Added support for >1D fits

src/wrappers/OsipiBase.py

@@ -47,7 +47,7 @@ def initialize(**kwargs):
         pass
 
     #def osipi_fit(self, data=None, bvalues=None, thresholds=None, bounds=None, initial_guess=None, **kwargs):
-    def osipi_fit(self, data, bvalues, **kwargs):
+    def osipi_fit(self, data, bvalues=None, **kwargs):
         """Fits the data with the bvalues
         Returns [S0, f, Dstar, D]
         """
@@ -68,7 +68,6 @@ def osipi_fit(self, data, bvalues, **kwargs):
         #kwargs["bvalues"] = use_bvalues
 
         #args = [data, use_bvalues, use_thresholds]
-        args = [data, use_bvalues]
         #if self.required_bounds or self.required_bounds_optional:
             #args.append(use_bounds)
         #if self.required_initial_guess or self.required_initial_guess_optional:
@@ -83,7 +82,12 @@ def osipi_fit(self, data, bvalues, **kwargs):
 
         #args = [data, use_bvalues, use_initial_guess, use_bounds, use_thresholds]
         #args = [arg for arg in args if arg is not None]
-        results = self.ivim_fit(*args, **kwargs)
+
+        # Assuming the last dimension of the data is the signal values of each b-value
+        results = np.empty(data.shape[:-1], dtype=object)
+        for ijk in np.ndindex(data.shape[:-1]):
+            args = [data[ijk], use_bvalues]
+            results[ijk] = self.ivim_fit(*args, **kwargs)
 
         #self.parameter_estimates = self.ivim_fit(data, bvalues)
         return results