def rdf_compare(plot_panel, filename1, filename2, ibi_iteration_number,
molecule1, molecule2):
i = 0
rdf_average = lmp_io.read_lammps_rdf(filename1,
4,
ibi_iteration_number,
timestep_limit=i,
write_rdf_files=False,
timestep_threshold=0)
rdf_average2 = lmp_io.read_lammps_rdf(filename2,
4,
ibi_iteration_number,
timestep_limit=i,
write_rdf_files=False,
timestep_threshold=0)
plot_panel.plot(range(0, len(rdf_average[molecule1][molecule2])),
rdf_average[molecule1][molecule2],
label="previous: target %d %d step %d" %
(molecule1, molecule2, i))
plot_panel.plot(range(0, len(rdf_average2[molecule1][molecule2])),
rdf_average2[molecule1][molecule2],
label="current: target %d %d step %d" %
(molecule1, molecule2, i))
pylab.legend(loc=4)
def rdf_compare(plot_panel, filename1, filename2, ibi_iteration_number, molecule1, molecule2):
i = 0
rdf_average = lmp_io.read_lammps_rdf(filename1, 4, ibi_iteration_number, timestep_limit=i, write_rdf_files=False, timestep_threshold=0)
rdf_average2 = lmp_io.read_lammps_rdf(filename2, 4, ibi_iteration_number, timestep_limit=i, write_rdf_files=False, timestep_threshold=0)
plot_panel.plot(range(0,len(rdf_average[molecule1][molecule2])),rdf_average[molecule1][molecule2], label="previous: target %d %d step %d" % (molecule1, molecule2, i))
plot_panel.plot(range(0,len(rdf_average2[molecule1][molecule2])),rdf_average2[molecule1][molecule2], label="current: target %d %d step %d" % (molecule1, molecule2, i))
pylab.legend(loc=4)
def txt_rdf(iter, molecule1, molecule2):
if molecule2 > 0:
d1, d2, d3, d4 = lmp_io.load_target_and_cg_rdf("rdf.%d.%d" % (molecule1, molecule2), "rdf_%d.%d.%d" % (iter-1, molecule1, molecule2))
print iter, " for particles ", molecule1, molecule2, "ave. diff: %f, quad. diff: %f" % (d3, d4)
else:
ave_diff_tot = 0.0
quad_diff_tot = 0.0
for i in xrange(1, molecule1+1):
for j in xrange(i, molecule1+1):
d1, d2, d3, d4 = lmp_io.load_target_and_cg_rdf("rdf.%d.%d" % (i, j), "rdf_%d.%d.%d" % (iter-1, i, j))
ave_diff_tot += d3
quad_diff_tot += d4
#print iter, i, j, "ave.: %f, quad.: %f" % (d3, d4)
print iter, "Average difference (total): %f, Squared difference (total): %f" % (ave_diff_tot, quad_diff_tot)
def basic_test_suite():
""" Simple testing of various functions in the script."""
print "read_lammps_data_file"
lattice, type_list = lmp_io.read_lammps_data_file("CG_first_interaction.lammps05")
print "read_lammps_rdf"
rdf_average = lmp_io.read_lammps_rdf("tmp.1.rdf", 3, 1)
print "read_CG_log_file"
final_pressure = read_CG_log_file("new_CG.prod1.log")
print "smooth_data"
smoothed = dm.smooth_data(rdf_average[1][1])
print "read_in_rdf_file"
rdf_array, numofbins, cutoff = read_in_rdf_file("rdf", 3, [[1,1],[1,2],[1,3],[2,2],[2,3],[3,3]])
print "read_in_interaction_files"
distance, potential, derivative, pot_file_list, cutoff = lmp_io.read_in_interaction_files("./pot", 3, [[1,1],[1,2],[1,3],[2,2],[2,3],[3,3]], 1)
def add_rdf_plot(iter, molecule1, molecule2, row, column, index, fig):
print "add_rdf_plot: ", iter, molecule1, molecule2, "d1: rdf.%d.%d" % (
molecule1, molecule2), "d2: rdf_%d.%d.%d" % (iter - 1, molecule1,
molecule2)
d1, d2, d3, d4 = lmp_io.load_target_and_cg_rdf(
"rdf.%d.%d" % (molecule1, molecule2),
"rdf_%d.%d.%d" % (iter - 1, molecule1, molecule2),
averaging="no")
ax2 = fig.add_subplot(row, column, index)
ax2.plot(d1[:, 0], d1[:, 2], label="target %d %d" % (molecule1, molecule2))
ax2.plot(d2[:, 0], d2[:, 1], label="fit iteration %d" % (iter - 1))
# ax2.plot(d2[:,0],np.absolute(d3), label="error iteration %d" % (iter-1)) #plot the error if needed:
y_coord = (pylab.ylim())[1] - 0.1
ax2.text(0.1,
y_coord,
"ave. diff: %f, quad. diff: %f" %
(np.average(np.absolute(d3)), np.average(d4)),
style='italic')
set_small_legend()
def basic_test_suite():
""" Simple testing of various functions in the script."""
print "read_lammps_data_file"
lattice, type_list = lmp_io.read_lammps_data_file(
"CG_first_interaction.lammps05")
print "read_lammps_rdf"
rdf_average = lmp_io.read_lammps_rdf("tmp.1.rdf", 3, 1)
print "read_CG_log_file"
final_pressure = read_CG_log_file("new_CG.prod1.log")
print "smooth_data"
smoothed = dm.smooth_data(rdf_average[1][1])
print "read_in_rdf_file"
rdf_array, numofbins, cutoff = read_in_rdf_file(
"rdf", 3, [[1, 1], [1, 2], [1, 3], [2, 2], [2, 3], [3, 3]])
print "read_in_interaction_files"
distance, potential, derivative, pot_file_list, cutoff = lmp_io.read_in_interaction_files(
"./pot", 3, [[1, 1], [1, 2], [1, 3], [2, 2], [2, 3], [3, 3]], 1)
def analyse_and_plot_lammps_rdf_file(plot_panel, filename, ibi_iteration_number, molecule1, molecule2):
for i in [0, 5000, 100000, 250000]:
print i
rdf_average = lmp_io.read_lammps_rdf(filename, 3, ibi_iteration_number, timestep_limit=i, write_rdf_files=False, timestep_threshold=0)
plot_panel.plot(range(0,len(rdf_average[molecule1][molecule2])),rdf_average[molecule1][molecule2], label="target %d %d step %d" % (molecule1, molecule2, i))
pylab.legend(loc=4)
def calc_ave_press(iter, tag="Press"):
d1,d2 = lmp_io.load_quantity_from_file("new_CG.prod%d.log" % (iter-1), tag)
offset = 100
pave = np.average(d2[100:])
pstdev = np.std(d2[100:])
print "READ: new_CG.prod%d.log" % (iter-1)
print "%s information iteration #%d, average pressure: %f, stdev: %f, initial values: %f %f %f" % (tag, iter-1,pave,pstdev,d2[0],d2[1],d2[2])
def txt_rdf(iter, molecule1, molecule2):
if molecule2 > 0:
d1, d2, d3, d4 = lmp_io.load_target_and_cg_rdf(
"rdf.%d.%d" % (molecule1, molecule2),
"rdf_%d.%d.%d" % (iter - 1, molecule1, molecule2))
print iter, " for particles ", molecule1, molecule2, "ave. diff: %f, quad. diff: %f" % (
d3, d4)
else:
ave_diff_tot = 0.0
quad_diff_tot = 0.0
for i in xrange(1, molecule1 + 1):
for j in xrange(i, molecule1 + 1):
d1, d2, d3, d4 = lmp_io.load_target_and_cg_rdf(
"rdf.%d.%d" % (i, j), "rdf_%d.%d.%d" % (iter - 1, i, j))
ave_diff_tot += d3
quad_diff_tot += d4
#print iter, i, j, "ave.: %f, quad.: %f" % (d3, d4)
print iter, "Average difference (total): %f, Squared difference (total): %f" % (
ave_diff_tot, quad_diff_tot)
def calc_ave_press(iter, tag="Press"):
d1, d2 = lmp_io.load_quantity_from_file("new_CG.prod%d.log" % (iter - 1),
tag)
offset = 100
pave = np.average(d2[100:])
pstdev = np.std(d2[100:])
print "READ: new_CG.prod%d.log" % (iter - 1)
print "%s information iteration #%d, average pressure: %f, stdev: %f, initial values: %f %f %f" % (
tag, iter - 1, pave, pstdev, d2[0], d2[1], d2[2])
def add_rdf_plot(iter, molecule1, molecule2, row, column, index, fig):
print "add_rdf_plot: ", iter, molecule1, molecule2, "d1: rdf.%d.%d" % (molecule1, molecule2), "d2: rdf_%d.%d.%d" % (iter-1, molecule1, molecule2)
d1, d2, d3, d4 = lmp_io.load_target_and_cg_rdf("rdf.%d.%d" % (molecule1, molecule2), "rdf_%d.%d.%d" % (iter-1, molecule1, molecule2), averaging="no")
ax2 = fig.add_subplot(row, column, index)
ax2.plot(d1[:,0],d1[:,2], label="target %d %d" % (molecule1, molecule2))
ax2.plot(d2[:,0],d2[:,1], label="fit iteration %d" % (iter-1))
# ax2.plot(d2[:,0],np.absolute(d3), label="error iteration %d" % (iter-1)) #plot the error if needed:
y_coord = (pylab.ylim())[1] - 0.1
ax2.text(0.1, y_coord, "ave. diff: %f, quad. diff: %f" % (np.average(np.absolute(d3)), np.average(d4)), style='italic')
set_small_legend()
def analyse_and_plot_lammps_rdf_file(plot_panel, filename,
ibi_iteration_number, molecule1,
molecule2):
for i in [0, 5000, 100000, 250000]:
print i
rdf_average = lmp_io.read_lammps_rdf(filename,
3,
ibi_iteration_number,
timestep_limit=i,
write_rdf_files=False,
timestep_threshold=0)
plot_panel.plot(range(0, len(rdf_average[molecule1][molecule2])),
rdf_average[molecule1][molecule2],
label="target %d %d step %d" %
(molecule1, molecule2, i))
pylab.legend(loc=4)
def read_in_rdf_file(filename, number_of_types, interaction_list):
""" subroutine to read in the RDF file - note, the format must be distance is first column, and RDF is the third column
ASSUMPTION: ALL RDF FILES HAVE THE SAME NUMBER OF BINS AND CUTOFF. """
index = 0
print "reading RDF %s" % (filename)
numofbins, cutoff, o = lmp_io.get_number_of_bins_and_cutoff("%s.1.1" % (filename), 0)
rdf_array = np.zeros((number_of_types+1, number_of_types+1, numofbins+1))
for i in interaction_list:
LIST_IN = open("%s.%d.%d" % (filename, i[0], i[1]), 'r')
index = 0
for line in LIST_IN:
NewRow = (line.strip()).split()
mystring = NewRow[0][0:1]
if mystring != "#":
if len(NewRow)>2:
index += 1
rdf_array[i[0]][i[1]][index] = float(NewRow[2])
LIST_IN.close()
return rdf_array, int(numofbins), float(cutoff)
def read_in_rdf_file(filename, number_of_types, interaction_list):
""" subroutine to read in the RDF file - note, the format must be distance is first column, and RDF is the third column
ASSUMPTION: ALL RDF FILES HAVE THE SAME NUMBER OF BINS AND CUTOFF. """
index = 0
print "reading RDF %s" % (filename)
numofbins, cutoff, o = lmp_io.get_number_of_bins_and_cutoff(
"%s.1.1" % (filename), 0)
rdf_array = np.zeros(
(number_of_types + 1, number_of_types + 1, numofbins + 1))
for i in interaction_list:
LIST_IN = open("%s.%d.%d" % (filename, i[0], i[1]), 'r')
index = 0
for line in LIST_IN:
NewRow = (line.strip()).split()
mystring = NewRow[0][0:1]
if mystring != "#":
if len(NewRow) > 2:
index += 1
rdf_array[i[0]][i[1]][index] = float(NewRow[2])
LIST_IN.close()
return rdf_array, int(numofbins), float(cutoff)
def production():
""" This script will create the next interation of coarse-grained potentials using the Iterative Boltzmann Inversion to
match to a user-supplied radial distribution function (normally from atomistic simulation). It will also attempt a correction
for the pressure. The script will also extrapolate the potentials at low distance values by fitting to a soft Lennard-Jones
potential. Note, this fitting is somewhat unstable (CurveFit.pm) and can cause the IBI to fail. """
print "ARGUMENTS TO THE IBI ARE: ", sys.argv
# user-supplied arguments to the IBI. Note, not all of these arguments are required depending on what analysis is need and files are provided.
lammps_input_file = "" # LAMMPS input file for the current CG iteration.
correct_rdf_base = "" # User-supplied Radial Distribution Function to match to (normally derived from atomistic simulation) - distance is column 1 and the RDF is column 3.
potential_base = "" # the file base-name for the potential energy files. The format is such: pot.<iteration_number>.new.<type1><type2>. In this case the base-name is "pot".
number = 0 # the current IBI iteration number
lammps_data_file = "" # LAMMPS CG data file
lammps_rdf_file = "" # the CG RDF file if calculated by LAMMPS - this is a series of snapshot values, which need to be averaged.
p_target = 1.0 # pressure target for the CG simulation.
p_flag = 0.0 # flag to indicate whether to apply pressure correction - set to one if a pressure target is set by the user.
CG_output_file = "" # LAMMPS thermodynamic log file for the current CG simulation. Used to calculate the current average CG pressure.
p_now = 0 # current CG pressure read from (and averaged) the CG lammps thermodynamic log file;
temperature = 300 # temperature the simulations are run at; default is 300K
LJ_file_flag = 0 # if this flag is set to one, the parameters used in the extrapolation by fitting to a Lennard-Jones potential are read from a file (called LJ_parameters) rather than computed from fitting to the potential / forces.
num_of_bins = 0
DeltaR = 0.0
number_of_arguments = len(sys.argv)
mode = "default"
num_of_types = 0
for i in xrange(0, number_of_arguments):
if sys.argv[i].lower() == "lammps_input_file":
lammps_input_file = sys.argv[i + 1]
print "THE LAMMPS INPUT FILE IS ", lammps_input_file
elif sys.argv[i].lower() == "lammps_output_file":
lammps_output_file = sys.argv[i + 1]
print "THE LAMMPS OUTPUT FILE IS ", lammps_input_file
elif sys.argv[i].lower() == "lammps_data_file":
lammps_data_file = sys.argv[i + 1]
print "THE LAMMPS DATA FILE IS ", lammps_data_file
elif ((sys.argv[i] == "potential_base")
or (sys.argv[i] == "potential")):
potential_base = sys.argv[i + 1]
elif sys.argv[i].lower() == "lammps_rdf_file":
lammps_rdf_file = sys.argv[i + 1]
print "THE RDFS WILL BE READ FROM LAMMPS OUTPUT", lammps_rdf_file
elif (sys.argv[i] == "correct_rdf_base"):
correct_rdf_base = sys.argv[i + 1]
print "THE RDFS TO MATCH TO HAVE THE FILE BASE ", correct_rdf_base
elif ((sys.argv[i] == "number") or (sys.argv[i] == "current_number")
or (sys.argv[i] == "iteration_number")):
number = int(sys.argv[i + 1])
print "THE CURRENT ITERATION NUMBER IS ", number
elif ((sys.argv[i] == "pressure_flag") or (sys.argv[i] == "p_flag")):
p_flag = float(sys.argv[i + 1])
print "THE PRESSURE FLAG is ", p_flag
elif ((sys.argv[i] == "pressure_target")
or (sys.argv[i] == "p_target")):
p_target = float(sys.argv[i + 1])
if abs(p_flag) < 0.00001:
p_flag = 1
print "THE PRESSURE TARGET is ", p_target
elif ((sys.argv[i] == "CG_log_file") or (sys.argv[i] == "CG_logfile")):
CG_output_file = sys.argv[i + 1]
p_now = read_CG_log_file(CG_output_file, label="Press")
#TODO: this is only a temp hack!
print "THE CURRENT PRESSURE WILL BE CALCULATED FROM THE LOG FILE ", CG_output_file, p_now
elif (sys.argv[i] == "temperature"):
temperature = float(sys.argv[i + 1])
elif (sys.argv[i] == "LJ_param_file"):
LJ_file_flag = 1
elif sys.argv[i].lower() == "numofbins":
num_of_bins = int(sys.argv[i + 1])
print "THE NUMBER OF BINS IS ", num_of_bins
elif sys.argv[i].lower() == "deltar":
DeltaR = float(sys.argv[i + 1])
print "DeltaR IS ", DeltaR
elif sys.argv[i] == "mode":
mode = sys.argv[i + 1]
elif sys.argv[i].lower() == "numoftypes":
num_of_types = int(sys.argv[i + 1])
# read in the lammps data file to identify the number of CG types and lattice parameters.
lattice, type_list = lmp_io.read_lammps_data_file(lammps_data_file)
num_of_types = len(type_list)
print "Num of types = ", num_of_types
#num_of_types = 4
number_of_types_array = np.zeros((num_of_types + 1))
for n in xrange(1, num_of_types + 1):
number_of_types_array[n] = len(type_list["%s" % n])
if mode == "pressure_correct":
num_of_bins, cutoff, offset = lmp_io.get_number_of_bins_and_cutoff(
potential_base, 1)
print "Potential numofbins and cutoff:", num_of_bins, cutoff
pots = (potential_base.strip()).split('.')
atom1 = int(pots[-2])
atom2 = int(pots[-1])
print "ATOMS are:", atom1, atom2
potential = np.zeros((num_of_bins + 1))
volume = float(lattice[0]) * float(lattice[1]) * float(lattice[2])
hist_rdf = lmp_io.read_lammps_rdf(lammps_rdf_file, num_of_types,
number)
pressure_pot = calc_pressure_correction(hist_rdf[atom1][atom2],
num_of_bins, DeltaR,
number_of_types_array[atom1],
number_of_types_array[atom2],
abs(p_flag), p_now, p_target,
volume, temperature)
old_distance, old_potential, old_derivative = lmp_io.read_in_interaction_file(
potential_base, num_of_bins)
potential = apply_pressure_correction(old_potential, pressure_pot,
num_of_bins + 1, old_distance[1],
DeltaR)
potential[0] = potential[
1] # TODO: change this temporary workaround into something more systematic. The workaround reduces anomalies in the derivative at the start of the potential.
new_derivatives = da.derivatives(np.arange(offset, cutoff, DeltaR),
potential)
print "dy lens:", num_of_bins, len(new_derivatives), len(
np.arange(offset - DeltaR, cutoff, DeltaR)), len(potential)
write_pot_file(
"%s/pot.%d.new.%d.%d" %
(os.path.dirname(lammps_output_file), number + 1, atom1, atom2),
new_derivatives[1:],
potential[1:],
num_of_bins,
DeltaR,
atom1,
atom2,
num_of_bins,
offset,
smoothing="no",
selection="no") #note: we use an offset here!
elif mode == "default":
# Either read an interaction list from a file in the atom_dir (useful if you want to parametrize only a subset of interactions), or generate one on the fly.
interaction_filename = os.path.dirname(
correct_rdf_base) + "/interaction_list"
if os.path.exists(interaction_filename):
interaction_list = interaction_list_from_file(interaction_filename)
else:
interaction_list = generate_interaction_list(num_of_types)
first_array, num_of_bins, cutoff2 = read_in_rdf_file(
correct_rdf_base, num_of_types,
interaction_list) # read in the rdfs to match to.
print "THE CUTOFF in the RDF files is", cutoff2, ", with", len(
first_array[1][1]) - 1, "number of bins "
print "THIS IS ITERATION NUMBER", number
deltaR = cutoff2 / num_of_bins # bin spacing from the RDF
previous_position, previous_potential, previous_derivative, old_pot_files, cutoff = lmp_io.read_in_interaction_files(
potential_base, num_of_types, interaction_list, number)
num_of_bins = int(cutoff / deltaR)
print deltaR, cutoff2, num_of_bins, correct_rdf_base
print "THE CUTOFF in the POS FILES is", cutoff, "and number of bins are", num_of_bins
# read in the RDFs of the CG calculated by LAMMPS.
hist_rdf = lmp_io.read_lammps_rdf(lammps_rdf_file, num_of_types,
number)
# print lammps_rdf_file, len(hist_rdf[1][1])
DeltaR = cutoff / num_of_bins
# calculate the IBI
compute_update(os.path.dirname(lammps_output_file), first_array,
hist_rdf, previous_position, previous_potential,
num_of_types, number, DeltaR, num_of_bins,
number_of_types_array, lattice, LJ_file_flag, p_flag,
p_now, p_target, temperature, interaction_list)
# modify the lammps input file, ready for the next iteration
modify_lammps_in_file(lammps_input_file, lammps_output_file, number,
interaction_list, num_of_types)
else:
print "ERROR: mode is incorrectly set in IBI.py. Should be e.g., default or pressure_correct"
sys.exit()
elif mode == "rdf_compare":
atom1 = int(sys.argv[3])
atom2 = int(sys.argv[4])
plot_panel = fig.add_subplot(1, 1, 1)
rdf_compare(plot_panel, "tmp.%d.rdf" % (iter), "tmp.1.rdf", iter, atom1, atom2)
elif mode == "lammps_rdf":
if len(sys.argv)>4:
atom1 = int(sys.argv[3])
atom2 = int(sys.argv[4])
plot_panel = fig.add_subplot(1, 1, 1)
analyse_and_plot_lammps_rdf_file(plot_panel, "tmp.%d.rdf" % (iter), iter, atom1, atom2)
elif len(sys.argv) == 4:
k=1
rdf_average = lmp_io.read_lammps_rdf("tmp.%d.rdf" % (iter), int(sys.argv[3]), iter, timestep_limit=200000, write_rdf_files=False, timestep_threshold=0)
for i in range(1,int(sys.argv[3])):
for j in range(i,int(sys.argv[3])):
# Create panel
plot_panel = fig.add_subplot(int(sys.argv[3]),(int(sys.argv[3])+1)/2, k)
# plot target RDF
d1 = np.loadtxt("rdf.%d.%d" % (i,j))
print "add_rdf_plot: ", iter, i, j, "d1: rdf.%d.%d" % (i,j)
#plot_panel.plot(d1[:,0],d1[:,2], label="target %d %d" % (i,j))
plot_panel.plot(xrange(0,len(d1)),d1[:,2], label="target %d %d" % (i,j))
# plot CG approximation
plot_panel.plot(xrange(0,len(rdf_average[i][j])),rdf_average[i][j], label="CG %d %d" % (i,j))
k += 1
pylab.legend(loc=4)
plot_panel = fig.add_subplot(1, 1, 1)
rdf_compare(plot_panel, "tmp.%d.rdf" % (iter), "tmp.1.rdf", iter,
atom1, atom2)
elif mode == "lammps_rdf":
if len(sys.argv) > 4:
atom1 = int(sys.argv[3])
atom2 = int(sys.argv[4])
plot_panel = fig.add_subplot(1, 1, 1)
analyse_and_plot_lammps_rdf_file(plot_panel, "tmp.%d.rdf" % (iter),
iter, atom1, atom2)
elif len(sys.argv) == 4:
k = 1
rdf_average = lmp_io.read_lammps_rdf("tmp.%d.rdf" % (iter),
int(sys.argv[3]),
iter,
timestep_limit=200000,
write_rdf_files=False,
timestep_threshold=0)
for i in range(1, int(sys.argv[3])):
for j in range(i, int(sys.argv[3])):
# Create panel
plot_panel = fig.add_subplot(int(sys.argv[3]),
(int(sys.argv[3]) + 1) / 2, k)
# plot target RDF
d1 = np.loadtxt("rdf.%d.%d" % (i, j))
print "add_rdf_plot: ", iter, i, j, "d1: rdf.%d.%d" % (i,
j)
#plot_panel.plot(d1[:,0],d1[:,2], label="target %d %d" % (i,j))
plot_panel.plot(xrange(0, len(d1)),
d1[:, 2],
def production():
""" This script will create the next interation of coarse-grained potentials using the Iterative Boltzmann Inversion to
match to a user-supplied radial distribution function (normally from atomistic simulation). It will also attempt a correction
for the pressure. The script will also extrapolate the potentials at low distance values by fitting to a soft Lennard-Jones
potential. Note, this fitting is somewhat unstable (CurveFit.pm) and can cause the IBI to fail. """
print "ARGUMENTS TO THE IBI ARE: ", sys.argv
# user-supplied arguments to the IBI. Note, not all of these arguments are required depending on what analysis is need and files are provided.
lammps_input_file = "" # LAMMPS input file for the current CG iteration.
correct_rdf_base = "" # User-supplied Radial Distribution Function to match to (normally derived from atomistic simulation) - distance is column 1 and the RDF is column 3.
potential_base = "" # the file base-name for the potential energy files. The format is such: pot.<iteration_number>.new.<type1><type2>. In this case the base-name is "pot".
number = 0 # the current IBI iteration number
lammps_data_file = "" # LAMMPS CG data file
lammps_rdf_file = "" # the CG RDF file if calculated by LAMMPS - this is a series of snapshot values, which need to be averaged.
p_target = 1.0 # pressure target for the CG simulation.
p_flag = 0.0 # flag to indicate whether to apply pressure correction - set to one if a pressure target is set by the user.
CG_output_file = "" # LAMMPS thermodynamic log file for the current CG simulation. Used to calculate the current average CG pressure.
p_now = 0 # current CG pressure read from (and averaged) the CG lammps thermodynamic log file;
temperature = 300 # temperature the simulations are run at; default is 300K
LJ_file_flag = 0 # if this flag is set to one, the parameters used in the extrapolation by fitting to a Lennard-Jones potential are read from a file (called LJ_parameters) rather than computed from fitting to the potential / forces.
num_of_bins = 0
DeltaR = 0.0
number_of_arguments = len(sys.argv)
mode = "default"
num_of_types = 0
for i in xrange(0, number_of_arguments):
if sys.argv[i].lower() == "lammps_input_file":
lammps_input_file = sys.argv[i+1]
print "THE LAMMPS INPUT FILE IS ", lammps_input_file
elif sys.argv[i].lower() == "lammps_output_file":
lammps_output_file = sys.argv[i+1]
print "THE LAMMPS OUTPUT FILE IS ", lammps_input_file
elif sys.argv[i].lower() == "lammps_data_file":
lammps_data_file = sys.argv[i+1]
print "THE LAMMPS DATA FILE IS ", lammps_data_file
elif ((sys.argv[i] == "potential_base") or (sys.argv[i] == "potential")):
potential_base = sys.argv[i+1]
elif sys.argv[i].lower() == "lammps_rdf_file":
lammps_rdf_file = sys.argv[i+1]
print "THE RDFS WILL BE READ FROM LAMMPS OUTPUT", lammps_rdf_file
elif (sys.argv[i] == "correct_rdf_base"):
correct_rdf_base = sys.argv[i+1]
print "THE RDFS TO MATCH TO HAVE THE FILE BASE ", correct_rdf_base
elif ((sys.argv[i] == "number") or (sys.argv[i] == "current_number") or (sys.argv[i] == "iteration_number")):
number = int(sys.argv[i+1])
print "THE CURRENT ITERATION NUMBER IS ", number
elif ((sys.argv[i] == "pressure_flag") or (sys.argv[i] == "p_flag")):
p_flag = float(sys.argv[i+1])
print "THE PRESSURE FLAG is ", p_flag
elif ((sys.argv[i] == "pressure_target") or (sys.argv[i] == "p_target")):
p_target = float(sys.argv[i+1])
if abs(p_flag) < 0.00001:
p_flag = 1
print "THE PRESSURE TARGET is ", p_target
elif ((sys.argv[i] == "CG_log_file") or (sys.argv[i] == "CG_logfile")):
CG_output_file = sys.argv[i+1]
p_now = read_CG_log_file(CG_output_file, label="Press")
#TODO: this is only a temp hack!
print "THE CURRENT PRESSURE WILL BE CALCULATED FROM THE LOG FILE ", CG_output_file , p_now
elif (sys.argv[i] == "temperature"):
temperature = float(sys.argv[i+1])
elif (sys.argv[i] == "LJ_param_file"):
LJ_file_flag = 1
elif sys.argv[i].lower() == "numofbins":
num_of_bins = int(sys.argv[i+1])
print "THE NUMBER OF BINS IS ", num_of_bins
elif sys.argv[i].lower() == "deltar":
DeltaR = float(sys.argv[i+1])
print "DeltaR IS ", DeltaR
elif sys.argv[i] == "mode":
mode = sys.argv[i+1]
elif sys.argv[i].lower() == "numoftypes":
num_of_types = int(sys.argv[i+1])
# read in the lammps data file to identify the number of CG types and lattice parameters.
lattice, type_list = lmp_io.read_lammps_data_file(lammps_data_file)
num_of_types = len(type_list)
print "Num of types = ", num_of_types
#num_of_types = 4
number_of_types_array = np.zeros((num_of_types+1))
for n in xrange(1, num_of_types+1):
number_of_types_array[n] = len(type_list["%s" % n])
if mode=="pressure_correct":
num_of_bins, cutoff, offset = lmp_io.get_number_of_bins_and_cutoff(potential_base, 1)
print "Potential numofbins and cutoff:", num_of_bins, cutoff
pots = (potential_base.strip()).split('.')
atom1 = int(pots[-2])
atom2 = int(pots[-1])
print "ATOMS are:", atom1, atom2
potential = np.zeros((num_of_bins+1))
volume = float(lattice[0]) * float(lattice[1]) * float(lattice[2])
hist_rdf = lmp_io.read_lammps_rdf(lammps_rdf_file, num_of_types, number)
pressure_pot = calc_pressure_correction(hist_rdf[atom1][atom2], num_of_bins, DeltaR, number_of_types_array[atom1], number_of_types_array[atom2], abs(p_flag), p_now, p_target, volume, temperature)
old_distance, old_potential, old_derivative = lmp_io.read_in_interaction_file(potential_base, num_of_bins)
potential = apply_pressure_correction(old_potential, pressure_pot, num_of_bins+1, old_distance[1], DeltaR)
potential[0]=potential[1] # TODO: change this temporary workaround into something more systematic. The workaround reduces anomalies in the derivative at the start of the potential.
new_derivatives = da.derivatives(np.arange(offset, cutoff, DeltaR), potential)
print "dy lens:", num_of_bins, len(new_derivatives), len(np.arange(offset-DeltaR, cutoff, DeltaR)), len(potential)
write_pot_file("%s/pot.%d.new.%d.%d" % (os.path.dirname(lammps_output_file), number+1, atom1, atom2), new_derivatives[1:] , potential[1:], num_of_bins, DeltaR, atom1, atom2, num_of_bins, offset, smoothing="no", selection="no") #note: we use an offset here!
elif mode=="default":
# Either read an interaction list from a file in the atom_dir (useful if you want to parametrize only a subset of interactions), or generate one on the fly.
interaction_filename = os.path.dirname(correct_rdf_base) + "/interaction_list"
if os.path.exists(interaction_filename):
interaction_list = interaction_list_from_file(interaction_filename)
else:
interaction_list = generate_interaction_list(num_of_types)
first_array, num_of_bins, cutoff2 = read_in_rdf_file(correct_rdf_base, num_of_types, interaction_list) # read in the rdfs to match to.
print "THE CUTOFF in the RDF files is", cutoff2, ", with", len(first_array[1][1])-1, "number of bins ";
print "THIS IS ITERATION NUMBER", number
deltaR = cutoff2 / num_of_bins # bin spacing from the RDF
previous_position, previous_potential, previous_derivative, old_pot_files, cutoff = lmp_io.read_in_interaction_files(potential_base, num_of_types,
interaction_list, number)
num_of_bins = int(cutoff / deltaR)
print deltaR, cutoff2, num_of_bins, correct_rdf_base
print "THE CUTOFF in the POS FILES is", cutoff, "and number of bins are", num_of_bins
# read in the RDFs of the CG calculated by LAMMPS.
hist_rdf = lmp_io.read_lammps_rdf(lammps_rdf_file, num_of_types, number)
# print lammps_rdf_file, len(hist_rdf[1][1])
DeltaR = cutoff / num_of_bins
# calculate the IBI
compute_update(os.path.dirname(lammps_output_file), first_array, hist_rdf, previous_position, previous_potential, num_of_types, number, DeltaR, num_of_bins, number_of_types_array, lattice, LJ_file_flag, p_flag, p_now, p_target, temperature, interaction_list)
# modify the lammps input file, ready for the next iteration
modify_lammps_in_file(lammps_input_file, lammps_output_file, number, interaction_list, num_of_types)
else:
print "ERROR: mode is incorrectly set in IBI.py. Should be e.g., default or pressure_correct"
sys.exit()
def update_one_file(out_path, target_g_r, new_g_r, old_distance, old_potential, num_of_types, number, DeltaR, numofbins, number_of_types1, number_of_types2,
lattice, LJ_file_flag, p_flag, p_now, p_target, temperature, atom1, atom2):
number = int(number)
potential = np.zeros((numofbins+1))
derivative = np.zeros((numofbins+1))
new_number = number + 1
volume = float(lattice[0]) * float(lattice[1]) * float(lattice[2])
print "Lengths: ", len(target_g_r), len(new_g_r), len(old_distance), len(old_potential), numofbins
index = length = 0
x_data = np.zeros((numofbins+1))
y_data = np.zeros((numofbins+1))
success = 0
# smooth the new CG radial distribution function and calculate where the old CG rdf starts (it will be zero at low distance values).
filtered_rdf = dm.smooth_data(new_g_r)
np.append(filtered_rdf, 1)
conversion_extrapolate_tmp = {}
pressure_pot = np.zeros((numofbins+1))
if abs(float(p_flag)) > 0.00001:
print "FabMD: P+) Applying pressure function."
pressure_pot = calc_pressure_correction(new_g_r, numofbins, DeltaR, number_of_types1, number_of_types2, abs(p_flag), p_now, p_target, volume, temperature)
else:
print "FabMD: P-) Not applying any pressure correction."
# use_data = 0
if float(p_flag) < -0.00001:
print "FabMD: I-) IBI is disabled!"
pot_write_threshold = -1 # slot where we start the pot functions
kB = 0.0019858775
for i in xrange(0, numofbins+1):
# print old_distance[1], i, i*DeltaR
if old_distance[1] <= ((i+0.1) * DeltaR): #0.1 is here to prevent tiny bugs in float comparisons, causing the list to get shorter and shorter...
if pot_write_threshold == -1:
pot_write_threshold = i
length += 1
# the IBI update to the potential
target_g_r_i = 1.0
if len(target_g_r) > i:
target_g_r_i = target_g_r[i]
fri = 1.0 #filtered rdf shorthand for beyond the cutoff.
if len(filtered_rdf) > i:
fri = filtered_rdf[i]
if float(p_flag) < -0.00001: # Disable IBI part.
print "old potential:", old_potential[length]
print "pressure modification:", pressure_pot[i-1]
potential[i] = old_potential[length] + pressure_pot[i-1]
#print i, (abs(target_g_r_i) > 0), (fri > 0.15), i*DeltaR, old_potential[length], pressure_pot[i]
if (abs(target_g_r_i) > 0) and (fri > 0.15):
if float(p_flag) > -0.00001: # Enable IBI part.
# print "FabMD: I+) IBI is enabled!"
potential[i] = old_potential[length] + (kB * temperature) * math.log(fri / target_g_r_i) + pressure_pot[i-1]
# Debug check
# if abs(old_distance[length] - i*DeltaR)>0.00001:
# print "Error: old_distance seems to be wrongly mapped!"
# exit()
x_data[index] = i * DeltaR
y_data[index] = potential[i]
index += 1
#print i, potential[i]
else:
# this array indicates which values we need to an extrapolation for the potential and for the forces (negative of the potential derivative) - defined as where
# the RDF is less than 0.15, yet is defined in the old potential file.
conversion_extrapolate_tmp[i] = 1
#exit()
x_data.resize((index))
y_data.resize((index))
dy = da.derivatives(x_data, y_data)
# print y_data, dy, len(y_data), len(dy)
# exit()
parameters = {}
square_residual = 0
if LJ_file_flag == 1:
# read in Lennard-Jones parameters from file if requested.
parameters = {}
LJ_IN = open("LJ_parameters", 'r')
for line in LJ_IN:
NewRow = (line.strip()).split()
if (NewRow[2] == atom1) and (NewRow[3] == atom2):
parameters[0][1] = NewRow[6]
parameters[1][1] = NewRow[9]
LJ_IN.close()
else:
# fitting the potential derivative (i.e. negative forces) using CurveFit.pm to a Lennard Jones 6 - 3 potential (i.e. 7 - 4 when differentiated )
fitfunc = lambda p, x: - 6 * (( ( 4 * p[0] * p[1]**6) / x**7) - ( (4 * p[0] * p[1]**3) / (2*x**4)) )
errfunc = lambda p, x, y: fitfunc(p, x) - y
#print "X_DATA = ", x_data, dy, y_data
p0 = np.array([0.5, 4.5]) #was 0.5,4.5
p1, success = scipy.optimize.leastsq(errfunc, p0[:], maxfev=5000, args=(x_data, dy)) #use [:int(4.0/DeltaR)] to optimize up to a cutoff of 4.
if success == 0:
print "Scipy.optimize did not manage to converge the fit on dataset", atom1, atom2, "! Exiting now."
exit()
LJ_OUT = open("%s/LJ_parameters" % (out_path),'w')
LJ_OUT.write("LJ PARAMETERS %d %d p0 = %f, p1 = %f\n" % (atom1, atom2, p1[0], p1[1]))
LJ_OUT.close()
for i in xrange(numofbins+1, 1, -1):
if i in conversion_extrapolate_tmp.keys(): #77-31
#print i
if conversion_extrapolate_tmp[i] > 0:
new_distance = i * DeltaR
# These Lennard-Jones forces are then numerically integrated to get the potential
derivative[i] = -np.abs(fitfunc(p1, new_distance))
diff = x_data[0] - new_distance
ave = 0.5 * fitfunc(p1, new_distance) - 0.5 * dy[0]
r_y = np.abs(y_data[0] - diff * ave)
potential[i] = r_y
# print i, derivative[i], potential[i], "!"
index = 0
for i in xrange(pot_write_threshold, numofbins+1):
if i not in conversion_extrapolate_tmp.keys():
derivative[i] = dy[index]
index += 1
index = 0
for i in xrange(0, numofbins+1):
if len(derivative) > i:
if abs(derivative[i]) > 0:
index += 1
# determining the number of potential values
lmp_io.write_pot_file("%s/pot.%d.new.%d.%d" % (out_path, new_number, atom1, atom2), derivative, potential, numofbins, DeltaR, atom1, atom2, index)
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='Modify a potential.')
parser.add_argument("-i", "--inputfile", help='name of the potential file to read')
parser.add_argument("-m", "--mode", default='smooth', help='type of filter used', choices=["smooth", "remove_tail", "append_zeroes", "amplify", "xshift", "yshift", "prepend", "append_linear"])
parser.add_argument("-r", "--repeat", help='number of repetitions', type=int, default=1)
parser.add_argument("-a", "--atom1", help='atom number 1', type=int, default=1)
parser.add_argument("-b", "--atom2", help='atom number 2', type=int, default=1)
parser.add_argument("-o", "--outputfile", help='name of the potential file to write')
parser.add_argument("parameter", type=float, help='specifies a key parameter to associate with the mode. Should be at least 1e-10.', default=0.0)
args = parser.parse_args()
print args
num_of_bins, cutoff, offset = lmp_io.get_number_of_bins_and_cutoff(args.inputfile, 1)
print "Potential numofbins and cutoff:", num_of_bins, cutoff
dis, pot, der = lmp_io.read_in_interaction_file(args.inputfile, num_of_bins)
dis = dis[1:]
pot = pot[1:]
der = der[1:]
for i in xrange(0,args.repeat):
if args.mode == "smooth":
pot = dm.smooth_data(pot,window_len=int(args.parameter),window='flat')
elif args.mode == "remove_tail":
pot, der = dm.remove_tail(pot, der)
elif args.mode == "append_zeroes":
if abs(args.parameter) < 1e-10:
args.parameter = 20
def update_one_file(out_path, target_g_r, new_g_r, old_distance, old_potential,
num_of_types, number, DeltaR, numofbins, number_of_types1,
number_of_types2, lattice, LJ_file_flag, p_flag, p_now,
p_target, temperature, atom1, atom2):
number = int(number)
potential = np.zeros((numofbins + 1))
derivative = np.zeros((numofbins + 1))
new_number = number + 1
volume = float(lattice[0]) * float(lattice[1]) * float(lattice[2])
print "Lengths: ", len(target_g_r), len(new_g_r), len(old_distance), len(
old_potential), numofbins
index = length = 0
x_data = np.zeros((numofbins + 1))
y_data = np.zeros((numofbins + 1))
success = 0
# smooth the new CG radial distribution function and calculate where the old CG rdf starts (it will be zero at low distance values).
filtered_rdf = dm.smooth_data(new_g_r)
np.append(filtered_rdf, 1)
conversion_extrapolate_tmp = {}
pressure_pot = np.zeros((numofbins + 1))
if abs(float(p_flag)) > 0.00001:
print "FabMD: P+) Applying pressure function."
pressure_pot = calc_pressure_correction(new_g_r, numofbins, DeltaR,
number_of_types1,
number_of_types2, abs(p_flag),
p_now, p_target, volume,
temperature)
else:
print "FabMD: P-) Not applying any pressure correction."
# use_data = 0
if float(p_flag) < -0.00001:
print "FabMD: I-) IBI is disabled!"
pot_write_threshold = -1 # slot where we start the pot functions
kB = 0.0019858775
for i in xrange(0, numofbins + 1):
# print old_distance[1], i, i*DeltaR
if old_distance[1] <= (
(i + 0.1) * DeltaR
): #0.1 is here to prevent tiny bugs in float comparisons, causing the list to get shorter and shorter...
if pot_write_threshold == -1:
pot_write_threshold = i
length += 1
# the IBI update to the potential
target_g_r_i = 1.0
if len(target_g_r) > i:
target_g_r_i = target_g_r[i]
fri = 1.0 #filtered rdf shorthand for beyond the cutoff.
if len(filtered_rdf) > i:
fri = filtered_rdf[i]
if float(p_flag) < -0.00001: # Disable IBI part.
print "old potential:", old_potential[length]
print "pressure modification:", pressure_pot[i - 1]
potential[i] = old_potential[length] + pressure_pot[i - 1]
#print i, (abs(target_g_r_i) > 0), (fri > 0.15), i*DeltaR, old_potential[length], pressure_pot[i]
if (abs(target_g_r_i) > 0) and (fri > 0.15):
if float(p_flag) > -0.00001: # Enable IBI part.
# print "FabMD: I+) IBI is enabled!"
potential[i] = old_potential[length] + (
kB * temperature) * math.log(
fri / target_g_r_i) + pressure_pot[i - 1]
# Debug check
# if abs(old_distance[length] - i*DeltaR)>0.00001:
# print "Error: old_distance seems to be wrongly mapped!"
# exit()
x_data[index] = i * DeltaR
y_data[index] = potential[i]
index += 1
#print i, potential[i]
else:
# this array indicates which values we need to an extrapolation for the potential and for the forces (negative of the potential derivative) - defined as where
# the RDF is less than 0.15, yet is defined in the old potential file.
conversion_extrapolate_tmp[i] = 1
#exit()
x_data.resize((index))
y_data.resize((index))
dy = da.derivatives(x_data, y_data)
# print y_data, dy, len(y_data), len(dy)
# exit()
parameters = {}
square_residual = 0
if LJ_file_flag == 1:
# read in Lennard-Jones parameters from file if requested.
parameters = {}
LJ_IN = open("LJ_parameters", 'r')
for line in LJ_IN:
NewRow = (line.strip()).split()
if (NewRow[2] == atom1) and (NewRow[3] == atom2):
parameters[0][1] = NewRow[6]
parameters[1][1] = NewRow[9]
LJ_IN.close()
else:
# fitting the potential derivative (i.e. negative forces) using CurveFit.pm to a Lennard Jones 6 - 3 potential (i.e. 7 - 4 when differentiated )
fitfunc = lambda p, x: -6 * (((4 * p[0] * p[1]**6) / x**7) -
((4 * p[0] * p[1]**3) / (2 * x**4)))
errfunc = lambda p, x, y: fitfunc(p, x) - y
#print "X_DATA = ", x_data, dy, y_data
p0 = np.array([0.5, 4.5]) #was 0.5,4.5
p1, success = scipy.optimize.leastsq(
errfunc, p0[:], maxfev=5000, args=(
x_data,
dy)) #use [:int(4.0/DeltaR)] to optimize up to a cutoff of 4.
if success == 0:
print "Scipy.optimize did not manage to converge the fit on dataset", atom1, atom2, "! Exiting now."
exit()
LJ_OUT = open("%s/LJ_parameters" % (out_path), 'w')
LJ_OUT.write("LJ PARAMETERS %d %d p0 = %f, p1 = %f\n" %
(atom1, atom2, p1[0], p1[1]))
LJ_OUT.close()
for i in xrange(numofbins + 1, 1, -1):
if i in conversion_extrapolate_tmp.keys(): #77-31
#print i
if conversion_extrapolate_tmp[i] > 0:
new_distance = i * DeltaR
# These Lennard-Jones forces are then numerically integrated to get the potential
derivative[i] = -np.abs(fitfunc(p1, new_distance))
diff = x_data[0] - new_distance
ave = 0.5 * fitfunc(p1, new_distance) - 0.5 * dy[0]
r_y = np.abs(y_data[0] - diff * ave)
potential[i] = r_y
# print i, derivative[i], potential[i], "!"
index = 0
for i in xrange(pot_write_threshold, numofbins + 1):
if i not in conversion_extrapolate_tmp.keys():
derivative[i] = dy[index]
index += 1
index = 0
for i in xrange(0, numofbins + 1):
if len(derivative) > i:
if abs(derivative[i]) > 0:
index += 1
# determining the number of potential values
lmp_io.write_pot_file(
"%s/pot.%d.new.%d.%d" % (out_path, new_number, atom1, atom2),
derivative, potential, numofbins, DeltaR, atom1, atom2, index)
type=int,
default=1)
parser.add_argument("-o",
"--outputfile",
help='name of the potential file to write')
parser.add_argument(
"parameter",
type=float,
help=
'specifies a key parameter to associate with the mode. Should be at least 1e-10.',
default=0.0)
args = parser.parse_args()
print args
num_of_bins, cutoff, offset = lmp_io.get_number_of_bins_and_cutoff(
args.inputfile, 1)
print "Potential numofbins and cutoff:", num_of_bins, cutoff
dis, pot, der = lmp_io.read_in_interaction_file(args.inputfile,
num_of_bins)
dis = dis[1:]
pot = pot[1:]
der = der[1:]
for i in xrange(0, args.repeat):
if args.mode == "smooth":
pot = dm.smooth_data(pot,
window_len=int(args.parameter),
window='flat')
elif args.mode == "remove_tail":
pot, der = dm.remove_tail(pot, der)
def production():
print "ARGUMENTS to the PMF calculation are: ", sys.argv
wham_correct_file = ""
number=""
atom_type1=""
atom_type2=""
out_path=""
CG_in_file_base=""
pot_base=""
wham_executable=""
lammps_input_file = ""
number_of_arguments = len(sys.argv)
for i in xrange(0, number_of_arguments):
if sys.argv[i].lower() == "wham_correct_file":
wham_correct_file = sys.argv[i+1]
print "THE WHAM CORRECT FILE IS ", wham_correct_file
elif ((sys.argv[i] == "potential_base") or (sys.argv[i] == "potential") or (sys.argv[i] == "pot_base")):
pot_base_full = os.path.split(sys.argv[i+1])
pot_base_file = pot_base_full[1]
pot_base_dir=pot_base_full[0]
pot_base=sys.argv[i+1]
elif ((sys.argv[i].lower() == "atom_type1") or (sys.argv[i].lower() == "atom1") or (sys.argv[i].lower() == "type1")):
atom_type1 = int(sys.argv[i+1])
elif ((sys.argv[i].lower() == "atom_type2") or (sys.argv[i].lower() == "atom2") or (sys.argv[i].lower() == "type2")):
atom_type2 = int(sys.argv[i+1])
elif ((sys.argv[i] == "number") or (sys.argv[i] == "current_number") or (sys.argv[i] == "iteration_number")):
number = int(sys.argv[i+1])
print "THE CURRENT ITERATION NUMBER IS ", number
elif ((sys.argv[i] == "CG_in_file") or (sys.argv[i] == "CG_lammps_file") or (sys.argv[i] == "CG_input")):
CG_in_file = sys.argv[i+1]
elif ((sys.argv[i].lower() == "lammps_in_file") or (sys.argv[i] == "lammps_input_file") or (sys.argv[i] == "lammps_input")):
lammps_input_file_full=os.path.split(sys.argv[i+1])
lammps_input_file = lammps_input_file_full[1]
lammps_input_file_dir=lammps_input_file_full[0]
lammps_input_file_full=sys.argv[i+1]
elif ((sys.argv[i] == "wham_executable") or (sys.argv[i] == "wham_executable_path") or (sys.argv[i] == "wham_path")):
wham_executable = sys.argv[i+1]
elif ((sys.argv[i] == "out_directory") or (sys.argv[i] == "out")):
out_path = sys.argv[i+1]
if not os.path.isdir(out_path):
os.makedirs(out_path)
#wham_correct_file=sys.argv[1]
#number=sys.argv[2]
#atom_type_1=int(sys.argv[3])
#atom_type_2=int(sys.argv[4])
#out_path=sys.argv[5]
#CG_in_file_base="in.CG.lammps.1"
wham_array_correct, wham_array_distance_correct, numofbins_wham, cutoff_wham= read_in_WHAM_file(wham_correct_file)
dir_value_dict, dir_spring_const_dict, dir_log_file_dict, dir_data_file_dict =find_dir_value_dict(CG_in_file,lammps_input_file_dir)
print "LAMMPS input_dir", lammps_input_file_dir
wham_spacing=wham_array_distance_correct[1]-wham_array_distance_correct[0]
wham_minimum=wham_array_distance_correct[0]-(wham_spacing/2)
wham_maximum=wham_array_distance_correct[numofbins_wham-1]+(wham_spacing/2)
wham_list_base="wham_list_CG"
wham_list=os.path.join(lammps_input_file_dir, str(wham_list_base + '.' + str(number)))
wham_output=os.path.join(lammps_input_file_dir, str("wham_output"+ '.' + str(number)))
print "WHAMLIST", wham_list, wham_output
pot_file=pot_base + str(number)
new_number=int(number)+1
print "NEW NUMBER", new_number, number
pot_new=pot_base_file+str(new_number)
pot_file_new=os.path.join(out_path,pot_new)
print "POT_FILE NEW", pot_file_new, out_path
print "NUMOFBINS IS ", numofbins_wham
create_WHAM_files(dir_value_dict, dir_spring_const_dict,dir_log_file_dict,dir_value_dict,wham_list,lammps_input_file_dir)
print "WHAM", wham_list, wham_output
call("'%s' '%f' '%f' '%d' 0.0001 500 0 '%s' '%s' " % (wham_executable, wham_minimum, wham_maximum, numofbins_wham, wham_list, wham_output), shell=True)
#2.875 20.125 69 0.0001 500 0 wham_list_CG.number wham_output.number')
wham_array_new, wham_array_distance_new, numofbins_new, cutoff_wham_new= read_in_WHAM_file(wham_output,lammps_input_file_dir)
num_of_pot_bins, cutoff_pot, offset_pot = lmp_io.get_number_of_bins_and_cutoff(pot_file,0)
pot_distance, pot_potential, pot_derivative=lmp_io.read_in_interaction_file(pot_file,num_of_pot_bins)
update_one_file_wham(number, num_of_pot_bins, wham_array_distance_new, wham_array_new, wham_array_distance_correct, wham_array_correct, pot_distance, pot_potential,atom_type1,atom_type2,dir_value_dict,pot_file_new,CG_in_file,out_path,lammps_input_file_dir)
new_lammps_input_file=os.path.join(out_path,lammps_input_file)
shutil.copy2(lammps_input_file_full, new_lammps_input_file)
def production():
print "ARGUMENTS to the PMF calculation are: ", sys.argv
wham_correct_file = ""
number = ""
atom_type1 = ""
atom_type2 = ""
out_path = ""
CG_in_file_base = ""
pot_base = ""
wham_executable = ""
lammps_input_file = ""
number_of_arguments = len(sys.argv)
for i in xrange(0, number_of_arguments):
if sys.argv[i].lower() == "wham_correct_file":
wham_correct_file = sys.argv[i + 1]
print "THE WHAM CORRECT FILE IS ", wham_correct_file
elif ((sys.argv[i] == "potential_base") or (sys.argv[i] == "potential")
or (sys.argv[i] == "pot_base")):
pot_base_full = os.path.split(sys.argv[i + 1])
pot_base_file = pot_base_full[1]
pot_base_dir = pot_base_full[0]
pot_base = sys.argv[i + 1]
elif ((sys.argv[i].lower() == "atom_type1")
or (sys.argv[i].lower() == "atom1")
or (sys.argv[i].lower() == "type1")):
atom_type1 = int(sys.argv[i + 1])
elif ((sys.argv[i].lower() == "atom_type2")
or (sys.argv[i].lower() == "atom2")
or (sys.argv[i].lower() == "type2")):
atom_type2 = int(sys.argv[i + 1])
elif ((sys.argv[i] == "number") or (sys.argv[i] == "current_number")
or (sys.argv[i] == "iteration_number")):
number = int(sys.argv[i + 1])
print "THE CURRENT ITERATION NUMBER IS ", number
elif ((sys.argv[i] == "CG_in_file")
or (sys.argv[i] == "CG_lammps_file")
or (sys.argv[i] == "CG_input")):
CG_in_file = sys.argv[i + 1]
elif ((sys.argv[i].lower() == "lammps_in_file")
or (sys.argv[i] == "lammps_input_file")
or (sys.argv[i] == "lammps_input")):
lammps_input_file_full = os.path.split(sys.argv[i + 1])
lammps_input_file = lammps_input_file_full[1]
lammps_input_file_dir = lammps_input_file_full[0]
lammps_input_file_full = sys.argv[i + 1]
elif ((sys.argv[i] == "wham_executable")
or (sys.argv[i] == "wham_executable_path")
or (sys.argv[i] == "wham_path")):
wham_executable = sys.argv[i + 1]
elif ((sys.argv[i] == "out_directory") or (sys.argv[i] == "out")):
out_path = sys.argv[i + 1]
if not os.path.isdir(out_path):
os.makedirs(out_path)
#wham_correct_file=sys.argv[1]
#number=sys.argv[2]
#atom_type_1=int(sys.argv[3])
#atom_type_2=int(sys.argv[4])
#out_path=sys.argv[5]
#CG_in_file_base="in.CG.lammps.1"
wham_array_correct, wham_array_distance_correct, numofbins_wham, cutoff_wham = read_in_WHAM_file(
wham_correct_file)
dir_value_dict, dir_spring_const_dict, dir_log_file_dict, dir_data_file_dict = find_dir_value_dict(
CG_in_file, lammps_input_file_dir)
print "LAMMPS input_dir", lammps_input_file_dir
wham_spacing = wham_array_distance_correct[
1] - wham_array_distance_correct[0]
wham_minimum = wham_array_distance_correct[0] - (wham_spacing / 2)
wham_maximum = wham_array_distance_correct[numofbins_wham -
1] + (wham_spacing / 2)
wham_list_base = "wham_list_CG"
wham_list = os.path.join(lammps_input_file_dir,
str(wham_list_base + '.' + str(number)))
wham_output = os.path.join(lammps_input_file_dir,
str("wham_output" + '.' + str(number)))
print "WHAMLIST", wham_list, wham_output
pot_file = pot_base + str(number)
new_number = int(number) + 1
print "NEW NUMBER", new_number, number
pot_new = pot_base_file + str(new_number)
pot_file_new = os.path.join(out_path, pot_new)
print "POT_FILE NEW", pot_file_new, out_path
print "NUMOFBINS IS ", numofbins_wham
create_WHAM_files(dir_value_dict, dir_spring_const_dict, dir_log_file_dict,
dir_value_dict, wham_list, lammps_input_file_dir)
print "WHAM", wham_list, wham_output
call("'%s' '%f' '%f' '%d' 0.0001 500 0 '%s' '%s' " %
(wham_executable, wham_minimum, wham_maximum, numofbins_wham,
wham_list, wham_output),
shell=True)
#2.875 20.125 69 0.0001 500 0 wham_list_CG.number wham_output.number')
wham_array_new, wham_array_distance_new, numofbins_new, cutoff_wham_new = read_in_WHAM_file(
wham_output, lammps_input_file_dir)
num_of_pot_bins, cutoff_pot, offset_pot = lmp_io.get_number_of_bins_and_cutoff(
pot_file, 0)
pot_distance, pot_potential, pot_derivative = lmp_io.read_in_interaction_file(
pot_file, num_of_pot_bins)
update_one_file_wham(number, num_of_pot_bins, wham_array_distance_new,
wham_array_new, wham_array_distance_correct,
wham_array_correct, pot_distance, pot_potential,
atom_type1, atom_type2, dir_value_dict, pot_file_new,
CG_in_file, out_path, lammps_input_file_dir)
new_lammps_input_file = os.path.join(out_path, lammps_input_file)
shutil.copy2(lammps_input_file_full, new_lammps_input_file)