Use IDs and names from ReturnData

CHANGELOG.md

@@ -67,6 +67,8 @@ See also our [versioning policy](https://amici.readthedocs.io/en/latest/versioni
 * Model output directory keyword arguments have been harmonized:
   What was previously `model_output_dir`, `output_dir`, `outdir` is now
   consistently called `output_dir` across the API.
+* Plotting functions have been moved from `amici.plotting` to
+  `amici.sim.sundials.plotting`. The `model` argument has been removed.
 
 **Features**
 

doc/examples/example_errors.ipynb

@@ -252,11 +252,10 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "42b79264344ba95f",
    "metadata": {},
+   "cell_type": "code",
    "outputs": [],
+   "execution_count": null,
    "source": [
     "# Create a copy of this simulation condition\n",
     "edata = ExpData(res[EDATAS][0])\n",
@@ -265,9 +264,10 @@
     "rdata = run_simulation(amici_model, amici_solver, edata)\n",
     "\n",
     "# Visualize state trajectories\n",
-    "plot_state_trajectories(rdata, model=amici_model)\n",
+    "plot_state_trajectories(rdata)\n",
     "plt.yscale(\"log\")"
-   ]
+   ],
+   "id": "fcebaf5d7951e1"
   },
   {
    "cell_type": "markdown",

doc/examples/example_petab/petab_v2.ipynb

@@ -113,7 +113,7 @@
    "source": [
     "rdata = result.rdatas[0]\n",
     "edata = result.edatas[0]\n",
-    "plot_observable_trajectories(rdata, model=simulator.model, edata=edata)"
+    "plot_observable_trajectories(rdata, edata=edata)"
    ],
    "id": "d6bbefc2e3d3ccbc"
   },
@@ -197,8 +197,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "plot_observable_trajectories(rdata, model=model, edata=edata)\n",
-    "plot_state_trajectories(rdata, model=model)"
+    "plot_observable_trajectories(rdata, edata=edata)\n",
+    "plot_state_trajectories(rdata)"
    ]
   },
   {

doc/examples/example_steady_states/ExampleEquilibrationLogic.ipynb

@@ -233,10 +233,10 @@
     "\n",
     "# plot trajectories\n",
     "fig, axes = plt.subplots(2, 2, figsize=(8, 8))\n",
-    "plot_state_trajectories(rdata, model=model, ax=axes[0, 0])\n",
-    "plot_observable_trajectories(rdata, model=model, ax=axes[1, 0])\n",
-    "plot_state_trajectories(rdata_reduced, model=model_reduced, ax=axes[0, 1])\n",
-    "plot_observable_trajectories(rdata_reduced, model=model_reduced, ax=axes[1, 1])\n",
+    "plot_state_trajectories(rdata, ax=axes[0, 0])\n",
+    "plot_observable_trajectories(rdata, ax=axes[1, 0])\n",
+    "plot_state_trajectories(rdata_reduced, ax=axes[0, 1])\n",
+    "plot_observable_trajectories(rdata_reduced, ax=axes[1, 1])\n",
     "fig.tight_layout()"
    ]
   },
@@ -386,7 +386,7 @@
     "timepoints = np.linspace(0, 14, 200)\n",
     "model.set_timepoints(timepoints)\n",
     "rdata = run_simulation(model, solver)\n",
-    "plot_state_trajectories(rdata, model=model)\n",
+    "plot_state_trajectories(rdata)\n",
     "\n",
     "for stst_value in steady_state:\n",
     "    plt.axhline(y=stst_value, color=\"gray\", linestyle=\"--\", linewidth=1)"
@@ -501,8 +501,8 @@
     "print(\"Initial state used for simulation:\", rdata_reduced[\"x0\"])\n",
     "\n",
     "\n",
-    "plot_state_trajectories(rdata_reduced, model=model_reduced)\n",
-    "plot_observable_trajectories(rdata_reduced, model=model_reduced)"
+    "plot_state_trajectories(rdata_reduced)\n",
+    "plot_observable_trajectories(rdata_reduced)"
    ]
   },
   {

doc/examples/getting_started/GettingStarted.ipynb

@@ -21,14 +21,15 @@
   },
   {
    "cell_type": "code",
+   "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
     "import amici\n",
+    "import numpy as np\n",
     "\n",
     "sbml_importer = amici.SbmlImporter(\"model_steadystate_scaled.xml\")"
-   ],
-   "outputs": [],
-   "execution_count": null
+   ]
   },
   {
    "cell_type": "markdown",
@@ -39,14 +40,14 @@
   },
   {
    "cell_type": "code",
+   "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
     "model_name = \"model_steadystate\"\n",
     "model_dir = \"model_dir\"\n",
     "sbml_importer.sbml2amici(model_name, model_dir)"
-   ],
-   "outputs": [],
-   "execution_count": null
+   ]
   },
   {
    "cell_type": "markdown",
@@ -58,17 +59,17 @@
   },
   {
    "cell_type": "code",
+   "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
     "# load the model module\n",
     "model_module = amici.import_model_module(model_name, model_dir)\n",
     "# instantiate model\n",
     "model = model_module.get_model()\n",
     "# instantiate solver\n",
     "solver = model.create_solver()"
-   ],
-   "outputs": [],
-   "execution_count": null
+   ]
   },
   {
    "cell_type": "markdown",
@@ -77,10 +78,12 @@
   },
   {
    "cell_type": "code",
+   "execution_count": null,
    "metadata": {},
-   "source": "model.set_free_parameter_by_name(\"p1\", 1e-3)",
    "outputs": [],
-   "execution_count": null
+   "source": [
+    "model.set_free_parameter_by_name(\"p1\", 1e-3)"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -89,10 +92,12 @@
   },
   {
    "cell_type": "code",
+   "execution_count": null,
    "metadata": {},
-   "source": "solver.set_absolute_tolerance(1e-10)",
    "outputs": [],
-   "execution_count": null
+   "source": [
+    "solver.set_absolute_tolerance(1e-10)"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -108,14 +113,14 @@
   },
   {
    "cell_type": "code",
+   "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
     "# set timepoints\n",
-    "model.set_timepoints([0, 1])\n",
+    "model.set_timepoints(np.linspace(0, 10, 101))\n",
     "rdata = model.simulate(solver=solver)"
-   ],
-   "outputs": [],
-   "execution_count": null
+   ]
   },
   {
    "cell_type": "markdown",
@@ -124,36 +129,51 @@
   },
   {
    "cell_type": "code",
+   "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
     "rdata.x"
-   ],
-   "outputs": [],
-   "execution_count": null
+   ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": "All results attributes are always ordered according to the model. For species, this means that the columns of `rdata.x` match the ordering of species in the model, which can be accessed as [Model.get_state_names](https://amici.readthedocs.io/en/latest/generated/amici.sim.sundials.html#amici.sim.sundials.Model.get_state_names)"
+   "source": "All results attributes are always ordered according to the model. For species, this means that the columns of `rdata.x` match the ordering of species in the model, which can be accessed as [Model.get_state_names](https://amici.readthedocs.io/en/latest/generated/amici.sim.sundials.html#amici.sim.sundials.Model.get_state_names) or as `ReturnData.state_names`:"
   },
   {
    "cell_type": "code",
+   "execution_count": null,
    "metadata": {},
-   "source": "model.get_state_names()",
    "outputs": [],
-   "execution_count": null
+   "source": [
+    "model.get_state_names()"
+   ]
   },
   {
-   "metadata": {},
    "cell_type": "markdown",
+   "metadata": {},
    "source": "For convenience, most results stored in `ReturnData` can also be retrieved as [xarray.DataArray](https://docs.xarray.dev/en/stable/index.html) objects that already include the respective row and column names. This can be accessed via the `xr` attribute of `ReturnData`. Here, we access the model state `x` as `DataArray` object to convert it to a `pandas.DataFrame`:"
   },
   {
+   "cell_type": "code",
+   "execution_count": null,
    "metadata": {},
+   "outputs": [],
+   "source": [
+    "rdata.xr.x.to_pandas()"
+   ]
+  },
+  {
    "cell_type": "code",
-   "source": "rdata.xr.x.to_pandas()",
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
-   "execution_count": null
+   "source": [
+    "from amici.sim.sundials.plotting import *\n",
+    "\n",
+    "plot_state_trajectories(rdata)"
+   ]
   },
   {
    "cell_type": "markdown",