.gitignore

 __pycache__
 .ipynb_checkpoints
-.coverage
+.coverage*
 *.pyc
 *.vti
 /build
@@ -18,4 +18,4 @@ pystencils/boundaries/createindexlistcython.c
 pystencils/boundaries/createindexlistcython.*.so
 pystencils_tests/tmp
 pystencils_tests/kerncraft_inputs/.2d-5pt.c_kerncraft/
-pystencils_tests/kerncraft_inputs/.3d-7pt.c_kerncraft/
+pystencils_tests/kerncraft_inputs/.3d-7pt.c_kerncraft/
\ No newline at end of file

binder/environment.yml

@@ -14,7 +14,7 @@
 name: pystencils
 dependencies:
   # Basic dependencies:
-  - python >= 3.6
+  - python >= 3.8
   - numpy
   - sympy >= 1.1
   - appdirs # to find default cache directory on each platform

doc/conf.py

@@ -51,7 +51,7 @@ nbsphinx_execute = 'never'
 nbsphinx_codecell_lexer = 'python3'
 
 # Example configuration for intersphinx: refer to the Python standard library.
-intersphinx_mapping = {'python': ('https://docs.python.org/3.6', None),
+intersphinx_mapping = {'python': ('https://docs.python.org/3.8', None),
                        'numpy': ('https://docs.scipy.org/doc/numpy/', None),
                        'matplotlib': ('https://matplotlib.org/', None),
                        'sympy': ('https://docs.sympy.org/latest/', None),

doc/notebooks/03_tutorial_datahandling.ipynb

 %% Cell type:code id: tags:
 
 ``` python
+import psutil
 from mpl_toolkits.mplot3d import Axes3D
 from matplotlib import pyplot, cm
 from pystencils.session import *
 from pystencils.boundaries import add_neumann_boundary, Neumann, Dirichlet, BoundaryHandling
 from pystencils.slicing import slice_from_direction
 import math
 import time
 %matplotlib inline
 ```
 
 %% Cell type:markdown id: tags:
 
 Test to see if pycuda is installed which is needed to run calculations on the GPU
 
 %% Cell type:code id: tags:
 
 ``` python
 try:
     import pycuda
 except ImportError:
     pycuda = None
     print('No pycuda installed')
 
 if pycuda:
     import pycuda.gpuarray as gpuarray
 ```
 
 %% Cell type:markdown id: tags:
 
 # Tutorial 03: Datahandling
 
 %% Cell type:markdown id: tags:
 
 This is a tutorial about the `DataHandling` class of pystencils. This class is an abstraction layer to
 - link numpy arrays to pystencils fields
 - handle CPU-GPU array transfer, such that one can write code that works on CPU and GPU
 - makes it possible to write MPI parallel simulations to run on distributed-memory clusters using the waLBerla library
 
 We will look at a small and easy example to demonstrate the usage of `DataHandling` objects. We will define an averaging kernel to every cell of an array, that writes the average of the neighbor cell values to the center.
 
 %% Cell type:markdown id: tags:
 
 ## 1. Manual
 
 ### 1.1. CPU kernels
 
 In this first part, we set up a scenario manually without a `DataHandling`. In the next sections we then repeat the same setup with the help of the data handling.
 
 One concept of *pystencils* that may be confusing at first, is the differences between pystencils fields and numpy arrays. Fields are used to describe the computation *symbolically* with sympy, while numpy arrays hold the actual values where the computation is executed on.
 
 One option to create and execute a *pystencils* kernel is listed below. For reasons that become clear later we call this the **variable-field-size workflow**:
 
 1. define pystencils fields
 2. use sympy and the pystencils fields to define an update rule, that describes what should be done on *every cell*
 3. compile the update rule to a real function, that can be called from Python. For each field that was referenced in the symbolic description the function expects a numpy array, passed as named parameter
 4. create some numpy arrays with actual data
 5. call the kernel - usually many times
 
 Now, lets see how this actually looks in Python code:
 
 %% Cell type:code id: tags:
 
 ``` python
 # 1. field definitions
 src_field, dst_field = ps.fields("src, dst:[2D]")
 
 # 2. define update rule
 update_rule = [ps.Assignment(lhs=dst_field[0, 0],
                              rhs=(src_field[1, 0] + src_field[-1, 0] +
                                   src_field[0, 1] + src_field[0, -1]) / 4)]
 
 # 3. compile update rule to function
 kernel_function = ps.create_kernel(update_rule).compile()
 
 # 4. create numpy arrays and call kernel
 src_arr, dst_arr = np.random.rand(30, 30), np.zeros([30, 30])
 
 # 5. call kernel
 kernel_function(src=src_arr, dst=dst_arr)  # names of arguments have to match names passed to ps.fields()
 ```
 
 %% Cell type:markdown id: tags:
 
 This workflow separates the symbolic and the numeric stages very cleanly. The separation also makes it possible to stop after step 3, write the C-code to a file and call the kernel from a C program. Speaking of the C-Code - lets have a look at the generated sources:
 
 %% Cell type:code id: tags:
 
 ``` python
 ps.show_code(kernel_function.ast)
 ```
 
 %% Output
 
 
 
 %% Cell type:markdown id: tags:
 
 Even if it looks very ugly and low-level :) lets look at this code in a bit more detail. The code is generated in a way that it works for different array sizes. The size of the array is passed in the `_size_dst_` variables that specifiy the shape of the array for each dimension. Also, the memory layout (linearization) of the array can be different. That means the array could be stored in row-major or column-major order - if we pass in the array strides correctly the kernel does the right thing. If you're not familiar with the concept of strides check out [this stackoverflow post](https://stackoverflow.com/questions/53097952/how-to-understand-numpy-strides-for-layman) or search in the numpy documentation for strides - C vs Fortran order.
 
 The goal of *pystencils* is to produce the fastest possible code. One technique to do this is to use all available information already on compile time and generate code that is highly adapted to the specific problem. In our case we already know the shape and strides of the arrays we want to apply the kernel to, so we can make use of this information. This idea leads to the **fixed-field-size workflow**. The main difference there is that we define the arrays first and therefore let *pystencils* know about the array shapes and strides, so that it can generate more specific code:
 
 1. create numpy arrays that hold your data
 2. define pystencils fields, this time telling pystencils already which arrays they correspond to, so that it knows about the size and strides
 
 in the other steps nothing has changed
 
 3. define the update rule
 4. compile update rule to kernel
 5. run the kernel
 
 %% Cell type:code id: tags:
 
 ``` python
 # 1. create arrays first
 src_arr, dst_arr = np.random.rand(30, 30), np.zeros([30, 30])
 
 # 2. define symbolic fields - note the additional parameter that link an array to each field
 src_field, dst_field = ps.fields("src, dst:[2D]", src=src_arr, dst=dst_arr)
 
 # 3. define update rule
 update_rule = [ps.Assignment(lhs=dst_field[0, 0],
                              rhs=(src_field[1, 0] + src_field[-1, 0] +
                                   src_field[0, 1] + src_field[0, -1]) / 4)]
 
 # 4. compile it
 kernel_function = ps.create_kernel(update_rule).compile()
 
 # 5. call kernel
 kernel_function(src=src_arr, dst=dst_arr)  # names of arguments have to match names passed to ps.fields()
 ```
 
 %% Cell type:markdown id: tags:
 
 Functionally, both variants are equivalent. We see the difference only when we look at the generated code
 
 %% Cell type:code id: tags:
 
 ``` python
 ps.show_code(kernel_function.ast)
 ```
 
 %% Output
 
 
 
 %% Cell type:markdown id: tags:
 
 Compare this to the code above! It looks much simpler. The reason is that all index computations are already simplified since the exact field sizes and strides are known. This kernel now only works on arrays of the previously specified size.
 
 Lets try what happens if we use a different array:
 
 %% Cell type:code id: tags:
 
 ``` python
 src_arr2, dst_arr2 = np.random.rand(40, 40), np.zeros([40, 40])
 try:
     kernel_function(src=src_arr2, dst=dst_arr2)
 except ValueError as e:
     print(e)
 ```
 
 %% Output
 
     Wrong shape of array dst. Expected (30, 30)
 
 %% Cell type:markdown id: tags:
 
 ### 1.2. GPU simulations
 
 Let's now jump to a seemingly unrelated topic: running kernels on the GPU.
-When creating the kernel, an additional parameter `target='gpu'` has to be passed. Also, the compiled kernel cannot be called with numpy arrays directly, but has to be called with `pycuda.gpuarray`s instead. That means, we have to transfer our numpy array to GPU first. From this step we obtain a gpuarray, then we can run the kernel, hopefully multiple times so that the data transfer was worth the time. Finally we transfer the finished result back to CPU:
+When creating the kernel, an additional parameter `target=ps.Target.GPU` has to be passed. Also, the compiled kernel cannot be called with numpy arrays directly, but has to be called with `pycuda.gpuarray`s instead. That means, we have to transfer our numpy array to GPU first. From this step we obtain a gpuarray, then we can run the kernel, hopefully multiple times so that the data transfer was worth the time. Finally we transfer the finished result back to CPU:
 
 %% Cell type:code id: tags:
 
 ``` python
 if pycuda:
-    kernel_function_gpu = ps.create_kernel(update_rule, target='gpu').compile()
+    config = ps.CreateKernelConfig(target=ps.Target.GPU)
+    kernel_function_gpu = ps.create_kernel(update_rule, config=config).compile()
     # transfer to GPU
     src_arr_gpu = pycuda.gpuarray.to_gpu(src_arr)
     dst_arr_gpu = pycuda.gpuarray.to_gpu(dst_arr)
 
     # run kernel on GPU, this is done many times in real setups
     kernel_function_gpu(src=src_arr_gpu, dst=dst_arr_gpu)
 
     # transfer result back to CPU
     dst_arr_gpu.get(dst_arr)
 ```
 
 %% Cell type:markdown id: tags:
 
 ###  1.3. Summary: manual way
 
 - Don't confuse *pystencils* fields and *numpy* arrays
     - fields are symbolic
     - arrays are numeric
 - Use the fixed-field-size workflow whenever possible, since code might be faster. Create arrays first, then create fields from arrays
 - if we run GPU kernels, arrays have to transferred to the GPU first
 
 As demonstrated in the examples above we have to define 2 or 3 corresponding objects for each grid:
 
 - symbolic pystencils field
 - numpy array on CPU
 - for GPU run also a pycuda.gpuarray to mirror the data on the GPU
 
 Managing these three objects manually is tedious and error-prone. We'll see in the next section how the data handling object takes care of this problem.
 
 %% Cell type:markdown id: tags:
 
 ## 2. Introducing the data handling - serial version
 
 ### 2.1. Example for CPU simulations
 
 The data handling internally keeps a mapping between symbolic fields and numpy arrays. When we create a field, automatically a corresponding array is allocated as well. Optionally we can also allocate memory on the GPU for the array as well. Lets dive right in and see how our example looks like, when implemented with a data handling.
 
 %% Cell type:code id: tags:
 
 ``` python
 dh = ps.create_data_handling(domain_size=(30, 30))
 
 # fields are now created using the data handling
 src_field = dh.add_array('src', values_per_cell=1)
 dst_field = dh.add_array('dst', values_per_cell=1)
 
 # kernel is created just like before
 update_rule = [ps.Assignment(lhs=dst_field[0, 0],
                              rhs=(src_field[1, 0] + src_field[-1, 0] + src_field[0, 1] + src_field[0, -1]) / 4)]
 kernel_function = ps.create_kernel(update_rule).compile()
 
 # have a look at the generated code - it uses
 # the fast version where array sizes are compiled-in
 #  ps.show_code(kernel_function.ast)
 ```
 
 %% Cell type:markdown id: tags:
 
 The data handling has methods to create fields - but where are the corresponding arrays?
 In the serial case you can access them as a member of the data handling, for example to initialize our 'src' array we can write
 
 %% Cell type:code id: tags:
 
 ``` python
 src_arr = dh.cpu_arrays['src']
 src_arr.fill(0.0)
 ```
 
 %% Cell type:markdown id: tags:
 
 This method is nice and easy, but you should not use it if you want your simulation to run on distributed-memory clusters. We'll see why in the last section. So it is good habit to not access the arrays directly but use the data handling to do so. We can, for example, initialize the array also with the following code:
 
 %% Cell type:code id: tags:
 
 ``` python
 dh.fill('src', 0.0)
 ```
 
 %% Cell type:markdown id: tags:
 
 To run the kernels with the same code as before, we would also need the arrays. We could do that accessing the `cpu_arrays`:
 
 %% Cell type:code id: tags:
 
 ``` python
 kernel_function(src=dh.cpu_arrays['src'],
                 dst=dh.cpu_arrays['dst'])
 ```
 
 %% Cell type:markdown id: tags:
 
 but to be prepared for MPI parallel simulations, again a method of the data handling should be used for this.
 Besides, this method is also simpler to use - since it automatically detects which arrays a kernel uses and passes them in.
 
 %% Cell type:code id: tags:
 
 ``` python
 dh.run_kernel(kernel_function)
 ```
 
 %% Cell type:markdown id: tags:
 
 To access the data read-only instead of using `cpu_arrays` the gather function should be used.
 This function gives you a read-only copy of the domain or part of the domain.
 We will discuss this function later in more detail when we look at MPI parallel simulations.
 For serial simulations keep in mind that modifying the resulting array does not change your original data!
 
 %% Cell type:code id: tags:
 
 ``` python
 read_only_copy = dh.gather_array('src', ps.make_slice[:, :], ghost_layers=False)
 ```
 
 %% Cell type:markdown id: tags:
 
 ### 2.2. Example for GPU simulations
 
 In this section we have a look at GPU simulations using the data handling. Only minimal changes are required.
 When creating the data handling we can pass a 'default_target'. This means for every added field an array on the CPU and the GPU is allocated. This is a useful default, for more fine-grained control the `add_array` method also takes additional parameters controlling where the array should be allocated.
 
 Additionally we also need to compile a GPU version of the kernel.
 
 %% Cell type:code id: tags:
 
 ``` python
-dh = ps.create_data_handling(domain_size=(30, 30), default_target='gpu')
+dh = ps.create_data_handling(domain_size=(30, 30), default_target=ps.Target.GPU)
 
 # fields are now created using the data handling
 src_field = dh.add_array('src', values_per_cell=1)
 dst_field = dh.add_array('dst', values_per_cell=1)
 
 # kernel is created just like before
 update_rule = [ps.Assignment(lhs=dst_field[0, 0],
                              rhs=(src_field[1, 0] + src_field[-1, 0] + src_field[0, 1] + src_field[0, -1]) / 4)]
-kernel_function = ps.create_kernel(update_rule, target=dh.default_target).compile()
+config = ps.CreateKernelConfig(target=dh.default_target)
+kernel_function = ps.create_kernel(update_rule, config=config).compile()
 
 dh.fill('src', 0.0)
 ```
 
 %% Cell type:markdown id: tags:
 
 The data handling provides function to transfer data between CPU and GPU
 
 %% Cell type:code id: tags:
 
 ``` python
 dh.to_gpu('src')
 dh.run_kernel(kernel_function)
 dh.to_cpu('dst')
 ```
 
 %% Cell type:markdown id: tags:
 
 usually one wants to transfer all fields that have been allocated on CPU and GPU at the same time:
 
 %% Cell type:code id: tags:
 
 ``` python
 dh.all_to_gpu()
 dh.run_kernel(kernel_function)
 dh.all_to_cpu()
 ```
 
 %% Cell type:markdown id: tags:
 
 We can always include the `all_to_*` functions in our code, since they do nothing if there are no arrays allocated on the GPU. Thus there is only a single point in the code where we can switch between CPU and GPU version: the `default_target` of the data handling.
 
 %% Cell type:markdown id: tags:
 
 ### 2.2 Ghost Layers and periodicity
 
 The data handling can also provide periodic boundary conditions. Therefor the domain is extended by one layer of cells, the so-called ghost layer or halo layer.
 
 %% Cell type:code id: tags:
 
 ``` python
 print("Shape of domain         ", dh.shape)
 print("Direct access to arrays ", dh.cpu_arrays['src'].shape)
 print("Gather                  ", dh.gather_array('src', ghost_layers=True).shape)
 ```
 
 %% Output
 
     Shape of domain          (30, 30)
     Direct access to arrays  (32, 32)
     Gather                   (32, 32)
 
 %% Cell type:markdown id: tags:
 
  So the actual arrays are 2 cells larger than what you asked for. This additional layer is used to copy over the data from the other end of the array, such that for the stencil algorithm effectively the domain is periodic. This copying operation has to be started manually though:
 
 %% Cell type:code id: tags:
 
 ``` python
 dh = ps.create_data_handling((4, 4), periodicity=(True, False))
 dh.add_array("src")
 
 # get copy function
 copy_fct = dh.synchronization_function(['src'])
 
 dh.fill('src', 0.0, ghost_layers=True)
 dh.fill('src', 3.0, ghost_layers=False)
 before_sync = dh.gather_array('src', ghost_layers=True).copy()
 
 copy_fct() # copy over to get periodicity in x direction
 
 after_sync = dh.gather_array('src', ghost_layers=True).copy()
 plt.subplot(1,2,1)
 plt.scalar_field(before_sync);
 plt.title("Before")
 plt.subplot(1,2,2)
 plt.scalar_field(after_sync);
 plt.title("After");
 ```
 
 %% Output
 
-
+
 
 %% Cell type:markdown id: tags:
 
 ## 3. Going (MPI) parallel - the parallel data handling
 
 ### 3.1. Conceptual overview
 To run MPI parallel simulations the waLBerla framework is used. waLBerla has to be compiled against your local MPI library and thus is a bit hard to install. We suggest to use the version shipped with conda for testing. For production, when you want to run on a cluster the best option is to build waLBerla yourself against the MPI library that is installed on your cluster.
 
 Now lets have a look on how to write code that runs MPI parallel.
 Since the data is distributed, we don't have access to the full array any more but only to the part that is stored locally. The domain is split into so called blocks, where one process might get one (or sometimes multiple) blocks. To do anything with the local part of the data we iterate over the **local** blocks to get the contents as numpy arrays. The blocks returned in the loop differ from process to process.
 
 Copy the following snippet to a python file and run with multiple processes e.g.:
 ``mpirun -np 4 myscript.py`` you will see that there are as many blocks as processes.
 
 %% Cell type:code id: tags:
 
 ``` python
 dh = ps.create_data_handling(domain_size=(30, 30), parallel=True)
 field = dh.add_array('field')
 for block in dh.iterate():
     # offset is in global coordinates, where first inner cell has coordiante (0,0)
     # and ghost layers have negative coordinates
     print(block.offset, block['field'].shape)
 
     # use offset to create a local array 'my_data' for the part of the domain
     #np.copyto(block[field.name], my_data)
 ```
 
 %% Output
 
-    (-1, -1) (32, 32)
+    ---------------------------------------------------------------------------
+    ValueError                                Traceback (most recent call last)
+    <ipython-input-20-779cf1a58f81> in <module>
+    ----> 1 dh = ps.create_data_handling(domain_size=(30, 30), parallel=True)
+          2 field = dh.add_array('field')
+          3 for block in dh.iterate():
+          4     # offset is in global coordinates, where first inner cell has coordiante (0,0)
+          5     # and ghost layers have negative coordinates
+    ~/git/pystencils/pystencils/datahandling/__init__.py in create_data_handling(domain_size, periodicity, default_layout, default_target, parallel, default_ghost_layers, opencl_queue)
+         38         assert not opencl_queue, "OpenCL is only supported for SerialDataHandling"
+         39         if wlb is None:
+    ---> 40             raise ValueError("Cannot create parallel data handling because walberla module is not available")
+         41
+         42         if periodicity is False or periodicity is None:
+    ValueError: Cannot create parallel data handling because walberla module is not available
 
 %% Cell type:markdown id: tags:
 
 To get some more interesting results here in the notebook we put multiple blocks onto our single notebook process. This makes not much sense for real simulations, but for testing and demonstration purposes this is useful.
 
 %% Cell type:code id: tags:
 
 ``` python
 from waLBerla import createUniformBlockGrid
 from pystencils.datahandling import ParallelDataHandling
 
 blocks = createUniformBlockGrid(blocks=(2,1,2), cellsPerBlock=(20, 10, 20),
                                 oneBlockPerProcess=False, periodic=(1, 0, 0))
 dh = ParallelDataHandling(blocks)
 field = dh.add_array('field')
 for block in dh.iterate():
     print(block.offset, block['field'].shape)
 ```
 
-%% Output
-
-    (-1, -1, -1) (22, 12, 22)
-    (19, -1, -1) (22, 12, 22)
-    (-1, -1, 19) (22, 12, 22)
-    (19, -1, 19) (22, 12, 22)
-
 %% Cell type:markdown id: tags:
 
 Now we see that we have four blocks with (20, 10, 20) block each, and the global domain is (40, 10, 40) big.
 All subblock also have a ghost layer around them, which is used to synchronize with their neighboring blocks (over the network). For ghost layer synchronization the same `synchronization_function` is used that we used above for periodic boundaries, because copying between blocks and copying the ghost layer for periodicity uses the same mechanism.
 
 %% Cell type:code id: tags:
 
 ``` python
 dh.gather_array('field').shape
 ```
 
-%% Output
-
-
-    $\displaystyle \left( 40, \  10, \  40\right)$
-    (40, 10, 40)
-
 %% Cell type:markdown id: tags:
 
 ### 3.2. Parallel example
 
 To illustrate this in more detail, lets run a simple kernel on a parallel domain. waLBerla can handle 3D domains only, so we choose a z-size of 1.
 
 %% Cell type:code id: tags:
 
 ``` python
 blocks = createUniformBlockGrid(blocks=(2,4,1), cellsPerBlock=(20, 10, 1),
                                 oneBlockPerProcess=False, periodic=(1, 1, 0))
 dh = ParallelDataHandling(blocks, dim=2)
 src_field = dh.add_array('src')
 dst_field = dh.add_array('dst')
 
 update_rule = [ps.Assignment(lhs=dst_field[0, 0 ],
                              rhs=(src_field[1, 0] + src_field[-1, 0] +
                                   src_field[0, 1] + src_field[0, -1]) / 4)]
 
 # 3. compile update rule to function
 kernel_function = ps.create_kernel(update_rule).compile()
 ```
 
 %% Cell type:markdown id: tags:
 
 Now lets initialize the arrays. To do this we can get arrays (meshgrids) from the block with the coordinates of the local cells. We use this to initialize a circular shape.
 
 %% Cell type:code id: tags:
 
 ``` python
 dh.fill('src', 0.0)
 for block in dh.iterate(ghost_layers=False, inner_ghost_layers=False):
     x, y = block.midpoint_arrays
     inside_sphere = (x -20)**2 + (y-25)**2  < 8 ** 2
     block['src'][inside_sphere] = 1.0
 plt.scalar_field( dh.gather_array('src') );
 ```
 
-%% Output
-
-
-
 %% Cell type:markdown id: tags:
 
 Now we can run our compute kernel on this data as usual. We just have to make sure that the field is synchronized after every step, i.e. that the ghost layers are correctly updated.
 
 %% Cell type:code id: tags:
 
 ``` python
 sync = dh.synchronization_function(['src'])
 for i in range(40):
     sync()
     dh.run_kernel(kernel_function)
     dh.swap('src', 'dst')
 ```
 
 %% Cell type:code id: tags:
 
 ``` python
 plt.scalar_field( dh.gather_array('src') );
 ```
-
-%% Output
-
-

doc/notebooks/06_tutorial_phasefield_dentritic_growth.ipynb

 %% Cell type:code id: tags:
 ``` python
 from pystencils.session import *
+import pystencils as ps
 sp.init_printing()
 frac = sp.Rational
 ```
 %% Cell type:markdown id: tags:
 # Tutorial 06: Phase-field simulation of dentritic solidification
 This is the second tutorial on phase field methods with pystencils. Make sure to read the previous tutorial first.
 In this tutorial we again implement a model described in **Programming Phase-Field Modelling** by S. Bulent Biner.
 This time we implement the model from chapter 4.7 that describes dentritic growth. So get ready for some beautiful snowflake pictures.
 We start again by adding all required arrays fields. This time we explicitly store the change of the phase variable φ in time, since the dynamics is calculated using an Allen-Cahn formulation where a term $\partial_t \phi$ occurs.
 %% Cell type:code id: tags:
 ``` python
 dh = ps.create_data_handling(domain_size=(300, 300), periodicity=True,
-                             default_target='cpu')
+                             default_target=ps.Target.CPU)
 φ_field = dh.add_array('phi', latex_name='φ')
 φ_field_tmp = dh.add_array('phi_temp', latex_name='φ_temp')
 φ_delta_field = dh.add_array('phidelta', latex_name='φ_D')
 t_field = dh.add_array('T')
 ```
 %% Cell type:markdown id: tags:
 This model has a lot of parameters that are created here in a symbolic fashion.
 %% Cell type:code id: tags:
 ``` python
 ε, m, δ, j, θzero, α, γ, Teq, κ, τ = sp.symbols("ε m δ j θ_0 α γ T_eq κ τ")
 εb = sp.Symbol("\\bar{\\epsilon}")
 φ = φ_field.center
 φ_tmp = φ_field_tmp.center
 T = t_field.center
 def f(φ, m):
     return φ**4 / 4 - (frac(1, 2) - m/3) * φ**3 + (frac(1,4)-m/2)*φ**2
 free_energy_density = ε**2 / 2 * (ps.fd.Diff(φ,0)**2 + ps.fd.Diff(φ,1)**2 ) + f(φ, m)
 free_energy_density
 ```
 %% Output
     $\displaystyle \frac{{{φ}_{(0,0)}}^{4}}{4} - {{φ}_{(0,0)}}^{3} \left(\frac{1}{2} - \frac{m}{3}\right) + {{φ}_{(0,0)}}^{2} \left(\frac{1}{4} - \frac{m}{2}\right) + \frac{ε^{2} \left({\partial_{0} {{φ}_{(0,0)}}}^{2} + {\partial_{1} {{φ}_{(0,0)}}}^{2}\right)}{2}$
        4                                  2 ⎛         2            2⎞
     φ_C       3 ⎛1   m⎞      2 ⎛1   m⎞   ε ⋅⎝D(φ[0,0])  + D(φ[0,0]) ⎠
     ──── - φ_C ⋅⎜─ - ─⎟ + φ_C ⋅⎜─ - ─⎟ + ────────────────────────────
      4          ⎝2   3⎠        ⎝4   2⎠                2
 %% Cell type:markdown id: tags:
 The free energy is again composed of a bulk and interface part.
 %% Cell type:code id: tags:
 ``` python
 plt.figure(figsize=(7,4))
 plt.sympy_function(f(φ, m=1), x_values=(-1.05, 1.5) )
 plt.xlabel("φ")
 plt.title("Bulk free energy");
 ```
 %% Output
 %% Cell type:markdown id: tags:
 Compared to last tutorial, this bulk free energy has also two minima, but at different values.
 Another complexity of the model is its anisotropy. The gradient parameter $\epsilon$ depends on the interface normal.
 %% Cell type:code id: tags:
 ``` python
 def σ(θ):
     return 1 + δ * sp.cos(j * (θ - θzero))
 θ = sp.atan2(ps.fd.Diff(φ, 1), ps.fd.Diff(φ, 0))
 ε_val = εb * σ(θ)
 ε_val
 ```
 %% Output
     $\displaystyle \bar{\epsilon} \left(δ \cos{\left(j \left(- θ_{0} + \operatorname{atan_{2}}{\left({\partial_{1} {{φ}_{(0,0)}}},{\partial_{0} {{φ}_{(0,0)}}} \right)}\right) \right)} + 1\right)$
     \bar{\epsilon}⋅(δ⋅cos(j⋅(-θ₀ + atan2(D(φ[0,0]), D(φ[0,0])))) + 1)
 %% Cell type:code id: tags:
 ``` python
 def m_func(T):
     return (α / sp.pi) * sp.atan(γ * (Teq - T))
 ```
 %% Cell type:markdown id: tags:
 However, we just insert these parameters into the free energy formulation before doing the functional derivative, to make the dependence of $\epsilon$ on $\nabla \phi$ explicit.
 %% Cell type:code id: tags:
 ``` python
 fe = free_energy_density.subs({
     m: m_func(T),
     ε: εb * σ(θ),
 })
 dF_dφ = ps.fd.functional_derivative(fe, φ)
 dF_dφ = ps.fd.expand_diff_full(dF_dφ, functions=[φ])
 dF_dφ
 ```
 %% Output
     $\displaystyle {{φ}_{(0,0)}}^{3} - \frac{{{φ}_{(0,0)}}^{2} α \operatorname{atan}{\left({{T}_{(0,0)}} γ - T_{eq} γ \right)}}{\pi} - \frac{3 {{φ}_{(0,0)}}^{2}}{2} + \frac{{{φ}_{(0,0)}} α \operatorname{atan}{\left({{T}_{(0,0)}} γ - T_{eq} γ \right)}}{\pi} + \frac{{{φ}_{(0,0)}}}{2} - \bar{\epsilon}^{2} δ^{2} \cos^{2}{\left(j θ_{0} - j \operatorname{atan_{2}}{\left({\partial_{1} {{φ}_{(0,0)}}},{\partial_{0} {{φ}_{(0,0)}}} \right)} \right)} {\partial_{0} {\partial_{0} {{φ}_{(0,0)}}}} - \bar{\epsilon}^{2} δ^{2} \cos^{2}{\left(j θ_{0} - j \operatorname{atan_{2}}{\left({\partial_{1} {{φ}_{(0,0)}}},{\partial_{0} {{φ}_{(0,0)}}} \right)} \right)} {\partial_{1} {\partial_{1} {{φ}_{(0,0)}}}} - 2 \bar{\epsilon}^{2} δ \cos{\left(j θ_{0} - j \operatorname{atan_{2}}{\left({\partial_{1} {{φ}_{(0,0)}}},{\partial_{0} {{φ}_{(0,0)}}} \right)} \right)} {\partial_{0} {\partial_{0} {{φ}_{(0,0)}}}} - 2 \bar{\epsilon}^{2} δ \cos{\left(j θ_{0} - j \operatorname{atan_{2}}{\left({\partial_{1} {{φ}_{(0,0)}}},{\partial_{0} {{φ}_{(0,0)}}} \right)} \right)} {\partial_{1} {\partial_{1} {{φ}_{(0,0)}}}} - \bar{\epsilon}^{2} {\partial_{0} {\partial_{0} {{φ}_{(0,0)}}}} - \bar{\epsilon}^{2} {\partial_{1} {\partial_{1} {{φ}_{(0,0)}}}}$
               2                               2
        3   φ_C ⋅α⋅atan(T_C⋅γ - T_eq⋅γ)   3⋅φ_C    φ_C⋅α⋅atan(T_C⋅γ - T_eq⋅γ)   φ_C
     φ_C  - ─────────────────────────── - ────── + ────────────────────────── + ───
                         π                  2                  π                 2
     
     
                      2  2    2
      - \bar{\epsilon} ⋅δ ⋅cos (j⋅θ₀ - j⋅atan2(D(φ[0,0]), D(φ[0,0])))⋅D(D(φ[0,0]))
     
     
     
                     2  2    2
     - \bar{\epsilon} ⋅δ ⋅cos (j⋅θ₀ - j⋅atan2(D(φ[0,0]), D(φ[0,0])))⋅D(D(φ[0,0])) -
     
     
     
                      2
      2⋅\bar{\epsilon} ⋅δ⋅cos(j⋅θ₀ - j⋅atan2(D(φ[0,0]), D(φ[0,0])))⋅D(D(φ[0,0])) -
     
     
     
                     2
     2⋅\bar{\epsilon} ⋅δ⋅cos(j⋅θ₀ - j⋅atan2(D(φ[0,0]), D(φ[0,0])))⋅D(D(φ[0,0])) - \
     
     
     
                  2                              2
     bar{\epsilon} ⋅D(D(φ[0,0])) - \bar{\epsilon} ⋅D(D(φ[0,0]))
     
 %% Cell type:markdown id: tags:
 Then we insert all the numeric parameters and discretize:
 %% Cell type:code id: tags:
 ``` python
 discretize = ps.fd.Discretization2ndOrder(dx=0.03, dt=1e-5)
 parameters = {
     τ: 0.0003,
     κ: 1.8,
     εb: 0.01,
     δ: 0.02,
     γ: 10,
     j: 6,
     α: 0.9,
     Teq: 1.0,
     θzero: 0.2,
     sp.pi: sp.pi.evalf()
 }
 parameters
 ```
 %% Output
     $\displaystyle \left\{ \pi : 3.14159265358979, \  T_{eq} : 1.0, \  \bar{\epsilon} : 0.01, \  j : 6, \  α : 0.9, \  γ : 10, \  δ : 0.02, \  θ_{0} : 0.2, \  κ : 1.8, \  τ : 0.0003\right\}$
     {π: 3.14159265358979, T_eq: 1.0, \bar{\epsilon}: 0.01, j: 6, α: 0.9, γ: 10, δ:
      0.02, θ₀: 0.2, κ: 1.8, τ: 0.0003}
 %% Cell type:code id: tags:
 ``` python
 dφ_dt = - dF_dφ / τ
 φ_eqs = ps.simp.sympy_cse_on_assignment_list([ps.Assignment(φ_delta_field.center,
                                                             discretize(dφ_dt.subs(parameters)))])
 φ_eqs.append(ps.Assignment(φ_tmp, discretize(ps.fd.transient(φ) - φ_delta_field.center)))
 temperature_evolution = -ps.fd.transient(T) + ps.fd.diffusion(T, 1) + κ * φ_delta_field.center
 temperature_eqs = [
     ps.Assignment(T, discretize(temperature_evolution.subs(parameters)))
 ]
 ```
 %% Cell type:markdown id: tags:
-When creating the kernels we pass as target (which may be 'cpu' or 'gpu') the default target of the target handling. This enables to switch to a GPU simulation just by changing the parameter of the data handling.
+When creating the kernels we pass as target (which may be 'Target.CPU' or 'Target.GPU') the default target of the target handling. This enables to switch to a GPU simulation just by changing the parameter of the data handling.
 The rest is similar to the previous tutorial.
 %% Cell type:code id: tags:
 ``` python
 φ_kernel = ps.create_kernel(φ_eqs, cpu_openmp=4, target=dh.default_target).compile()
 temperature_kernel = ps.create_kernel(temperature_eqs, target=dh.default_target).compile()
 ```
 %% Cell type:code id: tags:
 ``` python
 def timeloop(steps=200):
     φ_sync = dh.synchronization_function(['phi'])
     temperature_sync = dh.synchronization_function(['T'])
     dh.all_to_gpu()  # this does nothing when running on CPU
     for t in range(steps):
         φ_sync()
         dh.run_kernel(φ_kernel)
         dh.swap('phi', 'phi_temp')
         temperature_sync()
         dh.run_kernel(temperature_kernel)
     dh.all_to_cpu()
     return dh.gather_array('phi')
 def init(nucleus_size=np.sqrt(5)):
     for b in dh.iterate():
         x, y = b.cell_index_arrays
         x, y = x-b.shape[0]//2, y-b.shape[0]//2
         bArr = (x**2 + y**2) < nucleus_size**2
         b['phi'].fill(0)
         b['phi'][(x**2 + y**2) < nucleus_size**2] = 1.0
         b['T'].fill(0.0)
 def plot():
     plt.subplot(1,3,1)
     plt.scalar_field(dh.gather_array('phi'))
     plt.title("φ")
     plt.colorbar()
     plt.subplot(1,3,2)
     plt.title("T")
     plt.scalar_field(dh.gather_array('T'))
     plt.colorbar()
     plt.subplot(1,3,3)
     plt.title("∂φ")
     plt.scalar_field(dh.gather_array('phidelta'))
     plt.colorbar()
 ```
 %% Cell type:code id: tags:
 ``` python
 timeloop(10)
 init()
 plot()
 print(dh)
 ```
 %% Output
         Name|      Inner (min/max)|     WithGl (min/max)
     ----------------------------------------------------
            T|            (  0,  0)|            (  0,  0)
          phi|            (  0,  1)|            (  0,  1)
     phi_temp|            (  0,  0)|            (  0,  0)
     phidelta|            (  0,  0)|            (  0,  0)
     
 %% Cell type:code id: tags:
 ``` python
 result = None
 if 'is_test_run' not in globals():
     ani = ps.plot.scalar_field_animation(timeloop, rescale=True, frames=600)
     result = ps.jupyter.display_as_html_video(ani)
 result
 ```
 %% Output
     <IPython.core.display.HTML object>
 %% Cell type:code id: tags:
 ``` python
 assert np.isfinite(dh.max('phi'))
 assert np.isfinite(dh.max('T'))
 ```

doc/notebooks/demo_wave_equation.ipynb

 %% Cell type:code id: tags:
 
 ``` python
+import psutil
+
 from pystencils.session import *
 
 import shutil
 ```
 
 %% Cell type:markdown id: tags:
 
 # Demo: Finite differences - 2D wave equation
 
 In this tutorial we show how to use the finite difference module of *pystencils* to solve a 2D wave equations. The time derivative is discretized by a simple forward Euler method.
 
 %% Cell type:markdown id: tags:
 
 $$ \frac{\partial^2 u}{\partial t^2} = \mbox{div} \left( q(x,y) \nabla u \right)$$
 
 %% Cell type:markdown id: tags:
 
 We begin by creating three *numpy* arrays for the current, the previous and the next timestep. Actually we will see later that two fields are enough, but let's keep it simple. From these *numpy* arrays we create *pystencils* fields to formulate our update rule.
 
 %% Cell type:code id: tags:
 
 ``` python
 size = (60, 70) #  domain size
 u_arrays = [np.zeros(size), np.zeros(size), np.zeros(size)]
 
 u_fields = [ps.Field.create_from_numpy_array("u%s" % (name,), arr)
             for name, arr in zip(["0", "1", "2"], u_arrays)]
 
 # Nicer display for fields
 for i, field in enumerate(u_fields):
     field.latex_name = "u^{(%d)}" % (i,)
 ```
 
 %% Cell type:markdown id: tags:
 
 *pystencils* contains already simple rules to discretize the a diffusion term. The time discretization is done manually.
 
 %% Cell type:code id: tags:
 
 ``` python
 discretize = ps.fd.Discretization2ndOrder()
 
 def central2nd_time_derivative(fields):
     f_next, f_current, f_last = fields
     return (f_next[0, 0] - 2 * f_current[0, 0] + f_last[0, 0]) / discretize.dt**2
 
 rhs = ps.fd.diffusion(u_fields[1], 1)
 
 wave_eq = sp.Eq(central2nd_time_derivative(u_fields), discretize(rhs))
 
 wave_eq = sp.simplify(wave_eq)
 wave_eq
 ```
 
 %% Output
 
     $$\frac{{{u^{(0)}}_{(0,0)}} - 2 {{u^{(1)}}_{(0,0)}} + {{u^{(2)}}_{(0,0)}}}{dt^{2}} = \frac{{{u^{(1)}}_{(-1,0)}} + {{u^{(1)}}_{(0,-1)}} - 4 {{u^{(1)}}_{(0,0)}} + {{u^{(1)}}_{(0,1)}} + {{u^{(1)}}_{(1,0)}}}{dx^{2}}$$
     u_0_C - 2⋅u_1_C + u_2_C   u_1_W + u_1_S - 4⋅u_1_C + u_1_N + u_1_E
     ─────────────────────── = ───────────────────────────────────────
                 2                                 2
               dt                                dx
 
 %% Cell type:markdown id: tags:
 
 The explicit Euler scheme is now obtained by solving above equation with respect to $u_C^{next}$.
 
 %% Cell type:code id: tags:
 
 ``` python
 u_next_C = u_fields[-1][0, 0]
 update_rule = ps.Assignment(u_next_C, sp.solve(wave_eq, u_next_C)[0])
 update_rule
 ```
 
 %% Output
 
     $${{u^{(2)}}_{(0,0)}} \leftarrow \frac{{{u^{(1)}}_{(-1,0)}} dt^{2} + {{u^{(1)}}_{(0,-1)}} dt^{2} - 4 {{u^{(1)}}_{(0,0)}} dt^{2} + {{u^{(1)}}_{(0,1)}} dt^{2} + {{u^{(1)}}_{(1,0)}} dt^{2} + dx^{2} \left(- {{u^{(0)}}_{(0,0)}} + 2 {{u^{(1)}}_{(0,0)}}\right)}{dx^{2}}$$
                      2           2             2           2           2     2
              u_1_W⋅dt  + u_1_S⋅dt  - 4⋅u_1_C⋅dt  + u_1_N⋅dt  + u_1_E⋅dt  + dx ⋅(-u
     u_2_C := ─────────────────────────────────────────────────────────────────────
                                                        2
                                                      dx
     
     
     _0_C + 2⋅u_1_C)
     ───────────────
     
     
 
 %% Cell type:markdown id: tags:
 
 Before creating the kernel, we substitute numeric values for $dx$ and $dt$.
 Then a kernel is created just like in the last tutorial.
 
 %% Cell type:code id: tags:
 
 ``` python
 update_rule = update_rule.subs({discretize.dx: 0.1, discretize.dt: 0.05})
 ast = ps.create_kernel(update_rule)
 kernel = ast.compile()
 
 ps.show_code(ast)
 ```
 
 %% Output
 
 
     
     FUNC_PREFIX void kernel(double * RESTRICT const _data_u0, double * RESTRICT const _data_u1, double * RESTRICT _data_u2)
     {
        for (int ctr_0 = 1; ctr_0 < 59; ctr_0 += 1)
        {
           double * RESTRICT _data_u2_00 = _data_u2 + 70*ctr_0;
           double * RESTRICT const _data_u1_00 = _data_u1 + 70*ctr_0;
           double * RESTRICT const _data_u0_00 = _data_u0 + 70*ctr_0;
           double * RESTRICT const _data_u1_01 = _data_u1 + 70*ctr_0 + 70;
           double * RESTRICT const _data_u1_0m1 = _data_u1 + 70*ctr_0 - 70;
           for (int ctr_1 = 1; ctr_1 < 69; ctr_1 += 1)
           {
              _data_u2_00[ctr_1] = 0.25*_data_u1_00[ctr_1 + 1] + 0.25*_data_u1_00[ctr_1 - 1] + 0.25*_data_u1_01[ctr_1] + 0.25*_data_u1_0m1[ctr_1] - 1.0*_data_u0_00[ctr_1] + 1.0*_data_u1_00[ctr_1];
           }
        }
     }
 
 %% Cell type:code id: tags:
 
 ``` python
 ast.get_parameters()
 ```
 
 %% Output
 
     [_data_u0, _data_u1, _data_u2]
 
 %% Cell type:code id: tags:
 
 ``` python
 ast.fields_accessed
 ```
 
 %% Output
 
     {u0, u1, u2}
 
 %% Cell type:markdown id: tags:
 
 To run simulation a suitable initial condition and boundary treatment is required. We chose an initial condition which is zero at the borders of the domain. The outermost layer is not changed by the update kernel, so we have an implicit homogenous Dirichlet boundary condition.
 
 %% Cell type:code id: tags:
 
 ``` python
 X,Y = np.meshgrid( np.linspace(0, 1, size[1]), np.linspace(0,1, size[0]))
 Z = np.sin(2*X*np.pi) * np.sin(2*Y*np.pi)
 
 # Initialize the previous and current values with the initial function
 np.copyto(u_arrays[0], Z)
 np.copyto(u_arrays[1], Z)
 # The values for the next timesteps do not matter, since they are overwritten
 u_arrays[2][:, :] = 0
 ```
 
 %% Cell type:markdown id: tags:
 
 One timestep now consists of applying the kernel once, then shifting the arrays.
 
 %% Cell type:code id: tags:
 
 ``` python
 def run(timesteps=1):
     for t in range(timesteps):
         kernel(u0=u_arrays[0], u1=u_arrays[1], u2=u_arrays[2])
         u_arrays[0], u_arrays[1], u_arrays[2] = u_arrays[1], u_arrays[2], u_arrays[0]
     return u_arrays[2]
 ```
 
 %% Cell type:markdown id: tags:
 
 Lets create an animation of the solution:
 
 %% Cell type:code id: tags:
 
 ``` python
 if shutil.which("ffmpeg") is not None:
     ani = plt.surface_plot_animation(run, zlim=(-1, 1))
     ps.jupyter.display_as_html_video(ani)
 else:
     print("No ffmpeg installed")
 ```
 
 %% Output
 
     <IPython.core.display.HTML object>
 
 %% Cell type:code id: tags:
 
 ``` python
 assert np.isfinite(np.max(u_arrays[2]))
 ```
 
 %% Cell type:markdown id: tags:
 
 __LLVM backend__
 
 The next cell demonstrates how to run the same simulation with the LLVM backend. The only difference is that in the `create_kernel` function the `target` is set to llvm.
 
 %% Cell type:code id: tags:
 
 ``` python
 try:
     import llvmlite
 except ImportError:
     llvmlite=None
     print('No llvmlite installed')
 
 if llvmlite:
-    kernel = ps.create_kernel(update_rule, target='llvm').compile()
+    kernel = ps.create_kernel(update_rule, backend=ps.Backend.LLVM).compile()
 
     X,Y = np.meshgrid( np.linspace(0, 1, size[1]), np.linspace(0,1, size[0]))
     Z = np.sin(2*X*np.pi) * np.sin(2*Y*np.pi)
 
     # Initialize the previous and current values with the initial function
     np.copyto(u_arrays[0], Z)
     np.copyto(u_arrays[1], Z)
     # The values for the next timesteps do not matter, since they are overwritten
     u_arrays[2][:, :] = 0
 
     def run_LLVM(timesteps=1):
         for t in range(timesteps):
             kernel(u0=u_arrays[0], u1=u_arrays[1], u2=u_arrays[2])
             u_arrays[0], u_arrays[1], u_arrays[2] = u_arrays[1], u_arrays[2], u_arrays[0]
         return u_arrays[2]
 
     assert np.isfinite(np.max(u_arrays[2]))
     if shutil.which("ffmpeg") is not None:
         ani = plt.surface_plot_animation(run_LLVM, zlim=(-1, 1))
         ps.jupyter.display_as_html_video(ani)
     else:
         print("No ffmpeg installed")
 ```
 
 %% Output
 
     <IPython.core.display.HTML object>
 
 %% Cell type:markdown id: tags:
 
 __Runing on GPU__
 
 We can also run the same kernel on the GPU, by using the ``pycuda`` package.
 
 %% Cell type:code id: tags:
 
 ``` python
 try:
     import pycuda
 except ImportError:
     pycuda=None
     print('No pycuda installed')
 
 
 res = None
 if pycuda:
-    gpu_ast = ps.create_kernel(update_rule, target='gpu')
+    gpu_ast = ps.create_kernel(update_rule, target=ps.Target.GPU)
     gpu_kernel = gpu_ast.compile()
     res = ps.show_code(gpu_ast)
 res
 ```
 
 %% Output
 
 
     
     FUNC_PREFIX __launch_bounds__(256) void kernel(double * RESTRICT const _data_u0, double * RESTRICT const _data_u1, double * RESTRICT _data_u2)
     {
        if (blockDim.x*blockIdx.x + threadIdx.x + 1 < 59 && blockDim.y*blockIdx.y + threadIdx.y + 1 < 69)
        {
           const int64_t ctr_0 = blockDim.x*blockIdx.x + threadIdx.x + 1;
           const int64_t ctr_1 = blockDim.y*blockIdx.y + threadIdx.y + 1;
           double * RESTRICT _data_u2_10 = _data_u2 + ctr_1;
           double * RESTRICT const _data_u1_10 = _data_u1 + ctr_1;
           double * RESTRICT const _data_u0_10 = _data_u0 + ctr_1;
           double * RESTRICT const _data_u1_11 = _data_u1 + ctr_1 + 1;
           double * RESTRICT const _data_u1_1m1 = _data_u1 + ctr_1 - 1;
           _data_u2_10[70*ctr_0] = 0.25*_data_u1_10[70*ctr_0 + 70] + 0.25*_data_u1_10[70*ctr_0 - 70] + 0.25*_data_u1_11[70*ctr_0] + 0.25*_data_u1_1m1[70*ctr_0] - 1.0*_data_u0_10[70*ctr_0] + 1.0*_data_u1_10[70*ctr_0];
        }
     }
 
 %% Cell type:markdown id: tags:
 
 The run function has to be changed now slightly, since the data has to be transfered to the GPU first, then the kernel can be executed, and in the end the data has to be transfered back
 
 %% Cell type:code id: tags:
 
 ``` python
 if pycuda:
     import pycuda.gpuarray as gpuarray
 
     def run_on_gpu(timesteps=1):
         # Transfer arrays to GPU
         gpuArrs = [gpuarray.to_gpu(a) for a in u_arrays]
 
         for t in range(timesteps):
             gpu_kernel(u0=gpuArrs[0], u1=gpuArrs[1], u2=gpuArrs[2])
             gpuArrs[0], gpuArrs[1], gpuArrs[2] = gpuArrs[1], gpuArrs[2], gpuArrs[0]
 
         # Transfer arrays to CPU
         for gpuArr, cpuArr in zip(gpuArrs, u_arrays):
             gpuArr.get(cpuArr)
 assert np.isfinite(np.max(u_arrays[2]))
 ```
 
 %% Cell type:code id: tags:
 
 ``` python
 if pycuda:
     run_on_gpu(400)
 ```

doc/sphinx/kernel_compile_and_call.rst

@@ -8,6 +8,10 @@ Creating kernels
 
 .. autofunction:: pystencils.create_kernel
 
+.. autofunction:: pystencils.CreateKernelConfig
+
+.. autofunction:: pystencils.create_domain_kernel
+
 .. autofunction:: pystencils.create_indexed_kernel
 
 .. autofunction:: pystencils.create_staggered_kernel

pystencils/__init__.py

 """Module to generate stencil kernels in C or CUDA using sympy expressions and call them as Python functions"""
+from .enums import Backend, Target
 from . import fd
 from . import stencil as stencil
 from .assignment import Assignment, assignment_from_stencil
 from .data_types import TypedSymbol
 from .datahandling import create_data_handling
-from .display_utils import show_code, get_code_obj, get_code_str, to_dot
+from .display_utils import get_code_obj, get_code_str, show_code, to_dot
 from .field import Field, FieldType, fields
 from .kernel_decorator import kernel
-from .kernelcreation import create_indexed_kernel, create_kernel, create_staggered_kernel
+from .kernelcreation import (
+    CreateKernelConfig, create_domain_kernel, create_indexed_kernel, create_kernel, create_staggered_kernel)
 from .simp import AssignmentCollection
 from .slicing import make_slice
+from .spatial_coordinates import x_, x_staggered, x_staggered_vector, x_vector, y_, y_staggered, z_, z_staggered
 from .sympyextensions import SymbolCreator
-from .spatial_coordinates import (x_, x_staggered, x_staggered_vector, x_vector,
-                                  y_, y_staggered, z_, z_staggered)
 
 try:
     import pystencils_autodiff
+
     autodiff = pystencils_autodiff
 except ImportError:
     pass
 
-
 __all__ = ['Field', 'FieldType', 'fields',
            'TypedSymbol',
            'make_slice',
-           'create_kernel', 'create_indexed_kernel', 'create_staggered_kernel',
+           'create_kernel', 'create_domain_kernel', 'create_indexed_kernel', 'create_staggered_kernel',
+           'CreateKernelConfig',
+           'Target', 'Backend',
            'show_code', 'to_dot', 'get_code_obj', 'get_code_str',
            'AssignmentCollection',
            'Assignment',
@@ -39,5 +42,6 @@ __all__ = ['Field', 'FieldType', 'fields',
            'stencil']
 
 from ._version import get_versions
+
 __version__ = get_versions()['version']
 del get_versions

pystencils/astnodes.py

@@ -7,6 +7,7 @@ import sympy as sp
 
 import pystencils
 from pystencils.data_types import TypedImaginaryUnit, TypedSymbol, cast_func, create_type
+from pystencils.enums import Target, Backend
 from pystencils.field import Field
 from pystencils.kernelparameters import FieldPointerSymbol, FieldShapeSymbol, FieldStrideSymbol
 from pystencils.sympyextensions import fast_subs
@@ -136,7 +137,6 @@ class Conditional(Node):
 
 
 class KernelFunction(Node):
-
     class Parameter:
         """Function parameter.
 
@@ -176,7 +176,9 @@ class KernelFunction(Node):
         def field_name(self):
             return self.fields[0].name
 
-    def __init__(self, body, target, backend, compile_function, ghost_layers, function_name="kernel", assignments=None):
+    def __init__(self, body, target: Target, backend: Backend, compile_function, ghost_layers,
+                 function_name: str = "kernel",
+                 assignments=None):
         super(KernelFunction, self).__init__()
         self._body = body
         body.parent = self
@@ -194,12 +196,12 @@ class KernelFunction(Node):
 
     @property
     def target(self):
-        """Currently either 'cpu' or 'gpu' """
+        """See pystencils.Target"""
         return self._target
 
     @property
     def backend(self):
-        """Backend for generating the code e.g. 'llvm', 'c', 'cuda' """
+        """Backend for generating the code: `Backend`"""
         return self._backend
 
     @property

pystencils/backends/cbackend.py

@@ -14,6 +14,7 @@ from pystencils.cpu.vectorization import vec_all, vec_any, CachelineSize
 from pystencils.data_types import (
     PointerType, VectorType, address_of, cast_func, create_type, get_type_of_expression,
     reinterpret_cast_func, vector_memory_access, BasicType, TypedSymbol)
+from pystencils.enums import Backend
 from pystencils.fast_approximation import fast_division, fast_inv_sqrt, fast_sqrt
 from pystencils.integer_functions import (
     bit_shift_left, bit_shift_right, bitwise_and, bitwise_or, bitwise_xor,
@@ -34,7 +35,7 @@ KERNCRAFT_NO_TERNARY_MODE = False
 
 def generate_c(ast_node: Node,
                signature_only: bool = False,
-               dialect='c',
+               dialect: Backend = Backend.C,
                custom_backend=None,
                with_globals=True) -> str:
     """Prints an abstract syntax tree node as C or CUDA code.
@@ -46,7 +47,7 @@ def generate_c(ast_node: Node,
     Args:
         ast_node: ast representation of kernel
         signature_only: generate signature without function body
-        dialect: 'c', 'cuda' or opencl
+        dialect: `Backend`: 'C', 'CUDA' or 'OPENCL'
         custom_backend: use own custom printer for code generation
         with_globals: enable usage of global variables
     Returns:
@@ -60,21 +61,21 @@ def generate_c(ast_node: Node,
             ast_node.global_variables = d.symbols_defined
     if custom_backend:
         printer = custom_backend
-    elif dialect == 'c':
+    elif dialect == Backend.C:
         try:
             instruction_set = ast_node.instruction_set
         except Exception:
             instruction_set = None
         printer = CBackend(signature_only=signature_only,
                            vector_instruction_set=instruction_set)
-    elif dialect == 'cuda':
+    elif dialect == Backend.CUDA:
         from pystencils.backends.cuda_backend import CudaBackend
         printer = CudaBackend(signature_only=signature_only)
-    elif dialect == 'opencl':
+    elif dialect == Backend.OPENCL:
         from pystencils.backends.opencl_backend import OpenClBackend
         printer = OpenClBackend(signature_only=signature_only)
     else:
-        raise ValueError("Unknown dialect: " + str(dialect))
+        raise ValueError(f'Unknown {dialect=}')
     code = printer(ast_node)
     if not signature_only and isinstance(ast_node, KernelFunction):
         if with_globals and global_declarations:
@@ -189,7 +190,7 @@ class CFunction(TypedSymbol):
 # noinspection PyPep8Naming
 class CBackend:
 
-    def __init__(self, sympy_printer=None, signature_only=False, vector_instruction_set=None, dialect='c'):
+    def __init__(self, sympy_printer=None, signature_only=False, vector_instruction_set=None, dialect=Backend.C):
         if sympy_printer is None:
             if vector_instruction_set is not None:
                 self.sympy_printer = VectorizedCustomSympyPrinter(vector_instruction_set)
@@ -228,7 +229,7 @@ class CBackend:
         function_arguments = [f"{self._print(s.symbol.dtype)} {s.symbol.name}" for s in node.get_parameters()
                               if not type(s.symbol) is CFunction]
         launch_bounds = ""
-        if self._dialect == 'cuda':
+        if self._dialect == Backend.CUDA:
             max_threads = node.indexing.max_threads_per_block()
             if max_threads:
                 launch_bounds = f"__launch_bounds__({max_threads}) "

pystencils/backends/cuda_backend.py

@@ -2,6 +2,7 @@ from os.path import dirname, join
 
 from pystencils.astnodes import Node
 from pystencils.backends.cbackend import CBackend, CustomSympyPrinter, generate_c
+from pystencils.enums import Backend
 from pystencils.fast_approximation import fast_division, fast_inv_sqrt, fast_sqrt
 from pystencils.interpolation_astnodes import DiffInterpolatorAccess, InterpolationMode
 
@@ -22,7 +23,7 @@ def generate_cuda(ast_node: Node, signature_only: bool = False, custom_backend=N
     Returns:
         CUDA code for the ast node and its descendants
     """
-    return generate_c(ast_node, signature_only, dialect='cuda',
+    return generate_c(ast_node, signature_only, dialect=Backend.CUDA,
                       custom_backend=custom_backend, with_globals=with_globals)
 
 
@@ -33,7 +34,7 @@ class CudaBackend(CBackend):
         if not sympy_printer:
             sympy_printer = CudaSympyPrinter()
 
-        super().__init__(sympy_printer, signature_only, dialect='cuda')
+        super().__init__(sympy_printer, signature_only, dialect=Backend.CUDA)
 
     def _print_SharedMemoryAllocation(self, node):
         dtype = node.symbol.dtype

pystencils/backends/opencl_backend.py

@@ -4,6 +4,7 @@ import pystencils.data_types
 from pystencils.astnodes import Node
 from pystencils.backends.cbackend import CustomSympyPrinter, generate_c
 from pystencils.backends.cuda_backend import CudaBackend, CudaSympyPrinter
+from pystencils.enums import Backend
 from pystencils.fast_approximation import fast_division, fast_inv_sqrt, fast_sqrt
 
 with open(join(dirname(__file__), 'opencl1.1_known_functions.txt')) as f:
@@ -12,7 +13,7 @@ with open(join(dirname(__file__), 'opencl1.1_known_functions.txt')) as f:
 
 
 def generate_opencl(ast_node: Node, signature_only: bool = False, custom_backend=None, with_globals=True) -> str:
-    """Prints an abstract syntax tree node (made for target 'gpu') as OpenCL code.
+    """Prints an abstract syntax tree node (made for `Target` 'GPU') as OpenCL code. # TODO Backend instead of Target?
 
     Args:
         ast_node: ast representation of kernel
@@ -23,7 +24,7 @@ def generate_opencl(ast_node: Node, signature_only: bool = False, custom_backend
     Returns:
         OpenCL code for the ast node and its descendants
     """
-    return generate_c(ast_node, signature_only, dialect='opencl',
+    return generate_c(ast_node, signature_only, dialect=Backend.OPENCL,
                       custom_backend=custom_backend, with_globals=with_globals)
 
 
@@ -36,7 +37,7 @@ class OpenClBackend(CudaBackend):
             sympy_printer = OpenClSympyPrinter()
 
         super().__init__(sympy_printer, signature_only)
-        self._dialect = 'opencl'
+        self._dialect = Backend.OPENCL
 
     def _print_Type(self, node):
         code = super()._print_Type(node)

pystencils/boundaries/boundaryhandling.py

 import numpy as np
 import sympy as sp
 
-from pystencils import create_indexed_kernel
+from pystencils import create_kernel, CreateKernelConfig, Target
 from pystencils.assignment import Assignment
 from pystencils.backends.cbackend import CustomCodeNode
 from pystencils.boundaries.createindexlist import (
@@ -84,7 +84,7 @@ class FlagInterface:
 class BoundaryHandling:
 
     def __init__(self, data_handling, field_name, stencil, name="boundary_handling", flag_interface=None,
-                 target='cpu', openmp=True):
+                 target: Target = Target.CPU, openmp=True):
         assert data_handling.has_data(field_name)
         assert data_handling.dim == len(stencil[0]), "Dimension of stencil and data handling do not match"
         self._data_handling = data_handling
@@ -442,9 +442,10 @@ class BoundaryOffsetInfo(CustomCodeNode):
     INV_DIR_SYMBOL = TypedSymbol("invdir", np.int64)
 
 
-def create_boundary_kernel(field, index_field, stencil, boundary_functor, target='cpu', **kernel_creation_args):
+def create_boundary_kernel(field, index_field, stencil, boundary_functor, target=Target.CPU, **kernel_creation_args):
     elements = [BoundaryOffsetInfo(stencil)]
     dir_symbol = TypedSymbol("dir", np.int64)
     elements += [Assignment(dir_symbol, index_field[0]('dir'))]
     elements += boundary_functor(field, direction_symbol=dir_symbol, index_field=index_field)
-    return create_indexed_kernel(elements, [index_field], target=target, **kernel_creation_args)
+    config = CreateKernelConfig(index_fields=[index_field], target=target, **kernel_creation_args)
+    return create_kernel(elements, config=config)

pystencils/cpu/kernelcreation.py

@@ -5,6 +5,7 @@ import numpy as np
 
 import pystencils.astnodes as ast
 from pystencils.assignment import Assignment
+from pystencils.enums import Target, Backend
 from pystencils.astnodes import Block, KernelFunction, LoopOverCoordinate, SympyAssignment
 from pystencils.cpu.cpujit import make_python_function
 from pystencils.data_types import StructType, TypedSymbol, create_type
@@ -70,7 +71,7 @@ def create_kernel(assignments: AssignmentOrAstNodeList, function_name: str = "ke
     loop_order = get_optimal_loop_ordering(fields_without_buffers)
     loop_node, ghost_layer_info = make_loop_over_domain(body, iteration_slice=iteration_slice,
                                                         ghost_layers=ghost_layers, loop_order=loop_order)
-    ast_node = KernelFunction(loop_node, 'cpu', 'c', compile_function=make_python_function,
+    ast_node = KernelFunction(loop_node, Target.CPU, Backend.C, compile_function=make_python_function,
                               ghost_layers=ghost_layer_info, function_name=function_name, assignments=assignments)
     implement_interpolations(body)
 
@@ -151,7 +152,7 @@ def create_indexed_kernel(assignments: AssignmentOrAstNodeList, index_fields, fu
         loop_body.append(assignment)
 
     function_body = Block([loop_node])
-    ast_node = KernelFunction(function_body, "cpu", "c", make_python_function,
+    ast_node = KernelFunction(function_body, Target.CPU, Backend.C, make_python_function,
                               ghost_layers=None, function_name=function_name, assignments=assignments)
 
     fixed_coordinate_mapping = {f.name: coordinate_typed_symbols for f in non_index_fields}

pystencils/datahandling/__init__.py

+import warnings
+
 from typing import Tuple, Union
 
 from .datahandling_interface import DataHandling
+from ..enums import Target
 from .serial_datahandling import SerialDataHandling
 
 try:
@@ -18,7 +21,7 @@ except ImportError:
 def create_data_handling(domain_size: Tuple[int, ...],
                          periodicity: Union[bool, Tuple[bool, ...]] = False,
                          default_layout: str = 'SoA',
-                         default_target: str = 'cpu',
+                         default_target: Target = Target.CPU,
                          parallel: bool = False,
                          default_ghost_layers: int = 1,
                          opencl_queue=None) -> DataHandling:
@@ -29,10 +32,16 @@ def create_data_handling(domain_size: Tuple[int, ...],
         periodicity: either True, False for full or no periodicity or a tuple of booleans indicating periodicity
                      for each coordinate
         default_layout: default array layout, that is used if not explicitly specified in 'add_array'
-        default_target: either 'cpu' or 'gpu'
+        default_target: `Target`
         parallel: if True a parallel domain is created using walberla - each MPI process gets a part of the domain
         default_ghost_layers: default number of ghost layers if not overwritten in 'add_array'
     """
+    if isinstance(default_target, str):
+        new_target = Target[default_target.upper()]
+        warnings.warn(f'Target "{default_target}" as str is deprecated. Use {new_target} instead',
+                      category=DeprecationWarning)
+        default_target = new_target
+
     if parallel:
         assert not opencl_queue, "OpenCL is only supported for SerialDataHandling"
         if wlb is None:

pystencils/datahandling/datahandling_interface.py

@@ -3,6 +3,7 @@ from typing import Callable, Dict, Iterable, Optional, Sequence, Tuple, Union
 
 import numpy as np
 
+from pystencils.enums import Target, Backend
 from pystencils.field import Field, FieldType
 
 
@@ -16,8 +17,8 @@ class DataHandling(ABC):
     'gather' function that has collects (parts of the) distributed data on a single process.
     """
 
-    _GPU_LIKE_TARGETS = ['gpu', 'opencl']
-    _GPU_LIKE_BACKENDS = ['gpucuda', 'opencl']
+    _GPU_LIKE_TARGETS = [Target.GPU, Target.OPENCL]
+    _GPU_LIKE_BACKENDS = [Backend.CUDA, Backend.OPENCL]
 
     # ---------------------------- Adding and accessing data -----------------------------------------------------------
 
@@ -56,7 +57,7 @@ class DataHandling(ABC):
             layout: memory layout of array, either structure of arrays 'SoA' or array of structures 'AoS'.
                     this is only important if values_per_cell > 1
             cpu: allocate field on the CPU
-            gpu: allocate field on the GPU, if None, a GPU field is allocated if default_target is 'gpu'
+            gpu: allocate field on the GPU, if None, a GPU field is allocated if default_target is 'GPU'
             alignment: either False for no alignment, or the number of bytes to align to
         Returns:
             pystencils field, that can be used to formulate symbolic kernels
@@ -91,7 +92,7 @@ class DataHandling(ABC):
             layout: memory layout of array, either structure of arrays 'SoA' or array of structures 'AoS'.
                     this is only important if values_per_cell > 1
             cpu: allocate field on the CPU
-            gpu: allocate field on the GPU, if None, a GPU field is allocated if default_target is 'gpu'
+            gpu: allocate field on the GPU, if None, a GPU field is allocated if default_target is 'GPU'
             alignment: either False for no alignment, or the number of bytes to align to
         Returns:
             Fields representing the just created arrays
@@ -280,7 +281,7 @@ class DataHandling(ABC):
             names: what data to synchronize: name of array or sequence of names
             stencil: stencil as string defining which neighbors are synchronized e.g. 'D2Q9', 'D3Q19'
                      if None, a full synchronization (i.e. D2Q9 or D3Q27) is done
-            target: either 'cpu' or 'gpu
+            target: `Target` either 'CPU' or 'GPU'
             kwargs: implementation specific, optional optimization parameters for communication
 
         Returns:

pystencils/datahandling/parallel_datahandling.py

@@ -7,16 +7,18 @@ import waLBerla as wlb
 
 from pystencils.datahandling.blockiteration import block_iteration, sliced_block_iteration
 from pystencils.datahandling.datahandling_interface import DataHandling
+from pystencils.enums import Backend
 from pystencils.field import Field, FieldType
 from pystencils.kernelparameters import FieldPointerSymbol
 from pystencils.utils import DotDict
+from pystencils import Target
 
 
 class ParallelDataHandling(DataHandling):
     GPU_DATA_PREFIX = "gpu_"
     VTK_COUNTER = 0
 
-    def __init__(self, blocks, default_ghost_layers=1, default_layout='SoA', dim=3, default_target='cpu'):
+    def __init__(self, blocks, default_ghost_layers=1, default_layout='SoA', dim=3, default_target=Target.CPU):
         """
         Creates data handling based on walberla block storage
 
@@ -27,8 +29,9 @@ class ParallelDataHandling(DataHandling):
             dim: dimension of scenario,
                  walberla always uses three dimensions, so if dim=2 the extend of the
                  z coordinate of blocks has to be 1
-            default_target: either 'cpu' or 'gpu' . If set to 'gpu' for each array also a GPU version is allocated
-                           if not overwritten in add_array, and synchronization functions are for the GPU by default
+            default_target: `Target`, either 'CPU' or 'GPU' . If set to 'GPU' for each array also a GPU version is
+                            allocated if not overwritten in add_array, and synchronization functions are for the GPU by
+                            default
         """
         super(ParallelDataHandling, self).__init__()
         assert dim in (2, 3)
@@ -94,7 +97,7 @@ class ParallelDataHandling(DataHandling):
         if ghost_layers is None:
             ghost_layers = self.default_ghost_layers
         if gpu is None:
-            gpu = self.default_target == 'gpu'
+            gpu = self.default_target == Target.GPU
         if layout is None:
             layout = self.default_layout
         if len(self.blocks) == 0:
@@ -230,7 +233,7 @@ class ParallelDataHandling(DataHandling):
             kernel_function(**arg_dict)
 
     def get_kernel_kwargs(self, kernel_function, **kwargs):
-        if kernel_function.ast.backend == 'gpucuda':
+        if kernel_function.ast.backend == Backend.CUDA:
             name_map = self._field_name_to_gpu_data_name
             to_array = wlb.cuda.toGpuArray
         else:
@@ -283,10 +286,10 @@ class ParallelDataHandling(DataHandling):
             self.to_gpu(name)
 
     def synchronization_function_cpu(self, names, stencil=None, buffered=True, stencil_restricted=False, **_):
-        return self.synchronization_function(names, stencil, 'cpu', buffered, stencil_restricted)
+        return self.synchronization_function(names, stencil, Target.CPU, buffered, stencil_restricted)
 
     def synchronization_function_gpu(self, names, stencil=None, buffered=True, stencil_restricted=False, **_):
-        return self.synchronization_function(names, stencil, 'gpu', buffered, stencil_restricted)
+        return self.synchronization_function(names, stencil, Target.GPU, buffered, stencil_restricted)
 
     def synchronization_function(self, names, stencil=None, target=None, buffered=True, stencil_restricted=False):
         if target is None:
@@ -299,12 +302,12 @@ class ParallelDataHandling(DataHandling):
             names = [names]
 
         create_scheme = wlb.createUniformBufferedScheme if buffered else wlb.createUniformDirectScheme
-        if target == 'cpu':
+        if target == Target.CPU:
             create_packing = wlb.field.createPackInfo if buffered else wlb.field.createMPIDatatypeInfo
             if buffered and stencil_restricted:
                 create_packing = wlb.field.createStencilRestrictedPackInfo
         else:
-            assert target == 'gpu'
+            assert target == Target.GPU
             create_packing = wlb.cuda.createPackInfo if buffered else wlb.cuda.createMPIDatatypeInfo
             names = [self.GPU_DATA_PREFIX + name for name in names]
 

pystencils/datahandling/serial_datahandling.py

@@ -8,6 +8,7 @@ from pystencils.datahandling.blockiteration import SerialBlock
 from pystencils.datahandling.datahandling_interface import DataHandling
 from pystencils.datahandling.pycuda import PyCudaArrayHandler, PyCudaNotAvailableHandler
 from pystencils.datahandling.pyopencl import PyOpenClArrayHandler
+from pystencils.enums import Target
 from pystencils.field import (
     Field, FieldType, create_numpy_array_with_layout, layout_string_to_tuple,
     spatial_layout_string_to_tuple)
@@ -22,7 +23,7 @@ class SerialDataHandling(DataHandling):
                  default_ghost_layers: int = 1,
                  default_layout: str = 'SoA',
                  periodicity: Union[bool, Sequence[bool]] = False,
-                 default_target: str = 'cpu',
+                 default_target: Target = Target.CPU,
                  opencl_queue=None,
                  opencl_ctx=None,
                  array_handler=None) -> None:
@@ -33,8 +34,9 @@ class SerialDataHandling(DataHandling):
             domain_size: size of the spatial domain as tuple
             default_ghost_layers: default number of ghost layers used, if not overridden in add_array() method
             default_layout: default layout used, if  not overridden in add_array() method
-            default_target: either 'cpu' or 'gpu' . If set to 'gpu' for each array also a GPU version is allocated
-                            if not overwritten in add_array, and synchronization functions are for the GPU by default
+            default_target: `Target` either 'CPU' or 'GPU'. If set to 'GPU' for each array also a GPU version is
+                            allocated if not overwritten in add_array, and synchronization functions are for the GPU by
+                            default
         """
         super(SerialDataHandling, self).__init__()
         self._domainSize = tuple(domain_size)
@@ -55,7 +57,7 @@ class SerialDataHandling(DataHandling):
             except Exception:
                 self.array_handler = PyCudaNotAvailableHandler()
 
-            if default_target == 'opencl' or opencl_queue:
+            if default_target == Target.OPENCL or opencl_queue:
                 self.array_handler = PyOpenClArrayHandler(opencl_queue)
         else:
             self.array_handler = array_handler
@@ -107,7 +109,7 @@ class SerialDataHandling(DataHandling):
         }
 
         if not hasattr(values_per_cell, '__len__'):
-            values_per_cell = (values_per_cell, )
+            values_per_cell = (values_per_cell,)
         if len(values_per_cell) == 1 and values_per_cell[0] == 1:
             values_per_cell = ()
 
@@ -266,17 +268,17 @@ class SerialDataHandling(DataHandling):
         return name in self.gpu_arrays
 
     def synchronization_function_cpu(self, names, stencil_name=None, **_):
-        return self.synchronization_function(names, stencil_name, target='cpu')
+        return self.synchronization_function(names, stencil_name, target=Target.CPU)
 
     def synchronization_function_gpu(self, names, stencil_name=None, **_):
-        return self.synchronization_function(names, stencil_name, target='gpu')
+        return self.synchronization_function(names, stencil_name, target=Target.GPU)
 
     def synchronization_function(self, names, stencil=None, target=None, functor=None, **_):
         if target is None:
             target = self.default_target
-        if target == 'opencl':
-            target = 'gpu'
-        assert target in ('cpu', 'gpu')
+        if target == Target.OPENCL:  # TODO potential misuse between Target and Backend
+            target = Target.GPU
+        assert target in (Target.CPU, Target.GPU)
         if not hasattr(names, '__len__') or type(names) is str:
             names = [names]
 
@@ -305,12 +307,12 @@ class SerialDataHandling(DataHandling):
             gls = self._field_information[name]['ghost_layers']
             values_per_cell = self._field_information[name]['values_per_cell']
             if values_per_cell == ():
-                values_per_cell = (1, )
+                values_per_cell = (1,)
             if len(values_per_cell) == 1:
                 values_per_cell = values_per_cell[0]
 
             if len(filtered_stencil) > 0:
-                if target == 'cpu':
+                if target == Target.CPU:
                     if functor is None:
                         from pystencils.slicing import get_periodic_boundary_functor
                         functor = get_periodic_boundary_functor
@@ -318,7 +320,8 @@ class SerialDataHandling(DataHandling):
                 else:
                     if functor is None:
                         from pystencils.gpucuda.periodicity import get_periodic_boundary_functor as functor
-                        target = 'gpu' if not isinstance(self.array_handler, PyOpenClArrayHandler) else 'opencl'
+                        target = Target.GPU if not isinstance(self.array_handler,
+                                                              PyOpenClArrayHandler) else Target.OPENCL
                     result.append(functor(filtered_stencil, self._domainSize,
                                           index_dimensions=self.fields[name].index_dimensions,
                                           index_dim_shape=values_per_cell,
@@ -328,7 +331,7 @@ class SerialDataHandling(DataHandling):
                                           opencl_queue=self._opencl_queue,
                                           opencl_ctx=self._opencl_ctx))
 
-        if target == 'cpu':
+        if target == Target.CPU:
             def result_functor():
                 for arr_name, func in zip(names, result):
                     func(pdfs=self.cpu_arrays[arr_name])
@@ -379,6 +382,7 @@ class SerialDataHandling(DataHandling):
                     raise NotImplementedError("VTK export for fields with more than one index "
                                               "coordinate not implemented")
             image_to_vtk(full_file_name, cell_data=cell_data)
+
         return writer
 
     def create_vtk_writer_for_flag_array(self, file_name, data_name, masks_to_name, ghost_layers=False):

pystencils/display_utils.py

@@ -3,6 +3,7 @@ from typing import Any, Dict, Optional, Union
 import sympy as sp
 
 from pystencils.astnodes import KernelFunction
+from pystencils.enums import Backend
 from pystencils.kernel_wrapper import KernelWrapper
 
 
@@ -45,12 +46,9 @@ def get_code_obj(ast: Union[KernelFunction, KernelWrapper], custom_backend=None)
     if isinstance(ast, KernelWrapper):
         ast = ast.ast
 
-    if ast.backend == 'gpucuda':
-        dialect = 'cuda'
-    elif ast.backend == 'opencl':
-        dialect = 'opencl'
-    else:
-        dialect = 'c'
+    if ast.backend not in {Backend.C, Backend.CUDA, Backend.OPENCL}:
+        raise NotImplementedError(f'get_code_obj is not implemented for backend {ast.backend}')
+    dialect = ast.backend
 
     class CodeDisplay:
         def __init__(self, ast_input):