Updated initialization

Author: etpeterson

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=True, thresholds=None, prior_in=None):
+    def __init__(self, bvalues=None, thresholds=None, bounds=([0, 0, 0.005, 0.7],[0.005, 0.7, 0.2, 1.3]), initial_guess=None, fitS0=True, prior_in=None):
         """
             Everything this algorithm requires should be implemented here.
             Number of segmentation thresholds, bounds, etc.
@@ -39,11 +39,11 @@ def __init__(self, bvalues=None, bounds=([0, 0, 0.005, 0.7],[0.005, 0.7, 0.2, 1.
             Args:
                  datain is a 2D array with values of D, f, D* (and S0) that will form the prior.
         """
-        super(OGC_AmsterdamUMC_Bayesian_biexp, self).__init__(bvalues, thresholds, bounds, initial_guess) #, fitS0, prior_in)
+        super(OGC_AmsterdamUMC_Bayesian_biexp, self).__init__(bvalues, bounds, initial_guess) #, fitS0, prior_in)
         self.OGC_algorithm = fit_bayesian
-        self.initialize(bounds, initial_guess, fitS0, thresholds, prior_in)
+        self.initialize(bounds, initial_guess, fitS0, prior_in)
 
-    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):
+    def initialize(self, bounds=([0, 0, 0.005, 0.7],[0.005, 0.7, 0.2, 1.3]), initial_guess=None, fitS0=True, 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=True, thresholds=None, prior_in=None, **kwargs):
+    def ivim_fit(self, signals, bvalues, initial_guess=None, **kwargs):
         """Perform the IVIM fit
 
         Args:
@@ -71,9 +71,10 @@ def ivim_fit(self, signals, bvalues, bounds=None, initial_guess=None, fitS0=True
         Returns:
             _type_: _description_
         """
-        self.initialize(bounds, initial_guess, fitS0, thresholds, prior_in)
+        if initial_guess is not None and len(initial_guess) == 4:
+            self.initial_guess = initial_guess
         bvalues=np.array(bvalues)
-        fit_results = fit_segmented(bvalues, signals,bounds=self.bounds,cutoff=150,p0=self.initial_guess)
+        fit_results = fit_segmented(bvalues, signals, bounds=self.bounds, cutoff=150, p0=self.initial_guess)
         fit_results=fit_results+(1,)
         fit_results = self.OGC_algorithm(bvalues, signals, self.neg_log_prior, x0=fit_results, fitS0=self.fitS0)
 

src/standardized/OGC_AmsterdamUMC_biexp.py

@@ -26,17 +26,16 @@ class OGC_AmsterdamUMC_biexp(OsipiBase):
     required_initial_guess = False
     required_initial_guess_optional = True
     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):
+    def __init__(self, bvalues=None, thresholds=None, bounds=([0, 0, 0.005, 0.7],[0.005, 0.7, 0.2, 1.3]), initial_guess=None, fitS0=False):
         """
             Everything this algorithm requires should be implemented here.
             Number of segmentation thresholds, bounds, etc.
 
             Our OsipiBase object could contain functions that compare the inputs with
             the requirements.
         """
-        super(OGC_AmsterdamUMC_biexp, self).__init__(bvalues, bounds,initial_guess,fitS0)
+        super(OGC_AmsterdamUMC_biexp, self).__init__(bvalues, bounds, initial_guess, fitS0)
         self.OGC_algorithm = fit_least_squares
         self.initialize(bounds, initial_guess, fitS0)
 
@@ -51,7 +50,7 @@ def initialize(self, bounds, initial_guess, fitS0):
             self.initial_guess = initial_guess
         self.fitS0=fitS0
 
-    def ivim_fit(self, signals, bvalues, bounds=None, initial_guess=None, fitS0=False, **kwargs):
+    def ivim_fit(self, signals, bvalues, initial_guess=None, **kwargs):
         """Perform the IVIM fit
 
         Args:
@@ -61,7 +60,8 @@ def ivim_fit(self, signals, bvalues, bounds=None, initial_guess=None, fitS0=Fals
         Returns:
             _type_: _description_
         """
-        self.initialize(bounds, initial_guess, fitS0)
+        if initial_guess is not None and len(initial_guess) == 4:
+            self.initial_guess = initial_guess
         bvalues=np.array(bvalues)
         fit_results = self.OGC_algorithm(bvalues, signals, p0=self.initial_guess, bounds=self.bounds, fitS0=self.fitS0)
 

src/standardized/OGC_AmsterdamUMC_biexp_segmented.py

@@ -26,7 +26,6 @@ class OGC_AmsterdamUMC_biexp_segmented(OsipiBase):
     required_initial_guess = False
     required_initial_guess_optional = True
     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, thresholds=150, bounds=([0, 0, 0.005],[0.005, 0.7, 0.2]), initial_guess=[0.001, 0.01, 0.01,1]):
         """
@@ -55,7 +54,7 @@ def initialize(self, bounds, initial_guess, thresholds=300):
         else:
             self.thresholds = thresholds
 
-    def ivim_fit(self, signals, bvalues, bounds=None, initial_guess=None, thresholds=None, **kwargs):
+    def ivim_fit(self, signals, bvalues, initial_guess=None, **kwargs):
         """Perform the IVIM fit
 
         Args:
@@ -65,6 +64,8 @@ def ivim_fit(self, signals, bvalues, bounds=None, initial_guess=None, thresholds
         Returns:
             _type_: _description_
         """
+        if initial_guess is not None and len(initial_guess) == 4:
+            self.initial_guess = initial_guess
         bvalues=np.array(bvalues)
         fit_results = self.OGC_algorithm(bvalues, signals, bounds=self.bounds, cutoff=self.thresholds, p0=self.initial_guess)
 

tests/IVIMmodels/unit_tests/test_ivim_synthetic.py

@@ -25,17 +25,18 @@ def test_generated(ivim_algorithm, ivim_data, SNR, rtol, atol, fit_count, rician
     Dp = data["Dp"]
     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.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)
+    if hasattr(fit, "accepts_priors") and fit.accepts_priors:
+        prior = [np.random.normal(D, D/3, 10), np.random.normal(f, f/3, 10), np.random.normal(Dp, Dp/3, 10), np.random.normal(1, 1/3, 10)]
+        # 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):
         # if "data" not in data:
         signal = gd.ivim_signal(D, Dp, f, S0, bvals, SNR, rician_noise)
         # 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])