adding jupyter notebook usage code and IVIM analysis

README.md

@@ -17,6 +17,9 @@ If you would like to contribute with code, please follow the instructions below:
 *   [Guidelines for IVIM code contribution](doc/guidelines_for_contributions.md)
 *   [Guidelines to creating a test file](doc/creating_test.md) 
 
+If you would like to use code from the repository and/or are new to Github or IVIM, please see the jupyter notebook below:
+*   [Introduction to TF2.4_IVIM-MRI_CodeCollection github and IVIM Analysis using Python](doc/Introduction_to_TF24_IVIM-MRI_CodeCollection_github_and_IVIM_Analysis_using_Python.ipynb)
+
 ## Repository Organization
 
 The repository is organized in four main folders along with configuration files for automated testing. 

src/wrappers/OsipiBase.py

@@ -3,6 +3,7 @@
 from scipy.stats import norm
 import pathlib
 import sys
+from tqdm import tqdm
 
 class OsipiBase:
     """The base class for OSIPI IVIM fitting"""
@@ -47,7 +48,7 @@ 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 +69,6 @@ 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 +83,13 @@ 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(list(data.shape[:-1])+[3]) # Create an array with the voxel dimensions + the ones required for the fit
+        for ijk in tqdm(np.ndindex(data.shape[:-1]), total=np.prod(data.shape[:-1])):
+            args = [data[ijk], use_bvalues]
+            fit = list(self.ivim_fit(*args, **kwargs))
+            results[ijk] = fit
 
         #self.parameter_estimates = self.ivim_fit(data, bvalues)
         return results