Merge pull request #13097 from anntzer/tupleless

QuLogic · web-flow · commit af8a720136be · 2019-01-04T15:42:01.000-05:00
Replace 1-tuples by scalars where possible.
diff --git a/examples/images_contours_and_fields/contour_label_demo.py b/examples/images_contours_and_fields/contour_label_demo.py
@@ -36,11 +36,8 @@
 
 class nf(float):
     def __repr__(self):
-        str = '%.1f' % (self.__float__(),)
-        if str[-1] == '0':
-            return '%.0f' % self.__float__()
-        else:
-            return '%.1f' % self.__float__()
+        s = f'{self:.1f}'
+        return f'{self:.0f}' if s[-1] == '0' else s
 
 
 # Basic contour plot
diff --git a/examples/misc/transoffset.py b/examples/misc/transoffset.py
@@ -38,7 +38,7 @@
                                        x=0.05, y=0.10, units='inches')
 
 for x, y in zip(xs, ys):
-    plt.plot((x,), (y,), 'ro')
+    plt.plot(x, y, 'ro')
     plt.text(x, y, '%d, %d' % (int(x), int(y)), transform=trans_offset)
 
 
@@ -49,7 +49,7 @@
                                        y=6, units='dots')
 
 for x, y in zip(xs, ys):
-    plt.polar((x,), (y,), 'ro')
+    plt.polar(x, y, 'ro')
     plt.text(x, y, '%d, %d' % (int(x), int(y)),
              transform=trans_offset,
              horizontalalignment='center',
diff --git a/examples/mplot3d/lorenz_attractor.py b/examples/mplot3d/lorenz_attractor.py
@@ -38,9 +38,9 @@ def lorenz(x, y, z, s=10, r=28, b=2.667):
 num_steps = 10000
 
 # Need one more for the initial values
-xs = np.empty((num_steps + 1,))
-ys = np.empty((num_steps + 1,))
-zs = np.empty((num_steps + 1,))
+xs = np.empty(num_steps + 1)
+ys = np.empty(num_steps + 1)
+zs = np.empty(num_steps + 1)
 
 # Set initial values
 xs[0], ys[0], zs[0] = (0., 1., 1.05)
diff --git a/lib/matplotlib/colorbar.py b/lib/matplotlib/colorbar.py
@@ -790,7 +790,7 @@ def _process_values(self, b=None):
         if self.values is not None:
             self._values = np.array(self.values)
             if self.boundaries is None:
-                b = np.zeros(len(self.values) + 1, 'd')
+                b = np.zeros(len(self.values) + 1)
                 b[1:-1] = 0.5 * (self._values[:-1] - self._values[1:])
                 b[0] = 2.0 * b[1] - b[2]
                 b[-1] = 2.0 * b[-2] - b[-3]
@@ -802,7 +802,7 @@ def _process_values(self, b=None):
         # make reasonable ones based on cmap and norm.
         if isinstance(self.norm, colors.NoNorm):
             b = self._uniform_y(self.cmap.N + 1) * self.cmap.N - 0.5
-            v = np.zeros((len(b) - 1,), dtype=np.int16)
+            v = np.zeros(len(b) - 1, dtype=np.int16)
             v[self._inside] = np.arange(self.cmap.N, dtype=np.int16)
             if self._extend_lower():
                 v[0] = -1
@@ -818,7 +818,7 @@ def _process_values(self, b=None):
             if self._extend_upper():
                 b = b + [b[-1] + 1]
             b = np.array(b)
-            v = np.zeros((len(b) - 1,), dtype=float)
+            v = np.zeros(len(b) - 1)
             bi = self.norm.boundaries
             v[self._inside] = 0.5 * (bi[:-1] + bi[1:])
             if self._extend_lower():
diff --git a/lib/matplotlib/colors.py b/lib/matplotlib/colors.py
@@ -412,14 +412,15 @@ def makeMappingArray(N, data, gamma=1.0):
         raise ValueError("data mapping points must have x in increasing order")
     # begin generation of lookup table
     x = x * (N - 1)
-    lut = np.zeros((N,), float)
     xind = (N - 1) * np.linspace(0, 1, N) ** gamma
     ind = np.searchsorted(x, xind)[1:-1]
 
     distance = (xind[1:-1] - x[ind - 1]) / (x[ind] - x[ind - 1])
-    lut[1:-1] = distance * (y0[ind] - y1[ind - 1]) + y1[ind - 1]
-    lut[0] = y1[0]
-    lut[-1] = y0[-1]
+    lut = np.concatenate([
+        [y1[0]],
+        distance * (y0[ind] - y1[ind - 1]) + y1[ind - 1],
+        [y0[-1]],
+    ])
     # ensure that the lut is confined to values between 0 and 1 by clipping it
     return np.clip(lut, 0.0, 1.0)
 
@@ -543,8 +544,7 @@ def __call__(self, X, alpha=None, bytes=False):
                 # If the bad value is set to have a color, then we
                 # override its alpha just as for any other value.
 
-        rgba = np.empty(shape=xa.shape + (4,), dtype=lut.dtype)
-        lut.take(xa, axis=0, mode='clip', out=rgba)
+        rgba = lut.take(xa, axis=0, mode='clip')
         if vtype == 'scalar':
             rgba = tuple(rgba[0, :])
         return rgba
diff --git a/lib/matplotlib/hatch.py b/lib/matplotlib/hatch.py
@@ -201,7 +201,7 @@ def get_path(hatchpattern, density=6):
         return Path(np.empty((0, 2)))
 
     vertices = np.empty((num_vertices, 2))
-    codes = np.empty((num_vertices,), np.uint8)
+    codes = np.empty(num_vertices, Path.code_type)
 
     cursor = 0
     for pattern in patterns:
diff --git a/lib/matplotlib/mlab.py b/lib/matplotlib/mlab.py
@@ -516,12 +516,12 @@ def _spectral_helper(x, y=None, NFFT=None, Fs=None, detrend_func=None,
     # zero pad x and y up to NFFT if they are shorter than NFFT
     if len(x) < NFFT:
         n = len(x)
-        x = np.resize(x, (NFFT,))
+        x = np.resize(x, NFFT)
         x[n:] = 0
 
     if not same_data and len(y) < NFFT:
         n = len(y)
-        y = np.resize(y, (NFFT,))
+        y = np.resize(y, NFFT)
         y[n:] = 0
 
     if pad_to is None:
@@ -1598,7 +1598,7 @@ def evaluate(self, points):
             raise ValueError("points have dimension {}, dataset has dimension "
                              "{}".format(dim, self.dim))
 
-        result = np.zeros((num_m,), dtype=float)
+        result = np.zeros(num_m)
 
         if num_m >= self.num_dp:
             # there are more points than data, so loop over data
diff --git a/lib/matplotlib/projections/polar.py b/lib/matplotlib/projections/polar.py
@@ -1122,12 +1122,10 @@ def set_theta_direction(self, direction):
            Theta increases in the counterclockwise direction
         """
         mtx = self._direction.get_matrix()
-        if direction in ('clockwise',):
+        if direction in ('clockwise', -1):
             mtx[0, 0] = -1
-        elif direction in ('counterclockwise', 'anticlockwise'):
+        elif direction in ('counterclockwise', 'anticlockwise', 1):
             mtx[0, 0] = 1
-        elif direction in (1, -1):
-            mtx[0, 0] = direction
         else:
             raise ValueError(
                 "direction must be 1, -1, clockwise or counterclockwise")
diff --git a/lib/matplotlib/quiver.py b/lib/matplotlib/quiver.py
@@ -306,9 +306,8 @@ def _init(self):
             # Hack: save and restore the Umask
             _mask = self.Q.Umask
             self.Q.Umask = ma.nomask
-            self.verts = self.Q._make_verts(np.array([self.U]),
-                                            np.zeros((1,)),
-                                            self.angle)
+            self.verts = self.Q._make_verts(
+                np.array([self.U]), np.zeros(1), self.angle)
             self.Q.Umask = _mask
             self.Q.pivot = _pivot
             kw = self.Q.polykw
diff --git a/lib/matplotlib/tests/test_axes.py b/lib/matplotlib/tests/test_axes.py
@@ -630,13 +630,13 @@ def test_const_xy():
     fig = plt.figure()
 
     plt.subplot(311)
-    plt.plot(np.arange(10), np.ones((10,)))
+    plt.plot(np.arange(10), np.ones(10))
 
     plt.subplot(312)
-    plt.plot(np.ones((10,)), np.arange(10))
+    plt.plot(np.ones(10), np.arange(10))
 
     plt.subplot(313)
-    plt.plot(np.ones((10,)), np.ones((10,)), 'o')
+    plt.plot(np.ones(10), np.ones(10), 'o')
 
 
 @image_comparison(baseline_images=['polar_wrap_180', 'polar_wrap_360'],
@@ -5198,7 +5198,7 @@ def test_violin_point_mass():
 
 
 def generate_errorbar_inputs():
-    base_xy = cycler('x', [np.arange(5)]) + cycler('y', [np.ones((5, ))])
+    base_xy = cycler('x', [np.arange(5)]) + cycler('y', [np.ones(5)])
     err_cycler = cycler('err', [1,
                                 [1, 1, 1, 1, 1],
                                 [[1, 1, 1, 1, 1],
diff --git a/lib/matplotlib/tests/test_mlab.py b/lib/matplotlib/tests/test_mlab.py
@@ -210,7 +210,7 @@ def check_window_apply_repeat(self, x, window, NFFT, noverlap):
         if np.iterable(window):
             windowVals = window
         else:
-            windowVals = window(np.ones((NFFT,), x.dtype))
+            windowVals = window(np.ones(NFFT, x.dtype))
 
         # do the ffts of the slices
         for i in range(n):
diff --git a/lib/matplotlib/tests/test_simplification.py b/lib/matplotlib/tests/test_simplification.py
@@ -49,7 +49,7 @@ def test_diamond():
 
 def test_noise():
     np.random.seed(0)
-    x = np.random.uniform(size=(50000,)) * 50
+    x = np.random.uniform(size=50000) * 50
 
     fig, ax = plt.subplots()
     p1 = ax.plot(x, solid_joinstyle='round', linewidth=2.0)
@@ -190,7 +190,7 @@ def test_angled_antiparallel(angle, offset):
 def test_sine_plus_noise():
     np.random.seed(0)
     x = (np.sin(np.linspace(0, np.pi * 2.0, 50000)) +
-         np.random.uniform(size=(50000,)) * 0.01)
+         np.random.uniform(size=50000) * 0.01)
 
     fig, ax = plt.subplots()
     p1 = ax.plot(x, solid_joinstyle='round', linewidth=2.0)
diff --git a/lib/mpl_toolkits/axes_grid1/colorbar.py b/lib/mpl_toolkits/axes_grid1/colorbar.py
@@ -596,8 +596,8 @@ def _process_values(self, b=None):
         if b is not None:
             self._boundaries = np.asarray(b, dtype=float)
             if self.values is None:
-                self._values = 0.5*(self._boundaries[:-1]
-                                        + self._boundaries[1:])
+                self._values = (self._boundaries[:-1]
+                                + self._boundaries[1:]) / 2
                 if isinstance(self.norm, colors.NoNorm):
                     self._values = (self._values + 0.00001).astype(np.int16)
                 return
@@ -606,7 +606,7 @@ def _process_values(self, b=None):
         if self.values is not None:
             self._values = np.array(self.values)
             if self.boundaries is None:
-                b = np.zeros(len(self.values)+1, 'd')
+                b = np.zeros(len(self.values) + 1)
                 b[1:-1] = 0.5*(self._values[:-1] - self._values[1:])
                 b[0] = 2.0*b[1] - b[2]
                 b[-1] = 2.0*b[-2] - b[-3]
@@ -617,22 +617,16 @@ def _process_values(self, b=None):
         # Neither boundaries nor values are specified;
         # make reasonable ones based on cmap and norm.
         if isinstance(self.norm, colors.NoNorm):
-            b = self._uniform_y(self.cmap.N+1) * self.cmap.N - 0.5
-            v = np.zeros((len(b)-1,), dtype=np.int16)
-            v = np.arange(self.cmap.N, dtype=np.int16)
-            self._boundaries = b
-            self._values = v
+            self._boundaries = (
+                self._uniform_y(self.cmap.N + 1) * self.cmap.N - 0.5)
+            self._values = np.arange(self.cmap.N, dtype=np.int16)
             return
         elif isinstance(self.norm, colors.BoundaryNorm):
-            b = np.array(self.norm.boundaries)
-            v = np.zeros((len(b)-1,), dtype=float)
-            bi = self.norm.boundaries
-            v = 0.5*(bi[:-1] + bi[1:])
-            self._boundaries = b
-            self._values = v
+            self._boundaries = np.array(self.norm.boundaries)
+            self._values = (self._boundaries[:-1] + self._boundaries[1:]) / 2
             return
         else:
-            b = self._uniform_y(self.cmap.N+1)
+            b = self._uniform_y(self.cmap.N + 1)
 
         self._process_values(b)
 
diff --git a/tools/memleak.py b/tools/memleak.py
@@ -18,12 +18,12 @@ def run_memleak_test(bench, iterations, report):
     starti = min(50, iterations // 2)
     endi = iterations
 
-    malloc_arr = np.empty((endi,), dtype=np.int64)
-    rss_arr = np.empty((endi,), dtype=np.int64)
-    rss_peaks = np.empty((endi,), dtype=np.int64)
-    nobjs_arr = np.empty((endi,), dtype=np.int64)
-    garbage_arr = np.empty((endi,), dtype=np.int64)
-    open_files_arr = np.empty((endi,), dtype=np.int64)
+    malloc_arr = np.empty(endi, dtype=np.int64)
+    rss_arr = np.empty(endi, dtype=np.int64)
+    rss_peaks = np.empty(endi, dtype=np.int64)
+    nobjs_arr = np.empty(endi, dtype=np.int64)
+    garbage_arr = np.empty(endi, dtype=np.int64)
+    open_files_arr = np.empty(endi, dtype=np.int64)
     rss_peak = 0
 
     p = psutil.Process()
