Merge remote-tracking branch 'upstream/master' into malloc_hooks

12 files changed, 10 insertions, 264 deletions

diff --git a/numpy/polynomial/chebyshev.py b/numpy/polynomial/chebyshev.py
index 6fbd5d346..00cac2527 100644
--- a/numpy/polynomial/chebyshev.py
+++ b/numpy/polynomial/chebyshev.py
@@ -1452,9 +1452,8 @@ def chebvander(x, deg) :
 
     x = np.array(x, copy=0, ndmin=1) + 0.0
     dims = (ideg + 1,) + x.shape
-    mask = x.flags.maskna
     dtyp = x.dtype
-    v = np.empty(dims, dtype=dtyp, maskna=mask)
+    v = np.empty(dims, dtype=dtyp)
     # Use forward recursion to generate the entries.
     v[0] = x*0 + 1
     if ideg > 0 :
@@ -1739,16 +1738,6 @@ def chebfit(x, y, deg, rcond=None, full=False, w=None):
         lhs = lhs * w
         rhs = rhs * w
 
-    # deal with NA. Note that polyvander propagates NA from x
-    # into all columns, that is rows for transposed form.
-    if lhs.flags.maskna or rhs.flags.maskna:
-        if rhs.ndim == 1:
-            mask = np.isna(lhs[0]) | np.isna(rhs)
-        else:
-            mask = np.isna(lhs[0]) | np.isna(rhs).any(0)
-        np.copyto(lhs, 0, where=mask)
-        np.copyto(rhs, 0, where=mask)
-
     # set rcond
     if rcond is None :
         rcond = len(x)*np.finfo(x.dtype).eps
diff --git a/numpy/polynomial/hermite.py b/numpy/polynomial/hermite.py
index 229ac26db..0b637f40a 100644
--- a/numpy/polynomial/hermite.py
+++ b/numpy/polynomial/hermite.py
@@ -1225,9 +1225,8 @@ def hermvander(x, deg) :
 
     x = np.array(x, copy=0, ndmin=1) + 0.0
     dims = (ideg + 1,) + x.shape
-    mask = x.flags.maskna
     dtyp = x.dtype
-    v = np.empty(dims, dtype=dtyp, maskna=mask)
+    v = np.empty(dims, dtype=dtyp)
     v[0] = x*0 + 1
     if ideg > 0 :
         x2 = x*2
@@ -1516,16 +1515,6 @@ def hermfit(x, y, deg, rcond=None, full=False, w=None):
         lhs = lhs * w
         rhs = rhs * w
 
-    # deal with NA. Note that polyvander propagates NA from x
-    # into all columns, that is rows for transposed form.
-    if lhs.flags.maskna or rhs.flags.maskna:
-        if rhs.ndim == 1:
-            mask = np.isna(lhs[0]) | np.isna(rhs)
-        else:
-            mask = np.isna(lhs[0]) | np.isna(rhs).any(0)
-        np.copyto(lhs, 0, where=mask)
-        np.copyto(rhs, 0, where=mask)
-
     # set rcond
     if rcond is None :
         rcond = len(x)*np.finfo(x.dtype).eps
diff --git a/numpy/polynomial/hermite_e.py b/numpy/polynomial/hermite_e.py
index ba41a4fc0..c5abe03ca 100644
--- a/numpy/polynomial/hermite_e.py
+++ b/numpy/polynomial/hermite_e.py
@@ -1222,9 +1222,8 @@ def hermevander(x, deg) :
 
     x = np.array(x, copy=0, ndmin=1) + 0.0
     dims = (ideg + 1,) + x.shape
-    mask = x.flags.maskna
     dtyp = x.dtype
-    v = np.empty(dims, dtype=dtyp, maskna=mask)
+    v = np.empty(dims, dtype=dtyp)
     v[0] = x*0 + 1
     if ideg > 0 :
         v[1] = x
@@ -1512,16 +1511,6 @@ def hermefit(x, y, deg, rcond=None, full=False, w=None):
         lhs = lhs * w
         rhs = rhs * w
 
-    # deal with NA. Note that polyvander propagates NA from x
-    # into all columns, that is rows for transposed form.
-    if lhs.flags.maskna or rhs.flags.maskna:
-        if rhs.ndim == 1:
-            mask = np.isna(lhs[0]) | np.isna(rhs)
-        else:
-            mask = np.isna(lhs[0]) | np.isna(rhs).any(0)
-        np.copyto(lhs, 0, where=mask)
-        np.copyto(rhs, 0, where=mask)
-
     # set rcond
     if rcond is None :
         rcond = len(x)*np.finfo(x.dtype).eps
diff --git a/numpy/polynomial/laguerre.py b/numpy/polynomial/laguerre.py
index 4401b5414..3533343b0 100644
--- a/numpy/polynomial/laguerre.py
+++ b/numpy/polynomial/laguerre.py
@@ -1225,9 +1225,8 @@ def lagvander(x, deg) :
 
     x = np.array(x, copy=0, ndmin=1) + 0.0
     dims = (ideg + 1,) + x.shape
-    mask = x.flags.maskna
     dtyp = x.dtype
-    v = np.empty(dims, dtype=dtyp, maskna=mask)
+    v = np.empty(dims, dtype=dtyp)
     v[0] = x*0 + 1
     if ideg > 0 :
         v[1] = 1 - x
@@ -1515,16 +1514,6 @@ def lagfit(x, y, deg, rcond=None, full=False, w=None):
         lhs = lhs * w
         rhs = rhs * w
 
-    # deal with NA. Note that polyvander propagates NA from x
-    # into all columns, that is rows for transposed form.
-    if lhs.flags.maskna or rhs.flags.maskna:
-        if rhs.ndim == 1:
-            mask = np.isna(lhs[0]) | np.isna(rhs)
-        else:
-            mask = np.isna(lhs[0]) | np.isna(rhs).any(0)
-        np.copyto(lhs, 0, where=mask)
-        np.copyto(rhs, 0, where=mask)
-
     # set rcond
     if rcond is None :
         rcond = len(x)*np.finfo(x.dtype).eps
diff --git a/numpy/polynomial/legendre.py b/numpy/polynomial/legendre.py
index 9470865a8..1216e29f4 100644
--- a/numpy/polynomial/legendre.py
+++ b/numpy/polynomial/legendre.py
@@ -1253,9 +1253,8 @@ def legvander(x, deg) :
 
     x = np.array(x, copy=0, ndmin=1) + 0.0
     dims = (ideg + 1,) + x.shape
-    mask = x.flags.maskna
     dtyp = x.dtype
-    v = np.empty(dims, dtype=dtyp, maskna=mask)
+    v = np.empty(dims, dtype=dtyp)
     # Use forward recursion to generate the entries. This is not as accurate
     # as reverse recursion in this application but it is more efficient.
     v[0] = x*0 + 1
@@ -1540,16 +1539,6 @@ def legfit(x, y, deg, rcond=None, full=False, w=None):
         lhs = lhs * w
         rhs = rhs * w
 
-    # deal with NA. Note that polyvander propagates NA from x
-    # into all columns, that is rows for transposed form.
-    if lhs.flags.maskna or rhs.flags.maskna:
-        if rhs.ndim == 1:
-            mask = np.isna(lhs[0]) | np.isna(rhs)
-        else:
-            mask = np.isna(lhs[0]) | np.isna(rhs).any(0)
-        np.copyto(lhs, 0, where=mask)
-        np.copyto(rhs, 0, where=mask)
-
     # set rcond
     if rcond is None :
         rcond = len(x)*np.finfo(x.dtype).eps
diff --git a/numpy/polynomial/polynomial.py b/numpy/polynomial/polynomial.py
index 7a5d3dbd5..324bec9c0 100644
--- a/numpy/polynomial/polynomial.py
+++ b/numpy/polynomial/polynomial.py
@@ -205,7 +205,7 @@ def polyadd(c1, c2):
 
     Returns the sum of two polynomials `c1` + `c2`.  The arguments are
     sequences of coefficients from lowest order term to highest, i.e.,
-    [1,2,3] represents the polynomial ``1 + 2*x + 3*x**2"``.
+    [1,2,3] represents the polynomial ``1 + 2*x + 3*x**2``.
 
     Parameters
     ----------
@@ -524,12 +524,9 @@ def polyder(c, m=1, scl=1, axis=0):
 
     """
     c = np.array(c, ndmin=1, copy=1)
-    if c.flags.maskna and isna(c).any():
-        raise ValueError("Coefficient array contains NA")
     if c.dtype.char in '?bBhHiIlLqQpP':
         # astype fails with NA
         c = c + 0.0
-    mna = c.flags.maskna
     cdt = c.dtype
     cnt, iaxis = [int(t) for t in [m, axis]]
 
@@ -555,7 +552,7 @@ def polyder(c, m=1, scl=1, axis=0):
         for i in range(cnt):
             n = n - 1
             c *= scl
-            der = np.empty((n,) + c.shape[1:], dtype=cdt, maskna=mna)
+            der = np.empty((n,) + c.shape[1:], dtype=cdt)
             for j in range(n, 0, -1):
                 der[j - 1] = j*c[j]
             c = der
@@ -641,12 +638,9 @@ def polyint(c, m=1, k=[], lbnd=0, scl=1, axis=0):
 
     """
     c = np.array(c, ndmin=1, copy=1)
-    if c.flags.maskna and isna(c).any():
-        raise ValueError("Coefficient array contains NA")
-    elif c.dtype.char in '?bBhHiIlLqQpP':
+    if c.dtype.char in '?bBhHiIlLqQpP':
         # astype doesn't preserve mask attribute.
         c = c + 0.0
-    mna = c.flags.maskna
     cdt = c.dtype
     if not np.iterable(k):
         k = [k]
@@ -677,7 +671,7 @@ def polyint(c, m=1, k=[], lbnd=0, scl=1, axis=0):
         if n == 1 and np.all(c[0] == 0):
             c[0] += k[i]
         else:
-            tmp = np.empty((n + 1,) + c.shape[1:], dtype=cdt, maskna=mna)
+            tmp = np.empty((n + 1,) + c.shape[1:], dtype=cdt)
             tmp[0] = c[0]*0
             tmp[1] = c[0]
             for j in range(1, n):
@@ -770,8 +764,6 @@ def polyval(x, c, tensor=True):
 
     """
     c = np.array(c, ndmin=1, copy=0)
-    if c.flags.maskna and isna(c).any():
-        raise ValueError("Coefficient array contains NA")
     if c.dtype.char in '?bBhHiIlLqQpP':
         # astype fails with NA
         c = c + 0.0
@@ -1062,9 +1054,8 @@ def polyvander(x, deg) :
 
     x = np.array(x, copy=0, ndmin=1) + 0.0
     dims = (ideg + 1,) + x.shape
-    mask = x.flags.maskna
     dtyp = x.dtype
-    v = np.empty(dims, dtype=dtyp, maskna=mask)
+    v = np.empty(dims, dtype=dtyp)
     v[0] = x*0 + 1
     if ideg > 0 :
         v[1] = x
@@ -1370,16 +1361,6 @@ def polyfit(x, y, deg, rcond=None, full=False, w=None):
         lhs = lhs * w
         rhs = rhs * w
 
-    # deal with NA. Note that polyvander propagates NA from x
-    # into all columns, that is rows for transposed form.
-    if lhs.flags.maskna or rhs.flags.maskna:
-        if rhs.ndim == 1:
-            mask = np.isna(lhs[0]) | np.isna(rhs)
-        else:
-            mask = np.isna(lhs[0]) | np.isna(rhs).any(0)
-        np.copyto(lhs, 0, where=mask)
-        np.copyto(rhs, 0, where=mask)
-
     # set rcond
     if rcond is None :
         rcond = len(x)*np.finfo(x.dtype).eps
diff --git a/numpy/polynomial/tests/test_chebyshev.py b/numpy/polynomial/tests/test_chebyshev.py
index 5e6d5632b..331cc17bc 100644
--- a/numpy/polynomial/tests/test_chebyshev.py
+++ b/numpy/polynomial/tests/test_chebyshev.py
@@ -436,36 +436,6 @@ class TestFitting(TestCase):
         wcoef2d = cheb.chebfit(x, np.array([yw,yw]).T, 3, w=w)
         assert_almost_equal(wcoef2d, np.array([coef3,coef3]).T)
 
-        #test NA
-        y = f(x)
-        y[10] = 100
-
-        xm = x.view(maskna=1)
-        xm[10] = np.NA
-        res = cheb.chebfit(xm, y, 3)
-        assert_almost_equal(res, coef3)
-
-        ym = y.view(maskna=1)
-        ym[10] = np.NA
-        res = cheb.chebfit(x, ym, 3)
-        assert_almost_equal(res, coef3)
-
-        y2 = np.vstack((y,y)).T
-        y2[10,0] = 100
-        y2[15,1] = 100
-        y2m = y2.view(maskna=1)
-        y2m[10,0] = np.NA
-        y2m[15,1] = np.NA
-        res = cheb.chebfit(x, y2m, 3).T
-        assert_almost_equal(res[0], coef3)
-        assert_almost_equal(res[1], coef3)
-
-        wm = np.ones_like(x, maskna=1)
-        wm[10] = np.NA
-        res = cheb.chebfit(x, y, 3, w=wm)
-        assert_almost_equal(res, coef3)
-
-
 class TestGauss(TestCase):
 
     def test_100(self):
diff --git a/numpy/polynomial/tests/test_hermite.py b/numpy/polynomial/tests/test_hermite.py
index 425ebe5e0..b5649a693 100644
--- a/numpy/polynomial/tests/test_hermite.py
+++ b/numpy/polynomial/tests/test_hermite.py
@@ -426,36 +426,6 @@ class TestFitting(TestCase):
         wcoef2d = herm.hermfit(x, np.array([yw,yw]).T, 3, w=w)
         assert_almost_equal(wcoef2d, np.array([coef3,coef3]).T)
 
-        #test NA
-        y = f(x)
-        y[10] = 100
-
-        xm = x.view(maskna=1)
-        xm[10] = np.NA
-        res = herm.hermfit(xm, y, 3)
-        assert_almost_equal(res, coef3)
-
-        ym = y.view(maskna=1)
-        ym[10] = np.NA
-        res = herm.hermfit(x, ym, 3)
-        assert_almost_equal(res, coef3)
-
-        y2 = np.vstack((y,y)).T
-        y2[10,0] = 100
-        y2[15,1] = 100
-        y2m = y2.view(maskna=1)
-        y2m[10,0] = np.NA
-        y2m[15,1] = np.NA
-        res = herm.hermfit(x, y2m, 3).T
-        assert_almost_equal(res[0], coef3)
-        assert_almost_equal(res[1], coef3)
-
-        wm = np.ones_like(x, maskna=1)
-        wm[10] = np.NA
-        res = herm.hermfit(x, y, 3, w=wm)
-        assert_almost_equal(res, coef3)
-
-
 class TestGauss(TestCase):
 
     def test_100(self):
diff --git a/numpy/polynomial/tests/test_hermite_e.py b/numpy/polynomial/tests/test_hermite_e.py
index f0ecb66b8..018fe8595 100644
--- a/numpy/polynomial/tests/test_hermite_e.py
+++ b/numpy/polynomial/tests/test_hermite_e.py
@@ -423,36 +423,6 @@ class TestFitting(TestCase):
         wcoef2d = herme.hermefit(x, np.array([yw,yw]).T, 3, w=w)
         assert_almost_equal(wcoef2d, np.array([coef3,coef3]).T)
 
-        #test NA
-        y = f(x)
-        y[10] = 100
-
-        xm = x.view(maskna=1)
-        xm[10] = np.NA
-        res = herme.hermefit(xm, y, 3)
-        assert_almost_equal(res, coef3)
-
-        ym = y.view(maskna=1)
-        ym[10] = np.NA
-        res = herme.hermefit(x, ym, 3)
-        assert_almost_equal(res, coef3)
-
-        y2 = np.vstack((y,y)).T
-        y2[10,0] = 100
-        y2[15,1] = 100
-        y2m = y2.view(maskna=1)
-        y2m[10,0] = np.NA
-        y2m[15,1] = np.NA
-        res = herme.hermefit(x, y2m, 3).T
-        assert_almost_equal(res[0], coef3)
-        assert_almost_equal(res[1], coef3)
-
-        wm = np.ones_like(x, maskna=1)
-        wm[10] = np.NA
-        res = herme.hermefit(x, y, 3, w=wm)
-        assert_almost_equal(res, coef3)
-
-
 class TestGauss(TestCase):
 
     def test_100(self):
diff --git a/numpy/polynomial/tests/test_laguerre.py b/numpy/polynomial/tests/test_laguerre.py
index 8b3fd5f8a..14fafe37d 100644
--- a/numpy/polynomial/tests/test_laguerre.py
+++ b/numpy/polynomial/tests/test_laguerre.py
@@ -421,36 +421,6 @@ class TestFitting(TestCase):
         wcoef2d = lag.lagfit(x, np.array([yw,yw]).T, 3, w=w)
         assert_almost_equal(wcoef2d, np.array([coef3,coef3]).T)
 
-        #test NA
-        y = f(x)
-        y[10] = 100
-
-        xm = x.view(maskna=1)
-        xm[10] = np.NA
-        res = lag.lagfit(xm, y, 3)
-        assert_almost_equal(res, coef3)
-
-        ym = y.view(maskna=1)
-        ym[10] = np.NA
-        res = lag.lagfit(x, ym, 3)
-        assert_almost_equal(res, coef3)
-
-        y2 = np.vstack((y,y)).T
-        y2[10,0] = 100
-        y2[15,1] = 100
-        y2m = y2.view(maskna=1)
-        y2m[10,0] = np.NA
-        y2m[15,1] = np.NA
-        res = lag.lagfit(x, y2m, 3).T
-        assert_almost_equal(res[0], coef3)
-        assert_almost_equal(res[1], coef3)
-
-        wm = np.ones_like(x, maskna=1)
-        wm[10] = np.NA
-        res = lag.lagfit(x, y, 3, w=wm)
-        assert_almost_equal(res, coef3)
-
-
 class TestGauss(TestCase):
 
     def test_100(self):
diff --git a/numpy/polynomial/tests/test_legendre.py b/numpy/polynomial/tests/test_legendre.py
index c7b71fc05..cdfaa96f1 100644
--- a/numpy/polynomial/tests/test_legendre.py
+++ b/numpy/polynomial/tests/test_legendre.py
@@ -424,36 +424,6 @@ class TestFitting(TestCase):
         wcoef2d = leg.legfit(x, np.array([yw,yw]).T, 3, w=w)
         assert_almost_equal(wcoef2d, np.array([coef3,coef3]).T)
 
-        #test NA
-        y = f(x)
-        y[10] = 100
-
-        xm = x.view(maskna=1)
-        xm[10] = np.NA
-        res = leg.legfit(xm, y, 3)
-        assert_almost_equal(res, coef3)
-
-        ym = y.view(maskna=1)
-        ym[10] = np.NA
-        res = leg.legfit(x, ym, 3)
-        assert_almost_equal(res, coef3)
-
-        y2 = np.vstack((y,y)).T
-        y2[10,0] = 100
-        y2[15,1] = 100
-        y2m = y2.view(maskna=1)
-        y2m[10,0] = np.NA
-        y2m[15,1] = np.NA
-        res = leg.legfit(x, y2m, 3).T
-        assert_almost_equal(res[0], coef3)
-        assert_almost_equal(res[1], coef3)
-
-        wm = np.ones_like(x, maskna=1)
-        wm[10] = np.NA
-        res = leg.legfit(x, y, 3, w=wm)
-        assert_almost_equal(res, coef3)
-
-
 class TestGauss(TestCase):
 
     def test_100(self):
diff --git a/numpy/polynomial/tests/test_polynomial.py b/numpy/polynomial/tests/test_polynomial.py
index 4854f95ee..bae711cbf 100644
--- a/numpy/polynomial/tests/test_polynomial.py
+++ b/numpy/polynomial/tests/test_polynomial.py
@@ -441,36 +441,6 @@ class TestMisc(TestCase) :
         wcoef2d = poly.polyfit(x, np.array([yw,yw]).T, 3, w=w)
         assert_almost_equal(wcoef2d, np.array([coef3,coef3]).T)
 
-        #test NA
-        y = f(x)
-        y[10] = 100
-
-        xm = x.view(maskna=1)
-        xm[10] = np.NA
-        res = poly.polyfit(xm, y, 3)
-        assert_almost_equal(res, coef3)
-
-        ym = y.view(maskna=1)
-        ym[10] = np.NA
-        res = poly.polyfit(x, ym, 3)
-        assert_almost_equal(res, coef3)
-
-        y2 = np.vstack((y,y)).T
-        y2[10,0] = 100
-        y2[15,1] = 100
-        y2m = y2.view(maskna=1)
-        y2m[10,0] = np.NA
-        y2m[15,1] = np.NA
-        res = poly.polyfit(x, y2m, 3).T
-        assert_almost_equal(res[0], coef3)
-        assert_almost_equal(res[1], coef3)
-
-        wm = np.ones_like(x, maskna=1)
-        wm[10] = np.NA
-        res = poly.polyfit(x, y, 3, w=wm)
-        assert_almost_equal(res, coef3)
-
-
     def test_polytrim(self) :
         coef = [2, -1, 1, 0]