def txgrpshift(infile, outfile, subtri, shift, grpmap):
'''
Open the input file infile as a WaveformSet and apply the specified
nonzero integer sample shift (positive is a time advance) to all
transmissions from subtriangle subtri. The modified WaveformSet will be
stored in outfile or, if outfile is None or an empty string, back to
infile.
If grpmap is not None, it specifies the location of a transmit-group
file that is used to map subtriangle indices to transmission numbers.
The grpmap must be provided if WaveformSet(infile).txgrps is not None
and must be omitted if WaveformSet(infile).txgrps is None.
'''
# Validate numeric inputs
try:
subtri = subtridx(subtri)
except (argparse.ArgumentTypeError, ValueError) as e:
raise ValueError('Invalid subtriangle index: %s' % (e, ))
try:
shift = nonzero(shift)
except (argparse.ArgumentTypeError, ValueError) as e:
raise ValueError('Invalid shift index: %s' % (e, ))
if not outfile:
outfile = infile
print('Will overwrite input file', infile)
else:
print('Will create new input file', outfile)
wset = WaveformSet.load(infile)
if wset.txgrps and not grpmap:
raise ValueError(
'Argument "grpmap" required when input uses transmit groups')
# Attempt to assign a group map
if grpmap: wset.groupmap = loadkeymat(grpmap)
# Map transmit subtriangle indices to record rows
txrows = [
wset.tx2row(wset.element2tx(i))
for i in range(64 * subtri, 64 * (subtri + 1))
]
# Perform the shifts for each receive-channel record
for hdr, data in wset.allrecords():
# Copy the tail of the data
tail = data[txrows, shift:].copy()
# Now do the cyclic shift
data[txrows, -shift:] = data[txrows, :shift]
data[txrows, :-shift] = tail
# Store the record (no need to make another copy)
wset.setrecord(hdr, data, copy=False)
# Store the output
wset.store(outfile)
def getatimes(atarg, freq=1, scalar=True, cols=None):
'''
Given a list of files or globs, try to open arrival-time maps matching
the globs with habis.formats.loadkeymat() and pull the columns
specified in the sequence cols. If cols is None, all columns will be
picked. Keys of each map should be transmit-receive pairs (t, r).
Files are loaded in lexical order. If the same key is present in
multiple files, the values for that key will a concatenation of the
values for individual files (each considered as a list) that preserves
the lexical ordering.
If the lengths of value lists for keys in the composite arrivla-time
map, only those keys with maximum-length values will be retained.
The times are scaled by the frequency to convert the times to samples.
If scalar is True, values in the returned map will be scalars if a
single column is pulled. Otherwise, the returned values will always be
arrays.
'''
# Try to load the files one-by-one
atfiles = sorted(matchfiles(atarg, forcematch=True))
# Concatenate values to accommodate repeat keys, track max column count
ncols = 0
atmap = defaultdict(list)
for atfile in atfiles:
for k, v in loadkeymat(atfile, scalar=False, nkeys=2).items():
atmap[k].extend(vv for vv in v)
ncols = max(ncols, len(atmap[k]))
if cols is None:
acols = list(range(ncols))
else:
acols = cols
print(f'Using columns {acols} from arrival-time records')
if scalar:
if len(acols) != 1:
raise ValueError(
'Scalar arrival-time map requires a single column specification'
)
acols = acols[0]
return {
k: freq * np.array(v)[acols]
for k, v in atmap.items() if len(v) == ncols
}
parser.add_argument('-b' ,'--backscatter', action='store_true',
help='Extract backscatter waveforms, not arbitrary T-R pairs')
parser.add_argument('-o', '--output', type=str, default=None,
help='Output file (default: replace extension with extract.wmz)')
parser.add_argument('inputs', type=str, nargs='+',
help='Input WaveformMap files from which to extract')
args = parser.parse_args(sys.argv[1:])
# Try to read all input WaveformMap files
infiles = matchfiles(args.inputs)
# Read a defined receive-to-transmit-list map
if args.trmap: args.trmap = loadkeymat(args.trmap, scalar=False)
# At first, clobber the output
append = False
for infile in infiles:
wmap = WaveformMap.load(infile)
# Build the appropriate subset of the WaveformMap
if not args.backscatter: wvs = trextract(wmap, args.trmap, args.random)
else: wvs = ((k, v) for k, v in wmap.items() if k[0] == k[1])
omap = WaveformMap(wvs)
if args.output:
# Save to common output and switch to append mode
omap.store(args.output, compression=args.compression, append=append)
def exdelayEngine(config):
'''
Use positions of elements and reflectors specified in the provided
config, combined with a specified sound speed and reflector radius, to
estimate the round-trip arrival times from every element to the
reflector and back.
'''
msection = 'measurement'
esection = 'exdelays'
try:
# Try to grab the input and output files
eltfiles = config.getlist(esection, 'elements')
rflfile = config.get(esection, 'reflectors')
timefile = config.get(esection, 'timefile')
except Exception as e:
err = 'Configuration must specify elements, reflectors and timefile in [%s]' % esection
raise HabisConfigError.fromException(err, e)
# Grab the sound speed and reflector radius
try:
c = config.get(msection, 'c', mapper=float)
r = config.get(msection, 'radius', mapper=float)
except Exception as e:
err = 'Configuration must specify c and radius in [%s]' % msection
raise HabisConfigError.fromException(err, e)
try:
# Read an optional global time offset
offset = config.get(esection, 'offset', mapper=float, default=0.)
except Exception as e:
err = 'Invalid optional offset in [%s]' % esection
raise HabisConfigError.fromException(err, e)
# Read the element and reflector positions
eltspos = dict(kp for efile in eltfiles
for kp in loadkeymat(efile).items())
reflpos = np.loadtxt(rflfile, ndmin=2)
nrefl, nrdim = reflpos.shape
times = {}
for elt, epos in eltspos.items():
nedim = len(epos)
if not nedim <= nrdim <= nedim + 2:
raise ValueError(
'Incompatible reflector and element dimensionalities')
# Determine one-way distances between element and reflector centers
dx = norm(epos[np.newaxis, :] - reflpos[:, :nedim], axis=-1)
# Use encoded wave speed if possible, otherwise use global speed
try:
lc = reflpos[:, nedim]
except IndexError:
lc = c
# Use encoded radius if possible, otherwise use global radius
try:
lr = reflpos[:, nedim + 1]
except IndexError:
lr = r
# Convert distances to round-trip arrival times
times[elt, elt] = 2 * (dx - lr) / lc + offset
# Save the estimated arrival times
savez_keymat(timefile, times)
type=str,
help='Integral output, keymap or binary matrix (with -c)')
args = parser.parse_args(sys.argv[1:])
# Load the tracer configuration
try:
config = HabisConfigParser(args.tracer)
except Exception as e:
err = f'Unable to load configuration {args.tracer}'
raise HabisConfigError.fromException(err, e)
tracer = PathTracer.fromconf(config)
# Load the element coordinates and target list
elements = loadkeymat(args.elements)
if args.trlist:
# Load the r-[t] keymap and flatten to a trlist
targets = loadkeymat(args.targets)
targets = [(t, r) for r, tl in targets.items() for t in tl]
else:
targets = mio.readbmat(args.targets)
s = np.load(args.slowness)
comm = MPI.COMM_WORLD
rank, size = comm.rank, comm.size
args.quiet = rank or args.quiet
times = tracetimes(tracer, s, elements, targets[rank::size], args.trlist,
def atimesEngine(config):
'''
Use habis.trilateration.ArrivalTimeFinder to determine a set of
round-trip arrival times from a set of one-to-many multipath arrival
times. Multipath arrival times are computed as the maximum of
cross-correlation with a reference pulse, plus some constant offset.
'''
asec = 'atimes'
msec = 'measurement'
ssec = 'sampling'
kwargs = {}
def _throw(msg, e, sec=None):
if not sec: sec = asec
raise HabisConfigError.fromException(f'{msg} in [{sec}]', e)
try:
# Read all target input lists
targets = sorted(k for k in config.options(asec)
if k.startswith('target'))
targetfiles = OrderedDict()
for target in targets:
targetfiles[target] = matchfiles(config.getlist(asec, target))
if len(targetfiles[target]) < 1:
raise HabisConfigError(f'Key {target} matches no inputs')
except Exception as e:
_throw('Configuration must specify at least one unique "target" key',
e)
try:
efiles = config.getlist(asec, 'elements', default=None)
if efiles:
efiles = matchfiles(efiles)
kwargs['elements'] = loadmatlist(efiles, nkeys=1)
except Exception as e:
_throw('Invalid optional elements', e)
# Grab the reference file
try:
reffile = config.get(msec, 'reference', default=None)
except Exception as e:
_throw('Invalid optional reference', e, msec)
# Grab the output file
try:
outfile = config.get(asec, 'outfile')
except Exception as e:
_throw('Configuration must specify outfile', e)
try:
# Grab the number of processes to use (optional)
nproc = config.get('general',
'nproc',
mapper=int,
failfunc=process.preferred_process_count)
except Exception as e:
_throw('Invalid optional nproc', e, 'general')
try:
# Determine the sampling period and a global temporal offset
dt = config.get(ssec, 'period', mapper=float)
t0 = config.get(ssec, 'offset', mapper=float)
except Exception as e:
_throw('Configuration must specify period and offset', e, ssec)
# Override the number of samples in WaveformMap
try:
kwargs['nsamp'] = config.get(ssec, 'nsamp', mapper=int)
except HabisNoOptionError:
pass
except Exception as e:
_throw('Invalid optional nsamp', e, ssec)
# Determine the oversampling rate to use when cross-correlating
try:
osamp = config.get(ssec, 'osamp', mapper=int, default=1)
except Exception as e:
_throw('Invalid optional osamp', e, ssec)
try:
neighbors = config.get(asec, 'neighbors', default=None)
if neighbors:
kwargs['neighbors'] = loadkeymat(neighbors, dtype=int)
except Exception as e:
_throw('Invalid optional neighbors', e)
# Determine the range of elements to use; default to all (as None)
try:
kwargs['minsnr'] = config.getlist(asec, 'minsnr', mapper=int)
except HabisNoOptionError:
pass
except Exception as e:
_throw('Invalid optional minsnr', e)
# Determine a temporal window to apply before finding delays
try:
kwargs['window'] = config.get(asec, 'window')
except HabisNoOptionError:
pass
except Exception as e:
_throw('Invalid optional window', e)
# Determine an energy leakage threshold
try:
kwargs['eleak'] = config.get(asec, 'eleak', mapper=float)
except HabisNoOptionError:
pass
except Exception as e:
_throw('Invalid optional eleak', e)
# Determine a temporal window to apply before finding delays
try:
kwargs['bandpass'] = config.get(asec, 'bandpass')
except HabisNoOptionError:
pass
except Exception as e:
_throw('Invalid optional bandpass', e)
# Determine peak-selection criteria
try:
kwargs['peaks'] = config.get(asec, 'peaks')
except HabisNoOptionError:
pass
except Exception as e:
_throw('Invalid optional peaks', e)
# Determine IMER criteria
try:
kwargs['imer'] = config.get(asec, 'imer')
except HabisNoOptionError:
pass
except Exception as e:
_throw('Invalid optional imer', e)
maskoutliers = config.get(asec, 'maskoutliers', mapper=bool, default=False)
optimize = config.get(asec, 'optimize', mapper=bool, default=False)
kwargs['negcorr'] = config.get(asec, 'negcorr', mapper=bool, default=False)
kwargs['signsquare'] = config.get(asec,
'signsquare',
mapper=bool,
default=False)
kwargs['flipref'] = config.get(asec, 'flipref', mapper=bool, default=False)
# Check for delay cache specifications as boolean or file suffix
cachedelay = config.get(asec, 'cachedelay', default=True)
if isinstance(cachedelay, bool) and cachedelay: cachedelay = 'delays.npz'
try:
# Remove the nearmap file key
guesses = shsplit(kwargs['peaks'].pop('nearmap'))
guesses = loadmatlist(guesses, nkeys=2, scalar=False)
except IOError as e:
guesses = None
print(f'WARNING - Ignoring nearmap: {e}', file=sys.stderr)
except (KeyError, TypeError, AttributeError):
guesses = None
else:
# Adjust delay time scales
guesses = {k: (v - t0) / dt for k, v in guesses.items()}
# Adjust the delay time scales for the neardefault, if provided
try:
v = kwargs['peaks']['neardefault']
except KeyError:
pass
else:
kwargs['peaks']['neardefault'] = (v - t0) / dt
try:
# Load the window map, if provided
winmap = shsplit(kwargs['window'].pop('map'))
winmap = loadmatlist(winmap, nkeys=2, scalar=False)
except IOError as e:
winmap = None
print(f'WARNING - Ignoring window map: {e}', file=sys.stderr)
except (KeyError, TypeError, AttributeError):
winmap = None
else:
# Replace the map argument with the loaded array
kwargs['window']['map'] = winmap
times = OrderedDict()
# Process each target in turn
for i, (target, datafiles) in enumerate(targetfiles.items()):
if guesses:
# Pull the column of the nearmap for this target
nearmap = {k: v[i] for k, v in guesses.items()}
kwargs['peaks']['nearmap'] = nearmap
if cachedelay:
delayfiles = buildpaths(datafiles, extension=cachedelay)
else:
delayfiles = [None] * len(datafiles)
times[target] = dict()
dltype = 'IMER' if kwargs.get('imer', None) else 'cross-correlation'
ftext = 'files' if len(datafiles) != 1 else 'file'
print(
f'Finding {dltype} delays for {target} ({len(datafiles)} {ftext})')
for (dfile, dlayfile) in zip(datafiles, delayfiles):
kwargs['cachefile'] = dlayfile
delays = finddelays(nproc, dfile, reffile, osamp, **kwargs)
# Note the receive channels in this data file
lrx = set(k[1] for k in delays.keys())
# Convert delays to arrival times
delays = {k: v * dt + t0 for k, v in delays.items()}
if any(dv < 0 for dv in delays.values()):
raise ValueError('Non-physical, negative delays exist')
if maskoutliers:
# Remove outlying values from the delay dictionary
delays = stats.mask_outliers(delays)
if optimize:
# Prepare the arrival-time finder
atf = trilateration.ArrivalTimeFinder(delays)
# Compute the optimized times for this data file
optimes = {(k, k): v for k, v in atf.lsmr() if k in lrx}
else:
# Just pass through the desired times
optimes = delays
times[target].update(optimes)
# Build the combined times list
for tmap in times.values():
try:
rxset.intersection_update(tmap.keys())
except NameError:
rxset = set(tmap.keys())
if not len(rxset):
raise ValueError(
'Different targets have no common receive-channel indices')
# Cast to Python float to avoid numpy dependencies in pickled output
ctimes = {i: [float(t[i]) for t in times.values()] for i in sorted(rxset)}
# Save the output as a pickled map
savez_keymat(outfile, ctimes)
def finddelays(nproc=1, *args, **kwargs):
'''
Distribute, among nproc processes, delay analysis for waveforms using
calcdelays(). All *args and **kwargs, are passed to calcdelays on each
participating process. This function explicitly sets the "queue",
"rank", "grpsize", and "delaycache" arguments of calcdelays, so *args
and **kwargs should not contain these values.
The delaycache argument is built from an optional file specified in
cachefile, which should be a map from transmit-receive pair (t, r) to a
precomputed delay, loadable with habis.formats.loadkeymat.
'''
forbidden = {'queue', 'rank', 'grpsize', 'delaycache'}
forbidden.intersection_update(kwargs)
if forbidden:
raise TypeError("Forbidden argument '{next(iter(forbidden))}'")
cachefile = kwargs.pop('cachefile', None)
# Try to read an existing delay map
try:
kwargs['delaycache'] = loadkeymat(cachefile)
except (KeyError, ValueError, IOError):
pass
# Create a result queue and a dictionary to accumulate results
queue = multiprocessing.Queue(nproc)
delays = {}
# Extend the kwargs to include the result queue
kwargs['queue'] = queue
# Extend the kwargs to include the group size
kwargs['grpsize'] = nproc
# Keep track of waveform statistics
stats = defaultdict(int)
# Spawn the desired processes to perform the cross-correlation
with process.ProcessPool() as pool:
for i in range(nproc):
# Pick a useful process name
procname = process.procname(i)
# Add the group rank to the kwargs
kwargs['rank'] = i
# Extend kwargs to contain the queue (copies kwargs)
pool.addtask(target=calcdelays,
name=procname,
args=args,
kwargs=kwargs)
pool.start()
# Wait for all processes to respond
responses, deadpool = 0, False
while responses < nproc:
try:
results = queue.get(timeout=0.1)
except pyqueue.Empty:
# Loosely join to watch for a dead pool
pool.wait(timeout=0.1, limit=1)
if not pool.unjoined:
# Note a dead pool, give read one more try
if deadpool: break
else: deadpool = True
else:
delays.update(results[0])
for k, v in results[1].items():
if v: stats[k] += v
responses += 1
if responses != nproc:
print(f'WARNING: Proceeding with {responses} of {nproc} '
'subprocess results. A subprocess may have died.')
pool.wait()
if stats:
print(f'For file {os.path.basename(args[0])} '
f'({len(delays)} identfied times):')
for k, v in sorted(stats.items()):
if v:
wfn = 'waveforms' if v > 1 else 'waveform'
print(f' {v} {k} {wfn}')
if len(delays) and cachefile:
# Save the computed delays, if desired
try:
savez_keymat(cachefile, delays)
except (ValueError, IOError):
pass
return delays
tfiles = matchfiles(config.getlist(tsec, 'timefile'), forcematch=False)
# Determine the local shares of every file
tfiles = flocshare(tfiles, MPI.COMM_WORLD)
# Pull out local share of locally available arrival times
atimes = {}
for tf, (st, ln) in tfiles.items():
atimes.update(
getatimes(tf, elements, 0, False, vclip, mask_outliers, st,
ln))
with watchConfigErrors('exclusions', tsec):
# Try to load a list of arrival times to exclude
efiles = matchfiles(config.getlist(tsec, 'exclusions', default=[]),
forcematch=False)
exclusions = {(t, r)
for f in efiles for r, tl in loadkeymat(f).items()
for t in tl}
if exclusions:
if not rank:
print(
f'{len(exclusions)} measurement pairs marked for exclusion'
)
goodpairs = set(atimes).difference(exclusions)
atimes = {k: atimes[k] for k in goodpairs}
# Convert the scalar slowness or file name into a matrix
try:
s = float(s)
except (ValueError, TypeError):
s = np.load(s).astype(np.float64)
else:
