def dump_history(self, save_dir=None):
if save_dir is None:
save_dir = os.getcwd()
logger.info('Dumping sampler history to %s' % save_dir)
dump_objects(os.path.join(save_dir, self.history.filename),
self.history)
def run_mpi_sampler(
sampler_name, model, sampler_args, keep_tmp, n_jobs,
loglevel='info', project_dir=''):
"""
Execute a sampling algorithm that requires the call of mpiexec
as it uses MPI for parallelization.
A run directory is created unter '/tmp/' where the sampler
arguments are pickled and then reloaded by the MPI sampler
script.
Parameters
----------
sampler_name : string
valid names see distributed.samplers for available options
model : :class:`pymc3.model.Model`
that holds the forward model graph
sampler_args : list
of sampler arguments, order is important
keep_tmp : boolean
if true dont remove the run directory after execution
n_jobs : number of processors to call MPI with
loglevel : string
loglevels defined by the logging package
project_dir : string
main directory path to the logfile, if left empty- written to tmp
directory
"""
from beat.info import project_root
from beat.utility import dump_objects
try:
sampler = samplers[sampler_name]
except KeyError:
raise NotImplementedError(
'Currently only samplers: %s supported!' %
list2string(list(samplers.keys())))
runner = MPIRunner(keep_tmp=keep_tmp)
args_path = pjoin(runner.tempdir, mpiargs_name)
model_path = pjoin(runner.tempdir, pymc_model_name)
dump_objects(model_path, model)
dump_objects(args_path, sampler_args)
samplerdir = pjoin(project_root, sampler)
logger.info('sampler directory: %s' % samplerdir)
runner.run(
samplerdir, n_jobs=n_jobs, loglevel=loglevel, project_dir=project_dir)
def main():
parser = OptionParser(usage=usage, description=description)
parser.add_option('--force',
dest='force',
action='store_true',
default=False,
help='allow recreation of output <directory>')
parser.add_option('--debug',
dest='debug',
action='store_true',
default=False,
help='print debugging information to stderr')
parser.add_option('--dry-run',
dest='dry_run',
action='store_true',
default=False,
help='show available stations/channels and exit '
'(do not download waveforms)')
parser.add_option('--continue',
dest='continue_',
action='store_true',
default=False,
help='continue download after a accident')
parser.add_option('--local-data',
dest='local_data',
action='append',
help='add file/directory with local data')
parser.add_option('--local-stations',
dest='local_stations',
action='append',
help='add local stations file')
parser.add_option('--selection',
dest='selection_file',
action='append',
help='add local stations file')
parser.add_option(
'--local-responses-resp',
dest='local_responses_resp',
action='append',
help='add file/directory with local responses in RESP format')
parser.add_option('--local-responses-pz',
dest='local_responses_pz',
action='append',
help='add file/directory with local pole-zero responses')
parser.add_option(
'--local-responses-stationxml',
dest='local_responses_stationxml',
help='add file with local response information in StationXML format')
parser.add_option(
'--window',
dest='window',
default='full',
help='set time window to choose [full, p, "<time-start>,<time-end>"'
'] (time format is YYYY-MM-DD HH:MM:SS)')
parser.add_option(
'--out-components',
choices=['enu', 'rtu'],
dest='out_components',
default='rtu',
help='set output component orientations to radial-transverse-up [rtu] '
'(default) or east-north-up [enu]')
parser.add_option('--out-units',
choices=['M', 'M/S', 'M/S**2'],
dest='output_units',
default='M',
help='set output units to displacement "M" (default),'
' velocity "M/S" or acceleration "M/S**2"')
parser.add_option(
'--padding-factor',
type=float,
default=3.0,
dest='padding_factor',
help='extend time window on either side, in multiples of 1/<fmin_hz> '
'(default: 5)')
parser.add_option(
'--zero-padding',
dest='zero_pad',
action='store_true',
default=False,
help='Extend traces by zero-padding if clean restitution requires'
'longer windows')
parser.add_option(
'--credentials',
dest='user_credentials',
action='append',
default=[],
metavar='SITE,USER,PASSWD',
help='user credentials for specific site to access restricted data '
'(this option can be repeated)')
parser.add_option(
'--token',
dest='auth_tokens',
metavar='SITE,FILENAME',
action='append',
default=[],
help='user authentication token for specific site to access '
'restricted data (this option can be repeated)')
parser.add_option(
'--sites',
dest='sites',
metavar='SITE1,SITE2,...',
default='geofon,iris,orfeus',
help='sites to query (available: %s, default: "%%default"' %
', '.join(g_sites_available))
parser.add_option(
'--band-codes',
dest='priority_band_code',
metavar='V,L,M,B,H,S,E,...',
default='B,H',
help='select and prioritize band codes (default: %default)')
parser.add_option(
'--instrument-codes',
dest='priority_instrument_code',
metavar='H,L,G,...',
default='H,L',
help='select and prioritize instrument codes (default: %default)')
parser.add_option('--radius-min',
dest='radius_min',
metavar='VALUE',
default=0.0,
type=float,
help='minimum radius [km]')
parser.add_option('--nstations-wanted',
dest='nstations_wanted',
metavar='N',
type=int,
help='number of stations to select initially')
(options, args) = parser.parse_args(sys.argv[1:])
print('Parsed arguments:', args)
if len(args) not in (10, 7, 6):
parser.print_help()
sys.exit(1)
if options.debug:
util.setup_logging(program_name, 'debug')
else:
util.setup_logging(program_name, 'info')
if options.local_responses_pz and options.local_responses_resp:
logger.critical('cannot use local responses in PZ and RESP '
'format at the same time')
sys.exit(1)
n_resp_opt = 0
for resp_opt in (options.local_responses_pz, options.local_responses_resp,
options.local_responses_stationxml):
if resp_opt:
n_resp_opt += 1
if n_resp_opt > 1:
logger.critical('can only handle local responses from either PZ or '
'RESP or StationXML. Cannot yet merge different '
'response formats.')
sys.exit(1)
if options.local_responses_resp and not options.local_stations:
logger.critical('--local-responses-resp can only be used '
'when --stations is also given.')
sys.exit(1)
try:
ename = ''
magnitude = None
mt = None
if len(args) == 10:
time = util.str_to_time(args[1] + ' ' + args[2])
lat = float(args[3])
lon = float(args[4])
depth = float(args[5]) * km
iarg = 6
elif len(args) == 7:
if args[2].find(':') == -1:
sname_or_date = None
lat = float(args[1])
lon = float(args[2])
event = None
time = None
else:
sname_or_date = args[1] + ' ' + args[2]
iarg = 3
elif len(args) == 6:
sname_or_date = args[1]
iarg = 2
if len(args) in (7, 6) and sname_or_date is not None:
events = get_events_by_name_or_date([sname_or_date],
catalog=geofon)
if len(events) == 0:
logger.critical('no event found')
sys.exit(1)
elif len(events) > 1:
logger.critical('more than one event found')
sys.exit(1)
event = events[0]
time = event.time
lat = event.lat
lon = event.lon
depth = event.depth
ename = event.name
magnitude = event.magnitude
mt = event.moment_tensor
radius = float(args[iarg]) * km
fmin = float(args[iarg + 1])
sample_rate = float(args[iarg + 2])
eventname = args[iarg + 3]
cwd = str(sys.argv[1])
event_dir = op.join(cwd, 'data', 'events', eventname)
output_dir = op.join(event_dir, 'waveforms')
except:
raise
parser.print_help()
sys.exit(1)
if options.force and op.isdir(event_dir):
if not options.continue_:
shutil.rmtree(event_dir)
if op.exists(event_dir) and not options.continue_:
logger.critical(
'directory "%s" exists. Delete it first or use the --force option'
% event_dir)
sys.exit(1)
util.ensuredir(output_dir)
if time is not None:
event = model.Event(time=time,
lat=lat,
lon=lon,
depth=depth,
name=ename,
magnitude=magnitude,
moment_tensor=mt)
if options.window == 'full':
if event is None:
logger.critical('need event for --window=full')
sys.exit(1)
low_velocity = 1500.
timewindow = VelocityWindow(low_velocity,
tpad=options.padding_factor / fmin)
tmin, tmax = timewindow(time, radius, depth)
elif options.window == 'p':
if event is None:
logger.critical('need event for --window=p')
sys.exit(1)
phases = list(map(cake.PhaseDef, 'P p'.split()))
emod = cake.load_model()
tpad = options.padding_factor / fmin
timewindow = PhaseWindow(emod, phases, -tpad, tpad)
arrivaltimes = []
for dist in num.linspace(0, radius, 20):
try:
arrivaltimes.extend(timewindow(time, dist, depth))
except NoArrival:
pass
if not arrivaltimes:
logger.error('required phase arrival not found')
sys.exit(1)
tmin = min(arrivaltimes)
tmax = max(arrivaltimes)
else:
try:
stmin, stmax = options.window.split(',')
tmin = util.str_to_time(stmin.strip())
tmax = util.str_to_time(stmax.strip())
timewindow = FixedWindow(tmin, tmax)
except ValueError:
logger.critical('invalid argument to --window: "%s"' %
options.window)
sys.exit(1)
if event is not None:
event.name = eventname
tfade = tfade_factor / fmin
tpad = tfade
tmin -= tpad
tmax += tpad
tinc = None
priority_band_code = options.priority_band_code.split(',')
for s in priority_band_code:
if len(s) != 1:
logger.critical('invalid band code: %s' % s)
priority_instrument_code = options.priority_instrument_code.split(',')
for s in priority_instrument_code:
if len(s) != 1:
logger.critical('invalid instrument code: %s' % s)
station_query_conf = dict(latitude=lat,
longitude=lon,
minradius=options.radius_min * km * cake.m2d,
maxradius=radius * cake.m2d,
channel=','.join('%s??' % s
for s in priority_band_code))
target_sample_rate = sample_rate
fmax = target_sample_rate
# target_sample_rate = None
# priority_band_code = ['H', 'B', 'M', 'L', 'V', 'E', 'S']
priority_units = ['M/S', 'M', 'M/S**2']
# output_units = 'M'
sites = [x.strip() for x in options.sites.split(',') if x.strip()]
for site in sites:
if site not in g_sites_available:
logger.critical('unknown FDSN site: %s' % site)
sys.exit(1)
for s in options.user_credentials:
try:
site, user, passwd = s.split(',')
g_user_credentials[site] = user, passwd
except ValueError:
logger.critical('invalid format for user credentials: "%s"' % s)
sys.exit(1)
for s in options.auth_tokens:
try:
site, token_filename = s.split(',')
with open(token_filename, 'r') as f:
g_auth_tokens[site] = f.read()
except (ValueError, OSError, IOError):
logger.critical('cannot get token from file: %s' % token_filename)
sys.exit(1)
fn_template0 = \
'data_%(network)s.%(station)s.%(location)s.%(channel)s_%(tmin)s.mseed'
fn_template_raw = op.join(output_dir, 'raw', fn_template0)
fn_stations_raw = op.join(output_dir, 'stations.raw.txt')
fn_template_rest = op.join(output_dir, 'rest', fn_template0)
fn_commandline = op.join(output_dir, 'beatdown.command')
ftap = (ffade_factors[0] * fmin, fmin, fmax, ffade_factors[1] * fmax)
# chapter 1: download
sxs = []
for site in sites:
try:
extra_args = {
'iris': dict(matchtimeseries=True),
}.get(site, {})
extra_args.update(station_query_conf)
if site == 'geonet':
extra_args.update(starttime=tmin, endtime=tmax)
else:
extra_args.update(startbefore=tmax,
endafter=tmin,
includerestricted=(site in g_user_credentials
or site in g_auth_tokens))
logger.info('downloading channel information (%s)' % site)
sx = fdsn.station(site=site,
format='text',
level='channel',
**extra_args)
except fdsn.EmptyResult:
logger.error('No stations matching given criteria. (%s)' % site)
sx = None
if sx is not None:
sxs.append(sx)
if all(sx is None for sx in sxs) and not options.local_data:
sys.exit(1)
nsl_to_sites = defaultdict(list)
nsl_to_station = {}
if options.selection_file:
logger.info('using stations from stations file!')
stations = []
for fn in options.selection_file:
stations.extend(model.load_stations(fn))
nsls_selected = set(s.nsl() for s in stations)
else:
nsls_selected = None
for sx, site in zip(sxs, sites):
site_stations = sx.get_pyrocko_stations()
for s in site_stations:
nsl = s.nsl()
nsl_to_sites[nsl].append(site)
if nsl not in nsl_to_station:
if nsls_selected:
if nsl in nsls_selected:
nsl_to_station[nsl] = s
else:
nsl_to_station[
nsl] = s # using first site with this station
logger.info('number of stations found: %i' % len(nsl_to_station))
# station weeding
if options.nstations_wanted:
nsls_selected = None
stations_all = [
nsl_to_station[nsl_] for nsl_ in sorted(nsl_to_station.keys())
]
for s in stations_all:
s.set_event_relative_data(event)
stations_selected = weeding.weed_stations(stations_all,
options.nstations_wanted)[0]
nsls_selected = set(s.nsl() for s in stations_selected)
logger.info('number of stations selected: %i' % len(nsls_selected))
if tinc is None:
tinc = 3600.
have_data = set()
if options.continue_:
fns = glob.glob(fn_template_raw % starfill())
p = pile.make_pile(fns)
else:
fns = []
have_data_site = {}
could_have_data_site = {}
for site in sites:
have_data_site[site] = set()
could_have_data_site[site] = set()
available_through = defaultdict(set)
it = 0
nt = int(math.ceil((tmax - tmin) / tinc))
for it in range(nt):
tmin_win = tmin + it * tinc
tmax_win = min(tmin + (it + 1) * tinc, tmax)
logger.info('time window %i/%i (%s - %s)' %
(it + 1, nt, util.tts(tmin_win), util.tts(tmax_win)))
have_data_this_window = set()
if options.continue_:
trs_avail = p.all(tmin=tmin_win, tmax=tmax_win, load_data=False)
for tr in trs_avail:
have_data_this_window.add(tr.nslc_id)
for site, sx in zip(sites, sxs):
if sx is None:
continue
selection = []
channels = sx.choose_channels(
target_sample_rate=target_sample_rate,
priority_band_code=priority_band_code,
priority_units=priority_units,
priority_instrument_code=priority_instrument_code,
timespan=(tmin_win, tmax_win))
for nslc in sorted(channels.keys()):
if nsls_selected is not None and nslc[:3] not in nsls_selected:
continue
could_have_data_site[site].add(nslc)
if nslc not in have_data_this_window:
channel = channels[nslc]
if event:
lat_, lon_ = event.lat, event.lon
else:
lat_, lon_ = lat, lon
try:
dist = orthodrome.distance_accurate50m_numpy(
lat_, lon_, channel.latitude.value,
channel.longitude.value)
except:
dist = orthodrome.distance_accurate50m_numpy(
lat_, lon_, channel.latitude, channel.longitude)
if event:
depth_ = event.depth
time_ = event.time
else:
depth_ = None
time_ = None
tmin_, tmax_ = timewindow(time_, dist, depth_)
tmin_this = tmin_ - tpad
tmax_this = float(tmax_ + tpad)
tmin_req = max(tmin_win, tmin_this)
tmax_req = min(tmax_win, tmax_this)
if channel.sample_rate:
try:
deltat = 1.0 / int(channel.sample_rate.value)
except:
deltat = 1.0 / int(channel.sample_rate)
else:
deltat = 1.0
if tmin_req < tmax_req:
logger.debug('deltat %f' % deltat)
# extend time window by some samples because otherwise
# sometimes gaps are produced
# apparently the WS are only sensitive to full seconds
# round to avoid gaps, increase safetiy window
selection.append(nslc +
(math.floor(tmin_req - deltat * 20.0),
math.ceil(tmax_req + deltat * 20.0)))
if options.dry_run:
for (net, sta, loc, cha, tmin, tmax) in selection:
available_through[net, sta, loc, cha].add(site)
else:
neach = 100
i = 0
nbatches = ((len(selection) - 1) // neach) + 1
while i < len(selection):
selection_now = selection[i:i + neach]
f = tempfile.NamedTemporaryFile()
try:
sbatch = ''
if nbatches > 1:
sbatch = ' (batch %i/%i)' % (
(i // neach) + 1, nbatches)
logger.info('downloading data (%s)%s' % (site, sbatch))
data = fdsn.dataselect(site=site,
selection=selection_now,
**get_user_credentials(site))
while True:
buf = data.read(1024)
if not buf:
break
f.write(buf)
f.flush()
trs = io.load(f.name)
for tr in trs:
tr.fix_deltat_rounding_errors()
logger.debug('cutting window: %f - %f' %
(tmin_win, tmax_win))
logger.debug(
'available window: %f - %f, nsamples: %g' %
(tr.tmin, tr.tmax, tr.ydata.size))
try:
logger.debug('tmin before snap %f' % tr.tmin)
tr.snap(interpolate=True)
logger.debug('tmin after snap %f' % tr.tmin)
tr.chop(tmin_win,
tmax_win,
snap=(math.floor, math.ceil),
include_last=True)
logger.debug(
'cut window: %f - %f, nsamles: %g' %
(tr.tmin, tr.tmax, tr.ydata.size))
have_data.add(tr.nslc_id)
have_data_site[site].add(tr.nslc_id)
except trace.NoData:
pass
fns2 = io.save(trs, fn_template_raw)
for fn in fns2:
if fn in fns:
logger.warn('overwriting file %s', fn)
fns.extend(fns2)
except fdsn.EmptyResult:
pass
except HTTPError:
logger.warn('an error occurred while downloading data '
'for channels \n %s' %
'\n '.join('.'.join(x[:4])
for x in selection_now))
f.close()
i += neach
if options.dry_run:
nslcs = sorted(available_through.keys())
all_channels = defaultdict(set)
all_stations = defaultdict(set)
def plural_s(x):
return '' if x == 1 else 's'
for nslc in nslcs:
sites = tuple(sorted(available_through[nslc]))
logger.info('selected: %s.%s.%s.%s from site%s %s' %
(nslc + (plural_s(len(sites)), '+'.join(sites))))
all_channels[sites].add(nslc)
all_stations[sites].add(nslc[:3])
nchannels_all = 0
nstations_all = 0
for sites in sorted(all_channels.keys(),
key=lambda sites: (-len(sites), sites)):
nchannels = len(all_channels[sites])
nstations = len(all_stations[sites])
nchannels_all += nchannels
nstations_all += nstations
logger.info('selected (%s): %i channel%s (%i station%s)' %
('+'.join(sites), nchannels, plural_s(nchannels),
nstations, plural_s(nstations)))
logger.info('selected total: %i channel%s (%i station%s)' %
(nchannels_all, plural_s(nchannels_all), nstations_all,
plural_s(nstations_all)))
logger.info('dry run done.')
sys.exit(0)
for nslc in have_data:
# if we are in continue mode, we have to guess where the data came from
if not any(nslc in have_data_site[site] for site in sites):
for site in sites:
if nslc in could_have_data_site[site]:
have_data_site[site].add(nslc)
sxs = {}
for site in sites:
selection = []
for nslc in sorted(have_data_site[site]):
selection.append(nslc + (tmin - tpad, tmax + tpad))
if selection:
logger.info('downloading response information (%s)' % site)
sxs[site] = fdsn.station(site=site,
level='response',
selection=selection)
sxs[site].dump_xml(filename=op.join(output_dir, 'stations.%s.xml' %
site))
# chapter 1.5: inject local data
if options.local_data:
have_data_site['local'] = set()
plocal = pile.make_pile(options.local_data, fileformat='detect')
logger.info(
'Importing local data from %s between %s (%f) and %s (%f)' %
(options.local_data, util.time_to_str(tmin), tmin,
util.time_to_str(tmax), tmax))
for traces in plocal.chopper_grouped(gather=lambda tr: tr.nslc_id,
tmin=tmin,
tmax=tmax,
tinc=tinc):
for tr in traces:
if tr.nslc_id not in have_data:
fns.extend(io.save(traces, fn_template_raw))
have_data_site['local'].add(tr.nslc_id)
have_data.add(tr.nslc_id)
sites.append('local')
if options.local_responses_pz:
sxs['local'] = epz.make_stationxml(
epz.iload(options.local_responses_pz))
if options.local_responses_resp:
local_stations = []
for fn in options.local_stations:
local_stations.extend(model.load_stations(fn))
sxs['local'] = resp.make_stationxml(
local_stations, resp.iload(options.local_responses_resp))
if options.local_responses_stationxml:
sxs['local'] = stationxml.load_xml(
filename=options.local_responses_stationxml)
# chapter 1.6: dump raw data stations file
nsl_to_station = {}
for site in sites:
if site in sxs:
stations = sxs[site].get_pyrocko_stations(timespan=(tmin, tmax))
for s in stations:
nsl = s.nsl()
if nsl not in nsl_to_station:
nsl_to_station[nsl] = s
stations = [nsl_to_station[nsl_] for nsl_ in sorted(nsl_to_station.keys())]
util.ensuredirs(fn_stations_raw)
model.dump_stations(stations, fn_stations_raw)
dump_commandline(sys.argv, fn_commandline)
# chapter 2: restitution
if not fns:
logger.error('no data available')
sys.exit(1)
p = pile.make_pile(fns, show_progress=False)
p.get_deltatmin()
otinc = None
if otinc is None:
otinc = nice_seconds_floor(p.get_deltatmin() * 500000.)
otinc = 3600.
otmin = math.floor(p.tmin / otinc) * otinc
otmax = math.ceil(p.tmax / otinc) * otinc
otpad = tpad * 2
fns = []
rest_traces_b = []
win_b = None
for traces_a in p.chopper_grouped(gather=lambda tr: tr.nslc_id,
tmin=otmin,
tmax=otmax,
tinc=otinc,
tpad=otpad):
rest_traces_a = []
win_a = None
for tr in traces_a:
win_a = tr.wmin, tr.wmax
if win_b and win_b[0] >= win_a[0]:
fns.extend(cut_n_dump(rest_traces_b, win_b, fn_template_rest))
rest_traces_b = []
win_b = None
response = None
failure = []
for site in sites:
try:
if site not in sxs:
continue
logger.debug('Getting response for %s' % tr.__str__())
response = sxs[site].get_pyrocko_response(
tr.nslc_id,
timespan=(tr.tmin, tr.tmax),
fake_input_units=options.output_units)
break
except stationxml.NoResponseInformation:
failure.append('%s: no response information' % site)
except stationxml.MultipleResponseInformation:
failure.append('%s: multiple response information' % site)
if response is None:
failure = ', '.join(failure)
else:
failure = ''
try:
if tr.tmin > tmin and options.zero_pad:
logger.warning(
'Trace too short for clean restitution in '
'desired frequency band -> zero-padding!')
tr.extend(tr.tmin - tfade, tr.tmax + tfade, 'repeat')
rest_tr = tr.transfer(tfade, ftap, response, invert=True)
rest_traces_a.append(rest_tr)
except (trace.TraceTooShort, trace.NoData):
failure = 'trace too short'
if failure:
logger.warn('failed to restitute trace %s.%s.%s.%s (%s)' %
(tr.nslc_id + (failure, )))
if rest_traces_b:
rest_traces = trace.degapper(rest_traces_b + rest_traces_a,
deoverlap='crossfade_cos')
fns.extend(cut_n_dump(rest_traces, win_b, fn_template_rest))
rest_traces_a = []
if win_a:
for tr in rest_traces:
try:
rest_traces_a.append(
tr.chop(win_a[0], win_a[1] + otpad, inplace=False))
except trace.NoData:
pass
rest_traces_b = rest_traces_a
win_b = win_a
fns.extend(cut_n_dump(rest_traces_b, win_b, fn_template_rest))
# chapter 3: rotated restituted traces for inspection
if not event:
sys.exit(0)
fn_template1 = \
'DISPL.%(network)s.%(station)s.%(location)s.%(channel)s'
fn_waveforms = op.join(output_dir, 'prepared', fn_template1)
fn_stations = op.join(output_dir, 'stations.prepared.txt')
fn_event = op.join(event_dir, 'event.txt')
fn_event_yaml = op.join(event_dir, 'event.yaml')
nsl_to_station = {}
for site in sites:
if site in sxs:
stations = sxs[site].get_pyrocko_stations(timespan=(tmin, tmax))
for s in stations:
nsl = s.nsl()
if nsl not in nsl_to_station:
nsl_to_station[nsl] = s
p = pile.make_pile(fns, show_progress=False)
deltat = None
if sample_rate is not None:
deltat = 1.0 / sample_rate
traces_beat = []
used_stations = []
for nsl, s in nsl_to_station.items():
s.set_event_relative_data(event)
traces = p.all(trace_selector=lambda tr: tr.nslc_id[:3] == nsl)
if options.out_components == 'rtu':
pios = s.guess_projections_to_rtu(out_channels=('R', 'T', 'Z'))
elif options.out_components == 'enu':
pios = s.guess_projections_to_enu(out_channels=('E', 'N', 'Z'))
else:
assert False
for (proj, in_channels, out_channels) in pios:
proc = trace.project(traces, proj, in_channels, out_channels)
for tr in proc:
tr_beat = heart.SeismicDataset.from_pyrocko_trace(tr)
traces_beat.append(tr_beat)
for ch in out_channels:
if ch.name == tr.channel:
s.add_channel(ch)
if proc:
io.save(proc, fn_waveforms)
used_stations.append(s)
stations = list(used_stations)
util.ensuredirs(fn_stations)
model.dump_stations(stations, fn_stations)
model.dump_events([event], fn_event)
from pyrocko.guts import dump
dump([event], filename=fn_event_yaml)
utility.dump_objects(op.join(cwd, 'seismic_data.pkl'),
outlist=[stations, traces_beat])
logger.info('prepared waveforms from %i stations' % len(stations))
def Metropolis_sample(n_stages=10, n_steps=10000, trace=None, start=None,
progressbar=False, stage=None, rm_flag=False,
step=None, model=None, n_jobs=1, update=None, burn=0.5, thin=2):
"""
Execute Metropolis algorithm repeatedly depending on the number of stages.
The start point of each stage set to the end point of the previous stage.
Update covariances if given.
"""
model = pm.modelcontext(model)
step.n_steps = int(n_steps)
if n_steps < 1:
raise Exception('Argument `n_steps` should be above 0.', exc_info=1)
if step is None:
raise Exception('Argument `step` has to be a TMCMC step object.')
if trace is None:
raise Exception('Argument `trace` should be path to result_directory.')
if n_jobs > 1:
if not (step.n_chains / float(n_jobs)).is_integer():
raise Exception('n_chains / n_jobs has to be a whole number!')
if start is not None:
if len(start) != step.n_chains:
raise Exception('Argument `start` should have dicts equal the '
'number of chains (step.N-chains)')
else:
step.population = start
if not any(
step.likelihood_name in var.name for var in model.deterministics):
raise Exception('Model (deterministic) variables need to contain '
'a variable %s '
'as defined in `step`.' % step.likelihood_name)
homepath = trace
util.ensuredir(homepath)
chains, step, update = init_stage(
homepath=homepath,
step=step,
stage=stage,
n_jobs=n_jobs,
progressbar=progressbar,
update=update,
model=model,
rm_flag=rm_flag)
# set beta to 1 - standard Metropolis sampling
step.beta = 1.
step.n_jobs = n_jobs
with model:
for s in range(int(stage), n_stages):
stage_path = os.path.join(homepath, 'stage_%i' % s)
logger.info('Sampling stage %s' % stage_path)
if s == 0:
draws = 1
else:
draws = n_steps
if not os.path.exists(stage_path):
chains = None
step.stage = s
sample_args = {
'draws': draws,
'step': step,
'stage_path': stage_path,
'progressbar': progressbar,
'model': model,
'n_jobs': n_jobs,
'chains': chains}
_iter_parallel_chains(**sample_args)
mtrace = backend.load(stage_path, model)
step.population, step.array_population, step.likelihoods = \
step.select_end_points(mtrace)
pdict, step.covariance = get_trace_stats(
mtrace, step, burn, thin)
if step.proposal_name == 'MultivariateNormal':
step.proposal_dist = choose_proposal(
step.proposal_name, scale=step.covariance)
if update is not None:
logger.info('Updating Covariances ...')
update.update_weights(pdict['dist_mean'], n_jobs=n_jobs)
mtrace = update_last_samples(
homepath, step, progressbar, model, n_jobs, rm_flag)
elif update is not None and stage == 0:
update.engine.close_cashed_stores()
step.chain_previous_lpoint = step.get_chain_previous_lpoint(mtrace)
outpath = os.path.join(stage_path, sample_p_outname)
outparam_list = [step, update]
utility.dump_objects(outpath, outparam_list)
get_final_stage(homepath, n_stages, model=model)
outpath = os.path.join(homepath, 'stage_final', sample_p_outname)
utility.dump_objects(outpath, outparam_list)
def dump_atmip_params(self, stage_number, outlist):
"""
Save atmip params to file.
"""
utility.dump_objects(self.atmip_path(stage_number), outlist)
def ATMIP_sample(n_steps,
step=None,
start=None,
trace=None,
chain=0,
stage=None,
n_jobs=1,
tune=None,
progressbar=False,
model=None,
update=None,
random_seed=None,
rm_flag=False):
"""
(C)ATMIP sampling algorithm
(Cascading - (C) not always relevant)
Samples the solution space with n_chains of Metropolis chains, where each
chain has n_steps iterations. Once finished, the sampled traces are
evaluated:
(1) Based on the likelihoods of the final samples, chains are weighted
(2) the weighted covariance of the ensemble is calculated and set as new
proposal distribution
(3) the variation in the ensemble is calculated and the next tempering
parameter (beta) calculated
(4) New n_chains Metropolis chains are seeded on the traces with high
weight for n_steps iterations
(5) Repeat until beta > 1.
Parameters
----------
n_steps : int
The number of samples to draw for each Markov-chain per stage
step : :class:`ATMCMC`
ATMCMC initialisation object
start : List of dictionaries
with length of (n_chains)
Starting points in parameter space (or partial point)
Defaults to random draws from variables (defaults to empty dict)
chain : int
Chain number used to store sample in backend. If `n_jobs` is
greater than one, chain numbers will start here.
stage : str
Stage where to start or continue the calculation. It is possible to
continue after completed stages (stage should be the number of the
completed stage + 1). If None the start will be at stage = 0.
n_jobs : int
The number of cores to be used in parallel. Be aware that theano has
internal parallelisation. Sometimes this is more efficient especially
for simple models.
step.n_chains / n_jobs has to be an integer number!
tune : int
Number of iterations to tune, if applicable (defaults to None)
trace : string
Result_folder for storing stages, will be created if not existing.
progressbar : bool
Flag for displaying a progress bar
model : :class:`pymc3.Model`
(optional if in `with` context) has to contain deterministic
variable name defined under step.likelihood_name' that contains the
model likelihood
update : :py:class:`models.Problem`
Problem object that contains all the observed data and (if applicable)
covariances to be updated each transition step.
rm_flag : bool
If True existing stage result folders are being deleted prior to
sampling.
References
----------
.. [Minson2013] Minson, S. E. and Simons, M. and Beck, J. L., (2013),
Bayesian inversion for finite fault earthquake source models
I- Theory and algorithm. Geophysical Journal International, 2013,
194(3), pp.1701-1726,
`link <https://gji.oxfordjournals.org/content/194/3/1701.full>`__
"""
model = pm.modelcontext(model)
step.n_steps = int(n_steps)
if n_steps < 1:
raise Exception('Argument `n_steps` should be above 0.', exc_info=1)
if step is None:
raise Exception('Argument `step` has to be a TMCMC step object.')
if trace is None:
raise Exception('Argument `trace` should be path to result_directory.')
if n_jobs > 1:
if not (step.n_chains / float(n_jobs)).is_integer():
raise Exception('n_chains / n_jobs has to be a whole number!')
if start is not None:
if len(start) != step.n_chains:
raise Exception('Argument `start` should have dicts equal the '
'number of chains (step.N-chains)')
else:
step.population = start
if not any(step.likelihood_name in var.name
for var in model.deterministics):
raise Exception('Model (deterministic) variables need to contain '
'a variable %s '
'as defined in `step`.' % step.likelihood_name)
homepath = trace
util.ensuredir(homepath)
if progressbar and n_jobs > 1:
progressbar = False
chains, step, update = init_stage(homepath=homepath,
step=step,
stage=stage,
n_jobs=n_jobs,
progressbar=progressbar,
update=update,
model=model)
with model:
while step.beta < 1.:
if step.stage == 0:
# Initial stage
logger.info('Sample initial stage: ...')
draws = 1
else:
draws = n_steps
logger.info('Beta: %f Stage: %i' % (step.beta, step.stage))
# Metropolis sampling intermediate stages
stage_path = os.path.join(homepath, 'stage_%i' % step.stage)
if not os.path.exists(stage_path):
chains = None
sample_args = {
'draws': draws,
'step': step,
'stage_path': stage_path,
'progressbar': progressbar,
'model': model,
'n_jobs': n_jobs,
'chains': chains
}
_iter_parallel_chains(**sample_args)
mtrace = backend.load(stage_path, model)
step.population, step.array_population, step.likelihoods = \
step.select_end_points(mtrace)
step.beta, step.old_beta, step.weights = step.calc_beta()
step.chain_previous_lpoint = step.get_chain_previous_lpoint(mtrace)
if update is not None:
logger.info('Updating Covariances ...')
mean_pt = step.mean_end_points()
update.update_weights(mean_pt, n_jobs=n_jobs)
if step.beta > 1.:
logger.info('Beta > 1.: %f' % step.beta)
step.beta = 1.
outpath = os.path.join(stage_path, sample_p_outname)
outparam_list = [step, update]
utility.dump_objects(outpath, outparam_list)
if stage == 'final':
chains = []
else:
chains = None
break
step.covariance = step.calc_covariance()
step.proposal_dist = choose_proposal(step.proposal_name,
scale=step.covariance)
step.resampling_indexes = step.resample()
outpath = os.path.join(stage_path, sample_p_outname)
outparam_list = [step, update]
utility.dump_objects(outpath, outparam_list)
step.stage += 1
del (mtrace)
# Metropolis sampling final stage
logger.info('Sample final stage')
stage_path = os.path.join(homepath, 'stage_final')
temp = np.exp((1 - step.old_beta) * \
(step.likelihoods - step.likelihoods.max()))
step.weights = temp / np.sum(temp)
step.covariance = step.calc_covariance()
step.proposal_dist = choose_proposal(step.proposal_name,
scale=step.covariance)
step.resampling_indexes = step.resample()
sample_args['step'] = step
sample_args['stage_path'] = stage_path
sample_args['chains'] = chains
_iter_parallel_chains(**sample_args)
outpath = os.path.join(stage_path, sample_p_outname)
outparam_list = [step, update]
utility.dump_objects(outpath, outparam_list)