Fix CuPy solver tolerance parameter and update tutorial installations

python/openimpala/_solver.py

@@ -415,7 +415,7 @@ def solve_tortuosity(
 
         # CuPy CG solve
         solution_gpu, info = cusp_linalg.cg(A_gpu, rhs_gpu, x0=x0_gpu,
-                                             tol=tol, maxiter=maxiter)
+                                             rtol=tol, maxiter=maxiter)
         solution = cp.asnumpy(solution_gpu)
         converged = info == 0
         # CuPy doesn't return iteration count directly; estimate from info

tutorials/02_digital_twin.ipynb

@@ -3,14 +3,14 @@
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": "<a href=\"https://colab.research.google.com/github/BASE-Laboratory/OpenImpala/blob/master/tutorials/02_digital_twin.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n\n# Tutorial 2 of 7: From 3D Image to Device Model\n\n*OpenImpala Tutorial Series — From first solve to HPC deployment*\n\n---\n\nA common workflow in electrochemical research is to characterise a 3D microstructure from X-ray tomography and feed the resulting transport parameters into a continuum device model such as [PyBaMM](https://pybamm.org/). This tutorial walks through that pipeline end-to-end.\n\n**What you will learn:**\n1. Load a real 3D TIFF image stack.\n2. Compute directional tortuosity (X, Y, Z) to quantify anisotropy.\n3. Visualise the 3D diffusion field using the C++ core API and `yt`.\n4. Export results to the BPX (Battery Parameter eXchange) JSON format.\n5. Import those parameters into PyBaMM and run a discharge simulation.\n\n**Prerequisites:** [Tutorial 1](01_hello_openimpala.ipynb) — basic OpenImpala workflow (volume fraction, percolation, tortuosity)."
+   "source": "<a href=\"https://colab.research.google.com/github/BASE-Laboratory/OpenImpala/blob/master/tutorials/02_digital_twin.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n\n# Tutorial 2 of 7: From 3D Image to Device Model\n\n*OpenImpala Tutorial Series \u2014 From first solve to HPC deployment*\n\n---\n\nA common workflow in electrochemical research is to characterise a 3D microstructure from X-ray tomography and feed the resulting transport parameters into a continuum device model such as [PyBaMM](https://pybamm.org/). This tutorial walks through that pipeline end-to-end.\n\n**What you will learn:**\n1. Load a real 3D TIFF image stack.\n2. Compute directional tortuosity (X, Y, Z) to quantify anisotropy.\n3. Visualise the 3D diffusion field using the C++ core API and `yt`.\n4. Export results to the BPX (Battery Parameter eXchange) JSON format.\n5. Import those parameters into PyBaMM and run a discharge simulation.\n\n**Prerequisites:** [Tutorial 1](01_hello_openimpala.ipynb) \u2014 basic OpenImpala workflow (volume fraction, percolation, tortuosity)."
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": "# Install OpenImpala, PyBaMM, and visualization utilities\n!pip install -q openimpala pybamm bpx tifffile matplotlib yt"
+   "source": "# Install OpenImpala (compiled C++ backend needed for low-level API in this tutorial)\n!pip install -q openimpala-cuda --find-links https://github.com/BASE-Laboratory/OpenImpala/releases/latest nvidia-cuda-runtime-cu12 nvidia-cublas-cu12 nvidia-cusparse-cu12 nvidia-curand-cu12 pybamm bpx tifffile matplotlib yt"
   },
   {
    "cell_type": "code",
@@ -168,7 +168,7 @@
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": "## Next Steps\n\nThis tutorial demonstrated the full pipeline from a 3D tomography image to a device-level simulation, with BPX as the interchange format.\n\n**Continue the series:**\n- [Tutorial 3: REV and Uncertainty](03_rev_and_uncertainty.ipynb) — How large does your sample need to be for statistically representative results?\n- [Tutorial 4: Effective Diffusivity and Field Visualisation](04_multiphase_and_fields.ipynb) — Extend to the cell-problem solver and multi-phase composites.\n- [Tutorial 7: Scaling to HPC](07_hpc_scaling.ipynb) — Analyse full-resolution synchrotron datasets with MPI.\n\n---\n\n## References & Further Reading\n\n1. **OpenImpala:** S. Mayner, F. Ciucci, *OpenImpala: open-source computational framework for microstructural analysis of 3D tomography data*, SoftwareX (2024). [GitHub](https://github.com/BASE-Laboratory/OpenImpala)\n2. **PyBaMM:** M. Sulzer et al., *Python Battery Mathematical Modelling (PyBaMM)*, J. Open Research Software 9(1), 14 (2021). [doi:10.5334/jors.309](https://doi.org/10.5334/jors.309)\n3. **BPX standard:** *Battery Parameter eXchange (BPX)*, an open standard for lithium-ion battery parameterisation. [GitHub](https://github.com/pybop-team/BPX)\n4. **Electrode tortuosity & anisotropy:** M. Ebner et al., *Tortuosity anisotropy in lithium-ion battery electrodes*, Advanced Energy Materials 4(5), 1301278 (2014). [doi:10.1002/aenm.201301278](https://doi.org/10.1002/aenm.201301278)\n5. **yt visualisation:** M. J. Turk et al., *yt: A multi-code analysis toolkit for astrophysical simulation data*, Astrophys. J. Suppl. 192, 9 (2011). [doi:10.1088/0067-0049/192/1/9](https://doi.org/10.1088/0067-0049/192/1/9)\n6. **Digital twin concept for batteries:** A. A. Franco et al., *Boosting rechargeable batteries R&D by multiscale modeling: myth or reality?*, Chemical Reviews 119(7), 4569–4627 (2019). [doi:10.1021/acs.chemrev.8b00239](https://doi.org/10.1021/acs.chemrev.8b00239)"
+   "source": "## Next Steps\n\nThis tutorial demonstrated the full pipeline from a 3D tomography image to a device-level simulation, with BPX as the interchange format.\n\n**Continue the series:**\n- [Tutorial 3: REV and Uncertainty](03_rev_and_uncertainty.ipynb) \u2014 How large does your sample need to be for statistically representative results?\n- [Tutorial 4: Effective Diffusivity and Field Visualisation](04_multiphase_and_fields.ipynb) \u2014 Extend to the cell-problem solver and multi-phase composites.\n- [Tutorial 7: Scaling to HPC](07_hpc_scaling.ipynb) \u2014 Analyse full-resolution synchrotron datasets with MPI.\n\n---\n\n## References & Further Reading\n\n1. **OpenImpala:** S. Mayner, F. Ciucci, *OpenImpala: open-source computational framework for microstructural analysis of 3D tomography data*, SoftwareX (2024). [GitHub](https://github.com/BASE-Laboratory/OpenImpala)\n2. **PyBaMM:** M. Sulzer et al., *Python Battery Mathematical Modelling (PyBaMM)*, J. Open Research Software 9(1), 14 (2021). [doi:10.5334/jors.309](https://doi.org/10.5334/jors.309)\n3. **BPX standard:** *Battery Parameter eXchange (BPX)*, an open standard for lithium-ion battery parameterisation. [GitHub](https://github.com/pybop-team/BPX)\n4. **Electrode tortuosity & anisotropy:** M. Ebner et al., *Tortuosity anisotropy in lithium-ion battery electrodes*, Advanced Energy Materials 4(5), 1301278 (2014). [doi:10.1002/aenm.201301278](https://doi.org/10.1002/aenm.201301278)\n5. **yt visualisation:** M. J. Turk et al., *yt: A multi-code analysis toolkit for astrophysical simulation data*, Astrophys. J. Suppl. 192, 9 (2011). [doi:10.1088/0067-0049/192/1/9](https://doi.org/10.1088/0067-0049/192/1/9)\n6. **Digital twin concept for batteries:** A. A. Franco et al., *Boosting rechargeable batteries R&D by multiscale modeling: myth or reality?*, Chemical Reviews 119(7), 4569\u20134627 (2019). [doi:10.1021/acs.chemrev.8b00239](https://doi.org/10.1021/acs.chemrev.8b00239)"
   }
  ],
  "metadata": {