Combine fixtures in test_constraints

Author: tsmbland

tests/test_constraints.py

@@ -3,7 +3,7 @@
 import numpy as np
 import pandas as pd
 import xarray as xr
-from pytest import approx, fixture, raises
+from pytest import approx, fixture, mark, raises
 
 from muse import examples
 from muse.constraints import (
@@ -65,7 +65,6 @@ def model_data():
     ).sel(year=INVESTMENT_YEAR).groupby("technology").sum("asset").rename(
         technology="asset"
     )
-
     # Remove un-demanded commodities
     market_demand = market_demand.sel(
         commodity=(market_demand > 0).any(dim=["timeslice", "asset"])
@@ -285,6 +284,9 @@ def test_lp_constraints_matrix_b_is_scalar(lpcosts, constraints):
     """B is a scalar - output should be equivalent to a single row matrix."""
     constraint = constraints["max_production"]
 
+    constraint = constraint_data["constraints"]["max_production"]
+    lpcosts = constraint_data["lp_costs"]
+
     for attr in ["capacity", "production"]:
         lpconstraint = lp_constraint_matrix(
             xr.DataArray(1), getattr(constraint, attr), getattr(lpcosts, attr)
@@ -300,6 +302,9 @@ def test_max_production_constraint_diagonal(lpcosts, constraints):
     """Test production side of max capacity production is diagonal."""
     constraint = constraints["max_production"]
 
+    constraint = constraint_data["constraints"]["max_production"]
+    lpcosts = constraint_data["lp_costs"]
+
     # Test capacity constraints
     result = lp_constraint_matrix(constraint.b, constraint.capacity, lpcosts.capacity)
     decision_dims = {f"d({x})" for x in lpcosts.capacity.dims}
@@ -329,6 +334,9 @@ def test_lp_constraint(lpcosts, constraints):
 
     constraint = constraints["max_production"]
 
+    constraint = constraint_data["constraints"]["max_production"]
+    lpcosts = constraint_data["lp_costs"]
+
     result = lp_constraint(constraint, lpcosts)
     constraint_dims = {
         f"c({x})" for x in set(lpcosts.production.dims).union(constraint.b.dims)
@@ -533,6 +541,13 @@ def test_scipy_solver(model_data, lp_inputs, constraints):
     """Test the scipy solver for demand matching."""
     from muse.investments import scipy_match_demand
 
+    constraints = [
+        constraint_data["constraints"]["max_production"],
+        constraint_data["constraints"]["max_capacity_expansion"],
+        constraint_data["constraints"]["demand"],
+        constraint_data["constraints"]["demand_limiting_capacity"],
+    ]
+
     solution = scipy_match_demand(
         costs=lp_inputs["capacity_costs"],
         commodities=lp_inputs["commodities"],