Updt. cost2d/optim2d x0 shape/colour, revert conftest win platform un…

…icode, add infeasible unit test

conftest.py

@@ -1,7 +1,6 @@
 import pytest
 import matplotlib
 import plotly
-import sys
 
 plotly.io.renderers.default = None
 matplotlib.use("Template")
@@ -43,10 +42,6 @@ def pytest_configure(config):
     config.addinivalue_line("markers", "plots: mark test as a plot test")
     config.addinivalue_line("markers", "notebook: mark test as a notebook test")
 
-    if sys.platform.startswith("win"):
-        # Set the output encoding to UTF-8 on Windows
-        sys.stdout = open(sys.stdout.fileno(), mode="w", encoding="utf8", buffering=1)
-
 
 def pytest_collection_modifyitems(config, items):
     options = {

pybop/plotting/plot_convergence.py

@@ -98,35 +98,35 @@ def plot_optim2d(optim, bounds=None, steps=10, show=True, **layout_kwargs):
     # Import plotly only when needed
     go = pybop.PlotlyManager().go
 
-    # Plot the initial guess
+    # Plot the optimisation trace
+    optim_trace = np.array([item for sublist in optim.log for item in sublist])
+    optim_trace = optim_trace.reshape(-1, 2)
     fig.add_trace(
         go.Scatter(
-            x=[optim.x0[0]],
-            y=[optim.x0[1]],
+            x=optim_trace[:, 0],
+            y=optim_trace[:, 1],
             mode="markers",
-            marker_symbol="x",
             marker=dict(
-                color="red",
-                line_color="midnightblue",
-                line_width=1,
-                size=12,
+                color=[i / len(optim_trace) for i in range(len(optim_trace))],
+                colorscale="YlOrBr",
                 showscale=False,
             ),
             showlegend=False,
         )
     )
 
-    # Plot the optimisation trace
-    optim_trace = np.array([item for sublist in optim.log for item in sublist])
-    optim_trace = optim_trace.reshape(-1, 2)
+    # Plot the initial guess
     fig.add_trace(
         go.Scatter(
-            x=optim_trace[:, 0],
-            y=optim_trace[:, 1],
+            x=[optim.x0[0]],
+            y=[optim.x0[1]],
             mode="markers",
+            marker_symbol="circle",
             marker=dict(
-                color=[i / len(optim_trace) for i in range(len(optim_trace))],
-                colorscale="YlOrBr",
+                color="mediumspringgreen",
+                line_color="mediumspringgreen",
+                line_width=1,
+                size=14,
                 showscale=False,
             ),
             showlegend=False,

tests/integration/test_parameterisations.py

@@ -91,7 +91,7 @@ def test_spm_optimisers(self, optimiser, spm_costs):
 
         if optimiser in [pybop.CMAES]:
             parameterisation.set_f_guessed_tracking(True)
-            parameterisation.cost.problem._model.allow_infeasible_solutions = False
+            parameterisation.cost.problem.model.allow_infeasible_solutions = False
             assert parameterisation._use_f_guessed is True
             parameterisation.set_max_iterations(1)
             x, final_cost = parameterisation.run()
@@ -115,7 +115,7 @@ def test_spm_optimisers(self, optimiser, spm_costs):
             x, final_cost = parameterisation.run()
 
         elif optimiser in [pybop.SciPyMinimize]:
-            parameterisation.cost.problem._model.allow_infeasible_solutions = False
+            parameterisation.cost.problem.model.allow_infeasible_solutions = False
             x, final_cost = parameterisation.run()
 
         else:

tests/unit/test_optimisation.py

@@ -122,6 +122,17 @@ def test_halting(self, cost):
         with pytest.raises(ValueError):
             optim.set_max_unchanged_iterations(1, threshold=-1)
 
+    @pytest.mark.unit
+    def test_infeasible_solutions(self, cost):
+        # Test infeasible solutions
+        for optimiser in [pybop.SciPyMinimize, pybop.GradientDescent]:
+            optim = pybop.Optimisation(
+                cost=cost, optimiser=optimiser, allow_infeasible_solutions=False
+            )
+            optim.set_max_iterations(1)
+            optim.run()
+            assert optim._iterations == 1
+
     @pytest.mark.unit
     def test_unphysical_result(self, cost):
         # Trigger parameters not physically viable warning