def plot(dec_inter1, dec_inter2, nat_inter1, nat_inter2, ax, pdb, title1, title2):
d1e = scorefileparse.get_energies(dec_inter1[pdb])
d2e = scorefileparse.get_energies(dec_inter2[pdb])
n1e = scorefileparse.get_energies(nat_inter1[pdb])
n2e = scorefileparse.get_energies(nat_inter2[pdb])
r = scorefileparse.get_rmsd(dec_inter1[pdb])
scatterplot.draw_actual_plot(ax, d1e, d2e, r, pdb, title1,title2)
#scatterplot.draw_actual_plot(ax, n1e, n2e, [], pdb, title1,title2)
scatterplot.plot_regression(ax, scorefileparse.get_energies(dec_inter1[pdb])+scorefileparse.get_energies(nat_inter1[pdb])
,scorefileparse.get_energies(dec_inter2[pdb])+scorefileparse.get_energies(nat_inter2[pdb]),False)
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 plot(disc_metrics_1, disc_metrics_2, title1, title2, output_pre, add_slash=True):
pdbs = sorted(disc_metrics_1.keys())
n_metrics = len(disc_metrics_1[pdbs[0]])
fig, axarr = conv.create_ax(n_metrics, 1)
for x_ind,metric_name in enumerate(disc_metrics_1[pdbs[0]].keys()):
x = []
y = []
ax = axarr[0,x_ind]
for pdb in pdbs:
x.append(disc_metrics_1[pdb][metric_name])
y.append(disc_metrics_2[pdb][metric_name])
scatterplot.draw_actual_plot(ax, x, y, 'b', metric_name, title1,title2, size=20, edgecolors='k')
scatterplot.plot_regression(ax,x,y,False)
if add_slash:
filename = output_pre + "/" + title1 + "_" + title2 + ".txt"
else:
filename = output_pre + title1 + "_" + title2 + ".txt"
suffix="disc_v_disc"
conv.save_fig(fig, filename, suffix, n_metrics*3, 3, size=9)
def plot_corr(c_1, c_2, ax, x_axis, y_axis):
scatterplot.draw_actual_plot(ax, c_1, c_2, 'b', "Counts vs. Counts", x_axis, y_axis, size=10)
scatterplot.plot_regression(ax, c_1, c_2)