科学计算和可视化

import numpy
print (‘使用列表生成一维数组‘)
data = [1,2,3,4,5]
x = numpy.array(data)
print (x) 
print (x.dtype)
print (x.ndim) 
print (x.shape)

print (‘使用列表生成二维数组‘)
data = [[1,0],[2,0],[3,0]]
x = numpy.array(data)
print (x) 
print (x.ndim) 
print (x.shape)

print (‘使用zero/ones/empty创建数组:根据shape来创建‘)
x = numpy.zeros(5)
print (x)
x = numpy.zeros((3,3))
print (x)
x = numpy.ones((2,3))
print (x)
x = numpy.empty((3,2))
print (x)

print (‘使用arrange生成连续元素‘)
print (numpy.arange(5))
print (numpy.arange(0,5,2))

import numpy

print (‘生成指定元素类型的数组:设置dtype属性‘)
x = numpy.array([1,2.6,3],dtype = numpy.int64)
print (x) 
print (x.dtype)
print (x.ndim) 
print (x.shape)
x = numpy.array([1,2,3],dtype = numpy.float64)
print (x) 
print (x.dtype)
print (x.ndim) 
print (x.shape)
x = numpy.array([-1,-2,-3,-4],dtype = numpy.uint16)
print (x) 
print (x.dtype)
print (x.ndim) 
print (x.shape)

print (‘使用astype复制数组，并转换类型‘)
x = numpy.array([1,2.6,3],dtype = numpy.float64)
y = x.astype(numpy.int32)
print (y)
print (x)
print (x.ndim) 
print (x.shape)
z = y.astype(numpy.float64)
print (z)
print (x.ndim) 
print (x.shape)

print (‘将字符串元素转换为数值元素‘)
x = numpy.array([‘1‘,‘2‘,‘3‘],dtype = numpy.string_)
y = x.astype(numpy.int32)
print (x)
print (x.ndim) 
print (x.shape)
print (y)

print (‘使用其他数组的数据类型作为参数‘)
x = numpy.array([ 1., 2.6,3. ],dtype = numpy.float32)
y = numpy.arange(3,dtype=numpy.int32)
print (y)
print (y.astype(x.dtype))

import numpy
print (‘ndarray数组与标量/数组的运算‘)
x = numpy.array([1,2,3]) 
print (x*2)
print (x**2)
print (x>2)
y = numpy.array([3,4,5])
print (x+y)
print (x^y)
print (x>y)

print (‘ndarray的花式索引:使用整型数组作为索引‘)
x = numpy.array([1,2,3,4,5,6])
print (x[[0,1,2]])
print (x[[-1,-2,-3]])
x = numpy.array([[1,2],[3,4],[5,6]])
print (x[[0,1]])
print (x[[0,1],[0,1]]) # [1,4] 打印x[0][0]和x[1][1]
print (x[[0,1]][:,[0,1]]) # 打印01行的01列 [[1,2],[3,4]]
# 使用numpy.ix_()函数增强可读性
print (x[numpy.ix_([0,1],[0,1])]) 
x[[0,1],[0,1]] = [0,0]
print (x)# [[0,2],[3,0],[5,6]]

import numpy
print (‘ndarray数组的转置和轴对换‘)
k = numpy.arange(9)
m = k.reshape((3,3)) # 改变数组的shape复制生成2维的，每个维度长度为3的数组
print (k)
print (m)
# 转置(矩阵)数组：T属性 : mT[x][y] = m[y][x]
print (m.T)
# 计算矩阵的内积 xTx
print (numpy.dot(m,m.T)) # numpy.dot点乘
# 高维数组的轴对象
k = numpy.arange(8).reshape(2,2,2)
print (k)
print (k[1][0][0])
# 轴变换 transpose 参数:由轴编号组成的元组
m = k.transpose((1,0,2))
print (m)
print (m[0][1][0])
# 轴交换 swapaxes (axes：轴)，参数:一对轴编号
m = k.swapaxes(0,1)
print (m)
print (m[0][1][0])
# 使用轴交换进行数组矩阵转置
m = numpy.arange(9).reshape((3,3))
print (m)
print (m.swapaxes(1,0))

import matplotlib.pyplot as plt
plt.rcParams[‘font.family‘]=[‘SimHei‘] #用来正常显示中文标签
plt.rcParams[‘axes.unicode_minus‘]=False #用来正常显示负号

import matplotlib.pyplot as plt
plt.rcParams[‘font.family‘]=[‘SimHei‘] 
plt.rcParams[‘axes.unicode_minus‘]=False 
labels=‘青蛙‘,‘猪‘,‘狗子‘,‘原木‘
sizes=25,20,45,60
colors=‘yellowgreen‘,‘gold‘,‘lightskyblue‘,‘lightcoral‘
explode=0,0.2,0,0
plt.pie(sizes,explode=explode,labels=labels,colors=colors,autopct=‘%1.2f%%‘,shadow=True,startangle=50)
plt.axis(‘equal‘)
plt.show()

import matplotlib.pyplot as plt
import numpy as np
import matplotlib
plt.rcParams[‘font.family‘]=‘SimHei‘

labels=np.array([‘总成绩‘,‘单选‘,‘程序题‘,‘剩余用时‘,‘复写率‘])
nAttr=5
data=np.array([7.4,8,6.8,2,9])
angles=np.linspace(0,2*np.pi,nAttr,endpoint=False)
data=np.concatenate((data,[data[0]]))
angles=np.concatenate((angles,[angles[0]]))
fig=plt.figure(facecolor=‘white‘)
plt.subplot(111,polar=True)
plt.plot(angles,data,‘b*-‘,color=‘y‘,linewidth=3)
plt.fill(angles,data,facecolor=‘g‘,alpha=0.25)
plt.thetagrids(angles*180/np.pi,labels)
plt.figtext(0.52,0.95,‘iconangle的成绩表‘,ha=‘center‘)
plt.grid(True)
plt.savefig(‘scorce_radar.JPG‘)
plt.show()

import matplotlib.pyplot as plt
import numpy as np

plt.rcParams[‘font.family‘]=‘SimHei‘
plt.title("在重力加速度为9.8m/s的时候单摆的周期（T）与摆长（L）关系")
plt.xlabel(‘摆长（L）‘)
plt.ylabel(‘周期（T）‘)
x=np.linspace(0.0,40.0,1000)
y=2*np.pi*np.sqrt(x/9.8)
plt.plot(x,y,‘k‘,color=‘g‘,linewidth=3,linestyle=‘-‘)
plt.show()