signal = []
signal = np.zeros((N, 3), dtype='complex')
fftsignal = []
fftsignal = np.zeros((N, 3), dtype='complex')
fftsignal1 = []
fftsignal1 = np.zeros((N, 3), dtype='complex')
tt, signal[:, 0] = sg.seno(fs, f0, N, a0, p0)
tt, signal[:, 1] = sg.square(fs, f0, N, a0, p0, duty)
tt, signal[:, 2] = sg.sawtooth(fs, f0, N, a0, p0, width)
ii = 0
while ii < 3:
fftsignal[:, ii] = np.fft.fft(signal[:, ii])
FFT.plotFFT(fftsignal[:, ii],
fs,
N,
tp='FFT',
c=ii,
db='off',
l=lsignal[ii])
fftsignal1[:, ii] = FFT.myDFT(signal[:, ii])
FFT.plotFFT(fftsignal1[:, ii],
fs,
N,
tp='DFT',
c=ii,
db='off',
l=lsignal[ii])
ii += 1
ii = 0
while ii < 8:
tt, signal = sg.seno(fs, f0, N[ii], a0, p0)
the_start = time()
np.fft.fft(signal)
the_end = time()
tiempofft[ii] = the_end - the_start
ii += 1
#%% calculando el tiempo de mi DFT
ii = 0
while ii < 8:
tt, signal = sg.seno(fs, f0, N[ii], a0, p0)
the_start = time()
FFT.myDFT(signal)
the_end = time()
tiempodft[ii] = the_end - the_start
ii += 1
import pandas as pd
index = ['16', '32', '64', '128', '256', '512', '1024', '2048', '4096', '8192']
data = {'N': index, 'FFT': tiempofft, 'myDFT': tiempodft}
df = pd.DataFrame(data)
df.set_index('N', inplace=True)
# select two columns
print(df)
plt.figure("Gráfico comparatvo de tiempo entre la FFT y la DFT")
