{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# SBML import, observation model, sensitivity analysis, data export and visualization\n", "\n", "This is an example using the [model_steadystate_scaled.xml] model to demonstrate:\n", "\n", "* SBML import\n", "* specifying the observation model\n", "* performing sensitivity analysis\n", "* exporting and visualizing simulation results" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# SBML model we want to import\n", "sbml_file = \"model_steadystate_scaled_without_observables.xml\"\n", "# Name of the model that will also be the name of the python module\n", "model_name = \"model_steadystate_scaled\"\n", "# Directory to which the generated model code is written\n", "model_output_dir = model_name\n", "\n", "import amici\n", "import libsbml\n", "import matplotlib.pyplot as plt\n", "import numpy as np" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The example model\n", "\n", "Here we use `libsbml` to show the reactions and species described by the model (this is independent of AMICI)." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Species: ['x1', 'x2', 'x3']\n", "\n", "Reactions:\n", " r1: 2 x1 -> x2\t\t[p1 * x1^2]\n", " r2: x1 + x2 -> x3\t\t[p2 * x1 * x2]\n", " r3: x2 -> 2 x1\t\t[p3 * x2]\n", " r4: x3 -> x1 + x2\t\t[p4 * x3]\n", " r5: x3 -> \t\t[k0 * x3]\n", " r6: -> x1\t\t[p5]\n" ] } ], "source": [ "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": "markdown", "metadata": {}, "source": [ "## Importing an SBML model, compiling and generating an AMICI module\n", "\n", "Before we can use AMICI to simulate our model, the SBML model needs to be translated to C++ code. This is done by `amici.SbmlImporter`." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# Create an SbmlImporter instance for our SBML model\n", "sbml_importer = amici.SbmlImporter(sbml_file)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this example, we want to specify fixed parameters, observables and a $\\sigma$ parameter. Unfortunately, the latter two are not part of the [SBML standard](https://sbml.org/). However, they can be provided to `amici.SbmlImporter.sbml2amici` as demonstrated in the following." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Constant parameters\n", "\n", "Constant parameters, i.e. parameters with respect to which no sensitivities are to be computed (these are often parameters specifying a certain experimental condition) are provided as a list of parameter names." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "constant_parameters = [\"k0\"]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Observation model\n", "\n", "Specifying the observation model (i.e., the quantities that are observed, as well as the respective error models) is beyond the scope of SBML. Here we define that manually.\n", "\n", "If you are looking for a more scalable way of defining observables, then checkout [PEtab](https://github.com/PEtab-dev/PEtab). Another possibility is using SBML's [`AssignmentRule`s](https://sbml.org/software/libsbml/5.18.0/docs/formatted/python-api/classlibsbml_1_1_assignment_rule.html) to specify model outputs within the SBML file.\n", "\n", "\n", "\n", "For model import in AMICI, the different types of measurements are represented as `MeasurementChannels`.\n", "The measurement channel is characterized by an ID, an optional name, the observation function (`MeasurementChannels.formula`), the type of noise distribution (`MeasurementChannels.noise_distribution`, defaults to a normal distribution), and the scale parameter of that distribution (`MeasurementChannels.sigma`).\n", "The symbols used in the observation function and for the scale parameter must already be defined in the model." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# Define observation model\n", "from amici import MeasurementChannel as MC\n", "\n", "observation_model = [\n", " MC(id_=\"observable_x1\", formula=\"x1\"),\n", " MC(id_=\"observable_x2\", formula=\"x2\"),\n", " MC(id_=\"observable_x3\", formula=\"x3\"),\n", " MC(id_=\"observable_x1_scaled\", formula=\"scaling_x1 * x1\"),\n", " MC(id_=\"observable_x2_offsetted\", formula=\"offset_x2 + x2\"),\n", " MC(\n", " id_=\"observable_x1withsigma\",\n", " formula=\"x1\",\n", " sigma=\"observable_x1withsigma_sigma\",\n", " ),\n", "]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Generating the module\n", "\n", "Now we can generate the python module for our model. `amici.SbmlImporter.sbml2amici` will symbolically derive the sensitivity equations, generate C++ code for model simulation, and assemble the python module. Standard logging verbosity levels can be passed to this function to see timestamped progression during code generation." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2021-11-30 16:57:57.997 - amici.sbml_import - INFO - Finished gathering local SBML symbols + (8.05E-03s)\n", "2021-11-30 16:57:58.047 - amici.sbml_import - INFO - Finished processing SBML parameters + (4.64E-02s)\n", "2021-11-30 16:57:58.054 - amici.sbml_import - INFO - Finished processing SBML compartments + (2.69E-04s)\n", "2021-11-30 16:57:58.063 - amici.sbml_import - INFO - Finished processing SBML species initials ++ (1.47E-03s)\n", "2021-11-30 16:57:58.067 - amici.sbml_import - INFO - Finished processing SBML rate rules ++ (7.65E-05s)\n", "2021-11-30 16:57:58.068 - amici.sbml_import - INFO - Finished processing SBML species + (9.09E-03s)\n", "2021-11-30 16:57:58.076 - amici.sbml_import - INFO - Finished processing SBML reactions + (4.32E-03s)\n", "2021-11-30 16:57:58.086 - 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amici.ode_export - INFO - Finished computing total_cl ++ (3.64E-03s)\n", "2021-11-30 16:57:59.052 - amici.ode_export - INFO - Finished writing total_cl.cpp + (7.19E-03s)\n", "2021-11-30 16:57:59.064 - amici.ode_export - INFO - Finished simplifying x_solver +++ (7.69E-05s)\n", "2021-11-30 16:57:59.065 - amici.ode_export - INFO - Finished computing x_solver ++ (4.62E-03s)\n", "2021-11-30 16:57:59.068 - amici.ode_export - INFO - Finished writing x_solver.cpp + (1.13E-02s)\n", "2021-11-30 16:57:59.079 - amici.ode_export - INFO - Finished generating cpp code (8.66E-01s)\n", "2021-11-30 16:58:07.834 - amici.ode_export - INFO - Finished compiling cpp code (8.75E+00s)\n" ] } ], "source": [ "import logging\n", "\n", "sbml_importer.sbml2amici(\n", " model_name,\n", " model_output_dir,\n", " verbose=logging.INFO,\n", " observation_model=observation_model,\n", " constant_parameters=constant_parameters,\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Importing the module and loading the model\n", "\n", "If everything went well, we can now import the newly generated Python module containing our model:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "model_module = amici.import_model_module(model_name, model_output_dir)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And get an instance of our model from which we can retrieve information such as parameter names:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "model = model_module.get_model()\n", "\n", "print(\"Model name: \", model.get_name())\n", "print(\"Model parameters: \", model.get_parameter_ids())\n", "print(\"Model outputs: \", model.get_observable_ids())\n", "print(\"Model state variables: \", model.get_state_ids())" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "## Running simulations and analyzing results\n", "\n", "After importing the model, we can run simulations using `amici.run_simulation`. This requires a `Model` instance and a `Solver` instance. Optionally you can provide measurements inside an `ExpData` instance, as shown later in this notebook." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Create Model instance\n", "model = model_module.get_model()\n", "\n", "# set timepoints for which we want to simulate the model\n", "model.set_timepoints(np.linspace(0, 60, 60))\n", "\n", "# Create solver instance\n", "solver = model.create_solver()\n", "\n", "# Run simulation using default model parameters and solver options\n", "rdata = amici.run_simulation(model, solver)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print(\n", " \"Simulation was run using model default parameters as specified in the SBML model:\"\n", ")\n", "print(dict(zip(model.get_parameter_ids(), model.get_parameters())))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Simulation results are provided as `numpy.ndarray`s in the returned dictionary:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ts: [ 0. 1.01694915 2.03389831 3.05084746 4.06779661 5.08474576\n", " 6.10169492 7.11864407 8.13559322 9.15254237 10.16949153 11.18644068\n", " 12.20338983 13.22033898 14.23728814 15.25423729 16.27118644 17.28813559\n", " 18.30508475 19.3220339 20.33898305 21.3559322 22.37288136 23.38983051\n", " 24.40677966 25.42372881 26.44067797 27.45762712 28.47457627 29.49152542\n", " 30.50847458 31.52542373 32.54237288 33.55932203 34.57627119 35.59322034\n", " 36.61016949 37.62711864 38.6440678 39.66101695 40.6779661 41.69491525\n", " 42.71186441 43.72881356 44.74576271 45.76271186 46.77966102 47.79661017\n", " 48.81355932 49.83050847 50.84745763 51.86440678 52.88135593 53.89830508\n", " 54.91525424 55.93220339 56.94915254 57.96610169 58.98305085 60. ]\n", " x: [[0.1 0.4 0.7 ]\n", " [0.57995052 0.73365809 0.0951589 ]\n", " [0.55996496 0.71470091 0.0694127 ]\n", " [0.5462855 0.68030366 0.06349394]\n", " [0.53561883 0.64937432 0.05923555]\n", " [0.52636487 0.62259567 0.05568686]\n", " [0.51822013 0.59943346 0.05268079]\n", " [0.51103767 0.57935661 0.05012037]\n", " [0.5047003 0.56191592 0.04793052]\n", " [0.49910666 0.54673518 0.0460508 ]\n", " [0.49416809 0.53349812 0.04443205]\n", " [0.48980687 0.52193767 0.04303399]\n", " [0.48595476 0.51182731 0.04182339]\n", " [0.48255176 0.50297412 0.04077267]\n", " [0.47954511 0.49521318 0.03985882]\n", " [0.47688833 0.48840304 0.03906254]\n", " [0.47454049 0.48242198 0.03836756]\n", " [0.47246548 0.47716502 0.0377601 ]\n", " [0.47063147 0.47254128 0.03722844]\n", " [0.46901037 0.46847202 0.03676259]\n", " [0.46757739 0.46488881 0.03635397]\n", " [0.46631065 0.46173207 0.03599523]\n", " [0.46519082 0.45894987 0.03568002]\n", " [0.46420083 0.45649684 0.03540285]\n", " [0.4633256 0.45433332 0.03515899]\n", " [0.4625518 0.45242457 0.03494429]\n", " [0.46186768 0.45074016 0.03475519]\n", " [0.46126282 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0.0333981 ]\n", " [0.45685771 0.43849478 0.0333906 ]\n", " [0.45683304 0.43843488 0.03338397]\n", " [0.45681123 0.4383819 0.0333781 ]\n", " [0.45679194 0.43833507 0.03337292]\n", " [0.45677488 0.43829365 0.03336833]\n", " [0.4567598 0.43825703 0.03336428]\n", " [0.45674646 0.43822466 0.0333607 ]\n", " [0.45673467 0.43819603 0.03335753]]\n", " x0: [0.1 0.4 0.7]\n", " x_ss: [nan nan nan]\n", " sx: None\n", " sx0: None\n", " sx_ss: None\n", " y: [[0.1 0.4 0.7 0.2 3.4 0.1 ]\n", " [0.57995052 0.73365809 0.0951589 1.15990103 3.73365809 0.57995052]\n", " [0.55996496 0.71470091 0.0694127 1.11992992 3.71470091 0.55996496]\n", " [0.5462855 0.68030366 0.06349394 1.092571 3.68030366 0.5462855 ]\n", " [0.53561883 0.64937432 0.05923555 1.07123766 3.64937432 0.53561883]\n", " [0.52636487 0.62259567 0.05568686 1.05272975 3.62259567 0.52636487]\n", " [0.51822013 0.59943346 0.05268079 1.03644027 3.59943346 0.51822013]\n", " [0.51103767 0.57935661 0.05012037 1.02207533 3.57935661 0.51103767]\n", " 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0.91398641 3.43882387 0.4569932 ]\n", " [0.45695285 0.43872584 0.03341618 0.9139057 3.43872584 0.45695285]\n", " [0.45691717 0.43863917 0.03340658 0.91383433 3.43863917 0.45691717]\n", " [0.45688561 0.43856254 0.0333981 0.91377123 3.43856254 0.45688561]\n", " [0.45685771 0.43849478 0.0333906 0.91371543 3.43849478 0.45685771]\n", " [0.45683304 0.43843488 0.03338397 0.91366609 3.43843488 0.45683304]\n", " [0.45681123 0.4383819 0.0333781 0.91362246 3.4383819 0.45681123]\n", " [0.45679194 0.43833507 0.03337292 0.91358388 3.43833507 0.45679194]\n", " [0.45677488 0.43829365 0.03336833 0.91354976 3.43829365 0.45677488]\n", " [0.4567598 0.43825703 0.03336428 0.9135196 3.43825703 0.4567598 ]\n", " [0.45674646 0.43822466 0.0333607 0.91349292 3.43822466 0.45674646]\n", " [0.45673467 0.43819603 0.03335753 0.91346934 3.43819603 0.45673467]]\n", " sigmay: [[1. 1. 1. 1. 1. 0.2]\n", " [1. 1. 1. 1. 1. 0.2]\n", " [1. 1. 1. 1. 1. 0.2]\n", " [1. 1. 1. 1. 1. 0.2]\n", " [1. 1. 1. 1. 1. 0.2]\n", " [1. 1. 1. 1. 1. 0.2]\n", " [1. 1. 1. 1. 1. 0.2]\n", " [1. 1. 1. 1. 1. 0.2]\n", " [1. 1. 1. 1. 1. 0.2]\n", " [1. 1. 1. 1. 1. 0.2]\n", " [1. 1. 1. 1. 1. 0.2]\n", " [1. 1. 1. 1. 1. 0.2]\n", " [1. 1. 1. 1. 1. 0.2]\n", " [1. 1. 1. 1. 1. 0.2]\n", " [1. 1. 1. 1. 1. 0.2]\n", " [1. 1. 1. 1. 1. 0.2]\n", " [1. 1. 1. 1. 1. 0.2]\n", " [1. 1. 1. 1. 1. 0.2]\n", " [1. 1. 1. 1. 1. 0.2]\n", " [1. 1. 1. 1. 1. 0.2]\n", " [1. 1. 1. 1. 1. 0.2]\n", " [1. 1. 1. 1. 1. 0.2]\n", " [1. 1. 1. 1. 1. 0.2]\n", " [1. 1. 1. 1. 1. 0.2]\n", " [1. 1. 1. 1. 1. 0.2]\n", " [1. 1. 1. 1. 1. 0.2]\n", " [1. 1. 1. 1. 1. 0.2]\n", " [1. 1. 1. 1. 1. 0.2]\n", " [1. 1. 1. 1. 1. 0.2]\n", " [1. 1. 1. 1. 1. 0.2]\n", " [1. 1. 1. 1. 1. 0.2]\n", " [1. 1. 1. 1. 1. 0.2]\n", " [1. 1. 1. 1. 1. 0.2]\n", " [1. 1. 1. 1. 1. 0.2]\n", " [1. 1. 1. 1. 1. 0.2]\n", " [1. 1. 1. 1. 1. 0.2]\n", " [1. 1. 1. 1. 1. 0.2]\n", " [1. 1. 1. 1. 1. 0.2]\n", " [1. 1. 1. 1. 1. 0.2]\n", " [1. 1. 1. 1. 1. 0.2]\n", " [1. 1. 1. 1. 1. 0.2]\n", " [1. 1. 1. 1. 1. 0.2]\n", " [1. 1. 1. 1. 1. 0.2]\n", " [1. 1. 1. 1. 1. 0.2]\n", " [1. 1. 1. 1. 1. 0.2]\n", " [1. 1. 1. 1. 1. 0.2]\n", " [1. 1. 1. 1. 1. 0.2]\n", " [1. 1. 1. 1. 1. 0.2]\n", " [1. 1. 1. 1. 1. 0.2]\n", " [1. 1. 1. 1. 1. 0.2]\n", " [1. 1. 1. 1. 1. 0.2]\n", " [1. 1. 1. 1. 1. 0.2]\n", " [1. 1. 1. 1. 1. 0.2]\n", " [1. 1. 1. 1. 1. 0.2]\n", " [1. 1. 1. 1. 1. 0.2]\n", " [1. 1. 1. 1. 1. 0.2]\n", " [1. 1. 1. 1. 1. 0.2]\n", " [1. 1. 1. 1. 1. 0.2]\n", " [1. 1. 1. 1. 1. 0.2]\n", " [1. 1. 1. 1. 1. 0.2]]\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: [[-2.04603669 0.57163267 2. ]\n", " [ 0.69437133 -0.62836733 2. ]\n", " [ 0.21909801 0.22836733 -3. ]]\n", " xdot: [-1.08967281e-05 -2.64534209e-05 -2.92761862e-06]\n", " status: 0.0\n", " llh: nan\n", " chi2: nan\n", " res: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n", " sres: None\n", " FIM: None\n", " w: [[3.4 0.1 0.2 0.4 0.1 0.7\n", " 0.01 0.02 0.16 1.4 0.7 0.1 ]\n", " [3.73365809 0.57995052 1.15990103 0.73365809 0.57995052 0.0951589\n", " 0.3363426 0.21274269 0.29346324 0.1903178 0.0951589 0.1 ]\n", " [3.71470091 0.55996496 1.11992992 0.71470091 0.55996496 0.0694127\n", " 0.31356076 0.20010373 0.28588036 0.13882541 0.0694127 0.1 ]\n", " [3.68030366 0.5462855 1.092571 0.68030366 0.5462855 0.06349394\n", " 0.29842785 0.18582001 0.27212146 0.12698788 0.06349394 0.1 ]\n", " [3.64937432 0.53561883 1.07123766 0.64937432 0.53561883 0.05923555\n", " 0.28688753 0.17390856 0.25974973 0.1184711 0.05923555 0.1 ]\n", " [3.62259567 0.52636487 1.05272975 0.62259567 0.52636487 0.05568686\n", " 0.27705998 0.16385625 0.24903827 0.11137372 0.05568686 0.1 ]\n", " [3.59943346 0.51822013 1.03644027 0.59943346 0.51822013 0.05268079\n", " 0.26855211 0.15531924 0.23977338 0.10536158 0.05268079 0.1 ]\n", " [3.57935661 0.51103767 1.02207533 0.57935661 0.51103767 0.05012037\n", " 0.2611595 0.14803652 0.23174264 0.10024074 0.05012037 0.1 ]\n", " [3.56191592 0.5047003 1.00940059 0.56191592 0.5047003 0.04793052\n", " 0.25472239 0.14179957 0.22476637 0.09586103 0.04793052 0.1 ]\n", " [3.54673518 0.49910666 0.99821331 0.54673518 0.49910666 0.0460508\n", " 0.24910746 0.13643958 0.21869407 0.0921016 0.0460508 0.1 ]\n", " [3.53349812 0.49416809 0.98833618 0.53349812 0.49416809 0.04443205\n", " 0.2442021 0.13181887 0.21339925 0.08886411 0.04443205 0.1 ]\n", " [3.52193767 0.48980687 0.97961374 0.52193767 0.48980687 0.04303399\n", " 0.23991077 0.12782433 0.20877507 0.08606799 0.04303399 0.1 ]\n", " [3.51182731 0.48595476 0.97190952 0.51182731 0.48595476 0.04182339\n", " 0.23615203 0.12436246 0.20473093 0.08364678 0.04182339 0.1 ]\n", " [3.50297412 0.48255176 0.96510352 0.50297412 0.48255176 0.04077267\n", " 0.2328562 0.12135552 0.20118965 0.08154533 0.04077267 0.1 ]\n", " [3.49521318 0.47954511 0.95909022 0.49521318 0.47954511 0.03985882\n", " 0.22996351 0.11873853 0.19808527 0.07971763 0.03985882 0.1 ]\n", " [3.48840304 0.47688833 0.95377667 0.48840304 0.47688833 0.03906254\n", " 0.22742248 0.11645686 0.19536122 0.07812507 0.03906254 0.1 ]\n", " [3.48242198 0.47454049 0.94908097 0.48242198 0.47454049 0.03836756\n", " 0.22518867 0.11446438 0.19296879 0.07673511 0.03836756 0.1 ]\n", " [3.47716502 0.47246548 0.94493095 0.47716502 0.47246548 0.0377601\n", " 0.22322363 0.112722 0.19086601 0.0755202 0.0377601 0.1 ]\n", " [3.47254128 0.47063147 0.94126293 0.47254128 0.47063147 0.03722844\n", " 0.22149398 0.1111964 0.18901651 0.07445688 0.03722844 0.1 ]\n", " [3.46847202 0.46901037 0.93802074 0.46847202 0.46901037 0.03676259\n", " 0.21997073 0.10985912 0.18738881 0.07352518 0.03676259 0.1 ]\n", " [3.46488881 0.46757739 0.93515478 0.46488881 0.46757739 0.03635397\n", " 0.21862862 0.10868575 0.18595552 0.07270794 0.03635397 0.1 ]\n", " [3.46173207 0.46631065 0.9326213 0.46173207 0.46631065 0.03599523\n", " 0.21744562 0.10765529 0.18469283 0.07199046 0.03599523 0.1 ]\n", " [3.45894987 0.46519082 0.93038164 0.45894987 0.46519082 0.03568002\n", " 0.2164025 0.10674963 0.18357995 0.07136003 0.03568002 0.1 ]\n", " [3.45649684 0.46420083 0.92840166 0.45649684 0.46420083 0.03540285\n", " 0.21548241 0.10595311 0.18259874 0.0708057 0.03540285 0.1 ]\n", " [3.45433332 0.4633256 0.92665119 0.45433332 0.4633256 0.03515899\n", " 0.21467061 0.10525213 0.18173333 0.07031797 0.03515899 0.1 ]\n", " [3.45242457 0.4625518 0.9251036 0.45242457 0.4625518 0.03494429\n", " 0.21395417 0.1046349 0.18096983 0.06988859 0.03494429 0.1 ]\n", " [3.45074016 0.46186768 0.92373536 0.45074016 0.46186768 0.03475519\n", " 0.21332175 0.10409116 0.18029606 0.06951039 0.03475519 0.1 ]\n", " [3.44925337 0.46126282 0.92252564 0.44925337 0.46126282 0.03458856\n", " 0.21276339 0.10361194 0.17970135 0.06917712 0.03458856 0.1 ]\n", " [3.44794075 0.46072804 0.92145608 0.44794075 0.46072804 0.03444166\n", " 0.21227033 0.10318943 0.1791763 0.06888332 0.03444166 0.1 ]\n", " [3.44678168 0.46025521 0.92051041 0.44678168 0.46025521 0.03431212\n", " 0.21183485 0.1028168 0.17871267 0.06862424 0.03431212 0.1 ]\n", " [3.44575804 0.45983714 0.91967427 0.44575804 0.45983714 0.03419784\n", " 0.21145019 0.10248805 0.17830322 0.06839569 0.03419784 0.1 ]\n", " [3.44485388 0.45946749 0.91893498 0.44485388 0.45946749 0.03409701\n", " 0.21111037 0.10219795 0.17794155 0.06819402 0.03409701 0.1 ]\n", " [3.44405514 0.45914065 0.91828131 0.44405514 0.45914065 0.03400802\n", " 0.21081014 0.10194188 0.17762206 0.06801603 0.03400802 0.1 ]\n", " [3.44334947 0.45885167 0.91770333 0.44334947 0.45885167 0.03392946\n", " 0.21054485 0.10171582 0.17733979 0.06785891 0.03392946 0.1 ]\n", " [3.44272595 0.45859615 0.91719229 0.44272595 0.45859615 0.03386009\n", " 0.21031042 0.10151621 0.17709038 0.06772018 0.03386009 0.1 ]\n", " [3.44217497 0.45837021 0.91674042 0.44217497 0.45837021 0.03379883\n", " 0.21010325 0.10133992 0.17686999 0.06759766 0.03379883 0.1 ]\n", " [3.44168805 0.45817043 0.91634087 0.44168805 0.45817043 0.03374473\n", " 0.20992015 0.1011842 0.17667522 0.06748945 0.03374473 0.1 ]\n", " [3.44125772 0.45799379 0.91598758 0.44125772 0.45799379 0.03369693\n", " 0.20975831 0.10104665 0.17650309 0.06739386 0.03369693 0.1 ]\n", " [3.44087738 0.4578376 0.9156752 0.44087738 0.4578376 0.03365471\n", " 0.20961527 0.10092512 0.17635095 0.06730942 0.03365471 0.1 ]\n", " [3.44054121 0.45769949 0.91539898 0.44054121 0.45769949 0.0336174\n", " 0.20948882 0.10081774 0.17621648 0.0672348 0.0336174 0.1 ]\n", " [3.44024405 0.45757737 0.91515474 0.44024405 0.45757737 0.03358444\n", " 0.20937705 0.10072286 0.17609762 0.06716887 0.03358444 0.1 ]\n", " [3.43998137 0.45746939 0.91493878 0.43998137 0.45746939 0.03355531\n", " 0.20927824 0.10063901 0.17599255 0.06711061 0.03355531 0.1 ]\n", " [3.43974917 0.45737391 0.91474782 0.43974917 0.45737391 0.03352956\n", " 0.20919089 0.1005649 0.17589967 0.06705912 0.03352956 0.1 ]\n", " [3.43954389 0.45728948 0.91457897 0.43954389 0.45728948 0.03350681\n", " 0.20911367 0.1004994 0.17581756 0.06701361 0.03350681 0.1 ]\n", " [3.43936242 0.45721483 0.91442966 0.43936242 0.45721483 0.0334867\n", " 0.2090454 0.10044151 0.17574497 0.06697339 0.0334867 0.1 ]\n", " [3.43920198 0.45714882 0.91429764 0.43920198 0.45714882 0.03346892\n", " 0.20898505 0.10039033 0.17568079 0.06693784 0.03346892 0.1 ]\n", " [3.43906014 0.45709045 0.91418091 0.43906014 0.45709045 0.03345321\n", " 0.20893168 0.1003451 0.17562406 0.06690641 0.03345321 0.1 ]\n", " [3.43893474 0.45703884 0.91407768 0.43893474 0.45703884 0.03343932\n", " 0.2088845 0.10030511 0.1755739 0.06687863 0.03343932 0.1 ]\n", " [3.43882387 0.4569932 0.91398641 0.43882387 0.4569932 0.03342704\n", " 0.20884279 0.10026976 0.17552955 0.06685407 0.03342704 0.1 ]\n", " [3.43872584 0.45695285 0.9139057 0.43872584 0.45695285 0.03341618\n", " 0.20880591 0.10023851 0.17549034 0.06683236 0.03341618 0.1 ]\n", " [3.43863917 0.45691717 0.91383433 0.43863917 0.45691717 0.03340658\n", " 0.2087733 0.10021088 0.17545567 0.06681317 0.03340658 0.1 ]\n", " [3.43856254 0.45688561 0.91377123 0.43856254 0.45688561 0.0333981\n", " 0.20874446 0.10018646 0.17542502 0.0667962 0.0333981 0.1 ]\n", " [3.43849478 0.45685771 0.91371543 0.43849478 0.45685771 0.0333906\n", " 0.20871897 0.10016486 0.17539791 0.0667812 0.0333906 0.1 ]\n", " [3.43843488 0.45683304 0.91366609 0.43843488 0.45683304 0.03338397\n", " 0.20869643 0.10014577 0.17537395 0.06676793 0.03338397 0.1 ]\n", " [3.4383819 0.45681123 0.91362246 0.4383819 0.45681123 0.0333781\n", " 0.2086765 0.10012889 0.17535276 0.0667562 0.0333781 0.1 ]\n", " [3.43833507 0.45679194 0.91358388 0.43833507 0.45679194 0.03337292\n", " 0.20865887 0.10011396 0.17533403 0.06674583 0.03337292 0.1 ]\n", " [3.43829365 0.45677488 0.91354976 0.43829365 0.45677488 0.03336833\n", " 0.20864329 0.10010077 0.17531746 0.06673667 0.03336833 0.1 ]\n", " [3.43825703 0.4567598 0.9135196 0.43825703 0.4567598 0.03336428\n", " 0.20862951 0.1000891 0.17530281 0.06672856 0.03336428 0.1 ]\n", " [3.43822466 0.45674646 0.91349292 0.43822466 0.45674646 0.0333607\n", " 0.20861733 0.10007878 0.17528986 0.06672139 0.0333607 0.1 ]\n", " [3.43819603 0.45673467 0.91346934 0.43819603 0.45673467 0.03335753\n", " 0.20860656 0.10006966 0.17527841 0.06671506 0.03335753 0.1 ]]\n", " preeq_wrms: nan\n", " preeq_t: nan\n", "preeq_numlinsteps: None\n", "preeq_numsteps: [[0 0 0]]\n", "preeq_numstepsB: 0.0\n", "preeq_status: [[0 0 0]]\n", "preeq_cpu_time: 0.0\n", "preeq_cpu_timeB: 0.0\n", " posteq_wrms: nan\n", " posteq_t: nan\n", "posteq_numlinsteps: None\n", "posteq_numsteps: [[0 0 0]]\n", "posteq_numstepsB: 0.0\n", "posteq_status: [[0 0 0]]\n", "posteq_cpu_time: 0.0\n", "posteq_cpu_timeB: 0.0\n", " numsteps: [ 0 100 144 165 181 191 200 207 213 218 223 228 233 237 241 245 249 252\n", " 255 258 261 264 266 269 272 275 278 282 286 290 293 296 299 303 307 311\n", " 314 317 321 325 328 333 337 340 342 344 346 348 350 352 354 356 358 359\n", " 360 361 362 363 364 365]\n", " numrhsevals: [ 0 114 160 193 212 227 237 248 255 260 267 272 277 282 287 292 296 300\n", " 303 306 309 312 315 318 322 325 329 333 337 342 345 348 352 358 365 369\n", " 372 376 381 385 389 395 400 403 405 407 409 411 413 415 417 419 421 422\n", " 424 426 427 428 429 430]\n", "numerrtestfails: [0 1 1 3 3 4 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 6 6 6\n", " 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6]\n", "numnonlinsolvconvfails: [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", " order: [0 5 5 5 5 5 4 5 4 5 4 4 4 4 4 4 5 5 5 5 5 5 5 5 4 4 5 5 5 4 4 4 5 5 5 4 4\n", " 4 4 5 5 4 5 5 5 5 5 5 5 4 4 4 4 4 4 4 4 4 4 4]\n", " cpu_time: 3.176\n", " numstepsB: None\n", "numrhsevalsB: None\n", "numerrtestfailsB: None\n", "numnonlinsolvconvfailsB: None\n", " cpu_timeB: 0.0\n" ] } ], "source": [ "# np.set_printoptions(threshold=8, edgeitems=2)\n", "for key, value in rdata.items():\n", " print(f\"{key:12s}\", value)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "# In particular for interactive use, ReturnDataView.by_id() and amici.evaluate provides a more convenient way to access slices of the result:\n", "# Time trajectory of observable observable_x1\n", "print(f\"{rdata.by_id('observable_x1')=}\")\n", "# Time trajectory of state variable x2\n", "print(f\"{rdata.by_id('x2')=}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": "Alternatively, those data can be accessed through `ReturnData.xr.*` as [xarray.DataArray](https://docs.xarray.dev/en/stable/index.html) objects, that contain additional metadata such as timepoints and identifiers. This allows for more convenient indexing and plotting of the results." }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "rdata.xr.x" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "rdata.xr.x.to_pandas()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plotting trajectories\n", "\n", "The simulation results above did not look too appealing. Let's plot the trajectories of the model states and outputs them using `matplotlib.pyplot`:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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\n", 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" }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import amici.plotting\n", "\n", "amici.plotting.plot_state_trajectories(rdata, model=None)\n", "amici.plotting.plot_observable_trajectories(rdata, model=None)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "We can also evaluate symbolic expressions of model quantities using `amici.numpy.evaluate`, or directly plot the results using `amici.plotting.plot_expressions`:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "amici.plotting.plot_expressions(\n", " \"observable_x1 + observable_x2 + observable_x3\", rdata=rdata\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Computing likelihood\n", "\n", "Often model parameters need to be inferred from experimental data. This is commonly done by maximizing the likelihood of observing the data given to current model parameters. AMICI will compute this likelihood if experimental data is provided to `amici.runAmiciSimulation` as optional third argument. Measurements along with their standard deviations are provided through an `amici.ExpData` instance." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Create model instance and set time points for simulation\n", "model = model_module.get_model()\n", "model.set_timepoints(np.linspace(0, 10, 11))\n", "\n", "# Create solver instance, keep default options\n", "solver = model.create_solver()\n", "\n", "# Run simulation without experimental data\n", "rdata = amici.run_simulation(model, solver)\n", "\n", "# Create ExpData instance from simulation results\n", "edata = amici.ExpData(rdata, 1.0, 0.0)\n", "\n", "# Re-run simulation, this time passing \"experimental data\"\n", "rdata = amici.run_simulation(model, solver, edata)\n", "\n", "print(f\"Log-likelihood {rdata['llh']:f}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": "The provided measurements can be visualized together with the simulation results by passing the `ExpData` to `amici.plotting.plot_observable_trajectories`:" }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "amici.plotting.plot_observable_trajectories(rdata, edata=edata)\n", "plt.legend(loc=\"center left\", bbox_to_anchor=(1.04, 0.5))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Simulation tolerances\n", "Numerical error tolerances are often critical to get accurate results. For the state variables, integration errors can be controlled using `set_relative_tolerance` and `set_absolute_tolerance`. Similar functions exist for sensitivities, steady states and quadratures. We initially compute a reference solution using extremely low tolerances and then assess the influence on integration error for different levels of absolute and relative tolerance." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "solver.set_relative_tolerance(1e-16)\n", "solver.set_absolute_tolerance(1e-16)\n", "solver.set_sensitivity_order(amici.SensitivityOrder.none)\n", "rdata_ref = amici.run_simulation(model, solver, edata)\n", "\n", "\n", "def get_simulation_error(solver):\n", " rdata = amici.run_simulation(model, solver, edata)\n", " return np.mean(np.abs(rdata[\"x\"] - rdata_ref[\"x\"])), np.mean(\n", " np.abs(rdata[\"llh\"] - rdata_ref[\"llh\"])\n", " )\n", "\n", "\n", "def get_errors(tolfun, tols):\n", " solver.set_relative_tolerance(1e-16)\n", " solver.set_absolute_tolerance(1e-16)\n", " x_errs = []\n", " llh_errs = []\n", " for tol in tols:\n", " getattr(solver, tolfun)(tol)\n", " x_err, llh_err = get_simulation_error(solver)\n", " x_errs.append(x_err)\n", " llh_errs.append(llh_err)\n", " return x_errs, llh_errs\n", "\n", "\n", "atols = np.logspace(-5, -15, 100)\n", "atol_x_errs, atol_llh_errs = get_errors(\"set_absolute_tolerance\", atols)\n", "\n", "rtols = np.logspace(-5, -15, 100)\n", "rtol_x_errs, rtol_llh_errs = get_errors(\"set_relative_tolerance\", rtols)\n", "\n", "fig, axes = plt.subplots(1, 2, figsize=(15, 5))\n", "\n", "\n", "def plot_error(tols, x_errs, llh_errs, tolname, ax):\n", " ax.plot(tols, x_errs, \"r-\", label=\"x\")\n", " ax.plot(tols, llh_errs, \"b-\", label=\"llh\")\n", " ax.set_xscale(\"log\")\n", " ax.set_yscale(\"log\")\n", " ax.set_xlabel(f\"{tolname} tolerance\")\n", " ax.set_ylabel(\"average numerical error\")\n", " ax.legend()\n", "\n", "\n", "plot_error(atols, atol_x_errs, atol_llh_errs, \"absolute\", axes[0])\n", "plot_error(rtols, rtol_x_errs, rtol_llh_errs, \"relative\", axes[1])\n", "\n", "# reset relative tolerance to default value\n", "solver.set_relative_tolerance(1e-8)\n", "solver.set_absolute_tolerance(1e-16)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Sensitivity analysis\n", "\n", "AMICI can provide first- and second-order sensitivities using the forward- or adjoint-method. The respective options are set on the Model and Solver objects." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Forward sensitivity analysis" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "model = model_module.get_model()\n", "model.set_timepoints(np.linspace(0, 10, 11))\n", "model.require_sensitivities_for_all_parameters() # sensitivities w.r.t. all parameters\n", "# model.set_parameter_list([1, 2]) # sensitivities\n", "# w.r.t. the specified parameters\n", "model.set_parameter_scale(\n", " amici.ParameterScaling.none\n", ") # parameters are used as-is (not log-transformed)\n", "\n", "solver = model.create_solver()\n", "solver.set_sensitivity_method(\n", " amici.SensitivityMethod.forward\n", ") # forward sensitivity analysis\n", "solver.set_sensitivity_order(\n", " amici.SensitivityOrder.first\n", ") # first-order sensitivities\n", "\n", "rdata = amici.run_simulation(model, solver)\n", "\n", "# print sensitivity-related results\n", "for key, value in rdata.items():\n", " if key.startswith(\"s\"):\n", " print(f\"{key:12s}\", value)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Adjoint sensitivity analysis" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Set model options\n", "model = model_module.get_model()\n", "p_orig = np.array(model.get_parameters())\n", "p_orig[\n", " list(model.get_parameter_ids()).index(\"observable_x1withsigma_sigma\")\n", "] = 0.1 # Change default parameter\n", "model.set_parameters(p_orig)\n", "model.set_parameter_scale(amici.ParameterScaling.none)\n", "model.set_timepoints(np.linspace(0, 10, 21))\n", "\n", "solver = model.create_solver()\n", "solver.set_max_steps(10**4) # Set maximum number of steps for the solver\n", "\n", "# simulate time-course to get artificial data\n", "rdata = amici.run_simulation(model, solver)\n", "edata = amici.ExpData(rdata, 1.0, 0)\n", "edata.fixed_parameters = model.get_fixed_parameters()\n", "# set sigma to 1.0 except for observable 5, so that p[7] is used instead\n", "# (if we have sigma parameterized, the corresponding ExpData entries must NaN, otherwise they will override the parameter)\n", "edata.set_observed_data_std_dev(\n", " rdata[\"t\"] * 0 + np.nan,\n", " list(model.get_observable_ids()).index(\"observable_x1withsigma\"),\n", ")\n", "\n", "# enable sensitivities\n", "solver.set_sensitivity_order(amici.SensitivityOrder.first) # First-order ...\n", "solver.set_sensitivity_method(\n", " amici.SensitivityMethod.adjoint\n", ") # ... adjoint sensitivities\n", "model.require_sensitivities_for_all_parameters() # ... w.r.t. all parameters\n", "\n", "# compute adjoint sensitivities\n", "rdata = amici.run_simulation(model, solver, edata)\n", "# print(rdata['sigmay'])\n", "print(f\"Log-likelihood: {rdata['llh']}\")\n", "print(f\"Gradient: {rdata['sllh']}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Finite differences gradient check\n", "\n", "Compare AMICI-computed gradient with finite differences" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from scipy.optimize import check_grad\n", "\n", "\n", "def func(x0, symbol=\"llh\", x0full=None, plist=[], verbose=False):\n", " p = x0[:]\n", " if len(plist):\n", " p = x0full[:]\n", " p[plist] = x0\n", " verbose and print(f\"f: p={p}\")\n", "\n", " old_parameters = model.get_parameters()\n", " solver.set_sensitivity_order(amici.SensitivityOrder.none)\n", " model.set_parameters(p)\n", " rdata = amici.run_simulation(model, solver, edata)\n", "\n", " model.set_parameters(old_parameters)\n", "\n", " res = np.sum(rdata[symbol])\n", " verbose and print(res)\n", " return res\n", "\n", "\n", "def grad(x0, symbol=\"llh\", x0full=None, plist=[], verbose=False):\n", " p = x0[:]\n", " if len(plist):\n", " model.set_parameter_list(plist)\n", " p = x0full[:]\n", " p[plist] = x0\n", " else:\n", " model.require_sensitivities_for_all_parameters()\n", " verbose and print(f\"g: p={p}\")\n", "\n", " old_parameters = model.get_parameters()\n", " solver.set_sensitivity_method(amici.SensitivityMethod.forward)\n", " solver.set_sensitivity_order(amici.SensitivityOrder.first)\n", " model.set_parameters(p)\n", " rdata = amici.run_simulation(model, solver, edata)\n", "\n", " model.set_parameters(old_parameters)\n", "\n", " res = rdata[f\"s{symbol}\"]\n", " if not isinstance(res, float):\n", " if len(res.shape) == 3:\n", " res = np.sum(res, axis=(0, 2))\n", " verbose and print(res)\n", " return res\n", "\n", "\n", "epsilon = 1e-4\n", "err_norm = check_grad(func, grad, p_orig, \"llh\", epsilon=epsilon)\n", "print(f\"sllh: |error|_2: {err_norm:f}\")\n", "# assert err_norm < 1e-6\n", "print()\n", "\n", "for ip in range(model.np()):\n", " plist = [ip]\n", " p = p_orig.copy()\n", " err_norm = check_grad(\n", " func, grad, p[plist], \"llh\", p, [ip], epsilon=epsilon\n", " )\n", " print(f\"sllh: p[{ip:d}]: |error|_2: {err_norm:f}\")\n", "\n", "print()\n", "for ip in range(model.np()):\n", " plist = [ip]\n", " p = p_orig.copy()\n", " err_norm = check_grad(func, grad, p[plist], \"y\", p, [ip], epsilon=epsilon)\n", " print(f\"sy: p[{ip}]: |error|_2: {err_norm:f}\")\n", "\n", "print()\n", "for ip in range(model.np()):\n", " plist = [ip]\n", " p = p_orig.copy()\n", " err_norm = check_grad(func, grad, p[plist], \"x\", p, [ip], epsilon=epsilon)\n", " print(f\"sx: p[{ip}]: |error|_2: {err_norm:f}\")\n", "\n", "print()\n", "for ip in range(model.np()):\n", " plist = [ip]\n", " p = p_orig.copy()\n", " err_norm = check_grad(\n", " func, grad, p[plist], \"sigmay\", p, [ip], epsilon=epsilon\n", " )\n", " print(f\"ssigmay: p[{ip}]: |error|_2: {err_norm:f}\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "eps = 1e-4\n", "op = model.get_parameters()\n", "\n", "\n", "solver.set_sensitivity_method(\n", " amici.SensitivityMethod.forward\n", ") # forward sensitivity analysis\n", "solver.set_sensitivity_order(\n", " amici.SensitivityOrder.first\n", ") # first-order sensitivities\n", "model.require_sensitivities_for_all_parameters()\n", "solver.set_relative_tolerance(1e-12)\n", "rdata = amici.run_simulation(model, solver, edata)\n", "\n", "\n", "def fd(x0, ip, eps, symbol=\"llh\"):\n", " p = list(x0[:])\n", " old_parameters = model.get_parameters()\n", " solver.set_sensitivity_order(amici.SensitivityOrder.none)\n", " p[ip] += eps\n", " model.set_parameters(p)\n", " rdata_f = amici.run_simulation(model, solver, edata)\n", " p[ip] -= 2 * eps\n", " model.set_parameters(p)\n", " rdata_b = amici.run_simulation(model, solver, edata)\n", "\n", " model.set_parameters(old_parameters)\n", " return (rdata_f[symbol] - rdata_b[symbol]) / (2 * eps)\n", "\n", "\n", "def plot_sensitivities(symbol, eps):\n", " fig, axes = plt.subplots(4, 2, figsize=(15, 10))\n", " for ip in range(4):\n", " fd_approx = fd(model.get_parameters(), ip, eps, symbol=symbol)\n", "\n", " axes[ip, 0].plot(\n", " edata.get_timepoints(), rdata[f\"s{symbol}\"][:, ip, :], \"r-\"\n", " )\n", " axes[ip, 0].plot(edata.get_timepoints(), fd_approx, \"k--\")\n", " axes[ip, 0].set_ylabel(f\"sensitivity {symbol}\")\n", " axes[ip, 0].set_xlabel(\"time\")\n", "\n", " axes[ip, 1].plot(\n", " edata.get_timepoints(),\n", " np.abs(rdata[f\"s{symbol}\"][:, ip, :] - fd_approx),\n", " \"k-\",\n", " )\n", " axes[ip, 1].set_ylabel(\"difference to fd\")\n", " axes[ip, 1].set_xlabel(\"time\")\n", " axes[ip, 1].set_yscale(\"log\")\n", "\n", " plt.tight_layout()\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plot_sensitivities(\"x\", eps)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plot_sensitivities(\"y\", eps)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Export as DataFrame\n", "\n", "Experimental data and simulation results can both be exported as pandas Dataframe to allow for an easier inspection of numeric values" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# run the simulation\n", "rdata = amici.run_simulation(model, solver, edata)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# look at the ExpData as DataFrame\n", "df = amici.get_data_observables_as_data_frame(model, [edata])\n", "df" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# from the exported dataframe, we can actually reconstruct a copy of the ExpData instance\n", "reconstructed_edata = amici.get_edata_from_data_frame(model, df)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# look at the States in rdata as DataFrame\n", "amici.get_residuals_as_data_frame(model, [edata], [rdata])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# look at the Observables in rdata as DataFrame\n", "amici.get_simulation_observables_as_data_frame(model, [edata], [rdata])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# look at the States in rdata as DataFrame\n", "amici.get_simulation_states_as_data_frame(model, [edata], [rdata])" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.7" }, "nbsphinx": { "execute": "always" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 2 }