如何在Python中的wave生成中使用队列实现多线程

Question

我有一个代码可以生成 1 Hz 到 7 Hz 的正弦波，采样率为 1000 Hz。要求是生成一个多线程程序，其中使用 10 次迭代的 for 循环来获取正弦波的值。每次循环得到 100 点，因此执行 for 循环 10 次将产生 100 x10 = 1000 点。这千个点必须存储在队列中，从而将队列值传输到下一个线程。

这是我生成正弦波点的代码

import numpy as np
import pandas as pd
def get_values_for_frequency(freq):
    # sampling information
    omega = 2*np.pi*freq # angular frequency for sine waves with amplitude 1
    t_vec = np.linspace(0,1,num=1000,endpoint=True) # time 
    y = np.sin(omega*t_vec) #sine wave generation
    return y
#columns are created for sine wave frequencies
df = pd.DataFrame(columns =['1Hz','2Hz', '3Hz', '4Hz', '5Hz', '6Hz', '7Hz'])
df['1Hz']=pd.Series(get_values_for_frequency(1))
df['2Hz']=pd.Series(get_values_for_frequency(2))
df['3Hz']=pd.Series(get_values_for_frequency(3))
df['4Hz']=pd.Series(get_values_for_frequency(4))
df['5Hz']=pd.Series(get_values_for_frequency(5))
df['6Hz']=pd.Series(get_values_for_frequency(6))
df['7Hz']=pd.Series(get_values_for_frequency(7))
df = df.round(decimals = 6)                  #Round a table values in DataFrame to3 decimal places
df['1Hz']=(2**15) *df['1Hz']                 #values are converted into int16 equivalent
df['2Hz']=(2**15) *df['2Hz']
df['3Hz']=(2**15) *df['3Hz']
df['4Hz']=(2**15) *df['4Hz']
df['5Hz']=(2**15) *df['5Hz']
df['6Hz']=(2**15) *df['6Hz']
df['7Hz']=(2**15) *df['7Hz']
df = df.astype(np.int16)                        #converting float datatype to int16
arr=df.to_numpy(dtype='int16')    

  The arr numpy array has the values for sine waves 1000X7 rows and  columns.

要求是将其实现为多线程程序，其中以下部分必须执行 10 次，每次必须收集 100 个值，在 10 次循环后，必须聚合所有值并将其发送到通过队列进行下一步处理。

def get_values_for_frequency(freq):
omega = 2*np.pi*freq # angular frequency for sine waves with amplitude 1
t_vec = np.linspace(0,1,num=1000,endpoint=True) # time 
y = np.sin(omega*t_vec) #sine wave generation
return y
#columns are created for sine wave frequencies
df = pd.DataFrame(columns =['1Hz','2Hz', '3Hz', '4Hz', '5Hz', '6Hz', '7Hz'])
df['1Hz']=pd.Series(get_values_for_frequency(1))
df['2Hz']=pd.Series(get_values_for_frequency(2))
df['3Hz']=pd.Series(get_values_for_frequency(3))
df['4Hz']=pd.Series(get_values_for_frequency(4))
df['5Hz']=pd.Series(get_values_for_frequency(5))
df['6Hz']=pd.Series(get_values_for_frequency(6))
df['7Hz']=pd.Series(get_values_for_frequency(7))

当我尝试实现时，我只得到 100x 7 ielast 迭代值。谁能指导我解决这个问题，

这是我尝试过的代码。

import threading
import numpy as np
from queue import Queue
import pandas as pd

def get_values_for_frequency(freq):
    omega = 2*np.pi*freq 
    t_vec = np.linspace(origin,end,num=100,endpoint=True) 
    y = np.sin(omega*t_vec)                              
    return y
    
def thread_task(in_q):
    global df, df1,end,origin
    df= pd.DataFrame(columns =['1Hz','2Hz', '3Hz', '4Hz', '5Hz', '6Hz', '7Hz'])
    end=1/10
    origin=0
    for _ in range(10):
        df['1Hz']=pd.Series(get_values_for_frequency(1))
        df['2Hz']=pd.Series(get_values_for_frequency(2))
        df['3Hz']=pd.Series(get_values_for_frequency(3))
        df['4Hz']=pd.Series(get_values_for_frequency(4))
        df['5Hz']=pd.Series(get_values_for_frequency(5))
        df['6Hz']=pd.Series(get_values_for_frequency(6))
        df['7Hz']=pd.Series(get_values_for_frequency(7))
        origin=end
        end+=end
    df = df.round(decimals = 6)                  
    df['1Hz']=(2**15) *df['1Hz']                 
    df['2Hz']=(2**15) *df['2Hz']
    df['3Hz']=(2**15) *df['3Hz']
    df['4Hz']=(2**15) *df['4Hz']
    df['5Hz']=(2**15) *df['5Hz']
    df['6Hz']=(2**15) *df['6Hz']
    df['7Hz']=(2**15) *df['7Hz']
    df = df.astype(np.int16) 
    in_q.put(df)
def output(out_q):
    while True:
        print(out_q.get())
def main_task():
    global df
    global df1
    global end
    global origin
    df= pd.DataFrame(columns =['1Hz','2Hz', '3Hz', '4Hz', '5Hz', '6Hz', '7Hz'])
    df1=pd.DataFrame()
    q = Queue()
    lock = threading.Lock()
    t1 = threading.Thread(target=thread_task, args=(q,))
    t2 = threading.Thread(target=output, args=(q,))
    t1.start()
    t2.start()
    t1.join()
    t2.join()
if __name__ == "__main__":
        main_task()