coord_system=args.coordinate_system,
vert_ref=args.vertical_reference,
add_cast_files=args.cast_profiles,
show_progress=args.show_progress,
parallel_write=args.parallel_write,
error_files=args.error_files,
logfiles=args.export_log,
weekstart_year=args.weekstart_year,
weekstart_week=args.weekstart_week,
override_datum=args.coordinate_system,
max_gap_length=args.max_navigation_gap)
elif funcname == 'intel_processing':
intel_process(args.files,
outfold=args.output_folder,
coord_system=args.coordinate_system,
vert_ref=args.vertical_reference,
parallel_write=args.parallel_write,
force_coordinate_system=args.force_coordinate_system,
process_mode=args.process_mode)
elif funcname == 'intel_service':
intel_process_service(
args.folder,
outfold=args.output_folder,
coord_system=args.coordinate_system,
vert_ref=args.vertical_reference,
parallel_write=args.parallel_write,
force_coordinate_system=args.force_coordinate_system,
process_mode=args.process_mode)
elif funcname == 'convert':
convert_multibeam(args.files,
input_datum=args.input_datum,
# Examples related to changing, subsetting, filtering and saving data, last updated 2/23/2022, Kluster 0.8.10
# uses the multibeam file hstb_kluster/test_data/0009_20170523_181119_FA2806.all
# Written by Eric Younkin
import numpy as np
from HSTB.kluster.fqpr_convenience import reload_data
from HSTB.kluster.fqpr_intelligence import intel_process
from HSTB.kluster import kluster_variables
# we start with one of the preferred processing steps from the data_processing example
_, fq = intel_process(r"C:\data_dir\0009_20170523_181119_FA2806.all")
fq = fq[
0] # this should just be a list of one element if you have just one sonar/day of data, take the first one to get the data
# or we can just reload if you have data from before
fq = reload_data(r"C:\data_dir\em2040_40111_05_23_2017")
# Build out a polygon in geographic coordinates to just get a subset of data from this dataset (lon, lat)
polygon = np.array([[-122.47798556, 47.78949665], [-122.47798556, 47.78895117],
[-122.47771027, 47.78895117], [-122.47771027,
47.78949665]])
# return soundings gets you the variables used in Points View, these are all 1d arrays of the same length
head, x, y, z, tvu, rejected, pointtime, beam = fq.return_soundings_in_polygon(
polygon)
assert head.shape == x.shape == y.shape == z.shape == tvu.shape == rejected.shape == pointtime.shape == beam.shape
assert x.shape == (1911, )
# rejected array is actually an array of integers that are the sounding flags kluster uses for rejecting/accepting soundings
print(kluster_variables.amplitude_detect_flag) # added in kluster 0.8.10
print(kluster_variables.phase_detect_flag) # added in kluster 0.8.10
print(kluster_variables.rejected_flag)
print(kluster_variables.accepted_flag)
# when you drag in new multibeam data into Kluster, it generates a new conversion action and organizes data into:
# sonarmodelnumber_serialnumber_dateofsurvey
# which it gets from the multibeam data itself. This is the Kluster Intelligence module that will basically perform
# the convert_multibeam and process_multibeam actions for you, putting data in the right place and doing only those
# steps that are required. For this reason it is recommended that you use the intelligence module rather than
# the core processing routines described in 1 and 2. Learn more here: https://kluster.readthedocs.io/en/latest/indepth/intel.html
# the intel process command will perform just like if you were to drag in new files in Kluster. You just provide all the
# files that you want to add, and Kluster Intelligence determines the type of file, how to add it and what processing
# steps to take. Those steps are all performed and you get the Fqpr object back
# just like all the others, either provide a list of files...
_, fq = intel_process([
r"C:\data_dir\0009_20170523_181119_FA2806.all", r'C:\data_dir\sbet.out',
r'C:\data_dir\smrmsg.out', r'C:\data_dir\export_log.txt',
r'C:\data_dir\mysvpfile.svp'
],
coord_system='WGS84',
vert_ref='ellipse')
# a single file or a path to a directory full of files:
_, fq = intel_process(r"C:\data_dir")
# it will behave much like step 2, you probably won't notice a difference during the processing
# what you should notice is that the output directory is now a folder with the sonarmodelnumber_serialnumber_dateofsurvey of the file
# - note: fq is a list of the converted fqpr objects, since data is organized by modelnumber,etc., you might end up with multiple containers
fq[0].output_folder
# Out[10]: 'C:\\data_dir\\em2040_40111_05_23_2017'
# the last thing to mention is the intel_process_service, which combines intel_process with folder monitoring. The service
# will monitor a folder and add/process any files that you add to that directory (or are already in there)
_, fq = intel_process_service(r"C:\data_dir")
# this will lock up the console until you force it to quit