change doctests output

giddy/markov.py

@@ -1119,7 +1119,7 @@ def chi2(T1, T2):
            [  1.,  92., 815.,  51.],
            [  1.,   0.,  60., 903.]])
     >>> chi2(T1,T2)
-    (23.39728441473295, 0.005363116704861337, 9)
+    (np.float64(23.39728441473295), np.float64(0.005363116704861337), np.int64(9))
 
     Notes
     -----
@@ -1468,20 +1468,20 @@ def spillover(self, quadrant=1, neighbors_on=False):
         >>> lm_random = LISA_Markov(pci, w, permutations=99)
         >>> r = lm_random.spillover()
         >>> (r['components'][:, 12] > 0).sum()
-        17
+        np.int64(17)
         >>> (r['components'][:, 13]>0).sum()
-        23
+        np.int64(23)
         >>> (r['spill_over'][:,12]>0).sum()
-        6
+        np.int64(6)
 
         Including neighbors of core neighbors
         >>> rn = lm_random.spillover(neighbors_on=True)
         >>> (rn['components'][:, 12] > 0).sum()
-        26
+        np.int64(26)
         >>> (rn["components"][:, 13] > 0).sum()
-        34
+        np.int64(34)
         >>> (rn["spill_over"][:, 12] > 0).sum()
-        8
+        np.int64(8)
 
         """
         n, k = self.q.shape
@@ -2081,7 +2081,7 @@ def sojourn_time(p, summary=True):
 
     >>> p = np.array([[.5, .25, .25], [.5, 0, .5],[ 0, 0, 0]])
     >>> sojourn_time(p)
-    Sojourn times are infinite for absorbing states! In this Markov Chain, states [2] are absorbing states.
+    Sojourn times are infinite for absorbing states! In this Markov Chain, states [np.int64(2)] are absorbing states.
     array([ 2.,  1., inf])
     """  # noqa E501
 

giddy/rank.py

@@ -469,10 +469,10 @@ class Tau_Local:
             0.48387097,  0.93548387,  0.61290323,  0.74193548,  0.41935484,
             0.61290323,  0.61290323])
     >>> tau_local.tau
-    0.6612903225806451
+    np.float64(0.6612903225806451)
     >>> tau_classic = Tau(r[:,0],r[:,1])
     >>> tau_classic.tau
-    0.6612903225806451
+    np.float64(0.6612903225806451)
 
     """
 
@@ -586,10 +586,10 @@ class Tau_Local_Neighbor:
     array([-1,  1,  1,  1,  1,  1,  1, -1,  1,  1,  1,  1,  1,  1,  1,  1,  1,
             1,  1, -1, -1, -1,  1,  1,  1,  1,  1,  1,  1,  1,  1, -1])
     >>> (res.tau_ln * res.tau_ln_weights).sum() #global spatial tau
-    0.39682539682539675
+    np.float64(0.39682539682539675)
     >>> res1 = SpatialTau(r[:,0],r[:,1],w,permutations=999)
     >>> res1.tau_spatial
-    0.3968253968253968
+    np.float64(0.3968253968253968)
 
     """