Add multiple fits for statistics

Author: etpeterson

conftest.py

@@ -43,6 +43,12 @@ def pytest_addoption(parser):
         type=float,
         help="Absolute tolerance",
     )
+    parser.addoption(
+        "--fitCount",
+        default=10,
+        type=int,
+        help="Number of fits to perform on the same parameters",
+    )
 
 
 @pytest.fixture(scope="session")
@@ -68,6 +74,10 @@ def rtol(request):
 def atol(request):
     return request.config.getoption("--atol")
 
+@pytest.fixture(scope="session")
+def fit_count(request):
+    return request.config.getoption("--fitCount")
+
 
 def pytest_generate_tests(metafunc):
     if "SNR" in metafunc.fixturenames:

tests/IVIMmodels/unit_tests/test_ivim_synthetic.py

@@ -5,29 +5,32 @@
 import pathlib
 import os
 import csv
+import random
 
 from src.wrappers.OsipiBase import OsipiBase
 from utilities.data_simulation.GenerateData import GenerateData
 
 #run using pytest <path_to_this_file> --saveFileName test_output.txt --SNR 50 100 200
 #e.g. pytest tests/IVIMmodels/unit_tests/test_ivim_synthetic.py --saveFileName test_output.txt --SNR 50 100 200
-def test_generated(ivim_algorithm, ivim_data, SNR, rtol, atol, save_file):
+def test_generated(ivim_algorithm, ivim_data, SNR, rtol, atol, fit_count, save_file):
     # assert save_file == "test"
+    random.seed(42)
     S0 = 1
     gd = GenerateData()
     name, bvals, data = ivim_data
     D = data["D"]
     f = data["f"]
     Dp = data["Dp"]
-    # if "data" not in data:
-    signal = gd.ivim_signal(D, Dp, f, S0, bvals, SNR)
-    # else:
-    #     signal = data["data"]
     fit = OsipiBase(algorithm=ivim_algorithm)
-    [f_fit, Dp_fit, D_fit] = fit.ivim_fit(signal, bvals)
-    if save_file:
-        save_results(save_file, ivim_algorithm, name, SNR, [f, D, Dp], [f_fit, Dp_fit, D_fit])
-    npt.assert_allclose([f, D, Dp], [f_fit, D_fit, Dp_fit], rtol, atol)
+    for idx in range(fit_count):
+        # if "data" not in data:
+        signal = gd.ivim_signal(D, Dp, f, S0, bvals, SNR)
+        # else:
+        #     signal = data["data"]
+        [f_fit, Dp_fit, D_fit] = fit.ivim_fit(signal, bvals)
+        if save_file:
+            save_results(save_file, ivim_algorithm, name, SNR, [f, D, Dp], [f_fit, Dp_fit, D_fit])
+        npt.assert_allclose([f, D, Dp], [f_fit, D_fit, Dp_fit], rtol, atol)
 
 
 def save_results(filename, algorithm, name, SNR, truth, fit):