diff --git a/Makefile b/Makefile
index 14186465..f2624531 100644
--- a/Makefile
+++ b/Makefile
@@ -78,6 +78,7 @@ docs: ## generate Sphinx HTML documentation, including API docs
 	jupytext demos/extract_currents.py -o docs/extract_currents.md
 	jupytext demos/tracking_values.py -o docs/tracking_values.md
 	jupytext demos/custom_stimulus_domain.py -o docs/custom_stimulus_domain.md
+	jupytext demos/patient_specific_lv.py -o docs/patient_specific_lv.md
 	mkdir -p docs/_build
 	cp -r benchmarks docs/_build/
 	jupyter book build -W docs
diff --git a/demos/geometries/patient.msh b/demos/geometries/patient.msh
new file mode 100644
index 00000000..09fc3016
--- /dev/null
+++ b/demos/geometries/patient.msh
@@ -0,0 +1,2503 @@
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+$EndElements
diff --git a/demos/patient_specific_lv.py b/demos/patient_specific_lv.py
new file mode 100644
index 00000000..1958d8fb
--- /dev/null
+++ b/demos/patient_specific_lv.py
@@ -0,0 +1,157 @@
+# # Patient specific geometry
+#
+# In this demo we show to use `simcardems` on a patient specific geometry coming from `gmsh`. We have uploaded a [gmsh file](https://github.com/ComputationalPhysiology/simcardems/blob/main/demos/geometries/patient.msh) of an LV geometry where the basal, endocardial and epicardial surfaces are marked. In order to run a simulation we need to first convert this geometry into FEniCS format and then generate some fiber orientations. To convert the mesh we will use a library called [`cardiac-geometries`](https://computationalphysiology.github.io/cardiac_geometries/) and we will generate rule-based fiber orientations using the [`ldrb algorithm`](https://finsberg.github.io/ldrb/README.html).
+#
+# First we make the necessary imports.
+
+from pathlib import Path
+import math
+import cardiac_geometries
+import ldrb
+import simcardems
+import pulse
+
+# Next we use `cardiac-geometries` to convert the gmsh file into FEniCS format.
+#
+
+msh_file = "geometries/patient.msh"
+geo = cardiac_geometries.gmsh2dolfin(msh_file)
+
+# Mesh is currently in centimeters. Let us make sure it in a unit so that we can use kPa directly without scaling it.
+#
+
+geo.mesh.scale(1 / math.sqrt(10))
+
+# The `geo` object now contains the markers, but the `ldrb` algorithm expects the markers in a dictionary with keys `base`, `lv` and `epi` so we create a translation for this.
+#
+
+ldrb_markers = {
+    "base": geo.markers["BASE"][0],
+    "lv": geo.markers["ENDO"][0],
+    "epi": geo.markers["EPI"][0],
+}
+
+# We also need to specify a function space for the fibers. In this example we will will first order discontinuous lagrange elements.
+
+fiber_space = "DG_1"
+
+# We also need so specify the fiber fiber orientations on the endo- and epicardium.
+
+angles = dict(
+    alpha_endo_lv=60,  # Fiber angle on the endocardium
+    alpha_epi_lv=-60,  # Fiber angle on the epicardium
+    beta_endo_lv=0,  # Sheet angle on the endocardium
+    beta_epi_lv=0,
+)
+
+# Now we can run the ldrb algorithm in order to get the fibers (`f0`), sheets (`s0`) and sheet-normal (`n0`) directions. For some reason we also need to allow these functions to be extrapolated.
+
+f0, s0, n0 = ldrb.dolfin_ldrb(
+    mesh=geo.mesh,
+    fiber_space=fiber_space,
+    ffun=geo.marker_functions.ffun,
+    markers=ldrb_markers,
+    **angles
+)
+f0.set_allow_extrapolation(True)
+s0.set_allow_extrapolation(True)
+n0.set_allow_extrapolation(True)
+
+
+# Next we define a directory where to store the output
+
+here = Path(__file__).absolute().parent
+outdir = here / "results_patient_specific_lv"
+outdir.mkdir(exist_ok=True)
+
+
+# and then we can load the geometry into simcardems.
+#
+
+geometry = simcardems.lvgeometry.LeftVentricularGeometry(
+    mechanics_mesh=geo.mesh,
+    microstructure=pulse.Microstructure(f0=f0, s0=s0, n0=n0),
+    ffun=geo.marker_functions.ffun,
+    markers=geo.markers,
+    parameters={"num_refinements": 1, "fiber_space": fiber_space},
+)
+
+# For this example we will use the Tor-Land model with some non-default initial conditions specified in the following file
+#
+
+# Specify path to the initial conditions for the cell model
+initial_conditions_path = (
+    here / "initial_conditions/fully_coupled_Tor_Land/init_5000beats.json"
+)
+
+# We will run the simulation for 1000 milliseconds. We apply a spring on the epicardium to mimic the pericardium and we set the traction on the endocardium to zero.
+
+config = simcardems.Config(
+    outdir=outdir,
+    coupling_type="fully_coupled_Tor_Land",
+    T=1000,
+    traction=0.0,  # Pressure on the endocardium
+    cell_init_file=initial_conditions_path,
+    mechanics_use_continuation=True,
+)
+
+# Now we can create the coupling.
+
+coupling = simcardems.models.em_model.setup_EM_model_from_config(
+    config=config,
+    geometry=geometry,
+)
+
+# And to make things move a little bit more we will scale the reference active tension to 60 kPa.
+
+coupling.mech_solver.material.active.T_ref.assign(10.0)
+
+# Now we create the runner
+
+runner = simcardems.Runner.from_models(config=config, coupling=coupling)
+
+# but before we run the EM simulations, we will inflate the LV to a cavity pressure of 3 kPa.
+#
+# xdmf = dolfin.XDMFFile(dolfin.MPI.comm_world, "u.xdmf")
+# u = dolfin.Function(coupling.mech_solver.state_space.sub(0).collapse())
+# xdmf.write_checkpoint(u, "u", 0.0, dolfin.XDMFFile.Encoding.HDF5, True)
+
+pulse.iterate.iterate(
+    problem=runner.coupling.mech_solver,
+    control=runner.coupling.mech_solver.bcs.neumann[0].traction,
+    target=3,  # Set it to 3 kPa
+    initial_number_of_steps=50,
+)
+
+# u.assign(coupling.mech_solver.state.split(deepcopy=True)[0])
+# xdmf.write_checkpoint(u, "u", 1.0, dolfin.XDMFFile.Encoding.HDF5, True)
+
+# V = dolfin.FunctionSpace(coupling.geometry.mechanics_mesh, "CG", 1)
+# Ta = dolfin.Function(V)
+
+
+# class _Land(coupling.mech_solver.material.active.__class__):
+#     Ta = Ta
+
+
+# coupling.mech_solver.material.active.__class__ = _Land
+# coupling.mech_solver._init_forms()
+
+# pulse.iterate.iterate(
+#     problem=runner.coupling.mech_solver,
+#     control=Ta,
+#     target=60.0,
+#     initial_number_of_steps=100,
+# )
+
+# u.assign(coupling.mech_solver.state.split(deepcopy=True)[0])
+# xdmf.write_checkpoint(u, "u", 2.0, dolfin.XDMFFile.Encoding.HDF5, True)
+# Now we run the EM simulation.
+
+runner.solve(T=config.T, save_freq=config.save_freq, show_progress_bar=True)
+
+
+# And save the output to xdmf-files that can be viewed in Paraview
+#
+
+simcardems.postprocess.make_xdmffiles(outdir.joinpath("results.h5"), names=["u"])
diff --git a/docs/_toc.yml b/docs/_toc.yml
index 1b4160e2..b2ef3fad 100644
--- a/docs/_toc.yml
+++ b/docs/_toc.yml
@@ -24,6 +24,7 @@ parts:
     - file: "extract_currents"
     - file: "tracking_values"
     - file: "custom_stimulus_domain"
+    - file: "patient_specific_lv"
     - file: "cli"
     - file: "docker"
     - file: "gui"
diff --git a/src/simcardems/models/fully_coupled_Tor_Land/active_model.py b/src/simcardems/models/fully_coupled_Tor_Land/active_model.py
index 12eba155..7f3c3998 100644
--- a/src/simcardems/models/fully_coupled_Tor_Land/active_model.py
+++ b/src/simcardems/models/fully_coupled_Tor_Land/active_model.py
@@ -45,6 +45,7 @@ def __init__(
         eta=0,
         scheme: Scheme = Scheme.analytic,
         dLambda_tol: float = 1e-12,
+        T_ref: float = 0.0,
         **kwargs,
     ):
         logger.debug("Initialize Land Model")
@@ -56,7 +57,7 @@ def __init__(
 
         self._eta = eta
         self.function_space = dolfin.FunctionSpace(coupling.mech_mesh, "CG", 1)
-
+        self.T_ref = dolfin.Constant(T_ref)
         self.XS = coupling.XS_mech
         self.XW = coupling.XW_mech
         if parameters is None:
@@ -235,7 +236,7 @@ def Wactive(self, F, **kwargs):
         self.update_Zetas()
         self.update_Zetaw()
         return pulse.material.active_model.Wactive_transversally(
-            Ta=self.Ta,
+            Ta=self.Ta * self.T_ref,
             C=C,
             f0=self.f0,
             eta=self.eta,
diff --git a/src/simcardems/newton_solver.py b/src/simcardems/newton_solver.py
index 7a384f0f..30f3ab8a 100644
--- a/src/simcardems/newton_solver.py
+++ b/src/simcardems/newton_solver.py
@@ -95,8 +95,9 @@ def default_solver_parameters():
 
     def converged(self, r, p, i):
         self._converged_called = True
-
+        print("converged")
         res = r.norm("l2")
+        print(f"Mechanics solver residual: {res}")
         logger.debug(f"Mechanics solver residual: {res}")
 
         if self.debug:
@@ -130,7 +131,9 @@ def solver_setup(self, A, J, p, i):
     def solve(self):
         logger.debug("Solving mechanics")
         self._solve_called = True
+        print("Solve before")
         ret = super().solve(self._problem, self._state.vector())
+        print("Solve after")
         self._state.vector().apply("insert")
         logger.debug("Done solving mechanics")
         self.save_residuals()
@@ -144,6 +147,10 @@ def check_overloads_called(self):
         assert getattr(self, "_update_solution_called", False)
         assert getattr(self, "_solve_called", False)
 
+    def update_solution(self, x, dx, rp, p, i):
+        print("Update solution")
+        return super().update_solution(x, dx, rp, p, i)
+
 
 class MechanicsNewtonSolver_ODE(MechanicsNewtonSolver):
     def update_solution(self, x, dx, rp, p, i):