Merge branch 'main' into reference_fitting_algorithm

Author: oliverchampion

.github/workflows/unit_test.yml

@@ -11,7 +11,7 @@ jobs:
       fail-fast: false
       matrix:
         os: [ubuntu-latest, macos-latest, windows-latest]
-        python-version: ["3.9", "3.10", "3.11", "3.12", "3.13"]
+        python-version: ["3.11", "3.12", "3.13"]
         exclude:
           - os: windows-latest
             python-version: "3.13"

.gitignore

@@ -16,6 +16,13 @@ md5sums.txt
 *.gz
 *.zip
 *.tmp
+*.asv
+src/original/ASD_MemorialSloanKettering/MRI-QAMPER_IVIM/test_data
+src/original/ASD_MemorialSloanKettering/MRI-QAMPER_IVIM/output_files
+tests/IVIMmodels/unit_tests/*.log
+junit/*
+ivim_simulation.bval
+ivim_simulation.bvec
 
 # Unit test / coverage reports
 .tox/

conftest.py

@@ -81,6 +81,24 @@ def pytest_addoption(parser):
         type=str,
         help="Drop this algorithm from the list"
     )
+    parser.addoption(
+        "--withmatlab",
+        action="store_true",
+        default=False,
+        help="Run MATLAB-dependent tests"
+    )
+
+
+@pytest.fixture(scope="session")
+def eng(request):
+    """Start and return a MATLAB engine session if --withmatlab is set."""
+    if not request.config.getoption("--withmatlab"):
+        return None
+    import matlab.engine
+    print("Starting MATLAB engine...")
+    eng = matlab.engine.start_matlab()
+    print("MATLAB engine started.")
+    return eng
 
 
 @pytest.fixture(scope="session")
@@ -149,25 +167,20 @@ def use_prior(request):
 def pytest_generate_tests(metafunc):
     if "SNR" in metafunc.fixturenames:
         metafunc.parametrize("SNR", metafunc.config.getoption("SNR"))
-    if "ivim_algorithm" in metafunc.fixturenames:
-        algorithms = algorithm_list(metafunc.config.getoption("algorithmFile"), metafunc.config.getoption("selectAlgorithm"), metafunc.config.getoption("dropAlgorithm"))
-        metafunc.parametrize("ivim_algorithm", algorithms)
     if "ivim_data" in metafunc.fixturenames:
         data = data_list(metafunc.config.getoption("dataFile"))
         metafunc.parametrize("ivim_data", data)
+    if "data_ivim_fit_saved" in  metafunc.fixturenames:
+        args = data_ivim_fit_saved(metafunc.config.getoption("dataFile"),metafunc.config.getoption("algorithmFile"))
+        metafunc.parametrize("data_ivim_fit_saved", args)
+    if "algorithmlist" in metafunc.fixturenames:
+        args = algorithmlist(metafunc.config.getoption("algorithmFile"))
+        metafunc.parametrize("algorithmlist", args)
+    if "bound_input" in metafunc.fixturenames:
+        args = bound_input(metafunc.config.getoption("dataFile"),metafunc.config.getoption("algorithmFile"))
+        metafunc.parametrize("bound_input", args)
 
 
-def algorithm_list(filename, selected, dropped):
-    current_folder = pathlib.Path.cwd()
-    algorithm_path = current_folder / filename
-    with algorithm_path.open() as f:
-        algorithm_information = json.load(f)
-    algorithms = set(algorithm_information["algorithms"])
-    algorithms = algorithms - set(dropped)
-    if len(selected) > 0 and selected[0]:
-        algorithms = algorithms & set(selected)
-    return list(algorithms)
-
 def data_list(filename):
     current_folder = pathlib.Path.cwd()
     data_path = current_folder / filename
@@ -178,3 +191,69 @@ def data_list(filename):
     bvals = bvals['bvalues']
     for name, data in all_data.items():
         yield name, bvals, data
+
+
+def data_ivim_fit_saved(datafile, algorithmFile):
+    # Find the algorithms from algorithms.json
+    current_folder = pathlib.Path.cwd()
+    algorithm_path = current_folder / algorithmFile
+    with algorithm_path.open() as f:
+        algorithm_information = json.load(f)
+    # Load generic test data generated from the included phantom: phantoms/MR_XCAT_qMRI
+    generic = current_folder / datafile
+    with generic.open() as f:
+        all_data = json.load(f)
+    algorithms = algorithm_information["algorithms"]
+    bvals = all_data.pop('config')
+    bvals = bvals['bvalues']
+    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)}
+            kwargs = algorithm_dict.get("options", {})
+            tolerances = algorithm_dict.get("tolerances", {})
+            skiptime=False
+            if first:
+                if algorithm_dict.get("fail_first_time", False):
+                    skiptime = True
+                    first = False
+            requires_matlab = algorithm_dict.get("requires_matlab", False)
+            yield name, bvals, data, algorithm, xfail, kwargs, tolerances, skiptime, requires_matlab
+
+def algorithmlist(algorithmFile):
+    # Find the algorithms from algorithms.json
+    current_folder = pathlib.Path.cwd()
+    algorithm_path = current_folder / algorithmFile
+    with algorithm_path.open() as f:
+        algorithm_information = json.load(f)
+
+    algorithms = algorithm_information["algorithms"]
+    for algorithm in algorithms:
+        algorithm_dict = algorithm_information.get(algorithm, {})
+        requires_matlab = algorithm_dict.get("requires_matlab", False)
+        yield algorithm, requires_matlab
+
+def bound_input(datafile,algorithmFile):
+    # Find the algorithms from algorithms.json
+    current_folder = pathlib.Path.cwd()
+    algorithm_path = current_folder / algorithmFile
+    with algorithm_path.open() as f:
+        algorithm_information = json.load(f)
+    # Load generic test data generated from the included phantom: phantoms/MR_XCAT_qMRI
+    generic = current_folder / datafile
+    with generic.open() as f:
+        all_data = json.load(f)
+    algorithms = algorithm_information["algorithms"]
+    bvals = all_data.pop('config')
+    bvals = bvals['bvalues']
+    for name, data in all_data.items():
+        for algorithm in algorithms:
+            algorithm_dict = algorithm_information.get(algorithm, {})
+            xfail = {"xfail": name in algorithm_dict.get("xfail_names", {}),
+                "strict": algorithm_dict.get("xfail_names", {}).get(name, True)}
+            kwargs = algorithm_dict.get("options", {})
+            tolerances = algorithm_dict.get("tolerances", {})
+            requires_matlab = algorithm_dict.get("requires_matlab", False)
+            yield name, bvals, data, algorithm, xfail, kwargs, tolerances, requires_matlab