Package genice_core
GenIce-core
Core algorithms of GenIce2
version 0.6
Requirements
- numpy
- networkx
Installation
GenIce-core is registered to PyPI (Python Package Index). Install with pip3.
pip3 install genice-core
Uninstallation
pip3 uninstall genice-core
API
API manual is here.
Examples
Make an ice graph from a given undirected graph.
import networkx as nx
import matplotlib
import genice_core
# np.random.seed(12345)
g = nx.dodecahedral_graph() # dodecahedral 20mer
pos = nx.spring_layout(g)
# set orientations of the hydrogen bonds.
dg = genice_core.ice_graph(g)
nx.draw_networkx(dg, pos)
Algorithms and how to cite them.
The algorithms to make a depolarized hydrogen-disordered ice are explained in these papers:
M. Matsumoto, T. Yagasaki, and H. Tanaka,"GenIce: Hydrogen-Disordered Ice Generator", J. Comput. Chem. 39, 61-64 (2017). DOI: 10.1002/jcc.25077
@article{Matsumoto:2017bk,
author = {Matsumoto, Masakazu and Yagasaki, Takuma and Tanaka, Hideki},
title = {GenIce: Hydrogen-Disordered Ice Generator},
journal = {Journal of Computational Chemistry},
volume = {39},
pages = {61-64},
year = {2017}
}
M. Matsumoto, T. Yagasaki, and H. Tanaka, “Novel Algorithm to Generate Hydrogen-Disordered Ice Structures.”, J. Chem. Info. Modeling 61 (6): 2542–46 (2021). DOI:10.1021/acs.jcim.1c00440
@article{Matsumoto:2021,
author = {Matsumoto, Masakazu and Yagasaki, Takuma and Tanaka, Hideki},
title = {Novel Algorithm to Generate Hydrogen-Disordered Ice Structures},
journal = {Journal of Chemical Information and Modeling},
volume = {61},
pages = {2542-2546},
year = {2021}
}
How to contribute
GenIce has been available as open source software on GitHub(https://github.com/vitroid/GenIce) since 2015. Feedback, suggestions for improvements and enhancements, bug fixes, etc. are sincerely welcome. Developers and test users are also welcome. If you have any ice that is publicly available but not included in GenIce, please let me know.
Expand source code
"""
.. include:: ../README.md
"""
"""
Optimizes the orientations of directed paths to reduce the net dipole moment.
"""
import numpy as np
import networkx as nx
from genice_core.topology import noodlize, split_into_simple_paths, balance
from genice_core.dipole import optimize, vector_sum
from typing import Union
from logging import getLogger
def ice_graph(
g: nx.Graph,
vertexPositions: Union[np.ndarray, None] = None,
isPeriodicBoundary: bool = False,
dipoleOptimizationCycles: int = 0,
fixedEdges: Union[nx.DiGraph, None] = nx.DiGraph(),
hook=None,
) -> nx.DiGraph:
"""Make a digraph that obeys the ice rules.
A new algorithm suggested by Prof. Sakuma, Yamagata University.
Args:
g (nx.Graph): A ice-like undirected graph.
vertexPositions (Union[nx.ndarray, None], optional): Positions of the vertices. Defaults to None.
isPeriodicBoundary (bool, optional): If True, the positions are considered to be in the fractional coordinate system. Defaults to False.
dipoleOptimizationCycles (int, optional): Number of iterations to reduce the net dipole moment. Defaults to 0 (no iteration).
fixed (Union[nx.DiGraph, None], optional): A digraph made of edges whose directions are fixed. All edges in fixed must also be included in g. Defaults to an empty graph.
Returns:
nx.DiGraph: An ice graph (fixed part is excluded).
"""
logger = getLogger()
logger.debug(g)
logger.debug(fixedEdges)
if fixedEdges is not None:
balance(fixedEdges, g, hook=hook)
# Divide the graph into noodle graph
dividedGraph = noodlize(g, fixedEdges)
# Simplify paths ( paths with least crossings )
paths = list(split_into_simple_paths(len(g), dividedGraph))
# arrange the orientations here if you want to balance the polarization
if vertexPositions is not None:
if fixedEdges is not None:
# Set the targetPol in order to cancel the polarization in fixed.
targetPol = -vector_sum(fixedEdges, vertexPositions, isPeriodicBoundary)
else:
targetPol = np.zeros(3)
paths = optimize(
paths,
vertexPositions,
isPeriodicBoundary=isPeriodicBoundary,
dipoleOptimizationCycles=dipoleOptimizationCycles,
targetPol=targetPol,
)
# paths to digraph
dg = nx.DiGraph()
for path in paths:
nx.add_path(dg, path)
return dg
Sub-modules
genice_core.dipole
-
Optimizes the orientations of directed paths to reduce the net dipole moment.
genice_core.topology
-
Arrange edges appropriately.
Functions
def ice_graph(g: networkx.classes.graph.Graph, vertexPositions: Optional[numpy.ndarray] = None, isPeriodicBoundary: bool = False, dipoleOptimizationCycles: int = 0, fixedEdges: Optional[networkx.classes.digraph.DiGraph] = <networkx.classes.digraph.DiGraph object>, hook=None) ‑> networkx.classes.digraph.DiGraph
-
Make a digraph that obeys the ice rules.
A new algorithm suggested by Prof. Sakuma, Yamagata University.
Args
g
:nx.Graph
- A ice-like undirected graph.
vertexPositions
:Union[nx.ndarray, None]
, optional- Positions of the vertices. Defaults to None.
isPeriodicBoundary
:bool
, optional- If True, the positions are considered to be in the fractional coordinate system. Defaults to False.
dipoleOptimizationCycles
:int
, optional- Number of iterations to reduce the net dipole moment. Defaults to 0 (no iteration).
fixed
:Union[nx.DiGraph, None]
, optional- A digraph made of edges whose directions are fixed. All edges in fixed must also be included in g. Defaults to an empty graph.
Returns
nx.DiGraph
- An ice graph (fixed part is excluded).
Expand source code
def ice_graph( g: nx.Graph, vertexPositions: Union[np.ndarray, None] = None, isPeriodicBoundary: bool = False, dipoleOptimizationCycles: int = 0, fixedEdges: Union[nx.DiGraph, None] = nx.DiGraph(), hook=None, ) -> nx.DiGraph: """Make a digraph that obeys the ice rules. A new algorithm suggested by Prof. Sakuma, Yamagata University. Args: g (nx.Graph): A ice-like undirected graph. vertexPositions (Union[nx.ndarray, None], optional): Positions of the vertices. Defaults to None. isPeriodicBoundary (bool, optional): If True, the positions are considered to be in the fractional coordinate system. Defaults to False. dipoleOptimizationCycles (int, optional): Number of iterations to reduce the net dipole moment. Defaults to 0 (no iteration). fixed (Union[nx.DiGraph, None], optional): A digraph made of edges whose directions are fixed. All edges in fixed must also be included in g. Defaults to an empty graph. Returns: nx.DiGraph: An ice graph (fixed part is excluded). """ logger = getLogger() logger.debug(g) logger.debug(fixedEdges) if fixedEdges is not None: balance(fixedEdges, g, hook=hook) # Divide the graph into noodle graph dividedGraph = noodlize(g, fixedEdges) # Simplify paths ( paths with least crossings ) paths = list(split_into_simple_paths(len(g), dividedGraph)) # arrange the orientations here if you want to balance the polarization if vertexPositions is not None: if fixedEdges is not None: # Set the targetPol in order to cancel the polarization in fixed. targetPol = -vector_sum(fixedEdges, vertexPositions, isPeriodicBoundary) else: targetPol = np.zeros(3) paths = optimize( paths, vertexPositions, isPeriodicBoundary=isPeriodicBoundary, dipoleOptimizationCycles=dipoleOptimizationCycles, targetPol=targetPol, ) # paths to digraph dg = nx.DiGraph() for path in paths: nx.add_path(dg, path) return dg