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 test_atom_pairs_featurizer():
with tempdir():
shell('msmb AtomIndices -o all.txt --all -d -p '
'%s/alanine_dipeptide/ala2.pdb' % get_data_home()),
shell("msmb AtomPairsFeaturizer "
"--trjs '{data_home}/alanine_dipeptide/*.dcd'"
" --transformed pairs --pair_indices all.txt"
" --top {data_home}/alanine_dipeptide/ala2.pdb"
.format(data_home=get_data_home()))
ds = dataset('pairs')
assert len(ds) == 10
assert ds[0].shape[1] == len(np.loadtxt('all.txt') ** 2)
print(ds.provenance)
def test_superpose_featurizer():
with tempdir():
shell('msmb AtomIndices -o all.txt --all -a -p '
'%s/alanine_dipeptide/ala2.pdb' % get_data_home()),
shell("msmb SuperposeFeaturizer "
"--trjs '{data_home}/alanine_dipeptide/*.dcd'"
" --transformed distances --atom_indices all.txt"
" --reference_traj {data_home}/alanine_dipeptide/ala2.pdb"
" --top {data_home}/alanine_dipeptide/ala2.pdb"
.format(data_home=get_data_home()))
ds = dataset('distances')
assert len(ds) == 10
assert ds[0].shape[1] == len(np.loadtxt('all.txt'))
print(ds.provenance)
def test_convert_chunked_project_1():
fetch_alanine_dipeptide()
with tempdir():
root = os.path.join(get_data_home(), 'alanine_dipeptide')
if sys.platform == 'win32':
pattern = "*.dcd"
else:
pattern = "'*.dcd'"
cmd = 'msmb ConvertChunkedProject out {root} --pattern {pattern} -t {root}/ala2.pdb'.format(
root=root, pattern=pattern)
shell(cmd)
assert set(os.listdir('out')) == set(
('traj-00000000.dcd', 'trajectories.jsonl'))
# check that out/traj-00000.dcd really has concatenated all of
# the input trajs
length = len(md.open('out/traj-00000000.dcd'))
assert length == sum(
len(md.open(f)) for f in glob.glob('%s/*.dcd' % root))
with open('out/trajectories.jsonl') as f:
record = json.load(f)
assert set(record.keys()) == set(('filename', 'chunks'))
assert record['filename'] == 'traj-00000000.dcd'
assert sorted(glob.glob('%s/*.dcd' % root)) == record['chunks']
def test_transform_command_1():
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 "
"--stride 2".format(data_home=get_data_home()))
def test_convert_chunked_project_1():
fetch_alanine_dipeptide()
with tempdir():
root = os.path.join(get_data_home(), 'alanine_dipeptide')
if sys.platform == 'win32':
pattern = "*.dcd"
else:
pattern = "'*.dcd'"
cmd = ('msmb ConvertChunkedProject out {root} --pattern {pattern} '
'-t {root}/ala2.pdb'
.format(root=root, pattern=pattern))
shell(cmd)
assert set(os.listdir('out')) == {'traj-00000000.dcd',
'trajectories.jsonl'}
# check that out/traj-00000.dcd really has concatenated all of
# the input trajs
length = len(md.open('out/traj-00000000.dcd'))
assert length == sum(len(md.open(f))
for f in glob.glob('%s/*.dcd' % root))
with open('out/trajectories.jsonl') as f:
record = json.load(f)
assert set(record.keys()) == {'filename', 'chunks'}
assert record['filename'] == 'traj-00000000.dcd'
assert sorted(glob.glob('%s/*.dcd' % root)) == record['chunks']
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 setup_module():
global DATADIR, HMM, t
DATADIR = tempfile.mkdtemp()
# 4 components and 3 features. Each feature is going to be the x, y, z
# coordinate of 1 atom
HMM = hmmlearn.hmm.GaussianHMM(n_components=4)
HMM.transmat_ = np.array([[0.9, 0.1, 0.0, 0.0],
[0.1, 0.7, 0.2, 0.0],
[0.0, 0.1, 0.8, 0.1],
[0.0, 0.1, 0.1, 0.8]])
HMM.means_ = np.array([[-10, -10, -10],
[-5, -5, -5],
[5, 5, 5],
[10, 10, 10]])
HMM.covars_ = np.array([[0.1, 0.1, 0.1],
[0.5, 0.5, 0.5],
[1, 1, 1],
[4, 4, 4]])
HMM.startprob_ = np.array([1, 1, 1, 1]) / 4.0
# get a 1 atom topology
topology = t.atom_slice([1]).topology
# generate the trajectories and save them to disk
for i in range(10):
d, s = HMM.sample(100)
traj = md.Trajectory(xyz=d.reshape(len(d), 1, 3), topology=topology)
traj.save(os.path.join(DATADIR, 'Trajectory%d.h5' % i))
assert os.path.exists(os.path.join("{}", "alanine_dipeptide").format(get_data_home()))
def test_convert_chunked_project_1():
with tempdir():
root = os.path.join(get_data_home(), 'alanine_dipeptide')
assert os.path.exists(root)
cmd = ("msmb ConvertChunkedProject out {root} --pattern *.dcd "
"-t {root}/ala2.pdb".format(root=root))
shell(cmd)
assert set(
os.listdir('out')) == {'traj-00000000.dcd', 'trajectories.jsonl'}
# check that out/traj-00000.dcd really has concatenated all of
# the input trajs
length = len(md.open('out/traj-00000000.dcd'))
assert length == sum(
len(md.open(f)) for f in glob.glob('%s/*.dcd' % root))
with open('out/trajectories.jsonl') as f:
record = json.load(f)
assert set(record.keys()) == {'filename', 'chunks'}
assert record['filename'] == 'traj-00000000.dcd'
assert sorted(glob.glob('%s/*.dcd' % root)) == record['chunks']
def setup_module():
global DATADIR, HMM
DATADIR = tempfile.mkdtemp()
# 4 components and 3 features. Each feature is going to be the x, y, z
# coordinate of 1 atom
HMM = hmmlearn.hmm.GaussianHMM(n_components=4)
HMM.transmat_ = np.array([[0.9, 0.1, 0.0, 0.0], [0.1, 0.7, 0.2, 0.0],
[0.0, 0.1, 0.8, 0.1], [0.0, 0.1, 0.1, 0.8]])
HMM.means_ = np.array([[-10, -10, -10], [-5, -5, -5], [5, 5, 5],
[10, 10, 10]])
HMM.covars_ = np.array([[0.1, 0.1, 0.1], [0.5, 0.5, 0.5], [1, 1, 1],
[4, 4, 4]])
HMM.startprob_ = np.array([1, 1, 1, 1]) / 4.0
# get a 1 atom topology
topology = md.load(get_mdtraj_fn('native.pdb')).atom_slice([1]).topology
# generate the trajectories and save them to disk
for i in range(10):
d, s = HMM.sample(100)
t = md.Trajectory(xyz=d.reshape(len(d), 1, 3), topology=topology)
t.save(os.path.join(DATADIR, 'Trajectory%d.h5' % i))
assert os.path.exists("{}/alanine_dipeptide".format(get_data_home()))
def test_transform_command_1():
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 "
"--stride 2".format(data_home=get_data_home()))
import tempfile
import hmmlearn.hmm
import mdtraj as md
import numpy as np
from mdtraj.testing import eq
# from mdtraj.testing import get_fn as get_mdtraj_fn
from msmbuilder.dataset import dataset
from msmbuilder.example_datasets import get_data_home, FsPeptide
from msmbuilder.utils import load
DATADIR = HMM = None
t = FsPeptide().get().trajectories[0][0]
fn = os.path.join("{}","fs_peptide", "fs-peptide.pdb").format(get_data_home())
def setup_module():
global DATADIR, HMM, t
DATADIR = tempfile.mkdtemp()
# 4 components and 3 features. Each feature is going to be the x, y, z
# coordinate of 1 atom
HMM = hmmlearn.hmm.GaussianHMM(n_components=4)
HMM.transmat_ = np.array([[0.9, 0.1, 0.0, 0.0],
[0.1, 0.7, 0.2, 0.0],
[0.0, 0.1, 0.8, 0.1],
[0.0, 0.1, 0.1, 0.8]])
HMM.means_ = np.array([[-10, -10, -10],
[-5, -5, -5],
[5, 5, 5],
[10, 10, 10]])