Attempted to fix, once and for all, the problems of using "isnan" by

astraw · astraw · commit 1cecd8a56883 · 2005-12-19T19:39:38.000Z
defining our own "MPL_isnan64" macro in C (modified from a similar
    solution in numarray). Also, checking in the unit test that
    demonstrates why we need isnan. -- ADS

svn path=/trunk/matplotlib/; revision=1922
diff --git a/src/MPL_isnan.h b/src/MPL_isnan.h
@@ -0,0 +1,33 @@
+// These definitions modified from numarray's Include/numarray/nummacro.h
+
+#if defined(SIZEOF_VOID_P)
+#if SIZEOF_VOID_P == 8
+#define MPL_LP64 1
+#else
+#define MPL_LP64 0
+#endif
+#else
+#define MPL_LP64 0
+#endif
+
+#if MPL_LP64
+typedef long int                 MPL_Int64;
+#else                  /* 32-bit platforms */
+#if defined(_MSC_VER)
+typedef __int64                  MPL_Int64;
+#else
+typedef long long                MPL_Int64;
+#endif
+#endif
+
+#if !defined(MPL_isnan64)
+#define MPL_U64(u) (* (MPL_Int64 *) &(u) )
+
+#if !defined(_MSC_VER)
+#define MPL_isnan64(u) \
+  ( (( MPL_U64(u) & 0x7ff0000000000000LL) == 0x7ff0000000000000LL)  && ((MPL_U64(u) & 0x000fffffffffffffLL) != 0)) ? 1:0
+#else
+#define MPL_isnan64(u) \
+  ( (( MPL_U64(u) & 0x7ff0000000000000i64) == 0x7ff0000000000000i64)  && ((MPL_U64(u) & 0x000fffffffffffffi64) != 0)) ? 1:0
+#endif
+#endif /* MPL_isnan64 */
diff --git a/src/_transforms.cpp b/src/_transforms.cpp
@@ -1,6 +1,7 @@
 #include <functional>
 #include "_transforms.h"
 #include "mplutils.h"
+#include "MPL_isnan.h"
 
 #ifdef NUMARRAY
 #   include "numarray/arrayobject.h"
@@ -444,7 +445,6 @@ Bbox::update(const Py::Tuple &args) {
 Py::Object
 Bbox::update_numerix(const Py::Tuple &args) {
   //update the box from the numerix arrays x and y
-  using std::isnan;
   _VERBOSE("Bbox::update_numerix");
 
   args.verify_length(3);
@@ -491,15 +491,15 @@ Bbox::update_numerix(const Py::Tuple &args) {
       thisy = *(double *)(y->data + i*y->strides[0]);
 
       if (!xok) {
-	if (!isnan(thisx)) {
+	if (!MPL_isnan64(thisx)) {
 	  minx=thisx;
 	  maxx=thisx;
 	  xok=1;
 	}
       }
 
       if (!yok) {
-	if (!isnan(thisy)) {
+	if (!MPL_isnan64(thisy)) {
 	  miny=thisy;
 	  maxy=thisx;
 	  yok=1;
diff --git a/unit/transforms_unit.py b/unit/transforms_unit.py
@@ -1,6 +1,6 @@
 #from __future__ import division
 
-from matplotlib.numerix import array, asarray, alltrue
+from matplotlib.numerix import array, asarray, alltrue, arange
 from matplotlib.numerix.mlab import rand
 from matplotlib.transforms import Point, Bbox, Value, Affine
 from matplotlib.transforms import multiply_affines
@@ -271,4 +271,35 @@ def rand_transform():
 polar = FuncXY(POLAR)
 assert( closeto_seq( polar.map(math.pi,1), (-1,0)) )
 assert( closeto_seq( polar.inverse(1,1), ( (math.pi/4), math.sqrt(2))) )
+
+
+
+# This unit test requires "nan", which numarray.ieeespecial
+# exports.  (But we can keep using the numerix module.)
+try:
+    from numarray.ieeespecial import nan
+    have_nan = True
+except ImportError:
+    have_nan = False
+
+if have_nan:
+    y1=array([  2,nan,1,2,3,4])
+    y2=array([nan,nan,1,2,3,4])
+
+    x1=arange(len(y1))
+    x2=arange(len(y2))
+
+    bbox1 = Bbox(Point(Value(0),Value(0)),
+                 Point(Value(1),Value(1)))
+
+    bbox2 = Bbox(Point(Value(0),Value(0)),
+                 Point(Value(1),Value(1)))
+
+    bbox1.update_numerix(x1,y1,1)
+    bbox2.update_numerix(x2,y2,1)
+
+    assert( closeto_seq( bbox1.get_bounds(), bbox2.get_bounds() ) )
+else:
+    print 'nan could not be imported from numarray.ieeespecial, test skipped'
+ 
 print 'all tests passed'
