numpy - DC Term in Python FFT - Amplitude of Constant Term -

i've created fft class/object takes signal stored in 2d array , produces subsequent fft of input, before printing matplotlib graph.

after great deal of reading, appreciate due windowing, need have ideally 2^x number of points , integer number of cycles in data set, amplitude of peaks never 100% accurate (but approximately right).

however, when add dc offset signal, reason, 0 hz frequency has peak double actual dc/constant offset! example, if add 2 sine wave of x hz, peak @ x hz on fft, , peak of 4 @ 0.

why - , can correct this?

thanks!

the code using below:

import numpy np import matplotlib.pyplot plt  class fft:     def __init__(self, time, signal, buff=1, scaling=2, centre=false):          self.signal = signal         self.buff = buff         self.time = time         self.scaling = scaling         self.centre = centre         if (centre):             self.scaling = 1     def fft(self):         self.y = np.fft.fft(self.signal, self.buff * len(self.signal))  # fft on signal , store         if (self.centre true):             self.y = np.fft.fftshift(self.y)  # centre 0 frequency in centre         self.__graph__()     def __graph__(self):         self.n = len(self.y) / self.scaling  # fft length (halved avoid reflection)         print (self.n)         self.fa = 1 / (self.time[1] - self.time[0])  # time interval & sampling frequency of fft         if (self.centre true):         self.t_axis = np.linspace(-self.fa / 2 * self.scaling, self.fa / 2 * self.scaling, self.n, endpoint=true)  # create x axis vector 0 nyquist freq. (fa/2) n values         else:             self.t_axis = np.linspace(0, self.fa / self.scaling, self.n, endpoint=true)  # create x axis vector 0 nyquist freq. (fa/2) n values     def show(self, absolute=true):          if absolute:             plt.plot(self.t_axis, ((2.0) * self.buff / (self.n * (self.scaling))) * np.abs(self.y[0:self.n]))         else:             plt.plot(self.t_axis, ((2.0) * self.buff / (self.ns * (self.scaling))) * self.y[0:self.n])  # multiply y axis 2/n actual values         plt.grid()         plt.show()  def sineexample(start=0, dur=128, samples=16384):         t = np.linspace(start, dur + start, samples, true)     print(t)     f = 10.0  # frequency in hz     = 10.0  # amplitude in unit     retarr = np.zeros(len(t))         retarr = np.column_stack((t, retarr))     row in range(len(retarr)):         retarr[row][1] = * np.sin(2 * np.pi * f * retarr[row][0]) + 2 # signal       print(retarr)     return retarr  htarray = sineexample() # plt.plot(htarray[:,0], htarray[:,1]) # plt.grid() # plt.show()  myfft = fft(htarray[:, 0], htarray[:, 1], scaling=2,centre=false) myfft.fft() myfft.show() 

actually, opposite. other frequencies in full complex fft result of strictly real data split 2 result bins , mirrored complex conjugated, half pure sinusoids amplitude when scaled 1/n, except dc component , n/2 cosine component, not split 2 fft results, , not halved.