note
import pytraj as pt
pt.pca(traj)
Most of the methods’ names start with ‘calc_‘ but use can ignore ‘calc_‘. For example:
pt.calc_pca(...) is the same as pt.pca(...)
pytraj.all_actions.distance(traj=None, mask='', frame_indices=None, dtype='ndarray', top=None, image=False, n_frames=None)¶compute distance between two maskes
| 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
|
|---|---|
| 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.all_actions.get_velocity(traj, mask=None, frame_indices=None)¶get velocity for specify frames with given mask
| Parameters: | traj : Trajectory-like or iterable that produces Frame mask : str, default None (use all atoms), optional
frame_indices : iterable that produces integer, default None, optional
|
|---|
Notes
Since pytraj has limited support for force and velocity info, if user wants to load both from disk, should iterate the TrajectoryIterator (got by pytraj.iterload method)
import pytraj as pt
forces = []
velocities = []
traj = pt.iterload(filename, topology_filename)
for frame in traj:
forces.append(frame.force)
velocities.append(frame.velocity)
# Note: pytraj can efficiently load arbitary frame indices to memory
for frame in pt.iterframe(traj, frame_indices=[0, 8, 8, 100, 1000]): pass
Examples
>>> vels = pt.get_velocity(traj, frame_indices=[0, 3])
pytraj.all_actions.multidihedral(traj=None, dhtypes=None, resrange=None, define_new_type=None, range360=False, dtype='dataset', top=None, frame_indices=None)¶perform dihedral search
| Parameters: | traj : Trajectory-like object dhtypes : dihedral type, default None
resrange : str | array-like
define_new_type : str
range360 : bool, default False
top : Topology | str, optional
|
|---|---|
| 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, dhtypes='phi psi')
>>> data = pt.multidihedral(traj, dhtypes='phi psi', resrange='3-7')
>>> data = pt.multidihedral(traj, dhtypes='phi psi', resrange=[3, 4, 8])
pytraj.all_actions.bfactors(traj=None, mask='', byres=True, top=None, dtype='ndarray', frame_indices=None)¶calculate pseudo bfactor
| 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]) |
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.calc_bfactors(traj, byres=True)
pytraj.all_actions.atomiccorr(traj, mask='', cut=None, min_spacing=None, byres=True, frame_indices=None, dtype='ndarray', top=None)¶compute average correlations between the motion of atoms in mask.
| Parameters: | traj : Trajectory-like mask : atom mask cut : {None, float}, default None
min_spacing : {None, float}, default None
byres : bool, default False
|
|---|
pytraj.all_actions.replicate_cell(traj=None, mask='', direction='all', top=None)¶create a trajectory where the unit cell is replicated in 1 or more direction (up to 27)
| Parameters: | traj : Trajectory-like or Frame iterator mask : str, default: “”
direction: {‘all’, ‘dir’} or list/tuple of <XYZ> (below)
top : Topology, optional, default: None |
|---|---|
| Returns: | traj : pytraj.Trajectory |
Examples
>>> import pytraj as pt
>>> traj = pt.datafiles.load_tz2_ortho()
>>> new_traj = pt.replicate_cell(traj, direction='all')
>>> new_traj = pt.replicate_cell(traj, direction='dir 001 dir 111')
>>> new_traj = pt.replicate_cell(traj, direction='dir 001 dir 1-10')
>>> new_traj = pt.replicate_cell(traj, direction='dir 001 dir 1-10')
>>> # similiar usage
>>> new_traj = pt.replicate_cell(traj, direction=('001', '0-10'))
>>> new_traj = pt.replicate_cell(traj, direction=['001', '0-10'])
pytraj.all_actions.wavelet(traj, command)¶wavelet analysis
| Parameters: | traj : Trajectory-like command : str, cpptraj command |
|---|---|
| Returns: | out : dict |
Notes
you need to type cpptraj command. Please check cpptraj manual for further info if you really want to use it.
so this method is only good for small trajectory that fit to your RAM.
version added: 1.0.6
Examples
>>> import pytraj as pt
>>> traj = pt.datafiles.load_dpdp()
>>> c0 = 'nb 10 s0 2 ds 0.25 type morlet correction 1.01 chival 0.25 :1-22'
>>> c1 = 'cluster minpoints 66 epsilon 10.0'
>>> command = ' '.join((c0, c1))
>>> wavelet_dict = pt.wavelet(traj, command)
pytraj.all_actions.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.calc_volume(traj, '@CA')
pytraj.all_actions.check_structure(traj, mask='', options='', frame_indices=None, top=None, dtype='ndarray')¶check if the structure is ok or not
| Parameters: | traj : Trajectory-like mask: str, default all atoms options : str, default ‘’
dtype : str, default ‘ndarray’ |
|---|---|
| Returns: | out : 1D-array
|
Examples
>>> import pytraj as pt
>>> traj = pt.datafiles.load_rna()
>>> failures = pt.check_structure(traj[0], top=traj.top)
pytraj.all_actions.diffusion(traj, mask='', tstep=1.0, individual=False, top=None, dtype='dataset', frame_indices=None)¶compute diffusion
| Parameters: | traj : Trajectory-like or iterator mask : str, default ‘’ (all atoms) tstep : float, time step between frames, default 1.0 ps individual : bool, default False top : Topology, optional dtype : str, default ‘dataset’ frame_indices : array or None |
|---|
Examples
>>> import pytraj as pt
>>> traj = pt.datafiles.load_tz2_ortho()
>>> data = pt.diffusion(traj, dtype='dict')
>>> data['X']
array([ 0. , 0.87027302, 1.64626022, 2.26262651, 2.98068114,
3.57075535, 4.07030655, 4.71894512, 5.42302306, 6.01310377])
pytraj.all_actions.atomicfluct(traj=None, mask='', top=None, dtype='ndarray', frame_indices=None)¶Examples
>>> import pytraj as pt
>>> traj = pt.datafiles.load_tz2_ortho()
>>> data = pt.atomicfluct(traj, '@CA')
>>> data[:3]
array([[ 5. , 0.61822273],
[ 16. , 0.5627449 ],
[ 40. , 0.53717119]])
pytraj.all_actions.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: | out : pytraj.DatasetList, shape=(1+n_residues, n_frames)
|
|---|
pytraj.all_actions.jcoupling(traj=None, mask='', top=None, kfile=None, dtype='dataset', frame_indices=None)¶compute j-coupling
| Parameters: | traj : any things that make frame_iter_master returning Frame object command : str, default “”
kfile : str, default None, optional
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.all_actions.lowestcurve(data, points=10, step=0.2)¶compute lowest curve for data
| Parameters: | data : 2D array-like points : number of lowest points in each bin, default 10 step : step size, default 0.2 |
|---|
pytraj.all_actions.principal_axes(traj=None, mask='*', dorotation=False, mass=True, top=None)¶compute principal axes
| 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) |
pytraj.all_actions.rmsd_nofit(traj=None, mask='', ref=0, mass=False, frame_indices=None, top=None, dtype='ndarray', **kwd)¶compute rmsd without fitting (translating and rotating)
| Parameters: | traj : Trajectory-like mask : str ref : Frame or int mass : bool, default False
frame_indices : 1D array-like, default None
|
|---|
Notes
This method is equal to pytraj.rmsd(traj, mask, ref, nofit=True, ...)
pytraj.all_actions.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.all_actions.translate(traj=None, command='', frame_indices=None, top=None)¶translate coordinate
Examples
>>> import pytraj as pt
>>> # load to mutable trajectory by `load` method
>>> from pytraj.testing import get_fn
>>> fn, tn = get_fn('tz2')
>>> traj = pt.load(fn, tn)
>>> # do transform traj and return itself
>>> traj = pt.translate(traj, '@CA x 120.')
pytraj.all_actions.pairwise_distance(traj=None, mask_1='', mask_2='', top=None, dtype='ndarray', frame_indices=None)¶compute pairwise distance between atoms in mask_1 and atoms in mask_2
| Parameters: | traj : Trajectory-like or iterable that produces Frame mask_1: string or 1D array-like mask_2: string or 1D array-like ... |
|---|---|
| Returns: | out_1 : numpy array, shape (n_frames, n_atom_1, n_atom_2) out_2 : atom pairs, shape=(n_atom_1, n_atom_2, 2) |
Notes
This method is only fast for small number of atoms.
Examples
>>> import pytraj as pt
>>> traj = pt.datafiles.load_tz2_ortho()
>>> mat = pt.calc_pairwise_distance(traj, '@CA', '@CB')
pytraj.all_actions.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: | traj : Trajectory-like solute_mask: solute mask solvent_mask: solvent mask lower : double, default 3.4
upper : double, default 5.0
image : bool, defaul 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.)
pytraj.all_actions.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: | 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’
|
|---|---|
| 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.all_actions.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: | traj : Trajectory-like mask : str or 1D array-like of string or 1D or 2D array-like
ref : {Frame, int}, default=0 (first frame)
ref_mask: str, optional
nofit : bool, default False
mass : bool, default False
update_coordinate : bool, default True
frame_indices : int 1D array-like, default None
top : {Topology, str}, default None, optional dtype : return data type, default=’ndarray’ |
|---|
Notes
if traj is mutable and update_coordinate=True, its coordinates will be updated.
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])
pytraj.all_actions.acorr(data, dtype='ndarray', option='')¶compute autocorrelation
| Parameters: | data : 1d array-like dtype: return type, default ‘ndarray’ covar : bool, default True option : str
|
|---|
Notes
Same as autocorr in cpptraj
pytraj.all_actions.vector(traj=None, command='', frame_indices=None, dtype='ndarray', top=None)¶perform vector calculation. See example below. Same as ‘vector’ command in cpptraj.
| Parameters: | traj : Trajectory-like or iterable that produces command : str or a list of strings, cpptraj command frame_indices : array-like, optional, default None
dtype : output’s dtype, default ‘ndarray’ top : Topology, optional, default None |
|---|---|
| Returns: | out : numpy ndarray, shape (n_frames, 3) if command is a string
|
Notes
It’s faster to calculate with a list of commands. For example, if you need to perform 3 calculations for ‘ucellx’, ‘boxcenter’, ‘box’ like below:
>>> data = pt.calc_vector(traj, "ucellx")
>>> data = pt.calc_vector(traj, "boxcenter")
>>> data = pt.calc_vector(traj, "box")
You should use a list of commands for faster calculation.
>>> comlist = ['ucellx', 'boxcenter', 'box']
>>> data = pt.calc_vector(traj, comlist)
Examples
>>> import pytraj as pt
>>> traj = pt.datafiles.load_tz2_ortho()
>>> data = pt.calc_vector(traj, "@CA @CB")
>>> data = pt.calc_vector(traj, [("@CA @CB"),])
>>> data = pt.calc_vector(traj, "principal z")
>>> data = pt.calc_vector(traj, "principal x")
>>> data = pt.calc_vector(traj, "ucellx")
>>> data = pt.calc_vector(traj, "boxcenter")
>>> data = pt.calc_vector(traj, "box")
pytraj.all_actions.strip(obj, mask)¶return a new Trajectory or FrameIterator or Topology with given mask.
Notes
This method is trying to be smart. If you give it an in-memory Trajectory, it will return a corresponding in-memory Trajectory. If you give it an out-of-memory TrajectoryIterator, it will give you a corresponding FrameIterator object (out-of-memory). If you give it a Topology, it will return a new stripped Topology.
Examples
>>> import pytraj as pt
>>> traj = pt.datafiles.load_tz2_ortho()
>>> traj.n_atoms
5293
>>> pt.strip(traj, '!@CA').n_atoms
12
>>> pt.strip(traj.top, '!@CA').n_atoms
12
>>> pt.strip('!@CA', traj.top).n_atoms
12
>>> t0 = traj[:3]
>>> pt.strip(t0, '!@CA').n_atoms
12
>>> fi = pt.iterframe(traj, stop=3)
>>> fi = pt.strip(fi, '!@CA')
>>> for frame in fi:
... print(frame)
<Frame with 12 atoms>
<Frame with 12 atoms>
<Frame with 12 atoms>
>>> # raise ValueError
>>> pt.strip(0, '@CA')
Traceback (most recent call last):
...
ValueError: object must be either Trajectory or Topology
pytraj.all_actions.volmap(traj, mask, grid_spacing, size=None, center=None, buffer=3.0, centermask='*', radscale=1.36, peakcut=0.05, top=None, dtype='ndarray', frame_indices=None)¶(combined with cpptraj doc) Grid data as a volumetric map, similar to the volmap command in VMD. The density is calculated by treating each atom as a 3-dimensional Gaussian function whose standard deviation is equal to the van der Waals radius
| Parameters: | mask : {str, array-like}, default all atoms
grid_spacing : tuple, grid spacing in X-, Y-, Z-dimensions, require size : {None, tuple}, default None
center : {None, tuple}, default None
buffer : float, default 3.0 Angstrom
centermask : str radscale : float, default 1.36 (to match to VMD calculation)
peakcut : float |
|---|
Examples
>>> import pytraj as pt
>>> # load all frames to memory
>>> traj = pt.datafiles.load_tz2_ortho()[:]
>>> # do fitting and centering before perform volmap
>>> traj = traj.superpose(mask=':1-13').center(':1-13 mass origin')
>>> data = pt.volmap(traj, mask=':WAT@O', grid_spacing=(0.5, 0.5, 0.5), buffer=2.0, centermask='!:1-13', radscale=1.36)
pytraj.all_actions.rotation_matrix(traj=None, mask='', ref=0, mass=False, frame_indices=None, top=None, with_rmsd=False)¶compute rotation matrix with/without rmsd
| Parameters: | traj : Trajectory-like ref : {int, Frame}, default 0 (first Frame)
mask : str, default all atoms mass : bool, default False
frame_indices : {None, array-like}
top : Topology, optional with_rmsd : bool, default False
|
|---|---|
| Returns: | out : if with_rmsd=False, return numpy array, shape (n_frames, 3, 3)
|
Examples
>>> import pytraj as pt
>>> traj = pt.datafiles.load_tz2()
>>> mat = pt.calc_rotation_matrix(traj, mask='@CA')
>>> mat.shape
(101, 3, 3)
pytraj.all_actions.pucker(traj=None, pucker_mask=("C1'", "C2'", "C3'", "C4'", "O4'"), resrange=None, top=None, dtype='dataset', range360=False, method='altona', use_com=True, amplitude=False, offset=None)¶compute pucker
| Parameters: | traj : Trajectory-like pucker_mask : str resrange : None or array of int top : Topology, optional dtype : str, return type range360: bool, use 360 or 180 scale method : {‘altona’, ‘cremer’}, default ‘altona’ use_com : bool amplitude : bool, default False offset : None or float |
|---|---|
| Returns: | Dataset |
pytraj.all_actions.make_structure(traj=None, mask='', top=None)¶pytraj.all_actions.align_principal_axis(traj=None, mask='*', top=None, frame_indices=None, mass=False)¶Notes
apply for mutatble traj (Trajectory, Frame)
pytraj.all_actions.search_neighbors(traj=None, mask='', frame_indices=None, dtype='dataset', top=None)¶search neighbors
| Returns: | 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.all_actions.autoimage(traj, mask='', frame_indices=None, top=None)¶perform autoimage and return the coordinate-updated traj
>>> import pytraj as pt
>>> traj = pt.datafiles.load_tz2_ortho()[:]
>>> traj = pt.autoimage(traj)
pytraj.all_actions.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.
>>> pt.calc_center_of_mass(traj(stop=2), ':3')
array([[-0.661702 , 6.69124347, 3.35159413],
[ 0.5620708 , 7.82263042, -0.72707798]])
pytraj.all_actions.native_contacts(traj=None, mask='', mask2='', ref=0, dtype='dataset', distance=7.0, image=True, include_solvent=False, byres=False, frame_indices=None, top=None)¶compute native contacts
Examples
>>> import pytraj as pt
>>> traj = pt.datafiles.load_tz2_ortho()
>>> # use 1st frame as reference, don't need specify ref Frame
>>> data = pt.native_contacts(traj)
>>> # explicitly specify reference, specify distance cutoff
>>> ref = traj[3]
>>> data = pt.native_contacts(traj, ref=ref, distance=8.0)
>>> # use integer array for mask
>>> data = pt.native_contacts(traj, mask=range(100), mask2=[200, 201], ref=ref, distance=8.0)
pytraj.all_actions.multivector(traj, resrange, names, top=None, dtype='dataset', frame_indices=None)¶| Parameters: | traj : Trajectory-like resrange : str, residue range names : {str, tuple of str} top : Topology, optional dtype : str, default ‘dataset’ frame_indices : {None, 1D array-like}, optional, default None |
|---|
Examples
>>> import pytraj as pt
>>> traj = pt.datafiles.load_tz2_ortho()
>>> vecs = pt.multivector(traj, resrange='1-5', names=('C', 'N'))
>>> vecs = pt.multivector(traj, resrange='1-5', names='C N')
pytraj.all_actions.distance_rmsd(traj=None, mask='', ref=0, top=None, dtype='ndarray', frame_indices=None)¶compute distance rmsd between traj and reference
| Parameters: | traj : Trajectory-like or iterator that produces Frame ref : {int, Frame}, default 0 (1st Frame) mask : str top : Topology or str, optional, default None dtype : return dtype, default ‘ndarray’ |
|---|---|
| Returns: | 1D ndarray if dtype is ‘ndarray’ (default) |
Examples
>>> import pytraj as pt
>>> # compute distance_rmsd to last frame
>>> traj = pt.datafiles.load_tz2_ortho()
>>> data = pt.distance_rmsd(traj, ref=-1)
>>> # compute distance_rmsd to first frame with mask = '@CA'
>>> data = pt.distance_rmsd(traj, ref=0, mask='@CA')
pytraj.all_actions.rdf(traj=None, solvent_mask=':WAT@O', solute_mask='', maximum=10.0, bin_spacing=0.5, image=True, density=0.033456, volume=False, center_solvent=False, center_solute=False, intramol=True, frame_indices=None, top=None)¶compute radial distribtion function. Doc was adapted lightly from cpptraj doc
| Parameters: | traj : Trajectory-like, require solvent_mask : solvent mask, default None, required bin_spacing : float, default 0.5, optional
maximum : float, default 10., optional
solute_mask: str, default None, optional
image : bool, default True, optional
density : float, default 0.033456 molecules / A^3, optional volume : determine density for normalization from average volume of input frames center_solvent : bool, default False, optional
center_solute : bool, default False, optional
intramol : bool, default True, optional
frame_indices : array-like, default None, optional |
|---|---|
| Returns: | a tuple of bin_centers, rdf values |
Notes
pytraj and libcpptraj with openmp to speed up calculationExamples
>>> import pytraj as pt
>>> traj = pt.datafiles.load_tz2_ortho()
>>> data = pt.rdf(traj, solvent_mask=':WAT@O', bin_spacing=0.5, maximum=10.0, solute_mask=':WAT@O')
>>> data[0]
array([ 0.25, 0.75, 1.25, ..., 8.75, 9.25, 9.75])
>>> data[1]
array([ 0. , 0. , 0. , ..., 0.95620052,
0.95267934, 0.95135242])
>>> # use array-like mask
>>> atom_indices = pt.select(':WAT@O', traj.top)
>>> data = pt.rdf(traj, solvent_mask=':WAT@O', bin_spacing=0.5, maximum=10.0, solute_mask=atom_indices)
pytraj.all_actions.pairwise_rmsd(traj=None, mask='', metric='rms', top=None, dtype='ndarray', mat_type='full', frame_indices=None)¶calculate pairwise rmsd with different metrics.
| Parameters: | traj : Trajectory-like or iterable object mask : mask
metric : {‘rms’, ‘dme’, ‘srmsd’, ‘nofit’}
top : Topology, optional, default=None dtype: ndarray
mat_type : str, {‘full’, ‘half’}
|
|---|
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.all_actions.xcorr(data0, data1, dtype='ndarray')¶compute cross correlation between two datasets
| Parameters: | data0 and data1: 1D-array like dtype : return datatype, default ‘ndarray’ |
|---|
Notes
Same as corr in cpptraj
pytraj.all_actions.dihedral(traj=None, mask='', top=None, dtype='ndarray', frame_indices=None, *args, **kwargs)¶compute dihedral angle between two maskes
| 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 |
Examples
>>> import pytraj as pt
>>> traj = pt.datafiles.load_tz2_ortho()
>>> # calculate dihedral angle for four atoms, using amber mask
>>> pt.dihedral(traj, '@1 @3 @10 @20')
array([ 23.32244362, 23.5386922 , 14.26831569, 14.58865946,
23.98675475, 26.18419185, 6.06982926, 13.57158505,
16.59013076, 29.99131573])
>>> # calculate dihedral angle for four groups of atoms, using amber mask
>>> data = pt.dihedral(traj, '@1,37,8 @2,4,6 @5,20 @21,22')
>>> # calculate dihedral angle for four residues, using amber mask
>>> data = pt.dihedral(traj, ':1 :10 :20 :22')
>>> # calculate multiple dihedral angles for four residues, using amber mask
>>> # angle for residue 1, 10, 20, 30; angle between residue 3, 20, 30, 40
>>> # (when using atom string mask, index starts from 1)
>>> pt.dihedral(traj, [':1 :10 :20 :30', ':3 :20 :30 :40'])
array([[-166.81829116, -160.19029669, -158.56560062, ..., -169.10064772,
-158.81655586, -165.28873555],
[ -0.60994639, 0.78261235, 1.86394369, ..., 1.21170489,
-1.16762607, -3.08412049]])
>>> # calculate dihedral angle for a series of atoms, using array for atom mask
>>> # dihedral angle for atom 1, 5, 8, 10, dihedral for atom 4, 10, 20, 30 (index starts from 0)
>>> data = pt.dihedral(traj, [[1, 5, 8, 10], [4, 10, 20, 30]])
pytraj.all_actions.grid(traj=None, command='', top=None, dtype='dataset')¶pytraj.all_actions.angle(traj=None, mask='', top=None, dtype='ndarray', frame_indices=None, *args, **kwargs)¶compute angle between two maskes
| 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 |
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.all_actions.scale(traj=None, command='', frame_indices=None, top=None)¶Examples
>>> import pytraj as pt
>>> # load to mutable trajectory by `load` method
>>> from pytraj.testing import get_fn
>>> fn, tn = get_fn('tz2')
>>> traj = pt.load(fn, tn)
>>> # do transform traj and return itself
>>> traj = pt.scale(traj, '@CA x 1.2')
pytraj.all_actions.randomize_ions(traj, mask, around, by, overlap, seed=1, top=None, frame_indices=None)¶randomize_ions for given Frame with Topology
| Parameters: | traj : Trajectory-like or a Frame
mask : str
frame_indices : {None, array-like}, optional top : Topology, optional (only needed if |
|---|
Examples
>>> pt.randomize_ions(traj, mask='@Na+', around=':1-16', by=5.0, overlap=3.0, seed=113698)
pytraj.all_actions.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.all_actions.set_dihedral(traj, resid='1', dihedral_type=None, deg=0, top=None)¶| Returns: | updated traj |
|---|
Examples
>>> import pytraj as pt
>>> traj = pt.datafiles.load_tz2()
>>> # make mutable traj by loading all frames to disk
>>> traj = traj[:]
>>> traj = pt.set_dihedral(traj, resid='3', dihedral_type='phi', deg=60)
>>> traj = pt.set_dihedral(traj, resid=2, dihedral_type='phi', deg=60)
pytraj.all_actions.center(traj=None, mask='', center='box', mass=False, top=None, frame_indices=None)¶| Parameters: | traj : Trajectory-like or Frame iterator mask : str, mask center : str, {‘box’, ‘origin’} mass : bool, default: False
top : Topology, optional, default: None |
|---|---|
| Returns: | updated traj |
See also
Examples
>>> import pytraj as pt
>>> traj = pt.datafiles.load_tz2_ortho()
>>> # load all frames to memory so we can 'mutate' them
>>> traj = traj[:]
>>> # all atoms, center to box center (x/2, y/2, z/2)
>>> traj = pt.center(traj)
>>> # center at origin, use @CA
>>> traj = pt.center(traj, '@CA', center='origin')
>>> # center to box center, use mass weighted
>>> traj = pt.center(traj, mass=True)
>>> traj = pt.center(traj, ':1', mass=True)
pytraj.all_actions.superpose(traj, *args, **kwd)¶pytraj.all_actions.surf(traj=None, mask='', dtype='ndarray', frame_indices=None, top=None)¶calc surf (LCPO method)
Examples
>>> import pytraj as pt
>>> traj = pt.datafiles.load_tz2_ortho()
>>> data = pt.surf(traj, '@CA')
pytraj.all_actions.mean_structure(traj, mask='', frame_indices=None, dtype='frame', autoimage=False, rmsfit=None, top=None)¶get mean structure for a given mask and given frame_indices
| Parameters: | traj : Trajectory-like or iterable that produces Frame mask : None or str, default None (all atoms) frame_indices : iterable that produces integer, default None, optional
dtype: str, {‘frame’, ‘traj’}, default ‘frame’
autoimage : bool, default False
rmsfit : object, {Frame, int, tuple, None}, default None
|
|---|
Notes
if autoimage=True and having rmsfit, perform autoimage first and do rmsfit
Examples
>>> import pytraj as pt
>>> traj = pt.datafiles.load_tz2_ortho()
>>> # get average structure from whole traj, all atoms
>>> frame = pt.mean_structure(traj)
>>> # get average structure from several frames, '@CA' atoms"
>>> frame = pt.mean_structure(traj, '@CA', frame_indices=range(2, 8, 2))
>>> # get average structure but do autoimage and rmsfit to 1st Frame
>>> frame = pt.mean_structure(traj(autoimage=True, rmsfit=0))
>>> # get average structure but do autoimage and rmsfit to 1st Frame.
>>> frame = pt.mean_structure(traj(autoimage=True, rmsfit=0, frame_indices=[0, 5, 6]))
pytraj.all_actions.pca(traj, mask, n_vecs=2, fit=True, ref=None, ref_mask=None, dtype='ndarray', top=None)¶perform PCA analysis by following below steps:
| Parameters: | traj : Trajectory
mask : str
n_vecs : int, default 2
fit : bool, default True
ref : {None, Frame, int}, default None
ref_mask : {None, str}, default None (use mask)
dtype : return datatype top : Topology, optional |
|---|---|
| Returns: | out1: projection_data, ndarray with shape=(n_vecs, n_frames) out2: tuple of (eigenvalues, eigenvectors) |
Examples
>>> import pytraj as pt
>>> traj = pt.datafiles.load_tz2()[:]
>>> # compute pca for first and second modes
>>> pca_data = pt.pca(traj, '!@H=', n_vecs=2)
>>> # get projection values
>>> pca_data[0]
array([[ 4.93425131, 13.80002308, 20.61605835, ..., -57.92280579,
-61.25728607, -52.85142136],
[ 4.03333616, -6.9132452 , -14.53991318, ..., -6.757936 ,
2.1086719 , -3.60922861]], dtype=float32)
>>> # get eigenvalues for first 2 modes
>>> pca_data[1][0]
array([ 1399.36472919, 240.42342439])
>>> # compute pca for all modes
>>> pca_data = pt.pca(traj, '!@H=', n_vecs=-1)
>>> # does not perform fitting
>>> data = pt.pca(traj, mask='!@H=', fit=False)
>>> # provide different mask for fitting
>>> data = pt.pca(traj, mask='!@H=', fit=True, ref=0, ref_mask='@CA')
pytraj.all_actions.rotate(traj=None, command='', frame_indices=None, top=None)¶Notes
rotate is an alias of do_rotation
Examples
>>> import pytraj as pt
>>> # load to mutable trajectory by `load` method
>>> from pytraj.testing import get_fn
>>> fn, tn = get_fn('tz2')
>>> traj = pt.load(fn, tn)
>>> # do transform traj and return itself
>>> traj = pt.rotate(traj, 'x 90')
pytraj.all_actions.get_average_frame(traj, mask='', frame_indices=None, dtype='frame', autoimage=False, rmsfit=None, top=None)¶get mean structure for a given mask and given frame_indices
| Parameters: | traj : Trajectory-like or iterable that produces Frame mask : None or str, default None (all atoms) frame_indices : iterable that produces integer, default None, optional
dtype: str, {‘frame’, ‘traj’}, default ‘frame’
autoimage : bool, default False
rmsfit : object, {Frame, int, tuple, None}, default None
|
|---|
Notes
if autoimage=True and having rmsfit, perform autoimage first and do rmsfit
Examples
>>> import pytraj as pt
>>> traj = pt.datafiles.load_tz2_ortho()
>>> # get average structure from whole traj, all atoms
>>> frame = pt.mean_structure(traj)
>>> # get average structure from several frames, '@CA' atoms"
>>> frame = pt.mean_structure(traj, '@CA', frame_indices=range(2, 8, 2))
>>> # get average structure but do autoimage and rmsfit to 1st Frame
>>> frame = pt.mean_structure(traj(autoimage=True, rmsfit=0))
>>> # get average structure but do autoimage and rmsfit to 1st Frame.
>>> frame = pt.mean_structure(traj(autoimage=True, rmsfit=0, frame_indices=[0, 5, 6]))
pytraj.all_actions.velocityautocorr(traj, mask='', maxlag=-1, tstep=1.0, direct=True, norm=False, usevelocity=False, dtype='ndarray', top=None, velocity_arr=None)¶| Parameters: | traj : Trajectory-like mask : str
maxlag : int, default -1
tstep : float, default 1.0 direct : bool, default True
norm : bool, default False
usevelocity : bool, default False
dtype : str, default ‘ndarray’
top : None or Topology, default None, optional velocity_arr : None or 3D like array, default None
|
|---|
Notes
If you create Trajectory by pytraj.load method, there is no velocity information. So if you want to use usevelocity=True, you need to provide 3D-array velocity_arr
pytraj.all_actions.projection(traj, mask='', eigenvalues=None, eigenvectors=None, scalar_type='covar', average_coords=None, frame_indices=None, dtype='ndarray', top=None)¶compute projection along given eigenvectors
| Parameters: | traj : Trajectory-like mask : atom mask, either string or array-like eigenvalues : 1D array-like eigenvectors : 2D array-like scalar_type : str, {‘covar’, ‘mwcovar’, }, default ‘covar’
average_coords : 3D array-like, optional, default None
frame_indices : array-like
dtype : str, return data type, default ‘ndarray’ top : Topology, optional, default None |
|---|---|
| Returns: | projection_data : ndarray, shape=(n_vecs, n_frames) |
Examples
>>> import pytraj as pt
>>> traj = pt.datafiles.load_tz2()
>>> mat = pt.matrix.covar(traj, '@CA')
>>> eigenvalues, eigenvectors = pt.matrix.diagonalize(mat, 2)
>>> # since we compute covariance matrix, we need to specify
>>> # scalar_type = 'covar'
>>> scalar_type = 'covar'
>>> data = pt.projection(traj, '@CA', eigenvalues=eigenvalues, eigenvectors=eigenvectors, scalar_type=scalar_type)
>>> data.shape
(2, 101)
pytraj.all_actions.timecorr(vec0, vec1, order=2, tstep=1.0, tcorr=10000.0, norm=False, dtype='ndarray')¶compute time correlation.
| 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 |
|---|
pytraj.all_actions.center_of_geometry(traj=None, mask='', top=None, dtype='ndarray', frame_indices=None)¶pytraj.all_actions.pairdist(traj, mask='*', mask2='', delta=0.1, dtype='ndarray', top=None, frame_indices=None)¶compute pair distribution function
| Parameters: | traj : Trajectory-like mask : str, default all atoms delta : float, default 0.1 dtype : str, default ‘ndarray’
top : Topology, optional |
|---|
Examples
>>> import pytraj as pt
>>> traj = pt.datafiles.load_tz2_ortho()
>>> data = pt.calc_pairdist(traj)
pytraj.all_actions.transform(traj, by, frame_indices=None)¶transform pytraj.Trajectory by a series of cpptraj’s commands
| Parameters: | traj : Mutable Trajectory by : list of cpptraj commands frame_indices : {None, array-like}, default None
|
|---|---|
| Returns: | transformed Trajectory. Trajectory’s coordinates will be inplace-updated |
Examples
>>> import pytraj as pt
>>> traj = pt.datafiles.load_tz2_ortho()
>>> # perform 'autoimage', then 'rms', then 'center'
>>> traj = pt.transform(traj[:], by=['autoimage', 'rms', 'center :1-5'])
pytraj.all_actions.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])