Standardized pv mumc triexp

src/original/PV_MUMC/triexp_fitting_algorithms.py

@@ -88,9 +88,9 @@ def monofit(bvalues, Dpar, Fp):
     Dpar1 = params[0]
     if IR:
         if not fitS0:
-            bounds = ([bounds[0][1] , bounds[0][2] , bounds[0][3] , bounds[0][4] , bounds[0][5] ],
+            newbounds = ([bounds[0][1] , bounds[0][2] , bounds[0][3] , bounds[0][4] , bounds[0][5] ],
                         [Dpar1          , bounds[1][2] , bounds[1][3] , bounds[1][4] , bounds[1][5] ])      
-            params, _ = curve_fit(tri_expN_noS0_IR, bvalues, dw_data, p0=[Dpar1, 0.0, (bounds[0][3]+bounds[1][3])/2, 0.05, (bounds[0][5]+bounds[1][5])/2], bounds=bounds)
+            params, _ = curve_fit(tri_expN_noS0_IR, bvalues, dw_data, p0=[Dpar1, 0.0, (bounds[0][3]+bounds[1][3])/2, 0.05, (bounds[0][5]+bounds[1][5])/2], bounds=newbounds)
             Dpar, Fint, Dint, Fmv, Dmv = params[0], params[1], params[2], params[3], params[4]
             #when the fraction of a compartment equals zero (or very very small), the corresponding diffusivity is non-existing (=NaN)
 
@@ -104,9 +104,9 @@ def monofit(bvalues, Dpar, Fp):
 
     else:
         if not fitS0:
-            bounds = ([bounds[0][1] , bounds[0][2] , bounds[0][3] , bounds[0][4] , bounds[0][5] ],
+            newbounds = ([bounds[0][1] , bounds[0][2] , bounds[0][3] , bounds[0][4] , bounds[0][5] ],
                         [Dpar1          , bounds[1][2] , bounds[1][3] , bounds[1][4] , bounds[1][5] ])      
-            params, _ = curve_fit(tri_expN_noS0, bvalues, dw_data, p0=[Dpar1, 0.0, (bounds[0][3]+bounds[1][3])/2, 0.05, (bounds[0][5]+bounds[1][5])/2], bounds=bounds)
+            params, _ = curve_fit(tri_expN_noS0, bvalues, dw_data, p0=[Dpar1, 0.0, (bounds[0][3]+bounds[1][3])/2, 0.05, (bounds[0][5]+bounds[1][5])/2], bounds=newbounds)
             Dpar, Fint, Dint, Fmv, Dmv = params[0], params[1], params[2], params[3], params[4]
             #when the fraction of a compartment equals zero (or very very small), the corresponding diffusivity is non-existing (=NaN)
 

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

src/standardized/PV_MUMC_triexp.py

@@ -0,0 +1,58 @@
+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
+    result_keys = ["Dpar", "Fint", "Dint", "Fmv", "Dmv"]
+    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)
+        results = {}
+        results["Dpar"] = fit_results[0]
+        results["Fint"] = fit_results[1]
+        results["Dint"] = fit_results[2]
+        results["Fmv"] = fit_results[3]
+        results["Dmv"] = fit_results[4]
+
+        return results

tests/IVIMmodels/unit_tests/algorithms.json

@@ -11,6 +11,7 @@
         "OGC_AmsterdamUMC_biexp_segmented",
         "OGC_AmsterdamUMC_biexp",
         "PV_MUMC_biexp",
+        "PV_MUMC_triexp",
         "PvH_KB_NKI_IVIMfit",
         "OJ_GU_seg"
     ],
@@ -163,5 +164,23 @@
                 "Dp": 1e-1
             }
         }
+    },
+    "PV_MUMC_triexp": {
+        "tolerances": {
+            "rtol": {
+                "Dpar": 5,
+                "Fint": 5,
+                "Dint": 25,
+                "Fmv": 25,
+                "Dmv": 25
+            },
+            "atol": {
+                "Dpar": 1e-2,
+                "Fint": 1e-2,
+                "Dint": 1e-1,
+                "Fmv": 1e-1,
+                "Dmv": 1e-1
+            }
+        }
     }
 }