Merge branch 'standardized_PV_MUMC_triexp' of github.com:OSIPI/TF2.4_IVIM-MRI_CodeCollection into standardized_PV_MUMC_triexp

Author: DKuppens

src/standardized wip/PV_MUMC_triexp.py

@@ -0,0 +1,53 @@
+import numpy as np
+from src.wrappers.OsipiBase import OsipiBase
+from src.original.PV_MUMC.triexp_fitting_algorithms import fit_least_squares_tri_exp, fit_NNLS
+
+
+class PV_MUMC_triexp(OsipiBase):
+    """
+    Tri-exponential least squares fitting algorithm by Paulien Voorter, Maastricht University
+    """
+    
+    # Some basic stuff that identifies the algorithm
+    id_author = "Paulien Voorter MUMC"
+    id_algorithm_type = "Tri-exponential fit"
+    id_return_parameters = "Dpar, Fint, Dint, Fmv, Dmv"
+    id_units = "seconds per milli metre squared or milliseconds per micro metre squared"
+    
+    # Algorithm requirements
+    required_bvalues = 4
+    required_thresholds = [0, 0] # Interval from "at least" to "at most", in case submissions allow a custom number of thresholds
+    required_bounds = False
+    required_bounds_optional = True # Bounds may not be required but are optional
+    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?
+    
+    def __init__(self, bvalues=None, thresholds=None, bounds=None, initial_guess=None, weighting=None, stats=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(PV_MUMC_triexp, self).__init__(bvalues, None, bounds, None)
+        self.PV_algorithm = fit_least_squares_tri_exp
+        
+    
+    def ivim_fit(self, signals, bvalues=None):
+        """Perform the IVIM fit
+
+        Args:
+            signals (array-like)
+            bvalues (array-like, optional): b-values for the signals. If None, self.bvalues will be used. Default is None.
+
+        Returns:
+            _type_: _description_
+        """
+        
+            
+        fit_results = self.PV_algorithm(bvalues, signals)
+        Dpar, Fint, Dint, Fmv, Dmv = fit_results
+        
+        return Dpar, Fint, Dint, Fmv, Dmv