Added intersections

ElM2D/ElM2D.py

@@ -448,5 +448,65 @@ def featurize(self, compositions, how="mean"):
 
         return np.array(vectors)
 
+    def intersect(self, y, X=None):
+        """
+        Takes in a second formula list, y, and computes the intersectional distance 
+        matrix between the two under the given metric. If a two formula lists
+        are given the intersection between the two is computed, returning a 
+        distance matrix of the form:
+
+              X_0             X_1            X_2            ...
+        y_0  ElMD(X_0, y_0)  ElMD(X_1, y_0)  ElMD(X_2, y_0)
+        y_1  ElMD(X_0, y_1)  ElMD(X_1, y_1)  ElMD(X_2, y_1)
+        f_2  ElMD(X_0, y_2)  ElMD(X_1, y_2)  ElMD(X_2, y_2)
+        ...
+        """
+        if X is None:
+            X = self.formula_list
+
+        intersection_dm = self._process_intersection(X, y, self.n_proc)
+
+        return intersection_dm
+
+
+    def _process_intersection(self, X, y, n_proc):
+        '''
+        Compute the 
+        '''
+        pool_list = []
+
+        n_elements = len(ElMD().periodic_tab[self.metric])
+        X_mat = np.ndarray(shape=(len(X), n_elements), dtype=np.float64)
+        y_mat = np.ndarray(shape=(len(y), n_elements), dtype=np.float64)
+
+        print("Parsing X Formula")
+        for i, formula in tqdm(list(enumerate(X))):
+            X_mat[i] = ElMD(formula, metric=self.metric).ratio_vector
+
+        print("Parsing Y Formula")
+        for i, formula in tqdm(list(enumerate(y))):
+            y_mat[i] = ElMD(formula, metric=self.metric).ratio_vector
+
+        # Create input pairings
+        print("Constructing joint compositional pairings")
+        for y in tqdm(range(len(y_mat))):
+            sublist = [(y, x) for x in range(len(X_mat))]
+            pool_list.append(sublist)
+
+
+        # Distribute amongst processes
+        if self.verbose: print("Creating Process Pool")
+        if self.verbose:
+            print("Scattering compositions between processes and computing distances")
+            distances = process_map(self._pool_ElMD, pool_list, chunksize=1)
+
+        if self.verbose: print("Distances computed closing processes")
+
+        # if self.verbose: print("Flattening sublists")
+        # Flattens list of lists to single list
+        # distances = [dist for sublist in scores for dist in sublist]
+
+        return np.array(distances, dtype=np.float64)
+
 if __name__ == "__main__":
     main()

setup.py

@@ -2,13 +2,13 @@
 setup(
   name = 'ElM2D',        
   packages = ['ElM2D'],  
-  version = '0.3.13',      
+  version = '0.3.14',      
   license='GPL3',       
   description = 'A high performance mapping class to embed large datasets of ionic compositions with respect to the ElMD metric.',  
   author = 'Cameron Hagreaves',              
   author_email = 'cameron.h@rgreaves.me.uk', 
   url = 'https://github.com/lrcfmd/ElM2D/',   
-  download_url = 'https://github.com/lrcfmd/ElM2D/archive/0.3.13.tar.gz',    
+  download_url = 'https://github.com/lrcfmd/ElM2D/archive/0.3.14.tar.gz',    
   keywords = ['ChemInformatics', 'Materials Science', 'Machine Learning', 'Materials Representation'],   
   install_requires=[            
           'cython',