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')