plt.plot(sun["ha"], sun["volts"])
plt.xlabel(r"Hour angle [h]", fontsize=18)
plt.ylabel(r"Power", fontsize=18)
plt.subplot(212)
trans = np.fft.fft(sun["volts"])
freqs = np.fft.fftfreq(len(trans), 2. * np.pi * 1.0 / 86164.)
plt.plot(np.fft.fftshift(freqs), np.fft.fftshift(abs(trans)**2))
plt.xlabel(r"Frequency [rad$^{-1}$]", fontsize=18)
plt.ylabel(r"Power", fontsize=18)
# Now we remove the dc offset, as well as high frequency noise
local_fringe_frequencies = fringe_freq(B, wl, sun["dec"], 2.*np.pi*sun["ha"]/24.)
bandpass = FourierFilter(min_freq=0.001, max_freq=max(local_fringe_frequencies))
sun["volts"] = np.real(bandpass(sun["t"], sun["volts"])[1])
sun.flatten_invalid_points()
sun["volts"] = sun.real_boxcar(200)
def boxcar(y, width):
""" Normalize each chunk of 200 points to the maximum over that range """
medianed = np.zeros(len(y))
for i, v in enumerate(y):
boxcar = y[max(0, i-width/2.):min(len(y), i+width/2.)]
medianed[i] = v - np.median(boxcar)
return medianed
'''
suncopy = sun.copyslice(3600, 7100)
suncopy["ha"] = suncopy["ha"]
positive_values = suncopy["volts"] >= 0.0
enveloper = FourierFilter(max_freq=0.01)
plt.figure()
plt.plot(B_to_try, s2s2, linewidth=2)
plt.xlabel(r"$B_y$ [m]", fontsize=18)
plt.ylabel(r"$\chi^2$", fontsize=18)
'''
# These frequencies are in rad^-1, so to apply the filter we must convert the time array to radians
bandpass = FourierFilter(min_freq=min_freq, max_freq=max_freq)
# crab.flatten_invalid_points()
# crab["volts"] = crab["volts"] / sorted(crab["volts"])[-10]
# crab.flatten_invalid_points()
# crab["volts"] = crab["volts"] / np.max(abs(crab["volts"]))
crab["volts"] = crab.real_boxcar(200)
plt.figure()
plt.subplot(211)
plt.plot(crab["ha"], crab["volts"])
plt.xlabel(r"Hour angle [h]", fontsize=18)
plt.ylabel(r"Power", fontsize=18)
plt.subplot(212)
trans = np.fft.fft(crab["volts"])
freqs = np.fft.fftfreq(len(trans), 2.*np.pi * 1.0 / 86164.)
plt.plot(np.fft.fftshift(freqs), np.fft.fftshift(abs(trans)**2))
plt.xlabel(r"Frequency [rad$^{-1}$]", fontsize=18)
plt.ylabel(r"Power", fontsize=18)
decs_to_try = np.arange(catalog_dec - np.deg2rad(10.0), catalog_dec + np.deg2rad(10.0), np.deg2rad(0.01))
