def rmsd_start_final_plots_annealing(pdb_file,
random_displacements_parameterization,
tst_tardy_pdb_params,
parameter_trial_table, cp_n_trials, rmsds,
write_separate_pages):
ttd = dict(parameter_trial_table)
plot_data = {}
for h in ttd["structure_factors_high_resolution"]:
plot_data[h] = {}
for e in ttd["emulate_cartesian"]:
plot_data[h][e] = {}
for d in ttd["real_space_gradients_delta_resolution_factor"]:
plot_data[h][e][d] = {}
for w in ttd["real_space_target_weight"]:
plot_data[h][e][d][w] = {}
for t in ttd["start_temperature_kelvin"]:
plot_data[h][e][d][w][t] = {}
for c in ttd["number_of_cooling_steps"]:
plot_data[h][e][d][w][t][c] = group_args(
rmsd_start=flex.double(),
rmsd_final=flex.double())
#
p = tst_tardy_pdb_params
for cp_i_trial in xrange(cp_n_trials):
tst_tardy_comprehensive.set_parameters(
params=p, trial_table=parameter_trial_table, cp_i_trial=cp_i_trial)
plot = plot_data[p.structure_factors_high_resolution][
p.emulate_cartesian][
p.real_space_gradients_delta_resolution_factor][
p.real_space_target_weight][p.start_temperature_kelvin][
p.number_of_cooling_steps]
for rmsd in rmsds[cp_i_trial]:
plot.rmsd_start.append(rmsd[0])
plot.rmsd_final.append(rmsd[-1])
#
extra_type = None
if (pdb_file == "1yjp_box.pdb"):
if (random_displacements_parameterization == "constrained"):
extra_type = 0
elif (pdb_file == "1yjp_no_water.pdb"):
if (random_displacements_parameterization == "cartesian"):
extra_type = 1
if (extra_type is not None):
extra_page = plot_grid(grid=(4, 3),
top_labels=[],
more_narrow_shift=30)
else:
extra_page = None
mpp = multi_page_plots(file_name="plots_h_e.pdf")
for i_h, h in enumerate(ttd["structure_factors_high_resolution"]):
for e in ttd["emulate_cartesian"]:
short_label = "h%.2f_e%d" % (h, int(e))
print short_label
top_labels = [
compose_top_label(pdb_file,
random_displacements_parameterization, e)
]
top_labels.append("high resol: %.2f, algorithm: annealing" % h)
page = plot_grid(grid=(4, 3), top_labels=top_labels)
plot_xy_max = iceil(h * 1.3)
for d in ttd["real_space_gradients_delta_resolution_factor"]:
for i_w, w in enumerate(ttd["real_space_target_weight"]):
for i_t, t in enumerate(ttd["start_temperature_kelvin"]):
for i_c, c in enumerate(
ttd["number_of_cooling_steps"]):
pd = plot_data[h][e][d][w][t][c]
rf = flex.mean(pd.rmsd_final)
label = "w%04.0f_t%.0f_c%d: %.2f" % (w, t, c, rf)
print " ", label
page.process(grid_ij=(i_t * 2 + i_c, i_w),
xy_max=plot_xy_max,
label=label,
data=zip(pd.rmsd_start,
pd.rmsd_final))
if (extra_type == 0 and h == 3.75 and t == 5000
and c == 500):
extra_label = "w_rs = %.0f" % w
extra_page.process(grid_ij=(i_w + 1, int(e)),
xy_max=plot_xy_max,
label=extra_label,
data=zip(
pd.rmsd_start,
pd.rmsd_final),
label_font_size=14)
elif (extra_type == 1 and w == 100
and (h == 3.75 or h == 5.00) and c == 500):
extra_label = u"resol. = %.2f \u00C5" % h
extra_page.process(grid_ij=(i_h, int(e)),
xy_max=plot_xy_max,
label=extra_label,
data=zip(
pd.rmsd_start,
pd.rmsd_final),
label_font_size=14)
if (write_separate_pages):
page.write_to_file(file_name="plot_%s.pdf" % short_label)
mpp.add_page(page=page)
mpp.write_to_file()
if (extra_page is not None):
from reportlab.graphics.shapes import String
if (extra_type == 0): ty = 540
else: ty = 372
extra_page.top_group.add(
String(120,
ty,
"Torsion-Angle SA",
fontSize=16,
textAnchor="middle"))
extra_page.top_group.add(
String(280 + 2 / 3,
ty,
"Cartesian SA",
fontSize=16,
textAnchor="middle"))
extra_page.write_to_file(file_name="plot_extra.pdf")
#
mms = min_mean_stats(algorithm="annealing",
random_displacements_parameterization=
random_displacements_parameterization,
pdb_file=pdb_file)
assert ttd["emulate_cartesian"] == (False, True)
assert len(ttd["real_space_gradients_delta_resolution_factor"]) == 1
for h in ttd["structure_factors_high_resolution"]:
for d in ttd["real_space_gradients_delta_resolution_factor"]:
for w in ttd["real_space_target_weight"]:
for t in ttd["start_temperature_kelvin"]:
for c in ttd["number_of_cooling_steps"]:
mms.collect(rmsd_t_c=[
plot_data[h][e][d][w][t][c].rmsd_final
for e in (False, True)
],
param_values=(h, w, t, c))
mms.finalize().show().pickle()
def rmsd_start_final_plots_annealing(
pdb_file,
random_displacements_parameterization,
tst_tardy_pdb_params,
parameter_trial_table,
cp_n_trials,
rmsds,
write_separate_pages):
ttd = dict(parameter_trial_table)
plot_data = {}
for h in ttd["structure_factors_high_resolution"]:
plot_data[h] = {}
for e in ttd["emulate_cartesian"]:
plot_data[h][e] = {}
for d in ttd["real_space_gradients_delta_resolution_factor"]:
plot_data[h][e][d] = {}
for w in ttd["real_space_target_weight"]:
plot_data[h][e][d][w] = {}
for t in ttd["start_temperature_kelvin"]:
plot_data[h][e][d][w][t] = {}
for c in ttd["number_of_cooling_steps"]:
plot_data[h][e][d][w][t][c] = group_args(
rmsd_start=flex.double(),
rmsd_final=flex.double())
#
p = tst_tardy_pdb_params
for cp_i_trial in xrange(cp_n_trials):
tst_tardy_comprehensive.set_parameters(
params=p,
trial_table=parameter_trial_table,
cp_i_trial=cp_i_trial)
plot = plot_data[
p.structure_factors_high_resolution][
p.emulate_cartesian][
p.real_space_gradients_delta_resolution_factor][
p.real_space_target_weight][
p.start_temperature_kelvin][
p.number_of_cooling_steps]
for rmsd in rmsds[cp_i_trial]:
plot.rmsd_start.append(rmsd[0])
plot.rmsd_final.append(rmsd[-1])
#
extra_type = None
if (pdb_file == "1yjp_box.pdb"):
if (random_displacements_parameterization == "constrained"):
extra_type = 0
elif (pdb_file == "1yjp_no_water.pdb"):
if (random_displacements_parameterization == "cartesian"):
extra_type = 1
if (extra_type is not None):
extra_page = plot_grid(
grid=(4,3), top_labels=[], more_narrow_shift=30)
else:
extra_page = None
mpp = multi_page_plots(file_name="plots_h_e.pdf")
for i_h,h in enumerate(ttd["structure_factors_high_resolution"]):
for e in ttd["emulate_cartesian"]:
short_label = "h%.2f_e%d" % (h, int(e))
print short_label
top_labels = [compose_top_label(
pdb_file, random_displacements_parameterization, e)]
top_labels.append("high resol: %.2f, algorithm: annealing" % h)
page = plot_grid(grid=(4,3), top_labels=top_labels)
plot_xy_max = iceil(h * 1.3)
for d in ttd["real_space_gradients_delta_resolution_factor"]:
for i_w,w in enumerate(ttd["real_space_target_weight"]):
for i_t,t in enumerate(ttd["start_temperature_kelvin"]):
for i_c,c in enumerate(ttd["number_of_cooling_steps"]):
pd = plot_data[h][e][d][w][t][c]
rf = flex.mean(pd.rmsd_final)
label = "w%04.0f_t%.0f_c%d: %.2f" % (w, t, c, rf)
print " ", label
page.process(
grid_ij=(i_t*2+i_c, i_w),
xy_max=plot_xy_max,
label=label,
data=zip(pd.rmsd_start, pd.rmsd_final))
if (extra_type == 0
and h == 3.75
and t == 5000
and c == 500):
extra_label = "w_rs = %.0f" % w
extra_page.process(
grid_ij=(i_w+1, int(e)),
xy_max=plot_xy_max,
label=extra_label,
data=zip(pd.rmsd_start, pd.rmsd_final),
label_font_size=14)
elif (extra_type == 1
and w == 100
and (h == 3.75 or h == 5.00)
and c == 500):
extra_label = u"resol. = %.2f \u00C5" % h
extra_page.process(
grid_ij=(i_h, int(e)),
xy_max=plot_xy_max,
label=extra_label,
data=zip(pd.rmsd_start, pd.rmsd_final),
label_font_size=14)
if (write_separate_pages):
page.write_to_file(file_name="plot_%s.pdf" % short_label)
mpp.add_page(page=page)
mpp.write_to_file()
if (extra_page is not None):
from reportlab.graphics.shapes import String
if (extra_type == 0): ty = 540
else: ty = 372
extra_page.top_group.add(String(
120, ty,
"Torsion-Angle SA",
fontSize=16,
textAnchor="middle"))
extra_page.top_group.add(String(
280+2/3, ty,
"Cartesian SA",
fontSize=16,
textAnchor="middle"))
extra_page.write_to_file(file_name="plot_extra.pdf")
#
mms = min_mean_stats(
algorithm="annealing",
random_displacements_parameterization=random_displacements_parameterization,
pdb_file=pdb_file)
assert ttd["emulate_cartesian"] == (False, True)
assert len(ttd["real_space_gradients_delta_resolution_factor"]) == 1
for h in ttd["structure_factors_high_resolution"]:
for d in ttd["real_space_gradients_delta_resolution_factor"]:
for w in ttd["real_space_target_weight"]:
for t in ttd["start_temperature_kelvin"]:
for c in ttd["number_of_cooling_steps"]:
mms.collect(
rmsd_t_c=[plot_data[h][e][d][w][t][c].rmsd_final
for e in (False, True)],
param_values=(h,w,t,c))
mms.finalize().show().pickle()
def rmsd_start_final_plots_minimization(pdb_file,
random_displacements_parameterization,
tst_tardy_pdb_params,
parameter_trial_table, cp_n_trials,
rmsds, rmsd_n_n, write_separate_pages):
ttd = dict(parameter_trial_table)
plot_data = {}
for h in ttd["structure_factors_high_resolution"]:
plot_data[h] = {}
for e in ttd["emulate_cartesian"]:
plot_data[h][e] = {}
for d in ttd["real_space_gradients_delta_resolution_factor"]:
plot_data[h][e][d] = {}
for w in ttd["real_space_target_weight"]:
plot_data[h][e][d][w] = group_args(
rmsd_start=flex.double(),
rmsd_final=flex.double(),
rmsd_n=flex.double())
#
p = tst_tardy_pdb_params
for cp_i_trial in xrange(cp_n_trials):
tst_tardy_comprehensive.set_parameters(
params=p, trial_table=parameter_trial_table, cp_i_trial=cp_i_trial)
plot = plot_data[p.structure_factors_high_resolution][
p.emulate_cartesian][
p.real_space_gradients_delta_resolution_factor][
p.real_space_target_weight]
for rmsd in rmsds[cp_i_trial]:
plot.rmsd_start.append(rmsd[0])
plot.rmsd_final.append(rmsd[-1])
plot.rmsd_n.append(rmsd[min(len(rmsd) - 1, rmsd_n_n)])
#
mpp = multi_page_plots(file_name="plots_h_e.pdf")
for e in ttd["emulate_cartesian"]:
short_label = "e%d" % int(e)
print short_label
top_labels = [
compose_top_label(pdb_file, random_displacements_parameterization,
e), "algorithm: minimization"
]
page = plot_grid(grid=(4, 3), top_labels=top_labels)
w_d_ranks_rn = {}
w_d_ranks_rf = {}
for d in ttd["real_space_gradients_delta_resolution_factor"]:
for w in ttd["real_space_target_weight"]:
w_d_ranks_rn[(w, d)] = []
w_d_ranks_rf[(w, d)] = []
for i_h, h in enumerate(ttd["structure_factors_high_resolution"]):
plot_xy_max = iceil(h * 1.3)
page_rn = []
page_rf = []
for d in ttd["real_space_gradients_delta_resolution_factor"]:
for i_w, w in enumerate(ttd["real_space_target_weight"]):
pd = plot_data[h][e][d][w]
rn = flex.mean(pd.rmsd_n)
rf = flex.mean(pd.rmsd_final)
page_rn.append((rn, (w, d)))
page_rf.append((rf, (w, d)))
label = "h%.2f_w%04.0f: %d=%.2f, %.2f" % (h, w, rmsd_n_n,
rn, rf)
print " ", label
page.process(grid_ij=(i_h, i_w),
xy_max=plot_xy_max,
label=label,
data=zip(pd.rmsd_start, pd.rmsd_final))
def cmp_rx(a, b):
result = cmp(a[0], b[0])
if (result == 0):
result = cmp(a[1], b[1])
return result
page_rn.sort(cmp_rx)
page_rf.sort(cmp_rx)
for i, (r, w_d) in enumerate(page_rn):
w_d_ranks_rn[w_d].append(i)
for i, (r, w_d) in enumerate(page_rf):
w_d_ranks_rf[w_d].append(i)
if (write_separate_pages):
page.write_to_file(file_name="plot_%s.pdf" % short_label)
mpp.add_page(page=page)
w_d_ranks_rn = w_d_ranks_rn.items()
w_d_ranks_rf = w_d_ranks_rf.items()
def cmp_w_d_ranks(a, b):
result = cmp(sum(a[1]), sum(b[1]))
if (result == 0):
result = cmp(sorted(a[1]), sorted(b[1]))
if (result == 0):
result = cmp(a[1], b[1])
if (result == 0):
result = cmp(a[0], b[0])
return result
w_d_ranks_rn.sort(cmp_w_d_ranks)
w_d_ranks_rf.sort(cmp_w_d_ranks)
print "emulate_cartesian = %s" % str(e)
for prefix, w_d_ranks in [("rn:", w_d_ranks_rn),
("rf:", w_d_ranks_rf)]:
for w_d, ranks in w_d_ranks:
print prefix, "%4.0f %4.2f" % w_d, "%2d" % sum(ranks), \
"[" + ", ".join(["%2d" % r for r in ranks]) + "]"
prefix = " "
print
mpp.write_to_file()
#
mms = min_mean_stats(algorithm="minimization",
random_displacements_parameterization=
random_displacements_parameterization,
pdb_file=pdb_file)
assert ttd["emulate_cartesian"] == (False, True)
assert len(ttd["real_space_gradients_delta_resolution_factor"]) == 1
for h in ttd["structure_factors_high_resolution"]:
for d in ttd["real_space_gradients_delta_resolution_factor"]:
for w in ttd["real_space_target_weight"]:
mms.collect(rmsd_t_c=[
plot_data[h][e][d][w].rmsd_final for e in (False, True)
],
param_values=(h, w))
mms.finalize().show().pickle()
def rmsd_start_final_plots_minimization(
pdb_file,
random_displacements_parameterization,
tst_tardy_pdb_params,
parameter_trial_table,
cp_n_trials,
rmsds,
rmsd_n_n,
write_separate_pages):
ttd = dict(parameter_trial_table)
plot_data = {}
for h in ttd["structure_factors_high_resolution"]:
plot_data[h] = {}
for e in ttd["emulate_cartesian"]:
plot_data[h][e] = {}
for d in ttd["real_space_gradients_delta_resolution_factor"]:
plot_data[h][e][d] = {}
for w in ttd["real_space_target_weight"]:
plot_data[h][e][d][w] = group_args(
rmsd_start=flex.double(),
rmsd_final=flex.double(),
rmsd_n=flex.double())
#
p = tst_tardy_pdb_params
for cp_i_trial in xrange(cp_n_trials):
tst_tardy_comprehensive.set_parameters(
params=p,
trial_table=parameter_trial_table,
cp_i_trial=cp_i_trial)
plot = plot_data[
p.structure_factors_high_resolution][
p.emulate_cartesian][
p.real_space_gradients_delta_resolution_factor][
p.real_space_target_weight]
for rmsd in rmsds[cp_i_trial]:
plot.rmsd_start.append(rmsd[0])
plot.rmsd_final.append(rmsd[-1])
plot.rmsd_n.append(rmsd[min(len(rmsd)-1, rmsd_n_n)])
#
mpp = multi_page_plots(file_name="plots_h_e.pdf")
for e in ttd["emulate_cartesian"]:
short_label = "e%d" % int(e)
print short_label
top_labels = [
compose_top_label(
pdb_file, random_displacements_parameterization, e),
"algorithm: minimization"]
page = plot_grid(grid=(4,3), top_labels=top_labels)
w_d_ranks_rn = {}
w_d_ranks_rf = {}
for d in ttd["real_space_gradients_delta_resolution_factor"]:
for w in ttd["real_space_target_weight"]:
w_d_ranks_rn[(w,d)] = []
w_d_ranks_rf[(w,d)] = []
for i_h,h in enumerate(ttd["structure_factors_high_resolution"]):
plot_xy_max = iceil(h * 1.3)
page_rn = []
page_rf = []
for d in ttd["real_space_gradients_delta_resolution_factor"]:
for i_w,w in enumerate(ttd["real_space_target_weight"]):
pd = plot_data[h][e][d][w]
rn = flex.mean(pd.rmsd_n)
rf = flex.mean(pd.rmsd_final)
page_rn.append((rn, (w,d)))
page_rf.append((rf, (w,d)))
label = "h%.2f_w%04.0f: %d=%.2f, %.2f" % (h, w, rmsd_n_n, rn, rf)
print " ", label
page.process(
grid_ij=(i_h,i_w),
xy_max=plot_xy_max,
label=label,
data=zip(pd.rmsd_start, pd.rmsd_final))
def cmp_rx(a, b):
result = cmp(a[0], b[0])
if (result == 0):
result = cmp(a[1], b[1])
return result
page_rn.sort(cmp_rx)
page_rf.sort(cmp_rx)
for i,(r,w_d) in enumerate(page_rn):
w_d_ranks_rn[w_d].append(i)
for i,(r,w_d) in enumerate(page_rf):
w_d_ranks_rf[w_d].append(i)
if (write_separate_pages):
page.write_to_file(file_name="plot_%s.pdf" % short_label)
mpp.add_page(page=page)
w_d_ranks_rn = w_d_ranks_rn.items()
w_d_ranks_rf = w_d_ranks_rf.items()
def cmp_w_d_ranks(a, b):
result = cmp(sum(a[1]), sum(b[1]))
if (result == 0):
result = cmp(sorted(a[1]), sorted(b[1]))
if (result == 0):
result = cmp(a[1], b[1])
if (result == 0):
result = cmp(a[0], b[0])
return result
w_d_ranks_rn.sort(cmp_w_d_ranks)
w_d_ranks_rf.sort(cmp_w_d_ranks)
print "emulate_cartesian = %s" % str(e)
for prefix,w_d_ranks in [("rn:", w_d_ranks_rn),
("rf:", w_d_ranks_rf)]:
for w_d,ranks in w_d_ranks:
print prefix, "%4.0f %4.2f" % w_d, "%2d" % sum(ranks), \
"[" + ", ".join(["%2d" % r for r in ranks]) + "]"
prefix = " "
print
mpp.write_to_file()
#
mms = min_mean_stats(
algorithm="minimization",
random_displacements_parameterization=random_displacements_parameterization,
pdb_file=pdb_file)
assert ttd["emulate_cartesian"] == (False, True)
assert len(ttd["real_space_gradients_delta_resolution_factor"]) == 1
for h in ttd["structure_factors_high_resolution"]:
for d in ttd["real_space_gradients_delta_resolution_factor"]:
for w in ttd["real_space_target_weight"]:
mms.collect(
rmsd_t_c=[plot_data[h][e][d][w].rmsd_final for e in (False, True)],
param_values=(h,w))
mms.finalize().show().pickle()
