Remove optional numba installation code

quantecon/graph_tools.py

@@ -12,6 +12,16 @@
 from fractions import gcd
 
 
+# Decorator for *_components properties
+def annotate_nodes(func):
+    def new_func(self):
+        list_of_components = func(self)
+        if self.node_labels is not None:
+            return [self.node_labels[c] for c in list_of_components]
+        return list_of_components
+    return new_func
+
+
 class DiGraph(object):
     r"""
     Class for a directed graph. It stores useful information about the
@@ -27,6 +37,11 @@ class DiGraph(object):
     weighted : bool, optional(default=False)
         Whether to treat `adj_matrix` as a weighted adjacency matrix.
 
+    node_labels : array_like(default=None)
+        Array_like of length n containing the labels associated with the
+        nodes, which must be homogeneous in type. If None, the labels
+        default to integers 0 through n-1.
+
     Attributes
     ----------
     csgraph : scipy.sparse.csr_matrix
@@ -38,16 +53,26 @@ class DiGraph(object):
     num_strongly_connected_components : int
         The number of the strongly connected components.
 
-    strongly_connected_components : list(ndarray(int))
+    strongly_connected_components_indices : list(ndarray(int))
+        List of numpy arrays containing the indices of the strongly
+        connected components.
+
+    strongly_connected_components : list(ndarray)
         List of numpy arrays containing the strongly connected
-        components.
+        components, where the nodes are annotated with their labels (if
+        `node_labels` is not None).
 
     num_sink_strongly_connected_components : int
         The number of the sink strongly connected components.
 
-    sink_strongly_connected_components : list(ndarray(int))
+    sink_strongly_connected_components_indices : list(ndarray(int))
+        List of numpy arrays containing the indices of the sink strongly
+        connected components.
+
+    sink_strongly_connected_components : list(ndarray)
         List of numpy arrays containing the sink strongly connected
-        components.
+        components, where the nodes are annotated with their labels (if
+        `node_labels` is not None).
 
     is_aperiodic : bool
         Indicate whether the digraph is aperiodic.
@@ -56,8 +81,14 @@ class DiGraph(object):
         The period of the digraph. Defined only for a strongly connected
         digraph.
 
-    cyclic_components : list(ndarray(int))
-        List of numpy arrays containing the cyclic components.
+    cyclic_components_indices : list(ndarray(int))
+        List of numpy arrays containing the indices of the cyclic
+        components.
+
+    cyclic_components : list(ndarray)
+        List of numpy arrays containing the cyclic components, where the
+        nodes are annotated with their labels (if `node_labels` is not
+        None).
 
     References
     ----------
@@ -70,7 +101,7 @@ class DiGraph(object):
 
     """
 
-    def __init__(self, adj_matrix, weighted=False):
+    def __init__(self, adj_matrix, weighted=False, node_labels=None):
         if weighted:
             dtype = None
         else:
@@ -83,6 +114,9 @@ def __init__(self, adj_matrix, weighted=False):
 
         self.n = n  # Number of nodes
 
+        # Call the setter method
+        self.node_labels = node_labels
+
         self._num_scc = None
         self._scc_proj = None
         self._sink_scc_labels = None
@@ -95,6 +129,26 @@ def __repr__(self):
     def __str__(self):
         return "Directed Graph:\n  - n(number of nodes): {n}".format(n=self.n)
 
+    @property
+    def node_labels(self):
+        return self._node_labels
+
+    @node_labels.setter
+    def node_labels(self, values):
+        if values is None:
+            self._node_labels = None
+        else:
+            values = np.asarray(values)
+            if (values.ndim < 1) or (values.shape[0] != self.n):
+                raise ValueError(
+                    'node_labels must be an array_like of length n'
+                )
+            if np.issubdtype(values.dtype, np.object_):
+                raise ValueError(
+                    'data in node_labels must be homogeneous in type'
+                )
+            self._node_labels = values
+
     def _find_scc(self):
         """
         Set ``self._num_scc`` and ``self._scc_proj``
@@ -170,21 +224,31 @@ def num_sink_strongly_connected_components(self):
         return len(self.sink_scc_labels)
 
     @property
-    def strongly_connected_components(self):
+    def strongly_connected_components_indices(self):
         if self.is_strongly_connected:
             return [np.arange(self.n)]
         else:
             return [np.where(self.scc_proj == k)[0]
                     for k in range(self.num_strongly_connected_components)]
 
     @property
-    def sink_strongly_connected_components(self):
+    @annotate_nodes
+    def strongly_connected_components(self):
+        return self.strongly_connected_components_indices
+
+    @property
+    def sink_strongly_connected_components_indices(self):
         if self.is_strongly_connected:
             return [np.arange(self.n)]
         else:
             return [np.where(self.scc_proj == k)[0]
                     for k in self.sink_scc_labels.tolist()]
 
+    @property
+    @annotate_nodes
+    def sink_strongly_connected_components(self):
+        return self.sink_strongly_connected_components_indices
+
     def _compute_period(self):
         """
         Set ``self._period`` and ``self._cyclic_components_proj``.
@@ -256,13 +320,18 @@ def is_aperiodic(self):
         return (self.period == 1)
 
     @property
-    def cyclic_components(self):
+    def cyclic_components_indices(self):
         if self.is_aperiodic:
             return [np.arange(self.n)]
         else:
             return [np.where(self._cyclic_components_proj == k)[0]
                     for k in range(self.period)]
 
+    @property
+    @annotate_nodes
+    def cyclic_components(self,):
+        return self.cyclic_components_indices
+
     def subgraph(self, nodes):
         """
         Return the subgraph consisting of the given nodes and edges
@@ -271,7 +340,7 @@ def subgraph(self, nodes):
         Parameters
         ----------
         nodes : array_like(int, ndim=1)
-           Array of nodes.
+           Array of node indices.
 
         Returns
         -------
@@ -282,7 +351,13 @@ def subgraph(self, nodes):
         adj_matrix = self.csgraph[nodes, :][:, nodes]
 
         weighted = True  # To copy the dtype
-        return DiGraph(adj_matrix, weighted=weighted)
+
+        if self.node_labels is not None:
+            node_labels = self.node_labels[nodes]
+        else:
+            node_labels = None
+
+        return DiGraph(adj_matrix, weighted=weighted, node_labels=node_labels)
 
 
 def _csr_matrix_indices(S):

quantecon/lss.py

@@ -9,10 +9,9 @@
 import numpy as np
 from numpy.random import multivariate_normal
 from scipy.linalg import solve
+from numba import jit
 
-#-Check if Numba is Available-#
-from .util import numba_installed, jit
-
+@jit
 def simulate_linear_model(A, x0, v, ts_length):
     """
     This is a separate function for simulating a vector linear system of 
@@ -55,8 +54,6 @@ def simulate_linear_model(A, x0, v, ts_length):
                 x[i, t+1] += A[i, j] * x[j, t]   #Dot Product
     return x
 
-if numba_installed:
-    simulate_linear_model = jit(simulate_linear_model)
 
 class LinearStateSpace(object):
     """

quantecon/markov/__init__.py

@@ -8,4 +8,4 @@
 from .random import random_markov_chain, random_stochastic_matrix, \
     random_discrete_dp
 from .approximation import tauchen
-from .ddp import DiscreteDP
+from .ddp import DiscreteDP, backward_induction