Merge branch 'master' of https://github.com/cmelab/cmeutils into tens…

…ile-test

.pre-commit-config.yaml

@@ -32,7 +32,7 @@ repos:
       exclude: 'cmeuitls/tests/assets/.* '
 
 - repo: https://github.com/pycqa/flake8
-  rev: 7.0.0
+  rev: 7.1.0
   hooks:
     - id: flake8
       args:

cmeutils/__version__.py

@@ -1 +1 @@
-__version__ = "1.1.1"
+__version__ = "1.3.0"

cmeutils/gsd_utils.py

@@ -129,11 +129,21 @@ def get_molecule_cluster(gsd_file=None, snap=None, gsd_frame=-1):
     n_query_points = n_points = snap.particles.N
     query_point_indices = snap.bonds.group[:, 0]
     point_indices = snap.bonds.group[:, 1]
-    distances = system.box.compute_distances(
-        system.points[query_point_indices], system.points[point_indices]
+    box = freud.box.Box(
+        snap.configuration.box[0],
+        snap.configuration.box[1],
+        snap.configuration.box[2],
+    )
+    vectors = box.wrap(
+        snap.particles.position[query_point_indices]
+        - snap.particles.position[point_indices]
     )
     nlist = freud.NeighborList.from_arrays(
-        n_query_points, n_points, query_point_indices, point_indices, distances
+        num_query_points=n_query_points,
+        num_points=n_points,
+        query_point_indices=query_point_indices,
+        point_indices=point_indices,
+        vectors=vectors,
     )
     cluster = freud.cluster.Cluster()
     cluster.compute(system=system, neighbors=nlist)

cmeutils/sampling.py

@@ -103,3 +103,23 @@ def is_equilibrated(data, threshold_fraction=0.50, threshold_neff=50, nskip=1):
         return [True, t0, g, Neff]
     else:
         return [False, None, None, None]
+
+
+def autocorr1D(array):
+    """Takes in a linear np array, performs autocorrelation
+    function and returns normalized array with half the length
+    of the input.
+
+    Parameters
+    ----------
+    data : numpy.typing.Arraylike, required
+        1-D series of data to perform autocorrelation on.
+
+    Returns
+    -------
+    1D np.array
+
+    """
+    ft = np.fft.rfft(array - np.average(array))
+    acorr = np.fft.irfft(ft * np.conjugate(ft)) / (len(array) * np.var(array))
+    return acorr[0 : len(acorr) // 2]  # noqa: E203

cmeutils/tests/test_gsd.py

@@ -50,7 +50,7 @@ def test_ellipsoid_gsd(self, butane_gsd):
     def test_create_rigid_snapshot(self):
         benzene = mb.load("c1ccccc1", smiles=True)
         benzene.name = "Benzene"
-        box = mb.fill_box(benzene, 5, box=[1, 1, 1])
+        box = mb.fill_box(benzene, 5, box=[3, 3, 3], seed=42)
         box.label_rigid_bodies(discrete_bodies="Benzene")
 
         rigid_init_snap = create_rigid_snapshot(box)
@@ -60,7 +60,7 @@ def test_create_rigid_snapshot(self):
     def test_update_rigid_snapshot(self):
         benzene = mb.load("c1ccccc1", smiles=True)
         benzene.name = "Benzene"
-        box = mb.fill_box(benzene, 5, box=[1, 1, 1])
+        box = mb.fill_box(benzene, 5, box=[3, 3, 3], seed=42)
         box.label_rigid_bodies(discrete_bodies="Benzene")
 
         rigid_init_snap = create_rigid_snapshot(box)

cmeutils/tests/test_sampling.py

@@ -1,7 +1,8 @@
 import numpy as np
 import pytest
+import scipy
 
-from cmeutils.sampling import equil_sample, is_equilibrated
+from cmeutils.sampling import autocorr1D, equil_sample, is_equilibrated
 from cmeutils.tests.base_test import BaseTest
 
 
@@ -82,3 +83,28 @@ def test_trim_high_threshold(self, correlated_data_tau100_n10000):
             [equil_data, uncorr_indices, prod_start, Neff] = equil_sample(
                 data, threshold_fraction=0.75, threshold_neff=10000
             )
+
+    def test_autocorr1D(self):
+        randoms = np.random.randint(100, size=(100))
+        integers = np.array(np.arange(100))
+
+        autocorrelation_randoms = autocorr1D(randoms)
+        autocorrelation_integers = autocorr1D(integers)
+
+        def expfunc(x, a):
+            return np.exp(-x / a)
+
+        x_values = np.array(range(len(autocorrelation_integers)))
+
+        exp_coeff_randoms = scipy.optimize.curve_fit(
+            expfunc, x_values, autocorrelation_randoms
+        )[0][0]
+        exp_coeff_int = scipy.optimize.curve_fit(
+            expfunc, x_values, autocorrelation_integers
+        )[0][0]
+
+        assert (
+            round(autocorrelation_integers[0], 10)
+            and round(autocorrelation_randoms[0], 10) == 1
+        )
+        assert exp_coeff_int > 5 and exp_coeff_randoms < 1

environment-dev.yml

@@ -2,15 +2,15 @@ name: cmeutils-dev
 channels:
   - conda-forge
 dependencies:
-  - freud =2.13.2
+  - freud >= 3.0
   - gmso >=0.11.2
   - fresnel >=0.13.5
   - gsd >=3.0
   - hoomd >=4.0
   - mbuild >=0.16.4
   - numpy
   - pip
-  - python >= 3.10
+  - python >= 3.10,<3.12
   - matplotlib
   - pre-commit
   - pymbar >= 4.0

environment.yml

@@ -2,15 +2,15 @@ name: cmeutils
 channels:
   - conda-forge
 dependencies:
-  - freud =2.13.2
+  - freud >= 3.0
   - gmso >=0.11.2
   - fresnel >=0.13.5
   - gsd >=3.0
   - hoomd >=4.0
   - mbuild >=0.16.4
   - numpy
   - pip
-  - python >= 3.10
+  - python >= 3.10,<3.12
   - matplotlib
   - pymbar >= 4.0
   - rowan