Put in fill_value in pivot_table

python/ribasim/ribasim/delwaq/generate.py

@@ -70,7 +70,9 @@ def _make_boundary(data, boundary_type):
  """
  bid = _boundary_name(data.node_id.iloc[0], boundary_type)
  piv = (
- data.pivot_table(index="time", columns="substance", values="concentration")
+ data.pivot_table(
+ index="time", columns="substance", values="concentration", fill_value=-999
+ )
  .reset_index()
  .reset_index(drop=True)
  )
@@ -79,9 +81,7 @@ def _make_boundary(data, boundary_type):
  boundary = {
  "name": bid,
  "substances": list(map(_quote, piv.columns[1:])),
- "df": piv.to_string(
- formatters={"time": _quote}, header=False, index=False, na_rep=-999
- ),
+ "df": piv.to_string(formatters={"time": _quote}, header=False, index=False),
  }
  substances = data.substance.unique()
  return boundary, substances
@@ -182,7 +182,7 @@ def _setup_graph(nodes, edge, use_evaporation=True):
  boundary_id -= 1
  node_mapping[node_id] = boundary_id
  else:
- raise Exception("Found unexpected node $node_id in delwaq graph.")
+ raise Exception(f"Found unexpected node {node_id} in delwaq graph.")
 
  nx.relabel_nodes(G, node_mapping, copy=False)
 
@@ -251,25 +251,18 @@ def _setup_boundaries(model):
  substances = set()
 
  if model.level_boundary.concentration.df is not None:
- model.level_boundary.concentration.df["concentration"].fillna(
- np.nan, inplace=True
- )
  for _, rows in model.level_boundary.concentration.df.groupby(["node_id"]):
  boundary, substance = _make_boundary(rows, "LevelBoundary")
  boundaries.append(boundary)
  substances.update(substance)
 
  if model.flow_boundary.concentration.df is not None:
- model.flow_boundary.concentration.df["concentration"].fillna(
- np.nan, inplace=True
- )
  for _, rows in model.flow_boundary.concentration.df.groupby("node_id"):
  boundary, substance = _make_boundary(rows, "FlowBoundary")
  boundaries.append(boundary)
  substances.update(substance)
 
  if model.basin.concentration.df is not None:
- model.basin.concentration.df["concentration"].fillna(np.nan, inplace=True)
  for _, rows in model.basin.concentration.df.groupby(["node_id"]):
  for boundary_type in ("Drainage", "Precipitation"):
  nrows = rows.rename(columns={boundary_type.lower(): "concentration"})