def plot_radar(ax, filename, num_samples, bounds=None, clim=None):
'''
ax: axis to plot to
filename: radargram to be plotted (in same file format as pik1)
bounds: [min_trace, max_trace, min_sample, max_sample] of region to plot.
If None, plot whole transect.
clim: [min_counts, max_counts] for the data. If None, use
automatically-generated bounds.
'''
num_traces = pik1_utils.get_num_traces(filename, num_samples)
radar_data = np.memmap(filename,
'>i4',
mode='r',
shape=(num_traces, num_samples))
# TODO: How important is the radar skip for generating images?
if bounds is None:
plot_data = radar_data
else:
min_trace, max_trace, min_sample, max_sample = bounds
plot_data = radar_data[min_trace:max_trace, min_sample:max_sample]
radar_plot = ax.imshow(plot_data.T, aspect='auto', interpolation='nearest')
if bounds is not None:
min_trace, max_trace, min_sample, max_sample = bounds
radar_plot.set_extent([min_trace, max_trace, max_sample, min_sample])
radar_plot.set_cmap('gray')
if clim is not None:
radar_plot.set_clim(clim)
def plot_radar(ax, filename, num_samples, bounds=None, clim=None):
'''
ax: axis to plot to
filename: radargram to be plotted (in same file format as pik1)
bounds: [min_trace, max_trace, min_sample, max_sample] of region to plot.
If None, plot whole transect.
clim: [min_counts, max_counts] for the data. If None, use
automatically-generated bounds.
'''
num_traces = pik1_utils.get_num_traces(filename, num_samples)
radar_data = np.memmap(filename, '>i4', mode='r', shape=(num_traces, num_samples))
# TODO: How important is the radar skip for generating images?
if bounds is None:
plot_data = radar_data
else:
min_trace, max_trace, min_sample, max_sample = bounds
plot_data = radar_data[min_trace:max_trace, min_sample:max_sample]
radar_plot = ax.imshow(plot_data.T, aspect='auto', interpolation='nearest')
if bounds is not None:
min_trace, max_trace, min_sample, max_sample = bounds
radar_plot.set_extent([min_trace, max_trace, max_sample, min_sample])
radar_plot.set_cmap('gray')
if clim is not None:
radar_plot.set_clim(clim)
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_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')
