2to3: Apply `raise` fixes. Closes #3077.

Replaces the

    raise Exception, msg:

form with

    raise Exception(msg):

9 files changed, 23 insertions, 23 deletions

diff --git a/numpy/core/code_generators/generate_numpy_api.py b/numpy/core/code_generators/generate_numpy_api.py
index 7cd4b9f6a..463fe1695 100644
--- a/numpy/core/code_generators/generate_numpy_api.py
+++ b/numpy/core/code_generators/generate_numpy_api.py
@@ -221,8 +221,8 @@ def do_generate_api(targets, sources):
         multiarray_api_dict[name] = TypeApi(name, index, 'PyTypeObject', api_name)
 
     if len(multiarray_api_dict) != len(multiarray_api_index):
-        raise AssertionError, "Multiarray API size mismatch %d %d" % \
-                        (len(multiarray_api_dict), len(multiarray_api_index))
+        raise AssertionError("Multiarray API size mismatch %d %d" %
+                        (len(multiarray_api_dict), len(multiarray_api_index)))
 
     extension_list = []
     for name, index in genapi.order_dict(multiarray_api_index):
diff --git a/numpy/core/records.py b/numpy/core/records.py
index 964b4a54e..2e55c48fb 100644
--- a/numpy/core/records.py
+++ b/numpy/core/records.py
@@ -436,7 +436,7 @@ class recarray(ndarray):
             fielddict = ndarray.__getattribute__(self, 'dtype').fields or {}
             if attr not in fielddict:
                 exctype, value = sys.exc_info()[:2]
-                raise exctype, value
+                raise exctype(value)
         else:
             fielddict = ndarray.__getattribute__(self, 'dtype').fields or {}
             if attr not in fielddict:
diff --git a/numpy/f2py/auxfuncs.py b/numpy/f2py/auxfuncs.py
index a12d92b7e..72af373db 100644
--- a/numpy/f2py/auxfuncs.py
+++ b/numpy/f2py/auxfuncs.py
@@ -447,7 +447,7 @@ class throw_error:
         self.mess = mess
     def __call__(self,var):
         mess = '\n\n  var = %s\n  Message: %s\n' % (var,self.mess)
-        raise F2PYError,mess
+        raise F2PYError(mess)
 
 def l_and(*f):
     l,l2='lambda v',[]
diff --git a/numpy/lib/polynomial.py b/numpy/lib/polynomial.py
index fad06f4df..421efb675 100644
--- a/numpy/lib/polynomial.py
+++ b/numpy/lib/polynomial.py
@@ -564,9 +564,9 @@ def polyfit(x, y, deg, rcond=None, full=False, w=None, cov=False):
     if w is not None:
         w = NX.asarray(w) + 0.0
         if w.ndim != 1:
-            raise TypeError, "expected a 1-d array for weights"
+            raise TypeError("expected a 1-d array for weights")
         if w.shape[0] != y.shape[0] :
-            raise TypeError, "expected w and y to have the same length"
+            raise TypeError("expected w and y to have the same length")
         lhs *= w[:, NX.newaxis]
         if rhs.ndim == 2:
             rhs *= w[:, NX.newaxis]
diff --git a/numpy/ma/extras.py b/numpy/ma/extras.py
index c5b3a3957..3133b168b 100644
--- a/numpy/ma/extras.py
+++ b/numpy/ma/extras.py
@@ -1865,9 +1865,9 @@ def polyfit(x, y, deg, rcond=None, full=False, w=None, cov=False):
     if w is not None:
         w = asarray(w)
         if w.ndim != 1:
-            raise TypeError, "expected a 1-d array for weights"
+            raise TypeError("expected a 1-d array for weights")
         if w.shape[0] != y.shape[0] :
-            raise TypeError, "expected w and y to have the same length"
+            raise TypeError("expected w and y to have the same length")
         m = mask_or(m, getmask(w))
 
     if m is not nomask:
diff --git a/numpy/ma/mrecords.py b/numpy/ma/mrecords.py
index 44d273d34..d3b2de3ba 100644
--- a/numpy/ma/mrecords.py
+++ b/numpy/ma/mrecords.py
@@ -252,7 +252,7 @@ class MaskedRecords(MaskedArray, object):
             optinfo = ndarray.__getattribute__(self, '_optinfo') or {}
             if not (attr in fielddict or attr in optinfo):
                 exctype, value = sys.exc_info()[:2]
-                raise exctype, value
+                raise exctype(value)
         else:
             # Get the list of names ......
             fielddict = ndarray.__getattribute__(self, 'dtype').fields or {}
diff --git a/numpy/oldnumeric/arrayfns.py b/numpy/oldnumeric/arrayfns.py
index 683ed309d..54992de57 100644
--- a/numpy/oldnumeric/arrayfns.py
+++ b/numpy/oldnumeric/arrayfns.py
@@ -20,7 +20,7 @@ def array_set(vals1, indices, vals2):
     vals1 = asarray(vals1)
     vals2 = asarray(vals2)
     if vals1.ndim != vals2.ndim or vals1.ndim < 1:
-        raise error, "vals1 and vals2 must have same number of dimensions (>=1)"
+        raise error("vals1 and vals2 must have same number of dimensions (>=1)")
     vals1[indices] = vals2
 
 from numpy import digitize
@@ -38,7 +38,7 @@ def interp(y, x, z, typ=None):
     if typ == 'f':
         return res.astype('f')
 
-    raise error, "incompatible typecode"
+    raise error("incompatible typecode")
 
 def nz(x):
     x = asarray(x,dtype=np.ubyte)
diff --git a/numpy/oldnumeric/random_array.py b/numpy/oldnumeric/random_array.py
index 777e2a645..7141e564b 100644
--- a/numpy/oldnumeric/random_array.py
+++ b/numpy/oldnumeric/random_array.py
@@ -201,20 +201,20 @@ def test():
     mt.set_state(obj)
     obj2 = mt.get_state()
     if (obj2[1] - obj[1]).any():
-        raise SystemExit, "Failed seed test."
+        raise SystemExit("Failed seed test.")
     print "First random number is", random()
     print "Average of 10000 random numbers is", np.sum(random(10000),axis=0)/10000.
     x = random([10,1000])
     if len(x.shape) != 2 or x.shape[0] != 10 or x.shape[1] != 1000:
-        raise SystemExit, "random returned wrong shape"
+        raise SystemExit("random returned wrong shape")
     x.shape = (10000,)
     print "Average of 100 by 100 random numbers is", np.sum(x,axis=0)/10000.
     y = uniform(0.5,0.6, (1000,10))
     if len(y.shape) !=2 or y.shape[0] != 1000 or y.shape[1] != 10:
-        raise SystemExit, "uniform returned wrong shape"
+        raise SystemExit("uniform returned wrong shape")
     y.shape = (10000,)
     if np.minimum.reduce(y) <= 0.5 or np.maximum.reduce(y) >= 0.6:
-        raise SystemExit, "uniform returned out of desired range"
+        raise SystemExit("uniform returned out of desired range")
     print "randint(1, 10, shape=[50])"
     print randint(1, 10, shape=[50])
     print "permutation(10)", permutation(10)
@@ -224,18 +224,18 @@ def test():
     s = 3.0
     x = normal(2.0, s, [10, 1000])
     if len(x.shape) != 2 or x.shape[0] != 10 or x.shape[1] != 1000:
-        raise SystemExit, "standard_normal returned wrong shape"
+        raise SystemExit("standard_normal returned wrong shape")
     x.shape = (10000,)
     mean_var_test(x, "normally distributed numbers with mean 2 and variance %f"%(s**2,), 2, s**2, 0)
     x = exponential(3, 10000)
     mean_var_test(x, "random numbers exponentially distributed with mean %f"%(s,), s, s**2, 2)
     x = multivariate_normal(np.array([10,20]), np.array(([1,2],[2,4])))
     print "\nA multivariate normal", x
-    if x.shape != (2,): raise SystemExit, "multivariate_normal returned wrong shape"
+    if x.shape != (2,): raise SystemExit("multivariate_normal returned wrong shape")
     x = multivariate_normal(np.array([10,20]), np.array([[1,2],[2,4]]), [4,3])
     print "A 4x3x2 array containing multivariate normals"
     print x
-    if x.shape != (4,3,2): raise SystemExit, "multivariate_normal returned wrong shape"
+    if x.shape != (4,3,2): raise SystemExit("multivariate_normal returned wrong shape")
     x = multivariate_normal(np.array([-100,0,100]), np.array([[3,2,1],[2,2,1],[1,1,1]]), 10000)
     x_mean = np.sum(x,axis=0)/10000.
     print "Average of 10000 multivariate normals with mean [-100,0,100]"
diff --git a/numpy/oldnumeric/rng.py b/numpy/oldnumeric/rng.py
index 28d3f16df..38b182eae 100644
--- a/numpy/oldnumeric/rng.py
+++ b/numpy/oldnumeric/rng.py
@@ -36,7 +36,7 @@ class Distribution(object):
 class ExponentialDistribution(Distribution):
     def __init__(self, lambda_):
         if (lambda_ <= 0):
-            raise error, "parameter must be positive"
+            raise error("parameter must be positive")
         Distribution.__init__(self, 'exponential', lambda_)
 
     def density(x):
@@ -51,7 +51,7 @@ class LogNormalDistribution(Distribution):
         m = float(m)
         s = float(s)
         if (s <= 0):
-            raise error, "standard deviation must be positive"
+            raise error("standard deviation must be positive")
         Distribution.__init__(self, 'lognormal', m, s)
         sn = math.log(1.0+s*s/(m*m));
         self._mn = math.log(m)-0.5*sn
@@ -69,7 +69,7 @@ class NormalDistribution(Distribution):
         m = float(m)
         s = float(s)
         if (s <= 0):
-            raise error, "standard deviation must be positive"
+            raise error("standard deviation must be positive")
         Distribution.__init__(self, 'normal', m, s)
         self._fac = 1.0/math.sqrt(2*math.pi)/s
 
@@ -84,7 +84,7 @@ class UniformDistribution(Distribution):
         b = float(b)
         width = b-a
         if (width <=0):
-            raise error, "width of uniform distribution must be > 0"
+            raise error("width of uniform distribution must be > 0")
         Distribution.__init__(self, 'uniform', a, b)
         self._fac = 1.0/width
 
@@ -106,7 +106,7 @@ class CreateGenerator(object):
         if dist is None:
             dist = default_distribution
         if not isinstance(dist, Distribution):
-            raise error, "Not a distribution object"
+            raise error("Not a distribution object")
         self._dist = dist
 
     def ranf(self):