def run(prefix):
"""
Exercise gradients match:
- small vs large box:
-- using clustering vs not using clustering.
Non-P1 case (P212121)
"""
data_file_prefix = "2olx"
common_args = ["restraints=cctbx", "mode=opt", "parallel.nproc=1"]
r = run_tests.run_cmd(
prefix,
args=common_args +
["clustering=true", "dump_gradients=cluster_true.pkl"],
pdb_name=os.path.join(qr_unit_tests_data, "%s.pdb" % data_file_prefix),
mtz_name=os.path.join(qr_unit_tests_data, "%s.mtz" % data_file_prefix))
r = run_tests.run_cmd(
prefix,
args=common_args +
["clustering=false", "dump_gradients=cluster_false.pkl"],
pdb_name=os.path.join(qr_unit_tests_data, "%s.pdb" % data_file_prefix),
mtz_name=os.path.join(qr_unit_tests_data, "%s.mtz" % data_file_prefix))
#
g1 = flex.vec3_double(easy_pickle.load("cluster_false.pkl"))
g2 = flex.vec3_double(easy_pickle.load("cluster_true.pkl"))
assert g1.size() == g2.size()
diff = g1 - g2
if (0):
for i, diff_i in enumerate(diff):
print i, diff_i #, g1[i], g2[i]
print
assert approx_equal(diff.max(), [0, 0, 0])
def run(prefix):
"""
Exercise standard (cctbx-based restraints) refinement with all defaults.
"""
xrs_good, xrs_poor, f_obs, r_free_flags = run_tests.setup_helix_example()
run_tests.run_cmd(prefix, args=["restraints=cctbx"])
# Check results
xrs_refined = iotbx.pdb.input(file_name=os.path.join(
prefix, "m00_poor_refined.pdb")).xray_structure_simple()
d = flex.sqrt((xrs_good.sites_cart() - xrs_poor.sites_cart()).dot())
assert flex.mean(d) > 0.10
d = flex.sqrt((xrs_good.sites_cart() - xrs_refined.sites_cart()).dot())
assert flex.mean(d) < 0.05
# Check R-factors
r_start, r_final = None, None
ofo = open("%s.log" % prefix, "r")
for l in ofo.readlines():
if (l.strip().endswith("n_fev: 0")):
r_start = float(l.split()[2])
if (l.strip().startswith("Best r_work:")):
r_final = float(l.split()[2])
assert r_start > 0.1
assert r_final < 0.04
# Make sure output model actually corresponds to reported R-factor
fmodel = mmtbx.f_model.manager(f_obs=f_obs,
r_free_flags=r_free_flags,
xray_structure=xrs_refined)
fmodel.update_all_scales()
assert fmodel.r_work() < 0.04
assert abs(r_final - fmodel.r_work()) < 0.0005, abs(r_final -
fmodel.r_work())
def run(prefix):
"""
Exercise standard (cctbx-based restraints) optimization (no data required).
"""
xrs_good, xrs_poor, f_obs, r_free_flags = run_tests.setup_helix_example()
run_tests.run_cmd(
prefix=prefix,
args=["restraints=cctbx mode=opt use_convergence_test=False"],
mtz_name="")
# Check results
pdb_inp = iotbx.pdb.input(
file_name=os.path.join(prefix, "m00_poor_refined.pdb"))
model_1 = mmtbx.model.manager(model_input=pdb_inp,
build_grm=True,
log=null_out())
s1 = model_1.geometry_statistics().result()
assert s1.bond.mean < 0.005
##########################
xrs_good, xrs_poor, f_obs, r_free_flags = run_tests.setup_helix_example()
run_tests.run_cmd(
prefix=prefix,
args=["restraints=cctbx mode=opt use_convergence_test=False"])
# Check results
pdb_inp = iotbx.pdb.input(
file_name=os.path.join(prefix, "m00_poor_refined.pdb"))
model_2 = mmtbx.model.manager(model_input=pdb_inp,
build_grm=True,
log=null_out())
s2 = model_2.geometry_statistics().result()
assert s2.bond.mean < 0.005
###########################
assert approx_equal(s1.bond.mean, s2.bond.mean, 0.0001)
def run(prefix):
"""
Exercise refinement match:
-- pdbs with altlocs
-- using subtract vs using average
"""
import multiprocessing
nproc = str(multiprocessing.cpu_count())
for data_file_prefix in ["h_altconf_complete", "h_altconf_2_complete"]:
for maxnum in ["15"]:
common_args = ["restraints=cctbx", "mode=refine", "parallel.nproc="+nproc, "clustering=true"] +\
["gradient_only=true", "maxnum_residues_in_cluster="+maxnum]
r = run_tests.run_cmd(prefix,
args = common_args+["altloc_method=subtract", "output_file_name_prefix=subtract-"+data_file_prefix],
pdb_name = os.path.join(qr_unit_tests_data,"%s.pdb"%data_file_prefix),
mtz_name = os.path.join(qr_unit_tests_data,"%s.mtz"%data_file_prefix))
r = run_tests.run_cmd(prefix,
args = common_args+["altloc_method=average", "output_file_name_prefix=average-"+data_file_prefix],
pdb_name = os.path.join(qr_unit_tests_data,"%s.pdb"%data_file_prefix),
mtz_name = os.path.join(qr_unit_tests_data,"%s.mtz"%data_file_prefix))
sites_cart_average = iotbx.pdb.input("%s/average-%s_refined.pdb"%(prefix,data_file_prefix)).\
construct_hierarchy().extract_xray_structure().sites_cart()
sites_cart_subtract = iotbx.pdb.input("%s/subtract-%s_refined.pdb"%(prefix,data_file_prefix)).\
construct_hierarchy().extract_xray_structure().sites_cart()
rmsd_diff = sites_cart_average.rms_difference(sites_cart_subtract)
assert approx_equal(rmsd_diff, 0, 0.2), 'rmsd diff between subtract and average is too large %0.3f' % rmsd_diff
def run(prefix):
"""
Exercise refine_sites=False.
"""
xrs_good, xrs_poor, f_obs, r_free_flags = run_tests.setup_helix_example()
run_tests.run_cmd(prefix, args=["restraints=cctbx", "refine_sites=False"])
xrs_start = iotbx.pdb.input(file_name=os.path.join(
qr_unit_tests, "data_files", "m00_poor.pdb")).xray_structure_simple()
xrs_refined = iotbx.pdb.input(file_name=os.path.join(
prefix, "m00_poor_refined.pdb")).xray_structure_simple()
d = flex.sqrt((xrs_start.sites_cart() - xrs_refined.sites_cart()).dot())
assert approx_equal(d.min_max_mean().as_tuple(), [0, 0, 0])
def run(prefix):
"""
Exercise standard (cctbx-based restraints) optimization.
Assert mode=opt is approximately equivalent to data_weight=0.
"""
xrs_good, xrs_poor, f_obs, r_free_flags = run_tests.setup_helix_example()
# Run optimization
run_tests.run_cmd(prefix, args=["restraints=cctbx", "mode=opt"])
assert get_bond_rmsd(file_name=os.path.join(qr_unit_tests, "data_files",
"m00_poor.pdb")) > 0.1
assert get_bond_rmsd(
file_name=os.path.join(prefix, "m00_poor_refined.pdb")) < 0.0009
#Run refinement without data term
run_tests.run_cmd(prefix, args=["restraints=cctbx", "data_weight=0"])
assert get_bond_rmsd(
file_name=os.path.join(prefix, "m00_poor_refined.pdb")) < 0.0009
def run(prefix="tst_18"):
"""
Exercise gradients match:
-- pdbs with altlocs
-- using clustering with less clusters vs not using clustering.
-- using clustering with more clusters vs not using clustering.
"""
import multiprocessing
nproc = str(multiprocessing.cpu_count())
for data_file_prefix in ["h_altconf_complete", "h_altconf_2_complete"]:
for maxnum in ["15", "2"]:
common_args = ["restraints=cctbx", "mode=opt", "parallel.nproc="+nproc] +\
["altloc_method=subtract","maxnum_residues_in_cluster="+maxnum]
r = run_tests.run_cmd(
prefix,
args=common_args +
["clustering=true", "dump_gradients=cluster_true.pkl"],
pdb_name=os.path.join(qr_unit_tests_data,
"%s.pdb" % data_file_prefix),
mtz_name=os.path.join(qr_unit_tests_data,
"%s.mtz" % data_file_prefix))
r = run_tests.run_cmd(
prefix,
args=common_args +
["clustering=false", "dump_gradients=cluster_false.pkl"],
pdb_name=os.path.join(qr_unit_tests_data,
"%s.pdb" % data_file_prefix),
mtz_name=os.path.join(qr_unit_tests_data,
"%s.mtz" % data_file_prefix))
#
g1 = flex.vec3_double(easy_pickle.load("cluster_false.pkl"))
g2 = flex.vec3_double(easy_pickle.load("cluster_true.pkl"))
assert g1.size() == g2.size()
diff = g1 - g2
if (0):
for i, diff_i in enumerate(diff):
print i, diff_i #, g1[i], g2[i]
assert approx_equal(diff.max(), [0, 0, 0],
[1.0E-3, 1.0E-3, 1.0E-3])
def run(prefix="tst_06"):
"""
Exercise altlocs.
"""
run_tests.assert_folder_is_empty(prefix=prefix)
xrs_good, xrs_poor, f_obs, r_free_flags = run_tests.setup_helix_example(
pdb_name="altlocs.pdb", mtz_name="altlocs.mtz")
r = run_tests.run_cmd(prefix,
args=["restraints=cctbx"],
pdb_name="altlocs.pdb",
mtz_name="altlocs.mtz")
#assert r.stdout_lines == \
# ['Sorry: Alternative conformations are not supported.']
run_tests.clean_up(prefix, mtz_name="altlocs.mtz")
def run(prefix):
"""
Exercise gradients match:
- small vs large box:
-- using clustering vs not using clustering.
"""
for restraints in ["cctbx", "qm"]:
#for restraints in ["cctbx",]:
# XXX qm option is not supposed to work fulfull the test with 2ona_box_S
# XXX qm option is currently suspected to be broken for 2ona_box_L
for data_file_prefix in ["2ona_box_L", "2ona_box_S"]:
if (restraints is "qm" and data_file_prefix is "2ona_box_S"):
continue
common_args = [
"restraints=%s" % restraints, "mode=opt", "parallel.nproc=1",
"quantum.engine_name=mopac", "two_buffers=true"
]
args = common_args + [
"clustering=false", "dump_gradients=cluster_false.pkl"
]
r = run_tests.run_cmd(
prefix,
args=args,
pdb_name=os.path.join(qr_unit_tests_data,
"%s.pdb" % data_file_prefix),
mtz_name=os.path.join(qr_unit_tests_data,
"%s.mtz" % data_file_prefix))
args = common_args + [
"clustering=true", "dump_gradients=cluster_true.pkl"
]
r = run_tests.run_cmd(
prefix,
args=args,
pdb_name=os.path.join(qr_unit_tests_data,
"%s.pdb" % data_file_prefix),
mtz_name=os.path.join(qr_unit_tests_data,
"%s.mtz" % data_file_prefix))
#
g1 = easy_pickle.load("cluster_false.pkl")
g2 = easy_pickle.load("cluster_true.pkl")
if (restraints is "cctbx"):
##tight comparison
## clustering cctbx should match the atomic gradients
## at x,y,z directions
g1 = g1.as_double()
g2 = g2.as_double()
assert g1.size() == g2.size()
diff = g1 - g2
if (0):
for i, diff_i in enumerate(diff):
print i + 1, diff_i, g1[i], g2[i]
print
assert approx_equal(g1, g2, 1.0E-4)
else:
## loose comparison
## clustering qm just checks the norm of gradients from
## x, y, z directions
assert approx_equal(g1.norm(), g2.norm(), g1.norm() * 0.05)
g1_norms = flex.sqrt(g1.dot())
g2_norms = flex.sqrt(g2.dot())
for i in range(g1_norms.size()):
#print i+1, g1_norms[i], g2_norms[i], g1_norms[i]*0.2
assert approx_equal(g1_norms[i], g2_norms[i],
g1_norms[i] * 0.2)