Fixed some bugs

Author: oliverchampion

src/standardized/OGC_AmsterdamUMC_Bayesian_biexp.py

@@ -28,7 +28,7 @@ class OGC_AmsterdamUMC_Bayesian_biexp(OsipiBase):
     accepted_dimensions = 1  # Not sure how to define this for the number of accepted dimensions. Perhaps like the thresholds, at least and at most?
     accepts_priors = True
 
-    def __init__(self, bvalues=None, bounds=([0, 0, 0.005, 0.7],[0.005, 0.7, 0.2, 1.3]), initial_guess=None, fitS0=False, thresholds=None, prior_in=None):
+    def __init__(self, bvalues=None, bounds=([0, 0, 0.005, 0.7],[0.005, 0.7, 0.2, 1.3]), initial_guess=None, fitS0=True, thresholds=None, prior_in=None):
         """
             Everything this algorithm requires should be implemented here.
             Number of segmentation thresholds, bounds, etc.
@@ -43,7 +43,7 @@ def __init__(self, bvalues=None, bounds=([0, 0, 0.005, 0.7],[0.005, 0.7, 0.2, 1.
         self.OGC_algorithm = fit_bayesian
         self.initialize(bounds, initial_guess, fitS0, thresholds, prior_in)
 
-    def initialize(self, bounds=([0, 0, 0.005, 0.7],[0.005, 0.7, 0.2, 1.3]), initial_guess=None, fitS0=False, thresholds=None, prior_in=None):
+    def initialize(self, bounds=([0, 0, 0.005, 0.7],[0.005, 0.7, 0.2, 1.3]), initial_guess=None, fitS0=True, thresholds=None, prior_in=None):
         if bounds is None:
             self.bounds=([0, 0, 0.005, 0.7],[0.005, 1, 0.2, 1.3])
         else:
@@ -61,7 +61,7 @@ def initialize(self, bounds=([0, 0, 0.005, 0.7],[0.005, 0.7, 0.2, 1.3]), initial
             self.initial_guess = initial_guess
         self.fitS0=fitS0
 
-    def ivim_fit(self, signals, bvalues, bounds=None, initial_guess=None, fitS0=False, thresholds=None, prior_in=None, **kwargs):
+    def ivim_fit(self, signals, bvalues, bounds=None, initial_guess=None, fitS0=True, thresholds=None, prior_in=None, **kwargs):
         """Perform the IVIM fit
 
         Args:

src/standardized/OGC_AmsterdamUMC_biexp_segmented.py

@@ -50,7 +50,10 @@ def initialize(self, bounds, initial_guess, thresholds=300):
             self.initial_guess = [0.001, 0.1, 0.03, 1]
         else:
             self.initial_guess = initial_guess
-        self.thresholds=thresholds
+        if thresholds is None:
+            self.thresholds = 150
+        else:
+            self.thresholds = thresholds
 
     def ivim_fit(self, signals, bvalues, bounds=None, initial_guess=None, thresholds=None, **kwargs):
         """Perform the IVIM fit

tests/IVIMmodels/unit_tests/algorithms.json

@@ -134,29 +134,17 @@
         }
     },
     "OGC_AmsterdamUMC_biexp_segmented": {
-        "xfail_names": {
-            "Vein": true,
-            "pericardium": true,
-            "small intestine": true,
-            "desc lower intestine": true,
-            "trans lower intestine": true,
-            "asc lower intestine": true,
-            "Artery": true,
-            "spleen": true,
-            "left kidney medulla": true,
-            "Left kidney cortex": true,
-            "right kidney medulla": true,
-            "Right kydney cortex": true,
-            "pancreas": true,
-            "st wall": true,
-            "esophagus cont": true,
-            "esophagus": true,
-            "Liver": true,
-            "Blood RA": true,
-            "Blood RV": true,
-            "myocardium ra": true,
-            "myocardium RV": true,
-            "Myocardium LV": true
+        "tolerances": {
+            "rtol": {
+                "f": 5,
+                "D": 5,
+                "Dp": 25
+            },
+            "atol": {
+                "f": 1e-2,
+                "D": 1e-2,
+                "Dp": 1e-1
+            }
         }
     }
 }

tests/IVIMmodels/unit_tests/test_ivim_synthetic.py

@@ -26,7 +26,7 @@ def test_generated(ivim_algorithm, ivim_data, SNR, rtol, atol, fit_count, rician
     fit = OsipiBase(algorithm=ivim_algorithm)
     # here try a prior, but it's not seeing it, why?
     # if hasattr(fit, "accepts_priors") and fit.accepts_priors:
-    prior = [np.repeat(D, 10), np.repeat(f, 10), np.repeat(Dp, 10)]  # D, f, D*
+    prior = [np.repeat(D, 10)+np.random.normal(0,D/3,np.shape(np.repeat(D, 10))), np.repeat(f, 10)+np.random.normal(0,f/3,np.shape(np.repeat(D, 10))), np.repeat(Dp, 10)+np.random.normal(0,Dp/3,np.shape(np.repeat(D, 10))),np.repeat(np.ones_like(Dp), 10)+np.random.normal(0,1/3,np.shape(np.repeat(D, 10)))]  # D, f, D*
     #     fit.initialize(prior_in=prior)
     time_delta = datetime.timedelta()
     for idx in range(fit_count):
@@ -35,7 +35,7 @@ def test_generated(ivim_algorithm, ivim_data, SNR, rtol, atol, fit_count, rician
         # else:
         #     signal = data["data"]
         start_time = datetime.datetime.now()
-        [f_fit, Dp_fit, D_fit] = fit.osipi_fit(signal, bvals)  #, prior_in=prior
+        [f_fit, Dp_fit, D_fit] = fit.osipi_fit(signal, bvals, prior_in=prior)
         time_delta += datetime.datetime.now() - start_time
         if save_file:
             save_results(save_file, ivim_algorithm, name, SNR, idx, [f, Dp, D], [f_fit, Dp_fit, D_fit])