Merge remote-tracking branch 'origin/nanobind' into nanobind-order

.github/workflows/build_wheels.yml

@@ -8,6 +8,7 @@ on:
   push:
     branches:
       - "main"
+      - "nanobind"
       - "next"
     tags:
       - "*"

.github/workflows/run_tests.yml

@@ -10,6 +10,7 @@ on:
   push:
     branches:
       - "main"
+      - "nanobind"
       - "next"
 
   workflow_dispatch:

.github/workflows/style_check.yml

@@ -10,6 +10,7 @@ on:
   push:
     branches:
       - "main"
+      - "nanobind"
 
   workflow_dispatch:
 

.github/workflows/update-conda-lockfiles.yaml

@@ -0,0 +1,15 @@
+name: Update conda lockfiles
+
+on:
+  schedule:
+    - cron: '0 12 1 * *'
+
+  workflow_dispatch:
+
+jobs:
+  update:
+    uses: glotzerlab/workflows/.github/workflows/update-conda-lockfiles.yaml@ea2e25d07af862a1c696a932c2bd6b242d142049 # 0.2.0
+    secrets: inherit
+    with:
+      branch: main
+      path: .github/workflows/env-files

doc/source/reference/credits.rst

@@ -405,11 +405,9 @@ Philipp Schönhöfer
 * Contributed code, design, documentation, and testing for ``freud.locality.FilterRAD`` class.
 
 Suraj Kannur
-
-* Ported ``Interface`` to pure python with Corwin Kerr.
-
 Trevor Teague
 Kate Jensen
+Jen Bradley
 
 Source code
 -----------

freud/data.py

@@ -81,7 +81,7 @@ def generate_system(self, num_replicas=1, scale=1, sigma_noise=0, seed=None):
                 if isinstance(n_repeats, int):
                     N = n_repeats ** dimension
                 else:
-                    N = np.product(n_repeats)
+                    N = np.prod(n_repeats)
                 indices = np.repeat(np.arange(len(uc.basis_positions)), N)
 
         Below is an example of expanding basis position properties (in this
@@ -92,7 +92,7 @@ def generate_system(self, num_replicas=1, scale=1, sigma_noise=0, seed=None):
             >>> uc = freud.data.UnitCell.bcc()
             >>> n_repeats = (10, 5, 4)
             >>> system = uc.generate_system(n_repeats)
-            >>> N = np.product(n_repeats)
+            >>> N = np.prod(n_repeats)
             >>> indices = np.repeat(np.arange(len(uc.basis_positions)), N)
             >>> # An array of types for all points
             >>> types = np.array([0, 1])[indices]

freud/diffraction.pyx

@@ -891,7 +891,7 @@ cdef class DiffractionPattern(_Compute):
             vmax = 0.7 * self.N_points
 
         norm = matplotlib.colors.LogNorm(vmin=vmin, vmax=vmax)
-        cmap = matplotlib.cm.get_cmap(cmap)
+        cmap = matplotlib.colormaps[cmap]
         image = cmap(norm(np.clip(self.diffraction, vmin, vmax)))
         return (image * 255).astype(np.uint8)