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Clean some docstrings (#1854)

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* add type of argument

* fix typos

* split lines for formatting

* reformat string, add ellipsis, remove r string

* make docstring stylistically consistent

* make docstrings a little more elaboratet

* reduce by 1 space

* make line wrap 120

* remove unnecessary line

* add returns to docstring

* add docstring, make code more pep8 and delete some unused print functions

* more pep8

* file docstring instead of comments

* delete unused variables, add file docstring and add some pep8 spring cleaning

* add file docstring, fix typos and add some pep8 correections

Co-authored-by: Dan <daniel.timbrell@ing.com>
This commit is contained in:
Dan Timbrell
2020-04-24 23:10:27 +02:00
committed by GitHub
parent f2c9793eb7
commit 3bd5ef71c2
7 changed files with 360 additions and 291 deletions
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46
gym/envs/classic_control/acrobot.py
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@@ -214,13 +214,17 @@ class AcrobotEnv(core.Env):
self.viewer = None
def wrap(x, m, M):
"""
:param x: a scalar
:param m: minimum possible value in range
:param M: maximum possible value in range
Wraps ``x`` so m <= x <= M; but unlike ``bound()`` which
"""Wraps ``x`` so m <= x <= M; but unlike ``bound()`` which
truncates, ``wrap()`` wraps x around the coordinate system defined by m,M.\n
For example, m = -180, M = 180 (degrees), x = 360 --> returns 0.
Args:
x: a scalar
m: minimum possible value in range
M: maximum possible value in range
Returns:
x: a scalar, wrapped
"""
diff = M - m
while x > M:
@@ -230,10 +234,14 @@ def wrap(x, m, M):
return x
def bound(x, m, M=None):
"""
:param x: scalar
Either have m as scalar, so bound(x,m,M) which returns m <= x <= M *OR*
"""Either have m as scalar, so bound(x,m,M) which returns m <= x <= M *OR*
have m as length 2 vector, bound(x,m, <IGNORED>) returns m[0] <= x <= m[1].
Args:
x: scalar
Returns:
x: scalar, bound between min (m) and Max (M)
"""
if M is None:
M = m[1]
@@ -248,17 +256,14 @@ def rk4(derivs, y0, t, *args, **kwargs):
This is a toy implementation which may be useful if you find
yourself stranded on a system w/o scipy. Otherwise use
:func:`scipy.integrate`.
*y0*
initial state vector
*t*
sample times
*derivs*
returns the derivative of the system and has the
signature ``dy = derivs(yi, ti)``
*args*
additional arguments passed to the derivative function
*kwargs*
additional keyword arguments passed to the derivative function
Args:
derivs: the derivative of the system and has the signature ``dy = derivs(yi, ti)``
y0: initial state vector
t: sample times
args: additional arguments passed to the derivative function
kwargs: additional keyword arguments passed to the derivative function
Example 1 ::
## 2D system
def derivs6(x,t):
@@ -278,6 +283,9 @@ def rk4(derivs, y0, t, *args, **kwargs):
yout = rk4(derivs, y0, t)
If you have access to scipy, you should probably be using the
scipy.integrate tools rather than this function.
Returns:
yout: Runge-Kutta approximation of the ODE
"""
try: