def gen_scatterplot(ax, x_axis, y_axis, z_axis, min_naive_by_pdb):
x_deltas = get_dist_deltas(x_axis, "All", min_naive_by_pdb)
y_deltas = get_dist_deltas(y_axis, "All", min_naive_by_pdb)
z_deltas = get_dist_deltas(z_axis, "All", min_naive_by_pdb)
#c_deltas = get_dist_deltas("All", None, min_naive_by_pdb)
scatterplot.draw_actual_plot(ax, x_deltas, y_deltas, 'k', x_axis, x_axis + " Delta to Min RMSD (A)", "Delta to Min RMSD (A)", size=15, label=y_axis)
scatterplot.draw_actual_plot(ax, x_deltas, z_deltas, 'r', x_axis, x_axis + " Delta to Min RMSD (A)", "Delta to Min RMSD (A)", size=15, label=z_axis)
scatterplot.add_x_y_line(ax)
conv.add_legend(ax)
def main(seq_file, canonical_file, output_prefix):
series = []
canonical_list_seq = seq_IO.read_sequences(canonical_file)
print "Beginning Script: {0}".format(datetime.datetime.now())
for canonical in canonical_list_seq:
with open(seq_file) as strings:
seq_list = strings.read().splitlines()
seq_ind_list = [ (seq, ind) for ind, seq in enumerate(seq_list) ]
orig_len = len(seq_ind_list)
if canonical not in seq_list:
one_away = gsconv.gen_hamdist_one(canonical)
one_away = [ o for o in one_away if o != canonical ] + [canonical]
seq_ind_list = seq_ind_list[:] + [ (o, ind) for (ind, o) in enumerate(one_away, len(seq_ind_list)) ]
edges = [(seq2,seq) for seq,seq2 in itertools.combinations(seq_ind_list,2) if gsconv.hamdist(seq2[0],seq[0]) < 2 ]
print len(seq_ind_list)
print "Generated Edges: {0}".format(datetime.datetime.now())
numpy.set_printoptions(threshold='nan')
canon_ind=[ i for (s, i) in seq_ind_list if s == canonical ][0]
T_mat = trans_matrix(seq_ind_list,edges)
#print raise_matrix(T_mat,1)
#print raise_matrix(T_mat,3)
#T = raise_matrix(T_mat,10)
#T = raise_matrix(T_mat,20)
x = [0]
y = [0]
print "Transformed Matrix: {0}".format(datetime.datetime.now())
x.append(1)
y.append(find_frac(T_mat, canon_ind, orig_len))
T_mat_new = T_mat
for i in range(2,23):
x.append(i)
T_mat_new, frac = square_matrix(T_mat_new,T_mat,canon_ind, orig_len)
y.append(frac)
print "Raised Matrix {0}: {1}".format(i, datetime.datetime.now())
series.append([x,y,canonical])
fig, ax = conv.create_ax(1, 1)
color=['orange', 'palevioletred', 'mediumaquamarine', 'deepskyblue']
scatterplot.plot_series( ax[0,0], series, title="", x_axis="Number of Steps", colors=color, y_axis="Fraction Cleaved Variants Reached", alpha=0.85, connect_dots=True, size=15, edgecolors='k', linewidth=0)
ax[0,0].set_xlim(xmin=1)
ax[0,0].set_ylim(ymin=0.0, ymax=1.0)
ax[0,0].set_xticks(xrange(1,23,3))
lgd = conv.add_legend(ax[0,0], location='upper center', bbox_to_anchor=(0.5, 1.05), ncol=2, size=8)
conv.save_fig(fig, output_prefix, "fraction_func", 2.5, 3, size=9.5, extra_artists=lgd)
print "Outputted Figure: {0}".format(datetime.datetime.now())
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)
def main(seq_file, canonical_file, output_prefix):
#canonical_list_seq = seq_IO.read_sequences(canonical_file)
canonical_list_seq = ["DEMEE", "DEMED"]
print "Beginning Script: {0}".format(datetime.datetime.now())
with open(seq_file) as strings:
seq_list = strings.read().splitlines()
seq_ind_list = [(seq, ind) for ind, seq in enumerate(seq_list)]
seq_ind_dict = {seq: ind for seq, ind in seq_ind_list}
orig_len = len(seq_ind_list)
edges = []
edges_set = set()
print "Read in Data: {0}".format(datetime.datetime.now())
for seq, seq_ind in seq_ind_dict.items():
neighbors = gsconv.gen_hamdist_one(seq)
edges_set.update([(seq, n) for n in neighbors if n in seq_ind_dict])
edges += [((seq, seq_ind), (n, seq_ind_dict[n])) for n in neighbors
if n in seq_ind_dict and (n, seq) not in edges_set]
print len(seq_ind_list)
print "Generated Edges: {0}".format(datetime.datetime.now())
numpy.set_printoptions(threshold='nan')
canon_ind_dict = {
canonical: [i for (s, i) in seq_ind_list if s == canonical][0]
for canonical in canonical_list_seq
}
T_mat = trans_matrix(seq_ind_list, edges)
#print raise_matrix(T_mat,1)
#print raise_matrix(T_mat,3)
#T = raise_matrix(T_mat,10)
#T = raise_matrix(T_mat,20)
print "Transformed Matrix: {0}".format(datetime.datetime.now())
canon_x = {can: [0, 1] for can in canonical_list_seq}
canon_y = {
can: [0.0, find_frac(T_mat, canon_ind_dict[can], orig_len)]
for can in canonical_list_seq
}
print "Made x and y dicts: {0}".format(datetime.datetime.now())
T_mat_new = T_mat
for i in range(2, 23):
T_mat_new = square_matrix(T_mat_new, T_mat)
for can in canonical_list_seq:
canon_x[can].append(i)
canon_y[can].append(
find_frac(T_mat_new, canon_ind_dict[can], orig_len))
print "Raised Matrix {0}: {1}".format(i, datetime.datetime.now())
series = [[canon_x[can], canon_y[can], can] for can in canonical_list_seq]
fig, ax = conv.create_ax(1, 1)
color = ['orange', 'palevioletred', 'mediumaquamarine', 'deepskyblue']
scatterplot.plot_series(ax[0, 0],
series,
title="",
x_axis="Number of Steps",
colors=color,
y_axis="Fraction Cleaved Variants Reached",
alpha=0.85,
connect_dots=True,
size=15,
edgecolors='k',
linewidth=0)
ax[0, 0].set_xlim(xmin=1)
ax[0, 0].set_ylim(ymin=0.0, ymax=1.0)
ax[0, 0].set_xticks(xrange(1, 23, 3))
lgd = conv.add_legend(ax[0, 0],
location='upper center',
bbox_to_anchor=(0.5, 1.05),
ncol=2,
size=8)
conv.save_fig(fig,
output_prefix,
"fraction_func",
2.5,
3,
size=9.5,
extra_artists=lgd)
print "Outputted Figure: {0}".format(datetime.datetime.now())
def main(seq_file, canonical_file, output_prefix):
#canonical_list_seq = seq_IO.read_sequences(canonical_file)
canonical_list_seq = ["DEMEE","DEMED"]
print "Beginning Script: {0}".format(datetime.datetime.now())
with open(seq_file) as strings:
seq_list = strings.read().splitlines()
seq_ind_list = [ (seq, ind) for ind, seq in enumerate(seq_list) ]
seq_ind_dict = { seq : ind for seq, ind in seq_ind_list }
orig_len = len(seq_ind_list)
edges = []
edges_set = set()
print "Read in Data: {0}".format(datetime.datetime.now())
for seq, seq_ind in seq_ind_dict.items():
neighbors = gsconv.gen_hamdist_one(seq)
edges_set.update([ (seq, n) for n in neighbors if n in seq_ind_dict ])
edges += [((seq, seq_ind), (n,seq_ind_dict[n])) for n in neighbors if n in seq_ind_dict and (n,seq) not in edges_set ]
print len(seq_ind_list)
print "Generated Edges: {0}".format(datetime.datetime.now())
numpy.set_printoptions(threshold='nan')
canon_ind_dict = { canonical : [ i for (s, i) in seq_ind_list if s == canonical ][0] for canonical in canonical_list_seq }
T_mat = trans_matrix(seq_ind_list,edges)
#print raise_matrix(T_mat,1)
#print raise_matrix(T_mat,3)
#T = raise_matrix(T_mat,10)
#T = raise_matrix(T_mat,20)
print "Transformed Matrix: {0}".format(datetime.datetime.now())
canon_x = { can : [0,1] for can in canonical_list_seq }
canon_y = { can : [0.0, find_frac(T_mat, canon_ind_dict[can], orig_len)] for can in canonical_list_seq }
print "Made x and y dicts: {0}".format(datetime.datetime.now())
T_mat_new = T_mat
for i in range(2,23):
T_mat_new = square_matrix(T_mat_new, T_mat)
for can in canonical_list_seq:
canon_x[can].append(i)
canon_y[can].append(find_frac(T_mat_new, canon_ind_dict[can], orig_len))
print "Raised Matrix {0}: {1}".format(i, datetime.datetime.now())
series = [ [canon_x[can],canon_y[can], can] for can in canonical_list_seq ]
fig, ax = conv.create_ax(1, 1)
color=['orange', 'palevioletred', 'mediumaquamarine', 'deepskyblue']
scatterplot.plot_series( ax[0,0], series, title="", x_axis="Number of Steps", colors=color, y_axis="Fraction Cleaved Variants Reached", alpha=0.85, connect_dots=True, size=15, edgecolors='k', linewidth=0)
ax[0,0].set_xlim(xmin=1)
ax[0,0].set_ylim(ymin=0.0, ymax=1.0)
ax[0,0].set_xticks(xrange(1,23,3))
lgd = conv.add_legend(ax[0,0], location='upper center', bbox_to_anchor=(0.5, 1.05), ncol=2, size=8)
conv.save_fig(fig, output_prefix, "fraction_func", 2.5, 3, size=9.5, extra_artists=lgd)
print "Outputted Figure: {0}".format(datetime.datetime.now())
def main(seq_file, canonical_file, output_prefix):
series = []
canonical_list_seq = seq_IO.read_sequences(canonical_file)
print "Beginning Script: {0}".format(datetime.datetime.now())
for canonical in canonical_list_seq:
with open(seq_file) as strings:
seq_list = strings.read().splitlines()
seq_ind_list = [(seq, ind) for ind, seq in enumerate(seq_list)]
orig_len = len(seq_ind_list)
if canonical not in seq_list:
one_away = gsconv.gen_hamdist_one(canonical)
one_away = [o for o in one_away if o != canonical] + [canonical]
seq_ind_list = seq_ind_list[:] + [
(o, ind) for (ind, o) in enumerate(one_away, len(seq_ind_list))
]
edges = [(seq2, seq)
for seq, seq2 in itertools.combinations(seq_ind_list, 2)
if gsconv.hamdist(seq2[0], seq[0]) < 2]
print len(seq_ind_list)
print "Generated Edges: {0}".format(datetime.datetime.now())
numpy.set_printoptions(threshold='nan')
canon_ind = [i for (s, i) in seq_ind_list if s == canonical][0]
T_mat = trans_matrix(seq_ind_list, edges)
#print raise_matrix(T_mat,1)
#print raise_matrix(T_mat,3)
#T = raise_matrix(T_mat,10)
#T = raise_matrix(T_mat,20)
x = [0]
y = [0]
print "Transformed Matrix: {0}".format(datetime.datetime.now())
x.append(1)
y.append(find_frac(T_mat, canon_ind, orig_len))
T_mat_new = T_mat
for i in range(2, 23):
x.append(i)
T_mat_new, frac = square_matrix(T_mat_new, T_mat, canon_ind,
orig_len)
y.append(frac)
print "Raised Matrix {0}: {1}".format(i, datetime.datetime.now())
series.append([x, y, canonical])
fig, ax = conv.create_ax(1, 1)
color = ['orange', 'palevioletred', 'mediumaquamarine', 'deepskyblue']
scatterplot.plot_series(ax[0, 0],
series,
title="",
x_axis="Number of Steps",
colors=color,
y_axis="Fraction Cleaved Variants Reached",
alpha=0.85,
connect_dots=True,
size=15,
edgecolors='k',
linewidth=0)
ax[0, 0].set_xlim(xmin=1)
ax[0, 0].set_ylim(ymin=0.0, ymax=1.0)
ax[0, 0].set_xticks(xrange(1, 23, 3))
lgd = conv.add_legend(ax[0, 0],
location='upper center',
bbox_to_anchor=(0.5, 1.05),
ncol=2,
size=8)
conv.save_fig(fig,
output_prefix,
"fraction_func",
2.5,
3,
size=9.5,
extra_artists=lgd)
print "Outputted Figure: {0}".format(datetime.datetime.now())