def generate_data_products():
'''
I need a variety of intermediate data products in order to create
the figures.
'''
TOT_bounds, VCD_bounds, THW_bounds = find_quiet_regions()
VCD_rawfile = WAIS + '/orig/xlob/VCD/JKB2g/DVD01a/RADnh3/bxds'
TOT_rawfile = WAIS + '/orig/xlob/TOT/JKB2d/X16a/RADnh3/bxds'
THW_rawfile = WAIS + '/orig/xlob/THW/SJB2/DRP02a/RADjh1/bxds1'
insamples = 3437
outsamples = 3200
channel = 2
blanking = 200
scale = 1000
num_sweeps = pik1_utils.get_num_traces(TOT_rawfile, insamples)
reference_chirp = pik1_utils.generate_reference_chirp()
def generate_data_products():
'''
I need a variety of intermediate data products in order to create
the figures.
'''
TOT_bounds, VCD_bounds, THW_bounds = find_quiet_regions()
VCD_rawfile = WAIS + '/orig/xlob/VCD/JKB2g/DVD01a/RADnh3/bxds'
TOT_rawfile = WAIS + '/orig/xlob/TOT/JKB2d/X16a/RADnh3/bxds'
THW_rawfile = WAIS + '/orig/xlob/THW/SJB2/DRP02a/RADjh1/bxds1'
insamples = 3437
outsamples = 3200
channel = 2
blanking = 200
scale = 1000
num_sweeps = pik1_utils.get_num_traces(TOT_rawfile, insamples)
reference_chirp = pik1_utils.generate_reference_chirp()
def plot_filter_freqs():
'''
Shows the various frequencies within the system, and what the filter does.
'''
# Each bin is one sample; sampled at 50Mhz
nbins = 3200
# Time of each sample, in us
tt = np.arange(0, nbins / 50., 1 / 50.)
# 70MHz local oscillator signal
y70 = np.sin(2 * np.pi * tt * 70)
fft70 = np.fft.fft(y70, n=nbins)
# 140MHz - LO's first harmonic
y140 = np.sin(2 * np.pi * tt * 140)
fft140 = np.fft.fft(y140, n=nbins)
# Reference chirp used for actual pik1 dechirping
ref_chirp = pik1_utils.generate_reference_chirp()
y_ref = np.zeros(nbins)
y_ref[100:100 + len(ref_chirp)] = ref_chirp
fft_ref = np.fft.fft(y_ref, n=nbins)
# theoretical reference chirp
theoretical_chirp = pik1_utils.generate_theoretical_chirp()
y_theory = np.zeros(nbins)
y_theory[100:100 + len(theoretical_chirp)] = theoretical_chirp
fft_theory = np.fft.fft(y_theory, n=nbins)
# Frequency of fft bins, in MHz
fftfreq = np.fft.fftfreq(nbins, 1 / 50.)
# ideal chirp frequency content
fft_ideal = [
1.0 if 2.5 <= np.abs(elem) <= 17.5 else 0.0 for elem in fftfreq
]
# Hanning filter
hfilter = pik1_utils.generate_hfilter(nbins)
fig = Figure((15, 3.5))
canvas = FigureCanvas(fig)
ax = fig.add_axes([0.05, 0.25, 0.9, 0.7])
ax.plot(fftfreq,
np.abs(fft_ref) / np.max(np.abs(fft_ref)),
linewidth=1,
color='black',
label='pik1 reference chirp')
ax.plot(fftfreq,
np.abs(fft_theory) / np.max(np.abs(fft_theory)),
linewidth=2,
color='darkgrey',
label='theoretical reference chirp')
ax.plot(fftfreq,
np.abs(fft_ideal) / np.max(np.abs(fft_ideal)),
linewidth=2,
color='lightgrey',
label='theoretical reference chirp')
ax.plot(fftfreq,
np.abs(fft70) / np.max(np.abs(fft70)),
linewidth=2,
color='blue',
label='FFT of 70MHz signal')
ax.plot(fftfreq,
np.abs(fft140) / np.max(np.abs(fft140)),
linewidth=2,
color='blue',
label='FFT of 1st harmonic of LO')
ax.plot(fftfreq,
hfilter / np.max(hfilter),
linewidth=2,
color='red',
label='Hamming filter')
ax.set_ylim([0, 1.05])
ax.set_xlim([-25, 25])
ax.set_xlabel('Frequency (MHz)', fontsize=24)
#ax.legend()
ax.tick_params(which='both',
bottom=True,
top=False,
left=False,
right=False,
labelbottom=True,
labeltop=False,
labelleft=False,
labelright=False,
labelsize=18)
for side in ['top', 'left', 'right']:
ax.spines[side].set_visible(False)
canvas.print_figure('../FinalReport/figures/filter_frequencies.png')
def plot_filter_freqs():
'''
Shows the various frequencies within the system, and what the filter does.
'''
# Each bin is one sample; sampled at 50Mhz
nbins = 3200
# Time of each sample, in us
tt = np.arange(0, nbins/50., 1/50.)
# 70MHz local oscillator signal
y70 = np.sin(2*np.pi*tt*70)
fft70 = np.fft.fft(y70, n=nbins)
# 140MHz - LO's first harmonic
y140 = np.sin(2*np.pi*tt*140)
fft140 = np.fft.fft(y140, n=nbins)
# Reference chirp used for actual pik1 dechirping
ref_chirp = pik1_utils.generate_reference_chirp()
y_ref = np.zeros(nbins)
y_ref[100:100+len(ref_chirp)] = ref_chirp
fft_ref = np.fft.fft(y_ref, n=nbins)
# theoretical reference chirp
theoretical_chirp = pik1_utils.generate_theoretical_chirp()
y_theory = np.zeros(nbins)
y_theory[100:100+len(theoretical_chirp)] = theoretical_chirp
fft_theory = np.fft.fft(y_theory, n=nbins)
# Frequency of fft bins, in MHz
fftfreq = np.fft.fftfreq(nbins, 1/50.)
# ideal chirp frequency content
fft_ideal = [1.0 if 2.5 <= np.abs(elem) <= 17.5 else 0.0 for elem in fftfreq]
# Hanning filter
hfilter = pik1_utils.generate_hfilter(nbins)
fig = Figure((15,3.5))
canvas = FigureCanvas(fig)
ax = fig.add_axes([0.05, 0.25, 0.9, 0.7])
ax.plot(fftfreq, np.abs(fft_ref)/np.max(np.abs(fft_ref)),
linewidth=1, color='black', label='pik1 reference chirp')
ax.plot(fftfreq, np.abs(fft_theory)/np.max(np.abs(fft_theory)),
linewidth=2, color='darkgrey', label='theoretical reference chirp')
ax.plot(fftfreq, np.abs(fft_ideal)/np.max(np.abs(fft_ideal)),
linewidth=2, color='lightgrey', label='theoretical reference chirp')
ax.plot(fftfreq, np.abs(fft70)/np.max(np.abs(fft70)),
linewidth=2, color='blue', label='FFT of 70MHz signal')
ax.plot(fftfreq, np.abs(fft140)/np.max(np.abs(fft140)),
linewidth=2, color='blue', label='FFT of 1st harmonic of LO')
ax.plot(fftfreq, hfilter/np.max(hfilter), linewidth=2, color='red',
label='Hamming filter')
ax.set_ylim([0, 1.05])
ax.set_xlim([-25, 25])
ax.set_xlabel('Frequency (MHz)', fontsize=24)
#ax.legend()
ax.tick_params(which='both', bottom=True, top=False, left=False, right=False,
labelbottom=True, labeltop=False, labelleft=False, labelright=False,
labelsize=18)
for side in ['top', 'left', 'right']:
ax.spines[side].set_visible(False)
canvas.print_figure('../FinalReport/figures/filter_frequencies.png')