{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d9b93a27",
   "metadata": {},
   "source": [
    "```{try_on_binder}\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d3521384",
   "metadata": {
    "load": "myst_code_init.py",
    "tags": [
     "remove-cell"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The pymor.discretizers.builtin.gui.jupyter extension is already loaded. To reload it, use:\n",
      "  %reload_ext pymor.discretizers.builtin.gui.jupyter\n"
     ]
    }
   ],
   "source": [
    "from IPython import get_ipython\n",
    "ip = get_ipython()\n",
    "if ip is not None:\n",
    "    ip.run_line_magic('load_ext', 'pymor.discretizers.builtin.gui.jupyter')\n",
    "    ip.run_line_magic('matplotlib', 'inline')\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\", category=UserWarning, module='torch')\n",
    "import pymor.tools.random\n",
    "pymor.tools.random._default_random_state = None\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9da0079",
   "metadata": {},
   "source": [
    "# Tutorial: Projecting a Model\n",
    "\n",
    "In this tutorial we will show how pyMOR builds a reduced-order model by\n",
    "projecting the full-order model onto a given reduced space. If you want to learn\n",
    "more about building a reduced space, you can find an introduction in\n",
    "{doc}`tutorial_basis_generation`.\n",
    "\n",
    "We will start by revisiting the concept of Galerkin projection and then manually\n",
    "project the model ourselves. We will then discuss offline/online decomposition of\n",
    "parametric models and see how pyMOR's algorithms automatically handle building\n",
    "an online-efficient reduced-order model. Along the way, we will take a look at\n",
    "some of pyMOR's source code to get a better understanding of how pyMOR's components\n",
    "fit together.\n",
    "\n",
    "## Model setup\n",
    "\n",
    "As a full-order {{ Model }}, we will use the same\n",
    "{meth}`thermal block <pymor.analyticalproblems.thermalblock.thermal_block_problem>` benchmark\n",
    "problem as in {doc}`tutorial_basis_generation`. In particular, we will use pyMOR's\n",
    "builtin {mod}`discretization toolkit <pymor.discretizers.builtin>`\n",
    "(see {doc}`tutorial_builtin_discretizer`) to construct the FOM. However, all we say\n",
    "works exactly the same when a FOM of the same mathematical structure is provided\n",
    "by an external PDE solver (see {doc}`tutorial_external_solver`).\n",
    "\n",
    "Since this tutorial is also supposed to give you a better overview of pyMOR's\n",
    "architecture, we will not import everything from the {mod}`pymor.basic` convenience\n",
    "module but directly import all classes and methods from their original locations in\n",
    "pyMOR's subpackages.\n",
    "\n",
    "Let's build a 2-by-2 thermal block {{ Model }} as our FOM:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ebe11ff3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "669df06bd6b74f57a5833b7574651da7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Accordion(children=(HTML(value='', layout=Layout(height='16em', overflow_y='auto', width='100%')),), selected_…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pymor.analyticalproblems.thermalblock import thermal_block_problem\n",
    "from pymor.discretizers.builtin import discretize_stationary_cg\n",
    "\n",
    "p = thermal_block_problem((2,2))\n",
    "fom, _ = discretize_stationary_cg(p, diameter=1/100)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b40004e4",
   "metadata": {},
   "source": [
    "To get started, we take a look at one solution of the FOM for some fixed {{ parameter_values }}."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "eca47d82",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c2f89e69a9964a3f87095d843d2bdc63",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Accordion(children=(HTML(value='', layout=Layout(height='16em', overflow_y='auto', width='100%')),), selected_…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "U = fom.solve([1., 0.1, 0.1, 1.])\n",
    "fom.visualize(U)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46cff0eb",
   "metadata": {},
   "source": [
    "To build the ROM, we will need a reduced space {math}`V_N` of small dimension {math}`N`.\n",
    "Any subspace of the {attr}`~pymor.models.interface.Model.solution_space` of the FOM will\n",
    "do for our purposes here. We choose to build a basic POD space from some random solution\n",
    "snapshots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5d65df0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "434e62a6073e48adb1cd2940d4689d4d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Accordion(children=(HTML(value='', layout=Layout(height='16em', overflow_y='auto', width='100%')),), selected_…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pymor.algorithms.pod import pod\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "snapshots = fom.solution_space.empty()\n",
    "for mu in p.parameter_space.sample_randomly(20):\n",
    "    snapshots.append(fom.solve(mu))\n",
    "basis, singular_values = pod(snapshots, modes=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a831c8f",
   "metadata": {},
   "source": [
    "The singular value decay looks promising:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "528bc80e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = plt.semilogy(singular_values)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9260bf24",
   "metadata": {},
   "source": [
    "## Solving the Model\n",
    "\n",
    "Now that we have our FOM and a reduced space {math}`V_N` spanned by `basis`, we can project\n",
    "the {{ Model }}. However, before doing so, we need to understand how actually\n",
    "solving the FOM works. Let's take a look at what\n",
    "{meth}`~pymor.models.interface.Model.solve` does:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a5586616",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       ".output_html .il { color: #666666 } /* Literal.Number.Integer.Long */</style><div class=\"highlight\"><pre><span></span>    <span class=\"k\">def</span> <span class=\"nf\">solve</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">mu</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">,</span> <span class=\"nb\">input</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">,</span> <span class=\"n\">return_error_estimate</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">,</span> <span class=\"o\">**</span><span class=\"n\">kwargs</span><span class=\"p\">):</span>\n",
       "<span class=\"w\">        </span><span class=\"sd\">&quot;&quot;&quot;Solve the discrete problem for the |parameter values| `mu`.</span>\n",
       "\n",
       "<span class=\"sd\">        This method returns a |VectorArray| with a internal state</span>\n",
       "<span class=\"sd\">        representation of the model&#39;s solution for given</span>\n",
       "<span class=\"sd\">        |parameter values|. It is a convenience wrapper around</span>\n",
       "<span class=\"sd\">        :meth:`compute`.</span>\n",
       "\n",
       "<span class=\"sd\">        The result may be :mod:`cached &lt;pymor.core.cache&gt;`</span>\n",
       "<span class=\"sd\">        in case caching has been activated for the given model.</span>\n",
       "\n",
       "<span class=\"sd\">        Parameters</span>\n",
       "<span class=\"sd\">        ----------</span>\n",
       "<span class=\"sd\">        mu</span>\n",
       "<span class=\"sd\">            |Parameter values| for which to solve.</span>\n",
       "<span class=\"sd\">        input</span>\n",
       "<span class=\"sd\">            The model input. Either a |NumPy array| of shape `(self.dim_input,)`,</span>\n",
       "<span class=\"sd\">            a |Function| with `dim_domain == 1` and `shape_range == (self.dim_input,)`</span>\n",
       "<span class=\"sd\">            mapping time to input, or a `str` expression with `t` as variable that</span>\n",
       "<span class=\"sd\">            can be used to instantiate an |ExpressionFunction| of this type.</span>\n",
       "<span class=\"sd\">            Can be `None` if `self.dim_input == 0`.</span>\n",
       "<span class=\"sd\">        return_error_estimate</span>\n",
       "<span class=\"sd\">            If `True`, also return an error estimate for the computed solution.</span>\n",
       "<span class=\"sd\">        kwargs</span>\n",
       "<span class=\"sd\">            Additional keyword arguments passed to :meth:`compute` that</span>\n",
       "<span class=\"sd\">            might affect how the solution is computed.</span>\n",
       "\n",
       "<span class=\"sd\">        Returns</span>\n",
       "<span class=\"sd\">        -------</span>\n",
       "<span class=\"sd\">        The solution |VectorArray|. When `return_error_estimate` is `True`,</span>\n",
       "<span class=\"sd\">        the estimate is returned as second value.</span>\n",
       "<span class=\"sd\">        &quot;&quot;&quot;</span>\n",
       "        <span class=\"n\">data</span> <span class=\"o\">=</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">compute</span><span class=\"p\">(</span>\n",
       "            <span class=\"n\">solution</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">,</span>\n",
       "            <span class=\"n\">solution_error_estimate</span><span class=\"o\">=</span><span class=\"n\">return_error_estimate</span><span class=\"p\">,</span>\n",
       "            <span class=\"n\">mu</span><span class=\"o\">=</span><span class=\"n\">mu</span><span class=\"p\">,</span>\n",
       "            <span class=\"nb\">input</span><span class=\"o\">=</span><span class=\"nb\">input</span><span class=\"p\">,</span>\n",
       "            <span class=\"o\">**</span><span class=\"n\">kwargs</span>\n",
       "        <span class=\"p\">)</span>\n",
       "        <span class=\"k\">if</span> <span class=\"n\">return_error_estimate</span><span class=\"p\">:</span>\n",
       "            <span class=\"k\">return</span> <span class=\"n\">data</span><span class=\"p\">[</span><span class=\"s1\">&#39;solution&#39;</span><span class=\"p\">],</span> <span class=\"n\">data</span><span class=\"p\">[</span><span class=\"s1\">&#39;solution_error_estimate&#39;</span><span class=\"p\">]</span>\n",
       "        <span class=\"k\">else</span><span class=\"p\">:</span>\n",
       "            <span class=\"k\">return</span> <span class=\"n\">data</span><span class=\"p\">[</span><span class=\"s1\">&#39;solution&#39;</span><span class=\"p\">]</span>\n",
       "</pre></div>\n"
      ],
      "text/latex": [
       "\\begin{Verbatim}[commandchars=\\\\\\{\\}]\n",
       "    \\PY{k}{def} \\PY{n+nf}{solve}\\PY{p}{(}\\PY{n+nb+bp}{self}\\PY{p}{,} \\PY{n}{mu}\\PY{o}{=}\\PY{k+kc}{None}\\PY{p}{,} \\PY{n+nb}{input}\\PY{o}{=}\\PY{k+kc}{None}\\PY{p}{,} \\PY{n}{return\\PYZus{}error\\PYZus{}estimate}\\PY{o}{=}\\PY{k+kc}{False}\\PY{p}{,} \\PY{o}{*}\\PY{o}{*}\\PY{n}{kwargs}\\PY{p}{)}\\PY{p}{:}\n",
       "\\PY{+w}{        }\\PY{l+s+sd}{\\PYZdq{}\\PYZdq{}\\PYZdq{}Solve the discrete problem for the |parameter values| `mu`.}\n",
       "\n",
       "\\PY{l+s+sd}{        This method returns a |VectorArray| with a internal state}\n",
       "\\PY{l+s+sd}{        representation of the model\\PYZsq{}s solution for given}\n",
       "\\PY{l+s+sd}{        |parameter values|. It is a convenience wrapper around}\n",
       "\\PY{l+s+sd}{        :meth:`compute`.}\n",
       "\n",
       "\\PY{l+s+sd}{        The result may be :mod:`cached \\PYZlt{}pymor.core.cache\\PYZgt{}`}\n",
       "\\PY{l+s+sd}{        in case caching has been activated for the given model.}\n",
       "\n",
       "\\PY{l+s+sd}{        Parameters}\n",
       "\\PY{l+s+sd}{        \\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}}\n",
       "\\PY{l+s+sd}{        mu}\n",
       "\\PY{l+s+sd}{            |Parameter values| for which to solve.}\n",
       "\\PY{l+s+sd}{        input}\n",
       "\\PY{l+s+sd}{            The model input. Either a |NumPy array| of shape `(self.dim\\PYZus{}input,)`,}\n",
       "\\PY{l+s+sd}{            a |Function| with `dim\\PYZus{}domain == 1` and `shape\\PYZus{}range == (self.dim\\PYZus{}input,)`}\n",
       "\\PY{l+s+sd}{            mapping time to input, or a `str` expression with `t` as variable that}\n",
       "\\PY{l+s+sd}{            can be used to instantiate an |ExpressionFunction| of this type.}\n",
       "\\PY{l+s+sd}{            Can be `None` if `self.dim\\PYZus{}input == 0`.}\n",
       "\\PY{l+s+sd}{        return\\PYZus{}error\\PYZus{}estimate}\n",
       "\\PY{l+s+sd}{            If `True`, also return an error estimate for the computed solution.}\n",
       "\\PY{l+s+sd}{        kwargs}\n",
       "\\PY{l+s+sd}{            Additional keyword arguments passed to :meth:`compute` that}\n",
       "\\PY{l+s+sd}{            might affect how the solution is computed.}\n",
       "\n",
       "\\PY{l+s+sd}{        Returns}\n",
       "\\PY{l+s+sd}{        \\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}}\n",
       "\\PY{l+s+sd}{        The solution |VectorArray|. When `return\\PYZus{}error\\PYZus{}estimate` is `True`,}\n",
       "\\PY{l+s+sd}{        the estimate is returned as second value.}\n",
       "\\PY{l+s+sd}{        \\PYZdq{}\\PYZdq{}\\PYZdq{}}\n",
       "        \\PY{n}{data} \\PY{o}{=} \\PY{n+nb+bp}{self}\\PY{o}{.}\\PY{n}{compute}\\PY{p}{(}\n",
       "            \\PY{n}{solution}\\PY{o}{=}\\PY{k+kc}{True}\\PY{p}{,}\n",
       "            \\PY{n}{solution\\PYZus{}error\\PYZus{}estimate}\\PY{o}{=}\\PY{n}{return\\PYZus{}error\\PYZus{}estimate}\\PY{p}{,}\n",
       "            \\PY{n}{mu}\\PY{o}{=}\\PY{n}{mu}\\PY{p}{,}\n",
       "            \\PY{n+nb}{input}\\PY{o}{=}\\PY{n+nb}{input}\\PY{p}{,}\n",
       "            \\PY{o}{*}\\PY{o}{*}\\PY{n}{kwargs}\n",
       "        \\PY{p}{)}\n",
       "        \\PY{k}{if} \\PY{n}{return\\PYZus{}error\\PYZus{}estimate}\\PY{p}{:}\n",
       "            \\PY{k}{return} \\PY{n}{data}\\PY{p}{[}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{solution}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{]}\\PY{p}{,} \\PY{n}{data}\\PY{p}{[}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{solution\\PYZus{}error\\PYZus{}estimate}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{]}\n",
       "        \\PY{k}{else}\\PY{p}{:}\n",
       "            \\PY{k}{return} \\PY{n}{data}\\PY{p}{[}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{solution}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{]}\n",
       "\\end{Verbatim}\n"
      ],
      "text/plain": [
       "    def solve(self, mu=None, input=None, return_error_estimate=False, **kwargs):\n",
       "        \"\"\"Solve the discrete problem for the |parameter values| `mu`.\n",
       "\n",
       "        This method returns a |VectorArray| with a internal state\n",
       "        representation of the model's solution for given\n",
       "        |parameter values|. It is a convenience wrapper around\n",
       "        :meth:`compute`.\n",
       "\n",
       "        The result may be :mod:`cached <pymor.core.cache>`\n",
       "        in case caching has been activated for the given model.\n",
       "\n",
       "        Parameters\n",
       "        ----------\n",
       "        mu\n",
       "            |Parameter values| for which to solve.\n",
       "        input\n",
       "            The model input. Either a |NumPy array| of shape `(self.dim_input,)`,\n",
       "            a |Function| with `dim_domain == 1` and `shape_range == (self.dim_input,)`\n",
       "            mapping time to input, or a `str` expression with `t` as variable that\n",
       "            can be used to instantiate an |ExpressionFunction| of this type.\n",
       "            Can be `None` if `self.dim_input == 0`.\n",
       "        return_error_estimate\n",
       "            If `True`, also return an error estimate for the computed solution.\n",
       "        kwargs\n",
       "            Additional keyword arguments passed to :meth:`compute` that\n",
       "            might affect how the solution is computed.\n",
       "\n",
       "        Returns\n",
       "        -------\n",
       "        The solution |VectorArray|. When `return_error_estimate` is `True`,\n",
       "        the estimate is returned as second value.\n",
       "        \"\"\"\n",
       "        data = self.compute(\n",
       "            solution=True,\n",
       "            solution_error_estimate=return_error_estimate,\n",
       "            mu=mu,\n",
       "            input=input,\n",
       "            **kwargs\n",
       "        )\n",
       "        if return_error_estimate:\n",
       "            return data['solution'], data['solution_error_estimate']\n",
       "        else:\n",
       "            return data['solution']"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pymor.tools.formatsrc import print_source\n",
    "print_source(fom.solve)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da5fdd49",
   "metadata": {},
   "source": [
    "This does not look too interesting. Actually, {meth}`~pymor.models.interface.Model.solve`\n",
    "is just a convenience method around {meth}`~pymor.models.interface.Model.compute` which\n",
    "handles the actual computation of the solution and various other associated values like\n",
    "outputs or error estimates. Next, we take a look at the implemenation of\n",
    "{meth}`~pymor.models.interface.Model.compute`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8145af97",
   "metadata": {},
   "outputs": [
    {
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       ".output_html .il { color: #666666 } /* Literal.Number.Integer.Long */</style><div class=\"highlight\"><pre><span></span>    <span class=\"k\">def</span> <span class=\"nf\">compute</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">solution</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">,</span> <span class=\"n\">output</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">,</span> <span class=\"n\">solution_d_mu</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">,</span> <span class=\"n\">output_d_mu</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">,</span>\n",
       "                <span class=\"n\">solution_error_estimate</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">,</span> <span class=\"n\">output_error_estimate</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">,</span>\n",
       "                <span class=\"n\">output_d_mu_return_array</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">,</span> <span class=\"n\">output_error_estimate_return_vector</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">,</span>\n",
       "                <span class=\"o\">*</span><span class=\"p\">,</span> <span class=\"n\">mu</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">,</span> <span class=\"nb\">input</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">,</span> <span class=\"o\">**</span><span class=\"n\">kwargs</span><span class=\"p\">):</span>\n",
       "<span class=\"w\">        </span><span class=\"sd\">&quot;&quot;&quot;Compute the solution of the model and associated quantities.</span>\n",
       "\n",
       "<span class=\"sd\">        This method computes the output of the model, its internal state,</span>\n",
       "<span class=\"sd\">        and various associated quantities for given |parameter values| `mu`.</span>\n",
       "\n",
       "<span class=\"sd\">        .. note::</span>\n",
       "\n",
       "<span class=\"sd\">            The default implementation defers the actual computations to</span>\n",
       "<span class=\"sd\">            the methods :meth:`_compute_solution`, :meth:`_compute_output`,</span>\n",
       "<span class=\"sd\">            :meth:`_compute_solution_error_estimate` and :meth:`_compute_output_error_estimate`.</span>\n",
       "<span class=\"sd\">            The call to :meth:`_compute_solution` is :mod:`cached &lt;pymor.core.cache&gt;`.</span>\n",
       "<span class=\"sd\">            In addition, |Model| implementors may implement :meth:`_compute` to</span>\n",
       "<span class=\"sd\">            simultaneously compute multiple values in an optimized way. The corresponding</span>\n",
       "<span class=\"sd\">            `_compute_XXX` methods will not be called for values already returned by</span>\n",
       "<span class=\"sd\">            :meth:`_compute`.</span>\n",
       "\n",
       "<span class=\"sd\">        Parameters</span>\n",
       "<span class=\"sd\">        ----------</span>\n",
       "<span class=\"sd\">        solution</span>\n",
       "<span class=\"sd\">            If `True`, return the model&#39;s internal state.</span>\n",
       "<span class=\"sd\">        output</span>\n",
       "<span class=\"sd\">            If `True`, return the model output.</span>\n",
       "<span class=\"sd\">        solution_d_mu</span>\n",
       "<span class=\"sd\">            If not `False`, either `True` to return the derivative of the model&#39;s</span>\n",
       "<span class=\"sd\">            internal state w.r.t. all parameter components or a tuple `(parameter, index)`</span>\n",
       "<span class=\"sd\">            to return the derivative of a single parameter component.</span>\n",
       "<span class=\"sd\">        output_d_mu</span>\n",
       "<span class=\"sd\">            If `True`, return the gradient of the model output w.r.t. the |Parameter|.</span>\n",
       "<span class=\"sd\">        solution_error_estimate</span>\n",
       "<span class=\"sd\">            If `True`, return an error estimate for the computed internal state.</span>\n",
       "<span class=\"sd\">        output_error_estimate</span>\n",
       "<span class=\"sd\">            If `True`, return an error estimate for the computed output.</span>\n",
       "<span class=\"sd\">        output_d_mu_return_array</span>\n",
       "<span class=\"sd\">            If `True`, return the output gradient as a |NumPy array|.</span>\n",
       "<span class=\"sd\">            Otherwise, return a dict of gradients for each |Parameter|.</span>\n",
       "<span class=\"sd\">        output_error_estimate_return_vector</span>\n",
       "<span class=\"sd\">            If `True`, return the output estimate as a |NumPy array|,</span>\n",
       "<span class=\"sd\">            where each component corresponds to the respective component</span>\n",
       "<span class=\"sd\">            of the :attr:`output_functional`.</span>\n",
       "<span class=\"sd\">            Otherwise, return the Euclidean norm of all components.</span>\n",
       "<span class=\"sd\">        mu</span>\n",
       "<span class=\"sd\">            |Parameter values| for which to compute the values.</span>\n",
       "<span class=\"sd\">        input</span>\n",
       "<span class=\"sd\">            The model input. Either a |NumPy array| of shape `(self.dim_input,)`,</span>\n",
       "<span class=\"sd\">            a |Function| with `dim_domain == 1` and `shape_range == (self.dim_input,)`</span>\n",
       "<span class=\"sd\">            mapping time to input, or a `str` expression with `t` as variable that</span>\n",
       "<span class=\"sd\">            can be used to instantiate an |ExpressionFunction| of this type.</span>\n",
       "<span class=\"sd\">            Can be `None` if `self.dim_input == 0`.</span>\n",
       "<span class=\"sd\">        kwargs</span>\n",
       "<span class=\"sd\">            Further keyword arguments to select further quantities that should</span>\n",
       "<span class=\"sd\">            be returned or to customize how the values are computed.</span>\n",
       "\n",
       "<span class=\"sd\">        Returns</span>\n",
       "<span class=\"sd\">        -------</span>\n",
       "<span class=\"sd\">        A dict with the computed values.</span>\n",
       "<span class=\"sd\">        &quot;&quot;&quot;</span>\n",
       "        <span class=\"c1\"># make sure no unknown kwargs are passed</span>\n",
       "        <span class=\"k\">assert</span> <span class=\"n\">kwargs</span><span class=\"o\">.</span><span class=\"n\">keys</span><span class=\"p\">()</span> <span class=\"o\">&lt;=</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">_compute_allowed_kwargs</span>\n",
       "        <span class=\"k\">assert</span> <span class=\"nb\">input</span> <span class=\"ow\">is</span> <span class=\"ow\">not</span> <span class=\"kc\">None</span> <span class=\"ow\">or</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">dim_input</span> <span class=\"o\">==</span> <span class=\"mi\">0</span>\n",
       "\n",
       "        <span class=\"c1\"># parse parameter values</span>\n",
       "        <span class=\"k\">if</span> <span class=\"ow\">not</span> <span class=\"nb\">isinstance</span><span class=\"p\">(</span><span class=\"n\">mu</span><span class=\"p\">,</span> <span class=\"n\">Mu</span><span class=\"p\">):</span>\n",
       "            <span class=\"n\">mu</span> <span class=\"o\">=</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">parameters</span><span class=\"o\">.</span><span class=\"n\">parse</span><span class=\"p\">(</span><span class=\"n\">mu</span><span class=\"p\">)</span>\n",
       "        <span class=\"k\">assert</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">parameters</span><span class=\"o\">.</span><span class=\"n\">assert_compatible</span><span class=\"p\">(</span><span class=\"n\">mu</span><span class=\"p\">)</span>\n",
       "\n",
       "        <span class=\"c1\"># parse input and add it to the parameter values</span>\n",
       "        <span class=\"n\">mu_input</span> <span class=\"o\">=</span> <span class=\"n\">Parameters</span><span class=\"p\">(</span><span class=\"nb\">input</span><span class=\"o\">=</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">dim_input</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">parse</span><span class=\"p\">(</span><span class=\"nb\">input</span><span class=\"p\">)</span>\n",
       "        <span class=\"nb\">input</span> <span class=\"o\">=</span> <span class=\"n\">mu_input</span><span class=\"o\">.</span><span class=\"n\">get_time_dependent_value</span><span class=\"p\">(</span><span class=\"s1\">&#39;input&#39;</span><span class=\"p\">)</span> <span class=\"k\">if</span> <span class=\"n\">mu_input</span><span class=\"o\">.</span><span class=\"n\">is_time_dependent</span><span class=\"p\">(</span><span class=\"s1\">&#39;input&#39;</span><span class=\"p\">)</span> <span class=\"k\">else</span> <span class=\"n\">mu_input</span><span class=\"p\">[</span><span class=\"s1\">&#39;input&#39;</span><span class=\"p\">]</span>\n",
       "        <span class=\"n\">mu</span> <span class=\"o\">=</span> <span class=\"n\">mu</span><span class=\"o\">.</span><span class=\"n\">with_</span><span class=\"p\">(</span><span class=\"nb\">input</span><span class=\"o\">=</span><span class=\"nb\">input</span><span class=\"p\">)</span>\n",
       "\n",
       "        <span class=\"c1\"># log output</span>\n",
       "        <span class=\"c1\"># explicitly checking if logging is disabled saves some cpu cycles</span>\n",
       "        <span class=\"k\">if</span> <span class=\"ow\">not</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">logging_disabled</span><span class=\"p\">:</span>\n",
       "            <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">logger</span><span class=\"o\">.</span><span class=\"n\">info</span><span class=\"p\">(</span><span class=\"sa\">f</span><span class=\"s1\">&#39;Solving </span><span class=\"si\">{</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">name</span><span class=\"si\">}</span><span class=\"s1\"> for </span><span class=\"si\">{</span><span class=\"n\">mu</span><span class=\"si\">}</span><span class=\"s1\"> ...&#39;</span><span class=\"p\">)</span>\n",
       "\n",
       "        <span class=\"c1\"># first call _compute to give subclasses more control</span>\n",
       "        <span class=\"n\">data</span> <span class=\"o\">=</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">_compute</span><span class=\"p\">(</span><span class=\"n\">solution</span><span class=\"o\">=</span><span class=\"n\">solution</span><span class=\"p\">,</span> <span class=\"n\">output</span><span class=\"o\">=</span><span class=\"n\">output</span><span class=\"p\">,</span>\n",
       "                             <span class=\"n\">solution_d_mu</span><span class=\"o\">=</span><span class=\"n\">solution_d_mu</span><span class=\"p\">,</span> <span class=\"n\">output_d_mu</span><span class=\"o\">=</span><span class=\"n\">output_d_mu</span><span class=\"p\">,</span>\n",
       "                             <span class=\"n\">solution_error_estimate</span><span class=\"o\">=</span><span class=\"n\">solution_error_estimate</span><span class=\"p\">,</span>\n",
       "                             <span class=\"n\">output_error_estimate</span><span class=\"o\">=</span><span class=\"n\">output_error_estimate</span><span class=\"p\">,</span>\n",
       "                             <span class=\"n\">mu</span><span class=\"o\">=</span><span class=\"n\">mu</span><span class=\"p\">,</span> <span class=\"o\">**</span><span class=\"n\">kwargs</span><span class=\"p\">)</span>\n",
       "\n",
       "        <span class=\"k\">if</span> <span class=\"p\">(</span><span class=\"n\">solution</span> <span class=\"ow\">or</span> <span class=\"n\">output</span> <span class=\"ow\">or</span> <span class=\"n\">solution_error_estimate</span>\n",
       "            <span class=\"ow\">or</span> <span class=\"n\">output_error_estimate</span> <span class=\"ow\">or</span> <span class=\"n\">solution_d_mu</span> <span class=\"ow\">or</span> <span class=\"n\">output_d_mu</span><span class=\"p\">)</span> \\\n",
       "           <span class=\"ow\">and</span> <span class=\"s1\">&#39;solution&#39;</span> <span class=\"ow\">not</span> <span class=\"ow\">in</span> <span class=\"n\">data</span><span class=\"p\">:</span>\n",
       "            <span class=\"n\">retval</span> <span class=\"o\">=</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">cached_method_call</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">_compute_solution</span><span class=\"p\">,</span> <span class=\"n\">mu</span><span class=\"o\">=</span><span class=\"n\">mu</span><span class=\"p\">,</span> <span class=\"o\">**</span><span class=\"n\">kwargs</span><span class=\"p\">)</span>\n",
       "            <span class=\"k\">if</span> <span class=\"nb\">isinstance</span><span class=\"p\">(</span><span class=\"n\">retval</span><span class=\"p\">,</span> <span class=\"nb\">dict</span><span class=\"p\">):</span>\n",
       "                <span class=\"k\">assert</span> <span class=\"s1\">&#39;solution&#39;</span> <span class=\"ow\">in</span> <span class=\"n\">retval</span>\n",
       "                <span class=\"n\">data</span><span class=\"o\">.</span><span class=\"n\">update</span><span class=\"p\">(</span><span class=\"n\">retval</span><span class=\"p\">)</span>\n",
       "            <span class=\"k\">else</span><span class=\"p\">:</span>\n",
       "                <span class=\"n\">data</span><span class=\"p\">[</span><span class=\"s1\">&#39;solution&#39;</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">retval</span>\n",
       "\n",
       "        <span class=\"k\">if</span> <span class=\"n\">output</span> <span class=\"ow\">and</span> <span class=\"s1\">&#39;output&#39;</span> <span class=\"ow\">not</span> <span class=\"ow\">in</span> <span class=\"n\">data</span><span class=\"p\">:</span>\n",
       "            <span class=\"c1\"># TODO use caching here (requires skipping args in key generation)</span>\n",
       "            <span class=\"n\">retval</span> <span class=\"o\">=</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">_compute_output</span><span class=\"p\">(</span><span class=\"n\">data</span><span class=\"p\">[</span><span class=\"s1\">&#39;solution&#39;</span><span class=\"p\">],</span> <span class=\"n\">mu</span><span class=\"o\">=</span><span class=\"n\">mu</span><span class=\"p\">,</span> <span class=\"o\">**</span><span class=\"n\">kwargs</span><span class=\"p\">)</span>\n",
       "            <span class=\"k\">if</span> <span class=\"nb\">isinstance</span><span class=\"p\">(</span><span class=\"n\">retval</span><span class=\"p\">,</span> <span class=\"nb\">dict</span><span class=\"p\">):</span>\n",
       "                <span class=\"k\">assert</span> <span class=\"s1\">&#39;output&#39;</span> <span class=\"ow\">in</span> <span class=\"n\">retval</span>\n",
       "                <span class=\"n\">data</span><span class=\"o\">.</span><span class=\"n\">update</span><span class=\"p\">(</span><span class=\"n\">retval</span><span class=\"p\">)</span>\n",
       "            <span class=\"k\">else</span><span class=\"p\">:</span>\n",
       "                <span class=\"n\">data</span><span class=\"p\">[</span><span class=\"s1\">&#39;output&#39;</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">retval</span>\n",
       "\n",
       "        <span class=\"k\">if</span> <span class=\"n\">solution_d_mu</span> <span class=\"ow\">and</span> <span class=\"s1\">&#39;solution_d_mu&#39;</span> <span class=\"ow\">not</span> <span class=\"ow\">in</span> <span class=\"n\">data</span><span class=\"p\">:</span>\n",
       "            <span class=\"k\">if</span> <span class=\"nb\">isinstance</span><span class=\"p\">(</span><span class=\"n\">solution_d_mu</span><span class=\"p\">,</span> <span class=\"nb\">tuple</span><span class=\"p\">):</span>\n",
       "                <span class=\"n\">retval</span> <span class=\"o\">=</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">_compute_solution_d_mu_single_direction</span><span class=\"p\">(</span>\n",
       "                    <span class=\"n\">solution_d_mu</span><span class=\"p\">[</span><span class=\"mi\">0</span><span class=\"p\">],</span> <span class=\"n\">solution_d_mu</span><span class=\"p\">[</span><span class=\"mi\">1</span><span class=\"p\">],</span> <span class=\"n\">data</span><span class=\"p\">[</span><span class=\"s1\">&#39;solution&#39;</span><span class=\"p\">],</span> <span class=\"n\">mu</span><span class=\"o\">=</span><span class=\"n\">mu</span><span class=\"p\">,</span> <span class=\"o\">**</span><span class=\"n\">kwargs</span><span class=\"p\">)</span>\n",
       "            <span class=\"k\">else</span><span class=\"p\">:</span>\n",
       "                <span class=\"n\">retval</span> <span class=\"o\">=</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">_compute_solution_d_mu</span><span class=\"p\">(</span><span class=\"n\">data</span><span class=\"p\">[</span><span class=\"s1\">&#39;solution&#39;</span><span class=\"p\">],</span> <span class=\"n\">mu</span><span class=\"o\">=</span><span class=\"n\">mu</span><span class=\"p\">,</span> <span class=\"o\">**</span><span class=\"n\">kwargs</span><span class=\"p\">)</span>\n",
       "            <span class=\"c1\"># retval is always a dict</span>\n",
       "            <span class=\"k\">if</span> <span class=\"nb\">isinstance</span><span class=\"p\">(</span><span class=\"n\">retval</span><span class=\"p\">,</span> <span class=\"nb\">dict</span><span class=\"p\">)</span> <span class=\"ow\">and</span> <span class=\"s1\">&#39;solution_d_mu&#39;</span> <span class=\"ow\">in</span> <span class=\"n\">retval</span><span class=\"p\">:</span>\n",
       "                <span class=\"n\">data</span><span class=\"o\">.</span><span class=\"n\">update</span><span class=\"p\">(</span><span class=\"n\">retval</span><span class=\"p\">)</span>\n",
       "            <span class=\"k\">else</span><span class=\"p\">:</span>\n",
       "                <span class=\"n\">data</span><span class=\"p\">[</span><span class=\"s1\">&#39;solution_d_mu&#39;</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">retval</span>\n",
       "\n",
       "        <span class=\"k\">if</span> <span class=\"n\">output_d_mu</span> <span class=\"ow\">and</span> <span class=\"s1\">&#39;output_d_mu&#39;</span> <span class=\"ow\">not</span> <span class=\"ow\">in</span> <span class=\"n\">data</span><span class=\"p\">:</span>\n",
       "            <span class=\"c1\"># TODO use caching here (requires skipping args in key generation)</span>\n",
       "            <span class=\"n\">retval</span> <span class=\"o\">=</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">_compute_output_d_mu</span><span class=\"p\">(</span><span class=\"n\">data</span><span class=\"p\">[</span><span class=\"s1\">&#39;solution&#39;</span><span class=\"p\">],</span> <span class=\"n\">mu</span><span class=\"o\">=</span><span class=\"n\">mu</span><span class=\"p\">,</span>\n",
       "                                               <span class=\"n\">return_array</span><span class=\"o\">=</span><span class=\"n\">output_d_mu_return_array</span><span class=\"p\">,</span>\n",
       "                                               <span class=\"o\">**</span><span class=\"n\">kwargs</span><span class=\"p\">)</span>\n",
       "            <span class=\"c1\"># retval is always a dict</span>\n",
       "            <span class=\"k\">if</span> <span class=\"nb\">isinstance</span><span class=\"p\">(</span><span class=\"n\">retval</span><span class=\"p\">,</span> <span class=\"nb\">dict</span><span class=\"p\">)</span> <span class=\"ow\">and</span> <span class=\"s1\">&#39;output_d_mu&#39;</span> <span class=\"ow\">in</span> <span class=\"n\">retval</span><span class=\"p\">:</span>\n",
       "                <span class=\"n\">data</span><span class=\"o\">.</span><span class=\"n\">update</span><span class=\"p\">(</span><span class=\"n\">retval</span><span class=\"p\">)</span>\n",
       "            <span class=\"k\">else</span><span class=\"p\">:</span>\n",
       "                <span class=\"n\">data</span><span class=\"p\">[</span><span class=\"s1\">&#39;output_d_mu&#39;</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">retval</span>\n",
       "\n",
       "        <span class=\"k\">if</span> <span class=\"n\">solution_error_estimate</span> <span class=\"ow\">and</span> <span class=\"s1\">&#39;solution_error_estimate&#39;</span> <span class=\"ow\">not</span> <span class=\"ow\">in</span> <span class=\"n\">data</span><span class=\"p\">:</span>\n",
       "            <span class=\"c1\"># TODO use caching here (requires skipping args in key generation)</span>\n",
       "            <span class=\"n\">retval</span> <span class=\"o\">=</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">_compute_solution_error_estimate</span><span class=\"p\">(</span><span class=\"n\">data</span><span class=\"p\">[</span><span class=\"s1\">&#39;solution&#39;</span><span class=\"p\">],</span> <span class=\"n\">mu</span><span class=\"o\">=</span><span class=\"n\">mu</span><span class=\"p\">,</span> <span class=\"o\">**</span><span class=\"n\">kwargs</span><span class=\"p\">)</span>\n",
       "            <span class=\"k\">if</span> <span class=\"nb\">isinstance</span><span class=\"p\">(</span><span class=\"n\">retval</span><span class=\"p\">,</span> <span class=\"nb\">dict</span><span class=\"p\">):</span>\n",
       "                <span class=\"k\">assert</span> <span class=\"s1\">&#39;solution_error_estimate&#39;</span> <span class=\"ow\">in</span> <span class=\"n\">retval</span>\n",
       "                <span class=\"n\">data</span><span class=\"o\">.</span><span class=\"n\">update</span><span class=\"p\">(</span><span class=\"n\">retval</span><span class=\"p\">)</span>\n",
       "            <span class=\"k\">else</span><span class=\"p\">:</span>\n",
       "                <span class=\"n\">data</span><span class=\"p\">[</span><span class=\"s1\">&#39;solution_error_estimate&#39;</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">retval</span>\n",
       "\n",
       "        <span class=\"k\">if</span> <span class=\"n\">output_error_estimate</span> <span class=\"ow\">and</span> <span class=\"s1\">&#39;output_error_estimate&#39;</span> <span class=\"ow\">not</span> <span class=\"ow\">in</span> <span class=\"n\">data</span><span class=\"p\">:</span>\n",
       "            <span class=\"c1\"># TODO use caching here (requires skipping args in key generation)</span>\n",
       "            <span class=\"n\">retval</span> <span class=\"o\">=</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">_compute_output_error_estimate</span><span class=\"p\">(</span>\n",
       "                <span class=\"n\">data</span><span class=\"p\">[</span><span class=\"s1\">&#39;solution&#39;</span><span class=\"p\">],</span> <span class=\"n\">mu</span><span class=\"o\">=</span><span class=\"n\">mu</span><span class=\"p\">,</span>\n",
       "                <span class=\"n\">return_vector</span><span class=\"o\">=</span><span class=\"n\">output_error_estimate_return_vector</span><span class=\"p\">,</span> <span class=\"o\">**</span><span class=\"n\">kwargs</span><span class=\"p\">)</span>\n",
       "            <span class=\"k\">if</span> <span class=\"nb\">isinstance</span><span class=\"p\">(</span><span class=\"n\">retval</span><span class=\"p\">,</span> <span class=\"nb\">dict</span><span class=\"p\">):</span>\n",
       "                <span class=\"k\">assert</span> <span class=\"s1\">&#39;output_error_estimate&#39;</span> <span class=\"ow\">in</span> <span class=\"n\">retval</span>\n",
       "                <span class=\"n\">data</span><span class=\"o\">.</span><span class=\"n\">update</span><span class=\"p\">(</span><span class=\"n\">retval</span><span class=\"p\">)</span>\n",
       "            <span class=\"k\">else</span><span class=\"p\">:</span>\n",
       "                <span class=\"n\">data</span><span class=\"p\">[</span><span class=\"s1\">&#39;output_error_estimate&#39;</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">retval</span>\n",
       "\n",
       "        <span class=\"k\">return</span> <span class=\"n\">data</span>\n",
       "</pre></div>\n"
      ],
      "text/latex": [
       "\\begin{Verbatim}[commandchars=\\\\\\{\\}]\n",
       "    \\PY{k}{def} \\PY{n+nf}{compute}\\PY{p}{(}\\PY{n+nb+bp}{self}\\PY{p}{,} \\PY{n}{solution}\\PY{o}{=}\\PY{k+kc}{False}\\PY{p}{,} \\PY{n}{output}\\PY{o}{=}\\PY{k+kc}{False}\\PY{p}{,} \\PY{n}{solution\\PYZus{}d\\PYZus{}mu}\\PY{o}{=}\\PY{k+kc}{False}\\PY{p}{,} \\PY{n}{output\\PYZus{}d\\PYZus{}mu}\\PY{o}{=}\\PY{k+kc}{False}\\PY{p}{,}\n",
       "                \\PY{n}{solution\\PYZus{}error\\PYZus{}estimate}\\PY{o}{=}\\PY{k+kc}{False}\\PY{p}{,} \\PY{n}{output\\PYZus{}error\\PYZus{}estimate}\\PY{o}{=}\\PY{k+kc}{False}\\PY{p}{,}\n",
       "                \\PY{n}{output\\PYZus{}d\\PYZus{}mu\\PYZus{}return\\PYZus{}array}\\PY{o}{=}\\PY{k+kc}{False}\\PY{p}{,} \\PY{n}{output\\PYZus{}error\\PYZus{}estimate\\PYZus{}return\\PYZus{}vector}\\PY{o}{=}\\PY{k+kc}{False}\\PY{p}{,}\n",
       "                \\PY{o}{*}\\PY{p}{,} \\PY{n}{mu}\\PY{o}{=}\\PY{k+kc}{None}\\PY{p}{,} \\PY{n+nb}{input}\\PY{o}{=}\\PY{k+kc}{None}\\PY{p}{,} \\PY{o}{*}\\PY{o}{*}\\PY{n}{kwargs}\\PY{p}{)}\\PY{p}{:}\n",
       "\\PY{+w}{        }\\PY{l+s+sd}{\\PYZdq{}\\PYZdq{}\\PYZdq{}Compute the solution of the model and associated quantities.}\n",
       "\n",
       "\\PY{l+s+sd}{        This method computes the output of the model, its internal state,}\n",
       "\\PY{l+s+sd}{        and various associated quantities for given |parameter values| `mu`.}\n",
       "\n",
       "\\PY{l+s+sd}{        .. note::}\n",
       "\n",
       "\\PY{l+s+sd}{            The default implementation defers the actual computations to}\n",
       "\\PY{l+s+sd}{            the methods :meth:`\\PYZus{}compute\\PYZus{}solution`, :meth:`\\PYZus{}compute\\PYZus{}output`,}\n",
       "\\PY{l+s+sd}{            :meth:`\\PYZus{}compute\\PYZus{}solution\\PYZus{}error\\PYZus{}estimate` and :meth:`\\PYZus{}compute\\PYZus{}output\\PYZus{}error\\PYZus{}estimate`.}\n",
       "\\PY{l+s+sd}{            The call to :meth:`\\PYZus{}compute\\PYZus{}solution` is :mod:`cached \\PYZlt{}pymor.core.cache\\PYZgt{}`.}\n",
       "\\PY{l+s+sd}{            In addition, |Model| implementors may implement :meth:`\\PYZus{}compute` to}\n",
       "\\PY{l+s+sd}{            simultaneously compute multiple values in an optimized way. The corresponding}\n",
       "\\PY{l+s+sd}{            `\\PYZus{}compute\\PYZus{}XXX` methods will not be called for values already returned by}\n",
       "\\PY{l+s+sd}{            :meth:`\\PYZus{}compute`.}\n",
       "\n",
       "\\PY{l+s+sd}{        Parameters}\n",
       "\\PY{l+s+sd}{        \\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}}\n",
       "\\PY{l+s+sd}{        solution}\n",
       "\\PY{l+s+sd}{            If `True`, return the model\\PYZsq{}s internal state.}\n",
       "\\PY{l+s+sd}{        output}\n",
       "\\PY{l+s+sd}{            If `True`, return the model output.}\n",
       "\\PY{l+s+sd}{        solution\\PYZus{}d\\PYZus{}mu}\n",
       "\\PY{l+s+sd}{            If not `False`, either `True` to return the derivative of the model\\PYZsq{}s}\n",
       "\\PY{l+s+sd}{            internal state w.r.t. all parameter components or a tuple `(parameter, index)`}\n",
       "\\PY{l+s+sd}{            to return the derivative of a single parameter component.}\n",
       "\\PY{l+s+sd}{        output\\PYZus{}d\\PYZus{}mu}\n",
       "\\PY{l+s+sd}{            If `True`, return the gradient of the model output w.r.t. the |Parameter|.}\n",
       "\\PY{l+s+sd}{        solution\\PYZus{}error\\PYZus{}estimate}\n",
       "\\PY{l+s+sd}{            If `True`, return an error estimate for the computed internal state.}\n",
       "\\PY{l+s+sd}{        output\\PYZus{}error\\PYZus{}estimate}\n",
       "\\PY{l+s+sd}{            If `True`, return an error estimate for the computed output.}\n",
       "\\PY{l+s+sd}{        output\\PYZus{}d\\PYZus{}mu\\PYZus{}return\\PYZus{}array}\n",
       "\\PY{l+s+sd}{            If `True`, return the output gradient as a |NumPy array|.}\n",
       "\\PY{l+s+sd}{            Otherwise, return a dict of gradients for each |Parameter|.}\n",
       "\\PY{l+s+sd}{        output\\PYZus{}error\\PYZus{}estimate\\PYZus{}return\\PYZus{}vector}\n",
       "\\PY{l+s+sd}{            If `True`, return the output estimate as a |NumPy array|,}\n",
       "\\PY{l+s+sd}{            where each component corresponds to the respective component}\n",
       "\\PY{l+s+sd}{            of the :attr:`output\\PYZus{}functional`.}\n",
       "\\PY{l+s+sd}{            Otherwise, return the Euclidean norm of all components.}\n",
       "\\PY{l+s+sd}{        mu}\n",
       "\\PY{l+s+sd}{            |Parameter values| for which to compute the values.}\n",
       "\\PY{l+s+sd}{        input}\n",
       "\\PY{l+s+sd}{            The model input. Either a |NumPy array| of shape `(self.dim\\PYZus{}input,)`,}\n",
       "\\PY{l+s+sd}{            a |Function| with `dim\\PYZus{}domain == 1` and `shape\\PYZus{}range == (self.dim\\PYZus{}input,)`}\n",
       "\\PY{l+s+sd}{            mapping time to input, or a `str` expression with `t` as variable that}\n",
       "\\PY{l+s+sd}{            can be used to instantiate an |ExpressionFunction| of this type.}\n",
       "\\PY{l+s+sd}{            Can be `None` if `self.dim\\PYZus{}input == 0`.}\n",
       "\\PY{l+s+sd}{        kwargs}\n",
       "\\PY{l+s+sd}{            Further keyword arguments to select further quantities that should}\n",
       "\\PY{l+s+sd}{            be returned or to customize how the values are computed.}\n",
       "\n",
       "\\PY{l+s+sd}{        Returns}\n",
       "\\PY{l+s+sd}{        \\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}}\n",
       "\\PY{l+s+sd}{        A dict with the computed values.}\n",
       "\\PY{l+s+sd}{        \\PYZdq{}\\PYZdq{}\\PYZdq{}}\n",
       "        \\PY{c+c1}{\\PYZsh{} make sure no unknown kwargs are passed}\n",
       "        \\PY{k}{assert} \\PY{n}{kwargs}\\PY{o}{.}\\PY{n}{keys}\\PY{p}{(}\\PY{p}{)} \\PY{o}{\\PYZlt{}}\\PY{o}{=} \\PY{n+nb+bp}{self}\\PY{o}{.}\\PY{n}{\\PYZus{}compute\\PYZus{}allowed\\PYZus{}kwargs}\n",
       "        \\PY{k}{assert} \\PY{n+nb}{input} \\PY{o+ow}{is} \\PY{o+ow}{not} \\PY{k+kc}{None} \\PY{o+ow}{or} \\PY{n+nb+bp}{self}\\PY{o}{.}\\PY{n}{dim\\PYZus{}input} \\PY{o}{==} \\PY{l+m+mi}{0}\n",
       "\n",
       "        \\PY{c+c1}{\\PYZsh{} parse parameter values}\n",
       "        \\PY{k}{if} \\PY{o+ow}{not} \\PY{n+nb}{isinstance}\\PY{p}{(}\\PY{n}{mu}\\PY{p}{,} \\PY{n}{Mu}\\PY{p}{)}\\PY{p}{:}\n",
       "            \\PY{n}{mu} \\PY{o}{=} \\PY{n+nb+bp}{self}\\PY{o}{.}\\PY{n}{parameters}\\PY{o}{.}\\PY{n}{parse}\\PY{p}{(}\\PY{n}{mu}\\PY{p}{)}\n",
       "        \\PY{k}{assert} \\PY{n+nb+bp}{self}\\PY{o}{.}\\PY{n}{parameters}\\PY{o}{.}\\PY{n}{assert\\PYZus{}compatible}\\PY{p}{(}\\PY{n}{mu}\\PY{p}{)}\n",
       "\n",
       "        \\PY{c+c1}{\\PYZsh{} parse input and add it to the parameter values}\n",
       "        \\PY{n}{mu\\PYZus{}input} \\PY{o}{=} \\PY{n}{Parameters}\\PY{p}{(}\\PY{n+nb}{input}\\PY{o}{=}\\PY{n+nb+bp}{self}\\PY{o}{.}\\PY{n}{dim\\PYZus{}input}\\PY{p}{)}\\PY{o}{.}\\PY{n}{parse}\\PY{p}{(}\\PY{n+nb}{input}\\PY{p}{)}\n",
       "        \\PY{n+nb}{input} \\PY{o}{=} \\PY{n}{mu\\PYZus{}input}\\PY{o}{.}\\PY{n}{get\\PYZus{}time\\PYZus{}dependent\\PYZus{}value}\\PY{p}{(}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{input}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{)} \\PY{k}{if} \\PY{n}{mu\\PYZus{}input}\\PY{o}{.}\\PY{n}{is\\PYZus{}time\\PYZus{}dependent}\\PY{p}{(}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{input}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{)} \\PY{k}{else} \\PY{n}{mu\\PYZus{}input}\\PY{p}{[}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{input}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{]}\n",
       "        \\PY{n}{mu} \\PY{o}{=} \\PY{n}{mu}\\PY{o}{.}\\PY{n}{with\\PYZus{}}\\PY{p}{(}\\PY{n+nb}{input}\\PY{o}{=}\\PY{n+nb}{input}\\PY{p}{)}\n",
       "\n",
       "        \\PY{c+c1}{\\PYZsh{} log output}\n",
       "        \\PY{c+c1}{\\PYZsh{} explicitly checking if logging is disabled saves some cpu cycles}\n",
       "        \\PY{k}{if} \\PY{o+ow}{not} \\PY{n+nb+bp}{self}\\PY{o}{.}\\PY{n}{logging\\PYZus{}disabled}\\PY{p}{:}\n",
       "            \\PY{n+nb+bp}{self}\\PY{o}{.}\\PY{n}{logger}\\PY{o}{.}\\PY{n}{info}\\PY{p}{(}\\PY{l+s+sa}{f}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{Solving }\\PY{l+s+si}{\\PYZob{}}\\PY{n+nb+bp}{self}\\PY{o}{.}\\PY{n}{name}\\PY{l+s+si}{\\PYZcb{}}\\PY{l+s+s1}{ for }\\PY{l+s+si}{\\PYZob{}}\\PY{n}{mu}\\PY{l+s+si}{\\PYZcb{}}\\PY{l+s+s1}{ ...}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{)}\n",
       "\n",
       "        \\PY{c+c1}{\\PYZsh{} first call \\PYZus{}compute to give subclasses more control}\n",
       "        \\PY{n}{data} \\PY{o}{=} \\PY{n+nb+bp}{self}\\PY{o}{.}\\PY{n}{\\PYZus{}compute}\\PY{p}{(}\\PY{n}{solution}\\PY{o}{=}\\PY{n}{solution}\\PY{p}{,} \\PY{n}{output}\\PY{o}{=}\\PY{n}{output}\\PY{p}{,}\n",
       "                             \\PY{n}{solution\\PYZus{}d\\PYZus{}mu}\\PY{o}{=}\\PY{n}{solution\\PYZus{}d\\PYZus{}mu}\\PY{p}{,} \\PY{n}{output\\PYZus{}d\\PYZus{}mu}\\PY{o}{=}\\PY{n}{output\\PYZus{}d\\PYZus{}mu}\\PY{p}{,}\n",
       "                             \\PY{n}{solution\\PYZus{}error\\PYZus{}estimate}\\PY{o}{=}\\PY{n}{solution\\PYZus{}error\\PYZus{}estimate}\\PY{p}{,}\n",
       "                             \\PY{n}{output\\PYZus{}error\\PYZus{}estimate}\\PY{o}{=}\\PY{n}{output\\PYZus{}error\\PYZus{}estimate}\\PY{p}{,}\n",
       "                             \\PY{n}{mu}\\PY{o}{=}\\PY{n}{mu}\\PY{p}{,} \\PY{o}{*}\\PY{o}{*}\\PY{n}{kwargs}\\PY{p}{)}\n",
       "\n",
       "        \\PY{k}{if} \\PY{p}{(}\\PY{n}{solution} \\PY{o+ow}{or} \\PY{n}{output} \\PY{o+ow}{or} \\PY{n}{solution\\PYZus{}error\\PYZus{}estimate}\n",
       "            \\PY{o+ow}{or} \\PY{n}{output\\PYZus{}error\\PYZus{}estimate} \\PY{o+ow}{or} \\PY{n}{solution\\PYZus{}d\\PYZus{}mu} \\PY{o+ow}{or} \\PY{n}{output\\PYZus{}d\\PYZus{}mu}\\PY{p}{)} \\PYZbs{}\n",
       "           \\PY{o+ow}{and} \\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{solution}\\PY{l+s+s1}{\\PYZsq{}} \\PY{o+ow}{not} \\PY{o+ow}{in} \\PY{n}{data}\\PY{p}{:}\n",
       "            \\PY{n}{retval} \\PY{o}{=} \\PY{n+nb+bp}{self}\\PY{o}{.}\\PY{n}{cached\\PYZus{}method\\PYZus{}call}\\PY{p}{(}\\PY{n+nb+bp}{self}\\PY{o}{.}\\PY{n}{\\PYZus{}compute\\PYZus{}solution}\\PY{p}{,} \\PY{n}{mu}\\PY{o}{=}\\PY{n}{mu}\\PY{p}{,} \\PY{o}{*}\\PY{o}{*}\\PY{n}{kwargs}\\PY{p}{)}\n",
       "            \\PY{k}{if} \\PY{n+nb}{isinstance}\\PY{p}{(}\\PY{n}{retval}\\PY{p}{,} \\PY{n+nb}{dict}\\PY{p}{)}\\PY{p}{:}\n",
       "                \\PY{k}{assert} \\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{solution}\\PY{l+s+s1}{\\PYZsq{}} \\PY{o+ow}{in} \\PY{n}{retval}\n",
       "                \\PY{n}{data}\\PY{o}{.}\\PY{n}{update}\\PY{p}{(}\\PY{n}{retval}\\PY{p}{)}\n",
       "            \\PY{k}{else}\\PY{p}{:}\n",
       "                \\PY{n}{data}\\PY{p}{[}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{solution}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{]} \\PY{o}{=} \\PY{n}{retval}\n",
       "\n",
       "        \\PY{k}{if} \\PY{n}{output} \\PY{o+ow}{and} \\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{output}\\PY{l+s+s1}{\\PYZsq{}} \\PY{o+ow}{not} \\PY{o+ow}{in} \\PY{n}{data}\\PY{p}{:}\n",
       "            \\PY{c+c1}{\\PYZsh{} TODO use caching here (requires skipping args in key generation)}\n",
       "            \\PY{n}{retval} \\PY{o}{=} \\PY{n+nb+bp}{self}\\PY{o}{.}\\PY{n}{\\PYZus{}compute\\PYZus{}output}\\PY{p}{(}\\PY{n}{data}\\PY{p}{[}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{solution}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{]}\\PY{p}{,} \\PY{n}{mu}\\PY{o}{=}\\PY{n}{mu}\\PY{p}{,} \\PY{o}{*}\\PY{o}{*}\\PY{n}{kwargs}\\PY{p}{)}\n",
       "            \\PY{k}{if} \\PY{n+nb}{isinstance}\\PY{p}{(}\\PY{n}{retval}\\PY{p}{,} \\PY{n+nb}{dict}\\PY{p}{)}\\PY{p}{:}\n",
       "                \\PY{k}{assert} \\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{output}\\PY{l+s+s1}{\\PYZsq{}} \\PY{o+ow}{in} \\PY{n}{retval}\n",
       "                \\PY{n}{data}\\PY{o}{.}\\PY{n}{update}\\PY{p}{(}\\PY{n}{retval}\\PY{p}{)}\n",
       "            \\PY{k}{else}\\PY{p}{:}\n",
       "                \\PY{n}{data}\\PY{p}{[}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{output}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{]} \\PY{o}{=} \\PY{n}{retval}\n",
       "\n",
       "        \\PY{k}{if} \\PY{n}{solution\\PYZus{}d\\PYZus{}mu} \\PY{o+ow}{and} \\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{solution\\PYZus{}d\\PYZus{}mu}\\PY{l+s+s1}{\\PYZsq{}} \\PY{o+ow}{not} \\PY{o+ow}{in} \\PY{n}{data}\\PY{p}{:}\n",
       "            \\PY{k}{if} \\PY{n+nb}{isinstance}\\PY{p}{(}\\PY{n}{solution\\PYZus{}d\\PYZus{}mu}\\PY{p}{,} \\PY{n+nb}{tuple}\\PY{p}{)}\\PY{p}{:}\n",
       "                \\PY{n}{retval} \\PY{o}{=} \\PY{n+nb+bp}{self}\\PY{o}{.}\\PY{n}{\\PYZus{}compute\\PYZus{}solution\\PYZus{}d\\PYZus{}mu\\PYZus{}single\\PYZus{}direction}\\PY{p}{(}\n",
       "                    \\PY{n}{solution\\PYZus{}d\\PYZus{}mu}\\PY{p}{[}\\PY{l+m+mi}{0}\\PY{p}{]}\\PY{p}{,} \\PY{n}{solution\\PYZus{}d\\PYZus{}mu}\\PY{p}{[}\\PY{l+m+mi}{1}\\PY{p}{]}\\PY{p}{,} \\PY{n}{data}\\PY{p}{[}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{solution}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{]}\\PY{p}{,} \\PY{n}{mu}\\PY{o}{=}\\PY{n}{mu}\\PY{p}{,} \\PY{o}{*}\\PY{o}{*}\\PY{n}{kwargs}\\PY{p}{)}\n",
       "            \\PY{k}{else}\\PY{p}{:}\n",
       "                \\PY{n}{retval} \\PY{o}{=} \\PY{n+nb+bp}{self}\\PY{o}{.}\\PY{n}{\\PYZus{}compute\\PYZus{}solution\\PYZus{}d\\PYZus{}mu}\\PY{p}{(}\\PY{n}{data}\\PY{p}{[}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{solution}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{]}\\PY{p}{,} \\PY{n}{mu}\\PY{o}{=}\\PY{n}{mu}\\PY{p}{,} \\PY{o}{*}\\PY{o}{*}\\PY{n}{kwargs}\\PY{p}{)}\n",
       "            \\PY{c+c1}{\\PYZsh{} retval is always a dict}\n",
       "            \\PY{k}{if} \\PY{n+nb}{isinstance}\\PY{p}{(}\\PY{n}{retval}\\PY{p}{,} \\PY{n+nb}{dict}\\PY{p}{)} \\PY{o+ow}{and} \\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{solution\\PYZus{}d\\PYZus{}mu}\\PY{l+s+s1}{\\PYZsq{}} \\PY{o+ow}{in} \\PY{n}{retval}\\PY{p}{:}\n",
       "                \\PY{n}{data}\\PY{o}{.}\\PY{n}{update}\\PY{p}{(}\\PY{n}{retval}\\PY{p}{)}\n",
       "            \\PY{k}{else}\\PY{p}{:}\n",
       "                \\PY{n}{data}\\PY{p}{[}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{solution\\PYZus{}d\\PYZus{}mu}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{]} \\PY{o}{=} \\PY{n}{retval}\n",
       "\n",
       "        \\PY{k}{if} \\PY{n}{output\\PYZus{}d\\PYZus{}mu} \\PY{o+ow}{and} \\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{output\\PYZus{}d\\PYZus{}mu}\\PY{l+s+s1}{\\PYZsq{}} \\PY{o+ow}{not} \\PY{o+ow}{in} \\PY{n}{data}\\PY{p}{:}\n",
       "            \\PY{c+c1}{\\PYZsh{} TODO use caching here (requires skipping args in key generation)}\n",
       "            \\PY{n}{retval} \\PY{o}{=} \\PY{n+nb+bp}{self}\\PY{o}{.}\\PY{n}{\\PYZus{}compute\\PYZus{}output\\PYZus{}d\\PYZus{}mu}\\PY{p}{(}\\PY{n}{data}\\PY{p}{[}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{solution}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{]}\\PY{p}{,} \\PY{n}{mu}\\PY{o}{=}\\PY{n}{mu}\\PY{p}{,}\n",
       "                                               \\PY{n}{return\\PYZus{}array}\\PY{o}{=}\\PY{n}{output\\PYZus{}d\\PYZus{}mu\\PYZus{}return\\PYZus{}array}\\PY{p}{,}\n",
       "                                               \\PY{o}{*}\\PY{o}{*}\\PY{n}{kwargs}\\PY{p}{)}\n",
       "            \\PY{c+c1}{\\PYZsh{} retval is always a dict}\n",
       "            \\PY{k}{if} \\PY{n+nb}{isinstance}\\PY{p}{(}\\PY{n}{retval}\\PY{p}{,} \\PY{n+nb}{dict}\\PY{p}{)} \\PY{o+ow}{and} \\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{output\\PYZus{}d\\PYZus{}mu}\\PY{l+s+s1}{\\PYZsq{}} \\PY{o+ow}{in} \\PY{n}{retval}\\PY{p}{:}\n",
       "                \\PY{n}{data}\\PY{o}{.}\\PY{n}{update}\\PY{p}{(}\\PY{n}{retval}\\PY{p}{)}\n",
       "            \\PY{k}{else}\\PY{p}{:}\n",
       "                \\PY{n}{data}\\PY{p}{[}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{output\\PYZus{}d\\PYZus{}mu}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{]} \\PY{o}{=} \\PY{n}{retval}\n",
       "\n",
       "        \\PY{k}{if} \\PY{n}{solution\\PYZus{}error\\PYZus{}estimate} \\PY{o+ow}{and} \\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{solution\\PYZus{}error\\PYZus{}estimate}\\PY{l+s+s1}{\\PYZsq{}} \\PY{o+ow}{not} \\PY{o+ow}{in} \\PY{n}{data}\\PY{p}{:}\n",
       "            \\PY{c+c1}{\\PYZsh{} TODO use caching here (requires skipping args in key generation)}\n",
       "            \\PY{n}{retval} \\PY{o}{=} \\PY{n+nb+bp}{self}\\PY{o}{.}\\PY{n}{\\PYZus{}compute\\PYZus{}solution\\PYZus{}error\\PYZus{}estimate}\\PY{p}{(}\\PY{n}{data}\\PY{p}{[}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{solution}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{]}\\PY{p}{,} \\PY{n}{mu}\\PY{o}{=}\\PY{n}{mu}\\PY{p}{,} \\PY{o}{*}\\PY{o}{*}\\PY{n}{kwargs}\\PY{p}{)}\n",
       "            \\PY{k}{if} \\PY{n+nb}{isinstance}\\PY{p}{(}\\PY{n}{retval}\\PY{p}{,} \\PY{n+nb}{dict}\\PY{p}{)}\\PY{p}{:}\n",
       "                \\PY{k}{assert} \\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{solution\\PYZus{}error\\PYZus{}estimate}\\PY{l+s+s1}{\\PYZsq{}} \\PY{o+ow}{in} \\PY{n}{retval}\n",
       "                \\PY{n}{data}\\PY{o}{.}\\PY{n}{update}\\PY{p}{(}\\PY{n}{retval}\\PY{p}{)}\n",
       "            \\PY{k}{else}\\PY{p}{:}\n",
       "                \\PY{n}{data}\\PY{p}{[}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{solution\\PYZus{}error\\PYZus{}estimate}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{]} \\PY{o}{=} \\PY{n}{retval}\n",
       "\n",
       "        \\PY{k}{if} \\PY{n}{output\\PYZus{}error\\PYZus{}estimate} \\PY{o+ow}{and} \\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{output\\PYZus{}error\\PYZus{}estimate}\\PY{l+s+s1}{\\PYZsq{}} \\PY{o+ow}{not} \\PY{o+ow}{in} \\PY{n}{data}\\PY{p}{:}\n",
       "            \\PY{c+c1}{\\PYZsh{} TODO use caching here (requires skipping args in key generation)}\n",
       "            \\PY{n}{retval} \\PY{o}{=} \\PY{n+nb+bp}{self}\\PY{o}{.}\\PY{n}{\\PYZus{}compute\\PYZus{}output\\PYZus{}error\\PYZus{}estimate}\\PY{p}{(}\n",
       "                \\PY{n}{data}\\PY{p}{[}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{solution}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{]}\\PY{p}{,} \\PY{n}{mu}\\PY{o}{=}\\PY{n}{mu}\\PY{p}{,}\n",
       "                \\PY{n}{return\\PYZus{}vector}\\PY{o}{=}\\PY{n}{output\\PYZus{}error\\PYZus{}estimate\\PYZus{}return\\PYZus{}vector}\\PY{p}{,} \\PY{o}{*}\\PY{o}{*}\\PY{n}{kwargs}\\PY{p}{)}\n",
       "            \\PY{k}{if} \\PY{n+nb}{isinstance}\\PY{p}{(}\\PY{n}{retval}\\PY{p}{,} \\PY{n+nb}{dict}\\PY{p}{)}\\PY{p}{:}\n",
       "                \\PY{k}{assert} \\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{output\\PYZus{}error\\PYZus{}estimate}\\PY{l+s+s1}{\\PYZsq{}} \\PY{o+ow}{in} \\PY{n}{retval}\n",
       "                \\PY{n}{data}\\PY{o}{.}\\PY{n}{update}\\PY{p}{(}\\PY{n}{retval}\\PY{p}{)}\n",
       "            \\PY{k}{else}\\PY{p}{:}\n",
       "                \\PY{n}{data}\\PY{p}{[}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{output\\PYZus{}error\\PYZus{}estimate}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{]} \\PY{o}{=} \\PY{n}{retval}\n",
       "\n",
       "        \\PY{k}{return} \\PY{n}{data}\n",
       "\\end{Verbatim}\n"
      ],
      "text/plain": [
       "    def compute(self, solution=False, output=False, solution_d_mu=False, output_d_mu=False,\n",
       "                solution_error_estimate=False, output_error_estimate=False,\n",
       "                output_d_mu_return_array=False, output_error_estimate_return_vector=False,\n",
       "                *, mu=None, input=None, **kwargs):\n",
       "        \"\"\"Compute the solution of the model and associated quantities.\n",
       "\n",
       "        This method computes the output of the model, its internal state,\n",
       "        and various associated quantities for given |parameter values| `mu`.\n",
       "\n",
       "        .. note::\n",
       "\n",
       "            The default implementation defers the actual computations to\n",
       "            the methods :meth:`_compute_solution`, :meth:`_compute_output`,\n",
       "            :meth:`_compute_solution_error_estimate` and :meth:`_compute_output_error_estimate`.\n",
       "            The call to :meth:`_compute_solution` is :mod:`cached <pymor.core.cache>`.\n",
       "            In addition, |Model| implementors may implement :meth:`_compute` to\n",
       "            simultaneously compute multiple values in an optimized way. The corresponding\n",
       "            `_compute_XXX` methods will not be called for values already returned by\n",
       "            :meth:`_compute`.\n",
       "\n",
       "        Parameters\n",
       "        ----------\n",
       "        solution\n",
       "            If `True`, return the model's internal state.\n",
       "        output\n",
       "            If `True`, return the model output.\n",
       "        solution_d_mu\n",
       "            If not `False`, either `True` to return the derivative of the model's\n",
       "            internal state w.r.t. all parameter components or a tuple `(parameter, index)`\n",
       "            to return the derivative of a single parameter component.\n",
       "        output_d_mu\n",
       "            If `True`, return the gradient of the model output w.r.t. the |Parameter|.\n",
       "        solution_error_estimate\n",
       "            If `True`, return an error estimate for the computed internal state.\n",
       "        output_error_estimate\n",
       "            If `True`, return an error estimate for the computed output.\n",
       "        output_d_mu_return_array\n",
       "            If `True`, return the output gradient as a |NumPy array|.\n",
       "            Otherwise, return a dict of gradients for each |Parameter|.\n",
       "        output_error_estimate_return_vector\n",
       "            If `True`, return the output estimate as a |NumPy array|,\n",
       "            where each component corresponds to the respective component\n",
       "            of the :attr:`output_functional`.\n",
       "            Otherwise, return the Euclidean norm of all components.\n",
       "        mu\n",
       "            |Parameter values| for which to compute the values.\n",
       "        input\n",
       "            The model input. Either a |NumPy array| of shape `(self.dim_input,)`,\n",
       "            a |Function| with `dim_domain == 1` and `shape_range == (self.dim_input,)`\n",
       "            mapping time to input, or a `str` expression with `t` as variable that\n",
       "            can be used to instantiate an |ExpressionFunction| of this type.\n",
       "            Can be `None` if `self.dim_input == 0`.\n",
       "        kwargs\n",
       "            Further keyword arguments to select further quantities that should\n",
       "            be returned or to customize how the values are computed.\n",
       "\n",
       "        Returns\n",
       "        -------\n",
       "        A dict with the computed values.\n",
       "        \"\"\"\n",
       "        # make sure no unknown kwargs are passed\n",
       "        assert kwargs.keys() <= self._compute_allowed_kwargs\n",
       "        assert input is not None or self.dim_input == 0\n",
       "\n",
       "        # parse parameter values\n",
       "        if not isinstance(mu, Mu):\n",
       "            mu = self.parameters.parse(mu)\n",
       "        assert self.parameters.assert_compatible(mu)\n",
       "\n",
       "        # parse input and add it to the parameter values\n",
       "        mu_input = Parameters(input=self.dim_input).parse(input)\n",
       "        input = mu_input.get_time_dependent_value('input') if mu_input.is_time_dependent('input') else mu_input['input']\n",
       "        mu = mu.with_(input=input)\n",
       "\n",
       "        # log output\n",
       "        # explicitly checking if logging is disabled saves some cpu cycles\n",
       "        if not self.logging_disabled:\n",
       "            self.logger.info(f'Solving {self.name} for {mu} ...')\n",
       "\n",
       "        # first call _compute to give subclasses more control\n",
       "        data = self._compute(solution=solution, output=output,\n",
       "                             solution_d_mu=solution_d_mu, output_d_mu=output_d_mu,\n",
       "                             solution_error_estimate=solution_error_estimate,\n",
       "                             output_error_estimate=output_error_estimate,\n",
       "                             mu=mu, **kwargs)\n",
       "\n",
       "        if (solution or output or solution_error_estimate\n",
       "            or output_error_estimate or solution_d_mu or output_d_mu) \\\n",
       "           and 'solution' not in data:\n",
       "            retval = self.cached_method_call(self._compute_solution, mu=mu, **kwargs)\n",
       "            if isinstance(retval, dict):\n",
       "                assert 'solution' in retval\n",
       "                data.update(retval)\n",
       "            else:\n",
       "                data['solution'] = retval\n",
       "\n",
       "        if output and 'output' not in data:\n",
       "            # TODO use caching here (requires skipping args in key generation)\n",
       "            retval = self._compute_output(data['solution'], mu=mu, **kwargs)\n",
       "            if isinstance(retval, dict):\n",
       "                assert 'output' in retval\n",
       "                data.update(retval)\n",
       "            else:\n",
       "                data['output'] = retval\n",
       "\n",
       "        if solution_d_mu and 'solution_d_mu' not in data:\n",
       "            if isinstance(solution_d_mu, tuple):\n",
       "                retval = self._compute_solution_d_mu_single_direction(\n",
       "                    solution_d_mu[0], solution_d_mu[1], data['solution'], mu=mu, **kwargs)\n",
       "            else:\n",
       "                retval = self._compute_solution_d_mu(data['solution'], mu=mu, **kwargs)\n",
       "            # retval is always a dict\n",
       "            if isinstance(retval, dict) and 'solution_d_mu' in retval:\n",
       "                data.update(retval)\n",
       "            else:\n",
       "                data['solution_d_mu'] = retval\n",
       "\n",
       "        if output_d_mu and 'output_d_mu' not in data:\n",
       "            # TODO use caching here (requires skipping args in key generation)\n",
       "            retval = self._compute_output_d_mu(data['solution'], mu=mu,\n",
       "                                               return_array=output_d_mu_return_array,\n",
       "                                               **kwargs)\n",
       "            # retval is always a dict\n",
       "            if isinstance(retval, dict) and 'output_d_mu' in retval:\n",
       "                data.update(retval)\n",
       "            else:\n",
       "                data['output_d_mu'] = retval\n",
       "\n",
       "        if solution_error_estimate and 'solution_error_estimate' not in data:\n",
       "            # TODO use caching here (requires skipping args in key generation)\n",
       "            retval = self._compute_solution_error_estimate(data['solution'], mu=mu, **kwargs)\n",
       "            if isinstance(retval, dict):\n",
       "                assert 'solution_error_estimate' in retval\n",
       "                data.update(retval)\n",
       "            else:\n",
       "                data['solution_error_estimate'] = retval\n",
       "\n",
       "        if output_error_estimate and 'output_error_estimate' not in data:\n",
       "            # TODO use caching here (requires skipping args in key generation)\n",
       "            retval = self._compute_output_error_estimate(\n",
       "                data['solution'], mu=mu,\n",
       "                return_vector=output_error_estimate_return_vector, **kwargs)\n",
       "            if isinstance(retval, dict):\n",
       "                assert 'output_error_estimate' in retval\n",
       "                data.update(retval)\n",
       "            else:\n",
       "                data['output_error_estimate'] = retval\n",
       "\n",
       "        return data"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print_source(fom.compute)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd853f7a",
   "metadata": {},
   "source": [
    "What we see is a default implementation from {class}`~pymor.models.interface.Model` that\n",
    "takes care of checking the input {{ parameter_values }} `mu`, {mod}`caching <pymor.core.cache>` and\n",
    "{mod}`logging <pymor.core.logger>`, but defers the actual computations to further private methods.\n",
    "Implementors can directly implement {meth}`~pymor.models.interface.Model._compute` to compute\n",
    "multiple return values at once in an optimized way. Our given model, however, just implements\n",
    "{meth}`~pymor.models.interface.Model._compute_solution` where we can find the\n",
    "actual code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "64a48f57",
   "metadata": {},
   "outputs": [
    {
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       ".output_html .il { color: #666666 } /* Literal.Number.Integer.Long */</style><div class=\"highlight\"><pre><span></span>    <span class=\"k\">def</span> <span class=\"nf\">_compute_solution</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">mu</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">,</span> <span class=\"o\">**</span><span class=\"n\">kwargs</span><span class=\"p\">):</span>\n",
       "        <span class=\"k\">return</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">operator</span><span class=\"o\">.</span><span class=\"n\">apply_inverse</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">rhs</span><span class=\"o\">.</span><span class=\"n\">as_range_array</span><span class=\"p\">(</span><span class=\"n\">mu</span><span class=\"p\">),</span> <span class=\"n\">mu</span><span class=\"o\">=</span><span class=\"n\">mu</span><span class=\"p\">)</span>\n",
       "</pre></div>\n"
      ],
      "text/latex": [
       "\\begin{Verbatim}[commandchars=\\\\\\{\\}]\n",
       "    \\PY{k}{def} \\PY{n+nf}{\\PYZus{}compute\\PYZus{}solution}\\PY{p}{(}\\PY{n+nb+bp}{self}\\PY{p}{,} \\PY{n}{mu}\\PY{o}{=}\\PY{k+kc}{None}\\PY{p}{,} \\PY{o}{*}\\PY{o}{*}\\PY{n}{kwargs}\\PY{p}{)}\\PY{p}{:}\n",
       "        \\PY{k}{return} \\PY{n+nb+bp}{self}\\PY{o}{.}\\PY{n}{operator}\\PY{o}{.}\\PY{n}{apply\\PYZus{}inverse}\\PY{p}{(}\\PY{n+nb+bp}{self}\\PY{o}{.}\\PY{n}{rhs}\\PY{o}{.}\\PY{n}{as\\PYZus{}range\\PYZus{}array}\\PY{p}{(}\\PY{n}{mu}\\PY{p}{)}\\PY{p}{,} \\PY{n}{mu}\\PY{o}{=}\\PY{n}{mu}\\PY{p}{)}\n",
       "\\end{Verbatim}\n"
      ],
      "text/plain": [
       "    def _compute_solution(self, mu=None, **kwargs):\n",
       "        return self.operator.apply_inverse(self.rhs.as_range_array(mu), mu=mu)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print_source(fom._compute_solution)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09371a54",
   "metadata": {},
   "source": [
    "What does this mean? If we look at the type of `fom`,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "de11f90b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pymor.models.basic.StationaryModel"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(fom)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf62c5c7",
   "metadata": {},
   "source": [
    "we see that `fom` is a {{ StationaryModel }} which encodes an equation of the\n",
    "form\n",
    "\n",
    "```{math}\n",
    "L(u(\\mu); \\mu) = F(\\mu)\n",
    "```\n",
    "\n",
    "Here, {math}`L` is a linear or non-linear parametric {{ Operator }} and {math}`F` is a\n",
    "parametric right-hand side vector. In {{ StationaryModel }}, {math}`L` is represented by\n",
    "the {attr}`~pymor.models.basic.StationaryModel.operator` attribute. So\n",
    "\n",
    "```\n",
    "self.operator.apply_inverse(X, mu=mu)\n",
    "```\n",
    "\n",
    "determines the solution of this equation for the {{ parameter_values }} `mu` and a right-hand\n",
    "side given by `X`. As you see above, the right-hand side of the equation is given by the\n",
    "{attr}`~pymor.models.basic.StationaryModel.rhs` attribute.\n",
    "However, while {meth}`~pymor.operators.interface.Operator.apply_inverse` expects a\n",
    "{{ VectorArray }},  we see that {attr}`~pymor.models.basic.StationaryModel.rhs` is actually\n",
    "an {{ Operator }}:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7bcd91b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NumpyMatrixOperator(<20201x1 dense>, range_id='STATE')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fom.rhs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8408ad7",
   "metadata": {},
   "source": [
    "This is due to the fact that {{ VectorArrays }} in pyMOR cannot be parametric. So to allow\n",
    "for parametric right-hand sides, this right-hand side is encoded by a linear {{ Operator }}\n",
    "that maps numbers to scalar multiples of the right-hand side vector. Indeed, we see that"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9e0a92b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NumpyVectorSpace(1)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fom.rhs.source"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab7fa9bd",
   "metadata": {},
   "source": [
    "is one-dimensional, and if we look at the base-class implementation of\n",
    "{meth}`~pymor.operators.interface.Operator.as_range_array`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0b5c41ff",
   "metadata": {},
   "outputs": [
    {
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       ".output_html .il { color: #666666 } /* Literal.Number.Integer.Long */</style><div class=\"highlight\"><pre><span></span>    <span class=\"k\">def</span> <span class=\"nf\">as_range_array</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">mu</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">):</span>\n",
       "<span class=\"w\">        </span><span class=\"sd\">&quot;&quot;&quot;Return a |VectorArray| representation of the operator in its range space.</span>\n",
       "\n",
       "<span class=\"sd\">        In the case of a linear operator with |NumpyVectorSpace| as</span>\n",
       "<span class=\"sd\">        :attr:`~Operator.source`, this method returns for given |parameter values|</span>\n",
       "<span class=\"sd\">        `mu` a |VectorArray| `V` in the operator&#39;s :attr:`~Operator.range`,</span>\n",
       "<span class=\"sd\">        such that ::</span>\n",
       "\n",
       "<span class=\"sd\">            V.lincomb(U.to_numpy()) == self.apply(U, mu)</span>\n",
       "\n",
       "<span class=\"sd\">        for all |VectorArrays| `U`.</span>\n",
       "\n",
       "<span class=\"sd\">        Parameters</span>\n",
       "<span class=\"sd\">        ----------</span>\n",
       "<span class=\"sd\">        mu</span>\n",
       "<span class=\"sd\">            The |parameter values| for which to return the |VectorArray|</span>\n",
       "<span class=\"sd\">            representation.</span>\n",
       "\n",
       "<span class=\"sd\">        Returns</span>\n",
       "<span class=\"sd\">        -------</span>\n",
       "<span class=\"sd\">        V</span>\n",
       "<span class=\"sd\">            The |VectorArray| defined above.</span>\n",
       "<span class=\"sd\">        &quot;&quot;&quot;</span>\n",
       "        <span class=\"k\">assert</span> <span class=\"nb\">isinstance</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">source</span><span class=\"p\">,</span> <span class=\"n\">NumpyVectorSpace</span><span class=\"p\">)</span> <span class=\"ow\">and</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">linear</span>\n",
       "        <span class=\"k\">assert</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">source</span><span class=\"o\">.</span><span class=\"n\">dim</span> <span class=\"o\">&lt;=</span> <span class=\"n\">as_array_max_length</span><span class=\"p\">()</span>\n",
       "        <span class=\"k\">return</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">apply</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">source</span><span class=\"o\">.</span><span class=\"n\">from_numpy</span><span class=\"p\">(</span><span class=\"n\">np</span><span class=\"o\">.</span><span class=\"n\">eye</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">source</span><span class=\"o\">.</span><span class=\"n\">dim</span><span class=\"p\">)),</span> <span class=\"n\">mu</span><span class=\"o\">=</span><span class=\"n\">mu</span><span class=\"p\">)</span>\n",
       "</pre></div>\n"
      ],
      "text/latex": [
       "\\begin{Verbatim}[commandchars=\\\\\\{\\}]\n",
       "    \\PY{k}{def} \\PY{n+nf}{as\\PYZus{}range\\PYZus{}array}\\PY{p}{(}\\PY{n+nb+bp}{self}\\PY{p}{,} \\PY{n}{mu}\\PY{o}{=}\\PY{k+kc}{None}\\PY{p}{)}\\PY{p}{:}\n",
       "\\PY{+w}{        }\\PY{l+s+sd}{\\PYZdq{}\\PYZdq{}\\PYZdq{}Return a |VectorArray| representation of the operator in its range space.}\n",
       "\n",
       "\\PY{l+s+sd}{        In the case of a linear operator with |NumpyVectorSpace| as}\n",
       "\\PY{l+s+sd}{        :attr:`\\PYZti{}Operator.source`, this method returns for given |parameter values|}\n",
       "\\PY{l+s+sd}{        `mu` a |VectorArray| `V` in the operator\\PYZsq{}s :attr:`\\PYZti{}Operator.range`,}\n",
       "\\PY{l+s+sd}{        such that ::}\n",
       "\n",
       "\\PY{l+s+sd}{            V.lincomb(U.to\\PYZus{}numpy()) == self.apply(U, mu)}\n",
       "\n",
       "\\PY{l+s+sd}{        for all |VectorArrays| `U`.}\n",
       "\n",
       "\\PY{l+s+sd}{        Parameters}\n",
       "\\PY{l+s+sd}{        \\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}}\n",
       "\\PY{l+s+sd}{        mu}\n",
       "\\PY{l+s+sd}{            The |parameter values| for which to return the |VectorArray|}\n",
       "\\PY{l+s+sd}{            representation.}\n",
       "\n",
       "\\PY{l+s+sd}{        Returns}\n",
       "\\PY{l+s+sd}{        \\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}}\n",
       "\\PY{l+s+sd}{        V}\n",
       "\\PY{l+s+sd}{            The |VectorArray| defined above.}\n",
       "\\PY{l+s+sd}{        \\PYZdq{}\\PYZdq{}\\PYZdq{}}\n",
       "        \\PY{k}{assert} \\PY{n+nb}{isinstance}\\PY{p}{(}\\PY{n+nb+bp}{self}\\PY{o}{.}\\PY{n}{source}\\PY{p}{,} \\PY{n}{NumpyVectorSpace}\\PY{p}{)} \\PY{o+ow}{and} \\PY{n+nb+bp}{self}\\PY{o}{.}\\PY{n}{linear}\n",
       "        \\PY{k}{assert} \\PY{n+nb+bp}{self}\\PY{o}{.}\\PY{n}{source}\\PY{o}{.}\\PY{n}{dim} \\PY{o}{\\PYZlt{}}\\PY{o}{=} \\PY{n}{as\\PYZus{}array\\PYZus{}max\\PYZus{}length}\\PY{p}{(}\\PY{p}{)}\n",
       "        \\PY{k}{return} \\PY{n+nb+bp}{self}\\PY{o}{.}\\PY{n}{apply}\\PY{p}{(}\\PY{n+nb+bp}{self}\\PY{o}{.}\\PY{n}{source}\\PY{o}{.}\\PY{n}{from\\PYZus{}numpy}\\PY{p}{(}\\PY{n}{np}\\PY{o}{.}\\PY{n}{eye}\\PY{p}{(}\\PY{n+nb+bp}{self}\\PY{o}{.}\\PY{n}{source}\\PY{o}{.}\\PY{n}{dim}\\PY{p}{)}\\PY{p}{)}\\PY{p}{,} \\PY{n}{mu}\\PY{o}{=}\\PY{n}{mu}\\PY{p}{)}\n",
       "\\end{Verbatim}\n"
      ],
      "text/plain": [
       "    def as_range_array(self, mu=None):\n",
       "        \"\"\"Return a |VectorArray| representation of the operator in its range space.\n",
       "\n",
       "        In the case of a linear operator with |NumpyVectorSpace| as\n",
       "        :attr:`~Operator.source`, this method returns for given |parameter values|\n",
       "        `mu` a |VectorArray| `V` in the operator's :attr:`~Operator.range`,\n",
       "        such that ::\n",
       "\n",
       "            V.lincomb(U.to_numpy()) == self.apply(U, mu)\n",
       "\n",
       "        for all |VectorArrays| `U`.\n",
       "\n",
       "        Parameters\n",
       "        ----------\n",
       "        mu\n",
       "            The |parameter values| for which to return the |VectorArray|\n",
       "            representation.\n",
       "\n",
       "        Returns\n",
       "        -------\n",
       "        V\n",
       "            The |VectorArray| defined above.\n",
       "        \"\"\"\n",
       "        assert isinstance(self.source, NumpyVectorSpace) and self.linear\n",
       "        assert self.source.dim <= as_array_max_length()\n",
       "        return self.apply(self.source.from_numpy(np.eye(self.source.dim)), mu=mu)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pymor.operators.interface import Operator\n",
    "print_source(Operator.as_range_array)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "beddd44d",
   "metadata": {},
   "source": [
    "we see all that {meth}`~pymor.operators.interface.Operator.as_range_array`\n",
    "does is to apply the operator to {math}`1`. (`NumpyMatrixOperator.as_range_array`\n",
    "has an optimized implementation which just converts the stored matrix to a\n",
    "{{ NumpyVectorArray }}.)\n",
    "\n",
    "Let's try solving the model on our own:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4d7bae57",
   "metadata": {
    "tags": [
     "raises-exception"
    ]
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "mu is not a Mu instance. (Use parameters.parse?)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[13], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m U2 \u001b[38;5;241m=\u001b[39m \u001b[43mfom\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moperator\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply_inverse\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfom\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrhs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mas_range_array\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmu\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmu\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1.\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0.1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0.1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1.\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/builds/pymor/pymor/src/pymor/operators/constructions.py:194\u001b[0m, in \u001b[0;36mLincombOperator.apply_inverse\u001b[0;34m(self, V, mu, initial_guess, least_squares)\u001b[0m\n\u001b[1;32m    192\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m U\n\u001b[1;32m    193\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 194\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply_inverse\u001b[49m\u001b[43m(\u001b[49m\u001b[43mV\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmu\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmu\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minitial_guess\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minitial_guess\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mleast_squares\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mleast_squares\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/builds/pymor/pymor/src/pymor/operators/interface.py:225\u001b[0m, in \u001b[0;36mOperator.apply_inverse\u001b[0;34m(self, V, mu, initial_guess, least_squares)\u001b[0m\n\u001b[1;32m    223\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m initial_guess \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mor\u001b[39;00m initial_guess \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msource \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(initial_guess) \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mlen\u001b[39m(V)\n\u001b[1;32m    224\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpymor\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01moperators\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mconstructions\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m FixedParameterOperator\n\u001b[0;32m--> 225\u001b[0m assembled_op \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43massemble\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmu\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    226\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m assembled_op \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28mself\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(assembled_op, FixedParameterOperator):\n\u001b[1;32m    227\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m assembled_op\u001b[38;5;241m.\u001b[39mapply_inverse(V, initial_guess\u001b[38;5;241m=\u001b[39minitial_guess, least_squares\u001b[38;5;241m=\u001b[39mleast_squares)\n",
      "File \u001b[0;32m/builds/pymor/pymor/src/pymor/operators/constructions.py:140\u001b[0m, in \u001b[0;36mLincombOperator.assemble\u001b[0;34m(self, mu)\u001b[0m\n\u001b[1;32m    138\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpymor\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01malgorithms\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlincomb\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m assemble_lincomb\n\u001b[1;32m    139\u001b[0m operators \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtuple\u001b[39m(op\u001b[38;5;241m.\u001b[39massemble(mu) \u001b[38;5;28;01mfor\u001b[39;00m op \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moperators)\n\u001b[0;32m--> 140\u001b[0m coefficients \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mevaluate_coefficients\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmu\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    141\u001b[0m \u001b[38;5;66;03m# try to form a linear combination\u001b[39;00m\n\u001b[1;32m    142\u001b[0m op \u001b[38;5;241m=\u001b[39m assemble_lincomb(operators, coefficients, solver_options\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msolver_options,\n\u001b[1;32m    143\u001b[0m                       name\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mname \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m_assembled\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
      "File \u001b[0;32m/builds/pymor/pymor/src/pymor/operators/constructions.py:78\u001b[0m, in \u001b[0;36mLincombOperator.evaluate_coefficients\u001b[0;34m(self, mu)\u001b[0m\n\u001b[1;32m     66\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mevaluate_coefficients\u001b[39m(\u001b[38;5;28mself\u001b[39m, mu):\n\u001b[1;32m     67\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Compute the linear coefficients for given |parameter values|.\u001b[39;00m\n\u001b[1;32m     68\u001b[0m \n\u001b[1;32m     69\u001b[0m \u001b[38;5;124;03m    Parameters\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     76\u001b[0m \u001b[38;5;124;03m    List of linear coefficients.\u001b[39;00m\n\u001b[1;32m     77\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m---> 78\u001b[0m     \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparameters\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43massert_compatible\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmu\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     79\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m [c\u001b[38;5;241m.\u001b[39mevaluate(mu) \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(c, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mevaluate\u001b[39m\u001b[38;5;124m'\u001b[39m) \u001b[38;5;28;01melse\u001b[39;00m c \u001b[38;5;28;01mfor\u001b[39;00m c \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcoefficients]\n",
      "File \u001b[0;32m/builds/pymor/pymor/src/pymor/parameters/base.py:205\u001b[0m, in \u001b[0;36mParameters.assert_compatible\u001b[0;34m(self, mu)\u001b[0m\n\u001b[1;32m    195\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21massert_compatible\u001b[39m(\u001b[38;5;28mself\u001b[39m, mu):\n\u001b[1;32m    196\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Assert that |parameter values| are compatible with the given |Parameters|.\u001b[39;00m\n\u001b[1;32m    197\u001b[0m \n\u001b[1;32m    198\u001b[0m \u001b[38;5;124;03m    Each of the parameter must be contained in  `mu` and the dimensions have to match,\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    203\u001b[0m \u001b[38;5;124;03m    Otherwise, an `AssertionError` will be raised.\u001b[39;00m\n\u001b[1;32m    204\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 205\u001b[0m     \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mis_compatible\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmu\u001b[49m\u001b[43m)\u001b[49m, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mwhy_incompatible(mu)\n\u001b[1;32m    206\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m\n",
      "File \u001b[0;32m/builds/pymor/pymor/src/pymor/parameters/base.py:217\u001b[0m, in \u001b[0;36mParameters.is_compatible\u001b[0;34m(self, mu)\u001b[0m\n\u001b[1;32m    209\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Check if |parameter values| are compatible with the given |Parameters|.\u001b[39;00m\n\u001b[1;32m    210\u001b[0m \n\u001b[1;32m    211\u001b[0m \u001b[38;5;124;03mEach of the parameter must be contained in  `mu` and the dimensions have to match,\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    214\u001b[0m \u001b[38;5;124;03m    mu[parameter].size == self[parameter]\u001b[39;00m\n\u001b[1;32m    215\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    216\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m mu \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(mu, Mu):\n\u001b[0;32m--> 217\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmu is not a Mu instance. (Use parameters.parse?)\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m    218\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m \\\n\u001b[1;32m    219\u001b[0m     mu \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mall\u001b[39m(\u001b[38;5;28mgetattr\u001b[39m(mu\u001b[38;5;241m.\u001b[39mget(k), \u001b[38;5;124m'\u001b[39m\u001b[38;5;124msize\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;241m==\u001b[39m v \u001b[38;5;28;01mfor\u001b[39;00m k, v \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mitems())\n",
      "\u001b[0;31mTypeError\u001b[0m: mu is not a Mu instance. (Use parameters.parse?)"
     ]
    }
   ],
   "source": [
    "U2 = fom.operator.apply_inverse(fom.rhs.as_range_array(mu), mu=[1., 0.1, 0.1, 1.])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "722531a8",
   "metadata": {},
   "source": [
    "That did not work too well! In pyMOR, all parametric objects expect the\n",
    "`mu` argument to be an instance of the {class}`~pymor.parameters.base.Mu`\n",
    "class. {meth}`~pymor.models.interface.Model.compute` and related methods\n",
    "like {meth}`~pymor.models.interface.Model.solve` are an exception: for\n",
    "convenience, they accept as a `mu` argument anything that can be converted\n",
    "to a {class}`~pymor.parameters.base.Mu` instance using the\n",
    "{meth}`~pymor.parameters.base.Parameters.parse` method of the\n",
    "{class}`~pymor.parameters.base.Parameters` class. In fact, if you look\n",
    "back at the implementation of {meth}`~pymor.models.interface.Model.compute`,\n",
    "you see the explicit call to {meth}`~pymor.parameters.base.Parameters.parse`.\n",
    "We try again:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3fd720a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "mu = fom.parameters.parse([1., 0.1, 0.1, 1.])\n",
    "U2 = fom.operator.apply_inverse(fom.rhs.as_range_array(mu), mu=mu)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81f60a8d",
   "metadata": {},
   "source": [
    "We can check that we get exactly the same result as from our earlier call\n",
    "to {meth}`~pymor.models.interface.Model.solve`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "682fa8f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(U-U2).norm()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84d1ad84",
   "metadata": {},
   "source": [
    "## Galerkin Projection\n",
    "\n",
    "Now that we understand how the FOM works, we want to build a reduced-order model\n",
    "which approximates the FOM solution {math}`U(\\mu)` in {math}`V_N`.\n",
    "To that end we call {math}`\\mathbb{V}_N` the matrix that has the vectors in\n",
    "`basis` as columns. The coefficients of the solution of the ROM w.r.t. these\n",
    "basis vectors will be called {math}`u_N(\\mu)`. We want that\n",
    "\n",
    "```{math}\n",
    "U_N := \\mathbb{V}_N \\cdot u_N(\\mu) \\approx u(\\mu).\n",
    "```\n",
    "\n",
    "Substituting {math}`\\mathbb{V}_N \\cdot u_N(\\mu)` for {math}`u(\\mu)` into the equation system\n",
    "defining the FOM, we arrive at:\n",
    "\n",
    "```{math}\n",
    "L(\\mathbb{V}_N\\cdot u_N(\\mu); \\mu) = F(\\mu).\n",
    "```\n",
    "\n",
    "However, this is an over-determined system: we have decreased the degrees of\n",
    "freedom of the solution, but did not change the number of constraints (the dimension\n",
    "of {math}`F(\\mu)`). So in general, this system will not have a solution.\n",
    "\n",
    "One approach to define {math}`u_N` from this ansatz is to choose {math}`u_N`\n",
    "as a minimizer of norm of the residual of the equations system, i.e. to minimize\n",
    "the defect by which {math}`u_N` fails to satisfy the equations:\n",
    "\n",
    "```{math}\n",
    "u_N(\\mu) := \\operatorname{arg\\,min}_{u \\in \\mathbb{R}^N} \\|F(\\mu) - L(\\mathbb{V}_N \\cdot u; \\mu)\\|.\n",
    "```\n",
    "\n",
    "While this is a feasible (and sometimes necessary) approach that can be realized with\n",
    "pyMOR as well, we choose here an even simpler method by requiring that the residual is\n",
    "orthogonal to our reduced space, i.e.\n",
    "\n",
    "```{math}\n",
    "(\\mathbb{V}_{N,i},\\, F(\\mu) - L(\\mathbb{V}_N \\cdot u_N; \\mu)) = 0 \\qquad i=1,\\ldots,N,\n",
    "```\n",
    "\n",
    "where the {math}`\\mathbb{V}_{N,i}` denote the columns of {math}`\\mathbb{V}_N`\n",
    "and {math}`(\\cdot, \\cdot)` denotes some inner product on our\n",
    "{attr}`~pymor.models.interface.Model.solution_space`.\n",
    "\n",
    "Let us assume that {math}`L` is actually linear for all parameter values {math}`\\mu`,\n",
    "and that {math}`\\mathbb{A}(\\mu)` is its matrix representation. Further assume\n",
    "that {math}`(\\cdot, \\cdot)` is the Euclidean inner product. Then we arrive at\n",
    "\n",
    "```{math}\n",
    "[\\mathbb{V}_N^T \\cdot \\mathbb{A}(\\mu) \\cdot \\mathbb{V}_N] \\cdot u_N =\n",
    "\\mathbb{V}_N^T \\cdot F(\\mu),\n",
    "```\n",
    "\n",
    "which is a {math}`N\\times N` linear equation system. In the common case that\n",
    "{math}`\\mathbb{A}(\\mu)` is positive definite, the reduced system matrix\n",
    "\n",
    "```{math}\n",
    "\\mathbb{A}_N(\\mu) := \\mathbb{V}_N^T \\cdot \\mathbb{A}(\\mu) \\cdot \\mathbb{V}_N\n",
    "```\n",
    "\n",
    "is positive definite as well, and {math}`u_N(\\mu)` is uniquely determined. We call\n",
    "{math}`U_N(\\mu)` the Galerkin projection of {math}`U(\\mu)` onto {math}`V_N`.\n",
    "\n",
    "You may know the concept of Galerkin projection from finite element methods. Indeed, if our\n",
    "equation system comes from the weak formulation of a PDE of the form\n",
    "\n",
    "```{math}\n",
    "a(v, U(\\mu); \\mu) = f(v; \\mu) \\qquad \\forall v \\in V_h,\n",
    "```\n",
    "\n",
    "the matrix of the bilinear form {math}`a(\\cdot, \\cdot; \\mu)` w.r.t. a finite element basis\n",
    "is {math}`\\mathbb{A}(\\mu)`, and {math}`F(\\mu)` is the vector representation of the linear\n",
    "functional {math}`f` w.r.t. the dual finite element basis, then\n",
    "\n",
    "```{math}\n",
    "\\mathbb{A}_N(\\mu) \\cdot u_N = \\mathbb{V}_N^T \\cdot F(\\mu)\n",
    "```\n",
    "\n",
    "is exactly the equation system obtained from Galerkin projection of the weak PDE formulation onto\n",
    "the reduced space, i.e. solving\n",
    "\n",
    "```{math}\n",
    "a(v, u_N(\\mu); \\mu) = f(v; \\mu) \\qquad \\forall v \\in V_N\n",
    "```\n",
    "\n",
    "for {math}`U_N(\\mu) \\in V_N`. As for finite element methods,\n",
    "[Cea's Lemma](<https://en.wikipedia.org/wiki/Cea's_lemma>) guarantees that when {math}`a(\\cdot, \\cdot, \\mu)`\n",
    "is positive definite, {math}`U_N` will be a quasi-best approximation\n",
    "of {math}`U(\\mu)` in {math}`V_N`. So, if we have constructed a good reduced space {math}`V_N`, then\n",
    "Galerkin projection will also give us a good ROM to actually find a good approximation in {math}`V_N`.\n",
    "\n",
    "Let's compute the Galerkin ROM for our FOM at hand with pyMOR. To compute {math}`\\mathbb{A}_N`\n",
    "we use the {meth}`~pymor.operators.interface.Operator.apply2` method of `fom.operator`.\n",
    "For computing the inner products {math}`\\mathbb{V}_N^T \\cdot F(\\mu)` we can simply compute the\n",
    "inner product with the `basis` {{ VectorArray }} using its {meth}`~pymor.vectorarrays.interface.VectorArray.inner`\n",
    "method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f3e4ab33",
   "metadata": {},
   "outputs": [],
   "source": [
    "reduced_operator = fom.operator.apply2(basis, basis, mu=mu)\n",
    "reduced_rhs = basis.inner(fom.rhs.as_range_array(mu))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50365d9e",
   "metadata": {},
   "source": [
    "Now we just need to solve the resulting linear equation system using {{ NumPy }} to obtain\n",
    "{math}`u_N(\\mu)`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8555f54b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-14.84054563],\n",
       "       [ -0.13841165],\n",
       "       [ -1.06434203],\n",
       "       [  6.7881616 ],\n",
       "       [  2.42928484],\n",
       "       [  0.15368485],\n",
       "       [  1.19058685],\n",
       "       [ -0.55125475],\n",
       "       [ -0.79001652],\n",
       "       [  0.09628444]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "u_N = np.linalg.solve(reduced_operator, reduced_rhs)\n",
    "u_N"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52529e75",
   "metadata": {},
   "source": [
    "To reconstruct the high-dimensional approximation {math}`\\mathbb{V}_N \\cdot u_N(\\mu)`\n",
    "from {math}`u_N(\\mu)` we can use the {meth}`~pymor.vectorarrays.interface.VectorArray.lincomb`\n",
    "method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "fb8f6f80",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NumpyVectorArray(\n",
       "    NumpyVectorSpace(20201, id='STATE'),\n",
       "    [[0.00000000e+00 0.00000000e+00 0.00000000e+00 ... 3.53535121e-04\n",
       "      2.30915396e-04 8.70792913e-05]],\n",
       "    _len=1)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "U_N = basis.lincomb(u_N.T)\n",
    "U_N"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ef91405",
   "metadata": {},
   "source": [
    "Let's see, how good our reduced approximation is:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f53860bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.0044354])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(U-U_N).norm(fom.h1_0_product) / U.norm(fom.h1_0_product)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4009d03b",
   "metadata": {},
   "source": [
    "With only 10 basis vectors, we have achieved a relative {math}`H^1`-error of 2%.\n",
    "We can also visually inspect our solution and the approximation error:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "bbfcbc0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1280x960 with 7 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fom.visualize((U, U_N, U-U_N), separate_colorbars=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b74e3d4a",
   "metadata": {},
   "source": [
    "## Building the ROM\n",
    "\n",
    "So far, we have only constructed the ROM in the form of {{ NumPy }} data structures:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "dd04a0b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(reduced_operator)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "046231a2",
   "metadata": {},
   "source": [
    "To build a proper pyMOR {{ Model }} for the ROM, which can be used everywhere a {{ Model }} is\n",
    "expected, we first wrap these data structures as pyMOR {{ Operators }}:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d7dcca45",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pymor.operators.numpy import NumpyMatrixOperator\n",
    "\n",
    "reduced_operator = NumpyMatrixOperator(reduced_operator)\n",
    "reduced_rhs = NumpyMatrixOperator(reduced_rhs)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8c73180",
   "metadata": {},
   "source": [
    "Galerkin projection does not change the structure of the model. So the ROM should again\n",
    "be a {{ StationaryModel }}. We can construct it easily as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "86d81a19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StationaryModel(\n",
       "    NumpyMatrixOperator(<10x10 dense>),\n",
       "    NumpyMatrixOperator(<10x1 dense>),\n",
       "    output_functional=ZeroOperator(NumpyVectorSpace(0), NumpyVectorSpace(10)),\n",
       "    products={})"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pymor.models.basic import StationaryModel\n",
    "rom = StationaryModel(reduced_operator, reduced_rhs)\n",
    "rom"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec835ac6",
   "metadata": {},
   "source": [
    "Let's check if it works as expected:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "4dee42bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2bcbc041574e420e91c6c06e0f44af40",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Accordion(children=(HTML(value='', layout=Layout(height='16em', overflow_y='auto', width='100%')),), selected_…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([[ 0.00000000e+00, -2.77555756e-16, -4.44089210e-16,\n",
       "         8.88178420e-16,  1.33226763e-15,  8.32667268e-17,\n",
       "         4.44089210e-16, -2.22044605e-16,  0.00000000e+00,\n",
       "         5.55111512e-17]])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "u_N2 = rom.solve()\n",
    "u_N.T - u_N2.to_numpy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b0b1796",
   "metadata": {},
   "source": [
    "We get exactly the same result, so we have successfully built a pyMOR ROM.\n",
    "\n",
    "## Offline/Online Decomposition\n",
    "\n",
    "There is one issue however. Our ROM has lost the parametrization since we\n",
    "have assembled the reduced-order system for a specific set of\n",
    "{{ parameter_values }}:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "8b30b454",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{diffusion: 4}\n",
      "{}\n"
     ]
    }
   ],
   "source": [
    "print(fom.parameters)\n",
    "print(rom.parameters)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a545266",
   "metadata": {},
   "source": [
    "Solving the ROM for a new `mu` would mean to build a new ROM with updated\n",
    "system matrix and right-hand side. However, if we compare the timings,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "440a7102",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "abae996af102415887f58a7bc96f2921",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Accordion(children=(HTML(value='', layout=Layout(height='16em', overflow_y='auto', width='100%')),), selected_…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FOM:          0.10538 (s)\n",
      "ROM assemble: 0.00449 (s)\n",
      "ROM solve:    0.00136 (s)\n"
     ]
    }
   ],
   "source": [
    "from time import perf_counter\n",
    "\n",
    "tic = perf_counter()\n",
    "fom.solve(mu)\n",
    "toc = perf_counter()\n",
    "fom.operator.apply2(basis, basis, mu=mu)\n",
    "basis.inner(fom.rhs.as_range_array(mu))\n",
    "tac = perf_counter()\n",
    "rom.solve()\n",
    "tuc = perf_counter()\n",
    "print(f'FOM:          {toc-tic:.5f} (s)')\n",
    "print(f'ROM assemble: {tac-toc:.5f} (s)')\n",
    "print(f'ROM solve:    {tuc-tac:.5f} (s)')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a99e19d",
   "metadata": {},
   "source": [
    "we see that we lose a lot of our speedup when we assemble the ROM\n",
    "(which involves a lot of full-order dimensional operations).\n",
    "\n",
    "To solve this issue we need to find a way to pre-compute everything we need\n",
    "to solve the ROM once-and-for-all for all possible {{ parameter_values }}. Luckily,\n",
    "the system operator of our FOM has a special structure:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "8734b258",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LincombOperator(\n",
       "    (NumpyMatrixOperator(<20201x20201 sparse, 400 nnz>, source_id='STATE', range_id='STATE', name='boundary_part'),\n",
       "     NumpyMatrixOperator(<20201x20201 sparse, 24801 nnz>, source_id='STATE', range_id='STATE', name='diffusion_0'),\n",
       "     NumpyMatrixOperator(<20201x20201 sparse, 24801 nnz>, source_id='STATE', range_id='STATE', name='diffusion_1'),\n",
       "     NumpyMatrixOperator(<20201x20201 sparse, 24801 nnz>, source_id='STATE', range_id='STATE', name='diffusion_2'),\n",
       "     NumpyMatrixOperator(<20201x20201 sparse, 24801 nnz>, source_id='STATE', range_id='STATE', name='diffusion_3')),\n",
       "    (1.0,\n",
       "     ProjectionParameterFunctional('diffusion', size=4, index=0, name='diffusion_0_0'),\n",
       "     ProjectionParameterFunctional('diffusion', size=4, index=1, name='diffusion_1_0'),\n",
       "     ProjectionParameterFunctional('diffusion', size=4, index=2, name='diffusion_0_1'),\n",
       "     ProjectionParameterFunctional('diffusion', size=4, index=3, name='diffusion_1_1')),\n",
       "    name='ellipticOperator')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fom.operator"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88dc4f69",
   "metadata": {},
   "source": [
    "We see that `operator` is a {{ LincombOperator }}, a linear combination of {{ Operators }}\n",
    "with coefficients that may either be a number or a parameter-dependent number,\n",
    "called a {{ ParameterFunctional }} in pyMOR. In our case, all\n",
    "{attr}`~pymor.operators.constructions.LincombOperator.operators` are\n",
    "{{ NumpyMatrixOperators }}, which themselves don't depend on any parameter. Only the\n",
    "{attr}`~pymor.operators.constructions.LincombOperator.coefficients` are\n",
    "parameter-dependent.  This allows us to easily build a parametric ROM that no longer\n",
    "requires any high-dimensional operations for its solution by projecting each\n",
    "{{ Operator }} in the sum separately:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d92e55cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "reduced_operators = [NumpyMatrixOperator(op.apply2(basis, basis))\n",
    "                     for op in fom.operator.operators]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c997b1d4",
   "metadata": {},
   "source": [
    "We could instantiate a new {{ LincombOperator }} of these `reduced_operators` manually.\n",
    "An easier way is to use the {meth}`~pymor.core.base.ImmutableObject.with_` method,\n",
    "which allows us to create a new object from a given {{ ImmutableObject }} by replacing\n",
    "some of its attributes by new values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "87b58c2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "reduced_operator = fom.operator.with_(operators=reduced_operators)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a59bc362",
   "metadata": {},
   "source": [
    "The right-hand side of our problem is non-parametric,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "f50b949c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Parameters({})"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fom.rhs.parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35bfb3cd",
   "metadata": {},
   "source": [
    "so we don't need to do anything special about it. We build a new ROM,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a7a63192",
   "metadata": {},
   "outputs": [],
   "source": [
    "rom = StationaryModel(reduced_operator, reduced_rhs)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa3a0f80",
   "metadata": {},
   "source": [
    "which now depends on the same {{ Parameters }} as the FOM:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "7dd46e4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Parameters({diffusion: 4})"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rom.parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7dcbe5f8",
   "metadata": {},
   "source": [
    "We check that our new ROM still computes the same solution:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "2d79abe0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d6fb839b293e4cf78a695a56f74871df",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Accordion(children=(HTML(value='', layout=Layout(height='16em', overflow_y='auto', width='100%')),), selected_…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([[ 0.00000000e+00, -2.77555756e-16, -4.44089210e-16,\n",
       "         8.88178420e-16,  1.33226763e-15,  8.32667268e-17,\n",
       "         4.44089210e-16, -2.22044605e-16,  0.00000000e+00,\n",
       "         5.55111512e-17]])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "u_N3 = rom.solve(mu)\n",
    "u_N.T - u_N3.to_numpy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b06cab3",
   "metadata": {},
   "source": [
    "Let's see if our new ROM is actually faster than the FOM:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "7d3ba976",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c3be5044c4c7417eb3155203a61ba4bb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Accordion(children=(HTML(value='', layout=Layout(height='16em', overflow_y='auto', width='100%')),), selected_…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FOM: 0.10720 (s)\n",
      "ROM: 0.00169 (s)\n"
     ]
    }
   ],
   "source": [
    "tic = perf_counter()\n",
    "fom.solve(mu)\n",
    "toc = perf_counter()\n",
    "rom.solve(mu)\n",
    "tac = perf_counter()\n",
    "print(f'FOM: {toc-tic:.5f} (s)')\n",
    "print(f'ROM: {tac-toc:.5f} (s)')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16977b0d",
   "metadata": {},
   "source": [
    "You should see a significant speedup of around two orders of magnitude.\n",
    "In model order reduction, problems where the {{ parameter_values }} only enter\n",
    "as linear coefficients are called parameter separable. Many real-life\n",
    "application problems are actually of this type, and as you have seen in this\n",
    "section, these problems admit an *offline/online decomposition* that\n",
    "enables the *online efficient* solution of the ROM.\n",
    "\n",
    "For problems that do not allow such an decomposition and also for non-linear\n",
    "problems, more advanced techniques are necessary such as\n",
    "{mod}`empiricial interpolation <pymor.algorithms.ei>`.\n",
    "\n",
    "## Letting pyMOR do the work\n",
    "\n",
    "So far we completely built the ROM ourselves. While this may not have been\n",
    "very complicated after all, you'd expect a model order reduction library\n",
    "to do the work for you and to automatically keep an eye on proper\n",
    "offline/online decomposition.\n",
    "\n",
    "In pyMOR, the heavy lifting is handled by the\n",
    "{meth}`~pymor.algorithms.projection.project` method, which is able to perform\n",
    "a Galerkin projection, or more general a Petrov-Galerkin projection, of any\n",
    "pyMOR {{ Operator }}. Let's see, how it works:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "0b475f5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pymor.algorithms.projection import project\n",
    "\n",
    "reduced_operator = project(fom.operator, basis, basis)\n",
    "reduced_rhs      = project(fom.rhs,      basis, None )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b11858b",
   "metadata": {},
   "source": [
    "The arguments of {meth}`~pymor.algorithms.projection.project` are the {{ Operator }}\n",
    "to project, a reduced basis for the {attr}`~pymor.operators.interface.Operator.range`\n",
    "(test) space and a reduced basis for the {attr}`~pymor.operators.interface.Operator.source`\n",
    "(ansatz) space of the {{ Operator }}. If no projection for one of these spaces shall be performed,\n",
    "`None` is passed.  Since we are performing Galerkin-projection, where test space into\n",
    "which the residual is projected is the same as the ansatz space in which the solution\n",
    "is determined, we pass `basis` twice when projecting `fom.operator`. Note that\n",
    "`fom.rhs` only takes scalars as input, so we do not need to project anything in the ansatz space.\n",
    "\n",
    "If we check the result,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "b0002d07",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LincombOperator(\n",
       "    (NumpyMatrixOperator(<10x10 dense>, name='boundary_part'),\n",
       "     NumpyMatrixOperator(<10x10 dense>, name='diffusion_0'),\n",
       "     NumpyMatrixOperator(<10x10 dense>, name='diffusion_1'),\n",
       "     NumpyMatrixOperator(<10x10 dense>, name='diffusion_2'),\n",
       "     NumpyMatrixOperator(<10x10 dense>, name='diffusion_3')),\n",
       "    (1.0,\n",
       "     ProjectionParameterFunctional('diffusion', size=4, index=0, name='diffusion_0_0'),\n",
       "     ProjectionParameterFunctional('diffusion', size=4, index=1, name='diffusion_1_0'),\n",
       "     ProjectionParameterFunctional('diffusion', size=4, index=2, name='diffusion_0_1'),\n",
       "     ProjectionParameterFunctional('diffusion', size=4, index=3, name='diffusion_1_1')),\n",
       "    name='ellipticOperator')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reduced_operator"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35b1bbb5",
   "metadata": {},
   "source": [
    "we see, that pyMOR indeed has taken care of projecting each individual {{ Operator }}\n",
    "of the linear combination. We check again that we have built the same ROM:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "d0753c73",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a64319745329479687ef10772dce9c6e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Accordion(children=(HTML(value='', layout=Layout(height='16em', overflow_y='auto', width='100%')),), selected_…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([[ 0.00000000e+00, -2.77555756e-16, -4.44089210e-16,\n",
       "         8.88178420e-16,  1.33226763e-15,  8.32667268e-17,\n",
       "         4.44089210e-16, -2.22044605e-16,  0.00000000e+00,\n",
       "         5.55111512e-17]])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rom = StationaryModel(reduced_operator, reduced_rhs)\n",
    "u_N4 = rom.solve(mu)\n",
    "u_N.T - u_N4.to_numpy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ab9b872",
   "metadata": {},
   "source": [
    "So how does {meth}`~pymor.algorithms.projection.project` actually work? Let's take\n",
    "a look at the source:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "e1ff3a52",
   "metadata": {},
   "outputs": [
    {
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       ".output_html .il { color: #666666 } /* Literal.Number.Integer.Long */</style><div class=\"highlight\"><pre><span></span><span class=\"k\">def</span> <span class=\"nf\">project</span><span class=\"p\">(</span><span class=\"n\">op</span><span class=\"p\">,</span> <span class=\"n\">range_basis</span><span class=\"p\">,</span> <span class=\"n\">source_basis</span><span class=\"p\">,</span> <span class=\"n\">product</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">):</span>\n",
       "<span class=\"w\">    </span><span class=\"sd\">&quot;&quot;&quot;Petrov-Galerkin projection of a given |Operator|.</span>\n",
       "\n",
       "<span class=\"sd\">    Given an inner product `( ⋅, ⋅)`, source vectors `b_1, ..., b_N`</span>\n",
       "<span class=\"sd\">    and range vectors `c_1, ..., c_M`, the projection `op_proj` of `op`</span>\n",
       "<span class=\"sd\">    is defined by ::</span>\n",
       "\n",
       "<span class=\"sd\">        [ op_proj(e_j) ]_i = ( c_i, op(b_j) )</span>\n",
       "\n",
       "<span class=\"sd\">    for all i,j, where `e_j` denotes the j-th canonical basis vector of R^N.</span>\n",
       "\n",
       "<span class=\"sd\">    In particular, if the `c_i` are orthonormal w.r.t. the given product,</span>\n",
       "<span class=\"sd\">    then `op_proj` is the coordinate representation w.r.t. the `b_i/c_i` bases</span>\n",
       "<span class=\"sd\">    of the restriction of `op` to `span(b_i)` concatenated with the</span>\n",
       "<span class=\"sd\">    orthogonal projection onto `span(c_i)`.</span>\n",
       "\n",
       "<span class=\"sd\">    From another point of view, if `op` is viewed as a bilinear form</span>\n",
       "<span class=\"sd\">    (see :meth:`apply2`) and `( ⋅, ⋅ )` is the Euclidean inner</span>\n",
       "<span class=\"sd\">    product, then `op_proj` represents the matrix of the bilinear form restricted</span>\n",
       "<span class=\"sd\">    to `span(b_i) / span(c_i)` (w.r.t. the `b_i/c_i` bases).</span>\n",
       "\n",
       "<span class=\"sd\">    How the projection is realized will depend on the given |Operator|.</span>\n",
       "<span class=\"sd\">    While a projected |NumpyMatrixOperator| will</span>\n",
       "<span class=\"sd\">    again be a |NumpyMatrixOperator|, only a generic</span>\n",
       "<span class=\"sd\">    :class:`~pymor.operators.constructions.ProjectedOperator` can be returned</span>\n",
       "<span class=\"sd\">    in general. The exact algorithm is specified in :class:`ProjectRules`.</span>\n",
       "\n",
       "<span class=\"sd\">    Parameters</span>\n",
       "<span class=\"sd\">    ----------</span>\n",
       "<span class=\"sd\">    range_basis</span>\n",
       "<span class=\"sd\">        The vectors `c_1, ..., c_M` as a |VectorArray|. If `None`, no</span>\n",
       "<span class=\"sd\">        projection in the range space is performed.</span>\n",
       "<span class=\"sd\">    source_basis</span>\n",
       "<span class=\"sd\">        The vectors `b_1, ..., b_N` as a |VectorArray| or `None`. If `None`,</span>\n",
       "<span class=\"sd\">        no restriction of the source space is performed.</span>\n",
       "<span class=\"sd\">    product</span>\n",
       "<span class=\"sd\">        An |Operator| representing the inner product.  If `None`, the</span>\n",
       "<span class=\"sd\">        Euclidean inner product is chosen.</span>\n",
       "\n",
       "<span class=\"sd\">    Returns</span>\n",
       "<span class=\"sd\">    -------</span>\n",
       "<span class=\"sd\">    The projected |Operator| `op_proj`.</span>\n",
       "<span class=\"sd\">    &quot;&quot;&quot;</span>\n",
       "    <span class=\"k\">assert</span> <span class=\"n\">source_basis</span> <span class=\"ow\">is</span> <span class=\"kc\">None</span> <span class=\"ow\">or</span> <span class=\"n\">source_basis</span> <span class=\"ow\">in</span> <span class=\"n\">op</span><span class=\"o\">.</span><span class=\"n\">source</span>\n",
       "    <span class=\"k\">assert</span> <span class=\"n\">range_basis</span> <span class=\"ow\">is</span> <span class=\"kc\">None</span> <span class=\"ow\">or</span> <span class=\"n\">range_basis</span> <span class=\"ow\">in</span> <span class=\"n\">op</span><span class=\"o\">.</span><span class=\"n\">range</span>\n",
       "    <span class=\"k\">assert</span> <span class=\"n\">product</span> <span class=\"ow\">is</span> <span class=\"kc\">None</span> <span class=\"ow\">or</span> <span class=\"n\">product</span><span class=\"o\">.</span><span class=\"n\">source</span> <span class=\"o\">==</span> <span class=\"n\">product</span><span class=\"o\">.</span><span class=\"n\">range</span> <span class=\"o\">==</span> <span class=\"n\">op</span><span class=\"o\">.</span><span class=\"n\">range</span>\n",
       "\n",
       "    <span class=\"n\">rb</span> <span class=\"o\">=</span> <span class=\"n\">product</span><span class=\"o\">.</span><span class=\"n\">apply</span><span class=\"p\">(</span><span class=\"n\">range_basis</span><span class=\"p\">)</span> <span class=\"k\">if</span> <span class=\"n\">product</span> <span class=\"ow\">is</span> <span class=\"ow\">not</span> <span class=\"kc\">None</span> <span class=\"ow\">and</span> <span class=\"n\">range_basis</span> <span class=\"ow\">is</span> <span class=\"ow\">not</span> <span class=\"kc\">None</span> <span class=\"k\">else</span> <span class=\"n\">range_basis</span>\n",
       "\n",
       "    <span class=\"k\">try</span><span class=\"p\">:</span>\n",
       "        <span class=\"k\">return</span> <span class=\"n\">ProjectRules</span><span class=\"p\">(</span><span class=\"n\">rb</span><span class=\"p\">,</span> <span class=\"n\">source_basis</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">apply</span><span class=\"p\">(</span><span class=\"n\">op</span><span class=\"p\">)</span>\n",
       "    <span class=\"k\">except</span> <span class=\"n\">NoMatchingRuleError</span><span class=\"p\">:</span>\n",
       "        <span class=\"n\">op</span><span class=\"o\">.</span><span class=\"n\">logger</span><span class=\"o\">.</span><span class=\"n\">warning</span><span class=\"p\">(</span><span class=\"s1\">&#39;Using inefficient generic projection operator&#39;</span><span class=\"p\">)</span>\n",
       "        <span class=\"k\">return</span> <span class=\"n\">ProjectedOperator</span><span class=\"p\">(</span><span class=\"n\">op</span><span class=\"p\">,</span> <span class=\"n\">range_basis</span><span class=\"p\">,</span> <span class=\"n\">source_basis</span><span class=\"p\">,</span> <span class=\"n\">product</span><span class=\"p\">)</span>\n",
       "</pre></div>\n"
      ],
      "text/latex": [
       "\\begin{Verbatim}[commandchars=\\\\\\{\\}]\n",
       "\\PY{k}{def} \\PY{n+nf}{project}\\PY{p}{(}\\PY{n}{op}\\PY{p}{,} \\PY{n}{range\\PYZus{}basis}\\PY{p}{,} \\PY{n}{source\\PYZus{}basis}\\PY{p}{,} \\PY{n}{product}\\PY{o}{=}\\PY{k+kc}{None}\\PY{p}{)}\\PY{p}{:}\n",
       "\\PY{+w}{    }\\PY{l+s+sd}{\\PYZdq{}\\PYZdq{}\\PYZdq{}Petrov\\PYZhy{}Galerkin projection of a given |Operator|.}\n",
       "\n",
       "\\PY{l+s+sd}{    Given an inner product `( ⋅, ⋅)`, source vectors `b\\PYZus{}1, ..., b\\PYZus{}N`}\n",
       "\\PY{l+s+sd}{    and range vectors `c\\PYZus{}1, ..., c\\PYZus{}M`, the projection `op\\PYZus{}proj` of `op`}\n",
       "\\PY{l+s+sd}{    is defined by ::}\n",
       "\n",
       "\\PY{l+s+sd}{        [ op\\PYZus{}proj(e\\PYZus{}j) ]\\PYZus{}i = ( c\\PYZus{}i, op(b\\PYZus{}j) )}\n",
       "\n",
       "\\PY{l+s+sd}{    for all i,j, where `e\\PYZus{}j` denotes the j\\PYZhy{}th canonical basis vector of R\\PYZca{}N.}\n",
       "\n",
       "\\PY{l+s+sd}{    In particular, if the `c\\PYZus{}i` are orthonormal w.r.t. the given product,}\n",
       "\\PY{l+s+sd}{    then `op\\PYZus{}proj` is the coordinate representation w.r.t. the `b\\PYZus{}i/c\\PYZus{}i` bases}\n",
       "\\PY{l+s+sd}{    of the restriction of `op` to `span(b\\PYZus{}i)` concatenated with the}\n",
       "\\PY{l+s+sd}{    orthogonal projection onto `span(c\\PYZus{}i)`.}\n",
       "\n",
       "\\PY{l+s+sd}{    From another point of view, if `op` is viewed as a bilinear form}\n",
       "\\PY{l+s+sd}{    (see :meth:`apply2`) and `( ⋅, ⋅ )` is the Euclidean inner}\n",
       "\\PY{l+s+sd}{    product, then `op\\PYZus{}proj` represents the matrix of the bilinear form restricted}\n",
       "\\PY{l+s+sd}{    to `span(b\\PYZus{}i) / span(c\\PYZus{}i)` (w.r.t. the `b\\PYZus{}i/c\\PYZus{}i` bases).}\n",
       "\n",
       "\\PY{l+s+sd}{    How the projection is realized will depend on the given |Operator|.}\n",
       "\\PY{l+s+sd}{    While a projected |NumpyMatrixOperator| will}\n",
       "\\PY{l+s+sd}{    again be a |NumpyMatrixOperator|, only a generic}\n",
       "\\PY{l+s+sd}{    :class:`\\PYZti{}pymor.operators.constructions.ProjectedOperator` can be returned}\n",
       "\\PY{l+s+sd}{    in general. The exact algorithm is specified in :class:`ProjectRules`.}\n",
       "\n",
       "\\PY{l+s+sd}{    Parameters}\n",
       "\\PY{l+s+sd}{    \\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}}\n",
       "\\PY{l+s+sd}{    range\\PYZus{}basis}\n",
       "\\PY{l+s+sd}{        The vectors `c\\PYZus{}1, ..., c\\PYZus{}M` as a |VectorArray|. If `None`, no}\n",
       "\\PY{l+s+sd}{        projection in the range space is performed.}\n",
       "\\PY{l+s+sd}{    source\\PYZus{}basis}\n",
       "\\PY{l+s+sd}{        The vectors `b\\PYZus{}1, ..., b\\PYZus{}N` as a |VectorArray| or `None`. If `None`,}\n",
       "\\PY{l+s+sd}{        no restriction of the source space is performed.}\n",
       "\\PY{l+s+sd}{    product}\n",
       "\\PY{l+s+sd}{        An |Operator| representing the inner product.  If `None`, the}\n",
       "\\PY{l+s+sd}{        Euclidean inner product is chosen.}\n",
       "\n",
       "\\PY{l+s+sd}{    Returns}\n",
       "\\PY{l+s+sd}{    \\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}}\n",
       "\\PY{l+s+sd}{    The projected |Operator| `op\\PYZus{}proj`.}\n",
       "\\PY{l+s+sd}{    \\PYZdq{}\\PYZdq{}\\PYZdq{}}\n",
       "    \\PY{k}{assert} \\PY{n}{source\\PYZus{}basis} \\PY{o+ow}{is} \\PY{k+kc}{None} \\PY{o+ow}{or} \\PY{n}{source\\PYZus{}basis} \\PY{o+ow}{in} \\PY{n}{op}\\PY{o}{.}\\PY{n}{source}\n",
       "    \\PY{k}{assert} \\PY{n}{range\\PYZus{}basis} \\PY{o+ow}{is} \\PY{k+kc}{None} \\PY{o+ow}{or} \\PY{n}{range\\PYZus{}basis} \\PY{o+ow}{in} \\PY{n}{op}\\PY{o}{.}\\PY{n}{range}\n",
       "    \\PY{k}{assert} \\PY{n}{product} \\PY{o+ow}{is} \\PY{k+kc}{None} \\PY{o+ow}{or} \\PY{n}{product}\\PY{o}{.}\\PY{n}{source} \\PY{o}{==} \\PY{n}{product}\\PY{o}{.}\\PY{n}{range} \\PY{o}{==} \\PY{n}{op}\\PY{o}{.}\\PY{n}{range}\n",
       "\n",
       "    \\PY{n}{rb} \\PY{o}{=} \\PY{n}{product}\\PY{o}{.}\\PY{n}{apply}\\PY{p}{(}\\PY{n}{range\\PYZus{}basis}\\PY{p}{)} \\PY{k}{if} \\PY{n}{product} \\PY{o+ow}{is} \\PY{o+ow}{not} \\PY{k+kc}{None} \\PY{o+ow}{and} \\PY{n}{range\\PYZus{}basis} \\PY{o+ow}{is} \\PY{o+ow}{not} \\PY{k+kc}{None} \\PY{k}{else} \\PY{n}{range\\PYZus{}basis}\n",
       "\n",
       "    \\PY{k}{try}\\PY{p}{:}\n",
       "        \\PY{k}{return} \\PY{n}{ProjectRules}\\PY{p}{(}\\PY{n}{rb}\\PY{p}{,} \\PY{n}{source\\PYZus{}basis}\\PY{p}{)}\\PY{o}{.}\\PY{n}{apply}\\PY{p}{(}\\PY{n}{op}\\PY{p}{)}\n",
       "    \\PY{k}{except} \\PY{n}{NoMatchingRuleError}\\PY{p}{:}\n",
       "        \\PY{n}{op}\\PY{o}{.}\\PY{n}{logger}\\PY{o}{.}\\PY{n}{warning}\\PY{p}{(}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{Using inefficient generic projection operator}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{)}\n",
       "        \\PY{k}{return} \\PY{n}{ProjectedOperator}\\PY{p}{(}\\PY{n}{op}\\PY{p}{,} \\PY{n}{range\\PYZus{}basis}\\PY{p}{,} \\PY{n}{source\\PYZus{}basis}\\PY{p}{,} \\PY{n}{product}\\PY{p}{)}\n",
       "\\end{Verbatim}\n"
      ],
      "text/plain": [
       "def project(op, range_basis, source_basis, product=None):\n",
       "    \"\"\"Petrov-Galerkin projection of a given |Operator|.\n",
       "\n",
       "    Given an inner product `( ⋅, ⋅)`, source vectors `b_1, ..., b_N`\n",
       "    and range vectors `c_1, ..., c_M`, the projection `op_proj` of `op`\n",
       "    is defined by ::\n",
       "\n",
       "        [ op_proj(e_j) ]_i = ( c_i, op(b_j) )\n",
       "\n",
       "    for all i,j, where `e_j` denotes the j-th canonical basis vector of R^N.\n",
       "\n",
       "    In particular, if the `c_i` are orthonormal w.r.t. the given product,\n",
       "    then `op_proj` is the coordinate representation w.r.t. the `b_i/c_i` bases\n",
       "    of the restriction of `op` to `span(b_i)` concatenated with the\n",
       "    orthogonal projection onto `span(c_i)`.\n",
       "\n",
       "    From another point of view, if `op` is viewed as a bilinear form\n",
       "    (see :meth:`apply2`) and `( ⋅, ⋅ )` is the Euclidean inner\n",
       "    product, then `op_proj` represents the matrix of the bilinear form restricted\n",
       "    to `span(b_i) / span(c_i)` (w.r.t. the `b_i/c_i` bases).\n",
       "\n",
       "    How the projection is realized will depend on the given |Operator|.\n",
       "    While a projected |NumpyMatrixOperator| will\n",
       "    again be a |NumpyMatrixOperator|, only a generic\n",
       "    :class:`~pymor.operators.constructions.ProjectedOperator` can be returned\n",
       "    in general. The exact algorithm is specified in :class:`ProjectRules`.\n",
       "\n",
       "    Parameters\n",
       "    ----------\n",
       "    range_basis\n",
       "        The vectors `c_1, ..., c_M` as a |VectorArray|. If `None`, no\n",
       "        projection in the range space is performed.\n",
       "    source_basis\n",
       "        The vectors `b_1, ..., b_N` as a |VectorArray| or `None`. If `None`,\n",
       "        no restriction of the source space is performed.\n",
       "    product\n",
       "        An |Operator| representing the inner product.  If `None`, the\n",
       "        Euclidean inner product is chosen.\n",
       "\n",
       "    Returns\n",
       "    -------\n",
       "    The projected |Operator| `op_proj`.\n",
       "    \"\"\"\n",
       "    assert source_basis is None or source_basis in op.source\n",
       "    assert range_basis is None or range_basis in op.range\n",
       "    assert product is None or product.source == product.range == op.range\n",
       "\n",
       "    rb = product.apply(range_basis) if product is not None and range_basis is not None else range_basis\n",
       "\n",
       "    try:\n",
       "        return ProjectRules(rb, source_basis).apply(op)\n",
       "    except NoMatchingRuleError:\n",
       "        op.logger.warning('Using inefficient generic projection operator')\n",
       "        return ProjectedOperator(op, range_basis, source_basis, product)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print_source(project)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46bfb29c",
   "metadata": {},
   "source": [
    "We see there is error checking and some code to handle the optional `product` {{ Operator }}\n",
    "used to project into the reduced {attr}`~pymor.operators.interface.Operator.range` space.\n",
    "The actual work is done by the {meth}`~pymor.algorithms.rules.RuleTable.apply` method\n",
    "of the `ProjectRules` object.\n",
    "\n",
    "`ProjectRules` is a {{ RuleTable }}, an ordered list of conditions with corresponding actions.\n",
    "The list is traversed from top to bottom, and the action of the first matching condition is\n",
    "executed. These {{ RuleTables }} can also be modified by the user to customize the behavior\n",
    "of an algorithm for a specific application. We will not go into the details of defining\n",
    "or modifying a {{ RuleTable }} here, but we will look at the rules of `ProjectRules` by looking\n",
    "at its string representation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "eca1f8e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pos  Match Type  Condition                      Action Name / Action           \n",
       "---  ----------  -----------------------------  -------------------------------\n",
       "                                                Description                    \n",
       "0    ALWAYS      None                           no_bases                       \n",
       "1    CLASS       ZeroOperator                   ZeroOperator                   \n",
       "2    CLASS       ConstantOperator               ConstantOperator               \n",
       "3    GENERIC     linear and not parametric      apply_basis                    \n",
       "4    CLASS       ConcatenationOperator          ConcatenationOperator          \n",
       "5    CLASS       AdjointOperator                AdjointOperator                \n",
       "6    CLASS       EmpiricalInterpolatedOperator  EmpiricalInterpolatedOperator  \n",
       "7    CLASS       AffineOperator                 AffineOperator                 \n",
       "8    CLASS       LincombOperator                LincombOperator                \n",
       "9    CLASS       SelectionOperator              SelectionOperator              \n",
       "10   CLASS       BlockOperatorBase              BlockOperatorBase              "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pymor.algorithms.projection import ProjectRules\n",
    "ProjectRules"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17062735",
   "metadata": {},
   "source": [
    "In the case of `fom.operator`, which is a {{ LincombOperator }}, the rule with index 8 will\n",
    "be the first matching rule. We can take a look at it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "c720d129",
   "metadata": {
    "tags": [
     "hide-code",
     "hide-output"
    ]
   },
   "outputs": [],
   "source": [
    "assert ProjectRules.rules[8].action_description == 'LincombOperator'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "0a5978b5",
   "metadata": {},
   "outputs": [
    {
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       ".output_html .il { color: #666666 } /* Literal.Number.Integer.Long */</style><div class=\"highlight\"><pre><span></span>    <span class=\"nd\">@match_class</span><span class=\"p\">(</span><span class=\"n\">LincombOperator</span><span class=\"p\">)</span>\n",
       "    <span class=\"k\">def</span> <span class=\"nf\">action_LincombOperator</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">op</span><span class=\"p\">):</span>\n",
       "        <span class=\"k\">return</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">replace_children</span><span class=\"p\">(</span><span class=\"n\">op</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">with_</span><span class=\"p\">(</span><span class=\"n\">solver_options</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">)</span>\n",
       "</pre></div>\n"
      ],
      "text/latex": [
       "\\begin{Verbatim}[commandchars=\\\\\\{\\}]\n",
       "    \\PY{n+nd}{@match\\PYZus{}class}\\PY{p}{(}\\PY{n}{LincombOperator}\\PY{p}{)}\n",
       "    \\PY{k}{def} \\PY{n+nf}{action\\PYZus{}LincombOperator}\\PY{p}{(}\\PY{n+nb+bp}{self}\\PY{p}{,} \\PY{n}{op}\\PY{p}{)}\\PY{p}{:}\n",
       "        \\PY{k}{return} \\PY{n+nb+bp}{self}\\PY{o}{.}\\PY{n}{replace\\PYZus{}children}\\PY{p}{(}\\PY{n}{op}\\PY{p}{)}\\PY{o}{.}\\PY{n}{with\\PYZus{}}\\PY{p}{(}\\PY{n}{solver\\PYZus{}options}\\PY{o}{=}\\PY{k+kc}{None}\\PY{p}{)}\n",
       "\\end{Verbatim}\n"
      ],
      "text/plain": [
       "    @match_class(LincombOperator)\n",
       "    def action_LincombOperator(self, op):\n",
       "        return self.replace_children(op).with_(solver_options=None)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ProjectRules.rules[8]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26bfabcd",
   "metadata": {},
   "source": [
    "The implementation of the action for {{ LincombOperators }} uses the\n",
    "{meth}`~pymor.algorithms.rules.RuleTable.replace_children` method of {{ RuleTable }},\n",
    "which will recursively apply `ProjectionRules` to all\n",
    "{meth}`children <pymor.algorithms.rules.RuleTable.get_children>` of the\n",
    "{{ Operator }}, collect the results and then return a new {{ Operator }} where\n",
    "the children have been replaced by the results of the applications of the\n",
    "{{ RuleTable }}. Here, the {meth}`children <pymor.algorithms.rules.RuleTable.get_children>`\n",
    "of an {{ Operator }} are all of its attribute that are either {{ Operators }} or lists or dicts\n",
    "of {{ Operators }}.\n",
    "\n",
    "In our case, `ProjectRules` will be applied to all {{ NumpyMatrixOperators }} held by\n",
    "`fom.operator`. These are linear, non-parametric operators, for which rule 3\n",
    "will apply:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "69e0fdb0",
   "metadata": {
    "tags": [
     "hide-code",
     "hide-output"
    ]
   },
   "outputs": [],
   "source": [
    "assert ProjectRules.rules[3].action_description == 'apply_basis'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "c1b66734",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       ".output_html .sx { color: #008000 } /* Literal.String.Other */\n",
       ".output_html .sr { color: #A45A77 } /* Literal.String.Regex */\n",
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       ".output_html .bp { color: #008000 } /* Name.Builtin.Pseudo */\n",
       ".output_html .fm { color: #0000FF } /* Name.Function.Magic */\n",
       ".output_html .vc { color: #19177C } /* Name.Variable.Class */\n",
       ".output_html .vg { color: #19177C } /* Name.Variable.Global */\n",
       ".output_html .vi { color: #19177C } /* Name.Variable.Instance */\n",
       ".output_html .vm { color: #19177C } /* Name.Variable.Magic */\n",
       ".output_html .il { color: #666666 } /* Literal.Number.Integer.Long */</style><div class=\"highlight\"><pre><span></span>    <span class=\"nd\">@match_generic</span><span class=\"p\">(</span><span class=\"k\">lambda</span> <span class=\"n\">op</span><span class=\"p\">:</span> <span class=\"n\">op</span><span class=\"o\">.</span><span class=\"n\">linear</span> <span class=\"ow\">and</span> <span class=\"ow\">not</span> <span class=\"n\">op</span><span class=\"o\">.</span><span class=\"n\">parametric</span><span class=\"p\">,</span> <span class=\"s1\">&#39;linear and not parametric&#39;</span><span class=\"p\">)</span>\n",
       "    <span class=\"k\">def</span> <span class=\"nf\">action_apply_basis</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">op</span><span class=\"p\">):</span>\n",
       "        <span class=\"n\">range_basis</span><span class=\"p\">,</span> <span class=\"n\">source_basis</span> <span class=\"o\">=</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">range_basis</span><span class=\"p\">,</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">source_basis</span>\n",
       "        <span class=\"k\">if</span> <span class=\"n\">source_basis</span> <span class=\"ow\">is</span> <span class=\"kc\">None</span><span class=\"p\">:</span>\n",
       "            <span class=\"k\">try</span><span class=\"p\">:</span>\n",
       "                <span class=\"n\">V</span> <span class=\"o\">=</span> <span class=\"n\">op</span><span class=\"o\">.</span><span class=\"n\">apply_adjoint</span><span class=\"p\">(</span><span class=\"n\">range_basis</span><span class=\"p\">)</span>\n",
       "            <span class=\"k\">except</span> <span class=\"ne\">NotImplementedError</span> <span class=\"k\">as</span> <span class=\"n\">e</span><span class=\"p\">:</span>\n",
       "                <span class=\"k\">raise</span> <span class=\"n\">RuleNotMatchingError</span><span class=\"p\">(</span><span class=\"s1\">&#39;apply_adjoint not implemented&#39;</span><span class=\"p\">)</span> <span class=\"kn\">from</span> <span class=\"nn\">e</span>\n",
       "            <span class=\"k\">if</span> <span class=\"nb\">isinstance</span><span class=\"p\">(</span><span class=\"n\">op</span><span class=\"o\">.</span><span class=\"n\">source</span><span class=\"p\">,</span> <span class=\"n\">NumpyVectorSpace</span><span class=\"p\">):</span>\n",
       "                <span class=\"kn\">from</span> <span class=\"nn\">pymor.operators.numpy</span> <span class=\"kn\">import</span> <span class=\"n\">NumpyMatrixOperator</span>\n",
       "                <span class=\"k\">return</span> <span class=\"n\">NumpyMatrixOperator</span><span class=\"p\">(</span><span class=\"n\">V</span><span class=\"o\">.</span><span class=\"n\">to_numpy</span><span class=\"p\">(),</span> <span class=\"n\">source_id</span><span class=\"o\">=</span><span class=\"n\">op</span><span class=\"o\">.</span><span class=\"n\">source</span><span class=\"o\">.</span><span class=\"n\">id</span><span class=\"p\">,</span> <span class=\"n\">name</span><span class=\"o\">=</span><span class=\"n\">op</span><span class=\"o\">.</span><span class=\"n\">name</span><span class=\"p\">)</span>\n",
       "            <span class=\"k\">else</span><span class=\"p\">:</span>\n",
       "                <span class=\"kn\">from</span> <span class=\"nn\">pymor.operators.constructions</span> <span class=\"kn\">import</span> <span class=\"n\">VectorArrayOperator</span>\n",
       "                <span class=\"k\">return</span> <span class=\"n\">VectorArrayOperator</span><span class=\"p\">(</span><span class=\"n\">V</span><span class=\"p\">,</span> <span class=\"n\">adjoint</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">,</span> <span class=\"n\">name</span><span class=\"o\">=</span><span class=\"n\">op</span><span class=\"o\">.</span><span class=\"n\">name</span><span class=\"p\">)</span>\n",
       "        <span class=\"k\">else</span><span class=\"p\">:</span>\n",
       "            <span class=\"k\">if</span> <span class=\"n\">range_basis</span> <span class=\"ow\">is</span> <span class=\"kc\">None</span><span class=\"p\">:</span>\n",
       "                <span class=\"n\">V</span> <span class=\"o\">=</span> <span class=\"n\">op</span><span class=\"o\">.</span><span class=\"n\">apply</span><span class=\"p\">(</span><span class=\"n\">source_basis</span><span class=\"p\">)</span>\n",
       "                <span class=\"k\">if</span> <span class=\"nb\">isinstance</span><span class=\"p\">(</span><span class=\"n\">op</span><span class=\"o\">.</span><span class=\"n\">range</span><span class=\"p\">,</span> <span class=\"n\">NumpyVectorSpace</span><span class=\"p\">):</span>\n",
       "                    <span class=\"kn\">from</span> <span class=\"nn\">pymor.operators.numpy</span> <span class=\"kn\">import</span> <span class=\"n\">NumpyMatrixOperator</span>\n",
       "                    <span class=\"k\">return</span> <span class=\"n\">NumpyMatrixOperator</span><span class=\"p\">(</span><span class=\"n\">V</span><span class=\"o\">.</span><span class=\"n\">to_numpy</span><span class=\"p\">()</span><span class=\"o\">.</span><span class=\"n\">T</span><span class=\"p\">,</span> <span class=\"n\">range_id</span><span class=\"o\">=</span><span class=\"n\">op</span><span class=\"o\">.</span><span class=\"n\">range</span><span class=\"o\">.</span><span class=\"n\">id</span><span class=\"p\">,</span> <span class=\"n\">name</span><span class=\"o\">=</span><span class=\"n\">op</span><span class=\"o\">.</span><span class=\"n\">name</span><span class=\"p\">)</span>\n",
       "                <span class=\"k\">else</span><span class=\"p\">:</span>\n",
       "                    <span class=\"kn\">from</span> <span class=\"nn\">pymor.operators.constructions</span> <span class=\"kn\">import</span> <span class=\"n\">VectorArrayOperator</span>\n",
       "                    <span class=\"k\">return</span> <span class=\"n\">VectorArrayOperator</span><span class=\"p\">(</span><span class=\"n\">V</span><span class=\"p\">,</span> <span class=\"n\">adjoint</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">,</span> <span class=\"n\">name</span><span class=\"o\">=</span><span class=\"n\">op</span><span class=\"o\">.</span><span class=\"n\">name</span><span class=\"p\">)</span>\n",
       "            <span class=\"k\">else</span><span class=\"p\">:</span>\n",
       "                <span class=\"kn\">from</span> <span class=\"nn\">pymor.operators.numpy</span> <span class=\"kn\">import</span> <span class=\"n\">NumpyMatrixOperator</span>\n",
       "                <span class=\"k\">return</span> <span class=\"n\">NumpyMatrixOperator</span><span class=\"p\">(</span><span class=\"n\">op</span><span class=\"o\">.</span><span class=\"n\">apply2</span><span class=\"p\">(</span><span class=\"n\">range_basis</span><span class=\"p\">,</span> <span class=\"n\">source_basis</span><span class=\"p\">),</span> <span class=\"n\">name</span><span class=\"o\">=</span><span class=\"n\">op</span><span class=\"o\">.</span><span class=\"n\">name</span><span class=\"p\">)</span>\n",
       "</pre></div>\n"
      ],
      "text/latex": [
       "\\begin{Verbatim}[commandchars=\\\\\\{\\}]\n",
       "    \\PY{n+nd}{@match\\PYZus{}generic}\\PY{p}{(}\\PY{k}{lambda} \\PY{n}{op}\\PY{p}{:} \\PY{n}{op}\\PY{o}{.}\\PY{n}{linear} \\PY{o+ow}{and} \\PY{o+ow}{not} \\PY{n}{op}\\PY{o}{.}\\PY{n}{parametric}\\PY{p}{,} \\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{linear and not parametric}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{)}\n",
       "    \\PY{k}{def} \\PY{n+nf}{action\\PYZus{}apply\\PYZus{}basis}\\PY{p}{(}\\PY{n+nb+bp}{self}\\PY{p}{,} \\PY{n}{op}\\PY{p}{)}\\PY{p}{:}\n",
       "        \\PY{n}{range\\PYZus{}basis}\\PY{p}{,} \\PY{n}{source\\PYZus{}basis} \\PY{o}{=} \\PY{n+nb+bp}{self}\\PY{o}{.}\\PY{n}{range\\PYZus{}basis}\\PY{p}{,} \\PY{n+nb+bp}{self}\\PY{o}{.}\\PY{n}{source\\PYZus{}basis}\n",
       "        \\PY{k}{if} \\PY{n}{source\\PYZus{}basis} \\PY{o+ow}{is} \\PY{k+kc}{None}\\PY{p}{:}\n",
       "            \\PY{k}{try}\\PY{p}{:}\n",
       "                \\PY{n}{V} \\PY{o}{=} \\PY{n}{op}\\PY{o}{.}\\PY{n}{apply\\PYZus{}adjoint}\\PY{p}{(}\\PY{n}{range\\PYZus{}basis}\\PY{p}{)}\n",
       "            \\PY{k}{except} \\PY{n+ne}{NotImplementedError} \\PY{k}{as} \\PY{n}{e}\\PY{p}{:}\n",
       "                \\PY{k}{raise} \\PY{n}{RuleNotMatchingError}\\PY{p}{(}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{apply\\PYZus{}adjoint not implemented}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{)} \\PY{k+kn}{from} \\PY{n+nn}{e}\n",
       "            \\PY{k}{if} \\PY{n+nb}{isinstance}\\PY{p}{(}\\PY{n}{op}\\PY{o}{.}\\PY{n}{source}\\PY{p}{,} \\PY{n}{NumpyVectorSpace}\\PY{p}{)}\\PY{p}{:}\n",
       "                \\PY{k+kn}{from} \\PY{n+nn}{pymor}\\PY{n+nn}{.}\\PY{n+nn}{operators}\\PY{n+nn}{.}\\PY{n+nn}{numpy} \\PY{k+kn}{import} \\PY{n}{NumpyMatrixOperator}\n",
       "                \\PY{k}{return} \\PY{n}{NumpyMatrixOperator}\\PY{p}{(}\\PY{n}{V}\\PY{o}{.}\\PY{n}{to\\PYZus{}numpy}\\PY{p}{(}\\PY{p}{)}\\PY{p}{,} \\PY{n}{source\\PYZus{}id}\\PY{o}{=}\\PY{n}{op}\\PY{o}{.}\\PY{n}{source}\\PY{o}{.}\\PY{n}{id}\\PY{p}{,} \\PY{n}{name}\\PY{o}{=}\\PY{n}{op}\\PY{o}{.}\\PY{n}{name}\\PY{p}{)}\n",
       "            \\PY{k}{else}\\PY{p}{:}\n",
       "                \\PY{k+kn}{from} \\PY{n+nn}{pymor}\\PY{n+nn}{.}\\PY{n+nn}{operators}\\PY{n+nn}{.}\\PY{n+nn}{constructions} \\PY{k+kn}{import} \\PY{n}{VectorArrayOperator}\n",
       "                \\PY{k}{return} \\PY{n}{VectorArrayOperator}\\PY{p}{(}\\PY{n}{V}\\PY{p}{,} \\PY{n}{adjoint}\\PY{o}{=}\\PY{k+kc}{True}\\PY{p}{,} \\PY{n}{name}\\PY{o}{=}\\PY{n}{op}\\PY{o}{.}\\PY{n}{name}\\PY{p}{)}\n",
       "        \\PY{k}{else}\\PY{p}{:}\n",
       "            \\PY{k}{if} \\PY{n}{range\\PYZus{}basis} \\PY{o+ow}{is} \\PY{k+kc}{None}\\PY{p}{:}\n",
       "                \\PY{n}{V} \\PY{o}{=} \\PY{n}{op}\\PY{o}{.}\\PY{n}{apply}\\PY{p}{(}\\PY{n}{source\\PYZus{}basis}\\PY{p}{)}\n",
       "                \\PY{k}{if} \\PY{n+nb}{isinstance}\\PY{p}{(}\\PY{n}{op}\\PY{o}{.}\\PY{n}{range}\\PY{p}{,} \\PY{n}{NumpyVectorSpace}\\PY{p}{)}\\PY{p}{:}\n",
       "                    \\PY{k+kn}{from} \\PY{n+nn}{pymor}\\PY{n+nn}{.}\\PY{n+nn}{operators}\\PY{n+nn}{.}\\PY{n+nn}{numpy} \\PY{k+kn}{import} \\PY{n}{NumpyMatrixOperator}\n",
       "                    \\PY{k}{return} \\PY{n}{NumpyMatrixOperator}\\PY{p}{(}\\PY{n}{V}\\PY{o}{.}\\PY{n}{to\\PYZus{}numpy}\\PY{p}{(}\\PY{p}{)}\\PY{o}{.}\\PY{n}{T}\\PY{p}{,} \\PY{n}{range\\PYZus{}id}\\PY{o}{=}\\PY{n}{op}\\PY{o}{.}\\PY{n}{range}\\PY{o}{.}\\PY{n}{id}\\PY{p}{,} \\PY{n}{name}\\PY{o}{=}\\PY{n}{op}\\PY{o}{.}\\PY{n}{name}\\PY{p}{)}\n",
       "                \\PY{k}{else}\\PY{p}{:}\n",
       "                    \\PY{k+kn}{from} \\PY{n+nn}{pymor}\\PY{n+nn}{.}\\PY{n+nn}{operators}\\PY{n+nn}{.}\\PY{n+nn}{constructions} \\PY{k+kn}{import} \\PY{n}{VectorArrayOperator}\n",
       "                    \\PY{k}{return} \\PY{n}{VectorArrayOperator}\\PY{p}{(}\\PY{n}{V}\\PY{p}{,} \\PY{n}{adjoint}\\PY{o}{=}\\PY{k+kc}{False}\\PY{p}{,} \\PY{n}{name}\\PY{o}{=}\\PY{n}{op}\\PY{o}{.}\\PY{n}{name}\\PY{p}{)}\n",
       "            \\PY{k}{else}\\PY{p}{:}\n",
       "                \\PY{k+kn}{from} \\PY{n+nn}{pymor}\\PY{n+nn}{.}\\PY{n+nn}{operators}\\PY{n+nn}{.}\\PY{n+nn}{numpy} \\PY{k+kn}{import} \\PY{n}{NumpyMatrixOperator}\n",
       "                \\PY{k}{return} \\PY{n}{NumpyMatrixOperator}\\PY{p}{(}\\PY{n}{op}\\PY{o}{.}\\PY{n}{apply2}\\PY{p}{(}\\PY{n}{range\\PYZus{}basis}\\PY{p}{,} \\PY{n}{source\\PYZus{}basis}\\PY{p}{)}\\PY{p}{,} \\PY{n}{name}\\PY{o}{=}\\PY{n}{op}\\PY{o}{.}\\PY{n}{name}\\PY{p}{)}\n",
       "\\end{Verbatim}\n"
      ],
      "text/plain": [
       "    @match_generic(lambda op: op.linear and not op.parametric, 'linear and not parametric')\n",
       "    def action_apply_basis(self, op):\n",
       "        range_basis, source_basis = self.range_basis, self.source_basis\n",
       "        if source_basis is None:\n",
       "            try:\n",
       "                V = op.apply_adjoint(range_basis)\n",
       "            except NotImplementedError as e:\n",
       "                raise RuleNotMatchingError('apply_adjoint not implemented') from e\n",
       "            if isinstance(op.source, NumpyVectorSpace):\n",
       "                from pymor.operators.numpy import NumpyMatrixOperator\n",
       "                return NumpyMatrixOperator(V.to_numpy(), source_id=op.source.id, name=op.name)\n",
       "            else:\n",
       "                from pymor.operators.constructions import VectorArrayOperator\n",
       "                return VectorArrayOperator(V, adjoint=True, name=op.name)\n",
       "        else:\n",
       "            if range_basis is None:\n",
       "                V = op.apply(source_basis)\n",
       "                if isinstance(op.range, NumpyVectorSpace):\n",
       "                    from pymor.operators.numpy import NumpyMatrixOperator\n",
       "                    return NumpyMatrixOperator(V.to_numpy().T, range_id=op.range.id, name=op.name)\n",
       "                else:\n",
       "                    from pymor.operators.constructions import VectorArrayOperator\n",
       "                    return VectorArrayOperator(V, adjoint=False, name=op.name)\n",
       "            else:\n",
       "                from pymor.operators.numpy import NumpyMatrixOperator\n",
       "                return NumpyMatrixOperator(op.apply2(range_basis, source_basis), name=op.name)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ProjectRules.rules[3]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b271134",
   "metadata": {},
   "source": [
    "This action has special cases for all possible combinations of given or not-given\n",
    "{attr}`~pymor.operators.interface.Operator.range` and {attr}`~pymor.operators.interface.Operator.source`\n",
    "bases. In our case, the `else` block of the second `else` block applies,\n",
    "where we see our familiar {meth}`~pymor.operators.interface.Operator.apply2` call.\n",
    "\n",
    "If you look at the rules of `ProjectRules` again, you see that\n",
    "{meth}`~pymor.algorithms.projection.project` can handle many more cases.\n",
    "If all rules fail, a `NoMatchingRuleError` will be raised, in which case,\n",
    "{meth}`~pymor.algorithms.projection.project` will return a\n",
    "{class}`~pymor.operators.constructions.ProjectedOperator`, which just stores the\n",
    "projection bases and performs the projection for each call to the {{ Operator }} interface\n",
    "methods. Thus, even when offline/online decomposition fails, still a mathematically correct\n",
    "representation of the projected {{ Operator }} is returned to allow testing the approximation\n",
    "quality of the ROM before taking care of online efficiency in a later step.\n",
    "\n",
    "## Using Reductors\n",
    "\n",
    "Instead of projecting each {{ Operator }} of our FOM separately and then instantiating\n",
    "the ROM with the projected {{ Operators }}, we can use a {mod}`reductor <pymor.reductors>`,\n",
    "which does all the work for us. For a simple Galerkin projection of a {{ StationaryModel }},\n",
    "we can use {class}`~pymor.reductors.basic.StationaryRBReductor`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "e5e7a390",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "767abfe3f6bf40f684f310a98c5c0114",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Accordion(children=(HTML(value='', layout=Layout(height='16em', overflow_y='auto', width='100%')),), selected_…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pymor.reductors.basic import StationaryRBReductor\n",
    "\n",
    "reductor = StationaryRBReductor(fom, basis)\n",
    "rom = reductor.reduce()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3c4b6d5",
   "metadata": {},
   "source": [
    "Again, we get the same ROM as before:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "50b65304",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.00000000e+00, -2.77555756e-16, -4.44089210e-16,\n",
       "         8.88178420e-16,  1.33226763e-15,  8.32667268e-17,\n",
       "         4.44089210e-16, -2.22044605e-16,  0.00000000e+00,\n",
       "         5.55111512e-17]])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "u_N5 = rom.solve(mu)\n",
    "u_N.T - u_N5.to_numpy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "456a21d0",
   "metadata": {},
   "source": [
    "As an additional feature, {meth}`~pymor.reductors.basic.StationaryRBReductor.reduce`\n",
    "allows to project the model onto a smaller dimensional subspace of {math}`V_N` by\n",
    "extracting the ROM from a previously computed ROM for the full {math}`V_N`. This\n",
    "is useful, in particular, when assessing the ROM for different basis sizes. The\n",
    "actual projection is handled in the\n",
    "{meth}`~pymor.reductor.basic.StationaryRBReductor.project_operators` method,\n",
    "where we can find some well-known code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "a09a0f42",
   "metadata": {},
   "outputs": [
    {
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       ".output_html .il { color: #666666 } /* Literal.Number.Integer.Long */</style><div class=\"highlight\"><pre><span></span>    <span class=\"k\">def</span> <span class=\"nf\">project_operators</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">):</span>\n",
       "        <span class=\"n\">fom</span> <span class=\"o\">=</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">fom</span>\n",
       "        <span class=\"n\">RB</span> <span class=\"o\">=</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">bases</span><span class=\"p\">[</span><span class=\"s1\">&#39;RB&#39;</span><span class=\"p\">]</span>\n",
       "        <span class=\"n\">projected_operators</span> <span class=\"o\">=</span> <span class=\"p\">{</span>\n",
       "            <span class=\"s1\">&#39;operator&#39;</span><span class=\"p\">:</span>          <span class=\"n\">project</span><span class=\"p\">(</span><span class=\"n\">fom</span><span class=\"o\">.</span><span class=\"n\">operator</span><span class=\"p\">,</span> <span class=\"n\">RB</span><span class=\"p\">,</span> <span class=\"n\">RB</span><span class=\"p\">),</span>\n",
       "            <span class=\"s1\">&#39;rhs&#39;</span><span class=\"p\">:</span>               <span class=\"n\">project</span><span class=\"p\">(</span><span class=\"n\">fom</span><span class=\"o\">.</span><span class=\"n\">rhs</span><span class=\"p\">,</span> <span class=\"n\">RB</span><span class=\"p\">,</span> <span class=\"kc\">None</span><span class=\"p\">),</span>\n",
       "            <span class=\"s1\">&#39;products&#39;</span><span class=\"p\">:</span>          <span class=\"p\">{</span><span class=\"n\">k</span><span class=\"p\">:</span> <span class=\"n\">project</span><span class=\"p\">(</span><span class=\"n\">v</span><span class=\"p\">,</span> <span class=\"n\">RB</span><span class=\"p\">,</span> <span class=\"n\">RB</span><span class=\"p\">)</span> <span class=\"k\">for</span> <span class=\"n\">k</span><span class=\"p\">,</span> <span class=\"n\">v</span> <span class=\"ow\">in</span> <span class=\"n\">fom</span><span class=\"o\">.</span><span class=\"n\">products</span><span class=\"o\">.</span><span class=\"n\">items</span><span class=\"p\">()},</span>\n",
       "            <span class=\"s1\">&#39;output_functional&#39;</span><span class=\"p\">:</span> <span class=\"n\">project</span><span class=\"p\">(</span><span class=\"n\">fom</span><span class=\"o\">.</span><span class=\"n\">output_functional</span><span class=\"p\">,</span> <span class=\"kc\">None</span><span class=\"p\">,</span> <span class=\"n\">RB</span><span class=\"p\">)</span>\n",
       "        <span class=\"p\">}</span>\n",
       "        <span class=\"k\">return</span> <span class=\"n\">projected_operators</span>\n",
       "</pre></div>\n"
      ],
      "text/latex": [
       "\\begin{Verbatim}[commandchars=\\\\\\{\\}]\n",
       "    \\PY{k}{def} \\PY{n+nf}{project\\PYZus{}operators}\\PY{p}{(}\\PY{n+nb+bp}{self}\\PY{p}{)}\\PY{p}{:}\n",
       "        \\PY{n}{fom} \\PY{o}{=} \\PY{n+nb+bp}{self}\\PY{o}{.}\\PY{n}{fom}\n",
       "        \\PY{n}{RB} \\PY{o}{=} \\PY{n+nb+bp}{self}\\PY{o}{.}\\PY{n}{bases}\\PY{p}{[}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{RB}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{]}\n",
       "        \\PY{n}{projected\\PYZus{}operators} \\PY{o}{=} \\PY{p}{\\PYZob{}}\n",
       "            \\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{operator}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{:}          \\PY{n}{project}\\PY{p}{(}\\PY{n}{fom}\\PY{o}{.}\\PY{n}{operator}\\PY{p}{,} \\PY{n}{RB}\\PY{p}{,} \\PY{n}{RB}\\PY{p}{)}\\PY{p}{,}\n",
       "            \\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{rhs}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{:}               \\PY{n}{project}\\PY{p}{(}\\PY{n}{fom}\\PY{o}{.}\\PY{n}{rhs}\\PY{p}{,} \\PY{n}{RB}\\PY{p}{,} \\PY{k+kc}{None}\\PY{p}{)}\\PY{p}{,}\n",
       "            \\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{products}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{:}          \\PY{p}{\\PYZob{}}\\PY{n}{k}\\PY{p}{:} \\PY{n}{project}\\PY{p}{(}\\PY{n}{v}\\PY{p}{,} \\PY{n}{RB}\\PY{p}{,} \\PY{n}{RB}\\PY{p}{)} \\PY{k}{for} \\PY{n}{k}\\PY{p}{,} \\PY{n}{v} \\PY{o+ow}{in} \\PY{n}{fom}\\PY{o}{.}\\PY{n}{products}\\PY{o}{.}\\PY{n}{items}\\PY{p}{(}\\PY{p}{)}\\PY{p}{\\PYZcb{}}\\PY{p}{,}\n",
       "            \\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{output\\PYZus{}functional}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{:} \\PY{n}{project}\\PY{p}{(}\\PY{n}{fom}\\PY{o}{.}\\PY{n}{output\\PYZus{}functional}\\PY{p}{,} \\PY{k+kc}{None}\\PY{p}{,} \\PY{n}{RB}\\PY{p}{)}\n",
       "        \\PY{p}{\\PYZcb{}}\n",
       "        \\PY{k}{return} \\PY{n}{projected\\PYZus{}operators}\n",
       "\\end{Verbatim}\n"
      ],
      "text/plain": [
       "    def project_operators(self):\n",
       "        fom = self.fom\n",
       "        RB = self.bases['RB']\n",
       "        projected_operators = {\n",
       "            'operator':          project(fom.operator, RB, RB),\n",
       "            'rhs':               project(fom.rhs, RB, None),\n",
       "            'products':          {k: project(v, RB, RB) for k, v in fom.products.items()},\n",
       "            'output_functional': project(fom.output_functional, None, RB)\n",
       "        }\n",
       "        return projected_operators"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print_source(reductor.project_operators)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65a73adb",
   "metadata": {},
   "source": [
    "We see that the reductor also takes care of projecting output functionals and\n",
    "inner products associated with the {{ Model }}. The construction of the ROM from\n",
    "the projected operators is performed by a separate method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "7763fb52",
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   "outputs": [
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       ".output_html .il { color: #666666 } /* Literal.Number.Integer.Long */</style><div class=\"highlight\"><pre><span></span>    <span class=\"k\">def</span> <span class=\"nf\">build_rom</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">projected_operators</span><span class=\"p\">,</span> <span class=\"n\">error_estimator</span><span class=\"p\">):</span>\n",
       "        <span class=\"k\">return</span> <span class=\"n\">StationaryModel</span><span class=\"p\">(</span><span class=\"n\">error_estimator</span><span class=\"o\">=</span><span class=\"n\">error_estimator</span><span class=\"p\">,</span> <span class=\"o\">**</span><span class=\"n\">projected_operators</span><span class=\"p\">)</span>\n",
       "</pre></div>\n"
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       "    \\PY{k}{def} \\PY{n+nf}{build\\PYZus{}rom}\\PY{p}{(}\\PY{n+nb+bp}{self}\\PY{p}{,} \\PY{n}{projected\\PYZus{}operators}\\PY{p}{,} \\PY{n}{error\\PYZus{}estimator}\\PY{p}{)}\\PY{p}{:}\n",
       "        \\PY{k}{return} \\PY{n}{StationaryModel}\\PY{p}{(}\\PY{n}{error\\PYZus{}estimator}\\PY{o}{=}\\PY{n}{error\\PYZus{}estimator}\\PY{p}{,} \\PY{o}{*}\\PY{o}{*}\\PY{n}{projected\\PYZus{}operators}\\PY{p}{)}\n",
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       "    def build_rom(self, projected_operators, error_estimator):\n",
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    "More advanced reductors, such as {class}`~pymor.reductors.coercive.CoerciveRBReductor`\n",
    "also assemble an a posteriori error estimator for the model order reduction error.\n",
    "In the case of {class}`~pymor.reductors.basic.StationaryRBReductor`, however,\n",
    "`error_estimator` is always `None`.\n",
    "\n",
    "Reductors also allow to compute {math}`U_N(\\mu)` from {math}`u_N(\\mu)` using\n",
    "the {meth}`~pymor.reductors.basic.StationaryRBReductor.reconstruct` method:"
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    {
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       "array([2.52399528e-15])"
      ]
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     "execution_count": 48,
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    "U_N5 = reductor.reconstruct(u_N5)\n",
    "(U_N - U_N5).norm()"
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   "cell_type": "markdown",
   "id": "ea578ed8",
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   "source": [
    "Again, if we look at the source code, we see a familiar expression:"
   ]
  },
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   "cell_type": "code",
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   "id": "3e2b290c",
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       "<span class=\"w\">        </span><span class=\"sd\">&quot;&quot;&quot;Reconstruct high-dimensional vector from reduced vector `u`.&quot;&quot;&quot;</span>\n",
       "        <span class=\"k\">return</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">bases</span><span class=\"p\">[</span><span class=\"n\">basis</span><span class=\"p\">][:</span><span class=\"n\">u</span><span class=\"o\">.</span><span class=\"n\">dim</span><span class=\"p\">]</span><span class=\"o\">.</span><span class=\"n\">lincomb</span><span class=\"p\">(</span><span class=\"n\">u</span><span class=\"o\">.</span><span class=\"n\">to_numpy</span><span class=\"p\">())</span>\n",
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    "Download the code:\n",
    "{download}`tutorial_projection.md`\n",
    "{nb-download}`tutorial_projection.ipynb`"
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