Updates for numpy 2 compatibility

CHANGELOG.md

@@ -24,7 +24,7 @@
 
 ## Bug Fixes
 
-
+- [#372](https://github.com/pybop-team/PyBOP/pull/372) - Converts `np.array` to `np.asarray` for Numpy v2.0 support.
 - [#165](https://github.com/pybop-team/PyBOP/issues/165) - Stores the attempted and best parameter values and the best cost for each iteration in the log attribute of the optimiser and updates the associated plots.
 - [#354](https://github.com/pybop-team/PyBOP/issues/354) - Fixes the calculation of the gradient in the `RootMeanSquaredError` cost.
 - [#347](https://github.com/pybop-team/PyBOP/issues/347) - Resets options between MSMR tests to cope with a bug in PyBaMM v23.9 which is fixed in PyBaMM v24.1.

examples/notebooks/multi_model_identification.ipynb

@@ -3958,7 +3958,7 @@
     }
    ],
    "source": [
-    "bounds = np.array([[5.5e-05, 8e-05], [7.5e-05, 9e-05]])\n",
+    "bounds = np.asarray([[5.5e-05, 8e-05], [7.5e-05, 9e-05]])\n",
     "for optim in optims:\n",
     "    pybop.plot2d(optim, bounds=bounds, steps=10, title=optim.cost.problem.model.name)"
    ]

examples/notebooks/multi_optimiser_identification.ipynb

@@ -925,7 +925,7 @@
    ],
    "source": [
     "# Plot the cost landscape with optimisation path and updated bounds\n",
-    "bounds = np.array([[0.5, 0.8], [0.55, 0.8]])\n",
+    "bounds = np.asarray([[0.5, 0.8], [0.55, 0.8]])\n",
     "for optim in optims:\n",
     "    pybop.plot2d(optim, bounds=bounds, steps=10, title=optim.name())"
    ]

examples/notebooks/optimiser_calibration.ipynb

@@ -677,7 +677,7 @@
    ],
    "source": [
     "# Plot the cost landscape with optimisation path and updated bounds\n",
-    "bounds = np.array([[0.6, 0.9], [0.5, 0.8]])\n",
+    "bounds = np.asarray([[0.6, 0.9], [0.5, 0.8]])\n",
     "for optim, sigma in zip(optims, sigmas):\n",
     "    pybop.plot2d(optim, bounds=bounds, steps=10, title=f\"Sigma: {sigma}\")"
    ]

examples/notebooks/spm_AdamW.ipynb

@@ -530,7 +530,7 @@
     "# Plot the cost landscape\n",
     "pybop.plot2d(cost, steps=15)\n",
     "# Plot the cost landscape with optimisation path and updated bounds\n",
-    "bounds = np.array([[0.6, 0.9], [0.5, 0.8]])\n",
+    "bounds = np.asarray([[0.6, 0.9], [0.5, 0.8]])\n",
     "pybop.plot2d(optim, bounds=bounds, steps=15);"
    ]
   },

examples/scripts/ecm_CMAES.py

@@ -101,5 +101,5 @@
 pybop.plot2d(cost, steps=15)
 
 # Plot the cost landscape with optimisation path and updated bounds
-bounds = np.array([[1e-4, 1e-2], [1e-5, 1e-2]])
+bounds = np.asarray([[1e-4, 1e-2], [1e-5, 1e-2]])
 pybop.plot2d(optim, bounds=bounds, steps=15)

examples/scripts/spm_AdamW.py

@@ -77,5 +77,5 @@ def noise(sigma):
 pybop.plot_parameters(optim)
 
 # Plot the cost landscape with optimisation path
-bounds = np.array([[0.5, 0.8], [0.4, 0.7]])
+bounds = np.asarray([[0.5, 0.8], [0.4, 0.7]])
 pybop.plot2d(optim, bounds=bounds, steps=15)

examples/scripts/spm_IRPropMin.py

@@ -51,5 +51,5 @@
 pybop.plot_parameters(optim)
 
 # Plot the cost landscape with optimisation path
-bounds = np.array([[0.5, 0.8], [0.4, 0.7]])
+bounds = np.asarray([[0.5, 0.8], [0.4, 0.7]])
 pybop.plot2d(optim, bounds=bounds, steps=15)

examples/scripts/spm_MAP.py

@@ -69,5 +69,5 @@
 pybop.plot2d(cost, steps=15)
 
 # Plot the cost landscape with optimisation path
-bounds = np.array([[0.55, 0.77], [0.48, 0.68]])
+bounds = np.asarray([[0.55, 0.77], [0.48, 0.68]])
 pybop.plot2d(optim, bounds=bounds, steps=15)

examples/scripts/spm_MLE.py

@@ -69,5 +69,5 @@
 pybop.plot2d(likelihood, steps=15)
 
 # Plot the cost landscape with optimisation path
-bounds = np.array([[0.55, 0.77], [0.48, 0.68]])
+bounds = np.asarray([[0.55, 0.77], [0.48, 0.68]])
 pybop.plot2d(optim, bounds=bounds, steps=15)

examples/scripts/spm_NelderMead.py

@@ -77,5 +77,5 @@ def noise(sigma):
 pybop.plot_parameters(optim)
 
 # Plot the cost landscape with optimisation path
-bounds = np.array([[0.5, 0.8], [0.4, 0.7]])
+bounds = np.asarray([[0.5, 0.8], [0.4, 0.7]])
 pybop.plot2d(optim, bounds=bounds, steps=15)

examples/scripts/spm_descent.py

@@ -57,5 +57,5 @@
 pybop.plot_parameters(optim)
 
 # Plot the cost landscape with optimisation path
-bounds = np.array([[0.5, 0.8], [0.4, 0.7]])
+bounds = np.asarray([[0.5, 0.8], [0.4, 0.7]])
 pybop.plot2d(optim, bounds=bounds, steps=15)

pybop/costs/_likelihoods.py

@@ -47,7 +47,7 @@ def set_sigma(self, sigma):
             )
 
         if not isinstance(sigma, np.ndarray):
-            sigma = np.array(sigma)
+            sigma = np.asarray(sigma)
 
         if not np.issubdtype(sigma.dtype, np.number):
             raise ValueError("Sigma must contain only numeric values")
@@ -74,7 +74,7 @@ def _evaluate(self, x, grad=None):
             if len(y.get(key, [])) != len(self._target.get(key, [])):
                 return -np.float64(np.inf)  # prediction doesn't match target
 
-        e = np.array(
+        e = np.asarray(
             [
                 np.sum(
                     self._offset
@@ -102,7 +102,7 @@ def _evaluateS1(self, x, grad=None):
                 dl = self._dl * np.ones(self.n_parameters)
                 return -likelihood, -dl
 
-        r = np.array([self._target[signal] - y[signal] for signal in self.signal])
+        r = np.asarray([self._target[signal] - y[signal] for signal in self.signal])
         likelihood = self._evaluate(x)
         dl = np.sum((self.sigma2 * np.sum((r * dy.T), axis=2)), axis=1)
         return likelihood, dl
@@ -148,7 +148,7 @@ def _evaluate(self, x, grad=None):
             if len(y.get(key, [])) != len(self._target.get(key, [])):
                 return -np.float64(np.inf)  # prediction doesn't match target
 
-        e = np.array(
+        e = np.asarray(
             [
                 np.sum(
                     self._logpi
@@ -181,7 +181,7 @@ def _evaluateS1(self, x, grad=None):
                 dl = self._dl * np.ones(self.n_parameters)
                 return -likelihood, -dl
 
-        r = np.array([self._target[signal] - y[signal] for signal in self.signal])
+        r = np.asarray([self._target[signal] - y[signal] for signal in self.signal])
         likelihood = self._evaluate(x)
         dl = sigma ** (-2.0) * np.sum((r * dy.T), axis=2)
         dsigma = -self.n_time_data / sigma + sigma**-(3.0) * np.sum(r**2, axis=1)

pybop/costs/fitting_costs.py

@@ -47,7 +47,7 @@ def _evaluate(self, x, grad=None):
             if len(prediction.get(key, [])) != len(self._target.get(key, [])):
                 return np.float64(np.inf)  # prediction doesn't match target
 
-        e = np.array(
+        e = np.asarray(
             [
                 np.sqrt(np.mean((prediction[signal] - self._target[signal]) ** 2))
                 for signal in self.signal
@@ -87,7 +87,7 @@ def _evaluateS1(self, x):
                 de = self._de * np.ones(self.n_parameters)
                 return e, de
 
-        r = np.array([y[signal] - self._target[signal] for signal in self.signal])
+        r = np.asarray([y[signal] - self._target[signal] for signal in self.signal])
         e = np.sqrt(np.mean(r**2, axis=1))
         de = np.mean((r * dy.T), axis=2) / (e + np.finfo(float).eps)
 
@@ -159,7 +159,7 @@ def _evaluate(self, x, grad=None):
             if len(prediction.get(key, [])) != len(self._target.get(key, [])):
                 return np.float64(np.inf)  # prediction doesn't match target
 
-        e = np.array(
+        e = np.asarray(
             [
                 np.sum(((prediction[signal] - self._target[signal]) ** 2))
                 for signal in self.signal
@@ -197,7 +197,7 @@ def _evaluateS1(self, x):
                 de = self._de * np.ones(self.n_parameters)
                 return e, de
 
-        r = np.array([y[signal] - self._target[signal] for signal in self.signal])
+        r = np.asarray([y[signal] - self._target[signal] for signal in self.signal])
         e = np.sum(np.sum(r**2, axis=0), axis=0)
         de = 2 * np.sum(np.sum((r * dy.T), axis=2), axis=1)
 

pybop/models/base_model.py

@@ -292,7 +292,7 @@ def reinit(
         if x is None:
             x = self._built_model.y0
 
-        sol = pybamm.Solution([np.array([t])], [x], self._built_model, inputs)
+        sol = pybamm.Solution([np.asarray([t])], [x], self._built_model, inputs)
 
         return TimeSeriesState(sol=sol, inputs=inputs, t=t)
 
@@ -303,7 +303,7 @@ def get_state(self, inputs: Inputs, t: float, x: np.ndarray) -> TimeSeriesState:
         if self._built_model is None:
             raise ValueError("Model must be built before calling get_state")
 
-        sol = pybamm.Solution([np.array([t])], [x], self._built_model, inputs)
+        sol = pybamm.Solution([np.asarray([t])], [x], self._built_model, inputs)
 
         return TimeSeriesState(sol=sol, inputs=inputs, t=t)
 

pybop/observers/observer.py

@@ -134,7 +134,7 @@ def get_current_covariance(self) -> Covariance:
 
     def get_measure(self, x: TimeSeriesState) -> np.ndarray:
         measures = [x.sol[s].data[-1] for s in self._signal]
-        return np.array([[m] for m in measures])
+        return np.asarray([[m] for m in measures])
 
     def get_current_time(self) -> float:
         """

pybop/observers/unscented_kalman.py

@@ -118,7 +118,7 @@ def observe(self, time: float, value: np.ndarray) -> float:
         if value is None:
             raise ValueError("Measurement must be provided.")
         elif isinstance(value, np.floating):
-            value = np.array([value])
+            value = np.asarray([value])
 
         dt = time - self.get_current_time()
         if dt < 0:
@@ -201,7 +201,7 @@ def __init__(
         zero_cols = np.logical_and(np.all(P0 == 0, axis=1), np.all(Rp == 0, axis=1))
         zeros = np.logical_and(zero_rows, zero_cols)
         ones = np.logical_not(zeros)
-        states = np.array(range(len(x0)))[ones]
+        states = np.asarray(range(len(x0)))[ones]
         bool_mask = np.ix_(ones, ones)
 
         S_filtered = linalg.cholesky(P0[ones, :][:, ones])
@@ -276,11 +276,11 @@ def gen_sigma_points(
 
         # Define the weights of the sigma points
         w_m0 = sigma / (L + sigma)
-        w_m = np.array([w_m0] + [1 / (2 * (L + sigma))] * (2 * L))
+        w_m = np.asarray([w_m0] + [1 / (2 * (L + sigma))] * (2 * L))
 
         # Define the weights of the covariance of the sigma points
         w_c0 = w_m0 + (1 - alpha**2 + beta)
-        w_c = np.array([w_c0] + [1 / (2 * (L + sigma))] * (2 * L))
+        w_c = np.asarray([w_c0] + [1 / (2 * (L + sigma))] * (2 * L))
 
         return (points, w_m, w_c)
 

pybop/plotting/plot2d.py

@@ -77,19 +77,19 @@ def plot2d(
     # Populate cost matrix
     for i, xi in enumerate(x):
         for j, yj in enumerate(y):
-            costs[j, i] = cost(np.array([xi, yj]))
+            costs[j, i] = cost(np.asarray([xi, yj]))
 
     if gradient:
         grad_parameter_costs = []
 
         # Determine the number of gradient outputs from cost.evaluateS1
-        num_gradients = len(cost.evaluateS1(np.array([x[0], y[0]]))[1])
+        num_gradients = len(cost.evaluateS1(np.asarray([x[0], y[0]]))[1])
 
         # Create an array to hold each gradient output & populate
         grads = [np.zeros((len(y), len(x))) for _ in range(num_gradients)]
         for i, xi in enumerate(x):
             for j, yj in enumerate(y):
-                (*current_grads,) = cost.evaluateS1(np.array([xi, yj]))[1]
+                (*current_grads,) = cost.evaluateS1(np.asarray([xi, yj]))[1]
                 for k, grad_output in enumerate(current_grads):
                     grads[k][j, i] = grad_output
 
@@ -103,7 +103,7 @@ def plot2d(
         flat_costs = costs.flatten()
 
         # Append the optimisation trace to the data
-        parameter_log = np.array(optim.log["x_best"])
+        parameter_log = np.asarray(optim.log["x_best"])
         flat_x = np.concatenate((flat_x, parameter_log[:, 0]))
         flat_y = np.concatenate((flat_y, parameter_log[:, 1]))
         flat_costs = np.concatenate((flat_costs, optim.log["cost"]))
@@ -140,7 +140,9 @@ def plot2d(
 
     if plot_optim:
         # Plot the optimisation trace
-        optim_trace = np.array([item for sublist in optim.log["x"] for item in sublist])
+        optim_trace = np.asarray(
+            [item for sublist in optim.log["x"] for item in sublist]
+        )
         optim_trace = optim_trace.reshape(-1, 2)
         fig.add_trace(
             go.Scatter(

tests/integration/test_optimisation_options.py

@@ -13,7 +13,7 @@ class TestOptimisation:
 
     @pytest.fixture(autouse=True)
     def setup(self):
-        self.ground_truth = np.array([0.55, 0.55]) + np.random.normal(
+        self.ground_truth = np.asarray([0.55, 0.55]) + np.random.normal(
             loc=0.0, scale=0.05, size=2
         )
 

tests/integration/test_spm_parameterisations.py

@@ -11,7 +11,7 @@ class Test_SPM_Parameterisation:
 
     @pytest.fixture(autouse=True)
     def setup(self):
-        self.ground_truth = np.array([0.55, 0.55]) + np.random.normal(
+        self.ground_truth = np.asarray([0.55, 0.55]) + np.random.normal(
             loc=0.0, scale=0.05, size=2
         )
 
@@ -160,7 +160,7 @@ def spm_two_signal_cost(self, parameters, model, cost_class):
         [
             pybop.SciPyDifferentialEvolution,
             pybop.IRPropMin,
-            pybop.CMAES,
+            pybop.XNES,
         ],
     )
     @pytest.mark.integration
@@ -218,7 +218,7 @@ def test_model_misparameterisation(self, parameters, model, init_soc):
         cost = pybop.RootMeanSquaredError(problem)
 
         # Select optimiser
-        optimiser = pybop.CMAES
+        optimiser = pybop.XNES
 
         # Build the optimisation problem
         optim = optimiser(cost=cost)

tests/integration/test_thevenin_parameterisation.py

@@ -11,7 +11,7 @@ class TestTheveninParameterisation:
 
     @pytest.fixture(autouse=True)
     def setup(self):
-        self.ground_truth = np.array([0.05, 0.05]) + np.random.normal(
+        self.ground_truth = np.asarray([0.05, 0.05]) + np.random.normal(
             loc=0.0, scale=0.01, size=2
         )