Merge pull request #12 from OSIPI/testing/wrapper

Testing/wrapper

Author: etpeterson

.gitignore

@@ -9,3 +9,4 @@ __pycache__/
 .cache
 nosetests.xml
 coverage.xml
+*.pyc

src/original/ETP_SRI/LinearFitting.py

@@ -1,9 +1,7 @@
 import numpy as np
 import numpy.polynomial.polynomial as poly
 
-from utils.data_simulation.GenerateData import GenerateData
-
-
+from utilities.data_simulation.GenerateData import GenerateData
 
 class LinearFit:
     """
@@ -17,6 +15,9 @@ def __init__(self, linear_cutoff=500):
             The b-value after which it can be assumed that the perfusion value is negligible
         """
         self.linear_cutoff = linear_cutoff
+
+    def accepted_dimensions(self):
+        return (1, 1)
 
     def linear_fit(self, bvalues, signal, weighting=None, stats=False):
         """

src/original/IAR_LundUniversity/ivim_fit_method_modified_topopro.py

@@ -8,7 +8,7 @@
 
 class IvimModelTopoPro(ReconstModel):
 
-    def __init__(self, gtab, bounds=[[0,1], [0.005, 0.1], [1e-5, 0.004]], \
+    def __init__(self, gtab, bounds=[[0, 0.005, 1e-5], [1, 0.1, 0.004]], \
         rescale_units=False, shgo_iters=5, rescale_results_to_mm2_s=False):
         r""" Initialize an IvimModelTP class.
         This particular script was modified as the DIPY version has stringent 

src/standardized wip/ETP_SRI_LinearFitting.py

@@ -0,0 +1,74 @@
+import numpy as np
+from src.wrappers.OsipiBase import OsipiBase
+from src.original.ETP_SRI.LinearFitting import LinearFit
+
+
+class ETP_SRI_LinearFitting(OsipiBase):
+    """WIP
+    Implementation and execution of the submitted algorithm
+    """
+    
+    # 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 = "Eric T. Peterson, SRI"
+    id_algorithm_type = "Linear fit"
+    id_return_parameters = "f, D*, D"
+    id_units = "seconds per milli metre squared"
+    
+    # Algorithm requirements
+    required_bvalues = 3
+    required_thresholds = [0,1] # Interval from 1 to 1, 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 = 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, thresholds=None, bounds=None, initial_guess=None, weighting=None, stats=False):
+        """
+            Everything this method 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(ETP_SRI_LinearFitting, self).__init__(bvalues, thresholds, bounds, initial_guess)
+        
+        # Could be a good idea to have all the submission-specfic variable be 
+        # defined with initials?
+        self.ETP_weighting = weighting
+        self.ETP_stats = stats
+        
+        # Check the inputs
+        
+    
+    def ivim_fit(self, signals, bvalues=None, linear_fit_option=False):
+        """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.
+            linear_fit_option (bool, optional): This fit has an option to only run a linear fit. Defaults to False.
+
+        Returns:
+            _type_: _description_
+        """
+        if bvalues is None:
+            bvalues = self.bvalues
+        
+        if self.thresholds is None:
+            ETP_object = LinearFit()
+        else:
+            ETP_object = LinearFit(self.thresholds[0])
+            
+        if linear_fit_option:
+            f, Dstar = ETP_object.linear_fit(bvalues, signals)
+            return f, Dstar
+        else: 
+            f, D, Dstar = ETP_object.ivim_fit(bvalues, signals)
+            return f, Dstar, D
+    

src/standardized wip/IAR_LU_linear.py

@@ -0,0 +1,82 @@
+import numpy as np
+from dipy.core.gradients import gradient_table
+from src.wrappers.OsipiBase import OsipiBase
+from src.original.IAR_LundUniversity.ivim_fit_method_linear import IvimModelLinear
+
+
+class IAR_LU_linear(OsipiBase):
+    """
+    Bi-exponential fitting algorithm by Ivan A. Rashid, Lund University
+    """
+    
+    # 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 = "Ivan A. Rashid, LU"
+    id_algorithm_type = "Linear fit"
+    id_return_parameters = "f, D"
+    id_units = "seconds per milli metre squared or milliseconds per micro metre squared"
+    
+    # Algorithm requirements
+    required_bvalues = 3
+    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=None, 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(IAR_LU_linear, self).__init__(bvalues, thresholds, bounds, initial_guess)
+        
+        # Check the inputs
+        
+        # Initialize the algorithm
+        if self.bvalues is not None:
+            bvec = np.zeros((self.bvalues.size, 3))
+            bvec[:,2] = 1
+            gtab = gradient_table(self.bvalues, bvec, b0_threshold=0)
+            
+            self.IAR_algorithm = IvimModelLinear(gtab)
+        else:
+            self.IAR_algorithm = None
+        
+    
+    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_
+        """
+        
+        if self.IAR_algorithm is None:
+            if bvalues is None:
+                bvalues = self.bvalues
+            else:
+                bvalues = np.asarray(bvalues)
+            
+            bvec = np.zeros((bvalues.size, 3))
+            bvec[:,2] = 1
+            gtab = gradient_table(bvalues, bvec, b0_threshold=0)
+            
+            self.IAR_algorithm = IvimModelLinear(gtab)
+            
+        fit_results = self.IAR_algorithm.fit(signals)
+        
+        f = fit_results.model_params[1]
+        D = fit_results.model_params[2]
+        
+        return f, D

src/standardized/IAR_LU_biexp.py

@@ -0,0 +1,83 @@
+import numpy as np
+from dipy.core.gradients import gradient_table
+from src.wrappers.OsipiBase import OsipiBase
+from src.original.IAR_LundUniversity.ivim_fit_method_biexp import IvimModelBiExp
+
+
+class IAR_LU_biexp(OsipiBase):
+    """
+    Bi-exponential fitting algorithm by Ivan A. Rashid, Lund University
+    """
+    
+    # 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 = "Ivan A. Rashid, LU"
+    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=None, 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(IAR_LU_biexp, self).__init__(bvalues, thresholds, bounds, initial_guess)
+        
+        # Check the inputs
+        
+        # Initialize the algorithm
+        if self.bvalues is not None:
+            bvec = np.zeros((self.bvalues.size, 3))
+            bvec[:,2] = 1
+            gtab = gradient_table(self.bvalues, bvec, b0_threshold=0)
+            
+            self.IAR_algorithm = IvimModelBiExp(gtab)
+        else:
+            self.IAR_algorithm = None
+        
+    
+    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_
+        """
+        
+        if self.IAR_algorithm is None:
+            if bvalues is None:
+                bvalues = self.bvalues
+            else:
+                bvalues = np.asarray(bvalues)
+            
+            bvec = np.zeros((bvalues.size, 3))
+            bvec[:,2] = 1
+            gtab = gradient_table(bvalues, bvec, b0_threshold=0)
+            
+            self.IAR_algorithm = IvimModelBiExp(gtab)
+            
+        fit_results = self.IAR_algorithm.fit(signals)
+        
+        f = fit_results.model_params[1]
+        Dstar = fit_results.model_params[2]
+        D = fit_results.model_params[3]
+        
+        return f, Dstar, D

src/standardized/IAR_LU_modified_mix.py

@@ -0,0 +1,83 @@
+import numpy as np
+from dipy.core.gradients import gradient_table
+from src.wrappers.OsipiBase import OsipiBase
+from src.original.IAR_LundUniversity.ivim_fit_method_modified_mix import IvimModelVP
+
+
+class IAR_LU_modified_mix(OsipiBase):
+    """
+    Bi-exponential fitting algorithm by Ivan A. Rashid, Lund University
+    """
+    
+    # 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 = "Ivan A. Rashid, LU"
+    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=None, 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(IAR_LU_modified_mix, self).__init__(bvalues, thresholds, bounds, initial_guess)
+        
+        # Check the inputs
+        
+        # Initialize the algorithm
+        if self.bvalues is not None:
+            bvec = np.zeros((self.bvalues.size, 3))
+            bvec[:,2] = 1
+            gtab = gradient_table(self.bvalues, bvec, b0_threshold=0)
+            
+            self.IAR_algorithm = IvimModelVP(gtab)
+        else:
+            self.IAR_algorithm = None
+        
+    
+    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_
+        """
+        
+        if self.IAR_algorithm is None:
+            if bvalues is None:
+                bvalues = self.bvalues
+            else:
+                bvalues = np.asarray(bvalues)
+            
+            bvec = np.zeros((bvalues.size, 3))
+            bvec[:,2] = 1
+            gtab = gradient_table(bvalues, bvec, b0_threshold=0)
+            
+            self.IAR_algorithm = IvimModelVP(gtab, rescale_results_to_mm2_s=True)
+            
+        fit_results = self.IAR_algorithm.fit(signals)
+        
+        f = fit_results.model_params[1]
+        Dstar = fit_results.model_params[2]
+        D = fit_results.model_params[3]
+        
+        return f, Dstar, D