FqprVisualizations

class HSTB.kluster.modules.visualizations.FqprVisualizations(fqpr)

Bases: object

Visualizations in Matplotlib built on top of FQPR class. Includes animations of beam vectors and vessel orientation.

Processed fqpr_generation.Fqpr instance is passed in as argument

Methods Summary

plot_backscatter_sample()	After running process_backscatter, we get an image for the first ping to see the different components of the backscatter processing.
plot_sound_velocity_map([filter_casts_by_time])	Plot a latitutde/longitude overview of all multibeam lines within the current Fqpr time range and all applicable casts.
plot_sound_velocity_profiles([filter_by_time])	Get all the sound velocity profiles attached to this fqpr instance and plot the values by depth/sv.
plot_surface_sv_vs_profiles([filter_by_time])
plot_thu_sample()	After running calculate_total_uncertainty, images are saved of the average ping thu for the first 1000 pings.
plot_tvu_sample()	After running calculate_total_uncertainty, images are saved of the average ping tvu for the first 1000 pings.
soundings_plot_2d([mode, color_by, ...])	Plots a 2d representation of the acrosstrack/depth values generated by sv correct.
soundings_plot_3d([mode, color_by, ...])	Plots a 3d representation of the alongtrack/acrosstrack/depth values generated by sv correct.
visualize_beam_pointing_vectors([corrected])	Use matplotlib funcanimation to build animated representation of the beampointingvectors/traveltimes across time
visualize_orientation_vector([system_index])	Use matplotlib funcanimation to build animated representation of the transmitter/receiver across time

Methods Documentation

plot_backscatter_sample()

After running process_backscatter, we get an image for the first ping to see the different components of the backscatter processing. This method will show the image for you to see.

plot_sound_velocity_map(filter_casts_by_time=False)

Plot a latitutde/longitude overview of all multibeam lines within the current Fqpr time range and all applicable casts. Plot cast positions as a scatter plot on top of the multibeam lines.

Parameters

filter_casts_by_time (bool) – if True, will only include casts within the time range of the dataset (use if Fqpr.subset_by_time and you only want to show the casts within the time range of the subset)

plot_sound_velocity_profiles(filter_by_time=False)

Get all the sound velocity profiles attached to this fqpr instance and plot the values by depth/sv. If the fqpr instance is a subset (see fqpr_generation.Fqpr.subset_by_time) then only get the casts within the dataset time range (with a small buffer applied)

Parameters

filter_by_time (bool) – if True, will only include casts within the time range of the dataset (use if Fqpr.subset_by_time and you only want to show the casts within the time range of the subset)

plot_surface_sv_vs_profiles(filter_by_time=False)

plot_thu_sample()

After running calculate_total_uncertainty, images are saved of the average ping thu for the first 1000 pings. That image is saved within the ping zarr folder. This method will show the image for you to see.

plot_tvu_sample()

After running calculate_total_uncertainty, images are saved of the average ping tvu for the first 1000 pings. That image is saved within the ping zarr folder. This method will show the image for you to see.

soundings_plot_2d(mode='svcorr', color_by='depth', start_time=None, end_time=None)

Plots a 2d representation of the acrosstrack/depth values generated by sv correct. If sector is provided, isolates that sector. If a time is provided, isolates that time.

Parameters

• mode (str) – str, either ‘svcorr’ to plot the svcorrected offsets, or ‘georef’ to plot the georeferenced soundings

• color_by (str) – str, either ‘depth’ or ‘sector’

• start_time (Optional[float]) – start time in utc seconds, optional if you want to subset by time

• end_time (Optional[float]) – end time in utc seconds, optional if you want to subset by time

Returns

matplotlib.pyplot.figure instance

Return type

plt.Figure

soundings_plot_3d(mode='svcorr', color_by='depth', start_time=None, end_time=None)

Plots a 3d representation of the alongtrack/acrosstrack/depth values generated by sv correct. If a time is provided, isolates that time.

Parameters

• mode (str) – str, either ‘svcorr’ to plot the svcorrected offsets, or ‘georef’ to plot the georeferenced soundings

• color_by (str) – str, either ‘depth’ or ‘sector’

• start_time (Optional[float]) – start time in utc seconds, optional if you want to subset by time

• end_time (Optional[float]) – end time in utc seconds, optional if you want to subset by time

Returns

matplotlib axes object for plot

Return type

plt.Axes

visualize_beam_pointing_vectors(corrected=False)

Use matplotlib funcanimation to build animated representation of the beampointingvectors/traveltimes across time

if corrected is True uses the ‘corr_pointing_angle’ variable that is corrected for mounting angles/attitude, otherwise plots the raw ‘beampointingangle’ variable that is uncorrected.

Parameters

corrected (bool) – if True uses the ‘corr_pointing_angle’, else raw beam pointing angle ‘beampointingangle’

visualize_orientation_vector(system_index=0)

Use matplotlib funcanimation to build animated representation of the transmitter/receiver across time

Receiver orientation is based on attitude at the average time of receive (receive time differs across beams)

Parameters

system_index (int) – int, optional will automatically choose the first (only matters with dual head, which would have two systems)