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01/gotodefine.py

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+# -*- coding: utf-8 -*-
+"""
+演示Wing IDE 101的goto define功能。使用Wing IDE 101或Spyder打开本程序之后，
+按住Ctrl的同时点击signal, pl, HasTraits等跳转到定义它们的程序。
+"""
+from scipy import signal
+import pylab as pl
+from enthought.traits.api import HasTraits, Instance
+from enthought.traits.ui.api import View, Item
+
+signal.lfilter
+pl.plot
+pl.title

01/ipython_debug.py

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+# -*- coding: utf-8 -*-
+"""
+本程序有BUG，不能正常运行。在IPython中执行本程序进行调试。
+"""
+import pylab as pl
+import numpy as np
+
+def test_debug():
+    x = np.linspace(1, 50, 10000)
+    img = np.sin(x*np.cos(x))
+    pl.imshow(img)
+    pl.show()
+
+test_debug()    

01/mymodel.py

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+# -*- coding: utf-8 -*-
+"""
+一个简单模块，在usemode.py中使用其中的x和y。
+"""
+x = 100
+y = 200

01/speedup_test.py

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+# -*- coding: utf-8 -*-
+"""
+载入几个重量级的扩展库。在IPython中使用run命令可以缩短
+第二次运行程序启动时间。
+"""
+import numpy as np
+from scipy import signal
+import pylab as pl
+
+t = np.linspace(0, 10, 1000)
+x = signal.chirp(t, 5, 10, 30)
+pl.plot(t, x)
+pl.show()

01/spyder_interface.py

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+# -*- coding: utf-8 -*-
+"""
+对图像进行浮雕处理
+"""
+import pylab as pl
+import scipy as sp
+img = sp.lena()
+pl.imshow(img, cmap=pl.cm.gray)
+img2 = img[:-2,1:-1]-img[2:,1:-1]+img[1:-1, :-2]-img[1:-1,2:]
+pl.figure()
+pl.imshow(img2, cmap=pl.cm.gray)
+pl.show()

01/spyder_workspace.py

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+# -*- coding: utf-8 -*-
+"""
+演示Spyder的Workspace，在Spyder中运行本程序之后，可以使用
+Workspace查看x, y和img数组。
+"""
+import numpy as np
+x = np.linspace(0.1, 10, 100)
+y = np.sin(x)/x
+img = np.random.random((10,10))

01/usemodel.py

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+# -*- coding: utf-8 -*-
+"""
+输出mymodel.py模块中的x和y，为了显示最新的值，使用reload()强制
+重新载入模块。
+"""
+import mymodel
+reload(mymodel)
+from mymodel import *
+
+print x, y

01/wingide_debug.py

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+# -*- coding: utf-8 -*-
+"""
+使用TraitsUI制作的简单的界面程序。使用Wing IDE 101可以查看
+按钮点击事件处理函数_button_fired的调用堆栈。
+"""
+from enthought.traits.api import HasTraits, Instance, Button, Int
+from enthought.traits.ui.api import View, Item
+
+class Demo(HasTraits):
+    count = Int(0)
+    button = Button("Click Me")
+    view = View(Item("button"), Item("count"))
+
+    def _button_fired(self):
+        self.count += 1
+
+demo = Demo()
+demo.configure_traits()

02/create_bmp.py

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+# -*- coding: utf-8 -*-
+"""
+创建一个1000*1000的24bit的BMP图像。
+"""
+header = 'BM\xf6\xc6-\x00\x00\x00\x00\x006\x00\x00\x00(\x00\x00\x00\xe8\x03\x00\x00\xe8\x03\x00\x00\x01\x00\x18\x00\x00\x00\x00\x00\xc0\xc6-\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00'
+f = file("tmp.bmp","wb")
+f.write(header)
+f.write("\x00"*(1000*1000*3))
+f.close()

02/height.csv

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+  18.0, 173.4
+   7.8, 126.2
+   8.5, 131.2
+  12.5, 155.0
+   7.4, 126.8
+  15.0, 170.4
+   7.1, 121.8
+  15.2, 169.3
+  19.2, 176.8
+  16.6, 175.2
+  18.7, 175.9
+  13.3, 160.4
+  19.1, 174.6
+  15.1, 169.9
+  16.7, 173.1
+  12.7, 155.8
+  19.3, 175.4
+  18.6, 174.4
+  11.8, 148.5
+  15.5, 172.5
+  17.2, 175.2
+  18.3, 175.6
+   7.1, 123.1
+  18.5, 170.8
+   7.4, 125.0
+   7.4, 128.4
+   9.8, 140.9
+  16.8, 175.8
+  10.0, 142.8
+  10.9, 146.3
+   9.4, 137.2
+  13.5, 163.2
+  15.8, 174.7
+  18.4, 174.3
+  10.4, 143.6
+  12.4, 153.3
+   7.1, 127.2
+  16.2, 171.9
+  12.2, 156.6
+   9.4, 135.4
+  16.6, 172.4
+  18.6, 176.8
+   9.9, 140.2
+  11.0, 148.0
+  18.3, 173.0
+  18.9, 172.0
+  10.1, 143.1
+  13.7, 165.0
+  15.2, 169.9
+  12.5, 153.6
+  15.9, 178.2
+  10.4, 143.7
+  17.2, 173.9
+  11.5, 151.1
+  12.5, 154.1
+  19.2, 178.8
+   8.6, 132.1
+  12.3, 153.6
+   9.3, 137.2
+  13.0, 161.0
+  18.3, 173.8
+  15.7, 176.3
+  13.0, 161.3
+  13.3, 160.0
+  18.8, 174.6
+  14.4, 166.6
+  14.0, 164.9
+  19.9, 173.9
+   8.8, 134.5
+  16.3, 171.4
+   8.0, 133.0
+  12.6, 153.2
+   7.9, 126.4
+   7.6, 131.2
+  13.4, 161.0
+  15.7, 172.7
+  10.7, 144.1
+  18.9, 175.7
+  15.6, 173.4
+  17.6, 175.3
+  17.8, 176.7
+  19.0, 173.0
+  10.2, 142.1
+  10.7, 143.5
+  11.5, 147.2
+   8.4, 130.6
+   9.6, 139.7
+  12.0, 151.4
+  12.1, 147.8
+   8.3, 131.0
+   9.4, 134.2
+   7.3, 123.5
+  13.7, 163.3
+  11.2, 145.9
+  13.8, 164.2
+  19.6, 175.9
+  19.0, 172.2
+  14.7, 169.1
+  15.8, 173.9
+  10.8, 145.0

02/numpy_access1d.py

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+# -*- coding: utf-8 -*-
+"""
+演示一维数组的下标存取。
+"""
+import numpy as np
+
+a = np.arange(10)
+print "a[5]", a[5]
+print "a[3:5]", a[3:5]
+print "a[:5]", a[:5]
+print "a[:-1]", a[:-1]
+a[2:4] = 100,101 
+print "a", a
+print "a[1:-1:2]", a[1:-1:2]
+print "a[::-1]", a[::-1]
+print "a[5:1:-2]", a[5:1:-2]
+
+b = a[3:7]
+print b
+b[2] = -10
+print b
+print a
+
+x = np.arange(10,1,-1)
+print x
+print "x[[3, 3, 1, 8]]", x[[3, 3, 1, 8]]
+b = x[np.array([3,3,-3,8])] 
+b[2] = 100
+print b
+print x
+x[[3,5,1]] = -1, -2, -3
+print x
+
+x = np.arange(5,0,-1)
+print x
+print x[np.array([True, False, True, False, False])] 
+print x[[True, False, True, False, False]]
+print x[np.array([True, False, True, True])]
+x[np.array([True, False, True, True])] = -1, -2, -3 
+print x
+
+x = np.random.rand(10)
+print x
+print x>0.5
+print x[x>0.5]

02/numpy_access2d.py

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+# -*- coding: utf-8 -*-
+"""
+演示二维数组的下标存取。
+"""
+import numpy as np
+
+a = np.arange(0, 60, 10).reshape(-1, 1) + np.arange(0, 6)
+print "a[0,3:5]=", a[0,3:5]
+print "a[4:,4:]=", a[4:,4:]
+print "a[:,2]=", a[:,2]
+print "a[2::2,::2]=", a[2::2,::2]
+
+b = a[0,3:5]
+b[0] = -b[0]
+print "a[0,3:5]=", a[0,3:5]
+b[0] = -b[0]
+
+idx = slice(None, None, 2), slice(2,None)
+print "idx = ", idx
+print "a[idx]=", a[idx] # 和a[::2,2:]相同
+print "a[idx][idx]=", a[idx][idx] # 和a[::2,2:][::2,2:]相同
+
+print a[(0,1,2,3,4),(1,2,3,4,5)]
+print a[3:, [0,2,5]]
+mask = np.array([1,0,1,0,0,1], dtype=np.bool)
+print a[mask, 2]
+mask = np.array([1,0,1,0,0,1])
+print a[mask, 2]

02/numpy_array_index.py

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+# -*- coding: utf-8 -*-
+"""
+多维数组的下标存取实例。
+
+有一个shape为(I, J, K)的三维数组v和一个shape为(I, J)的二维数组idx，idx的每个值
+都是0到K-L的整数。通过下标运算得到一个数组r，对于第0和1轴的每个下标i和j都满足下
+面条件：
+
+    r[i,j,:] = v[i,j,idx[i,j]:idx[i,j]+L]
+"""
+import numpy as np
+
+I, J, K, L = 6, 7, 8, 3
+_, _, v = np.mgrid[:I, :J, :K]
+idx = np.random.randint(0, K-L, size=(I,J))
+
+idx_k = idx.reshape(I,J,1) + np.arange(3)
+idx_i, idx_j, _ = np.ogrid[:I, :J, :K]
+
+r = v[idx_i, idx_j, idx_k]
+i, j = 2,3
+print r[i,j,:]
+print v[i,j,idx[i,j]:idx[i,j]+L]

02/numpy_array_index_demo.py

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+# -*- coding: utf-8 -*-
+"""
+使用Mayavi绘制多为数组下标存取的演示图像。
+"""
+import numpy as np
+from enthought.mayavi import mlab
+
+x, y, z = np.mgrid[:6,:7,:8]
+c = np.zeros((6, 7, 8), dtype=np.int)
+c.fill(1)
+k = np.random.randint(2,5,size=(6, 7))
+
+idx_i, idx_j, _ = np.ogrid[:6, :7, :8]
+idx_k = k[:,:, np.newaxis] + np.arange(3)
+c[idx_i, idx_j, idx_k] = np.random.randint(2,6, size=(6,7,3))
+
+mlab.points3d(x[c>1], y[c>1], z[c>1], c[c>1], mode="cube", scale_factor=0.8, 
+    scale_mode="none", transparent=True, vmin=0, vmax=8, colormap="Greys")
+
+mlab.points3d(x[c==1], y[c==1], z[c==1], c[c==1], mode="cube", scale_factor=0.8,
+    scale_mode="none", transparent=True, vmin=0, vmax=8, colormap="Greys", opacity = 0.2)
+mlab.gcf().scene.background = (1,1,1)
+
+mlab.figure()
+x, y, z = np.mgrid[:6,:7,:3]
+mlab.points3d(x, y, z, c[idx_i, idx_j, idx_k], mode="cube", scale_factor=0.8, 
+    scale_mode="none", transparent=True, vmin=0, vmax=8, colormap="Greys", opacity = 1)
+mlab.gcf().scene.background = (1,1,1)
+mlab.show()

02/numpy_change_bmp.py

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+# -*- coding: utf-8 -*-
+"""
+使用内存映射数组修改BMP文件。
+"""
+import numpy as np
+
+# shape = 高，宽，颜色
+bmp = np.memmap("tmp.bmp", offset=54, shape=(1000,1000,3)) 
+
+# 产生一组从0到255的渐变值
+tmp = np.linspace(0, 255, 1000).astype(np.uint8) 
+
+# 水平蓝色渐变 
+bmp[:, :, 0] = tmp  
+# 垂直绿色渐变
+bmp[:, :, 1] = tmp.reshape(-1,1)  
+# 红色成分
+bmp[:, :, 2] = 127  
+
+bmp.flush()