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Use sphinx_autodoc_typehints
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@basnijholt
basnijholt committed Dec 4, 2022
1 parent 6c69e88 commit f7816ba
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Showing 5 changed files with 150 additions and 131 deletions.
46 changes: 29 additions & 17 deletions adaptive/learner/learner1D.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -6,7 +6,7 @@
from copy import copy, deepcopy
from numbers import Integral as Int
from numbers import Real
from typing import Any, Callable, Dict, List, Sequence, Tuple, Union
from typing import TYPE_CHECKING, Any, Callable, Dict, List, Sequence, Tuple, Union

import cloudpickle
import numpy as np
Expand All @@ -24,12 +24,22 @@
partial_function_from_dataframe,
)

if TYPE_CHECKING:
import holoviews

try:
from typing import TypeAlias
except ImportError:
# Remove this when we drop support for Python 3.9
from typing_extensions import TypeAlias

try:
from typing import Literal
except ImportError:
# Remove this when we drop support for Python 3.7
from typing_extensions import Literal


try:
import pandas

Expand Down Expand Up @@ -145,7 +155,7 @@ def resolution_loss_function(
Returns
-------
loss_function : callable
loss_function
Examples
--------
Expand Down Expand Up @@ -230,12 +240,12 @@ class Learner1D(BaseLearner):
Parameters
----------
function : callable
function
The function to learn. Must take a single real parameter and
return a real number or 1D array.
bounds : pair of reals
bounds
The bounds of the interval on which to learn 'function'.
loss_per_interval: callable, optional
loss_per_interval
A function that returns the loss for a single interval of the domain.
If not provided, then a default is used, which uses the scaled distance
in the x-y plane as the loss. See the notes for more details.
Expand Down Expand Up @@ -356,15 +366,15 @@ def to_dataframe(
Parameters
----------
with_default_function_args : bool, optional
with_default_function_args
Include the ``learner.function``'s default arguments as a
column, by default True
function_prefix : str, optional
function_prefix
Prefix to the ``learner.function``'s default arguments' names,
by default "function."
x_name : str, optional
x_name
Name of the input value, by default "x"
y_name : str, optional
y_name
Name of the output value, by default "y"
Returns
Expand Down Expand Up @@ -403,16 +413,16 @@ def load_dataframe(
Parameters
----------
df : pandas.DataFrame
df
The data to load.
with_default_function_args : bool, optional
with_default_function_args
The ``with_default_function_args`` used in ``to_dataframe()``,
by default True
function_prefix : str, optional
function_prefix
The ``function_prefix`` used in ``to_dataframe``, by default "function."
x_name : str, optional
x_name
The ``x_name`` used in ``to_dataframe``, by default "x"
y_name : str, optional
y_name
The ``y_name`` used in ``to_dataframe``, by default "y"
"""
self.tell_many(df[x_name].values, df[y_name].values)
Expand Down Expand Up @@ -795,17 +805,19 @@ def _loss(
loss = mapping[ival]
return finite_loss(ival, loss, self._scale[0])

def plot(self, *, scatter_or_line: str = "scatter"):
def plot(
self, *, scatter_or_line: Literal["scatter", "line"] = "scatter"
) -> holoviews.Overlay:
"""Returns a plot of the evaluated data.
Parameters
----------
scatter_or_line : str, default: "scatter"
scatter_or_line
Plot as a scatter plot ("scatter") or a line plot ("line").
Returns
-------
plot : `holoviews.Overlay`
plot
Plot of the evaluated data.
"""
if scatter_or_line not in ("scatter", "line"):
Expand Down
85 changes: 45 additions & 40 deletions adaptive/learner/learner2D.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -5,7 +5,7 @@
from collections import OrderedDict
from copy import copy
from math import sqrt
from typing import Callable, Iterable
from typing import TYPE_CHECKING, Callable, Iterable

import cloudpickle
import numpy as np
Expand All @@ -22,6 +22,9 @@
partial_function_from_dataframe,
)

if TYPE_CHECKING:
import holoviews

try:
import pandas

Expand All @@ -40,11 +43,11 @@ def deviations(ip: LinearNDInterpolator) -> list[np.ndarray]:
Parameters
----------
ip : `scipy.interpolate.LinearNDInterpolator` instance
ip
Returns
-------
deviations : list
deviations
The deviation per triangle.
"""
values = ip.values / (ip.values.ptp(axis=0).max() or 1)
Expand Down Expand Up @@ -79,11 +82,11 @@ def areas(ip: LinearNDInterpolator) -> np.ndarray:
Parameters
----------
ip : `scipy.interpolate.LinearNDInterpolator` instance
ip
Returns
-------
areas : numpy.ndarray
areas
The area per triangle in ``ip.tri``.
"""
p = ip.tri.points[ip.tri.simplices]
Expand All @@ -99,11 +102,11 @@ def uniform_loss(ip: LinearNDInterpolator) -> np.ndarray:
Parameters
----------
ip : `scipy.interpolate.LinearNDInterpolator` instance
ip
Returns
-------
losses : numpy.ndarray
losses
Loss per triangle in ``ip.tri``.
Examples
Expand Down Expand Up @@ -136,7 +139,7 @@ def resolution_loss_function(
Returns
-------
loss_function : callable
loss_function
Examples
--------
Expand Down Expand Up @@ -173,11 +176,11 @@ def minimize_triangle_surface_loss(ip: LinearNDInterpolator) -> np.ndarray:
Parameters
----------
ip : `scipy.interpolate.LinearNDInterpolator` instance
ip
Returns
-------
losses : numpy.ndarray
losses
Loss per triangle in ``ip.tri``.
Examples
Expand Down Expand Up @@ -217,11 +220,11 @@ def default_loss(ip: LinearNDInterpolator) -> np.ndarray:
Parameters
----------
ip : `scipy.interpolate.LinearNDInterpolator` instance
ip
Returns
-------
losses : numpy.ndarray
losses
Loss per triangle in ``ip.tri``.
"""
dev = np.sum(deviations(ip), axis=0)
Expand All @@ -241,15 +244,15 @@ def choose_point_in_triangle(triangle: np.ndarray, max_badness: int) -> np.ndarr
Parameters
----------
triangle : numpy.ndarray
triangle
The coordinates of a triangle with shape (3, 2).
max_badness : int
max_badness
The badness at which the point is either chosen on a edge or
in the middle.
Returns
-------
point : numpy.ndarray
point
The x and y coordinate of the suggested new point.
"""
a, b, c = triangle
Expand All @@ -267,17 +270,17 @@ def choose_point_in_triangle(triangle: np.ndarray, max_badness: int) -> np.ndarr
return point


def triangle_loss(ip):
def triangle_loss(ip: LinearNDInterpolator) -> list[float]:
r"""Computes the average of the volumes of the simplex combined with each
neighbouring point.
Parameters
----------
ip : `scipy.interpolate.LinearNDInterpolator` instance
ip
Returns
-------
triangle_loss : list
triangle_loss
The mean volume per triangle.
Notes
Expand Down Expand Up @@ -311,13 +314,13 @@ class Learner2D(BaseLearner):
Parameters
----------
function : callable
function
The function to learn. Must take a tuple of two real
parameters and return a real number.
bounds : list of 2-tuples
bounds
A list ``[(a1, b1), (a2, b2)]`` containing bounds,
one per dimension.
loss_per_triangle : callable, optional
loss_per_triangle
A function that returns the loss for every triangle.
If not provided, then a default is used, which uses
the deviation from a linear estimate, as well as
Expand Down Expand Up @@ -424,19 +427,19 @@ def to_dataframe(
Parameters
----------
with_default_function_args : bool, optional
with_default_function_args
Include the ``learner.function``'s default arguments as a
column, by default True
function_prefix : str, optional
function_prefix
Prefix to the ``learner.function``'s default arguments' names,
by default "function."
seed_name : str, optional
seed_name
Name of the seed parameter, by default "seed"
x_name : str, optional
x_name
Name of the input x value, by default "x"
y_name : str, optional
y_name
Name of the input y value, by default "y"
z_name : str, optional
z_name
Name of the output value, by default "z"
Returns
Expand Down Expand Up @@ -475,18 +478,18 @@ def load_dataframe(
Parameters
----------
df : pandas.DataFrame
df
The data to load.
with_default_function_args : bool, optional
with_default_function_args
The ``with_default_function_args`` used in ``to_dataframe()``,
by default True
function_prefix : str, optional
function_prefix
The ``function_prefix`` used in ``to_dataframe``, by default "function."
x_name : str, optional
x_name
The ``x_name`` used in ``to_dataframe``, by default "x"
y_name : str, optional
y_name
The ``y_name`` used in ``to_dataframe``, by default "y"
z_name : str, optional
z_name
The ``z_name`` used in ``to_dataframe``, by default "z"
"""
data = df.set_index([x_name, y_name])[z_name].to_dict()
Expand Down Expand Up @@ -538,7 +541,7 @@ def interpolated_on_grid(
Parameters
----------
n : int, optional
n
Number of points in x and y. If None (default) this number is
evaluated by looking at the size of the smallest triangle.
Expand Down Expand Up @@ -611,14 +614,14 @@ def interpolator(self, *, scaled: bool = False) -> LinearNDInterpolator:
Parameters
----------
scaled : bool
scaled
Use True if all points are inside the
unit-square [(-0.5, 0.5), (-0.5, 0.5)] or False if
the data points are inside the ``learner.bounds``.
Returns
-------
interpolator : `scipy.interpolate.LinearNDInterpolator`
interpolator
Examples
--------
Expand Down Expand Up @@ -755,7 +758,9 @@ def remove_unfinished(self) -> None:
if p not in self.data:
self._stack[p] = np.inf

def plot(self, n=None, tri_alpha=0):
def plot(
self, n: int = None, tri_alpha: float = 0
) -> holoviews.Overlay | holoviews.HoloMap:
r"""Plot the Learner2D's current state.
This plot function interpolates the data on a regular grid.
Expand All @@ -764,16 +769,16 @@ def plot(self, n=None, tri_alpha=0):
Parameters
----------
n : int
n
Number of points in x and y. If None (default) this number is
evaluated by looking at the size of the smallest triangle.
tri_alpha : float
tri_alpha
The opacity ``(0 <= tri_alpha <= 1)`` of the triangles overlayed
on top of the image. By default the triangulation is not visible.
Returns
-------
plot : `holoviews.core.Overlay` or `holoviews.core.HoloMap`
plot
A `holoviews.core.Overlay` of
``holoviews.Image * holoviews.EdgePaths``. If the
`learner.function` returns a vector output, a
Expand Down
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