55from collections import defaultdict
66from copy import deepcopy
77from math import hypot
8- from numbers import Integral as Int
9- from numbers import Real
108from typing import Callable , DefaultDict , Iterable , List , Sequence , Tuple
119
1210import numpy as np
1614
1715from adaptive .learner .learner1D import Learner1D , _get_intervals
1816from adaptive .notebook_integration import ensure_holoviews
17+ from adaptive .types import Int , Real
1918from adaptive .utils import assign_defaults , partial_function_from_dataframe
2019
2120try :
@@ -99,7 +98,7 @@ def __init__(
9998 if min_samples > max_samples :
10099 raise ValueError ("max_samples should be larger than min_samples." )
101100
102- super ().__init__ (function , bounds , loss_per_interval )
101+ super ().__init__ (function , bounds , loss_per_interval ) # type: ignore[arg-type]
103102
104103 self .delta = delta
105104 self .alpha = alpha
@@ -110,7 +109,7 @@ def __init__(
110109
111110 # Contains all samples f(x) for each
112111 # point x in the form {x0: {0: f_0(x0), 1: f_1(x0), ...}, ...}
113- self ._data_samples = SortedDict ()
112+ self ._data_samples : SortedDict [ float , dict [ int , Real ]] = SortedDict ()
114113 # Contains the number of samples taken
115114 # at each point x in the form {x0: n0, x1: n1, ...}
116115 self ._number_samples = SortedDict ()
@@ -124,14 +123,14 @@ def __init__(
124123 # form {xi: ((xii-xi)^2 + (yii-yi)^2)^0.5, ...}
125124 self ._distances : dict [Real , float ] = decreasing_dict ()
126125 # {xii: error[xii]/min(_distances[xi], _distances[xii], ...}
127- self .rescaled_error : dict [Real , float ] = decreasing_dict ()
126+ self .rescaled_error : ItemSortedDict [Real , float ] = decreasing_dict ()
128127
129128 def new (self ) -> AverageLearner1D :
130129 """Create a copy of `~adaptive.AverageLearner1D` without the data."""
131130 return AverageLearner1D (
132131 self .function ,
133132 self .bounds ,
134- self .loss_per_interval ,
133+ self .loss_per_interval , # type: ignore[arg-type]
135134 self .delta ,
136135 self .alpha ,
137136 self .neighbor_sampling ,
@@ -163,7 +162,7 @@ def to_numpy(self, mean: bool = False) -> np.ndarray:
163162 ]
164163 )
165164
166- def to_dataframe (
165+ def to_dataframe ( # type: ignore[override]
167166 self ,
168167 mean : bool = False ,
169168 with_default_function_args : bool = True ,
@@ -201,10 +200,10 @@ def to_dataframe(
201200 if not with_pandas :
202201 raise ImportError ("pandas is not installed." )
203202 if mean :
204- data = sorted (self .data .items ())
203+ data : list [ tuple [ Real , Real ]] = sorted (self .data .items ())
205204 columns = [x_name , y_name ]
206205 else :
207- data = [
206+ data : list [ tuple [ int , Real , Real ]] = [ # type: ignore[no-redef]
208207 (seed , x , y )
209208 for x , seed_y in sorted (self ._data_samples .items ())
210209 for seed , y in sorted (seed_y .items ())
@@ -217,7 +216,7 @@ def to_dataframe(
217216 assign_defaults (self .function , df , function_prefix )
218217 return df
219218
220- def load_dataframe (
219+ def load_dataframe ( # type: ignore[override]
221220 self ,
222221 df : pandas .DataFrame ,
223222 with_default_function_args : bool = True ,
@@ -257,7 +256,7 @@ def load_dataframe(
257256 self .function , df , function_prefix
258257 )
259258
260- def ask (self , n : int , tell_pending : bool = True ) -> tuple [Points , list [float ]]:
259+ def ask (self , n : int , tell_pending : bool = True ) -> tuple [Points , list [float ]]: # type: ignore[override]
261260 """Return 'n' points that are expected to maximally reduce the loss."""
262261 # If some point is undersampled, resample it
263262 if len (self ._undersampled_points ):
@@ -310,18 +309,18 @@ def _ask_for_new_point(self, n: int) -> tuple[Points, list[float]]:
310309 new point, since in general n << min_samples and this point will need
311310 to be resampled many more times"""
312311 points , (loss_improvement ,) = self ._ask_points_without_adding (1 )
313- points = [(seed , x ) for seed , x in zip (range (n ), n * points )]
312+ seed_points = [(seed , x ) for seed , x in zip (range (n ), n * points )]
314313 loss_improvements = [loss_improvement / n ] * n
315- return points , loss_improvements
314+ return seed_points , loss_improvements # type: ignore[return-value]
316315
317- def tell_pending (self , seed_x : Point ) -> None :
316+ def tell_pending (self , seed_x : Point ) -> None : # type: ignore[override]
318317 _ , x = seed_x
319318 self .pending_points .add (seed_x )
320319 if x not in self .data :
321320 self ._update_neighbors (x , self .neighbors_combined )
322321 self ._update_losses (x , real = False )
323322
324- def tell (self , seed_x : Point , y : Real ) -> None :
323+ def tell (self , seed_x : Point , y : Real ) -> None : # type: ignore[override]
325324 seed , x = seed_x
326325 if y is None :
327326 raise TypeError (
@@ -492,7 +491,7 @@ def _calc_error_in_mean(self, ys: Iterable[Real], y_avg: Real, n: int) -> float:
492491 t_student = scipy .stats .t .ppf (1 - self .alpha , df = n - 1 )
493492 return t_student * (variance_in_mean / n ) ** 0.5
494493
495- def tell_many (
494+ def tell_many ( # type: ignore[override]
496495 self , xs : Points | np .ndarray , ys : Sequence [Real ] | np .ndarray
497496 ) -> None :
498497 # Check that all x are within the bounds
@@ -577,10 +576,10 @@ def tell_many_at_point(self, x: Real, seed_y_mapping: dict[int, Real]) -> None:
577576 self ._update_interpolated_loss_in_interval (* interval )
578577 self ._oldscale = deepcopy (self ._scale )
579578
580- def _get_data (self ) -> dict [Real , dict [Int , Real ]]:
579+ def _get_data (self ) -> dict [Real , dict [Int , Real ]]: # type: ignore[override]
581580 return self ._data_samples
582581
583- def _set_data (self , data : dict [Real , dict [Int , Real ]]) -> None :
582+ def _set_data (self , data : dict [Real , dict [Int , Real ]]) -> None : # type: ignore[override]
584583 if data :
585584 for x , samples in data .items ():
586585 self .tell_many_at_point (x , samples )
@@ -615,7 +614,7 @@ def plot(self):
615614 return p .redim (x = {"range" : plot_bounds })
616615
617616
618- def decreasing_dict () -> dict :
617+ def decreasing_dict () -> ItemSortedDict :
619618 """This initialization orders the dictionary from large to small values"""
620619
621620 def sorting_rule (key , value ):
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