{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AMICI documentation example of the steady state solver logic\n",
    "\n",
    "This is an example to document the internal logic of the steady state solver, which is used in preequilibration and postequilibration.\n",
    "\n",
    "\n",
    "**NOTE**: This notebook is somewhat outdated.\n",
    "For a more recent theoretical overview and benchmarking of AMICI's steady-state and sensitivities at steady-state capabilities, see [this paper](https://doi.org/10.1371/journal.pone.0312148).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Steady states of dynamical system\n",
    "\n",
    "Not every dynamical system needs to run into a steady state. Instead, it may exhibit\n",
    "\n",
    "  * continuous growth, e.g., $$\\dot{x} = x, \\quad x_0 = 1$$\n",
    "  * a finite-time blow up, e.g., $$\\dot{x} = x^2, \\quad x_0 = 1$$\n",
    "  * oscillations, e.g., $$\\ddot{x} = -x, \\quad x_0 = 1$$\n",
    "  * chaotic behaviour, e.g., the Lorentz attractor\n",
    "\n",
    "If the considered dynamical system has a steady state for positive times, then integrating the ODE long enough will equilibrate the system to this steady state. However, this may be computationally more demanding than other approaches and may fail, if the maximum number of integration steps is exceeded before reaching the steady state.\n",
    "\n",
    "In general, Newton's method will find the steady state faster than forward simulation. However, it only converges if started close enough to the steady state. Moreover, it will not work, if the dynamical system has conserved quantities which were not removed prior to steady state computation: Conserved quantities will cause singularities in the Jacobian of the right hand side of the system, such that the linear problem within each step of Newton's method can not be solved."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logic of the steady state solver\n",
    "\n",
    "If AMICI has to equilibrate a dynamical system, it can do this either via simulating until the right hand side of the system becomes small, or it can try to find the steady state directly by Newton's method.\n",
    "Amici decides automatically which approach is chosen and how forward or adjoint sensitivities are computed, if requested. However, the user can influence this behavior, if prior knowledge about the dynamical is available.\n",
    "\n",
    "The logic which AMICI will follow to equilibrate the system works as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "\n",
    "fig = Image(filename=\"../../../doc/gfx/steadystate_solver_workflow.png\")\n",
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The example model\n",
    "\n",
    "We will use the example model `model_constant_species.xml`, which has conserved species. Those are automatically removed in the SBML import of AMICI, but they can also be kept in the model to demonstrate the failure of Newton's method due to a singular right hand side Jacobian."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Species:  ['substrate', 'enzyme', 'complex', 'product']\n",
      "\n",
      "Reactions:\n",
      "creation:             -> substrate\t\t[compartment * (synthesis_substrate + k_create)]\n",
      "binding:  substrate +  enzyme <->   complex\t\t[compartment * (k_bind * substrate * enzyme - k_unbind * complex)]\n",
      "conversion:    complex  -> enzyme +  product\t\t[compartment * k_convert * complex]\n",
      "decay:    product  ->          \t\t[compartment * k_decay * product]\n"
     ]
    }
   ],
   "source": [
    "import libsbml\n",
    "import amici\n",
    "import os\n",
    "import numpy as np\n",
    "\n",
    "# SBML model we want to import\n",
    "sbml_file = \"model_constant_species.xml\"\n",
    "\n",
    "# Name of the models that will also be the name of the python module\n",
    "model_name = \"model_constant_species\"\n",
    "model_reduced_name = model_name + \"_reduced\"\n",
    "\n",
    "# Directories to which the generated model code is written\n",
    "model_output_dir = model_name\n",
    "model_reduced_output_dir = model_reduced_name\n",
    "\n",
    "# Read the model and give some output\n",
    "sbml_reader = libsbml.SBMLReader()\n",
    "sbml_doc = sbml_reader.readSBML(sbml_file)\n",
    "sbml_model = sbml_doc.getModel()\n",
    "dir(sbml_doc)\n",
    "\n",
    "print(\"Species: \", [s.getId() for s in sbml_model.getListOfSpecies()])\n",
    "\n",
    "print(\"\\nReactions:\")\n",
    "for reaction in sbml_model.getListOfReactions():\n",
    "    reactants = \" + \".join(\n",
    "        [\n",
    "            \"{} {}\".format(\n",
    "                int(r.getStoichiometry()) if r.getStoichiometry() > 1 else \"\",\n",
    "                r.getSpecies(),\n",
    "            )\n",
    "            for r in reaction.getListOfReactants()\n",
    "        ]\n",
    "    )\n",
    "    products = \" + \".join(\n",
    "        [\n",
    "            \"{} {}\".format(\n",
    "                int(r.getStoichiometry()) if r.getStoichiometry() > 1 else \"\",\n",
    "                r.getSpecies(),\n",
    "            )\n",
    "            for r in reaction.getListOfProducts()\n",
    "        ]\n",
    "    )\n",
    "    reversible = \"<\" if reaction.getReversible() else \"\"\n",
    "    print(\n",
    "        \"%3s: %10s %1s->%10s\\t\\t[%s]\"  # noqa: UP031\n",
    "        % (\n",
    "            reaction.getId(),\n",
    "            reactants,\n",
    "            reversible,\n",
    "            products,\n",
    "            libsbml.formulaToL3String(reaction.getKineticLaw().getMath()),\n",
    "        )\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create an SbmlImporter instance for our SBML model\n",
    "sbml_importer = amici.SbmlImporter(sbml_file)\n",
    "\n",
    "# specify observables and constant parameters\n",
    "constantParameters = [\"synthesis_substrate\", \"init_enzyme\"]\n",
    "observables = {\n",
    "    \"observable_product\": {\"name\": \"\", \"formula\": \"product\"},\n",
    "    \"observable_substrate\": {\"name\": \"\", \"formula\": \"substrate\"},\n",
    "}\n",
    "sigmas = {\"observable_product\": 1.0, \"observable_substrate\": 1.0}\n",
    "\n",
    "# import the model\n",
    "sbml_importer.sbml2amici(\n",
    "    model_reduced_name,\n",
    "    model_reduced_output_dir,\n",
    "    observables=observables,\n",
    "    constant_parameters=constantParameters,\n",
    "    sigmas=sigmas,\n",
    ")\n",
    "sbml_importer.sbml2amici(\n",
    "    model_name,\n",
    "    model_output_dir,\n",
    "    observables=observables,\n",
    "    constant_parameters=constantParameters,\n",
    "    sigmas=sigmas,\n",
    "    compute_conservation_laws=False,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# import the models and run some test simulations\n",
    "model_reduced_module = amici.import_model_module(\n",
    "    model_reduced_name, os.path.abspath(model_reduced_output_dir)\n",
    ")\n",
    "model_reduced = model_reduced_module.getModel()\n",
    "\n",
    "model_module = amici.import_model_module(\n",
    "    model_name, os.path.abspath(model_output_dir)\n",
    ")\n",
    "model = model_module.getModel()\n",
    "\n",
    "\n",
    "# simulate model with conservation laws\n",
    "model_reduced.setTimepoints(np.linspace(0, 2, 100))\n",
    "solver_reduced = model_reduced.getSolver()\n",
    "rdata_reduced = amici.runAmiciSimulation(model_reduced, solver_reduced)\n",
    "\n",
    "# simulate model without conservation laws\n",
    "model.setTimepoints(np.linspace(0, 2, 100))\n",
    "solver = model.getSolver()\n",
    "rdata = amici.runAmiciSimulation(model, solver)\n",
    "\n",
    "# plot trajectories\n",
    "import amici.plotting\n",
    "\n",
    "amici.plotting.plotStateTrajectories(rdata_reduced, model=model_reduced)\n",
    "amici.plotting.plotObservableTrajectories(rdata_reduced, model=model_reduced)\n",
    "\n",
    "amici.plotting.plotStateTrajectories(rdata, model=model)\n",
    "amici.plotting.plotObservableTrajectories(rdata, model=model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inferring the steady state of the system (postequilibration)\n",
    "\n",
    "First, we want to demonstrate that Newton's method will fail with the unreduced model due to a singular right hand side Jacobian."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          ts:  [inf]\n",
      "           x:  [[0.11       2.         0.2        2.00000002]]\n",
      "          x0:  [5. 2. 0. 0.]\n",
      "        x_ss:  [nan nan nan nan]\n",
      "          sx:  None\n",
      "         sx0:  None\n",
      "       sx_ss:  None\n",
      "           y:  [[2.00000002 0.11      ]]\n",
      "      sigmay:  [[1. 1.]]\n",
      "          sy:  None\n",
      "     ssigmay:  None\n",
      "           z:  None\n",
      "          rz:  None\n",
      "      sigmaz:  None\n",
      "          sz:  None\n",
      "         srz:  None\n",
      "     ssigmaz:  None\n",
      "        sllh:  None\n",
      "       s2llh:  None\n",
      "           J:  [[-20.    0.   20.    0. ]\n",
      " [ -1.1   0.    1.1   0. ]\n",
      " [  1.    0.  -11.   10. ]\n",
      " [  0.    0.    0.   -1. ]]\n",
      "        xdot:  [ 0.00000000e+00  0.00000000e+00  2.22044605e-16 -2.24170307e-08]\n",
      "      status:  0.0\n",
      "         llh:  nan\n",
      "        chi2:  nan\n",
      "         res:  [0. 0.]\n",
      "        sres:  None\n",
      "         FIM:  None\n",
      "           w:  [[2.         2.         2.         2.00000002]]\n",
      "  preeq_wrms:  nan\n",
      "     preeq_t:  nan\n",
      "preeq_numlinsteps:  None\n",
      "preeq_numsteps:  [[0 0 0]]\n",
      "preeq_numstepsB:  12.0\n",
      "preeq_status:  [[0 0 0]]\n",
      "preeq_cpu_time:  0.0\n",
      "preeq_cpu_timeB:  0.0\n",
      " posteq_wrms:  0.5604257578208488\n",
      "    posteq_t:  19.2252094591474\n",
      "posteq_numlinsteps:  None\n",
      "posteq_numsteps:  [[  0 417   0]]\n",
      "posteq_numstepsB:  0.0\n",
      "posteq_status:  [[-3  1  0]]\n",
      "posteq_cpu_time:  2.315\n",
      "posteq_cpu_timeB:  0.0\n",
      "    numsteps:  None\n",
      " numrhsevals:  None\n",
      "numerrtestfails:  None\n",
      "numnonlinsolvconvfails:  None\n",
      "       order:  None\n",
      "    cpu_time:  0.0\n",
      "   numstepsB:  None\n",
      "numrhsevalsB:  None\n",
      "numerrtestfailsB:  None\n",
      "numnonlinsolvconvfailsB:  None\n",
      "   cpu_timeB:  0.0\n"
     ]
    }
   ],
   "source": [
    "# Call postequilibration by setting an infinity timepoint\n",
    "model.setTimepoints(np.full(1, np.inf))\n",
    "\n",
    "# set the solver\n",
    "solver = model.getSolver()\n",
    "solver.setNewtonMaxSteps(10)\n",
    "solver.setMaxSteps(1000)\n",
    "rdata = amici.runAmiciSimulation(model, solver)\n",
    "\n",
    "# np.set_printoptions(threshold=8, edgeitems=2)\n",
    "for key, value in rdata.items():\n",
    "    print(f\"{key:12s}: \", value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The fields `posteq_status` and `posteq_numsteps` in rdata tells us how postequilibration worked:\n",
    "\n",
    "  * the first entry informs us about the status/number of steps in Newton's method (here 0, as Newton's method did not work)\n",
    "  * the second entry tells us, the status/how many integration steps were taken until steady state was reached\n",
    "  * the third entry informs us about the status/number of Newton steps in the second launch, after simulation\n",
    "\n",
    "The status is encoded as an Integer flag with the following meanings:\n",
    "\n",
    "  * ` 1`: Successful run\n",
    "  * ` 0`: Did not run\n",
    "  * `-1`: Error: No further specification is given, the error message should give more information.\n",
    "  * `-2`: Error: The method did not converge to a steady state within the maximum number of steps (Newton's method or simulation).\n",
    "  * `-3`: Error: The Jacobian of the right hand side is singular (only Newton's method)\n",
    "  * `-4`: Error: The damping factor in Newton's method was reduced until it met the lower bound without success (Newton's method only)\n",
    "  * `-5`: Error: The model was simulated past the timepoint `t=1e100` without finding a steady state. Therefore, it is likely that the model has not steady state for the given parameter vector.\n",
    "\n",
    "Here, only the second entry of `posteq_status` contains a positive integer: The first run of Newton's method failed due to a Jacobian, which oculd not be factorized, but the second run (simulation) contains the entry 1 (success). The third entry is 0, thus Newton's method was not launched for a second time.\n",
    "More information can be found in`posteq_numsteps`: Also here, only the second entry contains a positive integer, which is smaller than the maximum number of steps taken (<1000). Hence, steady state was reached via simulation, which corresponds to the simulated time written to `posteq_time`.\n",
    "\n",
    "We want to demonstrate a complete failure if inferring the steady state by reducing the number of integration steps to a lower value:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Status of postequilibration: [[-3 -2 -3]]\n",
      "Number of steps employed in postequilibration: [[  0 100   0]]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Warning] AMICI:simulation: AMICI simulation failed:\n",
      "Steady state computation failed. First run of Newton solver failed: RHS could not be factorized. Simulation to steady state failed: No convergence was achieved. Second run of Newton solver failed: RHS could not be factorized.\n",
      "Error occured in:\n",
      "0          0x1060f7913 amici::SteadystateProblem::handleSteadyStateFailure(amici::Solver const*, amici::Model*) + 531\n",
      "1          0x1060f6b3c amici::SteadystateProblem::findSteadyState(amici::Solver*, amici::NewtonSolver*, amici::Model*, int) + 332\n",
      "2          0x1060f6882 amici::SteadystateProblem::workSteadyStateProblem(amici::Solver*, amici::Model*, int) + 322\n",
      "3          0x1060a4615 amici::AmiciApplication::runAmiciSimulation(amici::Solver&, amici::ExpData const*, amici::Model&, bool) + 405\n",
      "4  \n"
     ]
    }
   ],
   "source": [
    "# reduce maxsteps for integration\n",
    "solver.setMaxSteps(100)\n",
    "rdata = amici.runAmiciSimulation(model, solver)\n",
    "print(\"Status of postequilibration:\", rdata[\"posteq_status\"])\n",
    "print(\n",
    "    \"Number of steps employed in postequilibration:\", rdata[\"posteq_numsteps\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, the same logic works, if we use the reduced model.\n",
    "For sufficiently many Newton steps, postequilibration is achieved by Newton's method in the first run. In this specific example, the steady state is found within one step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Status of postequilibration: [[1 0 0]]\n",
      "Number of steps employed in postequilibration: [[2 0 0]]\n"
     ]
    }
   ],
   "source": [
    "# Call postequilibration by setting an infinity timepoint\n",
    "model_reduced.setTimepoints(np.full(1, np.inf))\n",
    "\n",
    "# set the solver\n",
    "solver_reduced = model_reduced.getSolver()\n",
    "solver_reduced.setNewtonMaxSteps(10)\n",
    "solver_reduced.setMaxSteps(100)\n",
    "rdata_reduced = amici.runAmiciSimulation(model_reduced, solver_reduced)\n",
    "\n",
    "print(\"Status of postequilibration:\", rdata_reduced[\"posteq_status\"])\n",
    "print(\n",
    "    \"Number of steps employed in postequilibration:\",\n",
    "    rdata_reduced[\"posteq_numsteps\"],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Postequilibration with sensitivities\n",
    "\n",
    "Equilibration is possible with forward and adjoint sensitivity analysis. As for the main simulation part, adjoint sensitivity analysis yields less information than forward sensitivity analysis, since no state sensitivities are computed. However, it has a better scaling behavior towards large model sizes.\n",
    "\n",
    "### Postequilibration with forward sensitivities\n",
    "\n",
    "If forward sensitivity analysis is used, then state sensitivities at the timepoint `np.inf` will be computed. This can be done in (currently) two different ways:\n",
    "\n",
    "1. If the Jacobian $\\nabla_x f$ of the right hand side $f$ is not (close to) singular, the most efficient approach will be solving the linear system of equations, which defines the steady state sensitivities:\n",
    "\n",
    "   $$0 = \\dot{s}^x = (\\nabla_x f) s^x + \\frac{\\partial f}{\\partial \\theta}\\qquad \\Rightarrow \\qquad(\\nabla_x f) s^x = - \\frac{\\partial f}{\\partial \\theta}$$\n",
    "\n",
    "   This approach will always be chosen by AMICI, if the option `model.SteadyStateSensitivityMode` is set to `SteadyStateSensitivityMode.newtonOnly`. Furthermore, it will also be chosen, if the steady state was found by Newton's method, as in this case, the Jacobian is at least not singular (but may still be poorly conditioned). A check for the condition number of the Jacobian is currently missing, but will soon be implemented.\n",
    "\n",
    "2. If the Jacobian is poorly conditioned or singular, then the only way to obtain a reliable result will be integrating the state variables with state sensitivities until the norm of the right hand side becomes small. This approach will be chosen by AMICI, if the steady state was found by simulation and the option `model.SteadyStateSensitivityMode` is set to `SteadyStateSensitivityMode.simulationFSA`. This approach is numerically more stable, but the computation time for large models may be substantial.\n",
    "\n",
    "Side remark:\n",
    "\n",
    "A possible third way may consist in a (relaxed) Richardson iteration type approach, which interprets the entries of the right hand side $f$ as residuals and minimizes the squared residuals $\\Vert f \\Vert^2$ by a Levenberg-Marquart-type algorithm. This approach would also work for poorly conditioned (and even for singular Jacobians if additional constraints are implemented as Lagrange multipliers) while being faster than a long forward simulation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want to demonstrate both possibilities to find the steady state sensitivities, as well as the failure of their computation if the Jacobian is singular and the `newtonOnly` setting was used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Status of postequilibration: [[-3  1  0]]\n",
      "Number of steps employed in postequilibration: [[   0 1026    0]]\n",
      "Computed state sensitivities:\n",
      "[[-1.10000000e-02  0.00000000e+00 -6.70507402e-18 -1.20114408e-11]\n",
      " [ 1.00000000e-02  0.00000000e+00 -8.22965063e-19  1.20114329e-11]\n",
      " [-1.00000000e-03  0.00000000e+00 -2.00000000e-02 -2.40228711e-11]\n",
      " [ 5.50000000e-02  0.00000000e+00  1.00000000e-01  9.99999999e-01]\n",
      " [ 0.00000000e+00  0.00000000e+00  0.00000000e+00 -2.00000004e+00]]\n"
     ]
    }
   ],
   "source": [
    "# Call simulation with singular Jacobian and integrateIfNewtonFails mode\n",
    "model.setTimepoints(np.full(1, np.inf))\n",
    "model.setSteadyStateSensitivityMode(\n",
    "    amici.SteadyStateSensitivityMode.integrateIfNewtonFails\n",
    ")\n",
    "solver = model.getSolver()\n",
    "solver.setNewtonMaxSteps(10)\n",
    "solver.setSensitivityMethod(amici.SensitivityMethod.forward)\n",
    "solver.setSensitivityOrder(amici.SensitivityOrder.first)\n",
    "solver.setMaxSteps(10000)\n",
    "rdata = amici.runAmiciSimulation(model, solver)\n",
    "\n",
    "print(\"Status of postequilibration:\", rdata[\"posteq_status\"])\n",
    "print(\n",
    "    \"Number of steps employed in postequilibration:\", rdata[\"posteq_numsteps\"]\n",
    ")\n",
    "print(\"Computed state sensitivities:\")\n",
    "print(rdata[\"sx\"][0, :, :])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Status of postequilibration: [[-2 -1  1]]\n",
      "Number of steps employed in postequilibration: [[  0 543   0]]\n",
      "Computed state sensitivities:\n",
      "[[0. 0. 0. 0.]\n",
      " [0. 0. 0. 0.]\n",
      " [0. 0. 0. 0.]\n",
      " [0. 0. 0. 0.]\n",
      " [0. 0. 0. 0.]]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Warning] AMICI:simulation: AMICI simulation failed:\n",
      "Steady state sensitvitiy computation failed due to unsuccessful factorization of RHS Jacobian\n",
      "Error occured in:\n",
      "0          0x1060f698b amici::SteadystateProblem::workSteadyStateProblem(amici::Solver*, amici::Model*, int) + 587\n",
      "1          0x1060a4615 amici::AmiciApplication::runAmiciSimulation(amici::Solver&, amici::ExpData const*, amici::Model&, bool) + 405\n",
      "2          0x1060a4474 amici::runAmiciSimulation(amici::Solver&, amici::ExpData const*, amici::Model&, bool) + 36\n",
      "3          0x106061005 _wrap_runAmiciSimulation(_object*, _object*) + 549\n",
      "4          0x1021b2309 cfunction_call_varargs + 320\n",
      "5          \n"
     ]
    }
   ],
   "source": [
    "# Call simulation with singular Jacobian and newtonOnly mode (will fail)\n",
    "model.setTimepoints(np.full(1, np.inf))\n",
    "model.setSteadyStateSensitivityMode(\n",
    "    amici.SteadyStateSensitivityMode.newtonOnly\n",
    ")\n",
    "solver = model.getSolver()\n",
    "solver.setSensitivityMethod(amici.SensitivityMethod.forward)\n",
    "solver.setSensitivityOrder(amici.SensitivityOrder.first)\n",
    "solver.setMaxSteps(10000)\n",
    "rdata = amici.runAmiciSimulation(model, solver)\n",
    "\n",
    "print(\"Status of postequilibration:\", rdata[\"posteq_status\"])\n",
    "print(\n",
    "    \"Number of steps employed in postequilibration:\", rdata[\"posteq_numsteps\"]\n",
    ")\n",
    "print(\"Computed state sensitivities:\")\n",
    "print(rdata[\"sx\"][0, :, :])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Status of postequilibration: [[1 0 0]]\n",
      "Number of steps employed in postequilibration: [[2 0 0]]\n",
      "Computed state sensitivities:\n",
      "[[-1.1e-02  0.0e+00 -0.0e+00 -0.0e+00]\n",
      " [ 1.0e-02  0.0e+00 -0.0e+00 -0.0e+00]\n",
      " [-1.0e-03  0.0e+00 -2.0e-02 -0.0e+00]\n",
      " [ 5.5e-02  0.0e+00  1.0e-01  1.0e+00]\n",
      " [-0.0e+00  0.0e+00 -0.0e+00 -2.0e+00]]\n"
     ]
    }
   ],
   "source": [
    "# Call postequilibration by setting an infinity timepoint\n",
    "model_reduced.setTimepoints(np.full(1, np.inf))\n",
    "model.setSteadyStateSensitivityMode(\n",
    "    amici.SteadyStateSensitivityMode.newtonOnly\n",
    ")\n",
    "solver_reduced = model_reduced.getSolver()\n",
    "solver_reduced.setNewtonMaxSteps(10)\n",
    "solver_reduced.setSensitivityMethod(amici.SensitivityMethod.forward)\n",
    "solver_reduced.setSensitivityOrder(amici.SensitivityOrder.first)\n",
    "solver_reduced.setMaxSteps(1000)\n",
    "rdata_reduced = amici.runAmiciSimulation(model_reduced, solver_reduced)\n",
    "\n",
    "print(\"Status of postequilibration:\", rdata_reduced[\"posteq_status\"])\n",
    "print(\n",
    "    \"Number of steps employed in postequilibration:\",\n",
    "    rdata_reduced[\"posteq_numsteps\"],\n",
    ")\n",
    "print(\"Computed state sensitivities:\")\n",
    "print(rdata_reduced[\"sx\"][0, :, :])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Postequilibration with adjoint sensitivities\n",
    "\n",
    "Postequilibration also works with adjoint sensitivities. In this case, it is exploited that the ODE of the adjoint state $p$ will always have the steady state 0, since it's a linear ODE:\n",
    "\n",
    "   $$\\frac{d}{dt} p(t) = J(x^*, \\theta)^T p(t),$$\n",
    "\n",
    "where $x^*$ denotes the steady state of the system state.\n",
    "Since the Eigenvalues of the Jacobian are negative and since the Jacobian at steady state is a fixed matrix, this system has a simple algebraic solution:\n",
    "\n",
    "   $$p(t) = e^{t J(x^*, \\theta)^T} p_{\\text{end}}.$$\n",
    "\n",
    "As a consequence, the quadratures in adjoint computation also reduce to a matrix-vector product:\n",
    "\n",
    "   $$Q(x, \\theta) = Q(x^*, \\theta) = p_{\\text{integral}} * \\frac{\\partial f}{\\partial \\theta}$$\n",
    "\n",
    "with\n",
    "\n",
    "   $$p_{\\text{integral}} = \\int_0^\\infty p(s) ds = (J(x^*, \\theta)^T)^{-1} p_{\\text{end}}.$$\n",
    "\n",
    "However, this solution is given in terms of a linear system of equations defined by the transposed Jacobian of the right hand side. Hence, if the (transposed) Jacobian is singular, it is not applicable.\n",
    "In this case, standard integration must be carried out."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Status of postequilibration: [[1 0 0]]\n",
      "Number of steps employed in postequilibration: [[2 0 0]]\n",
      "Number of backward steps employed in postequilibration: 0.0\n",
      "Computed gradient: [-1.85900e-02  1.69000e-02 -1.69000e-03 -3.16282e+00  1.60000e+01]\n"
     ]
    }
   ],
   "source": [
    "# Call adjoint postequilibration by setting an infinity timepoint\n",
    "# and create an edata object, which is needed for adjoint computation\n",
    "edata = amici.ExpData(2, 0, 0, np.array([float(\"inf\")]))\n",
    "edata.setObservedData([1.8] * 2)\n",
    "edata.fixedParameters = np.array([3.0, 5.0])\n",
    "\n",
    "model_reduced.setSteadyStateSensitivityMode(\n",
    "    amici.SteadyStateSensitivityMode.newtonOnly\n",
    ")\n",
    "solver_reduced = model_reduced.getSolver()\n",
    "solver_reduced.setNewtonMaxSteps(10)\n",
    "solver_reduced.setSensitivityMethod(amici.SensitivityMethod.adjoint)\n",
    "solver_reduced.setSensitivityOrder(amici.SensitivityOrder.first)\n",
    "solver_reduced.setMaxSteps(1000)\n",
    "rdata_reduced = amici.runAmiciSimulation(model_reduced, solver_reduced, edata)\n",
    "\n",
    "print(\"Status of postequilibration:\", rdata_reduced[\"posteq_status\"])\n",
    "print(\n",
    "    \"Number of steps employed in postequilibration:\",\n",
    "    rdata_reduced[\"posteq_numsteps\"],\n",
    ")\n",
    "print(\n",
    "    \"Number of backward steps employed in postequilibration:\",\n",
    "    rdata_reduced[\"posteq_numstepsB\"],\n",
    ")\n",
    "print(\"Computed gradient:\", rdata_reduced[\"sllh\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we carry out the same computation with a system that has a singular Jacobian, then `posteq_numstepsB` will not be `0` any more (which indicates that the linear system solve was used to compute backward postequilibration).\n",
    "Now, integration is carried out and hence `posteq_numstepsB > 0`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Status of postequilibration: [[-3 -1  1]]\n",
      "Number of steps employed in postequilibration: [[  0 479   0]]\n",
      "Number of backward steps employed in postequilibration: 3076.0\n",
      "Computed gradient: [-1.85899987e-02  1.68999988e-02 -1.69000055e-03 -3.16282001e+00\n",
      "  1.60000000e+01]\n"
     ]
    }
   ],
   "source": [
    "# Call adjoint postequilibration with model with singular Jacobian\n",
    "model.setSteadyStateSensitivityMode(\n",
    "    amici.SteadyStateSensitivityMode.newtonOnly\n",
    ")\n",
    "solver = model.getSolver()\n",
    "solver.setNewtonMaxSteps(10)\n",
    "solver.setSensitivityMethod(amici.SensitivityMethod.adjoint)\n",
    "solver.setSensitivityOrder(amici.SensitivityOrder.first)\n",
    "rdata = amici.runAmiciSimulation(model, solver, edata)\n",
    "\n",
    "print(\"Status of postequilibration:\", rdata[\"posteq_status\"])\n",
    "print(\n",
    "    \"Number of steps employed in postequilibration:\", rdata[\"posteq_numsteps\"]\n",
    ")\n",
    "print(\n",
    "    \"Number of backward steps employed in postequilibration:\",\n",
    "    rdata[\"posteq_numstepsB\"],\n",
    ")\n",
    "print(\"Computed gradient:\", rdata[\"sllh\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preequilibrating the model\n",
    "\n",
    "Sometimes, we want to launch a solver run from a steady state which was inferred numerically, i.e., the system was preequilibrated. In order to do this with AMICI, we need to pass an ExpData object, which contains fixed parameter for the actual simulation and for preequilibration of the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create edata, with 3 timepoints and 2 observables:\n",
    "edata = amici.ExpData(2, 0, 0, np.array([0.0, 0.1, 1.0]))\n",
    "edata.setObservedData([1.8] * 6)\n",
    "edata.fixedParameters = np.array([3.0, 5.0])\n",
    "edata.fixedParametersPreequilibration = np.array([0.0, 2.0])\n",
    "edata.reinitializeFixedParameterInitialStates = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create the solver object and run the simulation\n",
    "solver_reduced = model_reduced.getSolver()\n",
    "solver_reduced.setNewtonMaxSteps(10)\n",
    "rdata_reduced = amici.runAmiciSimulation(model_reduced, solver_reduced, edata)\n",
    "\n",
    "amici.plotting.plotStateTrajectories(rdata_reduced, model=model_reduced)\n",
    "amici.plotting.plotObservableTrajectories(rdata_reduced, model=model_reduced)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also combine pre- and postequilibration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Change the last timepoint to an infinity timepoint.\n",
    "edata.setTimepoints(np.array([0.0, 0.1, float(\"inf\")]))\n",
    "\n",
    "# run the simulation\n",
    "rdata_reduced = amici.runAmiciSimulation(model_reduced, solver_reduced, edata)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preequilibration with sensitivities\n",
    "\n",
    "Beyond the need for an ExpData object, the steady state solver logic in preequilibration is the same as in postequilibration, also if sensitivities are requested. The computation will fail for singular Jacobians, if `SteadyStateSensitivityMode` is set to `newtonOnly`, or if not enough steps can be taken.\n",
    "However, if forward simulation with steady state sensitivities is allowed, or if the Jacobian is not singular, it will work."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prequilibration with forward sensitivities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          preeq_wrms:  0.5604257578208488\n",
      "             preeq_t:  19.2252094591474\n",
      "   preeq_numlinsteps:  None\n",
      "      preeq_numsteps:  [[  0 417   0]]\n",
      "     preeq_numstepsB:  0.0\n",
      "        preeq_status:  [[-3  1  0]]\n",
      "      preeq_cpu_time:  1.723\n",
      "     preeq_cpu_timeB:  0.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Warning] AMICI:simulation: AMICI simulation failed:\n",
      "Steady state sensitvitiy computation failed due to unsuccessful factorization of RHS Jacobian\n",
      "Error occured in:\n",
      "0          0x1060f698b amici::SteadystateProblem::workSteadyStateProblem(amici::Solver*, amici::Model*, int) + 587\n",
      "1          0x1060a456f amici::AmiciApplication::runAmiciSimulation(amici::Solver&, amici::ExpData const*, amici::Model&, bool) + 239\n",
      "2          0x1060a4474 amici::runAmiciSimulation(amici::Solver&, amici::ExpData const*, amici::Model&, bool) + 36\n",
      "3          0x106061005 _wrap_runAmiciSimulation(_object*, _object*) + 549\n",
      "4          0x1021b2309 cfunction_call_varargs + 320\n",
      "5          \n"
     ]
    }
   ],
   "source": [
    "# No postquilibration this time.\n",
    "edata.setTimepoints(np.array([0.0, 0.1, 1.0]))\n",
    "\n",
    "# create the solver object and run the simulation, singular Jacobian, enforce Newton solver for sensitivities\n",
    "model.setSteadyStateSensitivityMode(\n",
    "    amici.SteadyStateSensitivityMode.newtonOnly\n",
    ")\n",
    "solver = model.getSolver()\n",
    "solver.setNewtonMaxSteps(10)\n",
    "solver.setSensitivityMethod(amici.SensitivityMethod.forward)\n",
    "solver.setSensitivityOrder(amici.SensitivityOrder.first)\n",
    "rdata = amici.runAmiciSimulation(model, solver, edata)\n",
    "\n",
    "for key, value in rdata.items():\n",
    "    if key[0:6] == \"preeq_\":\n",
    "        print(f\"{key:20s}:\", value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          preeq_wrms:  0.9920376238481097\n",
      "             preeq_t:  21.270502326483026\n",
      "   preeq_numlinsteps:  None\n",
      "      preeq_numsteps:  [[   0 1026    0]]\n",
      "     preeq_numstepsB:  0.0\n",
      "        preeq_status:  [[-3  1  0]]\n",
      "      preeq_cpu_time:  12.439\n",
      "     preeq_cpu_timeB:  0.0\n"
     ]
    }
   ],
   "source": [
    "# Singular Jacobian, use simulation\n",
    "model.setSteadyStateSensitivityMode(\n",
    "    amici.SteadyStateSensitivityMode.integrateIfNewtonFails\n",
    ")\n",
    "solver = model.getSolver()\n",
    "solver.setNewtonMaxSteps(10)\n",
    "solver.setSensitivityMethod(amici.SensitivityMethod.forward)\n",
    "solver.setSensitivityOrder(amici.SensitivityOrder.first)\n",
    "rdata = amici.runAmiciSimulation(model, solver, edata)\n",
    "\n",
    "for key, value in rdata.items():\n",
    "    if key[0:6] == \"preeq_\":\n",
    "        print(f\"{key:20s}:\", value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          preeq_wrms:  0.0\n",
      "             preeq_t:  nan\n",
      "   preeq_numlinsteps:  None\n",
      "      preeq_numsteps:  [[2 0 0]]\n",
      "     preeq_numstepsB:  0.0\n",
      "        preeq_status:  [[1 0 0]]\n",
      "      preeq_cpu_time:  0.036\n",
      "     preeq_cpu_timeB:  0.0\n"
     ]
    }
   ],
   "source": [
    "# Non-singular Jacobian, use Newton solver\n",
    "solver_reduced = model_reduced.getSolver()\n",
    "solver_reduced.setNewtonMaxSteps(10)\n",
    "solver_reduced.setSensitivityMethod(amici.SensitivityMethod.forward)\n",
    "solver_reduced.setSensitivityOrder(amici.SensitivityOrder.first)\n",
    "rdata_reduced = amici.runAmiciSimulation(model_reduced, solver_reduced, edata)\n",
    "\n",
    "for key, value in rdata_reduced.items():\n",
    "    if key[0:6] == \"preeq_\":\n",
    "        print(f\"{key:20s}:\", value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prequilibration with adjoint sensitivities\n",
    "\n",
    "\n",
    "When using preequilibration, adjoint sensitivity analysis can be used for simulation. This is a particularly interesting case: Standard adjoint sensitivity analysis requires the initial state sensitivities `sx0` to work, at least if data is given for finite (i.e., not exclusively postequilibration) timepoints:\n",
    "For each parameter, a contribution to the gradient is given by the scalar product of the corresponding state sensitivity vector at timepoint $t=0$, (column in `sx0`), with the adjoint state ($p(t=0)$). Hence, the matrix `sx0` is needed. This scalar product \"closes the loop\" from forward to adjoint simulation.\n",
    "\n",
    "By default, if adjoint sensitivity analysis is called with preequilibration, the initial state sensitivities are computed in just the same way as if this way done for forward sensitivity analysis. The only difference in the internal logic is that, if the steady state gets inferred via simulation, a separate solver object is used in order to ensure that the steady state simulation does not interfere with the snapshotting of the forward trajectory from the actual time course.\n",
    "\n",
    "However, also an adjoint version of preequilibration is possible: In this case, the \"loop\" from forward to adjoint simulation needs no closure: The simulation time is extended by preequilibration: forward from $t = -\\infty$ to $t=0$, and after adjoint simulation also backward from $t=0$ to $t = -\\infty$. Similar to adjoint postequilibration, the steady state of the adjoint state (at $t=-\\infty$) is $p=0$, hence the scalar product (at $t=-\\infty$) for the initial state sensitivities of preequilibration with the adjoint state vanishes. Instead, this gradient contribution is covered by additional quadratures $\\int_{-\\infty}^0 p(s) ds \\cdot \\frac{\\partial f}{\\partial \\theta}$. In order to compute these quadratures correctly, the adjoint state from the main adjoint simulation must be passed on to the initial adjoint state of backward preequilibration.\n",
    "\n",
    "However, as the adjoint state must be passed on from backward computation to preequilibration, it is currently not allowed to alter (reinitialize) states of the model at $t=0$, unless these states are constant, as otherwise this alteration would lead to a discontinuity in the adjoints state as well and hence to an incorrect gradient."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          preeq_wrms:  0.0\n",
      "             preeq_t:  nan\n",
      "   preeq_numlinsteps:  None\n",
      "      preeq_numsteps:  [[2 0 0]]\n",
      "     preeq_numstepsB:  0.0\n",
      "        preeq_status:  [[1 0 0]]\n",
      "      preeq_cpu_time:  0.039\n",
      "     preeq_cpu_timeB:  0.0\n",
      "Gradient: [-0.05528395  0.0461776  -0.03354519 -2.34602219  6.314481  ]\n"
     ]
    }
   ],
   "source": [
    "# Non-singular Jacobian, use Newton solver and adjoints with initial state sensitivities\n",
    "solver_reduced = model_reduced.getSolver()\n",
    "solver_reduced.setNewtonMaxSteps(10)\n",
    "solver_reduced.setSensitivityMethod(amici.SensitivityMethod.adjoint)\n",
    "solver_reduced.setSensitivityOrder(amici.SensitivityOrder.first)\n",
    "rdata_reduced = amici.runAmiciSimulation(model_reduced, solver_reduced, edata)\n",
    "\n",
    "for key, value in rdata_reduced.items():\n",
    "    if key[0:6] == \"preeq_\":\n",
    "        print(f\"{key:20s}:\", value)\n",
    "print(\"Gradient:\", rdata_reduced[\"sllh\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          preeq_wrms:  0.8470065245264354\n",
      "             preeq_t:  19.213162474372176\n",
      "   preeq_numlinsteps:  None\n",
      "      preeq_numsteps:  [[  0 426   0]]\n",
      "     preeq_numstepsB:  0.0\n",
      "        preeq_status:  [[-2  1  0]]\n",
      "      preeq_cpu_time:  1.753\n",
      "     preeq_cpu_timeB:  0.0\n",
      "Gradient: [-0.05528395  0.0461776  -0.03354519 -2.34602226  6.3144812 ]\n"
     ]
    }
   ],
   "source": [
    "# Non-singular Jacobian, use simulation solver and adjoints with initial state sensitivities\n",
    "solver_reduced = model_reduced.getSolver()\n",
    "solver_reduced.setNewtonMaxSteps(0)\n",
    "solver_reduced.setSensitivityMethod(amici.SensitivityMethod.adjoint)\n",
    "solver_reduced.setSensitivityOrder(amici.SensitivityOrder.first)\n",
    "rdata_reduced = amici.runAmiciSimulation(model_reduced, solver_reduced, edata)\n",
    "\n",
    "for key, value in rdata_reduced.items():\n",
    "    if key[0:6] == \"preeq_\":\n",
    "        print(f\"{key:20s}:\", value)\n",
    "print(\"Gradient:\", rdata_reduced[\"sllh\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          preeq_wrms:  0.0\n",
      "             preeq_t:  nan\n",
      "   preeq_numlinsteps:  None\n",
      "      preeq_numsteps:  [[2 0 0]]\n",
      "     preeq_numstepsB:  0.0\n",
      "        preeq_status:  [[1 0 0]]\n",
      "      preeq_cpu_time:  0.042\n",
      "     preeq_cpu_timeB:  0.009\n",
      "Gradient: [-0.05528395  0.0461776  -0.03354519 -2.34602219  6.314481  ]\n"
     ]
    }
   ],
   "source": [
    "# Non-singular Jacobian, use Newton solver and adjoints with fully adjoint preequilibration\n",
    "solver_reduced = model_reduced.getSolver()\n",
    "solver_reduced.setNewtonMaxSteps(10)\n",
    "solver_reduced.setSensitivityMethod(amici.SensitivityMethod.adjoint)\n",
    "solver_reduced.setSensitivityMethodPreequilibration(\n",
    "    amici.SensitivityMethod.adjoint\n",
    ")\n",
    "solver_reduced.setSensitivityOrder(amici.SensitivityOrder.first)\n",
    "rdata_reduced = amici.runAmiciSimulation(model_reduced, solver_reduced, edata)\n",
    "\n",
    "for key, value in rdata_reduced.items():\n",
    "    if key[0:6] == \"preeq_\":\n",
    "        print(f\"{key:20s}:\", value)\n",
    "print(\"Gradient:\", rdata_reduced[\"sllh\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As for postquilibration, adjoint preequilibration has an analytic solution (via the linear system), which will be preferred. If used for models with singular Jacobian, numerical integration will be carried out, which is indicated by `preeq_numstepsB`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          preeq_wrms:  0.9986067660342685\n",
      "             preeq_t:  36.94272314329062\n",
      "   preeq_numlinsteps:  None\n",
      "      preeq_numsteps:  [[  0 417   0]]\n",
      "     preeq_numstepsB:  1371.0\n",
      "        preeq_status:  [[-3  1  0]]\n",
      "      preeq_cpu_time:  2.488\n",
      "     preeq_cpu_timeB:  5.016\n",
      "Gradient: [-0.05528395  0.04617759 -0.03354518 -2.34602224  6.3144811 ]\n"
     ]
    }
   ],
   "source": [
    "# Non-singular Jacobian, use Newton solver and adjoints with fully adjoint preequilibration\n",
    "solver = model.getSolver()\n",
    "solver.setNewtonMaxSteps(10)\n",
    "solver.setSensitivityMethod(amici.SensitivityMethod.adjoint)\n",
    "solver.setSensitivityMethodPreequilibration(amici.SensitivityMethod.adjoint)\n",
    "solver.setSensitivityOrder(amici.SensitivityOrder.first)\n",
    "rdata = amici.runAmiciSimulation(model, solver, edata)\n",
    "\n",
    "for key, value in rdata.items():\n",
    "    if key[0:6] == \"preeq_\":\n",
    "        print(f\"{key:20s}:\", value)\n",
    "print(\"Gradient:\", rdata[\"sllh\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Controlling the error tolerances in pre- and postequilibration\n",
    "\n",
    "When solving ODEs or DAEs, AMICI uses the default logic of CVODES and IDAS to control error tolerances. This means that error weights are computed based on the absolute error tolerances and the product of current state variables of the system and their respective relative error tolerances. If this error combination is then controlled.\n",
    "\n",
    "The respective tolerances for equilibrating a system with AMICI can be controlled by the user via the getter/setter functions `[get|set][Absolute|Relative]ToleranceSteadyState[Sensi]`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "ODE solver steps, which were necessary to reach steady state:\n",
      "lax tolerances:  [[  0 733   0]]\n",
      "strict tolerances:  [[   0 1031    0]]\n",
      "\n",
      "simulation time correpsonding to steady state:\n",
      "6.002011407974004\n",
      "31.0689293433781\n",
      "\n",
      "computation time to reach steady state:\n",
      "7.646\n",
      "7.837\n"
     ]
    }
   ],
   "source": [
    "# Non-singular Jacobian, use simulation\n",
    "model_reduced.setSteadyStateSensitivityMode(\n",
    "    amici.SteadyStateSensitivityMode.integrateIfNewtonFails\n",
    ")\n",
    "solver_reduced = model_reduced.getSolver()\n",
    "solver_reduced.setNewtonMaxSteps(0)\n",
    "solver_reduced.setSensitivityMethod(amici.SensitivityMethod.forward)\n",
    "solver_reduced.setSensitivityOrder(amici.SensitivityOrder.first)\n",
    "\n",
    "# run with lax tolerances\n",
    "solver_reduced.setRelativeToleranceSteadyState(1e-2)\n",
    "solver_reduced.setAbsoluteToleranceSteadyState(1e-3)\n",
    "solver_reduced.setRelativeToleranceSteadyStateSensi(1e-2)\n",
    "solver_reduced.setAbsoluteToleranceSteadyStateSensi(1e-3)\n",
    "rdata_reduced_lax = amici.runAmiciSimulation(\n",
    "    model_reduced, solver_reduced, edata\n",
    ")\n",
    "\n",
    "# run with strict tolerances\n",
    "solver_reduced.setRelativeToleranceSteadyState(1e-12)\n",
    "solver_reduced.setAbsoluteToleranceSteadyState(1e-16)\n",
    "solver_reduced.setRelativeToleranceSteadyStateSensi(1e-12)\n",
    "solver_reduced.setAbsoluteToleranceSteadyStateSensi(1e-16)\n",
    "rdata_reduced_strict = amici.runAmiciSimulation(\n",
    "    model_reduced, solver_reduced, edata\n",
    ")\n",
    "\n",
    "# compare ODE outputs\n",
    "print(\"\\nODE solver steps, which were necessary to reach steady state:\")\n",
    "print(\"lax tolerances: \", rdata_reduced_lax[\"preeq_numsteps\"])\n",
    "print(\"strict tolerances: \", rdata_reduced_strict[\"preeq_numsteps\"])\n",
    "\n",
    "print(\"\\nsimulation time corresponding to steady state:\")\n",
    "print(rdata_reduced_lax[\"preeq_t\"])\n",
    "print(rdata_reduced_strict[\"preeq_t\"])\n",
    "\n",
    "print(\"\\ncomputation time to reach steady state:\")\n",
    "print(rdata_reduced_lax[\"preeq_cpu_time\"])\n",
    "print(rdata_reduced_strict[\"preeq_cpu_time\"])"
   ]
  }
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