Extract all structures of Gaussian 09 molecular dynamics calculation using babel?

Question

Is there a way to easily extract all the structures and energies from an ADMP calculation done in Gaussian 09? GaussView can plot them but not export them in a useful way and babel just gives me the last structure in there.

I tried

babel <G09output> all -oxyz <output.xyz> 

but I get just

==============================
*** Open Babel Error  in OpenAndSetFormat
  Cannot open all
1 molecule converted
1 errors 16 audit log messages 

Or is there a different easy way to do this? Otherwise I guess I have to write my own script or look into cclib, but that one is using babel too if I understand it correct?

Example output file: http://dl.dropboxusercontent.com/u/6111391/Tz_TCO_dyn6.txt

Input:

%nprocshared=16
%mem=56GB
%chk=Tz_TCO_dyn6.chk
# 6-31g(d) scrf=check geom=(connectivity,crowd)admp=(stepsize=2500,maxpoints=400) rm062x IOp(1/44=990210)

[No Title]

0 1
 C                  1.08049600   -0.58720100   -2.07918800
 C                 -1.08049600    0.58720100   -2.07918800
 C                 -0.58945000    0.34949100    0.13978700
 H                 -0.52250100    1.43457800    0.23933400
 C                  0.58945000   -0.34949100    0.13978700
 H                  0.52250100   -1.43457800    0.23933400
 N                 -1.13318600   -0.73433600   -2.39136300
 N                  0.00078600   -1.35005000   -2.39250200
 N                 -0.00078600    1.35005000   -2.39250200
 N                  1.13318600    0.73433600   -2.39136300
 C                  1.84657900    0.28440600    0.66016000
 H                  2.75382900   -0.14516700    0.22100400
 H                  1.84124500    1.35503200    0.42328200
 C                 -1.84657900   -0.28440600    0.66016000
 H                 -2.75382900    0.14516700    0.22100400
 H                 -1.84124500   -1.35503200    0.42328200
 C                  1.87373600    0.08444000    2.19307200
 H                  2.72499800    0.64701600    2.59375900
 H                  2.07520100   -0.97273600    2.41061700
 C                 -1.87373600   -0.08444000    2.19307200
 H                 -2.72499800   -0.64701600    2.59375900
 H                 -2.07520100    0.97273600    2.41061700
 C                 -0.58945000   -0.50859700    2.93023900
 H                 -0.86607100   -0.70606500    3.97132400
 H                 -0.24860000   -1.47389400    2.53270700
 C                  0.58945000    0.50859700    2.93023900
 H                  0.86607100    0.70606500    3.97132400
 H                  0.24860000    1.47389400    2.53270700
 H                  2.02862900   -1.10167200   -1.96939700
 H                 -2.02862900    1.10167200   -1.96939700

 1 8 1.5 10 1.5 29 1.0
 2 7 1.5 9 1.5 30 1.0
 3 4 1.0 5 2.0 14 1.0
 4
 5 6 1.0 11 1.0
 6
 7 8 1.5
 8
 9 10 1.5
 10
 11 12 1.0 13 1.0 17 1.0
 12
 13
 14 15 1.0 16 1.0 20 1.0
 15
 16
 17 18 1.0 19 1.0 26 1.0
 18
 19
 20 21 1.0 22 1.0 23 1.0
 21
 22
 23 24 1.0 25 1.0 26 1.0
 24
 25
 26 27 1.0 28 1.0
 27
 28
 29
 30

Answer 1

If you have the latest copy of cclib installed, it can extract both the energies and geometries and write the geometries to an XYZ trajectory file that you can open with Avogadro or VMD.

To print the HF or DFT energies to the screen,

$ ccget scfenergies admp.out

but be warned that the energies are in eV, not hartree.

To dump geometries to XYZ files, here's a simplified version of a script I use all the time:

#!/usr/bin/env python

"""cclib_extract_geom.py: Extract geometries from a quantum chemical
output file using cclib.
"""


from __future__ import print_function

import os.path

from cclib.io import ccopen, ccwrite


def getargs():
    """Get command-line arguments."""

    import argparse

    parser = argparse.ArgumentParser()

    parser.add_argument('outputfilename', nargs='+')

    parser.add_argument('--trajectory',
                        action='store_true',
                        help="""Extract all possible geometries into a trajectory-like file?""")
    parser.add_argument('--suffix')

    args = parser.parse_args()

    return args


if __name__ == '__main__':

    args = getargs()

    for outputfilename in args.outputfilename:

        job = ccopen(outputfilename)
        data = job.parse()

        stub = os.path.splitext(outputfilename)[0]
        if args.suffix:
            xyzfilename = ''.join([stub, '.', args.suffix, '.xyz'])
        else:
            xyzfilename = ''.join([stub, '.xyz'])

        ccwrite(data, outputdest=xyzfilename, allgeom=args.trajectory)

To write the energies in to a text file in hartree, you might add something like

        from cclib.parser.utils import convertor # put me at the top of the file
        with open(stub + '.energies', 'w') as fh:
            for energy in data.scfenergies:
                fh.write(str(convertor(energy, 'eV', 'hartree')) + '\n')

Answer 2

If, like me, you just want to extract the optimized structures from a potential energy scan, you can use this messy script:

#!/usr/bin/env python
# python Extract_Optimized_From_Gaussian.py filename

from __future__ import print_function
import sys, os

def extract_all(text, target):
    linenums = []
    # Start count at 1 because files start at line 1 not 0
    count = 1
    for line in text:
        if (line.find(target)) > -1:
            linenums.append(count)
        count += 1
    return linenums

code = {"1" : "H", "2" : "He", "3" : "Li", "4" : "Be", "5" : "B", \
"6"  : "C", "7"  : "N", "8"  : "O", "9" : "F", "10" : "Ne", \
"11" : "Na" , "12" : "Mg" , "13" : "Al" , "14" : "Si" , "15" : "P", \
"16" : "S"  , "17" : "Cl" , "18" : "Ar" , "19" : "K"  , "20" : "Ca", \
"21" : "Sc" , "22" : "Ti" , "23" : "V"  , "24" : "Cr" , "25" : "Mn", \
"26" : "Fe" , "27" : "Co" , "28" : "Ni" , "29" : "Cu" , "30" : "Zn", \
"31" : "Ga" , "32" : "Ge" , "33" : "As" , "34" : "Se" , "35" : "Br", \
"36" : "Kr" , "37" : "Rb" , "38" : "Sr" , "39" : "Y"  , "40" : "Zr", \
"41" : "Nb" , "42" : "Mo" , "43" : "Tc" , "44" : "Ru" , "45" : "Rh", \
"46" : "Pd" , "47" : "Ag" , "48" : "Cd" , "49" : "In" , "50" : "Sn", \
"51" : "Sb" , "52" : "Te" , "53" : "I"  , "54" : "Xe" , "55" : "Cs", \
"56" : "Ba" , "57" : "La" , "58" : "Ce" , "59" : "Pr" , "60" : "Nd", \
"61" : "Pm" , "62" : "Sm" , "63" : "Eu" , "64" : "Gd" , "65" : "Tb", \
"66" : "Dy" , "67" : "Ho" , "68" : "Er" , "69" : "Tm" , "70" : "Yb", \
"71" : "Lu" , "72" : "Hf" , "73" : "Ta" , "74" : "W"  , "75" : "Re", \
"76" : "Os" , "77" : "Ir" , "78" : "Pt" , "79" : "Au" , "80" : "Hg", \
"81" : "Tl" , "82" : "Pb" , "83" : "Bi" , "84" : "Po" , "85" : "At", \
"86" : "Rn" , "87" : "Fr" , "88" : "Ra" , "89" : "Ac" , "90" : "Th", \
"91" : "Pa" , "92" : "U"  , "93" : "Np" , "94" : "Pu" , "95" : "Am", \
"96" : "Cm" , "97" : "Bk" , "98" : "Cf" , "99" : "Es" ,"100" : "Fm", \
"101": "Md" ,"102" : "No" ,"103" : "Lr" ,"104" : "Rf" ,"105" : "Db", \
"106": "Sg" ,"107" : "Bh" ,"108" : "Hs" ,"109" : "Mt" ,"110" : "Ds", \
"111": "Rg" ,"112" : "Uub","113" : "Uut","114" : "Uuq","115" : "Uup", \
"116": "Uuh","117" : "Uus","118" : "Uuo"}

logfile_fn = os.path.basename(sys.argv[1])
logfile_bn = os.path.splitext(logfile_fn)[0]
logfile_fh = open(logfile_fn, 'r')
text = logfile_fh.readlines()
logfile_fh.close()

# Find all lines that contain "!   Optimized Parameters   !"
opt_param_line_nums = extract_all(text, '!   Optimized Parameters   !')
# Then find all lines that have "Input orientation:"
input_orient_line_nums = extract_all(text, 'Standard orientation')
# And all lines that have the ---- which we will use to find the ends of the coordinate sections
end_coor_line_nums = extract_all(text, '---------')
# Add 5 because the first actual coordinate is +5 lines
input_orient_line_nums = [x+5 for x in input_orient_line_nums]
# Make sure they're sorted
opt_param_line_nums.sort()
input_orient_line_nums.sort()
end_coor_line_nums.sort()

new_input_lns = []
count = 0
for x in reversed(input_orient_line_nums):
    if count > len(opt_param_line_nums)-1:
        break
    if x < list(reversed(opt_param_line_nums))[count]:
        new_input_lns.append(x)
        count += 1

new_input_lns.reverse()

new_end_lns = []
count = 0
for x in end_coor_line_nums:
    if count > len(new_input_lns)-1:
        break
    if x > new_input_lns[count]:
        new_end_lns.append(x)
        count += 1

intervals = zip(new_input_lns, new_end_lns)

# Now we convert each interval into a .xyz
outfile = open(logfile_bn+'.xyz','w')
for intvl in intervals:
    n_atoms = intvl[1] - intvl[0]
    outfile.write(str(n_atoms)+"\n\n")
    for x in range(intvl[0]-1, intvl[1]-1):
        column = text[x].split()
        print(code[column[1]],float(column[3]),float(column[4]),float(column[5]), file=outfile)

outfile.close()

I adapted this from here. I've tested it with Gaussian 09. No idea if it works with other versions. The resulting file loads into VMD. I don't know about other visualization software.

Edit: I checked the original script I had posted here against GaussView, and my script was wrong. Specifically, it was the structures titled "Standard orientation" that came before each "! Optimized Parameters !" line that I needed to extract, not the ones that came after. I have updated the script above to do that, and the output now matches that of GaussView. I removed the original script, but I have copied it to Pastbin here in case anyone still wants access to it (they're not too different).