Merge pull request #84 from avmarchenko/master

Fixes for PCF integration

Author: avmarchenko

exatomic/__init__.py

@@ -18,7 +18,7 @@
 
 .. _atomic: https://en.wikipedia.org/wiki/Atomic_units
 """
-__exatomic_version__ = (0, 3, 5)
+__exatomic_version__ = (0, 3, 6)
 __version__ = '.'.join((str(v) for v in __exatomic_version__))
 
 

exatomic/algorithms/neighbors.py

@@ -72,7 +72,10 @@ def _slice_atoms_molecules(universe, sources, restrictions, n):
     if isinstance(n, (int, np.int32, np.int64)):
         n = [n]
     labels = universe.atom.get_atom_labels()
-    universe.atom['label'] = labels
+    del_label = False
+    if 'label' not in universe.atom.columns:
+        del_label = True
+        universe.atom['label'] = labels
     labels = labels.unique()
     symbols = universe.atom['symbol'].unique()
     classification = universe.molecule['classification'].unique()
@@ -121,7 +124,8 @@ def _slice_atoms_molecules(universe, sources, restrictions, n):
                 other_atoms = other_atoms[other_atoms['symbol'].isin(syms)]
             if len(lbls) > 0:
                 other_atoms = other_atoms[other_atoms['label'].isin(lbls)]
-    del universe.atom['label']
+    if del_label:
+        del universe.atom['label']
     return source_atoms, other_atoms, source_molecules, other_molecules, n
 
 

exatomic/algorithms/pcf.py

@@ -79,7 +79,7 @@ def radial_pair_correlation(universe, a, b, dr=0.05, start=1.0, stop=13.0,
     nb = universe.atom[universe.atom["symbol"] == b].groupby("frame").size().mean()
     if a == b:
         nb -= 1
-    n = hist.cumsum()/nn*na*nb                           # Compute pair count
+    n = hist.cumsum()/nn*na*nb*4/3*np.pi*bmax**3/v_cell
     r = (bins[1:] + bins[:-1])/2*Length["au", length]
     unit = "au"
     if length in ["A", "angstrom", "ang"]:
@@ -90,5 +90,6 @@ def radial_pair_correlation(universe, a, b, dr=0.05, start=1.0, stop=13.0,
     df = pd.DataFrame.from_dict({rlabel: r, glabel: g, nlabel: n})
     if window > 1:
         df = df.rolling(window=window).mean()
+        df = df.iloc[window:]
     df.set_index(rlabel, inplace=True)
     return df

exatomic/basis.py

@@ -542,23 +542,27 @@ class SlaterTypeBasisFunction(SymbolicFunction):
     expr = r**kr * x**kx * y**ky * z**kz * sy.exp(-zeta*r)
 
     @classmethod
-    def eval(cls, xa=None, ya=None, za=None, kx=None, ky=None, kz=None, kr=None, zeta=None):
+    def eval(cls, xa=None, ya=None, za=None, kx=None, ky=None, kz=None,
+             kr=None, zeta=None):
+        """
+        """
         subs = {}
-        if xa:
+        if xa is not None:
             subs[cls.xa] = xa
-        if ya:
+        if ya is not None:
             subs[cls.ya] = ya
-        if za:
+        if za is not None:
             subs[cls.za] = za
-        if kr:
+        if kr is not None:
             subs[cls.kr] = kr
-        if kx:
+        if kx is not None:
             subs[cls.kx] = kx
-        if ky:
+        if ky is not None:
             subs[cls.ky] = ky
-        if kz:
+        if kz is not None:
             subs[cls.kz] = kz
-        if zeta:
+        if zeta is not None:
             subs[cls.zeta] = zeta
+        print(subs)
         expr = cls.expr.subs(subs)
         return super().new_expression(expr, "vectorize")

meta.yaml

@@ -1,9 +1,9 @@
 package:
     name: exatomic
-    version: "0.3.5"
+    version: "0.3.6"
 
 source:
-    git_rev: v0.3.5
+    git_rev: v0.3.6
     git_url: https://github.com/exa-analytics/exatomic.git
 
 requirements:

requirements.txt

@@ -1,2 +1,18 @@
+numpy
+scipy
+matplotlib
+sqlalchemy
+pandas
+networkx
+sympy
+seaborn
+traitlets
+jupyter
+notebook
+ipywidgets
+ipyparallel
+sphinx
+nose
+numba
 sphinxcontrib-autoanysrc
 exa