loads.ipynb

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    {
      "cell_type": "markdown",
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        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/mifm/examples/blob/master/loads.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "markdown",
      "metadata": {
        "id": "1qfKRmyE1gdh",
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      "source": [
        "# Loads\n",
        "Topfarm supports load constraints by evaluating a trained surrogate model at each iteration. There are currently two different high-level load workflows implemented, namely the Dynamic Wake Meandering (DWM) and the Frandsen approach.\n",
        "### DWM\n",
        "In DWM the loads are calculated for all turbines for all flowcases and for all turbine interactions. This means that for each turbine it will look at the wakes coming from all other turbines (individually). The results are subsequently reduced to only one load per turbine by applying a soft max. This is done to create a differentiable expression for the max load instead of just taking the max load, which would not work well in a gradient-based optimization.\n",
        "### Frandsen\n",
        "In the Frandsen implementation, the turbulence is aggregated from different sectors with the WÃ¶hler exponent as described in the IEC. Loads are then calculated based on the effective turbulence. Alternatively, if one would like to improve the fidelity by trading off some memory consumption the loads could be calculated based on the all wind direction sectors and subsequently aggregated with the WÃ¶hler exponent.\n",
        "\n",
        "## Load Surrogates\n",
        "Topfarm can utilize surrogates trained with a range of different algorithms and softwares, namely scikit-learn, OpenTURNS, TensorFlow and the artificial neural networks code (wind2loads ANN) developed at DTU. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eYeTRElS1gdl",
        "colab_type": "text"
      },
      "source": [
        "[Try this yourself](https://colab.research.google.com/github/DTUWindEnergy/TopFarm2/blob/master/docs/notebooks/loads.ipynb) (requires google account)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DQ3AEKlG1gdn",
        "colab_type": "text"
      },
      "source": [
        "## Install wind2loads\n",
        "In this exercisw we will use the wind2loads artificial neural networks code to predict the turbine loads of the DTU 10MW reference turbine."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v9acy8caO4b4",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "dc882510-1ae7-44b0-b007-e79590b81d7c"
      },
      "source": [
        "%%capture\n",
        "!pip install git+https://gitlab.windenergy.dtu.dk/TOPFARM/wind2loads.git\n",
        "import wind2loads\n",
        "import w2l\n",
        "!pip install git+https://gitlab.windenergy.dtu.dk/TOPFARM/workshop-december-2019.git\n",
        "import numpy as np\n",
        "import topfarm\n",
        "from topfarm import TopFarmProblem\n",
        "from topfarm.easy_drivers import EasyScipyOptimizeDriver\n",
        "from topfarm.constraint_components.spacing import SpacingConstraint\n",
        "from topfarm.constraint_components.boundary import XYBoundaryConstraint\n",
        "from topfarm.plotting import XYPlotComp\n",
        "import workshop\n",
        "!git clone https://gitlab.windenergy.dtu.dk/TOPFARM/workshop-december-2019.git\n",
        "workshop.w2l_path = 'workshop-december-2019/workshop/wind2loads_ann'\n",
        "import workshop.workflow as wf"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting git+https://gitlab.windenergy.dtu.dk/TOPFARM/wind2loads.git\n",
            "  Cloning https://gitlab.windenergy.dtu.dk/TOPFARM/wind2loads.git to /tmp/pip-req-build-dmy364hs\n",
            "  Running command git clone -q https://gitlab.windenergy.dtu.dk/TOPFARM/wind2loads.git /tmp/pip-req-build-dmy364hs\n",
            "Requirement already satisfied (use --upgrade to upgrade): Wind2Loads==0.0.1 from git+https://gitlab.windenergy.dtu.dk/TOPFARM/wind2loads.git in ./workshop-december-2019/wind2loads\n",
            "Requirement already satisfied: topfarm in /usr/local/lib/python3.6/dist-packages (from Wind2Loads==0.0.1) (2.2.1)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.6/dist-packages (from topfarm->Wind2Loads==0.0.1) (0.22.2.post1)\n",
            "Requirement already satisfied: openmdao==2.6 in /usr/local/lib/python3.6/dist-packages (from topfarm->Wind2Loads==0.0.1) (2.6.0)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from topfarm->Wind2Loads==0.0.1) (1.18.2)\n",
            "Requirement already satisfied: pytest in /usr/local/lib/python3.6/dist-packages (from topfarm->Wind2Loads==0.0.1) (3.6.4)\n",
            "Requirement already satisfied: py-wake>=1.0.11 in /usr/local/lib/python3.6/dist-packages (from topfarm->Wind2Loads==0.0.1) (1.0.11)\n",
            "Requirement already satisfied: sphinx-rtd-theme in /usr/local/lib/python3.6/dist-packages (from topfarm->Wind2Loads==0.0.1) (0.4.3)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.6/dist-packages (from topfarm->Wind2Loads==0.0.1) (3.2.1)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.6/dist-packages (from topfarm->Wind2Loads==0.0.1) (1.4.1)\n",
            "Requirement already satisfied: sphinx in /usr/local/lib/python3.6/dist-packages (from topfarm->Wind2Loads==0.0.1) (1.8.5)\n",
            "Requirement already satisfied: pytest-cov in /usr/local/lib/python3.6/dist-packages (from topfarm->Wind2Loads==0.0.1) (2.8.1)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.6/dist-packages (from scikit-learn->topfarm->Wind2Loads==0.0.1) (0.14.1)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from openmdao==2.6->topfarm->Wind2Loads==0.0.1) (1.12.0)\n",
            "Requirement already satisfied: pyparsing in /usr/local/lib/python3.6/dist-packages (from openmdao==2.6->topfarm->Wind2Loads==0.0.1) (2.4.6)\n",
            "Requirement already satisfied: networkx>=2.0 in /usr/local/lib/python3.6/dist-packages (from openmdao==2.6->topfarm->Wind2Loads==0.0.1) (2.4)\n",
            "Requirement already satisfied: pyDOE2 in /usr/local/lib/python3.6/dist-packages (from openmdao==2.6->topfarm->Wind2Loads==0.0.1) (1.3.0)\n",
            "Requirement already satisfied: pluggy<0.8,>=0.5 in /usr/local/lib/python3.6/dist-packages (from pytest->topfarm->Wind2Loads==0.0.1) (0.7.1)\n",
            "Requirement already satisfied: attrs>=17.4.0 in /usr/local/lib/python3.6/dist-packages (from pytest->topfarm->Wind2Loads==0.0.1) (19.3.0)\n",
            "Requirement already satisfied: more-itertools>=4.0.0 in /usr/local/lib/python3.6/dist-packages (from pytest->topfarm->Wind2Loads==0.0.1) (8.2.0)\n",
            "Requirement already satisfied: py>=1.5.0 in /usr/local/lib/python3.6/dist-packages (from pytest->topfarm->Wind2Loads==0.0.1) (1.8.1)\n",
            "Requirement already satisfied: atomicwrites>=1.0 in /usr/local/lib/python3.6/dist-packages (from pytest->topfarm->Wind2Loads==0.0.1) (1.3.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from pytest->topfarm->Wind2Loads==0.0.1) (46.0.0)\n",
            "Requirement already satisfied: xarray in /usr/local/lib/python3.6/dist-packages (from py-wake>=1.0.11->topfarm->Wind2Loads==0.0.1) (0.15.0)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.6/dist-packages (from py-wake>=1.0.11->topfarm->Wind2Loads==0.0.1) (3.13)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib->topfarm->Wind2Loads==0.0.1) (0.10.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->topfarm->Wind2Loads==0.0.1) (1.1.0)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->topfarm->Wind2Loads==0.0.1) (2.8.1)\n",
            "Requirement already satisfied: docutils>=0.11 in /usr/local/lib/python3.6/dist-packages (from sphinx->topfarm->Wind2Loads==0.0.1) (0.15.2)\n",
            "Requirement already satisfied: babel!=2.0,>=1.3 in /usr/local/lib/python3.6/dist-packages (from sphinx->topfarm->Wind2Loads==0.0.1) (2.8.0)\n",
            "Requirement already satisfied: requests>=2.0.0 in /usr/local/lib/python3.6/dist-packages (from sphinx->topfarm->Wind2Loads==0.0.1) (2.21.0)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.6/dist-packages (from sphinx->topfarm->Wind2Loads==0.0.1) (20.3)\n",
            "Requirement already satisfied: imagesize in /usr/local/lib/python3.6/dist-packages (from sphinx->topfarm->Wind2Loads==0.0.1) (1.2.0)\n",
            "Requirement already satisfied: snowballstemmer>=1.1 in /usr/local/lib/python3.6/dist-packages (from sphinx->topfarm->Wind2Loads==0.0.1) (2.0.0)\n",
            "Requirement already satisfied: Jinja2>=2.3 in /usr/local/lib/python3.6/dist-packages (from sphinx->topfarm->Wind2Loads==0.0.1) (2.11.1)\n",
            "Requirement already satisfied: Pygments>=2.0 in /usr/local/lib/python3.6/dist-packages (from sphinx->topfarm->Wind2Loads==0.0.1) (2.1.3)\n",
            "Requirement already satisfied: alabaster<0.8,>=0.7 in /usr/local/lib/python3.6/dist-packages (from sphinx->topfarm->Wind2Loads==0.0.1) (0.7.12)\n",
            "Requirement already satisfied: sphinxcontrib-websupport in /usr/local/lib/python3.6/dist-packages (from sphinx->topfarm->Wind2Loads==0.0.1) (1.2.1)\n",
            "Requirement already satisfied: coverage>=4.4 in /usr/local/lib/python3.6/dist-packages (from pytest-cov->topfarm->Wind2Loads==0.0.1) (5.0.4)\n",
            "Requirement already satisfied: decorator>=4.3.0 in /usr/local/lib/python3.6/dist-packages (from networkx>=2.0->openmdao==2.6->topfarm->Wind2Loads==0.0.1) (4.4.2)\n",
            "Requirement already satisfied: pandas>=0.25 in /usr/local/lib/python3.6/dist-packages (from xarray->py-wake>=1.0.11->topfarm->Wind2Loads==0.0.1) (0.25.3)\n",
            "Requirement already satisfied: pytz>=2015.7 in /usr/local/lib/python3.6/dist-packages (from babel!=2.0,>=1.3->sphinx->topfarm->Wind2Loads==0.0.1) (2018.9)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests>=2.0.0->sphinx->topfarm->Wind2Loads==0.0.1) (2019.11.28)\n",
            "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests>=2.0.0->sphinx->topfarm->Wind2Loads==0.0.1) (3.0.4)\n",
            "Requirement already satisfied: idna<2.9,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests>=2.0.0->sphinx->topfarm->Wind2Loads==0.0.1) (2.8)\n",
            "Requirement already satisfied: urllib3<1.25,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests>=2.0.0->sphinx->topfarm->Wind2Loads==0.0.1) (1.24.3)\n",
            "Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.6/dist-packages (from Jinja2>=2.3->sphinx->topfarm->Wind2Loads==0.0.1) (1.1.1)\n",
            "Building wheels for collected packages: Wind2Loads\n",
            "  Building wheel for Wind2Loads (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for Wind2Loads: filename=Wind2Loads-0.0.1-cp36-none-any.whl size=7693 sha256=d1fdd7eac65459a6008f9effa0f60e23e7ef9870b5d5f94dae9cbd27318cc308\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-0wk2y0n5/wheels/f7/0a/79/6ea6bfdabcf701663fb668cb3b4e7ea612a51546aaeb11d5eb\n",
            "Successfully built Wind2Loads\n",
            "Collecting git+https://gitlab.windenergy.dtu.dk/TOPFARM/workshop-december-2019.git\n",
            "  Cloning https://gitlab.windenergy.dtu.dk/TOPFARM/workshop-december-2019.git to /tmp/pip-req-build-tie0_qo3\n",
            "  Running command git clone -q https://gitlab.windenergy.dtu.dk/TOPFARM/workshop-december-2019.git /tmp/pip-req-build-tie0_qo3\n",
            "Requirement already satisfied (use --upgrade to upgrade): workshop==0.0.0 from git+https://gitlab.windenergy.dtu.dk/TOPFARM/workshop-december-2019.git in ./workshop-december-2019\n",
            "Building wheels for collected packages: workshop\n",
            "  Building wheel for workshop (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for workshop: filename=workshop-0.0.0-cp36-none-any.whl size=15439 sha256=10fc8d1c7e1c46b703d9f908601b5554a1dc8bb96163269141e2fd1f42aae70c\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-5vypz9rj/wheels/d0/22/67/401502bc288128f6355f24f9c9c9acb1905eadf464028a51e4\n",
            "Successfully built workshop\n",
            "fatal: destination path 'workshop-december-2019' already exists and is not an empty directory.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:144: FutureWarning: The sklearn.preprocessing.data module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.preprocessing. Anything that cannot be imported from sklearn.preprocessing is now part of the private API.\n",
            "  warnings.warn(message, FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/base.py:318: UserWarning: Trying to unpickle estimator MinMaxScaler from version 0.21.3 when using version 0.22.2.post1. This might lead to breaking code or invalid results. Use at your own risk.\n",
            "  UserWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jXbLNyrY1gd9",
        "colab_type": "text"
      },
      "source": [
        "## Set up the problem\n",
        "In this example we will import most of the code for setting up the workflow."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8PvNh7dJ1gd-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def get_problem(maxiter=10, allowable_load=1.0, tol=1e-2):\n",
        "    problem = TopFarmProblem(\n",
        "            design_vars={topfarm.x_key: wf.site.initial_position[:, 0],\n",
        "                         topfarm.y_key: wf.site.initial_position[:, 1]},\n",
        "            cost_comp=wf.get_load_cost_comp(),\n",
        "            driver=EasyScipyOptimizeDriver(maxiter=maxiter, tol=tol),\n",
        "            constraints=[SpacingConstraint(wf.min_spacing),\n",
        "                         XYBoundaryConstraint(wf.boundary),],\n",
        "                post_constraints=[(ls, allowable_load) for ls in wf.load_signals],\n",
        "            plot_comp=XYPlotComp(),\n",
        "            approx_totals={'step':10},\n",
        "            expected_cost=1e-6,)\n",
        "    cost, state = problem.evaluate()\n",
        "    max_load = {ls: np.array([problem[ls+'_abs'].max()]) for ls in wf.load_signals}\n",
        "    problem.cost_comp.analytical_group.lifetime_comp.options['max_load'] = max_load\n",
        "    return problem"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2y9Owr7T1geD",
        "colab_type": "text"
      },
      "source": [
        "## Run the optimization\n",
        "In this example there is the option to change the maximum number of iterations, the tolerance of the optimization as well as the maximum allowable load. The maximum allowable load is set as a percentage of the loads calculated for the initial layout, meaning e.g. that allowable_load=1.0 is 100% of the initial loads. We will optimize on AEP of the wind farm."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LSyEjdTA1geF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 851
        },
        "outputId": "62531bd5-4a0d-4c4b-d98f-60c270b6682e"
      },
      "source": [
        "problem = get_problem(5, 1.0)\n",
        "cost, state, recorder = problem.optimize(disp=True)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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CsMIov9LY3m7kbxIRUUqtBL5VSn2E/QmlPbCrCro0lSQmJobRo0czY8YMMves\nZtI9o+g8qANF593574L15F3IdahRAIwfP57mzZsz4/9W0NZ6HFIO0LNuS9b8uIe/fTyHl19+2aF6\nNm7cyFNPPcWcOXPYPHczO87t4FD6ISz1LayZtwZPd0/8mvg5TI/NZuOOO+5gyJAhvLp8OQ2PH6fg\n8GGG+PuzbcMGvlmyhMcee8xhegB++OEH3n33XT7+eA4dmvcm43Qutzfrwp6jkdx552h+/vln3Nyc\nM4ZFxyuqQGWHURlPJO8Ah7EPnf0a+/DYttgv9seAH4C6RlkvY/uYkd+2TD1vAsexD5393dW0rYfO\n1gwRERHSrVs3AeT555+XVq1aSffu3WXRokUOnWNRQnZ2trzyyisSEBAgb731lgwePFj8/Pxk4sSJ\ncubMGYfrERFZtWqVDBo0SJo2bSpz5syR5s2bS//+/WX58uVO0XPu3DmZMmWKNGrUSGbNmiU9evSQ\nxo0by9SpU38zH8RRfPvtt9KrVy9p06aNfPjhhxIYGCgjRoyQ9evXO0WPiEhmZqYopSQ8PNxpGpwN\nVRg6W+luKGeju6FqjmeffZaFCxdy8OBBCgoK6NChg31UkhPJy8sjIyODtLQ02rRpQ4MGDZyqByAx\nMZG6deuSn59Pu3btnC0Hi8XChQsXSE5OpnPnztSrV8+pekSExMREPDw8EBFuuukmp+pZtWoV99xz\nDyaTieHDhztVi7NwVjeUxkUp+b/t4OBgZ0spxdvbm5YtW9KyZYUD5ZyCI+MAV4Ovry++vr40bdrU\n2VIAUErVChMtwWQyUbduXfr37+9sKdcl+rehNBdx/vx5Dh48eMPeeWlcl8jISAYMGKDjFZVEm4Xm\nIvT/V2hckaysLPbt26fP6yqgzUJzESaTiXr16hEaWqluTY2mVrJlyxZsNps2iyqgzUJzEZGRkQwZ\nMsRpM2w1mpqgJF4xYMAAZ0u5btFmoSklNTWVuLg4ffelcTlMJpOOV1QRbRaaUnS8QuOKZGdn63hF\nNaDNQlOKjldoXJGSeIUe4Vc1tFloSjGZTAwePFjHKzQuhclkwtPTU8crqog2Cw1g/zE6Ha/QuCIl\n8Qpvb29nS7mu0WahAXS8QuOaZGdns3fvXn1eVwPaLDSAjldoXBM9v6L60GahAf4Xr/D09HS2FI2m\n2oiMjNTximpCm4WGtLQ0Dhw4oEeLaFwOk8lE//79dbyiGtBmodHxCo1LYjab2bNnjz6vqwltFhpM\nJhPe3t707dvX2VI0mmpDxyuqF20WGiIjI3W8QuNy6PkV1Ys2ixuc9PR0YmNj9d2XxuUoiVc4+x8D\nXQVtFjc4Ol6hcUXMZrOeXyMJnIoAACAASURBVFHNaLO4wdHxCo0rsnXrVqxWqx7hV41os7jBMZlM\nDBo0SMcrNC6FyWTCw8ODgQMHOluKy6DN4gYmIyODAwcO6Ed1jcuh4xXVjzaLG5ioqChERJuFxqWw\nWCx6fkUNoM3iBkbHKzSuSEm8QptF9aLN4gbEZrMB/4tX1K1b16l6RKRUU22htunRx+jq0fGKmkGb\nxQ3EwYMH+eMf/0jLli3JyMggNja29NUZFBQU8Omnn9K9e3e++OILLBYLQ4cO5YsvvqC4uNgpmqKj\no3n00Ufp3r07AO+++y5TpkwhISHBKXpycnKYPXs2N998MytXruT06dMMHTqUb775xmkX66ioKO6/\n/37CwsIAmDp1Kq+88gqnTp1yip7ymEwm+vXrp+MV1Y2IXJdLSEiIaK6e9evXS0BAgLz//vty7tw5\n+emnnwSQiRMnSmBgoKxYscKhegoKCmT48OFy++23S0REhNhsNrFarbJ27VoZNmyYjB49WgoKChyq\naenSpdKkSRP56KOPJCMjQ0REkpOTZcaMGRIYGCiRkZEO1WM2m6VXr17y+9//Xnbs2CE2m02Kiork\np59+kpCQEBk/frxYrVaHapo7d64EBQXJ/PnzxWKxiIjI8ePH5aWXXpJmzZpJTEyMQ/WUx2w2i5ub\nm7z55ptO1VFbAaKlktfcKl2wAT9gGXAYOAQMBPyB9UCC8drIKKuAfwDHgFigT5l6JhjlE4AJV9O2\nNotr49y5cxIdHS0iIlZrsUyf9EcZ1aOTHNq+RXbt3Cmvv/66wzWtXbtWrFar2Kw2uRCfJtkbfpUL\n8WlSWFAoU6ZMkcTERIfqSUpKkvj4eLFarXL48GExmUxy+PBhsVqtsn79evnwww8dqsdms8kvv/wi\nNptNbLZiSU3dKImJ/5DU1I2Sm2uRp556Ss6fP+9QTXFxcfLrr79Ksc0ma1Oz5G8nzsra1Cwpttnk\nu+++kwULFjhUT3nWrFkjgKxfv96pOmorVTEL9yo+mPwd+EVEHlBKeQL1gDeAjSIyUyk1HZgOTAN+\nB7Q3lv7APKC/UsofeBsIBQTYo5RaKSKZVdSmMVixYgWdOnUiJCQEm83K46Nuw7sgl5Ed2/Lp26+T\nKe4s/mU9FosFX1/fGteTkZHB8uXL+eMf/4jYhLcffIluPm0Z1KIX287EEJebyD9/+CeFRYUUFBQ4\nJKayZMkShgwZQseOHRk3bhy+vr4EBQWRnJyMxWJhyZIl3HrrrQ47RklJSezcuZOHH34YESsvvtiX\n4DaZ9OjhRmyslaQTjfjii93k5RVgtVpxc3OrcU0LFy7krrvuwj8ggN5Tp5EW3AHpGYraH01A0lH2\nffwhbko57BhVhI5X1ByVjlkopRoCw4AvAUSkUESygDHAIqPYIuBeY30MsNgwuB2An1KqOXAHsF5E\nMgyDWA+MqqwuzW959913SU1NBeDEvj34UcySnTGsPXiUhZE7aUQxUSv/w+DBgx2iZ926dfz8888A\n5B/JoJtPWyYvm8FfN3/J5GUz6ObTlvwjGTz44INs2rTJIZpee+018vPzSUhIwNfXlyVLlhAREcGS\nJUvw9fUlISGBqKgo7r333itXVg2sWLGCjRs3ApCeHklwm0zeCT/JVwvTeSf8JMFtMklPj+SWW25h\n//79Na5HRJg0aRKenp5sTDeTFtyBlPBXsSycS0r4q6QGd2Bjuplly5YxceLEGtdzKSIjI+nXrx8+\nPj5O0+CqVCXA3QZIBRYqpfYppRYopXyApiJy1iiTAjQ11lsCZSNgyUbapdJ/g1JqolIqWikVXXLx\n01yZnJwcGjduDMD5pOO0adSAge1asyH+GAPbtSbYvyEX0s5jNpsdpsff3x+AojO5DGrRi8d638vf\nty3isd73MqhFL4rO5OLu7k5ubq5DNaWkpBAUFERoaChRUVGEhoYSFBRESkoKHh4e5OTkOERPbm5u\n6WdmsRykRw837r67Af/+dxZ3392AHj3csFjicXd3d4imoqIirFYrDRo0IC4nD+kZSr17HiT36y+o\nd8+D0DOUgzl5Dv3MypOTk8Pu3bv1T3zUEFUxC3egDzBPRHoDudi7nEox+sikCm1chIh8LiKhIhIa\nGBhYXdW6PG3atCkd8dQkuB1Hz6ez/fhJbu1yM9uPnyQpI5uM/ELatGnjcD0eLXzYdiaGr/f9hxcG\nTeDrff9h25kY3JvX48CBAwQHBztEU3BwMLGxsTRr1ozk5GSio6MZNmwY0dHRJCcn06xZM2JjYx2u\nB8DXtyuxsVZWrTIzfrwfq1aZiY21UrfuzRw5coSbbrqpxvV4enrStGlTDh06RLf63qj90VxY+QM+\njz3NhZU/wP5outb3dugxKo+eX1GzVCVmkQwki8hOY3sZdrM4p5RqLiJnjW6m80b+aaBVmf2DjLTT\nwIhy6aYq6NKUY/78+TRtan/Aa9M7hEMpaTzSvzcdmwfSqWVzMnHHu1lLXn75ZYfoGTFiBN9++y0A\nXh39ictNZN4D7zGoRS8GtQshLjeR25q5cc899xASEuIQTcuWLaNVq1a4u7tjsVgYN24cQUFBtG/f\nHovFQvv27dm2bRvPPvusQ/Tcd999DBo0CIDGjYeTdKIRb4dDjx5u9AnxI+lEI2y2LkyaNMkhZgH2\neEDbtm3pWKcOAUlHkfDZ0DMU3z79CEw6ysjGDTjo68t9993nED0V6XN3dy89bppqprKRcftDA5uB\njsZ6ODDbWKYbadOBWcb6ncAa7KOiBgC7jHR/4ATQyFhOAP5XaluPhro2oqOj5a233pK0tDSpo5S8\n/8qLsn3ZEjm2e4f89+efHa7HarXKiy++KAkJCb8ZDRUXe0CSkpIcrmnTpk3y17/+VYqLiy8aDVVQ\nUCBr1651uJ78/Hx55pln5PTp02VGQ30iqakbZceO7ZKamupwTT/++KMsWLCgdDTUR8ZoqJwLF2Tj\nxo0O11OWAQMGyKBBg5yqobZDFUZDVXVS3nPAN0qpWKAX8BdgJnCbUioBuNXYBlgNJGIfOvsFMMUw\nqwzgPWC3sbxrpGmqkeDgYCIjI+nevTs2EZp27MqWE8nc9dgT/OWDD7hw4YJD9dSpU4fOnTvTt29f\nJjwxgVjzMVaejeIP7zzD0OHDiImJcagegPbt2/Ptt9/Su3dvfvnlF+rXr8+yZcvo2LEj//jHP7Ba\nrQ7V4+npSYsWLejatSuTJk3hxAlfoqJ8eeqpv3PnnXcRHx/vUD0AXbt2ZdasWQweOJDklT/SN/kY\nm//+V9q3a8eiRYtKbiIdTkm8QndB1RzKWR9uVQkNDZXo6Ghny7iuEBHGjRvHsmXL+PLLL9mxYweP\nPvoogwcPRinlFE3p6el89dVXtG7dmuXLlzNo0CAef/xxGjZs6BQ9IsKmTZtYsWIFvXv3Zu/evUyY\nMIHQ0FCn6AE4e/YsX375JR06dGDFihWEhYUxbtw4p434sVqtrFmzhi1bttC8eXNOnjzJk08+Sdeu\nXZ2iB+wj7O644w7WrVvHbbfd5jQdtR2l1B4RqdTJrM3iBqNPnz74+fk5bEiqRuMI3njjDWbPnk1W\nVpYeNnsZqmIW+rehbiAyMzOJiYnRQws1LofJZKJv377aKGoQbRY3EJs3b0ZE/3+FxrXQ8QrHoM3i\nBiIyMpK6devSv39/Z0vRaKqNbdu2UVxcrM2ihtFmcQNhMpkYOHAgXl5ezpai0VQbkZGRen6FA9Bm\ncYOQlZXFvn379N2XxuUoiVfUr1/f2VJcGm0WNwg6XqFxRXJzc9m1a5cetOEAtFncIJhMJh2v0Lgc\nOl7hOLRZ3CCYTCYGDBig4xUal8JkMuHm5uawn9e/kdFmcQOQlZVFTEyMvvvSuBw6XuE4tFncAGzZ\nsgWbzabNQuNS5Obm6vkVDkSbxQ1ASbxiwIABzpai0VQb27dvp6ioSAe3HYQ2ixsAHa/QuCI6XuFY\ntFm4ONnZ2ezbt0/ffWlcDpPJRGhoKL6+vs6WckOgzcLF0fEKjStSMr9Cn9eOQ5uFi2MymfD09NTx\nCo1LURKv0GbhOLRZuDgl8Qpvb29nS9Foqo3IyEgdr3Aw2ixcmOzsbPbu3avvvjQuh8lkIiQkRMcr\nHIg2CxdGxys0rsiFCxfYuXOnPq8djDYLF0bHKzSuiI5XOAdtFi5MZGQk/fv31/EKjUuh51c4B20W\nLorZbGbPnj367kvjckRGRhISEkKDBg2cLeWGQpuFi6LjFRpXRMcrnIc2CxdFxys0rsiOHTsoLCzU\nv0jgBLRZuCgmk4l+/fpRr149Z0vRaKoNk8lEnTp1GDJkiLOl3HBos3Ah9u3bx5w5c0rjFT179nSq\nHrPZzPz584mLi6O4uJiVK1dSXFzsND0iwrZt2/jss88A+6ia5ORkp+kBSE9PZ86cOfz666/k5OSw\ndu1abDab0/SICBs2bODrr78G7Bfn1NRUp+kpT8n8Ch2vcDzaLFyAtLQ0brvtNsaMGUNhYSFbt27F\nZrPx9ddf8/jjj5OXl+dwTZ999hnBwcFs2LABT09Pzp07x6xZs2jbti1bt251uJ5Tp04xcOBAJkyY\nUJoWGRlJjx49eP755x1uYiLCBx98wM0338yePXtwc3MjOTmZ119/nc6dOxMbG+tQPQCHDx+me/fu\nvPTSS7i7uwPw3//+lw4dOvDWW28hIg7XVJa8vDwdr3AmInJdLiEhIaKx869//UteffVVKSoqEhGR\n1157TTw8PCQ1NVUeeOABmTJlikP1ZGdny6233ipHjx79Td7q1aslMDBQUlJSHKpp5syZ8uGHH4rV\nar0oPTMzU0aOHCnh4eEO1XP69Gm5/fbbJTk5+aJ0m80m33zzjbRs2VJycnIcqumVV16RBQsWiM1m\nuyg9JSVFQkNDZd68eQ7VU55NmzYJIP/973+dquN6BoiWSl5zlVTxbkEp5QZEA6dF5C6lVBvgO6Ax\nsAd4TEQKlVJ1gcVACJAOPCwiSUYdrwNPAVbgeRFZe6V2Q0NDJTo6ukraXYG8vLzSeRRWm2A6cp7n\n356Nd34aO/+zEGtxEVlZWTRp0sQheoqKigDw8PCwJ9iskLAeUmKhWQ9ofxvJZ84SFBTkED1Q/hhZ\n2XJ6C4cyDtHZvzNDWg4h70IexcXF+Pn5OURPQUEB7u7uuLm5ASBWKzlRUeQfOoRX587UHzaM02ed\nd4xsNuFkXDqppywEtvKldbfGZGZmUK9ePafO2Xn77bd5//33yczM1N1QlUQptUdEQiuzb3V0Q70A\nHCqz/SHwsYjcDGRiNwGM10wj/WOjHEqpLsBYoCswCphrGJDmKggLC2PHjh1YbULIg8/x5F8WkhM8\nnLM+NxPy4HO4uXtgsViYM2eOQ/SsWLGCRx55xL5hszJrXHciZj8KEX8hYvajzBrXnaAWzZk5cyZn\nzpxxiKbu3btz/PhxrDYrAycOZNL8ScyNmcuk+ZMYOHEg3vW8OXnyJAsWLHCIngULFvDCCy8AdqN4\no39/lk/8E2mf/JPlE//EG/3707J5c15//XVycnJqXE9BQQFt2rQhIyMDm034w5gX+ST83+xadYJP\nwv/NH8a8SKNG/uzdu5fvvvuuxvVcCpPJRJ8+fbRROIkqmYVSKgi4E1hgbCvgFmCZUWQRcK+xPsbY\nxsgfaZQfA3wnIgUicgI4BvSriq4biSNHjtC+fXtMR86T6dOK5B8/IHvrt6SsmE2mTytMR85z/vx5\nh33Jjx49ys0332zfSFhPX99zPLQkk7ci8nhoSSZ9fc9BwnpWrVpFYmJijespKioiKSmJtm3bsuX0\nFixNLSR8kkDK8hQSPknA3NTMltNbSExMZNWqVTWuB+zHqF27dgDkREXRKdvM1BOJfJJ6nqknEumU\nbSYnKoply5Y5xFBTU1OpU6cO/v7+nIxLJ8CjDZ+vCefn3Qv5fE04AR5tOBmXzsGDB9m4cWON66mI\nvLw8duzYoeMVTqSqTxZzgNeAkuEbjYEsESmJFiYDLY31lsApACM/2yhfml7BPhehlJqolIpWSkXX\nphEazsTb2xuz2czBM2bqtOiGb+/RZG/7Dt/eo6nTohvxZ8yYzWaH/aWql5cXZrPZvpESS1iQlcmh\nHrwXVcjkUA/CgqyQcgCLxeIQTW5ubiiluHDhAocyDuHRwQP/MH9SV6biH+aPZwdPDmccdugxKvnM\nAPIPHaKfuztj/fyYl57OWD8/+rm7k3/IcZq8vLzIzc3FarWSespC2yY9GNLlbn7Z+2+GdLmbtk16\nkJZscegxKk/J/AptFs6j0mahlLoLOC8ie6pRz2URkc9FJFREQgMDAx3VbK3mhRdewGKx0LVFA2xn\n4rDsW03DQWOx7FuN7UwcXVo0YMWKFdx5550O0TN69GiaNWtm32jWg4hkN+ZFFzFjmCfzoouISHYj\nRQWSkZHhkKG9derUYfr06Zw7d47O/p0pOlpERkQGgfcEkhGRQeHRQjr5d3LoMRozZgz+/v4AeHXu\nzK7iYr7LymJy48Z8l5XFruJifq2jaNKkCa1atapxPQEBATz55JOkp6cT2MqXxPOxbIlfxag+49kS\nv4rE87EEBPk69BiVR8+vcD6VDnArpT4AHgOKAS+gAfATcAfQTESKlVIDgXARuUMptdZY366UcgdS\ngEBgOoCIfGDUW1rucu3rAPf/yMvLIzUtnbtf+Atn3ZpQr10ocuYgjXJPsXvp37FZi7FarQ6doHf8\n+HHatQlm1rju9PU9R1iQlYhkN3ZZmjBtSRyZ2WYaNWrkMD1ZWVkUFBVw9+t3Y25qxrODJ4VHC2lw\nrgFb52+lsKAQd3d36tat6zBNx48fp21wMG/070+nbDP93N3ZVVzM4YYN+MvOnWSZHXuMzp07h5eX\nN8+Pn0GARxvaNulB4vlY0opO8OVPH5GTY6FBgwbUqeP4EfcjRowgNzeX3bt3O7xtV8IpAW4ReV1E\ngkQkGHuAepOIPApEAA8YxSYAK4z1lcY2Rv4mYyjXSmCsUqquMZKqPbCrsrpuRH7++WeGDB7ErW3r\nkbNvNfe2deeT5x9kwtD2DBs6BHd3d4caRUFBAXfeeSd/mjyF+95dTtir3yBhb9DkzjfYkNacVf9d\n7dCLIMC3337L0MFDeeWOV/j06U95ptczzBo3i3va3sNdd95FvXr1HGoUmZmZjBgxgtemT2fSDz9w\n/+efEfDcswRPmkSkuzs7d+92+DGaO3cuI0YM58E/3c4zbz1C/3va8MhzvyOoe30ee2w8fn5+TjGK\n/Px8duzYoX/iw9lUdsxt2QUYAfxsrLfFfrE/BvwA1DXSvYztY0Z+2zL7vwkcB44Av7uaNvU8i4tZ\nt26dtGnTRgB59dVXxdfXVx5++GHZs2ePU/SkpqbKtGnTJDAwUN555x0JDAyUjh07yt///vfS+SCO\nZvny5TJs2DDp2rWrTJo0SRo0aCBPPPGExMfHO0XPqVOn5Pnnn5dGjRrJe++9J/7+/tKzZ09ZsGDB\nb+aDOAKbzSaLFy+WkJAQGTFihPz+978XPz8/mTJlipw4ccLhekqIiIgQQFatWuU0Da4Czpxn4Sx0\nN9RvKfnRwNWrV+Ph4VEr/nKysLCQgoICLBYLzZs3xz4AzrlkZ2dTVFSEt7c3Pj4+zpZDfn4+hYWF\nXLhwgaZNm9aaY1RQUICvr6/T/w8lPDyc9957j/T0dIfNhXFVqtIN5V7dYjTOwWKxEB0dzbRp00qD\np7UBT09PPD09a4VxldCwYUNnS7gILy8vvLy8atX8gdp0jEwmE71799ZG4WT0b0O5CNu2bcNqteqh\nhRqXoiReoc9r56PNwkUwmUy4u7szaNAgZ0vRaKqNnTt3UlBQoM2iFqDNwkUo+f+K2tAHr9FUFyaT\nCaWUnl9RC9Bm4QLk5OSwe/duffelcTl0vKL2oM3CBdi6dStWq1WPQ9e4FPn5+Wzfvl3fBNUStFm4\nADpeoXFFdLyidqHNwgWIjIykb9++1K9f39lSNJpqIzIyEqUUQ4cOdbYUDdosrnt0vELjquh4Re1C\nm8V1zrZt2yguLtZmoXEpSuIVOg5Xe9BmcZ2j4xUaV2TXrl3k5+frm6BahDaL6xyTyURoaKiOV2hc\nipL5FTpeUXvQZnEdU/L7/vruS+NqmEwmevXq5fCfaddcGm0W1zE6XqFxRQoKCvT8ilqINovrGJPJ\nhJubG4MHD3a2FI2m2tDxitqJNovrGJPJpOdXaFwOHa+onWizuE7Jzc1l165d+u5L43KYTCZ69uyp\n4xW1DG0W1ykl8Qo9Dl3jShQUFLBt2zZ9E1QL0WZxnRIZGanjFRqXY/fu3TpeUUvRZnGdUjK/ojb9\nXalGU1V0vKL2os3iOkTHKzSuislkokePHrXqf+Q1drRZXIds376doqIibRYal0LHK2o32iyuQ/T8\nCo0rsnv3bvLy8rRZ1FK0WVyHmEwmQkJCdLxC41KUxCuGDRvmbCmaCtBmcR1hNpu5cOFCrYlX5OXl\nUVhYiIggIs6Wg4hgNptL12sDubm5FBcX1xo9IoLFYildr01ERkbqeEUtRpvFdcCyZcsYNGgQv//9\n70vjFdHR0cTHxztFz6lTp3jmmWdo0aIFsbGx7N27l549e/LFF19gs9kcrkdE+Oqrr+jTpw+TJ08G\nYMyYMfzpT3/ixIkTDtcDcPToUZ588klatGjBqVOnWLNmDf3792fJkiVOuUgXFxfzz3/+k86dO/P2\n228jIgwbNoypU6dy9uxZh+spT2FhIVu3bq0VN0GaS1ByV3i9LSEhIXIjMHPmTOnYsaP89NNPUlRU\nJH/+85/Fzc1N3nnnHQkICJDNmzc7VM/p06elRYsWMm3aNElOThYREZvNJhs2bJABAwbI+PHjxWq1\nOlTTSy+9JCEhIfLLL7+Utn327FmZMWOGNGvWTPbv3+9QPfHx8RIYGCjvv/++nD9/XkREiouLZdWq\nVdKtWzeZOnWq2Gw2h2oaP368DB8+XKKiokrbPnHihLz44ovSunVrSUxMdKie8mzZskUAWb58uVN1\nuDpAtFTymlvpizXQCogA4oGDwAtGuj+wHkgwXhsZ6Qr4B3AMiAX6lKlrglE+AZhwNe3fKGaRlJQk\naWlpYrUWy7HonfLUqJEyZvgQsVqLZd26dfL99987VI/NZpO9e/fa1602uRCfJtkbfpUL8WmSm5Mr\nr732muTm5jpU05EjR8RisYjVapXDhw+LyWSSw4cPi9VqlaVLl8q6descqqe4uFhiYmJERMRmK5bU\n1I2SmPgPSU3dKBkZaTJt2jQpKipyqKYDBw5IQUGBFNtssjY1S/524qysTc2SYptNPv/8c9m5c6dD\n9ZTn/fffF0DS0tKcqsPVqYpZuFfhoaQYeFlE9iqlfIE9Sqn1wBPARhGZqZSaDkwHpgG/A9obS39g\nHtBfKeUPvA2EAmLUs1JEMqugzSWYPXs248ePp1EjPx4fdRt+FNPRzxe3/FweH3Ubi39ZjwicOXOG\nFi1a1LieXbt2kZiYyNixYxGb8PaDL9HNpy2DWvRi25kY4nITmfnDR5w5ewZvb2+UUjWu6Z133uHF\nF1+kXr16jBs3Dl9fX4KCgkhOTsZisbBkyRKsVivnzp2jadOmNa5nw4YNFBYWMnr0aESsvPhiX4Lb\nZNKjhxuxsVaSTjRizpzdnD59lqCgoBrXY7VamTFjBuHh4bh5eNB76jTSgjsgPUNR+6MJSDrKvo8/\npKiggLS0NAICAmpcU0WUzK9o3LixU9rXXJlKxyxE5KyI7DXWLcAhoCUwBlhkFFsE3GusjwEWGwa3\nA/BTSjUH7gDWi0iGYRDrgVGV1eUq2Gw23njjDRo2bMiJfXtopIr5Kmon6w4e5Zvte2lEMSf27eH7\n779n6tSpDtH0448/kpCQAED+kQy6+bRl8rIZ/HXzl0xeNoNuPm3JP5JBv379OHXqVI3rSUtL4+OP\nP6Z+/fokJCTg6+vLkiVLiIiIYMmSJfj6+pKQkMCnn37K//3f/9W4HoBvvvmm9L2np0cS3CaTd8JP\n8tXCdN4JP0lwm0zOp0bQvn178vPza1zPkSNH+P777/H09GRjupm04A6khL+KZeFcUsJfJTW4AxvT\nzbz77rvMnz+/xvVUhI5XXB9US4BbKRUM9AZ2Ak1FpCRilgKU3M61BMpeQZKNtEulV9TORKVUtFIq\nOjU1tTqk11qKi4sB8Pb25nzScYIbNWRgu9ZsiD/GwHatCfZvSGpSIt7e3g656ADk5+eXDtctOpPL\noBa9eKz3vfx92yIe630vg1r0ouhMLt7e3hQUFDhEj4+PD25ubqSkpBAUFERoaChRUVGEhoYSFBRE\nSkqK046RxXKQHj3cuPvuBvz731ncfXcDevRw40LuYTw8PBx2jEr0xOXkIT1DqXfPg+R+/QX17nkQ\neoZyMCfPoceoPNHR0Xp+xXVAlc1CKVUf+BF4UUTMZfOMPrJqG/ohIp+LSKiIhAYGBlZXtbUST09P\ngoKCOHjwIE2C25GUmc324ye5tcvNbD9+kqSMbAKD27Jz5046dOjgEE0dO3YkJiYGAI8WPmw7E8PX\n+/7DC4Mm8PW+/7DtTAw53oWkp6c7pFusSZMmFBQUkJKSQrNmzUhOTiY6Opphw4YRHR1NcnIyzZo1\nY9euXQ47Rh06dCg9Rr6+XYmNtbJqlZnx4/1YtcpMbKyVzMz6+Pj4OGSeTHBwML/++iu5ubl0q++N\n2h/NhZU/4PPY01xY+QPsj6ZrfW+HnkflMZlMAHp+RS2nKjELlFIe2I3iGxFZbiSfU0o1F5GzRjfT\neSP9NPageAlBRtppYES5dFNVdLkKO3fupEmTJthsVtJtbozt14NOzZvSqWVzMnGnTe8QOsQedNiP\nrj3xxBM8/vjjAHh19CcuN5F5D7zHoBa9GNQuhLjcRPo2rcPs2bPx8fGpcT2enp7Ex8fTpEkTmjRp\ngsViYdy4cQQFBdG+fXssFgvt27ene/fuPProozWuB+Dll1/G09MTgMaNh5N0ohFvh0OPHm70CfEj\n6UQjbh3Zm7/97W/U2b2PVgAAE99JREFUqVPzI9f9/f05fPgwPj4+jKwnBCQdRcJnQ89QfPv0IzDp\nKLf4+7JrwAAeeOCBGtdTESaTie7du+t4RW2nspFx7KObFgNzyqXPBqYb69OBWcb6ncAaY78BwK7/\nb+/+o6Mq7zyOv5/8IPwKIWBQfsUGDBBMEQISyrKwgCilCnrStWJldYV21YVuRc9ZCF1FqotitaWA\nVG0tIEW2gVXUtmgUZkQxwfBDCkSFiBvAQCZBCISQZDLf/WNudMCEUZy5M3f8vs65J3eemUk+95l7\n73fufe5M5Iurpw4CqdZ0EOgS7O9/W66GWrFihSxcuFBef+01yereTVb+90NyoKRYqqqqbL8SSkTk\n+PHjkpeXJ+Xl5edcDVW7t0oK/lwQkatZnnjiCXn66afF6/WeczXU4cOHZcOGDbbnOXTokNx8883i\n8XgCroZaIseOFcrq1avk9OnTtmfKz8+XgoICaWxqktc8J+RJ62qoD/fvl8LCQtvzNKuvr5f27dvL\nrFmzIpbh24RvcDXUN3lr8w/ANGCcMWaXNU0CHgUmGGP2A9dYtwH+CnyM/9LZZ4F7rGJ1HPgl8J41\nLbDaFDB+/HheeeUVbv7Rj/jgqIe2fQfw2NO/54orrqCoqKi5QNumc+fODB8+nEGDBvHj235M0dHd\nLC1ezYhbxvLAgw9QVVVlax6A66+/nmXLljFo0CBcLhfHjh3jkUceITs7mz179tiep0ePHvTp04fM\nzEymT/8Je/fGs2pVNaNG3cNvfvNbTpw4YXumvLw85syZw4jhw6ku/Cupro08e/cMRgwfzoEDB2zP\n06ykpIQzZ87oeIUDGLt3NqEybNgwKSkpiXQMW4gIOTk5VFVVsWDBAjweD9OmTaN79+4Ry/TZZ5+x\nevVq+vTpw+bNm5k4cSLjx4+35XLZlogIbreb0tJSvF4vTU1NTJs2LaKnNiorK3n++ecZMGAAbreb\nKVOmMHLkyIj1kc/n47XXXqOyspJPP/2U1NRUbr31Vjp16hSRPAALFy4kPz8fj8cTsct2v02MMdtF\nZNhFPVeLRfSrq6ujc+fO/OxnP+Pxxx+PdBylQua6666joqKC3bt3RzrKt8I3KRb63VAOUFRUREND\ngx6qq5jS2NjI22+/reu1Q2ixcACXy0VcXByjRo2KdBSlQqZ5vGLMmDGRjqK+Ai0WDuByucjJySEl\nJSXSUZQKGf18hbNosYhydXV1FBUV6aG6ijkul4vs7Gxi/QO2sUKLRZRrHq/QQ3UVSxobG/X7oBxG\ni0WUc7vdOl6hYs727dupra3VYuEgWiyinMvlYsiQIXTu3DnSUZQKGR2vcB4tFlHs7NmzOl6hYpLL\n5eLKK6/U8QoH0WIRxYqKiqivr9dioWKKfr7CmbRYRDGXy4UxRscrVEzR8Qpn0mIRxdxut45XqJjj\ndrsBHa9wGi0WUers2bO8++67+u5LxZzm8Ypu3bpFOor6GrRYRKni4mIdr1Axp3m8Qj835DxaLKJU\n83iFXf8FTyk77Nixg9OnT+ubIAfSYhGl9PMVKhY1f75CjyycR4tFFGoer9ANSsUat9vNwIEDdbzC\ngbRYRKFt27bpeIWKOV6vly1btuh67VBaLKKQjleoWKTjFc6mxSIKuVwuBg8eTGpqaqSjKBUy+n1Q\nzqbFIsro5ytUrHK5XGRlZXHppZdGOoq6CFososy2bds4e/asFgsVU3S8wvm0WEQZHa9QsWjnzp06\nXuFwWiyijNvt5qqrrtLxChVT9PMVzqfFIorU19ezdetWffelYo6OVzifFosIq6+vZ82aNSxduvTz\n8YrMzMyIZtq7dy/33XcfHo+H8vJyCgoKaGxsjFieM2fO8Ic//IGVK1cC8MILL1BeXh6xPOD/mu1Z\ns2ZRX19PaWkpr776Kk1NTRHLc/LkSZYsWcKLL74IwMqVKzl69GjE8gRqHq/Qowpn02IRQcXFxfTt\n25c//vGP9O3b9/PxigceeIC77roLr9dra56GhgZuv/12JkyYQPv27UlKSuL48eMsXbqUzMxM3n//\nfVvzABQWFnL55ZezYcMGMjIyEBF27NjBkCFDmDdvHiJia57Tp08zefJk8vLyuOyyywCorq7moYce\nYtCgQZSVldmaB6CgoICMjAzeeecd0tPT8Xq9vPPOO2RlZfHkk0/anud8O3fu5NSpU3rE7HQi4shp\n6NCh4nQLFiyQl19++fPb48aNk8GDB0tNTY2MGzdOli1bZmuejz76SO688045c+bMl+5bu3at9O/f\nXxobG23NdO+998pbb731pXaPxyNDhw6VdevW2ZqnpKREZs6cKQ0NDV+6b8mSJZKbmys+n8+2PD6f\nT2bMmCG7du360n3l5eXSr1+/FvvPTosWLRJAKioqIppDiQAlcpH7XCM2vzNrjTFmIrAYiAd+LyKP\nXujxw4YNk5KSEluyhUNlZeXn34/T5BMK9xzhtllzuWZof/70q3mcrTtDUlISCQkJtuQ5ceIE7dq1\nIykpyd/ga4L9hXB0N1w2CDIncPLUaVJSUmzJA+f3URNvH3mb0uOlZHXJYlTPUdSeriU5ORljjC15\nqqurSUlJ+fw1kaYmTr/1FmdLS2mblUXH0aOpOR25PvL5hPI91XgOnSKtdzLp2V05darG1jwtuf76\n6ykrK6O0tDSiORQYY7aLyLCLea49e6IgjDHxwDJgAnAYeM8Y87KI7ItssvCoq6ujX79+HDx4kE4p\nnRn6z7OobtuTDrk3s+Xwbobk3cObzz7Mxo0bOXz4MNOmTQt7pvnz59O2bVvmzJkDviaevmsUuSke\nxvb2sflQHMUn05ixzM3NP/kJTz31FHFx4T2DWVlZyciRI9m3bx/xifFMvH8itd1radO/DQ0fNtCh\nogMbf7WR9SvWAzBlypSw5gGYOXMmOTk5TJ8+HWlq4rFrr2XgqdPkJiZS3NjIvuSO3L1uHTNnzmTx\n4sVhz/PBBx8wdepUduzYgQjce/t80pL60LfbVZRVvo+n/mN+vXI+ixcvpnfv3hE5DeTz+diyZQu3\n3nqr7X9bhdjFHpKEcgK+B7wWcHsuMPdCz3Hyaajy8nLp2bOniIi8se+opE97VOLadZKUkbdIXLtO\nkvbDB6Vd36sFiMj0g8wEeXVqO7mkvZH/Gt1GLmlv5NWp7eQHmQkRyZN8VbKk35su8cnxkjY5TeKT\n4yX93nRJvio5Yn00pkMHWd6zl6TGx8vdXbtKany8LO/ZS8Z06BCRPNnpI+SuiY9Ix7YpMjHnNunY\nNkXumviIZKePiFgfBU5r166N8FanRL7ZaaioOLIAegKHAm4fBnLPf5Ax5qfATwHS09PtSRYGHTt2\npKamhsbGRvZ+WkNcj2ySh0zi5Na1pIy8hfZ9h3HN4D7UvPtnamtruemmm8Keac2aNfTq1YvRo0dz\ndd1mcus2cfewJn75VgP/NboN3++XSEr2BDY+8BcWLVoU9tNjx44dY/ny5Tz44IPsbLuT7UnbqRtb\nh+dlD2mT0+g0qBNj+4/lkxc+ITk5mQkTJoQ1D8AzzzzD1VdfzZAhQ8goKaHvtve4pXNnlldXc3fX\nrozu2JGuI7/He3/5CwsXLgx7ngMHDrBhwwbuu+8+jCcN4+nGqIE3sHHHaibm3MaVl+cycFgGK/7a\nQP/+/RkxYkTYM7WkXbt2tqzDKswutsqEcgJ+iH+covn2NGDphZ7j5CMLEZGHH35YqqqqWjyySJ/2\nqLyx76iMGTNG3G63LXncbre89NJL/hsf/E02Te9yzpHFpuldpPj5h+SGG26wJY/P55Nf/OIXUltb\nK65ylwzIH3DOkUX//P6yuXyzDBw4UEpLS23J9Morr8ibb74pIiI1mzbJiisyzzmyWHFFpqyfmy/T\np0+3JU99fb3k5+eL1+uVg+97ZPZNvz7nyGL2Tb+Wj7ZXSO/evXVwWYlIDAxwG2O+B8wXkeus23MB\nRKTVt2dOH+AGKCsro0PHZCbes4DPOvQmrkc2vk/3kFp7iK2rf0VCfByJiYm2DeD6fD5KSkoYPmwo\ni6Z+l6uTjzG2VxObD8dTXNONOWv30OBtok2bNrbkAf9nPnr27sm1s6+l5tIa2vRrQ8NHDXQ61gnX\nEhcJ8Qm25mlsbGT37t3kDB5Mfm4uA07WMDwhgW1eL6Wdklm4bRuNTfb20c6dOxkwIIu7bv5PLknM\noE+3QXxcuZuqxoM8XbCIuDhjax4VvRw/wA28B2QaYzKAI8AtQMyPiK1bt45nn32Wufnz6J4znrLq\neto3pLF1/TP8y7TbWLduna15jhw5Ql5eHpMnT2bWgwUMiD9EfXkJ9aVV/H7xBvLKPrb9A4PPPfcc\nr7/+Ovnz8knLTaOspoy4XnEUPlPI/ffdz1NPPWVrnv379zNp0iTuuOMO/m3tWtIOHeLEzp0kHDpE\nwUsvMbu62vb/AvfEE09w4MAB5s7N57uXj6D6SC3pJ+NYvrqQRx9dyPz5823No2LUxR6ShHoCJgEf\nAWXAvGCPd/ppqGYbN26USZMmyciRI2X8+PHSo0cPmT9/vng8nojkqaiokHnz5km3bt1kzpw5kpiY\nKDfeeKNs2rQpInl8Pp+sX79exo4dK5MnT5bs7Gzp06ePLFq0SE6ePBmRTJ988onMnj1bUlNTZe7c\nuZKUlCRTp06V4uLiiOTxer2yatUqyc3NlRkzZkhaWppkZWXJkiVLWvzMjPr2wumnoS5GLJyGCuTz\n+WhqaiIxMTHSUQD/m4impiaMMcTHx0c6DuDvI6/XGzWnVEQEr9dLfHx82C8l/qqirY9UdImF01Df\nenFxcVGzwwEwxtj2gcCvKi4uLqp2gsaYqCnuzaKtj1TsiJ69k1JKqailxUIppVRQWiyUUkoFpcVC\nKaVUUFoslFJKBaXFQimlVFBaLJRSSgWlxUIppVRQWiyUUkoFpcVCKaVUUFoslFJKBeXYLxI0xniA\n/7vAQy4BqmyKEypOzAzOzO3EzKC57eTEzHDh3JeLSNrF/FLHFotgjDElF/vtipHixMzgzNxOzAya\n205OzAzhy62noZRSSgWlxUIppVRQsVwsnol0gIvgxMzgzNxOzAya205OzAxhyh2zYxZKKaVCJ5aP\nLJRSSoWIFgullFJBObJYGGMeN8Z8YIzZbYx50RjT2Wr/jjGmzhizy5p+F/CcocaYvxtjDhhjfmuM\nMVZ7F2NMoTFmv/UzNULLNNEY86GVb04kMgRk6W2M2WyM2WeM2WuM+Q+rfb4x5khA/04KeM5cK/uH\nxpjrAtptXS5jzCfW67zLGFNitbX4Ghu/31rZdhtjcgJ+z+3W4/cbY24PY97+Af25yxhTY4z5eTT2\ntTHmOWNMpTFmT0BbyPq2tW00DJmjfv/RSu6QrRPGmAxjTLHV/j/GmOD/uF1EHDcB1wIJ1vxjwGPW\n/HeAPa08ZxswAjDA34DvW+2LgDnW/Jzm32Xz8sQDZUAfoA3wPjAwgv3bHcix5pOBj4CBwHzg/hYe\nP9DKnARkWMsSH4nlAj4BLjmvrcXXGJhkrQvGWjeKrfYuwMfWz1RrPtWm9eAocHk09jUwGsgJ3MZC\n2betbaNhyBz1+49WcodsnQD+DNxizf8OuDtYJkceWYjI6yLitW4WAb0u9HhjTHegk4gUib93VgE3\nWndPAVZa8ysD2u00HDggIh+LSAOw1soVESJSISI7rPlTQCnQ8wJPmQKsFZF6ETkIHMC/TNGyXK29\nxlOAVeJXBHS21pXrgEIROS4inwGFwEQbco4HykTkQt9MELG+FpG3gOMt5PnGfRtkGw1pZifsP1rp\n69Z8rXXCOioaB6z7OrkdWSzOcyf+St8swxiz0xjjNsb8o9XWEzgc8JjDfLHzu1REKqz5o8ClYU3b\nsp7AoYDbgfkiyhjzHWAIUGw1zbQO358LOORuLX8klkuA140x240xP7XaWnuNoyk3wC3ACwG3o72v\nIXR9e6FtNJyctv8IxTrRFTgRUDC/Ul9HbbEwxrxhjNnTwjQl4DHzAC/wJ6upAkgXkSHAbGCNMabT\nV/2b1rsGvZbYYozpCKwHfi4iNcByoC8wGH9fPxHBeK0ZJSI5wPeBfzfGjA68M1pfY+uc8WSgwGpy\nQl+fI1r7tjUO3H9EdJ1IsPOPfR0ics2F7jfG3AFcD4y3XiREpB6ot+a3G2PKgH7AEc491OxltQEc\nM8Z0F5EK63CzMqQL8tUcAXoH3A7MFxHGmET8heJPIvK/ACJyLOD+Z4FXrZsXym/rconIEetnpTHm\nRfyH4q29xq3lPgL803ntrnDmxl/cdjT3sRP62hKqvr3QNhpyTtx/hHCdqMZ/WjDBOrr4an0disEY\nuyf854/3AWnntacB8dZ8H6sDukjLA1STrPbHOXeAalEElicB/0BfBl8MRF0Zwf41+M/L/ua89u4B\n8/fiP08KcCXnDrB9jH9wzdblAjoAyQHzW611pcXXGPgB5w7CbrPauwAH8Q/AplrzXcLc52uBf432\nvua8QeBQ9m1r22gYMjti/9FC7pCtE/iPYAMHuO8JmiecG0C4JvwDOIeAXdb0O6s9D9hrte0Abgh4\nzjBgD/6rA5byxafXuwJvAvuBNwjzTuECyzQJ/1VHZcC8CPfvKPyH07sD+ngS8Dzwd6v95fNW3nlW\n9g8JuIrFzuWyNvD3rWlv899r7TW2NvxlVra/A8MCfted1np2gICdeJhyd8D/bi8loC3q+hr/eEoF\n0Ij/PPf0UPZta9toGDJH/f6jldwhWyesbWWb1RcFQFKwTPp1H0oppYKK2gFupZRS0UOLhVJKqaC0\nWCillApKi4VSSqmgtFgopZQKSouFUkqpoLRYKKWUCur/ASYHKJ5SizTiAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-3-d3c2d6935637>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mproblem\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_problem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1.0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mcost\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrecorder\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mproblem\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptimize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdisp\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/topfarm/_topfarm.py\u001b[0m in \u001b[0;36moptimize\u001b[0;34m(self, state, disp)\u001b[0m\n\u001b[1;32m    420\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msetup\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    421\u001b[0m         \u001b[0mt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 422\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_driver\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    423\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcleanup\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    424\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mdisp\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/openmdao/core/problem.py\u001b[0m in \u001b[0;36mrun_driver\u001b[0;34m(self, case_prefix, reset_iter_counts)\u001b[0m\n\u001b[1;32m    564\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_clear_iprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    565\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_scaled_context_all\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 566\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdriver\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    567\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    568\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mrun_once\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/openmdao/drivers/scipy_optimizer.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    429\u001b[0m                                   \u001b[0mtol\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'tol'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    430\u001b[0m                                   \u001b[0;31m# callback=None,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 431\u001b[0;31m                                   options=self.opt_settings)\n\u001b[0m\u001b[1;32m    432\u001b[0m             \u001b[0;32melif\u001b[0m \u001b[0mopt\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'basinhopping'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    433\u001b[0m                 \u001b[0;32mfrom\u001b[0m \u001b[0mscipy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptimize\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mbasinhopping\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/scipy/optimize/_minimize.py\u001b[0m in \u001b[0;36mminimize\u001b[0;34m(fun, x0, args, method, jac, hess, hessp, bounds, constraints, tol, callback, options)\u001b[0m\n\u001b[1;32m    616\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mmeth\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'slsqp'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    617\u001b[0m         return _minimize_slsqp(fun, x0, args, jac, bounds,\n\u001b[0;32m--> 618\u001b[0;31m                                constraints, callback=callback, **options)\n\u001b[0m\u001b[1;32m    619\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mmeth\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'trust-constr'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    620\u001b[0m         return _minimize_trustregion_constr(fun, x0, args, jac, hess, hessp,\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/scipy/optimize/slsqp.py\u001b[0m in \u001b[0;36m_minimize_slsqp\u001b[0;34m(func, x0, args, jac, bounds, constraints, maxiter, ftol, iprint, disp, eps, callback, **unknown_options)\u001b[0m\n\u001b[1;32m    421\u001b[0m             \u001b[0;31m# Compute the derivatives of the objective function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    422\u001b[0m             \u001b[0;31m# For some reason SLSQP wants g dimensioned to n+1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 423\u001b[0;31m             \u001b[0mg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfprime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    424\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    425\u001b[0m             \u001b[0;31m# Compute the normals of the constraints\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/scipy/optimize/optimize.py\u001b[0m in \u001b[0;36mfunction_wrapper\u001b[0;34m(*wrapper_args)\u001b[0m\n\u001b[1;32m    325\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mfunction_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mwrapper_args\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    326\u001b[0m         \u001b[0mncalls\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 327\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mfunction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwrapper_args\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    328\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    329\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mncalls\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunction_wrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/openmdao/drivers/scipy_optimizer.py\u001b[0m in \u001b[0;36m_gradfunc\u001b[0;34m(self, x_new)\u001b[0m\n\u001b[1;32m    671\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    672\u001b[0m             grad = self._compute_totals(of=self._obj_and_nlcons, wrt=self._dvlist,\n\u001b[0;32m--> 673\u001b[0;31m                                         return_format='array')\n\u001b[0m\u001b[1;32m    674\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_grad_cache\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgrad\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    675\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/openmdao/core/driver.py\u001b[0m in \u001b[0;36m_compute_totals\u001b[0;34m(self, of, wrt, return_format, global_names)\u001b[0m\n\u001b[1;32m    772\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    773\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 774\u001b[0;31m                 \u001b[0mtotals\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtotal_jac\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompute_totals\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    775\u001b[0m             \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    776\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_recording_iter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstack\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/openmdao/core/total_jac.py\u001b[0m in \u001b[0;36mcompute_totals\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1253\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1254\u001b[0m         \u001b[0;31m# Linearize Model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1255\u001b[0;31m         \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_linearize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_assembled_jac\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msub_do_ln\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_linear_solver\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_linearize_children\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1256\u001b[0m         \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_linear_solver\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_linearize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1257\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/openmdao/core/group.py\u001b[0m in \u001b[0;36m_linearize\u001b[0;34m(self, jac, sub_do_ln)\u001b[0m\n\u001b[1;32m   1673\u001b[0m                 do_ln = sub_do_ln and (subsys._linear_solver is not None and\n\u001b[1;32m   1674\u001b[0m                                        subsys._linear_solver._linearize_children())\n\u001b[0;32m-> 1675\u001b[0;31m                 \u001b[0msubsys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_linearize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjac\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msub_do_ln\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdo_ln\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1676\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1677\u001b[0m             \u001b[0;31m# Update jacobian\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/openmdao/core/group.py\u001b[0m in \u001b[0;36m_linearize\u001b[0;34m(self, jac, sub_do_ln)\u001b[0m\n\u001b[1;32m   1664\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_unscaled_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_outputs\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1665\u001b[0m                 \u001b[0;32mfor\u001b[0m \u001b[0mapproximation\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mitervalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_approx_schemes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1666\u001b[0;31m                     \u001b[0mapproximation\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompute_approximations\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mjac\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mjac\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtotal\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1667\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1668\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_assembled_jac\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/openmdao/approximation_schemes/finite_difference.py\u001b[0m in \u001b[0;36mcompute_approximations\u001b[0;34m(self, system, jac, total)\u001b[0m\n\u001b[1;32m    281\u001b[0m                     \u001b[0;32mfor\u001b[0m \u001b[0mdelta\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcoeff\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdeltas\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcoeffs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    282\u001b[0m                         self._run_point(system, wrt, idx, delta, out_tmp, in_tmp, result_array,\n\u001b[0;32m--> 283\u001b[0;31m                                         total)\n\u001b[0m\u001b[1;32m    284\u001b[0m                         \u001b[0mresult_array\u001b[0m \u001b[0;34m*=\u001b[0m \u001b[0mcoeff\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    285\u001b[0m                         \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mresult_array\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/openmdao/approximation_schemes/finite_difference.py\u001b[0m in \u001b[0;36m_run_point\u001b[0;34m(self, system, in_name, idxs, delta, out_tmp, in_tmp, result_array, total)\u001b[0m\n\u001b[1;32m    359\u001b[0m             \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    360\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 361\u001b[0;31m         \u001b[0mrun_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    362\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    363\u001b[0m         \u001b[0mresult_array\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresults_vec\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/openmdao/core/system.py\u001b[0m in \u001b[0;36mrun_solve_nonlinear\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   2633\u001b[0m         \"\"\"\n\u001b[1;32m   2634\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_scaled_context_all\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2635\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_solve_nonlinear\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2636\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2637\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mrun_apply_linear\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvec_names\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscope_out\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscope_in\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/openmdao/core/group.py\u001b[0m in \u001b[0;36m_solve_nonlinear\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1560\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1561\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mRecording\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m'._solve_nonlinear'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miter_count\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1562\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_nonlinear_solver\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msolve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1563\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1564\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_guess_nonlinear\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/openmdao/solvers/nonlinear/nonlinear_runonce.py\u001b[0m in \u001b[0;36msolve\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     38\u001b[0m             \u001b[0;31m# If this is not a parallel group, transfer for each subsystem just prior to running it.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     39\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 40\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_gs_iter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     41\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     42\u001b[0m             \u001b[0mrec\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mabs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/openmdao/solvers/solver.py\u001b[0m in \u001b[0;36m_gs_iter\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    648\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    649\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0msubsys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mloc\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 650\u001b[0;31m                 \u001b[0msubsys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_solve_nonlinear\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    651\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    652\u001b[0m             \u001b[0msystem\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_reconf_update\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msubsys\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/openmdao/core/group.py\u001b[0m in \u001b[0;36m_solve_nonlinear\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1560\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1561\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mRecording\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m'._solve_nonlinear'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miter_count\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1562\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_nonlinear_solver\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msolve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1563\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1564\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_guess_nonlinear\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/openmdao/solvers/nonlinear/nonlinear_runonce.py\u001b[0m in \u001b[0;36msolve\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     38\u001b[0m             \u001b[0;31m# If this is not a parallel group, transfer for each subsystem just prior to running it.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     39\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 40\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_gs_iter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     41\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     42\u001b[0m             \u001b[0mrec\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mabs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/openmdao/solvers/solver.py\u001b[0m in \u001b[0;36m_gs_iter\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    648\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    649\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0msubsys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mloc\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 650\u001b[0;31m                 \u001b[0msubsys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_solve_nonlinear\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    651\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    652\u001b[0m             \u001b[0msystem\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_reconf_update\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msubsys\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/openmdao/core/explicitcomponent.py\u001b[0m in \u001b[0;36m_solve_nonlinear\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    218\u001b[0m                                      self._discrete_outputs)\n\u001b[1;32m    219\u001b[0m                     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 220\u001b[0;31m                         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_inputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_outputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    221\u001b[0m                 \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    222\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_inputs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_only\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/py_wake/wake_model.py\u001b[0m in \u001b[0;36mcalc_wake\u001b[0;34m(self, x_i, y_i, h_i, type_i, wd, ws)\u001b[0m\n\u001b[1;32m    174\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    175\u001b[0m             \u001b[0;31m# generate indexes of up wind(n_uw) and down wind(n_dw) turbines\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 176\u001b[0;31m             \u001b[0mn_uw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mindices\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0muwi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ml\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0muwi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ml\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdw_order_indices_dl\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0mj\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi_wt_l\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi_wd_l\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    177\u001b[0m             \u001b[0mn_dw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mindices\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdwi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ml\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mdwi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ml\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdw_order_indices_dl\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mj\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi_wt_l\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi_wd_l\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    178\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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gTAkJkZLiYlm4cKEcOHDAKpoGDx4smzdvFqUcZFDQSBl/81vyz0dXy9N3/EPu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\nJtosNBqNRtMm2iw0Go1G0yb/D+2F5oZZ9M2qAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z2XeIPXn1geM",
        "colab_type": "text"
      },
      "source": [
        "## Post Process\n",
        "During the optimization all variable are recorded. After it is finished one can see all the recorded variables by typing `recorder.keys()` and plot them by writing `plt.plot([your variable],'.')`. We can use this procedure to examine how the load constraint has developed during the optimization:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hHdUuxlF1geN",
        "colab_type": "text"
      },
      "source": [
        "First we show the relative loads meaning the ratio between the load at each iteration and the initial loads:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ov4kb2vF1geO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.figure()\n",
        "for ls in wf.load_signals:\n",
        "    plt.plot(np.max(recorder[ls], axis=1),'.',label=ls)\n",
        "plt.grid()\n",
        "plt.title('Load constraints')\n",
        "plt.legend()\n",
        "plt.ylabel('Load / Initial Load')\n",
        "plt.xlabel('iteration')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AqO2-NS01geU",
        "colab_type": "text"
      },
      "source": [
        "Secondly we can also plot the absolute magnitude of the loads as a function of the iterations:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MfuyzedT1geV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "plt.figure()\n",
        "for ls in wf.load_signals:\n",
        "    ls += '_abs'\n",
        "    plt.plot(np.max(recorder[ls], axis=1),'.',label=ls)\n",
        "plt.grid()\n",
        "plt.title('Load constraints')\n",
        "plt.legend()\n",
        "plt.ylabel('Load [kNm]')\n",
        "plt.xlabel('iteration')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_IgJNoxZ1gec",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}