Refactor checking for cython availability

GPy/kern/src/coregionalize.py

@@ -9,10 +9,10 @@ from ...util.config import config # for assesing whether to use cython
 
 try:
     from . import coregionalize_cython
-    cython_coregionalize_working = True
+    use_coregionalize_cython = config.getboolean('cython', 'working')
 except ImportError:
     print('warning in coregionalize: failed to import cython module: falling back to numpy')
-    cython_coregionalize_working = False
+    use_coregionalize_cython = False
 
 
 class Coregionalize(Kern):
@@ -64,7 +64,7 @@ class Coregionalize(Kern):
         self.B = np.dot(self.W, self.W.T) + np.diag(self.kappa)
 
     def K(self, X, X2=None):
-        if cython_coregionalize_working and config.getboolean('cython', 'working'):
+        if use_coregionalize_cython:
             return self._K_cython(X, X2)
         else:
             return self._K_numpy(X, X2)
@@ -95,7 +95,7 @@ class Coregionalize(Kern):
             index2 = np.asarray(X2, dtype=np.int)
 
         #attempt to use cython for a nasty double indexing loop: fall back to numpy
-        if cython_coregionalize_working and config.getboolean('cython', 'working'):
+        if use_coregionalize_cython:
             dL_dK_small = self._gradient_reduce_cython(dL_dK, index, index2)
         else:
             dL_dK_small = self._gradient_reduce_numpy(dL_dK, index, index2)

GPy/kern/src/stationary.py

@@ -14,10 +14,10 @@ from paramz.transformations import Logexp
 
 try:
     from . import stationary_cython
-    cython_stationary_working = True
+    use_stationary_cython = config.getboolean('cython', 'working')
 except ImportError:
     print('warning in stationary: failed to import cython module: falling back to numpy')
-    cython_stationary_working = False
+    use_stationary_cython = False
 
 
 class Stationary(Kern):
@@ -197,7 +197,7 @@ class Stationary(Kern):
 
             tmp = dL_dr*self._inv_dist(X, X2)
             if X2 is None: X2 = X
-            if cython_stationary_working and config.getboolean('cython', 'working'):
+            if use_stationary_cython:
                 self.lengthscale.gradient = self._lengthscale_grads_cython(tmp, X, X2)
             else:
                 self.lengthscale.gradient = self._lengthscale_grads_pure(tmp, X, X2)
@@ -240,7 +240,7 @@ class Stationary(Kern):
         """
         Given the derivative of the objective wrt K (dL_dK), compute the derivative wrt X
         """
-        if cython_stationary_working and config.getboolean('cython', 'working'):
+        if use_stationary_cython:
             return self._gradients_X_cython(dL_dK, X, X2)
         else:
             return self._gradients_X_pure(dL_dK, X, X2)

GPy/util/choleskies.py

@@ -7,10 +7,10 @@ from .config import config
 
 try:
     from . import choleskies_cython
-    cython_choleskies_working = True
+    use_choleskies_cython = config.getboolean('cython', 'working')
 except ImportError:
     print('warning in choleskies: failed to import cython module: falling back to numpy')
-    cython_choleskies_working = False
+    use_choleskies_cython = False
 
 
 def safe_root(N):
@@ -103,7 +103,7 @@ def indexes_to_fix_for_low_rank(rank, size):
     return np.setdiff1d(np.arange((size**2+size)/2), keep)
 
 
-if cython_choleskies_working and config.getboolean('cython', 'working'):
+if use_choleskies_cython:
     triang_to_flat = _triang_to_flat_cython
     flat_to_triang = _flat_to_triang_cython
     backprop_gradient = choleskies_cython.backprop_gradient_par_c