Fix examples in docs of mixture.py, bound.py and continuous.py

pymc3/distributions/bound.py

@@ -176,13 +176,15 @@ class Bound(object):
 
     Examples
     --------
-    with pm.Model():
-        NegativeNormal = pm.Bound(pm.Normal, upper=0.0)
-        par1 = NegativeNormal('par2', mu=0.0, sd=1.0, testval=1.0)
+    .. code-block:: python
 
-        # or you can define it implicitly within the model context
-        par2 = pm.Bound(pm.Normal, lower=-1.0, upper=1.0)(
-                'par2', mu=0.0, sd=1.0, testval=1.0)
+        with pm.Model():
+            NegativeNormal = pm.Bound(pm.Normal, upper=0.0)
+            par1 = NegativeNormal('par2', mu=0.0, sd=1.0, testval=1.0)
+
+            # or you can define it implicitly within the model context
+            par2 = pm.Bound(pm.Normal, lower=-1.0, upper=1.0)(
+                    'par2', mu=0.0, sd=1.0, testval=1.0)
     """
 
     def __init__(self, distribution, lower=None, upper=None):

pymc3/distributions/continuous.py

@@ -275,11 +275,13 @@ class Normal(Continuous):
 
     Examples
     --------
-    with pm.Model():
-        x = pm.Normal('x', mu=0, sd=10)
-
-    with pm.Model():
-        x = pm.Normal('x', mu=0, tau=1/23)
+    .. code-block:: python
+
+        with pm.Model():
+            x = pm.Normal('x', mu=0, sd=10)
+
+        with pm.Model():
+            x = pm.Normal('x', mu=0, tau=1/23)
     """
 
     def __init__(self, mu=0, sd=None, tau=None, **kwargs):
@@ -362,11 +364,13 @@ class HalfNormal(PositiveContinuous):
 
     Examples
     --------
-    with pm.Model():
-        x = pm.HalfNormal('x', sd=10)
-
-    with pm.Model():
-        x = pm.HalfNormal('x', tau=1/15)
+    .. code-block:: python
+
+        with pm.Model():
+            x = pm.HalfNormal('x', sd=10)
+
+        with pm.Model():
+            x = pm.HalfNormal('x', tau=1/15)
     """
 
     def __init__(self, sd=None, tau=None, *args, **kwargs):
@@ -861,12 +865,14 @@ class Lognormal(PositiveContinuous):
 
     Example
     -------
-    # Example to show that we pass in only `sd` or `tau` but not both.
-    with pm.Model():
-        x = pm.Lognormal('x', mu=2, sd=30)
+    .. code-block:: python
 
-    with pm.Model():
-        x = pm.Lognormal('x', mu=2, tau=1/100)
+        # Example to show that we pass in only `sd` or `tau` but not both.
+        with pm.Model():
+            x = pm.Lognormal('x', mu=2, sd=30)
+
+        with pm.Model():
+            x = pm.Lognormal('x', mu=2, tau=1/100)
     """
 
     def __init__(self, mu=0, sd=None, tau=None, *args, **kwargs):
@@ -964,11 +970,13 @@ class StudentT(Continuous):
 
     Examples
     --------
-    with pm.Model():
-        x = pm.StudentT('x', nu=15, mu=0, sd=10)
-
-    with pm.Model():
-        x = pm.StudentT('x', nu=15, mu=0, lam=1/23)
+    .. code-block:: python
+
+        with pm.Model():
+            x = pm.StudentT('x', nu=15, mu=0, sd=10)
+
+        with pm.Model():
+            x = pm.StudentT('x', nu=15, mu=0, lam=1/23)
     """
 
     def __init__(self, nu, mu=0, lam=None, sd=None, *args, **kwargs):
@@ -1633,12 +1641,14 @@ class HalfStudentT(PositiveContinuous):
 
     Examples
     --------
-    # Only pass in one of lam or sd, but not both.
-    with pm.Model():
-        x = pm.HalfStudentT('x', sd=10, nu=10)
-
-    with pm.Model():
-        x = pm.HalfStudentT('x', lam=4, nu=10)
+    .. code-block:: python
+
+        # Only pass in one of lam or sd, but not both.
+        with pm.Model():
+            x = pm.HalfStudentT('x', sd=10, nu=10)
+
+        with pm.Model():
+            x = pm.HalfStudentT('x', lam=4, nu=10)
     """
     def __init__(self, nu=1, sd=None, lam=None, *args, **kwargs):
         super(HalfStudentT, self).__init__(*args, **kwargs)

pymc3/distributions/mixture.py

@@ -42,28 +42,30 @@ class Mixture(Distribution):
 
     Example
     -------
-    # 2-Mixture Poisson distribution
-    with pm.Model() as model:
-        lam = pm.Exponential('lam', lam=1, shape=(2,))  # `shape=(2,)` indicates two mixtures.
+    .. code-block:: python
 
-        # As we just need the logp, rather than add a RV to the model, we need to call .dist()
-        components = pm.Poisson.dist(mu=lam, shape=(2,))  
+        # 2-Mixture Poisson distribution
+        with pm.Model() as model:
+            lam = pm.Exponential('lam', lam=1, shape=(2,))  # `shape=(2,)` indicates two mixtures.
 
-        w = pm.Dirichlet('w', a=np.array([1, 1]))  # two mixture component weights.
+            # As we just need the logp, rather than add a RV to the model, we need to call .dist()
+            components = pm.Poisson.dist(mu=lam, shape=(2,))  
 
-        like = pm.Mixture('like', w=w, comp_dists=components, observed=data)
+            w = pm.Dirichlet('w', a=np.array([1, 1]))  # two mixture component weights.
 
-    # 2-Mixture Poisson using iterable of distributions.
-    with pm.Model() as model:
-        lam1 = pm.Exponential('lam1', lam=1)
-        lam2 = pm.Exponential('lam2', lam=1)
+            like = pm.Mixture('like', w=w, comp_dists=components, observed=data)
 
-        pois1 = pm.Poisson.dist(mu=lam1)
-        pois2 = pm.Poisson.dist(mu=lam2)
+        # 2-Mixture Poisson using iterable of distributions.
+        with pm.Model() as model:
+            lam1 = pm.Exponential('lam1', lam=1)
+            lam2 = pm.Exponential('lam2', lam=1)
 
-        w = pm.Dirichlet('w', a=np.array([1, 1]))
+            pois1 = pm.Poisson.dist(mu=lam1)
+            pois2 = pm.Poisson.dist(mu=lam2)
 
-        like = pm.Mixture('like', w=w, comp_dists = [pois1, pois2], observed=data)
+            w = pm.Dirichlet('w', a=np.array([1, 1]))
+
+            like = pm.Mixture('like', w=w, comp_dists = [pois1, pois2], observed=data)
     """
     def __init__(self, w, comp_dists, *args, **kwargs):
         shape = kwargs.pop('shape', ())