# Using a plurality of tasks concurrently yield achieved threaded # primer: Builder Review # DEF func1 (): # Print ( '. 1') # yield # Print ( '2') # yield # Print ( '. 3') # yield # = func1 RES () # Print (RES) # <Object Generator func1 AT 0x109032e60> # Next (RES). 1 # # Next (RES) # 2 # Next (RES). 3 # # ---------- -------------------------------------------- # official for single use yield thread concurrency multiple tasks # Import Time # # that # summing function is a function concurrent # def func1(): # a = 1 # for i in range(1000): # a += 1 # print("a run") # yield # # def func2(): # res = func1() # b = 1 # for i in range(1000): # b += 1 # print("b run") # next(res) # # start_time = time.time() # func2() # end_time = time.time() # print("耗时>>>%s"%(end_time-start_time)) #>>> .007849931716918945 # --------------------------------------------- ---------- # refrain yield concurrent program (default operation) # Import Time # # # DEF func1 (): # A. 1 = # for I in Range (1000): # A. 1 = + # Print ( "A RUN") # # # DEF func2 (): # B =. 1 # for I in Range (1000): # B +. 1 = # Print ( "RUN B") # # # START_TIME the time.time = ( ) # func1 () #func2 () # END_TIME the time.time = () # Print ( "% S Processed >>>"% (END_TIME-START_TIME)) # # Processed >>> .006770133972167969 # we have found that by comparing the numerical # using the yield force generator instead of a single thread concurrency function leads to increased time to slow down with