Fix integration pipeline

This MR fixes several issues with the integration pipeline.

1. !349 (closed).

2. Fixes an oversight introduced in !344 (merged). The issue was with expressions like:

   a: scalar float = CastFunc(b: vector float, float)

   The mentioned MR used get_type_of_expression to determine whether an expression will be vectorized. That works in most cases due to type collation but fails for CastFuncs, which are always vectorized. This MR replaces get_type_of_expression by a small helper function that checks whether an expression is entirely scalar or requires additional vector casts.

pystencils/gpu/kernelcreation.py

-from typing import Union
-
 from pystencils.astnodes import Block, KernelFunction, LoopOverCoordinate, SympyAssignment
 from pystencils.config import CreateKernelConfig
 from pystencils.typing import StructType, TypedSymbol
@@ -9,15 +7,13 @@ from pystencils.enums import Target, Backend
 from pystencils.gpu.gpujit import make_python_function
 from pystencils.node_collection import NodeCollection
 from pystencils.gpu.indexing import indexing_creator_from_params
-from pystencils.simp.assignment_collection import AssignmentCollection
 from pystencils.slicing import normalize_slice
 from pystencils.transformations import (
     get_base_buffer_index, get_common_field, parse_base_pointer_info,
     resolve_buffer_accesses, resolve_field_accesses, unify_shape_symbols)
 
 
-def create_cuda_kernel(assignments: Union[AssignmentCollection, NodeCollection],
-                       config: CreateKernelConfig):
+def create_cuda_kernel(assignments: NodeCollection, config: CreateKernelConfig):
 
     function_name = config.function_name
     indexing_creator = indexing_creator_from_params(config.gpu_indexing, config.gpu_indexing_params)
@@ -114,31 +110,24 @@ def create_cuda_kernel(assignments: Union[AssignmentCollection, NodeCollection],
     return ast
 
 
-def created_indexed_cuda_kernel(assignments: Union[AssignmentCollection, NodeCollection],
-                                config: CreateKernelConfig):
+def created_indexed_cuda_kernel(assignments: NodeCollection, config: CreateKernelConfig):
 
     index_fields = config.index_fields
     function_name = config.function_name
     coordinate_names = config.coordinate_names
     indexing_creator = indexing_creator_from_params(config.gpu_indexing, config.gpu_indexing_params)
-
     fields_written = assignments.bound_fields
     fields_read = assignments.rhs_fields
-    assignments = assignments.all_assignments
-
-    assignments = add_types(assignments, config)
 
     all_fields = fields_read.union(fields_written)
     read_only_fields = set([f.name for f in fields_read - fields_written])
-
-    for index_field in index_fields:
-        index_field.field_type = FieldType.INDEXED
-        assert FieldType.is_indexed(index_field)
-        assert index_field.spatial_dimensions == 1, "Index fields have to be 1D"
-
+    # extract the index fields based on the name. The original index field might have been modified
+    index_fields = [idx_field for idx_field in index_fields if idx_field.name in [f.name for f in all_fields]]
     non_index_fields = [f for f in all_fields if f not in index_fields]
     spatial_coordinates = {f.spatial_dimensions for f in non_index_fields}
-    assert len(spatial_coordinates) == 1, "Non-index fields do not have the same number of spatial coordinates"
+    assert len(spatial_coordinates) == 1, f"Non-index fields do not have the same number of spatial coordinates " \
+                                          f"Non index fields are {non_index_fields}, spatial coordinates are " \
+                                          f"{spatial_coordinates}"
     spatial_coordinates = list(spatial_coordinates)[0]
 
     def get_coordinate_symbol_assignment(name):