rm red

src/impetuous/clustering.py

@@ -628,58 +628,15 @@ else :
                 R2[i][j] = np.sum((P[i]-Q[j])**2)
         return ( R2 )
 
-
 def select_from_distance_matrix(boolean_list,distance_matrix):
     return ( np.array( [ d[boolean_list] for d in distance_matrix[boolean_list]] ) )
 
-
-def calculate_rdf ( particles_i=None , particles_o=None , nbins=100 ,
-                    distance_matrix = None , bInGroup = None , bNotInGroup = None ,
-                    n_dimensions = 3 , xformat="%.3f" , bLiquidState=True, constant=4.0/3.0) :
-
-    import operator
-    crit1 = particles_i is None and particles_o is None
-    crit2 = bInGroup is None and distance_matrix is None and bNotInGroup is None
-
-    if not crit2 :
-        particles_i = distance_matrix_to_absolute_coordinates ( \
-                         select_from_distance_matrix ( bInGroup    , distance_matrix ) ,
-                         n_dimensions = n_dimensions )
-        particles_o = distance_matrix_to_absolute_coordinates ( \
-                         select_from_distance_matrix ( bNotInGroup , distance_matrix ) ,
-                         n_dimensions = n_dimensions )
-    else :
-        particles_i = particles_i.T
-        particles_o = particles_o.T
-
-    if operator.xor( not crit1, not crit2 ) :
-        rdf_p = pd.DataFrame ( exclusive_pdist ( particles_i.T , particles_o.T ) ).apply( np.sqrt ).values.reshape(-1)
-        rmax  = np.max ( rdf_p )
-        Y_ , X = np.histogram ( rdf_p , bins=nbins )
-        X_ = 0.5*(X[1:]+X[:1])
-        bUse  = [ x<rmax/2.0 for x in X_ ]
-        rd = X_
-        norm = len(rdf_p)
-        if bLiquidState :
-            norm = constant * np.pi * ( ( X_ + np.diff(X) )**n_dimensions - X_**n_dimensions )*len(rdf_p)
-        dd = Y_ / norm
-        rd = [ r for r,b in zip(rd,bUse) if b ]
-        dd = [ y for y,b in zip(dd,bUse) if b ]
-        bar_source = {'density_values': dd, 'density_ids':[ xformat % (d) for d in rd ] }
-        return ( bar_source , rdf_p )
-    else :
-        print ( """calculate_rdf ( particles_i=None , particles_o=None , Nbins=100 ,
-                    distance_martix = None, bInGroup = None, bNotInGroup = None ,
-                    n_dimensions = 3 , xformat="%.3f" , bLiquidState=True, constant=4.0/3.0 )""")
-        exit ( 1 )
-
 def diar ( n ):
     if n>1:
         return ( np.sqrt(n)*diar(n-1) )
     else:
         return ( 1. )
 
-
 def calculate_rdf ( particles_i = None , particles_o = None , nbins=100 ,
                     distance_matrix = None , bInGroup = None , bNotInGroup = None ,
                     n_dimensions = 3 , xformat="%.3f" ,