def plot_trace():
# Trace 3900 in pik1 looks pretty nice.
trace_idx = 3900
num_in_samples = 3437
num_out_samples = 3200
raw_y = 2 #(where to plot the raw data ...)
raw_filename = WAIS + '/orig/xlob/TOT/JKB2d/X16a/RADnh3/bxds'
# For plotting, I tried raw dB of trace ... I don't like it as much
# If I try it again, gotta be careful - abs(elem) can cause overflow errors if we don't cast to float first!
sweep_gen = pik1_utils.load_raw_nh3_subset_gen(raw_filename, 2,
[50 * trace_idx],
num_in_samples,
num_out_samples, 0)
raw_trace = [elem for elem in sweep_gen][0]
raw_min = np.min(raw_trace)
raw_range = 1.0 * np.max(raw_trace) - np.min(raw_trace)
norm_raw_trace_ch2 = [
raw_y + (1.0 * elem - raw_min) / raw_range for elem in raw_trace
]
sweep_gen = pik1_utils.load_raw_nh3_subset_gen(raw_filename, 1,
[50 * trace_idx],
num_in_samples,
num_out_samples, 0)
raw_trace = [elem for elem in sweep_gen][0]
raw_min = np.min(raw_trace)
raw_range = 1.0 * np.max(raw_trace) - np.min(raw_trace)
norm_raw_trace_ch1 = [
raw_y + 1 + (1.0 * elem - raw_min) / raw_range for elem in raw_trace
]
pik1_filename = 'TOT_LO'
num_traces = pik1_utils.get_num_traces(pik1_filename, num_in_samples)
pik1_data = np.memmap(pik1_filename,
'>i4',
mode='r',
shape=(num_traces, num_out_samples))
processed_trace = pik1_data[trace_idx, :]
trace_min = np.min(processed_trace)
trace_range = np.max(processed_trace) - np.min(processed_trace)
norm_processed_trace = [
1.0 * (elem - trace_min) / trace_range for elem in processed_trace
]
fig = Figure((30, 15))
canvas = FigureCanvas(fig)
ax = fig.add_axes([0, 0, 1, 1])
ax.minorticks_off()
ax.tick_params(which='both',
bottom=False,
top=False,
left=False,
right=False,
labelbottom=False,
labeltop=False,
labelleft=False,
labelright=False)
for side in ['bottom', 'top', 'left', 'right']:
ax.spines[side].set_visible(False)
ax.set_xlim([0, 3200])
ylim = [0, raw_y + 2]
ax.set_ylim(ylim)
bar_y1 = 1.15
bar_y2 = 1.25
bar_y3 = 1.35
text_y1 = 1.0
text_y2 = 1.3
text_y3 = 1.4
fontsize = 50
xshift = 66 # WTF. Why do I have to offset the raw pulse to make it line up with the dechirped?
# reference chirp 50-100
ax.plot([50, 100], [1.65, 1.65], color='red', linewidth=15)
ax.text(25,
1.7,
'chirp',
fontsize=50,
color='red',
horizontalalignment='left',
verticalalignment='bottom')
# "main bang"
ax.plot([100, 540], [1.35, 1.35], '--', color='red', linewidth=15)
ax.plot([100, 400], [1.35, 1.35], color='red', linewidth=15)
ax.text(250,
1.4,
'main bang',
fontsize=50,
color='red',
horizontalalignment='center',
verticalalignment='bottom')
# hardware blanking from 1-134 (in theory ... I don't trust this from the resulting plots
ax.plot([xshift + 0, xshift + 134], [1.15, 1.15],
color='red',
linewidth=15)
ax.text(xshift,
0.9,
'HW blanking',
fontsize=50,
color='red',
horizontalalignment='left',
verticalalignment='bottom')
# software blanking from 1-200
ax.plot([0, 200], [0.85, 0.85], color='red', linewidth=15)
ax.text(25,
0.6,
'SW blanking',
fontsize=50,
color='red',
horizontalalignment='left',
verticalalignment='bottom')
# Surface at 555
ax.plot([555, 555], ylim, color='lightgray', linewidth=15)
ax.text(575,
1.3,
'ice surface',
fontsize=50,
color='black',
horizontalalignment='left',
verticalalignment='bottom')
# surface multiple at 950
ax.plot([950, 950], ylim, color='lightgray', linewidth=15)
ax.text(970,
0.9,
'surface \nmultiple',
fontsize=50,
color='black',
horizontalalignment='left',
verticalalignment='bottom')
# Crevasses at 1262
ax.plot([1262, 1262], ylim, color='lightgray', linewidth=15)
ax.text(1282,
1.7,
'crevasse',
fontsize=50,
color='black',
horizontalalignment='left',
verticalalignment='bottom')
# water at 1378
ax.plot([1378, 1378], ylim, color='lightgray', linewidth=15)
ax.text(1398,
0.85,
'water',
fontsize=50,
color='black',
horizontalalignment='left',
verticalalignment='bottom')
# off-nadir energy at 1400 - 1850
ax.plot([1400, 2100], [bar_y2, bar_y2], '--', color='red', linewidth=15)
ax.plot([1500, 1850], [bar_y2, bar_y2], color='red', linewidth=15)
ax.text(1750,
text_y2,
'off-nadir energy',
fontsize=50,
color='red',
horizontalalignment='center',
verticalalignment='bottom')
# bed multiple at 2204
ax.plot([2204, 2204], ylim, color='lightgray', linewidth=15)
ax.text(2224,
0.5,
'bed multiple',
fontsize=50,
color='black',
horizontalalignment='left',
verticalalignment='bottom')
# noise at 2400 - 3200 (Can I call this receiver noise?)
ax.plot([2250, 3200], [bar_y2, bar_y2], '--', color='red', linewidth=15)
ax.plot([2400, 3200], [bar_y2, bar_y2], color='red', linewidth=15)
ax.text(2725,
text_y2,
'receiver noise',
fontsize=50,
color='red',
horizontalalignment='center',
verticalalignment='bottom')
# Ideas:
# * have 3 different y values, for things that apply to one, the other, or both?
# * shade the background to make it clearer what's going on?
# or at least draw light vertical grey bars for the point events?
# * Be sure to show where this trace comes from on a transect ...
# ax1 = fig.add_axes([0.0, 0.05, 0.5, 0.9])
# ax1.set_ylim([3200, 0])
# ax1.minorticks_off()
# ax1.tick_params(which='both',
# bottom=False, top=False, left=False, right=False,
# labelbottom=False, labeltop=False, labelleft=False, labelright=False)
# for side in ['bottom', 'top', 'left', 'right']:
# ax1.spines[side].set_visible(False)
# ax2 = fig.add_axes([0.5, 0.05, 0.5, 0.9])
# ax2.plot(processed_trace, range(len(processed_trace)), 'k', markersize=1)
# ax2.set_ylim([3200, 0])
# ax2.minorticks_on()
# ax2.tick_params(which='both', direction='inout', labelsize=18,
# bottom=False, top=False, left=True, right=False,
# labelbottom=False, labeltop=False, labelleft=True, labelright=False)
# for side in ['bottom', 'top', 'right']:
# ax2.spines[side].set_visible(False)
ax.plot(range(len(norm_processed_trace)),
norm_processed_trace,
'k.',
markersize=6)
#ax.plot(range(len(norm_processed_trace)), norm_processed_trace, color='lightgrey', linewidth=1)
ax.plot(np.arange(xshift, xshift + len(norm_raw_trace_ch2)),
norm_raw_trace_ch2,
'k.',
markersize=6)
ax.plot(np.arange(xshift, xshift + len(norm_raw_trace_ch1)),
norm_raw_trace_ch1,
'k.',
markersize=6)
canvas.print_figure('../FinalReport/figures/trace.jpg')
def plot_trace():
# Trace 3900 in pik1 looks pretty nice.
trace_idx = 3900
num_in_samples = 3437
num_out_samples = 3200
raw_y = 2 #(where to plot the raw data ...)
raw_filename = WAIS + '/orig/xlob/TOT/JKB2d/X16a/RADnh3/bxds'
# For plotting, I tried raw dB of trace ... I don't like it as much
# If I try it again, gotta be careful - abs(elem) can cause overflow errors if we don't cast to float first!
sweep_gen = pik1_utils.load_raw_nh3_subset_gen(
raw_filename, 2, [50*trace_idx], num_in_samples, num_out_samples, 0)
raw_trace = [elem for elem in sweep_gen][0]
raw_min = np.min(raw_trace)
raw_range = 1.0*np.max(raw_trace) - np.min(raw_trace)
norm_raw_trace_ch2 = [raw_y + (1.0*elem - raw_min) / raw_range for elem in raw_trace]
sweep_gen = pik1_utils.load_raw_nh3_subset_gen(
raw_filename, 1, [50*trace_idx], num_in_samples, num_out_samples, 0)
raw_trace = [elem for elem in sweep_gen][0]
raw_min = np.min(raw_trace)
raw_range = 1.0*np.max(raw_trace) - np.min(raw_trace)
norm_raw_trace_ch1 = [raw_y + 1 + (1.0*elem - raw_min) / raw_range for elem in raw_trace]
pik1_filename = 'TOT_LO'
num_traces = pik1_utils.get_num_traces(pik1_filename, num_in_samples)
pik1_data = np.memmap(pik1_filename, '>i4', mode='r', shape=(num_traces, num_out_samples))
processed_trace = pik1_data[trace_idx,:]
trace_min = np.min(processed_trace)
trace_range = np.max(processed_trace) - np.min(processed_trace)
norm_processed_trace = [1.0*(elem - trace_min) / trace_range for elem in processed_trace]
fig = Figure((30, 15))
canvas = FigureCanvas(fig)
ax = fig.add_axes([0, 0, 1, 1])
ax.minorticks_off()
ax.tick_params(which='both',
bottom=False, top=False, left=False, right=False,
labelbottom=False, labeltop=False, labelleft=False, labelright=False)
for side in ['bottom', 'top', 'left', 'right']:
ax.spines[side].set_visible(False)
ax.set_xlim([0, 3200])
ylim = [0, raw_y + 2]
ax.set_ylim(ylim)
bar_y1 = 1.15
bar_y2 = 1.25
bar_y3 = 1.35
text_y1 = 1.0
text_y2 = 1.3
text_y3 = 1.4
fontsize=50
xshift = 66 # WTF. Why do I have to offset the raw pulse to make it line up with the dechirped?
# reference chirp 50-100
ax.plot([50, 100], [1.65, 1.65], color='red', linewidth=15)
ax.text(25, 1.7, 'chirp', fontsize=50, color='red',
horizontalalignment='left', verticalalignment='bottom')
# "main bang"
ax.plot([100, 540], [1.35, 1.35], '--', color='red', linewidth=15)
ax.plot([100, 400], [1.35, 1.35], color='red', linewidth=15)
ax.text(250, 1.4, 'main bang', fontsize=50, color='red',
horizontalalignment='center', verticalalignment='bottom')
# hardware blanking from 1-134 (in theory ... I don't trust this from the resulting plots
ax.plot([xshift+0, xshift+134], [1.15, 1.15], color='red', linewidth=15)
ax.text(xshift, 0.9, 'HW blanking', fontsize=50, color='red',
horizontalalignment='left', verticalalignment='bottom')
# software blanking from 1-200
ax.plot([0, 200], [0.85, 0.85], color='red', linewidth=15)
ax.text(25, 0.6, 'SW blanking', fontsize=50, color='red',
horizontalalignment='left', verticalalignment='bottom')
# Surface at 555
ax.plot([555, 555], ylim, color='lightgray', linewidth=15)
ax.text(575, 1.3, 'ice surface', fontsize=50, color='black',
horizontalalignment='left', verticalalignment='bottom')
# surface multiple at 950
ax.plot([950, 950], ylim, color='lightgray', linewidth=15)
ax.text(970, 0.9, 'surface \nmultiple', fontsize=50, color='black',
horizontalalignment='left', verticalalignment='bottom')
# Crevasses at 1262
ax.plot([1262, 1262], ylim, color='lightgray', linewidth=15)
ax.text(1282, 1.7, 'crevasse', fontsize=50, color='black',
horizontalalignment='left', verticalalignment='bottom')
# water at 1378
ax.plot([1378, 1378], ylim, color='lightgray', linewidth=15)
ax.text(1398, 0.85, 'water', fontsize=50, color='black',
horizontalalignment='left', verticalalignment='bottom')
# off-nadir energy at 1400 - 1850
ax.plot([1400, 2100], [bar_y2, bar_y2], '--', color='red', linewidth=15)
ax.plot([1500, 1850], [bar_y2, bar_y2], color='red', linewidth=15)
ax.text(1750, text_y2, 'off-nadir energy', fontsize=50, color='red',
horizontalalignment='center', verticalalignment='bottom')
# bed multiple at 2204
ax.plot([2204, 2204], ylim, color='lightgray', linewidth=15)
ax.text(2224, 0.5, 'bed multiple', fontsize=50, color='black',
horizontalalignment='left', verticalalignment='bottom')
# noise at 2400 - 3200 (Can I call this receiver noise?)
ax.plot([2250, 3200], [bar_y2, bar_y2], '--', color='red', linewidth=15)
ax.plot([2400, 3200], [bar_y2, bar_y2], color='red', linewidth=15)
ax.text(2725, text_y2, 'receiver noise', fontsize=50, color='red',
horizontalalignment='center', verticalalignment='bottom')
# Ideas:
# * have 3 different y values, for things that apply to one, the other, or both?
# * shade the background to make it clearer what's going on?
# or at least draw light vertical grey bars for the point events?
# * Be sure to show where this trace comes from on a transect ...
# ax1 = fig.add_axes([0.0, 0.05, 0.5, 0.9])
# ax1.set_ylim([3200, 0])
# ax1.minorticks_off()
# ax1.tick_params(which='both',
# bottom=False, top=False, left=False, right=False,
# labelbottom=False, labeltop=False, labelleft=False, labelright=False)
# for side in ['bottom', 'top', 'left', 'right']:
# ax1.spines[side].set_visible(False)
# ax2 = fig.add_axes([0.5, 0.05, 0.5, 0.9])
# ax2.plot(processed_trace, range(len(processed_trace)), 'k', markersize=1)
# ax2.set_ylim([3200, 0])
# ax2.minorticks_on()
# ax2.tick_params(which='both', direction='inout', labelsize=18,
# bottom=False, top=False, left=True, right=False,
# labelbottom=False, labeltop=False, labelleft=True, labelright=False)
# for side in ['bottom', 'top', 'right']:
# ax2.spines[side].set_visible(False)
ax.plot(range(len(norm_processed_trace)), norm_processed_trace, 'k.', markersize=6)
#ax.plot(range(len(norm_processed_trace)), norm_processed_trace, color='lightgrey', linewidth=1)
ax.plot(np.arange(xshift, xshift+len(norm_raw_trace_ch2)), norm_raw_trace_ch2, 'k.', markersize=6)
ax.plot(np.arange(xshift, xshift+len(norm_raw_trace_ch1)), norm_raw_trace_ch1, 'k.', markersize=6)
canvas.print_figure('../FinalReport/figures/trace.jpg')
def plot_LO_fft():
# pik1_utils does all of this ...
filename = WAIS + '/orig/xlob/TOT/JKB2d/X16a/RADnh3/bxds'
trace_start, trace_end = 405450, 415450 # This is 8109 - 8309 in pik1 traces
correction = pik1_utils.find_LO_params(filename, 2, 3437, trace_start,
trace_end)
print "LO correction: ", correction
print "mag = %r, phs = %r" % (np.abs(correction), np.angle(correction))
# Plot the fft of the full traces
input_sweeps = pik1_utils.load_raw_nh3_subset_gen(
filename, 2, np.arange(trace_start, trace_end), 3437, 3200, 200)
input_stacked = pik1_utils.coherent_stack_gen(input_sweeps, 50)
radar_data = np.array([trace for trace in input_stacked])
radar_fft = np.fft.fft(radar_data, n=3200)
fftfreq3200 = np.fft.fftfreq(3200, 1 / 50.)
fig2 = Figure((10, 8))
canvas2 = FigureCanvas(fig2)
ax2 = fig2.add_axes([0, 0, 1, 1])
ax2.imshow(np.abs(radar_fft), cmap='gray', aspect='auto')
canvas2.print_figure('../FinalReport/figures/data_fft.jpg')
# Plot the fft of the bottom portion only
input_sweeps = pik1_utils.load_raw_nh3_subset_gen(
filename, 2, np.arange(trace_start, trace_end), 3437, 3437, 200)
input_stacked = pik1_utils.coherent_stack_gen(input_sweeps, 50)
noise_data = np.array([trace[-800:] for trace in input_stacked])
noise_fft = np.fft.fft(noise_data, n=800)
fftfreq800 = np.fft.fftfreq(800, 1 / 50.)
fig3 = Figure((10, 8))
canvas3 = FigureCanvas(fig3)
ax3 = fig3.add_axes([0, 0, 1, 1])
ax3.imshow(np.abs(noise_fft), cmap='gray', aspect='auto')
canvas3.print_figure('../FinalReport/figures/noise_fft.jpg')
# TODO: Maybe just to nice clean line plots showing full transect and bottom?
fig4 = Figure((15, 5))
canvas4 = FigureCanvas(fig4)
ax4 = fig4.add_axes([0.01, 0.2, 0.98, 0.8])
abs_noise_fft = np.sum(np.abs(noise_fft), axis=0)
# cancel out constant term.
abs_noise_fft[0] = 0
plot_noise_fft = abs_noise_fft / np.max(abs_noise_fft)
ax4.plot(fftfreq800[1:400], plot_noise_fft[1:400], color='black')
ax4.plot(fftfreq800[401:800], plot_noise_fft[401:800], color='black')
# Cancel out the two biggest peaks.
abs_noise_fft[160] = 0
abs_noise_fft[640] = 0
plot_noise_fft2 = abs_noise_fft / np.max(abs_noise_fft)
ax4.plot(fftfreq800[1:400], plot_noise_fft2[1:400], color='darkgrey')
ax4.plot(fftfreq800[401:800], plot_noise_fft2[401:800], color='darkgrey')
abs_radar_fft = np.sum(np.abs(radar_fft), axis=0)
plot_radar_fft = abs_radar_fft / np.max(abs_radar_fft)
# Plot in two chunks to avoid the awkward line across the figure.
ax4.plot(fftfreq3200[1:1600], plot_radar_fft[1:1600], color='red')
ax4.plot(fftfreq3200[1601:3200], plot_radar_fft[1601:3200], color='red')
ax.set_ylim([0, 1.05])
ax4.set_xlim([-25, 25])
ax4.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']:
ax4.spines[side].set_visible(False)
ax4.set_xlabel('Frequency (MHz)', fontsize=24)
canvas4.print_figure('../FinalReport/figures/LO_fft.jpg')
def plot_LO_fft():
# pik1_utils does all of this ...
filename = WAIS + '/orig/xlob/TOT/JKB2d/X16a/RADnh3/bxds'
trace_start, trace_end = 405450, 415450 # This is 8109 - 8309 in pik1 traces
correction = pik1_utils.find_LO_params(filename, 2, 3437, trace_start, trace_end)
print "LO correction: ", correction
print "mag = %r, phs = %r" % (np.abs(correction), np.angle(correction))
# Plot the fft of the full traces
input_sweeps = pik1_utils.load_raw_nh3_subset_gen(
filename, 2, np.arange(trace_start, trace_end), 3437, 3200, 200)
input_stacked = pik1_utils.coherent_stack_gen(input_sweeps, 50)
radar_data = np.array([trace for trace in input_stacked])
radar_fft = np.fft.fft(radar_data, n=3200)
fftfreq3200 = np.fft.fftfreq(3200, 1/50.)
fig2 = Figure((10, 8))
canvas2 = FigureCanvas(fig2)
ax2 = fig2.add_axes([0, 0, 1, 1])
ax2.imshow(np.abs(radar_fft), cmap='gray', aspect='auto')
canvas2.print_figure('../FinalReport/figures/data_fft.jpg')
# Plot the fft of the bottom portion only
input_sweeps = pik1_utils.load_raw_nh3_subset_gen(
filename, 2, np.arange(trace_start, trace_end), 3437, 3437, 200)
input_stacked = pik1_utils.coherent_stack_gen(input_sweeps, 50)
noise_data = np.array([trace[-800:] for trace in input_stacked])
noise_fft = np.fft.fft(noise_data, n=800)
fftfreq800 = np.fft.fftfreq(800, 1/50.)
fig3 = Figure((10, 8))
canvas3 = FigureCanvas(fig3)
ax3 = fig3.add_axes([0, 0, 1, 1])
ax3.imshow(np.abs(noise_fft), cmap='gray', aspect='auto')
canvas3.print_figure('../FinalReport/figures/noise_fft.jpg')
# TODO: Maybe just to nice clean line plots showing full transect and bottom?
fig4 = Figure((15, 5))
canvas4 = FigureCanvas(fig4)
ax4 = fig4.add_axes([0.01, 0.2, 0.98, 0.8])
abs_noise_fft = np.sum(np.abs(noise_fft), axis=0)
# cancel out constant term.
abs_noise_fft[0] = 0
plot_noise_fft = abs_noise_fft/np.max(abs_noise_fft)
ax4.plot(fftfreq800[1:400], plot_noise_fft[1:400], color='black')
ax4.plot(fftfreq800[401:800], plot_noise_fft[401:800], color='black')
# Cancel out the two biggest peaks.
abs_noise_fft[160] = 0
abs_noise_fft[640] = 0
plot_noise_fft2 = abs_noise_fft/np.max(abs_noise_fft)
ax4.plot(fftfreq800[1:400], plot_noise_fft2[1:400], color='darkgrey')
ax4.plot(fftfreq800[401:800], plot_noise_fft2[401:800], color='darkgrey')
abs_radar_fft = np.sum(np.abs(radar_fft), axis=0)
plot_radar_fft = abs_radar_fft/np.max(abs_radar_fft)
# Plot in two chunks to avoid the awkward line across the figure.
ax4.plot(fftfreq3200[1:1600], plot_radar_fft[1:1600], color='red')
ax4.plot(fftfreq3200[1601:3200], plot_radar_fft[1601:3200], color='red')
ax.set_ylim([0, 1.05])
ax4.set_xlim([-25, 25])
ax4.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']:
ax4.spines[side].set_visible(False)
ax4.set_xlabel('Frequency (MHz)', fontsize=24)
canvas4.print_figure('../FinalReport/figures/LO_fft.jpg')
