def load_centers(iterable):
dict_ = CIDict()
for row in iterable:
if row[0] != '':
dict_.update({row[0]:row[1]})
for key, value in dict_.items():
# Turns '(1,2,3)' etc, that is, textual representations of vectors,
# into Vector objects. Will cut off the last digit of the third
# componant, but I don't care because the third componant will
# always be 0.0
dict_[key] = np.array([float(y[:-1]) for y in value[1:].split()])
return dict_
def moments(path, selections, centers):
with open('published params.csv', 'rb') as f:
reader = csv.reader(f)
new_calc = ezb.Calculator(reader, normalize = False)
moments = CIDict()
for name in structures.keys():
if name.upper() == '1QD5':
continue
moments.update({name:
ezb.moment(structures[name], selections[name],
centers[name], new_calc,
paramless_option = '0',
old_style_gly = True) })
with open(path, 'wb') as f:
target = csv.writer(f)
for name, moment in moments.items():
row = [name.upper()] + [str(component) for component in moment]
target.writerow(row)
def selections_by_index(iterable):
selections = CIDict()
test_sequences = CIDict()
for line in iterable:
selections.update({line[0]: [int(x) for x in line[2:]]})
test_sequences.update({line[0]: line[1]})
return selections, test_sequences
def selections_by_resi(iterable):
selections = CIDict()
for line in iterable:
selections.update({line[0]: [int(x) for x in line[1:]]})
return selections
with warnings.catch_warnings():
warnings.simplefilter('ignore')
structures = [(name, parser.get_structure(name, path)) \
for name, path in structure_files.items()]
structures = CIDict(structures)
with open('cored 1 selections with 1qd5.csv', 'rb') as f:
reader = csv.reader(f)
inclusive_selections = ezb.selections_by_resi(reader)
with open('beta_selections.csv', 'rb') as f:
reader = csv.reader(f)
beta_selections = ezb.selections_by_resi(reader)
exclusive_selections = CIDict()
for name, selection in beta_selections.items():
exclusive_selections.update(((name, list(set(selection).intersection(
set(inclusive_selections[name])))),))
with open('cored 1 centers with 1qd5.csv', 'rb') as f:
reader = csv.reader(f)
inc_centers = ezb.load_centers(reader)
with open('exc centers.csv', 'rb') as f:
exc_centers = ezb.load_centers(csv.reader(f))
def moments(path, selections, centers):
with open('published params.csv', 'rb') as f:
# or thousands of warnings when loading them. The following code
# suppresses warnings
with warnings.catch_warnings():
warnings.simplefilter('ignore')
structures = [(name, parser.get_structure(name, path)) \
for name, path in structure_files.items()]
structures = CIDict(structures)
# Load the selections that should have been used
with open('exc centers/exc selections.csv', 'rb') as f:
reader = csv.reader(f)
beta_selections = ezb.selections_by_resi(reader)
right_selections = CIDict()
for name, selection in beta_selections.items():
right_selections.update(((name, selection),))
with open('exc centers.csv', 'rb') as f:
exc_centers = ezb.load_centers(csv.reader(f))
def moments(path, selections, centers):
with open('published params.csv', 'rb') as f:
reader = csv.reader(f)
new_calc = ezb.Calculator(reader, normalize = True)
moments = CIDict()
for name in structures.keys():
if name.upper() == '1QD5':
with warnings.catch_warnings():
warnings.simplefilter('ignore')
structures = [(name, parser.get_structure(name, path)) \
for name, path in structure_files.items()]
structures = CIDict(structures)
with open('cored 1 selections with 1qd5.csv', 'rb') as f:
reader = csv.reader(f)
inclusive_selections = ezb.selections_by_resi(reader)
with open('beta_selections.csv', 'rb') as f:
reader = csv.reader(f)
beta_selections = ezb.selections_by_resi(reader)
exclusive_selections = CIDict()
for name, selection in beta_selections.items():
exclusive_selections.update(((name, list(set(selection).intersection(
set(inclusive_selections[name])))),))
with open('cored 1 centers with 1qd5.csv', 'rb') as f:
reader = csv.reader(f)
inc_centers = ezb.load_centers(reader)
with open('exc centers.csv', 'rb') as f:
exc_centers = ezb.load_centers(csv.reader(f))
def moments(path, selections, centers):
with open('published params.csv', 'rb') as f: