Prepare for amici>=1.0.0

Author: dweindl

doc/example/amici.ipynb

@@ -108,18 +108,18 @@
     "# load amici module (the usual starting point later for the analysis)\n",
     "sys.path.insert(0, os.path.abspath(model_output_dir))\n",
     "model_module = importlib.import_module(model_name)\n",
-    "model = model_module.getModel()\n",
-    "model.requireSensitivitiesForAllParameters()\n",
-    "model.setTimepoints(np.linspace(0, 10, 11))\n",
-    "model.setParameterScale(amici.ParameterScaling.log10)\n",
-    "model.setParameters([-0.3, -0.7])\n",
-    "solver = model.getSolver()\n",
-    "solver.setSensitivityMethod(amici.SensitivityMethod.forward)\n",
-    "solver.setSensitivityOrder(amici.SensitivityOrder.first)\n",
+    "model = model_module.get_model()\n",
+    "model.require_sensitivities_for_all_parameters()\n",
+    "model.set_timepoints(np.linspace(0, 10, 11))\n",
+    "model.set_parameter_scale(amici.ParameterScaling.log10)\n",
+    "model.set_parameters([-0.3, -0.7])\n",
+    "solver = model.create_solver()\n",
+    "solver.set_sensitivity_method(amici.SensitivityMethod.forward)\n",
+    "solver.set_sensitivity_order(amici.SensitivityOrder.first)\n",
     "\n",
     "# how to run amici now:\n",
-    "rdata = amici.runAmiciSimulation(model, solver, None)\n",
-    "amici.plotting.plotStateTrajectories(rdata)\n",
+    "rdata = amici.run_simulation(model, solver, None)\n",
+    "amici.plotting.plot_state_trajectories(rdata)\n",
     "edata = amici.ExpData(rdata, 0.2, 0.0)"
    ]
   },
@@ -329,7 +329,7 @@
     ")\n",
     "\n",
     "# run prediction\n",
-    "prediction = predictor(x=model.getUnscaledParameters())"
+    "prediction = predictor(x=model.get_unscaled_parameters())"
    ]
   },
   {

doc/example/conversion_reaction.ipynb

@@ -523,7 +523,7 @@
    "outputs": [],
    "source": [
     "# Set gradient computation method to adjoint\n",
-    "problem.objective.amici_solver.setSensitivityMethod(\n",
+    "problem.objective.amici_solver.set_sensitivity_method(\n",
     "    amici.SensitivityMethod.adjoint\n",
     ")"
    ]

doc/example/getting_started.ipynb

@@ -17,21 +17,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
    "metadata": {},
-   "outputs": [],
    "source": [
     "# install if not done yet\n",
     "# !apt install libatlas-base-dev swig\n",
     "# %pip install pypesto[amici,petab] --quiet\n",
     "# %pip install git+https://github.com/Benchmarking-Initiative/Benchmark-Models-PEtab.git@master#subdirectory=src/python --quiet"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "code",
-   "execution_count": null,
    "metadata": {},
-   "outputs": [],
    "source": [
     "import os\n",
     "from pprint import pprint\n",
@@ -43,7 +41,9 @@
     "import pypesto.optimize as optimize\n",
     "import pypesto.petab\n",
     "import pypesto.visualize as visualize"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "markdown",
@@ -68,11 +68,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
    "metadata": {
     "scrolled": true
    },
-   "outputs": [],
    "source": [
     "# a collection of models that can be simulated\n",
     "\n",
@@ -92,7 +90,9 @@
     "\n",
     "# create a petab problem\n",
     "petab_problem = petab.Problem.from_yaml(yaml_config)"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "markdown",
@@ -110,23 +110,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
    "metadata": {
     "scrolled": true
    },
-   "outputs": [],
    "source": [
     "importer = pypesto.petab.PetabImporter(petab_problem, simulator_type=\"amici\")\n",
     "factory = importer.create_objective_creator()\n",
     "\n",
     "model = factory.create_model(verbose=False)\n",
     "\n",
     "# some model properties\n",
-    "print(\"Model parameters:\", list(model.getParameterIds()), \"\\n\")\n",
-    "print(\"Model const parameters:\", list(model.getFixedParameterIds()), \"\\n\")\n",
-    "print(\"Model outputs:   \", list(model.getObservableIds()), \"\\n\")\n",
-    "print(\"Model states:    \", list(model.getStateIds()), \"\\n\")"
-   ]
+    "print(\"Model parameters:\", list(model.get_parameter_ids()), \"\\n\")\n",
+    "print(\"Model const parameters:\", list(model.get_fixed_parameter_ids()), \"\\n\")\n",
+    "print(\"Model outputs:   \", list(model.get_observable_ids()), \"\\n\")\n",
+    "print(\"Model states:    \", list(model.get_state_ids()), \"\\n\")"
+   ],
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "markdown",
@@ -151,15 +151,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
    "metadata": {},
-   "outputs": [],
    "source": [
     "importer = pypesto.petab.PetabImporter.from_yaml(\n",
     "    yaml_config, simulator_type=\"amici\"\n",
     ")\n",
     "problem = importer.create_problem()  # creating the problem from the importer. The objective can be found at problem.objective"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "markdown",
@@ -170,11 +170,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
    "metadata": {
     "scrolled": true
    },
-   "outputs": [],
    "source": [
     "import libsbml\n",
     "\n",
@@ -185,10 +183,12 @@
     "obj = factory.create_objective()\n",
     "\n",
     "# for some models, hyperparameters need to be adjusted\n",
-    "# obj.amici_solver.setMaxSteps(10000)\n",
-    "# obj.amici_solver.setRelativeTolerance(1e-7)\n",
-    "# obj.amici_solver.setAbsoluteTolerance(1e-7)"
-   ]
+    "# obj.amici_solver.set_max_steps(10000)\n",
+    "# obj.amici_solver.set_relative_tolerance(1e-7)\n",
+    "# obj.amici_solver.set_absolute_tolerance(1e-7)"
+   ],
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "markdown",
@@ -199,9 +199,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
    "metadata": {},
-   "outputs": [],
    "source": [
     "ret = obj(\n",
     "    petab_problem.x_nominal_scaled,\n",
@@ -210,7 +208,9 @@
     "    return_dict=True,\n",
     ")\n",
     "pprint(ret)"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "markdown",
@@ -221,12 +221,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
    "metadata": {},
-   "outputs": [],
    "source": [
     "problem = importer.create_problem(obj)"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "markdown",
@@ -237,13 +237,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
    "metadata": {},
-   "outputs": [],
    "source": [
     "print(f\"{problem.x_fixed_indices=}\")\n",
     "print(f\"{problem.x_free_indices=}\")"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "markdown",
@@ -254,22 +254,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
    "metadata": {},
-   "outputs": [],
    "source": [
     "objective = problem.objective\n",
     "fval, gradient = objective(\n",
     "    petab_problem.x_nominal_free_scaled, sensi_orders=(0, 1)\n",
     ")\n",
     "print(f\"{fval=}\\n{gradient=}\")"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "code",
-   "execution_count": null,
    "metadata": {},
-   "outputs": [],
    "source": [
     "eps = 1e-4\n",
     "\n",
@@ -291,7 +289,9 @@
     "for i, (g, f) in enumerate(zip(gradient, fdval)):\n",
     "    print(f\"{i=}: {g=:9f},\\t{f=:9f},\\t{g - f=:9f}\")\n",
     "print(f\"l2 difference: {np.linalg.norm(gradient - fdval):.2e}\")"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "markdown",
@@ -309,11 +309,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
    "metadata": {
     "scrolled": true
    },
-   "outputs": [],
    "source": [
     "optimizer = optimize.ScipyOptimizer()\n",
     "\n",
@@ -324,7 +322,9 @@
     "result = optimize.minimize(\n",
     "    problem=problem, optimizer=optimizer, n_starts=10, engine=engine\n",
     ")"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "markdown",
@@ -342,19 +342,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
    "metadata": {
     "collapsed": false,
     "jupyter": {
      "outputs_hidden": false
     }
    },
-   "outputs": [],
    "source": [
     "problem = importer.create_problem(\n",
     "    startpoint_kwargs={\"check_fval\": True, \"check_grad\": True}\n",
     ")"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "markdown",
@@ -372,12 +372,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
    "metadata": {},
-   "outputs": [],
    "source": [
     "result.optimize_result.fval"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "markdown",
@@ -388,17 +388,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
    "metadata": {},
-   "outputs": [],
    "source": [
     "ref = visualize.create_references(\n",
     "    x=petab_problem.x_nominal_scaled, fval=obj(petab_problem.x_nominal_scaled)\n",
     ")\n",
     "\n",
     "visualize.waterfall(result, reference=ref, scale_y=\"lin\")\n",
     "visualize.parameters(result, reference=ref);"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "markdown",
@@ -413,21 +413,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
    "metadata": {
     "pycharm": {
      "name": "#%%\n"
     }
    },
-   "outputs": [],
    "source": [
     "# we need to explicitly import the method\n",
     "from pypesto.visualize.model_fit import visualize_optimized_model_fit\n",
     "\n",
     "visualize_optimized_model_fit(\n",
     "    petab_problem=petab_problem, result=result, pypesto_problem=problem\n",
     ");"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": null
   }
  ],
  "metadata": {