Add formatters module

Reference: #130

The problem before is that we tend to have many parameters in our
plotting functions. The formatters module changes that. We have
three types of formatters: Designer, Animator, and Mesher. There
are defaults present, but the user can change and pass them to the
ploting functions whenever needed.

Signed-off-by: Lester James V. Miranda <ljvmiranda@gmail.com>

pyswarms/utils/plotters/formatters.py

@@ -0,0 +1,65 @@
+# -*- coding: utf-8 -*-
+
+"""
+Plot Formatters
+
+This module implements helpful classes to format your plots or create meshes.
+"""
+
+# Import modules
+import numpy as np
+from attr import (attrs, attrib)
+from attr.validators import instance_of
+
+@attrs
+class Designer(object):
+    """Designer class for specifying a plot's formatting and design"""
+    # Overall plot design
+    figsize = attrib(type=tuple, validator=instance_of(tuple), default=(10,8))
+    title_fontsize = attrib(validator=instance_of((str, int, float)),
+                            default='large')
+    text_fontsize = attrib(validator=instance_of((str, int, float)),
+                           default='medium')
+    label = attrib(validator=instance_of((str, list, tuple)), default='Cost')
+    limits = attrib(validator=instance_of((list, tuple)), 
+                    default=[(-1,1),(-1,1)])
+
+@attrs
+class Animator(object):
+    """Animator class for specifying animation behavior"""
+    interval = attrib(type=int, validator=instance_of(int), default=80)
+    repeat_delay = attrib(default=None)
+    repeat = attrib(type=bool, validator=instance_of(bool), default=True)
+
+@attrs
+class Mesher(object):
+    """Mesher class for plotting contours of objective functions"""
+    func = attrib()
+    # For mesh creation
+    delta = attrib(type=float, default=0.001)
+    limits = attrib(validator=instance_of((list, tuple)),
+                    default=[(-1,1),(-1,1)])
+    levels = attrib(type=list, default=np.arange(-2.0,2.0,0.070))
+    # Surface transparency
+    alpha = attrib(type=float, validator=instance_of(float), default=0.3)
+
+    def compute_history_3d(self, pos_history):
+        """Computes a 3D position matrix
+
+        The first two columns are the 2D position in the x and y axes
+        respectively, while the third column is the fitness on that given
+        position.
+
+        Parameters
+        ----------
+        pos_history : numpy.ndarray
+            Two-dimensional position matrix history of shape
+            :code:`(iterations, n_particles, 2)`
+
+        Returns
+        -------
+        numpy.ndarray
+            3D position matrix of shape :code:`(iterations, n_particles, 3)`
+        """
+        fitness = np.array(list(map(self.func, pos_history)))
+        return np.dstack((pos_history, fitness))