def test_delete():
tica_mdl = load(os.path.join(base_dir,"dihedral_mdl/tica_mdl.pkl"))
tica_data = load(os.path.join(base_dir,"dihedral_mdl/tica_features.pkl"))
df = pd.read_pickle(os.path.join(base_dir,"./dihedral_mdl/feature_descriptor.pkl"))
with enter_temp_directory():
cur_dir = os.path.abspath(os.path.curdir)
TicaMetadSim(base_dir=cur_dir, tica_data=tica_data,tica_mdl=tica_mdl,
data_frame=df, grid=False, interval=False,render_scripts=True,
delete_existing=True)
f = open("tic_0/rand.txt",'w')
f.writelines("t")
f.close()
TicaMetadSim(base_dir=cur_dir, tica_mdl=tica_mdl,tica_data=tica_data,
data_frame=df, grid=False, interval=False,
delete_existing=False)
assert os.path.isfile("tic_0/rand.txt")
TicaMetadSim(base_dir=cur_dir, tica_mdl=tica_mdl,tica_data=tica_data,
data_frame=df, grid=False, interval=False,
delete_existing=True)
assert not os.path.isfile("tic_0/rand.txt")
def _test_plumed_run():
tica_mdl = load(os.path.join(base_dir, "dihedral_mdl/tica_mdl.pkl"))
df = pd.read_pickle(
os.path.join(base_dir, "./dihedral_mdl/feature_descriptor.pkl"))
starting_coordinates_folder = os.path.join(base_dir,
"starting_coordinates")
with enter_temp_directory():
cur_dir = os.path.abspath(os.path.curdir)
TicaMetadSim(base_dir=cur_dir,
starting_coordinates_folder=starting_coordinates_folder,
tica_mdl=tica_mdl,
data_frame=df,
grid=False,
interval=False,
wall=False,
platform='CPU',
n_iterations=1,
swap_rate=5,
sim_save_rate=10,
pace=1,
stride=1)
meta_sim = load("./metad_sim.pkl")
run_meta_sim("./metad_sim.pkl")
for i in range(1):
for j in [meta_sim.bias_file, meta_sim.hills_file,\
"speed_report.txt","trajectory.dcd","plumed_script.dat",\
"checkpt.chk"]:
print(i, j)
assert os.path.isfile(os.path.join(cur_dir, "tic_%d" % i, j))
assert not os.path.isfile("swap_log.txt")
def get_plumed_dict(metad_sim):
if type(metad_sim)==str:
metad_sim = load(metad_sim)
if not hasattr(metad_sim,"nrm"):
metad_sim.nrm = None
if not hasattr(metad_sim,"walker_id"):
metad_sim.walker_id = None
metad_sim.walker_n = None
if not hasattr(metad_sim, "multiple_tics"):
metad_sim.multiple_tics = None
if not hasattr(metad_sim, "vde_mdl"):
metad_sim.vde_mdl = None
if type(metad_sim.tica_mdl)==str:
tica_mdl = load(metad_sim.tica_mdl)
return render_tica_plumed_file(tica_mdl=tica_mdl,
df = metad_sim.data_frame,
n_tics=metad_sim.n_tics,
grid=metad_sim.grid,
interval=metad_sim.interval,
wall_list=metad_sim.wall_list,
grid_list=metad_sim.grid_list,
interval_list=metad_sim.interval_list,
pace=metad_sim.pace,
height=metad_sim.height, biasfactor=metad_sim.biasfactor,
temp=metad_sim.temp, sigma=metad_sim.sigma,
stride=metad_sim.stride, hills_file=metad_sim.hills_file,
bias_file=metad_sim.bias_file, label=metad_sim.label,
nrm = metad_sim.nrm, walker_id = metad_sim.walker_id,
walker_n=metad_sim.walker_n,
multiple_tics=metad_sim.multiple_tics,
vde_mdl = metad_sim.vde_mdl)
def process_all_replicas(file_loc, redo=True, stride=1):
sim_mdl = load(file_loc)
os.chdir(sim_mdl.base_dir)
top_loc = glob.glob(
os.path.join(sim_mdl.starting_coordinates_folder, "0.pdb"))[0]
for i in range(sim_mdl.n_tics):
if redo:
concatenate_folder("tic_%d" % i, top_loc, stride)
sim_mdl.pace = 1000000000
sim_mdl.height = 0
sim_mdl.stride = 1
sim_mdl.bias_file = "{{fname}}"
plumed_scripts_dict = get_plumed_dict(sim_mdl)
full_dict = {}
for r1 in range(sim_mdl.n_tics):
for r2 in range(sim_mdl.n_tics):
full_dict["%d_%d" % (r1, r2)] = Template(
plumed_scripts_dict[r1]).render(fname="r%d_t%d.bias" %
(r1, r2))
p = Pool(sim_mdl.n_tics)
jobs = [(r1, r2, full_dict["%d_%d" % (r1, r2)])
for r1 in range(sim_mdl.n_tics) for r2 in range(sim_mdl.n_tics)]
p.map(process_folder, jobs)
p.close()
return
def test_load_legacy():
# Used to save joblib files
data = dict(name="Fancy_name", arr=np.random.rand(10, 5))
with tempdir():
jl_dump(data, 'filename', compress=1)
data2 = load('filename')
eq(data, data2)
def test_load_legacy():
# Used to save joblib files
data = dict(name="Fancy_name", arr=np.random.rand(10, 5))
with tempdir():
jl_dump(data, 'filename', compress=1)
data2 = load('filename')
eq(data, data2)
def test_setup():
tica_mdl = load(os.path.join(base_dir,"dihedral_mdl/tica_mdl.pkl"))
tica_data = load(os.path.join(base_dir,"dihedral_mdl/tica_features.pkl"))
df = pd.read_pickle(os.path.join(base_dir,"./dihedral_mdl/feature_descriptor.pkl"))
with enter_temp_directory():
cur_dir = os.path.abspath(os.path.curdir)
TicaMetadSim(base_dir=cur_dir,tica_data=tica_data, tica_mdl=tica_mdl,
data_frame=df, grid=False, interval=False,wall=False,
render_scripts=True,
delete_existing=True)
metad_sim = load("./metad_sim.pkl")
assert eq(tica_mdl.components_, metad_sim.tica_mdl.components_)
for i in range(metad_sim.n_tics):
assert os.path.isdir("tic_%d"%i)
assert os.path.isfile(("tic_%d/plumed.dat"%i))
assert os.path.isfile("sub.sh")
def test_transform_command_1():
with tempdir():
shell("msmb KCenters -i {data_home}/alanine_dipeptide/*.dcd "
"-o model.pkl --top {data_home}/alanine_dipeptide/ala2.pdb "
"--metric rmsd".format(data_home=get_data_home()))
shell("msmb TransformDataset -i {data_home}/alanine_dipeptide/*.dcd "
"-m model.pkl -t transformed.h5 --top "
"{data_home}/alanine_dipeptide/ala2.pdb".format(data_home=get_data_home()))
eq(dataset('transformed.h5')[0], load('model.pkl').labels_[0])
def __init__(self, file_loc="metad_sim.pkl"):
from tica_metadynamics.load_sim import create_simulation
self.file_loc = file_loc
self.metad_sim = load(self.file_loc)
self.beta = 1/(boltzmann_constant * self.metad_sim.temp)
#get
self.rank = rank
self.size = size
self.host_name = socket.gethostname()
self.gpu_index = get_gpu_index()
#setup MSM swap stuff
if self.metad_sim.msm_swap_folder is not None:
self.setup_msm_swap()
print("Hello from rank %d running tic %d on "
"host %s with gpu %d"%(self.rank, self.rank,
self.host_name, self.gpu_index))
# if multi walkers
if hasattr(self.metad_sim,"n_walkers") and self.metad_sim.n_walkers > 1:
cbd = self.metad_sim.base_dir
walker_index = int(os.path.split(cbd)[1].strip("walker_"))
print("I am walker %d running tic%d"%(walker_index,self.rank))
self.metad_sim.walker_index = walker_index
if self.metad_sim.plumed_script is not None:
#self.plumed_force_dict = self.metad_sim.plumed_dict
self.plumed_force_dict = {0: self.metad_sim.plumed_script}
else:
self.plumed_force_dict = get_plumed_dict(self.metad_sim)
# last replica is the neutral replica
if self.metad_sim.neutral_replica and self.rank==self.size-1:
from tica_metadynamics.load_sim import create_neutral_simulation
self.sim_obj = create_neutral_simulation(self.metad_sim.base_dir,
self.metad_sim.starting_coordinates_folder,
self.gpu_index,
self.metad_sim.sim_save_rate,
self.metad_sim.platform)
else:
self.sim_obj, self.force_group = create_simulation(self.metad_sim.base_dir,
self.metad_sim.starting_coordinates_folder,
self.gpu_index,
self.rank,
self.plumed_force_dict[self.rank],
self.metad_sim.sim_save_rate,
self.metad_sim.platform)
if self.rank ==0 and self.size > 1:
self.log_file = open("../swap_log.txt","a")
header = ["Iteration","S_i","S_j","Eii","Ejj","Eij","Eji",
"DeltaE","Temp","Beta","Probability","Accepted"]
self.log_file.writelines("#{}\t{}\t{}\t{}\t{}\t{}"
"\t{}\t{}\t{}\t{}\t{}\t{}\n".format(*header))
def test_dump():
# gh-713
sequence = [0, 0, 0, 1, 1, 1, 0, 0, 2, 2, 0, 1, 1, 1, 2, 2, 2, 2, 2]
model = ContinuousTimeMSM(verbose=False)
model.fit([sequence])
d = tempfile.mkdtemp()
try:
utils.dump(model, '{}/cmodel'.format(d))
m2 = utils.load('{}/cmodel'.format(d))
np.testing.assert_array_almost_equal(model.transmat_, m2.transmat_)
finally:
shutil.rmtree(d)
def test_dump():
# gh-713
sequence = [0, 0, 0, 1, 1, 1, 0, 0, 2, 2, 0, 1, 1, 1, 2, 2, 2, 2, 2]
model = PESContinuousTimeMSM(verbose=False)
model.fit([sequence])
d = tempfile.mkdtemp()
try:
utils.dump(model, '{}/cmodel'.format(d))
m2 = utils.load('{}/cmodel'.format(d))
np.testing.assert_array_almost_equal(model.transmat_, m2.transmat_)
finally:
shutil.rmtree(d)
def _component_type(self, spec):
if spec is None:
return None
spec_split = spec.split(':')
if len(spec_split) > 1:
fn = ':'.join(spec_split[:-1])
comp_i = int(spec_split[-1])
else:
fn = spec_split[0]
comp_i = 0
obj = utils.load(fn)
component = obj.components_[comp_i]
component = component ** 2
component = component / np.max(component)
return component
def test_transform_command_1():
with tempdir():
shell("msmb KCenters -i {data_home}/alanine_dipeptide/*.dcd "
"-o model.pkl --top {data_home}/alanine_dipeptide/ala2.pdb "
"--metric rmsd".format(data_home=get_data_home()))
shell("msmb TransformDataset -i {data_home}/alanine_dipeptide/*.dcd "
"-m model.pkl -t transformed.h5 --top "
"{data_home}/alanine_dipeptide/ala2.pdb"
.format(data_home=get_data_home()))
eq(dataset('transformed.h5')[0], load('model.pkl').labels_[0])
with tempdir():
shell("msmb KCenters -i {data_home}/alanine_dipeptide/trajectory-0.dcd "
"-o model.pkl --top {data_home}/alanine_dipeptide/ala2.pdb "
"--metric rmsd".format(data_home=get_data_home()))
def pull_features(yaml_file, prt, skip=1, feature_indices=None):
"""
Simple utility to pull certain features from the feature_folder object
:param prt: Protein model to use
:param skip: skip for each file(defaults to 1)
:param feature_indices: which indices to pull
:return: dictionary keyed on file name with feature values as arrays
"""
yaml_file = load_yaml_file(yaml_file)
all_f ={}
with enter_protein_data_dir(yaml_file, prt.name):
feature_file_list = glob.glob("./%s/*.jl"%yaml_file["feature_dir"])
for i in feature_file_list:
all_f[os.path.basename(i)]=load(i)[:, feature_indices]
return all_f
def validate_plumed_script(sim_obj_loc="metad_sim.pkl",
featurizer=None,
traj=None):
sim_obj = load(sim_obj_loc)
if featurizer is None and not hasattr(sim_obj, featurizer):
raise ValueError("Featuizer cant be none if sim_obj doesnt "
"have featurizer object")
if traj is None:
warnings.warn("No test trj found")
# with enter_temp_directory():
# for i in range()
f = open("./plumed.dat", 'w')
f.writelines(globals()["plumed_%d" % tic_index].format(bias="r%dt%d.bias" %
(tic_index, i)))
f.close()
cmd = [
"plumed", "--no-mpi", "driver", "--mf_xtc",
"../tic_%s/tic_%s.xtc" % (i, i)
]
ret_code = call(cmd)
return
import os
from msmbuilder import example_datasets, cluster, msm, featurizer, lumping, utils, dataset, decomposition
sysname = os.path.split(os.getcwd())[-1]
dt = 0.25
tica_lagtime = 400
#regularization_string = ""
regularization_string = "_012"
dih = dataset.NumpyDirDataset("./dihedrals/")
X = dataset.dataset("./tica/tica%d%s.h5" %
(tica_lagtime, regularization_string))
tica_model = utils.load("./tica/tica%d%s.pkl" %
(tica_lagtime, regularization_string))
Xf = np.concatenate(X)
hexbin(Xf[:, 0], Xf[:, 1], bins='log')
tica_model.timescales_
title("tICA: lagtime %d (%.3f)" % (tica_lagtime, dt * tica_lagtime))
xlabel("Slowest tIC")
ylabel("Second Slowest tIC")
savefig("./%s_tica_lag%d%s.png" %
(sysname, tica_lagtime, regularization_string),
bbox_inches="tight")
#Execute like python importing_plumed.py >tic{TICnumber}.dat
from tica_metadynamics.plumed_writer import render_tica_plumed_file
from msmbuilder.featurizer import DihedralFeaturizer
import pandas as pd
import mdtraj as md
from msmbuilder.utils import load
#python imports
import os, glob
import numpy as np
import pickle
tica_model = load("./tica_mdl_flapchi1angle.pkl")
#a = np.arange(1027,1357)
a = np.arange(1118, 1276)
top = md.load("prot.pdb", atom_indices=a)
# swap this for whatever you have. The code for now supports contacts, dihedral, and angles.
feat = DihedralFeaturizer(types=['chi1', 'chi2'])
# this basically maps every feature to atom indices.
df1 = pd.DataFrame(feat.describe_features(top))
#print(df1)
output = render_tica_plumed_file(tica_mdl=tica_model,
df=df1,
n_tics=5,
multiple_tics=None,
vde_mdl=None)
#Printing TIC1
#print(output[0])
import matplotlib
matplotlib.use('Agg')
from msmbuilder import example_datasets, cluster, msm, featurizer, lumping, utils, dataset, decomposition
from sklearn.pipeline import make_pipeline
import numpy as np
import matplotlib.pyplot as plt
import mdtraj as md
tica_lagtime = 1600
dih = dataset.NumpyDirDataset("./dihedrals/")
X = dataset.dataset("./tica%d.h5" % tica_lagtime)
Xf = np.concatenate(X)
tica_model = utils.load("./tica%d.pkl" % tica_lagtime)
#Load trajectory with ensembler models
t_models = md.load("../ensembler-models/traj-refine_implicit_md.xtc", top = "../ensembler-models/topol-renumbered-implicit.pdb")
#Now make dihedrals of this.
dihedrals_models = featurizer.DihedralFeaturizer(types=["phi", "psi", "chi1", "chi2"]).transform([t_models])
x_models = tica_model.transform(dihedrals_models)
#Now plot on the slow MSM features found before.
plt.plot(x_models[0][:, 0], x_models[0][:, 1], 'o', markersize=5, label="ensembler models", color='white')
plt.title("Dihedral tICA Analysis - Abl")
plt.xlabel("Slowest Coordinate")
plt.ylabel("Second Slowest Coordinate")
plt.legend()
import scipy.spatial
from msmbuilder import example_datasets, cluster, msm, featurizer, lumping, utils, dataset, decomposition
from sklearn.pipeline import make_pipeline
dih = dataset.NumpyDirDataset("./dihedrals/")
X = dataset.dataset("./tica.h5")
tica_model = utils.load("./tica.pkl")
dih_model = utils.load("./dihedrals/model.pkl")
clusterer = utils.load("./cluster.pkl")
microstate_model = utils.load("./msm.pkl")
labels = microstate_model.transform(clusterer.labels_)
n_macrostates = 5
pcca = lumping.PCCAPlus.from_msm(microstate_model, n_macrostates=n_macrostates)
macrostate_model = msm.MarkovStateModel()
macrostate_model.fit(pcca.transform(labels))
pipeline = make_pipeline(clusterer, microstate_model, pcca, macrostate_model)
s = pipeline.transform(X)
sf = np.concatenate(s)
Xf = np.concatenate(X)
for i in range(n_macrostates):
figure()
f = hexbin(Xf[:, 0], Xf[:, 1], bins='log')
hull = scipy.spatial.ConvexHull(Xf[sf == i, 0:2])
scipy.spatial.convex_hull_plot_2d(hull, ax=f.axes)
def test_dump_load():
data = dict(name="Fancy_name", arr=np.random.rand(10, 5))
with tempdir():
dump(data, 'filename')
data2 = load('filename')
eq(data, data2)
import mdtraj as md
from msmbuilder import example_datasets, cluster, msm, featurizer, lumping, utils, dataset, decomposition
from sklearn.pipeline import make_pipeline
trj0 = md.load("traj-refine_implicit_md.xtc", top="topol-renumbered-implicit.pdb")
trj0 = trj0[0:50]
X = dataset.dataset("./tica.h5")
Xf = np.concatenate(X)
dih_model = utils.load("./dihedrals/model.pkl")
tica_model = utils.load("./tica.pkl")
pipeline = make_pipeline(dih_model, tica_model)
x0 = pipeline.transform([trj0])[0]
hexbin(Xf[:, 0], Xf[:, 1], bins='log')
plot(x0[:, 0], x0[:, 1], 'kx')
map(lambda k: annotate(k, xy=x0[k, 0:2], fontsize=14), arange(len(x0)))
cfg = ConfigParser(interpolation=None)
cfg.read(os.environ.get("CONFIG"))
# Load optional env variables files
if os.environ.get("GEN"):
prevgen = int(os.environ.get("GEN"))
else:
prevgen = cfg.getint("production", "generation") - 1
if os.environ.get("STRIDE"):
stride = int(os.environ.get("STRIDE"))
else:
stride = 1
if os.environ.get("MSM"):
mmsm = load(os.environ.get("MSM"))
else:
mmsm = load(
os.path.join(cfg["system"]["rootdir"], "production", str(prevgen),
"mmsm_G%d.pkl" % prevgen))
if os.environ.get("CLUST"):
clust = load(os.environ.get("CLUST"))
else:
clust = load(
os.path.join(cfg["system"]["rootdir"], "production", str(prevgen),
"testing.mcluster.pkl"))
clust = [c[::stride] for c in clust]
# Make directory structure
if os.environ.get("CDIR"):
outdir = os.environ.get("CDIR")
import pandas as pd
from msmbuilder import example_datasets, cluster, msm, featurizer, lumping, utils, dataset, decomposition
from sklearn.pipeline import make_pipeline
import mdtraj as md
tica_lagtime = 1600
trajectories = dataset.MDTrajDataset("./trajectories/*.h5")
t0 = trajectories[0][0]
dih = dataset.NumpyDirDataset("./dihedrals/")
X = dataset.dataset("./tica/tica%d.h5" % tica_lagtime)
Xf = np.concatenate(X)
tica_model = utils.load("./tica/tica%d.pkl" % tica_lagtime)
dih_model = utils.load("./dihedrals/model.pkl")
d = dih_model.describe_features(t0)
d = pd.DataFrame(d)
d.ix[argsort(tica_model.eigenvectors_[:, 0])[0:5]]
d.ix[argsort(tica_model.eigenvectors_[:, 0])[-5:]]
def __init__(self,
base_dir="./",
starting_coordinates_folder="./starting_coordinates",
n_tics=1,
tica_mdl="tica_mdl.pkl",
tica_data="tica_data.pkl",
data_frame="feature_descriptor.pkl",
featurizer=None,
kmeans_mdl=None,
nrm=None,
wt_msm_mdl=None,
grid=False,
interval=False,
wall=False,
pace=2500,
stride=2500,
temp=300,
biasfactor=10,
height=1.0,
sigma=0.2,
delete_existing=False,
hills_file="HILLS",
bias_file="BIAS",
label="metad",
sim_save_rate=50000,
swap_rate=25000,
n_iterations=1000,
platform='CUDA',
grid_mlpt_factor=.3,
render_scripts=False,
msm_swap_folder=None,
msm_swap_scheme='random',
n_walkers=1,
neutral_replica=False,
multiple_tics=False,
plumed_script=None,
vde_mdl=None):
self.base_dir = os.path.abspath(base_dir)
self.starting_coordinates_folder = starting_coordinates_folder
self.n_tics = n_tics
if type(featurizer) == str:
self.featurizer = load(featurizer)
else:
self.featurizer = featurizer
self.tica_mdl = tica_mdl
if type(tica_data) == str:
self.tica_data = load(tica_data)
else:
self.tica_data = tica_data
if type(data_frame) == str:
self.data_frame = load(data_frame)
else:
self.data_frame = data_frame
if type(kmeans_mdl) == str:
self.kmeans_mdl = load(kmeans_mdl)
else:
self.kmeans_mdl = kmeans_mdl
if type(nrm) == str:
self.nrm = load(nrm)
else:
self.nrm = nrm
if type(wt_msm_mdl) == str:
self.wt_msm_mdl = load(wt_msm_mdl)
else:
self.wt_msm_mdl = wt_msm_mdl
# load plumed file
if type(plumed_script) == str:
self.plumed_script = open(plumed_script, 'r').read()
#TODO: write multiple scripts into dictionary separately?
# self.n_tics = len(list(self.plumed_dict.keys()))
else:
self.plumed_script = None
self.grid = grid
self.grid_mlpt_factor = grid_mlpt_factor
self.interval = interval
self.wall = wall
self.delete_existing = delete_existing
self.n_iterations = n_iterations
self.platform = platform
self.grid_list = self.interval_list = self.wall_list = None
self.render_scripts = render_scripts
self.walker_n = n_walkers
self.multiple_tics = multiple_tics
self.vde_mdl = vde_mdl
if self.grid:
if len(self.grid) < 2:
raise ValueError("grid must length at least 2 (like [0, 100]")
if len(self.grid) == 2 and type(self.grid[0]) in [float, int]:
# assume user meant us to specify
self.grid_list = get_interval(self.tica_data, self.grid[0],
self.grid[1])
print(self.grid_list)
#add extra mulplicative factor because these these tend to fail
self.grid_list = [(k[0]-self.grid_mlpt_factor*abs(k[0]),\
k[1]+self.grid_mlpt_factor*abs(k[1])) for k in self.grid_list]
print(self.grid_list)
else:
self.grid_list = self.grid
if self.interval:
if len(self.interval) < 2:
raise ValueError("interval must length 2(like [0, 100] for "
"calculating percentiles")
if len(self.interval) == 2 and type(
self.interval[0]) in [float, int]:
self.interval_list = get_interval(self.tica_data,
self.interval[0],
self.interval[1])
else:
self.interval_list = self.interval
if self.wall:
if len(self.wall) < 2:
raise ValueError("interval must length 2(like [0, 100] for "
"calculating percentiles")
if len(self.wall) == 2 and type(self.wall[0]) in [float, int]:
self.wall_list = get_interval(self.tica_data, self.wall[0],
self.wall[1])
else:
self.wall_list = self.wall
self.pace = pace
self.stride = stride
self.temp = temp
self.biasfactor = biasfactor
self.height = height
self.sigma = sigma
self.hills_file = hills_file
self.bias_file = bias_file
self.label = label
self.sim_save_rate = sim_save_rate
self.swap_rate = swap_rate
self.plumed_scripts_dict = None
self.msm_swap_folder = msm_swap_folder
self.msm_swap_scheme = msm_swap_scheme
self.neutral_replica = neutral_replica
self.tica_data = None
if self.walker_n > 1:
print("Multiple walkers found. Modifying current model")
os.chdir(self.base_dir)
# base_dir, has n_walker folders called walker_0 ... walkers
c_base_dir = self.base_dir
self._setup_walkers_folder()
for w in range(self.walker_n):
os.chdir(c_base_dir)
self.base_dir = os.path.join(c_base_dir, "walker_%d" % w)
self.walker_id = w
os.chdir(base_dir)
self._setup()
self._write_scripts_and_dump()
# make
else:
self._setup()
self._write_scripts_and_dump()
import scipy.spatial
from msmbuilder import example_datasets, cluster, msm, featurizer, lumping, utils, dataset, decomposition
from sklearn.pipeline import make_pipeline
dih = dataset.NumpyDirDataset("./dihedrals/")
X = dataset.dataset("./tica.h5")
tica_model = utils.load("./tica.pkl")
dih_model = utils.load("./dihedrals/model.pkl")
clusterer = utils.load("./cluster.pkl")
microstate_model = utils.load("./msm.pkl")
labels = microstate_model.transform(clusterer.labels_)
n_macrostates = 5
pcca = lumping.PCCAPlus.from_msm(microstate_model, n_macrostates=n_macrostates)
macrostate_model = msm.MarkovStateModel()
macrostate_model.fit(pcca.transform(labels))
pipeline = make_pipeline(clusterer, microstate_model, pcca, macrostate_model)
s = pipeline.transform(X)
sf = np.concatenate(s)
Xf = np.concatenate(X)
for i in range(n_macrostates):
figure()
f = hexbin(Xf[:, 0], Xf[:, 1], bins='log')
import os
from msmbuilder import example_datasets, cluster, msm, featurizer, lumping, utils, dataset, decomposition
sysname = os.path.split(os.getcwd())[-1]
dt = 0.25
tica_lagtime = 400
#regularization_string = ""
regularization_string = "_012"
dih = dataset.NumpyDirDataset("./dihedrals/")
X = dataset.dataset("./tica/tica%d%s.h5" % (tica_lagtime, regularization_string))
tica_model = utils.load("./tica/tica%d%s.pkl" % (tica_lagtime, regularization_string))
Xf = np.concatenate(X)
hexbin(Xf[:, 0], Xf[:, 1], bins='log')
tica_model.timescales_
title("tICA: lagtime %d (%.3f)" % (tica_lagtime, dt * tica_lagtime))
xlabel("Slowest tIC")
ylabel("Second Slowest tIC")
savefig("./%s_tica_lag%d%s.png" % (sysname, tica_lagtime, regularization_string), bbox_inches="tight")
from msmbuilder.utils import load, dump
from msmbuilder.featurizer import DihedralFeaturizer
import os, glob
from msmbuilder.decomposition import tICA
import mdtraj as md
import pandas as pd
from msmbuilder.msm import MarkovStateModel
from msmbuilder.cluster import KMeans
trj_list = load("traj_list.pkl")
print("Found %d trajs" % len(trj_list))
f = DihedralFeaturizer(sincos=False)
dump(f, "raw_featurizer.pkl")
feat = f.transform(trj_list)
dump(feat, "raw_features.pkl")
f = DihedralFeaturizer()
dump(f, "featurizer.pkl")
df1 = pd.DataFrame(f.describe_features(trj_list[0]))
dump(df1, "feature_descriptor.pkl")
feat = f.transform(trj_list)
dump(feat, "features.pkl")
t = tICA(lag_time=100, n_components=1, kinetic_mapping=False)
tica_feat = t.fit_transform(feat)
import numpy as np
from msmbuilder.utils import load
import os
plot_feat = load("./raw_features.pkl")
train_feat = load("./features.pkl")
df = load("./feature_descriptor.pkl")
from sklearn.linear_model import PassiveAggressiveClassifier
#Perception based model generation
X=np.vstack(plot_feat)
train_X=np.vstack(train_feat)
train_Y=np.concatenate([np.zeros(len(plot_feat[0])),
np.ones(len(plot_feat[0]))])
if not os.path.isfile("./pasag_model_bpti.pkl"):
train =True
else:
clf = load("./pasag_model_bpti.pkl")
train =False
if train:
clf = PassiveAggressiveClassifier(max_iter=1000)
clf.fit(train_X, train_Y)
#Dumping the Model
if train:
from msmbuilder.utils import dump
from msmbuilder.cluster import KMeans
flist = glob.glob("./run*.xtc")
top = md.load("../top.pdb")
trj_list = [md.load(i, top=top) for i in flist]
print("Found %d trajs" % len(trj_list))
f = DihedralFeaturizer(sincos=False)
dump(f, "raw_featurizer.pkl")
feat = f.transform(trj_list)
dump(feat, "raw_features.pkl")
f = load("../featurizer.pkl")
df1 = pd.DataFrame(f.describe_features(trj_list[0]))
dump(df1, "feature_descriptor.pkl")
feat = f.transform(trj_list)
dump(feat, "features.pkl")
t = load("../tica_mdl.pkl")
t.commute_mapping = False
tica_feat = t.transform(feat)
dump(t, "tica_mdl.pkl")
dump(tica_feat, "tica_features.pkl")
kmeans_mdl = load("../kmeans_mdl.pkl")
ass = kmeans_mdl.predict(tica_feat)
def run_meta_sim(file_loc="metad_sim.pkl"):
from tica_metadynamics.load_sim import create_simulation
metad_sim = load(file_loc)
if metad_sim.msm_swap_folder is not None:
print("Found MSM state folder. Will swap all replicas with the MSM "
"occasionally",flush=True)
#beta is 1/kt
beta = 1/(boltzmann_constant * metad_sim.temp)
#get
my_host_name = socket.gethostname()
my_gpu_index = get_gpu_index()
print("Hello from rank %d running tic %d on "
"host %s with gpu %d"%(rank, rank, my_host_name, my_gpu_index))
plumed_force_dict = get_plumed_dict(metad_sim)
sim_obj, force_group = create_simulation(metad_sim.base_dir, metad_sim.starting_coordinates_folder,
my_gpu_index, rank, plumed_force_dict[rank],
metad_sim.sim_save_rate, metad_sim.platform)
if rank ==0 and size>1:
log_file = open("../swap_log.txt","a")
header = ["Iteration","S_i","S_j","Eii","Ejj","Eij","Eji",
"DeltaE","Temp","Beta","Probability","Accepted"]
log_file.writelines("#{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\n".format(*header))
for step in range(metad_sim.n_iterations):
#2fs *3000 = 6ps
sim_obj.step(metad_sim.swap_rate)
if metad_sim.msm_swap_folder is not None and np.random.random() < 0.5:
sim_obj = swap_with_msm_state(sim_obj, metad_sim.msm_swap_folder, force_group,beta)
#get old energy for just the plumed force
old_energy = sim_obj.context.getState(getEnergy=True,groups={force_group}).\
getPotentialEnergy().value_in_unit(kilojoule_per_mole)
#write the chckpt
with open("checkpt.chk",'wb') as f:
f.write(sim_obj.context.createCheckpoint())
old_state = os.path.abspath("checkpt.chk")
#send state and energy
data = comm.gather((old_state,old_energy), root=0)
if size >1:
if rank==0:
#rnd pick 2 states
i,j = np.random.choice(np.arange(size), 2, replace=False)
s_i_i,e_i_i = data[i]
s_j_j,e_j_j = data[j]
#swap out states
data[j], data[i] = data[i],data[j]
else:
data = None
#get possible new state
new_state = None
new_state,energy = comm.scatter(data,root=0)
#set state
with open(new_state, 'rb') as f:
sim_obj.context.loadCheckpoint(f.read())
# return new state and new energies
new_energy = sim_obj.context.getState(getEnergy=True,groups={force_group}).\
getPotentialEnergy().value_in_unit(kilojoule_per_mole)
data = comm.gather((new_state,new_energy), root=0)
if rank==0:
s_i_j, e_i_j = data[i]
s_j_i, e_j_i = data[j]
delta_e = e_i_i+e_j_j - e_i_j - e_j_i
probability = np.min((1,np.exp(beta*delta_e)))
print(e_i_i,e_j_j,e_i_j,e_j_i,probability)
if np.random.random() < probability :
accepted= 1
print("Swapping out %d with %d"%(i,j),flush=True)
else:
accepted= 0
print("Failed Swap of %d with %d"%(i,j),flush=True)
#go back to original state list
data[i], data[j] = data[j] , data[i]
header = [step, i, j, e_i_i,e_j_j,e_i_j,e_j_i,delta_e,metad_sim.temp,beta,probability,accepted]
log_file.writelines("{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\n".format(*header))
log_file.flush()
else:
data = None
#get final state for iteration
new_state,energy = comm.scatter(data,root=0)
#print(rank,new_state)
with open(new_state, 'rb') as f:
sim_obj.context.loadCheckpoint(f.read())
#barrier here to prevent
comm.barrier()
if rank==0 and size >1:
log_file.close()
return
flist = glob.glob("../trajectory.xtc")
top = md.load("../top.pdb")
trj_list = [md.load(i, top=top) for i in flist]
print("Found %d trajs" % len(trj_list))
f = DihedralFeaturizer(sincos=False)
dump(f, "raw_featurizer.pkl")
feat = f.transform(trj_list)
dump(feat, "raw_features.pkl")
f = load("./featurizer.pkl")
dump(f, "featurizer.pkl")
df1 = pd.DataFrame(f.describe_features(trj_list[0]))
dump(df1, "feature_descriptor.pkl")
feat = f.transform(trj_list)
dump(feat, "features.pkl")
t = tICA(lag_time=100, n_components=2, kinetic_mapping=False)
tica_feat = t.fit_transform(feat)
dump(t, "tica_mdl.pkl")
dump(tica_feat, "tica_features.pkl")
kmeans_mdl = KMeans(50)
def test_dump_load():
data = dict(name="Fancy_name", arr=np.random.rand(10, 5))
with tempdir():
dump(data, 'filename')
data2 = load('filename')
eq(data, data2)
