def test_gist():
traj = pt.iterload(tz2_ortho_trajin, tz2_ortho_top, frame_slice=(0, 10))
traj.autoimage('origin')
command = "doorder doeij refdens 0.033422885325 gridcntr 1.44 0.67 0.29 \
griddim 10 12 10 gridspacn 2.0"
data = pt.all_actions.gist(traj,
do_order=True,
do_eij=True,
reference_density=0.033422885325,
grid_center=(1.44, 0.67, 0.29),
grid_dim=(10, 12, 10),
grid_spacing=2.0,
dtype='cpptraj_dataset')
state_command = """
parm {}
trajin {}
autoimage origin
gist {}
""".format(tz2_ortho_top, tz2_ortho_trajin, command)
state = pt.load_cpptraj_state(state_command)
with capture_stdout() as (out, _):
state.run()
data_dict = data.to_dict()
data_state_dict = state.data[1:].to_dict()
for key, state_key in zip(sorted(data_dict.keys()),
sorted(data_state_dict.keys())):
aa_eq(data_dict[key], data_state_dict[state_key])
def test_crank(self):
traj = pt.iterload(fn('tz2.nc'), fn('tz2.parm7'))
dihedrals = pt.dihedral(
traj, [':1@C :2@N :2@CA :2@C', ':2@C :3@N :3@CA :3@C'])
state = pt.load_cpptraj_state(cm)
with capture_stdout() as (out, _):
state.run()
data = pt.crank(dihedrals[0], dihedrals[1], mode='angle')
assert out.read() == data
def test_rotdif(self):
traj = pt.load(trajin, parm)
ref = pt.load(reference, parm)
mat = pt.rotation_matrix(traj, ref=ref, mask='@CA,C,N,O')
data = pt.all_actions.rotdif(mat, short_cm)
state = pt.load_cpptraj_state(cm)
with capture_stdout() as (out, _):
state.run()
assert data == out.read()
def test_cluster_hieragglo():
command = """
parm {}
trajin {}
createcrd crd1
cluster crdset crd1 C0 !@H hieragglo epsilon 0.8 averagelinkage
""".format(tz2_top, tz2_trajin)
with tempfolder():
state = pt.load_cpptraj_state(command)
with capture_stdout() as (cpp_out, _):
state.run()
traj = pt.iterload(tz2_trajin, tz2_top)
data = pt.cluster.hieragglo(traj,
mask='!@H=',
options='epsilon 0.8 averagelinkage')
aa_eq(state.data[-2], data.cluster_index)
def test_cluster_dbscan():
command = """
parm {}
trajin {}
createcrd crd1
cluster crdset crd1 C0 @CA dbscan epsilon 1.7 minpoints 5
""".format(tz2_top, tz2_trajin)
with tempfolder():
state = pt.load_cpptraj_state(command)
with capture_stdout() as (out, _):
state.run()
traj = pt.iterload(tz2_trajin, tz2_top)
data = pt.cluster.dbscan(traj,
mask='@CA',
options='epsilon 1.7 minpoints 5')
aa_eq(state.data[-2], data.cluster_index)
def _cluster(traj,
algorithm,
mask="",
frame_indices=None,
dtype='dataset',
top=None,
options=''):
"""clustering. Limited support.
Parameters
----------
traj : Trajectory-like or any iterable that produces Frame
mask : str
atom mask
dtype : str
return data type
top : Topology, optional
options: str
more cpptraj option
Notes
-----
Call `pytraj._verbose()` to see more output. Turn it off by `pytraj._verbose(False)`
cpptraj manual::
Algorithms:
[hieragglo [epsilon <e>] [clusters <n>] [linkage|averagelinkage|complete]
[epsilonplot <file>]]
[dbscan minpoints <n> epsilon <e> [sievetoframe] [kdist <k> [kfile <prefix>]]]
[dpeaks epsilon <e> [noise] [dvdfile <density_vs_dist_file>]
[choosepoints {manual | auto}]
[distancecut <distcut>] [densitycut <densitycut>]
[runavg <runavg_file>] [deltafile <file>] [gauss]]
[kmeans clusters <n> [randompoint [kseed <seed>]] [maxit <iterations>]
[{readtxt|readinfo} infofile <file>]
Distance metric options: {rms | srmsd | dme | data}
{ [[rms | srmsd] [<mask>] [mass] [nofit]] | [dme [<mask>]] |
[data <dset0>[,<dset1>,...]] }
[sieve <#> [random [sieveseed <#>]]] [loadpairdist] [savepairdist] [pairdist <name>]
[pairwisecache {mem | none}]
Output options:
[out <cnumvtime>] [gracecolor] [summary <summaryfile>] [info <infofile>]
[summarysplit <splitfile>] [splitframe <comma-separated frame list>]
[clustersvtime <filename> cvtwindow <window size>]
[cpopvtime <file> [normpop | normframe]] [lifetime]
[sil <silhouette file prefix>]
Coordinate output options:
[ clusterout <trajfileprefix> [clusterfmt <trajformat>] ]
[ singlerepout <trajfilename> [singlerepfmt <trajformat>] ]
[ repout <repprefix> [repfmt <repfmt>] [repframe] ]
[ avgout <avgprefix> [avgfmt <avgfmt>] ]
Experimental options:
[[drawgraph | drawgraph3d] [draw_tol <tolerance>] [draw_maxit <iterations]]
Cluster structures based on coordinates (RMSD/DME) or given data set(s).
<crd set> can be created with the 'createcrd' command.
"""
# Note: do not use super_dispatch here. We use get_iterator_from_dslist
ana = c_analysis.Analysis_Clustering()
# need to creat `dslist` here so that every time `do_clustering` is called,
# we will get a fresh one (or will get segfault)
crdname = 'DEFAULT_NAME'
dslist, _top, mask2 = get_iterator_from_dslist(traj,
mask,
frame_indices,
top,
crdname=crdname)
if 'summary' not in options.split():
options += ' summary'
# do not output cluster info to STDOUT
command = ' '.join(
(algorithm, mask2, "crdset {0}".format(crdname), options))
with capture_stdout() as (out, _):
ana(command, dslist)
# remove frames in dslist to save memory
dslist.remove_set(dslist[crdname])
return ClusteringDataset(
(get_data_from_dtype(dslist[:1], dtype='ndarray'), out.read()))
