Analysis¶

Try pytraj online:

Submodules

pytraj.all_actions

pytraj.distance(traj=None, mask='', frame_indices=None, dtype='ndarray', top=None, image=False, n_frames=None)¶

compute distance between two maskes

Parameters:

Parameters:	traj : Trajectory-like, list of Trajectory, list of Frames mask : str or a list of string or a 2D array-like of integers frame_indices : array-like, optional, default None dtype : return type, default ‘ndarray’ top : Topology, optional image : bool, default False n_frames : int, optional, default None only need to provide n_frames if `traj` does not have this info
Returns:	1D ndarray if mask is a string 2D ndarray, shape (n_atom_pairs, n_frames) if mask is a list of strings or an array

traj : Trajectory-like, list of Trajectory, list of Frames

mask : str or a list of string or a 2D array-like of integers

frame_indices : array-like, optional, default None

dtype : return type, default ‘ndarray’

top : Topology, optional

image : bool, default False

n_frames : int, optional, default None

only need to provide n_frames if traj does not have this info

Returns:

1D ndarray if mask is a string

2D ndarray, shape (n_atom_pairs, n_frames) if mask is a list of strings or an array

Notes

Be careful with Topology. If your topology has Box info but your traj does not, you would get weird output ([0.0, ...]). Make sure to use image=False in this method or set_nobox for Topology.

Examples

>>> import pytraj as pt
>>> # calculate distance for two atoms, using amber mask
>>> traj = pt.datafiles.load_tz2_ortho()
>>> dist = pt.distance(traj, '@1 @3')

>>> # calculate distance for two groups of atoms, using amber mask
>>> dist = pt.distance(traj, '@1,37,8 @2,4,6')

>>> # calculate distance between two residues, using amber mask
>>> dist = pt.distance(traj, ':1 :10')

>>> # calculate multiple distances between two residues, using amber mask
>>> # distance between residue 1 and 10, distance between residue 3 and 20
>>> # (when using atom string mask, index starts from 1)
>>> dist = pt.distance(traj, [':1 :10', ':3 :20'])

>>> # calculate distance for a series of atoms, using array for atom mask
>>> # distance between atom 1 and 5, distance between atom 4 and 10 (index starts from 0)
>>> dist = pt.distance(traj, [[1, 5], [4, 10]])

pytraj.angle(traj=None, mask='', top=None, dtype='ndarray', frame_indices=None, *args, **kwargs)¶

compute angle between two maskes

Parameters:

Parameters:	traj : Trajectory-like, list of Trajectory, list of Frames mask : str or array top : Topology, optional dtype : return type, defaul ‘ndarray’
Returns:	1D ndarray if mask is a string 2D ndarray, shape (n_atom_pairs, n_frames) if mask is a list of strings or an array

traj : Trajectory-like, list of Trajectory, list of Frames

mask : str or array

top : Topology, optional

dtype : return type, defaul ‘ndarray’

Returns:

1D ndarray if mask is a string

2D ndarray, shape (n_atom_pairs, n_frames) if mask is a list of strings or an array

Examples

>>> import pytraj as pt
>>> traj = pt.datafiles.load_tz2_ortho()
>>> # calculate angle for three atoms, using amber mask
>>> pt.angle(traj, '@1 @3 @10')
array([  98.06193365,   95.75979717,  105.59626997,  107.64079091,
         94.93516228,  102.06028369,  109.3479057 ,  110.11532478,
        101.86104127,  110.48992512])

>>> # calculate angle for three groups of atoms, using amber mask
>>> angles = pt.angle(traj, '@1,37,8 @2,4,6 @5,20')

>>> # calculate angle between three residues, using amber mask
>>> angles = pt.angle(traj, ':1 :10 :20')

>>> # calculate multiple angles between three residues, using amber mask
>>> # angle between residue 1, 10, 20, angle between residue 3, 20, 30
>>> # (when using atom string mask, index starts from 1)
>>> angles = pt.angle(traj, [':1 :10 :20', ':3 :20 :30'])

>>> # calculate angle for a series of atoms, using array for atom mask
>>> # angle between atom 1, 5, 8, distance between atom 4, 10, 20 (index starts from 0)
>>> angles = pt.angle(traj, [[1, 5, 8], [4, 10, 20]])

pytraj.rmsd(traj=None, mask='', ref=0, ref_mask='', nofit=False, mass=False, update_coordinate=True, frame_indices=None, top=None, dtype='ndarray')¶

compute rmsd

Parameters:

Parameters:	traj : Trajectory-like mask : str or 1D array-like of string or 1D or 2D array-like Atom mask/indices ref : {Frame, int}, default=0 (first frame) Reference frame or index. ref_mask: str, optional if given, use it instead of mask nofit : bool, default False if False, perform fitting (rotation and translation). if `traj` is mutable, its coordinates will be updated if True, not fitting. mass : bool, default False if True, compute mass-weighted rmsd update_coordinate : bool, default True if True, coordinates will be updated. But this only apply to mutable Trajectory if False (same as nomod in cpptraj), no modification frame_indices : int 1D array-like, default None if not None, only compute rmsd for given frame indices top : {Topology, str}, default None, optional dtype : return data type, default=’ndarray’

traj : Trajectory-like

mask : str or 1D array-like of string or 1D or 2D array-like

Atom mask/indices

ref : {Frame, int}, default=0 (first frame)

Reference frame or index.

ref_mask: str, optional

if given, use it instead of mask

nofit : bool, default False

if False, perform fitting (rotation and translation). if traj is mutable, its coordinates will be updated if True, not fitting.

mass : bool, default False

if True, compute mass-weighted rmsd

update_coordinate : bool, default True

if True, coordinates will be updated. But this only apply to mutable Trajectory if False (same as nomod in cpptraj), no modification

frame_indices : int 1D array-like, default None

if not None, only compute rmsd for given frame indices

top : {Topology, str}, default None, optional

dtype : return data type, default=’ndarray’

Notes

if traj and ref has diffrent n_atoms, make sure to update ref.top
you can use pytraj.rmsd to superpose structure (use update_coordinate=True)

Examples

>>> import pytraj as pt
>>> traj = pt.datafiles.load_trpcage()
>>> # all atoms, do fitting, using ref=traj[-3]
>>> data = pt.rmsd(traj, ref=-3)
>>> # rmsd for 3 maskes, do fitting, using ref=traj[0] (defaul)
>>> data = pt.rmsd(traj, mask=['@CA', '@C', ':3-18@CA'], dtype='dataset')
>>> # rmsd to first frame, use mass ':3-13' but do not perorm fitting
>>> data= pt.rmsd(traj, ref=traj[0], mask=':3-13', nofit=True)
>>> # use atom indices for mask
>>> data= pt.rmsd(traj, ref=traj[0], mask=range(40), nofit=True)
>>> # compute rmsd (and align) with reference having different atoms
>>> trpcage_traj = pt.datafiles.load_trpcage()[:]
>>> tz2_traj = pt.datafiles.load_tz2()[:1]
>>> data = pt.rmsd(trpcage_traj, mask='@1-10', ref=tz2_traj, ref_mask='@11-20')
>>> data
array([ 2.16203842,  2.28859396,  2.15817654, ...,  2.20767189,
        2.30087764,  1.92654945])
Notes
-----
if ``traj`` is mutable and update_coordinate=True, its coordinates will be updated.

pytraj.rmsd_perres(traj=None, mask='', ref=0, mass=False, resrange=None, perres_mask=None, perres_center=False, perres_invert=False, frame_indices=None, top=None, dtype='dataset', **kwd)¶

superpose traj to ref with mask, then calculate nofit rms for residues in resrange with given perresmask

Returns:

Returns:	out : pytraj.DatasetList, shape=(1+n_residues, n_frames) out[0]: regular rmsd out[1:]: perres rmsd for all given residues out.values will return corresponding numpy array

out : pytraj.DatasetList, shape=(1+n_residues, n_frames)

out[0]: regular rmsd out[1:]: perres rmsd for all given residues out.values will return corresponding numpy array

pytraj.pairwise_rmsd(traj=None, mask='', metric='rms', top=None, dtype='ndarray', mat_type='full', frame_indices=None)¶

Calculate pairwise rmsd with different metrics.

Parameters:

Parameters:	traj : Trajectory-like or iterable object mask : mask if mask is “”, use all atoms metric : {‘rms’, ‘dme’, ‘srmsd’, ‘nofit’} if ‘rms’, perform rms fit if ‘dme’, use distance RMSD if ‘srmsd’, use symmetry-corrected RMSD if ‘nofit’, perform rmsd without fitting top : Topology, optional, default=None dtype: ndarray return type mat_type : str, {‘full’, ‘half’} if ‘full’: return 2D array, shape=(n_frames, n_frames) if ‘half’: return 1D array, shape=(n_frames*(n_frames-1)/2, )

traj : Trajectory-like or iterable object

mask : mask

if mask is “”, use all atoms

metric : {‘rms’, ‘dme’, ‘srmsd’, ‘nofit’}

if ‘rms’, perform rms fit if ‘dme’, use distance RMSD if ‘srmsd’, use symmetry-corrected RMSD if ‘nofit’, perform rmsd without fitting

top : Topology, optional, default=None

dtype: ndarray

return type

mat_type : str, {‘full’, ‘half’}

if ‘full’: return 2D array, shape=(n_frames, n_frames) if ‘half’: return 1D array, shape=(n_frames*(n_frames-1)/2, )

Notes

Install libcpptraj with openmp to get benefit from parallel

Examples

>>> import pytraj as pt
>>> traj = pt.datafiles.load_tz2_ortho()
>>> arr = pt.pairwise_rmsd(traj(0, 1000, mask='@CA'))
>>> # calculate pairwise rmsd for all frames using CA atoms, use `dme` (distance RMSD)
>>> # convert to numpy array
>>> arr_np = pt.pairwise_rmsd(traj, "@CA", metric="dme", dtype='ndarray')
>>> # calculate pairwise rmsd for all frames using CA atoms, nofit for RMSD
>>> # convert to numpy array
>>> arr_np = pt.pairwise_rmsd(traj, "@CA", metric="nofit", dtype='ndarray')
>>> # calculate pairwise rmsd for all frames using CA atoms
>>> # use symmetry-corrected RMSD, convert to numpy array
>>> arr_np = pt.pairwise_rmsd(traj, "@CA", metric="srmsd", dtype='ndarray')
>>> # use different dtype
>>> arr_np = pt.pairwise_rmsd(traj, "@CA", metric="srmsd", dtype='dataset')

pytraj.dssp(traj=None, mask='', frame_indices=None, dtype='ndarray', simplified=False, top=None)¶

return dssp profile for frame/traj

Parameters:

Parameters:	traj : Trajectory-like mask: str atom mask frame_indices : {None, array-like}, default None, optional specify frame numbers for calculation. if None, do all frames dtype : str, default ‘ndarray’ return data type, for regular user, just use default one (ndarray). use dtype=’dataset’ if wanting to get secondary structure in integer format simplified : bool, default False if True, use simplified codes, only has ‘H’, ‘E’ and ‘C’ if False, use all DSSP codes
Returns:	out_0: ndarray, shape=(n_residues,) residue names out_1: ndarray, shape=(n_frames, n_residues) DSSP for each residue out_2 : pytraj.DatasetList average value for each secondary structure type

traj : Trajectory-like

mask: str

atom mask

frame_indices : {None, array-like}, default None, optional

specify frame numbers for calculation. if None, do all frames

dtype : str, default ‘ndarray’

return data type, for regular user, just use default one (ndarray). use dtype=’dataset’ if wanting to get secondary structure in integer format

simplified : bool, default False

if True, use simplified codes, only has ‘H’, ‘E’ and ‘C’ if False, use all DSSP codes

Returns:

out_0: ndarray, shape=(n_residues,)

residue names

out_1: ndarray, shape=(n_frames, n_residues)

DSSP for each residue

out_2 : pytraj.DatasetList

average value for each secondary structure type

Notes

Character	Integer	DSSP_Char	Secondary structure type
0	0	‘0’	None
b	1	‘E’	Parallel Beta-sheet
B	2	‘B’	Anti-parallel Beta-sheet
G	3	‘G’	3-10 helix
H	4	‘H’	Alpha helix
I	5	‘I’	Pi (3-14) helix
T	6	‘T’	Turn
S	7	‘S’	Bend

Simplified codes:

- 'H': include 'H', 'G', 'I' (helix)
- 'E': include 'E', 'B' (strand)
- 'C': include 'T', 'S' or '0' (coil)

Simplified codes will be mostly used for visualization in other packages.

Examples

>>> import pytraj as pt
>>> traj = pt.load_pdb_rcsb('1l2y')
>>> residues, ss, _ = pt.dssp(traj, ":2-10")
>>> residues 
array(['LEU:2', 'TYR:3', 'ILE:4', 'GLN:5', 'TRP:6', 'LEU:7', 'LYS:8',
       'ASP:9', 'GLY:10'],
      dtype='<U6')
>>> ss 
array([['0', 'H', 'H', ..., 'H', 'T', '0'],
       ['0', 'H', 'H', ..., 'H', 'T', '0'],
       ['0', 'H', 'H', ..., 'H', 'T', '0'],
       ...,
       ['0', 'H', 'H', ..., 'H', 'T', '0'],
       ['0', 'H', 'H', ..., 'H', 'H', '0'],
       ['0', 'H', 'H', ..., 'H', 'T', '0']],
      dtype='<U1')

>>> residues, ss, _ = pt.dssp(traj, mask=range(100))

>>> traj = pt.fetch_pdb('1l2y')
>>> residues, ss, _ = pt.dssp(traj, simplified=True)
>>> ss[0].tolist() # first frame
['C', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'C', 'C', 'H', 'H', 'H', 'H', 'C', 'C', 'C', 'C', 'C', 'C']

pytraj.multidihedral(traj=None, dihedral_types=None, resrange=None, define_new_type=None, range360=False, dtype='dataset', top=None, frame_indices=None)¶

perform dihedral search

Parameters:

Parameters:	traj : Trajectory-like object dihedral_types : dihedral type, default None if None, calculate all supported dihedrals resrange : str \| array-like residue range for searching. If resrange is string, use index starting with 1 (cpptraj convertion) if resrange is array-like, use index starting from 0 (python convention) define_new_type : str define new type for searching range360 : bool, default False if True: use 0-360 top : Topology \| str, optional only need to have ‘top’ if can not find it in traj
Returns:	pytraj.DatasetList (use values attribute to get raw numpy array)

traj : Trajectory-like object

dihedral_types : dihedral type, default None

if None, calculate all supported dihedrals

resrange : str | array-like

residue range for searching. If resrange is string, use index starting with 1 (cpptraj convertion) if resrange is array-like, use index starting from 0 (python convention)

define_new_type : str

define new type for searching

range360 : bool, default False

if True: use 0-360

top : Topology | str, optional

only need to have ‘top’ if can not find it in traj

Returns:

pytraj.DatasetList (use values attribute to get raw numpy array)

Notes

Dataset lables show residue number in 1-based index

Examples

>>> import pytraj as pt
>>> traj = pt.datafiles.load_tz2_ortho()
>>> data = pt.multidihedral(traj)
>>> data = pt.multidihedral(traj, 'phi psi')
>>> data = pt.multidihedral(traj, resrange=range(8))
>>> data = pt.multidihedral(traj, range360=True)
>>> data = pt.multidihedral(traj, resrange='1,3,5')
>>> data = pt.multidihedral(traj, dihedral_types='phi psi')
>>> data = pt.multidihedral(traj, dihedral_types='phi psi', resrange='3-7')
>>> data = pt.multidihedral(traj, dihedral_types='phi psi', resrange=[3, 4, 8])

pytraj.bfactors(traj=None, mask='', byres=True, top=None, dtype='ndarray', frame_indices=None)¶

calculate pseudo bfactor

Parameters:

Parameters:	traj: Trajectory-like mask: str, mask
Returns:	if dtype is ‘ndarray’ (default), return a numpy array with shape=(n_atoms/n_residues, 2) ([atom_or_residue_idx, value])

traj: Trajectory-like

mask: str, mask

Returns:

if dtype is ‘ndarray’ (default), return a numpy array

with shape=(n_atoms/n_residues, 2) ([atom_or_residue_idx, value])

Notes

This is NOT getting bfactor from xray, but computing bfactor from simulation.

Examples

>>> import pytraj as pt
>>> from pytraj.testing import get_fn
>>> fn, tn = get_fn('tz2')
>>> traj = pt.load(fn, tn, mask='!:WAT')
>>> traj = pt.superpose(traj)
>>> bfactor = pt.bfactors(traj, byres=True)

pytraj.radgyr(traj=None, mask='', top=None, nomax=True, frame_indices=None, dtype='ndarray')¶

compute radius of gyration

Examples

>>> import pytraj as pt
>>> traj = pt.datafiles.load_tz2_ortho()
>>> data = pt.radgyr(traj, '@CA')
>>> data = pt.radgyr(traj, '!:WAT', nomax=False)
>>> data = pt.radgyr(traj, '@CA', frame_indices=[2, 4, 6])

pytraj.molsurf(traj=None, mask='', probe=1.4, offset=0.0, dtype='ndarray', frame_indices=None, top=None)¶

calc molsurf

Examples

>>> import pytraj as pt
>>> traj = pt.datafiles.load_tz2_ortho()
>>> pt.molsurf(traj, '@CA')
array([ 458.51409637,  459.64784573,  456.54690793,  467.72939574,
        462.45908781,  458.70327554,  454.40514806,  455.15015576,
        468.70566447,  456.0058624 ])
>>> pt.molsurf(traj, '!:WAT')
array([ 1079.1395679 ,  1090.79759341,  1069.65127413,  1096.0810919 ,
        1091.65862234,  1091.68906298,  1085.53105392,  1069.22510187,
        1079.70803583,  1075.8151414 ])

pytraj.center_of_mass(traj=None, mask='', top=None, dtype='ndarray', frame_indices=None)¶

compute center of mass

Examples

>>> import pytraj as pt
>>> traj = pt.datafiles.load_tz2()
>>> # compute center of mass residue 3 for first 2 frames.
array([[-0.661702  ,  6.69124347,  3.35159413],
       [ 0.5620708 ,  7.82263042, -0.72707798]])

pytraj.center_of_geometry(traj=None, mask='', top=None, dtype='ndarray', frame_indices=None)¶

pytraj.search_hbonds(traj, mask='', solvent_donor=None, solvent_acceptor=None, distance=3.0, angle=135.0, image=False, series=True, options='', dtype='hbond', frame_indices=None, top=None)¶

(combined with cpptraj doc) Searching for Hbond donors/acceptors in region specified by mask. Hydrogen bond is defined as A-HD, where A is acceptor heavy atom, H is hydrogen, D is donor heavy atom. Hydrogen bond is formed when A to D distance < distance cutoff and A-H-D angle > angle cutoff; if angle < 0 it is ignored.

Parameters:

Parameters:	traj : Trajectory-like mask : {str, 1D array-like} Atom mask for searching hbond. If this mask is specify, cpptraj will automatically search for donors and acceptors. solvent_donor : {None, str}, default None solvent_acceptor: {None, str}, deafult None if solvent_acceptor and solvent_donor are None, cpptraj only search hbond for if solvent_donor and solvent_acceptor are NOT None, cpptraj will search for hbond between solute and solvent too. distance : float, default 3.0 (angstrom) hbond distance cut off angle : float, 135.0 degree hbond angle cut off dtype : return output’s type, default ‘hbond’ image : bool, default False series : bool, default True output time series (array of 1 and 0) for hbond or not (highly recommend to use this default value) if False, you must specify dtype=’dataset’ options : str additional cpptraj options. For example you can explicitly specify donormask and acceptormask. If `donormask` is specified but not `acceptormask`, acceptors will be automatically searched for in `mask`. If `acceptormask` is specified but not donormask, donors will be automatically search for in `mask`. If both `donormask` and `acceptormask` are specified no automatic searching will occur.
Returns:	out : DatasetHBond if series is True else return ‘DatasetList’

traj : Trajectory-like

mask : {str, 1D array-like}

Atom mask for searching hbond. If this mask is specify, cpptraj will automatically search for donors and acceptors.

solvent_donor : {None, str}, default None

solvent_acceptor: {None, str}, deafult None

if solvent_acceptor and solvent_donor are None, cpptraj only search hbond for if solvent_donor and solvent_acceptor are NOT None, cpptraj will search for hbond between solute and solvent too.

distance : float, default 3.0 (angstrom)

hbond distance cut off

angle : float, 135.0 degree

hbond angle cut off

dtype : return output’s type, default ‘hbond’

image : bool, default False

series : bool, default True

output time series (array of 1 and 0) for hbond or not (highly recommend to use this default value)

if False, you must specify dtype=’dataset’

options : str

additional cpptraj options. For example you can explicitly specify donormask and acceptormask.

If donormask is specified but not acceptormask, acceptors will be automatically searched for in mask.

If acceptormask is specified but not donormask, donors will be

automatically search for in mask.

If both donormask and acceptormask are specified no automatic searching will occur.

Returns:

out : DatasetHBond if series is True else return ‘DatasetList’

See also

to_amber_mask

Notes

pytraj use ‘series’ as default. In cpptraj, you need to explicitly specify ‘series’.
if ‘series’ is False, the ‘dtype’ argument will be ignored.

Examples

>>> import pytraj as pt
>>> traj = pt.load_sample_data('tz2')
>>> # search hbond without including solvent
>>> data = pt.search_hbonds(traj, ':5,8')
>>> data
<pytraj.hbonds.DatasetHBond
donor_acceptor pairs : 2>
>>> data.donor_acceptor
['LYS8_O-GLU5_N-H', 'GLU5_O-LYS8_N-H']

>>> # get raw data, ndarray with shape=(n_hbonds+1, n_frames)
>>> # first array shows the total solute hbonds and other arrays shows
>>> # if hbond exists (1) or non-exists (0) for each frame
>>> data.values
array([[2, 2, 0, ..., 1, 1, 1],
       [1, 1, 0, ..., 1, 1, 1],
       [1, 1, 0, ..., 0, 0, 0]], dtype=int32)

>>> # search hbond including solvent
>>> hbonds = pt.search_hbonds(traj, ':5,8', solvent_donor=':WAT@O', solvent_acceptor=':WAT')
>>> hbonds
<pytraj.hbonds.DatasetHBond
donor_acceptor pairs : 8>

>>> hbonds.donor_acceptor
['LYS8_O-GLU5_N-H', 'GLU5_O-LYS8_N-H', 'GLU5_OE2-V', 'GLU5_O-V', 'LYS8_HZ2-V', 'GLU5_OE1-V', 'LYS8_HZ1-V', 'LYS8_HZ3-V']
>>> # 'GLU5_O-V' mean non-specific hbond between GLU5_O and solvent (:WAT in this case)

pytraj.jcoupling(traj=None, mask='', top=None, kfile=None, dtype='dataset', frame_indices=None)¶

compute j-coupling

Parameters:

Parameters:	traj : any things that make frame_iter_master returning Frame object command : str, default “” cpptraj’s command/mask kfile : str, default None, optional Dir for Karplus file. If “None”, use $AMBERHOME dir dtype : str, {‘dataset’, ...}, default ‘dataset’

traj : any things that make frame_iter_master returning Frame object

command : str, default “”

cpptraj’s command/mask

kfile : str, default None, optional

Dir for Karplus file. If “None”, use $AMBERHOME dir

dtype : str, {‘dataset’, ...}, default ‘dataset’

Examples

>>> import pytraj as pt
>>> traj = pt.datafiles.load_tz2()
>>> data = pt.calc_jcoupling(traj, ':1-12', kfile='data/Karplus.txt')

pytraj.density(traj, mask='*', density_type='number', delta=0.25, direction='z', dtype='dict')¶

Compute density (number, mass, charge, electron) along a coordinate

Parameters:

Parameters:	traj : Trajectory-like mask : str or list of str, default ‘’ required mask density_type* : str, {‘number’, ‘mass’, ‘charge’, ‘electron’}, default ‘number’ delta : float, default 0.25 resolution (Angstrom) direction : str, default ‘z’ dtype : str, default ‘dict’ return data type. Please always using default value, others are for debugging.
Returns:	out : dict of average density and std for each frame

traj : Trajectory-like

mask : str or list of str, default ‘*’

required mask

density_type : str, {‘number’, ‘mass’, ‘charge’, ‘electron’}, default ‘number’

delta : float, default 0.25

resolution (Angstrom)

direction : str, default ‘z’

dtype : str, default ‘dict’

return data type. Please always using default value, others are for debugging.

Returns:

out : dict of average density and std for each frame

Notes

Syntax might be changed

Examples

>>> def func():
...     import pytraj as pt
...     fn = "data/DOPC.rst7"
...     tn = "data/DOPC.parm7" 
...     traj = pt.load("data/DOPC.rst7", "data/DOPC.parm7")

... delta = ‘0.25’ ... density_type = ‘charge’ ... masks = [”:PC@P31”, ”:PC@N31”, ”:PC@C2”, ”:PC | :OL | :OL2”] ... density_dict = pt.density(traj, mask=masks, density_type=density_type, delta=delta) ... return density_dict >>> density_dict = func() # doctest: +SKIP

pytraj.volume(traj=None, mask='', top=None, dtype='ndarray', frame_indices=None)¶

compute volume

Examples

>>> import pytraj as pt
>>> traj = pt.datafiles.load_tz2_ortho()
>>> vol = pt.volume(traj, '@CA')

pytraj.mindist(traj=None, command='', top=None, dtype='ndarray', frame_indices=None)¶

compute mindist

Examples

>>> import pytraj as pt
>>> traj = pt.datafiles.load_tz2()
>>> data = pt.mindist(traj, '@CA @H')

pytraj.acorr(data, dtype='ndarray', option='')¶

compute autocorrelation

Parameters:

Parameters:	data : 1d array-like dtype: return type, default ‘ndarray’ covar : bool, default True option : str more cpptraj options

data : 1d array-like

dtype: return type, default ‘ndarray’

covar : bool, default True

option : str

more cpptraj options

Notes

Same as autocorr in cpptraj

pytraj.xcorr(data0, data1, dtype='ndarray')¶

compute cross correlation between two datasets

Parameters:

Parameters:	data0 and data1: 1D-array like dtype : return datatype, default ‘ndarray’

data0 and data1: 1D-array like

dtype : return datatype, default ‘ndarray’

Notes

Same as corr in cpptraj

pytraj.timecorr(vec0, vec1, order=2, tstep=1.0, tcorr=10000.0, norm=False, dtype='ndarray')¶

compute time correlation.

Parameters:

Parameters:	vec0 : 2D array-like, shape=(n_frames, 3) vec1 : 2D array-like, shape=(n_frames, 3) order : int, default 2 tstep : float, default 1. tcorr : float, default 10000. norm : bool, default False

vec0 : 2D array-like, shape=(n_frames, 3)

vec1 : 2D array-like, shape=(n_frames, 3)

order : int, default 2

tstep : float, default 1.

tcorr : float, default 10000.

norm : bool, default False

pytraj.principal_axes(traj=None, mask='*', dorotation=False, mass=True, top=None)¶

compute principal axes

Parameters:

Parameters:	traj : Trajectory-like mask : str, default ‘’ (all atoms) mass: bool, defaul True* if `dorotation`, the system will be aligned along principal axes (apply for mutable system) top : Topology, optional
Returns:	out_0 : numpy array, shape=(n_frames, 3, 3) out_1: numpy array with shape=(n_frames, 3)

traj : Trajectory-like

mask : str, default ‘*’ (all atoms)

mass: bool, defaul True

if `dorotation`, the system will be aligned along principal axes (apply for mutable system)

top : Topology, optional

Returns:

out_0 : numpy array, shape=(n_frames, 3, 3)

out_1: numpy array with shape=(n_frames, 3)

pytraj.randomize_ions(traj, mask, around, by, overlap, seed=1, top=None, frame_indices=None)¶

randomize_ions for given Frame with Topology

Parameters:

Parameters:	traj : Trajectory-like or a Frame `traj` must be mutable mask : str cpptraj command frame_indices : {None, array-like}, optional top : Topology, optional (only needed if `traj` does not have it)

traj : Trajectory-like or a Frame

traj must be mutable

mask : str

cpptraj command

frame_indices : {None, array-like}, optional

top : Topology, optional (only needed if traj does not have it)

Examples

>>> pt.randomize_ions(traj, mask='@Na+', around=':1-16', by=5.0, overlap=3.0, seed=113698) 

pytraj.crank(data0, data1, mode='distance', dtype='ndarray')¶

Parameters:

Parameters:	data0 : array-like data1 : array-like mode : str, {‘distance’, ‘angle’} dtype : str

data0 : array-like

data1 : array-like

mode : str, {‘distance’, ‘angle’}

dtype : str

Notes

Same as crank in cpptraj

Examples

>>> import pytraj as pt
>>> traj = pt.datafiles.load_tz2()
>>> distances = pt.distance(traj, [':3 :7', ':8 :12'])
>>> out = pt.crank(distances[0], distances[1])

pytraj.autoimage(traj, mask='', frame_indices=None, top=None)¶

perform autoimage and return the updated-coordinate traj

>>> import pytraj as pt
>>> traj = pt.datafiles.load_tz2_ortho()[:]
>>> traj = pt.autoimage(traj)

pytraj.closest(traj=None, mask='*', solvent_mask=None, n_solvents=10, frame_indices=None, dtype='iterator', top=None)¶

return either a new Trajectory or a frame iterator. Keep only n_solvents closest to mask

Parameters:

Parameters:	traj : Trajectory-like \| list of Trajectory-like/frames \| frame iterator \| chunk iterator *mask: str, default ‘’ (all solute atoms) top : Topology-like object, default=None, optional dtype** : {‘iterator’, ‘trajectory’}, default ‘iterator’ if ‘iterator’, return a tuple of Frame iterator and new Toplogy. ‘iterator’ is good for streaming large trajectory. if ‘trajectory’, return a new Trajectory.
Returns:	out : (check above)

traj : Trajectory-like | list of Trajectory-like/frames | frame iterator | chunk iterator

mask: str, default ‘*’ (all solute atoms)

top : Topology-like object, default=None, optional

dtype : {‘iterator’, ‘trajectory’}, default ‘iterator’

if ‘iterator’, return a tuple of Frame iterator and new Toplogy. ‘iterator’ is good for streaming large trajectory. if ‘trajectory’, return a new Trajectory.

Returns:

out : (check above)

Examples

>>> import pytraj as pt
>>> traj = pt.datafiles.load_tz2_ortho()
>>> # obtain new traj, keeping only closest 100 waters
>>> # to residues 1 to 13 (index starts from 1) by distance to the first atom of water
>>> t = pt.closest(traj, mask='@CA', n_solvents=10)

pytraj.search_neighbors(traj=None, mask='', frame_indices=None, dtype='dataset', top=None)¶

search neighbors

Returns:

Returns:	pytraj.DatasetList, is a list of atom index arrays for each frame. Those arrays might not have the same lenghth

pytraj.DatasetList, is a list of atom index arrays for each frame.

Those arrays might not have the same lenghth

Examples

>>> import pytraj as pt
>>> traj = pt.datafiles.load_tz2_ortho()
>>> indices = pt.search_neighbors(traj, ':5<@5.0') # around residue 5 with 5.0 cutoff

pytraj.watershell(traj=None, solute_mask='', solvent_mask=':WAT', lower=3.4, upper=5.0, image=True, dtype='dataset', frame_indices=None, top=None)¶

(adapted from cpptraj doc): Calculate numbers of waters in 1st and 2nd solvation shells (defined by <lower cut> (default 3.4 Ang.) and <upper cut> (default 5.0 Ang.)

Parameters:

Parameters:	traj : Trajectory-like solute_mask: solute mask solvent_mask: solvent mask lower : double, default 3.4 lower cut distance upper : double, default 5.0 upper cut distance image : bool, defaul True do autoimage if True dtype : return type, defaul ‘dataset’ top : Topology, optional

traj : Trajectory-like

solute_mask: solute mask

solvent_mask: solvent mask

lower : double, default 3.4

lower cut distance

upper : double, default 5.0

upper cut distance

image : bool, defaul True

do autoimage if True

dtype : return type, defaul ‘dataset’

top : Topology, optional

Examples

>>> import pytraj as pt
>>> traj = pt.datafiles.load_tz2_ortho()
>>> data = pt.watershell(traj, solute_mask='!:WAT')
>>> data = pt.watershell(traj, solute_mask='!:WAT', lower=5.0, upper=10.)