def __init__(self,
source_sites,
polynomial_order,
weighting_power=2,
epsilon_multiplier=1.5):
self.last_target_site = np.zeros((1, ))
self.kokkos_obj = pycompadre.KokkosParser()
assert len(
source_sites.shape
) == 2, "2D array must be given to GMLS for source_sites (#sites x spatial dimension)"
self.input_dimensions = source_sites.shape[1]
self.polynomial_order = polynomial_order
# initialize 3rd order reconstruction using 2nd order basis in 3D (GMLS)
self.gmls_obj = pycompadre.GMLS(polynomial_order,
self.input_dimensions, "QR",
"STANDARD")
self.gmls_obj.setWeightingPower(weighting_power)
self.weighting_power = weighting_power
self.gmls_obj.setWeightingType("power")
# neighbor search
self.epsilon_multiplier = epsilon_multiplier
self.gmls_helper = pycompadre.ParticleHelper(self.gmls_obj)
self.gmls_helper.generateKDTree(source_sites)
self.gmls_obj.addTargets(
pycompadre.TargetOperation.ScalarPointEvaluation)
self.gmls_obj.addTargets(
pycompadre.TargetOperation.PartialXOfScalarPointEvaluation)
self.gmls_obj.addTargets(
pycompadre.TargetOperation.PartialYOfScalarPointEvaluation)
import pycompadre
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.widgets import Slider, Button, RadioButtons, CheckButtons
''' Interactive example of weighting kernels in pycompadre
The weighting kernels used in the GMLS class in the function Wab allow users
to vary two parameters. This tool gives users the ability to vary these two
parameters and weighting type and see the effect visually.
'''
# initialize Kokkos
kp = pycompadre.KokkosParser()
# initialize parameters
wt = pycompadre.WeightingFunctionType.Power
x = np.linspace(-2.0, 2.0, 700)
h = 1.0
p = 2
n = 1
def approximate(wt, h, p, n):
y = np.array([pycompadre.Wab(xin,h,wt,p,n) for xin in x], dtype='f8')
return y
# get initial data
y = approximate(wt, h, p, n)
# plot initial data
fig, ax = plt.subplots()
def setUpClass(cls):
KokkosTestCase.instances += 1
if KokkosTestCase.instances == 1:
KokkosTestCase.shared_resource = pycompadre.KokkosParser(sys.argv)
def safe_instantiate_kokkos():
if KokkosDevice.instance_count == 0:
KokkosDevice.kokkos_obj=pycompadre.KokkosParser()
KokkosDevice.instance_count += 1
from unittest import TestCase
import numpy as np
import math
import random
import pycompadre
kokkos_obj=pycompadre.KokkosParser()
# function used to generate sample data
def exact(coord,order,dimension):
x = coord[0]
y = coord[1] if (dimension>1) else 0
z = coord[2] if (dimension>2) else 0
if order==1:
return 1 + x + y + z
elif order==2:
return 1 + x + y + z + x*x + x*y + x*z + y*y + y*z + z*z
elif order==3:
return 1 + x + y + z + x*x + x*y + x*z + y*y + y*z + z*z + x*x*x + x*x*y + x*x*z + x*y*y + x*y*z + x*z*z + y*y*y + y*y*z + y*z*z + z*z*z
# function used to get analytic gradient
def grad_exact(coord,component,order,dimension):
x = coord[0]
y = coord[1] if (dimension>1) else 0
z = coord[2] if (dimension>2) else 0
if (component==0):
if order==1:
return 1
elif order==2:
return 1 + 2*x + y + z
elif order==3: