s = fakesignal.evaluate(t) + 0.05*np.random.randn(t.size)
# subtract average, random numbers may not average to 0
s -= np.average(s)
s_original = np.zeros_like(s)
s_original[:] = s[:]
# cosine bell
hann = np.hanning(s.size)
# Need to know Nyquist (more or less)
nyuquist = 0.5/((t[1] - t[0]))
fittedsignal = seismo.fitting.signal()
freqs0, amps0 = seismo.deeming(t, s)
noise = 4*np.median(amps0)
# Find and subtract signal, until no signals above 4*median amplitude in
# DFT is found
while True:
# calculate the DFT
# apply hanning / cosine bell first
freqs, amps = seismo.deeming(t, s)
# find peak and fit it
fmax, amax = seismo.timeseries.find_peak(freqs, amps)
if amax < noise:
break
fittedsignal.add_component(seismo.fitting.sinewave(amax, fmax, 3.14, 0))
s = fakesignal.evaluate(t) + 0.05 * np.random.randn(t.size)
# subtract average, random numbers may not average to 0
s -= np.average(s)
s_original = np.zeros_like(s)
s_original[:] = s[:]
# cosine bell
hann = np.hanning(s.size)
# Need to know Nyquist (more or less)
nyuquist = 0.5 / ((t[1] - t[0]))
fittedsignal = seismo.fitting.signal()
freqs0, amps0 = seismo.deeming(t, s)
noise = 4 * np.median(amps0)
# Find and subtract signal, until no signals above 4*median amplitude in
# DFT is found
while True:
# calculate the DFT
# apply hanning / cosine bell first
freqs, amps = seismo.deeming(t, s)
# find peak and fit it
fmax, amax = seismo.timeseries.find_peak(freqs, amps)
if amax < noise:
break
fittedsignal.add_component(seismo.fitting.sinewave(
import matplotlib
from matplotlib.pyplot import subplot, plot, show
from numpy import linspace, sin, pi
import seismo
matplotlib.style.use('ggplot')
x = linspace(0, 0.05, 500)
y = 0.6*sin(2*pi*240*x)\
+ 0.15*sin(2*pi*1303*x + 0.4)\
+ 0.1*sin(2*pi*3000*x)
f, a = seismo.deeming(x, y)
subplot(211)
plot(x, y, '.')
subplot(212)
plot(f, a)
show()
component = seismo.sinewave(amplitude, frequency, phase, 0)
# add component to signal
signal.add_component(component)
# evaluate signal at times
s = signal.evaluate(t) + 0.025*np.random.randn(t.size)
# now calculate the DFT
# Need to know Nyquist (more or less)
nyuquist = 0.5/((t[1] - t[0]))
# need to know freq resolution and oversample
fres = 1.0/(t[-1] - t[0])
freqs = np.linspace(0, nyuquist, 5*int(nyuquist/fres))
freqs, amps = seismo.deeming(t, s, freqs)
fig = plt.figure()
# make some plots
plt.subplot(211)
plt.plot(t, s, '.')
plt.xlabel("Time (days)")
plt.ylabel("Magnitude")
ymin, ymax = plt.ylim()
plt.ylim(2*ymax, 2*ymin)
plt.subplot(212)
plt.plot(freqs, amps)
plt.xlabel(r"Frequency (day$^{-1}$)")
plt.ylabel("Amplitude (mag)")
component = seismo.sinewave(amplitude, frequency, phase, 0)
# add component to signal
signal.add_component(component)
# evaluate signal at times
s = signal.evaluate(t) + 0.025 * np.random.randn(t.size)
# now calculate the DFT
# Need to know Nyquist (more or less)
nyuquist = 0.5 / ((t[1] - t[0]))
# need to know freq resolution and oversample
fres = 1.0 / (t[-1] - t[0])
freqs = np.linspace(0, nyuquist, 5 * int(nyuquist / fres))
freqs, amps = seismo.deeming(t, s, freqs)
fig = plt.figure()
# make some plots
plt.subplot(211)
plt.plot(t, s, '.')
plt.xlabel("Time (days)")
plt.ylabel("Magnitude")
ymin, ymax = plt.ylim()
plt.ylim(2 * ymax, 2 * ymin)
plt.subplot(212)
plt.plot(freqs, amps)
plt.xlabel(r"Frequency (day$^{-1}$)")
plt.ylabel("Amplitude (mag)")
