added code description PV_MUMC

Author: paulienvoorter

src/original/PV_MUMC/two_step_IVIM_fit.py

@@ -50,11 +50,11 @@ def fit_least_squares_array(bvalues, dw_data, fitS0=True, bounds=([0.9, 0.0001,
     Fmv = np.zeros(len(dw_data))
     for i in tqdm.tqdm(range(len(dw_data)), position=0, leave=True):
         # fill arrays with fit results on a per voxel base:
-        Dpar[i], Fmv[i], Dmv[i], S0[i] = fit_least_squares(bvalues, dw_data[i, :], S0_output=True, fitS0=fitS0, bounds=bounds)
+        Dpar[i], Fmv[i], Dmv[i], S0[i] = fit_least_squares(bvalues, dw_data[i, :], S0_output=False, fitS0=fitS0, bounds=bounds)
     return [Dpar, Fmv, Dmv, S0]
 
 
-def fit_least_squares(bvalues, dw_data, IR=True, S0_output=False, fitS0=True,
+def fit_least_squares(bvalues, dw_data, IR=False, S0_output=False, fitS0=True,
                       bounds=([0.9, 0.0001, 0.0, 0.0025], [1.1, 0.0025, 0.2, 0.2]), cutoff=200):
     """
    This is the LSQ implementation, in which we first estimate Dpar using a curve fit to b-values>=cutoff;

src/standardized/PV_MUMC_biexp.py

@@ -0,0 +1,56 @@
+import numpy as np
+from src.wrappers.OsipiBase import OsipiBase
+from src.original.PV_MUMC.ivim_fit_method_biexp import fit_least_squares_array
+
+
+class PV_MUMC_biexp(OsipiBase):
+    """
+    Bi-exponential fitting algorithm by Paulien Voorter, Maastricht University
+    """
+    
+    # Some basic stuff that identifies the algorithm
+    id_author = "Paulien Voorter MUMC"
+    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 = 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=([0.9, 0.0001, 0.0, 0.0025], [1.1, 0.0025, 0.2, 0.2]), 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, self).__init__(bvalues, None, bounds, None)
+        self.PV_algorithm = fit_least_squares_array
+        
+    
+    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)
+        
+        f = fit_results[1]
+        Dstar = fit_results[2]
+        D = fit_results[0]
+        
+        return f, Dstar, D