请查看我的代码并帮助我。 我正在编写一个近似于函数导数的代码。为了直观地检查我的近似值与真实导数的接近程度，我正在将它们绘制在一起。
我的问题是当函数未定义为零时，例如1 / x，导数为'1 / x ^ 2.
提前致谢。
# -*- coding: utf-8 -*-
from pylab import *
import math
def Derivative(f,x, Tol = 10e-5, Max = 20):
try:
k = 2
D_old = (f(x+2.0**(-k))-f(x-2.0**-k))/(2.0**(1-k))
k = 3
D_new = (f(x+2.0**(-k))-f(x-2.0**-k))/(2.0**(1-k))
E_old = abs(D_new - D_old)
while True:
D_old = D_new
k+=1
D_new = (f(x+2.0**(-k))-f(x-2.0**-k))/(2.0**(1-k))
E_new = abs(D_old - D_new)
if E_new < Tol or E_new >= E_old or k >= Max:
return D_old
except:
return nan
def Fa(x):
return math.sin(2*math.pi*x)
def Fap(x):
return 2*math.pi*math.cos(2*math.pi*x)
def Fb(x):
return x**2
def Fbp(x):
return 2*x
def Fc(x):
return 1.0/x
def Fcp(x):
if abs(x)<0.01:
return 0
else:
return -1.0/x**2
def Fd(x):
return abs(x)
def Fdp(x):
return 1 #since it's x/sqrt(x**2)
# Plot of Derivative Fa
xx = arange(-1, 1, 0.01) # A Numpy vector of x-values
yy = [Derivative(Fa, x) for x in xx] # Vector of f’ approximations
plot(xx, yy, 'r--', linewidth = 5) # solid red line of width 5
yy2 = [Fap(x) for x in xx]
plot(xx, yy2, 'b--', linewidth = 2) # solid blue line of width 2
# Plot of Derivative Fb
yy = [Derivative(Fb, x) for x in xx] # Vector of f’ approximations
plot(xx, yy, 'g^', linewidth = 5) # solid green line of width 5
yy2 = [Fbp(x) for x in xx]
plot(xx, yy2, 'y^', linewidth = 2) # solid yellow line of width 2
答案 0 :(得分：1)
＆＃34; 1 / X＆＃34;是一个无限函数，你不能绘制一个未定义为零的函数。 您只能使用断轴绘制函数。 对于断轴，您可以在评论中遵循wwii的建议，或者您可以对tutorial进行matplotlib。
本教程展示了如何使用两个子图创建所需的效果。
这里是示例代码：
"""
Broken axis example, where the y-axis will have a portion cut out.
"""
import matplotlib.pylab as plt
import numpy as np
# 30 points between 0 0.2] originally made using np.random.rand(30)*.2
pts = np.array([ 0.015, 0.166, 0.133, 0.159, 0.041, 0.024, 0.195,
0.039, 0.161, 0.018, 0.143, 0.056, 0.125, 0.096, 0.094, 0.051,
0.043, 0.021, 0.138, 0.075, 0.109, 0.195, 0.05 , 0.074, 0.079,
0.155, 0.02 , 0.01 , 0.061, 0.008])
# Now let's make two outlier points which are far away from everything.
pts[[3,14]] += .8
# If we were to simply plot pts, we'd lose most of the interesting
# details due to the outliers. So let's 'break' or 'cut-out' the y-axis
# into two portions - use the top (ax) for the outliers, and the bottom
# (ax2) for the details of the majority of our data
f,(ax,ax2) = plt.subplots(2,1,sharex=True)
# plot the same data on both axes
ax.plot(pts)
ax2.plot(pts)
# zoom-in / limit the view to different portions of the data
ax.set_ylim(.78,1.) # outliers only
ax2.set_ylim(0,.22) # most of the data
# hide the spines between ax and ax2
ax.spines['bottom'].set_visible(False)
ax2.spines['top'].set_visible(False)
ax.xaxis.tick_top()
ax.tick_params(labeltop='off') # don't put tick labels at the top
ax2.xaxis.tick_bottom()
# This looks pretty good, and was fairly painless, but you can get that
# cut-out diagonal lines look with just a bit more work. The important
# thing to know here is that in axes coordinates, which are always
# between 0-1, spine endpoints are at these locations (0,0), (0,1),
# (1,0), and (1,1). Thus, we just need to put the diagonals in the
# appropriate corners of each of our axes, and so long as we use the
# right transform and disable clipping.
d = .015 # how big to make the diagonal lines in axes coordinates
# arguments to pass plot, just so we don't keep repeating them
kwargs = dict(transform=ax.transAxes, color='k', clip_on=False)
ax.plot((-d,+d),(-d,+d), **kwargs) # top-left diagonal
ax.plot((1-d,1+d),(-d,+d), **kwargs) # top-right diagonal
kwargs.update(transform=ax2.transAxes) # switch to the bottom axes
ax2.plot((-d,+d),(1-d,1+d), **kwargs) # bottom-left diagonal
ax2.plot((1-d,1+d),(1-d,1+d), **kwargs) # bottom-right diagonal
# What's cool about this is that now if we vary the distance between
# ax and ax2 via f.subplots_adjust(hspace=...) or plt.subplot_tool(),
# the diagonal lines will move accordingly, and stay right at the tips
# of the spines they are 'breaking'
plt.show()