Merge pull request #9517 from charris/rebase-9508

MAINT/DOC: Use builtin when np.{x} is builtins.{x}.

10 files changed, 57 insertions, 57 deletions

diff --git a/numpy/lib/arraysetops.py b/numpy/lib/arraysetops.py
index aa3a05e12..ededb9dd0 100644
--- a/numpy/lib/arraysetops.py
+++ b/numpy/lib/arraysetops.py
@@ -451,11 +451,11 @@ def in1d(ar1, ar2, assume_unique=False, invert=False):
     # This code is significantly faster when the condition is satisfied.
     if len(ar2) < 10 * len(ar1) ** 0.145:
         if invert:
-            mask = np.ones(len(ar1), dtype=np.bool)
+            mask = np.ones(len(ar1), dtype=bool)
             for a in ar2:
                 mask &= (ar1 != a)
         else:
-            mask = np.zeros(len(ar1), dtype=np.bool)
+            mask = np.zeros(len(ar1), dtype=bool)
             for a in ar2:
                 mask |= (ar1 == a)
         return mask
diff --git a/numpy/lib/function_base.py b/numpy/lib/function_base.py
index f5e9ff2a5..c185f9639 100644
--- a/numpy/lib/function_base.py
+++ b/numpy/lib/function_base.py
@@ -717,7 +717,7 @@ def histogram(a, bins=10, range=None, normed=False, weights=None,
         # At this point, if the weights are not integer, floating point, or
         # complex, we have to use the slow algorithm.
         if weights is not None and not (np.can_cast(weights.dtype, np.double) or
-                                        np.can_cast(weights.dtype, np.complex)):
+                                        np.can_cast(weights.dtype, complex)):
             bins = linspace(mn, mx, bins + 1, endpoint=True)
 
     if not iterable(bins):
@@ -1541,7 +1541,7 @@ def gradient(f, *varargs, **kwargs):
 
     Examples
     --------
-    >>> f = np.array([1, 2, 4, 7, 11, 16], dtype=np.float)
+    >>> f = np.array([1, 2, 4, 7, 11, 16], dtype=float)
     >>> np.gradient(f)
     array([ 1. ,  1.5,  2.5,  3.5,  4.5,  5. ])
     >>> np.gradient(f, 2)
@@ -1557,7 +1557,7 @@ def gradient(f, *varargs, **kwargs):
 
     Or a non uniform one:
 
-    >>> x = np.array([0., 1., 1.5, 3.5, 4., 6.], dtype=np.float)
+    >>> x = np.array([0., 1., 1.5, 3.5, 4., 6.], dtype=float)
     >>> np.gradient(f, x)
     array([ 1. ,  3. ,  3.5,  6.7,  6.9,  2.5])
 
@@ -1565,7 +1565,7 @@ def gradient(f, *varargs, **kwargs):
     axis. In this example the first array stands for the gradient in
     rows and the second one in columns direction:
 
-    >>> np.gradient(np.array([[1, 2, 6], [3, 4, 5]], dtype=np.float))
+    >>> np.gradient(np.array([[1, 2, 6], [3, 4, 5]], dtype=float))
     [array([[ 2.,  2., -1.],
             [ 2.,  2., -1.]]), array([[ 1. ,  2.5,  4. ],
             [ 1. ,  1. ,  1. ]])]
@@ -1575,7 +1575,7 @@ def gradient(f, *varargs, **kwargs):
 
     >>> dx = 2.
     >>> y = [1., 1.5, 3.5]
-    >>> np.gradient(np.array([[1, 2, 6], [3, 4, 5]], dtype=np.float), dx, y)
+    >>> np.gradient(np.array([[1, 2, 6], [3, 4, 5]], dtype=float), dx, y)
     [array([[ 1. ,  1. , -0.5],
             [ 1. ,  1. , -0.5]]), array([[ 2. ,  2. ,  2. ],
             [ 2. ,  1.7,  0.5]])]
@@ -1592,7 +1592,7 @@ def gradient(f, *varargs, **kwargs):
     The `axis` keyword can be used to specify a subset of axes of which the
     gradient is calculated
 
-    >>> np.gradient(np.array([[1, 2, 6], [3, 4, 5]], dtype=np.float), axis=0)
+    >>> np.gradient(np.array([[1, 2, 6], [3, 4, 5]], dtype=float), axis=0)
     array([[ 2.,  2., -1.],
            [ 2.,  2., -1.]])
 
@@ -2600,7 +2600,7 @@ class vectorize(object):
     >>> out = vfunc([1, 2, 3, 4], 2)
     >>> type(out[0])
     <type 'numpy.int32'>
-    >>> vfunc = np.vectorize(myfunc, otypes=[np.float])
+    >>> vfunc = np.vectorize(myfunc, otypes=[float])
     >>> out = vfunc([1, 2, 3, 4], 2)
     >>> type(out[0])
     <type 'numpy.float64'>
@@ -3029,7 +3029,7 @@ def cov(m, y=None, rowvar=True, bias=False, ddof=None, fweights=None,
     # Get the product of frequencies and weights
     w = None
     if fweights is not None:
-        fweights = np.asarray(fweights, dtype=np.float)
+        fweights = np.asarray(fweights, dtype=float)
         if not np.all(fweights == np.around(fweights)):
             raise TypeError(
                 "fweights must be integer")
@@ -3044,7 +3044,7 @@ def cov(m, y=None, rowvar=True, bias=False, ddof=None, fweights=None,
                 "fweights cannot be negative")
         w = fweights
     if aweights is not None:
-        aweights = np.asarray(aweights, dtype=np.float)
+        aweights = np.asarray(aweights, dtype=float)
         if aweights.ndim > 1:
             raise RuntimeError(
                 "cannot handle multidimensional aweights")
diff --git a/numpy/lib/index_tricks.py b/numpy/lib/index_tricks.py
index 950f77175..650b37f25 100644
--- a/numpy/lib/index_tricks.py
+++ b/numpy/lib/index_tricks.py
@@ -842,7 +842,7 @@ def diag_indices(n, ndim=2):
 
     And use it to set the diagonal of an array of zeros to 1:
 
-    >>> a = np.zeros((2, 2, 2), dtype=np.int)
+    >>> a = np.zeros((2, 2, 2), dtype=int)
     >>> a[d3] = 1
     >>> a
     array([[[1, 0],
diff --git a/numpy/lib/npyio.py b/numpy/lib/npyio.py
index 187a6722a..17b585ee5 100644
--- a/numpy/lib/npyio.py
+++ b/numpy/lib/npyio.py
@@ -737,7 +737,7 @@ def _getconv(dtype):
         return np.longdouble
     elif issubclass(typ, np.floating):
         return floatconv
-    elif issubclass(typ, np.complex):
+    elif issubclass(typ, complex):
         return lambda x: complex(asstr(x))
     elif issubclass(typ, np.bytes_):
         return asbytes
@@ -1902,16 +1902,16 @@ def genfromtxt(fname, dtype=float, comments='#', delimiter=None,
             # If the dtype is uniform, don't define names, else use ''
             base = set([c.type for c in converters if c._checked])
             if len(base) == 1:
-                (ddtype, mdtype) = (list(base)[0], np.bool)
+                (ddtype, mdtype) = (list(base)[0], bool)
             else:
                 ddtype = [(defaultfmt % i, dt)
                           for (i, dt) in enumerate(column_types)]
                 if usemask:
-                    mdtype = [(defaultfmt % i, np.bool)
+                    mdtype = [(defaultfmt % i, bool)
                               for (i, dt) in enumerate(column_types)]
         else:
             ddtype = list(zip(names, column_types))
-            mdtype = list(zip(names, [np.bool] * len(column_types)))
+            mdtype = list(zip(names, [bool] * len(column_types)))
         output = np.array(data, dtype=ddtype)
         if usemask:
             outputmask = np.array(masks, dtype=mdtype)
@@ -1937,7 +1937,7 @@ def genfromtxt(fname, dtype=float, comments='#', delimiter=None,
             # Now, process the rowmasks the same way
             if usemask:
                 rowmasks = np.array(
-                    masks, dtype=np.dtype([('', np.bool) for t in dtype_flat]))
+                    masks, dtype=np.dtype([('', bool) for t in dtype_flat]))
                 # Construct the new dtype
                 mdtype = make_mask_descr(dtype)
                 outputmask = rowmasks.view(mdtype)
@@ -1968,9 +1968,9 @@ def genfromtxt(fname, dtype=float, comments='#', delimiter=None,
             output = np.array(data, dtype)
             if usemask:
                 if dtype.names:
-                    mdtype = [(_, np.bool) for _ in dtype.names]
+                    mdtype = [(_, bool) for _ in dtype.names]
                 else:
-                    mdtype = np.bool
+                    mdtype = bool
                 outputmask = np.array(masks, dtype=mdtype)
     # Try to take care of the missing data we missed
     names = output.dtype.names
diff --git a/numpy/lib/tests/test__iotools.py b/numpy/lib/tests/test__iotools.py
index a7ee9cbff..03192896c 100644
--- a/numpy/lib/tests/test__iotools.py
+++ b/numpy/lib/tests/test__iotools.py
@@ -257,7 +257,7 @@ class TestMiscFunctions(object):
 
     def test_has_nested_dtype(self):
         "Test has_nested_dtype"
-        ndtype = np.dtype(np.float)
+        ndtype = np.dtype(float)
         assert_equal(has_nested_fields(ndtype), False)
         ndtype = np.dtype([('A', '|S3'), ('B', float)])
         assert_equal(has_nested_fields(ndtype), False)
diff --git a/numpy/lib/tests/test_function_base.py b/numpy/lib/tests/test_function_base.py
index 4ecb02821..ad840f8ef 100644
--- a/numpy/lib/tests/test_function_base.py
+++ b/numpy/lib/tests/test_function_base.py
@@ -2663,28 +2663,28 @@ class TestInterp(object):
 
             incres = interp(incpts, xp, yp)
             decres = interp(decpts, xp, yp)
-            inctgt = np.array([1, 1, 1, 1], dtype=np.float)
+            inctgt = np.array([1, 1, 1, 1], dtype=float)
             dectgt = inctgt[::-1]
             assert_equal(incres, inctgt)
             assert_equal(decres, dectgt)
 
             incres = interp(incpts, xp, yp, left=0)
             decres = interp(decpts, xp, yp, left=0)
-            inctgt = np.array([0, 1, 1, 1], dtype=np.float)
+            inctgt = np.array([0, 1, 1, 1], dtype=float)
             dectgt = inctgt[::-1]
             assert_equal(incres, inctgt)
             assert_equal(decres, dectgt)
 
             incres = interp(incpts, xp, yp, right=2)
             decres = interp(decpts, xp, yp, right=2)
-            inctgt = np.array([1, 1, 1, 2], dtype=np.float)
+            inctgt = np.array([1, 1, 1, 2], dtype=float)
             dectgt = inctgt[::-1]
             assert_equal(incres, inctgt)
             assert_equal(decres, dectgt)
 
             incres = interp(incpts, xp, yp, left=0, right=2)
             decres = interp(decpts, xp, yp, left=0, right=2)
-            inctgt = np.array([0, 1, 1, 2], dtype=np.float)
+            inctgt = np.array([0, 1, 1, 2], dtype=float)
             dectgt = inctgt[::-1]
             assert_equal(incres, inctgt)
             assert_equal(decres, dectgt)
diff --git a/numpy/lib/tests/test_io.py b/numpy/lib/tests/test_io.py
index 4bc2a1b1b..f2fd37230 100644
--- a/numpy/lib/tests/test_io.py
+++ b/numpy/lib/tests/test_io.py
@@ -373,7 +373,7 @@ class TestSaveTxt(object):
         # Test the functionality of the header and footer keyword argument.
 
         c = BytesIO()
-        a = np.array([(1, 2), (3, 4)], dtype=np.int)
+        a = np.array([(1, 2), (3, 4)], dtype=int)
         test_header_footer = 'Test header / footer'
         # Test the header keyword argument
         np.savetxt(c, a, fmt='%1d', header=test_header_footer)
@@ -485,7 +485,7 @@ class TestLoadTxt(object):
         c.write('1 2\n3 4')
 
         c.seek(0)
-        x = np.loadtxt(c, dtype=np.int)
+        x = np.loadtxt(c, dtype=int)
         a = np.array([[1, 2], [3, 4]], int)
         assert_array_equal(x, a)
 
@@ -721,7 +721,7 @@ class TestLoadTxt(object):
         # Test using an explicit dtype with an object
         data = """ 1; 2001-01-01
                    2; 2002-01-31 """
-        ndtype = [('idx', int), ('code', np.object)]
+        ndtype = [('idx', int), ('code', object)]
         func = lambda s: strptime(s.strip(), "%Y-%m-%d")
         converters = {1: func}
         test = np.loadtxt(TextIO(data), delimiter=";", dtype=ndtype,
@@ -751,11 +751,11 @@ class TestLoadTxt(object):
         # IEEE doubles and floats only, otherwise the float32
         # conversion may fail.
         tgt = np.logspace(-10, 10, 5).astype(np.float32)
-        tgt = np.hstack((tgt, -tgt)).astype(np.float)
+        tgt = np.hstack((tgt, -tgt)).astype(float)
         inp = '\n'.join(map(float.hex, tgt))
         c = TextIO()
         c.write(inp)
-        for dt in [np.float, np.float32]:
+        for dt in [float, np.float32]:
             c.seek(0)
             res = np.loadtxt(c, dtype=dt)
             assert_equal(res, tgt, err_msg="%s" % dt)
@@ -765,7 +765,7 @@ class TestLoadTxt(object):
         c = TextIO()
         c.write("%s %s" % tgt)
         c.seek(0)
-        res = np.loadtxt(c, dtype=np.complex)
+        res = np.loadtxt(c, dtype=complex)
         assert_equal(res, tgt)
 
     def test_universal_newline(self):
@@ -1190,7 +1190,7 @@ M   33  21.99
         # Test using an explicit dtype with an object
         data = """ 1; 2001-01-01
                    2; 2002-01-31 """
-        ndtype = [('idx', int), ('code', np.object)]
+        ndtype = [('idx', int), ('code', object)]
         func = lambda s: strptime(s.strip(), "%Y-%m-%d")
         converters = {1: func}
         test = np.genfromtxt(TextIO(data), delimiter=";", dtype=ndtype,
@@ -1200,7 +1200,7 @@ M   33  21.99
             dtype=ndtype)
         assert_equal(test, control)
 
-        ndtype = [('nest', [('idx', int), ('code', np.object)])]
+        ndtype = [('nest', [('idx', int), ('code', object)])]
         try:
             test = np.genfromtxt(TextIO(data), delimiter=";",
                                  dtype=ndtype, converters=converters)
@@ -1337,7 +1337,7 @@ M   33  21.99
         test = np.mafromtxt(data, dtype=None, **kwargs)
         control = ma.array([(0, 1), (2, -1)],
                            mask=[(False, False), (False, True)],
-                           dtype=[('A', np.int), ('B', np.int)])
+                           dtype=[('A', int), ('B', int)])
         assert_equal(test, control)
         assert_equal(test.mask, control.mask)
         #
@@ -1345,7 +1345,7 @@ M   33  21.99
         test = np.mafromtxt(data, **kwargs)
         control = ma.array([(0, 1), (2, -1)],
                            mask=[(False, False), (False, True)],
-                           dtype=[('A', np.float), ('B', np.float)])
+                           dtype=[('A', float), ('B', float)])
         assert_equal(test, control)
         assert_equal(test.mask, control.mask)
 
@@ -1414,7 +1414,7 @@ M   33  21.99
                             missing_values='-999.0', names=True,)
         control = ma.array([(0, 1.5), (2, -1.)],
                            mask=[(False, False), (False, True)],
-                           dtype=[('A', np.int), ('B', np.float)])
+                           dtype=[('A', int), ('B', float)])
         assert_equal(test, control)
         assert_equal(test.mask, control.mask)
 
@@ -1682,7 +1682,7 @@ M   33  21.99
         kwargs = dict(delimiter=",", missing_values="N/A", names=True)
         test = np.recfromtxt(data, **kwargs)
         control = np.array([(0, 1), (2, 3)],
-                           dtype=[('A', np.int), ('B', np.int)])
+                           dtype=[('A', int), ('B', int)])
         assert_(isinstance(test, np.recarray))
         assert_equal(test, control)
         #
@@ -1690,7 +1690,7 @@ M   33  21.99
         test = np.recfromtxt(data, dtype=None, usemask=True, **kwargs)
         control = ma.array([(0, 1), (2, -1)],
                            mask=[(False, False), (False, True)],
-                           dtype=[('A', np.int), ('B', np.int)])
+                           dtype=[('A', int), ('B', int)])
         assert_equal(test, control)
         assert_equal(test.mask, control.mask)
         assert_equal(test.A, [0, 2])
@@ -1701,7 +1701,7 @@ M   33  21.99
         kwargs = dict(missing_values="N/A", names=True, case_sensitive=True)
         test = np.recfromcsv(data, dtype=None, **kwargs)
         control = np.array([(0, 1), (2, 3)],
-                           dtype=[('A', np.int), ('B', np.int)])
+                           dtype=[('A', int), ('B', int)])
         assert_(isinstance(test, np.recarray))
         assert_equal(test, control)
         #
@@ -1709,7 +1709,7 @@ M   33  21.99
         test = np.recfromcsv(data, dtype=None, usemask=True, **kwargs)
         control = ma.array([(0, 1), (2, -1)],
                            mask=[(False, False), (False, True)],
-                           dtype=[('A', np.int), ('B', np.int)])
+                           dtype=[('A', int), ('B', int)])
         assert_equal(test, control)
         assert_equal(test.mask, control.mask)
         assert_equal(test.A, [0, 2])
@@ -1717,12 +1717,12 @@ M   33  21.99
         data = TextIO('A,B\n0,1\n2,3')
         test = np.recfromcsv(data, missing_values='N/A',)
         control = np.array([(0, 1), (2, 3)],
-                           dtype=[('a', np.int), ('b', np.int)])
+                           dtype=[('a', int), ('b', int)])
         assert_(isinstance(test, np.recarray))
         assert_equal(test, control)
         #
         data = TextIO('A,B\n0,1\n2,3')
-        dtype = [('a', np.int), ('b', np.float)]
+        dtype = [('a', int), ('b', float)]
         test = np.recfromcsv(data, missing_values='N/A', dtype=dtype)
         control = np.array([(0, 1), (2, 3)],
                            dtype=dtype)
@@ -1827,7 +1827,7 @@ M   33  21.99
 
         assert_equal(test.dtype.names, ['f0', 'f1', 'f2'])
 
-        assert_(test.dtype['f0'] == np.float)
+        assert_(test.dtype['f0'] == float)
         assert_(test.dtype['f1'] == np.int64)
         assert_(test.dtype['f2'] == np.integer)
 
@@ -1919,7 +1919,7 @@ class TestPathUsage(object):
             kwargs = dict(delimiter=",", missing_values="N/A", names=True)
             test = np.recfromtxt(path, **kwargs)
             control = np.array([(0, 1), (2, 3)],
-                               dtype=[('A', np.int), ('B', np.int)])
+                               dtype=[('A', int), ('B', int)])
             assert_(isinstance(test, np.recarray))
             assert_equal(test, control)
 
@@ -1933,7 +1933,7 @@ class TestPathUsage(object):
             kwargs = dict(missing_values="N/A", names=True, case_sensitive=True)
             test = np.recfromcsv(path, dtype=None, **kwargs)
             control = np.array([(0, 1), (2, 3)],
-                               dtype=[('A', np.int), ('B', np.int)])
+                               dtype=[('A', int), ('B', int)])
             assert_(isinstance(test, np.recarray))
             assert_equal(test, control)
 
diff --git a/numpy/lib/tests/test_regression.py b/numpy/lib/tests/test_regression.py
index 1567219d6..74c47df7c 100644
--- a/numpy/lib/tests/test_regression.py
+++ b/numpy/lib/tests/test_regression.py
@@ -108,13 +108,13 @@ class TestRegression(object):
     def test_polydiv_type(self):
         # Make polydiv work for complex types
         msg = "Wrong type, should be complex"
-        x = np.ones(3, dtype=np.complex)
+        x = np.ones(3, dtype=complex)
         q, r = np.polydiv(x, x)
-        assert_(q.dtype == np.complex, msg)
+        assert_(q.dtype == complex, msg)
         msg = "Wrong type, should be float"
-        x = np.ones(3, dtype=np.int)
+        x = np.ones(3, dtype=int)
         q, r = np.polydiv(x, x)
-        assert_(q.dtype == np.float, msg)
+        assert_(q.dtype == float, msg)
 
     def test_histogramdd_too_many_bins(self):
         # Ticket 928.
@@ -123,11 +123,11 @@ class TestRegression(object):
     def test_polyint_type(self):
         # Ticket #944
         msg = "Wrong type, should be complex"
-        x = np.ones(3, dtype=np.complex)
-        assert_(np.polyint(x).dtype == np.complex, msg)
+        x = np.ones(3, dtype=complex)
+        assert_(np.polyint(x).dtype == complex, msg)
         msg = "Wrong type, should be float"
-        x = np.ones(3, dtype=np.int)
-        assert_(np.polyint(x).dtype == np.float, msg)
+        x = np.ones(3, dtype=int)
+        assert_(np.polyint(x).dtype == float, msg)
 
     def test_ndenumerate_crash(self):
         # Ticket 1140
@@ -234,7 +234,7 @@ class TestRegression(object):
 
     def test_nansum_with_boolean(self):
         # gh-2978
-        a = np.zeros(2, dtype=np.bool)
+        a = np.zeros(2, dtype=bool)
         try:
             np.nansum(a)
         except Exception:
diff --git a/numpy/lib/tests/test_type_check.py b/numpy/lib/tests/test_type_check.py
index 259fcd4e5..d863e5924 100644
--- a/numpy/lib/tests/test_type_check.py
+++ b/numpy/lib/tests/test_type_check.py
@@ -374,7 +374,7 @@ class TestNanToNum(object):
         vals = nan_to_num(1)
         assert_all(vals == 1)
         vals = nan_to_num([1])
-        assert_array_equal(vals, np.array([1], np.int))
+        assert_array_equal(vals, np.array([1], int))
 
     def test_complex_good(self):
         vals = nan_to_num(1+1j)
@@ -420,7 +420,7 @@ class TestArrayConversion(object):
     def test_asfarray(self):
         a = asfarray(np.array([1, 2, 3]))
         assert_equal(a.__class__, np.ndarray)
-        assert_(np.issubdtype(a.dtype, np.float))
+        assert_(np.issubdtype(a.dtype, float))
 
 if __name__ == "__main__":
     run_module_suite()
diff --git a/numpy/lib/type_check.py b/numpy/lib/type_check.py
index 9d369aa9f..b2de153d3 100644
--- a/numpy/lib/type_check.py
+++ b/numpy/lib/type_check.py
@@ -433,12 +433,12 @@ def real_if_close(a,tol=100):
     -----
     Machine epsilon varies from machine to machine and between data types
     but Python floats on most platforms have a machine epsilon equal to
-    2.2204460492503131e-16.  You can use 'np.finfo(np.float).eps' to print
+    2.2204460492503131e-16.  You can use 'np.finfo(float).eps' to print
     out the machine epsilon for floats.
 
     Examples
     --------
-    >>> np.finfo(np.float).eps
+    >>> np.finfo(float).eps
     2.2204460492503131e-16
 
     >>> np.real_if_close([2.1 + 4e-14j], tol=1000)