fix typeguard errors

Author: basnijholt

adaptive/learner/average_learner1D.py

@@ -3,7 +3,17 @@
 from collections import defaultdict
 from copy import deepcopy
 from math import hypot
-from typing import Callable, DefaultDict, Dict, List, Optional, Sequence, Set, Tuple
+from typing import (
+    Callable,
+    DefaultDict,
+    Dict,
+    Iterable,
+    List,
+    Optional,
+    Sequence,
+    Set,
+    Tuple,
+)
 
 import numpy as np
 import scipy.stats
@@ -356,7 +366,7 @@ def _update_losses_resampling(self, x: Real, real=True) -> None:
         if (b is not None) and right_loss_is_unknown:
             self.losses_combined[x, b] = float("inf")
 
-    def _calc_error_in_mean(self, ys: Sequence[Real], y_avg: Real, n: int) -> float:
+    def _calc_error_in_mean(self, ys: Iterable[Real], y_avg: Real, n: int) -> float:
         variance_in_mean = sum((y - y_avg) ** 2 for y in ys) / (n - 1)
         t_student = scipy.stats.t.ppf(1 - self.alpha, df=n - 1)
         return t_student * (variance_in_mean / n) ** 0.5

adaptive/learner/base_learner.py

@@ -5,14 +5,14 @@
 from adaptive.utils import _RequireAttrsABCMeta, load, save
 
 
-def uses_nth_neighbors(n):
+def uses_nth_neighbors(n: int):
     """Decorator to specify how many neighboring intervals the loss function uses.
 
     Wraps loss functions to indicate that they expect intervals together
     with ``n`` nearest neighbors
 
     The loss function will then receive the data of the N nearest neighbors
-    (``nth_neighbors``) aling with the data of the interval itself in a dict.
+    (``nth_neighbors``) along with the data of the interval itself in a dict.
     The `~adaptive.Learner1D` will also make sure that the loss is updated
     whenever one of the ``nth_neighbors`` changes.
 

adaptive/learner/learner1D.py

@@ -740,7 +740,7 @@ def _set_data(self, data: Dict[float, float]) -> None:
     def __getstate__(self):
         return (
             cloudpickle.dumps(self.function),
-            self.bounds,
+            tuple(self.bounds),
             self.loss_per_interval,
             dict(self.losses),  # SortedDict cannot be pickled
             dict(self.losses_combined),  # ItemSortedDict cannot be pickled

adaptive/tests/test_average_learner.py

@@ -7,8 +7,12 @@
 from adaptive.runner import simple
 
 
+def f_unused(seed):
+    raise NotImplementedError("This function shouldn't be used.")
+
+
 def test_only_returns_new_points():
-    learner = AverageLearner(lambda x: x, atol=None, rtol=0.01)
+    learner = AverageLearner(f_unused, atol=None, rtol=0.01)
 
     # Only tell it n = 5...10
     for i in range(5, 10):
@@ -25,7 +29,7 @@ def test_only_returns_new_points():
 
 @flaky.flaky(max_runs=5)
 def test_avg_std_and_npoints():
-    learner = AverageLearner(lambda x: x, atol=None, rtol=0.01)
+    learner = AverageLearner(f_unused, atol=None, rtol=0.01)
 
     for i in range(300):
         # This will add 5000 points at random values of n.
@@ -63,7 +67,7 @@ def constant_function(seed):
 
 def test_zero_mean():
     # see https://github.com/python-adaptive/adaptive/issues/275
-    learner = AverageLearner(None, rtol=0.01)
+    learner = AverageLearner(f_unused, rtol=0.01)
     learner.tell(0, -1)
     learner.tell(1, 1)
     learner.loss()

adaptive/tests/test_average_learner1d.py

@@ -16,8 +16,8 @@ def almost_equal_dicts(a, b):
 
 def test_tell_many_at_point():
     f = generate_random_parametrization(noisy_peak)
-    learner = AverageLearner1D(f, bounds=[-2, 2])
-    control = AverageLearner1D(f, bounds=[-2, 2])
+    learner = AverageLearner1D(f, bounds=(-2, 2))
+    control = AverageLearner1D(f, bounds=(-2, 2))
     learner._recompute_losses_factor = 1
     control._recompute_losses_factor = 1
     simple_run(learner, 100)