1 Scipy简介

Scipy依赖于Numpy
Scipy提供了真正的矩阵
Scipy包含的功能：最优化、线性代数、积分、插值、拟合、特殊函数、快速傅里叶变换、信号处理、图像处理、常微分方程求解器等
Scipy是高端科学计算工具包
Scipy由一些特定功能的子模块组成

2 图片消噪:傅里叶变换

#模块用来计算快速傅里叶变换
import scipy.fftpack as fftpack
import matplotlib.pyplot as plt
%matplotlib inline
#读取图片
data = plt.imread(\'moonlanding.png\')
#
data2 = fftpack.fft2(data)

data3 = np.where(np.abs(data2)>8e2,0,data2)

data4 = fftpack.ifft2(data3)

data5 = np.real(data4)

plt.figure(figsize=(12,9))

plt.imshow(data5,cmap = \'gray\')

3 图片灰度处理

最大值法: R=G=B=max(R,G,B) 这种方法灰度亮度比较高

data2 = data.mean(axis = 2)

平均值法: R=G=B=(R+G+B)/3 这种方法灰度图像比较柔和

加权平均值 : R=G=B=(w1R+w2G+w3*B) 根据不同的权重得到不同底色的图片

data3 = np.dot(data,[0.299,0.587,0.114])

4 Matplotlib中的绘图技巧

单条曲线

x = np.arange(0.0,6.0,0.01)
plt.plot(x, x**2)
plt.show()

多条曲线

x = np.arange(1, 5,0.01)
plt.plot(x, x**2)
plt.plot(x, x**3.0)
plt.plot(x, x*3.0)
plt.show()

x = np.arange(1, 5)
plt.plot(x, x*1.5, x, x*3.0, x, x/3.0)
plt.show()

标题与标签

plt.plot([1, 3, 2, 4])
plt.xlabel(\'This is the X axis\')
plt.ylabel(\'This is the Y axis\')
plt.show()

plt.plot([1, 3, 2, 4])
plt.title(\'Simple plot\')
plt.show()

根据线型绘制图片

numpy.random.randn(d0, d1, …, dn)是从标准正态分布中返回一个或多个样本值。

numpy.random.rand(d0, d1, …, dn)的随机样本位于[0, 1)中。

numpy.random.standard_normal(size=None)：随机一个浮点数或N维浮点数组，标准正态分布随机样本

cumsum ：计算轴向元素累加和，返回由中间结果组成的数组 , 重点就是返回值是“由中间结果组成的数组”

plt.plot(np.random.randn(1000).cumsum(), linestyle = \':\',marker = \'.\', label=\'one\')
plt.plot(np.random.randn(1000).cumsum(), \'r--\', label=\'two\') 
plt.plot(np.random.randn(1000).cumsum(), \'b.\', label=\'three\')
plt.legend(loc=\'best\') # loc=\'best\'
plt.show()

5 scipy积分求圆周率

绘制圆

f = lambda x : (1 - x**2)**0.5
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(-1,1,1000)
plt.figure(figsize = (4,4))
plt.plot(x,f(x),\'-\',x,-f(x),\'-\',color = \'r\')

使用Scipy.integrate.quad()来进行计算

#integrate.quad(函数,区间端点) ,返回值为面积与精度
from scipy import integrate
def g(x):
    return (1- x**2)**0.5
area,err = integrate.quad(g,-1,1)
print(area,err)

6 scipy文件的输入与输出

保存二进制文件

from scipy import io as spio
import numpy as np
a = np.ones((3,3))
#mat文件是标准的二进制文件
spio.savemat(\'./data/file.mat\',mdict={\'a\':a})

读取图片

from scipy import misc
data = misc.imread(\'./data/moon.png\')

读取保存的文件

data = spio.loadmat(\'./data/file.mat\')
data[\'a\']

保存图片

#模糊，轮廓，细节，edge_enhance，edge_enhance_more， 浮雕，find_edges，光滑，smooth_more，锐化
misc.imsave(\'./data/save.png\',arr=data)

7 使用ndimage处理图片

导包提取数据处理数据

misc.face(gray=True,cmap=\'gray\') 读取图片并可以进行灰度预处理

ndimage.rotate(图片,角度) 旋转图片

ndimage.zoom(图片,比例) 缩放图片

face[0:400,450:900] 切割图片,一维从0-400,二维从450-900

from scipy import misc,ndimage
#原始图片
face = misc.face(gray=True)
#移动图片坐标
shifted_face = ndimage.shift(face, (50, 50))
#移动图片坐标，并且指定模式
shifted_face2 = ndimage.shift(face, (-200, 0), mode=\'wrap\')
#旋转图片
rotated_face = ndimage.rotate(face, -30)
#切割图片
cropped_face = face[10:-10, 50:-50]
#对图片进行缩放
zoomed_face = ndimage.zoom(face, 0.5)
faces = [shifted_face,shifted_face2,rotated_face,cropped_face,zoomed_face]

绘制图片

plt.figure(figsize = (12,12))
for i,face in enumerate(faces):
    plt.subplot(1,5,i+1)
    plt.imshow(face,cmap = plt.cm.gray)
    plt.axis(\'off\')

图片的过滤

#导包处理滤波
from scipy import misc,ndimage
import numpy as np
import matplotlib.pyplot as plt
face = misc.face(gray=True)
face = face[:512, -512:]  # 做成正方形
#图片加噪
noisy_face = np.copy(face).astype(np.float)
#噪声图片
noisy_face += face.std() * 0.5 * np.random.standard_normal(face.shape)
#高斯过滤
blurred_face = ndimage.gaussian_filter(noisy_face, sigma=1)
#中值滤波
median_face = ndimage.median_filter(noisy_face, size=5)

#signal中维纳滤波
from scipy import signal
wiener_face = signal.wiener(noisy_face, (5, 5))

titles = [\'noisy\',\'gaussian\',\'median\',\'wiener\']
faces = [noisy_face,blurred_face,median_face,wiener_face]

绘制图片

plt.figure(figsize=(12,12))
plt.subplot(141)
plt.imshow(noisy_face,cmap = \'gray\')
plt.title(\'noisy\')
plt.subplot(142)
plt.imshow(blurred_face,cmap = \'gray\')
plt.title(\'gaussian\')
plt.subplot(143)
plt.imshow(median_face,cmap = \'gray\')
plt.title(\'median\')
plt.subplot(144)
plt.imshow(wiener_face,cmap = \'gray\')
plt.title(\'wiener\')
plt.show()

8 pandas绘图函数

线型图

#采用Series做法
import numpy as np
import pandas as pd
from pandas import Series,DataFrame
import matplotlib.pyplot as plt
np.random.seed(0)
s = Series(np.random.randn(10).cumsum(),index = np.arange(0,100,10))
s.plot()
plt.show(s.plot())

#DataFrame图标实例
np.random.seed(0)
df = DataFrame(np.random.randn(10,4).cumsum(0),
              columns= [\'A\',\'B\',\'C\',\'D\'],
              index = np.arange(0,100,10))
plt.show(df.plot())

柱状图

#水平与垂直柱状图Series
fig,axes = plt.subplots(2,1)
data = Series(np.random.rand(16),index = list(\'abcdefghijklmnop\'))
data.plot(kind = \'bar\',ax = axes[0],color = \'b\',alpha = 0.9)
data.plot(kind = \'barh\',ax = axes[1],color = \'b\',alpha = 0.9)

#DataFrame柱状图
df = DataFrame(np.random.rand(6,4),
              index = [\'one\',\'two\',\'three\',\'four\',\'five\',\'six\'],
              columns = pd.Index([\'A\',\'B\',\'C\',\'D\'],name = \'Genus\'))
plt.show(df.plot(kind = \'bar\'))

df = DataFrame(np.random.rand(6,4),
              index = [\'one\',\'two\',\'three\',\'four\',\'five\',\'six\'],
              columns = pd.Index([\'A\',\'B\',\'C\',\'D\'],name = \'Genus\'))
plt.show(df.plot(kind = \'bar\',stacked = True))

直方图与密度图

a = np.random.random(10)
b = a/a.sum()
s = Series(b)
plt.show(s.hist(bins = 100)) #bins直方图的柱数

#密度图
a = np.random.random(10)
b = a/a.sum()
s = Series(b)
plt.show(s.plot(kind = \'kde\'))

带有密度估计的规格化直方图

%matplotlib inline
comp1 = np.random.normal(0,1,size = 200)
comp2 = np.random.normal(10,2,size = 200)
values = Series(np.concatenate([comp1,comp2]))
p1 = values.hist(bins = 100,alpha = 0.3,color = \'k\',density = True)

p2 = values.plot(kind = \'kde\',style = \'--\',color = \'r\')

散布图

#简单的散布图
df = DataFrame(np.random.randint(0,100,size = 100).reshape(50,2),columns = [\'A\',\'B\'])
df.plot(\'A\',\'B\',kind = \'scatter\',title = \'x Vs y\')

散步矩阵图

import numpy as np
import pandas as pd
from pandas import Series,DataFrame
%matplotlib inline
df = DataFrame(np.random.randn(200).reshape(50,4),columns = [\'A\',\'B\',\'C\',\'D\'])
pd.plotting.scatter_matrix(df,diagonal = \'kde\',color = \'k\')