apply more pylint recommendations
This commit is contained in:
@ -264,7 +264,7 @@ max-bool-expr = 5
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max-branches = 50
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max-branches = 50
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# Maximum number of locals for function / method body.
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# Maximum number of locals for function / method body.
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max-locals = 20
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max-locals = 25
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# Maximum number of parents for a class (see R0901).
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# Maximum number of parents for a class (see R0901).
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max-parents = 7
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max-parents = 7
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@ -282,7 +282,7 @@ max-returns = 15
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max-statements = 500
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max-statements = 500
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# Minimum number of public methods for a class (see R0903).
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# Minimum number of public methods for a class (see R0903).
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min-public-methods = 2
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min-public-methods = 0
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[tool.pylint.exceptions]
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[tool.pylint.exceptions]
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# Exceptions that will emit a warning when caught.
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# Exceptions that will emit a warning when caught.
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@ -11,8 +11,10 @@
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# See the README file in the top-level LAMMPS directory.
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# See the README file in the top-level LAMMPS directory.
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# -------------------------------------------------------------------------
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# -------------------------------------------------------------------------
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# various symbolic constants to be used
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"""
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# in certain calls to select data formats
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various symbolic constants to be used
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in certain calls to select data formats
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"""
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# these must be kept in sync with the enums in src/library.h, src/lmptype.h,
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# these must be kept in sync with the enums in src/library.h, src/lmptype.h,
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# tools/swig/lammps.i, examples/COUPLE/plugin/liblammpsplugin.h,
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# tools/swig/lammps.i, examples/COUPLE/plugin/liblammpsplugin.h,
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@ -55,9 +57,11 @@ LMP_BUFSIZE = 1024
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# -------------------------------------------------------------------------
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# -------------------------------------------------------------------------
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def get_ctypes_int(size):
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def get_ctypes_int(size):
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"""return ctypes type matching the configured C/C++ integer size in LAMMPS"""
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# pylint: disable=C0415
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from ctypes import c_int, c_int32, c_int64
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from ctypes import c_int, c_int32, c_int64
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if size == 4:
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if size == 4:
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return c_int32
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return c_int32
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elif size == 8:
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if size == 8:
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return c_int64
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return c_int64
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return c_int
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return c_int
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@ -10,7 +10,9 @@
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#
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#
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# See the README file in the top-level LAMMPS directory.
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# See the README file in the top-level LAMMPS directory.
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# -------------------------------------------------------------------------
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# -------------------------------------------------------------------------
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# Python wrapper for the LAMMPS library via ctypes
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"""
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Python module wrapping the LAMMPS library via ctypes
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"""
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# avoid pylint warnings about naming conventions
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# avoid pylint warnings about naming conventions
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# pylint: disable=C0103
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# pylint: disable=C0103
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@ -11,12 +11,12 @@
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# See the README file in the top-level LAMMPS directory.
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# See the README file in the top-level LAMMPS directory.
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# -------------------------------------------------------------------------
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# -------------------------------------------------------------------------
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################################################################################
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"""
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# LAMMPS data structures
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Data structures for LAMMPS Python module
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# Written by Richard Berger <richard.berger@temple.edu>
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Written by Richard Berger <richard.berger@temple.edu>
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################################################################################
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"""
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class NeighList(object):
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class NeighList:
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"""This is a wrapper class that exposes the contents of a neighbor list.
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"""This is a wrapper class that exposes the contents of a neighbor list.
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It can be used like a regular Python list. Each element is a tuple of:
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It can be used like a regular Python list. Each element is a tuple of:
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@ -38,6 +38,7 @@ class NeighList(object):
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self.idx = idx
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self.idx = idx
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def __str__(self):
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def __str__(self):
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# pylint: disable=C0209
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return "Neighbor List ({} atoms)".format(self.size)
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return "Neighbor List ({} atoms)".format(self.size)
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def __repr__(self):
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def __repr__(self):
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@ -11,14 +11,15 @@
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# See the README file in the top-level LAMMPS directory.
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# See the README file in the top-level LAMMPS directory.
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# -------------------------------------------------------------------------
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# -------------------------------------------------------------------------
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################################################################################
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"""
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# LAMMPS output formats
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Output formats for LAMMPS python module
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# Written by Richard Berger <richard.berger@temple.edu>
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Written by Richard Berger <richard.berger@temple.edu>
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# and Axel Kohlmeyer <akohlmey@gmail.com>
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and Axel Kohlmeyer <akohlmey@gmail.com>
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################################################################################
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"""
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import re
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import re
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# pylint: disable=C0103
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has_yaml = False
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has_yaml = False
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try:
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try:
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import yaml
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import yaml
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@ -32,6 +33,7 @@ except ImportError:
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pass
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pass
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class LogFile:
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class LogFile:
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# pylint: disable=R0903
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"""Reads LAMMPS log files and extracts the thermo information
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"""Reads LAMMPS log files and extracts the thermo information
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It supports the line, multi, and yaml thermo output styles.
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It supports the line, multi, and yaml thermo output styles.
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@ -55,7 +57,7 @@ class LogFile:
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yamllog = ""
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yamllog = ""
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self.runs = []
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self.runs = []
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self.errors = []
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self.errors = []
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with open(filename, 'rt') as f:
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with open(filename, 'rt', encoding='utf-8') as f:
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in_thermo = False
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in_thermo = False
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in_data_section = False
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in_data_section = False
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for line in f:
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for line in f:
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@ -72,9 +74,9 @@ class LogFile:
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elif re.match(r'^(keywords:.*$|data:$|---$| - \[.*\]$)', line):
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elif re.match(r'^(keywords:.*$|data:$|---$| - \[.*\]$)', line):
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if not has_yaml:
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if not has_yaml:
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raise Exception('Cannot process YAML format logs without the PyYAML Python module')
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raise RuntimeError('Cannot process YAML format logs without the PyYAML Python module')
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style = LogFile.STYLE_YAML
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style = LogFile.STYLE_YAML
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yamllog += line;
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yamllog += line
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current_run = {}
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current_run = {}
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elif re.match(r'^\.\.\.$', line):
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elif re.match(r'^\.\.\.$', line):
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@ -134,9 +136,13 @@ class AvgChunkFile:
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:ivar chunks: List of chunks. Each chunk is a dictionary containing its ID, the coordinates, and the averaged quantities
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:ivar chunks: List of chunks. Each chunk is a dictionary containing its ID, the coordinates, and the averaged quantities
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"""
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"""
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def __init__(self, filename):
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def __init__(self, filename):
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with open(filename, 'rt') as f:
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with open(filename, 'rt', encoding='utf-8') as f:
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timestep = None
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timestep = None
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chunks_read = 0
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chunks_read = 0
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compress = False
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coord_start = None
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coord_end = None
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data_start = None
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self.timesteps = []
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self.timesteps = []
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self.total_count = []
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self.total_count = []
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@ -145,24 +151,24 @@ class AvgChunkFile:
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for lineno, line in enumerate(f):
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for lineno, line in enumerate(f):
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if lineno == 0:
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if lineno == 0:
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if not line.startswith("# Chunk-averaged data for fix"):
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if not line.startswith("# Chunk-averaged data for fix"):
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raise Exception("Chunk data reader only supports default avg/chunk headers!")
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raise RuntimeError("Chunk data reader only supports default avg/chunk headers!")
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parts = line.split()
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parts = line.split()
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self.fix_name = parts[5]
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self.fix_name = parts[5]
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self.group_name = parts[8]
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self.group_name = parts[8]
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continue
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continue
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elif lineno == 1:
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if lineno == 1:
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if not line.startswith("# Timestep Number-of-chunks Total-count"):
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if not line.startswith("# Timestep Number-of-chunks Total-count"):
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raise Exception("Chunk data reader only supports default avg/chunk headers!")
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raise RuntimeError("Chunk data reader only supports default avg/chunk headers!")
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continue
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continue
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elif lineno == 2:
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if lineno == 2:
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if not line.startswith("#"):
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if not line.startswith("#"):
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raise Exception("Chunk data reader only supports default avg/chunk headers!")
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raise RuntimeError("Chunk data reader only supports default avg/chunk headers!")
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columns = line.split()[1:]
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columns = line.split()[1:]
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ndim = line.count("Coord")
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ndim = line.count("Coord")
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compress = 'OrigID' in line
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compress = 'OrigID' in line
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if ndim > 0:
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if ndim > 0:
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coord_start = columns.index("Coord1")
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coord_start = columns.index("Coord1")
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coord_end = columns.index("Coord%d" % ndim)
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coord_end = columns.index(f"Coord{ndim}")
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ncount_start = coord_end + 1
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ncount_start = coord_end + 1
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data_start = ncount_start + 1
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data_start = ncount_start + 1
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else:
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else:
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@ -216,8 +222,8 @@ class AvgChunkFile:
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assert chunk == chunks_read
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assert chunk == chunks_read
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else:
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else:
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# do not support changing number of chunks
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# do not support changing number of chunks
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if not (num_chunks == int(parts[1])):
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if not num_chunks == int(parts[1]):
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raise Exception("Currently, changing numbers of chunks are not supported.")
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raise RuntimeError("Currently, changing numbers of chunks are not supported.")
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timestep = int(parts[0])
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timestep = int(parts[0])
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total_count = float(parts[2])
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total_count = float(parts[2])
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@ -11,12 +11,13 @@
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# See the README file in the top-level LAMMPS directory.
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# See the README file in the top-level LAMMPS directory.
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# -------------------------------------------------------------------------
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# -------------------------------------------------------------------------
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################################################################################
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"""
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# NumPy additions
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NumPy additions to the LAMMPS Python module
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# Written by Richard Berger <richard.berger@temple.edu>
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Written by Richard Berger <richard.berger@temple.edu>
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################################################################################
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"""
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from ctypes import POINTER, c_void_p, c_char_p, c_double, c_int, c_int32, c_int64, cast
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from ctypes import POINTER, c_void_p, c_char_p, c_double, c_int, c_int32, c_int64, cast
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import numpy as np
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from .constants import LAMMPS_AUTODETECT, LAMMPS_INT, LAMMPS_INT_2D, LAMMPS_DOUBLE, \
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from .constants import LAMMPS_AUTODETECT, LAMMPS_INT, LAMMPS_INT_2D, LAMMPS_DOUBLE, \
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LAMMPS_DOUBLE_2D, LAMMPS_INT64, LAMMPS_INT64_2D, LMP_STYLE_GLOBAL, LMP_STYLE_ATOM, \
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LAMMPS_DOUBLE_2D, LAMMPS_INT64, LAMMPS_INT64_2D, LMP_STYLE_GLOBAL, LMP_STYLE_ATOM, \
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@ -26,6 +27,7 @@ from .constants import LAMMPS_AUTODETECT, LAMMPS_INT, LAMMPS_INT_2D, LAMMPS_DOU
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from .data import NeighList
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from .data import NeighList
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class numpy_wrapper:
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class numpy_wrapper:
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# pylint: disable=C0103
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"""lammps API NumPy Wrapper
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"""lammps API NumPy Wrapper
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|
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This is a wrapper class that provides additional methods on top of an
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This is a wrapper class that provides additional methods on top of an
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@ -46,10 +48,9 @@ class numpy_wrapper:
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# -------------------------------------------------------------------------
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# -------------------------------------------------------------------------
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def _ctype_to_numpy_int(self, ctype_int):
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def _ctype_to_numpy_int(self, ctype_int):
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import numpy as np
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if ctype_int == c_int32:
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if ctype_int == c_int32:
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return np.int32
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return np.int32
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elif ctype_int == c_int64:
|
if ctype_int == c_int64:
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return np.int64
|
return np.int64
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return np.intc
|
return np.intc
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@ -102,9 +103,9 @@ class numpy_wrapper:
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if dtype in (LAMMPS_DOUBLE, LAMMPS_DOUBLE_2D):
|
if dtype in (LAMMPS_DOUBLE, LAMMPS_DOUBLE_2D):
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return self.darray(raw_ptr, nelem, dim)
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return self.darray(raw_ptr, nelem, dim)
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elif dtype in (LAMMPS_INT, LAMMPS_INT_2D):
|
if dtype in (LAMMPS_INT, LAMMPS_INT_2D):
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return self.iarray(c_int32, raw_ptr, nelem, dim)
|
return self.iarray(c_int32, raw_ptr, nelem, dim)
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elif dtype in (LAMMPS_INT64, LAMMPS_INT64_2D):
|
if dtype in (LAMMPS_INT64, LAMMPS_INT64_2D):
|
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return self.iarray(c_int64, raw_ptr, nelem, dim)
|
return self.iarray(c_int64, raw_ptr, nelem, dim)
|
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return raw_ptr
|
return raw_ptr
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|
|
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@ -133,7 +134,7 @@ class numpy_wrapper:
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if ctype == LMP_TYPE_VECTOR:
|
if ctype == LMP_TYPE_VECTOR:
|
||||||
nrows = self.lmp.extract_compute(cid, cstyle, LMP_SIZE_VECTOR)
|
nrows = self.lmp.extract_compute(cid, cstyle, LMP_SIZE_VECTOR)
|
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return self.darray(value, nrows)
|
return self.darray(value, nrows)
|
||||||
elif ctype == LMP_TYPE_ARRAY:
|
if ctype == LMP_TYPE_ARRAY:
|
||||||
nrows = self.lmp.extract_compute(cid, cstyle, LMP_SIZE_ROWS)
|
nrows = self.lmp.extract_compute(cid, cstyle, LMP_SIZE_ROWS)
|
||||||
ncols = self.lmp.extract_compute(cid, cstyle, LMP_SIZE_COLS)
|
ncols = self.lmp.extract_compute(cid, cstyle, LMP_SIZE_COLS)
|
||||||
return self.darray(value, nrows, ncols)
|
return self.darray(value, nrows, ncols)
|
||||||
@ -142,13 +143,12 @@ class numpy_wrapper:
|
|||||||
ncols = self.lmp.extract_compute(cid, cstyle, LMP_SIZE_COLS)
|
ncols = self.lmp.extract_compute(cid, cstyle, LMP_SIZE_COLS)
|
||||||
if ncols == 0:
|
if ncols == 0:
|
||||||
return self.darray(value, nrows)
|
return self.darray(value, nrows)
|
||||||
else:
|
|
||||||
return self.darray(value, nrows, ncols)
|
return self.darray(value, nrows, ncols)
|
||||||
elif cstyle == LMP_STYLE_ATOM:
|
elif cstyle == LMP_STYLE_ATOM:
|
||||||
if ctype == LMP_TYPE_VECTOR:
|
if ctype == LMP_TYPE_VECTOR:
|
||||||
nlocal = self.lmp.extract_global("nlocal")
|
nlocal = self.lmp.extract_global("nlocal")
|
||||||
return self.darray(value, nlocal)
|
return self.darray(value, nlocal)
|
||||||
elif ctype == LMP_TYPE_ARRAY:
|
if ctype == LMP_TYPE_ARRAY:
|
||||||
nlocal = self.lmp.extract_global("nlocal")
|
nlocal = self.lmp.extract_global("nlocal")
|
||||||
ncols = self.lmp.extract_compute(cid, cstyle, LMP_SIZE_COLS)
|
ncols = self.lmp.extract_compute(cid, cstyle, LMP_SIZE_COLS)
|
||||||
return self.darray(value, nlocal, ncols)
|
return self.darray(value, nlocal, ncols)
|
||||||
@ -189,7 +189,7 @@ class numpy_wrapper:
|
|||||||
if ftype == LMP_TYPE_VECTOR:
|
if ftype == LMP_TYPE_VECTOR:
|
||||||
nlocal = self.lmp.extract_global("nlocal")
|
nlocal = self.lmp.extract_global("nlocal")
|
||||||
return self.darray(value, nlocal)
|
return self.darray(value, nlocal)
|
||||||
elif ftype == LMP_TYPE_ARRAY:
|
if ftype == LMP_TYPE_ARRAY:
|
||||||
nlocal = self.lmp.extract_global("nlocal")
|
nlocal = self.lmp.extract_global("nlocal")
|
||||||
ncols = self.lmp.extract_fix(fid, fstyle, LMP_SIZE_COLS, 0, 0)
|
ncols = self.lmp.extract_fix(fid, fstyle, LMP_SIZE_COLS, 0, 0)
|
||||||
return self.darray(value, nlocal, ncols)
|
return self.darray(value, nlocal, ncols)
|
||||||
@ -197,7 +197,7 @@ class numpy_wrapper:
|
|||||||
if ftype == LMP_TYPE_VECTOR:
|
if ftype == LMP_TYPE_VECTOR:
|
||||||
nrows = self.lmp.extract_fix(fid, fstyle, LMP_SIZE_ROWS, 0, 0)
|
nrows = self.lmp.extract_fix(fid, fstyle, LMP_SIZE_ROWS, 0, 0)
|
||||||
return self.darray(value, nrows)
|
return self.darray(value, nrows)
|
||||||
elif ftype == LMP_TYPE_ARRAY:
|
if ftype == LMP_TYPE_ARRAY:
|
||||||
nrows = self.lmp.extract_fix(fid, fstyle, LMP_SIZE_ROWS, 0, 0)
|
nrows = self.lmp.extract_fix(fid, fstyle, LMP_SIZE_ROWS, 0, 0)
|
||||||
ncols = self.lmp.extract_fix(fid, fstyle, LMP_SIZE_COLS, 0, 0)
|
ncols = self.lmp.extract_fix(fid, fstyle, LMP_SIZE_COLS, 0, 0)
|
||||||
return self.darray(value, nrows, ncols)
|
return self.darray(value, nrows, ncols)
|
||||||
@ -222,7 +222,6 @@ class numpy_wrapper:
|
|||||||
:return: the requested data or None
|
:return: the requested data or None
|
||||||
:rtype: c_double, numpy.array, or NoneType
|
:rtype: c_double, numpy.array, or NoneType
|
||||||
"""
|
"""
|
||||||
import numpy as np
|
|
||||||
value = self.lmp.extract_variable(name, group, vartype)
|
value = self.lmp.extract_variable(name, group, vartype)
|
||||||
if vartype == LMP_VAR_ATOM:
|
if vartype == LMP_VAR_ATOM:
|
||||||
return np.ctypeslib.as_array(value)
|
return np.ctypeslib.as_array(value)
|
||||||
@ -242,7 +241,6 @@ class numpy_wrapper:
|
|||||||
:return: the requested data as a 2d-integer numpy array
|
:return: the requested data as a 2d-integer numpy array
|
||||||
:rtype: numpy.array(nbonds,3)
|
:rtype: numpy.array(nbonds,3)
|
||||||
"""
|
"""
|
||||||
import numpy as np
|
|
||||||
nbonds, value = self.lmp.gather_bonds()
|
nbonds, value = self.lmp.gather_bonds()
|
||||||
return np.ctypeslib.as_array(value).reshape(nbonds,3)
|
return np.ctypeslib.as_array(value).reshape(nbonds,3)
|
||||||
|
|
||||||
@ -260,7 +258,6 @@ class numpy_wrapper:
|
|||||||
:return: the requested data as a 2d-integer numpy array
|
:return: the requested data as a 2d-integer numpy array
|
||||||
:rtype: numpy.array(nangles,4)
|
:rtype: numpy.array(nangles,4)
|
||||||
"""
|
"""
|
||||||
import numpy as np
|
|
||||||
nangles, value = self.lmp.gather_angles()
|
nangles, value = self.lmp.gather_angles()
|
||||||
return np.ctypeslib.as_array(value).reshape(nangles,4)
|
return np.ctypeslib.as_array(value).reshape(nangles,4)
|
||||||
|
|
||||||
@ -278,7 +275,6 @@ class numpy_wrapper:
|
|||||||
:return: the requested data as a 2d-integer numpy array
|
:return: the requested data as a 2d-integer numpy array
|
||||||
:rtype: numpy.array(ndihedrals,5)
|
:rtype: numpy.array(ndihedrals,5)
|
||||||
"""
|
"""
|
||||||
import numpy as np
|
|
||||||
ndihedrals, value = self.lmp.gather_dihedrals()
|
ndihedrals, value = self.lmp.gather_dihedrals()
|
||||||
return np.ctypeslib.as_array(value).reshape(ndihedrals,5)
|
return np.ctypeslib.as_array(value).reshape(ndihedrals,5)
|
||||||
|
|
||||||
@ -296,7 +292,6 @@ class numpy_wrapper:
|
|||||||
:return: the requested data as a 2d-integer numpy array
|
:return: the requested data as a 2d-integer numpy array
|
||||||
:rtype: numpy.array(nimpropers,5)
|
:rtype: numpy.array(nimpropers,5)
|
||||||
"""
|
"""
|
||||||
import numpy as np
|
|
||||||
nimpropers, value = self.lmp.gather_impropers()
|
nimpropers, value = self.lmp.gather_impropers()
|
||||||
return np.ctypeslib.as_array(value).reshape(nimpropers,5)
|
return np.ctypeslib.as_array(value).reshape(nimpropers,5)
|
||||||
|
|
||||||
@ -317,7 +312,6 @@ class numpy_wrapper:
|
|||||||
:return: requested data
|
:return: requested data
|
||||||
:rtype: numpy.array
|
:rtype: numpy.array
|
||||||
"""
|
"""
|
||||||
import numpy as np
|
|
||||||
nlocal = self.lmp.extract_setting('nlocal')
|
nlocal = self.lmp.extract_setting('nlocal')
|
||||||
value = self.lmp.fix_external_get_force(fix_id)
|
value = self.lmp.fix_external_get_force(fix_id)
|
||||||
return self.darray(value,nlocal,3)
|
return self.darray(value,nlocal,3)
|
||||||
@ -339,10 +333,9 @@ class numpy_wrapper:
|
|||||||
:param eatom: per-atom potential energy
|
:param eatom: per-atom potential energy
|
||||||
:type: numpy.array
|
:type: numpy.array
|
||||||
"""
|
"""
|
||||||
import numpy as np
|
|
||||||
nlocal = self.lmp.extract_setting('nlocal')
|
nlocal = self.lmp.extract_setting('nlocal')
|
||||||
if len(eatom) < nlocal:
|
if len(eatom) < nlocal:
|
||||||
raise Exception('per-atom energy dimension must be at least nlocal')
|
raise RuntimeError('per-atom energy dimension must be at least nlocal')
|
||||||
|
|
||||||
c_double_p = POINTER(c_double)
|
c_double_p = POINTER(c_double)
|
||||||
value = eatom.astype(np.double)
|
value = eatom.astype(np.double)
|
||||||
@ -366,12 +359,11 @@ class numpy_wrapper:
|
|||||||
:param eatom: per-atom potential energy
|
:param eatom: per-atom potential energy
|
||||||
:type: numpy.array
|
:type: numpy.array
|
||||||
"""
|
"""
|
||||||
import numpy as np
|
|
||||||
nlocal = self.lmp.extract_setting('nlocal')
|
nlocal = self.lmp.extract_setting('nlocal')
|
||||||
if len(vatom) < nlocal:
|
if len(vatom) < nlocal:
|
||||||
raise Exception('per-atom virial first dimension must be at least nlocal')
|
raise RuntimeError('per-atom virial first dimension must be at least nlocal')
|
||||||
if len(vatom[0]) != 6:
|
if len(vatom[0]) != 6:
|
||||||
raise Exception('per-atom virial second dimension must be 6')
|
raise RuntimeError('per-atom virial second dimension must be 6')
|
||||||
|
|
||||||
c_double_pp = np.ctypeslib.ndpointer(dtype=np.uintp, ndim=1, flags='C')
|
c_double_pp = np.ctypeslib.ndpointer(dtype=np.uintp, ndim=1, flags='C')
|
||||||
|
|
||||||
@ -422,8 +414,8 @@ class numpy_wrapper:
|
|||||||
# -------------------------------------------------------------------------
|
# -------------------------------------------------------------------------
|
||||||
|
|
||||||
def iarray(self, c_int_type, raw_ptr, nelem, dim=1):
|
def iarray(self, c_int_type, raw_ptr, nelem, dim=1):
|
||||||
|
# pylint: disable=C0116
|
||||||
if raw_ptr and nelem >= 0 and dim >= 0:
|
if raw_ptr and nelem >= 0 and dim >= 0:
|
||||||
import numpy as np
|
|
||||||
np_int_type = self._ctype_to_numpy_int(c_int_type)
|
np_int_type = self._ctype_to_numpy_int(c_int_type)
|
||||||
ptr = None
|
ptr = None
|
||||||
|
|
||||||
@ -440,15 +432,15 @@ class numpy_wrapper:
|
|||||||
if dim > 1:
|
if dim > 1:
|
||||||
a.shape = (nelem, dim)
|
a.shape = (nelem, dim)
|
||||||
else:
|
else:
|
||||||
a.shape = (nelem)
|
a.shape = nelem
|
||||||
return a
|
return a
|
||||||
return None
|
return None
|
||||||
|
|
||||||
# -------------------------------------------------------------------------
|
# -------------------------------------------------------------------------
|
||||||
|
|
||||||
def darray(self, raw_ptr, nelem, dim=1):
|
def darray(self, raw_ptr, nelem, dim=1):
|
||||||
|
# pylint: disable=C0116
|
||||||
if raw_ptr and nelem >= 0 and dim >= 0:
|
if raw_ptr and nelem >= 0 and dim >= 0:
|
||||||
import numpy as np
|
|
||||||
ptr = None
|
ptr = None
|
||||||
|
|
||||||
if dim == 1:
|
if dim == 1:
|
||||||
@ -464,7 +456,7 @@ class numpy_wrapper:
|
|||||||
if dim > 1:
|
if dim > 1:
|
||||||
a.shape = (nelem, dim)
|
a.shape = (nelem, dim)
|
||||||
else:
|
else:
|
||||||
a.shape = (nelem)
|
a.shape = nelem
|
||||||
return a
|
return a
|
||||||
return None
|
return None
|
||||||
|
|
||||||
@ -485,9 +477,6 @@ class NumPyNeighList(NeighList):
|
|||||||
:param idx: neighbor list index
|
:param idx: neighbor list index
|
||||||
:type idx: int
|
:type idx: int
|
||||||
"""
|
"""
|
||||||
def __init__(self, lmp, idx):
|
|
||||||
super(NumPyNeighList, self).__init__(lmp, idx)
|
|
||||||
|
|
||||||
def get(self, element):
|
def get(self, element):
|
||||||
"""
|
"""
|
||||||
Access a specific neighbor list entry. "element" must be a number from 0 to the size-1 of the list
|
Access a specific neighbor list entry. "element" must be a number from 0 to the size-1 of the list
|
||||||
|
|||||||
Reference in New Issue
Block a user