numpy - FFT iOS double the values? -

hi running fft function in simulator on ios platform

like

-(matrix*) spectraldensitywithvec:(matrix*) vector {      nslog(@"vector:%@",vector);     int samplesize = [vector rows];      double *r  = [vector array]; // arc handle memory      float * f = (float*) malloc(samplesize * sizeof(float));      (int = 0;i< samplesize;i++) {         f[i] = r[i];     }      // working 128 samples 2^7     // 7     vdsp_length log2n = log2([self nextpowerof2withnumber:samplesize]);      fftsetup fftsetup = vdsp_create_fftsetup(log2n,fft_radix2);      int nover2 = samplesize/2;      complex_split a;      a.realp = (float *) malloc(nover2*sizeof(float));     a.imagp = (float *) malloc(nover2*sizeof(float));      vdsp_ctoz((complex*)f, 2, &a, 1, nover2);      vdsp_fft_zrip(fftsetup, &a,1 ,log2n, fft_forward);      matrix* psd = [matrix matrixofrows:samplesize/2 columns:1];      matrix* fftresult = [matrix matrixofrows:samplesize columns:1];      // obtain imaginary / real parts     (int = 0;i < samplesize/2;i++) {         float realp = a.realp[i];         float imagp = a.imagp[i];          nslog(@"real:%g img:%g",realp,imagp);          float val = sqrtf((realp * realp) + (imagp * imagp)) * 2.0f /(float)samplesize ;          [psd setvalue:val row:i column:0];         [fftresult setvalue:realp row:i column:0];         [fftresult setvalue:imagp row:i*2+1 column:0];     }      nslog(@"fft result kw energy: %@",fftresult);      vdsp_destroy_fftsetup(fftsetup);      // release memory     if (f) {        free(f);         f = null;     }     if (a.realp) {         free(a.realp);         a.realp = null;     }     if (a.imagp) {         free(a.imagp);         a.imagp = null;     }      return psd; } 

with vector result of

[1.012817,     1.022833,     1.022028,     1.017877,     1.023680,     1.016974,     1.017746,     1.019263,     1.016417,     1.020745,     1.018643,     1.019521,     1.020260,     1.018041,     1.020829,     1.018644,     1.019398,     1.020399,     1.019222,     1.022093,     1.020433,     1.020534,     1.021503,     1.019931,     1.020480,     1.018945,     1.019238,     1.019989,     1.018917,     1.019762,     1.019021,     1.017887,     1.018136,     1.019543,     1.020242,     1.018757,     1.019534,     1.019862,     1.019060,     1.020717,     1.021055,     1.020178,     1.018108,     1.014776,     1.015374,     1.018429,     1.019895,     1.018647,     1.016387,     1.017053,     1.019572,     1.021097,     1.019488,     1.017218,     1.019876,     1.022022,     1.020574,     1.021588,     1.022298,     1.019369,     1.016980,     1.016774,     1.016079,     1.015292] 

the result on vector above:

   130.456100 -0.017152 -0.013957 -0.020526 -0.043666 -0.003648 -0.018351 -0.006409 -0.000965 -0.015068 -0.001338 0.011731 -0.042658 -0.012478 0.018228 -0.006718 -0.026859 -0.013061 -0.019885 -0.003351 -0.005669 -0.002971 -0.023977 -0.019806 -0.036644 -0.055367 -0.015106 0.000406 -0.002309 -0.002857 -0.002926 -0.002933 0.000000 -0.005997 0.000000 -0.013211 0.000000 -0.030161 0.000000 -0.021531 0.000000 -0.028181 0.000000 -0.011275 0.000000 -0.009881 0.000000 -0.009062 0.000000 -0.010197 0.000000 0.003035 0.000000 0.038075 0.000000 0.016340 0.000000 0.008624 0.000000 0.005861 0.000000 0.003673 0.000000 0.001765 

how ever numpy fft result:

   [65.22805000000001,  0.0,  -0.0085759487914117867,  -0.015603049865923341,  -0.0069788925854868157,  0.015041138788651551,  -0.010263059930525575,  0.012321275391622862,  -0.021832981634872219,  -0.026161796455543486,  -0.0018237156004342254,  -0.013884928458450145,  -0.0091755911990841002,  -0.016388522893239089,  -0.003204667797210365,  -0.033305086561749964,  -0.00048255506211134121,  -0.015783138731002323,  -0.0075337096722261337,  -0.0071538868820418761,  -0.00066829905037151319,  -0.017467879005406146,  0.0058655519004814438,  0.011289870623693908,  -0.021329700649694319,  0.0018507094402767038,  -0.0062391410740094827,  -0.013248826390988894,  0.0091139373764349274,  -0.0076327732856707516,  -0.003359005157638993,  0.0064289012241005097,  -0.013430000000003162,  -0.0029959999999995546,  -0.0065307135571379222,  -0.006605572447413598,  -0.0099422652522889073,  -0.015080331241097611,  -0.0016757131155144375,  -0.010765855482435674,  -0.0028346121922537357,  -0.014091061276246888,  -0.0014857985251928623,  -0.0056375351611077946,  -0.011988911430201157,  -0.0049400354875454924,  -0.0099028010321836699,  -0.0045315332341146703,  -0.018321444937884485,  -0.0050991387310010752,  -0.027683215530298805,  0.0015174037330767526,  -0.0075530851759402738,  0.019037454812601148,  0.00020298122212324523,  0.0081699113196296563,  -0.0011547055231802498,  0.0043124328279300489,  -0.0014283591672329676,  0.0029302575544578394,  -0.001462892683058185,  0.0018357150212062624,  -0.0014666841715850814,  0.00088237880086386444,  -0.0014699999999976399,  0.0] 

i have tried multiple things , out of ideas know doing wrong? have never worked fft function on ios before checked documentation appears not helpful in case.

well spotted.

the scaling factor of 2 is property of apple's real fft implementation. not sure why it's way, maybe it's more efficient.