finished testing

docs/source/userguide/settings.rst

@@ -6,7 +6,7 @@ Settings
 
 The settings of a FESTIM simulation are defined with a :class:`festim.Settings` object.
 
-.. code-block:: python
+.. testcode::
 
     import festim as F
 

docs/source/userguide/sources.rst

@@ -10,7 +10,7 @@ Volumetric sources
 
 Volumetric sources can be set in a simulation by using the :class:`festim.Source` class.
 
-.. code:: python
+.. testcode::
 
     import festim as F
 
@@ -28,7 +28,7 @@ Implantation flux
 
 Hydrogen implanted in a material can be simulated by a Gaussian-shaped volumetric source with the :class:`festim.ImplantationFlux` class.
 
-.. code:: python
+.. testcode::
 
     import festim as F
 
@@ -51,7 +51,7 @@ Radioactive decay
 
 Radioactive decay can be simulated by a volumetric source with the :class:`festim.RadioactiveDecay` class.
 
-.. code:: python
+.. testcode::
 
     import festim as F
 
@@ -61,6 +61,6 @@ Radioactive decay can be simulated by a volumetric source with the :class:`festi
         F.RadioactiveDecay(
             decay_constant=1.78e-9,
             volume=1,
-            fields="all",
+            field="all",
             ),
         ]

docs/source/userguide/stepsize.rst

@@ -7,14 +7,14 @@ For transient problems, a :class:`festim.Stepsize` is required.
 It represents the time discretisation of the problem.
 Here is an example creating a stepsize of 1.2 seconds:
 
-.. code-block:: python
+.. testcode::
 
     import festim as F
     my_stepsize = F.Stepsize(initial_value=1.2)
 
 To use the adaptive time stepping implemented in FESTIM, the arguments ``stepsize_change_ratio`` needs to be set to a value above 1.
 
-.. code-block:: python
+.. testcode::
 
     my_stepsize = F.Stepsize(initial_value=1.2, stepsize_change_ratio=1.5)
 
@@ -25,7 +25,7 @@ Moreover, if the solver doesn't converge, the stepsize will be reduced and the s
 Setting the ``dt_min`` argument will prevent the stepsize from becoming too small and will stop the simulation when this happens.
 To cap the stepsize after some time, the parameters ``t_stop`` and ``stepsize_stop_max`` can be used.
 
-.. code-block:: python
+.. testcode::
 
     my_stepsize = F.Stepsize(
         initial_value=1.2, 
@@ -41,7 +41,7 @@ To cap the stepsize after some time, the parameters ``t_stop`` and ``stepsize_st
 Another option for controlling the stepsize is to use the ``max_stepsize`` parameter. This parameter defines the maximal value of the stepsize during simulations, 
 and it can be set as a constant or a callable function of time:
 
-.. code-block:: python
+.. testcode::
 
     def max_stepsize(t):
         if t <= 5:
@@ -60,7 +60,7 @@ and it can be set as a constant or a callable function of time:
 The ``milestones`` argument can be used to make sure the simulation passes through specific times.
 This will modify the stepsize as needed.
 
-.. code-block:: python
+.. testcode::
 
     my_stepsize = F.Stepsize(
         initial_value=1.2,

docs/source/userguide/temperature.rst

@@ -2,6 +2,10 @@
 Temperature
 ===========
 
+.. testsetup::
+
+    from festim import HeatTransferProblem, TemperatureFromXDMF
+
 Definition of a temperature field or problem is essential for hydrogen transport 
 and FESTIM as a whole.
 Regardless of how you define the temperature of the problem, it is passed to the :code:`T` attribute of the :class:`festim.Simulation` object.
@@ -12,7 +16,7 @@ Analytical expressions
 
 The temperature can be defined as a constant value in Kelvin (K):
 
-.. code-block:: python
+.. testcode::
 
     my_temperature = 300
 
@@ -40,7 +44,7 @@ More complex expressions can be expressed with sympy:
 
 would be passed to FESTIM as:
 
-.. code-block:: python
+.. testcode::
 
     from festim import x, t
     import sympy as sp
@@ -49,12 +53,12 @@ would be passed to FESTIM as:
 
 Conditional expressions are also possible:
 
-.. code-block:: python
+.. testcode::
 
     from festim import x, t
     import sympy as sp
 
-    my_temp = sp.Piecewise((400, F.t < 10), (300, True))
+    my_temp = sp.Piecewise((400, t < 10), (300, True))
 
 ---------------------------
 From a heat transfer solver
@@ -64,7 +68,7 @@ Temperature can also be obtained by solving the heat equation.
 Users can define heat transfer problems using :class:`festim.HeatTransferProblem`.
 
 
-.. code-block:: python
+.. testcode::
 
     my_temp = HeatTransferProblem()
 

docs/source/userguide/traps.rst

@@ -16,7 +16,7 @@ A trap in FESTIM is defined by:
 * its density
 * the materials where it is located
 
-.. code-block:: python
+.. testcode::
 
     import festim as F
 
@@ -26,7 +26,7 @@ A trap in FESTIM is defined by:
 
 If the trap is located in several materials, instead of creating another :class:`festim.Trap` object, simply use a list of materials:
 
-.. code-block:: python
+.. testcode::
 
     import festim as F
 
@@ -37,7 +37,7 @@ If the trap is located in several materials, instead of creating another :class:
 
 The trap density can be a function of space and time. For example:
 
-.. code-block:: python
+.. testcode::
 
     import festim as F
 
@@ -52,7 +52,7 @@ The trap density can be a function of space and time. For example:
 
 Boolean expressions can also be used to restrict the trap to certain regions:
 
-.. code-block:: python
+.. testcode::
 
     import festim as F
 
@@ -76,7 +76,7 @@ Extrinsic traps
 An extrinsic trap is defined as a trap with a density evolving over time.
 If the temporal evolution of the trap's density is known `a priori`, then a "normal" trap can be used with a time dependent expression as density (see above).
 
-.. code-block:: python
+.. testcode::
 
     import festim as F
 
@@ -94,7 +94,7 @@ Let's imagine a case where you have two subdomains. Trap 1 is defined only in th
 It would be possible to define one trap in each subdomain.
 Grouping traps together helps save computational time by reducing the number of degrees of freedom.
 
-.. code-block:: python
+.. testcode::
 
     import festim as F
 

docs/source/userguide/troubleshooting.rst

@@ -23,7 +23,7 @@ The first thing to check is the details of the Newton solver iterations.
 To do so, you must set the ``log_level`` to ``20`` (default is ``40``).
 This will provide more information during the solving stage.
 
-.. code-block:: python
+.. testcode::
 
     import festim as F
 
@@ -47,7 +47,7 @@ This is often due to an excessively high absolute tolerance.
 The Newton solver then converges in zero iterations. In other words, nothing is solved.
 First, check that this is the case by setting the log level to 20:
 
-.. code-block:: python
+.. testcode::
 
     import festim as F
 
@@ -57,9 +57,9 @@ First, check that this is the case by setting the log level to 20:
 
 Then increase the absolute tolerance of the solver:
 
-.. code-block:: python
+.. testcode::
 
     my_model.settings = F.Settings(
-        ...,
         absolute_tolerance=1e10,
+        relative_tolerance=1e-10,
     )