我在验证我写的一个简单的IDFT程序-

###############################################################
#My IDFT Routines
###############################################################
def simple_idft(data_f):
    data_t_r = []
    data_t_i = []
    for ii in range(0,len(data_f)):
      tmp_r=0.00
      tmp_i=0.00
      scale = 1.00/len(data_f)
      for jj in range(0,len(data_f)):
        tmp_r += data_f[jj].real*math.cos(2.00*math.pi*ii*jj/len(data_f)) - data_f[jj].imag*math.sin(2.00*math.pi*ii*jj/len(data_f))
        tmp_i += data_f[jj].real*math.sin(2.00*math.pi*ii*jj/len(data_f)) + data_f[jj].imag*math.cos(2.00*math.pi*ii*jj/len(data_f))
      tmp_r *=scale
      tmp_i *=scale
      data_t_r.append(tmp_r)
      data_t_i.append(tmp_i)
   return data_t_r, data_t_i
    

def rms_idft(data_t_r, data_t_i):
    rms = []
    for ii in range(0,len(data_t_r)):
        rms.append(math.sqrt(data_t_r[ii]**2 + data_t_i[ii]**2))
    return rms

def do_idft(data_t):
    data_t_r, data_t_i = simple_idft(data_t)
    rms = rms_idft(data_t_r, data_t_i)
    return(rms)

与愚蠢的IDFT程序进行对比-

################################################################
#Transform OFDM Data to time domain
################################################################
def IDFT(OFDM_data):
    return np.fft.ifft(OFDM_data)

当我运行这些（64点数据）时，我似乎得到了非常不同的结果-

OFDM_time = IDFT(OFDM_data)
print ("Number of OFDM samples in time-domain before CP: ", len(OFDM_time))
print(OFDM_time)
plt.plot(OFDM_time)
plt.show()

rms = []
rms = do_idft(OFDM_data)
plt.plot(rms,label='raj')
plt.legend()
plt.show()

你能看出我的算法有什么错误吗？