Upgrade pre-commit hook and fix resulting complaints

pnkraemer · pnkraemer · commit e96a8d78caf7 · 2026-02-16T08:19:05.000+01:00
diff --git a/docs/examples_advanced/neural_ode.py b/docs/examples_advanced/neural_ode.py
@@ -33,7 +33,7 @@ def main(num_data=100, epochs=500, print_every=50, hidden=(20,), lr=0.2):
     data = jnp.sin(2.5 * jnp.pi * grid) * jnp.pi * grid
     stdev = 1e-1
     output_scale = 1e4
-    vf, u0, (t0, t1), f_args = vf_neural_ode(hidden=hidden, t0=0.0, t1=1)
+    vf, u0, (t0, _t1), f_args = vf_neural_ode(hidden=hidden, t0=0.0, t1=1)
 
     # Create a loss (this is where probabilistic numerics enters!)
     loss = loss_log_marginal_likelihood(vf=vf, t0=t0)
diff --git a/docs/examples_advanced/solve_pde.py b/docs/examples_advanced/solve_pde.py
@@ -59,7 +59,7 @@ def vf(y, *, t):  # noqa: ARG001
     simulate = simulator(save_at=save_at, errorest=errorest, solver=solver)
     (u, u_std) = simulate(init)
 
-    fig, axes = plt.subplots(
+    _fig, axes = plt.subplots(
         nrows=2, ncols=len(u), figsize=(2 * len(u), 3), tight_layout=True
     )
     for t_i, u_i, std_i, ax_i in zip(save_at, u, u_std, axes.T):
diff --git a/docs/examples_benchmarks/convergence-rates-lotka-volterra.py b/docs/examples_benchmarks/convergence-rates-lotka-volterra.py
@@ -73,7 +73,7 @@ def main():
         results[label] = param_to_wp(tolerances)
 
     layout = [["values", "trends"]]
-    fig, ax = plt.subplot_mosaic(
+    _fig, ax = plt.subplot_mosaic(
         layout,
         figsize=(8, 3),
         constrained_layout=True,
@@ -209,7 +209,7 @@ def parameter_list_to_workprecision(list_of_args, /):
         works_std = []
         precisions = []
         for arg in list_of_args:
-            x, num_steps = fun(arg)
+            _x, num_steps = fun(arg)
 
             precision = precision_fun(fun(arg)[0].block_until_ready())
             times = timeit_fun(lambda: fun(arg)[0].block_until_ready())  # noqa: B023
diff --git a/docs/examples_benchmarks/taylor-init-fitzhughnagumo.py b/docs/examples_benchmarks/taylor-init-fitzhughnagumo.py
@@ -55,7 +55,7 @@ def main(max_time=0.25, repeats=2):
             algo, timeit_fun=timeit_fun, max_time=max_time
         )
 
-    fig, (axis_perform, axis_compile) = plt.subplots(
+    _fig, (axis_perform, axis_compile) = plt.subplots(
         ncols=2, figsize=(8, 3), dpi=150, sharex=True, sharey=True
     )
 
diff --git a/docs/examples_benchmarks/taylor-init-node.py b/docs/examples_benchmarks/taylor-init-node.py
@@ -55,7 +55,7 @@ def main(max_time=0.55, repeats=2):
             algo, timeit_fun=timeit_fun, max_time=max_time
         )
 
-    fig, (axis_perform, axis_compile) = plt.subplots(
+    _fig, (axis_perform, axis_compile) = plt.subplots(
         ncols=2, figsize=(8, 3), dpi=150, sharex=True, sharey=True
     )
 
diff --git a/docs/examples_benchmarks/taylor-init-pleiades.py b/docs/examples_benchmarks/taylor-init-pleiades.py
@@ -54,7 +54,7 @@ def main(max_time=0.5, repeats=2):
             algo, timeit_fun=timeit_fun, max_time=max_time
         )
 
-    fig, (axis_perform, axis_compile) = plt.subplots(
+    _fig, (axis_perform, axis_compile) = plt.subplots(
         ncols=2, figsize=(8, 3), dpi=150, sharex=True, sharey=True
     )
 
diff --git a/docs/examples_benchmarks/work-precision-hires.py b/docs/examples_benchmarks/work-precision-hires.py
@@ -42,7 +42,7 @@ def main(start=1.0, stop=9.0, step=1.0, repeats=2, use_diffrax: bool = False):
     # Simulate once to plot the state
     ts, ys = solve_ivp_once()
 
-    fig, ax = plt.subplots(figsize=(5, 3))
+    _fig, ax = plt.subplots(figsize=(5, 3))
     ax.plot(ts, ys)
     ax.set_title("Hires problem")
     ax.set_xlabel("Time")
@@ -80,7 +80,7 @@ def main(start=1.0, stop=9.0, step=1.0, repeats=2, use_diffrax: bool = False):
         param_to_wp = workprec(algo, precision_fun=precision_fun, timeit_fun=timeit_fun)
         results[label] = param_to_wp(tolerances)
 
-    fig, ax = plt.subplots(figsize=(5, 3))
+    _fig, ax = plt.subplots(figsize=(5, 3))
     for label, wp in results.items():
         ax.loglog(wp["precision"], wp["work_mean"], label=label)
 
diff --git a/docs/examples_benchmarks/work-precision-lotka-volterra.py b/docs/examples_benchmarks/work-precision-lotka-volterra.py
@@ -42,7 +42,7 @@ def main(start=3.0, stop=12.0, step=1.0, repeats=2, use_diffrax: bool = False):
     # Simulate once to plot the state
     ts, ys = solve_ivp_once()
 
-    fig, ax = plt.subplots(figsize=(5, 3))
+    _fig, ax = plt.subplots(figsize=(5, 3))
     ax.plot(ts, ys)
     ax.set_title("Lotka-Volterra problem")
     ax.set_xlabel("Time")
@@ -87,7 +87,7 @@ def main(start=3.0, stop=12.0, step=1.0, repeats=2, use_diffrax: bool = False):
         param_to_wp = workprec(algo, precision_fun=precision_fun, timeit_fun=timeit_fun)
         results[label] = param_to_wp(tolerances)
 
-    fig, ax = plt.subplots(figsize=(7, 3))
+    _fig, ax = plt.subplots(figsize=(7, 3))
     for label, wp in results.items():
         ax.loglog(wp["precision"], wp["work_mean"], label=label)
 
diff --git a/docs/examples_benchmarks/work-precision-pleiades.py b/docs/examples_benchmarks/work-precision-pleiades.py
@@ -41,9 +41,9 @@ def main(start=3.0, stop=11.0, step=1.0, repeats=2, use_diffrax: bool = False):
     jax.config.update("jax_enable_x64", True)
 
     # Simulate once to plot the state
-    ts, ys = solve_ivp_once()
+    _ts, ys = solve_ivp_once()
 
-    fig, ax = plt.subplots(figsize=(5, 3))
+    _fig, ax = plt.subplots(figsize=(5, 3))
     ax.plot(ys[:, :7], ys[:, 7:14], linestyle="solid", marker="None")
     ax.plot(ys[0, :7], ys[0, 7:14], linestyle="None", marker=".", markersize=4)
     ax.plot(ys[-1, :7], ys[-1, 7:14], linestyle="None", marker="*", markersize=8)
@@ -92,7 +92,7 @@ def main(start=3.0, stop=11.0, step=1.0, repeats=2, use_diffrax: bool = False):
         param_to_wp = workprec(algo, precision_fun=precision_fun, timeit_fun=timeit_fun)
         results[label] = param_to_wp(tolerances)
 
-    fig, ax = plt.subplots(figsize=(7, 3))
+    _fig, ax = plt.subplots(figsize=(7, 3))
     for label, wp in results.items():
         ax.loglog(wp["precision"], wp["work_mean"], label=label)
 
diff --git a/docs/examples_benchmarks/work-precision-vanderpol.py b/docs/examples_benchmarks/work-precision-vanderpol.py
@@ -42,7 +42,7 @@ def main(start=2.0, stop=8.0, step=1.0, repeats=2, use_diffrax: bool = False):
     # Simulate once to plot the state
     ts, ys = solve_ivp_once()
 
-    fig, ax = plt.subplots(figsize=(5, 3))
+    _fig, ax = plt.subplots(figsize=(5, 3))
     ax.plot(ts, ys)
     ax.set_ylim((-6, 6))
     ax.set_title("Van-der-Pol problem")
@@ -80,7 +80,7 @@ def main(start=2.0, stop=8.0, step=1.0, repeats=2, use_diffrax: bool = False):
         param_to_wp = workprec(algo, precision_fun=precision_fun, timeit_fun=timeit_fun)
         results[label] = param_to_wp(tolerances)
 
-    fig, ax = plt.subplots(figsize=(7, 3))
+    _fig, ax = plt.subplots(figsize=(7, 3))
     for label, wp in results.items():
         ax.loglog(wp["precision"], wp["work_mean"], label=label)
 
diff --git a/probdiffeq/ivpsolve.py b/probdiffeq/ivpsolve.py
@@ -516,7 +516,7 @@ def step_extract_timestep_state(self, state: _RejectionLoopState) -> TimeStepSta
 
     def interp_skip(self, args):
         """If step_from.t < t1, don't interpolate."""
-        state, t1 = args
+        state, _t1 = args
         solution = state.step_from
         return solution, state
 
diff --git a/probdiffeq/probdiffeq.py b/probdiffeq/probdiffeq.py
@@ -402,7 +402,7 @@ def log_marginal_likelihood_terminal_values(
             Expected to correspond to a solution of an ODE with shape (d,).
         """
         [u_leaves], u_structure = tree.tree_flatten(u)
-        [std_leaves], std_structure = tree.tree_flatten(standard_deviation)
+        [_std_leaves], std_structure = tree.tree_flatten(standard_deviation)
 
         if u_structure != std_structure:
             msg = (
diff --git a/tests/test_probdiffeq/test_calibration_mle.py b/tests/test_probdiffeq/test_calibration_mle.py
@@ -25,7 +25,7 @@ def solver_to_solution(solver_fun, strategy_fun):
         solve = ivpsolve.solve_fixed_grid(solver=solver)
         return solve(init, grid=grid)
 
-    return solver_to_solution, ssm
+    return solver_to_solution
 
 
 @testing.case()
@@ -47,7 +47,7 @@ def solver_to_solution(solver_fun, strategy_fun):
         solve = ivpsolve.solve_adaptive_save_at(errorest=errorest, solver=solver)
         return solve(init, save_at=save_at, dt0=dt0, atol=1e-2, rtol=1e-2)
 
-    return solver_to_solution, ssm
+    return solver_to_solution
 
 
 @testing.case()
@@ -69,7 +69,7 @@ def solver_to_solution(solver_fun, strategy_fun):
         )
         return solve(init, t0=t0, t1=t1, dt0=dt0, atol=1e-2, rtol=1e-2)
 
-    return solver_to_solution, ssm
+    return solver_to_solution
 
 
 @testing.fixture(name="uncalibrated_and_mle_solution")
@@ -83,9 +83,8 @@ def solver_to_solution(solver_fun, strategy_fun):
     ],
 )
 def fixture_uncalibrated_and_mle_solution(solver_to_solution, strategy_fun):
-    solve, ssm = solver_to_solution
-    uncalib = solve(probdiffeq.solver, strategy_fun)
-    mle = solve(probdiffeq.solver_mle, strategy_fun)
+    uncalib = solver_to_solution(probdiffeq.solver, strategy_fun)
+    mle = solver_to_solution(probdiffeq.solver_mle, strategy_fun)
     return (uncalib, mle)
 
 
diff --git a/tests/test_probdiffeq/test_strategy_filter_vs_smoother.py b/tests/test_probdiffeq/test_strategy_filter_vs_smoother.py
@@ -65,7 +65,7 @@ def test_compare_filter_smoother_rmse(filter_solution, smoother_solution):
 
 
 def _reference_solution(ts):
-    vf, (u0,), (t0, t1) = ode.ivp_lotka_volterra()
+    vf, (u0,), (_t0, _t1) = ode.ivp_lotka_volterra()
     return ode.odeint_and_save_at(vf, (u0,), save_at=ts, atol=1e-10, rtol=1e-10)
 
 
diff --git a/tests/test_probdiffeq/test_strategy_warnings_for_wrong_strategies.py b/tests/test_probdiffeq/test_strategy_warnings_for_wrong_strategies.py
@@ -7,10 +7,10 @@
 
 @testing.parametrize("fact", ["isotropic"])  # no dense/blockdiag because no impl test
 def test_warning_for_fixedpoint_in_save_every_step_mode(fact):
-    vf, (u0,), (t0, t1) = ode.ivp_lotka_volterra()
+    vf, (u0,), (t0, _t1) = ode.ivp_lotka_volterra()
 
     tcoeffs = taylor.odejet_padded_scan(lambda y: vf(y, t=t0), (u0,), num=2)
-    init, ibm, ssm = probdiffeq.prior_wiener_integrated(tcoeffs, ssm_fact=fact)
+    _init, ibm, ssm = probdiffeq.prior_wiener_integrated(tcoeffs, ssm_fact=fact)
 
     ts0 = probdiffeq.constraint_ode_ts0(ssm=ssm)
     strategy = probdiffeq.strategy_smoother_fixedpoint(ssm=ssm)
@@ -25,10 +25,10 @@ def test_warning_for_fixedpoint_in_save_every_step_mode(fact):
 
 @testing.parametrize("fact", ["isotropic"])  # no dense/blockdiag because no impl test
 def test_warning_for_smoother_in_save_at_mode(fact):
-    vf, (u0,), (t0, t1) = ode.ivp_lotka_volterra()
+    vf, (u0,), (t0, _t1) = ode.ivp_lotka_volterra()
 
     tcoeffs = taylor.odejet_padded_scan(lambda y: vf(y, t=t0), (u0,), num=2)
-    init, ibm, ssm = probdiffeq.prior_wiener_integrated(tcoeffs, ssm_fact=fact)
+    _init, ibm, ssm = probdiffeq.prior_wiener_integrated(tcoeffs, ssm_fact=fact)
     ts0 = probdiffeq.constraint_ode_ts0(ssm=ssm)
     strategy = probdiffeq.strategy_smoother_fixedinterval(ssm=ssm)
     solver = probdiffeq.solver(

Original file line number	Diff line number	Diff line change
`@@ -59,7 +59,7 @@ def vf(y, *, t): # noqa: ARG001`
`59`	`59`	`simulate = simulator(save_at=save_at, errorest=errorest, solver=solver)`
`60`	`60`	`(u, u_std) = simulate(init)`
`61`	`61`
`62`		`- fig, axes = plt.subplots(`
	`62`	`+ _fig, axes = plt.subplots(`
`63`	`63`	`nrows=2, ncols=len(u), figsize=(2 * len(u), 3), tight_layout=True`
`64`	`64`	`)`
`65`	`65`	`for t_i, u_i, std_i, ax_i in zip(save_at, u, u_std, axes.T):`
Original file line number	Diff line number	Diff line change
`@@ -55,7 +55,7 @@ def main(max_time=0.25, repeats=2):`
`55`	`55`	`algo, timeit_fun=timeit_fun, max_time=max_time`
`56`	`56`	`)`
`57`	`57`
`58`		`- fig, (axis_perform, axis_compile) = plt.subplots(`
	`58`	`+ _fig, (axis_perform, axis_compile) = plt.subplots(`
`59`	`59`	`ncols=2, figsize=(8, 3), dpi=150, sharex=True, sharey=True`
`60`	`60`	`)`
`61`	`61`
Original file line number	Diff line number	Diff line change
`@@ -55,7 +55,7 @@ def main(max_time=0.55, repeats=2):`
`55`	`55`	`algo, timeit_fun=timeit_fun, max_time=max_time`
`56`	`56`	`)`
`57`	`57`
`58`		`- fig, (axis_perform, axis_compile) = plt.subplots(`
	`58`	`+ _fig, (axis_perform, axis_compile) = plt.subplots(`
`59`	`59`	`ncols=2, figsize=(8, 3), dpi=150, sharex=True, sharey=True`
`60`	`60`	`)`
`61`	`61`
Original file line number	Diff line number	Diff line change
`@@ -54,7 +54,7 @@ def main(max_time=0.5, repeats=2):`
`54`	`54`	`algo, timeit_fun=timeit_fun, max_time=max_time`
`55`	`55`	`)`
`56`	`56`
`57`		`- fig, (axis_perform, axis_compile) = plt.subplots(`
	`57`	`+ _fig, (axis_perform, axis_compile) = plt.subplots(`
`58`	`58`	`ncols=2, figsize=(8, 3), dpi=150, sharex=True, sharey=True`
`59`	`59`	`)`
`60`	`60`