easytrajh5 0.2.14

MD trajectory library

EasyTrajH5

Trajectory management for mdtraj H5 files with atom selection language and efficient data operations via the h5py library.

Installation

pip install easytrajh5

Quick Guide

Our main file object EasyTrajH5 is a drop-in replacement for mdtraj.H5TrajectryFile:

from easytrajh5.traj import EasyTrajH5File
 
h5 = EasyTrajH5File('traj.h5')
traj = h5.read_as_traj()

This loads the data progressively in chunks, allowing online streaming in advanced usage.

Load individual frames

last_frame_traj = h5.read_frame_as_traj(-1)

As we use the h5py library, we can use efficient fancy indexing to load just certain atoms:

atom_indices = [100, 115, 116]
three_atom_traj = h5.read_as_traj(atom_indices=atom_indices)

We provide atom selection using a new selection language (described in detail below). This is particular efficient as it only loads the atoms you want, without requiring the entire trajectory to be loaded into memory:

from easytrajh5.traj import EasyTrajH5File
 
mask = "intersect {mdtraj name CA} {protein}"
ca_trace_traj = EasyTrajH5File('traj.h5', atom_mask=mask).read_as_traj()

Drop in replacement for mdtraj.reporters.HDF5Reporter in openmm that uses EasyTrajH5File:

from easytrajh5.traj import EasyTrajH5Reporter

Atom Selection Language

Why another atom selection language (we have AMBER and MDTRAJ)? Two main reasons.

First, we wanted user-defined residue selections. These are stored in easytrajh5/data/select.yaml. Edit this file to create any new residue selections.

Second, we wanted to fix residue selection. The problem is that AMBER uses residue numbering (:3,5,10-12) defined in the PDB file and not 0-based residue indexing. This means that in PDB files with multiple chains, the residue number is not unique. MDTRAJ on the other hand, uses 0-based indexing, but only allows you to use ranges (resi 10 to 15).

We've combined these ideas to provide our new flexible 0-based residue indexing resi 3,5,10-12,100-150,300.

We also allow you to easily drop in to AMBER and MDTRAJ simply by using the amber and mdtraj keywords. When combined with set operations, everything is now at your disposal.

Some useful masks:

• no solvent: not {solvent}
• just the protein: protein
• ligand and specific residues: ligand resi 5,1,22-200
• heavy protein atoms: diff {protein} {amber @/H}
• no hydrogens: not {amber @/H}
• ligand and 6 closest residues: pocket ligand
• specified ligand with 10 closest neighbours: resname UNL near UNL 10

User-defined and operator keywords

If more than one keyword is specified, it is assumed they are joined with "or" operation (i.e. ligand protein will return both ligand and protein atom indices).

This default keywords are:

• ligand, protein, water, lipid, salt, solvent, lipid, nucleic
• as defined in easytrajh5/data/select.yaml
• ligand will find the residues LIG, UNL, UNK

Special operator keywords:

• pocket will find the closest 6 residues to the ligand group.
• near will require a following resname, with an optional integer, e.g.: near ATP near ATP 5
• resname identifies a single residue type resname LEU
• resi for 0-indexed residue selections resi 0,10-13 - selects atoms in the first and 11th to 14th residues
• atom for 0-indexed atoms selections atom 0,55,43,101-105 - selects the first, 56th, 44th, 102 to 106th atom

AMBER-style atom selection

• https://parmed.github.io/ParmEd/html/amber.html#amber-mask-syntax
• amber :ALA,LYS - selects all alanine and lysine residues

MDTraj-style atom selection

• https://mdtraj.org/1.9.4/atom_selection.html
• mdtraj protein and water - selects protein and water

Set operations

Selections can be combined with set operators: not, intersect, merge, diff:

• intersect {not {amber :ALA}} {protein}
• diff {protein} {not {amber :ALA}}
• not {resname LEU}
• merge {near BSM 8} {amber :ALA}

Use in python

In your python code, there is a select_mask fn that operates on parmed.Structure objects:

from easytrajh5.traj import EasyTrajH5File
from easytrajh5.select import select_mask
from easytrajh5.struct import slice_parmed

pmd = EasyTrajH5File("traj.h5").get_topology_parmed()
i_atoms = select_mask(pmd, "not {solvent}")
sliced_pmd = slice_parmed(pmd, i_atoms)

Some common conversions and loaders in easytrajh5.struct for parmed.Structure and mdtraj.Trajectory objects:

import parmed, mdtraj

def dump_parmed(pmd: parmed.Structure, fname: str): 
def load_parmed(fname: str) -> parmed.Structure:
def get_parmed_from_pdb(pdb: str) -> parmed.Structure:
def get_parmed_from_parmed_or_pdb(pdb_or_parmed: str) -> parmed.Structure:
def get_parmed_from_mdtraj(traj: mdtraj.Trajectory, i_frame=0) -> parmed.Structure:
def get_parmed_from_openmm(openmm_topology, openmm_positions=None) -> parmed.Structure:
def get_mdtraj_from_parmed(pmd: parmed.Structure) -> mdtraj.Trajectory:
def get_mdtraj_from_openmm(openmm_topology, openmm_positions) -> mdtraj.Trajectory:

Use as H5

There are convenience functions to insert different types of data.

To save/load strings:

h5.set_str_dataset('my_string', 'a string')
h5.flush()
new_str = h5.get_str_dataset('my_string')

To save/load json:

h5.set_json_dataset('my_obj', {"a", "b"})
h5.flush()
new_obj = h5.get_json_dataset('my_obj')

To insert/extract binary files:

h5.insert_file_to_dataset('blob', 'blob.bin')
h5.flush()
h5.extract_file_from_dataset('blob', 'new_blob.bin')

We can get information about the h5 file:

schema_json = h5.get_schema()
dataset_keys = h5.get_dataset_keys()
attr_keys = h5.get_attr_keys()

We can extract data

dataset = h5.get_dataset("coordinates")
value_list = dataset[:]
last_value = dataset[-1]

# if the attrs are set
value = h5.get_attr('user')

Convenience function to append values to an h5 file without worrying about file or dataset creation:

from easytrajh5.h5 import dump_value_to_h5, EasyH5File

dump_value_to_h5('new.h5', [1,2], 'my_data_set')
dump_value_to_h5('new.h5', [3,4], 'my_data_set')
dump_value_to_h5('new.h5', [5,7], 'my_data_set')

return_values = EasyH5File('new.h5').get_dataset("my_data_set")[:]
# [[1,2], [3,4], [5,6]]

Command-line utility easyh5

easyh5 provides a bunch of useful cli subcommands to interrogate h5 and related files:

Usage: easyh5 [OPTIONS] COMMAND [ARGS]...

  h5: preprocessing and analysis tools

Options:
  --help  Show this message and exit.

Commands:
  dataset        Examine contents of h5
  insert-parmed  Insert parmed into dataset:parmed of an H5
  mask           Explore residues/atoms of H5/PDB/PARMED using mask
  merge          Merge a list of H5 files
  parmed         Extract parmed from dataset:parmed of an H5 with...
  pdb            Extract PDB of a frame of an H5
  schema         Examine layout of H5
  show-chimera   Use CHIMERA to show H5/PDB/PARMED with mask, needs PARMED
  show-pymol     Use PYMOL to show H5/PDB/PARMED with mask
  show-vmd       Use VMD to show H5/PDB/PARMED with mask

To get a schema of the dataset layout and attributes:

> easyh5 schema traj.h5
# {
# │   'datasets': [
# ....
# │   │   {
# │   │   │   'key': 'coordinates',
# │   │   │   'shape': [200, 3340, 3],
# │   │   │   'chunks': [3, 3340, 3],
# │   │   │   'is_extensible': True,
# │   │   │   'frame_shape': [3340, 3],
# │   │   │   'n_frame': 200,
# │   │   │   'dtype': 'float32',
# │   │   │   'attr': {'CLASS': 'EARRAY', 'EXTDIM': 0, 'TITLE': None, 'VERSION': '1.1', 'units': 'nanometers'}
# │   │   },
#
# ...
#
# │   │   {
# │   │   │   'key': 'topology',
# │   │   │   'shape': [1],
# │   │   │   'dtype': 'string(217329)',
# │   │   │   'attr': {'CLASS': 'ARRAY', 'FLAVOR': 'python', 'TITLE': None, 'VERSION': '2.4'}
# │   │   }
# │   ],
# │   'attr': {
# │   │   'CLASS': 'GROUP',
# │   │   'FILTERS': 65793,
# │   │   'PYTABLES_FORMAT_VERSION': '2.1',
# │   │   'TITLE': None,
# │   │   'VERSION': '1.0',
# │   │   'application': 'MDTraj',
# │   │   'conventionVersion': '1.1',
# │   │   'conventions': 'Pande',
# │   │   'program': 'MDTraj',
# │   │   'programVersion': '1.9.7',
# │   │   'title': 'title'
# │   }
# }

Or as a quick summary table:

> easyh5 dataset examples/trajectory.h5 
# Warning: importing 'simtk.openmm' is deprecated.  Import 'openmm' instead.
# 
#                   sims/high_bf/trajectory.h5                  
#                                                               
#   dataset           shape              dtype       size (MB)  
#  ──────────────────────────────────────────────────────────── 
#   cell_angles       (1500, 3)          float32       0.02 MB  
#   cell_lengths      (1500, 3)          float32       0.02 MB  
#   coordinates       (1500, 25767, 3)   float32     442.32 MB  
#   kineticEnergy     (1500,)            float32         <1 KB  
#   potentialEnergy   (1500,)            float32         <1 KB  
#   temperature       (1500,)            float32         <1 KB  
#   time              (1500,)            float32         <1 KB  
#   topology          (1,)               |S2083249     1.99 MB  
#                                                               
#   total                                            444.36 MB  

To get an overview of a dataset:

> easyh5 dataset examples/trajectory.h5 coordinates
# Warning: importing 'simtk.openmm' is deprecated.  Import 'openmm' instead.
# 
#   examples/trajectory
#      dataset=coordinates
#      shape=(1500, 25767, 3)
# 
# [[[1.291678   7.558739   1.5199517 ]
#   [1.368739   7.5888386  1.4620152 ]
#   [1.2175218  7.6268845  1.5275735 ]
#   ...
#   [2.375777   0.09478953 4.0356894 ]
#   [3.107005   3.3255231  2.8464174 ]
#   [3.0329072  3.9307644  1.3600407 ]]
# 
#  ...
# 
#  [[2.9693408  7.1466036  1.4656581 ]
#   [2.9327238  7.198606   1.3871984 ]
#   [3.0665123  7.171176   1.4781022 ]
#   ...
#   [4.944392   0.56028575 4.301907  ]
#   [2.6180382  0.3969128  1.4842175 ]
#   [3.281546   4.9666233  2.4855924 ]]]

Or to focus on a selected frames, use a numbered lis:

> easyh5 dataset examples/trajectory.h5 coordinates 1,3,4-10
# Warning: importing 'simtk.openmm' is deprecated.  Import 'openmm' instead.
# 
#   sims/high_bf/trajectory.h5
#      dataset=coordinates
#      shape=(1500, 25767, 3)
# 
# frames(1,3,4-10)=
# [[[1.2958181  7.5481067  1.5513833 ]
#   [1.2766361  7.4586782  1.5085387 ]
#   [1.3654946  7.58469    1.4880756 ]
#   ...
#   [2.0149727  0.20826703 3.712016  ]
#   [3.3603299  3.6615734  2.6487541 ]
#   [3.1595583  4.0199933  1.509442  ]]
# 
#  ...
# 
#  [[1.2550778  7.4836254  1.5989571 ]
#   [1.278228   7.403505   1.5419852 ]
#   [1.2919694  7.571082   1.5644412 ]
#   ...
#   [2.6036768  5.9193387  3.7148886 ]
#   [4.2752028  3.8813443  2.6205144 ]
#   [2.343824   3.9689744  0.05281828]]]
# 

To check atom selections of the protein:

> easyh5 mask sims/high_bf/trajectory.h5 "amber :PRO" --res
# Warning: importing 'simtk.openmm' is deprecated.  Import 'openmm' instead.
# EasyTrajH5File: fname='sims/high_bf/trajectory.h5' mode='a' atom_mask='' is_dry_cache=False
# open connection:started...
# open connection:finished in <1ms
# loading topology:started...
# loading topology:finished in 134ms
# select_mask "amber :PRO" -> 112 atoms, 8 residues
# <Residue PRO[7]; chain=1>
# <Residue PRO[12]; chain=1>
# <Residue PRO[42]; chain=1>
# <Residue PRO[48]; chain=1>
# <Residue PRO[50]; chain=1>
# <Residue PRO[87]; chain=1>
# <Residue PRO[129]; chain=1>
# <Residue PRO[167]; chain=1>

To extract that as PDB:

> easyh5 mask sims/high_bf/trajectory.h5 "amber :PRO" --pdb pro.pdb

There are three sub-commands that help visualize selections in standard viewers:

• easyh5 show-pymol <PDB> <MASK1> <MASK2>
• easyh5 show-vmd <PDB|PARMED|H5> <MASK1> <MASK2>
• easyh5 show-chimera <PDB|PARMED|H5> <MASK1> <MASK2>

It will open the structure or trajectory in the corresponding viewers with the first selection colored in green, and the second selection in pink.

A configuration file in your systems config directory rseed.binary.yaml will be created that list the full path name of PYMOL/VMD/CHIMERA. Change this if your copy of the viewer is in a different location.

Miscellaneous utility

In easytrajh5.quantity we have some useful transforms to handle those pesky unit objects from openmm. These transforms are used in our yaml and json convenience functions

from easytrajh5 import quantity
from parmed import unit

x = 5 * unit.nanosecond
d = quantity.get_dict_from_quantity(x)
# {
#│   'type': 'quantity',
#│   'value': 5,
#│   'unit': 'nanosecond',
#│   'unit_repr': 'Unit({BaseUnit(base_dim=BaseDimension("time"), name="nanosecond", symbol="ns"): 1.0})'
#}
y = quantity.get_quantity_from_dict(d)
# Quantity(value=5, unit=nanosecond)