def load_tables():
tables = {}
for residue_type in ["ala", "gly", "prepro", "pro"]:
file_name = libtbx.env.find_in_repositories(
relative_path="chem_data/rotarama_data/%s.rama.combined.data" %
residue_type,
test=os.path.isfile)
f = open(file_name, "r")
data = flex.double()
for line in f.readlines():
val, phi, psi = line.split()
assert ((int(phi) % 2 == 1) and (int(psi) % 2 == 1))
data.append(float(val))
t = lookup_table(data, 180)
tables[residue_type] = t
return tables
def load_tables(params=None):
if (params is None):
params = master_phil.fetch().extract()
if (params.scale_allowed <= 0.0):
raise Sorry("Ramachandran restraint parameter scale_allowed must be " +
"a positive number (current value: %g)." %
params.scale_allowed)
tables = {}
for residue_type in ["ala", "gly", "prepro", "pro"]:
file_name = libtbx.env.find_in_repositories(
relative_path="chem_data/rotarama_data/%s.rama.combined.data" %
residue_type,
test=os.path.isfile)
f = open(file_name, "r")
data = flex.double()
for line in f.readlines():
val, phi, psi = line.split()
assert ((int(phi) % 2 == 1) and (int(psi) % 2 == 1))
data.append(float(val))
t = lookup_table(data, 180, params.scale_allowed)
tables[residue_type] = t
return tables
def load_tables (params=None) :
import boost.python
if (params is None) :
params = master_phil.fetch().extract()
if (params.scale_allowed <= 0.0) :
raise Sorry("Ramachandran restraint parameter scale_allowed must be "+
"a positive number (current value: %g)." % params.scale_allowed)
tables = {}
for residue_type in ["ala", "gly", "prepro", "pro"] :
file_name = libtbx.env.find_in_repositories(
relative_path="chem_data/rotarama_data/%s.rama.combined.data" %
residue_type,
test=os.path.isfile)
f = open(file_name, "r")
data = flex.double()
for line in f.readlines() :
val, phi, psi = line.split()
assert ((int(phi) % 2 == 1) and (int(psi) % 2 == 1))
data.append(float(val))
t = lookup_table(data, 180, params.scale_allowed)
tables[residue_type] = t
return tables
def load_emsley8k_tables():
tables = {}
name_to_file = [("general", "rama8000-general-noGPIVpreP.data", 0),
("glycine", "rama8000-gly-sym.data", 1),
("cis-proline", "rama8000-cispro.data", 2),
("trans-proline", "rama8000-transpro.data", 3),
("pre-proline", "rama8000-prepro-noGP.data", 4),
("isoleucine or valine", "rama8000-ileval-nopreP.data", 5)]
tmp = OrderedDict()
rr = [i for i in range(-179, 180, 2)]
for i in rr:
for j in rr:
tmp[(i, j)] = 0
R = ramachandran_eval.RamachandranEval()
outlier = ramalyze.RAMALYZE_OUTLIER
favored = ramalyze.RAMALYZE_FAVORED
allowed = ramalyze.RAMALYZE_ALLOWED
for (rama_key, file_name, selfstore) in name_to_file:
file_name = libtbx.env.find_in_repositories(
relative_path="chem_data/rotarama_data/%s" % (file_name),
test=os.path.isfile)
di = {}
outlier_vals = flex.double()
favored_vals = flex.double()
allowed_vals = flex.double()
status = {}
with open(file_name, "r") as f:
lines = f.readlines()
for line in lines:
if line[0] == "#": continue
phi, psi, val = line.split()
phi = int(float(phi))
psi = int(float(psi))
val = float(val)
di[(phi, psi)] = val
rama_score = R.rama_eval.get_score(selfstore, phi, psi)
evaluation = R.rama_eval.evaluate_score(selfstore, rama_score)
if (evaluation == outlier):
outlier_vals.append(val)
status[(phi, psi)] = outlier
elif (evaluation == favored):
favored_vals.append(val)
status[(phi, psi)] = favored
elif (evaluation == allowed):
allowed_vals.append(val)
status[(phi, psi)] = allowed
else:
raise RuntimeError("Not supposed to be here.")
data = flex.double()
max_outlier = flex.max(outlier_vals)
max_favored = flex.max(favored_vals)
min_favored = flex.min(favored_vals)
max_allowed = flex.max(allowed_vals)
for k, v in zip(tmp.keys(), tmp.values()):
try:
val = di[k]
if (status[k] == outlier): val = -1 + val / max_outlier
elif (status[k] == favored):
val = val #math.exp(val)**0.5/2.71828182846**0.5 #math.exp(val)**3
elif (status[k] == allowed):
val = val #math.exp(val)**0.5/2.71828182846**0.5 #math.exp(val/max_allowed)**3
except KeyError:
val = -1
data.append(val)
t = lookup_table(data, 180)
tables[rama_key] = t
return tables