# Path: fastpm/force/kernels.py
# def laplace(k, v):
# def gradient(dir, order=0):
#         def kernel(k, v):
#         def kernel(k, v):
# def longrange(r_split):
#         def kernel(k, v):
#         def kernel(k, v):


from fastpm.force import kernels
from numpy.testing import assert_allclose
from pmesh.pm import ParticleMesh

pm = ParticleMesh(BoxSize=128., Nmesh=[8, 8, 8])

def test_gradient():
    # asserts the gradient is hermitian

    data = pm.generate_whitenoise(type='real', seed=333)
    for d in range(pm.ndim):

# Path: fastpm/force/kernels.py
# def laplace(k, v):
# def gradient(dir, order=0):
#         def kernel(k, v):
#         def kernel(k, v):
# def longrange(r_split):
#         def kernel(k, v):
#         def kernel(k, v):


# Path: fastpm/tests/test_kernel.py
from fastpm.force import kernels
from numpy.testing import assert_allclose
from pmesh.pm import ParticleMesh


pm = ParticleMesh(BoxSize=128., Nmesh=[8, 8, 8])

def test_gradient():
    # asserts the gradient is hermitian

    data = pm.generate_whitenoise(type='real', seed=333)
    for d in range(pm.ndim):

        g1 = data.r2c().apply(kernels.gradient(d))