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LIGO-G040107-00-ZLSC Meeting March 2004 Search Pipelines for Binary Inspiral Duncan Brown Inspiral Working Group University of Wisconsin-Milwaukee LIGO-G040107-00-Z.

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Presentation on theme: "LIGO-G040107-00-ZLSC Meeting March 2004 Search Pipelines for Binary Inspiral Duncan Brown Inspiral Working Group University of Wisconsin-Milwaukee LIGO-G040107-00-Z."— Presentation transcript:

1 LIGO-G ZLSC Meeting March 2004 Search Pipelines for Binary Inspiral Duncan Brown Inspiral Working Group University of Wisconsin-Milwaukee LIGO-G Z

2 LSC Meeting March 2004 S2 Inspiral Pipeline lalapps_tmpltbank

3 LIGO-G ZLSC Meeting March 2004 S2 Inspiral Pipeline lalapps_inspiral

4 LIGO-G ZLSC Meeting March 2004 S2 Inspiral Pipeline lalapps_inca

5 LIGO-G ZLSC Meeting March 2004 S2 Inspiral Pipeline lalapps_inca

6 LIGO-G ZLSC Meeting March 2004 S2 Inspiral Pipeline lalapps_inspiral

7 LIGO-G ZLSC Meeting March 2004 S2 Inspiral Pipeline lalapps_inca

8 LIGO-G ZLSC Meeting March 2004 S2 Inspiral Pipeline lalapps_inca

9 LIGO-G ZLSC Meeting March 2004 S2 Inspiral Pipeline LIGO_LW XML file

10 LIGO-G ZLSC Meeting March 2004 Pipeline Infrastructure Requirements •Ensure that all data is analyzed •Automate pipeline as much as possible •Provide flexible pipeline for testing and tuning •Allow easy construction of complex workflows •Simple reusable infrastructure •Easy to debug

11 LIGO-G ZLSC Meeting March 2004 Pipeline Implementation •Condor to manage job submission to cluster •lalapps code to execute components of pipeline » Use LAL functions for GW analysis •Condor DAGman to manage execution of pipeline •Standard file types for I/O » Read AS_Q and calibration from frame data » Writes triggers as LIGO_LW XML » Can write r(t), x 2 (t), PSD, filter data as frames

12 LIGO-G ZLSC Meeting March 2004 Creation of the DAG •Simple Python modules in lalapps to build scripts that write pipeline •lalapps/src/lalapps/pipeline.py » Read segwizard files » Manipulate science segments (union, intersection, inverse) » Create Condor Jobs and DAGs •lalapps/src/inspiral/inspiral.py » Construction of DAG nodes specific to inspiral •lalapps/src/inspiral/inspiral_pipe.in » Use building blocks to construct pipeline

13 LIGO-G ZLSC Meeting March 2004 Putting It All Together data = pipeline.ScienceData() data.read(‘segwizard.txt’,2048) data.make_chunks(length,overlap,isplay) dag = pipeline.CondorDAG(‘mydag.dag’) datafind_job = pipeline.LSCDataFindJob() inspiral_job = inspiral.InspiralJob() for seg in data: df = pipeline.LSCDataFindNode() df.set_start(seg.start()) df.set_end(seg.end()) for chunk in seg: insp = inspiral.InspiralNode() insp.set_start(chunk.start()) insp.set_end(chunk.end()) insp.add_parent(df) dag.write()

14 LIGO-G ZLSC Meeting March 2004 Putting It All Together data = pipeline.ScienceData() data.read(‘segwizard.txt’,2048) data.make_chunks(length,overlap,isplay) dag = pipeline.CondorDAG(‘mydag.dag’) datafind_job = pipeline.LSCDataFindJob() inspiral_job = inspiral.InspiralJob() for seg in data: df = pipeline.LSCDataFindNode() df.set_start(seg.start()) df.set_end(seg.end()) for chunk in seg: insp = inspiral.InspiralNode() insp.set_start(chunk.start()) insp.set_end(chunk.end()) insp.add_parent(df) dag.write()

15 LIGO-G ZLSC Meeting March 2004 Putting It All Together data = pipeline.ScienceData() data.read(‘segwizard.txt’,2048) data.make_chunks(length,overlap,isplay) dag = pipeline.CondorDAG(‘mydag.dag’) datafind_job = pipeline.LSCDataFindJob() inspiral_job = inspiral.InspiralJob() for seg in data: df = pipeline.LSCDataFindNode() df.set_start(seg.start()) df.set_end(seg.end()) for chunk in seg: insp = inspiral.InspiralNode() insp.set_start(chunk.start()) insp.set_end(chunk.end()) insp.add_parent(df) dag.write()

16 LIGO-G ZLSC Meeting March 2004 Putting It All Together data = pipeline.ScienceData() data.read(‘segwizard.txt’,2048) data.make_chunks(length,overlap,isplay) dag = pipeline.CondorDAG(‘mydag.dag’) datafind_job = pipeline.LSCDataFindJob() inspiral_job = inspiral.InspiralJob() for seg in data: df = pipeline.LSCDataFindNode() df.set_start(seg.start()) df.set_end(seg.end()) for chunk in seg: insp = inspiral.InspiralNode() insp.set_start(chunk.start()) insp.set_end(chunk.end()) insp.add_parent(df) dag.write()

17 LIGO-G ZLSC Meeting March 2004 Putting It All Together data = pipeline.ScienceData() data.read(‘segwizard.txt’,2048) data.make_chunks(length,overlap,isplay) dag = pipeline.CondorDAG(‘mydag.dag’) datafind_job = pipeline.LSCDataFindJob() inspiral_job = inspiral.InspiralJob() for seg in data: df = pipeline.LSCDataFindNode() df.set_start(seg.start()) df.set_end(seg.end()) for chunk in seg: insp = inspiral.InspiralNode() insp.set_start(chunk.start()) insp.set_end(chunk.end()) insp.add_parent(df) dag.write()

18 LIGO-G ZLSC Meeting March 2004 Putting It All Together data = pipeline.ScienceData() data.read(‘segwizard.txt’,2048) data.make_chunks(length,overlap,isplay) dag = pipeline.CondorDAG(‘mydag.dag’) datafind_job = pipeline.LSCDataFindJob() inspiral_job = inspiral.InspiralJob() for seg in data: df = pipeline.LSCDataFindNode() df.set_start(seg.start()) df.set_end(seg.end()) for chunk in seg: insp = inspiral.InspiralNode() insp.set_start(chunk.start()) insp.set_end(chunk.end()) insp.add_parent(df) dag.write()

19 LIGO-G ZLSC Meeting March 2004 Putting It All Together data = pipeline.ScienceData() data.read(‘segwizard.txt’,2048) data.make_chunks(length,overlap,isplay) dag = pipeline.CondorDAG(‘mydag.dag’) datafind_job = pipeline.LSCDataFindJob() inspiral_job = inspiral.InspiralJob() for seg in data: df = pipeline.LSCDataFindNode() df.set_start(seg.start()) df.set_end(seg.end()) for chunk in seg: insp = inspiral.InspiralNode() insp.set_start(chunk.start()) insp.set_end(chunk.end()) insp.add_parent(df) dag.write()

20 LIGO-G ZLSC Meeting March 2004 S2 Inspiral DAG

21 LIGO-G ZLSC Meeting March 2004 Conclusions •Use of Condor DAGman has been very successful » Simplifies management of analysis workflow » More time to concentrate on scientific questions •Infrastructure written in lalapps is simple to use » Python modules are documented in lalapps documentation •Reusable code » LIGO/TAMA inspiral analysis (Steve Fairhurst) » Stochastic lalapps pipeline (Adam Mercer) •Fast, simple, efficient!


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