1. Data transfer between matlab and python
1 import scipy.io as sio 2 import numpy as np 3 4 # ##The following is an explanation of how python reads .mat files and how to process the results ### 5 load_fn = ' xxx.mat ' 6 load_data = sio.loadmat (load_fn) 7 load_matrix = load_data[ ' matrix ' ] #Assume that the character variable stored in the file is matrix, such as save(load_fn, 'matrix') in matlab; of course, multiple save(load_fn, 'matrix_x', 'matrix_y' can be saved ', ...); 8 load_matrix_row = load_matrix[0] #Take the first row of the matrix in matlab at that time, and the array row in python is arranged 9 10 # ##The following is an explanation of how python saves .mat files for use by matlab programs# ## 11save_fn = ' xxx.mat ' 12 save_array = np.array([1,2,3,4 ]) 13 sio.savemat(save_fn, { ' array ' : save_array}) #Same as above, there is an array variable First line 14 15 save_array_x = np.array([1,2,3,4 ]) 16 save_array_y = np.array([5,6,7,8 ]) 17 sio.savemat(save_fn, { ' array_x ' : save_array_x, ' array_x ' : save_array_x}) #Similarly , just
2. Python drawing
1 import matpylib.pyplot as plt 2 3 a=np.arange(0,4,0.01).reshape(400,1) 4 5 figure1=plt.figure() 6 plt.plot(np.linspace(0，400，400)，a,'b-',label='ckc') 7 plt.title("ckc") 8 plt.xlabel("c") 9 plt.ylabel("x") 10 plt.legend() 11 plt.show()
3. Array creation operation in python
1 1 #数组的初始化 2 2 >>> import numpy as np 3 3 >>> a = np.arange(15).reshape(3, 5) 4 4 >>> a 5 5 array([[ 0, 1, 2, 3, 4], 6 6 [ 5, 6, 7, 8, 9], 7 7 [10, 11, 12, 13, 14]]) 8 8 >>> a.shape 9 9 (3, 5) 10 10 >>> a.ndim 11 11 2 12 12 >>> a.dtype.name 13 13 'int64' 14 14 >>> a.itemsize 15 15 8 16 16 >>> a.size 17 17 15 18 18 >>> type(a) 19 19 <type 'numpy.ndarray'> 20 20 >>> b = np.array([6, 7, 8]) 21 21 >>> b 22 22 array([6, 7, 8]) 23 23 >>> type(b) 24 24 <type 'numpy.ndarray'> zeros: all 0s 2828ones: all 1s 272726 2625 2529 29 empty: random number, depends on memory 30 30 31 31 >>> np.zeros( (3,4 ) ) 32 32 array([[ 0., 0., 0., 0.], 33 33 [ 0., 0., 0., 0.], 34 34 [ 0., 0., 0., 0.]]) 35 35 >>> np.ones( (2,3,4), dtype=np .int16 ) # dtype can also be specified 36 36 array([[[ 1, 1, 1, 1 ], 37 37 [ 1, 1, 1, 1 ], 38 38 [ 1, 1, 1, 1 ]], 39 39 [[ 1, 1, 1, 1 ], 40 40 [ 1, 1, 1, 1], 41 41 [ 1, 1, 1, 1]]], dtype=int16) 42 42 >>> np.empty( (2,3) ) # uninitialized, output may vary 43 43 array([[ 3.73603959e-262, 6.02658058e-154, 6.55490914e-260], 44 44 [ 5.30498948e-313, 3.14673309e-307, 1.00000000e+000]]) 45 45 46 46 #np.arange()的用法 47 47 >>> np.arange( 10, 30, 5 ) 48 48 array([10, 15, 20, 25]) 49 49 >>> np.arange( 0, 2, 0.3 ) # it accepts float arguments 50 50 array([ 0. , 0.3, 0.6, 0.9, 1.2, 1.5, 1.8]) 51 51 52 52 >>> np.linspace( 0, 2, 9 ) # 9 numbers from 0 to 2 53 53 array([ 0. , 0.25, 0.5 , 0.75, 1. , 1.25, 1.5 , 1.75, 2. ]) 54 54 >>> x = np.linspace( 0, 2*pi, 100 )
4.python gets .mat from malab
1 import scipy.io as sio # io related modules to operate. 2 curcwd= os.getcwd() 3 4 mat_theory= ' noise_784.mat ' 5 data_theory= sio.loadmat(mat_theory) 6 load_matrix=data_theory[ ' noise_784 ' ] 7 8 signal=load_matrix[0] #Take the first line 9 signal =np.reshape(signal,(784,1))
5. python generates random numbers
1 # rand function, generate random numbers from 0 to 1, the parameter is shape 2 np.random.rand(3,4 ) 3 >> Generate random numbers from 0 to 1, shape is 3 rows and 4 columns 4 5 # randn function, Generate a standard normal distribution with a mean of 0 and a variance of 1. The parameters are also shape 6 np.random.randn 7 # randint function, which generates random integers in the specified range. The first two parameters represent the range, and the last parameter is size=( shape) 8 np.random.randint(0,3,size=(3,4 )) 9 10 # numpy.random can generate random numbers of a specific distribution, such as normal distribution, uniform distribution, poisson distribution, etc. 11 The front of these functions Several parameters are the parameters of the distribution function, the last parameter is shape 12 , such as normal distribution, normal is the mean and variance, uniform is the upper and lower bounds, and Poisson distribution is 13 14 np.random.normal(mean, variance, size=(3 ,4 )) 15 16 np.random.uniform(2,3,size=(3,4)) #The first two parameters are uniform distribution in the range 17 18 np.random.pession(2,size=()) #Poisson distribution
6. Notes on python file reading
1 file=open( ' abc.tex ' , ' w ' ) >> Note that writing ' w ' once will erase the previous 2 >> continue to write ' a ' 3 file=open( " abc.txt " . ' a ' ) 4 Note that the file must be opened 5 file.close() >> otherwise the writing operation will encounter problems 6 7 # ###Get the elements of each line and put them in the array 8 file = open( ' text_c.txt ' ) 9 10 lines = file. readlines() 11aa= [] 12 for line in lines: 13 temp=line.replace( ' \n ' , '' ) #Remove the newline character of each line. 14 aa.append(temp)