Fix CI failure

.test-conda-env-py3.yml

@@ -23,3 +23,4 @@ dependencies:
 - cython
 - gmsh
 - pyvkfft
+- mpi4py

examples/off-surface-eval.py

@@ -66,7 +66,8 @@ def main():
         fmm_order=fmm_order)
 
     from sumpy.kernel import LaplaceKernel
-    op = pytential.sym.D(LaplaceKernel(2), pytential.sym.var("sigma"), qbx_forced_limit=-2)
+    op = pytential.sym.D(
+        LaplaceKernel(2), pytential.sym.var("sigma"), qbx_forced_limit=-2)
 
     if mpi_rank == 0:
         sigma = layer_pot_source.density_discr.zeros(actx) + 1
@@ -107,5 +108,6 @@ def main():
         plt.colorbar()
         plt.show()
 
+
 if __name__ == "__main__":
     main()

pytential/qbx/distributed.py

@@ -29,7 +29,6 @@
 from boxtree.tools import DeviceDataRecord
 from boxtree.timing import TimingRecorder
 from pytools import memoize_method
-import six
 
 
 @dataclass
@@ -671,7 +670,7 @@ def exec_compute_potential_insn_fmm(self, actx: PyOpenCLArrayContext,
             calibration_params = AbstractQBXCostModel.get_unit_calibration_params()
 
             kernel_args = {}
-            for arg_name, arg_expr in six.iteritems(insn.kernel_arguments):
+            for arg_name, arg_expr in insn.kernel_arguments.items():
                 kernel_args[arg_name] = evaluate(arg_expr)
 
             boxes_time, _ = cost_model.qbx_cost_per_box(
@@ -773,8 +772,8 @@ def exec_compute_potential_insn_fmm(self, actx: PyOpenCLArrayContext,
                         output_and_expansion_dtype,
                         self.qbx_order,
                         self.fmm_level_to_order,
-                        source_extra_kwargs=source_extra_kwargs,
-                        kernel_extra_kwargs=kernel_extra_kwargs,
+                        source_extra_kwargs,
+                        kernel_extra_kwargs,
                         _use_target_specific_qbx=self._use_target_specific_qbx)
 
         if self.comm.Get_rank() == 0:

pytential/symbolic/execution.py

@@ -376,7 +376,7 @@ def exec_assign(self, actx: PyOpenCLArrayContext, insn, bound_expr, evaluate):
         if self.comm.Get_rank() == 0:
             return super().exec_assign(actx, insn, bound_expr, evaluate)
         else:
-            return dict()
+            return {}
 
     def exec_compute_potential_insn(
             self, actx: PyOpenCLArrayContext, insn, bound_expr, evaluate):