Fix (or ignore) new mypy errors

optimagic-dev · Jan 21, 2025 · 9026fd9 · 9026fd9
1 parent 7d73b0b
commit 9026fd9
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Showing 9 changed files with 18 additions and 17 deletions.
diff --git a/.tools/envs/testenv-linux.yml b/.tools/envs/testenv-linux.yml
@@ -23,7 +23,7 @@ dependencies:
   - scipy>=1.2.1  # run, tests
   - sqlalchemy  # run, tests
   - seaborn  # dev, tests
-  - mypy=1.13  # dev, tests
+  - mypy=1.14.1  # dev, tests
   - pyyaml  # dev, tests
   - jinja2  # dev, tests
   - annotated-types  # dev, tests

diff --git a/.tools/envs/testenv-numpy.yml b/.tools/envs/testenv-numpy.yml
@@ -21,7 +21,7 @@ dependencies:
   - scipy>=1.2.1  # run, tests
   - sqlalchemy  # run, tests
   - seaborn  # dev, tests
-  - mypy=1.13  # dev, tests
+  - mypy=1.14.1  # dev, tests
   - pyyaml  # dev, tests
   - jinja2  # dev, tests
   - annotated-types  # dev, tests

diff --git a/.tools/envs/testenv-others.yml b/.tools/envs/testenv-others.yml
@@ -21,7 +21,7 @@ dependencies:
   - scipy>=1.2.1  # run, tests
   - sqlalchemy  # run, tests
   - seaborn  # dev, tests
-  - mypy=1.13  # dev, tests
+  - mypy=1.14.1  # dev, tests
   - pyyaml  # dev, tests
   - jinja2  # dev, tests
   - annotated-types  # dev, tests

diff --git a/.tools/envs/testenv-pandas.yml b/.tools/envs/testenv-pandas.yml
@@ -21,7 +21,7 @@ dependencies:
   - scipy>=1.2.1  # run, tests
   - sqlalchemy  # run, tests
   - seaborn  # dev, tests
-  - mypy=1.13  # dev, tests
+  - mypy=1.14.1  # dev, tests
   - pyyaml  # dev, tests
   - jinja2  # dev, tests
   - annotated-types  # dev, tests

diff --git a/src/optimagic/differentiation/derivatives.py b/src/optimagic/differentiation/derivatives.py
@@ -328,7 +328,7 @@ def first_derivative(
         f0 = np.array(f0, dtype=np.float64)
 
     # convert the raw evaluations to numpy arrays
-    raw_evals = _convert_evals_to_numpy(
+    raw_evals_arr = _convert_evals_to_numpy(
         raw_evals=raw_evals,
         unpacker=unpacker,
         registry=registry,
@@ -337,9 +337,9 @@ def first_derivative(
     )
 
     # apply finite difference formulae
-    evals_data = np.array(raw_evals).reshape(2, n_steps, len(x), -1)
-    evals_data = np.transpose(evals_data, axes=(0, 1, 3, 2))
-    evals = Evals(pos=evals_data[0], neg=evals_data[1])
+    evals_data = np.array(raw_evals_arr).reshape(2, n_steps, len(x), -1)
+    evals_data_transposed = np.transpose(evals_data, axes=(0, 1, 3, 2))
+    evals = Evals(pos=evals_data_transposed[0], neg=evals_data_transposed[1])
 
     jac_candidates = {}
     for m in ["forward", "backward", "central"]:

diff --git a/src/optimagic/examples/criterion_functions.py b/src/optimagic/examples/criterion_functions.py
@@ -114,7 +114,7 @@ def rosenbrock_gradient(params: PyTree) -> PyTree:
     l4 = np.delete(x, [0])
     l4 = np.append(l4, 0)
     l5 = np.full((len(x) - 1), 2)
-    l5 = np.append(l5, 0)
+    l5 = np.append(l5, 0)  # type: ignore[assignment]
     flat = 100 * (4 * (l1**3) + 2 * l2 - 2 * (l3**2) - 4 * (l4 * x)) + 2 * l1 - l5
     return _unflatten_gradient(flat, params)
 

diff --git a/src/optimagic/logging/logger.py b/src/optimagic/logging/logger.py
@@ -187,7 +187,10 @@ def _build_history_dataframe(self) -> pd.DataFrame:
 
         times = np.array(history["time"])
         times -= times[0]
-        history["time"] = times.tolist()
+        # For numpy arrays with ndim = 0, tolist() returns a scalar, which violates the
+        # type hinting list[Any] from above. As history["time"] is always a list, this
+        # case is safe to ignore.
+        history["time"] = times.tolist()  # type: ignore[assignment]
 
         df = pd.DataFrame(history)
         df = df.merge(

diff --git a/src/optimagic/optimization/multistart.py b/src/optimagic/optimization/multistart.py
@@ -313,9 +313,11 @@ def run_explorations(
 
     """
     internal_problem = internal_problem.with_step_id(step_id)
-    x_list = list(sample)
+    x_list: list[NDArray[np.float64]] = list(sample)
 
-    raw_values = np.array(internal_problem.exploration_fun(x_list, n_cores=n_cores))
+    raw_values = np.asarray(
+        internal_problem.exploration_fun(x_list, n_cores=n_cores), dtype=np.float64
+    )
 
     is_valid = np.isfinite(raw_values)
 

diff --git a/src/optimagic/optimizers/scipy_optimizers.py b/src/optimagic/optimizers/scipy_optimizers.py
@@ -799,11 +799,7 @@ def _solve_internal_problem(
             )
         raw_res = scipy.optimize.brute(
             func=problem.fun,
-            ranges=tuple(
-                map(
-                    tuple, np.column_stack((problem.bounds.lower, problem.bounds.upper))
-                )
-            ),
+            ranges=tuple(zip(problem.bounds.lower, problem.bounds.upper, strict=False)),
             Ns=self.n_grid_points,
             full_output=True,
             finish=self.polishing_function,