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# 16点FFT频率与钻头扭转输出大量毁灭
进口matplotlib。pyplot作为plt
进口numpy np
从scipy。fftpack进口fft

#输入系数
x0 = 1 + 1 j
x1 = 2 + 1 j
x2 = 1 - 2 j
x3 = 2 - 1 j
x4 = 2 + 3 j
x5 = 3 + 2 j
x6 = 1 + 3 j
x7 = 3 + 1 j
×8 = 3 + 3 j
x9 = 3 - 3 j
x10 = 1 - 1 j
x11 = 3 - 3 j
x12 = 3 - 3 j
* 13 = 1 - 1 j
x14 = 3 - 3 j
连接= 1 + 1 j
x = (x0, x1, x2, x3, x4, x5, x6, x7,出数,x9, x10, x11, x12, * 13, x14,连接)

#抚弄因素
#第三阶段
tc0 = 1.0000
tc1 = 0.9239 - 0.3827 j
tc2 = 0.7071 - 0.7071 j
tc3 = 0.3827 - 0.9239 j
tc4 = 0.0000 - 1.0000 j
tc5 = -0.3827 - 0.9239 j
tc6 = -0.7071 - 0.7071 j
tc7 = -0.9239 - 0.3827 j

#抚弄因素
# 2阶段
tb0 = 1.0000
tb1 = 0.7071 - 0.7071 j
tb2 = 0.0000 - 1.0000 j
tb3 = -0.7071 - 0.7071 j

# 1阶段
ta0 = 1.0000
ta1 = 0.0000 - 1.0000 j

#第三阶段输出
s3_0 = x0 +的混合体
s3_1 = x1 + x9
s3_2 = x2 + x10
s3_3 = x3 + x11
s3_4 = x4 + x12
s3_5 = x5 + * 13
s3_6 = x6 + x14
s3_7 = x7 +连接
s3_8 = (x0 -×8) * tc0
s3_9 = (x1 - x9) * tc1
s3_10 = (x2 - x10) * tc2
s3_11 = (x3 - x11) * tc3
s3_12 = (x4 - x12) * tc4
s3_13 = (x5 - * 13) * tc5
s3_14 = (x6 - x14英寸)* tc6
s3_15 = (x7 - x15) * tc7

Stage3 = [s3_0 s3_1、s3_2 s3_3, s3_4, s3_5, s3_6, s3_7, s3_8, s3_9, s3_10, s3_11, s3_12, s3_13, s3_14, s3_15]

#第二阶段输出
s2_0 = s3_0 + s3_4
s2_1 = s3_1 + s3_5
s2_2 = s3_2 + s3_6
s2_3 = s3_3 + s3_7
s2_4 = (s3_0 - s3_4) * tb0
s2_5 = (s3_1 - s3_5) * tb1
s2_6 = (s3_2 - s3_6) * tb2
s2_7 = (s3_3 - s3_7) * tb3
s2_8 = s3_8 + s3_12
s2_9 = s3_9 + s3_13
s2_10 = s3_10 + s3_14
s2_11 = s3_11 + s3_15
s2_12 = (s3_8 - s3_12) * tb0
s2_13 = (s3_9 - s3_13) * tb1
s2_14 = (s3_10 - s3_14) * tb2
s2_15 = (s3_11 - s3_15) * tb3

Stage2 = [s2_0 s2_1、s2_2 s2_3, s2_4, s2_5, s2_6, s2_7, s2_8, s2_9, s2_10, s2_11, s2_12, s2_13, s2_14, s2_15]
#第1阶段
s1_0 = s2_0 + s2_2
s1_1 = s2_1 + s2_3
s1_2 = (s2_0 - s2_2) * ta0
s1_3 = (s2_1 - s2_3) * ta1
s1_4 = s2_4 + s2_6
s1_5 = s2_5 + s2_7
s1_6 = (s2_4 - s2_6) * ta0
s1_7 = (s2_5 - s2_7) * ta1
s1_8 = s2_8 + s2_10
s1_9 = s2_9 + s2_11
s1_10 = (s2_8 - s2_10) * ta0
s1_11 = (s2_9 - s2_11) * ta1
s1_12 = s2_12 + s2_14
s1_13 = s2_13 + s2_15
s1_14 = (s2_12 - s2_14) * ta0
s1_15 = (s2_13 - s2_15) * ta1

#阶段0
s0_0 = s1_0 + s1_1
s0_1 = s1_0 - s1_1
s0_2 = s1_2 + s1_3
s0_3 = s1_2 - s1_3
s0_4 = s1_4 + s1_5
s0_5 = s1_4 - s1_5
s0_6 = s1_6 + s1_7
s0_7 = s1_6 - s1_7
s0_8 = s1_8 + s1_9
s0_9 = s1_8 - s1_9
s0_10 = s1_10 + s1_11
s0_11 = s1_10 - s1_11
s0_12 = s1_12 + s1_13
s0_13 = s1_12 - s1_13
s0_14 = s1_14 + s1_15
s0_15 = s1_14 - s1_15

s0_2, Y = [s0_0 s0_1 s0_3, s0_4, s0_5, s0_6, s0_7, s0_8, s0_9, s0_10, s0_11, s0_12, s0_13, s0_14, s0_15]
Y = [Y Y [0], [8], [4], [12], [2], [10], [6], [14], [1], [9], [5], [13], [3], [11], [7], Y [15]]
# FFT使用python的内置函数
Y_fft = fft (x, 16)
Y = (abs(避署)避署的Y)
Y_fft = (Y_fft abs(避署)避署)

日元= np。减去(Y, Y_fft)

plt。情节(Y, r *) # 16点FFT输出阴谋
plt。标题(“16点FFT频率使用大量毁灭”)
plt.show ()
plt。情节(Y_fft, y ^) #建于16点FFT输出阴谋
plt。使用内置的标题(“16点FFT scipy FFT函数”)
plt.show ()
plt。情节(“g”日元)#区别两个输出
plt。标题(“区别上述两方法”)
plt.show ()