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| GW170814 | [LALInferenceNest samples](https://ldas-jobs.ligo.caltech.edu/~jacob.lange/LALInference/O2_PE/C02-cleaned-final/G297595-C02-cleaned-H1L1V1-IMRPv2-rererun/lalinferencenest/IMRPhenomPv2pseudoFourPN/1186741861.53-0/V1H1L1/posterior_samples.dat) | [comparison page](https://ldas-jobs.ligo.caltech.edu/~charlie.hoy/pe/bilby_lalinference_comparison/GW170814/home.html) | review-ready (missing L.I. MCMC run) | |
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| GW170817 | | | TBD| |
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| GW170818 | | | TBD| |
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| GW170823 | | | TBD| | |
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| GW170823 | | | TBD| |
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## Instructions for running
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These are the top-level instructions for PE-group members running bilby on the known events and comparing the results with LALInference. If you have questions or comments, we plan to coordinate on the [bilby-devel](https://chat.ligo.org/ligo/channels/bilby-devel) channel of chat.ligo.
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1. Clone the [bilby event samples repository](https://git.ligo.org/gregory.ashton/bilby_pe_event_samples), this is where all results will ultimately be hosted and has detailed instructions for running bilby.
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2. Identify a python installation with `bilby>0.5.4`, and `bilby_pipe>0.2.3` (or install these in your own environment)
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3. Run bilby on your event using the configuration files and priors listed above
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4. Import the results using the `import_results.py` script
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4. Identify the set of LALInference posterior samples (ideally one for MCMC and one for Nest) that you'll compare against.
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5. Create a pesummary page comparing the reweighted bilby samples and the L.I. results |
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