Cleaned up model.py, made it comply with pep8, and fixed lint error on distribution.py.

Author: lucianopaz

pymc3/distributions/distribution.py

@@ -5,7 +5,7 @@
 import theano
 from ..memoize import memoize
 from ..model import (
-    Model, modelcontext, FreeRV, ObservedRV, MultiObservedRV,
+    Model, modelcontext, FreeRV, ObservedRV,
     not_shared_or_constant_variable, DependenceDAG
 )
 from ..vartypes import string_types
@@ -35,12 +35,14 @@ def __new__(cls, name, *args, **kwargs):
         if isinstance(name, string_types):
             data = kwargs.pop('observed', None)
             if isinstance(data, ObservedRV) or isinstance(data, FreeRV):
-                raise TypeError("observed needs to be data but got: {}".format(type(data)))
+                raise TypeError("observed needs to be data but got: {}".
+                                format(type(data)))
             total_size = kwargs.pop('total_size', None)
             dist = cls.dist(*args, **kwargs)
             return model.Var(name, dist, data, total_size)
         else:
-            raise TypeError("Name needs to be a string but got: {}".format(name))
+            raise TypeError("Name needs to be a string but got: {}".
+                            format(name))
 
     def __getnewargs__(self):
         return _Unpickling,
@@ -64,12 +66,14 @@ def __init__(self, shape, dtype, testval=None, defaults=(),
         self.conditional_on = None
 
     def default(self):
-        return np.asarray(self.get_test_val(self.testval, self.defaults), self.dtype)
+        return np.asarray(self.get_test_val(self.testval, self.defaults),
+                          self.dtype)
 
     def get_test_val(self, val, defaults):
         if val is None:
             for v in defaults:
-                if hasattr(self, v) and np.all(np.isfinite(self.getattr_value(v))):
+                if (hasattr(self, v) and
+                        np.all(np.isfinite(self.getattr_value(v)))):
                     return self.getattr_value(v)
         else:
             return self.getattr_value(val)
@@ -132,7 +136,8 @@ class NoDistribution(Distribution):
     def __init__(self, shape, dtype, testval=None, defaults=(),
                  transform=None, parent_dist=None, *args, **kwargs):
         super(NoDistribution, self).__init__(shape=shape, dtype=dtype,
-                                             testval=testval, defaults=defaults,
+                                             testval=testval,
+                                             defaults=defaults,
                                              *args, **kwargs)
         self.parent_dist = parent_dist
 
@@ -161,7 +166,8 @@ def __init__(self, shape=(), dtype=None, defaults=('mode',),
             else:
                 dtype = 'int64'
         if dtype != 'int16' and dtype != 'int64':
-            raise TypeError('Discrete classes expect dtype to be int16 or int64.')
+            raise TypeError('Discrete classes expect dtype to be int16 or '
+                            'int64.')
 
         if kwargs.get('transform', None) is not None:
             raise ValueError("Transformations for discrete distributions "
@@ -174,7 +180,8 @@ def __init__(self, shape=(), dtype=None, defaults=('mode',),
 class Continuous(Distribution):
     """Base class for continuous distributions"""
 
-    def __init__(self, shape=(), dtype=None, defaults=('median', 'mean', 'mode'),
+    def __init__(self, shape=(), dtype=None,
+                 defaults=('median', 'mean', 'mode'),
                  *args, **kwargs):
         if dtype is None:
             dtype = theano.config.floatX
@@ -195,12 +202,15 @@ class DensityDist(Distribution):
             with pm.Model():
                 mu = pm.Normal('mu',0,1)
                 normal_dist = pm.Normal.dist(mu, 1)
-                pm.DensityDist('density_dist', normal_dist.logp, observed=np.random.randn(100), random=normal_dist.random)
+                pm.DensityDist('density_dist', normal_dist.logp,
+                               observed=np.random.randn(100),
+                               random=normal_dist.random)
                 trace = pm.sample(100)
 
     """
 
-    def __init__(self, logp, shape=(), dtype=None, testval=0, random=None, *args, **kwargs):
+    def __init__(self, logp, shape=(), dtype=None, testval=0, random=None,
+                 *args, **kwargs):
         if dtype is None:
             dtype = theano.config.floatX
         super(DensityDist, self).__init__(
@@ -213,7 +223,8 @@ def random(self, *args, **kwargs):
             return self.rand(*args, **kwargs)
         else:
             raise ValueError("Distribution was not passed any random method "
-                            "Define a custom random method and pass it as kwarg random")
+                             "Define a custom random method and pass it as "
+                             "kwarg random")
 
 
 def draw_values(params, point=None, size=None, model=None):
@@ -462,6 +473,7 @@ def to_tuple(shape):
         shape = tuple(shape)
     return shape
 
+
 def _is_one_d(dist_shape):
     if hasattr(dist_shape, 'dshape') and dist_shape.dshape in ((), (0,), (1,)):
         return True
@@ -471,6 +483,7 @@ def _is_one_d(dist_shape):
         return True
     return False
 
+
 def generate_samples(generator, *args, **kwargs):
     """Generate samples from the distribution of a random variable.