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

This is the case for x in {int, bool, str, float, complex, object}.
Using the np.{x} version is deceptive as it suggests that there is a
difference. This change doesn't affect any external behaviour. The
`long` type is missing in python 3, so np.long is still useful

Author: eric-wieser

examples/event_handling/viewlims.py

@@ -36,8 +36,8 @@ def __call__(self, xstart, xend, ystart, yend):
         self.y = np.linspace(ystart, yend, self.height).reshape(-1, 1)
         c = self.x + 1.0j * self.y
         threshold_time = np.zeros((self.height, self.width))
-        z = np.zeros(threshold_time.shape, dtype=np.complex)
-        mask = np.ones(threshold_time.shape, dtype=np.bool)
+        z = np.zeros(threshold_time.shape, dtype=complex)
+        mask = np.ones(threshold_time.shape, dtype=bool)
         for i in range(self.niter):
             z[mask] = z[mask]**self.power + c[mask]
             mask = (np.abs(z) < self.radius)

examples/images_contours_and_fields/contour_corner_mask.py

@@ -14,7 +14,7 @@
 z = np.sin(0.5 * x) * np.cos(0.52 * y)
 
 # Mask various z values.
-mask = np.zeros_like(z, dtype=np.bool)
+mask = np.zeros_like(z, dtype=bool)
 mask[2, 3:5] = True
 mask[3:5, 4] = True
 mask[7, 2] = True

examples/images_contours_and_fields/tricontour_smooth_delaunay.py

@@ -75,7 +75,7 @@ def experiment_res(x, y):
 ntri = tri.triangles.shape[0]
 
 # Some invalid data are masked out
-mask_init = np.zeros(ntri, dtype=np.bool)
+mask_init = np.zeros(ntri, dtype=bool)
 masked_tri = random_gen.randint(0, ntri, int(ntri * init_mask_frac))
 mask_init[masked_tri] = True
 tri.set_mask(mask_init)

lib/matplotlib/axes/_axes.py

@@ -4769,9 +4769,9 @@ def fill_between(self, x, y1, y2=0, where=None, interpolate=False,
             y2 = np.ones_like(x) * y2
 
         if where is None:
-            where = np.ones(len(x), np.bool)
+            where = np.ones(len(x), bool)
         else:
-            where = np.asarray(where, np.bool)
+            where = np.asarray(where, bool)
 
         if not (x.shape == y1.shape == y2.shape == where.shape):
             raise ValueError("Argument dimensions are incompatible")
@@ -4930,9 +4930,9 @@ def fill_betweenx(self, y, x1, x2=0, where=None,
             x2 = np.ones_like(y) * x2
 
         if where is None:
-            where = np.ones(len(y), np.bool)
+            where = np.ones(len(y), bool)
         else:
-            where = np.asarray(where, np.bool)
+            where = np.asarray(where, bool)
 
         if not (y.shape == x1.shape == x2.shape == where.shape):
             raise ValueError("Argument dimensions are incompatible")
@@ -4954,7 +4954,7 @@ def fill_betweenx(self, y, x1, x2=0, where=None,
                 continue
 
             N = len(yslice)
-            Y = np.zeros((2 * N + 2, 2), np.float)
+            Y = np.zeros((2 * N + 2, 2), float)
             if interpolate:
                 def get_interp_point(ind):
                     im1 = max(ind - 1, 0)

lib/matplotlib/colors.py

@@ -1443,7 +1443,7 @@ def hsv_to_rgb(hsv):
     g = np.empty_like(h)
     b = np.empty_like(h)
 
-    i = (h * 6.0).astype(np.int)
+    i = (h * 6.0).astype(int)
     f = (h * 6.0) - i
     p = v * (1.0 - s)
     q = v * (1.0 - s * f)

lib/matplotlib/finance.py

@@ -58,27 +58,27 @@ def md5(x):
     (str('year'), np.int16),
     (str('month'), np.int8),
     (str('day'), np.int8),
-    (str('d'), np.float),     # mpl datenum
-    (str('open'), np.float),
-    (str('high'), np.float),
-    (str('low'), np.float),
-    (str('close'), np.float),
-    (str('volume'), np.float),
-    (str('aclose'), np.float)])
+    (str('d'), float),     # mpl datenum
+    (str('open'), float),
+    (str('high'), float),
+    (str('low'), float),
+    (str('close'), float),
+    (str('volume'), float),
+    (str('aclose'), float)])
 
 
 stock_dt_ochl = np.dtype(
     [(str('date'), object),
      (str('year'), np.int16),
      (str('month'), np.int8),
      (str('day'), np.int8),
-     (str('d'), np.float),     # mpl datenum
-     (str('open'), np.float),
-     (str('close'), np.float),
-     (str('high'), np.float),
-     (str('low'), np.float),
-     (str('volume'), np.float),
-     (str('aclose'), np.float)])
+     (str('d'), float),     # mpl datenum
+     (str('open'), float),
+     (str('close'), float),
+     (str('high'), float),
+     (str('low'), float),
+     (str('volume'), float),
+     (str('aclose'), float)])
 
 
 def parse_yahoo_historical_ochl(fh, adjusted=True, asobject=False):

lib/matplotlib/mlab.py

@@ -3141,12 +3141,12 @@ def get_justify(colname, column, precision):
             length = max(len(colname), fixed_width)
             return 0, length+padding, "%s"  # left justify
 
-        if np.issubdtype(ntype, np.int):
+        if np.issubdtype(ntype, int):
             length = max(len(colname),
                          np.max(list(map(len, list(map(str, column))))))
             return 1, length+padding, "%d"  # right justify
 
-        if np.issubdtype(ntype, np.float):
+        if np.issubdtype(ntype, float):
             fmt = "%." + str(precision) + "f"
             length = max(
                 len(colname),

lib/matplotlib/quiver.py

@@ -982,12 +982,12 @@ def _find_tails(self, mag, rounding=True, half=5, full=10, flag=50):
         # If rounding, round to the nearest multiple of half, the smallest
         # increment
         if rounding:
-            mag = half * (mag / half + 0.5).astype(np.int)
+            mag = half * (mag / half + 0.5).astype(int)
 
-        num_flags = np.floor(mag / flag).astype(np.int)
+        num_flags = np.floor(mag / flag).astype(int)
         mag = np.mod(mag, flag)
 
-        num_barb = np.floor(mag / full).astype(np.int)
+        num_barb = np.floor(mag / full).astype(int)
         mag = np.mod(mag, full)
 
         half_flag = mag >= half

lib/matplotlib/streamplot.py

@@ -599,14 +599,14 @@ def interpgrid(a, xi, yi):
 
     Ny, Nx = np.shape(a)
     if isinstance(xi, np.ndarray):
-        x = xi.astype(np.int)
-        y = yi.astype(np.int)
+        x = xi.astype(int)
+        y = yi.astype(int)
         # Check that xn, yn don't exceed max index
         xn = np.clip(x + 1, 0, Nx - 1)
         yn = np.clip(y + 1, 0, Ny - 1)
     else:
-        x = np.int(xi)
-        y = np.int(yi)
+        x = int(xi)
+        y = int(yi)
         # conditional is faster than clipping for integers
         if x == (Nx - 2):
             xn = x

lib/matplotlib/tests/test_image.py

@@ -705,7 +705,7 @@ def test_mask_image():
 
     ax1.imshow(A, interpolation='nearest')
 
-    A = np.zeros((5, 5), dtype=np.bool)
+    A = np.zeros((5, 5), dtype=bool)
     A[1:2, 1:2] = True
     A = np.ma.masked_array(np.ones((5, 5), dtype=np.uint16), A)