Merge branch 'main' into remove_edge_sources

core/src/allocation_optim.jl

@@ -706,11 +706,9 @@ function save_allocation_flows!(
     priority::Int32,
     optimization_type::OptimizationType.T,
 )::Nothing
-    (; flow, problem, subnetwork_id) = allocation_model
+    (; flow, subnetwork_id, sources) = allocation_model
     (; allocation, graph) = p
     (; record_flow) = allocation
-    F_basin_in = problem[:F_basin_in]
-    F_basin_out = problem[:F_basin_out]
 
     edges_allocation = keys(flow.data)
 
@@ -765,7 +763,7 @@ function save_allocation_flows!(
     for node_id in graph[].node_ids[subnetwork_id]
         if node_id.type == NodeType.Basin &&
            has_external_demand(graph, node_id, :level_demand)[1]
-            flow_rate = JuMP.value(F_basin_out[node_id]) - JuMP.value(F_basin_in[node_id])
+            flow_rate = sources[(node_id, node_id)].basin_flow_rate
             push!(record_flow.time, t)
             push!(record_flow.edge_id, 0)
             push!(record_flow.from_node_type, string(NodeType.Basin))
@@ -885,6 +883,15 @@ function optimize_per_source!(
 )::Nothing
     (; problem, sources, subnetwork_id, flow) = allocation_model
     (; priorities) = allocation
+    F_basin_in = problem[:F_basin_in]
+    F_basin_out = problem[:F_basin_out]
+
+    # Start the cumulative basin flow rates at 0
+    for source in values(sources)
+        if source.type == AllocationSourceType.basin
+            source.basin_flow_rate = 0.0
+        end
+    end
 
     priority = priorities[priority_idx]
 
@@ -931,6 +938,15 @@ function optimize_per_source!(
             end
         end
 
+        # Add to the basin cumulative flow rate
+        for (edge, source) in sources
+            if source.type == AllocationSourceType.basin
+                node_id = edge[1]
+                source.basin_flow_rate +=
+                    JuMP.value(F_basin_out[node_id]) - JuMP.value(F_basin_in[node_id])
+            end
+        end
+
         # Adjust allocated flow to basins
         increase_allocateds!(p.basin, problem)
     end

core/src/parameter.jl

@@ -136,12 +136,15 @@ edge: The outflow edge of the source
 type: The type of source (edge, basin, main_to_sub, user_return, buffer)
 capacity: The initial capacity of the source as determined by the physical layer
 capacity_reduced: The capacity adjusted by passed optimizations
+basin_flow_rate: The total outflow rate of a basin when optimized over all sources for one priority.
+    Ignored when the source is not a basin.
 """
 @kwdef mutable struct AllocationSource
     const edge::Tuple{NodeID, NodeID}
     const type::AllocationSourceType.T
     capacity::Float64 = 0.0
     capacity_reduced::Float64 = 0.0
+    basin_flow_rate::Float64 = 0.0
 end
 
 function Base.show(io::IO, source::AllocationSource)

docs/tutorial/natural-flow.ipynb

@@ -48,7 +48,7 @@
    "source": [
     "# Crystal River Basin\n",
     "We will examine a straightforward example of the Crystal river basin, which includes a main river and a single tributary flowing into the sea (see @fig-crystal-basin).\n",
-    "An average discharge of $44.45 \\text{ m}^3/\\text{s}$ is measured at the confluence.\n",
+    "Between 2014 and 2023 an average discharge of $44.45 \\text{ m}^3/\\text{s}$ is measured at the confluence.\n",
     "In this module, the basin is free of any activities, allowing the model to simulate the natural flow.\n",
     "The next step is to include a demand (irrigation) that taps from a canal out of the main river.\n",
     "\n",
@@ -89,7 +89,9 @@
    "metadata": {},
    "source": [
     "### Setup paths and model configuration\n",
-    "Reference the paths of the Ribasim installation and model directory and define the time period (2022-01-01 until 2023-01-01) for the model simulation.\n",
+    "Reference the paths of the Ribasim installation and model directory and define the time period.\n",
+    "The used simulation period is defined by the `starttime` and `endtime` of the model, not by the input timeseries.\n",
+    "For now we will look into the period from 2022-01-01 until 2023-01-01 for the model simulation.\n",
     "The coordinate reference system (CRS) is also required, and set to [EPSG:4326](https://epsg.io/4326), which means all coordinates are interpreted as latitude and longitude values.\n",
     "The CRS is important for correctly placing Ribasim models on the map, but since this is a fictional model, it is not important."
    ]
@@ -135,8 +137,7 @@
     "data[\"total\"] = data[\"minor\"] + data[\"main\"]\n",
     "display(data)\n",
     "\n",
-    "# Average and max inflow of the total inflow data timeseries\n",
-    "# From 2014 - 2023\n",
+    "# Average and max inflow of the total inflow data over 2022\n",
     "print(\"Average inflow [m3/s]:\", data[\"total\"].mean())\n",
     "print(\"Maximum inflow [m3/s]:\", data[\"total\"].max())\n",
     "\n",

python/ribasim/ribasim/config.py

@@ -80,6 +80,7 @@ class Results(ChildModel):
 class Solver(ChildModel):
     """
     Defines the numerical solver options.
+
     For more details see <https://docs.sciml.ai/DiffEqDocs/stable/basics/common_solver_opts/#solver_options>.
 
     Attributes

python/ribasim/ribasim/delwaq/__init__.py

@@ -7,7 +7,6 @@
     "generate",
     "parse",
     "run_delwaq",
-    "plot",
     "add_tracer",
     "plot_fraction",
     "plot_spatial",

python/ribasim/ribasim/delwaq/generate.py

@@ -57,8 +57,8 @@ def _quote(value):
 def _make_boundary(data, boundary_type):
     """
     Create a Delwaq boundary definition with the given data and boundary type.
-    Pivot our data from long to wide format, and convert the time to a string.
 
+    Pivot our data from long to wide format, and convert the time to a string.
     Specifically, we go from a table:
         `node_id, substance, time, concentration`
     to
@@ -300,7 +300,6 @@ def generate(
     output_path: Path = output_path,
 ) -> tuple[nx.DiGraph, set[str]]:
     """Generate a Delwaq model from a Ribasim model and results."""
-
     # Read in model and results
     model = ribasim.Model.read(toml_path)
     results_folder = toml_path.parent / model.results_dir

python/ribasim/ribasim/geometry/edge.py

@@ -75,8 +75,10 @@ def add(
         edge_id: Optional[NonNegativeInt] = None,
         **kwargs,
     ):
-        """Add an edge between nodes. The type of the edge (flow or control)
-        is automatically inferred from the type of the `from_node`.
+        """
+        Add an edge between nodes.
+
+        The type of the edge (flow or control) is automatically inferred from the type of the `from_node`.
 
         Parameters
         ----------
@@ -181,7 +183,6 @@ def plot(self, **kwargs) -> Axes:
         **kwargs : Dict
             Supported: 'ax', 'color_flow', 'color_control'
         """
-
         assert self.df is not None
         kwargs = kwargs.copy()  # Avoid side-effects
         ax = kwargs.get("ax", None)

python/ribasim/ribasim/model.py

@@ -300,7 +300,6 @@ def write(self, filepath: str | PathLike[str]) -> Path:
         filepath : str | PathLike[str]
             A file path with .toml extension.
         """
-
         if self.use_validation:
             self._validate_model()
 
@@ -351,7 +350,6 @@ def _has_valid_neighbor_amount(
         nodes,
     ) -> bool:
         """Check if the neighbor amount of the two nodes connected by the given edge meet the minimum requirements."""
-
         is_valid = True
 
         # filter graph by edge type
@@ -419,7 +417,6 @@ def _add_source_sink_node(
 
         Specify that their occurrence in from_node table or to_node table is 0.
         """
-
         # loop over nodes, add the one that is not the downstream (from) or upstream (to) of any other nodes
         for index, node in enumerate(nodes):
             if nodes.index[index] not in node_info[f"{direction}_node_id"].to_numpy():
@@ -463,7 +460,6 @@ def _reset_contextvar(self) -> "Model":
 
     def plot_control_listen(self, ax):
         """Plot the implicit listen edges of the model."""
-
         df_listen_edge = pd.DataFrame(
             data={
                 "control_node_id": pd.Series([], dtype="int32[pyarrow]"),
@@ -598,7 +594,6 @@ def to_xugrid(self, add_flow: bool = False, add_allocation: bool = False):
         add_allocation : bool
             add allocation results (Optional, defaults to False)
         """
-
         if add_flow and add_allocation:
             raise ValueError("Cannot add both allocation and flow results.")
 

python/ribasim/ribasim/utils.py

@@ -32,6 +32,7 @@ def __getattr__(
 
 def _node_lookup_numpy(node_id) -> Series[Int32]:
     """Create a lookup table from from node_id to the node dimension index.
+
     Used when adding data onto the nodes of an xugrid dataset.
     """
     return pd.Series(
@@ -43,6 +44,7 @@ def _node_lookup_numpy(node_id) -> Series[Int32]:
 
 def _node_lookup(uds) -> Series[Int32]:
     """Create a lookup table from from node_id to the node dimension index.
+
     Used when adding data onto the nodes of an xugrid dataset.
     """
     return pd.Series(
@@ -57,7 +59,6 @@ def _edge_lookup(uds) -> Series[Int32]:
 
     Used when adding data onto the edges of an xugrid dataset.
     """
-
     return pd.Series(
         index=uds["edge_id"],
         data=uds[uds.grid.edge_dimension],

python/ribasim_api/tests/test_bmi.py

@@ -175,8 +175,9 @@ def test_get_value_ptr_allocation(libribasim, user_demand, tmp_path):
 
 def test_err_unknown_var(libribasim, basic, tmp_path):
     """
-    Unknown or invalid variable address should trigger Python Exception,
-    print the kernel error, and not crash the library
+    Unknown or invalid variable address.
+
+    Should trigger Python Exception, print the kernel error, and not crash the library.
     """
     basic.write(tmp_path / "ribasim.toml")
     config_file = str(tmp_path / "ribasim.toml")

python/ribasim_testmodels/ribasim_testmodels/__init__.py

@@ -7,6 +7,7 @@
 import ribasim_testmodels
 from ribasim_testmodels.allocation import (
     allocation_example_model,
+    allocation_training_model,
     fair_distribution_model,
     flow_demand_model,
     level_demand_model,
@@ -63,6 +64,7 @@
 from ribasim_testmodels.two_basin import two_basin_model
 
 __all__ = [
+    "allocation_training_model",
     "allocation_example_model",
     "backwater_model",
     "basic_arrow_model",