inspiral_pipe module

class inspiral_pipe.LIGOLWContentHandler(document, start_handlers={})

Bases: LIGOLWContentHandler

startColumn(parent, attrs)

startStream(parent, attrs, __orig_startStream=<function LIGOLWContentHandler.startStream>)

startTable(parent, attrs, __orig_startTable=<function use_in.<locals>.startTable>)

inspiral_pipe.adapt_gstlal_inspiral_output(inspiral_nodes, options, segsdict)

inspiral_pipe.aggregator_layer(dag, jobs, options, job_tags)

inspiral_pipe.analysis_segments(analyzable_instruments_set, allsegs, boundary_seg, max_template_length, min_instruments=2)

get a dictionary of all the disjoint 2+ detector combination segments

inspiral_pipe.build_bank_groups(cachedict, numbanks=[2], maxjobs=None)

! given a dictionary of bank cache files keyed by ifo from .e.g., parse_cache_str(), group the banks into suitable size chunks for a single svd bank file according to numbanks. Note, numbanks can be should be a list and uses the algorithm in the group() function

inspiral_pipe.cache_to_db(cache, jobs)

inspiral_pipe.calc_rank_pdf_layer(dag, jobs, marg_nodes, options, boundary_seg, instrument_set, with_zero_lag=False)

inspiral_pipe.clean_merger_products_layer(dag, jobs, plotnodes, dbs_to_delete, margfiles_to_delete)

clean intermediate merger products

inspiral_pipe.compute_far_layer(dag, jobs, margnodes, injdbs, noninjdb, final_sqlite_nodes, options, with_zero_lag=False)

compute FAPs and FARs

inspiral_pipe.create_svd_bank_strings(svd_nodes, instruments=None)

inspiral_pipe.dq_monitor_layer(dag, jobs, options)

inspiral_pipe.event_plotter_layer(dag, jobs, options)

inspiral_pipe.event_upload_layer(dag, jobs, options, job_tags)

inspiral_pipe.expected_snr_layer(dag, jobs, ref_psd_parent_nodes, options, num_split_inj_snr_jobs)

inspiral_pipe.final_marginalize_layer(dag, jobs, rankpdf_nodes, rankpdf_zerolag_nodes, options, with_zero_lag=False)

inspiral_pipe.get_bank_params(options, verbose=False)

inspiral_pipe.get_rank_file(instruments, boundary_seg, n, basename, job=None)

inspiral_pipe.get_svd_bank_params(svd_bank_cache, online=False)

inspiral_pipe.get_svd_bank_params_online(svd_bank_cache)

inspiral_pipe.get_threshold_values(template_mchirp_dict, bgbin_indices, svd_bank_strings, options)

Calculate the appropriate ht-gate-threshold values according to the scale given

inspiral_pipe.horizon_dist_layer(dag, jobs, marg_nodes, output_dir, instruments)

calculate horizon distance from marginalize diststats

inspiral_pipe.inj_psd_layer(segsdict, options)

inspiral_pipe.injection_template_match_layer(dag, jobs, parent_nodes, options, instruments)

inspiral_pipe.inputs_to_db(jobs, inputs, job_type='toSqlite')

inspiral_pipe.inspiral_layer(dag, jobs, psd_nodes, svd_nodes, segsdict, options, channel_dict, template_mchirp_dict)

inspiral_pipe.likelihood_layer(dag, jobs, marg_nodes, lloid_output, lloid_diststats, options, boundary_seg, instrument_set)

inspiral_pipe.lnlrcdf_signal_layer(dag, jobs, parent_nodes, inj_tmplt_match_nodes, options, boundary_seg, instrument_set)

inspiral_pipe.load_analysis_output(options)

inspiral_pipe.load_bank_cache(options)

inspiral_pipe.load_reference_psd(options)

inspiral_pipe.load_svd_dtdphi_map(options)

inspiral_pipe.make_mc_vtplot_layer(dag, jobs, parent_nodes, add_parent_node, options, instrument_set, output_dir, injdbs=None)

inspiral_pipe.marginalize_layer(dag, jobs, svd_nodes, lloid_output, lloid_diststats, options, boundary_seg, instrument_set, model_node, model_file, ref_psd, svd_dtdphi_map, idq_file=None)

inspiral_pipe.mass_model_layer(dag, jobs, parent_nodes, instruments, options, seg, psd)

mass model node

inspiral_pipe.median_psd_layer(dag, jobs, parent_nodes, options, boundary_seg, instruments)

inspiral_pipe.merge_cluster_layer(dag, jobs, parent_nodes, db, db_cache, sqlfile, input_files=None)

merge and cluster from sqlite database

inspiral_pipe.online_inspiral_layer(dag, jobs, options)

inspiral_pipe.parse_cache_str(instr)

! A way to decode a command line option that specifies different bank caches for different detectors, e.g.,

>>> bankcache = parse_cache_str("H1=H1_split_bank.cache,L1=L1_split_bank.cache,V1=V1_split_bank.cache")
>>> bankcache
{'V1': 'V1_split_bank.cache', 'H1': 'H1_split_bank.cache', 'L1': 'L1_split_bank.cache'}

inspiral_pipe.ref_psd_layer(dag, jobs, parent_nodes, segsdict, channel_dict, options)

inspiral_pipe.set_up_scripts(options)

inspiral_pipe.sim_tag_from_inj_file(injections)

inspiral_pipe.snrchi2_pdf_plot_layer(dag, jobs, marg_nodes, output_dir)

create snrchi2 PDF plot for each template bank bin

inspiral_pipe.sql_cluster_and_merge_layer(dag, jobs, likelihood_nodes, ligolw_add_nodes, options, boundary_seg, instruments, with_zero_lag=False)

inspiral_pipe.subdir_path(dirlist)

inspiral_pipe.summary_page_layer(dag, jobs, plotnodes, options, boundary_seg, injdbs, output_dir)

create a summary page

inspiral_pipe.summary_plot_layer(dag, jobs, farnode, options, injdbs, noninjdb, output_dir)

inspiral_pipe.svd_bank_cache_maker(svd_bank_strings, injection=False)

inspiral_pipe.svd_layer(dag, jobs, parent_nodes, psd, bank_cache, options, seg, output_dir, template_mchirp_dict)

inspiral_pipe.webserver_url()

! The stupid pet tricks to find webserver on the LDG.