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_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)
