From 4b28aa3a4c4a2bdf5dca0b034f075dd92459e392 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Anna=20St=C3=B6riko?= <a.storiko@tudelft.nl>
Date: Mon, 8 Jun 2026 18:26:47 +0200
Subject: [PATCH 1/4] Add explaining text to the pathogen transport example

TODO: Make sure that units and parameter values are consistent
throughout the notebook.
---
 notebooks/pathogen-model.ipynb | 349 +++++++++++++++++++++++++--------
 1 file changed, 269 insertions(+), 80 deletions(-)

diff --git a/notebooks/pathogen-model.ipynb b/notebooks/pathogen-model.ipynb
index 0ac01db..3aede63 100644
--- a/notebooks/pathogen-model.ipynb
+++ b/notebooks/pathogen-model.ipynb
@@ -9,25 +9,49 @@
    "source": [
     "import jax\n",
     "import jax.numpy as jnp\n",
-    "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
     "\n",
-    "from reactix import (\n",
-    "    Advection,\n",
-    "    Cells,\n",
-    "    Dispersion,\n",
-    "    FixedConcentrationBoundary,\n",
-    "    TransportSystem,\n",
-    "    make_solver,\n",
-    "    declare_species,\n",
-    "    KineticReaction,\n",
-    "    reaction,\n",
-    "    SpatiallyConst,\n",
-    "    SpatiallyVarying,\n",
-    "    user_system_parameters\n",
-    ")\n",
+    "import reactix as rx\n",
+    "\n",
+    "jax.config.update(\"jax_enable_x64\", True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "beb577fd",
+   "metadata": {},
+   "source": [
+    "# Pathogen transport model\n",
+    "\n",
+    "This notebook demonstrates how to set up a reactive-transport model that simulates transport of a pathogen (modeled as a colloid) in porous media. The model couples advective-dispersive transport with attachment, detachment, and decay processes.\n",
+    "\n",
+    "## Model equations\n",
+    "\n",
+    "The transport of mobile pathogens and attached pathogens is governed by the following system of coupled partial differential equations, assuming constant coefficients.\n",
     "\n",
-    "from dataclasses import dataclass"
+    "Mobile phase pathogen (aqueous concentration, mol/L of water):\n",
+    "\n",
+    "$$\\frac{\\partial C_m}{\\partial t} + \\frac{\\rho_b}{\\theta}\\frac{\\partial S}{\\partial t} = -v\\frac{\\partial C_m}{\\partial x} + D\\frac{\\partial^2 C_m}{\\partial x^2} - k_a C_m + k_d \\frac{S}{\\rho_b/\\theta} - \\lambda_m C_m$$\n",
+    "\n",
+    "Solid phase pathogen (attached concentration, mol/kg of solids):\n",
+    "\n",
+    "$$\\frac{\\partial S}{\\partial t} = k_a \\frac{\\theta}{\\rho_b} C_m - k_d S - \\lambda_s S$$\n",
+    "\n",
+    "Where:\n",
+    "- $C_m$ = mobile pathogen concentration (mol/L of water)\n",
+    "- $S$ = attached pathogen concentration (mol/kg of solids)\n",
+    "- $\\theta$ = porosity (fraction)\n",
+    "- $\\rho_b$ = bulk density of porous medium (kg/m³)\n",
+    "- $v$ = pore water velocity (m/s)\n",
+    "- $D$ = hydrodynamic dispersion coefficient (m²/s)\n",
+    "- $k_a$ = attachment rate coefficient (1/s)\n",
+    "- $k_d$ = detachment rate coefficient (1/s)\n",
+    "- $\\lambda_m$ = decay rate for mobile pathogens (1/s)\n",
+    "- $\\lambda_s$ = decay rate for attached pathogens (1/s)\n",
+    "\n",
+    "## Simulated species\n",
+    "\n",
+    "The following code set up three species that will be used in the simulation – mobile and attached pathogens, and a conservative tracer for comparison. The second line indicates for each species if transport calculations are carried out – for the attached pathogens this is deactivated.\n"
    ]
   },
   {
@@ -37,10 +61,65 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "Species = declare_species([\"tracer\", \"mobile_pathogen\", \"attached_pathogen\"])\n",
+    "Species = rx.declare_species([\"tracer\", \"mobile_pathogen\", \"attached_pathogen\"])\n",
     "species_is_mobile = Species(tracer=True, mobile_pathogen=True, attached_pathogen=False)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "ad80ee54",
+   "metadata": {},
+   "source": [
+    "## System-wide parameters\n",
+    "\n",
+    "Converting between solid-phase and aqueous-phase concentrations in the reaction terms requires knowing the porosity and bulk density of the porous medium.\n",
+    "These are both system-wide properties rather than being specific to one reaction.\n",
+    "While the porosity is a native property of a `System` in Reactix (because it is also required for transport calculations), the bulk density is, by default, not stored in the `System`\n",
+    "\n",
+    "However, users can define a custom class that holds system-wide parameters and pass it to the `System`. Parameter `x` can be accessed as `system.parameters.x` where `system` is an `System` object.\n",
+    "\n",
+    "The custom parameter class needs to be defined using the `user_system_parameters` decorator. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c3af3f9a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "@rx.user_system_parameters\n",
+    "class SystemParameters:\n",
+    "    # Provide parameter names as attributes of the class\n",
+    "    solid_density: jax.Array\n",
+    "\n",
+    "    # Parameters can also be calculated from other system parameters\n",
+    "    def bulk_density(self, system):\n",
+    "        return (1 - system.porosity) * self.solid_density"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ba7b12df",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a SystemParameters instance to store the parameter values\n",
+    "# Solid density typical for quartz sand: 2.65 g/cm³\n",
+    "system_parameters = SystemParameters(solid_density=rx.SpatiallyConst(jnp.array(2.65)))  # g/cm³"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "82519479",
+   "metadata": {},
+   "source": [
+    "## Reaction kinetics and stoichiometry\n",
+    "\n",
+    "Each reaction term is defined in a separate class, providing a custom rate expression and the stoichiometric coefficients. Note how we can use the same variable name for the decay parameter of the decay reaction of attached and mobile pathogens because they are contained by different classes."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -48,8 +127,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "@reaction\n",
-    "class MobilePathogenDecay(KineticReaction):\n",
+    "@rx.reaction\n",
+    "class MobilePathogenDecay(rx.KineticReaction):\n",
     "    decay_coefficient: jax.Array\n",
     "\n",
     "    def rate(self, time, state, system):\n",
@@ -60,8 +139,8 @@
     "            \"mobile_pathogen\": -1,\n",
     "        }\n",
     "\n",
-    "@reaction\n",
-    "class AttachedPathogenDecay(KineticReaction):\n",
+    "@rx.reaction\n",
+    "class AttachedPathogenDecay(rx.KineticReaction):\n",
     "    decay_coefficient: jax.Array\n",
     "\n",
     "    def rate(self, time, state, system):\n",
@@ -72,8 +151,8 @@
     "            \"attached_pathogen\": -1,\n",
     "        }\n",
     "\n",
-    "@reaction\n",
-    "class Attachment(KineticReaction):\n",
+    "@rx.reaction\n",
+    "class Attachment(rx.KineticReaction):\n",
     "    attachment_coefficient: jax.Array\n",
     "\n",
     "    def rate(self, time, state, system):\n",
@@ -86,8 +165,8 @@
     "            \"attached_pathogen\": system.porosity / bulk_density\n",
     "        }\n",
     "    \n",
-    "@reaction\n",
-    "class Detachment(KineticReaction):\n",
+    "@rx.reaction\n",
+    "class Detachment(rx.KineticReaction):\n",
     "    detachment_coefficient: jax.Array\n",
     "\n",
     "    def rate(self, time, state, system):\n",
@@ -102,44 +181,36 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "add50723",
+   "cell_type": "markdown",
+   "id": "8079009a",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "n_cells = 200\n",
-    "reactions = [\n",
-    "    Attachment(attachment_coefficient=0.02),\n",
-    "    Detachment(detachment_coefficient=1e-2),\n",
-    "    MobilePathogenDecay(decay_coefficient=1e-5),\n",
-    "    AttachedPathogenDecay(decay_coefficient=1e-4),\n",
-    "]"
+    "After defining the form of the reaction rates in the reaction classes, specific parameter values are provided when creating an instance of each reaction class."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "01746b6c",
+   "id": "add50723",
    "metadata": {},
    "outputs": [],
    "source": [
-    "@user_system_parameters\n",
-    "class SystemParameters:\n",
-    "    solid_density: jax.Array\n",
-    "\n",
-    "    def bulk_density(self, system):\n",
-    "        return (1 - system.porosity) * self.solid_density"
+    "reactions = [\n",
+    "    Attachment(attachment_coefficient=0.005),  # 1/hr\n",
+    "    Detachment(detachment_coefficient=5e-5),  # 1/hr\n",
+    "    MobilePathogenDecay(decay_coefficient=1e-5),  # 1/hr\n",
+    "    AttachedPathogenDecay(decay_coefficient=5e-5),  # 1/hr\n",
+    "]"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "9e965afb",
+   "cell_type": "markdown",
+   "id": "1a1233f6",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "system_parameters = SystemParameters(solid_density=SpatiallyConst(jnp.array(2.65)))  # g/cm3"
+    "## Discretization and transport parameters\n",
+    "\n",
+    "The simulation is set up on a uniform grid, and parameters and options for the dispersion and advection terms are provided."
    ]
   },
   {
@@ -149,47 +220,90 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "jax.config.update(\"jax_enable_x64\", True)\n",
-    "interface_areas = jnp.ones(n_cells + 1)\n",
-    "#interface_areas = interface_areas.at[100:].set(2)\n",
-    "cells = Cells.equally_spaced(10, n_cells, interface_area=interface_areas)\n",
-    "dispersion = Dispersion.build(\n",
+    "n_cells = 200\n",
+    "column_length = 10.0  # meters\n",
+    "interface_areas = jnp.ones(n_cells + 1)  # Uniform cross-sectional area\n",
+    "cells = rx.Cells.equally_spaced(column_length, n_cells, interface_area=interface_areas)\n",
+    "\n",
+    "\n",
+    "dispersion = rx.Dispersion.build(\n",
     "    cells=cells,\n",
-    "    dispersivity=jnp.array(0.1),\n",
+    "    dispersivity=jnp.array(0.1),  # m\n",
     "    pore_diffusion=Species(\n",
     "        tracer=jnp.array(1e-9 * 3600 * 24),\n",
     "        mobile_pathogen=jnp.array(1e-9 * 3600 * 24),\n",
     "        attached_pathogen=jnp.array(1e-9 * 3600 * 24),\n",
     "    ),\n",
     ")\n",
-    "advection = Advection.build(\n",
+    "advection = rx.Advection.build(\n",
     "    limiter_type=\"minmod\",\n",
-    ")\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b7ba644e",
+   "metadata": {},
+   "source": [
+    "## Boundary conditions\n",
+    "\n",
+    "The model simulates a pulse injection experiment. A rectangular pulse of pathogen-bearing water is injected from the left boundary, followed by a flushing period with pathogen-free water.\n",
+    "\n",
+    "In total, we need to set up four boundary conditions – one per side for the tracer and the mobile pathogens. Since the attached pathogens are not transported, no boundary condition is needed for them.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "45181e68",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t_stop_injection = 4000.0\n",
     "bcs = [\n",
-    "    FixedConcentrationBoundary(\n",
+    "    rx.FixedConcentrationBoundary(\n",
     "        boundary=\"left\",\n",
     "        species_selector=lambda s: getattr(s, \"tracer\"),\n",
-    "        fixed_concentration=lambda t: jnp.array(10.0),\n",
+    "        fixed_concentration=lambda t: jnp.select([t < t_stop_injection], [jnp.array(10.0)], default=jnp.array(0.0)),\n",
     "    ),\n",
-    "    FixedConcentrationBoundary(\n",
+    "    rx.FixedConcentrationBoundary(\n",
     "        boundary=\"right\",\n",
     "        species_selector=lambda s: getattr(s, \"tracer\"),\n",
     "        fixed_concentration=lambda t: jnp.array(0.0),\n",
     "    ),\n",
-    "    FixedConcentrationBoundary(\n",
+    "    rx.FixedConcentrationBoundary(\n",
     "        boundary=\"left\",\n",
     "        species_selector=lambda s: getattr(s, \"mobile_pathogen\"),\n",
-    "        fixed_concentration=lambda t: jnp.array(10.0),\n",
+    "        fixed_concentration=lambda t: jnp.select([t < t_stop_injection], [jnp.array(10.0)], default=jnp.array(0.0)),\n",
     "    ),\n",
-    "    FixedConcentrationBoundary(\n",
+    "    rx.FixedConcentrationBoundary(\n",
     "        boundary=\"right\",\n",
     "        species_selector=lambda s: getattr(s, \"mobile_pathogen\"),\n",
     "        fixed_concentration=lambda t: jnp.array(0.0),\n",
     "    )\n",
-    "]\n",
+    "]\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1732a3d1",
+   "metadata": {},
+   "source": [
+    "## System set-up\n",
+    "\n",
+    "All previously defined components of the model are now passed to a `System` object to set up the model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7ec6bc86",
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "\n",
     "porosity= jnp.ones(n_cells) * 0.3\n",
-    "system = TransportSystem.build(\n",
+    "system = rx.TransportSystem.build(\n",
     "    porosity=porosity,\n",
     "    # velocity=lambda t: jnp.array(1 / 365) * jnp.sin(np.pi * 2 * 1 / 5000 * t),\n",
     "    discharge=lambda t: jnp.array(1 / 365) * 0.3,\n",
@@ -203,6 +317,16 @@
     ")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "85d731ef",
+   "metadata": {},
+   "source": [
+    "## Initial conditions\n",
+    "\n",
+    "Lastly, all concentrations are set to zero throughout the domain as the initial value."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -210,18 +334,23 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "t_points = jnp.linspace(0, 8000, 123)\n",
-    "solver = make_solver(t_max=8000, t_points=t_points, rtol=1e-3, atol=1e-3)\n",
-    "val0 = jnp.zeros(cells.n_cells)\n",
-    "#val0 = val0.at[slice(10,20)].set(10.0)\n",
-    "\n",
-    "state = Species(\n",
-    "    tracer=val0,\n",
-    "    mobile_pathogen=val0,\n",
-    "    attached_pathogen=val0\n",
+    "zeros = jnp.zeros(cells.n_cells)\n",
+    "\n",
+    "initial_state = Species(\n",
+    "    tracer=zeros,\n",
+    "    mobile_pathogen=zeros,\n",
+    "    attached_pathogen=zeros\n",
     ")\n"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "252471c9",
+   "metadata": {},
+   "source": [
+    "## Provide solver options and run the simulation"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -229,10 +358,26 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "solution = solver(state, system)\n",
+    "t_points = jnp.linspace(0, 8000, 123)\n",
+    "solver = rx.make_solver(t_max=8000, t_points=t_points, rtol=1e-3, atol=1e-3)\n",
+    "solution = solver(initial_state, system)\n",
     "#%timeit solution = solver(state, system)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "75f177ae",
+   "metadata": {},
+   "source": [
+    "## Simulation results\n",
+    "\n",
+    "This section presents the results of the reactive-transport simulation, showing how the three species (conservative tracer, mobile pathogen, and attached pathogen) evolve in space and time under the imposed pulse-injection boundary conditions.\n",
+    "\n",
+    "### Tracer concentration profiles at several times\n",
+    "\n",
+    "The first plot shows how the conservative tracer moves quickly through the entire domain."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -241,18 +386,54 @@
    "outputs": [],
    "source": [
     "plt.plot(cells.centers[:], solution.ys.tracer.T[:,0::10]);\n",
-    "plt.show()"
+    "plt.xlabel(\"Distance (m)\")\n",
+    "plt.ylabel(\"Tracer Concentration\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "73c8f0c4",
+   "metadata": {},
+   "source": [
+    "### Mobile pathogen concentration profiles\n",
+    "\n",
+    "In contrast to the conservative tracer, the mobile pathogens are subject to attachment and filtration on the porous medium surfaces. The plot below shows significant attenuation in the first several meters of the domain, where the high attachment coefficient leads to rapid removal from the mobile phase."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "e193781c",
+   "id": "eba0f796",
    "metadata": {},
    "outputs": [],
    "source": [
-    "plt.plot(cells.centers[:], solution.ys.attached_pathogen.T[:,0::10]);\n",
-    "plt.show()"
+    "plt.plot(cells.centers[:], solution.ys.mobile_pathogen.T[:, 0::10]);\n",
+    "plt.xlabel(\"Distance (m)\")\n",
+    "plt.ylabel(\"Mobile Pathogen Concentration\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "424c7b6f",
+   "metadata": {},
+   "source": [
+    "### Breakthrough curve of the mobile pathogens\n",
+    "\n",
+    "The breakthrough curve (BTC) plots the concentration of mobile pathogens at a fixed location (5 m into the column) as a function of time.\n",
+    "After the stop of the injection, it shows the typical tailing behaviour caused by detachment of pathogens from the solid phase."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "202350f1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.plot(solution.ts, solution.ys.mobile_pathogen.T[5,:]);\n",
+    "plt.xlabel(\"Time\")\n",
+    "plt.ylabel(\"Mobile Pathogen Concentration\")\n",
+    "#plt.yscale(\"log\")"
    ]
   },
   {
@@ -280,11 +461,19 @@
     "ani = animation.ArtistAnimation(fig=fig, artists=artists, interval=40)\n",
     "plt.show()"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4ebcb174",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "default",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -298,7 +487,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.13.5"
+   "version": "3.13.9"
   }
  },
  "nbformat": 4,

From c103e1f681854200b5d692f97621a6d8349dd9b2 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Anna=20St=C3=B6riko?= <a.storiko@tudelft.nl>
Date: Thu, 2 Jul 2026 11:37:00 +0200
Subject: [PATCH 2/4] Update parameter values and plots in pathogen transport
 example
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Parameter values are now roughly oriented at the ones in Schijven et el.
(1999)

Schijven, J. F., Hoogenboezem, W., Hassanizadeh, M., & Peters, J. H. (1999). Modeling removal of bacteriophages MS2 and PRD1 by dune recharge at Castricum, Netherlands. Water Resources Research, 35(4), 1101–1111. https://doi.org/10.1029/1998WR900108
---
 notebooks/pathogen-model.ipynb | 45 ++++++++++++++++++----------------
 1 file changed, 24 insertions(+), 21 deletions(-)

diff --git a/notebooks/pathogen-model.ipynb b/notebooks/pathogen-model.ipynb
index 3aede63..927b948 100644
--- a/notebooks/pathogen-model.ipynb
+++ b/notebooks/pathogen-model.ipynb
@@ -42,12 +42,12 @@
     "- $S$ = attached pathogen concentration (mol/kg of solids)\n",
     "- $\\theta$ = porosity (fraction)\n",
     "- $\\rho_b$ = bulk density of porous medium (kg/m³)\n",
-    "- $v$ = pore water velocity (m/s)\n",
-    "- $D$ = hydrodynamic dispersion coefficient (m²/s)\n",
-    "- $k_a$ = attachment rate coefficient (1/s)\n",
-    "- $k_d$ = detachment rate coefficient (1/s)\n",
-    "- $\\lambda_m$ = decay rate for mobile pathogens (1/s)\n",
-    "- $\\lambda_s$ = decay rate for attached pathogens (1/s)\n",
+    "- $v$ = pore water velocity (m/d)\n",
+    "- $D$ = hydrodynamic dispersion coefficient (m²/d)\n",
+    "- $k_a$ = attachment rate coefficient (1/d)\n",
+    "- $k_d$ = detachment rate coefficient (1/d)\n",
+    "- $\\lambda_m$ = decay rate for mobile pathogens (1/d)\n",
+    "- $\\lambda_s$ = decay rate for attached pathogens (1/d)\n",
     "\n",
     "## Simulated species\n",
     "\n",
@@ -196,10 +196,10 @@
    "outputs": [],
    "source": [
     "reactions = [\n",
-    "    Attachment(attachment_coefficient=0.005),  # 1/hr\n",
-    "    Detachment(detachment_coefficient=5e-5),  # 1/hr\n",
-    "    MobilePathogenDecay(decay_coefficient=1e-5),  # 1/hr\n",
-    "    AttachedPathogenDecay(decay_coefficient=5e-5),  # 1/hr\n",
+    "    Attachment(attachment_coefficient=1),  # 1/d\n",
+    "    Detachment(detachment_coefficient=1e-3),  # 1/d\n",
+    "    MobilePathogenDecay(decay_coefficient=5e-2),  # 1/d\n",
+    "    AttachedPathogenDecay(decay_coefficient=5e-2),  # 1/d\n",
     "]"
    ]
   },
@@ -228,9 +228,9 @@
     "\n",
     "dispersion = rx.Dispersion.build(\n",
     "    cells=cells,\n",
-    "    dispersivity=jnp.array(0.1),  # m\n",
+    "    dispersivity=jnp.array(0.05),  # m\n",
     "    pore_diffusion=Species(\n",
-    "        tracer=jnp.array(1e-9 * 3600 * 24),\n",
+    "        tracer=jnp.array(1e-9 * 3600 * 24), # m²/d\n",
     "        mobile_pathogen=jnp.array(1e-9 * 3600 * 24),\n",
     "        attached_pathogen=jnp.array(1e-9 * 3600 * 24),\n",
     "    ),\n",
@@ -259,7 +259,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "t_stop_injection = 4000.0\n",
+    "t_stop_injection = 20\n",
     "bcs = [\n",
     "    rx.FixedConcentrationBoundary(\n",
     "        boundary=\"left\",\n",
@@ -306,7 +306,7 @@
     "system = rx.TransportSystem.build(\n",
     "    porosity=porosity,\n",
     "    # velocity=lambda t: jnp.array(1 / 365) * jnp.sin(np.pi * 2 * 1 / 5000 * t),\n",
-    "    discharge=lambda t: jnp.array(1 / 365) * 0.3,\n",
+    "    discharge=lambda t: jnp.array(1),\n",
     "    cells=cells,\n",
     "    advection=advection,\n",
     "    dispersion=dispersion,\n",
@@ -358,8 +358,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "t_points = jnp.linspace(0, 8000, 123)\n",
-    "solver = rx.make_solver(t_max=8000, t_points=t_points, rtol=1e-3, atol=1e-3)\n",
+    "t_points = jnp.linspace(0, 100, 300)\n",
+    "solver = rx.make_solver(t_max=100, t_points=t_points, rtol=1e-6, atol=1e-8)\n",
     "solution = solver(initial_state, system)\n",
     "#%timeit solution = solver(state, system)"
    ]
@@ -385,7 +385,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "plt.plot(cells.centers[:], solution.ys.tracer.T[:,0::10]);\n",
+    "plt.plot(cells.centers[:], solution.ys.tracer.T[:,:120:3]);\n",
     "plt.xlabel(\"Distance (m)\")\n",
     "plt.ylabel(\"Tracer Concentration\")"
    ]
@@ -407,9 +407,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "plt.plot(cells.centers[:], solution.ys.mobile_pathogen.T[:, 0::10]);\n",
+    "plt.plot(cells.centers[:], solution.ys.mobile_pathogen.T[:, 0:30]);\n",
     "plt.xlabel(\"Distance (m)\")\n",
-    "plt.ylabel(\"Mobile Pathogen Concentration\")"
+    "plt.ylabel(\"Mobile Pathogen Concentration\")\n",
+    "#plt.yscale(\"log\")\n",
+    "#plt.ylim(bottom=1e-3)"
    ]
   },
   {
@@ -430,10 +432,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "plt.plot(solution.ts, solution.ys.mobile_pathogen.T[5,:]);\n",
+    "plt.plot(solution.ts, solution.ys.mobile_pathogen.T[100,:]);\n",
     "plt.xlabel(\"Time\")\n",
     "plt.ylabel(\"Mobile Pathogen Concentration\")\n",
-    "#plt.yscale(\"log\")"
+    "plt.yscale(\"log\")\n",
+    "plt.ylim(bottom=1e-4)"
    ]
   },
   {

From 93d3b6e45eb3b91e6860f8057acc81896a1653fa Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Anna=20St=C3=B6riko?= <a.storiko@tudelft.nl>
Date: Thu, 9 Jul 2026 12:08:15 +0200
Subject: [PATCH 3/4] Start document the PyMC example notebook

---
 notebooks/transport_with_pymc.ipynb | 277 +++++++++++++++++++++++-----
 1 file changed, 235 insertions(+), 42 deletions(-)

diff --git a/notebooks/transport_with_pymc.ipynb b/notebooks/transport_with_pymc.ipynb
index 8be59ca..5955f88 100644
--- a/notebooks/transport_with_pymc.ipynb
+++ b/notebooks/transport_with_pymc.ipynb
@@ -1,5 +1,22 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "c1aa613a",
+   "metadata": {},
+   "source": [
+    "# Parameter estimation with PyMC\n",
+    "\n",
+    "This notebook demonstrates how Reactix can be coupled with PyMC for Bayesian inference of reaction parameter values.\n",
+    "\n",
+    "[PyMC](https://www.pymc.io/) is a library for Bayesian modeling and parameter inference. It can be used to define the prior distributions and likelihood, and offers a range of modern samplers, among them the No-U-turn sampler (NUTS). Using this sampler requires the computation of gradients of the posterior with respect to parameters. Since Reactix is built on JAX, this can be achieved through automatic differentiation. All this is done under the hood by PyMC, so the user only needs to define the model and run the sampler.\n",
+    "\n",
+    "This notebooks shows a toy example with a simple first-order decay reaction coupled to advective-dispersive transport.\n",
+    "Based on an artificial concentration measurement, we will estimate the decay constant.\n",
+    "\n",
+    "## Package imports"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -7,6 +24,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "# Ensure that that the ODE solver return NaN values when it fails instead of raising an error.\n",
+    "# This is important to prevent the NUTS sampler from crashing when the ODE solver fails to integrate the system of equations,\n",
+    "# but rather registering a divergence for the given parameter combination.\n",
     "%env EQX_ON_ERROR=nan"
    ]
   },
@@ -22,6 +42,8 @@
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
     "import dataclasses\n",
+    "import pandas as pd\n",
+    "import xarray as xr\n",
     "\n",
     "from reactix import (\n",
     "    Advection,\n",
@@ -33,20 +55,7 @@
     "    declare_species,\n",
     "    KineticReaction,\n",
     "    reaction,\n",
-    "    SpatiallyConst,\n",
-    "    SpatiallyVarying\n",
     ")\n",
-    "\n",
-    "from dataclasses import dataclass"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "06bc296f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
     "import pytensor.tensor as pt\n",
     "import pytensor\n",
     "import pymc as pm\n",
@@ -60,10 +69,25 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "\n",
+    "# Enable 64-bit floating-point precision in JAX\n",
+    "# See here: https://docs.kidger.site/diffrax/usage/how-to-choose-a-solver/#stiff-problems\n",
     "jax.config.update(\"jax_enable_x64\", True)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "3ee0ad2f",
+   "metadata": {},
+   "source": [
+    "## Define the reactive-transport model\n",
+    "\n",
+    "In this example, we will use a simple reactive transport model with a reactive compound that is degraded with first-order decay kinetics.\n",
+    "\n",
+    "### Reaction kinetics\n",
+    "\n",
+    "We define a custom first-order decay reaction with Reactix:"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -86,6 +110,19 @@
     "Species = declare_species([\"tracer\", \"reactive_tracer\"])"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "2752e270",
+   "metadata": {},
+   "source": [
+    "### Transport system\n",
+    "\n",
+    "We set up a system with constant flow velocity and uniform discretization. At the inflow boundary, a constant-concentration boundary is applied with a time-invariant inflow concentration.\n",
+    "\n",
+    "To keep the code in the PyMC model definition concise, we define a function for setting up the reactive-transport system that takes all the parameters that we want to estimate as inputs. In this case, we want to estimate the first-order decay constant.\n",
+    "Note that `make_system` takes an argument `decay_coefficient` which is then passed as a value to the `FirstOrderDecay` reaction object."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -94,6 +131,7 @@
    "outputs": [],
    "source": [
     "def make_system(decay_coefficient):\n",
+    "    # Define the set of species and reactions\n",
     "    species_is_mobile = Species(tracer=True, reactive_tracer=True)\n",
     "    n_cells = 200\n",
     "    reactions = [\n",
@@ -101,8 +139,11 @@
     "            decay_coefficient=decay_coefficient\n",
     "        )\n",
     "    ]\n",
+    "    # Define the cell geometry\n",
     "    interface_areas = jnp.ones(n_cells + 1)\n",
     "    cells = Cells.equally_spaced(10, n_cells, interface_area=interface_areas)\n",
+    "\n",
+    "    # Set transport process options\n",
     "    dispersion = Dispersion.build(\n",
     "        cells=cells,\n",
     "        dispersivity=jnp.array(0.1),\n",
@@ -114,6 +155,7 @@
     "    advection = Advection.build(\n",
     "        limiter_type=\"upwind\",\n",
     "    )\n",
+    "    # Define the boundary conditions\n",
     "    bcs = [\n",
     "        FixedConcentrationBoundary(\n",
     "            boundary=\"left\",\n",
@@ -137,6 +179,7 @@
     "        )\n",
     "    ]\n",
     "\n",
+    "    # Set up the system\n",
     "    porosity= jnp.ones(n_cells) * 0.3\n",
     "    #porosity = porosity.at[100:].set(0.1)\n",
     "    return TransportSystem.build(\n",
@@ -152,6 +195,48 @@
     "    )"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "a3b11448",
+   "metadata": {},
+   "source": [
+    "## Define the PyMC model\n",
+    "\n",
+    "### Measurement point\n",
+    "\n",
+    "Below, we create an concentration data point that will be used for parameter estimation.\n",
+    "We set a concentration of 0.5 at the 40th cell at the 20th time point.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2da0d311",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "idx_time = np.array([20,])\n",
+    "idx_space = np.array([40,])\n",
+    "\n",
+    "measurement = xr.DataArray(\n",
+    "    data=[0.5],\n",
+    "    name=\"concentration_measurement\",\n",
+    "    dims=\"measurement_nr\",\n",
+    "    coords={\"measurement_nr\": pd.RangeIndex(len(idx_time))}\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c17ef2e7",
+   "metadata": {},
+   "source": [
+    "### Passing coordinates and defining prior distributions\n",
+    "\n",
+    "PyMC return an `InferenceData` object with named arrays that have dimensions and coordinates (based on Xarray).\n",
+    "In order to save the time as a coordinate for the simulated concentrations, we need to create a dictionary with all the coodinates."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -160,18 +245,62 @@
    "outputs": [],
    "source": [
     "t_points = jnp.linspace(0, 8000, 123)\n",
+    "\n",
     "coords = {\n",
     "    \"time_dense\": np.array(t_points),\n",
-    "}\n",
+    "    **measurement.coords\n",
+    "}\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2939797b",
+   "metadata": {},
+   "source": [
+    "We can then pass these coordinates to the PyMC model. The first step in the model is to define prior distributions for all parameters that we would like to estimate.\n",
+    "\n",
+    "Here, we chose a half-normal distribution with a standard deviation of 0.01 for the decay coefficient and pass it to the function that builds the reactive-transport system.\n",
+    "\n",
+    "Finally, we also add the cell center locations as a coordinate to the PyMC `model`. This can only be done after creating the `system`, when the cell geometry is known."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "96e3a15e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "\n",
     "with pm.Model(coords=coords) as model:\n",
     "    decay_coefficient = pm.HalfNormal(\"k_dec\", 0.01)\n",
     "    system = make_system(decay_coefficient)\n",
     "    model.add_coord(\"x\", np.array(system.cells.centers))\n",
-    "    val0 = jnp.zeros(system.cells.n_cells)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6b096196",
+   "metadata": {},
+   "source": [
+    "### Setting up the solver"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "605c6710",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "with model:\n",
+    "    # Initial conditions: all species start at zero concentration\n",
+    "    zeros = jnp.zeros(system.cells.n_cells)\n",
     "    initial_state = Species(\n",
-    "        tracer=val0,\n",
-    "        reactive_tracer=val0\n",
+    "        tracer=zeros,\n",
+    "        reactive_tracer=zeros\n",
     "    )\n",
     "    \n",
     "    @pytensor.wrap_jax\n",
@@ -179,8 +308,36 @@
     "        solve_fn_dense = make_solver(t_max=8000, t_points=t_points, rtol=1e-6, atol=1e-6)\n",
     "        return solve_fn_dense(y0, system).ys\n",
     "    \n",
-    "    solution = solve_pt(initial_state, system)\n",
+    "    solution = solve_pt(initial_state, system)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "359f9a7e",
+   "metadata": {},
+   "source": [
+    "### Saving outputs and defining the likelihood"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "737d3152",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t_points[idx_time]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fdbefc0a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "\n",
+    "with model:\n",
     "    fields = dataclasses.fields(solution)\n",
     "    for field in fields:\n",
     "        pm.Deterministic(\n",
@@ -188,7 +345,15 @@
     "            getattr(solution, field.name),\n",
     "            dims=(\"time_dense\", \"x\"),\n",
     "        )\n",
-    "    pm.Normal(\"made_up_point\", mu=solution.reactive_tracer[20, 40], sigma=0.1, observed=0.5)\n"
+    "    pm.Normal(\"made_up_point\", mu=solution.reactive_tracer[idx_time, idx_space], sigma=0.1, observed=measurement.values, dims=(\"measurement_nr\"))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5eeb5d52",
+   "metadata": {},
+   "source": [
+    "## Sample the prior distribution"
    ]
   },
   {
@@ -202,96 +367,124 @@
     "    prior = pm.sample_prior_predictive(draws=50)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "7d25cdb3",
+   "metadata": {},
+   "source": [
+    "### Prior predictive checks\n",
+    "\n",
+    "#### Concentration profile at the time of measurement"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "fca11dc7",
+   "id": "276fcb22",
    "metadata": {},
    "outputs": [],
    "source": [
-    "compiled = nutpie.compile_pymc_model(model, backend=\"jax\", gradient_backend=\"jax\")\n",
-    "sampler = nutpie.sample(compiled, chains=2, blocking=False)"
+    "prior.prior.reactive_tracer.isel(chain=0, time_dense=idx_time[0]).plot.line(x=\"x\", hue=\"draw\", add_legend=False);\n",
+    "plt.scatter(system.cells.centers[idx_space], measurement.values, color=\"k\", label=\"measurement\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4ce025a8",
+   "metadata": {},
+   "source": [
+    "#### Breakthrough curve at the measurement location"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "d492fbb1",
+   "id": "c8813155",
    "metadata": {},
    "outputs": [],
    "source": [
-    "trace = sampler.inspect()\n"
+    "prior.prior.reactive_tracer.isel(chain=0, x=idx_space[0]).plot.line(x=\"time_dense\", hue=\"draw\", add_legend=False);\n",
+    "plt.scatter(t_points[idx_time], prior.observed_data.made_up_point, color=\"k\", label=\"measurement\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a9159ded",
+   "metadata": {},
+   "source": [
+    "## Sample the posterior distribution"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "c9230034",
+   "id": "fca11dc7",
    "metadata": {},
    "outputs": [],
    "source": [
-    "sampler.abort()"
+    "compiled = nutpie.compile_pymc_model(model, backend=\"jax\", gradient_backend=\"jax\")\n",
+    "sampler = nutpie.sample(compiled, chains=2, blocking=False)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "35abfdbf",
+   "id": "d492fbb1",
    "metadata": {},
    "outputs": [],
    "source": [
-    "trace.warmup_sample_stats.diverging.plot.line(x=\"draw\", hue=\"chain\")"
+    "trace = sampler.inspect()\n"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "9c1c306d",
+   "id": "c9230034",
    "metadata": {},
    "outputs": [],
    "source": [
-    "trace.warmup_posterior.k_dec.isel(chain=0, draw=slice(100, None)).plot.line(x=\"draw\")"
+    "sampler.abort()"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "63a3c605",
+   "id": "35abfdbf",
    "metadata": {},
    "outputs": [],
    "source": [
-    "trace.warmup_sample_stats.step_size.plot.line(x=\"draw\")\n",
-    "plt.yscale(\"log\")"
+    "trace.warmup_sample_stats.diverging.plot.line(x=\"draw\", hue=\"chain\")"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "a12de2d8",
+   "id": "9c1c306d",
    "metadata": {},
    "outputs": [],
    "source": [
-    "trace.warmup_posterior.reactive_tracer.isel(chain=0, time_dense=20).plot.line(x=\"x\", hue=\"draw\", add_legend=False);"
+    "trace.warmup_posterior.k_dec.plot.line(x=\"draw\")"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "430a404d",
+   "id": "63a3c605",
    "metadata": {},
    "outputs": [],
    "source": [
-    "prior.prior.reactive_tracer.isel(chain=0, time_dense=20).plot.line(x=\"x\", hue=\"draw\", add_legend=False);"
+    "trace.warmup_sample_stats.step_size.plot.line(x=\"draw\")\n",
+    "plt.yscale(\"log\")"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "6895010e",
+   "id": "a12de2d8",
    "metadata": {},
    "outputs": [],
    "source": [
-    "prior.prior.reactive_tracer.isel(chain=0, x=-1).plot.line(x=\"time_dense\", hue=\"draw\", add_legend=False);"
+    "trace.warmup_posterior.reactive_tracer.isel(chain=0, time_dense=20).plot.line(x=\"x\", hue=\"draw\", add_legend=False);"
    ]
   },
   {
@@ -305,7 +498,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "reactix",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -319,7 +512,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.15"
+   "version": "3.13.9"
   }
  },
  "nbformat": 4,

From 514caaa21d607db787eaf7561f84440d131cc673 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Anna=20St=C3=B6riko?= <a.storiko@tudelft.nl>
Date: Thu, 23 Jul 2026 10:56:19 +0200
Subject: [PATCH 4/4] Finish tidying the notebook with the PyMC example

---
 notebooks/transport_with_pymc.ipynb | 343 ++++++++++++++++++++++------
 1 file changed, 269 insertions(+), 74 deletions(-)

diff --git a/notebooks/transport_with_pymc.ipynb b/notebooks/transport_with_pymc.ipynb
index 5955f88..7a64ba6 100644
--- a/notebooks/transport_with_pymc.ipynb
+++ b/notebooks/transport_with_pymc.ipynb
@@ -19,10 +19,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "a015cae5-632b-451f-a107-3902af5dfe97",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "env: EQX_ON_ERROR=nan\n"
+     ]
+    }
+   ],
    "source": [
     "# Ensure that that the ODE solver return NaN values when it fails instead of raising an error.\n",
     "# This is important to prevent the NUTS sampler from crashing when the ODE solver fails to integrate the system of equations,\n",
@@ -32,7 +40,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
+   "id": "8f4b2f0f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pytensor\n",
+    "pytensor.config.exception_verbosity = \"high\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
    "id": "9ff039d9-af08-4303-a907-5db4a96050e6",
    "metadata": {},
    "outputs": [],
@@ -57,14 +76,13 @@
     "    reaction,\n",
     ")\n",
     "import pytensor.tensor as pt\n",
-    "import pytensor\n",
     "import pymc as pm\n",
     "import nutpie"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "858c1ec4",
    "metadata": {},
    "outputs": [],
@@ -90,7 +108,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "id": "15bb6a7f",
    "metadata": {},
    "outputs": [],
@@ -125,7 +143,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "id": "1721c501-5617-420d-afd0-012d4ab732d4",
    "metadata": {},
    "outputs": [],
@@ -210,7 +228,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "id": "2da0d311",
    "metadata": {},
    "outputs": [],
@@ -239,7 +257,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "id": "853ceb90",
    "metadata": {},
    "outputs": [],
@@ -266,7 +284,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "id": "96e3a15e",
    "metadata": {},
    "outputs": [],
@@ -284,12 +302,14 @@
    "id": "6b096196",
    "metadata": {},
    "source": [
-    "### Setting up the solver"
+    "### Setting up the solver\n",
+    "\n",
+    "PyMC is built around the PyTensor library, whereas Reactix uses JAX. In order to be able to use JAX together with PyMC, the JAX code needs to be wrapped with the `pytensor.wrap_jax` decorator."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "id": "605c6710",
    "metadata": {},
    "outputs": [],
@@ -302,12 +322,15 @@
     "        tracer=zeros,\n",
     "        reactive_tracer=zeros\n",
     "    )\n",
-    "    \n",
+    "\n",
+    "\n",
+    "    # Wrap the solver function with PyTensor to make it compatible with PyMC\n",
     "    @pytensor.wrap_jax\n",
     "    def solve_pt(y0, system):\n",
     "        solve_fn_dense = make_solver(t_max=8000, t_points=t_points, rtol=1e-6, atol=1e-6)\n",
     "        return solve_fn_dense(y0, system).ys\n",
     "    \n",
+    "    # Solve the system of ODEs using the initial state and the system definition\n",
     "    solution = solve_pt(initial_state, system)\n"
    ]
   },
@@ -321,23 +344,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "737d3152",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "t_points[idx_time]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "id": "fdbefc0a",
    "metadata": {},
    "outputs": [],
    "source": [
     "\n",
     "with model:\n",
+    "    # Save all concentrations as deterministic variables in the PyMC model for later analysis\n",
     "    fields = dataclasses.fields(solution)\n",
     "    for field in fields:\n",
     "        pm.Deterministic(\n",
@@ -345,6 +359,8 @@
     "            getattr(solution, field.name),\n",
     "            dims=(\"time_dense\", \"x\"),\n",
     "        )\n",
+    "\n",
+    "    # Define a normal likelihood for the observations, using the model's predicted concentration at the specified time and space indices as the mean\n",
     "    pm.Normal(\"made_up_point\", mu=solution.reactive_tracer[idx_time, idx_space], sigma=0.1, observed=measurement.values, dims=(\"measurement_nr\"))\n"
    ]
   },
@@ -358,10 +374,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "id": "ee25de4d",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling: [k_dec, made_up_point]\n"
+     ]
+    }
+   ],
    "source": [
     "with model:\n",
     "    prior = pm.sample_prior_predictive(draws=50)"
@@ -374,15 +398,38 @@
    "source": [
     "### Prior predictive checks\n",
     "\n",
+    "The following plots show several draws from the prior of the simulated concentrations. This is a good check to assess if the chosen prior distributions for the parameters are reasonable. The measured concentrations is plotted alongside as a black dot.\n",
+    "\n",
     "#### Concentration profile at the time of measurement"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "id": "276fcb22",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x741e6f48bb60>"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "prior.prior.reactive_tracer.isel(chain=0, time_dense=idx_time[0]).plot.line(x=\"x\", hue=\"draw\", add_legend=False);\n",
     "plt.scatter(system.cells.centers[idx_space], measurement.values, color=\"k\", label=\"measurement\")"
@@ -398,10 +445,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "id": "c8813155",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x741e6c267250>"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "prior.prior.reactive_tracer.isel(chain=0, x=idx_space[0]).plot.line(x=\"time_dense\", hue=\"draw\", add_legend=False);\n",
     "plt.scatter(t_points[idx_time], prior.observed_data.made_up_point, color=\"k\", label=\"measurement\")"
@@ -412,85 +480,212 @@
    "id": "a9159ded",
    "metadata": {},
    "source": [
-    "## Sample the posterior distribution"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "fca11dc7",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "compiled = nutpie.compile_pymc_model(model, backend=\"jax\", gradient_backend=\"jax\")\n",
-    "sampler = nutpie.sample(compiled, chains=2, blocking=False)"
+    "## Sample the posterior distribution\n",
+    "\n",
+    "Finally, we can sample the posterior distribution. We can use a state-of-the-art HMC sampler since gradient calculations are provided through automatic differentiation by JAX under the hood. Here we use the Nutpie sampler for PyMC. Note that you need to specify that JAX should be used as a backend for the posterior and gradient evaluation.\n",
+    "\n",
+    "Note that sampling can take a while since thousands of model runs are needed. In order to be able to see intermediate results while the sampler is still running, we let the sampler run in the background by using nutpie's option `blocking=False`. "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "id": "d492fbb1",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "    :root {\n",
+       "        --column-width-1: 40%; /* Progress column width */\n",
+       "        --column-width-2: 15%; /* Chain column width */\n",
+       "        --column-width-3: 15%; /* Divergences column width */\n",
+       "        --column-width-4: 15%; /* Step Size column width */\n",
+       "        --column-width-5: 15%; /* Gradients/Draw column width */\n",
+       "    }\n",
+       "\n",
+       "    .nutpie {\n",
+       "        max-width: 800px;\n",
+       "        margin: 10px auto;\n",
+       "        font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;\n",
+       "        //color: #333;\n",
+       "        //background-color: #fff;\n",
+       "        padding: 10px;\n",
+       "        box-shadow: 0 4px 6px rgba(0,0,0,0.1);\n",
+       "        border-radius: 8px;\n",
+       "        font-size: 14px; /* Smaller font size for a more compact look */\n",
+       "    }\n",
+       "    .nutpie table {\n",
+       "        width: 100%;\n",
+       "        border-collapse: collapse; /* Remove any extra space between borders */\n",
+       "    }\n",
+       "    .nutpie th, .nutpie td {\n",
+       "        padding: 8px 10px; /* Reduce padding to make table more compact */\n",
+       "        text-align: left;\n",
+       "        border-bottom: 1px solid #888;\n",
+       "    }\n",
+       "    .nutpie th {\n",
+       "        //background-color: #f0f0f0;\n",
+       "    }\n",
+       "\n",
+       "    .nutpie th:nth-child(1) { width: var(--column-width-1); }\n",
+       "    .nutpie th:nth-child(2) { width: var(--column-width-2); }\n",
+       "    .nutpie th:nth-child(3) { width: var(--column-width-3); }\n",
+       "    .nutpie th:nth-child(4) { width: var(--column-width-4); }\n",
+       "    .nutpie th:nth-child(5) { width: var(--column-width-5); }\n",
+       "\n",
+       "    .nutpie progress {\n",
+       "        width: 100%;\n",
+       "        height: 15px; /* Smaller progress bars */\n",
+       "        border-radius: 5px;\n",
+       "    }\n",
+       "    progress::-webkit-progress-bar {\n",
+       "        background-color: #eee;\n",
+       "        border-radius: 5px;\n",
+       "    }\n",
+       "    progress::-webkit-progress-value {\n",
+       "        background-color: #5cb85c;\n",
+       "        border-radius: 5px;\n",
+       "    }\n",
+       "    progress::-moz-progress-bar {\n",
+       "        background-color: #5cb85c;\n",
+       "        border-radius: 5px;\n",
+       "    }\n",
+       "    .nutpie .progress-cell {\n",
+       "        width: 100%;\n",
+       "    }\n",
+       "\n",
+       "    .nutpie p strong { font-size: 16px; font-weight: bold; }\n",
+       "\n",
+       "    @media (prefers-color-scheme: dark) {\n",
+       "        .nutpie {\n",
+       "            //color: #ddd;\n",
+       "            //background-color: #1e1e1e;\n",
+       "            box-shadow: 0 4px 6px rgba(0,0,0,0.2);\n",
+       "        }\n",
+       "        .nutpie table, .nutpie th, .nutpie td {\n",
+       "            border-color: #555;\n",
+       "            color: #ccc;\n",
+       "        }\n",
+       "        .nutpie th {\n",
+       "            background-color: #2a2a2a;\n",
+       "        }\n",
+       "        .nutpie progress::-webkit-progress-bar {\n",
+       "            background-color: #444;\n",
+       "        }\n",
+       "        .nutpie progress::-webkit-progress-value {\n",
+       "            background-color: #3178c6;\n",
+       "        }\n",
+       "        .nutpie progress::-moz-progress-bar {\n",
+       "            background-color: #3178c6;\n",
+       "        }\n",
+       "    }\n",
+       "</style>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [],
+      "text/plain": [
+       "<nutpie.sample._BackgroundSampler at 0x741e5c16ba10>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "trace = sampler.inspect()\n"
+    "compiled = nutpie.compile_pymc_model(model, backend=\"jax\", gradient_backend=\"jax\")\n",
+    "sampler = nutpie.sample(compiled, chains=2, blocking=False)\n",
+    "\n"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c9230034",
+   "cell_type": "markdown",
+   "id": "fd562ddc",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "sampler.abort()"
+    "After a while, we can use `sampler.inspect()` to obtain the intermediate results of the sampler and inspect them."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "35abfdbf",
+   "execution_count": 36,
+   "id": "c13d2092",
    "metadata": {},
    "outputs": [],
    "source": [
-    "trace.warmup_sample_stats.diverging.plot.line(x=\"draw\", hue=\"chain\")"
+    "# Inspect intermediate results of the sampler\n",
+    "trace = sampler.inspect()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "9c1c306d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
+   "execution_count": 37,
+   "id": "8b5a51b9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x741df29e7890>,\n",
+       " <matplotlib.lines.Line2D at 0x741df29e79d0>]"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot the evolution of the decay coefficient during the warmup phase of the sampler\n",
     "trace.warmup_posterior.k_dec.plot.line(x=\"draw\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "63a3c605",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "trace.warmup_sample_stats.step_size.plot.line(x=\"draw\")\n",
-    "plt.yscale(\"log\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a12de2d8",
+   "execution_count": 40,
+   "id": "76644c9c",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "trace.warmup_posterior.reactive_tracer.isel(chain=0, time_dense=20).plot.line(x=\"x\", hue=\"draw\", add_legend=False);"
+    "# Plot concentration profiles in the warmup posterior\n",
+    "trace.warmup_posterior.reactive_tracer.isel(chain=0, time_dense=20, draw=slice(20, None)).plot.line(x=\"x\", hue=\"draw\", add_legend=False);"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "cecd4b29",
+   "id": "e0781b8b",
    "metadata": {},
    "outputs": [],
    "source": []