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Commit f27bf7e

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basnijholtbasnijholt
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Remove types in doc-strings in learner2D.py
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‎adaptive/learner/learner2D.py

Copy file name to clipboardExpand all lines: adaptive/learner/learner2D.py
+45-40Lines changed: 45 additions & 40 deletions
Original file line numberDiff line numberDiff line change
@@ -5,7 +5,7 @@
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from collections import OrderedDict
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from copy import copy
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from math import sqrt
8-
from typing import Callable, Iterable
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from typing import TYPE_CHECKING, Callable, Iterable
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import cloudpickle
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import numpy as np
@@ -22,6 +22,9 @@
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partial_function_from_dataframe,
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)
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25+
if TYPE_CHECKING:
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import holoviews
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try:
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import pandas
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@@ -40,11 +43,11 @@ def deviations(ip: LinearNDInterpolator) -> list[np.ndarray]:
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Parameters
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----------
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ip : `scipy.interpolate.LinearNDInterpolator` instance
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ip
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Returns
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-------
47-
deviations : list
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deviations
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The deviation per triangle.
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"""
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values = ip.values / (ip.values.ptp(axis=0).max() or 1)
@@ -79,11 +82,11 @@ def areas(ip: LinearNDInterpolator) -> np.ndarray:
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Parameters
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----------
82-
ip : `scipy.interpolate.LinearNDInterpolator` instance
85+
ip
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Returns
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-------
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areas : numpy.ndarray
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areas
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The area per triangle in ``ip.tri``.
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"""
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p = ip.tri.points[ip.tri.simplices]
@@ -99,11 +102,11 @@ def uniform_loss(ip: LinearNDInterpolator) -> np.ndarray:
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Parameters
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----------
102-
ip : `scipy.interpolate.LinearNDInterpolator` instance
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ip
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Returns
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-------
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losses : numpy.ndarray
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losses
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Loss per triangle in ``ip.tri``.
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Examples
@@ -136,7 +139,7 @@ def resolution_loss_function(
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Returns
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-------
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loss_function : callable
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loss_function
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Examples
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--------
@@ -173,11 +176,11 @@ def minimize_triangle_surface_loss(ip: LinearNDInterpolator) -> np.ndarray:
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Parameters
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----------
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ip : `scipy.interpolate.LinearNDInterpolator` instance
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ip
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Returns
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-------
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losses : numpy.ndarray
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losses
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Loss per triangle in ``ip.tri``.
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Examples
@@ -217,11 +220,11 @@ def default_loss(ip: LinearNDInterpolator) -> np.ndarray:
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Parameters
219222
----------
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ip : `scipy.interpolate.LinearNDInterpolator` instance
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ip
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Returns
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-------
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losses : numpy.ndarray
227+
losses
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Loss per triangle in ``ip.tri``.
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"""
227230
dev = np.sum(deviations(ip), axis=0)
@@ -241,15 +244,15 @@ def choose_point_in_triangle(triangle: np.ndarray, max_badness: int) -> np.ndarr
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Parameters
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----------
244-
triangle : numpy.ndarray
247+
triangle
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The coordinates of a triangle with shape (3, 2).
246-
max_badness : int
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max_badness
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The badness at which the point is either chosen on a edge or
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in the middle.
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Returns
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-------
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point : numpy.ndarray
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point
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The x and y coordinate of the suggested new point.
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"""
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a, b, c = triangle
@@ -267,17 +270,17 @@ def choose_point_in_triangle(triangle: np.ndarray, max_badness: int) -> np.ndarr
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return point
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270-
def triangle_loss(ip):
273+
def triangle_loss(ip: LinearNDInterpolator) -> list[float]:
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r"""Computes the average of the volumes of the simplex combined with each
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neighbouring point.
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Parameters
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----------
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ip : `scipy.interpolate.LinearNDInterpolator` instance
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ip
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Returns
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-------
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triangle_loss : list
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triangle_loss
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The mean volume per triangle.
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Notes
@@ -311,13 +314,13 @@ class Learner2D(BaseLearner):
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Parameters
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----------
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function : callable
317+
function
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The function to learn. Must take a tuple of two real
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parameters and return a real number.
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bounds : list of 2-tuples
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bounds
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A list ``[(a1, b1), (a2, b2)]`` containing bounds,
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one per dimension.
320-
loss_per_triangle : callable, optional
323+
loss_per_triangle
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A function that returns the loss for every triangle.
322325
If not provided, then a default is used, which uses
323326
the deviation from a linear estimate, as well as
@@ -424,19 +427,19 @@ def to_dataframe(
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Parameters
426429
----------
427-
with_default_function_args : bool, optional
430+
with_default_function_args
428431
Include the ``learner.function``'s default arguments as a
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column, by default True
430-
function_prefix : str, optional
433+
function_prefix
431434
Prefix to the ``learner.function``'s default arguments' names,
432435
by default "function."
433-
seed_name : str, optional
436+
seed_name
434437
Name of the seed parameter, by default "seed"
435-
x_name : str, optional
438+
x_name
436439
Name of the input x value, by default "x"
437-
y_name : str, optional
440+
y_name
438441
Name of the input y value, by default "y"
439-
z_name : str, optional
442+
z_name
440443
Name of the output value, by default "z"
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Returns
@@ -475,18 +478,18 @@ def load_dataframe(
475478
476479
Parameters
477480
----------
478-
df : pandas.DataFrame
481+
df
479482
The data to load.
480-
with_default_function_args : bool, optional
483+
with_default_function_args
481484
The ``with_default_function_args`` used in ``to_dataframe()``,
482485
by default True
483-
function_prefix : str, optional
486+
function_prefix
484487
The ``function_prefix`` used in ``to_dataframe``, by default "function."
485-
x_name : str, optional
488+
x_name
486489
The ``x_name`` used in ``to_dataframe``, by default "x"
487-
y_name : str, optional
490+
y_name
488491
The ``y_name`` used in ``to_dataframe``, by default "y"
489-
z_name : str, optional
492+
z_name
490493
The ``z_name`` used in ``to_dataframe``, by default "z"
491494
"""
492495
data = df.set_index([x_name, y_name])[z_name].to_dict()
@@ -538,7 +541,7 @@ def interpolated_on_grid(
538541
539542
Parameters
540543
----------
541-
n : int, optional
544+
n
542545
Number of points in x and y. If None (default) this number is
543546
evaluated by looking at the size of the smallest triangle.
544547
@@ -611,14 +614,14 @@ def interpolator(self, *, scaled: bool = False) -> LinearNDInterpolator:
611614
612615
Parameters
613616
----------
614-
scaled : bool
617+
scaled
615618
Use True if all points are inside the
616619
unit-square [(-0.5, 0.5), (-0.5, 0.5)] or False if
617620
the data points are inside the ``learner.bounds``.
618621
619622
Returns
620623
-------
621-
interpolator : `scipy.interpolate.LinearNDInterpolator`
624+
interpolator
622625
623626
Examples
624627
--------
@@ -755,7 +758,9 @@ def remove_unfinished(self) -> None:
755758
if p not in self.data:
756759
self._stack[p] = np.inf
757760

758-
def plot(self, n=None, tri_alpha=0):
761+
def plot(
762+
self, n: int = None, tri_alpha: float = 0
763+
) -> holoviews.Overlay | holoviews.HoloMap:
759764
r"""Plot the Learner2D's current state.
760765
761766
This plot function interpolates the data on a regular grid.
@@ -764,16 +769,16 @@ def plot(self, n=None, tri_alpha=0):
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Parameters
766771
----------
767-
n : int
772+
n
768773
Number of points in x and y. If None (default) this number is
769774
evaluated by looking at the size of the smallest triangle.
770-
tri_alpha : float
775+
tri_alpha
771776
The opacity ``(0 <= tri_alpha <= 1)`` of the triangles overlayed
772777
on top of the image. By default the triangulation is not visible.
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Returns
775780
-------
776-
plot : `holoviews.core.Overlay` or `holoviews.core.HoloMap`
781+
plot
777782
A `holoviews.core.Overlay` of
778783
``holoviews.Image * holoviews.EdgePaths``. If the
779784
`learner.function` returns a vector output, a

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