def subdivide(seg, length, min_len=0):
"""
Subdivide a segment into smaller segments based on a given length. Enforce a given minimum length at the end, if necessary. If the remainder segment is smaller than the minimum length, then the last two segments will span the remainder plus penultimate segment, with the span divided evenly between the two.
Input segment: (0, 10] subdivide 3 min 2
Output segment(s): (0, 3], (3, 6], (6, 8], (8, 10]
"""
assert length >= min_len
if abs(seg) < min_len:
return segmentlist([])
if abs(seg) <= length:
return segmentlist([seg])
subsegl = segmentlist([])
for i in range(int(float(abs(seg))/length)):
st = seg[0]
subsegl.append(segment(st+length*i, st+length*(i+1)))
# Make an attempt to subdivide evenly.
if float(abs(seg)) % length <= min_len:
s1 = subsegl.pop()
rem_len = float(abs(s1)) + (float(abs(seg)) % length)
s2 = segment(seg[1]-rem_len/2, seg[1])
s1 = segment(s1[0], seg[1]-rem_len/2)
subsegl.append(s1)
subsegl.append(s2)
else:
subsegl.append(segment(subsegl[-1][1], seg[1]))
return subsegl
def setup_psd_calculate(workflow, frame_files, ifo, segments,
segment_name, out_dir, tags=None):
make_analysis_dir(out_dir)
tags = [] if not tags else tags
if workflow.cp.has_option_tags('workflow-psd', 'parallelization-factor', tags=tags):
num_parts = int(workflow.cp.get_opt_tags('workflow-psd',
'parallelization-factor',
tags=tags))
else:
num_parts = 1
# get rid of duplicate segments which happen when splitting the bank
segments = segmentlist(frozenset(segments))
segment_lists = list(chunks(segments, num_parts))
psd_files = FileList([])
for i, segs in enumerate(segment_lists):
seg_file = SegFile.from_segment_list('%s_%s' %(segment_name, i),
segmentlist(segs), segment_name, ifo,
valid_segment=workflow.analysis_time,
extension='xml', directory=out_dir)
psd_files += [make_psd_file(workflow, frame_files, seg_file,
segment_name, out_dir,
tags=tags + ['PART%s' % i])]
if num_parts > 1:
return merge_psds(workflow, psd_files, ifo, out_dir, tags=tags)
else:
return psd_files[0]
def setup_psd_calculate(workflow, frame_files, ifo, segments,
segment_name, out_dir,
gate_files=None, tags=None):
make_analysis_dir(out_dir)
tags = [] if not tags else tags
if workflow.cp.has_option_tags('workflow-psd', 'parallelization-factor', tags=tags):
num_parts = int(workflow.cp.get_opt_tags('workflow-psd',
'parallelization-factor',
tags=tags))
else:
num_parts = 1
# get rid of duplicate segments which happen when splitting the bank
segments = segmentlist(frozenset(segments))
segment_lists = list(chunks(segments, num_parts))
psd_files = FileList([])
for i, segs in enumerate(segment_lists):
seg_file = segments_to_file(segmentlist(segs),
out_dir + '/%s-INSPIRAL_DATA-%s.xml' % (ifo, i),
'INSPIRAL_DATA', ifo=ifo)
psd_files += [make_psd_file(workflow, frame_files, seg_file,
segment_name, out_dir,
gate_files=gate_files,
tags=tags + ['PART%s' % i])]
if num_parts > 1:
return merge_psds(workflow, psd_files, ifo, out_dir, tags=tags)
else:
return psd_files[0]
def run_query_segments(
doc,
proc_id,
engine,
gps_start_time,
gps_end_time,
included_segments_string,
excluded_segments_string=None,
write_segments=True,
start_pad=0,
end_pad=0,
):
"""Runs a segment query. This was originally part of ligolw_query_segments, but now is also
used by ligolw_segments_from_cats.
The write_segments option is provided so callers can coalesce segments obtained over
sever invocations (as segments_from_cats does).
"""
if write_segments:
all_ifos = {}
for ifo, segment_name, version in split_segment_ids(included_segments_string.split(",")):
all_ifos[ifo] = True
new_seg_def_id = add_to_segment_definer(doc, proc_id, "".join(all_ifos.keys()), "result", 0)
add_to_segment_summary(doc, proc_id, new_seg_def_id, [[gps_start_time, gps_end_time]])
result = segmentlist([])
for ifo, segment_name, version in split_segment_ids(included_segments_string.split(",")):
sum_segments, seg_segments = build_segment_list(
engine, gps_start_time, gps_end_time, ifo, segment_name, version, start_pad, end_pad
)
seg_def_id = add_to_segment_definer(doc, proc_id, ifo, segment_name, version)
add_to_segment_summary(doc, proc_id, seg_def_id, sum_segments)
# and accumulate segments
result |= seg_segments
# Excluded segments are not required
if excluded_segments_string:
excluded_segments = segmentlist([])
for ifo, segment_name, version in split_segment_ids(excluded_segments_string.split(",")):
sum_segments, seg_segments = build_segment_list(
engine, gps_start_time, gps_end_time, ifo, segment_name, version
)
excluded_segments |= seg_segments
result = result - excluded_segments
result.coalesce()
# Add the segments
if write_segments:
add_to_segment(doc, proc_id, new_seg_def_id, result)
return result
def generated_vdb_ascii(json_dict,filepath):
#res_dict=json.loads(json_str)
res_dict=json_dict
active_list=res_dict['active']
active_segments=segments.segmentlist([segments.segment(x[0],x[1]) for x in active_list])
active_segments.coalesce()
known_list=res_dict['known']
known_segments=segments.segmentlist([segments.segment(x[0],x[1]) for x in known_list])
known_segments.coalesce()
query_start=res_dict['query_information']['start']
query_stop=res_dict['query_information']['end']
if query_start!=0 and query_stop!=0:
requested_span=segments.segmentlist([segments.segment(query_start,query_stop)])
else:
requested_span=segments.segmentlist([segments.segment(0,9999999999)])
active_segments_string=',1 \n'.join([str(i[0])+","+str(i[1]) for i in active_segments])+",1 \n"
unknown_segments=requested_span-known_segments
unknown_segments_string=',-1 \n'.join([str(i[0])+","+str(i[1]) for i in unknown_segments])+",-1 \n"
known_not_active_segments=known_segments-active_segments
known_not_active_segments_string=',0 \n'.join([str(i[0])+","+str(i[1]) for i in known_not_active_segments])+",0 \n"
output_fileh=open(filepath,'a')
query_info_string=json.dumps(res_dict['query_information'], indent=1)
output_fileh.writelines(query_info_string)
output_fileh.write('\n')
output_fileh.writelines(active_segments_string)
output_fileh.writelines(unknown_segments_string)
output_fileh.writelines(known_not_active_segments_string)
output_fileh.close()
return filepath
def inj_seg(self, exclude_coinc_flags=None):
""" Returns a segmentlist that is the union of all excitation,
segdb and bitmasked channels.
"""
if exclude_coinc_flags is None:
exclude_coinc_flags = []
tmp_list = segments.segmentlist([])
for key in self.exc_dict.keys():
if key[3:] not in exclude_coinc_flags:
tmp_list.extend(self.exc_dict[key])
for key in self.seg_dict.keys():
if key[3:] not in exclude_coinc_flags:
tmp_list.extend(self.seg_dict[key])
for key in self.bitmask_dict.keys():
if key[3:] not in exclude_coinc_flags:
tmp_list.extend(self.bitmask_dict[key])
if self.schedule_time:
seg = segments.segment(self.schedule_time, self.schedule_time + 1)
seg_list = segments.segmentlist([seg])
tmp_list.extend(seg_list)
for time in self.gracedb_time:
seg = segments.segment(time, time + 1)
seg_list = segments.segmentlist([seg])
tmp_list.extend(seg_list)
return tmp_list
def generated_vdb_ascii(json_str, filepath):
res_dict = json.loads(json_str)
active_list = res_dict["active"]
active_segments = segments.segmentlist([segments.segment(x[0], x[1]) for x in active_list])
known_list = res_dict["known"]
known_segments = segments.segmentlist([segments.segment(x[0], x[1]) for x in known_list])
query_start = res_dict["query_information"]["start"]
query_stop = res_dict["query_information"]["end"]
if query_start != 0 and query_stop != 0:
requested_span = segments.segmentlist([segments.segment(query_start, query_stop)])
else:
requested_span = segments.segmentlist([segments.segment(0, 9999999999)])
active_segments_string = ",1 \n".join([str(i[0]) + "," + str(i[1]) for i in active_segments]) + ",1 \n"
unknown_segments = requested_span - known_segments
unknown_segments_string = ",-1 \n".join([str(i[0]) + "," + str(i[1]) for i in unknown_segments]) + ",-1 \n"
known_not_active_segments = known_segments - active_segments
known_not_active_segments_string = (
",0 \n".join([str(i[0]) + "," + str(i[1]) for i in known_not_active_segments]) + ",0 \n"
)
output_fileh = open(filepath, "w+")
query_info_string = json.dumps(res_dict["query_information"], indent=1)
output_fileh.writelines(query_info_string)
output_fileh.write("\n")
output_fileh.writelines(active_segments_string)
output_fileh.writelines(unknown_segments_string)
output_fileh.writelines(known_not_active_segments_string)
output_fileh.close()
return filepath
def fetch(self, channel, start, end):
"""
Retrieve data, caching file locations and the files themselves.
"""
seg = segment(start, end)
if not self._query(channel, start, end):
raise ValueError("%s not found in cache" % repr(segmentlist([seg]) - self._remotecoverage))
# Need to cache files locally
# Note: seg *will* be in self._cachecoverage if self.scratchdir is None.
if seg not in self._cachecoverage:
for f,s in zip(self._remotefiles, self._remotesegs):
if seg.intersects(s) and s not in self._cachecoverage:
dest = os.path.join(self._scratchdir, os.path.split(f)[-1])
if self._verbose:
print "Copying %s -->\n %s." % (f, dest)
shutil.copy(f, dest)
ind = bisect_right(self._cachedsegs, s)
self._cachedfiles.insert(ind, dest)
self._cachedsegs.insert(ind, s)
self._cachecoverage |= segmentlist([s])
assert seg in self._cachecoverage
# Finally, return the cached data
return self._fetch(channel, start, end)
def __init__(self, active = (), valid = (), instruments = (), name = None, version = None, comment = None): """ Initialize a new LigolwSegmentList instance. active and valid are sequences that will be cast to segments.segmentlist objects. They can be generator expressions. The "active" sequence is what is usually thought of as the segment list, the "valid" sequence identifies the intervals of time for which the segment list's state is defined. """ # if we've only been passed an argument for active, see if # it's an object with the same attributes as ourselves and # if so initialize ourself as a copy of it. if not valid and not instruments and name is None and version is None and comment is None: try: self.valid = segments.segmentlist(active.valid) self.active = segments.segmentlist(active.active) self.instruments = set(active.instruments) self.name = active.name self.version = active.version self.comment = active.comment return except AttributeError: pass # we had more than one argument or it didn't have the # correct attributes, so do a normal initialization. make # copies of mutable objects to avoid confusion self.valid = segments.segmentlist(valid) self.active = segments.segmentlist(active) self.instruments = set(instruments) self.name = name self.version = version self.comment = comment
def __init__(self,ifo,name,version):
self.known=segments.segmentlist([])
self.active=segments.segmentlist([])
# self.metadata={}
self.flagDict={}
self.ifo=ifo
self.name=name
self.version=version
def __init__(self, instrument):
self.fig, self.axes = SnglBurstUtils.make_burst_plot("%s Confidence" % instrument, "Coincident Event Rate (Hz)")
self.instrument = instrument
self.foreground = []
self.background = []
self.foreground_segs = segments.segmentlist()
self.background_segs = segments.segmentlist()
self.axes.loglog()
def fromsegmentxml(file, dict=False, id=None):
"""
Read a glue.segments.segmentlist from the file object file containing an
xml segment table.
Arguments:
file : file object
file object for segment xml file
Keyword Arguments:
dict : [ True | False ]
returns a glue.segments.segmentlistdict containing coalesced
glue.segments.segmentlists keyed by seg_def.name for each entry in the
contained segment_def_table. Default False
id : int
returns a glue.segments.segmentlist object containing only those
segments matching the given segment_def_id integer
"""
# load xmldocument and SegmentDefTable and SegmentTables
xmldoc, digest = utils.load_fileobj(file, gz=file.name.endswith(".gz"))
seg_def_table = table.get_table(xmldoc, lsctables.SegmentDefTable.tableName)
seg_table = table.get_table(xmldoc, lsctables.SegmentTable.tableName)
if dict:
segs = segments.segmentlistdict()
else:
segs = segments.segmentlist()
seg_id = {}
for seg_def in seg_def_table:
seg_id[int(seg_def.segment_def_id)] = str(seg_def.name)
if dict:
segs[str(seg_def.name)] = segments.segmentlist()
for seg in seg_table:
if dict:
segs[seg_id[int(seg.segment_def_id)]]\
.append(segments.segment(seg.start_time, seg.end_time))
continue
if id and int(seg.segment_def_id)==id:
segs.append(segments.segment(seg.start_time, seg.end_time))
continue
segs.append(segments.segment(seg.start_time, seg.end_time))
if dict:
for seg_name in seg_id.values():
segs[seg_name] = segs[seg_name].coalesce()
else:
segs = segs.coalesce()
xmldoc.unlink()
return segs
def coalesceResultDictionary(result_dict):
out_result_dict=result_dict
active_seg_python_list=[seg.segment(i[0],i[1]) for i in result_dict[0]['active']]
active_seg_list=seg.segmentlist(active_seg_python_list)
active_seg_list.coalesce()
out_result_dict[0]['active']=active_seg_list
known_seg_python_list=[seg.segment(i[0],i[1]) for i in result_dict[0]['known']]
known_seg_list=seg.segmentlist(known_seg_python_list)
known_seg_list.coalesce()
out_result_dict[0]['known']=known_seg_list
return out_result_dict
def test_optimized_query(engine):
res = segmentdb_utils.query_segments( engine, 'segment_summary', [ ('H1','DMT-TESTSEG_2',1,924900000,924900016,0,0),
('H1','DMT-TESTSEG_3',1,924900000,924900016,0,0) ] )
if res[0] != segmentlist([segment(924900000, 924900010)]):
return False
if res[1] != segmentlist([segment(924900008, 924900016)]):
return False
return True
def add_cache(self, cache_entries):
"""
Add information from some cache entries.
"""
newentries = [entry for entry in cache_entries \
if entry.path not in self._remotefiles]
newfiles = [entry.path for entry in newentries]
newsegs = segmentlist([entry.segment for entry in newentries])
self._remotefiles.extend(newfiles)
self._remotesegs.extend(newsegs)
self._remotecoverage |= segmentlist(newsegs)
self._remotecoverage.coalesce()
def test_basic_seg_summary(engine):
res = segmentdb_utils.query_segments( engine, 'segment_summary', [ ('H1','DMT-TESTSEG_1',1,924900000,924900016,0,0) ] )
if res != [ segmentlist( [segment(924900000, 924900016)] ) ]:
return False
res = segmentdb_utils.query_segments( engine, 'segment_summary', [ ('H1','DMT-TESTSEG_1',2,924900000,924900016,0,0) ] )
if res != [ segmentlist( [segment(924900008, 924900010)] ) ]:
return False
return True
def get_manually(gps_start_time, gps_end_time):
db_location = os.environ["S6_SEGMENT_SERVER"]
segment_connection = segmentdb_utils.setup_database(db_location)
engine = query_engine.LdbdQueryEngine(segment_connection)
# 1. Get v1 science segments
sql = "SELECT segment.start_time, segment.end_time "
sql += "FROM segment_definer, segment "
sql += "WHERE segment.segment_def_id = segment_definer.segment_def_id "
sql += "AND segment_definer.ifos = 'H1' "
sql += "AND segment.segment_def_cdb = segment_definer.creator_db "
sql += "AND segment_definer.name = 'DMT-SCIENCE' "
sql += "AND segment_definer.version = 1 "
sql += "AND NOT (%s > segment.end_time OR segment.start_time > %s)" % (gps_start_time, gps_end_time)
v1_science_segments = segmentlist([segment(row[0], row[1]) for row in engine.query(sql)]).coalesce()
# 2. Get v2 science summaries
sql = "SELECT segment_summary.start_time, segment_summary.end_time "
sql += "FROM segment_definer, segment_summary "
sql += "WHERE segment_summary.segment_def_id = segment_definer.segment_def_id "
sql += "AND segment_definer.ifos = 'H1' "
sql += "AND segment_summary.segment_def_cdb = segment_definer.creator_db "
sql += "AND segment_definer.name = 'DMT-SCIENCE' "
sql += "AND segment_definer.version = 2 "
sql += "AND NOT (%s > segment_summary.end_time OR segment_summary.start_time > %s)" % (gps_start_time, gps_end_time)
v2_science_summaries = segmentlist([segment(row[0], row[1]) for row in engine.query(sql)]).coalesce()
# 1. Get v2 science segments
sql = "SELECT segment.start_time, segment.end_time "
sql += "FROM segment_definer, segment "
sql += "WHERE segment.segment_def_id = segment_definer.segment_def_id "
sql += "AND segment_definer.ifos = 'H1' "
sql += "AND segment.segment_def_cdb = segment_definer.creator_db "
sql += "AND segment_definer.name = 'DMT-SCIENCE' "
sql += "AND segment_definer.version = 2 "
sql += "AND NOT (%s > segment.end_time OR segment.start_time > %s)" % (gps_start_time, gps_end_time)
v2_science_segments = segmentlist([segment(row[0], row[1]) for row in engine.query(sql)]).coalesce()
result = (v1_science_segments - v2_science_summaries) + v2_science_segments
result.coalesce()
result &= segmentlist([segment(gps_start_time, gps_end_time)])
return result
def find_frame_urls(self, site, frametype, gpsstart, gpsend,
match=None, urltype=None, on_gaps="warn"):
"""Query the LDR host for the most all frame files of the given
site and frametype in the given [gpsstart, gpsend) interval.
Use urltype to restrict th returned frames to the given
scheme (e.g. "file").
If on_gaps="error" is given, raises RuntimeError if there are
gaps in the found frame list, otherwise prints warning if
"warn" is given or nothing if "ignore" is given.
Use match to return only those frames matching the given
regular expression.
Returns glue.lal.Cache.
"""
if on_gaps not in ("warn", "error", "ignore"):
raise ValueError("on_gaps must be 'warn', 'error', or 'ignore'.")
url = ("%s/gwf/%s/%s/%s,%s"
% (_url_prefix, site, frametype, gpsstart, gpsend))
# if a URL type is specified append it to the path
if urltype:
url += "/%s" % urltype
# request JSON output
url += ".json"
# append a regex if input
if match:
url += "?match=%s" % match
# make query
response = self._requestresponse("GET", url)
urllist = decode(response.read())
out = lal.Cache([lal.CacheEntry.from_T050017(x,
coltype=self.LIGOTimeGPSType) for x in urllist])
if on_gaps == "ignore":
return out
else:
span = segments.segment(gpsstart, gpsend)
seglist = segments.segmentlist(e.segment for e in out).coalesce()
missing = (segments.segmentlist([span]) - seglist).coalesce()
if span in seglist:
return out
else:
msg = "Missing segments: \n%s" % "\n".join(map(str, missing))
if on_gaps=="warn":
sys.stderr.write("%s\n" % msg)
return out
else:
raise RuntimeError(msg)
def build_segment_list_one(
engine, gps_start_time, gps_end_time, ifo, segment_name, version=None, start_pad=0, end_pad=0
):
"""Builds a list of segments satisfying the given criteria """
seg_result = segmentlist([])
sum_result = segmentlist([])
# Is there any way to get segment and segement summary in one query?
# Maybe some sort of outer join where we keep track of which segment
# summaries we've already seen.
sql = "SELECT segment_summary.start_time, segment_summary.end_time "
sql += "FROM segment_definer, segment_summary "
sql += "WHERE segment_summary.segment_def_id = segment_definer.segment_def_id "
sql += "AND segment_definer.ifos = '%s' " % ifo
if engine.__class__ == query_engine.LdbdQueryEngine:
sql += "AND segment_summary.segment_def_cdb = segment_definer.creator_db "
sql += "AND segment_definer.name = '%s' " % segment_name
sql += "AND segment_definer.version = %s " % version
sql += "AND NOT (%s > segment_summary.end_time OR segment_summary.start_time > %s)" % (gps_start_time, gps_end_time)
rows = engine.query(sql)
for sum_start_time, sum_end_time in rows:
sum_start_time = (sum_start_time < gps_start_time) and gps_start_time or sum_start_time
sum_end_time = (sum_end_time > gps_end_time) and gps_end_time or sum_end_time
sum_result |= segmentlist([segment(sum_start_time, sum_end_time)])
# We can't use queries paramaterized with ? since the ldbd protocol doesn't support it...
sql = "SELECT segment.start_time + %d, segment.end_time + %d " % (start_pad, end_pad)
sql += "FROM segment, segment_definer "
sql += "WHERE segment.segment_def_id = segment_definer.segment_def_id "
if engine.__class__ == query_engine.LdbdQueryEngine:
sql += "AND segment.segment_def_cdb = segment_definer.creator_db "
sql += "AND segment_definer.ifos = '%s' " % ifo
sql += "AND segment_definer.name = '%s' " % segment_name
sql += "AND segment_definer.version = %s " % version
sql += "AND NOT (%s > segment.end_time OR segment.start_time > %s)" % (gps_start_time, gps_end_time)
rows = engine.query(sql)
for seg_start_time, seg_end_time in rows:
seg_start_time = (seg_start_time < gps_start_time) and gps_start_time or seg_start_time
seg_end_time = (seg_end_time > gps_end_time) and gps_end_time or seg_end_time
seg_result |= segmentlist([segment(seg_start_time, seg_end_time)])
engine.close()
return sum_result, seg_result
def __init__(self,ifo,name,version,hackDec11=False):
self.known=segments.segmentlist([])
self.active=segments.segmentlist([])
# self.metadata={}
self.flagDict={}
self.ifo=ifo
self.name=name
self.version=version
self.temp_process_ids={} # Used to hold the data
# # associated with a process_id
if hackDec11:
self.insert_history={}
else:
self.insert_history=[] # holds the process_metadatas and insertion_metadatas # Note that this assumes that proper dictionaries are appended to this list
def get_science_segs_from_datafind_outs(datafindcaches):
"""
This function will calculate the science segments that are covered in
the OutGroupList containing the frame files returned by various
calls to the datafind server. This can then be used to check whether this
list covers what it is expected to cover.
Parameters
----------
datafindcaches : OutGroupList
List of all the datafind output files.
Returns
--------
newScienceSegs : Dictionary of ifo keyed glue.segment.segmentlist instances
The times covered by the frames found in datafindOuts.
"""
newScienceSegs = {}
for cache in datafindcaches:
if len(cache) > 0:
groupSegs = segments.segmentlist(e.segment for e in cache).coalesce()
ifo = cache.ifo
if not newScienceSegs.has_key(ifo):
newScienceSegs[ifo] = groupSegs
else:
newScienceSegs[ifo].extend(groupSegs)
newScienceSegs[ifo].coalesce()
return newScienceSegs
def find_segments(doc, key, use_segment_table = True):
key_pieces = key.split(':')
while len(key_pieces) < 3:
key_pieces.append('*')
filter_func = lambda x: str(x.ifos) == key_pieces[0] and (str(x.name) == key_pieces[1] or key_pieces[1] == '*') and (str(x.version) == key_pieces[2] or key_pieces[2] == '*')
# Find all segment definers matching the critieria
seg_def_table = lsctables.SegmentDefTable.get_table(doc)
seg_defs = filter(filter_func, seg_def_table)
seg_def_ids = map(lambda x: str(x.segment_def_id), seg_defs)
# Find all segments belonging to those definers
if use_segment_table:
seg_table = lsctables.SegmentTable.get_table(doc)
seg_entries = filter(lambda x: str(x.segment_def_id) in seg_def_ids, seg_table)
else:
seg_sum_table = lsctables.SegmentSumTable.get_table(doc)
seg_entries = filter(lambda x: str(x.segment_def_id) in seg_def_ids, seg_sum_table)
# Combine into a segmentlist
ret = segmentlist(map(lambda x: segment(x.start_time, x.end_time), seg_entries))
ret.coalesce()
return ret
def expand_version_number(engine, segdef):
ifo, name, version, start_time, end_time, start_pad, end_pad = segdef
if version != '*':
return [segdef]
# Start looking at the full interval
intervals = segmentlist([segment(start_time, end_time)])
# Find the maximum version number
sql = "SELECT max(version) FROM segment_definer "
sql += "WHERE segment_definer.ifos = '%s' " % ifo
sql += "AND segment_definer.name = '%s' " % name
rows = engine.query(sql)
try:
version = len(rows[0]) and rows[0][0] or 1
except:
version = None
results = []
while version > 0:
for interval in intervals:
segs = query_segments(engine, 'segment_summary', [(ifo, name, version, interval[0], interval[1], 0, 0)])
for seg in segs[0]:
results.append( (ifo, name, version, seg[0], seg[1], 0, 0) )
intervals.coalesce()
intervals -= segs[0]
version -= 1
return results
def get_by_name(self, name, clip_to_valid = False):
"""
Retrieve the active segmentlists whose name equals name.
The result is a segmentlistdict indexed by instrument. All
segmentlist objects within it will be copies of the
contents of this object, modifications will not affect the
contents of this object. If clip_to_valid is True then the
segmentlists will be intersected with their respective
intervals of validity, otherwise they will be the verbatim
active segments.
NOTE: the intersection operation required by clip_to_valid
will yield undefined results unless the active and valid
segmentlist objects are coalesced.
"""
result = segments.segmentlistdict()
for seglist in self:
if seglist.name != name:
continue
segs = seglist.active
if clip_to_valid:
# do not use in-place intersection
segs = segs & seglist.valid
for instrument in seglist.instruments:
if instrument in result:
raise ValueError("multiple '%s' segmentlists for instrument '%s'" % (name, instrument))
result[instrument] = segments.segmentlist(segs)
if not result:
raise KeyError("no segmentlists named '%s'" % name)
return result
def find_times(self, site, frametype, gpsstart=None, gpsend=None):
"""Query the LDR for times for which frames are avaliable
Use gpsstart and gpsend to restrict the returned times to
this semiopen interval.
@returns: L{segmentlist<glue.segments.segmentlist>}
@param site:
single-character name of site to match
@param frametype:
name of frametype to match
@param gpsstart:
integer GPS start time of query
@param gpsend:
integer GPS end time of query
@type site: L{str}
@type frametype: L{str}
@type gpsstart: L{int}
@type gpsend: L{int}
"""
if gpsstart and gpsend:
url = ("%s/gwf/%s/%s/segments/%s,%s.json"
% (_url_prefix, site, frametype, gpsstart, gpsend))
else:
url = ("%s/gwf/%s/%s/segments.json"
% (_url_prefix, site, frametype))
response = self._requestresponse("GET", url)
segmentlist = decode(response.read())
return segments.segmentlist(map(segments.segment, segmentlist))
def segmentlistdict(self):
"""
A segmentlistdict object describing the instruments and time
spanned by this CacheEntry. A new object is constructed each time
this attribute is accessed (segments are immutable so there is no
reason to try to share a reference to the CacheEntry's internal
segment; modifications of one would not be reflected in the other
anyway).
Example:
>>> c = CacheEntry("H1 S5 815901601 576.5 file://localhost/home/kipp/tmp/1/H1-815901601-576.xml")
>>> c.segmentlistdict['H1']
[segment(LIGOTimeGPS(815901601, 0), LIGOTimeGPS(815902177, 500000000))]
The \"observatory\" column of the cache entry, which is frequently
used to store instrument names, is parsed into instrument names for
the dictionary keys using the same rules as
glue.ligolw.lsctables.instrumentsproperty.get().
Example:
>>> c = CacheEntry("H1H2, S5 815901601 576.5 file://localhost/home/kipp/tmp/1/H1H2-815901601-576.xml")
>>> c.segmentlistdict['H1H2']
[segment(LIGOTimeGPS(815901601, 0), LIGOTimeGPS(815902177, 500000000))]
"""
# the import has to be done here to break the cyclic
# dependancy
from glue.ligolw.lsctables import instrumentsproperty
instruments = instrumentsproperty.get(self.observatory) or (None,)
return segments.segmentlistdict((instrument, segments.segmentlist(self.segment is not None and [self.segment] or [])) for instrument in instruments)
def get_slide_coincs_from_cache(cachefile, pattern, match, verb, coinc_stat):
full_coinc_table = []
cache = cachefile.sieve(description=pattern, exact_match=match)
found, missed = cache.checkfilesexist()
files = found.pfnlist()
if not len(files):
print >>sys.stderr, "cache contains no files with " + pattern + " description"
return None
# split the time slide files into 105 groups to aid with I/O
num_files=len(files)
#Changed by Tristan Miller as a memory fix
#groups_of_files = split_seq(files,105)
groups_of_files = split_seq(files,50)
for filegroup in groups_of_files:
if filegroup:
# extract the coinc table
coinc_table = SnglInspiralUtils.ReadSnglInspiralFromFiles(filegroup, mangle_event_id=False, verbose=verb, non_lsc_tables_ok=False)
segDict = SearchSummaryUtils.GetSegListFromSearchSummaries(filegroup)
rings = segments.segmentlist(iterutils.flatten(segDict.values()))
rings.sort()
for k,ring in enumerate(rings):
rings[k] = segments.segment(rings[k][0], rings[k][1] + 10**(-9))
shift_vector = {"H1": 0, "H2": 0, "L1": 5, "V1": 5}
if coinc_table:
SnglInspiralUtils.slideTriggersOnRingWithVector(coinc_table, shift_vector, rings)
full_coinc_table.extend(CoincInspiralUtils.coincInspiralTable(coinc_table,coinc_stat))
return full_coinc_table
def convert_json_list_to_segmentlist(jsonlist):
"""
Helper function used to convert json list of lists type object to a
segmentlist object
"""
segment_list=segments.segmentlist([segments.segment(x[0],x[1]) for x in jsonlist])
return segment_list
def __init__(self, active = (), valid = (), instruments = set(), name = None, version = None, comment = None): """ Initialize a new LigolwSegmentList instance. active and valid are sequences that will be cast to segments.segmentlist objects. They can be generator expressions. The "active" sequence is what is usually thought of as the segment list, the "valid" sequence identifies the intervals of time for which the segment list's state is defined. """ self.valid = segments.segmentlist(valid) self.active = segments.segmentlist(active) self.instruments = instruments self.name = name self.version = version self.comment = comment
def tosegmentxml(file, segs):
"""
Write the glue.segments.segmentlist object segs to file object file in xml
format with appropriate tables.
"""
# generate empty document
xmldoc = ligolw.Document()
xmldoc.appendChild(ligolw.LIGO_LW())
xmldoc.childNodes[-1].appendChild(lsctables.New(lsctables.ProcessTable))
xmldoc.childNodes[-1].appendChild(lsctables.New(lsctables.ProcessParamsTable))
# append process to table
process = ligolw_process.append_process(xmldoc,\
program='pylal.dq.dqSegmentUtils',\
version=__version__,\
cvs_repository='lscsoft',\
cvs_entry_time=__date__)
gpssegs = segments.segmentlist()
for seg in segs:
gpssegs.append(segments.segment(LIGOTimeGPS(seg[0]), LIGOTimeGPS(seg[1])))
# append segs and seg definer
segments_tables = ligolw_segments.LigolwSegments(xmldoc)
segments_tables.add(ligolw_segments.LigolwSegmentList(active=gpssegs))
# finalise
segments_tables.coalesce()
segments_tables.optimize()
segments_tables.finalize(process)
ligolw_process.set_process_end_time(process)
# write file
utils.write_fileobj(xmldoc, file, gz=False)
ifo_times = "".join(grb_ifolist)
if offSourceSegment is None:
print(
"Warning: insufficient multi-IFO data to construct an off-source segment for GRB %s; skipping"
% grb.event_number_grb,
file=sys.stderr)
continue
elif opts.verbose:
print("Sufficient off-source data has been found in", ifo_times,
"time.")
# write out the segment list to a segwizard file
offsource_segfile = idirectory + "/offSourceSeg.txt"
segmentsUtils.tosegwizard(open(offsource_segfile, "w"),
segments.segmentlist([offSourceSegment]))
onsource_segfile = idirectory + "/onSourceSeg.txt"
segmentsUtils.tosegwizard(file(onsource_segfile, "w"),
segments.segmentlist([onSourceSegment]))
segLen = abs(onSourceSegment)
bufferSegment = segments.segment( onSourceSegment[0]-opts.number_buffer_left*segLen,\
onSourceSegment[1]+opts.number_buffer_right*segLen)
buffer_segfile = idirectory + "/bufferSeg.txt"
segmentsUtils.tosegwizard(file(buffer_segfile, "w"),
segments.segmentlist([bufferSegment]))
if opts.verbose:
print("on-source segment: ", onSourceSegment)
print("off-source segment: ", offSourceSegment)
############################################################################
##############################################################################
# get the pad and chunk lengths from the values in the ini file
paddata = int(cp.get('data', 'pad-data'))
n = int(cp.get('data', 'segment-length'))
s = int(cp.get('data', 'number-of-segments'))
r = int(cp.get('data', 'sample-rate'))
o = int(cp.get('inspiral', 'segment-overlap'))
length = (n * s - (s - 1) * o) / r
overlap = o / r
minsciseg = length + 2 * paddata
##############################################################################
# Based on the start and end time, generate a list of epochs to
# analyze. An entire hipe dag will be run for each of these epochs.
search_epochs = segments.segmentlist()
istart = opts.start_time
while (istart < opts.end_time):
iend = istart + opts.interval
if iend > opts.end_time:
iend = opts.end_time
search_epochs.append(segments.segment(istart, iend))
istart += opts.interval
# FIXME: the writing out of the segments should be done at the end so
# that successfully generated dags, etc can be maintained from run to
# run
segmentsUtils.tosegwizard(file("multi_hipe_selectedsegs.txt", 'w'),
search_epochs)
##############################################################################
# Read in all the segment lists
def find_frame_urls(self,
site,
frametype,
gpsstart,
gpsend,
match=None,
urltype=None,
on_gaps="warn"):
"""Find the framefiles for the given type in the [start, end) interval
frame
@param site:
single-character name of site to match
@param frametype:
name of frametype to match
@param gpsstart:
integer GPS start time of query
@param gpsend:
integer GPS end time of query
@param match:
regular expression to match against
@param urltype:
file scheme to search for (e.g. 'file')
@param on_gaps:
what to do when the requested frame isn't found, one of:
- C{'warn'} (default): print a warning,
- C{'error'}: raise an L{RuntimeError}, or
- C{'ignore'}: do nothing
@type site: L{str}
@type frametype: L{str}
@type gpsstart: L{int}
@type gpsend: L{int}
@type match: L{str}
@type urltype: L{str}
@type on_gaps: L{str}
@returns: L{Cache<glue.lal.Cache>}
@raises RuntimeError: if gaps are found and C{on_gaps='error'}
"""
if on_gaps not in ("warn", "error", "ignore"):
raise ValueError("on_gaps must be 'warn', 'error', or 'ignore'.")
url = ("%s/gwf/%s/%s/%s,%s" %
(_url_prefix, site, frametype, gpsstart, gpsend))
# if a URL type is specified append it to the path
if urltype:
url += "/%s" % urltype
# request JSON output
url += ".json"
# append a regex if input
if match:
url += "?match=%s" % match
# make query
response = self._requestresponse("GET", url)
urllist = decode(response.read())
out = lal.Cache([
lal.CacheEntry.from_T050017(x, coltype=self.LIGOTimeGPSType)
for x in urllist
])
if on_gaps == "ignore":
return out
else:
span = segments.segment(gpsstart, gpsend)
seglist = segments.segmentlist(e.segment for e in out).coalesce()
missing = (segments.segmentlist([span]) - seglist).coalesce()
if span in seglist:
return out
else:
msg = "Missing segments: \n%s" % "\n".join(map(str, missing))
if on_gaps == "warn":
sys.stderr.write("%s\n" % msg)
return out
else:
raise RuntimeError(msg)
def __init__(self, ifo):
self.fig, self.axes = SnglBurstUtils.make_burst_plot(
"GPS Time (s)", "Frequency (Hz)")
self.ifo = ifo
self.nevents = 0
self.seglist = segments.segmentlist()
def plotdutycycle(segdict, outfile, binlength=3600, keys=None, t0=None,\
showmean=False, **kwargs):
"""
Plot the percentage duty cycle each flag in the given
glue.segments.segmentlistdict, binned over the given duration.
"""
# get time limits
xlim = kwargs.pop("xlim", None)
if xlim is None:
try:
extents = [seg.extent() for seg in segdict.values()]
start = min(s[0] for s in extents)
end = max(s[1] for s in extents)
except ValueError:
start = 0
end = 1
xlim = start, end
else:
start, end = xlim
# get unit for plot
unit, timestr = plotutils.time_axis_unit(end - start)
# set xlabel and renomalize time
xlabel = kwargs.pop("xlabel", None)
if not xlabel:
if not t0:
t0 = start
unit, timestr = plotutils.time_axis_unit(end - start)
t0 = LIGOTimeGPS(float(t0))
if t0.nanoseconds == 0:
xlabel = datetime.datetime(*date.XLALGPSToUTC(t0)[:6])\
.strftime("%B %d %Y, %H:%M:%S %ZUTC")
xlabel = "Time (%s) since %s (%s)" % (timestr, xlabel, int(t0))
else:
xlabel = datetime.datetime(*date.XLALGPSToUTC(\
LIGOTimeGPS(t0.seconds))[:6])\
.strftime("%B %d %Y, %H:%M:%S %ZUTC")
xlabel = "Time (%s) since %s (%s)"\
% (timestr,
xlabel.replace(" UTC", ".%.3s UTC" % t0.nanoseconds),\
t0)
t0 = float(t0)
xlim[0] = (start - t0) / unit
xlim[1] = (end - t0) / unit
ylabel = kwargs.pop("ylabel", "")
title = kwargs.pop("title", "")
subtitle = kwargs.pop("subtitle", "")
# get other parameters
loc = kwargs.pop("loc", 0)
legalpha = kwargs.pop("alpha", 0.8)
labels_inset = kwargs.pop("labels_inset", False)
bbox_inches = kwargs.pop("bbox_inches", "tight")
hidden_colorbar = kwargs.pop("hidden_colorbar", False)
# escape underscores for latex text
if not keys:
keys = segdict.keys()
if pylab.rcParams["text.usetex"]:
newdict = segments.segmentlistdict()
for i, key in enumerate(keys):
newkey = re.sub('(?<!\\\\)_', '\_', key)
keys[i] = newkey
newdict[newkey] = segdict[key]
segdict = newdict
#
# generate duty cycle info
#
# generate bins
binlength = float(binlength)
if int(end - start) % binlength == 0:
numbins = int(end - start) / binlength
else:
numbins = float(end - start) // binlength + 1
bins = numpy.arange(float(start), float(end), binlength) + binlength / 2
duty = dict((key, numpy.zeros(numbins)) for key in keys)
bs = float(start)
for i in range(numbins):
be = float(bs + binlength)
seg = segments.segmentlist([segments.segment(bs, be)])
for key in keys:
duty[key][i] = float(abs(segdict[key] & seg)) / abs(seg) * 100
bs += binlength
if showmean:
mean = dict((key, numpy.zeros(numbins)) for key in keys)
for key in keys:
mean[key] = [duty[key][:i + 1].mean() for i in range(numbins)]
#
# generate plot
#
bins = (bins - t0) / unit
plot = plotutils.BarPlot(xlabel, ylabel, title, subtitle)
for i, key in enumerate(keys):
if showmean:
thislabel = plotutils.display_name(
key) + ' (%.2f\%%)' % (mean[key][-1])
else:
thislabel = plotutils.display_name(key)
plot.add_content(bins, duty[key], label=thislabel,\
alpha=0.8, width=binlength/unit)
plot.finalize(loc=loc, alpha=legalpha)
# add running mean
if showmean:
for i, key in enumerate(keys):
print i, key
plot.ax.plot(bins, mean[key], linestyle='--')
plot.ax.get_legend().get_frame().set_alpha(0.5)
# add colorbar
if hidden_colorbar:
plotutils.add_colorbar(plot.ax, visible=False)
# set limits
plot.ax.autoscale_view(tight=True, scalex=True)
if xlim:
plot.ax.set_xlim(map(float, xlim))
plot.ax.set_ylim(0, 100)
# set grid
plot.ax.grid(True, which="both")
plotutils.set_time_ticks(plot.ax)
plotutils.set_minor_ticks(plot.ax, x=False)
# save figure
plot.savefig(outfile, bbox_inches=bbox_inches,\
bbox_extra_artists=plot.ax.texts)
def grab_segments(start, end, flag,\
segment_url='https://segdb.ligo.caltech.edu',\
segment_summary=False):
"""
Returns a segmentlist containing the segments during which the given flag
was active in the given period.
Arguments:
start : int
GPS start time
end : int
GPS end time
flag : string
'IFO:NAME:VERSION' format string
Keyword arguments:
segment_url : string
url of segment database to query, default https://segdb.ligo.caltech.edu
segment_summary : [ True | False ]
also return the glue.segments.segmentlist defining the valid span of the
returned segments
"""
# set times
start = int(math.floor(start))
end = int(math.ceil(end))
# set query engine
connection = segmentdb_utils.setup_database(segment_url)
engine = query_engine.LdbdQueryEngine(connection)
# format flag name
if isinstance(flag, basestring):
flags = flag.split(',')
else:
flags = flag
segdefs = []
for f in flags:
spec = f.split(':')
if len(spec) < 2 or len(spec) > 3:
raise AttributeError, "Included segements must be of the form "+\
"ifo:name:version or ifo:name:*"
ifo = spec[0]
name = spec[1]
if len(spec) is 3 and spec[2] is not '*':
version = int(spec[2])
if version < 1:
raise AttributeError, "Segment version numbers must be greater than zero"
else:
version = '*'
# expand segment definer
segdefs += segmentdb_utils.expand_version_number(engine, (ifo, name, version, \
start, end, 0, 0))
# query database and return
segs = segmentdb_utils.query_segments(engine, 'segment', segdefs)
segs = [s.coalesce() for s in segs]
if segment_summary:
segsums = segmentdb_utils.query_segments(engine, 'segment_summary',
segdefs)
#segsums = reduce(operator.or_, segsums).coalesce()
segsums = [s.coalesce() for s in segsums]
segsummap = [segments.segmentlist() for f in flags]
for segdef, segsum in zip(segdefs, segsums):
try:
fidx = flags.index(':'.join(map(str, segdef[:3])))
except ValueError:
fidx = flags.index(':'.join(segdef[:2]))
segsummap[fidx].extend(segsum)
if flag == flags[0]:
return segs[0], segsummap[0]
else:
return segs, segsummap
if flag == flags[0]:
return segs[0]
else:
return segs
def __init__(self, *files, **kwargs):
sample = kwargs.get('sample', 'foreground')
# Open the files and split them into coinc files, bank files, psds,
# and triggers.
key = itemgetter(0)
files = [_hdf_file(f) for f in files]
files = sorted([(_classify_hdf_file(f, sample), f) for f in files],
key=key)
files = {
key: list(v[1] for v in value)
for key, value in groupby(files, key)
}
try:
coinc_file, = files['coincs']
except (KeyError, ValueError):
raise ValueError('You must provide exactly one coinc file.')
try:
bank_file, = files['bank']
except (KeyError, ValueError):
raise ValueError(
'You must provide exactly one template bank file.')
try:
psd_files = files['psds']
except KeyError:
raise ValueError('You must provide PSD files.')
try:
trigger_files = files['triggers']
except KeyError:
raise ValueError('You must provide trigger files.')
self._bank = bank_file
key_prefix = 'detector_'
detector_nums, self._ifos = zip(*sorted(
(int(key[len(key_prefix):]), value)
for key, value in coinc_file.attrs.items()
if key.startswith(key_prefix)))
coinc_group = coinc_file[sample]
self._timeslide_interval = coinc_file.attrs.get(
'timeslide_interval', 0)
self._template_ids = coinc_group['template_id']
self._timeslide_ids = coinc_group.get('timeslide_id',
np.zeros(len(self)))
self._trigger_ids = [
coinc_group['trigger_id{}'.format(detector_num)]
for detector_num in detector_nums
]
triggers = {}
for f in trigger_files:
(ifo, group), = f.items()
triggers[ifo] = [
group['snr'], group['coa_phase'], group['end_time']
]
self._triggers = tuple(triggers[ifo] for ifo in self._ifos)
psdseglistdict = {}
for psd_file in psd_files:
(ifo, group), = psd_file.items()
psd = [group['psds'][str(i)] for i in range(len(group['psds']))]
psdseglistdict[ifo] = segmentlist(
_psd_segment(*segargs) for segargs in zip(
psd, group['start_time'], group['end_time']))
self._psds = [psdseglistdict[ifo] for ifo in self._ifos]
def InsertMultipleDQXMLFileThreaded(filenames,
logger,
server='http://slwebtest.virgo.infn.it',
hackDec11=True,
debug=True,
threads=1,
testing_options={}):
"""
Inserts multiple dqxml files of data into the DQSEGDB.
- filenames is a list of string filenames for DQXML files.
- hackDec11 is used to turn off good features that the server doesn't
yet support.
returns True if it completes sucessfully
- options is a dictionary including (optionally):offset(int),synchronize(time in 'HH:MM' format (string))
"""
logger.info(
"Beginning call to InsertMultipleDQXMLFileThreaded. This message last updated April 14 2015, Ciao da Italia!"
)
from threading import Thread
from Queue import Queue
import sys
# Make a call to server+'/dq':
protocol = server.split(':')[0]
serverfqdn = server.split('/')[-1]
apiResult = queryAPIVersion(protocol, serverfqdn, False)
# If the API change results in a backwards incompatibility, handle it here with a flag that affects behavior below
if apiResult >= "2.1.0":
# S6 style comments are needed
new_comments = True
else:
# Older server, so don't want to supply extra comments...
new_comments = False
if apiResult >= "2.1.15":
# Alteration to insertion_metadata from uri to comment to accomodate s6 data conversion
use_new_insertion_metadata = True
else:
use_new_insertion_metadata = False
if 'offset' in testing_options:
offset = int(testing_options['offset'])
else:
offset = 0
if 'synchronize' in testing_options:
synchronize = testing_options['synchronize']
xmlparser = pyRXP.Parser()
lwtparser = ldbd.LIGOLwParser()
flag_versions = {}
# flag_versions, filename, server, hackDec11, debug are current variables
# This next bunch of code is specific to a given file:
if len(filenames) < 1:
print "Empty file list sent to InsertMultipleDQXMLFileThreaded"
raise ValueError
for filename in filenames:
segment_md = setupSegment_md(filename, xmlparser, lwtparser, debug)
# segment_md, flag_versions, filename, server, hackDec11, debug are current variables
flag_versions_numbered = {}
for j in range(len(segment_md.table['segment_definer']['stream'])):
flag_versions_numbered[j] = {}
for i, entry in enumerate(
segment_md.table['segment_definer']['orderedcol']):
#print j,entry,segment_md.table['segment_definer']['stream'][j][i]
flag_versions_numbered[j][entry] = segment_md.table[
'segment_definer']['stream'][j][i]
# parse process table and make a dict that corresponds with each
# process, where the keys for the dict are like "process:process_id:1"
# so that we can match
# these to the flag_versions from the segment definer in the next
# section
# Note: Wherever temp_ preceeds a name, it is generally an identifier
# field from the dqxml, that is only good for the single dqxml file
# being parsed
process_dict = {}
# Going to assign process table streams to process_dict with a key
# matching process_id (process:process_id:0 for example)
for j in range(len(segment_md.table['process']['stream'])):
process_id_index = segment_md.table['process']['orderedcol'].index(
'process_id')
temp_process_id = segment_md.table['process']['stream'][j][
process_id_index]
# Now we're going to assign elements to process_dict[process_id]
process_dict[temp_process_id] = {}
for i, entry in enumerate(
segment_md.table['process']['orderedcol']):
#print j,entry,segment_md.table['process']['stream'][j][i]
process_dict[temp_process_id][entry] = segment_md.table[
'process']['stream'][j][i]
# Note that the segment_md.table['process']['stream'][0] looks like this:
#0 program SegGener
#0 version 6831
#0 cvs_repository https://redoubt.ligo-wa.caltech.edu/
#0 svn/gds/trunk/Monitors/SegGener/SegGener.cc
#0 cvs_entry_time 1055611021
#0 comment Segment generation from an OSC condition
#0 node l1gds2
#0 username [email protected]
#0 unix_procid 24286
#0 start_time 1065916603
#0 end_time 1070395521
#0 process_id process:process_id:0
#0 ifos L0L1
# So now I have all of that info stored by the process_id keys
# Eventually I have to map these elements to the process_metadata
# style.. maybe I can do that now:
process_dict[temp_process_id]['process_metadata'] = {}
if hackDec11:
process_dict[temp_process_id]['process_metadata'][
'process_start_time'] = process_dict[temp_process_id][
'start_time']
else: # This is for the newer server APIs: (April 24 2015 we checked it (it probably changed before ER6 finally))
process_dict[temp_process_id]['process_metadata'][
'process_start_timestamp'] = process_dict[temp_process_id][
'start_time']
if new_comments:
process_dict[temp_process_id][
'process_comment'] = process_dict[temp_process_id][
'comment']
process_dict[temp_process_id]['process_metadata'][
'uid'] = process_dict[temp_process_id]['username']
process_dict[temp_process_id]['process_metadata']['args'] = [
] ### Fix!!! dqxml has no args???
process_dict[temp_process_id]['process_metadata'][
'pid'] = process_dict[temp_process_id]['unix_procid']
process_dict[temp_process_id]['process_metadata'][
'name'] = process_dict[temp_process_id]['program']
process_dict[temp_process_id]['process_metadata'][
'fqdn'] = process_dict[temp_process_id][
'node'] ### Fix!!! Improvement: not really fqdn, just the node name
# So now I have process_dict[temp_process_id]['process_metadata'] for each
# process_id, and can add it to a flag version when it uses it; really I
# should group it with the segment summary info because that has the
# insertion_metadata start and stop time
### Fix!!! Get the args from the *other* process table... yikes
### Double check what is done below works!
# First pass:
#if debug:
# import pdb
# pdb.set_trace()
temp_process_params_process_id = None
try:
len(segment_md.table['process_params']['stream'])
except:
logger.info("No process_params table for file: %s" % filename)
else:
for j in range(len(segment_md.table['process_params']['stream'])):
process_id_index = segment_md.table['process_params'][
'orderedcol'].index('process_id')
temp_process_params_process_id = segment_md.table[
'process_params']['stream'][j][process_id_index]
# This next bit looks a bit strange, but the goal is to pull off only the param and value from each row of the process_params table, and then put them into the process_metadata
# Thus we loop through the columns in each row and toss out everything but the param and value entries, and then outside the for loop, append them to the args list
for i, entry in enumerate(
segment_md.table['process_params']['orderedcol']):
if entry == "param":
temp_param = str(
segment_md.table['process_params']['stream'][j][i])
if entry == "value":
temp_value = str(
segment_md.table['process_params']['stream'][j][i])
process_dict[temp_process_params_process_id][
'process_metadata']['args'].append(str(temp_param))
process_dict[temp_process_params_process_id][
'process_metadata']['args'].append(str(temp_value))
#if debug:
# import pdb
# pdb.set_trace()
temp_id_to_flag_version = {}
for i in flag_versions_numbered.keys():
ifo = flag_versions_numbered[i]['ifos']
name = flag_versions_numbered[i]['name']
version = flag_versions_numbered[i]['version']
if (ifo, name, version) not in flag_versions.keys():
if new_comments == True:
flag_versions[(ifo, name, version)] = InsertFlagVersion(
ifo, name, version)
else:
flag_versions[(ifo, name, version)] = InsertFlagVersionOld(
ifo, name, version)
if new_comments:
flag_versions[(ifo, name, version)].flag_description = str(
flag_versions_numbered[i]['comment']
) # old segment_definer comment = new flag_description
# OUTDATED PLACEHOLDER: flag_versions[(ifo,name,version)].version_comment=str(flag_versions_numbered[i]['comment'])
else:
flag_versions[(ifo, name, version)].flag_comment = str(
flag_versions_numbered[i]['comment'])
flag_versions[(ifo, name, version)].version_comment = str(
flag_versions_numbered[i]['comment'])
flag_versions[(
ifo, name,
version)].temporary_definer_id = flag_versions_numbered[i][
'segment_def_id']
flag_versions[(
ifo, name, version
)].temporary_process_id = flag_versions_numbered[i]['process_id']
# Populate reverse lookup dictionary:
temp_id_to_flag_version[flag_versions[(
ifo, name, version)].temporary_definer_id] = (ifo, name,
version)
# ways to solve the metadata problem:
# Associate each insertion_metadata block with a process, then group
# them and take the min insert_data_start and max insert_data_stop
# parse segment_summary table and associate known segments with
# flag_versions above:
## Note this next line is needed for looping over multiple files
for i in flag_versions.keys():
flag_versions[i].temp_process_ids = {}
for j in range(len(segment_md.table['segment_summary']['stream'])):
#flag_versions_numbered[j] = {}
seg_def_index = segment_md.table['segment_summary'][
'orderedcol'].index('segment_def_id')
#print "associated seg_def_id is: "+ segment_md.table['segment_summary']['stream'][j][seg_def_index]
(ifo, name, version
) = temp_id_to_flag_version[segment_md.table['segment_summary']
['stream'][j][seg_def_index]]
seg_sum_index = segment_md.table['segment_summary'][
'orderedcol'].index('segment_sum_id')
# Unneeded:
#flag_versions[(ifo,name,version)].temporary_segment_sum_id = segment_md.table['segment_summary']['stream'][j][seg_sum_index]
start_time_index = segment_md.table['segment_summary'][
'orderedcol'].index('start_time')
end_time_index = segment_md.table['segment_summary'][
'orderedcol'].index('end_time')
start_time = segment_md.table['segment_summary']['stream'][j][
start_time_index] + offset
end_time = segment_md.table['segment_summary']['stream'][j][
end_time_index] + offset
comment_index = segment_md.table['segment_summary'][
'orderedcol'].index('comment')
seg_sum_comment = segment_md.table['segment_summary']['stream'][j][
comment_index]
new_seg_summary = segments.segmentlist(
[segments.segment(start_time, end_time)])
flag_versions[(ifo, name, version)].appendKnown(new_seg_summary)
# Now I need to build up the insertion_metadata dictionary for this
# summary:
# Now I need to associate the right process with the known
# segments here, and put the start and end time into the
# insertion_metadata part of the
# insert_history dict
# Plan for processes and affected data:
# Loop through segment summaries
# If we haven't seen the associated process before, create it:
# First, append the temp_process_id to temp_process_ids
# Then, each temp_process_ids entry is a dictionary, where the one
# element is start_affected time, and the other is end_affected
# time, and later we will combine this with the correct
# process_metadata dictionary
process_id_index = segment_md.table['segment_summary'][
'orderedcol'].index('process_id')
temp_process_id = segment_md.table['segment_summary']['stream'][j][
process_id_index]
if temp_process_id in flag_versions[(
ifo, name, version)].temp_process_ids.keys():
# We don't need to append this process metadata, as it already
# exists We do need to extend the affected data start and stop
# to match
if start_time < flag_versions[(
ifo, name, version
)].temp_process_ids[temp_process_id]['insert_data_start']:
flag_versions[(ifo, name, version)].temp_process_ids[
temp_process_id]['insert_data_start'] = start_time
if end_time > flag_versions[(
ifo, name, version
)].temp_process_ids[temp_process_id]['insert_data_stop']:
flag_versions[(ifo, name, version)].temp_process_ids[
temp_process_id]['insert_data_stop'] = end_time
else:
# Need to make the dictionary entry for this process_id
if seg_sum_comment != None:
flag_versions[(ifo, name,
version)].provenance_url = seg_sum_comment
else:
flag_versions[(ifo, name, version)].provenance_url = ''
flag_versions[(
ifo, name,
version)].temp_process_ids[temp_process_id] = {}
flag_versions[(ifo, name, version)].temp_process_ids[
temp_process_id]['insert_data_start'] = start_time
flag_versions[(ifo, name, version)].temp_process_ids[
temp_process_id]['insert_data_stop'] = end_time
# Now, I need to append an insert_history element to the flag_versions
# for this ifo,name, version, as I have the correct insertion_metadata
# and the correct
# process_metadata (from the process_dict earlier
if debug:
t1 = time.time()
for i in flag_versions.keys():
for pid in flag_versions[i].temp_process_ids.keys():
start = flag_versions[i].temp_process_ids[pid][
'insert_data_start']
stop = flag_versions[i].temp_process_ids[pid][
'insert_data_stop']
if new_comments:
flag_versions[i].flag_version_comment = process_dict[pid][
'process_comment']
insert_history_dict = {}
try:
insert_history_dict['process_metadata'] = process_dict[
pid]['process_metadata']
except:
raise
# import pdb
# pdb.set_trace()
insert_history_dict['insertion_metadata'] = {}
insert_history_dict['insertion_metadata'][
'insert_data_stop'] = stop
insert_history_dict['insertion_metadata'][
'insert_data_start'] = start
ifo = flag_versions[i].ifo
version = flag_versions[i].version
name = flag_versions[i].name
if use_new_insertion_metadata == True:
insert_history_dict['insertion_metadata'][
'comment'] = '/dq/' + '/'.join(
[str(ifo), str(name),
str(version)]
) # FIX make dq a constant string in case we ever change it
else:
insert_history_dict['insertion_metadata'][
'uri'] = '/dq/' + '/'.join(
[str(ifo), str(name),
str(version)]
) # FIX make dq a constant string in case we ever change it
#print ifo,name,version
insert_history_dict['insertion_metadata'][
'timestamp'] = _UTCToGPS(time.gmtime())
insert_history_dict['insertion_metadata'][
'auth_user'] = process.get_username()
#if hackDec11:
# # note that this only uses one insert_history...despite
# all that hard work to get the list right...
# # so this might break something...
# flag_versions[i].insert_history=insert_history_dict
#else:
# flag_versions[i].insert_history.append(insert_history_dict)
flag_versions[i].insert_history.append(insert_history_dict)
# parse segment table and associate known segments with flag_versions
# above:
try:
for j in range(len(segment_md.table['segment']['stream'])):
#flag_versions_numbered[j] = {}
seg_def_index = segment_md.table['segment'][
'orderedcol'].index('segment_def_id')
#print "associated seg_def_id is: "+
# segment_md.table['segment']['stream'][j][seg_def_index]
(ifo, name, version) = temp_id_to_flag_version[
segment_md.table['segment']['stream'][j][seg_def_index]]
#seg_sum_index = segment_md.table['segment']['orderedcol'].index('segment_sum_id')
start_time_index = segment_md.table['segment'][
'orderedcol'].index('start_time')
end_time_index = segment_md.table['segment'][
'orderedcol'].index('end_time')
start_time = segment_md.table['segment']['stream'][j][
start_time_index] + offset
end_time = segment_md.table['segment']['stream'][j][
end_time_index] + offset
new_seg = segments.segmentlist(
[segments.segment(start_time, end_time)])
flag_versions[(ifo, name, version)].appendActive(new_seg)
except KeyError:
logger.info("No segment table for this file: %s" % filename)
if debug:
print "No segment table for this file: %s" % filename
except:
print "Unexpected error:", sys.exc_info()[0]
raise
for i in flag_versions.keys():
flag_versions[i].coalesceInsertHistory()
if threads > 1:
# Call this after the loop over files, and we should be good to go
concurrent = min(threads, len(i)) # Fix!!! why did I do len(i) ???
q = Queue(concurrent *
2) # Fix!!! Improvement: remove hardcoded concurrency
for i in range(concurrent):
t = Thread(target=threadedPatchWithFailCases,
args=[q, server, debug, logger])
t.daemon = True
t.start()
for i in flag_versions.values():
i.buildFlagDictFromInsertVersion()
#i.flagDict
url = i.buildURL(server)
if debug:
print url
logger.debug("json.dumps(i.flagDict):")
logger.debug("%s" % json.dumps(i.flagDict))
#if hackDec11:
# if len(i.active)==0:
# print "No segments for this url"
# continue
q.put(i)
q.join()
else:
for i in flag_versions.values():
i.buildFlagDictFromInsertVersion()
#i.flagDict
url = i.buildURL(server)
if debug:
logger.debug("Url for the following data: %s" % url)
#print url
logger.debug("json.dumps(i.flagDict):")
logger.debug("%s" % json.dumps(i.flagDict))
#if hackDec11:
# if len(i.active)==0:
# print "No segments for this url"
# continue
patchWithFailCases(i, url, debug, logger, testing_options)
if debug:
logger.debug(
"If we made it this far, no errors were encountered in the inserts."
)
#print "If we made it this far, no errors were encountered in the inserts."
### Fix!!! Improvement: Should be more careful about error handling here.
if debug:
t2 = time.time()
logger.debug("Time elapsed for file %s = %d." % (filename, t2 - t1))
#print "Time elapsed for file %s = %d." % (filename,t2-t1)
return True
def fromsegmentxml(xml_file, return_dict=False, select_seg_def_id=None):
""" Read a glue.segments.segmentlist from the file object file
containing an xml segment table.
Parameters
-----------
xml_file : file object
file object for segment xml file
return_dict : boolean, optional (default = False)
returns a glue.segments.segmentlistdict containing coalesced
glue.segments.segmentlists keyed by seg_def.name for each entry in the
contained segment_def_table.
select_seg_def_id : int, optional (default = None)
returns a glue.segments.segmentlist object containing only those
segments matching the given segment_def_id integer
Returns
--------
segs : glue.segments.segmentlist instance
The segment list contained in the file.
"""
# load XML with SegmentDefTable and SegmentTable
xmldoc, digest = utils.load_fileobj(xml_file,
gz=xml_file.name.endswith(".gz"),
contenthandler=ContentHandler)
seg_def_table = table.get_table(xmldoc,
lsctables.SegmentDefTable.tableName)
seg_table = table.get_table(xmldoc, lsctables.SegmentTable.tableName)
if return_dict:
segs = segments.segmentlistdict()
else:
segs = segments.segmentlist()
seg_id = {}
for seg_def in seg_def_table:
# encode ifo, channel name and version
full_channel_name = ':'.join(
[str(seg_def.ifos),
str(seg_def.name),
str(seg_def.version)])
seg_id[int(seg_def.segment_def_id)] = full_channel_name
if return_dict:
segs[full_channel_name] = segments.segmentlist()
for seg in seg_table:
seg_obj = segments.segment(
lal.LIGOTimeGPS(seg.start_time, seg.start_time_ns),
lal.LIGOTimeGPS(seg.end_time, seg.end_time_ns))
if return_dict:
segs[seg_id[int(seg.segment_def_id)]].append(seg_obj)
elif select_seg_def_id is not None:
if int(seg.segment_def_id) == select_seg_def_id:
segs.append(seg_obj)
else:
segs.append(seg_obj)
if return_dict:
for seg_name in seg_id.values():
segs[seg_name] = segs[seg_name].coalesce()
else:
segs = segs.coalesce()
xmldoc.unlink()
return segs
def __init__(self, channel_name, seglist=None, description="", bitmask=""):
seglist = segments.segmentlist([]) if seglist is None else seglist
self.channel_name = channel_name
self.segmentlist = seglist
self.description = description
self.bitmask = bitmask
def make_grb_segments_plot(wkflow, science_segs, trigger_time, trigger_name,
out_dir, coherent_seg=None, fail_criterion=None):
ifos = wkflow.ifos
if len(science_segs.keys()) == 0:
extent = segments.segment(int(wkflow.cp.get("workflow", "start-time")),
int(wkflow.cp.get("workflow", "end-time")))
else:
pltpad = [science_segs.extent_all()[1] - trigger_time,
trigger_time - science_segs.extent_all()[0]]
extent = segments.segmentlist([science_segs.extent_all(),
segments.segment(trigger_time - pltpad[0],
trigger_time + pltpad[1])]).extent()
ifo_colors = {}
for ifo in ifos:
ifo_colors[ifo] = ifo_color(ifo)
if ifo not in science_segs.keys():
science_segs[ifo] = segments.segmentlist([])
# Make plot
fig, subs = plt.subplots(len(ifos), sharey=True)
plt.xticks(rotation=20, ha='right')
plt.subplots_adjust(bottom=0.15)
for sub, ifo in zip(subs, ifos):
for seg in science_segs[ifo]:
sub.add_patch(Rectangle((seg[0], 0.1), abs(seg), 0.8,
facecolor=ifo_colors[ifo], edgecolor='none'))
if coherent_seg:
if len(science_segs[ifo]) > 0 and \
coherent_seg in science_segs[ifo]:
sub.plot([trigger_time, trigger_time], [0, 1], '-',
c='orange')
sub.add_patch(Rectangle((coherent_seg[0], 0),
abs(coherent_seg), 1, alpha=0.5,
facecolor='orange', edgecolor='none'))
else:
sub.plot([trigger_time, trigger_time], [0, 1], ':',
c='orange')
sub.plot([coherent_seg[0], coherent_seg[0]], [0, 1], '--',
c='orange', alpha=0.5)
sub.plot([coherent_seg[1], coherent_seg[1]], [0, 1], '--',
c='orange', alpha=0.5)
else:
sub.plot([trigger_time, trigger_time], [0, 1], ':k')
if fail_criterion:
if len(science_segs[ifo]) > 0:
style_str = '--'
else:
style_str = '-'
sub.plot([fail_criterion[0], fail_criterion[0]], [0, 1], style_str,
c='black', alpha=0.5)
sub.plot([fail_criterion[1], fail_criterion[1]], [0, 1], style_str,
c='black', alpha=0.5)
sub.set_frame_on(False)
sub.set_yticks([])
sub.set_ylabel(ifo, rotation=45)
sub.set_xlim([float(extent[0]), float(extent[1])])
sub.get_xaxis().get_major_formatter().set_useOffset(False)
sub.get_xaxis().get_major_formatter().set_scientific(False)
sub.get_xaxis().tick_bottom()
if not sub is subs[-1]:
sub.get_xaxis().set_ticks([])
sub.get_xaxis().set_ticklabels([])
else:
sub.tick_params(labelsize=10, pad=1)
xmin, xmax = fig.axes[-1].get_xaxis().get_view_interval()
ymin, ymax = fig.axes[-1].get_yaxis().get_view_interval()
fig.axes[-1].add_artist(Line2D((xmin, xmax), (ymin, ymin), color='black',
linewidth=2))
fig.axes[-1].set_xlabel('GPS Time')
fig.axes[0].set_title('Science Segments for GRB%s' % trigger_name)
fig.subplots_adjust(hspace=0)
plot_name = 'GRB%s_segments.png' % trigger_name
plot_url = 'file://localhost%s/%s' % (out_dir, plot_name)
fig.savefig('%s/%s' % (out_dir, plot_name))
return [ifos, plot_name, extent, plot_url]
# name the DQ flag
optic = channel[0].split('_')[2]
flag_name = 'L1:DCH-EQ_%s_GT_%s:1' % (optic, thresh)
# grab all observing (or whatever is defined) time
active = DataQualityFlag.query_dqsegdb(args.science, args.start, args.end).active
# grab only data for the STS channel in observing time
data = get_timeseries_dict(channel, active, frametype='L1_M')
# find times above threshold
time = [j.times[j > thresh] for j in data[channel[0]]]
times = numpy.concatenate(time)
# put all times above threshold in to segments
segs = segments.segmentlist()
segs.extend([segments.segment(int(t.value), int(t.value)+args.stride) for t in times])
segs = segs.coalesce()
# set up the xml file by making a list of the start and end times of the flag
start_time = []
start_time.extend([t[0] for t in segs])
end_time = []
end_time.extend([t[1] for t in segs])
# put in to dq flag object
flag = DataQualityFlag(flag_name, active=zip(start_time,end_time), known=[[args.start,args.end]])
# write flag to xml
flag.write(segment_file)
def query_segments(engine, table, segdefs):
# each segdef is a list containing:
# ifo, name, version, start_time, end_time, start_pad, end_pad
# The trivial case: if there's nothing to do, return no time
if len(segdefs) == 0:
return [segmentlist([])]
#
# For the sake of efficiency we query the database for all the segdefs at once
# This constructs a clause that looks for one
#
def make_clause(table, segdef):
ifo, name, version, start_time, end_time, start_pad, end_pad = segdef
sql = " (segment_definer.ifos = '%s' " % ifo
sql += "AND segment_definer.name = '%s' " % name
sql += "AND segment_definer.version = %s " % version
sql += "AND NOT (%d > %s.end_time OR %s.start_time > %d)) " % (
start_time, table, table, end_time)
return sql
clauses = [make_clause(table, segdef) for segdef in segdefs]
sql = 'SELECT segment_definer.ifos, segment_definer.name, segment_definer.version, '
sql += ' %s.start_time, %s.end_time ' % (table, table)
sql += ' FROM segment_definer, %s ' % table
sql += ' WHERE %s.segment_def_id = segment_definer.segment_def_id AND ' % table
if engine.__class__ == query_engine.LdbdQueryEngine:
sql += " %s.segment_def_cdb = segment_definer.creator_db AND " % table
sql += '( ' + ' OR '.join(clauses) + ' )'
rows = engine.query(sql)
#
# The result of a query will be rows of the form
# ifo, name, version, start_time, end_time
#
# We want to associate each returned row with the segdef it belongs to so that
# we can apply the correct padding.
#
# If segdefs were uniquely spcified by (ifo, name, version) this would
# be easy, but it may happen that we're looking for the same segment definer
# at multiple disjoint times. In particular this can happen if the user
# didn't specify a version number; in that case we might have version 2
# of some flag defined over multiple disjoint segment_definers.
#
results = []
for segdef in segdefs:
ifo, name, version, start_time, end_time, start_pad, end_pad = segdef
search_span = segment(start_time, end_time)
search_span_list = segmentlist([search_span])
# See whether the row belongs to the current segdef. Name, ifo and version must match
# and the padded segment must overlap with the range of the segdef.
def matches(row):
return (row[0].strip() == ifo and row[1] == name
and int(row[2]) == int(version) and search_span.intersects(
segment(row[3] + start_pad, row[4] + start_pad)))
# Add the padding. Segments may extend beyond the time of interest, chop off the excess.
def pad_and_truncate(row_start, row_end):
tmp = segmentlist(
[segment(row_start + start_pad, row_end + end_pad)])
# No coalesce needed as a list with a single segment is already coalesced
tmp &= search_span_list
# The intersection is guaranteed to be non-empty if the row passed match()
# PR 2969: The above comment is incorrect. Negative padding may cause
# an empty intersection.
if len(tmp) == 0:
return segment(0, 0)
else:
return tmp[0]
# Build a segment list from the returned segments, padded and trunctated. The segments will
# not necessarily be disjoint, if the padding crosses gaps. They are also not gauranteed to
# be in order, since there's no ORDER BY in the query. So the list needs to be coalesced
# before arithmatic can be done with it.
result = segmentlist([
pad_and_truncate(row[3], row[4]) for row in rows if matches(row)
]).coalesce()
# This is not needed: since each of the segments are constrained to be within the search
# span the whole list must be as well.
# result &= search_span_list
results.append(result)
return results
def build_segment_list_one(engine,
gps_start_time,
gps_end_time,
ifo,
segment_name,
version=None,
start_pad=0,
end_pad=0):
"""Builds a list of segments satisfying the given criteria """
seg_result = segmentlist([])
sum_result = segmentlist([])
# Is there any way to get segment and segement summary in one query?
# Maybe some sort of outer join where we keep track of which segment
# summaries we've already seen.
sql = "SELECT segment_summary.start_time, segment_summary.end_time "
sql += "FROM segment_definer, segment_summary "
sql += "WHERE segment_summary.segment_def_id = segment_definer.segment_def_id "
sql += "AND segment_definer.ifos = '%s' " % ifo
if engine.__class__ == query_engine.LdbdQueryEngine:
sql += "AND segment_summary.segment_def_cdb = segment_definer.creator_db "
sql += "AND segment_definer.name = '%s' " % segment_name
sql += "AND segment_definer.version = %s " % version
sql += "AND NOT (%s > segment_summary.end_time OR segment_summary.start_time > %s)" % (
gps_start_time, gps_end_time)
rows = engine.query(sql)
for sum_start_time, sum_end_time in rows:
sum_start_time = (sum_start_time <
gps_start_time) and gps_start_time or sum_start_time
sum_end_time = (sum_end_time >
gps_end_time) and gps_end_time or sum_end_time
sum_result |= segmentlist([segment(sum_start_time, sum_end_time)])
# We can't use queries paramaterized with ? since the ldbd protocol doesn't support it...
sql = "SELECT segment.start_time + %d, segment.end_time + %d " % (
start_pad, end_pad)
sql += "FROM segment, segment_definer "
sql += "WHERE segment.segment_def_id = segment_definer.segment_def_id "
if engine.__class__ == query_engine.LdbdQueryEngine:
sql += "AND segment.segment_def_cdb = segment_definer.creator_db "
sql += "AND segment_definer.ifos = '%s' " % ifo
sql += "AND segment_definer.name = '%s' " % segment_name
sql += "AND segment_definer.version = %s " % version
sql += "AND NOT (%s > segment.end_time OR segment.start_time > %s)" % (
gps_start_time, gps_end_time)
rows = engine.query(sql)
for seg_start_time, seg_end_time in rows:
seg_start_time = (seg_start_time <
gps_start_time) and gps_start_time or seg_start_time
seg_end_time = (seg_end_time >
gps_end_time) and gps_end_time or seg_end_time
seg_result |= segmentlist([segment(seg_start_time, seg_end_time)])
engine.close()
return sum_result, seg_result
def start_end_to_segments(start, end):
return segmentlist([segment(s, e) for s, e in zip(start, end)])
def run_query_segments(doc,
proc_id,
engine,
gps_start_time,
gps_end_time,
included_segments_string,
excluded_segments_string=None,
write_segments=True,
start_pad=0,
end_pad=0):
"""Runs a segment query. This was originally part of ligolw_query_segments, but now is also
used by ligolw_segments_from_cats.
The write_segments option is provided so callers can coalesce segments obtained over
sever invocations (as segments_from_cats does).
"""
if write_segments:
all_ifos = {}
for ifo, segment_name, version in split_segment_ids(
included_segments_string.split(',')):
all_ifos[ifo] = True
new_seg_def_id = add_to_segment_definer(doc, proc_id,
''.join(list(all_ifos.keys())),
'result', 0)
add_to_segment_summary(doc, proc_id, new_seg_def_id,
[[gps_start_time, gps_end_time]])
result = segmentlist([])
for ifo, segment_name, version in split_segment_ids(
included_segments_string.split(',')):
sum_segments, seg_segments = build_segment_list(
engine, gps_start_time, gps_end_time, ifo, segment_name, version,
start_pad, end_pad)
seg_def_id = add_to_segment_definer(doc, proc_id, ifo, segment_name,
version)
add_to_segment_summary(doc, proc_id, seg_def_id, sum_segments)
# and accumulate segments
result |= seg_segments
# Excluded segments are not required
if excluded_segments_string:
excluded_segments = segmentlist([])
for ifo, segment_name, version in split_segment_ids(
excluded_segments_string.split(',')):
sum_segments, seg_segments = build_segment_list(
engine, gps_start_time, gps_end_time, ifo, segment_name,
version)
excluded_segments |= seg_segments
result = result - excluded_segments
result.coalesce()
# Add the segments
if write_segments:
add_to_segment(doc, proc_id, new_seg_def_id, result)
return result
def ReadMultiInspiralTimeSlidesFromFiles(fileList,generate_output_tables=False):
"""
Read time-slid multiInspiral tables from a list of files
@param fileList: list of input files
"""
if not fileList:
return multiInspiralTable(), None
multis = None
timeSlides = []
segmentDict = {}
for thisFile in fileList:
doc = utils.load_filename(thisFile,
gz=(thisFile or "stdin").endswith(".gz"), contenthandler = lsctables.use_in(ligolw.LIGOLWContentHandler))
# Extract the time slide table
timeSlideTable = lsctables.TimeSlideTable.get_table(doc)
slideMapping = {}
currSlides = {}
# NOTE: I think some of this is duplicated in the glue definition of the
# time slide table. Probably should move over to that
for slide in timeSlideTable:
currID = int(slide.time_slide_id)
if currID not in currSlides.keys():
currSlides[currID] = {}
currSlides[currID][slide.instrument] = slide.offset
elif slide.instrument not in currSlides[currID].keys():
currSlides[currID][slide.instrument] = slide.offset
for slideID,offsetDict in currSlides.items():
try:
# Is the slide already in the list and where?
offsetIndex = timeSlides.index(offsetDict)
slideMapping[slideID] = offsetIndex
except ValueError:
# If not then add it
timeSlides.append(offsetDict)
slideMapping[slideID] = len(timeSlides) - 1
# Get the mapping table
segmentMap = {}
timeSlideMapTable = lsctables.TimeSlideSegmentMapTable.get_table(doc)
for entry in timeSlideMapTable:
segmentMap[int(entry.segment_def_id)] = int(entry.time_slide_id)
# Extract the segment table
segmentTable = lsctables.SegmentTable.get_table(doc)
for entry in segmentTable:
currSlidId = segmentMap[int(entry.segment_def_id)]
currSeg = entry.get()
if not segmentDict.has_key(slideMapping[currSlidId]):
segmentDict[slideMapping[currSlidId]] = segments.segmentlist()
segmentDict[slideMapping[currSlidId]].append(currSeg)
segmentDict[slideMapping[currSlidId]].coalesce()
# extract the multi inspiral table
try:
multiInspiralTable = lsctables.MultiInspiralTable.get_table(doc)
# Remap the time slide IDs
for multi in multiInspiralTable:
newID = slideMapping[int(multi.time_slide_id)]
multi.time_slide_id = ilwd.ilwdchar(\
"time_slide:time_slide_id:%d" % (newID))
if multis: multis.extend(multiInspiralTable)
else: multis = multiInspiralTable
# except: multiInspiralTable = None
except: raise
if not generate_output_tables:
return multis,timeSlides,segmentDict
else:
# Make a new time slide table
timeSlideTab = lsctables.New(lsctables.TimeSlideTable)
for slideID,offsetDict in enumerate(timeSlides):
for instrument in offsetDict.keys():
currTimeSlide = lsctables.TimeSlide()
currTimeSlide.instrument = instrument
currTimeSlide.offset = offsetDict[instrument]
currTimeSlide.time_slide_id = ilwd.ilwdchar(\
"time_slide:time_slide_id:%d" % (slideID))
currTimeSlide.process_id = ilwd.ilwdchar(\
"process:process_id:%d" % (0))
timeSlideTab.append(currTimeSlide)
# Make a new mapping table
timeSlideSegMapTab = lsctables.New(lsctables.TimeSlideSegmentMapTable)
for i in range(len(timeSlides)):
currMapEntry = lsctables.TimeSlideSegmentMap()
currMapEntry.time_slide_id = ilwd.ilwdchar(\
"time_slide:time_slide_id:%d" % (i))
currMapEntry.segment_def_id = ilwd.ilwdchar(\
"segment_def:segment_def_id:%d" % (i))
timeSlideSegMapTab.append(currMapEntry)
# Make a new segment table
newSegmentTable = lsctables.New(lsctables.SegmentTable)
segmentIDCount = 0
for i in range(len(timeSlides)):
currSegList = segmentDict[i]
for seg in currSegList:
currSegment = lsctables.Segment()
currSegment.segment_id = ilwd.ilwdchar(\
"segment:segment_id:%d" %(segmentIDCount))
segmentIDCount += 1
currSegment.segment_def_id = ilwd.ilwdchar(\
"segment_def:segment_def_id:%d" % (i))
currSegment.process_id = ilwd.ilwdchar(\
"process:process_id:%d" % (0))
currSegment.set(seg)
currSegment.creator_db = -1
currSegment.segment_def_cdb = -1
newSegmentTable.append(currSegment)
return multis,timeSlides,segmentDict,timeSlideTab,newSegmentTable,\
timeSlideSegMapTab
from glue import segments
from glue.ligolw import ligolw, lsctables, table, utils
from glue.ligolw.utils import segments as ligolw_segments
from glue.segmentdb import query_engine, segmentdb_utils
from glue.ligolw.utils import process as ligolw_process
from pylal.dq.dqTriggerUtils import def_get_time
from scipy.stats import poisson
LIGOTimeGPS = lsctables.LIGOTimeGPS
# Some boilerplate to make segmentlists picklable
import copy_reg
copy_reg.pickle(type(segments.segment(0, 1)), \
lambda x:(segments.segment, (x[0], x[1])))
copy_reg.pickle(type(segments.segmentlist([])), lambda x:
(segments.segmentlist, ([y for y in x], )))
from glue import git_version
__author__ = "Andrew P Lundgren <*****@*****.**>, Duncan Macleod <*****@*****.**>"
__version__ = "git id %s" % git_version.id
__date__ = git_version.date
"""
This module provides useful segment and veto tools for data quality investigations.
"""
# ==============================================================================
# Function to load segments from an xml file
# ==============================================================================
])
return fig
fig = newfig(options.segment)
axes = fig.gca()
rate.filter_array(trigger_rate.array,
rate.gaussian_window(bins_per_filterwidth))
axes.plot(trigger_rate.centres()[0], trigger_rate.at_centres())
axes.set_xlim(list(options.segment))
axes.grid(True)
for seg in ~seglist & segments.segmentlist([options.segment]):
axes.axvspan(seg[0], seg[1], facecolor="k", alpha=0.2)
axes.set_title(
"%s Excess Power Trigger Rate vs. Time\n(%d Triggers, %g s Moving Average)"
% (options.instrument, num_triggers, options.window))
ticks = make_xticks(options.segment)
axes.set_xticks(ticks[0])
axes.set_xticklabels(ticks[1],
horizontalalignment="right",
fontsize=10,
rotation=10)
axes.set_xlabel("UTC")
#axes.yticks(fontsize = 10)
axes.set_ylabel("Trigger Rate (Hz)")
def setup_analysislogging(workflow, segs_list, insps, args, output_dir,
program_name="workflow", tags=[]):
"""
This module sets up the analysis logging xml file that contains the
following information:
* Command line arguments that the code was run with
* Segment list of times marked as SCIENCE
* Segment list of times marked as SCIENCE and "OK" ie. not CAT_1 vetoed
* Segment list of times marked as SCIENCE_OK and present on the cluster
* The times that will be analysed by the matched-filter jobs
Parameters
-----------
workflow : pycbc.workflow.core.Workflow
The Workflow instance.
segs_list : pycbc.workflow.core.FileList
A list of Files containing the information needed to generate the
segments above. For segments generated at run time the associated
segmentlist is a property of this object.
insps : pycbc.workflow.core.FileList
The output files from the matched-filtering module. Used to identify
what times have been analysed in this workflow.
output_dir : path
Directory to output any files to.
program_name : string (optional, default = "workflow")
The program name to stick in the process/process_params tables.
tags : list (optional, default = [])
If given restrict to considering inspiral and segment files that
are tagged with all tags in this list.
"""
logging.info("Entering analysis logging module.")
make_analysis_dir(output_dir)
# Construct the summary XML file
outdoc = ligolw.Document()
outdoc.appendChild(ligolw.LIGO_LW())
# Add process and process_params tables
proc_id = process.register_to_xmldoc(outdoc, program_name,
vars(args) ).process_id
# Now add the various segment lists to this file
summ_segs = segmentlist([workflow.analysis_time])
# If tags is given filter by tags
if tags:
for tag in tags:
segs_list = segs_list.find_output_with_tag(tag)
insps = insps.find_output_with_tag(tag)
for ifo in workflow.ifos:
# Lets get the segment lists we need
seg_ifo_files = segs_list.find_output_with_ifo(ifo)
# SCIENCE
sci_seg_file = seg_ifo_files.find_output_with_tag('SCIENCE')
if len(sci_seg_file) == 1:
sci_seg_file = sci_seg_file[0]
sci_segs = sci_seg_file.segmentList
sci_def_id = segmentdb_utils.add_to_segment_definer(outdoc, proc_id,
ifo, "CBC_WORKFLOW_SCIENCE", 0)
segmentdb_utils.add_to_segment(outdoc, proc_id, sci_def_id,
sci_segs)
segmentdb_utils.add_to_segment_summary(outdoc, proc_id, sci_def_id,
summ_segs, comment='')
elif sci_seg_file:
# FIXME: While the segment module is still fractured (#127) this
# may not work. Please update when #127 is resolved
pass
#err_msg = "Got %d files matching %s and %s. Expected 1 or 0." \
# %(len(sci_seg_file), ifo, 'SCIENCE')
#raise ValueError(err_msg)
# SCIENCE_OK
sci_ok_seg_file = seg_ifo_files.find_output_with_tag('SCIENCE_OK')
if len(sci_ok_seg_file) == 1:
sci_ok_seg_file = sci_ok_seg_file[0]
sci_ok_segs = sci_ok_seg_file.segmentList
sci_ok_def_id = segmentdb_utils.add_to_segment_definer(outdoc,
proc_id, ifo, "CBC_WORKFLOW_SCIENCE_OK", 0)
segmentdb_utils.add_to_segment(outdoc, proc_id, sci_ok_def_id,
sci_ok_segs)
segmentdb_utils.add_to_segment_summary(outdoc, proc_id,
sci_ok_def_id, summ_segs, comment='')
elif sci_ok_seg_file:
# FIXME: While the segment module is still fractured (#127) this
# may not work. Please update when #127 is resolved
pass
#err_msg = "Got %d files matching %s and %s. Expected 1 or 0." \
# %(len(sci_ok_seg_file), ifo, 'SCIENCE_OK')
#raise ValueError(err_msg)
# SCIENCE_AVAILABLE
sci_available_seg_file = seg_ifo_files.find_output_with_tag(\
'SCIENCE_AVAILABLE')
if len(sci_available_seg_file) == 1:
sci_available_seg_file = sci_available_seg_file[0]
sci_available_segs = sci_available_seg_file.segmentList
sci_available_def_id = segmentdb_utils.add_to_segment_definer(\
outdoc, proc_id, ifo, "CBC_WORKFLOW_SCIENCE_AVAILABLE", 0)
segmentdb_utils.add_to_segment(outdoc, proc_id,
sci_available_def_id, sci_available_segs)
segmentdb_utils.add_to_segment_summary(outdoc, proc_id,
sci_available_def_id, summ_segs, comment='')
elif sci_available_seg_file:
# FIXME: While the segment module is still fractured (#127) this
# may not work. Please update when #127 is resolved
pass
#err_msg = "Got %d files matching %s and %s. Expected 1 or 0." \
# %(len(sci_available_seg_file), ifo, 'SCIENCE_AVAILABLE')
#raise ValueError(err_msg)
# ANALYSABLE - This one needs to come from inspiral outs
ifo_insps = insps.find_output_with_ifo(ifo)
analysable_segs = ifo_insps.get_times_covered_by_files()
analysable_def_id = segmentdb_utils.add_to_segment_definer(outdoc,
proc_id, ifo, "CBC_WORKFLOW_ANALYSABLE", 0)
segmentdb_utils.add_to_segment(outdoc, proc_id, analysable_def_id,
analysable_segs)
segmentdb_utils.add_to_segment_summary(outdoc, proc_id,
analysable_def_id, summ_segs, comment='')
summ_file = File(workflow.ifos, "WORKFLOW_SUMMARY",
workflow.analysis_time, extension=".xml",
directory=output_dir)
summ_file.PFN(summ_file.storage_path, site='local')
utils.write_filename(outdoc, summ_file.storage_path)
return FileList([summ_file])
def calculate_combined_result(includedJSON, excludedJSON, startTime, endTime,
ifo):
"""
Calculate the result of the union of the active times for the included flag less the intersection of that result with the union of the excluded flags
Inputs are 2 lists of python dictionaries representing the JSON (already have run json.loads() on the JSON), a start time, and end time, and the ifo name (it does not make sense to include/exclude across multiple ifos)
Parameters
----------
startTime : `int`
Ex: 999999999
endTime : `int`
Ex: 999999999
ifo : `string`
Ex: 'L1'
"""
total_active_list = segments.segmentlist([])
for flag in includedJSON:
#result=json.loads(flag)
#flagDict=result['flags'][0]
active_list = flag['active']
active_segments = segments.segmentlist(
[segments.segment(x[0], x[1]) for x in active_list])
total_active_list = total_active_list + active_segments
total_active_list.coalesce()
for flag in excludedJSON:
#result=json.loads(flag)
#flagDict=result['flags'][0]
active_list = flag['active']
active_segments = segments.segmentlist(
[segments.segment(x[0], x[1]) for x in active_list])
total_active_list = total_active_list - active_segments
total_active_list.coalesce()
# Now, total_active_list contains a segmentlist object with segments spanning the expected result # includedJSON and excludedJSON contain lists of JSON text blobs (not parsed with json.loads yet)
## Note: About known segments for the result: We just report the start and end time of the period queried! If you wanted to report the actual validity of multiple segments, it's somewhat undefined if the excluded ones and/or some of the included flags aren't known about for a time when the included ones are; Technically since exclusion trumps all inclusions, if an excluded segment is known and active at any given time, the result is known for that time explicitly.
result_known_segment_list = segments.segmentlist(
[segments.segment(startTime, endTime)])
## Now we have to build the JSON for this flag
# JSON flag objects looks like this (each is a dictionary!):
# {
# "ifo" : "ifo",
# "name" : "flag",
# "version" : n,
# "comment" : "description", # "provenance_url" : "aLog URL",
# "deactivated" : false|true,
# "active_indicates_ifo_badness" : true|false|null,
# // known segments returned for both /active and /known URIs, no segments are returned for the /metadata or /report/flags queries
# // aka S6 summary segments
# "known" : [ [ts,te], [ts,te], ... ]
# // active segments returned only for /active URI:
# "active" : [ [ts,te], [ts,te], ... ]
# // \textcolor{red}{Comment: or "segment" : [ [ts,te,value], [ts,te,value], ...] (where value can be -1,0 or +1)}
# // inactive == (known - active)
# // unknown == (all_time - known)
# },
## Make the json-ready flag dictionary for the combined result:
ifo = ifo # replicating old behavoir from ligolw_segment_query
# Note: This just uses the ifo of the last excluded flag!
name = 'RESULT'
version = 1
known_segments = result_known_segment_list
active_segments = total_active_list
result_flag = jsonhelper.buildFlagDict(ifo, name, version, known_segments,
active_segments)
return result_flag
dag.add_node(df)
# modify the start and end time by pad seconds
log_fh.write("gps_start_time = %d\n" % gps_start_time)
log_fh.write("gps_end_time = %d\n" % gps_end_time)
# Don't need to do these, since we'll pad each segment
# gps_start_time += pad
# gps_end_time -= pad
# log_fh.write("gps_start_time + pad = %d\n" % gps_start_time)
# log_fh.write("gps_end_time - pad = %d\n" % gps_end_time)
# Determine the times to analyze
job_segs = segments.segmentlist()
analysis_segs = segments.segmentlist()
for seg in available_segments:
seg_start = seg[0] + pad
seg_end = seg[1] - pad
if seg_end - seg_start < length:
"Segment from %d to %d is too short, skipping" % (seg_start, seg_end)
continue
while seg_start + length < seg_end:
job_segs.append( segments.segment(seg_start, seg_start + length) )
analysis_segs.append( segments.segment(seg_start + overlap/2, seg_start + length - overlap/2) )
seg_start += length - overlap
def compute_thinca_livetime(on_instruments, off_instruments, rings,
vetoseglistdict, offsetvectors):
"""
@on_instruments is an iterable of the instruments that must be on.
@off_instruments is an iterable of the instruments that must be off.
on_instruments and off_instruments must be disjoint.
@rings is a list of segments defining the analysis ring boundaries. They
can overlap, and do not need to be ordered.
@vetoseglistdict is a coalesced glue.segments.segmentlistdict object
providing the veto segments for whatever instruments have vetoes defined
for them. This can include veto lists for instruments other than those
listed in on_ and off_instruments (extra veto lists will be ignored), and
it need not provide lists for all instruments (instruments for which
there are no veto segment lists are assumed to be on at all times).
@offsetvectors is an iterable of dictionaries of instrument-offset pairs.
Each dictionary must contain entries for all instruments in the union of
on_instruments and off_instruments (it is allowed to name others as well,
but they will be ignored). An example of one dictionary of
instrument-offset pairs: {"H1": 0.0, "H2": 5.0, "L1": 10.0}.
The return value is a float giving the livetime in seconds.
"""
# local copies so they can be modified and iterated over more than once
# (in case generator expressions have been passed in)
on_instruments = set(on_instruments)
off_instruments = set(off_instruments)
# check that the on and off instruments are disjoint
if on_instruments & off_instruments:
raise ValueError, "on_instruments and off_instruments not disjoint"
# instruments that are not vetoed are assumed to be on
on_instruments &= set(vetoseglistdict.keys())
# performance aid: only need offsets for instruments whose state is
# important
all_instruments = on_instruments | off_instruments
offsetvectors = tuple(
dict((key, value) for key, value in offsetvector.items()
if key in all_instruments) for offsetvector in offsetvectors)
# performance aid: if there are no offset vectors to consider, the
# livetime is trivial
if not offsetvectors:
return []
# check that each offset vector provides values for all instruments of
# interest
for offsetvector in offsetvectors:
if not set(offsetvector.keys()).issuperset(all_instruments):
raise ValueError, "incomplete offset vector %s; missing instrument(s) %s" % (
repr(offsetvector),
", ".join(all_instruments - set(offsetvector.keys())))
# initialize the livetime sums
live_time = [0.0] * len(offsetvectors)
# the livetime is trivial if an instrument that must be off is never
# vetoed
if not set(vetoseglistdict.keys()).issuperset(off_instruments):
return live_time
# performance aid: don't need veto segment lists for instruments whose
# state is unimportant, nor veto segments that don't intersect the rings
coalesced_rings = segments.segmentlist(rings).coalesce()
vetoseglistdict = segments.segmentlistdict(
(key,
segments.segmentlist(seg for seg in seglist
if coalesced_rings.intersects_segment(seg)))
for key, seglist in vetoseglistdict.items() if key in all_instruments)
# tot up the time when exactly the instruments that must be on are on
for ring in rings:
# don't do this in loops
ring = segments.segmentlist([ring])
# performance aid: this is done in the loop, inside
# slideSegListDictOnRing(), but we can make that go faster by doing it
# here first
clipped_vetoseglistdict = segments.segmentlistdict(
(key, seglist & ring) for key, seglist in vetoseglistdict.items())
# performance aid: if an instrument that must be vetoed is never
# vetoed in this ring, the livetime is zero
if not all(clipped_vetoseglistdict[key] for key in off_instruments):
continue
# iterate over offset vectors
for n, offsetvector in enumerate(offsetvectors):
# apply the offset vector to the vetoes, wrapping around the ring
slidvetoes = slideSegListDictOnRing(ring[0],
clipped_vetoseglistdict,
offsetvector)
# slidvetoes = times when instruments are vetoed,
# slidvetoes.union(on_instruments) = times when an instrument that
# must be on is vetoed
#
# ~slidvetoes = times when instruments are not vetoed,
# (~slidvetoes).union(off_instruments) = times when an instrument
# that must be off is not vetoed
live_time[n] += float(
abs(ring - slidvetoes.union(on_instruments) -
(~slidvetoes).union(off_instruments)))
# done
return live_time
def getSegments(seglistin, interval):
seglistout = segments.segmentlist([s for s in seglistin \
if (s[1] > interval[0] and s[0] < interval[1]) ])
return seglistout
def calculate_versionless_result(jsonResults,
startTime,
endTime,
ifo_input=None):
"""
Construct output segments lists from multiple JSON objects.
The jsonResults input is a list of json ojbects and
are expected to be in order of decreasing versions.
"""
debug = False
active_results = {}
segment_known_results = {}
affected_results = {}
total_active_list = segments.segmentlist([])
total_query_time = segments.segmentlist(
[segments.segment(startTime, endTime)])
total_known_list = segments.segmentlist([])
for resultin in jsonResults:
#result=json.loads(resultin)
result = resultin
# old : flagDict=result['flags'][0] # Our queries above each return 1 flag
version = int(result['version'])
deactivated_state = result['metadata']['deactivated']
if str(deactivated_state) in ["False", "false"]:
known_list = result['known']
known_segments = segments.segmentlist([
segments.segment(x[0], x[1]) for x in known_list
]) # make a segment list object to do arithmetic
known_segments.coalesce()
segment_known_results[version] = known_segments
active_list = result['active']
active_segments = segments.segmentlist([
segments.segment(x[0], x[1]) for x in active_list
]) # make a segment list object to do arithmetic
active_segments.coalesce()
active_results[version] = active_segments
if debug:
print "Active results for version %d" % version
print active_results[version]
# Now I have 2 dictionaries of known and active segments with versions as keys in case I want/need them later...
# This next step might seem a bit confusing:
# We need to take the active segments for this version only during times that were not known by higher segment versions
# Thus we need to take the intersection (& operator) of the unknown segments across all higher versions with the known segments for this version, then take the intersection of that result with the active segments for this version, and then add that to the current list of total active segments... phew:
total_active_list |= (total_query_time - total_known_list
) & known_segments & active_segments
if debug:
import pdb
print "Running pdb to see what is in total_active_list"
pdb.set_trace()
total_active_list.coalesce()
# The S6 clients want to know about the range of times affected by a given version explicitly, so those are calculated here:
affected_results[version] = (total_query_time -
total_known_list) & known_segments
# Note that the order matters here! we use the total_known_list from the previous iteration of the loop step to figure out which active segments to use in this iteration of the loop, so the above line must come before the next
total_known_list |= known_segments
total_known_list.coalesce()
if ifo_input == None:
if len(jsonResults) == 0:
import exceptions
exceptions.RuntimeError(
"No versions for flag in versionless query")
else:
ifo = result['ifo']
else: #Only use ifo_input if we can't extract the ifo from the json result (usually because json result is empty)
try:
ifo = result['ifo']
except:
ifo = ifo_input
name = 'RESULT' # Fix!!! Executive decision to make this clear that this is not a specific IFO:FLAG:VERSION resource, but rather a contrived result
version = 1 # Fix!!! Executive decision to make this match what old clients expect
# I would prefer that this is more clear that this is not a specific IFO:FLAG:VERSION resource, but rather a contrived result, possibly by making it version 0
total_active_list.coalesce()
total_known_list.coalesce()
result_flag = jsonhelper.buildFlagDict(ifo, name, version,
total_known_list, total_active_list)
return result_flag, affected_results
def setup_datafind_workflow(workflow,
scienceSegs,
outputDir,
seg_file=None,
tags=None):
"""
Setup datafind section of the workflow. This section is responsible for
generating, or setting up the workflow to generate, a list of files that
record the location of the frame files needed to perform the analysis.
There could be multiple options here, the datafind jobs could be done at
run time or could be put into a dag. The subsequent jobs will know
what was done here from the OutFileList containing the datafind jobs
(and the Dagman nodes if appropriate.
For now the only implemented option is to generate the datafind files at
runtime. This module can also check if the frameFiles actually exist, check
whether the obtained segments line up with the original ones and update the
science segments to reflect missing data files.
Parameters
----------
workflow: pycbc.workflow.core.Workflow
The workflow class that stores the jobs that will be run.
scienceSegs : Dictionary of ifo keyed glue.segment.segmentlist instances
This contains the times that the workflow is expected to analyse.
outputDir : path
All output files written by datafind processes will be written to this
directory.
seg_file : SegFile, optional (default=None)
The file returned by get_science_segments containing the science
segments and the associated segment_summary. This will
be used for the segment_summary test and is required if, and only if,
performing that test.
tags : list of string, optional (default=None)
Use this to specify tags. This can be used if this module is being
called more than once to give call specific configuration (by setting
options in [workflow-datafind-${TAG}] rather than [workflow-datafind]).
This is also used to tag the Files returned by the class to uniqueify
the Files and uniqueify the actual filename.
FIXME: Filenames may not be unique with current codes!
Returns
--------
datafindOuts : OutGroupList
List of all the datafind output files for use later in the pipeline.
sci_avlble_file : SegFile
SegFile containing the analysable time after checks in the datafind
module are applied to the input segment list. For production runs this
is expected to be equal to the input segment list.
scienceSegs : Dictionary of ifo keyed glue.segment.segmentlist instances
This contains the times that the workflow is expected to analyse. If
the updateSegmentTimes kwarg is given this will be updated to reflect
any instances of missing data.
sci_avlble_name : string
The name with which the analysable time is stored in the
sci_avlble_file.
"""
if tags == None:
tags = []
logging.info("Entering datafind module")
make_analysis_dir(outputDir)
cp = workflow.cp
# Parse for options in ini file
datafindMethod = cp.get_opt_tags("workflow-datafind", "datafind-method",
tags)
if cp.has_option_tags("workflow-datafind", "datafind-check-segment-gaps",
tags):
checkSegmentGaps = cp.get_opt_tags("workflow-datafind",
"datafind-check-segment-gaps", tags)
else:
checkSegmentGaps = "no_test"
if cp.has_option_tags("workflow-datafind", "datafind-check-frames-exist",
tags):
checkFramesExist = cp.get_opt_tags("workflow-datafind",
"datafind-check-frames-exist", tags)
else:
checkFramesExist = "no_test"
if cp.has_option_tags("workflow-datafind",
"datafind-check-segment-summary", tags):
checkSegmentSummary = cp.get_opt_tags(
"workflow-datafind", "datafind-check-segment-summary", tags)
else:
checkSegmentSummary = "no_test"
logging.info("Starting datafind with setup_datafind_runtime_generated")
if datafindMethod == "AT_RUNTIME_MULTIPLE_CACHES":
datafindcaches, datafindouts = \
setup_datafind_runtime_cache_multi_calls_perifo(cp, scienceSegs,
outputDir, tags=tags)
elif datafindMethod == "AT_RUNTIME_SINGLE_CACHES":
datafindcaches, datafindouts = \
setup_datafind_runtime_cache_single_call_perifo(cp, scienceSegs,
outputDir, tags=tags)
elif datafindMethod == "AT_RUNTIME_MULTIPLE_FRAMES":
datafindcaches, datafindouts = \
setup_datafind_runtime_frames_multi_calls_perifo(cp, scienceSegs,
outputDir, tags=tags)
elif datafindMethod == "AT_RUNTIME_SINGLE_FRAMES":
datafindcaches, datafindouts = \
setup_datafind_runtime_frames_single_call_perifo(cp, scienceSegs,
outputDir, tags=tags)
elif datafindMethod == "FROM_PREGENERATED_LCF_FILES":
ifos = scienceSegs.keys()
datafindcaches, datafindouts = \
setup_datafind_from_pregenerated_lcf_files(cp, ifos,
outputDir, tags=tags)
else:
msg = "Entry datafind-method in [workflow-datafind] does not have "
msg += "expected value. Valid values are "
msg += "AT_RUNTIME_MULTIPLE_FRAMES, AT_RUNTIME_SINGLE_FRAMES "
msg += "AT_RUNTIME_MULTIPLE_CACHES or AT_RUNTIME_SINGLE_CACHES. "
msg += "Consult the documentation for more info."
raise ValueError(msg)
using_backup_server = False
if datafindMethod == "AT_RUNTIME_MULTIPLE_FRAMES" or \
datafindMethod == "AT_RUNTIME_SINGLE_FRAMES":
if cp.has_option_tags("workflow-datafind",
"datafind-backup-datafind-server", tags):
using_backup_server = True
backup_server = cp.get_opt_tags("workflow-datafind",
"datafind-backup-datafind-server",
tags)
cp_new = copy.deepcopy(cp)
cp_new.set("workflow-datafind", "datafind-ligo-datafind-server",
backup_server)
cp_new.set('datafind', 'urltype', 'gsiftp')
backup_datafindcaches, backup_datafindouts =\
setup_datafind_runtime_frames_single_call_perifo(cp_new,
scienceSegs, outputDir, tags=tags)
backup_datafindouts = datafind_keep_unique_backups(\
backup_datafindouts, datafindouts)
datafindcaches.extend(backup_datafindcaches)
datafindouts.extend(backup_datafindouts)
logging.info("setup_datafind_runtime_generated completed")
# If we don't have frame files covering all times we can update the science
# segments.
if checkSegmentGaps in ['warn', 'update_times', 'raise_error']:
logging.info("Checking science segments against datafind output....")
newScienceSegs = get_science_segs_from_datafind_outs(datafindcaches)
logging.info("Datafind segments calculated.....")
missingData = False
msg = "Any errors directly following this message refer to times that"
msg += " the segment server says are science, but datafind cannot find"
msg += "frames for:"
logging.info(msg)
for ifo in scienceSegs.keys():
# If no data in the input then do nothing
if not scienceSegs[ifo]:
msg = "No input science segments for ifo %s " % (ifo)
msg += "so, surprisingly, no data has been found. "
msg += "Was this expected?"
logging.warning(msg)
continue
if not newScienceSegs.has_key(ifo):
msg = "IFO %s's science segments " % (ifo)
msg += "are completely missing."
logging.error(msg)
missingData = True
if checkSegmentGaps == 'update_times':
scienceSegs[ifo] = segments.segmentlist()
continue
missing = scienceSegs[ifo] - newScienceSegs[ifo]
if abs(missing):
msg = "From ifo %s we are missing frames covering:" % (ifo)
msg += "\n%s" % "\n".join(map(str, missing))
missingData = True
logging.error(msg)
if checkSegmentGaps == 'update_times':
# Remove missing time, so that we can carry on if desired
logging.info("Updating science times for ifo %s." % (ifo))
scienceSegs[ifo] = scienceSegs[ifo] - missing
if checkSegmentGaps == 'raise_error' and missingData:
raise ValueError("Workflow cannot find needed data, exiting.")
logging.info("Done checking, any discrepancies are reported above.")
elif checkSegmentGaps == 'no_test':
# Do nothing
pass
else:
errMsg = "checkSegmentGaps kwArg must take a value from 'no_test', "
errMsg += "'warn', 'update_times' or 'raise_error'."
raise ValueError(errMsg)
# Do all of the frame files that were returned actually exist?
if checkFramesExist in ['warn', 'update_times', 'raise_error']:
logging.info("Verifying that all frames exist on disk.")
missingFrSegs, missingFrames = \
get_missing_segs_from_frame_file_cache(datafindcaches)
missingFlag = False
for ifo in missingFrames.keys():
# If no data in the input then do nothing
if not scienceSegs[ifo]:
continue
# If using a backup server, does the frame exist remotely?
if using_backup_server:
# WARNING: This will be slow, but hopefully it will not occur
# for too many frames. This could be optimized if
# it becomes necessary.
new_list = []
for frame in missingFrames[ifo]:
for dfout in datafindouts:
dfout_pfns = list(dfout.pfns)
dfout_urls = [a.url for a in dfout_pfns]
if frame.url in dfout_urls:
pfn = dfout_pfns[dfout_urls.index(frame.url)]
dfout.removePFN(pfn)
if len(dfout.pfns) == 0:
new_list.append(frame)
else:
msg = "Frame %s not found locally. "\
%(frame.url,)
msg += "Replacing with remote url(s) "
msg += "%s." \
%(str([a.url for a in dfout.pfns]),)
logging.info(msg)
break
else:
new_list.append(frame)
missingFrames[ifo] = new_list
if missingFrames[ifo]:
msg = "From ifo %s we are missing the following frames:" % (
ifo)
msg += '\n'.join([a.url for a in missingFrames[ifo]])
missingFlag = True
logging.error(msg)
if checkFramesExist == 'update_times':
# Remove missing times, so that we can carry on if desired
logging.info("Updating science times for ifo %s." % (ifo))
scienceSegs[ifo] = scienceSegs[ifo] - missingFrSegs[ifo]
if checkFramesExist == 'raise_error' and missingFlag:
raise ValueError("Workflow cannot find all frames, exiting.")
logging.info("Finished checking frames.")
elif checkFramesExist == 'no_test':
# Do nothing
pass
else:
errMsg = "checkFramesExist kwArg must take a value from 'no_test', "
errMsg += "'warn', 'update_times' or 'raise_error'."
raise ValueError(errMsg)
# Check if there are cases where frames exist, but no entry in the segment
# summary table are present.
if checkSegmentSummary in ['warn', 'raise_error']:
logging.info("Checking the segment summary table against frames.")
dfScienceSegs = get_science_segs_from_datafind_outs(datafindcaches)
missingFlag = False
# NOTE: Should this be overrideable in the config file?
sci_seg_name = "SCIENCE"
if seg_file is None:
err_msg = "You must provide the science segments SegFile object "
err_msg += "if using the datafind-check-segment-summary option."
raise ValueError(err_msg)
if seg_file.seg_summ_dict is None:
err_msg = "The provided science segments SegFile object must "
err_msg += "contain a valid segment_summary table if using the "
err_msg += "datafind-check-segment-summary option."
raise ValueError(err_msg)
seg_summary_times = seg_file.seg_summ_dict
for ifo in dfScienceSegs.keys():
curr_seg_summ_times = seg_summary_times[ifo + ":" + sci_seg_name]
missing = (dfScienceSegs[ifo] & seg_file.valid_segments)
missing.coalesce()
missing = missing - curr_seg_summ_times
missing.coalesce()
scienceButNotFrame = scienceSegs[ifo] - dfScienceSegs[ifo]
scienceButNotFrame.coalesce()
missing2 = scienceSegs[ifo] - scienceButNotFrame
missing2.coalesce()
missing2 = missing2 - curr_seg_summ_times
missing2.coalesce()
if abs(missing):
msg = "From ifo %s the following times have frames, " % (ifo)
msg += "but are not covered in the segment summary table."
msg += "\n%s" % "\n".join(map(str, missing))
logging.error(msg)
missingFlag = True
if abs(missing2):
msg = "From ifo %s the following times have frames, " % (ifo)
msg += "are science, and are not covered in the segment "
msg += "summary table."
msg += "\n%s" % "\n".join(map(str, missing2))
logging.error(msg)
missingFlag = True
if checkSegmentSummary == 'raise_error' and missingFlag:
errMsg = "Segment_summary discrepancy detected, exiting."
raise ValueError(errMsg)
elif checkSegmentSummary == 'no_test':
# Do nothing
pass
else:
errMsg = "checkSegmentSummary kwArg must take a value from 'no_test', "
errMsg += "'warn', or 'raise_error'."
raise ValueError(errMsg)
# Now need to create the file for SCIENCE_AVAILABLE
sci_avlble_dict = segments.segmentlistdict()
# NOTE: Should this be overrideable in the config file?
sci_avlble_name = "SCIENCE_AVAILABLE"
for ifo in scienceSegs.keys():
sci_avlble_dict[ifo + ':' + sci_avlble_name] = scienceSegs[ifo]
sci_avlble_file = SegFile.from_segment_list_dict(
'SCIENCE_AVAILABLE',
sci_avlble_dict,
ifo_list=scienceSegs.keys(),
valid_segment=workflow.analysis_time,
extension='.xml',
tags=tags,
directory=outputDir)
logging.info("Leaving datafind module")
return FileList(
datafindouts), sci_avlble_file, scienceSegs, sci_avlble_name
try: bit_mask = int(value) except ValueError: bit_mask = int(value, 16) # try base 16 in case it's hex # # Are we looking outside the range rather than inside? # if min_threshold is not None and max_threshold is not None: invert = min_threshold >= max_threshold else: invert = False if opts.verbose: print "Inverted? %s"% str(invert) seglist = segmentlist([]) for path in cache.pfnlist(): # # Read data # data, start, _, dt, _, _ = Fr.frgetvect1d(path, channel) # # Apply conditions and transform samples to segments # if equals is not None: seglist.extend(dqsegs.equality_data_to_seglist(data, start, dt, equality=equals)) if bit_mask is not None: seglist.extend(dqsegs.mask_data_to_seglist(data, start, dt, mask_on=bit_mask)) else: seglist.extend(dqsegs.threshold_data_to_seglist(data, start, dt, min_threshold=min_threshold, max_threshold=max_threshold, invert=invert))
#!/usr/bin/env python
import operator
import unittest
import numpy as np
np.seterr(all="raise")
from glue.segments import segment, segmentlist
from pylal import metaarray
TimeSeries_metadata = {
"name": "test",
"dt": 0.1,
"segments": segmentlist([segment(1, 5)]),
"comments": []
}
class test_TimeSeries(unittest.TestCase):
def test_identity(self):
"""
See that the TimeSeries wrapping doesn't touch array data
"""
arr = np.arange(100, dtype=np.float32)
spec = metaarray.TimeSeries(arr, TimeSeries_metadata)
self.assertTrue((arr == spec.A).all())
arr = np.arange(100, dtype=np.float64)
spec = metaarray.TimeSeries(arr, TimeSeries_metadata)
self.assertTrue((arr == spec.A).all())
arr = np.ones(100, dtype=np.bool8)
# FIXME: This should be imported from pycbc.
DYN_RANGE_FAC = 5.9029581035870565e+20
class psd_segment(segment):
def __new__(cls, psd, *args):
return segment.__new__(cls, *args)
def __init__(self, psd, *args):
self.psd = psd
psdseglistdict = {}
for psd_file in opts.psd_files:
(ifo, group), = h5py.File(psd_file, 'r').items()
psd = [group['psds'][str(i)] for i in range(len(group['psds']))]
psdseglistdict[ifo] = segmentlist(
psd_segment(*segargs)
for segargs in zip(psd, group['start_time'], group['end_time']))
def reference_psd_for_sngl(sngl):
psd = psdseglistdict[sngl.ifo]
try:
psd = psd[psd.find(sngl.get_end())].psd
except ValueError:
raise ValueError(
'No PSD found for detector {0} at GPS time {1}'.format(
sngl.ifo, sngl.get_end()))
flow = psd.file.attrs['low_frequency_cutoff']
df = psd.attrs['delta_f']
kmin = int(flow / df)
def frominjectionfile(file, type, ifo=None, start=None, end=None):
"""
Read generic injection file object file containing injections of the given
type string. Returns an 'Sim' lsctable of the corresponding type.
Arguments:
file : file object
type : [ "inspiral" | "burst" | "ringdown" ]
Keyword arguments:
ifo : [ "G1" | "H1" | "H2" | "L1" | "V1" ]
"""
# read type
type = type.lower()
# read injection xml
xml = re.compile('(xml$|xml.gz$)')
if re.search(xml,file.name):
xmldoc,digest = utils.load_fileobj(file)
injtable = table.get_table(xmldoc,'sim_%s:table' % (type))
# read injection txt
else:
cchar = re.compile('[#%<!()_\[\]{}:;\'\"]+')
#== construct new Sim{Burst,Inspiral,Ringdown}Table
injtable = lsctables.New(lsctables.__dict__['Sim%sTable' % (type.title())])
if type=='inspiral':
columns = ['geocent_end_time.geocent_end_time_ns',\
'h_end_time.h_end_time_ns',\
'l_end_time.l_end_time_ns',\
'v_end_time.v_end_time_ns',\
'distance']
for line in file.readlines():
if re.match(cchar,line):
continue
# set up siminspiral object
inj = lsctables.SimInspiral()
# split data
sep = re.compile('[\s,=]+')
data = sep.split(line)
# set attributes
inj.geocent_end_time = int(data[0].split('.')[0])
inj.geocent_end_time_ns = int(data[0].split('.')[1])
inj.h_end_time = int(data[1].split('.')[0])
inj.h_end_time_ns = int(data[1].split('.')[1])
inj.l_end_time = int(data[2].split('.')[0])
inj.l_end_time_ns = int(data[2].split('.')[1])
inj.v_end_time = int(data[3].split('.')[0])
inj.v_end_time_ns = int(data[3].split('.')[1])
inj.distance = float(data[4])
injtable.append(inj)
if type=='burst':
if file.readlines()[0].startswith('filestart'):
# if given parsed burst file
file.seek(0)
snrcol = { 'G1':23, 'H1':19, 'L1':21, 'V1':25 }
for line in file.readlines():
inj = lsctables.SimBurst()
# split data
sep = re.compile('[\s,=]+')
data = sep.split(line)
# set attributes
# gps time
if 'burstgps' in data:
idx = data.index('burstgps')+1
geocent = LIGOTimeGPS(data[idx])
inj.time_geocent_gps = geocent.seconds
inj.time_geocent_gps_ns = geocent.nanoseconds
else:
continue
#inj.waveform = data[4]
#inj.waveform_number = int(data[5])
# frequency
if 'freq' in data:
idx = data.index('freq')+1
inj.frequency = float(data[idx])
else:
continue
# SNR a.k.a. amplitude
if ifo and 'snr%s' % ifo in data:
idx = data.index('snr%s' % ifo)+1
inj.amplitude = float(data[idx])
elif 'rmsSNR' in data:
idx = data.index('rmsSNR')+1
inj.amplitude = float(data[idx])
else:
continue
if 'phi' in data:
idx = data.index('phi' )+1
inj.ra = float(data[idx])*24/(2*math.pi)
if 'theta' in data:
idx = data.index('theta' )+1
inj.ra = 90-(float(data[idx])*180/math.pi)
if ifo and 'hrss%s' % ifo in data:
idx = data.index('hrss%s' % ifo)+1
inj.hrss = float(data[idx])
elif 'hrss' in data:
idx = data.index('hrss')+1
inj.hrss = float(data[idx])
# extra columns to be added when I know how
#inj.q = 0
#inj.q = float(data[11])
#h_delay = LIGOTimeGPS(data[41])
#inj.h_peak_time = inj.time_geocent_gps+h_delay.seconds
#inj.h_peak_time_ns = inj.time_geocent_gps_ns+h_delay.nanoseconds
#l_delay = LIGOTimeGPS(data[43])
#inj.l_peak_time = inj.time_geocent_gps+l_delay.seconds
#inj.l_peak_time_ns = inj.time_geocent_gps_ns+l_delay.nanoseconds
#v_delay = LIGOTimeGPS(data[43])
#inj.v_peak_time = inj.time_geocent_gps+v_delay.seconds
#inj.v_peak_time_ns = inj.time_geocent_gps_ns+v_delay.nanoseconds
injtable.append(inj)
else:
# if given parsed burst file
file.seek(0)
for line in file.readlines():
inj = lsctables.SimBurst()
# split data
sep = re.compile('[\s,]+')
data = sep.split(line)
# set attributes
geocent = LIGOTimeGPS(data[0])
inj.time_geocent_gps = geocent.seconds
inj.time_geocent_gps_ns = geocent.nanoseconds
injtable.append(inj)
injections = table.new_from_template(injtable)
if not start: start = 0
if not end: end = 9999999999
span = segments.segmentlist([ segments.segment(start, end) ])
get_time = dqTriggerUtils.def_get_time(injections.tableName)
injections.extend(inj for inj in injtable if get_time(inj) in span)
return injections
