Journey 02 — Core path (Rössler)

import solvsrk
import numpy as np
 
def rossler(t, y):
    x, z, w = y
    return np.array([-z - w, x + 0.2*z, 0.2 + w*(x - 5.7)])
 
# Prefer SolvScout token when installed; else bundled tuned preset:
cfg = solvsrk.rossler_config()  # or run_profile(token, ...)
 
result = solvsrk.run(cfg, ndim=3, t0=0.0, t_end=100.0, y0=(1,1,1), rhs_fn=rossler)
print(result['survived'], result['t_reached'], result['y_final'])

Rössler attractor | ndim=3 | nonlinear chaos

-- Act 1 - The problem --
  -> The Rössler system is a three-state nonlinear ODE with a strange attractor. It is not stiff in the Robertson sense, but it is a realistic nonlinear trajectory — the kind of model you hand to an integrator when there is no closed form for y(t_end).

-- Act 2 - The call --
  -> solvsrk.run() takes a tuned config, the dimension, the time span, y0, and your rhs(t, y). Knobs come from SolvScout recommend_product() when installed (journey 04); otherwise we use a bundled tuned preset — not raw config_defaults().
  knob_source: SolvScout tuned (solvsrk, high)

-- Act 3 - Read the result --
  -> survived tells you it reached t_end; phase counters in the result dict show where the work happened. For chaotic systems you care that the integration finished with a receipt, not that y_final matches a textbook number.
  survived: True
  failure_mode: ok
  t_reached: 100
  y_final: [9.65822, -3.53339, 0.813287]
  total_rhs_evals: 20170
  front_phase: steps=0 rhs=0
  stiff_phase: steps=18121 rhs=20170
  t_handoff: 0
  wall_s: 0.05695
  ek_steps: 0
  cvode_steps: 18121

-- Run complete --
  next_journey: 03_stiff_chemistry.py

cd python
# Guided tour — narrated scenario walkthrough (recommended first run):
python examples/journeys/02_core_path.py
 
# Production one-liner — same API call you ship:
python examples/journeys/02_core_path.py