have to wrap delta with np.array to make sure it is ndarray and result stays CoordinateFrame

src/mdevaluate/__init__.py

@@ -16,7 +16,7 @@ from . import reader
 from . import system
 from . import utils
 from . import extra
-from .logging_util import logger
+from .logging import logger
 
 
 def open(

src/mdevaluate/autosave.py

@@ -5,7 +5,7 @@ from typing import Optional, Callable, Iterable
 
 import numpy as np
 from .checksum import checksum
-from .logging_util import logger
+from .logging import logger
 
 autosave_directory: Optional[str] = None
 load_autosave_data = False
@@ -166,10 +166,8 @@ def autosave_data(
         @functools.wraps(function)
         def autosave(*args, **kwargs):
             description = kwargs.pop("description", "")
-            autosave_dir_overwrite = kwargs.pop("autosave_dir_overwrite", None)
-            autosave_dir = autosave_dir_overwrite if autosave_dir_overwrite is not None else autosave_directory
             autoload = kwargs.pop("autoload", True) and load_autosave_data
-            if autosave_dir is not None:
+            if autosave_directory is not None:
                 relevant_args = list(args[:nargs])
                 if kwargs_keys is not None:
                     for key in [*posargs_keys, *kwargs_keys]:

src/mdevaluate/checksum.py

@@ -1,14 +1,9 @@
 import functools
 import hashlib
-from .logging_util import logger
+from .logging import logger
 from types import ModuleType, FunctionType
 import inspect
 from typing import Iterable
-import ast
-import io
-import tokenize
-import re
-import textwrap
 
 import numpy as np
 
@@ -33,46 +28,19 @@ def version(version_nr: int, calls: Iterable = ()):
     return decorator
 
 
-def strip_comments(source: str) -> str:
-    """Removes docstrings, comments, and irrelevant whitespace from Python source code."""
-
-    # Step 1: Remove docstrings using AST
-    def remove_docstrings(node):
-        if isinstance(node, (ast.FunctionDef, ast.AsyncFunctionDef, ast.ClassDef, ast.Module)):
-            if (doc := ast.get_docstring(node, clean=False)):
-                first_stmt = node.body[0]
-                if isinstance(first_stmt, ast.Expr) and isinstance(first_stmt.value, ast.Constant):
-                    node.body.pop(0)  # Remove the docstring entirely
-        for child in ast.iter_child_nodes(node):
-            remove_docstrings(child)
-
-    tree = ast.parse(textwrap.dedent(source))
-    remove_docstrings(tree)
-    code_without_docstrings = ast.unparse(tree)
-
-    # Step 2: Remove comments using tokenize
-    tokens = tokenize.generate_tokens(io.StringIO(code_without_docstrings).readline)
-    result = []
-    last_lineno = -1
-    last_col = 0
-
-    for toknum, tokval, (srow, scol), (erow, ecol), line in tokens:
-        if toknum == tokenize.COMMENT:
+def strip_comments(s: str):
+    """Strips comment lines and docstring from Python source string."""
+    o = ""
+    in_docstring = False
+    for l in s.split("\n"):
+        if l.strip().startswith(("#", '"', "'")) or in_docstring:
+            in_docstring = l.strip().startswith(('"""', "'''")) + in_docstring == 1
             continue
-        if srow > last_lineno:
-            last_col = 0
-        if scol > last_col:
-            result.append(" " * (scol - last_col))
-        result.append(tokval)
-        last_lineno, last_col = erow, ecol
-
-    code_no_comments = ''.join(result)
-
-    # Step 3: Remove empty lines (whitespace-only or truly blank)
-    return "\n".join([line for line in code_no_comments.splitlines() if line.strip() != ""])
+        o += l + "\n"
+    return o
 
 
-def checksum(*args, csum=None, _seen=None):
+def checksum(*args, csum=None):
     """
     Calculate a checksum of any object, by sha1 hash.
 
@@ -92,15 +60,7 @@ def checksum(*args, csum=None, _seen=None):
         csum = hashlib.sha1()
         csum.update(str(SALT).encode())
 
-    if _seen is None:
-        _seen = set()
-
     for arg in args:
-        obj_id = id(arg)
-        if obj_id in _seen:
-            continue
-        _seen.add(obj_id)
-
         if hasattr(arg, "__checksum__"):
             logger.debug("Checksum via __checksum__: %s", str(arg))
             csum.update(str(arg.__checksum__()).encode())
@@ -117,15 +77,15 @@ def checksum(*args, csum=None, _seen=None):
             for key in sorted(merged):  # deterministic ordering
                 v = merged[key]
                 if v is not arg:
-                    checksum(v, csum=csum, _seen=_seen)
+                    checksum(v, csum=csum)
         elif isinstance(arg, functools.partial):
             logger.debug("Checksum via partial for %s", str(arg))
-            checksum(arg.func, csum=csum, _seen=_seen)
+            checksum(arg.func, csum=csum)
             for x in arg.args:
-                checksum(x, csum=csum, _seen=_seen)
+                checksum(x, csum=csum)
             for k in sorted(arg.keywords.keys()):
                 csum.update(k.encode())
-                checksum(arg.keywords[k], csum=csum, _seen=_seen)
+                checksum(arg.keywords[k], csum=csum)
         elif isinstance(arg, np.ndarray):
             csum.update(arg.tobytes())
         else:

src/mdevaluate/coordinates.py

@@ -1,6 +1,6 @@
 from functools import partial, wraps
 from copy import copy
-from .logging_util import logger
+from .logging import logger
 from typing import Optional, Callable, List, Tuple
 
 import numpy as np

src/mdevaluate/correlation.py

@@ -18,7 +18,7 @@ def log_indices(first: int, last: int, num: int = 100) -> np.ndarray:
     return np.unique(np.int_(ls) - 1 + first)
 
 
-@autosave_data(nargs=2, kwargs_keys=('selector', 'segments', 'skip', 'window', 'average', 'points',), version=1.0)
+@autosave_data(2)
 def shifted_correlation(
     function: Callable,
     frames: Coordinates,
@@ -199,7 +199,7 @@ def msd(
         raise ValueError('Parameter axis has to be ether "all", "x", "y", or "z"!')
 
 
-def isf_raw(
+def isf(
     start_frame: CoordinateFrame,
     end_frame: CoordinateFrame,
     q: float = 22.7,
@@ -216,59 +216,29 @@ def isf_raw(
         displacements = displacements_without_drift(start_frame, end_frame, trajectory)
     if axis == "all":
         distance = (displacements**2).sum(axis=1) ** 0.5
-        return np.sinc(distance * q / np.pi)
+        return np.sinc(distance * q / np.pi).mean()
     elif axis == "xy" or axis == "yx":
         distance = (displacements[:, [0, 1]]**2).sum(axis=1) ** 0.5
-        return np.real(jn(0, distance * q))
+        return np.real(jn(0, distance * q)).mean()
     elif axis == "xz" or axis == "zx":
         distance = (displacements[:, [0, 2]]**2).sum(axis=1) ** 0.5
-        return np.real(jn(0, distance * q))
+        return np.real(jn(0, distance * q)).mean()
     elif axis == "yz" or axis == "zy":
         distance = (displacements[:, [1, 2]]**2).sum(axis=1) ** 0.5
-        return np.real(jn(0, distance * q))
+        return np.real(jn(0, distance * q)).mean()
     elif axis == "x":
         distance = np.abs(displacements[:, 0])
-        return np.cos(np.abs(q * distance))
+        return np.mean(np.cos(np.abs(q * distance)))
     elif axis == "y":
         distance = np.abs(displacements[:, 1])
-        return np.cos(np.abs(q * distance))
+        return np.mean(np.cos(np.abs(q * distance)))
     elif axis == "z":
         distance = np.abs(displacements[:, 2])
-        return np.cos(np.abs(q * distance))
+        return np.mean(np.cos(np.abs(q * distance)))
     else:
         raise ValueError('Parameter axis has to be ether "all", "x", "y", or "z"!')
 
 
-def isf(
-    start_frame: CoordinateFrame,
-    end_frame: CoordinateFrame,
-    q: float = 22.7,
-    trajectory: Coordinates = None,
-    axis: str = "all",
-) -> float:
-    """
-    Incoherent intermediate scattering function averaged over all particles.
-    See isf_raw for details.
-    """
-    return isf_raw(start_frame, end_frame, q=q, trajectory=trajectory, axis=axis).mean()
-
-
-def isf_mean_var(
-    start_frame: CoordinateFrame,
-    end_frame: CoordinateFrame,
-    q: float = 22.7,
-    trajectory: Coordinates = None,
-    axis: str = "all",
-) -> float:
-    """
-    Incoherent intermediate scattering function averaged over all particles and the
-    variance.
-    See isf_raw for details.
-    """
-    values = isf_raw(start_frame, end_frame, q=q, trajectory=trajectory, axis=axis)
-    return values.mean(), values.var()
-
-
 def rotational_autocorrelation(
     start_frame: CoordinateFrame, end_frame: CoordinateFrame, order: int = 2
 ) -> float:
@@ -460,11 +430,10 @@ def non_gaussian_parameter(
     end_frame: CoordinateFrame,
     trajectory: Coordinates = None,
     axis: str = "all",
-    full_output = False,
 ) -> float:
-    r"""
+    """
     Calculate the non-Gaussian parameter.
-    .. math:
+    ..math:
       \alpha_2 (t) =
         \frac{3}{5}\frac{\langle r_i^4(t)\rangle}{\langle r_i^2(t)\rangle^2} - 1
     """
@@ -473,41 +442,27 @@ def non_gaussian_parameter(
     else:
         vectors = displacements_without_drift(start_frame, end_frame, trajectory)
     if axis == "all":
-        r2 = (vectors**2).sum(axis=1)
+        r = (vectors**2).sum(axis=1)
         dimensions = 3
     elif axis == "xy" or axis == "yx":
-        r2 = (vectors[:, [0, 1]]**2).sum(axis=1)
+        r = (vectors[:, [0, 1]]**2).sum(axis=1)
         dimensions = 2
     elif axis == "xz" or axis == "zx":
-        r2 = (vectors[:, [0, 2]]**2).sum(axis=1)
+        r = (vectors[:, [0, 2]]**2).sum(axis=1)
         dimensions = 2
     elif axis == "yz" or axis == "zy":
-        r2 = (vectors[:, [1, 2]]**2).sum(axis=1)
+        r = (vectors[:, [1, 2]]**2).sum(axis=1)
         dimensions = 2
     elif axis == "x":
-        r2 = vectors[:, 0] ** 2
+        r = vectors[:, 0] ** 2
         dimensions = 1
     elif axis == "y":
-        r2 = vectors[:, 1] ** 2
+        r = vectors[:, 1] ** 2
         dimensions = 1
     elif axis == "z":
-        r2 = vectors[:, 2] ** 2
+        r = vectors[:, 2] ** 2
         dimensions = 1
     else:
         raise ValueError('Parameter axis has to be ether "all", "x", "y", or "z"!')
-    
-    m2 = np.mean(r2)
-    m4 = np.mean(r2**2)
-    if m2 == 0.0:
-        if full_output:
-            return 0.0, 0.0, 0.0
-        else:
-            return 0.0
-
-    alpha_2 = (m4 / ((1 + 2 / dimensions) * m2**2)) - 1
-    if full_output:
-        return alpha_2, m2, m4
-    else:
-        return alpha_2
-
 
+    return (np.mean(r**2) / ((1 + 2 / dimensions) * (np.mean(r) ** 2))) - 1

src/mdevaluate/pbc.py

@@ -7,7 +7,7 @@ from numpy.typing import ArrayLike, NDArray
 
 from itertools import product
 
-from .logging_util import logger
+from .logging import logger
 
 if TYPE_CHECKING:
     from mdevaluate.coordinates import CoordinateFrame
@@ -163,7 +163,7 @@ def nojump(frame: CoordinateFrame, usecache: bool = True) -> CoordinateFrame:
                 ).T
     
     delta = delta[selection, :]
-    delta = delta @ frame.box
+    delta = np.array(delta @ frame.box)
     return frame - delta
 
 

src/mdevaluate/reader.py

@@ -19,13 +19,13 @@ import MDAnalysis
 from scipy import sparse
 
 from .checksum import checksum
-from .logging_util import logger
+from .logging import logger
 from . import atoms
 from .coordinates import Coordinates
 
 CSR_ATTRS = ("data", "indices", "indptr")
 NOJUMP_MAGIC = 2016
-Group_RE = re.compile(r"\[ ([-+\w]+) \]")
+Group_RE = re.compile("\[ ([-+\w]+) \]")
 
 
 class NojumpError(Exception):
@@ -275,7 +275,7 @@ def load_nojump_matrices(reader: BaseReader):
                 "Loaded Nojump matrices: {}".format(nojump_load_filename(reader))
             )
         else:
-            logger.info("Invalid Nojump Data: {}".format(nojump_load_filename(reader)))
+            logger.info("Invlaid Nojump Data: {}".format(nojump_load_filename(reader)))
     except KeyError:
         logger.info("Removing zip-File: %s", zipname)
         os.remove(nojump_load_filename(reader))

src/mdevaluate/utils.py

@@ -14,7 +14,7 @@ from scipy.ndimage import uniform_filter1d
 from scipy.interpolate import interp1d
 from scipy.optimize import curve_fit
 
-from .logging_util import logger
+from .logging import logger
 from .functions import kww, kww_1e
 
 
@@ -357,37 +357,6 @@ def quick1etau(t: ArrayLike, C: ArrayLike, n: int = 7) -> float:
     return tau_est
 
 
-def quicknongaussfit(t, C, width=2):
-    """
-    Estimates the time and height of the peak in the non-Gaussian function.
-    C is C(t) the correlation function
-    """
-    def ffunc(t,y0,A_main,log_tau_main,sig_main):
-        main_peak = A_main*np.exp(-(t - log_tau_main)**2 / (2 * sig_main**2))
-        return y0 + main_peak
-    
-    # first rough estimate, the closest time. This is returned if the interpolation fails!
-    tau_est = t[np.argmax(C)]
-    nG_max = np.amax(C)
-    try:
-        with np.errstate(invalid='ignore'):
-            corr = C[t > 0]
-            time = np.log10(t[t > 0])
-            tau = time[np.argmax(corr)]
-            mask = (time>tau-width/2) & (time<tau+width/2)
-            time = time[mask] ; corr = corr[mask]
-            nG_min = C[t > 0].min()
-            guess = [nG_min, nG_max-nG_min, tau, 0.6]
-            popt = curve_fit(ffunc, time, corr, p0=guess, maxfev=10000)[0]
-            tau_est = 10**popt[-2]
-            nG_max = popt[0] + popt[1]
-    except:
-        pass
-    if np.isnan(tau_est):
-        tau_est = np.inf
-    return tau_est, nG_max
-
-
 def susceptibility(
     time: NDArray, correlation: NDArray, **kwargs
 ) -> tuple[NDArray, NDArray]: