Use standard NumPy import in pylab examples.

QuLogic · QuLogic · commit e53876ae5a66 · 2017-05-29T04:01:49.000-04:00
diff --git a/examples/animation/image_slices_viewer.py b/examples/animation/image_slices_viewer.py
@@ -5,7 +5,8 @@
 
 """
 from __future__ import print_function
-import numpy
+
+import numpy as np
 import matplotlib.pyplot as plt
 
 
@@ -24,9 +25,9 @@ def __init__(self, ax, X):
     def onscroll(self, event):
         print("%s %s" % (event.button, event.step))
         if event.button == 'up':
-            self.ind = numpy.clip(self.ind + 1, 0, self.slices - 1)
+            self.ind = np.clip(self.ind + 1, 0, self.slices - 1)
         else:
-            self.ind = numpy.clip(self.ind - 1, 0, self.slices - 1)
+            self.ind = np.clip(self.ind - 1, 0, self.slices - 1)
         self.update()
 
     def update(self):
@@ -37,7 +38,7 @@ def update(self):
 
 fig, ax = plt.subplots(1, 1)
 
-X = numpy.random.rand(20, 20, 40)
+X = np.random.rand(20, 20, 40)
 
 tracker = IndexTracker(ax, X)
 
diff --git a/examples/event_handling/pick_event_demo2.py b/examples/event_handling/pick_event_demo2.py
@@ -7,13 +7,13 @@
 mean vs stddev.  When you click on one of the mu, sigma points, plot the raw
 data from the dataset that generated the mean and stddev.
 """
-import numpy
+import numpy as np
 import matplotlib.pyplot as plt
 
 
-X = numpy.random.rand(100, 1000)
-xs = numpy.mean(X, axis=1)
-ys = numpy.std(X, axis=1)
+X = np.random.rand(100, 1000)
+xs = np.mean(X, axis=1)
+ys = np.std(X, axis=1)
 
 fig, ax = plt.subplots()
 ax.set_title('click on point to plot time series')
diff --git a/examples/event_handling/zoom_window.py b/examples/event_handling/zoom_window.py
@@ -14,7 +14,7 @@
 points**2, so their size is independent of the zoom
 """
 from matplotlib.pyplot import figure, show
-import numpy
+import numpy as np
 figsrc = figure()
 figzoom = figure()
 
@@ -23,7 +23,7 @@
                              autoscale_on=False)
 axsrc.set_title('Click to zoom')
 axzoom.set_title('zoom window')
-x, y, s, c = numpy.random.rand(4, 200)
+x, y, s, c = np.random.rand(4, 200)
 s *= 200
 
 
diff --git a/examples/pylab_examples/ellipse_demo.py b/examples/pylab_examples/ellipse_demo.py
@@ -5,20 +5,22 @@
 
 """
 import matplotlib.pyplot as plt
-import numpy.random as rnd
+import numpy as np
 from matplotlib.patches import Ellipse
 
 NUM = 250
 
-ells = [Ellipse(xy=rnd.rand(2)*10, width=rnd.rand(), height=rnd.rand(), angle=rnd.rand()*360)
+ells = [Ellipse(xy=np.random.rand(2)*10,
+                width=np.random.rand(), height=np.random.rand(),
+                angle=np.random.rand()*360)
         for i in range(NUM)]
 
 fig, ax = plt.subplots(subplot_kw={'aspect': 'equal'})
 for e in ells:
     ax.add_artist(e)
     e.set_clip_box(ax.bbox)
-    e.set_alpha(rnd.rand())
-    e.set_facecolor(rnd.rand(3))
+    e.set_alpha(np.random.rand())
+    e.set_facecolor(np.random.rand(3))
 
 ax.set_xlim(0, 10)
 ax.set_ylim(0, 10)
diff --git a/examples/pylab_examples/pcolor_demo.py b/examples/pylab_examples/pcolor_demo.py
@@ -10,15 +10,15 @@
 """
 import matplotlib.pyplot as plt
 import numpy as np
-from numpy.random import rand
 from matplotlib.colors import LogNorm
 from matplotlib.mlab import bivariate_normal
 
+
 ###############################################################################
 # A simple pcolor demo
 # --------------------
 
-Z = rand(6, 10)
+Z = np.random.rand(6, 10)
 
 plt.subplot(2, 1, 1)
 c = plt.pcolor(Z)
diff --git a/examples/pylab_examples/spy_demos.py b/examples/pylab_examples/spy_demos.py
@@ -7,15 +7,15 @@
 """
 
 from matplotlib.pyplot import figure, show
-import numpy
+import numpy as np
 
 fig = figure()
 ax1 = fig.add_subplot(221)
 ax2 = fig.add_subplot(222)
 ax3 = fig.add_subplot(223)
 ax4 = fig.add_subplot(224)
 
-x = numpy.random.randn(20, 20)
+x = np.random.randn(20, 20)
 x[5] = 0.
 x[:, 12] = 0.
 
diff --git a/examples/pylab_examples/tricontour_vs_griddata.py b/examples/pylab_examples/tricontour_vs_griddata.py
@@ -9,16 +9,15 @@
 import matplotlib.pyplot as plt
 import matplotlib.tri as tri
 import numpy as np
-import numpy.random as rnd
 import matplotlib.mlab as mlab
 import time
 
-rnd.seed(0)
+np.random.seed(0)
 npts = 200
 ngridx = 100
 ngridy = 200
-x = rnd.uniform(-2, 2, npts)
-y = rnd.uniform(-2, 2, npts)
+x = np.random.uniform(-2, 2, npts)
+y = np.random.uniform(-2, 2, npts)
 z = x*np.exp(-x**2 - y**2)
 
 # griddata and contour.
diff --git a/examples/pylab_examples/vline_hline_demo.py b/examples/pylab_examples/vline_hline_demo.py
@@ -9,7 +9,6 @@
 import matplotlib.pyplot as plt
 from matplotlib.transforms import blended_transform_factory as btf
 import numpy as np
-import numpy.random as rnd
 
 
 def f(t):
@@ -19,7 +18,7 @@ def f(t):
 
 t = np.arange(0.0, 5.0, 0.1)
 s = f(t)
-nse = rnd.normal(0.0, 0.3, t.shape) * s
+nse = np.random.normal(0.0, 0.3, t.shape) * s
 
 fig = plt.figure(figsize=(12, 6))
 vax = fig.add_subplot(121)
diff --git a/examples/subplots_axes_and_figures/subplot_toolbar.py b/examples/subplots_axes_and_figures/subplot_toolbar.py
@@ -5,17 +5,17 @@
 
 """
 import matplotlib.pyplot as plt
-import numpy.random as rnd
+import numpy as np
 
 fig = plt.figure()
 plt.subplot(221)
-plt.imshow(rnd.random((100, 100)))
+plt.imshow(np.random.random((100, 100)))
 plt.subplot(222)
-plt.imshow(rnd.random((100, 100)))
+plt.imshow(np.random.random((100, 100)))
 plt.subplot(223)
-plt.imshow(rnd.random((100, 100)))
+plt.imshow(np.random.random((100, 100)))
 plt.subplot(224)
-plt.imshow(rnd.random((100, 100)))
+plt.imshow(np.random.random((100, 100)))
 
 plt.subplot_tool()
 plt.show()
diff --git a/examples/units/units_sample.py b/examples/units/units_sample.py
@@ -15,9 +15,9 @@
 """
 from basic_units import cm, inch
 import matplotlib.pyplot as plt
-import numpy
+import numpy as np
 
-cms = cm * numpy.arange(0, 10, 2)
+cms = cm * np.arange(0, 10, 2)
 
 fig = plt.figure()
 
diff --git a/examples/user_interfaces/histogram_demo_canvasagg_sgskip.py b/examples/user_interfaces/histogram_demo_canvasagg_sgskip.py
@@ -15,16 +15,15 @@
 from matplotlib.backends.backend_agg import FigureCanvasAgg
 from matplotlib.figure import Figure
 from matplotlib.mlab import normpdf
-from numpy.random import randn
-import numpy
+import numpy as np
 
 fig = Figure(figsize=(5, 4), dpi=100)
 ax = fig.add_subplot(111)
 
 canvas = FigureCanvasAgg(fig)
 
 mu, sigma = 100, 15
-x = mu + sigma*randn(10000)
+x = mu + sigma * np.random.randn(10000)
 
 # the histogram of the data
 n, bins, patches = ax.hist(x, 50, normed=1)
@@ -52,7 +51,7 @@
 
 if 0:
     # convert to a numpy array
-    X = numpy.fromstring(s, numpy.uint8).reshape((h, w, 3))
+    X = np.fromstring(s, np.uint8).reshape((h, w, 3))
 
 if 0:
     # pass off to PIL
diff --git a/examples/userdemo/annotate_text_arrow.py b/examples/userdemo/annotate_text_arrow.py
@@ -5,16 +5,16 @@
 
 """
 
-import numpy.random
+import numpy as np
 import matplotlib.pyplot as plt
 
 fig, ax = plt.subplots(figsize=(5, 5))
 ax.set_aspect(1)
 
-x1 = -1 + numpy.random.randn(100)
-y1 = -1 + numpy.random.randn(100)
-x2 = 1. + numpy.random.randn(100)
-y2 = 1. + numpy.random.randn(100)
+x1 = -1 + np.random.randn(100)
+y1 = -1 + np.random.randn(100)
+x2 = 1. + np.random.randn(100)
+y2 = 1. + np.random.randn(100)
 
 ax.scatter(x1, y1, color="r")
 ax.scatter(x2, y2, color="g")
