swap actual and desired in test

.github/workflows/unit_test.yml

@@ -34,3 +34,4 @@ jobs:
         run: |
           pip install pytest pytest-cov
           python -m pytest --doctest-modules --junitxml=junit/test-results.xml --cov=. --cov-report=xml --cov-report=html
+          python -m pytest --doctest-modules --junitxml=junit/test-results-brain.xml --cov=. --cov-report=xml --cov-report=html --dataFile tests/IVIMmodels/unit_tests/generic_brain.json

.gitignore

@@ -5,6 +5,8 @@
 __pycache__/
 *.nii.gz
 *.nii
+*.bval
+*.bvec
 *.dcm
 *.mat
 *.raw

conftest.py

@@ -166,7 +166,15 @@ def algorithm_list(filename, selected, dropped):
     algorithms = algorithms - set(dropped)
     if len(selected) > 0 and selected[0]:
         algorithms = algorithms & set(selected)
-    return list(algorithms)
+    options = []
+    for algorithm in algorithms:
+        if algorithm in algorithm_information:
+            option = algorithm_information[algorithm]
+        else:
+            option = {}
+        options.append(option)
+    for algorithm, option in zip(list(algorithms), options):
+        yield algorithm, option
 
 def data_list(filename):
     current_folder = pathlib.Path.cwd()

src/wrappers/OsipiBase.py

@@ -268,7 +268,7 @@ def osipi_author():
         """Author identification"""
         return ''
 
-    def osipi_simple_bias_and_RMSE_test(self, SNR, bvalues, f, Dstar, D, noise_realizations=100):
+    def osipi_simple_bias_and_RMSE_test(self, SNR, bvalues, f, Dstar, D, noise_realizations=100, print_results=True):
         # Generate signal
         bvalues = np.asarray(bvalues)
         signals = f*np.exp(-bvalues*Dstar) + (1-f)*np.exp(-bvalues*D)
@@ -282,7 +282,10 @@ def osipi_simple_bias_and_RMSE_test(self, SNR, bvalues, f, Dstar, D, noise_reali
             noised_signal = np.array([norm.rvs(signal, sigma) for signal in signals])
 
             # Perform fit with the noised signal
-            f_estimates[i], Dstar_estimates[i], D_estimates[i] = self.ivim_fit(noised_signal, bvalues)
+            fit = self.osipi_fit(noised_signal, bvalues)
+            f_estimates[i] = fit['f']
+            Dstar_estimates[i] = fit['Dp']
+            D_estimates[i] = fit['D']
 
         # Calculate bias
         f_bias = np.mean(f_estimates) - f
@@ -293,9 +296,13 @@ def osipi_simple_bias_and_RMSE_test(self, SNR, bvalues, f, Dstar, D, noise_reali
         f_RMSE = np.sqrt(np.var(f_estimates) + f_bias**2)
         Dstar_RMSE = np.sqrt(np.var(Dstar_estimates) + Dstar_bias**2)
         D_RMSE = np.sqrt(np.var(D_estimates) + D_bias**2)
-
-        print(f"f bias:\t{f_bias}\nf RMSE:\t{f_RMSE}")
-        print(f"Dstar bias:\t{Dstar_bias}\nDstar RMSE:\t{Dstar_RMSE}")
-        print(f"D bias:\t{D_bias}\nD RMSE:\t{D_RMSE}")
-
-
+
+        if print_results:
+            print()
+            print(f"\tBias\t\tRMSE")
+            print(f"f\t{f_bias:.3}\t\t{f_RMSE:.3}")
+            print(f"D*\t{Dstar_bias:.3}\t\t{Dstar_RMSE:.3}")
+            print(f"D\t{D_bias:.3}\t{D_RMSE:.3}")
+            print()
+        else:
+            return {'f':f_bias,'Dstar':Dstar_bias,'D':D_bias}, {'f':f_RMSE,'Dstar':Dstar_RMSE,'D':D_RMSE}

tests/IVIMmodels/unit_tests/generic_brain.json

@@ -0,0 +1,73 @@
+{
+    "Gray matter": {
+        "noise": 0.0005,
+        "D": 0.81e-3,
+        "f": 0.044,
+        "Dp": 84e-3,
+        "data": [
+            1.00021099, 
+            0.9668462, 
+            0.94870507, 
+            0.93594347, 
+            0.92702605,
+            0.89596427, 
+            0.8748837, 
+            0.85339213, 
+            0.83335988, 
+            0.81319343,
+            0.74911377, 
+            0.69206656, 
+            0.63748683, 
+            0.58790883, 
+            0.54206431,
+            0.50051437, 
+            0.46152624
+        ]
+    },
+        "White matter": {
+        "noise": 0.0005,
+        "D": 0.86e-3,
+        "f": 0.033,
+        "Dp": 76e-3,
+        "data": [
+                0.99968445, 
+                0.97478753, 
+                0.95818188, 
+                0.94546804, 
+                0.93605795,
+                0.90321907, 
+                0.88051331, 
+                0.85720661, 
+                0.83480693, 
+                0.81373184,
+                0.74707338, 
+                0.6854536, 
+                0.62867824, 
+                0.57723924, 
+                0.53090183,
+                0.48602603, 
+                0.44600491
+        ]
+    },
+    "config": {
+        "bvalues": [
+            0.0,
+            10.0, 
+            20.0,
+            30.0, 
+            40.0, 
+            80.0, 
+            110.0, 
+            140.0, 
+            170.0, 
+            200.0, 
+            300.0, 
+            400.0, 
+            500.0, 
+            600.0, 
+            700.0, 
+            800.0, 
+            900.0
+        ]
+    }
+}

tests/IVIMmodels/unit_tests/test_ivim_fit.py

@@ -59,15 +59,16 @@ def data_ivim_fit_saved():
                     first = False
             yield name, bvals, data, algorithm, xfail, kwargs, tolerances, skiptime
 
-@pytest.mark.parametrize("name, bvals, data, algorithm, xfail, kwargs, tolerances, skiptime", data_ivim_fit_saved())
-def test_ivim_fit_saved(name, bvals, data, algorithm, xfail, kwargs, tolerances,skiptime, request, record_property):
-    if xfail["xfail"]:
-        mark = pytest.mark.xfail(reason="xfail", strict=xfail["strict"])
-        request.node.add_marker(mark)
+#@pytest.mark.parametrize("name, bvals, data, algorithm, xfail, kwargs, tolerances, skiptime", data_ivim_fit_saved())
+#def test_ivim_fit_saved(name, bvals, data, algorithm, xfail, kwargs, tolerances,skiptime, request, record_property):
+def test_ivim_fit_saved(ivim_algorithm, ivim_data, record_property):
+    algorithm, options = ivim_algorithm
+    name, bvals, data = ivim_data
+
     signal = signal_helper(data["data"])
-    tolerances = tolerances_helper(tolerances, data)
+    tolerances = tolerances_helper(options.get("tolerances", {}), data)
     start_time = time.time()  # Record the start time
-    fit = OsipiBase(algorithm=algorithm,  **kwargs)
+    fit = OsipiBase(algorithm=algorithm)
     fit_result = fit.osipi_fit(signal, bvals)
     elapsed_time = time.time() - start_time  # Calculate elapsed time
     def to_list_if_needed(value):
@@ -85,14 +86,15 @@ def to_list_if_needed(value):
         "atol": tolerances["atol"]
     }
     record_property('test_data', test_result)
-    npt.assert_allclose(fit_result['f'],data['f'], rtol=tolerances["rtol"]["f"], atol=tolerances["atol"]["f"])
+    npt.assert_allclose(data['f'], fit_result['f'], rtol=tolerances["rtol"]["f"], atol=tolerances["atol"]["f"])
     if data['f']<0.80: # we need some signal for D to be detected
-        npt.assert_allclose(fit_result['D'],data['D'], rtol=tolerances["rtol"]["D"], atol=tolerances["atol"]["D"])
+        npt.assert_allclose(data['D'], fit_result['D'], rtol=tolerances["rtol"]["D"], atol=tolerances["atol"]["D"])
     if data['f']>0.03: #we need some f for D* to be interpretable
-        npt.assert_allclose(fit_result['Dp'],data['Dp'], rtol=tolerances["rtol"]["Dp"], atol=tolerances["atol"]["Dp"])
+        npt.assert_allclose(data['Dp'], fit_result['Dp'], rtol=tolerances["rtol"]["Dp"], atol=tolerances["atol"]["Dp"])
     #assert fit_result['D'] < fit_result['Dp'], f"D {fit_result['D']} is larger than D* {fit_result['Dp']} for {name}"
+    skiptime = False
     if not skiptime:
-        assert elapsed_time < 0.5, f"Algorithm {name} took {elapsed_time} seconds, which is longer than 2 second to fit per voxel" #less than 0.5 seconds per voxel
+        assert elapsed_time < 0.5, f"Algorithm {algorithm} took {elapsed_time} seconds, which is longer than 2 second to fit per voxel" #less than 0.5 seconds per voxel
 
 
 def algorithms():

tests/IVIMmodels/unit_tests/test_ivim_synthetic.py

@@ -15,6 +15,7 @@
 #e.g. pytest -m slow tests/IVIMmodels/unit_tests/test_ivim_synthetic.py  --saveFileName test_output.csv --SNR 10 50 100 200 --fitCount 20
 @pytest.mark.slow
 def test_generated(ivim_algorithm, ivim_data, SNR, rtol, atol, fit_count, rician_noise, save_file, save_duration_file, use_prior):
+    algorithm, _ = ivim_algorithm
     # assert save_file == "test"
     rng = np.random.RandomState(42)
     # random.seed(42)
@@ -24,7 +25,7 @@ def test_generated(ivim_algorithm, ivim_data, SNR, rtol, atol, fit_count, rician
     D = data["D"]
     f = data["f"]
     Dp = data["Dp"]
-    fit = OsipiBase(algorithm=ivim_algorithm)
+    fit = OsipiBase(algorithm=algorithm)
     # here is a prior
     if use_prior and hasattr(fit, "accepts_priors") and fit.accepts_priors:
         prior = [rng.normal(D, D/3, 10), rng.normal(f, f/3, 10), rng.normal(Dp, Dp/3, 10), rng.normal(1, 1/3, 10)]
@@ -40,11 +41,11 @@ def test_generated(ivim_algorithm, ivim_data, SNR, rtol, atol, fit_count, rician
         fit_result = fit.osipi_fit(signal, bvals) #, prior_in=prior
         time_delta += datetime.datetime.now() - start_time
         if save_file is not None:
-            save_file.writerow([ivim_algorithm, name, SNR, idx, f, Dp, D, fit_result["f"], fit_result["Dp"], fit_result["D"], *signal])
+            save_file.writerow([algorithm, name, SNR, idx, f, Dp, D, fit_result["f"], fit_result["Dp"], fit_result["D"], *signal])
             # save_results(save_file, ivim_algorithm, name, SNR, idx, [f, Dp, D], [f_fit, Dp_fit, D_fit])
         npt.assert_allclose([f, Dp, D], [fit_result["f"], fit_result["Dp"], fit_result["D"]], rtol, atol)
     if save_duration_file is not None:
-        save_duration_file.writerow([ivim_algorithm, name, SNR, time_delta/datetime.timedelta(microseconds=1), fit_count])
+        save_duration_file.writerow([algorithm, name, SNR, time_delta/datetime.timedelta(microseconds=1), fit_count])
         # save_duration(save_duration_file, ivim_algorithm, name, SNR, time_delta, fit_count)
 
 

utilities/data_simulation/Download_data.py

@@ -12,16 +12,20 @@ def unzip_file(zip_file_path, extracted_folder_path):
             zip_ref.extract(file_info, extracted_folder_path)
 
 
-def download_data(force=False):
+def download_data(force=False,folder=None):
     # Check if the folder exists, and create it if not
     curdir=os.getcwd()
-    base_folder = os.path.abspath(os.path.dirname(__file__))
-    base_folder = os.path.split(os.path.split(base_folder)[0])[0]
-    if not os.path.exists(os.path.join(base_folder,'download')):
-        os.makedirs(os.path.join(base_folder,'download'))
-        print(f"Folder '{'download'}' created.")
-        # Change to the specified folder
-    os.chdir(os.path.join(base_folder,'download'))
+    if folder is None:
+        base_folder = os.path.abspath(os.path.dirname(__file__))
+        base_folder = os.path.split(os.path.split(base_folder)[0])[0]
+        if not os.path.exists(os.path.join(base_folder,'download')):
+            os.makedirs(os.path.join(base_folder,'download'))
+            print(f"Folder '{'download'}' created.")
+        download_folder = os.path.join(base_folder,'download')
+    else:
+        download_folder = folder
+    # Change to the specified folder        
+    os.chdir(download_folder)
     subprocess.check_call(["zenodo_get", 'https://zenodo.org/records/14605039'])
     # Open the zip file
     if force or not os.path.exists('Data'):