DOC: Fix documentation warnings (#653)

quantecon/discrete_rv.py

@@ -61,7 +61,7 @@ def draw(self, k=1, random_state=None):
         q[i].
 
         Parameters
-        -----------
+        ----------
         k : scalar(int), optional
             Number of draws to be returned
 

quantecon/filter.py

@@ -24,15 +24,17 @@ def hamilton_filter(data, h, p=None):
         Must be greater than h.
         If not supplied, random walk process is assumed.
 
-    Note: For seasonal data, it's desirable for p and h to be integer multiples
-          of the number of obsevations in a year.
-          e.g. For quarterly data, h = 8 and p = 4 are recommended.
-
     Returns
     -------
     cycle : array of cyclical component
     trend : trend component
 
+    Notes
+    -----
+    For seasonal data, it's desirable for p and h to be integer multiples of
+    the number of obsevations in a year. E.g. for quarterly data, h = 8 and p =
+    4 are recommended.
+
     """
     # transform data to array
     y = np.asarray(data, float)

quantecon/game_theory/logitdyn.py

@@ -95,8 +95,8 @@ def play(self, init_actions=None, player_ind_seq=None, num_reps=1,
         random_state : np.random.RandomState, optional(default=None)
             Random number generator used.
 
-        Return
-        ------
+        Returns
+        -------
         tuple(int)
             The action profile after iterations.
 
@@ -137,8 +137,8 @@ def time_series(self, ts_length, init_actions=None, random_state=None):
         random_state : np.random.RandomState, optional(default=None)
             Random number generator used.
 
-        Return
-        ------
+        Returns
+        -------
         ndarray(int)
             The array representing the time series of action profiles.
 

quantecon/game_theory/support_enumeration.py

@@ -49,7 +49,7 @@ def support_enumeration_gen(g):
         NormalFormGame instance with 2 players.
 
     Yields
-    -------
+    ------
     tuple(ndarray(float, ndim=1))
         Tuple of Nash equilibrium mixed actions.
 

quantecon/game_theory/vertex_enumeration.py

@@ -59,7 +59,7 @@ def vertex_enumeration_gen(g, qhull_options=None):
         manual <http://www.qhull.org>`_  for details.
 
     Yields
-    -------
+    ------
     tuple(ndarray(float, ndim=1))
         Tuple of Nash equilibrium mixed actions.
 
@@ -124,7 +124,7 @@ def _vertex_enumeration_gen(labelings_bits_tup, equations_tup, trans_recips):
 
 
 class _BestResponsePolytope:
-    """
+    r"""
     Class that represents a best response polytope for a player in a
     two-player normal form game.
 

quantecon/inequality.py

@@ -60,7 +60,7 @@ def gini_coefficient(y):
     Implements the Gini inequality index
 
     Parameters
-    -----------
+    ----------
     y : array_like(float)
         Array of income/wealth for each individual.
         Ordered or unordered is fine
@@ -88,13 +88,13 @@ def shorrocks_index(A):
     Implements Shorrocks mobility index
 
     Parameters
-    -----------
+    ----------
     A : array_like(float)
         Square matrix with transition probabilities (mobility matrix) of
         dimension m
 
     Returns
-    --------
+    -------
     Shorrocks index: float
         The Shorrocks mobility index calculated as
 
@@ -105,7 +105,7 @@ def shorrocks_index(A):
         An index equal to 0 indicates complete immobility.
 
     References
-    -----------
+    ----------
     .. [1] Wealth distribution and social mobility in the US:
        A quantitative approach (Benhabib, Bisin, Luo, 2017).
        https://www.econ.nyu.edu/user/bisina/RevisionAugust.pdf

quantecon/kalman.py

@@ -34,7 +34,7 @@ class Kalman:
 
 
     Parameters
-    -----------
+    ----------
     ss : instance of LinearStateSpace
         An instance of the quantecon.lss.LinearStateSpace class
     x_hat : scalar(float) or array_like(float), optional(default=None)
@@ -241,7 +241,7 @@ def update(self, y):
         self.filtered_to_forecast()
 
     def stationary_values(self, method='doubling'):
-        """
+        r"""
         Computes the limit of :math:`\Sigma_t` as t goes to infinity by
         solving the associated Riccati equation. The outputs are stored in the
         attributes `K_infinity` and `Sigma_infinity`. Computation is via the

quantecon/lss.py

@@ -47,7 +47,7 @@ def simulate_linear_model(A, x0, v, ts_length):
         The length of the time series
 
     Returns
-    --------
+    -------
     x : np.ndarray
         Time series with ts_length columns, the t-th column being :math:`x_t`
     """

quantecon/optimize/nelder_mead.py

@@ -52,7 +52,7 @@ def nelder_mead(fun, x0, bounds=np.array([[], []]).T, args=(), tol_f=1e-10,
         The maximum number of allowed iterations.
 
     Returns
-    ----------
+    -------
     results : namedtuple
         A namedtuple containing the following items:
         ::
@@ -78,7 +78,7 @@ def nelder_mead(fun, x0, bounds=np.array([[], []]).T, args=(), tol_f=1e-10,
                                  [0.99999814, 0.99999756]]))
 
     Notes
-    --------
+    -----
     This algorithm has a long history of successful use in applications, but it
     will usually be slower than an algorithm that uses first or second
     derivative information. In practice, it can have poor performance in
@@ -170,7 +170,7 @@ def _nelder_mead_algorithm(fun, vertices, bounds=np.array([[], []]).T,
         The maximum number of allowed iterations.
 
     Returns
-    ----------
+    -------
     results : namedtuple
         A namedtuple containing the following items:
         ::
@@ -312,7 +312,7 @@ def _initialize_simplex(x0, bounds):
         Sequence of (min, max) pairs for each element in x0.
 
     Returns
-    ----------
+    -------
     vertices : ndarray(float, ndim=2)
         Initial simplex with shape (n+1, n).
 
@@ -396,7 +396,7 @@ def _check_bounds(x, bounds):
         Sequence of (min, max) pairs for each element in x.
 
     Returns
-    ----------
+    -------
     bool
         `True` if `x` is within `bounds`, `False` otherwise.
 
@@ -434,7 +434,7 @@ def _neg_bounded_fun(fun, bounds, x, args=()):
         Extra arguments passed to the objective function.
 
     Returns
-    ----------
+    -------
     scalar
         `-fun(x, *args)` if x is within `bounds`, `np.inf` otherwise.
 

quantecon/quadsums.py

@@ -87,7 +87,7 @@ def m_quadratic_sum(A, B, max_it=50):
         The maximum number of iterations
 
     Returns
-    ========
+    -------
     gamma1: array_like(float, ndim=2)
         Represents the value :math:`V`
 

quantecon/util/random.py

@@ -17,9 +17,9 @@ def check_random_state(seed):
 
     Otherwise raise ValueError.
 
-    .. Note
-       ----
-        1. This code was sourced from scikit-learn
+    Notes
+    -----
+    This code was sourced from scikit-learn.
 
     """
     if seed is None or seed is np.random: