reacnetgenerator package#

ReacNetGenerator is an automatic reaction network generator for reactive molecular dynamics simulation.[Rb972b5c58b34-1]_.

References#

[1]

Jinzhe Zeng, Liqun Cao, Chih-Hao Chin, Haisheng Ren, John Z. H. Zhang, Tong Zhu, ReacNetGenerator: an automatic reaction network generator for reactive molecular dynamic simulations, Phys. Chem. Chem. Phys., 2020, 22 (2): 683-691, doi: 10.1039/C9CP05091D.

class reacnetgenerator.ReacNetGenerator(**kwargs: Any)[source]#

Bases: object

Use ReacNetGenerator for trajectory analysis.

Parameters:

inputfiletype: str

The type of the input file. The following type is allowed:

dump: LAMMPS dump file, which can be outputed by using dump 1 all custom 100 dump.reaxc id type x y z. See https://lammps.sandia.gov/doc/dump.html for details.
bond: LAMMPS ReaxFF bond file. See https://lammps.sandia.gov/doc/fix_reaxc_bonds.html for details.
xyz: XYZ file, which can also be outputed by LAMMPS using dump.

inputfilename: str or list of strs

The filename(s) of the input file, which can be either relative path or absolute path. If it is a list, the files will be read in order.

atomname: tuple of strs

The list of the atomic names in the input file, such as (‘C’, ‘H’, ‘O’). It should match the order of that in the input file.

runHMM: bool, optional, default: True

Process trajectory with Hidden Markov Model (HMM) or not. If the user find too many species are filtered, they can turn off this option.

miso: int, optional, default: 0

Merge the isomers and the highest frequency is used as the representative. 0, off two available levels: 1, merge the isomers with same atoms and same bond-network but different bond levels; 2, merge the isomers with same atoms with different bond-network.

pbc: bool, optional, default: True

Use periodic boundary conditions (PBC) or not.

cell: (3,3) array_like or (3,) array_like or (9,) array_like, optional, default: None

The cell (box size) of the system. If None (default), the cell will be read from the input file. If the input file doesn’t have cell information, this parameter will be necessary.

nproc: int, optional, default: None

The number of processors used for analysis. If None (default), the program will try to use all processors.

selectatoms: str, optional, default: None

Select an element from the atomic names, such as C, and only show species with this element in the reaction network. If None (default), the network will show all elements.

split: int, optional, default: None

Split number for the time axis. For example, if set to 10, the whole trajectroy will be divided into 10 parts and reactions of each part will be shown.

a: (2,2) array_like, optional, default: [[0.999, 0.001], [0.001, 0.009]]

Transition matrix A of HMM parameters. It is recommended for users to choose their own parameters. See the paper for details.

b: (2,2) array_like, optional, default: [[0.6, 0.4], [0.4, 0.6]]

Emission matrix B of HMM parameters. It is recommended for users to choose their own parameters. See the paper for details.

Examples

>>> from reacnetgenerator import ReacNetGenerator
>>> rng=ReacNetGenerator(inputfiletype="dump", inputfilename="dump.ch4", atomname=['C', 'H', 'O'])
>>> rng.runanddraw()

class Status(value)[source]#

Bases: Enum

ReacNetGenerator status.

The ReacNetGenerator consists of several modules and algorithms to process the information from the given trajectory, including:

DOWNLOAD: Download trajectory from urls
DETECT: Read bond information and detect molecules
HMM: HMM filter
MISO: Merge isomers
PATH: Indentify isomers and collect reaction paths
MATRIX: Reaction matrix generation
NETWORK: Draw reaction network
REPORT: Generate analysis report

DETECT = 'Read bond information and detect molecules'#

DOWNLOAD = 'Download trajectory'#

HMM = 'HMM filter'#

INIT = 'Init'#

MATRIX = 'Reaction matrix generation'#

MISO = 'Merge isomers'#

NETWORK = 'Draw reaction network'#

PATH = 'Indentify isomers and collect reaction paths'#

REPORT = 'Generate analysis report'#

draw() → None[source]#: Draw the reaction network, i.e. NETWORK step.

hmmfilename: str#

moleculetemp2filename: str#

moleculetempfilename: str#

originfilename: str#

report() → None[source]#: Generate the analysis report, i.e. REPORT step.

resultfilename: str#

run() → None[source]#: Process MD trajectory, including DOWNLOAD, DETECT, HMM, PATH, and MATRIX steps.

runanddraw(run: bool = True, draw: bool = True, report: bool = True) → None[source]#

Analyze the trajectory from MD simulation.

Parameters:

runbool, optional, default: True: Process the trajectory or not, including DOWNLOAD, DETECT, HMM, PATH, and MATRIX steps.
drawbool, optional, default: True: Draw the reaction network or not, i.e. NETWORK step.
reportbool, optional, default: True: Generate the analysis report, i.e. NETWORK step.

urls: dict#

Submodules#

reacnetgenerator.commandline module#

Command line interface for reacnetgenerator.

reacnetgenerator.commandline.main_parser() → ArgumentParser[source]#

Return main parser.

Returns:

argparse.ArgumentParser: reacnetgenerator cli parser

reacnetgenerator.commandline.parm2cmd(pp: dict) → List[str][source]#

Convert a parameter dictionary to command line arguments.

Parameters:

ppdict: Parameter dictionary

Returns:

List[str]: Command line arguments

reacnetgenerator.dps module#

Connect molecule with Depth-First Search.

reacnetgenerator.dps.dps(bonds, levels)#

Connect molecule with Depth-First Search.

Parameters:

bondslist of list: The bonds of molecule.
levelslist of int: The levels of atoms.

Returns:

list of list: The connected atoms in each molecule.
list of list: The connected bonds in each molecule.

reacnetgenerator.dps.dps_reaction(reactdict)#

Find A+B->C+D reactions.

Parameters:

reactdictlist of dict of list: Two dictionaries of reactions.

Returns:

list of list of list: List of reactions. The secons axis matches the position of species (left or right).

reacnetgenerator.gui module#

reacnetgenerator.reacnetgen module#

The main module of ReacNetGenerator.

reacnetgenerator.tools module#

Useful methods to futhur process ReacNetGenerator results.

reacnetgenerator.tools.calculate_rate(specfile: str | Path, reacfile: str | Path, cell: ndarray, timestep: float) → Dict[str, float][source]#

Calculate the rate constant of each reaction.

The rate constants are calculated by the method developed in [1]. The time interval of the trajectory is assumed to be uniform.

Parameters:

specfilestr: The species file.
reacfilestr: The reactions file.
cellnp.ndarray: The cell with the shape (3, 3). Unit: Angstrom.
timestepfloat: The time step. Unit: femtosecond.

Returns:

ratesDict[str, float]: The rate of each reaction. The dict key is the reaction SMILES. The value is in unit of [(cm^3/mol)^(n-1)s^(-1)], where n is the reaction order.

References

[1]

Yanze Wu, Huai Sun, Liang Wu, Joshua D. Deetz, Extracting the mechanisms and kinetic models of complex reactions from atomistic simulation data, J. Comput. Chem. 40, 16, 1586-1592.

Examples

>>> cell = np.eye(3) * 3.7601e1 # in unit Angstrom
>>> timestep = 0.1 # in unit fs
>>> rates = calculate_rate('methane.species', 'methane.reactionabcd', cell, timestep)

reacnetgenerator.tools.read_reactions(reacfile: str | Path) → List[Tuple[int, Counter, str]][source]#

Read reactions from the reactions file (ends with .reaction or .reactionsabcd).

For accuracy, HMM filter should be disabled.

Parameters:

reacfilestr or Path: The reactions file.

Returns:

occsList[Tuple[int, Counter, str]]: The number of occurences of each reaction. The tuple is (occurence, counter_reactants, reaction).

reacnetgenerator.tools.read_species(specfile: str | Path) → Tuple[ndarray, Dict[str, ndarray]][source]#

Read species from the species file (ends with .species).

For accuracy, HMM filter should be disabled.

Parameters:

specfilestr or Path: The species file.

Returns:

step_idxnp.ndarray: The index of the step.
n_speciesDict[str, np.ndarray]: The number of species in each step. The dict key is the species SMILES.

Examples

Plot the number of methane in each step.

>>> from reacnetgenerator.tools import read_species
>>> import matplotlib.pyplot as plt
>>> step_idx, n_species = read_species('methane.species')
>>> plt.plot(step_idx, n_species['[H]C([H])([H])[H]'])
>>> plt.savefig("methane.svg")

reacnetgenerator.utils module#

Provide utils for ReacNetGenerator.

class reacnetgenerator.utils.SCOUROPTIONS[source]#

Bases: object

Scour (SVG optimization) options.

enable_viewboxing = True#

newlines = False#

remove_descriptions = True#

remove_descriptive_elements = True#

remove_metadata = True#

remove_titles = True#

shorten_ids = True#

strip_comments = True#

strip_ids = True#

strip_xml_prolog = True#

strip_xml_space_attribute = True#

class reacnetgenerator.utils.SharedRNGData(rng: reacnetgenerator.ReacNetGenerator, usedRNGKeys: List[str], returnedRNGKeys: List[str], extraNoneKeys: List[str] | None = None)[source]#

Bases: object

Share ReacNetGenerator data with a class of the submodule.

Parameters:

rng: reacnetgenerator.ReacNetGenerator: The centered ReacNetGenerator class.
usedRNGKeys: list of strs: Keys that needs to pass from ReacNetGenerator class to the submodule.
returnedRNGKeys: list of strs: Keys that needs to pass from the submodule to ReacNetGenerator class.
extraNoneKeys: list of strs, optional, default: None: Set keys to None, which will be used in the submodule.

returnkeys() → None[source]#: Return back keys to ReacNetGenerator class.

class reacnetgenerator.utils.WriteBuffer(f: IO, linenumber: int = 1200, sep: AnyStr | None = None)[source]#

Bases: object

Store a buffer for writing files.

It is expensive to write to a file, so we need to make a buffer.

Parameters:

f: fileObject: The file object to write.
linenumber: int, default: 1200: The number of contents to store in the buffer. The buffer will be flushed if it exceeds the set number.
sep: str or bytes, default: None: The separator for contents. If None (default), there will be no separator.

append(text: AnyStr) → None[source]#

Append a text.

Parameters:

textstr or bytes: The text to be appended.

check() → None[source]#

Check if the number of stored contents exceeds.

If so, the buffer will be flushed.

extend(text: Iterable) → None[source]#

Extend texts.

Parameters:

textlist of strs or bytes: Texts to be extended.

flush() → None[source]#: Flush the buffer.

reacnetgenerator.utils.appendIfNotNone(f: WriteBuffer | ExitStack, wbytes: AnyStr | None) → None[source]#

Append a line to a file if the line is not None.

Parameters:

fWriteBuffer: The file to write.
wbytesstr or bytes: The line to write.

reacnetgenerator.utils.bytestolist(x: bytes) → Any[source]#

Convert a compressed line to an object.

Parameters:

xbytes: The compressed line.

Returns:

object: The decompressed object.

reacnetgenerator.utils.checksha256(filename: str, sha256_check: str | List[str])[source]#

Check sha256 of a file is correct.

Parameters:

filenamestr: The filename.
sha256_checkstr or list of strs: The sha256 to be checked.

Returns:

bool: Indicate whether sha256 is correct.

reacnetgenerator.utils.compress(x: str | bytes) → bytes[source]#

Compress the line.

This function reduces IO overhead to speed up the program. The functions will use lz4 to compress, since lz4 has better performance that any others.

The compressed format is size + data + size + data + …, where size is a 64-bit little-endian integer.

Parameters:

xstr or bytes: The line to compress.

Returns:

bytes: The compressed line, with a linebreak in the end.

reacnetgenerator.utils.decompress(x: bytes, isbytes: bool = False) → str | bytes[source]#

Decompress the line.

Parameters:

xbytes: The line to decompress.
isbytesbool, optional, default: False: If the decompressed content is bytes. If not, the line will be decoded.

Returns:

str or bytes: The decompressed line.

async reacnetgenerator.utils.download_file(urls: str | List[str], pathfilename: str, sha256: str) → str[source]#

Download files from remote urls if not exists.

Parameters:

urls: str or list of strs: The url(s) that is available to download.
pathfilename: str: The downloading path of the file.
sha256: str: Sha256 of the file. If not None and match the file, the download will be skiped.

Returns:

pathfilename: str: The downloading path of the file.

reacnetgenerator.utils.download_multifiles(urls: List[dict]) → None[source]#

Download multiple files from dicts.

Parameters:

urlslist of dicts

The information of download files. Each dict should contain the following key:

url: str or list of strs
The url(s) that is available to download.
pathfilename: str
The downloading path of the file.
sha256: str, optional, default: None
Sha256 of the file. If not None and match the file, the download will be skiped.

async reacnetgenerator.utils.gather_download_files(urls: List[dict]) → None[source]#

Asynchronously download files from remote urls if not exists.

See download_multifiles function for details.

reacnetgenerator.utils_np module#

reacnetgenerator.utils_np.check_zero_signal(signal)#

Check if the given signal contains only zeros.

Parameters:

signal1D array: The signal to check.

Returns:

bool: True if the signal contains only zeros, False otherwise.

Notes

This method uses Cython to speed up the computation.

Benchmark for 1,000,000 loops (1000/2000):

Cython: 1.45 s
Python: 3.67 s

reacnetgenerator.utils_np.idx_to_signal(idx, step)#

Converts an index array to a signal array.

Parameters:

idxarray_like: Index array.
stepint: Step size.

Returns:

signalndarray: Signal array.

Notes

This method uses Cython to speed up the computation.

Benchmark for 1,000,000 loops (step=250000):

Cython: 18.61 s
Python: 31.30 s