def setUpClass(self):
from htmd.simlist import simlist, simfilter
from glob import glob
from htmd.projections.metric import Metric
from moleculekit.projections.metricdistance import MetricDistance
from moleculekit.projections.metricdihedral import MetricDihedral
from moleculekit.util import tempname
from htmd.home import home
from os.path import join
sims = simlist(
glob(join(home(dataDir="adaptive"), "data", "*", "")),
glob(join(home(dataDir="adaptive"), "input", "*")),
)
fsims = simfilter(sims, tempname(), "not water")
metr = Metric(fsims)
metr.set(
MetricDistance(
"protein and resid 10 and name CA",
"resname BEN and noh",
periodic="selections",
metric="contacts",
groupsel1="residue",
threshold=4,
)
)
self.data1 = metr.project()
metr.set(MetricDihedral())
self.data2 = metr.project()
def _algorithm(self):
logger.info('Postprocessing new data')
sims = simlist(glob(path.join(self.datapath, '*', '')),
glob(path.join(self.inputpath, '*', 'structure.pdb')),
glob(path.join(self.inputpath, '*', '')))
if self.filter:
sims = simfilter(sims, self.filteredpath, filtersel=self.filtersel)
metr = Metric(sims, skip=self.skip)
metr.set(self.projection)
#if self.contactsym is not None:
# contactSymmetry(data, self.contactsym)
if self.ticadim > 0:
# tica = TICA(metr, int(max(2, np.ceil(self.ticalag)))) # gianni: without project it was tooooo slow
tica = TICA(metr.project(), int(max(2, np.ceil(self.ticalag))))
datadr = tica.project(self.ticadim)
else:
datadr = metr.project()
datadr.dropTraj(
) # Preferably we should do this before any projections. Corrupted sims can affect TICA
datadr.cluster(
self.clustmethod(n_clusters=self._numClusters(datadr.numFrames)))
self._model = Model(datadr)
self._model.markovModel(self.lag, self._numMacrostates(datadr))
if self.save:
self._model.save('adapt_model_e' + str(self._getEpoch()) + '.dat')
relFrames = self._getSpawnFrames(self._model, datadr)
self._writeInputs(datadr.rel2sim(np.concatenate(relFrames)))
def setUpClass(self):
from htmd.simlist import simlist, simfilter
from glob import glob
from htmd.projections.metric import Metric
from moleculekit.projections.metricdistance import MetricDistance
from moleculekit.projections.metricdihedral import MetricDihedral
from moleculekit.util import tempname
from htmd.home import home
from os.path import join
sims = simlist(glob(join(home(dataDir='adaptive'), 'data', '*', '')),
glob(join(home(dataDir='adaptive'), 'input', '*')))
fsims = simfilter(sims, tempname(), 'not water')
metr = Metric(fsims)
metr.set(
MetricDistance('protein and resid 10 and name CA',
'resname BEN and noh',
metric='contacts',
groupsel1='residue',
threshold=4))
self.data1 = metr.project()
metr.set(MetricDihedral())
self.data2 = metr.project()
def _algorithm(self):
logger.info('Postprocessing new data')
sims = simlist(glob(path.join(self.datapath, '*', '')),
glob(path.join(self.inputpath, '*', 'structure.pdb')),
glob(path.join(self.inputpath, '*', '')))
if self.filter:
sims = simfilter(sims, self.filteredpath, filtersel=self.filtersel)
metr = Metric(sims, skip=self.skip)
metr.set(self.projection)
# if self.contactsym is not None:
# contactSymmetry(data, self.contactsym)
if self.ticadim > 0:
# tica = TICA(metr, int(max(2, np.ceil(self.ticalag)))) # gianni: without project it was tooooo slow
tica = TICA(metr.project(), int(max(2, np.ceil(self.ticalag))))
datadr = tica.project(self.ticadim)
else:
datadr = metr.project()
datadr.dropTraj(
) # Preferably we should do this before any projections. Corrupted sims can affect TICA
datadr.cluster(
self.clustmethod(n_clusters=self._numClusters(datadr.numFrames)))
model = Model(datadr)
self._model = model
self._model.markovModel(self.lag, self._numMacrostates(datadr))
if self.save:
self._model.save('adapt_model_e' + str(self._getEpoch()) + '.dat')
# Undirected component
uc = -model.data.N # Lower counts should give higher score hence the -
if self.statetype == 'micro':
uc = uc[model.cluster_ofmicro]
if self.statetype == 'macro':
uc = macroAccumulate(model, uc[model.cluster_ofmicro])
# Calculating the directed component
dc = self._calculateDirectedComponent(sims, model.data.St,
model.data.N)
if self.statetype == 'micro':
dc = dc[model.cluster_ofmicro]
if self.statetype == 'macro':
dc = macroAccumulate(model, dc[model.cluster_ofmicro])
uc = self._featScale(uc)
dc = self._featScale(dc)
reward = dc + self.ucscale * uc
relFrames = self._getSpawnFrames(reward, self._model, datadr)
self._writeInputs(datadr.rel2sim(np.concatenate(relFrames)))
def fitBaselineWithMetrics(projected_simlist,
base_simlist,
metric,
ticalag=25,
ticadim=4,
ticaunits='frames',
tica=False):
from htmd.projections.tica import TICA
from htmd.projections.metric import Metric
from htmd.model import Model
"""
Implement a MetricB that returns the TICA tranformation of a MetricA
1) Calculate MetricA for each trajectory
2) TICA transform based on a basetica (basetica.tic.t)
"""
basetica_metric = Metric(base_simlist)
basetica_metric.set(metric)
basetica = TICA(basetica_metric, ticalag, units=ticaunits)
basetica.tic.set_params(dim=ticadim)
def metricToTica(mol, metric, tica):
metric_data = metric.project(mol)
return tica.tic.transform(metric_data)
tica_metric = Metric(projected_simlist)
tica_metric.set((metricToTica, (metric, basetica)))
projectdata = tica_metric.project().dat
if tica:
return projectdata, basetica
return projectdata
def get_data(model, metr, skip=1):
""" Returns the projected data of metric applied to a model
Parameters
----------
mod : htmd.model.Model
Model to get the simlist
metric : htmd.projections.MetricData
MetricData with the metric we want to project
skip : int
Frames to skip while projecting the data. Default = 1
"""
from htmd.model import Model
from htmd.projections.metric import Metric
if isinstance(model, Model):
simlist = model.data.simlist
elif isinstance(model, np.ndarray):
simlist = model
else:
raise TypeError(
"Model should be either an htmd.model.Model or a simlist")
metric = Metric(simlist, skip=skip)
metric.set(metr)
data = metric.project()
return data
def _getGoalData(self, sims):
from htmd.projections.metric import Metric
logger.debug('Starting projection of directed component')
metr = Metric(sims, skip=self.skip)
metr.set(self.goalfunction)
data = metr.project()
logger.debug('Finished calculating directed component')
return data
def _getGoalData(self, sims):
from htmd.projections.metric import Metric
logger.debug('Starting projection of directed component')
metr = Metric(sims, skip=self.skip)
metr.set(self.goalfunction)
data = metr.project()
logger.debug('Finished calculating directed component')
return data
def _getData(self, sims):
metr = Metric(sims, skip=self.skip)
metr.set(self.projection)
# if self.contactsym is not None:
# contactSymmetry(data, self.contactsym)
if self.ticadim > 0:
# tica = TICA(metr, int(max(2, np.ceil(self.ticalag)))) # gianni: without project it was tooooo slow
data = metr.project()
data.dropTraj() # Drop before TICA to avoid broken trajectories
ticalag = int(
np.ceil(max(2, min(np.min(data.trajLengths) / 2, self.ticalag)))) # 1 < ticalag < (trajLen / 2)
tica = TICA(data, ticalag)
datadr = tica.project(self.ticadim)
else:
datadr = metr.project()
datadr.dropTraj() # Preferably we should do this before any projections. Corrupted sims can affect TICA
return datadr
def _getData(self, sims):
metr = Metric(sims, skip=self.skip)
metr.set(self.projection)
# if self.contactsym is not None:
# contactSymmetry(data, self.contactsym)
if self.ticadim > 0:
# tica = TICA(metr, int(max(2, np.ceil(self.ticalag)))) # gianni: without project it was tooooo slow
data = metr.project()
data.dropTraj() # Drop before TICA to avoid broken trajectories
ticalag = int(
np.ceil(max(2, min(np.min(data.trajLengths) / 2, self.ticalag)))) # 1 < ticalag < (trajLen / 2)
tica = TICA(data, ticalag)
datadr = tica.project(self.ticadim)
else:
datadr = metr.project()
datadr.dropTraj() # Preferably we should do this before any projections. Corrupted sims can affect TICA
return datadr
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 _computeChiDihedrals(self, fstep=0.1, skip=1):
chis = []
protmol = self.mol.copy()
protmol.filter('protein')
caidx = self.mol.atomselect('protein and name CA')
resids = self.mol.resid[caidx]
resnames = self.mol.resname[caidx]
for residue, resname in zip(resids, resnames):
ch = Dihedral.chi1(protmol, residue)
if ch is not None:
chis.append(ch)
metr = Metric(self.model.data.simlist, skip=skip)
metr.set(MetricDihedral(chis, sincos=False))
data = metr.project()
data.fstep = fstep
self.chi = data
def _computeChiDihedrals(self, fstep=0.1, skip=1):
chis = []
protmol = self.mol.copy()
protmol.filter('protein')
caidx = self.mol.atomselect('protein and name CA')
resids = self.mol.resid[caidx]
resnames = self.mol.resname[caidx]
for residue, resname in zip(resids, resnames):
ch = Dihedral.chi1(protmol, residue)
if ch is not None:
chis.append(ch)
metr = Metric(self.model.data.simlist, skip=skip)
metr.set(MetricDihedral(chis, sincos=False))
data = metr.project()
data.fstep = fstep
self.chi = data
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 removeCorrupted():
from htmd.simlist import simlist
from htmd.projections.metric import Metric
from os import path
from glob import glob
import shutil
print("Removing Corrupted Simulations")
try:
sims = simlist(glob("./filtered/*/"), "./filtered/filtered.pdb")
except:
return
met = Metric(sims)
met.set(corruptMetric)
dat = met.project()
for i, s in zip(dat.dat, dat.simlist):
if np.sum(i):
pt = path.dirname(s.trajectory[0])
shutil.move(pt, f"/tmp/{pt}")
def _precalculateData(self, metricData, folder, skip=1, fstep=None):
from htmd.projections.metric import Metric
max_epoch = max(self.epoch_analysis)
max_epoch_sim = np.concatenate(
np.array([
self.epoch_sim_indexes[i] for i in range(1, max_epoch + 1)
if i in list(self.epoch_sim_indexes.keys())
]))
if self.test:
sims = self._sims[0:100]
else:
sims = np.array([self._sims[i] for i in max_epoch_sim])
metr = Metric(sims, skip=skip)
metr.set(metricData)
data = metr.project()
if fstep:
data.fstep = fstep
return data
def analyze_folder(folder=None,
out_folder="/tmp",
skip=1,
metrics=None,
clu=500,
tica=True,
ticadim=5,
tica_lag=20,
model_lag=10,
model_units='ns',
macro_N=10,
bulk_split=False,
fes=True,
rg_analysis=True,
save=True,
data_fstep=None):
"""Analysis script for create a Markov State Model
Creates and returns a Markov State Model given a data folder.
Intented to follow up the evolution of an adaptive sampling run.
Allows to save the model ans several informative plots
Parameters
----------
folder : str
Data folder where adaptive is running
out_folder : str
Output folder to store derived data
skip : int
Number of frames to skip while projecting the MD data
metrics : [:class: `Metric` object]
Metric array used to project the data
clu : int
Number of cluster to create using the MiniBatchKMeans method.
tica: bool
Wether to use TICA of GWPCA for dimensionality reduction
ticadim : int
Number of TICA dimension to project the data. If None, the model will be created using the raw projected data
tica_lag : int, optional
Description
model_lag : int
Number of ns used to create the model
model_units : str, optional
Description
macro_N : int
Number of macrostate to split the final Markov State Model
fes : bool, optional
If true it will save a plot projecting the first two TICA dimension. Requires ticadim to be defined
rg_analysis : bool, optional
If true, a plot with information relative to the radious of gyration of the molecule will be created.
save : bool, optional
If true, the model will be saved in the outputs folder
Returns
-------
:class:`Model`
Final model
"""
from htmd.model import Model
from htmd.molecule.molecule import Molecule
from htmd.simlist import simlist
from htmd.projections.metric import Metric
from sklearn.cluster import MiniBatchKMeans
from IDP_htmd.IDP_model import plot_RG
from IDP_htmd.model_utils import create_bulk
from glob import glob
import os
try:
os.mkdir(out_folder)
except:
print("Folder already exists")
try:
fsims = np.load(f"{folder}/simlist.npy", allow_pickle=True)
print(f"Loaded {folder}/simlist.npy")
except:
print("Creating simlist")
sims = glob(folder + 'filtered/*/')
fsims = simlist(sims, folder + 'filtered/filtered.pdb')
metr = Metric(fsims, skip=skip)
metr.set(metrics)
#Check if this gives problems to ITS
try:
model = Model(file=f"{out_folder}/model.dat")
out_data = model.data
print(f"Loading model: {out_folder}/model.dat")
except:
if tica and ticadim:
from htmd.projections.tica import TICA
print("Projecting TICA")
tica = TICA(metr, tica_lag)
out_data = tica.project(ticadim)
elif not tica and ticadim:
from htmd.projections.gwpca import GWPCA
data = metr.project()
data.dropTraj()
print("using GWPCA")
gwpca = GWPCA(data, tica_lag)
out_data = gwpca.project(ticadim)
else:
print("Not using TICA")
data = metr.project()
data.dropTraj()
out_data = data
#Avoid some possibles error while clustering
if data_fstep: out_data.fstep = data_fstep
x = True
while x:
try:
out_data.cluster(MiniBatchKMeans(n_clusters=clu), mergesmall=5)
x = False
except Exception as e:
raise Exception("Error " + str(e))
model = Model(out_data)
model.plotTimescales(plot=False, save=f"{out_folder}/1_its.png")
if macro_N:
model.markovModel(model_lag, macro_N, units=model_units)
if bulk_split:
try:
print("Starting bulk splitting")
create_bulk(model, bulk_split)
except Exception as e:
print("Could not perform the bulk splitting")
print(e)
model.eqDistribution(plot=False,
save=f"{out_folder}/1.2_eqDistribution.png")
if rg_analysis:
from IDP_htmd.IDP_analysis import rg_analysis
mol = Molecule(model.data.simlist[0].molfile)
rg_data = rg_analysis(model, skip=skip)
plot_RG(rg_data, mol, save=f"{out_folder}/1.4_rg.png")
# if fes and ticadim:
# model.plotFES(0, 1, temperature=310, states=True,
# plot=False, save=f"{out_folder}/1.3_fes.png")
if save:
model.save(f"{out_folder}/model.dat")
return model
def _algorithm(self):
from htmd.kinetics import Kinetics
sims = self._getSimlist()
metr = Metric(sims, skip=self.skip)
metr.set(self.projection)
data = metr.project()
data.dropTraj() # Drop before TICA to avoid broken trajectories
if self.goalfunction is not None:
goaldata = self._getGoalData(data.simlist)
if len(data.simlist) != len(goaldata.simlist):
raise RuntimeError('The goal function was not able to project all trajectories that the MSM projection could. Check for possible errors in the goal function.')
goaldataconcat = np.concatenate(goaldata.dat)
if self.save:
makedirs('saveddata', exist_ok=True)
goaldata.save(path.join('saveddata', 'e{}_goaldata.dat'.format(self._getEpoch())))
# tica = TICA(metr, int(max(2, np.ceil(self.ticalag)))) # gianni: without project it was tooooo slow
if self.ticadim > 0:
ticalag = int(np.ceil(max(2, min(np.min(data.trajLengths) / 2, self.ticalag)))) # 1 < ticalag < (trajLen / 2)
tica = TICA(data, ticalag)
datatica = tica.project(self.ticadim)
if not self._checkNFrames(datatica): return False
self._createMSM(datatica)
else:
if not self._checkNFrames(data): return False
self._createMSM(data)
confstatdist = self.conformationStationaryDistribution(self._model)
if self.actionspace == 'metric':
if not data.K:
data.cluster(self.clustmethod(n_clusters=self._numClusters(data.numFrames)))
data_q = data.copy()
elif self.actionspace == 'goal':
data_q = goaldata.copy()
elif self.actionspace == 'tica':
data_q = datatica.copy()
elif self.actionspace == 'ticapcca':
data_q = datatica.copy()
for traj in data_q.trajectories:
traj.cluster = self._model.macro_ofcluster[traj.cluster]
data_q.K = self._model.macronum
if self.recluster:
print('Reclustering with {}'.format(self.reclusterMethod))
data_q.cluster(self.reclusterMethod)
numstates = data_q.K
print('Numstates: {}'.format(numstates))
currepoch = self._getEpoch()
q_values = np.zeros(numstates, dtype=np.float32)
n_values = np.zeros(numstates, dtype=np.int32)
if self.random: # If random mode respawn from random action states
action_sel = np.zeros(numstates, dtype=int)
N = self.nmax - self._running
randomactions = np.bincount(np.random.randint(numstates, size=N))
action_sel[:len(randomactions)] = randomactions
if self.save_qval:
makedirs('saveddata', exist_ok=True)
np.save(path.join('saveddata', 'e{}_actions.npy'.format(currepoch)), action_sel)
relFrames = self._getSpawnFrames_UCB(action_sel, data_q)
self._writeInputs(data.rel2sim(np.concatenate(relFrames)))
return True
if self.goalfunction is not None:
## For every cluster in data_q, get the max score and initialize
if self.goal_preprocess is not None:
goaldataconcat = self.goal_preprocess(goaldataconcat)
qstconcat = np.concatenate(data_q.St)
statemaxes = np.zeros(numstates)
np.maximum.at(statemaxes, qstconcat, np.squeeze(goaldataconcat))
if not self.pucb:
goalenergies = -Kinetics._kB * self.temperature * np.log(1-statemaxes)
q_values = goalenergies
n_values += int((self.nframes / self._numClusters(self.nframes)) * self.goal_init) ## Needs nframes to be set properly!!!!!!!!
rewardtraj = np.arange(data_q.numTrajectories) # Recalculate reward for all states
rewards = self.getRewards(rewardtraj, data_q, confstatdist, numstates, self.reward_method, self.reward_mode, self.reward_window)
for i in range(numstates):
if len(rewards[i]) == 0:
continue
q_values[i] = updatingMean(q_values[i], n_values[i], rewards[i])
n_values += np.array([len(x) for x in rewards])
if self.save_qval:
makedirs('saveddata', exist_ok=True)
np.save(path.join('saveddata', 'e{}_qval.npy'.format(currepoch)), q_values)
np.save(path.join('saveddata', 'e{}_nval.npy'.format(currepoch)), n_values)
if self.pucb:
ucb_values = np.array([self.count_pucb(q_values[clust], self.exploration, statemaxes[clust], currepoch + 1, n_values[clust]) for clust in range(numstates)])
else:
ucb_values = np.array([self.count_ucb(q_values[clust], self.exploration, currepoch + 1, n_values[clust]) for clust in range(numstates)])
if self.save_qval:
makedirs('saveddata', exist_ok=True)
np.save(path.join('saveddata', 'e{}_ucbvals.npy'.format(currepoch)), ucb_values)
N = self.nmax - self._running
if self.actionpool <= 0:
self.actionpool = N
topactions = np.argsort(-ucb_values)[:self.actionpool]
action = np.random.choice(topactions, N, replace=False)
action_sel = np.zeros(numstates, dtype=int)
action_sel[action] += 1
while np.sum(action_sel) < N: # When K is lower than N repeat some actions
for a in action:
action_sel[a] +=1
if np.sum(action_sel) == N:
break
if self.save_qval:
np.save(path.join('saveddata', 'e{}_actions.npy'.format(currepoch)), action_sel)
relFrames = self._getSpawnFrames_UCB(action_sel, data_q)
self._writeInputs(data.rel2sim(np.concatenate(relFrames)))
return True
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)
_getPlumedRoot()
except:
print("Tests in %s skipped because plumed executable not found." % __file__)
sys.exit()
# Simlist
dd = htmd.home.home(dataDir="adaptive")
fsims = simlist([dd + '/data/e1s1_1/', dd + '/data/e1s2_1/'],
dd + '/generators/1/structure.pdb')
metr = Metric(fsims)
metr.set(MetricPlumed2(
['d1: DISTANCE ATOMS=2,3',
'd2: DISTANCE ATOMS=5,6']))
data2 = metr.project()
# One simulation
testpath=os.path.join(htmd.home.home(), 'data', '1kdx')
mol = Molecule(os.path.join(testpath, '1kdx_0.pdb'))
mol.read(os.path.join(htmd.home.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])
assert np.all(np.abs(ref - data[:, 0]) < 0.01), 'Plumed demo calculation is broken'
from glob import glob
from htmd.projections.metric import Metric
from htmd.projections.metricdistance import MetricDistance
from htmd.projections.metricdihedral import MetricDihedral
from htmd.util import tempname
from htmd.home import home
from os.path import join
testfolder = home(dataDir='adaptive')
sims = simlist(glob(join(testfolder, 'data', '*', '')), glob(join(testfolder, 'input', '*', 'structure.pdb')))
fsims = simfilter(sims, tempname(), 'not water')
metr = Metric(fsims)
metr.set(MetricDistance('protein and resid 10 and name CA', 'resname BEN and noh', metric='contacts',
groupsel1='residue', threshold=4))
data1 = metr.project()
metr.set(MetricDihedral())
data2 = metr.project()
# Testing combining of metrics
data1.combine(data2)
# Testing dimensions
assert np.array_equal(data1.description.shape, (897, 3)), 'combine not working correct'
assert np.array_equal(data1.trajectories[0].projection.shape, (6, 897)), 'combine not working correct'
assert np.array_equal(np.where(data1.description.type == 'contact')[0], [0, 1, 2, 3, 4, 5, 6, 7, 8]), 'combine not working correct'
# Testing dimension dropping / keeping
datatmp = data1.copy()
data1.dropDimensions(range(9))
assert np.array_equal(data1.description.shape, (888, 3)), 'dropDimensions not working correct'
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
])
assert np.all(
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.set(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)),
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.')
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.set(MetricRmsd(ref, 'protein and name CA'))
data2 = metr2.project()
assert data2.trajectories[0].projection.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
from htmd.home import home
from os.path import join
testfolder = home(dataDir='adaptive')
sims = simlist(glob(join(testfolder, 'data', '*', '')),
glob(join(testfolder, 'input', '*', 'structure.pdb')))
fsims = simfilter(sims, tempname(), 'not water')
metr = Metric(fsims)
metr.set(
MetricDistance('protein and resid 10 and name CA',
'resname BEN and noh',
metric='contacts',
groupsel1='residue',
threshold=4))
data1 = metr.project()
metr.set(MetricDihedral())
data2 = metr.project()
# Testing combining of metrics
data1.combine(data2)
# Testing dimensions
assert np.array_equal(data1.description.shape,
(897, 3)), 'combine not working correct'
assert np.array_equal(data1.trajectories[0].projection.shape,
(6, 897)), 'combine not working correct'
assert np.array_equal(
np.where(data1.description.type == 'contact')[0],
[0, 1, 2, 3, 4, 5, 6, 7, 8]), 'combine not working correct'
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)))
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