Format code using black and add pre-commit hooks

.flake8

@@ -0,0 +1,5 @@
+[flake8]
+ignore = E203, E266, E501, W503, F403, F401
+max-line-length = 79
+max-complexity = 18
+select = B,C,E,F,W,T4,B9

.pre-commit-config.yaml

@@ -0,0 +1,10 @@
+repos:
+-   repo: https://github.com/ambv/black
+    rev: stable
+    hooks:
+    - id: black
+      language_version: python3.6
+-   repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v1.2.3
+    hooks:
+    - id: flake8

CONTRIBUTING.rst

@@ -77,13 +77,15 @@ Ready to contribute? Here's how to set up `pyswarms` for local development.
 
    Now you can make your changes locally.
 
-5. When you're done making changes, check that your changes pass flake8 and the tests, including testing other Python versions with tox::
+5. When you're done making changes, check that your changes pass flake8 and the tests, including testing other Python versions with tox. In addition, ensure that your code is formatted using black::
 
     $ flake8 pyswarms tests
+    $ black pyswarms tests
     $ python setup.py test or py.test
     $ tox
 
-   To get flake8 and tox, just pip install them into your virtualenv.
+   To get flake8, black, and tox, just pip install them into your virtualenv. If you wish,
+   you can add pre-commit hooks for both flake8 and black to make all formatting easier.
 
 6. Commit your changes and push your branch to GitHub::
 

README.md

@@ -8,6 +8,7 @@
 [![Documentation Status](https://readthedocs.org/projects/pyswarms/badge/?version=master)](https://pyswarms.readthedocs.io/en/master/?badge=development)
 [![License: MIT](https://img.shields.io/badge/license-MIT-blue.svg )](https://raw.githubusercontent.com/ljvmiranda921/pyswarms/master/LICENSE)
 [![DOI](http://joss.theoj.org/papers/10.21105/joss.00433/status.svg)](https://doi.org/10.21105/joss.00433)
+[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/ambv/black)
 [![Gitter Chat](https://badges.gitter.im/Join%20Chat.svg)](https://gitter.im/pyswarms/Issues)
 
 PySwarms is an extensible research toolkit for particle swarm optimization

pyproject.toml

@@ -0,0 +1,16 @@
+[tool.black]
+line-length = 79
+include = '\.pyi?$'
+exclude = '''
+/(
+    \.git
+  | \.hg
+  | \.mypy_cache
+  | \.tox
+  | \.venv
+  | _build
+  | buck-out
+  | build
+  | dist
+)/
+'''

pyswarms/__init__.py

@@ -11,14 +11,10 @@
 """
 
 __author__ = """Lester James V. Miranda"""
-__email__ = '[email protected]'
-__version__ = '0.2.0'
+__email__ = "[email protected]"
+__version__ = "0.2.0"
 
 from .single import global_best, local_best
 from .discrete import binary
 
-__all__ = [
-    'global_best',
-    'local_best',
-    'binary'
-    ]
+__all__ = ["global_best", "local_best", "binary"]

pyswarms/backend/__init__.py

@@ -6,4 +6,6 @@
 
 from .generators import *
 from .operators import *
-from .swarms import *
+from .swarms import *
+
+__all__ = ["generators", "operators", "swarms"]

pyswarms/backend/generators.py

@@ -16,7 +16,9 @@
 from .swarms import Swarm
 
 
-def generate_swarm(n_particles, dimensions, bounds=None, center=1.00, init_pos=None):
+def generate_swarm(
+    n_particles, dimensions, bounds=None, center=1.00, init_pos=None
+):
     """Generates a swarm
 
     Parameters
@@ -44,25 +46,37 @@ def generate_swarm(n_particles, dimensions, bounds=None, center=1.00, init_pos=N
         if (init_pos is not None) and (bounds is None):
             pos = init_pos
         elif (init_pos is not None) and (bounds is not None):
-            if not (np.all(bounds[0] <= init_pos) and np.all(init_pos <= bounds[1])):
-                raise ValueError('User-defined init_pos is out of bounds.')
+            if not (
+                np.all(bounds[0] <= init_pos) and np.all(init_pos <= bounds[1])
+            ):
+                raise ValueError("User-defined init_pos is out of bounds.")
             pos = init_pos
         elif (init_pos is None) and (bounds is None):
-            pos = center * np.random.uniform(low=0.0, high=1.0, size=(n_particles, dimensions))
+            pos = center * np.random.uniform(
+                low=0.0, high=1.0, size=(n_particles, dimensions)
+            )
         else:
             lb, ub = bounds
-            min_bounds = np.repeat(np.array(lb)[np.newaxis, :], n_particles, axis=0)
-            max_bounds = np.repeat(np.array(ub)[np.newaxis, :], n_particles, axis=0)
-            pos = center * np.random.uniform(low=min_bounds, high=max_bounds,
-                                             size=(n_particles, dimensions))
+            min_bounds = np.repeat(
+                np.array(lb)[np.newaxis, :], n_particles, axis=0
+            )
+            max_bounds = np.repeat(
+                np.array(ub)[np.newaxis, :], n_particles, axis=0
+            )
+            pos = center * np.random.uniform(
+                low=min_bounds, high=max_bounds, size=(n_particles, dimensions)
+            )
     except ValueError:
         raise
     else:
         return pos
 
-def generate_discrete_swarm(n_particles, dimensions, binary=False, init_pos=None):
+
+def generate_discrete_swarm(
+    n_particles, dimensions, binary=False, init_pos=None
+):
     """Generates a discrete swarm
-    
+
     Parameters
     ----------
     n_particles : int
@@ -78,19 +92,22 @@ def generate_discrete_swarm(n_particles, dimensions, binary=False, init_pos=None
     try:
         if (init_pos is not None) and binary:
             if not len(np.unique(init_pos)) == 2:
-                raise ValueError('User-defined init_pos is not binary!')
+                raise ValueError("User-defined init_pos is not binary!")
             pos = init_pos
         elif (init_pos is not None) and not binary:
             pos = init_pos
         elif (init_pos is None) and binary:
             pos = np.random.randint(2, size=(n_particles, dimensions))
         else:
-            pos = np.random.random_sample(size=(n_particles, dimensions)).argsort(axis=1)
+            pos = np.random.random_sample(
+                size=(n_particles, dimensions)
+            ).argsort(axis=1)
     except ValueError:
         raise
     else:
         return pos
 
+
 def generate_velocity(n_particles, dimensions, clamp=None):
     """Initializes a velocity vector
 
@@ -111,19 +128,29 @@ def generate_velocity(n_particles, dimensions, clamp=None):
         velocity matrix of shape (n_particles, dimensions)
     """
     try:
-        min_velocity, max_velocity = (0,1) if clamp==None else clamp
-        velocity = ((max_velocity - min_velocity) 
-                 * np.random.random_sample(size=(n_particles, dimensions)) 
-                 + min_velocity)
+        min_velocity, max_velocity = (0, 1) if clamp is None else clamp
+        velocity = (max_velocity - min_velocity) * np.random.random_sample(
+            size=(n_particles, dimensions)
+        ) + min_velocity
     except (ValueError, TypeError):
         raise
     else:
         return velocity
 
-def create_swarm(n_particles, dimensions, discrete=False, binary=False,
-                options={}, bounds=None, center=1.0, init_pos=None, clamp=None):
+
+def create_swarm(
+    n_particles,
+    dimensions,
+    discrete=False,
+    binary=False,
+    options={},
+    bounds=None,
+    center=1.0,
+    init_pos=None,
+    clamp=None,
+):
     """Abstracts the generate_swarm() and generate_velocity() methods
-    
+
     Parameters
     ----------
     n_particles : int
@@ -156,10 +183,17 @@ def create_swarm(n_particles, dimensions, discrete=False, binary=False,
         a Swarm class
     """
     if discrete:
-        position = generate_discrete_swarm(n_particles, dimensions, binary=binary)
+        position = generate_discrete_swarm(
+            n_particles, dimensions, binary=binary
+        )
     else:
-        position = generate_swarm(n_particles, dimensions, bounds=bounds,
-                                  center=center, init_pos=init_pos)
+        position = generate_swarm(
+            n_particles,
+            dimensions,
+            bounds=bounds,
+            center=center,
+            init_pos=init_pos,
+        )
 
     velocity = generate_velocity(n_particles, dimensions, clamp=clamp)
-    return Swarm(position, velocity, options=options)
+    return Swarm(position, velocity, options=options)

pyswarms/backend/operators.py

@@ -17,9 +17,10 @@
 # Create a logger
 logger = logging.getLogger(__name__)
 
+
 def compute_pbest(swarm):
     """Takes a swarm instance and updates the personal best scores
-    
+
     You can use this method to update your personal best positions.
 
     .. code-block:: python
@@ -37,7 +38,7 @@ def compute_pbest(swarm):
     It updates your :code:`current_pbest` with the personal bests acquired by
     comparing the (1) cost of the current positions and the (2) personal
     bests your swarm has attained.
-    
+
     If the cost of the current position is less than the cost of the personal
     best, then the current position replaces the previous personal best
     position.
@@ -58,25 +59,28 @@ def compute_pbest(swarm):
         # Infer dimensions from positions
         dimensions = swarm.dimensions
         # Create a 1-D and 2-D mask based from comparisons
-        mask_cost = (swarm.current_cost < swarm.pbest_cost)
-        mask_pos = np.repeat(mask_cost[:, np.newaxis], swarm.dimensions, axis=1)
+        mask_cost = swarm.current_cost < swarm.pbest_cost
+        mask_pos = np.repeat(mask_cost[:, np.newaxis], dimensions, axis=1)
         # Apply masks
         new_pbest_pos = np.where(~mask_pos, swarm.pbest_pos, swarm.position)
-        new_pbest_cost = np.where(~mask_cost, swarm.pbest_cost, swarm.current_cost)
+        new_pbest_cost = np.where(
+            ~mask_cost, swarm.pbest_cost, swarm.current_cost
+        )
     except AttributeError:
-        msg = 'Please pass a Swarm class. You passed {}'.format(type(swarm))
+        msg = "Please pass a Swarm class. You passed {}".format(type(swarm))
         logger.error(msg)
         raise
     else:
         return (new_pbest_pos, new_pbest_cost)
 
+
 def compute_velocity(swarm, clamp):
     """Updates the velocity matrix
 
     This method updates the velocity matrix using the best and current
     positions of the swarm. The velocity matrix is computed using the
     cognitive and social terms of the swarm.
-    
+
     A sample usage can be seen with the following:
 
     .. code-block :: python
@@ -107,33 +111,43 @@ def compute_velocity(swarm, clamp):
     try:
         # Prepare parameters
         swarm_size = swarm.position.shape
-        c1 = swarm.options['c1']
-        c2 = swarm.options['c2']
-        w = swarm.options['w']
+        c1 = swarm.options["c1"]
+        c2 = swarm.options["c2"]
+        w = swarm.options["w"]
         # Compute for cognitive and social terms
-        cognitive = (c1 * np.random.uniform(0,1, swarm_size) * (swarm.pbest_pos - swarm.position))
-        social = (c2 * np.random.uniform(0, 1, swarm_size) * (swarm.best_pos - swarm.position))
+        cognitive = (
+            c1
+            * np.random.uniform(0, 1, swarm_size)
+            * (swarm.pbest_pos - swarm.position)
+        )
+        social = (
+            c2
+            * np.random.uniform(0, 1, swarm_size)
+            * (swarm.best_pos - swarm.position)
+        )
         # Compute temp velocity (subject to clamping if possible)
         temp_velocity = (w * swarm.velocity) + cognitive + social
 
         if clamp is None:
             updated_velocity = temp_velocity
         else:
             min_velocity, max_velocity = clamp
-            mask = np.logical_and(temp_velocity >= min_velocity,
-                                temp_velocity <= max_velocity)
+            mask = np.logical_and(
+                temp_velocity >= min_velocity, temp_velocity <= max_velocity
+            )
             updated_velocity = np.where(~mask, swarm.velocity, temp_velocity)
     except AttributeError:
-        msg = 'Please pass a Swarm class. You passed {}'.format(type(swarm))
+        msg = "Please pass a Swarm class. You passed {}".format(type(swarm))
         logger.error(msg)
         raise
     except KeyError:
-        msg = 'Missing keyword in swarm.options'
+        msg = "Missing keyword in swarm.options"
         logger.error(msg)
         raise
     else:
         return updated_velocity
 
+
 def compute_position(swarm, bounds):
     """Updates the position matrix
 
@@ -160,15 +174,20 @@ def compute_position(swarm, bounds):
 
         if bounds is not None:
             lb, ub = bounds
-            min_bounds = np.repeat(np.array(lb)[np.newaxis, :], swarm.n_particles, axis=0)
-            max_bounds = np.repeat(np.array(ub)[np.newaxis, :], swarm.n_particles, axis=0)
-            mask = (np.all(min_bounds <= temp_position, axis=1)
-                * np.all(temp_position <= max_bounds, axis=1))
+            min_bounds = np.repeat(
+                np.array(lb)[np.newaxis, :], swarm.n_particles, axis=0
+            )
+            max_bounds = np.repeat(
+                np.array(ub)[np.newaxis, :], swarm.n_particles, axis=0
+            )
+            mask = np.all(min_bounds <= temp_position, axis=1) * np.all(
+                temp_position <= max_bounds, axis=1
+            )
             mask = np.repeat(mask[:, np.newaxis], swarm.dimensions, axis=1)
             temp_position = np.where(~mask, swarm.position, temp_position)
         position = temp_position
     except AttributeError:
-        msg = 'Please pass a Swarm class. You passed {}'.format(type(swarm))
+        msg = "Please pass a Swarm class. You passed {}".format(type(swarm))
         logger.error(msg)
         raise
     else: