def generate_table(self):
"""Generate a normalised table of the results.
The table is stored as a :class:`numpy.recarray` in the
attribute :attr:`~HydrogenBondAnalysis.table` and can be used
with e.g. `recsql`_.
Columns:
0. "time"
1. "donor_idx"
2. "acceptor_idx"
3. "donor_resnm"
4. "donor_resid"
5. "donor_atom"
6. "acceptor_resnm"
7. "acceptor_resid"
8. "acceptor_atom"
9. "distance"
10. "angle"
.. _recsql: http://pypi.python.org/pypi/RecSQL
"""
from itertools import izip
if self.timeseries is None:
msg = "No timeseries computed, do run() first."
warnings.warn(msg, category=MissingDataWarning)
logger.warn(msg)
return
num_records = numpy.sum([len(hframe) for hframe in self.timeseries])
dtype = [("time", float), ("donor_idx", int), ("acceptor_idx", int),
("donor_resnm", "|S4"), ("donor_resid", int),
("donor_atom", "|S4"), ("acceptor_resnm", "|S4"),
("acceptor_resid", int), ("acceptor_atom", "|S4"),
("distance", float), ("angle", float)]
self.table = numpy.recarray((num_records, ), dtype=dtype)
# according to Lukas' notes below, using a recarray at this stage is ineffective
# and speedups of ~x10 could be achieved by filling a standard array
# (perhaps at the cost of less clarity... but that might just be my code ;-) -- orbeckst)
cursor = 0 # current row
for t, hframe in izip(self.timesteps, self.timeseries):
if len(hframe) == 0:
continue # not really necessary, should also work without
self.table[cursor:cursor + len(hframe)].time = t
for donor_idx, acceptor_idx, donor, acceptor, distance, angle in hframe:
r = self.table[cursor]
r.donor_idx = donor_idx
r.donor_resnm, r.donor_resid, r.donor_atom = parse_residue(
donor)
r.acceptor_idx = acceptor_idx
r.acceptor_resnm, r.acceptor_resid, r.acceptor_atom = parse_residue(
acceptor)
r.distance = distance
r.angle = angle
cursor += 1
assert cursor == num_records, "Internal Error: Not all HB records stored"
logger.debug(
"HBond: Stored results as table with %(num_records)d entries.",
vars())
def timesteps_by_type(self):
"""Frames during which each hydrogen bond existed, sorted by hydrogen bond.
Processes :attr:`HydrogenBondAnalysis.timeseries` and returns
a :class:`numpy.recarray` containing atom indices, residue
names, residue numbers (for donors and acceptors) and a list
of timesteps at which the hydrogen bond was detected.
:Returns: a class:`numpy.recarray`
"""
from itertools import izip
if self.timeseries is None:
msg = "No timeseries computed, do run() first."
warnings.warn(msg, category=MissingDataWarning)
logger.warn(msg)
return
hbonds = defaultdict(list)
for (t, hframe) in izip(self.timesteps, self.timeseries):
for donor_idx, acceptor_idx, donor, acceptor, distance, angle in hframe:
donor_resnm, donor_resid, donor_atom = parse_residue(donor)
acceptor_resnm, acceptor_resid, acceptor_atom = parse_residue(
acceptor)
# generate unambigous key for current hbond
# (the donor_heavy_atom placeholder '?' is added later)
hb_key = (donor_idx, acceptor_idx, donor_resnm, donor_resid,
"?", donor_atom, acceptor_resnm, acceptor_resid,
acceptor_atom)
hbonds[hb_key].append(t)
out_nrows = 0
# count number of timesteps per key to get length of output table
for ts_list in hbonds.itervalues():
out_nrows += len(ts_list)
# build empty output table
dtype = [('donor_idx', int), ('acceptor_idx', int),
('donor_resnm', 'S4'), ('donor_resid', int),
('donor_heavy_atom', 'S4'), ('donor_atom', 'S4'),
('acceptor_resnm', 'S4'), ('acceptor_resid', int),
('acceptor_atom', 'S4'), ('time', float)]
out = numpy.empty((out_nrows, ), dtype=dtype)
out_row = 0
for (key, times) in hbonds.iteritems():
for tstep in times:
out[out_row] = key + (tstep, )
out_row += 1
# return array as recarray
# The recarray has not been used within the function, because accessing the
# the elements of a recarray (3.65 us) is much slower then accessing those
# of a ndarray (287 ns).
r = out.view(numpy.recarray)
# patch in donor heavy atom names (replaces '?' in the key)
h2donor = self._donor_lookup_table_byindex()
r.donor_heavy_atom[:] = [h2donor[idx - 1] for idx in r.donor_idx]
return r
def timesteps_by_type(self):
"""Frames during which each hydrogen bond existed, sorted by hydrogen bond.
Processes :attr:`HydrogenBondAnalysis.timeseries` and returns
a :class:`numpy.recarray` containing atom indices, residue
names, residue numbers (for donors and acceptors) and a list
of timesteps at which the hydrogen bond was detected.
:Returns: a class:`numpy.recarray`
"""
from itertools import izip
if self.timeseries is None:
msg = "No timeseries computed, do run() first."
warnings.warn(msg, category=MissingDataWarning)
logger.warn(msg)
return
hbonds = defaultdict(list)
for (t,hframe) in izip(self.timesteps, self.timeseries):
for donor_idx, acceptor_idx, donor, acceptor, distance, angle in hframe:
donor_resnm, donor_resid, donor_atom = parse_residue(donor)
acceptor_resnm, acceptor_resid, acceptor_atom = parse_residue(acceptor)
# generate unambigous key for current hbond
# (the donor_heavy_atom placeholder '?' is added later)
hb_key = (donor_idx, acceptor_idx,
donor_resnm, donor_resid, "?", donor_atom,
acceptor_resnm, acceptor_resid, acceptor_atom)
hbonds[hb_key].append(t)
out_nrows = 0
# count number of timesteps per key to get length of output table
for ts_list in hbonds.itervalues():
out_nrows += len(ts_list)
# build empty output table
dtype = [('donor_idx', int), ('acceptor_idx', int),
('donor_resnm', 'S4'), ('donor_resid', int), ('donor_heavy_atom', 'S4'), ('donor_atom', 'S4'),
('acceptor_resnm', 'S4'), ('acceptor_resid', int), ('acceptor_atom', 'S4'),
('time', float)]
out = numpy.empty((out_nrows,), dtype=dtype)
out_row = 0
for (key, times) in hbonds.iteritems():
for tstep in times:
out[out_row] = key + (tstep,)
out_row += 1
# return array as recarray
# The recarray has not been used within the function, because accessing the
# the elements of a recarray (3.65 us) is much slower then accessing those
# of a ndarray (287 ns).
r = out.view(numpy.recarray)
# patch in donor heavy atom names (replaces '?' in the key)
h2donor = self._donor_lookup_table_byindex()
r.donor_heavy_atom[:] = [h2donor[idx-1] for idx in r.donor_idx]
return r
def generate_table(self):
"""Generate a normalised table of the results.
The table is stored as a :class:`numpy.recarray` in the
attribute :attr:`~HydrogenBondAnalysis.table` and can be used
with e.g. `recsql`_.
Columns:
0. "time"
1. "donor_idx"
2. "acceptor_idx"
3. "donor_resnm"
4. "donor_resid"
5. "donor_atom"
6. "acceptor_resnm"
7. "acceptor_resid"
8. "acceptor_atom"
9. "distance"
10. "angle"
.. _recsql: http://pypi.python.org/pypi/RecSQL
"""
from itertools import izip
if self.timeseries is None:
msg = "No timeseries computed, do run() first."
warnings.warn(msg, category=MissingDataWarning)
logger.warn(msg)
return
num_records = numpy.sum([len(hframe) for hframe in self.timeseries])
dtype = [
("time", float), ("donor_idx", int), ("acceptor_idx", int),
("donor_resnm", "|S4"), ("donor_resid", int), ("donor_atom", "|S4"),
("acceptor_resnm", "|S4"), ("acceptor_resid", int), ("acceptor_atom", "|S4"),
("distance", float), ("angle", float)]
self.table = numpy.recarray((num_records,), dtype=dtype)
# according to Lukas' notes below, using a recarray at this stage is ineffective
# and speedups of ~x10 could be achieved by filling a standard array
# (perhaps at the cost of less clarity... but that might just be my code ;-) -- orbeckst)
cursor = 0 # current row
for t, hframe in izip(self.timesteps, self.timeseries):
if len(hframe) == 0:
continue # not really necessary, should also work without
self.table[cursor:cursor + len(hframe)].time = t
for donor_idx, acceptor_idx, donor, acceptor, distance, angle in hframe:
r = self.table[cursor]
r.donor_idx = donor_idx
r.donor_resnm, r.donor_resid, r.donor_atom = parse_residue(donor)
r.acceptor_idx = acceptor_idx
r.acceptor_resnm, r.acceptor_resid, r.acceptor_atom = parse_residue(acceptor)
r.distance = distance
r.angle = angle
cursor += 1
assert cursor == num_records, "Internal Error: Not all HB records stored"
logger.debug("HBond: Stored results as table with %(num_records)d entries.", vars())
def count_by_type(self):
"""Counts the frequency of hydrogen bonds of a specific type.
Processes :attr:`HydrogenBondAnalysis.timeseries` and returns
a :class:`numpy.recarray` containing atom indices, residue
names, residue numbers (for donors and acceptors) and the
fraction of the total time during which the hydrogen bond was
detected.
:Returns: a class:`numpy.recarray`
"""
if self.timeseries is None:
msg = "No timeseries computed, do run() first."
warnings.warn(msg, category=MissingDataWarning)
logger.warn(msg)
return
hbonds = defaultdict(int)
for hframe in self.timeseries:
for donor_idx, acceptor_idx, donor, acceptor, distance, angle in hframe:
donor_resnm, donor_resid, donor_atom = parse_residue(donor)
acceptor_resnm, acceptor_resid, acceptor_atom = parse_residue(acceptor)
# generate unambigous key for current hbond
# (the donor_heavy_atom placeholder '?' is added later)
hb_key = (
donor_idx, acceptor_idx,
donor_resnm, donor_resid, "?", donor_atom,
acceptor_resnm, acceptor_resid, acceptor_atom)
hbonds[hb_key] += 1
# build empty output table
dtype = [
('donor_idx', int), ('acceptor_idx', int),
('donor_resnm', 'S4'), ('donor_resid', int), ('donor_heavy_atom', 'S4'), ('donor_atom', 'S4'),
('acceptor_resnm', 'S4'), ('acceptor_resid', int), ('acceptor_atom', 'S4'),
('frequency', float)
]
out = numpy.empty((len(hbonds),), dtype=dtype)
# float because of division later
tsteps = float(len(self.timesteps))
for cursor, (key, count) in enumerate(hbonds.iteritems()):
out[cursor] = key + (count / tsteps,)
# return array as recarray
# The recarray has not been used within the function, because accessing the
# the elements of a recarray (3.65 us) is much slower then accessing those
# of a ndarray (287 ns).
r = out.view(numpy.recarray)
# patch in donor heavy atom names (replaces '?' in the key)
h2donor = self._donor_lookup_table_byindex()
r.donor_heavy_atom[:] = [h2donor[idx - 1] for idx in r.donor_idx]
return r
def check_parse_residue(rstring, residue):
assert_equal(util.parse_residue(rstring), residue)
