{
 "cells": [
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     "status": "completed"
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     "remove-input",
     "active-ipynb",
     "remove-output"
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   "outputs": [],
   "source": [
    "try:\n",
    "    from openmdao.utils.notebook_utils import notebook_mode  # noqa: F401\n",
    "except ImportError:\n",
    "    !python -m pip install openmdao[notebooks]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "ee8e8873",
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     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Using the Case Viewer\n",
    "\n",
    "The CaseViewer is a tool for interactive viewing of recorded cases data in Jupyter notebooks.\n",
    "This is currently an experimental feature of OpenMDAO but it's useful enough in its current state that we've included it in the package."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "c48021c2",
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   "source": [
    "## Example: Queen Dido's Problem\n",
    "\n",
    "Queen Dido's problem is an optimization problem that is also a legend about the founding of Carthage.\n",
    "Supposedly Queen Dido was allowed all the land she could enclose within the hide of an ox.\n",
    "Cutting the hide into very thin strips provided her with a long, but finite, length of \"fencing.\"\n",
    "What arrangement of this fencing provides the greatest possible enclosed area?\n",
    "\n",
    "If we assume a straight edge along one side of the fence (such as a riverbank or shoreline), how should the fencing be arranged to capture the greatest possible area?\n",
    "\n",
    "The following component takes the distance of the river bank, $L$, as well as the distance of the \"fence posts\" from the riverbank, $y$, and computes the enclosed area and the perimeter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ab5dfa22",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import openmdao.api as om\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "83f44aa7",
   "metadata": {
    "execution": {
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     "status": "completed"
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    "tags": []
   },
   "outputs": [],
   "source": [
    "class Fence(om.ExplicitComponent):\n",
    "\n",
    "    def initialize(self):\n",
    "        self.options.declare('n', types=int, default=50, desc='number of fenceposts')\n",
    "\n",
    "    def setup(self):\n",
    "        n = self.options['n']\n",
    "        self.add_input('L', val=10.0, units='m')\n",
    "        self.add_input('y', val=np.zeros(n), units='m')\n",
    "\n",
    "        self.add_output('perimeter', val=0.0, units='m')\n",
    "        self.add_output('area', val=0.0, units='m**2')\n",
    "        self.add_output('x', val=np.linspace(0, 100, n), units='m')\n",
    "\n",
    "        self.declare_partials(of='*', wrt='*', method='cs')\n",
    "\n",
    "    def compute(self, inputs, outputs):\n",
    "        n = self.options['n']\n",
    "        L = inputs['L']\n",
    "        y = inputs['y']\n",
    "\n",
    "        x = np.linspace(0, L, n)\n",
    "\n",
    "        dx = x[1:] - x[:-1]\n",
    "        dy = y[1:] - y[:-1]\n",
    "\n",
    "        mean_y = 0.5 * (y[1:] + y[:-1])\n",
    "\n",
    "        outputs['perimeter'] = np.sum(np.sqrt(dx**2+dy**2))\n",
    "        outputs['area'] = np.sum(dx * mean_y)\n",
    "        outputs['x'] = x"
   ]
  },
  {
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   "id": "6c9a5aa8",
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    "tags": []
   },
   "source": [
    "The optimization problem is to arrange these fence posts such that the perimeter $p$ does not exceed some value while maximizing the enclosed area $A$.\n",
    "\n",
    "In addition, the first and last fenceposts should be placed along the river's edge.\n",
    "\n",
    "\\begin{align}\n",
    "\\mathrm{Minimize}\\;J &= -A \\\\\n",
    "\\mathrm{Subject\\;to:} \\\\\n",
    "& 0 \\le y_i \\quad \\mathrm{for} \\quad i=1 ... N \\\\\n",
    "& y[0] = 0 \\; \\mathrm{m}\\\\\n",
    "& y[N] = 0 \\; \\mathrm{m}\\\\\n",
    "& p = 300 \\; \\mathrm{m}\n",
    "\\end{align}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "98d0c683",
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    "execution": {
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     "status": "completed"
    },
    "tags": [
     "remove-cell"
    ]
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def plot_fence(p, idx=0, fig=None, axes=None):\n",
    "    if axes is None:\n",
    "        fig, axes = plt.subplots(3, 1, figsize=(12, 6), tight_layout=True, sharex=True)\n",
    "    axes[idx].plot(p.get_val('fence.x'), p.get_val('fence.y'), 'ko-')\n",
    "    axes[idx].fill_between(p.get_val('fence.x'), p.get_val('fence.y'), color='tab:green')\n",
    "    axes[idx].fill_between([-1, 12], [-2, -2], color='tab:blue')\n",
    "    A = p.get_val('fence.area', units='m**2')[0]\n",
    "    perimeter = p.get_val('fence.perimeter', units='m')[0]\n",
    "    L = p.get_val('fence.L', units='m')[0]\n",
    "    axes[idx].annotate(f'p = {perimeter:6.2f} $m$', (4, 2.5), fontsize=16, color='white')\n",
    "    axes[idx].annotate(f'A = {A:6.2f} $m^2$', (4, 0.5), fontsize=16, color='white')\n",
    "    ht = -1\n",
    "    w = L\n",
    "    axes[idx].annotate('', xy=(0, ht), xytext=(w, ht), textcoords=axes[idx].transData, arrowprops=dict(arrowstyle='<->', color='white'))\n",
    "    axes[idx].annotate('', xy=(0, ht), xytext=(w, ht), textcoords=axes[idx].transData, arrowprops=dict(arrowstyle='|-|', color='white'))\n",
    "    bbox=dict(fc='tab:blue', ec='none')\n",
    "    axes[idx].text(w/2, ht, f'L={L:6.2f} m', ha='center', va='center', color='white', fontsize=16, bbox=bbox)\n",
    "    return fig, axes\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "959c9662",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": [
     "remove-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1790952307.966949] [runnervm8df0l:9368 :0]        ib_iface.c:1269 UCX  ERROR mana_0: iface 0x561fb7c69470 failed to create UD QP TX wr:256 sge:6 inl:64 resp:0 RX wr:4096 sge:1 resp:0 failed: Operation not supported\n",
      "[1790952307.967195] [runnervm8df0l:9368 :0]      ucp_worker.c:1412 UCX  ERROR uct_iface_open(ud_verbs/mana_0:1) failed: Input/output error\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[runnervm8df0l:09368] pml_ucx.c:313  Error: Failed to create UCP worker\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "N = 4\n",
    "\n",
    "prob = om.Problem()\n",
    "\n",
    "prob.model.add_subsystem('fence', Fence(n=N))\n",
    "\n",
    "prob.model.add_design_var('fence.L', lower=1)\n",
    "prob.model.add_design_var('fence.y', lower=0, indices=om.slicer[1:-1])\n",
    "prob.model.add_constraint('fence.perimeter', equals=100 * np.pi, ref=100 * np.pi)\n",
    "prob.model.add_objective('fence.area', ref=-1000.0)\n",
    "\n",
    "prob.driver = om.pyOptSparseDriver(optimizer='SLSQP')\n",
    "\n",
    "prob.setup(force_alloc_complex=True)\n",
    "\n",
    "prob.set_val('fence.L', val=10)\n",
    "\n",
    "fig = None\n",
    "axes = None\n",
    "\n",
    "for i, inp in enumerate(((10, [0, 3, 5, 0]),\n",
    "                         (11, [0, 4, 7, 0]),\n",
    "                         (9, [0, 7, 7, 0]))):\n",
    "    L, ys = inp\n",
    "    prob.set_val('fence.L', val=L)\n",
    "    prob.set_val('fence.y', val=ys)\n",
    "    prob.run_model()\n",
    "    fig, axes = plot_fence(prob, idx=i, fig=fig, axes=axes)\n",
    "\n",
    "fig.suptitle('Potential arrangements of 4 Fence posts and the resulting perimeter and enclosed area.', fontsize=16)\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "20413b8b",
   "metadata": {
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     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Solving the Optimization Problem and Recording the Driver Iterations\n",
    "\n",
    "In the following code, we set up the optimization problem to find the location of 20 fence posts which maximize area while limiting perimeter to $300$ meters.\n",
    "A recorder is added to the driver that records the design variables, constraints, and objectives."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c6d905fa",
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    "execution": {
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     "status": "completed"
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    "tags": []
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Optimization Problem -- Optimization using pyOpt_sparse\n",
      "================================================================================\n",
      "    Objective Function: _objfunc\n",
      "\n",
      "    Solution: \n",
      "--------------------------------------------------------------------------------\n",
      "    Total Time:                    1.6459\n",
      "       User Objective Time :       1.5425\n",
      "       User Sensitivity Time :     0.0791\n",
      "       Interface Time :            0.0184\n",
      "       Opt Solver Time:            0.0059\n",
      "    Calls to Objective Function :      35\n",
      "    Calls to Sens Function :           32\n",
      "\n",
      "\n",
      "   Objectives\n",
      "      Index  Name                  Value\n",
      "          0  fence.area    -8.218499E-01\n",
      "\n",
      "   Variables (c - continuous, i - integer, d - discrete)\n",
      "      Index  Name         Type      Lower Bound            Value      Upper Bound     Status\n",
      "          0  fence.L_0       c     1.000000E+00     1.069717E+01     1.000000E+20           \n",
      "          1  fence.y_0       c     0.000000E+00     2.185646E+00     1.000000E+20           \n",
      "          2  fence.y_1       c     0.000000E+00     3.139145E+00     1.000000E+20           \n",
      "          3  fence.y_2       c     0.000000E+00     3.783755E+00     1.000000E+20           \n",
      "          4  fence.y_3       c     0.000000E+00     4.258652E+00     1.000000E+20           \n",
      "          5  fence.y_4       c     0.000000E+00     4.617809E+00     1.000000E+20           \n",
      "          6  fence.y_5       c     0.000000E+00     4.885983E+00     1.000000E+20           \n",
      "          7  fence.y_6       c     0.000000E+00     5.077885E+00     1.000000E+20           \n",
      "          8  fence.y_7       c     0.000000E+00     5.202154E+00     1.000000E+20           \n",
      "          9  fence.y_8       c     0.000000E+00     5.263328E+00     1.000000E+20           \n",
      "         10  fence.y_9       c     0.000000E+00     5.263328E+00     1.000000E+20           \n",
      "         11  fence.y_10      c     0.000000E+00     5.202154E+00     1.000000E+20           \n",
      "         12  fence.y_11      c     0.000000E+00     5.077885E+00     1.000000E+20           \n",
      "         13  fence.y_12      c     0.000000E+00     4.885983E+00     1.000000E+20           \n",
      "         14  fence.y_13      c     0.000000E+00     4.617809E+00     1.000000E+20           \n",
      "         15  fence.y_14      c     0.000000E+00     4.258652E+00     1.000000E+20           \n",
      "         16  fence.y_15      c     0.000000E+00     3.783755E+00     1.000000E+20           \n",
      "         17  fence.y_16      c     0.000000E+00     3.139145E+00     1.000000E+20           \n",
      "         18  fence.y_17      c     0.000000E+00     2.185646E+00     1.000000E+20           \n",
      "\n",
      "   Constraints (i - inequality, e - equality)\n",
      "      Index  Name            Type          Lower           Value           Upper    Status  Lagrange Multiplier (N/A)\n",
      "          0  fence.perimeter    e   1.000000E+00    1.000000E+00    1.000000E+00              9.00000E+100\n",
      "\n",
      "\n",
      "   Exit Status\n",
      "      Inform  Description\n",
      "           0  Optimization terminated successfully.\n",
      "--------------------------------------------------------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "N = 20\n",
    "p = om.Problem()\n",
    "\n",
    "p.model.add_subsystem('fence', Fence(n=N))\n",
    "\n",
    "p.model.add_design_var('fence.L', lower=1)\n",
    "p.model.add_design_var('fence.y', lower=0, indices=om.slicer[1:-1])\n",
    "p.model.add_constraint('fence.perimeter', equals=100 * np.pi, ref=100 * np.pi)\n",
    "p.model.add_objective('fence.area', ref=-1000.0)\n",
    "\n",
    "p.driver = om.pyOptSparseDriver(optimizer='SLSQP')\n",
    "\n",
    "p.driver.add_recorder(om.SqliteRecorder('driver_cases.db'))\n",
    "p.driver.recording_options['includes'] = ['*']\n",
    "p.driver.recording_options['record_desvars'] = True\n",
    "p.driver.recording_options['record_constraints'] = True\n",
    "p.driver.recording_options['record_objectives'] = True\n",
    "\n",
    "p.setup(force_alloc_complex=True)\n",
    "\n",
    "p.set_val('fence.y', val=np.zeros(N))\n",
    "\n",
    "p.run_driver();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0395ddec",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2026-10-02T14:45:10.273556Z"
    },
    "papermill": {
     "duration": 0.009821,
     "end_time": "2026-10-02T14:45:10.275040+00:00",
     "exception": false,
     "start_time": "2026-10-02T14:45:10.265219+00:00",
     "status": "completed"
    },
    "tags": [
     "remove-cell"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(1.21185263400333e-07)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from openmdao.utils.assert_utils import assert_near_equal\n",
    "\n",
    "assert_near_equal(p.get_val('fence.area'), 821.85, tolerance=1.0E-2)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "ee1f3c8b",
   "metadata": {
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     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Invoking the CaseViewer\n",
    "\n",
    "In the following line we invoke the CaseViewer.\n",
    "It will not be displayed in the static documentation here, but executing this notebook in Jupyter will provide a GUI like that shown in the figure below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "40f6ef90",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "scrolled": false,
    "tags": [
     "remove-output"
    ]
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d312d052142547bea359dbc63c4ab0e9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(Dropdown(description='Source', layout=Layout(height='auto', width='30%'), options=(), value=Non…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "eb9493733b6743f09df2b48dd3c76cf0",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": 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",
      "text/html": [
       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img 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' width=900.0/>\n",
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "om.CaseViewer(p.get_outputs_dir() / 'driver_cases.db');"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "d4b829b8",
   "metadata": {
    "papermill": {
     "duration": 0.002127,
     "end_time": "2026-10-02T14:45:10.563601+00:00",
     "exception": false,
     "start_time": "2026-10-02T14:45:10.561474+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Plotting a scalar vs. the case index\n",
    "\n",
    "To see how the value of a variable evolves over the iteration history, use `Case Index` as the X-Axis variable.\n",
    "\n",
    "For instance, to see how the area (the objective function) changes over iterations, choose `outputs -> fence.area` as the Y-Axis variable.\n",
    "\n",
    "![Figure 1](./images/case_viewer_area.png)"
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    "## Plotting an array vs. the case index\n",
    "\n",
    "When an array-valued variable is plotted against the Case Index, each value in the array is plotted at the X-Axis value corresponding to each case.\n",
    "The color of each point represents its index in the array, with warmer colors being higher indices.\n",
    "\n",
    "For instance, to see how the values of `fence.y` at each iteration, choose `inputs -> fence.y` as the Y-Axis variable.\n",
    "\n",
    "If this type of plot is too noisy for your data, you can use the `Y Transform` options to retrieve things like the maximum value in the array, the minimum value in the array, or the norm of the array.\n",
    "\n",
    "![Figure 2](./images/case_viewer_y_vs_case.png)"
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    "## Plotting one array vs. another\n",
    "\n",
    "Sometimes it's useful to visualize the evolution of a solution as the plot of one array-shaped variable vs. another.\n",
    "In our case, the shape of the fence is represented by plotting `outputs -> fence.x` on the X-Axis and `inputs -> fence.y` on the Y-Axis.\n",
    "\n",
    "\n",
    "When plotting arrays, the X-Axis is required to be viewable as a flattened array.\n",
    "The Y-Axis variable must have the same number of points as the X-Axis variable in its first dimension.\n",
    "In our use-case here, `fence.x` and `fence.y` have the same shape.\n",
    "If they did not, then the slice text boxes could be used to reshape the variable in the appropriate way.\n",
    "The default value, `[...]`, plots the entire array.\n",
    "\n",
    "When plotting one array vs. another, the CaseViewer will connect the points in each case using a line, and color that line based on the index of the case in the selected cases box at the top of the GUI.\n",
    "Scrolling the slider will highlight a particular case.\n",
    "\n",
    "![Figure 1](./images/case_viewer_x_vs_y.png)"
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    "## Current limitations of the CaseViewer\n",
    "\n",
    "CaseViewer is currently limited to one instance per notebook.  Instantiating more than one will cause the plotting interface to fail."
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",
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