Fix #86: Correct bounds/initial_guess passing in 5 wrapped algorithms

[Devguru-codes]

Fix #86: Correct bounds/initial_guess passing in 5 wrapped algorithms

Description

This PR addresses Issue #86, where several wrapped IVIM algorithms crashed or returned mathematically incorrect results when processing real-world MRI data (which, unlike simulated test data, heavily tests boundary validation and fallback paths).

Root Causes & Bug Fixes

During my investigation of the algorithms acting irregularly on real data, I discovered two distinct classes of Python bugs in the wrappers:

1. PV_MUMC_biexp.py — UnboundLocalError Crash

   • Bug: The ivim_fit() method had an if/else block where the local bounds variable was only assigned in the else branch. Under OsipiBase, self.bounds defaults to a Python dictionary, meaning the code executed the if branch, never created the bounds variable, and crashed.
   • Fix: Restructured the condition to always extract the self.bounds dictionary values into the ([S0_min, D_min...], [S0_max, D_max...]) array formats expected by the underlying fit_least_squares algorithm.

2. 4 IAR_LU_* Algorithms — Invalid Constraints (Dict vs List-of-Lists)

   • Bug: When bvalues were provided at init, the wrappers passed the self.bounds Python dictionary directly to the dipy IvimModel* constructors. Internally, DIPY expects arrays and indexes them via bounds[0] and bounds[1]. Indexing a dictionary by integer yields its keys ("S0", "f"), completely breaking the optimizer constraints and silently producing garbage results.
   • Fix: Explicitly convert the dictionary into a [[lower...], [upper...]] array structure whenever the IAR_algorithm is initialized.
   • Affected files: IAR_LU_biexp.py, IAR_LU_segmented_2step.py, IAR_LU_segmented_3step.py, IAR_LU_subtracted.py.

Edge Case Hardening Added

To ensure these algorithms robustly handle diverse real-world data and usage patterns, I added several layers of error handling and edge-case protection:

• Stale-Model Guard: IAR_LU algorithms now automatically detect if the bvalues array changes between init and ivim_fit(), and gracefully rebuild their internal gradient table/model to prevent silent corruption.
• Missing D_and_Ds_swap: Added the missing parameter order swap check to IAR_LU_segmented_2step.
• bvalues=None Guards: All 5 algorithms now properly fall back to self.bvalues, raising a clear error if neither is available.
• Graceful Fallbacks: Wrapped underlying scipy optimization calls in robust try/except blocks to print a warning and return default initial guesses (fail gracefully) if max evaluations are exceeded or optimization fails.

Testing & Validation

• New Regression Test: Added 4 explicit sub-tests to test_init_bvalues in tests/IVIMmodels/unit_tests/test_ivim_fit.py. It specifically catches the init failure path, the osipi_fit crash path, result key validity, and the stale-model guard.
• Test Suite Results: 1148 passed, 0 failures (Verified manually through full suite execution natively). Note: TCML_TechnionIIT_lsqBOBYQA bounds x-fails are pre-existing, tolerated algorithm limitations (strict=False) and are completely unrelated to these fixes.

[oliverchampion]

I've approved the tests and asked @IvanARashid to take a look as he has been working on the bounds and as you've been changing most of his wrappers.

[Devguru-codes]

Comprehensive Verification of PR #142 — Fix #86

Hi @oliverchampion @IvanARashid,

I ran a comprehensive validation of this PR across all 5 fixed algorithms with 13 test scenarios each (65 tests total), on both main and fix-issue-86 branches. Here are the full results:

---

Issue Reproduction on main (before fix)

I ran the 65-test suite on main to confirm the bugs exist:

Algorithm	Tests Passed	Failures
PV_MUMC_biexp	12/13	bvalues=None at fit → TypeError crash
IAR_LU_biexp	9/13	Init with bvalues+bounds → KeyError: 0 (×4)
IAR_LU_segmented_2step	13/13	Passes, but dict bounds silently passed to dipy (garbage)
IAR_LU_segmented_3step	9/13	Init with bvalues+bounds → KeyError: 0 (×4)
IAR_LU_subtracted	13/13	Passes, but dict bounds silently passed to dipy (garbage)
TOTAL	56/65	9 failures

Root cause confirmed: OsipiBase always stores bounds as a Python dict (e.g. {"S0": [0.7, 1.3], "f": [0, 1], ...}), but dipy's IvimModel* constructors do bounds[0] / bounds[1] expecting a list-of-lists. Indexing a dict by integer raises KeyError: 0.

---

Verification on fix-issue-86 (after fix)

Algorithm	Tests Passed	Status
PV_MUMC_biexp	13/13	✅ All pass
IAR_LU_biexp	13/13	✅ All pass
IAR_LU_segmented_2step	13/13	✅ All pass
IAR_LU_segmented_3step	13/13	✅ All pass
IAR_LU_subtracted	13/13	✅ All pass
TOTAL	65/65	✅ All pass

---

All 13 Test Scenarios Covered

#	Test Scenario	What It Validates
1	Init with bvalues + bounds dict	Dict→list conversion at init (Bug B)
2	Init without bvalues (lazy init)	Deferred model construction
3	Init with defaults only	force_default_settings works
4	Fit realistic IVIM signal (f=0.15, D=0.001, Dp=0.03)	Core fitting accuracy
5	Fit mono-exponential signal (f≈0)	Edge case: no perfusion component
6	Fit noisy signal (SNR ≈ 20)	Robustness to noise
7	3 repeated calls	Object state stability
8	Stale-model guard (different bvalues on 2nd call)	Gradient table rebuild
9	bvalues=None at fit time (fall back to self.bvalues)	None-guard
10	No bvalues anywhere (should raise ValueError)	Error handling
11	Tight bounds → results within bounds	Bounds enforcement
12	Constant signal (no diffusion)	Edge case: flat signal
13	Full pytest regression (4 sub-tests from test_init_bvalues)	Exact reproduction

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Recorded API Responses (fix branch)

Realistic bi-exponential signal (f=0.15, D=0.001, Dp=0.03):

Algorithm	f	D	Dp
PV_MUMC_biexp	0.1497	0.001001	0.030093
IAR_LU_biexp	0.1497	0.001001	0.030093
IAR_LU_segmented_2step	0.1497	0.001001	0.030105
IAR_LU_segmented_3step	0.1497	0.001001	0.030105
IAR_LU_subtracted	0.1495	0.001001	0.030233

All results closely match the ground truth (f=0.15, D=0.001, Dp=0.03). ✅

Noisy signal (SNR ≈ 20):

Algorithm	f	D	Dp
PV_MUMC_biexp	0.2105	0.000758	0.016730
IAR_LU_biexp	0.2152	0.000749	0.016002
IAR_LU_segmented_2step	0.2003	0.000758	0.016122
IAR_LU_segmented_3step	0.2022	0.000758	0.015228
IAR_LU_subtracted	0.1947	0.000758	0.015134

Results are physiologically reasonable despite noise — fit degrades gracefully. ✅

---

Pytest Results

$ python -m pytest tests/IVIMmodels/unit_tests/test_ivim_fit.py::test_init_bvalues -v
21 passed, 5 skipped in 4.12s

$ python -m pytest tests/IVIMmodels/unit_tests/test_ivim_fit.py::test_default_bounds_and_initial_guesses -v
21 passed, 5 skipped in 2.49s

(5 skips = 2 deep learning + 3 MATLAB algorithms, as expected)

---

Before vs After Comparison

Scenario	main branch	fix-issue-86 branch
IAR_LU_biexp(bvalues=bvals, bounds=dict)	❌ KeyError: 0	✅ Init OK, fit returns valid params
IAR_LU_segmented_3step(bvalues=bvals, bounds=dict)	❌ KeyError: 0	✅ Init OK, fit returns valid params
PV_MUMC_biexp.ivim_fit(signal, None)	❌ TypeError crash	✅ Falls back to self.bvalues
IAR_LU_segmented_2step bounds handling	⚠️ Dict passed to dipy (silent garbage)	✅ Dict converted to list-of-lists
IAR_LU_subtracted bounds handling	⚠️ Dict passed to dipy (silent garbage)	✅ Dict converted to list-of-lists
Stale-model detection	❌ Not implemented	✅ Rebuilt when bvalues change
Graceful error handling	❌ Unhandled exceptions	✅ try/except with fallback

---

Environment

Python:  3.11.3
numpy:   2.4.2
scipy:   1.17.1
dipy:    1.11.0
pytest:  9.0.2
OS:      Windows 11

I have implemented checks on my end. Simulated issue on master branch and tested this branch. If there is something you want me to improve, let me know i will fix it.