Add black to documentation

Author: AntoinePrv

.pre-commit-config.yaml

@@ -23,6 +23,12 @@ repos:
     rev: 19.10b0
     hooks:
       - id: black
+  - repo: https://github.com/asottile/blacken-docs
+    rev: v1.10.0
+    hooks:
+    - id: blacken-docs
+      args: [ '--line-length', '100' ]
+      additional_dependencies: [black==21.7b0]
   - repo: https://github.com/Lucas-C/pre-commit-hooks
     rev: v1.1.9
     hooks:

README.rst

@@ -36,7 +36,7 @@ meant to mimic the `OpenAi Gym <https://gym.openai.com/>`_ API (as much as possi
    for _ in range(10):
        obs, action_set, reward_offset, done, info = env.reset(next(instances))
        while not done:
-          obs, action_set, reward, done, info = env.step(action_set[0])
+           obs, action_set, reward, done, info = env.step(action_set[0])
 
 
 Documentation

docs/howto/create-environments.rst

@@ -59,7 +59,6 @@ example, so we do not override it.
 
 
    class SimpleBranchingDynamics(ecole.dynamics.BranchingDynamics):
-
        def reset_dynamics(self, model):
            # Share memory with Ecole model
            pyscipopt_model = model.as_pyscipopt()
@@ -95,7 +94,6 @@ To do so, we will take parameters in the constructor.
    :skipif: pyscipopt is None
 
    class SimpleBranchingDynamics(ecole.dynamics.BranchingDynamics):
-
        def __init__(self, disable_presolve=True, disable_cuts=True, *args, **kwargs):
            super().__init__(*args, **kwargs)
            self.disable_presolve = disable_presolve

docs/howto/create-functions.rst

@@ -31,7 +31,6 @@ maximum absolute value.
 
 
    class ScaledNodeBipartite(NodeBipartite):
-
        def extract(self, model, done):
            # Call parent method to get the original observation
            obs = super().extract(model, done)
@@ -55,7 +54,6 @@ This will illustrate how the class paradigm is useful to saving information betw
 .. testcode::
 
    class MovingScaledNodeBipartite(NodeBipartite):
-
        def __init__(self, alpha, *args, **kwargs):
            # Construct parent class with other parameters
            super().__init__(*args, **kwargs)
@@ -123,8 +121,7 @@ For instance, we can create a ``StochasticReward`` function that will wrap any g
 
 
    class StochasticReward:
-
-       def __init__(self, reward_function, probability = 0.05):
+       def __init__(self, reward_function, probability=0.05):
            self.reward_function = reward_function
            self.probability = probability
 
@@ -135,7 +132,7 @@ For instance, we can create a ``StochasticReward`` function that will wrap any g
            # Unconditionally getting reward as reward_funcition.extract may have side effects
            reward = self.reward_function.extract(model, done)
            if random.random() < probability:
-               return 0.
+               return 0.0
            else:
                return reward
 

docs/howto/observation-functions.rst

@@ -51,7 +51,7 @@ you can explicitly pass ``None`` to the environment constructor.
    >>> env = ecole.environment.Branching(observation_function=None)
    >>> env.observation_function  # doctest: +SKIP
    ecole.observation.nothing()
-   >>> obs, _, _, _, _= env.reset("path/to/problem")
+   >>> obs, _, _, _, _ = env.reset("path/to/problem")
    >>> obs is None
    True
 
@@ -64,8 +64,8 @@ To use multiple observation functions, wrap them in a ``list`` or ``dict``.
 .. doctest::
 
    >>> obs_func = {
-   ...    "some_name": ecole.observation.NodeBipartite(),
-   ...    "other_name": ecole.observation.Nothing(),
+   ...     "some_name": ecole.observation.NodeBipartite(),
+   ...     "other_name": ecole.observation.Nothing(),
    ... }
    >>> env = ecole.environment.Branching(observation_function=obs_func)
    >>> obs, _, _, _, _ = env.reset("path/to/problem")
@@ -78,9 +78,7 @@ Similarily with a tuple
 
 .. doctest::
 
-   >>> obs_func = (
-   ...    ecole.observation.NodeBipartite(), ecole.observation.Nothing()
-   ... )
+   >>> obs_func = (ecole.observation.NodeBipartite(), ecole.observation.Nothing())
    >>> env = ecole.environment.Branching(observation_function=obs_func)
    >>> obs, _, _, _, _ = env.reset("path/to/problem")
    >>> obs  # doctest: +SKIP

docs/howto/reward-functions.rst

@@ -86,7 +86,7 @@ Arithmetic operations are even allowed between different reward functions,
 
    from ecole.reward import LpIterations, IsDone
 
-   4.0 * LpIterations()**2 - 3 * IsDone()
+   4.0 * LpIterations() ** 2 - 3 * IsDone()
 
 which is especially powerful because in this normally it would *not* be possible to pass both
 :py:class:`~ecole.reward.LpIterations` and :py:class:`~ecole.reward.IsDone` to the
@@ -96,7 +96,7 @@ All operations that are valid between scalars are valid between reward functions
 
 .. testcode::
 
-   - IsDone() ** abs(LpIterations() // 4)
+   -IsDone() ** abs(LpIterations() // 4)
 
 In addition, not all commonly used mathematical operations have a dedicated Python operator: to
 accomodate this, Ecole implements a number of other operations as methods of reward functions.
@@ -110,7 +110,7 @@ This also works with rewards functions created from arithmetic expressions.
 
 .. testcode::
 
-   (3 - 2*LpIterations()).exp()
+   (3 - 2 * LpIterations()).exp()
 
 Finally, reward functions have an ``apply`` method to compose rewards with any
 function.