def test_order_1():
with tempdir():
with dataset('ds1.h5', 'w', 'hdf5') as ds1:
for i in range(20):
ds1[i] = np.random.randn(10)
assert list(ds1.keys()) == list(range(20))
with dataset('ds1/', 'w', 'dir-npy') as ds1:
for i in range(20):
ds1[i] = np.random.randn(10)
assert list(ds1.keys()) == list(range(20))
def test_order_1():
with tempdir():
with dataset('ds1.h5', 'w', 'hdf5') as ds1:
for i in range(20):
ds1[i] = np.random.randn(10)
assert list(ds1.keys()) == list(range(20))
with dataset('ds1/', 'w', 'dir-npy') as ds1:
for i in range(20):
ds1[i] = np.random.randn(10)
assert list(ds1.keys()) == list(range(20))
def test_mdtraj_1():
ds = dataset(get_fn('') + '*.pdb', fmt='mdtraj', verbose=True)
print(ds.keys())
print(ds.get(0))
print(ds.provenance)
ds = dataset(get_fn('') + '*.pdb', fmt='mdtraj', atom_indices=[1, 2],
verbose=True)
print(ds.keys())
print(ds.get(0))
print(ds.provenance)
def test_union_3():
with tempdir():
# This doesn't work with py2.6
with dataset('ds1/', 'w', 'dir-npy') as ds1, \
dataset('ds2/', 'w', 'dir-npy') as ds2:
ds1[0] = np.random.randn(10, 2)
ds1[1] = np.random.randn(10)
ds2[0] = np.random.randn(10, 4)
# Uneven length!
with assert_raises(ValueError):
mds = dataset(['ds1', 'ds2'])
def test_hdf5_3():
with tempdir():
with dataset('ds.h5', 'w', 'hdf5') as ds:
ds[0] = np.random.randn(10)
ds[1] = np.random.randn(10)
ref_sum = _sum_helper(ds)
iter_args = (dataset('ds.h5') for _ in range(5))
sums = Parallel(n_jobs=2)(delayed(_sum_helper)(a) for a in iter_args)
assert all(s == ref_sum for s in sums)
def test_union_3():
with tempdir():
# This doesn't work with py2.6
with dataset('ds1/', 'w', 'dir-npy') as ds1, \
dataset('ds2/', 'w', 'dir-npy') as ds2:
ds1[0] = np.random.randn(10, 2)
ds1[1] = np.random.randn(10)
ds2[0] = np.random.randn(10,4)
# Uneven length!
with assert_raises(ValueError):
mds = dataset(['ds1', 'ds2'])
def test_union_2():
with tempdir():
# This doesn't work with py2.6
with dataset('ds1/', 'w', 'dir-npy') as ds1, \
dataset('ds2/', 'w', 'dir-npy') as ds2:
ds1[0] = np.random.randn(10, 2)
ds1[1] = np.random.randn(10)
ds2[0] = np.random.randn(10, 4)
ds2[1] = np.random.randn(10, 4)
mds = dataset(['ds1', 'ds2'])
mds_out = mds.create_derived('derived', fmt='dir-npy')
assert len(mds_out.provenance.split('\n')) > 0
def test_mdtraj_1():
ds = dataset(get_fn('') + '*.pdb', fmt='mdtraj', verbose=True)
print(ds.keys())
print(ds.get(0))
print(ds.provenance)
ds = dataset(get_fn('') + '*.pdb',
fmt='mdtraj',
atom_indices=[1, 2],
verbose=True)
print(ds.keys())
print(ds.get(0))
print(ds.provenance)
def test_uneven_n():
with tempdir():
# This doesn't work with py2.6
with dataset('ds1/', 'w', 'dir-npy') as ds1, \
dataset('ds2/', 'w', 'dir-npy') as ds2:
ds1[0] = np.random.randn(10, 2)
ds1[1] = np.random.randn(5, 2)
ds2[0] = np.random.randn(10, 4)
# Uneven number of trajs!
fu = FeatureUnion(normalize=False)
with assert_raises(ValueError):
fu.fit((ds1, ds2))
def test_hdf5_3():
with tempdir():
with dataset('ds.h5', 'w', 'hdf5') as ds:
ds[0] = np.random.randn(10)
ds[1] = np.random.randn(10)
ref_sum = _sum_helper(ds)
iter_args = (dataset('ds.h5') for _ in range(5))
sums = Parallel(n_jobs=2)(
delayed(_sum_helper)(a) for a in iter_args)
assert all(s == ref_sum for s in sums)
def test_uneven_n():
with tempdir():
# This doesn't work with py2.6
with dataset('ds1/', 'w', 'dir-npy') as ds1, \
dataset('ds2/', 'w', 'dir-npy') as ds2:
ds1[0] = np.random.randn(10, 2)
ds1[1] = np.random.randn(5, 2)
ds2[0] = np.random.randn(10, 4)
# Uneven number of trajs!
fu = FeatureUnion(normalize=False)
with assert_raises(ValueError):
fu.fit((ds1, ds2))
def test_union_2():
with tempdir():
# This doesn't work with py2.6
with dataset('ds1/', 'w', 'dir-npy') as ds1, \
dataset('ds2/', 'w', 'dir-npy') as ds2:
ds1[0] = np.random.randn(10, 2)
ds1[1] = np.random.randn(10)
ds2[0] = np.random.randn(10,4)
ds2[1] = np.random.randn(10,4)
mds = dataset(['ds1', 'ds2'])
mds_out = mds.create_derived('derived', fmt='dir-npy')
assert len(mds_out.provenance.split('\n')) > 0
def test_assign(self):
with open(os.devnull) as dn:
subprocess.call(
[
'msmb', 'SolventShellsAssigner', '--trjs', self.traj_fn,
'--solute_indices', self.ute_fn, '--solvent_indices',
self.vent_fn, '--n_shells', '3', '--shell_width', '1',
'--out', self.outfn, '--chunk', '2'
], stdout=dn, stderr=dn
)
data = dataset(self.outfn)[0]
should_be = np.array([
[0, 0, 0, 0],
[0, 1, 0, 0],
[1, 0, 0, 1],
[1, 1, 0, 1],
[2, 0, 0, 2],
[2, 1, 0, 2],
# 3
# 4
[5, 1, 0, 0],
[6, 1, 0, 1],
[7, 1, 0, 2],
# 8
])
np.testing.assert_array_equal(data, should_be)
def test_assign(self):
with open(os.devnull) as dn:
subprocess.call([
'msmb', 'SolventShellsAssigner', '--trjs', self.traj_fn,
'--solute_indices', self.ute_fn, '--solvent_indices',
self.vent_fn, '--n_shells', '3', '--shell_width', '1', '--out',
self.outfn, '--chunk', '2'
],
stdout=dn,
stderr=dn)
data = dataset(self.outfn)[0]
should_be = np.array([
[0, 0, 0, 0],
[0, 1, 0, 0],
[1, 0, 0, 1],
[1, 1, 0, 1],
[2, 0, 0, 2],
[2, 1, 0, 2],
# 3
# 4
[5, 1, 0, 0],
[6, 1, 0, 1],
[7, 1, 0, 2],
# 8
])
np.testing.assert_array_equal(data, should_be)
def test_1():
path = tempfile.mkdtemp()
shutil.rmtree(path)
try:
X = np.random.randn(10, 2)
ds = dataset(path, 'w', 'dir-npy')
ds[0] = X
assert set(os.listdir(path)) == set(('PROVENANCE.txt', '00000000.npy'))
np.testing.assert_array_equal(ds[0], X)
assert_raises(IndexError, lambda: ds[1])
assert len(ds) == 1
Y = np.zeros((10, 1))
Z = np.ones((2, 2))
ds[1] = Y
ds[2] = Z
np.testing.assert_array_equal(ds[1], Y)
np.testing.assert_array_equal(ds[2], Z)
assert len(ds) == 3
for i, item in enumerate(ds):
np.testing.assert_array_equal(item, [X, Y, Z][i])
except:
raise
finally:
shutil.rmtree(path)
def test_2():
path1 = tempfile.mkdtemp()
path2 = tempfile.mkdtemp()
shutil.rmtree(path1)
shutil.rmtree(path2)
try:
X = np.random.randn(10, 2)
Y = np.random.randn(10, 2)
ds1 = dataset(path1, 'w', 'dir-npy')
ds1[0] = X
ds2 = ds1.create_derived(path2)
ds2[0] = Y
np.testing.assert_array_equal(ds1[0], X)
np.testing.assert_array_equal(ds2[0], Y)
assert len(ds1) == 1
assert len(ds2) == 1
prov2 = ds2.provenance
print(prov2)
assert 2 == sum([s.startswith(' Command') for s in prov2.splitlines()])
except:
raise
finally:
shutil.rmtree(path1)
shutil.rmtree(path2)
def test_2():
path1 = tempfile.mkdtemp()
path2 = tempfile.mkdtemp()
shutil.rmtree(path1)
shutil.rmtree(path2)
try:
X = np.random.randn(10, 2)
Y = np.random.randn(10, 2)
ds1 = dataset(path1, 'w', 'dir-npy')
ds1[0] = X
ds2 = ds1.create_derived(path2)
ds2[0] = Y
np.testing.assert_array_equal(ds1[0], X)
np.testing.assert_array_equal(ds2[0], Y)
assert len(ds1) == 1
assert len(ds2) == 1
prov2 = ds2.provenance
print(prov2)
assert 2 == sum(
[s.startswith(' Command') for s in prov2.splitlines()])
except:
raise
finally:
shutil.rmtree(path1)
shutil.rmtree(path2)
def test_1():
path = tempfile.mkdtemp()
shutil.rmtree(path)
try:
X = np.random.randn(10, 2)
ds = dataset(path, 'w', 'dir-npy')
ds[0] = X
assert set(os.listdir(path)) == set(('PROVENANCE.txt', '00000000.npy'))
np.testing.assert_array_equal(ds[0], X)
assert_raises(IndexError, lambda: ds[1])
assert len(ds) == 1
Y = np.zeros((10, 1))
Z = np.ones((2, 2))
ds[1] = Y
ds[2] = Z
np.testing.assert_array_equal(ds[1], Y)
np.testing.assert_array_equal(ds[2], Z)
assert len(ds) == 3
for i, item in enumerate(ds):
np.testing.assert_array_equal(item, [X, Y, Z][i])
except:
raise
finally:
shutil.rmtree(path)
def test_partial_transform(self):
with open(os.devnull) as dn:
subprocess.call(
[
'msmb', 'SolventShellsFeaturizer', '--trjs', self.traj_fn,
'--solute_indices', self.ute_fn, '--solvent_indices',
self.vent_fn, '--n_shells', '3', '--shell_width', '1',
'--out', self.outfn
], stdout=dn, stderr=dn
)
data = dataset(self.outfn)[0]
norm = np.asarray([4 * np.pi * r ** 2 for r in [0.5, 1.5, 2.5]])
should_be = np.array([
[2, 0, 0],
[0, 2, 0],
[0, 0, 2],
[0, 0, 0],
[0, 0, 0],
[1, 0, 0],
[0, 1, 0],
[0, 0, 1],
[0, 0, 0]
]) / norm
np.testing.assert_array_equal(data, should_be)
def start(self):
if os.path.exists(self.out):
self.error('File exists: %s' % self.out)
print(self.instance)
if os.path.exists(os.path.expanduser(self.top)):
top = os.path.expanduser(self.top)
else:
top = None
traj_dataset = MDTrajDataset(self.trjs, topology=top,
stride=self.instance.stride, verbose=False)
with dataset(self.assignments, mode='r') as assn_dataset:
out_dataset = assn_dataset.create_derived(self.out, fmt='dir-npy')
pbar = ProgressBar(widgets=[Percentage(), Bar(), ETA()],
maxval=len(assn_dataset)).start()
for tr_key, as_key in pbar(
zip(traj_dataset.keys(), assn_dataset.keys())
):
out_dataset[as_key] = self.instance.partial_transform(
(traj_dataset[tr_key], assn_dataset[as_key])
)
out_dataset.close()
print("\nSaving transformed dataset to '%s'" % self.out)
print("To load this dataset interactive inside an IPython")
print("shell or notebook, run\n")
print(" $ ipython")
print(" >>> from msmbuilder.dataset import dataset")
print(" >>> ds = dataset('%s')\n" % self.out)
def save_dataset(data, path):
if os.path.exists(path):
cmd = "rm -rf %s" % path
subprocess.call(cmd, shell=True)
ds = dataset(path, 'w', 'dir-npy')
for i in range(0, len(data)):
ds[i] = data[i]
ds.close()
def featurizing_the_conformations(featurizer, xtc_traj_folder, traj_list_array, pdb_name):
#we usually use dihedral, rmsd or pairwise distance featurizers in MSM
output_features=[]
for trajfile in traj_list_array:
xyz=dataset(trajectory_dir+trajfile, topology=trajectory_dir+pdb_name) #xyz is the coordinates for the frames in the trajectory
temp=featurizer.fit_transform(xyz) #we transform the coordinates into pairwise distaneces
print("loaded %s into (%d,%d) dimensional file"%(trajfile, len(temp[0]), len(temp[0][0])))
output_features.append(temp[0])
return output_features
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_append_dirnpy():
path = tempfile.mkdtemp()
shutil.rmtree(path)
try:
with dataset(path, 'w', 'dir-npy') as ds:
ds[0] = np.random.randn(10, 2)
with dataset(path, 'a', 'dir-npy') as ds:
ds[1] = np.random.randn(10, 2)
with dataset(path, 'a', 'dir-npy') as ds:
ds[2] = np.random.randn(10, 2)
with dataset(path, 'a', 'dir-npy') as ds:
# Overwrite
ds[2] = np.random.randn(10, 2)
np.testing.assert_array_equal(ds[:][0], ds[0])
np.testing.assert_array_equal(ds[:][1], ds[1])
np.testing.assert_array_equal(ds[:][2], ds[2])
finally:
shutil.rmtree(path)
def test_hdf5_1():
with tempdir():
ds = dataset('ds.h5', 'w', 'hdf5')
print(ds.provenance)
ds[0] = np.zeros(10)
np.testing.assert_array_equal(ds.get(0), np.zeros(10))
assert list(ds.keys()) == [0]
assert len(ds) == 1
ds[0] = np.random.randn(10, 1)
ds[1] = np.random.randn(10, 2)
ds[2] = np.random.randn(10, 3)
np.testing.assert_array_equal(ds[:][0], ds[0])
np.testing.assert_array_equal(ds[:][1], ds[1])
np.testing.assert_array_equal(ds[:][2], ds[2])
ds.close()
with dataset('ds.h5') as ds:
assert ds[0].shape == (10, 1)
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'"
" --out 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_hdf5_1():
with tempdir():
ds = dataset('ds.h5', 'w', 'hdf5')
print(ds.provenance)
ds[0] = np.zeros(10)
np.testing.assert_array_equal(ds.get(0), np.zeros(10))
assert list(ds.keys()) == [0]
assert len(ds) == 1
ds[0] = np.random.randn(10, 1)
ds[1] = np.random.randn(10, 2)
ds[2] = np.random.randn(10, 3)
np.testing.assert_array_equal(ds[:][0], ds[0])
np.testing.assert_array_equal(ds[:][1], ds[1])
np.testing.assert_array_equal(ds[:][2], ds[2])
ds.close()
with dataset('ds.h5') as ds:
assert ds[0].shape == (10, 1)
def test_union():
with tempdir():
# This doesn't work with py2.6
with dataset('ds1.h5', 'w', 'hdf5') as ds1, \
dataset('ds2.h5', 'w', 'hdf5') as ds2:
ds1[0] = np.random.randn(10, 2)
ds1[1] = np.random.randn(10)
ds2[0] = np.random.randn(10, 4)
ds2[1] = np.random.randn(10, 4)
# Compare row sums
rs1 = np.sum(ds1[0], axis=1) + np.sum(ds2[0], axis=1)
rs2 = ds1[1] + np.sum(ds2[1], axis=1)
mds = dataset(['ds1.h5', 'ds2.h5'])
assert len(mds) == 2
assert mds[0].shape == (10, 6)
assert mds[1].shape == (10, 5)
np.testing.assert_array_almost_equal(np.sum(mds[0], axis=1), rs1)
np.testing.assert_array_almost_equal(np.sum(mds[1], axis=1), rs2)
def test_4():
path = tempfile.mkdtemp()
shutil.rmtree(path)
try:
ds = dataset(path, 'w', 'dir-npy')
ds[0] = np.random.randn(10, 2)
v = ds.get(0, mmap=True)
assert isinstance(v, np.memmap)
np.testing.assert_array_equal(ds[0], v)
del v # close the underlying file
finally:
shutil.rmtree(path)
def test_union():
with tempdir():
# This doesn't work with py2.6
with dataset('ds1.h5', 'w', 'hdf5') as ds1, \
dataset('ds2.h5', 'w', 'hdf5') as ds2:
ds1[0] = np.random.randn(10, 2)
ds1[1] = np.random.randn(10)
ds2[0] = np.random.randn(10,4)
ds2[1] = np.random.randn(10,4)
# Compare row sums
rs1 = np.sum(ds1[0], axis=1) + np.sum(ds2[0], axis=1)
rs2 = ds1[1] + np.sum(ds2[1], axis=1)
mds = dataset(['ds1.h5', 'ds2.h5'])
assert len(mds) == 2
assert mds[0].shape == (10, 6)
assert mds[1].shape == (10, 5)
np.testing.assert_array_almost_equal(np.sum(mds[0], axis=1), rs1)
np.testing.assert_array_almost_equal(np.sum(mds[1], axis=1), rs2)
def Get_rawposition_features_villin():
import os
import shutil
os.chdir('/homes/anuginueni/traj_villin')
if(os.path.isdir('./rawpositions')):
shutil.rmtree('./rawpositions')
from msmbuilder.dataset import dataset
xyz = dataset( "/homes/anuginueni/traj_villin/*.xtc",topology='/homes/anuginueni/traj_villin/filtered.pdb',stride=5)
from msmbuilder.featurizer import RawPositionsFeaturizer #for raw positions
featurizer = RawPositionsFeaturizer() #for raw positions
rawpositions = xyz.fit_transform_with(featurizer, 'rawpositions/', fmt='dir-npy') #for rawpositions
return rawpositions
def test_4():
path = tempfile.mkdtemp()
shutil.rmtree(path)
try:
ds = dataset(path, 'w', 'dir-npy')
ds[0] = np.random.randn(10, 2)
v = ds.get(0, mmap=True)
assert isinstance(v, np.memmap)
np.testing.assert_array_equal(ds[0], v)
del v # close the underlying file
finally:
shutil.rmtree(path)
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'"
" --out 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_append_dirnpy():
path = tempfile.mkdtemp()
shutil.rmtree(path)
try:
with dataset(path, 'w', 'dir-npy') as ds:
ds[0] = np.random.randn(10, 2)
with dataset(path, 'a', 'dir-npy') as ds:
ds[1] = np.random.randn(10, 2)
with dataset(path, 'a', 'dir-npy') as ds:
ds[2] = np.random.randn(10, 2)
with dataset(path, 'a', 'dir-npy') as ds:
# Overwrite
ds[2] = np.random.randn(10, 2)
np.testing.assert_array_equal(ds[:][0], ds[0])
np.testing.assert_array_equal(ds[:][1], ds[1])
np.testing.assert_array_equal(ds[:][2], ds[2])
finally:
shutil.rmtree(path)
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_dataset():
with tempdir():
# This doesn't work with py2.6
with dataset('ds1.h5', 'w', 'hdf5') as ds1, \
dataset('ds2.h5', 'w', 'hdf5') as ds2:
ds1[0] = np.random.randn(10, 2)
ds1[1] = np.random.randn(5, 2)
ds2[0] = np.random.randn(10, 4)
ds2[1] = np.random.randn(5, 4)
# Compare row sums
rs1 = np.sum(ds1[0], axis=1) + np.sum(ds2[0], axis=1)
rs2 = np.sum(ds1[1], axis=1) + np.sum(ds2[1], axis=1)
fu = FeatureUnion(normalize=False)
mds = fu.fit_transform((ds1, ds2))
assert len(mds) == 2
assert mds[0].shape == (10, 6)
assert mds[1].shape == (5, 6)
np.testing.assert_array_almost_equal(np.sum(mds[0], axis=1), rs1)
np.testing.assert_array_almost_equal(np.sum(mds[1], axis=1), rs2)
def start(self):
ds = dataset(self.dataset, mode='r')
assert len(ds) == 1, "Only support one at a time for now"
ds = ds[0]
dat_fn = "{}.txt".format(self.out_prefix)
tcl_fn = "{}.tcl".format(self.out_prefix)
np.savetxt(dat_fn, ds, fmt="%.5f")
with open(tcl_fn, 'w') as f:
f.write(VMDSCRIPT.format(
traj_fn=self.traj, step=self.stride, top_fn=self.top,
dat_fn=dat_fn
))
def test_dataset():
with tempdir():
# This doesn't work with py2.6
with dataset('ds1.h5', 'w', 'hdf5') as ds1, \
dataset('ds2.h5', 'w', 'hdf5') as ds2:
ds1[0] = np.random.randn(10, 2)
ds1[1] = np.random.randn(5, 2)
ds2[0] = np.random.randn(10, 4)
ds2[1] = np.random.randn(5, 4)
# Compare row sums
rs1 = np.sum(ds1[0], axis=1) + np.sum(ds2[0], axis=1)
rs2 = np.sum(ds1[1], axis=1) + np.sum(ds2[1], axis=1)
fu = FeatureUnion(normalize=False)
mds = fu.fit_transform((ds1, ds2))
assert len(mds) == 2
assert mds[0].shape == (10, 6)
assert mds[1].shape == (5, 6)
np.testing.assert_array_almost_equal(np.sum(mds[0], axis=1), rs1)
np.testing.assert_array_almost_equal(np.sum(mds[1], axis=1), rs2)
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 main():
import argparse, textwrap
parser = argparse.ArgumentParser(
usage=textwrap.dedent(
'''Use "python %(prog)s -h" for more information.'''),
formatter_class=argparse.RawTextHelpFormatter)
parser.add_argument(
'pdbpath',
help=textwrap.dedent('''[required] Path to pdb trajectories.'''))
parser.add_argument('target',
help=textwrap.dedent('''[required] Path to target pdb.
Note: The target pdb should have the same number of atoms in structure with that in pdb trajectories. '''
))
args = parser.parse_args()
from msmbuilder.dataset import dataset
coords = dataset(args.pdbpath)
print '%i trajectories found. ' % len(coords)
## featurize
features = featurize_trajectories(coords, 'ContactFeaturizer')
#print "features: (n_samples, n_features) = (%i, %i) for each trajectory \n" % (features[0].shape[0], features[0].shape[1])
import mdtraj as md
target = md.load(args.target)
native_contact_dists, native_contact_pairs = md.compute_contacts(
target, scheme='ca')
native_contact_pairs = native_contact_pairs[np.where(
native_contact_dists[0] <= 0.75)]
n_native_contact = len(native_contact_pairs)
print "Target structure has %i pairs of CA-CA contact in total. \n" % n_native_contact
from msmbuilder.featurizer import ContactFeaturizer
native_contact_to_target = np.concatenate(
ContactFeaturizer(
contacts=native_contact_pairs,
scheme='ca').fit_transform(coords)) # (n_samples, n_pairs)
native_contact_to_target = np.select(
[native_contact_to_target <= 0.75, native_contact_to_target > 0.75],
[1, 0])
native_contact_to_target = np.sum(native_contact_to_target, axis=1)
with open(
'%s.%s.number_native_contact.dat' %
(get_basename_no_ext(args.target), get_basename_no_ext(args.pdbpath)),
'w') as f:
for e in native_contact_to_target:
print >> f, '%i %i %.3f' % (n_native_contact, e,
e * 1. / n_native_contact)
def test_3():
path = tempfile.mkdtemp()
shutil.rmtree(path)
try:
ds = dataset(path, 'w', 'dir-npy')
ds[0] = np.random.randn(10, 2)
ds[1] = np.random.randn(10, 2)
ds[2] = np.random.randn(10, 2)
np.testing.assert_array_equal(ds[:][0], ds[0])
np.testing.assert_array_equal(ds[:][1], ds[1])
np.testing.assert_array_equal(ds[:][2], ds[2])
finally:
shutil.rmtree(path)
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 test_3():
path = tempfile.mkdtemp()
shutil.rmtree(path)
try:
ds = dataset(path, 'w', 'dir-npy')
ds[0] = np.random.randn(10, 2)
ds[1] = np.random.randn(10, 2)
ds[2] = np.random.randn(10, 2)
np.testing.assert_array_equal(ds[:][0], ds[0])
np.testing.assert_array_equal(ds[:][1], ds[1])
np.testing.assert_array_equal(ds[:][2], ds[2])
finally:
shutil.rmtree(path)
def Get_dihedral_features_villin():
import os
import shutil
import mdtraj as md
os.chdir('/homes/anuginueni/traj_villin')
if(os.path.isdir('./diheds')):
shutil.rmtree('./diheds')
from msmbuilder.dataset import dataset
t=md.load( "/homes/anuginueni/traj_villin/trajectory-331.xtc",top='/homes/anuginueni/traj_villin/filtered.pdb',stride=5)
xyz = dataset( "/homes/anuginueni/traj_villin/*.xtc",topology='/homes/anuginueni/traj_villin/filtered.pdb',stride=5)
from msmbuilder.featurizer import DihedralFeaturizer #for dihedrals
featurizer = DihedralFeaturizer(types=['phi', 'psi']) #for dihedrals
diheds = xyz.fit_transform_with(featurizer, 'diheds/', fmt='dir-npy') #for dihedrals
des_feat=featurizer.describe_features(t)
res = [ sub['resids'] for sub in des_feat ]
print(str(res))
return diheds
def Get_contacts_features_villin():
import os
import shutil
import mdtraj as md
os.chdir('/homes/anuginueni/traj_villin')
if(os.path.isdir('./contacts')):
shutil.rmtree('./contacts')
from msmbuilder.dataset import dataset
xyz = dataset( "/homes/anuginueni/traj_villin/*.xtc",topology='/homes/anuginueni/traj_villin/filtered.pdb',stride=5)
t=md.load( "/homes/anuginueni/traj_villin/trajectory-331.xtc",top='/homes/anuginueni/traj_villin/filtered.pdb',stride=5)
from msmbuilder.featurizer import ContactFeaturizer #for contacts
featurizer = ContactFeaturizer(scheme='ca') #for contacts
des_feat=featurizer.describe_features(t)
res = [ sub['resids'] for sub in des_feat ]
print(str(res))
contacts = xyz.fit_transform_with(featurizer, 'contacts/', fmt='dir-npy') #for contacts
return contacts
def test_items():
with tempdir():
ds = dataset('ds.h5', 'w', 'hdf5')
ds[0] = np.random.randn(10, 1)
ds[1] = np.random.randn(10, 2)
ds[5] = np.random.randn(10, 3)
# NOTE!
# ds[:] does not work for non-contiguous keys.
keys = [0, 1, 5]
for i, (k, v) in enumerate(ds.items()):
assert k == keys[i]
np.testing.assert_array_equal(ds[k], v)
ds.close()
def test_MSMBuilderDatasetLoader_1():
from msmbuilder.dataset import dataset
path = tempfile.mkdtemp()
shutil.rmtree(path)
try:
x = np.random.randn(10, 2)
ds = dataset(path, 'w', 'dir-npy')
ds[0] = x
loader = MSMBuilderDatasetLoader(path, fmt='dir-npy')
X, y = loader.load()
assert np.all(X[0] == x)
assert y is None
finally:
shutil.rmtree(path)
def Get_combined_features_villin():
from msmbuilder.featurizer import DihedralFeaturizer
from msmbuilder.featurizer import ContactFeaturizer
diheds= DihedralFeaturizer()
contacts=ContactFeaturizer()
features=[("di_villin",diheds),("con_villin",contacts)]
import os
import shutil
os.chdir('/homes/anuginueni/traj_villin')
if(os.path.isdir('/homes/anuginueni/traj_villin/combined')):
shutil.rmtree('/homes/anuginueni/traj_villin/combined')
from msmbuilder.dataset import dataset
xyz = dataset( "/homes/anuginueni/traj_villin/*.xtc",topology='/homes/anuginueni/traj_villin/filtered.pdb',stride=5)
from msmbuilder.feature_selection import FeatureSelector
comb_features=FeatureSelector(features)
co=xyz.fit_transform_with(comb_features, '/homes/anuginueni/traj_villin/combined/', fmt='dir-npy')
return co
def test_partial_transform(self):
with open(os.devnull) as dn:
subprocess.call([
'msmb', 'SolventShellsFeaturizer', '--trjs', self.traj_fn,
'--solute_indices', self.ute_fn, '--solvent_indices',
self.vent_fn, '--n_shells', '3', '--shell_width', '1', '--out',
self.outfn
],
stdout=dn,
stderr=dn)
data = dataset(self.outfn)[0]
norm = np.asarray([4 * np.pi * r**2 for r in [0.5, 1.5, 2.5]])
should_be = np.array([[2, 0, 0], [0, 2, 0], [0, 0, 2], [0, 0, 0],
[0, 0, 0], [1, 0, 0], [0, 1, 0], [0, 0, 1],
[0, 0, 0]]) / norm
np.testing.assert_array_equal(data, should_be)
def test_items():
with tempdir():
ds = dataset('ds.h5', 'w', 'hdf5')
ds[0] = np.random.randn(10, 1)
ds[1] = np.random.randn(10, 2)
ds[5] = np.random.randn(10, 3)
# NOTE!
# ds[:] does not work for non-contiguous keys.
keys = [0, 1, 5]
for i, (k, v) in enumerate(ds.items()):
assert k == keys[i]
np.testing.assert_array_equal(ds[k], v)
ds.close()
def test_MSMBuilderDatasetLoader_1():
from msmbuilder.dataset import dataset
path = tempfile.mkdtemp()
shutil.rmtree(path)
try:
x = np.random.randn(10, 2)
ds = dataset(path, 'w', 'dir-npy')
ds[0] = x
loader = MSMBuilderDatasetLoader(path, fmt='dir-npy')
X, y = loader.load()
assert np.all(X[0] == x)
assert y is None
finally:
shutil.rmtree(path)
def test_items():
with tempdir():
ds = dataset('ds.h5', 'w', 'hdf5')
ds[0] = np.random.randn(10, 1)
ds[1] = np.random.randn(10, 2)
ds[5] = np.random.randn(10, 3)
keys = [0, 1, 5]
for i, (k, v) in enumerate(ds.items()):
assert k == keys[i]
np.testing.assert_array_equal(ds[k], v)
np.testing.assert_array_equal(ds[:][0], ds[0])
np.testing.assert_array_equal(ds[:][1], ds[1])
np.testing.assert_array_equal(ds[:][2], ds[5])
ds.close()
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 test_hdf5_2():
with tempdir():
with dataset('ds.h5', 'w', 'hdf5') as ds:
ds2 = ds.create_derived('ds2.h5')
print(ds2.provenance)
ds2.close()
def load(self):
from msmbuilder.dataset import dataset
ds = dataset(self.path, mode='r', fmt=self.fmt, verbose=self.verbose)
print('Dataset provenance:\n')
print(ds.provenance)
return ds, None
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)))
def test_union_no_longer_exists():
with assert_raises_regexp(ValueError,
r".*[Uu]se msmbuilder\.featurizer\.FeatureUnion.*"):
mds = dataset(['ds1.h5', 'ds2.h5'], fmt='hdf5-union')
import matplotlib
matplotlib.use('Agg')
from msmbuilder.dataset import dataset
import matplotlib.pyplot as plt
import numpy as np
trajs = dataset('tica_trajs.h5') #Load file
trajs = np.concatenate(trajs) # Flatten list of trajectories
plt.hexbin(trajs[:,0],trajs[:,1], bins='log', mincnt=1)
plt.savefig('msm_fig.png')
