Adds plotting methods

CHANGELOG.md

@@ -1,7 +1,12 @@
 # [Unreleased](https://github.com/pybop-team/PyBOP)
 
+## Features
+- [#114](https://github.com/pybop-team/PyBOP/issues/114) - Adds standard plotting class `pybop.StandardPlot()` via plotly backend
+- [#114](https://github.com/pybop-team/PyBOP/issues/114) - Adds `quick_plot()`, `plot_convergence()`, and `plot_cost2d()` methods
 - [#116](https://github.com/pybop-team/PyBOP/issues/116) - Adds PSO, SNES, XNES, ADAM, and IPropMin optimisers to PintsOptimisers() class
 
+## Bug Fixes
+
 # [v23.11](https://github.com/pybop-team/PyBOP/releases/tag/v23.11)
 - Initial release
 - Adds Pints, NLOpt, and SciPy optimisers

conftest.py

@@ -1,6 +1,8 @@
 import pytest
 import matplotlib
+import plotly
 
+plotly.io.renderers.default = None
 matplotlib.use("Template")
 
 
@@ -13,24 +15,30 @@ def pytest_addoption(parser):
     )
 
 
+def pytest_terminal_summary(terminalreporter, exitstatus, config):
+    """Add additional section to terminal summary reporting."""
+    total_time = sum([x.duration for x in terminalreporter.stats.get("passed", [])])
+    num_tests = len(terminalreporter.stats.get("passed", []))
+    print(f"\nTotal number of tests completed: {num_tests}")
+    print(f"Total time taken: {total_time:.2f} seconds")
+
+
 def pytest_configure(config):
     config.addinivalue_line("markers", "unit: mark test as a unit test")
     config.addinivalue_line("markers", "examples: mark test as an example")
 
 
 def pytest_collection_modifyitems(config, items):
-    def skip_marker(marker_name, reason):
-        skip = pytest.mark.skip(reason=reason)
+    if config.getoption("--unit") and not config.getoption("--examples"):
+        skip_examples = pytest.mark.skip(
+            reason="need --examples option to run examples tests"
+        )
         for item in items:
-            if marker_name in item.keywords:
-                item.add_marker(skip)
-
-    if config.getoption("--unit"):
-        skip_marker("examples", "need --examples option to run")
-        return
+            if "examples" in item.keywords:
+                item.add_marker(skip_examples)
 
-    if config.getoption("--examples"):
-        skip_marker("unit", "need --unit option to run")
-        return
-
-    skip_marker("unit", "need --unit option to run")
+    if config.getoption("--examples") and not config.getoption("--unit"):
+        skip_unit = pytest.mark.skip(reason="need --unit option to run unit tests")
+        for item in items:
+            if "unit" in item.keywords:
+                item.add_marker(skip_unit)

examples/scripts/spm_CMAES.py

@@ -1,7 +1,7 @@
 import pybop
 import numpy as np
-import matplotlib.pyplot as plt
 
+# Define model
 parameter_set = pybop.ParameterSet("pybamm", "Chen2020")
 model = pybop.lithium_ion.SPM(parameter_set=parameter_set)
 
@@ -19,13 +19,15 @@
     ),
 ]
 
+# Generate data
 sigma = 0.001
 t_eval = np.arange(0, 900, 2)
 values = model.predict(t_eval=t_eval)
 CorruptValues = values["Terminal voltage [V]"].data + np.random.normal(
     0, sigma, len(t_eval)
 )
 
+# Form dataset for optimisation
 dataset = [
     pybop.Dataset("Time [s]", t_eval),
     pybop.Dataset("Current function [A]", values["Current [A]"].data),
@@ -38,18 +40,21 @@
 optim = pybop.Optimisation(cost, optimiser=pybop.CMAES)
 optim.set_max_iterations(100)
 
+# Run the optimisation
 x, final_cost = optim.run()
 print("Estimated parameters:", x)
 
-# Show the generated data
-simulated_values = problem.evaluate(x)
-
-plt.figure(dpi=100)
-plt.xlabel("Time", fontsize=12)
-plt.ylabel("Values", fontsize=12)
-plt.plot(t_eval, CorruptValues, label="Measured")
-plt.fill_between(t_eval, simulated_values - sigma, simulated_values + sigma, alpha=0.2)
-plt.plot(t_eval, simulated_values, label="Simulated")
-plt.legend(bbox_to_anchor=(0.6, 1), loc="upper left", fontsize=12)
-plt.tick_params(axis="both", labelsize=12)
-plt.show()
+# Plot the timeseries output
+pybop.quick_plot(x, cost, title="Optimised Comparison")
+
+# Plot convergence
+pybop.plot_convergence(optim)
+
+# Plot the parameter traces
+pybop.plot_parameters(optim)
+
+# Plot the cost landscape
+pybop.plot_cost2d(cost, steps=15)
+
+# Plot the cost landscape with optimisation path
+pybop.plot_cost2d(cost, optim=optim, steps=15)

examples/scripts/spm_IRPropMin.py

@@ -1,7 +1,7 @@
 import pybop
 import numpy as np
-import matplotlib.pyplot as plt
 
+# Define model
 parameter_set = pybop.ParameterSet("pybamm", "Chen2020")
 model = pybop.lithium_ion.SPM(parameter_set=parameter_set)
 
@@ -41,15 +41,17 @@
 x, final_cost = optim.run()
 print("Estimated parameters:", x)
 
-# Show the generated data
-simulated_values = problem.evaluate(x)
-
-plt.figure(dpi=100)
-plt.xlabel("Time", fontsize=12)
-plt.ylabel("Values", fontsize=12)
-plt.plot(t_eval, CorruptValues, label="Measured")
-plt.fill_between(t_eval, simulated_values - sigma, simulated_values + sigma, alpha=0.2)
-plt.plot(t_eval, simulated_values, label="Simulated")
-plt.legend(bbox_to_anchor=(0.6, 1), loc="upper left", fontsize=12)
-plt.tick_params(axis="both", labelsize=12)
-plt.show()
+# Plot the timeseries output
+pybop.quick_plot(x, cost, title="Optimised Comparison")
+
+# Plot convergence
+pybop.plot_convergence(optim)
+
+# Plot the parameter traces
+pybop.plot_parameters(optim)
+
+# Plot the cost landscape
+pybop.plot_cost2d(cost, steps=15)
+
+# Plot the cost landscape with optimisation path
+pybop.plot_cost2d(cost, optim=optim, steps=15)

examples/scripts/spm_SNES.py

@@ -1,7 +1,7 @@
 import pybop
 import numpy as np
-import matplotlib.pyplot as plt
 
+# Define model
 parameter_set = pybop.ParameterSet("pybamm", "Chen2020")
 model = pybop.lithium_ion.SPM(parameter_set=parameter_set)
 
@@ -41,15 +41,17 @@
 x, final_cost = optim.run()
 print("Estimated parameters:", x)
 
-# Show the generated data
-simulated_values = problem.evaluate(x)
-
-plt.figure(dpi=100)
-plt.xlabel("Time", fontsize=12)
-plt.ylabel("Values", fontsize=12)
-plt.plot(t_eval, CorruptValues, label="Measured")
-plt.fill_between(t_eval, simulated_values - sigma, simulated_values + sigma, alpha=0.2)
-plt.plot(t_eval, simulated_values, label="Simulated")
-plt.legend(bbox_to_anchor=(0.6, 1), loc="upper left", fontsize=12)
-plt.tick_params(axis="both", labelsize=12)
-plt.show()
+# Plot the timeseries output
+pybop.quick_plot(x, cost, title="Optimised Comparison")
+
+# Plot convergence
+pybop.plot_convergence(optim)
+
+# Plot the parameter traces
+pybop.plot_parameters(optim)
+
+# Plot the cost landscape
+pybop.plot_cost2d(cost, steps=15)
+
+# Plot the cost landscape with optimisation path
+pybop.plot_cost2d(cost, optim=optim, steps=15)

examples/scripts/spm_XNES.py

@@ -1,7 +1,7 @@
 import pybop
 import numpy as np
-import matplotlib.pyplot as plt
 
+# Define model
 parameter_set = pybop.ParameterSet("pybamm", "Chen2020")
 model = pybop.lithium_ion.SPM(parameter_set=parameter_set)
 
@@ -41,15 +41,17 @@
 x, final_cost = optim.run()
 print("Estimated parameters:", x)
 
-# Show the generated data
-simulated_values = problem.evaluate(x)
-
-plt.figure(dpi=100)
-plt.xlabel("Time", fontsize=12)
-plt.ylabel("Values", fontsize=12)
-plt.plot(t_eval, CorruptValues, label="Measured")
-plt.fill_between(t_eval, simulated_values - sigma, simulated_values + sigma, alpha=0.2)
-plt.plot(t_eval, simulated_values, label="Simulated")
-plt.legend(bbox_to_anchor=(0.6, 1), loc="upper left", fontsize=12)
-plt.tick_params(axis="both", labelsize=12)
-plt.show()
+# Plot the timeseries output
+pybop.quick_plot(x, cost, title="Optimised Comparison")
+
+# Plot convergence
+pybop.plot_convergence(optim)
+
+# Plot the parameter traces
+pybop.plot_parameters(optim)
+
+# Plot the cost landscape
+pybop.plot_cost2d(cost, steps=15)
+
+# Plot the cost landscape with optimisation path
+pybop.plot_cost2d(cost, optim=optim, steps=15)

examples/scripts/spm_adam.py

@@ -1,6 +1,5 @@
 import pybop
 import numpy as np
-import matplotlib.pyplot as plt
 
 # Parameter set and model definition
 parameter_set = pybop.ParameterSet("pybamm", "Chen2020")
@@ -45,15 +44,17 @@
 x, final_cost = optim.run()
 print("Estimated parameters:", x)
 
-# Show the generated data
-simulated_values = problem.evaluate(x)
-
-plt.figure(dpi=100)
-plt.xlabel("Time", fontsize=12)
-plt.ylabel("Values", fontsize=12)
-plt.plot(t_eval, corrupt_values, label="Measured")
-plt.fill_between(t_eval, simulated_values - sigma, simulated_values + sigma, alpha=0.2)
-plt.plot(t_eval, simulated_values, label="Simulated")
-plt.legend(bbox_to_anchor=(0.6, 1), loc="upper left", fontsize=12)
-plt.tick_params(axis="both", labelsize=12)
-plt.show()
+# Plot the timeseries output
+pybop.quick_plot(x, cost, title="Optimised Comparison")
+
+# Plot convergence
+pybop.plot_convergence(optim)
+
+# Plot the parameter traces
+pybop.plot_parameters(optim)
+
+# Plot the cost landscape
+pybop.plot_cost2d(cost, steps=15)
+
+# Plot the cost landscape with optimisation path
+pybop.plot_cost2d(cost, optim=optim, steps=15)

examples/scripts/spm_descent.py

@@ -1,6 +1,5 @@
 import pybop
 import numpy as np
-import matplotlib.pyplot as plt
 
 # Parameter set and model definition
 parameter_set = pybop.ParameterSet("pybamm", "Chen2020")
@@ -46,15 +45,17 @@
 x, final_cost = optim.run()
 print("Estimated parameters:", x)
 
-# Show the generated data
-simulated_values = problem.evaluate(x)
-
-plt.figure(dpi=100)
-plt.xlabel("Time", fontsize=12)
-plt.ylabel("Values", fontsize=12)
-plt.plot(t_eval, corrupt_values, label="Measured")
-plt.fill_between(t_eval, simulated_values - sigma, simulated_values + sigma, alpha=0.2)
-plt.plot(t_eval, simulated_values, label="Simulated")
-plt.legend(bbox_to_anchor=(0.6, 1), loc="upper left", fontsize=12)
-plt.tick_params(axis="both", labelsize=12)
-plt.show()
+# Plot the timeseries output
+pybop.quick_plot(x, cost, title="Optimised Comparison")
+
+# Plot convergence
+pybop.plot_convergence(optim)
+
+# Plot the parameter traces
+pybop.plot_parameters(optim)
+
+# Plot the cost landscape
+pybop.plot_cost2d(cost, steps=15)
+
+# Plot the cost landscape with optimisation path
+pybop.plot_cost2d(cost, optim=optim, steps=15)

examples/scripts/spm_nlopt.py

@@ -1,6 +1,5 @@
 import pybop
 import pandas as pd
-import matplotlib.pyplot as plt
 
 # Form dataset
 Measurements = pd.read_csv("examples/scripts/Chen_example.csv", comment="#").to_numpy()
@@ -36,18 +35,22 @@
 cost = pybop.RootMeanSquaredError(problem)
 
 # Build the optimisation problem
-parameterisation = pybop.Optimisation(cost=cost, optimiser=pybop.NLoptOptimize)
+optim = pybop.Optimisation(cost=cost, optimiser=pybop.NLoptOptimize)
 
 # Run the optimisation problem
-x, final_cost = parameterisation.run()
-
-# Show the generated data
-simulated_values = problem.evaluate(x)
-
-plt.figure()
-plt.xlabel("Time")
-plt.ylabel("Values")
-plt.plot(dataset[0].data, dataset[2].data, label="Measured")
-plt.plot(dataset[0].data, simulated_values, label="Simulated")
-plt.legend(bbox_to_anchor=(0.6, 1), loc="upper left", fontsize=12)
-plt.show()
+x, final_cost = optim.run()
+
+# Plot the timeseries output
+pybop.quick_plot(x, cost, title="Optimised Comparison")
+
+# Plot convergence
+pybop.plot_convergence(optim)
+
+# Plot the parameter traces
+pybop.plot_parameters(optim)
+
+# Plot the cost landscape
+pybop.plot_cost2d(cost, steps=15)
+
+# Plot the cost landscape with optimisation path
+pybop.plot_cost2d(cost, optim=optim, steps=15)

examples/scripts/spm_pso.py

@@ -1,7 +1,7 @@
 import pybop
 import numpy as np
-import matplotlib.pyplot as plt
 
+# Define model
 parameter_set = pybop.ParameterSet("pybamm", "Chen2020")
 model = pybop.lithium_ion.SPM(parameter_set=parameter_set)
 
@@ -41,15 +41,17 @@
 x, final_cost = optim.run()
 print("Estimated parameters:", x)
 
-# Show the generated data
-simulated_values = problem.evaluate(x)
-
-plt.figure(dpi=100)
-plt.xlabel("Time", fontsize=12)
-plt.ylabel("Values", fontsize=12)
-plt.plot(t_eval, CorruptValues, label="Measured")
-plt.fill_between(t_eval, simulated_values - sigma, simulated_values + sigma, alpha=0.2)
-plt.plot(t_eval, simulated_values, label="Simulated")
-plt.legend(bbox_to_anchor=(0.6, 1), loc="upper left", fontsize=12)
-plt.tick_params(axis="both", labelsize=12)
-plt.show()
+# Plot the timeseries output
+pybop.quick_plot(x, cost, title="Optimised Comparison")
+
+# Plot convergence
+pybop.plot_convergence(optim)
+
+# Plot the parameter traces
+pybop.plot_parameters(optim)
+
+# Plot the cost landscape
+pybop.plot_cost2d(cost, steps=15)
+
+# Plot the cost landscape with optimisation path
+pybop.plot_cost2d(cost, optim=optim, steps=15)