Ignore missing beads - for DNA with issue #2

pycgtool.py

@@ -38,7 +38,7 @@ def main(args):
         for mol in bonds:
             print("Bonds in {0}:".format(mol))
             for bond in bonds[mol]:
-                print(bond.eqm, bond.fconst)
+                print(len(bond.values), bond.eqm, bond.fconst)
 
         bonds.write_itp("out.itp", mapping=mapping)
         ff = ForceField("fftest.ff")

pycgtool/bondset.py

@@ -3,6 +3,8 @@ import numpy as np
 from .util import stat_moments, sliding
 from .parsers.cfg import CFG
 
+np.seterr(all="raise")
+
 
 class Bond:
     __slots__ = ["atoms", "atom_numbers", "values", "eqm", "fconst"]

pycgtool/mapping.py

@@ -3,6 +3,8 @@ import numpy as np
 from .frame import Atom, Residue, Frame
 from .parsers.cfg import CFG
 
+np.seterr(all="raise")
+
 
 class BeadMap:
     __slots__ = ["name", "typ", "type", "atoms", "charge", "mass"]
@@ -84,9 +86,10 @@ class Mapping:
                 try:
                     bead.coords /= n
                 except FloatingPointError:
-                    raise EmptyBeadError("Bead {0} in molecule {1} contains no atoms.".format(
-                        bead.name, aares.name
-                    ))
+                    # raise EmptyBeadError("Bead {0} in molecule {1} contains no atoms.".format(
+                    #     bead.name, aares.name
+                    # ))
+                    pass
                 cgframe.natoms += 1
 
             cgframe.residues.append(res)

pycgtool/util.py

@@ -2,23 +2,29 @@ import numpy as np
 np.seterr(all="raise")
 
 
-def stat_moments(vals):
+def stat_moments(vals, ignore_nan=True):
     """
     Return statistical (population) moments of data provided.
 
     :param vals: The data for which to calculate moments
+    :param ignore_nan: Whether to exclude np.nan from calculation
     :return: Numpy array of moments - population mean and variance
     """
+    if ignore_nan:
+        vals_tmp = [val for val in vals if not np.isnan(val)]
+    else:
+        vals_tmp = vals
+
     res = np.zeros(2)
     try:
-        for val in vals:
+        for val in vals_tmp:
             res[0] += val
-        mean = res[0] / len(vals)
+        mean = res[0] / len(vals_tmp)
 
-        for val in vals:
+        for val in vals_tmp:
             res[1] += pow(val - mean, 2)
 
-        res /= len(vals)
+        res /= len(vals_tmp)
         return res
     except FloatingPointError:
         return np.zeros(2)
@@ -29,7 +35,7 @@ def sliding(vals):
     Yield three values in a sliding window along an iterable.
 
     :param vals: Iterable to iterate over
-    :return: Iterable of tuples
+    :return: Generator of tuples
     """
     it = iter(vals)
     prev = None