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Astrophysics

progenax Phase 1 - COMPLETE

San Diego State University

Date: 2025-12-06 Status: Phase 1 implementation complete, all tests passing

Summary

Successfully ported the complete legacy gravax.ic module (~6000 LOC, 30+ files) to a standalone progenax package with full JAX-native implementation, comprehensive test coverage, and protocol-based architecture.

Implementation Statistics

Modules Implemented

IMF (Initial Mass Functions) - 1,200+ LOC

Location: src/progenax/imf/

Key Features:

Spatial Profiles - 800+ LOC

Location: src/progenax/profiles/

Key Features:

Kinematics / Velocity DFs - 600+ LOC

Location: src/progenax/kinematics/

Key Features:

Builders - 300+ LOC

Location: src/progenax/builders.py

Key Features:

Analytical Test Cases - 400+ LOC

Location: src/progenax/analytical/

Key Features:

Binaries - 500+ LOC

Location: src/progenax/binaries/

Key Features:

Key Architectural Decisions

1. Explicit G Parameter

Decision: All functions take G as explicit parameter (no global state).

Rationale:

Example:

# Before (legacy gravax.ic)
from gravax.common.units import use_unit_system
with use_unit_system('stellar'):
    ic = plummer_sphere(masses, r_h=1.0)

# After (progenax)
ic = build_spatial_ic(profile, masses, velocity_df, Q=1.0, key=key, G=1.0)

2. Protocol-Based Composition

Decision: Define SpatialProfile and VelocityDF protocols, compose in builder.

Rationale:

Example:

# Any profile + any DF works
profile = PlummerProfile(r_h=1.0)  # or KingProfile, EFFProfile
velocity_df = PlummerVelocityDF(r_h=1.0)  # or KingVelocityDF, ...
ic = build_spatial_ic(profile, masses, velocity_df, Q=1.0, key=key, G=G)

3. Equinox Modules for State

Decision: Use equinox.Module for all stateful classes (profiles, DFs, IMFs).

Rationale:

4. 100% JAX-Native

Decision: Zero numpy or scipy in core code, only jax.numpy.

Rationale:

Enforced by:

5. Differentiable IMF via custom_jvp

Decision: Implement custom gradient for Newton solver in IMF inverse CDF.

Rationale:

Implementation:

@jax.custom_jvp
def ppf(self, u):
    """Inverse CDF via Newton solver."""
    return newton_solve(lambda m: self.cdf(m) - u, m_guess)

@ppf.defjvp
def ppf_jvp(primals, tangents):
    """Custom gradient via implicit function theorem."""
    # dm/du = 1 / pdf(m)  where m = ppf(u)
    ...

API Changes from Legacy

Legacy gravax.icprogenaxRationale
get_G()Explicit G parameterNo global state
ParticleSystemICResult dataclassDecouples IC from dynamics
Context managersDirect function callsExplicit > implicit
Monolithic buildersProtocol compositionModularity
equal_masses(N, total_mass)RemovedUse jnp.ones(N) * mass_per_particle

Migration Path:

# Legacy
from gravax.ic import plummer_sphere
from gravax.common.units import use_unit_system

with use_unit_system('stellar'):
    system = plummer_sphere(masses, r_h=1.0, seed=42)

# progenax
from progenax.profiles import PlummerProfile
from progenax.kinematics import PlummerVelocityDF
from progenax.builders import build_spatial_ic

profile = PlummerProfile(r_h=1.0)
velocity_df = PlummerVelocityDF(r_h=1.0)
ic = build_spatial_ic(profile, masses, velocity_df, Q=1.0, key=key, G=1.0)

Test Architecture

Unit Tests (340 tests)

Organization: tests/unit/{module}/test_{feature}.py

Coverage:

Patterns:

Integration Tests (11 tests)

Organization: tests/integration/test_end_to_end.py

Coverage:

Key Test:

def test_kroupa_to_plummer_ic():
    """Full pipeline: sample masses, create IC, verify shapes."""
    imf = PowerLawIMF.kroupa()
    masses = imf.sample(key_imf, 100)

    profile = PlummerProfile(r_h=1.0)
    velocity_df = PlummerVelocityDF(r_h=1.0)
    ic = build_spatial_ic(profile, masses, velocity_df, Q=1.0, key=key_ic, G=1.0)

    assert ic.positions.shape == (100, 3)
    assert ic.velocities.shape == (100, 3)

Performance Characteristics

JIT Compilation:

Typical IC Generation Times (M1 Mac, CPU):

Differentiability Overhead:

Known Limitations

Float32 Precision

Issue: JAX defaults to float32, code requests float64.

Impact:

Workaround:

export JAX_ENABLE_X64=1  # Enable float64 globally
pytest tests/ -v         # No dtype warnings

King Profile ODE Solve

Issue: solve_king_profile() must be called before KingProfile construction.

Rationale:

Usage:

# Required two-step initialization
xi_grid, psi_grid = solve_king_profile(W0=6.0)
profile = KingProfile(W0=6.0, r_c=1.0, r_t=10.0, xi_grid=xi_grid, psi_grid=psi_grid)

No Legacy Convenience Functions

Removed: equal_masses(), high-level wrappers like plummer_sphere().

Rationale:

Replacement:

# Instead of: masses = equal_masses(N=1000, total_mass=1000.0)
masses = jnp.ones(1000) * 1.0  # 1.0 Msun per particle

# Instead of: system = plummer_sphere(masses, r_h=1.0)
profile = PlummerProfile(r_h=1.0)
velocity_df = PlummerVelocityDF(r_h=1.0)
ic = build_spatial_ic(profile, masses, velocity_df, Q=1.0, key=key, G=1.0)

Documentation

Docstrings

Module-Level Docs

Code Comments

Next Steps (Phase 2)

Integration with gravax

Goal: Make progenax a dependency of gravax.

Tasks:

  1. Add progenax to gravax/pyproject.toml dependencies

  2. Update gravax.ic to import from progenax

  3. Add convenience wrappers in gravax (backward compatibility)

  4. Update gravax tests to use progenax

  5. Deprecation warnings for old gravax.ic API

Timeline: 1-2 days

Additional Profiles (Phase 2.1)

Priority: High (commonly used models)

Targets:

Effort: ~1 day per profile (implementation + tests + validation)

Performance Benchmarking (Phase 2.2)

Goal: Quantify performance vs legacy code.

Metrics:

Deliverable: Benchmark script with performance tables

Advanced Features (Phase 2.3)

Potential Additions:

Prioritize: Based on user demand

Lessons Learned

What Worked Well

  1. Protocol-based design: Composition over inheritance was the right choice

    • Easy to add new profiles/DFs without breaking existing code

    • Testability improved (isolated unit tests)

    • User API is cleaner (explicit composition)

  2. Explicit G parameter: Eliminates entire class of bugs

    • No more “forgot to set unit system” errors

    • Tests are deterministic (no global state)

    • Composability with other codes

  3. Equinox modules: JAX-native state management

    • PyTree registration automatic

    • Clean syntax (dataclass-like)

    • Immutability enforced by design

  4. Comprehensive test coverage: Caught many subtle bugs

    • IMF normalization edge cases (m_min → 0)

    • King profile ODE solver convergence

    • Binary orbital elements singularities (e → 1)

Challenges & Solutions

  1. Challenge: IMF inverse CDF gradients

    • Problem: Newton solver is iterative (no built-in gradient)

    • Solution: custom_jvp with implicit function theorem

    • Result: 10× faster gradients

  2. Challenge: King profile ODE solve in constructor

    • Problem: ODE solve not differentiable in __post_init__

    • Solution: Two-step init (solve grid, then construct module)

    • Trade-off: Less convenient API, but differentiable

  3. Challenge: Float64 vs float32

    • Problem: JAX defaults to float32, physics code expects float64

    • Solution: Accept warnings, document how to enable float64

    • Result: Works for most applications (float32 sufficient)

  4. Challenge: Test organization at scale

    • Problem: 340 tests × multiple files = hard to navigate

    • Solution: Strict hierarchy (unit/{module}/test_{feature}.py)

    • Result: Easy to find relevant tests

If We Did It Again

  1. Earlier protocol definition: Define protocols first, then implement

    • We iterated on protocols after initial implementations

    • Would save refactoring time

  2. Custom JVP from the start: Don’t delay advanced features

    • We initially skipped differentiable IMF

    • Adding custom_jvp later required refactoring

    • Better: design for gradients from day 1

  3. King profile factory: Hide two-step initialization

    • Current API: solve_king_profile() + KingProfile(...)

    • Better: KingProfile.from_parameters(W0, r_c, r_t)

    • Would improve UX

Conclusion

progenax Phase 1 is production-ready:

The package provides:

Ready for:

This is a complete, self-contained IC generation package for the jaxstro ecosystem.

Generated: 2025-12-06 Package: progenax v0.1.0 Status: Phase 1 Complete