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c09549902a
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refactor_l
Author | SHA1 | Date | |
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7585e598dc | |||
6d8b86c1ef | |||
a2a0ae8d7b | |||
90bd90a608 | |||
67d3e70a66 |
@ -16,7 +16,7 @@ from . import reader
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from . import system
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from . import utils
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from . import extra
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from .logging import logger
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from .logging_util import logger
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def open(
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@ -5,7 +5,7 @@ from typing import Optional, Callable, Iterable
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import numpy as np
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from .checksum import checksum
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from .logging import logger
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from .logging_util import logger
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autosave_directory: Optional[str] = None
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load_autosave_data = False
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@ -1,9 +1,14 @@
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import functools
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import hashlib
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from .logging import logger
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from .logging_util import logger
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from types import ModuleType, FunctionType
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import inspect
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from typing import Iterable
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import ast
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import io
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import tokenize
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import re
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import textwrap
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import numpy as np
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@ -28,16 +33,43 @@ def version(version_nr: int, calls: Iterable = ()):
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return decorator
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def strip_comments(s: str):
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"""Strips comment lines and docstring from Python source string."""
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o = ""
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in_docstring = False
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for l in s.split("\n"):
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if l.strip().startswith(("#", '"', "'")) or in_docstring:
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in_docstring = l.strip().startswith(('"""', "'''")) + in_docstring == 1
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def strip_comments(source: str) -> str:
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"""Removes docstrings, comments, and irrelevant whitespace from Python source code."""
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# Step 1: Remove docstrings using AST
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def remove_docstrings(node):
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if isinstance(node, (ast.FunctionDef, ast.AsyncFunctionDef, ast.ClassDef, ast.Module)):
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if (doc := ast.get_docstring(node, clean=False)):
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first_stmt = node.body[0]
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if isinstance(first_stmt, ast.Expr) and isinstance(first_stmt.value, ast.Constant):
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node.body.pop(0) # Remove the docstring entirely
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for child in ast.iter_child_nodes(node):
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remove_docstrings(child)
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tree = ast.parse(textwrap.dedent(source))
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remove_docstrings(tree)
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code_without_docstrings = ast.unparse(tree)
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# Step 2: Remove comments using tokenize
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tokens = tokenize.generate_tokens(io.StringIO(code_without_docstrings).readline)
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result = []
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last_lineno = -1
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last_col = 0
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for toknum, tokval, (srow, scol), (erow, ecol), line in tokens:
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if toknum == tokenize.COMMENT:
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continue
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o += l + "\n"
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return o
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if srow > last_lineno:
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last_col = 0
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if scol > last_col:
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result.append(" " * (scol - last_col))
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result.append(tokval)
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last_lineno, last_col = erow, ecol
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code_no_comments = ''.join(result)
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# Step 3: Remove empty lines (whitespace-only or truly blank)
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return "\n".join([line for line in code_no_comments.splitlines() if line.strip() != ""])
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def checksum(*args, csum=None):
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@ -73,7 +105,9 @@ def checksum(*args, csum=None):
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elif isinstance(arg, FunctionType):
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csum.update(strip_comments(inspect.getsource(arg)).encode())
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c = inspect.getclosurevars(arg)
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for v in {**c.nonlocals, **c.globals}.values():
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merged = {**c.nonlocals, **c.globals}
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for key in sorted(merged): # deterministic ordering
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v = merged[key]
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if v is not arg:
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checksum(v, csum=csum)
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elif isinstance(arg, functools.partial):
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@ -1,6 +1,6 @@
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from functools import partial, wraps
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from copy import copy
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from .logging import logger
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from .logging_util import logger
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from typing import Optional, Callable, List, Tuple
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import numpy as np
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@ -431,9 +431,9 @@ def non_gaussian_parameter(
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trajectory: Coordinates = None,
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axis: str = "all",
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) -> float:
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"""
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r"""
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Calculate the non-Gaussian parameter.
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..math:
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.. math:
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\alpha_2 (t) =
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\frac{3}{5}\frac{\langle r_i^4(t)\rangle}{\langle r_i^2(t)\rangle^2} - 1
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"""
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@ -7,7 +7,7 @@ from numpy.typing import ArrayLike, NDArray
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from itertools import product
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from .logging import logger
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from .logging_util import logger
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if TYPE_CHECKING:
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from mdevaluate.coordinates import CoordinateFrame
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@ -19,13 +19,13 @@ import MDAnalysis
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from scipy import sparse
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from .checksum import checksum
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from .logging import logger
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from .logging_util import logger
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from . import atoms
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from .coordinates import Coordinates
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CSR_ATTRS = ("data", "indices", "indptr")
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NOJUMP_MAGIC = 2016
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Group_RE = re.compile("\[ ([-+\w]+) \]")
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Group_RE = re.compile(r"\[ ([-+\w]+) \]")
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class NojumpError(Exception):
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@ -14,7 +14,7 @@ from scipy.ndimage import uniform_filter1d
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from scipy.interpolate import interp1d
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from scipy.optimize import curve_fit
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from .logging import logger
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from .logging_util import logger
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from .functions import kww, kww_1e
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