/
filter_util_no_flex.py
91 lines (88 loc) · 3.02 KB
/
filter_util_no_flex.py
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from __future__ import division
def import_ptms(ptms_outfile):
chain_id = []
resid = []
resname = []
goto_atom = []
short_name = []
full_name = []
cc = []
d_ref = []
d_mid = []
d_new_in_ref = []
d_new_diff = []
d_far = []
ratio = []
score = []
model_id = []
d_min = []
b_factor = []
imported_ptms = (chain_id, resid, resname, goto_atom, short_name, full_name, cc,
d_ref, d_mid, d_new_in_ref, d_new_diff, d_far, ratio, score, model_id, d_min, b_factor)
with open(ptms_outfile, "r") as out:
for line in out.readlines():
items = line.split()
try:
for array, coerce_lambda in zip(imported_ptms,
(lambda x: x, lambda x: int(x), lambda x: x, lambda x: x, lambda x: x, lambda x: x,
lambda x: float(x), lambda x: float(x), lambda x: float(x), lambda x: float(x),
lambda x: float(x), lambda x: float(x), lambda x: float(x), lambda x: float(x),
lambda x: int(x), lambda x: float(x), lambda x: float(x))):
array.append(coerce_lambda(items.pop(0)))
except ValueError:
raise Exception("line could not be parsed: %s" % line)
return imported_ptms
def import_ptms_with_predictions(ptms_outfile):
chain_id = []
resid = []
resname = []
goto_atom = []
short_name = []
full_name = []
cc = []
d_ref = []
d_mid = []
d_new_in_ref = []
d_new_diff = []
d_far = []
ratio = []
score = []
model_id = []
d_min = []
b_factor = []
predictions = []
probabilities = []
imported_ptms = (chain_id, resid, resname, goto_atom, short_name, full_name, cc,
d_ref, d_mid, d_new_in_ref, d_new_diff, d_far, ratio, score, model_id, d_min, b_factor,
predictions, probabilities)
with open(ptms_outfile, "r") as out:
for line in out.readlines():
items = line.split()
for array, coerce_lambda in zip(imported_ptms,
(lambda x: x, lambda x: int(x), lambda x: x, lambda x: x, lambda x: x, lambda x: x,
lambda x: float(x), lambda x: float(x), lambda x: float(x), lambda x: float(x),
lambda x: float(x), lambda x: float(x), lambda x: float(x), lambda x: float(x),
lambda x: int(x), lambda x: float(x), lambda x: float(x),
lambda x: int(x), lambda x: float(x))):
array.append(coerce_lambda(items.pop(0)))
return imported_ptms
def import_synthetic_ptms(synthetic_ptms_outfile):
chain_id = []
resid = []
resname = []
goto_atom = []
short_name = []
full_name = []
model_id = []
d_min = []
b_factor = []
imported_synthetic_ptms = (chain_id, resid, resname, goto_atom, short_name, full_name,
model_id, d_min, b_factor)
with open(synthetic_ptms_outfile, "r") as out:
for line in out.readlines():
items = line.split()
for array, coerce_lambda in zip(imported_synthetic_ptms,
(lambda x: x, lambda x: int(x), lambda x: x, lambda x: x, lambda x: x, lambda x: x,
lambda x: int(x), lambda x: float(x), lambda x: None if x == 'None' else float(x))):
array.append(coerce_lambda(items.pop(0)))
return imported_synthetic_ptms