Change type to ssize_t for all index and size variables

skimage/graph/heap.pxd

@@ -1,29 +1,29 @@
 """ This is the definition file for heap.pyx.
 It contains the definitions of the heap classes, such that
 other cython modules can "cimport heap" and thus use the
-C versions of pop(), push(), and value_of(): pop_fast(), push_fast() and 
+C versions of pop(), push(), and value_of(): pop_fast(), push_fast() and
 value_of_fast()
 """
 
 # determine datatypes for heap
 ctypedef double VALUE_T
-ctypedef Py_ssize_t REFERENCE_T
+ctypedef ssize_t REFERENCE_T
 ctypedef REFERENCE_T INDEX_T
 ctypedef unsigned char BOOL_T
 ctypedef unsigned char LEVELS_T
 
 cdef class BinaryHeap:
     cdef readonly INDEX_T count
-    cdef readonly LEVELS_T levels, min_levels    
+    cdef readonly LEVELS_T levels, min_levels
     cdef VALUE_T *_values
     cdef REFERENCE_T *_references
     cdef REFERENCE_T _popped_ref
-    
+
     cdef void _add_or_remove_level(self, LEVELS_T add_or_remove)
     cdef void _update(self)
     cdef void _update_one(self, INDEX_T i)
     cdef void _remove(self, INDEX_T i)
-    
+
     cdef INDEX_T push_fast(self, VALUE_T value, REFERENCE_T reference)
     cdef VALUE_T pop_fast(self)
 
@@ -32,8 +32,7 @@ cdef class FastUpdateBinaryHeap(BinaryHeap):
     cdef INDEX_T *_crossref
     cdef BOOL_T _invalid_ref
     cdef BOOL_T _pushed
-    
+
     cdef VALUE_T value_of_fast(self, REFERENCE_T reference)
-    cdef INDEX_T push_if_lower_fast(self, VALUE_T value, 
+    cdef INDEX_T push_if_lower_fast(self, VALUE_T value,
                                     REFERENCE_T reference)
-    

skimage/measure/_find_contours.pyx

@@ -1,10 +1,7 @@
-# -*- python -*-
 # cython: cdivision=True
-
 import numpy as np
 cimport numpy as np
 
-np.import_array()
 
 cdef inline double _get_fraction(double from_value, double to_value,
                                  double level):
@@ -14,7 +11,7 @@ cdef inline double _get_fraction(double from_value, double to_value,
 
 
 def iterate_and_store(np.ndarray[double, ndim=2] array,
-                      double level, int vertex_connect_high):
+                      double level, ssize_t vertex_connect_high):
     """Iterate across the given array in a marching-squares fashion,
     looking for segments that cross 'level'. If such a segment is
     found, its coordinates are added to a growing list of segments,
@@ -27,7 +24,7 @@ def iterate_and_store(np.ndarray[double, ndim=2] array,
         raise ValueError("Input array must be at least 2x2.")
 
     cdef list arc_list = []
-    cdef int n
+    cdef ssize_t n
 
     # The plan is to iterate a 2x2 square across the input array. This means
     # that the upper-left corner of the square needs to iterate across a
@@ -39,17 +36,17 @@ def iterate_and_store(np.ndarray[double, ndim=2] array,
     # index varies the fastest).
 
     # Current coords start at 0,0.
-    cdef int[2] coords
+    cdef ssize_t[2] coords
     coords[0] = 0
     coords[1] = 0
 
     # Calculate the number of iterations we'll need
-    cdef int num_square_steps = (array.shape[0] - 1) * (array.shape[1] - 1)
+    cdef ssize_t num_square_steps = (array.shape[0] - 1) * (array.shape[1] - 1)
 
     cdef unsigned char square_case = 0
     cdef tuple top, bottom, left, right
     cdef double ul, ur, ll, lr
-    cdef int r0, r1, c0, c1
+    cdef ssize_t r0, r1, c0, c1
 
     for n in range(num_square_steps):
         # There are sixteen different possible square types, diagramed below.

skimage/measure/_moments.pyx

@@ -7,7 +7,7 @@ cimport numpy as np
 
 def central_moments(np.ndarray[np.double_t, ndim=2] array, double cr, double cc,
                      int order):
-    cdef int p, q, r, c
+    cdef ssize_t p, q, r, c
     cdef np.ndarray[np.double_t, ndim=2] mu
     mu = np.zeros((order + 1, order + 1), 'double')
     for p in range(order + 1):
@@ -18,7 +18,7 @@ def central_moments(np.ndarray[np.double_t, ndim=2] array, double cr, double cc,
     return mu
 
 def normalized_moments(np.ndarray[np.double_t, ndim=2] mu, int order):
-    cdef int p, q
+    cdef ssize_t p, q
     cdef np.ndarray[np.double_t, ndim=2] nu
     nu = np.zeros((order + 1, order + 1), 'double')
     for p in range(order + 1):