updated requirements, removed unnecessary numpy patch

.circleci/config.yml

@@ -21,7 +21,7 @@ jobs:
     # The executor is the environment in which the steps below will be executed - below will use a python 3.8 container
     # Change the version below to your required version of python
     docker:
-      - image: cimg/python:3.9
+      - image: cimg/python:3.10
     # Checkout the code as the first step. This is a dedicated CircleCI step.
     # The python orb's install-packages step will install the dependencies from a Pipfile via Pipenv by default.
     # Here we're making sure we use just use the system-wide pip. By default it uses the project root's requirements.txt.

.github/workflows/python-package.yml

@@ -12,10 +12,10 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: ["3.9", "3.11"]
+        python-version: ["3.10", "3.11", "3.12"]
         include:
-          - python-version: "3.9"
-            numpy-version: "1.26.4"
+          - python-version: "3.10"
+            numpy-version: "2.1.3"
 
     steps:
     - uses: actions/checkout@v2
@@ -29,9 +29,9 @@ jobs:
       run: |
         python -m pip install --upgrade pip
         python -m pip install flake8 pytest
-        python -m pip install mdtraj
+        #python -m pip install mdtraj # removed as long as mdtraj is not supporting numpy2
         python -m pip install .
-                if [ -f requirements.txt ]; then pip install --upgrade -r requirements.txt; fi
+        if [ -f requirements.txt ]; then pip install --upgrade -r requirements.txt; fi
     #- name: Lint with flake8
      # run: |
      #   # stop the build if there are Python syntax errors or undefined names

pytim/__init__.py

@@ -10,9 +10,11 @@
 from . import observables, utilities, datafiles
 from .version import __version__
 import warnings
-from .patches import patchNumpy, patchMDTRAJ_ReplacementTables
-patchNumpy()
-patchMDTRAJ_ReplacementTables()
+try: # waiting for numpy2 support in mdtraj>=1.10.2
+    from .patches import patchMDTRAJ_ReplacementTables
+    patchMDTRAJ_ReplacementTables()
+except:
+    pass
 
 warnings.filterwarnings(
     "ignore",

pytim/examples/example_mdtraj.py

@@ -6,12 +6,14 @@
     loaded with MDTraj (http://mdtraj.org/)
     (see also the openmm interoperability)
 """
+try:
+    import mdtraj
+    import pytim
+    from pytim.datafiles import WATER_GRO, WATER_XTC
 
-import mdtraj
-import pytim
-from pytim.datafiles import WATER_GRO, WATER_XTC
-
-t = mdtraj.load_xtc(WATER_XTC, top=WATER_GRO)
-inter = pytim.ITIM(t)
-for step in t[:]:
-    print("surface atoms: "+repr(inter.atoms.indices))
+    t = mdtraj.load_xtc(WATER_XTC, top=WATER_GRO)
+    inter = pytim.ITIM(t)
+    for step in t[:]:
+        print("surface atoms: "+repr(inter.atoms.indices))
+except:
+    pass # for package testing, in case mdtraj is not available or has compatibility issues

pytim/interface.py

@@ -541,7 +541,10 @@ def _():
 
         >>> # mdtraj
         >>> try:
+        ...     from packaging.version import Version
         ...     import mdtraj
+        ...     if Version(mdtraj.__version__) < Version('1.10.2'): # numpy2 support
+        ...         pass
         ...     try:
         ...         import numpy as np
         ...         import MDAnalysis as mda

pytim/itim.py

@@ -132,11 +132,14 @@ class ITIM(Interface):
 
 
         >>> # pytim can be used also on top of mdtraj (MDAnalysis must be present,though)
-        >>> import mdtraj
-        >>> import pytim
-        >>> from pytim.datafiles import WATER_GRO, WATER_XTC
-        >>> t = mdtraj.load_xtc(WATER_XTC,top=WATER_GRO)
-        >>> inter = pytim.ITIM(t)
+        >>> try:
+        ...     import mdtraj
+        ...     import pytim
+        ...     from pytim.datafiles import WATER_GRO, WATER_XTC
+        ...     t = mdtraj.load_xtc(WATER_XTC,top=WATER_GRO)
+        ...     inter = pytim.ITIM(t)
+        ... except (ModuleNotFoundError,ValueError): # ValueError to handle mdtraj not supporting numpy2
+        ...     pass
 
 
         .. _MDAnalysis: http://www.mdanalysis.org/

pytim/observables/basic_observables.py

@@ -159,12 +159,12 @@ class Distance(Observable):
         >>> u = mda.Universe(pytim.datafiles.WATER_GRO)
         >>> d1 = pytim.observables.Distance().compute(u.atoms[:9],u.atoms[:9])
         >>> d2 = pytim.observables.RelativePosition(spherical=True).compute(u.atoms[:9],u.atoms[:9])[:,0]
-        >>> np.all(np.isclose(d1,d2))
+        >>> all(np.isclose(d1,d2))
         True
 
         >>> d1 = pytim.observables.Distance('xy').compute(u.atoms[:9],u.atoms[:9])
         >>> d2 = pytim.observables.RelativePosition('xy',spherical=True).compute(u.atoms[:9],u.atoms[:9])[:,0]
-        >>> np.all(np.isclose(d1,d2))
+        >>> all(np.isclose(d1,d2))
         True
 
     """

pytim/observables/contactangle.py

@@ -52,24 +52,24 @@ class ContactAngle(object):
         >>> for ts in u.trajectory[::]:
         ...     CA.sample()
         >>> # Instantaneous contact angle (last frame) by fitting a circle...
-        >>> np.round(CA.contact_angle,2)
+        >>> print(np.round(CA.contact_angle,2))
         90.58
 
         >>> 
         >>> # ... and using an elliptical fit:
         >>> left, right = CA.contact_angles
         >>> # left angle
-        >>> np.round(np.abs(left),2)
+        >>> print(np.round(np.abs(left),2))
         79.95
 
         >>> # right angle
-        >>> np.round(right,2)
+        >>> print(np.round(right,2))
         83.84
 
         >>> # Contact angles from the averaged binned statistics of
         >>> # surface atoms' radial distance as a function of the azimuthal angle
-        >>> list(np.round(CA.mean_contact_angles,2))
-        [96.2, 100.68]
+        >>> np.around(CA.mean_contact_angles,1).tolist()
+        [96.2, 100.7]
 
    """
 

pytim/observables/correlator.py

@@ -138,7 +138,7 @@ def sample(self, group):
                 RuntimeError(
                     'Cannot compute survival probability without a reference')
             sampled = self.observable.compute(group)
-            self.timeseries.append(list(sampled.flatten()))
+            self.timeseries.append(sampled.flatten().tolist())
 
         if self.shape is None:
             self.shape = sampled.shape
@@ -149,13 +149,13 @@ def _sample_intermittent(self, group):
         # the residence function (1 if in the reference group, 0 otherwise)
         mask = np.isin(self.reference, group)
         # append the residence function to its timeseries
-        self.maskseries.append(list(mask))
+        self.maskseries.append(mask.tolist())
         if self.observable is not None:
             # this copies a vector of zeros with the correct shape
             sampled = self.reference_obs.copy()
             obs = self.observable.compute(group)
             sampled[np.where(mask)] = obs
-            self.timeseries.append(list(sampled.flatten()))
+            self.timeseries.append(sampled.flatten().tolist())
         else:
             self.timeseries = self.maskseries
             if self.shape is None:
@@ -249,7 +249,7 @@ def correlation(self, normalized=True, continuous=True):
             >>> print (np.allclose(corr, [ c0, c1, c2, c3]))
             True
             >>> # check normalization
-            >>> np.all(vv.correlation(continuous=False) == corr/corr[0])
+            >>> print(np.all(vv.correlation(continuous=False) == corr/corr[0]))
             True
             >>> # not normalizd, continuous
             >>> corr = vv.correlation(normalized=False,continuous=True)
@@ -258,7 +258,7 @@ def correlation(self, normalized=True, continuous=True):
             >>> print (np.allclose(corr, [ c0, c1, c2, c3]))
             True
             >>> # check normalization
-            >>> np.all(vv.correlation(continuous=True) == corr/corr[0])
+            >>> print(np.all(vv.correlation(continuous=True) == corr/corr[0]))
             True
 
         """

pytim/observables/distributionfunction.py

@@ -304,7 +304,7 @@ def sample(self, g1=None, g2=None, kargs1=None, kargs2=None):
         self.count += count
 
         box = self.universe.dimensions
-        self.volume += np.product(box[:3])
+        self.volume += np.prod(box[:3])
         if self.g2 is None or len(self.g2) == 0:
             self.n_normalize += len(self.g1)
         else:

pytim/observables/free_volume.py

@@ -48,11 +48,11 @@ class FreeVolume(object):
         >>> FV = pytim.observables.FreeVolume(u,npoints = nsamples)
         >>> np.random.seed(1) # ensure reproducibility of test
         >>> free, err = FV.compute()
-        >>> np.isclose(free,1.0-0.6802,rtol=1e-3)
+        >>> print(np.isclose(free,1.0-0.6802,rtol=1e-3))
         True
         >>> np.random.seed(1) # ensure reproducibility of test
         >>> lst, _ = FV._compute()
-        >>> np.isclose(free,1.0-len(lst)*1.0/nsamples, rtol=1e-6)
+        >>> print(np.isclose(free,1.0-len(lst)*1.0/nsamples, rtol=1e-6))
         True
 
     """

pytim/observables/orientation.py

@@ -109,7 +109,7 @@ def compute(self, inp, **kargs):
         >>> condition = np.logical_and(u.atoms.sides==0,u.atoms.layers==1)
         >>> group = u.atoms[condition]
         >>> costheta, phi = biv.compute(group)
-        >>> np.all(np.isclose([costheta[0],phi[0]], [0.6533759236335754, 0.10778185716460659]))
+        >>> print(all(np.isclose([costheta[0],phi[0]], [0.6533759236335754, 0.10778185716460659])))
         True
     """
 

pytim/observables/profile.py

@@ -311,18 +311,19 @@ def _():
         >>> inter = pytim.ITIM(u,cluster_cut=3.5,alpha=2.5)
         >>> print(inter.normal)
         2
-        >>> np.set_printoptions(precision=8)
         >>> np.random.seed(1) # for the MC normalization
         >>> stdprof = pytim.observables.Profile()
         >>> stdprof.sample(u.atoms)
-        >>> print(stdprof.get_values(binwidth=0.5)[2][:6])
-        [0.09229169 0.10959639 0.08075523 0.10959639 0.09805993 0.09805993]
+        >>> vals = stdprof.get_values(binwidth=0.5)[2]
+        >>> print(np.around(vals[:6],decimals=3))
+        [0.092 0.11  0.081 0.11  0.098 0.098]
+
 
         >>> prof = pytim.observables.Profile(interface=inter)
         >>> prof.sample(u.atoms)
         >>> vals = prof.get_values(binwidth=0.5)[2]
-        >>> print(vals[len(vals)//2-3:len(vals)//2+3])
-        [0.07344066 0.04300743 0.02803522        inf 0.         0.        ]
+        >>> print(np.around(vals[len(vals)//2-3:len(vals)//2+3],decimals=3))
+        [0.073 0.043 0.028   inf 0.    0.   ]
 
 
 
@@ -353,15 +354,15 @@ def _():
         >>> np.random.seed(1) # for the MC normalization
         >>> stdprof = pytim.observables.Profile()
         >>> stdprof.sample(u.atoms)
-        >>> print(stdprof.get_values(binwidth=0.5)[2][:6])
-        [0.09229169 0.10959639 0.08075523 0.10959639 0.09805993 0.09805993]
+        >>> vals = stdprof.get_values(binwidth=0.5)[2]
+        >>> print(np.around(vals[:6],decimals=3))
+        [0.092 0.11  0.081 0.11  0.098 0.098]
 
         >>> prof = pytim.observables.Profile(interface=inter)
         >>> prof.sample(u.atoms)
         >>> vals = prof.get_values(binwidth=1.0)[2]
-        >>> print(vals[len(vals)//2-4:len(vals)//2+2])
-        [0.09554818 0.09796541 0.05555127 0.                inf 0.        ]
-
+        >>> print(np.around(vals[len(vals)//2-4:len(vals)//2+2],decimals=3))
+        [0.096 0.098 0.056 0.      inf 0.   ]
 
         """
 

pytim/patches.py

@@ -2,25 +2,14 @@
 # vim: tabstop=4 expandtab shiftwidth=4 softtabstop=4
 from __future__ import print_function
 
-
-def patchNumpy():
-    # this try/except block patches numpy and provides _validate_lengths
-    # to skimage<=1.14.1
-    import numpy
-    try:
-        numpy.lib.arraypad._validate_lengths
-    except AttributeError:
-        def patch_validate_lengths(ar, crop_width):
-            return numpy.lib.arraypad._as_pairs(crop_width, ar.ndim, as_index=True)
-        numpy.lib.arraypad._validate_lengths = patch_validate_lengths
-
-
 def patchTrajectory(trajectory, interface):
     """ Patch the MDAnalysis trajectory class
 
         this patch makes the layer assignement being automatically
         called whenever a new frame is loaded.
     """
+    from importlib.metadata import version
+    if  int(version('numpy').split('.')[0])<2 : return
     try:
         trajectory.interface
         trajectory.interface = interface
@@ -110,11 +99,14 @@ def patchMDTRAJ(trajectory, universe):
 
         Example:
 
-        >>> import mdtraj
-        >>> import pytim
-        >>> from pytim.datafiles import WATER_GRO, WATER_XTC
-        >>> t = mdtraj.load_xtc(WATER_XTC,top=WATER_GRO)
-        >>> inter = pytim.ITIM(t)
+        >>> try:
+        ...     import mdtraj
+        ...     import pytim
+        ...     from pytim.datafiles import WATER_GRO, WATER_XTC
+        ...     t = mdtraj.load_xtc(WATER_XTC,top=WATER_GRO)
+        ...     inter = pytim.ITIM(t)
+        ... except:
+        ...     pass
 
 
     """

pytim/sanity_check.py

@@ -77,7 +77,7 @@ def _check_universe(self, input_obj):
                 patchMDTRAJ(input_obj, self.interface.universe)
                 os.remove(_file.name)
                 return 'mdtraj'
-        except ImportError:
+        except (ImportError,ValueError): # ValueError to handle mdtraj not supporting numpy2
             pass
         try:
             import os

pytim/utilities_dbscan.py

@@ -104,7 +104,7 @@ def _():
     >>> print (np.sort(c2)[-2:])
     [   0 9335]
 
-    >>> print ((np.all(c1==c2), np.all(l1==l2)))
+    >>> print ((all(c1==c2), all(l1==l2)))
     (True, True)
 
     """

pytim/utilities_geometry.py

@@ -62,12 +62,12 @@ def polygonalArea(points):
         >>> s1 = np.sin(2*np.pi/5.) ; s2 = np.sin(4*np.pi/5.)
         >>> pentagon = np.array([[1,0,0],[c1,s1,0],[-c2,s2,0],[-c2,-s2,0],[c1,-s1,0]])
         >>> A = 0.25 * np.sqrt(25+10*np.sqrt(5)) * 100./ (50+10*np.sqrt(5))
-        >>> np.isclose(pytim.utilities.polygonalArea(pentagon),A)
+        >>> print(np.isclose(pytim.utilities.polygonalArea(pentagon),A))
         True
 
         >>> # now let's rotate it:
         >>> rotated = np.dot(EulerRotation(0,np.pi/2.,0),pentagon.T).T
-        >>> np.isclose(pytim.utilities.polygonalArea(rotated),A)
+        >>> print(np.isclose(pytim.utilities.polygonalArea(rotated),A))
         True
 
      """

requirements.testing.txt

@@ -1,4 +1,4 @@
-mdtraj
 pytest==8.1.1
 codecov==2.1.13
 pytest-cov==5.0.0 
+packaging

requirements.txt

@@ -1,10 +1,11 @@
 cython>=0.24.1
-numpy>=1.26.4,<2.0.0
+numpy>=2.1.3
 scipy>=1.6.0
 gsd>3.0.0
-MDAnalysis>=2.7.0
+setuptools
+MDAnalysis>=2.8.0
 PyWavelets>=0.5.2
-scikit-image>=0.14.2
+scikit-image>=0.24.0
 sphinx>=1.4.3 
 matplotlib
 pytest

setup.py

@@ -125,14 +125,14 @@ def run_tests(self):
     # requirements files see:
     # https://packaging.python.org/en/latest/requirements.html
     install_requires=[
-        'numpy>=1.26.4,<2.0.0', 'cython>=0.24.1','gsd>=3.0.0','MDAnalysis>=2.7.0'
+        'numpy>=2.1.3', 'cython>=0.24.1','gsd>=3.0.0','MDAnalysis>=2.8.0'
     ],
 
     # List additional groups of dependencies here (e.g. development
     # dependencies). You can install these using the following syntax,
     # for example:
     # $ pip install -e .[dev,test]
-    tests_require=['nose>=1.3.7', 'coverage'],
+    tests_require=['nose>=1.3.7', 'coverage', 'scikit-image'],
     # If there are data files included in your packages that need to be
     # installed, specify them here.  If using Python 2.6 or less, then these
     # have to be included in MANIFEST.in as well.