notebooks/FenicsSolve.ipynb

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   "source": [
    "import numpy as np\n",
    "import dolfin as fin\n",
    "import matplotlib.pyplot as plt\n",
    "from mud.examples.fenics import fenics_poisson_solve, run_fenics\n",
    "\n",
    "__author__ = \"Carlos del-Castillo-Negrete\"\n",
    "__copyright__ = \"Carlos del-Castillo-Negrete\"\n",
    "__license__ = \"mit\"\n",
    "\n",
    "# Matplotlib plotting options\n",
    "plt.backend = \"Agg\"\n",
    "plt.rcParams[\"mathtext.fontset\"] = \"stix\"\n",
    "plt.rcParams[\"font.family\"] = \"STIXGeneral\"\n",
    "plt.rcParams[\"figure.figsize\"] = (10, 10)\n",
    "plt.rcParams[\"font.size\"] = 16\n",
    "plt.rcParams[\"text.usetex\"] = True\n",
    "plt.rcParams[\"text.latex.preamble\"] = r\"\\usepackage{bm}\"\n",
    "plt.rcParams[\"text.latex.preamble\"] = r\"\\usepackage{amsfonts}\""
   ]
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    {
     "ename": "NameError",
     "evalue": "name 'fin' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Input \u001b[0;32mIn [2]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mfin\u001b[49m\u001b[38;5;241m.\u001b[39mset_log_level(\u001b[38;5;241m50\u001b[39m)\n\u001b[1;32m      2\u001b[0m _logger \u001b[38;5;241m=\u001b[39m logging\u001b[38;5;241m.\u001b[39mgetLogger(\u001b[38;5;18m__name__\u001b[39m)\n\u001b[1;32m      5\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mpiecewise_eval_from_vector\u001b[39m(u, d\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m):\n",
      "\u001b[0;31mNameError\u001b[0m: name 'fin' is not defined"
     ]
    }
   ],
   "source": [
    "fin.set_log_level(50)\n",
    "_logger = logging.getLogger(__name__)\n",
    "\n",
    "\n",
    "def piecewise_eval_from_vector(u, d=1):\n",
    "    \"\"\"\n",
    "    Takes an iterable `u` with y-values (on interior of equispaced unit domain)\n",
    "    and returns the string for an expression\n",
    "    based on evaluating a piecewise-linear approximation through these points.\n",
    "    \"\"\"\n",
    "    n = len(u)\n",
    "    dx = 1 / (n + 1)\n",
    "    xvals = [i * dx for i in range(n + 2)]\n",
    "    yvals = [0] + list(u) + [1]\n",
    "\n",
    "    s = \"\"\n",
    "    for i in range(1, len(xvals)):\n",
    "        start = xvals[i - 1]\n",
    "        end = xvals[i]\n",
    "        diff = start - end\n",
    "        s += f\" ((x[{d}] >= {start}) && (x[{d}] < {end}))*\"\n",
    "        s += f\"({yvals[i-1]}*((x[{d}]-{end})/{diff}) + (1 - ((x[{d}]-{end})/{diff}))*{yvals[i]} ) +\"\n",
    "\n",
    "    return s[1:-1]\n",
    "\n",
    "\n",
    "def fenics_poisson_solve(gamma=-3, lam=None):\n",
    "    # Define mesh\n",
    "    mesh = fin.UnitSquareMesh(36, 36)\n",
    "\n",
    "    # Initialize mesh function for interior domains\n",
    "    domains = fin.MeshFunction(\"size_t\", mesh, mesh.topology().dim())\n",
    "    domains.set_all(0)\n",
    "\n",
    "    # Initialize mesh function for boundary domains\n",
    "    boundaries = fin.MeshFunction(\"size_t\", mesh, mesh.topology().dim() - 1)\n",
    "    boundaries.set_all(0)\n",
    "\n",
    "    # Create classes for defining parts of the boundaries and the interior\n",
    "    # of the domain\n",
    "    class Left(fin.SubDomain):\n",
    "        def inside(self, x, on_boundary):\n",
    "            return fin.near(x[0], 0.0)\n",
    "\n",
    "    class Right(fin.SubDomain):\n",
    "        def inside(self, x, on_boundary):\n",
    "            return fin.near(x[0], 1.0)\n",
    "\n",
    "    class Bottom(fin.SubDomain):\n",
    "        def inside(self, x, on_boundary):\n",
    "            return fin.near(x[1], 0.0)\n",
    "\n",
    "    class Top(fin.SubDomain):\n",
    "        def inside(self, x, on_boundary):\n",
    "            return fin.near(x[1], 1.0)\n",
    "\n",
    "    # Initialize sub-domain instances\n",
    "    left = Left()\n",
    "    top = Top()\n",
    "    right = Right()\n",
    "    bottom = Bottom()\n",
    "    left.mark(boundaries, 1)\n",
    "    top.mark(boundaries, 2)\n",
    "    right.mark(boundaries, 3)\n",
    "    bottom.mark(boundaries, 4)\n",
    "\n",
    "    if lam is None:\n",
    "        cons = gamma * 823543 / 12500\n",
    "        g_L = fin.Expression(f\"pow(x[1], 2) * pow(1 - x[1], 5) * {cons}\", degree=3)\n",
    "    else:\n",
    "        g_L = fin.Expression(piecewise_eval_from_vector(lam), degree=2)\n",
    "\n",
    "    g_R = fin.Constant(0.0)\n",
    "    f = fin.Expression(\n",
    "        \"10*exp(-(pow(x[0] - 0.5, 2) + pow(x[1] - 0.5, 2)) / 0.02)\", degree=2\n",
    "    )\n",
    "\n",
    "    # Define function space and basis functions\n",
    "    V = fin.FunctionSpace(mesh, \"Lagrange\", 1)\n",
    "    u = fin.TrialFunction(V)\n",
    "    v = fin.TestFunction(V)\n",
    "\n",
    "    # Define Dirichlet boundary (x = 0 or x = 1)\n",
    "    def top_bottom_boundary(x):\n",
    "        return x[1] < fin.DOLFIN_EPS or x[1] > 1.0 - fin.DOLFIN_EPS\n",
    "\n",
    "    dirichlet_bc = fin.Constant(0.0)  # top and bottom\n",
    "    bcs = fin.DirichletBC(\n",
    "        V, dirichlet_bc, top_bottom_boundary\n",
    "    )  # this is required for correct solutions\n",
    "\n",
    "    # Define new measures associated with the interior domains and\n",
    "    # exterior boundaries\n",
    "    dx = fin.Measure(\"dx\", domain=mesh, subdomain_data=domains)\n",
    "    ds = fin.Measure(\"ds\", domain=mesh, subdomain_data=boundaries)\n",
    "\n",
    "    # Define variational form\n",
    "    a0 = fin.Constant(1.0)\n",
    "    F = (\n",
    "        fin.inner(a0 * fin.grad(u), fin.grad(v)) * dx(0)\n",
    "        - g_L * v * ds(1)\n",
    "        - g_R * v * ds(3)\n",
    "        - f * v * dx(0)\n",
    "        - f * v * dx(1)\n",
    "    )\n",
    "\n",
    "    # Separate left and right hand sides of equation\n",
    "    a, L = fin.lhs(F), fin.rhs(F)\n",
    "\n",
    "    # Solve problem\n",
    "    u = fin.Function(V)\n",
    "    fin.solve(a == L, u, bcs)\n",
    "\n",
    "    return u\n",
    "\n",
    "\n",
    "def run_samples(\n",
    "    num_samples=10,\n",
    "    num_sensors=20,\n",
    "    mins=[-4, -4],\n",
    "    maxs=[0, 0],\n",
    "    sensor_low=[0, 0],\n",
    "    sensor_high=[1, 1],\n",
    "    gamma=-3,\n",
    "    save_data=True,\n",
    "):\n",
    "    if len(mins) != len(maxs):\n",
    "        raise ValueError(\"min/max arrays must be of same length\")\n",
    "\n",
    "    sensors = np.random.uniform(low=sensor_low, high=sensor_high, size=(num_sensors, 2))\n",
    "    lams = np.random.uniform(low=mins, high=maxs, size=(num_samples, len(mins)))\n",
    "    data = np.zeros((num_samples, num_sensors))\n",
    "    true_vals = np.zeros((num_sensors, 1))\n",
    "\n",
    "    for i in tqdm(range(len(lams))):\n",
    "        u = fenics_poisson_solve(lam=lams[i])\n",
    "        for j, s in enumerate(sensors):\n",
    "            data[i, j] = u(s)\n",
    "\n",
    "    # Solve true solution\n",
    "    u_true = fenics_poisson_solve(gamma=gamma)\n",
    "    for j, s in enumerate(sensors):\n",
    "        true_vals[j] = u_true(s)\n",
    "\n",
    "    # Store coordinates and grid where true solution solved for plotting later\n",
    "    c = u_true.function_space().mesh().coordinates()\n",
    "    v = np.array([u_true(c[i, 0], c[i, 1]) for i in range(len(c))])\n",
    "\n",
    "    full_res = {\n",
    "        \"lam\": lams,\n",
    "        \"data\": data,\n",
    "        \"true_vals\": true_vals,\n",
    "        \"domain\": list(zip(mins, maxs)),\n",
    "        \"sensors\": sensors,\n",
    "        \"u\": (c, v),\n",
    "    }\n",
    "\n",
    "    ts = datetime.now().strftime(\"%Y_%m_%d-%I:%M:%S_%p\")\n",
    "    if save_data:\n",
    "        with open(f\"s{num_samples}_n{num_sensors}_d{len(mins)}_res-{ts}\", \"wb\") as fp:\n",
    "            pickle.dump(full_res, fp)\n",
    "\n",
    "    return full_res"
   ]
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   "cell_type": "markdown",
   "id": "ccdd8044-9465-47ab-a178-ed883c5c312d",
   "metadata": {},
   "source": [
    "## Functions for solving Inverse Problem"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58318f1e-7bbe-48e4-be6a-84a09c28850f",
   "metadata": {},
   "source": [
    "## Notebooks Settings\n",
    "\n",
    "- Matplotlib widget: for manipulating images interactively \n",
    "- Figure dictionary: storing which figures have been opened"
   ]
  },
  {
   "cell_type": "code",
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   "outputs": [],
   "source": [
    "%matplotlib widget\n",
    "figs = {}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20d0d851-15d8-4443-87e9-59d9ec53311c",
   "metadata": {},
   "source": [
    "# Poisson Problem"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbe59d8f-b5b7-4253-a8aa-fb4f5d5b7bbd",
   "metadata": {},
   "source": [
    "Define boundary conditions.\n",
    "\n",
    "- Dirichlet on the upper and lower boundaries. \n",
    "- Neumann boundary on right"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e59db21-9e43-44c5-87e3-cd5eb23f9e86",
   "metadata": {},
   "source": [
    "## True Solution"
   ]
  },
  {
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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=1000.0/>\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": [
    "u_true = fenics_poisson_solve(gamma=-3)\n",
    "\n",
    "# Plot solution\n",
    "plt.figure()\n",
    "fin.plot(u_true, title=\"Solution u\")\n",
    "\n",
    "# Show plots\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46235e69-8ba7-4fbe-aed0-0d3e6435d8f1",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Using a Linear Spline Approximation to $g_L$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "66441970-5f8a-41ef-b668-40b8599fdea1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-20T16:18:48.312384Z",
     "iopub.status.busy": "2022-10-20T16:18:48.311708Z",
     "iopub.status.idle": "2022-10-20T16:18:59.695596Z",
     "shell.execute_reply": "2022-10-20T16:18:59.695214Z",
     "shell.execute_reply.started": "2022-10-20T16:18:48.312321Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "659e34b10b834c1e8bf05d86de9afff7",
       "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=1000.0/>\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": [
    "u_approx = fenics_poisson_solve(lam=[-3, -1])\n",
    "\n",
    "# Plot solution\n",
    "plt.figure()\n",
    "fin.plot(u_approx, title=\"Solution u\")\n",
    "\n",
    "# Show plots\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5449182-6cf9-4862-a40b-92683c74bae5",
   "metadata": {},
   "source": [
    "## Comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1dbfc4e6-ad2b-4b0e-afd6-7fe536f51548",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-20T16:19:21.738897Z",
     "iopub.status.busy": "2022-10-20T16:19:21.738375Z",
     "iopub.status.idle": "2022-10-20T16:19:21.925399Z",
     "shell.execute_reply": "2022-10-20T16:19:21.924837Z",
     "shell.execute_reply.started": "2022-10-20T16:19:21.738861Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x18e795e10>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c12fffa27efe44349561dd3ddfc62994",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": 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g2haGJ4Npe6nugNvb23d3XftXr/Terzz3/fowVsJvOtSl5K98l0SoI7G+enuv3s9/pJubm7XvVzE8R/6z//L39Pz9v7RWXW++/H/6F3/rj5Zul23byuVyE1t9zypft45Wq6Vyuazr6+uxPVE6nY5evHih6+vrQNsWFubQVxTGvHoc5tSZTwewac1mU9lsdqJ8WNbpdAKto16vK5vNTmxwls1m5Xne2LFBtC0sBPoakhrqSceqdyDeer2e0un01OcMw1C32w20DsdxJjYykzR6/cNjg2hbWHY+0D/6zpU+/ugXK78+iaFOLx3AJs0KWElKpVI6Pz+PpI6hh8cGWW/Qdj7Qhwj1cUkPdXrpQPRub2/HHnd3k2t2Ht/A6zHDMBYe47eOdDo99fbew2Me/+86bQsTq9wf+PijX+jHP/m1lV6bxNXvSV/5zqp3YLHP/uKv6umX6/2B/9X/uZP0Rzo6Ohorf/nypV69erVW3UEol8uqVCoT5bPu7hlX9NAfoacOAOG4vLzUzc3N6FGtVieOebww7bFlesB+6zg7O1M2m1Wr1Zpa17C+INoWJgJ9CkL9Gwy9AwjK/v7+2GNvz//5xXXdhcG6Sh0XFxfq9/tqNBqjRz6fl+d5Oj4+jqxt62DIfQaG3wFgMwzDmDqnLWnpgF2ljse35h72uIcbzATVtrDQQ58jbj31TaGXDiBKp6enchxn5vOWZUVSx3AO/eHub0HUGxYCfYF1Qj1ozKeHh1AH4qNQKMi27Yk56WHAmqYZaB2dTkcffvjhxLHNZlP1en1sGD2ItoWFQA9RkubTk95LBxAfpmkqn8+rVquNldfrdbXb7Yl56kwmMzYs7rcO13WVTqfHyiqVigzD0NnZ2Vpti9LO7+X+t//dP9HTX10cVqvOp0vJ2vc9yZexSezzju0Wxl7uy54j57br/9zpP//Df+m7Xcve0SyXyymVSk3dpW3ZOiqVylivO5fLzb3RShzvtkag+/iyxinUCfRwEOjYZkkLdPiz80Puf/cv/6+lj43TIjmG3sPBXDqAbbXzgS5Jf//b/3PpY+O0SA4AgCECPUL00rcDvXQA24hA/xq9dADANiPQV7RqqNNLBwCEgUB/wE8vfR1J2kUuqRh2B7BtCPRHtnHonV46AIBA3xB66fFHLx3ANiHQp6CXvjx66QAQDwR6AOKyQA4AsLsI9BmiWiAXJO7GFjyG3QFsCwI9ILvcS2fYHQA2j0Cfg146JHrpALYDgR4geukAgE0h0BfYxl46AGD3EOgJxLB78Bh2BxB3Ox/o/+CD/77wmCiuS0/CsDsAYHN2PtARHObRAWBzCHRJ/2j/vy48Ztvm0hl2Dx7D7gDijEAPAcPuAICoEehfW6aXjsUYdgeAzSDQfYhi2D3IXjrD7sFj2B1AXBHoIYnLXdgAALuBQAcAIAEI9AdY7R4M5tEBIHoEeohY7Z5MzKMDiCMCHQCABCDQAQBIAAL9Ea5HBwBsIwIdAIAEINBXwAYziyV9pTsL4wDEDYEeMjaYAQBE4emmGwAAwDSNRkNXV1c6PDxUv9+XZVnK5/OB12Hbtmq1mur1utLp9MI6LctSNptVsVhUNpuV4zhqNpvyPE/NZtNX+4JEoE/xj/b/q/7t7d/cdDMAYGeVy2VlMhnV6/VRmWVZcl1XpVIp0Docx1Gn01Gn05lZV7PZHL3GdV01Gg01Go3R86ZpqtvtLv37hYFABwDEim3barVaGgwGY+X1el25XG6pQPdTh+M4Mk1zau/cdV05jjN2fDqdVrFY1OvXr5VOp2VZlkzT9PtrBo5AX9Hf//b/1H/42V/bdDMAIHGazaay2exE+bCs0+ksHHr3U8fV1dXM3nW5XNYnn3wyVpZKpXR2drb4F4kYi+IAALHS6/VmzmUbhrHU0LafOizLmnpcp9ORZVkyDGNxo2OAQAdWxKVrQDgcx1EqlZr6XCqV0vn5eaB1TBsudxxHr1+/njsSMBzWt217YXuiQKADACJxe3s79ri7u5s4xvO8uXUYhrHwmCDqqFQqY4vpHnJdV5VKZbS4znVd5XI5OY4zt86wMYcegY8/+oV+/JNf8/26u4/vtffjZyG0KBo3mT0d9Cf/gwWwPX7y00O99yvP16rj7f99I0k6OjoaK3/58qVevXq1Vt1h6HQ6Ojk5mfm8ZVlji+RM01SxWJRlWer3+1E0cSp66DtiE7vFAcBDl5eXurm5GT2q1erEMYvmqxf1rIOoo1KpzB1qn7bK3jTN0eVvm0KgAwAisb+/P/bY2/O/RbTrumsvUptXh23bchxnqQ1mHhoev8lr0SMfcg9i5x9JarVaY0MbmUxm6c0GAADxZRiGXNed+pzneTo+Pg6tjmazOTfMC4WCHMfRxcXF1OdnvWcUIg30IHb+8TxPhUJBhUJhVI9t2yoUCjo+Pp563SEAYHucnp7OXck+6zKzIOro9XpzRwBs2565AY2kuXPvYYtsyH24vP/xxfj1el3lcnnpel68eKF0Oj32B4DruoEMwwAANq9QKMi27Ym57l6vJ2n6ZWZB1THvcjdJyufzU4fVh3PnmxwpjizQl9m1ZxHbttXpdFSpVMbKTdPU9fW17zkPAED8mKapfD6vWq02Vl6v19Vutyc6b5lMRplMZq06pOUW3FWr1YlO6PDmLrPqjUpkQ+69Xm/mcPhw155Fc+m1Wk2GYRDcAJBw7XZbjUZDlUpltOaqXC5PzQnDMKb2qv3UMazHMIy5U7eGYYxGlg3DGF17/tlnn218yjeyQB9ufj+Nn51/0um0PM9Tq9WS9G4PXhbEAUDyLLtf+qwFan7qGLq+vl54jGEYG71N6iyRBHoQu/ZI74Y1TNOcmIsvFAq6uLiY+wHf3d2N7Up0e3u78P2S5IM/Hyw+CACwtbbmOvRh4Pd6vYnhknq9rlarNVrsME2tVtPBwcHo8XjHojCtskucpK3eJQ4AEK1IAj3InX/S6fTEHPrw53k99Gq1OrZD0eXl5cL3xHrY9hUAohOLvdxd1116odusPw4eLk6YZm9vb6VdiWbhXugAgDiJbMg9iJ1/hgviZtURlH97+zcDqwsAgChEFuinp6dze9DL7PxjmubcbfWW+aMAAIAkiizQg9j5p1KpyPO8iT8MhjeX97PjHAAASRJZoAex8086ndbZ2dlEcL948UJnZ2cbv6gfAIBNiXRRXBA7/wwvUSsUCkqlUnJdV9VqdaU7tgEAkBSRr3IPYuefUqnEznAAADywNRvLxImfS9bisKkMu8QBQPIR6I9wyRoAYBsR6AgFu8QBQLQIdAAAEoBA92nb5s8BALuBQH8gifPnLIgDgN1AoAMAkAAEOgK3Kwvinv3pTzbdBAAYIdABAEgAAv1ry8yfb9uCOObPAWB3EOgAACQAga5krm7flF2ZPweAuCHQl8RwOwAgznY+0P/9F39j003AFmKFO4C42flAX8a29c43heF2ANgcAj2BGG4HgN1DoC/gp3cOAMCmEOgBYrgdALApBPoc29g7Z7g9fCyIAxBHBHpAdrl3DgDYPAJ9Bnrny2O4HQA2j0APAL1zAMCmPd10A+IoiuvOg8bceTSYPwei02g0dHV1pcPDQ/X7fVmWpXw+H2odrVZL/X5/9HMmk1GpVAqlbUEj0DckKb1zhtsBhKFcLiuTyaher4/KLMuS67pTA3bdOjzPU6FQUKFQGB1v27YKhYKOj4+VzWYDbVsYCPRH6J0DwGbZtq1Wq6XBYPzcVq/XlcvllgpNv3W8ePFC6XR6rNx1XbmuK8MwAm1bWJhD3wB659uJ4XYgGs1mc6xHPDQs63Q6gdZh27Y6nY4qlcrYsaZp6vr6Wul0OtC2hYVAf4DeOQBsXq/XGwvRhwzDULfbDbSOWq0mwzBmHh9028LCkPvXogpzeucAMJ/jODJNc+pzqVRK5+fngdbhOI7S6bQ8z1Or1ZIkXV1dTV0QF0TbwkKgK7przoMOc3rn0WG4HYiG53lznzcMY+ExfuuwbVumaarVauns7GxUXigUdHFxoWazGVjbwsSQu09xGWrfJHrnAFZxe3s79ri72/y5ZBjAvV5v4rKzer2uVqulXq+3gZb5t/M99M/+4q/q6a+G/z5J6Z0T5sBueXb5TN96vt7565dv3kqSjo6OxspfvnypV69ejZU9XFE+zTI9YD91DI9Np9MTc+PDn5vNpkzTDKRtYdr5QPcjLjvCMdQeLYbbgWBcXl5qf39/9PPe3p7vOlzXXWrxmt86ZoW1YRhyHCeytq2DQF8SYU7vHMB69vf3xwJ9FsMw5Lru1Oc8z9Px8XGgdQwXxM06Nui2hYU59CUwb7676J0D0Ts9PZ3bK7YsK9A6TNOcGdKSxkI6iLaFhUBfIE6XqNE7B7ALCoWCbNue6B0PF6fNumxs1ToqlYo8z5sIatu2Jb3b6jXItoWFQJ+DMN9t9M6BzTBNU/l8XrVabay8Xq+r3W5PzHdnMhllMpmV60in0zo7OxsLbunddrBnZ2djO8P5bVuUngweb0i7I25vb3VwcKC//e/+iZ7+6uTCjDiFuUTvfBMIdGybr97eq/fzH+nm5mapuep5hufIzO/9ob71/Pladf3yzRv1//D3fLdr2Tua5XI5pVKpqbu0+bkrWqvVUrfbVSqVkuu6KhaLM4+N493WCPQpgU6Yf4MwB7ZH0gId/jDkHiDCHACwKQT6I3Fa0c68OQBgWQT6A3Eaat90mO9y75zhdgDbiED/GmH+jV0OcwDYVgS6CPOHdj3M6Z0D2FY7v/XrT356qPd+xf/rkrYATiLMAWCb0UNfAWGeTPTOAWwzAt0nwjyZCHMA245A94EwBwDEFYG+JMI8ueidA0iCnV8Ut0gYQS4R5nFBmANICnrocxDmAIBtQQ99hiQOsUuE+UP0zgEkCT30KQjz5CPMASQNPfQHkhrkEmH+EGEOIIkIdCV3rlwiyAFgV+x8oD+7fCY9D7bOOAS5RJhPQ+8cQFIxhx4wwjy+CHMASbbzPfSgEOTxRpgDSDp66AEgzOONMAewC+ihryEuQS4R5rMQ5gB2BYG+gjgFuUSYAwAIdF8I8u1C7xzALok80BuNhq6urnR4eKh+vy/LspTP59eqM5fL6eLiIqAWTiLItw9hDmDXRBro5XJZmUxG9Xp9VGZZllzXValUWrlO27aDauIYgnw7EeYAdlFkgW7btlqtlgaD8ZCs1+vK5XIrBbpt2zo/Pw+qiSME+fYizAHsqsguW2s2m8pmsxPlw7JOp+O7zk8//VTFYnHttg198OeDWIX5Qf+OMPeBMAewyyIL9F6vp3Q6PfU5wzDU7XZ91ddoNFStVtdu1weXA4I8AQhzALsusiF3x3FkmubU51KplK+hc9u2lU6nZRhGQK3bPAJ8NQQ5ALwTSaB7njf3ecMwFh7z0Keffjq2sG4Zd3d3urv7JjRvb299vT5oBPj6CHMA+MbWbf266lB7rVbTwcHB6HF0dBRC6+YbDqUT5usjzAFgXCSBvmhofNne+TpD7dVqVTc3N6PH5eWl7zpWRYgH59mf/oQwB4ApYrFTnOu6MxfMPbTKUPvQ3t6e9vb2VnrtqgjxYBHkwG4JYiOydeqYtWmZZVnKZrMqFovKZrNyHEfNZlOe56nZbPpqX5AiC3TDMOS67tTnPM/T8fHx3Nd3Oh3Ztq1yuTxWPlxMNyyv1+sbWyxHgIeDIAd2TxAbka1Tx7xNy1zXVaPRUKPRGJWZpun7aq2gRRbop6enc1eyW5Y19/X5fH7qX1XDD30TfxUR4OEjzIHdE8RGZOvUsWjTsnQ6rWKxqNevXyudTsuyrJlXcUUpskAvFApqtVryPG+sB93r9SQpFh/GIgR4tAhzYDctsxHZomHzdeoYblo2q4eeSqV0dnY29/03IbJV7qZpKp/Pq1arjZXX63W12+2JYfJMJqNMJrOw3lnD+EF4uCqdMI/GcNEbYQ7sriA2Ilu1jqA2LduESC9ba7fbOjw8VKVSUaPRULlcVrlcnvpXkmEYcxfKtVotFQqF0ZaxuVxuYn59FQT45hDiAKR3G5GlUqmpzy27Edkqdfi9kmo4rB/WDcL8inyV+7LDFItuh1oqlVa+Q9tD+5/f6enTJ2vXg9UR5ACGgtiIbNU6lr2SynVdVSoVWZalUqmkXq+nXC6ndru91BVbYYnFZWvYTQQ5sFse79C5icuJZ/Ez1D4M8iHTNFUsFmVZlvr9flhNXIhAR+QIcmB7fHA50LeerXfzql/ev3v94x06X758qVevXo2VBbERmd86/A61TxsdNk1TlUplqQV7YSHQERmCHNhtl5eX2t/fH/28Su982Y3I/NSxzqZlQ8P6ut0ugY5kIsQBDO3v748F+izrbkTmpw6/m5YVCgU5jjNznVeYV14tQqAjcIQ4gHWsuxGZnzr8blo2HJ5/bBjkJycnC9sWlq272xri5+G144Q5gHUVCgXZtj0x1+1nI7Ig6pgmn89PvYZ9eAl1EFdfrYpAh28EOIAwBbERmd86Hps1dF6tVieG523bVq1WW6reMDHkjoUIbQBRa7fbajQaqlQqozulzduIbNomMn7qGGq1Wup2u2Oblh0fH4+G3g3DUL1eV7lclmEYchxHkvTZZ59N3Wo2Sk8Gj3eu3xG3t7c6ODjQb//W7+vp0+ebbs7GEdrA9vvq7b16P/+Rbm5ullp8Ns/wHPkbv/MH+taz9c6Rv7x/o//2b/5pIO3CbDvfQ3/2Zz/V0/eebboZC91//6OZzxHGAICdD/RtQWgDAOZhURwAAAlAoAMAkAAEOgAACUCgAwCQAAQ6AAAJQKADAJAABDoAAAlAoAMAkAAEOgAACUCgAwCQAAQ6AAAJQKADAJAABDoAAAlAoAMAkAAEOgAACbDzgX7/V76j++9/tOlmAACwlqebbkBcPAz1Z3/6kw22BAAA/3a+hz7N/fc/otcOANgqBPochDoAYFsQ6AsQ6gCAbUCgL4EheABA3BHoPhDqAIC4ItB9orcOAIijnQ/02+/t6Saz5/t1BDsAIE52PtCHVgl1iWAHAMQDgf7AqqEuMb8OANgsAv0RQh0AsI0I9CkIdQDAtiHQZyDUAQDbhECfg1AHAGwL7ra2wE1mTwf9u5Vee//9j7hzGwCsqNFo6OrqSoeHh+r3+7IsS/l8PpQ6PM9TrVbT4eGhrq6uZNu2LMvS2dlZaG0LGoEOAIidcrmsTCajer0+KrMsS67rqlQqBVqH53mqVCpqNptjZd/73vf0+vVrtdvtwNsWhieDwWCwsXffoNvbWx0cHOg3fucP9K1nzxcev2ovXeL+6gCi8dXbe/V+/iPd3Nxof39/rbr8niPn+eX9G/23f/NPl26XbdvK5XJ6HE+zyteto9VqqVwuq9vtyjTNUXmhUFCn01G/31c6nQ6sbWHZ+Tn0L46eLHXcuvPpzKkDwHKazaay2exE+bCs0+kEWsfx8bEMw5g4NpVKSXrXWw+ybWHZ+UCXpC++G36oSyyUA4Bl9Hq9UY/4McMw1O12A60jm83q+vp6rHf+sI6HAR5E28JCoH8tqlAHAMznOM6od/xYKpXS+fl56HV0Oh25rjsxfx5E28LCorgHvvjuE33w54vnP1j5DgDheDi8PY1hGAuPWbUOx3HU6XT0+vVreZ6nzz//fGwoPoi2hYke+gYw9A5gF93e3o497u5WX2wchnQ6rbOzM33yySeyLEu5XE62bW+6WUujh/5IFL10ANgW+5/f6enT5aYkZ/nqq3fnyqOjo7Hyly9f6tWrV2Nl0xanPbRMD3jdOgzD0NnZmfr9vnK53GiVexBtCxM99A2hlw5g11xeXurm5mb0qFarvutwXXdhsAZVR6FQkCRVKpVA6w0LgT4FC+QAIHj7+/tjj7296edQwzDkuu7U5zzP0/Hx8cL38lNHLpeTZVkTxw0Xvz0cdg+ibWEh0DeIXjoATDo9PZXjODOfnxa+69Rh2/bUY4fB/fCytSDaFhYCfQZ66QCwGYVCQbZtT8xJ93o9SZq4XnzdOkzT1MXFxUQdw2vKi8VioG0LC4G+YfTSAWCcaZrK5/Oq1Wpj5fV6Xe12e2KeOpPJKJPJrFxHs9nUixcvxkLacRw1Gg2VSqWxm674bVuUdn4v98zv/aG+9Xz6PsXLrHYfYq93AJsWxl7uv/1bv6+nT9fby/2rr97oP/6nf+67Xcve0SyXyymVSk3dpc3P3dYqlcroWnLHcVQul2feQS2Od1sj0OcEurR8qK97CRuhDmBdSQt0+LPzQ+73R/eB1MNcOgBgk3Y+0BdZdnHcuphLBwCsg0APEHdjAwBsCoEu6e7jYIbdAQDYlMj3cl93ZaDnearVaqNViK7rqlqtbnx1IQAAmxRpoJfLZWUyGdXr9VGZZVlyXVelUmnh64eXFdTr9dG1frZtK5fLKZ/PT9y3NijL3rAlCNxeFQCwisiG3G3bVqvV0tnZ2Vh5vV5XuVxeqo5arTYW5tK7Lfnq9bo6nc5op55NYrU7AGATIgv0ZrM5th/u0LCs0+ksrKPT6SiXy02UD7faC6uHDgBA3EUW6L1eT+l0eupzhmFM3eHnsXQ6PfUuN8Me+6w74GwbVrsDAPyKLNAdxxndiu6xVCql8/PzhXV0u11dX19PlA9vbXdycrJy+1jpDgDYZpEsint8V5rHhnvnrqrZbMowjLkL6+7u7nR39832rLe3t77eI8qFcQAA+LX116H3ej31ej198sknc+9yU6vVdHBwMHocHR2F1qYgFsYx7A4A8COSQF90O7l1eueFQkHNZnPhdejValU3Nzejx+Xl5crvCQBA3ES+scw0ruvOXDA3T6FQULVaXeoa9r29Pe3tcUkZACCZIhtyNwxj5ip0z/N0fHzsq75KpaKTk5OJ69oBANhFkQX66empHMeZ+bxlWUvX1Wq1dHh4OBHmrVZr5fYBALDNIgv0QqEg27Yn5suHu7sNN4dZpNfryfO8qT3zdebiAQDYZpHNoZumqXw+P9q+daher6vdbk8snMtkMpKkfr8/KnMcR+VyWaZpqlKpSPomxIfPrWrvx89Wfi0AAJsW6aK4drutRqOhSqUyuttauVyeukLdMIyJjWgsy5LjODOH1h/+oZAE3KgFALCsyFe5L7uI7eLiYqLsYW89amwqAwCIs63fWAYAABDoAAAkAoEOAEACEOgAACQAgQ4AQAIQ6OIadADA9iPQQ3DQv1t8EAAAASLQl8A16ACAuNv5QH92yXA7AGD77XygB43hdgDAJhDoCzDcDgDYBgR6jHFjFgDAsgj0OeidAwC2ReR3WwMAYBmNRkNXV1ej221bljX1dttB1uF5nl68eKFisTjzOMuylM1mVSwWlc1m5TiOms2mPM9Ts9n01b4gEegBYkEcAASjXC4rk8moXq+PyizLkuu6KpVKgddRKBSUSqUkSZ1OR8VicWa9ruuq0Wio0WiMykzTVLfbXapdYSHQZ2C4HQA2w7ZttVotDQbj5+F6va5cLrdUoPuto91uS5Icx1Gr1ZpbdzqdVrFY1OvXr5VOp2VZlkzTXOZXCxWBDgCIlWazqWw2O1E+LOt0OguH3oOoY5ZUKqWzs7OVXhsmFsVNsUrvnOF2AAhGr9dTOp2e+pxhGEsNbQdRx7Yh0GOKS9YA7CrHcUbz2Y+lUimdn59HUsciw2F927bXrisIBPoj9M4BIBy3t7djj7u7yXOn53lz6zAMY+ExQdQxj+u6qlQqo8V1rusql8vJcZyV6wwCc+hrIswBJNmzP/upnr633j0v3nt7L0k6OjoaK3/58qVevXq1Vt2bYFnW2KI60zRVLBZlWZb6/f7G2kUP/YG4rGxnuB1AEl1eXurm5mb0qFarE8cYhjG3jmV61kHUMc+0VfamacpxHHU6nbXqXgeB/jWG2gEgXPv7+2OPvb0933W4rrswsKOo47HhArxNLrbb+UD/4HIQqzCndw5g1xmGIdd1pz7neZ6Oj48jqWOaQqGgXC438/lZ7xmFnQ90AEC8nJ6ezl1gZllWJHVMY9v21NXzwyA/OTlZqd4gEOgrYKgdAMJTKBRk2/bEXHev15OkpXZlC6KOafL5/NRh9eHc+bLb0oaBQI8RhtsB4F3Y5vN51Wq1sfJ6va52uz0x/53JZJTJZNaqY2j4B8CsofNqtapyuTxWZtu2arXa3Hqj8GTweKPbHXF7e6uDgwP9xu/8gb717PnSrwuzd06gA1jHV2/v1fv5j3Rzc6P9/f216hqeI81f/921L1tbtV3L3iktl8splUpN7TkvW0elUpHjOLJtW47jyDAMmaapVCo1cQc1z/NUqVRkGMZoWL9arU7dajZKBLqPQCfMAcRZ0gId/jDkviTCHAAQZ+wUt0DYC+AIcwBAEOihz8FqdgDAtqCHPkVUQU7vHAAQFAL9AXrkAIBtRaBrM0FO7xwAEKSdn0Pf/5wwBwBsv50P9KgR5gCAMBDoESLMAQBhIdAjQpgDAMLEoriQEeQAgCjQQw8RYQ4AiAqBHhLCHAAQJQI9BIQ5ACBqzKEHiCAHAGwKgb4mQhwAEAcE+ooIcgBAnBDoPhDiAIC4ItAXIMQBANtg5wP92Z/9VE/fe7bpZgAAsBYuWwMAIAEIdAAAEoBABwAgAQh0AAASgEAHACABCHQAABKAQAcAIAEIdAAAEoBABwAgAQh0AAASgEAHACABCHQAABIg8puzNBoNXV1d6fDwUP1+X5ZlKZ/PR14HACDeos6LsI6NSqSBXi6XlclkVK/XR2WWZcl1XZVKpcjqAADEW9R5EdaxUXoyGAwGUbyRbdvK5XJ6/HazysOqY+j29lYHBwcyf/13uX0qgET46u29ej//kW5ubrS/v79WXUGeI/22K+q8COvYqEU2h95sNpXNZifKh2WdTieSOgAA8RZ1XoR1bNQiC/Rer6d0Oj31OcMw1O12I6kDABBvUedFWMdGLbJAdxxHqVRq6nOpVErn5+eR1AEAiLeo8yKsY6MWyaI4z/PmPm8YxsJj1q3j7u5Od3d3o59vbm4kvZvbAYAkGJ7PgpzH/WpwL70NoA69m5d/aG9vT3t7e2NlUedFWMduQuSXrW1KrVbTD3/4w4nyP/7Fv95AawAgPFdXVzo4OFirjmfPnunb3/62/vhnwZwj33//fR0dHY2VvXz5Uq9evQqkfkQU6IZhzH1+mb9o1q2jWq3qBz/4wdjx3/3ud/XjH/947S/+rri9vdXR0ZEuLy/XXkG7S/jc/OMzW83NzY0+/vjjmUPCfjx//lyff/657u+DGcUcDAZ68uTJWNnj3rkUfV6EdewmxKKH7rruzEUGQdUxbWhHkg4ODjhh+LS/v89ntgI+N//4zFbz3nvBLI96/vy5nj9/HkhdQYkiL6I4NgyRLYozDEOu6059zvM8HR8fR1IHACDeos6LsI6NWmSBfnp6KsdxZj5vWVYkdQAA4i3qvAjr2MgNItLtdgeSBtfX10uVh1XH0Js3bwYvX74cvHnzZunX7Do+s9XwufnHZ7aapHxuUedFWMdGLbJAHwwGg3w+Pzg7OxsrM01z0G63J45Np9ODdDq9Vh0AgO0UdV6EdWyUItvLfWjZO9TkcjmlUqmpu+7E8S43AIBgRZ0X2363tcgDHQAABC+yRXEAACA8sbgOfRVxvvF9nK37e3iep1qtJs/z5DiOXNdVtVqdWodlWcpmsyoWi8pms3IcR81mU57nqdlsBvlrhWrdz8zv58B37d2tKGu1mur1+lLX9Sbhu+Z5nl68eKFisbjSv/cuns/wyEZn8FdUKpUG9Xp9rMw0zUGz2QyljiDeLw7W/T2ur68HpVJpbBXnxcXFQNIgn89PHJ/NZgeSxh6maa71O0QtiH97P58D37V32u32xGf2+PGwrm3+ruXz+UGpVBqUSqWBpJUWVu3i+QyTtm4OPc43vo+zIH6PSqWiarU6sf1ho9FQpVJRt9uVaZqj8kKhoJOTE71+/VrpdFqWZY09H3dB/dsv+znwXftGo9FQt9ud2jt3XVeO4+ji4mJUtu3fNendXbwymYza7bav3vIuns8wQ7R/P6yvVCoNstns1Oe05F+3fuoI4v3iIIjfY9alIcNeeqlUmnjPbRbUv/2ynwPftW88viTocf2Pr/Xd9u/aYDAY9Pv9lf6dd/F8hum2blFcnG98H2dB/B7pdHrqlofDHvus7RC3VdT/9nzXvjFrt61OpyPLshbeJGOX7OL5DNNtXaDH+cb3cRbE79HtdnV9fT1Rbtu2JOnk5GTq62zbVqvVGh23LYL+t1/0OfBd+8a04XLHcfT69eu5w9Hb+l1bxy6ezzDdVgV6nG98H2dh/x7NZlOGYahUKo2Vu66rSqUi13VVKpXkuq5yudzcfZDjIsjPbJnPge/aYpVKRfV6fepz2/xdW8cuns8w29ZetoZ46PV66vV6arfbE8OglmWNhbxpmioWi7IsS/1+P+KWbg6fw/o6nc7MESCJzxiQtqyHHucb38dZmL9HoVBQs9mcOgz6uMcuvTvROo6jTqez8ntGIcjPbJnPge/afJVKZe5Q+zZ/19axi+czzLZVgb6I67prL5bxU0cQ7xcHq/4ehUJB1Wp16sl0luGCnG1ffLPuv73fz2GXv2u2bctxnKU2mHkoKd+1dezi+WyXbV2gx/nG93EW9O9RqVR0cnKis7Ozqc8XCgXlcrmZr9+GFfFBfGZ+Pge+a9M1m825YZ6E79o6dvF8hum2LtDjfOP7OAvy92i1Wjo8PJwI81arNfr/tm1PXU07PJnMmw+NiyA+Mz+fA9+16Xq93tyeYxK+a+vYxfMZZtj0hfB+xfnG93EW1O/R7XYnto0celg+a2OQer2+NZ9bEJ+Zn8+B79p0WrCNaxK+a4PB6hvL7OL5DNNtXaAPBvG+8X2crfu59fv9QTqdHpRKpcHZ2dng7OxstAf143qG+74/dHFxMTAMY6s+t3U/M7+fA9+1cdfX1wsDPSnfteGOi/P2VOd8hnm2bi/3oTjf+D7O1vncMpnM3OG6i4sLZbPZ0c+e56lSqcgwjNHrqtXq2DHbYN3vmt/Pge/auA8//FClUmnmNejSdn/XKpWKHMcZLf4zDEOmaSqVSk3cKY7zGebZ2kAHAADf2LpFcQAAYBKBDgBAAhDoAAAkAIEOAEACEOgAACQAgQ4AQAIQ6AAAJACBDgBAAhDoAAAkAIEOAEACEOgAACQAgQ4AQAIQ6AAAJACBDgBAAhDoAAAkAIEOAEACEOgAACQAgQ4AQAIQ6AAAJACBDgBAAhDoAAAkAIEOAEACEOgAACQAgQ4AQAIQ6AAAJACBDgBAAhDoAAAkAIEOAEACEOgAACTA/weO67NvyuCUWAAAAABJRU5ErkJggg==",
      "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=500.0/>\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": [
    "c = u_true.function_space().mesh().coordinates()\n",
    "v = np.array(\n",
    "    [\n",
    "        np.abs(u_true(c[i, 0], c[i, 1]) - u_approx(c[i, 0], c[i, 1]))\n",
    "        for i in range(len(c))\n",
    "    ]\n",
    ")\n",
    "\n",
    "fig = plt.figure(figsize=(5, 5))\n",
    "tcf = plt.tricontourf(c[:, 0], c[:, 1], v)\n",
    "\n",
    "fig.colorbar(tcf)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e27fae9f-2c50-4c70-99b0-e4930e5c5465",
   "metadata": {},
   "source": [
    "# Run trials"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91c075f6-1965-4f4e-b30f-fb3197943343",
   "metadata": {},
   "source": [
    "## Two dim"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f53b9ab-bf8e-4c45-b1da-48f0303557a0",
   "metadata": {},
   "source": [
    "### Creating Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e7ae3eba-e67d-4df5-ac86-0e0c8a6eb8b7",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "mins = [-4, -4]\n",
    "maxs = [0, 0]\n",
    "res_2d = run_samples(num_samples=1000, num_sensors=100, mins=mins, maxs=maxs)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "184065eb-9398-42db-9d40-720bdd9cfb96",
   "metadata": {},
   "source": [
    "### Loading Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1ca88a49-113a-4341-811c-059f567bae6f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-02T13:12:27.959017Z",
     "iopub.status.busy": "2022-08-02T13:12:27.957506Z",
     "iopub.status.idle": "2022-08-02T13:12:27.974503Z",
     "shell.execute_reply": "2022-08-02T13:12:27.973491Z",
     "shell.execute_reply.started": "2022-08-02T13:12:27.958881Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "with open(\"s1000_n100_d2\", \"rb\") as df:\n",
    "    res_2d = pickle.load(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f881fd64-078b-45aa-a9c4-04711210b826",
   "metadata": {},
   "source": [
    "## Four dim"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a65d3123-1221-4d66-85ef-79e36838d814",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Creating Data-Set\n",
    "\n",
    "Takes about 45 mins for 1000 samples x 100 sensors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6bce4bb6-bcc1-4224-b20a-febf4b7d39f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "mins = [-4, -4, -4, -4]\n",
    "maxs = [0, 0, 0, 0]\n",
    "res_4d = run_samples(num_samples=1000, num_sensors=100, mins=mins, maxs=maxs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "21e38c5b-7f0a-402e-9f56-51b4014c9e24",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-02T13:49:27.315988Z",
     "iopub.status.busy": "2022-08-02T13:49:27.315755Z",
     "iopub.status.idle": "2022-08-02T13:49:27.329937Z",
     "shell.execute_reply": "2022-08-02T13:49:27.329490Z",
     "shell.execute_reply.started": "2022-08-02T13:49:27.315956Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[0.        , 0.        ],\n",
       "        [0.02777778, 0.        ],\n",
       "        [0.05555556, 0.        ],\n",
       "        ...,\n",
       "        [0.94444444, 1.        ],\n",
       "        [0.97222222, 1.        ],\n",
       "        [1.        , 1.        ]]),\n",
       " array([0., 0., 0., ..., 0., 0., 0.]))"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with open(\"s1000_n100_d4-v2\", \"rb\") as fp:\n",
    "    ds = pickle.load(fp)\n",
    "ds[\"u\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "53eb23e8-7ef1-4b30-ae43-e970971cc977",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-02T13:49:30.427047Z",
     "iopub.status.busy": "2022-08-02T13:49:30.426391Z",
     "iopub.status.idle": "2022-08-02T13:49:30.436097Z",
     "shell.execute_reply": "2022-08-02T13:49:30.435288Z",
     "shell.execute_reply.started": "2022-08-02T13:49:30.427000Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "full_res = {\n",
    "    \"lam\": ds[\"lam\"],\n",
    "    \"data\": ds[\"data\"],\n",
    "    \"true_vals\": tv[\"true_vals\"],\n",
    "    \"domain\": ds[\"domain\"],\n",
    "    \"sensors\": ds[\"sensors\"],\n",
    "    \"u\": ds[\"u\"],\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "57c116c8-360a-4d22-acfd-4fc7627a17de",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-02T13:50:03.962382Z",
     "iopub.status.busy": "2022-08-02T13:50:03.961078Z",
     "iopub.status.idle": "2022-08-02T13:50:03.975341Z",
     "shell.execute_reply": "2022-08-02T13:50:03.974841Z",
     "shell.execute_reply.started": "2022-08-02T13:50:03.962329Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[0.        , 0.        ],\n",
       "        [0.02777778, 0.        ],\n",
       "        [0.05555556, 0.        ],\n",
       "        ...,\n",
       "        [0.94444444, 1.        ],\n",
       "        [0.97222222, 1.        ],\n",
       "        [1.        , 1.        ]]),\n",
       " array([0., 0., 0., ..., 0., 0., 0.]))"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with open(\"s1000_n100_d4-v3\", \"wb\") as fp:\n",
    "    pickle.dump(full_res, fp)\n",
    "ds[\"u\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "60c49b7c-6e72-45aa-88eb-cb4428226f6b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-02T13:48:42.163292Z",
     "iopub.status.busy": "2022-08-02T13:48:42.162107Z",
     "iopub.status.idle": "2022-08-02T13:48:42.178251Z",
     "shell.execute_reply": "2022-08-02T13:48:42.177676Z",
     "shell.execute_reply.started": "2022-08-02T13:48:42.163251Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100, 1)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with open(\"lam_4d_res_tv\", \"rb\") as fp:\n",
    "    tv = pickle.load(fp)\n",
    "tv[\"true_vals\"].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3e16e9f-fe25-424d-ba3a-bb1fb2c0db85",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Loading Data-Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "fd6b7cd8-3a3e-4800-8f92-93e2b4b9f863",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-02T23:29:23.562119Z",
     "iopub.status.busy": "2022-08-02T23:29:23.560757Z",
     "iopub.status.idle": "2022-08-02T23:29:23.581175Z",
     "shell.execute_reply": "2022-08-02T23:29:23.579659Z",
     "shell.execute_reply.started": "2022-08-02T23:29:23.562065Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df = \"s1000_n100_d4_res-2022_08_02-03_00_52_PM\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "8b534d0f-3972-442a-a57d-52e763eb8c0f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-02T23:29:24.589302Z",
     "iopub.status.busy": "2022-08-02T23:29:24.588765Z",
     "iopub.status.idle": "2022-08-02T23:29:24.639218Z",
     "shell.execute_reply": "2022-08-02T23:29:24.638729Z",
     "shell.execute_reply.started": "2022-08-02T23:29:24.589255Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "raw_data, poisson_prob = load_poisson_prob(df, std_dev=0.05, seed=11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "6b729ffe-304f-4959-8e3c-3c5a82756c74",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-02T23:29:26.123094Z",
     "iopub.status.busy": "2022-08-02T23:29:26.121416Z",
     "iopub.status.idle": "2022-08-02T23:29:26.389749Z",
     "shell.execute_reply": "2022-08-02T23:29:26.389089Z",
     "shell.execute_reply.started": "2022-08-02T23:29:26.123005Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "12b4293f0e5849a6b9afba124cbb1e8b",
       "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=500.0/>\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": [
    "figs = poisson_solution_surface(raw_data, figsize=(9, 9), figs=figs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "08709e61-cf44-440d-a47e-e673ed213be3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-02T15:51:03.959612Z",
     "iopub.status.busy": "2022-08-02T15:51:03.959175Z",
     "iopub.status.idle": "2022-08-02T15:51:04.712373Z",
     "shell.execute_reply": "2022-08-02T15:51:04.708134Z",
     "shell.execute_reply.started": "2022-08-02T15:51:03.959557Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "operands could not be broadcast together with shapes (9801,2) (4,) ",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Input \u001b[0;32mIn [19]\u001b[0m, in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0m figs \u001b[38;5;241m=\u001b[39m \u001b[43mpoisson_pca_solution\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpoisson_prob\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfigs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfigs\u001b[49m\u001b[43m)\u001b[49m\n",
      "Input \u001b[0;32mIn [15]\u001b[0m, in \u001b[0;36mpoisson_pca_solution\u001b[0;34m(poisson_prob, num_components, density_figname, contour_figname, vector_figname, pca_contour_figname, figs, figsize, **kwargs)\u001b[0m\n\u001b[1;32m    132\u001b[0m domain_y \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray([[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m25.0\u001b[39m, \u001b[38;5;241m25.0\u001b[39m], [\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m50.0\u001b[39m, \u001b[38;5;241m50.0\u001b[39m]])\n\u001b[1;32m    134\u001b[0m ax \u001b[38;5;241m=\u001b[39m fig\u001b[38;5;241m.\u001b[39madd_subplot(\u001b[38;5;241m2\u001b[39m, \u001b[38;5;241m2\u001b[39m, \u001b[38;5;241m1\u001b[39m)\n\u001b[0;32m--> 135\u001b[0m \u001b[43mqpca_mud_prob\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot_param_space\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    136\u001b[0m \u001b[43m    \u001b[49m\u001b[43max\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43max\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    137\u001b[0m \u001b[43m    \u001b[49m\u001b[43mx_range\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdomain_x\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    138\u001b[0m \u001b[43m    \u001b[49m\u001b[43mparam_idx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    139\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtrue_val\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpoisson_prob\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_closest_to_reference\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    140\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    141\u001b[0m ax\u001b[38;5;241m.\u001b[39mlegend()\n\u001b[1;32m    142\u001b[0m ax\u001b[38;5;241m.\u001b[39mset_xlabel(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m$\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124mlambda_1$\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "File \u001b[0;32m~/repos/mud/src/mud/base.py:486\u001b[0m, in \u001b[0;36mDensityProblem.plot_param_space\u001b[0;34m(self, param_idx, true_val, ax, x_range, aff, in_opts, up_opts, win_opts, mud_opts, true_opts)\u001b[0m\n\u001b[1;32m    484\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m in_opts:\n\u001b[1;32m    485\u001b[0m     io\u001b[38;5;241m.\u001b[39mupdate(in_opts)\n\u001b[0;32m--> 486\u001b[0m     \u001b[43mplot_dist\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_in_dist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx_range\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43midx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mparam_idx\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    487\u001b[0m \u001b[43m             \u001b[49m\u001b[43max\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43max\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msource\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mpdf\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maff\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maff\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mio\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    488\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m up_opts:\n\u001b[1;32m    489\u001b[0m     uo\u001b[38;5;241m.\u001b[39mupdate(up_opts)\n",
      "File \u001b[0;32m~/repos/mud/src/mud/plot.py:176\u001b[0m, in \u001b[0;36mplot_dist\u001b[0;34m(dist, domain, ax, idx, source, aff, **kwargs)\u001b[0m\n\u001b[1;32m    172\u001b[0m \u001b[38;5;66;03m# TODO: Add check that domain size matches dimension of dist\u001b[39;00m\n\u001b[1;32m    173\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m source \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpdf\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m    174\u001b[0m     \u001b[38;5;66;03m# Compute observed distribution using stored pdf\u001b[39;00m\n\u001b[1;32m    175\u001b[0m     plot_y \u001b[38;5;241m=\u001b[39m margins(\n\u001b[0;32m--> 176\u001b[0m             np\u001b[38;5;241m.\u001b[39mreshape(\u001b[43mdist\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpdf\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgrid\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mT\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mprod(axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m), mesh[idx]\u001b[38;5;241m.\u001b[39mshape\n\u001b[1;32m    177\u001b[0m                 ))[idx]\u001b[38;5;241m.\u001b[39mreshape(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m    178\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m source \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mkde\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m    179\u001b[0m     \u001b[38;5;66;03m# Compute observed distribution using kernel density estimate\u001b[39;00m\n\u001b[1;32m    180\u001b[0m     plot_y \u001b[38;5;241m=\u001b[39m margins(\n\u001b[1;32m    181\u001b[0m             np\u001b[38;5;241m.\u001b[39mreshape(dist(grid), mesh[idx]\u001b[38;5;241m.\u001b[39mshape)\u001b[38;5;241m.\u001b[39mT)[idx]\u001b[38;5;241m.\u001b[39mreshape(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m)\n",
      "File \u001b[0;32m/opt/miniconda3/envs/chg-prod/lib/python3.10/site-packages/scipy/stats/_distn_infrastructure.py:453\u001b[0m, in \u001b[0;36mrv_frozen.pdf\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m    452\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mpdf\u001b[39m(\u001b[38;5;28mself\u001b[39m, x):   \u001b[38;5;66;03m# raises AttributeError in frozen discrete distribution\u001b[39;00m\n\u001b[0;32m--> 453\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdist\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpdf\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/opt/miniconda3/envs/chg-prod/lib/python3.10/site-packages/scipy/stats/_distn_infrastructure.py:1904\u001b[0m, in \u001b[0;36mrv_continuous.pdf\u001b[0;34m(self, x, *args, **kwds)\u001b[0m\n\u001b[1;32m   1902\u001b[0m args \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtuple\u001b[39m(\u001b[38;5;28mmap\u001b[39m(asarray, args))\n\u001b[1;32m   1903\u001b[0m dtyp \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mfind_common_type([x\u001b[38;5;241m.\u001b[39mdtype, np\u001b[38;5;241m.\u001b[39mfloat64], [])\n\u001b[0;32m-> 1904\u001b[0m x \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39masarray((\u001b[43mx\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mloc\u001b[49m)\u001b[38;5;241m/\u001b[39mscale, dtype\u001b[38;5;241m=\u001b[39mdtyp)\n\u001b[1;32m   1905\u001b[0m cond0 \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_argcheck(\u001b[38;5;241m*\u001b[39margs) \u001b[38;5;241m&\u001b[39m (scale \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m)\n\u001b[1;32m   1906\u001b[0m cond1 \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_support_mask(x, \u001b[38;5;241m*\u001b[39margs) \u001b[38;5;241m&\u001b[39m (scale \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m)\n",
      "\u001b[0;31mValueError\u001b[0m: operands could not be broadcast together with shapes (9801,2) (4,) "
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f66689a9032c49a8b5664474d0131a39",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": 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fu7/X6934nP39/RoMBtU0TU2n02uPliwtLdXy8vKlBwAAfMsEyByaprm03bZt9Xq92ZGNT+9yNRwOa3V1dRYfh4eHPgcEAACq6snFxcXFfU/iIWjbtg4ODmp9fb2Oj49rd3d3FhWbm5u1vr5eOzs71bZtPXv27NJzO51Offjw4bN/xtnZWa2srNTp6amjIcCd8DoDwH0TIAvEGwPgrnmdAeC+OQULAACIESAAAECMAAEAAGIECAAAECNAAACAGAECAADECBAAACBGgAAAADECBAAAiBEgAABAjAABAABiBAgAABAjQAAAgBgBAgAAxAgQAAAgRoAAAAAxAgQAAIgRIAAAQIwAAQAAYgQIAAAQI0AAAIAYAQIAAMQIEAAAIEaAAAAAMQIEAACIESAAAECMAAEAAGIECAAAECNAAACAGAECAADECBAAACBGgAAAADECBAAAiBEgAABAjAABAABiBAgAABAjQAAAgBgBAgAAxAgQAAAgRoAAAAAxAgQAAIgRIAAAQIwAAQAAYgQIAAAQI0AAAICY7+97Ag9F27Y1HA6raZpq27a2traq0+l88VgAAHhMBMicNjc36+TkpKp+C4znz5/X0dHRF48FAIDHxClYc2jb9tJ20zQ1Go2+eCwAADw2AmQOo9Gout3upX3dbrfG4/EXjQUAgMfGKVhzmE6n1+6fTCZfNPb8/LzOz89n26enp1VVdXZ2dvtJAszh4+vLxcXFPc8EgMdKgHyBm2Jj3rF7e3v14sWLK/ufPn36BbMC+Lxff/21VlZW7nsaADxCAmQOnU7nyhGMyWRy7Z2tbjN2d3e3fvnll9n2dDqtf/zjH/Wf//znm3xjcHZ2Vk+fPq3379/X8vLyfU/nTnzra7S+h+/09LR+/PHHK6eKAkCKAJlDv9+vg4ODK/t7vd4XjV1aWqqlpaUr+1dWVr7ZNz9VVcvLy9/0+qq+/TVa38P33XcuAQTgfvg/0Byaprm03bZt9Xq92VGN8Xg8u/vV58YCAMBj5gjInI6OjmowGNT6+nodHx9f+lyPvb29Wl9fr52dnc+OBQCAx0yAzKlpmnr58mVVVW1sbFz62qeB8Udj/8jS0lL9+9//vva0rG/Bt76+qm9/jdb38D2GNQKw2J5cuBcjAAAQ4hoQAAAgRoAAAAAxrgEJa9u2hsNhNU1TbdvW1tbWjXfIus3YRXKbeY/H4xqNRlVVdXx8XK9fv174Nf7Z78tgMKjd3d1vbn2j0ajatp3dAa7f74dm+ufd9r/D0WhU3W632ratjY2NK3e7WzTj8bieP39eJycnfzjuob7GAPDAXRC1uro6+/W7d+8uNjY2vsrYRXKbeb98+fLSr3//3EX1Z74vJycnF1V18eHDhzuc2ddxm/W9ffv2Ymtraza2aZo7n9/X8Gf/jl5cXMzWu6iOjo5mf98+56G+xgDwsDkFK+jjZ4V81DTN7Kf/XzJ2kdxm3uPxuPb29mbbGxsblz5TZRH92e/L748QLLLbrm97e3t2x7emaert27d3Or+v4bZrfPPmzV1P6ava2Nio1dXVz457qK8xADx8AiTo42kcv9ftdms8Hn/R2EVym3mvrq7W69evZ9vT6XQ2flH9me/LcDi81e2Y79Nt1te2bU0mk+p0OjUej2s6nT6IyLrt97Db7dba2trsVKyffvopMc0791BfYwB4+ARI0Mc32J+aTCZfNHaR3Hbev39j/ubNm+r3+wt9Dvpt1zedThd6PZ+6zfrG43F1u93ZNQSvXr2q4XB4xzP8crf9Hn78nJ9nz57V0dHRg4nJz3morzEAPHwuQl8AN70R+NKxi+Rz855OpzUcDj970eyiuml9h4eHtbW1lZ3MHbhufZPJpNq2nUXj1tZW/fDDD3XxQD9a6Kbv4Wg0qpcvX1bbtrW9vV1VVQcHB8GZZT3U1xgAHg5HQII6nc6Vny5+PIXlS8Yukj8778FgUG/fvv2m1jcajernn38OzezruM36mqapTqcz+9rHfy76KTy3WWPbtnV8fFz9fr+2trbq3bt3dXh4uNDXKc3rob7GAPDwCZCgm25P2uv1vmjsIvkz897f36/BYFBN09R0Ol3on8Dedn2Hh4f16tWrevXqVbVtW3t7ewv9Bv0263sI13tc5zZrHI/Htb6+PttumqZ2d3cX+u/ovB7qawwAD58ACfr0DVvbttXr9S795PjjT1Y/N3ZR3WaNVb9doL26ujqLj8PDw4Ve423W9/Gn5h8fVb/dNWqeOxTdl9v+He31erM34x/v9LXI66u63RpXV1fr+Pj40vhff/114df40aeh9C28xgDw8D25eKgnbD9QbdvWwcFBra+v1/Hx8aUPptvc3Kz19fXa2dn57NhFNu8a27atZ8+eXXpup9OpDx8+3MOs53eb72HVb28CX716VYPBoLa2thY+Qm6zvul0WoPBoNbW1urk5GR2JGvR3WaNo9GoxuPx7Ov9fn+h1zgajert27e1v79fOzs7tb6+Prtw/lt5jQHgYRMgAABAjFOwAACAGAECAADECBAAACBGgAAAADECBAAAiBEgAABAjAABAABiBAgAABAjQAAAgBgBAgAAxAgQAAAgRoAAAAAxAgQAAIgRIAAAQIwAAQAAYgQIAAAQI0AAAIAYAQIAAMQIEAAAIEaAAAAAMQIEAACIESAAAECMAAEAAGIECAAAECNAAACAGAECAADECBAAACBGgAAAADECBAAAiBEgAABAjAABAABiBAgAABAjQAAAgBgBAgAAxAgQAAAgRoAAAAAxAgQAAIgRIAAAQIwAAQAAYgQIAAAQI0AAAIAYAQIAAMQIEAAAIEaAAAAAMQIEAACIESAAAECMAAEAAGIECAAAECNAAACAGAECAADECBAAACBGgAAAADECBAAAiBEgAABAjAABAABiBAgAABAjQAAAgBgBAgAAxAgQAAAgRoAAAAAxAgQAAIgRIAAAQIwAAQAAYgQIAAAQI0AAAIAYAQIAAMQIEAAAIEaAAAAAMQIEAACIESAAAECMAAEAAGIECAAAECNAAACAGAECAADECBAAACBGgAAAADECBAAAiBEgAABAjAABAABiBAgAABAjQAAAgBgBAgAAxAgQAAAgRoAAAAAxAgQAAIgRIAAAQIwAAQAAYgQIAAAQI0AAAIAYAQIAAMQIEAAAIEaAAAAAMQIEAACIESAAAECMAAEAAGIECAAAECNAAACAGAECAADECBAAACBGgAAAADECBAAAiBEgAABAjAABAABiBAgAABAjQAAAgBgBAgAAxAgQAAAgRoAAAAAxAgQAAIgRIAAAQIwAAQAAYgQIAAAQI0AAAIAYAQIAAMQIEAAAIEaAAAAAMQIEAACIESAAAECMAAEAAGIECAAAECNAAACAGAECAADECBAAACBGgAAAADECBAAAiBEgAABAjAABAABiBAgAABAjQAAAgBgBAgAAxAgQAAAgRoAAAAAxAgQAAIgRIAAAQIwAAQAAYgQIAAAQI0AAAIAYAQIAAMQIEAAAIEaAAAAAMQIEAACIESAAAECMAAEAAGIECAAAECNAAACAGAECAADECBAAACBGgAAAADECBAAAiBEgAABAjAABAABiBAgAABAjQAAAgBgBAgAAxAgQAAAgRoAAAAAxAgQAAIgRIAAAQIwAAQAAYgQIAAAQI0AAAIAYAQIAAMQIEAAAIEaAAAAAMQIEAACIESAAAECMAAEAAGIECAAAECNAAACAGAECAADECBAAACBGgAAAADECBAAAiBEgAABAjAABAABiBAgAABAjQAAAgBgBAgAAxAgQAAAgRoAAAAAxAgQAAIgRIAAAQIwAAQAAYgQIAAAQI0AAAIAYAQIAAMQIEAAAIEaAAAAAMQIEAACIESAAAECMAAEAAGIECAAAECNAAACAGAECAADECBAAACBGgAAAADECBAAAiBEgAABAjAABAABiBAgAABAjQAAAgBgBAgAAxAgQAAAgRoAAAAAxAgQAAIgRIAAAQIwAAQAAYgQIAAAQI0AAAIAYAQIAAMQIEAAAIEaAAAAAMQIEAACIESAAAECMAAEAAGIECAAAECNAAACAGAECAADECBAAACBGgAAAADECBAAAiBEgAABAjAABAABiBAgAABAjQAAAgBgBAgAAxAgQAAAgRoAAAAAxAgQAAIgRIAAAQIwAAQAAYgQIAAAQI0AAAIAYAQIAAMQIEAAAIEaAAAAAMQIEAACIESAAAECMAAEAAGIECAAAECNAAACAGAECAADECBAAACBGgAAAADECBAAAiBEgAABAjAABAABiBAgAABAjQAAAgBgBAgAAxAgQAAAgRoAAAAAxAgQAAIgRIAAAQIwAAQAAYgQIAAAQI0AAAIAYAQIAAMQIEAAAIEaAAAAAMQIEAACIESAAAECMAAEAAGIECAAAECNAAACAGAECAADECBAAACBGgAAAADECBAAAiBEgAABAjAABAABiBAgAABAjQAAAgBgBAgAAxAgQAAAgRoAAAAAxAgQAAIgRIAAAQIwAAQAAYgQIAAAQI0AAAIAYAQIAAMQIEAAAIEaAAAAAMQIEAACIESAAAECMAAEAAGIECAAAECNAAACAGAECAADECBAAACBGgAAAADECBAAAiBEgAABAjAABAABiBAgAABAjQAAAgBgBAgAAxAgQAAAgRoAAAAAxAgQAAIgRIAAAQIwAAQAAYgQIAAAQI0AAAIAYAQIAAMQIEAAAIEaAAAAAMQIEAACIESAAAECMAAEAAGIECAAAECNAAACAGAECAADECBAAACBGgAAAADECBAAAiBEgAABAjAABAABiBAgAABAjQAAAgBgBAgAAxAgQAAAgRoAAAAAxAgQAAIgRIAAAQIwAAQAAYgQIAAAQI0AAAIAYAQIAAMQIEAAAIEaAAAAAMQIEAACIESAAAECMAAEAAGIECAAAECNAAACAGAECAADECBAAACBGgAAAADECBAAAiBEgAABAjAABAABiBAgAABAjQAAAgBgBAgAAxAgQAAAgRoA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       "                    Figure\n",
       "                </div>\n",
       "                <img src='data:image/png;base64,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' width=800.0/>\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": [
    "figs = poisson_pca_solution(poisson_prob, figs=figs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ddce71b3-ab1a-45d8-a49b-cd3db0c31b7f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "citation-manager": {
   "items": {}
  },
  "kernelspec": {
   "display_name": "fenics",
   "language": "python",
   "name": "fenics"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}