def _algorithm(self):
logger.info('Postprocessing new data')
datalist = simlist(glob(path.join(self.datapath, '*', '')), glob(path.join(self.inputpath, '*', 'structure.pdb')),
glob(path.join(self.inputpath, '*', '')))
filtlist = simfilter(datalist, self.filteredpath, filtersel=self.filtersel)
if hasattr(self, 'metricsel2') and self.metricsel2 is not None:
proj = MetricDistance(self.metricsel1, self.metricsel2, metric=self.metrictype)
else:
proj = MetricSelfDistance(self.metricsel1, metric=self.metrictype)
metr = Metric(filtlist, skip=self.skip)
metr.projection(proj)
data = metr.project()
#if self.contactsym is not None:
# contactSymmetry(data, self.contactsym)
data.dropTraj()
if self.ticadim > 0:
tica = TICA(data, int(max(2, np.ceil(20/self.skip))))
datadr = tica.project(self.ticadim)
else:
datadr = data
K = int(max(np.round(0.6 * np.log10(datadr.numFrames/1000)*1000+50), 100)) # heuristic
if K > datadr.numFrames / 3: # Freaking ugly patches ...
K = int(datadr.numFrames / 3)
datadr.cluster(self.clustmethod(n_clusters=K), mergesmall=5)
replacement = False
if datadr.K < 10:
datadr.cluster(self.clustmethod(n_clusters=K))
replacement = True
model = Model(datadr)
macronum = self.macronum
if datadr.K < macronum:
macronum = np.ceil(datadr.K / 2)
logger.warning('Using less macrostates than requested due to lack of microstates. macronum = ' + str(macronum))
from pyemma.msm import timescales_msm
timesc = timescales_msm(datadr.St.tolist(), lags=self.lag, nits=macronum).get_timescales()
macronum = min(self.macronum, max(np.sum(timesc > self.lag), 2))
model.markovModel(self.lag, macronum)
p_i = self._criteria(model, self.method)
(spawncounts, prob) = self._spawn(p_i, self.nmax-self.running)
logger.debug('spawncounts {}'.format(spawncounts))
stateIdx = np.where(spawncounts > 0)[0]
_, relFrames = model.sampleStates(stateIdx, spawncounts[stateIdx], statetype='micro', replacement=replacement)
logger.debug('relFrames {}'.format(relFrames))
self._writeInputs(datadr.rel2sim(np.concatenate(relFrames)))
def _algorithm(self):
""" Select random frames for respawning
"""
from htmd.projections.metric import Metric
from htmd.molecule.molecule import Molecule
from htmd.projections.metriccoordinate import MetricCoordinate
from htmd.simlist import simlist
sims = simlist(glob(path.join(self.datapath, '*', '')), glob(path.join(self.inputpath, '*', 'structure.pdb')),
glob(path.join(self.inputpath, '*', '')))
metr = Metric(sims)
metr.projection(MetricCoordinate(Molecule(sims[0].molfile), 'protein and name CA', 'protein and name CA'))
data = metr.project()
simframes = data.abs2sim(np.random.randint(0, data.numFrames, self.nmax-self.running))
self._writeInputs(simframes)
def _algorithm(self):
""" Select random frames for respawning
"""
from htmd.projections.metric import Metric
from htmd.molecule.molecule import Molecule
from htmd.projections.metriccoordinate import MetricCoordinate
from htmd.simlist import simlist
sims = simlist(glob(path.join(self.datapath, '*', '')),
glob(path.join(self.inputpath, '*', 'structure.pdb')),
glob(path.join(self.inputpath, '*', '')))
metr = Metric(sims)
metr.projection(
MetricCoordinate(Molecule(sims[0].molfile), 'protein and name CA',
'protein and name CA'))
data = metr.project()
simframes = data.abs2sim(
np.random.randint(0, data.numFrames, self.nmax - self.running))
self._writeInputs(simframes)
from htmd.molecule.molecule import Molecule
from htmd.home import home
import numpy as np
from os import path
mol = Molecule(path.join(home(), 'data', 'metricdistance', 'filtered.pdb'))
mol.read(path.join(home(), 'data', 'metricdistance', 'traj.xtc'))
ref = mol.copy()
ref.coords = np.atleast_3d(ref.coords[:, :, 0])
metr = MetricTMscore(ref, 'protein and name CA')
data = metr.project(mol)
lasttm = np.array([0.9633381, 0.96441294, 0.96553609, 0.96088852, 0.96288511, 0.95677591, 0.96544727, 0.96359811,
0.95658912, 0.96893117, 0.96623924, 0.96064913, 0.96207041, 0.95947848, 0.96657048, 0.95993426,
0.96543296, 0.96806875, 0.96437248, 0.96144066], dtype=np.float32)
assert np.all(np.abs(data[-20:].flatten() - lasttm) < 0.001), 'Coordinates calculation is broken'
from htmd.simlist import simlist
from htmd.projections.metric import Metric
dd = home(dataDir="adaptive")
fsims = simlist([path.join(dd, 'data', 'e1s1_1'), path.join(dd, 'data', 'e1s2_1')],
path.join(dd, 'generators', '1', 'structure.pdb'))
ref = Molecule(path.join(dd, 'generators', '1', 'structure.pdb'))
metr2 = Metric(fsims)
metr2.projection(MetricTMscore(ref, 'protein and name CA'))
data2 = metr2.project()
assert data2.trajectories[0].projection.shape == (6, 1)
return datatica
if __name__ == '__main__':
from htmd.simlist import simlist
from glob import glob
from htmd.projections.metricdistance import MetricSelfDistance
from htmd.home import home
from os.path import join
testfolder = home(dataDir='villin')
sims = simlist(glob(join(testfolder, '*', '')), join(testfolder, 'filtered.pdb'))
met = Metric(sims[0:2])
met.projection(MetricSelfDistance('protein and name CA'))
data = met.project()
data.fstep = 0.1
tica = TICA(data, 2, dimensions=range(2, 10))
datatica = tica.project(2)
tica5 = TICA(data, 0.2, units='ns', dimensions=range(2, 10))
datatica5 = tica5.project(2)
expected = [[ 3.69098878, -0.33862674, 0.85779184],
[ 3.77816105, -0.31887317, 0.87724227],
[ 3.83537507, -0.11878026, 0.65236956]]
assert np.allclose(np.abs(datatica.trajectories[0].projection[-3:, -3:]), np.abs(np.array(expected, dtype=np.float32)), rtol=0, atol=0.01)
assert np.allclose(np.abs(datatica5.trajectories[0].projection[-3:, -3:]), np.abs(np.array(expected, dtype=np.float32)), rtol=0, atol=0.01)
assert np.all(datatica.description.iloc[[587, 588]].type == 'tica')
assert np.all(datatica.description.iloc[range(587)].type == 'distance')
print('In-memory TICA with subset of dimensions passed test.')
def _algorithm(self):
logger.info('Postprocessing new data')
datalist = simlist(
glob(path.join(self.datapath, '*', '')),
glob(path.join(self.inputpath, '*', 'structure.pdb')),
glob(path.join(self.inputpath, '*', '')))
filtlist = simfilter(datalist,
self.filteredpath,
filtersel=self.filtersel)
if hasattr(self, 'metricsel2') and self.metricsel2 is not None:
proj = MetricDistance(self.metricsel1,
self.metricsel2,
metric=self.metrictype)
else:
proj = MetricSelfDistance(self.metricsel1, metric=self.metrictype)
metr = Metric(filtlist, skip=self.skip)
metr.projection(proj)
data = metr.project()
#if self.contactsym is not None:
# contactSymmetry(data, self.contactsym)
data.dropTraj()
if self.ticadim > 0:
tica = TICA(data, int(max(2, np.ceil(20 / self.skip))))
datadr = tica.project(self.ticadim)
else:
datadr = data
K = int(
max(np.round(0.6 * np.log10(datadr.numFrames / 1000) * 1000 + 50),
100)) # heuristic
if K > datadr.numFrames / 3: # Freaking ugly patches ...
K = int(datadr.numFrames / 3)
datadr.cluster(self.clustmethod(n_clusters=K), mergesmall=5)
replacement = False
if datadr.K < 10:
datadr.cluster(self.clustmethod(n_clusters=K))
replacement = True
model = Model(datadr)
macronum = self.macronum
if datadr.K < macronum:
macronum = np.ceil(datadr.K / 2)
logger.warning(
'Using less macrostates than requested due to lack of microstates. macronum = '
+ str(macronum))
from pyemma.msm import timescales_msm
timesc = timescales_msm(datadr.St.tolist(),
lags=self.lag,
nits=macronum).get_timescales()
macronum = min(self.macronum, max(np.sum(timesc > self.lag), 2))
model.markovModel(self.lag, macronum)
p_i = self._criteria(model, self.method)
(spawncounts, prob) = self._spawn(p_i, self.nmax - self.running)
logger.debug('spawncounts {}'.format(spawncounts))
stateIdx = np.where(spawncounts > 0)[0]
_, relFrames = model.sampleStates(stateIdx,
spawncounts[stateIdx],
statetype='micro',
replacement=replacement)
logger.debug('relFrames {}'.format(relFrames))
self._writeInputs(datadr.rel2sim(np.concatenate(relFrames)))
import numpy as np
from os import path
mol = Molecule(path.join(home(), 'data', 'metricdistance', 'filtered.pdb'))
mol.read(path.join(home(), 'data', 'metricdistance', 'traj.xtc'))
ref = mol.copy()
ref.coords = np.atleast_3d(ref.coords[:, :, 0])
metr = MetricRmsd(ref, 'protein and name CA')
data = metr.project(mol)
lastrmsd = np.array([1.30797791, 1.29860222, 1.25042927, 1.31319737, 1.27044261,
1.40294552, 1.25354612, 1.30127883, 1.40618336, 1.18303752,
1.24414587, 1.34513164, 1.31932807, 1.34282494, 1.2261436 ,
1.36359048, 1.26243281, 1.21157813, 1.26476419, 1.29413617], dtype=np.float32)
assert np.all(np.abs(data[-20:] - lastrmsd) < 0.001), 'Coordinates calculation is broken'
from htmd.simlist import simlist
from htmd.projections.metric import Metric
dd = home(dataDir="adaptive")
fsims = simlist([dd + '/data/e1s1_1/', dd + '/data/e1s2_1/'],
dd + '/generators/1/structure.pdb')
ref = Molecule(dd+"/generators/1/structure.pdb")
metr2 = Metric(fsims)
metr2.projection(MetricRmsd(ref, 'protein and name CA'))
data2 = metr2.project()
assert data2.trajectories[0].projection.shape == (6,1)
pass
mol.read(path.join(home(), 'data', 'metricdistance', 'traj.xtc'))
ref = mol.copy()
ref.coords = np.atleast_3d(ref.coords[:, :, 0])
metr = MetricRmsd(ref, 'protein and name CA')
data = metr.project(mol)
lastrmsd = np.array([
1.30797791, 1.29860222, 1.25042927, 1.31319737, 1.27044261, 1.40294552,
1.25354612, 1.30127883, 1.40618336, 1.18303752, 1.24414587, 1.34513164,
1.31932807, 1.34282494, 1.2261436, 1.36359048, 1.26243281, 1.21157813,
1.26476419, 1.29413617
],
dtype=np.float32)
assert np.all(
np.abs(data[-20:] -
lastrmsd) < 0.001), 'Coordinates calculation is broken'
from htmd.simlist import simlist
from htmd.projections.metric import Metric
dd = home(dataDir="adaptive")
fsims = simlist([dd + '/data/e1s1_1/', dd + '/data/e1s2_1/'],
dd + '/generators/1/structure.pdb')
ref = Molecule(dd + "/generators/1/structure.pdb")
metr2 = Metric(fsims)
metr2.projection(MetricRmsd(ref, 'protein and name CA'))
data2 = metr2.project()
assert data2.dat[0].shape == (6, 1)
pass
An array containing the null data.
"""
trajlen = mol.numFrames
data = np.zeros((trajlen, self._ndim), dtype=np.float32)
return data
if __name__ == "__main__":
import htmd.home
from htmd.simlist import simlist
from htmd.projections.metric import Metric
import htmd.projections.metricnull
dd = htmd.home.home(dataDir="adaptive")
fsims = simlist([dd + '/data/e1s1_1/', dd + '/data/e1s2_1/'],
dd + '/generators/1/structure.pdb')
metr2 = Metric(fsims)
metr2.projection(htmd.projections.metricnull.MetricNull(2))
data2 = metr2.project()
assert data2.trajectories[0].projection.shape == (6, 2)
metr1 = Metric(fsims)
metr1.projection(htmd.projections.metricnull.MetricNull(1))
data1 = metr1.project()
assert data1.trajectories[0].projection.shape == (6, 1)
pass
import doctest
doctest.testmod()
# One simulation
mol = Molecule(os.path.join(htmd.home(), 'data', '1kdx', '1kdx_0.pdb'))
mol.read(os.path.join(htmd.home(), 'data', '1kdx', '1kdx.dcd'))
metric = MetricPlumed2(['d1: DISTANCE ATOMS=1,200',
'd2: DISTANCE ATOMS=5,6'])
# metric = MetricPlumed2(['']) # to test exceptions
data = metric.project(mol)
ref = np.array([0.536674, 21.722393, 22.689391, 18.402114, 23.431387, 23.13392, 19.16376, 20.393544,
23.665517, 22.298349, 22.659769, 22.667669, 22.484084, 20.893447, 18.791701,
21.833056, 19.901318])
assert np.all(np.abs(ref - data[:, 0]) < 0.01), 'Plumed demo calculation is broken'
# Simlist
# datadirs=glob(os.path.join(home(), 'data', 'adaptive', 'data', '*' )
# fsims=simlist(glob(os.path.join(home(), 'data', 'adaptive', 'data', '*', '/')),
# os.path.join(home(), 'data', 'adaptive', 'generators', '1','structure.pdb'))
dd = htmd.home(dataDir="adaptive")
fsims = htmd.simlist([dd + '/data/e1s1_1/', dd + '/data/e1s2_1/'],
dd + '/generators/1/structure.pdb')
metr = Metric(fsims)
metr.projection(MetricPlumed2(
['d1: DISTANCE ATOMS=2,3',
'd2: DISTANCE ATOMS=5,6']))
data2 = metr.project()
pass
from htmd.molecule.molecule import Molecule
from htmd.home import home
import numpy as np
from os import path
mol = Molecule(path.join(home(), 'data', 'metricdistance', 'filtered.pdb'))
mol.read(path.join(home(), 'data', 'metricdistance', 'traj.xtc'))
ref = mol.copy()
ref.coords = np.atleast_3d(ref.coords[:, :, 0])
metr = MetricTMscore(ref, 'protein and name CA')
data = metr.project(mol)
lasttm = np.array([0.9633381, 0.96441294, 0.96553609, 0.96088852, 0.96288511, 0.95677591, 0.96544727, 0.96359811,
0.95658912, 0.96893117, 0.96623924, 0.96064913, 0.96207041, 0.95947848, 0.96657048, 0.95993426,
0.96543296, 0.96806875, 0.96437248, 0.96144066], dtype=np.float32)
assert np.all(np.abs(data[-20:].flatten() - lasttm) < 0.001), 'Coordinates calculation is broken'
from htmd.simlist import simlist
from htmd.projections.metric import Metric
dd = home(dataDir="adaptive")
fsims = simlist([path.join(dd, 'data', 'e1s1_1'), path.join(dd, 'data', 'e1s2_1')],
path.join(dd, 'generators', '1', 'structure.pdb'))
ref = Molecule(path.join(dd, 'generators', '1', 'structure.pdb'))
metr2 = Metric(fsims)
metr2.projection(MetricTMscore(ref, 'protein and name CA'))
data2 = metr2.project()
assert data2.dat[0].shape == (6, 1)
An array containing the null data.
"""
trajlen = mol.numFrames
data = np.zeros((trajlen, self._ndim), dtype=np.float32)
return data
if __name__ == "__main__":
import htmd.home
from htmd.simlist import simlist
from htmd.projections.metric import Metric
import htmd.projections.metricnull
dd = htmd.home.home(dataDir="adaptive")
fsims = simlist([dd + '/data/e1s1_1/', dd + '/data/e1s2_1/'],
dd + '/generators/1/structure.pdb')
metr2 = Metric(fsims)
metr2.projection(htmd.projections.metricnull.MetricNull(2))
data2 = metr2.project()
assert data2.trajectories[0].projection.shape == (6, 2)
metr1 = Metric(fsims)
metr1.projection(htmd.projections.metricnull.MetricNull(1))
data1 = metr1.project()
assert data1.trajectories[0].projection.shape == (6, 1)
pass
try:
_getPlumedRoot()
except:
print("Tests in %s skipped because plumed executable not found." %
__file__)
sys.exit()
import doctest
doctest.testmod()
# Simlist
dd = htmd.home(dataDir="adaptive")
fsims = htmd.simlist([dd + '/data/e1s1_1/', dd + '/data/e1s2_1/'],
dd + '/generators/1/structure.pdb')
metr = Metric(fsims)
metr.projection(
MetricPlumed2(['d1: DISTANCE ATOMS=2,3', 'd2: DISTANCE ATOMS=5,6']))
data2 = metr.project()
# One simulation
testpath = os.path.join(htmd.home(), 'data', '1kdx')
mol = Molecule(os.path.join(testpath, '1kdx_0.pdb'))
mol.read(os.path.join(htmd.home(), 'data', '1kdx', '1kdx.dcd'))
metric = MetricPlumed2(
['d1: DISTANCE ATOMS=1,200', 'd2: DISTANCE ATOMS=5,6'])
data = metric.project(mol)
ref = np.array([
0.536674, 21.722393, 22.689391, 18.402114, 23.431387, 23.13392,
19.16376, 20.393544, 23.665517, 22.298349, 22.659769, 22.667669,
22.484084, 20.893447, 18.791701, 21.833056, 19.901318
])
if self.dimensions is not None: # If TICA is done on a subset of dims
datatica.map = keepdimdesc.append(datatica.map, ignore_index=True)
return datatica
if __name__ == "__main__":
from htmd import *
from htmd.home import home
from os.path import join
testfolder = home(dataDir="villin")
sims = simlist(glob(join(testfolder, "*", "")), join(testfolder, "filtered.pdb"))
met = Metric(sims[0:2])
met.projection(MetricSelfDistance("protein and name CA"))
data = met.project()
data.fstep = 0.1
tica = TICA(data, 2, dimensions=range(2, 10))
datatica = tica.project(2)
tica5 = TICA(data, 0.2, units="ns", dimensions=range(2, 10))
datatica5 = tica5.project(2)
expected = [
[3.69098878, -0.33862674, 0.85779184],
[3.77816105, -0.31887317, 0.87724227],
[3.83537507, -0.11878026, 0.65236956],
]
assert np.allclose(
np.abs(datatica.dat[0][-3:, -3:]), np.abs(np.array(expected, dtype=np.float32)), rtol=0, atol=0.01
)
