7da9e3eL+xQUFIjrGjVqUGBgYJUc14ABAygzM5NWrFhBaoZgCAAAKmTlypVitXrOKPDq9QMHDqSzZ89a3H/jxo2iu4ZXq2/VqhX5+/uLbpgjR46U2HfVqlXUtGlTCg4OprvvvpsuX75skhnp27cvRUZGUlhYGN1xxx20b9++Eq+xadMmys3NpYkTJ1JGRgZt27bN5PHx48dTmzZt6JdffhFdT/xajz76qPjxV/DtJ554gmJjY8Xlyy+/pF69etGrr75qsZ1paWn07LPPiqCFA5w777yTDh48WOZnOX/+fPH5GRs2bBgNGjSIPvroI4qJiaHGjRuX2k124sQJcR7482zWrBmtXbtWfM6LFy82eb1z585R7969RSDVunVr2r59u+G8jBw5ktLT08Xz+MKfDeOs2j333COOT80QDAEAuBpJIirIds6F39tK2dnZNGbMGNq1axctWbJErFj/4IMPltul8vrrr9MXX3whghoOGDgIKCwsNDyek5NDn3/+uQhSNm/eTImJifTaa6+ZBChDhw6lrVu30o4dO6hRo0biB9s4iFFqcIYMGUI+Pj7imu+b4+CNg4alS5eKCwdQn3zyieFxbh8HUdyNxAHali1bSg28jD3yyCOUkpIisil79+6ldu3a0V133UU3b960+BxuS4cOHUpsX7duHZ08eZLWrFkjjs+cTqcTARMHODt37qTvvvuO3nnnnVLfg7fz58i1Q7fddpv4TIqKiqhbt26iO5EDNw46+WL8eXfq1Em0W828nX0AAABgpjCH6OMY57z328lEvkFW7frQQw+Jaw5+atasKYKNqKgoOnbsGLVo0cLi87guhjM77KeffqK4uDhatGgR/fvf/xbbODCaOXMmNWjQQNwfNWqUyO4oONNijAMALrrmQOa+++4T2zgTxF1PSvaDszs9evSgr776SmSbFHzsXLQdEhIi7j/55JMiAOFsDAdXfHxz584V2ScOFmbPni2yNGUFNRwccjDk5+cntnFgxwEXH09ptT6cSeKsTGmvGxQURD/88AP5+vqW+n4cJHFAx9md6OhosY2PXfl8jXGAc++994rbEyZMoObNm9OZM2dEYMRt44yQ8hrG+LguXrwoPisOeNVIna0CAACHO336tMguNGzYkGrXri26yhhncsrStWtXw23uYuPun+PHjxu2cZZDCYRYrVq1RHChuHr1Ko0YMUJkhLhri3/Is7KyTN73t99+E6/B3UGMu8Pq1KlDCxYsMDkW7nJSAiHz9+JuJQ7MODOi4PdTuqtKw91hfCzVq1cXQZdyOX/+vMUuRO7KY9zNZa5ly5YWAyHGWaP4+HiTIMb4eI1x16RxO5nx52pJQECACITy8/NJrZAZAgBwNT6BcobGWe9tJe7e4gDj22+/FQEJBzH8g6sU+lb4EHx8TO5zxkIy6r7jLrIbN26ILA+/P2dgOMAyfl/OUh09epS8vW/9zPEPOhdSP/PMM2W+V2VGTnEgxIEGZ2rMWZoygAMnft/U1NRSM0P2YtxWfj9mTVtv3rwpjoODIrVCMAQA4Gr4h8rKripn4WCEsxLff/+9GAHF3VKHDh2y6rlc58OZJMYBwKlTp0SxtLX++ecfmj59uqgTYtyFc/36dcPjhw8fpj179oiAxHi4OP+oc/EzFxw3adKk3PfhTBcHEErBNuPuLD7enj17lvocrg+6cuWKCMI462QNzvxw4TN3L1ozz5AxzlJx+zlbxl2UjI/XVj4+PqL+qDRc4N62bVtSM3STAQCAzcLDw0VGg+t1uO6EC52Ni27LwvU/XJfDP7I8YopHhXERsLW4e4yLq7lrjYuGH3/8cZOsBWeFuKuIAxauXVIufL9jx46lFlKXhrvPOAv1xhtviAJizjRxVonrZpTMirk+ffqILBW3Z/Xq1WLCQy7A5uJlDtAs6d+/v6g3shUHadwdyMfJwSgHijw3ELN0jKWpXbu2yGrxeeHAkovYFdx2W4M0d4NgCNzCpdQc+nbTWbqZfSsNfiYlk65m5Dn1uAC0igMCHm7No6W4a+ztt9+mTz/91Krn8mit0aNHU/v27UUW5e+//y6zLsYcBzOcUeIsDBc8v/LKK6KAm3FXGRc8K8Xd5nj7zz//bDJ6rSxTpkwRw/95yD0HBJwF4yxWafU9SgCyfPlyEXgNHz5cFCfzcy9cuGDI3JSGgyx+HmeebMFD37k4mwMZDvR4SL8ymszSMZamc+fO9Pzzz9PgwYPFCL/PPvtMbE9KShLBHLdF1SQ3cPHiRe4sFtf2VFBQIC1evFhcq5Ua2likK5b6f7lJqvPGUqnX5A1S4o1sae6OBKnum0ulzh+tkRb+6d7tU/v503obrWlfbm6udOzYMXHtjnQ6nZSamiquy7Jhwwbxbznv667ty8rKksLCwqQffvjB7u/z8MMPSx9//HGlX2fr1q3icz5z5kylz9/YsWOlESNGlPn8sr6/jvr9tjfUDIHL+33vRTpxRZ4/5Pz1bOo/dTPlFMh921cy8mn3NQ96wMnHCADqtH//flHLwzU9XFPz4Ycfiu0PPGD/f3V4yRDOktmKpyXgEWvcfchdlpx14wyW8Yi8iqpZs6aYa0ntEAyBy5v9T4K4fv6O+rTp5DURGHl6EIX4+1B6biGtS/akicXWTxQHAGAL7irjYnHuyuOuPa6h4Tone+OC65dfftnm5/F8SFzXxFML8HFx3RJPamkP//3vf0kLEAyBS0vNLjBkhZ7rUZ9G9m5IC3ZdpK4NqlO9yCBqM3E1Xc8jSkrPpfo1ra85AICqxyO5jIfIuwMeRcWjs3i0HE8f4IqTDj711FPiAhVXobM6bdo0EcFycRYXXfFsm2Xh2TV53ROee4Hng+CCMi4UAyjPrgR5+vqGNYOperAfhfr70Iie9alFbBgF+XlT7Qh5TpSEG7dGPgAAgPuZNGmSKALnUXzcPccj8jgjVxaePVxZT0252FI4XuFgiGfv5P5Dnk6d12fh2T15SKClWSy5sp+H/vHwQp6KXJmXghe8AzCmK6Wra+c5ORjqXO/WXCHG6lWXg6ELCIZABdwtawJgz+8tL6fCiROeh4qXGeERfzyCj9fAK4vxmmp84ZF7Du8m475TngZdGWbH68fwCsQ8q+ebb75ZYn/ezhNd8dA8ZfZLayeicjSP5P0UmXmUPM4HE3l7kRp5FOncoo1rj1+ln7ZdoOHd69KdTeQhsizv1GHq5plD9wbnEZ27UeJ5d/gkUI7nVfI4n0IUc5HUeP7CchJsWjwT3A8Pj1b+eFTzLL+gTjn6OYnMZ/O21cqVK0tkfThDxNM3WJrkkllaU81hwRD/j8oH9dZbbxm2cf8pF2spi+GZ++uvv8QEVBzt8arGPH/BY489Joq9lH8AzPH6J8ZroCgrEefl5ZlMBFVZvivfpO6X9xKdIdXiE9ydb7h4G/vwhf8/4h5Xo17Xj/k/XAr0j/5i5km+8OO85E/py/64/fnrxf/YHI6mwqYDSY141WxlfSZr535RW/v4L2v+IeG/avm2K9allIWPmf9tVrop1AbtK/t5PEkjZ2dKW7uMf7eV33Guu1JwyYyykK0lypxLxrOIl4bnWOJlWXhpEZ576uOPPxaL0DosGOIG89BC84mj+D5Pb14aXuhu/fr1YoZQrhPiYX8vvfSS+EeBu9os9RvyirrmuIiN176xl3Z5fhTmH2e31wOwN//CVPLVZVPCnjV0+pLt/eDuZMOGDaRm1ravvBoJAHeSo09g8NQExvj3f/z48Rafx4HNq6++KqYI4NnDy1qOhHugeOJPDp4+//xz6tatm5gtPC7O+t93D55syNqdk5OTRa0Pd3kZrzo8duxY0dfH06Kb42Jpjgx5xV4lE8RdbTyfAv8VZE1miGfA5A+SV0iOiYkhe/7Fxv9A9e7d22QxPzVxhzbe9802SkrPo/fuaUIfrThBXDq04NlONGvbBVp17Co9270OjexV+nwZyTez6d4ZO8nLw4N2vNmLvHnMvZNMWnmS/m9vEsWG+dPHDzanzaeu04/bLlCgjxctfrEL1Qi59VcQ/2/3x/5kWnroMnWsG05PdalDIf4lz4/3qjfJ9+BPlN/lVdL1fIPUyB2+o1XVPv5e8B+K7lY7xG3kf/95QI1azyHaVxJnkXj/sjKZHDfw/Ec8V5NxrXB5maEXX3yRVqxYIZYosSWo4f9/eIbwIUOG0AcffGD182w6qzx/AQc0vCCcMb5vqb+OR5Bx+te4S4wPlKdg52630qZgN/+QlNQaV4jbMzOkpKy5j76yfZ2uytXbyEPnz93kwNeD7m9fh3ZcyBABwuR15+jwpXTK13lQ72axFs97bW9vUU6ToyO6nitR/Rr2Wdxy57kb9Paiw9SxbgSNurMhxYWX/b07ey2L5u5OJknyoA//1Ya6NIykTg2iRXv2JabRt9su0ccPtjTs/+WaU/TVutPi9q7ETNqWkEG/jehC/j6mXce6gGBx7U2F5GfH774rcfXvaGWpvX1KG7nXgFdlV2Mb0b6KU0Z28Qgx7kqzxqhRo2jp0qVivTtbAiHGx8/TIXAvlC1s6phWJpzihdyMU1l83zhTZIxTXHxQvJ+CV/zlIMmWtWhAnY4ky33CdasHimHzHHj4ennSP2duUEZekQhu2sRXK/Mvkwh93HwpNdcux8QTOY6ef4DOXsum+bsv0oCpW2jOP+cpPcdyPcsPW86JoKxP0yjq1lCejM3T04Ne699Y3P77YDLlFcqzZqdk5tGMjXKBU+/GNSgswIf2J6bRB0uPlXxhb33XWCHWYAMAdZMkSQRCPKM2l9fUq1fP5tfgoO7w4cMixrCFzVV6PKyeh8b/9NNPYsVgTmXxsDdldBlP/GRcYM2P82gynh6cgyAeecbFTVxQDXA4SQ6GmseGiesm0aE07fF25OPlQdGh/vTT8E7k7VX21zTcT+5SuJxun2Bo5qazdEW/AGyT6BDKzC+i8X8fox6fracFuxNLdGGkZOTRH/uSDLNkG+tSrzrFhPlTZl4RbTghTz8x558EKtAVU/s64TR7eCea9lg7sZ0Drws3zIaQ+sjZII9C+7QNAMBVjRw5UiyyO2/ePJFJ4h4kvvDgA4V5jDFx4kRavXq1qE/m6X6eeOIJMbSeF6y1hc2dn7yi7bVr1+j9998XB9mmTRsxHE4pqubpwI37D+Pj42nVqlX0n//8RxQ4cZ8hB0Y8mgzgiD4YaqkPhljfZlG09Y07RQ1NoG/5X9FwfYIxOc0+2ZP1x+Wg5X9D2tI9LaJp7o4L9OvORDqdkkVv/HFYBHCv928iMjrsmw1nqKBIDm461Ak3eS3ODt3fJlYEWL/vvUS3N4qkX3bIc2A831MOnHjbHbfVoE2nrtHX68/Q54+0LpkZKkIwBADqNmPGDMNM5cZmz55Nw4YNKzXGSE1NFdP9cDwSHh4ueq+4rtm8YLs8FaoE4zQWX0qzcePGEtu4C40nUQIwd/yyPG1C8xjTvuSoUOtHTlXTZ4aS0yofMHAX1smrmcSjS29vGCmyUsO616MnutShbzefo8mrTtLcHYn0x94kMR9S6/gw+m1Xonjua/0alzos9eH2cSIYWnciRXSFZeq7/7hLTfFqn0YiGFpyIInG9m9MNfXtl3z0c84UoZsMANRNsmLggHmM8eWXX4pLZbnXZBagKoW6Ykq8mWNYbqOiwvU1Q5fTKx8wbDtzwxCcRQTdqmnjoIjXRftxaAfRdZZbqKNlhy/Tx8tPUKFOorua1BTrpZWG29avmRz4/N+eS4asEGeNFG1ry1klfq2ft18opWYImSEAAEdR3xhBcBscCPESHAE+XhQVUvE5dG51k1U+YNh65rq47q4vgjZ3V9MokRHirrLlh6/Q/sRUkUF6todprZA5DqTWHL8qiqxbxIbSoLYll6N5tkc92nMhlX7eniBm4ua12AiZIQAAh0MwBE5z7ppcLMyrzxtnSWylFFAnp+eKNGtlZojddV5eC61r/dKzPIxfv1VcNXGxVuv4arTope6itohHx/l6l0zK9m0WTc1qhdKxyxn04bLj9MUjrSk5i6g2z16cnUn2mTQAAADMoZsMnObctSxxXdm5garpM0N5hcWUVsbwd2vqhThbxbFUO7NCaHvgIKhTvYhSAyHm5elBHwySp5BftD+J2kxcTWP/lofgJ19Po09WlD7LOwAAVA6CIXCa89flzFD9GhWvF2IcW0QGyxFRUiW6yvYmpIrrxlEhYs4jZ2hfJ4Im/aslBfp6iXmWCjzkgqgAj3xRhL39bMnFagEAoHLQTQZO7yZrYIdZo2uF+dP1rAJRN9TCaJi+Lbheh/EQeWca0qk29W8eLeYcql9Ui+hnojBvHVE+0dfrT1ss1AYAgIpBZgic5tz1LEPNUGXFVZMLjZXRaRWxJ+GmSwRDjEey8QizwCD5swnylLv/difcpKx8eRV0AACwDwRD4BRcn8OZHK7PqcywekXtCHmm5gs3KhYMXcvMp0P6CSBdKvOiH03mUZRLdaoHiqH3/+hHvAEAgH0gGAKnTrZYr3qQVbNMl6dOdTlouFDBzNA6/bD3VnFhVCtMP5zdFXjrgyFJR3c2kjNWG09ec/JBAQCoC4IhcIrjlzPEdVOzmacrmxlKNF/by0qrj10V18rkiC5DmWeIp6ivL2fQtp9FZggAwJ4QDIFTgyGeV8eewRCvXF+kK7bpuUeT02nzKTnb0q95NLkUL1+SSJ43qXWUPLIs4UYOpVdiCgEAADCFYAicmxmqFWKX14sK8RPz9xQVSzYty3HwYhq98tt+8bz+zaPotij7HI/deHiQzlOeNqCaT5Eh6OMZsAEAwD4QDEGVyykoorP6YfVN7ZQZ4hms48PlLqUEK7vK1hy7Sg9O/0ccC89T9NGDLckVKcEQL8nRMk6eNuDgpTTnHhQAgIogGIIqx0EIr0nGo6OibVidvjx1qwcZupHKk3A9m0bP30/FEomM0JJRt1MkrwXmgnQe+gkgC3OptT4YOnwJmSEAAHtBMARV7u+DyeJ6YKuYSq0jZq6RvotL6YIrC68Mn1Ogo051I+ibx9pRrH6eIlek89QHaYW5hvXQDiEzBABgNwiGoErdzC6gTfpi5fvbxNj1tXlYvDVZk0JdMf11MEncfv6O+uTj5dr/G+g89ZmhInl2bY4fk9PzxNxIAABQea79KwCq8+2ms2LiwJaxYXYvVubXZCeuZFB+kc7ifltOXxMTPlYP8qWet9UgV2ecGQr286YG+rXcDichOwQAYA8IhqDKXM3IoznbEsTtMX1vs/vrx4UHUHigjwi2TugndSzN0kOXxfXA1jEunxVixYaaIXmUXCt90HfwIuqGAADswfV/CUA1Vh+9QvlFxdQ6vhr1amz/jAzXH7VUamosDD3nOYjWHU8Rtwe0cLE5hcobTVaYY9odiOH1AAB2gWAIqszO8/JCqHc1qWnXwmljbeLlYGitfkbpMymZtOFEiqhVYrsSblJ6bqFYCNUVFmS1dWg9MwR8l9JI4jVEAACgUiq/KBSAFfhHe5c+GOpUL8Jh7/NQu1j6ev1pUaT9zqLD9OvORLE9xN+bFr7QlRbtSzIEZN5u0EVmmhnKFVfNY0LJy9ND1D3xBJMxLjwSDgDAHbjHrwG4PZ77JyUzn3y9PA3ZG0eoUz2I+jSV1xdTAiGWmVdEd0/dQgv3XhL3B3eMJ3dhHgz5+3hRo5pyEfURdJUBAFQagiGoErsT5KxQ6/gw8WPuSK/c2YhC/b0pJsyfXu/fmPa/15fqVpeXsWAPtImhDnUdl52yN52H0k0mB0OMh9izI8nlz6kEAABlQzcZVAme8dmey2+UhZesODS+v8m2xSO70+x/Eujc9Wx6775m5E7MM0PKNAK/772EzBAAgB0gGIIqcUW/eGqtMOfUt1QL9KX/OGA4f1UoNhRQ35pksUWsHFQiGAIAqDx0k0GVSE6Xsxq1wuy3FplWGNYmMwqGOMPm6UGiDislQw40AQCgYhAMQRVnhhAM2arYsBzHraAn0PfWTNRHkpEdAgCoDARDUCXD6nkIuDO7ydzZrbXJTDNAShH14UsoogYAqAwEQ+BwqTmFYuZpFhWmX2cLbF+Ow0IwhMwQAEDlIBgCh0tOk+uFIoP9yM/bscPq1Z0ZMl2lvkUMiqgBAOwBwRA4HOqFHJMZaq7PDHEX5PUs00AJAACsh2AIHO4yRpI5JDMU7OdN9SODxG1khwAAKg7BEDjcreJpBEP2zAwZ1w0dxUzUAAAVhmAIHO5GlrxifI0QFE/bMzNkPPni4UvIDAEAVBSCIXC4tFw5GAoL1M+kDHbPDGFEGQBAxSEYAodLzy0U19UC9D/qYLfMUPMYORi6lJpLqdly0AkAALZBMAQOl5YjB0NhCIbsnhniz7R2RKC4jbohAICKQTAEVZcZCkQwVKnMUHERka6oxOO8gj07jBFlAAAVgmAIqrCbDDVDFVHsYfS56UrpKtMXUR9F3RAAQIUgGAKHKigqppwCnbiNbrJKZoYsjSjT1w2hmwwAoGIQDEGVZIU8PIhC/L2dfTjuycOTJAuLtbLm+mU5zl/Ppsw8+fMGAADrVejXadq0aTR58mS6cuUKtW7dmr7++mvq1KlTqfvOmTOHhg8fbrLNz8+P8vJK/qMO6pOuH1Yf6u9Dnp4ezj4c9+XtR1RQWGowVD3YT0xoyZNbHr+cSZ3qRTjsMK5l5tPBi2l0LSufbmYXUE5BEeUXFlNekY50xRJ5eHgQn2ZvT08R/HI2sFqgL0WF+lGdiCCKqeZP3l74GwwASpo0aRL9+eefdOLECQoICKBu3brRp59+So0bNyZrzJ8/n4YMGUIPPPAALV68mBwaDC1YsIDGjBlDM2fOpM6dO9PUqVOpf//+dPLkSapZs2apzwkNDRWPK/gfTNDWSDIUT1eStz9RQVap3WTKEHsOhnhZDnsHQxz0/Lw9gZYdukynU7Iq9Vrenh4UFx5AdaoHUcOaweJSN8KfspHQAtC8TZs20ciRI6ljx45UVFREb7/9NvXr14+OHTtGQUHy0kOWJCQk0GuvvUY9evSo0HvbHAxNmTKFRowYYcj2cFC0bNkymjVrFr355pulPoeDn+jo6AodILg3zDFkx8wQKyUzpMxEvfb4VbtPvsgB0DuLDxuCWtY4KkQENBFBvhTk503+Pl7k5+0pAh2Ja5yKJSoqLqbMvCJx/lNzCulyWi5duJkjasgSbuSIy6ZT14wbSF8c3yiCo0ZRwdSwBl+HiPs1Q/zwBxSABqxcubJEzxInWfbu3Us9e/a0+DydTkePP/44TZgwgbZs2UJpaWmODYYKCgrEQb311luGbZ6entSnTx/avn27xedlZWVRnTp1qLi4mNq1a0cff/wxNW/e3OaDBfej/IiGIhiyUzBkOTPEjtmpiFqSJPpm/Rn6Ys0pcb9JdAi9cEcD6nlbDREEVURxsURXM/Mo4ToHQ9l0JiVLXE5fzaTk9Dy6kV1AN87fpJ3nb5o8j7vb4sIDRVegcqkZ6i+64Lj7VVwHeJOft5cIyLy9PEQ3HV97enhQoa6YijhA0xVToU4O1Ip0kmE7B2gF/FiR/DhvF/f5Wmzj+5L+8Vv3+bFiSTL5zG7dvnX8uuJiOp/gSQdWnBT/XjIltOMYjwM9Q6jH9/X3xGP6a/khD6Pb8gMmr2PpefobFl/HDnTFOjqR5EFJW8+Tl6cXOYPRR253/GN7MsmDLm05T15ezmkfVUH72mfkUVx11/m3Oj1d/uMuIqLsbPfEiRNF0PTMM8+IYKgibAqGrl+/Lj60qKgok+18n/v4SsN9fZw1atWqlWjY559/LvoBjx49SnFxcaU+Jz8/X1wUmZmZ4prrjHJycsheOA3HcnNzqbBQnXl6Z7cxNSub/LwkCvf3tOu5c5X2OZrSvmJPX+J/gvOy06m4lM+xYYSv+JwTb2RSakaWyNRUxvdbE2j6pnPk50X0VOfa9PKdDUSgQVREOTkl5zqyVpgPUetaAeJCFGlo48q1G6hO8w6UmJYvCsHPX8+hc9ez6WJaLhUUFtK5lHRxcV8etD0lgdTNk1YlycGzOqm/fYNT0ikiwL7BnlIfzL/jGRkZJrXDfLGEkyevvvoqde/enVq0aGFxv61bt9KPP/5IBw4cqNRxekjGf86UIzk5mWJjY2nbtm3UtWtXw/axY8eKvr6dO3eW+xr8g9W0aVNR5PTBBx+Uus/48eNFusvcvHnzKDBQnm0XQEt6nhxP4TnnaEf9MXQ1rI2zDwcAwCr8R/Bjjz1WYvu4cePEb70lL774Iq1YsUIEO5YSJxxgcaJl+vTpNGDAALFt2LBhopvMoQXUkZGRIkV49epVk+1839qaIB8fH2rbti2dOXPG4j7cDcdF2oqkpCRq1qyZKKqKiYkhe+G/SDds2EC9e/cmb291Dvt2dhs/XXWK5u+5RM92r0MjezVQXfscTWlfSHgkUc45atuyKeka9y1135d+O0Dbz92kdwY0pofbxVbo/Tgb88SPuym3qJie7lpHZIQczV7nkP+u47/suAtMdIsVS6J+yceo28xL3y1VldT+HdVCG9G+iuMkCuMiaE6mKMrKCo0aNYqWLl1KmzdvthgIsbNnz4rC6YEDB5pklBi3gwduNWhg3b9hNrXa19eX2rdvT+vWraNBgwYZ3pjv88Fbg7vZDh8+TPfcc4/FfczTZ0pqzd/f366ZIaVbhYfwcZCmRs5uY3q+RPk6DwoPCXJIVs/Z7XM0pX2evvJn5+cpEVn4HJvGRtDG06l0PCWvQp811/S8v3Q/peVLdHvDGvTaPS3IqwqmQ9DKOVRr+7TQRrSv4vh3m4WEhIiR5eX9QfPyyy/TokWLaOPGjVSvXr0y92/SpImIJ4y9++67ImP01VdfUXx8vNXHaXMIyBmboUOHUocOHcTcQjy0Pjs72zC67KmnnhLRH88XoBQ2denShRo2bChSVzw/0YULF+jZZ5+19a3BjUeTcaErOG40mfEaZTwPUEX8uiuR9iWmUbCfN01+pFWVBEIAAAoeVs/lMEuWLBHBE89lyMLCwkSgZh5jcKBlXk9UrVo1cV1WnZFdgqHBgwfTtWvX6P333xcH2qZNGzEcTimqTkxMNIyYYKmpqWIoPu8bHh4uMktcc8TdXqB+PLya8WgfqOQ8Q2WMJmPt64SL6xNXMigrv0gENdbimau/1I8ce63fbVQrTP6HBwCgqsyYMUNc9+rVy2T77NmzRS1QaTGGvVToF4q7xCx1i3Fqy9iXX34pLqDtYCjYD5khR2eGokL9KbZaACWl5YrsUPeG8mgta8zcdFZMrli/RhA93qWOPY4YAMAm1oznMo8xzPHcRBWBefHBoThDwbAuWSV5lT3PkHl2aO+FVKtf+nJ6Lv2w5by4/ebdTcgHy2UAgMbgXz1wqAz9wqHBCIYqRTJ0k5W9pl+HunIwtMeGYGjK6lOUX1RMHeuGU99mpnOIAQBoAYIhcGjKE5mhqusmYx3ryjO17j5/k/IKdeW+LNcX/b7vkrj99j1NsewFAGgSgiFwmOwCnWFZghDUDDl0OQ4FL5vBK8TnFupol9myFqWZtPyEOEf3tqxFbWvLWSUAAK1BMAQOk6UvnuZlHPx98FWrFCu7yTiz07txTXF7/YmUMvfdeDJFLJbKkxKOvbux/Y4VAMDN4BcKHIaHayv1Quh+qZrMEOtlFAxZGp3Bi5Z+tOy4uD20a12qUz3InkcLAOBWEAyBw2ToM0OoF7IDL+syQ+z2RpEU4ONFiTdzaHdC6YXUv+1KpNMpWRQe6EMv39XI3kcLAOBWEAyBwyjF05hjqPIkGzJDPNniA23kNfx+3XmhxONpOQU0RT/B4ph+jSksAOcHALQNwRA4vJsMmSE78LE+M8Qe7yxPnLji8BW6eDPH5LHxfx2l1JxCui0qmIZ0tH7tHgAAtUIwBA4voA6xYVkIKK+brPzMEGsZF0Zd61enAl0xTVx6zLB90f5LtPhAMvGyY5893Jq8McEiAACCIXD8UhzIDFXdPEPGJj7QXIzkW3PsKn2+6iT9fTCZ3vhDXuF5ZO+G1CZeXtAQAEDr8CsFDpOp1AwhGKo8G2qGFI2iQujNAU3ow2XH6ZsNZwzb+zSNov/0uc0RRwkA4JbwKwVVUDOEAt2qmmfI3LM96lOgrzd9t1leiPWJLnXolbsakSf3kwEAgIBgCKpgxXp8zapyNJm5xzrXFhcAACgdaobA4QXUoegmc1pmCAAAyodgCBwmMx8r1ttNJTJDAABQNgRDUAVD61EzVGleSjCUS4bVbwEAwC4QDIHja4aQGbJfN5lUTFQsf64AAGAfCIbAYdJz5W4yLPdgx24yhrohAAC7QjAEDsGrpWfoh9YjGLJ3MIS6IQAAe0IwBA6RV1hMhTq5tiUUwVDleXgSefnKt5EZAgCwKwRD4NAuMi9PDwry9XL24ahseD0yQwAA9oRgCBxC6SLjOYY8PDDbsbPWJwMAgPIhGAKHyNBnhtBFZkeYeBEAwCEQDIFDu8lCsS6Z/WDiRQAAh0AwBA6BkWQOgMwQAIBDIBgCh8jI1a9LFoAJF+0GmSEAAIdAMAQ2yS/S0eZT12h/YmqZ+6GbzAGQGQIAcAj82Q5Wy84von9N30Ynr2aKIfNr/tOT6tcILrOAGt1kdoSh9QAADoHMEFjtr4PJIhBiumKJftuVWP7QegRD9oPMEACAQyAYAquX15i744K43aV+hLheuPcS5RXqyukmQ/LRblAzBADgEAiGwConrmTS0eQM8vX2pG8ea0cxYf6UllNI285eL6eAGpkhu0FmCADAIRAMgVW2nb0hrrs1qE6RwX7Uo1ENcX/n+Zul7o9uMgdAZggAwCEQDIFVdp2Xg6HO9aqL60715K6yXRaCIYwmcwBkhgAAHALBEFhVL7Q7IdUkCFKuD19Kp5wCuUus9NFkqBmyG2SGAAAcAsEQlOvstSy6mV1A/j6e1DI2TGyLjwik2GoBVFQs0f7ENJP9uag6I08OkLhLDewEmSEAAIdAMATl2ntBzgq1ia8mCqgVSnZo5zm5C01xNUP+sebgCfMM2RFWrQcAcAgEQ1Cu45fluYVaxMhZoRLBkFnd0OV0+ce6VlgAeXh4VNlxaiYzVIhgCADAnhAMQbmOX84Q101qhZYaDO2/mCaW6TDPDEWH6n+8wT6QGQIAcAgEQ1Bu8TTPMcSa1goxeax+ZJCoCSooKqZDl9JLZIaiwxAM2RWW4wAAcAgEQ1AmDmx4mLy3pwc1rGm6Dhl3gXXWZ4e2nblVN3QFwZBjIDMEAOAQCIagTCeuyF1kDWoEk5+3V4nH77hNnnxxzfErJYKhWgiG7AuZIQAA1wmGpk2bRnXr1iV/f3/q3Lkz7dq1y6rnzZ8/X2QTBg0aVJG3BScWTzcx6yJT3NW0Jnl6EB1JyqCktFyx7TJqhhwDQ+sBQMUmTZpEHTt2pJCQEKpZs6aIFU6ePFnmc/7880/q0KEDVatWjYKCgqhNmzb0yy+/OD4YWrBgAY0ZM4bGjRtH+/bto9atW1P//v0pJSWlzOclJCTQa6+9Rj169LD5IMEFiqejTYunFdWD/ahDHbmrbPVROTt0Fd1kjoFuMgBQsU2bNtHIkSNpx44dtGbNGiosLKR+/fpRdna2xedERETQO++8Q9u3b6dDhw7R8OHDxWXVqlWODYamTJlCI0aMEG/WrFkzmjlzJgUGBtKsWbMsPken09Hjjz9OEyZMoPr169v6luBEloqnjfVvES2uF+65REW6YkrJRDDkEOgmAwAVW7lyJQ0bNoyaN28uEi1z5syhxMRE2rt3r8Xn9OrVix588EFq2rQpNWjQgEaPHk2tWrWirVu32vTeNq2VUFBQIA7qrbfeMmzz9PSkPn36iKjMkokTJ4qU1zPPPENbtmwp933y8/PFRZGZKf8g5+XlUU5ODtlLUZE8S3Jubq6IQNWoMm3kUWJJNzPJz4uoXjUfi5/9gKYR9NVqDzqbkk7/W3OMfDwlCvL1oiBPnV3PlxbPoXH7iookCiCi4sI8ynPw51qVtHQO1dg+LbQR7as4/t1WfsczMuSeBubn5ycuZUlPTzdkf6wd/bx+/XrRtfbpp5/adJweEj/bSsnJyRQbG0vbtm2jrl27GraPHTtWpLd27txZ4jkcnT366KN04MABioyMFFFfWloaLV682OL7jB8/XmSRzM2bN09koQC0KDgvme46/iYVeAXRilYznH04AADl4j+IH3vssRLbudSGf+stKS4upvvvv1/EC+VleTho4tiEkyheXl40ffp0evrpp8kWDl1FkyPBJ598kr7//nsRCFmLM09cl6RISkoSXXJcWBUTE2PXaHjDhg3Uu3dv8vZW54KilWnjkoOXafzS49Spbjh9+3jbMvdNvJlDQ37YTTmF8uSLf7/UleLCOY/hWGo/h8bt88m+THScyMdDR3379iW10NI5VGP7tNBGtK/iOInCjh07JgIWRXlZIa4dOnLkiFXdXVxwzQmXrKwsWrdunYgfuCSHu9CsZVOrOaDhqOvq1asm2/l+dLRcN2Ls7NmzonB64MCBJtGeeGNvb5HK4j4+c+bpMyW1xqPX7JkZUtKBAQEB5OOjzjW0KtPGU9fzKF/nQY2iw8v93JsEBtLvI3vQxL+PiZmpb4utTlVB7efQpH0e1cRtj6J8CgwI4ImeSA00dQ5V2D4ttBHtqzj+3VYCltDQ0gfimBs1ahQtXbqUNm/eTHFxceXuz+U6DRs2FLd5NNnx48fFyDSHBUO+vr7Uvn17EXkpw+M5uOH7fPDmmjRpQocPHzbZ9u6774qM0VdffUXx8fG2vD1UsROXyy+eNsZzEf30dCcHH5WGKaPJSCLSFRJ5+5JLuXmO6NgSopQTRD4BRPGdiZreR+Rn3fcHALRNkiR6+eWXadGiRbRx40aqV69ehV6H4xLjumNr2JwP4/TT0KFDxbj+Tp060dSpU8WwNx5dxp566imRCuOojCPCFi1amDyf5wJg5tvBFZfhkDNyTc3WJAMnjyZThte7SjDEgdm6CUQ7ZhAVy4WYwt7ZRKvCiXq/Q9TxWdVksgDAMbhrjGuDlyxZIjJJV67I07WEhYWJrJV5jMH4muMR7mXiAGj58uVinqEZM2Y4NhgaPHgwXbt2jd5//31xoJyS4uFwUVFR4nEeBscpK3BvVzPyKTWnkLxKWYYDnJ0ZcqHh9YV5RL8NJjq3Ub5f7w6iej2J8jOJjv8lZ4uWv0Z0dj3Rv74n8sN3CQBKpwQw5t1bs2fPFoOvSosxOBnz0ksv0aVLl0TAxD1Sc+fOFbGKLSpUKcVdYqV1izFObZWF5w0A13dcnxXixVj9fUouwwFOwJkVLz8iXb5rTLzI9X+LnpMDId9gogdnEjW9VR9Id75HtOdHotXvEZ1cTvTTQKKnFhP5hznzqAHARUlWDG43jzE+/PBDcakspHCg7Jmn0UXmWlxp4sXd38s1Ql6+REPmmwZCzMubqPPzRMOWEQVEECXvI5r3KFGBeuZIAgB1QDAEdimeBo0tyXHjLNGa9+Xb/T8mqlfGMjvxHeWMkF8oUeI2ooVD5TojAAAXgWAISnUkWZ75E8XTLsZVMkMcCHFAxjVCXBxdnlqtiR77PyLvAKLTq4lW3prFHgDA2RAMQQmZeYV0/rq8MF7LWNR3uBRXyAxd2EZ0YimRhxfRgM+sHyVWpyvRw7NudbHt+9mhhwkAYC0EQ1DC0eQM4jq2mDB/igwue5ZQcFZmyInB0ObJ8nW7J4lqNrHtuU3ukYfas2X/Jbq42/7HBwBgIwRDUMKRJLmLrGUcskKumxlyUjfZ5YPyMHnOCt3+n4q9Ro/XiJrcR6QrIFrwBFHmZXsfJQCATRAMQQmHLumDIXSRuR5nZ4Z2zJSvmz9IFF63Yq/Bc4TwMPwaTYmyrpDXH8PJsxgF1QDgPAiGoIzMkDxbOLgQZ2aG8jKIji2Wb/OQ+crgJToe/VXMOeSZtIdaXvrFLocIAFARCIbAREZeIZ1D8bTrcmZm6OifRIU5RJG3EcV1rPzrVW9A9NAsksiD6t7YSJ77MCErADgHgiEwcTRJnmwxtloARQS5yNpXcIuPE4fWH/o/+brtE/ZbZ6xRHyru/a646bnqLaLEHfZ5XQAAGyAYAhOHk9LENbJCLspZmaGsFHlIPWvxkF1furjrK5RUrSN5cN3QgieJ0hLt+voAAOVBMAQmDuszQxhJ5qKcVTN0YhmvHEQU044oLM6+r+3hQftrjyCpZjOi7BSiXx8hypWDcgCAqoBgCEwcviT/CLVCMOSanJUZOv63fG2+/pid6Lz8qWjwb0QhtYiunZCH3Dt7lm0A0AwEQ2CQnltICTfkRTRbxCAYcknOyAwV5hIlbJVvN77Hce8TGkv0+EIi3xCihC1E/zeUqKjAce8HAKDnrdwAOKofUh8fEUDhKJ52Tc7IDHFRsy5fztrUaOzY94puKQ+5n/dvolMriH4fTvTIHCIvH3Kqghyi6yeJrp0iSk8kykgmSk8iyr1JVJBNlJ9FVJAlj7bj6dtFgbmHuPb28qH+RR7knTCOyCdAfwmUz6Vyn2+L+3zN9/1KbvfyJfLw1L+up/5Ct26LO9ybqSOSiomKi41u66zYzteSfF/c1u+nbOduUgs8dTq67cop8txyTJ5HSmU8i3V02+XT5LnlKJGnl/1e2F4DESrJU3/+KKMNUfU6pEUIhsDgkDK/EIqnXZczMkPnNsrX9XtVzT/e9e8genQe0W9D5DXQODDigMi/ir6X/ON/9QhR4k6iizuIkvcT3TxfZjBQFo9CIhHCpqq3DorDg6Z847LK23eFVN2+osznEAwBHDYEQ5hs0WU5IzNkCIZ6V917NryLaMg8eXQZL//xYz+iIfOJIuo55v0yrxKdWUt0ejXR2Q1E+fL/CyYCqxNxkXe1OkShMURhsUSBkUR+wUS+fAmSMz4cMBplUgrzc2jrhrXUo0t78pYK5W5HziAV5snXfC75Nl+L27lysFuUa7qdly8RmRvO2BTLry9uG2/j6MtLzs6IjBHf9jK77VFyO197Gt/2MtuuZJ9KpyuWKDExkWrXrk1e5pkhF8l+VIauuNhy+ypKfEdcq31x/H3WKARDYHAYy3C4vqpetT4/U16PjNXrQVWqYR+ip1cSzXtULqqeeTtR34lE7YdXviuGu4ou7yc6uZLo9KpbbVRw3VJ8R6LaXeUJJqNaEAXXqNh7FRZSRuBpkuI6Efk4ubvPQYoLC+nQ8uUUd8895KXCNhraN0Dl7Quv4BI7KoBgCISb2QWUeFMunkYw5A6ZoSrqJkvaJ2cgwuLlbEhVq9WaaMQ6ooXD5S6rZWOIDi8kumOsnKmyJevAtT2c5Tq5Qs4AZV01fTymLVHDvkSN+hHFtrNvbQgAuDQEQyCsOCJ39jetFUphger7y0c1qjozdGmXfG2P5TcqioOw4cuJdn1HtHYCUeJ2ol8eJIpqKQ/15y41Luzm9c4UPAot/SJRynE5iOL6n8sH5K4m4+xPwzuJbrtbzkIF13RK8wDA+RAMgbBkf7K4HtTGCX/9g+tmhi7tcX4wxDhL0+VFOfjZ9g3Rvp+Irh6WLxs/lvfxC5ODRQ548jNu1dAY43qfxgOIbutPVOd2Im+MmgQABEOalleoo1n/nKd5OxPpUmqu6HG4H8GQa6vKzBAXeF7aLd+O70QugWe/HvCJ3E12/C+iM+vkOZB4iDsXPRvHiFzMHFGfKK6DXPsT35mIayJUUNALAPaFYEjDftx6niavOmm4f3fzaKoVFuDUYwIXygzxGmE5N4g8feT5f1xJYARR+2HyRSn05nl/iovk+Xh4GD53eyHwAQArIBjSsD0JN8X1093r0dBudSg+PNDZhwSuNLSe59phNZrcyki5Kq4XqtnE2UcBAG4KwZBGSZJEB/VD6blrrE71IGcfErjapItX9MFQdAvHvxcAgBOpb950sArXCPFweh8vD2pay2gUDrg2Z2SGeI4dAAAVQzCkUQcuyksDNKsVSn7emE/FbVRlZsgQDDV3/HsBADgRgiGNOqgPhlrHY+kNt80MOXI6f154VKzHpV88FQBAxRAMaVTCDXm26UZR6CJzK4ZCZolIV+i49+HlL/g9gqOIgrS7XhEAaAOCIY26kpErrmuF6jMN4F6ZIUfXDV0/JV9H3ua49wAAcBEIhjTqSrpccxIdhmDIrfAcOgpH1g1dPy1fIxgCAA1AMKRBhbpiupGNYMgt8SSCVTGi7IYSDDVy3HsAALgIBEMalJKZL2pveVh9RCDWZnI7VTGizJAZQjAEAOqHYEiDrqTLGYWaIf7k6YnlCtyOozNDuiKim+fk29URDAGA+iEY0qCrGfKPKLrI3JSjM0NpF+SV3znoCot3zHsAALgQBEMazgxFYySZe3J0ZujGGfk6ogGRJ/6JAAD1w790Gs4MRSEYcvPMkIOCodQE+bp6fce8PgCAi0EwpEGXlcxQmIuvRA7lZIbyHRsMhdd1zOsDALgYBEMahMyQm3N0N5kSDFWr45jXBwBwMQiGNOhGdoG4rhGCzJBbcnQBdeoF+Tq8nmNeHwDAxSAY0qAbWfKPaPUgBENuyZGZIZ6ACt1kAKAxCIY0pkhXTKk58gKf1YMx4aJbcmQBdfZ1osJsnuqaqBqG1QOANlQoGJo2bRrVrVuX/P39qXPnzrRr1y6L+/7555/UoUMHqlatGgUFBVGbNm3ol19+qcwxQyUogRCv6hCO2afdkyMzQ0pWKDTmVtAFAFAFJk2aRB07dqSQkBCqWbMmDRo0iE6ePFnmc77//nvq0aMHhYeHi0ufPn3KjEnsFgwtWLCAxowZQ+PGjaN9+/ZR69atqX///pSSklLq/hEREfTOO+/Q9u3b6dChQzR8+HBxWbVqlc0HC5WnrEnGy3B4YfZp9+TImiGecJGhiwwAqtimTZto5MiRtGPHDlqzZg0VFhZSv379KDubs9Wl27hxIw0ZMoQ2bNgg4oz4+HjxnKSkJJve29vWg50yZQqNGDFCBDRs5syZtGzZMpo1axa9+eabJfbv1auXyf3Ro0fTTz/9RFu3bhVBFFStG1ly8XREELJCbqsqMkPVatv/tQEAyrBy5UqT+3PmzBEZor1791LPnj1Lfc6vv/5qcv+HH36gP/74g9atW0dPPfUUOSQYKigoEAf11ltvGbZ5enqKtBRHZOWRJInWr18v0l6ffvqpxf3y8/PFRZGZmSmu8/LyKCcnh+ylqKhIXOfm5ooIVI3M25iSmkl+XhLVDPay62fpLGo/h6W1z0fyJB8iKszNokI7n0Ofmxfk1w6MsvtrW6LFc6g2am8j2ldx/Lut/I5nZGQYtvv5+YlLWdLT0w09TNbi3zVugy3PYR4SRyhWSk5OptjYWNq2bRt17drVsH3s2LEivbVz506LDeLncYDj5eVF06dPp6efftri+4wfP54mTJhQYvu8efMoMDDQ2sMFUKXGlxdRkyuL6HzknXQofphdX7vz2S8oOuMgHYgfThcie9v1tQFAe3Jycuixxx4rsZ1Lbfi33pLi4mK6//77KS0tTfQkWeull14SZThHjx4Vdc0O6yarCC6GOnDgAGVlZYnUFdcc1a9fv0QXmoIzT7yPgvv+mjVrJgqrYmJi7BoNcz9j7969ydu7Sj6KKmfexmkbz9IP/1ygwR3i6M3+t5G7U/s5LK193jtPEl1ZRPHRNSiqb1+7vp//pU/EdZPOd9Ft9e+iqqDFc6g2am8j2ldxnERhx44dE0kRRXlZIa4dOnLkiE2B0CeffELz588XdUS2BELMplZHRkaKzM7Vq1dNtvP96Ohoi8/jrrSGDRuK2zya7Pjx46Jq3FIwZJ4+U1Jr3Dh7ZoaUdGBAQAD5+HDngPqYt/FmHlG+zoOqhwSpIsum9nNYavsCQsSVNxWRt73PYab8D5d/jQZEVfT90OQ5VBm1txHtqzglKOGkSGhoqFXPGTVqFC1dupQ2b95McXFxVj3n888/F8HQ2rVrqVWrVo4dTebr60vt27cX2R3jVBbfN+42Kw8/x7gmCJww4SLmGHJfjhpNVpBNlJd2a2g9AEAV4qodDoQWLVok6ovr1bNuFvzPPvuMPvjgA1GAzVP5VITN+TDuvho6dKh4w06dOtHUqVPFsDdldBlXb3MqjDM/jK953wYNGogAaPny5WKeoRkzZlTogME+S3FUx2gy9+Wo0WTp+qGovsFE/mH2fW0AgHJw1xjXBi9ZskRkkq5cuSK2h4WFiaxVaTEGD8Z6//33xfN4/kPlOcHBweLisGBo8ODBdO3aNfHm/Kbc7cXRWFRUlHg8MTFRdIspOFDigqZLly6JxjRp0oTmzp0rXgecmRnChHpuy1GZoQx9MBQaK8/KCQBQhZQkiXkJzezZs2nYsGGlxhj8HB7p/vDDD9tUoG2uQpVSnMbiS2m4cMnYhx9+KC7gYpkhdJO5L0dlhpRgKOxWkSMAQFWxZnC7eYyRkKCfG62SsDaZhuQX6SgzT55PAt1kbsxRa5OlG2WGAAA0BMGQhtzUZ4W8PT0o1F99IyK0lxlyYDcZAICGIBjS6FIcnliXzH05qpssUy48pBDL02QAAKgRgiFN1guheNqtOaqAOksJhmrZ93UBAFwcgiEtjiRDvZB7c1hmSD+Zaog8MhQAQCsQDGmwZggjydycIzJDxTqi7BT5NjJDAKAxCIY05Lq+Zqh6ELrJVJMZsn6d5bJlXyOSiok8PImCatjnNQEA3ASCIQ3BUhwqywwxnRzg2q14mgMhTy/7vCYAgJtAMKTFbjLUDKkjM2TPuiGMJAMADUMwpCHXMZpMHbw4mNVPjVCYZ9+RZMEIhgBAexAMabCbjOcZAjfG64YZ6oZy7TySDMEQAGgPgiENdpNFombI/fn42zczlHlZvkYwBAAahGBII7LziyinQCduo5tMBbwD7JsZytJnhoIxxxAAaA+CIY24pu8iC/T1omA/b2cfDrhaZoiH1jMEQwCgQQiGNCIlUw6GaoYgK6QK9s4MKcFQUKR9Xg8AwI0gGNKIa5lyvVDNEKNh2eC+7J4ZuiFfY8JFANAgzQdDp9M96Nlf9tHFmzmkhcxQjVBkhlTBnpmhwlyigkz5NjJDAKBBmg+GvjnmRZtOXafnf9lLanYN3WTqYs/MUPZ1+drTh8gvtPKvBwDgZlBJq3fscobFx3IKiuj45Qw6dy2bEm7wJYeuZeTTjex8Ss0pFI/7enlSkJ83NYoKob5Na9KgtrEU4u9DrhcMoZtMFew5z5ChXqiGPIcRAIDGaD4Y8iSJipXZfPUkSRLB0d8HL9PWM9fo+OVM0hWXvSBmXmExZeQV0eX0PNp86hr9b/0Z+mhQC+rX3DXmbUEBtcr4BNgvM5Sj1AuhiwwAtEnzwVCkP1GK/vekuFii1ceu0NS1p+nEFX0NhV5UqB81rBlMdasHUb3IIIoK9RdrfEUE+1KQrzcV6IopPbeQ9l1Ipbk7Lojs0fNz99LE+5vTk13rkssEQ6gZUgdHZYYAADRI88FQhJ9EKXlyZuiRb7fT3gup4raftyfd2aQm3d0imjrWjaCYavq/xMvRrnY4Pdm1Dn2w9BjN3ZFI7y05SjVC/MXruMI8QxzEgQrYMzOEYfUAoHGaD4aMSyQ4EPL29KAX7mhAI3rWp7CAitX8+Hl70QcPtCBPDw/6efsFGvN/B6hZrZ5Uu3ogOUNhMVF6bpG4jW4ytWWG7FhAjcwQAGiU5keT6cxKgaY/3o5e69+4woGQwsPDg96/rxl1qhshlsF4449DohbJGTLkKYZEkXdl2wUulhmyazCEzBAAaJPmgyHj+KRRzWC7Fjx7e3nS5Edakb+PJ20/d4OWHEgmZ0jTB0PRYf4iSAMVZYbs2k2GzBAAaJPmg6FiSQ4Obm8YSX+81M3ur1+nehC9fGcjcfuzlScor1BeLLUqpebLbawVhnoh9WWG7FBAnaPPDAUiMwQA2oRgSH/9RJc6FOqgeYGeub0exYT5U3J6nhhpVtVS9Zkha4vAQWuZIdQMAYC2IRjSd5N5eTqu+8jfx4teuUvODn27+VyVZ4fS9JmhmGrIDKmGvTJD3E+M0WQAoHEIhvTBEI8ic6R/tYuj2GoBYibohXsukjNqhmqFITOkGvbKDBVk3SrCRjAEABqFYEgfDHk6OBjy9fakET3qiduztyWICR6rumaIgzFQCXtlhpQuMp9AIt+gyh8XAIAbQjCkv/aqglFWD3eIpxA/b7HG2ZYz+h+hKqwZqoVuMvWwV2YIw+oBABAMSVVQM6QI9vOmRzrEi9tVVUjNi8jmFCk1Q8gMqYbdMkMYVg8AoPlgqCoKqI092kkOhjaeTKGb2fqUjQNdTpeX4Qjy83LYaDlw48wQhtUDACAYuhUMVc373RYVQi1jw6hQJ9HfBx0/CWNyupw5qIU1ydQFmSEAALtBMKS/5nXEqsq/2sWK6z/3XXL4eyWlypmD2HB0kakKaoYAAOwGwZBhaH3VfRT3t44RQ/kPXkqnMymZDn2vpDQ5cxCPYEidmSFdPlGxEtJXADJDAAAIhm4Nra+696we7Ee9GtcUt//Yl+TQ97qYKgdDGFav0sxQZRdrRWYIAADBkGFofRUVUCse0neVLd6f5NA5h5TMUBwyQ+rMDDEEQwAAlYJgqIpmoDbXu0lNMefQ5fQ82puY6rD3SdJnhuKQGVIXTy8iT/3owMJKFFGjmwwAAMGQMs9QVRZQK+uV9W8RLW7/dcAxo8p4DbRrWfLwfWSG1DyiLK/iX/6cG/LtwOr2Oy4AADej+WCoqucZMjawdYy4Xn74MhXpKlEEa8ElfVbIz0uisABvu78+uMqIsgpmhvIziYoL5dsIhgBAwxAMOTEY6tagOkUE+dKN7ALadlb/F7odXUrNEdfV/Yg8qjjzBVXAp5LBUO5N+do7wLQGCQBAYyoUDE2bNo3q1q1L/v7+1LlzZ9q1a5fFfb///nvq0aMHhYeHi0ufPn3K3F8rBdTMx8uT7mkpd5U5YgJGJTMU4Vd1i8JCFeLFVSsz8WKOPhgKjLDfMQEAVNCkSZOoY8eOFBISQjVr1qRBgwbRyZMny3zO0aNH6aGHHhIxCf/RP3Xq1KoJhhYsWEBjxoyhcePG0b59+6h169bUv39/SklJKXX/jRs30pAhQ2jDhg20fft2io+Pp379+lFSkmOHlNucGXJS5mRgK7mrbOXRK5RfpHNQMGTXlwVXoWRzKpsZCkAwBADOt2nTJho5ciTt2LGD1qxZQ4WFhSJeyM7OtvicnJwcql+/Pn3yyScUHS0nF6okGJoyZQqNGDGChg8fTs2aNaOZM2dSYGAgzZo1q9T9f/31V3rppZeoTZs21KRJE/rhhx+ouLiY1q1bR84mSRJJ5OG0zBDrWDeCokP9KTOviDad1I/ssfOw+nBkhtSdGSqUu0NtlqMfxRgYbr9jAgCooJUrV9KwYcOoefPmItEyZ84cSkxMpL1791p8DmeSJk+eTI8++ij5+VX8L3+bqmoLCgrEQb311luGbZ6enqLri7M+1uAojqO9iAjLf43m5+eLiyIzU56lOS8vTzzfXvIKCkRxMSvIz6ccT/tmZqx1X/Ma9MuuRFp64CLdXi/Ubq97NTVTtC/SX6Lc3FzxuatNUVGRuNZi+/w8fcmL/3/JTiddBf6/8E6/Qr78Hr5hVGDH/69speVzqBZqbyPaV3H8u638jmdkZBi2c+BSXvCSnp4ursuKF+zFQ+L0iJWSk5MpNjaWtm3bRl27djVsHzt2rEhv7dy5s9zX4CzRqlWrRD8f1xyVZvz48TRhwoQS2+fNmyeyUABA1PHc/ygmfQ8djBtKCTXusvn5jS//SU2uLKbzkXfSofhhDjlGANA2TmA89thjJbZzqQ3/1lvCPUj3338/paWl0datW616L64bevXVV8XFVlU63pr79ObPny/qiCwFQowzT1yXpOD6Iu6S43RYTIxcY2MPGTn5dMeX/4jb28beQQE+/Hd21eN49IEZO8TSGZ8Mak79m0dV+jULdRJ1/nSDmErmvbY6ur9fb/L29lblXzRcj9a7t/ba57t0CVH6HmrasC416tTX5tf2WbOB6ApRXKOWFNXD9ufbi5bPoVqovY1oX8VxEoUdO3ZMJFMU5WWFuHboyJEjVgdClWVTqyMjI8nLy4uuXr1qsp3vl1e49Pnnn4tgaO3atdSqVasy9zVPnympNQ6g7JkZytN5UL5OrhUKCgwUEyE6y90t4+ibDWdo2bEb9GDHepV+vQs3simvyIP8fTwp2FtHAQEB5OOjn7FYRZSUribbFxAirnw9isi3Iv9fFMr/X/mERpGPEzOumj6HKqH2NqJ9FackPniEWGiodWUgo0aNoqVLl9LmzZspLi6OqoJNBdS+vr7Uvn17k+JnpRjauNvM3GeffUYffPCBKI7q0KEDuYpiox5CZxVQm0/AuOlUCqXnFNptJFlMWABhiiGVqnQBNUaTAYDr4F4SDoQWLVpE69evp3r1Kp8YcNhoMu6+4rmDfvrpJzp+/Di9+OKLYtgbjy5jTz31lEmB9aeffkrvvfeeGG3G/XlXrlwRl6ysLHK2IqMFUp01tF7RODqEGkeFiO6tVUev2G1Nsthqlrsjwc3Za2g9Zp8GABfAXWNz584V9cGcSVLiBS7sVpjHGDyw68CBA+LCt7mshm+fOXPGscHQ4MGDRZfX+++/L4bL85tyxicqSq5z4WFwly9fNuw/Y8YMcYAPP/ww1apVy3Dh13A2ZbV4joM8nZwZYgNb1xLXfx+q/ASMl/TD6mOwQKsGgqHKDq1HZggAnI/jBR5B1qtXL5N4gec3VJjHGFyT1LZtW3Hh7Rxb8O1nn33WpveuUKUUp7H4UhoujjaWkJBArkqn7yZzdlbIuKvs89Wn6J8z1+l6Vj5FBvvZJzNkeb4qUEU3WWUnXcQ8QwDgfNYMbjePMbjHyYZB8RZpem0ynT4z5Ox6IUWd6kHUOi5MzIrNi7dWRlKanC1AZkjFKtNNVlRAVKDvqkZmCAA0DsGQCwVDxoXUlV2rTJl9GjVDKuYTVPFuMiUr5OFJ5Bdm3+MCAHAzmg6GlNFkni7STcbuaxUjaph2J6QaApqKBHmX0+RZP2ORGVKvymSGDCPJwrlgzr7HBQDgZjT9r2CRTg6GvF0oMxQd5i/WK2NLK5gdSsnMEyPluF01Q7BKq2pVZmg9FmkFADDQdDBkyAy52KcwsJU8qmzFkSuVKp7mwMqVugDBBTNDqBcCANB2MKQrJpcaTabo11yezfvAxTS6ki53d1WsXghdZKpWmWAImSEAAAONB0OuV0DNokL9qW3tauL2muOmS5/YMvt0XDgWtVW1ynST5dyQr5EZAgDQeDAkuWYwxPrrs0Mrj1yucDAUG47MkKrZq4AaAEDjtB0MFbveaDLFvS3luqFtZ29Qso2jyi6lypmCOHSTaSczZOukY7mYfRoAQIFgyMVGkyniIwKpU70I8Ru3aH+STc9NvCkHQ7Wro5tME5mhimSHsEgrAICBpoOhW6PJXC8YYg+3ixPX83cnUl6hzqrnFOmKDaPJ6iAYUrfKBEOGRVoRDAEAVGhtMrVQVq13tdFkintb1aLPV5+kizdz6cNlx6hJdCgdupQmCqOfvr0eBfuVPH2X0+U5hny9PCkqxJ90uiKnHDtUAU8vIi8/Il2+vojahtXnkRkCADDQdDCkrFrvigXULMjPmyY+0IJemLuX5u5INHnsWHIGzXiiHXmYBXJKF1lcRIDIeOmsSyiBO2eHRDCEzBAAQEVpupvMlUeTKe5uEU0T7m9OzWNCqVmtUHq0Y7zYvvLolVJriZRgqE4Eusg0oSLD64uLbxVQIzMEAKDtzJBO55ozUJsb2q2uuCjiwgPo89Wn6Jv1Z2hQm1iTmqcLN/TF0wiGtKEiw+vz04kk/YyjyAwBACAzxLxdPRoyM6x7PQr196Zz17NprdmkjBf1mSEejQYaUJHMkFIv5BtM5I216wAA3CsKcNg8Q+RWuHD68S51xO3/rT9tqH1iJ69miuu61YOcdnzg4pkhdJEBAJhAMOTiNUOWPKsfTXYkKYOWHpZnqb6WmU9nUrLE7XZ1MLOwtoKhCmSGAvEdAQBg2g6G9AkVVx1aX5bqwX70fM/64vbHy45Tek4h7TgnrzfVJDqEIoJ8nXyEUCW4q4sVZFv/HCzSCgBgQtPBkKsPrS/Psz3qU73IILqSkUdvLz4slu5gXRvYMN8MuDffQNuDISzSCgBgQtPBkDLpoqvOQF2eAF8vmvLv1iKYW3boMv22S56LqGt9BEOa4RtU8W4yZIYAAARNB0PKchzu2E2maFs7nD5/pBUpTWgZG0Y9GtVw9mFBlXeTybViVsGEiwAAJrQ9z5Cbd5MpHmwbR9GhAZSdX0S9m9R0+/ZABYbWFyAzBABQUQiGVBAMMdQJabybrCIF1MgMAQAImu4m06mgmww0zhAM2dBNloN5hgAAjGk6GHL30WQAFSqgNmSGMM8QAABpPRhSRpN5afpTAM11k6FmCADAhKbDgGI3H1oPQD42BkO8bEeRfukO1AwBAAiaDoaUGai9EQyBVjJDSlbI05vIL9RxxwUA4Ea0HQwVF4trTxRQg1aCIcNSHOFkmJwKAEDjNB4MydcooAbNFFCjXggAoARNB0OGGagRDIEahtbrv89lwhxDAAAlaDoYMowmQ3cBuPsM1FIxUVG+DYu0YpJOAACFpoMhjCYD1WSGrK0bUrrJkBkCADDQdDCkzECN0WTgtjy9iLwDrJ+FGpkhAIAStB0MKZkhdJOBO/MNtL6I2pAZQjAEAKBAMIQZqEFLw+uRGQIAKEHTYcCt0WSa/hjA3fkGy9cIhgAAKkTTUcCt0WTOPhIAO4wos6mAGsEQAIBC08EQRpOB5iZeVDJDPAM1AAAImg6GlBmoMZoMVDPxYnmLtBbqs0fIDAEAGGg8GEJmCDRUQK10kXl4EfmHOf64AADUHAxNmzaN6tatS/7+/tS5c2fatWuXxX2PHj1KDz30kNjfw8ODpk6dSq42zxBmoAZVFFDnZ1m5FEd1LNIKAC5n0qRJ1LFjRwoJCaGaNWvSoEGD6OTJk+U+b+HChdSkSRMRk7Rs2ZKWL1/u+GBowYIFNGbMGBo3bhzt27ePWrduTf3796eUlJRS98/JyaH69evTJ598QtHR0eSaQ+vxwwBuzC9Evs7PKHs/jCQDABe2adMmGjlyJO3YsYPWrFlDhYWF1K9fP8rOtpz13rZtGw0ZMoSeeeYZ2r9/vwig+HLkyBHHBkNTpkyhESNG0PDhw6lZs2Y0c+ZMCgwMpFmzZpW6P0d5kydPpkcffZT8/PzIlSAYAlXwD5WvEQwBgBtbuXIlDRs2jJo3by4SLXPmzKHExETau3evxed89dVXdPfdd9Prr79OTZs2pQ8++IDatWtH33zzjU3v7W3LzgUFBeKg3nrrLcM2T09P6tOnD23fvp3sJT8/X1wUmZmZ4jovL09kmuymuJD8vCSSigrt+7oupKioSFzn5uaKKFtt0D4ibw9/8uV9s9OooIzvsXfaFXk/v7Ay96tqOIfuT+1tRPsqjn+3ld/xjIxbf7BxcqS8BEl6erq4joiwvJYixx7cW2WMe6sWL17suGDo+vXrpNPpKCoqymQ73z9x4gTZs99wwoQJJbbv3r1bZKHs5YFI+UI3j9OaNcdJzTZs2EBqpuX2xd+4QO2I6EZyAu1Ys8bifo0v76YmRHTpRg4dLGM/Z9HyOVQLtbcR7bOdkmjgniRjXGozfvx4i88rLi6mV199lbp3704tWrSwuN+VK1dKjUl4u8OCoarCmSfjSC8pKUl8kNzlFhMTY7f3eW7uPtp9IY0m3teYBraOJTXiiJ+/4L179yZvb5c83ZWC9hF5nS4iSvyeIkN8qW/fvhZfy2ftRqIrRLGNWlLNnpb3q2o4h+5P7W1E+youOTlZXB87doxiY2/9zpaXFeLaIa772bp1K1UFm1odGRlJXl5edPXqVZPtfN+exdHm6TMltcaV4vbMDBVKnpSv8yBfXz+7vq4rUVKeAQEB5OPjQ2qD9hFRCKc3ibwKs8v+HhfIKWefsGjycaHvO86h+1N7G9G+iuPfbcYjxEJD9fWN5Rg1ahQtXbqUNm/eTHFxcWXuy7GHPWISmwqofX19qX379rRu3TqTVBbf79q1K7kbFFCDtkaTYSkOAHBdkiSJQGjRokW0fv16qlevXrnP4djDOCZhPBLN1pjE5nwYd18NHTqUOnToQJ06dRLzBvGwNx5dxp566imRCuO6H6XomtNjym3u8jpw4AAFBwdTw4YNyZkQDIEqKBMo5ssDDSzCaDIAcGEjR46kefPm0ZIlS0QmSan7CQsLE1mr0mKM0aNH0x133EFffPEF3XvvvTR//nzas2cPfffdd44NhgYPHkzXrl2j999/XxxomzZtxHA4pYCJh8HxCDPj/sK2bdsa7n/++efiwge/ceNGciZMugjqygxlcqqWh3iWkxmyPDIDAMBZZsyYIa579eplsn327NliyH1pMUa3bt1EAPXuu+/S22+/TY0aNRIjycoqui5NhSqlOI3Fl9KYBzg88zSnvlzRnKHtafmqNXR7Q/ylDCoIhkiS1ydT5h2yuEgrgiEAcD2SFbFCaUmURx55RFwqQ9Nrk4X4+1CID5Gfj5ezDwWg4rz9iTx9yu4qK8ghKsqVb6ObDADAhKaDIQBV4G7e8oqolawQB02GTBIAADAEQwBqqxsqr3gaNXIAACYQDAFoYX0yjCQDALAIwRCAGvjpg6E8C8FQbqp8jZFkAAAlIBgC0Fo3GQAAmEAwBKAG1hZQIzMEAFACgiEANXWTlZcZwhxDAAAlIBgCUFNmyFLNUFaKfB0szxQPAAC3IBgCUNP6ZHlp5QRDNavumAAA3ASCIQA1UAqjlfXHzGVdla+RGQIAKAHBEICqgiF9bZA5ZIYAACxCMASg9mCIi6oLs+XbyAwBAJSAYAhADZQh87k3LWeFfIKI/IKr9rgAANwAgiEANWWG8tKJdIUW6oXQRQYAUBoEQwBq4F+Nl683XXpDgeJpAIAyIRgCUAMvb6KAaqWPKFO6yUIQDAEAlAbBEIBaKLNLmxdRIzMEAFAmBEMAah9RhpohAIAyIRgCUHswlKkPhoIQDAEAlAbBEIDagiHz4fXpl+TrsNiqPyYAADeAYAhALQLDSxZQSxJRWqJ8u1od5xwXAICLQzAEoOZuMh5mr8w+HRbnnOMCAHBxCIYA1BYMZV+/tS3twq2RZD4BzjkuAAAXh2AIQC1CY+TrjKRb29Iuytdh8c45JgAAN4BgCEAtlJogrhHiWiHltnistvOOCwDAxSEYAlALJftTkHVrSQ4EQwAA5UIwBKAWPv5EwdHy7dQE+Tpd301WDd1kAACWIBgCUBMlA6RkhFL1BdQYVg8AYBGCIQC1BkNF+UTXT8n3Ixs59bAAAFwZgiEAtQZDKceIiguJ/KshMwQAUAYEQwBqEm40oiz5gHw7pg2Rh4dTDwsAwJV5O/sAAMABmaHrJ4lCa8m3a7Vx6iEBALg6ZIYA1CS2A5GXnzya7MBvtzJDAABgEYIhADXxDyVqeJd8W5cvX8e0deohAQC4OgRDAGrTbNCt2y0eIgqv68yjAQBweagZAlCbxgOIQmOJAsKJ7pvq7KMBAHB5CIYA1NhVNvoQEUlEXj7OPhoAAJeHYAhAjbzwvzYAgLVQMwQAAACahmAIAAAANK1CwdC0adOobt265O/vT507d6Zdu3aVuf/ChQupSZMmYv+WLVvS8uXLK3q8AAAAoFKbN2+mgQMHUkxMDHl4eNDixYutikmaNm1KAQEB1LhxY/r5558dHwwtWLCAxowZQ+PGjaN9+/ZR69atqX///pSSklLq/tu2baMhQ4bQM888Q/v376dBgwaJy5EjR2w+WAAAAFCv7OxsEVdwgGONGTNm0FtvvUXjx4+no0eP0oQJE2jkyJH0999/OzYYmjJlCo0YMYKGDx9OzZo1o5kzZ1JgYCDNmjWr1P2/+uoruvvuu+n1118XkdsHH3xA7dq1o2+++cbWtwYAAAAVGzBgAH344Yf04IMPWrX/L7/8Qs8//zwNHjyY6tevT48++ig999xz9OmnnzouGCooKKC9e/dSnz59br2Ap6e4v3379lKfw9uN92ecSbK0PwAAAIA18vPzRQmOMe4u4/KdwsJCspZN42+vX79OOp2OoqKiTLbz/RMnTpT6nCtXrpS6P28vq3F8UaSnp4vrhIQEysvLI3vhD4rbdObMGfLxUed8LGpvI9rn/tTeRrW3TwttRPsqTvmt59/x0NBQw3Y/Pz9xqSxOrvzwww+i/IZ7nThhw/eVNtWqpV+wuhwuORnJpEmTRL+fuR49ejjleAAAAKDiWrRoYXKf6465zqey3nvvPRFwdenShSRJEsmWoUOH0meffSZ6rqxlUzAUGRlJXl5edPXqVZPtfD86OrrU5/B2W/ZnXAzFRdqKoqIiOn78OMXHx9vUuPJkZmaKuqdjx45RSEgIqZHa24j2uT+1t1Ht7dNCG9G+iisuLqbExETx+t7et0IOe2SFlC4xrln+9ttvRWzBmaDvvvtOtKNGjRqOCYZ8fX2pffv2tG7dOpGSUhrK90eNGlXqc7p27Soef/XVVw3b1qxZI7ZbUlr6rHv37mRvGRkZ4jo2NtYkfacmam8j2uf+1N5GtbdPC21E+yqndu3a5GjcvRcXFyduz58/n+677z7HZYYYZ2w4BdWhQwfq1KkTTZ06VQyF49Fl7KmnnhIfKHd1sdGjR9Mdd9xBX3zxBd17773iIPfs2SMiNwAAAABFVlaWqF1SnD9/ng4cOEAREREiqOKeo6SkJMNcQqdOnRLF0jznYWpqqhjxzlP3/PTTT2QLm4MhHr527do1ev/990U/XZs2bWjlypWGImlOhxlHY926daN58+bRu+++S2+//TY1atRITKJk3n8IAAAA2rZnzx7q3bu34b5SMsNJmDlz5tDly5dFnKHgQV2cbDl58qTIDvFzeX5DnhjaFhUqoOYuMUvdYhs3biyx7ZFHHhEXV8NdcVzEZa++S1ek9jaife5P7W1Ue/u00Ea0r+r06tVLFEJbwgGRMZ6/kCd0riwPqax3BQAAAFA5LNQKAAAAmoZgCAAAADQNwRAAAABoGoIhAAAA0DRNB0PTpk0Tw+94kTeeo4DnKnAHPIW5h4eHyaVJkyaGx3n9tpEjR1L16tUpODiYHnrooRKzgPPQRJ73KTAwkGrWrEmvv/66mOnbGTZv3kwDBw6kmJgY0RaeesEY1/jzVA48syjPNsoL/54+fdpkn5s3b9Ljjz8uJgyrVq0aPfPMM2K+CmOHDh0SS7rw+ebZzHm6dldo37Bhw0qcz7vvvttt2sdzinXs2FHM+MrfJZ6QlYe5GrPXd5JHq/L6QzzqpWHDhiVGljizjTwKxvw8vvDCC27RxhkzZlCrVq3E94svPCnuihUrVHP+ymufO5+70nzyySeiDcaTHbv7OXQ4SaPmz58v+fr6SrNmzZKOHj0qjRgxQqpWrZp09epVydWNGzdOat68uXT58mXD5dq1a4bHX3jhBSk+Pl5at26dtGfPHqlLly5St27dDI8XFRVJLVq0kPr06SPt379fWr58uRQZGSm99dZbTmkPv/8777wj/fnnnzyyUVq0aJHJ45988okUFhYmLV68WDp48KB0//33S/Xq1ZNyc3MN+9x9991S69atpR07dkhbtmyRGjZsKA0ZMsTweHp6uhQVFSU9/vjj0pEjR6TffvtNCggIkL799lunt2/o0KHi+I3P582bN032ceX29e/fX5o9e7Z43wMHDkj33HOPVLt2bSkrK8uu38lz585JgYGB0pgxY6Rjx45JX3/9teTl5SWtXLnSJdp4xx13iH9HjM8jnxd3aONff/0lLVu2TDp16pR08uRJ6e2335Z8fHxEe9Vw/sprnzufO3O7du2S6tatK7Vq1UoaPXq0Ybu7n0NH02ww1KlTJ2nkyJGG+zqdToqJiZEmTZokuUMwxD+MpUlLSxP/ky9cuNCw7fjx4+JHePv27eI+f8k9PT2lK1euGPaZMWOGFBoaKuXn50vOZB4sFBcXS9HR0dLkyZNN2ujn5yd+8Bn/T8nP2717t2GfFStWSB4eHlJSUpK4P336dCk8PNykfW+88YbUuHFjqSpZCoYeeOABi89xp/axlJQUcbybNm2y63dy7Nix4o8AY4MHDxaBirPbqPygGv/4mHO3NvL36YcfflDl+TNun5rOXWZmptSoUSNpzZo1Jm1S6zm0J012kxUUFNDevXtFd4uCZ83m+9u3byd3wN1E3O1Sv3590X2izMjJ7SosLDRpG3eh8TTmStv4umXLloZZw1n//v3F+jRHjx4lV8JTsfNM58btCQsLE92axu3hriNeIkbB+/M53blzp2Gfnj17ivX1jNvMXR08hbuzceqZ09KNGzemF198kW7cuGF4zN3al56eLq55+nx7fid5H+PXUPZxxv+z5m1U/Prrr2JBa55hn5cNyMnJMTzmLm3kGX152SReZom7k9R2/szbp6Zzx91g3M1lfhxqO4eOUKEZqN3d9evXxf8Qxied8f0TJ06Qq+NAgPtp+YeTpyafMGGCqBXh9Vg4cOAfRP7xNG8bP8b4urS2K4+5EuV4Sjte4/ZwIGGMV0fmHyrjferVq1fiNZTHwsPDyVm4Puhf//qXOL6zZ8+KZWsGDBgg/oHx8vJyq/bxws1cp8ALKytL7tjrO2lpH/7HOjc3V9STOauN7LHHHqM6deqIP1K4fuuNN94Qweiff/5Z5vErjzm7jYcPHxbBAdeWcE3JokWLxErjvC6UGs6fpfap4dwxDvD27dtHu3fvLvGY2v4fdARNBkPujn8oFVwUyMER/4/8f//3f279ZdSqRx991HCb/zLjc9qgQQORLbrrrrvInfBfphyUb926ldTKUhufe+45k/PIBf98/jjA5fPp6viPKw58OOv1+++/i7WgNm3aRGphqX0cELn7ubt48aJYFH3NmjViAAXYTpPdZJwK5b+4zSvp+X50dDS5G472b7vtNrHSLx8/dwOmpaVZbBtfl9Z25TFXohxPWeeKr1NSUkwe5xEQPALLHdvMXZ/8HVVWbnaX9vF6hUuXLqUNGzZQXFycYbu9vpOW9uHRQVX1R4ClNpaG/0hhxufRldvImQMeHdS+fXsxeq5169b01Vdfqeb8WWqfGs4dd4PxvxE8youzxnzhQO9///ufuM3ZGzWcQ0fSZDDE/1Pw/xDr1q0zSX3zfeM+ZHfBQ6z5Lxj+a4bbxSv3GreN071cU6S0ja85ZWz8A8t/UfAXWkkbuwru+uH/AY3bwylZrpUxbg//T87/ICjWr18vzqnyjxrvw0Pcud/cuM3816Izu8hKc+nSJVEzxOfTHdrHdeEcJHC3Ax+XeXedvb6TvI/xayj7VMX/s+W1sTSchWDG59GV22iOv1/5+fmqOH9ltU8N546zWHx8fNzKhWsMuZ5Uua3Gc2hXkoaH1vOIpDlz5ojROs8995wYWm9cSe+q/vvf/0obN26Uzp8/L/3zzz9iKCQPgeQRLsoQSh72u379ejGEsmvXruJiPoSyX79+YpgwD4usUaOG04bW8wgIHsrJF/5KTpkyRdy+cOGCYWg9n5slS5ZIhw4dEiOvShta37ZtW2nnzp3S1q1bxYgK46HnPJqCh54/+eSTYjgtn38eIloVQ8/Lah8/9tprr4kRHXw+165dK7Vr104cf15enlu078UXXxRTH/B30nhock5OjmEfe3wnlWG9r7/+uhgJM23atCob1lteG8+cOSNNnDhRtI3PI39X69evL/Xs2dMt2vjmm2+KkXF87Pz/GN/n0YqrV69Wxfkrq33ufu4sMR8h5+7n0NE0GwwxniOBvxw83xAPtec5XNwBD2WsVauWOO7Y2Fhxn/+HVnCQ8NJLL4mho/zFffDBB8U/3MYSEhKkAQMGiLloOJDiAKuwsNAJrZGkDRs2iCDB/MJDzpXh9e+99574secA9q677hJzhRi7ceOGCA6Cg4PFUNDhw4eLQMMYz1F0++23i9fgz42DLGe3j39M+R8f/keHh77WqVNHzHdiHpS7cvtKaxtfeF4ee38n+bNs06aN+O7zD5bxezizjYmJieLHMyIiQnz+PA8U/2AYz1Xjym18+umnxXeP35O/i/z/mBIIqeH8ldU+dz931gZD7n4OHc2D/2PfXBMAAACA+9BkzRAAAACAAsEQAAAAaBqCIQAAANA0BEMAAACgaQiGAAAAQNMQDAEAAICmIRgCAAAATUMwBABVZvz48dSmTRtnHwYAgAkEQwCgSh4eHrR48WJnHwYAuAEEQwAAAKBpCIYAVKpXr15ipXW+hIWFUWRkJL333ntiBXY2ffp0atSoEfn7+1NUVBQ9/PDDJit6T5o0SazOHhAQQK1bt6bff//d8PicOXOoWrVqJu/HWRjOxhj75JNPxGuHhITQM888Q3l5eSaP8/tMnDiR4uLiyM/PT3ShrVy50qr2FRQUiLbxyuLchjp16ohjZnXr1hXXDz74oDgm5T5bsmQJtWvXTjynfv36NGHCBCoqKjI8zvvPmDGDBgwYINrO+xi3HQBUyOGrnwGA0xZq5IVdebHGEydOSHPnzhULNH733XfS7t27xWrT8+bNE4sz7tu3T/rqq68Mz/3www+lJk2aiNWoz549KxZj5EUsedV2xvd5FXdjixYtEouXKhYsWCCe88MPP4j3f+edd6SQkBCpdevWhn2mTJkiFp797bffxD5jx44VC9aeOnWq3PZNnjxZio+PlzZv3izasGXLFtEelpKSYlhIlRej5PuM9+X3mzNnjmgXL9ZZt25dafz48YbX5edVr15d+v7778WCwO+++674rI4dO1ap8wEArgvBEICKg6GmTZtKxcXFhm1vvPGG2PbHH3+IoCAjI6PE8/Ly8kTQtG3bNpPtzzzzjDRkyBCrg6GuXbuKVbKNde7c2SQYiomJkT766COTfTp27FjieaV5+eWXpTvvvNOkfcb4WPiYjPFq5R9//LHJtl9++UWqVauWyfNeeOGFEsf94osvlntMAOCe0E0GoGJdunQx6brq2rUrnT59mu666y7RrcRdQE8++ST9+uuvlJOTI/Y5c+aMuN23b18KDg42XH7++Wc6e/as1e99/Phx6ty5s8k2fn9FRkYGJScnU/fu3U324fv83PIMGzaMDhw4QI0bN6ZXXnmFVq9eXe5zDh48KLrljNs1YsQIunz5sqH95sep3LfmmADAPXk7+wAAoOpxELBv3z7auHGjCCLef/99Mex99+7dlJWVJfZZtmwZxcbGmjyP63qYp6enofZIUVhYWIUtIFH3c/78eVqxYgWtXbuW/v3vf1OfPn3KrO/htnGN0L/+9a8Sj3ENEQBoEzJDACq2c+dOk/s7duwQRdNeXl7k7e0tgofPPvuMDh06RAkJCbR+/Xpq1qyZCHoSExOpYcOGJpf4+HjxOjVq1KDMzEzKzs42vDZnaYw1bdq01PdXhIaGUkxMDP3zzz8m+/B9PgZr8GsMHjyYvv/+e1qwYAH98ccfdPPmTfGYj48P6XS6EgHUyZMnS7SLLxzglXacyn1uDwCoEzJDACrGAc2YMWPo+eefF5mgr7/+mr744gtaunQpnTt3jnr27Enh4eG0fPlyMbKLu5x45Ndrr71G//nPf8S222+/ndLT00WQwsHH0KFDRfdXYGAgvf3226KLioMeHmFmbPTo0aIrq0OHDqLri7vijh49KrrmFK+//jqNGzeOGjRoIEaSzZ49WwRVvG95pkyZIkaStW3bVgQyCxcupOjoaMMoNx5Btm7dOvHeHNxxOzkDdt9991Ht2rXF6Dl+HnedHTlyhD788EPDa/Nr8XFz2/lYdu3aRT/++KNdzw0AuBBnFy0BgOMKqLkQmYuBuVg6PDxcevvtt0XBMY+84sd5W0BAgNSqVSsx+kvB+0ydOlVq3LixGN1Vo0YNqX///tKmTZsM+3BxcsOGDcXz77vvPjFKzfyfFC6OjoyMFKPahg4dKkaLGRdQ63Q6MZIrNjZWvA8/tmLFCqvax+/Xpk0bKSgoSLSPi6N5VJzir7/+Esfn7e0t1alTx7CdR8h169ZNHDc/r1OnTuK1FNyGadOmSX379hWj4Xi0mfFnAwDq48H/cXZABgCOmWeIsy1Tp0519qG4FS44X7RoEQ0aNMjZhwIAVQQ1QwAAAKBpCIYAwCV9/PHHJkPgjS88OzQAgL2gmwwAXBKPClNGhpnjZTLMh/0DAFQUgiEAAADQNHSTAQAAgKYhGAIAAABNQzAEAAAAmoZgCAAAADQNwRAAAABoGoIhAAAA0DQEQwAAAKBpCIYAAACAtOz/AY5DCBn+ThXhAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = None\n",
+    "ax = history.plot(\n",
+    "    x=\"pseudo_step\",\n",
+    "    y=\"CL\",\n",
+    "    ax=ax,\n",
+    "    title=\"Alpha controller history\",\n",
+    "    yticks=[0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7],\n",
+    "    ylim=[0, 0.7],\n",
+    "    grid=True,\n",
+    ")\n",
+    "history.plot(\n",
+    "    x=\"pseudo_step\",\n",
+    "    y=\"alphaAngle\",\n",
+    "    ax=ax,\n",
+    "    secondary_y=True,\n",
+    "    title=\"Alpha controller history\",\n",
+    "    yticks=[1.9, 2.0, 2.1, 2.2, 2.3, 2.4, 2.5, 2.6],\n",
+    "    ylim=[1.9, 2.6],\n",
+    "    grid=True,\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/ruff.toml b/ruff.toml
index efc7d50..753adba 100644
--- a/ruff.toml
+++ b/ruff.toml
@@ -29,6 +29,7 @@ ignore = [
     "E722",   # we'll allow bare excepts in notebooks
     "E731",   # allow lambda assignment
     "F401",   # imported but unused (common in notebooks for exploration)
+    "F403",   # unable to detect undefined names (when using from x import *)
     "NPY201", # allow numpy<2 for now
     "UP006",  # type annotation with Tuple[float] messes up pydantic
     "UP007",  # use x | y instead of union[x,y] (does not work)