Mods for notebook integration.

src/soap/soapy/elements.py

@@ -19,7 +19,7 @@ class PeriodicTable(object):
    "H","He",
    "Li","Be","B","C","N","O","F","Ne",
    "Na","Mg","Al","Si","P","S","Cl","Ar",
-   "K ","Ca","Sc","Ti","V","Cr","Mn","Fe","Co","Ni","Cu","Zn","Ga","Ge", "As","Se","Br","Kr",
+   "K","Ca","Sc","Ti","V","Cr","Mn","Fe","Co","Ni","Cu","Zn","Ga","Ge", "As","Se","Br","Kr",
    "Rb","Sr","Y","Zr","Nb","Mo","Tc","Ru","Rh","Pd","Ag","Cd","In","Sn", "Sb","Te","I","Xe",
    "Cs","Ba",
    "La","Ce","Pr","Nd","Pm","Sm","Eu","Gd","Tb","Dy","Ho","Er","Tm","Yb","Lu",

src/soap/soapy/kernel.py

@@ -232,7 +232,7 @@ class KernelAdaptorGlobalSpecific(object):
     def adaptScalar(self, atomic):
         xnklab_atomic = Xnklab(atomic, self.types)
         X = xnklab_atomic.reduce()
-        x_norm = X/np.dot(X,X)**0.5
+        X_norm = X/np.dot(X,X)**0.5
         return X, X_norm
 
 class KernelAdaptorGeneric(object):

src/soap/soapy/lagraph.py

@@ -696,10 +696,11 @@ class ParticleGraph(object):
         return P, positions
         
 def mp_create_graph(config, options, log):
-    soap.soapy.util.MP_LOCK.acquire()
-    log << log.item << "Graph('%s') PID=%d" % (\
-        config.config_file, mp.current_process().pid) << log.endl
-    soap.soapy.util.MP_LOCK.release()
+    if log:
+        soap.soapy.util.MP_LOCK.acquire()
+        log << log.item << "Graph('%s') PID=%d" % (\
+            config.config_file, mp.current_process().pid) << log.endl
+        soap.soapy.util.MP_LOCK.release()
     graph = ParticleGraph(config.config_file, config.atoms, options)
     return graph        
         

src/soap/soapy/lamatch.py

@@ -6,19 +6,21 @@ import kernel as kern
 import lagraph
 
 def compare_graphs_average(g1, g2, options):
-    P1 = g1.P
+    # TODO Use KernelFunctionFactory here
+    xi = options['basekernel']['kernel.xi']
     P1_avg = np.average(g1.P, axis=0)
     P1_avg = P1_avg / np.dot(P1_avg, P1_avg)**0.5
-    P2 = g2.P
     P2_avg = np.average(g2.P, axis=0)
     P2_avg = P2_avg / np.dot(P2_avg, P2_avg)**0.5
-    print P1.shape, P2.shape, P1_avg.shape, P2_avg.shape
-    return np.dot(P1_avg, P2_avg)
+    #print P1.shape, P2.shape, P1_avg.shape, P2_avg.shape
+    return np.dot(P1_avg, P2_avg)**xi
 
 def compare_graphs_global(g1, g2, options):
+    # TODO Use KernelFunctionFactory here
+    xi = options['basekernel']['kernel.xi']
     assert g1.P.shape[0] == 1 # Global descriptor computed?
     assert g2.P.shape[0] == 1 # Global descriptor computed?
-    return np.dot(g1.P[0], g2.P[0])
+    return np.dot(g1.P[0], g2.P[0])**xi
 
 def compare_graphs_rematch(g1, g2, options):
     if options['graph']['hierarchical']:

src/soap/soapy/momo.py

@@ -4,7 +4,10 @@ import commands
 import argparse
 import time
 import numpy as np
-from lxml import etree
+try:
+    from lxml import etree
+except ImportError:
+    pass
 
 boolean_dict = \
     {'true' : True,   '1' : True,  'yes' : True,

src/soap/tools/extract.py

 import numpy as np
 import numpy.linalg
 
-from momo import osio, endl, flush
+from ..soapy import momo
+
+osio = momo.osio
+endl = momo.endl
+flush = momo.flush
 
 class Xnklab(object):
     def __init__(self, atomic, types_global):

src/soap/tools/inverse.py

 import numpy as np
 import numpy.linalg
 
-from momo import osio, endl, flush
+from ..soapy import momo
 
+osio = momo.osio
+endl = momo.endl
+flush = momo.flush
 
 def compute_X(Qr):
     # Q is N x (2*L+1) real-valued matrix