极坐标与对数坐标的绘制
 
 
import numpy as np
 import matplotlib.pyplot as plt
 
 
-------------------------------对数坐标-------------------------------------------------
 #绘制低通滤波器的频率响应
 #用三种对数坐标表示，半对数坐标，全对数坐标。
 w=np.linspace(0.1,1000,1000)
 p=np.abs(1/(1+0.1j*w)) #低通滤波的频率响应
 
 
#plt.subplot(221)
 #plt.plot(w,p,linewidth=2)
 #plt.ylim(0,1.5)
 #
 #plt.subplot(222)
 #plt.semilogx(w,p,linewidth=2)
 #plt.ylim(0,1.5)
 #
 #plt.subplot(223)
 #plt.semilogy(w,p,linewidth=2)
 #plt.ylim(0,1.5)
 #
 #plt.subplot(224)
 #plt.loglog(w,p,linewidth=2)
 #plt.ylim(0,1.5)
 
 

 -------------------------------极坐标--------------------------------------------------
 theta=np.arange(0,2*np.pi,0.02)
 
 
plt.subplot(121,polar=True) #创建一个极坐标子图
 #plt.polar
 plt.plot(theta,1.6*np.ones_like(theta),linewidth=2)
 plt.plot(3*theta,theta/3,'--',linewidth=2)
 
 
plt.subplot(122,polar=True)
 plt.plot(theta,1.4*np.cos(5*theta),'--',linewidth=2)
 plt.plot(theta,1.8*np.cos(4*theta),linewidth=2)
 plt.rgrids(np.arange(0.5,2,0.5),angle=45) #设置栅格的半径大小和文字标注的角度
 #0.5 1 1.5 都在45度线上
 plt.thetagrids([0,45])  #设置放射栅格的角度
 
 
#自己试验一下怎么画极坐标
 plt.figure(2)
 theta_=np.array([0,np.pi/4,np.pi/2,np.pi])
 r_=np.array([1,2,3,4])
 plt.polar(theta_,r_,marker='*',linewidth=3,color='b',mfc='r',ms=10)
 #mfc是*的颜色  
 

 calculus：微积分
 
极坐标图的绘制
 
将半径r绘制为极角q的函数
 
阿基米德螺旋线：,是一个常数
 
我们必须使用处理自变量的方法来处理
 
polar():绘制极坐标图
 
 
 
 
 
>> a=2;
theta=[0:pi/90:2*pi];
r=a*theta;
polar(theta,r),title('spiral of archimedes'),grid
 
 
 
 
 
 
 >> theta=[0:pi/90:2*pi];
 >> r=1+2*cos(theta);
 >> polar(theta,r),grid
 
 
 
 
>> polar(theta,r,'g--'),grid
 
 
 
 
 
 
 对数坐标图绘制
 
loglog(x,y)
 
complex variable :复变量
 
 
 
>> s=[1:100]*i;
 RC=0.25;
 f=abs(1./(1+RC*s));
 loglog(imag(s),f),xlabel('frequency(rad/s)'),grid,ylabel('output/input ratio')...
 ,title('requency responce')
 
 
 
 
 
 
 >> x=[0:0.1:20];
 >> y=exp(-10*x.^2);
 >> plot(x,y)
 
 
 
 
>> loglog(x,y)
 
 
 
 
 Semilogx(x,y):会生成一个带有对数x和直线y轴的绘图
 
Semilogy(x,y):会生成一个带有直线x和对数y轴的绘图
 
 
 

 

 
I have a pandas DataFrame that has the following values in a Series
 
x = [2, 1, 76, 140, 286, 267, 60, 271, 5, 13, 9, 76, 77, 6, 2, 27, 22, 1, 12, 7, 19, 81, 11, 173, 13, 7, 16, 19, 23, 197, 167, 1]
 
I was instructed to plot two histograms in a Jupyter notebook with Python 3.6. No sweat right?
 
x.plot.hist(bins=8)
 
plt.show()
 
I chose 8 bins because that looked best to me.
 
I have also been instructed to plot another histogram with the log of x.
 
x.plot.hist(bins=8)
 
plt.xscale('log')
 
plt.show()
 
This histogram looks TERRIBLE. Am I not doing something right? I've tried fiddling around with the plot, but everything I've tried just seems to make the histogram look even worse. Example:
 
x.plot(kind='hist', logx=True)
 
I was not given any instructions other than plot the log of X as a histogram.
 
I really appreciate any help!!!
 
For the record, I have imported pandas, numpy, and matplotlib and specified that the plot should be inline.
 
解决方案
 
Specifying bins=8 in the hist call means that the range between the minimum and maximum value is divided equally into 8 bins. What is equal on a linear scale is distorted on a log scale.
 
What you could do is specify the bins of the histogram such that they are unequal in width in a way that would make them look equal on a logarithmic scale.
 
import pandas as pd
 
import numpy as np
 
import matplotlib.pyplot as plt
 
x = [2, 1, 76, 140, 286, 267, 60, 271, 5, 13, 9, 76, 77, 6, 2, 27, 22, 1, 12, 7,
 
19, 81, 11, 173, 13, 7, 16, 19, 23, 197, 167, 1]
 
x = pd.Series(x)
 
# histogram on linear scale
 
plt.subplot(211)
 
hist, bins, _ = plt.hist(x, bins=8)
 
# histogram on log scale.
 
# Use non-equal bin sizes, such that they look equal on log scale.
 
logbins = np.logspace(np.log10(bins[0]),np.log10(bins[-1]),len(bins))
 
plt.subplot(212)
 
plt.hist(x, bins=logbins)
 
plt.xscale('log')
 
plt.show()
 

import os
import networkx as nx
import matplotlib.pyplot as plt
import math
import numpy as np

BA = nx.random_graphs.barabasi_albert_graph(5000, 3)


degree = nx.degree_histogram(BA)
#生成x轴序列，从1到最大度
x = range(len(degree))
#将频次转换为频率
y = [z / float(sum(degree)) for z in degree]

#在双对数坐标轴上绘制度分布曲线
plt.loglog(x, y, '.', color="blue", Marker='.')
#x,y后的点代表散点
#显示图表
plt.show()