def tests():
# Currently assume some relative path stuff. This is apt to change once we
# make this into a module.
sys.path.insert(0, "..") # Make this the first thing since we want to override
from XYZ import XYZ # XYZ class
from reax_connection_table import Connection_Table
# Test files location:
structure_file = "tests/a10a_ph7.xyz"
connection_table_file = "tests/a10a_ph7.connect"
# Read in XYZ file. Store all the coordinates.
simulation_atoms = XYZ()
simulation_atoms.load(structure_file) # simulation_atoms.rows contains the data
print "Structure file loaded successfully: " + structure_file
# Read in connection table (ReaxFF fort.7 style, see ReaxFF manual for more info)
connection_table = Connection_Table()
connection_table.load(connection_table_file)
connection_table.next()
print "Connection table file loaded successfully: " + connection_table_file
molecule_helper = Molecule_Helper()
molecule_helper.simulation_atoms_class = simulation_atoms
molecule_helper.connection_table_class = connection_table
molecule_helper.bondorder_cutoff = 0.6
assert molecule_helper.atom_label_list_to_formula(["H", "O", "H"]) == "H2O"
# print molecule_helper.atom_label_list_to_formula(['H', 'O', 'H'])
assert molecule_helper.atom_label_list_to_formula(["H"]) == "H"
assert molecule_helper.atom_label_list_to_formula(["H", "H", "Ti", "O", "Ti", "P"]) == "H2OPTi2"
all_molecules = molecule_helper.get_all_molecules()
# We compare this to molfra.out file generated by ReaxFF with bond order
# cutoff of 0.6
assert molecule_helper.molecule_list_to_frequency_dict(all_molecules) == {
"HO20Ti10": 1,
"H2O81Ti40": 1,
"H3O41Ti20": 1,
"H2O": 149,
"O20Ti10": 6,
"HO": 1,
"H2O40Ti20": 1,
"HO21Ti10": 3,
}
print "All tests completed successfully!"
sys.exit(0)
def tests():
#Currently assume some relative path stuff. This is apt to change once we
#make this into a module.
sys.path.insert(0, "..") #Make this the first thing since we want to override
from XYZ import XYZ #XYZ class
from reax_connection_table import Connection_Table
#Test files location:
structure_file = 'tests/a10a_ph7.xyz'
connection_table_file = 'tests/a10a_ph7.connect'
#Read in XYZ file. Store all the coordinates.
simulation_atoms = XYZ()
simulation_atoms.load(structure_file) #simulation_atoms.rows contains the data
print 'Structure file loaded successfully: '+structure_file
#Read in connection table (ReaxFF fort.7 style, see ReaxFF manual for more info)
connection_table = Connection_Table()
connection_table.load(connection_table_file)
connection_table.next()
print 'Connection table file loaded successfully: '+connection_table_file
molecule_helper = Molecule_Helper()
molecule_helper.simulation_atoms_class = simulation_atoms
molecule_helper.connection_table_class = connection_table
molecule_helper.bondorder_cutoff = 0.6
assert molecule_helper.atom_label_list_to_formula(['H', 'O', 'H']) == 'H2O'
#print molecule_helper.atom_label_list_to_formula(['H', 'O', 'H'])
assert molecule_helper.atom_label_list_to_formula(['H']) == 'H'
assert molecule_helper.atom_label_list_to_formula(['H', 'H', 'Ti', 'O', 'Ti', 'P']) == 'H2OPTi2'
all_molecules = molecule_helper.get_all_molecules()
#We compare this to molfra.out file generated by ReaxFF with bond order
#cutoff of 0.6
assert molecule_helper.molecule_list_to_frequency_dict(all_molecules) == \
{'HO20Ti10': 1, 'H2O81Ti40': 1, 'H3O41Ti20': 1, 'H2O': 149, 'O20Ti10': 6, 'HO': 1, 'H2O40Ti20': 1, 'HO21Ti10': 3}
print 'All tests completed successfully!'
sys.exit(0)
def main():
#Read in XYZ file. Store all the coordinates.
global simulation_atoms, simulation_atoms_dict
simulation_atoms = XYZ()
simulation_atoms.load(
structure_file) #simulation_atoms.rows contains the data
simulation_atoms_dict = simulation_atoms.return_sorted_by_atoms_dict()
#NOTE: Now we have two tables, simulation_atoms and simulation_atoms_dict
#Read in connection table (ReaxFF fort.7 style, see ReaxFF manual for more info)
global connection_table #So that other functions can access this
connection_table = Connection_Table()
connection_table.load(connection_table_file)
#Testing
#tests()
#Go through each of the target molecules and do isolation stuff:
for target_molecule in target_molecules:
#Select an atom to work with. We just select the first one. Used for the 'exclude'
#case so that we don't have to check every single atom in simulation_atoms:
an_atom = target_molecule[0]
#Loop through all the atoms matching the an_atom type:
for atom_number, each_atom in enumerate(simulation_atoms.rows):
#Correction to the atom_number since XYZ list starts at 0 but atom numbers
#start at 1:
atom_number += 1
#Somewhat of a hack: Do a quick check here to see if this atom has been
#nullified in the XYZ object. If so, we can just skip it since this atom
#has been "deleted".
if each_atom == None:
continue #skip this atom since it has been deleted
#If the isolate_method is 'include' then we have to check all atoms because
#there are cases of single atoms and/or atoms not connected to the target
#molecule atoms that we defined which we won't catch unless we check every
#atom. If the isolate method is 'exclude' then we just have to check atoms that
#are connected to an atom in the defined target molecule.
if isolate_method == 'exclude' and each_atom[0] != an_atom:
#Skip this atom for now. If it's part of the target molecule, we'll
#automatically come back to it later.
continue
#For the current atom, get the molecule that corresponds to it:
molecule_parts = get_molecule_for_atom(atom_number)
#Check this molecule against our isolation specification see if match exists:
same_molecules_q = are_molecules_the_same( \
list(target_molecule),
molecule_parts_to_list(molecule_parts),
isolate_criteria
)
#Get the molecule numbers so that we can feed it into the nullify atoms func.
molecule_atom_numbers = molecule_parts.keys()
#Now keep/remove depending on criteria:
if isolate_method == 'include':
if same_molecules_q != True:
#print molecule_parts
#The molecules are not the same so we need to delete it
nullify_atoms_in_XYZ(molecule_atom_numbers)
elif isolate_method == 'exclude':
if same_molecules_q == True:
#This molecule is in the excluded list so we need to delete it
nullify_atoms_in_XYZ(molecule_atom_numbers)
else:
print 'ERROR: isolate_method option invalid!'
sys.exit(0)
#Cool, we now ran through the whole XYZ file. Let's save the changed version:
simulation_atoms.export(output_xyz_file)
print 'Processed XYZ file exported: ' + output_xyz_file
def main():
#Read in XYZ file. Store all the coordinates.
simulation_atoms = XYZ()
simulation_atoms.load(
structure_file) #simulation_atoms.rows contains the data
#Pre-sort the atoms by atom type (into a dictionary). This way, we don't
#have to loop through the whole file every time we calculate distances for
#a given atom.
simulation_atoms_dict = {}
for atom_number, each_row in enumerate(simulation_atoms.rows):
#Correction to the atom_number since it starts at 1 instead of 0:
atom_number += 1
#We want our new list to be in the format:
#atom_number x y z
temp_list = [atom_number] #Put it in a list first. For some reason,
temp_list.extend(each_row[1:]) #can't combine these two on same line.
#Now save it:
try:
simulation_atoms_dict[each_row[0]].append(temp_list)
except KeyError:
#This means that the dictionary entry for this atom has not been
#created yet. So we create it:
simulation_atoms_dict[each_row[0]] = [temp_list] #New list
#Read in connection table (ReaxFF fort.7 style, see ReaxFF manual for more info)
connection_table = Connection_Table()
connection_table.load(connection_table_file)
#Loop through each pair of atoms.
distance_histogram = {
} #Dictionary instead of array so we can add entries at any element.
#TODO: I should add a try to this outer loop too to catch if from_atom
# doesn't exist in the dictionary
for from_atom_row in simulation_atoms_dict[from_atom]:
#Calculate interatomic distances. Use the minimum image convention here to
#take care of any periodic conditions. We start by checking through all the
#other atoms to see if they match our to_atom:
try:
for to_atom_row in simulation_atoms_dict[to_atom]:
#Make sure this isn't the same atom by comparing atom number:
if from_atom_row[0] == to_atom_row[0]:
continue
#Make sure this to_atom isn't part of the same molecule. We figure
#this out by using the molecule column of the connection table:
if exclude_atoms_from_same_molecule:
if connection_table.rows[from_atom_row[0]][-1] == \
connection_table.rows[to_atom_row[0]][-1]:
#We have the same molecule. Don't do anything for this to_atom.
continue
#pass
#Otherwise, calculate the interatomic distance:
from_atom_coord = (from_atom_row[1], from_atom_row[2],
from_atom_row[3])
to_atom_coord = (to_atom_row[1], to_atom_row[2],
to_atom_row[3])
interatomic_distance = calc_interatomic_distance(
from_atom_coord, to_atom_coord)
#print interatomic_distance
#Now figure out which bin this distance goes in. The algorithm in
#Simulation of Liquids by Allen (p182) differs from the algorithm in
#Understanding Molecular Simulation by Frenkel (p86) in that Allen rounds
#up (by adding 1) while Frenkel doesn't round up. Adri's RDF script
#follows Allen's method. I will take the middle road and round the number:
#(NOTE: This method is used because FORTRAN only had arrays so the bins
# have to be integers. We do it in this case because floats can't
# be exactly represented in any prog. language. So this int method
# is actually not too bad.
distance_bin = int(round(interatomic_distance / bin_size))
try:
#We add two for the contribution of both atoms because we just want
#to find the number of atoms within a certain distance of each other
#(also see p86 of Frenkel).
distance_histogram[distance_bin] += 2
except KeyError:
#This is the first entry for this key. So we'll create it:
distance_histogram[distance_bin] = 2
except KeyError:
#to_atom was not found in the atoms dictionary
print 'ERROR: ' + to_atom + ' was not found in the structure file: ' + structure_file
sys.exit(1)
#print distance_histogram
#Normalize the histrogram by comparing to ideal gas.
#We want to compute (p184 Allen):
# g(r + 1/2 * delta_r) = n(b)/n_id(b) , no clue what b is though
#where according to p183 Allen and corroborated by p86 Frenkel and Adri's RDF script:
# n(b) = n_his(b)/(N * t_run)
#In our case, we are only calculating from one static frame, so t_run = 1. The reason
#why we divide by N is to get the "average". It sort of doesn't make sense to me since
#this would make n(b) always fractional. But if n_id(b) is also fractional, then it
#would work out.
#Also according to p184 Allen:
# n_id(b) = (4 pi rho)/3 * [(r + dr)^3 - r^3]
#which makes sense. NOTE: To convert from the bin index to length (in angstroms) we
#reverse what we do by multiplying by the bin_size (aka delta_r).
#Also, rho (the number density) is defined by wikipedia and MatDL wiki as:
# rho = N/V ; (num of particles)/(volume of system)
#where N is the number of particles we are considering.
#But Adri's RDF script doesn't calculate rho this way. He does some sort of ratio
#of the two atom populations...
#total_number_atoms = len(simulation_atoms.rows)
number_from_atoms = len(simulation_atoms_dict[from_atom])
number_to_atoms = len(simulation_atoms_dict[to_atom])
if from_atom == to_atom:
total_number_analyzed_atoms = number_from_atoms
#No combination correction factor:
combination_correction_factor = 1
else:
total_number_analyzed_atoms = number_from_atoms + number_to_atoms
#Calculate combination correction factor. This factor allows the ideal
#gas approx to match the number of combinations that the n_his makes.
#For more information, see p77 of Vol 2 of my notebook.
heterogeneous_combinations = number_from_atoms * number_to_atoms
homogeneous_combinations = \
(total_number_analyzed_atoms * (total_number_analyzed_atoms-1)) / 2
combination_correction_factor = \
heterogeneous_combinations/float(homogeneous_combinations)
total_number_molecules = connection_table.rows[-1][-1]
total_volume = float(unit_cell[0]) * unit_cell[1] * unit_cell[2]
rho = total_number_analyzed_atoms / total_volume
g = {
} #This is our pair correlation function results. However, have to store as bins, not angstroms
for distance_bin, n_his in distance_histogram.iteritems():
n = float(n_his) #/total_number_atoms
#n = float(n_his)/total_number_molecules
r = float(distance_bin
) * bin_size #convert from bin index to length (angstroms)
n_id = ((4 * math.pi * rho) / 3) * ((r + bin_size)**3 - r**3)
#Additional correction to n_id.
n_id *= number_from_atoms + number_to_atoms
n_id *= combination_correction_factor
#print n, n_id
g[distance_bin] = n / n_id
#Now print it out with angstroms!
#print "r \t g(r)"
#for distance_bin, gr in g.iteritems():
# #The format syntax is that we have _____._____ (5 spots before the . and 5 spots
# #after). This will cover like 99.99% of all numbers we encounter.
# print "%10.5f \t %10.5f" % (float(distance_bin) * bin_size, gr)
# #pass
#Output to file:
try:
output_f = file(output_file, 'w')
except (NameError, IOError):
print 'ERROR: Could not write output to ' + output_file
sys.exit(1)
#Alternative method for printing it out. We print only for r <= 1/2 L, where L is
#the average length of all the sides of the box. According to Deserno's paper on
#calculating g(r) in 3-D, once we assume minimum image convention, the n_id term
#no longer increases like how we defined it above. So g(r) is only accurate to 1/2 L.
average_L = (float(unit_cell[0]) + unit_cell[1] + unit_cell[2]) / 3
max_bin = int(round((average_L / 2) / bin_size)) #Convert to integer bin
#print "r \t g(r)"
output_f.write("r \t g(r)\n")
for distance_bin in xrange(0, max_bin):
#The format syntax is that we have _____._____ (5 spots before the . and 5 spots
#after). This will cover like 99.99% of all numbers we encounter.
try:
#print "%10.5f \t %10.5f" % (float(distance_bin) * bin_size, g[distance_bin])
output_f.write("%10.5f \t %10.5f\n" %
(float(distance_bin) * bin_size, g[distance_bin]))
except KeyError:
#g doesn't have this distance_bin entry. No worries, since we know it is zero:
#print "%10.5f \t %10.5f" % (float(distance_bin) * bin_size, 0.0)
output_f.write("%10.5f \t %10.5f\n" %
(float(distance_bin) * bin_size, 0.0))
output_f.close()
print 'RDF as tab separated values written to: ' + output_file
#Add in GNUPlot input file generation. Use below for smoothing:
'''
plot "[rdf_tsv_file]" u 1:2 with linespoints, \
"[rdf_tsv_file]" u 1:2 smooth bezier
'''
gnuplot_template = '''
reset
set title "Radial Distribution Function"
set xlabel "r (angstrom)"
set ylabel "g(r)"
set nokey
plot "[rdf_tsv_file]" u 1:2 with linespoints
set term png
set output "[output_image_file]"
replot
'''
try:
gnuplot_template = gnuplot_template.replace('[rdf_tsv_file]',
output_file)
output_image_file = os.path.splitext(gnuplot_file)[0] + '.png'
gnuplot_template = gnuplot_template.replace('[output_image_file]',
output_image_file)
#Write to file:
gnuplot_f = open(gnuplot_file, 'w')
gnuplot_f.write(gnuplot_template)
gnuplot_f.close()
print 'GNUPlot file written to: ' + gnuplot_file
#Now generate the graphs
os.system('gnuplot ' + gnuplot_file)
print 'GNUPlot graph generated!'
time.sleep(2)
os.system('gqview')
except IOError:
print 'ERROR: Could not write gnuplot file to: ' + gnuplot_file
except NameError:
#Do nothing
pass
def main():
#Read in XYZ file. Store all the coordinates.
simulation_atoms = XYZ()
simulation_atoms.load(structure_file) #simulation_atoms.rows contains the data
#Pre-sort the atoms by atom type (into a dictionary). This way, we don't
#have to loop through the whole file every time we calculate distances for
#a given atom.
simulation_atoms_dict = {}
for atom_number, each_row in enumerate(simulation_atoms.rows):
#Correction to the atom_number since it starts at 1 instead of 0:
atom_number += 1
#We want our new list to be in the format:
#atom_number x y z
temp_list = [atom_number] #Put it in a list first. For some reason,
temp_list.extend(each_row[1:]) #can't combine these two on same line.
#Now save it:
try:
simulation_atoms_dict[each_row[0]].append(temp_list)
except KeyError:
#This means that the dictionary entry for this atom has not been
#created yet. So we create it:
simulation_atoms_dict[each_row[0]] = [temp_list] #New list
#Read in connection table (ReaxFF fort.7 style, see ReaxFF manual for more info)
connection_table = Connection_Table()
connection_table.load(connection_table_file)
#Loop through each pair of atoms.
distance_histogram = {} #Dictionary instead of array so we can add entries at any element.
#TODO: I should add a try to this outer loop too to catch if from_atom
# doesn't exist in the dictionary
for from_atom_row in simulation_atoms_dict[from_atom]:
#Calculate interatomic distances. Use the minimum image convention here to
#take care of any periodic conditions. We start by checking through all the
#other atoms to see if they match our to_atom:
try:
for to_atom_row in simulation_atoms_dict[to_atom]:
#Make sure this isn't the same atom by comparing atom number:
if from_atom_row[0] == to_atom_row[0]:
continue
#Make sure this to_atom isn't part of the same molecule. We figure
#this out by using the molecule column of the connection table:
if exclude_atoms_from_same_molecule:
if connection_table.rows[from_atom_row[0]][-1] == \
connection_table.rows[to_atom_row[0]][-1]:
#We have the same molecule. Don't do anything for this to_atom.
continue
#pass
#Otherwise, calculate the interatomic distance:
from_atom_coord = (from_atom_row[1], from_atom_row[2], from_atom_row[3])
to_atom_coord = (to_atom_row[1], to_atom_row[2], to_atom_row[3])
interatomic_distance = calc_interatomic_distance(from_atom_coord, to_atom_coord)
#print interatomic_distance
#Now figure out which bin this distance goes in. The algorithm in
#Simulation of Liquids by Allen (p182) differs from the algorithm in
#Understanding Molecular Simulation by Frenkel (p86) in that Allen rounds
#up (by adding 1) while Frenkel doesn't round up. Adri's RDF script
#follows Allen's method. I will take the middle road and round the number:
#(NOTE: This method is used because FORTRAN only had arrays so the bins
# have to be integers. We do it in this case because floats can't
# be exactly represented in any prog. language. So this int method
# is actually not too bad.
distance_bin = int(round(interatomic_distance / bin_size))
try:
#We add two for the contribution of both atoms because we just want
#to find the number of atoms within a certain distance of each other
#(also see p86 of Frenkel).
distance_histogram[distance_bin] += 2
except KeyError:
#This is the first entry for this key. So we'll create it:
distance_histogram[distance_bin] = 2
except KeyError:
#to_atom was not found in the atoms dictionary
print 'ERROR: '+to_atom+' was not found in the structure file: '+structure_file
sys.exit(1)
#print distance_histogram
#Normalize the histrogram by comparing to ideal gas.
#We want to compute (p184 Allen):
# g(r + 1/2 * delta_r) = n(b)/n_id(b) , no clue what b is though
#where according to p183 Allen and corroborated by p86 Frenkel and Adri's RDF script:
# n(b) = n_his(b)/(N * t_run)
#In our case, we are only calculating from one static frame, so t_run = 1. The reason
#why we divide by N is to get the "average". It sort of doesn't make sense to me since
#this would make n(b) always fractional. But if n_id(b) is also fractional, then it
#would work out.
#Also according to p184 Allen:
# n_id(b) = (4 pi rho)/3 * [(r + dr)^3 - r^3]
#which makes sense. NOTE: To convert from the bin index to length (in angstroms) we
#reverse what we do by multiplying by the bin_size (aka delta_r).
#Also, rho (the number density) is defined by wikipedia and MatDL wiki as:
# rho = N/V ; (num of particles)/(volume of system)
#where N is the number of particles we are considering.
#But Adri's RDF script doesn't calculate rho this way. He does some sort of ratio
#of the two atom populations...
#total_number_atoms = len(simulation_atoms.rows)
number_from_atoms = len(simulation_atoms_dict[from_atom])
number_to_atoms = len(simulation_atoms_dict[to_atom])
if from_atom == to_atom:
total_number_analyzed_atoms = number_from_atoms
#No combination correction factor:
combination_correction_factor = 1
else:
total_number_analyzed_atoms = number_from_atoms + number_to_atoms
#Calculate combination correction factor. This factor allows the ideal
#gas approx to match the number of combinations that the n_his makes.
#For more information, see p77 of Vol 2 of my notebook.
heterogeneous_combinations = number_from_atoms * number_to_atoms
homogeneous_combinations = \
(total_number_analyzed_atoms * (total_number_analyzed_atoms-1)) / 2
combination_correction_factor = \
heterogeneous_combinations/float(homogeneous_combinations)
total_number_molecules = connection_table.rows[-1][-1]
total_volume = float(unit_cell[0]) * unit_cell[1] * unit_cell[2]
rho = total_number_analyzed_atoms/total_volume
g = {} #This is our pair correlation function results. However, have to store as bins, not angstroms
for distance_bin, n_his in distance_histogram.iteritems():
n = float(n_his)#/total_number_atoms
#n = float(n_his)/total_number_molecules
r = float(distance_bin) * bin_size #convert from bin index to length (angstroms)
n_id = ((4 * math.pi * rho)/3) * ( (r + bin_size)**3 - r**3 )
#Additional correction to n_id.
n_id *= number_from_atoms + number_to_atoms
n_id *= combination_correction_factor
#print n, n_id
g[distance_bin] = n/n_id
#Now print it out with angstroms!
#print "r \t g(r)"
#for distance_bin, gr in g.iteritems():
# #The format syntax is that we have _____._____ (5 spots before the . and 5 spots
# #after). This will cover like 99.99% of all numbers we encounter.
# print "%10.5f \t %10.5f" % (float(distance_bin) * bin_size, gr)
# #pass
#Output to file:
try:
output_f = file(output_file, 'w')
except (NameError, IOError):
print 'ERROR: Could not write output to '+output_file
sys.exit(1)
#Alternative method for printing it out. We print only for r <= 1/2 L, where L is
#the average length of all the sides of the box. According to Deserno's paper on
#calculating g(r) in 3-D, once we assume minimum image convention, the n_id term
#no longer increases like how we defined it above. So g(r) is only accurate to 1/2 L.
average_L = (float(unit_cell[0]) + unit_cell[1] + unit_cell[2])/3
max_bin = int(round((average_L/2) / bin_size)) #Convert to integer bin
#print "r \t g(r)"
output_f.write("r \t g(r)\n")
for distance_bin in xrange(0, max_bin):
#The format syntax is that we have _____._____ (5 spots before the . and 5 spots
#after). This will cover like 99.99% of all numbers we encounter.
try:
#print "%10.5f \t %10.5f" % (float(distance_bin) * bin_size, g[distance_bin])
output_f.write("%10.5f \t %10.5f\n" % (float(distance_bin) * bin_size, g[distance_bin]))
except KeyError:
#g doesn't have this distance_bin entry. No worries, since we know it is zero:
#print "%10.5f \t %10.5f" % (float(distance_bin) * bin_size, 0.0)
output_f.write("%10.5f \t %10.5f\n" % (float(distance_bin) * bin_size, 0.0))
output_f.close()
print 'RDF as tab separated values written to: '+output_file
#Add in GNUPlot input file generation. Use below for smoothing:
'''
plot "[rdf_tsv_file]" u 1:2 with linespoints, \
"[rdf_tsv_file]" u 1:2 smooth bezier
'''
gnuplot_template = '''
reset
set title "Radial Distribution Function"
set xlabel "r (angstrom)"
set ylabel "g(r)"
set nokey
plot "[rdf_tsv_file]" u 1:2 with linespoints
set term png
set output "[output_image_file]"
replot
'''
try:
gnuplot_template = gnuplot_template.replace('[rdf_tsv_file]', output_file)
output_image_file = os.path.splitext(gnuplot_file)[0]+'.png'
gnuplot_template = gnuplot_template.replace('[output_image_file]', output_image_file)
#Write to file:
gnuplot_f = open(gnuplot_file, 'w')
gnuplot_f.write(gnuplot_template)
gnuplot_f.close()
print 'GNUPlot file written to: '+gnuplot_file
#Now generate the graphs
os.system('gnuplot '+gnuplot_file)
print 'GNUPlot graph generated!'
time.sleep(2)
os.system('gqview')
except IOError:
print 'ERROR: Could not write gnuplot file to: '+gnuplot_file
except NameError:
#Do nothing
pass
def main(): #Read in XYZ file. Store all the coordinates. global simulation_atoms, simulation_atoms_dict simulation_atoms = XYZ() simulation_atoms.load(structure_file) #simulation_atoms.rows contains the data simulation_atoms_dict = simulation_atoms.return_sorted_by_atoms_dict() #NOTE: Now we have two tables, simulation_atoms and simulation_atoms_dict #Read in connection table (ReaxFF fort.7 style, see ReaxFF manual for more info) global connection_table #So that other functions can access this connection_table = Connection_Table() connection_table.load(connection_table_file) #Testing #tests() #Go through each of the target molecules and do isolation stuff: for target_molecule in target_molecules: #Select an atom to work with. We just select the first one. Used for the 'exclude' #case so that we don't have to check every single atom in simulation_atoms: an_atom = target_molecule[0] #Loop through all the atoms matching the an_atom type: for atom_number, each_atom in enumerate(simulation_atoms.rows): #Correction to the atom_number since XYZ list starts at 0 but atom numbers #start at 1: atom_number += 1 #Somewhat of a hack: Do a quick check here to see if this atom has been #nullified in the XYZ object. If so, we can just skip it since this atom #has been "deleted". if each_atom == None: continue #skip this atom since it has been deleted #If the isolate_method is 'include' then we have to check all atoms because #there are cases of single atoms and/or atoms not connected to the target #molecule atoms that we defined which we won't catch unless we check every #atom. If the isolate method is 'exclude' then we just have to check atoms that #are connected to an atom in the defined target molecule. if isolate_method == 'exclude' and each_atom[0] != an_atom: #Skip this atom for now. If it's part of the target molecule, we'll #automatically come back to it later. continue #For the current atom, get the molecule that corresponds to it: molecule_parts = get_molecule_for_atom(atom_number) #Check this molecule against our isolation specification see if match exists: same_molecules_q = are_molecules_the_same( \ list(target_molecule), molecule_parts_to_list(molecule_parts), isolate_criteria ) #Get the molecule numbers so that we can feed it into the nullify atoms func. molecule_atom_numbers = molecule_parts.keys() #Now keep/remove depending on criteria: if isolate_method == 'include': if same_molecules_q != True: #print molecule_parts #The molecules are not the same so we need to delete it nullify_atoms_in_XYZ(molecule_atom_numbers) elif isolate_method == 'exclude': if same_molecules_q == True: #This molecule is in the excluded list so we need to delete it nullify_atoms_in_XYZ(molecule_atom_numbers) else: print 'ERROR: isolate_method option invalid!' sys.exit(0) #Cool, we now ran through the whole XYZ file. Let's save the changed version: simulation_atoms.export(output_xyz_file) print 'Processed XYZ file exported: '+output_xyz_file
