Merge pull request #196 from weikang9009/dtype

address CI testing failures

giddy/markov.py

@@ -551,7 +551,7 @@ class Spatial_Markov(object):
     the second quintile, 0.204 (0.190) in the third, 0.0684 (0.255) in the
     fourth and 0.029 (0.337) in the fifth quintile.
 
-    >>> sm.S
+    >>> sm.S.astype(float).round(8)
     array([[0.43509425, 0.2635327 , 0.20363044, 0.06841983, 0.02932278],
            [0.13391287, 0.33993305, 0.25153036, 0.23343016, 0.04119356],
            [0.12124869, 0.21137444, 0.2635101 , 0.29013417, 0.1137326 ],
@@ -567,7 +567,7 @@ class Spatial_Markov(object):
     fourth quintile after 28.473 years.
 
     >>> for f in sm.F:
-    ...     print(f)
+    ...     print(f.round(8))
     [[  2.29835259  28.95614035  46.14285714  80.80952381 279.42857143]
      [ 33.86549708   3.79459555  22.57142857  57.23809524 255.85714286]
      [ 43.60233918   9.73684211   4.91085714  34.66666667 233.28571429]
@@ -1327,7 +1327,7 @@ class LISA_Markov(Markov):
     >>> lm_random.significant_moves[0, :3]
     array([11, 11, 11])
     >>> lm_random.significant_moves[0,-3:]
-    array([59, 43, 27])
+    array([59, 59, 43])
 
 
     Any value less than 49 indicates at least one of the LISA end points was

giddy/rank.py

@@ -95,7 +95,7 @@ def __init__(self, y, regime, permutations=999):
         n, k = y.shape
         ranks_d = ranks[:, list(range(1, k))] - ranks[:, list(range(k - 1))]
         self.ranks_d = ranks_d
-        regimes = sp.unique(regime)
+        regimes = np.unique(regime)
         self.regimes = regimes
         self.total = sum(abs(ranks_d))
         self.max_total = sum([abs(i - n + i - 1) for i in range(1, n + 1)])
@@ -152,15 +152,15 @@ class Tau:
     >>> x1 = [12, 2, 1, 12, 2]
     >>> x2 = [1, 4, 7, 1, 0]
     >>> kt = Tau(x1,x2)
-    >>> kt.tau
-    -0.47140452079103173
-    >>> kt.tau_p
-    0.24821309157521476
+    >>> print("%.5f" % kt.tau)
+    -0.47140
+    >>> print("%.5f" % kt.tau_p)
+    0.24821
     >>> tau, p = kendalltau(x1,x2)
-    >>> tau
-    -0.4714045207910316
-    >>> p
-    0.2827454599327748
+    >>> print("%.5f" % tau)
+    -0.47140
+    >>> print("%.5f" % p)
+    0.28275
 
     """
 

giddy/sequence.py

@@ -191,7 +191,8 @@ def __init__(self, y, subs_mat=None, dist_type=None, indel=None, cluster_type=No
                             "hamming distance cannot be calculated for "
                             "sequences of unequal lengths!"
                         )
-                    hamming_dist = d.pdist(y_int, metric="hamming") * y_int.shape[1]
+
+                    hamming_dist = d.pdist(y_int.astype(int), metric="hamming") * y_int.shape[1]
                     self.seq_dis_mat = d.squareform(hamming_dist)
 
                 elif dist_type.lower() == "arbitrary":