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Pydocstyle utils vector docstring (#2788)
* Added pydocstyle to pre-commit * Added docstrings for tests and updated the tests for autoreset * Add pydocstyle exclude folder to allow slowly adding new docstrings * Add docstrings for setup.py and gym/__init__.py, core.py, error.py and logger.py * Check that all unwrapped environment are of a particular wrapper type * Reverted back to import gym.spaces.Space to gym.spaces * Fixed the __init__.py docstring * Fixed autoreset autoreset test * Updated gym __init__.py top docstring * Fix examples in docstrings * Add docstrings and type hints where known to all functions and classes in gym/utils and gym/vector * Remove unnecessary import * Removed "unused error" and make APIerror deprecated at gym 1.0 * Add pydocstyle description to CONTRIBUTING.md * Added docstrings section to CONTRIBUTING.md * Added :meth: and :attr: keywords to docstrings * Added :meth: and :attr: keywords to docstrings * Imported annotations from __future__ to fix python 3.7 * Add __future__ import annotations for python 3.7 * isort * Remove utils and vectors for this PR and spaces for previous PR * Update gym/envs/classic_control/acrobot.py Co-authored-by: Markus Krimmel <montcyril@gmail.com> * Update gym/envs/classic_control/acrobot.py Co-authored-by: Markus Krimmel <montcyril@gmail.com> * Update gym/envs/classic_control/acrobot.py Co-authored-by: Markus Krimmel <montcyril@gmail.com> * Update gym/spaces/dict.py Co-authored-by: Markus Krimmel <montcyril@gmail.com> * Update gym/utils/env_checker.py Co-authored-by: Markus Krimmel <montcyril@gmail.com> * Update gym/utils/env_checker.py Co-authored-by: Markus Krimmel <montcyril@gmail.com> * Update gym/utils/env_checker.py Co-authored-by: Markus Krimmel <montcyril@gmail.com> * Update gym/utils/env_checker.py Co-authored-by: Markus Krimmel <montcyril@gmail.com> * Update gym/utils/env_checker.py Co-authored-by: Markus Krimmel <montcyril@gmail.com> * Update gym/utils/ezpickle.py Co-authored-by: Markus Krimmel <montcyril@gmail.com> * Update gym/utils/ezpickle.py Co-authored-by: Markus Krimmel <montcyril@gmail.com> * Update gym/utils/play.py Co-authored-by: Markus Krimmel <montcyril@gmail.com> * Pre-commit * Updated docstrings with :meth: * Updated docstrings with :meth: * Update gym/utils/play.py * Update gym/utils/play.py * Update gym/utils/play.py * Apply suggestions from code review Co-authored-by: Markus Krimmel <montcyril@gmail.com> * pre-commit * Update gym/utils/play.py Co-authored-by: Markus Krimmel <montcyril@gmail.com> * Updated fps and zoom parameter docstring * Update play docstring * Apply suggestions from code review Added suggested corrections from @markus28 Co-authored-by: Markus Krimmel <montcyril@gmail.com> * Pre-commit magic * Update the `gym.make` docstring with a warning for `env_checker` * Updated and fixed vector docstrings * Update test names for reflect the project filename style Co-authored-by: Markus Krimmel <montcyril@gmail.com>
This commit is contained in:
Mark Towers
GitHub
@@ -1,5 +1,7 @@
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"""Numpy utility functions: concatenate space samples and create empty array."""
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from collections import OrderedDict
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from functools import singledispatch
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from typing import Iterable, Union
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import numpy as np
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@@ -9,36 +11,29 @@ __all__ = ["concatenate", "create_empty_array"]
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@singledispatch
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def concatenate(space, items, out):
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def concatenate(
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space: Space, items: Iterable, out: Union[tuple, dict, np.ndarray]
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) -> Union[tuple, dict, np.ndarray]:
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"""Concatenate multiple samples from space into a single object.
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Parameters
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----------
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items : iterable of samples of `space`
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Samples to be concatenated.
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Example::
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out : tuple, dict, or `np.ndarray`
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>>> from gym.spaces import Box
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>>> space = Box(low=0, high=1, shape=(3,), dtype=np.float32)
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>>> out = np.zeros((2, 3), dtype=np.float32)
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>>> items = [space.sample() for _ in range(2)]
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>>> concatenate(space, items, out)
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array([[0.6348213 , 0.28607962, 0.60760117],
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[0.87383074, 0.192658 , 0.2148103 ]], dtype=float32)
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Args:
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space: Observation space of a single environment in the vectorized environment.
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items: Samples to be concatenated.
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out: The output object. This object is a (possibly nested) numpy array.
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Returns:
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The output object. This object is a (possibly nested) numpy array.
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space : `gym.spaces.Space` instance
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Observation space of a single environment in the vectorized environment.
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Returns
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-------
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out : tuple, dict, or `np.ndarray`
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The output object. This object is a (possibly nested) numpy array.
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Example
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-------
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>>> from gym.spaces import Box
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>>> space = Box(low=0, high=1, shape=(3,), dtype=np.float32)
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>>> out = np.zeros((2, 3), dtype=np.float32)
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>>> items = [space.sample() for _ in range(2)]
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>>> concatenate(items, out, space)
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array([[0.6348213 , 0.28607962, 0.60760117],
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[0.87383074, 0.192658 , 0.2148103 ]], dtype=float32)
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"""
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assert isinstance(items, (list, tuple))
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raise ValueError(
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f"Space of type `{type(space)}` is not a valid `gym.Space` instance."
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)
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@@ -76,38 +71,30 @@ def _concatenate_custom(space, items, out):
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@singledispatch
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def create_empty_array(space, n=1, fn=np.zeros):
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def create_empty_array(
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space: Space, n: int = 1, fn: callable = np.zeros
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) -> Union[tuple, dict, np.ndarray]:
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"""Create an empty (possibly nested) numpy array.
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Parameters
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----------
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space : `gym.spaces.Space` instance
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Observation space of a single environment in the vectorized environment.
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Example::
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n : int
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Number of environments in the vectorized environment. If `None`, creates
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an empty sample from `space`.
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>>> from gym.spaces import Box, Dict
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>>> space = Dict({
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... 'position': Box(low=0, high=1, shape=(3,), dtype=np.float32),
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... 'velocity': Box(low=0, high=1, shape=(2,), dtype=np.float32)})
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>>> create_empty_array(space, n=2, fn=np.zeros)
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OrderedDict([('position', array([[0., 0., 0.],
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[0., 0., 0.]], dtype=float32)),
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('velocity', array([[0., 0.],
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[0., 0.]], dtype=float32))])
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fn : callable
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Function to apply when creating the empty numpy array. Examples of such
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functions are `np.empty` or `np.zeros`.
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Args:
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space: Observation space of a single environment in the vectorized environment.
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n: Number of environments in the vectorized environment. If `None`, creates an empty sample from `space`.
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fn: Function to apply when creating the empty numpy array. Examples of such functions are `np.empty` or `np.zeros`.
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Returns
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-------
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out : tuple, dict, or `np.ndarray`
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Returns:
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The output object. This object is a (possibly nested) numpy array.
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Example
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-------
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>>> from gym.spaces import Box, Dict
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>>> space = Dict({
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... 'position': Box(low=0, high=1, shape=(3,), dtype=np.float32),
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... 'velocity': Box(low=0, high=1, shape=(2,), dtype=np.float32)})
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>>> create_empty_array(space, n=2, fn=np.zeros)
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OrderedDict([('position', array([[0., 0., 0.],
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[0., 0., 0.]], dtype=float32)),
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('velocity', array([[0., 0.],
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[0., 0.]], dtype=float32))])
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"""
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raise ValueError(
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f"Space of type `{type(space)}` is not a valid `gym.Space` instance."
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