Add one line installer and pulsatile case - #6
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Feb 19, 2026
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- Add a one line installer for the svFSGe
- Add a pulsatile case using mean wss
- Add converted pulsatile Q waveform (pulsatile_flow.dat) from VMR source .flow files, converted from cm/s to mm/s to match kg/mm/s unit system - Update steady_full.xml inlet BC to use Unsteady time dependence with the pulsatile temporal values file - Add static BC file copy step in svfsi.py setup_files so pulsatile_flow.dat is staged into the run directory automatically - Include source .flow files from Vascular Model Repository Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
- Increase Number_of_time_steps from 10 to 960 (10 cardiac cycles at dt=0.01s) - Add run_fluid_only.py script for standalone fluid simulation without FSG coupling, handles mesh/BC file staging and runs svFSI directly Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
- Container named 'fsg-dev' now persists after exit - Rerunning the script reconnects to existing container - Uses 'sleep infinity' + 'docker exec' pattern for persistence - Updated pip install to use requirements.txt (includes meshio) - Shows container management commands on completion Users can now: - Exit and reconnect without losing setup - Stop/start the container as needed - See the container in 'docker ps -a' Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
Instead of requiring requirements.txt, the script now has the package list hardcoded. This makes it work even if requirements.txt is missing or the repo state is inconsistent. Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
Creates a bootstrap script that can be downloaded and run via curl. Handles repo cloning and Docker setup in one command. Usage: curl -fsSL https://raw.githubusercontent.com/Eleven7825/svFSGe/master/scripts/install.sh | bash Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
- Prominently featured at the top of Quickstart section - Explains prerequisites and what the command does - Updated Quick Setup Script section to mention persistent container Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
- Add setup_singularity.sh for automated Singularity environment setup - Add install_singularity.sh for one-command installation on HPC - Add run_simulation.sh helper script for easy simulation execution - Update README.md with HPC installation instructions - Support for read-only containers with user-space Python package installation - Include verification step to ensure all dependencies are properly installed
…ive VTU copying - Copies folder structure and all files except VTU/BIN from pulsatile/steady/gr_restart - Copies only last 400 pulsatile VTU files to minimize transfer size - Copies only last 10 steady VTU files - Excludes all gr_restart VTU files completely - Uses SSH ControlMaster for single authentication - Strips carriage returns from remote SSH output for proper path handling
…from the json file
…be read from the json file" This reverts commit 7c671a3.
…_full_coarse.json
Informational prints in extract_pulsatile_time_average and extract_pulsatile_amplitude are now gated behind verbose=False by default. Enable with "debug": true in the JSON config. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- fsg.py/svfsi.py: add iqn_ils_debug option (off by default) to save V, W, Q, R, column counts before/after QR filtering, and cc coefficients each IQN-ILS call; saved to debug_qr.npy on archive - post.py: add plot_cc() to plot cc norm and heatmap with load-step transition lines; called automatically from main_arg when debug_qr.npy exists - in_sim/partitioned_full_steep.json: new steep profile case (nmax=5, profile_beta with beta_min=2/beta_max=10, iqn_ils_debug enabled) - in_svfsi_plus/gr_full_restart_steep.xml: solid XML with n_t_end=4 for steeper GR ramp (full load reached at t=3 out of 5 steps) Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
…verbose output - fsg.py/svfsi.py: save load step t and sub-iter n alongside each IQN-ILS debug entry, removing need for fragile formula to reconstruct transitions - post.py: derive transition lines directly from saved t array; fix path resolution for plot_cc in main_arg (two levels up from partitioned/converged) - svfsi.py: suppress "extracted from N geometries" print unless debug=true - in_sim/partitioned_full_debug.json: normal progressive profile case with iqn_ils_debug enabled for comparison against steep profile Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Draw a step line at ncols_after - 0.5 on the heatmap to clearly show the boundary between active coefficients and NaN, making filtering events (drops in the line) immediately visible. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
When iqn_ils_reset=true, mat_V and mat_W are cleared at n=0 of each new load step so IQN-ILS starts from scratch without reusing vectors from previous time steps. Default is false (existing behavior). Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- fsg.py: add iqn_ils_debug saving (cc, t, n per call), iqn_ils_reset flag to clear history vectors at each new load step, fallback to relaxation when mat_V is empty after reset - post.py: add plot_cc() with 3-panel GridSpec figure (|c| heatmap log scale, ||c|| norm, coupling residual before/after IQN update); called from main_arg; use pcolormesh + dedicated colorbar column to fix x-axis alignment - in_sim/partitioned_full_steep.json: set q=5, reset=true, profile_beta=true
…ot_cc - Dummy invisible subplots in colorbar column for all rows so column widths match - MaxNLocator(integer=True) for integer x-axis ticks - Fix sharex() call (replace deprecated get_shared_x_axes().join()) - Remove legend from heatmap and norm subplots; residual shows only before/after/tol - Sparser y-ticks on heatmap (every 2)
Saves and restores all state needed to resume from a converged load step: - IQN-ILS history (mat_W, mat_V, dk, res, dtk) - Coupling error and relaxation history (err, omega) - Current and all previous converged solutions - svFSI binary restart files (stFile_last.bin) snapshotted at checkpoint time to avoid corruption from subsequent failed load steps Usage: set "save_restart": true in JSON to enable checkpointing. Restart via "restart": "path/to/restart.npz" in JSON or -restart CLI flag. Tested on steady case (nmax=2 + restart to nmax=3): sub-iteration counts, IQN-ILS residuals, and displacement fields match to floating-point precision.
The load profile (how the G&R insult is ramped over the load steps) was
hard-coded inside svMultiPhysics' gr_equilibrated.cpp and could not be
changed from here. Expose it through the GR_equilibrated material params
and inject it from the simulation JSON:
- svfsi.py set_gr_load(): reads an optional "gr_load" section and patches
the solid solver XML (<load_profile>/<load_steep>/<load_file>) per run,
using the same regex-on-XML approach as the pulsatile step-count patch.
profile: linear | tanh (default) | power | file
steep: tanh steepness / power exponent
curve: for "file", the load factor per step ([step, factor] pairs or
a plain list); step 0 = pre-stress, 1..nloads = G&R loads.
Omitting "gr_load" leaves the XML untouched -> svFSI defaults (tanh, 2.0),
reproducing the historical behavior.
- Rename the top-level G&R load-step count nmax -> nloads (it clashed with
coup["nmax"], the coupling-iteration cap). The old "nmax" is still
accepted for backward compatibility. coup["nmax"] is unchanged.
Verified: fsg.py in_sim/partitioned_test.json matches test_reference/nmax_2
with both the tanh default and an equivalent file curve.
Requires the matching gr_equilibrated.cpp parser changes in svMultiPhysics
(FSGe branch); without them an older binary rejects the injected XML tags.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Replaces the per-sub-iteration MESH+FLUID svFSI solves with a DeepONet WSS + pressure surrogate. neural_operator.py: - New "direct" backend (lddmm_backend: "direct"): the svFSGe solid interface nodes coincide with the cylinder template, so the displacement is projected straight onto the SVD basis (cached exact-NN reorder) and the solid reference positions are used as the trunk — no MATLAB/fshapesTk, no per-call LDDMM, and consistent with FSG-direct-trained models. - Refactor: geometry encoding extracted into _encode_geometry dispatch with two interchangeable strategies, _encode_direct and _encode_registration (wrapping the existing matlab/matlab_engine/python LDDMM backends); predict_wss_and_pressure is now backend-agnostic with a single shared NN forward-pass tail. fsg.py: - Wire in NeuralOperator (import, init, _neural_operator_step); skip the fluid step when the surrogate is active. - _wss_relax_beta: optional WSS growth under-relaxation (constant or residual/sub-iteration ramp), defaulting to 1.0 = no-op. predictor_relax knob, also defaulting to original behaviour. - _save_failure_case: dump geometry to failed_cases/ on a failed solve. svfsi_docker.sh: - Purge leaked /dev/shm OpenMPI sm_segment.* before each solver call (these accumulate over a long run and exhaust the container's 64M /dev/shm). Verified: drives a 3-step FSG loop on the rebuilt svFSI with the archived FSG-direct models, reproducing the prior growth trajectory bit-for-bit. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
The FSG coupling consumes only |WSS| (svfsi.py reduces the vector via np.linalg.norm before the solid G&R solve), so a single-output magnitude model is both the correct target and more accurate than predicting the 3-vector and norming it (0.37% vs 0.64% |WSS| val error on the n833 set). If cfg["wss_magnitude_pt_file"] is set, load a 1-output magnitude model and deliver the scalar as a vector with the magnitude in the axial component, so the downstream norm recovers |WSS| exactly. Falls back to the 3-vector model (cfg["pt_file"]) otherwise — backward compatible. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Companion to the load-profile work: the aneurysm insult (the spatial
elastin/stimulus knock-down that localizes the lesion) was a super-Gaussian
hard-coded in svMultiPhysics' gr_equilibrated.cpp. Expose it and inject it
from the simulation JSON.
- svfsi.py set_gr_insult(): reads an optional "gr_insult" section and patches
the solid XML with the GR_equilibrated insult params, writing
gr_insult_curve.dat for profile=file. Refactor the XML-patch and curve-write
logic out of set_gr_load() into shared _patch_solid_xml()/_write_curve()
helpers used by both.
profile: gaussian (default) | file (axial shape; azimuth stays gaussian)
mag: peak elastin loss fraction
z_loc/z_wid/z_exp: axial center/width (frac of length)/exponent
asym, theta_wid/theta_exp: azimuthal localization
curve: for "file", axial factor f_axi vs normalized position z/lo
([z/lo, factor] pairs or a plain list)
Knock-down at a point = mag * f_axi(z) * f_cir(azimuth). Omitting "gr_insult"
leaves the XML untouched -> svFSI defaults reproduce the historical insult.
- partitioned_test.json: add an explicit gaussian gr_insult so the CI test
also exercises the injection path (behavior unchanged).
Verified: fsg.py in_sim/partitioned_test.json matches test_reference/nmax_2
with the injected gaussian insult.
Requires the matching gr_equilibrated.cpp parser changes in svMultiPhysics
(FSGe branch); an older binary would reject the injected insult_* XML tags.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
gen_insult_configs.py: Latin-Hypercube sample the configurable gr_insult shape params (mag, z_loc/wid/exp, theta_wid/exp) around the vanilla default, centered at (theta,z)=(0,0), one real-CFD FSG trajectory config per sample. fsg_insult_array.sbatch: run the N trajectories as a Singularity array job, each in its own working dir to avoid mesh-scratch collisions. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01M6MmCkf7dn8MKZarvtyZYw
Mirror the existing nmax=2 standard-FSI CI test for the neural-operator
direct backend so the NN branch is gated before merge.
- in_sim/partitioned_test_n660.json: nloads=2 test config, neural_operator
direct backend pointing at vendored assets.
- test_assets/n660/: self-contained NN deps (wss/pressure .pt, SVD basis,
cylinder.vtk template, shearStressNN_coeff model module) so CI needs
nothing outside the repo (torch is already in simvascular/solver:latest).
- test_reference/n660_nmax_2/: reference baseline (reference_results.json +
tube_002.vtu); iterations [2, 18, 36], reproducible bit-for-bit.
- test-fsg.yml: run both configs in one Docker build, copy to fixed
test_output/{standard,n660} dirs, compare each via compare_results.py.
- .gitignore: allow test_assets/**/*.vtk past the *.vt* rule.
- post.py: force headless Agg backend; close figures to avoid leaks.
- svfsi.py: optional set_gr_growth (tau_ratio_floor) G&R stabilization.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
The base simvascular/solver:latest image has no torch, so CI failed at `from neural_operator import NeuralOperator` (fsg.py top-level) for BOTH the standard and n660 tests. - fsg.py: import NeuralOperator lazily inside __init__, only when a neural_operator surrogate is enabled. The standard FSI path now runs with no torch installed. - test-fsg.yml: install the CPU torch wheel before the n660 test only. Verified in a pristine simvascular/solver:latest container: `import fsg` succeeds without torch, and NeuralOperator loads the vendored test_assets after the CPU torch install. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Add a partitioned continuation driver that traverses growth limit points by solving for the growth-load factor lambda instead of prescribing it, on top of the new svFSI explicit-restart capability. - fsg.py: _run_arclength() — single-loop Crisfield spherical arc-length in (interface displacement d, growth-load factor lambda), solving the partitioned NN-FSG equilibrium R(d,lambda)=solid(NN(d),lambda)-d=0 with a spherical constraint so a step can be re-evaluated at a different lambda without compounding G&R history. Opt-in via JSON "arc_length". - svfsi.py: pass --restart-in/--restart-out to the solid solver so each arc-length trial re-solves a load step from an exact checkpoint (no compounding); read the most-recently-written gr_restart output when the arc-length resets decouple solver cTS from the coupling counter. - neural_operator.py, post.py: supporting changes (WSS handling; headless plotting / arc-length convergence output). Requires the matching svFSI --restart-in/--restart-out support (svMultiPhysics FSGe branch). Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
The n660 comparison uses a very tight tolerance (Displacement rtol 1e-8, Velocity 1e-6), but the neural-operator (torch CPU) prediction is not bit-reproducible across machines: the committed reference was generated on a workstation, so it passes there but fails on the GitHub runner (the standard FSI test, which has no NN, passes on the runner — confirming the solid solver is consistent and this is NN cross-machine drift). Add a workflow_dispatch input `regenerate_n660` that re-generates the reference from the runner's own n660 output and commits it back (curated convergence.error + tube_002.vtu, matching the documented reference format), then skips the strict comparison for that run. Normal push/PR runs still compare strictly against the committed reference. Trigger it once from the Actions tab to bless a runner-generated baseline; subsequent CI runs on the same runner class then compare against a matching reference. Adds `permissions: contents: write` (used only by the regenerate path). Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
…ntinuation, explicit restart # Conflicts: # svfsi.py
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