feat: more conversion functions

Author: ntamas

src/codegen/internal_functions.py.in

@@ -20,4 +20,5 @@ from .types import (
 from .wrappers import _Graph, _Matrix, _MatrixInt, _Vector, _VectorBool, _VectorInt
 
 # fmt: off
+# flake8: noqa: E743
 # The rest of this file is generated by Stimulus

src/codegen/types.yaml

@@ -108,6 +108,15 @@ VERTEX:
     IN: "%C% = vertexlike_to_igraph_integer_t(%I%)"
     OUT: "%C% = igraph_integer_t(0)"
 
+VERTEX_COLOR:
+  PY_TYPE: Iterable[int]
+  PY_RETURN_TYPE: npt.NDArray[np_type_of_igraph_integer_t]
+  INCONV:
+    IN: "%C% = vertex_colors_to_igraph_vector_int_t_view(%I%, %I1%)"
+    INOUT: "%C% = vertex_colors_to_igraph_vector_int_t(%I%, %I1%)"
+    OUT: "%C% = _VectorInt.create(0)"
+  OUTCONV: "%I% = igraph_vector_int_t_to_numpy_array(%C%)"
+
 VERTEX_INDICES:
   PY_TYPE: Iterable[VertexLike]
   PY_RETURN_TYPE: npt.NDArray[np_type_of_igraph_integer_t]
@@ -146,6 +155,15 @@ EDGE:
     IN: "%C% = edgelike_to_igraph_integer_t(%I%)"
     OUT: "%C% = igraph_integer_t(0)"
 
+EDGE_COLOR:
+  PY_TYPE: Iterable[int]
+  PY_RETURN_TYPE: npt.NDArray[np_type_of_igraph_integer_t]
+  INCONV:
+    IN: "%C% = edge_colors_to_igraph_vector_int_t_view(%I%, %I1%)"
+    INOUT: "%C% = edge_colors_to_igraph_vector_int_t(%I%, %I1%)"
+    OUT: "%C% = _VectorInt.create(0)"
+  OUTCONV: "%I% = igraph_vector_int_t_to_numpy_array(%C%)"
+
 EDGE_INDICES:
   PY_TYPE: Iterable[EdgeLike]
   PY_RETURN_TYPE: npt.NDArray[np_type_of_igraph_integer_t]
@@ -171,6 +189,15 @@ EDGEWEIGHTS:
     OUT: "%C% = _Vector.create(0)"
   OUTCONV: "%I% = igraph_vector_t_to_numpy_array(%C%)"
 
+EDGE_CAPACITY:
+  PY_TYPE: Iterable[float]
+  PY_RETURN_TYPE: npt.NDArray[np_type_of_igraph_real_t]
+  INCONV:
+    IN: "%C% = edge_capacities_to_igraph_vector_t_view(%I%, %I1%)"
+    INOUT: "%C% = edge_capacities_to_igraph_vector_t(%I%, %I1%)"
+    OUT: "%C% = _Vector.create(0)"
+  OUTCONV: "%I% = igraph_vector_t_to_numpy_array(%C%)"
+
 BIPARTITE_TYPES:
   PY_TYPE: Iterable[Any]
   PY_RETURN_TYPE: npt.NDArray[np_type_of_igraph_bool_t]

src/igraph_ctypes/_internal/conversion.py

@@ -72,6 +72,10 @@
 __all__ = (
     "any_to_igraph_bool_t",
     "edgelike_to_igraph_integer_t",
+    "edge_capacities_to_igraph_vector_t",
+    "edge_capacities_to_igraph_vector_t_view",
+    "edge_colors_to_igraph_vector_t",
+    "edge_colors_to_igraph_vector_t_view",
     "edge_selector_to_igraph_es_t",
     "edge_weights_to_igraph_vector_t",
     "edge_weights_to_igraph_vector_t_view",
@@ -98,6 +102,8 @@
     "vertexlike_to_igraph_integer_t",
     "vertex_pairs_to_igraph_vector_int_t",
     "vertex_selector_to_igraph_vs_t",
+    "vertex_colors_to_igraph_vector_t",
+    "vertex_colors_to_igraph_vector_t_view",
     "vertex_qty_to_igraph_vector_t",
     "vertex_qty_to_igraph_vector_t_view",
 )
@@ -164,6 +170,26 @@ def edge_weights_to_igraph_vector_t_view(
     return iterable_to_igraph_vector_t_view(weights) if weights is not None else None
 
 
+# Currently we handle capacities the same way as weights; this might change in
+# the future
+edge_capacities_to_igraph_vector_t = edge_weights_to_igraph_vector_t
+edge_capacities_to_igraph_vector_t_view = edge_weights_to_igraph_vector_t_view
+
+
+def edge_colors_to_igraph_vector_t(colors: Iterable[int], graph: _Graph) -> _VectorInt:
+    """Converts a Python iterable of integers to a vector of edge colors."""
+    return iterable_to_igraph_vector_int_t(colors)
+
+
+def edge_colors_to_igraph_vector_t_view(
+    colors: Iterable[int], graph: _Graph
+) -> _VectorInt:
+    """Converts a Python iterable of integers to a vector of edge colors,
+    possibly creating a shallow view if the input is an appropriate NumPy array.
+    """
+    return iterable_to_igraph_vector_int_t_view(colors)
+
+
 def iterable_edge_indices_to_igraph_vector_int_t(
     indices: Iterable[EdgeLike],
 ) -> _VectorInt:
@@ -514,6 +540,22 @@ def vertex_selector_to_igraph_vs_t(
         return _VertexSelector.create_with(igraph_vs_1, index)
 
 
+def vertex_colors_to_igraph_vector_t(
+    colors: Iterable[int], graph: _Graph
+) -> _VectorInt:
+    """Converts a Python iterable of integers to a vector of vertex colors."""
+    return iterable_to_igraph_vector_int_t(colors)
+
+
+def vertex_colors_to_igraph_vector_t_view(
+    colors: Iterable[int], graph: _Graph
+) -> _VectorInt:
+    """Converts a Python iterable of integers to a vector of vertex colors,
+    possibly creating a shallow view if the input is an appropriate NumPy array.
+    """
+    return iterable_to_igraph_vector_int_t_view(colors)
+
+
 def vertex_qty_to_igraph_vector_t(weights: Iterable[float], graph: _Graph) -> _Vector:
     """Converts a Python iterable of floating-point numbers to a vector of
     vertex-related quantities.
@@ -531,9 +573,7 @@ def vertex_qty_to_igraph_vector_t_view(
     When the input is `None`, the return value will also be `None`, which is
     interpreted by the C core of igraph as all edges having equal weight.
     """
-    return (
-        vertex_qty_to_igraph_vector_t(weights, graph) if weights is not None else None
-    )
+    return iterable_to_igraph_vector_t_view(weights) if weights else None
 
 
 ################################################################################

src/igraph_ctypes/_internal/functions.py

@@ -20,6 +20,7 @@
 from .wrappers import _Graph, _Matrix, _MatrixInt, _Vector, _VectorBool, _VectorInt
 
 # fmt: off
+# flake8: noqa: E743
 # The rest of this file is generated by Stimulus
 
 
@@ -4911,23 +4912,150 @@ def induced_subgraph_map(graph: _Graph, vids: VertexSelector, impl: SubgraphImpl
     # Construct return value
     return res, map, invmap
 
-# igraph_gomory_hu_tree: no Python type known for type: EDGE_CAPACITY
 
-# igraph_maxflow: no Python type known for type: EDGE_CAPACITY
+def gomory_hu_tree(graph: _Graph, capacity: Optional[Iterable[float]] = None) -> Tuple[_Graph, npt.NDArray[np_type_of_igraph_real_t]]:
+    """Type-annotated wrapper for ``igraph_gomory_hu_tree``."""
+    # Prepare input arguments
+    c_graph = graph
+    c_tree = _Graph()
+    c_flows = _Vector.create(0)
+    c_capacity = edge_capacities_to_igraph_vector_t_view(capacity, graph) if capacity is not None else None
+
+    # Call wrapped function
+    igraph_gomory_hu_tree(c_graph, c_tree, c_flows, c_capacity)
+
+    # Prepare output arguments
+    tree = c_tree.mark_initialized()
+    flows = igraph_vector_t_to_numpy_array(c_flows)
+
+    # Construct return value
+    return tree, flows
+
+# igraph_maxflow: no Python type known for type: MAXFLOW_STATS
+
+# igraph_maxflow_value: no Python type known for type: MAXFLOW_STATS
+
+
+def mincut_value(graph: _Graph, capacity: Optional[Iterable[float]] = None) -> float:
+    """Type-annotated wrapper for ``igraph_mincut_value``."""
+    # Prepare input arguments
+    c_graph = graph
+    c_res = igraph_real_t()
+    c_capacity = edge_capacities_to_igraph_vector_t_view(capacity, graph) if capacity is not None else None
+
+    # Call wrapped function
+    igraph_mincut_value(c_graph, c_res, c_capacity)
+
+    # Prepare output arguments
+    res = c_res.value
+
+    # Construct return value
+    return res
+
+
+def st_mincut(graph: _Graph, source: VertexLike, target: VertexLike, capacity: Optional[Iterable[float]] = None) -> Tuple[float, npt.NDArray[np_type_of_igraph_integer_t], npt.NDArray[np_type_of_igraph_integer_t], npt.NDArray[np_type_of_igraph_integer_t]]:
+    """Type-annotated wrapper for ``igraph_st_mincut``."""
+    # Prepare input arguments
+    c_graph = graph
+    c_value = igraph_real_t()
+    c_cut = _VectorInt.create(0)
+    c_partition1 = _VectorInt.create(0)
+    c_partition2 = _VectorInt.create(0)
+    c_source = vertexlike_to_igraph_integer_t(source)
+    c_target = vertexlike_to_igraph_integer_t(target)
+    c_capacity = edge_capacities_to_igraph_vector_t_view(capacity, graph) if capacity is not None else None
+
+    # Call wrapped function
+    igraph_st_mincut(c_graph, c_value, c_cut, c_partition1, c_partition2, c_source, c_target, c_capacity)
+
+    # Prepare output arguments
+    value = c_value.value
+    cut = igraph_vector_int_t_to_numpy_array(c_cut)
+    partition1 = igraph_vector_int_t_to_numpy_array(c_partition1)
+    partition2 = igraph_vector_int_t_to_numpy_array(c_partition2)
+
+    # Construct return value
+    return value, cut, partition1, partition2
+
+
+def st_mincut_value(graph: _Graph, source: VertexLike, target: VertexLike, capacity: Optional[Iterable[float]] = None) -> float:
+    """Type-annotated wrapper for ``igraph_st_mincut_value``."""
+    # Prepare input arguments
+    c_graph = graph
+    c_res = igraph_real_t()
+    c_source = vertexlike_to_igraph_integer_t(source)
+    c_target = vertexlike_to_igraph_integer_t(target)
+    c_capacity = edge_capacities_to_igraph_vector_t_view(capacity, graph) if capacity is not None else None
+
+    # Call wrapped function
+    igraph_st_mincut_value(c_graph, c_res, c_source, c_target, c_capacity)
+
+    # Prepare output arguments
+    res = c_res.value
+
+    # Construct return value
+    return res
+
+
+def mincut(graph: _Graph, capacity: Optional[Iterable[float]] = None) -> Tuple[float, npt.NDArray[np_type_of_igraph_integer_t], npt.NDArray[np_type_of_igraph_integer_t], npt.NDArray[np_type_of_igraph_integer_t]]:
+    """Type-annotated wrapper for ``igraph_mincut``."""
+    # Prepare input arguments
+    c_graph = graph
+    c_value = igraph_real_t()
+    c_partition1 = _VectorInt.create(0)
+    c_partition2 = _VectorInt.create(0)
+    c_cut = _VectorInt.create(0)
+    c_capacity = edge_capacities_to_igraph_vector_t_view(capacity, graph) if capacity is not None else None
+
+    # Call wrapped function
+    igraph_mincut(c_graph, c_value, c_partition1, c_partition2, c_cut, c_capacity)
+
+    # Prepare output arguments
+    value = c_value.value
+    partition1 = igraph_vector_int_t_to_numpy_array(c_partition1)
+    partition2 = igraph_vector_int_t_to_numpy_array(c_partition2)
+    cut = igraph_vector_int_t_to_numpy_array(c_cut)
+
+    # Construct return value
+    return value, partition1, partition2, cut
+
 
-# igraph_maxflow_value: no Python type known for type: EDGE_CAPACITY
+def residual_graph(graph: _Graph, capacity: Iterable[float], flow: Iterable[float]) -> Tuple[_Graph, npt.NDArray[np_type_of_igraph_real_t]]:
+    """Type-annotated wrapper for ``igraph_residual_graph``."""
+    # Prepare input arguments
+    c_graph = graph
+    c_capacity = edge_capacities_to_igraph_vector_t_view(capacity, graph)
+    c_residual = _Graph()
+    c_residual_capacity = _Vector.create(0)
+    c_flow = iterable_to_igraph_vector_t_view(flow)
 
-# igraph_mincut_value: no Python type known for type: EDGE_CAPACITY
+    # Call wrapped function
+    igraph_residual_graph(c_graph, c_capacity, c_residual, c_residual_capacity, c_flow)
 
-# igraph_st_mincut: no Python type known for type: EDGE_CAPACITY
+    # Prepare output arguments
+    residual = c_residual.mark_initialized()
+    residual_capacity = igraph_vector_t_to_numpy_array(c_residual_capacity)
 
-# igraph_st_mincut_value: no Python type known for type: EDGE_CAPACITY
+    # Construct return value
+    return residual, residual_capacity
 
-# igraph_mincut: no Python type known for type: EDGE_CAPACITY
 
-# igraph_residual_graph: no Python type known for type: EDGE_CAPACITY
+def reverse_residual_graph(graph: _Graph, capacity: Iterable[float], flow: Iterable[float]) -> _Graph:
+    """Type-annotated wrapper for ``igraph_reverse_residual_graph``."""
+    # Prepare input arguments
+    c_graph = graph
+    c_capacity = edge_capacities_to_igraph_vector_t_view(capacity, graph)
+    c_residual = _Graph()
+    c_flow = iterable_to_igraph_vector_t_view(flow)
 
-# igraph_reverse_residual_graph: no Python type known for type: EDGE_CAPACITY
+    # Call wrapped function
+    igraph_reverse_residual_graph(c_graph, c_capacity, c_residual, c_flow)
+
+    # Prepare output arguments
+    residual = c_residual.mark_initialized()
+
+    # Construct return value
+    return residual
 
 # igraph_st_vertex_connectivity: no Python type known for type: VCONNNEI
 
@@ -5079,7 +5207,23 @@ def dominator_tree(graph: _Graph, root: VertexLike, mode: NeighborMode = Neighbo
 
 # igraph_all_st_mincuts: no Python type known for type: EDGESET_LIST
 
-# igraph_even_tarjan_reduction: no Python type known for type: EDGE_CAPACITY
+
+def even_tarjan_reduction(graph: _Graph) -> Tuple[_Graph, npt.NDArray[np_type_of_igraph_real_t]]:
+    """Type-annotated wrapper for ``igraph_even_tarjan_reduction``."""
+    # Prepare input arguments
+    c_graph = graph
+    c_graphbar = _Graph()
+    c_capacity = _Vector.create(0)
+
+    # Call wrapped function
+    igraph_even_tarjan_reduction(c_graph, c_graphbar, c_capacity)
+
+    # Prepare output arguments
+    graphbar = c_graphbar.mark_initialized()
+    capacity = igraph_vector_t_to_numpy_array(c_capacity)
+
+    # Construct return value
+    return graphbar, capacity
 
 
 def is_separator(graph: _Graph, candidate: VertexSelector) -> bool:
@@ -5206,11 +5350,11 @@ def isoclass_create(size: int, number: int, directed: bool = True) -> _Graph:
     # Construct return value
     return graph
 
-# igraph_isomorphic_vf2: no Python type known for type: VERTEX_COLOR
+# igraph_isomorphic_vf2: no Python type known for type: ISOCOMPAT_FUNC
 
-# igraph_count_isomorphisms_vf2: no Python type known for type: VERTEX_COLOR
+# igraph_count_isomorphisms_vf2: no Python type known for type: ISOCOMPAT_FUNC
 
-# igraph_get_isomorphisms_vf2: no Python type known for type: VERTEX_COLOR
+# igraph_get_isomorphisms_vf2: no Python type known for type: VECTOR_INT_LIST
 
 
 def subisomorphic(graph1: _Graph, graph2: _Graph) -> bool:
@@ -5229,15 +5373,15 @@ def subisomorphic(graph1: _Graph, graph2: _Graph) -> bool:
     # Construct return value
     return iso
 
-# igraph_subisomorphic_vf2: no Python type known for type: VERTEX_COLOR
+# igraph_subisomorphic_vf2: no Python type known for type: ISOCOMPAT_FUNC
 
-# igraph_subisomorphic_function_vf2: no Python type known for type: VERTEX_COLOR
+# igraph_subisomorphic_function_vf2: no Python type known for type: ISOMORPHISM_FUNC
 
-# igraph_count_subisomorphisms_vf2: no Python type known for type: VERTEX_COLOR
+# igraph_count_subisomorphisms_vf2: no Python type known for type: ISOCOMPAT_FUNC
 
-# igraph_get_subisomorphisms_vf2: no Python type known for type: VERTEX_COLOR
+# igraph_get_subisomorphisms_vf2: no Python type known for type: VECTOR_INT_LIST
 
-# igraph_canonical_permutation: no Python type known for type: VERTEX_COLOR
+# igraph_canonical_permutation: no Python type known for type: BLISSSH
 
 
 def permute_vertices(graph: _Graph, permutation: Iterable[int]) -> _Graph:
@@ -5256,11 +5400,11 @@ def permute_vertices(graph: _Graph, permutation: Iterable[int]) -> _Graph:
     # Construct return value
     return res
 
-# igraph_isomorphic_bliss: no Python type known for type: VERTEX_COLOR
+# igraph_isomorphic_bliss: no Python type known for type: BLISSSH
 
-# igraph_automorphisms: no Python type known for type: VERTEX_COLOR
+# igraph_automorphisms: no Python type known for type: BLISSSH
 
-# igraph_automorphism_group: no Python type known for type: VERTEX_COLOR
+# igraph_automorphism_group: no Python type known for type: VERTEXSET_LIST
 
 # igraph_subisomorphic_lad: no Python type known for type: VERTEXSET_LIST
 
@@ -5695,7 +5839,7 @@ def tree_game(n: int, directed: bool = False, method: RandomTreeMethod = RandomT
     # Construct return value
     return graph
 
-# igraph_vertex_coloring_greedy: no Python type known for type: VERTEX_COLOR
+# igraph_vertex_coloring_greedy: no Python type known for type: GREEDY_COLORING_HEURISTIC
 
 
 def deterministic_optimal_imitation(graph: _Graph, vid: VertexLike, quantities: Iterable[float], strategies: Iterable[int], optimality: Optimality = Optimality.MAXIMUM, mode: NeighborMode = NeighborMode.OUT) -> None: