fixed bugs

- fixed initial guesses and bounds such that it passes tests
- changed matlab huild for github, hopefully overcoming errors.

Author: oliverchampion

.github/workflows/unit_test.yml

@@ -34,8 +34,11 @@ jobs:
         uses: actions/checkout@v4
       - name: Set up MATLAB
         uses: matlab-actions/setup-matlab@v2
-      - name: Run build
-        uses: matlab-actions/run-build@v2
+        with:
+          release: R2025a 
+      - name: Install MATLAB Engine for Python
+        run: |
+          python -m pip install matlabengine==25.1.2
       - name: Test with pytest
         run: |
           pip install pytest pytest-cov

.gitignore

@@ -19,7 +19,7 @@ md5sums.txt
 *.asv
 src/original/ASD_MemorialSloanKettering/MRI-QAMPER_IVIM/test_data
 src/original/ASD_MemorialSloanKettering/MRI-QAMPER_IVIM/output_files
-
+tests/IVIMmodels/unit_tests/*.log
 
 
 # Unit test / coverage reports

src/standardized/ASD_MemorialSloanKettering_QAMPER_IVIM.py

@@ -33,7 +33,7 @@ class ASD_MemorialSloanKettering_QAMPER_IVIM(OsipiBase):
     supported_initial_guess = True
     supported_thresholds = False
 
-    def __init__(self, bvalues=None, thresholds=None, bounds=None, initial_guess=None):
+    def __init__(self, bvalues=None, thresholds=None, bounds=None, initial_guess=None,eng=None):
         """
             Everything this algorithm requires should be implemented here.
             Number of segmentation thresholds, bounds, etc.
@@ -44,7 +44,14 @@ def __init__(self, bvalues=None, thresholds=None, bounds=None, initial_guess=Non
         #super(OGC_AmsterdamUMC_biexp, self).__init__(bvalues, bounds, initial_guess, fitS0)
         super(ASD_MemorialSloanKettering_QAMPER_IVIM, self).__init__(bvalues=bvalues, bounds=bounds, initial_guess=initial_guess)
         self.initialize(bounds, initial_guess)
-        self.eng=matlab.engine.start_matlab()
+        if eng is None:
+            print('initiating matlab; this may take some time. For repeated testing one could use the optional input eng as an already initiated matlab engine')
+            self.eng=matlab.engine.start_matlab()
+            self.keep_alive=False
+        else:
+            self.eng = eng
+            self.keep_alive=True
+
 
 
     def algorithm(self,dwi_arr, bval_arr, LB0, UB0, x0in):
@@ -61,12 +68,12 @@ def algorithm(self,dwi_arr, bval_arr, LB0, UB0, x0in):
     def initialize(self, bounds, initial_guess):
         if bounds is None:
             print('warning, no bounds were defined, so algorithm-specific default bounds are used')
-            self.bounds=([1e-6, 0, 1e-6, 0],[0.003, 1.0, 5e-2, 5])
+            self.bounds=([1e-6, 0, 0.004, 0],[0.003, 1.0, 0.2, 5])
         else:
             self.bounds=bounds
         if initial_guess is None:
             print('warning, no initial guesses were defined, so algorithm-specific default initial guess is used')
-            self.initial_guess = [0.001, 0.001, 0.2, 1]
+            self.initial_guess = [0.001, 0.2, 0.01, 1]
         else:
             self.initial_guess = initial_guess
             self.use_initial_guess = True
@@ -85,10 +92,10 @@ def ivim_fit(self, signals, bvalues, **kwargs):
         """
 
         bvalues=np.array(bvalues)
-        LB = np.array(self.bounds[0])
-        UB = np.array(self.bounds[1])
+        LB = np.array(self.bounds[0])[[1,0,2,3]]
+        UB = np.array(self.bounds[1])[[1,0,2,3]]
 
-        fit_results = self.algorithm(np.array(signals)[:,np.newaxis], bvalues, LB, UB, np.array(self.initial_guess))
+        fit_results = self.algorithm(np.array(signals)[:,np.newaxis], bvalues, LB, UB, np.array(self.initial_guess)[[1,0,2,3]])
 
         results = {}
         results["D"] = fit_results[0]
@@ -99,20 +106,22 @@ def ivim_fit(self, signals, bvalues, **kwargs):
 
 
     def __del__(self):
-        if hasattr(self, "eng") and self.eng:
-            try:
-                self.eng.quit()
-            except Exception as e:
-                print(f"Warning: Failed to quit MATLAB engine cleanly: {e}")
+        if not self.keep_alive:
+            if hasattr(self, "eng") and self.eng:
+                try:
+                    self.eng.quit()
+                except Exception as e:
+                    print(f"Warning: Failed to quit MATLAB engine cleanly: {e}")
 
 
     def __enter__(self):
         return self
 
 
     def __exit__(self, exc_type, exc_val, exc_tb):
-        if hasattr(self, "eng") and self.eng:
-            try:
-                self.eng.quit()
-            except Exception as e:
-                print(f"Warning: Failed to quit MATLAB engine cleanly: {e}")
+        if not self.keep_alive:
+            if hasattr(self, "eng") and self.eng:
+                try:
+                    self.eng.quit()
+                except Exception as e:
+                    print(f"Warning: Failed to quit MATLAB engine cleanly: {e}")

src/standardized/OGC_AmsterdamUMC_biexp.py

@@ -55,7 +55,7 @@ def initialize(self, bounds, initial_guess, fitS0):
             self.bounds=bounds
         if initial_guess is None:
             print('warning, no initial guesses were defined, so default bounds are used of  [0.001, 0.001, 0.01, 1]')
-            self.initial_guess = [0.001, 0.001, 0.01, 1]
+            self.initial_guess = [0.001, 0.1, 0.01, 1]
         else:
             self.initial_guess = initial_guess
             self.use_initial_guess = True

tests/IVIMmodels/unit_tests/algorithms.json

@@ -15,6 +15,10 @@
         "PvH_KB_NKI_IVIMfit",
         "OJ_GU_seg"
     ],
+    "ASD_MemorialSloanKettering_QAMPER_IVIM": {
+        "requieres_matlab": true,
+        "fail_first_time": true
+    },
     "IAR_LU_biexp": {
         "fail_first_time": true
     },

tests/IVIMmodels/unit_tests/test_ivim_fit.py

@@ -6,6 +6,9 @@
 import time
 from src.wrappers.OsipiBase import OsipiBase
 #run using python -m pytest from the root folder
+import matlab.engine
+
+eng=matlab.engine.start_matlab()
 
 def signal_helper(signal):
     signal = np.asarray(signal)
@@ -44,9 +47,9 @@ def data_ivim_fit_saved():
     algorithms = algorithm_information["algorithms"]
     bvals = all_data.pop('config')
     bvals = bvals['bvalues']
-    first=True
-    for name, data in all_data.items():
-        for algorithm in algorithms:
+    for algorithm in algorithms:
+        first = True
+        for name, data in all_data.items():
             algorithm_dict = algorithm_information.get(algorithm, {})
             xfail = {"xfail": name in algorithm_dict.get("xfail_names", {}),
                 "strict": algorithm_dict.get("xfail_names", {}).get(name, True)}
@@ -56,7 +59,9 @@ def data_ivim_fit_saved():
             if first == True:
                 if algorithm_dict.get("fail_first_time", {}) == True:
                     skiptime = True
-                    first = False
+                    first = False # this will only work for 1 algorithm... If two algorithms use fail_first_time, the second will not see it is the first time.
+            if algorithm_dict.get("requieres_matlab", {}) == True:
+                kwargs={**kwargs,'eng': eng}
             yield name, bvals, data, algorithm, xfail, kwargs, tolerances, skiptime
 
 @pytest.mark.parametrize("name, bvals, data, algorithm, xfail, kwargs, tolerances, skiptime", data_ivim_fit_saved())