Source code for

Convenience classes for accessing hdf5 trigger files

import h5py
import numpy as np
import logging
import inspect
import pickle

from itertools import chain
from io import BytesIO
from lal import LIGOTimeGPS, YRJUL_SI

from ligo.lw import ligolw
from ligo.lw import lsctables
from ligo.lw import utils as ligolw_utils

from import (
from pycbc import events, conversions, pnutils
from import ranking, veto
from import mean_if_greater_than_zero

[docs]class HFile(h5py.File): """ Low level extensions to the capabilities of reading an hdf5 File """
[docs] def select(self, fcn, *args, chunksize=10**6, derived=None, group='', return_data=True, premask=None): """ Return arrays from an hdf5 file that satisfy the given function Parameters ---------- fcn : a function A function that accepts the same number of argument as keys given and returns a boolean array of the same length. args : strings A variable number of strings that are keys into the hdf5. These must refer to arrays of equal length. chunksize : {10**6, int}, optional Number of elements to read and process at a time. derived : dictionary Dictionary keyed on argument name (must be given in args), values are a tuple of: the function to be computed, and the required datasets. The function must take in a dictionary keyed on those dataset names. group : string, optional The group within the h5py file containing the datasets, e.g. in standard offline merged trigger files, this would be the IFO. This can be included in the args manually, but is required in the case of derived functions, e.g. newsnr. return_data : bool, optional, default True If True, return the data for elements passing the function. premask : array of boolean values, optional The pre-mask to apply to the triggers at read-in. Returns ------- indices: np.ndarray An array of indices of elements passing the function. return_tuple : tuple of np.ndarrays A variable number of arrays depending on the number of args provided, If return_data is True, arrays are the values of each arg. If return_data is False, this is None. >>> f = HFile(filename) >>> snr = snr: snr > 6, 'H1/snr') """ # Required datasets are the arguments requested and datasets given # for any derived functions derived = derived if derived is not None else {} dsets = [a for a in list(args) if a not in derived] for _, rqd_list in derived.values(): dsets += rqd_list # remove any duplicates from req_dsets dsets = list(set(dsets)) # Get the pointers to the h5py Datasets, # check they can all be used together refs = {} size = None for ds in dsets: refs[ds] = self[group + '/' + ds] if (size is not None) and (refs[ds].size != size): raise RuntimeError(f"Dataset {ds} is {self[ds].size} " "entries long, which does not match " f"previous input datasets ({size}).") size = refs[ds].size # Apply any pre-masks if premask is None: mask = np.ones(size, dtype=bool) else: mask = premask if not mask.dtype == bool: # mask is an array of indices rather than booleans, # make it a bool array new_mask = np.zeros(size, dtype=bool) new_mask[mask] = True mask = new_mask if not mask.size == size: raise RuntimeError(f"Using premask of size {mask.size} which " f"does not match the input datasets ({size}).") # datasets being returned (possibly) data = {} indices = np.array([], dtype=np.uint64) for arg in args: data[arg] = [] # Loop through the chunks: i = 0 while i < size: r = i + chunksize if i + chunksize < size else size if not any(mask[i:r]): # Nothing allowed through the mask in this chunk i += chunksize continue if all(mask[i:r]): # Everything allowed through the mask in this chunk submask = np.arange(r - i) else: submask = np.flatnonzero(mask[i:r]) # Read each chunk's worth of data partial_data = {arg: refs[arg][i:r][mask[i:r]] for arg in dsets} partial = [] for a in args: if a in derived.keys(): # If this is a derived dataset, calculate it derived_fcn = derived[a][0] partial += [derived_fcn(partial_data)] else: # otherwise, just read from the file partial += [partial_data[a]] # Find where it passes the function keep = fcn(*partial) # Keep the indices which pass the function: indices = np.concatenate([indices, submask[keep] + i]) if return_data: # Store the dataset results that pass the function for arg, part in zip(args, partial): data[arg].append(part[keep]) i += chunksize if return_data: return_tuple = tuple(np.concatenate(data[arg]) for arg in args) else: return_tuple = None return indices.astype(np.uint64), return_tuple
[docs]class DictArray(object): """ Utility for organizing sets of arrays of equal length. Manages a dictionary of arrays of equal length. This can also be instantiated with a set of hdf5 files and the key values. The full data is always in memory and all operations create new instances of the DictArray. """ def __init__(self, data=None, files=None, groups=None): """ Create a DictArray Parameters ---------- data: dict, optional Dictionary of equal length numpy arrays files: list of filenames, optional List of hdf5 file filenames. Incompatibile with the `data` option. groups: list of strings List of keys into each file. Required by the files option. """ # Check that input fits with how the DictArray is set up if data and files: raise RuntimeError('DictArray can only have data or files as ' 'input, not both.') if data is None and files is None: raise RuntimeError('DictArray needs either data or files at' 'initialization. To set up an empty instance' 'use DictArray(data={})') if files and not groups: raise RuntimeError('If files are given then need groups.') = data self.groups = groups if files: = {} for g in groups:[g] = [] for f in files: d = HFile(f) for g in groups: if g in d:[g].append(d[g][:]) d.close() for k in if not len([k]) == 0:[k] = np.concatenate([k]) for k in setattr(self, k,[k]) def _return(self, data): return self.__class__(data=data) def __len__(self): return len([tuple([0]]) def __add__(self, other): if == {}: logging.debug('Adding data to a DictArray instance which' ' was initialized with an empty dict') return self._return(data=other) data = {} for k in try: data[k] = np.concatenate([[k],[k]]) except KeyError:'%s does not exist in other data' % k) return self._return(data=data)
[docs] def select(self, idx): """ Return a new DictArray containing only the indexed values """ data = {} for k in # Make sure each entry is an array (not a scalar) data[k] = np.array([k][idx]) return self._return(data=data)
[docs] def remove(self, idx): """ Return a new DictArray that does not contain the indexed values """ data = {} for k in data[k] = np.delete([k], np.array(idx, dtype=int)) return self._return(data=data)
[docs] def save(self, outname): f = HFile(outname, "w") for k in self.attrs: f.attrs[k] = self.attrs[k] for k in f.create_dataset(k,[k], compression='gzip', compression_opts=9, shuffle=True) f.close()
[docs]class StatmapData(DictArray): def __init__(self, data=None, seg=None, attrs=None, files=None, groups=('stat', 'time1', 'time2', 'trigger_id1', 'trigger_id2', 'template_id', 'decimation_factor', 'timeslide_id')): super(StatmapData, self).__init__(data=data, files=files, groups=groups) if data: self.seg=seg self.attrs=attrs elif files: f = HFile(files[0], "r") self.seg = f['segments'] self.attrs = f.attrs def _return(self, data): return self.__class__(data=data, attrs=self.attrs, seg=self.seg)
[docs] def cluster(self, window): """ Cluster the dict array, assuming it has the relevant Coinc colums, time1, time2, stat, and timeslide_id """ # If no events, do nothing if len(self.time1) == 0 or len(self.time2) == 0: return self from import cluster_coincs interval = self.attrs['timeslide_interval'] cid = cluster_coincs(self.stat, self.time1, self.time2, self.timeslide_id, interval, window) return
[docs] def save(self, outname): super(StatmapData, self).save(outname) with HFile(outname, "w") as f: for key in self.seg.keys(): f['segments/%s/start' % key] = self.seg[key]['start'][:] f['segments/%s/end' % key] = self.seg[key]['end'][:]
[docs]class MultiifoStatmapData(StatmapData): def __init__(self, data=None, seg=None, attrs=None, files=None, ifos=None): groups = ['decimation_factor', 'stat', 'template_id', 'timeslide_id'] for ifo in ifos: groups += ['%s/time' % ifo] groups += ['%s/trigger_id' % ifo] super(MultiifoStatmapData, self).__init__(data=data, files=files, groups=groups, attrs=attrs, seg=seg) def _return(self, data): ifolist = self.attrs['ifos'].split(' ') return self.__class__(data=data, attrs=self.attrs, seg=self.seg, ifos=ifolist)
[docs] def cluster(self, window): """ Cluster the dict array, assuming it has the relevant Coinc colums, time1, time2, stat, and timeslide_id """ # If no events, do nothing pivot_ifo = self.attrs['pivot'] fixed_ifo = self.attrs['fixed'] if len(['%s/time' % pivot_ifo]) == 0 or len(['%s/time' % fixed_ifo]) == 0: return self from import cluster_coincs interval = self.attrs['timeslide_interval'] cid = cluster_coincs(self.stat,['%s/time' % pivot_ifo],['%s/time' % fixed_ifo], self.timeslide_id, interval, window) return
[docs]class FileData(object): def __init__(self, fname, group=None, columnlist=None, filter_func=None): """ Parameters ---------- group : string Name of group to be read from the file columnlist : list of strings Names of columns to be read; if None, use all existing columns filter_func : string String should evaluate to a Boolean expression using attributes of the class instance derived from columns: ex. 'self.snr < 6.5' """ if not fname: raise RuntimeError("Didn't get a file!") self.fname = fname self.h5file = HFile(fname, "r") if group is None: if len(self.h5file.keys()) == 1: group, = self.h5file.keys() else: raise RuntimeError("Didn't get a group!") self.group_key = group = self.h5file[group] self.columns = columnlist if columnlist is not None \ else list( self.filter_func = filter_func self._mask = None
[docs] def close(self): self.h5file.close()
@property def mask(self): """ Create a mask implementing the requested filter on the datasets Returns ------- array of Boolean True for dataset indices to be returned by the get_column method """ if self.filter_func is None: raise RuntimeError("Can't get a mask without a filter function!") else: # only evaluate if no previous calculation was done if self._mask is None: # get required columns into the namespace as numpy arrays for column in self.columns: if column in self.filter_func: setattr(self, column,[column][:]) self._mask = eval(self.filter_func) return self._mask
[docs] def get_column(self, col): """ Method designed to be analogous to legacy pylal.SnglInspiralUtils functionality Parameters ---------- col : string Name of the dataset to be returned Returns ------- numpy array Values from the dataset, filtered if requested """ # catch corner case with an empty file (group with no datasets) if not len( return np.array([]) vals =[col] if self.filter_func: return vals[self.mask] else: return vals[:]
[docs]class DataFromFiles(object): def __init__(self, filelist, group=None, columnlist=None, filter_func=None): self.files = filelist = group self.columns = columnlist self.filter_func = filter_func
[docs] def get_column(self, col): """ Loop over files getting the requested dataset values from each Parameters ---------- col : string Name of the dataset to be returned Returns ------- numpy array Values from the dataset, filtered if requested and concatenated in order of file list """'getting %s' % col) vals = [] for f in self.files: d = FileData(f,, columnlist=self.columns, filter_func=self.filter_func) vals.append(d.get_column(col)) # Close each file since h5py has an upper limit on the number of # open file objects (approx. 1000) d.close()'- got %i values' % sum(len(v) for v in vals)) return np.concatenate(vals)
[docs]class SingleDetTriggers(object): """ Provides easy access to the parameters of single-detector CBC triggers. """ def __init__(self, trig_file, detector, bank_file=None, veto_file=None, segment_name=None, premask=None, filter_rank=None, filter_threshold=None, chunksize=10**6, filter_func=None): """ Create a SingleDetTriggers instance Parameters ---------- trig_file : string or os.pathtype, required HDF file containing trigger information detector : string, required The detectior being used, this is used to access the triggers in trig_file bank_file: string or os.pathtype, optional hdf file containing template bank information veto_file: string or os.pathtype, optional File used to define vetoes segment_name : string, optional Segment name being used in the veto_file premask : array of indices or boolean, optional Array of used triggers filter_rank : string, optional The ranking, as defined by to compare to filter_threshold filter_threshold: float, required if filter_rank is used Threshold to filter the ranking values chunksize : int , default 10**6 Size of chunks to read in for the filter_rank / threshold. """'Loading triggers') self.trigs_f = HFile(trig_file, 'r') self.trigs = self.trigs_f[detector] self.ntriggers = self.trigs['end_time'].size self.ifo = detector # convenience attributes self.detector = detector if bank_file:'Loading bank') = HFile(bank_file, 'r') else: # empty dict in place of non-existent hdf file = {} # Apply some masks to start off with - here we should try and apply # them in the order which cuts most things earliest. # Apply any pre-masks if premask is None: self.mask = np.ones(self.ntriggers, dtype=bool) else: self.mask = None self.apply_mask(premask) if filter_rank: assert filter_threshold is not None"Applying threshold of %.3f on %s", filter_threshold, filter_rank) fcn_dsets = (ranking.sngls_ranking_function_dict[filter_rank], ranking.required_datasets[filter_rank]) idx, _ = lambda rank: rank > filter_threshold, filter_rank, derived={filter_rank: fcn_dsets}, return_data=False, premask=self.mask, group=detector, chunksize=chunksize, )"%d triggers remain", idx.size) # If self.mask already has values, need to take these into account: self.and_masks(idx) if filter_func: # Apply a filter on the triggers which is _not_ a ranking statistic for rank_str in ranking.sngls_ranking_function_dict.keys(): if f'self.{rank_str}' in filter_func: logging.warning('Supplying the ranking (%s) in ' 'filter_func is inefficient, suggest to ' 'use filter_rank instead.', rank_str)'Setting up filter function') for c in self.trigs.keys(): if c in filter_func: setattr(self, '_'+c, self.trigs[c][:]) for c in if c in filter_func: # get template parameters corresponding to triggers setattr(self, '_'+c, np.array([c])[self.trigs['template_id'][:]]) filter_mask = eval(filter_func.replace('self.', 'self._')) # remove the dummy attributes for c in chain(self.trigs.keys(), if c in filter_func: delattr(self, '_'+c) self.apply_mask(filter_mask)'%i triggers remain after cut on %s', sum(self.mask), filter_func) if veto_file:'Applying veto segments') # veto_mask is an array of indices into the trigger arrays # giving the surviving triggers'%i triggers before vetoes', self.mask_size) veto_mask, _ = events.veto.indices_outside_segments( self.end_time, [veto_file], ifo=detector, segment_name=segment_name) # Update mask accordingly self.apply_mask(veto_mask)'%i triggers remain after vetoes', self.mask_size) def __getitem__(self, key): # Is key in the TRIGGER_MERGE file? try: return self.get_column(key) except KeyError: pass # Is key in the bank file? try: self.checkbank(key) return[key][:][self.template_id] except (RuntimeError, KeyError) as exc: err_msg = "Cannot find {} in input files".format(key) raise ValueError(err_msg) from exc
[docs] def checkbank(self, param): if == {}: return RuntimeError("Can't get %s values without a bank file" % param)
[docs] def trig_dict(self): """Returns dict of the masked trigger values""" mtrigs = {} for k in self.trigs: if len(self.trigs[k]) == len(self.trigs['end_time']): if self.mask is not None: mtrigs[k] = self.trigs[k][self.mask] else: mtrigs[k] = self.trigs[k][:] return mtrigs
[docs] @classmethod def get_param_names(cls): """Returns a list of plottable CBC parameter variables""" return [m[0] for m in inspect.getmembers(cls) \ if type(m[1]) == property]
[docs] def apply_mask(self, logic_mask): """Apply a mask over the top of the current mask Parameters ---------- logic_mask : boolean array or numpy array of indices """ if self.mask is None: self.mask = np.zeros(self.ntriggers, dtype=bool) self.mask[logic_mask] = True elif hasattr(self.mask, 'dtype') and (self.mask.dtype == 'bool'): orig_indices = np.flatnonzero(self.mask)[logic_mask] self.mask[:] = False self.mask[orig_indices] = True else: self.mask = list(np.array(self.mask)[logic_mask])
[docs] def and_masks(self, logic_mask): """Apply a mask to be combined as a logical and with the current mask. Parameters ---------- logic_mask : boolean array or numpy array/list of indices """ if self.mask_size == self.ntriggers: # No mask exists, just update to use the given mask self.apply_mask(logic_mask) return # Use intersection of the indices of True values in the masks if hasattr(logic_mask, 'dtype') and (logic_mask.dtype == 'bool'): new_indices = np.flatnonzero(logic_mask) else: new_indices = np.array(logic_mask) if hasattr(self.mask, 'dtype') and (self.mask.dtype == 'bool'): orig_indices = np.flatnonzero(self.mask) else: orig_indices = np.array(self.mask) self.mask[:] = False and_indices = np.intersect1d(new_indices, orig_indices) self.mask[and_indices.astype(np.uint64)] = True
[docs] def mask_to_n_loudest_clustered_events(self, rank_method, ranking_threshold=6, n_loudest=10, cluster_window=10): """Edits the mask property of the class to point to the N loudest single detector events as ranked by ranking statistic. Events are clustered so that no more than 1 event within +/- cluster_window will be considered. Can apply a threshold on the ranking using ranking_threshold """ sds = rank_method.single(self.trig_dict()) stat = rank_method.rank_stat_single((self.ifo, sds)) if len(stat) == 0: # No triggers at all, so just return here self.apply_mask(np.array([], dtype=np.uint64)) return times = self.end_time if ranking_threshold: # Threshold on sngl_ranking # Note that we can provide None or zero to do no thresholding # but the default is to do some keep = stat >= ranking_threshold stat = stat[keep] times = times[keep] self.apply_mask(keep) if len(stat) == 0: logging.warning("No triggers after thresholding") return else:"%d triggers after thresholding", len(stat)) index = stat.argsort()[::-1] new_times = [] new_index = [] # Loop through triggers - loudest first for curr_idx in index: curr_time = times[curr_idx] for time in new_times: # Have we already got a louder trigger within the window? if abs(curr_time - time) < cluster_window: break else: # Store if no other triggers within cluster window new_index.append(curr_idx) new_times.append(curr_time) if len(new_index) >= n_loudest: # We have as many triggers as we want now break # For indexing, indices need to be a numpy array, in order index = np.array(new_index) index.sort() # Apply to the existing mask self.apply_mask(index)
@property def mask_size(self): if self.mask is None: return self.ntriggers if isinstance(self.mask, list): return len(self.mask) return np.count_nonzero(self.mask) @property def template_id(self): return self.get_column('template_id').astype(int) @property def mass1(self): self.checkbank('mass1') return['mass1'][:][self.template_id] @property def mass2(self): self.checkbank('mass2') return['mass2'][:][self.template_id] @property def spin1z(self): self.checkbank('spin1z') return['spin1z'][:][self.template_id] @property def spin2z(self): self.checkbank('spin2z') return['spin2z'][:][self.template_id] @property def spin2x(self): self.checkbank('spin2x') return['spin2x'][:][self.template_id] @property def spin2y(self): self.checkbank('spin2y') return['spin2y'][:][self.template_id] @property def spin1x(self): self.checkbank('spin1x') return['spin1x'][:][self.template_id] @property def spin1y(self): self.checkbank('spin1y') return['spin1y'][:][self.template_id] @property def inclination(self): self.checkbank('inclination') return['inclination'][:][self.template_id] @property def f_lower(self): self.checkbank('f_lower') return['f_lower'][:][self.template_id] @property def approximant(self): self.checkbank('approximant') return['approximant'][:][self.template_id] @property def mtotal(self): return self.mass1 + self.mass2 @property def mchirp(self): return conversions.mchirp_from_mass1_mass2(self.mass1, self.mass2) @property def eta(self): return conversions.eta_from_mass1_mass2(self.mass1, self.mass2) @property def effective_spin(self): # FIXME assumes aligned spins return conversions.chi_eff(self.mass1, self.mass2, self.spin1z, self.spin2z) # IMPROVEME: would like to have a way to access all get_freq and/or # other pnutils.* names rather than hard-coding each one # - eg make this part of a fancy interface to the bank file ? @property def f_seobnrv2_peak(self): return pnutils.get_freq('fSEOBNRv2Peak', self.mass1, self.mass2, self.spin1z, self.spin2z) @property def f_seobnrv4_peak(self): return pnutils.get_freq('fSEOBNRv4Peak', self.mass1, self.mass2, self.spin1z, self.spin2z) @property def end_time(self): return self.get_column('end_time') @property def template_duration(self): return self.get_column('template_duration') @property def snr(self): return self.get_column('snr') @property def sgchisq(self): return self.get_column('sg_chisq') @property def u_vals(self): return self.get_column('u_vals') @property def rchisq(self): return self.get_column('chisq') \ / (self.get_column('chisq_dof') * 2 - 2) @property def psd_var_val(self): return self.get_column('psd_var_val') @property def newsnr(self): return ranking.newsnr(self.snr, self.rchisq) @property def newsnr_sgveto(self): return ranking.newsnr_sgveto(self.snr, self.rchisq, self.sgchisq) @property def newsnr_sgveto_psdvar(self): return ranking.newsnr_sgveto_psdvar(self.snr, self.rchisq, self.sgchisq, self.psd_var_val) @property def newsnr_sgveto_psdvar_threshold(self): return ranking.newsnr_sgveto_psdvar_threshold(self.snr, self.rchisq, self.sgchisq, self.psd_var_val)
[docs] def get_ranking(self, rank_name, **kwargs): return ranking.get_sngls_ranking_from_trigs(self, rank_name, **kwargs)
[docs] def get_column(self, cname): """ Read columns while applying the mask """ # Fiducial value that seems to work, not extensively tuned. MFRAC = 0.3 # If the mask accesses few enough elements then directly use it # This can be slower than reading in all the elements if most of them # will be read. if isinstance(self.mask, list) or \ self.mask_size < (self.ntriggers * MFRAC): return self.trigs[cname][self.mask] # We have a lot of elements to read so we resort to readin the entire # array before masking. elif self.mask is not None: return self.trigs[cname][:][self.mask] else: return self.trigs[cname][:]
[docs]class ForegroundTriggers(object): # Injection files are expected to only have 'exclusive' IFAR/FAP values, # should use has_inc=False for these. def __init__(self, coinc_file, bank_file, sngl_files=None, n_loudest=None, group='foreground', has_inc=True): self.coinc_file = FileData(coinc_file, group=group) if 'ifos' in self.coinc_file.h5file.attrs: self.ifos = self.coinc_file.h5file.attrs['ifos'].split(' ') else: raise ValueError("File doesn't have an 'ifos' attribute!", coinc_file) self.sngl_files = {} if sngl_files is not None: for sngl_file in sngl_files: curr_dat = FileData(sngl_file) curr_ifo = curr_dat.group_key self.sngl_files[curr_ifo] = curr_dat if not all([ifo in self.sngl_files.keys() for ifo in self.ifos]): print("sngl_files: {}".format(sngl_files)) print("self.ifos: {}".format(self.ifos)) raise RuntimeError("IFOs in statmap file not all represented " "by single-detector trigger files.") if not sorted(self.sngl_files.keys()) == sorted(self.ifos): logging.warning("WARNING: Single-detector trigger files " "given for IFOs not in the statmap file") self.bank_file = HFile(bank_file, "r") self.n_loudest = n_loudest self._inclusive = has_inc self._sort_arr = None self._template_id = None self._trig_ids = None self.get_active_segments() @property def sort_arr(self): if self._sort_arr is None: if self._inclusive: try: ifar = self.coinc_file.get_column('ifar') except KeyError: logging.warning("WARNING: Can't find inclusive IFAR!" "Using exclusive IFAR instead ...") ifar = self.coinc_file.get_column('ifar_exc') self._inclusive = False else: ifar = self.coinc_file.get_column('ifar_exc') sorting = ifar.argsort()[::-1] if self.n_loudest: sorting = sorting[:self.n_loudest] self._sort_arr = sorting return self._sort_arr @property def template_id(self): if self._template_id is None: template_id = self.get_coincfile_array('template_id') self._template_id = template_id.astype(int) return self._template_id @property def trig_id(self): if self._trig_ids is not None: return self._trig_ids self._trig_ids = {} for ifo in self.ifos: self._trig_ids[ifo] = self.get_coincfile_array(ifo + '/trigger_id') return self._trig_ids
[docs] def get_coincfile_array(self, variable): return self.coinc_file.get_column(variable)[self.sort_arr]
[docs] def get_bankfile_array(self, variable): try: return self.bank_file[variable][:][self.template_id] except IndexError: if len(self.template_id) == 0: return np.array([]) raise
[docs] def get_snglfile_array_dict(self, variable): return_dict = {} for ifo in self.ifos: try: tid = self.trig_id[ifo] lgc = tid == -1 # Put in *some* value for the invalid points to avoid failure # Make sure this doesn't change the cached internal array! tid = np.copy(tid) tid[lgc] = 0 # If small number of points don't read the full file if len(tid) < 1000: curr = [] hdf_dataset = self.sngl_files[ifo].group[variable] for idx in tid: curr.append(hdf_dataset[idx]) curr = np.array(curr) else: curr = self.sngl_files[ifo].get_column(variable)[tid] except IndexError: if len(self.trig_id[ifo]) == 0: curr = np.array([]) lgc = curr == 0 else: raise return_dict[ifo] = (curr, np.logical_not(lgc)) return return_dict
[docs] def get_active_segments(self): self.active_segments = {} for ifo in self.ifos: starts = self.sngl_files[ifo].get_column('search/start_time') ends = self.sngl_files[ifo].get_column('search/end_time') self.active_segments[ifo] = veto.start_end_to_segments(starts, ends)
[docs] def get_end_time(self): times_gen = (self.get_coincfile_array('{}/time'.format(ifo)) for ifo in self.ifos) ref_times = np.array([mean_if_greater_than_zero(t)[0] for t in zip(*times_gen)]) return ref_times
[docs] def get_ifos(self): """ Returns ------- ifos_list List of lists of ifo names involved in each foreground event. Ifos will be listed in the same order as self.ifos """ # Ian thinks this could be coded more simply and efficiently # Note also that effectively the same thing is done as part of the # to_coinc_hdf_object method ifo_or_minus = [] for ifo in self.ifos: ifo_trigs = np.where(self.get_coincfile_array(ifo + '/time') < 0, '-', ifo) ifo_or_minus.append(ifo_trigs) ifos_list = [list(trig[trig != '-']) for trig in iter(np.array(ifo_or_minus).T)] return ifos_list
[docs] def to_coinc_xml_object(self, file_name): outdoc = ligolw.Document() outdoc.appendChild(ligolw.LIGO_LW()) ifos = sorted(self.sngl_files) proc_table = create_process_table( outdoc, program_name='pycbc', detectors=ifos ) proc_id = proc_table.process_id search_summ_table = lsctables.New(lsctables.SearchSummaryTable) coinc_h5file = self.coinc_file.h5file try: start_time = coinc_h5file['segments']['coinc']['start'][:].min() end_time = coinc_h5file['segments']['coinc']['end'][:].max() except KeyError: start_times = [] end_times = [] for ifo_comb in coinc_h5file['segments']: if ifo_comb == 'foreground_veto': continue seg_group = coinc_h5file['segments'][ifo_comb] start_times.append(seg_group['start'][:].min()) end_times.append(seg_group['end'][:].max()) start_time = min(start_times) end_time = max(end_times) num_trigs = len(self.sort_arr) search_summary = return_search_summary(start_time, end_time, num_trigs, ifos) search_summ_table.append(search_summary) outdoc.childNodes[0].appendChild(search_summ_table) sngl_inspiral_table = lsctables.New(lsctables.SnglInspiralTable) coinc_def_table = lsctables.New(lsctables.CoincDefTable) coinc_event_table = lsctables.New(lsctables.CoincTable) coinc_inspiral_table = lsctables.New(lsctables.CoincInspiralTable) coinc_event_map_table = lsctables.New(lsctables.CoincMapTable) time_slide_table = lsctables.New(lsctables.TimeSlideTable) # Set up time_slide table time_slide_id = lsctables.TimeSlideID(0) for ifo in ifos: time_slide_row = lsctables.TimeSlide() time_slide_row.instrument = ifo time_slide_row.time_slide_id = time_slide_id time_slide_row.offset = 0 time_slide_row.process_id = proc_id time_slide_table.append(time_slide_row) # Set up coinc_definer table coinc_def_id = lsctables.CoincDefID(0) coinc_def_row = lsctables.CoincDef() = "inspiral" coinc_def_row.description = \ "sngl_inspiral<-->sngl_inspiral coincidences" coinc_def_row.coinc_def_id = coinc_def_id coinc_def_row.search_coinc_type = 0 coinc_def_table.append(coinc_def_row) bank_col_names = ['mass1', 'mass2', 'spin1z', 'spin2z'] bank_col_vals = {} for name in bank_col_names: bank_col_vals[name] = self.get_bankfile_array(name) coinc_event_names = ['ifar', 'time', 'fap', 'stat'] coinc_event_vals = {} for name in coinc_event_names: if name == 'time': coinc_event_vals[name] = self.get_end_time() else: coinc_event_vals[name] = self.get_coincfile_array(name) sngl_col_names = ['snr', 'chisq', 'chisq_dof', 'bank_chisq', 'bank_chisq_dof', 'cont_chisq', 'cont_chisq_dof', 'end_time', 'template_duration', 'coa_phase', 'sigmasq'] sngl_col_vals = {} for name in sngl_col_names: sngl_col_vals[name] = self.get_snglfile_array_dict(name) sngl_event_count = 0 for idx in range(len(self.sort_arr)): # Set up IDs and mapping values coinc_id = lsctables.CoincID(idx) # Set up sngls sngl_mchirps = [] sngl_mtots = [] net_snrsq = 0 triggered_ifos = [] for ifo in ifos: # If this ifo is not participating in this coincidence then # ignore it and move on. if not sngl_col_vals['snr'][ifo][1][idx]: continue triggered_ifos += [ifo] event_id = lsctables.SnglInspiralID(sngl_event_count) sngl_event_count += 1 sngl = return_empty_sngl() sngl.event_id = event_id sngl.ifo = ifo net_snrsq += sngl_col_vals['snr'][ifo][0][idx]**2 for name in sngl_col_names: val = sngl_col_vals[name][ifo][0][idx] if name == 'end_time': sngl.end = LIGOTimeGPS(val) elif name == 'chisq': # Use reduced chisquared to be consistent with Live dof = 2. * sngl_col_vals['chisq_dof'][ifo][0][idx] - 2. sngl.chisq = val / dof else: setattr(sngl, name, val) for name in bank_col_names: val = bank_col_vals[name][idx] setattr(sngl, name, val) sngl.mtotal, sngl.eta = pnutils.mass1_mass2_to_mtotal_eta( sngl.mass1, sngl.mass2) sngl.mchirp, _ = pnutils.mass1_mass2_to_mchirp_eta( sngl.mass1, sngl.mass2) sngl.eff_distance = (sngl.sigmasq)**0.5 / sngl.snr # If exact match is not used, get masses from single triggers sngl_mchirps += [sngl.mchirp] sngl_mtots += [sngl.mtotal] sngl_inspiral_table.append(sngl) # Set up coinc_map entry coinc_map_row = lsctables.CoincMap() coinc_map_row.table_name = 'sngl_inspiral' coinc_map_row.coinc_event_id = coinc_id coinc_map_row.event_id = event_id coinc_event_map_table.append(coinc_map_row) # Take the mean if exact match is not used sngl_combined_mchirp = np.mean(sngl_mchirps) sngl_combined_mtot = np.mean(sngl_mtots) # Set up coinc inspiral and coinc event tables coinc_event_row = lsctables.Coinc() coinc_inspiral_row = lsctables.CoincInspiral() coinc_event_row.coinc_def_id = coinc_def_id coinc_event_row.nevents = len(triggered_ifos) # NB, `coinc_event_row.instruments = triggered_ifos does not give a # correct result with ligo.lw 1.7.1 coinc_event_row.instruments = ','.join(sorted(triggered_ifos)) coinc_inspiral_row.instruments = triggered_ifos coinc_event_row.time_slide_id = time_slide_id coinc_event_row.process_id = proc_id coinc_event_row.coinc_event_id = coinc_id coinc_inspiral_row.coinc_event_id = coinc_id coinc_inspiral_row.mchirp = sngl_combined_mchirp coinc_inspiral_row.mass = sngl_combined_mtot coinc_inspiral_row.end = LIGOTimeGPS(coinc_event_vals['time'][idx]) coinc_inspiral_row.snr = net_snrsq**0.5 coinc_inspiral_row.false_alarm_rate = coinc_event_vals['fap'][idx] coinc_inspiral_row.combined_far = 1./coinc_event_vals['ifar'][idx] # Transform to Hz coinc_inspiral_row.combined_far = \ coinc_inspiral_row.combined_far / YRJUL_SI coinc_event_row.likelihood = coinc_event_vals['stat'][idx] coinc_inspiral_row.minimum_duration = 0. coinc_event_table.append(coinc_event_row) coinc_inspiral_table.append(coinc_inspiral_row) outdoc.childNodes[0].appendChild(coinc_def_table) outdoc.childNodes[0].appendChild(coinc_event_table) outdoc.childNodes[0].appendChild(coinc_event_map_table) outdoc.childNodes[0].appendChild(time_slide_table) outdoc.childNodes[0].appendChild(coinc_inspiral_table) outdoc.childNodes[0].appendChild(sngl_inspiral_table) ligolw_utils.write_filename(outdoc, file_name)
[docs] def to_coinc_hdf_object(self, file_name): ofd = h5py.File(file_name,'w') # Some fields are special cases"Outputting search results") time = self.get_end_time() # time will be used later to determine active ifos ofd['time'] = time if self._inclusive: ofd['ifar'] = self.get_coincfile_array('ifar') ofd['p_value'] = self.get_coincfile_array('fap') ofd['ifar_exclusive'] = self.get_coincfile_array('ifar_exc') ofd['p_value_exclusive'] = self.get_coincfile_array('fap_exc') # Coinc fields for field in ['stat']: ofd[field] = self.get_coincfile_array(field)"Outputting template information") # Bank fields for field in ['mass1','mass2','spin1z','spin2z']: ofd[field] = self.get_bankfile_array(field) mass1 = self.get_bankfile_array('mass1') mass2 = self.get_bankfile_array('mass2') ofd['chirp_mass'], _ = pnutils.mass1_mass2_to_mchirp_eta(mass1, mass2)"Outputting single-trigger information")"reduced chisquared") chisq_vals_valid = self.get_snglfile_array_dict('chisq') chisq_dof_vals_valid = self.get_snglfile_array_dict('chisq_dof') for ifo in self.ifos: chisq_vals = chisq_vals_valid[ifo][0] chisq_valid = chisq_vals_valid[ifo][1] chisq_dof_vals = chisq_dof_vals_valid[ifo][0] rchisq = chisq_vals / (2. * chisq_dof_vals - 2.) rchisq[np.logical_not(chisq_valid)] = -1. ofd[ifo + '_chisq'] = rchisq # Single-detector fields for field in ['sg_chisq', 'end_time', 'sigmasq', 'psd_var_val']: try: vals_valid = self.get_snglfile_array_dict(field) except KeyError: + " is not present in the " "single-detector files") for ifo in self.ifos: # Some of the values will not be valid for all IFOs, # the `valid` parameter out of get_snglfile_array_dict # tells us this, and we set the values to -1 vals = vals_valid[ifo][0] valid = vals_valid[ifo][1] vals[np.logical_not(valid)] = -1. ofd[f'{ifo}_{field}'] = vals snr_vals_valid = self.get_snglfile_array_dict('snr') network_snr_sq = np.zeros_like(snr_vals_valid[self.ifos[0]][0]) for ifo in self.ifos: vals = snr_vals_valid[ifo][0] valid = snr_vals_valid[ifo][1] vals[np.logical_not(valid)] = -1. ofd[ifo + '_snr'] = vals network_snr_sq[valid] += vals[valid] ** 2.0 ofd['network_snr'] = np.sqrt(network_snr_sq)"Triggered detectors") # Create a n_ifos by n_events matrix, with the ifo letter if the # event contains a trigger from the ifo, empty string if not triggered_matrix = [[ifo[0] if v else '' for v in snr_vals_valid[ifo][1]] for ifo in self.ifos] # Combine the ifo letters to make a single string per event triggered_detectors = [''.join(triggered).encode('ascii') for triggered in zip(*triggered_matrix)] ofd.create_dataset('trig', data=triggered_detectors, dtype='<S3')"active detectors") # Create a n_ifos by n_events matrix, with the ifo letter if the # ifo was active at the event time, empty string if not active_matrix = [[ifo[0] if t in self.active_segments[ifo] else '' for t in time] for ifo in self.ifos] # Combine the ifo letters to make a single string per event active_detectors = [''.join(active_at_time).encode('ascii') for active_at_time in zip(*active_matrix)] ofd.create_dataset('obs', data=active_detectors, dtype='<S3') ofd.close()
[docs]class ReadByTemplate(object): # Default assignment to {} is OK for a variable used only in __init__ def __init__(self, filename, bank=None, segment_name=None, veto_files=None, gating_veto_windows={}): self.filename = filename self.file = h5py.File(filename, 'r') self.ifo = tuple(self.file.keys())[0] self.valid = None = h5py.File(bank, 'r') if bank else {} # Determine the segments which define the boundaries of valid times # to use triggers key = '%s/search/' % self.ifo s, e = self.file[key + 'start_time'][:], self.file[key + 'end_time'][:] self.segs = veto.start_end_to_segments(s, e).coalesce() if segment_name is None: segment_name = [] if veto_files is None: veto_files = [] for vfile, name in zip(veto_files, segment_name): veto_segs = veto.select_segments_by_definer(vfile, ifo=self.ifo, segment_name=name) self.segs = (self.segs - veto_segs).coalesce() if self.ifo in gating_veto_windows: gating_veto = gating_veto_windows[self.ifo].split(',') gveto_before = float(gating_veto[0]) gveto_after = float(gating_veto[1]) if gveto_before > 0 or gveto_after < 0: raise ValueError("Gating veto window values must be negative " "before gates and positive after gates.") if not (gveto_before == 0 and gveto_after == 0): autogate_times = np.unique( self.file[self.ifo + '/gating/auto/time'][:]) if self.ifo + '/gating/file' in self.file: detgate_times = self.file[self.ifo + '/gating/file/time'][:] else: detgate_times = [] gate_times = np.concatenate((autogate_times, detgate_times)) gating_veto_segs = veto.start_end_to_segments( gate_times + gveto_before, gate_times + gveto_after ).coalesce() self.segs = (self.segs - gating_veto_segs).coalesce() self.valid = veto.segments_to_start_end(self.segs)
[docs] def get_data(self, col, num): """Get a column of data for template with id 'num'. Parameters ---------- col: str Name of column to read num: int The template id to read triggers for Returns ------- data: numpy.ndarray The requested column of data """ ref = self.file['%s/%s_template' % (self.ifo, col)][num] return self.file['%s/%s' % (self.ifo, col)][ref]
[docs] def set_template(self, num): """Set the active template to read from. Parameters ---------- num: int The template id to read triggers for. Returns ------- trigger_id: numpy.ndarray The indices of this templates triggers. """ self.template_num = num times = self.get_data('end_time', num) # Determine which of these template's triggers are kept after # applying vetoes if self.valid: self.keep = veto.indices_within_times(times, self.valid[0], self.valid[1]) #'applying vetoes') else: self.keep = np.arange(0, len(times)) if != {}: self.param = {} if 'parameters' in for col in['parameters']: self.param[col] =[col][self.template_num] else: for col in self.param[col] =[col][self.template_num] # Calculate the trigger id by adding the relative offset in self.keep # to the absolute beginning index of this templates triggers stored # in 'template_boundaries' trigger_id = self.keep + \ self.file['%s/template_boundaries' % self.ifo][num] return trigger_id
def __getitem__(self, col): """ Return the column of data for current active template after applying vetoes Parameters ---------- col: str Name of column to read Returns ------- data: numpy.ndarray The requested column of data """ if self.template_num is None: raise ValueError('You must call set_template to first pick the ' 'template to read data from') data = self.get_data(col, self.template_num) data = data[self.keep] if self.valid else data return data
chisq_choices = ['traditional', 'cont', 'bank', 'max_cont_trad', 'sg', 'max_bank_cont', 'max_bank_trad', 'max_bank_cont_trad']
[docs]def get_chisq_from_file_choice(hdfile, chisq_choice): f = hdfile if chisq_choice in ['traditional','max_cont_trad', 'max_bank_trad', 'max_bank_cont_trad']: trad_chisq = f['chisq'][:] # We now need to handle the case where chisq is not actually calculated # 0 is used as a sentinel value trad_chisq_dof = f['chisq_dof'][:] trad_chisq /= (trad_chisq_dof * 2 - 2) if chisq_choice in ['cont', 'max_cont_trad', 'max_bank_cont', 'max_bank_cont_trad']: cont_chisq = f['cont_chisq'][:] cont_chisq_dof = f['cont_chisq_dof'][:] cont_chisq /= cont_chisq_dof if chisq_choice in ['bank', 'max_bank_cont', 'max_bank_trad', 'max_bank_cont_trad']: bank_chisq = f['bank_chisq'][:] bank_chisq_dof = f['bank_chisq_dof'][:] bank_chisq /= bank_chisq_dof if chisq_choice == 'sg': chisq = f['sg_chisq'][:] elif chisq_choice == 'traditional': chisq = trad_chisq elif chisq_choice == 'cont': chisq = cont_chisq elif chisq_choice == 'bank': chisq = bank_chisq elif chisq_choice == 'max_cont_trad': chisq = np.maximum(trad_chisq, cont_chisq) elif chisq_choice == 'max_bank_cont': chisq = np.maximum(bank_chisq, cont_chisq) elif chisq_choice == 'max_bank_trad': chisq = np.maximum(bank_chisq, trad_chisq) elif chisq_choice == 'max_bank_cont_trad': chisq = np.maximum(np.maximum(bank_chisq, cont_chisq), trad_chisq) else: err_msg = "Do not recognize --chisq-choice %s" % chisq_choice raise ValueError(err_msg) return chisq
[docs]def save_dict_to_hdf5(dic, filename): """ Parameters ---------- dic: python dictionary to be converted to hdf5 format filename: desired name of hdf5 file """ with h5py.File(filename, 'w') as h5file: recursively_save_dict_contents_to_group(h5file, '/', dic)
[docs]def recursively_save_dict_contents_to_group(h5file, path, dic): """ Parameters ---------- h5file: h5py file to be written to path: path within h5py file to saved dictionary dic: python dictionary to be converted to hdf5 format """ for key, item in dic.items(): if isinstance(item, (np.ndarray, np.int64, np.float64, str, int, float, bytes, tuple, list)): h5file[path + str(key)] = item elif isinstance(item, dict): recursively_save_dict_contents_to_group(h5file, path + key + '/', item) else: raise ValueError('Cannot save %s type' % type(item))
[docs]def load_hdf5_to_dict(h5file, path): """ Parameters ---------- h5file: h5py file to be loaded as a dictionary path: path within h5py file to load: '/' for the whole h5py file Returns ------- dic: dictionary with hdf5 file group content """ dic = {} for key, item in h5file[path].items(): if isinstance(item, h5py.Dataset): dic[key] = item[()] elif isinstance(item, h5py.Group): dic[key] = load_hdf5_to_dict(h5file, path + key + '/') else: raise ValueError('Cannot load %s type' % type(item)) return dic
[docs]def combine_and_copy(f, files, group): """ Combine the same column from multiple files and save to a third""" # ensure that the files input is stable for iteration order assert isinstance(files, (list, tuple)) f[group] = np.concatenate([fi[group][:] if group in fi else \ np.array([], dtype=np.uint32) for fi in files])
[docs]def name_all_datasets(files): assert isinstance(files, (list, tuple)) datasets = [] for fi in files: datasets += get_all_subkeys(fi, '/') return set(datasets)
[docs]def get_all_subkeys(grp, key): subkey_list = [] subkey_start = key if key == '': grpk = grp else: grpk = grp[key] for sk in grpk.keys(): path = subkey_start + '/' + sk if isinstance(grp[path], h5py.Dataset): subkey_list.append(path.lstrip('/')) else: subkey_list += get_all_subkeys(grp, path) # returns an empty list if there is no dataset or subgroup within the group return subkey_list
# # ============================================================================= # # Checkpointing utilities # # ============================================================================= #
[docs]def dump_state(state, fp, path=None, dsetname='state', protocol=pickle.HIGHEST_PROTOCOL): """Dumps the given state to an hdf5 file handler. The state is stored as a raw binary array to ``{path}/{dsetname}`` in the given hdf5 file handler. If a dataset with the same name and path is already in the file, the dataset will be resized and overwritten with the new state data. Parameters ---------- state : any picklable object The sampler state to dump to file. Can be the object returned by any of the samplers' `.state` attribute (a dictionary of dictionaries), or any picklable object. fp : h5py.File An open hdf5 file handler. Must have write capability enabled. path : str, optional The path (group name) to store the state dataset to. Default (None) will result in the array being stored to the top level. dsetname : str, optional The name of the dataset to store the binary array to. Default is ``state``. protocol : int, optional The protocol version to use for pickling. See the :py:mod:`pickle` module for more details. """ memfp = BytesIO() pickle.dump(state, memfp, protocol=protocol) dump_pickle_to_hdf(memfp, fp, path=path, dsetname=dsetname)
[docs]def dump_pickle_to_hdf(memfp, fp, path=None, dsetname='state'): """Dumps pickled data to an hdf5 file object. Parameters ---------- memfp : file object Bytes stream of pickled data. fp : h5py.File An open hdf5 file handler. Must have write capability enabled. path : str, optional The path (group name) to store the state dataset to. Default (None) will result in the array being stored to the top level. dsetname : str, optional The name of the dataset to store the binary array to. Default is ``state``. """ bdata = np.frombuffer(, dtype='S1') if path is not None: dsetname = path + '/' + dsetname if dsetname not in fp: fp.create_dataset(dsetname, shape=bdata.shape, maxshape=(None,), dtype=bdata.dtype) elif bdata.size != fp[dsetname].shape[0]: fp[dsetname].resize((bdata.size,)) fp[dsetname][:] = bdata
[docs]def load_state(fp, path=None, dsetname='state'): """Loads a sampler state from the given hdf5 file object. The sampler state is expected to be stored as a raw bytes array which can be loaded by pickle. Parameters ---------- fp : h5py.File An open hdf5 file handler. path : str, optional The path (group name) that the state data is stored to. Default (None) is to read from the top level. dsetname : str, optional The name of the dataset that the state data is stored to. Default is ``state``. """ if path is not None: fp = fp[path] bdata = fp[dsetname][()].tobytes() return pickle.load(BytesIO(bdata))
__all__ = ('HFile', 'DictArray', 'StatmapData', 'MultiifoStatmapData', 'FileData', 'DataFromFiles', 'SingleDetTriggers', 'ForegroundTriggers', 'ReadByTemplate', 'chisq_choices', 'get_chisq_from_file_choice', 'save_dict_to_hdf5', 'recursively_save_dict_contents_to_group', 'load_hdf5_to_dict', 'combine_and_copy', 'name_all_datasets', 'get_all_subkeys', 'dump_state', 'dump_pickle_to_hdf', 'load_state')