Implemented basic osipi_fit method to the base class

My thinking is that this is the default function for fitting. It basically passes arguements to the ivim_fit method of standardized version of the algorithm submission. But before a fit is performed, checks should be made to make sure algorithm requirements are fulfilled. This is yet to be implemented

src/standardized/ETP_SRI_LinearFitting.py

@@ -75,13 +75,15 @@ def ivim_fit(self, signals, bvalues=None, linear_fit_option=False):
 
 
 ## Simple test code... 
-#bvalues = np.array([0, 200, 500, 800])
+bvalues = np.array([0, 200, 500, 800])
 
-#def ivim_model(b, S0=1, f=0.1, Dstar=0.03, D=0.001):
-    #return S0*(f*np.exp(-b*Dstar) + (1-f)*np.exp(-b*D))
+def ivim_model(b, S0=1, f=0.1, Dstar=0.03, D=0.001):
+    return S0*(f*np.exp(-b*Dstar) + (1-f)*np.exp(-b*D))
 
-#signals = ivim_model(bvalues)
+signals = ivim_model(bvalues)
 
-#model = ETP_SRI_LinearFitting()
+model = ETP_SRI_LinearFitting(thresholds=[200])
 #results = model.ivim_fit(signals, bvalues)
+results = model.osipi_fit(signals, bvalues)
+print(results)
 #test = model.osipi_simple_bias_and_RMSE_test(SNR=20, bvalues=bvalues, f=0.1, Dstar=0.03, D=0.001, noise_realizations=10)

src/wrappers/OsipiBase.py

@@ -27,9 +27,9 @@ def osipi_fit(self, data=None, bvalues=None, thresholds=None, bounds=None, initi
         use_initial_guess = initial_guess if self.initial_guess is None else self.initial_guess
 
         args = [data, use_bvalues, use_thresholds]
-        if reqired_bounds or required_bounds_optional:
+        if self.required_bounds or self.required_bounds_optional:
             args.append(use_bounds)
-        if required_initial_guess or required_initial_guess_optional:
+        if self.required_initial_guess or self.required_initial_guess_optional:
             args.append(use_initial_guess)
 
         # Run a check_requirements method that makes sure that these inputs fulfil the requirements
@@ -44,7 +44,7 @@ def osipi_fit(self, data=None, bvalues=None, thresholds=None, bounds=None, initi
         f, Dstar, D = self.ivim_fit(*args)
 
         #self.parameter_estimates = self.ivim_fit(data, bvalues)
-        pass
+        return f, Dstar, D
 
     def osipi_print_requirements(self):
         """