[Scipy-svn] r5252 - trunk/doc/source/tutorial

[scipy-svn at scipy.org]

Author: jarrod.millman
Date: 2008-12-14 05:51:25 -0600 (Sun, 14 Dec 2008)
New Revision: 5252

Modified:
   trunk/doc/source/tutorial/interpolate.rst
Log:
remove * import


Modified: trunk/doc/source/tutorial/interpolate.rst
===================================================================
--- trunk/doc/source/tutorial/interpolate.rst	2008-12-14 11:21:11 UTC (rev 5251)
+++ trunk/doc/source/tutorial/interpolate.rst	2008-12-14 11:51:25 UTC (rev 5252)
@@ -28,14 +28,14 @@
 
 .. plot::
 
-   >>> from numpy import *
+   >>> import numpy as np
    >>> from scipy import interpolate
 
-   >>> x = arange(0,10)
-   >>> y = exp(-x/3.0)
+   >>> x = np.arange(0,10)
+   >>> y = np.exp(-x/3.0)
    >>> f = interpolate.interp1d(x, y)
 
-   >>> xnew = arange(0,9,0.1)
+   >>> xnew = np.arange(0,9,0.1)
    >>> import matplotlib.pyplot as plt
    >>> plt.plot(x,y,'o',xnew,f(xnew),'-')
 
@@ -91,20 +91,20 @@
 
 .. plot::
 
-   >>> from numpy import *
+   >>> import numpy as np
    >>> import matplotlib.pyplot as plt
    >>> from scipy import interpolate
 
    Cubic-spline
 
-   >>> x = arange(0,2*pi+pi/4,2*pi/8)
-   >>> y = sin(x)
+   >>> x = np.arange(0,2*np.pi+np.pi/4,2*np.pi/8)
+   >>> y = np.sin(x)
    >>> tck = interpolate.splrep(x,y,s=0)
-   >>> xnew = arange(0,2*pi,pi/50)
+   >>> xnew = np.arange(0,2*np.pi,np.pi/50)
    >>> ynew = interpolate.splev(xnew,tck,der=0)
 
    >>> plt.figure()
-   >>> plt.plot(x,y,'x',xnew,ynew,xnew,sin(xnew),x,y,'b')
+   >>> plt.plot(x,y,'x',xnew,ynew,xnew,np.sin(xnew),x,y,'b')
    >>> plt.legend(['Linear','Cubic Spline', 'True'])
    >>> plt.axis([-0.05,6.33,-1.05,1.05])
    >>> plt.title('Cubic-spline interpolation')
@@ -114,7 +114,7 @@
 
    >>> yder = interpolate.splev(xnew,tck,der=1)
    >>> plt.figure()
-   >>> plt.plot(xnew,yder,xnew,cos(xnew),'--')
+   >>> plt.plot(xnew,yder,xnew,np.cos(xnew),'--')
    >>> plt.legend(['Cubic Spline', 'True'])
    >>> plt.axis([-0.05,6.33,-1.05,1.05])
    >>> plt.title('Derivative estimation from spline')
@@ -123,8 +123,8 @@
    Integral of spline
 
    >>> def integ(x,tck,constant=-1):
-   >>>     x = atleast_1d(x)
-   >>>     out = zeros(x.shape, dtype=x.dtype)
+   >>>     x = np.atleast_1d(x)
+   >>>     out = np.zeros(x.shape, dtype=x.dtype)
    >>>     for n in xrange(len(out)):
    >>>         out[n] = interpolate.splint(0,x[n],tck)
    >>>     out += constant
@@ -132,7 +132,7 @@
    >>>
    >>> yint = integ(xnew,tck)
    >>> plt.figure()
-   >>> plt.plot(xnew,yint,xnew,-cos(xnew),'--')
+   >>> plt.plot(xnew,yint,xnew,-np.cos(xnew),'--')
    >>> plt.legend(['Cubic Spline', 'True'])
    >>> plt.axis([-0.05,6.33,-1.05,1.05])
    >>> plt.title('Integral estimation from spline')
@@ -145,14 +145,14 @@
 
    Parametric spline
 
-   >>> t = arange(0,1.1,.1)
-   >>> x = sin(2*pi*t)
-   >>> y = cos(2*pi*t)
+   >>> t = np.arange(0,1.1,.1)
+   >>> x = np.sin(2*np.pi*t)
+   >>> y = np.cos(2*np.pi*t)
    >>> tck,u = interpolate.splprep([x,y],s=0)
-   >>> unew = arange(0,1.01,0.01)
+   >>> unew = np.arange(0,1.01,0.01)
    >>> out = interpolate.splev(unew,tck)
    >>> plt.figure()
-   >>> plt.plot(x,y,'x',out[0],out[1],sin(2*pi*unew),cos(2*pi*unew),x,y,'b')
+   >>> plt.plot(x,y,'x',out[0],out[1],np.sin(2*np.pi*unew),np.cos(2*np.pi*unew),x,y,'b')
    >>> plt.legend(['Linear','Cubic Spline', 'True'])
    >>> plt.axis([-1.05,1.05,-1.05,1.05])
    >>> plt.title('Spline of parametrically-defined curve')
@@ -203,14 +203,14 @@
 
 .. plot::
 
-   >>> from numpy import *
+   >>> import numpy as np
    >>> from scipy import interpolate
    >>> import matplotlib.pyplot as plt
 
    Define function over sparse 20x20 grid
 
-   >>> x,y = mgrid[-1:1:20j,-1:1:20j]
-   >>> z = (x+y)*exp(-6.0*(x*x+y*y))
+   >>> x,y = np.mgrid[-1:1:20j,-1:1:20j]
+   >>> z = (x+y)*np.exp(-6.0*(x*x+y*y))
 
    >>> plt.figure()
    >>> plt.pcolor(x,y,z)
@@ -220,7 +220,7 @@
 
    Interpolate function over new 70x70 grid
 
-   >>> xnew,ynew = mgrid[-1:1:70j,-1:1:70j]
+   >>> xnew,ynew = np.mgrid[-1:1:70j,-1:1:70j]
    >>> tck = interpolate.bisplrep(x,y,z,s=0)
    >>> znew = interpolate.bisplev(xnew[:,0],ynew[0,:],tck)