python 科学计算(一)--转

https://www.cnblogs.com/bovine/archive/2012/11/09/2763374.html

python 科学计算(一)

使用python的科学计算库，达到快速计算的效果。

标准的Python中用列表(list)保存一组值，可以当作数组使用。但由于列表的元素可以是任何对象，因此列表中保存的是对象的指针。这样一来，为了保存一个简单的列表[1,2,3]，就需
要有三个指针和三个整数对象。对于数值运算来说，这种结构显然比较浪费内存和 CPU 计算时间。

使用numpy的array模块可以解决这个问题。细节不在此赘述。这里主要记录一些matplotlib的基本使用方法

• first plot

#first plot with matplotlib

import matplotlib.pyplot as plt

plt.plot([1,3,2,4])

plt.show()

in order to avoid pollution of global namespace, it is strongly recommended to never import like:

from <module> import *

• simple plot

import matplotlib as mpl

import matplotlib.pyplot as plt

import numpy as np

x = np.arange(0.0,6.0,0.1)

plt.plot(x, [xi**2 for xi in x],label = 'First',linewidth = 4,color = 'black')

plt.plot(x, [xi**2+2 for xi in x],label = 'second',color = 'red')

plt.plot(x, [xi**2+5 for xi in x],label = 'third')

plt.axis([0,7,-1,50])

plt.xlabel(r"$alpha$",fontsize=20)

plt.ylabel(r'y')

plt.title('simple plot')

plt.legend(loc = 'upper left')

plt.grid(True)

plt.savefig('simple plot.pdf',dpi = 200)

print mpl.rcParams['figure.figsize']       #return 8.0,6.0

print mpl.rcParams['savefig.dpi']          #default to 100              the size of the pic will be 800*600

#print mpl.rcParams['interactive']

plt.show()

• Decorate plot with styles and types

import matplotlib as mpl

import matplotlib.pyplot as plt

import numpy as np

x = np.arange(0.0,6.0,0.1)

plt.plot(x, [xi**2 for xi in x],label = 'First',linewidth = 4,color = 'black')   #using color string to specify color

plt.plot(x, [xi**2+2 for xi in x],'r',label = 'second')                          #using abbreviation to specify color

plt.plot(x, [xi**2+5 for xi in x],color = (1,0,1,1),label = 'Third')             #using color tuple to specify color

plt.plot(x, [xi**2+9 for xi in x],color = '#BCD2EE',label = 'Fourth')             #using hex string to specify color

plt.xticks(np.arange(0.0,6.0,2.5))

plt.xlabel(r"$alpha$",fontsize=20)

plt.ylabel(r'y')

plt.title('simple plot')

plt.legend(loc = 'upper left')

plt.grid(True)

plt.savefig('simple plot.pdf',dpi = 200)

print mpl.rcParams['figure.figsize']       #return 8.0,6.0

print mpl.rcParams['savefig.dpi']          #default to 100              the size of the pic will be 800*600

#print mpl.rcParams['interactive']

plt.show()

• types of graph

• Bars

import matplotlib.pyplot as plt 

import numpy as np 

dict = {'A': 40, 'B': 70, 'C': 30, 'D': 85} 

for i, key in enumerate(dict): plt.bar(i, dict[key]);

plt.xticks(np.arange(len(dict))+0.4, dict.keys());

plt.yticks(dict.values());

plt.grid(True)

plt.show()

• Pies

import matplotlib.pyplot as plt 

plt.figure(figsize=(10,10));

x = [4, 9, 21, 55, 30, 18] 

labels = ['Swiss', 'Austria', 'Spain', 'Italy', 'France', 

'Benelux'] 

explode = [0.2, 0.1, 0, 0, 0.1, 0] 

plt.pie(x, labels=labels, explode=explode, autopct='%1.1f%%'); 

plt.show()

• Scatter

import matplotlib.pyplot as plt

import numpy as np

x = np.random.randn(12,20)

y = np.random.randn(12,20)

mark = ['s','o','^','v','>','<','d','p','h','8','+','*']

for i in range(0,12):

    plt.scatter(x[i],y[i],marker = mark[i],color =(np.random.rand(1,3)),s=50,label = str(i+1))

plt.legend()

plt.show()