created wrapper for PvH

Author: oliverchampion

src/standardized/PvH_KB_NKI_IVIMfit.py

@@ -0,0 +1,59 @@
+from src.wrappers.OsipiBase import OsipiBase
+from src.original.PvH_KB_NKI.DWI_functions_standalone import generate_IVIMmaps_standalone
+import numpy as np
+
+class PvH_KB_NKI_IVIMfit(OsipiBase):
+    """
+    Bi-exponential fitting algorithm by Petra van Houdt and Koen Baas, NKI
+    """
+
+    # I'm thinking that we define default attributes for each submission like this
+    # And in __init__, we can call the OsipiBase control functions to check whether
+    # the user inputs fulfil the requirements
+
+    # Some basic stuff that identifies the algorithm
+    id_author = "Petra van Houdt and Koen Baas, NKI"
+    id_algorithm_type = "Bi-exponential fit"
+    id_return_parameters = "f, D*, D"
+    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 = False  # Bounds may not be required but are optional
+    required_initial_guess = False
+    required_initial_guess_optional =False
+    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, bminADC=150, bmaxADC=1000,):
+        """
+            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(PvH_KB_NKI_IVIMfit, self).__init__(bvalues, bminADC,bmaxADC)
+        self.NKI_algorithm = generate_IVIMmaps_standalone
+
+
+    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_
+        """
+        bvalues=np.array(bvalues)
+        fit_results = self.NKI_algorithm(signals,bvalues)
+
+        D = fit_results[0]
+        f = fit_results[1]
+        Dstar = fit_results[2]
+
+        return f, Dstar, D