Hướng dẫn how do you code a sine wave in python? - làm thế nào để bạn mã hóa một làn sóng sin trong python?

Để vẽ sóng sin trong Python, chúng tôi sử dụng thư viện Numpy và thư viện matplotlib., we use NumPy library and Matplotlib library.

Numpy là gói cơ bản để điện toán khoa học với Python. Matplotlib là một thư viện âm mưu 2D Python tạo ra các số liệu chất lượng xuất bản trong nhiều định dạng hardcopy và môi trường tương tác trên các nền tảng. is the fundamental package for scientific computing with Python. Matplotlib is a Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms.

Trong chương trình Python này, chúng tôi sử dụng Numpy để tạo các giá trị

from math import*
Fs=8000
f=500
sample=16
a=[0]*sample
for n in range(sample):
    a[n]=sin(2*pi*f*n/Fs)

from math import*
Fs=8000
f=500
sample=16
a=[0]*sample
for n in range(sample):
    a[n]=sin(2*pi*f*n/Fs)

Mã nguồn Python: Hàm sin

# Importing Required Libraries
import numpy as np
import matplotlib.pyplot as plt

# Generating time data using arange function from numpy
time = np.arange(-3*np.pi, 3*np.pi, 0.01)

# Finding amplitude at each time
amplitude = np.sin(time)

# Plotting time vs amplitude using plot function from pyplot
plt.plot(time, amplitude)

# Settng title for the plot in blue color
plt.title('Sine Wave', color='b')

# Setting x axis label for the plot
plt.xlabel('Time'+ r'$\rightarrow$')

# Setting y axis label for the plot
plt.ylabel('Sin(time) '+ r'$\rightarrow$')

# Showing grid
plt.grid()

# Highlighting axis at x=0 and y=0
plt.axhline(y=0, color='k')
plt.axvline(x=0, color='k')

# Finally displaying the plot
plt.show()

Đầu ra sóng sin

Sóng sin python: exaplanation (bước khôn ngoan)

1. Bước 1: Nhập mã thư viện

   from math import*
   Fs=8000
   f=500
   sample=16
   a=[0]*sample
   for n in range(sample):
       a[n]=sin(2*pi*f*n/Fs)
   

   7 và

   from math import*
   Fs=8000
   f=500
   sample=16
   a=[0]*sample
   for n in range(sample):
       a[n]=sin(2*pi*f*n/Fs)
   

   8 được sử dụng để nhập thư viện Numpy và matplotlib.pyplot. Code

   from math import*
   Fs=8000
   f=500
   sample=16
   a=[0]*sample
   for n in range(sample):
       a[n]=sin(2*pi*f*n/Fs)
   

   7 and

   from math import*
   Fs=8000
   f=500
   sample=16
   a=[0]*sample
   for n in range(sample):
       a[n]=sin(2*pi*f*n/Fs)
   

   8 are used to import numpy and matplotlib.pyplot Library.
2. Bước 2: Tạo mã dữ liệu thời gian

   from math import*
   Fs=8000
   f=500
   sample=16
   a=[0]*sample
   for n in range(sample):
       a[n]=sin(2*pi*f*n/Fs)
   

   9 được sử dụng để tạo mẫu dữ liệu -3*π đến 3*π trong khoảng bằng 0,01. Hàm

   import matplotlib.pyplot as plt
   import numpy as np
   
   
   Fs = 8000
   f = 5
   sample = 8000
   x = np.arange(sample)
   y = np.sin(2 * np.pi * f * x / Fs)
   plt.plot(x, y)
   plt.xlabel('sample(n)')
   plt.ylabel('voltage(V)')
   plt.show()
   

   0 được xác định trong Numpy. Code

   from math import*
   Fs=8000
   f=500
   sample=16
   a=[0]*sample
   for n in range(sample):
       a[n]=sin(2*pi*f*n/Fs)
   

   9 is used to generate data form -3*π to 3*π in equal interval of 0.01. Function

   import matplotlib.pyplot as plt
   import numpy as np
   
   
   Fs = 8000
   f = 5
   sample = 8000
   x = np.arange(sample)
   y = np.sin(2 * np.pi * f * x / Fs)
   plt.plot(x, y)
   plt.xlabel('sample(n)')
   plt.ylabel('voltage(V)')
   plt.show()
   

   0 is defined in numpy.
3. Bước 3: Tạo mã dữ liệu biên độ

   import matplotlib.pyplot as plt
   import numpy as np
   
   
   Fs = 8000
   f = 5
   sample = 8000
   x = np.arange(sample)
   y = np.sin(2 * np.pi * f * x / Fs)
   plt.plot(x, y)
   plt.xlabel('sample(n)')
   plt.ylabel('voltage(V)')
   plt.show()
   

   1 được sử dụng để tính giá trị sin tại mỗi dữ liệu thời gian được tạo. Hàm

   import matplotlib.pyplot as plt
   import numpy as np
   
   
   Fs = 8000
   f = 5
   sample = 8000
   x = np.arange(sample)
   y = np.sin(2 * np.pi * f * x / Fs)
   plt.plot(x, y)
   plt.xlabel('sample(n)')
   plt.ylabel('voltage(V)')
   plt.show()
   

   2 cũng được xác định trong Numpy. Code

   import matplotlib.pyplot as plt
   import numpy as np
   
   
   Fs = 8000
   f = 5
   sample = 8000
   x = np.arange(sample)
   y = np.sin(2 * np.pi * f * x / Fs)
   plt.plot(x, y)
   plt.xlabel('sample(n)')
   plt.ylabel('voltage(V)')
   plt.show()
   

   1 is used to calculate sine value at each generated time data. Function

   import matplotlib.pyplot as plt
   import numpy as np
   
   
   Fs = 8000
   f = 5
   sample = 8000
   x = np.arange(sample)
   y = np.sin(2 * np.pi * f * x / Fs)
   plt.plot(x, y)
   plt.xlabel('sample(n)')
   plt.ylabel('voltage(V)')
   plt.show()
   

   2 is also defined in numpy.
4. Bước 4: Đồ thị mã

   import matplotlib.pyplot as plt
   import numpy as np
   
   
   Fs = 8000
   f = 5
   sample = 8000
   x = np.arange(sample)
   y = np.sin(2 * np.pi * f * x / Fs)
   plt.plot(x, y)
   plt.xlabel('sample(n)')
   plt.ylabel('voltage(V)')
   plt.show()
   

   3 được sử dụng để vẽ dữ liệu được tạo về thời gian và biên độ. Thời gian ở đây là dữ liệu trục x và biên độ là dữ liệu trục y. Code

   import matplotlib.pyplot as plt
   import numpy as np
   
   
   Fs = 8000
   f = 5
   sample = 8000
   x = np.arange(sample)
   y = np.sin(2 * np.pi * f * x / Fs)
   plt.plot(x, y)
   plt.xlabel('sample(n)')
   plt.ylabel('voltage(V)')
   plt.show()
   

   3 is used to plot generated data of time and amplitude. Here time is x-axis data and amplitude is y-axis data.
5. Bước 5: Đặt mã tiêu đề

   import matplotlib.pyplot as plt
   import numpy as np
   
   
   Fs = 8000
   f = 5
   sample = 8000
   x = np.arange(sample)
   y = np.sin(2 * np.pi * f * x / Fs)
   plt.plot(x, y)
   plt.xlabel('sample(n)')
   plt.ylabel('voltage(V)')
   plt.show()
   

   4 được sử dụng để đặt tiêu đề của hình được tạo bằng màu xanh.

   import matplotlib.pyplot as plt
   import numpy as np
   
   
   Fs = 8000
   f = 5
   sample = 8000
   x = np.arange(sample)
   y = np.sin(2 * np.pi * f * x / Fs)
   plt.plot(x, y)
   plt.xlabel('sample(n)')
   plt.ylabel('voltage(V)')
   plt.show()
   

   5 chỉ ra tiêu đề có màu xanh lam. Code

   import matplotlib.pyplot as plt
   import numpy as np
   
   
   Fs = 8000
   f = 5
   sample = 8000
   x = np.arange(sample)
   y = np.sin(2 * np.pi * f * x / Fs)
   plt.plot(x, y)
   plt.xlabel('sample(n)')
   plt.ylabel('voltage(V)')
   plt.show()
   

   4 is used to set title of the generated figure in blue color.

   import matplotlib.pyplot as plt
   import numpy as np
   
   
   Fs = 8000
   f = 5
   sample = 8000
   x = np.arange(sample)
   y = np.sin(2 * np.pi * f * x / Fs)
   plt.plot(x, y)
   plt.xlabel('sample(n)')
   plt.ylabel('voltage(V)')
   plt.show()
   

   5 indicates title is in Blue Color.
6. Bước 6: Đặt mã nhãn

   import matplotlib.pyplot as plt
   import numpy as np
   
   
   Fs = 8000
   f = 5
   sample = 8000
   x = np.arange(sample)
   y = np.sin(2 * np.pi * f * x / Fs)
   plt.plot(x, y)
   plt.xlabel('sample(n)')
   plt.ylabel('voltage(V)')
   plt.show()
   

   6 và

   import matplotlib.pyplot as plt
   import numpy as np
   
   
   Fs = 8000
   f = 5
   sample = 8000
   x = np.arange(sample)
   y = np.sin(2 * np.pi * f * x / Fs)
   plt.plot(x, y)
   plt.xlabel('sample(n)')
   plt.ylabel('voltage(V)')
   plt.show()
   

   7 được sử dụng để đặt nhãn cho trục x và trục y. Ở đây

   import matplotlib.pyplot as plt
   import numpy as np
   
   
   Fs = 8000
   f = 5
   sample = 8000
   x = np.arange(sample)
   y = np.sin(2 * np.pi * f * x / Fs)
   plt.plot(x, y)
   plt.xlabel('sample(n)')
   plt.ylabel('voltage(V)')
   plt.show()
   

   8 được sử dụng để tạo mũi tên phải → được nối với thời gian và biên độ bằng toán tử +. Code

   import matplotlib.pyplot as plt
   import numpy as np
   
   
   Fs = 8000
   f = 5
   sample = 8000
   x = np.arange(sample)
   y = np.sin(2 * np.pi * f * x / Fs)
   plt.plot(x, y)
   plt.xlabel('sample(n)')
   plt.ylabel('voltage(V)')
   plt.show()
   

   6 and

   import matplotlib.pyplot as plt
   import numpy as np
   
   
   Fs = 8000
   f = 5
   sample = 8000
   x = np.arange(sample)
   y = np.sin(2 * np.pi * f * x / Fs)
   plt.plot(x, y)
   plt.xlabel('sample(n)')
   plt.ylabel('voltage(V)')
   plt.show()
   

   7 are used to set labels for x-axis and y-axis. Here

   import matplotlib.pyplot as plt
   import numpy as np
   
   
   Fs = 8000
   f = 5
   sample = 8000
   x = np.arange(sample)
   y = np.sin(2 * np.pi * f * x / Fs)
   plt.plot(x, y)
   plt.xlabel('sample(n)')
   plt.ylabel('voltage(V)')
   plt.show()
   

   8 is used to generate right arrow → which is concatenated with Time and Amplitude using + operator.
7. Bước 7: Hiển thị các lưới trong mã hình

   import matplotlib.pyplot as plt
   import numpy as np
   
   
   Fs = 8000
   f = 5
   sample = 8000
   x = np.arange(sample)
   y = np.sin(2 * np.pi * f * x / Fs)
   plt.plot(x, y)
   plt.xlabel('sample(n)')
   plt.ylabel('voltage(V)')
   plt.show()
   

   9 được sử dụng để hiển thị lưới trong biểu đồ được tạo. Code

   import matplotlib.pyplot as plt
   import numpy as np
   
   
   Fs = 8000
   f = 5
   sample = 8000
   x = np.arange(sample)
   y = np.sin(2 * np.pi * f * x / Fs)
   plt.plot(x, y)
   plt.xlabel('sample(n)')
   plt.ylabel('voltage(V)')
   plt.show()
   

   9 is used to display grid in generated plot.
8. Bước 8: Làm nổi bật mã dòng gốc

   import matplotlib.pyplot as plt # For ploting
   import numpy as np # to work with numerical data efficiently
   
   fs = 100 # sample rate 
   f = 2 # the frequency of the signal
   
   x = np.arange(fs) # the points on the x axis for plotting
   # compute the value (amplitude) of the sin wave at the for each sample
   y = np.sin(2*np.pi*f * (x/fs)) 
   
   #this instruction can only be used with IPython Notbook. 
   % matplotlib inline
   # showing the exact location of the smaples
   plt.stem(x,y, 'r', )
   plt.plot(x,y)
   

   0 và

   import matplotlib.pyplot as plt # For ploting
   import numpy as np # to work with numerical data efficiently
   
   fs = 100 # sample rate 
   f = 2 # the frequency of the signal
   
   x = np.arange(fs) # the points on the x axis for plotting
   # compute the value (amplitude) of the sin wave at the for each sample
   y = np.sin(2*np.pi*f * (x/fs)) 
   
   #this instruction can only be used with IPython Notbook. 
   % matplotlib inline
   # showing the exact location of the smaples
   plt.stem(x,y, 'r', )
   plt.plot(x,y)
   

   1 được sử dụng để ánh sáng cao x = 0 dòng và y = 0 dòng trong biểu đồ được tạo. Code

   import matplotlib.pyplot as plt # For ploting
   import numpy as np # to work with numerical data efficiently
   
   fs = 100 # sample rate 
   f = 2 # the frequency of the signal
   
   x = np.arange(fs) # the points on the x axis for plotting
   # compute the value (amplitude) of the sin wave at the for each sample
   y = np.sin(2*np.pi*f * (x/fs)) 
   
   #this instruction can only be used with IPython Notbook. 
   % matplotlib inline
   # showing the exact location of the smaples
   plt.stem(x,y, 'r', )
   plt.plot(x,y)
   

   0 and

   import matplotlib.pyplot as plt # For ploting
   import numpy as np # to work with numerical data efficiently
   
   fs = 100 # sample rate 
   f = 2 # the frequency of the signal
   
   x = np.arange(fs) # the points on the x axis for plotting
   # compute the value (amplitude) of the sin wave at the for each sample
   y = np.sin(2*np.pi*f * (x/fs)) 
   
   #this instruction can only be used with IPython Notbook. 
   % matplotlib inline
   # showing the exact location of the smaples
   plt.stem(x,y, 'r', )
   plt.plot(x,y)
   

   1 are used to high light x=0 line and y=0 line in generated plot.
9. Bước 9: Cuối cùng hiển thị mã hình

   import matplotlib.pyplot as plt # For ploting
   import numpy as np # to work with numerical data efficiently
   
   fs = 100 # sample rate 
   f = 2 # the frequency of the signal
   
   x = np.arange(fs) # the points on the x axis for plotting
   # compute the value (amplitude) of the sin wave at the for each sample
   y = np.sin(2*np.pi*f * (x/fs)) 
   
   #this instruction can only be used with IPython Notbook. 
   % matplotlib inline
   # showing the exact location of the smaples
   plt.stem(x,y, 'r', )
   plt.plot(x,y)
   

   2 được sử dụng để hiển thị biểu đồ cuối cùng trong đầu ra. Code

   import matplotlib.pyplot as plt # For ploting
   import numpy as np # to work with numerical data efficiently
   
   fs = 100 # sample rate 
   f = 2 # the frequency of the signal
   
   x = np.arange(fs) # the points on the x axis for plotting
   # compute the value (amplitude) of the sin wave at the for each sample
   y = np.sin(2*np.pi*f * (x/fs)) 
   
   #this instruction can only be used with IPython Notbook. 
   % matplotlib inline
   # showing the exact location of the smaples
   plt.stem(x,y, 'r', )
   plt.plot(x,y)
   

   2 is used to display final plot in output.