diff --git a/CHANGELOG.md b/CHANGELOG.md
new file mode 100644
index 0000000000000000000000000000000000000000..f0202916a5eb02291cdef773543f59df68298c9a
--- /dev/null
+++ b/CHANGELOG.md
@@ -0,0 +1,6 @@
+# Change Log
+
+## Unreleased
+
+### Removed
+* Removing OpenCL support because it is not supported by pystencils anymore
diff --git a/doc/notebooks/08_tutorial_shanchen_twophase.ipynb b/doc/notebooks/08_tutorial_shanchen_twophase.ipynb
index 74e5605cd0d4d66cffb87e9cd0a52745373a0899..2a7351cd542b0ffa520b0aa2d700881d321dba41 100644
--- a/doc/notebooks/08_tutorial_shanchen_twophase.ipynb
+++ b/doc/notebooks/08_tutorial_shanchen_twophase.ipynb
@@ -62,11 +62,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "dim = len(stencil[0])\n",
+    "dh = ps.create_data_handling((N,) * stencil.D, periodicity=True, default_target=ps.Target.CPU)\n",
     "\n",
-    "dh = ps.create_data_handling((N,)*dim, periodicity=True, default_target=ps.Target.CPU)\n",
-    "\n",
-    "src = dh.add_array('src', values_per_cell=len(stencil))\n",
+    "src = dh.add_array('src', values_per_cell=stencil.Q)\n",
     "dst = dh.add_array_like('dst', 'src')\n",
     "\n",
     "ρ = dh.add_array('rho')"
@@ -105,7 +103,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "zero_vec = sp.Matrix([0] * dh.dim) \n",
+    "zero_vec = sp.Matrix([0] * stencil.D) \n",
     "\n",
     "force = sum((psi(ρ[d]) * w_d * sp.Matrix(d)\n",
     "            for d, w_d in zip(stencil, weights)), zero_vec) * psi(ρ.center) * -1 * g_aa"
@@ -133,11 +131,10 @@
     "stream = create_stream_pull_with_output_kernel(collision.method, src, dst, {'density': ρ})\n",
     "\n",
     "\n",
-    "opts = {'cpu_openmp': False, \n",
-    "        'target': dh.default_target}\n",
+    "config = ps.CreateKernelConfig(target=dh.default_target, cpu_openmp=False)\n",
     "\n",
-    "stream_kernel = ps.create_kernel(stream, **opts).compile()\n",
-    "collision_kernel = ps.create_kernel(collision, **opts).compile()"
+    "stream_kernel = ps.create_kernel(stream, config=config).compile()\n",
+    "collision_kernel = ps.create_kernel(collision, config=config).compile()"
    ]
   },
   {
@@ -158,7 +155,7 @@
     "                                             pdfs=src.center_vector, density=ρ.center)\n",
     "\n",
     "\n",
-    "init_kernel = ps.create_kernel(init_assignments, ghost_layers=0).compile()"
+    "init_kernel = ps.create_kernel(init_assignments, ghost_layers=0, config=config).compile()"
    ]
   },
   {
@@ -235,7 +232,7 @@
    "outputs": [
     {
      "data": {
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       "text/plain": [
        "<Figure size 3200x1200 with 2 Axes>"
       ]
@@ -251,34 +248,6 @@
     "plot_ρs()"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Check the first time step against reference data\n",
-    "\n",
-    "The reference data was obtained with the [sample code](https://github.com/lbm-principles-practice/code/blob/master/chapter9/shanchen.cpp) after making the following changes:\n",
-    "```c++\n",
-    "const int nsteps = 1000;\n",
-    "const int noutput = 1;\n",
-    "```\n",
-    "\n",
-    "Remove the next cell if you changed the parameters at the beginning of this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "init()\n",
-    "time_loop(1)\n",
-    "ref = np.array([0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.136756, 0.220324, 1.2382, 2.26247, 2.26183, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.26183, 2.26247, 1.2382, 0.220324, 0.136756, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15])\n",
-    "\n",
-    "assert np.allclose(dh.gather_array(ρ.name)[N//2], ref)"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -288,12 +257,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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       "text/plain": [
        "<Figure size 3200x1200 with 2 Axes>"
       ]
@@ -309,6 +278,15 @@
     "time_loop(1000)\n",
     "plot_ρs()"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert np.isfinite(dh.gather_array(ρ.name)).all()"
+   ]
   }
  ],
  "metadata": {
diff --git a/doc/notebooks/09_tutorial_shanchen_twocomponent.ipynb b/doc/notebooks/09_tutorial_shanchen_twocomponent.ipynb
index 526db1ebfa4268bddb45c2df44b95f0de8c20df0..077e53f808699e1caea33ea85eac33da29ff2b33 100644
--- a/doc/notebooks/09_tutorial_shanchen_twocomponent.ipynb
+++ b/doc/notebooks/09_tutorial_shanchen_twocomponent.ipynb
@@ -255,6 +255,9 @@
     "\n",
     "    dh.fill(ρ_b.name, 0.9, slice_obj=ps.make_slice[:, :0.5])\n",
     "    dh.fill(ρ_b.name, 0.1, slice_obj=ps.make_slice[:, 0.5:])\n",
+    "    \n",
+    "    dh.fill(f_a.name, 0.0)\n",
+    "    dh.fill(f_b.name, 0.0)\n",
     "\n",
     "    dh.run_kernel(init_a_kernel)\n",
     "    dh.run_kernel(init_b_kernel)\n",
diff --git a/lbmpy/advanced_streaming/communication.py b/lbmpy/advanced_streaming/communication.py
index 5d67f68d5f478d2792106909434fafaf7949214a..2345b32a6aa6e88fbc7e0e680f8fa1e78bd055e8 100644
--- a/lbmpy/advanced_streaming/communication.py
+++ b/lbmpy/advanced_streaming/communication.py
@@ -104,8 +104,7 @@ def get_communication_slices(
 
 
 def periodic_pdf_copy_kernel(pdf_field, src_slice, dst_slice,
-                             domain_size=None, target=Target.GPU,
-                             opencl_queue=None, opencl_ctx=None):
+                             domain_size=None, target=Target.GPU):
     """Copies a rectangular array slice onto another non-overlapping array slice"""
     from pystencils.gpucuda.kernelcreation import create_cuda_kernel
 
@@ -136,9 +135,6 @@ def periodic_pdf_copy_kernel(pdf_field, src_slice, dst_slice,
     if target == Target.GPU:
         from pystencils.gpucuda import make_python_function
         return make_python_function(ast)
-    elif target == Target.OPENCL:
-        from pystencils.opencl import make_python_function
-        return make_python_function(ast, opencl_queue, opencl_ctx)
     else:
         raise ValueError('Invalid target:', target)
 
@@ -147,22 +143,17 @@ class LBMPeriodicityHandling:
 
     def __init__(self, stencil, data_handling, pdf_field_name,
                  streaming_pattern='pull', ghost_layers=1,
-                 opencl_queue=None, opencl_ctx=None,
                  pycuda_direct_copy=True):
         """
             Periodicity Handling for Lattice Boltzmann Streaming.
 
-            **On the usage with cuda/opencl:** 
+            **On the usage with cuda:**
             - pycuda allows the copying of sliced arrays within device memory using the numpy syntax,
             e.g. `dst[:,0] = src[:,-1]`. In this implementation, this is the default for periodicity
             handling. Alternatively, if you set `pycuda_direct_copy=False`, GPU kernels are generated and
             compiled. The compiled kernels are almost twice as fast in execution as pycuda array copying,
             but especially for large stencils like D3Q27, their compilation can take up to 20 seconds. 
             Choose your weapon depending on your use case.
-
-            - pyopencl does not support copying of non-contiguous sliced arrays, so the usage of compiled
-            copy kernels is forced on us. On the positive side, compilation of the OpenCL kernels appears
-            to be about four times faster.
         """
         if not isinstance(data_handling, SerialDataHandling):
             raise ValueError('Only serial data handling is supported!')
@@ -172,7 +163,7 @@ class LBMPeriodicityHandling:
         self.dh = data_handling
 
         target = data_handling.default_target
-        assert target in [Target.CPU, Target.GPU, Target.OPENCL]
+        assert target in [Target.CPU, Target.GPU]
 
         self.pdf_field_name = pdf_field_name
         self.ghost_layers = ghost_layers
@@ -180,8 +171,6 @@ class LBMPeriodicityHandling:
         self.inplace_pattern = is_inplace(streaming_pattern)
         self.target = target
         self.cpu = target == Target.CPU
-        self.opencl_queue = opencl_queue
-        self.opencl_ctx = opencl_ctx
         self.pycuda_direct_copy = target == Target.GPU and pycuda_direct_copy
 
         def is_copy_direction(direction):
@@ -205,7 +194,7 @@ class LBMPeriodicityHandling:
                                                            ghost_layers=ghost_layers)
             self.comm_slices.append(list(chain.from_iterable(v for k, v in slices_per_comm_dir.items())))
 
-        if target == Target.OPENCL or (target == Target.GPU and not pycuda_direct_copy):
+        if target == Target.GPU and not pycuda_direct_copy:
             self.device_copy_kernels = []
             for timestep in timesteps:
                 self.device_copy_kernels.append(self._compile_copy_kernels(timestep))
@@ -227,9 +216,7 @@ class LBMPeriodicityHandling:
         kernels = []
         for src, dst in self.comm_slices[timestep.idx]:
             kernels.append(
-                periodic_pdf_copy_kernel(
-                    pdf_field, src, dst, target=self.target,
-                    opencl_queue=self.opencl_queue, opencl_ctx=self.opencl_ctx))
+                periodic_pdf_copy_kernel(pdf_field, src, dst, target=self.target))
         return kernels
 
     def _periodicity_handling_gpu(self, prev_timestep):
diff --git a/lbmpy/boundaries/boundaryconditions.py b/lbmpy/boundaries/boundaryconditions.py
index 96251e0e858ddd09c1398ba9e0a02878dd7e6783..b21208cfa2fd5e97b6dcc022633742402b4b9cf8 100644
--- a/lbmpy/boundaries/boundaryconditions.py
+++ b/lbmpy/boundaries/boundaryconditions.py
@@ -120,9 +120,10 @@ class FreeSlip(LbBoundary):
 
     Args:
         stencil: LBM stencil which is used for the simulation
-        normal_direction: optional normal direction. If the Free slip boundary is applied to a certain side in the
-                          domain it is not necessary to calculate the normal direction since it can be stated for all
-                          boundary cells. This reduces the memory space for the index array significantly.
+        normal_direction: optional normal direction pointing from wall to fluid.
+                          If the Free slip boundary is applied to a certain side in the domain it is not necessary
+                          to calculate the normal direction since it can be stated for all boundary cells.
+                          This reduces the memory space for the index array significantly.
         name: optional name of the boundary.
     """
 
@@ -182,7 +183,12 @@ class FreeSlip(LbBoundary):
                     normal_direction[i] = direction[i]
                     ref_direction = MirroredStencilDirections.mirror_stencil(ref_direction, i)
 
-            ref_direction = inverse_direction(ref_direction)
+            # convex corner special case:
+            if all(n == 0 for n in normal_direction):
+                normal_direction = direction
+            else:
+                ref_direction = inverse_direction(ref_direction)
+
             for i, cell_name in zip(range(dim), self.additional_data):
                 cell[cell_name[0]] = -normal_direction[i]
             cell['ref_dir'] = self.stencil.index(ref_direction)
@@ -208,13 +214,14 @@ class FreeSlip(LbBoundary):
 
     def get_additional_code_nodes(self, lb_method):
         if self.normal_direction:
-            return [MirroredStencilDirections(self.stencil, self.mirror_axis)]
+            return [MirroredStencilDirections(self.stencil, self.mirror_axis), NeighbourOffsetArrays(lb_method.stencil)]
         else:
-            return []
+            return [NeighbourOffsetArrays(lb_method.stencil)]
 
     def __call__(self, f_out, f_in, dir_symbol, inv_dir, lb_method, index_field):
+        neighbor_offset = NeighbourOffsetArrays.neighbour_offset(dir_symbol, lb_method.stencil)
         if self.normal_direction:
-            normal_direction = self.normal_direction
+            tangential_offset = tuple(offset + normal for offset, normal in zip(neighbor_offset, self.normal_direction))
             mirrored_stencil_symbol = MirroredStencilDirections._mirrored_symbol(self.mirror_axis)
             mirrored_direction = inv_dir[sp.IndexedBase(mirrored_stencil_symbol, shape=(1,))[dir_symbol]]
         else:
@@ -222,10 +229,11 @@ class FreeSlip(LbBoundary):
             for i, cell_name in zip(range(self.dim), self.additional_data):
                 normal_direction.append(index_field[0](cell_name[0]))
             normal_direction = tuple(normal_direction)
+            tangential_offset = tuple(offset + normal for offset, normal in zip(neighbor_offset, normal_direction))
 
             mirrored_direction = index_field[0]('ref_dir')
 
-        return Assignment(f_in(inv_dir[dir_symbol]), f_in[normal_direction](mirrored_direction))
+        return Assignment(f_in.center(inv_dir[dir_symbol]), f_out[tangential_offset](mirrored_direction))
 
 
 # end class FreeSlip
@@ -283,7 +291,7 @@ class UBB(LbBoundary):
         Returns:
             list containing LbmWeightInfo and NeighbourOffsetArrays
         """
-        return [LbmWeightInfo(lb_method), NeighbourOffsetArrays(lb_method.stencil)]
+        return [LbmWeightInfo(lb_method, self.data_type), NeighbourOffsetArrays(lb_method.stencil)]
 
     @property
     def velocity_is_callable(self):
@@ -312,7 +320,8 @@ class UBB(LbBoundary):
             velocity = [eq.rhs for eq in shifted_vel_eqs.new_filtered(cqc.first_order_moment_symbols).main_assignments]
 
         c_s_sq = sp.Rational(1, 3)
-        weight_of_direction = LbmWeightInfo.weight_of_direction
+        weight_info = LbmWeightInfo(lb_method, data_type=self.data_type)
+        weight_of_direction = weight_info.weight_of_direction
         vel_term = 2 / c_s_sq * sum([d_i * v_i for d_i, v_i in zip(neighbor_offset, velocity)]) * weight_of_direction(
             dir_symbol, lb_method)
 
@@ -595,10 +604,11 @@ class DiffusionDirichlet(LbBoundary):
         name: optional name of the boundary.
     """
 
-    def __init__(self, concentration, name=None):
+    def __init__(self, concentration, name=None, data_type='double'):
         if name is None:
             name = "Diffusion Dirichlet " + str(concentration)
         self.concentration = concentration
+        self._data_type = data_type
 
         super(DiffusionDirichlet, self).__init__(name)
 
@@ -611,10 +621,11 @@ class DiffusionDirichlet(LbBoundary):
         Returns:
             list containing LbmWeightInfo
         """
-        return [LbmWeightInfo(lb_method)]
+        return [LbmWeightInfo(lb_method, self._data_type)]
 
     def __call__(self, f_out, f_in, dir_symbol, inv_dir, lb_method, index_field):
-        w_dir = LbmWeightInfo.weight_of_direction(dir_symbol, lb_method)
+        weight_info = LbmWeightInfo(lb_method, self._data_type)
+        w_dir = weight_info.weight_of_direction(dir_symbol, lb_method)
         return [Assignment(f_in(inv_dir[dir_symbol]),
                            2 * w_dir * self.concentration - f_out(dir_symbol))]
 
diff --git a/lbmpy/boundaries/boundaryhandling.py b/lbmpy/boundaries/boundaryhandling.py
index 71bded3b415ac7874d110ec568173337cd9693cd..60c00dfee531f5189f4570ea652b398da2100694 100644
--- a/lbmpy/boundaries/boundaryhandling.py
+++ b/lbmpy/boundaries/boundaryhandling.py
@@ -38,7 +38,7 @@ class LatticeBoltzmannBoundaryHandling(BoundaryHandling):
         self._prev_timestep = None
 
     def add_fixed_steps(self, fixed_loop, **kwargs):
-        if self._inplace:   # Fixed Loop can't do timestep selection
+        if self._inplace:  # Fixed Loop can't do timestep selection
             raise NotImplementedError("Adding to fixed loop is currently not supported for inplace kernels")
         super(LatticeBoltzmannBoundaryHandling, self).add_fixed_steps(fixed_loop, **kwargs)
 
@@ -52,10 +52,12 @@ class LatticeBoltzmannBoundaryHandling(BoundaryHandling):
         if boundary_obj not in self._boundary_object_to_boundary_info:
             sym_index_field = Field.create_generic('indexField', spatial_dimensions=1,
                                                    dtype=numpy_data_type_for_boundary_object(boundary_obj, self.dim))
-            kernels = [self._create_boundary_kernel(
-                self._data_handling.fields[self._field_name], sym_index_field, boundary_obj, Timestep.EVEN).compile(),
-                self._create_boundary_kernel(
-                self._data_handling.fields[self._field_name], sym_index_field, boundary_obj, Timestep.ODD).compile()]
+
+            ast_even = self._create_boundary_kernel(self._data_handling.fields[self._field_name], sym_index_field,
+                                                    boundary_obj, Timestep.EVEN)
+            ast_odd = self._create_boundary_kernel(self._data_handling.fields[self._field_name], sym_index_field,
+                                                   boundary_obj, Timestep.ODD)
+            kernels = [ast_even.compile(), ast_odd.compile()]
             if flag is None:
                 flag = self.flag_interface.reserve_next_flag()
             boundary_info = self.InplaceStreamingBoundaryInfo(self, boundary_obj, flag, kernels)
@@ -84,6 +86,7 @@ class LatticeBoltzmannBoundaryHandling(BoundaryHandling):
             self.boundary_object = boundary_obj
             self.flag = flag
             self._kernels = kernels
+
     #   end class InplaceStreamingBoundaryInfo
 
     # ------------------------------ Force On Boundary ------------------------------------------------------------
@@ -148,29 +151,32 @@ class LatticeBoltzmannBoundaryHandling(BoundaryHandling):
 
         return dh.reduce_float_sequence(list(result), 'sum')
 
+
 # end class LatticeBoltzmannBoundaryHandling
 
 
 class LbmWeightInfo(CustomCodeNode):
+    def __init__(self, lb_method, data_type='double'):
+        self.weights_symbol = TypedSymbol("weights", data_type)
+        data_type_string = "double" if self.weights_symbol.dtype.numpy_dtype == np.float64 else "float"
+
+        weights = [str(w.evalf(17)) for w in lb_method.weights]
+        if data_type_string == "float":
+            weights = "f, ".join(weights)
+            weights += "f"  # suffix for the last element
+        else:
+            weights = ", ".join(weights)
+        w_sym = self.weights_symbol
+        code = f"const {data_type_string} {w_sym.name} [] = {{{ weights }}};\n"
+        super(LbmWeightInfo, self).__init__(code, symbols_read=set(), symbols_defined={w_sym})
 
-    # --------------------------- Functions to be used by boundaries --------------------------
-
-    @staticmethod
-    def weight_of_direction(dir_idx, lb_method=None):
+    def weight_of_direction(self, dir_idx, lb_method=None):
         if isinstance(sp.sympify(dir_idx), sp.Integer):
-            return lb_method.weights[dir_idx].evalf()
+            return lb_method.weights[dir_idx].evalf(17)
         else:
-            return sp.IndexedBase(LbmWeightInfo.WEIGHTS_SYMBOL, shape=(1,))[dir_idx]
+            return sp.IndexedBase(self.weights_symbol, shape=(1,))[dir_idx]
 
-    # ---------------------------------- Internal ---------------------------------------------
 
-    WEIGHTS_SYMBOL = TypedSymbol("weights", "double")
-
-    def __init__(self, lb_method):
-        weights = [str(w.evalf()) for w in lb_method.weights]
-        w_sym = LbmWeightInfo.WEIGHTS_SYMBOL
-        code = "const double %s [] = { %s };\n" % (w_sym.name, ",".join(weights))
-        super(LbmWeightInfo, self).__init__(code, symbols_read=set(), symbols_defined={w_sym})
 # end class LbmWeightInfo
 
 
diff --git a/lbmpy/lbstep.py b/lbmpy/lbstep.py
index 15e73fe69ff3d48ed65270f5dbe7d2d6fc93c469..44aea72a2c5ca7f7f3fa99f09d06c438c59d3fae 100644
--- a/lbmpy/lbstep.py
+++ b/lbmpy/lbstep.py
@@ -74,7 +74,7 @@ class LatticeBoltzmannStep:
         self.density_data_name = name + "_density" if density_data_name is None else density_data_name
         self.density_data_index = density_data_index
 
-        self._gpu = target == Target.GPU or target == Target.OPENCL
+        self._gpu = target == Target.GPU
         layout = lbm_optimisation.field_layout
 
         alignment = False
diff --git a/lbmpy/macroscopic_value_kernels.py b/lbmpy/macroscopic_value_kernels.py
index 3f7a26033b83304917b3c26d06d816b6c0fce15d..4ec4f31ae4e44f0468ec58a191ea9843c8072a97 100644
--- a/lbmpy/macroscopic_value_kernels.py
+++ b/lbmpy/macroscopic_value_kernels.py
@@ -8,12 +8,16 @@ from lbmpy.advanced_streaming.utility import get_accessor, Timestep
 
 
 def pdf_initialization_assignments(lb_method, density, velocity, pdfs,
-                                   streaming_pattern='pull', previous_timestep=Timestep.BOTH):
+                                   streaming_pattern='pull', previous_timestep=Timestep.BOTH,
+                                   set_pre_collision_pdfs=False):
     """Assignments to initialize the pdf field with equilibrium"""
     if isinstance(pdfs, Field):
-        previous_step_accessor = get_accessor(streaming_pattern, previous_timestep)
-        field_accesses = previous_step_accessor.write(pdfs, lb_method.stencil)
-    elif streaming_pattern == 'pull':
+        accessor = get_accessor(streaming_pattern, previous_timestep)
+        if set_pre_collision_pdfs:
+            field_accesses = accessor.read(pdfs, lb_method.stencil)
+        else:
+            field_accesses = accessor.write(pdfs, lb_method.stencil)
+    elif streaming_pattern == 'pull' and not set_pre_collision_pdfs:
         field_accesses = pdfs
     else:
         raise ValueError("Invalid value of pdfs: A PDF field reference is required to derive "
@@ -28,11 +32,15 @@ def pdf_initialization_assignments(lb_method, density, velocity, pdfs,
 
 
 def macroscopic_values_getter(lb_method, density, velocity, pdfs,
-                              streaming_pattern='pull', previous_timestep=Timestep.BOTH):
+                              streaming_pattern='pull', previous_timestep=Timestep.BOTH,
+                              use_pre_collision_pdfs=False):
     if isinstance(pdfs, Field):
-        previous_step_accessor = get_accessor(streaming_pattern, previous_timestep)
-        field_accesses = previous_step_accessor.write(pdfs, lb_method.stencil)
-    elif streaming_pattern == 'pull':
+        accessor = get_accessor(streaming_pattern, previous_timestep)
+        if use_pre_collision_pdfs:
+            field_accesses = accessor.read(pdfs, lb_method.stencil)
+        else:
+            field_accesses = accessor.write(pdfs, lb_method.stencil)
+    elif streaming_pattern == 'pull' and not use_pre_collision_pdfs:
         field_accesses = pdfs
     else:
         raise ValueError("Invalid value of pdfs: A PDF field reference is required to derive "
diff --git a/lbmpy/moment_transforms/centralmomenttransforms.py b/lbmpy/moment_transforms/centralmomenttransforms.py
index 3d6ec0f485ad2dd1f808983494a031a0dde57302..700445dd9a6afba5d7aa7c37cbaa90e7b4295f9f 100644
--- a/lbmpy/moment_transforms/centralmomenttransforms.py
+++ b/lbmpy/moment_transforms/centralmomenttransforms.py
@@ -268,7 +268,7 @@ class FastCentralMomentTransform(AbstractCentralMomentTransform):
         ac = AssignmentCollection(main_assignments, subexpressions=subexpressions,
                                   subexpression_symbol_generator=symbol_gen)
         if simplification:
-            ac = self._simplify_lower_order_moments(ac, monomial_symbol_base)
+            ac = self._simplify_lower_order_moments(ac, monomial_symbol_base, return_monomials)
             ac = simplification.apply(ac)
         return ac
 
@@ -335,14 +335,19 @@ class FastCentralMomentTransform(AbstractCentralMomentTransform):
             'backward': backward_simp
         }
 
-    def _simplify_lower_order_moments(self, ac, moment_base):
+    def _simplify_lower_order_moments(self, ac, moment_base, search_in_main_assignments):
         if self.cqe is None:
             return ac
 
-        f_to_cm_dict = ac.main_assignments_dict
-        f_to_cm_dict_reduced = ac.new_without_subexpressions().main_assignments_dict
-
         moment_symbols = [sq_sym(moment_base, e) for e in moments_up_to_order(1, dim=self.dim)]
+
+        if search_in_main_assignments:
+            f_to_cm_dict = ac.main_assignments_dict
+            f_to_cm_dict_reduced = ac.new_without_subexpressions().main_assignments_dict
+        else:
+            f_to_cm_dict = ac.subexpressions_dict
+            f_to_cm_dict_reduced = ac.new_without_subexpressions(moment_symbols).subexpressions_dict
+
         cqe_subs = self.cqe.new_without_subexpressions().main_assignments_dict
         for m in moment_symbols:
             m_eq = fast_subs(fast_subs(f_to_cm_dict_reduced[m], cqe_subs), cqe_subs)
@@ -351,8 +356,12 @@ class FastCentralMomentTransform(AbstractCentralMomentTransform):
                 m_eq = subs_additive(m_eq, cqe_sym, cqe_exp)
             f_to_cm_dict[m] = m_eq
 
-        main_assignments = [Assignment(lhs, rhs) for lhs, rhs in f_to_cm_dict.items()]
-        return ac.copy(main_assignments=main_assignments)
+        if search_in_main_assignments:
+            main_assignments = [Assignment(lhs, rhs) for lhs, rhs in f_to_cm_dict.items()]
+            return ac.copy(main_assignments=main_assignments)
+        else:
+            subexpressions = [Assignment(lhs, rhs) for lhs, rhs in f_to_cm_dict.items()]
+            return ac.copy(subexpressions=subexpressions)
 
     def _split_backward_equations_recursive(self, assignment, all_subexpressions,
                                             stencil_direction, subexp_symgen, known_coeffs_dict,
diff --git a/lbmpy/oldroydb.py b/lbmpy/oldroydb.py
new file mode 100644
index 0000000000000000000000000000000000000000..16148f7010e3e917529fb8bcdb645dd844164f94
--- /dev/null
+++ b/lbmpy/oldroydb.py
@@ -0,0 +1,209 @@
+import pystencils as ps
+import sympy as sp
+import numpy as np
+
+from pystencils.boundaries.boundaryconditions import Boundary
+from pystencils.stencil import inverse_direction_string, direction_string_to_offset
+
+
+class OldroydB:
+    def __init__(self, dim, u, tau, F, tauflux, tauface, lambda_p, eta_p, vof=True):
+        assert not ps.FieldType.is_staggered(u)
+        assert not ps.FieldType.is_staggered(tau)
+        assert not ps.FieldType.is_staggered(F)
+        assert ps.FieldType.is_staggered(tauflux)
+        assert ps.FieldType.is_staggered(tauface)
+        
+        self.dim = dim
+        self.u = u
+        self.tau = tau
+        self.F = F
+        self.tauflux = tauflux
+        self.tauface_field = tauface
+        self.lambda_p = lambda_p
+        self.eta_p = eta_p
+        
+        full_stencil = ["C"] + self.tauflux.staggered_stencil + \
+            list(map(inverse_direction_string, self.tauflux.staggered_stencil))
+        self.stencil = tuple(map(lambda d: tuple(ps.stencil.direction_string_to_offset(d, self.dim)), full_stencil))
+        full_stencil = ["C"] + self.tauface_field.staggered_stencil + \
+            list(map(inverse_direction_string, self.tauface_field.staggered_stencil))
+        self.force_stencil = tuple(map(lambda d: tuple(ps.stencil.direction_string_to_offset(d, self.dim)),
+                                   full_stencil))
+        
+        self.disc = ps.fd.FVM1stOrder(self.tau, self._flux(), self._source())
+        if vof:
+            self.vof = ps.fd.VOF(self.tauflux, self.u, self.tau)
+        else:
+            self.vof = None
+    
+    def _flux(self):
+        return [self.tau.center_vector.applyfunc(lambda t: t * self.u.center_vector[i]) for i in range(self.dim)]
+    
+    def _source(self):
+        gradu = sp.Matrix([[ps.fd.diff(self.u.center_vector[j], i) for j in range(self.dim)] for i in range(self.dim)])
+        gamma = gradu + gradu.transpose()
+        return self.tau.center_vector * gradu + gradu.transpose() * self.tau.center_vector + \
+            (self.eta_p * gamma - self.tau.center_vector) / self.lambda_p
+    
+    def tauface(self):
+        return ps.AssignmentCollection([ps.Assignment(self.tauface_field.staggered_vector_access(d),
+                                        (self.tau.center_vector + self.tau.neighbor_vector(d)) / 2)
+                                        for d in self.tauface_field.staggered_stencil])
+    
+    def force(self):
+        full_stencil = self.tauface_field.staggered_stencil + \
+            list(map(inverse_direction_string, self.tauface_field.staggered_stencil))
+        dtau = sp.Matrix([sum([sum([
+                          self.tauface_field.staggered_access(d, (i, j)) * direction_string_to_offset(d)[i]
+                          for i in range(self.dim)]) / sp.Matrix(direction_string_to_offset(d)).norm()
+                         for d in full_stencil]) for j in range(self.dim)])
+        A0 = sum([sp.Matrix(direction_string_to_offset(d)).norm() for d in full_stencil])
+        return ps.AssignmentCollection(ps.Assignment(self.F.center_vector, dtau / A0 * 2 * self.dim))
+    
+    def flux(self):
+        if self.vof is not None:
+            return self.vof
+        else:
+            return self.disc.discrete_flux(self.tauflux)
+    
+    def continuity(self):
+        cont = self.disc.discrete_continuity(self.tauflux)
+        tau_copy = sp.Matrix(self.dim, self.dim, lambda i, j: sp.Symbol("tau_old_%d_%d" % (i, j)))
+        tau_subs = {self.tau.center_vector[i, j]: tau_copy[i, j] for i in range(self.dim) for j in range(self.dim)}
+        return [ps.Assignment(tau_copy[i, j], self.tau.center_vector[i, j])
+                for i in range(self.dim) for j in range(self.dim)] + \
+               [ps.Assignment(a.lhs, a.rhs.subs(tau_subs)) for a in cont]
+
+
+class Flux(Boundary):
+    inner_or_boundary = True  # call the boundary condition with the fluid cell
+    single_link = False  # needs to be called for all directional fluxes
+    
+    def __init__(self, stencil, value=None):
+        self.stencil = stencil
+        self.value = value
+
+    def __call__(self, field, direction_symbol, **kwargs):
+        assert ps.FieldType.is_staggered(field)
+        
+        assert all([s == 0 for s in self.stencil[0]])
+        accesses = [field.staggered_vector_access(ps.stencil.offset_to_direction_string(d))
+                    for d in self.stencil[1:]]
+        conds = [sp.Equality(direction_symbol, d + 1) for d in range(len(accesses))]
+        
+        if self.value is None:
+            val = sp.Matrix(np.zeros(accesses[0].shape, dtype=int))
+        else:
+            val = self.value
+        
+        # use conditional
+        conditional = None
+        for a, c, d in zip(accesses, conds, self.stencil[1:]):
+            d = ps.stencil.offset_to_direction_string(d)
+            assignments = []
+            for i in range(len(a)):
+                fac = 1
+                if ps.FieldType.is_staggered_flux(field) and type(a[i]) is sp.Mul and a[i].args[0] == -1:
+                    fac = a[i].args[0]
+                    a[i] *= a[i].args[0]
+                assignments.append(ps.Assignment(a[i], fac * val[i]))
+            if len(assignments) > 0:
+                conditional = ps.astnodes.Conditional(ps.data_types.type_all_numbers(c, "int"),
+                                                      ps.astnodes.Block(assignments),
+                                                      conditional)
+        return [conditional]
+
+    def __hash__(self):
+        return hash((Flux, self.stencil, self.value))
+
+    def __eq__(self, other):
+        return type(other) == Flux and other.stencil == self.stencil and self.value == other.value
+
+
+class Extrapolation(Boundary):
+    inner_or_boundary = True  # call the boundary condition with the fluid cell
+    single_link = False  # needs to be called for all directional fluxes
+
+    def __init__(self, stencil, src_field, order):
+        self.stencil = stencil
+        self.src = src_field
+        if order == 0:
+            self.weights = (1,)
+        elif order == 1:
+            self.weights = (sp.Rational(3, 2), - sp.Rational(1, 2))
+        elif order == 2:
+            self.weights = (sp.Rational(15, 8), - sp.Rational(10, 8), sp.Rational(3, 8))
+        else:
+            raise NotImplementedError("weights are not known for extrapolation orders > 2")
+
+    def __call__(self, field, direction_symbol, **kwargs):
+        assert ps.FieldType.is_staggered(field)
+        
+        assert all([s == 0 for s in self.stencil[0]])
+        accesses = [field.staggered_vector_access(ps.stencil.offset_to_direction_string(d))
+                    for d in self.stencil[1:]]
+        conds = [sp.Equality(direction_symbol, d + 1) for d in range(len(accesses))]
+        
+        # use conditional
+        conditional = None
+        for a, c, o in zip(accesses, conds, self.stencil[1:]):
+            assignments = []
+            src = [self.src.neighbor_vector(tuple([-1 * n * i for i in o])) for n in range(len(self.weights))]
+            interp = self.weights[0] * src[0]
+            for i in range(1, len(self.weights)):
+                interp += self.weights[i] * src[i]
+            for i in range(len(a)):
+                fac = 1
+                if ps.FieldType.is_staggered_flux(field) and type(a[i]) is sp.Mul and a[i].args[0] == -1:
+                    fac = a[i].args[0]
+                    a[i] *= a[i].args[0]
+                assignments.append(ps.Assignment(a[i], fac * interp[i]))
+            if len(assignments) > 0:
+                conditional = ps.astnodes.Conditional(ps.data_types.type_all_numbers(c, "int"),
+                                                      ps.astnodes.Block(assignments),
+                                                      conditional)
+        return [conditional]
+
+    def __hash__(self):
+        return hash((Extrapolation, self.stencil, self.src, self.weights))
+
+    def __eq__(self, other):
+        return type(other) == Extrapolation and other.stencil == self.stencil and \
+            other.src == self.src and other.weights == self.weights
+
+
+class ForceOnBoundary(Boundary):
+    inner_or_boundary = False  # call the boundary condition with the boundary cell
+    single_link = False  # needs to be called for all directional fluxes
+    
+    def __init__(self, stencil, force_field):
+        self.stencil = stencil
+        self.force_field = force_field
+        
+        assert not ps.FieldType.is_staggered(force_field)
+
+    def __call__(self, face_stress_field, direction_symbol, **kwargs):
+        assert ps.FieldType.is_staggered(face_stress_field)
+        
+        assert all([s == 0 for s in self.stencil[0]])
+        accesses = [face_stress_field.staggered_vector_access(ps.stencil.offset_to_direction_string(d))
+                    for d in self.stencil[1:]]
+        conds = [sp.Equality(direction_symbol, d + 1) for d in range(len(accesses))]
+        
+        # use conditional
+        conditional = None
+        for a, c, o in zip(accesses, conds, self.stencil[1:]):
+            assignments = ps.Assignment(self.force_field.center_vector,
+                                        self.force_field.center_vector + 1 * a.transpose() * sp.Matrix(o))
+            conditional = ps.astnodes.Conditional(ps.data_types.type_all_numbers(c, "int"),
+                                                  ps.astnodes.Block(assignments),
+                                                  conditional)
+        return [conditional]
+
+    def __hash__(self):
+        return hash((ForceOnBoundary, self.stencil, self.force_field))
+
+    def __eq__(self, other):
+        return type(other) == ForceOnBoundary and other.stencil == self.stencil and \
+            other.force_field == self.force_field
diff --git a/lbmpy/simplificationfactory.py b/lbmpy/simplificationfactory.py
index cbc58565bef3f5cc51921899afe85623bbbcec8c..ad89fe60b99faf946e3d36171412dbfed800a14f 100644
--- a/lbmpy/simplificationfactory.py
+++ b/lbmpy/simplificationfactory.py
@@ -2,6 +2,7 @@ import sympy as sp
 
 from lbmpy.innerloopsplit import create_lbm_split_groups
 from lbmpy.methods.momentbased.momentbasedmethod import MomentBasedLbMethod
+from lbmpy.methods.momentbased.centralmomentbasedmethod import CentralMomentBasedLbMethod
 from lbmpy.methods.centeredcumulant import CenteredCumulantBasedLbMethod
 from lbmpy.methods.momentbased.momentbasedsimplifications import (
     factor_density_after_factoring_relaxation_times, factor_relaxation_rates,
@@ -22,6 +23,8 @@ def create_simplification_strategy(lb_method, split_inner_loop=False):
             else:
                 # General MRT methods with population-space collision
                 return _mrt_population_space_simplification(split_inner_loop)
+    elif isinstance(lb_method, CentralMomentBasedLbMethod):
+        return _moment_space_simplification(split_inner_loop)
     elif isinstance(lb_method, CenteredCumulantBasedLbMethod):
         return _moment_space_simplification(split_inner_loop)
     else:
diff --git a/lbmpy_tests/advanced_streaming/test_fully_periodic_flow.py b/lbmpy_tests/advanced_streaming/test_fully_periodic_flow.py
index 9804a1e36a23df79a848fc0e618a224bd7f78e8c..0c37cfc37c52506d1a373815fa9d1d849bf03cd5 100644
--- a/lbmpy_tests/advanced_streaming/test_fully_periodic_flow.py
+++ b/lbmpy_tests/advanced_streaming/test_fully_periodic_flow.py
@@ -25,27 +25,15 @@ try:
 except Exception:
     pass
 
-try:
-    import pystencils.opencl.autoinit
-    from pystencils.opencl.opencljit import get_global_cl_queue
-    if get_global_cl_queue() is not None:
-        targets += [Target.OPENCL]
-except Exception:
-    pass
-
 
 @pytest.mark.parametrize('target', targets)
 @pytest.mark.parametrize('stencil', [Stencil.D2Q9, Stencil.D3Q19, Stencil.D3Q27])
 @pytest.mark.parametrize('streaming_pattern', streaming_patterns)
 @pytest.mark.longrun
 def test_fully_periodic_flow(target, stencil, streaming_pattern):
-
-    if target == Target.OPENCL:
-        opencl_queue = get_global_cl_queue()
-    else:
-        opencl_queue = None
-
-    gpu = target in [Target.GPU, Target.OPENCL]
+    gpu = False
+    if target == Target.GPU:
+        gpu = True
 
     #   Stencil
     stencil = LBStencil(stencil)
@@ -59,8 +47,7 @@ def test_fully_periodic_flow(target, stencil, streaming_pattern):
     domain_size = (30,) * stencil.D
     periodicity = (True,) * stencil.D
 
-    dh = create_data_handling(domain_size=domain_size, periodicity=periodicity,
-                              default_target=target, opencl_queue=opencl_queue)
+    dh = create_data_handling(domain_size=domain_size, periodicity=periodicity, default_target=target)
 
     pdfs = dh.add_array('pdfs', stencil.Q)
     if not inplace:
diff --git a/lbmpy_tests/advanced_streaming/test_periodic_pipe_with_force.py b/lbmpy_tests/advanced_streaming/test_periodic_pipe_with_force.py
index 7ee8a608a0b6080ad76229c9385b90f1f89e7e2c..42b6671a35f61167e63cba0171b12e08dd022b7b 100644
--- a/lbmpy_tests/advanced_streaming/test_periodic_pipe_with_force.py
+++ b/lbmpy_tests/advanced_streaming/test_periodic_pipe_with_force.py
@@ -26,14 +26,6 @@ try:
 except Exception:
     pass
 
-try:
-    import pystencils.opencl.autoinit
-    from pystencils.opencl.opencljit import get_global_cl_queue
-    if get_global_cl_queue() is not None:
-        targets += [Target.OPENCL]
-except Exception:
-    pass
-
 
 class PeriodicPipeFlow:
     def __init__(self, stencil, streaming_pattern, wall_boundary=None, target=Target.CPU):
@@ -42,7 +34,7 @@ class PeriodicPipeFlow:
             wall_boundary = NoSlip()
 
         self.target = target
-        self.gpu = target in [Target.GPU, Target.OPENCL]
+        self.gpu = target in [Target.GPU]
 
         #   Stencil
         self.stencil = stencil
diff --git a/lbmpy_tests/centeredcumulant/test_periodic_pipe_flow.ipynb b/lbmpy_tests/centeredcumulant/test_periodic_pipe_flow.ipynb
index cddc784870ea4b92f4fdf9b1b4abdbe3e515c0a2..ef7082dc03ffeaa2c1bec67a876345285218bc0d 100644
--- a/lbmpy_tests/centeredcumulant/test_periodic_pipe_flow.ipynb
+++ b/lbmpy_tests/centeredcumulant/test_periodic_pipe_flow.ipynb
@@ -260,7 +260,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.colorbar.Colorbar at 0x122dae4f0>"
+       "<matplotlib.colorbar.Colorbar at 0x7f7e7bd42370>"
       ]
      },
      "execution_count": 7,
@@ -269,7 +269,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 1152x432 with 2 Axes>"
       ]
@@ -316,7 +316,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -327,7 +327,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -337,7 +337,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -352,7 +352,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 1152x432 with 2 Axes>"
       ]
@@ -371,7 +371,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -387,7 +387,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -404,7 +404,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -415,7 +415,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -425,7 +425,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -440,7 +440,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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fPAAAAAC4uDI9Te+Q9H5JP7T9VLHsE5I+Lekrtj8k6UeSbl+TFgIAAADoS4M2zK6qZYumiPiutOT0Fu9a3eYA6OYNG9LZ1mSzRDZ/gJubzOXak+30NkcnZtPZzRvOp7NXjp9JZ7eNvZHOXjNyKp3dOpxvw6bG2XR2wvnXrOn8vshqRSOdPRsj6ezEUP73GnMrnS2jFenTezU9l/+czczm51qamc69vnNn89ss8zkvc/wYK3FcAnBpC9VnIohSs+cBAAAAgCQp5mfQq4P8n9cAAAAAoMtaX6fJ9i7bz9k+YvveRZ637c8Xzz9t+23F8mttf9v2YdvP2v5o1zpbbB+0/Xzx7+bl2kHRBAAAAKDv2G5Iuk/SrZJukHSn7RsWxG6VtKO47ZF0f7F8TtLHI+KXJd0s6e6ude+V9HhE7JD0ePG4J4omAAAAAKWF1vw6TTdJOhIRL0TErKRHJO1ekNkt6aGY9z1Jmy5cFikifiBJEXFG0mFJ27vWebC4/6Ck9yzXEM5pAgAAAFDBiq/TdIXtQ12P90bE3q7H2yW91PX4qKS3L9jGYpntko79tJX2dZLeKun7xaKrIuKYJBXXnL1yuYZSNAEAAACoZIUTQbwSETt7PL9YRbbwJ/bM2J6U9FVJH4uI0+WbOI/heQAAAAD60VFJ13Y9vkbSy9mM7abmC6aHI+JrXZnjtrcVmW2STizXEIomAAAAAJWs8TlNT0jaYft62yOS7pC0f0Fmv6QPFLPo3SzpjWLInSV9UdLhiPjsIuvcVdy/S9I3lmsIw/MAAAAAlBahbPFTcfsxZ/seSY9JakjaFxHP2v5I8fwDkg5Iuk3SEUnnJH2wWP0dkt4v6Ye2nyqWfSIiDkj6tKSv2P6QpB9Jun25tlA0AQAAAKhkhRNBLKsocg4sWPZA1/2QdPci631Xi5/vpIh4VdK7yrSDoglYZ7cMLfvHjJ9q3PCL6WxrY/7j3JrIH+DmxpNneI6109vcMDabzm4aPZ/Obh2ZSmevaJ7Jb3e4TDZ/jummoel0dqPn0tnmGnx/tUqc6HsmWuls0/n3TRnT0Uxnz7fz2dOj4/ns2Gg6OzM2ksrNjTfS2yzzOS9z/Bjbuuw1IH+qzPHuYOfRdBZA/1jhRBADg3OaAAAAAKAHepoAAAAAVLKW5zT1E4omAAAAAKWF0rPgDTyKJgAAAACV1OSUJoomAAAAABWs8ZTj/YSJIAAAAACgB3qaAAAAAFRTk/F5FE0AAAAAKqnL8DyKJgAAAACVcHFbAAAAAAA9TcB6G9qwIZ1tT46ms3Pj+e7xdn6z6ozm/oTUGG2nt7lhpJXOXjZyPp8dnk5ntzSm0tlNjbP57FCJNgzNpbMTzv+Nq1kim9WKTv7nR/73kvKvV6vRSGfPdkbS2TeG85/JMu/HDSMT6ezp5Ocn+3mUpPZo/phQ6vhR4rhU5ngHYPCEGJ4HAAAAAEsLSRRNAAAAALC0upzTRNEEAAAAoJqaFE1MBAEAAAAAPdDTBAAAAKACMxEEAAAAAPRUk+F5FE0AAAAAyov6TDnOOU0AAAAA0AM9TQAAAACqYXgeAAAAAPRSj+F5FE3Aems00tHOSD7bbuYPWp1mOqpo5v6E1Bxup7c53mzls418dkNjJp2dGJrNZ53PbvRcie3mR0hvGMrvtGHl3zdZTef3rzr5fdYq8XqdK7EfyuzfMu+bMu/HMu/z4eTnZzb5eZTKfc5LHT9KHJeGShzvAAwoepoAAAAAoIeaFE1MBAEAAAAAPdDTBAAAAKC8kFSTKccpmgAAAABUEjUZnkfRBAAAAKAaiiYAAAAA6KEmw/OYCAIAAAAAeqCnCQAAAEAlZngeAAAAACwhxDlNAAAAALA01+acJoomYJ3ZJQ4ujXw2GvnNRomzGWMo9yekoWROkhrupLPNEtkxz5XYbplsu0Q2HVXT+R0xrPwObpTYblqJvySWe73KvBfKbDe/f8u9b/LtLfM+z35+sp/H+Ww6Wur4Uea4VOp4BwB9jKIJAAAAQDU1GZ637N+hbO+zfcL2M13LPmn7x7afKm63rW0zAQAAAPSdWMFtgGQ6778kadciyz8XETcWtwOr2ywAAAAAfa8mRdOyw/Mi4ju2r1uHtgAAAAAYFKHaTASxkrOF77H9dDF8b/NSIdt7bB+yfejkyZMr+HEAAAAAsP6qFk33S/p5STdKOibpM0sFI2JvROyMiJ1bt26t+OMAAAAA9BtH9dsgqVQ0RcTxiGhHREfSFyTdtLrNAgAAAND3anJOU6Wiyfa2rofvlfTMUlkAAAAAGGTLTgRh+8uS3inpCttHJf2hpHfavlHzNeKLkj68dk0EAAAA0I8GbZhdVZnZ8+5cZPEX16AtAAAAANB3li2aAAAAAGBRNZlynKIJAAAAQHkDOKFDVRRNAAAAAKqpSdG0kovbAgAAAMAlj6IJAAAAQCVrfXFb27tsP2f7iO17F3netj9fPP+07bd1PbfP9gnbzyxY55O2f2z7qeJ223LtoGgCAAAAUM0aXtzWdkPSfZJulXSDpDtt37AgdqukHcVtj6T7u577kqRdS2z+cxFxY3E7sFxbKJoAAAAAVLOGRZOkmyQdiYgXImJW0iOSdi/I7Jb0UMz7nqRNtrdJUkR8R9Kplfx6F1A0AQAAAChtJUPziuF5V9g+1HXbs+BHbJf0Utfjo8WyspnF3FMM59tne/NyYYomAAAAABfDKxGxs+u2d8Hzi10EamEfVSaz0P2Sfl7SjZKOSfrMcg1lynEAAAAA1aztxW2PSrq26/E1kl6ukPlHIuL4hfu2vyDpm8s1hKIJWGcRJS5o0M5n3c5v1p0y2dzBsJPMSVI78p3crRLZ6cgf0lqlso0S2XRUrcjviGaZHbwG18yYU/7nl/m9yr1eZfZDfv+We9/k349l3ufZz0/28zifTUdLHT/KHJdKHe8ADKa1/Zg/IWmH7esl/VjSHZJ+Z0Fmv+aH2j0i6e2S3oiIY702antbV+a9kp7plZcomgAAAABUlJ06vIqImLN9j6THJDUk7YuIZ21/pHj+AUkHJN0m6Yikc5I++NO22V+W9E7Nnzt1VNIfRsQXJf2x7Rs1X/K9KOnDy7WFogkAAABANWvcoVxMB35gwbIHuu6HpLuXWPfOJZa/v2w7mAgCAAAAAHqgpwkAAABAebG2w/P6CUUTAAAAgGoomgAAAACgh5oUTZzTBAAAAAA90NMEAAAAoJK6nNNETxMAAAAA9EBPEwAAAIBqatLTRNEErLd2Ox0dms1nG638UWuo5XTWyezcXCO9zfOtZj7bzmfPtUfT2bOdkXw28tkz0UpnmzGXzqpTYrvOv2+yWtFJZ8+WyJ6J/NdQmf1QZv+Wed+UeT+WeZ9nPz/Zz6MkDeXfMuWOHyWOS2WOdwAGUI2mHGd4HgAAAAD0QE8TAAAAgGpq0tNE0QQAAACgGoomAAAAAFicVZ9zmiiaAAAAAFRTk6KJiSAAAAAAoAd6mgAAAACUV6MpxymaAAAAAFRD0QQAAAAAPdSkaOKcJgAAAADogZ4mYJ11zp1LZxtTM+ns8PkN+e3mN6uhGadyczON9DbPzTbT2dOz4/nsyFg6e2p4Mp2dGJpNZ5tup7PSdDrZ8lyJNnRKtCH580v8JfFM5L9aXu/k99nr7Yl09lQ7v39Pz+XbUOb9WOZ93k5+foaTn0ep3Od8+Hx+B5c5LrVLHO8ADCbOaQIAAACAXiiaAAAAAGAJIYomAAAAAOilLsPzmAgCAAAAAHqgpwkAAABANTXpaaJoAgAAAFBJXYbnUTQBAAAAqIaiCQAAAACWUKPZ85gIAgAAAAB6oKcJAAAAQGkubnVA0QSss4OdR9PZXVf9Xjrb/LmN+ezZfCfz8Pnc4XBuupHe5rnpkXT29bHxdPbkyGQ6O95opbNjzmfLaDVKvGaeTWebbldpTk+tyLf1bJTYv+2JdPbkXP49/kornz05m3/fvD6Tfz+WeZ8r+fnJfh4lqXk2P2ameWYundXJ19LRMsc7AAOqJsPzKJoAAAAAVFKX2fOW/XOz7X22T9h+pmvZFtsHbT9f/Lt5bZsJAAAAABdHZozOlyTtWrDsXkmPR8QOSY8XjwEAAADUSazgNkCWLZoi4juSTi1YvFvSg8X9ByW9Z3WbBQAAAKDv1aRoqnpO01URcUySIuKY7StXsU0AAAAA+l1wTtOqsb3H9iHbh06ePLnWPw4AAADAeqlJT1PVoum47W2SVPx7YqlgROyNiJ0RsXPr1q0VfxwAAAAAXBxVi6b9ku4q7t8l6Rur0xwAAAAAg8JR/TZIMlOOf1nSX0r6JdtHbX9I0qcl3WL7eUm3FI8BAAAA1ElNhuctOxFERNy5xFPvWuW2AAAAABggg9ZjVFXV2fMArIM4dy6dbU61SmSb6ezwlFO5xkQjvc2ZsZF09rWR8XR2bHhjOtt0J50tYzryr+3ZTv51mBiaTWebnktns1qR/7oo83udak+ms6+08vv32PSb0tkT5/Pbfe1c/v04czb/OjSmcp+f4an0JtWcyv9Ppszxo8xxCQAuFRRNAAAAAMobwGF2VVE0AQAAAKiGogkAAAAAFmdxThMAAAAA9FaToqnqdZoAAAAAoBYomgAAAABU4ojKt9T27V22n7N9xPa9izxv258vnn/a9tu6nttn+4TtZxass8X2QdvPF/9uXq4dFE0AAAAAylvJhW0TNZPthqT7JN0q6QZJd9q+YUHsVkk7itseSfd3PfclSbsW2fS9kh6PiB2SHi8e90TRBAAAAKASR/Vbwk2SjkTECxExK+kRSbsXZHZLeijmfU/SJtvbJCkiviPp1CLb3S3pweL+g5Les1xDKJoAAAAAVLOGPU2Stkt6qevx0WJZ2cxCV0XEMUkq/r1yuYYwex4AAACAi+EK24e6Hu+NiL1dj73IOgvLrUxmxSiagD7WOXs2nW28OpXOjm0aTWdbk4sdi35WezyXk6Tp0fyhZ6o5ls6eGOqks2W0It8pf77dTGffGN6Qzm5ozKSzY55LZ7OmI7/PzrXz76/Tc/n9e3J2Mp09cX5jPjuV3+7UVL69PpN/zUZO5z4/o2/k/x8w9lo7nS1z/GiXOC4BuPSt8DpNr0TEzh7PH5V0bdfjayS9XCGz0HHb2yLiWDGU78RyDWV4HgAAAIBq1nZ43hOSdti+3vaIpDsk7V+Q2S/pA8UsejdLeuPC0Lse9ku6q7h/l6RvLNcQiiYAAAAA5a1gEohMD1VEzEm6R9Jjkg5L+kpEPGv7I7Y/UsQOSHpB0hFJX5D0exfWt/1lSX8p6ZdsH7X9oeKpT0u6xfbzkm4pHvfE8DwAAAAAfSkiDmi+MOpe9kDX/ZB09xLr3rnE8lclvatMOyiaAAAAAFSz6lMu9CeKJgAAAAClWSueCGJgUDQBAAAAqCbqUTVRNAEAAACopC49TcyeBwAAAAA90NMEAAAAoLz89ZYGHkUTAAAAgErcudgtWB8UTUAfO9h5NJ199+j70tmxibF0tjWxMZWbG2ukt9lp5kcGzw6NpLNvpJNSu5Nvw/RcM509PTqezl42cj6dHW+00tnmGnyDtSL/ep1vl3i9ZvOv1+sz+exr5/LZqan85yFez78fR17Pv2Yjr+dyY6fy+3bsJ2fT2c6LR9PZMsclADVATxMAAAAALI2JIAAAAAAA9DQBAAAAqCDEdZoAAAAAoJe6DM+jaAIAAABQTU2KJs5pAgAAAIAe6GkCAAAAUJrF8DwAAAAAWFoEE0EAAAAAQC/0NAEAAABALxRNAAbJt2YeTmd3bf1wOjs+OZLKtUfH0tuMRpk5aPLZ2U6urZJ0upXf7sxs/lB5emw0nd0wMpHOjjdb6WzDnXQ2qx351+t8q5nOnpstkZ3O79+Zs/msz+T378jr+ddh9JTT2fFXc/ts/PhMept+6Sfp7GMljh8AUEcUTQAAAAAqYXgeAAAAACwlJHXqUTVRNAEAAACoph41E0UTAAAAgGrqMjyvzNnYAAAAAFA79DQBAAAAqIaL2wIAAADA0uoyPI+iCQAAAEB5odpMBME5TQAAAADQAz1NAAAAAEqzJHNOE4BLVfvVU+ls8/imVG7DSKNEC5rppNv5DvGhVr4NszP57c5M57c7MzaSzp4ebaezw8P57NDQ6n+BdTpOZ+fm8q9Xe6bE+6bEfmhM5bMjp/O/28jr6ajGX+2ksxt+0krlmsdPp7dZ5nMOAJXlD3UDjaIJAAAAQCX0NCXYflHSGUltSXMRsXM1GgUAAACgz9VoIojV6Gn69Yh4ZRW2AwAAAAB9h+F5AAAAACqI2lzcdqVTjoekb9l+0vaexQK299g+ZPvQyZMnV/jjAAAAAPQLR/XbIFlpT9M7IuJl21dKOmj7byPiO92BiNgraa8k7dy5c8BeHgAAAABLoqdpeRHxcvHvCUlfl3TTajQKAAAAQJ8LyZ3qt0FSuWiyPWF744X7kt4t6ZnVahgAAAAA9IOVDM+7StLXbV/Yzn+LiD9flVYBAAAA6H81GZ5XuWiKiBck/doqtgUAAADAIKlHzcSU40AdHew8ms6+e+TOVG60xM8fmr0snW3M5Lc8PZ0fcdw473R27mz+UDk33khnO6P5b5rZZj4bQ6v/DeZO/vVyK58dnimRLbHPhqfSUY2+kX+9xk7lB+GPH59JZ5vHT6dynb/7UXqbZT7nAFCVa9LTtNIpxwEAAADgkkZPEwAAAIBqatLTRNEEAAAAoLyQNGBTh1dF0QQAAACgNCtqc04TRRMAAACAampSNDERBAAAAAD0QE8TAAAAgGroaQIAAACAJVyYCKLqLcH2LtvP2T5i+95FnrftzxfPP237bcuta/uTtn9s+6nidtty7aCnCQAAAEAlazkRhO2GpPsk3SLpqKQnbO+PiL/pit0qaUdxe7uk+yW9PbHu5yLiT7JtoWgC0NO3Zr+cyt0ydHt6m81TW9LZ4amr89s9O5HPTjXS2dak89mJfLY9ms92mumoYg3GELjElLJDrXy2MZPPNs/mv5ibU/ns2GvtfPYnZ9NZv/STdLb96qlU7mDn0fQ2AWBdrO3wvJskHYmIFyTJ9iOSdkvqLpp2S3ooIkLS92xvsr1N0nWJddMYngcAAADgYrjC9qGu254Fz2+X9FLX46PFskxmuXXvKYbz7bO9ebmG0tMEAAAAoIJYaU/TKxGxs8fziw3JWPgDl8r0Wvd+SZ8qHn9K0mck/W6vhlI0AQAAACgvtNbD845Kurbr8TWSXk5mRpZaNyKOX1ho+wuSvrlcQxieBwAAAKCatZ097wlJO2xfb3tE0h2S9i/I7Jf0gWIWvZslvRERx3qtW5zzdMF7JT2zXEPoaQIAAADQdyJizvY9kh6T1JC0LyKetf2R4vkHJB2QdJukI5LOSfpgr3WLTf+x7Rs131f2oqQPL9cWiiYAAAAAlazllOOSFBEHNF8YdS97oOt+SLo7u26x/P1l20HRBAAAAKCaNS6a+gVFEwAAAIDyQlKHogkAAAAAlrDiKccHBrPnAQAAAEAP9DQBWBUHO4+uyXbfPfq+dHb87DXp7Mjlk+lsa7KZz27MH1bnxhe77t7i2s18NhrpaJrb+Wyjlf+r4/D5fLZ5Zi6fnWqls41Xp9LZzotH09nHZh5OZwFgYNWkp4miCQAAAEA1FE0AAAAAsAQmggAAAACAXkKKzsVuxLpgIggAAAAA6IGeJgAAAADVcE4TAAAAACyBc5oAAAAAYBk16WninCYAAAAA6IGeJgAAAADV1KSniaIJAAAAQAVB0QQA/eBbMw+ns7cM3Z7ODr08kc6ObdiQz27dnM62J0fT2c5II51Vw/lsVjv/pTg0205nG1Mz+TacfC0djXPn0tn22bPp7MHOo+ksAFzyQlKnHtdpomgCAAAAUE1NepqYCAIAAAAAeqCnCQAAAEA1NelpomgCAAAAUEFwcVsAAAAAWFJIEfWYCIJzmgAAAACgB3qaAAAAAFTD8DwAAAAA6IGJIAAAAABgCRFc3BYAAAAAeqKnCQAGy8HOoxe7Cbpl6PZ0dmjDhny20UhnbaezWVHmS7HdzkfPnUtn+2H/AgDqiaIJAAAAQCVRk+F5K5py3PYu28/ZPmL73tVqFAAAAIB+F/PD86reBkjlnibbDUn3SbpF0lFJT9jeHxF/s1qNAwAAANCnQkw5nnCTpCMR8YIk2X5E0m5JFE0AAABAHQTD85azXdJLXY+PFsv+Edt7bB+yfejkyZMr+HEAAAAAsP5WUjQtNj3Tz/TPRcTeiNgZETu3bt26gh8HAAAAoF+EpOhE5dsgWcnwvKOSru16fI2kl1fWHAAAAAADIaI2w/NWUjQ9IWmH7esl/VjSHZJ+Z1VaBQAAAKDvDVqPUVWVi6aImLN9j6THJDUk7YuIZ1etZQAAAADQB1Z0cduIOCDpwCq1BQAAAMAgqcnwPMc6XljK9klJf79uP7DerpD0ysVuBNLYX4OHfTZ42GeDh302WNhfK/NPImKgZk2z/eea3+9VvRIRu1arPWtpXYsmrB/bhyJi58VuB3LYX4OHfTZ42GeDh302WNhfuJStZMpxAAAAALjkUTQBAAAAQA8UTZeuvRe7ASiF/TV42GeDh302eNhng4X9hUsW5zQBAAAAQA/0NAEAAABADxRNAAAAANADRdMlxPbttp+13bG9c8Fzv2/7iO3nbP/WxWojfpbtXcV+OWL73ovdHvws2/tsn7D9TNeyLbYP2n6++HfzxWwj/oHta21/2/bh4pj40WI5+6xP2R6z/Ve2/7rYZ39ULGef9THbDdv/x/Y3i8fsL1yyKJouLc9I+leSvtO90PYNku6Q9CuSdkn6z7Yb6988LFTsh/sk3SrpBkl3FvsL/eVLmv/sdLtX0uMRsUPS48Vj9Ic5SR+PiF+WdLOku4vPFfusf81I+o2I+DVJN0raZftmsc/63UclHe56zP7CJYui6RISEYcj4rlFntot6ZGImImIv5N0RNJN69s6LOEmSUci4oWImJX0iOb3F/pIRHxH0qkFi3dLerC4/6Ck96xnm7C0iDgWET8o7p/R/H/qtot91rdi3lTxsFncQuyzvmX7Gkn/QtJ/6VrM/sIli6KpHrZLeqnr8dFiGS4+9s3guioijknz/0mXdOVFbg8WYfs6SW+V9H2xz/paMdTrKUknJB2MCPZZf/tTSf9eUqdrGfsLl6zhi90AlGP7f0q6epGn/iAivrHUaossY675/sC+AdaI7UlJX5X0sYg4bS/2cUO/iIi2pBttb5L0ddu/epGbhCXY/m1JJyLiSdvvvMjNAdYFRdOAiYjfrLDaUUnXdj2+RtLLq9MirBD7ZnAdt70tIo7Z3qb5v46jT9huar5gejgivlYsZp8NgIh43fZfaP48QvZZf3qHpH9p+zZJY5Ius/1fxf7CJYzhefWwX9IdtkdtXy9ph6S/ushtwrwnJO2wfb3tEc1P2LH/IrcJOfsl3VXcv0vSUj29WGee71L6oqTDEfHZrqfYZ33K9taih0m2xyX9pqS/FfusL0XE70fENRFxnea/t/5XRPxrsb9wCXMEI4EuFbbfK+k/Sdoq6XVJT0XEbxXP/YGk39X8rFIfi4j/cbHaiX+s+Evdn0pqSNoXEf/x4rYIC9n+sqR3SrpC0nFJfyjpv0v6iqQ3S/qRpNsjYuFkEbgIbP9zSf9b0g/1D+dbfELz5zWxz/qQ7X+q+YkDGpr/g+5XIuI/2L5c7LO+VgzP+3cR8dvsL1zKKJoAAAAAoAeG5wEAAABADxRNAAAAANADRRMAAAAA9EDRBAAAAAA9UDQBAAAAQA8UTQAAAADQA0UTAAAAAPTw/wHZg+MgaL11+gAAAABJRU5ErkJggg==",
       "text/plain": [
        "<Figure size 1152x432 with 2 Axes>"
       ]
@@ -459,7 +459,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -484,7 +484,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.7"
+   "version": "3.8.2"
   }
  },
  "nbformat": 4,
diff --git a/lbmpy_tests/shan_chen/test_shan_chen_two_component.py b/lbmpy_tests/shan_chen/test_shan_chen_two_component.py
new file mode 100644
index 0000000000000000000000000000000000000000..451527195ff7287c252c92b170e56f248381d4ee
--- /dev/null
+++ b/lbmpy_tests/shan_chen/test_shan_chen_two_component.py
@@ -0,0 +1,175 @@
+"""
+Test Shan-Chen two-component implementation against reference implementation
+"""
+
+import lbmpy
+
+import pystencils as ps
+import sympy as sp
+import numpy as np
+
+
+def test_shan_chen_two_component():
+    from lbmpy.enums import Stencil
+    from lbmpy import LBMConfig, ForceModel, create_lb_update_rule
+    from lbmpy.macroscopic_value_kernels import macroscopic_values_setter
+    from lbmpy.creationfunctions import create_stream_pull_with_output_kernel
+    from lbmpy.maxwellian_equilibrium import get_weights
+
+    N = 64
+    omega_a = 1.
+    omega_b = 1.
+
+    # interaction strength
+    g_aa = 0.
+    g_ab = g_ba = 6.
+    g_bb = 0.
+
+    rho0 = 1.
+
+    stencil = lbmpy.LBStencil(Stencil.D2Q9)
+    weights = get_weights(stencil, c_s_sq=sp.Rational(1, 3))
+
+    dim = stencil.D
+    dh = ps.create_data_handling((N, ) * dim, periodicity=True, default_target=ps.Target.CPU)
+
+    src_a = dh.add_array('src_a', values_per_cell=stencil.Q)
+    dst_a = dh.add_array_like('dst_a', 'src_a')
+
+    src_b = dh.add_array('src_b', values_per_cell=stencil.Q)
+    dst_b = dh.add_array_like('dst_b', 'src_b')
+
+    ρ_a = dh.add_array('rho_a')
+    ρ_b = dh.add_array('rho_b')
+    u_a = dh.add_array('u_a', values_per_cell=stencil.D)
+    u_b = dh.add_array('u_b', values_per_cell=stencil.D)
+    u_bary = dh.add_array_like('u_bary', u_a.name)
+
+    f_a = dh.add_array('f_a', values_per_cell=stencil.D)
+    f_b = dh.add_array_like('f_b', f_a.name)
+
+    def psi(dens):
+        return rho0 * (1. - sp.exp(-dens / rho0))
+
+    zero_vec = sp.Matrix([0] * stencil.D)
+
+    force_a = zero_vec
+    for factor, ρ in zip([g_aa, g_ab], [ρ_a, ρ_b]):
+        force_a += sum((psi(ρ[d]) * w_d * sp.Matrix(d)
+                        for d, w_d in zip(stencil, weights)),
+                       zero_vec) * psi(ρ_a.center) * -1 * factor
+
+    force_b = zero_vec
+    for factor, ρ in zip([g_ba, g_bb], [ρ_a, ρ_b]):
+        force_b += sum((psi(ρ[d]) * w_d * sp.Matrix(d)
+                        for d, w_d in zip(stencil, weights)),
+                       zero_vec) * psi(ρ_b.center) * -1 * factor
+
+    f_expressions = ps.AssignmentCollection([
+        ps.Assignment(f_a.center_vector, force_a),
+        ps.Assignment(f_b.center_vector, force_b)
+    ])
+
+    # calculate the velocity without force correction
+    u_temp = ps.Assignment(u_bary.center_vector,
+                           (ρ_a.center * u_a.center_vector
+                            - f_a.center_vector / 2 + ρ_b.center * u_b.center_vector
+                            - f_b.center_vector / 2) / (ρ_a.center + ρ_b.center))
+
+    # add the force correction to the velocity
+    u_corr = ps.Assignment(u_bary.center_vector,
+                           u_bary.center_vector
+                           + (f_a.center_vector / 2 + f_b.center_vector / 2) / (ρ_a.center + ρ_b.center))
+
+    lbm_config_a = LBMConfig(stencil=stencil, relaxation_rate=omega_a, compressible=True,
+                             velocity_input=u_bary, density_input=ρ_a, force_model=ForceModel.GUO,
+                             force=f_a, kernel_type='collide_only')
+
+    lbm_config_b = LBMConfig(stencil=stencil, relaxation_rate=omega_b, compressible=True,
+                             velocity_input=u_bary, density_input=ρ_b, force_model=ForceModel.GUO,
+                             force=f_b, kernel_type='collide_only')
+
+    collision_a = create_lb_update_rule(lbm_config=lbm_config_a,
+                                        optimization={'symbolic_field': src_a})
+
+    collision_b = create_lb_update_rule(lbm_config=lbm_config_b,
+                                        optimization={'symbolic_field': src_b})
+
+    stream_a = create_stream_pull_with_output_kernel(collision_a.method, src_a, dst_a,
+                                                     {'density': ρ_a, 'velocity': u_a})
+    stream_b = create_stream_pull_with_output_kernel(collision_b.method, src_b, dst_b,
+                                                     {'density': ρ_b, 'velocity': u_b})
+
+    config = ps.CreateKernelConfig(target=dh.default_target)
+
+    stream_a_kernel = ps.create_kernel(stream_a, config=config).compile()
+    stream_b_kernel = ps.create_kernel(stream_b, config=config).compile()
+    collision_a_kernel = ps.create_kernel(collision_a, config=config).compile()
+    collision_b_kernel = ps.create_kernel(collision_b, config=config).compile()
+
+    force_kernel = ps.create_kernel(f_expressions, config=config).compile()
+    u_temp_kernel = ps.create_kernel(u_temp, config=config).compile()
+    u_corr_kernel = ps.create_kernel(u_corr, config=config).compile()
+
+    init_a = macroscopic_values_setter(collision_a.method, velocity=(0, 0),
+                                       pdfs=src_a.center_vector, density=ρ_a.center)
+    init_b = macroscopic_values_setter(collision_b.method, velocity=(0, 0),
+                                       pdfs=src_b.center_vector, density=ρ_b.center)
+    init_a_kernel = ps.create_kernel(init_a, ghost_layers=0).compile()
+    init_b_kernel = ps.create_kernel(init_b, ghost_layers=0).compile()
+
+    sync_pdfs = dh.synchronization_function([src_a.name, src_b.name])
+    sync_ρs = dh.synchronization_function([ρ_a.name, ρ_b.name])
+
+    dh.fill(ρ_a.name, 0.1, slice_obj=ps.make_slice[:, :0.5])
+    dh.fill(ρ_a.name, 0.9, slice_obj=ps.make_slice[:, 0.5:])
+
+    dh.fill(ρ_b.name, 0.9, slice_obj=ps.make_slice[:, :0.5])
+    dh.fill(ρ_b.name, 0.1, slice_obj=ps.make_slice[:, 0.5:])
+
+    dh.fill(u_a.name, 0.0)
+    dh.fill(u_b.name, 0.0)
+    dh.fill(f_a.name, 0.0)
+    dh.fill(f_b.name, 0.0)
+    dh.run_kernel(u_temp_kernel)
+
+    dh.run_kernel(init_a_kernel)
+    dh.run_kernel(init_b_kernel)
+
+    for i in range(1000):
+        sync_ρs()
+        dh.run_kernel(force_kernel)
+        dh.run_kernel(u_corr_kernel)
+        dh.run_kernel(collision_a_kernel)
+        dh.run_kernel(collision_b_kernel)
+
+        sync_pdfs()
+        dh.run_kernel(stream_a_kernel)
+        dh.run_kernel(stream_b_kernel)
+        dh.run_kernel(u_temp_kernel)
+
+        dh.swap(src_a.name, dst_a.name)
+        dh.swap(src_b.name, dst_b.name)
+    
+    # reference generated from https://github.com/lbm-principles-practice/code/blob/master/chapter9/shanchen.cpp with
+    # const int nsteps = 1000;
+    # const int noutput = 1000;
+    # const int nfluids = 2;
+    # const double gA = 0;
+
+    ref_a = np.array([0.213948, 0.0816724, 0.0516763, 0.0470179, 0.0480882, 0.0504771, 0.0531983, 0.0560094, 0.0588071,
+                      0.0615311, 0.064102, 0.0664467, 0.0684708, 0.070091, 0.0712222, 0.0718055, 0.0718055, 0.0712222,
+                      0.070091, 0.0684708, 0.0664467, 0.064102, 0.0615311, 0.0588071, 0.0560094, 0.0531983, 0.0504771,
+                      0.0480882, 0.0470179, 0.0516763, 0.0816724, 0.213948, 0.517153, 0.833334, 0.982884, 1.0151,
+                      1.01361, 1.0043, 0.993178, 0.981793, 0.970546, 0.959798, 0.949751, 0.940746, 0.933035, 0.926947,
+                      0.922713, 0.920548, 0.920548, 0.922713, 0.926947, 0.933035, 0.940746, 0.949751, 0.959798,
+                      0.970546, 0.981793, 0.993178, 1.0043, 1.01361, 1.0151, 0.982884, 0.833334, 0.517153])
+    ref_b = np.array([0.517153, 0.833334, 0.982884, 1.0151, 1.01361, 1.0043, 0.993178, 0.981793, 0.970546, 0.959798,
+                      0.949751, 0.940746, 0.933035, 0.926947, 0.922713, 0.920548, 0.920548, 0.922713, 0.926947,
+                      0.933035, 0.940746, 0.949751, 0.959798, 0.970546, 0.981793, 0.993178, 1.0043, 1.01361, 1.0151,
+                      0.982884, 0.833334, 0.517153, 0.213948, 0.0816724, 0.0516763, 0.0470179, 0.0480882, 0.0504771,
+                      0.0531983, 0.0560094, 0.0588071, 0.0615311, 0.064102, 0.0664467, 0.0684708, 0.070091, 0.0712222,
+                      0.0718055, 0.0718055, 0.0712222, 0.070091, 0.0684708, 0.0664467, 0.064102, 0.0615311, 0.0588071,
+                      0.0560094, 0.0531983, 0.0504771, 0.0480882, 0.0470179, 0.0516763, 0.0816724, 0.213948])
+    assert np.allclose(dh.gather_array(ρ_a.name)[0], ref_a)
+    assert np.allclose(dh.gather_array(ρ_b.name)[0], ref_b)
diff --git a/lbmpy_tests/shan_chen/test_shan_chen_two_phase.py b/lbmpy_tests/shan_chen/test_shan_chen_two_phase.py
new file mode 100644
index 0000000000000000000000000000000000000000..18a7dd2e32e52f50af8463adc634571b1657c025
--- /dev/null
+++ b/lbmpy_tests/shan_chen/test_shan_chen_two_phase.py
@@ -0,0 +1,93 @@
+"""
+Test Shan-Chen two-phase implementation against reference implementation
+"""
+
+import lbmpy
+
+import pystencils as ps
+import sympy as sp
+import numpy as np
+
+
+def test_shan_chen_two_phase():
+    from lbmpy.enums import Stencil
+    from lbmpy import LBMConfig, ForceModel, create_lb_update_rule
+    from lbmpy.macroscopic_value_kernels import macroscopic_values_setter
+    from lbmpy.creationfunctions import create_stream_pull_with_output_kernel, create_lb_method
+    from lbmpy.maxwellian_equilibrium import get_weights
+
+    N = 64
+    omega = 1.
+    g_aa = -4.7
+    rho0 = 1.
+
+    stencil = lbmpy.LBStencil(Stencil.D2Q9)
+    weights = get_weights(stencil, c_s_sq=sp.Rational(1, 3))
+
+    dh = ps.create_data_handling((N, ) * stencil.D, periodicity=True, default_target=ps.Target.CPU)
+
+    src = dh.add_array('src', values_per_cell=stencil.Q)
+    dst = dh.add_array_like('dst', 'src')
+
+    ρ = dh.add_array('rho')
+
+    def psi(dens):
+        return rho0 * (1. - sp.exp(-dens / rho0))
+
+    zero_vec = sp.Matrix([0] * stencil.D)
+
+    force = sum((psi(ρ[d]) * w_d * sp.Matrix(d)
+                 for d, w_d in zip(stencil, weights)), zero_vec) * psi(ρ.center) * -1 * g_aa
+
+    lbm_config = LBMConfig(stencil=stencil, relaxation_rate=omega, compressible=True,
+                           force_model=ForceModel.GUO, force=force, kernel_type='collide_only')
+
+    collision = create_lb_update_rule(lbm_config=lbm_config,
+                                      optimization={'symbolic_field': src})
+
+    stream = create_stream_pull_with_output_kernel(collision.method, src, dst, {'density': ρ})
+
+    config = ps.CreateKernelConfig(target=dh.default_target, cpu_openmp=False)
+
+    stream_kernel = ps.create_kernel(stream, config=config).compile()
+    collision_kernel = ps.create_kernel(collision, config=config).compile()
+
+    method_without_force = create_lb_method(LBMConfig(stencil=stencil, relaxation_rate=omega, compressible=True))
+    init_assignments = macroscopic_values_setter(method_without_force, velocity=(0, 0),
+                                                 pdfs=src.center_vector, density=ρ.center)
+
+    init_kernel = ps.create_kernel(init_assignments, ghost_layers=0, config=config).compile()
+
+    for x in range(N):
+        for y in range(N):
+            if (x - N / 2)**2 + (y - N / 2)**2 <= 15**2:
+                dh.fill(ρ.name, 2.1, slice_obj=[x, y])
+            else:
+                dh.fill(ρ.name, 0.15, slice_obj=[x, y])
+
+    dh.run_kernel(init_kernel)
+
+    sync_pdfs = dh.synchronization_function([src.name])
+    sync_ρs = dh.synchronization_function([ρ.name])
+
+    for i in range(1000):
+        sync_ρs()
+        dh.run_kernel(collision_kernel)
+
+        sync_pdfs()
+        dh.run_kernel(stream_kernel)
+
+        dh.swap(src.name, dst.name)
+
+    # reference generated from https://github.com/lbm-principles-practice/code/blob/master/chapter9/shanchen.cpp with
+    # const int nsteps = 1000;
+    # const int noutput = 1000;
+
+    ref = np.array([0.185757, 0.185753, 0.185743, 0.185727, 0.185703, 0.185672, 0.185636, 0.185599, 0.185586, 0.185694,
+                    0.186302, 0.188901, 0.19923, 0.238074, 0.365271, 0.660658, 1.06766, 1.39673, 1.56644, 1.63217,
+                    1.65412, 1.66064, 1.66207, 1.66189, 1.66123, 1.66048, 1.65977, 1.65914, 1.65861, 1.6582, 1.6579,
+                    1.65772, 1.65766, 1.65772, 1.6579, 1.6582, 1.65861, 1.65914, 1.65977, 1.66048, 1.66123, 1.66189,
+                    1.66207, 1.66064, 1.65412, 1.63217, 1.56644, 1.39673, 1.06766, 0.660658, 0.365271, 0.238074,
+                    0.19923, 0.188901, 0.186302, 0.185694, 0.185586, 0.185599, 0.185636, 0.185672, 0.185703, 0.185727,
+                    0.185743, 0.185753])
+    assert np.allclose(dh.gather_array(ρ.name)[N // 2], ref)
diff --git a/lbmpy_tests/test_boundary_handling.py b/lbmpy_tests/test_boundary_handling.py
index fa285f6b6a6d43cf92329f6e273742021410d835..d94cfa63f6e3c4049165eda12b4ee8744eace162 100644
--- a/lbmpy_tests/test_boundary_handling.py
+++ b/lbmpy_tests/test_boundary_handling.py
@@ -4,7 +4,7 @@ import pytest
 from lbmpy.boundaries import NoSlip, UBB, SimpleExtrapolationOutflow, ExtrapolationOutflow, \
     FixedDensity, DiffusionDirichlet, NeumannByCopy, StreamInConstant, FreeSlip
 from lbmpy.boundaries.boundaryhandling import LatticeBoltzmannBoundaryHandling
-from lbmpy.creationfunctions import create_lb_function, create_lb_method, LBMConfig, LBMOptimisation
+from lbmpy.creationfunctions import create_lb_function, create_lb_method, LBMConfig
 from lbmpy.enums import Stencil, Method
 from lbmpy.geometry import add_box_boundary
 from lbmpy.lbstep import LatticeBoltzmannStep
@@ -22,22 +22,18 @@ def mirror_stencil(direction, mirror_axis):
     return tuple(direction)
 
 
-@pytest.mark.parametrize("target", [Target.GPU, Target.CPU, Target.OPENCL])
+@pytest.mark.parametrize("target", [Target.GPU, Target.CPU])
 def test_simple(target):
     if target == Target.GPU:
         import pytest
         pytest.importorskip('pycuda')
-    elif target == Target.OPENCL:
-        import pytest
-        pytest.importorskip('pyopencl')
-        import pystencils.opencl.autoinit
 
     dh = create_data_handling((4, 4), parallel=False, default_target=target)
     dh.add_array('pdfs', values_per_cell=9, cpu=True, gpu=target != Target.CPU)
     for i in range(9):
         dh.fill("pdfs", i, value_idx=i, ghost_layers=True)
 
-    if target == Target.GPU or target == Target.OPENCL:
+    if target == Target.GPU:
         dh.all_to_gpu()
 
     lbm_config = LBMConfig(stencil=LBStencil(Stencil.D2Q9), compressible=False, relaxation_rate=1.8)
@@ -57,7 +53,7 @@ def test_simple(target):
     bh.prepare()
     bh()
 
-    if target == Target.GPU or target == Target.OPENCL:
+    if target == Target.GPU:
         dh.all_to_cpu()
     # left lower corner
     assert (dh.cpu_arrays['pdfs'][0, 0, 6] == 7)
@@ -116,6 +112,122 @@ def test_simple(target):
     assert (all(dh.cpu_arrays['pdfs'][0, 2:4, 8] == 5))
 
 
+@pytest.mark.parametrize("given_normal", [True, False])
+def test_free_slip(given_normal):
+    # check if Free slip BC is applied correctly
+
+    stencil = LBStencil(Stencil.D2Q9)
+    dh = create_data_handling(domain_size=(4, 4),)
+    src1 = dh.add_array('src1', values_per_cell=stencil.Q)
+    dh.fill('src1', 0.0, ghost_layers=True)
+
+    shape = dh.gather_array('src1', ghost_layers=True).shape
+
+    num = 0
+    for x in range(shape[0]):
+        for y in range(shape[1]):
+            for direction in range(shape[2]):
+                dh.cpu_arrays[src1.name][x, y, direction] = num
+                num += 1
+
+    method = create_lb_method(lbm_config=LBMConfig(stencil=stencil, method=Method.SRT, relaxation_rate=1.8))
+
+    bh = LatticeBoltzmannBoundaryHandling(method, dh, 'src1', name="bh1")
+    if given_normal:
+        free_slipN = FreeSlip(stencil=stencil, normal_direction=(0, -1))
+        free_slipS = FreeSlip(stencil=stencil, normal_direction=(0, 1))
+        free_slipE = FreeSlip(stencil=stencil, normal_direction=(-1, 0))
+        free_slipW = FreeSlip(stencil=stencil, normal_direction=(1, 0))
+
+        bh.set_boundary(free_slipN, slice_from_direction('N', dh.dim))
+        bh.set_boundary(free_slipS, slice_from_direction('S', dh.dim))
+        bh.set_boundary(free_slipE, slice_from_direction('E', dh.dim))
+        bh.set_boundary(free_slipW, slice_from_direction('W', dh.dim))
+    else:
+        free_slip = FreeSlip(stencil=stencil)
+
+        bh.set_boundary(free_slip, slice_from_direction('N', dh.dim))
+        bh.set_boundary(free_slip, slice_from_direction('S', dh.dim))
+        bh.set_boundary(free_slip, slice_from_direction('E', dh.dim))
+        bh.set_boundary(free_slip, slice_from_direction('W', dh.dim))
+
+    bh()
+
+    mirrored_dirN = {6: 8, 1: 2, 5: 7}
+    mirrored_dirS = {7: 5, 2: 1, 8: 6}
+    mirrored_dirE = {6: 5, 4: 3, 8: 7}
+    mirrored_dirW = {5: 6, 3: 4, 7: 8}
+
+    # check North
+    assert dh.cpu_arrays[src1.name][1, -1, mirrored_dirN[6]] == dh.cpu_arrays[src1.name][1, -2, 6]
+    assert dh.cpu_arrays[src1.name][1, -1, mirrored_dirN[1]] == dh.cpu_arrays[src1.name][1, -2, 1]
+
+    for i in range(2, 4):
+        assert dh.cpu_arrays[src1.name][i, -1, mirrored_dirN[6]] == dh.cpu_arrays[src1.name][i, -2, 6]
+        assert dh.cpu_arrays[src1.name][i, -1, mirrored_dirN[1]] == dh.cpu_arrays[src1.name][i, -2, 1]
+        assert dh.cpu_arrays[src1.name][i, -1, mirrored_dirN[5]] == dh.cpu_arrays[src1.name][i, -2, 5]
+
+    assert dh.cpu_arrays[src1.name][4, -1, mirrored_dirN[1]] == dh.cpu_arrays[src1.name][4, -2, 1]
+    assert dh.cpu_arrays[src1.name][4, -1, mirrored_dirN[5]] == dh.cpu_arrays[src1.name][4, -2, 5]
+
+    # check East
+    assert dh.cpu_arrays[src1.name][-1, 1, mirrored_dirE[6]] == dh.cpu_arrays[src1.name][-2, 1, 6]
+    assert dh.cpu_arrays[src1.name][-1, 1, mirrored_dirE[4]] == dh.cpu_arrays[src1.name][-2, 1, 4]
+
+    for i in range(2, 4):
+        assert dh.cpu_arrays[src1.name][-1, i, mirrored_dirE[6]] == dh.cpu_arrays[src1.name][-2, i, 6]
+        assert dh.cpu_arrays[src1.name][-1, i, mirrored_dirE[4]] == dh.cpu_arrays[src1.name][-2, i, 4]
+        assert dh.cpu_arrays[src1.name][-1, i, mirrored_dirE[8]] == dh.cpu_arrays[src1.name][-2, i, 8]
+
+    assert dh.cpu_arrays[src1.name][-1, 4, mirrored_dirE[4]] == dh.cpu_arrays[src1.name][-2, 4, 4]
+    assert dh.cpu_arrays[src1.name][-1, 4, mirrored_dirE[8]] == dh.cpu_arrays[src1.name][-2, 4, 8]
+
+    # check South
+    assert dh.cpu_arrays[src1.name][1, 0, mirrored_dirS[8]] == dh.cpu_arrays[src1.name][1, 1, 8]
+    assert dh.cpu_arrays[src1.name][1, 0, mirrored_dirS[2]] == dh.cpu_arrays[src1.name][1, 1, 2]
+
+    for i in range(2, 4):
+        assert dh.cpu_arrays[src1.name][i, 0, mirrored_dirS[7]] == dh.cpu_arrays[src1.name][i, 1, 7]
+        assert dh.cpu_arrays[src1.name][i, 0, mirrored_dirS[2]] == dh.cpu_arrays[src1.name][i, 1, 2]
+        assert dh.cpu_arrays[src1.name][i, 0, mirrored_dirS[8]] == dh.cpu_arrays[src1.name][i, 1, 8]
+
+    assert dh.cpu_arrays[src1.name][4, 0, mirrored_dirS[2]] == dh.cpu_arrays[src1.name][4, 1, 2]
+    assert dh.cpu_arrays[src1.name][4, 0, mirrored_dirS[7]] == dh.cpu_arrays[src1.name][4, 1, 7]
+
+    # check West
+    assert dh.cpu_arrays[src1.name][0, 1, mirrored_dirW[5]] == dh.cpu_arrays[src1.name][1, 1, 5]
+    assert dh.cpu_arrays[src1.name][0, 1, mirrored_dirW[3]] == dh.cpu_arrays[src1.name][1, 1, 3]
+
+    for i in range(2, 4):
+        assert dh.cpu_arrays[src1.name][0, i, mirrored_dirW[5]] == dh.cpu_arrays[src1.name][1, i, 5]
+        assert dh.cpu_arrays[src1.name][0, i, mirrored_dirW[3]] == dh.cpu_arrays[src1.name][1, i, 3]
+        assert dh.cpu_arrays[src1.name][0, i, mirrored_dirW[7]] == dh.cpu_arrays[src1.name][1, i, 7]
+
+    assert dh.cpu_arrays[src1.name][0, 4, mirrored_dirW[3]] == dh.cpu_arrays[src1.name][1, 4, 3]
+    assert dh.cpu_arrays[src1.name][0, 4, mirrored_dirW[7]] == dh.cpu_arrays[src1.name][1, 4, 7]
+
+    if given_normal:
+        # check corners --> determined by the last boundary applied there.
+        # SouthWest --> West
+        assert dh.cpu_arrays[src1.name][0, 0, mirrored_dirW[5]] == dh.cpu_arrays[src1.name][1, 0, 5]
+        # NorthWest --> West
+        assert dh.cpu_arrays[src1.name][0, -1, mirrored_dirW[7]] == dh.cpu_arrays[src1.name][1, -1, 7]
+        # NorthEast --> East
+        assert dh.cpu_arrays[src1.name][-1, -1, mirrored_dirE[8]] == dh.cpu_arrays[src1.name][-2, -1, 8]
+        # SouthEast --> East
+        assert dh.cpu_arrays[src1.name][-1, 0, mirrored_dirE[6]] == dh.cpu_arrays[src1.name][-2, 0, 6]
+    else:
+        # check corners --> this time the normals are calculated correctly in the corners
+        # SouthWest --> Normal = (1, 1); dir 7 --> 6
+        assert dh.cpu_arrays[src1.name][0, 0, 6] == dh.cpu_arrays[src1.name][1, 1, 7]
+        # NorthWest --> Normal = (1, -1); dir 8 --> 5
+        assert dh.cpu_arrays[src1.name][0, -1, 8] == dh.cpu_arrays[src1.name][1, -2, 5]
+        # NorthEast --> Normal = (-1, -1); dir 7 --> 6
+        assert dh.cpu_arrays[src1.name][-1, -1, 7] == dh.cpu_arrays[src1.name][-2, -2, 6]
+        # SouthEast --> Normal = (-1, 1); dir 5 --> 8
+        assert dh.cpu_arrays[src1.name][-1, 0, 5] == dh.cpu_arrays[src1.name][-2, 1, 8]
+
+
 def test_free_slip_index_list():
     stencil = LBStencil(Stencil.D2Q9)
     dh = create_data_handling(domain_size=(4, 4), periodicity=(False, False))
@@ -185,6 +297,50 @@ def test_free_slip_index_list():
             assert normal == normal_north_east
 
 
+def test_free_slip_index_list_convex_corner():
+    stencil = LBStencil(Stencil.D2Q9)
+    dh = create_data_handling(domain_size=(4, 4))
+    src = dh.add_array('src', values_per_cell=len(stencil))
+    dh.fill('src', 0.0, ghost_layers=True)
+
+    lbm_config = LBMConfig(stencil=stencil, method=Method.SRT, relaxation_rate=1.8)
+    method = create_lb_method(lbm_config=lbm_config)
+
+    def bh_callback(x, y):
+        radius = 2
+        x_mid = 2
+        y_mid = 2
+        return (x - x_mid) ** 2 + (y - y_mid) ** 2 > radius ** 2
+
+    bh = LatticeBoltzmannBoundaryHandling(method, dh, 'src', name="bh")
+
+    free_slip = FreeSlip(stencil=stencil)
+    bh.set_boundary(free_slip, mask_callback=bh_callback)
+
+    bh.prepare()
+    for b in dh.iterate():
+        for b_obj, idx_arr in b[bh._index_array_name].boundary_object_to_index_list.items():
+            index_array = idx_arr
+
+    # correct index array for this case with convex corners
+    test = [(2, 1, 2, 0, 1, 2), (2, 1, 3, 1, 0, 3), (2, 1, 7, 1, 1, 7),
+            (2, 1, 8, 0, 1, 7), (3, 1, 2, 0, 1, 2), (3, 1, 4, -1, 0, 4),
+            (3, 1, 7, 0, 1, 8), (3, 1, 8, -1, 1, 8), (1, 2, 2, 0, 1, 2),
+            (1, 2, 3, 1, 0, 3), (1, 2, 5, 1, 0, 7), (1, 2, 7, 1, 1, 7),
+            (2, 2, 7, 1, 1, 7), (3, 2, 8, -1, 1, 8), (4, 2, 2, 0, 1, 2),
+            (4, 2, 4, -1, 0, 4), (4, 2, 6, -1, 0, 8), (4, 2, 8, -1, 1, 8),
+            (1, 3, 1, 0, -1, 1), (1, 3, 3, 1, 0, 3), (1, 3, 5, 1, -1, 5),
+            (1, 3, 7, 1, 0, 5), (2, 3, 5, 1, -1, 5), (3, 3, 6, -1, -1, 6),
+            (4, 3, 1, 0, -1, 1), (4, 3, 4, -1, 0, 4), (4, 3, 6, -1, -1, 6),
+            (4, 3, 8, -1, 0, 6), (2, 4, 1, 0, -1, 1), (2, 4, 3, 1, 0, 3),
+            (2, 4, 5, 1, -1, 5), (2, 4, 6, 0, -1, 5), (3, 4, 1, 0, -1, 1),
+            (3, 4, 4, -1, 0, 4), (3, 4, 5, 0, -1, 6), (3, 4, 6, -1, -1, 6)]
+
+    for i, cell in enumerate(index_array):
+        for j in range(len(cell)):
+            assert cell[j] == test[i][j]
+
+
 def test_free_slip_equivalence():
     # check if Free slip BC does the same if the normal direction is specified or not
 
@@ -218,7 +374,7 @@ def test_free_slip_equivalence():
     bh1()
     bh2()
 
-    assert np.array_equal(dh.cpu_arrays['src1'], dh.cpu_arrays['src2'])
+    assert np.array_equal(dh.gather_array('src1'), dh.gather_array('src2'))
 
 
 def test_exotic_boundaries():
diff --git a/lbmpy_tests/test_code_hashequivalence.py b/lbmpy_tests/test_code_hashequivalence.py
deleted file mode 100644
index 06ef0dedc28f7da6627879849daaaaf656f717a1..0000000000000000000000000000000000000000
--- a/lbmpy_tests/test_code_hashequivalence.py
+++ /dev/null
@@ -1,22 +0,0 @@
-from hashlib import sha256
-
-from pystencils import Backend, CreateKernelConfig, Target
-from lbmpy.creationfunctions import create_lb_ast
-from lbmpy.enums import Stencil, Method
-from lbmpy.creationfunctions import LBMConfig
-from lbmpy.stencils import LBStencil
-
-
-def test_hash_equivalence_llvm():
-    import pytest
-    pytest.importorskip("llvmlite")
-    from pystencils.llvm.llvmjit import generate_llvm
-
-    ref_value = "f1b1879e304fe8533977c885f2744516dd4964064a7e4ae64fd94b8426d995bb"
-
-    lbm_config = LBMConfig(stencil=LBStencil(Stencil.D2Q9), method=Method.SRT)
-    config = CreateKernelConfig(target=Target.CPU, backend=Backend.LLVM)
-    ast = create_lb_ast(lbm_config=lbm_config, config=config)
-    code = generate_llvm(ast)
-    hash_value = sha256(str(code).encode()).hexdigest()
-    assert hash_value == ref_value
diff --git a/lbmpy_tests/test_compiled_in_boundaries.ipynb b/lbmpy_tests/test_compiled_in_boundaries.ipynb
index 1a0adbd9a18406864f9aadd3583126f4554cad7f..559ad60b015c1823c88fbcc5b87e36535d751d5a 100644
--- a/lbmpy_tests/test_compiled_in_boundaries.ipynb
+++ b/lbmpy_tests/test_compiled_in_boundaries.ipynb
@@ -107,7 +107,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.colorbar.Colorbar at 0x11d5a7820>"
+       "<matplotlib.colorbar.Colorbar at 0x7fddd3f7a880>"
       ]
      },
      "execution_count": 6,
@@ -116,7 +116,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 1152x432 with 2 Axes>"
       ]
@@ -150,7 +150,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.colorbar.Colorbar at 0x11dcca970>"
+       "<matplotlib.colorbar.Colorbar at 0x7fddd28835e0>"
       ]
      },
      "execution_count": 7,
@@ -159,7 +159,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 1152x432 with 2 Axes>"
       ]
@@ -205,7 +205,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.7"
+   "version": "3.8.2"
   }
  },
  "nbformat": 4,
diff --git a/lbmpy_tests/test_conserved_quantity_relaxation_invariance.py b/lbmpy_tests/test_conserved_quantity_relaxation_invariance.py
index af29332fecfeb47bae52f64940bd859fe8ac143d..8881a7d39d6215fbbb605811c1ef035af2f29230 100644
--- a/lbmpy_tests/test_conserved_quantity_relaxation_invariance.py
+++ b/lbmpy_tests/test_conserved_quantity_relaxation_invariance.py
@@ -58,7 +58,7 @@ def check_for_collision_rule_equivalence(collision_rule1, collision_rule2, use_n
     for eq1, eq2 in zip(collision_rule1.main_assignments, collision_rule2.main_assignments):
         diff = sp.cancel(sp.expand(eq1.rhs - eq2.rhs))
         if use_numeric_subs:
-            assert math.isclose(diff, 0, rel_tol=0.0, abs_tol=1e-12)
+            assert math.isclose(diff, 0, rel_tol=0.0, abs_tol=1e-10)
         else:
             assert diff == 0
 
@@ -78,8 +78,8 @@ def test_cumulant():
     original_method = create_with_default_polynomial_cumulants(stencil, [sp.Symbol("omega")])
     changed_method = __change_relaxation_rate_of_conserved_moments(original_method)
 
-    check_method_equivalence(original_method, changed_method, True, True)
-    check_method_equivalence(original_method, changed_method, False, True)
+    check_method_equivalence(original_method, changed_method, True, use_numeric_subs=True)
+    check_method_equivalence(original_method, changed_method, False, use_numeric_subs=True)
 
 
 @pytest.mark.longrun
@@ -89,8 +89,8 @@ def test_srt():
                                  maxwellian_moments=True)
     changed_method = __change_relaxation_rate_of_conserved_moments(original_method)
 
-    check_method_equivalence(original_method, changed_method, True)
-    check_method_equivalence(original_method, changed_method, False)
+    check_method_equivalence(original_method, changed_method, True, use_numeric_subs=True)
+    check_method_equivalence(original_method, changed_method, False, use_numeric_subs=True)
 
 
 def test_srt_short():
@@ -99,8 +99,8 @@ def test_srt_short():
                                  maxwellian_moments=True)
     changed_method = __change_relaxation_rate_of_conserved_moments(original_method)
 
-    check_method_equivalence(original_method, changed_method, True)
-    check_method_equivalence(original_method, changed_method, False)
+    check_method_equivalence(original_method, changed_method, True, use_numeric_subs=False)
+    check_method_equivalence(original_method, changed_method, False, use_numeric_subs=False)
 
 
 @pytest.mark.parametrize('stencil_name', [Stencil.D2Q9, Stencil.D3Q19, Stencil.D3Q27])
@@ -117,8 +117,8 @@ def test_trt(stencil_name, continuous_moments):
 
 
 @pytest.mark.parametrize('method_name', [Method.TRT_KBC_N1, Method.TRT_KBC_N2, Method.TRT_KBC_N3, Method.TRT_KBC_N4])
-@pytest.mark.parametrize('dim', [2, 3])
-def test_trt_kbc_long(method_name, dim):
+def test_trt_kbc(method_name):
+    dim = 2
     method_nr = method_name.name[-1]
     original_method = create_trt_kbc(dim, sp.Symbol("omega1"), sp.Symbol("omega2"),
                                      method_name='KBC-N' + method_nr,
@@ -127,3 +127,15 @@ def test_trt_kbc_long(method_name, dim):
     check_method_equivalence(original_method, changed_method, True)
     check_method_equivalence(original_method, changed_method, False)
 
+
+@pytest.mark.parametrize('method_name', [Method.TRT_KBC_N1, Method.TRT_KBC_N2, Method.TRT_KBC_N3, Method.TRT_KBC_N4])
+@pytest.mark.longrun
+def test_trt_kbc_long(method_name):
+    dim = 3
+    method_nr = method_name.name[-1]
+    original_method = create_trt_kbc(dim, sp.Symbol("omega1"), sp.Symbol("omega2"),
+                                     method_name='KBC-N' + method_nr,
+                                     maxwellian_moments=False)
+    changed_method = __change_relaxation_rate_of_conserved_moments(original_method)
+    check_method_equivalence(original_method, changed_method, True)
+    check_method_equivalence(original_method, changed_method, False)
diff --git a/lbmpy_tests/test_diffusion.py b/lbmpy_tests/test_diffusion.py
index 69215f25014b2f3a7f94b2559218b8694fd4fc8c..df390b948f947a3ca68fc22b512c6cd6a2e73849 100644
--- a/lbmpy_tests/test_diffusion.py
+++ b/lbmpy_tests/test_diffusion.py
@@ -75,8 +75,8 @@ def test_diffusion():
         C(x,y) = 1 * erfc(y / sqrt(4Dx/u))
 
       The hydrodynamic field is not simulated, instead a constant velocity is assumed.
-  """
-
+    """
+    pytest.importorskip("pycuda")
     # Parameters
     domain_size = (1600, 160)
     omega = 1.38
@@ -84,9 +84,10 @@ def test_diffusion():
     velocity = 0.05
     time_steps = 50000
     stencil = LBStencil(Stencil.D2Q9)
+    target = ps.Target.GPU
 
     # Data Handling
-    dh = ps.create_data_handling(domain_size=domain_size)
+    dh = ps.create_data_handling(domain_size=domain_size, default_target=target)
 
     vel_field = dh.add_array('vel_field', values_per_cell=stencil.D)
     dh.fill('vel_field', velocity, 0, ghost_layers=True)
@@ -96,7 +97,9 @@ def test_diffusion():
     dh.fill('con_field', 0.0, ghost_layers=True)
 
     pdfs = dh.add_array('pdfs', values_per_cell=stencil.Q)
+    dh.fill('pdfs', 0.0, ghost_layers=True)
     pdfs_tmp = dh.add_array('pdfs_tmp', values_per_cell=stencil.Q)
+    dh.fill('pdfs_tmp', 0.0, ghost_layers=True)
 
     # Lattice Boltzmann method
     lbm_config = LBMConfig(stencil=stencil, method=Method.MRT, relaxation_rates=[1, 1.5, 1, 1.5, 1],
@@ -104,21 +107,24 @@ def test_diffusion():
                            weighted=True, kernel_type='stream_pull_collide')
 
     lbm_opt = LBMOptimisation(symbolic_field=pdfs, symbolic_temporary_field=pdfs_tmp)
+    config = ps.CreateKernelConfig(target=dh.default_target, cpu_openmp=True)
 
     method = create_lb_method(lbm_config=lbm_config)
     method.set_conserved_moments_relaxation_rate(omega)
 
     lbm_config = replace(lbm_config, lb_method=method)
     update_rule = create_lb_update_rule(lbm_config=lbm_config, lbm_optimisation=lbm_opt)
-    kernel = ps.create_kernel(update_rule).compile()
+    kernel = ps.create_kernel(update_rule, config=config).compile()
 
     # PDF initalization
     init = pdf_initialization_assignments(method, con_field.center,
                                           vel_field.center_vector, pdfs.center_vector)
     dh.run_kernel(ps.create_kernel(init).compile())
 
+    dh.all_to_gpu()
+
     # Boundary Handling
-    bh = LatticeBoltzmannBoundaryHandling(update_rule.method, dh, 'pdfs', name="bh")
+    bh = LatticeBoltzmannBoundaryHandling(update_rule.method, dh, 'pdfs', name="bh", target=dh.default_target)
     add_box_boundary(bh, boundary=NeumannByCopy())
     bh.set_boundary(DiffusionDirichlet(0), slice_from_direction('W', dh.dim))
     bh.set_boundary(DiffusionDirichlet(1), slice_from_direction('S', dh.dim))
@@ -129,6 +135,7 @@ def test_diffusion():
         dh.run_kernel(kernel)
         dh.swap("pdfs", "pdfs_tmp")
 
+    dh.all_to_cpu()
     # Verification
     x = np.arange(1, domain_size[0], 1)
     y = np.arange(0, domain_size[1], 1)
diff --git a/lbmpy_tests/test_force.py b/lbmpy_tests/test_force.py
index 4215e957b8ee1948d52383e1bbbdf59ce01b933c..cc6b664de4393af61687e1cb9a3989a69a8ad4ec 100644
--- a/lbmpy_tests/test_force.py
+++ b/lbmpy_tests/test_force.py
@@ -7,7 +7,7 @@ from pystencils import Target
 
 from lbmpy.creationfunctions import create_lb_method, create_lb_update_rule, LBMConfig, LBMOptimisation
 from lbmpy.enums import Stencil, Method, ForceModel
-from lbmpy.macroscopic_value_kernels import macroscopic_values_setter
+from lbmpy.macroscopic_value_kernels import macroscopic_values_setter, macroscopic_values_getter
 from lbmpy.moments import is_bulk_moment
 from lbmpy.stencils import LBStencil
 from lbmpy.updatekernels import create_stream_pull_with_output_kernel
@@ -37,17 +37,13 @@ def test_total_momentum(method_enum, force_model, omega):
     u = dh.add_array('u', values_per_cell=stencil.D)
 
     lbm_config = LBMConfig(method=method_enum, stencil=stencil, relaxation_rate=omega,
-                           compressible=True, force_model=force_model, force=F, kernel_type='collide_only')
+                           compressible=True, force_model=force_model, force=F, streaming_pattern='pull')
     lbm_opt = LBMOptimisation(symbolic_field=src)
 
     collision = create_lb_update_rule(lbm_config=lbm_config, lbm_optimisation=lbm_opt)
 
-    stream = create_stream_pull_with_output_kernel(collision.method, src, dst,
-                                                   {'density': ρ, 'velocity': u})
-
     config = ps.CreateKernelConfig(cpu_openmp=True, target=dh.default_target)
 
-    stream_kernel = ps.create_kernel(stream, config=config).compile()
     collision_kernel = ps.create_kernel(collision, config=config).compile()
 
     def init():
@@ -55,24 +51,28 @@ def test_total_momentum(method_enum, force_model, omega):
         dh.fill(u.name, 0)
 
         setter = macroscopic_values_setter(collision.method, velocity=(0,) * dh.dim,
-                                           pdfs=src.center_vector, density=ρ.center)
-        kernel = ps.create_kernel(setter, ghost_layers=0).compile()
+                                           pdfs=src, density=ρ.center,
+                                           set_pre_collision_pdfs=True)
+        kernel = ps.create_kernel(setter).compile()
         dh.run_kernel(kernel)
 
     sync_pdfs = dh.synchronization_function([src.name])
 
+    getter = macroscopic_values_getter(collision.method, ρ.center, u.center_vector, src, use_pre_collision_pdfs=True)
+    getter_kernel = ps.create_kernel(getter).compile()
+
     def time_loop(steps):
         dh.all_to_gpu()
-        for i in range(steps):
+        for _ in range(steps):
             dh.run_kernel(collision_kernel)
-            sync_pdfs()
-            dh.run_kernel(stream_kernel)
             dh.swap(src.name, dst.name)
+            sync_pdfs()
         dh.all_to_cpu()
 
     t = 20
     init()
     time_loop(t)
+    dh.run_kernel(getter_kernel)
     total = np.sum(dh.gather_array(u.name), axis=(0, 1))
     assert np.allclose(total / np.prod(L) / F / t, 1)
 
diff --git a/lbmpy_tests/test_free_slip.ipynb b/lbmpy_tests/test_free_slip.ipynb
index 179b8f36058551f69d11d9788b6c8d3102f71b23..08069b409d80d3dbe5c6048164729d95bf7ebf74 100644
--- a/lbmpy_tests/test_free_slip.ipynb
+++ b/lbmpy_tests/test_free_slip.ipynb
@@ -161,7 +161,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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O6AMAAAAA6KCuDjjPiogZETEj5/xiSmn7iHiyDdfXR8QVEfGNnPODTZy/LqV0TURcHREVEXFeSuk1OefcwXEvzTk/0ME2AAAAAIAu1qUBZ875fzp4/Z0RcWcrdf6SUroqIo6JiJ0iYs+IuK8j/QIAAAAAxfBq2UX9prLyTj02CgAAAACgW71aAs5+ZeX6HhsFAAAAANCtXi0B54Fl5Yc7ob0DUkr/SSmtSimtTik9mVK6PKX0zpRS6oT2AQAAAIBO0J27qHeJlNJrI+LtpcPZzWxG1FY7bHS8fel1XETckVL675zz3LY2mlLaupUqY9raJgAAAABszgodcKaU+kXEBdGwg3pExJc62OS6iPhrRFwXEQ9ExLKIGBYR+0TEhyNim4h4Y0RMTyntk3Ne1sb2n+3g+AAAAACAMoUOOCPipxGxd6n8u5zzXzvY3tSc89ImPr85pfTTiPhjRBwWEZMi4n8i4swO9gcAAAAAdEBhA86U0hcj4tTS4ayI+GhH22wm3Gw8tyKldFxEPB4RIyLigymlL+Sc17Whi21aOT8mIma0oT0AAAAA2KwVMuBMKZ0eEf9bOpwTEW/NOa/q6n5zzstSSpdFQ5g6MBpmj97Zhuufa+m8/YsAAAAAoG0Kt4t6SumEiPh56fDpiDg057ygG4dQvonRuG7sFwAAAADYSKECzpTSURFxcTSM+/mIOKS1WZFdMYxu7g8AAAAAaEZhAs6U0iERcUU0PFa/KCLenHN+vAeGsktZeV4P9A8AAAAAlBQi4Ewp7RsRf4mIfhGxPCIOzznP7oFxDI2I/y4dro6Imd09BgAAAADgJb0+4Ewp7RkRf4+GTX1WRcTbcs6z2tHOzSmlXHpt38T5t6SU+rdw/eBomEE6ovTRb3LOa9s6DgAAAACg83TpLuoppf0iYnzZRyPLyuNTSieV1885X7TR9TtFxLURMaz00VciYllKabcWup2fc57fjuF+ISIuTSldFRG3R8TjEbGy1Pc+EfHhiNimVHdORHy9HX0AAAAAAJ2oSwPOiDg1Ik5s5twbS69yF210vH9EbFl2/KNN6PMb0f7wcYtoGPOpLdS5NSLenXNe3M4+AAAAAIBO0tUBZ5F8JiIOiYbZmhOiYbbpsGhYa3NeRNwdEX+IiOtyzrmHxggAAAAAlOnSgDPnfFJEnNSB6y+KV87qbG9bB7VyfmbYNAgAAAAACqXXbzIEAAAAANAcAScAAAAAUFgCTgAAAACgsAScAAAAAEBhCTgBAAAAgMIScAIAAAAAhSXgBAAAAAAKS8AJAAAAABSWgBMAAAAAKCwBJwAAAABQWAJOAAAAAKCwBJwAAAAAQGEJOAEAAACAwhJwAgAAAACFJeAEAAAAAApLwAkAAAAAFJaAEwAAAAAoLAEnAAAAAFBYAk4AAAAAoLAEnAAAAABAYQk4AQAAAIDCEnACAAAAAIUl4AQAAAAACkvACQAAAAAUloATAAAAACgsAScAAAAAUFgCTgAAAACgsAScAAAAAEBhCTgBAAAAgMIScAIAAAAAhSXgBAAAAAAKS8AJAAAAABSWgBMAAAAAKCwBJwAAAABQWAJOAAAAAKCwBJwAAAAAQGEJOAEAAACAwhJwAgAAAACFJeAEAAAAAApLwAkAAAAAFJaAEwAAAAAoLAEnAAAAAFBYAk4AAAAAoLAEnAAAAABAYQk4AQAAAIDCEnACAAAAAIUl4AQAAAAACkvACQAAAAAUloATAAAAACgsAScAAAAAUFgCTgAAAACgsLo04EwpbZlSOiKldFZK6ZqU0sKUUi69LmpHe29JKV2VUnoupbS29H5VSuktnTzuESmlb6SU/p1SWpZSWl4qfyOlNKIz+wIAAAAA2q+yi9t/sTMaSSmliPhlRHxwo1PjIuJdEfGulNL5EfGhnHPuYF9TIuIvEbHVRqf2KL1OTSm9I+c8syP9AAAAAAAd152PqD8bEde189pvxUvh5n0RcUJETC2931f6/IMR8c2ODDClNC4i/hYN4eb6iPheRBxQen2v9NnYiJhWqgsAAAAA9KCunsF5VkTMiIgZOecXU0rbR8STbWkgpTQ+Ij5XOpwZEQfknNeUjmeklP4aEbdExN4R8fmU0oU558fbOd5vR8ToUvndOecry87dllKaGRFXlOp8MyJOaWc/AAAAAEAn6NIZnDnn/8k5T8s5d+RR9U/FS0Hsx8vCzcY+VkfEx0uHlRHxyfZ0klIaHRHvLR1eu1G42djXlRFxbenw/aVrAAAAAIAe0qt3US+tvfmO0uHDOed/NlWv9Pmc0uE7S9e11VERUVEqX9hCvYtK7xWlawAAAACAHtKrA86I2CEaNhKKaHgMvSWN57eOiO3b0df+TbTVUj8REfu1ox8AAAAAoJN09RqcHTWprPxwK3XLz0+KNq71WdbXspzzC81Vyjk/n1JaHhFDNhpfq1JKW7dSZUxb2gMAAACAzV1vDzi3KSs/10rdZ5u5rq19tdZPY1+7tqOfZ1uvAgAAAABsqt7+iPrgsvLKVuquKisP6kBfrfVT3ld7+gEAAAAAOklvn8FZXVZe10rdtWXl/h3oq7V+yvtqaz+tzfgcExEz2tgmAAAAAGy2envAWVNW7ttK3X5l5TXt7GvAJvRT3leb+sk5t/j4e/s2fwcAAACAzVdvf0R9RVm5tcfBB5aVN+Ux8+b62pTHzhv7ak8/AAAAAEAn6e0BZ/mMx9Z2IC9//Ls9m/k09tVaP+V92TQIAAAAAHpQbw84HywrT2ylbvn5hzrQ19CU0pjmKqWUtoqIIR3oBwAAAADoJL094HwyIuaVyge2UveA0vvciHiqHX3dXlZuqa/yc3e0ox8AAAAAoJP06oAz55wj4i+lw4kppTc0Va/0eeMMzr+Urmurv0ZEfal8cgv1Tiq915euAQAAAAB6SK8OOEt+HBHrS+XzUkr9y0+Wjs8rHa4v1X+FlNLNKaVcem2/8fmc8wsRcWnp8PCU0rFNtPFfEXF46fCS0jUAAAAAQA+p7MrGU0r7RcT4so9GlpXHp5ROKq+fc75o4zZyzo+klH4QEV+IiL0j4o6U0ncj4vGI2CkiPh8RrytV/37O+dEODPnLEfGWiBgVEX9IKe0dEdNK546IiE+Xygsi4isd6AcAAAAA6ARdGnBGxKkRcWIz595YepW7qJm6X46ILSPilGgIMy9ros5vooOhY8752ZTSkRHx54gYEw3h6ec3qvZCRLwz5/xcAAAAAAA9qgiPqEfOuT7n/IGIeHs0rMk5LyLWld7/EhFvyzmfmnOub6GZTe3r7ojYPSK+FREPRMTK0uv+0me7leoAAAAAAD2sS2dw5pxPipc25emM9v5fRPy/dl57UBvqLoyIr5ZeAAAAAEAvVYgZnAAAAAAATRFwAgAAAACFJeAEAAAAAApLwAkAAAAAFJaAEwAAAAAoLAEnAAAAAFBYAk4AAAAAoLAEnAAAAABAYQk4AQAAAIDCEnACAAAAAIUl4AQAAAAACkvACQAAAAAUloATAAAAACgsAScAAAAAUFgCTgAAAACgsAScAAAAAEBhCTgBAAAAgMIScAIAAAAAhSXgBAAAAAAKS8AJAAAAABSWgBMAAAAAKCwBJwAAAABQWAJOAAAAAKCwBJwAAAAAQGEJOAEAAACAwhJwAgAAAACFJeAEAAAAAApLwAkAAAAAFJaAEwAAAAAoLAEnAAAAAFBYAk4AAAAAoLAEnAAAAABAYQk4AQAAAIDCEnACAAAAAIUl4AQAAAAACkvACQAAAAAUloATAAAAACgsAScAAAAAUFgCTgAAAACgsAScAAAAAEBhCTgBAAAAgMIScAIAAAAAhSXgBAAAAAAKS8AJAAAAABSWgBMAAAAAKKzKnh4AAAAA8Ooya9asyoiYGhGHRsRuETGoZ0cE9LCVEfFARFwfEfdMnjx5fWc2LuAEAAAAOs2sWbMGRMS5EbFnSqlvRUXFwIio6OFhAT2rrq6ubmLO+aiI+NesWbM+MXny5NWd1biAEwAAAOgUpZmb50bEXn379t26T58+A1NKuU+fPnUppdzT4wO6X8451dfXV1RWVqb6+vpV69at6xMR586aNetDnTWTU8AJAAAAdJapEbFn3759t66qqqoePXr03KFDh67o06ePcBM2Y/X19WnZsmWDX3zxxTERsfW6devqI2JKRNzVGe3bZAgAAADoLIemlPr26dNn4OjRo18YPnz4cuEm0KdPnzx8+PDlo0ePfqE0s7tvNKzR2zntd1ZDAAAAwGZvt4qKioEppTx06NAVPT0YoHcZOnToipRSLq3Nu3tntdvrA86U0s0ppdzG10Ht6OfrXdk+AAAAbAYGRURFnz596szcBDbWp0+f3KdPn7po2HhsUKe121kN9SL1EfFoTw8CAAAANlc2FAKa0xX3hyJsMnRyRAxspc4uEXF5qXxDznluB/tsbYrskx1sHwAAAADoBL0+4Mw5txomppTeV3Z4cSf0+UBH2wAAAAAAul7hH1FPKfWJiPeUDldGxFU9OBwAAAAAoBsVPuCMiEMiYlyp/Mec8+qeHAwAAAAA0H1eDQHn+8vKHX48HQAAAODV5Nxzzx2RUpqcUpo8Z86cvj09np42derUCSmlyVOnTp3Q02Ohc/T6NThbklIaFBHvKh0+ExE3d1K70yNir4gYHBFLI+LBiPhHRPwq57ykA+1u3UqVMe1tGwAAAIrkzT+8ZdLi1euqenocXWmLAX1rp5954EM9PQ54tSt0wBkRx8RLO6xfknPurG3mDy0rj4qIA0uvz6eUTso5/6Wd7T7b4ZEBAADAq8Di1euqFq18dQecQPcoesDZ2Y+n3x8Rf46IeyJiXkRURcSEaNjE6LCIGBYRf0opHZlzvqYT+gMAAACgG91zzz1zenoMdK7CBpylx70PKh3+M+f8SAeb/HHO+etNfH53RFycUjo9In4ZERURcUFKaXzOeU0b+9imlfNjImJGG9sEAAAAgM1WYQPOiHhvvLRJ0u862ljOeWkr53+VUto7Ik6NiLERcXREXNrGPp5r6XxKqS3NAQAAAMBmr8i7qL+v9L42Ii7vpj5/VVY+sJv6BAAAAGjWggULKj7ykY+M22GHHXatrq7ea4sttnjtvvvuu/Nvf/vb4Zvaxpw5c/p+4AMf2Gb8+PG7Dhw48HX9+/d/3Xbbbbfbu9/97u3uueee/i1d27hD+5lnnjk2IuJvf/vb4EMPPXSnLbfcco/q6uq9dtxxx10/+9nPbrV8+fKX5VCXX3750AMPPHB8Y72ddtpp1y9+8Ytjampqmp0BVlNTk37/+98Pff/737/tbrvtNmnIkCF7VlZW7jVs2LA999hjj4lnnnnm2Oeff77FCX0t7aI+Z86cvo3f59xzzx0REXH11VcPedOb3jR+5MiRr+3bt+9e48aN2/0973nPto8//rg1ZHuJQs7gLM2k3KV0OK0jO5u30YNl5XHd1CcAAABAk2bNmlX91re+decFCxZsCNvWrl1beddddw2+6667Bv/jH/9YuN9++61sqY2f/vSnIz796U9vt27dupcFi88880y/Z555pt8VV1wx8rOf/ezcs88++4XWxvOlL31pzHe+851x5ftAP/nkk9U/+MEPxt5www1Db7rppkcGDx5cf+qpp25z4YUXbll+7RNPPFH9ne98Z9wdd9wx+MYbb3y0svKVsdV73vOe7a666qoRG3++bNmyivvvv3/g/fffP/DCCy8cdfnllz922GGHrWptvK35yEc+Mu4Xv/jFmPLP5s2b1/f3v//9qGuuuWb49ddfP2evvfaq6Wg/dEwhA854+eZCHX48vQ08Qw4AAAD0CosWLap4+9vfviHcfPvb377k/e9//8Ktttpq/YMPPlh93nnnjb7yyitHPvTQQwOaa+Oyyy4b+olPfGL7nHMMGDCg/vTTT3/x8MMPX15ZWZlvu+22QT/5yU/GLF26tPI73/nOuGHDhtV9/vOfX9BcW9dff/2Q+++/f+Cee+656kMf+tD8XXbZpWb+/PmV55577pa33nrr0Pvuu2/gV7/61TFbbLFF3YUXXrjlAQccsOyUU05ZuNNOO617+umnq77//e9v9e9//3vgbbfdNuSHP/zhqM997nOv6Gv9+vVp6623XvvWt7516dSpU1ftsMMO66qqqvITTzzRd/r06UOuvPLKkUuXLq08/vjjx99///2zx40bt769f74XXXTRqPvuu2/glClTVn7gAx9YsMsuu9QsXry44ne/+92Iq6++esSSJUsqTznllO3/9a9/PdzePugcqTxRL4KUUlVEzI2IURGxICLG5pzb/WNtY99TomGH9YiIC3LOp3Vy+1tHxLMRETccEzFmYGe23rK3bTWt+zoDAACgWYMnfaHb+qpdXBtzztywofQ2re0d0ZpZs2b9v8rKyl2qq6uHTZgw4bGW6k7+1vQ9Fq1c96p+xHfEoL61s77y5v90VfunnXba1hdccMHoiIgvfOELr5hhuXbt2nTIIYeMv+OOO4Y0fvbwww/fP2HChHWN57fddtvd58+fXzVgwID66dOnP7zvvvu+bEPlRx55pO9+++03ccGCBVXV1dX1TzzxxP1bbbXVy3KYlNLkxvLhhx++ZNq0aU+Uz75cv3597L333hP//e9/Dxw4cGD9+vXr03ve854Fv/nNb54tb2fFihV9Jk6cuOu8efP67rzzzmvmzJlT/iRtRETMnj2736RJk9b26dP0qov33HNP/4MPPnji6tWr+3ziE594/ic/+cm8jetMnTp1wowZMwZNmTJl5cY7qs+ZM6fvxIkTd288Pv744xdeeumlT2/c3/HHH7/d5ZdfPjIi4vbbb3/wjW98Y1s3ot5szZkzZ3xNTc3S9evXPzh58uS3dUabRVyD863REG5GRPy+u8LNktPLyrd0Y78AAAAAG6xZsyZdccUVIyMidt555zXf+ta3XvH4eL9+/fLvfve7pyorK5uc3XbJJZcMmz9/flVExBlnnPH8xuFmqe113/jGN56LiKipqenz85///BWPhzeqrq6u/93vfvf0xo+WV1ZWximnnLIgImLVqlV9hg8fXvvzn//8FWH64MGD64877rhFERGPPPJI/0WLFlVsXGfXXXdtNtyMiJg6deqa448/fmFExDXXXDOs2YqbYNSoUbW//e1vn2mqvy9+8Ysb/rxvuummwR3ph44rYsBZ/nj6xZtyQUrppJRSLr2+3sT53VNK41tp4/SI+EDp8IWIuHoTxwsAAADQqW6//fYBy5cvr4iIOOGEExZVVLwiC4yIiJ122ql2v/32W97UuRtuuGFIRERKKT760Y8ubK6vk08+ecmgQYPqIiJuuummIc3Ve+Mb37h89OjRdU2d22uvvTaEp29961uX9uvXr8nQdc8991zdWJ4zZ07f5vpqtGDBgorZs2f3mzlzZvWMGTOqZ8yYUT1s2LD1ERGPP/54/7Vr17Z7ucG3ve1tS/r379/kOF/72teuHTBgQH1ExBNPPNGvvX3QOQq1BmdKaXhEHFE6fCDnfG8nNT05Ii5IKd0UEddExP0RsSga/nwmRsR7I+LNpbp1EXF6zrnDC9UCAAAAtMe//vWvDTubv+ENb2gxo5g8efKqm2++eejGnz/88MP9IyLGjh27rqW1Kqurq/Muu+yy+p577hn8yCOPNLuj+vjx49c2d26LLbbY0P7OO+/c7KY8w4cP3xCQNga4G7vnnnv6f//73x998803D1m4cGGzyxzU19fHwoULK9q7DufEiRNb3DxoyJAh61evXt135cqVRZxA+KpSqIAzIv47IhpT8U2avdkGFRFxaOnVnEUR8YGc8187uW8AAACATbZkyZINmc5WW21V21Ld0aNHN3l+6dKlFRERI0eObPH6iIgtt9yyNiJi+fLlzWZJjTMam1I+w7SleuWPg69fv/4Vsy9/9KMfjfzsZz+7bV1d3SbNzFy1alW7w8eWxhnx0lg3dSx0naIlzO8rvddFxKWd2O7/i4bHzy+IiFkR8VxErImImoiYFw2zOs+IiB1zzn/pxH4BAAAA2qx80+iUWs7Xcs4tVmjt+k1pozvcd9991Y3h5hZbbLH+q1/96nO33XbbQy+88MK/ampq7s05z8o5z/rRj370VOM1Rdtcm/Yp1AzOnPMb23ndRRFxUQvn50fEb0svAAAAgF6t/JHvefPmVe2xxx7NPh4+f/78JvOfYcOG1UVELFiwoNXd7BcsWFAZ0fBYdttH2zl+/etfj6irq0sVFRUxffr0OXvttVeTj5CXz25l81C0GZwAAAAAm70999xzw6Y9//znPwe2VPfee+9t8vzEiRPXRETMmzev79y5c5sNBdeuXZsefPDBARENO7a3b8Qd17hm6IQJE1Y3F25GRNx7770Dum9U9AYCTgAAAICC2W+//VYPGTKkLiLi8ssvH1Ff3/RykU8++WTV7bff3uTO54cccsjyiIbHuH/2s5+NbK6viy66aPjKlSsrIiIOPvjgJndk7w6Na3KuWbOm2TzrmWeeqbz++uuHddug6BUEnAAAAAAF079//3zcccctjGiY2fi1r31t9MZ1amtr46STTtqutra2yfUz3/e+9y0dNWpUbUTEueeeu9Vdd931ih3SH3vssaqvfvWrW0dEVFdX13/kIx9Z1LnfZNPtuOOONRERTz/9dPUNN9zwilmpK1as6HPcccftWFNTI+/azPg/OAAAAEABfec733m+cYf0b3/721sfeeSRO/zxj38ccvvttw84//zzh++1116Tbr311qG77bbb6qau79evXz733HOfTinFqlWr+hx66KETP/OZz2x13XXXDbzxxhsHfuMb39jy9a9//S6Na3R+/etff26rrbbqsTU4Tz755EUREfX19XH00Ue/5ktf+tKYa665ZtBNN9004Lvf/e6o3XfffZe777578F577bWyp8ZIz7DoKgAAAEABjRgxom7atGmPvPWtb9154cKFVdOmTdti2rRpW5TXOfbYYxftv//+K84444ztm2rj+OOPX7ZgwYKnPvOZz2y3evXqPuecc87Yc845Z2x5nYqKivjsZz879/Of//yCLvw6rTrwwANXf/rTn553zjnnjF2+fHnF2WefPe7ss89+WZ3TTjvtxd12223NvffeO6iHhkkPMIMTAAAAoKD23nvvmgceeGD2hz70oRe22267tX379s3Dhg1b//rXv37FL3/5yyevvPLKp1pr4+Mf//ii//znPw+cfPLJ83fcccea/v3711dXV9dvs802a48//viFd95554Nnn332C93wdVr1gx/84PnLLrvssTe+8Y3LhwwZUldVVZVHjx5de9hhhy296qqrHj3//POf6+kx0v1Szrmnx0BJSmnriHg2IuKGYyLGtLgHWud621bTuq8zAAAAmjV40he6ra/axbUx58w5jYfb5Jw7FA7NmjXr/1VWVu5SXV09bMKECY+1VHfyt6bvsWjluqqO9NfbjRjUt3bWV978n54eB/Qmc+bMGV9TU7N0/fr1D06ePPltndGmR9QBAACAbrfFgL61PT2GrrY5fEfoDQScAAAAQLebfuaBD/X0GIBXB2twAgAAAACFJeAEAAAAAApLwAkAAAAAFJaAEwAAAAAoLAEnAAAAAFBYAk4AAAAAoLAEnAAAAABAYQk4AQAAAIDCEnACAAAAAIUl4AQAAAAACkvACQAAAAAUloATAAAAACgsAScAAAAAUFgCTgAAAACgsAScAAAAAEBhCTgBAAAAgMIScAIAAAAAhSXgBAAAAAAKq7KnBwAAAABsfh7/0m6T1q9YUNXT4+hKlYNH1e70vw881FXtH3PMMdtfddVVIyIiHn744fsnTJiwrrVrxo0bt/u8efP6jh07dt3cuXPvLz+XUprc3HX9+vXLw4YNW7/LLrusPuaYYxaffvrpiysrm4+VGvtp6lxVVVUeOnTo+p133nnNUUcdtfSjH/3owkGDBuXWxg7NEXACAAAA3W79igVVdcvnv6oDzleTtWvXphdffLHqxRdfHHrTTTcN/cUvfjH6uuuue3Ts2LHr29pWbW1tWrhwYdXChQur7rzzziE///nPR0+bNu3R3XfffW1XjJ1XPwEnAAAAABvsuuuuqy+88MInyz9bvnx5xb///e/+559//pZz5szpP3v27AHveMc7dpoxY8acltoaNWpU7d///vdHyj9bvXp1n9mzZ1dfeOGFo2bOnDnomWee6XfkkUe+5qGHHprdv39/MzlpMwEnAAAAABsMGDCgfsqUKTUbf37IIYesOu200xa/9rWvnfTkk09Wz5w5c9CNN9448E1vetOq5tqqqqrKTbV14IEHrj799NMX77vvvjvfc889g59++ul+//d//zfstNNOW9LZ34dXP5sMAQAAALBJBg8eXH/aaafNbzy+8847B7a3rYqKivjUpz71YuPx3Xff3e622LwJOAEAAADYZDvssMOGzYxqampSR9oaP378hnU3a2pq5FS0ix8OAAAAAJvsySef3LA7+rbbbtvqzu0teeyxx/p1VltsvgScAAAAAGySlStXpt/85jdbRkRUV1fXH3HEESva21Z9fX385Cc/GR0RkVKKd73rXUs7aZhsZmwyBAAAAMAGq1ev7jNjxozq8s9WrlzZp3EX9ccff7w6pRRf+cpX5o4dO3Z9S23V1tamjdtas2ZNnwcffLD6oosuGnn33XcPjog49dRTX2xqMyLYFAJOAAAAADaYPXv2gKlTp+7a3Pl99913+Wc/+9kX3vnOd7Y6e3PBggVVLbW12267rT7zzDNfOPnkk+2eTrt5RB0AAACATXbPPfcM/uUvfznqySefrOpoWw8++OCA3/zmNyNnzpxZ3XptaJqAEwAAAIANpkyZsjLnPKv8VVNTc+/DDz98/9lnn/3MoEGD6q699trh++yzz6T//Oc//Vpqa+zYses2bmvdunWznnjiif/89Kc/fXLMmDHr7rjjjiFvetObJk6fPn1gd31HXl0EnAAAAAAFlFJq8zU553b11a9fvzxhwoR1X/jCFxZMnz59TmVlZX7xxRerTjnllO3b2lZVVVXssMMOtR/96EcX33nnnQ8PHTq0bsWKFRUnn3zyjrW1te0aH5s3AScAAABAAVVXV9c3lleuXLlJGc+aNWv6REQMGDCgvrW6zdl7771rDjzwwGUREbNmzRp0//33tziLsyXbbbdd7dFHH70oImLu3Ll9p02bNqS9bbH5EnACAAAAFNAWW2yxYQfzuXPntroe5po1a9KKFSsqIyKGDh3a4u7nrdl555037Hh+77339u9IWxMnTtzQ1r///e8OtcXmScAJAAAAUEB77LHHmsbyzJkzB7RW/5///Gf/urq6iIjYZZdd1rRSvUXr16/f8Hz8unXr2v6s/Mvb2lCura3tUFtsngScAAAAAAV02GGHraioqMgREX/605+2qK9v+anz3/3udyMay4ceeujyjvR93333bdgQaPvtt1/XkbZmzpy5oa1tttmmQ22xeRJwAgAAABTQtttuu/6tb33rkoiIBx98cMCXv/zlMc3V/etf/zr40ksvHRXRsLP5CSecsLS9/V522WVDZ86cOSgiYtiwYesPOuigVe1t6/bbbx/w97//fYuIiKqqqnzkkUd2KHhl81TZ0wMAAAAAoH1++tOfPnfnnXcOWbx4ceV3vvOdcbfddtvgE044YdGkSZPWVlVV5aeffrrv3/72t6F/+tOfRtTV1aU+ffrEL3/5y6eqqppfsnP16tV9ZsyYUV3+2bp169IzzzzTd9q0aUMvv/zykY2ff+UrX5nbUlu1tbVp47bWr1+fnn/++arrrrtuyO9+97tRjY+4f+hDH3ph3LhxHVoblM2TgBMAAACgoLbbbrvaG2+88eGjjz56/BNPPFF9xx13DLnjjjua3Il88ODBdeeff/6TRx555IqW2pw9e/aAqVOn7tpSncrKyvz5z39+7qc//emFLdVbsGBBVWttpZTipJNOmv/jH/94Xkv1oDkCTgAAAIACe+1rX7v24Ycfnn3BBRds8de//nXYf/7zn4GLFy+urKurS0OHDl3/mte8pubQQw9ddsYZZywcMWJEXXv6qKioiEGDBtVtu+22Nfvtt9+Kj3zkIwt32223te1pq0+fPjFgwIC6bbbZZu2UKVNWnn766Qv33XffDm16xOZNwAkAAABQcFVVVfHhD3948Yc//OHF7W0j5zyrs8Yzd+7c+zurLWiNgBMAAADodpWDR9X29Bi62ubwHaE3KETAmVLKm1j1lpzzQZ3Q3/ERcXJE7BERwyPihYi4LSJ+lnP+Z0fbBwAAgM3dTv/7wEM9PQbg1aEQAWd3SSlVR8SVEXHERqe2K73enVL6es75m90+OAAAAADgFYoWcP4iIn7ewvlVHWz/N/FSuHlTRPwkIuZFxO4R8aWI2CkizkopPZ9zvqCDfQEAAAAAHVS0gHN+zvmBrmg4pXRgRLy7dPi3iHhXzrlxZ7EZKaW/RsSsiNg2Ir6XUvpjznlpV4wFAAAAANg0fXp6AL3I50rvdRHxkbJwMyIics4LI+LzpcPhEfGBbhwbAAAAANAEAWdEpJQGRcQhpcPpOefnmql6VUQsL5WP7vKBAQAAAAAtEnA2mBoR/UrlW5qrlHNeFxGNu6hPTSlVdfXAAAAAAIDmFS3g/K+U0pyU0pqU0oqU0qMppd+llA7uYLuTysoPt1K38XxlRLymg/0CAAAAAB1QtE2GdtnoeHzp9f6U0p8j4qSc87J2tLtNWbm5x9MbPbvRdQ9uaicppa1bqTJmU9sCAAAAAIoTcK6OiL9GxA3RMINyZUSMiogDI+JDETEiIt4ZEX9JKb0551zbxvYHl5VXtlJ3VVl5UBv7ebb1KgAAAADApipKwDku57y0ic+np5TOi4hrIuJ10RB4fjgizm1j+9Vl5XWt1F1bVu7fxn4AAAAAgE5UiICzmXCz8dyLKaVjI+KhiOgbER+PtgecNWXlvq3U7VdWXtPGfrZp5fyYiJjRxjYBAAAAYLNViICzNTnnJ1JK0yPi7RExPqU0Nuc8rw1NrCgrt/bY+cCycmuPs79MzrnF9T1TSm1pDgAAAAA2e0XbRb0l5Zv9jGvjteXBY2sbAZXPwrSmJgAAAAD0oFdTwNmR6Y/l4ejEVuo2nl8fEY91oE8AAAAAoINeTQHnLmXltjyeHtGw7mXj5kIHNlcppdQ3It7QeE3OubUNiQAAAACALvSqCDhTSjtGxJtLh0/knOe25fqc84qIuKF0eGhKqbnH1I+OiCGl8tVtHigAAAAA0Kl6fcCZUjoypdTsZkgppdER8ceIqCp99LMm6pyUUsql19ebaeoHpffKiPhZSqliozZGRsR3S4dLI+KCTf4SAAAAAECX6PUBZ0ScFxFPp5TOTSmdkFLaJ6W0Z0rp0JTStyJidkS8rlT39mgi4NwUOecbI+Ky0uFRETE9pXRUSmnvlNLJEfHPiNi2dP4LOecl7f5GAAAAALzMypUr06c//emtJkyYsEv//v1fl1KanFKafMopp2wTEXHuueeOaPxszpw5fXt6vPQezc6M7GXGRsTHS6/m/CkiTs05r+1AP6dEwyPob4uIg0uvcvUR8c2c86860AcAAABs9t75l3dOWlKzpKr1msU1vHp47Z/f8eeHuqu/RYsWVZx//vlb/OMf/xj62GOP9V+8eHFlZWVlHjFixPo99thj1ZFHHrn05JNPXlJZ2fvioLVr16YDDjhgwn333Tewp8fSW82ZM6fvxIkTd2/LNYcccsjS66+//vGuGlNv0ft+0a90YjRs/LNPROwYESOjIYRcGRHPRsSdEfG7nPNdHe0o57wmIt6eUnp3RJwUEa+NiGER8WJE3BYRP+2MfgAAAGBzt6RmSdXimsWv6oCzO/3whz8c+c1vfnPc0qVLX5H1rFy5suLpp5/u97e//W2L7373uzU/+9nPnj788MNXdvWYUkqTIyI+9alPPf/DH/6wxQ2hL7zwwuGN4eYxxxyz6KSTTlo0evTo2oiI0aNHr+/qsVJsvT7gzDnfEhG3dLCNiyLiojbU/31E/L4jfQIAAAB0h9NPP33r888/f3REREVFRT7iiCOWHHXUUUt32GGHtevWrevz4IMPVl955ZVb3HXXXYMff/zx6qOOOmrnX/7yl0+efPLJvWb5vRtuuGFIRMTIkSNrL7vssqd64yzT3uSQQw5ZevbZZ7e6yfawYcPqu2M8Pc2vBQAAAKCgzj777FGN4eaYMWPWXXXVVY/ts88+a8rrHH744Ss/9alPLfz1r389/KMf/egO69atS6effvoOEyZMqNl3333XNN1y93r++eerIiK22WabdcLN1g0dOrRuypQpNT09jt6iCJsMAQAAALCRRx55pO83vvGNbSIiBgwYUD99+vRHNg43y5122mlLfvrTnz4ZEVFbW5tOOumkHerre8cEv3Xr1vWJiKisrMw9PRaKR8AJAAAAUEDf/e53R69duzZFRHz605+et9tuu7W68fIHP/jBJQcddNCyiIhHH320/2WXXTZ04zrjxo3bPaU0+Zhjjtm+pbaOOeaY7VNKk8eNG/eyjW8ar288/tGPfrRV4+7nja9jjjlm+zlz5vRtPJ4xY8agiIgZM2YMKq+3cdubYt68eZWf+MQnxk6aNGmXwYMH79mvX7+9xo0bt/s73/nOHa699tpBzV3T2Of3v//9kU3V+e///u/tGuuceOKJ2zRV56yzztoypTS5srJy8uLFi+Vu3cQfNAAAAEDB1NfXx5/+9KcRERHV1dX1Z5xxxsJNvfbjH//4/MbyRRdd1GSYV1RXXXXVkAkTJux23nnnbfXwww/3X7lyZcW6devSvHnz+v7lL3/Z4i1vecuE97///dvW1dW97LqxY8eu32mnnWoiIm655ZYhTbV91113DW4s33nnnU3WufXWWwdHREycOHH1FltssWF6bGPoWx780nksagAAAABQMLNmzapetmxZRUTE3nvvvXLEiBF1rV3T6KijjlpeXV1dX1NT02fmzJlNzmjsiGuuueaRtWvXpqlTp+4aEfGe97xnwRlnnDG/vM7IkSPrxo4du/6ee+6ZHRFx8skn7zB79uwBu+666+oLL7zwycZ6/fr12+RH1u+8887+xx9//Pja2tpUWVmZ3//+9y9417vetXTw4MF1M2bMGPDjH/94q7lz5/a95JJLRg0cOLDuF7/4xcs26XnDG96w4vHHH6++++67X/Fn8uSTT1Y9++yz/RqPH3/88ep58+ZVjh07dsMO7/X19TFz5szBERH77rvvik0dNx0n4AQAAAAomJkzZw5oLL/2ta9d3ZZrKysrY+LEiWv+9a9/DVyyZEnlU089VbX99tvXdtbY9thjj5c9Kr/llluub25DnMbPBwwYUN/43t7Nc04//fTta2trU0VFRVx++eWPHX300csbzx144IGrTzzxxCX77LPPxMcff7z6/PPPH/OBD3xg0d57772hr4MOOmjFpZdeOmrhwoVV9913X/XrXve6DeeuueaawRER48ePr6mpqUnPPfdcv2uvvXZw+U70//znP/s3hs4HHXRQlwacy5Ytq5gxY0Z1a/UmTJiwbsiQIb1jodUuJOAEAAAAKJiFCxduyHTGjBnT5nBy5MiRG66ZP39+ZWcGnD3hpptuGvDAAw8MiIg47rjjFpSHm41GjRpV9/Of//ypww8/fGJ9fX385Cc/2fKSSy55pvH84YcfviGUnD59+uDygLPx0fN99tlnRU1NTbryyiv73XTTTS8LOG+44YbBERF9+vSJww47bGXXfNMNfQ2bOnXqsNbq/e1vf3vkiCOOeNXPJrUGJwAAAEDBrFixoqKxPHDgwDbP0GucMRkRsXTp0sLnQ9dee+2GNTFPP/30ZtcjPeyww1btuOOONRERt91228vW0dxmm23W77DDDjURLwWajRrX3zz44INXHHjggSvKP2vUeM2ECRNWjxw58mVLBsydO/f+nPOsnPOs9nw/WmYGJwAAAEDBDB48eEOAtnLlyjYHlKtWrdpwzbBhwwr/CPPs2bP7R0RUVVXlfffdt8VH9l/3uteteuKJJ6qfeeaZfjU1Nam6unrDOp9veMMbVjz55JMvW4fzqaeeqnrmmWf6pZTi8MMPX7FmzZoU8fJ1OEvrbw6K6J71N48++uhFf/rTn57q6n6KovAJPQAAAMDmZsSIERs2t3nhhReq2nr9woULN1yz5ZZbrm+pbhEsXbq0MiJi6NCh66uqWv7jGD16dG1ERM45FixYUFF+rnHtzMZ1OCNeWn9zp512qinttl679dZbr805x7XXXjs4IuLuu+/u3ziGgw46qEsfT+eVBJwAAAAABbP33nuvaSz/5z//GdBS3Y2tX78+5syZ0z8iYvjw4euLvv5muZRSq3Vybn5j9sMPP3xDODl9+vTBES9ff7PxXGP5pptuGhzx0vqbjbM82zd62kvACQAAAFAwe++995qhQ4fWRUTMnDlz0KJFiypau6bRX/7ylyE1NTV9Su28YrZhY0hYX9/yk+urV6/uNbnSsGHD1kc0zOSsrW05r50/f35VRMP3HDVq1MvWytxuu+1qt9tuu7URLwWb5etvNtbbeB3Oxro777zzmo3bpOv1mh8iAAAAAJumT58+cfTRRy+KiKipqenzk5/8ZOSmXvvTn/50y8byiSee+IoNeQYOHFgXEbFs2bIW9255/PHHqzd9xF1r1113XRMRUVtbm+68884WZ7T+61//GhgRse22264tX3+zUePszLvvvnvQU089VfX000/323hm5lve8pYVEQ1/BnPnzq2cMWNGt62/ySsJOAEAAAAK6HOf+9yLffv2zRER55xzztgHHnigX2vXnH/++cNvvvnmoRENa0oef/zxyzaus80226yNiHjggQcGNDeLc8aMGdWPPvpo/5b66tevX46IWLt2bevPjXfQ4Ycfvryx/Ktf/arZsPf6668f2BjM7r///subqtM4O3PhwoVVP/7xj0dFvLT+ZmOd8nU4v//9729Ztv6mgLMHCDgBAAAACmjixInrvvrVrz4X0fC4+GGHHbbzXXfd1WzoeMEFFwz/2Mc+tkNEw27jF1100ZMVFa98sn3//fdfERGxYMGCqvPPP3+Ljc8vWbKkzymnnLJDa+MbOXJkbUTEk08+2Wrw2lEHH3zw6t122211RMQVV1wx6uqrrx6ycZ1FixZVfOQjH9kuomEG7Cc+8Yn5TbXVODszIuI3v/nNlhEvX3+zUeNnjXVSSnHYYYc1GXCOGzdu95TS5JTS5LZ/O1rT4lRjAAAAAHqvr3zlK/OfeOKJfhdeeOGWzz//fN/9999/0pFHHrn4qKOOWrbjjjuuXbduXZo9e3b1FVdcMaJxvciqqqr8s5/97Mn99ttvdVNtnnbaaYt/8IMfjF25cmXFJz/5ye0fe+yxfm9729uWp5Ty3XffPfDnP//56Pnz5/edNGnS6oceeqjZx8EnT568cu7cuVvccMMNw77//e+PPOigg1b2798/R0QMHz68bty4cZ26e/uvfvWrpw466KBJtbW16bjjjht/0kknzX/HO96xbMiQIXUzZswY8OMf/3jMc8891y8i4oMf/OALU6ZMqWmqne2337522223XfvMM8/0W7lyZUXEy9ffbHTggQeuuPLKK0c21hk/fvyaMWPGdMv6m8uWLauYMWNGq0sEVFRUxF577dXk93w1EXACAAAAFNhvf/vbZydOnFjzv//7v+OWLVtW8ec//3nEn//85xFN1d1hhx1qzjvvvKff/va3v2JzoUZjx45d/5Of/OTpD37wgzuuXbs2nXPOOWPPOeecsY3n+/Xrl3/+858/+fe//31oSwHn5z//+Rf+8Y9/DF+3bl363Oc+t135uaOPPnrRn/70p6fa8XWbte+++6657LLLHjvxxBN3XLlyZcUFF1ww+oILLhi9cb33ve99C37605/ObamtN7zhDSueeeaZfhHN74xePtOz1H+3PZ5+ww03DJs6deqw1uoNGjSobsWKFf/q+hH1LI+oAwAAABTc5z73uQWPPfbY/d/+9ref2X///ZePGTNmXb9+/fKAAQPqt9lmm7VHHHHE4l/96ldPzJkzZ3ZL4WajU045Zcn06dMfevOb37x0+PDh66uqqvKYMWPWHX300Ytuv/32B0855ZQlrbWx7777rrnxxhsfOuKIIxZvtdVW6xrXC+1KRx999PI5c+Y88LGPfeyFiRMnrhk0aFBd375981ZbbbXuqKOOWvyPf/xjzsUXX/xMU4/ml2tchzPiletvln1e27heaYT1N3tSyrnLf1tsopTS1hHxbETEDcdEjBnYfX2/batp3dcZAAAAzRo86Qvd1lft4tqYc+acxsNtcs7PdaS9WbNm/b/Kyspdqqurh02YMOGxluq+8y/vnLSkZklVR/rr7YZXD6/98zv+/FBPjwN6kzlz5oyvqalZun79+gcnT578ts5o0yPqAAAAQLcT/AGdxSPqAAAAAEBhCTgBAAAAgMIScAIAAAAAhSXgBAAAAAAKS8AJAAAAABSWgBMAAAAAKCwBJwAAAABQWAJOAAAAAKCwBJwAAAAAQGEJOAEAAACAwhJwAgAAAACFJeAEAAAAAApLwAkAAAAAFJaAEwAAAAAoLAEnAAAAAFBYAk4AAAAAoLAEnAAAAABAYQk4AQAAAIDCEnACAAAA0KmOOeaY7VNKk8eNG7d7U+fHjRu3e0pp8jHHHLN9Nw+NV6HKnh4AAAAAsPl5/IgjJ9UtWVLV0+PoShXDh9fuNO1vD/X0OODVTsAJAAAAdLu6JUuq6hYtelUHnED38Ig6AAAAAFBYAk4AAAAAoLAEnAAAAABAYQk4AQAAAArmoIMOGp9SmrznnntObOr8tddeOyilNDmlNHnw4MF7rl+//hV1FixYUNGnT5/JKaXJZ5999qjGz+vq6uKvf/3r4A9+8INb77XXXhOHDx/+2srKyr0GDx6858SJE3f54Ac/uPWjjz7atwu/HrSJgBMAAACgYPbff/8VEREPPPDAgGXLlr0i37nhhhsGN5ZXrlxZceeddw7YuM4//vGPwTnniIg49NBDVzR+/tnPfnbsO97xjp1//etfj77vvvsGLl26tLKuri6tXLmyYs6cOf1//etfj95jjz12vfjii4d1xXeDthJwAgAAABTMm970phUREXV1dWn69OmDNj5/++23Dy4/vv766wdvXOemm24aHBExbNiw9ZMnT65p/Hz9+vUxatSo2ve+970Lfvaznz153XXXPXzbbbc9dMkllzz+oQ996IUBAwbU19TU9PngBz+447333lvd+d8O2kbACQAAAFAw+++//+qBAwfWR0TceOONLwsv165dm+67776BERFvetOblkZE3Hbbba8IOO+8887BERFTp05d2afPSxHRRz/60YXPPvvs/ZdccskzH/nIRxa/+c1vXrXffvutfu9737v0F7/4xdz//Oc/D2y55Za1a9euTWedddZWXfctYdMIOAEAAAAKprKyMvbaa6+VERF33HHHy8LLW265ZUBNTU2fQYMG1X3qU5+aHxExc+bMQeXrcC5YsKDi0Ucf7R8RccABB6wov37ChAnr+vXrl5vre6eddqr92Mc+9kJExA033DC0vr6+074XtIeAEwAAAKCAGtfhnD179svW4Wxcf3Pvvfdeeeihh66srq6u33gdzmuvvXZwYzBZvv5mUxYvXtzn4Ycf7jtz5szqGTNmVM+YMaN6wIAB9REN63s+/PDDNhyiRwk4AQAAAAqouXU4Gx9H33///VdUV1fnPffcc1XEy9fhbFx/c+jQoXVTpkxZs3HbjzzySN8TTzxxm3Hjxu0+YsSI102aNGn3KVOm7Dp16tRdp06duutnPvOZ7Rrrvvjii5Vd9y2hdYUIOFNKe6WUvpRSuial9GxKaW1KaWVK6ZGU0kUppf07qZ+vp5TyJr4O6ow+AQAAANrjgAMOWNU4k7JxHc7S+puDIl6ambnffvutiHj5OpyN629OmTJlRfn6mxERV1xxxZDXvva1u1588cVbzps3r9XZmatXry5EvsSrV6//AaaUbomIWRHx7Yh4S0RsHRF9I2JgRLwmIk6MiFtTShenlEyJBgAAADYLVVVV8brXve5l63DeeuutG9bf3HfffVdHRBxyyCErIl5ah3PhwoUVjzzySJPrb77wwgsVp5566o41NTV9BgwYUH/mmWfOu/766x+eO3fuv9esWXNvznlWznnWX/7yl0car8k5p+76ztCUIkwhHld6nxcRV0bEbRHxTERURMQ+EfHpUp33RcP3eXcn9bt7K+ef7KR+AAAAANpl//33X3HHHXcMaVyHs3z9zcrKhtjnoIMOWlW+Duezzz5b1bj+ZmP42ejiiy/eYsWKFRUREf/3f//3+Lve9a7lTfW7ePHiiq78XtAWvX4GZ0Q8HBH/HRHb5pw/mXP+U855Rs75nznnH0XEnhHR+F8NTuisx9Vzzg+08lrVGf0AAAAAtNfG63CWr7/ZWGfjdTgb198cMmRI3etf//qXrb85e/bs6oiGtTmbCzcjImbMmDGw878NtE+vDzhzzkfknK/IOdc1c35hNMzibHRs94wMAAAAoGcdcMABq/v3718fEXHttdcO2Xj9zUbl63A2rr+59957r6ioePlEzPXr16eIiHXr1qW6uiajmFixYkWfK6+8ckRnfxdor14fcG6im8vKO/XUIAAAAAC6U79+/fKee+65MiLisssuG7lmzZqXrb/ZqPFR9HvuuWfwnDlzBkS8cv3NiIjXvOY1ayMi1qxZ0+fCCy8cvvH59evXx7vf/e7tFixYUNUV3wfa49UScJZvLlTfY6MAAAAA6GaNj6OvXLmyIuLl6282alyHc/Xq1X3K1t9cuXFb73vf+xb37ds3R0R8/OMf3/5jH/vYuL/+9a+Db7311gHnnXfeiD333HPStGnTtthrr71ecS30lFdLwHlgWfnhzmgwpTQ9pbQopbQupTQ/pXRzSukLKaVX/NeLNrS5dUuviBjTGWMHAAAANh9vetObXhY2lq+/2ah8Hc6IiEGDBtXts88+qzeut9NOO9V+73vfe7pPnz5RU1PT52c/+9mYd7zjHTsfeOCBkz7xiU9sP3v27AFvf/vbl3zta1+b1zXfBtqu8AFnSqlPRHyh7KMrOqnpQyNii4ioiohR0RCinh0RT6SU3tHONp9t5TWjg2MGAAAANjMHHnjgqurq6g1PtG68/maj8uBzypQpKzdef7PRGWecsegf//jHw4ceeujS4cOHr6+srMyjRo2q3X///Zf/+te/fmLatGlPNHct9ITK1qv0ep+KiKml8tU555kdbO/+iPhzRNwTEfOiIeCcEBHviYjDImJYRPwppXRkzvmaDvYFAAAAm6WK4cNre3oMXa27vmN1dXVes2bNfa3V+973vvf89773vec3pc03v/nNq9785jc/3tz5I444YkXOeVZz5//0pz89FRFPNXd+7ty592/KOGBTFDrgTCkdGBHfKR3Oj4gPd7DJH+ecv97E53dHxMUppdMj4pcRURERF6SUxuec17Sh/W1aOT8mzOIEAABgM7DTtL891NNjAF4dChtwppR2jYiro+E7rI2I43LOL3akzZzz0lbO/yqltHdEnBoRYyPi6Ii4tA3tP9fS+ZTSpjYFAAAAAERB1+BMKe0QEddFxPCIqIuIE3LOt3RT978qKx/YbC0AAAAAoMsVLuBMKY2NiOujYQZljohTcs5Xd+MQHiwrj+vGfgEAAACAjRQq4EwpjYyI6RGxY+mjj+ecL+7uYXRzfwAAAABAMwoTcKaUhkbEtRGxS+mjL+Scf9YDQ9mlrDyvB/oHAAAAAEoKEXCmlAZExN8jYq/SR9/OOX+3h4Zzelm5u9b9BAAAAACa0OsDzpRS32jYLf2NpY9+knP+SjvaOSmllEuvrzdxfveU0vhW2jg9Ij5QOnyhNC4AAAAAoIdU9vQANsEfIuKwUvnGiPhNSmm3Fuqvyzk/0o5+JkfEBSmlmyLimoi4PyIWRcOf0cSIeG9EvLlUty4iTs85r2pHPwAAAABAJylCwHl0WflNEfGfVuo/HRHbt7Oviog4tPRqzqKI+EDO+a/t7AMAAAAA6CRFCDi7y/+LhsfP94mI10XE6IgYEQ27pi+OiH9HxD8i4qKc8/KeGiQAAAAA8JJeH3DmnFMntXNRRFzUwvn5EfHb0gsAAAAAKIBev8kQAAAAAEBzBJwAAAAAQGEJOAEAAACAwhJwAgAAAACFJeAEAAAAAApLwAkAAAAAFJaAEwAAAAAoLAEnAAAAAJRMnTp1Qkpp8tSpUyc0dT6lNDmlNPnMM88c291jo2mVPT0AAAAAYPPzh2/cPWnNynVVPT2OrtR/UN/aE/7n9Q91VfvTpk0bfOSRR+68qfV/8pOfPPWJT3xiUVeNp7dYvnx5n/PPP3+LadOmDXvooYcGLF26tLKioiJvscUW60eOHFm7yy67rD7ooINWHH744Su322672p4eb08799xzR5xxxhnbNx5/+MMffuHnP//53JaumTp16oQZM2YMGjt27Lq5c+fe3+WDbIWAEwAAAOh2a1auq1qzovZVHXDS/W666aYB733ve3eaN29e341Opblz5/adO3du33//+98D//CHP4waMWLE+oULF/67Rwbai1100UVbfvnLX35x3Lhx63t6LJtKwAkAAABQcO95z3sWnHHGGfNbqrPDDju8qmcrPvDAA/2OOuqonVeuXFkREfGmN71p6dFHH71k0qRJa/v161c/f/78qvvuu6//jTfeOOTuu+8e3N5+cs6zOm/Uvc+aNWv6/M///M+YCy644LmeHsumEnACAAAAFNyWW265fsqUKTU9PY6e9PnPf35cY7j54x//+Kkzzjhj48fx17zrXe9aftZZZ704b968yt/97nfDe2CYvdqwYcPWL126tPL//u//tvzqV7/6YlEe4bfJEAAAAACFtn79+rjxxhuHRkTsuuuuq5sIN19m7Nix67/4xS8u6J7RFcfHPvaxFyIi1q5dm772ta+N6enxbCoBJwAAAMBm5MwzzxzbuBN4RMSiRYsqPvvZz241adKkXQYPHrxnSmnyueeeO2Lj666++uoh73jHO3YYN27c7tXV1XsNGjTodRMmTNjlQx/60NZPP/30Jq2netNNNw04/vjjt9t+++13GzBgwOv69+//uh122GHX973vfdvef//9/dr7nebNm1dZU1PTJyJihx126NKZrC3ton7uueeOaDw/Z86cvmvWrElf+9rXRu+yyy6TBg8evOegQYNet8cee0w8++yzR61f3/uWuHz961+/+uCDD14WEfGHP/xh1GOPPVaIdXI9og4AAACwmbr//vv7veUtb9m5iU15Nli+fHmfY489dofp06cPK/987dq16ZFHHun/yCOP9L/kkktGXXDBBU+ccMIJy5pqo7a2Nk4++eRtL7300lEbn3vqqaeqn3rqqerLLrts5He+851nPv3pTy9s6/fo169fbiw/+uij/dt6fVdYuHBhxTHHHLPT7NmzB5R/fv/99w+8//77B/7xj3/c4vrrr390+PDh9e1pf86cOX0nTpy4e0TElClTVt5zzz1zOmPc3/rWt+but99+Q2tra9PXvva1sb///e+f7ox2u5IZnAAAAACbqWOPPXan+fPnV5144onzr7766kduvfXWh371q189scsuu9RENDz6fdhhh42fPn36sJRSHHHEEYt/+9vfPnHrrbc+dP311z/8zW9+89mtttpq3erVq/uceOKJO912220Dmurnv//7v7dvDDcPOOCAZT//+c+fvPnmmx+65ZZbHjrnnHOeHj9+fM369evTZz7zme1+//vfD23r9xg9enTd2LFj10VEzJkzp/+Xv/zlMXV1dR35o+mwD33oQ9vPnj17wNvf/vYll19++aO33nrrQ7/85S+f3G233VZHRNx7772DjjnmmB17dJBN2Hfffde8+c1vXhoRceWVV454+OGHmw2/ewszOAEAAAAKbv78+ZUzZsyobu782LFj148bN+4Vz0Q/9thj/f/4xz8++q53vWt542f777//6sbyN7/5zdF333334MrKynzppZc+dtxxxy0vv/6QQw5Zdfrppy/ad999Jz722GPVn/zkJ7eZNWvWy2YSXnTRRcOuvvrqERER55xzztNnnnnmy2ZoHnDAAas/9KEPLTrkkENe889//nPwZz/72W3/67/+6/6qqrY9HX3qqafOP+uss7aOiPjf//3fcRdddNGoQw89dOk+++yzav/991+16667rm1Tgx30wAMPDPjCF74w9+yzz36h8bP9999/9SmnnLL4TW9602tuv/32ITfddNPQyy67bOjxxx/f5MzXnvKtb31r7vXXXz9s/fr16atf/erYK6+88qmeHlNLzOAEAAAAKLhLL7101NSpU3dt7nXOOeds2dR1xxxzzMLycLPc2rVr0y9+8YvREREnnnji/I3DzUajRo2q+/a3v/1sRMOsxAceeOBla2n+4Ac/2Coi4s1vfvPSjcPNRgMGDMg/+9nPnomImDdvXt+///3vgzf1uzf62te+9uJ//dd/bWh/3rx5fS+++OItP/zhD++w22677TZy5MjXHnHEETv+/ve/H1pf366nwttk5513XvOtb33rhY0/r6qqiosuuuipysrKHBHxy1/+8hWP7fe0KVOm1Lz1rW9dEhFx9dVXj+jI+qjdQcAJAAAAsJl673vfu7i5c7fccsuABQsWVEVEnHDCCUtaauctb3nLyrLrBjaWn3zyyarGNSiPPfbYZvuKiNhrr71qhg0btj4i4o477hi0ad/gJRUVFXHFFVc8feWVVz667777Lu/T5+Wx16JFiyr//ve/D3/Pe94zfo899pg0e/bsLg3tTjjhhEUVFRVNnttpp51q99tvv+UREXfffffg9mw4NGHChHU551k551mdtf5muW9+85vzKioqoq6uLr7yla+8YkOl3sQj6gAAAAAF96lPfer5H/7wh/Paet2UKVPWNHfu7rvv3hBUHnrooRM3tc0XXnhhw7Pld9xxx4Y2Tj/99B1PP/30NrfRVscee+zyY489dvmCBQsqrr/++kEzZswY+K9//WvAjBkzBq1cubIiImL27NkDDj744AkzZsx4aLvttqttb18tecMb3rCqpfOTJ09edfPNNw+tqanp89BDD/Xbfffdu/UR+ta87nWvqznyyCMX/fnPfx4xbdq0Le69997n99prry7dob69zOAEAAAA2EyNGjWq2amD8+fPb1fIuHr16g1504svvtiuyXVr1qzpcGY1atSouhNOOGHZD3/4w3k33njjY/Pnz//3j3/846eGDBlSFxGxYMGCqs997nNdNjNxq622ajE4HT169IbzCxYs6JWTEL/1rW89X1FRkevr6+OrX/1qr53F2Sv/8AAAAADoepWVzUdD5buQT5s2bc6WW265Sc9Rjx07dkO9urq61Fj+xS9+8eTkyZNXN33Vy40cObLTt0Dv379/PuOMMxZtvfXWtccee+xrIiL+8Y9/DK+rq3u6uUfJOyKl1OL5nHPLFXqBXXfdde3RRx+96Morrxx5zTXXDL/77rv7v/71r2921m9PEXACAAAA8AojRozYEFT269cvT5kypc2PJ48cOXJDGymlaE8bne2YY45ZPmbMmHUvvPBC3+XLl1e8+OKLleWhbGeZN29e1R577NHsY+fz58/fkMu1NJO2p33zm998/uqrrx6xfv369JWvfGXs9OnTH+/pMW3MI+oAAAAAvEL5bMtrrrlmSHvamDp16oY2pk+f3q42usKWW2654fHwPn365K7o45///OfAls7fe++9AyMiqqur6ydNmtSr1t8sN2HChHXHHXfcwoiI66+/ftjtt98+oKfHtDEBJwAAAACvcNhhh60cOnRoXUTExRdfPGrx4sVtzpF22223tTvttFNNRMS0adO2ePTRR/t29jjbasWKFX0ef/zx/hERgwYNqttyyy07/XH4iIjLL798RH19fZPnnnzyyarbb799SETE61//+hUtLRXQG5x11lnP9+3bN0dEr1yLU8AJAAAAwCsMGDAgf/jDH34hImLhwoVV73rXu3Zcvnx5s1nSkiVL+vzv//7vqI0//+xnP/t8RMTatWvTu971rp3mzZvXbJq3Zs2a9J3vfGfU6tWr27Q+5bJly/rsscceE//whz8MLV87dGN1dXXxgQ98YJtVq1b1iYg45JBDlvXp0zXx2MMPP9z/a1/72uiNP6+trY2TTjppu9ra2hQRcfrppy9oT/tz5szpm1KanFKaPHXq1AkdHW9Ldtppp9oTTjhhQUTEzTffPPTRRx+t7sr+2qp3x8MAAAAA9JhvfvObL9xyyy1D7rrrrsG33nrr0IkTJ+564oknLnjjG9+4aosttli/fPnyigcffLD6lltuGTx9+vRh/fr1q//Sl770ssDu9NNPX3zdddcNueqqq0bMnj17wK677rrr+973vgUHH3zwytGjR9euXLmyzyOPPFJ9++23D/rHP/4xfNmyZRUf/vCHF0VEmx4dv//++we++93vHv/JT36y9vDDD1+6zz77rNxxxx3XDRkypG7x4sUVs2bNGvB///d/Ix999NENsze/+93vzu3EP66X2XXXXVd/+9vf3vrf//73gBNPPHHRmDFj1j/00EP9zjvvvNH333//wIiIgw8+eNkJJ5ywrKvG0JnOOuus5y+//PKRNTU1fZYuXdqrMsVeNRgAAAAAeo/Kysq47rrrHn3/+9+/3dVXXz3i+eef7/ud73xnXHP1t9hiiyY3y7n88suf+vjHP17761//eszSpUsrzzvvvK3OO++8Jtvo379/fUVFRZvCzaqqqjxy5MjahQsXVs2fP7/qkksuGXXJJZe8YjZpo+22227tJZdc8sSECRPWtaWftvjVr3711Gmnnbb9tGnTtpg2bdoWG5/fa6+9Vv7pT396oqv672zbbrvt+ve9730Lfv3rX79iVmpP84g6AAAAAM0aNGhQvuqqq5669dZbH3rPe96zYPz48TWDBg2qq6ioyIMHD66bOHHimuOOO27hhRde+Pijjz76QFNtVFZWxi9+8Yu599577wOnnXbai5MmTVo9ZMiQuoqKihg4cGD9+PHja4466qjF55133lNz587996BBg9oUcA4YMCC/8MIL/7nuuuse/vSnPz3vgAMOWLb11luvLYWlMWjQoLoddtih5u1vf/uSX/7yl08+/PDDs/fff//VrbfcfiNHjqybOXPmw1/84hfnTpw4cc3AgQPrBwwYUL/bbrut/va3v/3M3XffPWf48OFNL9LZS339619/oX///r1uzCnnLtkoinZIKW0dEc9GRNxwTMSYFvfa6lxv22pa93UGAABAswZP+kK39VW7uDbmnDmn8XCbnPNzHWlv1qxZ/6+ysnKX6urqYRMmTHispbp/+Mbdk9asXFfVkf56u/6D+tae8D+vf6inx0H3Offcc0ecccYZ20dEPPzww/d35QzRopozZ874mpqapevXr39w8uTJb+uMNj2iDgAAAHQ7wR/QWTyiDgAAAAAUloATAAAAACgsAScAAAAAUFgCTgAAAACgsAScAAAAANAJPvGJTyzKOc/KOc+yg3r3EXACAAAAAIUl4AQAAAAACkvACQAAAAAUloATAAAAACgsAScAAADQqXLOqafHAPROXXF/EHACAAAAnWVlRNTV19dX1NfXCzmBl6mvr0/19fUVEVEXDfeLTiHgBAAAADrLA3V1datyzmnZsmWDe3owQO+ybNmywTnnVFdXtyoi7u+sdgWcAAAAQGe5Pue8rr6+ftWLL744ZsmSJUPM5ATq6+vTkiVLhrz44otj6uvrV+Wc10XE9Z3VfmVnNQQAAABs9u6JiH+tW7euT0RsPW/evHHPP/987tOnT11KKff04IDul3NO9fX1FaX3VevWrXsuIv4VETM6qw8BJwAAANApJk+evH7WrFmfiIhz161bV59S6ltRUTEwIip6emxAj6orLV+xLhrCzU9Mnjx5fWc1nnL2H1B6i5TS1hHxbETEjTvuFGOqqnp4RAAAALyavVBbG2964vHGw21yzs91RruzZs2qjIgpEXFoROweEYM6o12gsFZGw5qb10fEjM4MNyPM4AQAAAA6WSm8uKv0AuhSNhkCAAAAAApLwAkAAAAAFJaAEwAAAAAorMIFnCmlbVNKP0gpPZRSWpVSWpxSuiel9JmU0oBO7Of4lNK1KaXnU0o1KaWnUkqXpJTe0Fl9AAAAAAAdU6hNhlJKb4+ISyNiaNnHA6JhZ7YpEXFqSultOecnOtBHdURcGRFHbHRqu9Lr3Smlr+ecv9nePgAAAACAzlGYGZwppddGxBXREG6ujIgvR8S+EXFIRPy6VG1CRPw9pTSoA139Jl4KN2+KiHdGxNSI+EBEPB4Nf2ZnpZRO7UAfAAAAAEAnKNIMzh9Hw2zN9RFxWM75rrJzN6aUHo2I70XExIg4MyLOamsHKaUDI+LdpcO/RcS7cs51peMZKaW/RsSsiNg2Ir6XUvpjznlpO74LAAAAANAJCjGDM6U0JSIOKh3+ZqNws9E5EfFQqfzJlFJVO7r6XOm9LiI+UhZuRkREznlhRHy+dDg8GmZ1AgAAAAA9pBABZzQ8Jt7owqYq5JzrI+Li0uHweCkQ3SSlx9oPKR1Ozzk/10zVqyJieal8dFv6AAAAAAA6V1ECzv1L76ui4RHx5txSVt6vjX1MjYh+TbTzMjnndRHxz8Zr2jlTFAAAAADoBEVZg3NS6f2xnPP6Fuo93MQ1be1j43aa6+ewaPjze01EPLgpHaSUtm6lyphNaQcAAAAAaNDrA86UUnVEjCwdNvfYeERE5JyXpJRWRcTAiNimjV2V12+xn4h4dqPrNing3Og6AAAAAKCDivCI+uCy8spNqL+q9D6oC/tZVVZuaz8AAAAAQCfp9TM4I6K6rLxuE+qvLb3378J+1paV29JPa7NKx0TEjDa0BwAAAACbtSIEnDVl5b6bUL9xo6A1XdhPv7LyJvfTws7sERGRUtrUpgAAAACAKMYj6ivKypvyOPjA0vumPM7e3n4GlpXb2g8AAAAA0El6fcCZc66JiIWlwxZ3IU8pDY+Xwse2buhTPruytd3Oyx81t3EQAAAAAPSQXh9wljxUeh+fUmrpsfqJTVyzqcp3Qp/YbK2Xn18fEY+1sR8AAAAAoJMUJeC8vfQ+MCImt1DvwLLyHW3sY0a8tLnQgc1VSin1jYg3NF6Tc96UjY8AAAAAgC5QlIDzz2Xlk5uqkFLqExHvLx0ujYib2tJBznlFRNxQOjw0pdTcY+pHR8SQUvnqtvQBAAAAAHSuQgScOed7IuK20uEHUkr7NFHt0xExqVT+Sc65tvxkSumklFIuvb7eTFc/KL1XRsTPUkoVG7UxMiK+WzpcGhEXtOmLAAAAAACdqhABZ8kZEbEmGsLH61JKX0wpvSGldHBK6VcR8b1SvUci4pz2dJBzvjEiLisdHhUR01NKR6WU9k4pnRwR/4yIbUvnv5BzXtLeLwMAAAAAdFxLG/b0Kjnn+1JK/x0R/xcNj4j/bxPVHomIt5ceN2+vU0rtvy0iDi69ytVHxDdzzr/qQB8AAAAAQCco0gzOyDn/LSL2iIgfRUOYuToaHhWfGRGfj4jX5Zw7tKt5znlNzvntEfGeiJgeEfOjYfOhZyPi9xGxX8756x3pAwAAAADoHCnn3NNjoKS0sdGzERE37rhTjKmq6uERAQAA8Gr2Qm1tvOmJxxsPt8k5P9eT4wFoj0LN4AQAAAAAKCfgBAAAAAAKqzCbDG1uXnPLzbH11lv39DAAAAB4FRv83HMR22zT08MA6BAzOAEAAACAwhJwAgAAAACFJeAEAAAAAApLwAkAAAAAFJaAEwAAAAAoLAEnAAAAAFBYAk4AAAAAoLAEnAAAAABAYQk4AQAAAIDCEnACAAAAAIUl4AQAAAAACkvACQAAAAAUloATAAAAACgsAScAAAAAUFgCTgAAAACgsAScAAAAAEBhCTgBAAAAgMIScAIAAAAAhSXgBAAAAAAKS8AJAAAAABSWgBMAAAAAKCwBJwAAAABQWAJOAAAAAKCwBJwAAAAAQGEJOAEAAACAwhJwAgAAAACFJeAEAAAAAApLwAkAAAAAFJaAEwAAAAAoLAEnAAAAAFBYAk4AAAAAoLAEnAAAAABAYQk4AQAAAIDCEnACAAAAAIUl4AQAAAAACkvACQAAAAAUloATAAAAACgsAScAAAAAUFgCTgAAAACgsAScAAAAAEBhCTgBAAAAgMIScAIAAAAAhSXgBAAAAAAKS8AJAAAAABSWgBMAAAAAKCwBJwAAAABQWAJOAAAAAKCwBJwAAAAAQGEJOAEAAACAwhJwAgAAAACFJeAEAAAAAApLwAkAAAAAFFZlTw+Al6loLDz//PM9OQ4AAAA2Axv9b8+K5uoB9GYp59zTY6AkpbR3RMzo6XEAAACwWZqSc57Z04MAaCuPqAMAAAAAhWUGZy+SUuoXEbuXDhdERF0bLh8TL83+nBIRL3Ti0Ng8+A3RUX5DdAa/IzrKb4iO8huiMxTpd1QREaNK5ftzzmt7cjAA7WENzl6k9BdJux4HSCmVH76Qc36uUwbFZsNviI7yG6Iz+B3RUX5DdJTfEJ2hgL+jp3t6AAAd4RF1AAAAAKCwBJwAAAAAQGEJOAEAAACAwhJwAgAAAACFJeAEAAAAAApLwAkAAAAAFJaAEwAAAAAorJRz7ukxAAAAAAC0ixmcAAAAAEBhCTgBAAAAgMIScAIAAAAAhSXgBAAAAAAKS8AJAAAAABSWgBMAAAAAKCwBJwAAAABQWAJOAAAAAKCwBJwAAAAAQGEJOF8FUkrbppR+kFJ6KKW0KqW0OKV0T0rpMymlAT09PnqnlFLexNfNPT1WekZKacuU0hEppbNSSteklBaW/S4uakd7b0kpXZVSei6ltLb0flVK6S1dMHx6gc74DaWUTmrD/eqkrv1GdLeU0l4ppS+Vfj/Plu4dK1NKj6SULkop7d/G9tyHNkOd8TtyL9p8pZSGpJSOTymdk1K6JaX0WEppWUppXUppfkrp5pTS51JKIzaxPfchgC6Qcs49PQY6IKX09oi4NCKGNlNlTkS8Lef8RPeNiiJIKW3q//PfknM+qCvHQu/Uym/kdznnkzaxnRQRv4yID7ZQ7fyI+FD2l9KrSmf8hkpBwYWb2OXJOeeLNrEuvVxK6ZaIOGATql4SEafmnNe10Jb70Gaqs35H7kWbr5TSoRExfROqLoyI9+acr22mHfchgC5U2dMDoP1SSq+NiCsiYkBErIyIsyPipojoHxHHR8RpETEhIv6eUpqSc17ZU2OlV/tFRPy8hfOrumsg9GrPRsRDEXFYO679Vrz0j/n7IuJ7EfF4ROwUEZ+LiNeVzi+IiK90eKT0Vh35DTU6PCLmtXD+uQ60Te8zrvQ+LyKujIjbIuKZiKiIiH0i4tOlOu+Lhn/TvruFttyHNl+d+Ttq5F60+Xk2Gv531qxS+floeBpy64g4NiKOjoiREfHX0v/u+k8TbbgPAXQhMzgLLKV0U0QcFBHrI+KAnPNdG53/bDT8xRkR8T8557O6d4T0ZmUzq76Rc/56T46F3iml9I2ImBERM3LOL6aUto+IJ0unN3X23fhoCLUqI2JmNNyr1pSdHxARt0TE3tFwL5uYc368M78HPaeTfkMnxUuzpnbIOT/V+SOlN0opTYuIiyPiTznnuibOj4yIOyJi59JHB+Scb2uinvvQZqwTf0cnhXvRZimlVNHUb2ejOu+MiKtLh1flnI/Z6Lz7EEAXswZnQaWUpkRDuBkR8ZuNw82Sc6LhL9KIiE+mlKq6Y2zAq0PO+X9yztNyzi92oJlPxUtPC3y8/B/zpT5WR8THS4eVEfHJDvRFL9NJvyE2UznnI3LOVzQXLOScF0bD7LtGxzbTlPvQZqwTf0dsploLN0t1/hwRD5cOm1oSwX0IoIsJOIvrnWXlJtcDyjnXR8N/sY6IGB4vBaIAXa601tQ7SocP55z/2VS90udzSofvLF0HsCluLivvtPFJ9yE20c1l5Vf8jmATNS7rVF3+ofsQQPcQcBZX426Pq6JhLZjm3FJW3q/rhgPwCjvES2uf3dJSxbLzW0fE9l01IOBVp29Zub6J8+5DbIrWfkfQopTSpIjYs3T48Ean3YcAuoGAs7gmld4fyzmvb6Fe+V+wk5qtxebsv1JKc1JKa1JKK1JKj6aUfpdSOrinB0bhld9zNv7H/sbcq9gUF6WUXkwprUspLUwp/TOl9K2U0rjWL+VV6sCyclP3GfchNkVrv6ONuRcRKaUBKaXXpJTOjIYNiCpKp36yUVX3IYBuIOAsoJRSdTTs0hfRyi6NOecl8dLjEtt05bgorF2iYWH96ogYFBHjI+L9EXFjSunqlNLQnhwchVZ+z2ltR9lnm7kOyh0YEVtGRFVEjIiI10fElyPisZTS6T05MLpfSqlPRHyh7KMrmqjmPkSLNvF3tDH3os1USumklFIubda5KiIeiYZ9D0aXqvwgIi7d6DL3IYBuUNl6FXqhwWXllZtQf1VEDIyG8AoarY6Iv0bEDdHwX4tXRsSoaPhH+4ei4R/s74yIv6SU3pxzru2hcVJcbblXrSoru1exsSci4qqIuCte+h9/O0bEMdGwIUh1RPwypZRzzuf3zBDpAZ+KiKml8tU555lN1HEfojWb8jtq5F5Ec/4VER/KOd/dxDn3IYBuIOAspvKFq9dtQv21pff+XTAWimtcznlpE59PTymdFxHXRMTroiHw/HBEnNuNY+PVoS33qrVlZfcqyl0dEb/LOeeNPp8REZenlI6IhsChKiJ+lFL6a875he4eJN0rpXRgRHyndDg/Gv6eaor7EM1qw+8owr2IBn+OiMYQvH80bEp1XES8KyIuTSl9Muc8baNr3IcAuoFH1Iuppqzct9laL+lXel/TBWOhoJoJNxvPvRgNMxEa/xH28e4YE686bblX9Ssru1exQc55WROBQvn5aRHxjdLhgIj4QLcMjB6TUto1GsKmymgIA44r/b3VFPchmtTG35F7ERHR8O/nnPMDpdeMnPNlOeejo2F5px2j4cmnkza6zH0IoBsIOItpRVl5Ux5dGFh635TH2SEiInLOT0TE9NLh+JTS2J4cD4XUlnvVwLKyexVt9euIaAweDmypIsWWUtohIq6LiOERURcRJ+ScW9qV2H2IV2jH72hTuRdtpnLOl0TEldHwv69/mlIaXnbafQigGwg4CyjnXBMRC0uHW7dUt/SXa+NflM+2VBea8GBZ2c6gtFX5Qvot3qvi5Qvpu1fRJjnn+fHS34vuVa9Spf/Qdn1EjI2GEOmUnPPVrVzmPsTLtPN3tEncizZ7fym9D4yIt5Z97j4E0A0EnMX1UOl9fEqppbVUJzZxDWyq1NMDoNDKA/KJzdZ65Xn3KtrD/epVLKU0MhqeKtix9NHHc84Xb8Kl7kNs0IHfUZu66eT2KI4FZeXtysruQwDdQMBZXLeX3gdGxOQW6pU/HnNH1w2HV6ldysrzemwUFNWT8dLvprVH9Q4ovc/9/+3dQYhdVxkH8P9XqjSF2IWhRRBS0i7qwoUSoaWCFeImbVyIhYILbSuhi4IGFwbRZQ3FTUJ0oU2xQletBUsVEa0ubA3oooJ0E2KJqAuDYKClrTX0c3HvMG8xk8xk3sy8m/f7QeCem/POnMXhmzv/d+69SS5s14S4PlXVrUk+PDbVqutMVd2S5FdZ/Z10vLt/sMGPq0Mk2fI62ujPUIuW2+yu3dnby9UhgB0g4Jyun80cP7xWh6q6IcMDr5PkUpLfbe+UuJ5U1YEknxubb3T3P3dzPkzP+DKGldu17qqqu9fqN55f2bHw4pVe4gDrOJrVXVPzeI4eC6Kqbk7yiySfHE890d1PbvTz6hDJ1tfRJqhFy+3BmeO/rByoQwA7Q8A5Ud39xyS/H5uPVtU9a3T7RpKPjcenuvt/OzI5Fl5VHbnSow2q6rYkP03ygfHUXHc4sFROJrk8Hp+uqj2z/zm2T4/Ny2N/SJJU1e1V9Ymr9HkgyXfG5rtJfrztE2NHVNUHM7zl+t7x1Knu/vY1DHUy6tDSmsc6UouWW1V9papuukqfY0kOj80LWb3bbsXJqEMA26p8MTRd44XWq0n2ZLgN4rsZdmnuSfJQhm+Rk+RckoPd/eZa47B8qupChvDyhSRnM1yIvZNkX5L7kjyW1VusXklyqLv/u9PzZHdV1aeT3Dlzal+S743HryY5M9u/u59ZZ5wTSY6PzdeSPJnkr0nuSPLNJCt/NJ7o7m/NY+4shq2uoaq6L8PvtbNJXkry5yQXM+yQOpDki+O/lR1Tj8/7llN2T1W9kOQLY/O3Sb6e1TdUr+W97j63zljq0JKaxzpSi5bbeN28N8N18ysZasdb47mPJ/lSVgP095Lc392/WWMcdQhgGwk4J66qjiR5NsmH1ulyLsMv2fM7NysW3Xihtv9q/TJcyH21uy9t64RYSFX1TJIvb7R/d6/5YoXxcRlPJXnkCh9/OsnR7n5/M3NksW11Dc2EClfzdpJj3f2jTUyPBVdVm71I/Vt3377OWOrQkprHOlKLltsmrpv/keSR7v71OuOoQwDbSMB5Haiq/Um+luT+JB/N8M3h+STPJ/l+d7+9i9NjAVXVZzI85PyeDDsP9mUIyd9K8vckf0jyk+4+u2uTZNfNK+CcGe9whp3ln8qw5v6d5E9Jftjdv7z2mbKo5hBw7k3y+Qy16mCSj2RYOzcm+U+S15O8nORMd1+cz6xZFPMMOGfGVIeWzJwCTrVoiVXVHUkOJflshsd/3ZbhTqd3k/wrw47enyd5biN/d6lDANtDwAkAAAAATJaXDAEAAAAAkyXgBAAAAAAmS8AJAAAAAEyWgBMAAAAAmCwBJwAAAAAwWQJOAAAAAGCyBJwAAAAAwGQJOAEAAACAyRJwAgAAAACTJeAEAAAAACZLwAkAAAAATJaAEwAAAACYLAEnAAAAADBZAk4AAAAAYLIEnAAAAADAZAk4AQAAAIDJEnACAAAAAJMl4AQAAAAAJkvACQAAAABMloATAAAAAJgsAScAAAAAMFkCTgAAAABgsgScAAAAAMBkCTgBAAAAgMkScAIAAAAAk/V/KhdDmgqBUoQAAAAASUVORK5CYII=\n",
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\n",
       "text/plain": [
        "<Figure size 1200x800 with 1 Axes>"
       ]
@@ -210,7 +210,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "timeloop(500)"
+    "timeloop(5000)"
    ]
   },
   {
@@ -230,7 +230,7 @@
     {
      "data": {
       "text/plain": [
-       "0.07442458175252653"
+       "0.07369491639715217"
       ]
      },
      "execution_count": 13,
@@ -250,7 +250,7 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x12c65b790>]"
+       "[<matplotlib.lines.Line2D at 0x7f35955717f0>]"
       ]
      },
      "execution_count": 14,
@@ -259,7 +259,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -287,13 +287,20 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "np.testing.assert_almost_equal(np.gradient(vel_profile)[-1], 0)"
+    "np.testing.assert_almost_equal(np.gradient(vel_profile)[-1], 0, decimal=3)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -307,7 +314,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.7"
+   "version": "3.8.12"
   }
  },
  "nbformat": 4,
diff --git a/lbmpy_tests/test_lbstep.py b/lbmpy_tests/test_lbstep.py
index e6a6bc633b331f69648cad81e2b98adcb3cc4135..86131385bf5942319182765fd64274bad3fda92b 100644
--- a/lbmpy_tests/test_lbstep.py
+++ b/lbmpy_tests/test_lbstep.py
@@ -50,40 +50,6 @@ def test_data_handling_3d():
         np.testing.assert_almost_equal(results[0], arr)
 
 
-def test_data_handling_2d_opencl():
-    pytest.importorskip('pyopencl')
-    import pystencils.opencl.opencljit
-    pystencils.opencl.opencljit.init_globally()
-    print("--- LDC 2D test ---")
-    results = []
-
-    # Since waLBerla has no OpenCL Backend yet, it is not possible to use the
-    # parallel Datahandling with OpenCL at the moment
-
-    # TODO: Activate parallel Datahandling if Backend is available
-    parallel = False
-    for gpu in [True, False] if gpu_available else [False]:
-        if parallel and gpu and not hasattr(wLB, 'cuda'):
-            continue
-
-        print(f"Testing parallel: {parallel}\tgpu: {gpu}")
-        config = CreateKernelConfig(target=Target.GPU if gpu else Target.CPU,
-                                    gpu_indexing_params=MappingProxyType({'block_size': (8, 4, 2)}))
-        if parallel:
-            from pystencils.datahandling import ParallelDataHandling
-            blocks = wLB.createUniformBlockGrid(blocks=(2, 3, 1), cellsPerBlock=(5, 5, 1),
-                                                oneBlockPerProcess=False)
-            dh = ParallelDataHandling(blocks, dim=2)
-            rho = ldc_setup(data_handling=dh, config=config)
-            results.append(rho)
-        else:
-            rho = ldc_setup(domain_size=(10, 15), parallel=False, config=config)
-            results.append(rho)
-    for i, arr in enumerate(results[1:]):
-        print(f"Testing equivalence version 0 with version {i + 1}")
-        np.testing.assert_almost_equal(results[0], arr)
-
-
 def test_data_handling_2d():
     print("--- LDC 2D test ---")
     results = []
diff --git a/lbmpy_tests/test_oldroydb.py b/lbmpy_tests/test_oldroydb.py
new file mode 100755
index 0000000000000000000000000000000000000000..a4a5228682917e8446c9dcb17918f2381bacc89a
--- /dev/null
+++ b/lbmpy_tests/test_oldroydb.py
@@ -0,0 +1,297 @@
+import pystencils as ps
+from lbmpy.stencils import get_stencil
+from lbmpy.updatekernels import create_stream_pull_with_output_kernel
+from lbmpy import create_lb_update_rule, relaxation_rate_from_lattice_viscosity, ForceModel, Method, LBStencil
+from lbmpy.macroscopic_value_kernels import macroscopic_values_setter
+from pystencils.boundaries.boundaryhandling import BoundaryHandling
+from pystencils.boundaries.boundaryconditions import Boundary, Neumann, Dirichlet
+from lbmpy.boundaries.boundaryhandling import LatticeBoltzmannBoundaryHandling
+from lbmpy.boundaries import NoSlip
+
+from lbmpy.oldroydb import *
+import pytest
+
+
+# # Lattice Boltzmann with Finite-Volume Oldroyd-B
+# # taken from the electronic supplement of https://doi.org/10.1140/epje/s10189-020-00005-6,
+# # available at https://doi.org/10.24416/UU01-2AFZSW
+
+pytest.importorskip('scipy.optimize')
+def test_oldroydb():
+    import scipy.optimize
+
+    # ## Definitions
+
+    # In[2]:
+
+
+    L = (34, 34)
+    lambda_p = sp.Symbol("lambda_p")
+    eta_p = sp.Symbol("eta_p")
+
+    lb_stencil = LBStencil("D2Q9")
+    fv_stencil = LBStencil("D2Q9")
+    eta = 1-eta_p
+    omega = relaxation_rate_from_lattice_viscosity(eta)
+
+    f_pre = 0.00001
+
+
+    # ## Data structures
+
+    # In[3]:
+
+
+    dh = ps.create_data_handling(L, periodicity=(True, False), default_target=ps.Target.CPU)
+
+    opts = {'cpu_openmp': True, 
+            'cpu_vectorize_info': None,
+            'target': dh.default_target}
+
+    src = dh.add_array('src', values_per_cell=len(lb_stencil), layout='c')
+    dst = dh.add_array_like('dst', 'src')
+    ρ = dh.add_array('rho', layout='c', latex_name='\\rho')
+    u = dh.add_array('u', values_per_cell=dh.dim, layout='c')
+    tauface = dh.add_array('tau_face', values_per_cell=(len(fv_stencil)//2, dh.dim, dh.dim), latex_name='\\tau_f',
+                           field_type=ps.FieldType.STAGGERED, layout='c')
+
+    tau = dh.add_array('tau', values_per_cell=(dh.dim, dh.dim), layout='c', latex_name='\\tau')
+    tauflux = dh.add_array('j_tau', values_per_cell=(len(fv_stencil)//2, dh.dim, dh.dim), latex_name='j_\\tau',
+                           field_type=ps.FieldType.STAGGERED_FLUX, layout='c')
+    F = dh.add_array('F', values_per_cell=dh.dim, layout='c')
+
+    fluxbh = BoundaryHandling(dh, tauflux.name, fv_stencil, name="flux_boundary_handling",
+                              openmp=opts['cpu_openmp'], target=dh.default_target)
+    ubh = BoundaryHandling(dh, u.name, lb_stencil, name="velocity_boundary_handling",
+                           openmp=opts['cpu_openmp'], target=dh.default_target)
+    taufacebh = BoundaryHandling(dh, tauface.name, fv_stencil, name="tauface_boundary_handling",
+                                 openmp=opts['cpu_openmp'], target=dh.default_target)
+
+
+    # ## Solver
+
+    # In[4]:
+
+
+    collision = create_lb_update_rule(stencil=lb_stencil,
+                                      method=Method.TRT,
+                                      relaxation_rate=omega, 
+                                      compressible=True,
+                                      force_model=ForceModel.GUO, 
+                                      force=F.center_vector+sp.Matrix([f_pre,0]),
+                                      kernel_type='collide_only',
+                                      optimization={'symbolic_field': src})
+
+    stream = create_stream_pull_with_output_kernel(collision.method, src, dst, {'density': ρ, 'velocity': u})
+
+    lbbh = LatticeBoltzmannBoundaryHandling(collision.method, dh, src.name,
+                                            openmp=opts['cpu_openmp'], target=dh.default_target)
+
+    stream_kernel = ps.create_kernel(stream, **opts).compile()
+    collision_kernel = ps.create_kernel(collision, **opts).compile()
+
+
+    # In[5]:
+
+
+    ob = OldroydB(dh.dim, u, tau, F, tauflux, tauface, lambda_p, eta_p)
+    flux_kernel = ps.create_staggered_kernel(ob.flux(), **opts).compile()
+    tauface_kernel = ps.create_staggered_kernel(ob.tauface(), **opts).compile()
+    continuity_kernel = ps.create_kernel(ob.continuity(), **opts).compile()
+    force_kernel = ps.create_kernel(ob.force(), **opts).compile()
+
+
+    # ## Set up the simulation
+
+    # In[6]:
+
+
+    init = macroscopic_values_setter(collision.method, velocity=(0,)*dh.dim, 
+                                     pdfs=src.center_vector, density=ρ.center)
+    init_kernel = ps.create_kernel(init, ghost_layers=0).compile()
+
+    # no-slip for the fluid, no-flux for the stress
+    noslip = NoSlip()
+    noflux = Flux(fv_stencil)
+    nostressdiff = Flux(fv_stencil, tau.center_vector)
+
+    # put some good values into the boundaries so we can take derivatives
+    noforce = Neumann() # put the same stress into the boundary cells that is in the nearest fluid cell
+    noflow = Dirichlet((0,)*dh.dim) # put zero velocity into the boundary cells
+
+    lbbh.set_boundary(noslip, ps.make_slice[:, :4])
+    lbbh.set_boundary(noslip, ps.make_slice[:, -4:])
+    fluxbh.set_boundary(noflux, ps.make_slice[:, :4])
+    fluxbh.set_boundary(noflux, ps.make_slice[:, -4:])
+    ubh.set_boundary(noflow, ps.make_slice[:, :4])
+    ubh.set_boundary(noflow, ps.make_slice[:, -4:])
+    taufacebh.set_boundary(nostressdiff, ps.make_slice[:, :4])
+    taufacebh.set_boundary(nostressdiff, ps.make_slice[:, -4:])
+
+    for bh in lbbh, fluxbh, ubh, taufacebh:
+        assert len(bh._boundary_object_to_boundary_info) == 1, "Restart kernel to clear boundaries"
+
+    def init():
+        dh.fill(ρ.name, np.nan, ghost_layers=True, inner_ghost_layers=True)
+        dh.fill(ρ.name, 1)
+        dh.fill(u.name, np.nan, ghost_layers=True, inner_ghost_layers=True)
+        dh.fill(u.name, 0)
+        dh.fill(tau.name, np.nan, ghost_layers=True, inner_ghost_layers=True)
+        dh.fill(tau.name, 0)
+        dh.fill(tauflux.name, np.nan, ghost_layers=True, inner_ghost_layers=True)
+        dh.fill(tauface.name, np.nan, ghost_layers=True, inner_ghost_layers=True)
+        dh.fill(F.name, np.nan, ghost_layers=True, inner_ghost_layers=True)
+        dh.fill(F.name, 0) # needed for LB initialization
+    
+        sync_tau() # force calculation inside the initialization needs neighbor taus
+        dh.run_kernel(init_kernel)
+        dh.fill(F.name, np.nan)
+
+
+    # In[7]:
+
+
+    sync_pdfs = dh.synchronization_function([src.name]) # needed before stream, but after collision
+    sync_u = dh.synchronization_function([u.name]) # needed before continuity, but after stream
+    sync_tau = dh.synchronization_function([tau.name]) # needed before flux and tauface, but after continuity
+
+    def time_loop(steps, lambda_p_val, eta_p_val):
+        dh.all_to_gpu()
+        vmid = np.empty((2,steps//10+1))
+        sync_tau()
+        sync_u()
+        ubh()
+        i = -1
+        for i in range(steps):
+            dh.run_kernel(flux_kernel)
+            fluxbh() # zero the fluxes into/out of boundaries
+            dh.run_kernel(continuity_kernel, **{lambda_p.name: lambda_p_val, eta_p.name: eta_p_val})
+        
+            sync_tau()
+            dh.run_kernel(tauface_kernel) # needed for force
+            taufacebh()
+            dh.run_kernel(force_kernel)
+        
+            dh.run_kernel(collision_kernel, **{eta_p.name: eta_p_val})
+            sync_pdfs()
+            lbbh() # bounce-back populations into boundaries
+            dh.run_kernel(stream_kernel)
+            sync_u()
+            ubh() # need neighboring us for flux and continuity
+        
+            dh.swap(src.name, dst.name)
+        
+            if i % 10 == 0:
+                if u.name in dh.gpu_arrays:
+                    dh.to_cpu(u.name)
+                uu = dh.gather_array(u.name)
+                uu = uu[L[0]//2-1:L[0]//2+1, L[1]//2-1:L[1]//2+1, 0].mean()
+                if np.isnan(uu):
+                    raise Exception(f"NaN encountered after {i} steps")
+                vmid[:, i//10] = [i, uu]
+        sync_u()
+        dh.all_to_cpu()
+    
+        return vmid[:,:i//10+1]
+
+
+    # ## Analytical solution
+    # 
+    # comes from Waters and King, Unsteady flow of an elastico-viscous liquid, Rheologica Acta 9, 345–355 (1970).
+
+    # In[9]:
+
+
+    def N(n):
+        return (2*n-1)*np.pi
+
+    def Alpha_n(N, El, eta_p):
+        return 1+(1-eta_p)*El*N*N/4
+
+    def Beta_n(alpha_n,N, El):
+        return np.sqrt(np.abs(alpha_n*alpha_n - El*N*N))
+
+    def Gamma_n(N, El, eta_p):
+        return 1-(1+eta_p)*El*N*N/4
+
+    def G(alpha_n,beta_n,gamma_n,flag,T):
+        if(flag):
+            return ((1.0 - gamma_n/beta_n)*np.exp(-(alpha_n+beta_n)*T/2) +
+                    (1.0 + gamma_n/beta_n)*np.exp((beta_n-alpha_n)*T/2))
+        else:
+            return 2*np.exp(-alpha_n*T/2)*(np.cos(beta_n*T/2) + (gamma_n/beta_n)*np.sin(beta_n*T/2))
+
+    def W(T, El, eta_p):
+        W_ = 1.5
+        for n in range(1,1000):
+            N_ = N(n)
+            alpha_n = Alpha_n(N_, El, eta_p)
+        
+            if(alpha_n*alpha_n - El*N_*N_ < 0):
+                flag_ = False
+            else:
+                flag_ = True
+
+            beta_n = Beta_n(alpha_n,N_, El)
+            gamma_n = Gamma_n(N_, El, eta_p)
+            G_=G(alpha_n,beta_n,gamma_n,flag_,T)
+
+            W_ -= 24*(np.sin(N_/2)/(N_*N_*N_))*G_
+
+        return W_
+
+
+    # ## Run the simulation
+
+    # In[11]:
+
+
+    lambda_p_val = 3000
+    eta_p_val = 0.9
+
+    init()
+    vmid = time_loop(lambda_p_val*4, lambda_p_val, eta_p_val)
+
+    actual_width = sum(dh.gather_array(lbbh.flag_array_name)[L[0]//2,:] == 1)
+    uref = float(f_pre*actual_width**2/(8*(eta+eta_p)))
+
+    Wi = lambda_p_val*uref/(actual_width/2)
+    Re = uref*(actual_width/2)/(eta+eta_p)
+    El = float(Wi/Re)
+
+    pref = 1/W(vmid[0,-1]/lambda_p_val, El, eta_p_val)
+
+    El_measured, pref_measured = scipy.optimize.curve_fit(lambda a, b, c: W(a, b, eta_p_val)*c,
+                                                          vmid[0,:]/lambda_p_val, vmid[1,:]/vmid[1,-1],
+                                                          p0=(El, pref))[0]
+    measured_width = np.sqrt(4*lambda_p_val*float(eta+eta_p)/El_measured)
+    
+    print(f"El={El}, El_measured={El_measured}")
+    print(f"L={actual_width}, L_measured={measured_width}")
+
+    assert abs(measured_width - actual_width) < 1, "effective channel width differs significantly from defined width"
+
+    an = W(vmid[0,:]/lambda_p_val, El, eta_p_val)*pref
+    an_measured = W(vmid[0,:]/lambda_p_val, El_measured, eta_p_val)*pref_measured
+
+    diff = abs(vmid[1,:]/vmid[1,-1]-an_measured)/an_measured
+    assert diff[lambda_p_val//5:].max() < 0.03, "maximum velocity deviation is too large"
+
+#    from pystencils import plot as plt
+#
+#     plt.xlabel("$t$")
+#     plt.ylabel(r"$u_{max}/u_{max}^{Newtonian}$")
+#     plt.plot(vmid[0,:], vmid[1,:]/vmid[1,-1] if vmid[1,-1] != 0 else 0, label='FVM')
+#     plt.plot(vmid[0,:], np.ones_like(vmid[0,:]), 'k--', label='Newtonian')
+#
+#     plt.plot(vmid[0,:], an, label="analytic")
+#     plt.plot(vmid[0,:], an_measured, label="analytic, fit width")
+#     plt.legend()
+#
+#     if eta_p_val == 0.1:
+#         plt.ylim(0.9, 1.15)
+#     elif lambda_p_val == 9000:
+#         plt.ylim(0.8, 1.5)
+#     elif eta_p_val == 0.3:
+#         plt.ylim(0.8, 1.4)
+#     plt.show()
diff --git a/lbmpy_tests/test_poisuille_channel.py b/lbmpy_tests/test_poisuille_channel.py
index d753d4de5f2315442e82be67ebaeaade5778acff..354b20579d537adb39d562343e07c7a2c2db7ea1 100644
--- a/lbmpy_tests/test_poisuille_channel.py
+++ b/lbmpy_tests/test_poisuille_channel.py
@@ -10,15 +10,11 @@ import pystencils as ps
 from poiseuille import poiseuille_channel
 
 
-@pytest.mark.parametrize('target', (ps.Target.CPU, ps.Target.GPU, ps.Target.OPENCL))
+@pytest.mark.parametrize('target', (ps.Target.CPU, ps.Target.GPU))
 @pytest.mark.parametrize('stencil_name', (Stencil.D2Q9, Stencil.D3Q19))
 def test_poiseuille_channel(target, stencil_name):
-    # OpenCL and Cuda
-    if target == ps.Target.OPENCL:
-        import pytest
-        pytest.importorskip("pyopencl")
-        import pystencils.opencl.autoinit
-    elif target == ps.Target.GPU:
+    # Cuda
+    if target == ps.Target.GPU:
         import pytest
         pytest.importorskip("pycuda")
 
diff --git a/lbmpy_tests/test_shear_flow.py b/lbmpy_tests/test_shear_flow.py
index a7c4f24a8d5e43836cf6e7852aee8c46a9229e90..ccb7fc200b948c92c4f8b44162dacc2d68e88a16 100644
--- a/lbmpy_tests/test_shear_flow.py
+++ b/lbmpy_tests/test_shear_flow.py
@@ -60,14 +60,11 @@ shear_velocity = 0.2  # scale by width to keep stable
 t_max = 2000
 
 
-@pytest.mark.parametrize('target', (ps.Target.CPU, ps.Target.GPU, ps.Target.OPENCL))
+@pytest.mark.parametrize('target', (ps.Target.CPU, ps.Target.GPU))
 @pytest.mark.parametrize('stencil_name', (Stencil.D2Q9, Stencil.D3Q19))
 def test_shear_flow(target, stencil_name):
-    # OpenCL and Cuda
-    if target == ps.Target.OPENCL:
-        pytest.importorskip("pyopencl")
-        import pystencils.opencl.autoinit
-    elif target == ps.Target.GPU:
+    # Cuda
+    if target == ps.Target.GPU:
         pytest.importorskip("pycuda")
 
     # LB parameters