Dataclasses for method creation

In lbmpy large dicts are created to manage all parameters for the lb method and the pystencils kernel parameters. This MR introduces dataclasses to manage all these parameters. Furthermore, a better integration with pystencils is achieved since pystencils introduced dataclasses lately.

Fixes #1 (closed)

lbmpy/creationfunctions.py

@@ -396,7 +396,7 @@ def create_lb_collision_rule(lb_method=None, lbm_config=None, lbm_optimisation=N
     return collision_rule
 
 
-def create_lb_method(lbm_config, **params):
+def create_lb_method(lbm_config=None, **params):
     """Creates a LB method, defined by moments/cumulants for collision space, equilibrium and relaxation rates."""
     lbm_config, _, _ = update_with_default_parameters(params, lbm_config=lbm_config)
 
@@ -557,28 +557,6 @@ def force_model_from_string(force_model_name, force_values):
     return force_model_class(force_values)
 
 
-def switch_to_symbolic_relaxation_rates_for_omega_adapting_methods(method_parameters, kernel_params, force=False):
-    """
-    For entropic kernels the relaxation rate has to be a variable. If a constant was passed a
-    new dummy variable is inserted and the value of this variable is later on passed to the kernel
-    """
-    if method_parameters['entropic'] or method_parameters['smagorinsky'] or force:
-        value_to_symbol_map = {}
-        new_relaxation_rates = []
-        for rr in method_parameters['relaxation_rates']:
-            if not isinstance(rr, sp.Symbol):
-                if rr not in value_to_symbol_map:
-                    value_to_symbol_map[rr] = sp.Dummy()
-                dummy_var = value_to_symbol_map[rr]
-                new_relaxation_rates.append(dummy_var)
-                kernel_params[dummy_var.name] = rr
-            else:
-                new_relaxation_rates.append(rr)
-        if len(new_relaxation_rates) < 2:
-            new_relaxation_rates.append(sp.Dummy())
-        method_parameters['relaxation_rates'] = new_relaxation_rates
-
-
 def update_with_default_parameters(params, opt_params=None, lbm_config=None, lbm_optimisation=None, config=None):
     # Fix CreateKernelConfig params
     pystencils_config_params = ['target', 'backend', 'cpu_openmp', 'double_precision', 'gpu_indexing',