def main(args):
#read in and rename arguments
inp_dir=args[1]
scoretype=args[2]
dec, nat = scorefileparse.read_dec_nat(inp_dir, [], scoretype)
disc = discparse.read_dir(inp_dir)
dec_norm = scorefileparse.norm_pdbs(dec)
nat_norm = scorefileparse.norm_pdbs(nat,dec)
[dec_inter, nat_inter, disc_inter] = scorefileparse.pdbs_intersect([dec_norm, nat_norm, disc])
#labels = ["Average","1.0","1.5","2.0","2.5","3.0","4.0","6.0"]
labels = ["Average"]
energy_gap = [[] for l in labels]
avg_disc = [[] for l in labels]
for pdb in dec_inter.keys():
for ind in xrange(0,len(labels)):
lowest_dec = min([ e[0] for e in dec_inter[pdb].values() ])
lowest_nat = min([ n[0] for n in nat_inter[pdb].values() if n[1] < 2.0 ])
energy_gap[ind].append(lowest_nat - lowest_dec)
avg_disc[ind].append(disc_inter[pdb][0])
fig, axarr = conv.create_ax(len(labels), 1)
for x_ind,l in enumerate(labels):
ax = axarr[0,x_ind]
scatterplot.draw_actual_plot(ax, avg_disc[x_ind], energy_gap[x_ind], [], l,"Disc","Energy Gap")
scatterplot.plot_regression(ax, avg_disc[x_ind], energy_gap[x_ind], False, False)
title = os.path.basename(inp_dir)
filename=inp_dir + "/test.txt"
conv.save_fig(fig, filename, "disc_v_egap", len(labels)*3, 4)
def main(args):
#read in and rename arguments
title1 = os.path.basename(args.input_dir_1)
title2 = os.path.basename(args.input_dir_2)
d1, n1 = scorefileparse.read_dec_nat(args.input_dir_1, [], args.scoretype1, True)
d2, n2 = scorefileparse.read_dec_nat(args.input_dir_2, [], args.scoretype2, True)
dec1 = scorefileparse.filter_pdbs_by_rmsd(d1, args.rmsd_cutoff)
nat1 = scorefileparse.filter_pdbs_by_rmsd(n1, args.rmsd_cutoff)
dec2 = scorefileparse.filter_pdbs_by_rmsd(d2, args.rmsd_cutoff)
nat2 = scorefileparse.filter_pdbs_by_rmsd(n2, args.rmsd_cutoff)
dec_norm1 = scorefileparse.norm_pdbs(dec1)
nat_norm1 = scorefileparse.norm_pdbs(nat1,dec1)
dec_norm2 = scorefileparse.norm_pdbs(dec2)
nat_norm2 = scorefileparse.norm_pdbs(nat2,dec2)
[dec_inter1, nat_inter1, dec_inter2, nat_inter2] = scorefileparse.pdbs_intersect([dec_norm1, nat_norm1, dec_norm2, nat_norm2])
[dec_inter1, dec_inter2] = scorefileparse.pdbs_scores_intersect([dec_inter1, dec_inter2])
[nat_inter1, nat_inter2] = scorefileparse.pdbs_scores_intersect([nat_inter1, nat_inter2])
dec_filt1 = scorefileparse.filter_norm_pdbs(dec_norm1)
nat_filt1 = scorefileparse.filter_norm_pdbs(nat_norm1)
dec_filt2 = scorefileparse.filter_norm_pdbs(dec_norm2)
nat_filt2 = scorefileparse.filter_norm_pdbs(nat_norm2)
[dec_finter1, dec_finter2] = scorefileparse.pdbs_scores_intersect([dec_filt1, dec_filt2])
[nat_finter1, nat_finter2] = scorefileparse.pdbs_scores_intersect([nat_filt1, nat_filt2])
fig, axarr = conv.create_ax(2, len(dec_inter1))
for x_ind,pdb in enumerate(sorted(dec_inter1.keys())):
ax = axarr[x_ind, 0]
plot(dec_inter1, dec_inter2, nat_inter1, nat_inter2, ax, pdb, title1, title2)
ax = axarr[x_ind, 1]
plot(dec_finter1, dec_finter2, nat_finter1, nat_finter2, ax, pdb, title1, title2)
def main(input_dir_rosetta_sf, input_dir_amber_sf, input_dir_rosetta_pdb, input_dir_amber_pdb, output_dir, n_results):
d1 = scorefileparse.read_dir(input_dir_rosetta_sf, 'rosetta', repl_orig=True)
d2 = scorefileparse.read_dir(input_dir_amber_sf, 'amber', repl_orig=True)
dec_norm1 = scorefileparse.norm_pdbs(d1)
dec_norm2 = scorefileparse.norm_pdbs(d2)
[dec_inter1, dec_inter2] = scorefileparse.pdbs_scores_intersect([dec_norm1, dec_norm2])
dec1_cp = copy.deepcopy(dec_inter1)
dec2_cp = copy.deepcopy(dec_inter2)
min_naive_by_pdb = {}
for pdb in sorted(dec_inter1.keys()):
for i in range(1,n_results+1):
rosetta_lowest_energy = find_lowest_energy( dec_inter1[pdb] )
amber_lowest_energy = find_lowest_energy( dec_inter2[pdb] )
pareto_lowest_energy = find_pareto(dec1_cp, dec2_cp, pdb)
#copy rosetta file
src = os.join(input_dir_rosetta_pdb, pdb, rosetta_lowest_energy + "_0001.pdb")
dst = os.join(output_dir,"rosetta","{0}_{1}.pdb".format(pdb,i))
copyfile(src, dst)
#copy amber file
src = os.join(input_dir_rosetta_pdb, pdb, "min_NoH_" + amber_lowest_energy + ".pdb")
dst = os.join(output_dir,"amber","{0}_{1}.pdb".format(pdb,i))
copyfile(src, dst)
#copy pareto file
src = os.join(input_dir_rosetta_pdb, pdb, pareto_lowest_energy + "_0001.pdb")
dst = os.join(output_dir,"combined","{0}_{1}.pdb".format(pdb,i))
copyfile(src, dst)
#delete from original scoredict so that next round will get the next-lowest
dec_inter1[pdb].pop(rosetta_lowest_energy)
dec_inter2[pdb].pop(amber_lowest_energy)
dec1_cp[pdb].pop(pareto_lowest_energy)
dec2_cp[pdb].pop(pareto_lowest_energy)
def main(input_dir_rosetta_sf, input_dir_amber_sf, input_dir_rosetta_pdb, input_dir_amber_pdb, output_dir, n_results, alternate_rmsd, start_ind_desc_amber, start_ind_desc_rosetta, end_ind_desc_amber, end_ind_desc_rosetta, add_pdb_to_path):
if alternate_rmsd is True:
d1 = scorefileparse.read_dir(input_dir_rosetta_sf, 'rosetta', repl_orig=False, rmsd='total_score', start_ind_desc=start_ind_desc_rosetta, end_ind_desc=end_ind_desc_rosetta)
d2 = scorefileparse.read_dir(input_dir_amber_sf, 'amber', repl_orig=False, rmsd='tot', start_ind_desc=start_ind_desc_amber, end_ind_desc=end_ind_desc_amber)
else:
d1 = scorefileparse.read_dir(input_dir_rosetta_sf, 'rosetta', repl_orig=False, start_ind_desc=start_ind_desc_rosetta, end_ind_desc=end_ind_desc_rosetta)
d2 = scorefileparse.read_dir(input_dir_amber_sf, 'amber', repl_orig=False, start_ind_desc=start_ind_desc_amber, end_ind_desc=end_ind_desc_amber)
dec_norm1 = scorefileparse.norm_pdbs(d1)
dec_norm2 = scorefileparse.norm_pdbs(d2)
[dec_inter1, dec_inter2] = scorefileparse.pdbs_scores_intersect([dec_norm1, dec_norm2])
dec1_cp = copy.deepcopy(dec_inter1)
dec2_cp = copy.deepcopy(dec_inter2)
try:
os.mkdir( os.path.join(output_dir,"rosetta") )
except OSError:
pass
try:
os.mkdir( os.path.join(output_dir,"amber") )
except OSError:
pass
try:
os.mkdir( os.path.join(output_dir,"combined") )
except OSError:
pass
for pdb in sorted(dec_inter1.keys()):
for i in range(1,n_results+1):
rosetta_lowest_energy = find_lowest_energy( dec_inter1[pdb] )
amber_lowest_energy = find_lowest_energy( dec_inter2[pdb] )
pareto_lowest_energy = find_pareto(dec1_cp, dec2_cp, pdb)
if add_pdb_to_path == "True":
path_p = pdb
else:
path_p = ""
if end_ind_desc_rosetta == 1000:
r_suffix = ".pdb"
else:
r_suffix = "_0001.pdb"
if start_ind_desc_amber is None:
a_prefix = "min_NoH_"
else:
a_prefix = "min_"
if end_ind_desc_amber is None:
a_suffix = ".pdb"
else:
a_suffix = ".pdb.pdb"
#copy rosetta file
src = os.path.join(input_dir_rosetta_pdb, path_p, rosetta_lowest_energy + r_suffix)
dst = os.path.join(output_dir,"rosetta","{0}_{1}.pdb".format(pdb,i))
copyfile(src, dst)
#copy amber file
src = os.path.join(input_dir_amber_pdb, path_p, a_prefix + amber_lowest_energy + a_suffix)
dst = os.path.join(output_dir,"amber","{0}_{1}.pdb".format(pdb,i))
copyfile(src, dst)
#copy pareto file
src = os.path.join(input_dir_rosetta_pdb, path_p, pareto_lowest_energy + r_suffix)
dst = os.path.join(output_dir,"combined","{0}_{1}.pdb".format(pdb,i))
copyfile(src, dst)
#delete from original scoredict so that next round will get the next-lowest
dec_inter1[pdb].pop(rosetta_lowest_energy)
dec_inter2[pdb].pop(amber_lowest_energy)
dec1_cp[pdb].pop(pareto_lowest_energy)
dec2_cp[pdb].pop(pareto_lowest_energy)
def main(input_dir_rosetta_sf, input_dir_amber_sf, input_dir_rosetta_pdb,
input_dir_amber_pdb, output_dir, n_results, alternate_rmsd,
start_ind_desc_amber, start_ind_desc_rosetta, end_ind_desc_amber,
end_ind_desc_rosetta, add_pdb_to_path):
if alternate_rmsd is True:
d1 = scorefileparse.read_dir(input_dir_rosetta_sf,
'rosetta',
repl_orig=False,
rmsd='total_score',
start_ind_desc=start_ind_desc_rosetta,
end_ind_desc=end_ind_desc_rosetta)
d2 = scorefileparse.read_dir(input_dir_amber_sf,
'amber',
repl_orig=False,
rmsd='tot',
start_ind_desc=start_ind_desc_amber,
end_ind_desc=end_ind_desc_amber)
else:
d1 = scorefileparse.read_dir(input_dir_rosetta_sf,
'rosetta',
repl_orig=False,
start_ind_desc=start_ind_desc_rosetta,
end_ind_desc=end_ind_desc_rosetta)
d2 = scorefileparse.read_dir(input_dir_amber_sf,
'amber',
repl_orig=False,
start_ind_desc=start_ind_desc_amber,
end_ind_desc=end_ind_desc_amber)
dec_norm1 = scorefileparse.norm_pdbs(d1)
dec_norm2 = scorefileparse.norm_pdbs(d2)
[dec_inter1,
dec_inter2] = scorefileparse.pdbs_scores_intersect([dec_norm1, dec_norm2])
dec1_cp = copy.deepcopy(dec_inter1)
dec2_cp = copy.deepcopy(dec_inter2)
try:
os.mkdir(os.path.join(output_dir, "rosetta"))
except OSError:
pass
try:
os.mkdir(os.path.join(output_dir, "amber"))
except OSError:
pass
try:
os.mkdir(os.path.join(output_dir, "combined"))
except OSError:
pass
for pdb in sorted(dec_inter1.keys()):
for i in range(1, n_results + 1):
rosetta_lowest_energy = find_lowest_energy(dec_inter1[pdb])
amber_lowest_energy = find_lowest_energy(dec_inter2[pdb])
pareto_lowest_energy = find_pareto(dec1_cp, dec2_cp, pdb)
if add_pdb_to_path == "True":
path_p = pdb
else:
path_p = ""
if end_ind_desc_rosetta == 1000:
r_suffix = ".pdb"
else:
r_suffix = "_0001.pdb"
if start_ind_desc_amber is None:
a_prefix = "min_NoH_"
else:
a_prefix = "min_"
if end_ind_desc_amber is None:
a_suffix = ".pdb"
else:
a_suffix = ".pdb.pdb"
#copy rosetta file
src = os.path.join(input_dir_rosetta_pdb, path_p,
rosetta_lowest_energy + r_suffix)
dst = os.path.join(output_dir, "rosetta",
"{0}_{1}.pdb".format(pdb, i))
copyfile(src, dst)
#copy amber file
src = os.path.join(input_dir_amber_pdb, path_p,
a_prefix + amber_lowest_energy + a_suffix)
dst = os.path.join(output_dir, "amber",
"{0}_{1}.pdb".format(pdb, i))
copyfile(src, dst)
#copy pareto file
src = os.path.join(input_dir_rosetta_pdb, path_p,
pareto_lowest_energy + r_suffix)
dst = os.path.join(output_dir, "combined",
"{0}_{1}.pdb".format(pdb, i))
copyfile(src, dst)
#delete from original scoredict so that next round will get the next-lowest
dec_inter1[pdb].pop(rosetta_lowest_energy)
dec_inter2[pdb].pop(amber_lowest_energy)
dec1_cp[pdb].pop(pareto_lowest_energy)
dec2_cp[pdb].pop(pareto_lowest_energy)
def main(input_dir_1, scoretype1, input_dir_2, scoretype2, rmsd_cutoff, output_pre ):
#read in and rename arguments
title1 = os.path.basename(input_dir_1)
title2 = os.path.basename(input_dir_2)
d1, n1 = scorefileparse.read_dec_nat(input_dir_1, scoretype1, repl_orig=False)
d2, n2 = scorefileparse.read_dec_nat(input_dir_2, scoretype2, repl_orig=False)
dec1 = scorefileparse.filter_pdbs_by_rmsd(d1, rmsd_cutoff)
nat1 = scorefileparse.filter_pdbs_by_rmsd(n1, rmsd_cutoff)
dec2 = scorefileparse.filter_pdbs_by_rmsd(d2, rmsd_cutoff)
nat2 = scorefileparse.filter_pdbs_by_rmsd(n2, rmsd_cutoff)
dec_norm1 = scorefileparse.norm_pdbs(dec1)
nat_norm1 = scorefileparse.norm_pdbs(nat1,dec1)
dec_norm2 = scorefileparse.norm_pdbs(dec2)
nat_norm2 = scorefileparse.norm_pdbs(nat2,dec2)
[dec_inter1, nat_inter1, dec_inter2, nat_inter2] = scorefileparse.pdbs_intersect([dec_norm1, nat_norm1, dec_norm2, nat_norm2])
[dec_inter1, dec_inter2] = scorefileparse.pdbs_scores_intersect([dec_inter1, dec_inter2])
[nat_inter1, nat_inter2] = scorefileparse.pdbs_scores_intersect([nat_inter1, nat_inter2])
dec_filt1 = scorefileparse.filter_norm_pdbs(dec_norm1)
nat_filt1 = scorefileparse.filter_norm_pdbs(nat_norm1)
dec_filt2 = scorefileparse.filter_norm_pdbs(dec_norm2)
nat_filt2 = scorefileparse.filter_norm_pdbs(nat_norm2)
[dec_finter1, dec_finter2] = scorefileparse.pdbs_scores_intersect([dec_filt1, dec_filt2])
[nat_finter1, nat_finter2] = scorefileparse.pdbs_scores_intersect([nat_filt1, nat_filt2])
fig, axarr = conv.create_ax(2, len(dec_inter1))
line_plot_data = {}
min_naive_by_pdb = {}
for x_ind,pdb in enumerate(sorted(dec_inter1.keys())):
ax = axarr[x_ind, 0]
plot_r_v_r(dec_inter1, dec_inter2, nat_inter1, nat_inter2, ax, pdb, title1, title2)
ax = axarr[x_ind, 1]
min_naive = plot_pareto(dec_inter1, dec_inter2, nat_inter1, nat_inter2, ax, pdb, title1, title2)
keys_to_include = ["Amber", "Rosetta","All","Pareto10"]
for key, (rank1, rank2, rmsd) in min_naive.items():
#if key not in keys_to_include:
# continue
if line_plot_data.get(key) is None:
line_plot_data[key] = ([],[])
line_plot_data[key][0].append(pdb)
line_plot_data[key][1].append(rmsd)
if min_naive_by_pdb.get(pdb) is None:
min_naive_by_pdb[pdb] = {}
min_naive_by_pdb[pdb][key] = rmsd
#organize data
indices = list(range(len(line_plot_data["All"][1])))
indices.sort(key=lambda x: line_plot_data["All"][1][x])
ranked_pdbs_by_rmsd_all = {}
for i, x in enumerate(indices):
ranked_pdbs_by_rmsd_all[line_plot_data["All"][0][x]] = i
for label, (pdbs, rmsds) in line_plot_data.items():
line_plot_data[label] = tuple(zip(*sorted(zip(pdbs,rmsds), key=lambda x: ranked_pdbs_by_rmsd_all[x[0]] )))
filename = output_pre + "/" + title1 + "_" + title2 + ".txt"
#suffix="rmsd_v_rmsd_{0}".format(rmsd_cutoff)
#conv.save_fig(fig, filename, suffix, 7, len(dec_inter1)*3)
#plot line plot
all_pareto_labels = []
for initial in ["R","A"]:
ordered_labels = ["All", "Amber", "Rosetta"]
for i in range(1,11):
ordered_labels.append("Pareto{0}{1}".format(initial,i))
all_pareto_labels.append("Pareto{0}{1}".format(initial,i))
lines = [ (line_plot_data[label][0], line_plot_data[label][1], label) for label in ordered_labels ]
fig2, axarr2 = conv.create_ax(1, len(ordered_labels), shx=True, shy=True)
for i, label in enumerate(ordered_labels):
line.plot_series(axarr2[i,0], lines[0:i+1], "RMSD vs. pdb", "PDB", "RMSD", linestyle='')
conv.add_legend(axarr2[i,0])
conv.save_fig(fig2, filename, "_line_{0}".format(initial), 10, len(ordered_labels)*5)
#plot histogram plot
hist_comp = [ ("Amber","All"), ("Rosetta", "All"), ("ParetoR10", "All"), ("ParetoA10", "All")]
hist_comp.extend([ ("ParetoR{0}".format(ind),"Rosetta") for ind in range(1,11) ])
hist_comp.extend([ ("ParetoR{0}".format(ind),"Amber") for ind in range(1,11) ])
hist_comp.extend([ ("ParetoA{0}".format(ind),"Rosetta") for ind in range(1,11) ])
hist_comp.extend([ ("ParetoA{0}".format(ind), "Amber") for ind in range(1,11) ])
fig3, axarr3 = conv.create_ax(2, len(hist_comp), shx=False, shy=False)
for ind, (top, bottom) in enumerate(hist_comp):
gen_dist_plot(axarr3[ind,0], axarr3[ind,1], top, bottom, min_naive_by_pdb)
conv.save_fig(fig3, filename, "_distdeltas", 7, len(hist_comp)*5, tight=False)
#plot scatterplot
fig4, axarr4 = conv.create_ax(10, 2)
for i in range(1,11):
gen_scatterplot(axarr4[0,i-1], "ParetoR{0}".format(i), "Rosetta", "Amber", min_naive_by_pdb)
gen_scatterplot(axarr4[1,i-1], "ParetoA{0}".format(i), "Rosetta", "Amber", min_naive_by_pdb)
conv.save_fig(fig4, filename, "_scattdeltas", 30, 6)
