Demos

RIFT ships several demonstration directories under MonteCarloMarginalizeCode/Code/demo. Use this page as a map from the problem you want to understand to the smallest existing example that exercises that workflow.

The demos are not all the same kind of artifact. Some are fast local smoke tests, some build Condor DAGs without submitting them, and some are advanced operator tutorials that assume LIGO cluster credentials or external software. Each entry below states what it is useful for before pointing you at the source files.

Pipeline-builder smoke tests

Path

MonteCarloMarginalizeCode/Code/demo/pipeline

Use this when

You want a fast, submission-free check that util_RIFT_pseudo_pipe.py can build a complete RIFT run directory and thread command-line options through CEPP into the generated Condor submit files.

What it demonstrates

The demo uses fake data and reference .ini / coinc.xml inputs to create run directories without submitting jobs or requiring real frames, PSDs, GPUs, or a Condor pool. It is especially useful for regression tests of argument plumbing: a flag supplied to util_RIFT_pseudo_pipe.py should survive into the correct args_*.txt file and *.sub file.

Primary files

• README.md — target descriptions and expected assertions.

• Makefile — baseline, grid, slices, all, and clean targets.

Typical command

From the demo directory, in a configured RIFT environment:

make all

or, using the repository Pixi environment from the README:

pixi run --manifest-path ../../../../../pixi.toml make all

Notes

The grid and slices targets exercise last-iteration extrinsic export behavior. They check that distance-grid or distance-slice flags land on the final extrinsic ILE stage without leaking into the intrinsic ILE jobs.

Zero-spin IMRPhenomD distance-grid validation

Path

MonteCarloMarginalizeCode/Code/demo/pipeline/zero_spin_phenomD

Use this when

You need a compact end-to-end validation of per-distance likelihood export, consolidation, and posterior reconstruction on a laptop-scale example.

What it demonstrates

This demo uses zero-spin IMRPhenomD, the AV sampler, fake zero-noise BBH inputs, and a small mass grid. It bypasses Condor for the local execution step but uses the same production code paths for building the pipeline, running integrate_likelihood_extrinsic_batchmode, consolidating .dgrid files, and reconstructing a joint intrinsic-plus-distance posterior.

Primary files

• README.md — full four-stage validation walkthrough.

• Makefile — build, run-extr, consolidate, posterior, all, and clean targets.

• zero_spin_phenomD.ini — minimal pseudo-pipe configuration.

Typical command

From the demo directory:

make all

Notes

The default settings intentionally evaluate only a few events so the test is fast. This is a code-path validation, not a scientific accuracy benchmark. Increase N_EVENTS if you want a more meaningful posterior check.

HyperPipe demos

Path

MonteCarloMarginalizeCode/Code/demo/hyperpipe

Use this when

You want to learn HyperPipe, test generalized likelihood drivers, compare baseline posterior resampling to tracer placement, or adapt a toy coordinate-free workflow into a real one.

What it demonstrates

The directory contains runnable YAML configurations for the same 3-D Gaussian toy likelihood. The variants exercise the iterative MARG -> CON -> UNIFY -> EOS_POST -> PUFF/placement -> TEST loop, OSG submit-host settings, coordinate transformation, and parsimonious/tracer placement.

Primary files

• README.md — detailed description of every configuration.

• technical_doc.txt — pedagogical implementation notes.

• hyperpipe_conf.yaml — baseline posterior-resampling workflow.

• hyperpipe_conf_tracer.yaml — tracer/parsimonious-placement workflow.

• hyperpipe_conf_osg.yaml — OSG/IGWN-oriented submit configuration.

• hyperpipe_conf_linear_uvw.yaml — fit in transformed coordinates while sampling in the original coordinates.

• example_gaussian*.py — toy likelihood drivers.

• Makefile — convenience targets such as rundir and rundir_tracer.

Typical commands

Baseline demo:

util_RIFT_hyperpipe.py --config ./hyperpipe_conf.yaml

Tracer-placement demo:

util_RIFT_hyperpipe.py --config ./hyperpipe_conf_tracer.yaml

Notes

Start here before writing a new HyperPipe configuration from scratch. The YAML files show the expected schema and the generated run directories expose the exact executable arguments in args_*.txt and Condor *.sub files.

Population-study demo

Path

MonteCarloMarginalizeCode/Code/demo/populations

Use this when

You want a worked outline for generating mock compact-binary populations with GWKokab and producing RIFT parameter estimates for those injections.

What it demonstrates

The README describes a multi-environment workflow: generate injections with GWKokab, validate the population inference setup, switch to a separate RIFT environment, prepare injections, generate MDC files, create RIFT run directories, submit PE jobs, and produce diagnostics.

Primary files

• README.md — full tutorial and environment notes.

• Makefile — workflow automation points.

• pop-example.ini — example RIFT configuration for the population run.

• injections.dat — example injection table.

• write_mdc.py and gwk_pop_conversion.py — conversion/setup helpers.

• plot_all.sh and collect_all.sh — post-processing helpers.

Typical command

This is an advanced, environment-dependent workflow. Read and edit the Makefile variables and pop-example.ini before running targets. The README starts with GWKokab setup and then moves into RIFT setup.

Notes

Keep GWKokab and RIFT in separate environments. The prior ranges in pop-example.ini must match the population used to generate injections.dat; otherwise the resulting PE runs are not meaningful.

Distance-grid export demo

Path

MonteCarloMarginalizeCode/Code/demo/rift/add_distance_grids

Use this when

You need to understand or validate the --export-marginal-distance-grid path and the generated .dgrid likelihood-density files.

What it demonstrates

The demo builds a small zero-spin RIFT workflow with distance-grid export enabled for ILE jobs. It reuses fake zero-noise CI assets and verifies that the generated ILE arguments include --export-marginal-distance-grid and --internal-use-lnL.

Primary files

• README.md — build/submit instructions and environment warning notes.

• PLAN_B_DESIGN.md — design notes for fixed-distance slice export and re-marginalization.

• Makefile — dag and submit targets.

• add_distance_grids.ini — zero-spin/fake-data configuration.

• validate_distance_grid.py and validate_distance_slices.py — helper validation scripts.

Typical commands

Build the DAG without submitting:

make dag

Submit the generated workflow after inspection:

make submit

Notes

LALSuite/SWIG compatibility warnings may appear in some environments. The README explains how to distinguish those warnings from distance-grid failures.

Numerical relativity with RIFT

Path

MonteCarloMarginalizeCode/Code/demo/nr_w_rift

Use this when

You want an advanced tutorial for comparing gravitational-wave data to numerical-relativity simulations rather than analytic waveform models.

What it demonstrates

The workflow obtains event data, constructs an NR simulation grid, builds a RIFT run directory through NR-specific pipeline tools, and runs a refine stage before the final CIP posterior construction.

Primary files

• README.md — cluster-oriented tutorial and required manual settings.

• Makefile — data, grid, and run-directory construction targets.

Typical commands

This tutorial assumes LIGO computing access and event-specific manual edits. Read the README first, then configure the event identifiers, channels, NR group, mass range, and event time in the Makefile before running targets such as make data, make grid, and make rundir.

Notes

This is not a quickstart. It assumes a working RIFT environment, LIGO data access, NR catalog access, and familiarity with production RIFT runs.

Internal and test-oriented demos

Some demo-like directories are primarily regression or development harnesses. They are useful for developers, but should not be presented as first-stop user quickstarts until their assumptions are documented.

Known examples include:

• MonteCarloMarginalizeCode/Code/demo/rift/test_frameworks/zero_likelihood — zero-likelihood HyperPipe/Condor smoke-test material.

When promoting one of these into a user-facing tutorial, first document:

• whether it requires Condor, GPUs, GraceDB, LIGO credentials, or external data;

• whether it submits jobs or only builds run directories;

• expected runtime and expected outputs;

• cleanup commands; and

• which scientific result, if any, should be trusted.

Choosing a starting point

Demo selection guide

If you want to…	Start with…	Why
Check pseudo-pipe argument plumbing quickly	demo/pipeline	Fast fake-data DAG construction without submission.
Validate distance-grid export end to end	demo/pipeline/zero_spin_phenomD	Exercises build, extrinsic likelihood, consolidation, and posterior reconstruction.
Learn HyperPipe	demo/hyperpipe	Small Gaussian likelihood with baseline, tracer, OSG, and coordinate transform variants.
Try tracer placement	demo/hyperpipe/hyperpipe_conf_tracer.yaml	Existing parsimonious-placement example with generated DAG output.
Explore population-study workflows	demo/populations	End-to-end GWKokab-to-RIFT outline, with environment caveats.
Inspect distance-grid export internals	demo/rift/add_distance_grids	Focused DAG build and validation helpers for .dgrid output.
Work with numerical-relativity simulations	demo/nr_w_rift	Advanced cluster-oriented NR workflow.

Related pages

• Evidence-weighted NR intrinsic and extrinsic posteriors

• HyperPipe

• Using the pipeline

• RIFT pipeline examples: the ini file

• Using the OSG

• Plotting with RIFT

• General Troubleshooting