%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import math
x = np.linspace(0, 3*np.pi, 500)
plt.plot(x, np.sin(x**2))
plt.title('A simple chirp');
x = np.linspace(0, 8*np.pi, 500)
y = np.sin(x + 0.0001) / np.abs(np.sin(x + 0.0001))
y = np.sin(1 * x - 0 * np.pi)
plt.plot(x, y)
plt.title('square wave')
<matplotlib.text.Text at 0x5cb98f0>
f1 = np.fft.fft(y)
# according to https://en.wikipedia.org/wiki/Discrete_Fourier_transform#Definition
amplitude = np.sqrt([x.imag * x.imag + x.real * x.real for x in f1])
phase = [math.atan2(x.imag, x.real) for x in f1]
plt.plot(x, phase)
plt.plot(x, amplitude / 100)
plt.title('wave ftt')
<matplotlib.text.Text at 0x5cf7c10>
def plot_phase(mult, phase0):
N = 2000
x = np.linspace(0, 4*np.pi, N)
y = np.sin(mult * x + phase0 - np.pi / 2 + 0.000000001)
y = y / abs(y)
f1 = np.fft.fft(y)
# according to https://en.wikipedia.org/wiki/Discrete_Fourier_transform#Definition
amplitude = np.sqrt([x.imag * x.imag + x.real * x.real for x in f1])
phase = [math.atan2(x.imag, x.real) for x in f1]
plt.plot(x, phase)
plt.plot(x, amplitude / N * 10)
index = [i for i, v in enumerate(amplitude) if v == max(amplitude)][0]
print("index: {}".format(index))
print("phase: {} amplitude: {} at index".format(phase[index] % np.pi, amplitude[index] / N))
print(amplitude[index -2:index + 3])
return plt.title('wave ftt')
plot_phase(1, 0)
index: 2 phase: 0.0031415926535887095 amplitude: 0.6366208195663385 at index [ 0. 0. 1273.24163913 0. 0. ]
<matplotlib.text.Text at 0x5d40590>
plot_phase(20, 0.5)
index: 40 phase: 0.5623409546597635 amplitude: 0.6357849394801928 at index [ 3.98766933 50.78563485 1271.56987896 50.78563485 3.98766933]
<matplotlib.text.Text at 0x5d59750>
plot_phase(1, 0.25)
index: 2 phase: 0.2528982124897716 amplitude: 0.6366184633713639 at index [ 2. 2. 1273.23692674 2. 2. ]
<matplotlib.text.Text at 0x6d30190>
plot_phase(1.5, 0)
index: 1997 phase: 3.1368802646094047 amplitude: 0.6366200341670444 at index [ 2.31290495 0. 1273.24006833 0. 2.30870347]
<matplotlib.text.Text at 0x6da8eb0>
x = np.linspace(0, 3 * np.pi, 200)
y1 = np.sin(x * 2)
y2 = np.sin(x * 3)
plt.plot(x, y1)
plt.plot(x, y2)
[<matplotlib.lines.Line2D at 0x6d9eb10>]
def plot_microphone(l):
N = len(l)
s = sum(l) / N
y = [x - s for x in l]
f1 = np.fft.fft(y)
amplitude = np.sqrt([x.imag * x.imag + x.real * x.real for x in f1])
phase = [math.atan2(x.imag, x.real) * 200 for x in f1]
plt.plot(phase)
plt.plot(amplitude / N * 10)
plt.plot(l)
index = [i for i, v in enumerate(amplitude) if v == max(amplitude)][0]
print("highest index: {} amplitude: {}".format(index, amplitude[index] / N))
if index > N / 2:
print("lower index: {} amplitude: {}".format(N - index, amplitude[N - index] / N))
#for index in range(12):
# print("phase: {} amplitude: {} at index {}".format(phase[index] % np.pi, amplitude[index] / N, index))
x = plt.title('wave ftt')
x.phase = phase
x.amplitude = amplitude
return x
plot_microphone([504, 503, -512, -512, -512, -512, 503, 504, 503, -512, -512, -512, 503, 503, 503, 503, -512, -512, -512, 502, 503, 503, 503, -512, -512, -512, 503, 503, 503, 504, -512, -512, -512, 503, 504, 503, -512, -512, -512, -512, 503, 503, 503, -512, -512, -512, -512, 503, 503, 503, -512, -512, -512, 502, 503, 503, 503, -512, -512, -512, 503, 503, 503, 503, -512, -512, -512, 503, 503, 503, 503, -512, -512, -512, 503, 503, 503, -512, -512, -512, -512, 503, 503, 503, -512, -512, -512, -512, 503, 504, 503, -512, -512, -512, -512, 503, 504, 503, -512, -512, -512, 503, 503, 504, 503, -512, -512, -512, 503, 504, 503, 503, -512, -512, -512, 503, 503, 504, -512, -512, -512, -512, 503, 503, 503, -512, -512, -512, -512, 503, 504, 503, -512, -512, -512, 324, 504, 504, 503, -512, -512, -512, 503, 503, 504, 503, -512, -512, -512, 503, 503, 503, 503, -512, -512, -512, 503, 503, 503, -512, -512, -512, -512, 503, 503, 503, -512, -512, -512, -512, 503, 504, 503, -512, -512, -512, 503, 503, 504, 503, -512, -512, -512, 504, 503, 504, 503, -512, -512, -512, 503, 503, 504, 503, -512, -512, -512, 503, 503, 504, -512, -512, -512, -512, 503, 503, 503, -512, -512, -512, -512, 503, 503, 503, -512, -512, -512, 504, 503, 503, 503, -512, -512, -512, 503, 503, 504, 503, -512, -512, -512, 503, 503, 503, -205, -512, -512, -512, 503, 503, 504, -512, -512, -512, -512, 503, 503, 504, -512, -512, -512, -512, 503, 503, -512]
)
highest index: 37 amplitude: 267.3080632561925
<matplotlib.text.Text at 0x5c9d5f0>
plot_microphone([500, -515, -515, -515, -515, 500, 500, 501, 500, -515, -515, -515, -515, 501, 502, 502, 504, 504, -515, -515, -515, -515, 501, 502, 502, 501, -515, -515, -515, -515, 502, 504, 505, 505, 506, -515, -515, -515, -515, 505, 505, 504, 503, -515, -515, -515, -515, -515, 501, 500, 500, 500, -515, -515, -515, -515, 500, 502, 501, 502, -515, -515, -515, -515, -515, 501, 501, 501, 501, -515, -515, -515, -515, 502, 501, 501, 501, 501, -515, -515, -515, -515, 501, 500, 500, 501, -515, -515, -515, -515, 500, 501, 500, 500, 500, -515, -515, -515, -515, 500, 500, 500, 500, -515, -515, -515, -515, 500, 501, 500, 500, 500, -515, -515, -515, -515, 500, 500, 500, 500, -515, -515, -515, -515, 500, 500, 500, 500, 501, -515, -515, -515, -515, 500, 500, 500, 500, -515, -515, -515, -515, 500, 500, 500, 500, 500, -515, -515, -515, -515, 499, 500, 500, 500, -515, -515, -515, -515, -515, 500, 501, 500, 500, -515, -515, -515, -515, 500, 500, 500, 500, -515, -515, -515, -515, -515, 500, 500, 500, 499, -515, -515, -515, -515, 500, 500, 500, 500, -515, -515, -515, -515, -515, 500, 500, 500, 500, -515, -515, -515, -515, 500, 500, 500, 500, 500, -515, -515, -515, -515, 501, 500, 500, 500, -515, -515, -515, -515, 500, 500, 500, 500, 500, -515, -515, -515, -515, 500, 500, 501, 500, -515, -515, -515, -515, 501, 500, 500, 500, 500, -515, -515, -515, -515, 500, 500, 500, 501, -515, -515, -515, -515, 465, 500, 500]
)
highest index: 30 amplitude: 314.7903553388149
<matplotlib.text.Text at 0x6f222f0>
plot_microphone([503, -512, -512, -512, -512, -512, 378, 504, 504, 503, 503, 503, -512, -512, -512, -512, -512, -512, 503, 504, 503, 504, 503, 503, -512, -512, -512, -512, -512, 503, 503, 503, 504, 503, 504, -512, -512, -512, -512, -512, -512, 503, 503, 503, 504, 503, -512, -512, -512, -512, -512, -512, 503, 504, 503, 503, 503, 503, -512, -512, -512, -512, -512, 503, 503, 503, 503, 503, 503, -512, -512, -512, -512, -512, -512, 503, 504, 503, 503, 503, 502, -512, -512, -512, -512, -512, 503, 503, 503, 503, 503, 503, -512, -512, -512, -512, -512, -512, 503, 503, 503, 504, 503, -512, -512, -512, -512, -512, -512, 502, 504, 503, 503, 503, 503, -512, -512, -512, -512, -512, 503, 502, 504, 503, 503, 503, -512, -512, -512, -512, -512, -512, 505, 507, 508, 508, 508, -512, -512, -512, -512, -512, -512, 504, 503, 504, 503, 504, 504, -512, -512, -512, -512, -512, 504, 503, 504, 503, 504, 504, -512, -512, -512, -512, -512, -512, 503, 503, 503, 503, 504, 503, -512, -512, -512, -512, -512, 503, 503, 504, 503, 503, 503, -512, -512, -512, -512, -512, -512, 503, 503, 503, 503, 503, -512, -512, -512, -512, -512, -512, 503, 504, 503, 504, 504, 503, -512, -512, -512, -512, -512, 503, 503, 503, 503, 504, 503, -512, -512, -512, -512, -512, -512, 503, 503, 503, 503, 504, 503, -512, -512, -512, -512, -512, 503, 503, 504, 503, 503, 504, -512, -512, -512, -512, -512, -512, 503, 503, 504, 503, 503, -512, -512, -512, -512]
)
highest index: 22 amplitude: 245.48687428225418
<matplotlib.text.Text at 0x700dd10>
# original frequency
plot_microphone([-504, -504, -504, -504, -504, -504, 512, 512, 511, 512, 511, 512, -504, -504, -504, -504, -504, -504, 511, 512, 513, 512, 512, -504, -504, -504, -504, -504, -504, 512, 513, 511, 512, 512, 513, -504, -504, -504, -504, -504, 511, 512, 512, 512, 512, 512, -504, -504, -504, -504, -504, -504, 512, 512, 512, 512, 512, -504, -504, -504, -504, -504, -504, 512, 512, 512, 512, 512, 512, -504, -504, -504, -504, -504, 512, 513, 512, 512, 512, 513, -504, -504, -504, -504, -504, -504, 512, 513, 512, 512, 512, -504, -504, -504, -504, -504, -504, 512, 512, 513, 512, 512, 513, -504, -504, -504, -504, -504, 230, 514, 514, 515, 517, 516, -504, -504, -504, -504, -504, -504, 514, 512, 512, 513, 511, 512, -504, -504, -504, -504, -504, 512, 513, 513, 514, 513, 512, -504, -504, -504, -504, -504, -504, 512, 512, 512, 512, 511, -504, -504, -504, -504, -504, -504, 511, 512, 512, 512, 512, 512, -504, -504, -504, -504, -504, -504, 512, 511, 512, 512, 512, -504, -504, -504, -504, -504, -504, 512, 513, 512, 511, 512, 511, -504, -504, -504, -504, -504, 511, 512, 512, 512, 512, 512, -504, -504, -504, -504, -504, -504, 512, 512, 512, 512, 512, -504, -504, -504, -504, -504, -504, 512, 512, 512, 512, 512, 512, -504, -504, -504, -504, -504, 512, 511, 512, 512, 513, 511, -504, -504, -504, -504, -504, -504, 511, 512, 511, 512, 512, 511, -504, -504, -504, -504, -504, 513, 512, 512, 512, 512, 512, -504, -504, -504, -504]
#[518, -504, -504, -504, -504, -504, 512, 512, 512, 511, 513, 512, -504, -504, -504, -504, -504, -504, 512, 512, 512, 513, 512, 512, -504, -504, -504, -504, -504, 511, 511, 512, 512, 511, 512, -504, -504, -504, -504, -504, -504, 511, 512, 511, 512, 511, -504, -504, -504, -504, -504, -504, 511, 511, 512, 511, 512, 511, -504, -504, -504, -504, -504, 511, 511, 512, 511, 511, 512, -504, -504, -504, -504, -504, -504, 511, 511, 512, 511, 512, 511, -504, -504, -504, -504, -504, 511, 512, 512, 511, 512, 512, -504, -504, -504, -504, -504, -504, 511, 511, 511, 512, 511, -504, -504, -504, -504, -504, -504, 511, 512, 512, 511, 513, 511, -504, -504, -504, -504, -504, 512, 511, 512, 512, 513, 512, -504, -504, -504, -504, -504, -504, 512, 512, 512, 513, 512, -504, -504, -504, -504, -504, -504, 512, 512, 512, 512, 511, 512, -504, -504, -504, -504, -504, -504, 511, 512, 512, 513, 512, -504, -504, -504, -504, -504, -504, 511, 512, 512, 512, 513, 512, -504, -504, -504, -504, -504, 511, 512, 512, 511, 512, 512, -504, -504, -504, -504, -504, -504, 511, 512, 511, 511, 512, -504, -504, -504, -504, -504, -504, 512, 512, 511, 511, 512, 511, -504, -504, -504, -504, -504, 512, 511, 511, 512, 511, 511, -504, -504, -504, -504, -504, -504, 512, 511, 512, 512, 512, -504, -504, -504, -504, -504, -504, 511, 512, 511, 511, 511, 511, -504, -504, -504, -504, -504, -504, 512, 512, 512, 512, 511, -504, -504, -504, -504]
#[-508, -508, -508, -508, -508, -508, 508, 507, 508, 507, 508, 507, -508, -508, -508, -508, -508, 507, 507, 507, 507, 507, 508, -508, -508, -508, -508, -508, -508, 507, 508, 508, 508, 508, 508, -508, -508, -508, -508, -508, 509, 508, 507, 508, 507, 508, -508, -508, -508, -508, -508, -508, 508, 507, 508, 507, 509, -508, -508, -508, -508, -508, -508, 508, 508, 508, 508, 508, 508, -508, -508, -508, -508, -508, 508, 507, 507, 508, 507, 508, -508, -508, -508, -508, -508, -508, 508, 507, 508, 507, 508, -508, -508, -508, -508, -508, -508, 508, 507, 507, 508, 507, 508, -508, -508, -508, -508, -508, 507, 508, 507, 507, 507, 508, -508, -508, -508, -508, -508, -508, 507, 507, 507, 507, 507, 508, -508, -508, -508, -508, -508, 507, 507, 507, 508, 507, 507, -508, -508, -508, -508, -508, -508, 507, 507, 507, 507, 507, -508, -508, -508, -508, -508, -508, 507, 507, 507, 508, 507, 508, -508, -508, -508, -508, -508, 507, 508, 507, 507, 508, 508, -508, -508, -508, -508, -508, -508, 507, 507, 508, 507, 507, 508, -508, -508, -508, -508, -508, 507, 508, 507, 508, 507, 507, -508, -508, -508, -508, -508, -508, 507, 507, 508, 508, 507, -508, -508, -508, -508, -508, -508, 507, 508, 507, 508, 507, 508, -508, -508, -508, -508, -508, 507, 508, 508, 508, 508, 507, -508, -508, -508, -508, -508, -508, 508, 508, 507, 508, 508, -498, -508, -508, -508, -508, -508, 507, 508, 507, 508, 507, 507, -508, -508, -508, -508]
)
highest index: 22 amplitude: 248.63465344720547
<matplotlib.text.Text at 0x704ad90>
#old
plot_microphone([510, -506, -506, -506, -506, 510, 509, 510, 510, -506, -506, -506, -506, 510, 510, 510, 510, 510, -506, -506, -506, -506, 509, 509, 510, 509, -506, -506, -506, -506, -506, 509, 509, 510, 509, -506, -506, -506, -506, 510, 509, 510, 510, -506, -506, -506, -506, -506, 509, 510, 510, 510, -506, -506, -506, -506, 510, 510, 511, 510, -506, -506, -506, -506, -506, 510, 510, 510, 510, -506, -506, -506, -506, 510, 510, 510, 510, 510, -506, -506, -506, -506, 510, 511, 510, 510, -506, -506, -506, -506, 510, 510, 510, 510, 510, -506, -506, -506, -506, 511, 510, 510, 510, -506, -506, -506, -506, 510, 510, 510, 510, 511, -506, -506, -506, -506, 510, 510, 510, 511, -506, -506, -506, -506, 515, 516, 516, 516, 516, -506, -506, -506, -506, 516, 515, 514, 516, -506, -506, -506, -506, -506, 515, 515, 515, 514, -506, -506, -506, -506, 515, 515, 516, 515, -506, -506, -506, -506, -506, 515, 516, 516, 515, -506, -506, -506, -506, 516, 516, 516, 515, -506, -506, -506, -506, -506, 515, 516, 515, 515, -506, -506, -506, -506, 515, 515, 515, 515, -389, -506, -506, -506, -506, 510, 510, 510, 509, -506, -506, -506, -506, 512, 510, 510, 511, 510, -506, -506, -506, -506, 510, 510, 510, 510, -506, -506, -506, -506, 510, 509, 511, 510, 509, -506, -506, -506, -506, 509, 510, 510, 510, -506, -506, -506, -506, 509, 510, 509, 510, 510, -506, -506, -506, -506, 510, 510, 510, 510, -506, -506, -506, -506, -506, 509, 516]
)
highest index: 30 amplitude: 314.9054716452421
<matplotlib.text.Text at 0x7088cd0>
#old
p = plot_microphone([-14, -15, -14, -500, -12, -12, 516, 517, 517, 517, -500, -500, -500, -16, -15, 517, 517, -13, -13, -13, 517, -16, -15, -500, -500, -500, -500, 517, 516, 516, -15, -13, -12, -500, -15, -16, -15, -500, -14, -13, 515, 516, 516, 517, -500, -500, -500, -18, -16, -16, 516, -14, -14, -14, 516, -17, -17, -500, -500, -500, -500, 516, 516, 516, -17, -14, -14, -500, -16, -16, -16, -500, -15, -13, -14, 516, 516, 516, -500, -500, -500, -500, -17, -17, 516, -15, -14, -14, 516, -18, -17, -16, -500, -500, -500, 516, 517, 516, 516, -15, -14, -500, -16, -16, -16, -500, -17, -13, -14, 516, 516, 516, -500, -500, -500, -500, -16, -16, 516, -14, -13, -13, 516, 517, -16, -16, -500, -500, -500, 516, 517, 516, 516, -14, -13, -500, -15, -14, -15, -15, -500, -12, -13, 516, 517, 516, -500, -500, -500, -500, -16, -15, 517, -13, -12, -12, -13, 518, -15, -15, -500, -500, -500, 516, 516, 517, 516, -14, -13, -500, -15, -14, -14, -14, -500, -12, -12, 516, 517, 517, 516, -500, -500, -500, -16, -15, 516, -14, -13, -13, -13, 517, -16, -15, -500, -500, -500, -500, 516, 517, 517, -15, -13, -500, -17, -15, -15, -15, -500, -14, -13, 516, 516, 516, 516, -500, -500, -500, -17, -16, 516, -18, -14, -14, -14, 516, -16, -16, -500, -500, -500, -500, 516, 516, 516, -15, -14, -499, -500, -16, -16, -16, -500, -14, -13, 516, 516, 517, 516, -500, -500, -500, -18, -17, -500]
).phase
p[22], p[37]
highest index: 218 amplitude: 134.00339192303932 lower index: 37 amplitude: 134.0033919230393
(-461.49175558953493, 83.53980949915874)
# find out the phase
l = [
[-510, -510, 506, 506, 505, 506, 506, 506, -510, -510, -510, -510, -510, -510, 507, 509, 510, 512, 511, -510, -510, -510, -510, -510, -510, 512, 513, 512, 512, 512, 513, -510, -510, -510, -510, -510, 513, 512, 512, 512, 513, 511, -510, -510, -510, -510, -510, -510, 511, 512, 511, 510, 511, 511, -510, -510, -510, -510, -510, 512, 511, 511, 510, 510, 511, -510, -510, -510, -510, -510, -510, 511, 511, 511, 512, 512, -510, -510, -510, -510, -510, -510, 512, 513, 512, 512, 513, 512, -510, -510, -510, -510, -510, 512, 512, 512, 512, 513, 512, -510, -510, -510, -510, -510, -510, 511, 511, 512, 510, 509, 507, -510, -510, -510, -510, -510, 506, 506, 507, 505, 506, 506, -510, -510, -510, -510, -510, -510, 506, 506, 506, 507, 506, -510, -510, -510, -510, -510, -510, 505, 505, 506, 506, 506, 505, -510, -510, -510, -510, -510, 506, 506, 506, 506, 505, 505, -510, -510, -510, -510, -510, -510, 507, 506, 506, 506, 506, -510, -510, -510, -510, -510, -510, 506, 506, 507, 506, 508, 509, -510, -510, -510, -510, -510, -510, 511, 512, 511, 512, 511, -510, -510, -510, -510, -510, -510, 511, 512, 511, 511, 512, 512, -510, -510, -510, -510, -510, 506, 506, 506, 505, 506, 506, -510, -510, -510, -510, -510, -510, 506, 506, 506, 506, 507, -510, -510, -510, -510, -510, -510, 506, 506, 506, 506, 506, 506, -510, -510, -510, -510, -510, 506, 506, 506, 506, 505, 506, -510, -510, -510, -510, -510, -510, 506, -510]
,
[509, -507, -507, -507, -507, -507, -507, 509, 510, 509, 509, 509, 509, -507, -507, -507, -507, -507, 509, 509, 510, 511, 514, 514, -507, -507, -507, -507, -507, -507, 515, 515, 515, 515, 515, -507, -507, -507, -507, -507, -507, 515, 515, 515, 515, 516, 514, -507, -507, -507, -507, -507, 514, 515, 516, 515, 515, 515, -507, -507, -507, -507, -507, -507, 510, 509, 510, 509, 509, 509, -507, -507, -507, -507, -507, 516, 515, 516, 515, 516, 515, -507, -507, -507, -507, -507, -507, 515, 514, 515, 515, 516, -507, -507, -507, -507, -507, -507, 515, 515, 515, 514, 516, 515, -507, -507, -507, -507, -507, 514, 514, 514, 513, 513, 513, -507, -507, -507, -507, -507, -507, 509, 509, 510, 509, 509, -332, -507, -507, -507, -507, -507, 510, 509, 510, 509, 510, 510, -507, -507, -507, -507, -507, -507, 514, 515, 515, 515, 516, -507, -507, -507, -507, -507, -507, 513, 514, 515, 515, 516, 514, -507, -507, -507, -507, -507, 515, 516, 516, 515, 515, 515, -507, -507, -507, -507, -507, -507, 515, 515, 515, 515, 516, -507, -507, -507, -507, -507, -507, 515, 515, 516, 515, 515, 514, -507, -507, -507, -507, -507, 514, 514, 515, 515, 515, 516, -507, -507, -507, -507, -507, -507, 515, 515, 515, 515, 515, -490, -507, -507, -507, -507, -507, 510, 509, 509, 509, 509, 509, -507, -507, -507, -507, -507, -507, 509, 511, 511, 510, 510, -507, -507, -507, -507, -507, -507, 515, 515, 515, 515, 515, 515, -507, -507, -507]
,
[496, 496, 497, 496, 497, 496, -519, -519, -519, -519, -519, -519, 496, 497, 496, 496, 496, -519, -519, -519, -519, -519, -519, 496, 496, 496, 496, 497, 496, -519, -519, -519, -519, -519, 497, 497, 497, 496, 497, 497, -519, -519, -519, -519, -519, -519, 496, 496, 497, 496, 497, -519, -519, -519, -519, -519, -519, 496, 497, 498, 496, 496, 496, -519, -519, -519, -519, -519, 497, 496, 496, 497, 497, 496, -519, -519, -519, -519, -519, -519, 497, 497, 497, 497, 498, 496, -519, -519, -519, -519, -519, 497, 497, 497, 497, 496, 496, -519, -519, -519, -519, -519, -519, 497, 497, 497, 497, 496, -519, -519, -519, -519, -519, -519, 497, 496, 498, 496, 497, 497, -519, -519, -519, -519, -519, 497, 496, 496, 497, 496, 497, -519, -519, -519, -519, -519, -519, 496, 497, 497, 496, 497, -519, -519, -519, -519, -519, -519, 497, 496, 496, 496, 496, 497, -519, -519, -519, -519, -519, 496, 497, 496, 496, 496, 497, -519, -519, -519, -519, -519, -519, 496, 496, 497, 496, 496, 496, -519, -519, -519, -519, -519, 496, 496, 496, 497, 496, 497, -519, -519, -519, -519, -519, -519, 496, 496, 497, 496, 496, -519, -519, -519, -519, -519, -519, 497, 496, 496, 497, 496, 497, -519, -519, -519, -519, -519, 496, 497, 496, 497, 497, 498, -519, -519, -519, -519, -519, -519, 497, 496, 497, 497, 497, 497, -519, -519, -519, -519, -519, 496, 496, 497, 497, 496, 497, -519, -519, -519, -519, -519, -519, 497, 496, 497, 496]
,
[496, 496, 497, 496, 497, 496, -519, -519, -519, -519, -519, -519, 496, 497, 496, 496, 496, -519, -519, -519, -519, -519, -519, 496, 496, 496, 496, 497, 496, -519, -519, -519, -519, -519, 497, 497, 497, 496, 497, 497, -519, -519, -519, -519, -519, -519, 496, 496, 497, 496, 497, -519, -519, -519, -519, -519, -519, 496, 497, 498, 496, 496, 496, -519, -519, -519, -519, -519, 497, 496, 496, 497, 497, 496, -519, -519, -519, -519, -519, -519, 497, 497, 497, 497, 498, 496, -519, -519, -519, -519, -519, 497, 497, 497, 497, 496, 496, -519, -519, -519, -519, -519, -519, 497, 497, 497, 497, 496, -519, -519, -519, -519, -519, -519, 497, 496, 498, 496, 497, 497, -519, -519, -519, -519, -519, 497, 496, 496, 497, 496, 497, -519, -519, -519, -519, -519, -519, 496, 497, 497, 496, 497, -519, -519, -519, -519, -519, -519, 497, 496, 496, 496, 496, 497, -519, -519, -519, -519, -519, 496, 497, 496, 496, 496, 497, -519, -519, -519, -519, -519, -519, 496, 496, 497, 496, 496, 496, -519, -519, -519, -519, -519, 496, 496, 496, 497, 496, 497, -519, -519, -519, -519, -519, -519, 496, 496, 497, 496, 496, -519, -519, -519, -519, -519, -519, 497, 496, 496, 497, 496, 497, -519, -519, -519, -519, -519, 496, 497, 496, 497, 497, 498, -519, -519, -519, -519, -519, -519, 497, 496, 497, 497, 497, 497, -519, -519, -519, -519, -519, 496, 496, 497, 497, 496, 497, -519, -519, -519, -519, -519, -519, 497, 496, 497, 496]
,
[500, 499, 499, 500, -516, -516, -516, -516, -516, 500, 499, 500, 500, 500, 500, -516, -516, -516, -516, -516, -516, 500, 500, 500, 500, 501, -516, -516, -516, -516, -516, -516, 500, 500, 500, 499, 500, 500, -516, -516, -516, -516, -516, 499, 500, 500, 499, 499, 500, -516, -516, -516, -516, -516, -516, 501, 503, 505, 505, 505, -516, -516, -516, -516, -516, -516, 506, 505, 506, 505, 505, 503, -516, -516, -516, -516, -516, -516, 501, 500, 500, 500, 501, -516, -516, -516, -516, -516, -516, 500, 500, 500, 499, 500, 501, -516, -516, -516, -516, -516, 500, 500, 500, 500, 500, 501, -516, -516, -516, -516, -516, -516, 501, 500, 501, 501, 500, -516, -516, -516, -516, -516, -516, 504, 503, 502, 501, 500, 500, -516, -516, -516, -516, -516, -516, 501, 499, 500, 500, 500, -516, -516, -516, -516, -516, -516, 501, 500, 500, 500, 500, 501, -516, -516, -516, -516, -516, 500, 500, 500, 501, 500, 501, -516, -516, -516, -516, -516, -516, 500, 500, 500, 500, 499, -516, -516, -516, -516, -516, -516, 499, 499, 499, 500, 500, 500, -516, -516, -516, -516, -516, 499, 501, 500, 500, 500, 500, -516, -516, -516, -516, -516, -516, 500, 500, 500, 500, 500, 501, -516, -516, -516, -516, -516, 500, 500, 499, 500, 499, 499, -516, -516, -516, -516, -516, -516, 499, 500, 499, 500, 500, -516, -516, -516, -516, -516, -516, 500, 500, 500, 501, 500, 500, -516, -516, -516, -516, -516, 500, 501, 500, 500, 500, 500, 500]
,
[-504, -504, 511, 512, 511, 511, 512, 511, -504, -504, -504, -504, -504, -504, 511, 512, 511, 511, 512, -504, -504, -504, -504, -504, -504, 511, 512, 511, 513, 511, 511, -504, -504, -504, -504, -504, -504, 512, 511, 511, 512, 512, -504, -504, -504, -504, -504, -504, 512, 513, 512, 512, 512, 512, -504, -504, -504, -504, -504, 511, 513, 511, 512, 512, 511, -504, -504, -504, -504, -504, -504, 511, 512, 512, 512, 511, -504, -504, -504, -504, -504, -504, 511, 511, 512, 512, 511, 512, -504, -504, -504, -504, -504, -504, 511, 511, 511, 512, 512, -504, -504, -504, -504, -504, -504, 511, 513, 512, 511, 511, 511, -504, -504, -504, -504, -504, 511, 512, 511, 512, 512, 512, -504, -504, -504, -504, -504, -504, 511, 511, 511, 512, 511, -504, -504, -504, -504, -504, -504, 511, 512, 511, 511, 511, 511, -504, -504, -504, -504, -504, 426, 511, 511, 511, 512, 511, -504, -504, -504, -504, -504, -504, 511, 512, 511, 512, 512, 511, -504, -504, -504, -504, -504, 511, 511, 512, 511, 512, 511, -504, -504, -504, -504, -504, -504, 511, 512, 512, 512, 511, -504, -504, -504, -504, -504, -504, 511, 513, 511, 511, 512, 511, -504, -504, -504, -504, -504, 512, 511, 512, 512, 512, 512, -504, -504, -504, -504, -504, -504, 511, 512, 511, 512, 512, -504, -504, -504, -504, -504, -504, 511, 511, 513, 511, 512, 512, -504, -504, -504, -504, -504, 512, 512, 512, 511, 512, 511, -504, -504, -504, -504, -504, -504, 511, -504]
,
[511, -505, -505, -505, -505, -505, -505, 511, 511, 511, 511, 511, 511, -505, -505, -505, -505, -505, 510, 512, 511, 511, 511, 512, -505, -505, -505, -505, -505, -505, 511, 511, 511, 511, 511, 511, -505, -505, -505, -505, -505, 511, 511, 510, 512, 511, 511, -505, -505, -505, -505, -505, -505, 511, 511, 512, 511, 511, -505, -505, -505, -505, -505, -505, 511, 511, 511, 511, 511, 511, -505, -505, -505, -505, -505, 510, 512, 511, 511, 511, 512, -505, -505, -505, -505, -505, -505, 511, 511, 512, 511, 511, -505, -505, -505, -505, -505, -505, 511, 511, 511, 512, 511, 511, -505, -505, -505, -505, -505, -505, 511, 511, 512, 511, 511, -505, -505, -505, -505, -505, -505, 511, 511, 511, 511, 511, 512, -505, -505, -505, -505, -505, 511, 512, 511, 511, 511, 512, -505, -505, -505, -505, -505, -505, 511, 511, 511, 511, 511, -505, -505, -505, -505, -505, -505, 511, 511, 511, 512, 511, 511, -505, -505, -505, -505, -505, 511, 511, 511, 511, 512, 511, -505, -505, -505, -505, -505, -505, 511, 511, 511, 512, 511, -505, -505, -505, -505, -505, -505, 511, 512, 511, 511, 511, 511, -505, -505, -505, -505, -505, -505, 511, 512, 510, 511, 511, -505, -505, -505, -505, -505, -505, 511, 512, 511, 511, 511, 511, -505, -505, -505, -505, -505, 511, 511, 511, 511, 512, 511, -505, -505, -505, -505, -505, -505, 511, 511, 511, 512, 511, -486, -505, -505, -505, -505, -505, 511, 511, 511, 511, 511, 511, -505, -505, -505]
,
[497, 497, 496, 497, 497, 497, -519, -519, -519, -519, -519, -519, 497, 497, 497, 497, 497, -519, -519, -519, -519, -519, -519, 497, 496, 496, 497, 497, 497, -519, -519, -519, -519, -519, 496, 496, 497, 497, 497, 497, -519, -519, -519, -519, -519, -519, 497, 497, 497, 497, 497, 497, -519, -519, -519, -519, -519, 497, 497, 497, 498, 497, 497, -519, -519, -519, -519, -519, -519, 497, 497, 497, 497, 497, -519, -519, -519, -519, -519, -519, 497, 498, 497, 498, 497, 498, -519, -519, -519, -519, -519, 501, 499, 498, 497, 497, 497, -519, -519, -519, -519, -519, -519, 497, 496, 496, 498, 496, -519, -519, -519, -519, -519, -519, 496, 497, 496, 497, 497, 497, -519, -519, -519, -519, -519, 496, 497, 497, 497, 497, 496, -519, -519, -519, -519, -519, -519, 496, 497, 496, 497, 496, 497, -519, -519, -519, -519, -519, 496, 496, 496, 497, 497, 497, -519, -519, -519, -519, -519, -519, 497, 496, 496, 497, 496, -519, -519, -519, -519, -519, -519, 496, 497, 496, 496, 496, 496, -519, -519, -519, -519, -519, 497, 496, 497, 496, 497, 497, -519, -519, -519, -519, -519, -519, 496, 497, 497, 496, 497, 496, -519, -519, -519, -519, -519, 496, 497, 497, 496, 497, 497, -519, -519, -519, -519, -519, -519, 497, 497, 497, 498, 497, -519, -519, -519, -519, -519, -519, 497, 497, 496, 497, 497, 497, -519, -519, -519, -519, -519, 497, 496, 496, 497, 497, 496, -519, -519, -519, -519, -519, -519, 496, 497, 497, 497]
,
[-505, -505, -505, -505, -505, 511, 512, 511, 512, 511, 511, -505, -505, -505, -505, -505, 511, 512, 511, 511, 512, 511, -505, -505, -505, -505, -505, -505, 510, 512, 511, 512, 511, -505, -505, -505, -505, -505, -505, 511, 512, 511, 511, 511, 511, -505, -505, -505, -505, -505, 511, 511, 511, 511, 511, 511, -505, -505, -505, -505, -505, -505, 511, 511, 511, 511, 511, -505, -505, -505, -505, -505, -505, 511, 511, 511, 511, 512, 511, -505, -505, -505, -505, -505, 511, 511, 511, 511, 511, 511, -505, -505, -505, -505, -505, -505, 510, 512, 511, 511, 511, 510, -505, -505, -505, -505, -505, 511, 511, 512, 511, 511, 511, -505, -505, -505, -505, -505, -505, 511, 511, 511, 511, 511, -505, -505, -505, -505, -505, -505, 510, 511, 511, 511, 512, 511, -505, -505, -505, -505, -505, 511, 511, 511, 511, 511, 511, -505, -505, -505, -505, -505, -505, 511, 511, 511, 511, 512, 512, -505, -505, -505, -505, -505, 511, 511, 512, 511, 511, 511, -505, -505, -505, -505, -505, -505, 511, 511, 512, 511, 511, -505, -505, -505, -505, -505, -505, 511, 511, 511, 511, 512, 511, -505, -505, -505, -505, -505, 512, 511, 512, 511, 511, 511, -505, -505, -505, -505, -505, -505, 511, 511, 511, 511, 511, -505, -505, -505, -505, -505, -505, 511, 511, 512, 511, 511, 511, -505, -505, -505, -505, -505, -505, 511, 511, 511, 511, 511, -505, -505, -505, -505, -505, -505, 511, 511, 511, 511, 511, 511, -505, -505, -505, -505, -505]
]
a = []
for l1 in l:
print(plot_microphone(l1).phase[22])
highest index: 22 amplitude: 248.18988474780159 -273.26290000171826 highest index: 22 amplitude: 243.95990662027165 485.41656211426306 highest index: 233 amplitude: 246.19754587129057 lower index: 22 amplitude: 246.19754587129054 -22.866500664974016 highest index: 233 amplitude: 246.19754587129057 lower index: 22 amplitude: 246.19754587129054 -22.866500664974016 highest index: 22 amplitude: 250.2276065555683 219.3750337241841 highest index: 22 amplitude: 244.69358518635522 -281.4902276104525 highest index: 22 amplitude: 241.99555425747198 470.63243913724637 highest index: 22 amplitude: 248.73499432692617 -38.647594337460674 highest index: 233 amplitude: 249.15639148477104 lower index: 22 amplitude: 249.156391484771 -542.8798942241993
def plot_signal(l):
N = len(l)
s = sum(l) / N
y = [x - s for x in l]
f1 = np.fft.fft(y)
amplitude = np.sqrt([x.imag * x.imag + x.real * x.real for x in f1])
phase = [math.atan2(x.imag, x.real) for x in f1]
plt.plot([200 * x for x in phase])
plt.plot(amplitude / N * 10)
plt.plot(l)
index = [i for i, v in enumerate(amplitude) if v == max(amplitude)][0]
print(amplitude)
print("highest index: {} amplitude: {}".format(index, amplitude[index] / N))
if index > N / 2:
print("lower index: {} amplitude: {}".format(N - index, amplitude[N - index] / N))
vs = []
for index, m in ((22, 3), (30, 4), (37, 5)):
verschiebung = phase[index] / np.pi / m
vs.append(verschiebung)
print("verschiebung: {} amplitude: {} at index {}".format(verschiebung, amplitude[index] / N, index))
print("verschiebung: {} {}".format((vs[1] - vs[0]) % 1, (vs[2] - vs[0]) % 1))
x = plt.title('wave ftt')
x.phase = phase
x.amplitude = amplitude
return x
p = plot_signal([632, -390, -390, -390, 630, 631, 630, 629, -390, -390, -390, 626, 627, 626, 627, -390, -390, -390, -390, -390, -390, -390, -390, -390, -390, -390, -390, -390, -390, -390, -390, -390, -390, -390, -390, -390, -390, -390, -390, -390, -390, -390, -390, -390, -390, -390, -390, -390, -390, -390, -390, -390, -390, -390, 506, 632, 632, 632, 633, 632, -390, -390, -390, -390, -390, -390, 632, 632, 633, 633, 632, 632, -390, -390, -390, -390, -390, 627, 627, 627, 627, 627, 627, -390, -390, -390, -390, 627, 627, 627, 627, -390, -390, -390, -390, -390, 627, 627, 627, 628, -390, -390, -390, -390, 626, 627, 627, 627, 627, -390, -390, -390, -390, 627, 626, 627, 626, -390, -390, -390, -390, 626, 626, 627, -390, -390, -390, 626, 626, 626, 626, -390, -390, -390, 626, 627, 626, 626, -390, -390, -390, 626, 626, 627, -390, -390, -390, -390, 626, 627, 626, -390, -390, -390, -390, -390, -390, -390, -390, -390, -390, -390, -390, -390, -390, -390, -390, -390, -390, -390, -390, -390, -390, -390, -390, -390, -390, -390, -390, -390, -390, -390, -390, -390, -390, -390, -390, -390, -390, -390, -390, 626, 626, 627, 626, 627, 626, -390, -390, -390, -390, -390, 626, 626, 627, 626, 627, 626, -390, -390, -390, -390, -390, -390, 626, 626, 627, 626, 627, 626, -390, -390, -390, -390, 627, 627, 627, 627, -390, -390, -390, -390, 626, 627, 627, 627, 627, -390, -390, -390, -390, 627, 627, 627, 627, -390, -390, -390, -390, 626, 628, 627, 627, 627, 626]
)
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p = plot_signal([-619, -619, -619, -2, 397, 397, 398, -619, -619, -619, -619, -619, -619, -619, -619, -619, -619, -619, -619, -619, -619, -619, -619, -619, -619, -619, -619, -619, -619, -619, -619, -619, -619, -619, -619, -619, -619, -619, -619, -619, -619, -619, -619, -619, -619, -619, -619, 397, 397, 397, 397, 397, -619, -619, -619, -619, -619, -619, 396, 397, 396, 397, 397, 397, -619, -619, -619, -619, -619, -619, 398, 397, 397, 396, 397, -619, -619, -619, -619, -619, 396, 397, 397, 397, -619, -619, -619, -619, 397, 396, 396, 397, -619, -619, -619, -619, -619, 396, 397, 397, 397, -619, -619, -619, -619, 396, 397, 397, 396, -619, -619, -619, -619, 397, 397, 397, -619, -619, -619, -619, 397, 397, 397, -619, -619, -619, -619, 397, 397, 397, -619, -619, -619, 396, 397, 397, 397, -619, -619, -619, 397, 397, 397, 398, -619, -619, -619, -619, -619, -619, -619, -619, -619, -619, -619, -619, -619, -619, -619, -619, -619, -619, -619, -619, -619, -619, -619, -619, -619, -619, -619, -619, -619, -619, -619, -619, -619, -619, -619, -619, -619, -619, -619, 390, 397, 397, 397, 397, 397, -619, -619, -619, -619, -619, -619, 397, 397, 397, 397, 397, 397, -619, -619, -619, -619, -619, 396, 397, 397, 397, 397, 397, -619, -619, -619, -619, 397, 397, 397, 397, -619, -619, -619, -619, -619, 397, 397, 397, 397, -619, -619, -619, -619, 397, 397, 397, 397, 397, -619, -619, -619, -619, 397, 397, 397, 396, -619, -619, -619, -619, 396, 396, 397, -619, 397]
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highest index: 2 amplitude: 117.28110870997261 verschiebung: -0.08872880228958868 amplitude: 60.38744422574047 at index 22 verschiebung: 0.2051536109753647 amplitude: 109.60587974233454 at index 30 verschiebung: 0.18450547163660383 amplitude: 51.038075979413385 at index 37 verschiebung: 0.29388241326495335 0.2732342739261925
p = plot_signal([-406, -406, -406, -406, -406, -406, -406, -406, -406, -406, -406, -406, -406, -406, -406, -406, -406, -406, -406, -406, -406, -406, -406, -406, -406, -406, -406, 610, 611, 610, 610, 610, 610, -406, -406, -406, -406, -406, 610, 610, 609, 609, 610, 610, -406, -406, -406, -406, -406, -406, 610, 610, 610, 611, 609, -406, -406, -406, -406, -406, 611, 610, 610, 610, -406, -406, -406, -406, 609, 611, 610, 610, -108, -406, -406, -406, -406, 609, 610, 609, 609, -406, -406, -406, -406, 610, 609, 609, 610, 609, -406, -406, -406, 609, 609, 609, -406, -406, -406, -406, 609, 610, 609, -406, -406, -406, -406, 609, 610, 609, -406, -406, -406, 610, 610, 609, 609, -406, -406, -406, 609, 609, 610, 610, -406, -406, -406, -406, -406, -406, -406, -406, -406, -406, -406, -406, -406, -406, -406, -406, -406, -406, -406, -406, -406, -406, -406, -406, -406, -406, -406, -406, -406, -406, -406, -406, -406, -406, -406, -406, -406, -406, -406, -406, 609, 610, 609, 610, 610, -406, -406, -406, -406, -406, -406, 610, 610, 610, 610, 611, 610, -406, -406, -406, -406, -406, 610, 610, 610, 610, 610, 610, -406, -406, -406, -406, 609, 610, 610, 610, 610, -406, -406, -406, -406, 610, 610, 610, 610, -406, -406, -406, -406, 610, 610, 609, 610, 610, -406, -406, -406, -406, 610, 609, 610, 610, -406, -406, -406, -406, 609, 609, 610, -406, -406, -406, 610, 610, 609, 610, -406, -406, -406, 610, 609, 609, 610, -406, -406, -406, 609, 609, 610, 609, -406]
)
highest index: 2 amplitude: 107.84882298525632 verschiebung: -0.27979193907504846 amplitude: 66.32901675813514 at index 22 verschiebung: -0.12362068755353707 amplitude: 72.17694211044868 at index 30 verschiebung: 0.1731327502155841 amplitude: 64.14216554160377 at index 37 verschiebung: 0.1561712515215114 0.45292468929063256
p = plot_signal([-401, -401, -401, -401, -401, -401, -401, -401, -401, -401, -401, -401, -401, -401, -401, -401, -401, -401, -401, -401, -401, -401, -401, -401, -401, -401, -401, -401, 614, 614, 615, 614, 615, 615, -401, -401, -401, -401, -401, 614, 614, 615, 614, 614, 615, -401, -401, -401, -401, -401, -401, 614, 615, 614, 614, 615, -401, -401, -401, -401, -401, 615, 614, 614, 614, -401, -401, -401, -401, 614, 615, 615, 614, -401, -401, -401, -401, -401, 614, 614, 615, 614, -401, -401, -401, -401, 616, 615, 615, 615, 614, -401, -401, -401, 615, 615, 615, -401, -401, -401, -401, 615, 615, 615, -401, -401, -401, -401, 615, 615, 615, -401, -401, -401, 615, 615, 616, 615, -401, -401, -401, 615, 615, 615, 615, -401, -401, -401, -401, -401, -401, -401, -401, -401, -401, -401, -401, -401, -401, -401, -401, -401, -401, -401, -401, -401, -401, -401, -401, -401, -401, -401, -401, -401, -401, -401, -401, -401, -401, -401, -401, -401, -401, -401, -401, 615, 615, 615, 615, 615, -401, -401, -401, -401, -401, -401, 615, 615, 614, 615, 616, 615, -401, -401, -401, -401, -401, 614, 615, 614, 615, 616, 614, -401, -401, -401, -401, 614, 615, 615, 615, 615, -401, -401, -401, -401, 615, 615, 614, 614, -401, -401, -401, -401, 614, 615, 614, 614, 614, -401, -401, -401, -401, 614, 615, 616, 615, -401, -401, -401, -401, 614, 614, 615, -401, -401, -401, 614, 615, 614, 616, -401, -401, -401, 615, 615, 615, 614, -401, -401, -401, 615, 614, 614, -401]
)
highest index: 2 amplitude: 109.4661728361966 verschiebung: 0.33097748272388827 amplitude: 69.34319681296937 at index 22 verschiebung: -0.181695092274558 amplitude: 75.92481681888778 at index 30 verschiebung: 0.11749413092233532 amplitude: 60.907736549668115 at index 37 verschiebung: 0.4873274250015538 0.786516648198447